Breast Cancer Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "40b675d9",
   "metadata": {},
   "source": [
    "# Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8783b0a7",
   "metadata": {},
   "source": [
    "link:-[Data_set](https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data?select=data.csv \"like\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7edaa83",
   "metadata": {},
   "source": [
    "## Importing the libraries and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "e6099a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "83e6d0ad",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
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       "      <th>concavity_mean</th>\n",
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       "      <th>...</th>\n",
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       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
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       "      <th>compactness_worst</th>\n",
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       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
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       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
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       "      <td>0.4000</td>\n",
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       "      <th>565</th>\n",
       "      <td>926682</td>\n",
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       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
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       "      <td>0.10340</td>\n",
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       "      <td>0.09791</td>\n",
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       "    <tr>\n",
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       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
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       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
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       "      <td>47.92</td>\n",
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      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
       "..        ...       ...          ...           ...             ...        ...   \n",
       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
       "566    926954         M        16.60         28.08          108.30      858.1   \n",
       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
       "568     92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760         0.30010              0.14710   \n",
       "1            0.08474           0.07864         0.08690              0.07017   \n",
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       "..               ...               ...             ...                  ...   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0    ...          17.33           184.60      2019.0           0.16220   \n",
       "1    ...          23.41           158.80      1956.0           0.12380   \n",
       "2    ...          25.53           152.50      1709.0           0.14440   \n",
       "3    ...          26.50            98.87       567.7           0.20980   \n",
       "4    ...          16.67           152.20      1575.0           0.13740   \n",
       "..   ...            ...              ...         ...               ...   \n",
       "564  ...          26.40           166.10      2027.0           0.14100   \n",
       "565  ...          38.25           155.00      1731.0           0.11660   \n",
       "566  ...          34.12           126.70      1124.0           0.11390   \n",
       "567  ...          39.42           184.60      1821.0           0.16500   \n",
       "568  ...          30.37            59.16       268.6           0.08996   \n",
       "\n",
       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0              0.66560           0.7119                0.2654          0.4601   \n",
       "1              0.18660           0.2416                0.1860          0.2750   \n",
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       "..                 ...              ...                   ...             ...   \n",
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       "565            0.19220           0.3215                0.1628          0.2572   \n",
       "566            0.30940           0.3403                0.1418          0.2218   \n",
       "567            0.86810           0.9387                0.2650          0.4087   \n",
       "568            0.06444           0.0000                0.0000          0.2871   \n",
       "\n",
       "     fractal_dimension_worst  Unnamed: 32  \n",
       "0                    0.11890          NaN  \n",
       "1                    0.08902          NaN  \n",
       "2                    0.08758          NaN  \n",
       "3                    0.17300          NaN  \n",
       "4                    0.07678          NaN  \n",
       "..                       ...          ...  \n",
       "564                  0.07115          NaN  \n",
       "565                  0.06637          NaN  \n",
       "566                  0.07820          NaN  \n",
       "567                  0.12400          NaN  \n",
       "568                  0.07039          NaN  \n",
       "\n",
       "[569 rows x 33 columns]"
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     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "815d2948",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "4181e948",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
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       "      <th>area_mean</th>\n",
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       "      <th>Unnamed: 32</th>\n",
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       "      <td>17.77</td>\n",
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       "      <td>1326.0</td>\n",
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       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb46386",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "caeed9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 33)"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "9aefa4ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "44035dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis'], dtype='object')"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.select_dtypes(include = 'object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "a4179aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset.select_dtypes(include = 'object').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "aeb3b7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
       "       'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.select_dtypes(include = ['float64','int64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "6cbfdba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset.select_dtypes(include = ['float64','int64']).columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac26f92",
   "metadata": {},
   "source": [
    "## Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "3e562fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.690000e+02</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  ...  texture_worst  perimeter_worst   area_worst  \\\n",
       "count     569.000000  ...     569.000000       569.000000   569.000000   \n",
       "mean        0.181162  ...      25.677223       107.261213   880.583128   \n",
       "std         0.027414  ...       6.146258        33.602542   569.356993   \n",
       "min         0.106000  ...      12.020000        50.410000   185.200000   \n",
       "25%         0.161900  ...      21.080000        84.110000   515.300000   \n",
       "50%         0.179200  ...      25.410000        97.660000   686.500000   \n",
       "75%         0.195700  ...      29.720000       125.400000  1084.000000   \n",
       "max         0.304000  ...      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "count            569.000000      569.000000               569.000000   \n",
       "mean               0.114606        0.290076                 0.083946   \n",
       "std                0.065732        0.061867                 0.018061   \n",
       "min                0.000000        0.156500                 0.055040   \n",
       "25%                0.064930        0.250400                 0.071460   \n",
       "50%                0.099930        0.282200                 0.080040   \n",
       "75%                0.161400        0.317900                 0.092080   \n",
       "max                0.291000        0.663800                 0.207500   \n",
       "\n",
       "       Unnamed: 32  \n",
       "count          0.0  \n",
       "mean           NaN  \n",
       "std            NaN  \n",
       "min            NaN  \n",
       "25%            NaN  \n",
       "50%            NaN  \n",
       "75%            NaN  \n",
       "max            NaN  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "3d916c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
       "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
       "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
       "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
       "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
       "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
       "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
       "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
       "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "f27e6e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "297a9437",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "569"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "ab794ee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 32'], dtype='object')"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns[dataset.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "99fe05b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset.columns[dataset.isnull().any()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "117a60d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Unnamed: 32'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "4c8725a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302         M        17.99         10.38          122.80     1001.0   \n",
       "1      842517         M        20.57         17.77          132.90     1326.0   \n",
       "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3    84348301         M        11.42         20.38           77.58      386.1   \n",
       "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
       "..        ...       ...          ...           ...             ...        ...   \n",
       "564    926424         M        21.56         22.39          142.00     1479.0   \n",
       "565    926682         M        20.13         28.25          131.20     1261.0   \n",
       "566    926954         M        16.60         28.08          108.30      858.1   \n",
       "567    927241         M        20.60         29.33          140.10     1265.0   \n",
       "568     92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760         0.30010              0.14710   \n",
       "1            0.08474           0.07864         0.08690              0.07017   \n",
       "2            0.10960           0.15990         0.19740              0.12790   \n",
       "3            0.14250           0.28390         0.24140              0.10520   \n",
       "4            0.10030           0.13280         0.19800              0.10430   \n",
       "..               ...               ...             ...                  ...   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
       "0    ...        25.380          17.33           184.60      2019.0   \n",
       "1    ...        24.990          23.41           158.80      1956.0   \n",
       "2    ...        23.570          25.53           152.50      1709.0   \n",
       "3    ...        14.910          26.50            98.87       567.7   \n",
       "4    ...        22.540          16.67           152.20      1575.0   \n",
       "..   ...           ...            ...              ...         ...   \n",
       "564  ...        25.450          26.40           166.10      2027.0   \n",
       "565  ...        23.690          38.25           155.00      1731.0   \n",
       "566  ...        18.980          34.12           126.70      1124.0   \n",
       "567  ...        25.740          39.42           184.60      1821.0   \n",
       "568  ...         9.456          30.37            59.16       268.6   \n",
       "\n",
       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0             0.16220            0.66560           0.7119   \n",
       "1             0.12380            0.18660           0.2416   \n",
       "2             0.14440            0.42450           0.4504   \n",
       "3             0.20980            0.86630           0.6869   \n",
       "4             0.13740            0.20500           0.4000   \n",
       "..                ...                ...              ...   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                  0.2654          0.4601                  0.11890  \n",
       "1                  0.1860          0.2750                  0.08902  \n",
       "2                  0.2430          0.3613                  0.08758  \n",
       "3                  0.2575          0.6638                  0.17300  \n",
       "4                  0.1625          0.2364                  0.07678  \n",
       "..                    ...             ...                      ...  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[569 rows x 32 columns]"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.drop(columns='Unnamed: 32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "ed382201",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.drop(columns='Unnamed: 32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "bbd4f8af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 32)"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "943317f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed35d38d",
   "metadata": {},
   "source": [
    "previously it was showing (569,33) dataset.shape, now after droping the column it is showing (569,32) this means the column with null value is get droped."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf32dc09",
   "metadata": {},
   "source": [
    "## Dealing with categorical data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "8966bf8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['diagnosis'], dtype='object')"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.select_dtypes(include = 'object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "30fa243f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['diagnosis'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "f9226b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['diagnosis'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e6831e",
   "metadata": {},
   "source": [
    "## one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "153bb49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.get_dummies(data = dataset, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "60d7e8a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>diagnosis_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "0         0.2419  ...          17.33           184.60      2019.0   \n",
       "1         0.1812  ...          23.41           158.80      1956.0   \n",
       "2         0.2069  ...          25.53           152.50      1709.0   \n",
       "3         0.2597  ...          26.50            98.87       567.7   \n",
       "4         0.1809  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  diagnosis_M  \n",
       "0          0.4601                  0.11890            1  \n",
       "1          0.2750                  0.08902            1  \n",
       "2          0.3613                  0.08758            1  \n",
       "3          0.6638                  0.17300            1  \n",
       "4          0.2364                  0.07678            1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69fa6485",
   "metadata": {},
   "source": [
    "## Countplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "5abcf455",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(dataset['diagnosis_M'],label= 'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "311bfb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "357"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B (0) values\n",
    "(dataset.diagnosis_M == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "135f0050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# M (1) values\n",
    "(dataset.diagnosis_M== 1).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e23e2a",
   "metadata": {},
   "source": [
    "## Correlation matrix and heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "a0f8c2b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_2 = dataset.drop(columns ='diagnosis_M' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "483800dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "cda95f90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'correlation with diagnosis_M'}>"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_2.corrwith(dataset['diagnosis_M']).plot.bar(\n",
    "    figsize=(20,10), title = 'correlation with diagnosis_M', rot = 45, grid = True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d8d81a",
   "metadata": {},
   "source": [
    "## Correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "ef5b4b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "f6a3d4e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>diagnosis_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074626</td>\n",
       "      <td>0.099770</td>\n",
       "      <td>0.073159</td>\n",
       "      <td>0.096893</td>\n",
       "      <td>-0.012968</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.050080</td>\n",
       "      <td>0.044158</td>\n",
       "      <td>-0.022114</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064720</td>\n",
       "      <td>0.079986</td>\n",
       "      <td>0.107187</td>\n",
       "      <td>0.010338</td>\n",
       "      <td>-0.002968</td>\n",
       "      <td>0.023203</td>\n",
       "      <td>0.035174</td>\n",
       "      <td>-0.044224</td>\n",
       "      <td>-0.029866</td>\n",
       "      <td>0.039769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_mean</th>\n",
       "      <td>0.074626</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.323782</td>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.170581</td>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.147741</td>\n",
       "      <td>...</td>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.007066</td>\n",
       "      <td>0.730029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_mean</th>\n",
       "      <td>0.099770</td>\n",
       "      <td>0.323782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>...</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.415185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_mean</th>\n",
       "      <td>0.073159</td>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>0.742636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_mean</th>\n",
       "      <td>0.096893</td>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>0.708984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_mean</th>\n",
       "      <td>-0.012968</td>\n",
       "      <td>0.170581</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>0.358560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_mean</th>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>0.596534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_mean</th>\n",
       "      <td>0.050080</td>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>...</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.696360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_mean</th>\n",
       "      <td>0.044158</td>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>...</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.776614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_mean</th>\n",
       "      <td>-0.022114</td>\n",
       "      <td>0.147741</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>0.330499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <td>-0.052511</td>\n",
       "      <td>-0.311631</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>0.767297</td>\n",
       "      <td>-0.012838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_se</th>\n",
       "      <td>0.143048</td>\n",
       "      <td>0.679090</td>\n",
       "      <td>0.275869</td>\n",
       "      <td>0.691765</td>\n",
       "      <td>0.732562</td>\n",
       "      <td>0.301467</td>\n",
       "      <td>0.497473</td>\n",
       "      <td>0.631925</td>\n",
       "      <td>0.698050</td>\n",
       "      <td>0.303379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194799</td>\n",
       "      <td>0.719684</td>\n",
       "      <td>0.751548</td>\n",
       "      <td>0.141919</td>\n",
       "      <td>0.287103</td>\n",
       "      <td>0.380585</td>\n",
       "      <td>0.531062</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.049559</td>\n",
       "      <td>0.567134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_se</th>\n",
       "      <td>-0.007526</td>\n",
       "      <td>-0.097317</td>\n",
       "      <td>0.386358</td>\n",
       "      <td>-0.086761</td>\n",
       "      <td>-0.066280</td>\n",
       "      <td>0.068406</td>\n",
       "      <td>0.046205</td>\n",
       "      <td>0.076218</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.128053</td>\n",
       "      <td>...</td>\n",
       "      <td>0.409003</td>\n",
       "      <td>-0.102242</td>\n",
       "      <td>-0.083195</td>\n",
       "      <td>-0.073658</td>\n",
       "      <td>-0.092439</td>\n",
       "      <td>-0.068956</td>\n",
       "      <td>-0.119638</td>\n",
       "      <td>-0.128215</td>\n",
       "      <td>-0.045655</td>\n",
       "      <td>-0.008303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_se</th>\n",
       "      <td>0.137331</td>\n",
       "      <td>0.674172</td>\n",
       "      <td>0.281673</td>\n",
       "      <td>0.693135</td>\n",
       "      <td>0.726628</td>\n",
       "      <td>0.296092</td>\n",
       "      <td>0.548905</td>\n",
       "      <td>0.660391</td>\n",
       "      <td>0.710650</td>\n",
       "      <td>0.313893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200371</td>\n",
       "      <td>0.721031</td>\n",
       "      <td>0.730713</td>\n",
       "      <td>0.130054</td>\n",
       "      <td>0.341919</td>\n",
       "      <td>0.418899</td>\n",
       "      <td>0.554897</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>0.556141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_se</th>\n",
       "      <td>0.177742</td>\n",
       "      <td>0.735864</td>\n",
       "      <td>0.259845</td>\n",
       "      <td>0.744983</td>\n",
       "      <td>0.800086</td>\n",
       "      <td>0.246552</td>\n",
       "      <td>0.455653</td>\n",
       "      <td>0.617427</td>\n",
       "      <td>0.690299</td>\n",
       "      <td>0.223970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.196497</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>0.125389</td>\n",
       "      <td>0.283257</td>\n",
       "      <td>0.385100</td>\n",
       "      <td>0.538166</td>\n",
       "      <td>0.074126</td>\n",
       "      <td>0.017539</td>\n",
       "      <td>0.548236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_se</th>\n",
       "      <td>0.096781</td>\n",
       "      <td>-0.222600</td>\n",
       "      <td>0.006614</td>\n",
       "      <td>-0.202694</td>\n",
       "      <td>-0.166777</td>\n",
       "      <td>0.332375</td>\n",
       "      <td>0.135299</td>\n",
       "      <td>0.098564</td>\n",
       "      <td>0.027653</td>\n",
       "      <td>0.187321</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.074743</td>\n",
       "      <td>-0.217304</td>\n",
       "      <td>-0.182195</td>\n",
       "      <td>0.314457</td>\n",
       "      <td>-0.055558</td>\n",
       "      <td>-0.058298</td>\n",
       "      <td>-0.102007</td>\n",
       "      <td>-0.107342</td>\n",
       "      <td>0.101480</td>\n",
       "      <td>-0.067016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_se</th>\n",
       "      <td>0.033961</td>\n",
       "      <td>0.206000</td>\n",
       "      <td>0.191975</td>\n",
       "      <td>0.250744</td>\n",
       "      <td>0.212583</td>\n",
       "      <td>0.318943</td>\n",
       "      <td>0.738722</td>\n",
       "      <td>0.670279</td>\n",
       "      <td>0.490424</td>\n",
       "      <td>0.421659</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143003</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>0.199371</td>\n",
       "      <td>0.227394</td>\n",
       "      <td>0.678780</td>\n",
       "      <td>0.639147</td>\n",
       "      <td>0.483208</td>\n",
       "      <td>0.277878</td>\n",
       "      <td>0.590973</td>\n",
       "      <td>0.292999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_se</th>\n",
       "      <td>0.055239</td>\n",
       "      <td>0.194204</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.228082</td>\n",
       "      <td>0.207660</td>\n",
       "      <td>0.248396</td>\n",
       "      <td>0.570517</td>\n",
       "      <td>0.691270</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.342627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100241</td>\n",
       "      <td>0.226680</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.168481</td>\n",
       "      <td>0.484858</td>\n",
       "      <td>0.662564</td>\n",
       "      <td>0.440472</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>0.439329</td>\n",
       "      <td>0.253730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_se</th>\n",
       "      <td>0.078768</td>\n",
       "      <td>0.376169</td>\n",
       "      <td>0.163851</td>\n",
       "      <td>0.407217</td>\n",
       "      <td>0.372320</td>\n",
       "      <td>0.380676</td>\n",
       "      <td>0.642262</td>\n",
       "      <td>0.683260</td>\n",
       "      <td>0.615634</td>\n",
       "      <td>0.393298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.086741</td>\n",
       "      <td>0.394999</td>\n",
       "      <td>0.342271</td>\n",
       "      <td>0.215351</td>\n",
       "      <td>0.452888</td>\n",
       "      <td>0.549592</td>\n",
       "      <td>0.602450</td>\n",
       "      <td>0.143116</td>\n",
       "      <td>0.310655</td>\n",
       "      <td>0.408042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_se</th>\n",
       "      <td>-0.017306</td>\n",
       "      <td>-0.104321</td>\n",
       "      <td>0.009127</td>\n",
       "      <td>-0.081629</td>\n",
       "      <td>-0.072497</td>\n",
       "      <td>0.200774</td>\n",
       "      <td>0.229977</td>\n",
       "      <td>0.178009</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.449137</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077473</td>\n",
       "      <td>-0.103753</td>\n",
       "      <td>-0.110343</td>\n",
       "      <td>-0.012662</td>\n",
       "      <td>0.060255</td>\n",
       "      <td>0.037119</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>0.389402</td>\n",
       "      <td>0.078079</td>\n",
       "      <td>-0.006522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_se</th>\n",
       "      <td>0.025725</td>\n",
       "      <td>-0.042641</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.005523</td>\n",
       "      <td>-0.019887</td>\n",
       "      <td>0.283607</td>\n",
       "      <td>0.507318</td>\n",
       "      <td>0.449301</td>\n",
       "      <td>0.257584</td>\n",
       "      <td>0.331786</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>-0.022736</td>\n",
       "      <td>0.170568</td>\n",
       "      <td>0.390159</td>\n",
       "      <td>0.379975</td>\n",
       "      <td>0.215204</td>\n",
       "      <td>0.111094</td>\n",
       "      <td>0.591328</td>\n",
       "      <td>0.077972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius_worst</th>\n",
       "      <td>0.082405</td>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.969476</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.185728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>0.776454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture_worst</th>\n",
       "      <td>0.064720</td>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.456903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter_worst</th>\n",
       "      <td>0.079986</td>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>...</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.782914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area_worst</th>\n",
       "      <td>0.107187</td>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.733825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness_worst</th>\n",
       "      <td>0.010338</td>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>0.421465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness_worst</th>\n",
       "      <td>-0.002968</td>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.590998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity_worst</th>\n",
       "      <td>0.023203</td>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>0.659610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points_worst</th>\n",
       "      <td>0.035174</td>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>0.793566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry_worst</th>\n",
       "      <td>-0.044224</td>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>0.416294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <td>-0.029866</td>\n",
       "      <td>0.007066</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.323872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis_M</th>\n",
       "      <td>0.039769</td>\n",
       "      <td>0.730029</td>\n",
       "      <td>0.415185</td>\n",
       "      <td>0.742636</td>\n",
       "      <td>0.708984</td>\n",
       "      <td>0.358560</td>\n",
       "      <td>0.596534</td>\n",
       "      <td>0.696360</td>\n",
       "      <td>0.776614</td>\n",
       "      <td>0.330499</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456903</td>\n",
       "      <td>0.782914</td>\n",
       "      <td>0.733825</td>\n",
       "      <td>0.421465</td>\n",
       "      <td>0.590998</td>\n",
       "      <td>0.659610</td>\n",
       "      <td>0.793566</td>\n",
       "      <td>0.416294</td>\n",
       "      <td>0.323872</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               id  radius_mean  texture_mean  perimeter_mean  \\\n",
       "id                       1.000000     0.074626      0.099770        0.073159   \n",
       "radius_mean              0.074626     1.000000      0.323782        0.997855   \n",
       "texture_mean             0.099770     0.323782      1.000000        0.329533   \n",
       "perimeter_mean           0.073159     0.997855      0.329533        1.000000   \n",
       "area_mean                0.096893     0.987357      0.321086        0.986507   \n",
       "smoothness_mean         -0.012968     0.170581     -0.023389        0.207278   \n",
       "compactness_mean         0.000096     0.506124      0.236702        0.556936   \n",
       "concavity_mean           0.050080     0.676764      0.302418        0.716136   \n",
       "concave points_mean      0.044158     0.822529      0.293464        0.850977   \n",
       "symmetry_mean           -0.022114     0.147741      0.071401        0.183027   \n",
       "fractal_dimension_mean  -0.052511    -0.311631     -0.076437       -0.261477   \n",
       "radius_se                0.143048     0.679090      0.275869        0.691765   \n",
       "texture_se              -0.007526    -0.097317      0.386358       -0.086761   \n",
       "perimeter_se             0.137331     0.674172      0.281673        0.693135   \n",
       "area_se                  0.177742     0.735864      0.259845        0.744983   \n",
       "smoothness_se            0.096781    -0.222600      0.006614       -0.202694   \n",
       "compactness_se           0.033961     0.206000      0.191975        0.250744   \n",
       "concavity_se             0.055239     0.194204      0.143293        0.228082   \n",
       "concave points_se        0.078768     0.376169      0.163851        0.407217   \n",
       "symmetry_se             -0.017306    -0.104321      0.009127       -0.081629   \n",
       "fractal_dimension_se     0.025725    -0.042641      0.054458       -0.005523   \n",
       "radius_worst             0.082405     0.969539      0.352573        0.969476   \n",
       "texture_worst            0.064720     0.297008      0.912045        0.303038   \n",
       "perimeter_worst          0.079986     0.965137      0.358040        0.970387   \n",
       "area_worst               0.107187     0.941082      0.343546        0.941550   \n",
       "smoothness_worst         0.010338     0.119616      0.077503        0.150549   \n",
       "compactness_worst       -0.002968     0.413463      0.277830        0.455774   \n",
       "concavity_worst          0.023203     0.526911      0.301025        0.563879   \n",
       "concave points_worst     0.035174     0.744214      0.295316        0.771241   \n",
       "symmetry_worst          -0.044224     0.163953      0.105008        0.189115   \n",
       "fractal_dimension_worst -0.029866     0.007066      0.119205        0.051019   \n",
       "diagnosis_M              0.039769     0.730029      0.415185        0.742636   \n",
       "\n",
       "                         area_mean  smoothness_mean  compactness_mean  \\\n",
       "id                        0.096893        -0.012968          0.000096   \n",
       "radius_mean               0.987357         0.170581          0.506124   \n",
       "texture_mean              0.321086        -0.023389          0.236702   \n",
       "perimeter_mean            0.986507         0.207278          0.556936   \n",
       "area_mean                 1.000000         0.177028          0.498502   \n",
       "smoothness_mean           0.177028         1.000000          0.659123   \n",
       "compactness_mean          0.498502         0.659123          1.000000   \n",
       "concavity_mean            0.685983         0.521984          0.883121   \n",
       "concave points_mean       0.823269         0.553695          0.831135   \n",
       "symmetry_mean             0.151293         0.557775          0.602641   \n",
       "fractal_dimension_mean   -0.283110         0.584792          0.565369   \n",
       "radius_se                 0.732562         0.301467          0.497473   \n",
       "texture_se               -0.066280         0.068406          0.046205   \n",
       "perimeter_se              0.726628         0.296092          0.548905   \n",
       "area_se                   0.800086         0.246552          0.455653   \n",
       "smoothness_se            -0.166777         0.332375          0.135299   \n",
       "compactness_se            0.212583         0.318943          0.738722   \n",
       "concavity_se              0.207660         0.248396          0.570517   \n",
       "concave points_se         0.372320         0.380676          0.642262   \n",
       "symmetry_se              -0.072497         0.200774          0.229977   \n",
       "fractal_dimension_se     -0.019887         0.283607          0.507318   \n",
       "radius_worst              0.962746         0.213120          0.535315   \n",
       "texture_worst             0.287489         0.036072          0.248133   \n",
       "perimeter_worst           0.959120         0.238853          0.590210   \n",
       "area_worst                0.959213         0.206718          0.509604   \n",
       "smoothness_worst          0.123523         0.805324          0.565541   \n",
       "compactness_worst         0.390410         0.472468          0.865809   \n",
       "concavity_worst           0.512606         0.434926          0.816275   \n",
       "concave points_worst      0.722017         0.503053          0.815573   \n",
       "symmetry_worst            0.143570         0.394309          0.510223   \n",
       "fractal_dimension_worst   0.003738         0.499316          0.687382   \n",
       "diagnosis_M               0.708984         0.358560          0.596534   \n",
       "\n",
       "                         concavity_mean  concave points_mean  symmetry_mean  \\\n",
       "id                             0.050080             0.044158      -0.022114   \n",
       "radius_mean                    0.676764             0.822529       0.147741   \n",
       "texture_mean                   0.302418             0.293464       0.071401   \n",
       "perimeter_mean                 0.716136             0.850977       0.183027   \n",
       "area_mean                      0.685983             0.823269       0.151293   \n",
       "smoothness_mean                0.521984             0.553695       0.557775   \n",
       "compactness_mean               0.883121             0.831135       0.602641   \n",
       "concavity_mean                 1.000000             0.921391       0.500667   \n",
       "concave points_mean            0.921391             1.000000       0.462497   \n",
       "symmetry_mean                  0.500667             0.462497       1.000000   \n",
       "fractal_dimension_mean         0.336783             0.166917       0.479921   \n",
       "radius_se                      0.631925             0.698050       0.303379   \n",
       "texture_se                     0.076218             0.021480       0.128053   \n",
       "perimeter_se                   0.660391             0.710650       0.313893   \n",
       "area_se                        0.617427             0.690299       0.223970   \n",
       "smoothness_se                  0.098564             0.027653       0.187321   \n",
       "compactness_se                 0.670279             0.490424       0.421659   \n",
       "concavity_se                   0.691270             0.439167       0.342627   \n",
       "concave points_se              0.683260             0.615634       0.393298   \n",
       "symmetry_se                    0.178009             0.095351       0.449137   \n",
       "fractal_dimension_se           0.449301             0.257584       0.331786   \n",
       "radius_worst                   0.688236             0.830318       0.185728   \n",
       "texture_worst                  0.299879             0.292752       0.090651   \n",
       "perimeter_worst                0.729565             0.855923       0.219169   \n",
       "area_worst                     0.675987             0.809630       0.177193   \n",
       "smoothness_worst               0.448822             0.452753       0.426675   \n",
       "compactness_worst              0.754968             0.667454       0.473200   \n",
       "concavity_worst                0.884103             0.752399       0.433721   \n",
       "concave points_worst           0.861323             0.910155       0.430297   \n",
       "symmetry_worst                 0.409464             0.375744       0.699826   \n",
       "fractal_dimension_worst        0.514930             0.368661       0.438413   \n",
       "diagnosis_M                    0.696360             0.776614       0.330499   \n",
       "\n",
       "                         ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "id                       ...       0.064720         0.079986    0.107187   \n",
       "radius_mean              ...       0.297008         0.965137    0.941082   \n",
       "texture_mean             ...       0.912045         0.358040    0.343546   \n",
       "perimeter_mean           ...       0.303038         0.970387    0.941550   \n",
       "area_mean                ...       0.287489         0.959120    0.959213   \n",
       "smoothness_mean          ...       0.036072         0.238853    0.206718   \n",
       "compactness_mean         ...       0.248133         0.590210    0.509604   \n",
       "concavity_mean           ...       0.299879         0.729565    0.675987   \n",
       "concave points_mean      ...       0.292752         0.855923    0.809630   \n",
       "symmetry_mean            ...       0.090651         0.219169    0.177193   \n",
       "fractal_dimension_mean   ...      -0.051269        -0.205151   -0.231854   \n",
       "radius_se                ...       0.194799         0.719684    0.751548   \n",
       "texture_se               ...       0.409003        -0.102242   -0.083195   \n",
       "perimeter_se             ...       0.200371         0.721031    0.730713   \n",
       "area_se                  ...       0.196497         0.761213    0.811408   \n",
       "smoothness_se            ...      -0.074743        -0.217304   -0.182195   \n",
       "compactness_se           ...       0.143003         0.260516    0.199371   \n",
       "concavity_se             ...       0.100241         0.226680    0.188353   \n",
       "concave points_se        ...       0.086741         0.394999    0.342271   \n",
       "symmetry_se              ...      -0.077473        -0.103753   -0.110343   \n",
       "fractal_dimension_se     ...      -0.003195        -0.001000   -0.022736   \n",
       "radius_worst             ...       0.359921         0.993708    0.984015   \n",
       "texture_worst            ...       1.000000         0.365098    0.345842   \n",
       "perimeter_worst          ...       0.365098         1.000000    0.977578   \n",
       "area_worst               ...       0.345842         0.977578    1.000000   \n",
       "smoothness_worst         ...       0.225429         0.236775    0.209145   \n",
       "compactness_worst        ...       0.360832         0.529408    0.438296   \n",
       "concavity_worst          ...       0.368366         0.618344    0.543331   \n",
       "concave points_worst     ...       0.359755         0.816322    0.747419   \n",
       "symmetry_worst           ...       0.233027         0.269493    0.209146   \n",
       "fractal_dimension_worst  ...       0.219122         0.138957    0.079647   \n",
       "diagnosis_M              ...       0.456903         0.782914    0.733825   \n",
       "\n",
       "                         smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "id                               0.010338          -0.002968         0.023203   \n",
       "radius_mean                      0.119616           0.413463         0.526911   \n",
       "texture_mean                     0.077503           0.277830         0.301025   \n",
       "perimeter_mean                   0.150549           0.455774         0.563879   \n",
       "area_mean                        0.123523           0.390410         0.512606   \n",
       "smoothness_mean                  0.805324           0.472468         0.434926   \n",
       "compactness_mean                 0.565541           0.865809         0.816275   \n",
       "concavity_mean                   0.448822           0.754968         0.884103   \n",
       "concave points_mean              0.452753           0.667454         0.752399   \n",
       "symmetry_mean                    0.426675           0.473200         0.433721   \n",
       "fractal_dimension_mean           0.504942           0.458798         0.346234   \n",
       "radius_se                        0.141919           0.287103         0.380585   \n",
       "texture_se                      -0.073658          -0.092439        -0.068956   \n",
       "perimeter_se                     0.130054           0.341919         0.418899   \n",
       "area_se                          0.125389           0.283257         0.385100   \n",
       "smoothness_se                    0.314457          -0.055558        -0.058298   \n",
       "compactness_se                   0.227394           0.678780         0.639147   \n",
       "concavity_se                     0.168481           0.484858         0.662564   \n",
       "concave points_se                0.215351           0.452888         0.549592   \n",
       "symmetry_se                     -0.012662           0.060255         0.037119   \n",
       "fractal_dimension_se             0.170568           0.390159         0.379975   \n",
       "radius_worst                     0.216574           0.475820         0.573975   \n",
       "texture_worst                    0.225429           0.360832         0.368366   \n",
       "perimeter_worst                  0.236775           0.529408         0.618344   \n",
       "area_worst                       0.209145           0.438296         0.543331   \n",
       "smoothness_worst                 1.000000           0.568187         0.518523   \n",
       "compactness_worst                0.568187           1.000000         0.892261   \n",
       "concavity_worst                  0.518523           0.892261         1.000000   \n",
       "concave points_worst             0.547691           0.801080         0.855434   \n",
       "symmetry_worst                   0.493838           0.614441         0.532520   \n",
       "fractal_dimension_worst          0.617624           0.810455         0.686511   \n",
       "diagnosis_M                      0.421465           0.590998         0.659610   \n",
       "\n",
       "                         concave points_worst  symmetry_worst  \\\n",
       "id                                   0.035174       -0.044224   \n",
       "radius_mean                          0.744214        0.163953   \n",
       "texture_mean                         0.295316        0.105008   \n",
       "perimeter_mean                       0.771241        0.189115   \n",
       "area_mean                            0.722017        0.143570   \n",
       "smoothness_mean                      0.503053        0.394309   \n",
       "compactness_mean                     0.815573        0.510223   \n",
       "concavity_mean                       0.861323        0.409464   \n",
       "concave points_mean                  0.910155        0.375744   \n",
       "symmetry_mean                        0.430297        0.699826   \n",
       "fractal_dimension_mean               0.175325        0.334019   \n",
       "radius_se                            0.531062        0.094543   \n",
       "texture_se                          -0.119638       -0.128215   \n",
       "perimeter_se                         0.554897        0.109930   \n",
       "area_se                              0.538166        0.074126   \n",
       "smoothness_se                       -0.102007       -0.107342   \n",
       "compactness_se                       0.483208        0.277878   \n",
       "concavity_se                         0.440472        0.197788   \n",
       "concave points_se                    0.602450        0.143116   \n",
       "symmetry_se                         -0.030413        0.389402   \n",
       "fractal_dimension_se                 0.215204        0.111094   \n",
       "radius_worst                         0.787424        0.243529   \n",
       "texture_worst                        0.359755        0.233027   \n",
       "perimeter_worst                      0.816322        0.269493   \n",
       "area_worst                           0.747419        0.209146   \n",
       "smoothness_worst                     0.547691        0.493838   \n",
       "compactness_worst                    0.801080        0.614441   \n",
       "concavity_worst                      0.855434        0.532520   \n",
       "concave points_worst                 1.000000        0.502528   \n",
       "symmetry_worst                       0.502528        1.000000   \n",
       "fractal_dimension_worst              0.511114        0.537848   \n",
       "diagnosis_M                          0.793566        0.416294   \n",
       "\n",
       "                         fractal_dimension_worst  diagnosis_M  \n",
       "id                                     -0.029866     0.039769  \n",
       "radius_mean                             0.007066     0.730029  \n",
       "texture_mean                            0.119205     0.415185  \n",
       "perimeter_mean                          0.051019     0.742636  \n",
       "area_mean                               0.003738     0.708984  \n",
       "smoothness_mean                         0.499316     0.358560  \n",
       "compactness_mean                        0.687382     0.596534  \n",
       "concavity_mean                          0.514930     0.696360  \n",
       "concave points_mean                     0.368661     0.776614  \n",
       "symmetry_mean                           0.438413     0.330499  \n",
       "fractal_dimension_mean                  0.767297    -0.012838  \n",
       "radius_se                               0.049559     0.567134  \n",
       "texture_se                             -0.045655    -0.008303  \n",
       "perimeter_se                            0.085433     0.556141  \n",
       "area_se                                 0.017539     0.548236  \n",
       "smoothness_se                           0.101480    -0.067016  \n",
       "compactness_se                          0.590973     0.292999  \n",
       "concavity_se                            0.439329     0.253730  \n",
       "concave points_se                       0.310655     0.408042  \n",
       "symmetry_se                             0.078079    -0.006522  \n",
       "fractal_dimension_se                    0.591328     0.077972  \n",
       "radius_worst                            0.093492     0.776454  \n",
       "texture_worst                           0.219122     0.456903  \n",
       "perimeter_worst                         0.138957     0.782914  \n",
       "area_worst                              0.079647     0.733825  \n",
       "smoothness_worst                        0.617624     0.421465  \n",
       "compactness_worst                       0.810455     0.590998  \n",
       "concavity_worst                         0.686511     0.659610  \n",
       "concave points_worst                    0.511114     0.793566  \n",
       "symmetry_worst                          0.537848     0.416294  \n",
       "fractal_dimension_worst                 1.000000     0.323872  \n",
       "diagnosis_M                             0.323872     1.000000  \n",
       "\n",
       "[32 rows x 32 columns]"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f1b87e3",
   "metadata": {},
   "source": [
    "## heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "29a6a65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2B5fuQq1S02FKD4JGdGbv4p8QZAJd5w5gvba/jNu7kDsGsUSVFv64+3ixsMVYygX48fGCQazS9pePZvfj/qkbbB6xErm5HHNry9x8pzYd4KROfzE83Pld6Zofgkzg/bn92dJ7EUlRSobunccDo1gslb/mbKVKW2Nbdv2XM1zcEsxHK4blKyNPloy+cweztPdclFEK5uxdwvXgy0To9JuaLWrj6ePFpBaf4xtQgX4LhjC3i2Y8nP3lJEe3HGTIitH5iQCgVpCmjPHNR+IbUJH+84cwp4vxpNSnU/pwaNM+Qvad47MFQ2nRvRXHth/ON392ZjYLe8wmU+v3Zv6ygIdnbtJr7qC3PhYOb/iTP1fsAqBV/450GtON7dM3vFPbeWzTfoK/N57wrxMUiJd3SYY1G0LFgEoMXzCCiZ3HG6XrN7U/ezf+yZl9pxm+cCStu7fh0PaDgGZyp9/U/lw/dd0o3/sDPuBF2Ats7G2MrtUOqoOXT0lGNh9KxYBKDJk/nCkmYqY+U/qxb9Nezu07w9AFw2nVvQ2Htx/k9rmbXA7W+PJylb0Zv3YSo1uNMMoPmrHwqTYmjI9SMKWAmHB2i9H4BFSgx4JBLNWJCVvmExNK/DeQPuf+H0AQhBTt/wVBENYIgnBPEIS/gBJvW1agfw0cHez/URll/f1QPItC+SIGVbaKG/suUM1gYqVa2zpc+U3zUPD8ehhW9jbYuzvppanQuDqKZ9HEv4r7W/dR2t9Pk197H7f3XTAKKlIVSby6FY46p2j7Rt+VbrpvVC1srBC1X40o7e+HUkeHWyZ0qNK2Dte18l7oyCvhV4oX18PIzshCrVLz9OJ9qrYznuCq/l4Dbu3Nm3yp06YeZ349CUDY9UfYONjiVML4i0rVGtXg4oHzAJz+9QSBbTXBfJIikfBbYaiy9eu0lF9pHl19QFZGFoIgkJmWSZX61fTSNOrcjMuHQlBExOWW9ZrGHzbjyz+XsODAVwxYOAxBZmz+6rSpR8i+s+Rk5RD7Iobop5H4+vsZpQPw9vcj7lk0Cm3dXtt3nppt6+qlqdm2Lpd+0wS0T6+HYm1vi4NBWxaEt78fsc+icmVc3XeeWkYyArmoI8NGR4aTpwvVW9bm3K5jRmV3ndmP3xftwPAjAXXb1OPUrycACL3+CNt82q96o5qEaN+Infr1OHXbNgDg0dUHpGrfSIdee4irlysACTHxuW/3M1LTeRX2EldPV+q3bcDxX48D8PD6Q2wdbHE2Ia9mo5qcPXAWgGO/HKNhuwaF1J5mxRGAmZkZcnMzPVW9/H1JeBpN4otY1Nkq7u8Lwa+N/thIUyQRdSscdbbx+LbzdMG3pT+3dp3U+92vTR3u/qq5z6L2/zM6/V8zfk4UmL964xpEP48i7pX+pJ0uXv6+xOvod29fCBXeQD/7fPTLjxL+viQ+jSbpuUZe2N4QfAxsTboiiZibpuXJzOSYWVkgyGWYWVuQFh2vd724bZmPvx8xz6KIexGDKjuHK/vOUcvAVtdqW5eQ304B8MRgfGdq+57cTI7cTJ5rkwG6zezPb4u2I+p0yHL+fsQa2JMaBmO9xj+0J7qU8fcjTscX3TThi6q2rcM1HV9kbcIX+Wl9UYLWF7mX9yL8ouZtdejZW9ToUC83bd029Tit9Q0F2ZZqjWoQoh0bp349QV0d3/DYhG/QpUbjmkQ/j8LR3Vmv/S7tO4e/QX36t63Lhd809xN+PRQbexsc3Z1IjE3gufYNeWZqBpGPX+Hs6QJAi17tOLjud3KyNKsEkhVJb6U+ZXI55lYWyOQyLKwtSNL2/9Azt1Gr1No8oThp76OsVt7r/nJ933mqG8ir3jaQy9r+8kwrz8HdCUs7a8rXq8LF3Ro7o8pWkZFU9C9yvitd86O0v6927MdqY7EQKpuIxSLyicWeXXpAemJKkXQr7+9H9LMoYl9Eo8rO4eK+s9Q26De129blnHbcP9b6XEetbg8v3SO1CLLqtKnHWe1YeFzAWKjaqAaXtC+3zvx6gjpt6xWaX9f2mJmb4eVX5p2MhQy9uNMStDbuXdrO/KjXtj4ntHHEo0LiiHPaOOL4L8do0K5h7rX3PnufCwfPk6hI0Mvj6ulKYKu6BO86Ylp2m/qc1PrsgmTXaFSTC9qY6cSvx6mntWuv4xQASxtLCvpw0+uYME479q6YiAlrtQ0kRDvun7xBTPifQK0uvv/+jyJN/Py3+BCoBNQABgPFthLoTXD0cCYhQpH774RIBY4ezgZpXPTSJEYpcdQ6o9f4d2rEjb3n9X5r3K8dXxxcwidLh2LtUPDnjx08nEnUkZEUqcTBw6WAHIXzLnVrP+ETZpxfQ+3OjXNX/5jSwdFAB00aZV6aKCUOns5EP3yBd73KWDvZYW5lQcUgfxy1D/Gv8a5XmdS4RBRPo3J/c/F0yZ14AVBGKXAxkGnvbE9qUmpuQKuIVODiWXDdvnj0nCr1q7Hk8Nd8d22LyWDA06ckto52TN81l3n7l9HkoxYAlPQrRf33GzO36zSmdxyPWq2mcZdmRvmdPV1QRurfu7Nnns69lw1nyoEltB/1EY4eLsTr1G28ibZ08nDWS5MQpcgN3EURPt82nUn7FtG4RyuTOjuZlOFiIk3ePcfryPh4Vn9+X7TdqK5qtK5DYrSSV/efGcl08XTVaz9FVBwuHvrtbu9sT1oR2q/lp224fvKa0e/upUvgU608D68/xNXTlbjIvAkMRVQcrp768hycHfT6S1ykfprKtSuz+tBq5mz5krIVy+b+LpPJ+ObgarZf38HTM7eJvJG39NvO05nkyLx+nxypxN6z6J98bzW7NycX7kRU69etvaczSTptpoxS4Fxo/4/L7WfOnq4oC8nf8IOmXNh7Ru+3On3bMODQQjouG4ylgw32b0G/Eyb0yw9bT2dSdOxISqQS2yLKS42K58Z3B+gb8jX9r64hKzmNF6fv6KUpbltmPPaUOBmMAycPF722SohS5D4UCTIZMw4sY/nVTdw/e4unN8IAqNk6kIRoJS8Nxp6Tgd037RsM/EeUIs83iDBi23Qm7ltEIwN70rRfOyYfXErPpcNy/Z6jQX0mRipwKMQXJRTBF0U9eklV7QRjzY4NcNKpZ2cD36DIxzfo2hZlEXyDLo0+aMK5vWdwNrSLkcbjyMlDf6zFRylxMrA9rqXdKVvVm/AbmhUHHuW9qFCvCtP+WMTE3V/iXdMX+Gf1mRQdz6nv9zPt/BpmXFpHRnIaoWduG+lWt1sLHmi3XRn2l0QT4yE/ea5lS5CiSKLH8uGM/2sR3RcPwUJnxU/Tfu2YeHAJn+YTJxWHrrrYe7iYiMWKbsveBGcPF5S68UukEmeDce/sYRzjOBv0m0LlmIiTDPunnYmxkOcn8s8vyGQsOPAV3177kdtnbpKelPpOxgLAhxN6sPT8ehp0bsofK3Zry3o3thOgRb/2zDy4nL5Lh2Oj0zc1cUSejnFRCqM4wt4gjlBExuGiTePi4UqDdg1zV//oMmjOELYs/CFfX+ji6UpchG4MozAZMxnGvLr3V79dA7459i3Tf5zFmonfmJQDpmNCpzeICbtpY0J1ESbTJP6dSBM//y2aATtFUVSJohgBHM8voSAIQwRBuCIIwpWNW3cW3x1qhBv9ZGijBJNp8hLJzeVUa12HmwfytkKc336URc3GsLLjFJJi4uk0o/ffuI9/aCzfkW4Ah5bvYX6jz7n25zka92tXpLLyTwOxjyM4vX4fA7ZPpd+WyUTdf4Zapf82reYHjbhpMAGVX3kGiQpPY0BE2Ev2rvsNVU4Ozx9oHpoMl93KzGT4VPdl+WcLWNJnLl1Gf4ynjxfVGtfEp4Yvc/cuZcGBr6jWqAYlynoYyTB1769v7Nsxq1jYfiIru83Gt25lKjSoaiJtUfTUJFrZdRZL3p/Ct/0X0bRvO3zrVTEur4D7KeieRVGkesvapCgSeXHnid41cysL2n/+Efu0QZqxyL8n0zBNtYY1aNm9NdsXbdH73crGignrJ/Pj3I2kp6QjmNhuadxf8hcXdieMAQ0/Y1T7UezfvI8Z3+dtfVGr1YzuMIr+9fvh5e+LW8XSOkW+eR98jW9Lf9IUSUTfeWp8sQh1U1C/MJUdvfFvRp3WdQn5K2/cBW8/xPpmX/BDh+mkxCTQamYvTFeaibJNUKB++VCkcZ8Plo42eLetzbZG49gSOAozG0sqfti4COW/Q1tWSDsUJA9AVKuZ33EiUxoOxbuWHyUrlsHcyoKOn3/EXlNj72/6BnTsybL3p7DOwJ6c3R7M3GajWdpxMokx8Xw4o0++8t7EfoHGF1VtXYdbOr7o50nf0ahPW0bvW4ClnbXe+Rnvyjfk3Y8ZdVrXI+Svc0Xy34WNNUsbK0asm8DuuZtzVzfI5XJsHexY2GUqvyzcxtC1X+RfWBHr09rBlmptAlncdDTz64/A3MaSgC5N9NK1HNkFtUrN9T/Oassq+N7zvSVRRC6XU7q6D+e2B/PVe1PJSs+k1fDOAJzbHsz8ZqNZ3nEKSTEJdDYVJ71jXYsk7h09uBbFzhTJxr8FOQWN94Lyi2o10zuOZ3SDwfj6++FS0q0IskzcZCFjAeD35TuZ1GgYIX+eoWW/9trCCi4r//sn9/4NbSfAqe1HmNFsFPM7TiQxJoGPZ/TNK8+kPy/aeAAYNGcwWxZtRm2wUiOwVV0S4hJ4fNv4vKCilJuXpuD7u3g4hNGtRrBk8EJ6jO9VgKy/3z+rt6xNsiKR5wYx4X8JUVQX23//V5HO+PnvUSTvJIriBmADQHZceLFODSdGKXEqmTcT7uTlmntQ8WsSohR6aV6/SXpN5Rb+vLzzhJS4vK0+un9f3HWcgZuMzxbQJSlKiaOODAcvF5IN7uNNeVe66XL9z3MM/GESR1b+olktZKCDoTxNmrw3Bg6eLiRr5V3dc5Kre04C0GZid5Ii8940yOQyqrWry9pO06nfpw11ewSRKYqE3wrFVSfQcPF0JT4m7y08QLIyCVsHW2RyGWqVGlcvV+Kj9dOY4uTuo5zcfRSA2XsWYGlrpXc9PlLBLWUymemZZKZn8uDSPcpW8QZB4MwvJ9izdIde+sB29flwzCcAbJz8rebtspfBvWvvKz5aiZNgTmZqBlf2nqN6y9p6e6SdvVxzDzZ8TUKUEmfd9vZ0JVFbt6/TpiiSuHX4Et61fHl86b5BfoVeflMy4qMUOJd0Ax5q0mhl1O7YgBqtA6kWFICZpQXWdtb0XzmKI+v/wK10CaYfXJZ7T3NOfE1iTDxZ2dmE3QrTaz9XTzeUBu2XpEzCxqD9lDp9tGzlcgxbMpKF/eaSkpB35ojcTM749VOIeRlDt9Hd6Tq6O6G3HuHm5a4vL1phJE+3v7h55aVJ1wlCr5y4wvD5I3BwdiApPin399SkVJ5fuI9Pi5rEPdKcM5AcpcTeK6/f23u5kFLIloPXlAqsiF/r2pRvUQu5pTnWTraMurmepFcKom6F46DTZpr+r1+ucf93I0Hbz5SRClwKyO/fojZP7oSTpDP+k+ISEdG8/by58wQf/zCeGztPGOmXXET9Smv189XqZ2lvzfurhrN/7Lp886REKrHTsSN2Xi5G27XyldekOskvYslQavrKk4NX8AyswKPf8w7XLC5b9hrDsevs5UKCwTiIj9K01etHAidP19x2fE16UhqPQu5Srbk/d0/fxLV0CWZqx56zpysz9i9lUZepRnbftG8w8B869iTJwJ6U09qTZJ1+cmHXcYZsmmyyPh1NyEs0vCcDX1SphT+vDHxR7OMINvZdBICbjyeB3Zoz9sAiskSRxwa+wTUf36BrW1yK6BsAArRjIzEuUccuanD2ciXBhO3UHWvOni657Sc3kzN8/QRC/jjDNZ2ztOKjFLn/fnIzDFEtYuti/4/q069JdZQvYkjV9v87hy5Trk7F3EmeOl2bUaVVABt6LsjNa9gXHL1cTPofU/JERBKjlDzXrqS4eeAirYZ/AOjHSRd2HWewiTjpXepqCtOxWEK+6f8JyiiF3kSJSz7j3rWkG6/XvejGCQXRqk97mvdoDSKEG/hZF0/j/lnQWFBGKgrNn5aUxv0Ld3ErU+KdjAVdLv55hjE/TGPvyj3vxHZGPHqhZ8vO7jrKpF/nM+PAMrJFNWG3QnHTid3cPF1RRhvHLYZ+93V9+tXwY8IaTV93cHGgTlAgqhwVFQMqUa9NfeoEBWJhaYGNvTVjVn3Bw2sPaPNpWwCN7JLugCZ+M2XXjGUb3x/AvUt38Sznhb2zPckmzmuLL0JMmGAiJkyIjiegYwNqtg6kukFMuHncaiT+O0grfv5bnAY+FQRBLgiCFxD0v74hU7y4+Rg3b09cSrsjN5fj36khd4Ov6qW5F3yNwI+aAlA2wI+M5DSSYxNyr/t/0Igb+/RXouieTVC9XV0iH70o8D5e3XyMq7cnztr7qNGpIQ8M7uP/im5u3p65f1dtXYeYxxEmdahpQocHwVcJ0MorE+BHZnJ6rjxbVwcAHEu6Uq19XW7qnOXj26Q6seERJEUpubgtmDUdpzG14ziuHLlI064tAPALqEhacqpRkAFw98Jt6nfU7DZs1jWIq8GXCqw7excHPLw1X9/x9PbCp6YfIfvO6aW5GnyJSvWqaM4OsLLA178iEWGvuHvuFvU6Nsw97NnW0Q7XUu5cOXyR6R3HM73jeJ7cfsy14Ms06NQEMwsz3MuUwNPHi8c3wpDJZdg5a86ukpnJqd6yNo8u3MHd2xNXbd3W7tSIW8FX9O7ndvAV6n2k2VLmHVCB9OQ0kmITsLC2zJ20srC2pHLTmkSY6I/Pbj6mhLdXrow6+ciob0LGn0t3Mr3hcGY2+ZwfRq3i4fk7bB63moiHL5gcOJiZTT5nZpPPSYhSMCdoDPPbjmdix3FcPhJC864a01ChkPZr0FGzKqN515a5hxO6lXRj4ndTWT1uFZFPIvTyDF86ildhL/hq2BImdhzH6A6juHA4hJZdWwJQKaASacmpRhMlALcv3KZJR82b4VYftyLkiEaek3vekv+KtSoiyASS4pNwcHHAVrsU3MLSgnJNqqMMy7ufyJvhOPt44ljGHZm5nCqdGhAWbLwtzRSnl+5hXYPRfNdkHPtGreXpmTusrjWMLR2nE3rkKtW6au7TL6Ai6clpJuvv3oU7uf2/adcgrmj7/9Wjl2mqrX9T+Rt90MRom5fu2RAV2wUS+/AlkTfDcdHRr+ob6Hdq6R6+bTCadU3GsXfUWp6dv1fgpA9AzM1wHL09sdfK8/ugAU+KKC/5lQKPAD/MrCwAKNW4GvGhr/TSFJcte83Tm2HasVcCubkZgZ0ac9Ng7N0MvkKDj5oD4KMz9uxcHLB20KxGNLe0oHLjmkQ9fkXEw+dMDBzE9CYjmd5kJPFRCua/P4mk2ASe33yMu45vqN2pEbcLsScZBdiT1/5N9wygmjp+76XWF72uz1qdGnLPhC+qreOL0ovgi17XsyAItPr8Q45+/RurOk5lcsdxXD5ykWZa31CQbbl34TYNtGOjuc7YKIzGHzTl/F7tGUg3w/Dw9sJN2371OjXmZvBlvfQ3gq/QULsduLy2/RK1+vVbMoLIsJcEb9I/FPv6kctUblgdAA8fL8zMzUhVJv+j+kyIiKNsQAXMtf3fr3H13IOSKzavRYthndg8aDnZGVm5Zb0w6C8BnRoZxRJ3g69SV9tfymnlJcUmkBybSEKEIvdrdhUaVydKO97y6y+6vCtd8+PVzXBcvD1xyo3FGvzjWCw/nhj0m/qdmnDdYBxeD75MY+249zXoNwVxbNshZnWcwPSO47l65BJNtGPBN6AiaQX4iXodNefQNO0axDVtH7529LLJ/PYuDtjo2J7qTWpy7+ytdzIWSujEnf6t6xL5WNOO78J2gn7f9G9Xj9BL95nfcSLjOowm5PAFgrRxRMWASqQmp+UbRzTWxhEtP27FxSMhAAxpMoghjQcypPFAzh84x3cz1nHxSAjblmxhYP3+DGk8kOWfL+XW+Vt8PXYFh7YeYHzHsYzvOJZLRy7SQuuzKwZUIi0f2Xcu3KahNmYK0omZPMt55aYpX708ZuZmJid9wDgmDDQRE94KvkID7bj3MYgJpzUczowmn7NJJyb8TyGd8SOt+PmP8TvQErgNPAJOvW0BE2cv5vL1WyQkJNGqS29GDOxD107t3qgMtUrN77M2M3jrVAS5jMt7ThId+pKGvVoDcGHHUe6fuE7lIH+mnFpFdnomuyd+l5vf3MqCik1q8Ou0jXrlvj+1JyWrlkMUIf5lLL8YXDd1H/tnbabf1imaz7nvOUlM6Cvq9tKcn3B5xzHs3B0Zvnc+lnbWiKJIowHt+abNJL3DlotDt46TP6VE+ZKo1SIJr2L5ZfqmXHn7Zm2m/9Ypms+ganWop9Xh0o5jPDxxg4pB/nxxaiXZ6Zn8piOv57qx2DjbocpRsXfmj2RoD+0FqNmpIbcMtnkBXD9+Ff+gOqw6vZ7M9Ey+m5C3X3nS5pl8P2kN8THx7Fy0lVFrxvPJhF48vRvOid3BADi6O7Fg33Ks7WwQ1SIdBnRiYutROJdw5svfFmsP+4ML+85wcf85WvbSvHU5vuMIEWGvuHXqOosOr0StFjm56ygvHz0H4OflO5m8bRaCTECVo2LzzO9RGByO+yr0BRf/OseSo9+g1qYR1WosrC2ZvG0WFmZmyOQyHpy7zdkdR1G8iGXk1mkIchkhe04SFfqSJtq2PLvjKHdPXKdaUACzT31NdnoW2ydqHp7t3RwZvEHzaW65XMaVP89x/5TxJ3PVKjW7Z/3A51unaz7xvOcEkaEvadqrDQBndgRz58R1qgXV5stT35CVnsW2id+a6npF5trxqwQEBbL69Hqy0jNZOyEvMJi6eSbrJ60lPkbJ9kVbGLdmAj0m9OLJ3XCOa9vv4zGfYudsz+B5QwFQqdRM6TSeyoFVaN41iGf3n7LswEoANi/dwpXjlwkMCuT7Mxs1n3OfsDJX3pzNc/hm8jcoo5X8uOhHJq+ZRO+JfQi/G86R3YcBaNKxMR36dESdoyIzI4ulny8FwKWEC+NWfIFMLkMmE3i2/wqPj9/ILVtUqTk6awvdtk5CkMu4vecUitBX+PfSBI83dhzH1t2RvvvmYWFnjahWEzigPZtaTyYrn/ENEH78BuWDarHy9Dpt/8+rv0mbZ7Bh0loSdPp/twk9eXb3Se5Kthva8WMqv4WVBdWb+rNx2no9mT2m9qVSVV8QRRJfxnFo2g+IKjVHZm2hu1a/W3tOEWdCv3775mnsl1a/jYXolx+iSs2ZmVvotF0j78HuU8Q/ekW13hp5d7cfx9rdkW5/5dVnzYHt2dlyMjE3HvP4wCW6HZyPWqUi7s4z7v50Qq/84rZlapWaXbM2MUY79s5px14z7dg7vSOYOyeuUSMogPmnVpOVnsWWiWsBcCzhRP+vPkcmkyHIBK7+dYHbxwueBFOr1Pwy6wdGbJ2GTMeeNNbak3M7jnJPa09mnfqarPQsdujYk0FaeyKTy7iqY086T+1FqareiKKI8mUsu6d9nyvvz1mbGbR1KjIdX9RAKy9kx1EeaH3R5FOryErP5GcDX1ShSQ1+M/BF/h80olEfjU2+c/gSV34+mXvt+vGrBATV4WutbVmn4xumbJ7Jd1rfsGPRVsasGU93rW84ruMbFun4ho4DOjG+9SjSU9KxsLKgRtNabJi2Lle/n2ZtZOzWGdr2O05E6Euaa/3FqR1HuH3iGjWCarPw1Bqy0jP5UWs7/QIr06hrc17ef8asA5rVWb8v/YnbJ69zds9xPls6gi8Paz4D/cP4Nf+4Pl/ceMztgxcZ89dC1DlqXt19ysWdmsNXu3zZHzMLcwZvnwZoDkrePX0japWaX2f9yFBtf7m45wRRoS9ppJV3XttfqgT5M/3U12SlZ7JrYp7d+HXOj/RZ9TlyczMUL2LYOUFzrdPUXpSsWg60/eVnE3HSu9I1P9QqNX/N2kzfrZORyWVc23OK2NBXBGrH/hVtLDY0NxZT02BAB9ZoY7GPvxmJT4Mq2DjbM/7Cak6s/IVre0yHv2qVmm2zNjJx60xkchmn9xznVegLgrT95sSOI9w8cY2aQbVZdmotmemZbNSOe4Dh34yjcoNq2Dnbs/LCBn5fuZvTe4z1u3H8KrWCavPV6W/J0n6O/TUTNk9n46RvSYiJZ9eibXy+5gu6TejJUwM/YSq/Uwlnhq4YpbU9Mi7uP8fNo1dQ56je+ljoOrk3nuVLIqpFFK9i2TZ9Q24dvgvb2XVqH8pobZniZSzbp+XZo6vHrxAYFMj6M9+TmZ7J6gmrcq/N3DyHtdo4YsuiH5mwZjK9JvYm/G44wbtNH9j8Jlw9foXaQXX49vR32s+559m16Ztn8e2kNcTHKNm2aDNfrJlIzwm9eXI3nKNau9awQ0Oad22JKjuHrMwsvhq5NF9Zmrr9gVHauj2fT0xYPag2c7Ux4dZ/GBNK/LsQ3tU+WYl/D8W51Wtq4PTCE71FCvqc+7sgo6iHbLwlLItZv3Cx6F8GeRvIi3nRopNgXqzy1MXcX2LFzGKVly7mFJ7oLdFMeDeHgebHDVILT/QWqUnBh9W/bRyK+YXWK3nxCowju1jlWRazLbMqZnnPxDefTPwn2AvF917TheL1C9nF7BcMP+f+rin2vklG4YneIqpifu6yEoq3PuXF3F+i1MVrW+TFWJ8eglXhid4i657uKd7GK2ayXt4utsFnUbrG/8m6lFb8SEhISEhISEhISEhISEhI/Dv5P3zocnEhnfEjISEhISEhISEhISEhISEh8S9FWvEjISEhISEhISEhISEhISHx70St+l/fwf8cacWPhISEhISEhISEhISEhISExL8UacWPRKEU54HLi64sKDZZAJdrTCxWeXVvLytWeaiK94DS7wLnFau8HjWNPzX7LrFuVr5Y5Qn+dYpV3trBIcUqb0rUicITvSX2LHizrwv+U9qviixWeRtHOxWrPMHNtVjl+U04WKzyFtgEFKu87GI+BrKhRUKxyhueWbwHsLrIrYtN1rfnZxebLIAzdeYXq7ymV6YWqzwxo3gPxl/aem3hid4iPe3jilWeS6Xi/WiD1QcNilXehpkvi1XeNaH4bNmKGaWLTdZ/AumMH2nFj4SEhISEhISEhISEhISEhMS/FWnFj4SEhISEhISEhISEhISExL8TtbTiR1rxIyEhISEhISEhISEhISEhIfEvRVrx8w8QBKEFMEEUxfcFQfgAqCqK4uL/7V29Gyo1r0XnWX2RyWVc3H2CE+v2GqXpPLsfVYL8yUrPYveEdby6+xT38l70XjM6N41rmRIcXvkLZ344SNuxXan/aUtSlEkAHFy6+43va8bCFZw+dwkXZyf+2L7+7yuog1OQPz5zB4BcRsxPx3i15ne9687t6lJ2Ug9QqxFVKp7M+pHkSw+wKOlKhW9GY17CCdQi0duDidz41z+6l3eh39mLV1n8zUZUahVd32vLoN4f611PTE5h5uJvePEqEksLC+ZNGU2F8uUA2PbzXn7dfwRRFPn4/bb0+aRzgbLKtqhJ0zl9EOQy7u08ybVv9+ldd/L1ovVXQ3Cv7k3Isp+5/t0Bze/lvWj37ee56RzLluDiV79wc9PhAuWZ16mH7ZBRIJORceQvMn7+Se+6WQ1/7GcuQB2tOX8l6/wZ0nduAcB2zGQs6jVEnRBP4sjPCpTzGplPdSxa9QSZjJybp8m5eMA4TZlKmjRyOWJaMpk7l2juJbAtZrWagSiijn1J1oFNoMopUN65e89Y+ttp1GqRDxtWZUCbQL3ryemZTN96hKj4ZHLUIn1bBtClQVWi4pOZsS0YRXIagiDQtVE1erXwL1Q/7+Y1aaltv9u7TnLJoP1cfL1ov3wIJap7c3bZz1zZkKf/4HMryUrNQFSpUatUbH9/VqHyAFaumEuH9i1JS09n4MBxXL9xxyjNhu+WU6dOLQQBQkOfMGDgWFJT06hUyZdN368kIKA6M2ctYcXK7wqUJfPWtp8gkHPrDDmX8mm/lj1AJkdMTyFzl7b96rTBrKa2/eJekXUw//YbPXckDVrWJzM9k0XjlvLoTqhRGq8ynsz+dgYOzvY8uh3K/NGLycnOwc7RjilfTaRUuZJkZWaxePwynjx8Shnf0sxZNzM3v7VfKbLP/k7OleC8e/+b/VNw8cTyg+G5aQQnd6OyTXEuPIalx+6gFkU+rFmWAQ0q6F3ffDGMA/dfAaBSizxRJHPi83Y4Wlvk/tZz62lK2Fmx+uP6Bcp6zdzFU2nZphnp6emMGzGdO7fuG6VZvWEJtfyrkZ2Tw42rt5k87ktycnL4sNt7jBgzEIC01DSmjp/HvTsPTcop1aIm9eb2QZDJCN15kttr9cdC+Q8bUX3E+wDkpGVwYepm4u89B6DxV4Mp3dqfjLgk/mxVtPNSSreoScMvNWPv4c6T3DSQ5+jrRfMVQ3Cr7s3lpT9z+7u8tq02sB2Ve7RAEAQe/HSCO4XYTQC7ZrXxmjUEZDLi9xwhbv0vetftW9fH44veiGoRVCoi531P2pV7ALgO6IzzJ21BhIxHT3k1cRVi1pudLzdGZ4wszGeMfNS/M90GdaW0Tyner/4hifFJhZY7+Msh1AkKJDM9k6/HryL8zmOjNCXKeDBxzSTsnOwJvxPGyrEryMnOG8t+NSuw9M/lLB+5lPMHzmFuac7Cn5dgbmGO3EyG3MEKVVKMXplnL11nydofUanVfNSxFYN6fKh3PTE5hVnLvuVFRBSWFhbMnTiCCj5lefLiFRPnrcxN9zIympH9u9On6/uF6voal6BaVJzfH0EuI2LHcZ6t/lPvulv7QMpP/gTUImKOikczt5B4yXS/z4+zl26w5Futfh1aMahHF2P9lq/jRUQ0lhbmzJ0wXKtfBBPn6+oXw8h+n9Cn63sFy7t6myUbfkKtVvNR22YM7KafPikllVmrfuBFVAyW5uZ8OWYAFbzzzkdRqdT0GPclJVydWTN7bIGyfJvXpN1szdi7vusk59fpjz1XXy8+WD4Uz2renFi+hxCt33PwcqHzyuHYuTsiqkWu/XScSz8WPvasGwfiNmUYglxO0q8HSdi0x2Q6y+oVKbVjFdETFpIafBYA93lfYNusPiplAi8+HFqoLAAz/3rYDPgcZHIyj/1F5u8GcVI1f+wmz0cVEwVA9sXTZPy8FcwtsJ/3NZibI8jlZF04RcbuzYXKO/c4mqXBtzR+oVY5BjSqpHd9c8gjDtzRnNOjUqs1fmHse/p+4ccTlLC3YvUnjQqVV655TZpr45a7u05yxSBucfb1os1yTdx5YdnPXNOJWywcbGi9dBCuFUuDKBI88XuiroXplfupHE7tPsZf6/SfDQB6zR5AraDaZKVn8f2E1Ty7+wSAGs396TVrADK5TC/viDVf4Fm+JAA2DrakJaUyq+MEbJ3sGLVuIlaBFVA9ukz2hd/y6vNJDEuP3dP62TIMqO+nX5+XHnPgXoSm7kQ1TxQpnBjZBitzOQN2XiBbpSZHLdK6ohcjmlQstD7/TYjSGT/SxI8pBEEQAEF8gx4iiuJewHg25F+AIBP4cO5nbOi9kMQoBWP2LuBe8FWiw17lpqncwh93H08WtxhH2QA/ui4YyDddZhIbHsnKjlNzy5l58VvuHL6cm+/0pgOc+l53cqTbG91bl45t6Nn1A6bNW/6PdMxFJqP8wsHc7T6XrEgFNQ8uQXnkMumP8g6PSzxzm5taHWyqlKPihvHcaDoaMUfF0y83k3r7CTJbK2odXkbC6Zt6ed+Ut62fSqVi/srv+H7FXDzdXek+ZDxBTerh6102N833236msp8P3yyYRvizlyxYuZ5Nq+YTGv6MX/cfYed3X2FuZsawiXNo1rAu5cqUNClLkAk0n9+PP3suJiVSySf75/Ik+CrxoRG5aTITUjk9exvl2+kfYpwQHsnu9tNzy+l/eTXhh64UrJxMhu3wsSTNGI86LhbHld+RHXIO1Ytnesly7t4i+Uvjh6/MowfJ2P8bdl9MK1hOroICFm36kLl7OWKyEqt+s1CF3UBU5OmHpTUWbfuQuWcFYrISbOw1We2cMKvTmoxN0yEnG4vOw5FXqY/qzrl8xanUahb9fJL1I7vg4WRHr+W7aV69PL5eLrlpdp+5RXlPF74Z2gllcjpdFmzjvcBKyGUyxn/YhCplSpCakUWPZbtpUKmsXl4j9WQCref34+dei0mOVNJ731weB19FodN+GQmpHJ+9Db92pg+h3tN9AenxKUWrT6BD+5ZU8POhctUm1K9Xm7VrFtGoSSejdOMnzCE5WVPu8qWzGTniM5YuW4tSmcDYcTPp3Ll94cIEAYs2vcnc85Wm/frMQvXYRPu17kPmLybar3ZrMn6coWm/TsORV66P6q5x+zVoWY/SPqXp2aQvVWtX4YtFYxjW6XOjdEOnD2bP979yfO8Jxi8ey3s9OvDn1n30GdWTsLthzBg0m7K+ZRi3cDTjuk/kxeOXDGyrCfRlMhkn7v+C6tE1A/3+Xv8UlVFkbJ6dW471iJX6ZZtApRZZdPQ26z9pgIe9Nb22nqG5nye+bva5afrX96O/Nkg9FRbF9ivhucE9wE9Xw/FxtSc1s2iTBi3bNMXHtxxN6nSgdmBNFn01i05tehil+/3n/YwaMhmAtRuX0bNvV7b+sJsXz17x8Xv9SUxMIqh1E5asnGMyvyATqL+gH0d6LCYtUsn7B+by/MhVEnXGQvKLWA59PJ+sxDRKBdWk0ZIB/NVpDgBhe05z/8dgmn5dtAczQSbQeH4/DvRcTGqkki5/zeXZkaskGNjO87O24W0w9pwrlaZyjxb88f5s1Nk5dNg+iefHb5D0JDp/gTIZJb8czpO+M8iJUlD+j5UkH71IZlje4fmp528SdvQiAJaVvSm7ejKhbYZj5uGKa79OhLYdgZiZRZnVk3Hs1IyEX48VSVfIGyM9tGNk/KIxDDUxRm5fvsv5oyF888uKIpVbJygQL++SDGs2hIoBlRi+YAQTO483Stdvan/2bvyTM/tOM3zhSFp3b8Oh7Qe1VSOj39T+XD91PTd9dmY2Mz+dRkZaBnIzOb882ImQkYyYpTn0VaVSseCbjWxYOgtPdxc+HTGFoIaB+HqXyS1j40+/UdnPm6/nTiL8+SsWfvM9G5fPwadMKX7ZsDy3nFbdh9KqSdEmQTU3LFBp8QCuf7KAzAgFdQ8vIu7wFVIf5cVr8advc0nrT+2qlqX6hrGENPmiyCJUKjULVm9iw5IZeLq78unIqQQ1CsS3XN5Ey8affqeyrzdffzlRo9/qTWxcNgufMiX55btlueW0+nQorZrUK1TewnXb2DB/Ah6uLvQYN5cW9f3xLVsqN833e/ZTqXwZVs0YxZMXkSxYt42NCyflXt+xNxifMl6kpmUUKEuQCbSf158dvRaRFKVk0N55PDp6jbjQvPpLT0jl0OytVDYYe2qVmuD5O4i68xQLWysG7Z9P+Nk7enmNkMlwnzGSiMFTyYmKo/Tu1aSeCCE7/LlROtdxA0k7d1Xv5+Q/jpD40148FhbxQyUyGTaDx5AydwJqRSz2S9aTffkc6pf6cVL2/dukLjKIk7KzSJ7zBWSkg1yO/fzVZF+7hCr0Xr7iVGqRRYdvsr5HYzwcrOn14wmaV/DC190hN03/BhXp30AzAXEqNJLtl8L0/cLlMI1fKMJksiATaDG/H7/30sSdn+6bS3jwVZQGccspE3EnQPM5fXh28hYHhn2DzFyOmbWlUblnoiOYs3cJ14MvExGWF9/XbFEbTx8vJrX4HN+ACvRbMIS5XaYiyGT0nTuYpb3nooxS6OX99vM8W/bp9H6kJ6dpqjozm1+/2smksf7InL306zP4Lus/qY+HvRW9tp2lua+Hvp+t50v/er6a+gyLZvvVJzhaWyCKIt93b4CNhRnZKjWf7bxAk/Lu1CzpXGi9Svx7kLZ6aREEwVsQhPuCIHwLXAM2CYJwRRCEu4IgfKmTrr0gCA8EQTgLfKTze39BENZo/94sCMLHOtdStP/3EgThtCAINwRBuCMIQtMC7idFEIQlgiBcFQThqCAI9QRBOCkIQrh2dRGCIMgFQVgmCMJlQRBuCYIwVPu7nSAIxwRBuCYIwm1BEDob6Pi9Vq8jgiAU+umLsv5+KJ5FoXwRgypbxY19F6jWVn+lQbW2dbjy2xkAnl8Pw8reBnt3J700FRpXR/EsmvhXb++LBoH+NXB0sC88YRGxC/Aj/WkUmc+jEbNziPvzLC7t6uqlUesEDjIbSxBFALJjEki9rZndV6dmkB76EgvP/B+si8Lb1u/2/VDKlvKiTElPzM3N6dCqKcfPXtRL8/jpCxrUqQVA+XKleRUVQ5wynvBnL6hZtRLWVpaYmckJ9K/GsTMX8pXl4e9L4tNokp7Hos5WEbo3hPJt9R1tuiKJmJvhqLNV+ZZTukk1kp7FkPxKUaBuZhWroIp4hToqEnJyyDx9HPMGTQqrklxy7t5CTE4ucnqZV3nEhBjExFhQq8i5fwl5Bf0vAZlVbYDq0TXNQzVAmk75MjmYWYAgQzCzQExJKFDenWfRlHF3orSbI+ZmctrVrsjJ2+F6aQQEUjOzEUWR9KwsHG2skMtkuDvaUqVMCQBsrSwo7+FMTGLBEzKe/r7EP40mUdt+D/aF4GvQfmmKJKJuhaPOyb/93oROndqxbYdmpcHFS9dwdHLE07OEUbrXkz4AVtZWiNoxGBur4MrVm2RnFx4gyrzKI8brtN+Di8j9/PXSmFVpgCr0auHtZ26BmJpgUk6Tdo05/MsRAO5du4+dox2uJYztQu3GAZz66xQAh34+QtN2jQHwrliOq2c1D53PH7/As7Qnzm76gVqdJgGoE2IQk/LGyD/un6/LKVfVqGxT3ImMp4yTLaWdbDGXy2hXpSQnw6LyTX/wfgTtq+Q9uEUnp3PmcQwf1Sybbx5D2nVsyS+7NO9brl25haOjPSU83IzSHQ8+k/v3jau38SrpAcCVSzdITNSsGrl2+Vbu74a4BfiS/DSaFO1YePJnCGUNHhpir4SSlagJ2mOvhWGjM6kaffEhWQlFnwB19/cl6Wk0yVp5j/8MoZzB2MtQJBF303jsOfmVJOb6Y1QZWYgqNZEhD/Bur++vDbGuVZHMZ5Fkv9D4vcT9p7Fvo/9lHj2/Z2312u0BIMjlyKwsQC5DsLYkJ1pZZF1BM0YOFWGMhN4NI+plARNYBtRrW58Tvx4H4NH1h9g62OJcwvghp2ajmpw7oFlBcfyXYzRo1zD32nufvc+Fg+dJVCTo5cnQ1ofczAzQ/yTb7QdhlC3lSZmSHho/G9SYE+cv66V5/Owl9QNqAFC+bCleRcUSp9SXcfH6bcqU9KCkh3uRdXao7Uf6k2gynsUgZquI/uM8bu31YxhVWt5XnjQxTJGL1+j3MIyyJV/rZ0aHFo04ca4I+sWb0s+zUP3uPAqnrFcJSnuWwNzcjPbN6nEi5LpemvDnEdSvVRUAnzJeRMTEoYhPBCAqTsnpyzf5qG2zQnUrqfV7CS80Y+/uvhAqtTH2e5G3wlEZxC0pMQlE3XkKQFZqBnFhEdh7FPxQbVmjEtnPI8h5GQU5OaQcPIlty4ZG6Rx7diYl+Cwqgz6ScfUO6sSixy1yv8qoo15pVj3n5JB99jgWdRsXOT8Z2i9ayc3AzIzCOs+dCCVlnG0p7az1C1VLczI0/y9eHrz7kvZV8yYQo5PSORMWzUf+3kW6PcO489E+03FntIm4xcLOmlL1KnF310kA1NkqspLSjMpVZedwcd9ZarfVH1e129bl3G8aH/74eig29rY4ujtR3t+P6GdRxL6IzjcvQL33GhGyV2OHstIzCb3ywGgV8Z3IBMo421DayUZTn5VLcjIsf5t48EEE7StrXs4KgoCNhWa9R45aJEelppg/Jvm/R60uvv/+jyJN/OhTCdgqimIAMF4UxUCgJtBcEISagiBYAd8DnYCmgOcblt8TOCyKoj9QC7hRQFpb4KQoinWAZGA+0Ab4EJirTTMQSBRFsS5QFxgsCIIPkAF8KIpibSAI+Eq7igmgArBWFMVqQALQtbCbdvRwJiEiL+hPiFTgaODMHD1c9NIkRilxNJj08O/UiBt7z+v91rhfO744uIRPlg7F2sG2sFt551h6upClMzGVFanEwtP4s8UuHerhf+YbqmybRtg440+BWpZ2x7aGDynXjJer/y+JiVPgWSLvwcjD3Y2YWP0Hukp+3hw9rZnQuX3vEZHRMUTHKvDzKcfVm3dJSEwiPSOTMyFXiYrJfxLP1tOZ5Ii8B4CUSCW2nm/+ZqHCBw159Gf+E0yvkbm6oY7LW26vjotF7mr8EGhWuRqOqzdh/+VS5GW93/h+XiPYOyMm5eknJisR7PT1E1w8wcoGyx6Tseo3G3k1zTJlMSWBnEuHsB6+HOvPVyFmpqN+erdAeTEJqXg62eX+28PJzmjy5tNmNXkSpaTNzB/4eNFOJnZtikym79pfKZJ48CqWGuUKNl/2JtqvsCBWD1Hk4+1T6P3XPGr2DCpSllIlPXn5Iu/N3KuXkZQqafo+N36/glcvblC5kh9r1v5Q9PvSItg55U14AGJyvHH7OXuClS2W3Sdh1WeWfvtdPoT10GVYj1iJmJmWb/u5eboRExGb++/YyFjcPPX7paOzAymJKahUaqM0YffCadZR846gin8lPEp74O6ln79l5yBU9/UncP9J/9TFrEp9o7JNEZOSgad93nsED3srYpJNv11Pz87h/JMYWlfMe5O57NhdxraogvAGkainVwkiXuVNLkVGROPpZXryBsDMzIyu3Ttx4thZo2uf9vmIE0fPmMgFNp7OpOqMhdRIJTYF2LIKn7bg1YlbRVHBJLZezqRE6siLUmLrVbSxF//wJV71K2HpZIfcyoIyLWthV9LYh+li7ulKdmReH82JjMPcwziPfduGVAheR7lNs3k1+WtN2mgFcRt/p+LZH6kcsg11chopZ68b5S0I9yKMkb+Dq6crcZF5PiouSoGrgT+3d3YgNSkVtXbsKSLjcNGmcfFwpUG7hrmrf3SRyWSsPPgNW69vR8xMyV3tAxATp8TTXdfPuhIdpz8ZVql8OY6e0Yyr2w9CiYyOJTpO3xcfPHGODi2L/vICwMrThQydWCwzQoGlib7q3qEuDc6uwH/7FO6NW/dGMmLilHiWyKtHD3dXohUG+vmW4+jZ1/qFafSL1U9z8MQ5OgQVPukQrYjHwz0vnvRwcyFGEa+XpqJPGY6d16yGuf0wnMgYBdHaNEs37OSLAZ8gEwp/5HHwdCEpMq/+kiKV2P+NuMWxtBue1crx6obx1kJdzEq4khOlM/ai4zArod/35SVcsW3ViKQ9/+zoAACZizvquDx5amUsgqvxxJtZparYf7URu+lLkJXx1ilAhv3yjTj98Ac5N6+gCjXeWqtLTHIGng66fsG6YL8QHk3rynkvBJYF32Jsy2pF9gt2JuIWuyLGLQ5l3UlXJtPmqyH0ODCfVksG5a74MSxXGanE2cBGOnu4oIjIszfKKAXOnq44e7ig1P3dRN5K9aqSFJdA9NP8J8UgHz+bkl99qjj/JJbWFfNiKJVa5JPNZ2i5NpgG3m7UkFb7/OeQJn70eSaKYoj2708EQbgGXAeqAVWBysATURRDRc0r5u1vWP5l4DNBEOYANURRLGiaPgs4pP37NnBKFMVs7d/e2t/bAn0FQbgBXARc0UzsCMBCQRBuAUeBUsDraPiJKIo3tH9f1SlLD0EQhmhXPF15lmE8myyKRulNpMlLJDeXU611HW4eyHuAOL/9KIuajWFlxykkxcTTaUZvU7dSvJjyLobKAsqDl7jRdDQPByzVnPejg8zGikqbJvJk1o+oUtKN8v4vEU3oYth2g3p9TFJyCl0HjGHHb/upXKE8crkcX+8yDOj5EYO/mMWwCbOp6OuDXC7PX5jJunyz+5WZy/FpU5uwvwp/8DQlz1CcKuwR8Z91J3HUQDL2/Yr9jAVvdkOFYjgw5Mg8vcn8ZSUZe77CvNEHCM4eYGmDvEIA6esnkb52HJhbIq9q/JZPv+TC2+78/edUKu1O8LwB7J78KYt/Pk1Kelbu9bTMLCZsOsDEj5pip7OU2iQmx3TBWXT5qetctr03g9/6LsO/b2tK16tUaJ7C7IgugwZ/QZlytbn/IJRPun1Q9BvLk2biNwNZMhkyj3Jk/raKjF9WYN6wU177+QWQvmEy6eu+0LZfAxPl5WdSjAyoqUQA7FizE3tHOzYd+Y6PBnxI6J1QVKq8N5Vm5mY0btuInAeXjcsoTL/8+meu/nLkfv5FKttUM+UXrJ8Oi8a/lEvucv7TYdE421hQ1dOpCDroll/0/gKwcPlMLp6/yqUL+tvWGjWpR4/eH7FwTj5biN7Alnk2qkKFHs25unBXvvdROH/fdiaERXDz2/103DmFDtsnobz3/O+tyDNRj8lHLhDaZjjPh87H4wuNv5Y52GLfuj6Pmg/kQcO+yKwtcezc4o1EFWmM/A0EE/VoWG5BsgfNGcyWRZtRm3h7q1arGddhNAPr90cwt0Yws8zLXwRbPbDHhySlpPLxkAn89PtBKlfwwUzHn2ZnZ3Py/BXaNivYL5gQVKRksQcvE9LkC271X47v5O5vJMJkHGFQ1wM/7aLRb+hEfvrjIJX9fDCT5z1yZGfncPLCVdo2N203C8OoPru9R1JqGt1GzWLn/qNU9i2LXCbj1KUbuDjZU9XP+2/JgTfvi+Y2lnRbP5Yjc7eRVVgMWISY023yMBQrN72dVQQm3Z6+vJzwRyQO+5Tk8YPIOPgbdpPn511Uq0meMIjEId2QV6iCrIxPgeJM1Vx+PfR0aBT+pV3z/EJoJM62llQt4qS3pvC/H7fIzOSUqO7NrW3H2NlxBtnpmQSO6FRAuUXz40XxUw0+aJK72qcg3qg+H0fjX8pZb9ucXCawp39TDg9rxZ3IBMJii75a7F+BqC6+//6PIp3xo08qgHbVzASgriiK8YIgbAastGmKYkJy0E6qaVfaWACIonhaEIRmwHvANkEQlomiuDWfMrLFPMugBjK1ZagFQXjdbgIwShRFvdPjBEHoD7gDdURRzBYE4anO/WfqJFUBJrd6iaK4AdgAsPqjWaKTzhtDJy9XkmL037YkRCnQTePo6UJSdF6ayi38eXnnCSlxibm/6f59cddxBm6aBLzZgZBvm8xIBRal8t62WHi5kFXAsvWkkHtYeXtg5mJPjjIZwUxOpU0Tif3tDMoDRZisKGY83N30VulEx8bh7qa/MsvO1ob5U8cAGufUrvtgSmvfond9vy1d328LwKoNW/XeahqSGqnEvmRe2XZeLqRGx+eb3hTlgmoRe+cp6XGFH+KpjotF5pa3LUjm5o5aob8iSUxPy/07+8pFGCFHcHBETErkTRGT4xEc8vQT7F2MtmuJyUrU6cmQnQXZWahfPkRWQnPOg5gYC+kap6t6dBVZKT9U9wrYOudkR5TOdpHohBTcDVbJ/XnxHgPa1EEQBMq6O1HK1YEnMUpqlPMkW6Vi/KaDdAysRKtafobFG5Fsov1SYorefqnRCYBmWXzY4at4+vvy0sQBosOH9WPgwF4AXLlyg9I6Z0aVKu1FRGT+y5jVajU//7yX8V8MZ8tW0wdi5oeYEo9gr9t+zibaLx51ekpe+714hMz9dfvF5bVf6DVkJf1Q3dO8NzALaMmmI5oHmgc3HlKiZN4bVXcvdxTR+m/2E5WJ2DnaIZfLUKnUuHu5E6dNk5aSxuIvluWm3R2yg8jneatcGgTVI/R2KNXS9MfIP+mfqnhNncvL10Qd/QzSCh9/HvZWRCXnPeREJ2fgbmdlMu2hBxG0r5LXzjdeKTkVFs3Z8KNkqdSkZmYzbf81Fr5f2yhvv0E96NVXs5P6xrU7lCyV9zbTq6QH0VExRnkAxk0ajqubM4P6zNH7vUq1iiz75kv6dBtGfLxpO5AWqcRWZyzYermQZsKWOVcpQ6NlgzjaZxmZb3C2lSGpkUrsdLaK2Xq6kBpV9LH3cNcpHu7SbDkInPwJqZH5+zCA7CgF5l55fdTMy43smPzzpF2+i0VZT+TODtg2qEH2y2hU2o80JB2+gE2dKiT+ebJAmR/260ynXh2Boo2RovK6XDkywm6F4qazOs7N0xWlgT9PUiZh62CLTC5DrVLj6uVGvDaNXw0/JqzRnBPj4OJAnaBAVDkqLh4Jyc2fmpSKOjMVwcoeMUUTXnm4uRIVq+tnFZRw1X94tbO1Yf6kkYDGz7bvNYJSOttaz1y6TpUKPri5OL2R/hmRCqx0YjHLkq5kFtB3EkLuY+3tgbmLPdnKoj0Eeri7EhWT1z756jdxBKDVr/fnpvVzdipcnquz3mqh6Dgl7gb1YmdjzbyxA3PldRg4kVKe7hw6fZGTF29w9sotMrOySU3PYOry71g0wfR5W0lRShy88urPwcuFFK0vKwoyMznd1o/l9h/neFDYuYRoV/h46ow9DzdyDFZhW1ariMcyzXk7cmdHbJvWQ1SpSDte+EpoQ9SKWGRuefJkLu6ISoOV2zpxUs61izB4HIK9I2Jynn0U01LIuXMD84B6ZL54kq88D3sropJ0/UI67vb5+IV7L2lfLW+b142XSk6FRnL2cTRZOSpSM3OY9ucVFnbOf+tqiqm4s4hxS0qkkpRIJdHaVVphBy4ROLyTyXJdvFxIMLCR8VEKXEu68Xqdv4unK/HRSuQWZriUdMs3r0wuo067+szuVPg5TR52b+Bn7+dt8zLEwcqcwDKunHsSg5/72ztOQuL/PtKKH9M4oJkEShQEwQPooP39AeAjCIKv9t/Gp0BqeAq83lTaGTAHEAShHBAjiuL3wCbAOKp9Mw4DwwVBeF1+RUEQbAFHrZxsQRCCgHL/RMiLm49x8/bEpbQ7cnM5/p0acjdY/4C5e8HXCPxIsx2hbIAfGclpJMcm5F73/6ARN/bpb/PSPQOoeru6RD56wf+alBthWPt4YVmmBIK5GW6dm6A8rO+8rbzzHjRsa/ggmJuRow2YfFeMID30JZHf6X9F4P8K1StX4PnLCF5GRJGdnc3BY2cIaqx/cGRSckruGSm/7j9CnVrVsLO1AUCh3aMfGR3LsdMX6NA6/z3z0TfDcfT2xL6MOzJzORU+aMCT4IIPiDWkQueGhBZhmxdAzqMHyEuVRubhCWZmWDZrSfZF/cN2Bec8x21WsTIIsr816QOgjnyC4FwCwdENZHLMqtRDFaa/xUEVdh1Z6YogyMDMAplXedSKSMQkJbKSvpozYgB5uar6h+6aoFpZD57HJvBKkUh2jorD1x7RvIb+2zYvZ3suPtQcNqhISuNpTDylXR0RRZEvfzqGj4czfVoGmCreiKib4Tj7eOKobb/KnRrwuIjtZ25tibmtVe7f5ZpWJ+6h6UPO163fQmDdtgTWbcvevYfp00vzUF+/Xm2SEpOIMvEg7+vrnfv3+++14eHDsCLdly6a9vPIa7/K9VGF3dBLo2m/Cjrt54NaGYmYrERWsnxe+5WtgqjIW6Kdc/04A9sOZWDboZw5fI52H2smS6vWrkJqUioKEw/V18/foPl7zQFo360tZ49o7KWdgy1m5pq5/vd7duTmxVukpeQF5q26tOToH8fz0e/v9c/XyKvWJ6cI27wAqnk58Tw+lVcJaWSr1By+H0FzP+NtesmZ2Vx9oSBI59ro5lU4MqINB4e1ZnGn2tQt62Zy0gdgy8adtG3WlbbNunL4wDE+/lSz2qt2YE2SklKIiTbeftqjT1datGrMyEET9d60liztxfdbv2bMsKmEP35mlO81cTfCcfDxxE47Fnw6N+DFEf2xYFvSlaDvx3JmzHqSwvM/26goxN7UyHttO307N+D5G9hOK1eH3Hvy6RDI4z/PF5g+/dYjLL1LYl7aA8HcDMf3m5F8VL/dLcrlbcuzquaLYG6OKj6J7IhYrP0rIVhpVrzYNqqldyh0fvy+5U8GtB3KAO0Yaa8zRlLyGSNF4XW54zqMJuTwBYK6tgSgYkAlUpPTiDfxEHj7wm0ad9RsqWr5cavciZ0hTQYxpPFAhjQeyPkD5/huxjouHgnBwcUBW+2ku4WlBTIrO8ScvHdq1Sv78exVJC8jozV+9sQ5WjTSP9MjKSU1z88eOEqdmlVy/SzAweNn33ibF0Dy9cfYlPfEqqw7grkcjy6NiDOIYay981b12WtjmKJO+gBUr+Sr1S+G7OwcDp48T4tG+g/jGv1ytPodo04NA/2KuM0LoFpFH55FxPAyKpbs7BwOnb5Ei/r6fiwpJS1P3uHT1K5WCTsba8b078bRLSs49MNylk4aTr2aVfKd9AGIuBmOi48nTtqxV61TAx4ZxLsF0WnpYOLCXnFxo/H2QFNk3nmIedlSmJXyADMz7Dq0IPVEiF6a5+378byd5r+UI2eInb/6b036AKjCHiLzKo2shCZOMm/Skqwr+vZBcMqLk+R+lREEATE5EcHBEcFGu93cwgKzmnVQvzI4hNqAaiWdeR6fwquEVI1fuPeS5hW8jNIlZ2Rz9XkcQTrXRgdV48ioDhwc2Y7FXepS19utwEkf0MSdTj6eOGjbr2KnBoQX0XamxSaSHKnEqbzmHso0roZSezC3brlyczPqd2rC9WD9cXU9+DKNP9L4cN+ACqQnp5EYm8CTm2F4eHvhVrqEybzVmtQkMvwV8VGF27xqXo76fvZBBM39jLc3J2dmc/WlkiCda8q0TJIyNDYnI1vFxWdx+LjaGeX9V6NWFd9//0eRVvyYQBTFm4IgXAfuAuHAOe3vGYIgDAH+EgQhDjgLVDdRxPfAn4IgXAKOoV1JBLQAJgqCkA2kAH3/4a1uRLNV65p2ZVEs0AXYAewTBOEKmnOEHvwTIWqVmt9nbWbw1qkIchmX95wkOvQlDXu1BuDCjqPcP3GdykH+TDm1iuz0THZPzPuMsrmVBRWb1ODXaRv1yn1/ak9KVi2HKEL8y1h+mbaR6geL+GUCLRNnL+by9VskJCTRqktvRgzsQ9dO7f6+sio14dM2UnXnTAS5jOhdx0l/9AKPvpqgNHrrEVzfa4B7txaI2TmoM7J4NEyzPcC+XmVKdGtB6r1n1ArWfJXj2aKfSDj+ZpMd71I/MzM508YOZeiEOajUaj7s2Bo/n7Ls/lMTpHTv3IHwZy+ZtmAlcrmM8uXKMHfK6Nz842YuJiExGTMzOdPHDcPRPn+nIarUnJ65hc7bJ2k+5777FMpHr6jWWxOI391+HBt3Rz75ax4WdtaIajW1BrZnR8vJZKekY2ZlQdmm1Tk5pYjnt6hVpK5bhcO85SCTkRl8ANXzp1h20DwYZh7ci2Xj5lh27AwqFWJWJilLc89tx27SLMxr+CM4OOK05WfSd/xI5hHjz1/nKagmK3gHlp+MB0FGzu0ziHERmPm3ACDnxklERSSqJ7exGjAXRJGcW6cR414hAqqHV7DqPwfUKtTRz8m5eapA9czkMqZ83Jzh3+5FrVbTuUFV/Lxc+fnsbQC6NanB4PZ1mbX9KB8v+gkRkbEfNMLZzprrjyPYf/khFUq68smSnQCMer8hTat556+eSs2xmVvoum0SMrmM27tPoXj0ilra9rupbb8++/Par87A9vzYajLWLnZ03jAW0LwBvf/HeZ6eKvzMkwMHj9G+fUse3j9HWno6gwblfWlm359bGTJsIlFRMfy4aRX2DnYIgsCtW/cY+bnmbaiHhzsXLxzEwcEOtVrN6FGDqVGrhd5h0Hrtd3Q7lh9/ofnc+e2ziIoIzGpp2+/mSURlJKond7DqPxdEtbaNte336ApWfWdr2i/mOTm3TLdfyLGLNGxZn53ntpGZnsEindU7S7cuZMnEr1BEK1i/4HvmfDuDQZM+I/RuGH/t1IzJchXKMf3ryahUap49esbiCXlf+LO0siSwWR2WT17JmM8MzmT5B/0TADML5N7VyDq0pdB2AzCTyZjSujrDfw5BLYp0rlEGPzd7fr7+FIBuAd4AHH8URUNvd6wt/nn4cezIaVq2aca5awdJT8/gi5Ezcq9t3bOOiaNnER0Vy+IVs3j5IoK9RzSfLT6w7yirlq1j3MRhOLs4snD5TE2d5OTQsaXxthdRpSZkxhba/DQJQSYjbPcpEh69olIfzVh4uO04tcZ9iKWzHQ0X9gdAnaNif8dZADRbOxLPhlWwcrGj25VvuLH8V0J35T/eRZWa8zO30GGHRt7D3aeIf/SKKtqxd3/7cazdHelyIG/sVR/Unl+CNLazzYYxWDrboc7J4dz0LbmHTueLSk3EnPV4b5mLIJMR/3MwmaHPce6peecV/9NBHNo3wunDlog5KsSMLF6MXgJA+s1HJB06h9++VYg5ajLuPSZ+16GCpBlx4dhFGrSsz65z28goYIx0HfAhPUd0x8Xdhc1Hvyfk+CWWTPwq33KvHr9CYFAg6898T2Z6JqsnrMq9NnPzHNZO/gZltJIti35kwprJ9JrYm/C74QTvPlLg/TqXcGHsinHI5DIEmQx1RgpiRt7EiZlczrRRgxg2eb7Gz3ZoiZ93Gfbs0yzO/qRTO8KfvWT6ktXIZDJ8y5XmywkjcvOnZ2Ry4eotZo0r2lfgdBFVah5O/YGAXdNALiNy50lSH76kVF9NvPZq61FKvF8fz27NEHNUqDOyuDNkVcGFGqDRbwDDpizQ6Nc+SKvfEa1+bQl//orpS9bk6Td+mLF+Y4cUXd6wXgyf9RUqtZoubZriV64Uew6c0MjrGMSTFxFMX/E9MrkM3zIl+XLMgDfS6TWiSs2hWZvpuXUyglzGzT2niA19Re1erQC4tuMYtu6ODNo3H0vt2Ks/oAPrWk/Co3IZanZtSvT95ww+sBCAE8t2E3biZv4CVWriFq7F67uFCHIZSb8fIfvxMxw+0XyuvrBzfUosnYJ13ZrInRwpd3Q7ym+3kfxbAZ+QV6tI2/g1djOXgUxG1vGDqF88xaKtJk7KOrIXi4bNsWz3AaJKBVlZpKzUHCsqc3bF5vOpmkPcBRlZ50+QfbXgCSgzmYwpbWsxfNc51GroXKscfu4O/HxNs0qoW23Ny6vjjyJo6FPiH/sFUaXm5MwtdNmmH3fW0NrO29q45VNt3IJajf/A9mxvNZmslHROztpC+2+GIzc3I/F5DMETNhiV+55c4PSe47wKfUFQL82zwYkdR7h54ho1g2qz7NRaMtMz2ThxrbbK1WybtZGJW2cik8ty876mfifT27yWn12HuZsdyOXIvauTefA7zBKiNX72l0uo1SKda5TW+NkbmhcX3fw17/mPh0bR0NtNrz7jUjKZefAmarWIGpG2lUrSzDf/M/Ek/p0Ib2MftcS/mwnePYqtkyy68rbPXCmYyzXebKLpn1L39rLCE71NVMW7de67wHnFKq9HzeJdJWbdrHyxyhP8TX8m/V2xdnBI4YneIlOiThSbrKQF/2BC+G/QflX+K0jeBYdGF/1rWG8Dwa3gA4PfNn4TivYG/W2xwKZoK+PeFtnF/HmVhhYJxSpveGbxnnfnIi/0g6VvjZ/PLyo2WQBn6swvPNFbpOmVacUqT8xILTzRW2Rpa+MPdLxLetq/vS/bFgWXSpmFJ3qLWH3w985t+rtsmGl6NfG74pqs+GzZ+hn/aMPGG2M9aMW/+kNfmQ9OFdvzrGXl5v8n61Ja8SMhISEhISEhISEhISEhIfHv5P/wocvFhTTx8z9GEISLgKXBz31EUbz9v7gfCQkJCQkJCQkJCQkJCQmJfw/SxM//GFEU6xeeSkJCQkJCQkJCQkJCQkJC4o1RSyt+pK96SUhISEhISEhISEhISEhISPxLkQ53liiUGd49i62TdMws3kPoivuw5as1JxSrPJW6eOd2V1kW72HSn2QV76coXdXFq59I8Z4N95d18cq7pTL+tPK74oss52KTBeDjkFSs8p4lORSrvEyheG3Lb1bF6xua5BTf4cAA8fJiFYdlMYd+xXkgKoBjMS5o76/KKDZZANX+GlF4orfIvfe/LVZ5OaritS27za2KVV7r9OIdfBYU7yqH4o5bTlkX7+YV82JsvuI+CNz3zuH/kwcSvy0y7wQX3+HO1dv8n6xLacWPhISEhISEhISEhISEhISExL8U6YwfCQkJCQkJCQkJCQkJCQmJfyfSGT//zRU/giA4CYLwt9bKCoLgLwhCx7d9TxISEhISEhISEhISEhISEhJvm//qih8nYATwdzYu+wOBwIGiZhAEQUBzntK/bqqxQvOadJzVF5lcxtXdJzi9bp/edTffkny0bCglq3kTvHwP577/641lOAX54zN3AMhlxPx0jFdrfte77tyuLmUn9QC1GlGl4smsH0m+9ACLkq5U+GY05iWcQC0SvT2YyI1vLl+XGQtXcPrcJVycnfhj+/p/VNZrHFsE4D1vAIJMRszOo0SY0K/0xB4giog5Kp7N/oHkSw8QLM2p9tt8BAtzBDMZyr8u8HL57kLlOQX5U37eZyCXEb3jGK/W/KF33aVdXcpO/hRRrQaVmvCZefVZcfUozN2dQBSJ2hZM5EbTw+CzOYOpHVSHzPRM1k74mid3wo3SlChTgrGrJ2LnZMeTO+GsHreSnOwcmnRpTpdhHwGQkZbB99PX8ez+U8wtzZm7ZyFmFubIzeSEHDgPS/T7m0dQTQLm9kGQywj/6SQP1+hfL/tRIyqN7ARATmoG16b8SOK953kJZAKtD80nPSqec32XF1qXevUWVIsK8z9DkMuI3HGMZ6v/1Lvu1j6Q8pO7I6o17Rg6czOJlx6+sYyK8/sjyGVE7DhuJMOjaxPKff4BAKrUDB5O2kTKvWcAlBncgZK9WwEQseM4LzaYbrvKzWvRZVY/ZHIZIbuPc3zdXqM0H87uR5WgALLSM9k5YR2v7j7FvbwXfdeMyU3jWqYEh1b+zOkfDlKrY33ajf2YEn6lWNV5Bi9v6/eHkV8Op17LemSmZ7D0i68IuxNmJNOzjAfT107D3smesDthLB6zlJzsHD4Z+jEtP2wJgNxMTlm/Mnzs353khGRsHWwZv3Qc3pW8cVHLuDVuPQlXQnPLdAuqRdX5/RDkMl7sOE74an1dS3ZtTPnc+szkzqSNJGv7i/fQjpTpGQRA8v3n3BqzHnVmwWdA2Tatg8eMoQhyGQl7DqPY8LPedbtWDXAf2wdENWKOmugF35F+9R4Azv064/RJOwRBIH7PIeI3/2lKhB5uQbWoMr8fyGW83HGcJwb6eenol5OayT0d/coN7UhprX4p959zuwj6lQiqSY15fUEu4/mOE4QajL/SHzXG73PN+FOlZnBz8g8kaeWZOdgQsGIw9pXKgChyfdwG4q+G5pbZwEzg1O5j/LXudyO5vWYPoFZQbbLSs/h+wmqe3X0CQI3m/vSaNQCZXGaUt3W/DrTu2wG1Ss2N41fZs3gbAGUql6P/wqG42toiiiJ/vDcLVWY2pVvUpOGXGtvycOdJbq7V183R14vmK4bgVt2by0t/5vZ3eeOr2sB2VO7RAkEQePDTCe5sOlxgPQL4NK9Jq9kaebd2neSigW918fWiw/IheFTz5szyn7lsMJ4FmUDf/fNIiYrn1wFfFSqvXPOaNJ+jkXd310mufKsvz9nXizbLh+Be3ZsLy37mmo48CwcbWi8dhGvF0iCKBE/8nqhrYXrlfirnrbVfmSrl6L9gKJY2VsS9jGX92FVkpKRj62THqHUT8anpy6VfTnH3+DU+mtUfmVzGhd3HObrOeMx0nd2fqlpbtmPCOl5qZc8+u5rMlAzUajXqHBXLP5iml6/l4PfpMr0PUwMGQZzxGT8OLQIoPWcwyGUodgYT/e2vetcd29aj5IReiGo1okrNyzkbSb18H4Bq5zegTk1HVGmuPXxvfMGNB5y9fo8lP/6GWq3mo1YNGfhhG73rSSlpzPr2J15Ex2FpbsaXI3pSoWxJAGZ9u4NTV+/i4mjP7yumFioLwL65Rj9BLkOxy4R+berhpdUPlZqXX+bpJ3ewpczSz7GuWBZEkWcTV5N2rWBf6NAigLJzByLIZMTuPErU2t/0rju1rUcpnTjp+ewfSNHKA0Amo+rBZWRHKQntt8Co/ErNa9FZG8te3H2CEyb8XufZ/agS5E9Weha7dfxe7zWjc9O4linB4ZW/cOaHg5SsWo6uCwZiZmmOOkfNbzN/4MXNx0blFred1qU44hZDeQXFMBp5n4BW3qOZW95Ynl/zmnScpbFl13af5IzRc4kXHy4bilc1b44t38O57/NsWZelg6nYMoBURRJr200pkjzf5jVpp7XV13ed5LyBPFdfLz5YPhTPat6cWL6HEK3tdPByofPK4di5OyKqRa79dJxLPxbuG6wbB+I2ZRiCXE7SrwdJ2LTHZDrL6hUptWMV0RMWkhp8FrmnOx4LJyJ3cwa1SNIvB0jc/keRdPy3IIqq//Ut/M/5r078LAZ8BUG4AQQDMcAngCXwuyiKswVB+BAYCbQBPIFTQGtgLmAtCEITYBFQBUgRRXE5gCAId4D3tXIOAieAhkAXQRA+MZRj6uYEQfAGDgFngQbATeBH4EugBNBLFMVLgiDYAquBGmjaco4oin9q828DbLVFfi6K4nlBEFoAc4A4oDpwFegt/s0TvgWZQKe5n/Fj70UkRSkYtnc+94OvERv2KjdNekIKf83ZQpW2gX9HBMhklF84mLvd55IVqaDmwSUoj1wm/dHL3CSJZ25z8/BlAGyqlKPihvHcaDoaMUfF0y83k3r7CTJbK2odXkbC6Zt6ed+ULh3b0LPrB0yb92aTAvkik+GzcDD3P/2SrEgF1Q8sJf7wZdJD9fWL19GvwnfjudlsNGJmNve6zUadloFgJqfaHwtIOH6dlGuPCpRXftEg7n4yl6xIJbUOLUZ55IpenSScuY1SR16lDV9wvekYxBwVT+ZsIfX2E+S2VtQ6spSE07eM6jMgqA5ePl6Maj6MCgEVGTx/ONO6TDS6lV5T+rF/017O7zvD4AXDadm9NUe2HyLmRTSzP5lGalIq/i1qM3TRSKZ1mUh2ZjZf9phJRloGcjM5835ZzPPg+yi1DxjIBGov7M/p7otIi1TS+uA8Io5cI/lRXn9MfR7LyY/mkZ2YhmfLWtRZNpDj7+UNwwqD25McGoGZ/Rse7CoTqLR4INc/mU9mhILAw4uIPXyFNB3Z8advc+nQFQBsq5al+oZxXGwy7g1lDOD6JwvIjFBQ9/Ai4g5fIVVHRsazGK51+ZKcxFRcW/pT+avBXOkwA9vKZSjZuxWX209DzMrBf9c04oKvkf4kSk+EIBP4aO4A1vdeQGKUgnF7F3I3+CrROmO6Sgt/3Hy8WNhiLOUC/Ph4wSC+7jKD2PBIvuo4Jbec2RfXcVvbjyIfvuDHYSvotnCwkVr1gupSyqcU/Zp+RpWAyoxZOIpRH4wxSjd46iB+3fgbJ/eeYszC0XT4tD37tu1nz3e/sOe7XwBo0Lo+XQd9RHJCMgAj5wzn8skrzB02n/GiG3JrS736rLZ4AJc+WUBGhILGhxcSc/gqKTr1mfYslpAuc8lJTMW9pT81vhrC+Q4zsPR0xntQe043HY86I5uADWPw6tKIV7tPFdB+MjznjOB5/+lkR8Xh8+sqko+HkBX2IjdJ6oUbpBwLAcCykjelvp5KePuhWFYoh9Mn7XjadRxidjZlN80j5cRlsp9FFCBPoOriAVzW6tdQq59uf0l/FstFrX5uLf2p9tUQQrT6lRvUnrNa/WoVST+Bmos+4/wni0iPVND80HyijMZfDOc+nEd2YiolWtbCf/kgTnecBUCN+X2JPn6Ty4O+RjCXa9pKp8yd8ZHM2buE68GXiQjLszk1W9TG08eLSS0+xzegAv0WDGFul6kIMhl95w5mae+5KKMUenkrN6xO7Tb1mNHhC3KycrB31RyMLZPLGLpyDN998TXlbsdg6WSHOjsHQSbQeH4/DvRcTGqkki5/zeXZkaskhObVf2ZCKudnbcO7XR29anGuVJrKPVrwx/uzUWfn0GH7JJ4fv0HSk+h8q1KQCbSe1489vRaTHKWk7965hB29ikJHXkZCKsdmb6OCgbzX1BnQHkVYBJZ2hdsyQSbQYn4/fu+1mJRIJZ/um0t48FWUBvJOzd5GeRPyms/pw7OTtzgw7Btk5nLMtONMt9wz0RFvrf0GLB7BroVbeHjxHk27taTjkM78tmIX2ZnZ/PrVTkpXKkv5SuXoNncAa3svICFKwYS9i7gTfIUoHVtWtYU/7j6ezGsxBu+ACnyyYCAruszIvb66x1xS45ON9HXycqVS05ooX8aarlCZjDLzhxLaczbZkQoq7V9OYvAlMkLzxnry2VvcP3IJAOvK5fBZN4l7QSNzrz/6ZAYqE7JNoVKpWbjpZzbMHImHixM9pi6nRWB1fMt45ab5/rcjVPIpxapJg3jyKpoFG39m4+zPAfigRX0+bd+M6Wu2F0nea/3Cemn122dCv3O3SAzW6GdVuRw+307ifkuNfqXmDCL55DWeDluCYG6GTNcu5yOv3IIhPOoxh6xIBVUPLCXhyCUydOKkpLO3SHhdn1XK4bt+Aneaj8q97jHofTJCXyK3tzEqXpAJfDj3Mzb0XkhilIIxexdwz8DvVdb2lcUtxlE2wI+uCwbyTZeZxIZHsrLj1NxyZl78ljtav/felJ4Ef/0rD07epHILf96f2pN1n84z0K2Y7bSB7HcetxjJKziG0ZVnV7Us1TeMJaTJF0UWIcgE3p/bny29F5EUpWTo3nk8MHouSeWvOVup0tbYll3/5QwXtwTz0YphRZbXfl5/dvTSyBu0dx6Pjl4jLlRf3qHZW6lsYDvVKjXB83cQdecpFrZWDNo/n/Czd/TyGiGT4T5jJBGDp5ITFUfp3atJPRFCdvhzo3Su4waSdu5q3m85KuKWbSDrfhiCjTWl96wh7fw147wS/2r+k1u9gCnAY1EU/dFM/FQA6qFZzVNHEIRmoij+DkShmfz5HpgtiuJzYBawWxRFf1EUC1teUQnYKopigPZvIzkF5PUDvgZqApWBnkATYALw+tXTdOC4KIp1gSBgmXYyKAZoI4pibaA78I1OuQHAWKAqUB5oXIgO+VLa3w/Fs2jiX8SgylZxe98FI0Oaqkji1a1w1Dl/b5bVLsCP9KdRZD6PRszOIe7Ps7i0q6uXRp2W97ZNZmMJ2nms7JgEUm9r3t6pUzNID32JhafL37qP1wT618DRwf4flaGLXYAfGU8jc/VT/HkW53b19NIY62d8TTCXI5ib5eqeH/YBfmQ8iSLzeQxidg6xf5wrsD7l+dSnKjWDtNBXJuuzbpt6nPr1BACh1x9h62CLUwnjLypVb1STkAPnADj163Hqtm0AwKOrD0hNStXkv/YQVy/X3DwZ2nuTm8mRm8v19HUJ8CXlaTSpz2MRs1W8+DOEUgaOVnEllOzENM3fV0Ox8cq7f2svF7xa+RP+04n8KzAfHGr7kfYkioxnMYjZKmL+OI97e/16VaXlfZVIt17fREb6k+hcGdF/nMfNQEbilUfkJGrqLvFqKJbaurOtUIrEq6Go07MQVWriz9/DvWM9Ixll/f2IexaFUjumr+87T3WDSdvqbQO58ttpAJ5dD8Pa3gZ7dye9NBUa19DYhleaL1LEPI4gNjzSpF6N2jYk+NejANy//gA7B1tcShj3K//GtTj91xkAjvwSTON2DY3StOwcxIk/TwJgY2dDjfo1OLjrEABitoqcpLTctE7aNkvX1mfkH+fxaK+va4JOfcZfDcVKp78IcjlyKwsEuQy5jSWZUQV/ocy6ZkWynkWQ/SIKsnNI+us09q30dRB1x7q1Fa8Hu4VvGTJuPETMyASVmrTLd7Bv26hAeYb6RRWiX0Ih+mUUop9zgB+pT6JJe66R9+qPC3gajL/4K6Fk59ZnWK48MztrXBtU5vlPJzX1oG0r3TJV2Tlc3HeW2m31+3zttnU595vmQefx9VBs7G1xdHeivL8f0c+iiH0RbZS3Va927F/3OzlZOQAkKzRfYKve1J8XD57y4r5mlVxmQgqiWsTd35ekp9EkP49Fna3i8Z8hlDPwdRmKJOJuGvs6J7+SxFx/jCpDM/YiQx7g3b7gFyFe/r4kPI0m8YVG3v19Ifi10ZeXpkgi6lY46mxj32rn6YJvS39u7TpZoJzXePj7kvg0miStfo/2hVDeQL90RRLRJny5hZ01pepV4q5WljpbRZZ2nOmW+zbbz6t8SR5e1KyEu3v2JoEdNL4jKz2T0CsPyM7Mxt7Nidhn0Si0tuzavvPUMJBdo21dLmlt2dProVjb2+JgYMtM8dHMvvy5aAcipm24rX8FMp9GkaX16/F7z+DYtiC/bvXG/kCXO2HPKOvpTmkPN8zNzWjfuDYnrtzWSxP+Mor61SsC4FPKg4hYBYoETb8PrOqHo53xhEh+2Bjqt6/o+snsrLGrVw3FrmAAxOwcVFp/nx+2ARXI1ImTlIXGSVZ6cZK5lytOreoQu/OoyfLL+vuh0PF7N/ZdoJqB36vWtg5XftP4n+fXw7Ay6feq6/k9EHMnXq0cbEiMNrahxW2ndSmOuMVQXmExjK48w3i3KJT290X5LJr4F7Ha55IQKpt4LonI57nk2aUHpCemFFleSX9f4p9Gk6C11Xf3hVDJhK2OvBWOysBWp8QkEHXnKQBZqRnEhUVg71Hw10cta1Qi+3kEOS+jICeHlIMnsW1pHAs59uxMSvBZVMqE3N9UcUqy7mtelIpp6WSHv8DMw63Iuv4rENXF99//Uf6rK350aav977r233ZoJmhOA6OAO0CIKIo7/0bZz0RRDCmCHFM8EUXxNoAgCHeBY6IoioIg3Aa8dcr8QBCE198ItwLKAhHAGkEQ/AEVUFGn3EuiKL7UlntDW9bZv6EbDh7OJEYocv+dFKmktL/f3ykqXyw9Xch6lfc5w6xIJXYBFYzSuXSoR9lpvTF3deB+n4XG5ZR2x7aGDynXQo2u/S+x8HQlS6cOsyIV2NU21s+5fX3KTuuFuasjD/rqLFOWyahxeBlW3p5Ebz5EyvWC9bPwciErQrc+FdibkOfSoR7lpvXC3M2B+70XGV23LOOOXXVvk/Xp4umKQkeGIioOFw9XEmLyAhJ7Z3vSklJRqzTGURGpwMXEJFLLT9tw/eQ1HXVlLNn/FZ7eXhzaeoDs63nLpq09XUh7lVeXaZFKXAN8860Lnx4tiDx+M/ff/nP7cGv+Tsxt3/wzzpaeLmTqtGNmhAIHE/Xq1qEuvtN7YuHmyE0T9VoQVp4uZBjJyH+8lewZhOL4DQBSHrzAd2p3zJztUGdk4dY6gKSbxtvvHD1cSNCRkRCppJzBmHYwTBOlxNHTheTYhNzfAjo15Pre80XSy83TjdiIvDfnsZFxuHm6ooxR5sl0diBFp7/ERcbh6qkfsFhaWRLYIpDVM9cC4FXWk0RlIhNXjMe3Snksbz7n3owtuYGlYX2mRyhxKqA+y/QMIlZbn5lR8TxZt5+ga2tRpWcRd+oWcaduFainmacrOZF54yI7Kg7rWpWM0tm3aYj7+P6YuTrxYrBmNVpm6DPcv+iH3MkedUYWts0Dybhd8Fi39HQhXUe/jAgljgXoV9pAv6fr9tP82lrUWv0Uhehn5eWsJy89UolzAfLK9mxBjHb82ZQrQZYimYCvh+JQtRyJt55we+ZWozKVkUp8/fXHlbOHi569UUYpcPZ0xdnDBaXu7zp5Pcp7UaleFT6e2IPszGx2LdjCk1uP8SzvhSjChK0z8XJ24vHeC9xa9xe2Xs6kROb1x9QoJSUKsC26xD98Sd3J3bB0siMnI4syLWsRd+tJgXnsPJ1J1pGXHKmkZBHlAbSa3ZuTC3diUYTVPrnyIvLkpUQq8fQvmjyHsu6kK5Np89UQ3KqUJeb2U07N2UZOeqZRuW+r/V4+ek5Am7pcD75M3Y6NcPEyfnixtLE0sGUKI1vm6OFsYMsUOHq6kBSbACKM2DYdRJFzPx3l/M5jAFRvXYeEaCUR2slBU5h7uur52exIBTYBFY3SObZvQKnJfTBzc+RxP52VICJU2PEliCKxOw6j+OlIvrIAopUJeLg65f7bw8WJ26H691fRuxTHLt6kdhVfboc+IzI2nmhFAq5ODgWWbQoLA/2yIhXY+pvQr10DSr7Wr79GP8uynuQoEyn71Wisq/iQdvsxr+Z8jzo90yh/njzjuMXWRH06ta9P6am9MXd15JHOdq6yXw7gxfwtyPMZD0b9wGRf0fd7iSb8nn+nRtzQ8Xt/frmVwVun0mlabwSZwJquxov8i9tOG8p+13GLLkWNYdw71MV3eg8s3By50XvxG8mw93Ax8VxSdNv5pjh4upAUqS+v1BvY6tc4lnbDs1o5Xt0w3gqoi1kJV3Ki8uKlnOg4rGpU1ksjL+GKbatGRAycjHt143ECYFbSA4sqvmTcevDG9yrx/zf/1RU/ugjAIu0KHn9RFP1EUdykvVYKUAMegiDkV1c56Nejlc7fuq8xCpJjCl0vqNb5t5q8CTsB6KpTZllRFO8D44BooBaa84gs8ilXRT6Tf4IgDBEE4YogCFeuJRuft6FNZPTT39w1lj8mZJh646A8eIkbTUfzcMBSzXk/OshsrKi0aSJPZv2IKiX97d7fP8WEeqbecMQfusjNZqN5OGAJZXT1U6u53WY81+oMxtbfD+tKZQuRV7Q2Ux68xPWmY3jw2VLKTv5U75rMxorKGycQPmuzyfoUitBmRUlTrWENWnZvzfZFW3J/U6vVTOw4jqENBuLnXxGHSqULUi3f/ujeqCo+PVtwe8EuALxaB5ARl0jCracm0xeKqXo10ZBxBy9zsck4bvdfRvnJ3f+xjPxwblyNkj1bEjZvBwBpoa94umYvAXtm4L9zGsl3nyGaeNtVlDo0eRs6aeTmcqq1rsONAyEmEhpTNJmF95eGbRpw9/Ld3G1ecjM5Far7sW/rfoZ1GElOWiblR3XWKdTU3ZjuLy6Nq1KmZxAP5v0EgJmjLSXa1+Fk3VEcrzUcuY0lJbs2yVfHfAWa6J/JwRcIbz+UFyPmac77AbIev0Cx4WfKbl5A2R/mkfngCaKqkFWUb6hf6Z5BPDLQ71TdUZzQ6udViH5FaaPXuDWuSrkeLbg7X/M+RWYmw7GGN083H+VUm2nkpGVS4fMPTJZpNKbzkVtQXrlcjo2DLXO7TGX3wq2MXDs+9/eKdSuzfswq9n44F+/2gZRsXA3TbWdSNSMSwiK4+e1+Ou6cQoftk1Dee17oCljBhLyiulbflv6kKZKI1r5JLhIm66poWWVmckpU9+bWtmPs7DiD7PRMAkd0KqDcf95+myZ9S+s+7fly31Ks7axQZecU6V6NReffZ1d2ncWy96ewrv8imvZth2+9KphbWdD28w85sML0mRp5BRdBOJB4KIR7QSMJH7QQrwm9cn9/9NEUHnT8grC+c3Hv1xG7+lULU834Fgx0G9ilNUmp6XSbsISdB09R2ac0crn8jcvVFG7iN1P6HQ7hfkuNfiVf62cmx6a6L3HbDvGw4zjU6Rl4jOhamDJFkpdw6CJ3mo8idOBizXk/gGPrQHLiEkm7bfyio6Dyi9JXRBN+7+aBi7m/Nezdhr3ztjG/0efsnbeNbkuGmJBt6obenZ02UMqE5LcctxQizxSxBy8T0uQLbvVfju8bynuTOPBd8abyzG0s6bZ+LEfmbiOrsGeUIowFt8nDUKzclO8XrARrKzxXzkSxZD1iaprJNP9a1Ori++//KP/ViZ9k4PV+ncPAAEEQ7AAEQSglCEIJQRDM0Jyr0xO4D3xhIi/AU6C2Nm9twCcfmSbl/EM9DgOjtIdHIwhCgPZ3RyBSe5h0H+CNvbsoihtEUQwURTGwtr3ptw9JUUocS+Ztw3HwciFZZ1XH2yAzUoFFqby3eRZeLmRFK/NNnxRyDytvD8xcNE0kmMmptGkisb+dQanjkP+vkBWpwEKnDi28XMmKyl+/5Iv3sCznmavfa1RJaSRduItTUEA+ObXyIhRYlNStT1eyClganBRy36g+K2+aYFSfnp+1p9bRZSw7sBJltBJXHRmunm56qzcAkpRJ2DjYIpNrTJCrlytKnWXQZSuXY9iSkSwdtJCUBONzDtKSUrl74TaeQTXzfotUYlMqry5tvFzIiE4wyutYpQyBXw3iXP8VZMVrlvS61qtIybZ16HhpFQ3Wf06JJlWpt2Z4vvViSGakAkuddrQsWXC9JoTcx9rbE3ODdiyIjEgFVgYyTG0vsqtalsorhnCz3zJy4vOWLEf+dILLbaZwrcscchJSSAuPMsqbEKXESUeGk5cLSQZjOtEwjaeL3hL2yi38eXXnKSlxifnq0rhPW8YfWMz6Q9+iiFbiXtI995q7lxsKgzGeqEzETqe/uHm5oYhW6KVp8UFzTuw9mfvv2Mg4YiNjeXBDczBk1L6LONbwzr2eEanUq0/rki4m69O+allqrBjK1X7LydbWp1uz6qQ/jyVLkYyYoyL6r0s41zX9Zu01OVFxmOmsTDD3dCMnJv+xnn75DuZlvZA7a97IJ/5yhCddRvOs5yRUCclkPy3gfB8gM1KJtY5+VvnoZ1e1LNVXDOWajn6uWv2y30C/9Ah9edZeLia3HThUKYP/V4O52P+rXHnpEUoyIpXEa1fwRey/iGNNb6MyXbxcSDCos/gohZ69cfF0JT5aiTJKgYvu7zp5lVEKrh7W2K/wm2GIahF7FweUUQoeXLxHSnwyqowsXhy/iVsNb1IjldjpbK+w9XQh9Q22VDzcdYrfO8xg/8fzyUhIJbGA830AkqOU2OvIs/dyIcXENhFTlAqsiF/r2gw9u5JOq0dStlFV3ltVsC1LiVRiXzJPnp2XC6lF9OUpkUpSIpVEa99Uhx24RInq3ibLfVvtF/n4Fcv6zmN2p0lc2HuWmGfGtiwzLdPAlrka2TIje+fpmmvLXqdNUSRx6/AlytXyxa2cB66lSzD54FJmn12Nk6crE/cvxsxgy092pL6fNfdyJbuAuCVF69flzhp/8DptjiKRxEMh2JhYTaOLh4sT0YqE3H9HKxNwd9FfyWNnY828kb34eflkFozqQ3xSCqVMbKktClmRxnFEdgG2LPXSPSzKavTLjowjKzKOtBuaswgTDpzHunrBKyRMyiukPq3KeWLmbI99YGWc2talZsh3+H47HvvGNSj/zVi99EY+zWRfUeilcfR0IcnA772880TP7wV2bcbtQ5pzh27+FULZWsZ6Fred1pf97uMWXYoaw+jL83gjeaafSxL+1v0WVZ6Dl768FBNxZ37IzOR0Wz+W23+c44H2bKOCyImOw8wzL14y83AjJ1Y/FrKsVhGPZVMpe3gLdm2b4j5jFDavt4OZyfFcNZPkv46TevRcke9T4t/Df3LiRxRFBXBOexBzG+An4IJ2G9UvaCZ2pgFnRFE8g2bSZ5AgCFXQHNZcVRCEG4IgdAd+BVy026aGAyZP1hVF8Ug+cv4J8wBz4JZWl9drhb8F+gmCEIJmm1fBG6j/Jq9uPsbV2xPn0u7IzeXU6NSQB8FXC8/4BqTcCMPaxwvLMiUQzM1w69wE5WF942jl7Zn7t20NHwRzM3KUmskC3xUjSA99SeR3+qfs/18h5UYYVjr6uXZuQvyRy3ppLHX0s6lRHplWPzMXB+QOmn35gpUFjk1rkq5zaKYpkm+EYV3eC8uyGnnuXRqjNJBXUH36rdTUZ8R3+/XyRP14iJutJzKx4zguHwmheVfNVyYqBFQkLTlVb5vXa+5euE2Djpojppp3bcnlYM2DmFtJNyZ+N5XV41YR+STv4dbBxQEbB8155RaWFtRsUovksLxzY+JvhGPn44lNGXcEczllOjcg4rB+f7Qu5UqjTWO5NGodKToTH3cW7uavOqM4UG8sIcPWEHP2Hpc+X1dgXeqSfP0xNuW9sCqrkV2iSyPiDPqptbdH7t92NXyQmZuRrSza4Z15MjxzZXiYkGFZypUaP4zn3si1pBucqWPu5pCbxr1jPaJ/N3b6L24+xt3bExftmA7o1Ig7BmP6TvBVAj/SHE9WLsCPjOQ0veXutT9ozLV9BQcU57Yd4auOUxjWfgTnDp+nTdfWAFQJqExqcprRRCHAjfM3afZeUwDaftyG80cu5F6ztbehZoOanD+ct8w+Pjae2Mg4SpfXrApza1pd7+DmxOuPsS3vibW2Pr26NCLaoL9YlXKl9g9fcHPkWlJ16jP9lQKn2n7IrDWLKV2bVieloAMZgfTbj7DwLol5aQ8wN8PhvWYkH9NfFWVeNu8wVquqvgjmZqjiNedwyF0cATDzcse+bSMS9xd8gGeitr+81s+zSyNiTOgX8MMX3Bq5ljQd/TJeKXB8Q/0Sbmjq00Yrr1SXhkQdMR5/dX8Yx9XPvyVVZ/xlxiaS/kqBna9Gf/em1Ul+9EqvTLm5GfU7NeF6sH6fvx58mcYfNQfAN6AC6clpJMYm8ORmGB7eXvw/9s47vIqia+C/vfem914gkJDQIYTeIaGjoiBdBBREsNJ7lS6iiKgUG72KivQOofdeEwIhpPdebtnvj70k96ajvPH9fPf3PD6Gu2fm7JndmTk75YxzZdciaa8eukjtlvUBcPPxQGmiIj0pjVsnr+NVqyqm+pgZHi1qkfwwkvgbYdj6uGPj5YLCRInvGy14evgq5cVcHzzaytMJn+5NeLSr9G2Q0TfCcPBxx06vr3aPFoSWU1/wku2sbPEpq9uMZfcn3/H07F32jim9LYu9EYa9jzu2en01erQgrJz6suJTSY9Owr6a9Oy8WtclSf+uGOb7Mp/f82DcgiDwxsd9OLap6Fao9IRUo7asUY9W3Cqk+9bhyzTTt2XeDauTk55FWnwKphZmmFlJi7dNLcyo1daf6IcRRD+IYHqT9/mszSd81uYTUmIS+eK1KWgM2j+AzBshmHl7YKrv1x1eb5sf6Pg5hv26Rb1qCKYqtMnpKCzMUOi3GysszLBp15CcByVvKwOo61eF8Oh4nsUmolZrOHDmKoFN6hvJpGVmodavjNp59ByNavtibfni25oBsm6EYOZjYF+PovaZVi3ePk18CuroBMyqVQLAprW/UVDo4si8bqzPsSw/qV41yW9JTufZ4o3caDKCmy1G8ujDL0k/c4uwT782Shtx4xHOBu9KQI+W3CnU7909fJUmb0r9T5Vi+r2A11txfbdxvU6LS8a3RW0A/FrVJeFJ0QHKim6nDakIv6WovtJ9GEN9Nnr/80X0Rd4Iw9HbHfv875IWL/27xJCoG2E4+rhjr2876/ZowcMX0NdjyQgSQiO58OP+csnn3n6ASZVKqCq5gUqFdfdAMo8b+xFPuw3laVfpv4xDp4ifv4KsY5K/5Dp3HHlhEaSu/6247P/9yDF+/ndj/Iii+Fahn5YX+vdcA9l0pADLz2laSLZLCWrqFdK5vBg9xd3bE8O0oii+U9w1URSzgZHFpA9BCgr9nKn6308AJwzkPi7rXkpDp9WxZ9Zahq6fIh3nvv0EcSGRNB0kHRl9adNRrF3s+ODP+ZhZWyCKIq2GdeObzpPILe+WK62OsGk/UmfLTASlgtitx8h+GIHbEKnIY9cfwunVFrj0DURUa9Dl5PFw1FcA2DSrhWvfQDLvhtPgsHQKV/iizaQcK7/DXpiJsxdz6dpNUlLS6NjzbT4cPpjePbr+5fzQ6ngy/UdqbZ6FoFQQt/Uo2Q8jcB0s2Re34RBOr7bEuU97RI0WXXYeIR9IR/Oaujngu/wTUCgQFAoSd58h5UgZHY6+POtumQFKBXFbjpH94Bnu+vKMWX8Ip9da4Nq3PTp9eT4YuQx4Xp7tpfI88gUATxdtJvnoNSMVV49doWFQE1YEryIvO5fvJqzIvzZ17UxWTfqO5LgkNi5ax9hvJzBwwiAe3wnj2DYp2GOf0QOwdrBhxDzp1dZqdUzpMR57Vwc+/moMCoUCQSFwbs8ZFEcKdItaHdemraXdlskISgWPt54k7WEk1YZI72PY+qPUGdsLUwcbGi16FwCdVsvRbjNf/LkVQtTqeDj1ZwK2TpeOKd1ynMwHz/AcIh2pG7X+MC6vtcC9bzvpOebkcfv9ZS+s48HUn2m4dRooFURvOUHmg2dUGiINmkSuP4LP+D6YOFhT8/PhUhqNlktdpVjw/j+Nw8TBBp1Gy4OpP+cHizREp9Xx26xfeH/9NBRKBRe3Hyc25BktB0k6zm06wr3j16gdFMC0k8tRZ+eyZeKq/PQm5qbUaFOfHdN+MMq3ftem9JrzDtaOtoz4eRKR98JZM0SKFXDh2EWadWjK+tO/kJudyxfjC46eXrBuHl9NWkZibBI/LvqJ6d9N492J7xB6O5T9WwuOPW3drTVXgq+QUyhOxLczv2PqismYmKiwfJLIzdEF9ypqddyZ+gvN9OX5bMtxMh48o4q+PJ+uP0L18b0xdbCm3ufD8svzTNfppF4NJWbPBdocXoSo1ZF26wkRG46W/gC1OmI+W4nXz/Ol49x/PURe6FPsB74CQMqWfdh2a41dz46IGqnuRY4piG9Q+dvpKB1sEdUaYj77Hl1a6QEoRa2Ou1N/ocnWaQgG9nnp7YtYfwRfvX11DOw7p7cvds8FWr2AfaJWx81pa2m5ZQqCUsHTLSdIfxCJt77+PVl/lJrj3sTUwYYGi9/NT3Oyq3SK0s3p62j8/UcIJiqywuO4Nma1UZ4NVALB248RGRJB0CCpvTq+6RA3jl/FP6gRX5z8jtzsXH6cKMV40ml1bJj1IxPXz0ShVOSnBQjefoz3lnzIgoPL0Kg1/DBeaqOy0jI5+ONu5vy5BAsdRBy/QYQ+nsbZmevovmkSgkLBg20nSX4YSe23OwBwb+MxLFzs6LlvHqbWFog6HfXe68avQZNRZ2TTec1ozBys0Wk0nJm+jrzU0pfXi1odR2ato+/6SQhKBbe2nyQxJJKAQZK+65uOYeVix5DdBfqaDOvGT50ml71VoAR9J2auo+cGSd/dbSdJehhJfb19tzYew9LFjgF7JH3odAQM78bGjpK+E7PW0e2bD1CaqEh9GsfhCWuK5Puq8uU9vxavt6XT4G4AXD54gVM7juXbsvT0SiysLTAxMUGdm8cnW2chanWc336CmJBntNa3ZWc2HeHu8WvUDWrIrJPLycvOY9NEaYDMxtmO99ZIoRMVSgVXdp3h3smCeHBlotURMXMNfhvnSMedbztKzsMInN+W7jlh4wHsu7fCsXdQfl1//KHUp6pc7Kn2g/6UKKWS5F3BpJ24VqIqAJVSybThffhgwfdodTp6BrXAz8uD7Yek8I39urTh8bNYpn+7EYVCwLeyO599UOACT/p6LZfvhJKSnkGnkTP5sN8rvFko8Hxh+57NXIPvBmP7nPT2JW48gP0revvUGsScPJ589EV+8mezfsD7m3EIJipyn8bwdMI3JWnK1/d0xg/U3DwbFAoS9PpcBku+V/yGgzi80hLnPoH5/eujD74sPU8DdFodv89ay4j1UxGUCi5tP1Fsv1crKIApJ79GnZ3Ltomr89M/7/d2TvvRKN8dU36g5+whKFRKNLlqdkw1vg4V304X1v2f9lsK6yvLh3F9rXkhfV+/kA6dVsfeWWsZsn4yCqWCq9tPEh8SSRP9d8ll/XfJyPzvEh0thnXnW/13SZ9vPsKnRW0sHWwYf24Fx5f9ytXtJU+yiFodB2at5a31kt95Q6+vkV7f1U1HsXKx473den06Hc2HdWdlp0m41fLCv3dbYu89ZcQ+KTbp8S+2EXq8lLZGqyNh4Xd4rF6IoFSQ9vsh1I/Cse33KgBp2/eWmNS8YV1sXu9E7sMwKv/6PQBJy38h69SlEtPI/PsQKnrvo8z/P2Z4v1VhL8kruSUH+PtP0PTWF2ULvUSu+E8oW+glotVV7KK+r83UFaqvX551hepz0lWsfWLxAQD+Y+y1qFh9N7Uvd2toaYzLK/20jJeNj21aheoLT3vxIK1/h9wSw979Z/jNvGL7hjaav7Ya4q+S/BfDrfxVzCrY9buqqNj4enYVOK/5jjanbKGXSN29H1aovruvfV+h+jTaim1btpmYly30EumUXbGVz5SKXX1Q0X7LSYuKXcNgUoGP7y2bhLKFXiK+tw9W7MOrYHKu/FFhT8+8cc//yrL8n13x89+AIAhOQHHD8x3129FkZGRkZGRkZGRkZGRkZGT+KroyDsX4H0Ae+PkH0Q/uBPzT9yEjIyMjIyMjIyMjIyMjI/PvRB74kZGRkZGRkZGRkZGRkZGR+XfyXxx0uaL4nzzVS0ZGRkZGRkZGRkZGRkZGRuZ/AXnFj0yZ5FBxkcz+7cGWG99cWqH60FZsMOJXG82rUH1dukSXLfQSMWlRp0L1CbUbVqi+kMHHyhZ6ifyUHFphugLndqgwXQCvflWxYdr2TrWrUH2CQ8UGyx4zrnzH3b4s3jCpW6H6rHUVGweyqX3FBg39M61iA9qqlVYVpqveufkVpgvgdOOK7WfbXKlY+8Tsv3Zc+F8luGPFBq+uXy2qQvVZ16rYOX7Tbm0rVF/o+JAK1XdLVXF+tef4BhWm638CnbziR17xIyMjIyMjIyMjIyMjIyMjI/MvRV7xIyMjIyMjIyMjIyMjIyMj8+9EjvEjr/iRkZGRkZGRkZGRkZGRkZGR+bfyP7/iRxCEUUCWKIrrX0Je00RRXPgSbuu/jprtG/DGrCEolAoubDvO8ZV/FpF5Y/ZQagcFkJedx7YJK4m88wSXah68/e2n+TJOXq4cXPYrp37eT9dxfanbuQmiqCMjIY1tE1a98H3NWPgVwWcu4uhgzx8bXzx9cdgFNsR73jAEhYK4LUeI+vZ3o+sOXZtSeeJAEEVEjZbw2T+TfvE+gpkJdX+bj2BqgqBSkLT3HM+Wbvtb9/KfsO/0hSss/uZHtDotvV/twntv9zG6npqewczF3xARGY2ZqSnzpnxK9WpVAdiw40927jmEKIr0ea0Lg/u9UaquSoH+NJs7GEGhIGTLCW59t9voerVeraj34WsAaLJyODd1Lcl3nwLQ+ssRVO4UQE5CGrs6Ti2Xbar6TTEf9BEoFKhP7iN371aj68paDbAaPRddfAwA6iunyd21QbpoaYXlsAkoKnkDItk/LkX76G6p+s48SeCLk/fR6UR61qvMsKY+RtfXXX7MvvuSLq2o43FSJsdGBpGt1jLz4C0SM/MQBOhdvzJvNaxapn1nboexZPtRdDodvdo0YFi3FkbX07Nzmf7TbmKS09BodQzp3Iyerf0B6D5tJVZmpigUClQKBZunDy1Tn1egP63nDEZQKri35QTXvzd+fva+HgR++T4u9by5+MUObqzel3/N/71u1BoQCIgk3n/GifFr0OaWvTf+8y9m0aVLIFnZ2Xw4chI3btwpIvPDT1/RsGF91BoNVy7fYMynM9BoNAC0aducRZ/PwMRERWJiMq92e6tEXQrvupgGDgCFAs2tU2guHSgqU7mGXkaJmJNO7nYpTpeqYUdU9dsCAppbwWiuHS1RzydzP6R5h2bkZOfy+dgvCLldNLaRu5c7s76fho29LSG3Qlg4+nM0ag1WNpZM+2YKbpVcUSqVbFv9Kwe2H8SrWmVmrZyRn97CzxP1+d1G96GoWhfT9v0k+26fRnP5YPH2te8n2ZedQe6vX0r2BXRAVa8NCIKUthT7nnPmUSxLDt9EJ4r0alCVYa1qGl1fe/4h+24/A0Cr0/E4MZ3jY17FzsJU/5vIW78cx9XGnBX9WpWprzCzF00msFMbcrJzmPDxTO7cvF9EZtmqhfg3rItareHG1dtMHzcv/90pDbcgf/znDUFQKniy6TgPvzWuC15vtqbGxz0A0GTmcH3yz6Tq27Kul5ajychG1OoQtTqOd51RJP/CeAT602ie1HY+2nKCe4X0Ve3VijofSfrUWTlcnvILKXefojAzodNvM1GYqlColDzde5HbS3eWqc+yTROcp44CpZK0X/eT8uP2YuXM6tWg8paviRm/kMxDpwFwnT8Oy/bN0SalEPHGyFL1fDT3Q5p3aEpudi5Lxi4tsS7M+H4aNvY2hNwKYfHoJfl1Yeo3U3Ct5IJSqWT76l85uP0QLh4uTFk+EQcXR0SdjrTkNFzdXcjLzmXVhBU8uR1WRIeLlyufrBiPtb01j2+H8f3Y5WjV0nswZM5wAoIaF0m//PRqsjOz0Wl16LRaZvSYCIDS1hWFlSOiVkqvTYvlVHAwn3/3C1qdjjdf6ch7A3sZ6U9Nz2DWF98TERWDmakpcyd+SHWfKjyOiGTivGX5cs+iY/nonf4M7v1aqeVqiGNQA6rPfxdBqSB601HCV+wyuu7crQnVJvdH1Ek+TMjMtaRefFDu/AFOX7xWofadvnKLz9dsRqfT8WaXdgzv+6rR9bSMTGZ9/TMRMXGYmZjw2ehhVPeunH9dq9UxcOxnuDo58O3sMaXq8m7vT5C+37u99QQXC/V7jr4edF36Pq71vDnzxQ4uryno9947s4y8zBxE/Tuy6bVZpeoCMGvRFLsxHyMoFWT+uY+MDVuKlTOpXROXH74laeY8co4HAyBYW+EwdQIqXx8QRVIWfEHe7dL9FlW9ppi/9aHkJwXvJ3dfIT+pZgOsPp2LLkGKoai+cprcPzdKFy2ssHx3PIrK3iCKZP+8FO2je6XqOxMSxZJ9V6R+oZEvw9oZx09be/ou+24+AaQ+4HF8Gscnv0lyVi6Ttp/Ol4tMzuCDIH/eblWrVH1egf60+kx6fve3nOD6d8X4LV+9j3M9by4u2cFNA7+l3vCu1B4YCILA/c3HufVT0T4ToHb7BvSZ9Q4KpYKz245xeOWuIjJ9Zr9D3aCG5GXnsmHCSp7deQzAZ6dXkJuRg06nQ6fRsuT1aQA0fKUFr4zpg5tfJZa+MZ2nt4q2WwBnwmJZcuQWOh30alCFYS1rGJfnhRD23Xnez4pSP/tpd+N+du1JqZ/t26JI/v9q5Bg//9sDP4IgqERRfDlf0xLTgBca+BEEQSmKovYl3sNLR1AI9Jr7LmveXkhqTCKj/1zA3cNXiA2NzJepFRiAi487iwPHUqWhH70XDOebnjOJD4tm2StT8/OZeeF7bh+8BMCJNXs4+NUOANq805XOo9984Xvr+Upn3ur9OtPmvaSgyQoFPgtHcG/AZ+RFJ1Jv3xKSD14iO+RZvkjqqVsk622wrF2V6qvHc6Pdp4i5au72nY0uKwdBpaTuHwtIOXaNjKsP//LtvGz7tFot85et5oev5uLu4kT/98cT1KYZvt5V8mV+2LCDWn4+fLNgGmHhz1iwbBU/fT2fkLBwdu45xJbVX2KiUjFq4hzatWxKVS/PYnUJCoHmC4ZyaOBisqKTeG3fXJ4eukJqSEFgw/SIeA70mU9eahaVgvxp9fkw9vaYA0Do9mDu/XKYtstL/5AwUIj5kE/JXDIJMSke6znfo752Dl1UuJGY5uFtspZNL5LcYtDHqG9dQv3tZ6BUgZlZ6WWpE1l8/B4r32yMm7U5g7acp301F3ydrPNlhjbxYWgTaTDoZFgcm66GY2duQp5Wx7h2Nantaktmnoa3Np+neRUno7RF9elYtOUwq8b0x83BhkGL1tHe3w9fT+d8mW3Hr1LNw5lvPu5DUnoWPWf9wKvN62KiUgLww/iBOFhbll2WSM+vzfyh7HlrMZnRSby5Zy7hh6+QbPD8clIyOTN7Az5dGxultXJ3oN67XdjWcTLaHDWdv/8Ev9db8GDHqVJ1du4SiK+vNw0bdKBJ0wC++nouHYN6F5Hbvu1PRgwfB8BPv3zN0Hf68dOPm7Gzs+HLZZ/Ru+e7PHsWjbOLUykGCph2eIvcncsQ05MxHzQd7aMbiEkGAcPNLDDtOIjc35YjpieBhY2U1MkTVf225GxeCFoNZm+ORvv4FmJKXBE1zTs0o5JPJd5u8w61G9Vm7KJP+bDHp0XkRk57jx0//MbxP08wdtFoXhnQjT837KHn0DcID3nK9HdnYedox/rgnzny+1Eiwp4xousoABQKBUfubUcbes3YvqCB5P72NWJGMuYDp6INu1nUvqCB5P7xDWJ6srF99dqQs3URaLWY9fq0RPueo9WJLDp4g1UDW+Nma8GgX47TvroHvi62+TLvtKjBOy0kJ/VkSDQbL4bmO6MAmy+F4uNkQ2beiwfPDOzUBu9qVQhq2oOAJvWZv3QGvbq8XURu16/7GDtKcrSXr1lM/8G92PTLjtIzVwg0WPQup/stIjs6kaAD84k+dJX0hwV9YObTOIJ7zUOdmolbhwY0XPoeJ14p+Og71XsBeUnlC1orKAQaL3yH4wMWkR2dRJd984g8eJW0EAN9EfEc6T0PdWoWHkENaLpkOIdfm40uV82xvgvQZOUiqJR0+mMW0cdukHi1lEDqCgUuMz4i8r2paGIT8Nq2gszj51E/elpEzmnccLLOXDH6Oe33Q6Ru+hPXxRNLtatZh6ZU9qnEkDbvUrtRLUYv+pSPi6kLI6YNZ6e+LoxZ9CndB3Rj94Y9vDH0dcJDwpmhrwtrg3/i6O/H0Gq1rJq7hpDbobTt3oYpyycx8/WJWFhbMmz+SGb1nFxEx8ApQ9j/027O7T7NsAWjCOrfkSMbDxIQ1Ah3H0/Gtf8Qv4Y1iqRfMGAm6clFn6M2PQFdhhQwW6vVsuCbH1mzZBbuLo4M+HAKQS2b4OvtlS//4+bfqOXnzfK5kwh7GsnCb37gx6Vz8PGqxK9rlubn07H/SDq2aV5quRqhEKi5eDjX+s0nNyqRJgcXEX/wMlkG72py8C0uHrgMgFWdKtRbM5YLbcaWW0VF26fV6li4cgNr5k/AzcmRgWPnEtg8AN8qlfJlfti+h5rVvPh6xic8johmwcoN/LhwUv71TX8exsfLg8ysnFJ1CQqBjvOH8uugxaRHJzFo91xCD18hyaDfy07J5NjsDfgV6vees6P/ArKTM0rVk49Cgf340SSMnog2Lh7Xn1eSc+osmifhReTsPnyf3AuXjX62H/sxOecvkTX9M1CpEMxL91sQFJgP/oTMpZMlP2nWd6ivn0UXZVzXNQ9vkbW86AC1xaCPUN++hPr7uZKfZFqWn6Rj0Z7LrBraQeoXVh+kfa3K+LoWHEbwTps6vNNGOjjj5P1nbDz3ADtLM+wszdj+4Sv5+XRZ+gcd6ngVq6fAPIHW84ey97nfsncuTw5dIaWw3zJrA96Fnp9DzcrUHhjI76/NRqvW8MrGSYQfu07a49giOvrNHca3by8gJSaRiX8u4tbhy8QYfBPV0X8TfRY4Gu+G1RmwYDhLexaU5/KBc8ks1I5EPYjgh1FfMnDhiFLKU2TRoZusGtAKNxsLBq09Sfvq7vg6G/SzzavzTvPqUnmGxLDx0iPjfvbyI3ycrcnMLXvCQ+bfx//7rV6CIHgLgnBfEIR1giDcFAThV0EQLAVBaCwIwklBEK4IgnBQEAQPvfwJQRAWCoJwEhgtCMIcQRAmGFxbJghCsCAI9wRBaCoIwm+CIIQIgjDfQOfbgiBcFAThuiAIqwVBUAqCsBiw0P+2qSQ5/e8ZgiDMFQThAtCyBLue6O/znCAIlwVBaKS345F+ldJzuYmCIFzS2/6Zwe9/6G2/IwjC+wa/ZwiCsEAQhBuCIJwXBMGtrDKuEuBHYngMSRFxaNVaru8+R90uTYxk6nZpzOXfpI+6p9dCMbexxMbF3kimeut6JIbHkhwpOUe5Gdn510wtzRHFFz/1o0lAfexsbV44XUlYN/Qj50k0uU9jEdUaEnedxqFrMyMZnYHjoLA0w/DQs+fXBBMlgokK/oJNhrxs+27dC6FKJQ+8PN0xMTGhe8e2HDt9wUjm0ZMIWjSWThKoVrUykTFxJCQlExYegX+dmliYm6FSKWkSUJejp86VqMu5oS/pT2LJeBqPTq3l8a7zVCnU0cZfDiEvNUv6+2oolh6O+ddiLzwgL6WczhOgrFYLXWwkYnw0aDWoLxzHpFE5Vw2YW6KqWR/1Sf3Mj1YDWZmlJrkdk4qXnSWV7SwxUSroWsOdE49K/jA+8CCGbjU9AHCxMqO2q9RRW5mq8HG0Ij4jt3R9j6PxcrWnsos9JiolXZvU5sQN49MsBAEyc/MQRZHs3DzsrMxRKv5aM+8a4Evak1jS9c/v0Z/n8e5i/PxyEtOIvxGGTl107FqhUqIyN0VQKlBZmJIZm1ymzldf68SWLdIKu8uXrmNnZ4ubm0sRucOHTuT/feXyDTwrSeXat9/r7P7zEM+eSYMbCfEln6ylcPdBTIlHTE0AnRbN/UsofQOMZFS1mqMNuSYN+gDoT5tROHqgiw4DTR6IOrTPHqL0K/4EttZdWnLo1yMA3Lt6DytbaxxdHYvINWwdwMm90izuwR2HaNO1NQCiKGJpZQGAhZUF6SnpaDXG5d2oTUN0qfEF9/ncvtQ4xDS9fQ8vo/Q1PiFEVbMZ2tDr0qCPkX3u6GIeg0ZdYF+hsinM7agkvBysqOxgJdWHOpU5EVLyqXv77zyjW52CGfnYtGxOhcbyZoB3qXpKonP3IH7bJs3sXr98C1s7G1zcnIvInThSMIN84+ptPDzL7AJxbOhH5uNYsp7GIaq1PPvjHB6F2rKkyyGoU6U2I+lKKBYeRZ9xeXFs6EvGk1gy9XXv6a7zVC6kL+FyCGp925lwNcSo7dRkSW2JwkSJwkRZZj9kXr8m6qdRaJ7FgFpDxv4TWHco6prYDXqDzMOn0SamGP2ec+U22tSyB7Vad2nFoV8PA3Dv6n2sba3KrAuHdhymdVepHRdFsLCSBq4N60JSXFL+yqEm7RsTHvIUBzcnQq89xNLWCnvXoifQ1W1Vnwv7zgJwaudxmnSRBh8ad27GqZ3HAUpNXxq37odSpZI7Xp5uUj8b1JrjZy8ZyTwKf0bzhvUBqFalEpEx8SQkpRjJXLh2Cy9PNzyLaQNLwraRH1mPY8gJl97VuD/O4tKtqZGMNqugr1Famr2wn1LR9t1+GEYVD1cqu7tiYqKiW7tmHD9/zUgm7GkUzRtIgwc+Xh5ExSWQmJwKQExCEsGXbvBml3Zl2uYe4EvKk1hS9XXvwe7z+BXq97IT04i9GYZO8/fnbE3r1ELzLBJtVDRoNGQdOYZ5u6J+i1XfXmSfCEabXNCPCpaWmAb4k7Vb77doNIgZpfstymo10cVFFfhJF09g0rB1+W7W3BJVjfqog/UnLGo1kF2Gn/QsES9Hayo7Wkt+S/2qnLj/rET5/bfC6Va/6OrnC2GxVHawxtO+9NP6CvstobtK8VsKPT8HP09irz1Ck5OHqNURff4+Pt2Mv3UAvAP8SAiPJVH/TXR191n8uxjXMf8uTbn4m9SGPbkWgoWNFbaFvokKE/sokriw0k+qvR2dLPWz9s/72UqcCIkpUX7/vWd0q1MwQBqbls2pR7G86V/2CvN/JTpdxf33X8r/+4EfPTWBNaIo+gNpwEfACqCPKIqNgZ+BBQby9qIothdF8cti8soTRbEdsArYpc+rHvCOIAhOgiDUBvoDrUVRDAC0wCBRFKcA2aIoBoiiOKgkOb0OK+C2KIrNRVE8TclEiKLYEjgFrAX6AC2AuQCCIHQBqgPNgACgsSAIz3u2YXrbmwCfCoLwfOrbCjgvimIDIBgoeWhZj52bAylRBR9QKdGJ2Lk5FJJxNJJJjUnCzt3YoQvo0Yrrf541+q3bhH7MOPstjd5onb/655/E1N2JPAM78qITMS3GgXfo1pwGwd9Qa/10Ho37tuCCQkH9w1/S+OYvpAbfIONaxR4zWRZxCYm4uxZ8DLm5OBNX6OO4pp83R4KlAZ1bdx8SHRtHbHwifj5VuXLjDimpaWTn5HLq/BVi4ko+EtjS3YHMqIIP0czoJCzdS3agqw8IJPL4zb9qGoKDM2JSfP6/dUnxCA5FP/yUfnWwnrcGy/GLUFSSOj+Fqwe69FQs3puE9dxVWAwbD6bmpeqLy8zBzaZAxs3GnPjM4gdvstVazj5JoGP1oh+ZUanZPIhPp5576cdxx6Wk4+5QMKvj5mBDXKGBsQFBjXgcnUjnSd/RZ+7PTOzfCYVCOiZaQOCDr7czcMFafg2+XqoukFbtZBg8v4zoJKxKeX6GZMYkc2P1Pt4+v5whV74lLz2LZ8G3y0zn4eFG5LOCmbmoqBg8Pd1LlFepVAwY2JMjh08C4Ovng729LXv2b+LkqV0MKLT9wBDB2t5ooETMSEawsTeWcXADc0vM+k7AfNAMlLWlj2FdYiSKyjXA3ApUpih96iPYFP+h7+zuTFxUwYBgQnQCzu7G76Wtgy0ZaRnotJKzEB+dgLO71GT/vnYXVapX4dcrW/n5yBq+nfV9kUHyDq8Hon1g/OElWNkXDOgAYnoyglUJ9vUZh/nAaShrS8u+dQlRKCpV19tnUqp9z4lLz8Hd1iL/3242FsSlFz+7nq3WcDYslk61ChzSLw7fZEyHugh/8VRzNw9XoiMLZmajo2Jx93AtUV6lUtGr32ucPHqmzLzNPRzINugXsqOTSh3Y8X4rkNhjNwp+EEXabJ1C0MEFeL/doUx9lu6OZBnoy4pOwsKj5LrnOzCQ6OMF+gSFQLfDC+l1cyUxwbdJvPaoVH1KNyfUMQVtpyYmAaWr8TuqdHXCulMrUrftLfP+S8LZ3Yn4qAI9hu/5c4qvC9K9/LF2F1Wre7H9yhZ+PLKa72atLFIXPL09cfF04dF1aaVtUkwiDm7Gz8rGwYbMtMx8HYnRCTjo78PB3Ykkg7I3TC8iMmXjbBbsWUqHgZ2N8lRaO6Fy9UPpUIm4xGTcXQz7WSdiE5KM5GtWq8qRU9Kky637IUTHxhObYNwX7z9+hu4d2hRfmCVg5u5IrsH950YlYuZe9F117t6U5qeX0WDjVO6NXflCOuISkirUvtjEZNxcCmxwc3YkLtF4IqGGjxdHz0or0W49CCM6LpFYvcySNVsYN6wfCqHsTx5rdwfSDfq99OgkrN1eYOBPFOm9cQpv751H/beCyhRXuDijjSvoH7RxCShdXIrIWLRvQ+bvxluWVJU80KWkYj9jEi7rVmM/dTyCeel+i+QnFeiT/KSiK2OVfnWw/mw1lmMXovDU+0kuej9p+ESs56zC4t1xZftJ6dm42xUM1rjZWhKXllWsbHaehrOh0XQqZlXPwVvhdC/HYIWlhwMZ0QZ+Z0wSVqW0nYYkPXiGR/OamNlbozI3pUqHBlh7Fi0bOzdHkg3qWHIx30T2bg5GMikxidjr66EowscbpjNp9yJaD+xYrnt7Tlx6Du42L9LPxtGpZsGq/C+O3mJMUF2Ev9rRyvy/59+y1StCFMXn3ttGpC1X9YDD+pdbCRgOo5YWeOV58JpbwB1RFKMBBEEIA7yANkBj4JI+bwuguGn+jqXIaYGyN90b34u1KIrpQLogCDmCINgDXfT/PZ/6sEYaCApGGux5/tXjpf89EcgD9uh/vwIYey969KuE3geY+85kaha6XniCqLhGxNAhU5ooqdupMfuWGO8lPrB0OweWbqfDh2/QemjX4m6lYimuLSxmMiz5wAWSD1zApnkdvCYN5F5//WIrnY5bncejtLWkxk+TsahZhewHT4tm8A9R3Kqqws/uvUF9WPzND/QeNprq1apSq3o1lEolvt5eDHvrTUaMm4WlhTk1fH1QKpUlKyuuYylhYtG9VW2qD2zP/l7zXsScQvqK+a2QvdonIaSPGwi5Oaj8m2H56VwyJg9FUChRVq1OzoYVaMPuYz7oI8xeG0Dub2tL1vcCk6TBYfEEeNpjZ25i9HtWnoYJe68zoX1NrM1Kb46LU1fY5LN3HlPTy5Ufxg0gIj6FUV9vo5FfZawtzFg7aRCu9jYkpWUyavk2fNydaFyjlGXTxdbpUm8xH1M7S7y7NGJTq7HkpWXRedUnVO/VmpDfS//ILqsdKcxXy+Zy5swlzp2Vlr+rVEoCAurx+muDMbcw58jRX7l06RqPQp8Up63oT4VVKRQo3KqSu+NLUJliPnAKuugwxKQY1JcOYN57LKI6F138M9AVP/tbrINVyKZi7db/v2lgE0LvPGJcv4l4enuydPNibnYZRVaG5DyrTFS06tISza+F6k55/DpBgcK1Crk7l4HKBPP+kyX7kmNQXz6I+ZtjEPNy0cVHlGhf4fstzy0Eh8QQUNkpf/l5cEg0DlZm1PFw4FJ4fAmpyjCl2GIu+d2Z98U0Lp67wqVCKweKz7vsZ/gc59Z1qDowkOA38hfgcrLHHHJiUzBztqX1tqmkh0aReL5o/KEChcX8VoI+11Z1qDYwkCM95xaI6kQOdJ6Gia0lbX8ai13NyqQ+KHmWvfjRNmN9LlNHkfDlT39vJrMcbUrxdUESkupCGOP7TcLT25Mlmxdzy6AumFuaU8O/BttWbiPbYEVxMUqKuQ+xpEv56ee8OZWUuGRsneyYunE2UY8iuX/xLtqMJLRpknuntHVDsLQvxnTjjIcP7MXi736hz/sTqO5ThVrVfVAZ9KdqtZoTZy8zevigwlmVTinlZ0jC/ksk7L+EfYvaVJvcn+t95xeRKYni8qsw+0rS1/dVPl+zmb6fzKK6d2Vq+VZBqVBw8uJ1HO1tqOPnzaViYn6VlS/wQn39lt5zyYxNwcLJlj6bJpMUGkVkafGTytG22I/5iNTv1hSte0olJjWqk/LlN6jv3sduzEdYDxlI+ppfSrnDsu3ThoeQPuEtAz/pMzKmvIOg1PtJm76V/KS3PsTs1QHk/r62RG3FNVslDToEP4gkwMsZO0vj7WNqjZaTDyL5tHODYtMZ5V2efr0EUkKjuP79Hl7dMgVNZg6Jd58Wu6qrXO9IKW3Mst6zSI1LxtrJlo83ziDmURSPLpYeJ6lATTF1rwTZ4NBYAio5FvSzoTE4WJpRx92eS+ElT9r+m/kvj6xSIfxbBn4K14R0pEGbYrdRAaWtTXw+Za8z+Pv5v1VIdWydKIplRZstTS6nnHF9ynMvi0RRXG2kWBACgU5AS1EUswRBOAE8H5ZXiwXesJYS3gFRFNcAawBWvDlLtDcY9bb3cCItzni2JSUmEUMZO3dH0gy2dtQKDODZ7cdkJKQWa+i1XWcY/vMk4MVjO7xM8qITMTWww9TDibyYpBLl0y/cxayqOypHGzQG8Ru0aVmknbuDfVDD/6qBHzcXZ6NVOrHxCbg4G88GWltZMn/qaEDqqLr2H0FlD2mlSu/XutD7tS4AfL1mvdGsX2GyopOw8izI28rDkaxitvs41Pai1RfvcWTwF+SWd198MYhJCQiOBTNlCkcXxJRCW31yCmaaNDcvIgwZjWBtiy45HjEpHm2Y5BiqLwVj9uqAUvW5WpsTazDTEpueg4tV8fvdDz4s2Ob1HLVWx4Q9N+hey4OOfmVvN3GztyEmOa1AX3I6LvbGMYF2nb3FsG4tEASBKq4OVHK243FMIvV9PHG1l7YMOtpaERRQg9tPokod+MmMTsLa4PlZl/D8iqNym3qkRcSTo68Tj/dfxr1J9WIHft57/22GvtMfgGtXblGpsifSmDR4eroTHR1bJA3A5Kmf4OTsyOi3CuI1RUXGkJiYTFZWNllZ2Zw9c5H69WsXO/AjrfApsE+wdkDMSDGWSU9Gl50hbenS5KGLDEHhUhltSiza26fR3pYWbJq07oWYUVA2qgaB/HBQWj1z/8YDXD1dASlItbOHMwmxxu9lalIq1rbWKJQKdFodLh7OJMZIMt37dWXzd9KAedSTKKIjYqji58X969LHRPOgpjy8FUrdLOOtNmJGCoJNwSykYOOAmFnIvoxkdDnF2ReH9s4ZtHek52XSqqeRfcXhZmNOTFrBx3ZsejYuNsXPBh+4+4xudQu2eV1/lsTJkGhOP4olT6MlM1fDtF2XWfhG0WX2hgwe3p8Bg6XYcDev3cGjUkE98vB0Izam+EGkTyeOxNHZgWlDyjfQnB2VhIVBv2Dh4Uh2TNHysK3tRaMvR3D2rc/JM2jLcmJTAMhNSCN6/2UcG/qWOvCTFZ2EpYE+Sw9HsmNSisjZ1/ai2dL3OPn2EiN9z1GnZRF37h4eQf6lDvxoYxIwcS9oO1XuzmjjjN9Rs7o1cP9ScmeUDnZYtmsGWi2ZR0ve7gtgN7AHtn27s1oLD248wMWzQI+LhzOJL1AXuvbrwtbvpHm7qCdRxETE4OXnRa0GNXh10Ct4VPHgycNwop8WbH1wdHciuZC/kp6UhpWtVb4OJw9nUmKlfj4pOhFHg7I3TJ+i/39aYiqXD17AN6A69y/eBV1BrAxtZhLulaoQE2/Yzybi6mS8IsDaypL5kz4CpH6226APqeResELt1MVr1K7ug7OjfanlW5jc6ETMDO7fzNOJvGLe1eeknL+Hhbc7Jo42qMsZg8rN2alC7XNzciA2vsAPi01IwqVQOmtLC+aNGZ6vr/vwiVRyd+FA8AVOXLjO6cs3yc1Tk5mdw9Slq1k0ofjYgenRSdgY9Hs2Ho5kxJWv3wPI1Nf17MQ0Qg9ewSPAt9SBH11cPErXgnJRujqjTTD+KDepVQPHeTMBUNjZYd6yOSlaLXm376KNj0d9V2pLso8HYzN4YKn3JybHIzgW6CuXnzT4U8lPSopHTC7sJ5Wuz83WgpjUgk+u2LQsXAxWrBhy4HY43fy9i/x+OiSaWh4OOFkXn86QzOgkrA1WY1q5O5JZyvtfmAdbT/Jgq7SCuNnkfkarh56TEpOIg0Edc/BwIrXIN1GSkYy9uxOpev/puWxGYho3D17Eu4FvuQd+3GwsiEl/gX62TqF+NjRG6me1Oqmf3X2FhT2Kj1Ul8+/k37LVq4ogCM8HeQYC5wGX578JgmAiCELdElO/GEeBPoIguOrzdhQE4fn6Q7UgCCblkHtZHASGCYJgrddRSa/PDkjWD/rUQtoe9peJuPEIZ293HCu7oDRREtCjJXcOGwd3vHv4Kk3ebAtAlYZ+5KRnkR6fkn894PVWXN9tvM3L2btgC0edTo2JexTFP03G9VDMfTww83JFMFHh9EYbkg8Zb6EwM7hvy/rVUJio0CSlo3K0RWkrxR8QzE2xa+tPdmgps6z/APVqVefpsyieRcWgVqvZf/QUQa2NAyumpWegVksDcDv3HKJxg7pY6+MqJCanABAdG8/R4HN071TynvmE62HY+rhj7eWCwkSJzxstiDh01UjGytOJoB/GcGr0KtLCSt6nXB60j++jdKuE4OwOShUmzYNQXzN+5wS7AudUWa0mKATEjDTE1GR0SfEo3KVOUlWnYZGg0IWp627L05QsIlOzUGt1HHwYQ6Bv0a0l6blqrjxLItC34INHFEU+O3IHH0crBjfyLpd9db09eBqXTGRCCmqNloOX79G+gZ+RjIejLRfuS/edmJbJk9gkKrvYk52bR2aONHacnZvHubuP8fMsPa5C3I0w7LzdsdE/P9/XW/Dk8NVS0zwnIzIRt4Z+qMylmaZKreuSbBCY1pAf12ykbasetG3Vgz17DjFQvz2rSdMA0tLSiY0t+vE+ZGg/OnZsx/B3Rxut6ti79witWjVFqVRiYWFO46YBPHhQ/DYXXcwTBHtXBFtnUChR1WqKNuyGkYz20XVpy5OgAJUpCncfdM+DIz8PhGzjiLJ6QzT3L+an09w4wYiuoxjRdRRnDpyhS59OANRuVJvM9EyS4oo6k9fO3qD9q1J96tq3C2cOSe9ubGQcjdpI8YMcnO3x8vUiKrxgAWuHN4I4tut4KfY5SfbVaIL2UWH7bqDwfG6fid6+mEL2OaD0a4im0FaywtT1dOBpcgaRKZlSfbj7jPbVPYrIpeeoufI0gSCDa58G1eXQJ93Z/1FXFvdsSlNv5zIHfQA2/LSNVwP782pgfw7tO86b/aVTrgKa1Cc9LYP42KKzmv3f7kW7Dq34dMSUcseVS77+COtq7lhWcUEwUVK5Z0uiDxn3gRaVnGjx81guf/w9GQZtmdLSDJWVef7fru3rk3Y/olR9SdfDsPFxx0pf96q80YJnhfRZVnKizY9jOP/pStIN9Jk52mCi74eU5ia4ta1LWmjpcSNybj/ApGolVJXcwESFdfdAMo+fN5IJ7zKU8M7SfxkHTxE/b0WZgz4AqVt2E/Hmh4zs+gFnDpylSx9pkXHtRrVKrAvXDepCl76dOXtI0hMXGUdDo7pQmejwaHat282ju2Hs33qAjV9vytfh17AG2elZ+QM2htw9d5vmr0ixVNr2DuLyYan+Xjlyiba9g4qkN7Mww1z/HM0szKjfLoCI55M6ioL5M4WFLfVq+hIeGc2z6Fipnz1+hsBWxjFA0jIyC/rZfUdo7F87v58F2H/s9Atv8wJIv/YIy2oemOvfVdeerUg4aBwQ2MK7YIDUur4PChNVuQd9AOrV8qtQ++rW8CE8Ko5nMfGo1RoOBF8ksLlxTLW0jCzU+lPZdh4MplHdmlhbWjD6nb4cWfcVB35eypJJH9DMv3aJgz4AMTfCsPdxx1Zf92r2aMGjcvZ7KgszTPTviMrCDO+29UgobaUdkHfvPiqvSig93EGlwrJTB3IKxU6M7T2I2DffIvbNt8g+fpKUpcvJCT6DLikZbWwcqirSBI5Zk0aoCweFLoT28QOUrgZ+UrPAon6SrYGf5FMTBIXkJ6UV9pMale0nVXLiaVI6kckZkt9yK5z2Blt8n5Oek8eVJ3EE1apc5NqBW0+KjftTHHE3wrDzKfBb/N5oQXg5nx+AuZO0nd7a0wnv7k0I3XW2iEz4jUe4eLvjpP8matSjFTcPG9exW4cv0+xNqQ3zblid7PQs0uJTMLUww0z/jphamFGrrT9RD0vvDwyp62HP06RMg342kvZ+RbfDp+eouRKRSFD1gmufBtbh0Edd2f9hFxa/3oSmVZ3/9wZ95Bg//5oVP/eAoYIgrAZCkOL7HAS+EQTBDsnOr3k+5fo3EEXxriAIM4BDgiAokJaofASEI62QuSkIwlV9nJ+S5F4Koige0scSOqdfepgBvA0cAEYJgnATeIA0EPaX0Wl1/D5rLSPWT0VQKri0/QSxIc9oOUj6mDm36Qj3jl+jVlAAU05+jTo7l20TCxYhmZibUqNNfXZO+9Eo31cmD8C1mic6nUhKZDy/Tv+J2nsnvNC9TZy9mEvXbpKSkkbHnm/z4fDB9O7xN7aMaXU8mf4jtTbPQlAqiNt6lOyHEbgOlla5xG04hNOrLXHu0x5Ro0WXnUfIB1KoKFM3B3yXfwIKBYJCQeLuM6QcuVKatgq3T6VSMm3MSEZOmINWp6PXK53w86nCtl1SsL7+b3QnLPwZ0xYsQ6lUUK2qF3OnFJy6MnbmYlJS01GplEwfOwo7m5JPoRK1Os7PWEfnzZMQFApCt50k5WEkNQdLMS4ebDhGg7G9MHOwpuXCdwDQabTs0Z+E0+67j3BvWRtzR2v6Xv6G60t3EqKfiSkWnY7sDSuwmvh5/jGlushwTIOkY2Lzju/BpGk7TDu8DlotYl4uWd8XLG/P3rgCi1HTEFQm6OKiyfpxSellqVAwOagWH/5+FZ0o8kbdSvg6WbPjptSJ9/WXnLHjoXG0qOqMhUlBc3s9KoW996Kp7mxN/42Sk/dxaz/a+pQ8GKNSKpgyoDMfLN+OTifyRuv6+Hm6sOOktFWlb/uGjHi1FbPW7qPPZz8hAmN6BeJgbcmz+BTGrfoNAI1WR/dmdWhdr1qp9olaHadnruPVjZMQlAoebDtJ8sNI6uhjlNzdeAwLFzt6752HqbUFok5H/eHd2NZhMnHXHxG27yK9989H1GpJuB3O3c1FBycKc+jgCbp0DeT6zWNkZefw0aiC03R27PyJTz6aSkxMHMuWzyPiaSSHj/0KwO4/D7Jk8bc8fPCII4eDOXthLzqdyPq127h3t4RT9UQdecc3Y9Z7jP7I8jOIiVGo/NtL5XTzJGJSDNontzEfMhtEEc2tU4iJ0gC1WY8PECysQKcl7+hmyC0+bsH5Yxdp3qE5G0+vIzcnl8/HFZzQt2j9ApZO/IrE2ETWLPyBmd9PZ/ikdwi5/Yh9W6Wj5Tcs38Tkryby05E1CMCahT+Spl/5ZWZuRuN2jflqytd8MrKQ8yfqyDu+FbNeo0FQoLlzBjEpGlV9yRnV3ApGTI5BG34H87dnSvbdOVNg32sjEcz19h3fUqJ9z1EpFEzp0oAPtp5Bp4M3GlTFz8WWHVelI2z7NpJOtzv2MIqWPq5YmL5c9+P44VMEdW7Dict7yM7OYdInBSdq/bz1W6aM+Yy4mHjmfzmDyIhofjuwHoADe46xYunqkrIFpLpwfdpaWm+ZgqBUEL7lBOkPIvEZIsVneLz+KLXHvYmpgw0Bi9/NT3O86wzMnO1o8Yt0YpJCpSTitzPElhHLTNTquDx9LYGbJyMoFYRtPUnaw0j8Bkv6Qjccpd7YXpg52NBkkaRPp9FyqPtMLNzsabF8FIJCAQqBp7svEHWkjO1sWh3xC77D84eFCAoFab8fIi80HNv+0pHZaWXE9XH7YgoWzfxR2tvhfWwjid9uIP23oscgXzh2keYdmrHh9FpycnL5wqAuLFw/ny8nfkVibBI/LPyRGd9P491JQwm9/Yj9+rqwcfkmJn01kR+OrEZA4IeFP5GWnEa9pnXp0qczYffCaNCyAc4eznx36SfSk9JZPWFFvo5Ja2ewZtJ3pMQls2XRej75djx9J7xF+J3HnNgmBWC/fuwKAUGNWRa8ktzs3Pz0ds72jF0jtUdKlZIzu05xU9/2Ku3cEUwtpH0tWjW65EimffIeoybPl/rZ7h3w8/Zi+26pTPr16EpY+DOmf74ChUKBb9XKfDbhw/z7zM7J5dyVm8waW84TLQ0QtToeTv2ZgK3TEZQKorYcJ/PBMzyHSINhUesP4/JaC9z7tpN8mJw8br+/rIxcjVEplRVqn0qpZNqoQXww60u0Oh09O7fFr2oltu+T+pR+rwTxOCKK6V/9gEKpwNfLk89GD3shm54janUcm7mO3hsmoVAquL3tJIkPI/HX93s3Nx7D0sWOt/cU9HuNhndjbcfJWDha8/qaMYBU1+//cZYnJ8uIW6jVkfLlCpy//hwUSjL37Efz+AmWvaRB7KxCcX0Kk/rVChzmTEMwUaGJjCZ5Qel+Czod2ZtWYDV+seQnnTqALioc00C9n3RC7ycF9ZD8JHUeWasM/aRvsXh/quQnxUeT9dMXpapTKRVMebUJH6w/Lvktjarh52rPjktS/Mu+TaXTp47de0ZLX/ci/UJ2nobzj2KY8XqzInkXx3O/5ZVNkt/53G+prX9+9/R+y5v7DPyW97qxPWgy6oxsuqwZjbmDNTqNhjPT1+UfPmJUhFod22f9zEfrpyEoFZzffoKYkGe00X8Tnd50hDvHr1E3qCGzTy5HnZ3HxolSHC0bZztGrJG+c5RKBZd3neHeSWlCxr9rU/rOeRdrR1tG/TyZyHvh8GiTcXkqFEzp4s8H285Jfqd/FamfvabvZxs+72ej/yP9rMz/f4S/cpLSfxOCIHgDe0RRrPdP38u/lQneAyvsJVl0eUHZQi+RK/4vNtD0d2l88yUdO19etBW7dW5To78Ro+cv0KtT6TPZLxuTFnUqVJ9Qu/iTov5TrBt8rEL1TU4ue8XAyyJ6btlBdV8mr371pEL17Z1Q+kDey0ZweLGTjv4udcbtr1B9X5q8rEXC5SNXUbHBNpvaV2yMh5FpFetruilLP/3nZbL2zNyyhV4ipxtXbD/b5srMCtUnZpd/9dHL4NuO31eovgGVKnZ1u3Wtit3cYdqtbYXqWz++Yg9RuaWqOL/6i9kVe/qWxbtL/tVRn7NP/FxhHZFF4LD/yrKUhwJlZGRkZGRkZGRkZGRkZGT+nYj/vVuwKor/9wM/oig+QTrB6/8lgiD8DvgU+nmyKIpF10nLyMjIyMjIyMjIyMjIyMjIvAD/7wd+/r8jimKvsqVkZGRkZGRkZGRkZGRkZGRemP/ioMsVxb/lVC8ZGRkZGRkZGRkZGRkZGRkZmULIK35kysSMCoxPVcHBiLW6Ch77rGD7UJpUqLqsCi5OdZymQvWZZGZXqL6yTlJ62WRWcCi6PG3FPT/B1qbCdAGYChXcvVpali3zMrF3rFB1lirzCtWXoazYxqyi5yGzs00rVJ9SqNi+T12RJVrB/bqmoudsK9g+wbRi63qWULGBx9MTK9Y+84TMCtVnkpxUofpSlRWqjlSx4uqDmFWxPuC/HjnGj7ziR0ZGRkZGRkZGRkZGRkZGRubfirziR0ZGRkZGRkZGRkZGRkZG5t+JHONHXvEjIyMjIyMjIyMjIyMjIyMj829FXvHzkhEEQSmKovafvo+XTfX2/rw6awgKpYLL244TvHJ3EZlXZw+hZlAA6uw8dk5YRdSdJwC0fLcbTQcEgSBweesxzv58AID+336CSzUPAMxtrchJywRyi+R7+sIVFn/zI1qdlt6vduG9t/sYXU9Nz2Dm4m+IiIzGzNSUeVM+pXq1qgBs2PEnO/ccQhRF+rzWhcH93ijTVvugAKrNexeUCmI3HSXy2z+Mrjt2bUqVyQMQdTrQ6gib+QvpF+9j6ulEjRWfYOJiD6JIzIbDRP+4r0x9FW1facxY+BXBZy7i6GDPHxtX/a28ALwC/WkzZzAKpYK7W05w7Xvj98be14MOX76PSz1vLnyxg+urC8rL/71u1BkQiIhI0v1nHBu/Bm3ui+2tNmncDKtRnyAoFOQc2Ev2js3G1+sHYDN7AbqYaAByz54ie/O6cuev8K6LaYeBICjQ3DqF5uL+ojJeNTEN6g8KJWJ2BrnbvgBA1agjKv92AGhunkJz9UiZ+s7cDWfJb8HodCK9WtZhWOcmRtfTs3OZvv4QMcnpaHQiQzo0pGeLOsQkpzNjw2ES07MQBIHereoyKDCgTH3e7f0JmjMYQang9tYTXCz0/Bx9Pei69H1c63lz5osdXF5T8PzeO7OMvMwcRK0OnVbLptdmlakP4Msv59C1axBZWdm8//4Erl+/XUTml1+W06hRfdRqDZcv3+Djj6ei0WiwtbXh55+/xsvLE5VKxddfr2HDhh0l6jrzOI4lR++iE0V6+XsxrLmf0fW1Fx+x724UAFpRx+PEDI5/1BlzEyXDtpxDrdWh0Yl0quHBh21qlKjng89G0axDU3Kyc/ly3JeE3n5URMbNy41p303Bxt6G0NuhLBm9FI1aioHk36I+o+aMRKVSkZqcxsS+kwCwsrVi7JIxeNesirmTKXknt6CLCzewL54vjkv29aznxbDmvkY6110KY9+9SMk+ncjjpAyOfdAJcxMlw7edJ0+rQ6sT6VTdnQ9al2xfsWX74BlLdp1HJ+ro1awmw4IaGF1Pz85j+tYTxKRkotHpGNKuPj2bvpgOgKkLxtGuYyuys3OY/uk87t16UETmrWF9GPz+AKr4eNG6dhdSklIBePfDt3mtd1cAlCol1ap707ZON1JT0ork4RnoT9O5gxEUCkK3nOD2d8Z1wadXK+p9+BoA6qwcLkxdS/LdpwC0+nIElToFkJOQxu6OU8tlV6VAf5rp9YVsOcGtQvqqGejTZOVwzkBf6y9HUFmvb1c59Vm3a4Tn7BGgUJC87TDxq341um7TuTlu4waBTkTUaIme9yNZl+9iWq0SVVZMypcz9XIndtkmEn/5s1g9H372AU07NCU3O5el474k9HZoERl3LzemfTcVG3sbQm6HsmT0F2jUGvqO7EOHXkGA9Ly8/LzoF9AfcwtzJn49EUcXB3Q6kX2b93F87YEi+b47ZwSNghqTm53LdxOW8/h2WBEZVy9XxqyYiLW9NY9vh7Fi7DI0ag1teran56g3AcjJyuGH6SsJv/ekxPI8fek6n69cj1an481uQbw3wLhvTk3PYNaXq4mIjsXM1JS540ZS3ccLgLSMTOZ8tYaQJ88QBJg7fiQBdUqvG05BDag1fyiCUsGzTcd4ssK4/N17t8bn49cB0GbmcnfSj2To35cqI7pT+e0OADzbdIyna4r2Y/+0fRWpz7e9P11nS/3eta0nOFvI33Xy9eD1pSNxr+vN8aXbOa/v92w9HHlj2QdYu9gh6kSubj7GxV8OllmWVm0b4zp9JIJSQcqOgyStMe6zrDu2wHn0YBB1iBodcQtXk33lLgAOQ97Avl9XEARSth8ged2uMvWZNGqG1YhPQKEg5/Becn419otU9QKwmbEAXazkF+WdO0X2Vskvsvp0MqZNW6JLTSb143fL1AVw5kk8X5y4h04HPetVZlizakbX111+zL77+n72eT80qgPZai0zD9wiMSsXAYHe9SvzViPvMvX5tPeno/753dx6ggsri/ot3Ze+j1tdb04t3cGlNcZ+uqAQGLJnHhkxyewc9mWRfNVKOLH1CHtW/l5E9+A5w2kQ1Ijc7FzWTPiWcH0bU799QwbPHoZCqTBK23v8QBp1boqoE0lLTGXN+BWkxCXTqmc7Xnn/DczdpDidgpMnOds/R0yI5MyTBL4IfiD163UrMayJj3F5XnnCvgfRBeWZnMmxEYHYmZsw58gdgh/H42hhyq9vtyqzLP91yDF+5IGfF0UQhD8AL8AcWC6K4hpBEDKAr4CuwHhBELyBTwFT4ALwoSiKWkEQVgJNAQvgV1EUZ5ei5wmwGQgCTID3gUWAH/CFKIqr9HITgX6AGfD78zyLu0/97xnAcuA1IBt4QxTF2FJtVgj0mPsuv7y9iLSYRD74cz73Dl8lPjQyX6ZGYADOPu58FTgOr4Z+vL5gGKt6zsK1RmWaDghi5Rsz0ao1DF03hQfHrpP4JIZtH6/IT999+iBy0rNoP8jfSLdWq2X+stX88NVc3F2c6P/+eILaNMPXu0q+zA8bdlDLz4dvFkwjLPwZC5at4qev5xMSFs7OPYfYsvpLTFQqRk2cQ7uWTanq5VmysQoF1Ra9x51+c8mLTqLBgcUkHbpM9sNn+SIpp26RdPASAJa1q1JzzTiutR2NqNHyeM46Mm89RmllToNDS0gJvmmUtjAVbl8Z9HylM2/1fp1p85b+5TyeIygE2s0fyu63FpMRnUSfPXN5cvgKySFR+TK5KZmcnr0Bn66NjdJauTvg/24XtnScjDZHTZfvP8Hv9RY82HGq/DegUGD90RhSp41HlxCP/fLV5F04g/ZpuJGY5vZN0uaU7+PI2EAB006DyN3xFWJ6MuZvz0D76DpiYnSBjJmFJPPr14jpSWApBRgWnD1R+bcjZ+MC0Gow6zMGbdhNxJS4EtVpdToW7TjBqo964mZvzaCl22hfrxq+HgVBdredukk1d0e+GdmDpPRsei7YwKtNaqJUKBjfqw21vVzJzMlj4BfbaFGzilHaIuYpBDrOH8qvgxaTHp3EoN1zCT18hSSD55edksmx2RvwK/T8nrOj/wKykzPKW6J07RqEr68P9eq1p1mzhnzzzXzatetZRG7r1j94993RAKxb9w3vvjuAH37YyMiRQ7h/P4Q+fYbj7OzIjRvH2br1D9TqogOGWp3IosN3WNWvOW425gzacJr2vm74OhcEgX6nmS/vNJMGS06GxrLxymPsLEwRRZEf+rfA0lSFWqvj3S3naFPNBX9PhyJ6mgY1pZKPJ++2HU6thrX4ZOHHjH59bBG596YO47cf/+Dknyf5dOHHdBvQlT0b9mJla8XHCz5m+uAZxEfFY+dkl5/mgzmjuHziMvNHLeD32XVBVRDMXasTWXz0Div7NJPs23SG9n6u+DoV2De0aTWGNpWc8JOPYtl05Um+fWv6Ns+3b9jWc7T2Kd6+4tDqdCz6/SyrRnTDzc6KQSv+pH2dKvi6FaTfdu4u1dzs+ebdLiRlZNPzi195taEvJqryR+ps27EVVX286N6iD/6N6zFrySQGdh9eRO7qxZucOHyGtb99b/T7L99v5JfvNwIQ2KUNQ0YOLHbQR1AINF8wlMMDF5MVncQr++YScegKqQZ1ISMinoN95pOXmoVnkD8tPh/G/h5zAAjdHsz9Xw7TevnIctn1XN8hvb7X9s3laSF96RHxHNDrqxTkT6vPh7HXQN+9Xw7Ttpz6UCjwnDuKx4NnoolJxHfXV6QduUBuaES+SOaZG4QevgCAeS1vvL6dTEinD8gLiyT01dH5+dQ6v5a0Q+eKVVNQF4ZRq2EtPl34MZ++PqaI3PCpw/ntx9858edJPl34SX5d2LH6V3aslgakWnRqzpvv9SI9JQMTU1PWzPuB0NuhWFhZ8N2+FYScucOzkIL7bxjUGA8fDz5pP4rqDWswYv4HTOs5sYjuQVOGsuenPzm7+xQjFnxAh/6dOLTxAHERsczuN43MtEwCAhsxctFHxaYH0Gp1LPj2F9Ysnoa7sxMDPplOUMvG+FatnC/z45Zd1PKtyvI54wl7GsnCb3/hxyUzAPj8+3W0btqAr2aNRa3WkJ1bdELMCIVA7cXDuNJvATlRibQ4uJD4g1fIfFjgo2WHx3Op51w0qZk4dwig7pfvc6H7DKxrVaby2x043206Yp6GRlunknD4GlmPY0pUV9H2VaQ+QSHQbd47bBq0iLSYJN77cx4Pj1wlIcSgLFMyOTB7PbUK9Xs6rY7D8zcRc/sJplbmvLdnPmGnbxulLYJCgdvsD4l4dzrqmAS8d35NxtHz5D0yqHvnrpNx9DwAZjW98Vw+lcfdRmJavSr2/brypM9YRLUar5/mkXHiEurwqJK0gUKB1agxpM0cjy4xHruvVqO+cAZtRCG/6O5N0ucW9Ytyj+4nZ+9vWI+dVrIOA7Q6kcXH7rLyzaZSP7T5HO19XfF1ss6XGdrEh6H6wYuTj+LYdO0Jduam5GlyGNeuJrXd7MjM0/DWprM0r+pslLYwgkKg07yhbB+0mPSYJIb8OZfQI1dINGg7c1IyOTp7A9VL8FsaD+tGYmgUZtYWxeZ7LiaSuX8u4eqRS0SFFPj3DYIa4ebjwYT2H+HbsAbvzn+fOT2nICgUDJ03gs8HfUZSTKJR2r2r/2Dnl1sA6PLOK/Qc3Y+101dz9o9gzv4RzOpJngiOnpi98j5iQqRUnifus7JXI9yszRm07QLtfVyMy7OxN0Mbe0vlGRbPpuvh2JlLvkGP2p709/di5qGiE2oy/xvIW71enGGiKDYGmgCfCoLgBFgBt0VRbA4kAv2B1qIoBgBaYJA+7XRRFJsA/kB7QRD8i+RuTIQoii2BU8BaoA/QApgLIAhCF6A60AwIABoLgtCulPtEf6/nRVFsAAQDI8oyuHKAH0nhsSRHxKFVa7m5+xy1uxg3mLW7NObab9JHecS1UMxtLLFxscfVrxIR10JR5+Sh0+p4cuEedbo2KaKj3qstuPlnUWfx1r0QqlTywMvTHRMTE7p3bMux0xeMZB49iaBFY2k2uVrVykTGxJGQlExYeAT+dWpiYW6GSqWkSUBdjp4q3iF9jk1DP3Iex5D7NA5RrSH+jzM4dm1qJKPLysn/W2lpBqJ0IoQ6LoXMW48B0GbmkBUSial76SffVLR9ZdEkoD52L+n0I9cAX1KfxJL2NB6dWkvon+fxKfTeZCemEXcjDJ266CI5hUqJytwUQalAZWFKVmzyC+lX1aiNNipSWs2j0ZB78himLdr8LZuM7s/dBzE5DjE1AXRaNPcvovQNML6H2s3RPrwqDfoAZKVLaR090EWFgSYPRB3aiIcoqzcqVd/t8Fi8XOyp7GyHiUpJ10Y1OHHLeMZaQCAzV40oimTn5WFnaY5SocDFzoraXq4AWJmbUs3NgbjU0gdk3AN8SXkSS6r++T3YfR6/Yp5f7M0wdJqXs8jxtdc6s3nzTgAuXryGnZ0t7u6uReQOHjye//flyzeoVElaOSiKItbWkgNkZWVFcnIKGk3xJ4fdjk7By8GSyvaWmCgVdK3lyYnQksfA99+PolstaVBVEAQsTaV5E41ORKPVlXj2YcsuLTiy8ygA96/dx8rWGkfXogMoDVo34NReqQ09/OsRWnZtCUBQz0DOHDhDfFQ8AKmJ0moVS2tL6jevx4Gt+hllnRbyCtqm2zEpeNkb2FfTo1T7DtyPolstj+Lt04kIQvmPfLsdEY+Xsy2VnWyld7VBNU7ceWokY/yuarCzNEOpeDGXpEO3dvy5Q1qdcPPKbWxsbXB2dSoid//2Q6Iioov8bsgrvbqw7/dDxV5zauhL+pNYMvR14cmu83gV+miIvxxCXqp0AkvC1VCsDAZV4y48IDel/AOgzoX0Pd51niql6Iu/Goqlgb7YCw/IewF9lg2qkxcejToiFlGtIXV3MLadmxvJGPZ7CouCfs8Q69YNpHwi44vV06pLSw4XqQtF+8iA1g0INqgLrboWnZkOfCOQ47tOAJAUl5S/cig7M5unoRE4uhnn27RzM07ulNqNkGsPsbK1wr6YelivlT/n950B4OTOYzTt0gKAh1fuk5kmnY4UcvUBTh5F37Pn3HoQShVPd7w83DAxUdG9fUuOn71sJPPo6TOaN6wHQLUqlYiMjSchOYWMzCyu3LrPm92klU0mJipsra1K1AVg18iPrMcxZIfHIaq1xPxxFtduxn5W6uWHaFKl+0+5EoKZ/n2xql6JlCsh6LLzELU6ks/ew/WVpkV0/JP2VaQ+zwBfkp/EkhIh1b07u89Ts7Nx3ctKTCP6ZhjaQn5LRlwKMbefAJCXmUNCaBQ2bqUPlpv71yAvPAp1RAyoNaTtDca6U0sjGdGg7gkW5vl1z8zXi+wbDxBzckGrI+vibWw6l76KQ1W9NtroSGk1j0ZDbvAxTJqX3y/S3LmJmJ5ebvmi/ZA7Jx6V0g89iKZbTakfcrE2p7abNNFhZarCx9Ga+IycEtMCeDz3W/TP797u8/gV8/xibhbvd1q7O+LbIYCbW0+UmK9WreH87tM07tzMSKZR52ac3imle3TtIZa2Vti5OuAb4Efsk2jiI2KLpM3JKDgp1szSvNh2VVWjMdqQKwDcjk2VytNOX57V3TkRVnx7C3DgYQzdarjn/7txJYf8QaD/SXS6ivvvvxR54OfF+VQQhBvAeaQVNdWRBnd26q93BBoDlwRBuK7/9/N1jf0EQbgKXAPqAnXK0PV8re4t4IIoiumiKMYDOYIg2ANd9P9dA64CtfT3U9J9AuQBe/R/XwG8yzLY1s2B1KjE/H+nRSdhV8ipkmQKjnBMi0nC1t2B2AcReDerhYW9NSbmptQICsCukMPk3awWmQmpJD4pOsMUl5CIu6tz/r/dXJyJi080kqnp582RYGnA49bdh0THxhEbn4ifT1Wu3LhDSmoa2Tm5nDp/hZi4hFJtNfVwJC+qQCYvOjHfQTLEsXszGp5aTu2NUwkd+32R62ZeLljX8ybjakip+iravorEyt2BDIN3IiM6CSv38q0YyIxJ5vrqfQw5v5x3rnxLXnoWEcEvNkOhcHZGF1+wgkaXEI/CybmInKp2Xey/+wnbuUtQVvEud/6CjQNiesFglJiRjGBjbJ/g4Abmlpj1n4j52zNR1mmpv5coFJWrg7kVqExRVqtfJG1h4lIycbcvmNVxs7cuMngzoJ0/j2OS6DzzZ/os2sLE3m1RKIw/2CMT07gfGU/9qu6UhrW7A+kGzy89OgnrMpxYI0SR3hun8PbeedR/K6hcSTw93Xn2rGBmLjIyBk9PtxLlVSoVAwe+yeHDJwBYtWodtWr5ERZ2icuXDzJhwmeIxThSAHEZObjbFMzoudmYE1eCU5mt1nL2cTydDBworU6k39pTdPjuMC28nalfwmoYZ3cn4g3alIToBJzcjd9DWwdbMtMy0Wl1+TLO7lI7WdmnMtZ21izZ/jnf7v2GTr07AuBexZ3UpFTGfzWO7/Z/i2m7/qAqOKI7LiMHN5uCY4PdbCyIzyh+pjtbreXskwQ6Vje2r//6U3RceYQWVZ2p72FfbNriiEvNwt2u4OPKzc6SuDTjI4UHtKrN49hUOs/fQp+vfmPi6y2KvKtl4erhQkxkwUdEbHQcbh4uL5QHgLmFGW2CWnB4z/Fir1u6O5BpUBeyopOwLKUt8xsQSOTxmy98HyXpyyxDX/W/qU/l7oQ6uuAdVcckYuJedGDDtksLqh9ZSdWfZxM5aXmR63avtSV1d3CJepzcnfIHMAESouNxKqTH1sGWDKO6EJ9fF55jZm5Gk8AmnN5/uogOt8pu+NX1JeT6Q6PfHd2dSDSoh4kxCTi6Gedr42BDloHuxOhEHIuZvOkwoDPXTlwt0c64hGTcXQrydnNxIjbReOKiZrWqHDktrRy+dT+U6NgEYuOTeBYTh4O9LTOWrqLvB1OY/dUasrJL/9g1d3ckx8BHy4lKwqyUSadKbwWRcOw6ABn3I3BoURsTB2sUFqY4dwrAvFLJg1r/hH0Vqc/W3ZG0aGN/16acfoshdpWdca9blcjrRbf1GmLi5oQmpuC91MQkYOJWtPytO7fE58BqvNZ8RvTUrwHIDQnHskk9FPY2COZmWLdvgolHUR/HEIWTM7oEA78oMR5lcX5RzbrYffMTNnNezC8qTFxGLm6G/ay1eTn6oaJ9flRqFg/i06jnbl+qPmt3B9Kjjf2WF3l+HWe/zYmFWxB1xn5D4XyTohNxKFTHHNwdSTJoY5JiEnF0c8TB3Ykkg3eqcNo+E9/i63NraNWzHTu/2lrknpR+jdCESAOdcRm5uFmb5V9zszYjPrOU8gxPoKNfyT6UzP8e8sDPCyAIQiDQCWipXzFzDWkrVY5BXB8BWCeKYoD+v5qiKM4RBMEHmAB0FEXRH9irT1saz2uzDuPgNzqkbXoCsMhAl58oij+Vcp8AarHgS0hLCdv9BEF4XxCEy4IgXH6SU3R0vvDHVHGzwaII8Y+iCF61m2EbpzJ03WRi7oWj0xqPsvu/3oobf54ttgCK+2grrOu9QX1IS8+g97DRbPptD7WqV0OpVOLr7cWwt95kxLhZjJowmxq+PiiVZWwjKNaOoveQtP8i19qO5v67S6gyeYDRNYWlObV+nEDYrLVoDUbz/yvsq0BKeifKg5mdJd5dGrGh1VjWNfkElaUZNXq1ftE7KFNC8+ghSUP7k/LRcLJ378R21oIX1FGIwgYqlCjcqpL723Jydi7DpOVrCA5uiEnRqC8ewLzvOMx6j0EXF1HmDIFI2e/K2XtPqVnZhcPzhrFt8gAW7wgmIzsv/3pWbh4TftrHxDfbYm1hWji7UvPW30S52dJ7LhtfncHOIV8QMKQTlZrVLDNN8e9MyUqXL5/PmTMXOHNGcvg7d27PzZt3qFatKc2bd2fZsrnY2BS/LLy4XEt6Y4IfxRJQyQE7gzJTKgS2v9OWg6M6cjs6hdD4EmZBy2FTaXYrVQqq16/OzKGzmPb2DN4aPZBKPpVQqpT41fNjz/q9fNT9Y0R1HiYBHUs3sDT7PIvat21IWw6+34HbMSmEJpR/lrf4si30rj6MpKanI4dnDGTbmF4s/uMcGTl5xaQsmcJ5QvnbGEMCu7Tl2qWbxW7zgherC26tauM3sD1XFxZ14MvNC+hzb1Wb6gPbc+Ul6yuu3qUdOk9Ipw8IH7kAt3FvG2dhosK2U3NS9atlildTnF3lrwvPadG5OXcv3SG90Komc0tzZq2ewco5q8ku1Pf+Vd2FZeq2rE+H/p3YuKjkWHDFt9XG/x7e/3XSMjLpM2oKm3cdpJafNyqlEq1Wy72Qx/R/rTM7Vi7GwtyMn7YVHy+pIPPi76I4HFrXodJbQYTMk+K6ZIZE8eTbP2m8fTqNt0wl/U44ouav9EX/OfsqvDwL63/BRsXE0oy+q8ZwaO4G8srwAYuv60X1ZRw+x+NuI3n24TxcxgwGIO9RBIk/7KDKLwvw+mkeOfcfI5a1+rYcfpn20UOSh/cn9dPh5Ozeic30v+kXFbmH4n8ODosjwNMeO3Nj3yQrT8OEPdeZ0L4W1malRyj5O32Cb4cAshLTiNWv2nrRfEtqu4oz1zDtr19sZkzL9zn7RzCdh3Y3klO4VQWNGjGp9BWrxRH8OJ4AD/v/7RU+hZFX/Mgxfl4QOyBZFMUsQRBqIW27KsxRYJcgCMtEUYwTBMERsAFsgUwgVRAEN6A7cOJv3s9BYJ4gCJtEUcwQBKESoC7nfZaKPibQGoBVb84W7TwLZiBsPRxJizOebUmNScLOs2AE29bdkXT91pwr209wZfsJADpP7G80m6JQKqjbtSnf9Zhe7H24uTgbrWKJjU/Axdl4lN3aypL5U0c/v2+69h9BZQ9phLv3a13o/VoXAL5esx53l9JnQ/KiEjH1LJAx9XAiL6bkLUZp5+9h7u2GytEGTVI6gkpJrZ8mEP/bKZL2XSgx3T9lX0WSEZ2EtcE7Ye3hWO7tWpXb1CM9Ip6cJOlj8/H+y7g3qc7D30v+qCiMLiEehUvBNiGFswu6ROMVUWJWVv7f6ksX4CMlgq0dYlpqmfmL6cYrfARrB8SMlCIyuuwMUOeBOg/ds4coXCqjTY5Fe/s02tvSjLVJm16IGaWXjZu9NTEGHzqxKRm42BovWd914S7DOjdGEASquNhTycmWx3FJ1K/qjlqrZfxP+3mlSU06NvArnH0R0qOTsDF4fjYejmTEle/5AWTGpgDSdrDQg1fwCPAl8mLRwLsjRw7h3XelwdMrV25SuXJBjKpKldyJji4+7tG0aaNxcXGkf/+COASDB/flyy+lFXhhYeE8eRJBzZq+XL58o0h6N2tzYtILnPLY9BxcrIsfiz9wr2CbV2FszU1o4uXEmcdx+LlI2ySVtVvz/YGPAHh44yEuBm2Ks4czSbHGq/pSk1KxsrVCoVSg0+pw9nAmMVaaXYyPTiA1KY3c7Fxys3O5deE21er4cPviHeKjE3hwXSpT7eMbRgM/rjbmxKYXzGzHpmfjYjBTaMjBB9H527wKY2NuQpPKTpx9HI+fc/m2gbrZWRKTWrDCJzY1CxdbSyOZXZcfMiyogfSuOttSydGGx3Gp1K9S+oqdge/2oc/bUmDX29fv4l6pYDbTzcOVuJiSl72XRPeenUvc5gXSihsrg7pgWUJbZl/bi1ZfvMeRwV+Q+wKxrQqTVUifVQn6HF6SPk10gtFKARN3JzSxSSXKZ128g2lVD5QOtmiTpcEy68DGZN95hCYhxUjWcfArOA7oykpR5MGNh7h4FjxfZw+X/Pf8OalJqVgb1YWiMoGvt+f4nyeMflOqlMxaM5NjfxznzIEz2CnM6DrkFToN6AxA6M1QnAzqoZO7M0lxxvmmJaVhaaDbycOJJINyr1KrKqM+/4iFQ+eSkVLyQKibsyMxBit3Y+MTcXU0XnVgbWXJ/AmjAKlf7zbkUyq5u5CTm4ebiyP+taU2unPb5vy0rfSAvTnRSZgb+Gjmno7kFuO3WNepQt2vRnJ14GLUBu9L5ObjRG6WVrv5TRtAblRikbT/pH0VqS8tJglbD2N/N0Pfl5UHhUpJ31VjuPXHGe4fuFymvDomAZXBClCVuzPquJLrXvbl25h4FdS91F8Pkfqr1HY5jxtqtHqoOHQJ8SicDfwiJxd0SYX8omwDv+jKBVCW3y8qjKu1GbGG/WxGDi5W5e+H1FodE/Zco3stD6MVqSWRHpOEjUchv6WcfmelJjXw69SIaoENUJqZYGZjwatff8DeMSuL5Ovo4URKoXYpKToRR4M2xtHdieS4ZFSmKhwN3qni0gKc3XWKCb9M57dl2/J/U/o1zl/tA/ryNFgxFZuRW3J5PoyhW82yy0zmfwt5xc+LcQBQCYJwE5iHtI3KCFEU7wIzgEN6ucOAhyiKN5BW3twBfgbK/wVbAqIoHkIKAH1OEIRbwK9Ig0xl3ueLEHnjEU7e7jhUdkFposS/R0vuH75iJHP/8BUavtkWAK+GfuSmZ5MenwKAlZMtAHaeTtTt1pQbBrF8fNvUIz4sirSY4ju6erWq8/RZFM+iYlCr1ew/eoqg1saxB9LSM/KDt+7cc4jGDepibSV9ZCQmS/cQHRvP0eBzdO/UjtJIvx6KRTUPzKq4IpiocOnZmqRDl4xkzL0LGlKr+j4IJio0+gEKv2Ufkh3yjKjVeygPFW1fRRJ3Iww7b3dsvFxQmCjxe70Fjw+XvDzekPTIRNwa+qHSz/xUal2X5NICJBaD5uF9lJ6VUbi5g0qFWfsO5J03rnaCQ0FHrqpRCwRFuZ0bXcwTBAc3BDtnUChR1WqG9pHxAIM29DqKStVBUIDKFIVHNXTPZ26eB3q2cURZvRGaexdL1Ve3ihtP41OITExFrdFy8OpD2tc3Ps3Bw8GGCw+kYIOJaVk8iUumspMdoijy2eaj+Lg5MLhDw3LZF3MjDHsfd2z1z69mjxY8KufzU1mYYWJlnv+3d9t6JDwoPsj56tXradHiFVq0eIXduw/x1lu9AWjWrCFpaenExBQd+HnnnQF07tyeIUM+MZqNjYiIJDBQWhnm6upMjRrVePz4aZH0AHU97HianElkShZqrY6D96NoX8yS6PRcNVeeJRFkcC0pK5e0HKlO5qi1XAhPwMcguKL23hk+7PYxH3b7mLMHz+Vvz6rVsBZZ6ZkkFTOAduPsTdq+KrWhnft04pw+QO65Q+ep16weCqUCM3MzajWsydPQCJLjk0mIjqdytUoAKCtVR5dcsF22rrsdT1MyiUzV2/cgmkDfku0LLGRfuqF9TxPwdiw9DochdSu78DQhjcikdOldvRFG+zpVjGQ87K25oA+4mZiezZP4VCo7lT2wtOWXX+ndcTC9Ow7m6P5gXu8rzZD6N65HRnoGCXGlf7QWxtrGiqYtG3LsQMlblBKvh2Hj4461vi54v9GCiEPGdcHK04nAH8ZwevQq0sNKDoxbHhKuh2FroM+nBH1BP4zh1OhVpP1NfVk3QzDz9sSkshuCiQq7Hu1IO2LcHplWLfggM6/ri2Ciyh/0AbDv0Y7UP08WyTtpwz5CXx3NB90+4uzBc3Q2qAuZ6ZlFBl9AqgvtiqkLAJY2ltRv4c+5g8bx7MZ9MZanIU/Z+cNv+b8dXL+Pia+MZeIrY7l06Dzte0tbTqs3rEFWeiYpxdTDO+du0eIVqQ1p37sDl/QBrZ09nZm4eiorxn5N9ONSgucC9Wr6Eh4Zw7PoONRqDftPniOwpXGckbSMTNT6U/t27j9G4/q1sbayxNnRHncXJx5HSDouXLuNb5XKRXQY5XXtEZbV3LGo4oJgosS9ZyviDhr7aOaVnAj4eRy3PvqOrDDj1QOmzrb5Mm6vNCX69+JXYP9T9lWkvqgbYTj6uGOvr3t1e7TgYSF/tzR6LBlBQmgkF34s+2Q0gJxbDzHV1z1MVNi+2i4/kPNzTKoU1D2zOr4IpgV1T+koxcBRebhg06UVaXuK1kFDNCGF/KJ2HVBfLOQX2Rv4RdVrgaL8flFh6rrb8TQ5y6AfiiGwWtG4fVI/lEygb8E1URT57PBtfBytGdzYp0ia4oi+EYaDjzt2+udXu0cLQsvptwQv2c7KFp+yus1Ydn/yHU/P3mXvmJVF8lWaqGjRow1XDxt/G1w9cok2vQMB8G1Yg6z0LFLjkgm7EYq7jwcuXq5F0rp5FzzbRp2bEvWowM8VBAGlX8P8+D4Add1seZqSRWRqtlSeITEEVis6WZKeq+ZKZHKxZf0/jairuP/+S5FX/LwAoijmIq3UKYx1IbltwLbCQqIovvMCurwN/l6LFNy5uGvLkU7pKkxx94koitYGf/+KNFhUKjqtjt2z1vLO+ikISgVXt58gLiSSZoMkB+7ipqM8OH6dGkEBjDu5DHV2Lr9NXJ2f/q2VY7B0sEar0fLnzF/0x7ZL+Pdoyc0StnkBqFRKpo0ZycgJc9DqdPR6pRN+PlXYtkvqVPu/0Z2w8GdMW7AMpVJBtapezJ3yaX76sTMXk5KajkqlZPrYUdiVsO0jH62OsGk/UnfLDFAqiNtyjOwHz3AfIq2qiVl/CKfXWuDatz06tQZdTh4PRi4DwKZZLVz7tifzbjgNjkhHdj9dtJnko9f+e+wrg4mzF3Pp2k1SUtLo2PNtPhw+mN49uv6lvEStjlMz19Fj4yQEpYL7206S/DCSuvpjY+9sPIaFix19987D1NoCUafDf3g3tnSYTNz1Rzzad5G+++ej02pJuB3Onc3Fx98oEZ2WjJVfYzd/KSgV5Bzah/bpE8xfeR2AnH1/YtamPeavvgFaLWJeLumLP3sBA3XkHd2MWe8xoFCguXUGMTEKVYP2AGhunERMikb75Dbm78wBUURz8xRiguSAmr3+AYKFNWi15B3dBLlZJesCVEoFU/q054Pv/0Sn0/FGizr4eTix4/QtAPq2qc+Ibk2ZtfEIfRZtRkRkzOutcLC24NqjKPZcekB1Tyf6fS6dIPHJay1pW9e7ZPO0Oo7NXEfvDZNQKBXc3naSxIeR+Ouf382Nx7B0sePtPQXPr9HwbqztOBkLR2teXzMGkGZA7/9xlicny45BcuDAMbp2DeLOnWCysrIZOXJC/rXff1/Lhx9OIjo6jhUrFvD0aSQnTkhHou7adYBFi75h8eJvWLPmSy5dOoggCEyfvpjExOJn+1QKBVM61eODXy+i04m8Ub8yfs427LgunW7SN6AqAMdCYmjp7YyFaUF3mZCRy8z9N9DpRHSIdKnpSbtiBlUALh67RNMOTfnl9M/kZufw5fhl+dfmrZvLsklfkxSbxE+Lfmbad1N4Z+IQQm8/4uBWaRY3IjSCyycus+rQSkRRx4EtBwl/IN3jdzNXMnnFJFQmJggWGeSd2GJk3+QOdflw50V0OnijXmV8nW3YcUNvXwPJvuMhsbSo6oyFiYF9mbnM2n8TnSiiE0U61/Qo0b5iy1apYMobLfngxwNS2TatgZ+7AzvO3ZN0t6zNiI4BzNoeTJ+vfkMURca80hQHq7J2PxsTfOQM7Tq2Yv+FneRk5zBj9Lz8ays3LWPWuAXExyYw6L1+DPtoMM6ujvx+fBPBR88ye9xCADq9EsiZkxfJNgigWhhRq+PijHV02jxJOs5920lSH0ZSY7BUFx5uOIb/2F6YOVjTfOE7AOg0Wva9MguAtt99hFvL2pg7WtP78jfcWLqT0K0lf6CJWh3nZ6yjs4G+lIeR1NTre7DhGA30+loa6Nuj19fuu49w1+vre/kbri/dSUgp+tDqiJq9Cp/1n0nHue84Qm7IUxzf6gZA0uYD2HZrhcObHRA1GsScPJ5+siQ/uWBuhnWbACKnf1eyDuDisYs069CUtad/lo5zH/9V/rX56+bylb4u/LjoJ6Z9N5WhE4fy6PajggDmQOturbkafIWc7IJZ77pN69K5TyfC7j1m5QHpHrZ9sYlrxws+mK4eu0LDoCasCF5FXnYu300oOFV06tqZrJr0HclxSWxctI6x305g4IRBPL4TxrFthwHoM3oA1g42jJgnnZSm1eqY0mN8sXaqlEqmffwOo6Ytkvr1roH4eXuxfY+UV7/XOhP2NJLpS1aiUCjwrVqJz8a9X3A/H73DlMXfotZoqOzuxrwJpZ/OJmp13J/6C422TkNQKojccpzMB8+oPKQTAM/WH6Ha+N6YOFhT+/NhUhqNlgtdpZXWDX4ah4mDNaJGy72pv+QHgS6JiravIvWJWh0HZq3lrfWTEZQKbmw/SXxIJI30/u7VTUexcrHjvd3zMdP3e82HdWdlp0m41fLCv3dbYu89ZcQ+qX05/sU2Qo8XXXGaj1ZH7NyVeP00H5QKUn89RF7oU+wHvAJAytZ92HRtjV3Pjvl1L2rM4vzklb6djtLeFlGjIfaz79GllbHyT6clc9XX2H62FBQKco9IfpFZN8kvyj3wJ2at22P2it4vys0lY0mBX2Q9YRYm9QMQbO2w/2UH2Zt/IffwvpK06fuhOnz422V0osgbdZ/3Q9KETN8G0oTA8dBYWlR1MuqHrkelsPdeFNWdrem/URqc+rh1Ddr6lLwqVNTqODJrHX3XS37nre0nSQyJJGCQ1HZe33QMKxc7huwu8FuaDOvGT50ml7otzzDfnkqB4O1HiQyJoMMg6dvg2KZD3Dh2hYCgRiwN/p687Fx+mPCtvsh1rJ/1IxPXz0KhVOSnBeg/5W08qlVCp9ORGBnPL9MKvp1qNq+DmJGCmFYwmaFSKJgcWJMPd12V+ta6nvg6WbPjlpRf3/peUnk+iqdFFScsTIzDP0w5cJMrz5JJyVHT9adgRrXwpVfdSiXaLfOfRRCEbkjf8ErgR1EUFxe6bgdsBKogjdksFUXxl7+l80X3rsr87zHd+60Ke0nmXCjxhPv/CBcbFL/F7D9Fsxsvea90WSgrdm/vDw1nVai+fvUiyhZ6iVh2rF620EtEqB9Qofq+H/G3Fge+MDPiT1WYrqQVvStMF0DPz+5VqL7fZ9aqUH2CW/Fbw/5TNHl/Z9lCL5HJqoqt6xU9P9jY9K/N4P9VxmvUFarPTlH89of/BJuCK9ZvOdFsSdlCL5HAi5MqVF9F83m7LytUX1+zFzuh9O/iUrP0gbyXjcWrARWq79tFL7bK8+9yi9In6V4mqycVv8X8P4XlR9++2AkL/8/I3vNVhX3PWrw2rtSyFARBCTwEOgPPgEvAQP3Ooecy0wA7URQnC4LgAjwA3EVRfLGAiAbIK37+YQRB+B0ovIZxsiiKB4uTl5GRkZGRkZGRkZGRkZGRKSf/XUGXmwGhoiiGAQiCsBV4A7hrICMCNoIUOdwaSAI0f0epPPDzDyOKYq9/+h5kZGRkZGRkZGRkZGRkZGT+41QCDLctPAOaF5L5FvgTiEKK4dtfFP9eACE5uLOMjIyMjIyMjIyMjIyMjMy/kwoM7iwIwvuCIFw2+O/9QndT3FawwlvRugLXAU8gAPhWEATbv1ME8oofGRkZGRkZGRkZGRkZGRkZmb+JKIprgDWliDwDvAz+XRlpZY8h7wKLRSkgc6ggCI+BWkDpxwCXghzcWaZMBlbtWWEvSWvd3zuV6kU5qUivUH2vaso+rvhlklXBa/pGXJtbofryVlVsMOncKxUbTFppXcFj8xX8vigsKi74+KXf/9YkyQuzx6JiC/PV7Irty5MVFftuVhZKPnHlP4FWV7HPLxbTCtVXy6pigztfyHGoUH2VNH8rDMILEaOq2LpwxiS3bKGXSKfcin03MxUVG1+2/+pGFaovbemfFaovJ61i309rj4oN5B4fYlWh+px9Ky5YdsQd+wrTBdDw6a5/d3Dn3xdXXHDnXlPKCu6sQgru3BGIRAru/JYoincMZFYCsaIozhEEwQ24CjQQRTHhr96XvOJHRkZGRkZGRkZGRkZGRkZG5j+MKIoaQRA+Bg4iHef+syiKdwRBGKW/vgqYB6wVBOEW0tawyX9n0AfkgR8ZGRkZGRkZGRkZGRkZGZl/K38vLvJLRxTFfcC+Qr+tMvg7CujyMnXKwZ1lZGRkZGRkZGRkZGRkZGRk/qX8T6/4EQTBG2gliuJm/b/fAZqIovjxP3lf/60MnfMeAUGNycvOZeWEb3hyO6yIjIuXK5+umICVvTVPbofx3div0ao1ePpWYuTST/Cp68u2pRvZu2YXACZmJszavgATUxOUKiVRey9z8avfjPKsEuhP2zmDEZQK7m45wdXvdxtdt/f1oNOX7+NSz5vzX+zg2mpp8NS+mgddvy94lHZVXLnw5a/c+Olgsfa9O2cEjYIak5udy3cTlvO4GPtcvVwZs2Ii1vbWPL4dxoqxy9CoNbTp2Z6eo94EICcrhx+mryT83hNMzEyYu30hKr19iXsuc/1LY/sqBfrTbO5gBIWCkC0nuPWdsX3VerWi3oevAaDJyuHc1LUk330KQOsvR1C5UwA5CWns6ji1WLsK4xXoT5s5g1Hoy/NaMeXZQV+eF77YwfXVBYPR/u91o86AQEREku4/49j4NWhz//p+7hkLvyL4zEUcHez5Y+OqshOUgbKaP6ZdB4OgQHP9BOqzu4vIKKrWxrTz2whKJWJWOjkbFkgXzCwxe+09FC6VAZHc3T+giwwtVZ9Jw2ZYDv8EFApyj+wl57fNRtdVdQOwnroAXVw0AHnnT5GzfR0KJxesRk9H4eCIqNORe3g3uXt2lmmfqn5TzAd/BAoF6hP7yN2z1dj+Wg2wGjsXXXwMAOrLp8n9YwMANl9tQszJAp0OUaslc/aH5dM3SK/v5D5y9xajb7SBviunyd0l6cPSCsthE1BU8gZEsn9civbR3VL1Kes0xrzfB5K+MwfIO7jd+HoNfyw+mI0uQdKnuXaGvH1SmZt07IVJ624giuiinpCz7kvQlP/ddAxqQPX57yIoFURvOkr4il1G1527NaHa5P6IOhFRoyVk5lpSLz4oM99a7RvQc9ZQFEoF57cd49jKovEdes0eSu2ghuRl57Jlwkoi7zwBwNzWkv6LR+JeszKIsHXSKsKvhuBZpyp9F7yHyswEnUbLzpk/w/nS31XHoAbUmP8OglJB1KZjJdjXD/T2PZy5rlz2GeIW5E/AXKmtfrz5BA++Na5/Xm+2ouZHPQDQZuZwdcovpOrbsu4Xv0aTkYOo1aHTajnWbWapuuwCG+I9bxiCQkHcliNEffu70XWHrk2pPHEgiJI94bN/Jv3ifQQzE+r+Nh/B1ARBpSBp7zmeLd1Wpm32QQFUm/cuKBXEbjpK5Ld/GF137NqUKpMHIOp0oNURNvMX0i/ex9TTiRorPsHExR5EkZgNh4n+cV+xOgxxC/LHf94QBKWCJ5uO87BIWbamxsdSWWoyc7g++ef8sux6aTmajGxErQ5Rq+N41xll6rNq1xj3me8jKBUkbztE4uodRtetO7XAdezb0vuh1RIzbw3ZV6T67PhuT+z7dQFEch+EEzVpGWJe+etepUB/muv7wIcl9IH1DfrAswZ9YHlxCmpArflDEZQKnm06xpMVxvXQvXdrfD5+HQBtZi53J/1Ihl5HlZGvUPmtIADS7z3lzuhV6Mro9zwD/Wmqtyl0ywluF7LJx6BfV2flcMHAplZfjqCSvl/fXUq/Xrd9AP1mvYtCqeD0tqMcXPlHEZn+s9+lXlAj8rJzWTvhOyLuPEZlZsKEbXNRmalQKpVc3X+e3csK2tqgod0IHNIdnVbLrWNXSftse5F83YL8aaiv62HF1PUqBnVdU6iuA6AQ6HRgPtkxyZwZsrTUsoSK9ZPO3H/Kkj/OotOJ9Gpei2EdGxpdT8/OZfrmY8QkZ6DRiQwJ9Kdns1oAzN56guB74ThaW7BzYr8ydQGYNmuGzccfg1JJ9t69ZG3eXKycqmZNHL//ntS5c8k9eRKllxd2s2fnX1d6eJD5yy9k/fprqfrMWzXFccKHoFSQ8ft+0tZuLVbOtE5N3Nd9Q8KU+WQdPQWmJrj/uAzB1ASUSrKOBpO6an25bHyOSeNmWL0v+Uw5h/aSs6OQz1Q/AJuZC9DF6n2ms6fI3rLuhXRYtW2M6/SRCEoFKTsOkrSmUFvWsQXOoweDqEPU6IhbuDq/LXMY8gb2/bqCIJCy/QDJ63YVp6J0+0Z9gqBQkHNgL9mF7DOpH4DN7AXoYiT7cs+eInvzi9ln074hleeMQFAqSNx6mNjvjX1Hu87N8JgwKL8vevbZj2ReugeA0tYKryUfY1GjCogi4RNXkHX1xfr5/9fo/rtW/PwT/E8P/ADewFtA8a2sTD4BQY1x9/FgbPsP8GtYg+HzRzGz56Qicm9NGcq+n/7k3O7TDF8wiqD+nTiy8QAZKRmsm/0jTbo2N5JX56qZP3AWuVk5KFVKlv76OeHHbxB77REAgkKg/fyh7HprMRnRSfTbM5fHh6+QHFIQ+Dw3JZPg2Ruo1rWxUd4pYdFs6zY9P593Lq0g7MDlYu1rGNQYDx8PPmk/iuoNazBi/gdM6zmxiNygKUPZ89OfnN19ihELPqBD/04c2niAuIhYZvebRmZaJgGBjRi56COm9ZyIOlfNZwNnkqO3b8WOJUQev0H81QL7mi8YyqGBi8mKTuK1fXN5eugKqQb2pUfEc6DPfPJSs6gU5E+rz4ext8ccAEK3B3Pvl8O0XT6yrEeYr6/d/KHs1pdnnz1zeVJMeZ6evQGfQuVp5e6A/7td2NJxMtocNV2+/wS/11vwYMepcukujp6vdOat3q8zbV7Zjl+ZCAKm3YeSs2kxYloS5sPnonl4BTHBIEi+mSVm3d4hZ8sSxLREsCwI+GvadTDaRzfJ3fkNKJRgYla6PoUCy/fHkD5nPLrEeGyXrCbv4hl0z8KNxDT3bpKxwNjZFHVastZ+hzYsBMwtsPvyB9TXLxdJa2yfAvOhn5L5+STEpHis536P+uo5dFGF9D24TdZX04vNInPheMSMtNLtMtQ35FMyl+j1zfke9bVi9D28TdayovosBn2M+tYl1N9+BkoVmJVRnoIC84EfkbV8GmJyApZTv0Fz8zy6aOMPPG3IbbK/n22c1N4J06A3yPzsfVDnYT5iGqqmgWjOHS6frQqBmouHc63ffHKjEmlycBHxBy+T9TAyXyQ5+BYX9e2HVZ0q1FszlgttxpZhksCbc4ex6u0FpMYkMvbPhdw5fIXY0IJ8awcG4OzjwcLAMVRt6EefBe+xvKf0od5r9lDun7zOug+XoTRRYmIhlWGPKYM4uHwn909cp3ZgAK9NHUT4G5+VYd8wrvVbQG5UIk0PLiLh4GUyS7DPuk4V6q0Zw/k248pXfnodDRe+w6n+i8iKTqLj/nlEHbpKuoGOrKfxnHxzHurULNw7NKDxF8M59mrBszzZZz55SRnl0KXAZ+EI7g34jLzoROrtW0LywUtkhzzLF0k9dYvkg5cAsKxdleqrx3Oj3aeIuWru9p2NLisHQaWk7h8LSDl2jYyrD0vVV23Re9zpN5e86CQaHFhM0qHLZD8s0Jdy6hZJBvpqrhnHtbajETVaHs9ZR+atxyitzGlwaAkpwTeN0hZXlg0WvcvpfovIjk4k6MB8oguVZebTOIJ7zUOdmolbhwY0XPoeJ14pCHZ/qvcC8pLKeXiBQoHHnA8IHzoDdUwC1X5fRvrR8+SFFgSzzzx7nbAj5wEwq+lN5RVTeNRlFCo3JxyH9uBR1w8Qc/Oo9M0UbHu0J3XnkXKpFhQCLRYM5aC+D+xRTB+YERHPfoM+DHrRaAABAABJREFUsPXnw9ij7wPLZ59A7cXDuNJvATlRibQ4uJD4g1eM3v/s8Hgu9ZyLJjUT5w4B1P3yfS50n4GZuwNV3+vGmbbj0eWo8V8zGveerYjadrJUm5ovGMphvU2v7JtLRDE2HdTb5BnkT4vPh7HfoF+//8thWpfSrwsKBQPnDufrt+eRHJPE1D8XcfPwZaJDC96reoENcfXxYGbgJ/g0rM6gBSNY3HMamlw1y976jNysHBQqJZN+ncftE9d4fC2EGi3r0qBzU+Z1H48mT4ONky3NCytXCDRa+A7B+rreqZi6nvk0nhOl1PXqI7qRHhKFysaijIdXsX6SVqdj0W9nWDXyVdzsrBj09W+0r+uNr3tBcPJtZ+5Qzc2Bb4Z3Jykjm56Lt/Fqo+qYqJS83rQGA9rUZcaW42XqAkChwGb0aFImTEAbH4/jqlXknjmDNjy8qNzIkeRdulRwrxERJL33Xv51519/JedUGf6YQoHj5E+I+3Aymth4PDZ+R/bJs6gfPy0i5zD6PXLOGfjMeWpiR05AzM4BlRL3n74m+8wl8m7dK7etVh+MIW3GeHQJ8dgtW436/Bm0EYV8ijs3Sf+sfBOZxelwm/0hEe9ORx2TgPfOr8k4ep68RwZt2bnrZBwtaMs8l0/lcbeRmFavin2/rjzpMxZRrcbrp3lknLiEOrzwQUsl67b+aAyp0yT77JevJu/CGbRPC9l3+yZpc/66fV7zRxI6aDbq6ERq7l5K6uGL5IQU2Jd+5iaph6VDn8xrVcXn+0nc6/ARAJXmvEf6ias8GfU5gokKhUUZfpnMv47/yq1egiBYCYKwVxCEG4Ig3BYEob8gCE8EQVgoCMI5QRAuC4LQSBCEg4IgPHoeCEmQ+EKf5pYgCP1L+x1YDLQVBOG6IAjPPXhPQRAOCIIQIgjCEoN7yhAEYYH+ns7ro2sjCIKLIAg7BUG4pP+vtf739vp8rwuCcE0QBBtBEDwEQQjW/3ZbEIS2pZRBhiAInwuCcEUQhCOCIDQTBOGEIAhhgiC8rpdR6u26JAjCTUEQRup/txYE4aggCFf19r6h/91bEIR7giD8IAjCHUEQDgmCUHavCzTu3IxTO08AEHrtIZa2Vti7Fj2lo26r+lzYdxaA4J3HadJFchnSElMJuxmKVq0tkiY3KwcApUqJQqUCg5jrbgG+pD6JJe1pPDq1lpA/z1Oti/GARHZiGnE3wtAVk/dzKrepS1p4HOmRicVeb9q5GSd3Sh11yLWHWJVgX71W/pzfdwaAkzuP0bRLCwAeXrlPZpoU6T/k6gOcPJzy0+QY2meiwvAgPeeGvqQ/iSVDb9/jXeepUmjAJf5yCHmpWdLfV0Ox9HDMvxZ74QF5KeX4UNLjWqg8Q/88j88LlKdCpURlboqgVKCyMCUrNrncuoujSUB97GxfzklnCk9fdEmxiCnxoNOivXMeVQ1j21T1WqF5cEka9AHI0g+CmFqgrFITzfUT0r91WsjNKlWfqnptdNGR0syURkPe6WOYNmtTrnsVk5OkQR+AnGy0z8JROLmUmkbpWwtdbCRifDRoNajPH8ekcaty6fsrKKsV0nfhOCaNyqnP3BJVzfqoT+pXNmg1kFX6SRgK75ro4qIRE2JAq0Fz6SQq/5blv2GFEkxMQaFAMDFDTCm+rheHbSM/sh7HkBMeh6jWEvfHWVy6NTWS0WYVnKSjtDSDcpyIWSXAj4TwGJIi4tCqtVzbfZZ6XZoYydTr0oTLvwUDEH4tFAsbS2xc7DGztqBas9pc2Ca1S1q1lpw06Z0UETG3lppuc1tL0sqoh7aN/Mh+HJtvX+wfZ3EuxT6FpZlRO1weHBv6kvEklsyn8YhqLRG7zuNZqC1LvByCWt+WJV4JwcKgLXsRrBv6kfMkmtynsYhqDYm7TuPQtZmRjE7f7hZnz/NrgokSwURV5rO0aehHzuMYcp/GIao1xP9xBseuxuVnqM/w/VDHpZB56zEgrXLKConE1L10ux0b+pH5OJasp9LzevbHOTwKlWXS5RDUqVKdSroS+pfLEsCiQQ3ywqNQR8SAWkPqnmBsOrUwkhGNytPcqDwFlRLB3BSUChQWZmhiy1/3CveBYcX0gXGl9IHlwU5fv7P173/MH2dx7WZcD1MvP+T/2Dvv8Ciq7/+/7uym957QQ4dQQq/SpNhFsYAIIlYUFRUUQVBBRLCLil0UEHsBpfcmvfcaSnqy6clmd2fu749Zkt10kM/q19++n4eH7MyZec85M/fcM3fOPddmt2f27pN4OXAIgwHF3u8ZfL0oTqm6vYWV0Snh923UraJfz9hzCj8HvrTtxymupl+PjW9M2rkUMi6koVpt7FqyhbZlfEvbgZ3Y9os+QHV270l8AvwIjAgGnOMug9HApRV+ew8fyPK5v2Gz6Kui5WWW/1BQUVuvXU1bd7xnPjGhxFwbz5lvazY44so46dD5NOqGBVInLBAPo4FB7Rqz3p6BeQlCCAqKrUgpKSq2EuTrhUHRX6c6NKpFoK93jbgAPJo3R01MRE3W4wjz2rV49ehRTs739tsxb9yIlp1d4Xk827dHTUxES02tks+zVTNsF5OwJep8BSvW49OnPF/A0MEUrNmEanLmk0V232k0grF63+kIY9MWqEmJeraLzUbxxrV4dK1ZzFRTeLdx9mW5f27Ev79zHOHoy4SPd4kOXo3qUrT/ONJcDKpG4Y5DBAyoeYxVTr8Na/G8yvr5xjehOCEFi73vy1qyiaCBVfV9pfop/j74d44j8zv9g5i02lBzXbdC2b8Cmua6f/9S/Fszfq4DkqSUNwIIIYKAWcAFKWU3IcQ7wDygB+ANHAY+Bm4H4oG2QDiwUwixEeheyfaJwHgp5U12nlF2uXZAMXBcCDFHSnkB8AO2SSkn2weEHgJeBd4D3pFSbhZC1EOvzt0CGA88LqXcIoTwB8zAw8AKKeUMIYQB8K3CBn7Aeinl80KIX+1cA4CWwNfAYuABIEdK2UkI4QVsEUKsBC4At0kpc4UQ4cA2IcSlvOYmwDAp5UNCiB+AIcCC6m5IaHQomUmlhcRNKZmERoWSnVYaAAWEBFCQW4Cm6g98ZnImodUEuKB/uXrtj7eIbhDN4a9Xk7rvdKkRokPISzKV/M5PNhHVrlG15yyLJrd048Tvf1W6PzQ6zEm/zJQMQqPCyulXWAP9+g0dwN71e0p+K4rCrD/eIrpBDCfnrSZjb6l+vtEhFDjoV5BsIqIK/ZoM7UPiugPVaFs5/KJDyL9CexakZLHvk6WM3PYeNrOFCxsPcmHjoSu+lqsNERCCzC3VTeaZUGo566aERoPBgPeIyeDpjW3HCmwHN6OERCAL8vC8+WGUqHpoyQlYVs4Ha+XL5orQcNSMtJLfWmY6xqYtyskZm8UR+PYXaFmZFM37CPVCgvM1RURjiG2C7UTV06BESDjSlF7KZ0rH0Kg8n6FxS/xnfIqWlYl50cdoiZe+Nkn8np8NUlK87g+s6/68enzTP0XLzsT8nc6nRMag5eXg8+BzGOo1RE04SdGCD8FiLnd8iR1CwtCyHPiyMzDENivP17AFvi9+hMzOpPjnz9GSzyGzM7Gs/gn/1+YjrcWoR/egHt1T7tjK4BUdSnFS6ctqcVImge2blJMLv74TjSbfg2d4EPvvnVnteYOiQsl2OG92son68Y2dZALLyqSYCIoORVNVCjJzGfrmGGq1qMfFg2f57ZWvsRQV89srX/PIN5O4edK9KIrg/SFTqSo89Y4OxVxOv8bl5CKu70SjycPwDA9i372vV6ufI3yiQylyGFgvSjYRWoVviR3Wh5S1+0s3SMk1300ECWfmr+HsgspfCj2jw7A46GNJzsS/gvsVcl0X6k0ajkdYEMdGzijdoSi0XvEG3g2iSZ23nPy9J6vUzTMmFItD/2BJziSgAr7Q6ztTf9JwPMIDOVrB8+FVNwL/Vg3I31M1n3dMCEVJZWxZwf26hAb39CG1jC17fjcRKeHs/DUkLFhbJZ8xKgxrcql+tpQMfNqWb3sBA7sROf4+jGHBnH/wZV02NZPMz3+h6aZ5aGYLBZv3ULB5b5V8jijbBxZW0wc2vYI+sOzzb04yEVSFPWvf05eMtfsAKE7JImHuH/Ta8yFakYXMDQfI3FA1f0U6hVehU+Mr0Ck4KpQsB52ykk3ExjcpJ2Ny8i2ZhESHkpuejVAUJv8xi4j60WyYv5yEffpU0aiGtWjSuQWDJwzDWmzlpxnfwM4LTuf1iQ6l0KGtFyabCKumrSc7PJ/x00Zw4NVFePjV6LujS+OktJxCooP9S35HBflx8Hyak8zQHnE89eUKBryygIJiC7NG9Ee5wiXolYgItHSHfi89HY+WLZ1lwsPx6tmTrGeewaN58wrP492vH+a1VbdzAGNEOLaUUn3UtHQ8Wzmf0xARhm/fHqQ+MgGvqWX8gKIQs/AjjHVrk/fD71gOHauWs+TQsHA0x5gpIx2PZhXETM3jCJrzBZopk8IvPkI9n1BjDo+oMGwp1fsy/wHdiHh2FMbQYC48rGeiFZ88R8TT96EEByDNFvx7d8R8qGpf7aRfeDhaurN+xor0axFH8IdfoGVmUvD55emn933OfZFffNNyckGDulLr+REYw4M4PWo6AF71orGZcqj31pP4tIil8OBpEl/+DK2o8jjXjf8e/pUZP8BBoL894+UaKWWOfftih/3bpZR5Usp0wCyECAZ6AouklKqUMhXYAHSqYntFWCOlzJFSmoEjQH37dgvwh/3v3ejTxAD6Ax8IIfbZry9QCBEAbAHeFkI8CQRLKW3ATuB+IcTLQGspZVV52BZguYO+G6SUVvvfl7gHAiPt3NuBMPSBHQG8JoQ4AKwGagNR9mPOSin3VaCHE4QQD9szq3adyk9AiPKdWrmB/prIVACpabxww9M83vVBouIbEdqsTpXnvNwv0YqHgdgB7Tn15/ZKZSrSr+zF10Qmrltr+t3dnwUzS+fsaprGhBue5pGuDxDerhHBV6hfdPcWNBnWm92vVTwfuyao0X2sBF5BvjQY2J753Z/m645PYPT1oult5b8U/WOoyJZloSgo0bGYv3sT87ez8LhmMCI0GhQDSkwDbLvXYP78RbAW49H95svnK2NL25kTZD98N7nPPEDxnz/jP3GGs4C3D/7PT6PwyzlQVHWGERWpV+bmqQknyXt6GPmTH8ay6ld8x00r2Zc/7SnypzxKwZsv4NX/VgzNWl8dvmeGkT/FzvekzicUA4b6TbCsXUz+1EeRxWa8bhpaNV9FhGX5zp8if/JICl99DMv6xfiMsU9t8fXH2KYbBS+OouD54eDpjbFzv2r4HKkraBcVNMSMZTvZ3vNpDo56g4bP311ufw1OW/JlvSoZpEQxGKjdKpatC1bx9o0vYCkqpt+YWwHoce8Afp/+DdO7P85v07/h7lnVTGGoSdsA0pftZFvPZzgw6k0a1UA/Z46K9agIEd1b0uCePhycUerL1t3yCmsGvsjme2bTaNQAwrtW/IJTOVf5TVnLt7O/15McHz2Lus8NK92haRwc8Cx7OjyEX3xjfJrVq5wLKunbyhOalu1g7zVPcez+2dR73vl5V3y9af75eM5MnYeaX1QNXfVt4RLCe7Sk/rA+HHp1Ucm2DTe/zNqBk9k6fBYN7x9AWFW21Amr3m9H3sq/OD3wUS48Op2Ip0cAoAT6E9C/Kyf7jOZE9xEIX2+Cbu1bo/Pp1DXvky71gbsutw+sUL2KSUJ6tKT2PX05OV2vAGAM8iPyug5s6vQEG9qOweDrRcyQqr/iV3z/KpaN6t6CxsN6s+dq6FSDmOWSiNQ0Xr1hAhO7PUKDto2p1bQuAIpBwTfQj9cHT+Ln1+bz8Iflp3vWxK9dQkT3lsQ6tPWY/u0wZ+SQfSChct1qRFix6N+Nkyry+2XZtx6/SLPaYax66V6+f/YOXv91C/lmyxXxVXwRztcQMHYs+Z9+WnkWgdGIV48eFK9fX/25a2DLkPGPkfX+5xXzaRrJwx7l4nVD8YprjkejBtVzVsFd1trqqRNk3X83OU88gHnJzwS8OKPcMZfLUZFDyV/1F2eve4SLj00nYpzuyyynL5D52Y/U+2oGdb+YjvnYWaSt8pkEFZBXK2E7fQLTfXeT/fgDFC35mcCpl6tfBdsq0C9nxTaO9nucMw++Rq3xw/WNRgO+rRqRMX85x294Gq3ITNRjQy6P//86pHTdv38p/pUDP1LKE0AH9EGOmUKISxPXLw1Lag5/X/ptpPJWdzlD8Y7nVSnNirLK0p7NcbsCdJNSxtv/1bYPSL0OPAj4oGfcNJdSbgR6AYnAfCHEyCquw5GvRF8p5SVdL+n1hAN3rJRyJTAciAA6SCnjgVT0zKiq9HOClPJTKeVXUkrjjxt/JSvVRFit8JL9odFhZKWZnI7JM+XiF+iHYtAfq7CYMLJSnWWqQmFuAYl/HaV+nzYl2wqSTQTUKs2q8Y8JpeAypxfV79uW9EMJFGU4pyy3vq8/byx9hzeWvoOpjH5h0eGYyuiXa8rFt4x+Jodrqde8Po/OepzZD75Gfnb5Mb3C3AJSth6ltoN+hckm/Bz084sJrXD6VEiLunR/40HWjn6H4qyaT+0qi/xkE/5l7FnT6Vp1erYi70I6ZlMemk3l7LJdRHcs/9X7n4LMNSECHdLzA0KRec66ybws1DMH9EyeonzU88dQouohc03IXBNakp6NZTu6AyW6QdV8mekYwiNLfithEWimDGehokIw6y951j3bwWhABATp+wwGAp6bhmXjaqzbqq+TJE0ZiNDS6WBKaET56UzmQijWs2ps+3cgDEaEv17H6JKszM3GumszhkZVvwxeNt+BUj4tKx1pSkc9o38NtO7ciKF+1c+KlpWBEuLAFxyOzC7jPxz41EM7wWBE+AVibN4OLTMVmZ8Dmopt75YKs5MqQ3FyJl61SqdnetUKw1LFdI7sbUfxaRCNR2jV0xSzU0wEO5w3OCaU3DTn8+aUlYkOJSc1i5yUTHJSTJy3f4nfv3Q7dVo1AKDjkN4cWK7P4d//5zbqta06a8+cnIl3Gf2qmq6i6xdVrX6OKEo24VO7lMMnJpSi1OxyckEt6tLhrQfZOuptLA6+zGyXLc7MJWnZLkLjG1bKZUnOxNNBH8+YMCwplfc1eduP4FU/GmMZfdTcQnL/Okxw33aVHGnnS8rE06F/0Pkqt1/utqN4N4gq4RNGA82/GE/6L5swLa38A8QlFCWZ8KlVxpYV8AW2qEv7tx5i26i3KrZlRi7Jy3ZVmXkF+ldxj5hS/YzR4VirmK5VuPMwnvWiMYQE4tcjHsuFVFRTLthU8lZsxad9zdteQZk+0LeKPrDHGw+y5gr6QHOyyen5964VWuHz79+yHnFvP8K++97EaucI69WKwvPpWDPzkDaV1D93ENyp/Bf2K9Ep2N6vr7sCnbJTTIQ46BQSE0p2mZglKyWTUCffEkZ2mZisKLeQE9sOE9c7vuS8e1foz2jC/lNITcMzzLndFCab8HVo674xoSXPnCOCWtSl41sPssWhrYd1bkqtgR24Yce7dP14LJE9W9L5gzFV6urKOCkqyI8Uh2lhqTkFRAT5Ocn8vvM417aORQhBvfAgaocGcDYt+4r4tPR0lAiHfi8iAjXDOY7waNaMoKlTCf/uO7x69yZw3Di8epYOPnp16YL1xAm0rOpjOVtaOsbo0rjFEBmBmu7c1r1aNiVi5mRq/7EA3/69CH3hSXz6OOeUyvwCzLv349O9sm/oFeiakY7iGDOFR6BlOusqHWOmXfaYKTCoxhzWlAyM0WV8WVrlfUPRrkN41I3BEKLHSTk/rSThtic5P/w51Jw8LDWt74Ndv4hq9Ct00G/n5eun933OfVFV+hXsOGL31QFYkzOwJGdQuE+vZ5e9dCs+rS5/BoUb/7fxrxz4EULUAgqllAuAN4H2NTx0I3C3vfZNBPogy44qtucBf7fAyEqgZOkoIUS8/f9GUsqDUspZwC6guRCiPpAmpfwM+OIy9KoMK4AxQggPO2dTIYQfEGTnsQoh+lKatXS5+BCIf+GGp9m1cjvXDOkDQON2TSnMK3CaBnUJh/86SJcb9A6i15C+7LYXGKsMAaGB+AbqnaqHlyd1r2lF1qlSR5u6/wxBDaIJqBuB4mGgyS1dObuq5lM4AJrc2o2TFUzzOvj1aibc8DQTbnianSu30XuI/pWySTX6db1Bz3LpPaQfO1fpAVJ4rXAmfPICc55+l+Szpdcf6KCfp5cnta5pRc7p0v0Z+84QGBuNv12/2Fu7cmGls35+tcLo+9k4Nj31MblnUi5L97JIK2PPxpdhz7zETKLaNcbo7QlA7R5xZJ1MrOYo10FLOoMSGo0IjgDFgCGuK7YTzrrZju/GULcZCAWMnhhqNULLSEIW5OgDR6ExABhi49AyqtbNdvIYSkwdlMhoMBrx7NkP684tTjIiuDRYNTRpDkJB5ukJjH6PP4968RzmxeVXTKkI6pljGKJrIyKiwWDEo2tfrHu2OvMFldalMjRsBkLoxZy9vMHbnlbv5Y2xdUe0MlPOyvGdPYYhqjYi3M7XpS/WvdXwKTqfzMlCM6WjROvZbcaW7coVhS4L7dxxlMhaiLAoMBgxduqN7cA2Z77AUj6lQVNdv4JcNFMahtjmJQW5jc3j0ZKdpydUhby9p/FtGIN3vQiEh4HIwd3JWOFcCN6nQVTJ3/6tY1E8jFirKZ57Yf9pIhpEE1onAoOHgXY3d+fQqt1OModW7abj7b0AqN+uMea8QvLSs8lLzyE7KZOIhvoz2bRHK1Lt7S03LYtGXfXpAE26tyI9oWq/oOsXXaJfVDX6BbSORdRAP0dk7TuDf2w0vnV1jrq3diV5hbOuPrXD6PbFOHY+MZd8B19m8PHC6Odd8ndU79bkHK+8+HH+vlN4x8bgVTcS4WEk7NaeZK3c6STj1SC65G/f1g1RPIzYTHkYQwMxBOqzrIW3J0HXtKHoVOVcAHn7TuHTMAavejpfxOAemMrweTvw+dntZ7Pbr/E7j1F08iJJn/xBTZC17zT+DaPxtd+vOoO7kbyyvC27fvk0u8Z+5GxLXwdb+noR2bs1uceqbgtFB07g2aA2HnWiwMNI0E29yF/jPEDlUT+mVNe4RggPI2pWLrakdHzimyG89bbn170txadr3vbK9oENK+kD+/2NPjDX/vz72O0ZPbg7aWWeTe/aYcR/+QwHH/+QwjPJJdvNiZkEt2+M4qP3e2HXtCK/mn4vc98ZAhx0alCJTn0+G8fmpz4m7wp0Sth/isgGMYTVicTgYaTjzT3Yv8q5Te9ftYuut/cGILZdE4ryCslNz8Y/NBAfexvw8PKkeY82pJzWddq3cgfNuunZoJGxMRg8jFgynf1ARW09qYK23v2Lcewo09YPvfY9f3Z4gqWdx7Ht0Q9I23yEHWPnVqmrK+OkuLqRnM/IITEzF6tNZcXeU/SOcw6hY4L92W5/BjLzCklIy6bOZQySO8J6/DiGOnVQovU4wrtfP4q3OvezGcOGkTF0KBlDh1K8YQO5775L8ebNJfu9r70W85o1NeKzHD6OsW5tjLV0Pr9BfSja4MyXePMIEm+6l8Sb7qVw9UZMM9+naP1WlOAghL8ezwovT7y7tMeaUPPV9WwnjmGoXQclSuf26tUP6/YyMVNIacxkbGqPmXJzyp6qUpgPnsCzQa0SXxZ4Y6+SQs6X4FGv1Jd5tWyE8NR9GYAhVB+EMcZEEDCwO7l/VF7EvUL9ajno17sflm1XV7/C/Sfxio3B0973hdx8TUkh50vwrF/aF/m0amjXLw9bejbW5Ay8GtYGIKBHG6ei0P9fwF3j519b46c18IYQQgOswBig6vUJdfwKdAP2o2cQPielTLHXyKloeyZgE0LsR68ZdCWVap8EPrRPqzKiDzI9CoyzD7qo6FPGlgFDgQlCCCuQD1SV8VMTfI4+VWuP0HN604HBwEJgiRBiF7APqPkk3Eqwd+1u4vt24N2NH1NcVMwn498v2ffcvCl89twHZKVlsWjmNzzxwbPcNX44CYfPsO57vYhYUEQwM5a8iY+/L1KTXD/6Zib0f4KQyBDGvP0UiqIgFMGFJTtJWLOv5NxS1dg45WtuXfCcvpz79xswnUgk7l59GsfhBWvxjQjirj+n4+nvg9Q02j5wHQv7PY81vwijtyf1rmnF+olfVqnfnrW7ade3I3M2foylqJgPx88p2ffCvCl8/NyHZKWZWDDza57+YDzDxg/n7OEzrLXrd8dTQ/EPCeCh6fqUC1XVmHjzswRHhjD27XEl+iUt3snF1c76bXvxawZ8+5y+7Ov3G8g+kUizEbp+x+evpe3Tt+EV4k+310YBoNlU/rCv3tLrw8eJ7tYC71B/7tz1Pvve/JmT31XeUUlVY9OUr7nZbs9j328gq4w9fSKCuNPBnm0euI5F/Z4nbd9pTi/dwZ3LXkVTVTIOneNwDYszVoYJL73Ozr0HyM7O5drB9/LYAyMYcvOgKzuZ1LAs/xrvYc+BomDbtwGZkYixva6bbc9aZGYS6ukD+Dw8E6SGdd96ZLr+0mdZ8TVeg8cgDEa07DSKl3xaNZ+mUvjZuwS89Ka+nPuapagXEvAapC8JXLxiMZ7deuN13a2gqmAppuAtfeUlY4vWePUdhC3hNIFvfw5A0YLP9KygSvk0ir6Zg9+EWfpy5xuXoSWew7OfvoStZe0feHTqhee1t4CmIi3FFH70KqAPmPiNs6/6pBiw/rUG28GdlTGV8s2vgK+vnW+dna/fLaA68+n6zMHn0UkIowdaWjKFn8+ujKmEz/z9R/g+OUPn27oSLfkcHtfcAIB101KM7Xvi0esmvfi2pZiiz/U6KlrCcWx7NuE7+QNQVbQLp7FuXlY1nwOkqnHihS+J/26yvtz5onUUHL9IrZEDAEj6ZhURN3Ul+s5eSJuKZrZw6OF3qj2vpmr8MvUrHv5mEopBYccP60g9eZFuw/sD8NfC1Rxdt5cWfeOZtOE9rEXFLJrwccnxv7z8Ffe+OxaDh5HMC2l8N17f98PETxn80n0YjAasxVZ+fOEz2laj3/EXvqTdd5PAoJC8aD0Fxy9Se6R+HYnfrCbypi5l9Hu3xva7xLFv0jyuWfS8vgT5dxvIPZFIw5HXAnDmmzW0fPo2PEMCaDfzfrt99GXbvSMC6falvr6CMBq48OtWUquq0aFqJEz+nObfTkUYFNK+W0PRiQtEjhgIQNr8lYTd2I3wO3rr+hRZODnmLQA8o0Jo9J6+nLBQFDKXbCF79e7Kuex8ZyZ9TtyiF8GgkLZoLUXHLxI9UudL+WYlYTd1JfLO3mhWG5rZwvFH9OcjoHNzIu/sTcGRc7Rd/QYA52d+S9aayuvgXLJlj0UTEQaFc4vWk3c8kVi7Lc9+s4YWz9yOZ0gA8a/fX3LMukEv4hUeRNevdFsqRgMXftlStS3t+qW8Mpd686YjFIXsn1ZRfPI8IcOuByBr0TICB/Ug6LZ+YFPRzMVcfHIWAEX7j5O3fAsNF7+HVFXMh8+Q/d3ltb1tL37NQHsfeLKCPjDe3gd2tfeB0qayxGEFs5pwHHvhK9p/NwlhUEi0t+869uf/4jerafjsEDxC/Gkxa3QJx/ZBk8nZc4rUP7bTbdVMpKqRezCBi/OrfsmWqsaOF7+mv0O/nnMikaZ2nU7MX0sbu05dHPr1pXadrvnwcaLs/fqQXe+z/82fOVWmX9dUje+mfsFT30xGMShs+WEdyScv0mu47rM2LlzFoXV7aN23Ha9umIOlyMLXEz4EICgymFFvjS2JS3b/+RcH1+oDKVt+WMd9s8cwdcVbqFYb8579kLoV6Ld30jx62dv62SraenuHtr7muik1vmdl+VwVJxkNChNv78mYT5eiScmtnZvRODqUH7fqNfju7N6Shwa0Z+p367njjR+RSMbd1IUQe7H9ifNXs+t0MtkFZgZOW8CYQR25rUsV2bWqSt577xHyxhv6EufLlqEmJOBzix5HFC1eXPmxAF5eeHboQO5bb9XMmKqGadYcIj98HRSF/MXLsZ45h/8QvV/P/7nywWlDRCjhrzwPBgWEoHDVBoo2VZ/BWAJNpWDuuwROt8dMq5aink/A63p7zLRsMV49euN1w60lMUX+7CpWq6xEv9Rpc6n7xatgUMj5aSWWU+cJHqrHEdnfLSVgUA+CBl+LtNmQZgtJ40rr2dX+YDKG4ECkzUbqKx+h5V5G5pimkj/3XYJefRMMCuaVun7eN+j6mZcuxqtnb7xvLNUv7/XL1+/ilE9pNP9lfTn379dgPnGBsHuvAyBzwXKCb+hO6JC+SKuuX8Ljb5QcfnHqZzR4/xmEh5Hi8ymcd3iXc+P/D4jK5uW64cYlDKs/2GUPSQ/Nv3qhq4gNSs2/aF8N3Gi7OitY1RSFLs7pe2jvtOqFriIsH9c8+L8aKN7t2q8jBn8Xj827+HlRfDxcxrXz10CXcQH84eNaY95Y5Nq+PEtx7bNZR1RdF+dqQ9Vce/9S8XQpX3O/mn9lvhrYbi6/Sub/ErVtNpdxpRhd2xa2eLi2GGv/Ytc+mwVXWCj5SnH3J383+f7ykPtmNYM5VxnmXNc+n/4xVpfypZ/0q17oKiK8ketWwrpwONhlXADtzv/u2sbnYhQtesllgZLPsFf+lbb8t2b8uOGGG2644YYbbrjhhhtuuOGGG278PfyLp2C5Cu6Bn38YQojtgFeZzSOklAf/ietxww033HDDDTfccMMNN9xwww03/jtwD/z8w5BSdvmnr8ENN9xwww033HDDDTfccMMNN/6TkO6Mn3/lql5uuOGGG2644YYbbrjhhhtuuOGGG278fbgzftyoFgYXjg8Oa+Pa4rlRe+q4lG/gwOTqha4irGmuK3AJri+27Pmoa4tJG85WvhLP/wIyJ92lfLY//nQpn8wzu4yrTXyqy7gAfjoeVb3QVUSH/hku5Ss441I65ifVcinfUy+7ls+6dZ9L+QwxodULXUXs/9y1xcc7dHRdX2vwd+03VP8NMdULXUUMuONKFry9cljOua54LoD1V9f2DUEzHnIpX2Bupkv5XI2Ij352KZ9P7yYu42rcJMVlXP9fwF3jx53x44YbbrjhhhtuuOGGG2644YYbbrjxX4U748cNN9xwww033HDDDTfccMMNN9z4b0K6NvP03wh3xk8FEEIECyEe+6evww033HDDDTfccMMNN9xwww033HDj78Cd8VMxgoHHgI/+4ev4p/AU8BAggM8qEmjRNY57p47G4GEgz5THjLunXBbBzY/dTp+7r0VTNb55+QsObtwHQODMd1FCw5CWYgByXxyPsXFT/B5+AhQF88o/Mf/4rdO5jK3jCZgyAy1Vn9Nv2bqJokVfA+D31PN4du6Glp1FzuP31+jaovq2od20EQiDwplv13P8gyVO++vd3p1mj98MgK3AzJ6JX5Fz5HypgCLov/xVilKy2DLyzWr5jK074T38cVAUrBuWUvznd077Dc3b4vfUNLR0fa6vdfdmin+fr+/09cN39HiU2g0ASdHnb6KePlIjPQE8OnTG79EnEIqCefmfFJWxrUfreAJemoGWotu2eOsmir79usbnNzRsg+egESAUbPvWY926pJyMUr8FngPuRRgMyMI8zPNn6Du8fPG66UGUiDqApHjJZ2iJp2rMXRFefO1tNm7ZQWhIML8t+PhvnQtgy/4TzJr/B5qmcVufTjxwS2+n/XmFZibN/YGUzGxsqsZ9N1zD4N4dAJi/bDO/rN+FENCkTjTTHh6Cl6dH1XyHE5j903qdr0crRg/s7MxXVMzkectIycrDpmqM7N+Rwd3iKLbaGP3OD1htKjZVo3+7Jjx2U/fL0tXQvD3etz8EQsG6bRWWNT+Vl2ncCq/bHgLFiCzIpeiDFy6Po1VHvIc9hhAKlk3LsCz73nl/szb4jp2GlmFvC3s2Y1myACWqDj6Pvlgip0REU/zb11hW/1pjbo+O9rZgUDAv+5OiH8q0hTbxBLzs0Ba2bKJoYfVtoWXvttw59X6EQWHr92tYOff3cjJ3vnQ/cX3bYS0q5pvxH3Hh8NmSfUIRTFzyOtkpJuY+MKtke5/7rqP3yOtQVZXDa/fApvfKndfYphM+I8aComBZv5TiJYuc97doi98z00t8i2XnJop/1X1L4LvfIs2FoGlIVSV/yphqdfXq2omgcWMRBoWCxUvJn7+oQjmPFs2I+OwDTFOmY163UdfT34+QF8ZjbBQLUpI94w0shyr3ZbG929D/pREoBoX9361n21xn3xLaKIYb33yYqLgGbHzzR3Z8utRpv1AEo/6YTl5KFj+Nfqta3bacSWX26oNoGtzWth6juzV12j9v+0mWHr4IgKpJzmbmse7J6wny8SzZds+8DUQGeDPnzq7V8hniOuI9dAxCUbBsWo5leZm20LQNvo+/gpbp0Bb+WKi3hUcml8gp4dEU//4NljVVtwVD47Z43nCf7qv3rMW6aXE5GaVBSzyvH1nqq790qLEmBN6PvobMzaJ44exq9Yvp04b200cgFIXTi9ZztEw/W/+27rS097PWQjO7Jn5F9pHzKF4e9P9lCoqnEcVo4PyfOzj0ZvU1Pjw6dHZpHGFs2wmfUWNBMWBZ+yfFv5dpey3b4jfhVbQ0e9vbsYnin78pFRAK/jM/RpoyKJg9qVq+qL5taDN9JMKgkLBwHSfK2LPu7T1oOrY0btn3/Jclccugne9hyy9CqhpS1Vg36MVy5y8LQ4sOeN/xiB63bF2BZdWPzvubtMbn4aklz6dt31Ysy3UbePS5FY/ug0AIrFuWY11f3ieWhUf7zvg9ZL9/q/7E/FOZ+9cqnoAXHe7fX5so+s5+/558Hs9O3dByssgZW/39c3U/tOXgKWZ9uwJNatx2TTseuLGn0/68QjOTPvuVlMxcbJrGfYO6MfiaeBKSM3ju49Jn/2J6Fo8N7sO9A6v2L1uOnmP2L5t1vq4tGd2/gzNfUTGTF6zW4whNY2Tfdgzu0oKUrDxeXLiGzNxChAJDusUxvHfbKrn+CT5XPisASoM4PPsN033nwU3YdiwrL1O3GZ597wbFgCzKp/j7N/RraX8txja9ALAd2IRtz+oacV6CoUV7vG9/WG+Hf63EsrqiuKw1Xrc/BAaDHpe9f3lx2X8K7ho///zAjxBiJDAekMAB4EXgSyACSAful1KeF0LMA4qA5kB94H7gPqAbsF1KOcp+vnzgE6AvkAUMlVKmCyEeAh4GPIFTwAgpZaEQIgr4GGhov6QxwJNAIyHEPmAV8CfwMpABtAJ2A/dKKaUQogPwNuBv3z9KSpkshHgSeBSwAUeklEOFEL2BS9G5BHpJKfMqsEkf4BUgFYgHfgEOog/I+ACDpZSnhRAR9muvZz90nJRyixCiM/CuXbbIbsPjQohRwC2AL9AI+FVK+VwZ+lbogz6dAQuwPKpBDKkJpYUSfQN9GfXqw8weOZ3MpAwCw4LKqlAlajWpQ9ebe/L8gKcIiQpl4sKXGd9nLNLeIPPeeBX11HFdWFHwGzOO3BefRctIJ+idT7Bu24J64ZzTOW2HD5D3SnlnVrx6GeY/fsH/meoDJ51P0P61UWy8eyaFySb6L5tO0so95J1ILBEpOJ/O+tunY80pJLpfWzq88QBrb3ypZH+Th64j72QSxgCf6vmEgvfIJymY/RzSlI7/yx9h3fsXWlIZ/U4covCdyeUO9xk+FuvBnVg/eAUMRvDyqpmeAIqC/+PjyJmk2zb4vU+wbN+Cer4M96ED5L58BR2FEHhefx/mha8jc014PzAN24ndyIykUhkvX7yuG4V50Wxkbib4Bpbs8hw0AvX0AYp/fh8UA3hchm6VYPANA7hnyC1Mml79gFx1UDWN175ezCcTRxMVGsg9Uz+iT4fmNKpdWtT3+1XbaFg7kjnPjsSUm8+tE97hxh5tMeUW8O3Kv/h11ji8PT2Y8P63LN92gFt7daiSb+YPa/n4iduJCg5g+Oxv6d26EY1iwkr5NuynYUwY748ZjCmvkMHT5nFjp+Z4Gg189uQd+Hp7YlVV7n/rB3rGxdImtoZFQoWC9x2PUjh3CjI7E99n3sZ2aDtaqkMxdh8/vO4YQ9HHLyOz0xH+l+cXEAo+w5+g4K3nkVkZ+E35ANu+v9CSzzuJ2U4epOh954FmLfUiBa88WnIe/7cWYd27pebcl9rCC/a2MOcTLNsqaQtTa94WhCK4e9oDvH/vq2SnZPL84pkcWLWLlFOl/iSuTzsiY6N5uc+TNGjXhKEzHuSNwaVtve/9N5ByKhFv/1J/0rRbHG0GdGTG9eOxWWz4hwXSqXk5cnxGPUXBzAlopnQCps/FumcrWmIZnY4fpODN8r4FIP/VZ5D5uTVTVlEIfvYpMp6agJqWTuSXczFv2oot4Vw5uaDHHqZ4+y6nzcFPj8W8bSeFk18BoxHhXXl7F4pg4PT7+G746+SlmBi1eBonV+8m82SpbzFnF7Dqpfk0HVRxm+o4+joyTiXh5V+9n1Y1ycyVB/h4aHeiAnwYPm8DvZtE0yi81F+N6tKEUV30op8bTqawYOfpkkEfgG93nSY23J+C4hoU3BcKPveMpeCdiXpbmDwH2/4K2sKpgxTNcS6or6VepGDamJLz+L/xbfVtQQg8bxqN+esZyNxMvB95Ddux3cj00ucUb1+8bhqNef5MZE4m+AU6ncLY7XpkehJ4VW9PoQg6vDaKdUNnUpRsYuDS6SSu2EPuSYd+9kI6q4fo/WxM37Z0mv0Aq256Ca3Yyto7Z2ArLEYYDfT/bSrJa/eTuaeKjwKujiOEgs/opyiYMQEtM52AmR9j3VVB2zt6sNJBHa8bhqAlnkf4+FbPpwjazryfzXfNpCg5k77LXyW5XNySxsbbpmPNKSCqX1vavfkg628ofXY2DZmBxVQuHK1UP++7HqPwg8nI7Ax8J7yL7eA2tBTnxTnU04cp+vhl50uNqY9H90EUvvE0qFZ8HpuO7fBO/dmpVD8Fv0fHkTvlWbTMdILe/gTr9gru35ED5E2r4P6tWYb5z1/wf7oG98/F/ZCqaby2YBmfPHuvHkdM+5w+8c1oVDuiROb7tTtpWCuCOU8Nw5RbwK2TP+TGbq1pEBPOD688UnKeAc+8Q7/2ZTuC8nwzf9rIx2NuISrYn+Fv/0jvVrE0ii4t9v795oM0jArh/YduxJRfxODXFnJjh6YYFIVnb+1Bi7oRFJgtDHvrB7o2q+t07D/N59JnBXTf2X84xT++jczLwvveF1FP70NmOhSX9/LRZX56F5lnAt8A/dDwWhjb9MK8YAaoNrzuGId65gAyO62G3Ared46h8MMX9bhs/Dt6XJZSJi67awxFc19CZl1BXObGfw7/6FQvIUQcMBnoJ6Vsiz6w8QHwjZSyDbAQeN/hkBCgH/A0sAR4B4gDWgsh4u0yfsAeKWV7YANw6Y38FyllJzvPUeAB+/b3gQ327e2Bw8BE4LSUMl5KOcEu1w4YB7REHyTqIYTwAOYAd0gpO6APWNnTFZgItLPrYe8JGA88LqWMB65BH5SpDJfs0RoYATSVUnYGPgeesMu8B7wjpewEDLHvAziGPqjUDpgKvOZw3njgbvt57xZC1C3D2wLYBhSiD1pt6Dioi5NA91t7sXP5NjKT9FVkcjNzSvb1uK0Xr/w+ixlL32L0a48ilPKPWIcBndm2ZDM2i430C2mkJiTTKL5xhUYwNm2BmpSof2W32SjeuBaPrj0rlK0ItsMHkHk1DGaA0HaNyE9IpeB8OtKqcuH3bdQu8+KQuesk1pxC/e/dJ/F1WB3FJyaUmGvjOfPtuhrxGRo2R0tNRKYng2rDun0dHu1rmInh7YuxWWusG+xfslUbFNZ8NYxytt2wFs/LsG11UGo1QjOlIrPTQVNRD2/D2NTZlsZW3bEd36kP+gAU2l8yPX0w1GuGbd96/bemQnHh376mjvGtCQoM+NvnATh0+iJ1o8KoExmKh9HIdV3bsH73UScZIaCwqBgpJYVmC0F+PhjsbUJVNYotVmyqSpHFSkRIYEU0pXwJKdSNCKZOeDAeRgODOjRj/YHT5fgKzBaklBQVWwny9cagKAgh8PXWX0JtqoZN0xCXoatSvwlaRjIyMxVUG7a9GzG2dvYLHu17Yzvwl36/AZmfU9GpKoWhYTO0tCRkRoreFnasx9ju8rKSAAwt26GlJSMzaxg8AcZmZdrC+rV4dvv7baFBfGPSz6WQeSEN1aqye8lW2g7s5CTTZmBHtv+iZ70k7D2Jb4AfgRHBAARHh9KqX3u2fLfG6Zhrhg9kxdzfsVn0QYT8zPKDM4ZGum/R7L7Fsm0tHh0u3541hWfL5tguJqIm6TYsXL0W717l+fzuvI2i9RtRs0pXChK+vnjGt6Fwid2X2WzI/Mp9WUx8I7ISUsm5kI5mVTmyZBtNBjj7lsLMXFIOnEGzquWOD4gOpVG/eA58t75Guh1KzqJuiB91gv3wMCgMalmb9ScrX21l2dGLXNeydsnv1NwiNp1O5fY29WvEZ4hthpbu0BZ2bsAYfwVtoUU7tPRkpKnqtqDUaYxmSkFmpYGqoh7cirF5RycZY+se2I7u0Ad9AApKnzkRGIqxaXusu9fW6Loc+1nNqnL+923UKdPPZjj0sxl7nPtZW6GeEax4GFA8DNXWbXB1HGFo3BwtNQktzd72tq7Fo1OPGh8vQsMxtuuKZW3NVlkMbdeYgrOpFJ5PQ1pVLv72FzFl7GnadRJrjt6mTLtP4fM3VnVTGjRFy0hCZurPp23PRoxtutXs2Oi6qAnHwVoMmoZ66hAebat+to1NWqAmJ+oZGpfuX5f/zf1zdT906EwidSNDqBMZgofRwHVd4li/77iTjBCCQnu/XljsHEdcwvYjZ6kbGUKt8OCq+c6lUTc8iDrhQXoc0a4J6w+edZIRCAqKrQ5xhBcGRSEiyI8WdfUBKT9vTxpGhZCWU3XM6Wo+Vz4rAEp0LDIrDZmTAZqK7dgODI3ina+pRRfUE3v0QR+AQv38SmgMWtIZsFlAaqgXTmBo0r7m3PWb6v79Uly2ZyPG1s7ZXh4demPbvxWZdWVx2X8Omua6f/9S/NM1fvoBP0kpMwCklCb0DJ5LeXnzAccWu0RKKdGzX1KllAellBr6YE0Du4wGXMrLXOBwfCshxCYhxEFgOPqA0aVrmGvnV6WUlbWKHVLKi3a+fXa+ZugZMqvs2UEvApfWBz8ALBRC3Is+gAKwBXjbng0ULKWs6tPfTillspSyGDgNrLRvP+iga3/gAzv3YiBQCBEABAE/CiEOUTo4dglrpJQ5UkozcAQ9e8oRh4BeQBh6ZtANYbXCnQSiY2vhF+TP5O+mMf2PN+h5ex8AajWuTZebejBtyCQm3/AsmqbRY3CvcoqFRIdiSi5detiUkklIdGnWgv/TEwma8zk+Q0eihIWjZZR2nFpGOoYw5+sBMDaPI2jOFwS8MhtDvQbl9tcUPtGhFCaWLn1ZmGzCJzqkUvnYYX1IXru/5Hf8tBEceHURaDUrICZCwpGm0iW7NVM6IqS8fobGLfGf/im+z85Eqa3fMiUyBi0vB58Hn8N/2sf4jH4WPL1rxAughIejpTvbVqnIti3iCP7wCwKnXZ5tRUAIMtdU8lvmmRABzrZUQqMR3n54j5iM9wPTMbbWm6sSEoEsyMPz5ofxfvBVPG988Kpk/FxNpGXlEB1a+vUkMjSI1CznF/ChA7pxJimN/mNf544X3ue5ETehKApRoUHcd0NPBj01m/5jZxLg60331lUvEZqWnU90SOmgVVSwP2nZ+c58veM5m2JiwKRPuWPGfCbc2QdF0Yd4VE3jrtcW0O/5T+javB6ta5rtAyhBYWhZpW1Wy85EBIU5y0TWQvj44zP2NXyffQdjp741Pj+ACA5Hc2gLMisDJbiCttCoJX4vf4zvuBkotcq/SHt07oN1R80GXkuuPayCthBeSVuY+wWBr87GUL9BtecNjgolK6nUn2QlZxIUFVqBTKlts1IyCbZ/0bxj6ih+nbkAWebFNrJhDI07N2fCbzN4+vuXqd+mUXmdQsPRHF46NFMGSkhEOTlD45YEvPYZfs/NtE8Z1SGlxG/iG/i/+jGefW+sVlclIhw1rZRPTcvAEBFRTsand08KfnWehmKsHYOWnUPwi88R8fUnBL/wLMK7cl8WEB1CXnKpb8lLNhFQhZ8ui2tfupd1ry1C1tBPp+WZiXbI4IwK8CEtz1yhbJHVxtYzafRvVroc/BtrDjKubxxC1Gy4tXxbSEcJDisnZ2jYEr+pc/F9spK20Kl3jdqCCAgtHdABZK4JEej8nCrhMbqvvn8q3o++hrHtNSX7PK+/D8uKhTUunOkbHUphUpl+Nqby+9doWB+S15X2s0IRXLfqNW47MJeUjYfI3Hu60mMBl8cR5dpeZjpKRf1605YEzP4cv4mvo9Qp5fO5byzmhZ+ArNnLg3dMCEUO9ixKNlU5sNPgnj6kOsQtSEnP7ybSd8UMGtzbr1q+cv1BVka5/gDAENsc34kf4DNmGkq0npiuJZ3D2LgV+AWAhxfGuI4VxjxOfGXvX2Yl969ZHEHvf0HAy1d+/1zdD6Vl5znHESGBpGY5DzwM7deJM8np9H/mHe6Y+jHPDRtU0q9fwvIdh7muS6vq+XLyiQ7xL/kdFexfbjBl6DWtOZuaxYCX5nHHrEVMuO2acnyJmbkcu5hB6/pRVAVX87nyWQF7nJtX+hFD5meVi3NFSJSeMXn3BLzvnYKhpT5IqmUkodRpAt5+YPTE0LB1uWOrghIchpbt8P6QXb4dKpG1Eb7++DwxE98J72LsVH37duO/jX96qpdAn/JUFRz3F9v/1xz+vvS7Ml0uHT8PfYrUfvuUpz6Xc6Fl+FQ7nwAOSykr+tRxI/oAyi3AFCFEnJTydSHEn8ANwDYhRH8p5bEa8Dnq66irAnSTUjplDgkh5gDrpJS3CSEaAOur0cMRR4FZqampey5cuBB64sSJIlHkHHwoRoXYVo2Yec9LeHh78vKvMzm19zhxPdoQ27oR0xbr8/s9vT3JzSg/jlZh8GsPGPPffBUtMwN8fAiYNB0RXN4Jln1g1FMnyLr/bjAX4dGxCwEvziD74eHlOWqAii+t4kc0ontLYu/pw7pb9ToHMf3bYc7IIftAAhHdWtSQsIJtZfjUhJPkPTMMis0Y23TG98lp5D9/H0IxYKjfBPP8OahnjuE9/HG8bhpK8S/zasZdg5wP2+kTmO6z27ZTFwKnziDrwRratiYvOYqCEh2LeeFMMHrgc//LqImnQDGgxDTAsuIbtKTTeA4cgUf3m7FuKD9/+Z9CRY9FWY23HjxB8/q1+HzSg1xINfHIrC9p36wBmpSs23OUpe+MJ8DXhwlzvuWPzXu5qWe7yvkq2Fa2LW09kkCzOhF89tQdXEjP4dEPfqZ9o9r4++hf0H6YdC+5hWae+XQJp5IyaFyr6oC7cs0obwDFgKFuIwo/ehHh4YXvuDdQE45XncLvrEwFG8u0hXOnyH9uuN4WWnfGZ+wrFEwaVSpgMGJs243in7+oGWdV3GXUs506gWmEQ1t4aQZZo6tpC1X4ulKR8jJSSlr1a09+Zg4XDp2lSdeWTvsNBgXfQH/eGDyZ+m0b8cCHT8PEoWXJq+W2JZwk9ym7b2nbBb9nppH37EgA8l95EpmdiQgMxn/iG6jJF1CPHfhbugaPe5ycDz8t/zXMYMCjaROy33of65FjBI17HP+Rw8j79KvKyCrgqvzSHNGoXzyFmbmkHkqgXtea+WlZwckr824bT6USXzu0ZJrXxlMphPh60TI6mJ3nMio5qiYnL9MWzp8if+K9+r1r1Qmfx16m4EWHmhSX2sIvX14ZXwXtW6nVEPO8V8HDE5+HpqFePIUSFoMsyEFLPovSoGUFJ7pCPjsiu7ek4bA+rB5cWk9IapLlAybhEejLNV88TVCzOuQcv1gFXwVtrMzvqxlH1MSX2c6eJPfxofr9i++C3/jp5I0bgbF9V2RuNurZExhbVl/PRKergX+2I7xHS+oP68PGW18p2bbh5pcxp2bjFR5Ij+9fIO9UEpnbKgtNqZmvvnCK/CmjwGLG0LIjPg9PoWDaQ2ipF7Cs+hHfsTOQxWbUxLOgls/Kq46vrHrq6RNkPWC/fx26EDB5BtmPXMH9c3E/VGEcUeYSth4+TfO60Xw+YSQX0rJ45K0FtG9aH38f/WOY1aayYd9xnhpS/Ut9xXFEGb5j52lWO5zPHr+VCxk5PDp3Me0b1cLfnjVcWGxh/FfLmXBbz5Jt/xY+lz4rlaEi3xlVn+If3wKjJ973vICWfAZpSsa6Yznedz6DtBSjpV34+5kiFcZljSn8YLIelz39JmrCsZrHZf811HAw/b+MfzrjZw1wlxAiDEAIEQpsBS5FsMOBzZd5TgW4w/73PQ7HBwDJ9ulZji18DXpdH4QQBiFEIJBnl68Ox4EIIUQ3+/EeQog4IYQC1JVSrgOeQy8W7S+EaGTPUpoF7EKvV/R3sBIYe+mHw3S3IODS5O5RV3DeL6Kioup37Ngx4J577vm8ONF5ik1WciYHNuyluKiY/Kw8ju04Qr0WDUAINv20jsk3PMvkG55lQr8n+OXd7+k4qAszlr7FjKVvEdu6EabkTEJjSl84Q6PDyErVv95qmfbAuKgIy4bVKCGhKOGRJbJKeESpjB2yqBDM+tiXddd2MBoQgVc2j7Uw2YRv7dIRc9+YUMyp2eXkglrUpeNbD7Jl1NtYsvSsi7DOTak1sAM37HiXrh+PJbJnSzp/UHVBVGnKQISWfhVXQiOQ2ZnOQuZCKNa/LtsO7EAYjAj/QLSsdKQpHfWMHqBZd27EUL/qrBFHaBnpKBHV2LbQwbY7L8+2Zb8ai4BQpy8jADIvC/XMAT3tuygf9fwxlKh6yFwTMteElqR/ybUd3YES3aDGurkCUaFBpJhKBzbTTDlElpmu9fuGPVzbsSVCCOpFh1E7IoSzyelsO3SK2hEhhAb642E0cG3HOPafPF+Wwpkv2J8Uhy+Bqdn5RAT5OfNtO8K18Y11vshgaocFcTbV2eaBvt50bFKHLUcSaqyrlpPh9MVaCQ5zyuYCkNmZ2I7tAUsxsiAX2+lDGGrF1phDZqWjOLQFERKOVlVbOLgDYTAg/EttbmzdCe38KWRudo154QrbgqH6tpCdkklIrVJ/EhITRk6a8/3ISskkxGEALiQ6jJzULBp1bEbr/h2ZvvkDRs8ZR7PurRj1zhP2Y0zsW7EdgHP7TyM1DRHgfC2aKR0lzEGn0HC07DIDD0UO9ty/vcS3ACV+SOZmY921GWPDqrsrLS0dQ2QpnyEyHDXDmc+jeVNCp08h6pdv8enbm+DxT+HdqwdqWjpqejrWI7ovK1q3Ec+mlfuyvBQTAQ4ZDQExoeSVec4rQ52OTWncvz1jNr/DLXMep373ltz0btV+OirAh5S80u8rqXlFRARUnJG0/MhFrmtZp+T3vosmNpxK4fqPVjJx8S52nstg0pLdVfLJrIwybSECLdu5vTm1hUM7y7eFVva2kJddJRfYfbXDl2IRWIGvzs1EPbVf99WFeagJx1Ci66HUa4qhWQd8np6D151PYoiNw2vI41XyFSab8K3l3M8WpZS/zuAWden85oNsur+0n3WENbeQtL+OEtO3TZV8egaf6+IILbNM2wuLQMsq48sc294+e9sLCMTYrBUeHboTOGcRvk9NxdiqHb5jq643UpRkwsfBnj4xoRSllG8PgS3q0v6th9g26i0ne16KcYozckletovQduUzCJ30yy7TH4SEI3PKPp9FYNH1U4/sAoMRYa8LZf1rJYWznqTo3eeQBXlo1byElrt/YRFopiru3+6a+eeK4Op+KCokwDmOyMolMtj59eP3zfu4tkNzvV+PCqV2eDBnHbLmNx88RfP6MYQF+VMdooL8SXG496nZ+UQElokjdhzj2jYNdb6IYGqHBZbEEVZV5dkvl3NDh6Zc27bq5+Sf4HPlswJ6DOuYpSP8Q5D52eVktITDYLVAUT7axRP2RUtAPbQZ8/zpFH8/G8wFyOzUGnNr2ZkowQ7vD8HhFcRlGdiOlonLatc8LnPjv4d/dOBHSnkYvSbOBiHEfvQiyU8C9wshDqDXtnnqMk9bAMQJIXajT+O69JloCrAdvViz46eMp4C+9ilgu4E4KWUmsEUIcUgI8UYV129BH2SaZb/+fUB3wAAssJ9zL3odnmxgnP2c+9Hr+5Qv/X55eBLoKIQ4IIQ4QmktodnATCHEFvu1XC4uec16wO1bf9/ktHP3qh0069wCxaDg6e1Jo/imJJ1K5PCWA3S+oVtJsWe/IH/Cakewa8X2ksGgswdPs2fVTrre3BOjp5GIupFEx8Zwet8pFINS6nwNBjw6dcO6fw+G2nVQoqLBaMSrVz+s252L5YmQ0hcAY9PmIBRk7pXNY83adwb/2Gh860YgPAzUvbUrSSucg3Sf2mF0/2IcO56YS/6Z0joPh177nj87PMHSzuPY9ugHpG0+wo6xc6vkU88ewxBVGxEeDQYjHl36Yt271Vm/oNJOxdCwGSgCmZ+LzMnSX+6i9Q7E2LJduaLQVcF24hiGWg627d0Py7arZ1st6Yw+lSs4Qv/qENcV24k9ztdwfDeGus1AKHqqa61Geu2Aghz9ZSRUn45kiI1Dy0isiOYfQ1zD2pxPyeBimgmrzcbybQfo3d45gyA6PIjth/XBq8ycPBKSM6gTGUp0WDAHTl2gqFift7/98Glia5efhuPEVz+a82lZJGbkYLWprNh9nN6tGzrJxIQEsP24XtgvM7eAhFQTdcKDMOUVkluoB6pmi43tx88TG1X5VICy0M6fRAmvhQiN0r9mtuuF7dAOJxnboW0YGsaBooCHF4b6zZyLP1cD9exxFMe20LkPtn1/OcmIwNK2oMTqz41j8WGPLn2xbr+8aV4AtuPHnP1Mn2raQrPmoFTfFs7tP01kgxjC6kRg8DDQ4ebuHFjlXNT44KpddLldnxLboF0TivIKyU3P5vfZi5jcbQxTeo7lyyfe5fjWQ8x7eg4AB1bupFk3Pa0/MjYGo4cRmed8LeqZYyjRtVEidHt6du2HdXcZezr5luYgdN+Clzd426c2eXljbN0R9aJzXYaysBw9hrFubQwxug19+/fDvMmZL3XIcFJvv4fU2++haN0Gst98D/PGLWimLNTUNIz19JJzXh3bYy1bFNoByfvPEBobTVDdCBQPAy1v7sqpVXsqlXfEhtk/8FHXJ5nb82kWP/Eh57Ye4Y9xVfvpuJhgzpsKSMwuwKpqrDiSSO/G0eXk8sxWdl/IpG+T0n1P9mnJyscHseyxgbx+S0c61Q/ntZsrL+IOoCYc11P0L7WFTr2x7a+iLTSooC107lvjKY9a4ulSX20wYGjdHdsx537PdnQXhvrN7e3bE0OdxmjpiVhXf0fRW49T9M4TFP/4PurZwxT//GGVfKZ9ZwiIjcbPfv/q3dqViyud+Xxrh9Hz83Fse3IueQ79rFdoAB6BesFjg7cHUdfEkXsqmapgO3HMpXGEerpM2+veD+uuKvr1Rs31fj0vF/Oiz8l97C5ynxhG4XvTsB3aS+EHr5WlcELWvtP4N4zGt54et9QZ3I3kleXjlq5fPs2usR85xS0GXy+Mft4lf0f2bk3usar9tnbuBEpELUSYvT9o3wvbgW3O+jm8DCv1m+q+xV4X6lKBWRESgbFtd6y7NlTJZztZJlbp1Q/rjjL3L9jh/jWpmX+uCK7uh+Jia3M+1cTF9CysNpXl2w/TO955xcDo0CC2H9H9b2ZOPgkpmdSJKL2GZdsPcX3n6qd5AcTVi+R8Rg6Jmbl6HLH3JL1bNXCSiQn2Z/sJPYMuM6+QhLRs6oQFIqXklUXriI0KYUTf+H8lnyufFQAtJQEREoUICgfFgLF5Z9TT+51k1FP7UGo3KYlzlZiGaCa7z7pU6DkgFEOT9tiO7ihLUTn3eXs7DHVohwe3O8nYDlYUl1WRHfkfh9Sky/79W/FPT/VCSvk1UHZN3HL5ipdW7bL/nYBeW6fcPvvvKegDPY7b5mKv5VNmeypwawXb7ymzab3DvrEOf+9Dn9JVFuWqiUkpn6hArhyklOvL8PWpaJ+9NtLdFRz/F+DYc0yxb5+HPuXtktxNlVzCz+g1fqzA44W5Bav7DR8IwNqFK0k6lciBDXuZueIdNE2y/rvVXDyhZyv8+OYinp8/FaEIVJvKvCmfkZmY7nTyxJMX2P7nFmatfh/NLiM1DU8fLwKnz9BXp1IUrPt2U7x8CVpaCoHT3wRFoXjVUtTzCXhdfwsAxcsW49WjN1433AqqirQUkz+7NIXZ/7mpeLSORwQGEfz1jxQt/Irilc7L+jrZTtXYO2kevRY9jzAonP1uA7knEmk48loAznyzhpZP34ZnSADtZ+pp9Zqqsua6y1vOvgSaRtH8OfhNmKXrvHEZWuI5PPvqt8ay7g88OvXCs98tJfoVfvRqyeFFC+bg8+gkhNEDLS2Zws+rX0a3lFslf+67BL36JhgUzCt123rfoNvWvHQxXj17431jqW3zXn+lmpM6QGpYln+N97DnQFGw7duAzEjE2F5v3rY9a5GZSainD+Dz8EyQGtZ965HpeqdkWfE1XoPHIAxGtOw0ipd8WnPuSjDhpdfZufcA2dm5XDv4Xh57YARDbh50RecyGgy8cN8tjJn9FZomGdy7A43rRPHDGr3jvevaLjw8uB9TPvmJIRPfQyIZd/cgQgL8CAnwY0DnVgx98QMMBoXm9WtxR9/O1fApTLyrH2M+/AVNk9zaLY7GtcL5cZMeZNx5TVseur4LU+ev4I4Z3yAljBt8DSH+PpxITGfKNyvQNIkmJQPbN6VXmUGjKqFpmH/+GN9HX9Gf0+2r0VLO49H9OgCsW5ejpV7EdnQ3vs/NASmxbluJllJ1FlM5joUf4Pv0TH0J680r0JLO4dFbbwvWDX9g7NgLzz43gaYiLRaKPplRerynF4aWHSj65t2ac5Zwq+R/+C5Br71pX+55Keq5BLxvtLeFPxfjdU1vvG+yt4XiYvJmVt8WNFXj+6lfMvabySgGhb9+WEfyyYtcM3wAAJsWruLQur3E9W3PKxvex1JkYf6Ej6o979Yf1jJi9mO8uOJNbFYbXz/7IaPKfsbRNIrmzcHv+Vn6ktIblqElJuB5rb6ks2XNEjw698arv923WIsp+ED3LUpgCH5P27+ZGAxYtq7BdmBn1RelamS/NYfwd3W+gj+WYTubgO9tOl9hmbo+ZZHz9hxCXp6E8DBiS0wma0blvkyqGiunfs3d3zyHMCgc+GEDGScTiR+u+5Z9C9fiFxHEfUum4+Xvg9Q0Oo6+js/7P48lv6o1FSqGUVGYOLANY77/C01Kbm1Tj8YRgfy4V38Zu7Od/gV17YlkusVG4uP5N0MrTcP87Qf4jntNX1J6y6W2oNdasm74E2OHa/S2oKpIq4WizxwGBzy9MLRsT9GCd2vMZ/nzK7xHTtJ99Z51yPSLGDv2B8C2azUyIwn15D58Hputt+89a5FpV/YCIVWNXZPn0edbvZ89Y+9nG4/Q+9lT89fQ6unb8AoJoOOlftamsvL6KfhEBdP1PfvCEYrg/JLtJK3eW41+KgVz33VZHIGmUfTl+/hNmg2KgmX9MrSLCXj2t7e91Uvw6NobrwG32n1ZMQXvTb8iW16y575J8+ixaCLCoHBu0XryjicSa49bzn6zhhbP3I5nSADxr99fcsy6QS/iFR5E16+eBkAxGrjwyxZS11UxpdOun/mHufg+/ioIpcTXe/S8AQDr5qUY2/XA45ob9WlcVgtFX80qOdz7wcl69o9qo/iHj6CofDaXM59KwcfvEviK/f6ttt+/6+z3b3mZ+1dc5v6Nd7h/X/1I0bdfUbyqkvvn4n7IaFB44d7rGfP2Qj2O6BlP49qR/LBO/0BwV9+OPHxzL6Z8+TtDpnysxxF3XktIgD74WVRsZdvhM0wZWX0dtkt8E4dcw5iPF+txRJcWNI4J48cthwC4s0crHhrUianfruGOWYv0OOLmboT4+7D3TBJ/7DpOk5gw7pr9HQBP3NSVa1o2+NfwufRZAT3OXfMtXkPG6b7z4BZkZhLGtr0BsO3fgDQloyYcwnvUyyAltgObSla39bplDMLHH1QVy5qFl7eIiaZh/uljfB+bpsdl21bp7bDH9QBYtywrjcsmfgCaxLptBVpyzT8Qu/Hfg6isfsn/VQgh8qWU1ec7ulFj3Fv/dpc9JO+1yqxe6Cpi7Z461QtdRQwcWPlKMP8LWNNqsHTwVYRPj7KLxP1v4fnotOqFriLUs9W8YFxlyJz06oWuImx/1GwVmasFWUmB3P8FLBctLuMCmHq86iKUVxuv9ahh/ZirhIIzLqVjflKt6oWuIp562bV81q37XMpn+BurOl0JFn9+JcnHV46B7Vz3Vdvg79rk+bUbal6Y/2pgwB3ZLuWznKv56qRXAx6R1dSNucrwHD3KpXwlq6b+R1H40c8u5fPpXfOSCn8X6nnXvjMEvP/H5Sz4+n8OhR8/5bL3Wd9H3/tX2vIfz/i52vi/NOgjhGiNvnKZI4qllF0qknfDDTfccMMNN9xwww033HDDDTfcuBz85wZ+/i9BSnkQiP+nr8MNN9xwww033HDDDTfccMMNN9z4b8I98OOGG2644YYbbrjhhhtuuOGGG278N+Fezv0fX87dDTfccMMNN9xwww033HDDDTfccMON/xHcGT9uVItg4eEyLp9el7HS0FVA2C4XFxHs2tK1fAWXv4LN34F5y0mX8hlcXGzZENvOpXxaatVLaF9tFB/PrV7oKuL0/jCXcbW637XFZX2Pu5bPo4VrixEH1XKt78z/0bVf6rSUVJfyGVvUdykfLl5uNsvgukLuAEUZrmt/odc2chkXQPD6K1t6+kphjG/uUj5D3SyX8pl+ce0qR2Fnj7iUTzRr71I+zJexMtVVQNZpb5fyCcV1ca53X9e2vf88/sXLrLsK7owfN9xwww033HDDDTfccMMNN9xww43/KNwZP2644YYbbrjhhhtuuOGGG2644cZ/E5q7xo8748cNN9xwww033HDDDTfccMMNN9xw4z8Kd8bPVYYQYhqwUUq5WggxDvhUSunaCa//Y7To3ZY7po5CMShs/X4tq+b+Xk7mjpdGEde3HZaiYuaPn8vFw3qtklc2z6E434ymaWg2ldm3TKqWT4lthee194CiYNu/Edv2peVl6jbTZQwGZGEexYtmAWDsOBBj214gJVr6RSxLvwDVVmNdQ/u2pcmr9yMMCskL13BujrOu4dd1pOHzdyM1ibSpnJwyj5wdx2t8foAtCRm8seEYmiYZ3KoOozvFOu3/etdZlh5LAUCVGmdNBax9pC9FVpUpKw6SWWBBCBjSug73tKu+ToTSIA7PfsNAKNgObsK2Y1l5mbrN8Ox7NygGZFE+xd+/AYCx/bUY2/QCwHZgE7Y9q6vk8mjXGd8HngBFoXj1n5h/+dZpvzEuHv8XZqClJQNg2bYJ8w9fo4RF4PfUZJSQUKSmUbxqCcV//Fytblv2n2DW/D/QNI3b+nTigVt6O+3PKzQzae4PpGRmY1M17rvhGgb37gDA/GWb+WX9LoSAJnWimfbwELw8/159qxdfe5uNW3YQGhLMbws+/lvnAti85zCzvvwRTZPc3r87D9w+yGl/bn4hUz+Yz4XUdLw8PHjl8RE0qV+LlAwTk9//moysXBRFYciAHtx7U7/L4vbo0Bm/R59AKArm5X9S9KPzvfRoHU/ASzPQUvR7Wbx1E0Xffn1ZHEF92lF/+miEopC2aDXJH/zqtD9kUCfqTBiGlHp7O/fSl+TvOFYqoCi0Wj4bS7KJE/e9Vi2foUk8njfer/uWXWuwbvytnIwS2xLPG+9HKLpvMX/+Ehg98H5oGhiMCMWA7fA2rGt+qJCjWe+23Dp1JIpBYfv361g3d3E5mVtfuo8WfeOxFFn4fvxcEg8nENEwhns/eLJEJqxuJCve+YlNX+rttcd9g+gxciCaqnF07V7I+L68fg3b4DlohN7W963HunVJef3qt8BzwL0Iu+80z5+h7/DyxeumB1Ei6gCS4iWfoSWeqtqezdrhdcsDoChYd6zGuu6XCq4pDs9bHwDFAAV5FH38IiIoDK+hT6EEhCClhm37Kqyb/yh3bJPebbjBbsvd369j49zy+tz40kia9o3HWmTh5/Efk3w4AYBu919Hx6F9QQh2fbeWv75cDkB0i3rcMuMBPH29yL6YwY/jPqQ4v3xtNEPD1nj2H64/K/s2YN32Z3lb1muOZ/97EIoRWZSHeeFMRGg0XoMfK5UJjsSy6RdsO1dWaUulQRyefYbqfAc3Ydu5vLxMnaZ2GQPSnEfxD28CYGx3LcbW1wAC28GN2PauqZKrhO9K+4UOAzC27gmg97PLv6q2n63bpw09Xx6BYlA4smg9ez9yvpfBjWLo99bDRLRqwPY3fmTfJ6X9fpsHr6Pl0D5IJKZjF1n77KeoxdYq+by6diL4mbEIRaFg8VLyvllUoZxHi2ZEfvEBphenU7R2IwDRv36LLCxEahqoKmmjxlTJBa6/f45wSdxyNo3Za46gScltbeoyuktjp/3zdpxm6ZEkwB63ZOaz7vEBeHsYGL3oL6yqhk2T9G8aw2M9m1bL58o40NXPypZTKcxesU+3ZbtYRvdwrusyb+txlh46D4CqSc5m5LLu2VsI8vHk+veX4udpRFEERkXh2wevrZ7PxXHSlkOnmbVoBZomue2aeB64oUd5vs9/J8WUg03TuG9gVwb3jAcgt9DMK1//wanEdATwyv0307ZRnSr5fHt2JPyFR8FgIPenZWR/XnHf7NWqKXUWvUvKs69RsHIzAJGvPoNv7y6opmwu3PpIlTyX4NGuM74P2ePcVX9i/rlMnNsqHv9JM9BSHeLc7/XYyO+J5/Ho2A0tJ4vcJ++vEZ8jlPpxePa+S28XhzZj27XCmbvDQIzNO+s/hIIIjaHok2eh+D/1WlpzuDN+3AM/VxtSyqkOP8cBC4D/TAsTiuCuaaP54N4ZZKdkMmHxTA6u2kXKqcQSmZZ94omIjeaVPk/RoF0Ths54gDcHv1iy/71h0yjIyqshocBzwAiKv38TmWfC+76pqKf2ITOTSmW8fPAcOILiH95G5pnAN0A/1D8YY4f+mL+YDDYrnreOwdCiC+qhLTXjVgTNXn+AvXe9SnFSJh1XzCR9xS4KT5TqmrXxIDuW7wLAr2U9Wn36NNt7Pl2z86N34q+vO8rc2zsQ5e/N8EXb6N0wgkZh/iUy93WM5b6O+mDQhjNpLNxzjiBvDyyqxjO9mtEiMpACi417vt1Gl3phTseWgxB49h9O8Y9vI/Oy8L73RdTT+5CZyaUyXj66zE/vOtszvBbGNr0wL5gBqg2vO8ahnjmAzE6rxH4Kvg+PI+/lZ9Ey0wmc/QmWHVvQLjoXUrQdPUD+jBectklNpXDeh6hnToK3D0FvfYZ1365yxzrbUuO1rxfzycTRRIUGcs/Uj+jToTmNakeVyHy/ahsNa0cy59mRmHLzuXXCO9zYoy2m3AK+XfkXv84ah7enBxPe/5bl2w5wa68OlduyBhh8wwDuGXILk6a/+bfOA6CqGq999j2fvvQkUWHBDHtuFn06taFR3ZgSmc9+Xk6z2Dq8O/ERzl5MYcZn3/P5K09hUAw8e98QWjaqR0GRmaHjX6db2xZOx1YJRcH/8XHkTHoWLSOd4Pc+wbJ9C+r5Mvfy0AFyX36hkpNUz9HgtYc4NvQVLMmZxC2dTfaKnRSdvFgikrPpIFkrdgLg06I+TT55lgO9SgdHoh+8kaKTFzH4+1bPJxQ8b34A81fTkbkmvMfMxHZ0FzK9lA9vX7xueQjzvBnInAzwC7QrasX8xStgMYNiwPvh6agn9qJdOFmGQnDbtPv59N7XyEnJ5KnFMziyajepDv6yud1fvt7naeq1a8yQGQ/w/uAppJ9J5p0bXig5z5TtH3HIrnujbi2JG9CBt65/HtViwz8skM63ldVP4Hn9fZgXvq7r98A0bCd2IzMcfacvXteNwrxoNjI3E3wDS3Z5DhqBevoAxT+/rw/SeHhVa0+v2x6m6NOXkTmZ+Dw5G9vhHci0Mva8/RGKPp+GzM5A+AXp2zUNyx/z0BLPgJc3vk+9he3EPqdjhSK4edr9fHXvTHJTMnl08ascXbWHdAdbNu0TT1hsNO/0eYY67Rpzy4zRfDJ4KpFN69BxaF8+vnUKqtXGfV9P5MTafWQmpDD49YdY/tpCErYfo/2dven58E2sefvH8rYcOBLzd7N1W456GdvJvWX6IV+8Bo3E/P2byNxSvylNKZi/nFpyHp+x76Ie312NLQWe/e6h+Od3dD89fDLq6f1IUxk/fe1win95T/fTPnY/HVYLY+trMH/7mu6nb38K9ezByv30Jb4r7Rf8gzG274f5q6l6P3vzIxiad0Y9vLVyOkXQ69X7WHLP6+Qnm7jjj2kkrNpN1slSexZnF7D5pfnEDnL2wX7RIbS5fyCLrn0e1Wxl4EdP0PiWrhz/cVPl+ikKIROeIv2JCahp6UTOm0vRpq3Yzp4rJxc09mHM23eVO0X6Y8+g5dSw+L2r75+TDq6JW2auOszHd3UhKsCb4fM307tRFI3CA0pkRnVuxKjOetHrDadSWbD7LEE+nkgp+ezurvh6GrGqGvcv+oueDSNoUyukckJXxoEuflZUTTJz+V4+Hn4NUYG+DP98Db2b1qJRRKkvHtW9GaO6N9NteSKJBdtPEuTjWbL/s5G9CfGtxj+X8Lk2TlI1jdcWLuOTZ4YTFRLIPa9+QZ/4pjSqFVHKt24XDWuFM+fJuzHlFXDr5Lnc2LU1HkYDsxetoEdcI94acwdWm0qRpeoBXhSFiBcfJ/HBF7ClZlD3+zkUrNuG9fT5cnJhzzxA4RZnX5z760pyFi4m8vUJNbInioLvI+PIe8ke575pj3MvlImNjhwg/9XysVHxmmWY//wFv3HVfwQvByHw7DuM4l/eReZn4T3sBT0md/Aztt0rse3WPzIYYttgbH/t/7+DPm4A/6GpXkKIkUKIA0KI/UKI+UKI+kKINfZta4QQ9exy84QQ7wshtgohzggh7nA4x3NCiIP2c7xu3/aQEGKnfdvPQghfIUSQECJBCKHYZXyFEBeEEB72898hhHgSqAWsE0KsE0I8IIR4x4HrISHE25Xo0kAIcUwI8bkQ4pAQYqEQor8QYosQ4qQQorNdzk8I8aX9+vYKIW51OH6TEGKP/V93+/Y+Qoj1Qoif7OdfKIQQl2PnBvGNyTiXSuaFNFSryp4lW2kzsJOTTJuBndjxi/71I2HvSXwC/AiMCL4cmhIoMQ2R2WnInHTQVGxHd2Bo4ryykrFlV9QTe/TOHqDQYVBJMYDRUx/pNnoi87NrzB3YvjGFZ1Mwn0tDWlXSfttKxHXOuqqFxSV/G3y9QF5exfhDKTnUDfKlTpAvHgaFQU2jWX+68gBv+fEUrmumv6xH+HnRIlIPDvw8jcSG+pGeX1zpsQBKdCwyK01/idVUbMd2YGgU7yRjbNGlQnsqoTFoSWfAZgGpoV44gaFJ5atFGJu0QEtO1L9y2GxYNq/Fs3PP6kwCgMwy6YM+AOYi1IvnUMIiqjzm0OmL1I0Ko05kKB5GI9d1bcP63UedZISAwqJipJQUmi0E+flgUHQ3qKoaxRYrNlUPLiJCAiuiuSx0jG9NUGBA9YI1wKFTCdSLiaBOdDgeHkau69mBdTv2O8mcuZBMlzZ6gBhbJ5qktEwys3OJCA2iZaN6APj5eBNbJ5q0zOwacxubtkBNStSzeWw2ijesxbNrze5lTeHfrjHmhGSKz6cirTZMv28mZFBnJxmtsHRlIL29le7zjAkj+NoOpH9bdRbaJSh1GqOZUpBZaaDaUA9swdiio5OMsW1PbIe36+0FoMAhmLfYr8Vg0P9V0PbrxTcm81wKJru/3LfkL+IGOnPEDezArl/0l9bze0/hHeBLQBl/2aRHKzLPpZKVqF9H9+EDWDd3MapF/2qdn1n+JUOp1QjNlIrM1n2nengbxqbOAbqxVXdsx3fqgz4AhfbzePpgqNcM2771+m9NrTZYVOo1QctIRppSQbVh27cZY5zz/TO264Xt4DZktq6HLNBXJ5J5WfqgD0CxGS3tIkqQ84pvdeIb6zaw2/Lgkr9oMdBZnxYDO7DPbsuLdlv6RwQT0bg2F/aewmq2oKkaZ7cfpcUg/T6EN4whYbueNXZ680Hirnf28botG6JlOdjy6HaMTZ19nzGuK7bju/VBH3Duh+wwNIhDZqeX2rsyW0bH6nIlfnpneT/dvAvqyb2lfrrIwU8nO/jpiycwNK56NcK/0y8AIC6vn42Mb0ROQiq559PRrCqnFm8jtsy9LMrMJW3/GTSrWv56jQaM3p4Ig4LRx5PC1KpXgfJs2RzbxUTUJN1/Fa1ai0+v7uXk/O+6jaJ1G9FMf29VKVffP0e4JG5JzqZuiC91gu1xS/NarD9V+Sp4y44lcV1zfdVBIQS+nvo3Z5smsaka1QWhrowDXf2sHEoyUTfEnzoh/rot4+qy/nhSpfLLDl3guri6V87n4jjp0Nkk6kaGUiciBA+jges6x7F+34kyfIJCs6UcX35RMbtPnue2a+IB8DAaCPStegUv79bNsJ5PwnYxBaw28petx79ft3JyQcNvpWDVZtQycZB59yHUnBp+mMYe56Y4xLmbah7ngj4gJPNrzucIJToWmZOGzLX7mRO7MDRqW6m8oVknbMd3XhHXfwZSuu7fvxT/iYwfIUQcMBnoIaXMEEKEAl8D30gpvxZCjAbeBwbbD4kBegLNgcXAT0KI6+37u0gpC+3nAPhFSvmZnedV4AEp5RwhxH6gN7AOuBlYIaW0XhpHkVK+L4R4BuhrvyY/4IAQ4jkppRW4H6gqj7AxcCfwMLATuMd+zbcAk+zXOhlYK6UcLYQIBnYIIVYDacAAKaVZCNEEWARcettoB8QBScAWoAewuaa2DooKJSupNGjNSs6kQbxzim9wVIiTTHZKJsHRoeSmZyMljJ0/GSklW75dzZZFVacwi4CQ0kAakHkmlBjnpVNFaDQoBryGPY/w9Ma6axXq4a3I/GxsO5bjM+ZNsFlRzx5CSzhcU1Xxig6l2EGP4qRMAts3KScXfn0nGk2+B8/wIPbfO7PG5wdIKzATFVDakUUFeHMopeKlWousKlsTMpjYt0W5fUk5RRxPz6NVdFCVfCIgBJlXGqjI/CyUmIbOMiFRuj3vnoDw8Ma6ZzXqkb/QMpLw6HkbePuBzYqhYWu0lITKuULDUTNKB7G0zHSMTctfu7FZHIFvf4GWlUnRvI9QLzifU4mIxhDbBNuJqpdATcvKITq0VP/I0CAOnr7gJDN0QDeefPsb+o99nQJzMbPHDkVRFKJCg7jvhp4Memo23p5GurVuQvfW5e/1P4nUzGyiwkq/ikaFhXDwZIKTTNMGdVizbR/tWzTm4MkEktNNpGZmExZcGpwlpmVy7OwFWjdtUGNuJTwcLd3hXmakY2xWwb1sEUfwh1+gZWZS8PlHqOcTyslUBs/oMCwO7c2SnIlfBe0t5Lou1J00HI+wII6PnFGyvf4rozn/6jcY/H1qxCcCQ5E5pXwy14RS15lPCasFBgPeD7wMXj7Ytv6Jbd9G+wkUvB+fhRIajXX7crSL5adBBUWFkO3oC5MzqV/GXwZFhTrJ5KSYCIoOJS89u2Rb/M3d2be4NIMivGE0sZ2bc/2Eu7EWW/ljxgLA+Qtjhb6zlrPvVEKjdf1GTAZPb2w7VmA7uBklJAJZkIfnzQ+jRNVDS07AsnI+WCsfWBaBoSUDOgAyJxOlnvMUDiWiFhiM+Dw6Hbx8sG7+A9vu9c7nCYlAqRWLet755SAwKoQcBzvlJpuoU8aWAVEh5CSV6pybYiIwOoS04xcYMP4ufIL9sZktNO0bT+IBfaAp7cRFmg/owLFVu4m7oStBMc4DTgDCv4a2VAx43zMRPH2w7VqJrUxWgaFFF2xHtpU7f3m+4NIXWC75aecpwCV++s7xer+3Zw3q0b/QMhOd/XRsa7TUqper/jv9gszPxrZrBT4Pz9L72YTDaOeq9tV+0SHkO9yn/GQTUe1qtiR6QUoW+z5Zysht72EzW7iw8SAXNh6q8hhDZDhqaqn/UtMy8Ixz9l9KRDg+vXuS/vizeE5uVuYMkvD33wAkBb8uoeC38tP8HOHq++cIl8Qt+WaiA0r9bFSANweTsyuULbKqbD2bzgvXxpVsUzXJsG82cyG7gLvb1ad1Vdk+uDYOdPWzkpZbRHSggy0DfTiYaKpQtshqY+vpFF64vnTQSwgYs3ATAhjSoSF3tG9Y4bElfC6Ok9Ky8oh2GByKDAng4Bnnga2h/Try5Jwf6D/+PZ3vkdtRFMHF9CxC/P2Y+tUSjl9IpWX9GJ4bNhBfL8+yNCUwRIVhTUkv+W1LycCrjfPUOUNkGP79u5N4//NEtqp+mmFVEGGXEee++wWaKZOir8rHuVfE7Rfs7LfzslCiYysWNnpgaBCHZV3F0xbd+P8H/4mBH6Af8JOUMgNASmkSQnQDbrfvnw/MdpD/TUqpAUeEEJfyG/sDX12qxyOlvOR5W9kHfIIBf+DSBMrvgbvRB36GAh9VdYFSygIhxFrgJiHEUcBDSnmwikPOXtovhDgMrJFSSiHEQaCBXWYgcIsQYrz9tzdQD31Q5wMhRDygAo6ebYeU8qL9vPvs5yo38COEeBh90Ik+oR2IC2h0aXsFypU7uCL9AXhnyFRy0rLwDwtk7IIXSTmdxOkdR8vJV40yhMKAEt2A4u9mg9ET73tfREs6jSzMw9CkHUUfPwfFhXje+hiGlt1Qj/xVM5qK9CinLGQs20nGsp0Ed21Bw+fvZt+dr16xKlVh45l04msFE+TtPJ+60GJj/J/7GN+7Gf5eV9Cky45MKwaUqPoU//iWbs97XkBLPoM0JWPdsRzvO59BWorR0i5UPV+2Bs+K7cwJsh++G8xFeLTvgv/EGeQ8PrxUwNsH/+enUfjlHCiqOuOgogH2slew9eAJmtevxeeTHuRCqolHZn1J+2YN0KRk3Z6jLH1nPAG+PkyY8y1/bN7LTT1r/qX1n0BZ/R64fSCzvviRO595jSb1a9E8tk7JlzqAwiIzz8z+lOdG34G/b80GSCpmKg/b6ROY7rPfy05dCJw6g6wHh1d7XJUUFdzTrOXbyVq+nYAuLanz3DCO3f0Kwf07YM3IofDgGQK6xZU/qMZ8ZQgNBpRaDTF/OQ08PPF5ZAbqhZP6FBipYf5gAnj74j18ArbIusg05wC6Yl9YVqRyfwlg8DAQ178DS2d/53BZBnwC/Xh/8BTqtm3EiA+fgq+fqZa7HBQFJToW88KZYPTA5/6XURNP6T4gpgGWFd+gJZ3Gc+AIPLrfjHXDT5Wfq8L2Xt63GGo3pOiTl8DDE9+xr6OeO1E6/czTG++Rz1O8+EsoLlNnpxo76SIV+5z000ls+ngJ9y94AUuBmZSj59BUPZPkl+c+5aaXRtL3yds5tno3qrWC2h811E2JboB50SwweuIzcgpq0ik9A8q+39ikHYXrfyx/rvKEFerhzKc4++lhE+1+OgXrzuV4D3kaaS1GS7+oZ2xdLmraLxTmYWgcT9FnE6G4CM+bH8XQoivq0coHuCp+5mt2WV5BvjQY2J753Z/GklvIwI+foOltPTjxa1VTuKu/f8FPP07Oh59W2KelPfQkWkYmSkgw4XPewJpwAcu+A5fJV+b3/+r+uSBuqehWVeZtNp5OJb52iNPUJIMi+GHUNeSarTzz2y5OpefROOJyM2P/V3Gga5+VCm1ZiTE3nkgmvm64ky3njepLZIAPpgIzjy7YRGxYAB3qV54d7eo4qaJnr6x+Ww+doXndKD4ffy8X0rJ45J2FtG9SD1XTOHY+mYn3DKJNw9rMWrSCL5dtZezgPpXyVWw852uIeOFRMt764irVe6lBnHv6BNkP2WOjDl3wnzSDnDGXERtdBnVlMDRsi5Z02j3Ny13j5z8z8COo/hXacb/jZ0vh8H9F55gHDJZS7hdCjAL62LcvBmbaM4M6AGtrcJ2fo2frHAO+qkbW8Ro1h98apfdNAEOklE5V+YQQLwOpQFv06Xxmh92O51Wp5BmQUn4KfAowtsHdJXbJTskkpFbpF9GQmDBy0pxTXbNTTE4ywdFh5NhTsS/J5mfmcmDFDhq0bVTlwI/My0IEhpb8FgGh5dJ0ZZ4JrSgPrBawWtAuHkeJ1FNhZU56SQq1emI3Su3GNR74KU7OxMtBD69aYVhSKk/rzd52FJ8G0XiEBmA11Sx1M9Lfm9S80tuTmmcmwq/iudorTpRO87oEq6ox/o/9XN88hmsbR1V4nCNkXhYioPTrmvAPqcCeWWhF+Q72PIESUQc1KxX10GbUQ/o4oUfP25D5ldtDZqZjCI8s+a2ERaCZMpyFHAZzrHu2wyMGREAQMi8HDAYCnpuGZeNqrNuqqN9gR1RoECmm0mypNFMOkWXSkH/fsIfRN/dCCEG96DBqR4RwNjmd5IxsakeEEBqo10e6tmMc+0+e/1cN/ESFBZOaWWrv1MwsIhy+3AH4+/ow/YmRgP5SfP2jU6gdpT/DVpvKM298xo29OtO/6+XppWWko0Q43MvwCLRM53spCx3u5c7t8LgBERiEzK04g60sLMmZeDq0N8+YMKwpFX/5BMjbfgSv+tEYQwMI6NSckIGdCL62PcLLA0OAL43mPMXpJ96r9HiZY0I4TCcSgaFOX5V1mUzUglw908VajJpwFCWmPqpj7RNzIerZwxiaxmMrM/CTk2Ii2NEXxoSRW85fZjrJBEWHkuswdaV5n3guHjpLfkaOwzEmDq3YAcCF/afRNKnXtHCY3iBzTeV9Z54zt8zLQi06UKrf+WN6hs/548hckx4oArajO/DofnMlliy1lQgOL+ULCqvenmePoNRqgJqRpGfLjHwO296NqIfKDxrkppgIcrBTYEwoeWVsqcuU6hzoYMvdP6xn9w/rARgw4W5ykvWsiIzTScwb+ToAYbHRNOtbvm3IvApsWdZv5ppQC0v7IfXCcZTIeqj2gR9DozZ65kZh+Wl55fjysxABDnxV+WmbBWwWtMSTup/OLuOne1Ttpy+d60r7BUCf0lSUD4B6cg9K7UZVDvzkJ5vwd7hP/jGh1U7XuoQ6PVuRdyEds72PPbtsF9Edm1Q58KOmpWOIKvVfhshw1Axn/+XZoimh06cAoAQH4d29C9KmYt64BS1Df1a0rGzM6zfjGde86pd5F98/R7gibony9yYlr3RgNjXPTIR/xVNwlh8tneZVFoHeHnSsG8aWs2lVDvy4Mg509bMSFehDSq6DLXOLiKgka3X54fLTvCLtmVehft70bV6LQ0mmKgd+XB0nRYUEkpJV6vPSsvKIDHa+179v2c/o67vrfFGh1A4P5mxyBjFhQUSFBNKmYW0ABnRowZfLKq8dBqCmZOARXaq/MTocNc15aq1XXFOi39Lr7RhCgvDt1RlUlYI1Nfwo7IDLjnN3l4lz/wZkfraz3w4IQRZkVyhraNoR2/Edf4vPjf8G/is1ftYAdwkhwgDsgzFb0TNxAIZT/XSmlcBoIYSvwzkAAoBkIYSH/TwASCnzgR3Ae8AfUsqKPsnk2Y+/dMx2oC76tK2rkW+3AnjiUp0eIcQl7xsEJNuzmkYAhqvABcC5/aeJaBBNWJ0IDB4G2t/cnQOrnIvbHVy1i8636ys/NWjXhKK8QnLTs/H08cLLTw8OPH28aH5NG5JOXCjH4Qgt+SwiJBIRFK5/MW3RGfXUXicZ9dRelDpNQShg9ESJaYiWmaxP3ajVSJ/bDRjqt3QuBlgN8vaexrdhDN71IhAeBiIHdydjhbOuPg1KB1v8W8eieBhrHDwBxEUHcj67kMScQqyqxooTKfRpFFlOLq/Yyu6LJvo0Ku3QpJS8svowsaF+jGjfoEZ8WkoCIiSq1J7NO6Oedq4To57ah1K7ibM9LxWLu1QwMSAUQ5P22I5W3pHYTh5DiamDEhkNRiOePfth3ekcnIvg0mDO0KQ5CKWkM/R7/HnUi+cwL654RYayiGtYm/MpGVxMM2G12Vi+7QC92zun3EaHB7H9sP4ym5mTR0JyBnUiQ4kOC+bAqQsUFevzzLcfPk1s7aprCrkacY3rcy45jYupGVitNpZv3k2fTm2cZHILCrHaMxZ+Xr2F9i0b4+/rg5SSlz6cT2ztaEbeUv2qH2VhO3EMQ606KFH6vfTq3Q/LtjL3MqT0Xhqb2u9lDQd9APL3ncI7NgavupEIDyOht/Yka6XzfHSvBtElf/u2bojiYcRmyuPCzIXs7fgQ+7o8yqkxb5O7+WCVgz4AWuIplLAYREgkGIwY2vTAdsy5fduO7sTQoAUoCnh4YqjbGC0tUS+C7G0vIG30xNCoDTI9sRzHhf2nCW8QTajdX8bf3I3Dq5yLSR5ZtYeOt18DQL12jTHnFTpP87qlO/uWOAe7h1fuorE9syk8Nhqjh7FcTRkt6QxKaDQiOELPtInriu3EHmf9ju/GULdZSVs31GqElpGELMjRB45C9YFmQ2wcWkZ5/Zz4LpxECS+1pzG+J+oR5/tnO7wDJbZliT2Vek2RqXoBZ6+7HkdLu4h1Y/lVzwAS958mrEE0IXZbtr65G8fK2PLoqt3E221Zp11jivOKyLfb0i9Mf7kJqhVGy+s6cWDxX07bhRD0GXsbOxaWrxGlJZ1FcfCbhhZdsJ107odsJ/dgqNu0nC0vwdiyK7bD1U/zArufDo5EBF7y051Qz5Tx06fL+Ono2FI/7ePop9thO1Z1wP93+gWZa9KnhZX0sy2ci0JXgLT9ZwhqEE1A3QgUDwONb+nK2VV7qjzmEvISM4lq1xijt85Xu0ccWSerfjYtR49hrFsbQ4zuv3wG9KNoo/NLXsptw0m57R5SbruHorUbyH7jPcwbtyC8vRH27Ejh7Y1Xl45YT5+tks/V98/JPq6IW2KCOJ9VQGK2PW45lkTvCj48XYpb+jrsMxUWk2vWC/SarSrbz2UQW9WCFLg2DnT1sxJXK4TzpnwSswp0Wx6+QO+m5RddyDNb2X0unb7NSgfRiiw2Cuyr2RVZbPx1JpXGEVVP93d1nBTXoBbnU01cTM/CalNZvuMwvds6T6+KDg1k+9Gzdr58ElJM1IkIITzIn6jQQBJS9IGb7UfP0rBWeDkOR5gPHcejfm2MtaPAw4j/9X0oWOfsd88NvI9zA/R/+Ss2kT59zhUN+kAFce41/bDuqCbOVZS/PegDjn4mTG8XTTuW89sAeHpjqNO04n3/v0GTrvv3L8V/IuNHSnlYCDED2CCEUIG9wJPAl0KICUA6ek2dqs6x3D41apcQwgIsRc/OmQJsRy+gcBCHgRz06V4/UpoFVBafAsuEEMlSyr72bT8A8VLKmn/CqRzTgXfRawcJIAG4CX3a2c9CiDvRp6IVXAUuADRV44epX/L4N5MQBoVtP6wn5eRFeg7vD8Dmhas5vG4vcX3b8dKG97AWWVgwYS4AAeFBPPSpPivNYFDY9fsWjm6oxhFJDcuqhXjd9SyXlpmVGUkY4/sAYNu3HpmZjHr2IN6jp4GU2A5sRGYkIgH1+C68R70MmoqWeh7b/g011lWqGide+JL47yYjDApJi9ZRcPwitUYOACDpm1VE3NSV6Dt7IW0qmtnCoYffuSx7GhWF5/s257Ff96BJya1xtWkU5s+PB/QBsTvb6F931p1Ko2v9cHw8SpvsvqRs/jyaTJNwf+5eoHdaY3s05prYKjpiqWFZ8y1eQ8bZl5ndgsxMwti2NwC2/RuQpmTUhEO63aTEdmBTyVQMr1vGIHz8QVWxrFlYddqoplL42bsEvPSmvszlmqWoFxLwGnQLAMUrFuPZrTde190KqgqWYgreekW3S4vWePUdhC3hNIFvfw5A0YLP9KygymxpMPDCfbcwZvZXaJpkcO8ONK4TxQ9r9GPuurYLDw/ux5RPfmLIxPeQSMbdPYiQAD9CAvwY0LkVQ1/8AINBoXn9WtzRt3OlXDXFhJdeZ+feA2Rn53Lt4Ht57IERDLl5UPUHVqLfpAfvZsy0D1A1jcHXdqNxvVr8sEKvOXPXoF6cvZjC5Pe/RlEUGtWJ5pXHRwCw99hp/tiwgyb1a3HnM/oy508Ov4VrOrSqGbmmkj/3XYJefRMMCuaVS1HPJ+B9g34vzUsX49WzN9436vdSWorJe/2Vy1NQ1UiY/DnNvp2KMCikf7eGohMXiBwxEIC0+SsJvbEb4Xf01ttbkYWTY966PA4nnTQsS77Ae9Rk3bfsWYdMu4ixs96+bTtWIdMTUU/sw+eJt0BqWHetQaZdQETVw+sOfblfhMB28C/U4+VfXDVV49ep83jomxcQBoWdP6wn9eRFutn95V8LV3N03V6a941n4oZ3sRYV8/2ET0qO9/D2pGnP1vw86XOn8+74YR13zX6U8StmY7Pa+O7ZuYwoO8NNaliWf433sOdKliCXGYkY2/fT9duzFpmZhHr6AD4Pz9T127e+ZFUzy4qv8Ro8BmEwomWnUbzk02rtWfzbZ/g89JJ9Ofc1aKkXMHbVn3fbthXItIuox/fi+8y7SCmxbV+FlnoepUELPDr0RU1OwOdpfc0Dy7IFqMdKbaqpGn9Mncd930zUl3P/YT1pJxPpNFwfyNy5cA0n1u2jad94ntnwDpaiYn5xsOWwuePwDfFHtaksmfIV5ly9W2xzS3e6jNDv+ZEVO9nzYwV9hNSwrJqP99AJ+rNi72OM7fRu3bZ3nd4PnTmIz4OvgpRY9+v2BvSBoNhWFC+fV7UNHfnW2f20ENgO2f10G7ufPrABaUrR/fTIl3Q/fXBTyQut181jED5+oKlY1nxbfXr/3+gXJHoWhfeIKSA1vZ89sLFqOlVj05SvuXnBcwiDwrHvN5B1IpG4e/Vn8/CCtfhEBHHnn9Px9PdBahptHriORf2eJ23faU4v3cGdy15FU1UyDp3j8LfrqtZP1ch+cw7h789CKAYKlizDdjYBv9v0LLaCX5dUeqgSGkLY7GkACIOBwhVrKN5WTYFUV98/R2oXxS0T+7dizE870DTJra3r0Dg8gB/36bWI7oyvD8Dakyl0axCOj2dp3JKRX8yUZfvRNImGZGCzWvRqVE22sivjQBc/K0ZFYeJ18Yz5dpMeA7ZtQOPIIH7crQ+83NlBL7Ow9ngi3RpGOdkys8DMMz/osZ9Nk1zfqi49GkeXJ3Hkc3GcZDQovHDPdYx5dxGapjG4RzyNa0fww3p90P6uPh14+OZrmPLlYoa89AlSwrgh/QgJ0D+sTBw2iBc++w2rTaVORDDT7q868xRVI33Gh9T67DWEopD760osp84RePeNAOR+X3XNpag3JuLTuQ2G4CAarF1A5gfzyftlReUHaCqFn75LwMtl4tzr7HHu8sV4du+N1/UOce6bpbGR37NT8WgVjwgMIviLHylc9BWW1Uur1vESpIZl3Xd43faU3i4Ob0GakjG21j+82w7qftjQuB3quSN6dqEb/99DlJ0j78b/FkKIP4B3pJRVVzX+F8Fxqtf/GrPH1GAZ5quIbW9ftTGxGqHry9VPx7qqKCiqXuYqwrzlZPVCVxG+k56sXugqwhDr2qlfWmrVXwuvNvLGT3Mp3+n95Qvp/q/Q6v6rlvhYI7y00LXfVV550LX6yRzX+s7Xf7ycmlR/Hy884lG90FWEqKJg6f8ELv4iOe89c/VCVxG31Kp5du/fRejdNStMfbWwbdbfzxa4HHSbUcelfDLzanwXrTlMv9S8ePbVQNgT5VeZ+l9CNKt89dX/CcyurSNz8ZEFLuULbeI6X+bdt3n1QlcRvuM+uayVnv+vofCN0S7r+HwnfPmvtOV/ZarXvx5CiGAhxAmg6P/SoI8bbrjhhhtuuOGGG2644YYbbrjxfxf/iale/xcgpczGeXUt7DWJKhoEulZKmVnBdjfccMMNN9xwww033HDDDTfccMONGsM98PMPwj64E/9PX4cbbrjhhhtuuOGGG2644YYbbvwn8S8uuuwquKd6ueGGG2644YYbbrjhhhtuuOGGG278R+HO+HGjWmi4boRUxHdwGReAZJNL+UQL1xYHvpzVQK4GDPtdW4xY5qS7lM/VxZaVqFiX8lnzXPstYIfBdcXcW0dWvWTw1UY2GS7lEyGuK5QNlCxb7Cqkku1SvsK1VS8RfrVRlOXacCy0j2vbQ6LBz6V8u89VvbrR1cTAVq7t11VqvjrpVUHTNi6lE7km1/L95triztqFiy7lUzTNtXz21dZcBbPZtYX4L+53XSH+JsNru4zr/wdIF7eFfyPcGT9uuOGGG2644YYbbrjhhhtuuOGGG/9RuDN+3HDDDTfccMMNN9xwww033HDDjf8m3DV+3Bk/brjhhhtuuOGGG2644YYbbrjhhhv/Vbgzfq4QQohawPtSyjuqkZskpXzNRZf1P0PL3m25c+r9CIPC1u/XsHLu7+Vk7nzpfuL6tsNaVMw34z/iwuHSeihCEUxc8jrZKSbmPjDL6bj+D93M7ZNHMKHdAxVybzlyjtm/bETTJLd1a8noAR2d9ucVFTP5m5WkZOVh0yQj+7VjcNeWpGTl8eL8VWTmFSKEYEj3OIb3ia9W19C+bWn66iiEQSFp4VrOzXHWNWpIT+qPvQUAtcDM8ee+IP+IPke87kPXU+veawFIWriWC58urZZvy6EzzP5hDZqmcVvPtoy+rmt5/b5YQkpWLjZVY+SAzgzuoc+5v37SXPy8PFEUBaOi8O3k+6rnc6E9ja074T3icVAUrOuXUvzHd077Dc3b4vf0NLT0FACsuzZT/Nt8AALeXog0F4KmIVWVgpceq163wwnM/mm9bsserRg9sHN53eYt03VTNUb278jgbnEUW22MfucHrDYVm6rRv10THrupe7V8m/ccZtaXP6Jpktv7d+eB2wc57c/NL2TqB/O5kJqOl4cHrzw+gib1a5GSYWLy+1+TkZWLoigMGdCDe2/qVy1fdXjxtbfZuGUHoSHB/Lbg4799Pq8unQh8aiwoBgr/+JOCBYsqlPNo3oywTz4k+6VpmNdvBED4+xH0/AQ8GsaClGTPnI318JEq+er1aUOvl0cgDApHFq1n90dLnPaHNIrh2rceJrJVA/5640f2fqK3r+CGMVz30dgSuaB6kWx76yf2f7Gixroq9Vvi2fsuEAq2w1uw7XI+1th+AMbm9udJKIjQGIo+HV9tHa243vEMm3o/ikFh0/drWDb3t3Iyw14aTeu+7bAUWfhy/AecP3yWkJgwHnj7CYIigtE0ycZFq1jzla5vhxu6ccu4u4hpXJsZt77AuYOnK+TekpDBGxuPo0nJ4LjajO7oXDPq690JLD2eDICqSc5mFbD2oT4EeXvw8urDbDybTqiPJz/dW31bAFAaxOHZZygoCraDm7DtXF5epk5Tu4wBac6j+Ic3ATC2uxZj62sAge3gRmx711Roy6EOtlxegS2HvnQ/rfu2x1JUzFfjPyyx5ei3xxIUEYzUJBsXrS6xZd2WDbh3xkN4eHmi2lQWTvmchP2nyp3Xo2Nn/B97AqEoFC37k6Lvv63QBsamzQl+/yNyZ7yCZZNei8XntiF4X38TCIF56R8U/fpTtbb07taJkPG67yz4bSm5X39XoZxny2ZEfTWHjEmvUrRmY+kORSF6/keoaZmkPz25Wj5Ds3Z43fqQ7qu3r8K67ufyMo1a4XnLA2AwQkEuRXMnI4LC8Ro2DiUgGCkltm0rsG7+o0KOJr3bcOPUkSgGhV3fr2Pj3CXlZG58aSTN+sZjLbLw8/iPSTqcAEC3+6+j09C+IAS7vlvL1i/1Z6vfuCF0GtqXAlMuACtn/8CJ9fvKnTeybxtaTx8JBoXzC9dx8gNn7jq396Dx2JsBvV/f//yX5B45D4Ax0Jd2bz9EQLO6ICV7n/6UrN0nq7TnliMJzP55g72fjWP0wE5O+/V+dgUppjxsmsbIa9szuKu9L3r3J70v0jT6xzfmsRu7VckFENa3Lc3scUviwrUklIlboof0pIFD3HK0TNxSxx63JC5cy/kaxS2nmf3dar2vvSae0dc7X2NeoVmPW0z2uGVQl5K4JbfQzLSvl3IqKR2B4OVRN9C2UZ2q+Y6dZ/ZvW3V7dmnO6Gud6yrlFRUz+du1pGTl63FLnzYM7twcgJe+W8/Go+cI9ffh5wl3VaubV9dOBI0bizAoFCxeSv78Svq9Fs2I+OwDTFOmY16nt72oX75FFhYiVQ1UlfTRY6rlMzRsjWf/4brf3LcB67Y/y8ko9Zrj2f8ehGJEFuVhXjgTERqN1+DSuEgJjsSy6RdsO1dWybfldAqzVx5Ak5Lb4hswunszp/3z/jrB0kMXAFCl5GxGLuuevokgH0+u/2A5fp5GFCEwKoJvH6g+btm89wizvvoFTdO4/dpuPHDbAKf9ufmFTP3oWy6kZuDlYeSVx+6hSb1aAEz9aCEbdh8mNCiAX99+oVouAP9e7YmZ+jAoClk/rCTjY2d/G9C/C1HP3IvUJKgqydM/o3CXHpuEjb6VkLsGggTziQQSJ7yLtFir5uvdntpTHwKDgun7VaTPdeYLHNCFqGeGg5RIm0rStM8p3HUEr4a1qffBcyVynnWjSX1nIRlfLq6Sb8vJJGb/uUu/fx0aM7pXnNP+eZuPsHR/AgCqpnE2PZd1E4cQ5OvF/K1H+XXXaYSAJlHBvHJbN7z+H3vnHR9F1f3/953d9N4LvXcIvVdpoiIgKIgCCipFBAVEQaoggiIKFkRFUZRmBaX33nuvoaQnm55NsmV+f8ySrdkExXyf5/nt5/XiRXbmzD1zzsy598y5557rpnLK738KsqvGjyvw8zchy3I84DToY8IU4L868CMkwTOzh7P4uTlkJKYxef08zm47TuJ1c/HLep0aE14lkpmdXqNy4xoMnDuCD/qYnc3OL/Qi8Xocnr7WBUGDokKo3b4BafccF+k1GI3MW7ebpWP6EBHoy+AP19CxflWqRQUX0azZd5aqkcEsfuUJNNla+sz9gcea1UIlSUzo2446FcLJzS9k0AdraFWrotW1dpAEtd5/kVNPz6UgPo3mW+aRuuU4uVfNsubfTuZkn1noM3MJ6RJD7YUvcfzRd/CpXYHo5x7hWM8pyIV6YlZPIXXbSbS3EotlZzAambdqG0vHP0NEkB+D562gY8PqVIsONcu36yRVo0JZ/Gp/NNl59Jn+FY+1rIebWumsv5owiCDf0hXJLVN9CgnPoa+RO/9NZE0KvrM/R3fyEMZ460KK+ivnyfvI8YdJ7nsTkHOySi/b2p0sHduPiEA/Bi/4iY4NqlEtylz0ds2eM1SNCmHxqD6KLmd/x2PNa+OuVvHVa/3x9nRHZzDwwsK1tKtXhYZVoornZzDy3ldrWDbjNSJCAhn05nw6NW9ItQrma776ZTO1qpTn47de4da9ROZ+tYavZ41DJamYMPQp6larSK42n4ET36d1ozpW1/4d9OnVjWef6s2Udz/8R+0AIEn4vzEOzeuTMCSnEPr1Ugr2H0Qfe9uOzm/UyxQcPWZ12H/cWAqOHCVj2kxQqxGeHk7ZCUnQac5Qfn/2fXISNDzz52xubjtB+rX4Ipr8jFz2zviBqj2si8Bn3Exgdc+pRe28cGwJNzcfL72sQuDeaRAFv32CnJOO58C3Mdw8i6xJKCLRn9yG/uQ2AFRVGqBu/EiJQR8hSQyePYKPnptNeqKGd9a/z+ltx0m4bi7u2aBTY8KrRDGl01iqNq7Bc3Nf5r0+b2PUG1g7ZwV3LtzCw8eTaRsWcHHfWRKu3yP+yh0+H/kBQ957pVjeBqPM+7sv80XfJkT4ejJ4zRE6VgmjWoi5qO/QppUZ2rQyAHtupvDj6dsEeCqFMp+oE80zDSswbev50uuwy7MU/LIIOTsdz8FTMdw4Y6VDPLxwf2QwBb9+gpytAS8/5dKQaNQN2pP/03tg0OPRbxyGW+eQM5KtdPns7OEseu5d0hM1TF0/jzM2uqxv0uVUky4Hz32JeX2mYNQbWDfnewtdzi/S5VNvPceGT9Zxfvdp6ndqTP+3n+PDgTOtZZMk/MaOJ2PyBIypKQR9+iWFhw5guGNvCz4jXqHwhNkWVJWr4Pno46SPHQk6PQHzFlB49BCGOCfFoyWJoMmvkTzmTQxJKUR+/zl5ew+hv2XPL3DsS+Qftn/X/Qb1Q3frDpJPKYoqCwmPvq+gXTYDOTMNr3Efor94FDnprpnG0wePfiPRfjUTOSMV4RugHDcaKNywHGPcTfDwwnv8QvTXzlhfi2KXT8x+gW+fm0dWYhqj1s/h0raTpFj4ETU7xRBaJZKPOr1BhcbV6T33RZb2mU54zfI0H9iZL56chkGnZ+iKt7iy8zRpscrYeuCbTez/yv5j2awnQcN5L3Dw6XloE9LouHkOiVtPkm0xrufeSeZA33fRZeYS3qURMR+OYG+v6QA0mDOEpJ1nODbiE4SbCpWX877MPM72VcbZD1bTsUFV67Fo7xnTONtbGYvmfM9jze6PRf3w9jCNRYvW0a5uZadjEZKg9vsvcvLpueTHp9FyyzxSbPwW7e1kjlv4LXUXvsRRk99S/rlHOGLyWxqb/Ja8kvyWn7ay9PWBRAT5M3jud3RsVMOx3zJ2gCLfO18W+S0LVm+jTf2qfDiqHzq9AW0JH9YGo5F5vx5g6SuPERHgw+CPf6VjvcpUiwwy8ztwgaoRQSwe/iiaHC193l/DY01q4KZW0bt5TQa2q8c7q3Y55aPoUiJwwjhSxynjXvjyL8jf53jcCxj9MgVH7G0vdcwbGDNL57cgBO7dh5C/egFylgbPYTPRXzuFnGYe9/DwxqPHEPLXfIicpQFvpd+UNYnkL59e1I7Xqx9juHLCKTuDUWbe5jMsfbYdEf5eDF6+i441oqgW5l9EM6x1TYa1rgnAnqsJrDx6nQAvc0Hjr55rT5C3cxso4mcw8t4361g2bQwRwYEMevtDOjWrb+0n/bqVWlXK8fGbI7gVl8Tcr9fx9QxlIqd3p5YM7NmBqZ+uLBU/JInoWaO4NeQd9IlpVP19Ednbj1Bw3dwf5R48w/XtRwDwqF2Ziksmc63bKNQRIYQMfYJr3UcjFxRSYclkAp7oQMYv9pMQlvzKzR7JreemoUtMo/r6j8jaZs0v58AZsrYp/DxrV6biZ5O5+sgoCm7Gca3XuKJ26hz5jswth5zr02hk3oZjLB3WhQh/bwYv3UzH2uWpFh5QRDOsXV2GtasLwJ7L91h58DIB3h4kZeWx6tAVfn3tcTzd1ExavY/N52J5skm10unWhf8J/GtLvYQQQ4QQZ4UQZ4QQP5iOVRJC7DAd3yGEqGg6/p0QYrEQ4qAQ4qYQor9FO28KIc6Z2nnfdOwlIcQx07FfhBDeQogAIUSsEEIy0XgLIe4KIdyEENWEEJuFECeEEPuEELUd3O9MIcQPQoidQohrQoiXTMeFEOIDIcR50308YzpeWQhx3vT3MCHEryYe14QQC0zH3we8hBCnhRA/CiF8hBB/me77/P22itFfrBDiPSHEISHEcSFEEyHEFiHEDSHESAu6SSZdnBVCzLI4/rtJ3gtCiJctjucIIeaa7uGwECKipGdZOaY6KbcTSbubjEFn4MSGgzSymb1q2L0ZR35VZjxiT13D288H/7BAAAIjg6nfpQkHVtt3nk9NG8pv836EYnYOO387iQphgZQPDcBNraJHk5rsPnfTWlcIcgt0yLKMtrCQAG9PVJJEWIAPdSqEA+Dj6U7ViCCSM3OcyurfpDraW0nk305G1hlI+v0goT2tZc08fhV9Zq7y94lreJicOZ8a5cg8cQ2jthDZYCT94EXCerWw42El360EKoQHUj4sUJGvWR12n7GeSRQCcgsKFfkKCgnwUeT7OyhLfaqq1caYFIeckgAGPbrDu3BrWrrMgb8lW2yiSTaTLpvWYvdZ60wIISA3/74udUWyCSHw9lQcG73BiN5oRJTE73osFaPCKB8Zipubmp7tmrLr6Bkrmpt3E2jZUJlNq1I+kvjkNNIysggLDqButYoA+Hh5UqV8JMlpGf9YB81iGhDg7/eP2wFwq1Mbw714DPEJoNej3b4Tj3Zt7ei8n+pL/p59GNMzio4Jb2/cGzVE+6dp5livR87JdcovIqYaGbFJZN1JwagzcHX9Yap2tw7waNOySD5zE6POUGw75dvVI/N2MtlxaaWWVYqojJyZjJyVCkYD+qvHUFUtficbVa3m6K+UHFiqElOd5NuJpN5NxqDTc3TDAWJs+s6Y7s059OtuAG6euoa3nzcBYYFkpmRwx5Q1WZCbT8KNOIIilSBrwo04km7G4wznkzKpEOhN+QBv3FQSPWpEsvtm8bvgbb6aSM+a5p2QmpYLKgoClQZSZBXkjBTkTJMOLx9DVS3GikZduyWGa6eUoA+ANlu5NjgKY8JN0BeCbMRw7yqq6tYz+lVM49B9XR7bcICY7tbZijHdm3P4VyXL5qZpHCpOl4GR9wPWMp6mwLm3vzcZSel2sqlr1cEQH4cxUbGF/N07cW/Tzo7O68l+FOzfg5xhbkNVsRK6yxehoACMBnRnz+DetoNTXbrXq43+bhyGOIVf3tZdeHe07zv9nulD3s59GDQZVsdV4aF4tW1Jzu8lZ24ASBVrYExLRNYkgUGP/vQ+1PWsxy51kw7ozx1CzlB2q5NzMpX/s9OVoA9AgRZj0j0kf/vJgPIx1dHcTiLd5Eec3XCIOjb2Xad7U079quy0effUdTz9vPELCyS8ejnunrqOLr8Qo8FI7JFL1O3RzI5HcQhqXJ3cW0nk3VHG9bjfDxFpEzxOP34NnWlcTz9xHU/ThIba14uQVrW589NuRV6dAX2W84Dv+dtJVAgNMI+zTR2Ms0IUPxZ5WIxFBiNCOB+NAppUJ+9WElqT35L4+0HC/lW/JZ4KYUGUDwtS5Gteh92nr9rIB7kFBYp8+Wa/JUdbwMmrd+nbrhEAbmoV/t6ezvndSaZCiD/lQ/wVfo2rs9uUCWalz/t+S4GOAG+PIj+pabXoEnnch3vd2ujvxRWNe3nbd+LZwd72fAb0Rbt7L4Z0+/7iQSBFV8WYnoSckQJGA4ZLR1DXbGJFo67XCv2VE0rQByAv264dVeV6Sv+b5XzcOx+voUKwD+WDfJRxoW55dl9NKJZ+08W79KznPBvLKb/rt6kYGUb5CJOf1LYJu46fs6K5eS+RlvWVQFOVchHEpyh+EkCzutUJKOXEJoBXo5oU3E5AdzcJWacn88+9+HWzzqI35uUX/S15eSJbfH4IlQrJ0x1UEsLLA32S8x3mvGNqUHg7gUITv4wNe/Hv3rJ4ft4eWDE0wbdtIwpvJ6CLc75T7fl7aVQI8aN8sJ9iCw0qsfvS3WLpN52NpWfDykW/DUaZAp2S2Z6v0xPmX3Y7q/5HwCiX3b//UPwrgR8hRD1gKtBFluVGgCmkyafA97IsNwR+BBZbXBYFtAMeB+4HeB4F+gAtTe0sMNH+Kstyc9OxS8BwWZYzgTNARxPNE8AWWZZ1wDJgrCzLTYGJwOfF3HpD4DGgNTDdtJyrHxADNAK6Ah8IIRxNvcQAzwANgGeEEBVkWX4L0MqyHCPL8mCgJxAvy3IjWZbrA/Z58Na4K8tya2Af8B1KhlErYLZJP92BGkALE/+mQoj7HuWLJnmbAa8JIe5PNfkAh0262wu8VMI9EBgRTHq8eTBJT0gjICLYAY15++L0xLQix7r/9GH8Nm8lsk1n16BrUzKTNMRdKn4rzeSMXCIDzTPUEYG+dsGGgR0acitRQ7dpy+k/bxWTnmqPJFk7SnFpWVyOS6FBJedbvHpGBpNvIWtBfBoeFrNKtoh+tjNpO08DkHP5LkGtaqMO8kXycie0a2M8yznfYjk5I5vIIPNMS0SQH8kZNvJ1bsKthDS6vfkZ/WcvZ9IzXYvkEwhGfbyWQXO/4+e9p53yUviVnT5FUCiyxjyIGTUpiKBQOzpV9br4zl2G98R5SOUqWZyR8Zm8AN/ZX+DW+bFSyJZDZJA56BER6Guvy44ximxTltF/7g9MGtCpSDaD0cjT762ky+QvaVW7Ig2czbACSWkZRISY342IkCCSNZlWNDUrl2fH4dMAnLsWS0KKhiSbAE9cchqXb92lQc3KJcpYllCFhWJINmdcGFNSUIVZPz8pNBTPDu3J+906NVkVHYUxI4OAKZMJXb6MgMkTEZ7OHW+fyCBy4s1OVk6CBl8ntlccavZuzbU/nM+a2UL4BiFnmx14OScD4VsMb7Ubqkr1MFw/WWK7Qbb9YkIaQXZ9Zwgay/41UUNgpHW/EVI+jIp1K3PztPPlJZZIzikgwtc8Kxvh60FKboFDWq3OwMHbqTxSvcR5gGIhfAPNAR1AzklH+AVa0wRFgKc3HgMm4jn4HVR1lOUhxrQ4pPI1wdMH1O6oqjRA+NmPMVZ6StAQGGGtpyBbGotx6D5CyodRoW4Vbpl0uXrWd/R/+3nmH/yC/lOG8OuCH+1kk0JDMaRY2EJqCqpQG1sICcW9XXvy/7S2BUPsLdwaNEL4+YOHB+4tWqEKC7fjYQlVeCiGJHPfqU9OQRVuzU8VFopXp3bk/GK/XCpowhjSFy9z+IHhCCIgpCigAyBnpCECrHUrhUaDly9eo+bgNX4h6qad7dsJCkcqVxXDnat25/wjgsi0eDZZCRo7P0KhMb9DWYka/CODSLpyl8otauMV6Iubpzs1O8cQYJE902pod8Zuep9+C17G098+w8kzKgitBW9tgqYosOMIFZ/tRPJOJYjvXSmcwrRsGn/yCh23vUfMwpdQlZDtUKqxqEMjbiWl0+2dr+k/70cmPdXReix6/0e6vP2VMhZVdu63eEQGU/AAfks5C78l9/JdAlvVxu2B/JYcIoNt/RbrYMTALk0Vv2XSEvrP+ppJA7shSYJ7KRkE+Xkz/du/eGb2cmat2Ii2oNA5v8w8a78lwIfkTOuJhIFt63ErKYNus1bS/8N1TOrTxs5vKQ0km3HPkJyKKizMjsarYztyf7O3PWSZkE8+IOzbpXg/WbLfInyDzAEdQM7WIPysn50UHInw9Mbz2bfwHDYLdX37CRhVnZboLx4ukV9ydj6RfubM+wh/L5KztQ5ptTo9B28k0bW2eUtxAYz6aT+DvtnJzydvObzOEkmaDCJCAs38ggNJTrP1k8qx44hib+eu3SYhJd3OTyot3CJD0CVY9J0JqbhF2L/Pft1bU2PbF1T6ZgZxkz9RaJPSSP36N2ru/5bah3/AmJ1Hzv5TzvlFhKCzGON1CWkO+fn3aEXNHV9QefkM7r35id35wCfak7F+r91xWyRnaYkMMAdrIgK8i39+hXoOXk+ga90KCq2/N0Pa1aHnwt/ptuBXfD3daVP9n2WZu/Dfh39rqVcX4GdZllMBZFm+36u1RgmkAPyAOZAD8Lssy0bgokUWSlfgW1mW82zaqS+EmAMEAr7A/WIMa1CCL7uAgcDnQghfoA2wzmLWpLhR+w9ZlrWAVgixCyWg0g5YJcuyAUgSQuwBmgNnba7dYQo+IYS4CFQCbMOw54APhRDzgT9lWd5XzH3cx30P8hzgK8tyNpAthMgXQgQC3U3/7vdMviiBoL0owZ6+puMVTMfTgELg/gL8E4D1YlsTTFlCLwNMH/YmdW0JbBxKRzNSsixTv0sTctIyuXv+FjVamVtx83Sn56v9WPL8HGfyIzvIBLLldfDSHWqVD+OrsX25m5rJyM/+oEnVcviaUlPzCgqZ+M1GJvVrX3SsWJQws2aJoLb1iH62C8d7K6m2edfiiP10PY3XvoMhN5/sC7eR9cVnJijyObgFm98HL9yiVoVwvnpjIHdTMhj58RqaVC+Pr5cH3705mPBAPzRZuYz8ZA1VIkNoWrOCE35lqE9HqrR5bwyx18h+fRAU5KNu1ALv8bPJmaTUKcqZPU75APEPxGfyAozxdzBcOeeg0fuyObgFW9kuxiqyjevP3ZRMRn76C02qlcPXS5kdXDvlObLy8nlj2Qaux6dSPdo+UOUMtiIP79ed+d+sY8Ab71GjUjS1q5S3ytbK0+bzxoJlvPlif3y9vfiPgiNbsHl+/uPGkL30SzBar5sWKhVuNWuS9fESdBcv4T/uVXyeG0TO1986YeeoD3mwW5bcVFTp1oSD7695sAsdoRjmqioNMcbfKHGZF+BQh7YBcIddjgWNh7cno7+YyJrZ35Gf49jB+6fYeyuFmKjAB8rwsYej98XmtyQhRVSiYN1CULvjOegtjAk3kTWJ6I5txvOp15F1BRhT7oHRuu8sSU/FEVmSeHh7MuqLiayZ/W2RLjs91521737Hyc1HaPZYa4bOH8Wi594tsV1b2XxHjyX3a3tbMNy5jXbNTwTMX4is1aK/eR3ZoHcgTAmwkTVowmgylnxlx8+zXSsMmnR0l6/h0bTRg/Mphh8qFary1dB+OQ3U7niPXYDh9hXkVFPmmbsnnkMnU/DH11Bg/54W5yOUTAMpN+LZu3QDL658m4LcfBIv3cZoUN6PIyu3sWvxryBD1wkD6PXOYH59c1mJ7RZn36Ft61JpUCf2PakkUEtqiYAGlTk35TvST92g/rtDqPFqby4vWOfweijlWHTpNrXKhfLV2H7KOPvpbzSpFm0ei94aTFZeAW98/WfJY9E/8FtyTX5LE5PfklMav8WB7oSN/St+SwRfTXiWuynpjPxoNU1qVMBgNHL5TiJvDepGg6rlmL96G8s3HWJMn452bRbxc+S32Pw+eOUetcqF8NWox7mblsXIL/+iSdUofD1L8PnsGi75XQkcP4bMz5bZ2R5AyiuvYUxNQwoKJPSTD9DfvkvhadvPhQfjh6RCiqxM/qr5oHbHa8g0DPHXlQw903l1jcbk7S7+nSxq2qEP6Jh277VEYsqHWC3z+m5oR8L9vNDk5jPypwNUCfWjacUH9JNsGA7v05X53/7KgInzqVExSvGTVA+x7oyD9zV76yGytx7Cu3k9It54jtjn30Hy98Gva0uudhyOISuXip++RcCTncj8Y7czYUrFL2vLYbK2HManhcLv1nPTzE24qfHv2pLEBd+XLEopbOE+9l65R0zFMAJMgeosbQG7L93jrzeexM/TnUmr9/HX6Vs8FlOlmBb+B+HAZv9/w78V+BEUt3bHGpY0llORwuJ/R+18B/SRZfmMEGIY0Ml0fD0wTwgRDDQFdqJkuGTIshzzgPdz/3dpR1TL+zfgQLeyLF8VQjQFepnuc6ssy7NL0abRpn2jqX0BzJNl+UvLi4QQnVCCZq1lWc4TQuwG7k+362TzqO3wPk33ugwlU4oP+k2Vg6LNEeygqBAyk63TW9MT0wiKDgWuKDSRIWQmpdOkVysadG1Gvc6NUXu44+XrxbBFY9m69HdCy4czddMHAARGhvD2n/Ph6nooNH9QRQT6kmgxU5aUkUOYzYzeH0cu8mK3pgghqBgWSLkQf24la2hQKRKdwcCEbzbRq1ktHmlU3ZGoVshPSMPTQlaP6BAKEu1TeX3rVqT2Ry9zetD76NPN95fw0y4SflLWkVebMpD8eOdpohGBfiSmm9eCJ6VnE2YxswXwx8FzvNizlSJfeBDlQgO4lZhGgyrRhAcqs4rB/j50jqnJ+dh4p4GfstSnrElFBJtnyqTgMOQMmzTkfPOz1p85ihg6DuHrj5yTVUQrZ2WgO74fVbXaTgM/EYG+JKabZx2TMnIIC7CR7fBFXuzezKTLQMqFBHArKd1qRtXf25NmNcpz4GKsU2c7IiSQpDTzu5GUlk5YcIAVja+3F++OHaLIIcs8OnIa5UyzQTq9gTc++IrHOrSgayvrZS3/CTAkp6AKN2cmSGFhGFKtn59brVoEzlQ+IKSAADxat0Q2GNBduIghJQXdxUsAaHftwfe5Z53yy0nQ4BttnoX3jQom18GyG2eo1LkRKedj0aaWsr6CCUp2inmWVfgGIudmOKRV1WyO/uoxh+dsYe4XFQRFhZDhoO8MtuxfI4PJMKWXq9QqRi2dyOHf93Fyy5HSigNAuK8HSTnmYSMpp4AwH8dzHluuJtKzlvOsgpKg6ND8/IRvEHJOhjVNdjpGbY6ypEtfiDHuGlJYeQwZSRjO78dwfj8Abm37IufY6kljraeoYDKSNTY0troMIdNKlxM48vs+Tm05WkTT+qlOrJ6lBCSP/3WIIe+PxBZKtpuFLYSGYUhLtaJR16iF/xSzLbg3b0W2wUDhwf3kb95I/mZl2ZXPiy9hSHGezm9ITkUVYe471eFhGFKsbc+9Tk1C33tH4RcYgFfbFmj0Btzr18arQxu82rZEuLsjfL0Jmf02adPnFctPzkxDBJrfUxEYYpWFAEoWkCE3CwoLoLAAw80LSNGVMaTGg6TCc+hb6E/uwXDecdZBZqKGAItn4x8VTJaNLSg05nfIPzKYbFMfcGLtbk6s3Q1At0nPkJWg6CPXwtaPrd7JkG8m2fHWxmvwsuDtFRVMvoNx3b9OBWIWvsShZ+ejM43r2ngN+Qka0k8py4bj/zxCjbG9Hcp4H6Uei7o1sx5n7cYiD5pVL8eBS7edjkUFCWl4lNJvqfvRy5wa9H6RfADxP+0i3uS3VC+N3xLkR6KmBL/lwFle7NnaNNYGUy40kFuJaUQF+xMe5E+DqkoWSbcmtVm+2XmGZkSAj7Xfkplrr89jV3ixS4zCLzSAcsF+3ErOoEFF59l1tjDajHuq8FAMqda27la7JsHvKh/uUkAAnq1bkmEwkL/3AEbTGGlMz0C7Zz/udWs7DfzI2RqExdJI4Rds329maTDkZYOuEHSFGO5eQQqviMEU+FFVa4gx6TbklTzuRfh5kWiRIZKUpSXM1/Gk0+YLd+lZz9qfDDdlCwX7eNK5VhTn4zVOAz8RwYFW2TtJmgzCLLLFwOQnjRmsyCrLPDpmFuXCndTidAJdYhpuURZ9Z1QouuTi3+e8YxdwrxiJKsgfn1YN0N1LwmB6t7O2HMK7aR2ngR9dYipuFrbpFhXilF/u0Qt4VIpCFeSPweT7+3Vqivb8DfSpGSXKF+HvTWKm2W9OyswjzK+Y53fuNj0bmLPoD99IpFyQL8E+yufgI3UrcPpuyv9fgR8X/rUaPzuAp+8vLzIFYgAOomTiAAwG9pfQzlbgRSGEt007fkCCEMLN1A4AsiznAEeBT1AyagyyLGcBt4QQA0xtCCFEcdNgTwohPE333Qk4hpI984wQQiWECAM6mHiUFjrTfd7fCSxPluWVwIdAE6dXlowtKPrxNbVfTggRDgQA6aagT22U5WF/G7fP3CC8chQh5cNQualo+kQbzm6zrm9xbttxWvZTVplVblwDbXYeWSkZ/LFgFVNbj2Jau1dZPvZjrhw8z3evLyH+yl0mN3uJae1eZVq7V8lITGPe45Otgj4A9SpGcCclg7i0THR6A1tOXqVjA+tOKirIjyNXlAKfaVl5xCanUz4kAFmWmfXTDqpEBPF8l9J9WGefuoF31Ug8K4Yh3FRE9GlD6hZrWT3KhdBg+QQujvkM7U3rtdFuof5FNGG9WpD02wGn/OpVjuJOcjpxqRmKfMcv0dEmoBIV7M+Ry7dN8uUSm6ShfFgg2oJCcvOVDzttQSGHLt6ienSYHQ8rfmWoT8PNy6giyyHCIkGlxq1VZ3QnD1rRiADzx7aqai0QQinm7OEJnqbBzMMTdYNmGO/GOpetUqRJlybZTlyhY4OqDmS7a5LNpMvQADTZeWSZ1mHnF+o5cuUOVSKcOx71qlfidkIy95JS0en0bN5/gk7NrevCZOXmodMps/u/bD9Ak7rV8fX2QpZlZnz2A1XKRTKk9yNO+fxfQXf5MqoK5VBFRYJajVfXLhQcsH5+KU8/S8qAQaQMGET+7j1kLfyYgn0HMGrSMSYno6qgOI0ezZqgj411yi/pzE0CK0fiXyEMyU1Fzd6tuLWt5OVUlqj5ZGuuPuAyLwBj0m1EYDjCP0SZPa3ZHMNNB866uyeq8jUw3Dhjf84BYs9cJ6JyFKHlw1G5qWnxRFvObLMOGp3edpzW/ToBUNXUd2amZAAwdP5oEq7fY9s3jndJcoZ6Ef7cycgjLlOLzmBky7VEOlW17x+yC3SciEunU9UH+0CyhTEx1qTDUEWHtZtjuGmtJ8ON00jlaoCQQO2OFFkF4/3iz/cLPfsFo6rRGP1l62E29sx1wi102fyJtpyxGYfObDtOq35K5oC9LkeRcD3OTpeZyRpqmrJRa7epT3KsfVFb/ZXLqMqVR4pUbMGzUxcKD1n37ZohA9E8r/wr2LeH7CWLKDyouDgiMBAAKSwc97btKdi13akuCy9exq1COVTRCj/v7p3R7rW2vfgnnyO+92Diew8mb8deNPMXo91zgMzPviH+sYHE9x5M6tQ5FBw77TToA2C8ew0pNAoRHA4qNeqY9hguWOtff+EIUpW6IEng5o5UqSZysjJOeDw9FmPSXXR7i9+NJu7MDUIqRxJk8iMaPtGay9usC9Fe3naCxv3aA1ChcXUKsrVkm56fT4gytgZEh1CvZ3POrFfs3M9USxCgbo/mJF29hy0yTt/Ap2ok3qZxvVyf1iRutebtVS6E5stf58Srn5N70/wOFKRkoo1Lw7easiQirH19q6LQjlA0zhaNRVftx6JgP45ctRiLktMtxiJlXFfGortUiXC+5DXLxm+J7NOGFBu/xbNcCI2WT+D8mM/IK8Zv8SwXQnivFiSW6LdEK2NtislvOXaJjo1q2Mjnz5HLsWb5ktIoHxpIaIAvkUF+xCYqAZIjl2OpGuU8Y6RehXDupGYSl5al8Dt1nY71KlnRRAX6cuSa8lzSsvOITc6gfPCD17srvHQZtcW45921C/n7rMeUpKcGk9TvWZL6PYt21x4yPvyE/L0HEJ6eCFPmrvD0xKNlM3Q3nS+HMsbfQgqKQAQo/aaqTkv016yXF+mvnURVoWZRv6mKrobxfqYdoK7bCv2Fkpd5AdSLDuKOJoe4jFxlXLh4j4417Zf7ZOfrOHEnlc4W57SFenILdEV/H7qZTPUwf7trrfhVr8jthBTuJaUpftKBk3Rq1sCKxspP2nGIJnWq/e0MaO3Zq3hUjsatfATCTU3A4x3I3m49aeJeySyTZ71qCDc3DOlZ6OJT8IqpVbQRhU+bRlZFmh0h78w13C34BT7Rgaxt1n2nJT+vetUQbuqioA9AYO8OZGzYUyr56pUL4U5aNnHpOYotnLtNx9r2NZiy8ws5EZtM5zrmwF1UgA9n76aiLdQjyzJHbiZSNSzA7tr/abhq/Pw7GT+yLF8QQswF9gghDChLkYYBrwHLhRCTgBTghRLa2SyEiAGOCyEKgY0ou2RNA44At1GWQVn27muAdZizgEAJDn0hhHgHcANWo9QDssVR4C+gIvCuLMvxQojfUJaonUHJAHpTluVEIUTlUilDyZo5K4Q4CXyPUiPICOiAkvd5dAJZlrcKIeoAh0ypkznAcyi1g0YKIc6ipOCUbkQoBkaDkTXTl/Pq91ORVBKH1u4i4do92g9WVont+3Eb53edol7nJszas5hCbSE/TCqujNKDQa2SeKt/R0Z9vh6j0ciTrepSPSqEdfuVzI8B7RrwUs/mTF+5nf7zfkJGZnzvNgT5enHqRjx/HrtCjegQnp6vbMc59vHWtK9XuVh+ssHIlbeX03j1FFBJJKzaTe6Ve5Qb0hWAuO+3U2VCf9yCfKk1X9l+XtYbONZjCgANv3kDtyA/jHoDV95eXlRM0al8A7sx6pO1GI0yT7ZtQPXoMNbtUQb+AR0b89JjbZj+3Ub6z/oGGRjftxNBvt7cS8ngjaW/AkoRyEdb1KVt/arFMytrfRqNaL9fgs+k+coWwXs3YYy7jXuXxwEo3Pknbs074P5IbzAakAsLyPtcWfon/IPwGW+qVS6p0B3agf6c8ywLtUrirae7MOqzXxVdtq5H9ehQ1u1TTH1A+0a89GhLpv+whf5zv0eWYXyf9gT5enE1LoVp32/BaJQxyjLdm9SkQ4OSdKliyohnGDX7UwxGI30eaU31itGs3aKs0366Rwdu3Utk6uIVSJJEtfKRzBrzPACnLt/gzz1HqVEpmgFvKJv+vTa4N+2b1nfKsyRMmvE+x06dJSMji0f6PMfo4c/z1BM9Sr7QEQxGsj5aTPBHC0CS0P61Cf2tWLyfVLY8zvvDQX0DC2QuWkzgjKkItRpDfAIZ8+Y7pZcNRvZMW0HvlW8iqSQurtmD5moc9Z9Ttos9v3In3mEBPPPXu7j7eiEbjcQM78nKLpPR5WhRe7pToX19dr21/MFllY0U7l6DR5/XlO3cLx5E1iSYthcH/TllVa6qWmMMty8qGSulgNFg5KfpXzP++3eQVBIH1u4k/to9Og7uDsCeH7dybtdJGnRuwnt7PlW2IDf1ndWb1abNUx25d+k20zcqmZG/LfiJc7tP0bhHCwbNHI5fsD/jlr/NnUuxcO5rK95qSWJyp1qM/uOkYg/1oqkW4su6c4ojO6CB4hDuupFCq4oheNls6frW5rOcuJdORr6OHt/sZWSravStV45iIRsp3PUTHk+NByHQnz+AnBaPuqESiNGf3YOsScQQex7PITNAltGf21e0e43HE6MQXj7KLlE7frJbSqfo8hvGfz8VoZI4sHaXSZfdTLrcZtJlY+buWUKhtpDvJn1WpMvWNrr8dcFPnN99iu/f+pKBM15AUkvoCnR8/7ZVAu195uR8+jEB8z5ESBL5WzZiuB2L5+NK5odtXR9bBEx/F+HvD3o9OZ9+jJzjfJMBDEY0HywhfMl8UEnkrt+E7uZtfJ9S+s6cXx48EOgURiMFvy3D66WZICR0x3ZgTLqLunVPAPSHNiMn38Nw5RTeExYjy0b0R7ZhTLyDVLkObs06Y4iPxev1RQAUblqJ4bJ1YMVoMLJh+ncM+/4thEri5NrdJF+Lo8VgJfB99McdXNl1mpqdY3hjzyJ02gJ+nWR+Fs9+MR7vIF8MegPrp31LfpYytvZ4exBRdSuBDOn3Uvhjyjd24skGI2enfEfrVQrvO6t2k30ljspDFN6x3++g1hv9cA/yo9H7LxRds6eHklF1duoKmn4+BuGmJu92MqfGO3hHLKBWSbw1oBOjPv8doyxbjLNKMHlAu4a81LMF01duo/97K5Vx/cl25rFo5TaMRiNGGbo3rkGHEsb1+35Lk9VTECqJeJPfUt7kt9z7fjtVTX5LHQu/5YjJb2lk8ltkvYHLpfVbnu3GqI9XK/K1bUj1cmGs260E6gd0asJLj7dl+rd/0n/m18iyzPinOhPkp9QmmTyoO1O+Xo9Ob6BcWCCzhzmvhaNWSbzVrx2jlm1U+LWoRfXIYNYdVLbgHtCmLi91a8L01bvp/8E6xW95vCVBpkyWt37YzvEbCWTk5tN99kpG9WhG35Z2e7woMBjJWLiE0I/ng6Qi90/TuNfXNO45qutjghQcRMj7piR+lYq8rTsoOFxCdqhspHDbD3gOnKSMPWf3IqfGoW6s1NDSn9qFnJaA4eY5vEbMAVlGd2YPcqop+Kh2R1WlPgWbv3PO574uJYm3esQwatUBZVxoVInqYf6sO6EUHx/QVHnXdl6Jp3XVCLzczZ+JabkFvPGz8jmhNxp5tF4F2lZznimqVqmYMrw/o+Z+rvhJnVtRvUIUa7cqQfGnu7fj1r0kpn66EkkSip80ypwd/ObH33H8wnUysnPo+so0Rj/di36PtC6eocFI/MylVF4xGyFJpK/bRsG1OwQ9+ygA6T9twr9nGwL7dkHWG5DzC7n7muKbaM9cJWvzAapv+BhZbyT/4g3SV5dQitVgJH76Uqp+PwtUEulrt1Nw7Q7Bg5W+U/PjZgIebUNQvy7Iej3G/EJuv2quciI8PfBtF8O9KZ8551OkT4m3Hm/GqBU7lefXpBrVIwJZd1SpqzaghVIke+fFu7SuFmX1/BpUCKVrvYoM+mITKklQOyqIp5qVvBLChf8tCEdrdf9/hBBiJpAjy/JD2Af5fwujKz9dZi/Jwi+d73bysHFwSElllh4u2qz5mx/hfxelqUPyEFG48rcy5ec+dECZ8pOiapRM9DD5RZRtCm5qn+Flyu/n204CCQ8Zwyf6lkz0EPHaotSSiR4iFk/6Z0u1HhgFjgtF/1sYtzijTPm9VyO5ZKKHCG36v7Xy3jGCO5WtPcz7uRTbyj9EtMgvmeZhofv39ju8/ZvY/3zpsgMeFtqte7RM+ZHlfKnZw4Zm7kMOpJaAwN7FL8f/NyCViy5bfjGdypTftT5LypSf0fjgRcP/LmosaF4y0UOE19PTy064/wPkTiu771mfd9f+R+ryX9vO3QUXXHDBBRdccMEFF1xwwQUXXHDBhf9blO0U038wZFme+X/B17SUzHZaf7Isy1sc0bvgggsuuOCCCy644IILLrjgggsulBauwM//MWRZ7lsylQsuuOCCCy644IILLrjgggsuuPDA+A8uulxWcC31csEFF1xwwQUXXHDBBRdccMEFF1z4H4WruLMLJWJApSfL7CVpKT/49pv/BPGSvkz51TSUbZJdbhmXFhvROaFkoocIKci7TPkVXMkqmeghQpddtrH50N/td8T5N6EZ4HRjx4eKSxf/2XblD4oDnu5lyq9DGRdbviN5lim/nDKepuoeklSm/FJTy7bYsp9P2b4vvkFlWG0ZuHc3sMx4nZHKdhyKVRvLlF+9wrI1vixVyTQPE73K2Na9AnVlyi8/q2z9TrkMix8DhL9QrUz5pf5wrcx4xceX7XbrreJ//Y8sSPywkPP2U2X2Pes775f/SF26Mn5ccMEFF1xwwQUXXHDBBRdccMEFF/5H4arx44ILLrjgggsuuOCCCy644IILLvxvwlXjx5Xx8zAhhOgjhKj7f30fLrjgggsuuOCCCy644IILLrjgggvgyvh52OgD/AlctD0hhFDLsly2BWUeMl6Y+RJNOjelQFvAZxM/4db5m3Y04RXCGb9kEr6Bvtw6f5Mlry9Cr9PTrk9H+ozsB0B+Xj5fTf2C25diCYkK5dVF4wkMC0Q2ylz7aQ8nl1vvZF+5Y0O6zHweoZI4t3o3Rz/fYHU+uFoUPT98mfD6ldn/wTqOL9tYdO6lA4sozM1HNhgxGgysfHy6Q9lqd2xEn+lDkVQSh9fsZOcX6+1o+s4YSp3OjSnUFrBq4hfEXYglrGoUQz4dV0QTUiGczYvWsXf5Jhr1akmP8f0Jr16Oj598h3vn7PUFUKFTQ9qa5Lu0ajenbeQLrBZFp4UvE1a/Mkc/WMeZL83yNRzRk9oDOwEyaZfvsXvCMgwFztePV+7YkM4mfueL0WcPkz4P2OhzhI0+fyxGn/ehbtAcz8FjQJLQ7dlIwV+rrc6rajfCZ9xsjCmJAOhO7Kfgjx+Uk94+eL84EalcZUBG+/WHGG7YmZZTqGo3wbPfSyAkdIe3UbjjZ3ua6vXx6PsSSGrk3Cy0n779QDzuw61pC3xGjkVIEvmb/0K77ifr8w1i8JsxF2OiUgep4OA+tD+teCAeHi2b4z/uVZBU5P35F7krVzm+l9q1CPnyMzJmzCZ/914AhK8PAZMn4Va1CsgyGfMWoLvwYPq0xTvvfcTeA0cJDgrk95VL/1FbAO7NW+D36lhQSWj/+ou8VT85pFPXqk3wZ5+TOXsWBXv3AODdfwBejz0Gsoz+5i0y578PusJS8w7uHEP1OS8gVBIJP+7gzpLfrc6H9GxGlckDwSgj6w1cn/YdmUcvP5B8VTs2pPsMxfZOr97NoS+sbS+kWhSPf/gKkfUqs/vDtRwx2Z7Kw40ha6ehclcjqVVc3niUvYt+KZFfUOcYqr6ryJT44w7ufWotU3CP5lSePBDZaEQ2GLk57VuyTDLVWDSa4G5N0aVmcrLTG6WSL6pTQ5q9+zxCkri+ajcXP7WWr3LfNtQd8zgA+rx8jr71HRkX7+AdHUzrT0biFR6AbJS5vnIXV77Z4ohFESp0akibWYouL6/azenPHPSbH71MaP3KHF2wjrMW/Wb94T2oM6gTCMHln3ZxrgReAF5tmhE8eTRCksj+bROZy9c4pHOvV5PoHxaT/OZc8rbvQxURRtjcN1GFBINsJPvnjWT99FuJ/Pw7Nabi7OEISSJl1XYSP/vVWr7uLSg3aRDIyvt4Z8Zyco5dMhNIEnU3fYAuUcO1oXNL5OfTvinhU19BqCQy1m1Bs2yd1XnfR1oROu55kI3IeiPJ732J9oTSfwQNeZLAp3uAEGSs3Uz6ij9K5OfRqjmBb7yKkCRy128k+/ti+rI6tQj/5lM077yLdqfSl0X+9hNyXh6y0QgGA8nDRpXIL6BTYyq/+yJCkkhetZ34T62fQVCP5pS30OftGcvJPnoZ4eFGvV/nINzdEGoJzV+HuPeh42dvibJ4P2t2bMjj04cgqSSOrdnFHpv+BOCJGUOo1TmGQm0hP09cSvyFWADavNCT5gM7I4Tg2OqdHFi+WeHdqyVdxz9FWPVoPn9yGnHnbjnkHdWpIc0tbP2CA1uvZ2HrRyxsvY2FrV8rha3f12e7mc8jqSQurtrNKQd+UheTn3Tkg3WctvGT6g7shIyM5vI9dpbgJ3m1aUbwm6NBksj5bROZ3xZv61HfLyZlsmLrwt2NyOUfIdzcQK0ib/s+Mr74vkTZ3Fu0wO/VV0GlUsa9n4ob92oR/PnnZM6eTcGePagqVCBgxoyi86qoKHK//Za8n+39HEt4tmlO8MTRoFLky/putUM697q1iFyxmNS35pC3Yx+4uxH59SKEuxuoVOTt2Evm0pLlK2t9WkKqVBf3jk+DkNBfOID+uPW7pm7SDXXtFsoPISGCo9AumwgFeaXm4dm6OUETFT839/eNZK0oXp8R3y4hdcoctDv2WtykROQPn2NITiPl9akl8ivrvux/Cq6Mn//uwI8QwgdYC5QHVMC7wMD7W6QLIboBo2RZ7ieEyAE+A7oC6cAUYAFQERgvy/J6IcQwlOCNCqgPLATcgeeBAqCXLMsaIUQ1U1thQB7wEhAM9AY6CiHeAZ4CvgEOAm2Bnab2a8qyrBNC+ANngRqyLNuNQEKI3cApoKmJzxDgbaABsEaW5XdMdM8Br5nu8wgwWpZlgxDiC6A54AX8LMvyDBN9LLACeAJwAwbIslzi10vjzk2JqhLF2I4jqdG4Ji/NGcWUPpPs6Aa/NZQ/v1nPwQ37eGnuKLo805WtKzeTfDeJGU9PITcrl5hOTXhl3him9JmEwWDg+znLuXX+Jp4+Xnzy5yJu7ztH2rV4RQ+SoOucoawb/D7ZCRqe2zCbG9tOFJ0HyM/IZeeMH6jeo6nDe1/7zFy06TnFyiYkQb/ZL7L0ublkJqbx+vr3uLDtBEnX44po6nSKIbRKFO91Gk+lxtXpP3cEn/R5h5SbCSzs9VZROzOOfMG5LccASLhyl29HfsSA915yyrvdnKH8+ez75CZo6PfnbG5vO0G6jXwHZvxAFRv5fCKDqP9Cd9Y8MhlDvo5un4+leu9WXFm3zym/R+YM5WeTPgdvmM31bSfQWPDTlqDPdSXo04IZnkNeI3fBm8iaFHxnfo7u1CGM8betyPRXz5O3yH6w8xr8Krpzx9B9OgtUavDwKJmnLf/+I8n7YhpyRhreb3yE/vwRjEl3LZj44NF/FNqlM5EzUhC+f7OQniThO2Y8mVMmYExNIfCTLyk8cgDDHRtZz58la+bfCywhSfi/MQ7N65MwJKcQ+vVSCvYfRB97247Ob9TLFBw9ZnXYf9xYCo4cJWPaTFCrEZ4PqE8H6NOrG88+1Zsp7374j9tCkvAbN56MSRMwpKQQvPRLCg4ewHDbgXwvv0LhMbN8Umgo3v2eInXYECgsJGDGTDy7dCF/y+ZS867x/nDOPP0uBfEamm6ZR+qW4+RdvVdEkrH3PMc3TwTAp25F6i17g6PtxpdaPCEJer47jJ8GzyMrUcOL69/l2vaTpF4z9zPajFy2zvieWja2ZyjQsXLQXHR5BUhqFUN+ns713WeIP3XdqUzV5o3g/NOzKUjQELP5fTRbbWTad46Tpv7Ku04l6ix7gxPtlUB20ppdxC/fRK0lY0stX/P3hrJz4PvkJWjouXE297acIMuib8m5m8L2p+ZQmJlHdOeGtFzwIlsen4lRb+Tk7J9IPxeL2seTRze/S8Lec1bX2vJqO2cof93vN/+aTezWE2TY9pvTf6CyjS6DapWnzqBO/Pb4DAw6Pb1WvsntnafJuuWkwKskETJlLImvTEaflEr0T5+St/sQupt37OiCx49Ae/CE+ZjBgObDLym8fB3h7UW51Z+jPXzC/lqbdirNfZmrg2ZSmJBG3Y0LyNh6lPxr5meXtf8sGVuPAuBVpxLVlk7kfEfzs4oY8Tj51+6h8itF4WFJImLGaO6+MBVdYiqVf/mYnB2HKbxh7itzD50mZ8dhADxqVSb6k7e51fMV3GtUIvDpHsT2fx1Zp6PCN++Ss/sYutuOn919fkGTxpEyVunLwr/7Au2+g+hv2dt6wKsvk3/kuF0TKaPfwJhZymL7kkSV917i0sBZFCakUX/jAtK3HENroc/MfedIt7CFGl9O4EyH15ALdFwcMANjXj5CraLe73PJ2HmKnJNXi2VXFu+nkAS9Z7/AN8/NIysxjTHr53Bp20mSLfyWWp1iCKkSyYed3qBC4+r0mfsin/eZTkTN8jQf2JnPn5yGQafnhRVvcXnnadJiE0m6cpeVIxfR973hTuVr8d5Qdphs/VGTrWfa2Po2C1tvteBFNj8+E9lk6xqTrffa/C6Je89ZXeuIX4c5Q9nw7PvkJGjo/+dsYm38pIKMXPYX4yc1fKE7q0x+UveS/CRJIvjtsSSNNNn6j5+St8exrQeNG4H2kNnW5UIdiS9NQtbmg1pF1LeL0O4/RsG5SxQLScJv3DgyJk40jXtLKThQzLj3ivW4Z7h7F82IEUXnQ3/+mfx9xft/RfJNHkvy6Mnok1KIWvkZ2j0H0d1yLF/+IQvbK9SR9MrEIvkiv/kY7YFjFJYgX5nq0xJC4N5pEAW/fYKck47nwLcx3DyLrDFvQqI/uQ39yW0AqKo0QN34kQcK+iBJBE1+jeQxb2JISiHy+8/J23vIYV8WOPYl8g/b92V+g/qhu3UHycenVPzKsi9z4X8P/+1LvXoC8bIsN5JluT6wGagjhAgznX8B+Nb0tw+wW5blpkA2MAfoBvQFZlu0WR94FmgBzAXyZFluDBxCCb4ALAPGmtqaCHwuy/JBYD0wSZblGFmWb5hoA2VZ7ijL8ixgN/CY6fhA4BdHQR8LFMqy3AFYCvwBjDHd3zAhRIgQog7wDNBWluUYwAAMNl07VZblZkBDlGBUQ4t2U2VZbgJ8Ybr/EtG8Wwv2/LILgGunruLj70NgeJAdXf02DTm88QAAe37ZSfPurQC4euIyuVm5yvUnrxASFQJARnJ6UeZQfq4WzfV4fCODi9qLjKlGemwSmXdSMOoMXN5wmGrdrQf2vLQsEs/exKg3lEYUO1SMqU7q7UQ0d5Mx6Ayc2nCQ+t2bWcvVvRnHf1Ui9LdPXcfLzxu/sEArmhptG5B2O4n0uFQAkm/Ek3LT+S5X4THVyIpNItsk3431h6lsI19+WhYpZ25i1NnLJ6lVqD3dESoJtZc7uUnpTvlFxlQjw0KfVzYcproNP21aFkn/QJ/3oapaG2NSHHJKAhj06I7swq1Jm9Jd7OmNulYDdHtMs3YGPeTlPhB/qVINjKkJyGlJYNCjP7UXdYOWVjRuTTqiP3sIOSMFADkn84F43Ie6Zh0M8XFKNo9eT8Genbi3ave32ioObnVqY7gXjyFe4aHdvhOPdm3t6Lyf6kv+nn0Y0zOKjglvb9wbNUT7p0mfej1yzoPp0xGaxTQgwP/h7MTnVlvRoSFBkS9/50482trr0LtvP/L37cGYYfOuq1QIDw+QlP+Naaml5u3fpDraW4nk305G1ulJ/v0AoT2t+wBDnnlXIpW3Jw+6I2Z0TDU0sUlk3FVs7+KGw9TsZt+XJZy9icGBrevylF2YJLUKlZsKSuDv17g6+bcSyb+jyJTy+wGCezS3ojFayeRh1WbW4UvoM0oR4DUhpHE1smOTyDH1Lbf/OEwFm4+w1OPXKMxUnOrUk9fxjlL6+vzkDNLPxQKgz80n83p80TlHsO03r//hpN+06ceCqkeTdOoG+vxCZIORhMOXqWLzrG3hUb8Wurvx6OMSQa8nd/NuvDvZ92X+g54kd/t+DJqMomOGVA2Fl5UAnZynpfDmHVThoU75+TSuQUFsAgV3kpB1ejR/7CeoRwsrGstnJ3l7gsXr4BYVQuAjTUlZtd0pn/vwbFiTwtvx6O4mgk5P1l978e3a2opGtuAnvDyL3hWPahXQnrmCnF8ABiN5R8/j1815P+9etzb6e3HmvmzbTrw62F/j+3RftLv2YtQ4H9dKgm/j6uRb6DOtRH16WOnz/jnhpkK4qUu0vbJ4PyvEVFf8DZPfcmbDIerY8KjTvSmnflUCAXdPXcfT5LeEVS/H3VPX0eUXYjQYuXXkEvV6KDxSbsSTWoLfYmvrsX8cpnwpbV2bnIHGxta9nNg6KPrMjE0i674+1x+migO/JbmUflKeEz/Jo34t9Ja2vqV4W8/bsR+jha0DSpACEGo1qNUljhNutWtjiLMd9xyM6/36kb93L8aMDPtGAPcmTTDExWFMcr5DmXv9WujvxaOPSyiSz6uTPT+/gX3I3bHPqi9zJF9JtlDW+rSEFFEZOTMZOSsVjAb0V4+hqtqwWHpVrebor9gHZpzBvV5t9HfjMJj0mbd1F94d7eXze6YPeTvt9akKD8WrbUtyft9od40jlHVf9j8H2Vh2//5D8d8e+DkHdBVCzBdCtJdlORP4AXhOCBEItAY2mWgLUQJD96/bYwq6nAMqW7S5S5blbFmWU4BMYIPFNZWFEL5AG2CdEOI08CUQ5eQeLfPovkYJRoF1UKo43F9vdA64IMtygizLBcBNoALwCEpG0DHTvTwCVDVd87QQ4iRK1lA9wLL20P2c8RNYy14sgiNDSIs3f0ilJaYSHBFiReMX5EdeVi5Gg/LCpyWkERxpP6B3GdiNU7tP2h0PKx9OeL1KJJy6UXTMLzKI7HhN0e+cBA1+EfYBp2Ihy/Rf+RbP/fUuDZ/t7JAkICKYjPi0ot8ZCRoCIqzv29+WJlFDgI1sjZ9ozan1B0t/byizUTk28vlElk6+3MR0zny5kecOf8KQE59SmJ3Hvb3nnV7ja6PP7AQNvg+oz6dM+mxQjD7vQwSFImtSin4bNSmIIPsPHlX1uvi+uwzvCfOQylUCQAqPwpidideIN/GdvRSvFyeA+4NtHy0FhGBMN7+zxow0RID1OyuFRyO8fPF69T28JyxC3dy5TMXyCg3FmJJs5pWaghRiL6u6Tj0CP/sG/9kLUFWs/EA8VGGhGJIteKSkoAqz5iGFhuLZoT15v1svVVRFR2HMyCBgymRCly8jYPJEhGfZbsddEqTQUIy28oXay+fRvj3a9dbyGVNTyV27mtA1awn75VeMubkUHi+9A+cRGUyBhX0XxGvwiAyxowt9tAUt9n9Mg5Vvc+X1L0rdPoBfZDDZCWYeWQka/Epp66DMeo/Y+B6vn/yCm/vOE3/6hlN6j6hgCiz67MKENDwcfGCFPNqCpvs+od7Kt7n6+uelvh9beEUGkWfRt+QlaPCKKl6+aoM6Eb/rrN1xn/KhBNevROrJ4uXzjgoiJ8HMKzdRg48TXpbQXLlHVMtaeAT6ovZ0p2KXRvhG2z9rS6jCQzEkmvsyQ3Iq6ohQG5oQvLu0I3vdn8W2o46OwKN2dQrOOU+ydY8MptDm2bk5eB8De7ak/p4l1FwxlVsTPi06XnHWi9ydswKMpXM+3SJC0Cea+ekTU3GLsOfn2601VTZ/SYVls0h4+2MACq7dxrtZfaRAP4SnB74dm+EW5TywpQoPxZBktnVDciqqsDArGiksFK+O7cj91X75EsiELv6A8BVL8enzmIPz1nCPDKHQwr4LE9Jwd2ALQT1b0mjvYmp/P5Ubb5j1iSTRYNtCmp79lsy9Z8g55Xw757J4P/0jgsiMt+5PbP2WgIggMixsMjNRg39kEElX7lKlRW28A31x83SnVucYAqKc24CVfA5s3fsf2HqaE1uHf+4nnf5yI0MOf8Iwk59014mfpAoPRW9h6/qkVLtArSo8BO/Oxdi6JBG9ZikVdq4j//BJCs87t3UpLAxjioWflJJibwuhoXi0a2c37lnCs0sX8nfudMoLQB0Wij7R0vZSUIVbP3tVWAjenduS87Nj+aJWLaX89p/JP3KiRPnKWp+WEL5ByNnmIJ+ck4HwLea9UbuhqlQPw3X7bxNnUPoyC/mSU+zlCwvFq1M7cn6x78uCJowhffGyUgdgyrovc+F/D//VS71kWb4qhGgK9ALmCSG2ogRXNgD5wDqLujo62RwqNqIs3UKWZaMQwlIPBRZ/Gy1+G1H0JQEZpgyb0qBoWl2W5QNCiMpCiI6ASpZl51/p1rxt70sNCGCFLMtWa0eEEFVQMnmay7KcLoT4DrD8yrvfloFi3gEhxMvAywBNghsihLAnsumoSkNTr3UDujzTlWlPWS938fT2ZOLSyeyatZLCHK1loyU16RQ/PTWb3KQMvEP86f/jZDTX47l39IrNfTu6bVvZHDRuQaNyU1Gva1P+WuB4bW+x+AfyuQd4U7l7E35s8zqFWXl0WzqWGn3bcu23A07YORK2tDcLq0z69LLQZ5yNPs3MHByzEc4Qe43sNwZBQT7qhi3wfm02OZOHIiQVqko1yP9hCYabl/EcPAaPxwdS8Ot3pb9ZRzdgq1xJhapCNfI+fwfh5oH3+A8wxF5BTnGyTKG0vGygv3EVzdBnIF+LW/OW+E+fS/qIwSVeZ2ZRsjz+48aQvfRLuw8+oVLhVrMmWR8vQXfxEv7jXsXnuUHkfF1S7LkMUYp302/MWHK+dCCfry+ebdqROmggck4OATNn4dm1G/nbt5WStyPW9oaRuukoqZuOEtCqDlUmP8OZAe+Wrv1i8CCzl7JR5uteU/Dw96b/stcJq1meFItlW3YoxfsCkLbpKGmbjuLfqg6VJg/k/NOz7a8rBR6kb4loU4dqgzqytY+1/tTeHrT/ehwnpq9EbzkO2PJy/MBKhYzr8Zz+/E8eW/UW+tx80i7eKTm70WE/bc0wZNJo0j/+uthgi/DyJHzhdNI++AI5t4SlBKV8dhmbj5Cx+Qi+LetSbtIgrg6cSUDXZuhTM8k7dxO/1vWc83lAfjnbDpGz7RBezeoTNv557g6bSuGNu6R9tY6K387FmJdP/uVbyCVmi5bML/D1MWR+tsyhPpNfeg1jahpSUCChSz5AF3uXwtP2gQVn7By9L+mbj5C++Qh+LetS4c1BXHpmlnLCaORctwmo/L2p+c1kvGpVRHul+KV6ZfJ+luKdLM6/SLkRz56lG3hx5dsU5uaTcOk2RsMDZPg+gN8S0aYO1Qd1ZIsDW+/w9TiOT1+JzomtK+z+vp/kYfKTfjD5Sd2XjqVm37ZcLc5PKoUtBE8aTfonxdi60Uj8MyOR/HwI+2gmbtUqo7sRW7qbLYaf36uvkrPMsS0AoFbj0bYtOV99VXLbpeingyaOJn1x8fIlDBqJ8PUhfOGskuX7T9CnE973oarSEGP8jQdb5lVKHkETRpOx5Cs7+TzbtcKgSUd3+RoeTRuVru0y7sv+5+Cq8fPfHfgRQkQDGlmWV5pq+AyTZTleCBEPvIOylOuhQpblLCHELSHEAFmW1wllRGooy/IZlCVkJa17+B5YhVKP6J9iB/CHEGKRLMvJQohgE39/lIBTphAiAngUZZlZqSHL8jKUGkAvxV64yfWz1wmJNkexQyJD0SRrrK7J0mTh7e+DpJIwGoyERIWgsUiprVi7EiPnj+G9obPJycguOq5Sq5iw9C32/b6HlM3Ws/TZCRr8os3RbN+oYHKSS5/2nZuUAShLKK5vOUFkTDW7wE9GooZAixm1wKhgsmx4ZNrSRAaTaSFb7U4xxJ2PJSf1wZYK5SZo8LWRz1kasiXKt6tP1t0U8jWKLm9tOk5ksxpOAz+2+vT7m/rUmvQZFVOt2MCPrElFBJtnrqTgMOSMNGuifPMgqz97FDFkHMLXH2N6CrImBcNNZXZHd2wvHo8NLPV9AhgzU3GzyDCSAkOQs6zfWTkjDX1uFhQWIBcWoL9xHlV0FfQPGPgxpqYghYWbeYWG2S01kvPMsuqOHYExKoR/AHJW6d4ZZWbOgkdYGIZUa3261apF4Eyl4LYUEIBH65bIBgO6CxcxpKSgu6isjdfu2oPvc88+kIz/NowpKUi28tno0K1WLQKmK/KJgAA8WrYiy2AAtRpDYgJypqLLgn37cKtfv9SBn4IEDR4W9u0RHUxhoqZY+szDl/CsHIlbsB86TXaxdJbITtTgZzGr7h8VTI7Jnh4EBVl53Dl0iaqdGjoN/BTEp+Fh0We7R4VQkFi8rWcdvoRX5QjUwX7oSymTJfISNHhb9C3eUcFoHfALrFOBlh+OYNdzH1BoUStMqFW0/3ocsb8e5O4m59lauQkafC1mOX0ig8l1Ipstrqzew5XVSlHwFpOftsrOcARDUgqqSHNfpgoPxZBsbXvu9WoQNn+Kcj4oAO/2zUk1GMjbdRDUKsI/mkHOxp3k7dhf4v0VJqThbvPsdEnF32POkYt4VopEHeSHX7PaBHZvTkCXpkgebkh+3lRdPJ6br31c7PW6xFTUkWZ+6shQdMnF89MeP49bhShUQf4Y0rPI/HkrmT9vBSD0jaFW2UOOYEhOQRVhtnVVeCiGVOtr3OvUJPjdaQBIgQF4tmmJrDeQv/cARlO/Z0zPIH/3ftzr1XYa+FH0abY996gQp/adfeQiHpUi7WzBkJVH1qELBHZu7PRjqSzez6xEDQHR1v2JY78lmPvVRgIig8k2+RfH1+7m+NrdAHSf9AxZCTZjsxM8iK23+nAEOx3YeodS2jooGT7/xE/KduAnFRf4MSSloLawdXVEKIYUG1uva7Z1KTAAr3bN4b6tm2DMziX/+Bm82jZzGqgwpqQgWWT4KON6SeNeS7IMBgr2K32JR8uW6K5exZhesk70ySmoIy1tL8xOPo+6NQmbN9VCvhbIBgPa3Wb55Jxc8k+cwatNc6fylbU+LSHnpCP8zBk+wjcQOTfDIa2qZnP0V485POcMhuRUVBEW8jnQp3udmoS+9w5gkq9tCzR6A+71a+PVoQ1ebVsi3N0Rvt6EzH6btOnziuVX1n2ZC/8uhBA9gU9Qagt/Lcvy+w5oOgEfo3yTp8qy3PGf8PxvX+rVADhqWuY0FaVuD8CPwF1Zlv/ZljXFYzAwXAhxBrgAPGk6vhqYJIQ4ZSoA7Qg/AkEowZ9/BJN87wBbhRBngW1AlCkIdcp0b8uB4iMBzvEZEDOp1+sc23qYjk8py2BqNK5JXnYuGQ4CBhcOnaNVL2W9cMenunBs2xEAQqNDmfTl2yx5/WMSbll/VI9aMJa463f582v7NNbEMzcJqhJJQIUwJDcVtZ9oxY1tpUvFdPPywM3Hs+jvSu3rk3rF/kPp7pkbhFWOJLh8GCo3FY2faMP5bSesaM5vO0Gzfh0AqNS4OvnZeWSnZBSdb9K7LSc3PLiak8/cJKByJH4m+ar1bkVsKeXLiUsjonF11J7uAJRrW490i0KxjpB45iaBVSLxN/Gr9QD6VFvoU+3lQeVi9HkfhluXUUWUQ4RGgkqNW8vO6E5ZL4UTAeZBWVW1FkgCOScLOTMdoyYFKbK8wq9uY7ui0CXBeOcaUmg0IjgCVGrUjTugP3/UikZ//jCqqvVAksDNA1WlWtbFn0sJ/dXLqKLLI0VEKrNvHbtQeNj6fRBBZsdVXbM2CKnUQR8A3eXLqCqUQxWl8PDq2oWCA9b6THn6WVIGDCJlwCDyd+8ha+HHFOw7gFGTjjE5GVWFCgB4NGuCPjb2geX8N6G7fBlVufJIkYp8nl26UHDQWoepzw4kdZDyr2DPHrI+XkTBgf0YkpNwq1u3qAC4e5Mm6G2LYzpB9qnreFWNwrNiOMJNTXiftqRusf4g8aocWfS3b4MqSG7qUgd9AOLP3CTYoi+r+0Qrrtr0M8XBO9gPD3+lSK/aw43K7eqRdt15HY7s09fxrBqFh0mmsD5t0Wy1dmw9LWTyaVAF4ab+W0EfgLTTN/GrEomPSb5KT7bi3lbrvsW7XAgdvh7PwdeWkn0z0epcq4UjyLoWz+VlmygJyWduElDF3G9Wf7IVt0vZjwF4hvgD4BsdQuVHm3H9D+dLdAsuXMGtYjnU5ZR306dnJ/L2HLKiuddrCPd6Pc+9Xs+Tu20fqXOXFH24hM6cgO7mHbJ+KHknNoDc09fwqBKFewXl2QU/2Y50m2fnYfHsvOtXVZ5dejb33l/JmWYvcbbVK9wYvZDsA+ecBn0A8s9dxb1yNG7lI8BNjf9jHYoKOd+HW0XzinaPutUQ7moM6UpxZVWwUhRfHRWGX/c2ZP25xym/wkuXUVv2Zd26oN1rrc/EvoNJ7PssiX2fRbtzDxkffEL+3gMIT0+EtxcAwtMTj5bN0N1wvPPUfeScvo5nlSg8TPoMKUmfDaoimWxBHeyPymR7wtOdgPYN0V53kmlH2byf987cILRyJEEmv6XRE625ZNOfXNp2gsb92gNQoXF18rO1RX6Lj4lHQHQI9Xo25/R6a/07g62tVy7G1jt+PZ4DDmy99cIRZF6L51IpbB3s/aTqvVtxq5T6zH5AP6ngwhXUFcuhjjbZeg97W497zGzredv3kfaeYutSUACSn1KgV3i449WyCbpbzv0J3ZUrqMrbjnvWzzt10CBSBw4kdeD9ce/joqAPgOcjj5C/Y0ep9FF44QrqCtbyafdY84t74nniHn+OuMefI2/7XjTzFqPdfRApMADha5bPs2UTdLHOgwZlrU9LGJNuIwLDEf4hIKlQ12yO4aaDALG7J6ryNTDcOFPqtu+j8OJl3CqUQ2WSz7t7Z7R7rfUZ/+RzxPceTHzvweTt2Itm/mK0ew6Q+dk3xD82kPjeg0mdOoeCY6edBn2g7Puy/zXIRrnM/pUEIYQK5Tv7UZRyLIOEEHVtaAKBz4HesizXAwb8Ux38V2f8yLK8BXC0D2Q74CsbWl+Lv2c6OifL8nfAdxbHK1v8XXROluVbKIWlbe/nANa1dDoVc28/y7Kc4eCcZVudLP7ejUXGjs25NVjXEbp/fFgx7Va2+Pt4Mfdoh5M7T9C4czOW7F1KobaAzyYuKTr39nfTWPrmZ6Qna1g5bwWvfzqRQRMHc+vCTXauUWbc+48biG+QHy+9+woABoORt56YQO1mdej4VGduX4rlg42L8EbFvgVrubVL6YBlg5Ed01bw1A9vIqkkzq3ZQ9rVOBo91wWAMyt34h0WwPN/vou7rxey0UjT4T359pHJeAX78uSy8YBS3O/S7weJ3WPf6RsNRn6d/i0vfz8FSSVxdO0ukq7do/XgrgAc+nE7l3adok7nGKbs+QSdtoBVk8xbV7t5ulOzXQPWTbFOs23Qozl9Zw7DN9ifl5a/Sdyl2+we/IH18zAY2T9tBY+tfBOhkriyZg/pV+Ooa5Lv4sqdeIUF8NRfZvkaDO/Jmi6TST59g5sbj/LUpjnIBgOp529z8addTp+jbDCy00Kf5036bGjid9akz+cs9NlkeE++M+mzt4U+LxejT7NijWh/WILPpPnKdu57N2GMu417Z2WL18Jdf+LWvAPuXXqDwYBcWEDe53OKLteuXILXyCkItRvG5ATyvl7gVDZH/PN/WYr3yFkK/yPbMSbewa2NYrq6g5sxJt1Df+kE3m8uAVlGd3grxsS/MfthNJDzxccEzPkQVBL5WzdiuBOLZ6/eAORvXI9Hu454PvZkkazZ7896MB4GI1kfLSb4owUgSWj/2oT+VizeTz4BQN4fjmphmJG5aDGBM6Yi1GoM8QlkzJv/4HLaYNKM9zl26iwZGVk80uc5Rg9/nqee6PH3GjMayF78MUELPgRJIn/TRgyxsXg9oehQu6H4+gb6S5fI37OHkGVfgcGA7tp1tH8614clZIORa29/Q8PVU5Xt3FftIu/KPaKHKEmj8d9vI/TxlkQO6IisN2DIL+Tiy4seSDzZYGTL9O8Y9P1kJJXEmbV7SL0WR5PBjwBw8scd+IQF8OKGOXiYbK/Fi4/yZdc38Q0P5ImPRiIkCSEJLv15hOs7TzlnaDByY8rX1F/1DkIlkbRqJ3lX7hE5pDsAid9vJfTxVoQP6Iis02PML+TyK2aZan0xnsA29VAH+9Hi5Jfc/mANSauKryEhG4wcn7qCLj8pfdmN1XvIvBpHjeeVvuXaDztp8Hpf3IN8aT5vmHKN3sDmR6cT1qImVQe0J/3iHR7dpmw9fmbeWuJ3OnbE7/ebvX58EyGZ+806pn7skqnf7LfRot8c0ZO1nSejy9HSfdk4PIN8Mer1HJi6oqgIrTNdps37lMgv5oEkkf37FnQ3buM3QOnLnNX18WhcD78nulF49SbRa5RxI33JcrT7jxZ7DQYjd975ilo/zQBJInXNDvKv3iXsecW2Un7YQlCv1oT274SsN2DML+TGqIXOZShBvqTZX1Dhmzmgksj8eSuF1+8QOLAXABmrN+LXoy0BfR5B1uuR8wuJH2+emCz36VRUgf7Iej1Jsz7HmFVCUXCDkYwPlxC6eD5CUpG7QenLfPoqfVnub8XbrhQcRMgCZTmiUKnI27KDgsMlzNQbjMRO/ZraP01HqCSSV+9Ae/Uu4c8rtpD8w1ZCHmtNaH/Fvo3aQq6Z9OkeEUS1T8aCJCEkibQNB8jY7jxgWxbvp9FgZP3073jx+7cQKonja3eTfC2OFqb+5OiPO7iy6zS1Oscwcc8idNoCfp70ZdH1g78Yj3eQL0a9gfXTviXftPlG3R7N6D1zKD7B/gxd/iYJl25zdKD12CsbjBybuoJHnNh6Q5Ott7Cw9U02tt7LZOunndj6fX77pq3gCZOfdNmkz3omfV4w6XOAhZ/UcHhPVpn8pBsbjzJg0xyMJj/pgjM/yWBE8/6nRJhsPecPk633N9m6o7o3JqhCgwl9V3nmSILcrXvR7jtSPC8Ag4HsTz4h6IMPTOPeJmXc620a95zU9QHAwwP3pk3JWlhK+zcY0cxfQvhn7yvyrd+M7uZtfJ9S5Mv5xYl8YcGEzpoMKgmEIG/bnlLIV8b6tIRspHD3Gjz6vKZs537xILImAXUDJRiqP6cUPldVa4zh9kXQF5a+bUv5PlhC+JL5oJLIXb+p1Pr8WyjjvsyFfxUtgOuyLN8EEEKsRkkksUxaeRb4VZblOwCyLCfbtfKAEA+6M8l/OoQQJ1CWOXUzFUL+j4EQYglKZK+XLMv/NfvnDaj0ZJm9JC3lh7NDUGkRL+lLJnqIqGko21hrbsmlZx4qRnR2noXwsCEFlWKr4oeIgiul3D74IUGXXbZJmaG/f1Om/DQDXiiZ6CHh0sXwkokeIg6YZpjLCh0Kyna4uyOVbXHwnDLOT+4e4nx3nIeN1FTfkokeIvx8yvZ98Q3KL5noIeLe3cAy43VGKttxKFZdtjvG1CssW+PLUpUpO3qVsa17BTrbzPfhIz+rbP1O2Vi2jmf4C8UtsPh3kPpD2RVEjo8PKDNeAK3ify3jr4ayRfZrj5fZ96zf4j+d6lII0R/oKcvyCNPv54GWsiy/akHzMcoSr3oopVw+kWX5+39yX//VGT+OYNpi/T8SsiyPtT0mhPgMsN1L8RNZlv+Dqq664IILLrjgggsuuOCCCy644IILzmC5SZIJy0z1c4tIHFxmG5hSo+ze/QjgBRwSQhz+J8kj/3OBn/82yLI85v/6HlxwwQUXXHDBBRdccMEFF1xwwYV/BlOQZ5kTkntABYvf5QHbnWXuoRR0zgVyhRB7gUbA3w78/LcXd3bBBRdccMEFF1xwwQUXXHDBBRdccAyjsez+lYxjQA0hRBUhhDswELAt6vUH0F4IoRZCeAMtgUv/RAWujB8XXHDBBRdccMEFF1xwwQUXXHDBhX8ZsizrhRCvomxSpQKWy7J8QQgx0nR+qSzLl4QQm4GzgBFly/fz/4Tv/1xxZxcePh6v+FiZvSSbE0+XFSsAOkXUL1N+x9Kvlym/QkPZFq9OHlqnTPnJOkOZ8ru0qWyLjx9VlW3R0KerP/hW9v8EwevKrpTZC00nlhkvgOZy2T67y9Lf2JHkn/DTp5cpvy/9yrbia0Srsu07s86XrS+WGO9fpvyEKFv56r4RVma8ys/YXWa8ANoE1SxTfudyy3ZcKDCUbfHjC23LtvB/QXrZLraIv1O2BYKPirItVN/UUMKujA8ZDRc2KDNeL7714FvM/xOsuv37/3Zx59GPll1x5883/Ufq0rXUywUXXHDBBRdccMEFF1xwwQUXXHDhfxSupV4uuOCCCy644IILLrjgggsuuODC/yaMrlVOrowfF1xwwQUXXHDBBRdccMEFF1xwwYX/UZSY8SOEeA0YBZyUZXnw32UkhBgGbJVl2XarMlu674A/ZVn+uRRtdgImyrL8uBCiN1BXluX3/+49/h0IIaKBxbIs9y9Lvv8XeHnWKzTr3IwCbQEfT1jEjfM37GgiKkTw5qeT8Qv05fr5G3w0fiF6nZ4GrRrwztfTSLqbBMDBzQdZ/ckq3DzcmL9uPm7ubkhqFd+v/YVZsxfatbvoo9k82rMLeVotw4e/zqnT9rWtln35IU2bNkIIuHbtFi8OH09ubh61alXjm68W0bhxfaZNn89Hi750KN+YWaNo0aUFBdp8FryxkOvn7evxRFaIYOpnU/AL9OP6+eu8P24Bep2ep1/pT5e+XQBQqVVUrF6B/jHPkJ2RjY+/DxMWvE7lWpXRGgoYM+otjh09ZdXu/A+m0717J/K0Wka/8iZnzlyw4/3VNx/RuHEDdHo9J46fYfxr76DXK3Uo2rVvybz57+DmpiYtLZ3Hej7rUMb7WLhwJj16dCYvT8vLL0/ktAN9fvvtJzRp0gCdTs/x42d49dW30ev1+Pv7sXz5x1SoEI1arebjj5fxww/rHPJR1W2K59OjQJLQHdhM4Za11udrNsRr1AyMqYkA6E8doHDjTwC4PdIXt7Y9QZYxxseSv2Ih6J2v/VfVb4bnoNEIIVG4bxOFm9ZYn6/VEO9XZxfx053cT+GGlUgR5fEa+U4RnRQWScHvKyjc/ptTfgGdGlPp3RcRkkTyqu0kfGpNH9SjOeUnDUKWZWS9gdszlpNz9LKZQJKov3kBhQkarg59zykvgIqdGtJh5vMIlcTFVbs58fkGa37Vonhk4cuE16/MoQ/WcerLjQAEVo2i5+evmu+7YjiHF/7MmW+2OOXn3rwFfq+OBZWE9q+/yFv1k0M6da3aBH/2OZmzZ1Gwdw8A3v0H4PXYYyDL6G/eInP++6D7+3Vo3nnvI/YeOEpwUCC/r1zqlFa4eaHyCQEhMOZnYdRm2tE8P3M4MZ2bUKAtYNnET4k9fxOAhh0b8/yMF5FUErtXb2fDF8oz9Qnw5dXPJhBWPoyUeyksGf0heVm5+Ab68trSSVRtWJ29P+/i++lfF/FoPWkAdZ5qh0eAD1/UGUGljg3paHp+F1bv5riD59ftw5cJMz2/k8s2Fp1z9/em64IRhNQsD7LMtklfkXjSec2wuh0b8fT0FxAqiQNrdrD1iz/saJ6e8QL1OjemUFvA9xM/5+6FW2Y9SoK3N7xPRqKGz4fPd8rrPsbOHk3LLi3I1xYw//UPuOawH41k+udT8Av059q5a7w3bj56nR7fAF/eXDiB6ErRFBYUsmDCQmKvxJaKr3e7poRPUfqazJ83k/71Wod0HvVrUnH1IhLemEfO1v2lahtAXb85ns+OVvqyvZso2Lja6ryqViN8XpuNMTUBAN2J/RSsX6mc9PLB+4UJSOUrgyyjXf4hhhvON+bwbN2cwAljQJLI/WMj2StWO6Rzr1uL8OVLSJsyB+3OvQBE/fEjxrw8ZWcRvYGkoaNLlM+/U2Mqzh6OkCRSVm0n8bNfrc4Hdm9BuUmDwNSX3ZmxnJxjFjJIEnU3fYAuUcO1oXNLx2/WCFBJpK7a5pBf9KRnwajwuzvzmyJ+DQ4tw5CrBYMRWW/g0mMl1++SKtfDvdNAkCT05/ahP7bZnqZ8TRONCjk/m4K1HwKgbvwI6gbtAYH+3F70p3aUyA9g3oJpdOveEa1Wy5iRkzl75qIdzZdfLySmSX30Oj0nT5zl9demodfraduuBT+uXsrt2/cA+HP9Vj6Y/6nd9f+GXwYw7oNxNH+kBZlpGXRo09uhfDPnTaZz1/ZotflMfHUa58/av9OfLJ1Hg8b10Ov0nDl5jrffeBe9Xk+1GpX5cMm71GtYhw/nLmHZZytK1Oec+VN4pFsHtNp8xo2ewjkH+vxs2QIaNVb0eerkWSaNn4ler6dHry5MnvoaRqMRg97AtLfncfTwyRJ5Arg1bYHPy2NBksjf+hf566zHQXWDGPymzcWYpNh+4cF9aFeVLM99eLRsTsD4V0GlIm/DX+T8sMrxfdSpReiyz0ifPpv8XYqtC18fAt+ehLpqFZBlMt5bgO68vV4sUda2V6FTQ9rNfB7J5Lecshn3AqtF0WWhMu4d+WAdp780j3sNR/Sk7sBOyMhoLt9j54RlGAqc+4F/1y8THm7U/XUOwt0NoZbQ/HWIuA/XFMPFjAPXEliw+SRGo0zfJlV5sX1dq/PfHbjExrO3ATAYZW6lZrFrUh/S8wp4c93BIrq49BxGdW7Ac61rOeQzdOYIYjo3pVBbwBcTFxf5LJYIqxDOa0sm4hPoS+z5m3z2+scYdHqiq5XjlQ/HUqVeNdZ8uJK/lim+gJuHG9PXzsXN3Q2VWgUwC5hRotD/pXDVNS7dUq/RwKOyLBd5g0IItSzLD1r5cBhwHvs96h8KZFlej/02aP86TIGs//mgT7POzYiuHM3LHV6iVuNajJ47hglPvmFHN+ztF/jj69/Zu2EvY94bQ7dnurNppdKJXzh2gdkvzLKi1xXomDJwCvl5+ajUKqaunsnmzbs4ctQ8ID/asws1qlehdt12tGzRhM8+nUebdk/Y8Z4wcSbZ2TkAfLhgBmNGv8CCDz5Do8lg/OvTePLJnsXK16Jzc8pVKcfQ9i9Qp3Ftxr03lrG9x9nRvfT2CH75+ld2r9/DuPde49GBPdnww5+s/fJn1n6pxCpbdW3JUyP6kZ2RDcCYmaM4tvs4s0fO4XTObby9Pa3a7Na9E9WqVaZxoy40ax7DRx/P5pHOT9nxXrtmPS8NV3T+zbcfM3TY03zz9U8EBPixcNEsnurzAvfuJRAaFlKsnAA9enSmWrUq1K/fkRYtGrN48Rw6dOhjR7d69e+88IKigxUrFvPCCwP56quVvPLKEC5fvkb//sMJDQ3mzJldrF79OzqdzWAsJDwHjSHvkynI6al4v70Y/dnDGBPuWJEZrp1H+7n1OCMCQ3Dv/CS5s14GXSGeL01B3bwT+kPbihdMSHgNHkvuwsnI6an4TPsU/elDdvz0186hXTzN6pgx6R65s0YWteO7cBW6UwecaBGQJCq/9xKXB86iMCGNehsXkLHlGNpr94pIMvedI33LMQC86lSixpcTONvhtaLzkSMeQ3vtHirfkgsBC0nQac5Qfn/2fXISNDzz52xubjtB+jVzl5qfkcveGT9QtUdTq2szbiawuufUonZeOLaEm5uPlyif37jxZEyagCElheClX1Jw8ACG27ft6V5+hcJjx8yHQkPx7vcUqcOGQGEhATNm4tmlC/lb7D+2Sos+vbrx7FO9mfLuhyXSqnxD0WcmgFGPOrAcxkLr4o+NOjchskoUEzqOoVrjmgyb8zIz+7yFkCSGvvsS7w+ehSYxjdnrF3Bi+zHir93jidF9uXjgLBu++I0nRvXlidH9WPP+D+gKdPz84SrK16pI+VoVrfjc2n6SMyu2MXTPh0XP77fByvMbuEF5fhqb57fHwfMD6DjzeW7vPsvGkYuR3FSovTyc6kBIgoGzh7P4uTmkJ6bx1vp5nN12nMTrcUU09To1JrxKJDM6vUaVxjUYNHcEC/pMLTrf5YVeJF6Pw9PXq0SdA7Ts0oJyVcrxXLth1GlSh9fnvcboJ16zo3tlygjWffUru9bv5vV54+g1sCfrf/iTwWMHcf3CDaaPmEWFahUYP3csEwa+WTJjSSJ82hjihk9Bl5RKpbWLyd11mMIbd+zowia8SN6BE6WSpwhCwvP5seR+OBlZk4Lv9M/QnT6IMd6mb7l6jrxP3rG73GvwGHTnj6H7fDao1ODu/NkhSQS9+RrJr76JISmFiBWfo917CP0te9sLePUl8g/b23LKyAkYM7NKJ58kUWnuy1wdNJPChDTqblxAxtaj5Fv0ZVn7z5Kx9agiT51KVFs6kfMdxxadjxjxOPnX7qHyK0VRc0mi4pxXuPrsDHQJadT564MS+VX9YhIXOpmD11cHvIM+Pbt08gmBe5dnKfhlEXJ2Op6Dp2K4cQZZk2Cm8fDC/ZHBFPz6CXK2BryUQv4iJBp1g/bk//QeGPR49BuH4dY55Ixkpyy7du9ItWqVaBbTlWbNY1i4aDbduti7iuvWrueVERMA+Gr5Ip4f+jTffqMEFg4dOs6gAS8Xy+Pf8ssAtq/bzp8r/uSNRfbtAXTu2o4qVSvRsfnjNG7WkDkfvkOf7vZzw7///BfjRr4NwOJl8xn4fD9WfruWjPQsZrz9Pj16dSlWPks80q0DVatWonWTnjRp1oj5C6fTq+tAO7pf1/3JmJeVPuOLrz9k8JD+rFi+mn17DrNl404A6tSrybJvF9G+xWMlM5YkfEaNJ+udCRhTUwhY9CW6wwcw3LW2Rf2Fs2TPertUsti2HzBxHGnjJmFITiHsm6Xk7zuIPtbe1v1Hv0zBkWNWhwPGj6Xg8FHSp84EtRrhWXLfUpa2JyRBhzlD2WDyW/r/OZtYG7+lICOX/TN+oIrNuOcTGUTDF7qz6pHJGPJ1dP98LNV7t+LKun1O5fu7fplcoOPSgBkY8/IRahV1f59L5s5T5Jy8Wiw7g9HIvI3HWfp8ZyL8vRj81TY61ipHtXBzQe1hbeswrK2y8cmeK3GsPHSFAG8PArw9WDuqZ1E73Reup0ud8g75xHRuSmSVKF7vOIrqjWsyfM5IpvWxHxuffWsoG79Zz6EN+xk+dySdn+nK9pWbycnIYcWMr2nWo6UVva5Ax5xB0ykwfYOtvPFLT2ATcLh4Jbvw3wynS72EEEuBqsB6IUSmEGKZEGIr8L0QorIQYp8Q4qTpXxuL694UQpwTQpwRQrwvhOgPNAN+FEKcFkJ4CSGmCyGOCSHOm9otVfVrIURPIcRlIcR+oJ/F8WFCiE9Nf38nhPhCCLFLCHFTCNFRCLFcCHHJlFF0/5ruQohDpvtfJ4RSil4IESuEmGU6fk4IUdt0vKPp/k8LIU4JIfxMejhvOu8phPjWdM0pIURni3v7VQixWQhxTQixoAQZc4QQ84UQJ4QQ24UQLYQQu02y9DbRqIQQH5h0eFYI8YrpuK8QYofFvT9pOl7ZJP9XQogLQoitQojSefNAy+6t2PmLMmheOXUFH38fgsKD7OgatmnI/o3KLOqOn3fQukerEtvOz8sHQK1Wo3Zzs4vIPvFED374UQmqHDl6koDAACIj7XdhuB/0AfD08ixqJyUljeMnztgHJizQpntrtv2yHYBLpy7j6+9DcHiwHV1M20bs/UsZdLb+vI22PVrb0XR5sjO7/tgNgLevNw1aNmDTauWDV6fTkZlpPWA+9nhXVq1SZiSOHztNQIA/ERH2O5Js27q76O8Tx88QXS4KgAFP92bD+q3cu6c4sakpacXKCfD449346adfADh69BQBAf4O9blly66iv48fP0M5Ez9ZlvH1VXZt8PHxIT09oyjzyBJS5VoYkxOQUxPBoEd/bA/qhvb6KhaSCtzcQZIQbh7IGc7lUlWthTE5voif7uhu1I3bOL3GYTt1Gyv3nebcsfdtXJ382AQK7iQh6/Ro/thPUI8WVjRG07sNoPL2AItX2z0qhMBHmpLy0/ZS3VdETDUyYpPIupOCUWfg6vrDVO1u7Shp07JIPnMTo5Pdzsq3q0fm7WSy45zr0612HQzxcRgSEkCvJ3/nTjzatrOj8+7bj/x9ezBm2OzypFIhPDxAUv43pqWWSs7i0CymAQH+Je+qJtQeyAYdGJV30liQi+TuY0XTtFsL9v+yG4Abp67i4+9DYHgQ1WKqkxSbQMrdJAw6PYc37KdptxZF1+wzXbPvl900664cL9AWcPX4ZXQOZiETT90gLzkDUJ5fpuXz2+D4+SWdvYlRb/383H29KNeiFhdWK/yNOgOFWc53MqkcU52U24mk3k3GoDNwfMNBGnVvbkXTqHszDv+qzBzfOnUNbz8f/MMCAQiMDKZ+lyYcWF267AaAtt1bs/VnUz968hI+/r4O+9HGbWPY85fCd8u6rbTr0Va55xqVOLlfyYa8e+MuEeUjCAoNLJGvZ8Na6O4koLuXCDo9WRv34NPFvq8JfK432dsOoE+zzwBzhqK+JSWhqG9xa9y2dBd7eqOu2QDd3k3Kb4MetLlOL3GvVxvd3TgMcYrt5W3bhVdH+77M95k+aHftw5ie8UDy2MKncQ0KHqAvk7w9rfoyt/t92arS9WU+MQq/Qgt+gd2tP0is+Hl5wj+YqZUiqyBnpCBnpoLRgP7yMVTVYqxo1LVbYrh2Sgn6AGiVcVoKjsKYcBP0hSAbMdy7iqp64xJ59nqsK6tX/Q4o47p/oJ/DcX371j1Ff588cYbochGlluvf9MsuHL1QNHnlCN0e7cwva5TMjVPHz+If4Ed4RKgd3a7t5qy6MyfPERWtyJeWquHsqQvodKWbQ+7RqwtrVytZCiePn8E/wJ9wB/rcsW1v0d+nLPjl5Zr7S29v71LP/KtrKuOgMVGxxYK9O3FrZT8O/l241a2N/l48hnilfe32nXi2t+9bfPr3tbN14e2Ne0xD8jaYMmT0euQc531LWdteuM24d339Yao8gN8iqVWoPd0RKgm1lzt5Sc53k/ynftn9c8JNhXBTl/ienI/TUCHYj/LBvripVfSoX5HdV+KKpd907jY9G1SyO37kZhLlg32JDvRxcJW1/3H91FW8TT6LLeq1acCRjUoW0d5fdtHM9Gyz0jK5efY6Bgc6LjDJbMr4ccNKI/9jMMpl9+8/FE4DP7Isj0TJ0OkMLAKaAk/KsvwskAx0k2W5CfAMsBhACPEo0AdoKctyI2CBadnWcWCwLMsxsixrgU9lWW4uy3J9wAt4vKSbFUJ4Al8BTwDtgUgn5EFAF+B1YIPp/usBDYQQMUKIUOAdoKtJhuOA5dRGqun4F8D9XMaJwBhZlmNM/LU2PMeY9NYAGASsMN0zQIxJTw2AZ4QQFZzcuw+wW5blpkA2MAfoBvQFZptohgOZsiw3B5oDLwkhqgD5QF/TvXcGFloE1WoAn8myXA/IAOzTSopBSGQIqQkpRb/TElMJibTOLPEP8ic3KxejwQhAaoI1Te0mtVmyeQkzV8yiYk3zzLgkSSzetISVp35kx469HD1mvQyqXHQk9+6aZwfi7iVQLtrxo//6q4+Iu3ua2rWq8+lny0srHqGRoaTEm+VLSUgl1IF8OXbyWTs6Hp4eNOvUjH2bFGcnqmIkmZpMJn00gaWbPmPJp+/h7W0db4uKiiDunlm++PhEoouRD5QA2cBBfdi+TXEYq1WvQmCgP39u+pE9+/5g4KC+TmWNjo7kngW/uLhEoqOLdzbVajWDBvVj27bdACxduoLatatz8+Yxjh/fwsSJsxwOjlJQCMZ0s06NGamIIPtsJFXVOni/8zler76LFKUMiHJGGoXbf8b3vR/wmf8Tcn4uhkvO07JFYChGjZmfnJ6KFGjviKqq1cVn5lK8x89FirYfgN1adEJ3dJfdcVu4R4ZQGG8OnhQmpOEWZf+RG9SzJQ33LqbW91O5+YY5Vb/SrBe5M+d75FIOED6RQeTEa4p+5yRo8I20H/hLQs3erbn2x6ES6aTQUIzJ5uCXMSUFVWioHY1H+/Zo11snWxpTU8ldu5rQNWsJ++VXjLm5FB4vIcPoYUFSFwV9AGSjXgkiWiAoMpi0eHMgSpOYRlBEMEGRIWgSzM9Uk5BGUKTyTP1DA8lIVpzOjOR0/EMfbItc38ggsm2fX0Tpnp9/xTC0mmy6LXyZQRvn8Mj8ESVm/ARGBJNu8X6mJ6QRGBHsgMash/TENAJN8g6YPozf5q3E+ABOf2hkKMnx5ncmNSGVUJs+UulHc4r6Ucu+9sbFm3R4VPmoqh1Ti8jyEYRFlbwttzo8BH2i2fb1Sam4RYTY0fh2bUPm6r9KLc99iKBQZI2FLWhSHPdl1eviO+tLvF9/r6hvkcKiMGZn4jV8Er4zl+L1whvg7ml3rVU7YaEYkszyGJJSUIWF2tF4dWpHzi8bbC8HWSbs0wVEfP8FPn1Lzmpwjwym0OI9KExIwy3SXr7Ani2pv2cJNVdM5dYEc19WcdaL3J2zQllaVgq4RwVTmGDBLzENdwd9Z2DPltTb/Sk1vn+HWAt+yDI1fppJnY0LCR3cvUR+wjfQHNAB5Jx0hF+gNU1QBHh64zFgIp6D30FVRwkcGtPikMrXBE8fULujqtIA4Wd/r7aIio4gLs6cURQfl1gUhHAEtVrN0wP7sGO7OZuheYsY9h5cz9pfvqZ27ep21/ybfllJiIwKJz4useh3YnwSEVHFb4uuVqvp9/QT7N5RQiZtMYiKirDilxCfSFQJ/Po/05tdO8yBp0cf78q+o3+xcu0XvP6qfWaeI0ghoRhTLWw/NQVViL1foa5dj4Al3+A3awGqipVL1Tbct3Vz+4YUe1uXQkPx7NievN+tx1lVuSiMGRkETp1M2HfLCHhrIsLTed9S1rbnyG/xKaXfkpuYzukvNzLk8CcMO/Ephdl53N1rX5bAEv/UL0OSqL9tIU3Ofkvm3jPknrrmlF9ylpZIf3OWY4S/F8lZtp+GCrSFeg5eT6Srg6yeLefv8Gj94u0v2IHPEmwznvsF+VnZelpCGsGRJfdVQpKYt3ERX55cAbANOFLiRS781+JBizuvNwVtQIkKfiWEOAesA+4vauwKfCvLch6ALMsa+2YA6CyEOGK6vgtKUKYk1AZuybJ8TVa+NFc6od1gojkHJMmyfE6WZSNwAagMtDLd8wEhxGlgKGD5FXh/0esJEz3AAeAjU92jQAfL3doBPwDIsnwZuA3UNJ3bIctypizL+cBFG162KATur4k4B+yRZVln+vv+vXQHhpju/QgQghLYEcB7QoizwHagHHDf27gly/JpB3LZQQjxshDiuBDi+J2cOwjsE7Lsvgcc5Gzdp7l+/jovtn6BsT3H8ud3G3jnK/OgazQaee3RsQxrOZTmzRpTr571+lZHyWDFReFHvPQGFSo14dLlazw9wPG6dEdwlG9my8NhUpoNTeturbhwzDxTplKrqFG/Ohu+/5ORj44hN0/L6xNGltius1mGjxbN5sCBYxw6qHxIq9UqYmLq8/RTI+jbZxhvTn6VatUrF3v9g/L75JM5HDhwhAMHlNTYbt06cvbsBapWbU7Llo+yaNFs/Px8HXGyP2TDx3DnOjlTh5A3ZzSFu9fjNWq6csLbF3XD1uS+M+t5JGoAAQAASURBVIzcyYPB3RN1ixJSwh0mDdrwu32dnDcHkztzJIU7/sDrVZsUd5UadaPW6I/voUSUzA6A9M1HONvhNa6+OJ/ybw4CILBrU3SpmeSds1+jXSw7h8+t1JcDILmpqNKtCdf+KsW47vB9t/7pN2YsOV9+affBJ3x98WzTjtRBA0np3w/h6Yln124PdrP/IoqzAYdppw9r4uYfPD9JrSK8fmXO/rCDVb3eQactoNlo++Wu1uxKYefF9Gn1uzQhOy2TO+dv2Z9/QJ62Qjq8L9P/P322Gt8AP77aspS+L/Th2vnrGPTFZ69ZNOqArTXfsLdHkrpweamDEzYM7A/ZqNJw+xrZE58lZ8YrFO74He/XlL5FqFSoKtWgcNcGcmaORC7Ix+Mx+yUqJbGz1WPgG6PJXPKVQ3mSRowj6fmRpIx7G9/+T+LRuEEJ/Ep+bgAZm49wvuNYrg1/X6n3AwR0bYb+AfsyRwI6soWMzUe40OlVrg+fR7lJ5rp1l/u+xaVHJ3Dt+dmED30U35Z17S8ugZ+dXUsSUkQlCn5bTP4vH+PW6jFEYASyJhHdsc14PvU6Hv3GYUy5B8aS38kHHWc/XDSTQweOcdg0rp89c5FGdTvRoU1vln35Az+s+sKBVP+eX1YSHlS+OR9M5cihExwrZV2df8rv/YXTOXzwOEcOmZd1bvpzO+1bPMYLg8cyear9EtRiGNvztfltuH6V9BeeIXPscPI3/ILfOyXXuLJgYN++jVwB48eQ9bmDcValwq1mTXJ/W0/KsJeR8/PxfX7Q3+BnT/WwbO+f+C0eAd5U7t6EH9q8zopmY1F7e1CzbwmZlv/ALwPAaOR8twmcavoSvjHV8arlPBgqO2i8uPUre6/GE1MxlABv60kbnd7AnitxdKtXPK9S6fFv6lo2Gnm71+uMaTUCoAVQv+Sr/kvhyvh54O3cLXMIXweSgEYoAaT7uXOCElxlUxbM50AzWZbvCiFmAs7D1GaUVpsFpv+NFn/f/60GDMA2WZaL6yXvX2Mw0SPL8vtCiL+AXsBhIURXzHKD4y7Htj2rNouBTjb3/EX3L8uyUQhx/zoBjJVl2ao6q1CKaIcBTWVZ1gkhYjHr1vYeil3qJcvyMpTg3ks3L9zk2tmrhFrMvIZEhqJJsl4qkqXJwsffB0klYTQYCY0y02hzzBHw47uOM2rOaPyD/MlKN9cgyM3KZc/eg/To3okO7VsxfLiyXvz48dOUrxBdRFeufBTxCUnF3TpGo5F169Yz4Y1RrPjecYFPgFEjhzLuleEAXD1zlbBos3xhUaGkJVnHLDM1mfjayJdmo4NOvTuya/3uot8pCamkJKRw+fQVAP74fROvvzGSES8/x9BhzwBw6sQ5ypWPRonFKRk5CcXIN/ntsYSEBjPuWXMdjvi4RNLS0snL05KXp+XggaM0aFCHG9dji2heeWUIL7ygfGycOHGW8uUt9FkukoQEx8uapkwZR1hYMM88Y167/vzzA1i48HMAbt68TWzsXWrVqsbx42esrjWmp+IWZNapFBiKnGETB843p18bzh+DQa8ifPxR1WqEMS0JOUdZkqE/dQBVtTroj+50eJ8AcnoKUrCZnwgKxWi7PMyCn/7cUTyfG4vw9UfOUd5DdYPmGO9cR87KKJbPfRQmpOEebZ45dY8KQZdYXJwbso9cxKNSJOpgP/ya1yaoe3MCH2mC8HBD5edNtSXjuDH2k2Kvz0nQ4Bttnr3xjQomt4S0Z1tU6tyIlPOxaFNLrv1hTElBCjfPqEphYRhslmu51apFwHQlWCcCAvBo2YosgwHUagyJCciZyvMr2LcPt/r1yd/upEbTw4JRr2T9mCAktd2HmiYhjZBo86xqcGQIGcnpqN3VBEeZn2lwVAjppn4gKzWDwPAgMpLTCQwPIiv1wZYL5SRo8LN9fsmle345CRpyEjQknVYKt17feJRmo5wHftIT0wiyeD+DokLItOGXkZhGUHQooPRPQZEhZCSl07hXKxp2bUb9zo1Re7jj5evFsEVj+e71JXZ8+gztzWPP9gLg8pkrhEeHo8yvQGhUKKk2faTSj/oW9aNhUaGkJSo0eTl5LJhgruG06tAPJNxNpCTok1JRR5ptXx0Rij7Z2hY969cgaqHSj6kC/fHp0BzZYCB3R8nZb3J6CiLYwhaCw+yXnlr2LWePIp5/DeHrj1GTgpyeguGmUtRdd2wvHo85/zgzJKeisljGoooIw5Bqzc+9Tk1C5iof6lJgAJ5tWoDBgHbPAYwmWmN6Btrd+3GvV5uCU+eK5af0ZWZ7cI8KQZdUfF+Wc+QinpUiUQf54desNoHdmxPQpSmShxuSnzdVF4/n5msfO+cXZcEv0nnfmXO/7wzyQ5+ejc7U7+nTMsnYfASfmBrkHCm+oK2S4WO2PeEbhJyTYU2TnY5Rm6Ms6dIXYoy7hhRWHkNGEobz+zGcVzJH3Nr2Rc5xbLfDXxrMkPvj+smzRcujAaLLRZJYzDj75luvEhIazOuvjSk6Zrl0ffvWPXz40UyCQ4Lo268Xo4YPASgTv8wSQ4Y/w8DnlUTxs6cuEF3OnJkcGR1BskXWnSXGTRpJcGgQbw+Z7fB8cXhhxLMMHqrURTp98rwVv6joSBKL4Tdh8mhCQoOY9NwMh+cPHzxO5SoVCA4ORKPJcHoPxtQUpFAL2w8Ns1u2LGvNtq87fgRGqxD+AchZJY8RhpQUVBHm9lVhYUX2ex9utWsRNFsZZ6WAADzatASDgcLzFzGkpKC7qBRezt+1B9/nnW/sUda258hvKWm51n2Ub1ef7Lsp5GuUidRbm44T2awGV38rPmvsn/hleo15aaMhK4+sQxcI6NwY7ZU7xV4f4e9NosWy66QsLWF+jj+tNp+/TU8HWT37rydQOyqIEF/rz2BVjRbM2zgMgJtnr9n5LOk2Y1y2ja2HWPgvpUFeVi7AbqAnSk1eF/4H8U+2cw8AEkxZNM8D93PptwIvCiG8AYQQ9y0+G7hfoOH+251qqqtT2uLIl4EqQohqpt8lhbad4TDQVghR3XSf3kKIms4uEEJUM2UOzUdZGlbbhmQvMNhEWxOoyH2P+uFjCzBKCOF2n58QwgfluSSbgj6dcZ5ZVBI+A2Jee3Qsh7YcpstTSsZFrca1yMvOJd3BR8u5Q+do10tJ1X+k/yMc3qpkFgSGmVM7azaqiZAEWelZ+Af74+OvrGl193DnkS7tuXLlBl8sXUGz5t1p1rw769dv4fnByivSskUTsjKzSEy0d6CqVatc9Pfjj3XjyhXnO958sXQFI3uOZmTP0RzYcpBuT3UFoE7j2uRm56FJtu8wTx88Q4fH2gPQvX83Dm41fzT4+HnTsFVDDm4xV+lPT0knJSGV8lWV1M6Ondpw5fJ1vl62kvZtnqB9myf488+tDDItz2rWPIasrGySkuwdmiFDn+aRRzow/IVxVjNCf/21nTZtmqNSqfDy8qRp8xiuXLHe2ePLL7+nVatetGrViw0btvLss4rz1qJFY7Kysh3qc9iwgXTr1pEhQ8Za8bt7N45OnZRZl/DwUGrWrMqtW/YDo/H2FaTwaERIhJJJ07wj+rPW9eKEv/m9kCrXBCGQc7MwapJRVakNbsrMiLp2DMaEu3Y8LGG4dQUpohwiNBJUatxadEJ/2vqjzopflVogpKKgD4Bby87ojpS8zAsg5/R1PKtE4VEhHPH/2Dvr8Ciu92/fs7tx9wQSIATX4E4SHFoKlBpQihYtRYpTpLjTUqRFKjiUtrQUd3dN0IQAAeLZuK7M+8csSTYOpfn213fu6+Jqs3PmfPY5Ns+eOec5Jiocu7Uk/rBx4EWzCjmOqmXtiihMVGjVyTxbsJUbDT/lZpNhhAxfTtLZwCInfQCiboViX8EdWy8XFCZKqrzTlMdHXu3taZVuzXhYgm1eAJr791GW9UTh7g4qFeZt2pB53tjhiu39EbG9pH+Zp06R9PUKMs+dRRcdhUmNGmAm1Z9p/fpo8waF/ocQtZkISpPsyR+FmRX6LOO4B9ePXqFlT38AfOpVIS05jYToeEJvheDu7YGLlytKExVNu7bk+pEr2fe0MtzTqqc/145cfqXvFXUrFHvvXPXXtSmhJay/tJhEkiPU2FeUfkh6taiJOrjwOAIAT289wrWCB06eLihNlDTs2pzbR4y3290+cpWm77YGwLteZdKT00iKSeCPxduZ2mw4X7b8jI2jvubB+aACJ30A9vz8J592HManHYdx7uA5OrxnGEfrVyc1ObXAcfTG+Vv4vSXpdny/A+cOS2Omla0VKhOp3t7q3ZnblwJJSyk6lhFARuADTMqXQVXWDUxU2HbxI/WE8VjzuH1/Hrfrx+N2/Ug+fJbo2atKNOkD0tiidDUeWzQ3zhulyT22KHONLWJSPHp1DAp36RmgqlEffXjRfSHr7n1MypVFWUbqe5btA0g/bawX0f1jIrr1IaJbH9KPnyZ+0UrST51DMDdHMGwnFszNMW/aEM2jJ0Xqpd4MxszbA9OSjmW1KiKYqNDGJ/N84RZuNfyU202H8mjEMpLPBRY56QOQeisY8zx6CXn6Uz49U0lPYWGGwkpyIRUWZti29i3yhxmAPvIJgr0rgq0zKJSoqjVCF2r8okL36CaKspVBUIDKFIW7N/qXwZ9fBnq2cURZuR7a+wX3/Y3rt+LX4h38WrzDvr+O8lGv7oDhuZ5Y8HO9b7/3adOuFZ8OGGv0nHV1zfmRV79BHRQKBeq4eDau38rnnUfxT/plhbFp4066+H9AF/8POLz/OD0/lCaf6zWsQ3JSMtFR+eO4ffTxu/i1ac6oTye98ok6P27YRrtW79Ku1bsc3HeMDz7qJpVHw7oGvfzl2bvve/i3acnwQeON9Cp45/zgrl23BiYmJsVO+gBoHxqeg25SXzRr3QbNJePnoOCQM7GhqlJN6vslmPQB0Ny7j8qzLEoPKX+Ldm3IOGvc16Pf6010z15E9+xFxolTJC79mozT59Cr49FFRaMsJ0WOMGtYH+3jJ0XqlXbfi74Vil0Fd2wMz71Kr+C3JL+Iw61eJVTmpgCUbVGT+GKee3/HL1M52qI0bNsSzE2xbVWHjJDnFEXNMo6ExSXzIj4FjVbHoaAw/KqWzW9LRhbXnsQQUC3/Nq+DgWEFxv3RBV9mSpexTOkylquHL2X7H5XqVSEtOTV763lu7lwIpEkXKR5c654BxfopNo62WBp+g5mYmYK0a+d+Uff8X0bUi6X279/Kq674yc0a4FdBEN4HTmBYDSSK4kFBEHyBq4IgZAH7ganAT8B3giCkA82QYvUEAk+AK3kzLwhRFDMEQRgC7BMEIRY4y2suSRNFMcawOma7IAgv1919CRQevh3GGCZTdEjbtQ4AHrmur0GyMRDQAv1FUcwscPn732cD0lat64YYPjFIsZW2AnsFQbgK3OQNdeCrx6/QMKAh689skI4NHb8i+9qsn2axctJK1FFqflzwI5NWTeTjCX0JvRPK4Z3SgqSWXVrQuW8X9FodmRlZLP5Mim/t6OrI2OXjUCgVKBQCP+74hX37jQNE7j9wjE6d2vDg3jnS0tMZPDgnFNPePzYxZNgEIiOj+XHj19jYWiMIArdv32XkZ9LbXTc3Fy5dOICtrbW0rWzUp9Su62/0Ru3S8cs0btOITWd/JDM9kyVf5BwpP+/nOSyfuIK4KDUbFmxk2uqpDJjQn5CgEA7syFlw1aJTC66dvkZGeu6FVbBq+mqmfDsJExMVd0MeMXK4cST+w4dO0qGjPzdvHyctPYORwyZlX/vl142MGjmFyMhoVnwzh2dhLzhyXAp0vffPQyxeuIqHDx5x9Mhpzl/ah14vsumnndy7W3gzPnjwOB07BnDnzmnS0tIZOjTnOM7ff/+JESMmEhERzbffziMs7AUnT/4OwB9/HGTBgpUsXLiSdeuWceXKIQRBYNq0hcTFFfAGR68nY+caLD+fJx2BfP4w+oinmLSSVgdozuxHVb8lJq3fllZkZGWSvmGBdOuTB2ivn8Fy2irQ6dA/e4Tm7IFCbcrW27oKy7ELEBQKss4eQh/+FBM/KXyY5tRfqBq2xtRf0hOzskj/PteSbFMzlDUakL7p66J1XqLT82TaBqpum4GgVBCz4xjpD5/h2lfa9x69+TCObzXD+T0/RK0OfXoWwcOXFZNp4Yg6Paem/8w7WyZKx6LuPIX64QtqfSw5/kFbjmPpYseH++Zgam2BqNfjO6gTW9pMQpOSjsrcFK9WtTgxuYSxr/Q6kld+jcPipdIxtgf2o3vyBIuu0hbK9L2FH6KovXePjFOncFq3HnQ6NMEhpP9VQCySV2DCzIVcuXGbhIQk2nb/mBGD+tKza8cC0+pSYlHZuQMC+oxk0GloY4hHcHzrYW4ev0bdgPosO72GLMNx7pLJen6esYGJm2agUCo4tesYL4KlCce9a35j1Jrx+H3YlrjwWFYOz1mZsuLsd1jYWKAyUdGwQxMW9v0KHqppMfUjqnZrjomFKQPOf82Li/fovnkiQq76q22ov0BD/X30l1R/vKy/tpPISknn5Iyf6bRyOEoTFYlh0RwZv67I8tLr9OyY8QOjNk1DoVRwftcJIoKf06qPtOXuzNYjBJ24Qa2A+sw+tZKs9Cw2TVjzt+ro4vHLNGnThC1nfyYzI5NF43LKaMGmeSydsJy4qDjWzV/P9DXTGDSxP8FBj9hvCH5fvlI5pnwzCb1Ox5PgMJaML2F/0emJmbsGzw3SWJP022GyQp5i96E01iTu3F9MBsWg15O+9VusvlgojWVnDqIPfyqNJUDWyb8wadQa04CuoNMharJI+25u9u3pW1ZhMWQKgsoEfUwEaRuXFGtP/OJvcVm5CEGpIOXPA2hDn2L1rqSX+ttfhd6qcHLAebFhm5lKSerBY2RcKMbF0ukJ+3I9VbfNBIWC2J3HyHj4DJe+Uv+K2XwIhy7NcH7PXxrLMrJ49DfGMnR6wqavp8rWmaBQErfzqKT3sUFvi6Tn1DPAoJdJqKG/qVzsqbRhsmSfUol6z2mSTt4oVAoAUU/WiW2Y9RwDgoA26BxiXDiqOn4AaG+fQlRHonsShPknM0EU0QaeQYyTYuGZdR2OYGEFeh1Zx7ZBZvGTkUcOnaR9Bz+u3TpGeno6nw2fnH1t5+71jP5sGpGR0Sz7ejbPwsI5dOwXIOfY9ne6d2Lg4N5otVoyMjIZPGBMPo1/yi8DmPDtRGo3q42tgy0XA4+wYuEadm79Pfv68SNnCGjfitNX90nHuY/KOSnzpx2rmThmFtGRMcxb9iUvnkXw+8HNABz86xgrl36Pi6sTe4/twNrGCr1ez8BhH9OueXdSkgsOTnz08Cnatm/NxRuHSE/LYMzIqdnXtu76nnGff0lUZAyLV8zk+bNw/joiHYm+f+9Rli9ew9vvdOD9j7qh0WrISM9k6MCCTyvLh15H6tqvsZ0jPQczj+xHF/YEs87SczDzwJ+YtfDDrEs3qe9nZZKyOP8paYWi05O4fCVOKxaDUkHaXwfQPn6CZXdpUi1tT9HPzcQVK3GYOU2aiA2PIGHeomL1SrPviTo9Z6b/TNct0nPv/s5TxD98QU3Dc+/OluNYuNjxfi6/pc6gTmxvM4nom494tP8y7x+Yi16nIzboKXe2FfNi7m/4ZSZuDvh8MwpBoQCFAvXecyQcLfoESJVSweQuDRi++RR6UU+3ehWp5GrHL1ekF8/vN5Jicx2/95xmPu5YmBr/7E7P0nIxNJIvuzYsUufG8Wv4BjTg69PfkZmeyffjV2Zfm/jTdNZPXEV8dDzbF2xi1Kov+GB8H57cCeXETmmVtZ2LPfP2LsXC2hJRL9J5YFcmtBuFg6sDw5ePRqFQICgEkGL8FP6Akfk/jyCfaS9THG+Xe6vUGsnByJulJQWAv1vpbmW9El/0KqQ3TZauZCdmvCmi+1UvVT2xiNOr/gnuHSj+VKk3yWVlCY5GfoN8UKnoVVVvGsdffiw1rQENxhef6A3SSCzduruvyCpdPe2rbTP8u3xvoyw+0RvErWnpjp1JQaXri0WG25aqniCUrn01xhUfFPxN4TnzZKlpATR3KHJx+hsnMLV0nwuZusJPYP0nuNOi8CDR/wSZ8X9ns8WrEx72agcR/F0uCwXFfPznaKArfiL2TVJnWTEx094gAyffKj7RG2T70z3/yEqFfwuJ/dqW2oPI7udj/8qyLN3RR0ZGRkZGRkZGRkZGRkZGRkam1Pg7W73+UQRB+B3wzvPxpLzBjP8vIwjCJSDvmbx9RVEsPAqjjIyMjIyMjIyMjIyMjIyMTAn51078iKLY43/9Hf5pRFFs8r/+DjIyMjIyMjIyMjIyMjIy/1n0/+sv8L9H3uolIyMjIyMjIyMjIyMjIyMj8x9FDu4sUyyLy39cao3ks89MS0sKgJNLSzconP+k0g0OLNiWrt6FaUUfffmmqeMbVap6Fg1LNwik4OpUqnqX58eVqt4G89ILzv3jtaXFJ3qDbK87o1T1Pro0qfhEb5Ks9FKVW+u/svhEb5DWJJeqXqa2dINXV2lUun392OX8xxj/k2hLMaxmt+mOxSd6g2ydV7qB1ft8WbrPIcGydAPjfzu9dINX+2aUbuB4T+vSHcs86hd8Mts/xdIrZUpVL5HS81vmt1WXmhaA7cYj/8qAxG+KhD5tSu33rP3W4//KspRX/MjIyMjIyMjIyMjIyMjIyMj8R/nXxviRkZGRkZGRkZGRkZGRkZGR+Vvo5V1O8oofGRkZGRkZGRkZGRkZGRkZmf8o8oqf/zGCIPgD40VRfFsQhHeAGqIoLvzffqui8farQ9uZfRGUCm7vOMmltXuNrjv6eNB56RDcalbgzNJfuLJuv9F1QSHwyV9zSImM59eBy4rVU1SohWnb3iAIaG+fQXt5f/40XlUxbdMLFErE9BQydywCQNWgPao6rUEU0ce+IOvARtAVvb/aOaAuNeb2Q1AqeLb1OKHf/ml0vUzPFlT87B0AdKmZBE3cQPLdMAAqDO2CV+8AAJLvhXF79HfoMzXF2FcTU/+PQKFAG3gG7ZWD+dN4VjGkUSJmJJO5S4pXoqrXFlXtVoCANvA02hvHitQCOPc4msXH7qIXRXrU8WJgk0pG13+6/Ij9d8Ml+0Q9j+NSODGyPeYmSgZuv4BGp0erF2lXxYMRLasUq5cbx4C6VJ47AEGpIGLrMZ5++4fRdedODak46UNEvYio1RE8/ScSLz94JY3cmDRsjNWwUQhKBRkH9pG+a5vx9Tq+2Myahz4yAoDMc2dI3/pzifNXVvbF9K0BUt1dPYbm9J58aRTeNTB9awCCQomYlkzGhpmgMsH809mgVCEolGjvXERzbNcr2aYoXwNTvw9AUKC9cw7t1UNG11X126Oq1lj6Q1AgOHqQvm48ZL5eXCvHAF8q5aq7sG+NbXXq1BDvSR+Boe5Cpv9E4uX7JboXoO+sQfgG1CczPZN141fxJCgUgDp+9eg7cyAKpYKTO46yd+3vAFjZWfPZ6i9w8XQh5nkM345YSlpSKtb21nz+3QQq1qnE6d0n2DRjQ06ZWTqgMLOR6ivuSZH2fjl/OafPXcbRwZ49W757rTLLSxn/OjSa3RdBoSBk+0mCVhuPnd49mlNrxNsAaNIyuDTlJ+INY0vzZZ9Stp0vGbFJ7G07pUR6Zy/fYNHqH9Hp9bzbpS2DexkflpmYnMKMJWt4Fh6JmakpsyeMoLJ3OR4/e8GEOSuy0z2PiGJk/w/p2/PtovWu3mbRd5slvU7+DP6gax69VGasWM+ziGjMTE2YPXYwlSt4AdCx31gsLc1RKhQolUp2rpxdpFZ5vzr4zZKeQ3d2nOTqGuOydPDxoP3SIbjUqsCFJb9wPddzyNTWknaLB+NUxRNEkSMT1hN5PaRIPRu/enjO+hRBqSBuxxGi1vxqdN2ufWM8xvdB1OtBp+f5VxtIvXIPAKWtFV6LP8OiSjkQRZ5O+Ja060WPa3b+9agwZyCCQkH09qOEr/rd2L6OjfCc0AtEqb89nfkDyZfvI5iZUPO3uQimJggqBep9F3i+dGeRWgAm9Rpj+ekoUCjIPLKPjF+Nx0pVLV+sp85DHyWNlVkXz5CxUxorrUZNwqRhM/SJ8SR9PqBYLQC3gDrUmy3VX+i2kzxYZVx/5d5tTtWRUvvRpmZwffKPJBr6AgAKgXYH55IeGc+5T4qP3+XhX4f6c6S+92j7Se7l0Svfozk1DHqatAyuTv6RhLthKMxMaPfbdBSmKhQqJWH7LhO09NeCJIw49ySWJafuo9eLdK/lycBG3kbXf776mP33IwHDc1adyvGhAaRrdEw/FEhcahaCAD1re9K7Xvli9Tz969DsK6k8H2w/ya08Y4udjwd+y4fgXKsCVxb/QuD3Of2h5qCOVOvljyAI3N92gqCNh/JmX4B9MSw5eQ+9Hsm+xhULsM/gR+hFHqtTOD6sjWTfwUDi0jIRECT76lcoXu9RJIsP35b8Ft8KDGxe1ej6Txcesj9IitWjE0UexyZxYuzb2FmY0nnVQaxMVSgEAZVCYNugNkVqlbaP6xRQl2oGn/P51uM8yeNzuvdsgXcun/PuxA2kGPpCuU874/mxZM/zrccJW3egWD2r1g1wnz4EQakgfudh4r7/xei6dbumuI79WHqW63REzllH+rW7ku0DumP/QQdAJPPBU8InrkDMKtrHVfk2xnLAZ6BQknlsH5l78owtNXyxnjQXXbTUHzSXTpOxexOYmGIz+xtQmSAolWRdPEXGrp8K1KjsV4e3ZnyCQqng6s4TnM5TZwBvzfyEqgG+aNKz+HX8d4TfeQJAswGdaPRRAAgCV3cc5/wPkg/eZkxPGn0UQKo6CYDDi3fx8OTNfPlW96vLezP6o1AqOL/zOEfW/pEvzXsz+1MzoB5Z6ZlsHr+W53ceA/DV2W/JTMlAr9ej1+pY/M7UIssSQFmrIea9RiAICrLOHCDrgPH4rqxaB8vPZqOPNZTn9bNk7d2Cws0Ti2FfZqdTuLiTuednso4aP1v+08ineskTP/8UgiAISMGzS9zMRFH8E/iz2IT/QwSFQLs5/djVZyHJkWo++XM2IUevERccnp0mIyGVYzM3U7ljgwLzaDCwE3Eh4ZhZW5RAUMC0/cdk7lqGmKzGvO8MdI9uIsbl6GFmgWm7vmTuXo6YrAZLKaCxYG2Pqn47Mn78ErQaTLsOR1mtCbo75wrXUwjUXDiQyx/MIyM8jhaH5hN96BopD19kJ0l7GsPF7rPRJqbi0saX2suGcL7zl5i5O1BhcCdOt/oCfYaGeutG49G9OS92niravja9yfx1BWJyPOZ9pqF7dAtRHWFsX9s+ZP72jWSfhcE+pzKoarciY9t80Gkxe3c0useBiAnRhcrp9CILjtzhuw+a4GZjTp/NZ/HzccPHOScIdP/GPvRv7APAqZAotlx7jJ2FKaIosv7DpliaqtDo9AzYfoGWFV2oU8ahcPvylG3VhYO48cFcMsPjaHhoATGHrpKWq2zjTwdy+eBVAKxqlKPWurFcajm2ZPnn01NgPXIMiVO+QB8bg/2335N18Ry6sKdGybRBt0maUbIf0kYICky7DiLjxzmISWrMhy9Ae+8qYkyuANfmlpi98ykZP81DTIwFK1uDqIaMjV9BVgYolJgPmYPu4Q30z4JLqC1g6t+LzN+/QUyJx/yjKehCbxu1G+31I2ivHwFA6V0bVb22rz3pg0JB5YWDuPXBHDLD1TQ4tIDYQ1dJe5hja8LpIK4eHA9IdVdz3TgutxxTonvrBtTH3duDL/xG4lOvCv3nDmFW98kICgX95nzKwj5foY6MY/afi7l29Arhwc/pOqIHd8/dZu/a3+k6vAddR7zLzoWb0WRq2L10O55Vy+FZtZyRGWJWGtr0JFSOXsWa3L1Le3r3fIepc95MUGhBIdBkXj+O9FpIWoSaLvtn8+zwNRJzjZ0pz2I49N5cshLTKBNQh6aLBnKg6ywAQnad5v6PR2jxzdAS6el0Ouat3MC6xTNwd3HkoxGTCWjWEJ8KObZv2PYb1SpV4JvZEwkNe8H8levZsHQW3l5l2b1uaXY+bT8cStuWTYrR0zNv9c+smz8Jd2dHPho9g4Am9fEpXzZHb+efVPMpxzczxhD6LJz5q39mw8KcvvfDwqk42BUfkF5QCPjP7cfvfRaSEqHmo72zCT1yDXWe59CpmZupWMBzyG9WX56evM3+YStRmChRWZgVLahQ4DV3KCF9ZqKJiKPq3qUkHrlMRnBOUNjkc7dJPHIZAPNq5fFeM5F7bUYCUHbWYJJPXufJsEUIJioUJdDznv8p9z76iqyIOGrtX0z8oSukB+f0mcQzgcQfugKAZfXyVP7+C261/hwxU8Pd92eiT8tAUCmpuWceCcdvkHL9YZF6lkPHkDzzC/RxMdgu/Z6sy+fQP8szVt69Tcrc/GNl5rEDZOz7Dasxxf9okfQE6s/vz+kPF5AWoabdgTmEH75Ocq5nQWpYDCffnYMmMQ33NnVpsGQQx9+amX298qedSA4OR2VTvB8hKAQazO/PiY8WkB6hpsP+Obw4dJ2k4Fx6z2I42lPS8wioS6PFgzjy9kz0mRqOvz8PbVomgkpJuz0ziDh+i7giJgp1epGFJ+6x9t0GuFmb02f7RfwquuDjZJ2dpl9Db/o1lCaDToVGs/X6U+zMTcjS6RnXuirVXW1JzdLSe9tFmpRzMrq3IPtazO3H/t4LSY1Q033fbJ4evkZCrv6QmZDK+RmbqZCnPzhU9aRaL3/2vD0TvUZL5y0TCTt+k6THhR+coNOLLDx+l7XvNpL8iG0X8PNxLdy+R9FsvfEEO3NTsrQZkn1udpJ9W8/TpLxzkfbp9CILDt7iu94tcbO1oM8PJ/Cr7IGPi212mv7NqtC/mfQi6tTDCLZcDsHOIuewkPUft8LBsph+x//Ax1UIVF84kGsGn7PpofnEHLpGaq6+kP40hisGn9O5jS81lw3hUucvsa7miefHbbjYaRpilpb6O6YQe+QGaY8ji9BT4DFrOE/7fYkmMpaKv68g+dhFskJyxrLU8zcJPXoRALOqFfD8djKPOgxD5eaEY7+uPOo4HDEzi7IrJ2Pb1Y/EX48WqWc5aDQpc8ajV8dgs+A7NFfPoX9uPLZo7gWSujDP2KLJIvmrcZCRDkolNnO+RXPjMrrgu0bJBIVA19kD+PHjBSRFxjH8z7ncO3KdmJCcMqzi74uztzvL/cfhVa8S78wbyHfdZ+BaxZNGHwWwttt0dBot/X6ezIPjN4l7IpXhuY0HOLt+X6HmCQqBD2YPZNXH80iIjGPCnwsIPHKVyFzaNfx9cfF25yv/0VSoV5mP5g1iafecCZhves0mNb6EAbgFBRZ9RpG6bBJifCxW01ehvXkBfUSYUTJtcCDpK6cbfaaPek7qV8Oy87Feth3NjSJ+D8n8J5G3er1BBEGoIAjCPUEQ1gDXgY2CIFwVBOGOIAhf5UrXSRCE+4IgnAXezfV5f0EQVhn+/ydBEN7LdS3F8F8PQRBOC4JwUxCEIEEQWhXyXZSGPIIEQQgUBGGs4XMfQRAOCoJwTRCEM4IgVHsVGz18fUh4EkXisxj0Gh339l6kUnvjh19aXBKRt0PRa/JHvrd2d8SnjS+3d5wskZ7CoyJifDRiYgzodWjvX0JZydcojap6U3TB16RJEYC0XAOoQgkqU2nFg4kpYmpCkXr29SuR9jiS9KfRiBodEXvO49apoVGahKsP0SZKpxbEXwvG3CPnRA9BqURpboqgVKC0NCMzsujTNxTu3ogJMdKkgF6H9v4VlD557KvWBF3wjRz70iX7FI4e6CNCQZsFoh7d84coK9UrUi8oIgEvB0s87S0xUSroWK0MJ0MKd/AO3A+nUzXpxARBELA0leaKtXoRrU7Pq4SstzWUbYahbKP3nMelUyOjNLq0zOz/V1qawd84dVBVtTq68BfSah6tlsyTxzFt1vK188uLwrMSenUkYnw06LTobp9DVd24rajqtkR755JUvwCpSTkXszKk/yqV0r9XsFXhVgExMRoxydBuHl5BWbFOoemVVRuhfXC1xPnnxbZ+JdKz605L9J5zOOfpF7q0jBw9S3NenhhZknsbtG/M2V9PAvDoxkOsbK2wd3XAx7cSUU8iiHkWhU6j5eLeszRo3zj7njOGe878epKGHaTPM9MzeXj1PpoCVtqJ2kwQS3YiR0Pf2ti9wVPxnOr5kPwkipQwaex88sdFvPL8cIi5GkxWojQ5F3s9BKtcY0v0pQdkJqSUWC/wfgjlyrrjVcYNExMTOge04MT5K0ZpHj19TpN6tQGoWK4sLyJjiFUnGKW5dCMQrzJulHFzKVrv4SPKlXHDy8MVExMVnf2acuLiNWO9sBc0qVtT0vMqw4uoWGLjE0ts00vcfH1IfBJFkqEsH+69SMUOxmWZHpdE1O1Q9Frj+ja1tqBs46rcMTyD9BodWUlFT4ha+lYm80kkWWFRiBot8XvPYGdoby/R52r/Ckvz7P6ssLbAunFN4nZIk7CiRosuqehTb6zrVSLjSQSZBr24P87i0LEoPTMQ818TTJQIJqpixxZV5eroI19Iq3m0WrLOHMe0ccnHSu3d24gpJT85yLGeDylPokgNi0HU6Hj2x0XK5ukLcVeD0Rj6Qty1YCxz9QULD0c82voSuu3EK+vpNTrC/riIZx692Fx6sdeN9bSG55LCRInCpPixOigyES87SzztDM/ZKu6cfFT4C5mDDyLpVNUDABcrM6q7ShMaVqYqvB2tiEnJLPReABdfH5KeRJFssO/RHxcpn6c/ZMQlEXsrf3+wr1SG6BuP0GVkIer0RFy8T4U843N++xLwss/lR1R15+Sjwv2Igw8icuyzNqe6m10u+6yJScko9F6AoHA1Xo5WeDpYSXo1PDn5MKLQ9AfuPqNTzdc7Ra60fVy7PD5n5J7zuOYp/8RcPmfCtWDMDG3TqnJZEq4Fo0+X6i7+/D1cuzTKp5Ebi7pVyHoajuZZJGi0JP51Gpt2TY3SiPnGspxrgkqJYG4KSgUKCzO0UUWfDqisVE0aW6KlsUVz7jimDVsUWy7ZZBhOj1SqpH8F9D1P30qon0YR/ywanUbH7b0XqJ6n/Vfv0IAbv50B4NmNEMxtLLFxsce1Ulme3QhBk5GFXqfnyaV71OhYdPvPTQXfSsQ+jSLOoH1973nqdDCugzodGnH5t9MAPLkRjIWNFbYu9iUvg1woK1ZFHx2OGBsJOi2ayydR1Wv+6vnUqIc+OgIxrvBx6b+IqBdL7d+/FXni581TFdgkimI94AtRFBsCdQA/QRDqCIJgDqwHugKtAPdXzL83cEgURV+gLnCzkHS+QFlRFGuJolgb+NHw+TpglCiKDYDxwJpXEbd2dyA5Iud4weQINTbuJVzxAbSd+TEn528vcacQrO1zJjwAMTkewdpYT3BwB3MrzD6ciHnfGShrSoOgmJKA9spBLIYuwWLECsTMNPRP7hSpZ+7uSEZ4zoMsPVyNmXvhR7V69Q4g5vhNADIj43m89i8Crq+mze3v0CSlEXvq9qvZlxKPYGOfxz43aeXI++Mx7/MlyurNANDHvUDhWQXMrUBlitK7NoJN0cfKRqdk4J7rDambjTnRhThd6Rod5x/H0K5KThPV6UU++OkMbVYfoWkFZ2qXdLUPYObuSGauss0MjyuwbJ07N6LJ2RXU3TKFe2PXljj/vCicnNHH5DzU9LExKJyd86VTVa+J/dqN2M5djLJ8hRLnL9g6Iibm2CMmqRHsjI+9VTiVQbCwwnzQLMxHLELl2zpXBgrMP1uC5ZSN6EJuo39e9FYTI21rB8TknElFMSUhX7/IRmWCsnxNdCHXS5x/XvLXnRoz9/xH/Dp3bkzjs19Te8sUHhjqriT3Org7Ehcem/23OjIOBzdHHNydUEfk3KuOiMPB0GZsne1JiJbKICE6Hltnu9e2rzSwdHcgNTynr6dFqLEsYuys9JE/L04UPX4URXSsGneXnPbu5uJEVKzx0bBVK5bn6JlLAATeDyYiKoaoWGNH/sCJc3RuU/wkQHRsPO4uOf3ZzdmRqDjjie+qFctx9Lw0ARn44BER0bHZ30kQYOi0RXwwajq/7D9epJa1uwPJucoyJUKNtVvJxiLbci6kq5Npv2wIvfbPpe2iwcWu+DF1dyIrV/vMiojDxC1/+7fr2JTqx1fj89N0nk74FgCzcu5o1YmUW/Y5VfevwGvRZ8Wu+JH0cuohKyIOU4/8Y6VDpybUPb2Sapum8WjcqpwLCgW1jyyjwe0fSTx9i5QbRa8kFJyc0cXmGivjYlA4FTBWVq2J7dcbsZ6xGKVXhSLzLAoLd0fSXuTYlxahxqKIvuDdy5+I47ey//ad3Zfbc7eXOFCnpbsjaeF59DwK1/Pp5U/EiRw9QSHQ6ch8etxeS+TpIOJuPCpSLzo1Azcb8+y/3WzMiUktePImXaPj/JNY2lZ2y3ctPDGdBzHJ1HIvemyz8nAgJZdflhqpxqoI+3IT/+A5Hk2qYmZvjdLcFK82dbEuU/Tx7dEpmbjl9iOszQudnCravjQexCRRy92+aL3kPH6LrQXRyemF6Gk5/yiKdtVyVhoKwPBtZ+m18Ti7rz8uUqu0fdy8PmdGMT5n2d4BxBp8zpT7z3BoWh0TB2sUFqY4t/PFvGzRdadyc0ITkTOWaSNjCxzLbDo0w+fwd5TbMIvwyV9LaaPiiNvwG1XO/ESVC1vQJ6eSevZGkXoKRxf0cTHZf+vVMQhO+V8iqKrUwGbJBqynLkLhWSFXBgpslmzAfuMetLevogu5l+9eWzcHEnOVYVKEGjs3xwLS5NRrUqQaW3cHoh48o0LjaljYW2NibkqVAF/sPHLKo2m/Dow6sJB3Fw/B3NYqn7admyPxubTjI+Kwy/MssndzMEqTEBmHvaGORRE+2zyNiXsX0KJX23z550Wwd0avzilPMT4WhX3+sVrpUwOrWd9hOWYeijL5t4qaNPZHc7lkE+cy/y3kiZ83z1NRFC8a/v8DQRCuAzeAmkANoBrwWBTFYFF6Jb7lFfO/AgwQBGEWUFsUxcJes4UCFQVB+FYQhE5AkiAI1kBz4BdBEG4C3wMeBd0sCMIQw2qlq5dScpxGoYA1HiVdqODTxpe0uCSigp6U7AaDYn7yCCoUKNzKk/nb12TsXo5Js67SZImZJcpK9UhfN4n0tePAxAxljaYF5FeMXF49A44tauDVO4D7c6T9yio7K1w7NeBko1EcrzscpaUZZXoW94OpAMG8ci/t+30lGb9+jUnTtxDs3RDVkWiuHMS851jM3h2NPuY56ItezVCQJYWt2jn9KArfsg5Gy6WVCoFd/VtxaFhbgiISCIkp+VtehALaTgHfKPbAFS61HEtg/yVUnPRhyfMvgV5eOW3IQ9R9PyRh+CDS//gV25nzXiH/Aj7L2xmUShRlKpKxaQEZP83FJOA9BCdDlxP1ZKyaQNrioSg9KyG4Fr/9qEgK6YhK7zrowx+9/jYvKKSZFlR3l7nccgxB/Rfj/bLuSnCvUFDbEMVX6Y7/egqysTBb3JpXp1IvP67P3/HaegXVT97vMKhXD5JSUnlvyHi2/X6AapW9USmV2dc1Gg0nz1+lQ+tmr6eXpwYHvd9V0hs5jW1/HqGaT3lUSskN2bRsBrtWzWXtnPHs+OsoVwPvFy5WYHsp9isCoFApca1Vgdubj7G9y5do0jNpOKJr0TeVpK8DiYcucq/NSEIHz6fM+D7ShyollrV8iN18kAddxqJPz8BtRM/X0Mv/UfzBS9xq/TkPBi7Ca2KvnAt6PYHtv+B6g0+x8q2ERZ4tjyUSzDtWPnpIwqcfkjRmEJn7fsV66iuMlXnVCpIrpAJdmtfAu7c/gfOkvuDRrh4ZsYkk3H7yCoIFfFaInmvzGlTs5c/NeTl9T9SLHGw/lT8ajMLJ1we7qsWsJnmFMep0aAy+ZeyxMzcx+jwtS8v4fTcZ71cVa7PiojKUfGzJS0JIOLfW/EWX7ZPpvGUi6rth+VYFlYhCHInTodEG+0yNPk/L0jL+r5uM96tWrH0Fj2WF6AVH4uvpZOS3/NTPjx2D27L6o+bsuhbKtbDYgm/mf+DjvsJDzqFFDcr2DiDY4HOmBofzZNWfNNg1jQbbp5B85ymitpjoEoUVXB6SD1/gUYdhPBs2B5exfQFQ2Fpj064pwf4Dedi8L4KlOXbdAkqUnxF5ClT7+CGJIz4iecJgMg78hvXEuTkX9XqSJwwmcej7KCtVR+HlTV4K8x+KTwMxj8I5/d1eBm6ZQr+fJxF57yl6ndT+L205wrLWY1jVZQrJ0Ql0+bJPibTzVV8R329Fzxksensya/ovoNUnHfFpXD1/fsXklVdQ9zSElIl9SJ01jKxjf2Dx2VfGyZUqVHWbob1aRBiK/yr6Uvz3L0We+HnzpAIIguCNtKKmrSiKdYB9wMtXQCV5jGgx1I8hXpApgCiKp4HWwAtgsyAInxR0syiK8Ugrgk4CI4ENhvwSRFH0zfWvwFFGFMV1oig2FEWxYRPrytmfJ0eqscn15tHGw5GUqKK3M72kbMMqVGpXn6FnV9D125GUa16Dt74eXuQ90gqYXFupbBwQUxKM0yTHo38cBJosSE9B/+whChcvlOVrSFts0pNBr0MXfB1FmUoURUaEGvNcb7ssyjgWuF3LpkY5ai8fyrV+S9HES9svnFvXIj0shqy4ZEStjqh9l3FoVHTw43z2WRdi35MgaUtXRgr6F8EoXCTHUxd0loytc8nctQQyUouM7wPSm7nIXG/KopIzcLE2LzDtwXs527zyYmtuQkMvJ849Lvky0cyIOMxyla1ZGSeyitgKl3DxHhYV3DFxfL3tNvrYGBQurtl/K5xd0McZO3xiWlr2UmLNlUugVCLYlmzliJhovMJHsHVETFLnSROHLvgmaDIhLRndk3soPPK8fclIQ/f4DsoqviW2TWo3OW+VBGv7QrcxKqs0QvvwSoHXSkpmhDpP3TmSFakuNH3ixXuYG+quJPeqI+JwKpPz1srR3YmE6HjUkXE45nr75ujhRHyUdG9SbAL2rlIZ2Ls6kBT76luGSpPUCDVWZXL6uqWHI2kFjJ321b1ovmQwJwauIDO+5Fu78uLm7ERkTE57j4qJw9XJ+E2ktZUlcyeOZPe6pcyfPIr4hCTKuuf0mTOXb1C9sjfOjvYl0HMkMianXqNi1bg6Gd9nbWXB3HFD2L16HvPHDyU+MZmybpLey+/mZG9H2+YNCXpQ+KqKlAg1NrnK0trDkdTokj2HUiLUpESoibop5R+y/zKutSoUeU9WRBymudqnqYcTmujC23/q5buYlnNH6WCDJiKWrIhY0m5KMXYS9p/HopZPCfRy2r2ph1OR/S350l3MyrujyjNW6pLSSLpwB/uAorcAi3ExKJ1zjZVOLujVeX4cp+caK68Zxkqb11tllxahxjLXygRLD0cyohLypbOr7kXDZYM51385WYa+4NS4CmU6NKDL5a9p+t1nuLasQeNVRfsRaRFqLMsY66VH5tezr+5F46WDOTMgRy83mqQ0oi/cwyOg8G21AK7W5kQl56ykjUrOwMWq4FVehx7mbPPK1tHpGf/XLTpX86BtpfwrZfKSGqHGOpdfZuXuSGox28xz82DHKX7v/CV/vTeXjIRUEouI7wPgam1GVG4/IqUI+x5E0KlaQfbdkOyrXPyidzcbC2O/JSkdl0Li5xy884xONY1forgaVgs5WpkTUNWDoPAi+lIp+7h5fU7zQnxO6xrlqLl8KDdz+ZwAL7ad4GL7KVzp/hWahFTSQgvfAgeGFT4eOWOZyt0ZTRHbtdKu3DGMZbZYtfAl61kUOnUSaHUkHzqPRf2iJyr06hgUuVb4KBxdEIsYW7Q3LkmHXuQZW8S0FLR3bmLia7zlFSAxUo1drjK09XAkKc/zQEqTU6+27o4kG+r12q6TrH57Ghs+nENaQipxhhhJqbFJ0rYdUeTKjuN41s0/bidExuGQS9vBw4nEPNoJkWqjNPbuTiQatF+mTYlL4vahy1QoQMOoHOJjUDjmlKfg4Iw+IU/9ZaRBpjT+aAMvIyiVCNY58bBUtRuhDwtBTEooUkvmv4k88fPPYYs0CZQoCIIb0Nnw+X3AWxCEl727V0E3A0+Al5tUuwEmAIIglAeiRVFcD2wE6hd0syAIzoBCFMVfgelAfVEUk4DHgiC8b0gjCIJQ91WMirgVioO3O3ZeLihMlFTv2pSQIyXbQnJ68S7WNv2c71uOZe+o1YSdv8u+MUVv5dFHPEZwcEOwcwaFUop3E3LTKI0u5AYKz8ogKEBlisLDG706AjFZjaJMRSnGD6AsVx0xruiHYuKNR1hVdMeinAuCiRKP7s2JOmQcp8K8rBP1fxjHrZGrSc31kE1/EYd9/UooDG+anFrVIiVX8MgC7Yt8gmDvimD70r5G6EJvGaXRPbqJomwu+9wl+4CcQM82jigr10N7/3KRejU97AiLT+VFQhoanZ5D98PxK8CxTM7UcO25moBc19RpmSRlSHFTMjQ6Lj2NxbuIgIz58rzxCMuKHpgbyta1e3NiDxnHnbGokKNnXdsbhYkKjfoVVhXlQvvgPsqynijc3EGlwsy/DVkXjQPZCQ45joCqajVQKBCTSjaBoH8RgsLJA8HBFZQqlHVaoL1vbI/23hWUFaqDQgEmpii9KqGPfgGWtmBuaRA2RelTBzGm6LZipB311NBunKR2U6URutACtgWZmqP0rIzu0a38116B5BshWFT0wLycK4KJCtfuLQqouxwHPnfdleTe60ev0LKnPwA+9aqQlpxGQnQ8obdCcPf2wMXLFaWJiqZdW3L9yJXse1oZ7mnV059rR4pu+/9r4m6GYuPtjrVh7KzQrSnPDhuPnVZlnPBfP4azo78jObSIAJ0loFa1Sjx9EcHziCg0Gg0HTpzDv7lx7IGklFQ0GqlP/7r/KA3qVMfayjL7+oHjZ0u0zQugVpWKPA2P5HlkNBqNlgOnLuLf1PjxJOlJpyr+evAkDWpXxdrKgrSMDFLTJMc/LSOD89cDqVSh8BVwUbdCsfd2x9ZQllW6NiW0hM+htJhEkiPU2FeUfox6taiJuphxOu1WMGbeHph6SW3YoWur7EDOLzEtn9P+LWpVRDBVoYtPRhuTgCYiFrOK0tYTmxZ1jIJCF0TKzRDMvT0wM+g5dWtJ/GHjyVuzXP3NsnZFFCYqtOpkVI62KG2lOhTMTbFrVYf0kOcUhTb4PgoPTxSu0lhp2qoNmst5xkr7nLFSWdkwVia/3mRr/M1QrL3dsfSSngVe3ZoSnuc5a1HWieYbx3B51FpScvWFoPk72ddgFPsbj+HisFVEn73L5c+K9iPUhr5nZWgv5bo15flhYz3Lsk603DCGi5+vNep7Zo42mBjKU2luglurmiSFFO1H1HS3JSwhjReJhufsw0j8fVzzpXv5nPX3yfkhJ4oiXx29g7ejFX1LcNoVQMytUGy93bEx2OfTrSlhJewPAOZOhphCZZzw7tyQR3+cL8Y+O8Lic9n3IBL/ioXZF29kuyiKfHUkCG9Ha/o2yL96o0C9Mg6EqVN4kZAq6d19jl+V/IvVkzM0XAuLJSDXtfQsLamGeG/pWVouhEZTKVdQ6LyUto+bdOMRlrl8TvfuzYkuwOf0/WEcgSNX55vYMXW2zU7j1qUREb8XXXfptx9iWqEsJp5uYKLC7u3WpBy7ZJTGpHxO+ZnX9EEwUaGLT0IbHoOFb1UEc2mSz6p5XTIfFT2W6UIeGI0tJi3akHXV+DsajS2VqiEoBMTkRARbOwRLg49paoqqTgP0L4yDGAO8uPUIpwruOHi6oDRRUqdrM+4fMS7D+0euUe9dKSSqV71KZCankxyTINlhaP92ZZyo2akRt/68AIBNrjg8NTo2Iuph/nH06a1HuFRwx8mgXb9rc24fMfZvAo9cpfG70jb/CvUqk56cRlJMAqYWZphZSS9eTS3MqNaqDuEPiynPxw9QuJVFcHYHpQqTxv5ob14wLk/bnBc8Cu+qICgQU3LiS5o0CUBz6f/PbV5yjB/5VK9/DFEUbwmCcAO4g7Tt6pzh8wxBEIYA+wRBiAXOArUKyGI98IcgCJeBYxhWEgH+wARBEDRAClDgih+gLPCjIAgvJ/dehsvvA6wVBOFLpMmkHUCJfxWKOj1HZ/zM+5smIigVBO46RVzwC3z7SMdJ3tx6HCsXOz7ZOwdTawtEvZ6GAzuxsd0kslIK3pNdtKCerKNbMHtvnOG487OIceGo6voDoL11ElEdge5xEOb9Z4OoRxt4BjH2BSKge3gV809mgl6HPjoM7e2ilzaKOj13pvxI4x1TQang+fYTpDx4TrlP2gEQtukolb/oiamDNbUWDZTu0eo413EaiddDiPzrEi2PLEDU6UkKfMKzzcUcry7qyTqxDbOeY6Tj6oPOSfbV8ZPsu30KUR2J7kmQZIcoSvYZTjUz6zocwcIK9Dqyjm0rdjuPSqFgcrtaDN99Gb1epFttTyo52/DLTemEhfd9pdUox4MjaVbBGQvTnCEiNiWT6QduodeL6BHpULUMrX2KfxuZu2wfTvkB3x3TEJQKwrefIPXBc8p80h6A8E1HcHm7Ke7vt0bU6tBnZBE0ZEUxuRaBXkfK6q+xm78UFAoyDu9H9/QJ5m9Jx6Jm7PsTs1Z+mL/dDXQ6xMxMkhd8VUymufPXk7V3I+b9p0lHql8/gRj9HFVjyR7t5SOIMS/QPbyJxahlIOrRXD2GGP0Mwa0cZu99hqBQSPUeeAHdg1eIwSPqyTq5E7Pun0vad88jqiNQ1ZYcG22gFMRQ6VMP3dO70mqxv4Go0xM8ZSN1DHUXsf0EaXnqzvntJri/74eo1aHLyOKuoe6KureNiZ7jWw9z8/g16gbUZ9npNWQZjnMH0Ov0/DxjAxM3zUChVHBq1zFeGH40713zG6PWjMfvw7bEhceycnjO6Vsrzn6HhY0FKhMVDTs0YWFfqV4Vlo4ozKwBAZVDOfSZyejTCn6bO2HmQq7cuE1CQhJtu3/MiEF96dm1498qw8tf/ky7bROl49x3niLx4Quq9JXGzoebj1NnbA/MHKxpMr+/ZL9Wx/4uMwBotXokbs2qY+5oTc+rK7m19FdCdhQ+nqmUSqaOGsywSXPR6fX06NyGShW82LVXOqr5g64dCX36nGmLvkWhUOBT3pOvxo/Ivj89I5ML124zY2zJThFTKZVMHf4Jw75cgk6np0eH1lQq78mufdIY+MFbbQl9Fs60pd9LeuXK8tWYwQDExScxZs7XgHQ6WBf/ZrRsWPiqClGn5+T0n+m+WXoO3d15CvXDF9Q2HGscuOU4li52fPSX9BxCr8d3UCe2tJWeQydn/EynlcNRmqhIDIvmyPh1RRun0/N8+jp8Ns+SjnPfeYyMh89w+riT9P23HMS+S3McewYgarSIGVk8Gbkk+/bnM9ZTYeU4BBMVmWGRhI1fWazek2kbqLZtBoJSQfSOY6Q/fIZr3w4ARG8+jNNbzXB+T+pv+vQsgodLx0abujng8410LLugUBC39xwJR68VpQZ6HWnrvsZmljRWZh7bj+7ZE8w6SWNl5sE/MW3uh1lnaawkK5PUpTljpdUXMzCp5Ytga4f9xl9I2/4jWUf3F6aGqNNzY+pPtN4+CUGp4PGOUyQ9fEHFT6QYF6GbjlFjbA9MHWyov2CA4SvqONZpeqF5FoWo03N12k/4b5P0Qg16lfpKeiGbj1FrbA/MHGxo+FJPq+Nw5+lYuNnT9Jth0litEAjbe4nwo0XHNVEpFEwKqMaI36+jF0W61SyLj5M1v9yWxq7360iTmidComla3hkLk5zn7M3wBPbdi6CyszUfbpF+0H3WohKtvAsPri7q9Jyf/jOdt0pjy4Odp4h/+ILqhv5wb8txLFzs6L4/xy+rNbgTuwMmoUlJp/260Zg5WKPXajk37efsAPNF2temBiN+u2qwzxMfZxt+uSX9KH+/bjmDfVE0Le9UgH3hBvvOGeyrUqR9KoWCyR19Gb79nOS31C1PJRdbfrkWKuk1kI6SP/4gnGYV3Yz8lrjUTMbtlqIvaPV6Otf0ooVP4auMStvHFXV67k/5kfo7piIoFbww+EWeBp/z+aajVPyiJyYO1lTP5XNe6jgNgLobx2HiYI2o1XFvyo/ZQaALRacn8qu1lPtpDoJCQcLuI2QGh+HQS3o3Hb/9ALYdW2DXow1odegzMnn++SIA0m89IPngOSr++Q2iTkfGnVASdhRzfLxeR9rGb7CetgQUCrJOHED//Amm7aWxJevIn5g29cOswzuIOh1kZZGyYjYACnsnLD+bIo1lgoKsCyfQXL9QgISevTN+ov+myQhKBdd3nSQ6+AWN+0j9+/LWYzw4cZMqAb6MO7UCTXomv034Pvv+3mvHYOlgjU6r48/pP5JhCL7fcUovPGqUBxHin8fwx9SNBWrvmvEDIzdJ9Xdx10kig5/Tso9Uf2e3HuXOiRvUDKjHzFPfoEnPYssEaTLQxtmOT9dJJ6EqlQqu/nGOe6eK+Tmm15OxdRWWYxcgKBRknT2EPvwpJn5vA6A59Reqhq0x9X8b9DrErCzSv8+1LdfUDGWNBqRv+rpoHZn/LEJh+6plZF6yuPzHpdZIPvvMtPhEb5CTS/9G3JPXwH/SmzslqCQIb/BUopJwYVrRb5bfNHV8i16S/qaxaJj/reY/ieBadKDGN83l+UWf0PGm2WD+GrEkXpMfr72ZY9lLyva6M0pV76NLk0pVj6zXmMj/G6z1L2ay5A3Tmtdbafi6ZGqVxSd6g1RpVLp9/djl1ztl6XXRvsqRk3+TbtOLPlThTbN1Xsm3cb0J+nxZus8hwdKy+ERvkG+nF73K4k3jm6EtVT1P69IdyzzqFzP59IZZeqXgkAT/FImUnt8yv23h2xL/CWw3HinFkbP0UXfzK7Xfs45/nPpXlqW81UtGRkZGRkZGRkZGRkZGRkbmP4q81es/gCAIl4C8kfX6iqIY+L/4PjIyMjIyMjIyMjIyMjIyMv8O5Imf/wCiKDb5X38HGRkZGRkZGRkZGRkZGZl/G+K/+Jj10kLe6iUjIyMjIyMjIyMjIyMjIyPzH0UO7ixTLL3L9yi1RvJcm1R8ojfI95Z5d8j9s4xI15SqnqlQuov6airtSlUvg9KdvrekdAOwJlC67cVbLN3+YC2WXuw7h9KLxwhAr1uzS1VvV53SDSYdbFK6vkMvi9INcnkzsXQD9iYpSzcOpHUpHzfb2DG2VPWCYxyKT/SGmKOKLjUtgJ/tTEpVb0Bi6QYjNhVK9zmrFEr3HXiWWLrlaSaUbntJ1JVu4H9dKfuB7irbUtOyKeW62/b0939lQOI3RdxbpRfc2WmfHNxZRkZGRkZGRkZGRkZGRkZGRqYUkWP8yMjIyMjIyMjIyMjIyMjI/CeRY/zIK35eC0EQ7AVBGPGa9/oKgtDlTX8nGRkZGRkZGRkZGRkZGRkZmbzIK35eD3tgBLDmNe71BRoC+0t6gyAIAlI8ptKeqxSAb5afWkNWeibfjf+WJ0Gh+RK5eLky6tsvsLa35nFQKGvGfoNOI+1h/mTWIHwDGhjd71GxDKNWjc++37WcG7uXb+fgD38xYNwnvN37LRLUCbi4O6PV6kiMS2TB2MU8DArOp+3h5c7MNV9i62DDw8Bg5n6+EK1Gi7WdNZOXTaBs+TJkZWax8IslPH7wBC8fT2atnZ59f8XyZYj5ZjPxP/2R/ZlVqwa4fTkUQakgYdch4tb9YqRp3bYpLmP6gqhH1OqJmvc96dfuAuDQrxv2H3REEATidx00yjcvo2aPoEmbxmSkZ7Jo7BKCg0LypXH3cmfGmqnY2NsSHBjM/NGL0Gq0WNlYMnXlZNzKuqJUKtn5/W4O7jqEV0VPZqz9Mqd8ynmwedlm3Mu507hNIzLSM1k2bhkhQY/yabl5uTF19WRs7G0ICQph8eilaA31WKdpbYbNGopKpSIxPokJ70+UysrWirGLx1ChanlEUeT3SeuxsLGk+4x+KJQKLu48zvG1f+bT6jGzH9UD6pGVnsn28Wt5cecJAOa2lny4cCjuVT1BhB0Tv+Pp9WDK1CjP+/MGozIzQa/V8ev0Hwi7JdlQw68u788YgKBUcH7nMQ6vzV/m788cQM2AemjSM9k0fg3P7jzOviYoBCbvXUhCpJq1gxZlf+7frxN+n3RCp9Nx5/h1fl+4FYCqfnXpNuMTFEoFl3ae4EQB9nWb2Y/qAb5kpWex02CfS0UPPl71eXYaJy9XDq3YzZkfDgDQol9HWnzSAb1Oz73jN9i3cBsANf186TVjAAqlgjM7j3Fg7Z58er1mDqR2QD2y0rP4Yfwqwu48xsHDiUHLR2HnYo9eL3J6+xGO/SgNOw26NOOdMR/gUaks87pN4Wlg/vYAUNGvDh1m9kVQKri54yQX1u41uu7k48HbS4fiXrMCJ5fu4tI6KX+lmQmf7JqO0lSFQqXk/v7LnF7xa4EauSnvVwe/WZLenR0nubrGWM/Bx4P2S4fgUqsCF5b8wvV1OcOoqa0l7RYPxqmKJ4giRyasJ/J6/j6VmzL+dWg0uy+CQkHI9pMErTbW8+7RnFoj3gZAk5bBpSk/EX83DIDmyz6lbDtfMmKT2Nt2SrG2FceX85dz+txlHB3s2bPlu7+dH4CHfx0azsmx7+4qY/sq9GhOjZGSfdq0DC5P/omEu2FYlnGk2TfDsHC1Q9SLhGw5wYONh4rVq+RXh04z+6JQKri+4yRn87QXZx8Pui0dikfNChxfuovzueqv25JPqdKmHqlxSazpMPmVbbVs2QC3acNAoSBx90HU638pMJ15rSqU27mc8HELSTl0tsT5uwfUod5sqW2GbjvJ/TxlWf7d5lQb2RUAbWoG1yb/SMLdMBRmJrT5XeoLgkrJs78uc2dp8X3B078Ozb6S9B5sP8mtPG3TzscDv+VDcK5VgSuLfyHw+5yyrDmoI9V6+SMIAve3nSCoBHXn4V+H+oa28mj7Se7lta9Hc2oY7NOkZXA1l33tfpuOwtDXw/ZdJqgE9lm0aIjz5GEISiVJvx4gYeOuAtOZ1apC2a1fEzV+PqlHpPpymTMOq9ZN0KkTeNZjaLFaAM4Bdak+tx8oFTzfepzH3xqP2x49W1Dxs3cA0KZmcnfiBpINfb380C549g4AIOVeGIGjv0OfWXz8tTGzP6NZmyZkpGcwrxAfpmf/7nwwuCee3mXpUqs7ifFSnMNyPl5MWzGRKrUqs27RD2z/vuDyeYlly4Y4TxkGSiVJuw+QsKHw8vTc/jWRX8wn9bBUnq5zx2HpZyjPbiUrz7yMnj2Spm2akJmeyfxCbH23fzfeN9j6dq0e2bYWxsjZI2jSphGZ6ZksHru0UB/pyzVTsbG3ITgwmIWjF2f7SFNWTsa1rAtKpZJd3+/m0K7DmJiZ8PWvyzAxNUGpVHJ6/xm2LJee7SO+Gk4jg97SccsIKVDPjamrp0h6QSEsHr0ErUbL+0Pfo00PqY0oVUq8Knnxge+HJCekAKBQKFi1byWxkXFM7j8NgFGzR9LU4AMuHLu4CB9wGrb2NjwMDGH+6Bwfd9Ky8ZQx+LiLv1jK4wdPcPFwYeo3k3B0cUCvF/lr2z7++uEvAIZ/Newf8QO7D+xG596dEBA4sP0gP63bni/fcXNG0axNUzLTM5gzdiEPAvO3j/cG9ODDwe/h5V2WjrW6kahOBKBjj3b0HdkLgLS0dBZPXkHI3YJ9lpd8MedzWrRpSkZ6Jl+NXcCDwIf50rw/4F16DX4PL29P2tXqmq3XumNLhk0YhCjq0Wp1LJ/5LbcuB+a7f8hXQ2gQ0JDM9Ey++eJrHhVSnhNWTcTG3oZHQSEsH7M8uzwBKtepzJI/lrJ45GLO7z8HSH71qMWfU75KOeyd7clMzyQ1IeWN/QYD6DyoKwEftUMU4dn9p3w/4Vs00pg2B+gG6IFooD8QXmRh/19CXvEjr/h5TRYCPoIg3BQEYYkgCBMEQbgiCMJtQRC+AhAEoYcgCEcFCQ9BEB4KglAOmA18aLj3Q0EQZgmCkD0LIghCkCAIFQz/7gmCsAa4DngVpFMQgiBYCYKwTxCEW4b8PjR83kAQhFOCIFwTBOGQIAgexdjZGag8zm8EG6asZeDcgh2CXpM/4cDGvYzzH0lqYioBH7YFwDegPu7eZch7f0RoOFO7jGNql3FMe3s8WemZXD10KTu/X9bvZv3Cjdy/9ZB363/AkknLGbdgdIHaQ6d9yq71v9K7ZT+SE1N4q1dnAPqO6k3InRAGtP+UeaMX8vnskQA8e/ScQR2GMqjDUD7tNBwxPYPkwxdyMlQocJ81gmeDZ/Co8zBs3/bDtJKXkWbqhZs87jqSx++MImLKCjzmSd/NrHJ57D/oyJOeYwntOhIb/8aYlC9T4Pdu0qYxZb3L8nHL/iyb9DVjF3xeYLqhUwfzy/rf6NuqP8mJKXT5qBMA3ft142lwGIM7DGPM++MZPmMIKhMVz0Kf82nHYXzacRhDO48gMz2DlMQUynqXYUCrQXwzaSWj5n9WoNbgKQP5bcMeBrYeTEpCCp0+6ghID6HP5n3GzIFfMaTdMOYOm5d9z/BZw7h68iqDA4YwvONIokPDeXf2QNb1X8ii9l9Q/50WuFUqa6RT3d8XZ28P5vuP4Zep63lv3uDsaz1m9uP+qZssavsFSztPJCrkBQBdJ/fh0De/sqzLZA4u/4W3p/QBpEmbD2cPYlX/+cxpP5aG77TAPY9eTf96uHq7M8v/c7ZOXcdHufQAAgZ0IdKg85IqzWpSp31D5nUez9wOX3Bk/d5svR6zB7Ch/yKWtB9PvXea57Ovmr8vLt7uLPQfy+6p6+k5bxAAMaERrOgyhRVdpvD121PJysgi6NAVAHya1aBm+wYs6zyJpR0mcGr9XwY9BX1mD+br/vOY3n4sjd9piUclTyO92v71cPX2YKr/KDZN/Y6P5w0BQK/VsWvuz0xvN4b5PaYQ0LdT9r3hD8JYM2wJwZfvFdgWXtraaU5/dvRbzPftJlLznWY4Vza2NT0hlcMzN3Fp/T6jz3WZGrb0mseGzlPZ0HkqFf3qUKZepUK1Xur5z+3Hnn6L2dx2IlXeaYpjZeP+k5GQyqmZm40mfF7iN6svT0/eZnObiWztNBV1SNG+iqAQaDKvH8c+XsyfAROp0L0pdnn0Up7FcOi9uextP5XbX++h6aKB2ddCdp3mWJ8lRWq8Ct27tOe75XPfWH6CQqDR/H6c6LOYv/wnUqFbU2wLsO9oz7nsbzeVoBV7aLJYsk+v1XN99jb+8pvEobdnUaV/u3z3FqTXZU5/tvZbzOp2E6n1TjNcCmgvB2Zu4nye9gJw85czbOm3+PWMVShwmzGS559O5/HbQ7F5yx9Tn3IFpnMeP4DUs9dfKXtBIdBgfn9O91nMQb+JlO/eDNsqxralhMVw/N05HGo7hTtf76HhEqnf6zM1nHxvHofaTeVQu6l4BNTBqX7xfaHF3H4c7LuY3QET8enWFPs85Z+ZkMr5GZu5/b1xX3Co6km1Xv7seXsmv3aYSrl29bD1diuRfSf7LGa//0TKd2uGbZ66S30Ww9GeczjQbgp3Vuyh0eIc+46/P4+D7adyoP1UPPyLtw+FApcvRxIx/EvC3vkU6y4BmFQsuL6cxg4i7dw1o4+T9xwmfNi0ojWM8hGosXAgV3sv5GyrL/Do0QKrPPWX/jSGS91ncy5gEo+W/0bNZdI4aubuQPnBnbjQcSrn/CaAQoFH9+bFSjZr0wRP77J82LIviyctZ/yCMQWmu30liNEfjSfiWaTR50kJyayYvqrYCR/JPqk8w4d+SVjXT7HpEoBJIe3faVz+8kz6/TARQ16hPPPQtE1jPL096dXyExZPWs4XhfhrgVfuMPajCflsLYjGbRrh6V2WT1oOYPmkrxldiI/06dRB/Lr+N/q1GkBKYgqdDT5St37v8DT4KUM6DGfc+xMYZvCRNJkavvhgIkM6DGdIx+E08m9EtXrVaBTQyOAnDeTrSd/weSF+0qApg/htw+8MaD3IyE/65fvdDO80kuGdRvLDwh8JvBiYPekD0GNQd8JCnmX/3aRNYzy9y9KnZT+WTVrB2MJ83Kmfsnv9r3zcqj8picl0+UjycT8e1ZuQO48Y1H4IC0Yv4rOvpE0IOp2ONbO/o1/AIEa8M4ru/bpRrnK5XPa9WT+wfNXydO7dic/fHsOwjiNo0rYxXt7GfatZmyZ4eXvyfos+LJi4jIkLxhaofftKIJ9/+EW+9hH+LILhPUfzcbtB/LhiE1MWf1Hg/S9p3qYp5bw9ebdFb+ZPXMLkBeMKTHfrSiAjPxxH+LMIo8+vnLlG73YD6NN+EHPGLeTLpRPz3dsgoCFlKpRhaOshrJ68iuHzCt4E0n9Kf/7Y8AdD/YaQkphK+w/bZ19TKBT0m9KfG6duGN3z6awhXD95jQ1zNvLoVgiTOox+o7/BHNwc6TjgLaa9PYFJHUajUCpo1rXly+yWAHWQFin8BZTuqREy/zjyxM/rMRl4JIqiL3AEqAw0RuooDQRBaC2K4u9AJDASWA/MFEUxDKkT7RRF0VcUxZ3F6FQFNomiWM/w//l0CrmvExAuimJdURRrAQcFQTABvgXeE0WxAfADMK+Q+1/SDdgEEHLjIZa2Vti75j8po2bz2lzafx6AM7+eoGGHJgA0aN+YM7+eoKj7a7WoTVRYJLEvYow+b9mxBYd2Hwbg7vV7WNtZ4+Sa/5SV+i3qcWrfKQAO/nKYVh1bAFChSnmunZUG07BHz3D3dMfB2Vi7Qct6ZIVFog3POZHDok4Vsp6Go3kWCRotSftOY9O2mdF9YlpG9v8rLMwBKUi8qY8XGTcfIGZkgk5P2pUgbDoU7CC26NCMw7uPAnDv+j2sbK1xLMC+ei18ObXvNACHfjlMS4N9oihiaWUhfWcrC5ITktFpjY8tqt+yHhFPI6jeoDpHfz0GwP0b9w1a+euxbou6nNl3BoAju4/SrKNkd0B3f84dPEdMuFRHiXHSWxFLa0tqN6nFwR3S22StRotrxTLEPo1E/SwanUbHjb3nqdWhoZFOrQ4NufqbZNPTGyFY2Fhi42KPmbUFFRtX59JOqc3oNDoyktIkexExt5bsNbe1JCkqHoAKvpWIeRpJnEHv2t7z1O3QyEivToeGXDLoPbkRjKWNFbYu9gDYuztSq019zu04ZnRPqz4dOLT2D7RZ0luTlDjDW1jfSsTlsu/m3gvUzGNfzQ4NuPqbVI5hN0IwN9iXm8otahH3NIr4F9JJN837tOfE2j/R5dHz9q1E9NNIYp9Fo9Noubz3HL557PPt0IgLv50EIPRGMJY2lti52JMYk0CYYWVTZmoGEY9e4OAutbGIRy+ICi16YqSMrw/qJ1EkPItBr9Fxd+9FqrRvYJQmLS6JiNuh6DT5j8zSpGUCoFApUZoooZgTJN18fUh8EkVSmKT3cO9FKnYw1kuPSyLqdij6PG3d1NqCso2rcmeHVA56jY4sQ9spDKd6PiQ/iSLFoPfkj4t4dTTWi7kaTFailE/s9RCsPHL6aPSlB2Tmcuz/Lg19a2Nna/PG8str39MC7IvNY5+lwb6M6ATiA58A0uqVxJDw7GuFUdbQXuKfxaDT6Ajae5GqedpLalwS4bdD0RfQXp5evk/6a5aneZ0qaMLC0TyXxu3k/aewbts0XzqHj98h5fA5dOqEV8rf0VCWqYayDPvjImXzlGXc1WA0hrKMuxaMRa7y0r7sCyZKFCZKijtN1cXXh6QnUSQb9B79cZHyefpCRlwSsbfy9wX7SmWIvvEIXUYWok5PxMX7VOhkPEYVZF9KHvs8C2grmuy2EmzUHvLaV1xfN6tdFU1YONrnkaDVknLgJFZtmuVLZ9e7GylHzuarr4xrQegTk4vUyI19/UqkPY4k/Wk0okZH5J7zuOUpk4SrD9Empkr/fy0Y81z2CUolSnNTBKUCpaUZGZHxxWq27Nicg7uPAHDn+j1sCvFhgu+EEPk8Kt/nCXEJ3L/1AG0BfSUv5rnLUyOVp3VB5dmnG6lHzqKLSzD6PONaELpXKM+8tOzYgoMl8NcKs7UgWnRozmFD+d27fh9rW6tifaTDvxyhRUfJ5xJFsLCyBPL7SBkGH06lUqFSSe21eYdmHMnnJ+XX821Rl9O5/KTmHfP7eP7d/Dnxx8nsv53dnWncphEHtx80su+Qwb671+9hXYhe/Vz2HczlA5avXJ7r+Xxce9TR6uyVQ+mp6TwNDsPZ3YlmHZr+I35guUpe3Lt+n8yMTPQ6PbcvBeLXuZVRnq07tmD/bslPvHP9bqHt42FQCBHP808KBl69Q3Ki9GwIun4XFw+XfGly49exJfsMekHX7xr6nlMBesEF6qWn5ZxKZmFpUeBw1rRDE47/ehyABzceYGVrhUMB5VmneR3O7ZdW1h3bfYymHXP65dsD3ub8gfMk5uqPFtYW1Gpck8M7DtO0QxNO7z5OWlLaG/8NplQqMTU3RaFUYGphRnxU9qmZuZfhWfHyB85/BFFfev/+rcgTP3+fDoZ/N5BW5lRDmqABGAVMATJFUcy/9rF4noqieLEEOnkJBNoJgrBIEIRWoigmIk0c1QKOCIJwE/gS8Czk/peUBbJfUagj43BwMx6sbRxsSE1KRa+TWnlcRCwO7tIA6+DuhDo8jqLub/ZOKy78ecbosx4DutO2WwDt322HtZ01ADERMTi7Oxuls3OwJSUxBZ1BO3eakLuhtO4iPXyq+1bFzdMNFw/j+9t0CyDpr5NGn6ncndBG5Bw7q4mMReWW/4Fh074ZFQ9+j9f6r4iY/DUAmcFPsWhUC6W9DYK5GVZ+DTHJ851f4uzuTHSuCafYiNh89tk62JKSlJJdtjERsTgbyvb3n/6gXOVy7L62gx+OrmPVjDX5fky0ecefk3+cwtndiZjwHJtiI2JxKkArdz3G5tLy9PbE2s6axbsWsWrfStr1lN4muJdzJ1GdyBfLx7H6wCrGLB6Nk5crCbnqPCFCjV2eOrd1czROE6nGzt0Rp3KupMYl8dHS4Yzbt4APFg7B1EI6XnzPVz/TdUofpp9fzTtTP2bfYqk72bs5Ep8rr/iIuHx6Upoc++Mj47A3TIC8N6M/vy/Ykq/sXCt6UKlxNSbsmcfYnbMoX8cHADs3hzz2xWHnZvwgtstjX6LBvtz4dm3OzT/PZ//tXNEd78bV+HzPHIbvnIFXnYqA9GbG6LtH5O9D9m7G/Sw+Uo29u3GbdfJ0oVyNCoTezL+8ujBs3B1JjsjJNylCjY17yY9IFhQCg/fPZ+z1tYSeCSL8ZtFLs63dHUgOzzmyOyVCjbVbyfRsy7mQrk6m/bIh9No/l7aLBqOyKPpoekt3B1Jz6aVFqLEswr5KH/nz4sTtEn2ffwMW7g6k5bHPwqNw+3x6+RNegH1Wns441ipP7PWi68/W3ZGkPO3F9hXay99B5eaMJiLn5YG2gHFb5eqEdfvmJOwo8S7rbCzcHUl/kWNbWoQaiyJsq9jLn8jjt7L/FhQCHY7Mp1vgWiJPBaG+UXRZWnk4kBKRU3epkWqsiqi73MQ/eI5Hk6qY2VujNDfFq01drMvkf4blxtLdkbTwPPYV01YiThjb1+nIfHrcXkvk6SDiirFP5eqENjJXfUXFonI1fiYpXZ2watucpF35V4e9KmbujqTnsi8jXI2Ze+ETmZ69A4g5fhOAzMh4nqz9C7/rqwm4/R3apDTiThU/DrjkecZHR8TgUog/8HdRujmhiTRu/8oCytO6XXMSd/798syLZGuOfkH+2qsi+S2588zxSV5SsI8k6e756Q/KV/Zi17XtbDj6PatnrM1+zisUCr4/tJZfb+3i2pnr3L/5AKc8erERMTgVqJfbT4rJ953MzM1o6N+QswdytpEOnzWUDfM3otfn+Bku7s557MvfPuwM9uly2edi0Ht09xGtOkurNKr5VsXd0y3fhIi7pxuVa1Xi/o0H/5gf+OTBU2o3qYWNvQ1m5mY0CmiEWxlXo3xd3F2M2kd0eAwu7kVP3hRG115vcfHE5SLTuLg7E5W774XH4PqK7dG/Uyt+Ob2ZFZsWMWfcwnzXndydiM31WyEuMq7Y9hIXEZudxtHNiWYdm3FwywGjeyS/Ookxy8bQqmtrWnRrjZnBl3lTv8Hio9TsW/cH315Yx5orP5CenErgmVu5s52H9NuvD/KKn/8c8sTP30cAFhhW8PiKolhJFMWNhmtlkXYUugmCUFhZazGuB/Nc/59aQh0jRFF8CDRAmgBaIAjCDMP9d3LdX1sUxQ6FGiUIQ06ePNmyY8eOG0JSnuTOPG/CgvQLu2R0v9JERYN2jbi4L+cH8J5Ne+nVvC+3LweRFJ/EyBnD8uVblPbL/Leu2o6NnTUbD3/PuwN7EBwUjE6X8+ZMZaKiRYfmJOV6OBsyLfI7vyT5yAVCOw3l2Yg5UrwfIOvRM+LW/UK5n+ZR7oc5ZN5/jKgr+G2dUMR3LyrNyxSN/BsScucR7zX4iMEdh/H53M+wtLY0sq95h2bSm6ki6qhILUMapUpB5dqVmd5vBlM//pLeo3tR1rssSpWSSrUq8demfYzs/BkZaRnU7ti4BFr5TUcUUSiVlK3lzfktR1j+1hSy0jNpM7wbAC0+bs8fczYxp/lI9szZxIeLhhaeWQltq9WmPilxiTwLepzvulKpwNLWmiXdp/Hb/M0MWj22UL38zbLo8laaKKnZrgG39udsb1QqlVjYWrGy+3T+mr+VvqtHF6FXsvJ8iZmlOSPWjmfn7J/ISEkvIHHJKW6lglFavciGLlNZ2XQUZXx9cKlSzDxzCcq2MBQqJa61KnB78zG2d/kSTXomDUd0LUauoLZTcFq35tWp1MuP6/N3lOwL/Qt4Vft8evlxY56xfSpLM1ptGM21GVvQvkbbeZX28sbJI+06dSgxS38A/Wu8iiumjxnpNK9Bxd7+3MpVlqJe5HD7qeytPwrHej7YVS3unUvJ6y4vCSHh3FrzF122T6bzlomo74blWxVUErki7evlz8089h1sP5U/GozCybcE9pVg3HaeNIy4FRtfr77y6RX0YcH2ObaogWfvAB7OkWKsqeyscO3UgFONRnGi7nCUlmZ49GxZ4L1GkiUYu98YBT8EjP5ymTKM2GVvqDxLIP+3bX3dZ63BbslHCuWDBr0Y0nE4o3L5SHq9nqEdh/Nho95U861KharlX98ny5Omafsm3L1yJ3ubV5O2jUmISyA4ME/8npK0jyJ8wG2rd2BjZ82GQ9/x7oDuBAeFGK36trA056t1M1k1aw1pKWn/mB/4LOQZu9b8woJt85m3ZQ6P74aizTPevKn2Ub+5L+/06sKqed8Xme5N9L2TB8/wfuu+TBg4jWETBxWkUrxGEXZ/OutTflrwE/o8/VGpUuJTy4f9m/dz98pdsjKzeGfEu7kzyKPx6r/BrGytaNChMaNbDmNk40GYWZjToodf7lTTAC9gK1DwnsD/o8grfuTgzq9LMvByTf4hYI4gCFtFUUwRBKEsoAHUwI9Ab+ATYBywNM+9AE+AtwEEQagPeBeiWaCOKIrReRMKglAGUIuiuEUQhBSk4FwLARdBEJqJonjBsPWriiiKd/LcPhL41DBw/OLv73+8d/ke2wAc3Z2IjzZe4pysTsLK1gqFUoFep8fJw5kEw5JBdUQcjrneNOa939e/Po+DQkmKlZaMtv+kMy0/agPA/ZsPCL0fStc+UuBRFw8X4qJyZq4BEtWJWNtZo1Qq0On0uHi4EGtIk5aSxsJxOfE3dl7cSkRYzpLOpgGNCQ4MxjrPkmdtZCyqXCuDTNyd0UarKYz0K0GYlPNA6WCLLj6JxN2HSTQseXYZ1w9tZM4bAYc+b7P+fWm/9P1bD3At4wpIxe/s4Zz93Y3ss7XOLlsXD2fiIqU0nT/oyLbVkuMd/iSciGeRlKvkxf2bDwD4fM5ITExNmL9lLg9vPcSlTI5Nzh7OqAvQyl2Pzh7OxBnqMSYilkR1EpnpmWSmZxJ4KYiKNbwJunyHmIhYHhg0z+4/i++UTzGzzpm7tPdwJClPm0mMVGOfq13YuzuSGBUPiCRGqgm7KTlIt/Zfou1wKdhmw55+/P7Vz9Ln+y7y4UIp/kJCZBwOufJy8HAiMY9efGQcDmWcAel7Org7kRgVT/0uTandriE1A+qhMjPFwtqC/itG8dPYb4mPVHPTEHfq6a1HiHo91o42+b+7h1M++xIi44zS2Lk7Zm9NAykG0POgx6QY2r10j5qgQ9JbrGe3HqHXi1g52uT67jn2JRRgX+5+5uDumN0HlSolw78bz8U9Z7ieK45WSUiOVGPjkZOvrYcjKVEJr5QHQGZSGmEX7lHRvw4xD58Xmi4lQo1NmZy3WdYejqTmsbWoe1Mi1EQZVhWF7L9Mw+FFT/ykRqixyqVn6eFIWlR+PfvqXjRfMpijfZeQGf/mtnb906RFqLHMY196AVtU7Kt70WTpYE58vISsXPYJKiWtNozmyW/neXbgarF6SZFqbPO0l+TXaC+vgzYqFpNcb7tV7s5oo43HOLNalSmzXAoarbS3xap1I9DqSDl2geJIj1BjUTbHNksPR9ILsM2uuheNlg3mVJ/FRmX5Ek1SGjHn7+EeUIfEB4X3hdQINda5thpZuTuSWoLtRS95sOMUD3ZIW6AbTvqA1IjCn2Hwsq3ksS8yIV86++peNF46mFMfF25f9IV7eBRjnzYqFlWuN/4qN2e0MXnqq2YV3JZIQdOVDnZYtWqMqNORdrz4+spLZoQai1z2mZdxJLOA8rSuUY5ay4dytddCNAb7nFrXIj0sBk2ctBUqat9lHBpVIeLX/IHB3+3XjXf6vAXAvZsvn/ESrrn8kzeNLjIWE3fj9q/L2/5rVsF9WU55WrZuDDodqSVo/wXRo183uvaRDqi9f/MBrmVy9Avy114lTwGBB7ce4GKUp3PBPmAhPlLHDzqwY7UUTSH8STiRzyLxquSV7a8AtOvRhnKVyjF/y3wuH79spOfs4ZLtAxnr5faT8qfxf8ePE3+ezP67ZsOaNG3flEYBjbGxt8bCyoK9Qb9zav+ZPPblbx8v7cvxcZ2JjczxcRd9sTQ77Y4LW7Jj4yhVSr5aN4vI55H0G/Mx/cf0/cf8wBePX3Bo52EO7ZT83gGT+vH8RQQ9+3enm8F/v3fzvlH7cC3jQmxULK9CpeoVmbp0AmM/nkRSAUHB3+/fg+4Gvbs37xutOnIt40LMa/a9G5duUbZ8Wewc7ejwThu693kbFUqCbwfjnOu3gpO7E+o8bSFJnWTUXpw8nLPTVK5diQmrpNhBto62NAhoiF6rw7OSJ3q9npELPiP4djApkfFUrCvFTHtTv8FqtaxL9LMoktVSOV45eJEqDapy7vdTec3fBuwDZr5W4cn8K5FX/LwGoijGAecEQQgC2iN1jguCIAQCu5EmdqYCZ0RRPIM06TNYEITqwAmgxsvgzsCvgKNh+9VwIH/oeUnzcCE6BVEbuGzIcxowVxTFLOA9YJEgCLeAm0BBAWhWI8UQ8gX2IE1aUaleFdKT0/L96AS4eyGIJl2krFr1DODqEekH7LWjV2jVUzrloKD7m7/T0mib15FNB5j48RQGdRjKmUPn6P7JOzx+8IQa9auTmpRKXAETMDfO38TvLWmmutP7HTh7WFo9ZG1rhcpEmtd8u3cXbl26Lb31MNC2exuO7jmeL7/0wIeYViiDiacbmKiwfas1yccuGqUxKZcTE9u8hg+CiQqd4UGkdLQDQOXhgk2H5iT+lTOQxm/9Kzvw8rmD5+jwXjsAqtevTmpyKuoC7buF31tSKKeO73fgnMG+qBfR1G9ZDwAHZ3u8fLwIf5oToM7KxopvZ6xhRKfPOH/oQvay3Gr1qpGWnIq6gHq8df42rd6Stse1f68dFwxBry8cvkitxrVQKBWYmZtRrV5VwkKeER8TT2xEDJ4VpUB+vi18eXo9GJcK7jh6uqA0UVKva3OCjhgHkgw6co2G70o2la9XiYzkNJJjEkiOSSQhPA6XilL5VmlRi6hgKehyUnQ8Pk1rAFC5eS1inkgOztNbj3Ct4IGTQa9B1+bcPmL8IzXwyFWaGPQq1KtMenIaSTEJ/LF4O9OaDWd6y8/4YdTXPDgfxE9jvwXg9uErVG1WCwBXbw9UJipS1Mk8u/UI51z2+XZtxp089t09cp2G70rlWC6XfS/xfac5N/eeN7rnzuGrVGpWEwBnb3dUJipS1ck8uRWCWwUPnD1dUZqoaNy1BbeOXDG69+aRqzR71x+Aigb7Eg16/RaNICLkOUc2/pWvvosj/FYojt7u2Hm5oDBRUqNrUx7msbUwLB1tMLOV3q6qzEyo0LImcSERRd4TdSsUe293bA16Vbo2JfRIyYLwpsUkkhyhxt7Qdrxa1EQd/KLIe+JuhmLj7Y61Qa9Ct6Y8O2ysZ1XGCf/1Yzg7+juSQ4sPSPpv4qV9Vgb7yndryvM89lmWdaL1hjGc/zy/fU2XDSYpOJz764yXohdG+K1QnLzdsfeS+katrk15UML28nfJCHyISfkymJSVxm2bLn6kHDcetx+3G0Bo2/6Etu1P8uGzRM1eXaJJHwB1nrIs160pLw4Z22ZZ1okWG8dwcdRaUnKVpZmTDSaGvqA0N8GtdU2SiukLMbdCsfV2x8ag59OtKWEl7AsA5k62gNR+vTs35NEf54tMX5B9zw/nt6/lhjFc/HytUVsxc8xjX6vi7csMeoBJubKoyrqBSoV1Z39STxjXV1infoR1lP6lHD5DzNxvX2vSByDxxiMsK7pjUc4FwUSJe/fmROepP/OyTtT7YRy3R64mLTTn+2e8iMOufiUUFqYAOLWqRUohY8tvP/9B/w5D6N9hCKcPnaXTe1Ig15r1q5NSiA/zJsgIeoBJeUN5mhRcnk879ONpe+lfyqEzxMz59rUnfQB+//kPBnYYykCDv9bpPWkBeY2/YevLPId2HM65g+fpYCi/6vWrFeoj3czlI3V4vz3nDX5L9Ito6hn5SJ5EPI3AztEOK1srAA7sPMSLxy/4ZvI3nD90gfa5/KTC9G6dv03rAvwkAEsbS2o3rcOFQzmf/bDoR/o07ssnzfsxc+BXXD5+ha61enD24Dk6GuyrUaQPeDPbvk65fMDcPu5bvbtw61Jgto87cel4wkKeMnPIbAZ3HPaP+oEAdk6S3+tSxoUWnVpweM8xfv1pD5+0H8wn7Qdz6uBZurwnvfSsWb/GK7cPt7KuLNgwh68+n8+z0IInlH/56Xf6tB9En/aDOHnwDG8Z9Gpl65V84sezQk5w6qq1q2BioiJRnZitMbrz51w8dIE2PaUX1VXrVSUtOS3fpAzA7QuBtOgirRBs+15bLh2W+uXgloMZ3GIQg1sM4vz+c6z9ci0XD19k95rdPLz5kMUjF3Hx0AVadG/Ni+Dnb/Q3WGx4DJXrVcHUXBrTaraow4uQ7HLNHULkHeB+iQvu/wDyih/piPD/9XeQ+fciAKuinkaOyEzP5Pvx3/LYcOzzxJ++ZN3E1SREx+Pq5caoVV9gZW/N0zuPWT1mRXZQ3P5zhlDXrx557zc1N+XbixsY02oY6ck5EzL9lo+gcg0fRBEsrS0QBIG0lDQWjFvCg9vSnNjiTfNZNGEZcVFxeJTzYNaaL6WjNe+EMHfUAjRZGmo2qMG0byah0+l5+vApC8cvJcUQHM7M3IzdV3fwUbOPWa7Tkhcrv4a4TTMc5777MHFrd2LfS3qzlbB9P05D3sOue1tErRZ9RhbRizZmH+defttilA62iBotUQvWk3bBaN8sI9JzjoAdPXcUjfwbkpmRyaJxS3losG/BpnksnbDcYJ870w1HeQYHPWL+5wvRZGlwcnNi0vIJOLk5IgDb1uzk6G/Hsu3beWUbfZr3RZOSBcDIuSNo6N+QzPQMln2xguDbUqyXOT/PZsXEr1FHqXEv557rGM9HLB69BE2W9H3fG9qTDh90QBT1HNx+iN837gGgYo2KjF0yGpWJCZFhEeybsJEK9SvTzXCc++VdJzi6eg/N+kiTXBe2SgGt3509gGp+vmjSM9k+4TueB4YCUKZGeT5cOASliYq4Z9HsGP8d6UmpeDesSveZ/VCqlGgyNfz65UaeBz0mAz01/evxnkHvwq4THFz9O636SA7Vma1S8MQPZw+ihl9dstKz2DxhDWEGvZdUblqDdp92zT7OXWmipO/iEXjWKI9Wo+W3eZt5eOEOliip5u9LtxmfICgVXNl1kmMF2Ndj9gCq+tVFk57JzgnfZ9tnYm7KlxdWsaD1aDKSc7bOKE2UfLB4GGUNen/N20rIhTskoKG2fz0+NBznfm7Xcfat/g2/PpKTfWqr9Jat9+zB1PLzJSs9kx8nrOFp4CMqNazG5N1zeX7vKXrDU+j3xdsIPHmDeh0b02vWIGwcbUlPSiXs3hO+/mQu3qJxXByfgLq0nyEdz31r1ynOrfqD+n0k5/H61mNYudgxcO9czKwtEPV6stIy+b7dROw9nem6fBiCQoGgELj31yXOrvw9X1+zFo3XIlcIqEvrmR8jKBXc3XmKK6v+pPbHknMVuOU4li52fPTXHEytLcCgt6XtJLJS0nGuUY52iwejNFGRGBbNkfHryEzMGVscCtjtUrZNXRp99bF03PnOUwSu/JMqfSW9h5uP02zJYMp1aUSqIQi3Xqtjfxdpy3ur1SNxa1Ydc0dr0mOTuLX0V0J25Ez09ro1O79gEUyYuZArN26TkJCEk6M9Iwb1pWfXjiW+f1ed/Fvxy7SpS4OvpPJ8tOMUd1b+SWWDfcGbj9Nk6WC8ctknanUc7DwDl8ZV6LBnBvF3w7KXjd9asIvwXHFrgk3y+w6VA+rSaYZ0BPmNXac4s+oPGhray9Wtx7B2sWNInvayut1EMlPS6blyJBWaVcfSwYbU2CROrNjNjZ25ytOi6B8KVq0b4Tp1CCiUJP56GPX3O7D7UBq3E3cax/VxXzCOlJOXizzO/WaicSwFjzZ1c45z33GKe9/8gc8nkm2PNh2j0dLBeL7VmNTnhrLU6TjSaTp21b1o8s0wBKXUF8L+vMTdFfn7QpLSuC94talLs1lS23yw8xQ3v/2T6oa+cG/LcSxc7Oi+X+oLol6PJi2T3QGT0KSk0/XX6Zg5WKPXarn41TbCz+Vd2AvWeuP682hTl/pf5dh3d+UfVOor2Rey+RiNlw7Gq0tjo75wuPN07Kt70fQbqa+jEAjbe4k7BdjX2NH4Db9lq0Y4TZLKJen3wySs247tB9JqmbxxfVzmfkHaqUvZx7m7Lp6MRaM6KO3t0MXFo16zmeTfjI+sD44xjlHk3NaX6nP6ISgVPN9+gtCv9+D1iTRuP9t0lJrLh+D+VmPSn+f0hQsdpZOuKk14D/duzRB1epICnxA07nvErBzfYY4q38JrAMbN+5ym/o3JSM9g/rjF3Dc845duWsDCCUuJjYrjvYE96DPiIxxdHEmIjefC8UssnLAMRxcHNh74DitrS/R6kfS0dPr4DyAtJY2f7UzyaVm2boTzZKkekn4/TPz327H90FCeeeL6uM77gtRTl7KPc3dbMhmLxjnlGbfKuDwHJOb3k/Iydt7nNPFvREZ6RqH+Ws+BPeg94sNsWy8ev8yiCcvy5WUqKAH4fO5nNPJvSEZGJkvGLeWhwW+Zv2kuyyYsJy5KjUe5nOPcQ4IeseDzRQYfyZGJyyfg6OaIgMAOg49Usbo3E1dMQKlUIAgKTv11im3fSDEDP5s7kob+DaTj3L9Ynu0nzf15NsuN/KQphuO5H7Fo9OJsP6n9++1p5N+A+SPzx4QBqNO0Du8N7Zl9nPvouaNo7N/I4APmlNnCTfNYku0DemQf5x4cFMI8gw9Yo351pn4zCb1Oz5Pgpywev4yUxBRqN6rFt79/zaN7oYiGbUQ/LdrElRNX/jE/cNmvS7Cxt0Wn1fL97PWcOp1/QnH8/NGGvpDJ3LGLuH9bWnm1fPNC5o9fQmxUHB8MepePh/fC0dWReENfmD9+CVOXTsC/S2siX0iBwXVaHQM655xwpSvgjO6J88fSzKA3e+wC7hn0vt68mLnjFxEbFceHg3rSd3gvnFwdiY9N4Nzxi8wbv5hPRvbmrfc6otVqyUjPZOXctUbHuburpIn1YXOGUd/QXr4Z/zUht6XV6jN/msW3k1aijlLjVs6NiasmYW1vTeidUJaNXpr9++glY5aN4fKxK9nHuXvX8GbU4s9RmaiwsrIEATJSM97ob7CeYz+i2dst0On0PLkTyvpJq9Fmadn29PffkGLC6oGnwDCg6Ldo/4eICvArtUkPtxOnCtxk/L9GnviRKZbe5XuUWiN5rs2/hPOf5HvLogPAvmlyT/yUBqZC6e7mrKm0K1W9jAIe+P8klihLVS+B0m0veSd+/mnyTvz8kxQ08fNP8qoTP3+XgiZ+/kkKmvj5Jylu4udNk3fi558m78TPP03eiZ9/mrwTP/80eSd+/kkKm/j5pyho4uefpCQTP2+SlxM/pYWy0BCc/wxZYumWp5lQuu0lUff3Ygm+KgVN/PyTvJz4KQ1sSrnutj39/V85WfGmiPL3L72Jn5Mn/5VlKcf4+T+MIAhOwLECLrU1bEeTkZGRkZGRkZGRkZGRkZH5/xh54uf/MIbJHd//9feQkZGRkZGRkZGRkZGRkZH5dyJP/MjIyMjIyMjIyMjIyMjIyPwn+TcHXS4t5FO9ZGRkZGRkZGRkZGRkZGRkZP6jyCt+ZIqlDlalprXhc/tS0wI4vbx0g9Dtm1K6wY+xtCxVuQvTw0tVr0G70g0YalK9TKnqCQ5Opap3bU7pHlm+zbz03j18c356qWlB6Qdb/uB26QaT1qtLt6+vavddqer1cC7dgL1JSealqufdNrVU9U4fci1VvUxF6Y0tRyZULj7RG2T70tKtu0PTS/c5JKhKN7jz13NKt6+305Zu/Tk5lLJe/dKtvxUnyxaf6A2SLpTespGpfqXbNv/riPp/ZbzlUkVe8SMjIyMjIyMjIyMjIyMjIyPzH0Ve8SMjIyMjIyMjIyMjIyMjI/OfRI7xI6/4kZGRkZGRkZGRkZGRkZGRkfnPIq/4eQMIgjAMSBNFcdMbyGuqKIrz38DX+sfw9qtDu5l9USgV3Npxkotr9xpdd/Tx4K2lQ3CrWYHTS3/h8rr9RtcFhUD/v+aQHBnP7oHLitVTeNfCtG1vUCjQ3jqN9tL+/Gm8qkpplErEtGQyty9CcHTH7J3hObr2LmjO/o726pEi9ZwD6lJ9bj9QKni+9TiPv/3T6LpHzxZU/OwdALSpmdyduIHku2EAlB/aBc/eAQCk3AsjcPR36DM1RdtXviamfh9I9gWdRXv1UP40nlUMaZSI6Slk7pbKTeXbBlWtliAI0r03jhWpBXDucQxLTtxFL4p0r+XFwCY+Rtd/vhLK/nsvANDpRR6rUzg+vB3mJkoG7bxIlk6PTi/SrrI7w1tUKVYvN44Bdakytz+CUkH41uM8/fYPo+vOnRpScdIHoBcRtToeTv+ZxMsPSpy/qk4jLPp+BgoFWSf3k7l3u/H16nWxGjcHfYwUyybryhkyf98MgO3X2xAz0kCvR9TpSJk+PF/+eVFWrINpx74gKNDePInm/N58aRTlq2Pa/mMEQ9vM2DxPumBmidnbg1G4eAIimXvXo38RUqTeuSexLDn9QKq7mmUZ2NDb6PrP156w/0EEYKi7+FSOf+qPnbkJs47e4fTjGBwtTNn9cfNibQNwCPCl4pwBCEoFkVuP8XzVHqPrjh0bUWHSR4h6PaJOT+j0H0m6fB+AyitG4Ni+AZrYRK77jyuRXg2/unwwQ9I7t/MYh9f+kS/NBzMHUDOgHlnpmWwav4Zndx5nXxMUAlP2LiQhUs2aQYuK1Tt7+QaLVv+ITq/n3S5tGdyrh9H1xOQUZixZw7PwSMxMTZk9YQSVvcvx+NkLJsxZkZ3ueUQUI/t/SN+ebxep5+Ffh4Zz+iIoFIRsP8ndVcbtpUKP5tQYKeWhTcvg8uSfSLgbhmUZR5p9MwwLVztEvUjIlhM82Jh/nHgVvpy/nNPnLuPoYM+eLW8mfs7Za4EsWr8dvV7k3fatGPR+F6PrSSmpzPjmR55FxmBmYsJXo/tTubxn9nWdTk+vcbNxdXRg1czRRWpV8KtDwKy+CEoFQTtOcnlN/udQx6VDcK1VgXNLfuFqrufQ4HMryErNQNTp0et0bH27+HhMFi0a4jRpOIJSQdJvB0ncuLPAdGY1q1Bm6zdET5hP6pEzKN1ccJ0/AaWzI+j1JO3eT9LWPcXq2fjVp+zMwQhKJXE7DhO99lej67btm+DxRZ/s8erFVxtIvXoPAKWtFV6LPsO8SnlAJGzCStKuFz2Oqmo3wrzPSFAo0JzaT+a+HUbXldXqYjV6dvbYqbl2lsw/pLETSyssB45HUbYCIJK+YSm6R3eL1HMJqEutOZ8gKBWEbT1ByCrj52zZd1tQKfs5m0HgpI0k3Q3DyseDBt9/np3OsrwrDxbv5vH6A0XquQXUwXe21F4ebzvJgzx9z+vd5lQd2RUAXWoG1yf/SKLhuQ6AQqDtwblkRMZz7pOlRWrlpbhnvKpBB1TVGkt/CAoERw/Sv/8CMtNeSeclZf3r0PQryUd7sP0kt1cb2+rTozl1RkjjjCY1g/NTfkJ9L6ygrArl3JMYlpy8h14P3Wt5MrBxRaPrP199zP77UhywbD9iWBvSNTqmHwwkLi0TAYGetT3pXb9C8XqPo1l8TPJbetTxYmCTSkbXf7r8iP13DXqinsdxKZwY2R5zEyUDt19Ao9Oj1Yu0q+LBiJZF+y0Vc/m4Nwvxcd82+Lincvm4SjMTPt71JUpTFQqVkgf7L3NmxW/F2mbrX49yswchKBTEbD9K5Grje+w7NKbshF4gSn5R2MwfSLlyLyeBQkGNA0vQRKoJ7jevWL3SHsv+zthis3Rrjl+m15E6a0SxepX86tDJUH/Xd5zkbJ76c/bxoNvSoXjUrMDxpbs4n+vZ0G3Jp1RpU4/UuCTWdJhcqEYVv7q8M0Mav67sPMHJtX/mS/POzH5UDfBFk57FrvFrCb/zBICWgzrT+MM2iKJI5INn/DLhO7SZGtqN6Unjj9qQqk4C4ODinSAefqPl+Tpj9X8JUZRj/MgTP38TQRBUoii+yaiTU4FXmvgRBEEpiqLuDX6HwrUUAh3m9GNHn4UkR6rp/+dsgo9eIy44J9BnRkIqR2ZupkrHBgXm0XBgJ2JDwjGztiiBoIBp+75k7lyKmKzGvN8MdCE3EeNyBRY1s8C0Q18ydy1HTFaDpQ0AojqSjJ9mZudjMWIFuofXi9ZTCNRYOJArH8wjIzyOZofmE33oGqkPX2QnSX8aw6Xus9EmpuLcxpeay4ZwsfOXmLk7UH5wJ862+gJ9hoa660bj0b05L3aeKtq+gF5k/vY1Yko85r2moAu9jaiOMLYvoBeZe1YiJseDhWSf4FQGVa2WZOxYADodZj0+R/c4EDGh8GBwOr3IwmN3WPteY9xszOmz9Rx+lVzxcbLJTtOvUUX6NZKcuFOPoth67Ql2FqaIosi695tgaapCo9MzcMcFWni7UKeMQ9Flmqtsqy4cyI0P5pEZHkejQwuIPXTVqGzjTwdy+eBVAKxrlKPWujFcbFmySQMEBRb9R5O6YAJ6dQw2c9aiuX4e/YunRsm0DwJJXTqtwCxS5o5DTEkqoZ6Aaed+ZGxdiJikxnzQbLQPryHG5m6blph16k/G9sWISXFgaZt9ybRjX3SPbpP560pQKMHErEg5nV5k4cn7rO1RHzdrc/rsvISftws+TtbZafo1qEC/BhUAOBUaw9abT7EzNwGga/UyfFjHi+mHg0pmn0KBz4LBBH0wm8wINb4HF6I+fJW0h8+zkyScCeT6oSsAWFYvT/V147jWSvrBHrXzBOE/HKDqt6NKJCcoBD6aPYiVH88lPjKOyX8u4PaRq0SG5LSPmv71cPV2Z6b/53jXq0yveYNZ3D2nLtsM6EJkyAvMSzC26HQ65q3cwLrFM3B3ceSjEZMJaNYQnwpe2Wk2bPuNapUq8M3siYSGvWD+yvVsWDoLb6+y7F63NDufth8OpW3LJsXa12h+P45/tJC0CDWd9s/m+aFrJOUaO1OexXC051yyEtMoE1CHJosHcujtWei1eq7P3kZ84BNUVuZ0PjiHiNOBRve+Kt27tKd3z3eYOufVfsQWhk6nZ/53W1k35wvcnBzoNW4O/k188SmXExB9/a59VK3oxdfTPuPxswjmfbeFDfMmZF/fuvcI3p5lSE0rOui+oBBoO7cfu/ssJDlCTZ+9swk5cg11rvJIT0jl+MzNVCrkOfTLh/NIj08pmXEKBc7TPiNiyGS0kbGU3fEtaScuoAkNy5fOcexg0s9fy10wxC1dR9a9EARLC8ruXE36hev5782Tj+ecoTzqMwNNZBxV/lxG4tHLZAY/y06Scu4WD45cAsC8WgUqrJ7I/bbSj6KyMz8l6dR1ngxfhGCiQmFR9NiCoMD8k89JXTwRUR2D9aw1aG5cQB+eZ+x8GETaivxjp0Wfz9AEXkGz6itQqsCsGD2FQO0FA7j4wXzSI+JodXAekYevkZLrWZAWFs35HrPRJKbi2qYudZZ+ytku00l9FMHpdlOy82l/cw2RB64Uq1dvfn/OfLiAtAg1bQ/MIfzwdZKN9GI49e4cNIlpuLepS4Mlgzj+1szs65U/7URycDgmNiXwW3JTgme89tphtNekH3hK7zqo6rd97UkfQSHQfG4/DvZeSGqEmnf2zSbs8DUScvWN5LAY9r0njTOeAXVosXgge7vOKrGGTi+y8Phd1r7bSPIjtl3Az8fV+FnU0Jt+hhcTpx5Fs/XGE+zMTcnSZjCudVWqu9mRmqWl99bzNCnvbHRvQXoLjtzhuw+aSHqbz+Ln44aPc47f0r+xD/0bSy+xToVEseXa42y/Zf2HTbP9lgHbL9CyYuF+S24fN6kYH7dynrFFl6lhW6/5aNIyUaiU9N09nUcnbxF+41HhhalQUH7eEB72mkVWRBw19i8m4fBlMoJznrNJZ2+TcPgyABbVy+Pz3XiC/HKeq26D3yYj+DlKmxIc5lHaY9nfHFsAUhd+UWK/TFAIdJnTn819FpAUqebTP+fw4Oh1YoJz+fAJqRyYuYlqBTwbbv5yhss/H6HH8mFFanSfPYANH88nMTKOz/6cx90j14jO5atU9ffF2dudJf5jKVevEj3mDWJ19+nYujnQon8nlrUbjzZTQ59Vo6nbtRnXdp8G4OzG/Zxevy9HzC+feOmO1TL/OeStXoAgCBUEQbgvCMLPgiDcFgRhtyAIloIgNBAE4ZQgCNcEQTgkCIKHIf1JQRDmC4JwChgtCMIsQRDG57q2QhCE04Ig3BMEoZEgCL8JghAsCMLcXJofC4JwWRCEm4IgfC8IglIQhIWAheGzrYWlM3yeIgjCbEEQLgHNCrFroSAIdw02LTV85iIIwq+CIFwx/GvxKmXl4etD/JMoEp/FoNfouLv3IpXbGw+eaXFJRN4ORa/JPxdl4+6ITxtfbu84WSI9hUdFxIRoxMQY0OvQ3ruMsnI9ozSqGk3RPbwuTfoApCXnz6d8DfQJ0dKP7yKwr1+JtMeRpD+NRtToiNxzHrdODY3SJFx9iDZROiUh4Vow5h6O2dcEpRKluSmCUoHS0oyMyPii7XP3RkyMRkyKlex7eBWlT11j+6o2lia7kg15pUv2KRzd0Uc+Bq0GRD265w9R+vgWqRcUmYCXvSWe9paYKBV0rOrByZCoQtMfvB9Op2oekm2CgKWpNFes1Yto9SKCUPLZc9v6lUh/HEWGoWyj9pzHuVMjozS6tMzs/1dYmoFY4uxR+lRDH/UCfUwE6LRkXTyOSYOSrWx5HRRlfNCroxATpLapu3MRVRXjvqCq1Rztgys57S7N4LyYWqAsVxXtzZPS33pdsY5+UFSiVHd2hrqr7M7J0JhC0x98GEmnKu7Zfzco65A9CVQSbOpVIuNxJBlh0YgaLTF7zuHY0bi+9GkZ2f+vtDQDMafCki7eQ5tQwh/WQAXfSsQ8jST2WTQ6jY6re89Tt4OxXt0ODbn4m+QgPb4RjKWNFbYu9gDYuztSq019zu0oftUbQOD9EMqVdcerjBsmJiZ0DmjBifPGPyAfPX1Ok3q1AahYriwvImOIVScYpbl0IxCvMm6UcXMpUs+png/JT6JICZPGzqd/XMQrj+MZezWYrESpHcReD8HSMLZkRCcQH/gEkFY/JIaEZ197XRr61sbO1qb4hCUkKDiUch6ueLq7YGKiolPrxpy4dMMoTeizcJrUqQGAt5cH4dFxxMUnAhAZq+b0ldu826FVsVruvj4kPIki0VCWD/ZepFIH47JMj0si6nYoeu3ffydiVrsqmrBwtM8jQasl9cAprALyjy22vbuRevQMulxtRBerJuuetJJPTEtH8zgMlZtzkXqWvpXJfBJB1rMoRI2W+L1nsGtvPLGYu+8pLM14OVgqrC2walLz/7F33vFRFO/jf+/dpfdeaAmhEyD00EMvinSVjogCSld6UzqIYgcRFRCkWQHpvfcaemghpOfS65X9/bFH7i53uQTFfPz6u/frlVeS3Zl99pmdZ+bZ2ZlnUG6WZraKKjWaDMu7+sgrS22nqGs7VWcPY9OglG2nvSOK6nVQHdV9NdeoIceyPI/6Vch+GE9OtNQXxP5+Gv/Oxv1s6oV7qHT9bOrFKKN+9hk+rULJeZRAbozl3R0964eQ9SiB7OgkRJWGJ3+cIbCI7aVcuIdKZ3spF+/hYCDPIcCTgPZhPPzpsEU55ihNH2+IvHpj1HdKGMiygE9YCBmPEsjU2caDP85QsYhtJF7UtzOJl6Jwes62xNSP8OfIfQt+xJ04ulSX/AgfZ3tq+km7mzrZKgj2dCYpK6/YvACRcWlU8DCQVyPQot+y+3YsXWpIA84mfotGiyWvJVDn46bpfNxbO85QzYyPG1eMj6vS+TAyhRyZjaJEH8apvmTr+dGSrSv/OIFH5yZGaYxt3d7omjYBXri3b0jSpgOWBeko67bsb7Utf4FyYSEoHyWQ+iQJjUpD5I4zVC/y/LJTMogt5vk9Pneb3BL8lgphVUh5HI9S56tc3XGaWp2M26/anRpy8dfjAERfjsLBxREXna8ik8uxsbdFJpdh42BLRoLl9wRDyrqt/q8hasvu59+KdeBHT3VgtSiKdYEM4F3gC6CvKIoNge8BwzmU7qIothFF0dxapQJRFFsDq4A/dNcKBYYJguAlCEJN4DWghSiKYYAGGCiK4jQgVxTFMFEUBxaXTifDCYgURbGpKIonit6AIAieQC+gtk6nZ4NOnwErRFFsDPQB1jxPIbn4e5AZpyz8PzNOiYt/KWd8AO3nDuLwok2I2tK90QsuHogZenliphLB2Vie4OkP9o7Y9Z+K/dC5yGubNoKKmk3R3Dpbojw7f09yY/WDQ3mxSuz8i3eKyg9oS9KhKwDkx6fyaOVO2lz6irbXVqHOyCHl6DXL+jm56wd0ADEzFcHJ3TiNh5+kX99J2PefgbxmOADa5Fhk5aqCvRMobJAH10FwsezAJWbl4eei3zbYz8WBpKx8s2lzVRpOPUqmfVX94IFGK/La+uO0X3mA8Ere1AlwN5vXHPb+nuQZlG1+bAp2ZuqOT9fGhJ/4hLAN07g5cWWpry/z9Eabop/tpFUmI/MwfRmXV6mFy6JvcZqyWDfdVUIURZymfYTzglXYtn2pRHlm66aLsT4yT38EeyfsB8/E/s35KOq0lI57+CBmZ2Lb/W3sRyzA9qURJc74SczKx89Zn8bP2Y6kbAvP7nEy7av4lahHcdgFeJIfq3+hKohLwc7MC4JX1yY0PP4ZtTdM5+7Er/+yPHc/T1IN6kdqXArufp5m0ujvKTU+BXedffabM4zfFm9AK5aubUlMVuLvo3da/Xy8SEhWGqWpXrkSB45L7cb12/eIS0giIdl48Hj34ZN0bdeyRHkO/h7kxOqvnxOnxCGg+LYzpH8EsYdN2w+n8t54hlYi+ZKFr8j/AxJS0vDz1j8vPy8PElPSjNJUC67AwdPSF+Trdx8Ql5hCQorU/i37djOT3uiHTFbyYLKzvweZscb9kLNf6fshRJE+G6Yx6M/51NEtzbWEwtcbdbx+kFWdkITcz3iLa7mvF07tW5Cx9c+i2fXXCfTDrkYV8q7dtijPxt8LVZy+nqvikrHxN91S261zODUOfk3lH+YQPflzAOwq+qNOSafi8vFU2/UpFZaOKXHGj+DhjajU66dVJiF4mL7QyavUwnn+ahzfW4ysXCUAZL4BaDPTcRgxBed5q3AY/h7YWt6a3j7Aw7ifjUvB3oItVBgQQaKunzUksGdznv5+yqIsAAd/T3Kf6uXlxilxsOC3BPePIP7Q1cL/680bzLUFm6CUfoshpenjC1HYIA+qjeZeCTOTLeAY4EG2gY+WE6/EyULZVns9ghgz7YwlErPy8TOY+eTnbF8KP8K0L4pNz+FOUgah/u4lyMvD31Ceiz2JxQwW5ao0nHqYRIdqxn7Lq2uP0+6r/YQHeVPHwixlZ38PMv6GjyvIBIbvWsj4S1/z8Ph1Yq9Ybqdt/T0pKNLPmrN19y5NCT36BdXWzeThe18WHq/44XCeLFgH2tK9aZZ1W/Z32hYJEafJy3D+cCU2ESX7Za7+nmTE6W09I06J63M8v9Lg5udBmkH7lR6XgluR/sfVz5N0wzTxSuneElI59u1Opp/6kpnnVpKXmcO949cL0zUb2pkJu5fSd9lIHFydTGSXdVtt5b+HdeBHzxNRFE/q/t4AdEYarNkvCMIVYBZQ3iC9+UWxEs8We14HboiiGCeKYj7wAKgAtAcaAud1124PVDZzHUvpNMAvZvI8IwPIA9YIgtAbeDadoAPwpe562wFXQRBMPvsKgvC2IAgXBEG4cC7rnuEZU0ml9IVC2oWRk5JBQuSj0mUoliICBTky/yDyf15B3taPsWn+ijRY8gyZHHmVMNS3S/EVzew7h3kFPVvUovyAttyd/xMACjcnfLs05GjjsRyuNxq5ox0BfUp4ISzNhBlBhsy3Ivm/f0neb59h06QbgrsvYmo8qgt7se89Abue49EmPZFmjljiOfzWY/cTCAv0wM3BtvCYXCawZUgr9r7djsj4NKKSTWdXFa9H6WYHJe0+z5mWk7g2bDkhU18r/fXN1k1jhdWP7pExvj+ZM94if+/vOE2aV3gu68NxZM0aSfayadh17Im8Rt0SxJVCH5kMmX8weZuXk/fTUmxa9ZQGKmVyZAFBqC8eJG/NLFDlY9O8e2mULBXHHiYRFuD+XDN8TDCnn5lBlZTd57jYajw331hGpamv/w1xpvLEovKKuafQdg3ITEknOvKh6fliEM0YQ9F7eLN/LzKysun79vv89NtualQNRiGXF55XqVQcOXWBTq3NTrq0eG3dTZjFr3lNQvq34fJC47X7Ckc7Wq0Zz8U5G1BnWV4OVeaYqRtFVX6zbzcysnLoN+4DNu04SI3KFZHL5Rw9dxVPNxdqVQkqlajnKUtzbOozjw0vzeKXIR8RNqQD5ZpUL0GgmWNF9PWaOhrlijXFvoAJDvb4rZhD8tKViNklLeMpne2l7z3D7fbv8PCtRVK8HwC5HMfQEJI37OZutwloc/Lwfafvc4srKk/z6B6Zk/qTNfttCvb/huM4qe0UZHLklapScGg7WXNGIebnYfdyCe3Aczw/rxa1qNi/LbcWGMdrE2zk+HdqSOz2kj/olEa/Z/g0r0XQgAiu62wvoEN98pPTSbv2qGQ5pZVdDPLK9dDG3v/Ly7yKE1jcWHhA85pUf70N54u0My9ILADHHiQSFuiOm72t0fGcAjXv77zC+21q4GxnOeqEudsvrliP3U8grJyp37J1WCv2jmpPZFwaUUnF+y3Cc5Sf2XvVinzfbSZfho8jMCwE72rlLWcoZT+btucskW3Gcu/NJVK8H8CtQyPUyenkXH9Q+hss67bsb7QtAFkLxpM1dxTZy6dj174H8up1LMszK+75B2wtYtZXKTkNooiDqxO1OjZiaatxLGz6DraOdtTvKb0nnNlwgGWtx/NZt2lkJqby0qxBZmSbuZ9/sq3+jyFqhTL7+bdijfGjp6jZZiIN2hTn0VuaH/fs04fW4O9n/yuQTHedKIrTS7gnS+nyLMX1EUVRLQhCE6TBoteBMUA7pMG+ZqIoWnxrEEVxNbAaYEmlQYVlkxmvxMXgq79LgCeZpZymWL5RNap0aEBIRD3kdjbYuTjw8qej2Tmh+FkdYmYqgqvBUioXT8SstCJplGhzM0FVAKoCtDF3kPlWQJMqTQWWV66LNuGxfpmNBfLjlDgE6r9+2Ad6km9muZZzrYqEfjKSC/2XoNLFifBqHUpudBKqFMmpSPjzHB6NqxH3i8mELP29Z6UZzRIRXDwQs4vol5WKNi8L1AWgLkD79B4yn/Jo0hLR3DiJ5oY0XmnTvCdiluVn4etiT0Km/ktZQmYuPs7mvwbvvRNXuMyrKC72NjQq78Wph0lU8S7dcpG8uBTsDcrWLtDLbNk+I+3MLRyC/LDxdEGlLHmASatMQublW/i/zNMbbVqRJQC5eidFffUsgnw8grMrYlYGYpr0dUbMSEN14QSKyjXQ3C7+S6iYoTStm5nG+oiZqWhyr4EqH1T5aKJvI/OriDb6DmKGUnLyAfWtcyUO/Pg625Fg8FU1ISsfH6dint3deLpU9zd7rrTkx6ZgF6j/kmQbYPl5Zeiel8LTBXUpnldRUuNT8DCoHx4BXqQnGstLi0/BI9AbkALVevh7kZaQSv1u4dTt0IjQtvVR2Nni4OzAsBVjWTvxi2Ll+Xl7EZ+krx8JSSn4ehl/tXN2cmTBlHcByXnsMvAdyvnr69jxc5epWTUYb0/3EvXLiVPiGKivL44BnuSaKU/3mhVounwEhwd9RIFBDBpBIafVmvE8+vUUT3ZfKFFeWePn7WE0YyohJRWfIuXi7OjA/AnDAak8u46YSjk/b/YcO8eRc1c5cfE6+QUqsnPymP7xtyx+7y2zsjLjlLgEGvdDWYml64cAshPSAGk5WNTeiwSEhfDUQhB5dUIyCn/97EGFnw+aROPZYXa1quG7bAYAcg83HFs2QdRoyDl0ChRy/FbMIevPQ+QcPElJqOKTsQnQ255NgDeqBGWx6bPP3cC2UgByDxdU8cmo4pLJuXIXgLRdp/B9p49FeaIyGcFTr5/M06ewPSwkz6DtvHYOYYjUdmpTkxCVSWgeSF/+VeePYfeS5ZeJvNgi/WyAl9ll0S41K1Lv47c5O0Dfzz7Dt10Y6dcfUpCcblEW6Gb4lNPLcwjwJFdXBwxxq1mBhh+P4MTAZYW259WkGgGdGuLfPgy5nQ0KFwcafzma82NKNxu1NH38M+TVGqG+c65U1y2OnDil0dItR39PcsyUrUfNCrRcNoK9gz8i/zmW5IKuL8rUu5AJWXnF90Vm/AiVRsv7Oy/TtUaA0Yzi4vBztifeUF5mHj7O5mcq7LmlX+ZVFFd7GxpV8OLkw0Sq+Jj3WzLjlbgW8XGznmMpzjPyM3KIPn2LyhF1STaIi1eUgrgUbIv0s5ZsPevsTewr+aPwcMGlUQ3cOzXGrV1DZHY2yFwcqfz5BB6M+7TY/GXdlv2dtsXIL8tMQ3XxBPLKNdDcuU5xZMQrcQ3Q27prgCeZZmz975Aer8TdoP1yC/Aio0j/kxGfgpthGt1snyotQ0l9kki2zkeK3HOeSg2rcfn3E2QZtGXnNh9i2HdToMikq7Juq63897DO+NFTURCEZ4M8/YEzgM+zY4Ig2AiCUPsFyToI9BUEwVd3bU9BEJ7NbVQJgmBTinQWEQTBGXATRXEXMAEI053ahzQI9CxdWNG8loi7+gDPYH/cKvggs5FTq3s4UftLNy356LKtfB0+jpUtJ7J97Fc8PnXT4qAPgDbuIYKHL4KbN8jkKGo2QRNlHDdCE3UZWflqIMhAYYssoDLaFH3gRHmtpqhLscwLIP3yfRwr++NQ0Uf6otizOYl7LxqlsS/nRf3vJ3Ht3a/IeaCXk/c0BbcGVZDpvjR5tQolyyCgnFn94h8huPsiuHpJ+lVrhOb+VaM0mvtXkQVW1elng8w/GK1SitZfGOjZxQN5lfolxgao7e9GdFo2T9NzUGm07L0TR0SI6RTszHwVF2OURBgsFVLm5JOZJ+1QlqfScDY6mSBP06moxZGpK1t7Xdn69WxO8l7jF1iHIL08lzrBCDaKUg36AGge3EbmXw6Zjz/IFdiGt0N18bRRGsFN74DLK9cAQZCCBtrZg71uKrmdPYo6jdDEWJ49oo19IC3lcveRZpXVDkddJHi4+s5F5BWqF9ZNeWAI2uRYxOx0aeDIU3KI5cG10SZbriu1/VyJTsvhaXqu9OzuxRNR2XQpW2a+iotPU4mo7GvmKqUn80oU9pUDsKvoi2CjwKdnC5T7jOuXfZDeaXfSPa+/MugD8PjqfXyDAvAq74PcRk6j7s25tt+4flzbf4Hw3q0BCK5fldzMHDKS0vhj2SZmNBvNrJZj+G7sp9w5FWlx0AcgtEYVHj+NIyYuAZVKxe7DJ4lobhxTKCMrG5VKqvO/7DpAw7o1cXbSB9DcfehEqZZ5AaRceYBLsD9OurazUo9wYvYZ1xfHcl60XjOBU+NWkfkg3uhc+McjyLgXy+3Vlncv+l9Ru2owj2MTiIlPQqVSs+fYOSKahBmlycjKQaVSA/DLvmM0qF0NZ0cHxg/tw4G1y9nz3TKWTRlJk7o1ih30AYi/+gD3YH9cdWVZvXs490vZDykc7LBxsi/8O6hVKMl3in8xA8iPvINNpXIoyvmDQoFT1zZkHzFuW550HcKTLtJP9v7jJC/8QnpRAnw+nITqQTTp6y1N0tWTc/UedsGB2FbwQ7BR4NG9FRn7jfsw20r6l2mH0MoINgo0qZmok9IoiEvGrnI5AFxa1DMKCm0OzcPbyP3KIXhLbadN07aoLhsvoTJuO6uDTGo7xfRUadDdX5rZoKhV3yTQaFHSrtzHyaCfDezZjPh9xv2sQzkvGn8/kctjviK7iC0AlOtVumVeAKlXHuAc7I9jBUlehR7hxO01ldfsuwmcH7uSLAN5kYu2sKvhWHY3mcDZUV+SdOJmqQd9oHR9PAC29sjLVzN/7jlIuvoA12B/nHW2UblHONFFbMMp0IsO307g6PhVZDw0LduSqO3vRnRqjoEfEW+2v5H8iFQiQvTnRFHkw/2RBHs6M7hhsEkes/IC3IhOzeZpmk7e7VjamFnG/MxvaVvEb8kw9FseJxNsIZB07NUHeBj4uDW7h3OvlG2Lg6cLdq5S/6CwsyGoZSjKKMsB+LOv3MMuOADbClI/69mjJalF+lk7g37WUWfr6tRMYpZs4Gqjt7gWPpL773xM5snrFgd9oOzbsr/TtmBr4JfZ2qMIbYQ25pFFebFXH+AV7I97BcmPCO0ezp39Fy3meV5irt7HK8gfD52vUq97M24VkXFz/yUa9pbi1VWsX4W8zBwyk9JIi02mYv2q2OhmwFVpEVoYFPpZDCCA2p0bk3DXtN0u67b6v4Yolt3PvxXrjB89t4ChgiB8A9xDiu+zF/hcEAQ3pLL6FLjxdwWJonhTEIRZwD5BEGSACikO0GOkWTbXBEG4pIvzU1y6knAB/hAEwR5p5tBE3fFxwFeCIFzT6XQMKD58fdF712jZN2cdr62fgiCXcW3rUZLvPSVsYDsArmw8hJOPG0N3zMfO2QFRq6XR8C6s6TCVgr+yNEHUUrB/I3avvidtmX39OGJyLIqwCADUV44gpsSheXgd++HzQBRRXzuG+OwlWmGLPKg2BXvWlVq/m9N/oNHmGQhyGTGbDpN1J4YKQzoA8GT9AULe64OthzO1luq+XKs1nO48k/RLUSTsPEvz/YsRNVoyrj/iyY8lBJoVtRQc3oxdr/GSfjdOIirjUNSRXm7V148hpsajeXwD+0GzJf1unCzc1czu5ZEI9k6g1VBweFOJU8QVMhlT29XmnV/OodVCj9DyhHi7sO2qVKX61ZPGFQ/fSyC8kjcONvomIjk7nzm7r6EVRbSiSMfqAbQ2M2hkqWzvTP+e+ptngFxG3KYjZN+JoZyubJ+uP4Dvy03x79caUa1Bm1dA5Nuflvr6aLXkrv0Cp6lLQSan4OhutE8fYdtemklTcHAHNk3aYNfhFdBoEFX5ZH8phb6SuXrgNFE3vVgup+DUQdTXSlgaKGop2LMO+/5TpG16rxxFTH6KooFkC+pLhxBTYtHcv4bD24tB1KK6cgQxSXrJLNi7DrueoxHkCrRpieTvWG1RnEImY2pEdd754xJarUiP2oGEeDmz7brkHPSrI+1Gdfh+EuEVvXCwkRvln7bnGhdjUknLU9H5u2OMCg+hV+1yxQvUaLk/Yw2hm2YhyGUkbDpEzp0Y/Id0AiB+/T68Xw7Ht18bRJUabV4Bt0fqtzivvnIC7s1ro/B0ocmlb3j80RYSNh0qVpxWo2XznO8Zu34mMrmMU1sPE3cvhlYDOwJwfON+Ig9fJrRtA+Yd/ZyC3ALWT/7rMYUUcjkzxo5g1NQFaLRaenVtR5WgCmzdIW21/Gr3zjx4HMPMpV8gk8kIqVSeD9/XbyWbm5fP6YvXmDNxZKnkiRotF2auo91PUtt5f/NR0u8+pepgqb7c+/EQdSb2wtbDmcaLh0l51Br2dJ2DT5NqVO7XitSb0XTdL4Wau7p4K7GH/vpL4uS5Szh/+RppaRm07zmId94cTJ/unf/y9RRyOTNGDWT03BVotFp6dmhJlUrl2Lr7CACvdo3gYUwsMz/5TirPioF8OG7YX5IlarQcmr2OPj9OQSaXEbnlKCl3n1J3kFSW1zYcwtHHjUE752Or64cavNmFte2n4uDpzCurJwBSANbbv5/iUQmx2NBoSV70Jf6rFiHIZWT+thfV/ce49JNiTmRuKz4Whl392ri80pH8uw8ot00aMFB+/j25xy20LxotMXO+ofL6DxDkMpRbD5B37wleA7sAkLJxD+5dm+HRpx2o1GjzC3j87rLC7E/nrqbSZ5MQbGwoiI4n+v3PLOun1ZL74xc4TV4qbRF8bDfap4+xbStt+V1weCc2jVtj207Xdhbkk/N14V4V5G74AodRMxAUNmgT48hZs6w4SYD0/CJnrCV803QEuYwnm46QdSeGSrq+4PH6A1Sd1BsbD2fqLBlemOd4Z2mXGrmDLT6t63BtculCFIoaLVdmrKXVpqkIchmPNh8l4+5TKg9pD8CD9QepNbEXth4u1F/8hlQkGg2Huswu1fUtCy+5jweQV6mP5vFNaWbv3xGn0XJ69jq6bJyCIJNxd8tR0u4+pYbONm5vOET9ib2wc3em+aJhAGjVGra/NKfUMiQ/ohbv/HoBrSjSo/YzP0La3alfvYoAHI5KILySl5EfcSU2jT9vxVLV25nXNkgzRsa0qEar4OKD4ytkMqZ1CGX0z+ekvq9Oeap4u7Dtis5vCZP8lkP34mkW5I2DrYHfkpXP7N1X0WpFtIh0qh5o0W8RNVr2z1nH60V83Po6H/eyzscdZuDjNh7ehW87TMXZ152XPxmJTCZDkAnc2nmWKDOxqYzQaIme9S3Vf5oLMhnJWw6Sd/cJPoOltjjpx714dGuGd9+IQr/o/mhzoUVLSVm3ZX+jbRHcPHAa96F0Hbkc1emDqK9b9su0Gi275qxl8HrJ1i9vPUrSvac0GijZ+oWNB3H2cePtHQsKn1/48K581WEK+Vm59Pn8XYKa1cTRw4VJZ77g8IqfuVxkZ16tRssfc9by5vrpyOQyzm89QsK9GJoOlNqvsxsPcPvwZaq3DWPK0U8pyM1n2+RvAHhy5T7Xd59l3J+L0Kq1xN54xNlN0ntCt+kDCKhVCURIjUni1xlrGFvrxZUnPH9bbeW/h/DC1z7+H0QQhCBgpyiKof/re/k3YrjU659m3Dt/IybJX+DYJ2UbJ6P1dLcylYdjKbb3fIGcnv3Xt5f+KzTsUPyuVv8ENjXNTyH/pxA83MtU3sX5z//19+/wk33ZTTr97MQLeIl7DrY1WVqm8l69Nq/kRC8QrbJsbf3LDqvKVF4vt8SSE71AMjLKNshmcETZ7uZybO/fm4H4vOQLZde2dJta/AySf4JNy8v22fWfZRps+J9EUMhLTvQC+XR+2dp6B3XZPj8vzzKW1+Dv76T4PKw48veWtT8vuULZbdk0o03Z1k23dQf/vcFpXgCPG3Qos/fZSpcO/CvL0rrUy4oVK1asWLFixYoVK1asWLFi5T+KdakXIIriI6QdvP5PIgjCb0DRxdJTRVHc+7+4HytWrFixYsWKFStWrFixYsXKvwPrwM9/AFEUe/2v78GKFStWrFixYsWKFStWrFj5t/Fv3ma9rLAu9bJixYoVK1asWLFixYoVK1asWPmPYp3xY6VEXMsujhmCd9kGEcwXyjZAqeDhUXKiF4m7Z5mKS5WVbSC67AdlKg63wLINkig4OpSpvGhZ2QaYva1OKDthBWUbyP2eTdlunFDWwZZlnmUb6DxOVrYBQ6MSy7btVFG2XyJdLuaXqbxIu7IN2Kul7OzvJW/vMpMlUcb9kItrmcrDvWz9QC1l2A8BqWq7MpXnlPP3dop7XsQ8VZnKyyrDYMsASspOP1Ft3YDpRWLdz8o648eKFStWrFixYsWKFStWrFixYuU/i3XGjxUrVqxYsWLFihUrVqxYsWLlP4k1xo91xo8VK1asWLFixYoVK1asWLFixcp/FuuMn38hgiDIRVEs24AGz0GFiLq0/GAwMrmMm5uOcPnrHUbn3UMCaPfx2/iEBnH2o21c+WZX4bm6I7pQ6/UIRESUt2M49N5qNPmW18uefJDIsoORaEWRXnUrMjy8qtH5tWej2HXrKQAarcjDlEwOj+mMm4Nt4bEB64/h62zPF32blqifb9u61Jk/BOQyojce5t6XxvqV792CKmO6S9fOzuPq1O/JuBkNgMLVkfqfvIVL9QogilyeuJrUi/cs63c/gWX7r0n61avE8ObVjfU7c5ddkTE6XbSSfhNeMtbvh8P4utjzxavNS9TPSPadGJb9cQatqKVXk+oMb1vP6HxmbgEzNx8hPi0btVbLkNZ16Nm4Wqmv79e2LmHzBiPIZTz86Qh3ipRlhd7Nqf6uviwvTfuBdF1Zdj33KeqsPESNFq1Gw6Eus0uUZxfeGLcJYxDkMrK37yLrx01m09nUrI7Pt1+inD2fvMPHABCcnfCY/j6KkGAQRdIWfkRB5E2L8uTV62P3ypsgk6E6dwDV4V9N01SujW2PN0Emh+xMclfNQnDzwu718chcPBBFLeqz+1Gd2FmifrKg2thGvA4yGerrx1Gf32Oapnw1XRo5Yl4m+VuXA6Co3x5FnVaAgPr6MdSXD5YoLyCiLo3mD0aQyYjadISbRZ5fUK/m1Hr3ZQDUOXmcm7aWtJvROAZ60uyzUTj4uiFqRaI2HObOd3tLlAcwdt47NG3XhLzcfJZO/Ih7kVEmafwr+DPn6xm4uLty7/o9Fo1filqlxtnNmSkfv0dgpUAK8gtY9t7HPLrzqFhZJy5cY+mqH9FotfTuEsGIV7sbnU/PzGbOim95EpeIna0N8yaOoGpQBQA6D52Io6M9cpkMuVzOls/nlahblTZ16TJXajsvbT7CiZXG5ekdEkCP5SMJqB3EoeVbObVa33b2+OgtqrWrT3ZKBl93mlaiLIATF6+z9NtNaLUivTu24s1+3YzOZ2RlM+ezH3gSn4SdjQ0fjh9G1UrlC89rNFr6T5qHr6cHX84dXyqZxTFr0SccO3kOTw93ft+w6i9do3qbevSYMwSZXMbZLYc5vHK7SZoec4dSs20YBbkFbHl/JU9vPMKncgCDvhxXmMargi97V/zM8e9302lCH5q+3o4sZQYAu5dt4faRKybX9WpbjxoLhiLIZcRsPMSjL4xl+/dpQfCYVwDQZOdzc8oasnRtWcWR3Sg/oC0AmbeiuTF+FdoS+j2ftvWotWAIglzGk42HuV9EXmCfFoQUysvj+pTvyLwZjVNIAPVX63V1rOTL3WU/82j1bovyHFo0wnvaKAS5nIxfdpP23Vaz6exCq1Fu46ckvL+I7P0nkPv74LdoMnJvD9CKZPy8i/QNv1uUBVC5TV066GzhyuYjnCliC54hAby8/G38agdxdPk2zulsQW5nw6Cts5DbKpAp5NzZdY7jK0zbXXPyOs2V+qIrm49wuog8r5AAXl4+Ev/aQRxZvpWzBvKGbJ1dKO/2rnMcW/FLifJORsWzbO8VqV+vH8zwFjWMzq89dYddkVL90GhFHiZncPi9V3BzsCUjr4B5Oy4SlZSBAHzwSiPqlS993JtyEXUJ/1Aq2zubjnDtK2NdQ3o1p+47Urutys7j1PS1KG9Fl/r6kn5xkn5anX4taxbR7za7rj/TT8vD5EwOv/8Kbg52Ov0uEJWYjiDAB90bU6+C5ThJJ29Fs+z3E5K88JoMb9/A6Hxmbj4zNx4kPjVL8lXahtGziVTmczcf5tjNR3g6O/DLlNdL1K2s60pZty1OrRviP/ttBLmM1C37SPlmm9F55w7h+E4cBFoRUaMhfv5qci9KvpDnsFdwf60zIJC2ZS/KtX+UqJ+iXmMcho0BmZyCQ3+S/4exX6aoVQ+nyQvQJsYDUHDuOPm/rNcnEGQ4L16FqEwme9kMszL+ib7hGW3eeonuMwcxp/7b5KRmAhDaJowBc95AkMs4vuUgu1b+biJvwNzh1Glbn4LcAr57/0uibzwE4I1l71CvXUMyUtKZ03lSYfoKtYIYsvBtbOxs0Kq1/Dj7W+CcaXnWbYzD4DEgk1FwZBf5O4qUZ816OE2ajzZJV57nj5P/248AuH76E2JeDmi1iBoNWbNHmy3P/yqiaJ3xYx34+R8gCMLvQAXAHvhMFMXVgiBkAZ8AnYH3BEEIAsYBtsBZ4B1RFDWCIKwEGgMOwM+iKM61IGcJ8AqgBvaJovi+IAg+wCqgoi7ZBFEUT5b63mUCrRcMZceAJWTFKem7cx6P9l8k9Z4+sGh+WjYn5v5IcOeGRnmd/D2o+0YnNrWfiiZPRaevx1LllXDubDterDyNVmTxgeusejUcPxcHBq4/Tpsq/oR4uxSmGda0CsOaVgHgaFQ8Gy48KBwUAfjp4gOCvVzILqEzBEAmUHfxG5x6dTG5cSm02bOA+H2XyLz7tDBJdnQiJ3vNR5WejW+7eoQtH8GxbnMAqLNgCAmHrnJ+xGcINnLkDpaD+Gm0Iov3XmVV/xb4uTow8IfDtKkaQIiPPpjisPBqDAuXBluO3otjw7koY/3OR0n6FTxfwDmNVsvi306x6q0u+Lk5MfCL7bSpVZEQP30A6i2nb1LZz53P3+iEMiuXnh/9zEv1Q7BRlCJQp0yg/qJhHH9tMTlxStrvnk9skbLMiU7iaO/5qNJz8G9Xj4Yfvcmhl/RV+mjfBRQos0qnkEyG+3vjSR4/GU1iEr7fryTv+CnUjx6bpHN7523yz14wOuw+cQx5Z86TM/NDUCgQ7EsIwCjIsOv1NrmrP0BMT8Fh3DLUN84hJsbo09g7Ytd7JLlr5iGmJSM4uUnHtVoKdq5F+/QB2NnjOP5j1HevGOc1kSdg224A+b+sQMxMxX7gTDT3ryIq4/Rp7BywbT+Q/F8/Q8xUgoNkJ4JXIIo6rcj7aRFo1Nj1Ho/m4XXEtOKDcQsygcaLhnLo9SXkxCnpsmseMXsvkmFg61lPkjjQZwEF6TkEtq1L02XD2fvyB2jVWi7N+4nU649QONnTdc984o5dN8prjqbtmlAuuByDWg6jZoOaTFw8jne6jzNJN3LGCLZ9+yuHtx9h4uLxdHu9C9t/3MnAsf2JunGfOSM+pEJIBSYsHMt7r08xK0uj0bLwq3WsXjQVf29PXh8/h7ZNGxBSqVxhmjVbtlMjpCKfzZnAgyexLPpqHWuWTC88//2SGXi4uZi7vNny7DZ/GD8OXExGvJK3ts/nzoFLJN3T20NuWja7566nRpG2E+DKtuOcW7efXp+MKpU8jUbLolUbWT3/Pfy8POg/aT4RTcMIqagPyvzt1j+pXrkCn84cw8MncSxctYE1CycXnt+4Yz/B5QPJzvn7gbF7duvIgD6vMGP+8r+UX5AJ9Jr3BqsHLSI9PoXx2xdyc/9FEqL05VcjIgyfYH+WREykYv0q9Fn4Jp/3nE3SgzhWdJteeJ3ZZ78mcu/5wnzHvtvF0W//LF64TKDmkuFcfHUhebEphO9dRNLei2QbtGW5j5M433Me6vRsvNuFUfvjtznbdRZ2/h5UGtGFk63eQ5unou7q8fj3bE7slqMW5dVe8gZnX11EXmwKLfcuJGHvRbKM5CVyWifPp1096nz8Fqe6zib7fhwn2k8vvE77q1+TsOt8MYKeyZPhM+tdYt+ajjo+mfJbviD78BlUD6JN0nlNfJOckxf1x9Qakj9aTcGtKARHB8pv/ZKcU5dM8xogyAQ6zR/K5oFLyIhXMmz7PO4duEiKQfuQl5bN/rk/UrWILWjyVfzUfxGqnHxkCjmDf57N/SNXib1836K8LvOH8ZPO9oZvn8+9A5dILmJ7++aup7oZeRv6LyyUN+TnOUQduUrsZdMB6cI8WpHFey6zamAr/FwdGbjmIG2qBRr3682rM0z3kefo3Vg2nL1X2K8v23uV5lX8Wd6vGSqNllyVulhZ5nRtvmAoewYsITtOySt/ziN630XSDMo2MzqJP/tK7Xb5tnVpsWw4O7p/UGoZGq2WxbsvsWpQG8lvWXOANtUDCfFxM9CvBsOaSwMvR+/EsuHsXdx0/tCyPZdpHuLP8n7NUWk05Kosf+fUaLUs/vU4q0Z1l3yVFb/QpnYQIf76AOxbTkZS2c+Dz0d0k3yVxZt4qUFVbBRyXmlcnddbhjLrp5I/dpR1XSn7tkVGwAejeTx0Fqr4ZCr/toLMg2coiHpSmCT71BUeHDgDgF31IMp/MY37nUZhV60S7q915mGvSYgqFRV/mE/WkfMUPLLQrwsyHIaPJ3vhZLQpSbgsXoXqwim0T439MvWt68UO6th164P2aTSCg2MxIv65vsEtwJNqreqQGpNkIE/GoHkj+HjQPJTxSuZsX8KV/ReIjdL7b3Ui6uMXHMD0iLFUrl+VIQvfZkFPSc7Jnw9zcN1uRnwy1kiPftMGs/2zbVw/cpk6EfXpN30wfFFk4EeQ4TBsPNmLJ6NVJuEyfyWqS2bK8851spfPNFteWQsmIWZlmD1n5b+PdanX/4bhoig2BBoB4wRB8AKcgEhRFJsCKcBrQAtRFMMADTBQl3emKIqNgLpAG0EQ6poTIAiCJ9ALqC2KYl1gge7UZ8AKURQbA32ANc9z475hIaQ/SiAjOgmtSkPU9jMEdzLu/HJTMki8+gCtmc5cppCjsLdFkMtQONiSk5BqUV5kXCoV3J0o7+6EjVxG55qBHImKLzb97luxdKmpf3FLyMzl+P1EetetWGweQzzqVyH7YQI50YmIKg1Pfz+Nf5HOPfXCPVTp0q4aqRejsA+QnA+FswNe4TWI/ukIAKJKgzojx7J+sUoqeDhR3kOnX63yHLkXV2z63Tdi6FJL/0U+ISOX41EJ9A4LKpV+RrKfJFHB25XyXq7YKOR0rleZIzeMnXUBgex8FaIoklugxs3RDrmsdM2GZ/0Qsh4lkB2dhKjS8OSPMwQWKcuUC/dQpUtllHLxHg4Bf30nHdtaNVDHPEUTGwdqNTkHDmHf2nQGlFO/XuQeOYYmVV/3BEdHbMPqkrNDN8NCrUbMsrxziqxiVbTJcYjKBNCoUV85gaJ2E6M0ivqtUV8/g5iWDICYnS79zkyVBn0A8vPQJsYgc7P8RVfmH4yYloSYngxaDerb55GHhBnLq9EUzb3L0qAPQK70dUrmGYA27gGoC0DUoom5i7xKfYvyvOqHkPkogSydrT/+4wwVijy/5Av3KNA9v+RLUTjqnl9eYhqp1x8BoM7OIz0qtvCcJVp0asa+nw8AcOvSLZxcnfH0Nc1Xv0UYR/+UZmrt3baPlp1bABBUtRKXTlwG4Mn9J/iV98PD292srOt371Mx0I8KAb7Y2Cjo2iacw2cuGqW5H/2UpvVqA1C5QiBPE5JJTk0vUQ9zlAsLQfkogdQnSWhUGiJ3nKF6R+PyzE7JIPaa+bbz8bnb5KaVchAUiLz3gIoBvpT398HGRkGX1k04fPayUZoHT2JpWrcWAMEVAohNTCFFp198spJj56/Ru1Or51XVLI3C6uDmWrpBMnNUDKtCyuN4lE8S0ag0XNlxmtqdGhmlqd2pIRd+lT4kRF+Owt7FERcfd6M0VVuEkvI4gdSnyaWW7dagCjkP48l9LPUL8b+fwreLsez0C3dR6/qFtIv3sDOo74JcjkzX78kd7ciPt9zvuReRF/v7afyKyEu9cK9QXurFKLNtp3erUHIeJZAbY1lXuzrVUUXHoo6JB7WarN1HcGrXzLQcBvQga/8JNMq0wmOaZCUFt6QXWzEnF9WDJyj8LM/eCAwLIfVRAmlPpLbl1o4zVCtiCzkpGcQVYwuqHGlHMplCjsxGQUkbeAXqbO+ZvJsW5GlKkCe3kZe4NYzUrztT3sNZ6tdrV+DIneJfjndHPqFLbWkmYVa+ikvRSfTS9ek2chmu9rbF5i2KT1gIGY8SyNS12w/+OEPFIj5a4kV9u514KQqn5+x3I58a6ienc+2KlvW7EU2XUEP9kulVP1inn7xE/SKjE6ng7ab3VepX4UjkI6M0Rr5KvsrIV2kYEoirY+l20irrulLWbYtDvWoUPI5F9SQeVGrSdx7DpUO4URoxJ6/wb5mjfaF92YZUIPfyHcS8fNBoyTl3HZdOpu2EIfIqNdAmxKJNjAONmoJTh7Bp3MJiHkMET28U9cMpOFT8wPw/2Tf0mD2EnYt/MmpiKodVIfFxPElPEtGo1JzdcZKwTo2NrlW/U2NO/XoEgAeX7+Ho4oibTt7dc7fITjfXl4vYO0s7uTq6OpKWoDRJIQ+pgTbhKdokXXmeOYRNw+eb6f//M6K27H7+rVgHfv43jBME4SpwBmnmT1WkwZ1nc0LbAw2B84IgXNH9X1l37lVBEC4Bl4HaQK1iZGQAecAaQRB6A89GIDoAX+quux1wFQSh1N64k78HWbH6xigrTomTf+m2KM+OT+XKN7sYcuYzhl38koLMHJ4ci7SYJzErD38X/ZbWfi72JGbmmU2bq1Jz6mEiHaoFFB776OANJkTURCjl7D77AA9yY1P014xTFg7smKPigAgSD10FpCn1BSmZ1P9sJG32LyLs47eQl+BsJGbm4e9qqJ+DZf0eJNChhn5g66P915jQrnap9TOSnZ6Dv5uTXrabI4kZxoMdrzevycOEdDou2ETfT35l8ivhyGSlE+bg70nuU+OydLBQV4L7RxCvK0sARJFWm6fRfu8Cgge1LVGezMcbTaJ+BosmMRm5j49JGoc2Lcn+zXjqtqJcANq0dNxnTcFn3Te4T38Pwd7y1uaCq2fhgA6AmJ6CUGTwRuYTCI7OOIyaj8P45SgaRphex8MHWWAwmui7luU5u+sHdAAxKxXBxb3ItfykWUb93sd+4CzkNSWnTJvyFFn5amDvBApb5MF1EFwsO/sO/h7kGNh6TpwSh4Din19I/whiD18zOe5U3hvP0EokXyr+i/wzvP29SYzVP8PkuGS8/Y1fIl09XMnKyEKrkXrWpLhkvP2lcr9/8wGtu7YEoEZYdfzL++ETYFwHnpGYnIq/j74M/Lw9SUgxdpqrV67IgVPSzLDrd+4Tl5hMQrJUJoIAI2cu5dWxs9m261CJurn6e5IRp7eHjDglrqVsO/8KCSlp+Hkb6OflQWJKmlGaasEVOHhaGuy6fvcBcYkphWWw7NvNTHqjX6nt/Z/Gzc+DNIO2OS0uBTc/jyJpPI3SpMcrcfM3rudh3ZtzZfspo2MthnZm0u6lvLpsJA6uThTF3t+TPIPr5sUqsfMv3n7KDWhL8qErAOTHp/Jo5U5aX/qKNtdWoc7IIeWoqZ0YyzPuh/JiU7C3UFekfuiKyfHAXs2J/e2UaYYiKHy9UMfrv2irE5JR+BrbndzXC6f2zcnYWvwLmCLQD9uaIeRdu21RnrO/Bxlx+rYlM06Jy3PYgiATGL5rIeMvfc3D49eJvWK5bXHx9ySziO09r7wRuxYx8dJKHhyPLFFeYkaucb/u6kBipvlZc7kqNafux9OhpvRBJyY1Gw9HO+Zsv8Brqw/w4Y4L5BaUfsaPY4AH2QZlmxOvxMlCu13t9QhizLTblkjMzMXfTT/7okT9ogz1y9Lpd57XVu/jwx3nS9QvMT0bf3cDX8XdicT0Ir5Ky1AeJqTS8YP19P1oC5N7tfxLbVdZ15WyblsUfl6o4vR+izo+GRs/049OLp2aEbJvFRXXfEDstE8leXcf49gkFLm7C4K9Hc5tGmFTTP/6DJmnN9oUfZ+uTUlC5mE6MCyvVguXZWtwmrYEWfmgwuMOQ8eQt/Ebi2/S/1TfUKtDQ9ITlMQVWQbp7ueJMlZfhqlxKXj4GV/Lw88LpYE8ZbwSD3/LH/c2ffgDr04fzPJTq3h1xhB+WbbRJI1JeSqTkXmYPgN5lVq4LPoWpymLkZULKjwuiiJO0z7CecEqbNu+ZPF+rPzzCILQRRCEO4IgRAmCUOwafkEQGguCoBEEoe/flWkd+CljBEGIQBp8aSaKYj2kARx7IM8gro8ArBNFMUz3U10UxQ8EQQgG3gfa62bx/KnLa4IoimqgCdJgUk/gWTAQmU72s2uXE0Ux08x9vi0IwgVBEC6cyLpneNyMrNLpbufmSFCnBvzYfCLrGo1F4WhHtV6WR/7NXbu4QY5jUQmElfMsnC59LCoBD0dbavm7l+4GMa9fcQp6t6hFpf4R3Fggra+VKWS41Qni0doDHO04A3VOPlV167KLw9yVi3NVjt2LJ6y8l16/e3F4ONlRy4JT9/yyjaWfuvuU6oGe7J/Vny0TerHk99Nk5RWUToA5RYopS5/mtQgaEMH1hZsLjx1+5UMOdprFiQHLCBnWEe/wGmbz6uWV/OzcJ7xL+lerQVvEiZDLsalWlexft5M0dCRibh7OQ/r/bXnI5MjLVSb3uwXkfvshtu37IXjrl9pga4/9kKnkb/8e8ktaTmNOXpH/ZTJkfpXI/+1z8n75FJvwlxDc/RCV8ajO78G+z0Tseo9HmxQDWsvT683bgvm0fs1rEtK/DZcNnh+AwtGOVmvGc3HOBtRZJS8XKo39mW2DdL9/+mozzm4ufLt3Fb3e6Mm9yCg0avN6imaUKVr/3+zXnYysbPq+O5Oftu+nRkglFHKp21z/8Ry2frmAlfPfZ/POA1y4bvll1+w9lLbx/CuYuXbRonuzbzcysnLoN+4DNu04SI3KFZHL5Rw9dxVPNxdqVQn65+7veSlF32O+f9InktvIqd2hIVd3nS08dmrDARa3Hs+KbtPISEyl+6xBZmSbuyHzz86jRS3KDWjLvfk/AaBwc8K3S0OONx7L0XqjkTvaEdCnpdm8BopYPm+AV4taVBjQltvzjeM8CDZy/Do1JG7H2WJyliCvSOF6Tx1FyorvTNvOZ5dwsMd/xWxSlq5CzLY807WonZkRZxFRK/J9t5l8GT6OwLAQvKuVLzmTibzSCxS1Imu6zeDz8LEEhoXgU4I8s31rcX7L3TjCKngbxOzTcjsujVcbVWbL2x2wt1Xw/cnnaVtKX7YBzWtS/fU2nC/SbpfEc/ktd2MJq+BVuMxLoxW5HZfKqw1D2PJ2J+xtFHx/8pZleaXwA0/deUL1ct7s/2AIW957lSW/Hi+9r1IC/2Rd+be2LZn7TnO/0yiejJqPz8TBABTcf0LKNz9Tcd0CKv4wj7zbDxGL6V8tyzPWT/3wHhnvvk7mlBHk7/kNp/fnS/o1CEfMSEPz0PJHsX+ib7Cxt6XDmJ7s/WSbSb6SriUlMr3NkupR20Gd2Tx/Le83H8Xm+Wt5Y+k7ZlKV3FarH90jY3x/Mme8Rf7e33GapI8/mPXhOLJmjSR72TTsOvZEXsPsopH/LFpRKLOfkhAEQQ58BXRFmsTRXxAEk8kcunRLgdIFyiwBa4yfsscNSBVFMUcQhBpAuJk0B4E/BEFYIYpiom7ZlgvgCmQD6YIg+CFVliPmhAiC4Aw4iqK4SxCEM8CzRcb7gDHAR7p0YaIoXimaXxTF1cBqgK8rDCpsVbLilDgH6ke2nQM8S1yu9YzyLUPJfJJEnlIaZ3q4+wL+japy97fiQwz5udgTb/AlKSEzDx9n8zMx9tyOpUtN/Uv1ladKjkYlcOLBAQo0WrLzVczYeYlFLzcwmx8gN1aJQ6B+VN4hwJM8M1NnXWtWIOzjtzg9YCmq1KzCvHlxSlJ1sQZid56l6ljLAz9+LvbEZxjql4uPSzH63YyhS229E3ElRsnRe3GcuJ9AgVpDdr6aGX9cYFGPRmbzm8h2cyTe4KtZQnoOPq7Ga6j/uHCX4W3rIQgCFb1dKefpwsPEdOpUtPyVB3QzfMoZl2VuQppJOreaFWj48QhODFxGQap++mueLm1+Sgaxuy/gGVaZ5DPFO8DaxCTkvr6F/8t9vdEkGy9xsKlRDc/5UpBomZsb9s2akqbRUBB5E01SEqqb0vVzDx/DZbDlgR8xPQXBXf/lSnDzQsxQmqTRZGeAKh9U+Wge3kQWGIQmORZkcuyHTEF9+RiayDMWZcGzGT4GU7ydPRCz0ozTZKaizc2SlnSpC9A+vYfMpzyatAQ0kSfQRJ6QyqFFL8Qsy3abE6fE0cDWHQM8yTVjC+41K9B0+QgOD/rI6PkJCjmt1ozn0a+neLL7gkm+Z/Qc+govDZCCDt++egffQF/gBgDeAd4kJ6QYpU9XpuPs6oxMLkOr0eIT4E1KvJQmJyuHZe/pY8hsOv0jcU/MLw318/YkPkn/vBKSlfh6uRulcXZyYMGktwHJaesybBLl/KQ65uslDbh6ubvRvnkjIu/cp1Gd4gcnM+KVuAbo7cE1wJNMM/bwovDz9iicnQSQkJKKj6e7URpnRwfmTxgOSPp1HTGVcn7e7Dl2jiPnrnLi4nXyC1Rk5+Qx/eNvWfzeW//Y/ZZEerwSd4O22T3Ai4xE4/qYFp9ilMbN35MMg/6pRkQYMZEPyUrWL9cz/Pvs5kO8+Z1pTKi8OCX2Bte1D/Q0u6TCuVZFan8ykkv9lxT2C16tQ8mJTkKVIvV7CX+ew71xNeJ+OVGsrnlxxv2QfaCX2X7IpVZF6nzyNucN5D3Dt30Y6dcfUpBU8tJEdUIyCn99m67w80adZGx3drWr4feRFKNC7uGGU6smiBoNOYdOg0KO/6ezyfzzENkHSg4ZmBmvxNVgJq1LgCdZpfQjDMnPyCH69C0qR9Ql+W7x8dEy45W4FLG9rL9ge4bykizI83N1MO7XM3LxcXYwm3bPDf0yLymvI76uDtTR9Z0da5bj+5N3Sn2POXFKo6Vbjv6e5JipOx41K9By2Qj2Dv6I/OdYQgrSzOT4dP3gXkJGLj4uxegX+YQuofql9n6uDpJ+5Z/pV77EgS0/dyfi0wx8lbRsfIrMzPvj3G2Gt68v+So+bpKvkpBKnUp+z6VbWdeVsm5b1PHJ2ATo/RaFvzeqIn2sITnnb2Bb0R+5hyua1AzStu0jbds+AHzfG4Iqvvi8oJvh46X3y2RePmhTi+TJ1dcl9ZWzCPIJCC6uKKqHYtOwOTZhTcHWFsHBEccxM8j5cpFR9n+ib/Cq5IdneR8m7V5amH7izkV83nMWqfEpeAbqy9AjwIu0IvKkNHp5nv6eZpduGdK8Txt++vB7AM7/eZphS0aTXySNVlmkPD290aYVWcprWJ5XzyLIxyM4uyJmZSCmSWUvZqShunACReUaaG4/34w/Ky+MJkCUKIoPAARB2Az0AIruKjMWaRJHY14A1hk/Zc8eQCEIwjVgPtJyLyNEUbwJzAL26dLtBwJEUbyKNEPoBvA9YMnDcgF26vIfBSbqjo8DGgmCcE0QhJtA6SKF6ki8+gC3IH9cKvggs5FT5ZVwHu6/VKq8mU9T8KtfBYVuPXe5FrVJNQiYZ47aAe5Ep2bzNC0HlUbL3luxtKnib3rtfBUXn6TQ1uDcuDY12fdOR3aP6sCS7g1oXNHb4qAPQNqV+zhV9sexog+CjZxyPZsRv8847odDOS8afz+Ri2O+JvuB/qUyPymd3KcpOIdIS818WoUaBTI2q1+gB9GpWTxNy5b0uxlDm6oBJuky81RcjE6mrcG5cW1rs29sV3a/25klPRvTOMi71IM+ALXL+xCdnMFTZSYqtYa9Vx/QppZxLKQAd2fO6oJCpmTm8igpnfJepVsZmHrlAc7B/jhWkMqyQo9w4vaalmWz7yZwfuxKsgzKUu5gh8LJvvBvvzZ1SL9jIfAxUHDrNooK5ZAH+INCgWOHduQdP22UJqHPQBJ6DyCh9wByDx8lbfln5B07iVaZiiYhEUVFyQG3a9QAVdGg0EXQPrmHzDsAwcMX5AoUYS3R3DQOoqq+cQ5ZcC2QycDGFlnFaogJkh52r76LNjEG1THT3SfMyot/hODui+DqDTI5ihqN0Ty4apRGc/8KsnJVQZCBwhaZfzDaZ8GfnwV6dvFEXrU+6tumu0UYknLlAS7B/jjpbL1Sj3Bi9hnbumM5L1qvmcCpcavIfGA8wBL+8Qgy7sVyu4TdhH5ft523Oo/irc6jOLnnJJ36dgCgZoOaZGdmo0w0dZYun7pKm5daA9C5XydO7pOmZzu5OqGwkb5nvDSgK9fOXicny/zsg9BqlXkcG09MfCIqlZrdR88QEW7cPmRkZaPSBVb9Zc8RGtapjrOTAzl5eYUBj3Py8jh16TpVgiqYyDAk9uoDvIL9ca/gg9xGTmj3cO7sv2gxz9+hdtVgHscmEBOfhEqlZs+xc0Q0CTNKk5GVo9dv3zEa1K6Gs6MD44f24cDa5ez5bhnLpoykSd0a/9NBH4AnV+/jHeSPZ3mp/MK6N+NGkfK7uf8SjXpLMYkq1q9CXmYOmUlphefDXmnOlR3GS58M4zyEdm5M3N0nFCXj8n0cK/vjoOsX/Hs2J7FIW2Zfzouw7ydx/d2vyHmgj9OW9zQF9wZVkOlmdHi1CiWrhH4v/bLUDz2TF9izGQlm5DX8fiJX3/3KqB96RmmXeQHkR97BpmI5FOX8QKHAuWsE2YeNXZPoLkOJ7iz9ZO07TtKCL6RBH8B33iQKHjwhfX3Ju2uBZAsewf646dqWmt3DuVdKP8LB0wU73QcKhZ0NQS1DUUZZDhofe/UBngbyanUP524pbc/RRF5tUqKKj8MHun5dmcXTVF2/fuMJbaoV068/TqJtdf0HK29ne/xdHXiULL3Mn32YSGWDoNAlkXT1Aa7B/jjrdK3cI5zoImXrFOhFh28ncHT8KjIeFh8zsVj9ynnq9MtCpdGw90Y0baoFmqTLzCvQ6adfnu7t7IC/qyOPkjN0+iWUqF/tCr5EJ6XxNCVD8lUuR9EmNMgoTYCHM2d1/lZKZg6PEtMp71X6cntGWdeVsm5bcq/dxTaoHDbl/cBGgdvLrck6aDwr0KaSvq7a1w5BsFGgSZWel9xLCuCtCPDBpXNz0ndYCCQNaO7fRuZfDpmPP8gV2DZvh+qCcbskuOlnrctDaoBMQMzMIG/TGjLeeZWMsf3J+Wwe6sjLJoM+8M/0DfF3nvBBo1EsajmORS3HkR6vZMXLM8hMSufh1Sj8ggLwLu+L3EZB0+4tuLLf2Pe7sv8CzXtHAFC5flVyMnNIN5BnjrTEVKqHSzEFazavQ8Ij07qjeVCkPMPbobpo7OcalWflGiAIUjBnO3uw1w3Q2tmjqNMITcxDi/f0X0MUhTL7MVw5o/t5u8jtlAMMHY4Y3bFCBEEohxSv969thWoG64yfMkYUxXykmTpFcS6SbguwxUz+YaWUE4c0mlj0eDJS4Oi/hKjRcnz2OrpvmIIgl3F7y1FS7z6l9qB2ANzYcAgHHzf6/TkfW2cHRK2Wum92YVO7qSReuc/9Xefot3sBWo2G5MjH3PjpsEV5CpmMaR1CGb3tDFpRpEedClTxdmHb5UcA9KsfBMChu/E0C/LBwfbvVWlRo+XajLU02zQNQS4jetMRMu88JWhIewAerT9I9Um9sfVwod6SNwrzHO08C4BrM9fR8Ot3EWwU5DxO5PKEb0rWr1M9Rm8+iVYLPepVooqPK9suSY1xvwbBOv1iaRbs+7f1M5ItlzGtRzNGr9mDVivSo3E1qvh7sO20NO26X7OavNU+jDlbj9H3k18RRZEJ3Rrj4WQ59s0zRI2WKzPW0mrTVAS5jEebj5Jx9ymVdWX5YP1Bak3sha2HC/UXS2X5bNt2ex9Xmn0vjVUKCjlPfjtFQklxCDRa0j7+Au9Pl4JMTvbO3agfPsKxl7RFd06RuD5FSf/kCzw+mIFgo0D9NI7Uhcssy9Nqyf/9Wxzemqvbzv0g2oQnKMI7A6A+sxcxMQbNncs4TvoUURRRn92PNiEaWVBNbBq2RRP3CIeJnwBQsHsDmtsWXn5ELQWHf8KuzwQQBNSRJxFTYlHUbSPJu3YUURmP5lEk9kPmgiiivn4cMUV6KbLrPhrBwQm0GgoO/gT5lpdjiBotF2auo91Pkq3f33yU9LtPqTpYsvV7Px6izsRe2Ho403jxMCmPWsOernPwaVKNyv1akXozmq77FwJwdfFWYg9dLU4cAGcOnaNpu6ZsOLGO/Lx8lk7Sz95ZvH4hyyd/QkpCCqsXfcvsr2fy5pRh3Iu8z67N0krWSlUqMv2zqWg1Gh7di+aj9z8uVpZCLmfG6CGMmvURGo2WXp1aU6VSebb+Ke388upL7XnwJJaZy79BJpMRUrEcH04YAUBKagYT5n8KSLtndYtoRstGlqdMazVads1Zy+D1kj1c3nqUpHtPaTRQsocLGw/i7OPG2zsWYKdrO8OHd+WrDlPIz8qlz+fvEtSsJo4eLkw68wWHV/zMZQu7tyjkcmaMGsjouSvQaLX07NCSKpXKsXX3EUm/rhE8jIll5iff6fQL5MNxwyzq8HeYPHcJ5y9fIy0tg/Y9B/HOm4Pp071zqfNrNVp+m7OWt9ZPR5DLOL/1CAn3Ymg2UBooPL3xALcOX6ZG2zCmHf0UVW4+Wybr218be1uqtazDLzOM9zN4efoAAmtVQhQhNSaJn4ucB8kWbk//gQabZyDIZTzddJjsOzGUHyLJjll/gMrv9cHGw5maS3UzqNQaznaeSfqlKBJ2nqXZ/sWIGi0Z1x8R86Pl3YVEjZbI6WtpslnSNWbTEbLuxFBRJy96/QGqvtcbWw9nahfK03Kys7SLi8zBFu/Wdbj+fin3btBoSV70FQHfLEKQy8j4bR+q+49xfVWKAWEpro99/dq4vNKB/LsPKP/z1wAoP/uBnOPF7yQmarTsn7OO19dLbcu1rUdJvveU+gOltuXyxkM4+bgxbMf8QltoPLwL33aYirOvOy9/MhKZTIYgE7i18yxRZuIbFZW3d85a+q+fikwu46pOXgOd7V3aeBAnHzeGG9hek+Fd+abDFJx93en+ySgEI3mXLcpTyGRM6xLG6J+OS35LvSCq+Lqx7aI0G7hfwxAADt15SrPKfib9+tQu9Znx+zlUGi3l3J2Y90rpP+iIGi2nZ6+jy8YpCDIZd7ccJe3uU2rofLTbGw5Rf2Iv7Nydab5oGABatYbtL80ptQyFTMa0rg0YvfGYpF9YsKTfBWlieb9G0i6rh24/pVmIGf261mfGb2cl/TycmPeKiatqLE8uY1rvVoxevVPyVZrUoIq/J9tOSTND+zWvzVsdGzFn0yH6LtuCiMiEl8Px0M2ymvbjfi5ExZKWnUenD9czunNjeoXXNCurrOtKWbctaLTEf7iSimvnI8hkpP28n/x70Xj0l15LUjftxrVzC9x6tQO1Bm1ePjHjlhZmr/DVDOTurohqNfEfrESbUcJsMa2W3O8/x2nGMt3247vRxjzCtoPklxUc2IFNeBvsOvYArQaxIJ/sz+ZbvmZREf9Q32BJ3oY5a5i0fhYyuYwTWw8Rey+GiIGdADiycR/XDl+ibtsGLDn6JQW5+Xw/+evC/CM/n0D18No4e7iw/PQ3/LFiC8e3HmLdtFX0n/sGcoUcVb6KddO/YbxbUeFactd+gdNUyc8tOLob7dNH2LbXlefBHdg0aYNdh1dAo0FU5ZP9pbS3j8zVA6eJumVfcjkFpw6ivlbCjo9W/jKGK2eKoeR1kPApMFW3q/cLuS/hH40xYOU/geFSr3+aNz4wnc3zT7JvpuUvhS+aTktLt7vYC8P9r++S9Vf4c5TlF/sXTbMgy1/TXjRuLYv2wv8sMoMp2WXBb8v//rbdz8MaWUKZydp7bGGZyQJY1PazMpU3ff/oMpUn8zT9wv9PMr2R+a1p/yk65Zatb6QqNkrKP0MNH8vLDl40WzNLXh78ItGWtNXXC2TSwqAykwXw0zTLs1FfNAOWBpepPNwtB8F90XzyTtm+/DbOKyEuzgumvLNJGM9/lMBGlj8qvWgWnC3b9wYlqjKT9UmLsm2n3Tce+nfs5vAPcbtatzLrGGrc3WWxLAVBaAZ8IIpiZ93/0wFEUVxskOYh+gEib6SNmt4WRfH3v3pf1hk//wEEQfgNKNozTxVF8YUEgrJixYoVK1asWLFixYoVK1as/G3OA1V1Gzc9BV4HBhgmEEWx8N1eEIS1wM6/M+gD1oGf/wSiKPb6X9+DFStWrFixYsWKFStWrFixYqV4RFFUC4IwBmm3LjnwvSiKNwRBGKU7/8Li+hhiHfixYsWKFStWrFixYsWKFStWrPwn+bdFtxFFcRewq8gxswM+pY3xWxLWXb2sWLFixYoVK1asWLFixYoVK1b+o1iDO1spkZlBA8qskqxNL9vgwB3capSpvGOZUWUqz1FRuh24XhTfycuVnOgFcsTGsUzlZQnaMpWXUIZBBAEaqe3KVF5bl+Qyk7U7u2wDZXdxTClTeTtzyjYgapysbAOULr5QtsG5L9V9v0zlJWvK1vY8ZAVlKs/Pq4Tdf14wckXZtdXhj2LKTBZAXZdKZSrvZnbZ6ucoL2u/JahM5S22Ldvgx2VNpia/TOWVZSB3gLY2ZRdM+rwmtcxkAex9svs/Hdz5ZshLZVZZat3/819ZltYZP1asWLFixYoVK1asWLFixYoVK/9RrDF+rFixYsWKFStWrFixYsWKFSv/SbTiv3ISTplinfFjxYoVK1asWLFixYoVK1asWLHyH8U64+dvIghCENBcFMWfdP8PAxqJojjmf3lfL5qqbery0pwhyOQyLmw5zLGVO0zSvDR3CNXbhqHKLeCX91cRe+MRAM3e6ELj19uCIHBh8yFOfb8HgNe+HItP5QAA7F2dyMvIZm2Ll8zKn7dkOu06tiY3N5eJ78wk8totkzRfrF5KvbDaqNRqrly8ztSJH6JWq+nV7yXeGf8mADnZOUx/bz6+fj7MWzwNZxt7jm45yJ8rfzO53sC5w6nXtgEFuQV8+/4XPL7xEIA6bcIYOGc4MrnMJG+HoV3pMKQrWo2WK4cusnXJjwBUqFGJYYtGonVUoNVq6dFhAAX55mMszF08lYgOLcnLzeP9MbO5ce22SZoVqxZRt35tVCo1Vy9FMnPSfNRqtdnrmWP6wkm0bt+c3Nw8Zo6bz63rd0zSDBjel8Fvv07F4Aq0qNmJNGU6AG+8M4iX+3QGQK6QU7lqEK1qdSE9LcPkGm4R9QmaPxxBJiNx0wFivzQuZ4/OjSk/uT+IIqJaw+O535N57jaCnQ21f12AYGuDoJCh/PM0Mcu3lKhXcJu6dJg7GJlcxtXNRzhTpJ56hgTw0vK38asdxLHl2zi32iiYPoJMYNjO+WTGp/Lz8I/Nyqjapi7ddLZw0YItVDOwhTgDW2hkYAundbbgX7Miryx8E1tHO9Jiktk24Svys3IBqN0mjNfnvIFMLuP4loPsWfm7ibzX575BnbYNKMjN54f3vyL6xkM8ArwY/skY3HzcEbUixzYd4OAPkr4VagUxaOFb2NjZolFr2Dh7DY+umsafqhBRl+YfDkaQy7i96QhXvjLW1T0kgIhP3sY7NIhzy7Zx7Rt9eYa+2Zma/SNAELj902Guf7fXbHkWh2PLhvjOGA0yGek/7yF1zVaz6exCq1Fx8wriJi0ma9+J55JRqU1d2nwg6Xdj8xEufG2sn0dIAB2Xv41PaBCnP9rGJYP6YuvqSIdlI/CqVh5Ekf2TvyX+UuljeDm2bIjfzFGF+im/3WY2nX1oNSpu+YTYSUvI2vt8+gW1qUtbnX6Rm49w7mtTe+i8/G18Q4M4+dE2LhjoN+LkCgqy8xA1WrQaDRtfnmNy/ept6tFDZwtntxzm8MrtJml6zB1KzbZhFOQWsOX9lTy98QifygEM+nJcYRqvCr7sXfEzx7/fTacJfWj6ejuylFJ7snvZFm4fufJcegPMWvQJx06ew9PDnd83/P1dUd0i6lPJoC2LK6YtEw3asqxzBm23TEbonmUUxCm5O3RRifJ82taj1oIhCHIZTzYe5v4XxmUb2KcFIWNeAUCTncf1Kd+ReTMap5AA6q/Wl61jJV/uLvuZR6t3W5Tn3jaM4HnDQS4j8aeDPDWjX8Up/UGrRdRoeDjnh8K2OvS3+chsbRAUclJ2nuZJKdpqhxaN8J42CkEuJ+OX3aR9V7x9l9v4KQnvLyJ7v1T/feZPwql1UzTKNJ70GlmiLACH5o3wnPIOyGRk/bab9B/M36Nt7WoErP+cpKkLyTlwHMHWBv/vP0GwsQGFnJwDx0lbub5UMhcsnUH7jq3Jzc1j/DszuH71pkmar1Yvo179UNQqNZcvXWPyhA9Qq9V07taOqTPHodVq0ag1zJ6+mHNnLpnkf+fD0TRu15j83HyWT/qYqEjTNsi/gh8zvpqOi7sL9yKjWDb+I9QqNf1G9qVdr7aA1JdXqFKBV8NeIzMti/Wn1pGbnYNWoyWrII+X2r9mct15i6fTrmMrcnPzmPhuMX7ZN0uo+8wvuxTJtGd+WV+9X5at88tu3TD1QwyZteh92nRoQW5OHtPGfcDNa6bpB735KkNH9qdScAWaVm9Pqs5vcXZxYvnK+QSW80eukPPd1xv4dZNp3w3g0TaMyvPfQJDLiN94kJgvfzc679m5MUFTX0fUahE1Wh7M/oEMna1XXfEOnh0bokpO51LEJIv6jPxwJI3bSs/uk/c+4X7kfZM0fhX8mPblNJzdnbkfeZ/lE5ajVqmpE16HOWvmEP8kHoBTe06x6bNNADi5OjF+2XgqVauEKIp8OvlTbl+6/Y/J6/lmTzr374woijy6/Yj5E5dQkF98fMLx894lvF1T8nPzWTRxGXcj75mk6T2sB/1G9KF8cDleDu1Feqqpf2mJCfPG0KxdU/Jy81hYjIw+w3ryqk5Gt9CehTIqhlRg5oopVAutyuql37PpG/NtkzlK8g+9QwLp/dFIAmsHsX/5Vk5++2eprz36w1E0adeYvNx8Pp70MVHFPL8ZX03Dxd2FqMgolo2Xnh9A3fA6jPpgJAqFgvTUDCb3m0L5yuWY8fV0w0tkAHOAT0t9Y/+HEK0zfqwDPy+AIGAA8NP/+D7+MQSZQPd5b/DDoMVkxKcwevsCbu2/RFLU08I01SLC8A7255OISVSoX4VXFg5nVc85+FYrT+PX27Kyx2w0KjVD103jzqErpDyKZ8uYLwrzd505kLxM8wHv2nVsRXBIJVo27EqDRnVZ/PEcunfsb5Lut207Gfv2VAC+WvMRA4b0Yf33W3jy+Cl9XxpGenoGbTu0ZOmKD/D0cqd/r7eome3OB9uXcnn/eWKj9AEM60Y0wD84gCkRYwipX5WhC99mXs/pCDIZQ+a9xbJB81DGpxjlrdEslAYdmzCr6yTUBWpcvFwBkMlljFwxnm8mfcbGcwdx93ArbIiLEtGhJUGVK9K2cXfCGtVhwfJZ9Oo0yCTdHz/vYuKoGQB8tnoJrw3uxcYfzL84FqVV++ZUCq5A1/C+1G0YypxlU+jf9U2TdJfOXePI/pOs/fVro+M/fL2BH77eIN1vp5YMGdnf7KAPMhnBi97i1usfUhCXQuiuZaTuPU/uPX05px+/Ture8wA41qxE1W/e42rrcYj5Km72m4s2Jw9BIaf27wtJO3SZrEt3i9VLkAl0mj+UzQOXkBmvZNj2edw7cJGUe7GFafLSstk/90eqdW5o9hqNhnchOSoWO2eHYmUY2sKoYmzBK9ifFRGTKK+zhW90ttDo9basMrCFuzpb6LnkLfYs2sijs7dp0K8NLd9+mYOfbEOQyRgw701WDJpParySmdsXc3X/BeIM6mpoRH18gwOYGTGWyvWrMnDhWyzuOQOtWsO2BeuJvvEQOyd7Zu9Yys3j14iLiqHPtEHs+GwbkUeuEBpRn77TB7H89Q9MdG2xYCh/DlhCdpyS3n/O49G+i6QVKc+Tc34kqEh5elQvT83+Efz28lw0KjXdNkzh8aErZDxMKPb5GSGT4Tv7XZ6+OQNVQjKVtn5O9uEzFNyPNknn895wck5eLN11i+gXsWAovw1cQlacktd3zOPB/osoi+h3dO6PVDZTX9p8MJjHR66xa9TnyGzkKByeIzivTIbfnHeJGa7Tb9tnZB06a1Y/7/ffIPuE6QtfafRrv2AoPw9cQmackoE75hFVRL/ctGwOzf2RKsXYw7bXFpKbaj4oryAT6DXvDVYPWkR6fArjty/k5v6LJBjYQo2IMHyC/VkSMZGK9avQZ+GbfN5zNkkP4ljRbXrhdWaf/ZpIXTsAcOy7XRx9DofYHD27dWRAn1eYMX/537oOADIZQYve4rauLau9axlpFtoyB11bdq21fgDGf8RL5N6LQe5cioD0MoHaS97g7KuLyItNoeXehSTsvUjWXX3Z5j5O5HTPeajTs/FpV486H7/Fqa6zyb4fx4n20wuv0/7q1yTsOl+MIL1+lRe9xY3X5lEQl0Ld3UtR7jtP7l1j/a4atNXVVr/HlVZSW32j7weFbXXoHwtIPXSJrEumL1qG8nxmvUvsW9NRxydTfssXZB8+g+qBaf33mvimiX1n/r6P9J+247docsllqbuO5/SxJIyaijohmcCNX5Jz9LRZeR7jR5B7Wi9PLFAR/9ZkxNw8UMgJ+GEFuSfOk3/ddJDDkPYdW1O5ciWaNehCg0b1WPrxHLp1eN0k3a/bdvLu21MAWLlmOQOH9GXd95s5fvQMe3cdAqBm7Wqs/mEFrZoYfxhr3LYx5YIDeaPVcGrUr8G4RWMY98oEExlvTn+TX9f8xpHtRxm3aCxdXu/Mzh//ZNs3P7Ptm58BCO/QlN4jepGZprf3ya9OJSM1w2xw53YdWhEcUpGWjbrp/LLZdO84wCTdb9v+ZOzIaQB8+e0y+g/uw48/bCE6+il9X9b7Zcs+nWs2/zPadGhBUOUKdGzSi3oNQ/lw2XT6dRlmku7iuasc3necH3//xuj4oDdfJerOQ0YNmoSHlzt7T//Cjp93oyrqi8lkhCweQeSr88iPUxK2ZwnKfRfIMbCFtOPXuWRgCzVXT+Jiq/EAJGw5TOz3u6n+xdhidQFo1LYR5YLKMaL1CKrXr86YhWOY2GOiSbrh04fz25rfOLbjGGMWjaHTa53YtUEaoL9x/gYfvPGBSZ6RH4zk4pGLLBq1CIWNAjsHu39MnpefF6+88Qqj2o+iIL+A6V9Pp32Pduzeav5DT3i7JpQPLk//lkOo1aAm7y0ez8jupt/Jr5+/wakDZ/j8508slqM5mrVrSvngcrzWcjC1G9Tk/cUTeLv7uybprp2P5OSB03z58wqj4xlpmayY/SWtu7R4Lrml8Q9z07L484N11OzU6Lmurbf1N6lRvwZjF41h/Cumz2/E9OH8uuZ3jm4/yrhFYwpt3cnViTELxzBz8CySYpNw83IDIObBU97pIpW/TCZj9+M/cwDTL+FW/jP8Z5d6CYLgJAjCn4IgXBUEIVIQhNcEQXgkCMIiQRBOC4JwQRCEBoIg7BUE4b4gCKN0+QRBED7S5bkuCMJrlo4DS4BWgiBcEQThmRUGCoKwRxCEe4IgLDO4pyxBEBbq7umMIAh+uuM+giD8IgjCed1PC93xNrrrXhEE4bIgCC6CIAQIgnBMdyxSEIRWxegvFwRhrcH9TtQdD9Hd20VBEI4LglDitlblw6qgfJxA6pNENCoN13acpmYn4xeFmp0acvnX4wA8uRyFvYsjLj7u+FYpx5PLUajyCtBqtDw6e4tanU0bvNCXwrm2/bRZ+Z27tePnzdLXzksXruHm5oKvn+kOPYf2Hy/8+8rF6wQE+gFw4dwV0tOlgYlL569RsVJ5Hj14QvTjGDQqNWd3nKBBp8ZG12rQqTEnfz0KwP3L93B0ccLNx53KYVVIeBxP0pMEk7ztB3Zm58rfUBdIjkRmiiQztFUYT24/4smtxwCkpaaj1ZrfcaRj17b8ukX6QnDlwnVc3VzwMaPrkQP6L/9XL0UW6loa2nVpzfZt0tffaxcjcXF1wdvXdEeg25F3iX0SZ/Fa3Xp1Ytdv+8yec65fhbxHceRHJyCq1KT8cQKPzk2M0mhz8gr/ljnaYbg5w7Nzgo0cwUYBJexAGBAWQuqjBNKfJKFVabi54wxVOxrX05yUDOKvPUCrMt2RyMXfk5B2YVzbfKRYGeXDqpBiYAvXi7GFKzpbiNHZgrOPOz5FbOHh2VvU1NmCd+UAHp2VvhjeP3Gd2l2lOhUcVoWkx/EkP0lEo1JzfsdJwoo4DGGdGnNGV1cfGNTV9KQ0onWz1PKz84i7/xR3f09dLhF73Quoo6sjaQmmO0f4hoWQ8SiBzGipPKP+OENQEV3zUjJIuvoArdq4PD2qBJJw+T7qvAJEjZa4M7cJ7lJ6R8e+bnVU0XGoYuJBpSZj11Gc2jUzSec+6BUy959EnZJe6ms/wy8shPRHCWTo9Lu74wyVi+iXm5JBwjVT/WydHSjXpDo3dHVFq9JQkFH6nVrs61ZDFR1bqF/mrqM4tw83Secx6BWy9p1Eo0x7bv38w0JIe5RAuk6/OzvOUKWU+pWGimFVSHkcj1JnC1d2nKZ2kbpZu1NDLuhsIdqgXzCkaotQyaaevtgd3hqF1cHN1eWFXKtoW6YsoS2TF2nLbAO8cG/fkKSfDpRKnnuDKuQ8jCf3cSKiSkPs76fxK2I/qRfuoU7Plv6+GIVDgKfJdbxbhZLzKIHcGMtl61y/CrmP4gv1S/7jBJ6djftE07ZaNDmnb6st62dXpzqq6FjUMfGgVpO1+4hZ+3Yb0IOs/SdM6n/exUi06ZmWhRjKC62O+kks6qeSvOy9R3CMaG6SzrV/D3IOnkBbRJ6Yq9NPoQCFgtLshtu5Wzu2bv4DgEsXruLq5oqvn49JuoP7jxX+ffmS3m/Jyda3J46OjmZlNu/UjP2/HATg9uXbOLk64+lrWg/CWtTj2J+SHe7/+QDNO5vqHtEjgsN/HClRr2d06tbWyC9zdS3GLztg4JcZ6HexiF8WEGDZh2nfpQ2/bZEGIK5ejMTFzQUfP1O/5db1Ozw147eIooiTrs9zcnIkPS0DtZl2z6V+FfIexpMXnYioUpP0+0mLtiAvYgsZZ26hTjM/WG5IeKdwDuqe3Z3Ld3BydcLD18MkXd3mdTmxS/L3Dvx8gGadTe3EEAdnB0KbhLJ3szTwolapyc7I/sfkgTRbzNbeFplchp2DHcnxxbc3LTu3YM/Pks9489ItnN2c8TJTZ+/diCI+ppQfikxkNGfPz/sBuHHpFi7PKSMtJY3bV++gNuMnWqI0/mF2SgZP/0Kf26xTOAdMbN30+dVrUY/jBrb+7Pm17RnByT0nSYpNAiDdjM8U1jIM4D7w+Llu7v8Qolh2P/9W/rMDP0AXIFYUxXqiKIYCe3THn4ii2Aw4DqwF+gLhwDzd+d5AGFAP6AB8JAhCgIXj04DjoiiGiaL4bNg4DHgNqAO8JghCBd1xJ+CMKIr1gGPAW7rjnwErRFFsDPQB1uiOvw+8K4piGNAKyEWaXbRXd6wecKUY/cOAcqIohoqiWAf4QXd8NTBWFMWGuut/XUz+Qlz9PEiP1W9NnBGnxM3P00wapT5NvBJXfw8S7jwhqEkNHNydsbG3pVrbMNwCjDvroCY1yE5OJ+VRvFn5/gG+xD7Vn4uLTcDfgpOgUCjo81p3Dh80XRbx+uDe3Lpxh9inesdAGafEo4gD4eHnSUqsvvNSxqfg4e+Fh58nSsPjBnn9KgdQvUlN5vy+mOlb5hFcN0S6/8oBiCK8v342Ow5tZuTYYcXeu1+AL3FP9R2RpKuvRV17vfoyRw+eLDZNUXwDfIg3kJEQl4hfgKlDWhL2Dna0bBvO/p2HzZ639feiwKDeFMSlYGvm5cSjS1PqHfucGutncn/Sl/oTMhl19n9Mw2s/kH7sKlmXLXxBBlz8PciM09fBzDglLv6mHWNxtJ87iMOLNiFqi2+xzdmCaxFbcCnGFhIt2ELi3Rhq6AapancLLzzu7ueJ0kBeapwSdzN11ShNfIrBAI+EV3kfKtQK5uEVqQw3f7iWvtMHs/TUSvrOGMKvyzaa6OoY4EGWQXlmxytxCihdeSrvxBDQtDp27s4o7G2p2K4ezoGl325c4euFOj6p8H91QjI2RfRW+Hrh3KE56Zv/2swQZ38PMg2eU1acEme/0unnWtGHXGUmHT9+m/67FtB+6YjnmvGj8PNGFWegX3wyCnP6dWxO2uZdRbOXiqL6ZT6HfgCIIn02TGPQn/OpM6CtyWk3Pw/SDOpdWlwKbkWu7+bnaZQmPV6JW5G6Gda9OVe2nzI61mJoZybtXsqry0bi4OpU+nv+hzDXltkU05bVPfY51dfP5IFBW1bpw+FEL1hvsW0xxN7fg1wDeXmxKdhbaMsqDogg8dAVk+OBvZoT+9sp0wxFsPP3pMBg4K0gTomtv6m9enZtQtjxz6n54wyiJn6lPyGTUW//chpf/570oyW31ebsW+FrPGgg9/XCqX1zMrb+vZlf0rW8TeTJzchzbNuSzG07TS8gkxG4ZRUVDm0j78wlCiJNl18XJSDAr4jfEk9ACX1539deMfJbur7cgePn/mTD1pVMHDPLJI+Xv1fhixxAclwSXkWem6uHK1kZ2Wg12sI03kXS2Nnb0SiiESd2G/hMosjijYv46s8vGDi0r4lsfxP9SuGXvdqdI8X4Zeb8NUP8AnyIj9XLS4hNwM+/+PIsyoY1WwmpFsyJyD3sOLaZhTOXmx1MswvwJD/W0BZSsDNj615dm9Dw+GfU3jCduxNLdKNN8Pb3JsmgD0iOT8bb37hOunq4km307JKNnm+NBjX4cs+XzFs3j4rVKgIQUDGAdGU6Ez+eyBe7vmD80vHYOdj9Y/JSElL4dfWvrDuzjo0XNpKdkc35Y8XPwPXx9ybRoM4mxSWZ3MffRZKRWPh/YlwSPi9YhjlK4x/+Vbz9vUgyqJfSsyn5+T2z9fLB5XF2c2bZ1qV8+efndOjT3kRGxCttADa9kBu28q/lvzzwcx3oIAjCUkEQWomi+Gx4c7vB+bOiKGaKopgE5AmC4A60BDaJoqgRRTEBOAo0tnDcHAdFUUwXRTEPuAlU0h0vAJ55FReRlomBNJD0pSAIV3T35yoIggtwEvhEEIRxgLsoimrgPPCGIAgfAHVEUSzus9cDoLIgCF8IgtAFyBAEwRloDmzTyfoGCDCXWRCEt3Wzoi48yjMdES/aYQqC6bpJUYSk+7EcW7WD4RumM3TdVOJvPUarMR7prvtKc65uL945NX/t4h3oRctnc/bURc6dNl4e0bxlE/oP6s1vZhw7k+uZkYkoWrwXuVyOo6sT83pOZ8ui9bz71XuFx6s1rsGq8Z/S76VhdHqpHc1bNzG5TvFii9d1/kczOHf6IufPXC42jYkMzD+r5yWiUysun79mfpmXJMgUM3JS95zlautx3Bm+lApTDJbwabVc7/gelxq+hVNYFRyqVyzhjswVXglZdIS0CyMnJYOEyEcliCi5LpqrI+hs4fiqHbxhxhZ+nbKa8MEdGb1jIXbO9mh008/NXcrkYRVje8+wc7Rn9Mr32TLvB/J0cYMiBnVi6/y1TG0+mq3z1zJ06WhTVf9GeaZFxXLl6528tGka3TZMIeVm9PN94SpFOftMH0Xyx99DMbPn/pqM0mWVKeT4hgZx7ceDbOo2C1VuPo3e6f7X7qNQuPG/vjNGkrT8r+tXXD0sLZv6zGPDS7P4ZchHhA3pQLkm1YsKML28SdW0/BzlNnJqd2jI1V1nC4+d2nCAxa3Hs6LbNDISU+k+y3Spa5nzHG3ZtdbjuDt8KeV1bZl7ByneR871B88hr/RxCLxa1KLCgLbcnm/srws2cvw6NSRux9licpYgz4wxKHef40qrcdwZvkyK9/MMrZarHd/nQoO3ca5fFcfqFUzyPq8876mjSFnx3V+37+eU5zn5HVI/W2NenlZL7GujiOncH9vQ6tiEBJVC5PP5LUs+nsOZUxc4a7DMbPfOA7Rq8hJvDBzL1JnjTPKYt/HS+GbGacI7NuXm+RtGy7wm9J7Eu93GMHPILIa+2Z+mzYxnLjy/XzaLs6cvmsQpat6yMa8P6s3CDywv6XleeUVp2a4ZtyLv0jK0Cz3aDmD24ik4OZsZVC6lLaTsPsfFVuO5+cYyKk01XcL3VzD1Q80lkn5FRUYxrNkwxnQZw/a125n97WxAmn1TJbQKu37cxdhuY8nLzePVd179x+Q5uzkT3jGcN1q8waDGg7B3tKdT7w7F6vi8Pu5f4e/Wlb8h+J+T+xd9z8L3E4WMqnWqMnvoHGYMmsWA8f0pF1yuMJ3CRkF4x6YApYsZ8X8UrSiU2c+/lf9sjB9RFO8KgtAQ6AYsFgTh2XqUfN1vrcHfz/5XYL7pw8JxcxheV4O+nFWi3lINj8uAZqIo5ha5zhJBEP7U6XBGEIQOoigeEwShNfAS8KMgCB+JomgSaVAUxVRBEOoBnYF3gVeBCUCabraQRURRXI00O4hVveeKbgZf610DPMlINF4akh6vxC1QP7Lt6u9Jpm75yMWtR7i49QgAHSe/RkacfkRcJpdRu3Njvuo+0+h6Q0f0Z+AQ6SvTlUuRBJbzLzwXEOhHQnwi5pg4ZTRe3h6MGPyB0fGatavx0ecfMrjfKDw83Xmld7fCc54BnqQlKo3Sp8an4BXozbPvlp7+XqQmKJHbKvAM9DabVxmfwsW9kpP94GoUolbExdMVZXwKt8/eJCs1k7zcPI7sP0Fo3ZqcOnYOgMFvvsbrg3sDcO3yDQLK6b+aSbrqv44YMm7ySDy9PZgxZL7Z84b0f6MvfQf1ACDyyk38DWT4BfiSWIwMS3Tt2bHYZV6gm+FjUG9sA7woiFcWmz7z7E3sKvmj8HRBrdSPZ2oycsg4fQP3tvXJvRNdfP54JS4GX+ZcAvR1sCTKN6pGlQ4NCImoh9zOBjsXB17+dDQ7J6w0SpcRr6SoLWQWsYUMM7aQUYwtpOtsIfl+LGuHLAHAK9if6m3rA5Aar8TTQJ5HMXXVKI2/F+kJUhq5Qs7oVe9x9vfjXN57rjBNsz4RbP5QmgR44c/TDFkyyqRMsuOUOBuUp5O/J9nxpStPgDubj3Jns7QErcnUV41mD5WEOiEZhb9+FprCzxt1Eb3tQ6sS8LEUy0Tu7opT68aIGg3ZB80vGS1KVpwSF4Pn5BzgSXZi6fTLilOSFack4YoUXDFq1zkajS79wI86IRkbg1l2Cn9v1IkpRmnsQqsS+IkUG+OZfqg1ZJVSv8wi+rkEeJJVSv0AshPSAGk5WNTeiwSEhfD0nD6Yanq8EneDeuce4GXSL6TFpxilcTOwBZBiAMVEPiQrWT/t3PDvs5sP8eZ3U0p9z/8U5toyVSnbMpfGNfDo1Bj39g0Q7GyQuzgS8sV47o/9rNj8eXFKHAzk2Qd6kWfG9lxqVaTOJ29zvv8SVEViMfm2DyP9+kMKkkpeBpkfl4JtOX2/ZhvgSUFC8fplnLmJfZCf2bY6/VQk7m3rk3PnSbH5zdp3UpH6X7safh/p7NvDDadWTRA1GnIOla7+G6JJSDKRpykiz7ZWVXyWSnHzZO5uOLRsDBoNOYf1H6W0mdnkXbiKQ4tGqO4/MpHzxogBhbNjTP0Wf+KL6Wffm/oOXt4eTB401+z5M6cuEBRcAU9Pd3r07saIN6RYOHeu3sUnUK+Xd4APKUWeW7oyHWdXJ2RyGVqN1myaiFfacHj7EaNjSl2atJR09vx5kLCGdahRqyoDdH7Z1cvP55d5enkwdeKHRsdr1qrGss/mMfjVUaSlmtbTgcP78ergngBcv3wT/0B/4CoAfoF+JCaU3m/p0787qz9fC0D0wxhiomMJqRrEtcs3jNLlx6ZgF2hoC17kW+j3Ms7cwsGMLZgj4I0u+A9szxeClnvX7uFj0Ad4+3uTkmBcJzOUGTgZPTt9mtws/evChcMXeHfBu7h6uJIcl0xyXDJ3rtzh5SEv07htY9y83Ti+8/g/Iq9u87rEP4knQxeM/+Sek4Q2qsW+X/XLWnsN7UH3gZLPffvKHXwN6qxPgI/JffwVeg/twSsDpRhYt67cwTdQPxvMN8CH5BcgoyRK4x8+D00Hd+SN11sDcPfqXXwM6qV3gDfKIjqlK9PNPD/JjpPikklXZpCfm09+bj7Xz0ZSuVYwTx9K8Ycat21EVOR9GrZu8NfW11n5P8N/dsaPIAiBQI4oihuA5UCDUmY9hrQ8Sy4Igg/QGjhn4Xgm8HcDCewDCqObCYIQpvsdIoridVEUlwIXgBqCIFQCEkVR/Bb4rji9BEHwBmSiKP4CzAYaiKKYATwUBKGfLo2gGxyyyNOr9/EK8sejvA9yGzl1uzfj9n7jqZy391+kfm8p3FCF+lXIz8wlMykNACddkGO3QC9qd2nMVYNYPiEtQ0l6EEtGESd63ZpNdGrdh06t+7B310H6vi7tXtKgUV0yMrJITDBdQ9x/cB8i2rfg3RGTjUbCA8sH8O36zxg/ajoP7j/myqVIgkMqUqFiOeQ2Cpp2b8nl/ReMrnV5/3la9G4j3WP9quRm5pCelMbDq1H4BQXgXd7XJO+lfeeo2awOAH7BAchtFGQqM7h+9AoValTC1t4WuVxOkxYNuXdH//X3x++28FLEa7wU8Rr7dh2m92vSC2RYozpkZmSRZEbX1wb1onW75ox7a1qpvihs+uFn+rQfTJ/2gzm4+xiv9OsKQN2GoWRlZpGc+HydorOLE42b1efQnmPFpsm6EoV9cAB2FXwRbBR49WhJ6j7jIKN2QXrH0bFOZWQ2CtTKTBSershdpfX4gr0tbq3qkmsQ0NgccVcf4Bnsj1sFH2Q2cmp1Dydqf+mC4h5dtpWvw8exsuVEto/9isenbpoM+oCpLdQxYwu39l8kTGcL5XW2kGXGFmp1aVwY1+rZcUEQiBjTi3MbJafp0dUofA3qW+PuLbhapK5e3X+BcF1drWxQVwGGLh1NXNRT9n9nPMstPVFJtfBaANRoHkqimWWWiVcf4Bbsj4uuPKv0COdxKcsTwF6nk3OgF0FdGxH1R8lLTp6Rd/0ONpUCUZTzAxsFrt3akH34jFGahx2H8bDDUB52GErmvhMkzvuy1IM+AAlXH+Ae7I+rTr9q3cN5UEr9cpLSyYxT4q7blbBCi9oo7z0tIZehfnexqRSIjU4/l25tyDoVYt1jAAEAAElEQVRURL8Ob/Cg/TAetB9G5r4TJMz7qtSDPgDxRfSr3j2c+6XUT+Fgh42TfeHfQa1CSb5jbH9Prt7HO8gfT50thHVvxo0itnBz/yUa6WyhYv0q5GXmFPYLAGGvNOfKDuN6YRgDKLRzY+LuFj+AUFYUbcs8n6Mte7J4I5cbvcWVpqOIGv0JGSeuWxz0AUi/fB+nyv44VPRBsJET2LMZCXuNy9a+nBcNv5/I1Xe/IvuBqf2WdpnXM/0cDPTz7tES5V7jdsbeQD+nOsEIz9pqL31bLbO3xb11XXKjLNtCfuQdbCqWk+xbocC5a4SJfUd3GUp0Z+kna99xkhZ88ZcGfQDyb9xBUbEcikB/UChw6hxBzlHjaz19aQgx3QYT020wOQeOk7LoC3IOn0Lm4YbMRZoZItjZ4tC0AaqH5uvkD2t+okOr3nRo1Zs9fx7k1deljy0NGtUjMyPT7EDFgMF9iWjXktFvvm/UlwcF62e41qlXCxsbG5TKNH5Y8xOju7zL6C7vcmrvaTrqlmzUqF+D7MxslImmA3ZXT12j9UuSHXbs24HT+/S6O7o4Uie8Lqf36o/ZO9jh4ORQ+Hfrts25c+se677bTOc2fencpi97/jxk5JdlWvDL2rRrwZi3phj7ZeX8+Xb9p4wfPZ2H982HFNn4/TZ6tB1Ij7YDObD7CL1ekwYP6jUMJSsji6TneJmPjYmnWStpprWXjyeVq1TiyWNTnyLzShT2lQOwqyjZgk/PFiiL2HpxtlAScT/s4XKHyYztOpbTe0/TXvfsqtevTnZmNqlmBgmunb5Gy24tAejQtwNn9kl24uGjX/pZrV41BJlARmoGqUmpJMUlUa5yOXau38nh3w+ze+Puf0xe0tMkajSogZ29tNQ5rEUYj+8Zf6D7bd0fDO80kuGdRnJ870m69O0EQK0GNcnKyCbFTJ19Xn5d9wfDOr3NsE5vc2zvCbr07QhA7RcooyRK4x8+D2d/3M87XcbwTpcxnNp7unB5Vo36NcjJzEZp5vldPXWNVmZs/fS+M4Q2CZXiMNnbUaN+daKj9O1YRI8IjjxHjK//q4iiUGY//1b+szN+kOLrfCQIghZQAaOBn0uR7zegGdJnBRGYIopivCAIxR1PAdSCIFxFihn0V4Z3xwFfCYJwDemZHANGARMEQWiLNDvoJrAbeB2YLAiCCsgChhRzzXLAD4IgPBvce7Zf30BgpSAIswAbYDPPPqEUg1ajZcectQxbPw1BLuPS1iMk3ntKk4FSI3Ru40HuHL5CtbZhTDq6AlVuPr9O1u+oMGDlBBw9nNGoNWyf/QN5GdmF5+p2b8Y1C8u8AA7uO0a7jq05eWk3ubl5THpXv9Z9/daVTB43h4T4JJZ8MoeYJ7Fs3ydtsLZrxwE+/WglEyePwsPTjUXLpamparWaWVMW8tMvq3G2sefY1kM8vfeEtgOlzujwxn1cPXyJum0b8NHRr8jPzWfN5K8Ky+LHOWuYvH42MrmsMC/Asa2HGLHsHRbuXYFapebb96Rdy3Iystm7ZgcfbF/G+5p8juw/zmGDQNSGHN5/nLYdW3Lkwk5yc/OYMla/hfL3m79k2oQPSYxPYsHHs3j6JI5f90iTvfbsPMQXy78xe82iHDtwktbtm7P77C/k5eYxa7x+xtDKjSuYM2khSQnJDBzxKsPfHYy3rye/Hd7IsYOnmDtJ2oa4Q7cITh49R65BkEMTNFoezVxDjZ/mIMhlJG4+SO7dJ/gOlso58cd9eL3UDO++bRDVGrS5BdwbLW2hbuvnQchnY0EmQ5DJSNlxkrQDljtQUaNl35x1vLZ+CoJcxrWtR0m+95Swge0AuLLxEE4+bgzdMR87ZwdErZZGw7uwpsNUCrKKTrYzj1ajZeectQxdP03arlNnC411tnB+40HuGthCQRFb6G9gCzsMbKHuK81pOlhyVG7uPc+lbUcL5f005zsmrJ+JIJdxcuthYu/F0GaglPboxv1cP3yJOm3rs/DoFxTkFrBWV1erNKpBsz5tiLn1mDm7PgLg12U/EXnkMuunfcPrc99AppChylexfrpp3RE1Wk7MXke3jVMQZDLubDlK6t2n1BwkleetDYdw8HGj96752OrKs86ILmxtOxVVVi6dVo/H3sMZrVrNyZnrKEgvffBjNFqSFnxN+TULQSYj49d9FEQ9xk3n+Kdv+Wtxb4rqd2T2Onr+KNWXm1uOorz7lDo6/a5vOISjjxuv75T0Q6sl7M0ubGgv1Zcjc9bR5fPRyG0UpEcnsv/91c+lX+L8lZT/bgHI5KT/so+CqOgXrt+h2evo8+MUZHIZkVuOknL3KXV1+l3T6Tdop/75NXizC2vbT8XB05lXVk8ApGVtt38/xaOj14yur9Vo+W3OWt5aPx1BLuP81iMk3Iuh2UBpmv/pjQe4dfgyNdqGMe3op6hy89liYAs29rZUa1mHX2asMbruy9MHEFirEqIIqTFJ/FzkfGmZPHcJ5y9fIy0tg/Y9B/HOm4Pp073zX7rWs7asuq4tSzLTlnkW05b9FUSNlsjpa2myWSrbmE1HyLoTQ8UhUtlGrz9A1fd6Y+vhTO2lw6U8ai0nO0szZ2UOtni3rsP190tZdhotD2asodam2QhyGQmbD5F79wl+QyT9Etbvw+ulcHz6RSCq1GjzCrg7SlqaY+vrQZXPxiDI5QgygeTtp0gtoa1GoyV50VcEfLMIQS4j47d9qO4/xvVV6Yt9SXF9fJdNw6FxXeTublQ6sAHl1z+S+av5XYSeyVMu+RK/lYul7dz/2Ivq/mNc+r4MQObPZuL66JB7e+I9X2oDkQlk7ztG7vGSl88d2HeU9h1bc+byXnJz8pjw7ozCcxu3fsOkcbNIiE9i2Yq5xDyJZed+aanerh0H+GTZ17z8Sif6vd4DlVpFXm4+I4ebbg1+7tA5mrRrzNoT30vbub+nXy61YN08PpnyKcoEJWsWf8eMr6YzdPJQ7kfeZ89mfVm16NKCS8cukpern6ju7uPB3G8l30Mul7Nl2x8cKRJH8ND+Y7Tr2IoTF3eTl5vLpDGzC8+t3/I1k8fPJSE+icUfzybmSRx/7JXiyO3eeYBPP1rFxCmjcfd0Y9FHkj+nVmvMbhn/jCP7T9KmQwsOnPud3Nw8po/Tzx76dtNnzJwwn8SEZAa/9RpvjRmCt68X249u5tiBk8ycuICvP17Dki8+YMfRzQiCwEfzvijc6t0IjZb7M9YQummWZAubDpFzJwZ/nS3Er9+H98vh+PZrU2gLt0euKMxefeUE3JvXRuHpQpNL3/D4oy0kbDpkIub8ofM0btuY745/R35uPive11/jw7Uf8tnUz1AmKPlh8Q9M/XIqQyYP4f6N++zdIj27Ft1a8NLgl9CoNRTkFbB0zNLC/KvmrGLK51NQ2CiIj45nxfsryErP+kfk3blyhxO7TvD5rs/RaDQ8uPGA7RuLt9/TB88S3q4pm0/+SF5uHosnfVR4btn6RSyd/DEpCSn0Gd6LAe+8hqePJ2sPfMuZQ+dYOrl0berpg2dp1q4pW09uIC83j0WTCvfYYfn6xSyZvJzkhBT6Du/FwHdex9PHk/UH1nD60FmWTP4YTx8Pvtu9CidnR7RakVff6sPAiDfIybLsw5TGP3T2cWP09gWSDyqKNB/ehc87TiG/BB/03KHzNG7XmB9OfE9+bh4fv6d/fvPXzWOFzta/W/w9M76axrDJQ4iKvM/ezdKs/CdRT7hw5AKr9q1EFLXs2bSXx3ekAVc7ezsatKrPZ9M+572PTdsZK/8thDJZ92jl/zQzgwaUWSVZm25xDOqF08GtxE3NXijHMqPKVJ6jwr5M5X0nL1dyohfIEZtSbI38AskSXkC8iecgAVWZymukfo4tyV8AbV1e7E5Oltid/c8HdzSki+M/P7XckJ05pQ+e/SKIkz3/TmB/h8UXFpapvEt13y9TecmasrU9D1lBmcrz8yp5p6MXiVxRdm11+CPLs1FfNHVdKpWc6AVibjv3fxJHeVn7LUFlKm+x7XN8BPk/SKYmv+RELxDt8wSvewG0tfEvOdEL4rzmry8V+yvsfbL73ztV5QVwqUKPMqssDZ788a8sy//sUi8rVqxYsWLFihUrVqxYsWLFipX/3/kvL/X6/wZBEM4CRT8XDhZF8fr/4n6sWLFixYoVK1asWLFixYoVK/8OrAM//wFEUWz6v74HK1asWLFixYoVK1asWLFi5d/Gv3mb9bLCutTLihUrVqxYsWLFihUrVqxYsWLlP4p1xo+VEkkuwwCzCx3rl5ksAJW6TMXRw6Z2mcrLkpft2K5GY2GXr3+A8R8Elqk8bXxCmcrLOVT6LcJfBFmJtmUqz7NB2QVlbH2w5O12XyRX0j3LVF4v78QylReVWLb6lXWw5QbXlpepPPWpX8tUHqllF1gd4PHCayUneoEELupYZrIWj75SZrIAksr4k+27tmVr68mKslUwRlu2wYF/HutapvLEjLINrJ59KrvkRC+QS5EBZSqvRa+0MpN1blvZ1pX/Ov/mbdbLCuuMHytWrFixYsWKFStWrFixYsWKlf8o1hk/VqxYsWLFihUrVqxYsWLFipX/JNYYP9aBn38MQRDcgQGiKH79v76XF0HtNmG8OucNZHIZJ7YcZO/K303SvDb3DULbNqAgN5+173/FkxsPUdjZ8P6WeSjsFMjlci7tPsOOFVuN8nV8qzt9Zw5hUv3hkFhgct1yEXVpMm8wgkzGvU1HuP7VDqPzlXs1J/SdlwFQ5+RxevpaUm9GA9Di47co3yGMvOQM/mg/vVS6lo+oS7MPByPIZdzZdISrReS5hQTQ5pO38Q4N4vyybVz/Zpe+nN7sTI3+EQiCwO2fDhP53d4S5fm1rUvd+UMQ5DIebTzM3S+N5VXo3YJqY7pL+mXncWXq96Tr9Ot8/jPUWbmIGi2iRsvhzrNKlBcYUZfGuvKM2nSEyCL6BRuUpyonj7MG5dn847copyvPHaUoT/e2YVSe/wbIZSRsPMjTL383Ou/ZuTEVp76OqNWCRsuD2T+Qee42toFeVPtiLDY+7iCKxP+4n7g1u8zKMOTkgwSWHbiOVgu96lVkeLNqRufXnr3HrhsxAGi0Ig9TMjk8rituDraFxwasPYqviz1f9AsvUZ68ch1sOwwEmQz1laOozvxpkkZWsQa2HQYgyBSIuZnkbVyM4OmPXc939GncfSk4/ivq8/ssyrNp1ATnd8YiyGTk7v6T3C0/mU2nqFYD98+/JmPhhxQcPwqAQ68+2Hd9GQSBvF07yf3t5xL1c2jeCM+p7yDIZGT+tpv077eYTWdbuxqBP35O4pSF5Bw4jtzPB5+FU5B7eYKoJfPnXWT89FuJ8hShjbEf8A7IZKiO7SZ/12aj8/Lq9XAaNw9tchwAqosnyN++QXezTji+8R6y8kEgiuR+vxzN/VsW5bm0qU/5D95CkMtI2byfhK9/MTrv1rEJAe8PLKyfMR+uIfu8dE25qxMVlo3BoVpFEEUeT/6CnEt3LMrzb1uX+vOktuXBT0e4XcTWK/VuTo139bZ+cdoPpN2MRmZnQ7vfZiO3VSAo5DzZeY4by38xJ8IIhxaN8Jo6GkEuI+PXPaR/Z/752dWuRuDGz0icvIjs/dLz8100Gbm3J2i1ZPy8i4yNv5coz6ttPWosGIoglxGz8RCPvthurH+fFgSPeQUATXY+N6esIUvXtlQc2Y3yA9oCkHkrmhvjV6HNL36ZsVtEfSrNH44gk5G46QBxXxrXL4/OjSk/uT+iKCKqNTye+z1Z527rE8hkhO5ZRkGckrtDF5WoW0nMWvQJx06ew9PDnd83rPrb1zPk5O0nLNt+Gq1WpFeT6gxvF2Z0PjO3gJmbDhOfloVaq2VIm7r0bFz9+WTcT2DZ/mtoRZFe9SoxvLlx/rVn7rIr8lnbqZXazgkvGbedPxyW2s5Xm5coz6lVQ3xnjkSQy0jbthfl6m1G553bh+M9fjCIWkS1lsRF35B78SYAHkN64P5qZxAE0rbuIXXdH8+naxmUpyH/j73zDo+q6P74Z+7uZtN7SEKH0GvoXYo0C4JdRECxYsWGHVSKiFixd0UFe0HpvfcuLaEF0rOb3rbc+f1xl2Q32WyC8u7r62+/z8ND9t6Z+d4z5cy5586caTCoE70cc+7xGmyYjk42zBanObeuaDawE5dO13TLgUXr2P6eK0dkQjyXzbuL2PZN2Tjve3Z+6DqfCkUw4fcZFGXk8uOkV2vlqze4Ex1nTACdQsrXa0mqossaXtOPFg67xV5cxv4nPqXAIZM+NJAur91JSOtGICV7H/6Q3N1JdZa1waBO9H5hPIrDRjtQpT4Tru5Lp/M2TLFWn+Yjda/P+EGd6DpDa68TC9dxpKqevrov7Rx62lpSxi4nPT30p+dQ/PQoeh0pf+zgUB30tNK0A36X3gxCYDuwEduO6raO0qg1fkPGgqJDlhZRvuhlAPTdhqHvdAlIiZqTimXpJ2D3HMNA16IzflfcBkLBtns11o3Vx4/StB1+l9+K0OmQxYWUffp85U0h8J88B1lgpvyrl2uVz69HT4LvewAUhbIlf1CyqAa7pXUbIua/S8HMFyjf4LBbrr2egMuvACmxnTpFwdw5YK3+nuCM6MGdaTtzIjjmoVNV5qH4a/vR3DEP2RzzUKGjbzZxmoeKjqRwsJZ5CEDXthv+192t2S1blmNZ6arLdC07EnDXNFRThsa5bwuWZQsBMAwajaGvpsusm5dhXVe7Losc3JlWM29F6BTSvl7DmfmueWKv7U+Tinm2jGNTP6Ho8BkAGt15GfVvuRSAtK/XcPbD2u1qH/5d8Dl+/nMIB+4F/ucdP0JRGPvi7bxxywxyM8w89dtLHFi5i/TkcxVpOgzqQr1m8Tw36AGadWnJuFl3MmfM09jKrbx+8wuUl5Sh6HVM/WEGh9bt5dRebZKPiI+i7YBOmM5l18At6DVrIivGzqEk3cyVS14kZcVu8pPSKtIUns1m2XUzseSX0GBwJ/q+PIk/Rj0PQPJ3Gzjy2UoGvHl3HWUV9Js5kSU3z6E43cyYP17kzIrd5DnxlecVs2XaApqO6OaSN6J1Q9qMHcQvV05Htdq47KuppKzZR8EpD3FhFEHnl25j0w0vUZpuYvCymaSv2EPh8crYLsUpWWy4egbW/GJih3Smy7w7WHf5tIr7G6+dhcVct/gl5+tzpaM+L1/yImer1GfR2WyWO+qz/uBO9H55Ekud6vPoZyvpV5f6VBSav3QHf97wIpZ0M52XzcG8Yhelxyv7Td7Gg5iX7wQgsG0TWn/4CHsHPIS02Tn1/BcUHzyFLsifzivmkrfhgEveqrCrkpdWHOD9m/oSGxLAuM/XM7BlHAnRlXukb+3Vklt7tQRgfVIGX+08UfHiAvDNrhM0iw6muLwOwZ+EwG/4BMoWzUUWmPG/9XlsSXuRpsq6xBiIccQEyr6dhywwQ2AIANKcQdmn0yrKCbj/DezHdtdanyEPTCHviUdRc7KJePsDLFs3Y085Uy1d0B13Y9m9s+KSrmkz/C+7ktwH7gGrjbCX5mLZsRV7qocYQopC1NMPkHH3E9gyc6j/zduUrNuK9WRKtXSRU+6gdIvT89vtmOd9gOVoMiIwgAaL3qV02+7qeZ0hFPzHP0DxvCeQ5myCp72Ddd8W1DTXPLbjByl5s7qDM2DcfVgP7cT67oug04OfsWYux3M3mnk3yeOmY0030XrxPPJX7qAs6WxFksLNB8hfuQMA/zZNaPbuVI4MuQ+ABs/fQeG6PZy+52WEQY8S4JlPKIJus29l3Y0vUZpuZtjSGaSt2EOB01gvSslmzTUzsOaXEDekM91fuZ1VV0xHLbey7rpZ2ErKEXodl/46jYw1+zHtSfYoX/Qz95N+15PYMnJosGg+JWtraL+Hq7efad6HWI442u/bdyjdusdz+ymCtnMmsfuGWZSlmei9fDbZy3dT7CRf6Zlsdo55EVt+MdFDEmn/6l1sv+xZjHERNLljJJsHPIpaZqXThw8RN6Yvad+ur1G2prPv5OhNL2BJN9F+yVzylu+kNKlSP+RvPEiuQ7cEtG1Cyw8e5cAlD1bcj7vjCkqTzqELDqxZpgvAmMuHcfO1V/H0jIsbG8iuqrz082bev+tyYsOCGPfWLwxs34SE2IiKNN9u+ZPmseG8NWkE5qJSxsz9niu6tMCg19WRQ/LS8v28P7YfsaEBjPtsLQNbxpMQ46Q7e7fi1t6aI319Ujpf7Uh21Z07k2kWFUKxpQ4xARWF2On3cva2Z7Bm5ND0xzcoWr0Ny4nKsVe8dR9Fq7cBYGzdlPpvPsWpkXfj17IJ4TeM4PR1DyOtVhp9MoOidTuxnkmria2KrP/5+nSGUAS9Z01kuWPOHeXGhik6m81SJxum38uT+N0x59aVY+iMiXw3bg6FGWYm/PYiyat2Y3LiKMsrZvX0BbSsYrecR7dJIzElp2EMDqidUBF0euk2tjjsloHLZpLhxm7Z7LBb6g3pTOK8O9jgsFs6zpxA5pr97LzjTYRBh64W3VlV1r4zJ7LMYaNd9YdWn842WmFKNn846rPh4E70mzuJxXWsz/N6eu1Nmp4evmQGqcv3UJDkJNvZbFZdq+np+MGd6TH3dlZeqenpNddX6umhv0wjvTY9LQR+w26h/LtXkYVm/MdPw35iXxU7IgC/oeMp/+E1ZGGlHSGCw9F3HUrZZ8+CzYrfqMno2vTC/udmz3yjbqfs85nIAhP+97yE7eguZLaTLeAfiHHUHZR9OQuZb4Ig1zgz+j6Xa+mNdekrCiEPTiF36qOo2dlEvPsB5Vs3Yz9T3W4JvvNuLLsq7RYlOprAq6/FNGkCWCyEPvc8/kOGULZ8mQc+Qbs5k9jpmIf6LJ9Nlpt5aHuVeWib0zy0yTEPdf7wIeLH9CW1pnkINLvlhnspefsZZF4OgY+/ge3gNtSMsy7J7Cf+pPT9510fNb4Jhr4jKHnlYbBbCbh3BrY/dyKzPegyRdB6ziT23jCL8jQTPZa/RM7yXS7ylZ3JYs+YF7DlFxM1JJE2r97JrsueJahNI+rfcik7Rz6NtNhIXPQ0OSv3UHoqo2a+fxm8G83rn4l/RYwfIcQEIcQBIcR+IcQCIUQTIcRqx7XVQojGjnSfCyHeE0KsFUKcFEIMFEJ8KoQ4IoT43Km8IiHEq0KIPY78MY7rdwohdjp4fhRCBDquxwohfnZc3y+E6AvMARKEEPuEEK8IIQYJIdYJIX4QQhwVQnwthBCO/N2EEOuFELuFEMuFEPGO6w8KIQ475FjkuDbQUeY+IcReIURIDXUSL4TY4Eh3SAgxwHF9uBBiq0O274UQwbXVb7PEFmSdySDnbBZ2q41dizfTeXh3lzSdh/dg20+acjy1N4mAkCBCY8IBKC/RAv7q9Dp0eh1SVg6965+7lZ9e+gpZw3CM7pJA4elMilKyUa12Tv26jcZVDJfsXUlY8ku0v/ckExhfGYgwc/sxLHl1D2wXk5hAwelMCh18J37dRpPhrnxlpgJy9p9Etdldroe3qE/W3hPYyyxIu0r6tqM0HelaT1UR2aUFxacyKUnJQlrtnPtlK/FV5DPvSsKarwXLM+9OJiD+rwdajKpSn6d/3UYjD/WZsyeZICe+rO3HKK9jfYZ0aUHZqQzKU7KQVhvZv2wmckQPlzRqSWUwaF2gERx9w5qVR/HBU4D2xaIkKRW/OM9yH0rPpVFEEA3DgzDoFEa0a8C6pJontKVHzjGyXYOK35kFpWw8kck1nZrUST6lfnPU3ExkXjaoduxHtqNv1dUljb59b2zHdmtOH4CS6g46XdP2yLxsZIHJI5++dVvsaamoGelgs1G2bg1+fftXSxcw+hrKN61H5uVWcjRugvXoYSgvB9WO9cB+/Ppd4pHP2KE11rNp2FIzwGajeNk6AgdV/5IfOnY0xas2YTfnVVyz55ixHNWMXVlSiuVkCrp60R75dM1bo2alIbPTwW7DumMdhi79POapgH8g+lYdsW5Y6ngAG5R6DjAZmNiS8tMZWFIykVYbuYs3Eja8p0sa5/6pBPpX9E8lOIDgnu0xLVqpyWi1YS/wzBfpGHvFjrGX8us2GlQZe6ZdSVgdY8+0O8llrNtKyjVugw7F4KpH3cHYsTXWlDRs5xztt3Q9QYPdtN/NoyletbF6+x2pbD/rqRT0sZ7bL6xrC0pOZVB6RtNlGb9soV4V/Ze/6zg2hy7L252E0Uk+odOh+PshdAq6QCPlGbnUhOAuLSg7nU65o+3Mv24iYkTNbafplsp7fvFRhF/ajexvVnmU6ULQPbEjYaFup+O/hUMp2TSKDqVhVCgGvY4RiQms+9P1pUkIQXG5FSklpRYrYYFGdErdzbtDaWZNd0ac150NWZeUXmP6pX+eY2S7hhW/MwtK2ZicyTWJTevE59+pFZYzaVjPZoDVRsEfGwge2scljXRqPxFQOfaMCY0o3X8MWVYOdpWSHYcIGVb7CqMKWb1Qn86oasOcdGPDZHmwYeqC+MQE8k5nkn9W4ziyeBsthrlylJgKyDhwEtVqr5Y/OC6ShCGJHFi0rk58EVXsltRfthJXRaZcJ7sld3cy/g6Z9MEBRPVuQ8o3Gpe02rEVlNRZ1qo22slft9G4io2WtbuyPrOq2DC1IbJLAkVV9HTDKrLlOOnpnD1JLu1VVU9Ti55W4psjc7OQ+ZodYTu6HV2LRJc0+ra9sSft1pw+4GpHKDrQ+4FQEAY/ZHGeZ76GLVBNGcjcLLDbsR/cgr6tq12m79Qf2+HtmtMHoLig4p4IjUTfqivWXas98lSU1aYtttRU1HTNbilfuwajO7tlzDWUb1yPmldF7+t0CKMRFB3C34ia4zkofbibeSi2yjyUV2Ue8q8yD+mc5qEyD/MQgNK0FWpOGtKUAXYbtj0b0Hfq4zFPRd64RthPHwNrOagq9uRDGDp71mWhXVtQeiqTMod8mb9sIXqka/s5z7P5u5MwxkcBENSyAfm7k1BLtXeU3C2Hibm8ZzUOH/7d+J93/Agh2gPPAEOklJ2Bh4C3gS+llJ2Ar4G3nLJEAEOAh4HFwOtAe6CjECLRkSYI2COl7AqsB6Y7rv8kpezh4DkC3O64/haw3nG9K/An8CRwQkqZKKV83JGuCzAFaAc0B/oJIQzAfOA6KWU34FNgliP9k0AXhxz3OK49BtwnpUwEBgClNVTNzcByR7rOwD4hRDTwLDDUIdsu4JEa8lcgPDaS3LTKl9LcdDPhsVHV0pid0uRlmIhwvKgLReHZJa8wb/cnHNl0gNP7tBeKTkO7k5dp5tyRKp5/JwTGRVCcZq74XZxuJjAuosb0LW8aROrav356SFB8BEXpTnwZZoLia+ZzRu6xc8T3ao0xPBidvx+NhnQmuH6Uxzz+8RGUOtVbabrZo2On6c2DyFyzv/KClPRf9CSDl8+i6S1Dan3GqvVZUkt9tvgb9ekXH4klrXKStqSbXF70ziPysp502fgmbb96iuSHqy+QMzaKIbhDU4r2eF4KnlVYRlxI5Reo2JAAsgrdnzJWarWx5WQWQ1tXngr2yuqDTBncHoc/tlaI4IhKhw4gC82IENe6VCLjEP6B+N/8JP63voC+Q3VHhq5tL2yHt9XKp0RHY8+uPLlJzclGF+36Mq5ERePXfwBlv7subbafPoWhY2dESCgYjfj17I0upp5HPl29aOwZlSvx7Fk51V7+dfWiCBzSn8Lvf6+xHH39WIxtWlB+8GiNaQBERDTS7CSfORsRUX386Fq0I/iFDwh8eDZKfc1Jp8TEoxbmE3D74wQ//z4Btz0Cfv4e+fzioqr1T0Nsdb6wEb1pu+YdEj5/jjOPzwfA2DgOmzmfxq8+SOslr9Po5ftrXfETEBdJaWrlWC9JNxPgYew1HzuIDKexLhTB8JWzGX3wPTLWH8K894RHPn29aGxO7WfLzEZXRT5dvSiCLu1HwXfVtyhWlONov7IDntvPPy6SMiddVpZmxujBWdvg5sHkrNkHQHlGLqff+51L9rzDwAPvYysowbS+Zr2jtV0llyXdhMGNbokY2YtOG96i9ZfPcPKRtyuuN3lhEikzv0R6+TSfv4KsgmLiwiu/z8SGBZGV7+pkvKlvO05l5TFsxtdc9+qPPD66D4pS91gGWYVlxIVeiO7MZGibSqf5KysPMGVIe+qoOjHERmHLqBx7towct2MveFgfmi37gEYfvkD6U28AUJ50hsDuHVDCQxD+RoIHdscQ79kp6Qxv1Kcz3M25QR7Gfau/MOcGx0VQ6GS3FKabCfHAURWXTr+FdbMX1nk8uLNb/D3YLY1vHkSWQ5cFNqmHxVRIlzfvZuDK2SS+eqfmmK0jAuMjKHaStaQWG63VTYM4dwH1GRgXSUlaFT3tofyEsYNIX+uqp0eunM3VB94jY8MhTLXoaREcXunQAWRhLiLYlU9ExIF/EMYbp+I/fhq69ppzQBblYdu5jIC7XyHg3teR5SWop//0zBcaWenQAWS+CRHi2nZKVDwiIBj/SdPxv2cO+sTKj0R+l9+KZcVXtTq0zkMXHY3qbLdkZ6NUtVuiozH2H0DpYle7Rc3JoeT7RUQt/I7o739CFhVj2b3LI58xLtKlb9Y2DzW8eTDZVeahgXveYXAd5iEAJSwK1emERDU3BxHmxm5p1obAJ98mYPKLKHGNtbRpZ9C36ABBIWAwom/fHRHhWZdVnWfL00wYPYz1+jcPxuSQr+joWSJ6t0EfEYwS4Ef00C74N/D8jvJvgyqF1/79U/E/7/hBc+L8IKXMAZBSmoE+wPlNpAsAZ/fyYql9Kj0IZEopD0opVTRnTVNHGhU4HwzhK6f8HYQQG4UQB4FxaA6j88/wnoPfLqXMr+FZd0gpzzn49jn4WgMdgJVCiH1ojpnzn9IOAF8LIW4Bzu892Qy8JoR4EAiXUta0J2UncJsQ4nmgo5SyEOiN5nTa7OCaCNS+vMFd/62i9N29LJ9PIlWVmZc/zpN97qZp5xbUb9UIg78fl99/Db+95j7mhFPBbrjdJ43r25aWYweye/Yi9wnqhLrzVUVechr73/2dyxc+yWVfTcV8OKXaqqBqbG7lq2H1U792NBk7iEMzF1ZcWz/qedYMf4Yt416m+W3DiOrd5i/wuU8b27ctLcYOZM9frU+3faI6mXnpDvYOeIijt82l8RM3udxTAv1p8/FjnJz2OfaimnycjrLdCFKT6t2QnElig8iKrQobkjOICDTSLi7cI4dr4XVoO0WHEteUsu9fo+zbVzD0uwoRGetyX9+yC7YjO/4in+vP4HsfoPjjD0BVXa7bU85Q+u03hL38KmGzX8F2MhlZSxyAurRf1OP3kvvGx9X4KooI8Kfeq9MwvfIesri2r7q1y2c/k0ThYzdTNP1uLKt/IfDBF7ScOh26Ji2xrF1M0fP3IMvLMF5xU/XyaqFzN/byl2/jyJD7OHnHbOo/Nk67qNcR2CGBnAXLOHb5w6ilZcTee+1F4QOo17cdzW8exP5ZlWNPqpIVw55mcdcHiOySQFjrhm7zXghf1BOTMb/uuf1iX59Gzst1aD+3g829fBH92tHg5sEkzdCmaX1YEPVGdmNjjwdY33kyukAj8ddW/yrskcsNVe6y7Ry45EGOT3qZhlPHAhA+tBvWnHxKDp70LM8/BO66SNWhueX4OVrXj2Llc+P49uFrmPPzZorKPMfBcOFwc61G3ZmUQWLDqErdmZRORJCRdnX8QKIVXrd5r2jlVk6NvJtz984gZsp4ACwnzmL66HsafzaLRp/MoOzoKWQt82wtNBe9Pl3Lrtk2qorzNsyuC5xzhZvWquN7OQlDEikxFZB56HTd+f6C3fKnw25R9AphHZty+vNVrB/2NLaSclo64pHUkb2u1MT3bUvrmwayc9YF1OeF6umxg9hXRU8vG/Y0v3Z7gKjEOujpuigzRUGJbUL5T29Q9sNrGPqMQkTEgjEQXYsulH74BKXvPQIGI7p2tcUmrAufDqV+M8oWzKHsy1kYBl2LiIpH16orsigfNe1ULRy18LmxW4o+qm63iOBgjH37Yxp3Ezk3XIMI8Mc4dNgF09U0D0X2a0fDmwdzvMo8tL7HA6ytyzwE7nVZFT772WSKnruVkjn3Y1n/GwF3PQeAmnkWy8rvCbx/FgH3zcCeegrsteiyunrXgYh+7al/8xCSZ3wNQElSKqff/o0u3z1L4sKnKfzzzAXpTh/+Hfg3xPgR1P5q7ny/3PG/6vT3+d811cf5/J8DY6SU+4UQtwKDLuRBq/DZHXwC+FNK6W5t4BXAJcBVwHNCiPZSyjlCiD+Ay4FtQoihUspqn2KllBuEEJc4ylgghHgFyAVWSinH1vagQoi7gLsA7hk+kf43XVpxLyI+krwss0v63AwTkfWjOP9tIzwuirxM1zSlBSUc3/Yn7Qcm8ueG/UQ1rMdzS1/RyoyL4tnf57LyiumUZlf6zUrSzQTVr/TWB8VHUpJZfellRNtG9H3lDlaNf4Xy3Lpv7aqK4nQzwU5froLiIimuZamnM44tWs+xRdqWt+5P3ODyZcodStPMBDitCgqIj6TUDV9o20Z0ffVOttz8MhYn+coy8wAozykgfekuIrskYNpW85f54ir1GVhDfYZfhPq0pJnwq1/59cIvPgqLh7os2HYE/6ax6CNDsJkLEXodbT55jOyfNmJesr1WvtiQADIKK51DmYWlxIS4X/Wx7LDrVoV958ysT85g04lMLHaV4nIbTy/ezexR7uMhgGOFT6jTEuGQSGRRnmuaAjP2kkItGKHVgv3sMZR6jbGbtbhPuoROqJlnoKSA2qBmZ7us0lGiY7CbXJc961u2JvRpLY6CEhaGX4/eFNrtWLZsomzZEsqWaYH8gibdiT3bfVyt87BnZqOLi6n4rasXjT3LdTuaX/uWxLz8tHY/IozAAT3IsdspWbsF9DrqvTadoiVrKFm9qVb5ZG42ItJJvsgYZF6V7W9llc4H24EdiPEPIoJDUc3ZyNxs7Ce1vm/duQHjFZ7VnCW9ev+0ZtU8Xot3HMavcRy6iBCs6TlY0nMo2XccgLwlW4id7NnxU5puJsDp61pgfCSljvHrjLC2jejx6h2sHzfXZayfh7WghOwtR4gb3In8YzXHvLJl5qB3aj99bAz2KvIZ27Wi3lyn9uvfE2m3U7JGa7/Y16dR9McaSlZ7iBnhQFm6GX8nXeZfP9Ltdq3gdo1p/9rd7Bk7B6tDvqhLOlCSko3VpG1hyPxjB+E9WpH+o/t+o7VdJZdffBTWjJrbrnD7YYxN4tBHhhDSow0Rw3sQfmlXhNGALiSQhPkPceKBN2uV8b+B2LAgMpy212bmFxMTGuSS5tedx5k0uDNCCBpHh9EgMoRTWXl0bOx5VV8FR4g/GQUXoDvbV9GdSema7rTZNd356y5mj655m7M1Iwd9XOXY08dFexx7pbsOYWgUjy4iFHtuAfk/rCD/By0QfvQjE11WD9Uqqxfq0xl1nXMj2jai3yt3sPIvzLmFGWZCnOyWkPhIitxwuEOD7q1oMbQrzQd1Rmc0YAwJ4Io3JvPHlPdqzOPObnG3JSa0bSMSX72TrTe/XDHWS9PMlKWbyXWshEn7fTstH6i746ck3eyydSswLpISN9wRbRvRf+4dLB//Sp23p58vP7B+FT2dkVctXXjbRvScdwfrb6lZT2dtPUJ8LXpaFuW6rLgRIRHV7YjCXNTSogo7Qj17HCWmkXYvPwdKNb1pT9qDUr8Fdg8riGWByWVFigiLQhbmVkuj2S3lYC3HfvoISlwTlPrN0LXpTkCrLqD3QxgDMF73AOU/zK+Rz56TjeJst8TEoFaxWwytWhP27DTH84Rh7NkbabcjdHrsGenIfO2doHzjRgztOlC+amWNfOXprn3T0zzU4bW72VVlHiqtMg9FeJiHANS8HAxOq3SUiGhkfhVdVlapW+2Hd4HuPkRQKLK4AOvWFVi3arrMb9REZJ5nXVaWbnKZZ431o2qUr81rd7Fv7BxsTv0z/Zu1pH+zFoCEp2+iLM3zO8q/DfIfvBLHW/g3rPhZDdwghIgCEEJEAluA8597xwG1v3W4QgGuc/x9s1P+ECDdsT1rXJVnmOzg1wkhQoFCR/racAyIEUL0ceQ3CCHaCyEUoJGUci0wFS1YdLAQIsGxSulltK1abpd4CCGaAFlSyo+AT9C2oG1D217WwpEmUAjRyl1+KeWHUsruUsruJJdSr2k8UQ3roTPo6T6qH/tXui633L9yF72vGQhAsy4tKS0soSA7j+DIUAJCteCZBqMfbfp1IuNEKmnHUni8+x080/8+nul/H7kZJmZeOdXF6QOQs+8koc3iCG4Ug2LQ0Wx0b86u2OOSJqh+FIM/msLGh96n4OTfC1KWvV/jC3HwJYzuTcrKPbVndMA/KrTimZpd1p0Tv27xmD533wmCm8cR2DgGYdDRcEwf0le4BvkNaBBF708fZtf971LkJJ8u0Ig+yL/i73oDO1Jw1DWgXFWY9p0kxKk+m9ZQn4M+msKmh96n8G/UZ+G+ZAKax2NsXA9h0BMzph/mFTtd0vg3javk7dgMYdBjcwSqbvH6vZQmnSPtg5q3ETmjfXw4KeZiUvOKsdpVlh9OZWCLuGrpCsus7D5rYnDLynsPDmrHivtGsPTe4cy5qjs9mkR7dPoAqGmnUCJiEWHRoOi0LVtJe13S2JL2oGvUCoQCej909RNQcyoD9+nb9cb2Z+3bvABsx46ia9AQJS4O9Hr8Bw3BstX1hdw84SbM47V/5RvXUzj/dSxbNPUlwsMBUGLq4ddvAOVrPcc3Kf/zGIbGDdA30PiCRg6iZP1WlzTnLp/AucvHc+7y8RSv3EjOrPma0weIfv5RrCdTKFhQ+6kmAPZTx9DVa4CIjgOdHkPPQVj3uo4fEVq5qkDXrDUIBVlUgCzIRTVno8RpL6T6dl1R02reQgpQsj8JY7N4/Bpp/TNi1ICKQM7n4dekso8EdGiO8NNjzy3Elp2HNT0HY3Ntu0tIv04uQaHdwewYe0GOsdd4dG9Sl7uO9cAGUfT7ZArbHnjPZawbo0IwOPSozt9A7CXtKUiuOQYLQPmhYxiaOLXfZQMpXufafmcvm8DZkdq/ivZbo9V5zAuPYD2ZQv6XdWu/gr0nCGweR4BDl8WN6UtWFfn8G0SR+OkjHLzvHUpOVj5/WaqJ8K4tUByrSKIGdKDIKZhqVRTtS8a/WTxGR9tFju5PbhXdYnTSLYEdm6M4dMvZl75mb/c72dfrHpInv0bBpoP/WKcPQPtGMaTkFJBqLsBqs7N83wkGtmvskiY+PJjtyZpeMRWWcDo7n4ZRoe6Kc89RP4KU3CIn3XmOgS3jq6UrLLOyOyWHwU73HhzcnhUPXMbS+0YwZ0wPejSN9uj0ASg7eBy/pvUxNIwFg57QKy6pCOR8HobGlRzGdgmOsac5yHWRYQDo42MIGd6Xgt89BF+tKqsX6tMZVW2Y5jXMuUP+hg2Tvv8kEc3iCHNwtB3Vm+Q62i0b5n7He70f5IP+D7P4gXdI2XLYo9MHIG/fCYKc7JYGY/qQ4cZu6fHpw+y+/12KnWQqz86nNNVEcILWvjEDOrgEha4N52005/qsaqMF1Y9i6EdTWP/Q+xRcYOBad3r63Irqerr/x1PY9uB7LjaSMbKKnh5Qu55W008hnOwIfZte2JP3uaSxJ+9Fadiywo5Q4puhmtORhWaU+s21GD+ArnFbpKkWvtQT2lau8BjQ6dB17IvtqKs9bzu6C12TNqAoYPBD17AFanYq1pULKZ03mdLX7qf8uzewnzrk0emjlXUUvZPdYhw8hPItrnaL6ZabMI3T/pVvWE/hW69j2bwJe1YmhrbtwKhtBTR07Vr9MIsqyK/jPNTl00c44GYeCruAeQhAPXMcJaY+IioWdHr0XS/BdsBVlzmHAFCatAIhkI64SSJY02UiIgZ9575Yd3nWZYUO+fwd8sWO6UvOctf2MzaIouOnj3L4vncoPenaHwyOw06MDaKIubwnmT/X/lHHh38X/udX/Egp/xRCzALWCyHswF7gQeBTIcTjQDZw2wUWWwy0F0LsBvKBGx3XnwO2A2fQtoqdd+w8BHwohLgdbSXPZCnlViHEZiHEIWAp4DaIgpTSIoS4DnhLCBGG1iZvAMeBrxzXBPC6lDJPCDFDCDHYwXPYUbY7DAIeF0JYgSJggpQy27FSaaEQ4vym6mcdXDVCtassmvYJD335DIpOYfN3a0lPOscl47Qllxu+XsmhtXvoOLgLM9fPx1Jq4YvH3wEgrF44t756P4qiIBTB7j+2cnBN3R0p0q6y7dkvGPbNVO348W/Xk3c8ldbjtXg2xxasofPDV2OMCKbP7Fu157XZ+d1xesQl79xHXJ+2+EcGc/2ut9g370eSFtWsWKVdZctzX3DZ1xrfsW/Xk3s8lbaO+DlHvlpDQEwYY5bMwC84AKmqdLhjJD8MfgJrUSnDPnwIY0Qwqs3G5me+qAgw6Ilv39Of02/hkwidwpmF6yg8lkqzCdoKq1NfrqbtI9fgFxFC4pzbKvKsHfEsxugwen/2MACKXsfZnzaTWctedmlX2fHsFwx1qs/846m0ctTn8QVr6OSoz15O9bnEUZ8D3rmPWEd9XrvrLfbP+5HkmurTrnLy6Y9pv/BZ0ClkLVxD6bFzxE0YDkDGlyuIurI39a4fiGq1oZZZOHb36wCE9GxDvesHUnz4DJ1XaavCUl76htzVe91zAXpF4cnhnZj87VZUKRndqTEtYkL5fq+2LPn6Ls0AWHM8nT7N6hHg9zfVn1SxrFyA/02Pa8eiHtiAzElF30U7CtS2dy3SlI795EEC7pgJUmLdvx6Z4zAk9H7omnWgfNnndeNT7RS9/QZhL81DKAply5dgP3Ma/yu1r6VV4/pURdi0GYjQULDZKHr7DWRRLV9B7Sqml94m7r2XQFEo/GU51hNnCLleOybXU1wfY5f2hIwahuX4Sep/qx1tnTv/U0o3edjSpqqUfj2foEfnaMeiblyGmnYGv0Ean2Xd7xh6XILf4FFgtyOtFkren1mRvfSrtwm46ymE3oCanU7JJ6/UKt+55z4kYcHz2nHu366m7PhZom4ZCYDpq2WEX96XyGsHI602ZJmF0/dVlnlu2kc0fesRhEFPeUoGKY+9VRMToI29PU9/zsCFT2jHuS9aT8HxVBIcY/3El6tp//DVGCNC6PbS+bFuZ+XI5/CvF06vN+9B6DQ9mvLbdtJX1TwWzsuXM/tt4t6fjdApFP58vv2uAKDw+5rj+hi7tCfkqmGUHz9Jg++1l0DzW59SunFnjXmkXeXoU5/RddHTCJ1C6sK1FB87R8MJQ7X6+nIVzR+9FkNEMG1fnqTlsdnZPuIZ8vckk/n7dvqsfAlpVyk4eJpzCzwED7WrnH7mY1p/Mw2hU8hetJrS42epN17TLVkLVhB5RR+irxuItNlRSy0kTa79iOq/g8enz2Hn3gPk5RVw6ZhbuPf28Vw7asTfLlevU3hyTF8mf7QUVZWM7tmaFnGRfL9VO9r8+j7tuHNoF6Z9u57rXv0BKWHK5T2JCPIc48qFQ1F4cnhnJi/ajKrC6M5NNN25x6E7u57XnWkXR3faVTJffI9Gn8wEnUL+DyuwJKcQftPlAOQtWkLIiH6EjbkUadPGXtqUORXZG7z9DLrwUKTNRuYL76IW1H1Fhzfq0xnnbZjhjjk3yY0Nk+iYc3s75lxps7PY6eTOunCsmvYF1385FaFTOPjdekxJqSSO0zj2fb2GoJgwJiyutFu6TxrJJ0OfwFLLFuqa+A48/Tl9HHZLisNuaerQZae/XE1rh93S2cluWT9CO43xwDNf0O3d+xAGPSVnstg75YML4t763BeMdNhoxx312cZhox39ag1dHr4aY3gwfZ1smN+uqFt9SrvKrmc+Z9A3rnq6xXhNtuQFq+ng0NPdHXpatdlZcdlzBMSG0/vNexCKAoogZfF20mrT01LFsuorjNc9AoqC7eAmpCkNfedBANj2r0Oa07GfOoT/rS+CVLEd3IjMSUUC9uO78J8wHVQ7alYKtgO1OEFVFcvvn+I/8RmNb89aZNY59D00e962cyUyOxV70j4C7psHUsW6ew0yy/OHjZr57BTOf4PwlzW7pXRp3e0W29EjlG9YT+T7H4Hdji05mdI/Fnukk3aVw099RnfHPHRu4VqKjp2jkWMeOvvlKhIevRa/iGDaOc1DW53mob5O89BZT/MQgKpS9t17BN43E4SCddsK1IwUDP01XWbdtAR9l34YBlyhbeOyWij97OWK7P53PIMICgW7jfLv3oVSz7pM2lWOPfUpXRY9DTqF9IXrKD52jgYO+VK/XEWzR6/DEBFM65dvr5Bv5whtZW+nTx7BEBGCarNz7KlPK4JA/3+B+03t/78gajsZ5P8jhBBFUspaT7v6/4K7m17vtU7Sx1r3IH8XA1Yvr/qLsnlX7RTpvLuoL8HuPiDofwpdX2ruVT41I9OrfCVrTnuVryjLr/ZEFxGRXb03/5xcHVR7oouI41bvTiE9oj1v3bvYSM7666cL/hWE6f5ajJW/iq4HLu6x7LXBtuUnr/KRW/ftURcDZ2b99UMX/gqazKv9sIOLhUWT93mNCyDby59sW5d7127J0XvXbgn0cpD30Q97186VF+AYvRgo3uJdO2nPoeqrE/+T6Hd97VvzLxZ2fH/xT4r0hEszv/1X74XaGHed1wb7gIwf/pF1+T+/4scHH3zwwQcffPDBBx988MEHH3zwwR1kjccW/P+Bz/HjBv9Lq32EEB3RTi5zRrmUstd/43l88MEHH3zwwQcffPDBBx988MGHfw58jp//cUgpDwKJ/+3n8MEHH3zwwQcffPDBBx988MGHfxq8vKvzH4l/w6lePvjggw8++OCDDz744IMPPvjggw8+uIHP8eODDz744IMPPvjggw8++OCDDz748C+Fb6uXD7XC6EX/oLdP2crVeZcvWPWugN4+ujAT754KZd2yz6t8+rZNvMpXmutdFZ2T493wZvpD3jsdo9zm3cFeoPPuWC8o+GvHTf9VWL0cJDHH7t2TcLx9ypa+7zVe5bOtrhoa8D+LrALvnqrXOCPVa1wWL9stBcK7M3uJ4l0BbV5lgxwv6+qiZSe8yhc0xLt2S1m+d+2WVIN3+XK3lXuNq1CEe43r/wNUX3Bn34ofH3zwwQcffPDBBx988MEHH3zwwYd/K3wrfnzwwQcffPDBBx988MEHH3zwwYd/JXzHufscPz7UEW0Hduaaabei6BS2fruGVe/9Wi3NtdNvpd3gLlhKy/n6sfc49+cpAKZvmk95URmqqqLa7My76mkALptyHX1uupQis7bd4/e5C2HVn9XKbTioE31eGI/QKRxbuI797yx2uR+WEM/A1+4iukNTds79noMfLKm41/72EbQZOwghBEe/WcuhT5bXKmuzgZ24dLrGd2DROra/58oXmRDPZfPuIrZ9UzbO+56dHy5xuS8UwYTfZ1CUkcuPk16tlS9+UCe6zhiPUBROLFzHkbdd+Zpc3Zd2940CwFpSxq4nPyPvcAqK0cDQn55D8dOj6HWk/LGDQ/N+rJWvwaBO9HxR40tauI6DVeqz+dV96XDvlQDYSsrY+tTn5B5OAaDfq3fScGgiZTkF/HrpU7VyxQ7uRKcZExA6hdNfr+V4FdkaXdOPVvdrstmKy9j3xKfkO7hG7HwTW1Ep0q4i7SprRzxbK5+ufXf8b5qMUBQsG5dhWfat6/1WnQi87wVUUwYA1j2bsPz+NUpsQwLufqYinRIdR/mvX2JZ/bNHPqVpe/wG3QSKgu3gRmw7l1VP07CVI40OWVZI+XfzANB3uRR9xwGAwHZwA7a9q2uVz79PDyIeuw8UheJfllDwxSK36fzatSb2s/nkPD2T0tUbnB5GIW7Bu9izTGQ//IzbvM4IHdSFxi/ejlAUsheuIuMd1+0v4cN70uDxsSAl0mYnZfqnFO084sLXbukrWDPMJE2cVSf5wh91yPfrEgo9yFfv0/mYnp5J6RpNvvhfv0YtKQFVBZudzIn31soXNqgLTWdMQigKWQtXkfa2a3tHjOhBQyf5zkz/lMIdRxFGA+1/monwMyD0CuY/tnJu3rc1sFTC27osZGBXGky/A6HTYVq0gqz3XPVD6LBexD86DlQVabeT+sLHFO/S2k8XGkSjl+/Hv1UTQJLy+FuU7DnmkS9mcGfazdTG+9mv13Ji/m8u9+tf24+E+68CwF5cxsGpn1B4OIWghHi6fPhgRbrAJvU4PvcHTn+49B/B5Q6bj55l7m9bUVXJ1T1bM2lIosv9wlILzyxcS0ZeETZVZcLATozp0fqCODzh2dmvsWHzDiIjwvnlq/f/dnmbk9KYu2Q3qpRc3TWBSZe0d7n/+abDLDlwGgC7KjmVXcDaJ64ht6Scqd9tqkiXmlvE5MGduKVvG498EYMTSZhxG0KnkPH1as6+/YvL/agR3WnyxE2gSqTdzonnPqdgx1EAWr0+mchh3bDm5LN70KN1k+9UNq+sPYwqJWM6NGJSrwSX+1/sPMmSI6mV8pmLWDN5KP4GHbd/uw2LXcWuSoa2jGNyv1a18jUa1Im+jrF+dOE69lUZ6+EJ8QxyjPUdc7/ngNNY73D7CNqOHQSOsX6wDmPdGS0HduLyaRNQdAq7v13Lhio2THRCfa555W7qt2/KynnfsfmjPy6ofPC+3eLN+mw8qBOXPK9xHV64jt3vunJFJMRz6at3Ua9DU7a+8j17HVzhzeMZ+e79FenCGtdj26s/sL8WPr8ePQm+7wFQFMqW/EHJom/cptO3bkPE/HcpmPkC5RvWAxBw7fUEXH4FSInt1CkK5s4Bq8Ujn655R/yG3wJCwbZvPdatv1dLozRug9/wcQhFhywpouyr2doNYyDGKyahxDQEoPz3j1FTkz3yedtucUaDQZ3o/cJ4FMece6BKv0m4ui+dHPautbiMLU99jvlIygVxeFu+eoM70dFhV5/5ei1JVcZew2v60dLJrt7/xKcUOOzq4TvfxFpUCnYV1a6yvg52tQ//LvgcP/9ACCFeBDZIKVcJIaYAH0opS/5rz6MIrn9xEu/cMou8DBOP/fYSh1buIiO5cg99u0GJxDSLY8agh2japSU3zLqd18ZUKpT5Y1+kOLewWtnrPvmDNR9VTjodMVTj7jdzIktunkNxupkxf7zImRW7yUtKq0hTnlfMlmkLaDqim0veiNYNaTN2EL9cOR3VauOyr6aSsmYfBacyPco6dMZEvhs3h8IMMxN+e5HkVbsxOfGV5RWzevoCWlbhO49uk0ZiSk7DGBxQI48zX7fZt7L2ppcoTTczfMkMUpfvoSCpsm6Lz2az6toZWPNLiB/cmR5zb2flldNRy62suX4WtpJyhF7H0F+mkb5mP6Y9NU/CQhH0mjWRFWPnUJJu5solL5KyYjf5TvIVns1m2XUzseSX0GBwJ/q+PIk/Rj0PQPJ3Gzjy2UoGvHl3rbKhCDq/dBubbniJ0nQTg5fNJH3FHgqPO8mWksWGq2dgzS8mdkhnusy7g3WXT6u4v/HaWVjM1ftNDcIRcPP9FL/+JDI3h6Bn5mPbvxU13XUStyUfpHT+NJdrauY5il+cXFFO8CvfYN27uRY+gd+Qmyn/8XVkYS7+457BfmI/0pxemcYYgN+l4yj/6U1koRkCQrSsUfXRdxxA2TezwW7DeM1D2E8dROZl1cynKEQ88SBZ903FnplN3JfvUrJhK7ZTZ6qlC3/gTsq27apWRMjYa7CeSkEJqkO8DUWhyay7OD72eSzpJtotmUveih2UJZ2rSFKw6QB5K3YAENC2CQnvP8ahgQ9U3I+940rKks6hCwmsE1/E1AfJul+TL/aLdymtQb6w+93Ll33Po6j5dYwbpCg0m30nR256AUu6iQ5L5pK7fCelTvLlbzxI7vKdAAS2bULLDx5l/yUPIsutHL5+OmpJGUKvo/0vs8hbs5eiPcdrpPO2LkNRaDjjbk6Mm4Y1w0Sr314lf9UOypPOViQp2ryfYyu3A+DfpilN35nK0Us1h1mD6XdSsH4Ppye/jDDoUQJqibGjCNrPuY3tN8ymLM1E/+WzyFy+myKn8V56JoutY17Ell9MzJDOdHz1TrZc9hzFJ9LZdN6RrAgu3f8umUt2/jO43MCuqrz082bev+tyYsOCGPfWLwxs34SE2IiKNN9u+ZPmseG8NWkE5qJSxsz9niu6tMCgvzixpsZcPoybr72Kp2fM+9tl2VWVl37fxfsThxAbGsC4D5YzsE1DEuqFVaS5tX87bu3fDoD1R8/x1dZjhAUaCQs08t29l1eUM3zeLwxp18gzoaLQ4qXbOXjDDMrTzXRZ9hKmFbsoOV459nI3HsK0/DEAgto2pu2Hj7BrwBQAMr9dR9qny2g9/353pbuRTzJn9Z+8d11PYkP8Gff1Zga2qEdCVEhFmok9mjOxR3NNvhOZfL37NGEBfkgp+fD6XgT66bHaVSYt2kq/ZjF0qh9RE13FWP/DMdav+eNFTlcZ62V5xWyuYay3HTuIn6+cjt1q4/KvpnKmtrFehXvUi7fx2S0vUZBh4p7fZnJk5R6yney10rwi/nj+C9oO716nMt1xeNtu8VZ9CkUwaOZEfrl5DkXpZm78/UVOrtxNbhWuDdMX0LwKV97JdBaNfKainNt2zufksurzlAsUhZAHp5A79VHU7Gwi3v2A8q2bsZ+pPu8F33k3ll2VukqJjibw6msxTZoAFguhzz2P/5AhlC2v/gGqUkCB38gJlH0zF1lgxn/SC9iS9iBzKuXDGIhx5ETKFs1DFpggsHKc+A2/BfvJg5T/9DYoOjDUNi942W5xFlUR9J05kWWOfnPVH5q969xvClOy+cNh7zYc3Il+cyex2GHv1glet8s0u3qzw64etGwmGVXs6pKULDY57Op6QzqTOO8ONjjZ1ZsvxK7+l8HbcU//ifDF+PkHQko5TUq5yvFzClCHt6b/HJoktiD7TCams1nYrXb2LN5Cx+E9XNJ0HN6DHT9pHuzTe5MICAkiNCb8b3PHJCZQcDqTwpRsVKudE79uo8lw18m2zFRAzv6TqDa7y/XwFvXJ2nsCe5kFaVdJ33aUpiM9GzrxiQnknc4k/6zGd2TxNloMc+UrMRWQceAkqtVeLX9wXCQJQxI5sGhdneSL7JJA0elMih3ypfy6jYZVjImcXUlY8zW/X86eJALjIyvu2Uq0IHOKQYdi0IGUHvmiuyRQeDqTIgffqV+30bgKX/auJCwOvuw9yS58mduPYckrqqNsLSg+lUlJShbSaufcL1uJr8Jl3pWENb9Y+3t3MgFOXBcKXbPWqNlpyJwMsNuw7lyPPrHvhZfTtgtqdjrS7MEJAyhxzZB52cj8HFDt2I7uRJeQ6JJG36YX9qS9mtMHoFSbbJXIeNT0k2CzgFSxnzuOrkUXj3x+7dtgO5uKPTUdbDZKVqwlcGB1+UJuHEPJmo3YzXmuctWLJqBfL4p+WVItjzsEdWlJ+el0ylMykVYb5l83ETGip0sataSssj4C/cGp+xniowi/tBvZC1dRF/i1b4PVWb6VawlwI1/wjWMoXbsRNTeveiEXgOAuLShzks9Uq3xGF/nO3xMGHcKgr3XseVuXBSZq7Wc5q8mXu3gjYcN6eZbPIaASHEBQr/aYF60EQFpt2AuKPfKFd21ByakMSs9o4z3tl63EVnnG3F1J2BzjPbeG8R49oAMlpzMpPZfzj+Byh0Mp2TSKDqVhVCgGvY4RiQms+9PV0BdCUFxuRUpJqcVKWKARnXLxTK7uiR0JCw2pPWEdcOiciUaRwTSMDNbk6diEdUfP1Zh+6cEzjOxYPWDs9pOZNIwIpn645xeYkC4tKD2VQVlKFtJqI/uXzUSNcG2/6rqlcnzlbzuCtY7zEMChjDwahQfSMDwQg05hROt41iXX7EhZdjSNkW3iAa0dA/20b6Q2VWJTJUJ43i5Qr8pYT/51G03djPVsN2M9okV9MveewOY01pvVMtad0TCxBaYzmeQ67LWDi7fStgp3samA1APVuesKb9st3qzPWIcNWODgOv7bNppX4So1FZC1370NeB4N+7cn/0wWhakmj7Lp27TFlpqKmq7Ne+Vr12Ds279auoAx11C+cT1qXq7rDZ0OYTSCokP4G1FzPOsypX4CqjkLmZcNqh374W3oW3V1faYOfbAd26U5fQBKHE4CP390jVtj26etNkK1Q7nnb9LetlucUXXOPfnrNhpXacus3ZX2btaeZIIu0Ab1tnwRXVpQVMWujvNgV9c09/nw/xc+x48ThBAThBAHhBD7hRALhBBNhBCrHddWCyEaO9J9LoR4SwixRQhxUghxnVMZU4UQBx1lzHFcu1MIsdNx7UchRKAQIkwIcVoIoTjSBAohzgohDI7yrxNCPAjUB9YKIdYKIW4XQrzuxHWnEOK1GmQJEkL84eA8JIS40XG9mxBivRBitxBiuRAivrZ6CY+NJC+tcvLKSzcRFuv6tSssNsI1TYaJsDiHspFw74JneHzxS/Qde6lLvgETR/DE0rncPPceAkKrG4tB8REUpZsrfhdnmAmKr/lLmzNyj50jvldrjOHB6Pz9aDSkM8H1ozzmCY6LoNCJrzDdTEhc3fgALp1+C+tmL0Sqng2Z8wiMi6TEqd5K0s0EeJAvYewg0tfur/gtFMHIlbO5+sB7ZGw4hGmv59MiAuMiKE5zqs90M4Ee5Gt50yBS1x6oiyjV4B8fQamTbKXpZo8TUNObB5G5plI2pKT/oicZvHwWTW8ZUiufCI9GNWdXZs/NRgmv3t665u0ImvYegQ/OQqlf/eXF0GMg1h1ra+cLDq906ACyKBcREu6aJiIW/AMxXv8Y/uOeRde2DwCqKRWlYSvwDwK9H7pmHREhnidnXb1o7JmV8tmystHVi3ZNExNNwKD+FP24uGp2Ih69j9y3PqzVyD4Pv7hILGmVRqQl3YQhrnp9ho/sRYf182n1xTOcevTtiuuNX5jE2ZlfaFuv6gBdjKt89sxsdDF1lw8piXl7LrFfvkfQ1VfUyucXF4XFqX9a0k34uemfESN70XnDW7T58hlOPFIpH4pCx5Wv0u3AZ+Rv2E/R3iSPfN7WZYa4KKzple1nTc9x235hI3rTZvW7NP9sGimPvwWAsXEcNlM+jec9RKslb9Do5ftrXfHjH+c63svSTPh70C2Nbx5E1pp91a7Xv7ovaT9v+cdwuUNWQTFx4ZUn4MWGBZGV7+oYu6lvO05l5TFsxtdc9+qPPD66D4qXT0eqK7IKS4kLq5x/Y0MDySpw/0JXarGxJTmdoW5W9Sw/eIbLOtV+gpAxPpJyp/YrTzfjF1+9b0Zd1pPuG9+gw1dPcezh9+oiiltkFZURG1J56l1sSADZRe5P5im12tlyOodLW8ZVXLOrkhu/3Mil762id5NoOsaHe+QL/Btj3ew01vX+fjSuw1h3RmhsBPlOdVuQbiY09uK++HndbvFifQbFRVDkZCMVpZsJvgAb8DxaXdWHpF+31ppOFx2Nml35kUnNzkaJdp33lOhojP0HULrYdTurmpNDyfeLiFr4HdHf/4QsKsay2/MKIxESgSysbDtZYEaEuMqnRMYh/IPwv+Up/Ce9gL5jP+16RD1kSQF+V96J/+0z8LtiEhg8n+TqbbvFGYHxERQ79ZuSWvpNq5sGce4C7V1vyxdQxa4uq8WublLFrpZS0nfRkwxaPosmdbCr/22QCK/9+6fCt9XLASFEe+AZoJ+UMkcIEQl8AXwppfxCCDEJeAsY48gSD/QH2gC/AT8IIS5z3O8lpSxxlAHwk5TyIwfPTOB2KeV8IcR+YCCwFhgFLJdSWs9/TZJSviWEeAQY7HimIOCAEGKqlNIK3AbUtOdmJJAmpbzCwRsmhDAA84HRUspshzNoFjCplsqpdqmqjnL7BcyR6PVrp1GQlUtwVCj3ffUsmSfSOLHjCJu+Wsmyt34ECZc/egNXPzueg498UpXcTbken7YCeclp7H/3dy5f+CTW4jLMh1Nq/cIl3PDVdb5JGJJIiamAzEOnadS7bd0yudMNNRDW69uO5mMHsWrMi5VJVcmyYU9jCA1kwCcPE9a6IfnHav5S664ta6rPuL5taTl2IEuvnuFBAE9UNfeJqoju144mYwexYfQLFdfWj3qessw8jNGh9Pv2KQqT0zBtO+qB0N1FVz57SjJFT94C5WXoO/Qg4N7nKX72tsoEOj36zn0o/+lTD5J5IKwqnqKgxDah/PtXQe+H/9gnUdNPIs0ZWHcuw//ah5HWctTsc9rXswtFlfqMePRe8uZ/VM3Z4t+/N3ZzLtajSRi7da5b2XVsv7xl28lbtp3gXu1o8PhYjt/0PGFDu2PLyafk4ElC+rSvXo5bPjfXqvCFP3Iv+W7kA8i84yHUHBNKRDgxb8/FdjqF8r0HL5Cv+qXcZdvJXbadkF7taDR1LEdudPRRVeXgsEfRhQbS6pMnCGjdmNJjnmIDeFeXueerTpi/fBv5y7cR1LM98Y+O48S4aaDTEdghgdTpH1Ky7zgNpt9BvXuvI+PVrz3Q1d3QierXjkY3D2bLVc+7FmHQETu8G0dnuY+R8F/hcgN3aqzqI205fo7W9aP46O4rOGsq4J4Pl9C1WRzB/p5flP4bcC+P+zrecCyVxEbRhAW6OgKtNjvrj6Xy4LA66Jc6znumpTswLd1BWO+2NH3iRg7e8NfmorqOM4ANJzJJrB9BWEBlO+kUwbcTBlBYZuWR33aTnFNIi+iaV1u5syMuZKzve/d3rlj4JLbiMkx1GuvO5O5smAt/afbM4ebaf9Bu8WZ9uuv3F1p9ikFHs2Fd2TKn9rhvdZkXgu99gKKPPqg274ngYIx9+2MadxOyqIiw6S9gHDqM8lUrL+yB3dkt8U0p+3oO6P0IuHUa9tQToOhQ4ppiWb4ANe0kfsPGYeg7Cuv62mM0ufL9B+0WF9S9LeP7tqX1TQP5/S/au55I/ht2Gbi3qzc67Go/h11dVJtd7cO/Dj7HTyWGAD9IKXMApJRmIUQf4BrH/QXAXKf0v0gpVeCwECLWcW0o8Nn5eDxSyvOu5g4Oh084EAycj/T2LXAjmuPnJuBdTw8opSwWQqwBrhRCHAEMUsqa3mwOAvOEEC8Dv0spNwohOgAdgJWOyU0HpLvLLIS4C7gL4L7ht9LnpkrPcHh8FAVZrstN8zLMhDt9RQmPiyI/U0tzPm2RqYADy3fQpHMCJ3YcoTAnvyL91kVruOuTJ6gqTHG6mWAnb3ZQXCTFGVWWunrAsUXrObZIW5ba/YkbXLz/7lCYYSbEiS8kPpKizLrxNejeihZDu9J8UGd0RgPGkACueGMyf0yp+UtlSbqZQKd6C4yPpDQjr1q68LaN6DnvDtbfMhdLbvUl7taCErK2HiF+cCePBlRJupmg+k71GR9JiRv5Ito2ou8rd7Bq/CuUu+GrC0rTzAQ4yRYQH0mpm7YLbduIrq/eyZabX3aRrSwzD4DynALSl+4iskuCxwlK5uagRMZU/BYRMah5Vdq7rPIrtu3QTvx1OkRwKLJIiwuj79ADNSUZWZhXq3zaCp/KuhTBEcgi13yyMBe1tEjb0mWzoKYmocQ0xJ6Xif3QJuyHtKCohn5XI4s89zN7Vg662Er59PVisGe7LiP3a9uK6NlabC0lPIyAfj0x2+z4dWhDwCV9CejXC+HnhwgOJOrFpzBNe6lGPku6Cb/6lV+u/OKjsGbWPH6Kth/Gv0kc+ogQQrq3IXx4D8KGdEMxGlBCAmn+1hROPvhGneXTxcZgz6kuX9SsSvn8+/YEu53S9ZtRHWnV3DxK123Cr30bj44fTb7K/ukXH4Ulo2b5CrcfxtgkDn1kCDan/fH2ghIKtv5J+OAuHh0/3tZl1owcDPGV7WeIj/bYfsU7/sSvSTy6iBCsGTlY03Mo2afFLMpbsoV6917rka8s3XW8+9ePosyNfCHtGtPxtbvYOXYO1iq6pd6lieQfPIUlO79avv8WlzvEhgWR4bTVKDO/mJgqK1Z/3XmcSYM7I4SgcXQYDSJDOJWVR8fG9S6Y7z+N2NAAMpxWLGUWlBAT4j5G3bJDZxjZqWm165uS0mkTH0FUHWLblaeZMTq1nzE+0uPYy992hICm1cdeXVEvxJ/MwsqtY5mFpcQEu1/BtvxYesU2r6oI8TfQvWEUW05le3T8XMyx3vOJG1xWu9SGggwzYU51GxofSWFW3bnrAm/bLd6sz6J0M8FONlJwfCTFdbQBz6PJ4M5kHzpNaU7t8ebsOdkoMZU6QYmJQTW5btcytGpN2LNajBYRFoaxZ2+k3Y7Q6bFnpCPzNR1WvnEjhnYdPDp+ZGEuIqSy7URoZDXbQxbkYi8p0oJEWy3YU46h1GuEevY4ssCMmnYSANvRnRj6XulZPi/bLc4oSTe7bN0KjIukxE2/iWjbiP5z72D5+Fcov4AtpP8N+ara1f4e7OouDrva6sautjjs6oha7Op/G3wxfnxbvZwhqP0bgvN953XCwul/d2V8DtwvpewIvACcX3P8G3CZY2VQN2BNHZ7zY+BWtNU+n9X4oFIed5R5EHhJCDHN8Xx/SikTHf86SimH15D/Qylldylld31yGTFN44hsGIPOoKPrqL4cXOm6nPTgyl30vOYSAJp2aUlZYQkF2Xn4BRgxBmni+gUYaTOgE+nHteCizjGAOo3oUXHdGdn7TxLaLI6QRjEoBh0Jo3uTsnJPHapJg39UKABB9aNodll3TvzqeVl/+v6TRDSLI8zB13ZUb5LryLdh7ne81/tBPuj/MIsfeIeULYc9On0AzPtOEtIsjiAHX+PRvTm3YrdLmsAGUfT/eArbHnyPwpMZFdeNkSEYQrXwTzp/A7ED2lOQ7NaPV4GcfVp9Bjv4mo3uzdkVrvIF1Y9i8EdT2PjQ+xQ48V0ocvedILh5HIGNYxAGHQ3H9CG9imwBDaLo/enD7Lr/XYqcuHSBRvSOfqMLNFJvYEcKjlbvH86wnz6GUq8BIjoOdHoMPQZi2++61FqEVi7zVZq2BqFUOH0ADD0H12mbF4CacRoRXg8RGg2KDn2bHthP7ndJYz+xD6VBSxAK6P1Q4pqhng/+fD7Qc0gkupZdsB3d4ZHPcvgohkYN0NWPA72ewOGDKd3g2p/TRt9C2lXjSLtqHCWrN2B++S1K128m/51PSLviJtKuGkfOMzMp37mvVuOpeF8Sxmbx+DWqhzDoiRzdn9wVrkFwjU0rt0MEdmiOMOix5RZybs5X7O9+Jwd6382Je1+lcPNBj06fCvkaO8k3rLp86WNuIX30ONJHj6N0zQZyHfIJf39EoPbCKfz98e/dHeuJ0x75ivYl498sHqNDvqja5OvYHMWgx2YuRB8Zis4x9oS/H2EDOlGa7GGlHd7XZSX7kzA2q49fo1iEQU/EqAEUOAI5n4dfk8oX3ABH+9lzC7Fl52FJz8HYvAEAIf06uwSFdof8vScIah5HgGO81x/Th8zlruPdv0EU3T59mP33vUOxG91S161X3uRyh/aNYkjJKSDVXIDVZmf5vhMMbNfYJU18eDDbk7UgoqbCEk5n59PQ0Yb/NLRvEEWKuZDU3CJNnoNnGNimQbV0hWUWdp/OYnCbhtXuLTt42m3cH3co3JdMQPN4/BtrYy9mTD9MK1xtCn+nsRfcsZmmW/5iQNL2cWGk5BWTml+C1a6y/Fg6gxJiq6UrLLey+5yZQS0q75lLyiksswJQZrWzPSWHppGeYxhl7T9JmNNYbzG6N2f+wlgPrh9F08u6k1zLWHdG6v4TRDWNI8Jhr3Uc1YejK3fXnvEC4G27xZv1mbn/JOFN4wh1cLW6qjenLoALoNXoPhyvwzYvANvRo+gbNESJ0+Y94+AhlG9xPVjCdMtNmMZp/8o3rKfwrdexbN6EPSsTQ9t2YNScmIauXbGnnHFHUwE17SRKZCwiTLNbdO16Yzu+1/WZju9B16hVhd2iq5+AakpDFudrW8MitbGpa9oeNTvNHU0FvG23OOP8nHve3m3uZs4Nqh/F0I+msP6h9yk4deH2rrfly3NjV2e4sat7fvowu+9/12Xuq2pXx9TBrvbh3wffip9KrAZ+FkK8LqU0OZwxW9BW4iwAxgGbPBUArACmCSG+Ob/Vy7HqJwRId2y1GgekAkgpi4QQO4A30VbluFt/WujIf34l0nYhRCOgK9CppgcRQtQHzFLKr4QQRWjOojlAjBCij5Ryq+N5Wkkpq5+h7gTVrvLDtE+598unUXQK275bR0bSOfqNGwrA5q9XcXjtXtoP7sK09W9iKbXw9eOawyMkOow7PtRO5lB0Crt/3cyR9drL8einxtGgXVOklJjPZfPt0x9R1WyUdpUtz33BZV9PRSgKx75dT+7xVNo69qYe+WoNATFhjFkyA7/gAKSq0uGOkfww+AmsRaUM+/AhjBHBqDYbm5/5oiKIW02QdpVV077g+i+nInQKB79bjykplcRxGt++r9cQFBPGhMWVfN0njeSToU9gKSr1WHZNfLue+ZxB3zyB0CmcXLSeguOptBivxUJKXrCaDg9fjTEihO4vaVuSVJudFZc9R0BsOL3fvAehKKAIUhZvJ23VXk90SLvKtme/YNg3Wn0mf7uevOOptB6vyXdswRo6P3w1xohg+sy+tYLvd8eJAJe8cx9xfdriHxnM9bveYt+8H0lyfElzx7Xv6c/pt/BJ7djJhesoPJZKswmabKe+XE3bR67BLyKExDm3VeRZO+JZjNFh9P7sYQAUvY6zP20ms7a916pK2TdvEzhlNkIoWDYvR007g2GgFu/Fuv4P9N0G4DfoSrDbkVYLpR/NrszvZ0TXriulX73hmadCQBXL2m8wXjsFhMB2aDPSlIa+00AAbAfWI80Z2E8fwn/CdO241YMbkSbNUDKOmowICALVjmX1N7UGScSuYn5lPvXmvww6heLflmI9eYbga7UvbkU/Vj+S9W/BrpLy7Ee0/mY6KAo5366m7PhZYsaPACB7wXIiLu9D9HWDkDY7apmFE5Nf/Vt8uXPnE/PWywidQtFvS7GdPEPQNZp8xT/VLJ8SFUH0XG05s9DrKF62mrKttZzUZFc5/czHtPlmGkKnkLVoNaXHz1JvvOYLz1qwgqgr+hB93UBNvlILSQ75/GIjSHhTO35XKAqmxZvJW+X55crbugy7yrlpH9D8y+cROgXzd6soSzpL1LiRAJi+Xkb4ZX2IuHYIWG2o5RbO3Fe5qDV1+oc0efMRhMGAJSWDlMferFW+Q099Ts9FTyF0CucWrqPo2DkaT9DmiZQvV9Hy0Wvwiwim/cva7mJpU9k8QjsFRwnwI/qSjhx87GPPcnmZyx30OoUnx/Rl8kdLUVXJ6J6taREXyfdbDwNwfZ923Dm0C9O+Xc91r/6AlDDl8p5EBPnXUnLd8fj0Oezce4C8vAIuHXML994+nmtHjfjr8lzRnclfrtXk6dqcFvXC+X6nFrfq+h4tAVhz5Bx9EuII8HM1HUstNradyODZq3pWK9st7CrJT39Ch4XPaMe5L1xLybFzxE8YBkD6lyuJvrIXsdcPRFo13XLk7orwhrR57yHC+rbHEBlCrz3vc+aV78hYWPN3M72i8MSQ9tz74w5UFUZ3aEhCdAjf79dekq/vrFkea5My6d0kmgBDpXw5xeVMW3oAVUpUKRnWOp5L3DiNnCHtKpue+4LLaxnr1ziN9Y53jOQ7x1gf/uFD+F/IWHeCalf5fdrnTPzySc3m+m4dWUmp9Binzbs7v15NcEwYk3+biTE4QIv5MWkkbw2bSnkdbZj/ht3irfqUdpX1z33BVV9NRdEpHP52PebjqXRwcB36ag2BMWHc+EclV+LtI/lqiMal9/ej0YAOrH2yLtvFAdVO4fw3CH95HkJRKF26BPuZ0/hfeRUAZb//VmNW29EjlG9YT+T7H4Hdji05mdI/3MS/cxFQxbL8S/zHTgVFYNu/AZmTir7rYK3MPWuRpjTsJw8ScOcskBLrvvXIbO3UKMuKBRjHTEYoOtS8bMp//8gzn7ftFmdR7Spbn/uCkY5+c9xh77ZxtOXRr9bQ5eGrMYYH09fJ3v3timkeSq0CL8sn7SoHnv6cvlXs6qYOu/r0l6tp7bCrOzvs6vPHthujw+jlsKuFXse5nzaT9RdjeP6vwrfiB8RF3/v7PwwhxETgccAO7AWeBz4FooFs4DYpZYoQ4nM0R80PjnxFUspgx99PAhMAC7BESvm0EGIyMBU4g7YCJ0RKeasj/XXA98AgKeV6x7WK8oUQDwD3AelSysFOHIlSyps8yDICeAWtn1uByVLKXUKIRLRYRWFojr83zscfqgkPNr3Ra52ko81Qe6KLiNyLc7JundHI6t3xVn4BsTAuBkLt3lWrQ0dd2Gk8fxf6tnX7on2xkPPNSa/yZaZ7d0VCXP06Hr1+EZCaGlZ7oouIg4p3D2PsofNeXQKkll3Y0br/axjyUbfaE11E6PteU3uiiwjb6gVe5dv50CGv8nV/sfqqpP8UFryYXXuii4izeu/Os+0t3rUjCrwcBN3L4nFjK++usgga4l27xfRzau2JLiJWZtR6Rs1FxbA4zyvULib2nPPsZL7YGJPxzT83KvFFwJLYm7z2EnZ55qJ/ZF36Vvw4QUr5BVpAZ2dUC3t+3mnj9DvY6e85aCtrnO+/B7jd8+NwHokq1251+ns+WkBmZ/QHXscDpJTLqYwl5Hx9H3CJp7w++OCDDz744IMPPvjggw8++PBvwD/5tC1vwRfj538IQohwIcRxoFRKufq//Tw++OCDDz744IMPPvjggw8++ODDPxu+FT//Q5BS5gGtnK8JIaLQ4hNVxaVSSpOb6z744IMPPvjggw8++OCDDz748P8Cqm/Bj8/x878Oh3Mn8b/9HD744IMPPvjggw8++OCDDz744MM/D76tXj744IMPPvjggw8++OCDDz744IMP/1L4Vvz4UCv8vegf7OOX5zUugNW2cK/y9Qj37ilUpaV+XuXTKd49bUQXH+lVPlTvnsoWOSi49kQXESW/lXuVLyPNe6eIterh3Z2vp3YGeJWv2aXFXuUL2e3dvpJp8u5YINe7utrbp2zpLx3vVT54wqts6nHvnYjY08ufUE/i3RMDuwTneZWvsNjoVb4j0rsnFFpLvHucrO2od0/Ziujv3f5p+tmrdJw+5z27s0OkL2LHxYTqC+7sW/Hjgw8++OCDDz744IMPPvjggw8++PBvhW/Fjw8++OCDDz744IMPPvjggw8++PCvhHfX7P8z4XP8+FAntBrYmdHTJiB0Cju+Xcu6936rluaq6RNpMzgRa6mF7x57j9Q/TxPTPJ5xbz9YkSayUT1WvP4Dmz5dSnzbxlwz63b8Av3JPZfNwinvQElhtXKDL+lK/LS7QFHI/W4FOe//4HI/ZGgvYh+5BalKsNtJn/ERJbsOAxA1aTQRNwwHCWXHT5P6+BtIi9WjrE0GdmLg8+MROoU/F61j17uLXe5HJMQzbN5dxHRoytZXvmfPh0sq7vmFBjJ07h1EtWoIUrLy8Y/I2JPskS+wf3ein7oHdDoKflhK3sffuU1n7NCKhgvfIOPR2RSv2ARAvZmPEDiwF3ZzHmdH3+2R5zyCL+lK/el3avX57Uqyq9bnsF7EPjIOVIm02Umf8TEluw7j17wBjedPrZS1URyZr3+N6bPqfeE8gi7pRtxzdyF0CrnfrsD0wfeuzzK0N/UevkXjstvJmPEhpbu1tou8bQzhNwwHJOXHzpA29fVa207XojN+l08EoWDbswbrxurPpjRth99lExA6HbKkkLJPX6y8KQT+98xGFuRS/vVcj1xaWe3xGzJW4zu4EduOpdXTNGqN3+AbQdEhS4so//YVAPTdhqHv2B8ANfsclmWfgd3mWb7WXTCO1trOun0l1rU/Vk+T0AG/q24HnR6KCyh97xlEWDTGsVNQQsKRUmLbthzrpt9rlS9oQDfqPXM3QqeQ9/1yzB9Wab9LexP90HiQKtKmkjX7g4r2i5gwmvAbRoAQ5H23jNwvfq2VL3RQFxq/eDtCUcheuIqMd35yuR8+vCcNHh8LUuubKdM/pWjnkcoEikK7pa9gzTCTNHFWrXyGLj0JvPMBUBTKV/5B2Y/fuNzXd0gk+OlZqJnpAFi2baTs2y+0unngCQzd+6Dm51Lw4G21cgHED+pE1xnjEYrCiYXrOPK2q25pcnVf2t03CgBrSRm7nvyMvMMpKEYDQ396DsVPj6LXkfLHDg7Nq972VaHv2AP/cfdp/WX9Esr/WORyX9emM0EPvYianaFx7t5E+a+OLUeBQQROegylQVNAUvrxPOwnDnvkC+jXnegn70HodBT8uJS8T2rWZQ2+foPMx2ZTvHITurgYYmc/ji46AlRJwQ9LyP/qF49c4YMTafbiJNApZH2zmtS3Xdf7R4zoQeOpY0FVkXY7p6Z9RuGOowijgQ4/z0DxMyD0Oky/b+XsvG89cgFsPpHJ3JUHUKXk6s5NmNS3tcv9z7cdZ8mhcwDYVZVTpkLWTrmCsAA/xzXJzZ+tpV6IP/Nv6Fs7X1Iac5fs1vi6JjDpkvaufJsOs+TA6YqyT2UXsPaJa8gtKWfqd5sq0qXmFjF5cCdu6dumVk5PeHb2a2zYvIPIiHB++er9v1UWQMTgRBJm3IbQKWR8vZqzb//icj9qRHeaPHFTxdxw4rnPKdhxFIBWr08mclg3rDn57B70aJ34/qruBDDe8AC6dt2RRfmUznuwWj53CBnYhYbP34nQKZgWrSTzXVe+sGE9iX9sHFJVwa5y7oWPKXboMl1oEI3m3k9Aq8YgJWcen0/JnmPVOFoN7MxVDrtspwe7rLWTXZb252kA+t9+GT1vHIKUkoxjZ/n+8fexlVu5+e0HiWkeD4B/aBBlBcUUjLm3Wrn/5rmh/qBO9HhR09PJC9dx6B1XPd3s6r50uPdKQNPT25/6nNzDKQTWj6T/m/fgHxMGquT412s5+snyWmUz9u5B2JT7ETqF4t+WULRgodt0hratifnobczPzaBs7QYAYn/6BllSgrSrYLeTPWlyrXz6jj3wH++YF9Ytofx3N/PCw07zwq5NlP+izQshr32NLCup0KvF06v3DU/QtemK/zV3glCwbluJZfUP1dO06IDx6jtB0SOLCyh9+6kL4mg2sBOXTtds+AOL1rH9Pdf2i0yI57J5dxHbvikb533PTicbHkAoggm/z6AoI5cfJ71aK5+3dZm33xl8+HfB5/j5L0IIUR94S0p5XS3pnpZSzvbSY1XnVwRXv3gbH90ym/wMEw/8NovDK3eTlVy5b7jNoESim8Uxd9DDNO7Sgqtn3c7bY54j+2Q6b1z+VEU5z25/l0PLdwJw3Zy7+GP215zcfoTu1w9i4F1XwhufupIrCvVfmMypCc9iyzDR/JfXKVy1nfLksxVJirfsJ3nVdgCMbZrSeP4TJA2bjD42iqiJo0gafi+y3EKj+U8QNuoS8n5c7VHWQTMn8vO4ORSlm7lp8YucXLkbc1JaRZqyvGLWT19A8xHdquUf+Px4zqw7wJJ73kIx6NAH1LJXXVGIefY+Uu94CltmDo2+nU/x2m1YT6RUSxf1yO2UbN7tcrng5xXkf/0b9eY87pnHqZz6L97DqfHPYcswkfDraxRUrc/N+0leqdWnf5umNHr7CZKGTsZyMpXkKx6qKKfNts8pWLHVI1f885M5M/FZrBk5NP/5dQpXb8Pi0nb7OLlqGwDG1k1pOP9JTgy/B31sFJETR3FixGRkuYUGbz1J6KiB5P+4qmY+IfC7chJlX8xCFpjwv3s2tqO7kdlO+9v9AzFeOYmyBS8h800Q5BpjRt/nMmR2GhjrEJ9FCPyGjqP8+9eQhbn43/Is9hP7kKb0yjTGAC3ND28gC80QGKJlDQ5H33UIZZ9NA5sVv1F3o2vTE/ufWzzwKRivvpvSD6cj800EPDQP2+EdyMzK+sQ/COM191D60fPIvBxEcJh2XbVjWfwpaupJMAYQOOVVbEn7XfNWhaIQO/1ezt72DNaMHJr++AZFq7dhOeHUflv3UbS6sv3qv/kUp0bejV/LJoTfMILT1z2MtFpp9MkMitbtxHomrSY2UBSazLqL42Ofx5Juot2SueSt2EFZ0rmKJAWbDpC3YgcAAW2bkPD+Yxwa+EDF/dg7rqQs6Ry6kDrEGFAUAu+eQuH0R1FN2YTO+wDLjs2oZ8+4JLMdPkDRzOqGZ/nqpZT98RNBU56unQtNt3SbfStrb3qJ0nQzw5fMIHX5HgqSKvtn8dlsVl07A2t+CfGDO9Nj7u2svHI6armVNdfPwlZSjtDrGPrLNNLX7MfkyaksFPwnPEjx3KlIczbBz7+Lde9W1LQq8h0/RMnrz1TLHjDufqwHd2J9+wXtRdhYN12WdudT2DJyaHhel510o8serqLLbHZyXvkQy5FkRGAADb97m5Ite6rndSqj+ew7+fPGF7Gkm+i09GXMK3ZSeryyr+RvPMh+x1wT2LYJrT58lH0DHkSWW/nzuudRS8oQeh0dfp1J7po9FO1JqlE0uyp5afl+3h/bj9jQAMZ9tpaBLeNJiKnUH7f2bsWtvVsBsD4pna92JFc4fQC+2ZlMs6gQimtxXmt8Ki/9vov3Jw7R+D5YzsA2DUmoF1bJ178dt/Zvp/EdPcdXW48RFmgkLNDId/deXlHO8Hm/MKRdo1o5a8OYy4dx87VX8fSMeX+7LBSFFi/dzsEbZlCebqbLspcwrdhFiVP75W48hGn5YwAEtW1M2w8fYdeAKQBkfruOtE+X0Xr+/XXj+zu6E7DuWo118x8Yx06ps3yNZt5N8rjpWNNNtF48j/yVOyhLquQr3HyA/JWaLvNv04Rm707lyJD7AGjw/B0UrtvD6XteRhj0KG7sCKEIxrx4Gx877LL73dhlrR122StOdtk7Y54jNDaCfreO5NWhj2ErtzLu7YfoPKoPu3/YwDf3v1WR/4pnbqGssITGbuT7t84NQhH0mjWRlWPnUJJu5vIlL3J2xW7ynWzAorPZLL9uJpb8EuoP7kTvlyexdNTzSJvKrhe+wXzoNPogf65cNoP0DQdd8rqTLfzRh8h56HHsWdnU+/Q9yjZuwXb6TLV0YffeRfn2XdWKyLnvEdT8Ao9yOQmI/8QHKX7ZMS+8+C7WPW7mhWOHKHmt+rwAUDz7UWRRHfmqcl93DyXvPYfMMxH4yGvYDm1HdR6HAUEYr5tM6fvPI/OyXcZh3SgEQ2dM5LtxcyjMMDPhtxdJXrUbUxUbfvX0BbR0Y8MDdJs0ElNyGsbgOtiB3tZl3n5n+JfBu1FI/5nwxfj5L0JKmVab08eBur1Z/IfQKLEFOWcyMJ/Nwm61s3/xVtoP7+6Spt3wbuz5aSMAKXuTCQgJJCQm3CVNi34dMJ3JJC9VC5oZ0zyek9u1LzJJmw7Q8bKe1bgDOrei/Ew61rOZSKuN/N83EDKst0sataSs4m8lwB/ptJZP6HQo/n6gUxABRmyZZo+yxiYmkH86k4KUbFSrneOLt9F8uOvkUGoqIPPASVSb3eW6X3AADXq25s9F67TnstqxFJR45PPv2BprShq2cxlgtVG0dB3BQ/pUSxc2bjTFKzdhN+W5XC/bfQh7fvVVUjUhsHNLLM71uXgDocN6uaRxrU8jLhXqQHC/zlo5qdk1cgV0boXlTBrWs5ps+b9vIGSoa9tJZ65Af5d1mEKvQzjaTgkwYsv0HOROadgC1ZyBzM0Cux37wS3o27j2U33HftiO7NCcPgDFlcaLCI1E36or1t1rPPJU8MU1Q+ZmIfNzQLVjO7oDXUKiK1/bXtiP79GcPuC6ok3oQO8HQkHo/ZBFeZ75GrdENWUgzZlgt2HbtxF9e9cxo+96CbaDW5F52hiTRfna/4W5mtMHoLwUNfMcSqjnAIX+nVzbr+CPDQQPde2bzu0nAvwr+ooxoRGl+48hy8rBrlKy4xAhwzyvcgjq0pLy0+mUp2h90/zrJiJGuMqneugvhvgowi/tRvZCD85BJ+hbtkXNSNVW89hsWDauwa9n/zrlBc0hJIvqPvYiuyRQdDqTYoduSfl1Gw2rGJ45u5Kw5ms6I2dPEoFOwcttJVowZcWgQzHo3I5LZ+iat0HNTEVmp4PdhnX7Wgxda19pAoB/IPrWHbGud3wJtdugxHPwaKOzLrNpuizInS67eTRFKzdhN+dVXLPnmLEc0ZxYsqQU68mz6GOja+QK7tKC0tMZFX0l59dNRI7o4ZLGta+46rHz94RBhzDoa13/fSjNTKOIIBpGBGHQKYxo15B1Sek1pl/65zlGtmtY8TuzoJSNyZlck9jUM9F5vnMmGkUG0zAyGINex4iOTVh39FyN6ZcePMPIjk2qXd9+MpOGEcHUD//7wWu7J3YkLDTkb5cDENKlBaWnMihLyUJabWT/spmoEa66uvpYr2yk/G1HsOYV1Znv7+hOAPXkYWRJ3fkCE1tSfjoDi6N/5i7eSNjw2nSZJp8SHEBwz/aYFq3UnsNqw15Qfew1SmyBqYpd1q6KXdZ+eDd212CXKTodBn8/FJ2CIcCPgszcahydrujNvt+qf4z4N88NUV0SKDydSZFDT5/+dRuNqujp7F1JWCr0dDJBDj1dmpWH+dBpAGzFZeQnpREY53me9WvXBtu5VOxp2jxUsmoN/pdUr4+g66+mdN0G7LnV2+lCoEuoMi9sW4uhWx3nhb8JpUlL1Jx0pMkxDvduQN/R1f40dB2I7cBWZJ5mWzqPw7ogPjGBvNOZ5J/V2u/I4m20GObafiWmAjIOnES12qvlD46LJGFIIgccdnxt8LYu8/Y7gw//PvyjV/wIISYAj6Gp8ANSyvFCiCbAp0AMkA3cJqVMEUJ8DhQA3YE4YKqU8gdHOVOB8WjOvqVSyieFEHcCdwF+QLLjvgHYDzSXUqpCiEDgGNAcaAy84+AtAe6UUh6t8rzPAwlAA6ARMFdK+ZEQQgBzgcscssyUUn4rhGgK/C6l7CCEuBW4Cgh0lPGzlHKqEGIOECCE2Af86Xjm74CGgA6YIaV0u07dkfcqwAaskFI+JoSIAd53yAMwRUq52VM7hMVGkJ9W+dKdn26iUWKLKmkiyXNKk5dhJiwuksLsvIpriaP6uhgRGcfP0W5YNw6v3E2ny3sTHh9FVTeCIS4Ka3rlVVt6DgGJramKkOF9iHt8ArqocM7c/oKWNtNEzsc/02rTZ8gyC0Wb9lK0aa8nUQmOi6AwrdI5VJRuJi4xwWOe8whtHEOpuZBhr95FdNvGZB08zfrnF2Arrfn0G11sFNYMJ/kycjB2cl2Sr6sXRfDQvqTe9gT1OrSq07PUBH1cFNb0ytNqrBkmAhOrlxk6vDexUyeijwrjzKQXqt0Pu3IA+Ys3eOaKdeWyZeQQ0Nl929V7bCL6qHBS7nheS5tpwvTxT7Ta+DlqmYXiTXsorqXtREhkpUMHkAVmlIau/VSJjgdFh/9t08Doj23rUmz7NcPY77KJWJZ/XbfVPoAIiUAWVhphsigXJb65a5qIWFB0GG98HGHwx7pnFfbDW5FFedh2LSfgrpfBZsV++k/UM5630YiwqIqXEgCZZ0Jp4tp2SnR90OkJmDwTjAFYN/6ObffaKs9UD6VBc+wpxz3yGWKjsGXU3n7Bw/oQ8+it6CPDOXvXdADKk84Q8/BElPAQZJmF4IHdKTtU84oKAL+4SCxplXyWdBNBXar3zfCRvWj41C0YosI47rRkv/ELkzg78wt0dflKB4ioaOw5WRW/VVM2+lZtq6XTt25P6BufoJpNlH72Lvazp+tUflUExkVS4qQjS9LNRHWtWbckjB1E+tr9lc+rCEYsn0Vw01iSPl+Jae8Jj3wiIhpprtQtqjkbXUJ1+XQt2hE840PUPBNli95HTT2DUi8etTCfgDumomvcHPvpJEq/egcsZdXyn4e+XhQ2Z12WmYN/x+q6LOjSvqTd/gQxNegyff1Y/NomUHbgqNv7AMa4SCypzn3FTHCXltXSRV7Wk8ZP34IhKpQj450WzSoKnZfPxb9ZHBmfLaNor+e+mVVYRlxoZb+KDQngYJr7F7BSq40tJzN5akTnimuvrDzAlCHtKbZ43spZyVdKXFilsyY2NJCD59yfMlZqsbElOZ2nruhe7d7yg2e4rFN1h9B/G8b4SMqdxkJ5upmQrtXbL+qynjR7+mYM0WEcuuWlv8x3sXRnXeEXF1Vdl7mZZ8NG9Kb+E+PRR4dx4tYZABgbx2Ez59P41QcJaNuMkoMnSH3+I9QqdkRYbISLzZWfbqJxFbssNDbS1XbLMBMaF0nqwZNs+Oh3ntryNtYyC0kbD5C08aBL3mY921CUk4/pdIZmJTvh3zw3BMZFUOxkA5akm4nuUrOebnHTIFLXHqh2PahhNJEdmpBTi55WYqKxZ1XOQ/asHPzat62WJmBgf3Luf5TwtlXqWUqi3nwFpKT4l8WU/PqHR74LmhdmfYiaa6JsoTYvOAgJemIuSEn52t+xrvXM5yJHWBSq02mJap4JXdVxWK8+KHoC7p+NMAZg2fAbtp11H4fBcREUple2X2G6mfoe2q8qLp1+C+tmL8SvjnaEt3WZt98Z/m1Qhe9Ur3/sih8hRHvgGWCIlLIz4NhjwtvAl1LKTsDXwFtO2eKB/sCVwBxHOZcBY4BejnLOB+74SUrZw3HtCHC7lDIfzfEz0JFmFLBcSmkFPgQekFJ2Q3NGvVvDo3cCrgD6ANMc27muARKBzsBQ4BUhRLybvInAjUBH4EYhRCMp5ZNAqZQyUUo5DhgJpEkpO0spOwDLaqi/SOBqoL2jrmY6br0JvC6l7AFcC3xcgxzOhVW/VvULqZs00smrrTPoaDe0GweWbK+49v3UD+g7fjgPLp6FMTgAm7VuRrG7L92FK7aSNGwyKXfPJPaRWwBQQoMIGdqL4wNv52ifCSgBRsJGD/Jctls56vZYil5HvQ5NObBgNQsvfxZraTnd7x11wXxVKzfmqXvIefUTUC/CIsVa2uk8ClZsI2noZM7cPauiPiuKMOgJHdqL/CUe/YU1yFYdhSu2cmL4PZy9ZwYxD2tHDCuhwYQM7U3SoEkc7zseEehP2OjBtfC5uVZVNkWHUr85ZV+9TNmXL2EYdA0iKh5dq67I4nzU9FN1euYa4Y4vtgnlP71J2Y+vY+hzpeYMMgaia5FI6UdPUvr+Y2Awomvb232ZF8Kn06FrmEDpJzMo/fB5/IbegIiuX3nfzx//iU9Q/uvHUF7quWy34756XylauZVTI+/m3L0ziJmitZ/lxFlMH31P489m0eiTGZQdPYW0Vf+69lf48pZt59DAB0i6fY4W0wEIG9odW04+JQcv5Ajn2vWa7cRx8u68kYIpt1P+x48EP1173KALoatJudTr247mYwexb1Zl7AWpSpYNe5pfuz1AVGICYa0bus17IXz200kUPjKWoufuwrLyZwIf1OJdCUWHrklLLGt+o2jaPcjyMoxX3lQLX+3tF/3EPZher1mXiQB/4l5/DtPL7yOLPayWrGNfMS/dwb4BD3Js0lwt3s95qCr7hz3Grq53EdylJYGtPW+FctdKNWm3DUkZJDaMqtjmtSEpnYggI+3iIzxyuPC5IRQ16NMNx1JJbBRNWKDrdiCrzc76Y6kMa19to85/H3UcC6alO9g1YAqHb5tL0yduvLjPcKG680JQR/nyl2/jyJD7OHnHbOo/Nk67qNcR2CGBnAXLOHb5w6ilZcTee60bjjrYKjWMk4DQINoN687LAx5kVq978Qs00mWM62rHzlf1dbvax1O5VfG/ODe4HWc12ICxfdvSYuxA9sx2jZGjDzQy6KOH2Dn9K6xFf3+eDZ9yH/nvfOhWb2bf/SDZt96N6ZEnCb52DH6JnWrhc3PN3bzw8FiKnnHMC1Mq4yAWvfgQRc/dQ/G8pzAOHY2udUfPfLWRu7GZdI0SKP3wBUrfn45x+E2ImLqPQ+GGo642fMKQREpMBWQ6Vm3VkbA6/pO6zNvvDD786/BPXvEzBPhBSpkDIKU878Ltg+ZIAVhApSMH4BcppQocFkLEOq4NBT6TUpZUKaeDEGImEA4EA+cjsH2L5nxZC9wEvCuECAb6At87TQo1BTz4VUpZCpQKIdYCPdGcUQullHYgUwixHugBVP1MsNrhfEIIcRhoAlQNwnEQmCeEeBlttdDGGp6jACgDPhZC/AGcj+Q6FGjnJEeoECJESumy9k8IcRfa6iIeGn4bPW+qfOkOi4+iIMv1a2d+honw+lEVv8PjIl2WDrcelEjqoVMU5VQu28w+kcbHEzTPd3SzONoMTqwmhDXDhCE+puK3Pj4aa1bN27VKdv6JX+M4dBGhBPXuiPVcJnaztp2nYPlWAru1Jf/XdTXmL0o3E1K/cmlucHwkxVVk9ZS3KN1M5j7tC0/ykh10n+zZ8WPPyMEQ5yRfXDT2LNctTcb2rYh7VYsxoosII/CSnmC3U7zaQ3ydGmBLz8EQX7mFwhAX5XH7W8mOP/FrEo8uIhR7rlaPwYO6UfrnCWw5eZ65Mly59HHRWD1s13Juu8DenbCcrWy7wuVbCOjalvxfa/7yIwvMiLDKPihCI11W5GhpTNhLCsFaDtZy7KePosQ1Rolvhq51NwJadgG9AWEMwHjtfZT/+E7NfIW5iJDKlzkRHFFtu5YszEUtLQKrBawW1HPHUWK0F3aZnwOl2hJfe9IelAYJ2I9sq5kv34QIr6xPER6FLHBtO5lnwl5cAJZysJRjP/knSv2m2HPStJVOE5/Etmc99kM185yHNSMHfVyV9vMw9kp3HcLQqLKv5P+wgvwfVgAQ/chEly/E7mBJN+FXv5LPLz4Kq4e+WbT9MP5N4tBHhBDSvQ3hw3sQNqQbitGAEhJI87emcPLBN2rML03Z6KLrVfxWomJQzVWesbTS+WDdvR3u1iFCwpCFF7b8HLQvx4FOOjIwPpLSjLxq6cLbNqLnvDtYf8tcLLnVl4BbC0rI2nqE+MGdyD9W8/Yfac5BRFbqFiUyBplXZfyVVcpnO7ADMeEhRHAoam420pyN/aS26sa6cwPGKzw7fmyZOeiddVlsNLbs6ros9pVKXRY0oCfSbqdkzVbQ64h74zkK/1hD8SrPTuXydBN+DZz7SiQWD32lYNth/JvGoo8MwWaunOrsBSXkbzlE+OAulByrOd5VbIg/GQWVL3CZhaXEhPi7Tbvs8DlGtq90yu07Z2Z9UjqbTmRisdkpLrfx9K+7mD26+gqdCr7QADLyK7f3ZBaUEBPi/gv0skNnGNmpabXrm5LSaRMfQVQdv1x7E+VpZoxOY8EYH4klo+b2y992hICmcdXar67427rzAuFWl3nQncU7DjvmvhCs6TlY0nMo2aetyMxbsoXYydUdP/kZZheby51dVpBhIsw5jcMua9G/A7lnsyh21OWhZTtp0q0Ve3/RAsAqOoUOI3oyf5T7KAP/5rmhON1MkJMNGBgfSYmbbXDhbRvR95U7WDX+Fcqd9LTQ6xj00UOc/HkLKUurx+OpCjUrG129ynlIVy8ae45rfRjatCJyxnMAKGFh+PfpRZ7dTtmGzag5mo5Vc/MoXb8Jv3ZtsOyrvgLpPC54Xti/AzFRmxdkUUFFWlmQh3XXJnQJbbAfc10tVqOs+TkYIirbUalhHNoc41BayrGdOISufjNs2XUbh4UZZkKctkiHxEdS5Kb93KFB91a0GNqV5oM6ozMaMIYEcMUbk/ljyns15vG2LvP2O8O/Db5Tvf7BK37Q/Kh1aSPnNM5rYYXT/+7K+Ry4X0rZEXgBOG/F/QZc5lgx0w1Yg1ZPeY5VN+f/VV8bWf15zv+u69oy5+e348YxJ6U87niug8BLQohpbh9CShua0+lHtBVP51cGKUAfJzkaVHX6OPJ/KKXsLqXs7p9cTnTTOCIaxqAz6Og8qg+HV7oGDDu8cg9drxkAQOMuLSgtLHHd5nVVX/Ytdv16FBSlBcYUQnDp/Vez7evqQZdLDxzH2LQ+hoaxCIOesCsvoXDVdpc0fk0qF0/5t09AGAzYcwuwpmUTkNga4a/56IL6dnYJYuwOmftPEt4sjtBGMSgGHa1G9ebkyj0e85xHSXY+helmwh0nYjTq1x6zU+BWdyg7dAxDkwboG8SCQU/wZYMoXuv6Un5m+ETODNP+FS3fSPaM+X9ZgZccSHKtz1GXULBqh0ua6vWpr3D6AISPuoT839bXylV64Dh+TRtgaKjJFnblJRStdm07Qw1cNndtd8Jz26mpJ1Ai4xDhMdrX2459sR117ae2I7vQNWkDigIGP3QNW6Bmp2JdtYjSV++j9PUHKP/+Leyn/vTo9AFQM04jImIRYdGg6NC36Yn9xH6XNPbkfSgNWoJQQO+HEt8c1ZyubUOLb67F+AF0Tdq6BoV2x3c2CSU6HhFZD3R69IkDsP/p2na2P7ejNGtXIZ/SpBUyS3MOGG94ADXzLNYNNZ/C5oyyg8fxc/QVDHpCr7ikIljneRgaV7afsV0Cwq+yr+gitaCM+vgYQob3peB3z32meF8Sxmbx+DWqhzDoiRzdn9wVO13SGJvGVfwd2KE5wqDHllvIuTlfsb/7nRzofTcn7n2Vws0HPTp9AGxJR1HiG6LUiwO9Hr8BQ7DucHU4iPBKA1LXUus3f8XpA2Ded5KQZnEEOXRL49G9ObfCtX8GNoii/8dT2PbgexSezKiUOzIEQ6gWlFTnbyB2QHsKkj33F/upo+hiGyCi40Cnx9BrMNa9rjpYhFU6LnXNW4MiNOM+PxfVnI0Spzkw9O26VAv+WRXlh45haOzQZXr3uixl5ERSRmj/ilZsJHvmfM3pA9R78REsJ8+S/+VP7op3QdG+ZAKaxWN09JXo0f0xL3d9yfJ36itBHZtpfcVciD4qFJ2jLhV/P8Iv6URpsmc93b5+BCm5RaTmFWO1qyw/fI6BLasv2i0ss7I7JYfBTvceHNyeFQ9cxtL7RjBnTA96NI326PQBaN8gihRzIam5RVhtdpYfPMPANg3c8FnYfTqLwW2qr/5advC027g//wQU7ksmoHk8/o219osZ0w/TiprbL9ip/f4K/q7uvFCU7HfVZRGjBlQEcj4PvyaV8gV0aO7QnYXYsvOwpudgbK61d0i/Ti5Boc/j3P4TRFWxy464scu6OdllZQ67LC8th8ZdWmLw1+afFv06uASFbtG/I9kn08iv4QX23zw3mBx6Otihp5uO7s3ZFa42YFD9KAZ9NIVND73voqcB+r56B3nJaRz5sPoJn+5gOXIUfaMG6OK1eShw6BDKNrrad5nXjiPzmpvJvOZmSteuJ2/em5Rt2Izw90cEao5d4e+PsVd3rCc9r1q2nzyKLq4BIsYxL/QejHVPLfOC0OYFjP7g73AkG/3Rd+yOegFbn9WUJJTo+ojIWG0cdrkE26Eq4/DQNnTN2zvGoRFdk9auwZ9rQfr+k0Q0iyPM0X5tR/UmuY42/Ia53/Fe7wf5oP/DLH7gHVK2HPbo9AHv6zJvvzP48O/DP3nFz2rgZyHE61JKkxAi0rFaZwvaSpwFwDhgk6dCgBVoW66+kVKWOJUTAqQLIQyOclIBpJRFQogdaFuifnes0ikQQpwSQlwvpfzeEbOnk5Ryvxu+0UKIl4AgYBDwJFosnruFEF8AkcAlwONUOptqg1UIYZBSWh1bx8xSyq+EEEXAre4yOFYpBUoplwghtqHFMTpfH/cDrzjSJUop93kiV+0qv077nDu+fApFp7Dzu3VkJp2j97ihAGz7ehVH1+6lzeBEnlj/BpbScr5//IOK/AZ/P1r278hPT7vuKku8qi99xw8H4NDyHez6fh3tqroi7Sppz79P0y9eRCgKud+vpDwphYibLwMg95ulhI7sS/jVQ5A2O7LMwtkHXwagdP9xCpZtpsXiN5A2lbLDJ8hd5HZnXAWkXWXdc18wZsFUhE7h8LfrMR9PpeMtQwA4+NUaAmPCuOn3GdoeYFUl8faRfHXpE1iKSlk37QtGvjUZnUFPfkoWKx/70CMfdpXsWe9Q/6PZCEWh4OcVWJLPEHrjFQAUfOt5/3TsK08S0LMTuvAwmq75CtPbCyj8ycPxoXaVtOnv0+zLF7Tj3L9fRXlSCpE3jwTA/M0yQkf2JeKaIUibDVlmIeWBykV1wt9IcP9EUp/x7BQ5z5Xxwns0/nwGQlHI+8HRdmMdbbdwKaEj+hF29RCw2VHLyjlX0XbHKFy2mea/vYm02yn78yR5i2oxpFQVyx+f4T/haVAUbHvWIrPPoe+u9VPbrlXInDTsSfsIuFfbo27ds+YvG/dIFcvqbzBeO0XjO7gZaUpD31nbKWrbvx5pTsd++hD+tz4PUmI7sBGZk4YE7Md34z/+OZAqamYKtgOeYyahqpT//CEBdz6vHYW6czVq5ln0fbS2s21dhsw6h/3YXgIffQspVWzbV6JmpKA0bYuh+2DsaacJePh1ACxLv8JexTHmArtK5ovv0eiTmaBTyP9hBZbkFMJv0k4Mylu0hJAR/Qgbc2lFX0mbMqcie4O3n0EXHoq02ch84V3UgloCGNpVUp79iNbfTAdFIefb1ZQdP0vM+BEAZC9YTsTlfYi+bhDSZkcts3Bicu1HrdZcn3ZKPnyDkOfnace5r16C/expjCOvAqB82W/49R2I8bLRYLeDpZzieZXxroIenYahQyIiNIzwT76nZOFnWFYtqYkNaVfZ9cznDPrmCYRO4eSi9RQcT6XF+EsBSF6wmg4PX40xIoTuL2nHw6s2Oysue46A2HB6v3kPQlFAEaQs3k7aKs8xr1BVShfMJ+jxl7VjezcsRU09g99g7Rhiy9rfMfS4BL8hV4HdjrSUU/LuzIrspV/NJ+CepxF6A2pWOiUfz62JSYNdJWf2O8R/MBuh03SZ9cQZQm9w6LLvatZl/l3aE3LVUMqPn6ThD9ouavObn1Gycaf7DHaVk09/TLuFzyF0CpmL1lB6/CyxE7T5JPPLFURd0ZuY6wchrTbUMgvH73kNAL96EbR4836ETodQBDm/bSF3lYdxAOgVhSeHd2byos2oKozu3IQWMaF8v0d7ybq+azMA1hxPo0+zegT4/T3TSq9TePKK7kz+ci2qKhndtTkt6oXz/U4tFsr1PbQYEmuOnKNPQlw1vlKLjW0nMnj2quoHJvxVPD59Djv3HiAvr4BLx9zCvbeP59pRI/5aYXaV5Kc/ocPCZ7QjkBeupeTYOeInDAMg/cuVRF/Zi9jrByKt2lg/cvfrFdnbvPcQYX3bY4gModee9znzyndkLPQQlP9v6E4A47hH0SV0QASFEvjsJ1hWLMS2w0OgYLvKuec+JGHB89px7g5dFnWLxmf6ahnhl/cl8trBSKumO0/f90pF9nPTPqLpW48gDHrKUzJIeeytahTn7bLbq9hlvRx22XaHXdZ6cCJTq9hlZ/ed4ODS7Tz4x2xUm0ran6fZvrDyw1vnUX1q3ublkO/fOjdIu8qOZ79g6DdTtePcv11P/vFUWo3XbMDjC9bQ6eGrMUYE02v2rYCmp5dcPo16PVqRcN0Acg+ncOUKbVvw3jnfkbrG3atCpWx5r84n+o2XQdFR/PtSbKdOE3i1tlq85OfFNWZVIiOImuPYhqXTUbJiNeXbatCZ56GqlH7pZl4Y4pgX1jjmhUuvAtV1XhChEQRNccyBig7r1tXYDtbCV4W77Mf3CbxHsz+t21ehZqRg6KuNC+uWZaiZ57Ad2U3g1PmajbZtRcU4rAukXWXVtC+4/kvNhj/43XpMSakkjtPab9/XawiKCWPCYs2Gl6pK90kj+WSoZsNfMLyty7z9zvAvg2/zGwh38T3+KRBCTERzkNiBvVLKWx0BkT8Foqke3Pl3p4DORVLKYMffTwITAAuwREr5tBBiMjAVOIO2eiZESnmrI/11wPfAICnlese1ZsB7aHGEDMAiKWXlxlcqgjvXRwvO3JgLD+7cXUp5v6Os34F5Usp1jm1dVwF7gC/RnDYqYAUmSymrrSd1xBD6Fc25JBxlfSGEiEYLUt0WzfG3QUp5j6d2mNp0rNc6yQTFu9HmV9vCvcp3RXDNp2D9J1Ba6ld7oosIneJdtdpk3MU5ZaauEKHBXuVTM7zbX879VvtR0xcThcW1HBF+EZHQ3fOJfhcby3fWEoPnIuPyYRm1J7qIyNnt3e9GmSbvjr0uc6oH6PyP4vyXdC9Bf+l4r/Jt7fCEV/m63mLxGlfSd16jAmCR4vlI8ouN2/z+2irHvwpvzgsAR/j7p95dCC5tfOHbB/8Oglt4N6CtEuXd/vnezxd25PvfRe/ymg9sudioH1lQe6KLiBaHl/+rox9/Gz/Oa++zN6Z//Y+sy3/yih+klF8AX1S5dhot/k/VtLdW+R3s9PccHMGena69h+bIccf7A1W2Z0kpT6EFVq4Nx6WUd1XJK9EcWI9XuX4a6OD4+3O07Wfn713p9PcTgLPVVKt7VkqZjrbVq+r1HLQYRj744IMPPvjggw8++OCDDz748K+G+o90xXgX/+QYPz744IMPPvjggw8++OCDDz744IMPPvwN/KNX/PyvQUr5/H+DVwjxM9CsyuUnpJT/fzZu+uCDDz744IMPPvjggw8++OBDFah1Pmvp3wuf4+dfACnl1f/tZ/DBBx988MEHH3zwwQcffPDBBx/+efBt9fLBBx988MEHH3zwwQcffPDBBx98+JfiH32qlw//DNzYZIzXOkmazbunejU3hHuV75yX5dMJ7/p2x6rRXuXz97L+ytV5d5loqs67p6RNjvXuyVAZqaFe4zpl9+7JLV2jcrzKdzgn0qt8h4w6r/LdEOLdE+6sFu/Kl1Xg3f7pbfQ59LJX+Y50f8hrXLdbvHvqVZjOuyfAlas2r/KpeHde/z/2zjs+qmL9/+/Z3fTeEyD0XhN6J6GL0rEACggo0qQjvRdBBJUqoCKCCIgFlBZ67zXUhNDTe0+2nN8fZ0l2s5vivd5cf/e7n9eLF5tznpnPzDPtmTkzz5RXld44BOAtbEuVL7eUL7GOkbJLlW+xdenWz+/UpXeL2ItS1uWPT3/9nz4Lta3Mu6XWubwbue0fqUvLjh8LLLDAAgsssMACCyywwAILLLDAgv9RWHz8WGCBBRZYYIEFFlhggQUWWGCBBf+TsFznbln4+UdBCNELeChJ0t3/dlrMYci84QQGNyInK4f1k7/icWiEiYyXvzfjVk/G0dWRx6ERrJnwBVq1hjJVyjJyxVgq1anCTyu28cfG3wHwq1yG8Wum5Icv7803K7awe/MvhaZj3ILRNG/fjJysHJZMWM7D0DATmT5DevLm8L6Uq1SWN+r2JiUp1eh906AmjFswGhulipM7j/Ln+l9N4hg4dygNghuSm5XLpsmreXrnMQD12gUwcM5QFEqFUVj/WhUYsngENva2xL+IY8P4L8hOz8LB1ZGx66dQqX4VDu4+zOpZawEYvWAUzdo3IScrh+UTVhAWGm6SBl9/X2atm4GTqxNht8P4dNxyNGoNDk72TP9qGt5lvVAqlez6+mcO7TqMl58X076cgpuXO5JOx/4fD/Dbt78zav5Imui5Vkz8nHCzXD7MWDtd5goNZ/m4z9CoNbw5oh/tewcDoFQp8a/qz1sBb2NrZ8uUL6bg7uWGTiex/8f9sPlsoeUGUDaoPs0WvIdQKHi44wS31+4zel+5d0vqjXoDAE1mNuembyHp7rMi4zSEX1B9Gi6U43+04wT31hjHX6F3S2qP7g6AOjObK9O+I/nuMxQ2VnT8ZTYKaxUKlZJnf14idMWeYvn8g+rTet57KJQK7u44wfV1xnyuVfxo//mHeNWtyMXPdnPj6/157+oP70rtd4KQkEi8/4JjkzaizVGbcFRrV5/X5wxCoVRwZedxTq3fZyLz+txB1AgOQJ2Vy57JG4i88wSAFu93pck7wSAEV346xrlvDwLQfnxfmrwTTEai3C4OL9/FwxM3TOK1ad4E14ljEAoFGXv3k7Z1h1k9WNWqgfc3a0ictZCsY6cA8P31R6TMTCSdDrRaYoeMLFafzkGBlJ8/HJQK4neEEL3WuB9w7dyUMlMGgE5C0mh5Pu8b0i/fA6De+Y1oM7JAq0PSaLn3+uRi+XyC6xO44D2EUkHEjyd4UKC+lO/Tkhr6+qLJyObatO9IMayPCkHHg4vIik7i7KAVxfLZtWqM57SPEEolqXsOkPzNLrNyNnWrU3b7F8RMXkJGyBkAvBZOxKFtM7SJyTzvPaJYLgCv4AbUXTgIoVTwbPtxwtfsNXpftk8rqo7pkZe/2598Q+rdZzhU8aPR1x/nydlX8ObB8p95vOlAkXyV29Wn41y5Pdz46QQXCtRV9yp+vLHiQ3zqVOTkit1c2ii3B6WNFe/umoVS3/4e7L/E6VWFjwFQ+rp0aNMI75kjEEoFybsPkbhxt9F7xw7N8Rz3Hkg6JI2O2CVfk3VVHsrdBvXE9a0uIATJuw6S9P3vxfK5BQdQZeH7CKWC6O1Heb7mN6P3Hl0aU+GTd+S2oNXyaPYWUi/dB6D6qpG4d2qEOj6Fq0GTSpS/0uYrDrOWrOTU2Uu4u7ny27YN/3Z8ju0aUnbOB6BUkLgzhLj1Pxu9d+7UDJ+JA0GS+5bIBZvJvHIXm8plKb9map6ctb8vMau2E//t3oIUJpi08GNatW9OdlYO8ycs5cHthyYyb77fh/7D++FfqRwd63YnJVE+Nta2S2s+mjIMSdKh0WhZOXc1Ny/dNgk/ev5ImrZvSk5WNsuLGNtnrpXtiPDQ8Dw74q0R/Wjfuz0gj+3lq/rTL+BtXNxdmLVuRl54v/K+pWKXFYfxC8bQon0zsrOyWVwIR98hvXhLz9Gtbq88jvJV/Jm5airV61Zj47Jv2fG1aX8xeN5wAoIbkau3cZ8UYuN+vHoyDq6OPAmNYK2BjTtCb+PuXLGNP/U2rpWNFXN2LcbK2gqlSsnF/ec49cVvRnHWbNeAPnMGo1AquLDzGEfWm9atPnMHUzs4EHVWDtsnr+eFfoy3c7bnnU9H4FejHJIEO6Zu4Mk1U73UbteAt+bI7fvszqMcXm/aB701933qBAeSm5XD1snreH7nMSobKybtnI/KRoVCqeT6gQv8sUru+4atGY9P5TIA2Dvbk5mayZJuU03iBRg27wMaBjcmJyuHNZO/IMKMbr39fZi4ejKOrk48Dn3ElxNWoVFraNKpGf0nDUTS6dBqtXw7fzP3r9wzywNyX+0zS99X7zpEgpm+2mt8fl8ds9igrx4s99VCCJJ2HSRpi/m+unq7BvScI4+tl3Ye54SZMusxdzA19XbZrsnreXnnCV6V/Ri4Jn9sdff35vCqnznz7QH8alegz+JhWNlYodPo+HX2t7Tv1TyvTm6YvLrQOjl29aS8ede6CV+iVcvH3wbNG2Y2vL2zPR8sG41/9fJIwMYpawi79gBgHvAB8Ops9Qxgf0FOC/7/hWXh55+FXsAfgMnCjxBCJUlS6R5kNUBAcCN8K/kxrt1IqgVWZ9iij5jVy7SDHzhtMPu/2cu5fWcYvvgj2r/dkZBtB0lPTmfL3M007tLMSD4qIpJPuk0AQCgUrL24mVMHzhSajubtm1KuUjn6tx5E7Ya1mLR0HCO6jzGRu335DueOXOCrn1eavFMoFExc/DET+k/FMV7NvL3LuB5ymcjwF3ky9YMa4lvJj6lBY6gSWI3Biz9kQa/pCIWCQQs+YPm7C0iMTjAKO/TTUfy05HseXLxLmzfb0+3Dnvyy8ifUOWr2fL6DcjXK41rNF4Cm7ZtQrlJZBrV+n1oNazJu6ceM6f6xSVo/mDGMPZt+4fjeE4xf+jGvvdOVfT/8Qc/BPXga9pRZ78/Bxd2FLae+4eivx9BqtWxYsJGw0HDsHOzYcGAt6lw1ZSuV4f02Q6kZWJOPl4zh4x7jTbiGTR/GL5t/5cTek3y8ZCxd3+nCHz/8ye6vf2b317KR3LxjM/oM701acjpW1tZsXLiJcD3X2v2ruXTyMSlhkWbLTigEzRcP5lD/T8mMSqT7/gU8O3zVSD79eRwH+i0iNyWTssH1abVsKH90n1dofSgYf6MlQzj+zlKyohLpvH8hLw9dIzXsZZ5MxvM4jvRdiDolE7/gBjRZPoyQN+aiy1Fz7M3FaDJzEColHX+bQ9SxmyRcMzWiDfnaLhrMvgGfkh6VSL8/FvAk5CpJBvnJSc7gzNwfqNSlkVFYB1836r/fmR0dPkGbrabzurFU7dGcB7tPm3B0X/A+3727lNToBEbuXcS9kGvEhefnqXpQAJ6VfFkZNBH/wKr0WDyUDb3m4F29HE3eCWZ9z9lo1RoGfz+NB8dukPBE9uNz9psDnNn0Z+EKVShwmzKOuLFT0MbG4b1lPVmnz6F5/NREzmXMh2RfvGISRdyoiehSSmjcKxSUXzSChwPmoo5KoNafn5F8+BLZYfntMvXMLZIPXwLArlYFKq+fwp2g/Pb/8M1ZaJJK6EdLIWi4ZAin3l5KZlQiHQ8sJPLwNdIeGtSXZ3Gc6CPXF9/2DWj02TCOvT437321D7qSFhaJyqkEPjcUCrxmjSbyg+loouMpt3M1GccvoI54ZiLnMWEYmWevGj1O++0wKT/uxWfJFEoEhaDe0ve58NYSsqISaHNwMdGHr5JukL/MZ7Gc670AdUoG3u0bUH/FB5zpNpuMR1Gc6jg9L55ON9YRfeBykXRCIei8cDA/DfyU1OhEhuxdQNiRqyQYtIfs5AxC5v5AtQLtQZuj5sf+S1Bn5qBQKXnv59k8OnGTyOuPCslbaetSgc/cUTx/fybq6Hgq7vmC9KMXyH30PE8k4/wN0o9eAMCmRkXKfDmdx11HYF2tAq5vdeFJvwlIajX+3ywk/cRl1E/N95Ov+KouHcbttxaSE5VI4MGlJBy+QubD/LaQdDqUhEPy4qZDrfLU2jiRK23GAxCz8wSR3x6kxmrTsfEfwVcC9OrWiQF9ezBjYfELqsVCoaDsgo94/O5s1NEJVN27ktSQi+SE55df+tmbpIZcBMC2ZkXKr/2Ehx1GkhPxkrBu4/LiqXVxCymHzhdL2bJ9c8pXKkefVgOo27A205ZO5P03PjKRu3n5NmdCzrFhz5dGzy+fvsqpQ7ItVLVWZZZ+PZ83275nJNM0uAllK5VlcJv3qRVYk3FLxjK2h6lvow+mD2fP5l84sfck45bk2xG7vv6ZXQZje9/hfUhLTiMtOY2Puo7SZ1nBjsvb/+N2WXFo0b4Z5SqV5e3W71GnYS0mLx3Ph91Hm8jduhzK2SPnWfPzKqPnqclprJq9hrZdW5mN/5WNO6HdSKrqbdzZZmzcAXob9/y+Mwxb/BHBb3fkiN7G/d6MjavOUbOo/xxyMrNRqpTM+3kpT0+G8vS6bFsIheDNBUNZ9+5ikqMTmLR3CbdDrhJjMMbXDgrAq5Ifi4LGUyGwKm8uHs6qXrMAeUHo3skbfDdqFUorJdZ2NiZpFgrBOwuG8dW7i0iKTmDa3qXcCrlCtAFHnaBAvCv5MjfoYyoFVqP/4uEs7zUTTY6aLwbMJ0ffN0/+eQF3Ttzg8fUwvhnzRV74vjPfIyst06xuGwY3wq9SGUa3G0H1wBp8uGgk03qZ9r3vTRvMvm/2cnbfaUYsHkmHtztxaNsBbp+9yWV926xQsyKT1k7l4w6jzHKhUOA7bxTPhsh9daU9X5B27AK54YX31WW/nE5E1xHYvOqr+8p9dflvFpJ+3LSvFgpB7wXvs+ndJaREJzB272Luhlwl1kCfNfV22fKgCZQPrErvxcNY02s2cRFRfNFtel48sy6uI/SQPLa+Pm0AR77cw4MTN6kZFEC/ZR8SEx3PxHajqBpYnaGLRjCn1ycmWe4/bRAHvtnH+X1nGLr4I4Lf7sCRbYcICG6Ib6UyZsMPmjucmyev8+XIz1BaqbCxszaMchXwN3S8/zyUrrerfyb+T/v4EUI4CCH+FELcFEKECiHeFkL8avC+kxDiF/3vdCHEMiHEVSHEESFEUyHECSFEhBCih15miBDiNyHEPiHEYyHEGCHERCHEdSHEBSGEu16uihDioD6u00KImkKIlkAP4DMhxA29zAkhxBIhxElgpj5OK30czkKIJ6/+NpO3j4UQd4UQt4QQPxnk91shxGV9mnqWVFdNOjXl1J4TAIRdf4iDswOu3m4mcnVa1uPC/nMAnNxznCad5UEwNSGFR7fC0aq1hXLUa1WfyKeRxLyMLVSmdZdWHPz5MAB3r93D0cURD29Tp6Zhd8KJfhFjNo5agTV5+eQlUc+i0Ko1XNx3hoadmxjJNOzchLO/nATg0fUw7J0ccPFypXJAVWKeRhP3PMYkrF/lMjy4KK/Z3Tlzk8avNQcgNyuHsCv3URvs5mjVuSWHfw4B4N61+zg6O+BuJh+BrQI4+ae8g+Lw7hBadWkJgCSBnYM9AHYOdqQlp6HVaEmMTczbOZSVkcWz8Oc079iMkD1HAbh//T4Ozo5muQJaNeDUn/LCQ8jPR2ip5zJEUM8gjv9+AoDE2MS8r4uvuBx8C3cw6xlYhbQnMaQ/i0On1hLx+wXKF5gAxl4JIzdFNh7iroVj71dyh7XugVVIfxJDhj7+Z79foFyB+OOvhKHWxx9/Lcwofk1mDgAKKyUKK6Ws5CLgHVCFlCcxpOr5wvdeoFJnY76shFRib0agM1PvFSolKltrhFKBys6azJgkE5lyAVVJfBpD0vNYtGott/adp1YBjlqdG3H9F7ncnl8Px9bJHicvV7yrluX59XDU2bnotDqeXLxH7S6Ni8yTIaxr10Tz4iXayCjQaMgKOYZdW9M64fhWb7KOn0KXaJr+vwKHgGrkPIki91kMklpD4u9ncO1sbETrMvMdHSrsbIsto6JgWF8ktZbnv1+gbIH6kmBQXxKuGtcXOz93/DoEEPHj8RLx2dSrgfpZJJoX0aDRkH7gBA7tW5jIuQzoSXrIGbSJyUbPs6+GokspuXN4t8CqZDyOJvNZLJJaS+Rv5/EtUP5JV8JQp2TIv6+GY2umvXm1qUvmkxiyXhTtrLpMQBWSnsSQ/FxuD/f2XaB6J2N9ZiakEnXLfHtQv2p/KiUKKxVF+XctbV3a1q9O7tNI1M+jQa0h9c9TOHY05pMM6qYwqJs2VfzJuvkAKTsHtDoyL4Xi1Mm0HRnCKbAqWY+jyX4Wi6TWEPfbWTwKlJ1RW7A3bgspF+6hTk4vcf5Km68kaBxQDxdnp78lLvuAauQ+jSL3udy3JO87hXNRfYu9jdm+xbFVA3KfRqF+Wbyj8XZdWvPnz4cACL12FycXRzy8PUzkHoaGEfXC1Kl+VmZW3m87ezuzXV3Lzi0I2XMEgHvXC7cjDMf2wz+H0KqLaVtp3zM4b2w3RGDrgFKxy4pD6y4tOai3me5cu6fXZ8k5khOSuX/zAZpCbNBGnZpyWm/jhl9/iH0RNu5FvY17as9xGhvYuBGF2Lg5+rqlVClRWimN+rYKAVWJexpNgn6Mv7bvHPU6G7e9up0bc/kX2Q58ej0cOyd7nL1csXG0o0rTWlzYKY9BWrWWrFTTxZeKeo54PceVfedoUMDmbdC5MRf0HI/1Nq+zl6s+/Tn56VcpMXcpUMPXW3B5r/kd3007NePEHjmND68/wMHZATczuq3Xsj7n98txHN9zjKZ63WYbtE0bexuKGhzszPTVTh0K76sVdrZ58VlX8Sf7hkFffTkUp86mfbV/QFXin0aTqNfnzX3nqVOgzGp3bsQ1vV32TF9mTnp9vkLVVnVJeBpD8kt5bJWQsHWUPyLZOttjZWvNab3eSlonTxvUSblOm4a3c7SjZrPanPhJ7ju0ag2ZZuqNBf95CCG6CiEeCCHChRDTzLwfqJ/H3xJCnBNCNPh3Of9PL/wAXYFISZIaSJJUFzgI1BJCeOnfvw98p//tAJyQJKkRkAYsAjoBvYEFBnHWBQYATYHFQKYkSYHAeWCQXmYjMFYf12RgnSRJ54C9wBRJkgIkSXr1qdNVkqR2kiTNB04Ar+ufvwPskSTJ9GyIjGlAoCRJ9YFXn5lmAsckSWoCBCMvMpXoKhE3X3cSIvMN/4ToBNx9jAddJzcnMlMz0GnlNdXEqATci1gIKIiWPVpz5LdjRcp4+XoSG5lvdMVFxeHp+9dukioYR2JUIm4+xgaZm49xfhOjE3Dz9cDNx51Ew+cGYV88fEZgJ3kwbdKtJe5+hafL09eDOKN8xOPpa5wGZzdn0lPT8/Qpy8hx/rbldypU82fX1R1sPvI1a+esNxmMfcr5ULVOFRRKpRFXfFQcHma58ssuPirOJD02tjY0DmrMGTNf/l5xxRX2hR6w93UjIzIx7+/MqEQcfE0HsVeo/k4QL4/fKvS9afzuZEYmGMVv51d4/FX6BxF1/Gbe30Ih6BqyhN631hN9KpSEIvIC8q6ddIP8pBeTH0NkRCdx4+v9DLrwJUOuriE3LZPnp0JN5Jx93EgxyFNqVCIuBdqdLJOfjtToRJx93Yh58JyKTWti5+qIla011YMDcPHLL9Pmgzsz9sCn9Fn+IbbOpt2A0tsTbUy+sa+NjUfp5WUko/DyxK5dazJ+MT1+BhKeX32G9/cbcOj1upn3xrD2cyc3Kr9t5UYnYG1mIcK1azPqnFhDta2zeDJpjQGdRLUf51Fr/+d4DuxcLJ+drzuZLwvUlyLKr1L/IKKO5deXgAXvcWvRDtCVbPFJ5e2BJjq/HWpi4lF5G/cRSm8PHDq0JHVXETuxSghbPzeyDOpOdlQCtkW0B/8BQcQeu2HyvEyvlrz87VyxfI6+bqRG5dfDtKhEnErYHkBuf0P3L2bctXU8Pn2byBuFt7/S1qWVjwea6Py6qYmOx8rHdBLv2KkFlQ5+jf/G+URN/wKAnLCn2Deui8LVCWFrg2O7xlgVMTYA2Pi5k2NQdjlRiVj7mfJ5vNaUxqe/oO626TyYsP5fzF3p85U2rHw8UBuM2+qoBLPl59ylOdWPrqfit3N5MfVLk/eu3duQvPdUiTi9fD2JiczvP2Mj4/D+i7ZKUNc27D71A6u2LmPhxE9N3nv6epbQjjAc2+PxKJCOV2P7aTNje3CPoFKxy4qDzGGgz6g4vP5GDndfU5vPnI2bYaDLhBLauEKhYOn+VXx97Xtun77J0xv5O4ldfNxJNmh7yWbGeNcCMinRibj4uuNZ3pv0hFQGrBjJlD+X8s6nH5rd8ePq406SQfikqARczXAkGeQ/KToBV33ehEIwY/9yll/dzL0zt3lyw3gndNWmtUiLTyHuiflbQd19PYg3qB/y/MG4nprTraGd2qxLc746uo6Z381hzZSvzPIAqHw90BjYEeroeFRm2rpTpxZUPvg1/pvmEzXtC0Duq+2a1EWp76sd2jXGykwdcylgl6VEJeDs41ZApkC56svMEAHdW3Jjb/7Yum/+VrpNH8iMc2t4fcZAkl7Gk2gQR2J0Am7F1sl43PR6c/P1MBveu7wPaQmpjFgxliX7P+eDZaOwMa43Y4BbwLdAyQfx/w8gleK/4iCEUAJrgdeA2kB/IUTtAmKPgXb6ufxC5PWDfwv/1xd+bgMd9Tt52kiSlAL8ALwrhHAFWgCvnBrkIi8MvQp3Ur/ochuoaBDncUmS0iRJigNSgH0GYSoKIRyBlsBuIcQN4GvAr4g07jT4vRl5MQqMF6XM4RawXQjxLvDqiFhnYJqe9wRgC5QvIo48CGHqEctk0b8kMoVAaaWiUcemHP+jaKPKDIXZrw9FR1KCOMwTFaIHOew3U9fR8b2uzN+3HDtH27wztubTULyuzHLpu5MmQY0JvxPBW43682GXkYxdNAZ7R/s8OVt7W+ZtnMP6eV/nDQhFkRWVr1do3qkZdy/fIa3Al11be1vmfD2L9fO+Rp2eRWEoUR3Sw7dlLar1b8eVJT8VGp8pgZlnhRB4t6xN5f5B3FicH7+kkzjYaQa/NxqLR0AVXGqUK5ru36jvNi72VOzckB9aTuD7xmNR2dtQvbfpFvSSlEth6Yh7FMmpDfsYum06g7//hOh7T9Fp5a+RF7eF8Hnb8azpNp202GS6zRpoLoemjwpwu04YTcrajaAzrWOxH3xM7OARxI+fhkO/XlgH1DfDUTSfOX0mH7zInaAxhA9bStkpA/Ke3+89jXuvTSLsvQV4D34Nx2YFx88CbH+hL/FqWZtKA4K4ra8vfh0DyY5PIfnWkyI5SkBo9KfnJx+RsOobs/r8yzDLZ17Uo1VtyvcP5t4iYx9OwkqJb+dGRO69WDxdCcuvMEg6iW+7zWRN848pE1AFz+pFtL9/hC5NM5cecp7HXUfwYtRC2YcEkPvoOQmbdlP+u8X4f7OQ7PuPkTSF73yV+cw8M8OXcOASV9qM5+77y6n4ydslyck/g6+0UcLySz10gYcdRvL0w8X4THzXOAorFc4dm5Gyv2g/dvmUxffdxeHEwdO82fY9pgydyUdTh5nhMA1TkvGhYN5bdGrOnct3SEs23gWnslLRolPz0rHLisHfoc+/Hr+JUPEyZiDpdEzvNoHRzYdTJaAafgZ9mzndmRqD5kQkFEol5epW4uy2ED57fTq5WTl0HGm6kb9Euiuinkg6iSXdpjKjxUdUbFCFMtX9jcSa9GhV6G6fwqP+azboxUMX+LjDKJZ9sIT+k8zZK3kxFZoPQ6SFnCei6wieF+yrN+6m/JbFlP92ITn3HyNpzfTVJRlbi8mP0kpJ7Y6NuLU/f2xt/m4n9i38gSUtx7Bv4Q+UqWVmelaCcnvFU1jdUiiVVKxbmSPbDjKj2yRyMnPoMarPK4n1QBUgAIgCPjcTiwV/D5oC4ZIkRUiSlAv8BBg1YEmSzkmS9Go7/QWg6IlJCfB/2sePJEkPhRCNgG7AUiHEYeTFlX1ANrDbwK+OWspvtTogRx+HTghhqMccg986g791yPpWAMmSJAWUMJkZBuk9K4SoKIRoByglSTLdIpCP14G2yMfHZgsh6iD3iH0lSXpQHKkQ4sNp06bNHDx4sNeCP5bxIvQpHmXyV749fD1Iik00CpOWmIq9swMKpQKdVoe7nwdJMYkFozaLwKCGPA6NICne9LhI78E96T6wGwD3bzzAu0z+rgMvPy8SYhJMwhSFuKh4ozjc/dxJLpCXpOgEPMp48spFnruvnBeltQp3Az0Yho169JLPBi0EwKeSHw2CjY851G5Zj7pBAdRtUpcHNx/gZZQPT5N8pCSm4OjsmKdPLz9PEqJlmS5vdeantfKaYOSTSKKfR+Nf1Z8HNx6gVClZ98dqHJwdeHf8AB7cfGjE5ennRUKBcpG58svOnExQj3Yc33vC6JlSpWTOxtkc++04Zw+epTyFf4HLiErEoUz+1wp7P3ezx5vcavnT6rPhhLz3GTlJJT8+kBmViH2Z/C879n7uZEUnm8i51vKn6YrhnHx3Oblm4lenZhJ7/h5+wfVJefDC5P0rpEcl4miQH8dC8mMO5VrXJe15HNmJsqH9+MAVfBtX4+GvxsZTSnQiLgZ5cvZzJzU2yYxMfjqcfd1J06fj6q4TXN11AoBOU94mNUquPxnx+X53Lv90jEHfmJ6318bGofTxzvtb6e2JNt74uI91req4L5wNgMLVBduWzZA0WrJPnUUXL3PpkpLJPnEG6zo1yb1R+A6u3KgErA12Qlj7eqCOLrz/SL94F5sKvqjcnNAkpaHW51mTkELywYs4BFQj/WLhfvIzoxKxL2tcX7Jjkk3kXGr50/jz4ZwemF9fPJpWp0znRvh1CEBpY4XKyY6ma0ZyaUzhuyA0MfGofPPbocrHE02ccZu3qVMdn8/k8/9KNxcc2jRF0mrJPFa8T5GCyI5MxM6g7tj6eZAdbVo/nWqVp8HnH3JxwKeoC7QH7/YBpNx+TG58SrF8adGJOBvs0HLycye9hO3BEDmpmTw7f4/KQfWJf2i+/ZW2LtXR8agMvvyqfD1RxxZeN7OuhGLl74fSzRltUiopPx8mRX8UxnPiYKPdQ+aQE5mIjUHZ2fi5k1tEW0i5cA+7ir6o3J3QJJb8CNt/i6+0oY6Ox8pg3Lby8yiy/DIu3cGmQn75ATgFNSIr9BGa+ORCw705pDe9BsqXE9y9cR+fMvn9p3cZL+L+oq3yCtcv3qRshbK4uLvQuUd7+gyUHbI/LDC2y3ZEcWO7qa1hbmwH2YdQWGh4qdhl5tBncE96DJR3i9678QBvQ336eRH/b3K8it8KJRG3woxsXPdCbFwHA116/AUbFyAzNYN750Op2S6AKH3flhydiKtB23P1cyelwBhfUMbF153UmCQkJJKjE/N2EN3Yf5GOI3uY8CZFJ+BmEN7Nz8MMRwJuZTwBeXrg5utBcoH+Oys1k7ALd6ndLoDIh7LPHIVSQUCXpiztbnxKpeugbnR6R955G34rDM8yXoDskNnc/CHVjG4Tzej27qU7+Fbww8nNiTQz/vw00fGoDOwIK19PNEX11ZdDsSpvvq/2KqSvLmiXufh5mLHLEozLVV9mr1AjKICXoY9JNxhbG/VtS1xEJOP3LwXAxtEed4M45DppzGNaJz1J1ustMSrBbHhJkkiMSuDRDXmGc3H/OcOFH8NzkpuQ/c7+z6A0b/USQnwIfGjwaKMkSYY7dsoCzw3+fgEYn0E2xjDyN6P8y/g/veNHCFEG+SjWNmRHVg0lSYoEIoFZwJa/m1OSpFTgsRDiTX0ahMGZvTSguEPtW4EdFLHbRwihAPwlSToOTAVcAUfgEDBW6JfWhRCBRaRz49KlSyvUrFnTfs4bn3D58EXa9g0CoFpgdTLTMkiONTUG7p6/TfNu8pnYdn2DuRJyqZjsyGjVow3nCtlC/ev3vzO08wiGdh7B6UNn6dpPHkxqN6xFemoGCUV06uZw/8Z9ylUqi5+/L0orFc26t+Z6iLFj2ushl2nVpx0AVQKrkZWWSUpcMo9vhuNT0Q/Pct4mYZ08nAH5y0XPMf04tv2wUZx3z93m+N4TjOgykrMHz9G5XycAajWsSUZaBolm8nHj3E3avd4WgM5vduLcYXnSEvsylsDWcvG5ebriX6UcUU+jAJi8YiJXTl3l7cYDGNl1NOcOnadT3w4A1AwsnOvmuVu0fb0NAJ36deT84fwJkr2TPfWa1+d8AaeWEz+bwLOwZ+zZVPQNPADxNyJwruSLo78XCisllXs25/nha0YyDmU8aL9pPKfHbSA1wvy24cKQeCMCp0q+OOjjL9+zOS8OGzt1tS/rQevN47nw8XrSDOK3cXfCylneMaW0tcKnTR1Sw6OK5Iu9GYFLRV+c9HxVezTncci1IsO8QtrLBHwCq6KylR3qlW1VhyQDJ9Sv8PLmIzwq+uJWzgullZL63VtwP8Q4T/dDrhLYRy43/8Cq5KRlkRaXDICDvk66lPGgTtcm3Nwrl5/hWfPaXZoQY2aCnXvvPir/sij9fEGlwq5Te7JOGZd/dO+BRPceQHTvAWQdO0nyZ1+SfeoswtYWYS+fVRe2ttg0a4z60eMidZJxMwzbSn5Y+3sjrFS492xNcoH+w6aib95v+7qVEdYqNElpKOxsUDjYAqCws8G5bQBZD4q+DS7pRgSOlXyx9/dCWCnx79mcyEPGurUr60HLb8Zzaex60g3qS+iSnfzZaCz7m47nwkdriD1zt8hFH4Cc0AdYlS+LqqwPqFQ4vhZExvELRjLPug7mWRf5X/rh08QtWv0vLVQAJN94hENlX+zKy/kr06sF0YdN89fk2wlcH7OWDDPtrWzvkh3zAoi8GYFbJV9c9O2hVvfmhJWwPdi5O2Gjb38qGysqtq5LYnjhzo9LW5fZtx9iXbEMVuV8wEqF8+tt85yDvoJV+fyNuza1qyCsVXmLBkp3Fzlvfl44dW5J6h8ni+RLuxGOXWU/bMvLbcGrVysSDhuPUbYGbcGxXiWElepfXoQpbb7SRubNsLzyE1YqXLu3JbVA32JdIb/87OpUQVjllx+Aa4+2JO8rutx2b/mVgZ2GMbDTME4cPM3r/boAULdhbb2tUvKFinIVy+b9rlGvOlZWKlISU9i95Vc+6jqKj7qO4uyhc3Tq2xGQfRdmpGUWake8Gts798u3IwAcnOyp37w+5w6ZtvNgA59+BfF322Xm8Mv3vzOk84cM6fwhpw6doaveZqrzN3G8in96twlcOXyRNnobt2oRNu6d87dpprdx2/YN5moxNq6TuzP2+qPUVjbW1G3dgNhH+X3bs5uP8Kroi7t+jG/YvSWhBcb40JCrNOkj24EVAquSnZZJalwyaXEpJEcm4F1ZrrvVW9Ul2owd8fTmI7wr+uGh52jcvSW3Cti8t0Ku0FzPUUlv86bGJePo7oSdvm+2srGiZqt6RD8ycGLcuh7REZEkF1goPrh1P5O6jWdSt/FcOnyRoL7yzbDVA2uQmZZpsoABEHr+Ni26yTufg/u2z3Po7GvQNivXrYzKSmV20Qcgy0xfnVZEX21b27itF+yrU8z01S9uPsLTwC5r0L0FdwuU2d2QazTU22XlA6uSlZaZZ5cBBPRoyY19xm0uNTaJmLAXfNFtOn8s2kZyZBxt9HqrGlidrLTMQuZdoXl1so3BvOvqkctmw6fEJZMQFY+f/ka2uq3q8zL/Eg3DEyi9gaI2GFhQBCRJ2ihJUmODfwWPaZndk2UuLiFEMPLCj6l377+I/9M7foB6yH5udIAaeHXf8HbA6z94rfpAYL0QYhZghby966b+/01CiI+BfoWE3Y7sX8j8vcoylMA2IYQLcsVaJUlSshBiIfAFcEu/+PMEeKMkCb5+7CqBwY348tSGvKsuX2Haltl8PXUNSbFJbF+6lXFrJvH25IE8uRPBsZ2yMz4XL1eW7luBnaM9kk6i29DuTOo4lqz0LKxtranXpgEbZxTvM+D80Ys0b9+Mn87+QHZWNksnfpb3bvnWJSyb8jkJMQn0HdqbAaPext3LnS1HNnHh2CWWTZF3LGq1OlbNWs3nPy7DRqni1K5jvAx7TrDeL8jx7Ye5efwa9YMb8tnJteRk5bB5inwFu06r44c5m5mydTYKpSIvLEDzHm3o+F5XAK4cusjp3fnn4lecWY+dox1KKxWturTkkwHTiXoaxQ9ntpCdncNnE/Md6C/ZuojPp6wkISaRTUs2M2vdDN6fOpjw0Ecc+Ek+bbjty+1MXTmFTUe+RiDYtOQbUpNSqdukDp37dSLiXgRfH1qPAL5dtoWoZ9FsOfOtfJ37pPwbNRZ9v4CVU78gMSaRzUu/Ycba6QyeMphHoY84+NOhPLlWXVtx7dRVsrPyN7TVaVKHTv06EnHvMesPyvqJ+PQXXhj4QTGEpNVxYdb3dP5xKkKhIGznSZIfvqTGe/J1sg9+OEbAhN7YuDnSfMkQOYxGy75uc4qtF6/ivzJzC0E/fiJfz/3TSVIfvqTqe/KiV/gPR6k7oTc2bk40XiqfmNRptBx+bTZ2Pq40//IjhEIBCsGzfReJPHK9WL7Ts7+n+7apCKWC+ztPkvTwJXXelfNzZ9sx7LxcePPPhVg72iHpdNQf1pUd7T8h9sYjHu2/xJsHFqHTaokPfcodM06CdVod++ZsYcjWaQilgmu7ThAb9pKmA+U8Xdp+lAfHb1A9OICJJ1ehzsrhlylf54UfsH489m6OaDVa9s7+juxUeQNhl+n98atdASRIehHH7zO+Mc2gVkfyitV4frUMoVCSse8AmsdPcOgtX2+e8as5vz4yFO5ueCyXXZ8JpZLMQ0fJuVD0rVBodTybvYnq2+eCQknCziNkP3yO17vy5Clu2yHcurXAo28wkkaLLjuHiJFyu1F5uVJ187Q8vsTfTpF6ovjyuz5jC213yPXlsb6+VB4k6zZi61FqT+iNtZsTDV/VF62Wo11nF52PIvIXv2Qtfl8vQSgVpP56GPWjpzi/JX/RLs4Xjffyadg1qY/S1YUKR7aRuO4H0n45VKi8pNUROmMLzXdMRygVPN9xgvQHL6gwSJ4oPt16hGoT+2Dl5ki9T4fmhTndZSYASjtrvNrW49aUzSXKnqTVETLne97ZKreHW7tOEh/2ksCBcnu4vv0YDl4uDNm3EBt9e2gytCubOn6Co7crb6wcgUKhQCgE9/64SLgZf0P/LV2i1RGzYD3+3ywCpYKUnw+TG/4M13fkHQ/JP+3HqUsrXHp1QNJokLJziRyf75Ol7JqZKF2dkTQaYuavQ5dazE5GrY7wGd9Qd8dM+Xr1HcfJfPACv0HyxDdqawiebzTD5812SGotuuxc7o1YlRe85vpxuLSsg5W7E82ubeDpZ7uI3lGEn5bS5isBpsz9lMvXb5GcnEqHXu8yath79O3e5V+LTKsjcs4GKm+dD0oFSbuOkBP2DPeB8niduP0gLq+1xK1PeySNBl12Lk/HLM8LLmxtcGwdwIsZa0tMefboBVp1aMGv53aQnZXDgglL89598cNyFk1eRnxMAm8P68t7I/vj4e3OjiPfcfbYBRZPXk7719vxer8uaDQasrNymDFyngnHxWOXaNq+CVvPfEdOVg6fTco/lbH4+4WsnLqKBP3YPnPtDN6fMoTw0HAOFBjbrxYY20H2+9OoTUO+mGbq66gg/g67rCQcLdo3Y9fZbWRnZbNkYn75rNi6lE+nrCA+JoF+Q3szcNQ7uHu5s/XIZs4fu8inUz7H3cuNbw5swMHRHp1O4q0P+jIw6H0y02WnttePXSUguBFfnNpATlYOXxvYuFO3zGaT3sbdsXQrY9dM4i29jXvcwMZdbGDjvja0O1M6jsXN242RK8fl9W0X/jjLnWP5C+I6rY49c75j5NYZ8nXuu44THfaCVgPlfvrs9iPcPX6d2sEBzD75JblZOfw4ZUNe+D3zvuO9L8agslIR/zyWHyfnvzPk+GnOt4zdOhOFUsG5XceJCntBm4Fy+z69PYTQ49epG9yQBSe/Ijcrl61T1sn58nZj8OejEQoFCoXg6p/nCTVIf+PurbhSxDEvgKvHrtAwuBHrTn2tv849X7czt8xh3dQ1JMUm8sPSLUxcM4UBk9/l8Z0Ijuh12+K1FrTr2x6tWkNuTi6fj15eGBVodUTPX4//t4vk69xf9dX99X31jv04d83vq3XZubw06KvLrZmJ0s0ZSa0hupC+WqfV8fucLQzfOh2FUsHlXSeICXtBc32ZXdh+hPvHr1MzOIBPTn5BblYOuw3sMitba6q1rscvM4zH1j3TNtFj7iAUKiWaHDXbx6ym1lttWHVqvb5Ors6TnbplFhunriXZoE6+OXkAT+885sRO2WnzDX2dNhf++7mbGP3lBFRWKmKfxRi+W458zEtCniOOKFzZ///hH3ar1wvA8NxkOeSNJ0YQQtRHPo30miRJ//ZWSvF3n8P9X4AQYg1wXZIkM7Oh/y6EEP2AnpIkvVes8N+Etyv0KrVKEqkp3S+Ila1cS5XvRSnnTylKd1Nff93f69CxONiWcv+VpCzFfaLAS2XpDlMjff7aLqt/F9EvnUuN67G2RH7s/zY09Cj6OM/fjbvxJXek/3cg1EZZqnxvORV/k9LfCXVu6eYvNrV062dpo0XoslLlu9d4XKlxDcst/ijk3wkXpV2p8uXoivBX+B+ArkSuUf8+lFeV3jgE4C1sS5Uvt5SnuzFSdvFCfyMWW5du/fxO7VJqXC9KWZc/Pv21dI3cUsamcu+WWufywYttRepS7ybmIdABeAlcBgZIknTHQKY8cAwYpL8E6t/G//UdPyYQQlxF9qsz6b+dloIQQqxG9v7d7b+dFgsssMACCyywwAILLLDAAgss+Kfjn7TjR5IkjRBiDLIbFiXwrSRJd4QQH+nfbwDmAB7AOr2XFo0kSY3/HV7Lwk8B6K9Y/0dCkqSxBZ8JIdYCBa8D+lKSpKJu/LLAAgsssMACCyywwAILLLDAAgtKGZIk7Qf2F3i2weD3cGD438lpWfj5/xySJI3+b6fBAgsssMACCyywwAILLLDAAgv+iZD+pw+ylQz/p2/1ssACCyywwAILLLDAAgsssMACCyz4X4Zl4ccCCyywwAILLLDAAgsssMACCyyw4H8UlqNeFhQLJ1F61cS9lG+rcCnlJqBWlu7NLepSdmVWNrd0b1do1DiqVPmy4kv3pp+rT31Lle/Fc9dS5as/2aPUuMJWZJYaF0BYnFup8uUoSvc7TmnfvKNUlW5fVmZJp1LlKx/9slT5dA8jSpWvNG/ZAqh1pfhryP8ulGtYunlzEFalylfan4hz0JYqXyudY6nydbBNLFU+rbZ0C/Bajmup8u0s5bFodvekUuNa+Efp3SD2fwH/JOfO/y1YdvxYYIEFFlhggQUWWGCBBRZYYIEFFvyPwrLjxwILLLDAAgsssMACCyywwAILLPifhGXHj2Xhx4ISok67APrPeR+FUsHpnUc5sP43E5n+c4dSLziQ3Kxcvp28hmd3HuPm58GwlWNx8XJFp5M4tSOEo9/l31zXfvBrtB/UFa1Wx+1jV1m/ZHPeuw/mf0ij4MbkZOXw5aQviAh9ZMLp7e/DlDVTcXR1IiI0nFXjV6JR5x83qlq/Gst/X8GK0cs5t/8sVjZWLNm9DCtrK5QqBXcOXObAqt3UateAPnOGoFAqOL/zGEfW/27C1XfuEGoHB5KblcP2yet5cecxAHPPrCYnPRudTodOo2VFjxlG4dp/8Aa9Zr7HuR1HqdyiNrlZOWyYvJonoaZb7b38vRm7ehKOro48Do1g3YQv0erzM2jeMAKCG5mE//LM12RlZKHT6tBptczqPkVO7/i3Ce7fidSEFAB+/Gwb149fBeD9eR/QMLgROVk5rJ38JY/NpMXb35vxq6fkpWX1hFVo1Bpa92pHr4/6AJCdmc2mmet5eu+JSXgAj+AG1Fw0GKFU8GL7MZ6s3mv03rdvKyqN6QGANiOHu1M3k373GQDlR3Sj3IBgANLuPePOuA3octRmeV7BqlFTHD4cCwoF2Yf/JHv3j0bvVfUCcJq9GF2MfEQs99xpsnZ8D4DDuE+wbtoCXXISKaPfL5LnFWyaN8F14hiEQkHG3v2kbd1hPl21auD9zRoSZy0k69gpOe+//oiUmYmk04FWS+yQkcXyeQfXp97CQaBU8Gz7ccLW7DN6X65PK6qO6Q6ANiObm598S6penypnewJXfoBTDX+QJK5P2EjS1bAi+VyCAqm4cChCoSB2xxEi1/xq9N6tSxPKTekPkoSk0fJ07rekXbqPsLGizi+LENZWCJWCxD/P82LFzmLzp6hYB+ugd0ChQHP7NJrLB01lylXXyyiRstPI2bVCzl9gB1T12gACze1TaK4fLZbPL6g+DRe+h1AoeLTjBPcK6LNC75bUHi3rU52ZzZVp35F89xkKGys6/jIbhbUKhUrJsz8vEbpiT7F8nsENqLVoMOjbw+MC7cGvbysq69uDRt8e0vTlV8GgPaTfe8btErQHn+D6BCx4D6FU8PjHEzwokD//Pi2pMTq/vlyb9h0pej4AFIIOBxeRHZ3E2UEris1f5Xb16TxX5rvx0wnOrzfm86jixxsrRuBbpyInVuzi4kZ5LFDaWDFo12yUen3e33+JU6uK1qddy8a4Tx0FCgXpvx4g5Tvz9cu6TnX8tn5F3CeLyTxyGmFthe+3KxFWVqBSknnkNMnrtxabt4I4e/85y/eeR6eT6N20BkPbBxi9T8vKZeaO40Qnp6PR6RjUrj69mtQoefyP4/js+F10kkSvuv4MbVbF6P33lyPYf08+HqbVSTxOTOfYyI7YWikZtvMCuVodWp1Ex2q+jGxVvVg+ZY1AbHp+AAoF6oshqI+b6l9ZpS7WPYaBUgUZqWStnwmAzVtjUdZujJSeQtaKj0uUP8d2DSk75wNQKkjcGULc+p+N3jt3aobPxIF5fUvkgs1kXrmLTeWylF8zNU/O2t+XmFXbif92b0GKEmPWkpWcOnsJdzdXftu2ofgARWDY/A9ppB9bV0/6slC7ZdKaKXq75RFf6u2Wpp2a0X/yQCSdhFar5dv5m7l3+S5lKpdl8lo5z+6+Hji5OZESl8znQ5cUakeMXj0RR1dHnoQ+Zr2BHfHevGEEBDckJyuHjZPX8CQ0AisbK2btWoRKbxdd2n+eX1bJ7an/jEEEdmiMRq0h9mkMKQnJ1G1V3yj8v8sP8MFnowlo35jUhBQmdh4L/GfsFCsbKxbsWqLPq5IL+88R8/kfRnGWD6pPm3lyP3Z3xwmurTPux1yr+NHx8w/xqluRC5/t5vrXcj/mWtmPLuvG5Mm5lPfm4uc/c/ObQybpNoRDm0b4zBqBUCpI3nWIhI27jd47dmiO1/j3QNIhaXTELP6arKt3AXAb3BPXt7oghCBp10GStpjargXh2LYhfnM+BIWCpF2Hid9g3PacOjbDZ+K7SDoJtFqiFm4i84rM5zG0J25vdQYJsh8+4eWUL5Byix6HygTVp8kCeZwN33GC0LXG+qzUuyV1R70ByOPsxelbSLr7DPsy7rT+8iNsvVxAJ/Fw+3HuF6NLgKrt6tN17nsolAqu/XSCMwXGIc8qfvRcMQK/OhU5tmIX5zbmz0l6fvYB1dsHkpGQyrrO04rlAlDWaoRtvxFy33nuELkhxuWnrFYPuw/noEuIBkBz4xy5B2Vb0SqoJ1Ytu4AQqM8eRH3CfPlVb9eAnnMGIZQKLu08zon1pv1dj7mDqRkcgDorl12T1/PyzhO8KvsxcE1+n+zu783hVT9z5tsD+NWuQJ/Fw7CysUKn0fHr7G9LlF8L/v9GqSz8CCE+BkYC1yRJGvhvxDMEOCxJUmQxcluAPyRJ+rkoOb1sEDBZkqQ3hBA9gNqSJH36r6bxX4EQogzwlSRJ/UqTt6QQCgUDFwxn5bsLSIpOZNbeT7kRcoWo8Bd5MvWCAvGu5MeMoLFUDqzGu4s/ZEmv6eg0WnYt+p5ndx5j42DL7H3LuXv6FlHhL6jRog4BnZow77VJaHI1OHk458XXKLgxfhXL8FHbD6keWIORi0cxpeckk7QNnj6EvZt/5/S+U4xcMpqOb3fi4LYDACgUCgZPH8L1k9fz5NU5ama/M4PszGyUKiUrf1nB/VM3eXPBUNa+u5jk6AQm711KaMgVosPzfS7UDgrAq5IvC4PGUTGwGm8tHsbKXrPy3q/uv4CMpDST9Ln6eVCjTX3S4lPwKO/NxHajqBpYnaGLRjCn1ycm8v2nDeLAN/s4v+8MQxd/RPDbHTiy7RABwQ3xrVSm0PCL35lNmhn+P77Zy76Nvxk9CwxuhF8lP8a2+4hqgdX5YNFIZvSaYhJ24LTB/PHNXs7tO80Hi0fS/u2OHN52kNjnMcx9awYZqRkEBDVkxNLRZsOjENT6dChX31pMdmQCzQ8tIe7QVTIe5us162kcl3stQJOSgWf7AOp8/iEXX5uFja8bFYZ35WybSeiy1dTfOA7fXi2J3HnSlCePT4HDyPGkzpqELj4Ol1Vfo75wFu3zp0Zimju3SJs/3SR4zpEDZP/xC44TZ5i8K4zPbco44sZOQRsbh/eW9WSdPofm8VMTOZcxH5J98YpJFHGjJqJLSS0hn6D+0vc599ZSsqISaHdwEdGHr5FmoM+MZ7Gc7b0QdUoG3u0bELBiOKe6zQGg3qJBxBy7yeXhXyKslCjtbIrNX6UlH3DvnfnkRiVQd/9ykg5dJissv92nnL5N0qHLANjXqkC1rydxs+3HSDlq7r45F11mNkKlpM5vi0k+dp30aw8L5xMC6/YDyNmzCiktCduBM9E+uomUaODHycYO6w4DyfnlS6S0RLBzkoN6lEFVrw3ZPy4BrQabPuPQPr6NlBxbOJ1C0GjJEI6/s5SsqEQ671/Iy0PXSA0z0OfzOI70XYg6JRO/4AY0WT6MkDfmostRc+zNxWgycxAqJR1/m0PUsZskXAsvQp+C2p8O5bK+PbQ4tIRYM+3hYoH2cMGgPZzRt4cGG8fh16slL4tsD4LAJUM4/fZSMqMS6XBgIZEF6kvmszhO9pHz59u+AY0+G8ax1+fmva/2QVfSwiKxcire95pQCLouHMKPA5eSGp3I0L0LCTtyjXgDfWYlZ3B47lZqdGlkFFabo2Zb/8WoM3NQqJQM+nkO4SduEnm9EH0qFLhPH0vMR5+giYmnzPY1ZJ48jzrimYmc27jhZJ2/mvdIylUT/cEUpKxsUCnx+24VWWcuk3P7XrF5zEuvTsfSX8+y4cNu+Lg4MPCr32hXpwJVfPL9Ou08d4fKPq58NbQLielZ9Fq+m9cDq2KlKt5PmFYn8enRO6zv1xQfJ1sGbj9Lu6reVPFwypMZ3KQyg5tUBuDkoxi2X32Ci501kiSx8c1m2FurUGt1DP3pPK0qeVG/TBE+p4QCm94jyNo4FyklAbtxK9DcvYQU8zxfxtYBmz4fkbVpHlJyPMIx3/eE+spR1Gf/xKb/+JIpUKGg7IKPePzubNTRCVTdu5LUkIvkhOfzpZ+9SWrIRZm6ZkXKr/2Ehx1GkhPxkrBu4/LiqXVxCymHzpeMtxD06taJAX17MGNh8YubRaFhcCPKVCzDqLYjqB5YgxGLR/JJz8kmcoOmD2Hf5t85s+80Hy0ZRYe3O3Fo2wFunb3JJX2eK9SsyOR1nzC2/UgiI14y8bVxNAxuxOvvd6dy7cp8N2sjQxZ9yLxeppPTd6a9x8Fv9nFh31neXzyCoLc7cHTbIRoEN8S3kh+T2o2mSmD1vPDqHDVL+s8lR28Xzf55MTdPXOfR9YfcPn2Tncu2odPqGLtuMvXaNGBim1FG4f9dfoBTu48T8v0BRqyUJ6n/KTtFnaNmfv/ZeTbgwp8/5caxe8RclxfohELQbtFgfh/wKelRibz1xwIeh1wlKSx/2pGTnMGpuT9QuUA/lhwRxc6uM/PiGXJ5NREHTcd9IygU+M4bxbMhM1FHx1NpzxekHbtArkFbyDh/g/SjFwCwqVGRsl9OJ6LrCGyqVcD1rS486TsBSa2m/DcLST9+GfXTIqZICgVl5o/k8aBZaKITqPzbKtKOGLe9jHM3CT8i10ObmhUpv/oTwjqNROXjgcfg7oR1HoWUk4v/6k9w6d6W5D2Ff2QRCkGzxYMJ6f8pmVGJdNu/gOeHr5JioM/053Ec6reI3JRMygTXp/myoRzoPg9Jo+PK/B9JDH2CysGWNw4uJOrUbaOw5vi6LRzCD/px6IO9C3lw5BpxBcahA3O3UrNA+QHc2H2aS9+H0HvlR4Xr0JgQ27dGkblmJlJyPPZTvkBz+wK66OdGYtpHd8jaMM/omcKvAlYtu5D52QTQqrEbtRDNnctIccb5EwpB7wXvs+ndJaREJzB272Luhlwl1mCOUjMoAM9KviwPmkD5wKr0XjyMNb1mExcRxRfdpufFM+viOkL1Ntvr0wZw5Ms9PDhxk5pBAXSbPqBkef7/GKXrDeqfidLy8TMK6Ga46CPEv+QxeAhQ5u9KVEFIkrS3tBd99LyR/9RFH4BKAVWJfRpN/PNYtGoNl/adJaBzEyOZgM5NOP/LCQAirodh72SPi5crKXHJPNPvjMnJyCbq0UvcfN0BCBrYhQPrf0WjdwiclpA/AW7auRnH9xwD4OH1Bzg4O+DmbWq41m9Zn7P7zwBw7OejNO/SIu/d6++/wfkD50hJSDYKk52ZDYBSpUKpUuFTtSxxT2NIeB6LVq3l2r5z1CuQv3qdm3DpF3mnxpPrYdg5OeDs5Vqs7vrMHsTvS7djZWvN9T9k4zT8+kPsnR1wNZOfOi3rcXH/OQBO7zlO487NAGjUqSmn9xwvNnxJ0KRTU07q4wq7/hCHQuKq27I+F/afBeDknmM06dwcgIdX75ORmiGHv/YADz/zDnpdGlYl83E0WU9jkdRaon87h3fXxkYyKVceokmR40q+GoaNn3veO6FUorC1RigVKO1tyIku2qGeqnottJEv0UVHgUZDzqljWDVvXRKVAPKCkJRmunhWGKxr10Tz4iXaSJkvK+QYdm1bmsg5vtWbrOOn0CX+ew4B3QKrkvE4hsxnsj5f/nYe3wKGS9KVMNR6fSZdDcdWr0+Vox0ezWvy7McTAEhqLZrUop0dOwZWJftJFDnPYpDUGhJ+P4Nbl6ZGMjp9WwJQ2NsYjaqv3gkrJcJKBVLRQ67CtxJSchxSSjzotGjuX0ZZJcBIRlWzGdqw6/KiD0CWXF4Kdz90URGgyQVJh/bFQ5RVA4vkcw+sQvqTGDKexaFTa3n2+wXKFdBn/JUw1CmynuKvhWFvUD81mTkyt5UShZWy2Py5mmkPPgXaQ3KB9mBboD0oDdpDdjHtwTB/klrL898vUKZA/hIM8pdwNQw7Az47P3f8OgTw+MfjRfK8QpmAKiQ+iSH5uazPu/suUL2TMV9mQipRtyLQqk2dt6pf6VOlRFmMPm3q1kDzPBLNy2jQaMg4dAL7INO259y/J5lHz6BLTDZ6LmXp66ZKBSoVUjFlVxChz+Lw93SmnIczViolXQKqcOKO8YKvEIKMHDWSJJGVq8bF3gZlCR1wh0Yn4+9qTzlXe6yUCrrU8ONEeEyh8gfvR9K1pl8er721bF5pdBIanYQQokg+Rflq6BKikRJjQKtBc+M0qjrGbV3VsC2a2+eRkuMBkNJT8t7pIu4iZaaXKG8A9gHVyH0aRe5zuW9J3ncKZ/1YlxenSd9iWkaOrRqQ+zQK9cu4EnObQ+OAerg4OxUvWAyadm5eIrulXsv6nNOPrcd/PkqzLvLYmm2QZ1szeW7auTkRoRHEPovh2uFLhY7dtVvW49J+2d44vec4jTrLZdmoU1PO7DkBwKMCY39Onl2kRGXQX4eevolOKx+QsHeyJykmyWz4f5f/waW7pCfnj7//STsl2yCvcl+TH59PQBVSnsSQqh8XwvZeoHJn434sKyGV2JsR6Mz0Y69QrnUdUp/GkvYyoVAZALv61cl9Gon6eTSoNaT+eQqnDi2MZCTDtmBny6sEW1fxJ/vGA6TsHNDqyLwcilNn037QiK9BdXKeRqHWt72UP07h1Km5kYyuAJ9hNXxll6FUIOxs0MQU7ajaI7AKaU9iSNfr88nvF/AvMA7FXQkjN2+cDcdBPw5lxSaTGPoEAE1GNilhkdj7ulMUyurHoaTncWjVWkL3XaBGgXEoIyGVyFvmy+/ppftkJZe8L1NUrI4uPhIpIVruO6+dQlW/RfEBAYWvP9onD0CdAzod2vBQrBqYlp9/QFXin0aTqJ+j3Nx3njqdjW2H2p0bce2X0wA8ux6OnZM9TgXmKFVb1SXhaQzJL/V9OBK2jvJHHVtne1JjSs9ptQX/PfzHF36EEBuAysBeIUSKEGKjEOIwsFUIUVEIcVoIcU3/r6VBuKlCiNtCiJtCiE+FEP2AxsB2IcQNIYSdEGKOEOKyECJUH2/R1k1+3F2FEPeFEGeAPgbPhwgh1uh/bxFCrBdCHBdCRAgh2gkhvhVC3NPvKHoVprMQ4rw+/buFEI7650+EEPP1z28LIWrqn7fTp/+GEOK6EMJJr4dQ/XtbIcR3+jDXhRDBBmn7RQhxUAgRJoRYXkT+lPr0h+rjmaB/XkUf/qpe7zVLoi83H3eSIuPz/k6KSsDNx7jzdfXxIDEyf4BLik7E1dd4QcCjnBfla1ck4oZ8vMSnsh/VmtZixm9LmbJzPhXr529l9/D1ID4qnzM+OgGPAvE5uTmTkZqRZ5QkRMXjrpdx9/GgeZcWebt/DKFQKFh14Cu2Xt/GgzO3yE7NJNkg7clRCbj4GBsYLj5uxjLRCbi8GoAkGPXDTKbsW0rL/h3yZOp2bERyTCKR956iUCpIjs4fIBOjTXXo5OZkkh83fX7cfI31axheQmLatrks/mMF7fsb30TTdVA3Vhz8kpGfjcXBWb5RzN3XgwSD8kyIjsfdp6Buncg0SksC7mYG3PbvdOL6iWsmzwFsfd3JNkhzdmQiNkUM2mUHBBN/7AYAOdFJPFn/B22vraXdrQ1oUjNJOHmr0LAACg9PdPH5Ozx08XEoPTxN5FQ16+Cy+huc5i9HWb5ikXEWBaW3J9qYfD5tbDxKLy/jNHl5YteuNRm/7CsYHJDw/OozvL/fgEOv14vls/VzI8tAn1lRiUYLAwVRfkAQscduAmBfwZvchDQCvxxBu5AlBHz+AUr7onf8WPt6kGvAlxuVgLUZPreuzWhw6itqbp3Jo4lr8l8oFNQL+ZxGt74j5dRN0q8XfaxMOLrmL+gAUnoSwsnVWMbNB2ztsXlzMrYDZ6GsJRtYuoSXKMpVB1sHUFmjrFQP4VS0gWjv606mQf4yoxKx8yt8MbVK/yCijt/MT4tC0DVkCb1vrSf6VCgJ102PdBjCxtfdqPyKaw/lBgQTV6A9tLu2luAStgc7X3eyXhrXFzvfwvNXqX8Q0cfy89dgwXvcWrQDdCVbFHHydSctKp8vNSoRpyL4CkIoBMP3L2HCtfVEnA4l8kbh+lR6e6KJzp/sa2LiUXp7FpDxwD64NWm7/ygYXP7qvXMD/sd2k33hGrmh90ucToDY1Ax8XfNvAvJxcSBWv2D3Cu+0rM3j2GQ6LdxOv8/3MKVnCxSKEpkoxKZn4+Nkmx+/kx1x6TlmZbPUWs49iadDtfwbALU6ibe3nqbD+iM0r+BJPT/XIvmEi0fegg6AlJyAcDEeExSeZcDOEbuRi7Ab/zmqRsElyos5WPl4oDYYg9RRCVj5mH5AcO7SnOpH11Px27m8mGp6S5dr9zYk7z31L6fj74aHrwcJUYZja0KeTfIKst2Snje2xkcZ2zbNujRn9bH1zNwylzVTjPPs4etBxVoVOb9XnuCZsyMcC4zdiVEJBnaEu9HYbxheKBQs3v856659x+3TN3l0w7S/Ll+rInfP3TYb/t/lL4j/pJ2iUCj4bP8qvrm2lVunbxBj0Nc4+LqRFpk/DqVHJeLwF/qxV6jWowUPfy9+J5rK1wONQZ1RR8ejMtMWnDq1oPLBr/HfNJ+oaV8AkBP2FLsmdVG6OiFsbXBo1xgrX1ObxxBWvh6oowz6zqh4s23PqXMLqoWsp8I3c3n5iVwPNTEJxG/+lepnvqPmhR/QpWWSfua6SVhD2Pu6kWGgz8yoROyL0GfVd4J4edx0bHMo54l73QrEFzPOOvu6k1pgHHL+F8qvpFC4eKBLyi8/XVK8Sd8JoKxUE/tpa7AbuQCFb3lZNvIpqqp1wcEJrGxQ1WmMcDMtPxcfN1IMbIeUqAScTeYo7gXmKIn5cxQ9Arq35Mbec3l/75u/lW7TBzLj3BpenzGQA8t/+ou5//8POlF6//6p+I8v/EiS9BEQCQQDq4BGQE9JkgYAsUAnSZIaAm8DXwEIIV4DegHNJElqACzXH9u6AgyUJClAkqQsYI0kSU0kSaoL2AFvFJceIYQtsAnoDrQBirov2Q1oD0wA9unTXweoJ4QIEEJ4ArOAjvo8XAEmGoSP1z9fD7za7zsZGC1JUoCeP6sA52i93uoB/YHv9WkGCNDrqR7wthDCv5B0BwBlJUmqq4/nO/3zjcBYSZIa6dOxroi858PMelrBL6Rml9wMZGzsbRm1fjI7F2whO13OslKpxMHZkSW9pvPzkh8YsTZfdYJ/jfOVzPB5H/D90i3odKauvHQ6HRNe+5hhzYZQoUFV3MqYdrQFPy6aXVPUC63qO4fP3pjG+iFLaTOoC1Wa1sLK1prOY3qzf+Uu03CFkxSan6L0O6/PdGa+PpllgxfSadBr1GxaG4CQbQcZ2/Yjprw2nuTYJAbNHlpsXkqS31eo06Ie7d/uyLal35vLHWaKkMI2Wrq1qk3ZAcGELZR98qhcHPDu2ojTTcZyssFIlPY2+PUtZveOOf0V+Fsb/pCk998mZewwsvftwWnW4qLjLJrQ9FEBHblOGE3K2o1gph7GfvAxsYNHED9+Gg79emEdUL9othKUySt4tqpNhf5B3FkknyNXqBS41KvIky1HONlpBprMHKrpfckUTmjmmRm6pIMXudn2Yx4MXYb/1P75L3Q6bneaxLVGH+AQUBW7GuWL5jOrzwJ/KxQofCqQ8+tXZO/5AqvmryNcfZASo1FfPoht3wnY9BmHLu4F6Iq5EriYPssQ3i1rU7l/EDcW5xtGkk7iYKcZ/N5oLB4BVXCpUe6v8xXSHtxb1abcgGAeFmgPJ5uM5XiJ24M5OvN8Xi1rU3FAELf1+fPrGEhOfArJt54UzVEM/spOGkknsbnbDL5qPpYyAVXwql6EPkvQFtynjCLpy81m2x46HZFvf8SLLv2xrlsDqyoVS5xOM1Rmk3Tu4QtqlPEgZPZAdk7ow6e/niU9O7eEBCVPy6lHMQSUccPFzjrvmVIh2DmoDYc+bE9odDLh8SXfyZifhgKJUCpRlqtC1jcLydo4D+uObyE8/8UN2CXsy1IPXeBhh5E8/XAxPhPfNY7CSoVzx2ak6Hd7/FPxV+wWgIuHLjC2/Ug+Hb6Y/pON86xQCGo1qc3FP8+ZDSvHX7huzb17FV7S6ZjZbRIfN/+AKgFVKVfduL/uMaYvkiRx+9SNYvL3r/EXxH/STtHpdEzpNoERzYdRNaA67oZ9t9k4zSaxUCislFTq1JDwPy+WQLpkbSEt5DwRXUfwfNRC2d8PkPvoOQkbd1N+y2LKf7uQnPuPkbTFjHvmYI7v8HnCOo3k2YhFeW1P4eyAU8dmPGw3jPstBqGws8GlZ1DRufsL+vRpWYuq/dtxbYnxAoTK3oagTeO4PHcb6vSCU6bi8Vd3dP4lmDfOjf7SPg8nffYQMj8dQ+7Jvdh9OBsAXcxzckN2Yz9mMXajF6J9+RjMlV9JdFhM21JaKandsRG39ufXyebvdmLfwh9Y0nIM+xb+wJvLPiw0mxb87+C/4dx5r37RBsAKWCOECAC0wCsPhB2B7yRJygSQJKmwvYTBQoipgD3gDtxBXqApCjWBx5IkhQEIIbYBhdX2fZIkSUKI20CMJEm39WHuABWBckBt4Ky+c7MGDJf4f9H/f5X8nUVngZVCiO3AL5IkvSjQMbYGVuvzfV8I8ZR8vRyVJClFn4a7QAXA+CCpjAigshBiNfAncFi/E6klsNuAr9BP/kKID1/p5cPOg2nzTv5OFjc/D5JjjbcEJkUn4F4mf5XbzdedZP0WUKVKycgNk7nw22muHbpoFObV349vhmNrb8dXIWvQanSE3wrD0y9/QcbT14PEAltKUxNTcXB2QKFUoNPq8PDzJEkvU7VeVSbrHUA6uzvTKLgxWo2Wi4cv5IXPSM0g7MJd3P29cDVIu6ufB6kF8pccnWgs4+tBin5b5CvZ9IRUbh26RIUGVchMSce3ajmWXN0EyMcX3v18FDc7PyAlLhl3Xw+SCnCkmcnPKx0mRhnr1zD8q7JITUjhyqGLVAmoxv1Ld0mNT0Gn92Gfm5NL6x5tqVynMuG3wvEwWOzy8PUkMdZUt/ZGafEg0WAbaPmaFfho2WiWDF5gtD3bENlRidgapNm2jLvZ41qOtctTZ+UIrvX/FHWSvMXWo21dMp/FoU6Q44758xKuTaoTteeMWS6Qd/goPL3z/lZ4eqFLiDeSkbLyjzepr1yEUUqEswtSagp/FdrYOJQ++XxKb0+08cZ81rWq475QHuQVri7YtmyGpNGSfeosunj564wuKZnsE2ewrlOT3BuF7+LIikzEzkCfdn7uZo/7ONfyJ+DzDzg/YFmePrMiE8mOSiRJ/7Us8o+LVBtb9MJPblQC1gZ81n4e5EYX1hVD2sW72FTwReXuhCYxv05oUzNJPX8H1+BAsh48KzS8vMPH4GiToxtSerKxTFoSuqx0+UiXJhfdyzAUXuXQJsegDT2DNlSuH1ateiOlF71tOTMqEXuD/Nn7uZMVnWwi51rLn6YrhnPy3eXkJpluAVenZhJ7/h5+wfVJefDC5P0r5EQZl19R7aHuyhFcKdAesgq0B7di2kNWVCJ2ZY3rS1aMaf5cavnT6PPhnBmYnz+PptXx69wI3w4BKG2sUDnZ0WTNSC6PWV8oX1p0Ik4Gxymc/dxJN8NXHHJSM3l2/h6Vg+oT99C8PrUxcah883fXqXw80cYZH6mwrl0Nr2Wyvy6Fqwt2rZuAVkvm8fxJsy4tg+wrN7Fr1Rj1oyclTqOPiwPRBscBYlIy8NLvqHyF3y8/ZGhwA4QQlPd0oay7E49jk6lX3rtgdCbwdrIlJi3/uEVMWhZejuaH60MPovKOeRWEk60Vjct5cO5xHFU9Cz/KJKUkIFzzxwTh6oGUatzWpeQEtBmpkJsDuTloI+6gKFMRbXyRLhfNQh0dj5XBGGTl54E6tvC+JePSHWwq+KF0c0abJB8JdwpqRFboIzTxyX+Z/++EwtaZlQfkHRHht8Lw8DMcWz3ybJJXkO0Wx7yx1dPP1LYBuHvpDr7l/ej1UV/a9mwLQEpCCnEv40iNl8crd19TWyytwNjt7pefhsSoBKOx31z4zNRM7p2/Q/2gQF48fEbHQV1546PeOLk5ceGPs8WG/3f4W/cJwqeCL5/tX1UqdkpmagZ3zt+mQlB9EvV9d0ZUIk5l8schRz93Mv7iEZgKwQ2IC31CVnzx/vs00fGoDOqMla8nmiLaQtblUKzK57eFlJ8Pk/LzYQC8Jg5GEx1faFgAdXQCVn4GfaefZ5FtL/PyHazL+6J0c8aheT3UL2LQJsr5Sj10HvtGtUj5/USh4TOiEnEw0Ke9nzuZZvTpWsuflp8N58h7n5FjMM4KlZKgTeOI+PUczw4U4y8JSI1OxLnAOJT2L4xDJYUuOR4rg106CjdPpJQC+szOX6zS3r0CytEIB2ekjFTU5w+jPi+Xn3X3wUY7L18hJToRFwPbwcXMHCUlOqHAHMXd6OhWjaAAXoY+Jj0+39Zt1Lcte+fLC6K3/rxAv08/+CtZ//8Sllu9Ss/HjyEM90NPAGKABsjHuF59shIUs8au3wWzDuin39WyCbAtKowBSrr8+2pvtc7g96u/Vfp0huh3IAVIklRbkqRhZsJr9fLofQgNR96hdMHMcauiNogZpiEvzoKQJCkJWacnkHcQbUYu62SDtAZIklSrMCJJkjZKktRYkqTGUngmPhX98CznjdJKRdPurbgZctlI/kbIFVr0CQKgcmA1stIySYlLBmDwslFEhb8g5BvjLffXD1+mZou6APhU8kOdk8vHncYw4bWPuXDoPMF92wNQPbAGGWmZJgslALfP36ZVN/nLd/t+HfIWdj5sPZwPWw3jw1bDOLf/LF/PWs/FwxdwdnfOO/JkbWNNjVZ1eXDmNl4VfXEv54XSSknD7i25HWI8wNwOuULTPrLxVTGwGtlpmaTGJWNtZ4ONg1ztrO1sqNmmPlEPnxP14DlT6w5hYvV3mVj9XdITU3l57ykpcclUDaxOVlqmicEEcPd8KM26ySce2/QN5krIJQCuHrlMm77y1nrD8DZ2Ntjq+W3sbKjXNoDn+sm14Xn47IxsLh++yJRuE7h8+ALt9HFVC6xOZlqG2bTcOX+b5t1aAdCub3su651OepbxZMrX01k94QuiHhdu9Kdef4R9ZV/synshrJT49mpJ7KGrRjK2ZT0I+HYit0evJTMi34lv9ssEXBtWRaH/iu3Rpi7pBs75zEHz8D7KsuVQ+PiCSoVN2/aoLxp/DRZu+QaIqnpNEIp/adEHIPfefVT+ZVH6yXx2ndqTdcp4a3d074FE9x5AdO8BZB07SfJnX5J96izC1hZhL5+tFra22DRrjPrR4yL5km88wqGyL/Z6fZbt1YLow8b6tCvrQZNvJ3B1zDoyIqLznufEpZD1MgHHKvIE0atNXSMnv+aQfiMc20p+2Ph7I6xUePRsTdJh43ZvUzF/w6R9vcoorFRoEtNQuTujdLbX588alzb1yQovfFEEQBf9BOHqjXD2BIUSVc0maCNuGsloH91AUbYaCAWorFH4VkL3yvnzK0fPTu4oqwWiuX+pSL7EGxE4VfLFwd8LhZWS8j2b86KAPu3LetB683gufLyeNAN92rg7YaXPn9LWCp82dUgNj6IopJSwPQR+O5FbZtqDy19sD0k3InCs5Iu9v8zn37M5UYdM60uLb8Zzeex60g3yF7pkJ/sbjeVA0/Fc/GgNcWfuFrnoAxB5MwL3Sr646PVZu3tzHoZcLTLMK9i7O2Gj16fKxoqKreuQUIQ+c+48QFW+LKoycttz6BJE5knjtvfy9UG86PYeL7q9R+aR0yQsWU3m8XMo3FxQOMljgLCxxq5ZQ9SPzX0/KRx1/L14Fp/Ky8RU1Both248ol1t4x0Sfq6OXAyX+8eEtEyexKVQzuACgyLj93XhWXIGL1MyUWt1HHoQRVAVHxO5tBw1V18kElQ1/11iZg5p2fItO9lqLRefxVPR3cEkrCF0z8NQePoh3L1BqUIV0AbtHeP2o7lzEUWl2qBQgJU1igrVkWKLbtOFIfNmGNYVy2BVzgdhpcK1e1tSQ4z5rCvkL2bZ1amCsFLlLfoAuPZoS/K+IpyblxJ02alMfG0cE18bx8VDF4zslsxC7JbQ87doqR9bg/t14NJheWz1Nchz5bpVUFmr+G3Dnrz4VdZWeQZilcDqZBZhRzTtJh+DbdM3mGt6e+3akcu07htkEt7J3Rl7ffuzsrGmbuv6ROr769inMeRkZjO+1UdcPnDBbPh/l/8Vzvxygpin0f9RO8XZ3Rl7AxuwfusGJIXnv4+5GYFLRV+c9P1YtR7NeRxi/jh7YajWswVhJTjmBZB1+2FeW8BKhfPrbUk7esFIxqp8fr2wrW3cFpTuspN1lZ8XTp1bkvJH0W0i69ZDbAzanssbbUk7YrwzybDt2dapgrCyQpuUijoyDruAGghbeRHaoWUDI6fQ5pCgH2cd9fqs2LM5zw8b69OhjAdBm8ZzZtwGo3EWoOXnw0kOj+TeRlO3DeYQeTMCj0q+uPrL9nzd7s15UMJx6F+B7ulDFF5lEB4+ct/ZsC2aW8blJ5zybXFFheogBFKGXH6vnOQLNy9UDVqivmJafi9uPsKzoi9u+jlKg+4tuFsgT3dDrtGwTxsAygdWJSstkzT9HAwgoEdLbuw7ZxQmNTaJys3laWDVlnWIf2Ksewv+N/Hfvs7dBXghSZJOCDEYeHXdxWFgjhDiR0mSMoUQ7vpdP2nAq89WrxZ54vW7WfoBxd7iBdwHKgkhqkiS9Aj5ONW/igvAWiFEVUmSwoUQ9kA5SZIKvbpGz3sbuC2EaIG8A+mGgcgpYCBwTAhRHSgPPAAaljRR+iNouZIk7RFCPAK2SJKUKoR4LIR4U5Kk3Xp/SPUlSbpZTHTotDp+nLOZ8VtnoVAqOLvrGJFhL2g3sDMAJ7cf5vbxa9QLbsiSk2vIzcrhuynyKbKqjWvSsm87Xtx7ypz9nwHw6/IfuX3iOmd2HeP95aOYf0i+yvTbSfn+Qa4eu0Lj4MZsOL1JvhZ18hd572ZvmcfaT74iMSaR75d+x+Q1nzBwyrtE3IkgZOfhIvPi5u3O+JUTUCgVCIWCO39eJPTIVXQaLaO2zkChVHBh1wmiw17QamBHAM5uP8Ld49epExzInJNfkpuVy/Yp8iTIydOF4RvlU3wKpYKrv5/l3klTlaqzc0l8EceqU+vJycrh68mr895N3TKLjVPXkhybxI6lWxm7ZhJvTh7A0zuPObHzCAA3jl0lILiRSXgXT1cmbJRv91KqlJz9/TS39LeY9Z8+iAq1KyJJEPcilq9nyGVy7dhVAoMbs/rUBnKzclhrkJbpW2azYepakmIT2bb0eyasmUz/yQN5fCeCYztDAOg37h0c3Zz4YOEIALRaHdO6m964Jml13J/+HQ1/moFQKni54zgZD15QbpCs1xdbj1B5Ul+s3ByptUw+hiZptFzsMpOUa+HE/HGRFiFLkbQ6Um8/4cUPxVzPrdOSsf4LnBeuAIWCnJD9aJ89weY1eWdLzoG92LRqh023nqDVIuXmkL58fl5wx6lzsKoXgHB2wfX73WRt/46cw/sLYwOtjuQVq/H8ahlCoSRj3wE0j5/g0Fu+Hjvj18I3Hyrc3fBYvgCQnSVmHjpKzoXLhcq/0uetGVtosWMaQqng2Y4TpD14ScVB8m68J1uPUmNiH6zdnGjw6ft5YU52kW+fuzXzexqtG42wUpH5NJbr478uWp9aHU9mbqbmj3MQSgWxPx0l6+FzvN+T233sD4fxeL0Fnv3aIWm06LJyCRv5OQDWPm5U+XIsKOR2lrDvLMlHijG+JB25x3/Epu94EAJN6FmkhEhU9dsBoLl1EikxGu2TUGwHzQVJQnP7NFKCbLTbdB+JsHMAnZbcoz9CTtHOqyWtjisztxD04ycIpYKIn06S+vAlVd+T9Rn+w1HqTuiNjZsTjZfK+tRptBx+bTZ2Pq40//IjhEIBCsGzfReJPFK0rwNJq+Pu9O9orG8PL3YcJ/3BC/z17eH51iNUmdQXazdHahu0h/MG7aGlQXt4Xkx7kLQ6bszYQpsdcv6e6PNXWV9fIrYepfaE3li7ORH4Kn9aLce6zi4y3qL4Ds3ZQv+tn6BQKri56yTxYS9pOFDmu7b9KA5eLgzdtwgbRzsknY6mQ1/j645TcfR2pftKWZ9CIbj3x0XCjxWhT62OxE/X4LN+qXyd+++HUD96ilM/+bR32s9m/ProofR0x3Ph1Lyyyzh8iqzTJTmSkQ+VUsG0Xi0ZuekAOp1Ez6Y1qOrrzu7z8pXHb7aozQcdA5mz8yT9Pv8ZSYLx3Zri5lCyb1MqhYJP2tdh1J5L6HTQs245qng6sfum7ED6zQYVADgeFkPzCp7YWeWbcvEZOcw5cAudJKGTJDrV8KOtmUUjI+h05Py6EbsP5oFQoL58FF3Mc1QtugKgOX8QKfYF2gfXsZ/0FZKkQ3MxBF20/JHBZuAklFXqIhycsZ/1DbmHd6C5dKRwPq2OyDkbqLx1PigVJO06Qk7YM9wHynyJ2w/i8lpL3Pq0R9Jo0GXn8nRMvltDYWuDY+sAXsxYWyJ9Focpcz/l8vVbJCen0qHXu4wa9h59u3f5y/FcPXaFRsGNWX96o95uyffRM2vLXNZ+spqkmES2Lt3CpDVTGTDlXR7fieCI3m5p0a0lQX3bo1VryM3O5fPR+Xm2trWhQo3yXDh4ns9PrSNXfx36K0zeMpPNU9eRHJvET0t/YMyaibw5eQBPCtgRDYIbmoR39XZjxMqxKPT99cU/znLjmNxfD14wHJW1FdO2ybf9KVXKv50fYPRXE6jVoi6Obk5suPANu1btIOZZzN9up7h6uzFm5Xh9XgXn/zhL3NEbeXFLWh2nZn9Pz21T5evcd54k8eFL6rwrL+jd2XYMey8X3vpzIdb6fqzBsK5sb/8J6vQsVLbWlG9TlxPTSng1tlZH9Pz1+H+7SL7O/efD5IY/w7V/NwCSd+zHuWsrXHp1yGsLL8fn30FTbs1MlG7OSGoN0fPXoUstxjGxVkfkvA1U/H4BQqEgaXcIOWHPcBvwGgBJPx7AuWtLXHu3R9JokbJzef7xMgCybj4k9eBZqu77AkmjI/vuI5J+OlgknaTVcWnW93T8Ue5zw3eeJOXhS6q/J+vz4Q/HqD+hNzZujjRbMgSQx9n93ebg3aQ6Vfq1IenuM944LB/Lv/7pLl4eK3zaotPq2D9nC+9tlce967tOEhf2ksb6cejK9qM4ernwocE41Hzoa6ztOJWc9Cz6fjWaii1qYe/mxMQLqzm+6meuF3V7pk5H9q712I9eJPedFw6ji36GVWu5/NRn9qMKbIVVm9flY1zqXLK+W5YX3Hb4TISDM2g15OxaB1mm5afT6vh9zhaGb52OQqng8q4TxIS9oLl+jnJh+xHuH79OzeAAPjn5BblZOeyekm/fWdlaU611PX6Zsdko3j3TNtFj7iAUKiWaHDV7pm9m3B9LCs/r/wAsO35A/EfPPr4iEeIJ8o6eMUC6JEkr9M+rAXuATOA4sv+ZV86RpwGDgFxgvyRJM4QQfYElyH5xWgAzgXeAJ8hHnp5KkjRPFHOduxCiK/AFEA+cAerqr3MfAjSWJGmMYRxCiIr633X14Q3ftQeWkX9sapYkSXtf5VmSpHghRGNghSRJQfrjV8HIO3buIt9U5vcqfv1Opg3IvpA0wERJko4bpk2fhj/0cZ4wk78GyH59Xu3omi5J0gEhRCVkf0N+yMfsfpIkaYE5HRlieMV+pXYDXpwuu3ihvxEVFEV/Cf27ES+V0MfD3wR1KXdzH2QXf+3z34lGjYveZfF3Iyu++KuY/05cfVqUC7K/Hz6K0m1/9SebvxHuP4HfVxS9EPR3w+1f8bXwbyBdUbp184F18TJ/Jwa6xRYv9DfCZ8Ffn/j/O5Cii97B9XdD9zCiVPkifi7dsajWFVNn0P8pvNVwXKlxATgIq1LlK23kULp9Zzvdv3+j219BB9uib8L6u6HVlu7hjms5rqXKF6EqtSkKAJO6l97tVwv/cCk1LoDlT3b8g90S//v4vPy7pVZZJj3b9o/UZans+JEkqaL+57wCz8MAQ4+m0w3efQp8WkB+D/JC0SvM0v8ryDekmPQcRN5pU/D5FmBLwTgkSXoC1DUXvyRJxwDju78xyjOSJF0BgvS/x5pJUl78kiRlIy8GFZo2/d+FOrLW7+Ix2SEkSdJjoGth4SywwAILLLDAAgsssMACCyyw4H8JpbtE+M/Ef8PHjwUWWGCBBRZYYIEFFlhggQUWWGCBBaWA/7aPn/8ohBC/ApUKPP5EkqRD/430/CcghLiI6e1c7726gcwCCyywwAILLLDAAgsssMACC/6vQvePPHxVuvifXviRJKn3fzsN/2lIktTsv50GCyywwAILLLDAAgsssMACCyyw4J8Jy1EvCyywwAILLLDAAgsssMACCyywwIL/UfxP7/ix4O+BO6V3g8S6c3NLjQsgtIWJb/D/KOqeX1SqfGjVpUr3U8uVpcqndCzdtWv3DlVKla9z3cBS5ds69Fyp8nWbe6LUuF4sL12/9p0W3SlVvpAp1UqV73VPz1LlqzTmWqnyLR15o1T5ckt5C3rTUv7s96EmpVT5ypXiTVu7rpXeDWIAdxqNL1W+Opc/L1U+tJpSpdvZaHGp8rmXLd0bJu2qle4VjFVeq12qfH+OLt2x9tM/XEuNa/5yk3uILPg3YLnO3bLjxwILLLDAAgsssMACCyywwAILLLDgfxaWHT8WWGCBBRZYYIEFFlhggQUWWGDB/yQs17lbFn4sKCGqt2tAzzmDEEoFl3Ye58T6vSYyPeYOpmZwAOqsXHZNXs/LO08AaDPsNZq83R4kiegHz9k1ZQOaHDWvTx9ArY4N0eZqSXgWw64pG8xyn7l0nWVrv0Or09GnWweG9zf22Z2Sls6cz9bxPDIaG2trFkwZRbVK5Xn8/CVTFq7Kk3sRFcPoIW/zXt83isyrc1Ag5eZ9AEoFCTtCiFm3x+i9S+emlJk8EEmnQ9LqeDFvMxmX7wFQ59xGdBlZSFr53YPXJxWr29LO35nLN1i2fqvM1zWY4e/0NOX7/GueR8XIfBNHUK2SPwCp6RnMW7mRsCcvEAIWTBpBQO3qhXKVCapPkwXvIRQKwnecIHTtPqP3lXq3pO4oOb3qzGwuTt9C0t1nALT8/APKdgwgOz6VfR2mF6NFGaoGTbAbMgYUSnKP/UnO7zuM39dugMOURehiowHIvXSanD1b8wWEAselG5AS48lYPqNYPkXFOlgHvQMKBZrbp9FcPmgqU666XkaJlJ1Gzq4VcloCO6Cq1wYQaG6fQnP9aLF8Z+8+Yfmek+h0Er1b1GFo5yZG79Oycpi59RDRiWlodDoGdWhIr+Z1yFFrGPrFz6g1WjQ6HR0DqjLq9RbF8vkH1afl/PcQSgX3d5zgRoHyc63iR9DKD/GsW5FLy3dz6+v9ee/qDutCrf5BVb6x0AABAABJREFUIAT3fzzO7W9Kdpni0uWz6dS5HVlZWYz+6BNu3bxrIvP15s8JaFgXjVrDtau3mPDxbDQaDa1aN2X7Txt4+vQFAH/sPcxny9YUynX2STyfnbyPTifRq245hjYxvgTy+yuP2X9fritaScfjxAyOjQgmS61l9qHbJGTkIgT0rVeOAYEVSpS/8QvG0KJ9M7Kzslk8YTkPQ8NMZPoO6cVbw/tSrlJZutXtRUpSKgDlq/gzc9VUqtetxsZl37Lj610l4gRQVKiDdbu35LoaegbNFePyUDXqjKpmU/kPoUC4+5H19STIKfmxhLPh0Sw/dAOdJNE7sBJDWxlvU99y7gH7Q+X2rdVJPI5P5fikHrjYWZOancuCfVcJj0tFAPN6NKZBOY9iORctm0GHTm3Jyspm3KgZ3DZTX9ZuXE6DQLm+XL92iynj56HRaOjSrT2fzPwYnU6HVqNl9vSlXLpQsiNlZYPq00zftz3ccYLbBdpG5d4tqafv2zSZ2Zwz6NtKgtJue07t5HFPKBUk/GRm3OvUFD/9uIdWx4v5+eOe0tkB/+VjsKteHiSJp1NWk3ntQbGckxZ+TKv2zcnOymH+hKU8uP3QRObN9/vQf3g//CuVo2Pd7qQkykfG2nZpzUdThiFJOjQaLSvnrubmJdPLTIfN/5BGwY3Iycph9aQviQh9ZCLj7e/DpDVTcHR1IiL0EV+OX4lGraFpp2b0nzwQSSeh1Wr5dv5m7l2+S5nKZZm8dmpeeJV7RXSZieiyU4vNM8CsJSs5dfYS7m6u/LbNvN3zV+HUriFl5w5HKJUk/HSY2PXG5efcqRl+kwaCToek1fJy/mYyrhiU37Ix2FavAEg8m/JVseV35uJVPv1yI1qdjr5vdGb4u28avU9JS2f20i94/jIaGxsrFk4bR7XKFQH4Yffv7Nl3CEmCft278N5bPc0wFOS7xqdrNqPV6uj7eieGD+xryrdsdZ6dtHDqGKpVlvvkrbv3sufPEASCapUrsOiTsdjYFH78yS+oPk0W5tstd9YYt72KvVtSZ3R+2744bQvJd59hX8adll9+hJ23C5JOImzbcR6UoO1ZN22K05gxoFSS9eefZP74o1k5VY0auK9bR8qCBeScPAmAfb9+2L3+upyWiAhSli2D3Nwi+ZR1GmP7zkiEQkHu6YPkHtxp/L56fexHz0eXII996mtnyP1jOwqfctiNmJknp/D0Jef3reQe/bVIvrMPXrL8j0uy3dKkGkOD6hm933IqlP03IgD9uBCbwvFZb+Nib8P2s3f55XIYkiTRp0l13m1d/DEyn+D6BCyQ+87HP57gQYHy8+/Tkhqju8t8Gdlcm/YdKYZ9s0LQ4eAisqOTODtohVmOau3q8/qcQSiUCq7sPM6p9ftMZF6fO4ga+jnRnskbiNTPiVq835Um7wSDEFz56RjnvpXtxo4T36RWp0ZIko70+FT2TDbfN5wNi2T5n1fkcbZRVYa2rWOszzN32X/ziV6fOh7HpXJ8Wl9c7G344dw9fr3yCCGgmo8r83u3wMZKWaxOLfjfgWXh59+EECIImCxJ0htCiB5AbUmSPv3vpqp46NOdK0lSsU49hELQe8H7bHp3CSnRCYzdu5i7IVeJDX+ZJ1MzKADPSr4sD5pA+cCq9F48jDW9ZuPs40arIV1Z0XEymhw1A9eMo0H3Flz9+RQPz9zmwPKf0Gl1vDatP8GjTAd/rVbL4q82s3H5HHy93Hln1DSCWzSmSkX/PJnNP/5CzaoV+XLBVCKevWTJV5vYvGIelfzL8vPGFXnxdHh7BB1aF3MJmkKB/6IRhA2YizoqgRp/rCAl5BLZYc/zRNLO3OLe4UsA2NWsQKX1U7kbPDrv/cO3ZqFNSitOrf+V/Gm1Ohav+Y6Nn87A19ODd8bOJLhFI6pUKJfPt+N3alapwJfzJsl8a75j83LZF9Kydd/TqkkDVs6ZgFqtISsnp1AuoRA0WzyYkP6fkhmVSLf9C3h++CopYZF5MunP4zjUbxG5KZmUCa5P82VDOdB9HgDhu05x/7sQWn05okS6RCiwGzqOjMVT0CXE4bR0A+or59C9fGokprl3u9BFHZtufdG9fIawsy8Bn8C6/QBy9qxCSkvCduBMtI9uIiVGGURoh3WHgeT88iVSWiLYOclBPcqgqteG7B+XgFaDTZ9xaB/fRkqOLZROq9OxdPcJNozujY+rIwM/+4l29SpTxS9/crzz1E0q+7rz1YgeJKZl0mvRVl5vXBNrlZJNH/fB3sYatVbL+6t207p2RepX8itCnYJWiwbz54BPyYhKpM+fC3hy+CrJBuWXnZzB2Tk/ULFLI6OwbjXKUat/EL++MRetWkO3bVN5euwGqY9jilRpx87tqFKlAo0DOtK4SQCfr1pAp/b9TOR279rLiOHyouqmb1fx3uC3+O4b2Vg+f/4K/d/8sEgeWZ8Snx6/x/o+jfBxtGXgjgu0q+xFFQ/HPJnBjSsxuLG8GHQyIpbt157iYmtFrlbHxLY1qOXtTEauhgE/XqBZeQ+jsObQon0zylUqy9ut36NOw1pMXjqeD7uPNpG7dTmUs0fOs+bnVUbPU5PTWDV7DW27tio2f0YQAuvg/uT88gVSehK2/aejjbhlVFc1Vw+juXoYAGWl+qgadvhLiz5ancTSg9fZMLANPs72DNx8lHbVy1DFyzlPZkjLGgxpWQOAkw8j2XYxDBc7eQK2/NBNWlb1ZcWbLVBrdWSpi/fz0aFTWypXrkCLhl1p2LgByz6fQ7eO75jI/bL7D0Z/KE/U129ewcBB/fj+2584ffICh/YfA6BWneps/G4VbZq+XiyvUAiaLx7MIX3f1n3/Ap6Z6dsO6Pu2ssH1abVsKH/o+7aSxF+qbU8/7oUP1I97+8yMe2dvkRIij3u2NStQad1U7rWX627ZecNJO3GNJx8tQ1ipUNjZFJvHlu2bU75SOfq0GkDdhrWZtnQi77/xkYnczcu3ORNyjg17jP3oXD59lVOHzgBQtVZlln49nzfbvmck0zC4EWUqlmFU2xFUD6zBiMUj+aTnZBOOQdOHsG/z75zZd5qPloyiw9udOLTtALfO3uRSyEUAKtSsyOR1nzC2/UgiI14y8bVxetUp+DliD7rckreVXt06MaBvD2YsND+p/MtQKCi3cASPBs5BHZ1A9b2fk3LkEjkG5Zd+9iYP9HmxrVmRimuncr/DKADKzv2A1JPXeDKyZOWn1WpZtHI9m1YtwtfLg7c/mEBwq2ZUqVQ+T2bT1l3UrFaZr5bMIuLpcxavXM83Xy4hLOIJe/YdYsfGlViprPho8hzatmhMBf+yRfN9+TWbVsyX+T6aQnCrpkZ20qZtP1OzaiW+WjSdiKcvWPzl13yzciExcQls3/MHv3+/GlsbGybNW86BY6fp9VoHs1xCIWi6ZDBH35Hb9mv7F/DikGnbDulrYLcsH8rBN+YhaXRcW/AjibefoHKwpdvBhUSfum0U1lzZOY0bR/LkyWjj4nDfsIGcs2fRPn1qKjdiBLmXL+c/8vTEvm9f4gcPhtxcXObOxbZ9e7IPmn6AMsggdgPGkLFqGlJSPA4zV6O5eR5dlPGitCb8Nlmr5xg908W8IGPByLx4HD/7EfX1s4Vzobdb9l5gw7DO8riw9k/a1fKnio9rnsyQtnUZ0rYuACfvPWfbmbu42NsQHp3EL5fD2DbqdayUCkZ/d4Q2NctRwdO5EDZAIQhcMoTTby8lMyqRDgcWEnn4GmkP8+crmc/iONlnIeqUTHzbN6DRZ8M49nq+f9FqH3QlLSwSKye7QlQo6L7gfb57dymp0QmM3LuIeyHXiDOYE1XXz4lWBk3EP7AqPRYPZUOvOXhXL0eTd4JZ33M2WrWGwd9P48GxGyQ8ieb0xj84snI3AC2GdKH9uD6A8RRNq9OxdN9lNgxpL+tzw0Ha1SxHFW+XfH22rs0Q/QLZyfsv2HbuPi72NsSkZrLj/AN++fgNbK1UTPnpNAdvP6Fnw9L1XfnfhM6y58fi46cwCBl/ST+SJO39py36CCEKW8oNAlqWJA7/gKrEP40m8XksWrWWm/vOU6dzYyOZ2p0bce2X0wA8ux6OnZM9Tl6uACiUSqxsrVEoFVjbWZMakwRA2Onb6LQ6fZgwXH3dTbhv3w+nfFlf/Mv4YGVlxWvBrTh+7rKRzKOnL2gWKH9BqFy+LC+j44hPTDaSuXj9Nv5lfCjj41VkXh0CqpHzJJrcZzFIag1Je0/j0rmpkYwuMzvvt8LeFqR/vSMp7fzdfhBO+TK++Pv5YGWl4rV2LTh+7oox37MXNAusm88XE0d8UjLpGZlcvX2fPl2DAbCyUuHs6FAol0dgFdKexJD+LA6dWsuT3y/gX2CSEncljNwU2WCOvxaOg19+HYi9+ICc5PQi82MIZdWa6GIi0cVGgVZD7rljWDUp+QRZuHuiCmxO7rE/SySv8K2ElByHlBIPOi2a+5dRVgkwklHVbIY27Lq86AOQJS8IKtz90EVFgCYXJB3aFw9RVi3akXPo0xj8PV0o5+mClUpJl0bVOXE7wjgPQpCRnYskSWTlqHGxt0WpUCCEwF7/hVOj1aHR6hCiaG+y3gFVSH0SQ5q+/MJ/v0DFzsbll52QStzNCHQardFzt6pliLn+CE12LpJWR9SF+1TqatxnmEO31zvy047fALhy+QbOrk74mKnTRw6fzPt97epNypT1KTbuggiNTsHfxZ5yLvZYKRV0qe7LiUeFL7wdfBBN1xryQpmXgw21vGXj08FaRSV3B+LSC18EfYXWXVpy8OcQAO5cu4eTiyMe3qb9XtidcKJfmE7UkxOSuX/zARq11uRdUVD4VkJKiUVK1dfVh1dQVmlQqLyyRhM0Dy4X+t4cQiMT8XdzpJybo6zPOv6ceFD4hOdA6HO61pEnbuk5aq49i6N3QEUArJQKnG2Ld0japVt7dv30OwDXrtzE2cUZbzP15WjIqbzf16/dxq+MXF8yM/In6/b29kgl7Ms9C/RtEb9foHyBvi3WoG+LuxaOvZ9pOReG0m579gXHvX0lH/cUjnY4Nq1Dwk9yvZbUGrSpGcXmsV2X1vz5s7wbIvTaXX1bMN3h9TA0jKgX0SbPszKz8n7b2duZHYabdm7O8T3ywt7D6w9wcHbAzdvNRK5ey/qc2y9PYI//fJRmXZoDkG2QZ1t7G7Njfb1WDZC0GtCV3CFx44B6uDg7lVi+OMjlF0Xuc4Py62T8Eci4/Gx4dehB4WiHQ7M6JP6F8rt97yHly/rhX8ZXtls6tOXYmQtGMo+ePKN5I7mPqVzBn5fRscQnJhHx9AX1a9fEztYWlUpJ44C6HD11vmi++2HGfO1bc+zsRWO+p89p3rC+nq+cni8ZAI1WS05OLhqNlqzsXLw8C2+L5uyWcgXadnwBu+VV286KTSbx9hOZMyOblPBI7Ipp91Y1a6J9+RJtVBRoNGQfO4ZNK1O7xb5PH7JPnUKXnGz8QqlE2NjI/9vaoouPL5JPWakGurhIpPho0GpQXz6JKqBEUwHjeGoFoouLQkosfMwECH0ej7+HM+XcnWS7pUElTtx7Xqj8gZuP6dpA/tgSEZdCfX8v7KxVqJQKGlXy4didondNugdWIf1JDBnP4pDUWp7/foEyBcov4UoYan35JVwNMyojOz93/DoE8PjH44VylAuoSuLTGJL0c6Jb+85Tq0D/XKtzI67r50TPr4djq58TeVcty/Pr4aizc9FpdTy5eI/aXeT+OSc9v0+zsrcx26eFvkjA38MpX5/1KhStz1tP6Fq/Yt7fWp1EjlqLRqsjW63By7kEHzkt+J+CZeHHAEKIikKIe0KIdcA14BshxBUhxB0hxHwDua5CiPtCiDNAH4PnQ4QQa/S/twgh+hm8S9f/7yeEOCWEuCGECBVCtCkkLW8JIVbqf48TQkTof1fR8yKE6CCEuC6EuC2E+FYIYaN//kQIMUcv96YQ4mMhxF0hxC0hxE9CiIrAR8AEfTrMpuEVXHzcSIlMyPs7JSoBZx+3AjLuJBvIJEcn4uLrTmpMEic3/cGMc2uYdWk92WmZhJ023Y7d5M0g7p+4afI8Nj4RX6/822N8vDyIiU80kqlRuQJHTstGwO37YUTFxBETn2Akc+D4WV5r37qobAJg5etBbmT+wKmOSsDK19QYdenanNrH11Ll+9k8nbw6/4UE1bbPp+afn+MxoHOxfKWdv9j4JHy98vPj4+VBTEKSKd+Zy3q+cKJi4omJS+RFdCxurs7MWrGBN0dOY+7KjWRmZVMY7H3dyIjMz0tmVCL2vqZG9ytUfSeIl8dvFZuHwqBw90SXkG+E6BLiULiZ3jykrF4bp+WbcZj2KYpyFfOe2w0eQ/b2r0Eqmd9/4eiav6ADSOlJCCdXYxk3H7C1x+bNydgOnIWyVgt92l6iKFcdbB1AZY2yUj2EU9EGYmxyOr5u+ZMFH1dHYgssjL3TtgGPY5LoNGsz/ZZuZ0rfdigU8gKPVqfjrU+30376JprXLE+9ir5F8tn7uZEelZ+/jOhEHPwKLz9DJD54gV+zGti4OqKytaZ8+wY4lin+2I5fGR9evszfhRL5Mjpvkm4OKpWKt97pxdEjp/OeNWkawKlze9m1ZzM1a1YtNGxsRjY+TrZ5f/s42RKXYX7xJkut5dyTeDpUM01LZEoWD+LSqOvrYiakMbx8PYmNzK+jsVFxePn+52/HEg6uSGn57VxKS0I4uJoXVlmhrFgHbdhfu0UrNjULX+f8L6Q+znbEpmWZlc1Sazj3KJqOteSdhi+SMnCzt2HO3iu8vfEI8/ddISu3+Im0n58PkS/zFwWiIqPx8/MuVF6lUtHv7R4cP3om79lrb3Tk9KU/2bZrPRPGlOyWR3N9m0MRfVv1v9i3lXbbsy4w7uVGJWDlY2bc69KcWsfWUmXLbJ5Okcc9m/K+aBJTKP/5x9TYvwr/ZWNKtOPHy9eTGMO2EBmH919sC0Fd27D71A+s2rqMhRNNv7l5+HqQEJWfr4ToBNwLjOdObs5kpKbnfYSKj0rAw0CmWZfmrD62nplb5rJmiuntXW16tEHKKfkHiv8ErHw9UEcZ2i3x5u2WLs2peXQdlb+bw7MpXwH68ktIofyKcVTf/0WJyi82LgFf7/wFVh8vT2IL2CQ1qlbiyEl5t8Ltuw+IioklJi6BqpUqcPVmKMkpqWRlZ3P6whWiY4terIiNM7WTYuMK2ElVKnLktLz4dPveQ6Ki44iJi8fHy4Mhb/ei41sfENz3fZwc7WnVpPCPLPa+bmQWtFuKaHtV+gcRaaZtO5TzxL1uBRKumR4tNITCywtdXFze37q4OJRexovXCk9PbFq3JmuvsYsFXXw8GTt34rlrF1579qBLTyf3ivGHvIIQrp7oEvP5pKQ4FK6mdUVZuTYOc9Zj//FiFGVMjzFbNWmH+lLhiyOvEJuaia9L/gdCH2d7YlPMLyxm5Wo49/AlHevKfFV9XLn6OIbkjGyycjWcefCSmELCvoKdrztZL/PrYlZUInZF9M2V+gcRfSx/7tFgwXvcWrQDdIV/BHAuMCdKjUrExcfdjEx+PUqNTsTZ142YB8+p2LQmdq6OWNlaUz04ABeDXdudJr/FlHOrCejZKm/3jyFiU7PwdclfrPFxsS98nM3VcC48io615Q8sPs72DGpdi66f/0an5b/gaGtNy6qF7/r+X4SuFP/9U2FZ+DFFDWCrJEmBwCRJkhoD9YF2Qoj6Qoj/x955h0dRdY//M7vpvZJGgJDQW+gdEjooHZEiUpWqgNJ7BxGsKIqogCDFLlJD7733UEJI771tmd8fsyS72U3h/eL6/nzn8zw+kp1758y557a5c+65NsA3QE+gLVD625MxQ4ADoigGAw2AayWkO6G7P7r/JwuC4Ae0AU7qnmMT8LooivWQtu2N18ufJ4piG1EUdwCzgIaiKNYHxomiGAF8BXwsimKwKIonKQ1TngHF+0QTaURRxNbJnjqdm7Cq7bssaz4BSztrGvYxXKDoMLEPWo2Wq7+fMr6HCbe84p4Kowf3JSMrmwFvT+PH3/ZRs1oAFsoiRyeVSsWxM5fo0q7smCaYcoIwseyevv8cd0In8njMCnymDS38/UG/Wdzr8R4P31yC5/AeODQvfT+yufUzLc/w79Gv95LkjZvFj38coGZQFSyUSjQaDXfDn/D6q535af0qbG2s+XancaynkvTQPYBJvFrVImhwe66s2FGmDqUINPGjoUD1k3AyJg4ic8YY8vf/hv20pQBYNGqBmJGG5olxjIlSBJYlDhQKFF6Vyf/tM/J++QTLFq8guHghpsShurgfm/5Tse43GW1iFGhL9+IwVXTFy/jM3afU8PMgbNkYds4awqqfjpGVKy1mKBUKds0ayoGlo7n1NJ6HMaVPtoXy6FcCaQ9juPblX7yyfRY9ts4g+U6kkWeCSZkl9CMlsebjRZw9fZFzOq+1G9fv0KB2CO1a9WLD1z/ww/b1JQt7AUe9E48TCfZ1wdnG0uD3nAI10/ZcY1r7GjhYl71z+kX1e2m8wFHhyqoN0MY8eqFtXlBS/TSd9sSDWIL9PQq3eWm0Wu7FpjGwSVV2vt0JGysLvjt9r0yZL1qeq9Yu4NyZS5w/e7nwt31/HaJts1cYOfQdZs59t0yZJcs1nda7VS2qDW7PpRfo28ze9so77h04x90O0rjn+3zcs1BiVzeQpB/2c7/HVLS5eXhN6G+U10jkS2gLx/af5LV2w5g+ai7jZowuV57iMkwOU3ppzh84xzsdxrNqzHIGT3vDIJ2FpQVNOzdHm1+2h9Pfi0kljH5KP3COex0n8OStFVK8HwClzn5b9/GgxxS0OXlUmGC8vdbg1iafwPAZxrzxGhmZ2fQf+Q7bfvmLmtUCUSoVBFbxZ9TQAbw1dT7jpi2kelAASr35jGl5Zc9bxgzpT0ZmFv1HT2Hbr3uoWa0qSqWS9Mwsjp6+wIEdX3Pkl+/Izc1j98FjJQt7gbZdOG9Zbti2LeysabdxMpcWbEWVZfqlvFSKCXScNImsDRtAa/g6KTg4YNO6NUmDBpHYvz+CrS02nTuXfm+TfbKhPE3kQ7JmvUH2kvEUHPkd2wmLDJMrLbBo0BL1pROURXnmLc85ce8ZwZUr4GwnLTxWreDCyPZ1GfddGBO/D6O6jytKRRmDWTn7MgDPVrWpMiSEmzr7+XRqSH5SOmk3IkoXUY6+q6QxIvFRDCe+2s2orbMZvnkmcXefotUU9c9ha3bxYat3uPbHaVoON/54bLItlPCcJ+5HEVzJs7A8M3LzOXY3ij3v9ebgjH7kFqjZc+1JaarK/AuRY/wY81QUxec+qwMFQXgbqZx8gNpIi2VPRFEMBxAEYStQdkCJIi4C3wmCYAn8LoriNVOJRFGMEwTBQRAER8Af+BFoh7QI9CvSAtUTURSfv6luBiYCn+j+1o/WdgPYJgjC78Dv5XlInd5vA0zuMpJmg0ILrzn7uJORYOglkh6XjIveV0UXnbdPUJu6pDxLIDtF2uJya/9FKjeuXrjI07h/O2p1bMiGIctNPoeXhztxiUUvqPGJyVRwN1y9d7C3Y9kMKdaAKIp0GzoBP++ir74nL1ylVrUAPNxcytRbFZuMlW/RlyVLH3dU8Sklps86fwfryt4oXR3RpGYWplUnp5O+/xx2wdXJOm8cbPSf0s/Lw424xKIvFfGJyVRwMyFv2rgieW++i5+3J3n5BXh5ulG/luRF0bltc77d+UeJsrJjU7D3LfoKYufjRk58qlE6l1r+tPpwDIeGfUh+6n/+5VSbnIjCvahcFO6eaFMNv0KSW/Qyq752HkE5BcHRCYsadbFs3ArL4OZgZYVga4fdpDnkrFtRojzJw6dIP8HBFTErzTBNZira3CxpS5e6AG10OArPimjS4tHcOoXmltQOLFv3RcwyLht9vFwciNOLHRWfloWns+FWuz/O3WFU5yYIgkAlTxf83J14Ep9q4N3jZGdNkyA/Tt99SpBvyV/Ys2NTcNBzgbb3diM7rvRn1Of+juPc3yFtyWo2c6CBB4M+o98aypsjXgfg6pUb+PkVfYHy9fMmLta0K/mMWZNw93Bj6rtFMXIyM4vqz6GDx1nz0SLc3F1JSTZ+7goONsRnFnmsxWfm4Wlv+kv3gQdF27yeo9JomfbXdbrX9KFjUMleSf2G96bXUCluzN1r96ngW1RHK/h4khSfXFLWl4aYlYbgWNTOBUdXxOw0k2mV1Zugvn/hhWV4OdkSl1H0khOfkYung+kYCftvF23zkvLaUcHJlnp+0hjSuZYf3502HVx25JghDB0uvZheu3ILX7+iuu3j601cXKLJfO/PnIC7hyvT31ho8vq5M5eoEuCPm5sLKcW20hanvH2bay1/Wn84hrAX7NvM1faeU1Bs3LPycUeVUHKe7At3sKokjXuq2CQKYpPIuSZNRdL2nsFrvOmFn9dG9KXPUCko7p1r9/DSbwu+niT+h23h6vnr+FX2w9nNmS69OtBn6KtYouThjXDcfYr0cvd2J7XYeJ6RkoG9kwMKpQKtRouHjzspJsb8Oxdu413JB0dXJzJ1wdYbhTTm8a1H1Kldtrff34kqLglLH/15i0ep85bsC7exquwj2S8uCVUx+1UoY+HOy9OduISidhafmGS0fcrB3o5lc6YA0jyi68DRVPSR2mr/V7vQ/1XppfaTrzfjXaF0Ty8vT+N5kkl5s94tkjfobSr6eHH64lX8fCrg5iLZqGO7lly7fY+eXUJMysqJTcGuWNvONdH2XGr502LNGI688SEFem1bsFDSbuNkIn49w7N9pXvfgOTho9Dz8FF4eqIptl3LskYNnBdI8XYEZ2esmzcnQ6MBpRJNbCxiuhTwPP/ECSzr1CEvLKxEeWJqEgq3InmCqyfatGJ1JU9vnnTrIjZKJYKDE2KWVO8t6jZFG/kQMTOtTP28nOyI0/PSic/IKXF70X69bV7P6du0Gn2bVgPgswNX8Cpja1JubAq2fkXvIrY+buTGGz+ncy1/Gq8dw6mhqwvt596sOj5dGuPdMRiltSUWjrY0XTeei5MMPyClx6XgrPe+4+TjZuKdKAVnvXrk5O1Gpm6MuLzrGJd3HQOg8/TXyYg17vdu/HGGN7+bDtdvG/wulWeRfeLTc/AsIRbR/ptP6VavyFvr3KM4/FwdcLOXPJ071vbn2rNEXgkOMJn/34gc4Uf2+DFFNoAgCAHANKCjzlNmD/B8X0B56o4aXfkK0tKvFYAoiieQFnCigR8EQXizlHucBUYC94GTSIs+LYHTlP0NV/8T1CvAF0Bj4LIgCGUu+ImiuEEUxSaiKDaxeZiPRxVvXCt6orRU0qBnS+6EXTZIfyfsCo36SQ5KlRoGkZuZQ2ZiGmkxSVRqWA1LXbyGoNZ1C4NCV2/fgJBxPdk0Zg2qPNOnENStGcTT6FiiYuNRqVTsO3qakFaGJxllZGWjUqkA+GXvIRrXr4WDfdHgsO/IqXJtgwLIvh6OdRUfrPwrIFha4NqrbWFAy+dY671E29atimBlgSY1E4WtNQp7qQNW2Frj2K4hefeLBej7h/WrWyOQp9FxRMUmoFKp2Xf8LCEtDfcmS/KkbRa/7DtC43qSPA83F7w93XnyTIrbcf7qLQIrVTSS8Zzka49xDPDGwd8ThaWSKr1b8Oyg4fYRe193Qr6ZwqnJX5H52DiOw4ugeXQPhbcfCk9vUFpg1aoDqkuGgfEE56KXX2VgTVAIiJkZ5G3fSMaEgWS8M5icT5egvnW11EUfAG1cBIJLBQQnD1AosajZFM1jw+2KmkfXUPhVA0EBFlYovAPQPg+o+zzQs6MbymoNUd8r/WW7TiUvIhPTiE5KR6XWcODyA9rXq2qQxsfNkfMPpP3eyRnZRCSkUtHDmZTMHDJyJM+fvAI15+8/I6DYds3iJFx/jHOAN446+wX1bsHTsPJv/7Fxl2LgOPi6U6V7Ex7+YTqO/LffbKN96160b92LPX8dYtDgPgA0aRpMRnom8fHGL/LDhr9Gh05teWvkVIMvbRX0XiAaNa6PQqEwuegDUMfbici0HKLTc1BptBx4EEdIoPE2ocx8FZejUggJLJosi6LI4kO3CXCzZ1ijKqWWw6+b/2BEl7cZ0eVtThw4RbcB0hfZOo1qkZWRTXIpL9gvi6K66i7V1epN0Dwy3lqLlQ3KitVNXyuDOr6uRKZkEZ2aLZXn7We0r27sRp6Zp+Ly00RCa/gW/ubhYIO3ky0RSdLC5vknCVTVCwqtz/cbf6RT2350atuP/XsOM1B3KmGjJg3IzMgkwUR9GTJsACEd2jB+9DSD+lJFLxhtvQa1sbS0LHPRByDp2mOc9Pq2qiX0bR2+mcLJyV+R8YJ9m7na3nNyrodjHaA37vU0HvesKpse99SJaahik7CuKgXndWxd3yAotD4/bfqNoZ1HM7TzaI7tP8krA7oCULdRbV1bKP/CT8UqRcGAa9SrjqWlBekp6YUy3us+mfMHzhHavwMA1RvWICczh9QE4/7g1tkbtOohxVUJHdCRCwelrdXelYvqb9W6gVhYWRQu+gC06d2Ok38c559Gsp8vVv5ehfbLCDOMgWOlp4tt3aoIlkX2KzCwXwODoNCmqFuzOpFRMUTFxEnzlsMnCC12sERGZlbRvGX3ARo3qFM4b0lOTQMgNj6BwyfO0r1T+9Ll1ahGZJTePOnIKUJbGcagMpC3J6xQnk8FT27ceUBuXj6iKHL+yg2qVi573mKvN2+JKta27fzcab9xCqffNZ63tFw7hvTwGO5u2FeqTs9R3b+PsmJFFN7eYGGBTYcO5J8xbK9JgweTNGgQSYMGkX/8OBmffEL+qVNoEhKwrF0brKUPFlaNGqEuHhS6GJqI+ygq+CF4SPMky6btUV83jLEkOBXNDRRVaoCgKFz0AbBsFlqubV4AdSp6EJmUQXRKpjRvuf6E9rWMyz8zr4DLT+IJre1v8HuKzmMqNi2LI7ef0r2MRYrUa49xCPDGzt8TwVKJf+8WxB4wfF+x9XOn5bdTuPjOerL07HdrxU72Nn6Hfc2mcH7cOhJP3TFa9AGIvv4Id713ovo9W3Kv2DvRvbDLNNS9E/k3DCI/M5fMxDQA7HX9s7OvO3W6NeX6n1L5u+u9W9Ts1IjER8Yx8ur4uROZnEl0apZUnjef0r5mCeUZkUBoraLy9HG258azJHIL1FJbeBxHVc9/dtFaxvzIHj8l44S0eJIuCIIX0B04BtwDAgRBCBRF8REwuIT8EUgLLbuA3oAlgCAIlYFoURS/EQTBHmgEbCnhHieAJbr/rgKhQK4oiumCINwDqgiCECSK4kNgGGA0A9EFqPYXRfGoLubPEMAByNTpWCZajZY/FmxizJbZKJQKLu46Rnx4FC2GdgLg3LZD3Dt6lZqhwcw8/gkFufn8NP1rAJ5de8TNfeeZvGcFWrWW6NsRnN8uHVvdZ/EILKwseWurdMJS5NWHRrItlErmvDOGcTOXodFq6du9A0FV/Nm1WwoKObBnVx4/jWLuB5+jUCgIrFyRxdMmFObPzcvn7OUbLJhazpOhNFqezd9A0NZF0rG2Ow+T9+AZHm90AyBp635curfCrX8oolqNNq+AJxM+lJ7V04Wq30jHjgtKJal/nCDj2NVSxZlbPwulkjmTRjBuzkpJXtcQSd5f0heiga925nFkNHNXr9fJ82Pxe0UObbMnjmDWqnWo1GoqenuxdFrJckWNlgvzNtPpxxnSsag7j5P+IJrqw6SJ+IMfjlB/al+sXR1ovmIEAFq1hr09pC9bbb+YiFfLWti4OdD/0mdcX/MLD3eUMsnWasn97jPs56wGhYKCY/vQRkVg1Uk6trPg0G4sW7THunNv0GoQC/LJ/nRpucrNtIJaCo7+iHX/KSAIqG+dRkyOwaK+NIlV3ziOmBKHJuIWNm8uBFFEffMkYrI0mFv3HI9gaw9aDQWHfyxza42FUsGs10IY/+XvaEWR3i1qE+Tjzk+npPgCr7Wpz1vdmrFgaxgDVmxFBKb0boOrgy0PohOZvzUMrVaLVoQuDavRrm7VUuWJGi2n5m+mxzbJfvd3Hif1QTS13pDsd3frEWw9nem3dylWDraIWi31xnRjV+hMVFm5dNkwGRtXB7RqNafnbi4MhlkaYQeO0blLey5fP0xubi6Txs8qvLbz52+YPGkucXEJrP1kCc8iYzhwWNoD//zY9l59ujFqzBDUajV5efmMGTml5PJUKJgZWpMJv12RyrOOH4HuDvx0Q3rpea2+NGE6+jCBFpU9sLUsGi6vxaSx524s1TwceH2rNGmb1DqItgGlB1c/e/g8LTs0Z9fpreTl5rHivdWF19ZsWcmq6WtIik9mwKi+DJ0wCDdPN7Yc2sjZI+dZNX0tbp6ufLvvK+wd7NBqRQa+1Z+hISPJySqjbEUtBUd3YN13MggK1LdPI6bEYlGvHQDqm5LLvjKoIZqndyQPtRfEQqFgVrdgxv94UirPBlUIquDMT5elGBevNZZODjlyP5qWVb2wtTKcfszs1pA5v19ApdHi52LPkl5lBwM/dPA4HTu349zVA+Tm5DFlYtFpfdt2fc17784jPi6R1R8vJOpZDH+FbQdg7+5DfLT6S17t1YXXBvVGpVaRl5vP2FHvlUtXUaPl3LzNdNH1beE7j5P2IJoaur7t/g9HCNb1bS10fZuo1rC7x4JS7mp4f7O2PY2WqPkbCPzBcNxz1417yVv349JDN+6p1Ih5BURM/LAwe9SCb6jy2XsIlhbkR8YROe2zMnU8ffgcrTu25Lcz28nLzWfJ1JWF1z75YTXLpn1AUnwyr4/uz7Dxg3Gv4Mb2Q99z+sg5lk9bTYdX2vPKgK5SW8/NZ874RUYyLh+5ROPQJqw/uUE6zn1aUYyeeZsW8sXMz0mNT2HLyk28v24GQ6a/wZPbjzm0UzrdrmWPVoT074BGpaYgr4C1E4vaq5WNNcFtg/lq9heMm92rTH31mb5wFRev3iAtLYOOfd5gwuhh9O/Z9YXuYYBGS9SCr6m6RbJfyq5D5IU/w32ozn7b9uPSvSWu/TuASo02v4CnerpEL9xA5U/fQ7C0pCAyjki9cjKFhYWSOVPHMfb9BdI84pXOBAVUZufvewF4vU8PHj99xpzlH6FUKKlaxZ8lsyYX5p86bwVp6ZlYWCiZO3Uczo6ln4ZoYaFkzuS3GDt9MRqthr7dOxEUUImdf0inV73euxuPI6OYs+JTlAqFJG/GJADq165O5/atGPjWeyiVSmpWC+C1V0sua1Gj5eLczXT8cQaCUsGjHdK8pZqubYfr5i1Wrg40WzlCyqPWsK/7AjybVafqa21JvRNJjzDJi/3ayl3EHCllIV2jIfPTT3H98ENQKMjbtw9NRAS2vaQ6VTyujz7qu3fJO34c92++AY0GVXg4uX/9VWpZotWS9+M67KasQBAUFJw+gDbmKZbtJa9U1fE9WDRui1XIq6DRIKoKyP1G7yOYlTXK2o3I3fpJ6XJ0WCgVzOrVnPHfHUIraundpBpBXq78dF7y6HytuXTK45HbkbSs5outleF26ve3HSM9Jx8LhYLZvVrgVEb8KVGj5dqcTbTdPhNBqSBix3EyHkRT9U3pFLfHWw5Te2pfrFwdabhypFQkGg1Hus0vlz5Sei27F2xixJZZCEoFV3YdIyE8mmZDJRkXth3m/tFrVA8N5r3jH6PKzedX3TsRwJD1U7BzdUCj1vDn/O/J0wVT7zJzEJ5VfRC1ImnRSfwx91vefd/Qm9hCqWDWq00Yv/kIWq1I70aBBHm58NMFyWPvtWbVpfK884yWgT4G42w9fw861anE4PX7UCoEavq40r9JyXEQ/438N8feMReCWeIL/H+CLujxX6Io1tX9vQloDjwG8oE/RVHcJAhCN6QtVUnAKaCu7jj3EUATURQn6RaL/kDy+jkMvCOKooMgCMOB6YAKyALeFEXR5CZLQRACgYdADVEUHwiCcBC4J4riu7rrHYE1SAt4F4HxoijmC4IQoXuOJN2WsqOAM5KX0FZRFFcJglAd+BmpHbxTWpyfGVUGm62SLDtl+pjtv4tbLcsXzPNlUffsMrPKQ6Myq7gdrT4yq7xebaLLTvQSsWpu3kFSqFv6SV8vmy2jSvcMeNnMTj9fdqKXRNTqbmaTBdB52e2yE71EwqZXM6s8wePvD0qtT8CkX80qb6V947ITvUQKXiAO08ugmSKz7EQvkbfV5d+q9jKoaGG+L9m7rpS+UPKyud14ilnl1bm41qzy0JT/hLSXwc7GpkMN/F10rh5lVnm21co+IfFlYtk9xKzy9kw071h71dp8783zVtc0mywA24ELzDwSmZdFlYeazXiLnm77ryxL2eNHD13Q47p6f48oId1+wKg1iqK4CSngMqIoxgMt9C7P1v2+GSkeT3me5xF6W7pEUexS7PphwOjNUBTFKnr/ViEFhC6e5gFS0GoZGRkZGRkZGRkZGRkZmX8l2v/KpRjzIsf4kZGRkZGRkZGRkZGRkZGRkfmXInv8/BcgCMJ5oPjG1WGiKN78J55HRkZGRkZGRkZGRkZGRubfgFY+10te+PlvQBTF5mWnkpGRkZGRkZGRkZGRkZGRkXkx5K1eMjIyMjIyMjIyMjIyMjIyMv9S5FO9ZMpkapVBZqskr+aatz62CRtlVnmnO39rVnlqM6/t/mKrMau8rnmWZSd6ibiI5j1tRIN5I9EdsVWaVd5NbbrZZPXWuppNFkCoU6JZ5R3PKP0o+f/f2SkkmVVeJ4V5Ty3LEMx70KzKzC7vlzUpZpXnrbA3m6xpmHdcqHP5E7PKu9tkctmJXiIFavOOQ/e1pR8n/7KpY5lhVnkKwbxtXSuad95yv8DRrPJUgvn0C60cYzZZAH5nj/yrwx/PrTLEbI1hecSP/5VlKXv8yMjIyMjIyMjIyMjIyMjIyPxLkWP8yMjIyMjIyMjIyMjIyMjI/Csxr1/tfyf/kws/giC4AENEUfzyP8gbDPiKorj3ZT+XuRAEoQ/wQBTFO+XNU7N9A/ouGI6gVHB+5xEOr//TKE3fhcOpFdoQVW4+26etJ+p2BAA2TnYMWjUW7xoVQYTtM77i6ZVwuk4ZQItBHchOkdxe96zeAfuulvocbqENqL5sBIJSQcy2Izz9/A+D6x7dmlB15kDQiohqDQ/mbyb9wv3yqgnAqat3+OD7X9FqtfTr2JLRfTsbXM/IymHBlz/yLD4Ja0sLFk8YQrVKvgAs+HIbxy/fxs3Zkd8+mv1Ccp/rV23ZSASlgthth0vQ73VEnX7h8ze9sH7uoQ2ouUyyZdS2I0R8bmhL7/6tCZjUCwBNdj53Zmwk604kAJXe6k7FNzoAELXtCJEb9hndv077YAYuGIlCqeDUzsMcWP+7UZrXF46kbmgjCnLz2TTtC57dfoKFtSXTdi7BwtoCpVLJlX3n2P3xrsI8ocO7EfJmd7QaDTePXOHXVVuN7usVWp/6S99EUCqI2HaUB+t2G1z379ea6pN6AqDOzuPazO9I1+nW9eKnqLNyETVaRI2Wo13nlbdIAfPZroau/kdvO0JEMRne/dtQpdB2edyd8S1Zd55Kur/VnYpvdAQgetsRIjeY7sKqt6/PqwveRKFUcHHnUY6v322UpufCN6kRGkxBbgE/T/uKGF1bbzWyG00HhSIIAhd3HOH0d/sBqNujOZ2m9MczyJcve88n+uYTg/u9vXgsTUKbkJ+bzyfvf8yjW4+MZHr5ezFj3UwcXRx4eOsRH01Zi1qlpl6LeszbOJ/4Z/EAnNl/hh2fbgdg8oeTadqxGenJaRzsaGzPiiH1abl4GIJSwf3tx7j+haGuzoE+tP/obTzqVuHi6p+4+XVRmdUZ3ZWag0MQBIF7Px7l1rcHTJanPnZtmuAxexwolWT8vI+0jbtMprOuW52K2z8h7v0VZB88BUCFZe9h1745mpQ0nvUeW6as4viF1KfF4mEodLreKKZrYN9W1J/wKgCq7DzOzN5Eyt3IF5bzd8ubsHg8TTs0JT83nzXvreXhrYdGabz9vZjzxWwcXRwJv/WQ1ZM/RK1S89rYAXToGwqA0kKJf5A/A4NfJzMtiy1nNpObnYNWo0Wj0fB7zyUG9wxoX5+OC6W6cmPHMc4XaxdugT50X/M2XnWqcHLNT1ws1r4EhcCbfy0lKy6VX0atLbsA9ajWvj49dG3y8s6jnCgm2yPQl34fjsW3ThXC1uzi9Dd7ynXf6u0b0GuB1F9e3HmUYybG9V4Lh1MjNBhVbgG7pq0vbOttRnen2esdEEWRuPvP+Gn6V6jzVQxZ9y6eVX0AsHGyJy8jm097FI2FExePp1mHZuTn5rG6FPvN/WIOji6OPLz1kFWTV6NWqRk4dgAd+krjj9JCSaUgfwYEv46zmzPzvpxTmL9ytUrkZuWSFp/ChmnriLj12EiGp38FJn7+Hg4uDkTcesL6qZ+iUUlbtoYtGk1waCPyc/ML81taWzJv1zIsrCxRWii4sPcsv368EwDfOSNw6tgMUaUm/2ksz6Z/hl3DGvgtHIOgVJK84yAJ638xkO/UuTk+7w8FrRZRoyF68UayL92VdHOyx/+DSdhUrwyIRE7/jJwrLzZW6DNvxUecOH0BN1cXft/61X98H30c2jfCb8FboFSQsjOMxPU/G1x36twcr/eGgiiNdzFLNpJz6Q7WVf2otG5GYTorf2/iP95G0nfGdc/gfiENqbR4DCgVJG0PI+6LXw2uu3Rphu/0IYVzv2eLviXrolSe9c5uQJOdCxotolrD3VemlSrLJ6Q+TZYOQ1AoeLj9GHeKzSOq9G1F7YlS36XOyePCrE2k3YlEYW1J51/nobSyQLBQErnnAjfX/GpKhAGO7RuZta6Y23bm1s87tD4Nl0h99eMfj3GvmP0q92tFzYlF88DLs74vtF+H3+YX2u/ZXxe4veYXUyIM8AmpT1O9+nLbRH2po1dfzuvqi52vG60+HYdtBWdErUj41qPcL8c8wrpFU5ynTEJQKsj+cy9ZP2w3mc6yVg08v1lHyvyl5B09AYDgYI/r7GlYBAaAKJK2/EMKbpX7VVDmX8D/5MIP4AJMAF544QcIBpoA5V74EQRBQIqnZNbFRkEQlKIomgq60gf4CyhXaxcUAv2XjOKrN5aTFpfM1D9XcCvsMvEPowvT1AoJxjPAhxUhU6jcMIgBy8fwSR/pRavfwuHcPX6NTRM+RmmpxNK26OT649/u5dg3fxX+HVjagygEaqwaxdWBy8mPSabpgZUkHbhE9oOi50g9cZML+y8B4FC7EnU3TOFcm/fKoyYAGo2WFd/+xIb5E/Fyc2Hw7DWENKlLoL9PYZpvfj1IjQA/PpkxhifR8Szf+BMbF04CoFdIcwZ1a8fcdcaLEmWiEKixajRXBy4jPyaZJgdWknjgEjkl6GdfuxJ1N0zlfJupLySj1qpRXB64nLyYZFocWEHigcsGZZj7NJGLfZagTs/Go0Mwdda+zfnu83CoWZGKb3TgXLe5iAVqGu2YTVLYVXKexBXmFRQKBi8ZzSdvLCU1LoXZf67kRtglYh9GFaapG9KQCgE+zA95h4CG1Ri6/C1W9ZmDOl/Fx0MWk5+Th8JCyYyfl3Lr2FWeXA2ness6NOjclKXd30ddoMbR3cmkbg1WjuTUwJXkxiYTun8ZsQevkKmnW3ZkAif6LkWVno1XhwY0XDOGYz0WFF4/2X85BSmZ5S9PPdnmsF3NVaO4orNdc50MQ9slcKnPYtTp2bh3CKb22re40H0e9jX9qfhGR853m4NYoKbhjjkkhV0xsB1Ibb3XkpF8+8ZKMuKSmfjnMu6GXSFBr63XCAnGPcCbNSHv4d8wiD7LR/FlnwV4Va9I00GhfNl7PhqVmpGbZ3HvyDWSI+KIv/+MreM+pu+K0UZqNQltgm8VX95u9xY1GtZgwvKJvN/buM2OmD2SPzb+zondJ5i4YiKdX+/Cvq1SN3z74m2WjFxslOfQT4f4a/NfvPex8f0EhUDrZcPZO2QV2bEp9NmzhKcHL5MWXrSnPj8tmzMLfqBK18YGeV1rVKTm4BB+f3UhWpWa7ltnEHnkGhlP4kuyHigUeM6bSPSY2ajjk/Df+TnZR8+hehRplM79vdHknL5s8HPGbwdJ3/YnFVZNL1lGCQgKgVbLhrNfp2uvPUuILKZrZmQiewYsoyA9h4qh9Wm9ehS7ey56YVl/p7ymoU3xC/BlZNtR1GxYk3dXTOLdXlOM0o2ePZpfN/7GsT+P8+6Kd+g2qCt//bCHn77+mZ++ll5yWnRqTr8xfclMyyrMN33gTDJSpQ8R+jF+BIVAp6XD2TV0FZlxKbz55xIeHrpMsp4+eWnZHF74A9WK1ZXnNB7VjeSHMVg72JZZfvoICoGeS0byva5NjtO1yUS9NpmblsWeRZup1aXJC923z5KRbHxjBelxyUz6czl3wi4btXWPAG8+DJlKpYZB9F0+mi/6zMfJy5XWI7qxttM01Pkqhq6bTIOeLbn88wl+nPRZYf5X5r5BXmZO4d/NQpviF+DH8LYjqdWwJpNXvMM7vYzjx7w1ewy/bPyVY38eZ/KKd+k+qBu7f/iLXV//zC49+/Uf04/MtEwy0zIZ120CAM07Nmfh1/OY/+p0XCq4MmLZ2yzqM8tIxqBZw9j/7W7O7T7NyOVjCXm9I4e3HqBBaCO8A3x4v/1EAhtWL8yvylexYvBC8nPyUFoomf/zcq4fu8qjqw/IPHmNmA+2gEaLz6zhVJg4AJfurXg0dAGquGSq/7mW9EMXyA9/Vig/6/R17oedB8CmZhWqfDGDex0lHfwWvkXG8StEjP8AwdIChd6c6T+hT4/ODOnfizlL1/yf7lOIQoHfknE8eWM+qrhkgv78iIyw8+Q/NNQvQ0+/Sl/M5EHH8eQ/jia8x+TC+9Q6v4n0A2fLlFdp2VgeDFmIKjaZWns+JO3gBfLCi+YVGadukHbwAgC2tSpTdf10bodMKrz+4LV5qFPLHtsFhUDTFcM5MmgVObEpdNu7hKgDl8nQa+tZzxI51F/qu3xD69N89SgOvLoIbb6Kw6+tQJ2Tj2ChpMvv84k5cp3kK8YfMvR1q7h0rPnqyj9gO3PqJygEGq8YwbHXV5Ibm0LnfUuJOXiFDL15UlZkIkf6LUWVnoN3hwY0+XA0h15ZiDZfxbEBywvt1/GPBcQduU7yFePFaX15zVYM57CuvnTX1Zf0YvUlTK++tFg9iv2vLkJUa7my5EdSbkZgYW9Dj/1LiTtx0yCvqfJ0eX8ySZOno0lIpMJ368k7eQZ1xFOjdM4T3ib//CWDn12mTiLv3EVy5i4GCwsEm/9b3/L/G/Jx7v+7MX5WAYGCIFwTBOFDQRCmC4JwURCEG4IgLAYQBKGvIAiHBAkfQRAeCIJQCVgCvK7L+7ogCIsEQSj8fCAIwi1BEKro/rsrCMKXwBXA35QcUwiCMEMQhHd1//5YEIQjun93FARhq+7fgwVBuKmT94Fe3ixBEJYIgnAeaCkIwipBEO7oZK4RBKEV0Av4UKdDqWstAJWCg0h6GkfyswQ0Kg1Xd5+hbrEJZt0uTbj4q7Si/PTqQ2wd7XDydMHawZaqzWpxfudRADQqDXkZOUYyyoNToyByn8ST9zQBUaUh/vczeHRrapBGk5Nf+G+FnTUv2sZvPXxKJW9PKnp5YGlpQbfWjTh66aZBmsdRcTSvWx2AAD8vYhKTSU6TXhaa1A7C2cHuP9BO0i/nSVyhfgm/n8GzFP2UdtbwgsHZnXUycnUy4n4/Q4VuhrZMv/QAdXo2AGmXw7H2cQPAvpofaZfD0eYWIGq0pJ65S4Uehs8XEBxEwtM4kp4loFGpubT7NA2K1ZUGXZpy7tfjADy5Go6toz1Oni4A5OfkSbpZKFFaKHkefL790C7sX/876gLpi2xmsnFwRLeGQWQ/iScnUtIt6vez+BR7CUu5FI5Kp1vK5YfY6nT7v2I+28Ub2K64DH3bpV8Ox9rHHZBsl25guzt49mhmJMM/OIjkp/Gk6tr69d1nqdXFsAxrdWnM1V9PAvDs6kNsHO1w9HTBM8iPZ1cfosorQKvR8uT8Xep0lWyf+CiGpMexJvVq3qUFR345AsD9q/exd7LHtYJxIOb6repzaq/k/XL458O07NqizDK7feE2mWmmJ/uewYFkRMSTGZmIVqXh0R/nqFxM17zkDJKuP0arNlw/dwnyJeHqIzR5UnnGnrtHlW6lv3Tb1KuBKjIGdVQcqNRk7TuGQ4eWRumch/YmO+wUmuQ0w2e5fAtN+n+wKGlC18d/nKNSMV0TLodTkC71zQlXHmL/f2gbf5e8Vl1aEvbLYQDuXb2HvZMDbhWM8wW3bsCJPVIdDfv5EK26tjJKE9I7hKN/HCuXPj7BgaRFxJP+TNLn7u5zBHU21CcnOYO4G4/Rqoy/tTh4uxHYIZgbO8onT5+KxdrkTRNtMjs5g+gbxvW0NKS2HkeKXluvXayvrtOlMZd1bT1SN6476vpqhVKJpY0VCqUCS1srMuJTjWTUf6UF1/48U/i3ZL9DANy9eg8HJ/sy7Xfw5zBadzVuJx16h5q0X69hrxAfFU9ydCKPrj7A3skeFxP9Se1W9biwV3ppPfnLURp3kfrDxp2bceoX6b7F8+uPTxaWFoV9eObJa6CRvuvlXL2Pba0A8iNiKXgWj6hSk7r7JM6dmxvI1+ruBbq5im6yonCwxb55HVJ2hAEgqtRoMrKNnv9FaBJcD2enlxcA1y64GgVPi/RL230Cpy5l6GdivHNo3YCCp7GooksPhG8fXE0qz0hJXsofp3ApTZ6tzQuPr89xbxhIZkQ8Wbq+6+kf5/AvNo9IulTUdyVdeYidXt+l1o3zCkslCkuLMuegds91M1NdMbftzK2fm85+2Tr7Rf5xDr9i9ku+FI5KZ7/ky+EG80BD+xXNQUuieH2J+OMcFctZX3IT0ki5GSHJzc4j/WFMmXNSq9o1UUdFo4mJBbWanENHsGlnPL7Zv9aX3GMn0KQW9cuCnR1WwfXJ2a3zW1CrEbP+b32LzP9//K8u/MwCHomiGAyEAdWAZkjePI0FQWgniuJvQBwwEfgGWCiKYiSwANgpimKwKIo7y5BTA9giimJD3b+N5JSQ7wTQVvfvJoCDIAiWQBvgpCAIvsAHQAfdvZrqtm8B2AO3RFFsjuTR0xeoI4pifWCZKIpngD+B6TodSvkUIeHi5UZaTHLh3+mxKTh7GXZOzsXSpMWl4OzthnulCmQlZzB4zXje37OS11e9jZXein3b4V2Zvu8DBq0ei61T6adw2Hi7kacnIz8mGWtv4wmdZ/emtDj1EcFbZ3Fn6vqy1DMgPiUNL3eXwr+93FxISDY8eah6FT8On78OwM3wp8QmphJf7CXtP8Ha2418I/2MBwGP7k1pfupjGmydzd0X1K94GebFpJiU8Ry/IaEkHbkGQNa9Z7i2qIWlqwMKWys8OgVj4+dukN7Fy41Uvfunxqbg4mWcJsWgriTjqnsGQaFg3t4PWXP5W+6eukHENelLi1dVX6o1q8Ws31fw/s7FVK5vvF5p4+NKrt59c2NTSh1EqwwJIf7I9aIfRJE2O2YRemA5VXTb2cqLOWxnWkbJJ1X5DQklWWe77HvPcGlRU892DY1sB+Dk5Uq6nowMk23dlbSYohN60uNScPJ2Jf7+MwKa1cTOxQFLGytqhAbj7GMsozju3u4kxRZNHpPjknD3Nszn5OpEdkY2Wt3LVVKsYZqajWry+f7PWbR5MZWqVypTJoC9jytZsUV6ZMelYO9TvpO/Uu9H4dO8BtYuDihtrPDv0AAH39J1VXq5o4or0lMdl4SyguHJUcoK7jh0akX6zvJt1Skvdj6uZOvpmlOGrtUHhRB19MZ/nTx3b3cSY4rKMCk20WRdyTKoK4l4FEtjbWNNk5AmnNp3quhHUWTlthV8sedzegzpbpDewduVTD19MmNTcCyl7RWn48I3OLZiO6L2xV9GTbVJJ6//+4K11I71x/VknL0MdXLycjOQLbV1NzLiUznxzV/MPrOOuRfWk5eZQ/hJww8kAc1qkpWUTnJEkVehh7eHgf0SY5OMbGNsvyTcvQ3byXP7ndS333O5tQI4f+Ri4d8pccm4FisvB1dHcvRkpMQm46p7DldvN5JjkkzmFxQKlu9dy5dXvufmyes8uhZuJN9tYCfyH0ejii26hyo2CUtv4/7BuWsLah7+kqrfLyByuuQpZV3JG3VyOpXWTKb63k/w/2DS/9nj52Vj6eWOKkZfv2QsvUyMJ11bUP3weqp8t5CoGZ8aXXfp2Za0P0+UKc/Kx40CvfIsiEvGysTY7tKtOXWOraPalnlEvL+u6IIoUu3HRdTauxaPoV1KlWXr7UqO3viWE5uCbSl9V+DgEGL0+i5BIdA9bDn9b3xJ7ImbJF8tfYpt6e1u1rpibtuZWz9bbzdyo4v6rJzYFGxL6aurDg4hTm8eKCgEuoStoPfN9cQdv0VKGfazM1Ff7F6gvjzHvqIHbnUrl+4dBig8PdAkJBT+rUlIQunpaZTGtn0bsn8z3HJm4eeDNi0dl3kz8Nz8NS6z30ewsSlV3r8N0Yz//bfyv7rwo08X3X9XkTxzaiIt0AC8A8wG8kVRNL2JsnSeiqJ4rhxyinMZaWHIEcgHziItALUFTgJNgWOiKCaKoqgGtgHPF5E0wPNNqRlAHrBREIR+wH/mamPqQLpiq+CmTjcURRGlUknFugGc3hrG2ldmU5CbT8fxvQE4vTWMZe3eZU2PWWQkpNF73htlPEf5TsZL3HeRc23e48aINQTOfL1ceUoXayh3dJ9OZGTn8tq0D9i+7zg1AyqiVL6E40dN6Cea6D6S9l3kfJup3BzxIVVfVD+TRWi6i3JtXRu/IaGEL/0RgOzwGCLW/UnjXXNpvH02mbefIqqL7V4sV10xoacuiajVsqzHdGa1HEuVBkH4VvcHQKFUYOdkz6o+c/hlxQ+8/YWJrTumK6FJ3Txa16by4BBuLStq1sd7LuJIl7mcGfoBVUd2xr1FTZN5TWIW25X/ZEjX1nXwHdKB8KXbAMgOjyZi3Z802jWPRtvnkHX7KaIp7wCTthHLkUby6jn+1W5GbZ3NyM0zib37FK2mbA8EwUSlMTJbKaZ9eOsho1qO5J1u7/DXpt3M+6a8sZlM3bR8OdMexnD9y7/osX0W3bfOIOVOZNneFibtZyjQc/Y4ktZ+C9qXvSu4HGWsw6dVLWoMas/F5Tv+6+SVp42b7l8M07To3Jw7F28bbPOa0u89JvaYxNw359FzeE8qNqtRdM8X0Kc4gR2CyUnOIP5WRPkyFKc8bfKl3bfsNIgitk721O7chA/avsvy5hOwsrOmYZ82Bska9Gpl4O1T8u3Ktl/xB2vZuQW3Lxp781lYWuDq4cq1M9f+Yxml1R9Rq2Vuj/d5t8VbBAYHUbHYIrPXpNcQ1RqyLpjYRW/CZukHznGv4wSevLVCinECoFRiVzeQpK37eNBjCtqcPCpMGGB8v3+Sco61GQfO8aDjeJ6+vRyv9wznd4KlBU6dmpO+93R5BJZHHGn7z3M7ZBIPR6/Eb/qQwt/v9Z3F3e7vEz5sCRWGd8ehee2SJZnUzXRar1a1CBzcnqt6fZeoFdnXeS6/NX4X9+BAnGtULFktSaIJeX9jXfkvsN3fq5+J30roLyu0qk3VISFcL2a/g53nsLvRO7g1LIf9ytOP6vBqVYugwe25Umyss7Czpt3GyVxasBVVVu4Lyysu0GXKRNK/2GA8j1Aqsaxejexf/yRx+FjE3Dwc3hxcujyZfx3/qzF+9BGAlaIofm3imh9SEHAvQRAUJcToUWO4gKa/fKrvQ1eaHANEUVQJghABjATOADeAUKQQOHeB6qVkz3se10cURbUgCM2AjsAgYBKSl1CZCILwNvA2wKQuI2kxqCibs48b6QmGbt1pcSm46H3xdtF9FRQRSY9LIVLnuXF973k6jpeCz2YlFXnSnN1xhLe+nVFq0KG82GRs9GRY+7qTH2fsXl74TOfuYlvFC0s3R1TljNvi5eZi4L0Tn5KGp5thPBkHO1uWTpQGJlEU6T5xMX4m3NVflPzYZKyL6VdQpn7eL6RfXmyKQRna+LqZLEOH2pWo89FYrgxehSq16OUo+sejRP8obdsLmjPIwAMFpHrgqnd/Vx830hJSDNKkxiXj5uvO8+8aLt7upMUbpsnNyOHBudvUaR9MzINnpMWlcPWAtAc84vpDRK0WBzcniCkaJHNjUrDVk23r40auCd2cavnTaO1bnBnyAQV6uuXFpwGQn5RB7L5LuDUMJPncPaP8pjCH7UzJKMl2tT96m6vFbBfz41Fi9GyXF5NilDcjLgVnPRlOPm5kFGvr6XEpuPi68XxHubO3G5m6bR6Xdh3j0q5jAHSZ/joZsYb14zkthnWm6eBQ8kQt4Tce4OFT9MXK3duDlHjDfBkpGdg72aNQKtBqtHj4FKXJ1ZsoXTp6ifHLJuDk6lQYq6UksmNTcND7amzv7UZ2KTYrzv0dx7m/Q9qy2GTmQAMPF1No4pKw9C7S08LbA02CoZ7WdarjvVYKhKt0dcauXTPQaMg+XEYchTLIiU0x2Epl5+1GjgldXWv502b1GA4M+5B8vUWRf1JereGdqDEklBDU3L/+AE/fojL08PEkuVjfkZ6SjoNBXTFOE9KrPUf/PGbwW4ouTVpyOmf2n6FicCBRuuDrmXEpOOrp4+jjRpaJrU2m8GtSnaBOjaga0gCltSXWjra88sl49kwpn8efqTaZmVD+eloS6cXGbGcfd6O2nhGXbCDbWTeuB7WpS+qzBLJ1fdet/Rep3Lg6V3+XPHAUSgV1uzbj855zaDmsM80GdyBH1PCgmP08fTzKYT8Pkov1B8Xt12t4T3oM7o69oz1pyWnY2hfFUXLzdietmF6ZKRnY6clw83EnVfccKbHJuPt6lJo/JyOHu2dvUz+kIVEPpBhdrv074NSxKQ8Hz8O2dgCWPkX3sPTxQBVfcv+QfeE2VpV9ULo6oopLQhWbRM61BwCk7T1DhQn9S8z7T6CKS8LSV18/d1QJpetnXdkHpasTGl2/7BjSmNxbj1AnpZUpryA2GSu98rTydkcVV7K8rPN3sK7sjYWrI+rUTFS6tqpOTidt/3nsg6uRdd70bDMnNgU7X72+q4R5hEstf5qvGcPRNz40mEc8R5WRQ8LZu/iG1if9fpTR9cJ0cUlmrSvmtp259cuNTcFWz5vZzseN3Hjj53Su5U/TtWM4PnR1ifZLPHMX7zLs9yL1pcWaMRwpVl8ECyXtNk4m4tczPNt3yShfcbQJiSgrVCj8W1nBA01SkkEay5rVcVs6HwCFszM2LZuTptFQcOsOmsREVHekeW3u0RM4DvvfWviRT/X63/X4yQSeb3g+AIwSBMEBQBAEP0EQKgiCYAF8DwxBWmx5z0RegAigkS5vIyCgBJkm5ZTyjCeAabr/nwTGAddE6dPTeaC9IAgegiAogcHA8eI30Mly1p1ANgVpW5gpHYwQRXGDKIpNRFFsYvUwD88q3rhV9ERpqaRhz1bcDjMMPHo77DJN+0lOR5UbBpGbmUNGYhqZiemkxSQXnvJRrXVd4sKlIGvP47oA1O/alNgHzyiNzKuPsKvqjU0lTwRLJV59WpF0wLCjtK3iVfhvx3oBCJYW5X6xBqgTVImnsYlExSejUqnZf/oKIU3qGaTJyM5BpTv945fDZ2lUKxAHuxcL2GkKST+fQv0qlKGfQ70AFC+oX4auDG11Mrz7tCLhgKEtbfzcCf7uPW5O/IKcYnFZrDycCtN49WhK7G+GX3Qjrj+kQhUf3CtWQGlpQZOerbkeZqjD9bBLtOjXHoCAhtUK64qDmxO2TlJ8JEtrK2q2rk/cI6muXDt4gRotJTtUCPBBaWlBVorhi33qtUc4VPXGTqdbxT4tiT1oqJutnzstvpvKpUlfkvW4aAuC0s4aC3ubwn9XaF+PjHul10d9zGk7Gz3bJRaTYePnToPv3ueWCdtZ6tmuQo9mxP1m/LUu6vojPKp446pr6w16tuRusbZ+N+wyDftJO1H9GwaRl5lLZmIaAPa6oNvOvu7U6daUa3+aXrA490MYn/eYw7vd3+HsgXN06C8tLNdoWIOczGxSTbzY3jx7kzY9JK+CjgM6cu6gtBDo4lnkVl29QXUEhVDmog9A4vXHOAV44+jvicJSSWDvFkSGXSkz33NsdLra+7oT0L0Jj/44U2r6vFv3sazsh4WfF1ha4NA9hOyj5wzSPO0ynKedpf+yDpwkcenn/+dFHyjS1UGna1UTutr7utPpmykcn/wVGcWCfv+T8u5uPsTvXecyvttEzhw4S+f+0sl0NRvWJDszmxQTLy3Xz9yg3StSHe08oBNnDxaVoZ2jHfVa1OesXlBSG1vrwsUCG1trGrVrRJLeZD/2+mNcA7xx1ulTq2cLHpazrpxYvYv1Ld7l6zZT2f3OF0SeuVPuRR+A6OuPcNdrk/V6tuResTb5nxBV7L6m2vqdsCs01rX1Sg2DyMvMITMxjbSYJCo1rIaljRUAQa3rGgSFDmpTj8THMaTHpXD2hzA+7TGbcd0mcPrAGTr37wRArYY1yc7MMWm/a2euF9qvy4DOnNGzn72jHfVb1OfMgaL29ufm3YzrNoF71+5xYs/JQhmBDauTk5ljtHADcOfsLZr1kGIHte0fypUwaXvYlUMXadM/xCi/o5sTdnrjU9029YnRHVrg2L4RXuP78Xj0MsS8AnKuh2Md4IuVvxeCpQWuPdsWBst9jlXlogMjbOtWRbC0QJOaiToxjYLYJKyr+kn3bt3AIBDufwM518OxquKLZUVJP5ee7cgIu2CQxkC/OoE6/Yr6ZZde7UjbbTRtNUn29XBsAnyw8q+AYGmBW+82pBWTZ13Fu/DfdnWrIlhZoE7NRGFrjUI3titsrXFqF0zu/ZJPD0y+9hjHAG/sdW29cu8WRB00bOt2fu602ziFM+9+RabePMLazRFLXR1R2lji3bYuGQ9LCdQLZq8r5radufVLKWa/Sr1bEF1sjmvn507rb6dw7p31BvNAa3dD+3m1q0PGQ9OxCZ9TvL5UKaG+tN84hdPF6gtAy7VjSA+P4a6JE3JNUXD3Hhb+fih9vMHCArtOHcg7aThHiO8/lPh+Q4jvN4Tco8dJW/MpeSdOo01JRROfgEUlyZPeukkjVMWDQsv86/mf9PgRRTFZEITTgiDcAvYBPwJndS6eWcAbSAstJ0VRPCkIwjXgoiAIe4CjwCzdbyuRtlW9+TwN8KAEmQcFQahlQk6CqfRIiz1zgbOiKGYLgpCn+w1RFGMFQZitexYB2CuK4h8m7uEI/CEIgo0u3fMjhHYA3+gCSA8oK86PVqPllwXfM3bLHBRKBed3HSUuPIpWQ6XJ1Zlth7hz9Cq1QoOZe/xTCnLz2TG96MjQXxZ9z7BPJqG0tCD5WQLbp0nXes4eim/tyiCKpEQl8tOcjYX71UyWoUbL/dnf0XDHHFAqiN1+jOz7Ufi9KT1H9JZDVHi1Od6vtUNUa9DmFXDr7U9KU80IC6WSOaMHMH75l2i0WvqEtiDI34dduiOVB3Zpw5OoeOau24pCIRBY0ZvF44tcimd8solLtx+SlplFp7HzmTCwB/06GgemLEm/B7O/I3jHXOm4+u1Hyb4fhe+b0nHyMVvC8Hy1RTH9Pn4h/USNlnuzv6fRjjnSkeA6GRV1ZRi15RBV3++PpasDtT4YJeVRazjfdS4ADb59D0tXB+lI1NnfFwYSfo5Wo2XHgm+ZvGUuCqWC07uOEhseRbuhkg4ntoVx6+gV6oU2ZNnxzynILWDz9C8AcK7gwoi1k1AoFAgKgct7znLziDSAnt51lOGrx7PgwFo0KjWb3v/CpG7X5myi9fZZCEoFT7cfI/N+NAFvSi+KT7YcptZ7/bBydSR41cjCPEe7zsPaw5kW30vNQ2Gh5Nmvp4l/gRgn5rLd/dnfFdouRlf/DW03QGc76fQsyXbSMceS7RwR1Rruzf7OyHYg2e/PBZsYtUUqw0u7jpEQHk2zoVIZXth2mPtHr1EjNJhpxz9GlZvPz9OLnBiHrp+CnasDWrWGP+d/T54uEGPtrk3otWg49m5ODP9uBrF3n/L9m6sAuHTkIk1Cm/DNyY3Sce7Tispl0aZFfDbzM1LiU/h+5ffMXDeDN6YP4/HtxxzcKR172qZHa7oP64FWrSE/r4DVk1YX5p/++QzqtayHk6sTgy9+xpW1vxR66YgaLWfmb6b7thkICgX3dx4n9UE0tXTxne5uPYKtpzN99i7FysEWUaul7phu/Bw6E1VWLp03TMba1QGtWs3puZsLgzeWiEZL4vIv8P1mBYJCQcZvByl4+BSn118BIKOMuD5eH87Ctll9lC7OVDmyleR1P5D5a9lHvz7X9ez8zXTT6fpg53HSHkRTU6frva1HaDi1L9YuDrRaMQJAsuErC0q5q/nlXThygWYdmrLp1HfSce7vf1R4bdnmJXw04xNS4lPYuPJb5nwxm+HTh/Po1iP27ygqp9bdWnPlxGXycouCrbt4urLwG0m2Uqnk6B9HSTxe1P5FjZZDCzbz2pYZCEoFN3cdJzk8muChkj7Xth3B3tOZN3cX1ZUmo7rxbaeZFJTlul8GWo2WvxZsYviWWdJx7ro22VTXJi9uO4yDpzPj/1yGtYMtoijSalQ3Pus8g/xSZGs1Wv5YsInRW2ajUCq4uOsY8eFRNNeN6+e3HeLe0avUCA1mxvFPKMjN5yddW3927RE3953n3T0r0Kq1xNyO4Pz2w4X3btCzpdE2L4DzOvttOfU9+bn5fPh+0bH2yzcv5aMZH5Oss9/cL+YwcvoIHt56yL5i9rtczH4gxf1p3LYRw1oPZ9TMkaw98SUFuuPYnzNt01w2zviStIRUdqz8gUnr3uO1aUOIuP2EYzuloNPXjlymQWgjo/wuFVwZ+9E7uvFJwfm/TnPtiPRCWXHJWAQrC4K2LgEg++p9ohZ8TdUtixCUClJ2HSIv/BnuQ7sBkLxtPy7dW+LavwOo1GjzC3g6sajfil64gcqfvodgaUlBZByR04xjrLwI0xeu4uLVG6SlZdCxzxtMGD2M/j27/uc31GiJWfAVVbcsBqWC1F2HyA+PxE2nX8q2/Th3b4Vrvw6IajXavAKe6vXLgo01Dm2CiZpjPI6XJC9y/jdU37YQFEqSdx4i78EzPN+QdEjcegDXHi1x7x+qG1/zeTxeOsHMwtOFoI3SqW6CUknK7yfIOHa1RFGiRsuluZvp8KPU1h/tOE76g2iqDZPaevgPR6g3tS9Wrg40XTlCyqPWsL/7Amy9XGj56VgE3Rzm6e7zRB+6VqZuZq0r/4DtzKmfqNFyZc4m2m+fKR3nvuM4GQ+iCdTNAx9tOUydqX2xdnWk8crn80ANYd3mY1PBheafjkNQSvaL/PM8sYdKrivP5V2cu5mOpdSX+rr60kyvvuzrvgDPZtWp+lpbUu9E0iNsOQDXVu4iRj/2pInyTFv7OR6ffAAKJdl/7UP9JAK7vtLx9DnF4voUJ/2jz3FdNAfB0gJ1dCypy1eXmv7fhnyql3TE+D/9DDL/5UytMshsleTVXPPWxzZho8wq73Tnb80qT21mp75fbMt/qszLoGuepVnluYhqs8rTmA7K9LdxxPYlxKp6AW5q08tO9JLorS1/MN6XQahT6aedvGyOZ3iWnej/Y3YKSWUneonoH+duDjIE8zqhq8w8Ab6sKX0r5MvGW1H6YREvk2mYd1yoc/kTs8q722SyWeUVqM07Dt3XOphVXh3Lsj1SXyYKwbxtXSuad95yv+DlnVhXHlQvEG/x/0po5dI9xl42fmePmNd4ZuY9M77PfhSx47+yLP8nPX5kZGRkZGRkZGRkZGRkZGT+/ciuLvLCzz+KIAjuwGETlzqKomg6KqqMjIyMjIyMjIyMjIyMjIxMOZEXfv5BdIs7wf/0c8jIyMjIyMjIyMjIyMjI/BuRT/X63z3VS0ZGRkZGRkZGRkZGRkZGRuZfj7zwIyMjIyMjIyMjIyMjIyMjI/MvRd7qJVMmlmY8WajtpdlmkwVwu/V/dlzxf0qby8vMKg+Nyqzi0lusLTvRS6TzgFSzyrMIrmlWeVSvb1Zx8YMOmVXej/nPzCZr1/Igs8kC6Lo01qzyDsx3N6s8wdHJrPIWvnPNrPImWrmZVV6OwrwHgDR0SDOrvDPZ5j35ypyfNetcNO+4Z+5Ttmpd+r8dJ/+iiAW5ZpX3pPEKs8qr3My8p3pZVjHviZbKDqFmlRc94qxZ5d20Md+pc27vdzCbrP8FRDm8s+zxIyMjIyMjIyMjIyMjIyMjI/NvRfb4kZGRkZGRkZGRkZGRkZGR+VciB3eWF35k/gOqt29A7wVvIigVXNh5lGPr/zRK02vhcGqGBqPKLWDXtPVE344AoO3o7jR9vQOIInH3n7Fr+leo80vfjnTqwjU++PJ7NFot/bp3ZMzgPgbX0zOzWLBmPc9i4rG2smTJtPFUC6jEk2cxTF/2cWG6qNgEJg4fyLD+r5Qqz7F9QyouegtBqSB5RxjxX/5icN25czN8pg1F1GpBoyVq8UayL94FQOlkj//qSdhWrwSiyNPpn5Nz5X4Z+l3lgy90+vXoyJjBfY31+/BLnsXEYW1lxZLpE3T6RTN9qb5+8Uwc8TrD+r9auryL1/hg/RZJXrdQxgzqbSxv7dc8i42X5L03lmoB/gBkZGWz6KMNhEdEIQiw5P2xBNeuXqIsr9D6NFwyDEGp4PGPx7i/brfB9Ur9WlFjYk8A1Nl5XJn1Pel3IosSKAQ67V9Gblwqp99cU6peAMpajbEZMBYUClRnDlAQ9pPh9Wr1sH17AdrkOEnmtTMU7N8OgGVIbyxbdQVBQHV6P6pjf5Qp7/STBFYfvoNWFOlb359RzQ23E2268Ii9d2IA0IhaniRncXRiZ2wslYzafhaVRotaK9Kpug8T2pRcjoXybj1i9Y5DaLVa+rYNZlT3lgbXM3PymPvtbuJSMlBrtLzZtTl9WkvbxTJy8liyeS8PYxIREFg0ogcNAiuWKs8npD5Nlw5DUCh4uP0Yt4vZr0rfVtSZKNU3dU4e52dtIu1OJHa+brT6dBy2FZwRtSLhW49y/9sDZeoHsGjlTEI7tSU3N49pk+Zz68ZdozSffrWSeg3roFapuX7lJrPfW4parSawWhXWfL6UOvVrsWb552z4YnOpsk5HJPLhsbtotdCnbkVGNatqcH3zpSfsvaezn1bkSUoWR8Z1IFelYf7+myTn5CMg0L9eRYY0qlIu/fSZvGQiLTo0Jz83nxVTV/PgVrhRmn4jevPamP5UDPDj1bp9SU8t/zYBc+t3+mEsqw9cQ6sV6dswgFFtahlc33TmHntvRurkaXmSlMnRab1wtrUmI6+AJbsv8TAhHUGART2b0sDfo0yZS1bOpkNnqb5MnTjXZH35/OtV1A+ug0qt5tqVW8yauhi1Wk3fAa8wYfJoALKzc5j9/lLu3jbdX1cIrU+9pW+CUkHktqOEF2sLFfu1JmiS1JdpsvO4PvM7MnR9mYWTHQ0/egvHGv4gilyduoHUy8a21scnpD6NdG3v0fZj3C0mr3LfVtTW9Z2qnDwuzfqetDuRKKwt6fTrfBRWFigslETuucCtNb+YEmGAfdvGVJg7FkGpIO2nA6RsMOw7HTq2wGPyMBC1iGotCSu+JvfyHQBc3+yNy0Cp70zbtZ/UzWX3ncX5u9rCsEWjCQ5tRH5uPhumrSPi1mOjNJ7+FZj4+Xs4uDgQcesJ66d+ikalLjX/Wx9OJLhDEzKS04F8o3ueOn+ZVZ9uQKPV0v/VLox54zWD6+mZWcxf+QnPouOwtrZk6azJVKtaBYAffvqDX3YfQBRhQM+uDBvY2+j+xXFo3wi/BW+BUkHKzjAS1/9scN2pc3O83hsKooio1hCzZCM5l+5gXdWPSutmFKaz8vcm/uNtJH1nPK8rL/NWfMSJ0xdwc3Xh961f/cf30efUhSt8sO47NBot/V7pxJgh/Qyup2dmsWD1uqJ54IyJVAuoDMCWn3bz655DCAJUq1qZpTMnYW1lVaIsc89bLIKbYTdyEiiU5B/eQ/7vPxperx2Mw8xlaBKkeYvq/Anyft4CllY4LvkULCwRlEoKzh0nb9emMuUpazTEutdoaZ504RCqo78ap6laB6veo0GhhOxMcr+ah+DsjvWgySgcXRFFLerzYahO/VWmvNN3n7L611NoRS19W9RmVKfGBtczc/OZu/UQcamZqLVa3gxtSJ/mtYhLzWTetsMkZ+QgKKB/yzoMbd+gTHmeoQ2ovUx6R3m27SiPPjesy779WxM4qRcg9dU3Z3xL5p1I7AN9aLjh3cJ0dpUr8GD1z0Rs2FeqvID29em0cBgKpYLrO45xbr1hfXEL9OGVNW/jVacKJ9b8xIUNew2uCwqBEX8tJTMulZ9Hlb1t9PSDaFbvuSSNs02CGNW+rsH1TSdvs/faE0k/rZYniRkcnfMaznbW/HD6Dr9deogAVPN2ZXG/Vlhbmm/rmsw/j7zwAwiCMA7IEUVxy0u41xxRFM27Yfg/QBCEYMBXFMW9ZaU1yKcQ6LtkJN+8sYL0uGTe+XM5d8Iuk/AwujBNzZBgPAK8WR0ylUoNg+i7fDTr+szHycuV1iO6sabTNNT5Koaum0yDni25/POJEuVpNFqWf/4tGz6Yh7enO4Mmzia0VRMCKxe9sG788TdqBlbh08XTeRwZzYrPv2XjhwsI8Pfl568/LLxPx0Fj6dimWekKKhT4LxvLw6ELUcUmU2P3GtLDLpAXXhSLJPP0DdLDLgBgU7MyAV/O4G6HiQD4LRpD5rErRIz7AMHSAoWtdaniNBoNyz/byIbVC/D2dGPQhFmEtmxCYBV/Pf1+pWZQFT5dMkPS77Nv2LhmEQH+fvy8YU3hfTq+PpaObZqXIU/L8nXfs2HVHLw93Bn0zlxCWzY2LM/tf1AzsDKfLnpfkrfuezaungfAB19upnXTBny0YCoqlZrcfOMJb1FZCjRaMYITr68kJzaFTvuWEnPwCpkPiupKdmQix/otRZWeg3eHBjT+cDRHXllYeL3aW93IDI/BwtG2VL0AEBTYDJxAzrq5iGlJ2E3/BPXNc2jjDOPIaB7dJverRYaP6lMZy1ZdyflwKmhU2E5Yivr2RcTEmJLLUiuyMuw2Xw1sjpejDUN/OEX7QC8CPRwL04xoFsiIZoEAHH8Yz9bLT3C2tUIURb55vQV2VhaoNFpGbj9Lm6qe1PcteS++Rqtl5Y8H+WrqILxcnRi6fBPtG1Qj0Lfo5Xjn0StU9fHgs3deIyUzhz7zvuaV5nWwtFCyekcYrepWZc34fqjUGnILSl9wFRQCzVYM5/CgVeTEptB97xKiDlwmPbyoTLKeJRLWfxkF6Tn4htanxepR7H91EaJay5UlP5JyMwILext67F9K3ImbBnlNEdqpDQFVK9O+6as0bFKfZWvm0afLUKN0v/+8h8njpHhgn234gEHD+rH1+12kpWawcPYquvYoe1+8Riuy6sgd1vdrKtnvx7O0D6xAoLtDYZrhTQIY3iQAgOOPEth2NQJnGysK1Hm8164GtbycyS5QM2TbGZpX9jDIWxYtOjSjYkBFBrd5k9qNavH+ysmM7TnJKN3Ni7c5c+gcn/38Ubnv/U/op9FqWbnvCl+90R4vJ1uGbjxE+xq+BHo6F6YZ0aomI1pJcbKO349h6/kHOOv6yNX7r9Iq0Js1r7VCpdGQq9KUqWOHTm0JCKxEmyY9aNSkPivXzqdn5yFG6X77aQ/vjJ0FwLpvVjN4WH9++H4nkZHRDHh1BOnpGYR2asPqTxaazI9CoP7KkZwZuJLc2GTa719GnFFflsDpvktRpWdToUMDgteM4UQPKYZcvWVvEn/kOhfHfIpgqURZxrggKAQarxjB0UEryY1NocvepUQfuEJGuJ68Z4kc6i/1nT6hDWi6ejRhry5Em6/iyGvLUefkI1go6fT7AmKPXCf5ysOSBSoUeC2cwLORc1HFJVHll0/IOnyOgkdFfWf22WtkHT4HgHWNKvh+Opsn3cZiVa0yLgO7EjFgKqJKhf+3S8k6dhHV09Lbuj5/V1toENoI7wAf3m8/kcCG1Rmx7G0W9ZlllG7QrGHs/3Y353afZuTysYS83pHDWw+Umv/ET0cJ27yPsR+9a3Q/jUbDso/W883Hy/D2dOf1t6YS2ro5gQGVCtN8s2UXNatV5bMV83j89BnLP1rPt5+uIPxxBL/sPsD2DR9haWHJuGkLaNeyCZX9/UpWVKHAb8k4nrwxH1VcMkF/fkRG2HnyHxbZL+v0dTLCzgNgU7MKlb6YyYOO48l/HE14j8mF96l1fhPpB/5vMVP69OjMkP69mLO07EWP8qDRaFj+6Tds+HChNA8cN4PQVk0N50nbfqFmUACfLp3F48goVnzyDRs/Wkx8YjI//rqH3zd9io21Ne8vWsO+I6fo062EMcLc8xaFArvRk8laOg1tSiKOK79Cdek02qinBslUd2+SvapYDExVAZmL34O8XFAqcVz6OaqrF9CE3ylZnqDAuu/b5G5YhJiejO27q1HfvoCYEFWUxsYO635jyd24BDEtCcFe14drtRT8tQlt9GOwtsFu8lrUD64Z5i2GRqtl5c8n+Gp8L7xcHBj60U+0rxtAoHdRDLWdp25S1cuVz956hZSsXPqs2MYrjaujVCh4v3dravl7kp1XwOC1u2hRw98gr3F5CtRZNZLzA1eQF5NMmwPLiT9wmSw9++U+TeBsnyWo07Px7NCAemvf4kz3+WQ/iuVUx9mF9+l4/Uvi914sWRZSX91l6XB2DF1FZlwKI/5cQvihyyTrzXXy0rIJW/gD1bs2NnmPJqO6kfQwBmuHsuuLRqtl5e4LfDWyE15Odgxdv4/2tSoSWMGlMM2ItnUY0bYOAMfvPmPrmbs421kTn57D9rP3+HVyL2wsLZi+/QT7b0bQu1FgmXL/LWjlGD9yjB9BECxEUfzqZSz66JjzHzzD37bcKghCSYt7wUCPF72ff3AQSU/jSHmWgEal4frus9Tp0sQgTe0ujbny60kAIq8+xNbRDkdPFwAUSiWWNlYolAqsbK3IiC89OO/N+w+p5OuNv68XlpYWdA9pxdHThh3xo6dRNG9YD4CqlfyIjkskKTXNIM35qzfx9/XG18uzVHl2wdXIj4ijIDIeUaUmdfdJnLsYLhZpc/IK/62wswFR6kgUDrY4NKtD8o4wAESVGk1Gdun63XtIJb/n+lnSPbQ1R8+UQ78UU/p5lalfYXn66MqzfUuOnrlkKC8yiuYN6xbJi5fKMys7h8s379GvmxS4z9LSAicH+xJluTUMJCsinuzIRESVhmd/nMOv2MCXfCkcVXqO9O/L4dj5FA3otj5u+HQM5vGPR0vV6TmKKtXRJsUgJseBRo36ygks6rcsOyOg8PZHE3EfVPmg1aJ5eAvLBq1KzXMrNg1/VzsquthhqVTQtaYvxx7Gl5h+370YutX0BUAQBOyspKap1oqoNdoyQ6jfehKDv6crFT1dsbRQ0rVpLY5de2CQRhAgOz8fURTJzSvA2d4GpUJBVm4+Vx48o28b6WuZpYUSJzubUuW5NwwkMyKerMhEtCoNEX+co2Ix+yVdCqdAZ7+kKw8L7ZebkEbKzQhJv+w80h/GYOtTdrDczt1D+WWn9LXs6qUbODk7UsHL2Ovj6KFThf++fuUmPr5eACQnpXDj6m1UqrIDyd6KS8PfRc9+Nbw59qhk++2/H0u3Gj4AeDrYUMtLmgzbW1kQ4OZAYlZeiXlN0aZra/b/fBCAO1fu4uDsgHsF4zIKv/2QuKiSn6skzK3fregU/F0dqOjqgKVSSdc6lTh2v+SX/323I+lWV3pxy8pXcSUyib4NpUUoS6USJ5uSv8g/p0uPUH7eIX3NvXLpBk5OpuvLkUMnC/99Ta++XL5wjfR0yWvkysUb+Ph4mZTj2jCI7Cfx5EQmIKo0RP9+Fu9ibSH1UjiqdKm/T738EBtdfbdwsMW9RU0ifzwGgKjSoM7IKVUv/b5Tq9IQWULbUxW2PcO+U50jLcgrLJUoLJWFY1RJ2NSvTsHTGFTP4kClJmPPCRw6Gfadot64J9gWjXvWgf7kXr+PmJcPGi05F27h2Ln0vrM4f1dbaNy5Gad+OQbAo6sPsHeyx6WC8eJ67Vb1uLBXWuw4+ctRGuvG/NLy379wh6y0TJNyb959QCU/H/x9vaVxvWM7jpw6Z5DmUUQkLRpL/XHVyv5ExyWQlJLK46dR1K9dE1sbGywslDQJrsvhE6UvxNgFV6PgaSwFz6R5S9ruEzh1MfwIZDhvsTZZJxxaN6DgaSyq6MRS5ZVFk+B6ODs5lp2wnNy895BKvnrl2aENR09fMEjzKOIZzRtJ3q1VK1UkOj6hcJ6k1mjIzy9ArdGQl59PBfeSxyJzz1uUQTXRxkWjTYgFtRrV6SNYNWldrryAtOgDoLSQ/iujrSsqVUObFIuYEi/Nk66dwqKO4RzXomE71DfPIaYlASBmp0v/z0yVFn0A8vPQJkShcC79UIFbTxPw93CmooezNG9pWI1jN58YpBEQyM5XSfOWfBXOdtYoFQo8ne2p5S/Nae1trKjq5UpCeulzapdGQeQ8iSP3qdRXx/x+Fq9uhu8oqZfCUev11abmJh5t65ITEU9uVFKp8nyCA0mNiCf9mdRX39l9jmqdDetLTnIGcTceozXxMcPR243ADsHc2HGsVDnPuRWVjL+bIxXdHKXyrF+ZY3dLPiRj340IutUPKPxboxXJV2lQa7TkqdR4lmdxUuZfxb9i4UcQhCqCINwTBGGzIAg3BEH4WRAEO0EQGguCcFwQhMuCIBwQBMFHl/6YIAgrBEE4DkwWBGGRIAjT9K59LAjCCUEQ7gqC0FQQhF8FQQgXBGGZnsw3BEG4IAjCNUEQvhYEQSkIwirAVvfbtpLS6X7PEgRhiSAI5wGjt1NBEJoJgvCr7t+9BUHIFQTBShAEG0EQHut+DxYE4ZxO598EQXAtQb/XBEG4JQjCdZ1eVsAS4HXdc71e3rJ29nIlPSa58O/02GScvFyLpXEjTS9NWlwKzt5uZMSncvybv5hzZh3zLqwnLzOH8JM3S5WXkJSCd4WigcXL05345BSDNDUCK3PolPQl6+a9h8TGJxKfaJhm39HTdA8tezC18nanIKaooy+ITcbSy3hgc+7aglpHviBw03yeTv8cAOtK3qhT0qm09l1q7P0Y/w8mlenxk5CUgrdn0YuKl6c78UnF9KtamUMnn+sXLumXlGyQZt/R03Tv0KZM/RKSUvH2LF6ehotvNapW5tCpizp5D4mNTyI+MYWouARcXZyYt+YrXhs/i4UfbSAnt+SXQVtvN3Kii54zJzYFW++SPVoCBocQe+R64d/BS4ZxY9l20JZvhV7h7I42tch22tQkBBOTEmVATexmrcN2/BIU3tIXWG3MUyyC6oK9I1haY1GnCYJr6dtMErLy8NYbNL0cbUgo4eU4V6XhzJNEOlX3LvxNoxUZuOkkHb4Io0UVD+qV4u0DkJCWhbdb0UlKXq6OJBR78RjUoTFPYpPpPP1zBizeyPRBnVEoBKIS03B1tGPB93t4fcl3LN68l9z8glLl2Xm7khNTVBdzYlOw8yn5GQMHhxBz9IbR7/YVPXCrW5nkK49KlQfg7VOBmOi4wr/jYuLx8qlQYnoLCwv6DezJscOny7x3cRKy8vHSt5+DDYlZpj3YclUazkQk0bGa8cJATHoO9xMzqOvt8kLyPb09SIgpesFKjE3Ew7vsrU3lxdz6JWTm4u1sVyTPyZaETNOn8+Sq1Jx5GEenWpKnYVRqFq521iz48yKvbzjI4t0XyS0oe/HO28fLoL7ExsTjXcLiDUj1pf/Anhw7fMro2qBh/Thq4ncAGx9XcvXGtNzYlMKFHVNUGhJCgq4vs6tcgYLkTBp+Opb2YSsIXvsWSrvSxwU7bzdyYor1nWW0vdijRX2noBDoFraCvjfWE3fiFslXS297ll7uqOOK+k51XJLJcc+hc0sC9n+N/4bFxM7+BID88KfYNamLwsURwcYah/ZNsPR5sXr8d7UFV283kvXG85S4ZFy9DO3m4OpITkY2Wo0U/SElNhlXb/dy5zdFQmIy3hWKPsJ4eXqQUGzMrhEUwKHjZwC4eec+sfEJxCcmExRQmcvXb5GWnkFuXh4nz10iLqH0l09LL3dUes+pKmHe4tS1BdUPr6fKdwuJmmF8UpdLz7ak/VmyB/Y/RUJSsvE8sPg8KbAKh05Ii2s374YTG5dIfGIyXp7ujBjYm86vj6VD/9E42NvRqmlwibLMPm9x80SbXFT3tSmJCO7GH/AsqtfG8cONOMz5AEXFKno3UOD44UZcvv0d9Y1LaB4ab3XVR3ByK1zQARDTk43mSQpPX7BzwHbcUmwnr8GicYjxfVw9UfgGoIl8YHRNn4T0LLxdizxFvVwcjBZvBrWtx5P4VDov3MSAD7YzvW9bFMVOOYxOzuBeVBL1KpfcvwPYeBv21XkxydiUYj+pr75m9Ltv31bE/HamVFkAjt6uZMYW1cXM2BQcS5FXnI4L3+Doiu2I5awvCRk5eDsXfXD1crInIb2EcbZAzZnwGDrVkea5Xs52vNmmNt0+/JXOq37GwcaSVtV8y/2s/wZEM/7338q/YuFHRw1ggyiK9YEMYCLwOTBAFMXGwHfAcr30LqIothdF0dSGygJRFNsBXwF/6O5VFxghCIK7IAi1gNeB1qIoBgMaYKgoirOAXFEUg0VRHFpSOp0Me+CWKIrNRVE0Ndu8AjTU/bstcAtoCjQHzut+3wLM1Ol8E1iol19fvwVAV1EUGwC9RFEs0P22U/esO0su1mIIJvwSitdwE2lEUcTWyZ46nZuwqu27LGs+AUs7axr2KX2xQjTx9UIo5hsxelAfMrKyGTB2Oj/+vo+aQQFYKIuqtkql5tjZy3Rp36JUWbqbm3oIo5/SD5zjboeJPB6zAt9pOpNaKLGrG0jSD/u532Mq2tw8vCb0L10/E92DUKz8Rg/uK+n39jR+/G0fNasFYKEschJTqVQcO3OJLu3K9m4xLc/w79Gv95LkjZvFj38coGZQFSyUSjQaDXfDn/D6q535af0qbG2s+XZnyXEATFaVEr5GebaqTcCQEG4u3wGAT6eG5CWlk3YjokydShVYTF/Ns4dkzR9BzqpJFBz/E9u35wOgjX9GQdhP2E1aju3EpWiin4Cm9K0mpjQpyWvnxKN4gv1ccbYt8mJQKgR2jWjLgXEduRWbxsNE01+PC+WVoy2cuf2EGv5ehH34DjsXjGLVjwfJys1Ho9VyLzKOgSEN2blgFDbWlny3rwx3fpPt2HRSr1a1CBrcnis6+z3Hws6adhsnc2nBVlRZZR/RW7zuSzJLHkKXfTiX82cvc/HclTLvXS5KMOCJxwkE+7rgXMwLJadAzbS/rjGtfU0crF9s5/SLtI+Xxt+o3wu1hwcxBPu7F27z0mhF7sWmMrBxIDvf7oKNpQXfnS79BQZevL6sWDOP82cvc6FYfWnVpimD3ujH8kWmtxCZklNSY/BoXZvKg0O4vUyKHaawUOBcrwoRmw5xvPMc1Dn5VNPFlyiRco5DABVa1abq4BCu6bU9USuyv/Mc/mj8Du7BgTjXKD2WVwmV0einrLCzPOk2lqgJS/GcMgyAgkfPSP7mJyp9vxz/b5eSd+8JorrsbXpli/+/t4Xy1I/SbPui9aswjalnKWbUMW+8RkZmNv1HvsO2X/6iZrVAlEoFgVX8GTV0AG9Nnc+4aQupHhSAUm+8N0k57Zdx4BwPOo7n6dvL8XrvDcNbWFrg1Kk56XtffBH978ZUkRvNW4b0IyMriwFj3uPH3/bq5kkK0jOzOHrmAvu3r+fwzxvJzctnd9jxEmWZfd5iimLy1E8ekD5hEJnTx5C371ccZiwruqjVkjl9DOljX0MZVAuFfwClUp66olCi9KtK7rfLyP1mMVYdX0Pw0FsgsLLB5s2Z5P/5HeSXPq6bbAvFHuHMvUhq+HkQtngEO6e/zqpfTpCVV/RhKie/gGnf72d63zY4lOUJanIeaBr31rXxHxLKvaXbDW9hqcSrS2Nid58vIadBauOfytl1BXYIJic5g/hbEeXLQPnawnNO3IsiuJInzroPDRm5+Ry7+4w90/pycNYAcgvU7LlmHPNM5t/NvynGzzNRFJ+PWFuRtlzVBcJ0g7cSiNVLX9pix/O32ZvAbVEUYwF0njb+QBugMXBRd29bIMHEfTqWkk4DlBhxURRFtSAID3WLR82Aj4B2Oj1OCoLgjLS483wE2wzoR2PU1+80sEkQhF2AcRQ3EwiC8DbwNkAXtyY0cJSC1qbHpeDsW/R1wNnHnYwEQ4+R9LhkXPTSuOi8fYLa1CXlWQLZKdIL7q39F6ncuDpXfzf9lRWkLztxCUWr9/GJyVRwN1xNd7C3Y9n0CYA0QHd7YxJ+3kVeAicvXKVWtQA8XF3K1LsgNhkrvZgpVj7uqBJSSkyffeEOVpW8Ubo6oopNoiA2iRzd9pu0vWfwGl/6wo+XhztxiUVfX0rUb8bEIv2GTjCtn1vZ+nl5uBGXWKw83UzImzauSN6b7+Ln7UlefgFenm7UryXVhc5tm/PtzpKDeObEpmDnV1QP7HzcyItPM0rnXMufJmvHcHLoagpSswBwb1Yd3y6N8ekYjNLaEgtHW5qtG8+FSetLlKdNS8JSz0tH4eqBmF7MdnlFkxTNnUugnIhg74SYnYHq7EFUZ6XtBlY9hxt8FTOFl4MNcXoeDfGZeXg6mN4+tf9u0Tav4jjZWNLE353TTxII8izZPd7L1ZG4lKJgpvGpmXi6GMZc+eP0DUZ1a4kgCFSq4IafhwtP4pLxcXOigqsT9apKcSI6N6rJd/tLX/jJiU3Bzrfo67adjxu5ccZbM11q+dNizRiOvPFhof0ABAsl7TZOJuLXMzzbd8ko33PeHP06g4ZJ7eTG1dv4+hV5RXn7epEQZ3rbweTp43DzcGX2m0tK1aMkKjhYE69vv6w8PO1Ne2IcuB9Lt5o+Br+pNFqm/XWV7jV96FjN22S+4vQd3pueQ6Udtveu3aeCb9GXXU8fT5Ljk0vK+sKYWz8vR1vi0ou2MMVn5JboRr7/1jO61S2Kd+LlZEsFJ1vqVZT6i861KvLd6Xsm8w4fPYghbw4A4PrVWwb1xcfXi/g4U0MxTJ0xHjd3V2ZOXWzwe63a1Vn96RKGDRxHWmq6yby5MSnY6o1ptj5u5JloC061/Ale+xZnh3yAStcWcmNSyItNIVXndRPz13mqvVP6wo/U9gz7zty4NKN0LrX8abZmDMffWG3Q9p6jysgh4exdfELrk36/5DgcqrgkLPQ8bCy8PUod93Iv3cLS3welqxOa1AzSfz5Ium6rlsd7ww28h0ri72oLz+9rhZLHNx7irjeeu3m7k1ZsvpKZkoGdkz0KpQKtRoubjzup8ZLuKbHJZeY3hTRvKeq34hOT8PQo5mlkb8eyOVMAaZztOnA0FX2kutz/1S70f7ULAJ98vRnvCqV7P6nikrDUe07LMuctt7GuXGQ/AMeQxuTeeoQ6Ka1M/cyN6XmgifKc+Q6gm7cMHoefjxenL17Dz9sLNxdp62qnts25fusePTu3NynL7POWlEQUeh4+CjdPxJRi7Se3qF9VXz0PY6YiODojZhb1V2JOFurb17AMbkb+M8OtVPqI6ckILkV1RXB2R8xIMUqjyc6Qtr6r8tE8uYPCtwqapBhQKLF5cwbqqyfQ3DpX/PZGeDk7EKfXN8WnZeHpZBgi4I8L9xjVsZE0b/F0wc/diSfxqdSr7IVKo+H97/bTo3F1OjYoOxZNXqxhX23j626yr3asXYl6H73NxcGrCvvq51ToGEz6zScUJJoeD/TJjEvBUc/709HHjcwyQlg8p2KT6gR1akRgSAOU1pZYO9ry6ifj+WtKyfXFy9mOOD2PqfiMbDydShhnb0TQrUHRQuC5h3H4uTrgZi/NUzvWqcS1p4m8ElzVZP5/I3KMn3+Xx09xa2YiLdoE6/6rJ4piF73rpW0Ufe4Pr8XwuAYt0mKZAGzWu3cNURQXmbhPaenyRFEs67PYSaA7oAIOIS04tQHK44tbqJ8oiuOAeUiLVtcEQSh9U66UZ4Moik1EUWzyfNEHIOr6IzyqeONa0ROlpZIGPVtyJ+yyQd47YVdo1K8tAJUaBpGbmUNmYhppMUlUalgNS92KfVDrugZBoU1Rt0YgT6NjiYpNQKVSs+/YGUJaGe7XzcjKLozp8cvewzSuVwsH+6ItB+Xd5gWQcz0c6wAfrPwrIFha4NqzbWEg5+dYVS560bCtWxXBygJNaibqxDRUsUlY616uHVvXNwgKbVK/mkE6/eJRqVTsO3qakFZNTein0ul3iMb1i+l35FS5tnnB8/KMKyrP42cJaWm4H9mgPPcdKSxPDzcXvD3defJMittx/uotAiuV/CU59dpjHAK8sfP3RLBU4t+7BTEHDOuKrZ87rb6dwoV31pP1uGjLxq0VO9nT+B32NpvCuXHrSDh1p9TJE4D26QMUnr4I7l6gtMCiUTvUNwwnJoJj0SKXonJ1EATEbGniKzhIE0PB1ROLBq1QXSr5qyBAHR9nIlOziU7LQaXRcuBeDO2DjN2QM/NVXI5KIVTvWkpOPhl5kk3zVBrOP00ioIzAwHWq+BKZkEp0YhoqtYYDF+/SvkE1gzQ+bk6cvxcBQHJGNhHxyVT0cMHD2QFvV0ci4qTJ8/l7EVQtYztG8rXHOAZ4Y+/vicJSSZXeLYg6aOgpYefnTvuNUzj97ldk6tkPoOXaMaSHx3C3jBMxtny7kx4hA+kRMpCDe4/Q/3XptJSGTeqTmZFJQrzxS+SgN/rRvkMr3nlr5n/sGVDH25nI1Byi03X2ux9HSFXjbWWS/VIJCSy6Jooii8NuEeDmwLDGZXxh1eO3zX8wqstYRnUZy8kDp+k2QBqSajeqRVZGNsmlvKy9KObWr46fG5EpWUSnZqHSaDhwO5L21Y0XOzPzCrj8NJHQGkXBaj0cbPF2siMiSWqL55/EU9XTySgvwOZvd9C1/QC6th/A/j1HGDBIWkRp1KQ+mRlZJuvL4GH9ad+hNZPemmFQX3z9vPlmyydMHj+bJ4+eGuV7Ttq1R9hX9cauktSX+fVpSdxB476s6XdTuTzpS7L12kJ+Yjq50ck4BOriJ7WtaxAo1hQpxdpepd4tiComz87PnTYbp3Du3fUGbc/azRFLJ2l8UNpY4tW2DhkPYymNvJsPsKrii2VFL7C0wOmVdoWBnJ9jWaloYdC6dqBu3JPspXST+k4LH08cu7Qi46/S+074+9rC8/vO7fE+lw9eoE3/EAACG1YnJzPH5MLNnbO3aNZD8pht2z+UK2HSVucrhy6WK39x6tasTmRUDFExcdK4fvgEocUOXsjIzCoa13cfoHGDOoXjerIuRmFsfAKHT5yleyfTixTPybkeXmg/wdICl57tyDCatxTZz7ZOIIJlkf0AXHq1I2132Xb7JzCaJx05Vfo8ac8hGtevjYO9HT4VPLhx5wG5eVLsu/NXbhJQ+b9n3qJ5eB+FT0UUFbzBwgLL1h0ouGS4xUhwKVpYUAbVRFAIiJnpCE7OCHa6eYOVFRb1G6ONjqQ0tM/CUXj4ILhWkOZJwW3Q3DGMK6m+fQFFQG1QKMDSCkWl6ojx0sKx9cCJaBOiUJ0o36lvdSpVIDIpnejkDGnecjWc9nWrGKTxcXHg/APp/smZOUQkpFHR3Ukah7YfJcDLlWGhweWSl35V6qttdX21b5+WxBezn42fO42/m8r1iV8Y9NXPKe82L4DY649xC/DGWddX1+7Zgodh5fNAPr56F1+2eJf1baby5ztf8PTMnVIXfQDq+LkTmZxJdEqmVJ43ntK+pr9Rusy8Ai5HxBNaq6iu+7jYceNZErkFaqktPIqjagVno7wy/27+TR4/lQRBaCmK4llgMHAOeOv5b4IgWALVRVG8/RJkHQb+EAThY1EUEwRBcAMcRVF8CqgEQbAURVFVRrrycAJpO9cWURQTdQs23kgLWqIgCKmCILQVRfEkMAwwOWoLghAoiuJ54LwgCD2RFoAygReOvqfVaPljwSbGbJmNQqng4q5jxIdH0WJoJwDObTvEvaNXqRkazMzjn1CQm89P078G4Nm1R9zcd57Je1agVWuJvh3B+e2HS5VnoVQy551RjJu1HI1WS99uoQRV8WfXbunr4sCeXXgcGc3cD9ahUCgIrFyRxe+PK8yfm5fP2cs3WDDl7fIpqNESNX8DgT8sko5z33mYvAfPcH+jGwDJW/fj0qMVbv1DEVVqxLwCIiZ+WJg9asE3VPnsPQRLC/Ij44ic9lk59BvDuJnLJP26d9Dpd0CnX1ceP41i7gefF+k3bYKxflPHlks9C6WSOZNGMG7OSkle1xBJ3l9SQOqBr3aWynP1ep08Pxa/V1R2syeOYNaqdajUaip6e7F0WslyRY2Wq3M20W77TASlgic7jpPxIJqqb3YE4PGWw9Se2hcrV0carRwJgFaj4XC3+eXSxQitlrxd67GbuAwEBapzB9HGRWLZRvqqrDq1F4uGrbFs+4q0jUtVQO73HxRmtxkzF8HeCTRq8nd9CbnGX9ANylKhYFanuoz/+QJarUjvehUJ8nDkp2tS834tWDpK9kh4HC2reGBrVdTdJmXlM3/fdbRaES0iXWr40i6w9L3rFkoFs4Z0ZvwnO9CKIr1b1yfIz5OfjkmTjNdCGvHWq61Z8P1fDFi0EVEUmdI/FFdH6WVi5uAuzNn4Jyq1Bj9PF5aMeKVUeaJGy8W5m+n44wwEpYJHO46T/iCaasOk01DCfzhC/al9sXJ1oNnKEVIetYZ93Rfg2aw6VV9rS+qdSHqESTtsr63cRYxeLARTHAk7SWjntpy4tEc6zv2dorqwaccXzJiyiIS4RJavnUf0s1h+2/8DAPv/Osxna77Gs4I7uw/vwMHRHq1Wy6hxb9CpVR+yMo3X+S0UCmZ2qM2EXy9J5VmnIoEejvx0XZo4v9ZA8kg5+jCeFpXdsbUsst+1mDT23I2hmocDr2+VHE0nta5O24DSg6vrc/bweVp0aM6O0z+Ql5vHyveK+pHVW1bwwfS1JMcn039UX4ZMeB03Tzc2HfqGc0cu8MH0so98Nbd+FgoFs7o3Yvy2E5K84ACCKjjz0yXpNKnXmkgfEI7ci6ZloJdBewCY2b0hc347j0qjxc/VniW9yjiBETgSdoIOndty6vI+8nJzeW9SUX3ZsvNLpk9eSHxcIivXzifqWSx/HNgGwL6/DvHJh18xdcZ4XNycWfGhdGqhWq3hlY7GIe9EjZYbczbRcvssBKWCyO3HyLwfTRVdXxax5TA13uuHlasjDVaNLMxzvKt03xtzN9P4y4kIlhbkPE3g6pSvS9VL1Gi5NHcTIT9KfedjXd8ZNEyS9/CHw9Sd2hdrV0eaPO871RoOdp+PrZcLLT4dh6BQgEIgcvd5Yg5dLb0gNVril6zH/9tloFSQ/vNBCh5G4jJI6jvTduzFsWtrnPt0RFRL417MlFWF2f3WzUXp4oSoVhO/+Eu0GaX3ncX5u9rCtSOXaRDaiLUnvqRAdxz7c6ZtmsvGGV+SlpDKjpU/MGnde7w2bQgRt59wbOehMvNP/GwqtVrWxcHVEaVCRJOdgpgnLaRYWCiZM3UcY99fII2zr3QmKKAyO3+XDlF9vU8PHj99xpzlH6FUKKlaxZ8lsyYX3nvqvBWkpWdiYaFk7tRxODuWcVqgRkvMgq+oumUxKBWk7jpEfngkbkOleUvKtv04d2+Fa78OiGo12rwCnk5aXZhdsLHGoU0wUXO+KMtU5WL6wlVcvHqDtLQMOvZ5gwmjh9G/Z9f/+H4WSiVz3h3DuBlLdPOkjgQFVGLXn7p5Ui/dPGnlZ9K8pUpFFk+XvKTr165O5/YtGfj2NCyUCmpWq8prr3YpUZb55y0acr79FIe5H4JCQcHRfWijIrDqLC1oF4T9iVWL9lh36YWo0UBBAVkfS16uChd37CbNBoUCQVBQcPYoqitlbOHWasn//Rts31qoO879MNr4Z1i0kOyjPncAMSEKzf2r2L33CaIooj4fhjY+EkWVWlg2DkUTG4HtVGlbbMG+rWjulbzQYaFUMKt/W8Z/9ac0T2peiyAfd346fQuA11rX5a2uTVnw42EGfLAdUYQpPVvi6mDL1ccx/HXpPtV83Bm4WtpO986rLWhbu0qJ8kSNlluzN9Fsx2wEpYKo7cfIuh9FpTeld5TILYeo9n4/rFwdqPPBKCmPWsvprnOlMrW1wqNdPW5O21h6OerJO7hgM69vkeZJN3YdJyk8muCh0jzp2rYj2Hs6M3z3UqwdbBG1WpqM6sbGTjMpKMf2d5Pl2bMZ4zcdlsbZRkEEebnw03lpp8FrzasDcOTOM1oG+WBrZVmYt56/J53qVGbwF3tQKgRq+rrRv2k1k3L+rWj/6Qf4L0D422MKmAFBEKoAe5EWSloB4UgLIdWBzwBnpEWuT0RR/EYQhGPANFEUL+nyLwKyRFFco39NEIQQ3b9f1aXTv/Y6MBvJa0oFTBRF8ZwgCB8AvYArujg/JaXLEkWx1NFcEARbIA3oKYriQUEQNgDeoij20l0PRopDZAc8BkaKophqQr9fgWpIHkiHgSmAK3AAsARWlhbnZ0aVwWarJMtOGh+1+ndyu/UCs8qrc2ZZ2YleJprSj+x+2fzRouwX0pdJt/7lc6l9WVgE1zSrPKrXN6u4XwYdMqu8ufnGwaD/Lu4uDzWbLICuS2+ZVd6B+XXNKk9wNO2R83dR7Z3fzCrvCyvztr2cYsFM/24aOpi373wr23Rg8b+Lyhbm+5L9/cUPyk70Ernb7D2zyqt1yTgQ9N+JWPDiL8T/F/5ovMKs8jq1Kd3j72VjWaX8wYZfBsoO5h1rj4woY7HrJXPT5m87iNmIyWvNO+e0HTDPvAORmXmrymtme5/9JuKn/8qy/Dd5/Gh1W5r0uYYUF8cAURRDiv29yNQ1URSPAcdKuLYTE3GCRFGcCcwsR7oyPuGAKIq5gLXe328Xu34NMIpYbEK/fiZun4IULFpGRkZGRkZGRkZGRkZG5l+JqQNu/tf4N8X4kZGRkZGRkZGRkZGRkZGRkZHR41/h8SOKYgTSCV7/XyIIwm9A8QiaM0VRPPBPPI+MjIyMjIyMjIyMjIyMzL8BOcbPv2Th5/93RFHs+08/g4yMjIyMjIyMjIyMjIyMzL8PeauXjIyMjIyMjIyMjIyMjIyMzL8U2eNHpkxszLg+KOYZH7v8d6LWmHftU8zNNKs8wcrGrPKyzXwyTcFT89YXpb95T8IRMlLMKi/DfIdVAJBvxlPnBDs7s8kCsBLMW5iChZmN5+JuVnF2SvP2ZUkW5h0b1GaVBpnZ1mUneoloyTOrvHw05hOmMa/1CtTmbevmPmVLsLI1qzxzz1syn5jXfk4WaWaVp0yIMau8RDOPfSmCGfuW9DTzyfofQA7uLHv8yMjIyMjIyMjIyMjIyMjIyPxrkT1+ZGRkZGRkZGRkZGRkZGRk/pXIwZ3lhZ//WQRBCAZ8RVHc+6J5g9rXp8eCYQhKBVd2HuPk+t0G1z0Cfej74Vh86lTh8JpdnP6mSESf1W9RvUNDspMz+KLrrHLJO3X5Jh9s+BGtVku/Lu0Y/dorBtczsrJZ8Ml3PItLwNrSksWTR1GtSsXC6xqNlsFTF1PB3ZV1C6eUKc8ppCGVloxGUChI3H6IuC9+Nbju0qUZftMHgygiqjVELvyOrIt3ixIoFNTe9yGquBTChy//r9Pv1MVrfLB+Cxqtln7dQhkzqLfB9fTMLBas/ZpnsfFYW1mx5L2xVAvwL3yWRR9tIDwiCkGAJe+PJbh29RJl+YXUp9mSYQgKBeHbj3HzC8O6UrVvK+pOeBUAdU4eZ2dvIvVOJACt175FxU7B5CVl8EfH2WXqBWDZqBn2b70DCgV5YXvI+/lHg+sWdYNxnLccbXwsAAVnT5K7YzMA9u/OxKppS7TpqaRPGlkueYqAulh1HAIKBerrJ1CfN25OCv8aUhqlEjEnk/ztH0jP0qQLFg3agSiiTYyiYO+3ZW4ZOH0vktW/n0GrFenbvCajOjY0uJ6Zm8/cH48Ql5qFWivyZkh9+jSrCcDCHcc4cfcpbg62/DJ9YLn08w+pT5tFw1AoFdzZfoyrXxrazyXQhw5r38azbhXOf/gT174u0r/+mG7UHhSCiEjKvSiOvL8BTX7ZW7uWfTCHjp3bkZubx+QJc7h5/Y5Rmi82rKZBw7qoVWquXrnB9CmLUKvVdO3RgZlz30Wr1aJRa5g/eyUXzl0pUdbpR3GsPngDrSjSN7gKo1rVMLi+6ewD9t56BoBGFHmSlMHRqa/ibGtF93X7sbeyQCEIWCgEfhzdoUQ5E5dMoHmHpuTn5rN66hrCbz00SuPt7828L+fg6OJI+M1wVk1ejVqlxt7RjtmfzaKCnydKpZJdX//MgV0HsbS25JNf1mJpZYlSqcQi+z7qK4YHQZ5+ksDqw3ck/er7M6p5kKF+Fx6x906MTj8tT5KzODqxMzaWSkZtP4tKo0WtFelU3YcJbUpu54Xy7kay+vdTUv1sUYtRHRsZXM/MzWfutsO6+qnlzdBgvfp5lBN3IqT6OWNQmbKeM2/FNNp3ak1uTh6z3l3EnRv3jdK8MXogw8cOpnKAP81rdCQ1JR0AB0d71qxfiq+fN0oLJd9+uZVft+82ym8Kv5D6tFgstY37249xo1jfFti3FfV1fZsqO48zszeRcjey3Hr5h9Sn1WJpnL23/RjXvjBueyEfvY1H3SpcWP0TN/TaXt3RXak1OAQEgXs/HuXmt2UfEGrucQ9gypJJtOzQnLzcPJZPXc2DW+FGafqP6MPAMf2pGOBHj7p9SE/NAKBSoD9zP55B9brV2PDBd2z/epdJGSMXvUWj0Mbk5+bzxbRPeXLrsVGaCv4VmPL5dBxcHHhy6zGfT/0YtUpNmz7t6TOuHwB5OXl8M3c9T+9GYGltyZJdK7CwskRpoUThYIs2K8ngnqfOX2HVuo1oNFr6v9KZMUP7G1xPz8xi/gef8ywmDmsrK5bOmES1qpUB2PLTn/yyJwwBgWpVK7Ns5jtYW1uVWpZOIQ2ptHgMKBUkbQ8zaT/f6UNAK9nv2aJvC+1X7+wGNNm5oNEiqjXcfWVaqbIATl24wgfrvkOj0dLvlU6MGdLPSL8Fq9fxLCYeaytLlsyYSLWA5/rt5tc9hxAEqFa1MktnTsLaqnT9SmPeio84cfoCbq4u/L71q//4Ps8x97zFpmVTXKdNBIWC7N/3krF5h8l0VrVr4PX95yTNWUbu4RNFFxQKvH/4Ek1CMolT55Ypz6JBU2zfnAQKJQVH95D/53bD67UaYD9tGdqEOAAKLp4k/9ctRQkEBQ4rvkJMSSL7wzllyjv9KJ7VYbpxtkFl43H23AP23ooCQKPV8iQ5k6NTXpHG2S8OGI6zo0LLlGdu+1Vv34DeC95EUCq4sPMox9b/aZSm18Lh1AwNRpVbwK5p64m+HQFA29Hdafp6BxBF4u4/Y9f0r1CXMU86/TiB1Ydv6cb1SoxqUc3g+qbzD9l7NxoAjVaUynNSV2lc//GMblzX0qmGLxPa1DAlQsZMCILQDfgUUAIbRVFcVey6oLveA8gBRoiiWPKkthzICz9/A4IgKEVRNOMm0JIRBMFCFEVTb5PBQBPghRZ+BIXAq0tGsPmNlWTEpTD2z6XcC7tC4sPowjS5adnsWbSFWl0aG+W/+vNJzm8Oo99H48olT6PRsmL9D2xYNg0vdzcGT11CSPNgAiv5Fab5Ztdf1Kjqzyfz3uHJs1iWr/+BjStmFF7f9mcYAf4+ZOeUI8aAQkHl5W/zYPAiCmKTqb13NWkHL5AXHlWYJOPUDdIOXgDAtlZlAr+axq327xRe9xrzKnnhUSgdy44pYm79NBoty9d9z4ZVc/D2cGfQO3MJbdmYwMpFC0kbt/9BzcDKfLrofR5HRrNi3fdsXD0PgA++3Ezrpg34aMFUVCo1ufn5JcoSFALNlw/n4OBV5MSm8OreJUQevEx6eNH+78xniewfsIyC9Bz8QuvT6oNR7Om5CICHu05w9/sw2n46tky9AFAosB83hYz576NNTsT5o69RnT+N5tlTg2TqOzfIXGI8oOcf3kfenl9xmFr2REZSUMCq8zDyd65BzEzBZvgCNA+vISbr7W+3tsWqyzDyd32EmJkCdo5SVgcXLBp3Iu/buaBWYdV7PMpazdHcOl2iOI1Wy8pfT/PV2FfwcrZn6Ce/0r5OFQK9XQvT7Dx9m6pernw2ujspWbn0WbWTVxpVw9JCSa+m1RnUpg7zth8tn3oKgXbLhrN7yCqyYlMY8NcSIsIuk6pnv/y0bE4t/IGAroZt3d7blfoju7C940w0eSq6fPkOQb1acP+nk6XK7Ni5HVWrVqZlo240atKAD9YuoEcn40WAX3/6i4lvS21g/cY1DH1zAJu/28HJ4+c4sPcIALXqVGfD9x/TttkrRvml8hRZuf86Xw1pg5eTLUO/O0r7aj4EejoVphnRsjojWkoLHscfxLL1wkOcbYteUL55oy2udqXHSmnWoSkVA/x4s81IajWqyeSV7zKp57tG6d6aM5pfvvmVo38eY8rK/8feecdHUXX//31nN733BAi919B7r6JSRBFBBQFFBARUmggoXcAKKooFla5YQHrvvYea0ALpvZct8/tjNtnd7GYT0CePv+e7n9eLF9mZM/cz584999w5c++5b/LEkD5s/fkv+g/vx/2I+7z3ymy8fL1Yffg79v2+H02+hrcHTyUvJw+VWsXO0yvRPbiBnHjfqN+eq6wc3JogD2eG/XyUzjWCqOHvYdSvVQ1GtKqh6BcZz5pzd/FycUSWZVY93wZXRzUanZ5X1p+gQ/UAGlfwsbhvY33qWfTbEVa+/rTSPj/ZbGifvkUyG4+FK+1zdF+lfS5ab9I+6zCkQ0PeW7fPZn2aonOP9lStHkrPVgNp0rwhHyyZwXN9RljInTt9iQO7j/DzH1+bHX9x1GAib97l9RffwsfPm10nNrP11x1oNLYDsEIStJs/nJ1DF5Mdm0K/bUrflmbat0Ulss3Qt1Xq2pj2S0ay1dC3lQYhCdrPH842Q/nPbJvLvWLl56Vlc2z2z1QtZns+dSpR74Uu/P7UHHQaLX3XTOX+/otk3I0vmbCc/R5A226tqVStIs93eIkGzerxzqJJvPb0OAu5y2fCObb3BCt+/cTseEZaJp/MWkGnPu1L5GjatTkh1UKY0Pl1ajWtzavzx/LugCkWcsOmD+ev77ZwfOsRXl0wlm7P92D3mp0kPIhnzuB3yc7IJqxLM8YsGse7A6agydfwwQuzimxv3fW1iPwsZI3ig3U6HfM/+5pVyz4gOMCP51+fQtf2rahRNbSIc9WaX6lbsxqfz5/BnfsPWfDZ13z38TziE5NZu/kv/vxxOc5OTrz9/hJ27D/CgCe6l1yZkkTl+WO4NXQOmthk6m1bWurzq/7VFK52GV90/tZz76FNLVsOQp1Ox4LPVvHN0jkEB/gx5PWpdG3X0ky/b9dupm7Nanw2bzp3oh6y8NNVfPvxB8QnJrPut238sfozg37L2LH/KAP6lBw4Lw0D+vZk6KB+vDtv2WOXUYj/xrjFZ9qbJIybii4+keCfviTn8Am0d+9byHlPeJW8k2ctivB44Rk0d6OQ3NzKoiAur0wke+EU9MmJeCxYiebccfTRxcZJN66UGNRxemIQ+ugohEsZxrh6mUW7LrHyhfaKn/3Bip9tU5sRbQx+NsKKnx3WoVQ/a1SvfJ+fkAQD577CqhcXkh6XzIQtC7i25xwJJu9EdbuE4V8tmCVdJlO5aU0GLhjFigGz8Azyof2IPizr8Q7afA3DVkykydNtOffr4RL5dHqZRXuvsHJwG4I8XBj20xE61ww29+utazLC8JHnUGQca87eMfr1IW2Nfn3dMTpUD7Tp1//XoJf/PTl+hBAq4AugJ/AQOCOE2CLLsunXzieAWoZ/rYGvDP8/Nuw5fh4DQog/hBDnhBBXhRCvGY5lCSHmCiFOAW2FEC8KIU4LIS4KIb42PGCEEF8JIc4arv3ABkcrIcRvhr/7CyFyhRCOQghnIcQdw/EwIcRJIcRlIcTvQggfw/GDQoiFQohDwEQhxHNCiHAhxCUhxGEhhCMwF3jecH/Pl1X3SmE1SLkfT+qDRHQaHVe2nqRusQBPdnIGMZfvoNdaxr7un75BbnpWWekIv3WHyiGBVAoOxMFBTZ9OrThw8oKZzJ2oGFo3qQ9AtdAQYhKSSE5VvujGJaVw+MwlnunVqUx8bk1rkX8vlvyoeGSNlpQ/j+LTu5WZjN4kwCK5OmOaK8whxA/v7s1JXL/3X6nflZuRVK4QTGhIEA4Oap7o3JYDx80HErejHtK6aUMAqleuSHR8IkmpaWRl53Duyg2e6aN8cXFwUOPpXvJAw79pDTLvxZMVlYheo+PunyepXOwlJfFsBAXpOcrf5yNxDTG+JMafuklBWtnbirpWPXSx0cpsHq2W/MP7cWjdoczXa69eRs4se/JtKaQ6cloCcnoi6HVor59GVct8Bo66fht0t84rQR+AHJPyJRWoHUFICLUjclaaTb7wqARC/Typ5OeJg1pF76Y1OWj4alQIIQTZ+RpkWSY3X4OXqxMqSenmm9eogKdr2RPkBobVIP1ePBmG5xe55STVitl6bnIGCZfuoNdY2rqkVqF2dkSoJNQujuTEl54Yu3ffbmza8CcA589ewtPLk8CgAAu5fXuMA6ML568QUiEIgJzsnKLjrq6uyDacfHhMCqG+blTyccNBJdG7fiUO3ootUX7HtQf0aVCpxPMloX2vduz+dQ8A18/fwN3TDd9AXwu5pu3DOLRN0Wv3L3to37sdALIMLm7KANvFzYXMtEx0hr41z9AXqdVqpT2ZdEbhsWmE+rhSydtV0a9uBQ5Glvzyv+NGDH3qVgCUduTqqHwX0upltDo9paU/DY9KINTfy7x9ht8zkxGU1j4fLeFw9z6d+X2j8u3i0rlwPLw8CAiyTDp9/cpNoh9YPltZlnFzV+rWzc2V9LQMtFb8VnEEhNUg4148mQbbuPPnSSoXs42Ec8a+LeF8JG4hls+8JAQWKz/yz5NULVZ+XnIGiZcs/axPzQrEX7iNNq8AWacn9uQNqvVpYZOvvP0eQIfe7dhpsIur56/j4eWOnxW7iLgaSdxDy3ablpzGjUs30VrpewrRsmcrDm1WAt0RF27h5umGd6DlS07Ddo05uV0Juh/avJ+WvdoAcOvcDbIzlA0EIs7fxC/E2LYKbU+lVqF8lDXiyo0IKlcMIbRCMA4ODjzRrQP7j50yk7l9/wFtmjUGoHqVSkTHJZCUkgaAVqcjP78ArVZHbl4BAf62245bmPL8Ckyen3cv83cDs+fn4qx0LI+JKzciqVzBXL8Dx06b63fvAa0L9atcieh4K/rpdOTl5xPoV3bbsIYWYY3w8vQoXbAMKO9xi2ODumgfRKOLVsYtObsP4Nq5nYWcx/MDyNl/BJ2hDguhCvTHpX1rsv4o2zdcVc266ONi0CfEgk5LwYn9OLQoOXhaHMLXH3XTNhQc2FYm+fCYFEJ9ivnZCBt+9upD+tR/dD9biPJ+fqFhNUm6H0fKgwR0Gh2Xtp6gQS/z/rZ+r+ac/0356BV1IRIXD1c8ArwBkFQqHJwdkVQSji6OZJQyTgqPTSXU241K3ob6rFeBg5FxJcrvuB5Dn3rKh2Rzv64vk1+34z+KVkCkLMt3ZFkuADYA/YvJ9Ad+khWcBLyFECF/h9Qe+Hk8jJRluTnKjJk3hRB+gBsQLstyayAZeB5oL8tyGKADhhmunSnLcgugMdBZCNG4BI7zQOFbZEcgHGiJEukrHEH8BEyTZbkxcAWYY3K9tyzLnWVZ/giYDfSWZbkJ0M/QwGYDG2VZDpNleWNZFfcI8iU9Jrnod0ZsCp5B/7locXxyKkEBxk45yN+XhGTzjrF2tVD2HT8HwJWbd4hNSCbeILPkm/W8NXIwkihbU3cM9qUgxjhluyA2GYdgyxcJ7z6taXhoObV/nMndt1cUHa/8wUgezP8R9GVbSVre+iUkpRIcYNQnKMCvqKxC1Klehb1Hzyh8NyKJjU8iPjGFh3EJ+Hh78t6ylTw3djpzPv6GnNySZxm5BvuQHWPclSo7NgXX4JLbSq0hXYg+cLlMeliD5OePPimh6Lc+ORGVn7+FnLpOA7w+/w6P95egqlz1sfmEhw+yya5bcmYKwt1cP+EbDM6uOL0wDefhc1A1MLzMZ6WhPb0Tl7HLcBn/KXJ+Lvp7V23yJaTnEOztXvQ7yMuNhHTzXc2GtG/A3fg0en6whmeX/cKUAe2QHnPHErdgH7JMnl9WbApuNp6fKbLjUrn49XZePvkZI86toCAzhweHw0u9LiQkiJho4yAmNiaOkJDAEuXVajXPPt+PA/uOFh174qkeHDm9jTWbvmLy+PdKvDYhM49gD+PuMUGeLiRkWt+9Jlej5fjteHrUNc7EE8DYdUd54bv9/Hr+bok8/sF+JMYkFv1OjE3Cv1if4unjSVZGFnqd3kRGabt/rP6TKrVC2XRuPd/u/ZovZn9VFNCSJImvd33F5kub0EXfQk40LiVKyCqmn4czCVnW7TVXo+P43UR61A4uOqbTywxefYRuX+yhTVV/GpXyVTAhPZtgb2MgOMjbSvvs0JC78an0fP8nnl26kSkDOzx2+wQICgkgLsbYXuJj4gkKLrm9FMeabzdRo3Y1jobvZOvhDSyYucxmsLAQriE+ZMcabSMnLgW3kJLrp/aQLjx8hL7NNcSHLJPys0sp3xQpNx8S0roOTt7uqJ0dqdytCe4VbO/AVt5+DyAg2J+EGGN/nRCbSECwZX/9d+Ab7EeyiV7JcUn4FgsMevh4kJORXWR7ybHJ+AZbBiK6DenJhYPGGfaSJLF0+yd8d/4n9CazfQASElMIDjDqEhTgR0Ki+Q6NdWpUZe+RkwBcuX6L2LhE4hOTCArwY8TzA+gx+FW6DnoFD3dX2rc0/6BQHI4hvhTEmjy/uGQcrQQavfu0psHBFdT66T3umTw/ZJla696n3vaP8B/WyyYXQEJSMsGBxcYRSVb0O1yoX4RBv2RFv8H96fn8GLoNGoW7myvtWoaVylleKO9xiyrQH1280T9oExJRBZrbgSrAH5cuHcjabLkM1eftcaR+/k2ZA3mSjz/6ZPNxkuRjaXeqWvXxWPwtbtMWI1WqWnTc5eXx5K37usy2npCZR7CnqR9yISGzJD+k5fgdK352/TFe+P4Av14o2c8Woryfn1eQj9k7UXpsssU7kVeQL2kmMmlxKXgF+5IRn8qhVX/x7vEVvHf6K/Iyc4g4csUmn1W/bqs+7ybQo7YxTqD49UN0W7GbNlUDSvXr/2uQy/GfEOI1w0SPwn+vFbudisADk98PDcceVeaRYA/8PB7eFEJcAk4CoShTsHTAZsP57kBzlGlbFw2/qxvODRZCnAcuAA2A+tYIDMuzIoUQ9VCigh8DnVCCQEeEEF4owZ1Dhkt+NJwvhGkw5xiwWgjxKso6wseGsDJGL8tA+Z9E8a9ro557kozsHJ6bMJv1f+2lbo3KqCSJQ6cv4uvtQf2aVR+lcMtjVvRL23mK8M4TiBi1WMl7AHj1aIE2KZ2cK5Y5BB4F/0n9rG1lWFzlUc/3IyMrm2dfn866P3dRt2ZV1CoVOp2O6xF3ef6pnvzy1WJcnJ34bqPlWuYSC1ZuwCqC29Wj1gudObfQ+tr2MsEKX/FHp7t9i9RRz5P+5ijytm7GY2bZclGUHcUIhQopuCr5v35C3qaPcGjXD+ETBE6uqGo1JXflVHK/mAwOTqjqty2lZCvPrtjv4zcfUqeiH3vmvMjGt59l8e/HyMoreCxNirdDKPtHYicvV6r2asbP7SbzY4sJqF2dqD2w9K+K1jlLJl380WxOHj/LqRPnio7t+GsvHVs9ySvDJjBtpuWSqqJyy2ALhTgcEUdYJT+z6eerh3dmw+jufDGkHZvO3eFcVJL1i8tQj1b1Ntxfyy4tiLx6h8HNX+C13mOZMH88roZZKnq9njG9x/J8y6FIAZURPsEm11u5Fet3yOHb8YRV9DHTTyUJNo3oyK7XuxMem0Zkou3ZcNYeU3G1jt98QJ2K/ux5/2U2vj2Yxb8deez2qZT/aO2lODp0a8v18Ft0aNiH/l2HMmvRVNxszGI0YbbCa10ypF096gzpzJkFZe/bhLUnVUa10iJjuPjlXzy5fjp910wl+VqU1dm35oTl7/f+7rN7XI7iepVFpkHbRnR7vgdrFv1YdEyv1zOl72TGtBmFcHABtXG2Wln6ltFDB5GRmcWgUZNY+9s26taqjkqlIj0ziwPHTrNrw9fs3/w9ubl5bN19sDRNS1MBUJ7f1S7jiRy1iIpThhYdvzFwOtefeJuIl+YSOPwJ3FtbHZbaLNtiHDH0GTKysnh29Fus+307dWtVQ62SFP2On2bn+q/Y9+u35Obls3XPIcsC/1so73GLNRSrYJ+33yBt+SqLYItzhzboUlLR3LDMjVUirDo5cz7tvQgyJgwhc/po8nf9jttb8wBQN22DnJGG7u6tMtM9kh+y5mdf7sSGUd344vlS/GxR4f/9cacFXwl9nYunGw16tmBxxzeZ3/oNHFydaDrA9iz1stheIQ5HxhNW0deKX+/MrrE9DX49wyafHY8PWZa/kWW5hcm/b4qJlG6MZZN5JNhz/DwihBBdgB5AW1mWc4QQBwFnIM8kr48AfpRleUaxa6sB7wAtZVlOFUKsNlxbEo6grO/TAHuB1SiBm9Iz70HRp1ZZll8XQrQGngQuGhI724QhMvkawJO+rWjmoawXzYhLwcvk66FniC+ZCWlluJ3HQ5CfD/EmX8rik1II8PU2k3F3dWHepFGA0pk+MWoKFYMD2Hn4FAdPXeTo2cvkF2jIzs1jxrKvWfROyWt3C2KTcaxg/PrhGOKHJj6lRPmsU9dwrhKM2scDjxZ18e7VEq9uzZGcHJA8XKn++STuvPnpv0a/IH9f4hKNXx7iE5MJ9DWP+Lu7uTL/ndeL+Pq8/CYVgwPIyy8gKMCXxvWUttCzY2u+2/hniVw5sSm4VTB+dXQL8bW63MenXijtlo5m70tLyU8t+xTb4tAnJSL5G7/2S34B6FPMBwlyrnEpkObcKVCpEJ5eyBnpj8wnZ6YiPI36CQ9fi+VacmYK+txM0BSApgD9w5tIgUoeBDk9EXKVl2ndrXNIFWuiu3aiRL4gLzfiTKYgx6dnE+Bl/pL655mbjOwWhhCCyv5eVPT14G5CGo0ql30WRCGyYlNwN3l+7iU8P2uo1KEhmQ8SyUtR9Lu74yzBLWpx63fLHEavjB7KsOHPAnDxfDgVKhqDFyEVgomLS7S4BuDtaW/g5+/DlBfnWD1/8vhZqlYLxdfXm5Ri0+NB+fIYZzLDJz4jlwB3Fws5gJ1XH9CnQajZsUDDVzdfN2e61gkhPCaF5pWVvkNVpw1f7xoNwM1LNwmoYFyuFhDiT3J8sllZ6SnpuHu6I6kk9Dq9IhOnyPQe3IsNXyhx/Jh7McQ9iCO0Zig3LxqTGGdnZKOPjURVqS7aVGUGTJC7s7l+mXkEuFt3NzuvG5d5FYenswMtQv04djeBmgElL6cI8nYjLs04wyc+LZsAz2Lt8/QNRnZvqrTPAEP7jE+lUZWgEsstjmEjn2PwSwMAuHLhGsEVgoFLyj1UCCIh3np7sYZBLzzNN5+vBiDq7kMeRsVQo1ZVLl+wPfsuJzbFbOmWa7AvOXHW+7YOS0az66Wl5D/C8oHs2BTcTcp3C/Yl20r5JeHmhkPc3KC8TLeaNths9pA1lJffe2Z4f/oNU3JuXb94k8AKxn4pMCSApGJ28Tgo5HBAIvJyJH4mevkF+5OSYK5XRkoGrp5uRbbnF+JHikk/V7luFV7/cBwLh88lK80y+JmTkY1ckIPk5IZeq+S8CwrwIy7R6HviE5Mtlmu5u7kyf7oSmJZlmd5DXqNSSBDHzlygYkggvt5eAHTv1JaLV2/wdK8uJepcEJuMY4jJ8wv2QxNn+/k5GZ6fNjUTjUFfbXI6aTtP4RZWi6xTlkn1CxEU4EdcQrFxhJ8V/aZNKNKvzwuvUzEkiGNnLlIxOKhIvx4dW3Mp/AZP9+xcIl95orzHLbqEJFQmy5nVgQHoEs3twLFebfwXKrNXJW8vXNq3IkWrw7FhXVw6tcOlfWuEoyPC3RW/uTNInr2oRD59SiKSX7FxUmoxuzMZJ2kvnkKMnITw8ERdpyEOzdrhENYaHBwRLq64jnuXnC8WlsgX5OFMXIapH8olwKMEP3TtocVyaqOfdaJr7QqEx6QW+VlrKO/nl17sncgrxI+MhNRiMsl4m8h4G2b71OzQkJQHCWQbxknhO89QpXltLvxxlJIQ5PEIfv1GDH3q2fDrlf04djeRmib5lv7Xof97MZN/Gg9RJo8UohIQ8xgyjwT7jJ9HhxeQagj61AXaWJHZBzwrhAgEEEL4CiGqAJ4oAZl0IUQQSlDHFg4Dk4ATsiwnAn5AXeCqLMvpQKoQoqNB9iXA6mcTIUQNWZZPybI8G0hCaUSZQImjeNNIZWHQByD60h18qwbjXSkAlYOKRk+34caecyUV87fRoHY17sck8DAuEY1Gy87Dp+nS2nzac0ZWTlEyzs27DtOsQR3cXV2YOOI59v74MTu/X8aSqWNp1biezaAIQPbFCJyqheAYGohwUOPbvwOpu8+YyThVNb6YujasjnBQo03N5OHiNVxq8SqX24zh9hsfkXnsis2gz39Dv4Z1anA/Oo6HsQloNFp2HDpBl7bm658zsrKNfDv207xRPdzdXPH39SY4wI+7D5Q+59SFcGpULnktdtLFO3hWC8Y9NADJQUW1/m14sNs8Gb1bBT+6rprEkYkrybhT8jrlskAbcQNVhUpIQcGgVuPUqRua0+aBBuFtHBCoa9UFSXqsoA+APvYuwicQ4eUPkgp1vVboIs3zM+kiLyBVqg1CArUjUkh19MmxyBkpSBVqKDl+AFWV+uZJoa2gQWggUUnpRCdnoNHq2HUhks4NqpjJhHi7cypCSSqYnJnDvYQ0Kvk+Xu6DhEt38KoajIfh+dXs14a7e8q2mUBmdDJBTWuidlb0q9i+AakR0VZlf/h2HT06PkOPjs+wc9s+Bht2mWvWogmZGZlWX+SHvvQsXbp1YOyod8xmCVStVrno70ZN6uPg4GA16APQoIIPUSlZRKdlo9Hp2XXtIZ1rWy6dzszTcC4qia4m53ILtGQbdt7ILdBy4k6C2eBJd/MkY3qPZUzvsRzbeZxez/YEoF6zumRnZlu8fAJcPH6Jzk8qkzZ7PdeT47uVIGBCdAJNOyh9go+/N6E1KhF7PxYvXy/cDIEVR2dHpIq10acZp/A3CPEiKjWb6LQcRb8bMXSuaRlgyczXcO5hCl1NzqXk5JORp+iXp9Fx6n4S1fzcLa41q8/QQKIS08zbZ8OqZjIhPu6cumXaPtOp5Pdog8613/9C/67D6N91GHt3HGTg830BaNK8IVkZWSQ+QvAg5mEcbTsquWz8AnypXrMKD+4/LOUqSLxk3rdV79+GqD2WfVuPVZM4NHElGXcfrW9LuHQHr2omtte/DffLaHsAzoY6da/gR9UnWhD553Gb8uXl93778U9G9HqNEb1e4/Cuo/Qx2EWDZvXIysgm2YpdPCoKOab0ncyZ3SfpPEjJSVeraW1yMrNJS7B8Cbx64gpt+iozEjsP6saZPcpqev8K/kz5egbLJ39K7F1j/+zp64lroe05OSKc3JC1xplrDevUIuphLA9j49FoNOzYf5Su7cxzJmVkZqHRKDa2edsemjdpgLubKyGBAVy+dovcvHxkWebU+ctUr2I750n2pQiciz2/tD3mOXcsnp+j8vwkFyckN+XFUXJxwrNTGLk3be8+17BuTe5Hm+vXpV1Lc/2ysk3020vzxvUN+vkX0+8K1UrRrzxR3uOWgms3cAitiKqCMm5x7dWV3MPm9hrT/0Vi+g0jpt8wcvYdJuXDz8k9dIz0L74j5skhxPQbRtLM+eSfuWgz6AOgu30DKbgiUkAwqNQ4tu2G5pw5n/AyfgxU1agLQiBnZpC34Vsyxg8m480XyPl8LtqrF2wGfcDgZ1OL+dlaNvxsLRt+9m5CqUGK8n5+Dy/dxr9qMD6Gd6ImT7flWrF3omt7ztPsGeVVrXLTmuRm5pCZmEZaTBKVm9bCwTBOqtm+oVlSaGtoEOJt7tevx9C5ZrCFXGa+hnMPkulqcs6qX/e17dft+I/iDFBLCFHNkHt3CFB8GcUW4GWhoA2QLstyyUmyygD7jJ9Hx07gdSHEZeAmynIvM8iyfE0I8R6wWwghoczYGSfL8kkhxAXgKnAHZQmWLZwCglACQACXgQTZ+KYzHFgphHA1lFfSHtRLhRC1UGYi7UP5PBoFTDcsRVtU1jw/ep2ebbNX8/JP05BUEuc3HSIxIpoWw5QdJ86u3Yd7gBdjtszHyd0FWdbTZuQTrOg5lfysXJ79fBzV2tTD1ceDt08s58Anv3J+U8nTfNUqFe++Poyxsz9Cp9czoGdHalapyKbtSsLGwX27cvdBDDM/XoWkkqgRWoEPJo4siyrWodMT9d4q6qybA5JE0sZ95N16QMBLvQFI/HkXPn3b4v9sF2StDn1eAbfHfvTYdOWtn1ql4t3xI3j93UXo9HoG9u5CzaqhbPpLSbI5+Kme3ImKZuaSr5AkiRpVKvLBW8ZlqTPGjWD64hVotFoqBQcxz0agSdbpOfnej/RcNxUhSURuPETarWjqvKTs3nHz5/00mTwQJx932i4cAYBeq+OvvrMB6PTFOILb1sPZ153nzn7OxWWbidhgY0q4Xkf2yk/x/GAZSBL5e7eji7qHU59+AOTv3IJT+8449e0POh1yfj5ZS4z51d3fmY1DozCEpxfeP/xC7rofyN9jI2GirKdgz1qcBr8NQkJ75QhyUgzqsC4AaC8eRE6ORXf3Cs4j54Iso718GDkpGhnQ3TyL84j3Qa9DHx+F9pLt6e5qlcT0Zzow9pvt6GWZ/q3qUDPYl1+OK19mn2tXn1d7NmP2hoM8u/QXZGQmPdUaH8Msluk/7+Xs7VjSsvPoNXcNY3u3YGDruiWrp9NzZNaPPL1mqrKl9MZDpN6KpsGLyvO7umY/LgFePLdtHo7uLsh6PY1H9WF9t2kkXLzN7e2neW7HfPQ6HUnh97m6rvTdxPbuPkT3np04eWEXuTl5TBpn3FVk7aaveevN94iPS2TJJ3N4+CCGv/Yo29Bu37qXj5d8yVP9evHckP5otBrycvMZM/KtkutTkpjeO4yx64+h18v0b1KFmgGe/HJOWbLyXHNlde7+mzG0rR6Ei6PRXSZn5/PWr0rXr9XreaJBKO1rWA6+AE7tP03rbq34+ehq8vLyWfqWceeZhT/N56MpH5Mcn8Kqhd/y3pfv8srU4USG32bHhp0ArPlsLVM/nsKqvV8jEKxa+B0ZqRlUr1eNqZ9MQaWSEEJCH30D/QPjV3q1JDG9R0PG/npa0a9RJWr6e/DLRWX3lufClKDh/og42lb1N9MvKSufWTsuodfL6JHpVacCnWrYnpWjtM+OjP3mL4WvVV1D+1RmzzzXrgGv9mzB7PX7eXbJRkP7bGPSPvdwNjJGaZ8f/MTY3i0Z2KaeTc6De47RuUd79p7+g9zcPGa8abTnVes/Y+akeSTEJ/HSq8/z6viX8Q/0Y8uhDRzee4yZk+fz5Uffsnj5+2w9tAEhBEvnLi/a6t0WZJ2eE7N+pM9apW+7Zejb6hps48aa/TSdPBAnb3famfRtW56cXWrZheUfnfUjfQ3l3zTYXj1D+dcNtvfMdqPtNRrdh01dp6HJyqXXNxNx9nFHr9VybOaPRYlMS0Q5+z2AE/tO0bZbazYdW0Nebh4L31pSdG7ZT4tYPGUZSfHJPDtyIMPeGIJvgC8/7f2WE/tPsXjKR/gG+PDdjpW4ubui18sMfnUQw7q8Qk6WUdfz+8/RtGsLlh9eSUFuPl+8s7zo3IzVs1g59QtSE1JYs+hHJq94hxfeGcbdq3fYv1Hxh89OHIK7jwevzlP8nE6nZ/rTb+Md6MP4jychSRJCEsj52cj5xlkDarWKdye+ypgpH6DT6xj4RA9qVqvMxj8Vm36+fx/uRD3k3YWfoZIkqlcNZe5UZYetxvVr07NzOwa/+hYqlYq6tarx3FO9bVemTk/UrFXUXjsHJBXJG/cqz+9Fw/Nbozw/v0FdDc8vnztjlX5IHeBNzW+nAyBUKlL+OEzGwQslUoFhHPHmaF6fOlcZRzzRnZrVKrNpyy4ABvfrzZ37D5m56HNlHFG1Eh9MGWeiX1sGv/YOapVE3VrVee6p0vMK2cKUOYs5c+EyaWkZdB/wIm+MeolBT5dSZyWg3MctOj0pS5cTuPxDUElkb9mB5s593Acp241nbf7rsfQoEXo9uas/x23GEpAkCg7uQP/wHo49ngagYO9WHFp3xqmnYZxUkE/25/Mem04tSUzv1YSxG46h12P0s4a8eM81qwbA/lsxtK0WaOlnNxf6WdngZ237ofJ+fnqdnj9nr2b0TzOQVBJnNh0kPuIhbYb1AODk2r3cOHCBul3DmHboUwpy8/llirK75IOLt7my4xQTty1Er9UTffUep9bb3tWyyK//clIZBzYKVfz6hXtKfTataqjPONpWDbD069svoJdl9DKKX7fyMeh/GdaW4f63IMuyVggxHtiFsprne1mWrwohXjecX4my83ZfIBJlO/eS3vPLDFHe+Vns+P8Ps6sOK7dGMnOP5Xau/0lc7rK0XPkaH7TcSvY/CeFY9l2c/gmsa/dxufI93exB6UL/IFw6VC9d6B+EaNCoXPl+GFv2WQX/BOZm/udmCxbH3c8HlBsXwFPvlp9uAH+937Bc+QipXLrMP4gmI9aUK980p5KDov8J2N5A/p9HMzm7dKF/EJN4vJmVj4sK6n9ml6eyYN3JxeXGBXCp5YzShf5BND79T+fCsw3haH3J7X8Ka5uULSD7T6FbyN/6YP/I8KxV9qTr/wSc+vyt3aYfGRvfLX2W5j+Jaw6l7/z4T+GDWX8rj+8jw2XUsv/pjb5eqDKg3N5n19//419Zl/YZP3bYYYcddthhhx122GGHHXbYYcf/JMo3BPrvhD3w8y+AEOJ3oFqxw9NkWd7137gfO+ywww477LDDDjvssMMOO+yw438D9sDPvwCyLA/8b9+DHXbYYYcddthhhx122GGHHXb8r+FftqvXfwX2Xb3ssMMOO+ywww477LDDDjvssMMOO/5HYQ/82GGHHXbYYYcddthhhx122GGHHXb8j8K+q5cdpWJ41UHl1khqyuW7C1W6KN9UXyF6Vbny5Yjyte93vmhernya33eUK19GePnt5gAgyjk0r8kt3/bpUbGg3Li+Cw8tNy6AvfqkcuXrKvmXK195T5nulJ9frnwPJady5UtSle8GIF7lnOVyhzqrdKF/EO317uXG5V2+boHy3irGsZzfE7Kl8tVw2KW55cqXO21MufJpU8rPzwJIruU7jtAmla8BOjf2LTeuxN3l229Wu7TnX7kT1T+FZ6v0K7fO7Nf7W/6VdWmf8WOHHXbYYYcddthhhx122GGHHXbY8T8Ke3JnO+ywww477LDDDjvssMMOO+yw438S9u3c7YEfOx4DjTqHMWz2SCSVxKGN+9j21e8WMsPmjKRJ12YU5Baw6p3l3L96F4BRS94grFsLMpLTmdl7cpn4anRuTO85LyFUEhc2HOT4V1vNzvvVCKHfsjEEN6jKgWWbOPnNdgA8Q3zp/8lY3AO8kPUy59ft5/QPu6xy1OnchP6zX0ZSSZzaeIADX22xkOk/Zzj1uoZRkFvAxne+IvrqPQKqh/DiijeN9xIayK5PfuXI9zuoUL8KgxaMQu3kgF6r57dZ36O9cM+i3KqdG9P1fUW/8A0HOf2luX6+NULovew1AhtW5djSXzhr0A9g9LFPKMjOQ9bp0et0rH1q9r+iPgtx7EYUS/44jl4vM7B1XUZ2b2p2PjM3n5nr9hOXmoVWL/Nyl8YMaFUXgDkbDnL4+n183V3YPGVwqXoBqBq2wPmFNxBCouDIDgp2bDQ/X6cxruPnok+KA0Bz/igFW9cgBVXC5fX3iuSkgGDy//iRgr2WbdsUTm1a4v3WeIQkkb1lO5k/rbcq51CvDoHfrSDlvXnk7j8MQPDv65BzcpD1etDpSBgxtlT9nNq0xGvSeIRK4cv6uWS+gFUrSJk1j7wDCl/QbwY+ncKXOLJ0Ppd2LfCd+gZIElm/7yD9h41W5Rwb1Cbkp89JnLaAnL1HEI4OBH//McLBAdQqcvYeIe2rn0rlM9OheSvcXpsAkkTe7m3k/bLO7Ly6URgesxagj48FoOD4EXLX//hIHNU6N6a7wRYubzjIqa8sbe+JZa8R1KAqR5b9whkT2wMQkuDlv+aRFZfK5pEflcjzxgdjadmtJfm5+Sx76yMiwyMtZIJDg3j3ixl4eHsQER7JkolL0Wq0PDfmWboN7AqASq0itGYog8OeJzNNmQIuSRIrtn0O8Zn8UuweqnduTI85LyGpJC5uOMhJK/o9ZdDv0LJfOG3QT+XkwIub3kPlqEZSq7i5/TRHPvmt1Pqs3rkxvQz1eXHDQU5Y6VueMvQtB5dt4pQJ38ubZhXx3dh+msOfbLbJ5dM1jOrzXkGoJOLW7uPhij/MdevdkqrThiDr9cg6PXdm/UDG6RsA1PrkDXx7NkeTlM75Lm+VqhdASJfGNJv3EkKSuL3+INdXmOtWZWA76o97GgBNTh5np/9A2rUoJCcHevw2C8mgW9S204Qvs60bQOUujelk8AvX1h/kXDG/4FMjhO4fKX7hxNJfuPC1Upfe1UPo8+X4IjmvyoGc/OhXLn1nu6+u0KUxLecq+kWuP0j4F+Z81Qa2o+EbTxXpd2rGalKvReFawZcOn72Oc4AX6GVurT3ADRtcw98fTVjX5hTk5vPVO59zL/yOhUxAaCBvLn8HN2937oXf4YvJn6LTaKlQoyJjlk2gWoMabFy2hm3f/AmAg5MDszctwMHRAZVaxantx4n96C+L+uxoUp/ni9Wnd40Qenz0GgENq3KyWH32Llafp8pQnyFdGtNynrE+rxZrL1UHtqPBOKU+tTl5nJq+mjRDfbb77HVcAhU/G7HmADdL4Srka2HCd80KX30TvtMGPsnJgZ6/KbYuDO3zyrLSbT2oa2OazlXq8866g9wsxlf5mXbUMdiDNjuP89N/IP1alFFAEvTYOZ/cuFSOvbysVL6KXRrTytA+I9Yf5Eqx9lndpH1qc/I4YWifAO0/epVKPcLIS8rgz+4zSuUqDe8t/JjDx07j6+PNH2tW/u3yynvc4tC0Fa6jFN+av3cbeb8V860NwnCfsQB9gsG3njxC3qYfkfwCcJs4E8nHF1mvJ3/PVvL/Kr0vUzduictL40GSKDi4nfyt5uMWdb0muL01D32iol/BmSPk//4zAJ6frkPOywG9HlmnI2tW6eMWhxatcHt9AkIlkbdjG7mb1lmVU9eui9enX5K58AMKjh4CwHnAIJyfeAqEIG/HX+T9/mupfKo6TXHqNwokCc3pvWgOWNqPqnoDHPuPAkkF2ZnkrnwP4eWH05CJSB4+yLIe7ak9aI7+ZYXBHC7tWuA77Q2EJJH5+w7Svy95XFbh589JmKqMy1RBAQQsmIrKzxdkPZm/bidjne22Ysf/Hv7PB36EEFWBdrIsrzP8HgG0kGV5vK3r/n+HEKILUCDL8vFHuk6SeHnuqyx5cS4pccm8v+VDLuw5Q0zkwyKZxl2aEVwthKldxlOjaS2GL3iNuQMUZ3v014Ps/XEHr338ZkkUxfgEfeaNYO2wRWTEpTB6yzxu7T1PUkR0kUxuWjY75/xE3d7m+WX0Oj175q8lLvwejm7OjP5rPneOhptdW8gxcO4rfPPiQtLjkpm4ZQHX9pwjPtIoV7dLGAHVglncZTKVm9Zk0IJRfD5gFol3Yvmk74yicmad+pLwXWcAeHL6UPZ8tpkbBy9Rt0sYT80Yyh+DF1pwd58/nF+HLSYzNoVhW+cSueccKRExZvrtn/MzNXtbz5/zy/MLyE0t2zrg8qjPQuj0ehb9doyVY54kyMuNYZ/+RucGVakR7FMks/HYVaoH+fD5qCdIycplwOKNPNmsFg5qFf1a1mZIhwa8t/5AmXRDSLgMm0D2R9OQU5Nwm7UC7cUT6GOjzMS0EVfI/XyWuW7xD8n+4PWictw/Wo/mwjHbfJKEz5SJJE6Ygi4hkcDVX5F75Djau/ct5LzGv0beqbMWRSS+8Rb69Iyy6SdJeL89kaSJBr7vvyLvyHG096zwvfEa+Vb4ksY9Gp/vjAnEvz4NbXwSFdauIOfQCTR3oizkfCaOJvfEuaJDcoGGuFenIOfmgVpFyA+fkHv0DPlXrpeZ223sJDLeext9UiJen3yN5uQxdA/MddVevUzmB483kBeSoMe84WwatpjMuBRe3jKXyL3nSDaxvby0bPbN+ZlaJdhe85F9SI6MwcndpUSell1bUrFaBV7pOJK6Tevy5sLxvNlvkoXcqBmj+O3b3zm45RBvLpxAnyG9+evnbfzy9a/88rUy+GzTozXPjB5YFPQBGDhqAFGRD6js4W2hX695w9kwbDEZcSmM2DKXCCv67bGiny5fw7oXFqLJyUdSq3jp11ncPniJmAu3bdZnn3kjWGfoW0ZumUeElb5l95yfqGOFb80LC4r4Xv51NpEHLxFzwTJABoAkUWPRaMIHzyU/NoWwnYtJ2X2WnFtGP5R25ArnDX2xa70q1PvmLc51nAhA/MYDxHy/gzrLJ5SoT3Hdmi8cwYEhi8iNTaHX9nlE7zpPholu2Q8S2TtoHpr0HEK6NqHlklHseWoO+nwN+59bgDYnH6FW0eOP2cTuv0Ty+RJ0M/B1mT+cP4YuJis2hef/msudPedILfbsDs/5merF6jLtTiwb+swsKueVM8u5s9OyLyjO13rBcPa8sJic2BT6bp/Lg93nSDfhy3qQyK5n51OQnkOFro1p8+FIdjz9PrJWz9kP1pESfg+1mzNP7ZxH7OErZtcWIqxrc4KrhTC581hqNq3NqPmvM2vAVAu5odOHs/27LZzYepRRC16n6/M92LtmJ1lpWfw451ta9G5tJq/J1zD/hdnk5+ShUqt4/9dF6PdfJ97QXoUk6Dx/OH8a6nPwX3O5W6w+823U50aT+hxRxvpstXA4+4Yo9fnE9rk83GVZn3sGmdTnkpHsfEqpz/Nz15FyRanPvjvnEVdCfZrytVw4nP0Gvj4GvoxifHtN+FovGcmup95Hn69h33MLi9pnrz9mEbP/EsnnS7Z1JEGzhSM4/PwicmJT6LFjHjG7z5N5y8QeohI5+IxiD8HdmtB86Sj2Pzmn6HytV/uQGRGD2qPkvtNUv9YLhrPb0D6f2j6XqGLtM/NBIjsN7bNi18a0+3Ak255+H4DITYe5/sMeOn72z+TWGdC3J0MH9ePdeaUHrErFf2Hc4vraJDLffxt9ciKeS76m4PQx9A+L+dbrl8laYO5bZb2OnNVfoLsTAc4ueH20Cs3FsxbXWug3YiLZi6agT0nEY95XaM4fRx9djO/mFbKXzbRaRNb8t5Czyj5ucR83ifQZytjBe/nXFJw8hi7KcpzkOmoMmnNnig6pqlTD+YmnSHvzddBo8Vy4hIJTJ9DHWB/jFurnNPA1cr95Hzk9GZc3l6C9eho5weiLcHbF6Zkx5H47FzktCeHmpRzX6yn4azX66Dvg5IzrxI/Q3rpofq0V/fzenUDcGMO4bN0Kcg5aH5f5ThpN7nHjuAydjpRlX1NwIxLh6kLFDV+Se/Kc5bX/w7DnNbbn+AGoCgz9b9/EfwpCiJKyrHUB2j1qedXDahJ/P47EB/HoNFpObT1Ks14tzWSa9WrJsd+U6PntCxG4erjhFeANwM3T18hOL3uysgphNUi9F0/ag0T0Gh1Xt56kTk/zgVlOcgaxl++g05gneMtKSCMu/B4ABdl5JEXG4BHkQ3FUDqtJ8v04Uh4koNPouLj1BA16tTCTadCrOWd/OwJA1IVInD1c8TDoVIha7RuSfD+e1OjCpK5y0Quhs6cr6fGpFtzBYTVIuxdPepSi382tJ6nZy1y/3OQM4i/fQa/9+wnsyqM+CxEelUConyeV/DxxUKvo3bQmB6/eM5MRQpCdr0GWZXLzNXi5OqGSlG6peY0KeLqWPdm3qnod9AkxyElxoNOiOX0QddNHbuKo6jdFnxCLnJxgU86xfl20D6PRxcSCVkvunv24dLLkcx88kNwDh9GnWD7/R0Fxvpy9+3G2wuf23EByDx5Gl/r3+Jwa1kH7IAZtdBxotWTvOohrF0s+zxf6k7PvKPqUNLPjcm4eAEKtBrX6kRyuunY9dDHR6OMUXfMP78ehTYe/pU9xhBTansEWrm89SU0rthB3+Q56jaXtuQf7UqNbGJc3HLTJ065XW/Zs3gfAjQs3cPN0xzfQMjlkWPsmHN6m9DF7ft1Lu96Wdd2lfxcO/Gnk8w/2p1W3luxcv9NCtritX996ktol2Lo1/TQ5SjJlSa1CclBTWi7nCmE1SDHhu2aDr3jfUpxP5aACG+3Fo2lN8u7GkReVgKzRkvjHMXx7m/shfU5e0d8qVyez8jJOXkebVnY/5Nu0Bln34sk29NNRf56kUrEAQdLZCDTpOcrf5yNwDTE+Y22hbg4qpFJ0AwgytM0MA9+tLSepbsUvJFyy/uwKUalDA9LvJ5AZnWyTz69pDTLvxZNl4Lv350lCi+mXeDaCgiL9InEz6JebkEaKwS9os/NIj4jBNdh68tPmPVtxZPNBACIv3MLV0w3vQEsf0qBdI05tV75JHd58gBa9lEBPRnI6dy5HWm0/+YbnrSpqP8ZzQWE1SDepz4i/UZ8Zj1mf1tqLaX26mtbnlXuAoT4jY3AJsZ1MtjjffSvPryQ+KN4+S7d1U3uQNToe/HmSisX4kk3sIfmcuT24hPgS0j2MO+vK9lHHv5h+d/88SWUb7TOxmH7xp25S8Aj2XhpahDXCy9PjHymrvMct6lr10MdGKzNltVoKju7HsVXZfKucmqIEfQDyctE9vI/kF2D7vmrURR8fjT4xFnRaCk7ux6H5o+tXVqjrFBs7HNyPY1tL/Zz7P0PB0UPo04zjJFXlKmivX4P8fNDr0Fy+hFP7Tjb5pMq10CfFIqfEg06L9uJR1A1amd9T005or5xETlPeDeTsdOX/zFQl6AOQn4c+4SGSl59NPqeGddCYjst2ljwuy957FJ3JuEyXlELBDeWDg5yTS8GdKFSB5bsphB3/ffxrAz9CCDchxDYhxCUhRLgQ4nkhxD0hxEIhxAkhxFkhRDMhxC4hxG0hxOuG64QQYqnhmitCiOdtHQcWAx2FEBeFEIVrjyoIIXYKISKEEEtM7ilLCLHAcE8nhRBBhuMBQojNQogzhn/tDcc7G8q9KIS4IITwEEKECCEOG46FCyE6lqD/YCHEx4a/Jwoh7hj+riGEOGr4u7uh3CtCiO+FEE6G4/eEELMNcs8JId4UQlwTQlwWQmwwzHJ6HZhsuA+r92ANPkG+pMQYd6tJiU3BJ8jPQibZVCYuGZ9g251ZSfAM9iUj1jjIyohNwSO45GBDSfCq5E9wgypEX7T8iuUV5ENajJEjLTYZr2IBDa8gXzOZ9LgUvIoNbsOebsfFLcYJVH9+8BNPzRjGe8dX8PS7w9ixZIMFt3uwD5kxKUW/M2NTcLcRTLGALDNozXRe3DaPRkO7lipeHvVZiIT0HIK9jTurBHm5kZCebSYzpH0D7san0fODNTy77BemDGiH9Jg7eghvf/QpiUW/5dQkJG9Lp6aqUR+391fiOmkBUoUqFucdWnVBc7r0Aakq0B9dvHGQpUtIQhVgPgiSAvxx6dyB7N+2Fr8ckPH/fCmBP67EbcCTpfJJAf7oEsrI97sVPlnG77OlBPywEtf+pfOpAv3RxhnrUxufZDFIUAX64dq1A5m/WJmeLElU2LiS0P2/kHfyPAXhN0rlLLrUzx99klFXfVIiKj/LZ6mu2wCv5d/h8cESVJWrlrl8MNherLntPYotdJ/zIgcXrkfW235L8gv2IzHGWI9JsYn4FesPPX08ycrIRq/TF8n4F5NxcnaiRZcWHN1xtOjY2PfH8O3C79BbuQf3YB8y/oZ+QhKM3L6Aiee/5O6RK8TYsHUAj2BfMv9G3yIkwejtC5l8/ivuHAm3yecU4ku+iY8piE3GycrLsd8TrWh+5DMarJnBrclflvleisM12Jcck/4/JzYFl5CSdavxQhdiD1wq+i0kQZ89Cxl4+SviDoeTbGPmFIBbsA9ZJn4hKzYF98fop2v3a0vEnydKlXMN9iHbhC8nNgVXG3w1h3Qh+sBli+NulfzxbViFpBL08w22HBv4Bpk/Nw8fD7JNbCE5NhnfEgJJphCSxKLtn/D1+R+5cuQS8Sbtx62Yn82KTcHtMeqzVr+23CpjfeYUr89S2kuMjfq0OfsGcLHCV1r7NOUTkuCJPQsYdPlLYg9fKbV9ugT7khNdzB5s1Ge1F7oQu99oD2FzX+Ly/PVQSt9ZiOLtM7uU9lmrhPb5b0R5j1uErz86U9+anIhkzbfWaYDnx9/hPmsJqtCqFuelgGBU1WqhvXXNJp/k64/eJBilT0lC8rEMFqlq1sdj4Srcpi5Cqmjkk2UZt+lLcZ+/EseuZRgn+fmjTzQfO0j+/hYyTu06krfNPKWD7t5dHBo1QXh4gpMTji3bIAUE2uQTnr5FAR0AOT0ZUSx4IwVUAFd3XF6fh8vEZaibd7EsxycAqUI1dFG3bPKpAv3RmYzLdAlJqIOsjMu6lTAuM0BdIQinujXJv1L2cdn/AvTI5fbv34p/81KvPkCMLMtPAgghvIAPgQeyLLcVQnwCrAbaA87AVWAl8AwQBjQB/IEzQojDKLNbrB2fDrwjy/JTBp4RBrmmQD5wUwixXJblB4AbcFKW5ZmGgNCrwHzgM+ATWZaPCiEqA7uAesA7wDhZlo8JIdyBPOA1YJcsywsMs3FcS9D/MDDF8HdHIFkIURHoABwRQjgb9O8uy/ItIcRPwFjgU8M1ebIsdzDoFANUk2U5XwjhLctymhBiJZAly/IjzVUVwvKl3OJLvhWZ0r5wPgoedaqeg6sTz62cxO65P1OQlWspYFWn4iK29VY5qGjQoznbTYI7bV/syZZ5P3Nl52maPNmG5z58ja1DPyy13EfpL9YPmkt2fBoufp48u3YaKZExRJ++WfYC+A/UZ2G5VhQpru3xmw+pU9GPVWOf4kFyBq9/vY1m1UNwd3Z8pHtSCrcWMDK/B939SLKmDoP8PNSNWuEy/gOy3x1hFFCpUTdpS/7m78pCaIXOnM978jjSv/gG9JYp5RJefRN9UjKSjzf+y5eiufeAgos2BqtlsCvvSSXzJY4x4ftsKdr7f5/Pd8obpH72rVU+9Hpinn8dycONgI/fx6FGVTS375XMVwp38daki7xF6ivPQ14uDi1a4/HeAtJeG1a28gFh5fmV1RRqdAsjJzmD+PB7hLapZ5unDPVYln61Tc/WXDtztWiZV+vurUhLTiPiSiSN2zS25P0b+gHIepnv+87EydOVQd9Mwr92JZJu2ZiCbq2MRyCU9TLf9n0XJ09Xnv1mMgG1K5FYEl8ZfUzyjtMk7ziNZ5t6VJk2hPDBj7lts9Wuxbpuge3qU/2FLuwdYOSS9TI7e76Lg6crHb+bjFedSqTfLLkurbeHR7tlyUFFtZ7NOL7Yev6H0vhK8kNB7epR84XO7Bo4z+y42tWJLqsmcmbOGjQl+IUy6fWYust6PTP6TsbV0423vplOVp1KpBTW8d/0s2CszxNlqM9H0aGoPgdY1menbydydnbJ9Wmke7TnV+OFzuw24ZP1Mjt6zsTB05VO300qQ/u0QleCggHt6lNtaBcO9FfsIaRHU/KS0km7fI+Atrb7TtuE1kWD29Wj1gud2VGsff5rUd7jljLUpfbOLdJeM/jWZq1xn76A9HEmvtXZBfdpc8n5fjnk5pRGaIXPnFB7L4KMiS8o+jVpjdtbc8l8+2UAsj54EzktGeHpjfv0pehiH6C78ajjFvOfbq9PIPu7ry3GLboH98nZtA6vRR8h5+WivRsJOm0p6pXBF0kqVBWrk/v1HHBwxHX8YnT3byEnGZYqOjrj/PI08rd8D/m2bd1632LO5zflDVI/LWFcBggXZwI/mk3y0q+Qs0t7fnb8r+HfHPi5AiwTQnwI/CXL8hGDc9tict5dluVMIFMIkSeE8EYJjKyXZVkHxAshDgEtbRy3tnB0nyzL6QBCiGtAFeABUAAUhlDPAT0Nf/cA6ps4X08hhAdwDPhYCLEW+E2W5YdCiDPA90IIB+APWZYvWlNeluU4IYS7oZxQYB3QCSUI9BtQB7gry3JhePhHYBzGwI/p6OQysFYI8QfwhzW+4hBCvIYSpKKNb1Nqe1QDDF/oKhijy74hvqQlpJhdmxqXjF8FfyIKZYL9SI03lykrMuJS8AwxRs89Q3zJik8r8/WSWsVzKydx5Y9j3ChhXX56XAreFYwc3iF+ZCSYL5NJi0s2k/EK9iXDZOlW3S5hPAy/S1ZSetGxFoM68ecHSrLZS9tO8tziVy24M2NT8Khg/JrpEeJLVjFuW8g21EVucgaRu84RElbDZuCnPOqzEEFebsSZTK+OT88mwMvNTObPMzcZ2S0MIQSV/b2o6OvB3YQ0GlW2/ZXFGuTURCRf45ck4eOPPq3YlPw8o5PTXjmN84sTEO6eRevH1Y1aoo+KRM5IK5VPl5CIKsh4n6pAf3RJSWYyjvVq4ztPWZcveXvh3K41slZH3uFj6JOUe9OnppF38CiODeraDMToExJRBdrmc6hrwuflhXPb1qTpLPlyDx3Fsb5tPl18IupgY32qg/zRJZrXp2P9WgR8+G6Rfi4dWoJOR84B48w3fWY2eWcv4dK+RZkDP8pXOqOukn8A+mRzXWWTAafm7Cl4Q4Xw9ELOSKcsyIxLwSOkmO1ZWY5pDRVb1KZmj2ZU79IElZMDTh4uPPnpWLZN+gqApi/3YNAQZSLlzUu3CKhgrEf/kACSi/WH6SnpuHu6Iakk9Dq9VZku/TpzYMvBot8NWjSgTc82tOzaCkcnBzw83Hj607FsNdxDZlwKno+pnynyM3KIOnGd6l0a2wz8KPX5+H2LNb6SAj/5Mck4mfghxxA/8uNK1i3j5HVcqgah9vVAm5L5yPeUE5uCq0n/7xriS25cmoWcd71QWi0bzaEXl1BgJe+aJiOHhBPXCena2OaLdVZsCu4mfsE9xJfsR3x2Vbo2ITH8HrlJpefGyI5Nwc2EzzXElxwrfN71Qmm3dDR7X1pKvol+Qq2iy6qJ3Pn9OFE7zP1CneE9qDWsK53Qc+dyBH6m44dgP1KLjR8yUzJwM7EFv5BHGz/kZGRz/UQ4Vbo0Lgr8ZBfzs//p+lTai3l95lppn971QmmzbDT7X1xq1l6EWkWnbydy77fjPNhh288+Kl/rZaM5UIyvEIXts0Ip7TMnNgXXiub2kGfF1r3qhdLio9EcGWa0B79WtanQqzkh3cNQOTmg9nCh1YqxnB7/lU0+0/bpVkL79Cmhff6bUd7jFjk5EZWpb/ULQJ9i7ltNgzma86dgjArh4YWcmQ4qFR5T51JweC+ak0dK5dOnJCL5mfD5+qNPK5lPe+kUQjWxSD/ZUBdyRhqas0dRV69rM/CjT0o0m6Vjbeygrl0HjxnKRiiSlxeOrdqQpdNRcOIo+bu2k79LSezu+sqr6BMTsQU5PRlhMkNLePkhZ6RYyOiyM0CTD5p8dHevIVWoii4pBiQVzi9PRXvhMLrwkza5QBmXqUzGZapAf3QJxcZlDYzjMpWPF64dW5JUOC5Tqwj8eA5Z2/eTs+8o/9dg39XrX7zUyxDQaI4S4FkkhCjcrijf8L/e5O/C32qsf5vDxnFrMC1XhzFAppGNoVXT4xLQVpblMMO/irIsZ8qyvBgYDbgAJ4UQdWVZPowSwIkGfhZCvGzjPk4ArwA3gSMoQZ+2KAGl0vQxXVPzJPAFSn2eE0KUGvCTZfkbWZZbyLLcojDoA3D3UiRBVUPwrxSIykFN66c7cGGP+cDkwp4ztH+mMwA1mtYiNzOH9MS00iitIubSHXyrBeMdGoDkoKLB0224tedc6Rca8PSSV0mKjObUtztKlHlw6Tb+VYPxrRSAykFF2NNtuVqM49qe87R4RnmRq9y0JnmZOWSa6BTWrx0Xt5rnyc5ISKWGYTZAzXYNSLoXZ8Edd+kO3tWC8TToV+fpNtzec75MuqldnHBwcy76u2rHhiTZGKxB+dRnIRqEBhKVlE50cgYarY5dFyLp3MB8inKItzunDAlSkzNzuJeQRiXfx1s7r7t7EymoIsI/GFRqHFp1QXvRfFq+8DROD5eq1QEhmSUNdGjdFc2psuUdKLh+A3VoRVQhwaBW49KzG7mHzfniBg4jbuBQ4gYOJXf/IdKWfkbe4WMIZ2eEq5L/STg749S6BZrbdx+Jz7VHN/KOmPPFDxpG/DNDiX9mKLkHDpG2zAbfHdt8+Vdvoq5cEXUFhc+tdxdyDpnzRT/5Mg/7vsTDvi+Rs/cIyQuXk3PgOJKPF5KHEuQTTo64tG6G5u6D0ivVAO2tG6gqVkIKUridOnVDc8o8aaXwMb4IqGvXVZ5lGYM+ALGX7uBTLRgvgy3Ue7oNkWW0vcNLNvFVmzf5usNktk74gqjj14qCPgAXftrL2D7jGNtnHMd3naDnoO4A1G1al+zMbFISLF9kLx2/TKcnlT6m57M9OLHbWNeuHq40atOYE7uMx77/8AeGtXqJl9sNZ+G4xdw/fq0o6AOKrRfXL6KM+rn4euDkqUxGVTs5ULVDQ1IiS04uW8jna8JX/xH6FlcLvgYkR8aWKJ95MRLn6iE4VQ5EOKgJGNCelN1nzGScqwYX/e3WqBrCQf1YQR+AlIt38KgWjJtBt8r92/Bwt7lurhX96PDtJE6++RWZd4x9vZOvBw4G3VTODgR1bECGDd0A4i/dwbuq0S/U7teGu2V8doWo3b9sy5IAkg36uRv4qvZvw4Pd5nxuFfzosmoSRyeuNNMPoN1Ho0mLjOH6N5Z+4eaPe/mr10xm9J3M2d2n6DioCwA1m9YmJzObNCsfOq6euELrvkreik6DunJuz2mb9+/h64mrp9LfODg50rBDE1JN2mv8pTt4VQ3Gw6Bfrceoz1r9y7ZsDoz16WZSnw+L1adrRT86fzuJY29a1mfbj0aTHmG9PsvCV6UEvk7fTuJ4Mb7i7TO4Y0MySrH11It3cK8WjGtoAMJBRWj/NsTsMrcHl4p+tPtuEqcnfEWWCV/4wo1saz6B7a0mcfL1FSQcvWYz6AOQdPEOnibts1oJ7bPrqkkcmbiSjDuWY61/K8p73KKNuIEUUgkpUPGtjh26oTlTzLd6G32rqpbBt2YqvtVt3DR0D++Tt2VT2fS7cwMpuCJSgKKfY5tuaM4V08/LqJ+qel0QQtHPyRmcDcm/nZxRN2qB7qHtcYv2ZrGxQ5duFJw01y91+JCif/lHDpG1/BMKThw13Is3AFJAII7tO5J/cK9NPv2DCCT/EIRPoDLzKqwDumvmvkh79TRStfogSeDgiFS5NnK8MlZ3GjwOfcJDNIctdxK2hvyrN3GoXBF1RcO4rI/luOxhX+O4LHvPEZIWLC/6GOf//tto7kSR8XPpu7HZ8b+Jf+2MHyFEBSBFluU1QogsYEQZLz0MjBFC/Aj4ogRZpqDoau14ReDvZmnbDYwHlhruPUyW5YtCiBqyLF8Brggh2gJ1hRC5QLQsy6uEEG5AM6CkfY4PA3MN/y4AXYFcWZbThRA3gKpCiJqyLEcCLwGHihcghJCAUFmWDxhy/gwF3IFMwPNRFdXr9Pw8+1um/DQLSSVxeNN+oiMe0HVYLwAOrN3NpQPnady1GUsPfUF+bj7fTvmi6Pqxn0+mbpsGuPt48MmJb/j9k40c3rSvRD5Zp2fn7NUM/WkaQiVxadMhEiOiaTZMeZE6v3YfbgFejN46Hyd3F2S9ntYjn+CrHlMJqhtK40Edib8exavbld20DizdSKRJ7oVCnX6fvZpXf5qBUEmc2XSQ+IiHtB3WA4ATa/dy/cAF6nYNY/qhT9Hk5rNxytdF1zs4O1K7QyM2v/utWbm/TF/FgDkvI6lVaPM1/DLD/Hyhfvtn/cign6ciqSTCNx4i+VY0jV/sBsDlNftxDfDixb/m4WjQr9moPqzuPg0XX3f6fTMJUGbi3PjjOPcO2V7XXh71WQi1SmL6Mx0Y+8129LJM/1Z1qBnsyy/HlTXhz7Wrz6s9mzF7w0GeXfoLMjKTnmqNjyEh9vSf93L2dixp2Xn0mruGsb1bMLB13ZKV0+vJW7sC18mLEJJEwdFd6GPu49DZsAXxob9Qt+iEY5enQK9DLigg9+sFxusdnVDVb07uT5/arMMi6PSkLVuO/+cfIiQV2Vt3oL17D7eByha2VvPsGCD5+uC3RJn6LlQqcnbtI//kmRLli/g+Wo7/px+CpCL7L4XP1cCXUxrfYsPSE5WKnN1l40tZvIKgrxYp27n/uQvN7ft4PKvUZ+avJa8fV/n74j9vKkKSQBJk7z5M7pFTtvlModeR/dWneM5bpmw5u2c7uqh7OD3RD4D8HVtwat8Zp779QadDLsgna8kHZS8fxRb2zv6R536ailBJXNl0iOSIaMKGKbZ3ce1+3AK8eHmr0fZajOzDdz2m2VziWByn95+mVbeWrD76vbKd+9sfF52b/+NcPp76KSnxKXy76Dve/WIGw6cM53b4bXZuMG7h3L5Pe84fPkdebr41ihL12zP7R4YY9Lu86RBJEdE0Neh3waDfiK3zimy95cg+rOoxDfdAb576eAySJCEkwfW/ThG5/2KpfLtmr+aFn6YhGfqWJCt9y0iTvqXVyCf4usdU3AO9efrj1xFmfBdKJtPpuf3utzRc/x5CJRG/fj85Nx8S/LLih+J+2o3/U20IfK4zskaLPq+AG2M+Kbq8zleT8G7XALWvB63Of839pRuJX7/fpm5nZ66myzql37yz4RAZt6Kp+ZKiW+TP+2g4eSBOPh60WPQKAHqtjt1PzMIlyJs2n71eZAtRW08Rs9eGbga+Q7N+pN8axS9c23iIlFvRNDT4hXCDX3h+m7Ftho3qw5pu09Bk5aJ2diS0Y0MOTP/eJo8p3+n3fqTHOsVmIzceIv1WNLVfUvhu/byfxpMH4uTjTuuFI4r02953NoEta1Pj2Y6kXoviqd1Kf3ph8Sai91v6hQv7zxHWtTmfHl5Jfm4+X7/zedG5qatnsWrqClITUlm/6CcmrHibwe8M497VOxzYuAcArwBvFmxdhou7K7Je5omRTzOlxwR8An0Y+/HEovZ68q9jxO+7aKbf4Vk/0n+NYguF9dnAUJ9XDfU52KQ+m4zqw1qT+qzcsSEHH6E+z8z8ke7rFL7bG5T6rGWozwhDfTr6uNNqkVKfslbHjidmE9CqNtWfU+qz7x6lPi8u2kSMlfo05Ts780e62eBrZOBracK384nZuAR50/azMUW2d3/rKaL3XiyRq5Dvwrur6bResYe7Bnuo/rJiD3d+2kf9yQNx9PGgWaE96HTs6zPLVrE2+U6+9yM9Tdpn2q1o6hj0u/nzfpoY2mdbk/b5V1/le3GnL8YR3LYezr7uPHf2cy4u20zEBovhcpkxZc5izly4TFpaBt0HvMgbo15i0NO9H6+w8h636HXkrPoUjzkG37pvO7oH93DqbfCtu7bg2LYzTn0U30pBPtkfKb5VXa8RTl17o713G8+PlfFs7ppVyqwgG/rlrl6O2zRl3FJwaAf66Hs4dlfGLQX7tuLQqjNOPfopvlyTT/aK+QBInj64TTaOWwqO70N7uZRxi15H1hef4rVQ0S9v93Z09+/h/KSiX/G8PsXhOXuekuNHpyV7xafIWaXMHNPryf9jFS6vzjFs574PffwD1G2U9qA9uQs54SG6mxdwfetTZFlGe2oP+vgopKr1cGjeFV3sPVwmK+OCgh1r0N2wEZTW6UletIJgw7gs8w/DuOw5w7jMRl4fp6YN8Hi6JwW37lBh40oAUpd/T+5R24H1/yVYS0Hxfw3i37q1mRCiN0ogRQ9oUPLX/Iqy1XqSKLbtuhDiHtACSAaWAE+grOycL8vyRqGsw7J23AHYiZL3ZzWQWqzcv4BlsiwfFEJkybLsbjj+LPCULMsjhBD+KDNq6qEEmA7Lsvy6EGI5SrBGB1xDCV4NQQk4aYAs4GVZlq2GsIUQNYBIoI4hj89u4IYsy28azncHlhk4zwBjDXl87pnUkwNwAPBCmSW0RpblxUKI2ob61AMTZFkucc7m8KqDyq2R1JTLvovTP4F0Ub4T/0L0JW2y9p9Bjihf+37nC+vbXv+noPm9bF9E/ylkhP/9ndUeBaKc52Rqcsu3fXpULCg3ru/CQ8uNC2CvPql0oX8QXaXy3Z2jvJMXdsove9Drn8BDyalc+ZJUj5fU/nHhVc5z3neoy3fpTXu9e+lC/xC8y9ctPNL09X8CjuX8npD9mBs8PC6GXXrM3F+Pidxp/8y28mWFNqX8/CyA5Fq+4whtUvkaoHPj0pPN/1NI3F2+/Wa1S3vKu3spVzxV+cly68z+itr2r6zLf+2MH1mWd6EkSTZFVZPzq1ECNYW/q5rITcGYGLnwvFzCcQ3QvRiPablPmfztbvL3ryiBE2RZTgKepxhkWZ5Q/BhKLp4frRy3gCzLtzHx8bIs9yp2fh9KEuri11U1+VuDkt+ouMwtwDIjqB122GGHHXbYYYcddthhhx12/I/g37zbVnnhX5vjxw477LDDDjvssMMOO+ywww477LDDjr+Hf+2Mn/9LEEKcAorPK3/JkB/IDjvssMMOO+ywww477LDDDjvseAz8W9PblCfsgZ9/AWRZbv3fvgc77LDDDjvssMMOO+ywww477LDjfw/2wI8dpUJXjhHSoR7lmxA1ItGndKF/EI2q294m9Z9GZnL5JsvOWFa2LSn/KXgteLVc+fzuXitXPv2Dh+XKl7Xrfrny5aeW32rjsDxtuXEBbHcoX74e2uxy5UvVlm/y40VOOeXK9+uER9708m8ha+ftcuXT5JRvAtZzsYHlytfdOaXcuHwrlm/bjL1bvm2zSquM0oX+QWTeLd+2Wd7Jll0+/Lp0oX8Quvu2d3v9pyFHR5Yrn/pwiXvT/Eeg6tK53Lj8c3eXG9f/BZTzngb/Sthz/Nhhhx122GGHHXbYYYcddthhhx12/I/CHvixww477LDDDjvssMMOO+ywww477PgfhX2plx122GGHHXbYYYcddthhhx122PE/Cdm+nbs98FMShBDewFBZlr/8b9/LfwJCiBHAblmWy5x05qX3RxHWtRn5ufl8884K7oXfsZAJCA1k3PK3cPd25174Xb6a/Bk6jbbE6x2cHHhv03zUjg6o1BKu+w6T+sXPZmW6tG+B//TXESoVGZt3kPbdJqv359SwNhXXfkr8OwvJ3nNUuZ95b+HWqTW6lDQeDCzbOm7/rk2oN384qCQert3P3eXmeWtCBrWn+vh+AGiz87k29Vsyr0UBUGVMXyoN7QpA1vUorkxciT5fY5PPqU1LvCaNR6gksrdsJ+vn9VblHOrVIWDVClJmzSPvwGEAhLsbPjPeQV2jGsgyaQuWUhBuOw+NW8fmBM4cg1BJpP2yi5RvfjE77969Df4TXwJZj6zVk7Dwa3LPKWX6vNwf78G9QQjSNu0k9cc/bXI5tmqFx/jxoFKRu20bOevWWZVT16mD75dfkj53LvmHDqEKDcVrzpyi86qQELJ/+IGcX3+1yXfsSiQfrtuFXtYzsGNTRj3Zwex8Zk4e7676nbjkDLR6PcN7t2VAxzDuxSYxdeXmIrmHiam8MaALL/ZqY5svMo4luy6il2UGNq3GyPZ1zc6vPn6T7eFK29DpZe4mZXDg7X54uTjyxOfbcXNUI0kCtSSxbnR3m1wAquqNcOwxDCQJ7cVDaE5us5CRKtfFscdQhKRGzs0kb+0ihG8wTgPeMMp4B1Jw5De0Z2yvHy/v5+fUWrEFVCpytm6zaQv+33xB6uy5ZrbgPWMK6uoGW1i4BE0ptuDXtQl15w9HGGz9XjFbDx7UnmoGW9cZbD3LYOuVX32CSi92A+Dh2v1EfbOjRJ4Jc8fRplsr8nLzWTx5CRHhljkQgkODmf3lTDy9Pbh1JZKFExej1Whx93Jn2kfvUKFKBQryC1jy9jLu3rxHQEgA7342Dd8AH/R6GdXafcR/95dZmZ5dmlJ57iiEJJG4fi9xX/xmdt67VysqTnkBZBlZqyNqzvdknbluFJAk6u9YiiYuhYjhC2zW5d+uT5O+M/N6FFdL6DvHfDCGll1bkp+bz8dvf8ztcMvcOEGhQUxfMR13b3duh99m2aRlaDVaGrVpxOxvZxP3IA6A4zuPs/4zpY25eboxcclEqtSugizLSKd/Rh9jLFuq2hDH7kNBCLSXj6A9vd2CVwqtg2O3F0BSIedmkb/hQwDUzXuibtwJZBl9UjQFO74Dne0cUI4tW+E+bgJIEnnbt5GzoSTbq4vP8i/JmP8B+YcPAeAy6Dlc+j4Jsoz27l0yliwGTYFNvr/jh4J+W4eck4Os04NOR+LIsTa5AOp2bsIzs4cjqSRObtzP3q8s88M9M2c49bs2RZObz9p3vuLh1XuKfp6uDFk8hpA6lZBlWD91JffOR9jkc+vYnKD3DH5v0y6Srfi9gElGvxe/wMTvDVf8nhCC1E07SV1t2+/B4/edAK7PPovLk08CoL1zh/QPP4QC28/Po3MzKs4ZjVCpSN6wm4SvNpud9+zZmpC3h4Fej6zTEf3Bt2SfVWxd5elG6Ifjca5dBZCJmvI5Oedv2uRTh7XC9ZXxIKnI37eN/D/M9VPXD8N92nx0CYqtaU4dJu/Xn8DBEY+5n4HaAaFSUXDyEHmbVtvkAnBu2xKfd8aBJJH9x3YyftxgVc6xfh2CflhO0rvzyd132HhCkgj++Ut0CckkTp5pk0vVsAXOL7yBEBIFR3ZQsGOj+fk6jXEdPxd9kkG380cp2LoGKagSLq+/Z6QMCCb/jx8p2Pt7qfrZwnsLP+bwsdP4+njzx5qVf6ssgGOXbvLhT1vQ62UGdm3JqH5dzc5n5uTy7hcbiUtOQ6vTMfzJTgzo0hKAtTuOsvnAaWRZZlC3Vrz4RMdH477xgCVbTijcreowsluYOXduATPXHyAuLQutXs/LnRszoGWdR+JQ1WmKU/9XQZLQnNqD5sBmS5kaDXHsNwpUasjOIPcrpU04DZ6Aqn4L5Kx0cpe9WTadrkex5I+jik5t6jGye7NiOuUzc+0+4lINOnUNY0ArZaw4Z8MBDl+7h6+7C5unDimbfvWa4/zsGEW/47so2GPel6lqNcLltdnok5X2qb14nIKdSn/u0KU/Du2UMbzm2E40B0vvy+z434I98FMyvIE3gP9vAz9CCAEIWZat5bMaAYQDZQr8NOnajOBqIbzdeRw1mtZmxPzXeH/AdAu5IdNfYud3Wzm59RivLBhDl+e7s2/NrhKv1+RrWPjCHPJz8lCpVXz9+zxyjpwh//INpUBJIuC9ccS8OgNtXBKVNi4n+8BJNHeizIklCb/Jo8g5ds7scOYfu0lft4WghVPKoiZIgvqLR3Jm8ALyYpJpu2shCbvOkX0rukgk934ipwbMRZuejX+3MBp89Bonn3gPp2Afqozuw9GOb6PP09Dkm4mEDGhH9MZDNvgkvN+eSNLEKegSEgn8/ivyjhxHe+++hZzXG6+Rf+qs2WHvyePJO3mGnJkfgFqNcC4l4aokETTnDR68MhNNXBJVN39K1r6TFNx+UCSSfeIiWftOAuBUpyoVPpvB3T5jcKxVBe/Bvbn37GRkjYbQ7+aRdfAMmvslNCFJwmPiRNLeeQddYiK+K1eSf+wYuvuWunmMGUPBmTNFh3QPHpAyenTRef9ffyXviO0Efjq9noVrdvD12y8S5OvJ0Lnf0iWsDjUqBhTJbNx/huoVAlg+8QVSMrLpP/MLnmzbiKoh/mz6YExROT3f+oRuzeqWRGWQk1m08wIrh3UkyNOVYd/uo3PtCtQIMCbeHNGuDiPaKYOWQ7diWHMqAi8Xx6Lzq17ujI9rGZPkCoFjr5fJ27AEOSMF5xHvo424gJxsUv9Orjj1fpm8jcuQM1LA1QMAOSWOvO9nF5XjMv5TdDfPWSExQTk/PyQJr3cmkmywhYDvVpZoC55vvEb+qTNmh70mTSD/5GlSZ75fRlsQ1Fs8knMGW2+zayGJVmz9TDFbP/XEe7jXrUSlF7txss9M5AItzTbMIGnPBXLuxlnQtO7WikrVKjKsw3DqN6vH5EUTeePpCRZyY959lV9XbWb/loO8tWgifYc8wZaft/LihKFEXr3NrNHvU7lGKBMXTODtIVPR6XR8OXclEeGRuLi58PuOr0k/fJG8iIdF9VRlwWvceuF9CmKTqb99CWm7TxvPAxlHL5O2+zQALvWqUGPlO4R3Nt5b0OinyIt4iMrD1XZd/s36LOw7jxn6zsbfTCR4QDtiivWdLbq2oGLViozuNJo6TeswfsF4JvefbHErI2eM5Pdvf+fw1sOMXzieXs/3YvsaJVBz9cxV3n/lfcv6f38M5w6eY+HrC1E7qNkwMdR4Uggce75I/qaPkDNTcH5pNrrbF4vZnguOPV4i/9ePkTONtifcvVE360HeD++BVoPj02NR1W2N7uoxG3Up4fHmJFKnvo0+MRGfL78m/4R123N/dQwFZ422IPn74zpwEMkjX4aCAjxnvY9zt27k7dppk+/v+CGApHFvoU8vWxJgIQmemzuSL19cQFpcMm9vWciVPeeIjzS2lfpdwgioFsL8LpOo0rQmzy0YzScDlJfqZ+YM5/qhi/zwxieoHFQ4upTu94Lff4OoEYrfq7b5UzL3n6QgsmS/V/GzGdzpMwanQr83SPF7lb+bR9YBG37PwPe4fafk74/roEEkDR8OBQV4zZmjPL+dtp9fpXljuD1sNpq4ZGpv+Yj0vafJjzDql3XsEjf3nALAuW5Vqn4xlRvdlY8BFee8Ssah89wb+yHCQY1Uhvp0HTWRrHnvoE9JxGPRSjRnj6F/aK6f5voVshfPML9WU0DmB29BXi6oVHjMW47mwml0ETaC9JKEz7Q3SRg3FV18IsE/fUnO4RNo71rWp/eEV8k7adk+PV54Bs3dKCQ3N9u6CQmXYRPI/mgacmoSbrNWoL14An2s+ZhTG3GF3M9nmR3Txz8k+4PXi8px/2g9mgs27LyMGNC3J0MH9ePdecv+dlk6vZ6FP/zB1zNGE+TnxdD3VtClWX1qVAoqktm4+wTVKwWyfMoIUjKy6P/2Mp7s0JR7sUlsPnCatfPG46BW8cbi7+kYVo8qIf5l5l70+zFWvtaXIC83hn3+B50bVKFGkHGTlY3Hr1I9yJvPR/YmJSuXAUt+4cmmNXFQlzHht5BwGjiG3G/mIKcn4zJxGdprp5HjjbaAsxtOz7xO7qr3kdOSEO5eRac0Z/ehObYNpxcmlV2n346w8vWnFZ0+2UznBlWpEexr1OlYONWDfPh8dF9Fp0XrebJZLRzUKvq1rMOQDg15b92+MuvnPPgNclbMRE5LwnXKp2ivnEQf98BMTHf7Krkr3zc7JoVUwaFdb3KWTgadBpc35qG9egY5sXw3nflvQm+f8fPvyPEjhHhZCHFZCHFJCPGzEKKKEGKf4dg+IURlg9xqIcRXQogDQog7QojOQojvhRDXhRCrTcrLEkJ8JIQ4b7g+wHD8VSHEGQPPZiGEq+F4kBDid8PxS0KIdsBioIYQ4qIQYqkQoosQ4qAQ4lchxA0hxFpDYAUhRHMhxCEhxDkhxC4hRIjh+JtCiGsGPTYYjnU2lHlRCHFBCOFRQp18KYToZ/j7dyHE94a/Rwkh5hv+fksIEW74N8lwrKqhPr4EzgOhhnoLF0JcEUJMFkI8C7QA1hruw6W0Z9S8ZyuObj4IwO0Lt3DzdMM70HJHrPrtGnF6+wkAjmw+QPNerUq9Pj8nDwCVWoVQq8BkFzGnRnXQRMWgfRgHWi1ZOw7i1q2tBa/X0P5k7TmKLiXN7HjeuXD06ZmlqVcE72Y1ybkbR+79BGSNjrg/jhPUp4WZTNrZW2jTlR100s5F4Bxi7OCFSoXK2RGhklC5OpEXl2qTz7F+XbQPo9HFxIJWS87e/Th3amch5/bcQHIPHkaXaixPuLriGNaYnK2GL89aLXKW7Z19nBvXpuB+DJoHcaDRkrHtMO49zOtTNjwPAOHiXPQ8nGqEknvpJnJePuj05JwOx6On5b0WwqFuXXTR0ehiFd3y9u/HqX17CznXZ54h7/Bh9GlpVstxbNYMXXQ0+vh4m7qF34kmNNCHSoE+OKhV9GndgIMXzb9aCiHIyStAlmVy8gvwcnNBJZl3g6eu3SU00IcK/t62+WJSCPVxp5KPOw4qid4NQjl4s2QHuiP8AX0ahJZ4vjRIFaqjT41HTksEvQ7d9VOoa5t/WVI3aIP25jkl6AOQY9n2VVUbIKclImck2+Qr7+fnUL8u2ocxRbaQu3c/zh0t+dyeHUjugSPoU418j2MLXlZsPbCYracXs3Ung6271apI2rkI9LkFyDo9qcevE9i3pVWe9r3asevXPQBcO38dd093fAN9LeSatQ/j0DblC/XOX3bTobeie5VaVTh/9AIAUbcfEFwpGB9/b1ISUopmDuVm55Ib8RDHYD9jPTWtRf69WPKj4pE1WlL+PIpP71ZmnHoTW5dcnTEdEzmE+OHdvTmJ6/farMdC/J36BKXvlEz6znwrfWebXm3Yt1kZIN+8cBM3Tzd8rPihxu0ac3S7Mutz7697advb0meYwsXdhYatGrJrwy4AtBot5OcWnZdCqiOnJiCnK7anvXEKVc0wszLU9dqgizinBH3A3PYkFagdQUgIB0fk7DSb96OuWw9tdDR6g+3lH9iPU7sOFnIuA54h/8gh9GnF6kqlQjg5gaRCODuhT7K9W+bf8UOPgyphNUm8H0fygwR0Gh3ntx6nUS/zttKwVwvO/KbYw/0Lkbh4uOIZ4I2Tuws1WtXj5MYDAOg0OnIzbO+q5WLF73l0L9nvSS7OFBqDY41Q8i6a+L0z4Xj0KtnvwT/QdxY+P5UK4exc6vNzDVNsveCBYuupW4/g1bO1mYy5rTsV6Se5u+DWugEpG5Q+StZo0WXY7jtVNeuij4tGn6Dopzm2H8cWlvqViDyDbanUyr9Sdo51bFAX7YNodNGG9rn7AK6dLZ+Bx/MDyNl/xGIcqAr0x6V9a7L+sJylVxyq6nXQJ8QgJ8WBTovm9EHUTW0/b6vl1G+KPiEWOTnhka8tjhZhjfDytPqq8MgIj3xAaJAflYL8cFCr6dO2CQfPmQfdhBDk5OYr46S8ArzcXVFJEnejE2hcszIuTo6oVSqa16vG/rPhZeeOSiTU35NKfp44qFX0DqvBwZFovuAAAQAASURBVKvmwTshBNn5GmRZJrdAg5erk8UYzRakyrXQJ8chp8SDTov24hHUDcz9nrpZJ7RXTiCnKXYlZ6UXndPfuYack/UIOiUQ6u9l1KlpTQ6G3zPXCROd8s11al6jAp5l/fgHSFVro0+KQU5W2qf2/GHUjW37t6Jrg0PR3bsJmnzQ69FFhuPQ5NHbth3/f+O/HvgRQjQAZgLdZFluAkwEVgA/ybLcGFgLfG5yiQ/QDZgMbAU+ARoAjYQQYQYZN+C8LMvNgENA4ZqD32RZbmnguQ6MMhz/HDhkON4MuApMB27Lshwmy3LhdJGmwCSgPlAdaC+EcACWA8/Kstwc+B4onBM/HWhq0MPwGYB3gHGyLIcBHQHj6NIchw3nASoaOAE6AEeEEM2BV4DWQBvgVSFEU4NMHUP9NQX8gYqyLDeUZbkR8IMsy78CZ4FhBv1Kuoci+AT7khxjHHykxCXjE2T+8uLu40FORjZ6nTLBKCU2GR/Di4it64UksWD7R3x5/gdyTlwg/4rxZV0d6Ic2LrHotzY+CXWg+dcFVaAfbt3bkbHJctnLo8Ip2JfcGOMLcV5MCk7Bli9phag0tCuJ+y8CkB+Xyr2v/qLz+S/oenkl2owckg/Z3kZTCvBHl2AcGOgSklAFBFjIuHTuQPbvW82OqyuGoE9Lx/u9qQT8+DXeM95GONvevt0hyA9tnPE5aOOScAjys5Bz79mWaju/JvSbD4id8amiX8R9XFs0RPL2QDg74d65BQ42vvRIAQHoE43PTp+YaKmbvz9OHTqQu6XkbeCdu3Ujb/9+m3oBJKRlEuxr/HIT6ONJfKp54GNIt5bciU2kx1uf8OzslUx9oTeSJMxkdp6+Sp/WDUvny8gl2NMYMw3ydCEh07op5Wq0HL8dR496lYqOCQFj1x7hhVV7+fW85bLJ4hDuPsaADiBnpiA8zF96Jd9ghLMrzkOn4zziA9QNLQfjqnqt0V47WSpfeT8/VYA/ungTW0hMRBVg3r4kf3+cO3ck5w9zPlXFEPRpaXjPnEbA6m/wmv5OqbbgHOxL3iPYesWhXUky2HrWjQf4tKmHg487kosj/j3CcK5oaUcAAcH+JMYY6zExNpGAYHO9vHw8ycrIQmfoOxNjkwgw9J23r92m4xPKS3/dsDoEVwoiIMT8OQRXCsK1YTWyLtwqOuYY7EuBSZ9bEJuMQ7DlPXr3aU3DQ8up/eNM7r69ouh45Q9G8mD+j6Av2waof6c+C/vOTue/oLONvtM/2J/EWGNdJsUl4V+sLj19PMk28UNJsUn4mehdt1ldVuxcwdwf51K5dmUAQiqHkJ6SzuSPJrN8+3ImfjgRHIwz84S7tzGgA8iZqQh3c9sTPsHKl+Tnp+L80mxUDZQBtZyVhvbMTlzGLMXljU+Q83PQ37taYr0AqPz90ScabUGfmIjkb2kLTh06krvV3Bb0SUnk/LIBv/Wb8P/lN+SsbArOWc6AMCvrb/ghRUkZv8+WEvDDSlz7P2mTC8AryJc0k7aSFpuCV7HxhHcxmfS4FLyCffGvHEhWcgZDl41lyrZFDFn8WqkzftTBfmhjjbagiUtCbcXvefRsS/WdXxO66gNip38KKH7PpWVDVAa/59a5BQ7Btmc4/J2+U5+URPbGjfhv2kTA5s3os7IoOGv7+TkE+6Ex1S82yaqte/VuQ919X1L9h9lETVGG1U6Vg9Emp1N52URqb/+U0A/HlzrjR/INQJ9sol9KIsIvwEJOXbs+Hku/xf3dD5EqVTUpQMJj6bd4f/cH2stn0UVet7jWFKpAf3TxJuPAhERUxceBAf64dOlA1mbL9unz9jhSP/+m1AATgPD2R59i5JJTk5C8LZ+3qkZ93N5fieukBUgVqlicd2jVBc3pA6XylTcSUtMJ9vMu+h3o60V8SrqZzJBe7bgTk0CPcQt4dtonTH35aSRJomZoEOdu3CUtM5vc/AKOXrxJXHI6ZUVCRjbB3u5Fv4O83EhINw8yDmlXn7sJafSct5ZnP9rMlP5tLcZotiC8/IoCOgByWjLCy9wWJP8K4OKOy9j5uEz6CHXzrsWLKTMS0rMJ9jbOIgvytqJTh4bcjU+l5/s/8ezSjUwZ2OGRdDK7dy8/9KlG/fSpSRb6Aaiq1cV1+gpcxs5FClb8nD7mPuqaDcHNAxycUDdogfAp22yt/xXIslxu//6t+K8HflCCOL/KspwEIMtyCtAWKFww/DNKsKMQW2WlRq8A8bIsXzEsZboKVDXI6IHCRblrTK5vKIQ4IoS4AgxDCRgV3sNXBn6dLMsl9WSnZVl+aOC7aOCrAzQE9gghLgLvAYVvd5dRZtW8CBQu6D8GfCyEeBPwlmW5pIX+R4COQoj6wDUg3jCTqC1w3KDT77IsZ8uynAX8hjFQdF+W5cI3uztAdSHEciFEH6Bsc7GLwTC5yQzFG7Y1mUJHa+t6Wa9nZt+3ebPNqzg3qoNjTRMnaqPMQvhPe53kT74r8wuKTVjti60bsG/7+lQa2pVb85SmqvZyI7BPcw61nMCBJmNRuToRMsjyK605X+n6eU8aR/oX31jqp1LhULsW2b9tIXH4GOTcPNxffuFv8wFk7TnB3T5jePjGPCXvAVBw+wHJq36h8g8LCP1uHnk37iJrdbb5SuHyGD+erG+s6FYItRqn9u3JP3jwUYsGLNU9fvU2dUOD2fvxZDa9P4ZFa3eSlZtfdF6j1XHo4k16tahPabDWKqxVL8DhW7GEhfqbLfNaPaIrG17twRdDO7DpzG3O3U+0frGtwosrLamQgquS98vH5G1cikP7fgjfILPz6lpN0V4/bZurJPwHn5814yvex3hNGkfGl19b8AmVCofatcn+fQuJI15DzsvD/aXSbMHaQeu27tO+PhWHdiXCYOvZETHcW7GF5ptm0nz9DDKv3kfWllAHZeg7rcoY/l/3xQY8vNz5dtdKnnllABHhkehM7M7F1ZkPvpnDgznfo88yCTyW0dbTdp4ivPMEIkYtVvL9AF49WqBNSifnSukBSSOftYNlq8/CvvNIywkcKmvfWchgUZcl30ZkeCQj2o5gfJ/xbFm9hVmrlKUaKrWKmg1rsv3n7UzoO4G83DwcWpkGMGwUWghJQgqqQv5vn5L368c4tH0a4RMETq6oajYl95tp5H71Fjg4oapvO3eYVb5idO5vTCBrlRVbcHfHqV0HkocNIWnwMwgXZ5x69CyF7m/4ISBxzJskjhhD8lvTcR80AMewxo9MZ9E2rd6SjKRSUalhNY6t2cPSJ2dQkJtPj7H9bfKVUJjFocw9J7jTZwwPivu9b36h8uoFVP5+Hvk37iLrHtHvWeErqe8U7u44t29P0pAhJA4ahHBxwblnKc+vjPql7zrJje5vcPfVhUq+HwCVCteGNUhas4NbfSehz8kj8I1nH0Uzq3zau7dIf2MImVNGk7fjN9ynzjee1OvJnDKa9DHPoapZDym02t/m83n7DdKWr7KoT+cObdClpKK5YTsHVBGsN06zX7r7kWRNHUb2+69TsO9PXMZ/YC6uUqNu0hbtWRvL/P9LsD5OMtf5+OWb1K1Sgb1fzGTTooksWv0nWTl5VK8YxCtPd2bMom9548PvqV0lBLWq7K+RZRqj3XpInQp+7Jk1jI2Tn2Hx78fIyrOd3+qRiVUqVJVqkPvdPHK/eR/HHoMR/hX+kaLBik43H1Cnoj973n+ZjW8PZvFvRx5fp7K0zweRZM0aQc7i8RQc2oLLa4qf08c/oGDPL7iOX4DLuHnoou/C4/Rldvx/jX9Djh9BSaNDI0zPF76l6U3+Lvxdkj6F168GBsiyfMmQ3LjLo9xoMT6dgU8AV2VZtjbX7kmgE9APmCWEaCDL8mIhxDagL3BSCNFDluUbFjcsy9FCCB+gD8rsH19gMJAly3KmsBplKUJRuFmW5VQhRBOgNzDOUMbI0hQVQrw2ffr0mcOHDw+Y/dciosOj8KtgjAz7BvuRlmA+3TszJQNXTzcklYRep8c3xI/UeOUraUpscqnX52TkkHvmEi4dWlIQqUz/1MYnoQ42fklSB/mjTTRfouLUoDZBS5V15CofL9w6tkLW6cjZf6I0NS2QH5uCSwVj9Ny5gq/VJQfu9SvT8OMxnH1hMZpUZVqoX6eG5EYloklWZpnEbzuNT8vaxG4+WiKfPiERVWBg0W9VoD+6YtO6HerWxnee0nFLXl44t21Nmk5HQfg1dImJaK4pzSf3wGE8SnnZ1cQloTb5WqkO9keTkFKifO7ZcBxCQ1D5eKJLzSD9192k/6okBPZ/a7jZ7CEL3RITkUy+ckoBAZa61amD12wl94zw8sKpdWsydDryjyp15tS6NZpbt9CXYWlBkI8HcSZfrhJSMwj0Np8e/efRi4zs2x4hBJWDfKno783d2CQaVa8IwNErkdStEoKflzulIcjThbgM44t2fEYuAe7WV03uvGq5zCvQQ5H1dXOma90KhMek0LyK5VfTQsiZKQhPk6UxHr7IWWnmMhkp6HIylUSumgJ0D24iBVZGl6Iss1LVaIw+/j7klB7/Le/np0tMRBVkYgsBAeiTzG3doW4dfOYqfJKXF07tWoOZLShfjvMOHML9paE2+fJiU3Auo603+HgM501sHSB63QGi1ylfdGu+O4R8k9kJoa/04tuXOgFw49ItAioY6zEgJICkeHO90lPScfd0R6WS0On0BIT4kxSnyORk5fDh28bcDhtOrCHWkJxYpVbxwTfvs/f3fbTaYT6LqyA2GUeTPtcxxA9NfMm2nnXqGs5VglH7eODRoi7evVri1a05kpMDkocr1T+fxJ03Py3x+r9Tn36dGpJTrO/0NvSdoa/0ouKL3VgudERcjjCb7eQf7E9ysbrMSMnAzcQP+YcYZXJNAmNnD5xl3PxxePp4khSbRFJsEjcNS0OPbj/KU51fLJKVs1IRHqa252Npe5mp6HOzimxP/+AWUoBi83J6EuQquukiziNVqInOxqw7XVIiUoDRFqSAAPTJxWyvdh283jOxvVZtkHU6hEqNLi4WOV3pC/OPHMGhfkPy9+4pke/v+KG8w8eK7FSfmkbuoaM41q9LwcWSZ7umxaXgbdJWvEN8SS82Higu4xXsS0Z8KjIyaXEp3L+oLHO8uP0UPcb2K5ELlJmtapPZqQ7B/mht+b0z4ThUtu73Akrxe/D3+k5UKnSxJs/v8GEcGjQgb0/Jz08Tl2Q2+9YhxN+mrWefvopjlRBUPh5o4pLQxCaRc1GZLZi2/TiBbwyyrV9KIpLJDB/JNwA5pVid5BqX32kvnILRkxEeXsiZRh8t52ShvXoRh7BW5D+4WyKfLiEJVZDJODAwAF2xcaBjvdr4L1RyQEneXri0b0WKVodjw7q4dGqHS/vWCEdHhLsrfnNnkDx7kVUuOTURydfIJXz80acVWxadZ6LbldM4vzgB4e6JnKX4VXWjluijIpEz0krU6b+FIF8v4pLTin4npKQT6ONpJvPnoXOM7NdFGScF+1MxwJe7MYk0qhnKM11b8UxXZenU5xt2EuTnRVkR5OVGXJrRh8anZxPgaZ5z6c8ztxjZtYnC7e9FRV8P7iak0ahyYPHirEJOT0aYzNAS3n5mM6VBmQWky86AgnwoyEd35ypSharokh49102QtxtxacYZPvFpVnQ6fYOR3ZsqOgUYdIpPpVGVoOLFlQp9WhIOJrN0JB9/5PRitp5n9HO6a2dBNQ7h5omcnYHmxG40J5S+zPHp4Wazo/4vwJ7j598x42cfMFgI4QcghPBFmdFSmN58GFDym7N1SEDhJ4uhJtd7ALGG5VnDit3DWAO/SgjhCWQa5EvDTSBACNHWcL2DEKKBEEICQmVZPgBMRUkW7S6EqGGYpfQhynIrWxlkT6AsLTuMMgPoHcP/GI4NEEK4CiHcgIEm54oghPAHJFmWNwOzUJayUZp+six/s2jRoip169Z1nfvUDM7tPk2HQV0AqNG0NjmZORaBG4BrJ8Jp1VeJgXUc1JXze5Skhef3nrF6vYevJ66eSuJQBydHXNo0Q3PXmKQsP/wmDpUroq4YBGo17k90IfuA+YA5qs9wonor/7J2HyFx/vLHCvoApF+4jWv1YFwqByAcVAQPaEfCLvMkuM4V/Wj6/VtcHvcFOXdii47nRSfj1awmkmFWh1/HhmRFRGMLBddvoA6tiCokGNRqXHt0I++I+b3HDxpG/DNDiX9mKLkHDpG27DNlsJ2Sii4+AXVl5eXCqUUzNMWTcRZD3pVbOFatgEOlIHBQ4/lkp6KEloVwqBxS9LdT/RoIRzW6VGVAozIspVKHBODRqx0Zf5X8RUtz8yaqSpWQghXdnLt1I//4cTOZpBdeIGnIEJKGDCH/0CEyPv20KGgA4Ny9O3n7ypb0rkG1ikTFp/AwMRWNVsfOU1fpHFbbTCbY14tT15QBZnJ6FvfikqkUYFyyseNUOE+0Kn2ZF0CDCj5EpWQRnZqNRqdn19UHdK4dYiGXmafh3P1EutYxflHKLdCSbdixKLdAy4k78dQMsD2A0sfcRfIJQnj5g6RSlmxFXDCT0UacRxVaG4QEakdUFWqgNxnMqOu3QXu19GVeUP7PT3P9BupKRltw6dGNvKPmfAnPDiVh0AskDHqBvAOHSF/2qZktqExsQXv3nk2+jDLaetj3b3GlmK0DOPp7FskE9W1J7O/Ge33ww25G936d0b1f5+jOY/R+VvliX79ZPbIzs0mx8tJ54fhFOj+pBIv6PNeLY7uV8tw93VA7KN80nhzal0unrpCTpbx4TF32DlGR9/llleWuJdkXI3CqFoJjaCDCQY1v/w6k7jZPiO1UNbjob9eG1REOarSpmTxcvIZLLV7lcpsx3H7jIzKPXbEZ9Pm79ZkXnYx3CX3ngx92c7L7dCY8MYETu07QfZCy+12dpnXIzswm1YofunziMh36KjOGejzbg5O7lTbvY2LrtZvURkiCjNQMUhNTSYxNpKIhABzWPgy9SeJmfexdhIntqeu2Rhd50YxTF3kBqVKtItuTQqqhT4lFzkxBqlBdyfEDqCrXQ042b0vFob1xA3VFo+05de1G/nHzJLHJLw4heZjyL//wITI//4SCY0fRJcTjUK8+OCnLdRyaNUMXZdsv/B0/JJydEa5KEFs4O+PUugWaOyW/xANEXbpNQNVgfCsFoHJQ0ezpdoTvMW8r4XvO0fIZxR6qNK1JXmYOGYlpZCamkxaTTGB1pa+t3b4hcaX42Vwrfi/Tht9zrl8D4VCy30u34ffg7/WduoQEHOobn59js2ZoiyeFLoacSxE4VauAY2gQwkGNz9MdyTAkci6EYxWjfi4GW9elZqJNTKMgNgknQ9v3aN/ELCm0NegibyKFVEIKVPRzaN+NgrPm+glvY6BUVbMuQhLImekITy+Eq+HDiqMj6sbN0UcX26yjGAqu3cAhtCKqCob22asruYfN+WL6v0hMv2HE9BtGzr7DpHz4ObmHjpH+xXfEPDmEmH7DSJo5n/wzF0sM+gDo7t5ECqqI8A8GlRqHVl3QXjS3BeFp7EekanVASEVBHwCH1l3RnPr3LfMCaFCjElFxyTxMSEGj1bLzxCU6N69nJhPs580pQ/645PRM7sUmUsmQly45XQncxCalsu9MOE+0bVJ27tAAopIyiE7JQKPVsevibTrXr2wmE+LtzqlIpe9NzszhXmI6lfw8rRVnFfoHEUj+IQjfQGXmVVhHdFfNZzhrr55CqlYfJAkcHJGq1EZOeFhCiaXpFEhUYhrRyQadLkTSuWFVc5183Dll2OQgOTOHewmPppOZfvdvIQVUQPgFKfo164T2snlfZpoCQKpSG4RAzlbaZ2Eia+ETgLpJOzT/wllpdvxn8V+f8SPL8lUhxALgkBBCB1wA3gS+F0JMARJRctk8CrKBBkKIc0A68Lzh+CzgFHAfZalYYeBjIvCNEGIUykyesbIsnxBCHBNChAM7AKsJZGRZLjAkS/5cCOGFUqefAreANYZjAvhEluU0IcQ8IURXA881Q9kl4QjQS5blSCHEfZRZP0cMvOcNCa0Le7RvZVm+IISoWqyMisAPhkAUQOEWC6uBlUKIXKBtaXl+Lu4/R5Ouzfjo8JcUGLZjL8Q7q2fy7dQvSUtIZcOinxm/4i2ee2co967e5eDGvTav9w70YczHE5AkCSFJ5O45SM4hkwGLTk/Swi8I+XohQiWR8ftuNLfv4zlYmYZfWl6fwCXTcWnZGJW3F1X2riHly5/J/G1XifKyTs+1GT/QYsO7ypbE6w+QdfMhoS/3AODBT3up8fYgHH3cqf+hMnFK1uo40Xsm6ecjif/rFO32LELW6cm4co8HP5fy0qvTk/bRcvw//RAkFdl/7UB79x6uA58GIMdaPgUTpH+8HJ/331Ve2KJjSV2wpFS++LlfEfrdfFBJpP+6m4LIKLyH9AUgbcN2PHq3x2tAd2StFjmvgJhJi4sur7hiJipvT2StlvgPvkSfYSMJnk5H5mef4bN0qbIl8Y4d6O7dw6Wf8nXWVl4YAJyccGzenIyPPrItZ4BaJTHjxScY+/Fa9HqZAR3CqFkxkE0HlPwIg7u24LWnOzHr+z8ZNGslMjKTnuuOj2HHotx8DSev3mHWy6XnqABQSxLT+4Qxdt0R9LJM/yZVqRnoxS/nlC2gn2teA4D9N6NpWz0IF0djd5ucncdbm5TBpFYv80TDUNrXDLYkMYWsp2DPzzgPmQJCQnv5MHJSNOqmyvp07YUDyMmx6O5cwWX0fJBlNJcOIScZXorUjqiqNSR/5+oy6Vfezw+dnvSPP8fvkyWgksgptIUBBlv4oxRb+ORzfObMVGwhJpa0BR/alJd1em7M+IFmBluPXn+A7JsPqWSw9Yc/7aX624Nw8HGnnomtn+qtbPva5Lu3cPBxR9bquD7jh6KkxcVxcv8pWndrxdqjP5Gfl8+Hby0tOrf4pwUsnfIxyfHJfL3wW2Z/OZNRU18hIjyS7RsU11C5ZmXe/Wwaep2eexH3WfKOUp+NWjak97M9uX39Dt/uWkkFVDxcvIb0/eeL6jPqvVXUWTcHJImkjfvIu/WAgJd6A5D48y58+rbF/9kuyFod+rwCbo8t47P6h+uzsO9sa9J3PrTSd57Zf4aWXVvy3ZHvyM/N55N3Pik698HqD/hs2mekxKfww6IfmLZiGi9PeZnbV2+za6PS57fv254nX3oSnVZHQV4BH443tpGVs1cy9fOpqB3UxEXFoTm51kQ5PQV71+D07FsgSWivHEVOjkHdpAsA2ksHkVNi0d0Nx3nEXJD1aK8cQU6KRgZ0t87i/PIc0OvQJ0ShvVzKYFuvI3P5p3h/uEzxizu2o7t/D+enFNvL+6tk29PeuE7+4UP4rlwFOh3ayEhyt9m2nb/jhyRfH/wWz1V+qFTk7N5H/skzJcor6unZPPsHxv70rrKd+6YDxEU8pP0wpa0cW7uXawcuUL9rGLMOfUZBbj7rphi3sd78/g+89Ol41A5qkh4ksO6dUra41umJ++ArQr+fr2znXuj3XjD4vfXb8exj9Hv6vAKiTfxepRUzUfl4Imu0xJXm9+Bv9Z3a69fJO3QIv1XK89NERJD711+l6vdw9tdU/+l9hEoiZdNe8iIe4DesDwDJa3fi/URbfAZ1A40WfX4B98cZxwrRc76hymdvIRwcKIiKI+qdz2zz6XXkfPcZ7jMV/QoO7ED/8B6OPRX9CvZswbFNZ5x69VOWxRUUkPWJ0kYkbz9cx88ASVK2Sz9xAM35Uj7U6fSkLF1O4PIPQSWRvWUHmjv3cR/0FPw/9s46zqri///POfdud+/S3bF0Kh2ilFgIAoKFYtIGHYINfBQVFQMxsJDu7u7u7e6bZ35/nMvuvXu3UNzP5+vvPn3wcO85c+Z1Zs7E+8yZeQ+Q/Usp+XMnqCqGZYvwfnUuQlEw7VqPGnsdt06alnn7KvQt78W98wOgWpEmE3mfzi643t0DXYMW5H3z4V27pfFT3+bg0ROkp2fSbcBQnh/1BIP69vpLcel1OiaP6M/ot79AVVUGdG5FrUqR/LRJGzx4pHtbnnmwG28t/olBEz9ASskrg+8jyDaLZeyH35KRnYtep+P1Jwfg71uGHR/ztRUmDWjP6M/XoqqS/q3rUisymJ/3as6lH27XgKe7N2PKj9t56L0VSAmv9GlNkE/J/vocUFWMv32G19PTQCiYD25GTbiJvp1WFyx71yETb2E9fxTvsQuQUsWyfyNqvDb46DFkLLqajRA+/ni/+QWmDcuxHCh+gwO9TmHSg/cw+rNVtjTV09K0R/Pj9nD7hjzdoyVTlm/hofk/anbnA20Jss0On/TtRg5diiU9x0DP6d8wulcrBratX6weqorhp0/wfmGWlr59G1Djb+DWUWvLzLvWoG/WAbd77teWcZlN5H1V0M95PvUGwscfrBaMP30MeWV3ZP1vQLpm/CD+lx0Q/VWEENlSytLXargoE0OrPlhuhWS6X8m7SdxtLiY57wjzT9K4xt/f4eFOyEq5gw7zLhBUoeTdVe42AbOfLlc9ebWELWf/AdSbf+0r1F8le33JX5bvNlbTX3Nw+Fc4dfnOp1X/Hea4le8U6nfk3dn1paykWcq+E8nd4EOPv+Se7i+z4sVSBmLvMtnrLpernjm3jNsj3yXmx5Vtqcbd4jm38isvwRXLt9+Lu/rXZgv8Vaq2Lt+6l3W1fMtmYNPyXfzgNe/TctWzXi95k5G7jYy5VK561h1Oix3+UXRd/roz6DvFsnZDuWkB+C1aU35G2X+BzpW6l9v77LZbm/4n8/K/PuPHhQsXLly4cOHChQsXLly4cOHin0D9F052uVP+lQM//5dm+wghGqPtXGaPUUrZ5r9xPy5cuHDhwoULFy5cuHDhwoWLfw//yoGf/0tIKU8C0f/t+3DhwoULFy5cuHDhwoULFy7+bbjm+/xv7OrlwoULFy5cuHDhwoULFy5cuHDh4h/ANePHRal4ivIbHwyuayw3LQD3JLVc9Xzrle9Yq2dy+TrLzo5xK1c9/8yUctUTdZuXq56ilm/5NGSWvC3y3SYpsfxW5VbyzSo3LQAPU/nWhZCg8q3rPrmmctXDXL5ysrSdm+4yPl2rlque5Vz51nVTXPm2ZVZr+fW1XrXdy00LQLlWvt+t3aqV7yYY/vr0ctWzpFrKVa+8nS3rqjYpVz1LTnq56pmvZ5SrnnL2VPlpRQaXm9b/D6iuOT+uGT8uXLhw4cKFCxcuXLhw4cKFCxf/VlwDPy5cuHDhwoULFy5cuHDhwoULF/9SXEu9XLhw4cKFCxcuXLhw4cKFCxf/SlxLvVwDPy7KSMNO0Qye8iSKTmHnj5tZ+8nvTmEGTx1J4y7NMOWZ+HLcIm6cvkpQVAij3n+RgLBAVFWyY/lGNn+1BoD+rz1Gsx6tUKVKVnImX45bBCQ7xauPbo33yDGg6DBuXo3xt+8dzzeMxnfiLKyJ8QCY9+/A8PM34OaO38yPwM0NodNh2rsdw49L7yjdwV2aUnvWkwidQtyyzVxf+IfD+dDeLakx8VGkKpEWKxffWkrGgfN3pKFv1ArPx58HRcG8Yy3GNT84nNfVbYrPSzNQk+O09B3ehXHld9pJLx+8nxyLUqkaSEnel+9ivXy2RD235q3xefpFUBQMG1djWFEoPxtF4/fmbNQETc+0dyd5P3wNgM9LE3Fv1Q41I42MMU+WmjbP9q0IHvc86BSyf1tL5tIfigzn3qAukV8vIHnSLHI37wR3NyKXfIBwdwOdjtzNO8hY/E2pervPXmf+r7tQpcrAtg0Y2b2Fw/msPCNvfLeJ+LQsLKrKsC7NGNCmPvFpWby5bDMpmbkIBQa1a8iQTk1L1zt+gXnfrkJVVQZ2bsWofp0c9XINvP7JT8SnpGOxqgzvcw8DOmn39O3aXfy67RBCQO1Kkcx4ZhAe7iX7hdl9OZ75G06gSsnA6GqMbF/X4fzSvRdYc+omAFYpuZqcydZXHyDAy537Fq3Dx12PIgR6RfD9qK6lpq+8n59/52ZUmf4U6BSSl28k/j+/OpwP7NmaCuMfB1t9uzntC7IPauW98d7PsObkgVVFWqycvX9cqXo+97Yg8q1nEDqFtB83kPLpzw7nfbu3JfzVoZqe1Ur8zM/IO3wGgOAnBxD4SE9AYjx/ndgJHyBNRTuiGT39OVp3bYUhz8h7r73HpVOXncJEVI7g9f9Mwi/Qj0unLjH/5XexmDXfE03aNua5ac+i1+vJSMtk/MMTABgwsj/3Pd4bgcDn9zVkfvebQ5xeHVoSMnE0QqeQ+es6Mr74scj782hYhwrLPiJx/BxyNu5EFxFG+Jzx6EKDQVXJXLGGzGW//7P5OaIfgY/2AgTpP64ndekfRSjAs9OfpVWXVhjzjLw/9n0uF5OXkxZNwjfQl8unLvPuK1peNm7bmClLphB/U+sr9qzbw/KPlmt5OWoAvQb3QkrJtXPX4NjnYCl4nrpaTXG//0kQCpbDmzHvdL4/pVoD3PuMQOh0yJwsDF9OKzgpBJ6j30ZmpmL8bl6peamr0Rj3nkM1vWPbMe9d5axXpR7uPYcgFB0yNxvDd3O0Ex7eeNw/EiWsEgDGVUtQYy6VqKdv3ArPJ17Q+qFtazCuKtQP1WuKz6szUJNs/eyhXRh//xYAv/eXIQ25oKpIq5Wcqc8XqdGgU1MemaL1p7t/3MyGT5zz8JGpT9KwSzNMeUa+GfcxN09fRe/hxtgfp6P30KPodBxdu49VH2hla9SiV4ioUQEAb39vcjNzmdNnglO8vvc2J2rKM6AopP20geTFKxzO+3VvQ8RrQ5GqBKuVuJmfk3tIK5shI/sT9EhPkGC4cI2Y8R8WW9fz86thSzwfG41QFEw712Fa51j3dHWa4P3CdNQUW34e2YVp1TKUiEp4PftGfjglNBLjH99g2uxYt53S16k5Fac8DTqF1B83kvSJY/r8e7Qh4rUhILW2M3bGEnIPncGjRkWqLCrIL/fKkSR8sIzkL1eWnL66zfDoN0orLwc2Yd76q3OYGg1x7z8KFB3kZJG3+E1EQAgej72M4heElCqW/Rsx73Iu24XRN22F1zDNDjRtXY1x5XLH8/Wb4jNuFqrNDjQd3InxV7s+Ryj4zlmMTE0m553XS9Rya9Ya71GajWTctBrDr0XYnJNnoybabKR9OzH89DVKSBg+L7+BEhSMVFWMG//EuOqXUtO2+/h55n2zElWVDOzSilH9ujicz8rN4/X//GizI6wMv/9eBnRuBcCytbv4ZesBpJQM6tqaoffdU6peabw553127D5AcFAgv3+3+G/Ht/vUZeb/sEmzk+6JZuR97RzOZ+UaeOOLP4lPzcRiVRnWqw0DOmh+iTJzDcz4eg2XYpMQCKaN6EPTmpVK1NNHt8b7Sbt3ht8LPb8GRbwzrLC9M8z4CPS2d4Z92zH8tLTU9CnVGuLedbDWVp/cieXAWucwlevi3uVRUHTIvGyMP76j3Uvzbuib3AuA5cROLEc2laqnq9EE915P2PqGbZj3/OmsV7U+7j2Gan1RbhaGb2drJzy88XjgKVvfIDH++XmpfYOLfxeugZ9/ACHEDGCHlHKTEOIV4DMpZe5/+bYcEEIMAC5IKc+UGlZRGDLjKd4fOoO0+FTeXPk2xzYeIu7SrfwwjTs3I7x6FK93fpEazWozdPYzzBkwGdVi5adZX3Pj9FU8fDx568/5nNl5grhLt1j/2R/88b5mXHYb0Ye+Lz8Mv85xFFcUvJ9+mewZ41BTkvCbtxjzwd2ot647BDOfPUnO3MmO15pNZE17DQx5oNPhN2sh5iMHsF4sNck2bUHdt0dx9JFZGGNTaLl+LknrD5F7ocApZtqOkxxYdwgAnwZVaPTZq+zv+GrZ4gcQCp5PvEjOuxORqUn4TvkP5mN7UGNvOASzXDhJ7kdvOl3uNeQFzKcOYv54Buj04O5RSpoUfJ57hcy3xqKmJBHw/qeY9+/GetMxPy1nTpA1Y7LT5cbNazGs/hXfV0s2nG5rBU98kcTnJ2JJSCLqu/+Qt30P5qs3nMIFvfwUhr2HCo6ZzCQ8Ow6ZZwC9jsgvPiRv90FMJ4sf1LKqKnNX7GDx6H5EBPoy5P2f6dSoOjXtnOP9uOskNSKCWPD0/aRm5zFgzjLub1EHnaIwtn8H6lcOI8dgYvB7P9G2bmWHa4vSm/P1Sj6dNJKIYH8en/IxnVvUo2bFiAK9jfuoUTGchWOHkZqZTf/xH3B/h6akZubw/Ya9/DbvFTzd3Ri/4HvW7TtB/3tblKAnmbvuOIsf70iEvxdDvtxKp9pR1Azzzw8zol0dRrSrA8D2C3F8d+ASAV4Fjkc/H3oPQd6llJHblPPzQ1GoMutZLjw+FXNcCvVXv0P6hgMYLha0M5m7TpC+4QAAXvWrUuOT8ZzuPCb//IWH38SSVkYnzopC1LTRXB/+Jub4ZGr89gFZm/dhunQzP0jOnmNc2bQPAI+61ai0cBKXez6HPiKE4OF9udxrNNJoouKCSfj37UTGL85GW6surahYvQJP3jOKes3q8eKcMbzcz7mNeGrySH5d8jvbV27npTlj6P1YL1Z9uxoffx/GzB7DG0+8SVJsEgEhAQBUrVuV+x7vzUsPvILZbOaXH2aSu2M/lhux+ekLfWMMcc9MwhKfTMUfFpK7dS/mK87PL/jVp8jbc7jgmNVKyrufYTp7CeHtRcUf/0Pe3iPO196l/PSoU5XAR3txdeBrSLOZKl/NJHvbQUzXYh0kWnZpScVqFXnq3qeo26wuY2aP4dX+znk5cvJIflvyGzv+3MGYOWPo+WhP1nynfXA4ffA0056c5hA+JCKEfk/247luz2Eympj88WT0jdtjObpdCyAE7n1HYVg6C5mZgudzc7GcO4RMsnOQ7OmNR9+nMHwzG5mRAj7+Dhr6dn208B5exefhbYTAvfcwDN/PR2am4jlyOpaLR5DJdvnh4Y1H7+EYfngXmZkC3n75p9x7DsV65STGXxdpL91updR5oeA5/CVy5k3Q+qEZH2M+shc1tlC/cP4Uue+/UWQUOXPGIrMzS5AQPDZjFAuGziItPoVJK+dyYuMh4i8V5GHDzs0Irx7J1M4vUb1ZbQbPfor5A97AYjTz4ePTMeYaUfQ6xq2Yweltx7h69CJfjPkw//pBbzxBXlYRppaiUGH6aK4OexNLfAo1fv+ArE37MTqUzeNc2rQfAI961aiycCIXe4xGHxFCyPC+XOz5PNJoovLCiQT0vZf0XzaXmJ9ej48h54NJyLRkfN5YiOX4XtS4Qv36pZPkLZzicExNuEXOjNH58fi+8z3mo7uL17Klr+KM57g69C3M8SnUWvk+mRsd05e9+ziZG7X0edarRpX/TORCt9EYr8Rwsc/L+fHU37+UjPV7S9YTCh4DnyHvs2nIjBS8XpqP5fQBZGJBW42nNx4PPkvekhnI9GSET4AtgSqmVUtRY66AhyfeL7+H5cIxx2uL0PN68mVy5ozX7MDZizEf3oMaU6h8njtZ7KCOx32DUGNuILy8S06bouD9zCtkTdNsJP/5n2I64GxzWs6eIHu2o40kVSu5S/+D9cpF8PQi4L3PMR875HStPVZVZc5Xv/Pp5KeICAng8TcX0bl5A2pWsrMjNuylRqVwFo4fodkRY9/l/o7NuBaXzC9bD7Bs5hjc9Dqef/tL7omuT9Wo0JLTWAoD+vTg8UH9eH3mu38rHrDZZd9vYPGrjxER5M+Q2Uvp1LQ2NSsU3OOPW49QIyqUBS8+TGpWLgPe/JT72zTETa9j/g8bad+oBu+OfhCzxUpeKQOuKAreo14me+Y41NQk/OYuxnyomHeGt4t4Z5hu984wcyHmo6W8MwiBe/chGH9+H5mVhufQN7FePoZMiSsI4+GlhVnxITIrNb+tFqEV0De5F8N3s8FqweOhV7BeOYFMTyxZ777hGJZpHxE8R83AcuFwEX3DCAzL59v6hoK+yL3XE1gvn8D4y4Ky9Q3/MqR0zfhx+fj5B5BSTpFS3n4DeAUopaf55xBC6Io5NQBoUJY4qkfXIvF6PMk3E7GaLRz4czfRPVs5hInu2Yq9v24D4MrRi3j7eRMQFkhGUjo3Tl8FwJhjIO5yDEG2l2lDdl7+9e7eHlBEhdTVqocaH6PNPrFYMO/agnurDmW5bQ2DTUOnB70e7mCan3/zWuRejcdwPRFptpL4+x7Cejum25pbsAuZrpg0lISuRl3UxFhkUhxYLZgPbMOtWRnT5+mNvk5jzDtsXxesFsgreWcffe36WOMK8tO4YwtubTqW+X4tp08gs8r2Yu3eqC6WW7FYYjStnPXb8OrsnDa/xwaQs3kn1tR0h+MyzwCA0NueXSl5e+p6IpVDA6gUGoCbXkevZrXZdvKqQxiBIMdoRkpJntFMgLcHOkUhLMCH+pXDAPDxdKdGRBCJGSXn5anLt6gcEUKl8GDc9Hp6t23CtsOOAxtCQG6eESkluQYTAT5e6BSt2bVaVYwmMxarZsyEBfkXJVOgF5tK5WAfKgX54KZT6NWgEtsuxBUbfu2Zm/RuWPKXsZIo7+fnE10b47U4TDcSkGYLqX/sIrBnG4cwaq4h/2/Fy/OO65s9Xk3rYLoei/lmPJgtZKzagV/3to5psNfz9nRoPoReh/B0B52C4uWBJaHoHeba9WzLJttL4rmj5/Dx9yU43HmXnKYdmrJz9U4ANq7YRLte2lfRLgM6s3vdbpJikwDISNF2MKlSqzJnj5zDaDCiWlUMh07i063g+Xg0rov5RiyWW/Ha81u7HZ8u7Z10/R/vT84mx+dnTU7FdPaSLQ/yMF+9gT6i5JeJv5Of7jUrk3f0PNJgBKtK7oGT+PV0/CoM0LZnWzbb8vL80fP4+PsQVEReNmnfhF1rdgGwyS4vS0Kn1+Hu6Y6iU/Dw8kBmphXca6VaqCnxyLREsFqxntyDvr5jX6Bv0hHLmf3aoA9ATsEgiPAPRl+nOeZDJQwW2KFUqImamohMTwLVivXMPvR1HHcU1Ddqh+X8Ic2wB8i1tcvunuiq1MVyzDZopVrBWPJ3J13NeqgJMQX90L6tuLVwLit/h2rRtUjKtyOsHPpzD00L2RFNe7Zk3687ALh69CLefj74hwUCYLT1tTq9Dp1eV6QB3/z+dhxc6TxI4tW0DsbrcZhvam1Lxqod+PVwLJuF2xb76IVOh2Kr68LLA0tCaolp1VWvi5oUi0yO1/Lz4Hb00Xeen7r6zVCT4pCpJbwIAt7RtTFdj8NkS1/6nzvwL6ntLMZW8e3QFNP1OMwxSSXqKVVqoybHIVMTwGrBcmwX+oatHcLom92L5eQ+ZLo2i1vmaO2WzErTBn0AjAbUxFsoASEl6ml2YKw2w8ZqwbR3C24ty24HiuBQ9M3aYtq6utSw+tr1Ue1sJNOuLbi3LpuNJNNStUEfAEMe1lvXUULCSrzm1KWbmh0REaLZEe2asu2w40CDEMLRjvD1RqcoXI1JpEmtKnh5uKPX6WhRvzpbDv39HadaRjcmwN+v9IBl4NTVWCqHBVEpLEizy1rVZ9uxCw5hhIAco5a+PIOJAB9PdIpCdp6RIxduMrCjNvvaTa/D39uzRL38d4ZE2zvD7i2430FZcXhn0JVutyiR1ZFpiciMZFCtWM4dQFcz2iGMvn4brBeOaIM+kN9WK8FRqLFXwGICqWK9eQFd7ZJ3jtX6hoSCvuH0PvR1HD8Y6hu1x3L+oF3fYOuL3L1sfcM27XcZ+gYX/z7+VTN+hBDDgHFo5uQJ4E3gSyAMSAKelFLeEEIsBTKBlkAkMEFKucIWxwTgCUAF1kopJwkhngaeAdyBS7bzbsBxoIaUUhVCeAPngRrA58AqoILt31YhRDLwHdBISvmqTetpoL6U8rUi0jIBMEgpFwghPgCaSim7CiG62dIxVAgxGHgdEMBqKeVE27XZwPtAL2CsEOIBoB9gATYAv9p+dxJCvAkMklI6z5e3ERQRTFpswRKstLgUakTXdggTGBFCamzBi09afCqBkSFkJKXnHwupFEaVBtW4cuxi/rGB4wbT7sFO5GXl8s7gaTSt7qitBIehJhcYIWpqErrazuNV+roN8HtvCTI1hdxvPkG9ec0WgYLf/M/QRVbEuO43rBdLXgZlj0dkMEa7NBljU/BvXtspXOh9raj5xuO4hwZwfOjcMscPIIJCHYw6NTUJXc16TuF0tRrgO/1T1PQUDD9+ihp7HSUsCjUrA69R49FVron1+gXyln0MJoPT9bdRQkJRk+30UpJwq1PfKZy+bkMCFnyBmppC7pcfY71x7Y7SBaAPC8USX6BlTUzCvZFj2nRhIXh36UDCs+PxmOK4bAlFIWrZx+grVyTrpz8wnTpXol5iRjaRQQXbg0cE+nLyeoJDmMfuaczLS9bQY+pScgwm5g3vhaIIhzAxKZmcu5VM46oRlERiWgaRwQH5v8ODAzh5+aZDmMd6tOOl97+h+5i3yTEYmT/mMRRFISI4gOF9OtLr5fl4uutp17g27Rs7ly0HvSwDkX4FswUi/L04GVP0C0ie2cKeywlM7hWdf0wAo7/fhRCCQc2q81Dz6kVee5vyfn7uUcGY4graGVN8Cr7NnPMksHcbKk56ArfQAC4Om1VwQkpqfz8NJCQtW0/ysg0lpy8iBLOdniU+Ga+mdZ3C+fVsR/i44ehDArnx1DQtbEIKKUt+pc7OpagGEzm7jpCz62iROqGRISTZtZ/JccmERIaSmlgwsOAf5E9OZg6qVc0PExqpvQxVql4JnZuO+T/Nw9vHi9+//INNv2zm2vnrjJgwHL9AP0wGE973tMJ4usCg1oeHYokvaDstCUl4NCn0/MJD8OnWgbhREwhr5Jx2AH2FCDzq1cJwouTn93fy03jhOuFjh6EL9EM1mPDt1BLDKefp56GRoSTFFaQpOT6Z0MhQ0krJy5DIghfLes3rsWjdIlITUlkyewk3LtwgJSGFXz/7la/3fY3JYOLIjiNYLxdsuSz8gwsGdACZkYJSybFsKiFRoNPjOXIquHth2bcGyzFtEMO9zwhMG74D9zLM9gGEXxAyy04vMxWlYk1HveBIUHR4Dp0M7p5YDm7AcnI3SlA4MjcT9weeRomoghp/VdM2m4rXCwpFphbqZ2s69wu6Wg3wnf0ZaloKhuWL7WZcSHwmzgcpMW5dhbmIF+zAiGDS7G2EuBSqO9kRhWyN+BQCI4PJTEpHKILJq+YRVjWS7d+u59oxx/JRq3V9spIzSLoW76TtFhmC2a7cWOKS8YouumxGjh+GLiSQ66Oma2ETUkhe8ht1dn2FNJjI3nWU7GLq+m1EYCiqXX7KtCR01Yvo12s0wGfKJ8j0VAwrPnOaYeXWqhPmA1tL1AJwiwjBbJdv5rgUvKPrOIXz79WWyAnD0YcEcG3kdKfzgX3vIX3ljlL1hH9w/oAO2OpDFUc9JawC6PR4PTcTPLww71qF5fA2x3iCwlAqVMd6w3EgoDBKUChqiqPdoq9VRPms3QC/t5egpiWTt2wx6q1rAHgNG4Ph+08RnqXXPxEcirWQjaQvxkbyf/8L1LQU8pZ+jPW2zXn7nsMi0VWvjeVCyTPME9MyiAwJzP8dHhzAyUuOM8Me69mel95bSvcXZpOTZ2T+S4+jKAq1Kkew8Kf1pGfl4OHuxq5j52lQ469/7PknSEzPJjK44KNWRJAfJ686zuR8rGsLXl60gh7jF2p22TMDUBTBraR0gvy8mfLVai7cSqRB1UgmPNYdLw/3wjL5KMFhqClleGeo0wC/d5Yg02zvDLaygqLgN8/uneFSye8MWltd0P/I7DSUqBqOYYIiQNHh8eh4hJsn5iObsJ7Zi5oci1vHgeDpAxYzuhqNUeOvla6XWWD3yaxUlApF9A06HZ5PvKH1DQfWYzm5CyUoDJmThXvfZ7S+Ie4apg3fgtlYWOZfi8vHz79o4EcI0RB4A+ggpUwWQgQDXwPfSCm/FkKMBBagzXQBiAI6AvWAlcAKIcR9tvNtpJS5tjgAfpVSfm7TmQWMklIuFEIcBzoBW4G+wHoppVkI7UXSNmjzGtDFdk8+wAkhxAQppRl4Eni2mCTtAMba7rkl4CGEcLPd804hRAVgHtACSAM2CCEGSCl/B3yAU1LKKbY0fAHUk1JKIUSglDJdCLESWHV7wKuUzHU6VPhrWxFBHEbKPbw9ef6Tcfw4Y6nDTJ/f3l3Ob+8u577nB9J1eG/Y8XEh7ZLjBbBcuUDGc4+BIQ998zb4TpxF5pih2klVJWvcUwhvX3wmzkSpXB315tUiIi2CotJdRKORvPYgyWsPEti2PjUmPsqxh2c5hSlBxPlQIQnr9YtkjXscjAb0TVrj/dJ0sidpfiR0VWtjWLYI65VzeD7+PB73P4bxt6V3lqbCepcvkDbqUTDk4daiDX5vzCb92SF3kKbitQqnLWjc86QtWAKq6hxWVYkb/BzC14fw96bjVrMa5svXipUrqjkvfAt7zt2gbsVQPn+hPzeTM3juk5U0r1kBX0/NkMg1mhj31TrGD+yYf6xYvSIEC6d4z8kL1KtagSWvP8XNhFSenfclzetWQ5WSrUfOsuaDcfh5ezF+4fes2nWUBzo2KyF9zoJF1jtgx8V4oiuFOCzzWjq8E+F+XqTmGHju+91UD/WjRZUSZnGU8/Mrqi4Ulcfp6/aTvm4/vm0aUHH841wYPBWAcwMnYU5IQx8SQJ3l0zBcukX2/pKnaJeFrA17ydqwF+9WDQl79QluDHsDxd8Xv+5tudh5JNbMHCotmkxA/y5k/FHES1qZ2s/iw+j0CrUb12biY5Pw8PTgwz/e5+yRc9y8dJOfPv6Zud/PwZCbh+n8FbDaPYcytJ0hE0eT+kExzw8QXp5EfDCF5HmfIHNK+TL4N/LTdPkmKZ+uoMrXs1BzDRjOXUVarGWKz2nmR5Hp1v536dQlRrQbgSHXQMsuLXnr87d4utPT+Ab40rZHW57s8CQ5mTm8/snr6Jreg/X4ztIjvY2iQ6lQHcNXM8HNHa9nZmG9eRElJAqZnYEaexWlWpkm2RaT0EK/FQUlqhqGZW+D3h2vEVOwxlzW7iOyGqb136LGXsG9xxDc2vfFvL0EXyNlKCvWaxfJenWw1g81bY33KzPIHj8cgOwZLyPTUxD+gfhMnI8aewPr+ZOOEmWoB0W3OVoYqUrm9JmAl783z346jgp1KhN7oWCgvVW/DkXO9imWIhoX+7IZ8dpQrj3xJoq/D37d23Ch0yismTlUWTSJgP6dyfhjW/FxF1kVCuXnjUtkTxqq5WejVng9P42cN+385un06Ju2w/jrl6WnpYR8sydz/T4y1+/Dp7WWvqtD3yqIwk2Pf/c2xM8v3RdbmfQUHbqKNcj7dCq4ueM95m2s1y8ULElx98Rz2ESMK78EY55zfKXpFcpPy7WLZL74mJaf0W3weW0mWa89gb5ZW2RmOtarF9DXL91vX1n6PcuVC6Q/Y7ORmrfBd9JsMl6ws5E8vfCdOIPcLxdCXsntZpF2RKF72HPivGZHvPEMNxNSeHbuEprXrU6NihE82bcTz85dgrenB3WqRqHX/W8t5ChqZp4oVEH2nL5K3coRfD72cW4mpfHc+z/QvHZlrKrKuRvxTBrcg8Y1KjLvh418uXYvLwzo5BRnKTfh8NNy9QIZz9veGZq1wXfCLDJfsntnGG97Zxh/h+8Mxeih6FAiqmL8+T3Qu+P5+GTUuCvI1DjMB9bh+fBrSJMRNfFmsX1xPmXpZxUFJbI6hmVzQe+G15PTsMZc0u4jqhqm9d+gxl7GvecTtr6h9NdAF/8e/rdaiL9HV2CFlDIZQEqZCrQDbnv1+hZt0OQ2v0spVZuPm9uf9rsDX932x2OLA6CREGKnEOIkMARoaDv+I/Co7e/HbL+LRUqZA2wBHhBC1APcpJQniwl+GGghhPADjMBetAGge4CdQCtgm5QySUppAZYB99qutQK3rbxMwAAsEUI8CJRpXp8Q4hkhxCEhxKFjl08SZLceNygqhHS7L6ygfZkLrlDwVTUoMph023RonV7H6MXj2Pf7To6s31+k3v4/dtKid1un42pKEkpowVRZJTgMmVrIAXRebv70TMuR/aDTI/wCHILI3Gwsp47h1sxxOnJJGONS8LBLk0eFEEzxacWGT993Fq9qkbgFl32KrExLQgSH5/9WgsOQ6YWWjBhywajN4rGcOIDQ6RG+/qipSci0JKxXtC/x5oM70FUtedaImpyEEmqnFxKGWig/pV1+mg/vB50O4e+Yn2XBkpiEPrJASxcehjXJMW0eDeoQNvcNKq76Du/u9xI8+SW8OjtOiZfZORgOH8erveOygMJEBPgSn5ad/zshPZswfx+HMH8cOEe3JjUQQlAlLJCKIf5cTdCeqdlqZeyX6+jTog7dmjp+QSlSLziA+NSM/N+JqRmEF1qu9cf2I3Rr2UDTiwyhYlgQV+OS2HfqEhXDggj298VNr6Nby4Ycv1iC/xQgws+L+KwCAzkhM48w36K/YK47fZPeDSs7HAu3zRYK9vGkS90oTsWWvFyhvJ+fKS4FdzvfBO6RIZjji7/H7P1n8KgaiT5Iq29m23O0pGSQvm4/PtEl1wVLfDJudnr6yFDMxSzXAsg9eBr3KpHogvzx6RCN6WYC1tRMsFjJWr8Hr+YFX4WDht7Px+sW8fG6RaQmpBBm136GRoWSWkgnIzUDH38fFJvRHhoVSoqt/UyKS+bQtkMY84xkpmVycv8pajTQZmut/3EDY/q8yLiHJmDNyMJ8vcBfiiUhGX1kQdupjwjDmuiYnx4N6hA+/3Uqr/sGnx73EPrGi3h3tT0/vY6ID6aQvXoLuZtLf6H+O/kJkP7zBq72f5nrgyeipmfl+/cJGno/Nf5cyMK1C0lNTCUsqiBNoZGhpBTSyEzNLCIvtTB52XkYbEteDm09hF6vxz/In+iO0cTfjCczNROrxcrudbvRVS6YwSAzUxB2y1FEQIjDV97bYawXj2tfT3OzsF47ixJZFaVqXXT1WuL12iI8HnkFXfVGeDz0Yol5KbPSEH52ev7ByOzCemlYL5/UZvLkZWO9cR4lvDIyMxWZmaotIQAs5w6iRFYtWS81GRFcqJ8tqR86XtAPAflhZWY65kO7ipy1mhafQpC9jRAVQkYhOyI9PsXR1ogMIT3BMUxeZi4X952hQafogvvVKUT3as3hVXuKTJ85PgU3u3KjjwrFnFh822JfNn07RGO+VVDXM9fvxbuF8wwQe2RaMopdfoqgMNT0Qnr2+XnqIEKny89P0DZ9UG9cQmall6ilpS8ZN7t8c4sKKTF9OQdO41E1Kr/uAfh1bkHeqctYkkvXkxkpiMACPREQ4jAL4XYY6/mjBfXh6hmUCtW0k4oOz2ETsBzdgfXUvlL11NQklJBCdktaofKZZ5efx/Yj9HqEnz/6uo1wa94e/wXL8X5pCvqGzfB+oXgfhTIlCV0pNpK9zWk+sh/0ugKbU6fDb8IMTDs2Yd63k9KICA4gPiU9/3fRdsRhurVqZLMjQqkYFsxV29LfB7u05sc5L/PVlOcI8PGmSuTf8+9zt4kI8iM+tWDZa0JaFmGBvg5h/th9gm7N6mrpCw+mYmggV+NTiAjyIzzIn8Y1KgLQo3k9zt5wnMVdGK2s3ME7w9ES3hlOH8MtuuR3Bq2tLlhuLHyDkNnpTmHUa6fz22r11oV8x/vWU7swfDsT44/zwZCDTC85fTIzFeFf4HtS+AU790VZaVivnNDqXl421hvnUCKq2PUN2gIPy9kDKJHVStT7tyHL8b//Vf5NAz+C0h242J+3n9sm7P5fVBxLgTFSysbAdOD2ItOVwH22WTUt0AZ1SmMJMAJtts9Xxd6oNiPomi3cHrTBni5ATeAsxXxTsmGQUlpt8ViA1mgDQQOAdWW4R6SUn0kpW0opW8pLuURUiyK0Ujg6Nz2t+3bg+MaDDuGPbTxEuwc7A1CjWW3ysnLzl3kNn/c8cZdusfELx50bwqtF5v8d3b0VcZdjKIz10nmUqEoo4ZGg1+PWsSumQ47GnQgsaAR1teohhEBmZSD8AxDetg7G3R19kxaoMSW/XNuTdfQy3jWi8KwShnDTET6gPcnrDzmE8apWsBzIt3F1FDc95tQyOpcFrFfPowuviAiNBJ0et9adMR8tlD7/gk5FV70uCAWZnYnMTNM6uUitA9E3aO40Vbwwlovn0FWohBKh5afHvV0xH3B8qbPPT33teqAoyMyMwlGViun0efSVK6KvoGn59OpM3nbHtMX0fYKYB4YS88BQcjftIHXuAvK27UEJDED4aoM2wsMdzzbNMV8r+dk1rBLOjeQMYlIyMVusrD96kU6NqjmEiQr0Zf8FzYlkSlYu1xLTqRTij5SS6cu3Uj0iiCe6RJcpfQ1rVORGfDK3ElMxWyys23eCTs0dXwgiQwPYf1rrZFMysrgWl0yl8GAiQwI5cekmeUYTUkr2n75M9Yol+wJoWCGIG6nZxKTnYLaqrD9zi051opzCZRnMHL6RTBe7c3kmCzlGc/7fe68kUivM3+lae8r7+eUcv4hn9SjcK4cj3PQE9+9I+sYDDmE87NoM70Y1EO56LGlZKF4eKD5as6x4eeB/bzR550vWyztxAfdqFXGrFAFuegIeuJfszY4D025VC/LQs2FNhJsea1omltgkvKLrIjw1x4g+7ZtitFvml/bdap7vPYbne49hz/q9dB/UDYB6zeqRm5XjsMzrNsf3nOCe+7UdWXo81J29GzQHq3s37KNR60aa7xlPD+o1q8sNm9PW246ewyqE4dO9I9lrC2YcGU+dx61qRfQVbc/vvk7kbHN02nrzvmHc7K39y9m4k+TZC8ndoj3jsOmvYb5yg4xvSt+V5u/mJ4DOlhZ9VBh+vdqT8ef2/Ly80vdFXrzvRfau30s3W17WbVaXnKwch2Vetzmx9wQd+2jfebo/1J19G7SXy6Cwgra0TtM6CEWQmZZJUkwS9ZrXw8P2PKM7RKPaOW5WYy6jhEQhAsNAp0PXuD2Wc459geXcIXRVtfYSN3d0lWqhJsVg3ricvHdHk/f+GIw/fYj16imMKxaWmJdq7BWU4AhEQKg2c6JBWywXHJcXWS4c0QanhAJ6d3QVaqKmxCJzMrSXg2CtruiqNURNii1KJh/rlXPoIisiwmz9UNsumI8U6ocC7PqhGnVBCM2Zs4cn3F5C4+GJvnHLgqXWdlw/fpnwalGEVApD56ajZd/2nNjomIcnNh6i7YPa96vqNjsiMykd32A/vPw1V4luHm7U69CYeDt7oV7HxsRfiSW9mIHivBMX8KhWAbdKEQhb2cza5Fg23Z3KphvWtEzMRdV1O6fJRWG9dh7Fvl9v1QnLcce6Z9+vK9UK+vXbuLXuUqZlXgC5xy/ibpe+wL73klmo7bRPn1ehugcQ2O9e0m11rjTUmxdRQqMQQeHazKTojljPONqEltMHUKo3yK8PSpU6yASt7/V45AXUxFuYd5S8c9htrJfPoURWRLGVT/d2XTEfLqF81qynlc+sTAw/LCFzzCNkvjSY3AUzsJw+Su5/5hSWKLjvi+ccbE73jl0xHyzeRtLVrqc9uyzNRvJ5YSLWW9cxrPypTGlrWLMSN+JTCuyIvcfpVGhgMTIkkP22pa+aHZFEpfBg22/tY1dcchqbD57ivnZlmNVUjjSsVoEbiWnEJKVrdtnBs3Rq6vhRJirYn/3nrgGQkpnDtYQUKoUGEhrgS2SQH9fitUG+/eeuUaMUx9VO7wwdyvDOoPz1dwY1/hoiqKCt1tdrjfXy8UL3dAylYu38tlqJqoGaavPPeNvRs18wutrNsZw9UFjCUS/2CkpwpNYXKTp0DdtiuXDEIYzl/GF0les69g3J9n2D1hboqjdETXZ+73Lx7+Zfs9QL2Az8JoT4QEqZYhuM2YM2E+dbtJk6u0qJYwMwRQjx/e2lXrZZP35AnG2p1RAgBkBKmS2EOAB8hLZsqqi56Vm262/PRNovhKgMNAealHI/O9B8Fo0ETqL57TlsW7K1H/hICBGKttRrMOBkTQohfAFvKeUaIcQ+NB9F9vdVKqpV5fspS3jlmzdRdAq7f9pC7MVbdBrSE4DtyzZwcusRGndpzpztizDlGflqvLZkq1bLerQf1IlbZ68zZY22feFv87/n5LajDJo4lMgaFZCqJCUmiW/f+Iw3Ci9PVq3kLvkI37feAUXBtGUt6s1ruPfsB4Bpw0rc23XCo1c/pNUKJhPZH8wAQAkKwXvMZM0ho1Aw7dmK+XApu1XYIa0qFyZ/SfQPbyB0CrHLt5Jz/hYVhvUAIPabjYQ90JbIh+9FWqyoBhOnnvmgzPFr6VPJW7YQn7Fva9ui7lyHGnsd984PaOnbtgq3Vvfi3qUvWK1Is4ncxQVLyfK+W4TXM5MRejfUpDhyv3inFD0rOYs/xH/6u7atStdgvXENj95afhrXrcSjQyc8+vTX9IxGsucX+ALwHTcFt8bRCP8AAr/6mbzvv8K4cU3RWlaV1HkLCf+PlrbsleswX7mO7yAtbdm/FL+Fqy4smNDpE0GngBDkbtxO3s6iZ4vdRq9TmDToHkYv1rZF7d+mPrWiQvh5t+bs8OEOjXi6VyumfL+Zh+YtR0p4pW87gny9OHolllWHzlM7KoRH5ms7zb34QFvuaVCtBD0dk4f3Y/T8r1BVyYBOLahVKYKfbC+7j3RrwzMDuvLWpysYNOkjJJJXHu1FkJ8PQX4+9GjdiMfeXIROp1CvagUe6lLylyW9ojCpVzSjl+/W0te0KrXC/Pn5sPZl/+EW2rryLedjaVcjAi/3guY9JcfIayu0l1+LqnJfw8p0qBnpLGJPOT8/rCo33vqcOsumgqIj5cdNGC7cJGxoLwCSvltPUJ92hAzqYqtvRq6M1nYd0YcFUmvJJEBzxJr6+w4yt5XshwOrSvz0T6iydCZCUUhfsRHjxRsEDb4PgLTla/Hv1YGAgV3BpnfrJW0b7rzj58lat5saKz9CWq0YTl8h/QfnLVwBDmw5SKuurfhq15cY8wy8N7agjZj59Qw+mPAhqQmpfDH3S17/zyRGjB/GpVOXWf+D5qPo5qWbHNp2iMUbPkFKlXXL13P9vDbAO+WzN/EL9MdqsZA8eyFqZsGMN6wqyXMWEbl4DkKnkPXbesyXr+P38P0AZP1cvKNTj2YN8evXA+OFK1T8+RMAUhd8Sd7Og8Ve83fyE6Dyf15HF+iPtFiIn/aJY1psHNxykFZdWvHFzi8w5hn5YFxBXk5fOp2PJn5EakIqX839iomLJjJs/DAun77M+h/XA9ChTwfuf+J+rBYrJoOJeWM0/fPHzrNrzS4WrFmA1WrlyukrWA7Z7dCmqphWfYnn8DdAUbAc2YpMvIW+ldYXWA5uRCbFYL14DK8X3gWpYj68BZlY8gBBsUgV0/pv8Bw8ARSB5fgOZHIM+ubaNs+WI1uRKbFYr5zE6+nZICXmY9vzdxkzbfgWjwGjEYoONT0J46rPS9ZTVfK+WYjP+HlaP7RjLWrMddy72vqhLbZ+qFs/UK1Ik5Hcj7V+SPgH4fOKrY9QdJj3bsZy0rmcqFaVH6Z8yYvfvIGiU9jz01biLt7iniFaHu5ctpFTW4/SqEtzZmxfgCnPxDc2OyIgPIjh772AUBQURXB49V5ObSl42WnZtwOHSlrmZVWJnbaYal/PQCgKaT/byubjtrL5/Vr8e7cncGBXpMWKNJi4mV/XL5C5bje1/vwQaVExnLlM2g+lfD9TVQzfL8L7lTma7bF7PWrsddw6aXXPvH01+hb3aP28rV/P+9xuMMLdA12D5uR992HJOvbpm7KYGt9MB51C2k+bMF68QfCQ3gCkLltHwH3tCXqwK9JiQTWYuD5mfv7lwtMD347R3Hr9P2XTU1WMv3+O19NTbdu5b0ZNuIm+rdZWW/atRybewnr+KN6vfYiUEsv+jagJN1Cq1cetRRescdfwevV9AExrv8N67kiJenlLF+Azeb5mB25bi3rrGu7d+2rXb/oTtzad8Ohhs1tMRnIWzCxbWpy0rOR+/iF+U2020uY1WG9ew6OXzUZab7M5e2tamIzkvKeVf339xnh06YXl2mX8318CQN53n2uzgopBr9MxeUR/Rr/9BaqqMqBzK2pViuQn2+6Hj3RvyzMPduOtxT8xaOIHSCl5ZfB9BNlmM4/98FsysnPR63S8/uQA/H3//l4y46e+zcGjJ0hPz6TbgKE8P+oJBvXt9Zfi0usUJj3eg9Ef/oAqJf07NKFWxTB+3qY974c7N+fpBzow5atVPDRtiZa+QV0I8tPSMXFwT15fshKzxUrFsEBmjLi/ZEHVSu4XH+H7hu2dYautrPSwvTNsXIl720549CzinSHQ9s6g2N4Z9m7FfKSUdwapYtr8PR6DXtH6hpO7kSmx6Jtqy9Esx7cjU+OwXjuF54hpICWWEzvzlzx69BuN8PIFqxXT5mWlO1uWKqZ1X9v6BgXLse22vqGrpndki9Y3XD6B1zNztb7o2DZkkjboalr/tdY36PSo6YkY//ysZL1/Ga5dvUD8mzJBCDEcGI+21OkoMA3NuXMozs6d8/3bCCGypZS+tr8nAcMAE7BGSvm6EGI0MAG4jjYA4yelHGEL/xDwM9BZSrnddiw/fiHEi8ALQJyUsoudRrSU8rFS0tMNbYZOoJQyRwhxAVgspXzfdv5xYDLa7J81UsoJRaQnCvgDbZaSAN61+TzqgOaE2gg8VJJz56eqPVRuheSdFsmlB7qLHN0VXnqgu0iLPiUvr7nbWJJL2fryLpMd41aueuGz+5WrngitWK568uyh0gPdRRI/LOHF/h8gKdG39EB3CW+P8q0Lr5pKWat/l/kksGw+ce4WhtzyrevjzOVrq/w8vPzKJgCeJe9Wc7exnCvfL72Tdpa8c9Pd5gWl/HarqdKteMfZ/wTXNpbvFsw1Hi7fb8SWW+nlqqcayret9n7z1XLV01Ut7Rvz3cVypnQH4XcTw8Kl5arn0aZWuWlJQ/GbtfwT+Lz5Xdmc9f0fpWXUPeVmSByK2/k/mZf/phk/SCm/RnPobE/XIsKNKPTb1+7vt4G3C53/BPikGM0VFFp2ZR+/lHIhzjNxOgKlTg2RUm5G2z3s9u86hc5/T4EPI/vj9umJQ1vqVTjMbsq4nbsLFy5cuHDhwoULFy5cuHDxfxHXrl7/Lh8///MIIQJts3bybIM6Lly4cOHChQsXLly4cOHChQsX/xj/qhk//+tIKdMBh1k7QogQNP9EhekmpSx+SxQXLly4cOHChQsXLly4cOHCRYn8m9zb/FVcAz//ZWyDO9H/7ftw4cKFCxcuXLhw4cKFCxcuXPz7cA38uCgVXYk7x99dPPu1LTctALnrarnqufe+p1z13NLK15k0fx4rX73yxlB+DkMBlOjO5aon1fJ1Jn1AlJ8D3cHNb5WbFkBGCZsM/ROENNeVq540lK+z7Kxd5bsyPWdPTrnqGTLK1xwL6vj3d/+5ExJk+TopPWIMLDetmveVr7tEdcO+ctXTde1SvnqJseWqZ9xQ8hbadxsZc6n0QHcRS056uerpG9xbrnrW7K/KVS9v+8Vy0/IeeV+5af3/gMvHj8vHjwsXLly4cOHChQsXLly4cOHCxb8W18CPCxcuXLhw4cKFCxcuXLhw4cLFvxTXUi8XLly4cOHChQsXLly4cOHCxb8S6Vrq5Rr4cVE2GnaK5pEpT6LoFHb9uJn1n/zuFObRqU/SqEtzTHlGlo77DzdPX0Xv4ca4H2eg99Cj0+k4snYff37wEwAPvPIwHR/rTnZqJgC/z/8eyHSKd/flBOZvPIEqJQObVmVk+7oO55fuu8CaU5r/DquqcjUli62v3E+Al7vtmOTxr7YS7ufJwkfa31G6g7s0pc6sEQidQuyyLVxf+IfD+dDeLakx8RFQJdJi5cJbX5Nx4Pwdaey+GMv8NYe19DWvych7Gzqmb9cZ1py4lp+Wq0mZbJ34IGm5Rib8tCs/XExaNqO7NGFo+3ol611L4p1tZ1FVGNCoEiNb13A4//Whq6w5F1ugl5rNlue6kme28ta6k6TkGhEIBjWuxOPNq91RWt1atMbnmRdBUTBsWI3h5+8dzusbR+P31mzUhDgATHt2krf86zLHv/vsdeb/ugtVqgxs24CR3Vs4nM/KM/LGd5uIT8vCoqoM69KMAW3qE5+WxZvLNpOSmYtQYFC7hgzp1LR0vVOXmbd8PaoqGXhPNKP6dHDUyzXw+pI/iE/NwKKqDO/ZlgEdowHIzDUw/etVXIpJQgDTn+xL05qVStTbdfQM8776FVVVebBbO0YN7OFwPjM7lykff8/NhGQ83PRMf/5xalepAMCUj5ex/fBpggP8+O39yaWmDcCrfUuCJzwPikL2b2vJ+OrHIsO5N6xD1DcLSJo4m9xNOxHubkR++T7CzQ30OnI37ST9k29K1avcuQkdpz2BolM4s3wbRz/+0+F8YM0our73DGGNqrH/nZ859uma/HNNnupNg8c6I5GknrvFlrGfYTWW7IdGH90a7yfHgKLDuHk1xt8LlccG0fhOnIU1MR4A8/4dGFZ8A27u+M34CPRuCJ0O077tGH5aWmr6AF6b+SLturbFmGdg5qtvc/6ks7+Ah54cyKNPPUTl6hXp1ag/GakZAPQa2J0nXhgMQG5uHvMnfcClM5eLT1/jVngOeQEUBfP2NRhX/+BwXlevKT4vz0BNsqXv8C6Mf3wLgN+7y5CGXFBVpGolZ9rzpaZN37QVXiO0/DRtWY3xj+WO5xs0xWf8LFRbfpoO7MT4i125EAq+cxcjU5PJmf96qXqFeXnGC7Tt2gZjnpE5r87nwinnvH1wRH8efmoQlapX5IFGA8lIc+5zisK9VWt8X7C1XWtWk/vD90WG09etR9DCj8mcNR3jju0AeA16GK8+94OUWK5eJXP+22A2lajn2a4VQeO0Z5fz+xoyv/6hyHDuDeoS8dVCkl+fRd7mHQUnFIXIbz/GmphC0qtvlCmNt9HVa47ng0+DUDDv24hp8wrnMLUa4THwaVD0yJxM8haVrU2xZ9S0p2nepSXGPCOLxn3IlVNXnMKEV47gtYXj8A304+qpy3z06gdYzBZa9WjD4LFDkKqK1Wrly+lLOHfobLFaFTo3odWMJxCKwqXl2zj1H8e2pfrA9jR6/gEAzLkG9k9eStqZG3hXCKbjR8/hGRYAquTCsq2c+2J9qWnbfT6G+asOaH1Dq9qM7NzY4fzSHadYc0xLr1WVXE3MYOubjxLg7cGy3Wf49eBFpJQ82KoOQzuW7kPIr1NzKk59CqHTkfLDBhI/+cXhvH+PNkSNHaLVZ6uVmOlLyLHll87fh8rzxuBZpyoguTF+AblHSrZjyr2v/Rt24H3/WY+Pux5FCPSK4PuRJfss0jdphdcTY0BRMG1bg/HPQu1Y/ab4vDYzv900HdyJ8Tet3fT/8PuCdtNqJfut0aWmzSmt524yf+Verey0rsvIrtEO57PyTLyxfCvx6dla3nZqwoBWdYuOrKj4T11m/g+bUFWVgfdEM/K+do7x5xp444s/iU/NxGJVGdarDQM6NAE0u2XG12u4FJuEQDBtRJ9S7ZbSeHPO++zYfYDgoEB+/27x34qrMG4tWuPz3IsIRcGwbjV5hWxOt8bR+E2djRqv2ZzGPTvJ+77sNieAW/PW+Dxt6xs2rsawopAd0Sgavzft7Nq9O8n7QdPweWki7q3aoWakkTHmyTLp7T5/i/l/7tfqQqs6jOzcxOH80u0n7doWVWtb3hqstS27TvPrwQtICQ+2rsPQjg2LknDxL8Y18PM3EEJUABZIKR8qJdzrUso55XRbZUIIEQ1UkFKuKTWsojB4xig+HDqTtPhUJq+cy4mNh4i7VOAstVHnZoRXj+Ktzi9SvVlthsx+mrcHvI7FaOaDx6djzDWg6HVMWDGTU9uOcvWoZpBv/mIVGz+3M8C61HTQtqqSueuPs3hwByL8vRjy1VY61Y6iZph/fpgRbeswom0dALZfjOO7A5fyB30Avj94ieohfuSY7tAZqSKo+/ZIjj4yG2NsCq3WzyV5/SFyLsTkB0nbcZID6zSHuL4NqtDos1fY1/G1MktYVZW5qw6xeHhXLX2frqdTvUrUDA8oSF/HBoywGX7bz93iu73nCfD2IMDbg5+e75MfT893f6drg8ql6Ene3nKGTx5sRYSfJ0O+30unmuHUDClwsju8ZXWGt6yu6V1OZNnRawR4umOyGHjt3rrUjwggx2Th8WV7aFM11OHaElEUfEa/QuabY1GTkwj44FPM+3ZjvXndIZjl9Amypt/5S4RVVZm7YgeLR/cjItCXIe//TKdG1akZGZwf5sddJ6kREcSCp+8nNTuPAXOWcX+LOugUhbH9O1C/chg5BhOD3/uJtnUrO1xblN6cZWv59LUhRAT58/isL+gcXYeaFcIK9LYeokaFUBa+9CipWTn0f+MT7m/bGDe9jvnL19OhYU3eG/0QZouVvFLKp9WqMueLn/nsrReICA5k8OR36dyyETUrR+WH+fzXDdStXpEPJzzF1ZgEZi/5mSVTxwDQr3MbHut9L28s+q5sGaooBE9+kYTnJmJJSKbCskXkbt+L+coNp3BBLz9F3t7D+YekyUz80+OReQbQ64j66gPydh3EeLL4FzOhCO6dNZw/H3+b7LhUHlo1g2sbD5N2scDRpzE9h11Tv6V6L8eXDJ/IIJo82ZPl3SZiNZjp+fGL1OrXlvM/7ywxfd6jXiZ75jjU1CT85i7GfGg36i3H8mg+e5KctwuVR7OJrOmvgSEPdDr8Zi7EfPQA1otnitcD2nVtQ+XqlXi4wxAaNm/AhLmvMuoB5wGVEwdPsnvjXj7+5UOH47E34xg96GWyMrJp16U1k+ePLfJ6AISC57CXyJk/AZmahO+0jzEf3YsaW6i+XThF7gdFDwzkvD0WmV22gRGEgtfIl8mZPR415XZ+7kGNKaR39mSxgzoefQahxtxAeN254+G2XVtTqXolBnccRoPm9Rk792We7TvGKdzJg6fZs2kfC1a8X/bIFQW/l14hbcJY1KQkgj7+FOPe3VivX3cK5/v0s5gOHSw4FBqK98BBpIwcBiYT/m9Nw7NrVwzr15WoFzTxJRJfmIA1IYnIbz4md8deLFed9QJffBrDPmen7H6DH8R89QaKj0/Z0wlauXnoOXI/eQuZnoL3a+9jObUfNeFmQRgvHzweGk3e4mnI9CSEb0Dx8RVD8y4tiKpegRc6PUudZnV5ZtZoJg0Y7xTuiUnD+fOLlez+cyfPzh5Nt0d7sP67tZzcfZyDG/cDULVeNcb+ZwIvdSu6LghF0Gb2cDYOfpvcuFT6rJnBzQ2HybBrW7JvJrH+oVmYMnKp0KUJbeeNZG3faUiLyqHp35N66hp6H08eWDeTuB0nHa4tjFVVmbtyH4tH9STC35sh/1lNp/qVqRkRmB9mxL2NGHFvIwC2n73Jd7vOEODtwaX4NH49eJHvnr8fN53CC19t4p56laga6l+MGqAoVJr5LJeHTMEcn0Kdle+RsekAxosFzyx793HO2/LLs141qv1nAuds+VVx6tNkbj/CtdHzEG56FC+P4rX4b/S1f98O/HxIR4K8S04XoLVjI14mZ+54rV+Y+QnmI0W0Y+dPkvNu0e1m9qzXyt5uFsKqqsz9bTeLn+lDRIAPQxb8TqeGVakZEZQf5sc9p6kREciCkb20vJ3/M/c3q4WbvnQH/1ZVZe73G1j86mNEBPkzZPZSOjWtTc0KoQXxbz1CjahQFrz4MKlZuQx481Pub9NQs1t+2Ej7RjV4d/SDZbJbysKAPj14fFA/Xp/57t+OywFFwfeFV8h4XbM5Az/6FNP+3VhvFHqWp06QOe3Obc7bGj7PvULmW2NRU5IIeP9TzPuLsGvPnCBrhrOGcfNaDKt/xffVsn3osKoqc//Yx+JRvYgI8GbIoj/pVL+KY9vSqTEjOmkDzdvP3OC7Xaft2pYLfPdCX1vbssHWttx5+/1/FdW1nbvLx8/fQUoZW9qgj407/3R5lxBCFDe4Fw30KUsc1aNrkXg9nuSbiVjNFg79uZumPVs6hGnasxX7ftW+bl49ehEvPx/8wwIBMOZqu3no9Dp0eh3yDireqdhUKgf5UCnIBzedQq8Gldh2Ma7Y8GtP36J3g4KvDwmZeey8lMCD0dXKrHkb/+a1yLuagOF6ItJsJeH3PYT2buUQxpprzP9b8fbgTmcRnrqVQuVgXyoF++Km19GrcVW2nSt+96G1J6/Tu3FVp+P7ryRQKciXCoElG/mn4tOpHOhNpUBvLT/rRrLtckKx4dedj6N3XW1gIczXk/oRWgfh466nerAvSdll36lFX6c+1tgY7cuKxYJxxxbc2nYs8/Wlcep6IpVDA6gUGqDlZbPabDvpuGubQJBjNCOlJM9oJsDbA52iEBbgQ/3K2oCNj6c7NSKCSMwoeVefU1djqRweTKWwINz0Onq3bsi2Yxcc9YQg12BCSkmuwUSAjxc6RSE7z8jhizcYeE80AG56Hf7eniXrXbpOlcgwKkWE4uamp3eH5mw9dNIhzJVb8bRppBm/1StGEJuUQkq6ZoC2bFCLAN+yv1B7NKqL5WYslph4sFjIWb8N787OM+b8B/cnd/Mu1NR0h+MyTysbQq8Hvb7Ueh8eXZOMawlk3khCNVu5tHIf1Xs6DvDkpWSSePwKqtnqdL2i16H3dEfoFPRe7uQmpJWop6tVDzU+BjVRK4/m3Vtwb9mhxGscMOTZItJr/8rQrt3bqwNrVmizBU4fOYNvgC8h4c4vPBdOXSLuVrzT8ZOHTpOVkQ3AqSNnCIsKcwpzG12NeqgJMcikOLBaMO/filvzO5vxeCfoatVDTYjV8tNqwbRnC26typ6fIjgUfbO2mLas/kv6HXt1YN2KDQCcOXK22Ly9ePoS8beKb/OKQl+vPpaYGNQ4W9u1dQse7Z3bLq8BD2LcuR01vVDZ0+kQHh6g6BCeHqjJySXquTesh+VmDNYYTS93w1a8Ozk/O79HB5C7ZSfWQnVPFx6KV4c2ZP9e6ncdJ5SqtVGT45ApCWC1YDm6A33jNg5h3Jp3wnJiLzI9CQCZnXHHOq17tGHbL1sBuHD0PD7+PgSFBzmFa9y+CXvXaFvkbf1lC617avdiyC3oezy8PSip8w1pVpOsawlk29qWa3/so3KhweOkQxcxZWi7NiYfuYRPlFZ28hLTST11DQBLjoGMi7F4lzBIAXDqZjKVQ/ypFOyn9UVNq7Pt7M1iw689fpXeTbWPLVeSMmhSOQwvdz16nUKL6hFsOX2j2GsBvKNrY7wWh+lmAtJsIe3PnQT0cHxmql1+KXb5pfh64dOmIak/bARAmi1YM0vp+8q7r/2bduCdoKuptZuqrd007duCW4t/rt0szKkbSVQO9adSiL+Wt9E12XbacRBBCLu8NRXkbZnivxpL5bCgfLulV6v6RdgtkGM0avEbTAT4eObbLUcu3GRgR22GVlnslrLQMroxAf5+fzuewjjZnNu34H4XbU4Afe36WONitNk8t+3aNmXXsJw+gczKKnN4rW3xo1LI7balBtvOFN8+rD1+ld7R2qz+K4nphdqWyFLbFhf/Pv7RgR8hxDAhxAkhxHEhxLe2Y1WFEJttxzcLIarYji8VQiwQQuwRQlwRQjxkF88EIcRJWzxv2449LYQ4aDv2ixDCWwgRIIS4JoRQbGG8hRA3hRBuQoiaQoh1QojDQoidQgin9TBCiGlCiG+FEFuEEBeFEE/bjgshxDtCiFO2+3jUdryaEOKU7e8RQohfbRoXhRDzbcffBryEEMeEEMuEED5CiNW2+z51O64i7qW1EOJX29/9hRB5Qgh3IYSnEOKK7Xi0EGKfLS9/E0IE2Y5vE0LMEUJsB14WQjxs0zouhNghhHAHZgCP2u6ryHu4TWBEMGmxKfm/0+JSCYwIcQqTahcmPT6FIJtxJBSFN9e8w7uHv+DsrhNcO1awlWXn4b15a+27DJs/Gm9/50GLxCwDkf5e+b8j/LxIzCp6sCHPbGHPlQS616uYf+ydjSd4pWtDxF/Ykd4zMhiDXZqMsSl4RDobpmH3taLtrveJ/m4SZ1795I40ErPyiAwoSHeEvzeJmUVvGZ5nsrDnUhzdi5jVs/7kde5r4jwg5KSXbSTCzy4/fT1JyjYWGTbPbGXPtWS61Y5wOhebkcv5pEwaRQaWqnkbJSQUNTkx/7eanIQuJNQpnL5eQwIWfoHf9PnoqlQrc/yJGdlEBhXMPooI9HUyKB+7pzFXE9LoMXUpD81bzviB96AojoUjJiWTc7eSaVzVOd0OemlZRAYVfHEMD/IjIc2xA3+sa0uuxCXTfdxHPDTtMyYM7omiCG4lpRHk68OUr/7kkemfM23pKnKNJS/9SEhNJyIksCB9wYEkpji+cNWpVpHN+48DcPLideKS0khISS8x3uLQhYdiiU/K/21JSEYXHlooTAjeXTqS9fMq5wgUhQo/Lqbylp8x7DuC6dS5EvV8IoPIjk3N/50dl4pPEfWtKHLi0zj26RqG7fuIEYcXYcrK5eaOUyVeowSHoaYUpE9NTUKEOA+k6Os0wO+dJfi+Pg+lUjWH9Pm9s4TAL37HcuIQ1kvFz2a6TVhkGImxBZqJsUmERRY/eFMSfQffz76txW9DLIJCkamF0hfkXN90tRrgO/MzvMfORalo34ZIfMbPx3f6J7h1vr/U+1GCQ1FT7Op3ShJKUXp1GuA3fwk+k952yE+v4WMwLPsUpFqqVlGERYY65G1SXBKhkc76fwVdaChqkl3akpJQQh3jVkJD8eh4D3l/rnQ4riYnk/vzD4Qs/4nQn39FZudgOuw8Q8dBLzwUa4Jd3UtMcq57YaF4de5I9i9/Fr6coLEvkLbgszINRhZGCQhBTSsYmFLTUxABjv29El4B4eWL15g5eI/9AH2rO9/uOzgyhGS755USn0JwIbvCL8iPnMwcVKtWJlLiUgiJLAjTpldbFmz+mDe+msKi8QuK1fKODCLHrm3JjUvFu4S2pdZjnYnZesLpuE+lUIIbVSX5aPHLKwESM3Od+/ViBjfyTBb2XIiheyOt7tWKCOTw1QTScwzkmSzsOh9DQikDI26RIZjjCp6ZOS4Zt8gQp3ABvdpSb/PH1PhqCjds+eVRJRJLSgZV3n2ZOms+pPK8MaXO+Cn3vvZv2oECGL18N4O/3MqKo1eLvO42Tu1YajJKkHMbravVAL85n+MzYS5KxWr5x6WU+Ex6B99Zi3HvUnq7WZjEzBwiA+3yNsDHOW/bN+BqYjo9Zi7jofd+YXz/dk55W2z86dlEBhfYLRFBfiSmF7ZbWnA1LoUe4xfy0PQljH+sh81uSSfIz5spX63m0RlfMv3rNeSVYrf8N1EKt9vJSShF2Zz1GxL4ny/wn3FnNicUYdemFGPX1m1IwIIv8Jt25xr2OLUtAd4kFjNQq7Utt+jeSNOrFRnE4Wv2bcstEtJLblv+bchy/O9/lX9sqZcQoiHwBtBBSpkshLj9iWQR8I2U8mshxEhgATDAdi4K6AjUA1YCK4QQ99nOt5FS5trF86uU8nOb1ixglJRyoRDiONAJ2Ar0BdZLKc1CiM+A56SUF4UQbYCPga5F3HoToC3gAxwVQqwG2qHNkGkKhAIHhRA7irg2GmgGGIHzQoiFUspJQogxUspo270OAmKllPfbfhc3x+6ILS6Ae4BTQCu0Z7bfdvwb4EUp5XYhxAxgKvCK7VyglLKTTeMk0EtKGSOECJRSmoQQU4CWUkrnufCFKao/KWRQiiJGVm4HkarKrD7j8fL3ZvSn46lQpzKxF26y/bsNrF7wC0hJv7GP8dCbwyBvg2McZbwdgB0X44muFJI/vXfHxTiCfDxoEBXEwetJxVxVAmUcLUpae5CktQcJbFufmhMf5ejDs8osUZRdXlReAuw4H0N05VACCk1XNlusbD8fw0s9Sl8nXyTFJHPHlUSiKwQS4OnucDzXZGHcqmOM61QPX487aEKKKiOFflsvXSDtyUfBkIdbyzb4vTmb9GeGlCn6IstKIck9525Qt2Ion7/Qn5vJGTz3yUqa16yAry2NuUYT475ax/iBHfOPFa/nrOikd+oK9SpHsGTcUG4mpvHsB8toXrsKVlXl3I04Jj3eiyY1KjJv+Xq+XLuHMQM6lymtBXqOgqMGdGfeV7/y8Lh51K4SRb3qldDpSp/+XUzkzscKFdjg8c+T9tESUIt4WVdVYh99DsXPh7D3p+FWsxrmy9dKkCu+DSkNjwBvqvVszrftX8WUmUvPxS9SZ2AHLvy2u2wRFCNouXqBjOcfA0Me+mZt8J0wi8yXhmonVZWs8U8hvH3xGT8TpXJ11Jslv1QUnaV3biA0bx9Nv8F9eGbAiyWIFXGskJb12kWyXhsMRgP6Jq3xfmkG2ROHA5A962VkegrCLxCfCfNR425gPX+yiEhv6xUp6PDLcvUimS88pulFt8Fn3EyyXnkCffO2yMx0rFcvoG/w19qxu5W3xcReROSOP32ff5Hszz91qgvC1xeP9h1JGfIYMjubgKnT8ejeA+OmjXd2C4XSEjT2edIXfu6k59mxLdbUNMznLuLR4q/kZen1HkWHrnJNcj9+E+Hmgfcr72C9dh6ZVPzyJyeVMjyvotuEgjD71+9j//p9NGjdkMFjhzB9yJRitEp/freJaF+fWoM7sX7gTIfjem8POn/+Mgenfoc5O6/oi0uIuth+/dxNoquG5/frNcIDebJTI577ciPe7nrqRAWhK/WlvgzPDMhYv4+M9fvwad2QqLFDuDxkCuh0eDeqSczUz8g9doGKU58i/PmHiH9v2R2mz/H33e1ri9ArJmxhOxBg6bB7CffzIjXHyHPLd1E9xI8WVYobFC49Ly3XLpL5sq3dbNoGn9dmkDV2GADZ01/S2k3/QHwnvYM17ibWc86DiMVRtE3o+HvPhVvUrRDC58/ez82UTJ77bA3Nq0eWmo9a/EXYLYXSvOf0VepWjuDzsY9zMymN597/gea1K9vslngmDe5B4xoVmffDRr5cu5cXBnQqc/rKl9JteMvlC6QOt9mcrdrgP2U2aU+VzebUJEq3W6yXL5A2yqbRog1+b8wm/dk70HCIu/Tnd5sdZ28QXTWiUNvSmOe+WI+3uxt1ooLL0La4+LfxT/r46QqskFImA0gpb39uaQc8aPv7W2C+3TW/SylV4IwQ4vYngO7AV1LK3ELxNLIN+AQCvsBtb3s/Ao+iDfw8BnwshPAF2gM/23W+xX3S+ENKmQfkCSG2Aq3RBqOWSymtQIJtJk0roHBrvllKmQEghDgDVAUKz+89CbwrhJgHrJJSFumEQkppEUJcEkLUt93D+8C9gA7YaRswCpRSbrdd8jXws10U9l5YdwNLhRA/Ab8Wk24HhBDPAM8APNdzOB0f65Z/LigqmPTEVIfwafEpBFcI4fZ3sMDIENITHMPkZeZyYd9pGnaKJvbCTbKSC2Yr7PphEy98MQn2OQ78RPh5Ep9ZYGQlZOUR5lf01NJ1Z27Ru2HB9N5jt1LZfjGOXZcTMFms5BgtvP7HIeb0b1nk9YUxxKXgWaHgq5lHhRCM8cUvH0nfdxavahG4BfthTi3b1M0Ify/i7b7mJGTmEmY3I8eedaeu07tJNafjuy7GUS8qiBDfoq+zJ9zXg4Qsu/zMNhDmU3RVWH8+jt71ohyOma0q41Yd5b56UXSrHVmqnj1qchJKaHj+byU0DDXFccmDzCuY7WQ+tB+e1yH8A5CZpS8liAjwJT4tO/93Qno2YYVmkf1x4BwjuzVHCEGVsEAqhvhzNSGNxlUjMFutjP1yHX1a1KFb05qFo3fWC/In3s4xbGJaFuGBjtOV/9h9nJH3tdf0IoKpGBrI1bhkokICiAjyp0kN7atkjxb1+XLtnpL1ggMdZu8kpKYTFuzo98HX24uZL2gGhZSS+16YTsUilruUBWtCEnq72Sj6iFCsSSkOYdwb1CZsnraSVQkMwKtjK7Bayd1akBY1KwfDoeN4dWhZ4sBPdlwqvhUK7tU3KrjU5Vq3qdSxEVk3kzDY6t3VtYeIbFm7xIEfNTUJxW6GjxIchkwttATHrjxaju6Hp15F+AUgswrKo8zNxnL6GG7RrTEWMfAzaMQA+g/RnMaePXaOcDsfUOEVwkhOKHnZT2Fq1a/B6++O59WhE8kswTGxTE1GBBdKX7rj88Ngl74TBxDDXkb4+iOzM/PDyqx0zId3oatRr8SBHzUlCSXErn6HhKGmFdKzz89j+xG6VxB+/ujrNsKtRXvcotuAuzvCyxvvMa+Tu6hk13gDh/en7xBtxfK5Y+cd8jYsKoyUhJTiLr0jrMlJKGF2aQtzbrvc6tQl4E1t4EEEBODRui3SakXo9Fjj45AZWpkx7tyJW4NGJQ78WBOT0UXY1b3wMOe6V78OoXPe1O4nMACvDq1JtVhxb1QPr3vb49WhDcLdHeHrTciMyaRMmVumtKoZybjZzdRSAkOQmY59uUxPwZKTCSYj0mTEcvkUugrVsZQy8NN7WB96PNYTgEsnLhJaIQzQZsqFRIaQVsiuyEzNxMffB0WnoFpVQqJCSC1kVwCcOXCayKpR+AX5kZXm3PfmxKXiY9e2eBfTtgTWr0z7d55i0xPvYLTrS4ReR+fPX+bKb3u4sbbk2VqgzfBx6tf9i15mu85umddtBraqzcBWtQFYsP4IEcVcextzfDJuUQXPzC0qFHMR+XSbnAOnca8ahS7ID3N8Mua4ZHJty33S1+wh/PlBJaevvPvav2EHAoTbbKpgHw+61KnAqdi0Ygd+tH7Brq4Hh6Kml9AvHN+P0BXRbmamYz60C32Nenc08BMR4EN8ul3eZuQ45+3BC4zs0lTL29AAKgb7cTUxncZVwgtH5xx/kB/xqQX9RkJaFmF2M4wA/th9gpG922nxh9vslvgUooL9CQ/yp/Ftu6V5Pb5ct7fMaStv1MLtdlE2Z66dzXlwP7xQdpszXyO0UL+XWoJde3i/tvT3DjTsiQjwcWxbMkppW6ILty11GNhKcwewYN1hIgLu3J/e/2VcPn7+2aVegrJ5PLEPY7/mRNj9v6h4lgJjpJSNgenA7V5gJXCfbWZQC2ALWjrTpZTRdv/ql+F+bv8u65Co/f1bKWJgTUp5wXZfJ4G5tpk3xbETuA8wA5vQBqA6AkXNNipMfssgpXwOeBOoDBwTQjjPAXa+z8+klC2llC25lEd4tShCKoWjc9PTsm8Hjm90NH6ObzxE2we1Uf/qzWqTl5VLZlI6vsH+eNkaJTcPd+p1aEL8Zc058m0fQADRvVoTe8F5DXzDCkHcSMsmJj0Hs1Vl/ZlbdKod5RQuy2Dm8I1kutide6lLQza8eB9rX+jF2wNa0apaaJkHfQCyjl7Gu0YknlXCEG46Iga0J3m9Y7q9qhVMUfZrXB3hpi/zoA9Aw4oh3EjNIiYtG7PFyvqT1+lkN0W5IH0mDl9LpEs953Xr605eK9LvT5F6kQHcSMslJiNXy8/z8XSu4WwsZBnNHL6VRueaBeeklEzfeIrqwb480aK60zWlYblwDl3FSigRkaDX43FvV8z7HV/MRVCBca6vUw+EUubOsWGVcG4kZxCTkqnl5dGLdLJNcb1NVKAv+y9oPpRSsnK5lphOpRB/LW3Lt1I9IognukSXTa9aBW4kpHIrKQ2zxcq6A6fp1LSOQ5jIYH/2n9UGA1IysrkWn0qlsCBCA3yJCPbnWrxmJO4/e5UaFUpeltKwVhWuxyVxKyEFs9nCut1H6NzScaeYzJxczGYLAL9s3kvz+jXx9S59QLAojKfPo69SEX0F7Xn59OpM7nZHIy/m/mHc6vMEt/o8Qe6mnaTMWUju1j0oQQEofpqxKjzc8WrTHPPV4n1cACQev0JAtUj8KoehuOmo1a8tVzceKdO9ZsWkENGsFnrbF8+KHRqSdjGmxGusl86jRFVCCdfS59ahK6ZDjoNvIrCgPOpq1UMoApmVgfAPQHjbjGV3d/RNWqDGFL1W/pelvzOsx1MM6/EU29ftos9DvQBo2LwB2Zk5pCQW/4JWmIiK4cxdMpPpL83h5pXifYEBWK+eQxdREREaCTo9bm26YD5aKH0BBctddDXqgiI0p6TunuBpKzfunugbtUS9da1kvcvnUCIrooRpeu7tu2IunJ/2ejXraXpZmRiWLyHz+UfIfHEwuR/NwHLqaKmDPgC/ff0HI3s+y8iez7Jz/W56P6QNKjRoXv+O87YkLOfOoa9YCSXS1nZ16Ypxj2PblTL0MVKGaP+MO7aTteADTLt3YU1MwK1+A/DQBtjdmjd3ci5aGNOZc7hVrojOVve8e3Yhb4djXsb2H0psvyHE9htC7uYdpM5bQN723WT85wti73+M2H5DSH5jFsaDx8o86AOg3riIEloBERwBOj36ZvdiOeW4pNByah+6Gg1BUcDNA13Vuo7On4th3TdrGNvnFcb2eYUDG/bTeZC2RKxOs7rkZuWSlug8GHNq70na2XZL7DKoa75D58iqBX19jUY10Lvpixz0AUg5dgW/6pH42tqWav3bcnODY9viUyGEzp+/wq6XF5N1xdG/Vvv3niL9UixnP1tbahoBGlYK5UZyJjGpWVpfdPwqneo7991ZBhOHrybQpdDy7VTbjKK49Gy2nL7OfdEl97e5xy/iUb0C7pUjEG56gvreQ+bG/Q5h3O3yy6tRDYSbHmtaFpakdExxyXjYXub9OjR1cApdZPrKu6/9G3ZgnslCjm13xzyThb1XE6kV5u907W2sVwq1Y227Yj7s2O85tpv1QNjaTQ+7dtPDE33jllhvlTwL1CmtlcNsZceWt8cu06lBFYcwUYG+7L+kDbKmZOVyLSmDSiElOP+2j79aBW4kphGTlK7Ff/AsnZrWdow/2J/9565p8WfmcC0hhUqhgYQG+BIZ5Fdgt5y7Ro2ou7Oc9p/AcuEcugp2Nmenrpj23T2bE8BysZDGvV0xHyikYWdH6GvXA+XONOxpWCmUGyn2bcsVOhXh/kFrW+LpUqjsOLUtTWs4Xevi380/OeNnM/CbEOIDKWWKECLYNltnD9pMnG+BIcCukiIBNgBThBDf317qZYvHD4gTQrjZ4okBkFJmCyEOAB+hzaixAplCiKtCiIellD8LbdpPEynl8SL0+gsh5qIt9eoMTEKbZfOsEOJrIBht5s14CgabSsMshHCzLTmrAKRKKb8TQmQDI0q4bgfacq5vpJRJtgGbSOC0lFIKIdKEEPfYZg09AWwvKhIhRE0p5X5gvxCiL9oAUBZaHpaKalX5YcoXvPzNGyg6hd0/bSXu4i3uHaJtJb1j2UZObT1C4y7NmLV9IaY8E1+P/w8AAeGBjHhvDIqiIBTB4dV7OblFM7gGTX6Cyg2qIaUk5VYS373+KY3GOI5J6RWFST2bMvqH3agq9G9alVph/vx8ROtMH26uGURbLsTSrno4Xu53r0hLq8r5yV/S7IfXQacQt3wbOedvUXFYdwBivtlE+ANtiHz4XqTFimowceqZD+9IQ69TmHR/S0Z/sxVVlfRvXoNa4YH8fFDb9exh21e/LWdv0a5mpFP68kwW9l2O581+rcumpyhM7NqA5389hCol/RtWomaoHz8f115aH26qdRJbLyXQtmoIXm4Fesdi01l9Npbaob48+p3WsY3pUId7qpfRR4lqJeeTD/Gf+S4oCsaNa7DeuIbHff0AMK5diUeHTnj06Q9WK9JkJHv+9LLFjS0vB93D6MUrtbxsU59aUSH8vFvz9fJwh0Y83asVU77fzEPzliMlvNK3HUG+Xhy9EsuqQ+epHRXCI/O1bZNffKAt9zSoVqLe5Md7M/rD5aiqyoAO0dSqGMZP27TdrR7p3IJn+t7DW1+uZNDUTzW9QV0J8tMGQicN7sXkz3/HbLFSKSyQGU/2LSV9Ol4f9RCjZ3+MVVUZ0KUttSpH8dMGrQl9pGdHrt5K4I1F36EogpqVIpk++vH86yd8uJRDpy+RnpVN92ff4vlH+vBgt3bFyYFVJfXtRUR8Mlfbzv2P9ZgvX8fvIW32StaKIvz62NCFBhM6cwJCUUAR5GzYQd7O/cWGB62+7Xzra/p+NwGhUzj343bSLsTQcKi2Ivf0d1vwCgvg4dUzcff1QqoqTUb1ZnnXiSQeu8zlNQd4eO0sVKuV5FPXOf391hL1UK3kfvERvm+8o23bu3Ut6q1ruPfQyqNp40rc23bCo2c/pNUKJhPZH8wAtFkQ3mMmg6IghIJp71bMR0r/8rln8z7ad2vDij3LMOQZmfXqvPxz73/7NnPGvUNyQgqPjHqQoaMHExwezHebvmDvlv3MGfcOo14dTkCQP+PnvgqA1WLlyfueLSZ9KnnfLsRn/DxtO/cda1FjruPeRXt+pq2rcGt1L+5d++XXt9yPtWWqIiAIn5dsdU+nw7x3M5aTB4vWsdf7cgE+r8+3bYNsy8/uWrk2bfoTt7ad8OjRH1RNL+ejmSXHeQfs3byftl3b8MPubzHkGZj72jv55+Z/M4d5498jJSGFQSMH8vjzjxIcFszSTZ+zb8sB5o1/r5S0Wcla+CGB895FKAp5a9dgvX4Nzwe0smJYtbLYSy3nzmLcsZ3gxZ+D1Yrl0iXyVjv75XHAqpL6zkLCF84DnULOyrWYr1zHd5D27LJ/Kb7u/W1UFcMvi/F+brpWbvZvQo2/gVv73gCY96xDTbiF5exhvCcsBCkx79uAGn9nTkIPbzlE8y4t+HjHp7bt3At89LyxdAofT1hEWmIq385dymuLxvP4uKFcPX2FTT9qM6Xa3deOToO6YjVbMBlNvPfC/OKkkFaVA29+TffvtTbp0o/bybgQQ50ntLblwrdbaPLqQDyCfGkzZ4SWDRYra/pMIbxVHWo+dA9pZ27wwIbZABx9+ydithRlPmrodQqT+rVh9JebUKVK/5a1qRURxM/7tS3SH26jbb295fQN2tWugJe7m8P1Y5dtIyPXiF5RmNyvLf6l+NzBqnJryqfU+GYaQqeQ+tMmDBdvEjJEe2Ypy9YReF87ggZ1BbMF1Wjiul1+xUz9jKofvYZwc8N0I54b4z4qUa7c+9q/YQem5Bh57Zd9AFhUyX0NK9OhZgk+hVSVvKUL8Zk4DxQdpu1rUWOu4d7N1o5t/hO31p3w6G5rN81GchZp7abiH4TPq1ofgU6Hac9mLCdKaTeLytsB7Rn9+Votb1vXpVZkMD/v1XaMfLhdA57u3owpP27nofdWaHnbpzVBPmV7HdHrFCY93oPRH/6g2YAdmlCrYhg/b9Ps8oc7N+fpBzow5atVPDRtCVJKXhnUJd9umTi4J68vWYnZYqViWCAzRty5H6PCjJ/6NgePniA9PZNuA4by/KgnGNS319+OF9VK9icfEjDrXdApGDZoNqdnH1u7vWYlHh074Xl/gc2Z9XbZbc7bGjmLP8R/us2u3WSza3vb7Np1hexao6Nd6ztuCm6NoxH+AQR+9TN533+FcWPxTvm1tqUto7/coJWP223LPs2H4sNtNfe1W05dp13tis5ty3dbycg1aG1L/7b4l2Wnu38R/8u+d8oLcffWwBcRuRDD0QZIrMBRKeUIIUQ14Es0XzlJwJNSyhtCiKVoAzUrbNdmSyl9bX9PAoYBJmCNlPJ1IcRoYAJwHW32jJ+UcoQt/ENoy546314KJYSoDnyC5kfIDfhBSjmj0P1OAyoANYEqwHwp5ee2gaL5aLNvJDBLSvmjLS2rpJSNhBAjsPOZI4RYBbwrpdxmW9bVD81vzzfAO4CKNpNntJSyyLnDQggvIB3oK6XcYPNTFCml7Gc7Hw0sBryBK7a8TBNCbAPG3Y7X5iS6NtrMpc1ofoCC0JbHuQFzpZT2S8MceLbaw+VWUz6cXvq037vJngl39jXm79J+QYNy1ZNpd+eLd1nJ+/NYuep5jyl5SvrdRviWzdnwXdMLKH3q9t0kbthd3k61FNaklOzU824yuG3JM2TuNvfdoXuhv8v6Hn/Rl9NfRBr+mhPmv0rfXeW7Cekv9SzlqmfI+Ce/wzkT1LF8p/gP/618fUkMsJRtBsTd4OGF5duvn391X7nq1V3cvVz1SCy7r6i7gXFD8U7y/wk8Hi3Thrp3j8DynZWjb3BvueplPPFkueoVu1fyP4D3yPvKTwzwGjjpX+30p154q3J7nz2XePB/Mi//0eIrpfwazfeM/bFrFOFU+fagjd1vX7u/3wbeLnT+E7SBnKJ0V1BoeZaU8irQuwy3fUFK+UyhayXaANb4QsevAY1sfy9FW352+9wDdn9PBCbaXbqeMmDzNeRh97vwfR1Dc0Rd+LrOhX4/WDgMkIrmp8iFCxcuXLhw4cKFCxcuXLj4V+Ly8fMPb+fuwoULFy5cuHDhwoULFy5cuHDh4r9H+c4t/h9HSjntv6ErhPgNKOy9b6KUskwzg1y4cOHChQsXLly4cOHChQsXzrh8/LgGfv4nkFIO/G/fgwsXLly4cOHChQsXLly4cOHi38c/6tzZxb+D/lUeKLdC0pXA8pICIEUp3/Jf0VK+vr4yytffK/08yteZdFjdnHLVS7tc1o387g4Gg1vpge4iNV523nL4n+TkvMRy01rpXr7PboOpfJ1J3+/mvKXrP0m2KF/nznvNCeWq95alfB2rx7iV73e4lHLuG/q6l2/f8KOp/BzxNzKVmxSg7ZZSnviq5VvXk/TlWzgfaFTy9vV3G8/GgeWqZ77+17YO/6tYs8vXrg749qty1ct5YVS5aaWeKWcb8OSG/0mHxHeLmqHNy61wXk4+8j+Zly4fPy5cuHDhwoULFy5cuHDhwoULF/9SXAM/Lly4cOHChQsXLly4cOHChQsX/0WEEMFCiI1CiIu2/ztNYRVCVBZCbBVCnBVCnBZCvFyWuF0DPy5cuHDhwoULFy5cuHDhwoWLfyWyHP/7m0wCNkspawObbb8LYwHGSinrA22BF4QQDUqL2OXc+S4ihBgAXJBSnvlv30tJCCGqAe2llN/fyXVPT3+GFl1aYswz8tHYD7ly6rJTmPDKEYxfNAHfQD+unLrEB6+8j8VsyT9fq0lt5v/xLu++MJ89a3bnH1cUhfdWfYBIyGTlk+85xFm1UxM6TXsCoVM4/cM2Dn38p8P5oJpR9Hj3GcIaVWPvOz9z5LM1+efc/b3pPv8pQupUAinZOP5z4o9cKjGdtTo1oc8UTe/Ij9vY+YmjXmjNKAa+8yxRDaux+d2f2P15gd6A+U9Tp2szclIy+U+vouqpM5U7N6H9dE3v3PJtHPuPo15gzSg6v/8MoY2qcWD+z5z4tECv0ahe1B/cGYTg3PdbOflF6RvBVe/UhG5TNb0TP2xjf6H0BdeM4r53nyGiYTV2vvszB+3yE0AogmGrZpIdn8YvIx2fVWF87mlB+BvPInQK6T+vJ/Wznx3O+3ZrS+jLT4BUkRaVxDmfkndYqz5Bw/oT+EgvEIL0n9aR9vUfpabNrXlrfJ5+ERQFw8bVGFY4FnF9o2j83pyNmhAHgGnvTvJ++Fq715cm4t6qHWpGGhljnixVC8C7Y0tCJz8HOh2ZK9aSvuSnIsN5NKpDpeUfEj92DjkbdgEQPus1vDu1wZqazs3+z5ZJz/fe5kRNeQYUhbSfNpC8eIXDeb/ubYh4bShSlWC1Ejfzc3IPafkZMrI/QY/0BAmGC9eIGf8h0mQuky6AUrUB7p0eAaFgOb0byyHHsqZv3gN9vdbaD6EggqPI+2wcGHPLrBHQuRlVZ45EKAqJyzcRt+g3h/NBvVpRafxgpJRIi5XrU78k+8A5hIcbDX6dhXB3Q+gVUlfvJebdH4vUqN2pCfdPGYaiUzj041Z2FCr/APdPHUbdLtGY80z8Mm4xsaevAdDuyd60eqwLCMGhH7aw58t1AHR9ZRCtHutCTmomABvm/8SGDUX7+Bk78yU6dG2LIc/I9Ffncv7kBacwDz/5IIOfeojK1SvRvVFfMlI1Hw339urIc+NHIaWKxWLl/akLOX7gZLH5WatTE3pPfQJFp3Dkh23sKqIt6/+u1pZtefcn9tjV9f7vFLRlH/csui2r26kp/W15uf/HrWz9ZKVTmP5Th1O/SzSmPBM/jvuEmNPXCKsRxdBFL+WHCakczvoPVrDzy7X5xzo9fT993xjKlGbPkJuWVWwaX5kxhnZd22DIMzD71flcOHXRKcygEQN45KlBVKpekT6NBpCRpj2nKjUr88YHE6jTqDafzfuS5Z8WXX8BQrs0pf6s4aBTuLVsC1cXOqY1alAHaozpB4Alx8iZCUvIOnMDgKrP9qHS410AyD57g5MvL0Y1lr3uVezchLbTted4fvk2ThTqI2oObE+T5x8AwJxjYM/kpaSevVHm+KF8+wXQ+oaIN219w0/rSSmibwh7paBvSJht1zcM1/oGIQRpP60jbWnpfcM/XRcKE9GlCdEztPy8+v02zi9y1Kv8YHvqvtAXAGuOgSOTviLjjN0zUwTd1s3CEJ/G7mHvlqoX2aUJzWx6V77fxrlCelUfbE89m54lx8DhSV+RfuYGiocbXX97C527HqHXcXPVAU6/+0upemFdmtJg1jCETuHmsq1cLlQfKgzqQE1bfbDmGDg54QuyztzAp2YUzT4rqPveVcO5MH8F1z5bS0lU7NyE1jOeQCgKF5dv42ShOlBjYHsa2eqAJdfA3slLSbPlZ4f3nqZS92gMyZn80W1yqWlza9kan+deROgUDGtXk/dT0aayvk49Aj78mKw50zHt2g6A54BBeN73AAiBYe0qDL+tKPJae3R1m+HR/2lQFMz7N2Le6pz/upqNcO83CnR6yMkk75M3APB45EV0DVoiszPIe/clp+uKvO/o1ng/OQYUHcbNqzH+XshOahCN78RZWBPjATDv34FhxTfg5o7fjI9A74bQ6TDt247hp6Vl0ryNWwtb3ioKhnWryfvZUdutcTR+U2ejxms2mnHPTvK+//qONErjzTnvs2P3AYKDAvn9u8V/Oz5901Z4jdDy07RlNcY/ljueb9AUn/GzUG35aTqwE+Mv3xQEEAq+cxcjU5PJmf96qXpeHVoSMnE0QqeQ+es6Mr4o2tbxaFiHCss+InH8HHI27kQXEUb4nPHoQoNBVclcsYbMZb//5XS7+EfpD3S2/f01sA2YaB9AShkHxNn+zhJCnAUqAiWOQbgGfu4uA4BVFJHpQgi9lNLidMU/SAma1YDHgTIP/LTo0pKoahV47t5nqNOsLqNnP8/4/mOdwg2fPIKVS/5g5587GD3nBbo/2oN132kduqIoDJ88gqPbjzpd98DIfty8dJMqfgGOaVAEnWcN57chb5Mdl8pjf87gysbDpF6MzQ9jSM9h+9RvqdGrhVO8naY9wfVtJ1jz3AIUNx16L48S0ykUwQMzRvD10Llkxqfy7MqZnNt4hKRLMflh8tJzWD3tG+r3dNY7umIn+7/eyIPvP1eijr1eh1nDWf342+TEpfLg6hlc23CY9ELp2z3lW6oVSl9Q3UrUH9yZ3x6YitVsoc93E7i+5RiZV4t3gioUQfeZw/lpyNtkxacybOUMLm06TEohvc1Tv6V2EfkJ0GJkb1IuxeLh61Vy4hSFiKnPc/PJNzDHJ1Ptlw/J3rwP0+UCR4o5e4+RvXkfAB51q1Hho8lc7f0s7rWrEvhIL6499CrSbKbyFzPJ3nYQ8/XY4tRAUfB57hUy3xqLmpJEwPufYt6/G+vN6w7BLGdOkDXD2fgzbl6LYfWv+L5aesd7Wy/szReIeWoyloRkKv+4kJyt+zBfvuEULuS1UeTuPuxwOPO3DWQsW0n42+PLrFdh+miuDnsTS3wKNX7/gKxN+zFessvPPce5tGk/AB71qlFl4UQu9hiNPiKEkOF9udjzeaTRROWFEwnoey/pv2wum7YQuHcejPG3j5DZaXg+NhnrlRPI1Lj8IJYjG7Ec2QiArnpj9M263dGgD4pCtTlPc+6x6ZjiUmi4Zj7p6w+Sd7FgACVj50nS1h8EwKt+VWp/OpYT976ENJo5+/BU1FwDQq+jwe+zydhylOwjjoMqQhH0nfEkXw2dS2Z8CqNXzuJsofpdp3M0odUjeb/za1RuVot+s0eyeMAUwutUotVjXfik/1tYzRaGfz2J81uOkXJNM+Z2f7GWXZ+vLjGJ7bu2pUr1SjzY4XEaNW/ApLmv8eQDzm3F8YMn2bVxD4t/+cjh+MGdh9mxXhs4rFW/BnM/nc7D9z5RpJZQBH1mjuDbIVpb9vTKmZzfdISki45t2dqp31CviLp+7OedHPh6IwOLacuEIhg440k+GzqHjPgUXl45mzMbD5Ngl5f1OkcTVj2Stzu/SpVmtRg0exQLBrxF0pU4PugzOT+et/Z/zCnbcwUIiAqmzj2NSbuVVFxWAtCuaxsqVa/Iox2foGHz+oyb+wrP9H3BKdyJg6fYvWkvi1Z84HA8Mz2LD95axL29O5SogyJo8PZIDj4yG0NsCu3WzyFx/WFyLtjl5fUk9g+YgSUjh9Cu0TR87xn23fcmHpFBVH2qN7vuGYtqMNP0s5eJGtCemB+3l6xpQyiC9rOGs87WR/RbPYMbhfqIrBtJrH5oFqaMXCp1aUKH+SP5s++0MsV/W6Pc+gUARSFy2vPcGKH1DdV/+ZCsLfswXSq+b6j40WSu9H4Wj9t9wyCtb6jyxUyyt5bcN/zTdcE5fYJmc0aw89G55Mal0m3tTGI3HCHLrrzk3khi+4MzMWfkEtm1KS3eGcWW+6fmn6/9dG+yLsbi5ld6fgpF0GLOCLY9Ope8uFR62PQy7fSybySxxU6v5Tuj2HT/VFSjmW0PzcaSa0TodXT7YwrxW46TUtIHMkXQ8O0n2f/IHAyxKXRcP5uE9YfJdqgPiey11Yewrk1p/N7T7LnvLXIux7Hr9uCLIuh2/GMS1hwsRqggfW1mD2fD4LfJjUvlgTVaHciwrwM3k1hnqwMVuzSh/byRrLbVgUs/7eDsVxu556MyfGBRFHxfeIWMyWNRk5MIXPgppn27sd647hTOe9SzmA8X3LuuanU873uA9JeeA7MF/znzMe3fixobQ7EIBY+Bz5L32VRkRgpeL7+L5cwBZIKdw2lPHzwefI68z6ch05MRvgV2svnQZsy7V+Mx+JXS05Z/3y+TPXMcamoSfnMXYz60G/WWY/rMZ0+S83YhO8lsImv6a2DIA50Ov5kLMR89gPViGb91387b1215+9GnmPY7563l1Akyp5U+QPdXGdCnB48P6sfrM0sfUC0VoeA18mVyZo9HTbmdn3tQYwql6ezJYgd1PPoMQo25gfDyLl1PUQh9Ywxxz0zCEp9MxR8Wkrt1L+YrznZn8KtPkbfHzu60Wkl59zNMZy8hvL2o+ON/yNt7xPnafzFSlp+jeiHEM8Azdoc+k1J+VsbLI2wDO0gp44QQJe42YZvQ0QzYX1rE/6eXegkhfIQQq4UQx4UQp4QQjwohfrM730MI8avt72whxDwhxGEhxCYhRGshxDYhxBUhRD9bmBFCiN+FEH8KIa4KIcYIIV4TQhwVQuwTQgTbwtUUQqyzxbVTCFFPCNEe6Ae8I4Q4ZguzTQgxRwixHXjDFqebLQ5/IcS1278LpStcCHHY9ndTIYQUQlSx/b4shPAWQlQVQmwWQpyw/f/2+aVCiPeFEFuBeUKITrb7OWZLhx/wNnCP7dirZcnr1j3bsPWXLQBcOHoeH38fgsKdd81o0r4Ju9doLyZbVmymba92+efuf/IB9q7dQ0ZKusM1IZEhtOzWio0/bHCKLyK6JhnXEsi8kYRqtnLhz33UKDTgkpeSScKJK6gWx70u3H29qNi6Lqd/2AaAarZiyiz5RbRSdE1SryeQdjMJq9nKyT/3Ua+QXk5KJrFF6AFcP3COvIzsEjXsCY+uSea1BLJs6bv0xz6qFdIzpGSSdNxZL6hWBRKOXsZiMCGtKnH7zlG9d8sS9aKia5J+LYGMm5re2T/3UauHo15uSibxJ66gmp3T5xsZTM2u0Zyw5WlJeDapg+l6LOab8WC2kLl6B77d2zmEkbmG/L+FlyfYdhn0qFmZvOPnkQYjWFVyD5zCr0f7EvX0tetjjYvRZvNYLBh3bMGtTcdS7/M2ltMnkFnFzyxwSl/juphvxGK5paUve+02fLu2cwoXMKQ/ORt3YS1U7g2HT2HNKLueV9M6GK/HYb6ZgDRbyFi1A78ebR3CqHb5qXh5Yr9po9DpUDzdQacgvDywJJR9lx0lohoyIxGZmQyqFcuFg+hqNCk2vK5uKyznD5U5fgDfZrUwXIvDeENLX+ofuwjq1dohjH36dN4e2M+mvX1OuOkQbnqK2rGyUnQtW/1OxGq2cuLPvU4DuPV7tuDorzsBuHn0Ep5+3viFBRJeqyI3j17CbDChWlWu7T9Lg14l17fCdOrVkdUrtJlSp46cwS/Al5DwEKdwF05dJO5WvNPxvNy8/L+9vL0oaVPOitE1Sb1W0Jad+nMfdXsU05YVUdevHzhHXnrxbVmV6FqkXI8n1ZaXx/7cS8OejvnRsGcLDtny8oZdXtpTu0MjUq4nkBaTnH+s/1vDWDX3+1InS3fs1Z51K7TBxtNHztryM9gp3MXTl4i/5Twgnp6Szrnj57EUkX57ApvXIvdqPHnXE5FmK/G/7yGiUFubfugClgxtl8H0wxfxjCq4D6HTofN0R+gUdN4eGOLTSklZAWGF+ogrf+yjSqEym3j4IqYMrW9LPHIJnyjnPCiJ8uwXALyK6Bv8uhXfNyhentyu7O41K2M4Ztc3HDyFX8+S+4Z/ui4UJrhZTbKvJZBzIwlptnLzj31UKDSglHLoImbbM0s5fBEvu2fmFRVMVLdorn6/tcx6WTY91Wzlxh/7qHgHepZcIwCKmw7FTVdk22lP4foQ+/tep/qQduhifn1IO3zJQe82ofc0IvdaAnm3kp3OOYSzpS/blr6rf+yjSqH0JR0qqANJRy7hbaeXsP88pjI+P33d+lhjY7QZJxYLxm1bcG/nbEd49n8Q067tqOkFdVlXpSqWs2fAaATVivnEcTw63FuinlKlNmpKPDI1AawWLMd2om/o2O/pm9+L5eReZLqWTzK7YJcu9coZZG7Zy6auVj3U+BjURC195t1bcG9ZysC3PQZbH6TTa//uYGdofZ1Cebt9C+5ty26j3S1aRjcmwN/vrsSlq1UPNSFWy0+rBdOeLbi1Knt+iuBQ9M3aYtpS8kej23jY250WCzlrt+PTxbn983+8PzmbdmJNTc8/Zk1OxXRWG9CVuXmYr95AHxFa5nt1cWdIKT+TUra0++cw6GMbizhVxL/+d6IjhPAFfgFekVJmlhb+//TAD9AbiJVSNpVSNgLWAfWFEGG2808Ct/f58wG2SSlbAFnALKAHMBCYYRdnI7TZMK2B2UCulLIZsBcYZgvzGfCiLa5xwMdSyj3ASmC8lDJaSnl7HVSglLKTlHI62lSt+23HHwN+kVI6zfeWUiYCnkIIf+Ae4BDaQE1VIFFKmQssAr6RUjYBlgEL7KKoA3SXUo613d8LUspoW1x5aGsFd9ru0/ETaDGERIaQHFfQOSfHpxAS6fjC4hfkT05mDqpVG1FNiUsm2BYmOCKEtr3a5c/+seepac/w9ZwvteUphfCNDCIrtuAFNTsuFd+Ism3T6l8ljLzULHq89wyD18yi27ynSp3x4xcRTEZsSv7vzLhU/Muo91fwjgoiO64gfTnxqfhElU0v9fwtotrUxSPQF72nO1W6NsW3gvNLpD2+kUFk2ellxaXiF1n29HWbOpRtc5YX+awK4xYRgiW+oMxY4pNxi3C+P98e7ai+7lMqfzaduMkfAmC8eB3vlo1QAv0Qnh74dmqJW1TJHZQSEoqaXLA9uJqShC7E+Rp93YYELPgCv2nz0VWpVmo6ikMXEYI5vmBGgiU+GV24o54uPATf7u3J+LFsnXpJuEWGYI6z04srOj/9eraj9sZPqPrFVGImajNGLAkpJC/5jTq7vqLevm9Rs3LJ3uU88644hG8QMqvAwJXZ6QjfYsqN3g1d1YZYLx0pc/wA7pEhmOzqnikuBbciXhaCerehyY4F1P3mDa68tqjghKLQaON7ND/xFRk7jpNz1HnJj39EkFP9DogILiJMQR3JjP9/7J11mFTV/8df585sd7G7dHcs3bFIiSIYqIiBYBEmIaAigoAgKioKNgKKYoPS3d3dud0dM/f8/rizuzM7swHC+NXffT3PPjD3nnve93Pqfu65J5LwDQsg9vRVqrepj4e/Ny7urtSNjMAvvCj92z3Ri+dXvsN9s57B3dfLoY0hYcHERhWV0bioeCqE3Zjj1a1PZ37asogPFs5k6ivvlBjONyyQtOhibdkN1PWy8AsNIMUqLVOiE/Er1lb6hQbahEmNScIvzDa9I/p14NCyHYW/G/ZoSWpsEtHlmKoUEhZMnHV6RscTcoPpWR7cwgLJtrIjJyoJt7CSO1cqPxJJ/IZDAOTGJHNp3p90PfAJkUfmY0rLInHzkXJre4YHkGnVZmeV8Yyo+3A3rm0sf/zg3OcCgDEsCJOVP5Efk4DRUVvWsz01V31GlS/eInr8HEB7Nni0bozB8mzw6toKlzLy/HbXheJ4hAWSfb1ILzs6CY9S9GoM6kbMhsOFv5tNeYwjby+BcqZncb2sMvRqFtMTiqDX2un0PzqPmM3HSDpoP43fGvewgGL1IRH3UvSqPtKNOEt9sKbivR2I+m2H/QXF8AwLINOqTc6MTsKzFL06D3fj+g3WgQKUoGDUeCs/IiEeJTjYLoxbh87k/GU7vc186SIuTZohfHzBzQ3X1u1QQkr9UI/wCyrs0AGQKYkIP9u6oARXBA9vPIa/jcdL72FsGXlTtgEogSGoiUV+hJoUjwgKsQtnrNsQn3e/xHviTJTK1a0iUPB590v8v/od05F9mM+dLL92sIO0deSjNWiE/ydf4Tvl7/lozkAJDEZNtPU7lQB7mwx1G+Iz60u8xr9jk54eT4wi57vPoJwjUYwVgjFZ+52x8RiKtZ2GCkF43dGRtKUl+53GiqG41a9NzpFT5dL9r6AinfZXFlLKHlLKxg7+/gBihRDhAJZ/4xzFYRk88gvwnZTy1/Kkwb+94+co0MMykqezlDIVWAQ8KoTwB9oDBT0NeWgdQwXXbbZ0uhxFm/pUwEYpZbqUMh5IBZZbXVPd0rPWAfhJCHEI+AwIL+UerSdffonWGQW2nVKO2AF0BLoA0y3/dga2Ws63p2iq1iLAutv8JyllwWer7cD7QogX0Dqhbmq6mUDYHSv+VUjYBykM89Tkp/l2xgJU1bZxa3VHa1ISUjh/tARHw0Gk5f3AoBgNVGhcnSOL1rOk7+vkZ+fSakS/Uq8pzYbbgaN0Le+aYCnnojj06Z/ctWQ8fRePI/HEFYejkMrSK695tbpHkJWYRuyxS+W7wHFi2h3KWLuTi32e5dqIqdqaDkDe+askfvETVb+ZRpWvppJz6iKyDNvKU1bM58+QPOwhUl8YRs7yX/B5bVr5bCmnXvHMC5nwHAnvfQXqbRpe6iA909fs5GzP4Vx59m1CX3kUAMXXC58ebTnTdRin2j+O4uGGX/9ut1wbwFCjKWrU+Rub5gU4qgqO6kLyqt0c6fICZ4bOpPK4QUUnVJVjPUdzsOXTeEfUxqNeVXsJh2WkeDvmuBzFn49iy/zlDF08gSe+fZWYk5dRzVqZ3L14Le91eYm5fSeQHpdC39cHOzaxHPplsWnVVgZ2eYyxQ1/juXHDbujaW9qWlaO+lWWvwcVAox4tObxCG53s4u5Kj1EDWP3+T3bXOb6Fv5+e5RNydNCxTmDHhlR+JJIzU7XHs9HPiwp9WrK59fNsbDYcg6cb4fffyFfu8rfZ4R0aUO/hruyd9sMNxO/k54JFsTyC6Wt3cqHPs1wt/mz4/CeqLphG1a+nknvqItJcxrPBAbfzue64LXOsF9KhIdUf6cZRS56F92hObkIqKUcu3Ra9Ch0aUvORbhy2KiNSlazpOZHlLZ4nsHkt/OpVLkPPYYVwSFDHhlR5JJJTU23XPREuBkJ7tSR6eZkzE0rwJRwHDevQgDqDurJ/+o3VgRvR8nrueTK/+szuuW6+epmspd/jN+M9/Ka9i+niOTDfhMtdPO8MBgyVa5H91VSyP5+Ma48HEcEVbzzecuqZLp4hdcTDpI99ipyVv+I97u2ik6pK+tinSH12IIbaDVCq1LgBobLLjen8GZKeeIiUkcPIXv4LvpP+ho/mDMrhB5ouniVt5MOkj3uK3FW/4TVmKgDGFu2QaSmYL9qv81eyniM5W72gV4eT9MGXJfqdwsOd0A8mkTBzHjLzBv00HWexDHjC8v8nALuF7ITmAH0FnJRSvl/eiP/Va/xIKc8IIVoCfYEZQog1aJ0ry4EctA6QglY3XxY96VUg1xKHKoSwTodcq/+rVr9VtPRSgBTLCJrykGl1v9uFENWFEF0Bg5TyWCnXbUXr6KmGluGvorUmf5YQ3rrmW2u+I4T4Cy2NdgkhepTnpoUQz4wfP/61J554ImTmX7O5cuwywVYjLoLDgkgqNlUkLSkNL18vFIOCalYJCg8m2RKmdpPajJk7DgDfQF9aRrbCbDJTt3k92vRsS8vIVri6ueLt40nvOcNZ/dI8QBvh41Ox6Muqd3ggmXHlGyafEZ1ERnQSsYe0TqVzK/bQanjpHT9pMUn4WY2a8Q0PJD0upVx6N0NmdBLeVqMavMICybyBaQCnf9jM6R+0tSLavPqgzeghR6THJOFjpecTHkhGbPn0KrWqS+0eLajZrRkGNxfcfDy4a85w/rLkVXHyYxIwWn2JNYYFkx9X8v1l7zuGS5VwDAG+mJPTSP15Dak/a9P/gl95wmb0kCO0L3NFX9eUoBDUJNtrZHbRQy5//24wGBC+fsi0VG4Uc0wCLmFFX8qMYcGY4xJtwrg1qkvYe9pcdUOAH55d2oDZTOb6nTeslx+TiEu4lV546emZtfc4rlXDMAT44tWuCfnXYjFbFh9OW70Tz5YNSP1jU7m0ZUYywqfoC6vw9kdmpjgMa6jbGtOZ0tdscERedCKuVnXPNTyI/JiS7UvffQK3amEYA30wJRVNmTOnZZG28zh+kc3JPm07aiTVQf1OK9aeaGGK6ohvWCDpljqyf+km9i/dBEDPsQ8VjiLITCgaXbv3hw08/lXRuk0Dh9zLgMHaoqMnDp0itGJRGa1QMYT4WNsyU14O7j5MpWqV8Av0K1z82Zq0mCR8w4u1ZbEpN6XliNSYJPyt0tI/PMguLVNiEm3C+IUFkmbV3tTvFsG1YxfJSNDuP6haKIGVQ3hl5czC8C//OZ2PBrwOUdpUrfue6M89g7WBsycPnaaCdXqGh5Bwk+lZGrnRSXhY2eFeMZBcB+20d8OqNH7/WfYNeof8ZG36RVCXxmRfiSc/USujsX/tIaB1XaJ/2VYu7azoJJupW55hgWQ50A5oUIVOs55i9WPvknsD05LAuc8F0EZHGq38CZewYEylPRv2HsOlquNnQ0g5ng23uy7Y3W90Eh6VivQ8wgPJdqDn16AKLd97im2DZ5FXUF7a1CW8V0vC7ojA4OaC0ceD1nOHs3dUyelZXM+zFL3W7z3FZis9a/LTsojfcZKwyKaknna8OD1Ajl19CHI4fdGnYVWavP8Me63qQwEV7ogg9ehF8uLLfvZmRSfhZdUme4UHkuWgfAY0qEKHd59i3WPvkuvAvvKgJsTbjNJRgkNQE23Ll7FuPXwmTNLO+/nh2qYdGWYzeTu3kbt6BbmrtYXBPZ98GjW+9HXKZGoiwr+oLgj/IGSabV2QKYmYM9MgLxfycjFfOI5SsTrmhFLWPCzJvqR4FKsRPkpgCLKYn4SVn2Q6uBueehnh44dML8ormZWB6fghXCLakHv1Yvm0y5G2MsvKR9u7G0bevI/mDNTEeJSgYn5ncrFnkHV6HtqNMLyE8PHFWK8xLi074BLRFlxdER6eeI6aSNbc6SXqmWITMFr7naEhmIu1nW4N61LBsp6QIcAPz05tkGYzWRt2gNFA6AeTyPhrA1nrt/P/jdva4X9reQdYKoQYBlwBBgIIISoCX0op+6INDnkMOGoZiAIwUUq5wkF8hfyrR/xYEiBLSrkYmA20kFJGAVHA68CCW61pmT93UQhRkAlCCNHMcjodKGvi6EJgCaWP9gHYAjwKnJXaalRJaJ03BTV1B9p0MYDBgEMvUghRS0p5VEo5E23KWP3y3KeU8vMZM2ZUq1+/vuerd41h1+qdRN7fHYC6zeuRmZ5FsoMOmKM7j9Kxr/Y1s/sDd7B7jbY44zOdnuKZjsN4puMwdqzYzmevz2P3ml0smvktw9oO4ZmOw5g9ahbXdpwo7PQBiD18Af8aYfhWCUFxMVC3XzsurC3fFJKs+FTSo5Pwr6kNyKrSsRFJZ0tZZA+4fvgCgdXD8K8cgsHFQJN+7Ti1dn+p1/wd4g5fwK9GGD4W+2r3b8flctoH4B7kC4B3xSCq39mKc3+UPmw6+vAFAmqE4WfRa9CvHefKqbdl1lLmtXuBzzq9zPLnP+HKjhOlOvc5R8/gWr0iLpVDwcWI711dChfrLMClatFgObeGtRCuRsyWHXcMgdoChsbwEHx6dSDtz9IXQzWdPYWhYmWU0DAwGnHr0p38PbYPNuFf5Dwa69QHRblphyLn2GlcqlXCWEmzz/vObmRutLXvcq8nuNxT+8tYvZX4qR/fVKcPQPaRM7hZ0lO4GPG7uwvp62y/lrpWK0pP90a1EC4umJPTyI+KxyOiHsJdm+ro1aGZzaLQZaHGXkb4V0D4BoFiwFi3NeYLDobSu7pjqFwH8/nD9ufKIOPQOdxrhONWpQLCxUhg/04kr7HtQHKrHlb4f88mNVFcjJiS0jEG+mLw1RZGFO6u+HZuSs45+xeX64fPE1Q9jABL/W7ar71d/T61dj/N7+sMQJXmtclNzyY9PgUAL0t986sYRKM+rTm8TMtL63VrGvZuTeyZIu2fFvzG4J7DGNxzGJtWbeWuB3oD0LhFQzLSMkmMK39HReXqlQr/X69JXVxcjA47fQCiDl8gqEYY/lU0Wxv3a8fpW9iWXT18nuDqYQRa0jKiX3uOF4v/xNoDtLKkZdXmtclJzypMS4CIezpwaHlRmxVz+iqTWz3H9E4vML3TC6TGJPHB3RNJt3o5/PXbPxjS6xmG9HqGLau30eeBngA0atHAkp7lX7uqvKQePI9nzTA8qoYgXAyEDehA3GpbW90rBdH861c4MvITsi4ULXqecz0Rvxa1UTxcAQjq3JiMMp5D1sQfvoBvjTC8LW12zf7tuFKszfaqGESPL15i84vzSbtovzZUWTjzuQCQ7eDZkF7Ks8G9YS2ES8nPhtQyng23uy4UJ/nQBbxrhOFZRSsvVfq3I7pYefGoFET7r15i7/PzyLhQlGfHpv/IipbPs7LNS+x+bi7x206U2ukDkHToAj41wvCy5F/V/u24XkzPs1IQHb96iV3F9NyCfHCxtJ0GdxdCuzQi7Vw0pZF68DxeVvWh4oD2xDqoDy2/fpnDIz8h84J9mSzvNC+AhEO2daBG/3ZcXWNfByK/eImtL84nzYFeeTGdPoWhkpUf0a07ebts/YjkJx4u/MvdupmMjz8gb6fmggs/fwCUkAq4duxM7qZ1peqpV8+iBIcjAiuAwYgxojPm43ts7+n4bpQaDUFRwMUVpVpdZFzJHXOlYT53GiW8MkoFzT6Xjt3J22ebD9Z+kqF2fYQikOmpCF8/hKe3dsLVFWPTlqjXy78wsOlMMR+tq33aigArH61ufRA376M5A/P5UyhhlVBCwsBgxLVDd/KLp6df0UczQ636oAhkeho5S74kbcSDpD0/iKwPp2A6drDUTh+A3EK/U0tDrzu7krnJ1qe8eufjXO2j/WWu3UrCtI+1Th8g5K1XyL9whdSFZe/cp/PPIaVMlFLeIaWsY/k3yXI8ytLpg5Rym5RSSCmbWpZuiSir0wf+5SN+gCZoiymrQD4w3HL8OyDkNm6rPhiYJ4R4HXABfgAOW/79wjKt6oESrv0ObX2hJSWcB0BKeckyjH2L5dA2oLKUsqC35QXgayHEWCCeoilkxXlJCBEJmNF2G1uJNnrJJIQ4DCwozzo/+zfso1VkK+Zv/YLc7Fw+HjOn8NwbCybzyasfkRSbxLczvmHM3FcZPPZRLhy/wNof7RdsvhGkWWXTG98yYNE4hEHhxI+bSTpznSaPap1QRxdvwDPEj4f/nIqrtweoKhHD+rD4jlfJy8hm06Rv6fPRcAwuRlKvxLF2TOkLqqtmlb8mLeDxha9q274u3Uz82eu0GnwHAPu+W493iB/PLnsbN28PpFRpN/RO5vYcR25GNg98NJIa7RrgGeDD6J0fs/GDnzmwtGSnVJpVtr3xLX2/G4dQFE7/uJnkM9dpYLHv5OINeIT4cd8KzT6pqjR5qg9LI18lPyObXp+/iHuAN6rJxPbXvi1c3LA0vXWTvmXgQi09jy7dTOLZ60QM1vQOfbcBrxA/Hl9epNdqaB++6qGl5w1hVomdMo8qX70NBoXUn9eQd+4K/g/3BSDlhxX49O6I34A7kCYTMiePqJeK1iypNPc1DP6+SJOJ2Lc+RU0r4wueaiZz/hx835oNikLuuhWYr1zCrY+2pWzuqmW4deyKW9/+YDYjc3PJmPVW4eXeYybh0iQC4euH/zc/kf39N+SuLaUNNavET/uEil9MRygKab+tIe/cZXwf0kYjpJWxrk/ou+PxaNMUg78f1TcsJnHuItJ/XV3yBWaVqMnzqf7tFISikPzTWnLPXiHgkTsBSP5+Jb59OuB/b3ekyYzMyePqC9rIiezDZ0hbtZ3ay+cgTSo5J86T/MOqkrWKI1XyNv2I24AXtO3cT+xAJkVjbKK91JuOajNQDbWaY758Akx55Y/byr5Lr31Jve8nIQwK8T+sJ/vMVSo81guAuEVrCLyrPcEPdEWazKjZeZwdrm0b7RIaQK0PtS1iURSSlm8nZZ39i51qVlk+aQFDFo5HGBQOLN1E3NnrtLHU7z3fref0xkPUjYzglc0fkJ+dy69jPyu8/pF5L+EZ4I3ZZGbZG9+Qk6YNruw9YRDhDauBhORr8fwx8SuHJm5fv4uOd7Tntx1LyMnOZcrLMwrPzVk0i7fHzCQhNpGHht3PY8MHEVQhkCXrvmH7hl1MGzOL7nd15a4HemMymcjJzmXi8MklJqdqVlkxaQGPLXwVYVA4WEJb9sxyS1umam3ZJz20tuz+j0ZSvb3Wlr2yS2vLDlrtRKWaVX6btICnF05AGBT2Lt1E7NlrtB+sDSrd+d06Tm48SP3ICMZvnkN+di4/WqWli7srdTs14ZeJX5ZcJspg5/rdtO/elqXbF5OTncP0V2YVnpu9cAbvjJ1NQmwiDwy9l8EjHiYwJJCF675k54bdvDP2PQJDAvhq5Xy8vD1RVcmDT9/P4G5PkpVh245Ks8qJCd/Q6oeJCIPCtSUbyTh9jSqPa7ZeXbiOWqPvxzXAm4Yzh2rXmMzs7P0aqQfOEfvnbjqsnYE0q6QdvcTVReXcTc+ivfONb+ljeUac+XEzKWeuU9/yjDi1eAPNX74XN39vOkwfAoBqMrPsrkk3pOG05wKAWSXmrXlU+fptbTv3gmfDIMuzYckKfPsUPRvUnDyuWz0bKs99DUOALzLfREw5ng23uy44Ss9DExfQeYmmd+mHzaSduU7NxzW9CwvX0/Dle3EN8KH5jCct92hmQ583bjwtLXoHJi6gq0XvgkWvlkXv/ML1NHr5XtwCfGhp0ZNmM2v7vIF7BX/afvgcwqAgFMGVZbuJXlf6+m/SrHJswgLa/DDBUh82kXH6GlUt9eHKwnXUGX0frgHeNCqsDyrbe2tbkCsergR3acLRMeWr+9Kssuv1b+n5vVYHzlnqQL3HtPJ5etEGmr18L24B3rS3qgN/9tXqQJdPRhLWvgHugd4M3PcRh2b/wtkfSsg/1UzGJ3Pwm675ETlrVmC+fAn3uzQ/ovi6PsXxnTRVW+PHbCJz7hxkRll+i0rub5/j8fRkEAr5e9ejxl7F2L4PAKadq5Bx1zCfPojn6I+QUsW0ey1qjNbh4jZ4NIZajRFevni+/hV5a5Zg2lNKZ5NqJuurD/F+7V1QFPI2rkS9dgnXnpp9eWuX4dquK2697tGmUOblkfGBtgyq4h+E56gJoCgIoZC3cyP5B27gQ5ZqJmPeHPzeng0GS9peuYR7X0varliGW6euuN9l8dHyckl/560yIr1xxr75DnsPHiElJY07BjzKiGGPcX+/3jcXmaqS/fVHeE2cpaXnJkt69tBmF+StW45Lu6649ewPqmZT5odTb/7mzSoJ0+cSNn86wqCQ/ttq8s9fxmeg5nem/1Sy3+nWvBE+9/Qk98wFKv2kdSYnffQ12VtvfIT2v5XyrL3zX0f8i4Y9lRshxFzgoJTSsff9DyKEeADoL6V0vAfv/yD9q97ttELSHX9nSQGQqDi3/FcylX9u/K0g1eBUOe5xu/Vf20sjpF5m2YFuIcnn3Z2ql5Njt+nfbaXmi2Ws7XCLOTrT4Xp1t4Vlrs7NuzV5N/dF9ma5y6WKU/UyhPO2RQXYmW+/K9ft5A1T6Yuy3mquuzj3O1yik58N/Vyd+2z4Me/2Ld5cnMY30c/9d7jxFY3+Ht63a426Eog3Ordw3t24/KNfbwXuTfydqpd/2bkjZswZzvWr/RaVNYHi1pI58sbW1vs7JJ1wsg94dI1zX1KcTOXAxk4rnNeSjv1PpuW/fcSPHZZt0DOB0f/0vRRHCPExcCfalC0dHR0dHR0dHR0dHR0dHZ3byH9xsMuN8p/r+LFssf4/iZTy+eLHhBCfoC3QZM2HUkrndmHr6Ojo6Ojo6Ojo6Ojo6Oj85/jPdfz825BSjvyn70FHR0dHR0dHR0dHR0dH57+Iqo/4+Xfv6qWjo6Ojo6Ojo6Ojo6Ojo6OjUzL6iB+dMjEI5/UPHhA3sUPI36C2dO6Cr0eN+U7VS5XO1Rtay7mLLXt0reNUPaGcdaretcOuTtVLWORc+5q+18lpWosmnHKaFkCY0depetlOXmw5Cee2LZEuYU7V63hvilP1knflOlXv0rXAsgPdQr4Rfk7Ve6NfctmBbhHv/OnvNC2ABnnO/WZ71N25iy0nCecuX/1AU+fWBUO3rk7VU04ec6pe9mbn+hHOXGwZwOsT5+0bdKLJWKdpAdR0qprzkfquXvqIHx0dHR0dHR0dHR0dHR0dHZ3/KnrHj46Ojo6Ojo6Ojo6Ojo6Ojs5/FH2ql46Ojo6Ojo6Ojo6Ojo6Ozn8SfTt3veNH5yYZNvlpWkS2Ijc7l7lj5nDh2AW7MBWqhPLKx2Pw9vfh4rHzfPjyB5jyTbTu2ZZBowcjVRWz2czXb32Jh5cHQ998CqPBwOYf1/PXvN/s4hv85lCaRbYgLzuPL8Z8zOXjFwFo0jWCwZOGohgUm2tHzH2FsJoVAfD09SIrLZNJfcfg5e/N8/PGUqNpLY7+vJVVk7610anVtSm933wMYVA4+MMmdsxbbnM+qFY498x+lrBG1dk4eym7Pl8BgG94IP0/GI53iB9SlRz4fgN7vlltZ0eDrs14YNIQFIPCjh83sHbeH3ZhHnhzCI0im5OXncuiMfO4ZrH1rW0fk5uRg6qqqCYzs+6ZCEDzvu3o+9IDhNauxOz+r+EV6MMDk4aAQbDph3X86SA9H5s8jGaRLcjNzuXzMXO5bMnDJl2b89ibWnpaX3v/6EG06NkaqUrSElP5fPTHpMQl02FAF/o+0x8Af3cThhq1SHn+acwXztlpurRsg9dzzyMUhZxVf5H90/e255tE4PPmNNSYaAByd2wl+/tv7eIpCaV6I1y7DwKhYDq6FdOelfZhqtTDNfIhUAzI7Axyf3wXAGOLOzA27QKA6chWTAfWlann0rwNnk8/D4pC7tq/yPnF1h5j4wi8J05DjdXsydu1lZwfNXu8nn8Vl1btUVOTSXvhyXLZ5921BZUmPQ0GhaQf1xI/72eb87492xL6ymCQEmkyEzXlS7L2ncCtZiWqzh1XGM61ShixH3xHwtfLStVzb9+agDEjQVHI/H0Fad/+4DCca8N6hH7zMQkT3yZ7/ZaiE4pC2KJPMcclEv/ya2Xat/1sNLNWHUBVJfe2qMnQzg1tzi/YfpIVRy4DYFYlFxPS2Dh2AMlZuYz7aUdhuOvJGQyPbMKj7euVqnc76qIjnnnrGVpa2ssPR8/h/LHzdmFCq4Qydu44fPx9OH/sHO+/9D6mfFPh+TpN6/DuH7OZNXIWO1ZsB8DL14vnZ71AtbpVcUVh95L1tHukB8KgsPfHjWyaZ5+/97z5BPUiI8jPzmPpmHlEHb8EQKdhd9Lmoe5IKYk5fZWfxs7HlJtPj5fup83D3clMSgNg1awfOb3pEACNu0bwyKQnEQaFrT+uZ8W83+30HnlzKE0im5OXncdXY+ZyxZJ+T84aQbPuLUlLTGVS71cKw1dpWJ3Hpz2Di5sLqkll0RtfcPGwfVtSnDpdm9J30uMoBoX9P25kS7F2O7hWRe5791kqNqrO2tlL2f7FX2XGaY2hQUvcH3gWFIX8HavJW/uT7fk6TfB4ZhJqYgwApkM7yFu1BACXbv1x6dAbhCB/+yryN9mXs+I4u+4FREZQa6qWlzHfrefq3N9tzgf1bkW1Vx8GVSLNZs6/sYC0Pdq6WXU/GE5gz5bkJ6Syv9voEjXqdm1G/0mPIwwKe0opn/Wtyuf145cIqRnO4LkvFIYJrFKBNR/8zLavVxLesBr3TRtWWF5+e+Nrrh62r1/OyL86XZtyl6UM7nNQBgHuevPxwvr3y5j5hfWv/ZN9aP1wJAjBvh82sOPrVQD0eGUgDXq2REqVjIQ0fhkzH66l2cUb3q0prac+hlAUzi3ZxPG5ttrV7+1Ao5F3a7Zl5bB7/AJSTlzBs2IgHT58Do8Kmt9ydvFGTn9l77cUp0bXpvR48zEUg8LhHzaxq5itgbXCuWv2M4Q2qs6W2T+xx+InFSAUwZA/p5Iek8zPQ98rU+9myw5A52F30vqh7mBp25Za2raSMNRrjts9w7Sysmcd+Rt/tQ9TsxGu/YeBYoDMdLLnv47wC8Lt4RdRfAKQUsW0ey352/4s07btJ68w6/dt2nOvXQOG3tHC5nx6di6vfbeemOQMTKrK45ERDGhTH4A3f9jIlhOXCPT24JdxD5epBf+An9SiDV4WPyln7V/k/GzvJ/m8buUn7dxK9g8WP+mFV3FtrflJqaPK5ycZm7XGY8goUAzkbfiL3D+W2J5v2AyvsW+jxml1PW/PVnJ/WVgUQCh4z5iPTEogc1bJz/Xy8Pr099myfQ+BAf78vnj+34qrAP/ICGpMGQoGhbjv13N9rq1/H9C7NVXHDQJVRZrNXJz0Del7TuFaMYg6H72ASwV/UCWxi9cS/eWNPQd1/v2Uq+NHCPECMBw4IKUcfLNiQoghwBopZVQZ4RYAf0opfy4tnCVsN2CMlPJuIcQ9QEMp5Ts3e483gxCiIvCRlPIBZ+reLJY0y5NS7igjqENaRLYkvEZFRnZ9lrrN6/HM28MZP8B+AbLHxj/B8q+WsX35Vp6dNpw7HurJ6sUrObr9MHvX7gagWv3qjP5kHEYXA28NnkRubBqTl83k4Nq9RJ27VhhX024tCKsRzrhuo6jVvA5PTHuGKQMmIBSFx6c8zaxHp5AUk2hz7aej3i+8/uHXniA7PQuA/Nx8fnlvCZXrVaVR3Rq2aaMI+kwdwneDZ5AWk8RTy6ZyZt0BEs5eLwyTnZLJqjcXUr93S5trVbPK2re/I+bYJVy93Hnqz7e5sO2YzbVCETw4ZShzH51GSkwiY5fN4OjafcScKwrTsFsEITXCeKvbi1RvXoeHpw1j9oDXC89/OGgKmcnpNtpRp6/yxXPvMWj60zYal6NjmbJsFgfW7SXqbFF6NotsQWiNcMZ0HUmt5nV58u1nmDxgPEJReGLq08wc/BZJMYk21/712e/88p72AO01pC8DXnyQBa99xo7ft7Djd+2F46O+fvhOmuaw0wdFwXvkS6ROHI2aEI//h5+Rt3s75iuXbYKZjh0hbfIE++vLQghcewwm96f3kenJuD/6Oubzh5CJ0UVh3Dy0MD/PQaYngaePdmlwRYxNu5CzeBqYTbg98BLmC0eQKXEl6ykKns++RPqbo1ET4/Gd/Rl5e7ajXi1mz4kjZLxtb0/u+pXk/PUrXi+V07FQFCpNeY6Lj75BfkwitZe9T9ra3eSeu1oYJGP7YdIsdcu9fnWqfvIqZ+4YTu6F65zt+2JhPA12LyB19c4y9QJefYG4keMwx8YTtvBTsrbsxHTxsl04/+efJmfXPrsofAbdR/7FKyheXmWaZ1ZVZqzYx/zHIgn19WDwF2vpWq8StSoULQo7pGMDhnRsAMDm09dZvPM0fp5u+Hm6sXR4n8J4er23jO4NKpeqd7vqYnFaRraiYvWKPNvlGeo1r8fwaSMY09/+5XjIhCH88eUfbF2+hRHTR9LzoZ6sXKw55Iqi8MSEIRzcfNDmmqcnP8OBTft557kZNPUIYPS69/jikWmkxiQyatk0TqzdT5yVPfW6RRBcI4x3u71M1ea1uXfaMD4Z8Aa+oQF0HNKH93qMwZSbz+C5L9KsX3v2/6zV621frWBLsY4SoSg8OuUp3nt0CkkxSUxa9g6H1u6zabebdGtOaI1wJnR7nprN6/D4tGd4e4BWF7b/vJH1367kqfeft4l34PjHWPbhTxzddJAm3ZozcMJjzHr4zVLTWCiCflOe5JtHZ5AWk8hzy97m5NoDxJ+zbrcz+GvytzTo1arUuEoQwP3BEWTNfQ2ZkoDn2DmYju5CjblqE8x8/jjZ8yfbHFPCq+HSoTdZ774M5nw8RkzFdHwvMr4UN8jJdQ9FofaMYRx9cCq50Uk0XzWDxDX7yDpTlJfJW4+RuHoMAF4NqtLg81fY1/klAGJ/3ETU16uo9/GoEiWEIrh3ypN88eh0UmMSed5B+axvKZ+zrMrn3AFvEH8hmjl9JxTG8/ruTzm2ei8Ad41/hHUf/sLpTYep3y2CvhMe4bOHpxYXv+35V7wMDndQButa7Hu/2ytUaV6be6YNZf6ASVSoW5nWD0cyr/8bmPNNPPHteE5vOETipRi2fv4n697XOqnaD+lN9xfv48zYBXbabaY/wfqH3yErOok7V0zh2ur9pJ4tuseMq/Gsvf9t8lKzqBjZlHazhrLq7slIk8qBKd+TdPQSRi93+q6aSsyWozbXOsrLXlOf4IfB75Aek8SQZVM4u24/iVbX5KRksvbNRdQt5icV0GpoHxLOReHm7VGijrXezZadgrZtdgltmwMx3O59huzPJyNTE/F4YRam43uQcUV1AXdP3O57luwvpyBTEhBelmeUqpL35wLU6xfAzR3PF9/DdOaQ7bXFMKsqM37dyvzn+hHq58XgD36ha6Pq1AorWnD6x+3HqBkawEdP9SUpI5sBM5ZwV4s6uBgN3NO6Hg93aszr368vMx01+5zvJ3k99xJpb2h+kt/7n5G/eztmB35S+pSS/STvl8vpJwkFj6EvkjltLGpiPD4z5pO/bwfq9WJ6J4+W2Knj1vd+1OtXEB6e5dMshQF9e/LI/fcwcersvx0XAIpCzelPc/yhKeRFJ9J05UyS1uwl26qtTt16lMOW9tGzQTXqfj6aQ51fQJrMXHprAZlHL6J4udNs9bukbDlsc+1/HVVf3Lnca/yMAPpad/oIIW5mtNAQoOJNXFcupJTLnN3pY9GN+l/s9BFClLQVQzegw83G26ZnWzb9shGAMwdP4+XrRUCFALtwTTo0Zafly/TGXzbQpldbAHKycgrDuHm64eruQvSlaGKvxmLON7F7+TZa9GptE1eLXq3Z/utmAM4fPIunjxd+If7UjKhN7OUY4ku5FqDNXR3YtWwbAHnZuZzdd4p8B198KkbUIvlSLClX41HzzRxfvot6PW0dl6zENKKPXMCcb7vzREZcCjHHLmkamTkknIvCJ9Q2XapH1CbhciyJV+Mw55s5sHwHTYvdb9Nerdnzq+aUXDp4Fg8fL3xD/O3u1ZrY89eJu6A9uMPrVLHSMLFr+TZa9mxjE75FzzZs+2UTAOcPnsHT1wu/CgHUiqhN7KXowvS0vjYno2jHNTdPd3AwZNKt6x3kbnbsgBjrNsAcdV0bzWMykbt5A67tbt2uTkpYDWRyHDI1AVQzplN7MNSKsL2HBm0xnzmgOTMAWdpLuxIYjhp1AUx5IFXMV89gqNOC0jDWaYAac137SmUykbd1A65tym+P6cQRZEbpnQbWeEbUIe9yNHlXY5H5JlKWb8HXUqcKUK3qluLp5jCPvDs2I+9yNPnX40vVc21UH9PV65iva/ZlrdmIZ1f7ZsPnoQFkbdiKOSnF5rihQjAeHduS8fsKu2sccex6ElUCfagc6I2L0UDvxlXZdPp6ieFXHr1MnybV7I7vvhBL5UBvKvqX/sJ7u+picdr1asuGXzYAcLqU9rJph6ZsX6G1Uet/Xk+73u0Lz9395N3sWLmD1MSUwmMe3h40btOINT+sAaBioxokXIwmyWLP4eU7aVisk6NRr5bs/3UrAFcOnsPDxxMfiz2KwYCLuyuKQcHFw5W02NJ3RaoZUZu4yzHEW9qZ3cu3E1Es/Zr3as2OXzcBcOHgWTx9PPGz6J3Zc5LM1AwHMUvcLS+Dnr6epMQmlXofAJUjapN4OZZki+1Hl++kQS/bdjszMY3rRy6gmm58xyClel3UhChkYgyYTZgObMHYtH3ZFwJKWBXMl05Dfi6oKuZzx3BpVvrj19l1z6d5bbIvxpBzJQ6ZbyL+9+0E9bYtO7Zti237n7rrJPkpjvKyiCoRtUm4HGNTPhsVK58Ne7XkQAnls4DaHRuTeDmWlOsJgLZLS0F5cff1dFhunZF/lSNqk2RVBo84KIMNerXkoMW+qwfP4W6xr0LtSlw9eI78nDxUs8ql3SdpaEn/XKvnrounm6MmnaDmtUi/FEvGFc1vufTHLioX63BJ2HeWvFTt41fCgXN4hmsdC9lxKSQdvQSAKTOH1HNReISXvstVuMVPSrX4SSeW76KOAz8p5sgF1Hz7+uYTFkit7hEc+WFTqToF/N2yY922uZbRtilV66AmRCOTYrWycmgbxka2/pOxeRdMR3chUyxlMDNV+zc9Wev0AcjNQY27huIXVKptx67EUSXYj8pBvtpzr3ltNln8yAIEgszcfKSUZOfm4+fphkHRXt9a1qqIr6dbqRo29v0DfpI5ushPyt2yAZe2N+AnHT+CTC+/n2SoXR81Ngo1LhrMJvJ2bMCldcdyXy8CgzE2b0fehlszEqZVRBP8fH1uSVwA3s1rk30phtwrmh+Y8Mc2AnvbPndL8gPz41LIPKqNuFUzc8g+ew3XMOfuaKfzz1Nmx48QYj7aDm/LhBCpQojPhRBrgIVCiOpCiK1CiAOWvw5W140TQhwVQhwWQrwjhHgAaAV8J4Q4JITwEEJMEkLsFUIcs8QrynPTQog+QohTQohtwH1Wx4cIIeZa/r9ACDFPCLFRCHFBCNFVCPG1EOKkZURRwTW9hBA7Lff/kxDC23L8khDiLcvxo0KI+pbjXS33f0gIcVAI4WNJh2OW8+5CiG8s1xwUQkRa3duvQohVQoizQohZpdj3oBDifcv/XxRCXLD8v5bFZoQQd1jiP2qxy83qvidZwg0UQrwghDghhDgihPhBCFEdeA542WJD5/KkuTWBYUEkRBW9NCbGJBIYavtw8wnwITMtE9WsbTGcGJ1IUFhRmLa92/HR+k957ZtJrPl+NYnRCYXnkqKTCCgWX0BoIIlRVmFiEgkICyIgNJCkqNKvrdemIWkJKcReiqYsfMMCSYtOLPydFp2ET5j9S1pZ+FUOJqxRNa4fsh1y7hcaSHJUUfzJ0Yn4Fesc8g8NsAmTEpOIv6VxlhJGLXqNcctn0HHQHQ61vYN8ba5Pik4koFjjHhBWLN1iEgkMDSQgLIik6JKvfWDsI8zZ+TkdBnThl/ftpx64dY0kd5Pjjh8lOBg1vujLkJoQjxIUbBfO2KAR/p98he+UWRiqVncYlyOETwAyvcihkxnJCB/btBUBodrXuofG4v7oGxgatrfcSxRK5Trg7gVGVww1m9hda6cXFIw5wcqexBLsqdcI3zlf4T1pFoYq5benOC6hQeRb5Vl+dCIuofZOpW/vdtRdP4/qX7/JtXEf2p3379eZlGUlfO20wlAhGHNsUT03xcVjqGBrnyEkGI9uncj4xX5KQ8DokSR/9LnDzidHxKVlE+Zb9IUt1NeDuLRsh2Gz80zsOBdDDwejelYfu8KdjauWqeeMuggQFBZEglX7lhhj2xYC+Ab4kmHTXiYUhgkMDaJ97/asWmw7HD+sahipSWm89N5LzFnxIb1eGUhaXErh+VQH9viGBpJqZU9qTJLW5sUms+WLP5mwYy6v7ZlHTnoWZ7ceLQzX/onevLRyJg/MehYPXy9L2ti2IcnRiQSEFmtnQoNIsm6LYpIICCv9RWjJW9/w4ITHmL1jPg9OfJxfZn1XanjNrgAbu9Kik/ANvXUOreIXhJpcZKuanIBw8EJnqFEfz/Fz8Rg+BSVMK4Nq1GWMtRuDlw+4uGFs1AoRYN9O2MTj5LrnFh5IrlX65UYn4Rpub1/QnW1otXUOjRdP4PTL88oVdwF+xfIoNToR32Ll0y80kBSb+paEX7FnV0S/DhxaVjRYeflbC+k7YTATd8zlromDWTnL/rnkjPxzVAb9ipVBLUxRR2ZaTBK+YQHEnr5K9Tb18fD3xsXdlbqREfhZpX/PMQ8ydsfHRPTvWDj6xxrPsACyrOLNik7CM7zk51etQd2I2njE7rhX5WACG1cj8YD9VDlrfMICSI8u0ku/QT/pjjcfZeP0JUi1fOXz75SdtNhkNn/xJxN3zOV1B21bcYRvYGGHDoBMTbQrK0pIRfD0xuO5qXi8OBtjy2728QSEoFSsgfnKmVJti0vNJMzqI0WovxdxqZk2YR7u1JiLscn0nLyQB979kbH3dkJRyvW6ZH9fTvaTlKBg1GJ+kqEEP8nvo6/wmXxjfp+dXmAwamIxv8xBfTXUbYjPrC/xGv8OSuUiPY8nRpHz3Wcg1Zu+h9uJW1ggedeLymdedBKuDp6pgXe2IWLrRzRYNJFzL39iH0/lELya1CDjwNnber//a0gpnfb3v0qZo3aklM8JIfoAkcAooB/QSUqZLYTwBHpKKXOEEHWAJUArIcSdwACgrZQySwgRKKVMEkKMQpuWtQ9ACDFXSjnF8v9FwN2AvRdjhRDCHfgC6A6cA34sJXiAJdw9lng7Ak8Be4UQEcA14HWgh5QyUwjxKvAKMMVyfYKUsoUQYgQwxnLtGGCklHK7pZMoB1tGWtKtiaWzaI0Qoq7lXATQHMgFTgshPpZSXsWeLUDB3KnOQKIQohLQCdhqSYMFwB1SyjNCiIVoU/HmWK7JkVJ2sqRXFFBDSpkrhPCXUqZYOvMypJQ3NfbQUfdc8ULuqA/POszu1bvYvXoXDds04qkpz3Du8NkSw5YiWqYOQLt7OhWO9rkZbrQCu3i6MXD+S6yZsoi8DNsXV4d9m8WjL8WmD+6fRGpcMt5Bvoxa/Dox56M4v+dkWZfb+f8lpZsjV8L62p/f/Z6f3/2efiPuo+cTd/LrB0XVr1ZEHWROLubLFx3EAjiM3RbT+TMkPfEQ5GTj0rotvpOmkfzUTc8utTdcMaCEViP3p/fA6Ir7IxNQoy8gk6LJ37MK94GvIPNyUeOuglrWg7/svDSdP0PK0xZ7WrbFe+I0UoffpD3lyVggbfUu0lbvwqtNI0JfeZSLj75RFIWLEd8ebYmZtdDuunJRTC9g9AhSPv7CLq3cO7XDnJRM/qmzuLVsVr6oHQzBLelTwJYzUURUDcav2JfOfJOZzaev80KPsjWdURctkZQYRylBCsM8PflpFsxYgFosjQ1GA7Ua1+KzSfM5c+gMs76dSniDasXiKNsepMTD14uGPVsxs/MLZKdl8einL9J8QCcO/r6NXYvXsf6jX0FCr9EDuev1R/l53GflantLs6skIh/tzQ9TF7B/1W5a39WeJ2eOYPajU0q9prR8uiU4LIi28ZuvniPjjSGQl4OhYSs8nnmDzClPo8ZeJW/tT3iOmobMzcF8/SKYb3zU0e2se2U2/BYSV+4hceUe/No1oPqrD3H0wakOLixJ4+/VNwCDi4GGPVradO60e7Qny6cu4tiqPTS9qx0DZz7DF49OL1v7FudfeeqD4zAQfz6KLfOXM3TxBHIzc4g5eRnVSmPt7KWsnb2ULiPuof0TvYiaWWzNvhLidURohwbUHtSV1QNs887o6UaXL19k36TF5Gc47nC3ErQ/VM7qVqt7BFmJacQeu0TVdg3Kd9HfKDsevl406tmKdxy0beXXsvcjDJVqkv3Zm+DiiueodzBfPoNMsEx1c3XH/fFXyV32NeSWnpaO8qn4Lew4fZV6lYL5YsQ9XE1I47nPltOiZjje7q6lxl1ubqefVI6yaT5/huRhRX6Sz2vTSHn2FvpJxQqL6eJZ0kY+DLk5GCPa4jVmKukvPYaxRTtkWgrmi2cwNixn2+lsyukHJq3cQ9LKPfi2a0jVcYM48dBbhecUT3fqfTWWi5O+wVxmXdf5r3Ez07WWSSkLSooLMNfSiWIGCjo4egDfSCmzAKSUJY3VjhRCjAM8gUDgOGV0/AD1gYtSyrMAQojFwDMlhF0upZRCiKNArJTyqOWa40B1oDLQENhueSC7AtYLXxSs6LafopFF24H3hRDfAb9KKa8Ve5h3Aj622H1KCHGZonRZL6VMtdzDCaAaYNfxI6WMEUJ4CyF8gCrA90AXtE6gX4F6ljQo+JTwLVqH0xzLb+vOsCNoo6x+B34vIZ3sEEI8gyVdIwKbMnzEcHo+3AuAc0fOElwxBNBecoLCgkiOs83itKQ0vHy9UAwKqlklKDyIJAdD9k/sOY5voB+hVcMKjwWGB5JSLL7kmESCKgZT0D0UGBZEcmwSBlcjgRWDS7xWMSi07N2WN/vZr0HkiLSYJHytvrT5hgeSEZtSrmsBFKOBgfNf4ujv2zm1yn7dhZSYRAIqFsUfEB5EalxysTBJNmH8w4JItQxNLgibkZjGkdV7qN6slt3LZnpCGnU7NC78HRgeZDddIik60TbdwoJIjkvG6Gok0Mp+R9cC7PhjK2O+ec2m46ddv04lTvMCywifkAqFv5XgENTEBJswMiur8P/5e3fDSAPC1w+ZllpivIXXptt+uRLeAciMFLswanYG5OdBfh7qtTMoIZUxJ8diPrYN8zHNGXTpdC8yo/SpLjIxHkOwlT1BIahJtvaQbWXP/t3wrAHh44dML9ue4uTHJOBilWcu4UHkx5XUtELmnuO4VQvHEOCLOVlbDNSnW0uyj53HlJBSpp45LgFDaEjhb2OFEMzxiTZhXBvUJXi6tuaN4u+HR8c2JJnMuDauj0eXDnh0bItwdUV4exI0ZQKJk2aUqBfq60lMWlF6xaZlE+LjeP2HVccu08fBqJ5t56KpHx5AkLd7mfbdzrrY5bFetHo4EoCzR84SHF6Ub0Fh9m1hWlIa3jbtZXBhmDpNajPWsjC3b6AvLSNboZrMnDp4ioToBM4c0h4DJ9ftp9crDxbG6RceRFoxe9JiEvGzsqfgi3jtTo1JvhpHZpI2pP7Yqr1Ua1mXg79vIyOhqKzu+WEDQ77S7iU5xrYNCQgPIqWYnhbGqj0JCyxz6laH+7vy/VtfA7D3r50MeWd4qeE1u5Js7PINDyQ9rvT6eyOoKQm4WH01VgKCkanF7MgpcqDNJ/aBYSTCyxeZmUb+zjXk79Sm5Ln2e8JmRIEjnF33cqOScLNKP7fwQPJiSs6n1F0n8agehjHQB1NS+aZhpBbLI0flMzUmEX+b+hZoMy2nXrcIrh+7aFMmW97fhWVvaQvBHvlrFw+887SdtjPyr7h9vuGBDuxLwq9i0Sgg37BA0i327V+6if1LNwHQc+xDNiOPCzjyxw4e/3qsXcdPVnQSnlbxeoYHkh1jX/79G1Sh3eyn2PDou+QlF03NE0YDXb58kUu/7uDqSnu/pTjpMUn4WE0H8wkvsqMsKreqS+0eLajVrRkGNxfcfDy4e85w/nyp5BFkf6fs1O7UmKQS2jZHyNREhH9RWRF+Qci0JLsw5sw0bfpffi7miydQKlbHnBAFigH3x8dhOrgF87FdZaZHqL8XMSlFI3xiUzIJ8bWdpvzHnlMMvaM5QgiqhvhRKdCHi7HJNKkWWmb8dvY52U9SE+JRyvCTZHE/yVB+v89OLzEeJaiYXnKxumSlZzq0G2F4CeHji7FeY1xadsAloi24uiI8PPEcNZGsucU6kv9BcqMTca1UVD5dwwPJK+WZmrbrBO7VQwvbamE0UO+rscT/upWkFbudccv/U6j/wyNxnEV51/ixxnoM4stALNAMbRpXQfezoIz+f8uolU+BB6SUTdBG8ZTtrWuUN+dyLf+qVv8v+G203OdaKWWE5a+hlHKYg+vNlvBY1hB6CvAAdhVMAbOitGEN1vdQGGcJ7ASeBE4DW9E6fdqjdTyVNXTCOo/uAj4BWgL7RTnXZpJSfi6lbCWlbFXDuxqrFq5gdN+XGN33Jfas2U23+7WXmrrN65GVnkWyAyf72M6jtO+rza2NvL974YLOYdXCC8PUbFwTKSUhlStQoUooBhcjbft14uBaW+fj4Nq9dLyvKwC1mtchOz2L1PgULh4+R2j1cIIrV3B4baNOTYm+cJ3kUpxYa6IOXyCwRhj+VUJQXAw06teOM2v3l+tagH6znibh3HV2f2m/SwLA5cPnCakeRlDlEAwuBlr068CRYrYeXbuPNvdpuyZUt9iaFp+Cq4cbbl5aFXH1cKN+56ZEnbEfMBZ99pqVhpF2/TpxYO1emzAH1u2l0/3dAKjVvC5Z6VmkxiVz4fA5wmqEE1Klgt21odWL8q1Fz9ZEnbdatFoI2tzVodSOH9OZUxgqVkYJDQOjEbeu3cnbtd0mjAgociaNdeuDUMr98FdjLiECQhF+waAYMNZvg/n8YZsw5nOHUCrVAaGA0RUlvCZqkmUKYMEChj6BGOq0wHRyT6l6prOnUMIro1TQ7HHt3J38PcXs8S+yx1CnPijKTXX6AGQdPotr9Yq4VA5FuBjx79eFtLW29+hqVbc8GtVCuBgLO30A/O/pQsryzeXSyztxCpcqlTBU1Ozz7BVJ9hbb9eCj+j9K1D2DibpnMFnrt5A08yOyN28n9ZOviLrrYaLuGUzCa2+Tu/dQqS+eAI0qBnIlMZ3ryRnkm8ysPnaFrvUq2YVLz8lj/6V4IuvbT/NadfSKw3V/HHE76+KWRWt48c4XePHOF9i1eifd7+8OQL1S2ssjO4/Ssa+29sEdD9zB7jXaS8NTnZ7iqY7DeKrjMHas2M681+exa80uUuJTSIhOoFJNLY18KwRgcDEQYLGnWb/2nCzWdp1Ye4CW92mze6s2r01Oehbp8SmkRCVQtXkdXCxfkGt3bFy4cKr1GiuNercm1mKnfdvbkUPF2plDa/fR4b5uANRsXkdrZ+JTSs2XlLhk6rVrBECDDk3KNUX3+uHzBFUPK7S9Sb/2nLqBdrss1MtnUEIqIoJCwWDE2KILpiO2L3XWL1NKtbogBDJTq3vCW1v8VQSEYGzWgfx9pddBZ9e99EPn8KgZjnvVCggXIyEDOpK4xrYuuFcv+jjj3aQGwsVY7k4fgGuHzxNslUfN+rXnhIPy2cKqfGZbymcBEfd04NBy23RIi0umpmXkSO0OjUi4FGOn7Yz8K14Gmzoog6fW7qe5xb4qzWuTm55daJ9XkC8AfhWDaNSnNYeXad8gg6zSvX6PFsSft190OfHQBXxqhOFl8Vuq92/HtTUHbMJ4Vgqi65cvsf2F+aRfsE2j9u89RerZKE5+7thvKU60xU/ys+g17NeOc2sPlH0hsHnWUj5t9wLzOr3Msuc/4fKOE6V2+sDfKzultW2OUK+eRQkORwRU0MpKRCfMJ2zbNdPxPSg1GoKigIsrStW6yFhtgVy3B0eixl0jf0vpO2YW0KhKBa7Ep3A9MU177h08R9fG1W3ChAd4s/uMds+J6VlcikulsqW83Cj/hJ9k4/d1Kd1PMhb4STfR6QNgPn8KJawSSkgYGIy4duhO/j7bNkP4FdV1Q636oAhkeho5S74kbcSDpD0/iKwPp2A6dvB/qtMHIOPQOTxqhONWRWurg/t3Iml1yW21V7G2utb7I8g+e43oz8oaY6HzX+XvbufuB1yTUqpCiCeAgsWE1wCThBDfW0/1AtKBglWuCjp5EixTph4AytzFCzgF1BBC1JJSngcG/Y373wV8IoSoLaU8Z5m6VtlqJI0dFt2jwFEhRHu0EUiHrIJsAQYDGyxTvKqidd6UvgKaPVvQppxNAQ6iTbXLllKmCiFOAdUL7ht4DLDzRIQQClBFSrnRsubPI4A3Wj7c3FMD2L9hHy0iW/Lpls8s27l/VHjutQWT+HTcXJLjklg0YwGvzB3LI2Me5eLxC6z7cS0A7e9sT9f7u2PON5GXm8d7I2fh6ePJpIWTMRoMbFm6getnrxI5WBthtPG7NRzeeICmkS14d/Mn5Gbn8uVYbc6qalZZNOlLxi58A8WgFF5bQNt+jqd5zd42Dw9vD1xdXKjXqxXfPfYOCWevI80qqyYt4JGFryIMCoeXbib+7HVaDNbW8Djw3Xq8Qvx4avnbuHl7IFWVtkPvZF6PcYTWr0LT+zsTe/IKT6/QHhYb3/2RcxuLHqqqWWXppK8ZuXAiwqCwa+kmYs5eo9PgHgBs+24dxzcepFFkc97c/CH52XksHqs5RT7Bfjz9ubarisGgsO+P7ZzcrMXdtHdrBk5+Eu9AX579ciwpMUmMXDgRaVDYsnQ9189epbslPTd8t4bDG/YTEdmC2Vs+JS87ly/GzC28v4WTvmTswkmW9FxfmJ4PjX+U8JqVUFWVxOvxfDPxs0K76rVtSFJ0Iv4xpbykqWYy5s3B7+3ZYFDIWbMC85VLuPe9B4CcFctw69QV97v6g9mMzMsl/Z23So6vOFIlb/33uN3/EigKpqPbkYlRGJtpHYamw5uRSdGYLx3DfchkkBLTka2Fw7Pd7hmO8PAGs5m89d9BblbJWhZ7sj6fg8/k2dp27utXYL56Cbc+mj25q5bh2qErbndq9pCXS+bsInu8Rk/CpXEEwtcP/69+ImvJN+StK2UxVrNK1KT51Fz4FhgUkpeuI/fsFQIHa7tZJX23Cr87OxBwX3ekyYSak8flUUVLiQl3N7w7RXBtov1875L0kt79mAofzwSDQuayleRfuIz3/dqWwBm/lL1N7Y1gNCiM79uS4Ys2o0qV/s1rUruCHz/t1XaIG9i6NgAbTl6jfa0wPFxtH1/ZeSZ2XYjh9X7l27XpdtXF4uzbsI9Wka34fOsX2nbuY+YUnntzwWQ+fvUjkmKTWDDjG8bNfZVHxz7KheMXWPPjmjJt+GzSfEZ/NAaji5Gsqwn8Ov5Lhi2cgGJQ2Lt0E7Fnr9HWYs/u79ZxauNB6kVGMG7zHPKyc/lprFaHrx46z9GVu3nhr+moJpWo45fYvUTrxO074RHCG1YDCcnX4vl14peF6bd40pe8svB1FIPCtqUbiDp7jW6WdmbTd2s4Ymm339k8l7zsXL4e+2nhvT/70UvUa9cI7wAfZu/8jD8++JGtSzfw7fj5DHrzSQxGA/m5+Xw74TPKQjWr/DlpAU8sHK9t5750E3Fnr9Pa0m7v/W493iF+DF9mabelpMPQPnzUc5zN4rklC6jkLJ2H58i3QSjk71qDGnMFl059AcjftgJj8464dL5Lq+v5eWR/M7PwcvenXkN4+YLZRO7STyG79IWQnV33MKucm/gVjZe8pm3nvmQjWaevEf54TwCiF64l+O62hA7sisw3o+bkcfLZDwovrz/vRfw6NMIl0Ie2B+Zz+d2lxCzZYCOhmlX+mLSAp4qVz3aW8rnLUj7rR0bwarHyCeDi7kqdTk0Ky18Bv4z/gnvefBzFaMCUm88vE2zPa+K3P/9Us8rySQsYsnA8wqBwwFIG21jK4J7v1nN64yHqRkbwyuYPyM/O5Vcr+x6Z9xKeAd6YTWaWvfENOWna97terz5MSM1wpCpJuZ7AH699RfHucGlW2fvat9zx/TiEQeH8D5tJPXOdOo9pHc5nF22g6cv34hrgTZsZQ7RrTGZW3jmJkDZ1qTmwM8knrtB37TQADs1YStQGx+1Zgd6aSd/y0EJN78jSzSScvU7EYE3v0Hcb8Arx44nlUwv9pFZD+/Blj1ftpr+Xh79Tdgrathctbdt1q7bNsZhK7u9f4PH0m5bt3Nejxl7F2K43AKZdq5Fx1zCfPojnK3OQUmLavRY19gpK9Qa4tIzEHH0Jj5e1XWXzVi7GfKrkTjGjQWH8fZ0Z/vmfqKqkf5v61A4L5KcdxwEY2KERT/dsxaQlG3hg1o9IJC/d3Y4Ay4Lm4xetZd+5KFIyc+j11kKG927NvaVNofsH/KTM+XPwfcviJ63T/D5rP8mtY1fc+lr8vtxcMmYV+UneYybh0sTiJ33zE9nff0Pu2lL8JFUl++uP8Jo4CxSFvE0rUa9dwrVHPy0/1i3HpV1X3Hr2B1XzMzM/vIEpqzfI2DffYe/BI6SkpHHHgEcZMewx7u/X++YjNKtcmPglDZe8gTAoxP6wgewzVwl9XHvuxi5cQ9Bd7QgZ2A2Zr/mBZ57TyqJPm/pUGNiNzBOXabZWW+nj8ozvSdlQvk7b/wL/y2vvOAtRnkQQQlxCG9EzCqu1YSzr+vwCZAEbgeellAWLI48HHgfygBVSyolCiPuB6UA22uiV14CHgUtoU54uSyknizK2c7esOTQHSAC2AY0t27kPAVpJKUdZx2FZ0PhPKWVjy/XW57oDM4GCxSJel1IuK7BZSpkghGgFzJZSdhNCfIzWCWMGTqDtVBZeEL9lJNN8tBE2JuAVS8dL4b1Z7uFPS5ybSrCxFtoaRvUs6/isAU5JKV+wnL8DmI3WebcXGG5Zx8f6vl0s+eKHNkposZTyHUuH1M9oI5+el1JudXQPBdxX7R6n1RQf4eIsKQBqy/IOMrs1xAqTU/VSpf3OZbeTDxraD0+/nXjeUcepejlbnbsQ3rXDN90/e1P4B5fhxN1iQkbful3dymLshFNO0wK4rGaWHegW0lC5dTuHlId4nNu2VCwcUOwcXr07xal6ybtyyw50C7l0zbm7ufzp/ne/M94Yb9x9cyMGboZ3/vR3mhZAg7ybGax/81xzrltGkriJNbD+BpMGFl+q8/Zi6NbVqXry5DGn6mVvdq6fZPB0bn3w+uQrp2ntbVK+ZSpuFR2if7m5VcP/JQR413ba+2xyxrn/ybQs77Sf6pb/Ti52/CzQ1OrQBKtz7wDvFAv/C1pHUQGvW/6K6w0p435WoY20KX58AdqixzZxSCkvAY2tfluf2wDY7f9tZTOWxai7Wf7/vINbKoxfSpmD1hlU4r1Zft/tIB7r8OexmtIlpexV7Px6tIWiS7vvfLQ1h4qHOYNtvuno6Ojo6Ojo6Ojo6Ojo/OdQy71SzH8X53aT6ujo6Ojo6Ojo6Ojo6Ojo6Og4DeeOvb1BhBC/ATWKHX5VSrn6n7if24EQYjdF08wKeKxgBzIdHR0dHR0dHR0dHR0dHZ2bQ1/j53+840dKee8/fQ+3Gyll23/6HnR0dHR0dHR0dHR0dHR0dP6blGtxZ53/3wyv/qDTCsn7r9tv0Xw7iZpz3Kl6FUc3c6qezHLuYr2nZ9pvL387qT3AuYtAGmraby9+OxEVnat3+KVDTtX70EV1mtZnPZy72PJz67ydqjevW5pT9aTJub7DQ1uKD4y9vYzLc+7C6unCUHagW0jjQOcuxD8p09OpepWF8zZueGtWKbso3QaSPtjkVL3A0d2dqkdqilPlYj4qeQez20FwZ+eulq2EOXchd6VR47ID3UKiJ20oO9AtJCbBeRsptD76rtO0AFyCa/5PLkh8q/D2rOE0xyUj6+L/ZFrqa/zo6Ojo6Ojo6Ojo6Ojo6Ojo/EfRO350dHR0dHR0dHR0dHR0dHR0/qP8T6/xo6Ojo6Ojo6Ojo6Ojo6Ojo3OzSH07d73jR+fGadi1GQ9OehJhUNj+43rWzPvDLsyDbz5Jo8jm5GXnsnDMp1w9frHwnFAEE5a/Q0pMEp8Om1mm3vaLccxafwJVSu5tWoWhbWvbnF+w5zwrTkQBYJYqFxMz2DiyJ+4uBoYu2Um+WcWkSnrUDWdEp7pl6nl0bEXw+OcQBgNpv6wk5aulDsO5Na5Lpe/mEDtmOplrt2EICyF0+lgMwQGgStJ+XkHq4t/Ltu9CLLPWHUVV4d5mVRna3vYeF+w+y4rj1zT7VMnFxHQ2vnAnfh6uhcceWbCZCj7ufDywXdl6lxJ4d8tpVCkZ0KgSQ1vZbpz37f5LrDgdXaSXnMmGp7vh5+7C5HXH2XIxnkAPV35+tEOZWj5dm1N58tMIg0LiD2uJ/fQXm/N+PdsQPmYwUlXBrHLtrS/J3HsSAIOvF1VmjcKjblWQkstjPybrwOkyNa0xNGiB+33PgKKQv3MNeet+tg9Tuwlu9z0NBgMyM43sjybckEYBSrVGuHZ9EBQF07FtmPbZbj5obNkLY/022g+hIALDyf5sNOSWfx2m7WejmPXXPq0utKzN0C6NbM4v2HaCFYcvAWBWVS7Gp7Fx/P34ebqxaMdJftt3HiGgTqg/b93bHjeX0tcV8evWnOpThyIUhbgl64ia+5vN+YDerak8dhBIiTSZufzm16TvOYVwc6HRr28jXF0QRoWkv3ZybfaPJeo8MfkpIiJbkpedy7wxH3Hp2AW7MCFVKvDCx2Pw8vfm0rELfPLyHMz5JirWqsSzs5+nRqNa/Dh7MX99rrVHLm4uTFo6DRdXFwxGA26nNpP7x0KbOA2NW+E+aARCKORtXUneStt7NNRriueoKagJMQDkH9hG3vLFKKGV8Xju9cJwSkgYub9/S946LX3cBo3g/TfakZedy/wxH5doz/Mfj8bb35uLxy7w6csfYs43AfD45GGF6WF9/Z3D+hH5cA+khKunLvPZ2I/Jz81n4OhBeN/bClQVNT2F7C9mIVMSMTZpjfvgkVr537yC3L9+sLWvfjO8XpyCGm+xb/82cv9YpJ309MJz6BiUStUBSfaXszGfP1FiHgIYm7bG47FRoCjkbVpB7vIltucbNMPrlamFenl7t5L7m6bnO+d7ZE4WqCrSbCbjjeEONYa/9RxturcmJzuX9155j3PHztuFCa0SysRPxuPj78O5Y+eY9eJsTJa0bdquCc9Nfhaj0UhqchpjB46jcs1KTPy0qN6HVQ3n+qyfuPr5isJjgZHNqPv2EIRBIeq7DVz+2Pa5F3p/J6qNugcAc2YOp8d9RcaJywBUefpOKj56BwBR322wibckKkQ2pcnUxxEGhcvfbeTs3OU25yvf15E6o/oBYMrM4fCrX5N24goAvfZ+SH5GNphVVLPK5t6v28VfHM9OrQie8BwYDKT9vJKUL0t+7lVeMoeY0dPJXLNNu9e3X8Gza1vMSSlc7f+s3TXBE4fzfqe2t7w+ePp68vTMkVSpWxUJfD52LmcPnOb+lx6ix6BemHLz8QsLJCslg20LVrFp3jI73XvefIL6kRHkZ+exdMw8rh+/REjNcAbPfaEwTGCVCqz54Ge2fb2S8IbVuG/aMFzcXFBNKr+98bXDdHJ2W+3WrjV+L41CGBQyl60gY9ESh+FcGtQj5Iu5JL0xlZyNWwAQ3l4ETBiDsVYNkJKUae+Sd6z0ur79zHXNPlVyb6vaDO1qu67Lgq3HWXHooq19Ewdq9m0/wW/7ziGAOmEBvHVfhzLt234hjlnrj1n8wKoMbVfHVm/3OVacvG7Rs/hJo3prfuD3Oyx+oEqPehUZ0aleqVoeHVoR+OoIhKKQ/ttKUr92/OxybVSXios+Im7cNLLWbcUQGkLItHEYggJBqqT/vIK0739zeK01hgYtcX/gWa2d3rGavLU/2Z6v0wSPZyahJmrtpunQDvJWafnr0q0/Lh16gxDkb19F/iZ7f9xOr2ZTXHs/BkLBdGgT+TuW24VRqjXAteejCIMBmZVOzqJp2gk3T9zufgolpDIgyV3+Ber1c6XqbT99jVnLd2t517ouQ7s1tTm/YPNRVhzS6rRZVbkYl8rGNwbh5+nGd9uO8+veM0gJ97Wpy6OdGjmSsMGjYyuCXh2OMCik/bqK1K8c559bo7pU/O5D4sZOJ3Otln8Vpo/FEBwIqkrazytI++73MvX8IyOoMWUoGBTivl/PdQd+UtVxgwqfbRcnfUP6nlO4Vgyizkcv4FLBH1RJ7OK1RH/5V5l6pfH69PfZsn0PgQH+/L54/t+KS+e/id7xUw6EEJOBDMAX2CKlXPcP3suUG70Hy/2/CdSRUp6zHHsZeB9oLaXcV+64FMHDU4bx0aNvkxyTyPhlMziydh8x564XhmnUrTkVaoTxZrcXqNG8DoOmPcWsAa8Vnu/+ZF9izl3H3dujTD2zKpmx9jjzH2xLqI87gxdto2utUGoFFy2uNqRNLYa0qQXA5nOxLN5/ET8PV6SUfPFQOzxdjeSbVZ5cspNONUNoWjGgZEFFIeT1kUQ9PQFTTAKVf/yYzI27yL9wxS5c0MvDyNq+v+iYyUzCu5+Td/IcwtODykvnkrXjgP21xe1bc4T5D3cg1MeDwQs207VOGLWCixYWHdK2DkPaak7O5rMxLN57vrDTB+D7feepEexNZq6pXOn5zqZTzLu3BaHe7gz+cTdda4RQK6hoIdonWlbniZbVNb0L8Xx36DJ+7tpihP0aVOShplV4Y82xMrVQFKq8/SznBr9JfnQi9ZbPJnXtHnLOFi0Anb79CKlr9wDgXr8aNT4dx8nuIwGoNPkp0jcd4NJzMxEuRhSPG1zcVSi4DxxO1ievI1MS8RzzAaZju1FjrBag9vDC7cHhZM97E5kcj/D2uzGNQi2Ba+Qgcn+dg8xIxn3QBMwXjiCToguDmPavwbR/DQCGGk0xtrjjhjp9zKrKjOV7mT+kO6G+ngyev4qu9StTq0LRPQ/p1JAhnRoCsPnUNRbvOIWfpxuxaVks2XmaX1+4G3cXI2N/2Mqqo5fo36JWyYKKQo3pT3Py4bfIi06k8YpZJK/eS/bZa4VBUrceJXn1XgA8G1SjzmejOdzlBWRuPicGvomalYMwGmj0+zRSNhwk48AZO5mIyJaE1Qjn5a7Dqd28LsPefo43BoyzC/fI+CdY8dUydi7fxrBpzxH5UA/WLV5FRkoG3775Ja16226QmJ+bz9uDJpGblYPBaODbtW9jOroX8wWtYxGh4DH4eTLfexWZnIDXG3MxHdqJGm1bX01nj5L90Rs2x9TYa2S+9VxhPN7vLSH/4HYAjE3aYAitxCtdR1C7eV2Gvv0skwa8amfPoPGPs/Kr5excvo2h054j8qE7WLd4NRGRLQirUdHu+oDQQHo/eRdj73iB/Nw8XvhkDO37dWLLzxv587Pf6b7/MwBce96LW//HyFn4Ee6Pv0DmrHHIpHi8J39K/sGdqFGXbe07c4ysD16zuz+PwaPIP7qX/LlvgcEIbmXUP6HgMeRFMmeMRU2Kx2fqPPIP7EC9Xkzv9FEyZ9vrAWS8/Qoyo+RFqltHtqZSjYo82XkY9ZvX5/npo3jxnpftwj01YSi/fvk7m5dt5oXpo+jzcG/+XPQXXr5ejJo2itcee534qHj8grS6c+3CdUb0GQWAoih8t3cR8Sv2FEWoCOq9M5SDD04jNyqR1qtnkLB6H5lnip57OZfjODDgLUypmQR1j6D+e0+z787X8apfhYqP3sHePhOReSYifphIwtoDZF+MKTktFUGzGU+y/cEZZEcn0m3V28SsOUC6lV7WlTi23TuV/NRMKnRvRsTsp9jSd1Lh+e33TyMvKb1kDRs97bl3/akJmGITqFLw3Dvv4Ln3SrHnHpD22xpSv1tGhXfG2kXt2aU1LtUqMeIW1weAx998isObD/Lh8HcxuBhxs3oubv16Je0H92B2jzGkxiTy/LJpnFi7nzgrX6V+twiCa4Qxq9vLVG1em3unDWPugDeIvxDNnL5aR6BQBK/v/pRjlnburvGPsO7DXzi96TD1u0XQd8IjcMX2g8I/0Vb7j36RhBfHYo6Lp8LX88jZugPTpct24fxGPEPubluXz//lUeTs2kvWa2+B0YhwL72ua/btYf6TPTT75q2ka4PK1KrgX2Rf50YM6ay9pG8+eZXFO05q9qVmsWTnKX598R7NviVbyrTPrEpmrDvK/AfbaX7Swq10rR1m6we2rc0Qy0fBzediWLzvQpEf+HD7Ij/w++10qlmhZD9QUQia+Dwxz76KKTaBit/PJWvTToc+YOBLT5G9w6oumM0kzf6MvFOaD1jph0/J3rW/VB8QoeD+4Aiy5r6GTEnAc+wcTEd32fopgPn8cbLnT7a9hfBquHToTda7L4M5H48RUzEd34uMjypFT+B65xPkfPcOMi0J92FTMJ3Zj0ywusbNE7c+Q8hZMguZlgieRf6oa+/HMJ8/Qu4vH4FiAJdylJU/djF/WG9C/TwZPHc5XRtUpVaof2GYIV2bMKRrEwA2n7jC4m3H8fN041xMMr/uPcPikf1wMSiM/GYNnetXplpwKX6aohD82iiinxmPKSaBSj98TNbGEvLvZfv8S5xd5MNX+vETsneW7sOjKNSc/jTHH5pCXnQiTVfOJGnNXrLP2PpJh638pLqfj+ZQ5xeQJjOX3lpA5tGLKF7uNFv9LilbDttce6MM6NuTR+6/h4lTZ990HP9lVH1DK32NnxtBSjnpn+z0+Zv3cBR42Or3A0Dpn3QcUD2iNvGXY0i4Goc538y+5Tto1qu1TZhmvVqx61ftS9LFg2fx9PHCN8QfAP+wQBp3b8H2H9aXS+9YdApVAjyp7O+Ji0Ghd/2KbDoXW2L4laei6FO/IgBCCDxdtb5NkyoxmVXKWmLdrUk98q9EYboWAyYTGSs34dW9vV04v0f6k7F2G+aklMJj5oQk8k5qXz5kVjb5F65iDA0uw75kqgR4UdnfS7OvYSU2nS35hWDlyWv0aVi001NsWjZbz8dyX9NqZVhm0YtNpYq/J5X9LOlZJ4xNF+JLDL/qTAx96oYV/m5ZKaCwE6gsPCPqkHsphrwrsch8E8nLt+LXq41NGDUrp/D/iqc7WBplxdsD7zaNSPxhLQAy34Q57cZ2ZVKq1UWNj0YmxoLZhOnAFoxNbEdEubTsiunwDmSylgYyI/WGNAq1wmogU+OQaQmgmjGd2YehVsk7uBnqtcZ0eu8NaRy7lkiVIB8qB/rgYjTQu0k1Np0seRe1lUcu0adp9cLfZlWSm2/GZFbJyTcR4lv6LjvezWuTcymaXEv+Jf6xjYDepeWfG9ajaAvOCRcDwsVYmLfFadmzDVt/2QTAuYNn8PT1wr+CvVPeqEMTdq/YAcCWXzbSqpfW0ZOWmMqFI+cw59vv8JZruQeD0aB1Xljdg6FmPdS4KGRCDJhN5O/ZhLF52aPYimNo2Bw1LhqZGAeAMaI9eTvW3ZA9W63s0dJjo8PrDQYDru6uKAYFVw83kmOTAMjOyC6MV7i5AxJDzfqosdeR8dGafbs34tKinPa5e2Ks14T8zZaRKWYTZJVe/wy1ND3Vope3awMuLW88PUujfa92rPtFe3acOngKL19vAh2kbbOOzdj611YA1v68jva9tTY8ckA3tq/aTnyUVt9TE+3re0SnCKIvR5NzLaHwmG+L2mRfjCXnchwy30zs7zsI7mP73EvddwZTqpZGqfvP4hYeBIBXnUqk7j+Lmp2HNKsk7zhBSF/belScgOa1ybgYS9YVTe/a7zsJ693SJkzSvrPkW/SS95/DI/zmd+5xt37u5WvPPW9Hz73B/clcuw1zYorN8Zz9xzCnOu5k8urenvQ/bn198PD2oH7bhmz6QYvbnG8iK62oI92/YhAJl2NIsvgqh5fvpFGvVjaaDXu15MCvWjm5cvAcHj6e+Fh8lQJqd2xM4uVYUq5r5UEiCz9auft6khabbGeLs9tq14b1MV27jjkqGkwmstZtwL2Lfd3zGngv2Zu2YE4uumfh6YlrRFOyllvqusmEzCi9rh+7lkiVQCv7mpbHvqKRxXb2+ZT+EfBYdDJV/K38pAYV2XSuND8pij4NND/J1g9Uy/QD3RrXI/9qFKbrmg+YuWoTnt3s09J3UH8y1znwAU8V+YB5F65gqFC6D6hUr4uaEIVMjCnyU5ra1z2H14ZVwXzpNOTngqpiPncMl2alt7lKxVqoSbHIlHhQzZiP78JY17ZtMTbugOn0Xq3TByDL0hnv6oGhaj1MhzZpv1VzmR+vjl1N0OpCkKWsNKvJphMld6SsPHyRPhE1AbgQl0LTKiF4uBoxGhRa1ghjw/FSOmGw9+EzV27GK9JB/j3Sn8x1W0v34S9eKdOH925em+xLMYV+UsIf2wjsbftssPeTND8kPy6FzKPaqDg1M4fss9dw/Zs7sLWKaIKfr/N2HdP596F3/JSAEOI1IcRpIcQ6oJ7l2AIhxAOW/08SQuwVQhwTQnwuhBCW462FEEeEEDuFEO8KIY5Zjg8RQvwqhFglhDgrhJhlpTVICHHUEtdMyzGDRe+Y5dzLDu7hHSHECYteWd27vwP9LdfVBFKBkt/4S8A/NJDkqKJtYJOjE/EPDXQQpshpTo5JxN/SmA2cNITfZiwud69rXEYOYVZOQaiPO3EZOQ7DZueb2XExnh5WHRVmVfLggq10/2Qt7aoH06S00T6AsUIQppiiZDHFJmAs9uA2VAjC644OpC0teUimsWIorg1qkXPkVOn2pRe3z4O49JLsM7HjQhw96lUsPPbu+qO8FNkIS/Erk7iMXEK9i77QhHq7EZ+ZW4KemR2XE7ijdmi54i6Oa1gQeVblIC86EZfQILtwfr3b0WDDJ9Ra8AaXx34MgFvVMExJqVR97wXqrfiAKjNH3fCIH8U/CDWlKC/VlASEn62+UqESwtMbj+dn4Dl2DsbWN7dtrfDyR6YXOdMyPRnh5e84sNEFQ/VGmM8euCGNuLRswvyKXgBC/TyJS892GDY7z8SOc9H0aFhFC+vryeOdGtDnvd/pOetXvN1d6VA7vFQ9Lf+K6npedCKuDl4uA/q0pdmWj6i/8DXOvzK36ISi0GTte7Q88g2pWw6TcfCsQ53AsEASrcpJUkwigcXaFJ8AHzLTMlHN2vbvidGJBJbDQRKKwowVH/DZgW8xnTiA+WJRfRT+wahJReVDJieg+Ns7eYZaDfGaPB/Pl6ahVLTvYHVp0438PRuL4g0IRibF2dgTUKY9CQSEaWUzICyIJKt0L7g+OTaJvz7/g493fs6ne78mOz2To1uLtiF2u38oPu8vwaX9HeT+usByH1blPykeEeDAvtoN8Z76OZ6jZ6BU0uxTKoSjpqfi8dQ4vKfMx2PoaHAtfXtsJTAYNbHIbjUpASUgxKGez/Qv8Bo3wzKNTENKidf4d/F+ez6ukXc51AgOCyLeqqwkRCcQFGZrk2+Ar03aJkQnEGxJ28o1KuPt582spTOZ+9dH9Lj/DjuNbvd0ZdMfm22OuYcFkmOVJ7lRibiFlfwsqfhIJIkbDgGQceoqAe3qYwzwRvFwJbhHc9wr2beOA5d/AAB3aklEQVSD1niEB5BtpZcTnVRqx061R7oRu6GoLEgp6fDDeLqtnka1R8tu0wyhQeRbP/diEuxeWA0VgvDu0YHUH29sKoKxQrDNM/VW1YcKVUNJT0zj2dnPM33Fezw9cwRuVs+IZv3aU6VpLQbOehYPXy9SoxPxDbXNM7/QQFKs4k6JScKvWLsS0a8Dh5btKPy9/K2F9J0wmIk75nLXxMGsnGU7fRKc31YrIcGY44rqnjkuAUNIiF0Yj66dyPzNdlqPsVI4akoq/q+PI+Tbz/CfMBrhXnpdj0vLIszPq8g+Xy/iUkux72wUPRpV1cL6efJ4p4b0efdXer7zM97uLnSoU9HhtYV6jvzA0vyki3H0qFuUZpofuJnuc9fQrnpIqX6goUIwZqvyao5LsHv5N1QIwrN7J9J/+rPEeIwVQ3GrX5vco6X7gIpfEGpyUZumJtv7KQCGGvXxHD8Xj+FTUMK0tFSjLmOs3Ri8fMDFDWOjVg7beGuETwAyLanwt0xPQvjYpocSGIZw98L9sddwHzYVY5NO2vGAEGRmOq79nsH9qbdxveupMkf82JUVP0/iSviIl51nYseZa/RoXB2A2mEB7L8US0pmDtl5JradvkZsSumdksXbG1NsPIZifqfmw3cs04d3q1+7TB/eLSyQvOvWfm4SrmH2+Rd4Zxsitn5Eg0UTOffyJ/bxVA7Bq0kNMg449pN0bg1SSqf9/a+id/w4QAjREm10THPgPqC1g2BzpZStpZSNAQ/gbsvxb4DnpJTtgeKfoCOAh4AmwENCiCpCiIrATKC75XxrIcQAy/8rSSkbSymbWOK1vsdA4F6gkZSyKfB2GWalAVeFEI2BQUDJC26UgqMOBrsC7qgTQkoad29BemIqV45dtD9fAo6qTkldHFvOxxJRKcBmGpRBESwd0pnVz93BsegUzsWXMfS9hHu3JvjV50j84CtQVcdReLgT9sEbJM6cj8ws/WuIo4XGSrTvXCwRlQIL7dtyLoYATzcahvmXqnGzbLkYT0S4f7lH+NjhyBAHjWHq6l2c7D6SC09Np+KYwdpBowHPxrVIWLSK031fRs3OIXTE/Td3H6XpKwYMVWqT/dlksj+dhFvvhxEhpTuhDilfvxsAhprNUKPO39A0L7jBsnL6GhFVQ/Dz1JyytOxcNp28xl+v9GfNuPvIzjPx16Ey6qHD/LM/lLxqN4e7vMDpoTOpMm5Q0QlV5WjP0Rxo+TReEbXxqFfVsYzDNsUuUNlhHN2uqjKh78uMbPcUhhr1bDoaHNb1YgaaL58jY9xgMic/R976P/AY9ZZtcIMRY7P2mPZZdRSUow1xbI8s5bYkXr5etOzVhhc7PcfINsNw83Cn471dC4Pk/vI16a8MIn/nelx7DChX/TNfOkv6K4PIeOMZ8tb+hucLU7R7UAwYqtUhb8MyMiY9h8zNwe3uhx1EaGNUmXqmS2dJe3EQ6ROfJnf173i9MqXwXMZbL5Dx+rNkzhqPW88BGOo3LR5bqelWFKTkMAajQp0mdXjjiUlMfPR1HnlxEJVqFI2gNLoYadezLVsso4VK0y2JgI6NqPhId85N/Q6ArLPXuTR3Gc2Xvk7EkomkH7+MNNmPTitTr4QCH9yxIdUGdeP420VrumztN5lNvV5jx+CZ1HyyJ0Ht6t+4XrG6EDLhORLeK/m5V3LcjqL++/VBMRio3rgm6xavYmLf0eRm5XLPiPsAWLt4FX9MWsCx1XtJi0vm7tcfdWRSmeXJ4GKgYY+WHFmxu/BYu0d7snzqIqZ3GMXyqYsYOPMZ+zic3laXXV78XxpJ6ief2+efwYBL3Tpk/rqM+CeeRWbn4P34IErDUVEsqYpsOeXIvqv8NeZe1ox/wGKf/ZpPN61XzE+CAj+wK6uH97T4gSVPJy1PGxM0dgTJc74s1Qes8N4kEt+dV6YPWK7n0NVzZLwxhKx3RpG3eRkez2hTj9XYq+St/QnPUdPwGDkV8/WLYL6JtqU4ioISVoOcH2aT8/1MXDoPQASGgWJACa+Oaf96cr58HfJzcenQr9SoHL0AixJqw5aTV4ioFlpYVmpW8OfJrk147qvVjPx6DXXDAzEoZdx/OdqboFeHk/RB6fkX+sEkEmbeZP45sDlp5R4OdX6B00Nnaev9WKF4ulPvq7FcnPQN5gzHHag6OrcKfY0fx3QGfpNSZgEIIexXBIRIIcQ4wBMIBI4LIbYCPlLKgs9D31PUIQSwXkqZaonzBFANCAI2SSnjLce/A7oAU4GaQoiPgb+ANcX004Ac4EshxF9AyZ8eivgBrUOrN3AH8GRJAYUQzwDPAHQJbElDH23oZXJMIgEVi3qzA8KDSI2zHeqcEpNIQMVgQFuINyAsiJTYZJr3bUfTHq1oHNkco5srHt4eDPngeRa8/HGJNxzq7U6M1Zey2PQcQrwdf41adbJomldxfN1daFUliO0X46gdUvIwSFNsAsawoi9lxtBgTPGJNmHcGtUl9F1t/r8hwA+vzm2QZjNZG3aC0UDYnDdI/2sDmeu2l6hTaJ+PRzH7sgnxKcG+E9fo07By4e9D15LYfC6GbedjyTOrZOaamLh8P9P7tXR4PUAFbzdiM4pG+MRm5BLi5fiLzeozMfSpF+bwXHnIi07EtWLR1yfX8CDy45JKDJ+55wSuVcMwBPiQH51AXnQCWYe0NWFSVuwgdPiNdfyoKYm4+BflpeIfbPOlC0CmJGDKTIO8XGReLqbzxzBUqoGptDnyDpAZKTZfzYRPADIzxWFYQ91WmE7vcXiuNEJ9PYlJLXJCYlOzShwiv+roZfo0KRqdsut8DJUCvAn00srWHQ2rcOhqPHdF1HB4PRTkX1Fddw0PIi+m5PxL330Ct2phGAN9MFmtLWJOyyJt53H8I5uTfVobph06pA8VBvdkBnDhyFmCrMpJYFgQycXKSXpSGl6+XigGBdWsEhQeVDjNqTxkpWViOn0YY+NW5F2/BIBMjkcJLCofIiAYNcW2rpNTlN6mo3twf/R5hLdv4To0xiatUa+cw9iyC65d+mr2XjqNCKwAXLayx7aNtLcnmBSLPUnRiQRapXvB9Y07NSPuaizpSZr23lW7qNuyHtt/sx2dkrdzPV6vTMd0ZC/Cyj4lMARZmn1H9iAefxHh7YuaHI9Misd8Qfvamb93C253ld7xoybFowRVsNILRk1JsA2UbaV3eDfC8GJhehbcm0xLIX/fNow162M+dQTXnv1xi7yLTzMEZw6fIcSqrASHB5MUa2tTalKqTdoGhweTaEnb+OgEUpPSyM3OJTc7l6O7j1GzYQ2uX9TWfWkd2Ypzx86TkpCCtqSfJZmiE3G3yhO3ikHkxthP8fFuWJX67z/DoUHvYErOKDwe/f1Gor/XRoXVmvgwOVGll93sqCQ8rPTcwwPJdqDn26AKzd97mh2PzCTfSi8nNgWAvIQ0olfuI6B5LRJ3lfzl2hyTgIv1cy8sGHOc/XMv7L2i555nlzZgNpO5fqddfH6D+uE78E7tXo6esTxTtc6LW1UfpJQkRSdy/pD2hXz3ih2FHT9pCamkRCfiFx7I+o9/5cmvxpFwKYa0YrqpMYn4W8XtHxZoM3WrXrcIrh+7SEZC0ZTAlvd3Ydlb3wJw5K9dPPDO08hijwtnt9VqXDyGCkV1z1AhGHOCbd1zqV+XwKlah4Hi54d7+7akmM3kHTuBOT6e/BNa+cjeuAWfx0rv+An18yQmtWjkRWxaJiG+Jdh35BJ9mhXd+65zxexrVJVDl+O5yzK9x6Gezw34gaei6NOgFD+wahDbL8ZTO8TXYRhzbDwGq7pgqGBfF1wb1SFk5kTtfIAfnp1bk2A2k7VxBxgNVHj/TTJWbCBr/bYSbSpATUnAxWqUjhIQjEwt1j7kFNluPrEPDCMRXr7IzDTyd64hf6f2euDa7wlk8Ta3GDItCeFbNKpN+ATajFYGbcSyOfuINoUsPxfzlVMooVVRr5xGpiVpH64A08k9ZXb8hPp52ZaV1KwSpy6uOnyRPsXK+b2t63Jva23Dk49W7SfUr/Rpj/Y+fAjmYv6EW8O6VJhllX+dCnx4Lf9CP5hExl8byFpftg+fG52IayVrPzeQvFJ8k7RdJ3CvHlroJwmjgXpfjSX+160kWXUw69we9F299BE/pVFi6RBCuAOfAg9YRuN8AbhT9nd/6zk1ZrSON4fXSCmTgWbAJmAk8GWx8yagDfALMABYVYY2wHLgMeCKlLKUTx4gpfxcStlKStmqoNMH4PLh81SoHk5Q5RAMLgZa9evAkbW2CwUeWbuPdvd1AaBG8zpkp2eRFp/CH7OWMLH9cF7vNIqvnp/D6R3HSu30AWgU7seV5Eyup2SRb1ZZfSqKrg6mHqXn5rP/WhKRVueSsnJJy8kHICffzO7LCdSwWsTYEbnHTuNStRLGSqFgNOJ9ZzcyN+6yCXOlzxNc6a39ZazZSvzbH2udPkCFKa+Qd+EqqQt/LVWnyD5/riRlcj0lU7PvxHW61rbvbEnPyWf/1UQi6xSde6FbQ9aM7M3KEb14555WtK4WXGqnD0CjUF+upGRxPTVb0zsbQ7ea9tMx0nPz2X89mW41KziIpXxkHT6LW41wXKtUQLgYCejXuXAh5wJcqxXZ49G4JsLViDk5HVN8CvnRCbjV1L7G+3RsarModHlQr5xBCamICAzVRma06ILpqO2D1XR0F4aajUBRwMUNQ7V6qLE3vrCeGnMJ4V8B4RsEigFj3VaYzx+2D+jqjqFyXcfnyqBRpSCuJKZzPTmDfJOZ1Ucv07V+Zbtw6Tl57L8UR2SDKoXHwv28OHI1gew8E1JKdl+IoWZI6QtZZxw6h3uNcNws+RfUvxPJa2zXJXKrXpR/nk1qorgYMSWlYwz0xWBx7oS7K36dm5J9rihdYxes4mjP0Uzo+zL71uym8/3dAKjdvC5Z6ZmkxNm/5B7feZS2fbW5+l3uj2T/2tI7z3wCffH01YaYu7i5YmzQAjW6qAyZL55GCa2ECA4DgxGXNt0wHbJ9iRW+RZ15So16IBSbxYdd2kaSv3sj+RuXkfnWc2S+9Rymg9tx7dCj0J7s9CyH9pzYeazQns73R7LPYs/+dXvpfH+k3fUJUfHUaV4XV3ftS3ajjk25bknTsOpF0xpcmndAjb6K+eIpDNb2tY0k/+AO61tA+BXZZ6hZDxShdcKkJmsdOWFa+TI2bG63KHRxzBdOoYRVQgnR9FzbdSd/f7H0tNGrD0LTw80d3C0vjm7uGJu0wnxN6yjIW/sH6ROfYUSfUexYvbNwelb95vXJSs8kyUHaHt5xhM53dQag5wM92LlGu4+da3bRuE1jFIOCm7sb9ZvX48q5ojLRrX83Nv2xyS6+9IPn8awZhnvVEISLgdABHUhYbfvcc6sURJOvR3Ni5CdkX4i2OediWazfrVIQIX3bEPtb6S8UKYfO410zDE+LXuUB7YlZY7ugskelINp8/TL7R31K5oWi9U4Mnm4YLS/VBk83Qro2Ie1U6W1nzrHTuFSzPPdcHD/3Lvd6gss9tb+M1VuJn/qxw04fgNQly7l63wiu3jeCzPU78Ol/6+tDanwKidEJhNfUXvIbd2zKdcvC8/4VArh2+DzB1cNo/VAksWev0axfe06stU3DE2sP0OI+rZxUbV6b7PQs0uNTCs9H3NOBQ8tt60xaXDI12zXQ7qdDIxIu2a814+y2Ou/kKYxVKmEIDwOjEc8e3cnZaps3sfcPJva+R4i97xGyN24mZfaH5GzZjpqUjDk2DmNV7R7cWrUgv/ii0CXZl5Su2XfkMl3rV7ELp9kXS2SDItvD/T1t7TsfQ80KpdvXKNzf1g88GeXYT8q1+ElW5xz6gYEl+4G5xwt8QC0tvfp0I2uzbVpe6/s41/o+xrW+j5G5disJ0z7WOn2A4Mmjyb9whbRFvziK3g71ssVPCbLyU47Y1j3rj0pKtbpau5mpPYcKNqQQASEYm3Ugf5/thwA7vagL2lQu/xBtxHOjdpjO2E47N53ej6GK9rzD6IqhYi1tHaLMVK3jKFB73hhqNEJNuO5IppBGlYO5kphWVFYOX6BrwxLKysUYIhvajgxOsoyAiU7JYMPxy9zZrOQOQrD48NWs8u/OrmRuss2/q3c+ztU+2l9h/m3Q8i/krVfIv3CF1IXly7+MQ+fwsPKTgvt3IqnYs8Hdyk/yalIDYfGTAGq9P4Lss9eI/sx+ZzUdnduBPuLHMVuABUKId9DSqB/wmdX5gk8NCUIIb7SFkn+WUiYLIdKFEO2klLuwXUy5JHYDHwohgoFktGlYH1t+50kpfxFCnAcWWF9k0fWUUq4QQuwCSt9PEZBSZgshXgXst9YpJ6pZ5YdJX/P8wtdQDAo7lm4k+uw1Og/uCcDW79ZybONBGke2YMrmj8jLzmPh2E9vVg6jojC+R2OG/7wHVZX0b1KZ2sE+/HRIc0wGRmhfyjacjaF99WA8XIuKdEJGLm+sPIyqSlQkvepVpEutMtarMaskTP+E8M+ma1tB/raG/POX8X1QW3OitDnB7s0b4XNPD3LPXKDyz5rNSR9+Q9bWkhfxNSoK43s1ZfiPO1GlpH/TqtQO8eWng9pLz8Dm2tePDWeiaV+jgo19N4NRUXi1Wz1G/HFAS89GFakV5M1PR7UXg4FNtAfyxvPxtKsahEexLVbHrzrC/mvJpOTk0/urLTzXrhb3NqpkpwNo27O/8Tm1Fk3WtnP/cT05Z64S9GgfABIXr8K/bwcC749E5puQOXlcGvlu4eXXJn1B9Y9eQbgYyb0Sw5UxH92YsapKzs/z8RwxRdsmddda1JgruHTUvkTnb1+JGnsN08n9eI6fC6okf9dq1OjSnV6HSJW8jT/gdu+L2hapx7cjk6IxNtE6QE1HtcXODbWbY758Akx5NyxhNCiMv7sVw7/doOVdi1rUDvXnpz1adR7YRvsqtuHEVdrXCrcpK02qBNOjUVUGzVuJQRHUDw/g/la1Sxc0q1x67Uvqfz8JYVCI+2E92WeuUuGxXgDELVpD0F3tCX6gK9JkRs3O4+zw9wBwDQ2g1ofPg6IgFIXE5dtJWbffoczBDfuJiGzJnC3zyc3O5TOrfB634A2+GDeX5LhklsxYyPNzR/PgmMFcOn6BjT9qC3/7hfgzbflsPLw9karkzqH9GNvjeQIqBDD8/RdRFAWhCEwnNmE6YtXxp6rkfDcXz5dnIBSFvG2rUaMu49JVG6SZv/lPjK264NrtblDNyLw8sj+bVnS9qxuGhi3JXjjHxh7TkT0Ym7Tlgy3zLPYUdW6PW/A6n4/7hBQrewaOeYTLxy+y6UdtkdpDlvQofv35Q2fZvWIn0/96D7NZ5dLxC2z4XvvS+/D4x/BuEgZSoibEkv3tHFBVshd9jNfYmVr537IS9fplXCM1+/I2/olL6y64dr8HzGZkXi5ZnxbNGM5e/DEez01EGF1Q46LJ+rJwWTrHqCrZCz7G69WZoBjI27wS9folXO/QvgjnrV+OS5uuuPWw6OXnkjlX01N8A/B62TLty2Agb8d6TEfs2809G/bSuntrvtn2NbnZObw3+oPCc1O/ncIH4+aQFJvEVzO+ZuIn4xky9nHOHTvP6h+0dLp67ir7Nu1j/pp5SKmyaslqLp/W6rubuxstOjfnw/H27Yw0q5ye8DXNf5gIBoXoJZvIPH2NSo9rHRrXF66jxugHcAnwpt7MYdo1JjN7e2tflZt+9QouAT6oJjOnJ3xduAh0SUizypGJC+iwZLy2nfuSTaSfvk71x7VOr0sL11PvlftwDfCh2TvawN2Cbdvdgv1o+42205kwGrj263biNh4pPe/MKvHTPqHiF9MRivbcyzt3Gd+HLM+9Mtb1CX13PB5tmmLw96P6hsUkzl1E+q+rAcjasgfPLq1veX0A+PbNLxj54csYXYzEXYktPDdowuPUblgTgaDz0L5kJqezZ8kGYs9eo91gLc92fbeOUxsPUj8yglc3zyEvO5efxha5eS7urtTp1IRfJ9p8c+OX8V9wz5uPoxgNmHLz+WXClzzzuO3ok3+irU5572OC52h1L/PPlZguXsLzXq3uZf1W+ktl6vsfEzB5ovZCej2a5Gml13WjQWF8vzYMX7Be81ta1Nbs222xr62VfbXD8XAtmi7epEoIPRpVY9Anf2n2VQzk/tZ1HOoU6hX4gT/t0vSaVNH8wIOXNL3m1TW9MzG0rx5i7weuOIgqJapE8wNLW7fQrJI4Yy5h82aAopD++2ryz1/GZ6DWbpa2ro9b80b49OtJ3pkLVPxR20o7+eOvyd5WykcKVSVn6Tw8R74NQiF/1xrNT+mkjSDN37YCY/OOuHS+S5vGlZ9H9jczCy93f+o1hJcvmE3kLv0UsjNKUtKQKnmrvsV90DhQFEyHNiMTrmNsoa0FZjqwAZkYhfn8ETyemQFSJf/QJmS81qmat/pb3AYMRxiMqClx5C7/vFQ5o0Fh/D3tGP71Gq0utKpD7dAAfrKMQBxomYa64dhl2tepZFNWAEYv3khqVg5GRWFC/3b4epax1qNZJWH6XMLmaz58+m8F+ae1Zek/ldyWuTVvhM89Pck9c4FKP80DIOmjr8kuxYfHrHJh4pc0XPIGwqAQ+8MGss9cJfRxzU+KXbiGoLvaETKwGzLfhJqTx5nn3gfAp019KgzsRuaJyzRbqy3TennG96RsuLH1H60Z++Y77D14hJSUNO4Y8Cgjhj3G/f1633R8/zX+l9fecRZCTwTHCCFeAx5HG69/DW0HrMbAn1LKn4UQb6N17FwCrgKXpZSThRBt0UYAZaKN1ukipewohBgCtJJSjrLE/ycwW0q5SQjxCDABbfTPCinlOCFEM7R1fQpGZU2QUq4UQixAm9a1HfiDopFGs6WU35Zgy2QgQ0o5u9jxTcCYsrZzH179QacVkvdft/8qdjuJmnPcqXoVR5e809PtQGbd2Doyf5fTM29sVM7fpfaAMuaz32IMNUvo5LpNiIrO1Tv80iGn6n3ocoPrhfwNPutxY7vC/V2eW1f66MJbzbxupQ7ivOVIk3N9h4e23Nji7n+XcXmOp4LcLtKFoexAt5DGgYllB7qFTMosfYrGraayKH2B4lvJW7MaOE0LIOmDTU7VCxx9c5se3DSpKU6Vi/noxkfg/h2CO9/kuok3ifI3d4q6Yb1GjZ2qFz1pg1P1YhKct2tW66Pvlh3oFuISXPMGVqz89+HqVtlpjkte7rX/ybTUR/yUgJRyGjCtlPOvA687OHXcstgyQojxwD5L+AVYjdqRUt5t9f/v0dYDso7/MNDCge4Qq5+l7wlbdM3kEo53K8/1Ojo6Ojo6Ojo6Ojo6Ojr/RvTBLnrHz+3gLiHEBLS0vQwM+WdvR0dHR0dHR0dHR0dHR0dH5/8resfPLUZK+SM3uVX638UyPW1gscM/WUYv6ejo6Ojo6Ojo6Ojo6Oj8v0If76N3/PynKGt6mo6Ojo6Ojo6Ojo6Ojo6Ozv8zpJT6n/53W/6AZ/6LWrqerqfr/f/R+y/bpuvperreP6f3X7ZN19P1dL1/Ts/Ztul//56/gh2jdHRuB8/8R7V0PV1P1/v/o/dftk3X0/V0vX9O779sm66n6+l6/5yes23T+Zegd/zo6Ojo6Ojo6Ojo6Ojo6Ojo/EfRO350dHR0dHR0dHR0dHR0dHR0/qPoHT86t5PP/6Naup6up+v9/9H7L9um6+l6ut4/p/dftk3X0/V0vX9Oz9m26fxLEFLqm5vp6Ojo6Ojo6Ojo6Ojo6Ojo/BfRR/zo6Ojo6Ojo6Ojo6Ojo6Ojo/EfRO350dHR0dHR0dHR0dHR0dHR0/qPoHT86Ojo6Ojo6OjolIoTw+qfvQUdH57+DEGJgeY7p6OjcOvSOHx0dHZ1/OUKIECHERCHE50KIrwv+/un70vnfx1kv9HrHwb8TIUQHIcQJ4KTldzMhxKf/8G39qxFCdCzPsVugU6M8x26DrlNe6P+JjgNn5Z0lXrfyHLvFms4sMxPKeexfiTPLio5OedEXd9a5JQgh7ivtvJTy19uk6wbcD1QHjFZ6U26DVgcHOgtvtU4xzRDgaQe6Q/+NOsU0nZZ3Fr1KQLViWltuh5aVplPKjBBiB7AV2A+YrbR+uQ1adYGx2Kdl91utZaXp1LJi0awLzANCpZSNhRBNgXuklG/fYp1QYDpQUUp5pxCiIdBeSvnVrdRxoNsB+BLwllJWFUI0A56VUo74N+oU03RK3lm0PIHRQFUp5dNCiDpAPSnln7day0rTaWVGCLEbeABYJqVsbjl2TErZ+FZrWeL2QEvL07cjfgd6TisrVpoHpJQtyjp2m3T2Sylb3kqdcuo6y75brvNPaf4P2XdLy4wQ4k6gL/Ag8KPVKV+goZSyza3SstJ8UUr5YVnHbrGms+pBqfFJKQ/cSj2dfzfGsoPo6JSLfpZ/KwAdgA2W35HAJuC2dPwAfwCpaC+8ubdJAyHEIqAWcIiiF2sJ3NaOHzT7tgLrrHT/zTrFNW973gEIIWYCDwEnsM2/29bx4+Qy4ymlfPU2xOuIn4D5wBf8B8uKFV+gdXB9BiClPCKE+B641S+EC4BvgNcsv8+gOcO3teMH+ADoDSwDkFIeFkJ0+RfrWOOsvAMt7/YD7S2/r6HVkdvW8YOTy4yU8qoQwvrQban3Qoh+wGzAFaghhIgApkgp77kdehacVlaEEO3R/KMQIcQrVqd8AcMt1KkPNAL8in2U8wXcb5WOA92CF/pKQoiPiuma/m06xTSdkncWrTCgEuAhhGgOFFQ+X8DzVmpZaTqzzEQB+4B70NrOAtKBl2+xVgFPAMU7eYY4OPa3cWZZsbAPOA7EF9yC1TkJ3LaPcjr/PvSOH51bgpTySQAhxJ9oPfbRlt/hwCe3UbqylLLPbYy/gFZodjl7iJyzXuid2XFQgLPyDmAA2ld4Z3UagHPLzJ9CiL5SyhVO0DJJKec5QccaZ5aVAjyllHuKvfDejpeKYCnlUiHEBAAppUkI4ZQONWe90DtLxwpn5R1ALSnlQ0KIQQBSymxRTPg24Mwyc9UyaksKIVyBF7BM+7oNTAbaoH0sQkp5SAhR/TZpFeDMsuIKeKP53j5Wx9PQRlXdKuoBdwP+FH2UA+3F+ulbqFMcZ73Q/xMdB87KO9A6yocAlYH3KHqRTwcm3mKtApxWZqSUh4HDQojvpZT5AEKIAKCKlDL5VmpZ2uVH0DqSl1md8gUSb6WWFc4sK6CNOL0fyAZ+AH6TUmbcBh2d/wB6x4/OraZ6QaePhVig7m3U2yGEaCKlPHobNQCOAWFAdFkBbzHOeqF3ZsdBAc7KO4ALgAvOGy0Czi0zLwIThRC5QD6aoyillL63QWu5EGIE8BtW6SmlTLoNWgU4s6wUkCCEqIX2xQwhxAPcnrzMFEIEWem0QxvddLtx1gu9MzsOCnBW3gHkWaYnFWjV4va3M84sM8+hfRWvhDaaaQ0w8jZpmaSUqbe/38wGp5UVKeVmYLMQYoGU8rJFT0GbBpl2C3X+AP4QQrSXUu68VfGWQ9cpL/TO7Diw0nRK3lm0vgW+FULcfzuma5eg+U+UmbVCiHvQ3kUPAfFCiM1SyldKv+yG2IFWn4PROtEKSAeO3EKdQpxZVix6HwAfCG0tpkHAeiHEZWC6lPL/2jvzuNvGuv+/P06RzJUGpPBIISTK1IAozUkkStTT+DyUSimVqETUI0oqIVIZKsNTIomQzEOJJxlK40+RY0r0+f3xvfY562z7nGNY17XPvs/3/Xrdr33vdd/3+qx1r2uv9b2+13e4rG+9ZLLJGj9Jr0g6GFgZ+BZhSL0euNb2f1fSuwr4D+B6wtgeTHjX6FnnTGAt4AJmnezWDEFH0nRgkaJZbULfSmdIs8m1K1onAGsCZzDr9du5b62O5ljGTG0kXT9is22vWFGz2VjpaK4IfIUI2b6laG9v+4aeddYGDgJWJ5yFSwNb2a5ilHZ0H0dM6F9E/D9PA3ax3esqaCudIc0m165obQbsAaxKnNuGwJtt/7RvrY7mWMZMbSQdRtyjP0SsYO8MPNL2OypqNhsrHc1jCIfafUTEyhLA52x/tmed/YiUtbuAU4ln4HtsH92nzgjdnxLRODMm9EDfE/pmOkOaTa5d0dqFSOmcTqQkrg18yPZpfWt1NJuNGUmX2n6WpLcSTruPS7qikg24CHCX7X8r6no9HfjhwHFYg5ZjpaO5GjHveiOwm+1ja2klk0k6fpLeKfnBzytvz7b9vYpaTxm1feBl71HnBbPROatPnfmJVteuaO0wG60j+9bqaDYdM2XFc2U6+fiuXLy6FS3HygjtRYAFbE+vqPEIItRewDU1jdH5iRbXrug8FliPuH7n2765pl7RbDJmGk8EH03ULdqcOK8fAXvbvrtvrRHaTcZK0brM9lqStgOeDXwQuLjCgtVA5zVEuvN7gTNtr9mnzgjdJhP6lo6DjmaTa1e0Lre9pqQXE1F2HwUOd93izs3GjKQric/6kcBHbF9Y0fFzMTEvWQo4n0gVvNP2dn1rdTRbfc5XJJw9rwJ+T6R7ndLivplMHpnqlfSOo4NXrWLOw1qDMMrHU7Fo4TgdPK0m9K0dB62uXdGq5uCZg2azMVMM312ImgCXEZPQn1OpqJ+k1YkIh+5YqVbovOVYGTC82lqiLHpfbVW0Hz7V9q8k7QGsLemTrtyJo9WEfhxRB62uXdHaELjM9v9K2p5IuTywplOy8ZjZ3PZuZSJ4E/A64Eyg9+tn+07C8fMRSdOARWpPXlqOlQ6PlPRIYmJ9sO1/SaqxCvvI8vpS4Fu2/642aXSPUNR33JqZBcgnWadLq2sHM2v7vJRw+Fyu+hew5Zj5BOHcPac4fVYEflNJS7bvlPQW4CDb+0m6tJLWgFZj5Voibe1Eoo7Q8sC7BtfN9ucqaCYTygLjPoBkaiDpnPI6XdJtna/pknrPae3ovlLSb4jw7LOAG4AfVtBZT9KFkm6XdI+k+2qeV0f3rUTnqR8x8yG556TqDGk2uXZFa2VJx0u6StJ1g68aWh3NlmNmF2Bd4EbbGwPPYmaHh16R9HEizeQgomvffkS4fTVajpUOO5V8/M2JboU7Ap+poPNR29MlbUQU9TySaC9dm83L+b2cmNA/jehuNKk6XVpdO4hrdaeiTf0HgBup3+2x5Zi530Swkg6SjpG0eIm++RVwjaSpNFYGfJm4hy0CnF0iGms8G06SdDXRaOAMSUsDLaIABjbEtZUn9K10urS6dgAXSzqN+Oz9SNJiwL8raQ1oMmaKY/fJttew/S4A29fZfm3fWjMltT6wHfC/ZVvt4IdWY2Uvoubiv4mi0osNfSXJDDLiJ+kF2xuV19Y3mb2J6IYfl5DfjYniZn1zMBFKeRzxQHwTER1Tm8GE/nzbGytabn5ignW6tLp2ECu6HydaS29MGPe1V85ajpm7bd8tCUkL2b5a0iqVtLYiojYutb2jpCcAX6ukNaDlWBnQarV10I3pZcAhtk+UtGcFnWFareyOI+qg5Ur5vbYt6VXAF2wfptmklvZIyzFzcpkI3kWsItd0Hqxq+zZFasQPKKkRQLWaGDSOqlAUef2L7WU7235HPJf61jmZcMzfZvs+SXcS6SDV6E7oB9tsX0fUbJo4nSHNJteu7FfAx4j6XdeVaJXHErZLFVqOmbLvVxI2WQt2AXYnOl79qjgJz6wl1nKs2N7zAR7T7rb36Vs/mSwy4ieZdP7lKBK6gKQFbA8K6vaO7WuBabbvs3048MIaOkPcPQh1H0zoiboOk6rTpdm1Axa2fQYR7ntjeVBWSYPq0nDM3CRpSeD7RKeME4mWtzW4y/a/gXslLQ78FahW2LnQcqwMaLXa+gdJhxLpCj+QtBBtns2DCX3taIBWOl1arpRPV7RV3x743zIhfeRc/ubh0mzM2P4QsD6wjqOO0CwTQUVx677opkacWPRqF6JsGlVR7p3/NbTNtnttIV90DrB9i+37yrY7bP+5T50RuvdROQK0pc6QZpNrN9gv8H3bl9i+tWz7mysWcB/DmDlP0sGSnidp7cFX3yLlnvwK26+0vS/MiC6q1tyj5Vh5ELxujNrJPEJG/CSTzq2SFgV+BnxT0l+BGjfWOxWtiC9T1Kz4ExG+WZvhCf0t1JnQt9Lp0uraAdxdVmB+I+m/gD8QYf01aTZmbL+mfLunopvYEkQ9lRpcVMbKV4nV+NuJzmU1aTlWBryFcC6NXG2VtJrtX/WgszXwEmB/27cqalbMSG+RtJQrtCi2/SFJ+zKblV1Jm9k+fVJ0hmh17QC2Ad4AvMX2nyUtT90IFWg8Zrr7sn0HcEfnx/sCfV2/Q4nUiMupn0YzoOVYGXC6pPcD36Hzv3T/aXSnSXot8N3iSGjFeYoOr8Pn13cNqlY6XVpdO4DzJa1r+8IK+54dLcfMBuV1r8420/OiXHnuPLvPfT5AWo6VB0KTAl/JvE129UomGpUWjcRq53bEhPeb7r8l8VOAvwALEl0OlgC+VCI6mqDoErUEUdTznimg0+TaFa11gV8DSxJpQ4sDn7V9ft9aHc2mY0ZR72Nl24eXqIpFbY9qvd6n5lOBxWuuQhadZmPlQRzTJa7YXaW1zrh0x3F+LTUl/dz2+i20Opotz+9S28+qtG8REZP3lvc7uHGh/hr/S0mj7su23WvkpKTpxGLDfcT9U0Vn8T51RuiOSqGx7V4n9K10hjSbXLuidRUReX0D4TgYXL+aXcvGMmZqI+kAItX+OGZ1wlRrRNNyrDwQxmVLJPMWGfGTTDS27ygT7JVtH6loBzutgs6NkhYGnmS7du2bWRgxoV+WKHI7kToDWl27onUhgCTbrpYjP6TZbMwoCi6vQxiJhxOpJkcDG1bQEuF8WdH2XpKWl/Qc29WiflqOlQdBq9Wzca3STeXza6nZpAvdEC3Pr9rqYYk46Eb27UIUsm5J7/9L2yv0vc/Z6IylsKujwcCU0RnSbHLtCls01ALajhlJSxC1F59fNp0F7GX7HxXkHgP8jVmjiUzFDsSNx8oDISN+kqzxk0w2kv4TOJ4IEYdwVny/gs4riDbZp5b3a0k6qW+dEbofJwpc7l42DSb0E6kzpNnk2hWt9cvq2a/L+zUlfamGVkez5Zh5DVHv4A4A23+kXjeHLxE1PwbFlacDX6ykBbQdKw+CVuGy4wrLncrn11Jzqp9fS8Yxcen9fynpkZJ2VnSaPF7SfylqG/WOoiPi/uXr5TU0RmguIelzki4qXweUSf5E6gxpNrt2tm8kopRfUb6WLNuq0nDMfJ2wH7YuX7cRC1e9Y3vHEV871dAa0HKsPECOG6N2Mo+Qjp9k0nk3EdVwG4Dt31CndsuewHOAW4vOZcBTK+gM02pC39JxMKDVtQP4H6Lt8d+K1uXMXGWqxZ60GzP3lNVxw4zUqFo81/a7KQV6S/2PBSvqQduxkiTJA+eGhlpTxaF1CPBswon+pfL9IX2LSPoMESV1VfnapWyrTasJfTPHQYcm1w5A0i7AN4ln3eOBoyX9dw2tjmbLMbOS7Y87Ci1fVyKjq6RBSVpO0vck/VXSXySdIGm5Glodmo0VAEn7SVq8OJzOkHSzpO0HP7f96VrayeSQqV7JpPNP2/eodF+V9AjqGIf32v6H6rchHuYe25ZUe0LfSqdLq2sHgO3fD12/+2b3uz3Rcswcq+jys2SJjtmJKL5cg38pumQMxsrSVOyCU2g6Vh4g1epfDdH7AFIUOl/P9nlz+LUb+tYds06XVtcOJjyVTdLriHpv0yXtAawNfHJQQNf2ln1pPZDDaag1oMZYWdf2mp33P5F0eQWdlwJrOToMIelI4FLgQxW0uqxku9tW/ROSLptgnS6trh1E4fHnOgqqoyiS/3PgoEp60HbM3CVpI9vnFK0NibpCNTgcOIaZna22L9v67Eo4TMuxArC57d0kvQa4iTjXM6kcvZ9MFhnxk0w6Z0n6MLCwoq3sccDJFXR+KekNwDRJK0s6CJjTpKkvhif0P6bOhL6VTpdW1w7g95I2ACxpQUWnhV9X0hrQbMzY3p9IhTqBqPPzMdu1jMMvAN8DHi/pU8A5QO2VpJZjBQgjdOAAlbR9SSl4yuDnttfrUWsjSTuW75eW1K0NsGlfOgOKUX/AXH6ntwm9pNUlbS3pTYOvGjodvZbXbpHiSEPS00qaRDec/419aXU095e02hx+pc8x89Hi9NmIiJo8knoRDnOr23VuJd1lJW0g6fmDr8HP+hwrHe6TtFJHf0XqLUQs2fm+ahpUh7vKeAGqTuhb6XRpee00tO/7aOP8XLLzfc0x807gi5JukHQjcDDw9kpaS9s+3Pa95esIYOlKWgNajhWIEg0QzrtveXzdw5J5mOzqlUw0xeB+C7A58UD8EfA19zywFcVkPzKks7ftu/vUmY32Zl1d99/6uKlOR6/JtStajwMOBF5UtE4DdnHFrlDjGDOSFqcTyVnrwS/p6cTkUsAZtqs60VqOlY7mFcCawBrAUcBhwJa2X9CzzozC3LafJmkZ4DjbvRfmHtL9BHAFldv2lvN7IbAq8AOiYOk5treqqNnk2hWti4HnAUsB5wMXAXfa3q5vrY7mW4mW448gVq2/Vakg6oyuXZL2Aa60fYwqdfJSdME5Hjjc9lV97382mvsC2xBpLYNJmW2/sqLmpsR1u464nz0F2NH2qC5VD0dnW+AzxKq/iPTm3W1/u0+dEbprEQ7CJYru34Ed3HP3x1Y6Q5pNrl3R2hXYgVhoAXg1cITt/+lbq6PZfMwUuwXbt1XU+DFwBPCtsmlb4rr1vrDS0Ww2VoreZ4gxchdRZmBJ4BTbz62hl0wm6fhJkgmg4YS+iU7SL5LeDuxFPPD/DTNasNbKl18KeDKzjpVLamiNC5XWp5I+BvzB9mGq09r5MuBZwCWDybSkK1yxZW/RaNK2V9KVhBPmUttrSnoC4bR7RZ86Q5pNrt2Q1n8DC9ver5ZjZIT2KoQDaFsiGuarFZwHpwB/IJzmzybGygVDKQx9aS0GvJ44pwWIGi7frjwhvAZYw/Y/a2nMRnchIjpTwNW19CU9CVi36PzC9p9r6MxGu/qEvqVOR6/JtStaawMbFa2zbV9aS6uj2WTMSPot4Sz/GXFu1Zy9kpYnIorWJ9LEzyMW/6oWy245VoreUsBttu8ri4+Lt/zMJ/M+WeMnmWgUHQf2Jjzpj6De5GUd4MNEcd7uZLf25GzkhJ6eC+C10hnSbHLtitYKwH9z/+tXc1W35Zh5P7Ca7Zsr7HsWJO0NvBn4LTPr7JhZ26T2rdlsrHSYLml3ohbA80saSo2OHOOor9Wybe9dtv8t6d4yQfsrFe8rhVbXDkCS1ge2I6LSoIFtVc7p6eXrZuByYFdJb7f9+h6ltgZeAuxv+9YyKfxAj/ufge3pRIrxV0u61beAz0s6noiWvLaC7HXE2Gjm+JH0M+BsYsJ7bkWnz1EDHdtX19CYje4sE3oimmpidYY0m1y7orVX0TlsUOenNo3HzKrAc4mIyf1LJPHltl9TQeuvNe29UTT8nG9i+yeStuxs6/5KtZb1yeSRET/JRCPpWmBLIgS9ZrrCNYSxeyWdQrYNVgt+A6xfe0LfSmdIs8m1K1qXE+kew9fvrIqazcaMpFOJVJY7+973CK1rgGfablYgt+VY6Wg+EXgDcKHtn5UVwxfa/kbPOu8HViaKTO5DFOY+xvVqNA10RTgrVrC9t6QnA0+yfUHPOl8iHKCvB94H3A5cZnvHPnWGNJtcu6L1AuK8zrW9b6nj8B7bO/et1dH8HNHe+SfEpPCCzs+usb1Kj1pH2X7j3Lb1pDUNeBkR8fNUIk3vm8TE8NO2n1ZB8wQiIu0MOs6fytdvRSKK43nAekX3Z7bf27POJh2dFYHLiMiKA/vUGaG7EDMn9BsSzsneJ/StdIY0m1y7orVT0Vqf6F42iIw5sW+tjmazMaNo0rAu8IKi+VjgCtu91/kpNsRfmOkkPLdWemxHs9Xn/BO2Py5pVEc7u3Lb+mSySMdPMtFIOhPY1KUDQUWdc2xvNPff7F23yYS+peOgo9nk2hWtX7TOc245ZiQ9i8gl/wWVJy9lovRO23/te99z0Gw2VlpSnC/LEROWZvW1ivYhhENyE9vPKCHip9let6LmU4nQ82o1OIrOIsDdJdz9acT/94e2/1VZdwFg0QapLTsRKVD3u19LWqLPCc1wilxxzlxpe9W+NDr7vo6oLXKYhzrOSfpCpfvZDqO22z6yb60h3ScRE97nARsDv7P9kgo604jJ9cbAO4gIvKf3rTOk2WRC39JxMKTb5Np19J5IRN69H1iqdrRmqzEj6U5iYexzwI9dseZi0VuemU7ClwK32l6rsmbTsZIkcyMdP8lEI2ldIgXkLGad8H6uZ51NiToKw6uCVUMoW03oWzoOOppNrl3RegMRVXHakFa1ujQtx4ykC4juWsPRRb1PXkoK24nAL5n1vGqmzTUbKx3N6cxMZVuQSAe53XavXU4kXWz72X3u8wHqDmrTXOqZtYUud8+1WzqRRSva3qsY30/sO7JoSLNZwWVJxxCTo/uAi4lCs5+z/dm+tTqaZ3ioKOmobQ9TY3ciUmthYOBgEtHe/Cu2d+9Lq+hNAz5ie68+9/sAtRcEBtFE1zRwEP6WSM87hohAuKyGU1vSGUQdr58XnXNaOOxbTehbOw6KZpNrV7S+RqRDDSJVziFqwd1bQ69oNhszkl5FOOyeQ9xXziOii86ooLUc8Ux4ARHh93fi3PbpW6uj2WysFL1dCDt+kDK7NvAh26fV0kwmj6zxk0w6nyJSBx5FTM5qsSOxavxIZk6sTf3c2UOJcP5ZJvQTrNOl1bUDeCbRVnkTZr1+1erS0HbM3Gt71wr7HcWRwL5M3bEC3L8GjqRXEwZq35wvaV3bF1bY95z4V5lsD2oLLU2d6/mlst9NiDpi04ETiBXlWsj2nZLeAhzkKLh8WSWtVW3fJmk7omvZBwkHUO+OH0mPAh4NPK5EaA0KOSwOLNOnVpkQ7SNpn76dPLPRu0/SxsQYaYakFxL3tBuI/+eTJe1g++yKsl8gJrzbEoXdz5J0tu3f9qxzBVGQe3XgH8Ctkn5uu3bL822J83sX8FZJtSb0rXS6tLp2EBFM04BbCUfFzTWdPoVmY6akrJ2oqO2zBfAeYDfC2dw3vwMuJFJG31Fh/6NoOVYAdrJ9oKQXA48nbNDDiQXPJAEy4ieZcCRdZHudBjpX2n5mbZ0RuufZ3mCq6AxpNrl2RetqonNLy7o0zcaMpE8BNwInM2tETO9d2SSd5Qptseei2WyszOU4zre9Xs/7vIqINrgRuANmFK6uXTh+O6KN9drExHcr4KO2j+1Zp0lk0ZDmpcRk8PPAW2z/qtbnUdKvgLWIVd2DbZ9V6/zKiu57CCfPHzs/uo3o6HVw35pFd1lmFlYHoIZjpNzHlgC+Q3wWBlo1IzMvBt5g+5ry/mnAt1pE4UlalJicvR9Yzva0BjpPtL1QDZ0Rut0J/eNt15jQN9MZ0mxy7YrWM4AXA+8FptlerpZWR7P6mClp42sB1xIRMT8juojdXUFrTcIJ83xgeeA3wFm2D+tba4R2q8/5FbbXkHQg8FPb31OjDpPJ5JARP8mk82NJmzcIZTxf0qqu2G5yNpwp6W3Un9C30unS6tpBdLxZkugo1IqWY+YN5bW7Ml+rK9vFkvYBTqJR2hxtxwoA6nTIIFpLr8PM1K8+2aLCPueK7W+WSe+mhLPp1bZ/XUGqVWRRl/cQn4XvFafPikTtmBocSkSLXA6cLekphCOmdxwFVg+U9N+uXPx7gKTPEIW5ryLS2SCuZY2ImMHiQzfqp3Zk5iMHTh8A2/8nqVYHOAAkHUBMQhclUmo+Rkx6+9b5LyK95dmEY/nrNXRG6A5P6N9EpJFPpM6QZpNrV7ReTly/5xNpqz+ppdXRbDlmPkOkrt036oeSNnNP9e5sX15Sr35LnN/2xP+1muOn5VgpXCzpNGAFYHdJi9EuKjuZEDLiJ5loSh2ORYgJ6L+gWjv3XwMrAdcXrVar8teP2Gzbfbdzb6IzpNnk2hWtnwJrEKG+rerSjGXMzOZYejOgFIWWh7Htmu3cm42Vjma3Q8a9xOT+q33XOyg1b+6H7d/1qTNCt0m3ptlEFu1h+7g+dWajvYgbtUEe0n1EjZQMjWjb28V16oddQ0RLVm93LmlF29fNbVvPml8nnEtHlU3bAY9w3a5zryNSkv4ym5+vZvtXPeh8gHDQXTxqPEpayvYtD1dnxH7XpcGEvpXO0D6bXLuyry8ys7X6H+f2+z1pjmXMzOZYZiks/zD3dRGwEFFH6BziGtbuyttsrJT9LUA4Qq+zfaukxwLLunIzhWSySMdPMqXp0YB6yqjtgwdHy4dhlxqGzTh1hjT7NKBGpia5bjv3eWbM9GlAPQCtHVy5I84IzV4NqJZIupKYeIqoYbQCUWB2tcq61bs1FUN0PaI+xSCy6IxKkUVd3fWJldxFbS9fwvzfbvtdFbSeAHwaWMb2FpJWBdavkUKgMbTtlfRD4HW2b+973yO07nefUuXi54qW4O8mVuZFTHq/1MLRNYdjanK/bvlcGIfuOM6v8bP257bXb6HV0Wx5fr2lKUla2vb/m8PPx2G39P6/lPRKIpIJIpXt5D73n0w+meqVTHWOIlaaHxYPYGXgjD50HgL7Ai0cMq10uvRy7WDuDp4aBtQ8NmY091/pjV2IyI6W9DZWJO3mKAZ8ECNSu9xzpzsP1Z2RtDZQrR2xOt2aJN3GzLFxD/CVPrVs/1vSAeWzdXWf+54L/0PUxDipHMflkp4/x7946BxBFND8SHn/f0SNmt4dP7Y/Xr596+yiHCpwJ3CZottPlY6PihotqwFLDEUzLU44Q6tRHDyfK1/zCq3u1y2fC+PQHcf5tdSs+tmYDS3Pr7fIhDk5fQrjsFt6/V+WtNx1gW+WTTtL2sANivMnk0M6fpKpzlQ2MFrqpgHVPxNpQD0AJn2sDKJRLupxnw8Y25eUFIZa+2/arQk4TdJrge+6YYix7d9LswyLWo6Sx9k+tjjUsH2vpNpOmeslnUo4mH5S+f96UvmqySrAy4k6bK/obJ8O/GcNQUnH2t66E3E3C+NIye3KTzGdcemO4/xaak7182vJOOyWvv+XLwXWcmkZL+lI4FJmrf2YzOek4yeZ6kxlA6Ol7lQ3MKb6+bVkov+Xg9DoVmHfknbtvF2AiFya2+pkH3xE0vbACrb3lvRk4Em2L+hZZ1eiPtO9ku6mQX0m4PeSNgAsaUFgZ2Y69PrmjlJLYVC8ej2iDXJNViEcJO8GDpN0CvBt2+f0LdTic+CZbZ3Xt/3z2nqFXcrryxvpJUny4LihodZUsceWJFKrITokJskspOMnSZJk6nNDQ61xRb/1gqSTmYMRWKEg+GKd7+8F/hc4oWeNUXyR6PixCbA3cHvZ1mu0ke3FJD0GWJl2kXXvAA4ElgVuAk4jnCQ12JWIiFlJ0rnA0kQB62rYvgs4FjhW0lLEuZ4F9NYmeEwRMX8rKWVPsL26pDWAV9r+ZN9Ctv9Uvr0ZuKukJT4NeDrww771HiT3NNLp/V49qOtl+7w5/NoNfeuOWadLq2sHEx5dW4ofn2p7uqQ9iEWPT7p0CLU9soh9Jcbxv+x7rOwDXFoacIio9ZPRPskspOMnmeqkAdUPrXS6pAH1QHfU0ICSNG0u9UXO7UvrQdDnWNm/vG4JPBE4urzfljqfg6uGO1yV61m769Vzba8t6VIA27eU6JhekfRWIrpiOeAyotjzeUSx5yrYvpnozlSdkpr3AiIKR0Rh7n/V1i2a2wBbEN0Kt+5ZYhwRMV8FPgAcCmD7CknHAL07fjqcDTyvONDOIFI8t6Hi+JG0IXCZ7TtK1N3awIGDunC21+tRayNgZduHS1qaKHg+6OLZ+2dwUNcLmG3NvJ6fR6sDq9JxKtv+Rt86Hb2W124RRjglO/eXXjswFs39gcPn0CyhzzHzUdvHlTH6YuLZewjw3B41gPHZLZKWBZ5CZ75t++zy2ttYKfv7lqKD7brEs+iDtv/cp0Yy+WRXr2SimdtDuGet2RpQkh5j++9z3sND0mzWtWFOBlQlvZbXbo4GlKTVbf+yZ805GlB9jhlJV9heo4zRfQgD6sO2axhQ1wPHE+d2Vd/7n4PubA2oSnpn237+3Lb1oDOqk1H1zimSfgFsAFxYHEBLA6f11UWlo3MlYYieb3utUsj3E7a36VNnSHNpojbMU5l1vPTe9arobTBCq+a983rCiXYscJIrt6wvncsGkWAX2P5rJZ0Lba/b7eYj6TLba9XQK/u/pIz//wYWLoXdL+37czCkeQWwJrAGUZj+MGBL2yO7Tz4MnY8D6wCr2H6apGWA42xv2KfOCN1PAFdQua5XOb8XEnbLDwgn6Dm2q0Xctbp2Reti4HnAUsD5hFPyTts1nZJvBXYk7mWHA9+yXSV1dfA5k7QP0VHymFqfvXHYLZL2JZzIVzGzxpwrRA13NZvaScnkkRE/yaRzCLCmol3vbsRD+BtANQOKeBg+kogE2BCghtOn0KQw6uwMKOJ/WYsm164wx1Xdvp0+hauBr0gaaUD1PGYGRsXLgENsnyhpzx7332UN4PXA10pU2teJ+iK3VdKbrQFFXNdaLC1pRdvXlWNYgUjj6QVJWxDFGJeV9IXOjxYnUr5q8wXge8DjJX2KSE/ao4LO3bbvloSkhWxfLWmVCjpdTgR+BvyYekWdAZB0FLAS4Yjpjs2a9841a37eukjaGvgs8FNiFfkgSR+wfXwFuZslrcTMeklbAX+a8588bCRpfeJZ8JayrbZtfK9tS3oVsdhxmKQdKui8BngWMIj8/KOkxeb8J70wqOt1n6S7qFfXayvCCXOp7R2Lg/JrPWsM0+raQSzO3ynpLcBBA6dkJS0AbH+NeLavQjiArigprF+1fWbPcn+QdCjwImBfSQsRde5q0NxuAV5NOF3/Obdf7IOOnfQrIo0b6ttJyYSRjp9k0kkDqh/SgOqZqWpA2Z5OpGR8VdEi+1vA5yUdD+xt+9oKsq+moQFVeC/wU0nXlfdPpd82638kHJCvBC7ubJ9etKti+5tlRXlT4r7yats1CiDfJGlJ4PvA6ZJuIc69Jo+2/cHKGgPWAVat6ZgfwT2S3k20QO9GaNaIaPoIsO4gyqdEU/2YWD3vm3cDXwGeLukPwPXA9hV0uuxC1MH4nu1fSVoR6Pv+PMx0RRe47YHnS5pGLCb1zT3lGTtwpC1SQeN+2G5hG8HMKN57JS0O/BVYsbJmq2sH43FKUs7p6eXrZuByYFdJb7f9+h6ltgZeAuxv+1ZJTyJSPXtnTHbLdcTYaGW3vJr2dlIyYaTjJ5l00oDqhzSg6ohOOQOqnNPLCGfWU4EDgG8SIek/AJ5WQba1AYXtUyWtTFw7gKv7NKhsXw5cLukM2zd1f1achbf0pTUH/kJExjwCWFjS2i51ofrC9mvKt3sqik4uAZzap8YITpH0Uts/qKwD8EuiFlTtyJQuRxERhS8G9iLua7W6li0wlNr1N+o5la8DXlSerwuUyVpVShrE2Z331xFd4GqyDfAG4C22/yxpeSKqqm+OLQsCS0r6T2AnYvJbFUkixmTtjoEXFafyVwnn+e1A3xrDtLp2AO+hsVNS0ueIjoE/AT7duWb7SrqmZ7lDbc+oU2T7T5L2I4rx98qY7JY7gcsUBetn2A62a91fmttJyeSRNX6SiUbSE4mH8IW2f1Yewi/su76CpPcTXWk2I2qo7AQcY/ugPnVG6DYxoCR9CfgwEQr7PsKAusz2jn3qDGk2uXZF6wXEeZ1re99iQL2n4gN42IA6rHvNJF1ju7d0F0lHdQ2o2W3rSes6wvg8zEOFxyV9ocb/VNIJRERaKwNqoFu97lUxpj9q+9jy/n3EpGLVPnVG6O4NvBn4LTO7Ntn2JjV1WyBpOhEp+U/gX1RsIV+cWWsRE87u2KxZx+FSR22MQW2vRwI/qnHtJH2WSJP4Vtm0DXBFjYiqMol/E/evl1TzPv004P0jNCf6c1Bsh+UIx/XmxGfgR7ZPb6B9CKVjoO1nKFKsT7Pda8fAIc2nAovbvqKWRtFZhEhfvU+jCy7X0l2AqCtZNcVT0k5ECtSdI362hHus96OhWnbFOXNljWffmOyWkRHsto/sW6vojcVOSiaLdPwkyVxIA6qaThpQ/Wk1MaDKfj9ie68+9/sAdJsaUEWzSeHQEp31FeBu4AlE5Mb7bN/ep84I3WuAZ9pu2T1vylGcyvfD9lkVNS+w/RxJZwPvAv5MFF2uEqUpaUtgI+LZd7bt71XSOY8oYnslM2tU1P6cXw58mYgYmVEPyvbFs/2jh685nZnO1gWJVfrbbS/Rs87Ftp/d5z4foO6gYPalnlmk+3Lba/asM1gYW9H2XmXx6IkVIou6ms0KLis62r2DGJcXE9GSn7NdK8KIEoG66dy2PUyN3YmFxoWJqBiIe8s9wFds99qCfFx2S9FekJnRRFU7Po7DTkomj0z1SiaaFgZUSfH6fjGgqjt7hmjVcvl+BpSk59Q0oGjYRneUASWpqgEFbGf760PHcYbtTfty+nQNKEkDR9YMA6oPjS7FSbcxkV7SDNtHtjSgCk3qXpXw9lOJkP5/A7vXdvoUfgksSaR1TjnUrrvJS4ejXxRFNqs5foii8UsRxbhPAhYFPlZR7zzi3vlvonV8LR5le9eK+x/FvbYPaSnooRRuSa8GnlNB6nxJ69quec1G8a8y2R6kxi9Nx5HXI18q+92EeCZNB05gZge6GoyqF3hZJa1Vbd8maTti8eGDhP3Su90i6VHAo4HHlXuLyo8WB5bpU8v2PsA+kvbp28kzG72x2C2SXggcCdxA/D+fLGmHSs+hdPAkD4h0/CQTTRpQvZEGVE9MZQOqcJ6kg4HvADPaSLvn2jBdWhtQhbvdoO6VpNOJ+jCrE5GFX1e0jX9/31pD7ANcKumXNEpRaoXadoHbjLifdNlixLbecBSOhzifqrXYFO2dP0akrA66eu017NTuiaMUtWhOYdYxWatrJsDJkt5FdLhrpTkLtr8v6UMVdr0x8HZJNxL36kHK4xoVtLqM6hj40Qo6TRbGhpDuXy9wWiWtR5Y0zlcDB9v+l0qdyQq8nagptAyliUnhNuCLNQRt797QQd/cbiHqCG1u+xqYkVb6LaBKFJ6kK5m5ED7gH8Si6idt/62GbjJZpOMnmVKkAfWQSQOqP6a6AbVBee2unplwGtaitQElogvbktQvHPpF298v399aPhMfrqAzzJHAvgyl1UwRXk3l7iaS3kmkWa0kqZsWuxgRIVMNSZ8G9rN9a3m/FJEeuEcFuQ8AzxpMGiQ9lji/Go6fewhn/Efo1J2irnNrkB7RLYZfVbOkzg1YgOgMV+N5tEWFfc4Vt+sY2GphrMt7aFdw+VBiseNy4GxJTyHsiN6xfSBwoKT/duXalQMkfYaoK9nCQT8Ou+WRA5sFwPb/FTu0Fj8k/o/HlPevJz5//wCOIGpOJvM5WeMnmWhmY0C9wPb6Pes8ZdR22zf2qTMb7acz04A6o4YBJekXxIPxwuIAWpqoJfSsvrU6ms0KLkvamViBv5zo7LA8cLTt5/Wt1dEcuwFVI3pD0oqOzjdz3Naz5hXDTtZR23rWnFEfQ5XrXknaCFjZ9uGSHgcsZvv6GlodzbNsj6xPM+lI+iHwupopc5KWIOp87AN0Fxum144WUad2SmfbLHW+etQ6A9jCpRZUWRD4ge0XVdD6LbEIcXPf+56XkHR45+29xOT+q561e1ofOsuP2m77d33qjNBt0mygRPBuA6xNOLK3AvawfVyfOrPRXsT2HXP/zd51H2H73gr73cT2T4Zs6hnY/m4FzWuANWo66Dta47Bbvk44l44qm7YDHuFKTVMknWt7w1HbJF1p+5k1dJPJIiN+kkmn68EeGFCvqqAzFg9px1i6esS2PhkVWVRj9XgGjuKnZymKPFdto2v7C8Q5Drix5Hz3zsCAAv4wyoiqYUABr6FyhEOH4wlDu8txVIq+KVwk6TBmNaCqFV8tzEjvtH1DLRFFEel1gFWAw4laZUcDG87p73rgYkn7EDViuikuNUPfqyLpIOJeXb2NrqNO1z8kHQj83aX1uKTFJD3X9i/60hrBNEkLDT7vkhYGFqqk9QfgF5JOJP63rwIukLQrgO3P9aj1K2YWe22CpEcDuwLL236bpJWJe+kptTRrTfxG8L/ENRPRmXAF4Bpgtcq6s+y/ROX0+nxQNGm4HtiN+pFFXd31gcOIulrLS1oTeLvtd1XQegLwaWAZ21tIWhUY6PfNC4h0zlFRIQZq2C0t24+Pw255J/Buwq4VEcn0pYp6i3afPZKeQ4xTiPlRkqTjJ5ls0oB6+KQB1TtT0oAqkWerEYWxuw6txem0O69EawMK2qV3vgZ4FiUt0PYfJS025z/phUHEyHqdbbVD32tzUXm9mHBodanlvD+EWScUd4zY1jdHA2eUyBEDOxERDzX4bfkacGJ5rTFG7yMcdmfSrh3x4cR4GaSC3ERMCHt3/EjardSyGzgoZ6Hv8xxe4Ze0NpGKXAXdv9nAoL5d780GSv21A0p099Vz/YP++B/gxZT7i+3LJT2/ktYRxPj8SHn/f0SNmt7tFtsfL9++1fZ9c/zl/qjuoB+n3VIc858rXy14K1EjcFHis3cb8NayuLpPo2NI5nHS8ZNMJGlA9UcaUP0yVQ0oIiLl5UQnqK5Tazrwnz3q3I8xGFDQrj7GPbatUnNqEAFXG9tVIt7GiUtXE0m7lJoVM5C0SyVZuZMzX+6nVW2r8uy7kpmO+r1t/6iS1idq7Hc2fL98tWQl29tI2hbA9l2SNLc/eogMFlMumuNvVcL2JZKqNWxw+2YDp0l6LfDd7mewNrZ/PzREaj3nH2f72GIPYvteSbVtiusVXSa/A/yk8v/1JO7voO+b5naLpGNtb63RxZaplaLuaD7zzJKGLJcacIVja2gmk0c6fpJJJQ2ofkkDqn+mlAFl+0TgREnr2/55Ta0B4zKgyr6r1+8qHCvpUGBJRUejnYiC0lWQtL3towepOsP0nLozLnYADhza9uYR2/rgOkUNsUFL8HcREXhVsf1DopjnlMHjaUd8T0mVGzheV6JS5KTtk8trk/Mc+owvQESh/b8G0h+RtD2wgu29JT0ZeJLtvovj7wosAtwr6W5mRmUu3rNOl99L2gBwqXe1MzPt0b65Q1FMfTA21yMK9dZkFcJB8m7gMEmnAN+2fU7fQi0+B+OwW4DBIsPLG+nNQNLLiAinRw1sa9tN29gn8zbp+EkmkjSg0oB6kKQB1R9/K5FFT7C9uqQ1gFfa/mQFrbEZUA1Zmqg/cBsxZj4G9F44t8MgoqhFOllTStTGG4AVJHUdoYsDtVrZvoOoH7YHcX85A3hbJS1gRlODfYHHE/fqFvfr6kh6ObA3M7sTtjivjwOnAk+W9E2ittabawhJOpk5pBy6/2L83c/4vUTK+gk9a4zii0R3rU2I63l72dbrYpntxSQ9BliZ+unGA95BOJCXJdICTyOe8TXYlVjQWUnSucSzYqtKWkBEvBHRIccqugUeCJxFjx1Xx7Sg08xusf2n8u3NwF0lCvRpwNOp6KyX9GXg0USa+teIsVKjE2kywWRXr2QiaW1AKQqwDhgUkT7B9t196ozQPYRiQNl+RnkQn2a792ijUQaUowBzFRTdiw4kJrkiDKhdXNoG96y1NnAQsDrwS4oB5UpdmkboDwyo7WxPtAEl6Syi9fGhLp2FJP3S9up9a3U0F2GEAWX7X7U0W6ER3ZhUv2PZNGBn25+vpTEOFN0XV2BEpy3gClfohjMOJF0LvKJ2HbbWlPPaEriyZeRpWRRYj3gOne9KXcUUnSwhzvGJRK0mgG2BG2x/uGe913mow9WobX0zuKep031O0uW21+xZ563E4sBywGXENTzP9qZ96oyTkja6CjE2r2nxzCvjdBsi3flC4Du2e3MYSnqS7T+pYbfcMdktFwPPI7o/nk9kKNxpe7tKelfYXqPzuigRxb95Db1kMsmIn2RS2b+8jjSgKuhdNcqAIopA1uS5AwMKwPYtJTqmV2ZnQBE1JKpQjOsqD8ARWpcUY2bcBtTWPUuMIyLm0bYvGErRqz2hPht4XnGgnUEYUNvQaPzUQNI7ibSgFSV1HZCLAefW1LZ9n6RXAlPK8VMmDDcC6ysKug8c5L+u5fSR9CjgLZTw+s6x7FRDr/CXVk6f4mg9hDYRfr8HftnS6VNYlohoeATwfElVui8OFlIk7W27W8/uZEln960H7M79bZRR2/rmX8W5PIiwXZpYwOqbXYjP+Pm2N1YU8q1ak6qcy38CT6Uzh6r4eX9OR2vtMja/UUkLSdcTNuCxwAdcoWX9ICLG9o1D9+kLbP+1b73COOwW2b5T0luAgxy12S6tqHdXeb1T0jJElOsKFfWSCSQdP8lEkgZU76QB1TNT2IC6udTBGIzJrYA/zflPHjatDagWHEOEfd8vOsX23xvonyfpYKIG1Yyx6Qlu5z6gOOX3B35KOHoPkvQB28dXkDuKKIr/YmAvwhlZ2ylzkaTvEIWQu8Xca3QM/CplpbxoXCHpGKCG42c34Adldb57XtXqTkn6OrAG0Up+8Gyt1X1xwNKSVrR9XTmGFYgo1F6QtAXwUmBZSV/o/Ghx2rR1/gLwPeDxkj5FpJzsUUHnbtt3S0LSQravlrRKBZ0uJwI/A35MvZqEAEg6CliJsCMGWgaq2S3AmrZvq7j/GUjaGvgsbe7TY7FbFN1rtyMWB6DuvPsUSUsS/9NLiHP9WkW9ZAJJx08y6aQB1Q9pQPXPVDWg3k10lnu6pD8A1wPbV9Dp0tqAqo7tfxB1prYd0yEM2ld3Cz9Oejv3AXsA6w6cn8XJ/GOillLf/Ift10l6le0ji1OkSoetDosTnfy6Ify1nBUtV8o/RdSDeRTQe2TrbFjP9qqNtAa8F/ippEER8KfSb5fQPxJRka8kWtUPmF60q2L7myXNZdB17tWVItRuKhPd7wOnS7qFOPeaPNr2BytrDFgHWLVxBNw9kt5NmwjGj9DuPj0Ou2UXYoH4e7Z/JWlF4MxaYrb3Lt+eoKgp+ahiZyTJDCbacE4S0oDqizSg+mdKGlDFyfoiRd2dBWxP71tjBE0NqPkBT8F27h0WGIp4+xtRlL8Gg5TRWyWtDvyZeA5Vw/aONfc/RMuV8seMoR7FzyWtavuqVoK2T5W0MlGrDOBq2711ErN9OXC5pDNs39T9WVnQuaUvrTnwF2Jh5xHAwpLW7jua0PZryrd7SjoTWIIo1F2TUyS91PYPKutA1CN8IvUjU7q0jGBsdp8eh91i+2wiTb17DDv3rSNpE9s/URT9H/5ZrUjQZELJ4s7JxCNpISoZUB2N5UYZULav6VtrhPZSwJOZNR2qWjpGqUuzBHCq7Xsq6nySKMRY3YCSdBxRzLaZAVU0rya6DM0woGzvMsc/fGhaV9p+Zuf9AsDl3W09ai0JvIn7p+j1btAk9SipgZ8GlrG9haRVgfVtHzbmQ3vYSPoskb7zrbJpG6K4c++OZkV9tBOK3uHAosBHbR/at1ZHczmiWP2GhEPmHKIw/k1z/MOHprUisVK+AeEwuJ4oUl+jAOtngJ/YPq3vfc9B8/nAyYTD7p/M7CRWrbh60V0dWJVZFwV6jUCVdA0xFo8t798HvKV2hJOkvYnOaL9lZtMB2574aEJJ04nOiP8knL7VOs8VZ9ZaRGembupj393fupqX2n6WZhYIfiTwoxrXrvF9ekka2y2K+mjvH6HZ6/9S0p6295R0OLM2+RiMzZr15pIJIx0/ycSTBtRkkgZUr1otDajziA4VV9KpN+WKLeVbGVDzE5J+SDgqPmJ7TUX3mEtrOAvHQVn93Ii4r5xt+3tjPqTekHQ6USPqqLJpe8IZs1kFrWmOYuDVV8pbPhM6mtcSbbOH72e9O7Y6mh8HXkjYLT8giv+fY7vXVt2SnkQ47e4GnkBEbrzP9u196ozQvQZ4Zs2Fo/kBzewCNwuu2231AtvPUdTKfBfhEL3A9oqV9Jrcp8dkt1wOfJnIFphRzsD2xbP9o4em8z5ifqDOK+X7qjXSkskjU72SiWZ2BhT91255IfAVRdHQgQH1nJ41RrE1sNJUNKBsL9ZQbs+GWgOapYDY/sCQAfWVihPdR9netdK+Z8dxhAH1NSrXg5qPeJztYyXtDmD7XklT6X97LvEZNOHwrYKiFfiezIy++Rmwt+2/1dIElrZ9eOf9EZLeU0nrekmnEkXAf1JJA2j+TBjwO9snNdbcCliTcLTuWKLvei/C6miZfSqRJvtvYPfaTp/CL4ElgVoNBsaKpGWBpzDrIkSNpiIvHV68kbQvUM3xQ9i5SxF10k4iIhg/VlHvPOKZ/m+i82ktxmG33Gv7kAY6i5bXVYgGHycSduAr6KSaJQmk4yeZfNKAmmDSgOqVVgbUUZL+EziFWaOnanaiamVAzU/cUZwWg9ot6xHFpicetS12/m3CuH5teb8d4SR5UQWtATdL2p6ZEX7bEvUxarAKMYF4N3BYKRr6bdvn9C0k6Xjg60SacY3ulaO4uhTkPpn6HdIG3G3735LulbQ48XzvPaKiRIb9CVgdWA74uqSzbb+/b60h9gEulfRLGkXYtqLYDdsAVzFro4gadstmwHDU7hYjtvWG7YH9fDYVxmSXkib7McKhPLhP72X76xXkxmG3nCzpXUSDlmqatj8BIOk0YO1BVKakPanfeTiZMDLVK5loJF1oe11FAeSNiaLLv7S9Ws86AwNqZ4oBRYSlVjWgJK1DeO/nGwOqxrlJusT22kPbrqhdx6EVIwyoFwBVDChFwepPAbcya/phNSOxGDB/pbIBNT8haW2iTsxqRCvrpYGtbF8x1gPrgRJiv5mHip3bXrOC1sW2nz207SLb6/St1dn/8sDBwPrEZ/A8oobZ72ppFt2lgAOJtLJpFfb/ImBHYD1iwnKE7av71hnSPHzE5mp1MSSJWJx6H/D68no7cJl7Ltot6dW2v995Pw34sGd2/6mCpF8Bh3L/tJqaCy1NKGlsa7hCLcmOxjuJNKuVgGs7P1qMqIu4XUXtTwP72b61vF+KSA/svZts+V9uMIiOLAsR59nuvaPsmOyW60dsrqYp6Wqim+w/y/uFiFqPT5/zXybzExnxk0wsxYC6ohRt+yqRR3s7dcL6v9gxoG5VtJb+cAWdYY4E9mXIgJoivBpYpZUBJak7oV2MmCxVo6UBBXwAeNawAUU4KPtmV6KF9c0V9j07diivH+hsM5VXJKc4VxGOtDsJh/n3gf8b5wH1SMuuXmdKej1wbHm/FfC/lbQG7A3sYPsWAEmPAfYHajkrXkA46bcgogm3rqFj+8fAjyUtQUQxnS7p98Tz/Wjb/5rjDh6aZssOadi2pLXKc+HLJZJ48RoOV9vfl7QRsHJJDVwKOLpvnRHcbPsLDXTGwXXAI+ksQFTgGOCHROTUhzrbpzdY7NjC9gzb1vYtkl5KRC73zU3Es2fAdOD3FXRgDHaL7RVaaRWOAi6Q9D3CPnoNMYdIkhlkxE8y0XRXWyU9lUoGVNn/DANK0uOAxWyP8uj3qXmW7ZEF/iadUlz2dTVT5soEYinGYEANijsPbbtf5FFPWmcQBts95f2CwA9s955uIukk4PW27+x730k7JB0L3AZ8s2zaFljK9uvGd1T9oNHFzq+0vVsFrUFB4oFjfgHgjvK9XadY/ah7y/229aR1PXAZ4dg6yfYdc/6Lh633WKJY9RuBPxLjcyOiWPALe9TZzfZ+kg5i1k44QPVuP18koplqpuQOaiCuQyywPE3SMsBxtjesrPs5wjFyErNGaFbrRlqbzjhZligvcAaznluNNt3rAb/qpO4sBqxq+xd9a3U0rwDW7USNLAxc1HcUfdn3N4BnElHtBl5FLNz+H/RblHgcdoukRxMOp+Vtv03SysRn8ZSKmmsDzytvz7Z9aS2tZDLJiJ9k0jlf0rq2L7R9Qy2RrgFFdMJZkFg5q2pAARdL2oepaUDdCVxWnBZVDCjb/wD+IelA4O9dA0rSc2saUMA0SQsNGVALVdL6A/ALSbMYUJJ2hd67OtxHXLczqWz4DhiHATUfsMpQ6tOZJUVq4nEUO38tcX+uWuzc4ylIvICkpYYifmrZc2vavq3SvmdB0neBpxMr16+w/afyo+9IuqhnuV+X1773+0DYGHi7pBsJJ2GtFvKvAZ4FXEII/LE4D2ozcECu19lmYJK7MA7GycWEPdal1gr6IUB3oeiOEdv65mjgDM1sDb4T9aJGflu+BpxYXmuM0eZ2CzFXuBjYoLy/iUhhrWa3lLnBxM4Pkvqk4yeZdNKAmjzSgOqflgbU98tXS5obUPMBl0paz/b5AJKeS3TCmhLYPqHUZnsEhHOkVpSfpDWIjn3dIvU1iwMfAJynKIZsIvXqU5W07in1MVYDHjXYWKkGzreJws63SdqjrF5/0vYlfddMsn1yeR1HKsQWjXTuKallgwLui7QQtb1xC52WDMaJpF1sH9j9maRdKsnKnbQMR0HwqvO2EgV3JbApYU/vbftHlbQ+UWO/s+H7tLdbVrK9jaRtAWzfVUpUJMnYSMdPMumkATVhpAFVRauZATWmiVIaUP3zXOBNkgYFgZcHfl3GbA3neTMkvR3YC7iLSMESlWpCSfo6kVb2K2amexmo5vix/Y0SAbMJcW5b2r6qktxRwNXAi4n/6XbMjJbpmz1sH1vSql9M1C06hBirvSLpZOaw0OCKDRRs31hr30McK+lQYElFR6OdiHpJVZC0ve2jB5Gmw/QceToudiAKnHd584htfXCdpJ2JzwBEvcLrKujMgu0fEjWGpgxjslvuKZHeg3nDStStDZUkcyUdP8lEkwbURJMG1AQi6eVEcdmnEM+QQZRd77VMOqQB1T8vGfcBVOT9wGqNCnmuZ3vVBjqzUBw9tZw9Xf7D9uskvcr2kYrW51Uc2Mzs7vgy4BDbJyo6+tVg//K6JfBEZhY93ha4oZJma5YGjidqea1CdH7sve5bh8GC2DjSH6tSFh3eAKxQ6sUMWJwoHl+DdwBfIAorm6gr9LZKWgBI2pJoKPJ44tne4vlenTHZLR8HTgWeLOmbROrxmyvqJclcScdPkjww0oDqiTSgJtuAAv6HmCxd2Y2iqkwaUD3T0Gk+Dn5L1BBrwc8lrVox4mbcDDpp3SppdeDPRFpbDf5QFlheBOyraEdcpRubS2txSXvbfn7nRydLOruG5hjYzPYHgdMHGyQdAHywhpjtQxUt42+z/fkaGmPkPOBPwOOIVMsB04EqDUUcnQlfX2Pfc2A/or5Wrai+cfE/NLZbbJ8u6RKiVIOAXRotRiTJbMmuXknyANCIbkySrqiZDlEMqJ2nmgEl6SnACozotAVcYfvesRxYz0i6liloQJXiiJva/vdcf7lf3ccy04A6Pw2oZHZIehZRF+oX1O+883zgZMIh8k/q1ZkbC5LeCpxAdN85AlgU+JjtL1fQejQRiXal7d9IehLRzeu0vrU6mr8GXmb7uvJ+BaIj4jNqadZG0juJqNYVmbX222LAuba3r6x/5lRMUx8g6QnAuuXtBcVBU0PnUcBbaFNfa6B5rit3fetoPY2Iwn6C7dVLrbRX2v5kBa1x2S2t678lyRxJx0+SzIE0oOqSBlRvWi0NqHWJkOmzmHVSXTX9MA2o5IEi6QLgHOBKZtbdqVLnoTh4dx2hNZUjqqYMkl4CfIWZqb9PBd5W09lUG0lLAEsxYnGlVoHzIf1PAUsA3yEaKQCT3Y10gKTXEWmCPyWcvM8DPmD7+ApaxxH1td5Ap76W7Vq1EFF0QH0iUQi5+3zv/Vkr6SzgA8Chtp9Vtv3S9uoVtJrbLbOr/1bT7kySuZGOnySZA2lA1SMNqF61WhpQpwG3c/+JbrUC02lAJQ8GSefZ3mDuv9mL1k9sT3KXxTki6dPAfrZvLe+XAt5ne4+xHliPlJSyp5e3V9v+Z+dnm9k+ffRfJqMo0RXDeCp8TiRdTqTQ/bW8Xxr4se01K2hdavtZg+hySY8EflTz/6joQjpMlWetpAttrzs4z7LtMttrVdAah91y1TjqvyXJnMgaP0kyB2z/A/gHUfBxHAwmL3t1tk16O/cBewDrDhtQRC2lvmlZoHTA4kSdkc0722p1+3m07QuGGl3VSpl7jO3N5/5rvTKWArrJxHKmpLcRKVhdp2sNZ/3V5X4yrDVVotG2sP3hwRvbt0h6KXH/nhIUR8/ls/nxvnRq5CRzZypHKQMLDEUm/41KdahoW18LANs71tz/EDeXRg2Dpg1bEXWUajAOu2Wq139LJpB0/CTJPEwaUL2RBlR//FjS5o1TIdKASh4MbyivuzNry+7e27kDCxMOnxYO3nEwTdJCgyiY0l1voTEfU0s0919JupQU7k8Dy9jeQtKqwPq2DxvzofXBqZJ+BHyrvN8G+EElra+UCLuPAicR9bU+WkkLAEnLAQcRDRRMpMzuYvumCnLvJtIsny7pD8D1RDR2DcZhtxxJ2C5Tsv5bMplkqleSzMNMZQNK0meJ9J2uAXVF6ULSt9agQOkaRNHXRYGP2j60b62OZjMDStKKhAG1AXALxYCqUWdE0nSi69w/CYda9W5lU72AbtIvkrYGTrV9m6SPAmsDe0+FFNnWSNoNeCVx3zSwE3CS7f3GemCNGNXYIZkzkn5IjJeP2F5T0iOAS20/c8yH1gulY+dGxHPobNvfG/Mh9Yak04FjgKPKpu0JW2KzClrTbN8naRFiIXB63xodrXHYLVn/LZnnSMdPkszDpAE1uUxVA2ocpAGVPBg6NTE2IhznBwAftv3cClotV8jHgqQtgE2J+/Rptmunyc4zpOPnwdOydss4KAtyzyE+7zWbUjwW2JOZ95afEQ7sv9XQK5r3u04V6+78DjiVqGH5E0+xCelUr/+WTCa10iqSJOmHx9k+ljLZdbQ6v2+8h9Qr5wJnAmeU76sg6bGSDpJ0iaSLJf1PMapqsrTtw23fW76OAJaupHW9pK8Q7c5vr6QBgKTjJb1UUsvnx+9sn2T7ets3Dr4a6ieTxeAe+TLgy7ZPBBaspHU4kYaxDLAsEZk2qkDqxGL7h7bfb/t985PTp3DDuA9gArmjPF8HqcfrEbUSJ54STXgBsBWwNfCLklpdg28DfwVeW/RuJpwkNblZ0vaSppWv7Yk0/BqsQtR1fDdhwxxcnPW9Mya75WpJx0jaVtKWg6+G+klyPzLiJ0nmYST9lHjon2577WJA7Wv7BeM9sodPMaA+S5uuXqcDZwNHl03bAS+0/aK+tTqaPwaOYGYq27bAjrY3raC1MPAK4PVEWsspwLdtn1NB60XAjoST6TjgCNtX960zpPklYEmmbgHdpEcknQL8AXgR8GzgLmJlvkbnnWYr5OOgTFT2BR5P3Kerp0i0YG4TsLy3PHQkrU1Ewa1GdGJcGtjK9hVjPbAeaNzV62Lbzx7adpHtdfrW6ux/eeBgYH3CcXcesLPt39XSLLpLAQcSUdHTKux/HHZLsw5pSfJAScdPkszDpAHVm1YaUP3rLEE4sz4C/B74KnC07X/N8Q8fmlYaUMkDRtKjgZcAV9r+jaQnAc+sUdizpYN3HJQ0y1fY/vW4j6VPZnNPGZD3loeBpEcB/wW8GJgO/Bw4yPbdYz2wHpB0ZTfVvkSQXF4j/V7S/sBFwLFl01bAarY/3rdWR/NI4D22bynvHwPsX+vzIOkFRH3HLYALge/YPqGGVtFrZrckybxIOn6SZB4mDajetNKA6lfrsUTNojcCfwS+SdRqeqbtF9bQTJJ5kXE5eFsh6VzbG477OJLJQdKxwG3EcwFior2U7deN76j6YTZNKa60vVsFrUFB4kFduwWAO8r3VaLuunWZ5rStJ63rgcsIu+wk23fM+S8etl4Tu0XSbrb3k3QQs3aVBMD2zn1pJcmDJR0/STIPkwZUb1ppQPWn9V3g6UTR6iNs/6nzs16jqNKASuZ1Wjt4WyPpQOCJwPeZommWkl5GRNU+arDN9l7jO6LJRtLlw5G7o7ZNKpJeSxRcnopNKS4n0uC797OzKi3ILW77tr73OxutlnbLK2yfLGmHUT+3fWRfWknyYHnEuA8gSZI5ssqQsXRmeTBPPLY/MGRAfaWWAWV7sRr7nQsLSFpqyICqdc9ds5UBRRScHLTK3qOkI37S9iUVUucG6SUX9bzfJOmLNQafcQDbf5fUu3N3jCwO3Als3tlmYEo4fiR9GXg0sDHwNSIa9IKxHtTkc6mk9WyfDyDpuVRs3tAa2yeUuoGPgHi22/57DS1JawBPpWM7VHa6HgCcJ+l44nO+NfCpSlr3SHo393e61nCaN7NbbJ9cXtPBk8xzZMRPkszDSDqC6ErTNaB2sP2usR5Yj0hanFmNmilhQEl6E7A7MIsBZfuoOf7hQ9N6FPAWGhhQmrVV9j7A/lRqlZ0k8zotV8iT/unczwaviwLftb35XP84GYmkXxMdmwbpjssTTvx/ExG2a4zr2B4ukt4O7EUUjP83M4udr1hB6+tEVPSvmBmtXL3+lKRVgU2IczvD9lWVdI4DrgbeQPxPtwN+bXuXClrN7BZJJzMiQnmA7Vf2rZkkD5SM+EmSeZvnAm+SNIsBJelKpqgBBTQzoKi4am37G5IuYqYBtWUtA4oIX76aqAU1w4CqpNVtlX2I7RMl7VlDKA2oZAJouULeHEnLEQ0GNiTO7xxgF9s3jfXA+uOu8nqnpGWI1tUrjPF4pgIvGfcBVOT9RH3AmxtorWd71QY6s1DslFq2Spf/sP06Sa+yfaSkY4AfVdJqZrcQTiWALYk02UE32W2BGyppJskDIh0/STJvkwZUP6QB1R9/kHQo0Sp7X0kLETWTapAGVDJP09jBOw4OB44BBnXlti/bNhvbEfXLKZKWBD4LXEI4t7421iOacGzfOO5jqMhvidTHFvxc0qpT7H7SZdBJ61ZJqwN/JqKya9DMbrF9FoCkvW0/v/OjkyWdXUMzSR4omeqVJMlYkHQqMUmqbkRJOgw4YKoaUJIusP2cYlS8izCgLqgUft6sVXZH8+whA2rktiRJ+kXSZbbXmtu2SUXSQrb/OfieSJW9e7AtSbqU+l2HA79g1mLnvTcakPR84GTief5PZqaVTWykdxdJbwVOAJ4JHAEsCnzM9pcraI3Dbvk18DLb15X3KwA/sP2MWppJMjcy4idJknGxO5EiUd2AAo4kVs+mpAEFfEXSUsAewEkUA6qGUHHUfbfz/k/An2b/F72wtKQVhwyopStrJkkCN0vanpndF7cl0qGmCj8H1gYozp5/SrpksC1JhjgU+AlwJTPTxmvxdaL1eAut5tgeRNadTYUU/yGtcdgt7wV+Kum68v6pwNsqaybJHEnHT5Ik4yINqJ5oaUCNiTSgkmQ87AQcDHyeSIM6r2ybaCQ9EVgWWLhEcaj8aHGiy1eSjOJe27s20vqd7ZMaaTVH0qeB/WzfWt4vBbzP9h5jPbCesH2qpJWJNvIAV3cjCSVtZvv08RxdMr+SqV5JkowFSefZ3qCR1k9sb9JCaxxMdQMKZqRhpAGVJMnDRtIOwJuBdYALmen4uQ04snLL7GRCkfQp4EYiBasbqdx7N1JJXwKWHKE1JcampEttP2to2yW254tou/npXJN5h3T8JEkyFtKA6o80oOafc02Slkg6kujidWt5vxRRL23io34AJL3W9gnjPo5kMpB0feftjAlUpXp6h4/YXL2deyskXQGs26mxtTBwke3VxntkbRhltyVJbTLVK0mScfGG8ro7s7bsrpGqtDDh8Nm8s61qO/fGTBsqUrowsNCYj6klmvuvJEnyEFhj4PQBsH1LSY2aKjxb0hlTOVoy6ZUPAqfavk3SR4laUHvXELK9Y439zkMcDZxRHFwmUkiPHO8hNSUjL5LmpOMnSZJxkQZUf6QBlSRJDRaQtJTtWwAkPYapZTtuYfvDgzfFsfVSolB+kgyzh+1jJW0EbAYcABwCPLdvIUnLAQcBGxLPuHOI6Lub+tYaB7b3k3QlsCmxeLO37R+N+bCSZEqzwLgPIEmS+ZY9itNnYEAdQRhQvSNpOUnfk/RXSX+RdEIxqqYEtvcDPgU8A1iNMKD2G+9RJUkyBTiA6L64t6S9iOLOU+neMq3UDwPmy2jJ5MFxX3l9GfBl2ycCC1bSOpzo0rkMUYj85LJtymD7h7bfb/t986HT54ZxH0Ay/5E1fpIkGQuD/GZJ+wBX2j6mVs6zpNOBY4Cjyqbtge1sb9a3VtIeSd+1veW4jyNJpiKSVgU2IVblz7B91ZgPqTck7Qa8kphQD6IlT0rHeTIKSacAfwBeBDwbuAu4wPaaFbQus73W3LZNKpK2BPYFHk/cW0TUMFp8rAf2MCnnNVumSm3JZDJJx0+SJGMhDaj+SAMqSZLkoSFpC2amm5w2H0YeJA8QSY8GXkIsVv1G0pOAZ9o+rYLWj4lI6G+VTdsCO9retG+tcSDpWuAVtn897mPpk9kU5R4wZYpzJ5NJOn6SJBkLaUD1RxpQSZIkSTJ1kLQ8cDCwPhGNdh6ws+3fjfXAekLSubY3HPdxJMn8RDp+kiSZ8qQBlSRJkgwjaT2igO4ziFot04A7Jj1aMpl8JB0JvGeosPr+U2XBQ9KBwBOB7xNdV4GpFckr6WVE3cVHDbbZ3mt8R5TM70ylzgxJkiSzY29gh2EDiqjnMBW4SNJ3SAMqSZLkwXAw8HrgOGAd4E3Af4z1iJIkWGNgswDY/ruk3msgjpHFgTuBzTvbDEwJu0XSl4FHAxsDXwO2Ai4Y60El8z3p+EmSZH4gDagJJg2oJElqYftaSdNs3wccLum8cR9TkgALSFpqaMFqyszbbO847mOozAa215B0he1PSDqAKWKTJZPLlLmBJEmSzIE0oCabNKCSJKnBnZIWBC6TtB/wJ2CRMR9TkgAcAJwn6XhiIWdr4FPjPaT+kLQckWa5IXF+5wC72L5prAfWH3eV1zslLQP8DVhhjMeTJCww7gNIkiRpwMCA2lvSXkSNnynTrlfScpK+J+mvkv4i6YRiVE0Vhg2of5EGVJIkD583ErbwfwF3AE8GXjvWI0oSwPY3iLH4F+D/AVvaPmq8R9UrhwMnAcsAywInl21ThVMkLQl8FrgEuAH49jgPKEmyuHOSJPMFklYFNiFa9p5h+6oxH1JvSDodOAYYGIXbA9vZ3mx8R9Ufkj5KrAxuCnyRWB38mu2PjvXAkiSZWCRNA460vf24jyVJ5jckXWZ7rbltm1QkLWT7n4PvifqEdw+2Jck4SMdPkiTJhJMGVJIkyYNH0o+AV9i+Z9zHkiTzE5J+DBwBfKts2hbY0famYzuoHpF0ie2157YtSVoyZWpcJEmSzMfcLGl7ZjWg/jbG4+mbnwNrAxRnzz8lXTLYliRJ8hC5AThX0klEqhcAtj83tiNKkvmDnYiuep8nonjPYwp0WpX0RCJ1beHSRETlR4sTTSqSZGyk4ydJkmTySQMqSZLkASLpKNtvBLYh7psLAIuN96iSZP7B9u+AV477OCrwYuDNwHJEfcmB3XIb8OExHVOSAJnqlSRJksyjSNqBMKDWAS5kVgPqSNvZ2StJkgeNpKuALYiCsi8c/rntv7c+piSZn5B0JNHF69byfingANsTv2gFIOm1tk8Y93EkSZeM+EmSJJlwpqoBZftI4Mg0oJIk6ZkvA6cS3QEv6mwXETW54jgOKknmI9YY2CwAtm8pkb1ThWdLOmPILnuf7T3Ge1jJ/Ey2c0+SJJl87mdAAVPNgFpy8EbSUpI+OcbjSZJkgrH9BdvPAA63vWLnawXb6fRJkvosUJwhAEh6DFMrIGGLEXbZS8d3OEmSjp8kSZKpQBpQSZIkDxLb7xz3MSTJfMoBwHmS9pa0F1GbcL8xH1OfTCtdSAGQtDCw0Bx+P0mqM5UmBkmSJPMrAwPqeCJNYWvgU+M9pF6ZNtTSPQ2oJEmSJJlQbH9D0kXAJkSK5Za2rxrzYfXJ0cAZkg4n7LKdgCPHe0jJ/E4Wd06SJJkCSFqVmQbUGVPJgJK0G9H9o2tAnWR7Kq0OJkmSJEkyRZC0BbApYZedZvtHYz6kZD4nHT9JkiTJPE8aUEmSJEmSJEny0EjHT5IkSZIkSZIkSZL0gKT1gIOAZwALAtOAO2wvPtYDS+ZrsrhzkiRJMk8jaT1JF0q6XdI9ku6TdNu4jytJkiRJkmQEBwPbAr8BFgbeSjiCkmRspOMnSZIkmddJAypJkiRJkonB9rXANNv32T4c2Hjcx5TM32RXryRJkmSex/a1kqbZvg84XNJ54z6mJEmSJEmSEdwpaUHgMkn7AX8CFhnzMSXzORnxkyRJkszrzGJASXovaUAlSZIkSTJv8kZinv1fwB3Ak4HXjvWIkvmeLO6cJEmSzNNIegrwF6JA4nuBJYAvlTDqJEmSJEmSeQJJ04AjbW8/7mNJki7p+EmSJEnmWdKASpIkSZJkkpD0I+AVtu8Z97EkyYCs8ZMkSZLMs9i+T9LSkhZMAypJkiRJkgngBuBcSScRqV4A2P7c2I4ome9Jx0+SJEkyr3MDaUAlSZIkSTIPI+ko228EtgE+T9T5WWy8R5UkQTp+kiRJknmSNKCSJEmSJJkgnl3qEv4OOGjcB5MkXdLxkyRJksyrpAGVJEmSJMmk8GXgVGAF4KLOdgEGVhzHQSUJZHHnJEmSZB5F0s7AOwkD6o/dHwG2nQZUkiRJkiTzFJIOsf3OcR9HknRJx0+SJEkyT5MGVJIkSZIkSZI8dNLxkyRJkiRJkiRJkiRJMkVZYNwHkCRJkiRJkiRJkiRJktQhHT9JkiRJkiRJkiRJkiRTlHT8JEmSJEmSJEmSJEmSTFHS8ZMkSZIkSZIkSZIkSTJFScdPkiRJkiRJkiRJkiTJFOX/Axw9iQMXLo+QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422f0771",
   "metadata": {},
   "source": [
    "## Splitting the dataset train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "719e1d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>diagnosis_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "0         0.2419  ...          17.33           184.60      2019.0   \n",
       "1         0.1812  ...          23.41           158.80      1956.0   \n",
       "2         0.2069  ...          25.53           152.50      1709.0   \n",
       "3         0.2597  ...          26.50            98.87       567.7   \n",
       "4         0.1809  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  diagnosis_M  \n",
       "0          0.4601                  0.11890            1  \n",
       "1          0.2750                  0.08902            1  \n",
       "2          0.3613                  0.08758            1  \n",
       "3          0.6638                  0.17300            1  \n",
       "4          0.2364                  0.07678            1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "8210c1c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# matrix of feature/independent variable\n",
    "x = dataset.iloc[:, 1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "a4f3cdcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "d3f592de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "0f079e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "fcf87455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569,)"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70973275",
   "metadata": {},
   "source": [
    "to split a dataset...we use a class from sklearn library...go to ...sklearn documentation...now use the spitter function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "ce959646",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "fe72ad68",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "a5caad19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455, 30)"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "cf6ffd20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 30)"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "b2aabe70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455,)"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "e1777035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114,)"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1337d04",
   "metadata": {},
   "source": [
    "## Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "d5ad4ef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "e408faff",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "22ee85e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "f5d35b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.15036482, -0.39064196, -1.12855021, ..., -0.75798367,\n",
       "        -0.01614761, -0.38503402],\n",
       "       [-0.93798972,  0.68051405, -0.94820146, ..., -0.60687023,\n",
       "         0.09669004, -0.38615797],\n",
       "       [ 0.574121  , -1.03333557,  0.51394098, ..., -0.02371948,\n",
       "        -0.20050207, -0.75144254],\n",
       "       ...,\n",
       "       [-1.32422924, -0.20048168, -1.31754581, ..., -0.97974953,\n",
       "        -0.71542314, -0.11978123],\n",
       "       [-1.24380987, -0.2245526 , -1.28007609, ..., -1.75401433,\n",
       "        -1.58157125, -1.00601779],\n",
       "       [-0.73694129,  1.14989702, -0.71226578, ..., -0.27460457,\n",
       "        -1.25895095,  0.21515662]])"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "b63ce958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.20175604,  0.3290786 , -0.13086754, ...,  1.3893291 ,\n",
       "         1.08203284,  1.54029664],\n",
       "       [-0.25555773,  1.46763319, -0.31780437, ..., -0.83369364,\n",
       "        -0.73131577, -0.87732522],\n",
       "       [-0.02619262, -0.8407682 , -0.09175081, ..., -0.49483785,\n",
       "        -1.22080864, -0.92115937],\n",
       "       ...,\n",
       "       [ 1.71811488,  0.09318356,  1.7286186 , ...,  1.57630515,\n",
       "         0.20317063, -0.15406178],\n",
       "       [ 1.18859296,  0.34352115,  1.19333694, ...,  0.56019755,\n",
       "         0.26991966, -0.27320074],\n",
       "       [ 0.26263752, -0.58080224,  0.28459338, ..., -0.19383705,\n",
       "        -1.15564888,  0.11231497]])"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72098181",
   "metadata": {},
   "source": [
    "# Part 2: Building the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476e27ce",
   "metadata": {},
   "source": [
    "# 1) Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "da6ad835",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "43caa9db",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifir_lr = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "e1cd9b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifir_lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "7042f95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifir_lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "d68598eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "e4807a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "ac42f2e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame([['Logistic Regression', acc, f1, prec, rec]],\n",
    "                      columns = ['Model','Accuracy', 'F1 Score', 'Precision', 'Recall'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "9ebe3d51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.964912</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.957447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  F1 Score  Precision    Recall\n",
       "0  Logistic Regression  0.964912  0.957447   0.957447  0.957447"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "f11f51ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "c9c4e11f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[65  2]\n",
      " [ 2 45]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f59ff8",
   "metadata": {},
   "source": [
    "### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "54353743",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "e9a26030",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies = cross_val_score(estimator=classifir_lr, X=x_train, y = y_train, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "a1af9e30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 97.81 %\n",
      "Standard Deviation is 1.98 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy is {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation is {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b00781",
   "metadata": {},
   "source": [
    "## 2) Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "3422b8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "042e7598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_rm = RandomForestClassifier(random_state=0)\n",
    "classifier_rm.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "cd0dd071",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier_rm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "1c280082",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "b02a8293",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "dfb6af4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_results = pd.DataFrame([['Random Forest', acc, f1, prec, rec]],\n",
    "                      columns = ['Model','Accuracy', 'F1 Score', 'Precision', 'Recall'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "e2e10867",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91999\\AppData\\Local\\Temp\\ipykernel_14404\\1490459329.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results=results.append(model_results, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "results=results.append(model_results, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "731ab8f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.964912</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.957447</td>\n",
       "      <td>0.957447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.964912</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.938776</td>\n",
       "      <td>0.978723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  F1 Score  Precision    Recall\n",
       "0  Logistic Regression  0.964912  0.957447   0.957447  0.957447\n",
       "1        Random Forest  0.964912  0.958333   0.938776  0.978723"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "14c8c406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[64  3]\n",
      " [ 1 46]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8da3d4f",
   "metadata": {},
   "source": [
    "### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "7fd61421",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 96.05 %\n",
      "Standard Deviation is 3.07 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "accuracies = cross_val_score(estimator=classifier_rm, X=x_train, y = y_train, cv=10)\n",
    "\n",
    "print(\"Accuracy is {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation is {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "1da71598",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sdf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [403]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msdf\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'sdf' is not defined"
     ]
    }
   ],
   "source": [
    "sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607bcb99",
   "metadata": {},
   "source": [
    "# Part 3: Randomized Search to find the best parameters (Logistic regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "eb64c597",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "e920d7f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'penalty':['l1','l2','elasticnet'],\n",
    "             'C':[0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],\n",
    "             'solver':['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
    "            }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "ac48dda7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'penalty': ['l1', 'l2', 'elasticnet'],\n",
       " 'C': [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],\n",
       " 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']}"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "3e0dde99",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_search = RandomizedSearchCV(estimator=classifir_lr, param_distributions=parameters, n_iter=5,\n",
    "                                  scoring='roc_auc', n_jobs= -1, cv=5, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "c4747743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:372: FitFailedWarning: \n",
      "5 fits failed out of a total of 25.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 447, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:969: UserWarning: One or more of the test scores are non-finite: [0.99519331        nan 0.99414838 0.99529781 0.99529781]\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(random_state=0), n_iter=5,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'C': [0.25, 0.5, 0.75, 1.0, 1.25, 1.5,\n",
       "                                              1.75, 2.0],\n",
       "                                        'penalty': ['l1', 'l2', 'elasticnet'],\n",
       "                                        'solver': ['newton-cg', 'lbfgs',\n",
       "                                                   'liblinear', 'sag',\n",
       "                                                   'saga']},\n",
       "                   scoring='roc_auc', verbose=3)"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "id": "2e15ac0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=2.0, penalty='l1', random_state=0, solver='saga')"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "e5edf4cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9952978056426331"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "3e88bedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'saga', 'penalty': 'l1', 'C': 2.0}"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aef1342",
   "metadata": {},
   "source": [
    "# Part 4: Final model(Logistic regression)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "b53c95c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "5a2ec7f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=2.0, penalty='l1', random_state=0, solver='saga')"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifir = LogisticRegression(C=2.0, penalty='l1', random_state=0, solver='saga')\n",
    "classifir.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "0aa77b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifir.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac864c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "prec = precision_score(y_test, y_pred)\n",
    "rec = recall_score(y_test, y_pred)\n",
    "\n",
    "model_results = pd.DataFrame([['Final Logistic Regression', acc, f1, prec, rec]],\n",
    "                      columns = ['Model','Accuracy', 'F1 Score', 'Precision', 'Recall'])\n",
    "\n",
    "results = results.append(model_results, ignore_index = True)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81edda8",
   "metadata": {},
   "source": [
    "### Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "38971ceb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "b44329a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 98.24 %\n",
      "Standard Deviation is 2.16 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91999\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "accuracies = cross_val_score(estimator=classifir, X=x_train, y=y_train, cv=10)\n",
    "\n",
    "print(\"Accuracy is {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation is {:.2f} %\".format(accuracies.std()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f0ff34",
   "metadata": {},
   "source": [
    "# Finally:      Predicting a single observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "fb3b5c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>diagnosis_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
       "0         0.2419  ...          17.33           184.60      2019.0   \n",
       "1         0.1812  ...          23.41           158.80      1956.0   \n",
       "2         0.2069  ...          25.53           152.50      1709.0   \n",
       "3         0.2597  ...          26.50            98.87       567.7   \n",
       "4         0.1809  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  diagnosis_M  \n",
       "0          0.4601                  0.11890            1  \n",
       "1          0.2750                  0.08902            1  \n",
       "2          0.3613                  0.08758            1  \n",
       "3          0.6638                  0.17300            1  \n",
       "4          0.2364                  0.07678            1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "38a9a009",
   "metadata": {},
   "outputs": [],
   "source": [
    "single_obs=[[17.99, 10.38, 122.8, 1001.0, 0.1184, 0.2776, 0.3001, 0.1471, 0.2419, 0.07871, 1.095, 0.9053, 8.589, 153.4, 0.006399 ,0.14994, 0.05373, 0.01587, 0.03003, 0.006193 ,25.38,\n",
    "             17.33, 184.6, 2019.0, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "003bce0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[17.99,\n",
       "  10.38,\n",
       "  122.8,\n",
       "  1001.0,\n",
       "  0.1184,\n",
       "  0.2776,\n",
       "  0.3001,\n",
       "  0.1471,\n",
       "  0.2419,\n",
       "  0.07871,\n",
       "  1.095,\n",
       "  0.9053,\n",
       "  8.589,\n",
       "  153.4,\n",
       "  0.006399,\n",
       "  0.14994,\n",
       "  0.05373,\n",
       "  0.01587,\n",
       "  0.03003,\n",
       "  0.006193,\n",
       "  25.38,\n",
       "  17.33,\n",
       "  184.6,\n",
       "  2019.0,\n",
       "  0.1622,\n",
       "  0.6656,\n",
       "  0.7119,\n",
       "  0.2654,\n",
       "  0.4601,\n",
       "  0.1189]]"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "e3e31974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=uint8)"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifir.predict(sc.transform(single_obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c913460",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd5c437a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b940c91d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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