{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "596_nIV4X39M",
      "metadata": {
        "id": "596_nIV4X39M"
      },
      "source": [
        "# CO2 Emissions by Vehicles - Regression Analysis for Lab 2\n",
        "\n",
        "This notebook analyzes the *CO2 Emissions Canada* dataset using multiple linear regression. The goal is to explain and predict **CO2 Emissions (g/km)** from vehicle characteristics.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "98d16db7",
      "metadata": {
        "id": "98d16db7"
      },
      "source": [
        "## Modeling principle\n",
        "\n",
        "The analysis emphasizes **regression interpretation**, not only prediction accuracy. Model choice is based on predictive performance, parsimony, and diagnostics.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "TC4YkMH-X39T",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:03.259905Z",
          "iopub.status.busy": "2026-03-24T12:15:03.259255Z",
          "iopub.status.idle": "2026-03-24T12:15:11.764070Z",
          "shell.execute_reply": "2026-03-24T12:15:11.762826Z"
        },
        "id": "TC4YkMH-X39T"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from IPython.display import display\n",
        "\n",
        "from sklearn.model_selection import train_test_split, KFold, cross_validate\n",
        "from sklearn.compose import ColumnTransformer, TransformedTargetRegressor\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import OneHotEncoder, PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "from statsmodels.stats.diagnostic import het_breuschpagan"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dzt__lv3X39W",
      "metadata": {
        "id": "dzt__lv3X39W"
      },
      "source": [
        "## 1. Load dataset\n",
        "\n",
        "Load the dataset and inspect the first rows.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "6DUsdUHzX39Y",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:11.768674Z",
          "iopub.status.busy": "2026-03-24T12:15:11.768237Z",
          "iopub.status.idle": "2026-03-24T12:15:11.860635Z",
          "shell.execute_reply": "2026-03-24T12:15:11.859785Z"
        },
        "id": "6DUsdUHzX39Y",
        "outputId": "c598120c-526f-49b1-f140-72da5ac5ac5c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Vehicle Class</th>\n",
              "      <th>Engine Size(L)</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>Fuel Consumption City (L/100 km)</th>\n",
              "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
              "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
              "      <th>Fuel Consumption Comb (mpg)</th>\n",
              "      <th>CO2 Emissions(g/km)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>1.6EL</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>9.2</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.1</td>\n",
              "      <td>35</td>\n",
              "      <td>186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>1.6EL</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>M5</td>\n",
              "      <td>X</td>\n",
              "      <td>8.5</td>\n",
              "      <td>6.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>37</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>3.2TL</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>AS5</td>\n",
              "      <td>Z</td>\n",
              "      <td>12.2</td>\n",
              "      <td>7.4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>3.5RL</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>A4</td>\n",
              "      <td>Z</td>\n",
              "      <td>13.4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>11.5</td>\n",
              "      <td>25</td>\n",
              "      <td>264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2000</td>\n",
              "      <td>ACURA</td>\n",
              "      <td>INTEGRA</td>\n",
              "      <td>SUBCOMPACT</td>\n",
              "      <td>1.8</td>\n",
              "      <td>4</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>33</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year   Make    Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
              "0  2000  ACURA    1.6EL       COMPACT             1.6          4           A4   \n",
              "1  2000  ACURA    1.6EL       COMPACT             1.6          4           M5   \n",
              "2  2000  ACURA    3.2TL      MID-SIZE             3.2          6          AS5   \n",
              "3  2000  ACURA    3.5RL      MID-SIZE             3.5          6           A4   \n",
              "4  2000  ACURA  INTEGRA    SUBCOMPACT             1.8          4           A4   \n",
              "\n",
              "  Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
              "0         X                               9.2   \n",
              "1         X                               8.5   \n",
              "2         Z                              12.2   \n",
              "3         Z                              13.4   \n",
              "4         X                              10.0   \n",
              "\n",
              "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
              "0                              6.7                               8.1   \n",
              "1                              6.5                               7.6   \n",
              "2                              7.4                              10.0   \n",
              "3                              9.2                              11.5   \n",
              "4                              7.0                               8.6   \n",
              "\n",
              "   Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
              "0                           35                  186  \n",
              "1                           37                  175  \n",
              "2                           28                  230  \n",
              "3                           25                  264  \n",
              "4                           33                  198  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"Fuel_Consumption_2000-2022.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "-SwyGddJX39b",
      "metadata": {
        "id": "-SwyGddJX39b"
      },
      "source": [
        "## 2. Basic information\n",
        "\n",
        "Check the dataset shape, variables, data types, and summary statistics.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "jF3wyKxKX39c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:11.865113Z",
          "iopub.status.busy": "2026-03-24T12:15:11.864534Z",
          "iopub.status.idle": "2026-03-24T12:15:11.871620Z",
          "shell.execute_reply": "2026-03-24T12:15:11.870604Z"
        },
        "id": "jF3wyKxKX39c",
        "outputId": "2d36940a-4d46-48bc-c8bc-51640e52ea0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape: (22556, 13)\n",
            "\n",
            "Columns:\n",
            "['Year', 'Make', 'Model', 'Vehicle Class', 'Engine Size(L)', 'Cylinders', 'Transmission', 'Fuel Type', 'Fuel Consumption City (L/100 km)', 'Fuel Consumption Hwy (L/100 km)', 'Fuel Consumption Comb (L/100 km)', 'Fuel Consumption Comb (mpg)', 'CO2 Emissions(g/km)']\n"
          ]
        }
      ],
      "source": [
        "print(\"Shape:\", df.shape)\n",
        "print(\"\\nColumns:\")\n",
        "print(df.columns.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "wWqIov7NX39d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:11.874632Z",
          "iopub.status.busy": "2026-03-24T12:15:11.874313Z",
          "iopub.status.idle": "2026-03-24T12:15:11.908371Z",
          "shell.execute_reply": "2026-03-24T12:15:11.907540Z"
        },
        "id": "wWqIov7NX39d",
        "outputId": "f69f2c7c-398b-4d0f-f414-4be8b5b16904"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 22556 entries, 0 to 22555\n",
            "Data columns (total 13 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   Year                              22556 non-null  int64  \n",
            " 1   Make                              22556 non-null  object \n",
            " 2   Model                             22556 non-null  object \n",
            " 3   Vehicle Class                     22556 non-null  object \n",
            " 4   Engine Size(L)                    22556 non-null  float64\n",
            " 5   Cylinders                         22556 non-null  int64  \n",
            " 6   Transmission                      22556 non-null  object \n",
            " 7   Fuel Type                         22556 non-null  object \n",
            " 8   Fuel Consumption City (L/100 km)  22556 non-null  float64\n",
            " 9   Fuel Consumption Hwy (L/100 km)   22556 non-null  float64\n",
            " 10  Fuel Consumption Comb (L/100 km)  22556 non-null  float64\n",
            " 11  Fuel Consumption Comb (mpg)       22556 non-null  int64  \n",
            " 12  CO2 Emissions(g/km)               22556 non-null  int64  \n",
            "dtypes: float64(4), int64(4), object(5)\n",
            "memory usage: 2.2+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "xkNn2xMNX39e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:11.911836Z",
          "iopub.status.busy": "2026-03-24T12:15:11.911208Z",
          "iopub.status.idle": "2026-03-24T12:15:11.960097Z",
          "shell.execute_reply": "2026-03-24T12:15:11.959343Z"
        },
        "id": "xkNn2xMNX39e",
        "outputId": "366797ef-24aa-4174-c3b1-809167f7fb94"
      },
      "outputs": [
        {
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>22556.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011.554442</td>\n",
              "      <td>6.298269</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>2022.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Make</th>\n",
              "      <td>22556</td>\n",
              "      <td>87</td>\n",
              "      <td>CHEVROLET</td>\n",
              "      <td>1725</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>22556</td>\n",
              "      <td>4242</td>\n",
              "      <td>JETTA</td>\n",
              "      <td>89</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class</th>\n",
              "      <td>22556</td>\n",
              "      <td>32</td>\n",
              "      <td>SUV</td>\n",
              "      <td>2640</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engine Size(L)</th>\n",
              "      <td>22556.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.356646</td>\n",
              "      <td>1.335425</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>22556.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.854141</td>\n",
              "      <td>1.819597</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transmission</th>\n",
              "      <td>22556</td>\n",
              "      <td>30</td>\n",
              "      <td>A4</td>\n",
              "      <td>3519</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Type</th>\n",
              "      <td>22556</td>\n",
              "      <td>5</td>\n",
              "      <td>X</td>\n",
              "      <td>11822</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption City (L/100 km)</th>\n",
              "      <td>22556.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.763513</td>\n",
              "      <td>3.500999</td>\n",
              "      <td>3.5</td>\n",
              "      <td>10.4</td>\n",
              "      <td>12.3</td>\n",
              "      <td>14.725</td>\n",
              "      <td>30.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
              "      <td>22556.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.919126</td>\n",
              "      <td>2.274764</td>\n",
              "      <td>3.2</td>\n",
              "      <td>7.3</td>\n",
              "      <td>8.4</td>\n",
              "      <td>10.2</td>\n",
              "      <td>20.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
              "      <td>22556.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.034341</td>\n",
              "      <td>2.91092</td>\n",
              "      <td>3.6</td>\n",
              "      <td>9.1</td>\n",
              "      <td>10.6</td>\n",
              "      <td>12.7</td>\n",
              "      <td>26.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption Comb (mpg)</th>\n",
              "      <td>22556.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.374534</td>\n",
              "      <td>7.376982</td>\n",
              "      <td>11.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>78.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CO2 Emissions(g/km)</th>\n",
              "      <td>22556.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>250.068452</td>\n",
              "      <td>59.355276</td>\n",
              "      <td>83.0</td>\n",
              "      <td>209.0</td>\n",
              "      <td>243.0</td>\n",
              "      <td>288.0</td>\n",
              "      <td>608.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    count unique        top   freq  \\\n",
              "Year                              22556.0    NaN        NaN    NaN   \n",
              "Make                                22556     87  CHEVROLET   1725   \n",
              "Model                               22556   4242      JETTA     89   \n",
              "Vehicle Class                       22556     32        SUV   2640   \n",
              "Engine Size(L)                    22556.0    NaN        NaN    NaN   \n",
              "Cylinders                         22556.0    NaN        NaN    NaN   \n",
              "Transmission                        22556     30         A4   3519   \n",
              "Fuel Type                           22556      5          X  11822   \n",
              "Fuel Consumption City (L/100 km)  22556.0    NaN        NaN    NaN   \n",
              "Fuel Consumption Hwy (L/100 km)   22556.0    NaN        NaN    NaN   \n",
              "Fuel Consumption Comb (L/100 km)  22556.0    NaN        NaN    NaN   \n",
              "Fuel Consumption Comb (mpg)       22556.0    NaN        NaN    NaN   \n",
              "CO2 Emissions(g/km)               22556.0    NaN        NaN    NaN   \n",
              "\n",
              "                                         mean        std     min     25%  \\\n",
              "Year                              2011.554442   6.298269  2000.0  2006.0   \n",
              "Make                                      NaN        NaN     NaN     NaN   \n",
              "Model                                     NaN        NaN     NaN     NaN   \n",
              "Vehicle Class                             NaN        NaN     NaN     NaN   \n",
              "Engine Size(L)                       3.356646   1.335425     0.8     2.3   \n",
              "Cylinders                            5.854141   1.819597     2.0     4.0   \n",
              "Transmission                              NaN        NaN     NaN     NaN   \n",
              "Fuel Type                                 NaN        NaN     NaN     NaN   \n",
              "Fuel Consumption City (L/100 km)    12.763513   3.500999     3.5    10.4   \n",
              "Fuel Consumption Hwy (L/100 km)      8.919126   2.274764     3.2     7.3   \n",
              "Fuel Consumption Comb (L/100 km)    11.034341    2.91092     3.6     9.1   \n",
              "Fuel Consumption Comb (mpg)         27.374534   7.376982    11.0    22.0   \n",
              "CO2 Emissions(g/km)                250.068452  59.355276    83.0   209.0   \n",
              "\n",
              "                                     50%     75%     max  \n",
              "Year                              2012.0  2017.0  2022.0  \n",
              "Make                                 NaN     NaN     NaN  \n",
              "Model                                NaN     NaN     NaN  \n",
              "Vehicle Class                        NaN     NaN     NaN  \n",
              "Engine Size(L)                       3.0     4.2     8.4  \n",
              "Cylinders                            6.0     8.0    16.0  \n",
              "Transmission                         NaN     NaN     NaN  \n",
              "Fuel Type                            NaN     NaN     NaN  \n",
              "Fuel Consumption City (L/100 km)    12.3  14.725    30.6  \n",
              "Fuel Consumption Hwy (L/100 km)      8.4    10.2    20.9  \n",
              "Fuel Consumption Comb (L/100 km)    10.6    12.7    26.1  \n",
              "Fuel Consumption Comb (mpg)         27.0    31.0    78.0  \n",
              "CO2 Emissions(g/km)                243.0   288.0   608.0  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe(include=\"all\").T"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Bilaeuo7X39f",
      "metadata": {
        "id": "Bilaeuo7X39f"
      },
      "source": [
        "## 3. Missing values and duplicates\n",
        "\n",
        "Check whether missing values or duplicated records are present before modeling.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ce81b8c",
      "metadata": {
        "id": "1ce81b8c"
      },
      "source": [
        "### 3.1 Missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "6ADr3G4sX39f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:11.964365Z",
          "iopub.status.busy": "2026-03-24T12:15:11.963567Z",
          "iopub.status.idle": "2026-03-24T12:15:11.988872Z",
          "shell.execute_reply": "2026-03-24T12:15:11.988122Z"
        },
        "id": "6ADr3G4sX39f",
        "outputId": "35ece430-87e4-4f90-e874-fa79b8b1940d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>missing_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Make</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engine Size(L)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transmission</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption City (L/100 km)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption Comb (mpg)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CO2 Emissions(g/km)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  missing_count\n",
              "Year                                          0\n",
              "Make                                          0\n",
              "Model                                         0\n",
              "Vehicle Class                                 0\n",
              "Engine Size(L)                                0\n",
              "Cylinders                                     0\n",
              "Transmission                                  0\n",
              "Fuel Type                                     0\n",
              "Fuel Consumption City (L/100 km)              0\n",
              "Fuel Consumption Hwy (L/100 km)               0\n",
              "Fuel Consumption Comb (L/100 km)              0\n",
              "Fuel Consumption Comb (mpg)                   0\n",
              "CO2 Emissions(g/km)                           0"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total missing values: 0\n",
            "Duplicate rows: 1\n"
          ]
        }
      ],
      "source": [
        "missing_summary = df.isnull().sum().sort_values(ascending=False)\n",
        "display(missing_summary.to_frame(\"missing_count\"))\n",
        "\n",
        "print(\"Total missing values:\", int(df.isnull().sum().sum()))\n",
        "print(\"Duplicate rows:\", int(df.duplicated().sum()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "I2CNVloRX39g",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:11.992180Z",
          "iopub.status.busy": "2026-03-24T12:15:11.991592Z",
          "iopub.status.idle": "2026-03-24T12:15:12.022988Z",
          "shell.execute_reply": "2026-03-24T12:15:12.022222Z"
        },
        "id": "I2CNVloRX39g",
        "outputId": "9b50cf7d-dd14-4068-d6d1-57bef8b897a2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Make</th>\n",
              "      <th>Model</th>\n",
              "      <th>Vehicle Class</th>\n",
              "      <th>Engine Size(L)</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>Fuel Consumption City (L/100 km)</th>\n",
              "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
              "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
              "      <th>Fuel Consumption Comb (mpg)</th>\n",
              "      <th>CO2 Emissions(g/km)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>377</th>\n",
              "      <td>2000</td>\n",
              "      <td>LAND ROVER</td>\n",
              "      <td>DISCOVERY SERIES II 4X4</td>\n",
              "      <td>SUV</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8</td>\n",
              "      <td>A4</td>\n",
              "      <td>Z</td>\n",
              "      <td>17.7</td>\n",
              "      <td>12.7</td>\n",
              "      <td>15.4</td>\n",
              "      <td>18</td>\n",
              "      <td>354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>2000</td>\n",
              "      <td>LAND ROVER</td>\n",
              "      <td>DISCOVERY SERIES II 4X4</td>\n",
              "      <td>SUV</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8</td>\n",
              "      <td>A4</td>\n",
              "      <td>Z</td>\n",
              "      <td>17.7</td>\n",
              "      <td>12.7</td>\n",
              "      <td>15.4</td>\n",
              "      <td>18</td>\n",
              "      <td>354</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Year        Make                    Model Vehicle Class  Engine Size(L)  \\\n",
              "377  2000  LAND ROVER  DISCOVERY SERIES II 4X4           SUV             4.0   \n",
              "378  2000  LAND ROVER  DISCOVERY SERIES II 4X4           SUV             4.0   \n",
              "\n",
              "     Cylinders Transmission Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
              "377          8           A4         Z                              17.7   \n",
              "378          8           A4         Z                              17.7   \n",
              "\n",
              "     Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
              "377                             12.7                              15.4   \n",
              "378                             12.7                              15.4   \n",
              "\n",
              "     Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
              "377                           18                  354  \n",
              "378                           18                  354  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "duplicate_examples = df[df.duplicated(keep=False)].sort_values(list(df.columns)).head(10)\n",
        "duplicate_examples"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "JoOrHqcvX39h",
      "metadata": {
        "id": "JoOrHqcvX39h"
      },
      "source": [
        "### 3.2 Remove Duplicates\n",
        "\n",
        "Remove duplicated rows so each observation is counted once.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "lTl3FXTjX39i",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:12.026119Z",
          "iopub.status.busy": "2026-03-24T12:15:12.025758Z",
          "iopub.status.idle": "2026-03-24T12:15:12.043064Z",
          "shell.execute_reply": "2026-03-24T12:15:12.042305Z"
        },
        "id": "lTl3FXTjX39i",
        "outputId": "6feba5b9-82bc-4a83-aaed-e92053623077"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape after removing duplicates: (22555, 13)\n",
            "Remaining duplicates: 0\n"
          ]
        }
      ],
      "source": [
        "df = df.drop_duplicates().reset_index(drop=True)\n",
        "print(\"Shape after removing duplicates:\", df.shape)\n",
        "print(\"Remaining duplicates:\", int(df.duplicated().sum()))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f69b0656",
      "metadata": {
        "id": "f69b0656"
      },
      "source": [
        "## 4. Preprocessing decisions\n",
        "\n",
        "For this analysis:\n",
        "\n",
        "- **CO2 Emissions(g/km)** is the response.\n",
        "- Numeric predictors are used directly.\n",
        "- Categorical predictors are one-hot encoded.\n",
        "- Preprocessing is included in the modeling pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "XdEBBuyYxXsG",
      "metadata": {
        "id": "XdEBBuyYxXsG"
      },
      "outputs": [],
      "source": [
        "df['Make'] = df['Make'].apply(lambda v: v.upper())\n",
        "df[\"Vehicle Class\"] = df[\"Vehicle Class\"].apply(lambda v: v.upper().replace(': ', ' - '))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "ed9457db",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "ed9457db",
        "outputId": "691fc221-326a-4cfd-ec98-7ae521ae6690"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Engine Size(L)</th>\n",
              "      <th>Cylinders</th>\n",
              "      <th>Fuel Consumption City (L/100 km)</th>\n",
              "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
              "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
              "      <th>Fuel Consumption Comb (mpg)</th>\n",
              "      <th>Vehicle Class</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Fuel Type</th>\n",
              "      <th>CO2 Emissions(g/km)</th>\n",
              "      <th>Engine Size x Fuel Consumption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>6.7</td>\n",
              "      <td>8.1</td>\n",
              "      <td>35</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>186</td>\n",
              "      <td>12.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.6</td>\n",
              "      <td>4</td>\n",
              "      <td>8.5</td>\n",
              "      <td>6.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>37</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>M5</td>\n",
              "      <td>X</td>\n",
              "      <td>175</td>\n",
              "      <td>12.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.2</td>\n",
              "      <td>6</td>\n",
              "      <td>12.2</td>\n",
              "      <td>7.4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>AS5</td>\n",
              "      <td>Z</td>\n",
              "      <td>230</td>\n",
              "      <td>32.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.5</td>\n",
              "      <td>6</td>\n",
              "      <td>13.4</td>\n",
              "      <td>9.2</td>\n",
              "      <td>11.5</td>\n",
              "      <td>25</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>A4</td>\n",
              "      <td>Z</td>\n",
              "      <td>264</td>\n",
              "      <td>40.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.8</td>\n",
              "      <td>4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8.6</td>\n",
              "      <td>33</td>\n",
              "      <td>SUBCOMPACT</td>\n",
              "      <td>A4</td>\n",
              "      <td>X</td>\n",
              "      <td>198</td>\n",
              "      <td>15.48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Engine Size(L)  Cylinders  Fuel Consumption City (L/100 km)  \\\n",
              "0             1.6          4                               9.2   \n",
              "1             1.6          4                               8.5   \n",
              "2             3.2          6                              12.2   \n",
              "3             3.5          6                              13.4   \n",
              "4             1.8          4                              10.0   \n",
              "\n",
              "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
              "0                              6.7                               8.1   \n",
              "1                              6.5                               7.6   \n",
              "2                              7.4                              10.0   \n",
              "3                              9.2                              11.5   \n",
              "4                              7.0                               8.6   \n",
              "\n",
              "   Fuel Consumption Comb (mpg) Vehicle Class Transmission Fuel Type  \\\n",
              "0                           35       COMPACT           A4         X   \n",
              "1                           37       COMPACT           M5         X   \n",
              "2                           28      MID-SIZE          AS5         Z   \n",
              "3                           25      MID-SIZE           A4         Z   \n",
              "4                           33    SUBCOMPACT           A4         X   \n",
              "\n",
              "   CO2 Emissions(g/km)  Engine Size x Fuel Consumption  \n",
              "0                  186                           12.96  \n",
              "1                  175                           12.16  \n",
              "2                  230                           32.00  \n",
              "3                  264                           40.25  \n",
              "4                  198                           15.48  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "target = \"CO2 Emissions(g/km)\"\n",
        "\n",
        "numeric_cols = [\n",
        "    \"Engine Size(L)\",\n",
        "    \"Cylinders\",\n",
        "    \"Fuel Consumption City (L/100 km)\",\n",
        "    \"Fuel Consumption Hwy (L/100 km)\",\n",
        "    \"Fuel Consumption Comb (L/100 km)\",\n",
        "    \"Fuel Consumption Comb (mpg)\"\n",
        "]\n",
        "\n",
        "categorical_cols = [\n",
        "    \"Vehicle Class\",\n",
        "    \"Transmission\",\n",
        "    \"Fuel Type\"\n",
        "]\n",
        "\n",
        "selected_df = df[numeric_cols + categorical_cols + [target]].copy()\n",
        "selected_df[\"Engine Size x Fuel Consumption\"] = (\n",
        "    selected_df[\"Engine Size(L)\"] * selected_df[\"Fuel Consumption Comb (L/100 km)\"]\n",
        ")\n",
        "selected_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5KYG84AKX39i",
      "metadata": {
        "id": "5KYG84AKX39i"
      },
      "source": [
        "## 5. Exploratory Data Analysis (EDA)\n",
        "\n",
        "Use simple plots to inspect the response distribution, relationships with key predictors, and overall correlation structure.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "-wibm_gfX39i",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:12.046030Z",
          "iopub.status.busy": "2026-03-24T12:15:12.045716Z",
          "iopub.status.idle": "2026-03-24T12:15:12.429361Z",
          "shell.execute_reply": "2026-03-24T12:15:12.428207Z"
        },
        "id": "-wibm_gfX39i",
        "outputId": "926c3cb9-296c-4f21-ac8f-74465765f215"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "plt.hist(df[\"CO2 Emissions(g/km)\"], bins=30)\n",
        "plt.title(\"Distribution of CO2 Emissions\")\n",
        "plt.xlabel(\"CO2 Emissions (g/km)\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cutHZGeFfIBu",
      "metadata": {
        "id": "cutHZGeFfIBu"
      },
      "source": [
        "- CO2 emissions have a rightward-skewed distribution, peaking at approximately 240 g/km and exhibiting a long tail.\n",
        "- The data focuses heavily on the 200-300g/km range.\n",
        "- Outliers focus on values ​​of 400 and above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "aaVV7qWHX39j",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:12.433451Z",
          "iopub.status.busy": "2026-03-24T12:15:12.433017Z",
          "iopub.status.idle": "2026-03-24T12:15:12.628374Z",
          "shell.execute_reply": "2026-03-24T12:15:12.627055Z"
        },
        "id": "aaVV7qWHX39j",
        "outputId": "59d7662d-960b-4ff4-aba6-8c4da60ae119"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(7, 5))\n",
        "plt.scatter(df[\"Engine Size(L)\"], df[\"CO2 Emissions(g/km)\"], alpha=0.6)\n",
        "plt.title(\"Engine Size vs CO2 Emissions\")\n",
        "plt.xlabel(\"Engine Size(L)\")\n",
        "plt.ylabel(\"CO2 Emissions(g/km)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "FhwS-JWliO7V",
      "metadata": {
        "id": "FhwS-JWliO7V"
      },
      "source": [
        "- There is a clear positive relationship: The larger the engine size, the higher the CO₂ emissions.\n",
        "- As Engine Size increases, the data becomes sparser and more dispersed.\n",
        "- Some outliers appear in the large engine (≈7–8L) with very high CO₂ levels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "HI6w4nsaX39j",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:12.632369Z",
          "iopub.status.busy": "2026-03-24T12:15:12.631990Z",
          "iopub.status.idle": "2026-03-24T12:15:12.795106Z",
          "shell.execute_reply": "2026-03-24T12:15:12.794077Z"
        },
        "id": "HI6w4nsaX39j",
        "outputId": "fa93a480-c2d6-44e0-8fb3-ee9e4b514567"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(7, 5))\n",
        "plt.scatter(df[\"Fuel Consumption Comb (L/100 km)\"], df[\"CO2 Emissions(g/km)\"], alpha=0.6)\n",
        "plt.title(\"Fuel Consumption (Combined) vs CO2 Emissions\")\n",
        "plt.xlabel(\"Fuel Consumption Comb (L/100 km)\")\n",
        "plt.ylabel(\"CO2 Emissions(g/km)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QgQnY9tqi9P_",
      "metadata": {
        "id": "QgQnY9tqi9P_"
      },
      "source": [
        "- There is a very strong linear relationship between Fuel Consumption and CO₂.\n",
        "- The data forms parallel lines, so it's possible the problem is due to different Fuel Type or Vehicle Class.\n",
        "- Because the dispersion is small, the prediction model will be very accurate.\n",
        "- There are a few slight misalignments and outliers, but nothing that affects the overall performance significantly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "FJwSzS3-X39k",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:12.799806Z",
          "iopub.status.busy": "2026-03-24T12:15:12.799377Z",
          "iopub.status.idle": "2026-03-24T12:15:13.195677Z",
          "shell.execute_reply": "2026-03-24T12:15:13.194534Z"
        },
        "id": "FJwSzS3-X39k",
        "outputId": "2e0a94e1-3c1a-4557-c53e-447d2b2f59b9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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AOx8N01uemZmB1ct+xN/b1iEhPhaB5YPRsftHKB9UBaYsOTkZC+bOwu5dO5CSnIxKVaqh19vvoZRfwD3PtbmzZ+Dwwf3qXKtdtx569HkPnp7FDK6/7rdVWLNqBWbPWwpzwPfogzlw6AjmLlyMK1evwsPdHa+0fhGvt30VFhb5z3v+Y/vfWLx8BSKuR8K7hBc6vN4OLZs301vnn917sWjZzwgNvwZPD3e0eLYpOr7eDjY2NjAXyclJWDxvJvb8s12da5UqV0e3Ph/e13t0/o/TcOTQXnWu1arTAN16f6D3f+i2zWsxc+q4PNs+37odevcdUCivh55MFtnZ2dmPeyeInmRxcXEqiA8MDMSCBQv0/gP+8MMPcezYMaxduxaurq4oCtbbGCeYs/fzRr31c+BSqTx2N+9aYIDv3bYlai2bjEtTF+DG5r/h/WoLlH6nIw51HoCInzVf3hzK+KHxvtWI3nMEl6cvhHNwOQSNHICwBavx7/v6wcWj5n1iN4xlwsjPcP7MCXTs/j4cHJ2waukcxMXFYNz0xXB2NnwOSTD15ce9kJyciDff6ovMjAwsmz8Djk5OGDVpPqytrSH/VYwc/B7CQy+jY/f31JeWtasW49K5U/hmykKU8PZ95K/FAsb572n8yIE4f+ZfdLpzzFYunauO2fjpiwo8ZkM+7q2OWYc7x2zpnWM2etI8dcwiI8LUoIiuhIQ4TB4zBCFVa2Lg0AkFfuF+GBlZxhu3/+7rT3D+7L/o0O1DddxWL5uN+NgYjJm2DE75HDetrKwsLJk7CZvXLkOjZq3zBPgLZk3A9q2/4c233kfxEr7YtGYJLp0/ha8nLURJH/9H/lpcbBJgDKO++gJnT5/CWz3fhqOjI5YvWYDY2FhMnTEXzi4uBreRYOGz/n2RlJSErt17IyMjEwvnzYajoxO+m/qDOtd0/b39D0ya8A08ixUv1AA/OdMexmJO71Evi0gUppOnz+Djz4egaeOn0axpE/x78hSW/rwSPd/qjI6vtze4zY5/dmPkmPFo+8pLqFurJv7ZsxfrNm7GF599jGefaazWOXj4CD4fOkI97gvPNcflq6GYM38RXmzZAh++26dQX1M07gbJhW3MiM9x7swJdOnRV73HViz5SZ1r332/EM7O+b1HMzD447fVe7Rzt3eQkZGBxfNnwtHRGeMmz8l5j/44YyKOHz2A9/v/T297dw9PlCj56AeSqlUogaJqT/16RnuuBnsLThCZI2bwiR4zCdxHjBiBd999Fz///DPefPNNtXzz5s34/fffMWfOnCIT3BuFhQX8urZByNhBEuHdl6CRHyPil0049ek36u+bv++EjYcbgoZ/lBPgl/usDzLiE3Gg3XsqY39j0w5kJqegyuQvcX7MTKSERsDUnT19HIf27cTAYd+pTJUIrlwdH/Vuj63rV6HNm90Nbrd35x+4fPEsxk1fAr8ATRVJ6bIVMeiDztizcxsaNX0e16+F4vSJI+jz4WA0fe5ltU6FkGp4t/OL+PvPjWjfsRdM+ZgNGvat3jHr1/s1/L5+Fdrmc8z23Dlm46cv1jlmFTDwgy45x6ykj5+66Zo4+n9wcnHFB58Mf+SBgzGdO30Mh/f/jU+HTkL12k+pZUGVauDjt9tg64Zf8OobPfPd9urlcyqAv3TuJGxt7fLcf+tGJP7YtBJd+nyCFi++ppZVrVkfn/V9DetWLkCvD76AKTp96gT2792NL7/6BrXr1lfLJIP/To9O2Lh+DV7v0MXgdv/8/RcuXjiPqTPnwj8gUC0rW64c+vXtpe575tkWallMTDSWLPwJWzaug4uL+fyfwffog5m/eBnKly2Dzz/pr/6uV7sWMjMyVZDf7pWXYGeX9z03d8EiNGn0FN7ro3nf1q1dE/EJCZi3aGlOgL9p6x8o4VVcPa6VlRVq16yB6OgYrPz1N/Tt3SPPQJMpOnPqXxzc9w/+N3w8atZpoJYFV66G93u9ic3rV6P9m28Z3G73zr9w6cI5fPf9AvjfOdcCy5bHJ+93w+6//0DjZ1uqZZcvnUO5CsGoGFzZiK+KnkScg09UBDz77LN45ZVXMH78eNy8eRMJCQkYOXIkOnXqhKeffhoXLlxAnz59ULNmTTRq1AiffPKJKt/XkgzQkCFD0LhxY1SuXBkNGzZUf0s5qNi7dy8qVaqEWbNmoX79+mjXrp3KoBVFrtWCUGX6Vwhb9CuOdB94z/UdSpeCc1AZXF/zu97yiFWb4VQhEI7lNSWcXs81QtTG7Xrl+NdXboKFlRW8WjaCOTh2aC/s7B1QrebdkXFXNw+EVKmJIwd3FbidT6mAnC/BQn739QtU5fgiPS1N/ZTsmZa9vQNsbG2REBcL8ztmNXDkYP6VF8cO7YNvvsfM8HaHD+zCvt1/4a3e/eCUTybIVBw/vEcdt6o1NIGq9rgFV66FowWca+KHScORnZWFYePnwtXdM8/9J45pytDrNGias8zGxhY16jS652MXZVJeb29vjxq16uQsc3NzR+Wq1XFw/978tzt0AKX8/HOCeyG/+/kH6G33y/LF6jkGfTEcdeprAmFzwPfo/UtLT8ex4//i6YZ335ei8dMNkZScrLL5uV2PjEJY+DU0aqC/TZOnGyI8IkLdp/0/QM5fCe61XF1dkJ6RoR7bHBw9tO/OuVY3Z5mbmwcqVamOw/mcM0LK8n39AnKCeyG/l/IvjUMH9qi/pQru6qULKFOmQiG/CtO5TJ6xbk8iBvhERYQE5PKfpwT5kydPhpOTEz777DNERkaqQL906dL45ZdfMHPmTDUAIJl+KQcTn3/+OU6ePIlp06apzL/M6//111+xfPnynMeXL8zbt29Xy0aNGgVLy6L59k++GoG/gp/Dqc/GIDMp5Z7rS6m9SDx3WW950oUrmvsrloGlvR0cA/2QeO6S3jppN6ORHhuv1jEH10Ivq1J5S50vYEIyVNfCrua7XXjYZRXg5+bt64eIcM12AWXKo3K12li9bC5Cr1xQ86IXz5mCtNQUNGyiySCaovDQKwaPmfd9HDNvg8esFK7dOWa65Mvd4rnT1GBL/af157Wa7LlWspTBcy0iXPPey8+7/b/Cl2NmIyDQ8Bfda2GXYO/gBHeP4nkeO/r2DTUf2xSFhV5FSW9fvQBJ+PiUQnh4aP7bXb0C31L6WWa1nW8phIfd3e6FVq9gxo8L0fDpJjAnfI/ev4jr11XA7VdKf8pUKV9N+XdomCZY13U1VHMO5d7G10ezTVh4uPr5SutWCL8WgZ9X/YqEhEQ1FWDVmnWoV6c2XPOZXmJqwsKuoKS3T573qDrXCniPyjnq45t36pBmO825dj0iXM3vP3/uFPq90wkdXm2qfm7ftqkQXgk96Uy/nobITLi5uWH48OH44IMPVMOaRYsWwcHBAT/88AO8vb3VAIDWpEmT0KBBA2zatEll4yXLX7duXQQFaebH+/n5qe3Pnj2r9xw9e/ZUc/2LsvToWHW7X9ZuzupnRpz+HFopx1f3uzrDxs3F4Dra9WQdc5CUlAAHh7sZdi0HB0c1DzXf7RIT4G3gy4m9bJd0d7ue7w3EmGEDMOgDTSmxlK++89EXqBhSDaZ8zBwNHDP7exyzZHXM/O55zLQO7tup+hd0e3uA+ZxrOtUc93vchH9g+QLvl2Nr6LHlPFb3JyWq5zE1SUmJat59bvI5n3xnsDa/7QwF+Op9rbOdZPTNEd+j9y8xUXM+5D7PHB0c1M8kA4Nj99omMUmTna9ZvSreaN8Gs+bOVzdRvlxZ/O8z0z1ehv4vNPjZ4+io3of5bpeUCB8D55rmva3Z7vLFc+pnVGQEuvV6H1ZW1tj+x2ZMmzgK6elpaPHCK3iSWFo9mZl1Y2GAT1SEtGjRAlWqVEGpUqVQvXp1tUwy8+fOnVPl+bpSU1NV6b6QDP8ff/yB1atX4/Llyzh//jzCwsJQtmxZvW2KenD/MCzuUYkgpcC45zqm12tUplhkZ2fd9+uwtMj/GBTUa1U7B1W++A4f+LbqKN3589FwcHJSc1xnT/0Gtnb2aNCoOUzzmGU91DHLKuCYWRqYt7tl3S9q/m/VGndLP02FweNW4Ov/b9VBBR1bYQoll5pjpv86CpoWVdBc74Le16ZwLB4E36P/zb36Zhs6z+71ftMeq8nTZ6p5+J3ffB01q1dDZFQUFixZhsFDR2D8qBGwt887t/9J+Vwr6BzVfkepVKUGPh86BpWr1VLT20SN2vURGxuN5YvnoPnzL5tkzwcqmhjgExUxMuIrN93/hCRbP2xY3k7vLi4u6v533nlHDQK89NJLaNWqlZqH/+WXX+ZZ31BzHVOXERuvflq76I+6a7PyGbEJOZn73Oto19M+hilZtWyu6pCvq97TzyI25naedSWDIAF5fqTTr6GyZ9nO0UlzHDesWabOtcEjp8DF1U0tq1qjHpIS4jFv5reqpLWofzmRYybdt3XVV8cs+iGOmVM+xywJDneOmVZCfBxOHj+kOnmbol+X/6guWaer7lPNEBtzy/Bxc/xvFTGa8zFvtkybrZX7izrpji83XU81amLwXJOsqvZ9Zoh0fZfSXsMVAfmfo6aI79H/xulOFj75TtZdK/FOpYdM/cuzjZNmm9zz6LVTAOX+mzdvYf3m39Hxjfbo0bVTzjrBFSug13v9sOn3bWjzciuYkl+WzsOKpT/pLWvwdFN1yVjD/xcWcK45Oef0PMp9DLXvUTd3D9Su93SedWrXbYjjRw4gJuY2PDwMX/bSHFkwg1+oGOATFXEVKlTAhg0b4OPjA1tbW7UsJiYGgwYNQo8ePVSQv2PHDtWBX5v1T09Px9WrV+Hv/+gvJ1XUJJzVzKt3LFcacUfuNhByKqdprpdw+gIyE5OQHHZdraPL1ssTNq7Oah1T0/z5V1Grrv6XhQN7duD4ob0qENftsXA9Igyl/PKv3vDxC8DlC/rTOYRcQqpchUrq95tR19U1p7XBvVZwlRrY+88fiIuJhptH3oZpRUmz519FTQPH7JiBYxYZEX4fx0xTcpnfMdM6elBzDe76jZ6FKXq2ZVvV4E7Xwb3bcfywgeN2PQy+/v+tUkj6QcgX6rjYaNVMLeexI8JQvISPqhgp6p5/8SXUraff6G7v7p04fPBAnmMWcS28wPJ6abB30cC5FnHtGioEBcOc8D363/j6eKtjJM3xdF27pvk7wC9vGbl/qVKadSIiUKHc3aq/8Ijrmm38/RF144bKblcJ0T/fSgf4q0Z7l6/m3wuhqJKS+Nr1NFcA0dq352/VaC/P/6HX5FzT//6gy7eUPy7dKcHXJf/3lq8Yon4/9e9RREZeQ9PmL+qtk5aaCktLq3wv90j0MIpmly0iyiHl9/Hx8fj0009x+vRpdRswYACOHz+OihUronjx4uryNBs3bkRoaKha3r9/f9VlP+1O53NzlnThKpIuhsKn3fN6y73btlTBf/IVTYOgm1v/QYlWTWFpa3N3nXbPIysjAzf/1HS5NSUexbxQtkKI3q1qzXoq0ydfhrUkSJLL28l9+ZH7roVdRtjVu00I5Xcpy9duJ8G9/C3N9XSdPXVcZS+cTeBSjp7FvFCuQojerVo+x+zUiSN6Xbtzq1azfr7HLPd25878C8/iJdT0BlOkOdcq6d3ksnWSZZdu+rrH7cyJw3qd9R9GlTvb79u1LWeZzFE9sn9nzn1FnVyDvnzFIL2bdM+Xc0063WvFxsbg5L/H9Drr5yb3SYO+0Kt3G4nK72GhV1CjZv7bmSK+R/8bSQJUq1IJO3ft0Ss3/3vXHpWJl4x7btKAz7tkSez4R79L/N+7dt+5rwR8fX1UwHv8hH4X/tCwcMTFxcPHuyRMjbxH5ZJ1urfqNeuqc02CfC0poT954iiq18p/6kb1mvVUo71QnXNNfpdlcp/49/ghTJ84Wq/Bowwk7P7nLwSFVFG9l54kMnXBWLcnETP4REWcZOGlYd63336Ljh07qu6utWrVwoIFC+DpqcmYjhkzBlOnTsXixYvh5eWFpk2bonv37mpevrmRMnvnSuVVYC9d8MW5UdNRfc4YpN+OQeTaP1DylebwfaMVDnXSXAdYXJjwI3zfbI26637EpUk/waliIIJGfozQH39GSqh+tsNUSffnSlVrYfq3w9Cp+wcq6F65ZA6cnJzRolU7vS+5GelpCCynacrYsHELrPl5PsYN/xgdumlKVJfNnwH/wHJo0Fgzt75Vmw74569NGDXkQ7z6ejdVJr1/91/YveN3dOnVTzUMMuVjNu3b4ejU/X1VofBLzjFrq3fMJMgsk3PMmqtjNtbgMdPvwB165SL8/mNWu6iRy+GFVKmNGd8NRYduH8DZxQ2rls2Go5MLmr/YPme98KsXkZ6RjsCymuN2PyRL36hZayyZMwnpaanw9g3AxjVLkJiYgJfadYWpksvhValWAxPHj0a3nm/DxdUVyxbPV+fai61e0QvepQqrbDlNMNaoSVN1CbwRX36Orj36qGULf5qN0oFl1X3mju/RByNz5AcOGY6RY8bjheea48SpM6rzfe9uXdU8eSnXv3I1VGX73d00FVldO76B8ZOmqm74DevXxa49+7D973/wxcBP1P2yXvtXX1aPI2rXrI7IqBtYuHQ5SpbwQuvnNdd5N3UyT75y1ZqYPGEEuvboqz7XViyZq861ljrnmgTvGenpKFOuovr7qSbNsGrFQowe9hk6d39XLVs8byYCAsviqcaaqpDnXngVWzb8ijEjPsebnXupqyZtXv8rQq9cwoixUx/TKyZzZZF9r44cREQ61tvc/xf1/8qzST003LYQu5t3xe0d+/SWHe31OcIWrM5ZN6DPmyg7oCfs/X1URv/CuFkIX7xG7/E8nq6NkLED4Vo9RA0OyP1nh09BdkZGob4O7xP5Xz/3UUtIiMOiH6eoslZpICQd7rv2/khl4LVGDn4PN6IiMGXO3eN360YkFsyeiONH9qtBJMnQynYenncvVXYt7AqWL5iBE8cOqqZCpQLK4KV2XVDvqcIJMiyQbeRj9jeysrMQpI5ZP71jNmLw++qYTZ2zSu+YzZ89KeeYSVYw9zETn77XCQGB5dBv4MhCfy0ZWcYbaElMiMPiOZNUub6caxVCqqNLzwHw0Tluo754FzejIjBxtv57UWtAn1cRXKUW3vlIv8eIBGrL50/D7h1bkJKShDLlgtGhez+UD6pSKK/FxSbvFTYKQ0J8PObO/h579/yjmucFV6qMXm+/h1J+d0v0vxg0AFGR1zF73tKcZTduRGHOD9Nw5PBBWFtZq6x+z7ffg6en4Tm7k78bi3+PHdF7jEctOdN4UyXM6T3qZRFZ6M8hGfz5S5YhLCwcxYoVw6utX8Tr7V5V9x059i8+/d+X+Kz/h3i+xd2BjnUbN2PFqjWIunlTZeQ7vt4ezzW7+9ku4cKq39ap9a5fj4Snpwdq16yBnm91zhkoKCzR0P/3KkwJCfGY/+NU7N8t51o2gkOqolufD/Xeo8M+/xA3oq7j+7krcpbdvBGJn2ZNwTF1rlmraoDufT7UO9ciroVhyfwfcOrEUVUpIFUDHbu+jZDKhXMlmmoVSqCoOtRcf9pXYaq1bSeeNAzwiajIBvjmwpgBvjkxVoBvTowZ4JsTYwX45sSYAb45MUaAb26MGeCbEwb4T26Az28CREREREREZBSW7KJfqJ7MzgNEREREREREZoYZfCIiIiIiIjIKC0tm8AsTM/hEREREREREZoAZfCIiIiIiIjKKJ/X69MbCo0tERERERERkBhjgExEREREREZkBlugTERERERGRUbDJXuFiBp+IiIiIiIjIDDCDT0REREREREZhacUMfmFiBp+IiIiIiIjIDDCDT0REREREREbBOfiFixl8IiIiIiIiIjPADD4REREREREZhYUlc8yFiUeXiIiIiIiIyAwwg09ERERERERGwTn4hYsZfCIiIiIiIiIzwAw+ERERERERGQUz+IWLGXwiIiIiIiIiM8AMPhERERERERkFM/iFixl8IiIiIiIiIjPADD4REREREREZhYUlc8yFiUeXiIiIiIiIyAwwg09ERERERERGYWnFOfiFiRl8IiIiIiIiIjPADD4REREREREZhSl00c/KysK0adOwYsUKxMfHo27duhg6dCj8/f3zrDt16lS1riHt2rXDN998o37v0aMHdu3apXd/vXr1sHDhwke67wzwiYiIiIiIiO74/vvvsWTJEowZMwbe3t4YP348evfujbVr18LW1ha6evbsiQ4dOugt++mnn7B06VJ07949Z9mZM2cwfPhwtGjRImeZjY0NHjUG+EREREREREQA0tLSMHfuXHz66ado2rSpWjZx4kQ0btwYW7ZswUsvvaS3vpOTk7ppnTx5EgsWLMDIkSMRFBSklt26dUvdqlevDi8vr0Ldfwb4RPRAvE/sfty7YHKuV274uHfBJLlXcX7cu2By3APcHvcumCQHL5fHvQumJzrpce+BSfokcPbj3gWTM+y8fmaU7tOaP1BUFfXL5J0+fRqJiYlo2PDu9zdXV1dUqlQJ+/fvzxPg5zZixAjUqVMHbdu21cveW1hYoEyZMihsDPCJiIiIiIjI7DRv3rzA+7dt25Zn2fXr19VPHx8fveUlSpTIuS8/f/75Jw4fPoxff/1Vb/nZs2fh4uKigv9//vkHjo6OeOGFF/Dee+/lKfn/rxjgExERERERkVEU9SZ7ycnJ6mfuwNvOzg6xsbEFbitz75999lmEhITkCfBTU1NRrVo11Wzv1KlTGDduHK5du6Z+PkoM8ImIiIiIiMjsbDOQob8Xe3v7nLn42t+FBOgODg75bifB+t69ezFr1qw890nmftCgQXBz00ylq1ixomqwN2DAAAwcOBDFixfHo1K0J0AQERERERGRWWXwjXV7GNrS/KioKL3l8nfJkiXz3W7r1q3w9PTE008/nec+a2vrnOBeq0KFCurnvcr+HxQDfCIiIiIiIiIAwcHBcHZ2Vtl4rbi4ONUdv27duvlud+DAAXVdewnmc+vatSsGDx6st+z48eMqix8YGPhI958l+kRERERERGQURb2Lvq2tLbp06YIJEyaojHypUqUwfvx4eHt7o2XLlsjMzMTt27dV0zzdEn4ZAGjfvr3Bx3z++ecxevRoNQe/UaNGKriXufe9evVSgwmPEgN8IiIiIiIiojv69euHjIwMDBkyBCkpKSpzP2fOHJVxDwsLU935v/nmG7Rr1067CW7cuAF3d3cYIgMGcpm8hQsXqkDfy8sL3bt3x9tvv41HzSI7Ozv7kT8qEZmtg2dvP+5dMDnXK9+9jirdP/cqj3ZE+0ngHqA/v4/uj5OXy+PeBZOTHJ30uHfBJA0LnP24d8HkDDvf83HvgkmqvOYPFFWh7xnOchcG/+9X4klTtOsjiIiIiIiIiOi+sESfiIiIiIiIjKKoz8E3dTy6RERERERERGaAGXwiIiIiIiIyDouHuz493R9m8ImIiIiIiIjMADP4REREREREZBQWlszgFyZm8ImIiIiIiIjMADP4REREREREZBTsol+4eHSJiIiIiIiIzAADfCIiIiIiIiIzwBJ9IiIiIiIiMgo22StczOATERERERERmQFm8ImIiIiIiMgo2GSvcPHoEhEREREREZkBZvCJiIiIiIjIKDgHv3Axg09ERERERERkBpjBJyIiIiIiIqNgBr9wMcAno+vatSv27duX7/27d++Gp6fnI3muZs2aoW3btvjwww9RWNLS0jBr1iysW7cOYWFhcHBwQLVq1dCnTx80aNBArSPLmzdvjgULFqB+/fqP7LnnzZunHnvIkCGYOnUqVq9ejT/++MPgupGRkejevTtWrFgBZ2dnFHUpyUlYOm869u36CykpyQiuXANde38EX7/SBW4XG30bC+dMxrFDe5CZmYkadZ5Cl1794OFZPGeduNgYLF8wA0cO7EJqagrKlA9G5x4fILBcEMyBfamSaHJkHQ60fx+3d+T/XhO+b7ZG+cF94VjWH8mXw3F+/CyEL/xVbx232lUQMnag+pkRl4iwBatwdsQ0ZKenw9S5N2iAgA/eg2PZcki/fQsRy1fg2sKF+a5vYWODgHffgVerF2Ht7o7kS5cRPn8+bm7eYngDKytUmzsH0bt2IfSHWTAXTjXqoGSXnrALCERGTDRub1iDW7/+nP8GlpYo3vZNuLd4ETaexZB6LRw3Vy5B3M6/9FZzfeoZFG/3Jmz9ApCVmICEo4cQOX82MmOjYersK9WAR9susPENQGZcDOL/3IC4LfrvNS3np5qheM+P8n2sG3MnIXHXn+p3v3FzYK3z+aZ1tX8XZCXEw9Q5Va8Nr049YOdfWp1r0Zt+w+3ffsl/A0tLFHv1dbg3fxHWHp5Ii7iGm6uWIn7XdsOr2zugzHc/4OaKRYj9M5/3sQmys7VAl5c8UL+qE+ztLHDyYgrmr7mNiBsZ+W4z7Qs/lPA0HBpE3UrHB6PDc/5+uakrWjRwQTF3a9yMzsDGnXHY/I9pn2/8XCNzwQCfHosXX3wRX3zxhcH7PDw8Htnz/PLLL7Czs0NhkuD62LFj+Pzzz1G+fHnEx8dj2bJl6NmzJ+bMmYOGDRvCx8cHO3fuhJub2yN73qtXr2Lu3LlYu3btfa1fsmRJtGrVCmPHjsXIkSNR1E2bMAznz5xAx+7vw8HRCauWzsHXX3yAcdMXw9nZ1eA2mZkZGDt8AJKTE9HzvYHIzMjAsvkzMGboRxg1aT6sra2RnZ2NSd8MRnjoZXTs/p4K/NeuWoyRg9/DN1MWooS3L0yZvZ836q2fAxt3w8dIl3fblqixYAIuTV2AG5v/hverLVBj7lhkpaYh4ucNah2HMn6ov+knRO85gkMd+8M5uByCRg6Ajac7/n1/GEyZc9UqCJkySQXnV7+fAdcaNRDYvx8srK0Q/tM8g9sEjfkGHo0bq0GAmH374BwSgvLDhsLGwwMRy5brrWtha4uKI0fApVpVFeCbC4eKIQgYMkp9iY1a/BMcK1VFyW5vw8LKCjdXLjW4TYmO3VG8fUfcWL4QSaeOw6VBY/h/NhRXMzMRv/tvtY5r42fh/+mXuL3pN0QumgtrDw+U6NQTgV9/i4sfv2PSA0p2ZSuiZL8hSNy/EzG/LoZdhUrweK2bOmaxG1fmWT/p2AFEjP4sz/Ji3T6Apb0jko8dVH9bOruo4P72zz8h9fxJvXWzkhJh6uwrhMB/8EjE7dqOG0vnwTGkCkp07aOO263V+u83La8330Kxth1UwJ50+gRc6j8Nv0+GICwrE/F7duqta+nkDP/Pv4JtSR+Ym4+6eKFCgB0WrY9GckoWXmvpjmF9vfHJ+GtITM4yuM2EeVGwsdLPrFYMtEO3Vz3x++67wbsMHLRq7Irlm2Nw/moqaoU4oFe7YsjIzMa2PQkwRfxcMzJ20S9UDPDpsbC3t4eXl1ehP8+jqgTIT0JCAn777TeVPW/atGnO8q+++gqnT5/G4sWLVYBvZWX1yF/v9OnT0bp16wcaNJDqiSZNmqB3794oXbrgTPjjdPb0cRzatxMDh32HGnUaqmXBlavjo97tsXX9KrR5s7vB7fbu/AOXL57FuOlL4BdQRi0rXbYiBn3QGXt2bkOjps/j+rVQnD5xBH0+HIymz72s1qkQUg3vdn4Rf/+5Ee079oJJsrCAX9c2CBk7CLjPyregkR8j4pdNOPXpN+rvm7/vhI2HG4KGf5QT4Jf7rA8y4hNxoN176ovIjU07kJmcgiqTv8T5MTOREhoBUxXw7rtIPH0G574cqv6O2bUbFtbW8OvZAxFLliIrNVVvfaegIBRr9iyuTJuOsDlz1bLYvfuQmZyMwH4fImrdemQmaL7cutasgbKfD4JtiRIwNyU6dUfKpfMIn6Q5bxIO71dfgou/1gm31q5Edlpanm3cW7yA2B1/4MbyBervxGOH4VCuAoq1bpPzRdjrtU6IP7AHETMm5WyXFh6KsuO/h0vdhojbtQOmyv2VTki7egk352heW/KJw+qYubV6DXFb1yI7Xf+YZSXEITUhTm+ZS/OXYOPjh4hvPlf3C1v/supn0uE9yLhxHebGq8Nb6ly7NmWs+jvxyAHAyhrF2nXE7fWrDZ5rbs1eQNzOP1WAL5KOH4Z92QrwePFVvQDfuU5DlOz1HiwdHGFuKpS2Q53Kjhg9OxJHTierZacupmD6F35o+ZQLVm+LNbjd5XD94+lgZ4GPuhTHwZNJWPOn5pzz8rDGS8+4Ys6q2zlB/4nzKSqTXyPIwWQDfH6ukTnh8AkVWVJeLxlwKa+vWbOmKm3/+uuvkZFxt7xMsuJSgl+1alW89NJLWLlyJYKCglTZuvYxJPgW8lNK1KWcXoJc2aZLly64cOFCzuNJ9v3LL79UpfW1a9fGW2+9hePHjxe4n5aWlmo/dPdLTJkyRT2WkP2R/dq7d6+6ye+GbtqpC1JOP2DAANSpU0e97nfffReXL1/OeWy5f/369aoS4kG4u7urAQcp7S/Kjh3aCzt7B1SrWS9nmaubB0Kq1MSRg7sK3M6nVEBOcC/kd1+/QFWOL9Lv/CctVQFa9vYOsLG1RUKc4S89psC1WhCqTP8KYYt+xZHuA++5vkPpUnAOKoPra37XWx6xajOcKgTCsbxmAMjruUaI2rhdL8twfeUm9cXHq2UjmCoptXerUxu3/tSUOWvd2roN1s7OcKlRI882DmU059XtHfpfyGL3H4CVoyPc6tTJWRYyaSJSI67jaKfOMCcW1jZwrFI9TyY0dtcOWDk6wTGkquHtbGyRlayfUc6Mj4OVy51KEwsLJBw5iOjN6/TWSQ0LVT9tTbmyxtoa9kFVVBCuK/HgLhVc2lUIuedDWLq6waNNZ8T/tQlpl87mLLf1L4Os5CSzDO7VuVa5GuL3/aO3PH73nXMtuIrB7SxtbJCZlJT/uSbrODrBb+AwJJ04htCRg2FuJNBOSc3C0TOa4F7EJ2bh5IUU1AxxuO/HafecO1ydrTBn1a2cZfWqOiItPRt/7tMvx5+08Aa+nX8Dpoifa8ZnYWFhtNuTiAE+FWmTJ09G3bp1VZZ84MCBWLRokZrrLk6dOoV33nlHBaxr1qxB3759Vfl5QQ4cOICDBw+qIH/JkiW4deuWyrYLKd2WefOhoaH44Ycf8PPPP6NGjRro2LEjTp7UL33UkrnsnTp1UiX5jRs3xieffKJ+l/J5KYmXW24yWCEDAtrb1q1bUaZMGTz11FOoVasWkpKSVKZdyOtduHChmrbwxhtvqMBebN++Ha6urmqu/4OSSoP85ukXFddCL6tSeUsrK73lJX38cC3sar7bhYddVgF+bt6+fogI12wXUKY8KlerjdXL5iL0ygUkxMdi8ZwpSEtNQcMmLWCqkq9G4K/g53DqszHITEq55/pSai8Sz90dOBJJF65o7q9YBpb2dnAM9EPiuUt666TdjEZ6bLxax1TZ+5WCpa0tkq9oXq9Wcqjmi5dDYN4Kl4yYGPXTzke/nNfB3y/nMbWO9+qDU/0HqCDfnNh4+8DSxhap4ZpBVK20CM3cXLtS/ga3u712JdyebQnnmnVVUOv2THM416qHmL+2albIzkbkTzMRv09/AE/Kq0XKVf3z1JTYeHmrAaX0yLvzl0VGlKb6xabk3fMmPx6vdAKysxCzerHectuAMshKjIdX30EImLIEAdOWweudz2Dl9uimuj0uNiU151raNf3jlnb9mvppm9+5tn413Jq2UPOp5VxzbdxMnXex27ferZBIS8XF/r0RMW08Mk14YDc/pUraIPJWhryt9Fy/mQHfEjb39RjF3K1UGf7av+JwMzozZ3mgr616nJBy9hgzwAdLxpVWc/ebNyj6vX3yw881Mjcs0afHQuaNb968Oc/yFi1aYPz48Tl/N2rUSGXRhb+/vwp2Dx06hDZt2qgsdJUqVVTgL8qWLasC9lGjRuX7vJJlHzduXE5Ze4cOHXKeb8+ePThy5Ij6KZlu8fHHH6vnk+Z4Y8aMyXcOvgwESPXAli1bcgYgZN9Hjx6dJ8i3tbXNKdeXQQWpUJARRhnMkDni0igvLi5O7Zf8LeQ1SeZfBh1kfdnPChUq4GHIdtevX0dERITqDVAUJSUlwMHhboZdy8HBUc2vz3e7xAR4++b9j9hettOZjyrz88cMG4BBH3RRf8vxf+ejL1Ax5MEHTIqK9OhYdbtf1m6aL2MZcfrllFKOr+53dYaNm4vBdbTryTqmyupOo8nMxFzZlzuZP2unvOdf7IEDSA4NQ9lBA3E+JQUJJ07CqWIFlO7XD9lZWbB0uJsZSzp/HuZIslkid9ZKssjC0tFwufOt336BQ1AllB5+dxA2+vcN+c6jFjbevvDu8S6SL55DwsG9MFXaEvCs5LvZVPV3iubve5WIW7q4wempZxG3ZU2e4y4ZfCv3YkjbsUWV+ksJv8erneD92ShcGzEA2Wn600xMiWTZRWZ+51o+x02CLjWf+ktNqbWI2bYRt9esuLtSRgbSrukHc+bE0d4Syal559nLMke7+8vttW7iivSMbGz4O9dUEWdLeLpZoV8nL6zYEoPwqHQ8XcMJ77yuafRoiiX6/FwzPgvOwS9UDPDpsZDS+U8//TTPcsdcH6LlymmyjFouLi5Iv1MqLFl1yXrrkmx/QYoXL643Z1338U6cOKEC7meffTZPl/zUXHNxc5PpAXJLSUnB4cOH8fvvv+cE4/IzPxLE79+/X3W2l4y89nXFxsbmeS2yD9rpBDdv3kSxYsXwX/oS3Lhxo0gE+FlZWcjO1v8ikp2VK+2gw9Ii//8U5N8vP9oyLWmuN3zg2/Aq4YPOn4+Gg5MT9uz8A7OnfgNbO3s0aNQcT4J7/ecqAeu9muAU9O9k8q/fwLmUnZGBk++/j/LDh6HKDzPVsrQbN3Bx3HgEjR2DrJR7V06YvHt9KTNw3KT8NXD0ZNh4euLa998hNewqHIMrw+uNLirIvf7j9DzbSHa29FfjkJ2ZidCxXxl8XJNRwGeWco/X5tL4OXW+SgCf263505GdlYm0y5oBpdRzJ5F+7Sp8Ph8L56eeVSX9ZnsZrVz/b6htrG1Q+uuJqnt+xMxJaq6zBGAyj1ren5Fzv4e5kf/aclchF1SVfD8f2zbWFmhWzwV/7o3P05DP2spCle1LQ759x5Ny5uAX97DG6y3dTTLA5+camRsG+PRYODk53VeTN8l25/fFWxrXSXD4IAw9npY8lpTcr1q16r63k6y6lLsPHjw4p3mgTBmQmwxOjBgxArdv3za4rQT1UhkgnfADAgL09kNK9mfMmJHvAIjM+5dLwD0M7TGTxygKVi2bqzrk66r39LOIjcl73CQLLwF5fhwdndXl9Qxt5+ikydhuWLNMHYPBI6fAxVUz2FO1Rj0kJcRj3sxvUf/pZk/EnK2MWM38SWsX/eOpzcpnxCbkZO5zr6NdT/sYpigjPkEvc6Olzdxrm+XllhIahn979VFd863d3ZB8NRR23t4qAMuINb9S39yy7lQ85M6eao9j7ooI4fpUYziULY/LQz9F4tFDapnMfc5MSoTvu/0RvWU9UnVKVWUurHQ2ly/Jl4d8jPQ7JdmmSpsVlMuxGc7sF9zt3rH2U0g+cSSnsZ6u1Itn8i47fxpZSQmw9TPdKTRCzg9hZe9oOLNv4CoBLg0awb5MOVz5aiCSjh1Wy5JOHlNXFPB+ux9ift+A1FDzKot+7Tl3vP68pupQa/fRRLi52BjM7Cel3Pt7U7Ugezg6WOLvQ3mPscztz8rKxuFT+hUp0syvRrAD3JwtEZvwYN/NHjd+rhXBATz6Txjgk8kKDg5Wl6fTJdnzh1WxYkXVFV8y+nK5O90SfHkuaciXm6wvUwXk8nPVq1fXu0+qAyTgl0EDmVeva9euXWru//Dhw1GvXr08+yE9BWR7bbZd9knm97/wwgvquaTEX7fp3oOQaQyiRBHp7t38+VdRq65mPprWgT07cPzQXhWI6w5EXI8IQym/wHwfy8cvAJcv3G1ApRUZEYZyFSqp329GXYevX+mc4F4ruEoN7P3nD8TFRMPNo3CvvlAUJJzVzKt3LFcacUdO5Sx3KqcZeEs4fQGZiUlIDruu1tFl6+UJG1dntY6pSgkLUxl57fx5LXt/zRSPpIv6fQeEpZ0dijVvhrgjR5F67RrSozXXMHYOCVY/E06fhrlLux6usk+2PvrzxrV/p4bp9zQQNl6aaUpJp/7VWy5fhoVcc1r7RVjmS5f6aBDSwq/iyleDkXH7JkxdRtR1dcysS+hXTGn/To/Iv1Tcyt0TdqXL4YaB7L2FgyOcajVE6qVzKmt/9w4LwMoGmfGmPeAkAZAcNxsf/UZk2sZkaQb6sWjPteTTJ/SWJ53UNMu19S9tdgH+1j3xqsu9rrpVHFE9yEGdCrpJ4pLFrREeee/LstUOcUTkrXRcDMvbOT7iZgYsLS1gbQWk6/QW1rbMkQZ8poafa2RuikYKj544UsouJeKGblISfz/kOvPS4X7ChAm4dOmSKouXzvXiYTKw0iQvJCREda+XefhXrlzBN998ozL6uacKaEk5vwTo0uBv6dKlaj/Onz+v5tHLXH9p2pc7+y9l9v369VPN+2Sqgu5rT0xMxCuvvKKmEcg6R48eVet//vnn2LFjh+q0L6S53pkzZ/JUMMhxlfVy32LuNAfTTgHw9fUtMgG+RzEvlK0QonerWrMekpOTVFd8rbjYaHV5O7kvP3LftbDLCLt6NziT36UsX7udBPfytzTX03X21HGV5Xe+M1XC3CVduIqki6Hwafe83nLvti1V8J98RdNc6ObWf1CiVVNY2t7NBnm3ex5ZGRm4+ad+V3BTIpc8ij10GJ7Nm+ktlwA+Iz4eCSf0AwSRlZ6uLn3n3b7d3YVWVvDp8KbK5CedM89597rkagqJJ47BtYH+FRRcGzZWVQ/JZ/MOcqSGaxoXOlbS73Eh1zQXadc1zeaca9eH34DBKji79PlHZvMlODsjHSlnT8CxVgO95RKcS6Y9Vacrfm52ZSuqn6nn7w7C5UhPh2end9Sl9nQ5Vq+nBqNSzhR8BRhTONck++5SX/9ck2uNZyYmIPlc3uoFKckXubueOwRXVj/TI033sp75iY7LVIG47u3Y2WSVrZcgX8vFyRKVytqr++7nMntnLhmemqjN3D9VU7/6SS7Ld/laGpJTTS/A5+faYyDJG2PdnkDM4NNjsXHjRnUzRJrNSab6XiTTPW3aNHz33Xcqiy5l7ZJll8vh2djcX5dYXVLyL+XyMi++f//+SE5OVoG9PIeU3Bsi2WXpyC+X85Ou/BLUS9At23300Ud47TX9L15iw4YN6nJ8Up4vN10ffPCBmrcv3fPlsXr16qVK8StXrqz2TTvQIAMDw4YNU8G6NBrUzc7LoEJu8jxyuT3ttALZviiTy+FVqloL078dhk7dP1BB98olc+Dk5IwWrdrpBe8Z6WkILKcZ+GjYuAXW/Dwf44Z/jA7d+qply+bPgH9gOTRorJlb36pNB/zz1yaMGvIhXn29myrr37/7L+ze8Tu69OoHKyvz/FiUMnvnSuVVYC9d8MW5UdNRfc4YpN+OQeTaP1DylebwfaMVDnXqn7PdhQk/wvfN1qi77kdcmvQTnCoGImjkxwj98WekhJr2l+WwH+eg8szvETRuLCLXrIFr9eoo1e0tXJkyVc3XtXJygkPZMirbnxEdI/NbEPHzCvh26oTUyEjVgd/njTfUdqc+/vSJmU958+dFKD1ivLrMWMzWjWreabG2byJywWzV1E1d+s2/tOp2Lh3KpYN00pmT6ktu1NL5SA2/CseKISj+ehfE7f0HKefPqC7zvh98oppa3VixWG2vK/3WDWTcMt0vxrHrf0bJj0fA692BSNi5FXblguH6fFtEr1qgBpss7B1g6+uP9KjreqX4NqVKIzs9zeBl8GTgIHbjSni06YTMuBgkHz8IW7/ScH+5g7okX8pp0w7wxc1fliBg2FiU+uRLxPyxCY5BlVDs1dcRtWhOzrkmrzk98s65dmA3ks+egu9Hg9S1ydUc/ArBKN6+szoPUwxUeJmjUxdT8e/5ZPTrXByL1kWrS+RJGb/Mp9+yK16v277Mt78cfjexIvkRWf7PYcNTR+RSewdOJKHbK56wt7XE1etpeKa2M4IC7TD+pyiYKn6ukTmxyC6oKxVRESbl+dJlvlIlTem1tjv///73P1Wqr+1Ab66kZF8y/UOHDr3vbaRKQIJ7mQIgVx14GAfPGu4p8KglJMRh0Y9TVLm+NOGTDvdde3+kMvBaIwe/hxtREZgyZ3XOsls3IrFg9kQcP7JfDdpUrVlfbefhqenwK66FXcHyBTNw4thB1UyuVEAZvNSuC+o91bRQXsv1yoYHiAqLZ5N6aLhtIXY374rbO/bpLTva63OELbh7vAL6vImyA3rC3t9HZfQvjJuF8MVr9B7P4+naCBk7EK7VQ9TggNx/dvgUVeJemNyrFH6Xfs9nn0XAu++oy+KlRUWpAP7awkXqPtfatVH1x1k4N3Q4otZqSqQtrK3h/3YflHjpJVi7uSLxzFmEzpqNmD35VzM8ffggrs78AaE/zCr01+MeoD/1pLDIXOcSHbupplHyBfX2hjW4dadLucw1LTNqIsInj0XMH5qrpciX4xJdeql5q1bOrkiLvIbYP39X28h55FS1JgK//jbf55Mv0DeWzS+01+PkpbliRGFyrNkA7q92VJfFy4i5hfg/N6jO+MI+qIrqfH9z7mQk7Lp7GVPPzu+oTH/oJ90NP6iFBVyeeR4uz7aCtZc3shLikbh3O2J+W6YGBgpTcnTefieFwaXe0yje4S3Y+voh4/YtRG/8DbfX/qLuc6xcDaVHfItr08Yj9s8tOeeaV6ceKtNv5eyisvax23/HrbUrVfd8Q6XW5Wcu0nuMwjQscDaMwcnBEm+94qHK9S0tLHD6cgrmr7mNiBt3j8Gwvt7w8rTGB6PuThNxdbbEj18FYPYvt/D7bsN9VmRQ4LWWbmhc2xmuTlYIi0zDyt9jsf/fwjknhp3vCWMwt8+1ymuK7iWRb3/9jtGey3PID3jSMMAnk7V8+XKVbR87dqwqrZeSeslqy7Xk87uknTmR6QCdOnXC+vXrc+bq38vEiRMRFRWlph48LGMF+ObE2AG+uTBGgG9ujBXgmxtjBPjmxlgBvrkxVoBvTowV4JsbBvhPboBv3ilOMmtvvPGGykjLteYjIyPVZeNat26t5q4/CWRKgpTwyxSC+8niyzHatGmT6t5PRERERETmhwE+mSxppCdz1uX2pOrdu/d9r1uyZEls3qwpKyMiIiIiehwsLJ7M5nfGwqNLREREREREZAaYwSciIiIiIiLjsHzwy1nT/WMGn4iIiIiIiMgMMINPRERERERERmFhyRxzYeLRJSIiIiIiIjIDzOATERERERGRUVhwDn6hYgafiIiIiIiIyAwwg09ERERERETGYcEcc2Hi0SUiIiIiIiIyA8zgExERERERkVFwDn7hYgafiIiIiIiIyAwwg09ERERERETGYckcc2Hi0SUiIiIiIiIyA8zgExERERERkVFYWHAOfmFiBp+IiIiIiIjIDDCDT0RERERERMbBOfiFikeXiIiIiIiIyAwwwCciIiIiIiIyAyzRJyIiIiIiIqOwsGSTvcLEDD4RERERERGRGWAGn4iIiIiIiIzDgjnmwsSjS0RERERERGQGmMEnIiIiIiIi4+Ac/ELFDD4RERERERGRGWAGn4iIiIiIiIzCgnPwCxWPLhEREREREZEZYAafiB6IBbIf9y6YHPcqzo97F0xSzL8Jj3sXTE7xisUf9y6YJFsXx8e9CyYnMy3jce+CSbK1t33cu2ByrGwZrpgdE5iDn5WVhWnTpmHFihWIj49H3bp1MXToUPj7+xtc/7fffsNnn32WZ/m2bdvg5+enft+4cSOmTp2KsLAwlC1bFoMGDULDhg0f+b4zg09ERERERER0x/fff48lS5Zg5MiRWLZsmQr4e/fujbS0NBhy5swZ1KtXDzt37tS7+fj4qPv37NmjBgA6dOiA1atXq8D+7bffxoULF/CoMcAnIiIiIiIio7CwtDTa7WFIED937lz069cPTZs2RXBwMCZOnIjr169jy5YtBrc5e/YsgoKC4OXlpXezsrJS98+ePRstWrTAW2+9hXLlyqnsfeXKlTF//nw8agzwiYiIiIiIiACcPn0aiYmJeuXzrq6uqFSpEvbv359vBl8Cd0Mk+3/o0KE85fj169fP9/H+C05qISIiIiIiIuOwMN4c/ObNmxd4v8yRz00y9UJbXq9VokSJnPt0xcbGIjIyEgcOHFBl/dHR0ahWrZoqyS9Tpgzi4uKQlJQEb2/v+3q8/4oZfCIiIiIiIiIAycnJ6qetrX5TTDs7O6SmpuZZ/9y5c+pndnY2vvnmG0yaNEmt16lTJ9y8eRMpKSkP9Hj/FTP4REREREREZBwPOTf+YWwzkKG/F3t7+5y5+NrfhQTjDg4OedavU6cOdu/eDQ8PD1jcqU6QDvwyf3/VqlV4/fXXcx5PV36P918xg09ERERERESEu6X5UVFResvl75IlSxrcxtPTMye4FxK4y+XxpHTf3d0djo6OD/R4/wUDfCIiIiIiIjIOCYSNdXsI0jXf2dkZe/fuzVkm8+hPnjyJunXr5ll/+fLlqmGezLPXSkhIwOXLl1G+fHkV+NeqVQv79u3T204eX7L/jxoDfCIiIiIiIiJo5sp36dIFEyZMUCX+0lV/wIABqkley5YtkZmZiRs3buTMrW/SpInqlD9w4EA1H//48eP48MMPVVa/Xbt2ap0ePXpg/fr1+Omnn3DhwgWMGzcOp06dQrdu3R75/jPAJyIiIiIiIqMozOveW+S6Pax+/frhtddew5AhQ9CxY0d1Pfs5c+bAxsYGERERaNSoETZs2JBT0j9v3jyVwZd1u3fvDhcXFyxYsEA10hOy/ujRo7F06VK0bdsWe/bswcyZM/O9tN5/YZEt7f6IiO7TobO3HvcumJzkN1s+7l0wSTH/JjzuXTA55dsFPu5dMEkuvp6PexdMTvKtuMe9CyZpmP/sx70LJmfI2e6PexdMUvCKLSiqkhd+bbTncug6BE8aZvCJiIiIiIiIzAAvk0dERERERETGYcEcc2Hi0SUiIiIiIiIyA8zgExERERERkXFYPtzl6+j+MINPREREREREZAaYwSciIiIiIiKjsOAc/ELFo0tERERERERkBpjBJyIiIiIiIuPgHPxCxQw+ERERERERkRlgBp+IiIiIiIiMg3PwCxWPLhEREREREZEZYAafiIiIiIiIjMOCc/ALEzP4RERERERERGaAGXwiIiIiIiIyDkvmmAsTA3wiKpJSkpOwZN732LfrL6SkJCOkcg107d0Pvn6lC9wuJvo2Fs2ZgqOH9iAzMxM16jRE11794OFZXN0/Y+LX2PHHhny3nzJnJbxK+MAUuTdogIAP3oNj2XJIv30LEctX4NrChfmub2Fjg4B334FXqxdh7e6O5EuXET5/Pm5u3mJ4AysrVJs7B9G7diH0h1kwJ/alSqLJkXU40P593N6xr8B1fd9sjfKD+8KxrD+SL4fj/PhZCF/4q946brWrIGTsQPUzIy4RYQtW4eyIachOT4c5cKxWC8U7dIedX2lkxMYgZvNaRK/7xeC6rs88B5/3Ps33sSKmj0fcjq0563q+/BpsSvoiI/oW4rb/jlurlwJZWTB1dkFV4dLqTVh7+yErPhaJO7cg8a/1Btd1qNsEHp365vtY0UtmIHn/DvW7jX9ZuL7SWf3MTklG0r7tiN/8C5CZCXPgUKUmPF97C7alApAZF4PYresRu3GVwXVdGrVAibcH5PtYUbO+Q/Lp4yj93U/5rhO343fc+HESTJ2drQU6PO+KupXtYW9rgdOX07B4QywibuZ/Xkz8tAS8PAyHBlG3M/Dxt1F5lj/XwAmtGjlhwIS895kax2q14dWxO+z873yubfoNt9ca/lxTLC3h+crrcG/2Aqw9iiHtejhurV6G+F3b9VazL1sBJd56G/blKiIzKQmxf23BzRULgYyMwn9R9ERigE90R0ZGBhYvXow1a9bg0qVLsLOzQ6VKlfD222+jQYMG99x+1apVGDx4MM6cOaP+btasGdq2bYsPP/zwofcpKCgI33zzDdq1a4cnzdQJw3H+zL/o1P19ODg6YeXSuRj5xYcYP30RnJ1dDW6TmZmBscM/RnJyInq9NxCZGRlYOn8GvhnaH6MnzYO1tTXadeiOFi+20dsuISEOk8cMQUjVmiju5Q1T5Fy1CkKmTFLB+dXvZ8C1Rg0E9u8HC2srhP80z+A2QWO+gUfjxmoQIGbfPjiHhKD8sKGw8fBAxLLleuta2Nqi4sgRcKlWVQX45sTezxv11s+Bjbvh80qXd9uWqLFgAi5NXYAbm/+G96stUGPuWGSlpiHiZ83AkUMZP9Tf9BOi9xzBoY794RxcDkEjB8DG0x3/vj8Mps6+QjD8Bo1A3K7tuLl8ARyDK8Orcy9YWFni9pqf86yfeGgfrgz5KM9y73cGwNLBEYmH96u/3V9sg5Ld+yJ+zw7cWDQbVq7uKP56V9iVLotr342EKbMpXR6evQci+chuxG9cAdsyQXB9uRMsrKyQsO23POunnjyMG5O+zLPc/c23YWHvoO4XVsVKoFjf/yHt8jlEz58M65Kl4NrqDVg6OSN2xRyYOrtyQfD5eBgS9v6N2ysXwaFiJRR7s4c6bjHrVuRZP/HoPoR99XGe5SV69YOFnGtH9yMrOcngOm4tXoJz/caI35HPAKeJef8ND5Tzt8GyTXFITs1G22Yu+F+v4hg0OQpJKdkGt5m0+DasrfXnRlfwt0WX1m74Y19SnvUbVLVH51auiI7LNIvPNf/B2s+1+XAIrgKvLr3VwPbtX/X/P9Qq/sZbKNb2TdxcsRjJp/+FS/2nUWrAFwjPzET83p1qHZsS3vAfOhbJZ08h/LuvYVcqAMU79oCVswsiZ0/BE4td9AsVA3wi+TKVmooePXogIiIC/fr1Q82aNZGSkoKVK1eq5ePGjcPLL7/8QI/5yy+/qEECenBnTx/HoX07MWjYtyoDL4IrV0e/3q/h9/Wr0PbN7ga327PzD1y+eBbjpy+GX0AZtax02QoY+EEX7Nm5DY2aPo+SPn7qpmvi6P/BycUVH3wyHBYm2vgl4N13kXj6DM59OVT9HbNrNyysreHXswcilixFVmqq3vpOQUEo1uxZXJk2HWFz5qplsXv3ITM5GYH9PkTUuvXITEhQy11r1kDZzwfBtkSJx/DKCpGFBfy6tkHI2EHAff6zB438GBG/bMKpT79Rf9/8fSdsPNwQNPyjnAC/3Gd9kBGfiAPt3lMZ+xubdiAzOQVVJn+J82NmIiU0AqZMgu6USxdwffp49XfS0QOAlTU823RA9IZfkZ2eprd+Znysuulyf+FV2Jbyx9UvB2jus7BE8fadkXj0IK5NHJWzXsqlcygzYRYcq9ZC0vFDMFUuL7yG9PDLiFn8vfo79fRRFTg4t3gVCTs2ArkqO7IS49VNl1Pj51UAf3Py0Jz7nJu9jOyUFNyeM0Fl7FNPHUF2Wirc2vdAwu+/IjPmFkyZZ7vOSL1yEVE/fKv+Tj5+UJ1rHi+/gdjNa/Kca1nxcUiNj9Nb5vbcy7Dx9Uf4iM/U/SL1gmYgXss2sLwK7m+tWICUsydh6sr726BWiD3Gzb+FY2c1n/1nLqepDH2LBk747S/NZ3tuVyL0M8oOdhZqoODw6RSs+/vuNq5OlnithQua1XNCfJLpV9dog3X5XIuYOk79nXjkgBpIKtZWPtdWIztN/1wTbs8+j7idf+LWL4vU30n/HlHZevcXXskJ8D3bvIms5GSEjRumMvYyoJmVloqSvd5X1UkZN28Y+ZXSk4DDJ0QAJk+erDLvS5YsUVn3wMBABAcH44svvkCbNm3w9ddfIzEx8YEe09PTE05OToW2z+bs2KG9sLN3QLWa9XKWubp5IKRKDRw5uLuA7fbBt1RATnAv5Hdfv0AcOWB4u8MHdmHf7r/wVu9+cHJ2gSmSUnu3OrVx688/9Zbf2roN1s7OcKlRI882DmU0x+j2Dk2Zr1bs/gOwcnSEW506OctCJk1EasR1HO3UGebEtVoQqkz/CmGLfsWR7gPvub5D6VJwDiqD62t+11sesWoznCoEwrG8ZvqI13ONELVxu145/vWVm9SXRa+WjWDKLKxt4FCpGhL2/6O3XDKsVo5OKut1L1Zu7ij+ZjfEbFmPlPOaQMvK3R1WLq5IOLRXb9200CvIiIuBU627nwUmx8oaduUrIeW4plJBK+XoXljaO8KuTPA9H8LS2Q0urd5A0j+/I/3qhZzldsHVkXLqsF45vjyuhaWlus+kWVvDIbgaEnN95ifu36kqP+wrVrrnQ0gViJT3x23bgNSL+kG9Lq+3+iItPBSxm/Sn2piqahXskZKahePn7g7sSiAuZfo1Ktrf9+O82tQFrs6WmLdWf4DulWecUbWCncr4S/Bv6uRzzbFyNcTv0/9ci99T8Oeapa0NspL0Kxsy4+PUZ5mWc/Xams81nXJ8eVwLSys4Vb/7/+wTx9LCeLcnEAN8euKlp6erTL2Uwfv45J173b9/f8yePVsNArRo0ULvvvj4eFSrVg1//fVXnu2kRH/q1Knqd/nZvXt3zJo1C02aNEHVqlXRpUsXXLhw94va9evX0bdvX1U9IOusXbs2z2P++eefaj/lOZ977jlMmjQJaTqjylLSP2XKFDz77LNo1KgRLl++jGPHjqFTp07qcevWraumDFy7dg1FWXjoFZTw9oWllZXecm8fP1wLu5r/dmGX4V0qIM9yb99SuBaed7vs7GwsnjsNIVVqov7TzWCq7P1KwdLWFslXrugtTw4NVT8dAvP2LciIiVE/7XKd8w7+fjmPqXW8Vx+c6j9ABfnmJPlqBP4Kfg6nPhuDzKR7f0mVUnuReO6y3vKkC5rj7lyxDCzt7eAY6IfEc5f01km7GY302Hi1jimzKekNSxtbpEWE6y1Pu675TLH11a+OMaT462/Jmw83l9+dOpKVmIjsjAzYeJXUW1dKza2cXGBbwjSnzgjrYiVUAJERpV+5kXEzUv20uo+eHy4vvqaOWdwGnSkQNjaw9vTK87gq+5+cBGsT7SWiZVPCRw1epl/XP9fSIzWv1yZXJZYhHu06IzsrC7dX5t+LxLl+E9iXD8bNxbOAbPPIRvt6WSMqOlNOGT2RtzLgU1z//9X8FHOzwvNPOWHD34m4FaNfgr9tXxI+/S4KB06afnCv97l2LeyBPtdur18N12dawKlGHTXo5NqoGZxq1EXcjm05U9ukRD/352VmXCwykxLv6/OS6GGwRJ+eeKGhoYiJiUGtWrUM3l+yZEl1s7W1xfz583HgwAHUuZPd3LBhA1xdXdG4cWM1d78gsp2U7EuQL4MKAwcOxFdffYUFCxao+f+9e/eGs7MzFi1apIJ2uU/Xjh071GCDzPN/6qmncPXqVYwcOVL1C5DBBy2pQpABCWkw5+/vrwL9N954A2PHjkVcXByGDh2K//3vf5g3z/C87KIgKSkBjg55qx/sHRzV/Pr8JCcmwNvAf5hqu6S82x3ctxPhoZfRrYCmTKbAytlZ/czMVWUizXyEtYFKktgDB5AcGoaygwbifEoKEk6chFPFCijdr5/6Qmzp4JCzbtL58zBH6dGx6na/rN00xzkjTr+8Vcrx1f2uzrBxczG4jnY9WceUWTpqziUJIHVp/5YvuQWxcnVTX4ij161Els57UsrK43dvh/vzLyM19DIS9u9S2VeZk5+dmQkLu/vPOhY1MvdbZKUm6y3PvvO3pf3d95ohls6ucKzTBAl/rUd2yt3jLtl/9Tgp+o+rfWyZq2/KtOdSnnMt5T7PNRc3uDRqjtiNq/XOtdzcW7VH8tkTSDl9HObC0d4CySl5BytkLr6D3f3l9l54ygkZGdnYtCvvZ1nETfNqDnevzzWrfM616HWrVF8I/y9G5yyL+WMTbv+2Qv9xDZx/8thSHUBUGBjg0xMvNlbzBd/Nza3A9aRkv3Llyvjtt99yAvzVq1fjlVdegVWuTLMhEsTLXH7t83To0AHjx2vmsO7evRvnzp3D77//joAATQZamuvJ9ACtmTNnqkBdthOyngwCdOvWDWFhYfDz0wS2r776qqoQ0L626OholChRAqVKlVIBv2T9b90qOvMys7KykJ0rayIBZn4sC2jMkpU7XaG3Xd4yrS3rflFz9KvWqAtTJuW4BZFKhTzLMjJw8v33UX74MFT5YaZalnbjBi6OG4+gsWOQlWIemRmjHmc5b++5Tv7nqFk0RrpHBtSt2YvqOMpc/dyuz56CEunpqvmeRd9PkJWaopr2yWCTDACYrHv19Sjgc0s4NnhWnVeJMlf/gf4tzP1cK/j1uTR9Xp1rMVvyH3y3Kx8CuzLlETFppEmfXrlPsYJOufv5CLKxBp6p44i/Dibl25DPnFhYPPj/oVKVEzDyO1i7e+L6D5OReu0qHCpWRvH2nZCVkoyon2bcs6dPQd91zB6b7BUqBvj0xJO58kKy+PfSvn17FSAPGTJENeQ7fPgwRo262xCqIMWLF9cbRHBxcVGZfHH27Fl1nza4FyEhIbC3v5u1OnnypCq3l+Z9uf/TkVJ/bYBfuvTdcmx5TKkMkEy/lO7L1QCeeeYZvPjiiygqVi2bqzrk66r/9LOIjYnOs65k4R0K6Gvg6OikLq+Xd7skODjpZ04T4uNw8vghdHgr/0tRmYqMeE2GJXc2QJu51zbLyy0lNAz/9uqjuuZbu7sh+Woo7Ly91ZfijDsDX3RXRqymsZm1S67jfCcrnxGbkJO5z72Odj3tY5gqbSYqd9bZ8k7FTe75qLm5NGiExKOH8jTdE9mpKYj8YSKi5s1QpfrpNyLVMrdmL+SUypoibYbdMlcVgjbDnjtrmJt99fpIPXMsT9M9bTbfQuf/Cd3HvtfjFnVZyfmda3cy+wVk5YVz3aeR9O/hnMZ6Btep9zQyE+KRdFS/P4IpafusC9o11+8fs/d4Mlyd8yYepGlecuq9g8qq5e3gaG+JXUfzVoeYIymXN1QVUlAGXj7L7APL4eqIQUg6rrmqRfLJ42pd7z4fImbrRqTfuJ5vtYksu9c5TPSwGODTE0+y2hJ8Hzp0CK1atcpzvwTPEsRLabx00pdSd5kLL0G5zIUvV04zL/depMQ/PzLKK5ns3OSyblpyvwTr0gQwNy8vr5zfdQcFxKeffqrm4G/fvl1VCkiw/+OPP+LXX38tcJ+Mpdnzr6Jm3af1lh3Ys0M12pPXbKmTEY2MCEcpv8B8H8vHLwCXL5zLszwyIgzlKug3ZDp6cI+axlC/0bMwdSlhYSojr50/r2Xv769+Jl3Unw8uLO3sUKx5M8QdOYrUa9eQHq0ZUHEO0TT8Sjh92ij7bkoSzmqOo2O50og7cipnuVM5zaBawukLyExMQnLYdbWOLlsvT9i4Oqt1TFl65DVVMm/j7au33PbO36kGel1oyXWi7ctUQMQGTeVSbk616qsgNvnMSaSFXckp6bf2LI6US6Y7TUTm2ssxsyqu30fA+s7fGZH683N1Wbp5wNavDKK358re35nWIF3ytY+Ts42zqyrfL+hxTYH0FlDnWkn9XgI2JTXnWto1TY8RQ6w8isEusDxi7nTfz49jjXpIPLRbr0mhqfljfyIOn9GvuKodYo9qFexUJl83+VyymDWuRd27vL5GsL267v2lcP2rO5gr7eea9nNMS/t3moHeP9bFNf1Cks+c0FuedEoz1cPOvzTSQi8j/daNPI8r049kQD6tgM9Ls2eiVywyFayPoCeeBJCvvfaauo69ZOVzk2D4+PHjqsRd5ttLczsppd+8efMjuz69ZOulYZ+U6WtJg7wEncxrhQoV1Hx7ydBrb9KYT8r+8+vwf/HiRQwbNgzFihVDx44dVRZfXo8MWpwuIgGcZzEvlKsQoneT7vnJyUkqyNeKi43GqRNH9Drr51atZn1cC7uMsKt3A1r5XebZ597u3Jl/4Vm8BLxMvBGVkMv3xB46DM/m+o0CJYDPiI9Hwgn9LyAiKz1dXfrOu73OOWxlBZ8Ob6pMftI50w2oCkvShatIuhgKn3bP6y33bttSBf/JVzQB1c2t/6BEq6aqw3LOOu2eR1ZGBm7+uQemTK4MIF9gXerpD8o512+EzMSEnK74+V1n2tAXYi33Fq3h1aWP3jKPVu1kdBOJubrrm5SMdKRdPA2HavpTgeyr1VNZat2u+LnZBpRXP9MuGT6uqWeOw65STdWpX/dxJVhJO2f4OJvSuZZy5l841XlKb7lTnafVuZZ68Wy+29qXC1I/U87lf8k7aeBo610KKWfvDtaZopj4LBWI696On0+Fg72l6nSv5eJoieBAW3XfvZT3t8XZK3kvC2eucj7X6ut/rrnc+VxLNvC5ph1gcgjWTInUcgyqrNcMUi796Vy7virpz3ncBo3UezTx3yOF8nqIGOATAXj33XfVpfEk0y2ZbWlgJ+XwkrWXvyXr7ejomFOmLwG+rNO6detH8vz169dH9erVVeO9I0eOqAEF+V03e92nTx81qDBt2jQV6Es2XvZPBgZ0M/i6PDw8sH79etVYT4J62U76BkjpftmyZVFUSVf7SlVrYdq3w/HH5t+wf/d2jBryEZycnNGiVVu94P2SzvWMGzZuDh9ff4wd/jH+2b5F3eR3/8ByaNBYP/gNvXIRfv75VwOYmrAf58ClShUEjRsL96efQsB7fVGq21vqGvcyn97KyQnOVavA2sNds0FWFiJ+XgGfjh3h/cbrcKtfD8HjxsK1enVcmvCt6c/ffQSkzN69fnXYFvfIWXZu1HT4vtEKVaYOg1fLxqgybbj6++zwu40uL0z4EXYliqHuuh9VoF+mf3dUmjAYoT/+jJTQvIOIpub2qiWq67jvgC9U9+hib7wFz5dfw+1fl6mssrqEWYVg1eRMl11AILLS0nK++OYWvelX1bDK66134FC5urqUnlyD+va6lfluYyrit6yGTUB5eHT7SF2+zuXF1+H87EtI2Kq5lruFnQNsSpeHpZN+qbW1b4C6P/NWlMHHTfhjLayc3VDsnc9VoO/0TCu4temKpN1/qOy+qYteswx2ZYNQ8oPBcKxWGx7tu8C9VTvErP1ZnWsyFcGuXBAsdS5LJmz9SqtzLSMq/yt/2N6pBku7Zn5ZVLnm/cmLqXjvdQ80reOIOpXs8XnPYmo+/da9iXrd9kv7WOdJrMry8PvI9JuTWyvvfK59PER1wpfPH89XXsetVUv1P9dcNZ9rCQd2I/nsKfj2GwT3li/BsXJ1dc37Et3eRvz+XUi5oBmAur1mhbo0qN8Xo1SVksdL7VGi27uI2boBGTdv4Ikl32+NdXsCPZmvmigXBwcH1b1egnfpQC+N6t555x1ERUVh4cKFeOGFF3LWbdiwoQqc5ZJ5ktF/FCSQ/+GHH1TQ3bNnT/XcMnig7Q8gZB8mTpyIrVu3qqkCn332meqQLwF/fmQ/5fWEh4erBn1S3i8N+X766SfVsb8oG/C/0ahTvzGW/DQdMyZ9rTL9X3w9Bc7Od4/53BkT8N3owTl/29jY4n8jJ6NM+WD8OH0cfpr5LSoEV8HgrybCSifDJWJjbpvsde8Nid2/H6c/HQiH0qUR8t238HrxBVyeNBnh8xeo+52Cg1F9wXx4Nmqcs03ozB9wbdEi+HXvrraRufgnP/wI0X///RhfSdHhWrMynt75swrStcIWrMbx94aiePOnUHvldHg2rosj3QciYsXdEurEMxex98WesHK0R63lU1Dmox64NHkeTgy4v34dRV3SiaO49t1I2Pr4wffTYerSUDcW/5jTOVqalpX+enKea9dbuXkgKykh/8c9dgjXJn8Dp2q14TdohKoKiPxpOm7m6tFhitLOn0D0vImwLuELz16fwKH204hbu0QF6MLGLxBe/UdqsvE6JHgvaC59RtQ13PphNCxsbOHZvT+cm7ZCwvaNiF09H+Yg+dQxRE4dDRvvUvD+6Eu4NHwWt5bNRcyGlep+KcP3G/YdnKobOtcKnt8sQZfISsz/nDRlco36Q6dS0PEFV7zd3h3RcZn4Zu5NvaZ5PV5xQ//Od79naDP91lYWSDTQhd+cJf17BOHfjlSXris1cBhcGzfDjYWzcz7X7MuUR+DoKXDWfq5lZSH068GI3/UXirXvDL//jYLbMy1wc+UShH/3tV6mP3TkYFja2qHUJ1/C86V2iF6/CpE/ff+4Xio9ASyyDbWGJKJ8STm8BNbTp09Xl6t70hw6a/pZIWNLfrPl494FkxTzr3l+8S5M5duZT1WKMbn46gc5dG/Jt/JvXkf5G+Y/+3HvgskZcrb7494FkxS8YguKqpT1mqv3GIN963fxpGGTPaL7JJec27NnDzZu3Kjm40smn4iIiIiIqKhggE90n6Tj+hdffKHK5uVSefe6vikREREREeViwVnihYkBPtF9ksD+wIEDj3s3iIiIiIiIDGKAT0RERERERMbxhHa3NxYeXSIiIiIiIiIzwAw+ERERERERGQf7WBUqZvCJiIiIiIiIzAAz+ERERERERGQc7KJfqHh0iYiIiIiIiMwAM/hERERERERkHJyDX6iYwSciIiIiIiIyA8zgExERERERkXFYMsdcmHh0iYiIiIiIiMwAA3wiIiIiIiIiM8ASfSIiIiIiIjKKbDbZK1TM4BMRERERERGZAWbwiYiIiIiIyDgsmGMuTDy6RERERERERGaAGXwiIiIiIiIyDmbwCxWPLhEREREREZEZYAafiIiIiIiIjIJd9AsXM/hEREREREREZoAZfCIiIiIiIjIOzsEvVDy6RERERERERGaAGXwiIiIiIiIyDs7BL1TM4BMRERERERGZAWbwiYiIiIiIyDgsmWMuTDy6RERERERERGaAGXwieiAZWfzYeFDuAW6PexdMUvGKxR/3Lpic86suP+5dMEmOgdcf9y6YnKTLKY97F0zS5RfOPe5dMDk3r9583LtAj1g25+AXKmbwiYiIiIiIiMwAU3FERERERERkHBbMMRcmHl0iIiIiIiKiO7KysjBlyhQ0btwYNWrUQJ8+fRAaGor8nDt3Dm+//Tbq16+Phg0bol+/frh27VrO/ZmZmahWrRqCgoL0blOnTsWjxgCfiIiIiIiI6I7vv/8eS5YswciRI7Fs2TIV8Pfu3RtpaWnILTo6Gj169IC9vT0WLlyI2bNn4/bt22r91NRUtc7ly5fV72vWrMHOnTtzbj179sSjxhJ9IiIiIiIiMorsIl6in5aWhrlz5+LTTz9F06ZN1bKJEyeqbP6WLVvw0ksv6a2/detWJCUlYdy4cSrIF+PHj1fbHjp0SGX0z5w5A2dnZwQHBxf6/hfto0tERERERERkJKdPn0ZiYqIKzLVcXV1RqVIl7N+/P8/6sp5k/LXBvbC01ITZcXFx6qcE+OXKlTPK/jODT0RERERERMZRxC+Td/265tKpPj4+estLlCiRc58uPz8/ddM1a9YsFfDXrVtX/X327FlkZGSgV69eagChZMmS6NatG1599dVHvv8M8ImIiIiIiMjsNG/evMD7t23blmdZcnKy+mlra6u33M7ODrGxsfd8TpmHv2jRIgwZMgSenp45TfhkHr803/P29sb27dsxePBgpKen47XXXsOjxACfiIiIiIiIjKKoz8G3v1NqL3PxdcvupUmeg4NDvttlZ2dj8uTJmDFjBvr27YuuXbvm3Ldu3TrVSd/JyUn9LXPxpcv+nDlzGOATERERERER3cs2Axn6e9GW5kdFRSEgICBnufwtl7YzRDLxkpGXQF5+du/eXe9+3YECrYoVK+K3337Do1a0h0+IiIiIiIjIvObgG+v2ECS7Lh3v9+7dm7NMmuWdPHkyZ059bgMHDsSmTZvw7bff5gnuZdt69eph1apVesuPHz+OChUq4FFjBp+IiIiIiIgImrn3Xbp0wYQJE9Qc+lKlSqnL3snc+ZYtW6pSe7nOvYuLi8rMS+C+YcMGFeRLIH/jxo2cx5J1pAN/gwYN1KX2ihUrhtKlS6vL7Un2/ocffnjk+88An4iIiIiIiIyjiM/BF9IMT7reS6O8lJQUlbmX+fI2NjYICwtTzfu++eYbtGvXTpXli3HjxqmbLu06o0ePxtSpUzFs2DDcunVLXTJvypQpaNy4MR41i2zpBkBEdJ/2nb5391DS5zSo7ePeBZNkbW/zuHfB5Jxfdflx74JJcgzMOzeSCpZ0OeVx74JJ+uaFWY97F0zOmKsDHvcumKRGRw+hqIo/sMloz+VS5wU8aZjBJyIiIiIiIqPIfsi58XR/in59BBERERERERHdEzP4REREREREZBwmMAfflPHoEhEREREREZkBZvCJiIiIiIjIKLLBOfiFiRl8IiIiIiIiIjPADD4REREREREZRTbn4BcqHl0iIiIiIiIiM8AMPhERERERERkHM/iFikeXiIiIiIiIyAwwwCciIiIiIiIyAyzRJyIiIiIiIqPItuBl8swig9+1a1cEBQUZvI0dO/aRPs/nn39e4DoZGRmYP38+2rVrh5o1a6JBgwbo2bMn9uzZgydBeno65s2bl/P31KlT0axZM6M89+rVq9GpUyfUqVNH3Tp27IjNmzfrrSP7IvsksrOz1Ta3bt36T8+7detW9O3bV/2+atUqdd7dS1RUlNpHOV661q1bZ/B4hYWF4Z133kGtWrXQqFEjTJo0CZmZmXrrLF68GM2bN0e1atXUcTh58mS+zy+PJ/u5d+9ePCoffPCBOhamICU5CfNmjsMH3V5A7zefwfgR/RERduWBHmPKmM/xw+Sv8izPzMzAL4tn4qOeL6HX640xcnAfnD/zL0ydU406KDvhe4T8vAEVZi1GsTZvFLyBpSWKt++I8jMWIGT5epSdOAuujZrmWc31qWfU4wYvW4eKc5bBt99AWLl5wBw4VquFgNFTUGHBGpSZOh8eL72W77quzzyHoOWb8725Nmmht27ghB9QYeFalJkyD8Xad1bH29zYlyqJljf2w7NJvXuu6/tmazQ5sg4vxB3FM8c2oFTXNnnWcatdBQ22LsDz0YfQ/MrfCBo5ABY2NjAHHo0aovbKxWhybA8a/LEB/r3eKnB9C1sblP2kHxru2Iwmx/eizq/LUKL1CwVu49etE549dxT2pXxhLoq3eBpP7/4FL8QewbNnt6HsgJ4Frm9pa4Ogrz9Gs4t/qXOt0b5V8HmjVZ71SrRqiqd3rcAL8cfQ7NJ2hEwYDCsnR5gLB3tLfPxueaxZ0BBbfm6E8cOqwL+Uwz23qxzkiimjqmPrL42wdmFDDO5XER7u+u9BRwcrvN+zLJbPqoffVzTC/Km10baVL0w9ZnNv2ADVFy9Ewz3/oM6GtSj1VtcC15fPptL9PkDdzRvQcO8u1Fi2GMVfaJlnPbd6dVF1zmw0+Psv1Nu2BcHfjoe9n18hvhJ60hk1g//iiy/iiy++yLPcweHeHziPSmpqKnr06IGIiAj069dPBfgpKSlYuXKlWj5u3Di8/PLLMGcSoH7zzTfo3r27+lsGNzp37lyozymBev/+/dUgyocffogRI0bAwsICW7ZswYABA9R9b7/9tlr3l19+gZ2dnfp9//79asBm27ZtD/3csbGxGDlyJBYsWPBA223fvh0NGzaEjc6XSwmO//e//6F48eJ668ogQK9evRAYGIhly5bh6tWr6ly3tLRU55mQgQo5v2RfKlWqhFmzZqlzbuPGjfD09IQxfPbZZ2oQTAYu3N3dUZR9/+2XOH/2X3To9iEcHJ2wetlsjB7SF2OmLYOTs2uB22ZlZWHJ3EnYv/sPNGrWOs/9i+dMwvatv+HNt95H8RK+2LRmCcYO/QBfT1qIkj7+MEUOFUMQMGQU4nb+hajFP8GxUlWU7PY2LKyscHPlUoPblOjYXQX4N5YvRNKp43Bp0Bj+nw3F1cxMxO/+W63j2vhZ+H/6JW5v+g2Ri+bC2sMDJTr1RODX3+Lix+8gO9cAmCmxrxAMv0EjELdrO24uXwDH4Mrw6twLFlaWuL3m5zzrJx7ahytDPsqz3PudAbB0cETi4f3qb/cX26Bk976I37MDNxbNhpWrO4q/3hV2pcvi2ncjYS7s/bxRb/0c2LgX/H4U3m1bosaCCbg0dQFubP4b3q+2QI25Y5GVmoaInzeodRzK+KH+pp8QvecIDnXsD+fgcirAt/F0x7/vD4Mpc61RFdV+mIqoDZtxadJ0uNWpiXIDB8DCyhpXZ801uE3lieNQ7NnGCJ2zANG798KlSiUEjx4OG08PhC/M+552CCytBgTMiXv96qi7ZiaurdiIs8Mmw+Pp2gge8xksrK1wYfxsg9vUXDwRJVo3xcXv5uLmH7vhVqsKqs0aBTsvT1yevkitU/LVFqj981Tc2r5PnWsyKFDhi/fg0bAmdjfpiOxcg/OmaNinISpY/37eRSQmZaJnx9KYOqo6ur5/APGJGQa3CanggqnfVMeV0CSMmngaqWlZ6NjWHz+Mr4keHx1UjyNGDKqE4AoumLv4Mq6EJaF2dQ/0f7s8XF2sMX/5VZgil6pVUWnqZNzcvAVXps+Aa80aCBzwkTrXwubeTYrpCh73DTwaN0b4goWI2bsPzpVCUGH4MNh4eCBi6XLN49aojiozpuPWX9txZvAXsHRwQMDbfVBt/lwcav8GMmJi8CTiZfLMKMC3t7eHl5cXHqfJkyfjzJkzKsj18fHJWS7BWEJCAr7++muVnXVycoK5kmBbl7zWwn69S5Yswe+//44VK1agcuXKOcslqy5Z7ilTpuCll16Cr6+vXrCbe18fhlRrVK9eHaVLl36g7Xbs2IEmTZqo37Xnhpw35cqVQ3x8vN66UoVw7do1/Pzzz3Bzc0PFihVV1YEE9O+++y5sbW0xc+ZMdOnSBa+88oraZvTo0WjRooU6JpL5NwY5BjVq1FDH5KOP8gYqRcW508dweP/f+HToJFSv/ZRaFlSpBj5+uw22bvgFr76Rfwbn6uVzWDBrAi6dOwlbW81Aka5bNyLxx6aV6NLnE7R4UZOtrVqzPj7r+xrWrVyAXh/kHYQ0BSU6dUfKpfMIn/SN+jvh8H4V3Bd/rRNurV2J7LS0PNu4t3gBsTv+wI3lmsGvxGOH4VCuAoq1bpMT4Hu91gnxB/YgYsaknO3SwkNRdvz3cKnbEHG7dsBUSdCdcukCrk8fr/5OOnoAsLKGZ5sOiN7wK7LT9Y9ZZnysuulyf+FV2Jbyx9UvB2jus5CqiM5IPHoQ1yaOylkv5dI5lJkwC45VayHp+CGYNAsL+HVtg5Cxg4D7zNgFjfwYEb9swqlPNefnzd93wsbDDUHDP8oJ8Mt91gcZ8Yk40O49NXB0Y9MOZCanoMrkL3F+zEykhEbAVJXp9x4STp3Gqc80ny+3/94FC2sblO7bC2HzFyMrNVVvfedKwfBq2QwXv5uKKzN+VMuid+1FZlIyyn76ESJ/XYcM3f+HLC0RMnYE0mNiYWXEpElhqzj0Q8QeOYWj3Qeqv29s+RuWNtYo9/m7arAoK0X/uLnWCIF3m+dw5suJ6pwRt/7YjcykJASP+gRhi9YgIzZePW7CqQvY17p3ziDl7Z0H8OyZ3+HXvR1C56yAKZPAvlH94vh0+HHsOXhbLTt2IhY//1gfbVv7YsHPhoPwt94IQGJiBvr972jOIMDBYzFYMqMuOrf3x6yFl1GxnDMa1PbEl2NO4M9/buas4+Jsjc7t/E02wA94710knj6Ns198qf6O2bVLnWt+vXri2uKled6jTsFBKNasGS5PnY6wH+eoZbF79yErOQWBH32IqHXrkRmfAL8e3ZF08RJOfzpQvtSq9eKPHEHdzRtR8pWX1eAA0aNWpIZPDJXX51524cIF9OnTR2XepQz6k08+wY0bN+7r8SXLKpl6Kc3XDe61JIs8e/ZsNRAhYmJi8NVXX+GZZ55RJdUdOnTQK5eWMnLJgksmVgLBqlWrqgBO9lE3CyzPJwGmZIPltUhGWchjSQm2lGJr5V4mr1+mMHz66ac5r3np0qU4ePAgXn31VfW4sl+XL1/WK+v+9ddfVcAs+/3GG2+o9bXl6YMHD1a/a8u/c5foS3WDPN/TTz+tgkHJTJ8+fTrnfnkNcpP9ktck+yABamRkZL7HXrLaTZs21Qvutbp166amDGiz4toSfdm3t97SlDBKWbsEz/J806ZNy/PYclxk6oWhig0pi5fqkQch58ru3btzAnw5rnJcJBiXoDy3AwcOqNcmwb2WTP2QgYFTp06pYF/+jWT/taytrVUmXaoU7oecV/JvMnDgQDUooj3/5Hg89dRT6vwYOnSo2k/595B/l+eeew5//fWX3uO0atVKDbjIsSmqjh/eAzt7B1StUT9nmaubB4Ir18LRg7sK3PaHScORnZWFYePnwtU9b2XEiWP71fGr0+BuKbqNjS1q1Gl0z8cuqiRQcKxSHfF7duotj921A1aOTnAMqWp4OxtbZCUn6i3LjI+DlcudjKyFBRKOHET05nV666SGhaqftt6mWwYsx8yhUjUk7P9Hb3nC3r/VMXMIrnLPx7Byc0fxN7shZst6pJw/o1nm7q6OX8Ih/ak1aaFXkBEXA6da9y5lL+pcqwWhyvSvELboVxy5E3gVxKF0KTgHlcH1Nb/rLY9YtRlOFQLhWF4z+Or1XCNEbdyuVxVyfeUmNVDl1bIRTJWU2rvXr4MbW/7QW35j0++wdnZW2fzcHMuVUT9v/rFdb3n0nv2wdnJUj6croHc32BYvhiszNYGGOZCsuucz9RH5a97zxsbVGZ5P186zjVR9iMj1f+otv/XXXlg7O6HYM5r3n3NwWdz4fafeuZYWdQsJpy+ixIt5pymZmvq1PJCUnIl9hzXBvYiJS8eRf2NUcJ6fQH9HHDsZq5fhT03Nwskz8WhYp1jOsjWbruHAUf3M89WwJDg6Wucp5zcFUmrvVqc2bv2hf97c/H2reo9KNj83xzKa9+jt7fqD3LH798PK0RFudTTv0fjj/+La4iU5wb1Iu3ETGQkJsPd/gsv0ZT6HsW5PoCIV4N+LBJAyb1mykFLGLRlRCaDefPNNJCUl3XP70NBQFbTLHGlDSpYsqQJiKysrFQBI6boEbuPHj1eBsWRlJdg9duxYzjZyvwTPEuRL0CSBnAwKiNu3b6s5z+3bt8eGDRtUICbBnGR1H8TChQsREhKC3377TQW6kkkePny4KhVftGiRmiv+7bff6m0zZswYlTmWsvCyZcuq1yKvX4I72U7s3LlTBYW65HjKvHg51jNmzFDBswx4yMBFeHh4znqSyZZjKc8vgyInTpxQc84NkUDy7Nmz+R53FxcXFehKlluX7Jt2Lr4E1pL5lpscB10ymCHLJWDOTf594uLicgL1+yX/plJNIOeECA4OVllv+Xcw5Pr16/D29tZbVqJECfVTAm65X+QeWJJ1tPcV5MqVKyqYl9ch/7Zyjmpf36VLl9QgxpAhQ7B8+XK89tprakBDzlmpNpDBGN1KCBmwkmOiHfQpiq6FXkaJkqVgeed1apX08UNEeMHz8N/t/xW+HDMbAYEVDD922CXYOzjB3aN4nseOvn1Dzf03NTbePrC0sUVq+N3BQpEWoXnP2pUyPO3g9tqVcHu2JZxr1lUl5m7PNIdzrXqI+etOn4bsbET+NBPx+/QHPlzqP61+plzVDCyaIpuS3uqYaY+RVtr1a+qnre+9v3gVf/0tdYxuLr9bvpmVmIjsjAzYeGk+O7QsnZxh5eQC2xL6nxOmKPlqBP4Kfg6nPhuDzKSUe66vDboSz+mfL0kXNO9l54plYGlvB8dAPySeu6S3TtrNaKTHxqt1TJWDvx8sbW2RdFn/syv5iibT6VgmMM826dGa4Cn3XHqHAL+cx9RyLF8OgR++i9ODhyEr5d7/HqbCsaw/rOxs85w3iec1x9EpKO85IeeLcAjQP26OZQNyHlOzXkyedSysreHg75Ozjikr7e+Ia9eTkZWlvzw8IhkBfvn3GZBBAO8SmiSXLl8fe/h6a5afvZCA8dPPIT5BP6nSuEFxRMekISbW9KZtyXx4eY9q35NayVc1g9kOgQbeo3dK6+199b/XaefW2/uVUj8lux/56xq9dVxr14KNmxuSLlx8xK+E6DEE+GvXrlVBm+6td+/e9729ZK4liJJARgKXKlWqqKBSgupNmzbdc3tt5lw3y5ofCX4laJXAuV69eihfvrwK3CtUqIA5c+6OkEvWWAJ2CQAlgy/Z9EOHNOWXEiSnpaWpQLFUqVKoXbu2GpSQrPyDkKBSBhb8/f1VoC3PKY9Rv3599ZwSzEkArUvms0sGX46TzPn28PBQGXAJ1iWgFjJdIndQLcFzdHS0msoggx3yuuQYyHYSRGrJY8g8enl8OT4ycKB93f/luOuSfdNuI2X7sg8yWCLB7uHDh9VyCW7ld6mSMOTIkSPq2D/oFAQpz5dA+H5JH4fcx1LbR0AGOJKTk3NeU+517pVJl+oBqWSQ/ZGyfpnXrzvXXM7LMmXKqGMj/85SOdCmTRv1byODNfLvqVvlIj0v/Pz8co5hUZSUlKDm3edm7+CI5FwZ59z8A8sXeH9youHHdnDQfOlJTir48YsiyTiL3Nn4rDuDFZaOhr/Q3frtFySdPI7Sw8ciZNk6+H38BWL+2IxbqzVzBw2x8faFd493kXzxHBIOProGkMZmmXPMkgwfszvnQ36sXN3g+kwLxGz+DVk650x2Wirid2+H+/Mvw7VpSxXY2/j4wbffYDWv18Iu75dnU5MeHYuU8PwrtnKzdnNWPzPiEvSWSzm+ut/VGTZuLgbX0a4n65gqaxfNvmcm6L+2zETNuSaZ5dxi9u5XwUWFLwfBo2E9WDk73Zm3319VKFk6asrwpbqh0vivEfHzasTsK7qDtg/D+s45kZ7rnMjUnjd3jqsumVOfeOEqKk8agmLPNoC1i5Oatx8y+lN13CSzKkLnrYRPu+dR9tM+sC3uAXt/H1SbPUo9p9WdY2vKnB2tVQY/N1nm5KA/cK5r/dbrCCrvgn69y6GYpy083W3Qt1sZBPo7wd4u/+1ef7kUalVzx6JfQnUT1SZDsvRCsuq6ZGqHsDLwHTJ2/wEkh4ai7KCBcKtXT63jWrMmAvt/pDnX8pkqY+3ujgrDvkRqVBSifluLJ5XMwTfW7Ulk1Dn4Unotpd+6tOXw90M6jp87dy5P1lkCJN2y+Pxo53ZL5vleJGCWIFay9lrSFE4yzRL8a0lZuW7gKttou65LYC5BtmTSJZiW8mopU5ey6QcREKAZedZtSCjBvu4xzN3pXYJ/LWkSJ4MhuQcB8nvd0ihOdx68PL4E+7rbyz7pNp/Tfd25STM3OXYSaP5X8u8hgxqStZfzQH7KvskAjCE3b95EsWJ3y8oeJMAfNuz+mzrJMZLBHF3awN3R0THnPDe0zr2aTEq1hhxbyf7LcdQlr835zn9M2ufSPV/ye17595VjUxTIIEV2tn6aoaDeC5b/8cM66x7fPiwsTbCc617d2Q28ZilRDxw9GTaenrj2/XdIDbuqaTL3RhdkpSTj+o/T82wjc81LfzVOBaqhY78y+Lgm417nUa5zMje3Zi/CwtJSzdXP7frsKSiRnq6a71n0/QRZqSmqaZ80V5IBgCeNHKeCyJfhe53D2Vnme64Zem3Z6Rk42qMvgsd8hRoLNM3kUiOjcO7rsag8ebya5ytK9+0Na1cXXJgwGU/aeZMnPa2OWzr2te6FarNHo8GW+WpZyrUonBjwNWotnaR6GIhzI6aq5mlBX/VDyDefIistDVfnrEDkb9vgXElTcWIq5GtB7v+2CjrlCnorrdtyHU4O1ujVORBvvOqHrKxs/LXrBn7bfA2tmxuuPmrX2hcf9i6HbX9HYfka/Soyk3Gv//cN/H8glVon+n6ACl8NQ9XZmn4PqVE3cHHsOASPH2uwmsameHHVcE9+/vtO35wBBCKTDvAli/qgjc5051VLICDZSUOBlzYrXRAJiiUgl0yzZJxzk0GCUaNGqTnq+QUYsly3FDx3RjY3yX6///77KmDctWuX6mIumXwp9zYk92XVhG4graWbxTUkd7m6PO69thH5vW459g/yunXJujLAkF+GX8rFZSqD3KQa4F4kUz1x4kTVGFGqQgqqApHXbOiYFkSa5Un1Re6BpIJIZUnuARSZOiGkzF9bmi/LJLOuu452GkB+2rZtqwY2pDRfBod0B50e5tx4kPPBGH5d/iNWL9M0kdKq+1QzxMbkvTSiZNcdHP9bJs/R0RkpBqoAtJUBcr+pkbJwQ1lnbWY/8879ulyfagyHsuVxeeinSDyqeW8mnTiGzKRE+L7bH9Fb1iNVpwRf5vj7f/6VCv4vD/kY6XdK2U2VNutuaa8/wGbpcCezf48vXi4NGqnjlrvpnshOTUHkDxMRNW+GKtVPvxGplrk1eyFnCsCTRJqaCcmm6tJm5TNiE3Iy97nX0a6nfQxTlBGfYDALKFl5dX+C4dcmGfzDnXqqQThpSJh8+SrsfL1V4JseG6sa8UmAf6z3+6qJpmTzcwZKrCw1vxsIgk2FTM0o6LzJndnXSrpwFXuadYGtlydsi7kj8dwV2Af4aI5btOb9KoOUZ774VgX6UpIvgwByjjX4YxHSb+d9TxdlPTqURs9O+iXkf+68AU/3vN/THB2tVBO9gkiQ/su6cJTysUdcXIYq2x8yIAhxuUryZWDh/R5l0aGtP7b8Fam67psqbXWNda73qPZv7Xs4t5TQUBzv2Vtd2cLazR3JV6/Czlv7Ho3TW9exfHnVpV8uxXjivQ+QcNz0L837X2Tfb4dWeihF4xu+TrAic8B1g0qZN64l5fEShEuwJAMFcpPsuZQt3092WgIamZ8sc5NlXnRuP/74I44fP65KuqUBnXRK131cCX5l3nJ+2eLcjh49qvZN5sBrm/HJ33KpOJlWoA3OdF+ztlnefyWvQ0uytzLdQNvgLncWWJe8btkH3evOS5b533//ve/XbYg0+pNBDtmP3OTydTKXXMrGczO0r1IVIfv0008/qSy0/J0fqZyQXggPc3k8Q3P681O3bl1VYaL7byn/zjKoJdMcJNMuZfS6TRpl8Epet2xbkNatW6veEzJIIoNPDzpgYYgcE22PgMft2ZZt8dWEeXo3n1KlcSMyQn0G6Iq8HgZf/7xz4R6ET6kANVAQF6tfURIZEYbiJXxga4Il1GnXw9UXVlsfzZw/Le3fqWF5+xZo54gnndL/kiFBvrALuHucXRs3Q+nh45Bx6wYuDfxQddE3demR19QxkykHurSNA1PD8+8Ebe1RDPZlKqjL4BniVKs+HIIqqaA+LeyK+ikl/daexdWVDp40CWc18+ody+kP8Dvd+Tvh9AVVrp4cdj3POhKkSUM1WcdUpVwNRVZGBhxK68/tdiitqbZKOq/fd0BY2tmh5Cut1Tze9Nu3kXThkjpfXSpr+sDEnziF4s2bqnnDkuFvevqQuoV8o+kB1HDbetRYMAumTAJ1OW7a80TLsbzmuEkX/Nykl0OpTq/AIdAPaTduq6Z5ctzcamq+/8Qe1nwH8WxSD8Wfa6Qu0yiPI8G9DJC4VqmI2MMnYUrWbI5ArwEH9W5Xw5PgU9I+T38xPx8HdVm7/ASVd0aThsWRmZmNq2HJKrgXFcu5qLn3WtbWFhj5eSUV3C9dHYoR355GpumOJSE5NExl5O11qmOFfYDm7+RLht+jXq1bwa6UL9JvR2vWycyEc0iwuj/x1Kmcdd3q1kG1eXPUVUeO9+iF+CNHC/010ZOtSAX40rH9n3/+UYGgzLOWueOS3dWSIEeCbinzl67ucpNrqEswq5vVLIiUy0sJujyWlHfL9cqlaZ4ETvK3PKeUOUtXdimxly79+/btUwMLMudcAn7p+n4/pHRaGu9Jkz55PbKtNNuT55e50rLP8lwS+Mt+/P333ypofRSkN4F0Tz9//rxqqidzwCXIFvKcQoJ2mTuu6+WXX1Yl9XJFATkucozleEsTQ2lm+LBkYKVx48bquu8yl18GEeSxpX/B9OnTVWd46VWQm3ZfZd3EO1lIqdaQTPb333+vmg66uuZ/DWbpJC8ZeUPTMuQ8y32TzL0E+A8y/15IZ30ZTJDjJvu6detWfPfdd6q5obbaQX6Xf19pfKj9d5HjL8fmfgan5NyUSzzKQNR/IVMl5JjI1IaiwKOYF8pWqKR3k8vWSZZduulrSUB+5sRhvc76D6PKne337dqWsyw9PQ1H9u/Muc/USFlq4oljcG2g32nctWFjlZlIPps3s5J6J0h3rKR/HjiGaLrHp13XDII6164PvwGDkXz6BC59/hEybheNqR2P4pglnToOl3qahoFazvUbITMxIacrviH2FTRf4JLP5B2wFO4tWsOrSx+9ZR6t2qlsamKu7vpPAgnUki6GqjnPurzbtlTBf/IVTaPDm1v/QYlWErTerUzybve8CvJu/nn3s8DUSPl37P5D8GrZXG+51/MtkB4Xh7hjeTN5WenpqDDsc/i+2T5nmQSgfl07IunKVSSeOYdry1fiQNuOerdLU2aodY+90w9nvhwJUybB9+2/D8C7rf60Rp+2zyM9Jg4x+4/l3SYtHZUnf4mA3prvO9rjFvh+F9WcL/64Jmnj0/55VJs5UjXW0/Lr0V5VSkSuudNk1ETcup2GM+cT9G77DkfDydEa9WvdnW7p7mqD6pXd1X35qVXVHcM+CYaz09359nVqeKBsaSfs2HP3s/+L/kFo0qA4Js8+j+lzTb9RnFTAxB46jGLN715RShRv0RwZcfGI//eEwfdouc8Hwbu9Tg8oKyv4duygqm8Sz53PuZyeZO5TIyNxrGs3Nta7g3PwzahE/14kAJJAV67PLUGRBD6SvdSWjUuJvXRtl7J3aR4mncSlM7tkgHXnjBdE5jvLY8ydO1d1f5dAR+YpV6pUSXWrlzn2Qh5b1pFLwUnpuGTBJYMql3OTgYj7IaXY0gVeuudLoC9BmkwxkOeV32UAQIL/CRMmqCkDkukdNGiQKun/r+T4yL7L65MgV16bNmMr+6C9vJ48vy4JnuX4SDm4VB0ImVIgDQ515/0/KHm9EsjLY0tHfPk3lAy5VGXI8ZFA3RAZBJFgWwLnjz/+WJ0jQprqSXl+fs31tCQ7LgMAkjl//nn9L5dyucXcpGGdrCuDOQ9CmuVJ4C3by0CKVJbIINJ7772Xs44slwEqGXyRAQc5nyTgv99zV46V7HNBx+t+yICVHJN7VQ48TnI5vJAqtTHju6Ho0O0DOLu4YdWy2XB0ckHzF+9+4Q2/ehHpGekILBt0348tWfpGzVpjyZxJSE9LhbdvADauWYLExAS81O7BGmAWJTd/XoTSI8bDb+AwxGzdqObTF2v7JiIXzFbzvqV8386/tCoRz4yLVZ3xk86cVMF71NL5KmPtWDEExV/vgri9/6gAVy4d5PvBJ6rx3I0Vi9X2utJv3UDGLdMN+G+vWgK/IWPgO+ALxP65GfYVK8Hz5ddwc+ncnGNm6xeA9OsReqX4Ut0gQVt6pOHrskdv+hX+X3wDr7feQcLBPXCqUgPF2nbArV+X57uNOZGSaudK5VVgr+1qfm7UdFSfMwbpt2MQufYPlHylOXzfaIVDnfrnbHdhwo/wfbM16q77EZcm/QSnioEIGvkxQn/8GSmhpn3cLn8/GzXm/4DKU8Yj4pdf4Varhrq03cUJk9VcXSnXdypfFslXw1Q2UAaDri3+GX7dOyP1eiSSLl1Gqc4d4FqrBv59b4Dqf5EWdUPddDlV1FTayQBASrjpTwc5P3oG6m/+CbWWTlaN8Twa1kTZT3rh9P++VX0I8pxrWVm4MnMJyvTrhpTw60g4cwmBfTvD46laONj+/Zy+IVdmLYN/rzdQfe4YhP70C1yrByN41Ce4tnw9bv99f5euLcqOnojFoWMxGPpJML6fdxFxcemqjD8hMQO/brimd1k8GxtLnLuoyc5v/jMSXV4LwIhBlbBkVRhKetnhw17l1KXzpAxfNKpfDM89UxJ/77mJk2fiUDlIf4qsZPrTM0yvZ0bo7B9R5YcZav68dL13qVEdpbq9hcuTp2reo05OcCxbFslhociQq1xkZSHi5xXw7dwRaZFRSLp8Gb4d3oBrjeo4OeCTnHOtwvChaiDp6vczYefto25a6dHRSNG5VDbRo2KRXVA3KzI50nFdgj8Z9NBttGduZJqFDJ5s27btnnPJZb6+ZNV/+OEHo+1fUfbOO++owSSpfnkY+04bZ35iYkIcFs+ZhIN7t6smfBVCqqNLzwHw8bsbZI764l3cjIrAxNn6l6DRGtDnVQRXqYV3PtLv2yEZ++Xzp2H3ji1ISUlCmXLB6NC9H8oH3fva5w/DaVBbGIPMCy/RsZtqhieB9+0Na3BrzYqcOfRlRk1E+OSxqlO+kAC2RJdeaj6+lbMr0iKvIfbP39U2Uq7oVLUmAr/WvwSnLhkYuLHMcD+RR8HavvCvp+xc9ykUf70rbHz9kHH7FmK2rEX0upXqPodK1RAwbDwivp+AuO13r8VdotcHKvN/4Z2O+T6uy1NNUaxdJ9iUKIn0m1HqcWM26V/iszCcX2XcSxdKqXPDbQuxu3lX3N6xT2/Z0V6fI2zB6px1A/q8ibIDeqqO5ZLRvzBuFsIX6793VcfzsQPhWj1EBWxy/9nhU9T5WJgcAwt/ak7x55qhTL++cCwbqBrmhS9ajtC5C9R97vXqoObiOTg16EtcX6U5TyQoCPzwHXi3eRnWbm5IOHUGl6f/gOidu/N9Du92ryBk7EjsbvpioQf4SZeNc0m+kq+2QMVh/eBUsQxSwyNxecZiNQCU37kmx63Clx/Ar8ursPF0Q9zR0zj39XRVIaKrePOnEDTqE7iElEPq9ZsIW7ga58f8UOjn2jcvGGfqhIuTNT7oXQ5NGhRT0x2Pn4rFlB8vIDRc02hQTB1dXV0W7/XedyuLgso5q+2CyjojPjED23fdxOzFl5F8pyu/zMd/oVn+l/t8rdceXI96tM1Ex1x9uO8rD6pYs2cR0PddOASWRlpUFCKW/4zwBYvUfW51aqPqnNk4++WwnO73cq75v/M2SrzcWl32LuHMGYT+MBsxuzUVR3alSqHuhvw75Ueu+Q3nhg4vtNfT6E5/naLoxknN/xfG4FXp3v29zA0DfDNj7gG+zOG/ePGiqgCQSwbez2UWJVsuFRJSxaDb4O5JJFNN5BKLMlVEpmIU5QDfnBgrwDc3xgjwzY2xA3xzYYwA39wYK8A3N8YK8M2JsQJ8c8MA/8kN8J/MiQlksuS69kOGDFFTDO63F4IEsl9++WWe6QhPIpkOMnTo0IcO7omIiIiI/otsWBrt9iQqUnPw6b+TTvTSiM1cde7cWd0e1IsvvqhuT7oZMzQNmIiIiIiIyPwwwCciIiIiIiKjyC7gkt303z2ZdQtEREREREREZoYBPhEREREREZEZYIk+ERERERERGUW2BXPMhYlHl4iIiIiIiMgMMINPRERERERERpENNtkrTMzgExEREREREZkBZvCJiIiIiIjIKDgHv3Dx6BIRERERERGZAWbwiYiIiIiIyCiyLTgHvzAxg09ERERERERkBpjBJyIiIiIiIqNgF/3CxQw+ERERERERkRlgBp+IiIiIiIiMgl30CxePLhEREREREZEZYAafiIiIiIiIjIJz8AsXM/hEREREREREZoAZfCIiIiIiIjIKzsEvXDy6RERERERERGaAGXwiIiIiIiIyCs7BL1zM4BMRERERERGZAWbwiYiIiIiIyCg4B79w8egSERERERERmQEG+ERERERERERmgCX6REREREREZBRssle4mMEnIiIiIiIiMgPM4BPRA3GxSXjcu2ByHLxcHvcumCRbF8fHvQsmxzHw+uPeBZOUdDnlce+CybF25VfIh2FtZ/u4d8Hk2DjaPO5doEcs24IZ/MLEDD4RERERERGRGeDwKxERERERERlFdjYz+IWJGXwiIiIiIiIiM8AMPhERERERERlFNnPMhYpHl4iIiIiIiOiOrKwsTJkyBY0bN0aNGjXQp08fhIaGIj/R0dH45JNPULduXdSrVw9fffUVkpOT9dbZuHEjWrVqhWrVqqFNmzbYvXs3CgMDfCIiIiIiIjKKbFgY7fawvv/+eyxZsgQjR47EsmXLVMDfu3dvpKWlGVy/X79+uHLlCubNm4fJkydj+/btGD58eM79e/bswWeffYYOHTpg9erVaNiwId5++21cuHABjxoDfCIiIiIiIiJABfFz585VQXvTpk0RHByMiRMn4vr169iyZUue9Q8fPox9+/Zh7NixqFy5sgreR4wYgTVr1iAyMlKtM3v2bLRo0QJvvfUWypUrh0GDBql158+f/8j3nwE+ERERERERGUVRz+CfPn0aiYmJKlDXcnV1RaVKlbB///486x84cABeXl4qcNeSMn0LCwscPHhQZf8PHTqk93iifv36Bh/vv2KAT0RERERERASoTL3w8fHRW16iRImc+3RJlj73ura2tnB3d0dERATi4uKQlJQEb2/v+3q8/4pd9ImIiIiIiMgo/svc+AfVvHnzAu/ftm1bnmXa5ngSpOuys7NDbGyswfVzr6tdPzU1FSkpKfk+ntz/qDGDT0RERERERATA3t5e/czdUE+CcQcHB4PrG2q+J+s7OjqqQP5BHu+/YgafiIiIiIiIzC6Dv81Ahv5etOX2UVFRCAgIyFkufwcFBeVZX0rvt27dqrdMgvmYmBhVhi+l+hLoy/a65O+SJUviUWMGn4iIiIiIiAhQXfOdnZ2xd+/enGUyj/7kyZPqOve5yTKZSy+XydOSrvqidu3aqtlerVq1cpZpyePXqVPnke8/M/hERERERERkFNnZxsvgPwyZK9+lSxdMmDABnp6eKFWqFMaPH68y9S1btkRmZiZu374NFxcXVZ5fvXp1FcAPGDAAw4cPVw31hg4dijZt2uRk6Hv06KGuey+d+Js0aYKVK1fi1KlTGDVqFB41ZvCJiIiIiIiI7ujXrx9ee+01DBkyBB07doSVlRXmzJkDGxsb1Rm/UaNG2LBhg1pXMvTTpk2Dn58funXrhv79+6sgXoJ9LVl/9OjRWLp0Kdq2bYs9e/Zg5syZepfWe1QssrOzsx/5oxKR2Tp1Ifxx74LJcfjmvce9CybJ1sXxce+CyTmz7uTj3gWTlHRZ0+GY7p+1K4tAH8Y3z/74uHfB5IwN7/+4d8Ek1d99t7y8qDl5/prRnqtSeV88afjpTERERERERGbXZO9JxBJ9IiIiIiIiIjPADD4REREREREZBTP4hYsZfCIiIiIiIiIzwAw+ERERERERGQUz+IWLGXwiIiIiIiIiM8AMPhERERERERlFdjYz+IWJGXwiIiIiIiIiM8AMPhERERERERlFFufgFyoG+ERUJCUnJ2PB3FnYvWsHUpKTUalKNfR6+z2U8gsocLuY6NuYO3sGDh/cj8zMTNSuWw89+rwHT89iBtdf99sqrFm1ArPnLYWps69UAx5tu8DGNwCZcTGI/3MD4rb8anBd56eaoXjPj/J9rBtzJyFx15/qd79xc2DtWTzPOlf7d0FWQjxMmV1QVbi0ehPW3n7Iio9F4s4tSPxrvcF1Heo2gUenvvk+VvSSGUjev0P9buNfFq6vdFY/s1OSkbRvO+I3/wJkZsIceDRqiLIDPoBThXJIu3kb4YuXIXTOgnzXt7C1QZkP+6Lkq61h4+GOpAuXcHX2PESt35TvNn7dOqHCkEHY3fRFpIRfg7mwL1USTY6sw4H27+P2jn0Fruv7ZmuUH9wXjmX9kXw5HOfHz0L4Qv33tFvtKggZO1D9zIhLRNiCVTg7Yhqy09NhDoo1fQoVhnwE56BySL1xC6FzluLy9HkFnmvlB70Pn9dfgq2nBxLPXsSlqXNxffVGvfXcG9RCxSH94VIlCOmx8YhavxXnRk9BZkISzIGDvSXe7lIaz9T3hIO9FY6eisP0eZcRei2lwO3KBDji3a4BCCnvgvSMLOw/GoMfFl7F/9m7D+goyi4MwC/pvdHTILTQe1VUFGygKCAIItJFBAFFiiDSBEEURERQOgIqKIICAgrSpPfeS3pCSO/1P/dbNtndbELAPyG7vM85eyCzM7szk0my97v3uxMdm3s9uTpbYWBPX7Ro5K7e59K1BCz44Rau3jDtc+favAW8B70D+ypVkB4VhfBff0HYmtX5b2BpiYpv9ES5lzvCukwZpAQGImTlCkTt+FtvtTLtO6j17Ly9kRYZicgtmxG8YrnZ/D2gR6hEv1evXvD39zf6mDlz5v/1fcaOHVvgOhkZGVixYgU6d+6MRo0aoWXLlujXrx8OHjyIR0F6ejqWL8/9Yzhv3jw888wzRfqe8j2R740xxfH+ht//1157DWfPnlVfy3vLPtzLJ598gu+//15vWWpqKjp27Ij169fnWX/16tVo27Yt6tevjzfeeAPnz5/Xez4oKAiDBg1C48aN0bp1a3z11VcqAH2Qc/ggZH/kPMj5MAWzP/8U+/ftRq8+AzF85FhE3YnEx2NHIiE+/4BSzueUT8bi8qULeGfoCAwaMgIXzp/DpPGjjR733t07sWzRApgD2yo1UH7Yx0gPC8Ltbz9D4qHdcH+tN1xf7GJ0/aTTRxE6fVSeR1rwLWTcuY3k08fUehZOziq4j1q7LM+6WUmJMGXWlarBY8BoZESEIHrZHCQf+xcuL78Bp7Ydja6fev4Ebn81Ic8jPTQQGdGR6nlhWbocSg8eh+z0NESvmIuEXZvh1KY9XDv3gTlwaVgP9b+bh6TrN3F2yAcI/2Mzqo5+H75v98t3mzpzPodP/7cQvnEzzgwahogt21Bz+iR49ephdH37ypVQZeQwmBs77wpovmUprN1c7rluhU7PoeHKL3D773/VYMCdPYfRcOlMVOzWPmcdez9vtNi6DJnJqTjeYwSuz1kKvxF9Ueerj2EOXJvWR+Mfv0Xiles42XsEQn/ZjBqTRsJv+IB8t2mw+AtUHtIHoes24XjPIQjbsBV15k6B78A3ctZx9K+Kpr8uRlZaGk71G4lrn3+Lil1fRv3vZsFcfDy8Otq0Ko3vVwdg+jdXUcbDBnMm1YGTo2W+27i7WmPOxNrq3xnzr+KbZTfRsLYLZo6vCUvL3IzrlFH+aN3MA0t/DsCUOVdQqlQpzJ1cBxXK2cJUOdWpixpffImUW7dw5aMxuLNtK3yHDEXFXm/lu413/wHwGfQOIrf+icujP0T86VOo/uk0uLd5Omed8t1eR9UJnyD51k1cHjsGwUsWoexLL6H61Gl41LvoF9fjUVSkGfwXX3wR48ePz7Pc3t4exUUCsr59+yI0NBTDhg1TAX5KSgp+/fVXtfzzzz/Hyy+/DHO2adMmfPbZZ+jTR/PhUgY3evbsiUfFkiVLUK1aNdStW/e+ttuzZw++++67nK/j4+MxYsQIXLp0Kc+6v/32m7qWpk6ditq1a6uBAbm+/vzzT3h4eKhBlv79+6Ny5cr46aefEBAQoH42LCws1HVZHGS/5DwsXrwY77zzDkqyixfO4cihA5gw+TM0adZCLZMM/qC+b+DPzRvRtfubRrf7d+8uXL92FfMWLoWPb2W1rErVqhg2uL967qmn26llMTHRWPPDMmz/cxOcne/9QdsUuHV8A2kBNxC55Cv1dfK5EyhlaQnX9q8h7u8/VLCpKyshDqkJcXrLnNu+BOuK3gj9bKx6Xtj4VFH/Jp04iIzbYTAnzi+8hvTgm4hZ/a36OvXiKZWRcWr3ChL2/Cmjo3rrZyXGq4cuxyeeh1V5L0TO/STnOadnXkZ2SgqilnyhMjSpF04iOy0Vrl36IuGvDciMuQNT5jfsXSRcuIgLozR/36P27kcpK2tUGtwfQStWIys1VW99p9o1Ufa5Z3B99jzcWrBYLYvefwiZScmo8uFwhG/YhAzdgTsLC9SaOQXpMbGwLMbPC0WqVCl493oVtWaOQWE/b/pP/QChv2zFhQ8/U19H/rUP1u6u8J80HKFrt6hlVUcNREZ8Io52fldl7G9v3YPM5BTUnTsBV2csREpgKExZtTFDEXfmAs4M/kh9HblzHyysrVDl/YG49d0PyErRv9ac69VE+Q7tcGXaXFyfrRmgj9p9UF1r1Se8j5Cf/0BGXDw8u74kXb5wotcwZCZqss6lrCxRZ/Yk2HlXREqQaZ+32jWc8HgzD4yedgGHT8SoZacvxOGn+Y3x6vMVsGp9sNHtHm/mDjdXa7w77gxCwjXnNiExA7Mm1EZdf2ecOh8H74p2aFDbBZ9/ew1/7ryt1jl7KR4bljbF80+VxYp1QTBFXgMGIunyJVybMkl9HXvwIEpZWcGrdx+Erf0Z2Qa/10TZl17Gne3bEbx0ifo67uhROPrXRIXXuiJ61z/qd5lXv/6IPXQIV8ePy9ku8dIl1F/9I1yaNUfckYKreIhKXJM9Ozs7lC1bNs/DyckJxWXu3LkqIFuzZg06deqkAqyaNWuq4OrVV1/Fp59+isRE085C3Ut2drbe146OjirofBRIUC7BtgTX90OuGTlvUnEidu7cqTL30dHRRtdfuHAh3nzzTbWOBNHTp09XA1nr1q1Tz2/btg0hISFqEKBGjRpo164dPvjgA1VZkpamH3gVJRnckfMh56Ukk/J6+f3RsHHTnGWurm6oU68Bjh05lP92x4/Cy9snJ7gX8n9vH1+97X75ebV6jzHjJ6Fpi1YweVZWsPOvq4JwXYnH9sPC3gG21Wvd8yUsXFzh/mpPxO/airQbl3OW2/j4ISs5yeyCe1hawbZabaScOaK3OOXUIVjYOcDWr+Y9X8LCyRXO7bsh6d+/kB5wLWe5bc0GSLlwQq/8Ul63lIWFes6USfmzW4umuL19p97y21v/gpWTE1ybNsqzjUNVP/Vv5M7desujDx6BlaODej1dvgN6w6ZMadxaqPnQbA5c6vuj7vzJCFq1ASf7jL7n+vaVvODk74ewjX/pLQ9dvw2O1SvDoVol9XXZZ1sj4s/deuX4Yb9uVYN7ZZ9rDVO/1jweb4aIzTv0lof9vh1Wzk5wb9k4zzZONaqqfyO27tJbfmfvIVg5OcCjdTP1tYWtLbIzMlTgr5UeFav+tfZwg6lr3tANycmZOHpKE9yL2LgMnDwfhxaN8z8+GxtNWJCYnPu7Ky5BU/3m4mxlsE5uVVxySibS0rJy1jE1payt4dK4MaJ26/+OivpnJywdHeFc3/jvbQtbG2QaVLJlxMXCylWTOLD28IC1qyui/92nt07y9etIj46G2+OP41Huol9cj0fRQ+2ib6y83nDZtWvXMHDgQJV5l7LmkSNH4vZtzYjhvUjWVDL1UppfsWLFPM9LNnbRokUqkBAxMTGYPHkynnrqKVVm3b17dxw6lBsUSFm3ZMElQHryySdRr149FdTJPmrt3r1bvV+DBg3QqlUrdSyxsZo/GvJaEjBKqbaW4TI5fpnC8OGHH+Yc848//ohjx47hlVdeUa8r+3Xz5k21vmwn22/YsAEvvfSS2u9u3bqp9YWUkn/0kWbkW9aT9zMskZfqBnm/xx9/HA0bNlTB8MWLF3Oel2OQh+yXHJPsg5Sah4eH479677339LLJ8r6yn5J11/rhhx/w7LPPqmBYzonMzdbKyspS3wspjzfm559/RoUKFVC9evX72i/5Psrrav3999/qvEv23dCdO3fU90POjZaVlRWaNm2KI0c0wcPRo0dRp04duLq65qwjU0USEhJw4cKFQu3TtGnT0KxZM5w+fVp9LedJjk+mA8i1KBUzx48fV8vatGmjpgLINS4VK1oyuODp6anWKcmCAgNQvoInLC31SwkrVvRCcHBg/tsF3IKnl3ee5RU9vRAclLvdC+07YsHiH9Dq8dzvsSmzLltBfUBJD9fPymREaLJQ1uW97vka7h3fALKzEPOb/s+Sja+fykyXHTwGvl+vge83P6HsoFGwdHWHKbMqXU5lnbXnSCsjUvN7zbJc3r8ZhpxffE1lAeO2rM1daG0NK4+yeV5XZf+Tk2BViNctyex9vGFhY4Okm7f0liffClD/OvjlDq5ppUdrggw7L0/91/L1znlNLYdqVVH5vXdw8aOJyNL53WXqkgNCsavms7gwagYyk+59XE41NYFq4hXN33qtpGua8+5Uww8WdrZwqOyNxCs39NZJi4xWc8plHVPmUNlHBVCJ1wzOwXXNteZYLe/xpd3RDMLb++hfaw5+mt4t9pV81L/BqzXT7Gp+OkZVRUjJftXRgxF/7hLiz+at0jM1vl72CIlIQVaW/vLgsBT4euZfFbNr/x1ERqVheH8/eLhZq5L7d3pVUsuOndZ8lr1+KwnHzsSi92s+8POxh7OTFd7tXQl2thbY+W8kTJGtp5f6vZYSoLm2tFLufja3r2S890/Yzz+jzAsvwrVlS1g6OKL0c8/DrUVLRG7V9BbJjI9HVkYGbCtU0NvO0tkZVs7OsPPUv06JHonb5EkAKcFLpUqV8Msvv6gsqQREr7/+OpKS7t3IIzAwUAXtEugYU758eRUQSxAhc3cluymB2KxZs1RgLMGQBLvagErI8xI8S5AvVQES3MmggIiKisLQoUPRpUsXbNmyBd98840K8CRrez8koK1VqxZ+//13NadbqgwmTZqEcePGYdWqVYiIiMCXX36pt82MGTNUoCyl4lWqVFHHIsffvn17tZ3Yt2+fCpB1yfns0aOHOtcLFixQAawMeMjARXBwsF6Zv5xLeX8ZFDl37pyaQ/5fPf300zh8+HDO/Oh///1XzeXSHVjZtWuXOg8ylUIGbbZv357z3P79+1VWXQY3jJHAXAZs7peU5+sG+JKRl0ENGxubPOuGhWkym4aDSOXKlct5Tv6VgQbD57UDLPci19DGjRuxbNkydc1qzZkzBwMGDFDPOTs7q2tAqgXk+pRpGXL82ioCLQn+d+zQz4iUNElJiXBwcMizXKoikgv42dds52hkOwe97SSjL4Mw5kKy9CJLZ/BLfZ2SrPd8vts7u8LxsacRt3MLspL1sxGSwbd0K420W9cQPu9TRK1dCrsadVBh1DSUsjHd+ZaltOcsVf+cZd/92sKu4NJwCycXODR9Eol7tyM7Jffakuy/ep27597wtUvd43VLOsmcisyEBL3l2jJnK6e8P38xh44gOSAQ1SeMgXur5rB0clSZ/qqjRyA7KwsWDppzIlnn2rM+Reja3xBzWDNIbS7So2ORElz4QXErV815zojTP89Sjq+ed3GCtauz0XW068k6pky7/xnxBtdaguYcWDrnvdai/j2CpBsBqDVjHDyebKHWUc30Jr6vrjXLu9dawsWruDTpSzUv/5mr+9F6/+/q2j3e/V3JHMDUOTlYISkpb48fyeo72Oc/Bz8qJh2zv7+Ox5q6Y/3ipvjp28aoWskRY6ZdQJJOVn/O99dhZ2eBZXMa4o/lzdClfUXMWnAd5y7lvRZNgVQfCcNsfObdzw2SxTcm9Kcf1bz7mnPmoumOnag2eQpu/7kFoatXqedlulLU33+h/GtdVTm/BPZ2vr6oNmUqsjMzYWEuU5AeAOfgm3CA/8cff6iAUvchwUhhSeZagqKPP/4YVatWVXOoJaiUoHrr3dGxgmgz57pZ0/xI8CtBqwTOzZs3V2XWErhL5lc3myyBqARbUuYvWVPJ6krWVEiQLOXWkiH18vJCkyZN1KDE/TZKk+BeBhZ8fHxUoC3vKa/RokWLnEzt5cu5JbTi7bffVkGunCeZB+7u7o61a9eqYF0CPyHTIwwDVBlEkABZpjJI4CjHJedAttPNistrTJkyRb2+nB8ZONAed35kMMTw+y8P3XntEmxKRv7kyZM5AbsE87KtHLcM5MgAgCyTaQVSeSD7rCUDGrLM2PdYsvtnzpxRAzX3Q8rXpSGfbka+INqKAsNza2trq3pACMmiG3teaNfJjwTxUokijRIN+wjIYJIcvwzqSIWHXPPSHFCO+fnnn1fX0pUrV/S2kWtazoucn5JA9kMG2HQfBe2bDADlJzsrO//tLMz4l3ype/wqN5imY8j5iWdV+bjM1Td0Z8V8hM4Yg9gtvyD1ynkk7NmOiAUzYF3BC06P5TYSMjkFXEeFOWcOLZ9W8ysTZa7+//F7UeLd4/iM/Qxmp2fgVN/BSA0NQ8OVi/Dkif2o89XnuPHVfPV8VrImo11p8ABYuTjj2hdz8aiTn8eCSLCKe65j2tdaqXv9LBm91tJxrOsgJAeFotlvS9Hu5mE0WPIlrk7/Rj2feffvtTTpq/PlRAQu+xlHXu2Hk/0+QEZCIpr+tgQ2ZY3fcaUk/yqztNB/FHTqCvoV1LZ1GUwd5Y/9R6Px4dTzGDfjIm4EJmHWhFrw9bTLqQ74dno9NTf/k1mXMHLyeWzeEYFRg6vgqVYmOv3zHp8PjP0sSdVc7QXfqTn3N2bOwPl330Hggm9Rut2zqDTig5z1bnw+E5HbtsLvo3Fouv1v1F3xAxLOnEHipYtmVaVEJUuRprAk8JDSb13acvjCdv2W4MQw6ywBkW5ZfH6088wl83wvEjBLEKsbDEogIWXWEvxrlSlTRi+YlG0kqywkmJIgW7KoEkxLybsEsFJefj98fX3zNCSUYF/3HGrfU0uCfy1ra2sVCBoOAuR33NKXQHdOvry+BPu628s+yesaO+78yD588cUXRisUZE67kPeVkn/J3Mt7SmAvz0vWXoJsGcyRTK62CkMC2sGDB6sqBlkuGeqvv/7a6PvL910GCUqXvr8/1tp9KWyvCO01bTiXXq5T7fdP1jH2vDCWqdaSgQ+pApHzZGyaiVS3aGnfS/f6Mfa+2qZ/cn5KQi+Gn9esVA9dj7V+ErExefsdJCUnwcEx/++Lg6MjkpOTCp3ZNxfarLth1jk3s19wnxGHJo8h+dzJnMZ6ulKv5y1XTb16EVlJCbDxNt0SYG2G3cJW/2+SNsMu5fQFsWvQAqmXTudpuqfN5pcy8rdOXvter1vSabOphhktycqr5/O5baJk8E+80U8zJ9XdFck3A2DrWUEFsumxsaoRnwT4pwcMQXZamsrm5wSwErHI/0vIoGRxyIjVnEcrgyx1TlY7NiEnc2+4jnY97WuYqvS4eKNVIdoqEmmWZ4xk8I+8LH0cPNR8epnWII3z1LUWE6uurSojByFk3R+4MCa3k3n0v0fwxLGtqDy0Ly5PzPvZpaTq3dUbfbrlfkYUuw7cUZ3wDTk4WCIhKf876fTp5o1zl+NVZ3yto6djsfKrBujfwxcTv7yMri9VVD+OH065kDM/X0r2pTv/iAFVsPtAFEyNtiLJ0uDzmPb3nGHFkvB4+mk41qiBC8OGIu7udMz4EyeQkZAAv1GjEfH7BjXXXirrbkyfhltzZqtS/dSwMLWs7Msdc6YAPIoe1bnxZhHgSzM33QCkMHRvZSVZPJmnPHHixDzrabPSBZGgWAJyyTRLxtmQDBLIvGaZo27YiE5LluuW8hor0dYl2e8hQ4aoEm/JRo8aNUpl8mX+uDHGbpOmG0hrSbf1ghiWG8vr3msbkd9xy7m/n+M2RoJLY99/w2y7DARJoC6VAS4uLiq4lkoFKdOXaQJSxq+diy09CeR7KlMG3Nzc1PqyrKBM7/1mqmX+/f2U9WsDbxl0kAoHLflapoEIqUQxHHCR54V2HWMk+JcqEOk9IVM1DKdmGCszv9f3XXs+CsqEF6fnX3wJzZrrV0scOrAPJ44dVfuqezyhIcGqvD4/0mDv+jX9igXNdiGo7n/vpmmmKiMiTJX7Gc7v1n6dHpr/hwhLNw/YVqqK20ay91LG7ti4FVJvXEF6SIBBysgamfGaKilTJHPt5ZxZltGfOmN19+sMg34Guixc3dXgRvTuP/P+Tk1LVV3yta+Ts42TiyrfL+h1TUFKQKCaU6qdy6ylnaOadFV/Pri2oVnZ59sh9vhJpAQFq/tLC+c6muaP8ecuoOyzz6g5sJLhN9Rqx2ZEHzqCk28WvgLQ1CVc1pxHh6qVEHcyt0+LY1XN39SEi9fUtIjkoDC1ji6bsh6wdnFS65iy5Juaa007f17LoYrm64TL1/NsI30Jyr/8LGIOnUByQDDSIjXXmkv92urf+FMXYF3GXTV3lHV0ybqJV2/AqWY1mJI//grHgWP6A+JyC7tmDVzVr2rdj3leFewQEJx3+pBW+bK22HtYP0CX5nmXriWiso99zjoBIck5wb3W6fPxePqxMmpgITq24ARQSZMSHKyaLtp56/9ek/vWi+S7fa902VbQ/H2N15nGq74+qbmu7P2qqABfGunJXUISTp9G8g3Nz7WVuztsypVT3fSJzG4OvgSyMgdcSz7My7xx3VJiCcIlgJJAUR4SHMp86MJkpyUwkPt+y3x6Y/Oc5XZhUqos5fTSsExKs3VfV4JfmW8v5fqFcerUKbVvUi6tbcYnXx88eFBlorWBu+4xa5vl/VdyHFqSsZXpBtLU7V6BnBy37IPsn25mWbLnhT3u/0oCfHm/v/76K6cs/rHHHlPnTTv/XksCfbn7gawrc82lLN2wEZuWTFOQcy69EQpLvud79+7Vm39/L1Ih4Ofnp9c3QAaqpBpBmuIJ+VcqUnS/93J8Mggm0yLyIxUlUsEiPRhkUEMGQv4r+V7LgI2cn5LAo3QZVKvhr/eQ7vmSiZdO91qxsTE4f/a0Xmd9Q/KcNOgLDMj9uZL/BwXeQsNG+W9n6rIz0pFy+RwcGrfUWy7BuWTaU3W64huyraKpWkq9aqTZY3o6PN4YpG61p8uhQXMVtKVcyv29Y3Iy0pF2/SLs62t+RrXs6jdXFQ+6XfEN2fhqfjem3TD+4Sz10hnY1m6kOvXrvq4MKKRdOQdTJvcNjz1yHGWfy/29LCSAT4+LQ9zps3m3SU9H9Ylj4fl6l5xlkkX17tUDSbcCkHjpCkJ+/hVHO/XQe9z4eoFa9/SgYbg0YSoeJUnXApB0PRAVOz+vt7xCp+dU8J98SzNQFPn3vyjXvg0sbHITAxU6P68C48h/9O+qYWqyUtMQfeAYyr+kub2plgTw6bFxiD2e9/dPVlo6as0cD+/eXfWuNZlrL8354s9fRtrtKKRFxcC9ZRO9bSXb71i1MpJvmVZW9U50ugrAdR9HTsXA0cEKzRrmdsx3dbFCg1ouOHIq/4FZCf7r+esn0GysS6F6Fcec2+bJOpW9HVRzPV11azqrsv24eNMK7oVUDcWdPAn3p9roLfdo87QKzhPP5/29Lfe1Fy4NGuot13bcTw3R/IyW69QZvkP1b4dc4fXuappNjEF3/UcJ5+AXrYfaZUo6tkvTMMl2S/Auc4zj4nJLRKXBnnT7ljL/d999Vy2TTu5yC7PCzquWcnkJ2OS1hg8frkq9pTRZ5vdL53mZ3yxZUskCS4m9ZEonTJiggjZpKCcBv7EKAmOkpFsa70lQKZ3sJVCWZntSAi/BlMy5lveSwF+6m9+6dUsd//+D9CaQzLa3t7fK+Mq8cNkH3RJwY0G7NK6TOfGyP1JtIIHf/Pnz1dx3aWZYHGSfZJBFmsHJPH8hgb40/ZNzKVMddMldCmRwRgL70aMLvt2QVAPIYIfcIlGXnHu57gwrDiTglve836770tRQqkHkOpbqA/key7x7GWAScls8+R7JeZbrWe5+MHv2bLVdYaojZMBBpn9IoC+DBYXpK5EfGWjQbdRXEsnt8OrWb4g5s6ajd7+34ezigp9Wr4CjoxNebN9RL3iX6QZVqmq+X62fbKNugTdlwlj06jtQLfth2SJUqlxFPWfOYjevRfkPpqDsO6ORsO9v2FatCZfnOyF6/UpNybOdPWw8fZAeEaZXim/tVQnZ6WlGb4MnAwexf/4K91ffQGZcDJLPHIONdyW4vdxd3ZIv5aIJB/iSadn+G0oPHgf33sORdGgXbPxqwOnplxC/+Sd1TkrZ2sOqghcyI8P1SvGtPH3V85l3NFU4hhJ2/oGyjR5D6UFjkbBrM6zKVoRLh9eRdGCnyu6bupvfLkLDFd+hztezEPrLBrg2bqhubXf9i7lqTqmU6ztWq4LkgCCkR0Wr0vqQ1Wvh3acnUsPCkXTjJrx6dodL44Y4++77KsWYFnFbPXQ51tD8vZIBgJTgEJgzKbN3ql1NBfbSBV9cmTYfDZbMQHpUDML/2InyHdvCs1t7HH9jRM52175YDM/XO6DZpsW48dUyONaoDP+pHyBw8VqkBJr2vdzF9S+/Q9P1i9Fg6WzV+d6teSNNCf2UOap3gzTRc/KviqQbgUiXDvqSKFr6EyoN6oWUkHCVkfft3wNuLRrhZK9hmnR2djauzZyvBgJkyknY79tg4+EOvxED1SDczfnLYepOX4jHibOx+HhYdXy36hZi4zNU+b2U52/clvu7vpK3PaytS+HqDc3UoaU/BeLT0f6YNLIGtuyIUM9JSX4ZDxt8+pWmOm7dHyF49skymD2xNlatD0JiUiaebOGh5u9/s/wmMk10Jk3I8qWo+fU3qDZtOm7/8Qec69dHxZ5vIvDb+apZnnTJt/fzQ0pwEDJiYhC9dy8Szp5F1YmTELR4EVJu3YJjnTrw6tMX0Xv2IPHuHZLC165Fzblfw3f4CMTs3QuXpk3h1bsPQlauQKpOM2siswnwJbgJCAhQgbcEORIMdejQIadsXErsJciWsmTp9C4BnQToK1euLPTcYZmXLK+xdOlS1f1d7kUugVzt2rXVXG+ZYy/ktWUdGUCQTviSBZc55DLoIAMRhSHl2XILOumeL4G+VBDIFAN5X/m/DABIh36Zly5TBiRzO2bMGFXS/1/J+ZF9l+OTOe1ybNou7bIP2tvryfsbTnWQ8yNd+KXqQMiUAhkA0Z33X9SkDF+mMci+Cjnn8n2S3gKGc9RlwESORyo+dEvijZHAWhrxGWsAKQ9dMsjQtWtXPPHEE/e9/zKYIhUgEsTLAJJcOzJ4o71OZXBHBiWkcaOsKwG6DDppB64KY/z48arBopTqG34f74dUGhgOeJREY8dPxtJF32L50u9Ug5uatetg1EefwElnes7C+XMRER6GRct/VF9bW9tg0rRZWPLdN/h23mxYWVqprH6/t9/Nt9LDXEiwfXvBTLi90gPlhoxDRswdRP+yHHHbN6rnpQxfOt9HLp2LhP259zC3dHFDlkHnYMOBg6yEWDg/3R7ObV5AVkI84ndvRczveW8ZaWrSrp5D9PI5cH6hKzz6j0RmbBTi/liDxF2b1fPW3pVRZugniF6zAMlHcgcELZ1cC5xLnxERgjvfTYfLyz3h0WeEGhxI2P0n4v/Uv6OFqYo5eBhnh46E37DBqLfgK6SGR+DazDkIXKrppeFcuxYarV6CC2MmIGy9pimqZOOzs7Pg+3ZfWLm6IuHCJZweOBTR+w485KMpGVwa1UGrHT/gVP+xCFqp+Zsl/8pt4qq83w/efbqojP7JPqMRui53akjipes49GI/1Jo5Go1//loNDtyYuxyXJxnvTWNqovYewsk+I1BtzBA0+mEeUkLDcWniF7j17Yqc0vvmvy/HmaHjEfLjBrXs6oz56m+G37D+sHZzRfzZizj++mDc2bU/53UDFq9RVQCV3+0Drzc6IS0qWlULnHxrmCrtNwcTZl3CkN6V1W3upJDz7KV4TJ59GQmJuVND3x/ohwplbdH9XU1ZuTTXGzv9Inq95qWa7SWlZOLi1QS8M/aMuj2eCI9Mw9DxZzGwpy9GvVNVvfatoGT1fnsPmd78e624Y8dwZdxYeA8YiBozP0fa7dsI+GYewn5co5538PdH7W8X4NrUKYjcslkNJl0cPgze77wDr779YOXigtSQEAQvX5azjYg9fAhXP5kAzz59Ue7VTkgLC8XNL79A+C/m8feASqZS2flNwiaTIJlgKWGXQQ/dRnvmTC5ZCdylOkMC8oJIsC1TAGTwQDLrjzqZyiEDOXKbPOlh8CAuXDOPDz/Fyf6zwg/kUC4b54Jv70d5Xdp0/mHvgklKuslu1vfLysV8bjVanD57evHD3gWTMzM4t2qFCq/FgdypoyXN4YvF18Onec0Hr3o1VQ91Dj7R/ZBSbJl3L2X8MoVAqj3uRYJYqRSRSgyCOg99+/Z94OCeiIiIiIhKLgb4ZDJkbryUp0ujOSlRL+j2crrefvtt3LhxA6cNOp0+aqQXgZyHQYMGPexdISIiIqJHVFYxPh5FrK8ycdJUT5oOPiqkYeL9kv4OcieFR53cVYHngYiIiIjIfDHAJyIiIiIiomKRnf1o3r6uuLBEn4iIiIiIiMgMMINPRERERERExSIbzOAXJWbwiYiIiIiIiMwAM/hERERERERULDgHv2gxg09ERERERERkBpjBJyIiIiIiomLBOfhFixl8IiIiIiIiIjPADD4REREREREVi6zsh70H5o0ZfCIiIiIiIiIzwAw+ERERERERFQvOwS9azOATERERERERmQFm8ImIiIiIiKhYZGczg1+UmMEnIiIiIiIiMgMM8ImIiIiIiIjMAEv0iYiIiIiIqFhk8zZ5RYoZfCIiIiIiIiIzwAw+ERERERERFYss3iavSDGDT0RERERERGQGmMEnIiIiIiKiYsHb5BUtZvCJiIiIiIiIzAAz+ERERERERFQs2EW/aDGDT0RERERERGQGmMEnIiIiIiKiYpHNLvpFihl8IiIiIiIiIjPADD4R3ZfkTLuHvQumJzrpYe+BScpMy3jYu2Bykm6mPOxdMElWLvw4dL8y4vjz+SAyUtMe9i6YnPSk9Ie9C/R/lsU5+EWKGXwiIiIiIiIiM8AhayIiIiIiIioW2dmcg1+UmMEnIiIiIiIiMgPM4BMREREREVGxyOYc/CLFDD4RERERERGRGWAGn4iIiIiIiIpFFjgHvygxg09ERERERERkBhjgExERERERUbHNwS+uR1FKTU3F5MmT0apVKzRq1AgjR45EVFRUgdscP34cvXr1QpMmTfDEE09g/PjxiImJyXk+PDwc/v7+eR7r168v9H4xwCciIiIiIiK6D5MmTcK+ffswb948rFixAtevX8ewYcPyXf/GjRvo37+/CtjXrl2LOXPm4PTp0xg+fHjOOhcvXoStrS327t2rXlv7aN++faH3i3PwiYiIiIiIiApJMu0bNmzAwoUL0bRpU7Vs9uzZeOGFF3DixAmV0Tck65crV05l7UuV0vQhmDhxInr27InAwED4+Pjg8uXLqFy5slrvQTHAJyIiIiIiomKRnW36TfaOHTum/m3ZsmXOMj8/P5QvXx5HjhwxGuB37NgRTz/9dE5wL7T/j42NVQH+pUuXULVq1f+0bwzwiYiIiIiIiO4jg+/u7q7K6XVJ5j0sLMzoNsYC90WLFqFs2bKqbF9IBl9eV7L6UtJfqVIlDB48GE8++WRhd40BPhERERERERWPrCJufqerbdu2KMiOHTuMLg8KCipwW5k3b2Njk2e5BPzSfK8wZs6ciV27duGbb76BtbU1MjIy1Dz+atWqYezYsXBycsLmzZvx9ttvY9myZaqZX2EwwCciIiIiIiK6S0rtt2zZgvzs3r0baWlpeZZLcG9vb4+CpKen45NPPlFz8qdOnYp27dqp5VZWVjh06BAsLS1hZ2enltWtWxdXrlzBkiVLGOATERERERFRyVLUt68rTIb+XiSjXtBceJkrL7e3kyBfN5MfERGhBgfyk5CQgKFDh+Lo0aOqKd+LL76o97yjo2OebapXr6466RcWb5NHREREREREVEhyH/usrKycZntC5szL3PxmzZoZ3UYGAwYNGqRujScZecPgXjL1jRs3Vll8XWfPnlVl+4XFDD4REREREREVi2yYfhf98uXLo0OHDvj4448xffp0VZYvt7xr3rw5GjZsmBPQS3d8V1dXleX/7rvv1IDAl19+iSpVquD27ds5ryfrSMWALJ8yZQomT56smu2tXbsWJ0+exK+//lrofWOAT0RERERERHQfZP68BPdSci+k070E/FonTpzAW2+9hZUrV6JFixbYtGkTsrOz8cEHH+R5Le06CxcuVAMAI0aMQFxcHGrXrq0a7NWoUaPQ+1UqW96FiKiQjl++87B3weTYf9T9Ye+CSbJxzNudlgp28cerD3sXTJKVC/Md9ysjLuNh74JJ+uyF7x/2LpicGQHvP+xdMEmtTx1HSfXLoaxie6/XWjx6M9IfvSMmIiIiIiIiMkMcsiYiIiIiIqJiwfrxosUMPhEREREREZEZYAafiIiIiIiIigUz+EWLGXwiIiIiIiIiM8AMPhERERERERWLrOxSD3sXzBoDfCIqkVKSk7Bm+bc4vH8XUlKSUatOQ/QaMAye3pUK3C4mOgqrlnyNU8cPIjMzEw2btkKv/sPg7lFGPb9gzqfYs3NLvtt/veRXlC1XEabIsUETlH2jL2x9KiEjJhrRW39H1O+/5L+BhQVKv9IVbm1fhJW7B9JCQxC5/kfE799tfHU7e/jN/g6R61Yh9p/tMAf2dRvB47W3YOPli8y4GMT+vRmxf643uq5z63Yo93b+t2uK+H42ki+eQaXZy/JdJ27PX7i9+CuYujLtHof/1PfhXLsaUsPv4NaC1bg+Z2m+61vYWKP6J+/B642OsCnjjoSL13Dti8UIXav/s1iufRtU/3gInOv5Iy0yGqG/bsXliXORmZgEU1e6zWOo/vFwOPlXRertOwhc8iNuzl+e7/qlbKxRbcwQVOz6Emw83JF4+TpuzFuKsN/+1FvPrWVj1Ph4BJzr+iM9Nh4Rm//GlelfIzPB9M+ZLjuv8njy5CYc7TIEUXsOF7iu5+sdUO2jwXCo4oPkm8G4Out7BP+wQW8d1yZ1UWvmaPVvRlwiglaux+Up3yA7PR3mwN7OAoP7VMFTj5WFvZ0lTp2LwdeLryEwOLnA7er4u2DQW36o7e+M5ORM7D9yBwtX3kB0TO55cbC3RN8elfBkyzLwcLdBSFgyNvwZig1/hph06bVbq5aoNHQIHKpWQXpUFEJ/WovglT/ku34pa2v4Dh6Ech3aw8rNDck3biBo+QpEbtX/++javBl8B70NxxrVkZWWhriTp3BzzlykBAUVw1HRo+i+AvxevXrh8GHjv1T79euHMWPG/F92St7Hy8sLM2bMyHedjIwMrF69Ghs3bsSNGzdga2uL2rVr4+2330bLli1h7tLT09Xx9+nTR309b948/Pbbb9i5c2eRv7e8z7p163D58mX1dfXq1dV+PP/880X6voW5LvK7Vrp3745Jkyahbt26eOaZZ9CpUye89957BW73ySefwNvbW11TWqmpqejatas63s6dO+utL9+PpUuX4vbt2+p9Pv74Y3VNagUFBWHq1Kk4cuQIHBwc8Nprr6l9sLS0NPr+Y8eORXBwMH74If8/Lvfj/Pnz6ph++uknWFmV/LG9eV9MwtVLZ/FGnyGwd3DErz8uxdTx72HW/FVwcnIxuk1mZgZmTvoAycmJ6P/uaGRmZODHFQvw2ScjMP2r5eq4O3fvg3Yvvqq3XUJCHObO+Bi16jVCmbIVYIrsqteCz0dTEbd/N27/uBwOteqiXK+BKGVpiTu//Wx0m7Kvv4XSnbqrgD3p4jk4t3gc3iM/RlBWJuIP7tNb18LRCT5jJ8OmvGkOfhhjW9UfFT+YiIRDexH16yrY16iN0q/3VecsZtO6POsnnjqMoMkf5Flerv8wlLJ3QOKpI8hKTjK6jmu7l+DU4gnE7zH9gRG3Fg3QbONChKz7UwXf7o83Qc0Zo1DKyhLXZi0yuk2j1XNQrkMbXJ+9FJE7D8C1cV3U/34abMt64Ob8VWqd8q+0Q5O183Bn92Ec7zFCMygw/l24t2qEA0/2QHZmJkyVa9P6aPzjtwjb8CeuTp+nCconjUQpKyvcmLvY6DYNFn+Bss89pQYB7uw5CNcGdVBn7hQ1QBKwaI1ax9G/Kpr+uhgxh47jVL+RsK1YTr2ufSUfnOg5BObCzrsCmm9eAms347/7dVXo9BwarvwCN+atxO1te1HhlXZouHQmslLTcgaU7P280WLrMkQfPKmuNaeaVdWAlbWHG84OmQhzMPHDWipY/3b5dSQmZaJfj0qYN60Beg05ivjEDKPb1KrujHmfNcCtwCRMm3MRqWlZ6NHJB9/NaoS+w4+p1xFTxtRGzerOWLr6Jm4FJaFJA3eMeLsaXJytsOLnAJgi53r1UHveXERu245b8xfApVFDVH5/uPq9FrTU+EBczc8/g/sTT6hBgJhDh+FUuxaqT5oIa3d3hP6o+bvr3LAB6i6Yjzu7duPSR+NhYW8P37cHov6KpTjepRsyYmLwKDLlgSBTcN+f8l988UWMHz8+z3J7e3sUFwmy+vbti9DQUAwbNgyNGjVCSkoKfv31V7X8888/x8svvwxztmnTJnz22Wc5Ab4MsPTs2bNI3zM7OxsjRozAwYMHVWA6ZcoUlCpVCtu3b8f777+vntMNhkuKJUuWoFq1airovh979uzBd999l/N1fHy8OsZLly4ZHfSQ604CeAnqv//+e3Ut/vnnn/Dw8FADMv3790flypVVgB0QEKB+jiwsLNQ1XBxkv+Q8LF68GO+88w5KsssXz+D44X0YM/FLlYEXNes0wLABr+GvzevR6XXNdW/o4L6duHn9MmbNXw1vXz+1rFKV6hg99E0c3LcDrds8j/IVvdVD15zp4+Do7IKhIyepa9oUle3+FlJuXEXI1zPV14knjwKWVijduQeiNv+G7LS0PNu4PvMC4vb9owJ8kXTmBOyqVIf7i6/oBfhOTVuhfP93YWHvAHPi0bknUm9dR8R3X6qvk88cU+fM/eVuiN22Ednp+ucsKz4OqfFxestcn30Z1p4+CJ4ySj0vUq/p/46wqVxNBfd31q1EyuXzMHU1PnkPsScv4FSf0err29v3wsLaClXHvqOCqqyUVL31XRrWQoVXn8WlCXNwdcZCtezOzgPITEpCzWkjEbRqIzJi49XrJly4hsMdBuRkUaP2HcXTl/6Cd5/OCFySd9DFVFQbMxRxZy7gzOCP1NeRO/epc1bl/YG49d0Pec6Zc72aKN+hHa5Mm4vrs79Xy6J2H0RmUjKqT3gfIT//gYy4eHh2fUl9Uj7Ra1hOlYMEJHVmT4Kdd0WkBIXCpJUqBe9er6LWzDFAIX81+0/9AKG/bMWFDz9TX0f+tQ/W7q7wnzQ8J8CvOmogMuITcbTzu+pau711DzKTU1B37gR1jaYEmvZ5k8C+dYsy+HDSGRw8FqWWnT4Xi7WLW6BTB0+sXGs8CH+rmy8SEzMwbNypnEGAY6djsGZBM/Ts4oPvf7iJGlWd0LKJBybMOId//o3MWcfZyQo9O/uYbIDv++47SLx4EZfHT1Bfx+zfr35Gvfv3Q8jqH5GVqv8z6ljTH6WfeQY3581H0OIlalnsocPISk5B5eHvIWLTZmTGJ8C7bx8kXb+Bix+Ozolq40+eRLNtf6J8x5cLrBAgKrYme3Z2dihbtmyeh5OTE4rL3LlzVZC1Zs0alYmVoKlmzZoqYHr11Vfx6aefIjExEeZMgm1djo6OKpAsSnK+//rrL5WlfvPNN1WwWLVqVQwePBjvvvsuvv76a4SEhKAkkaBcgm0Jru+HXF9yjv39/dXXUhnRsWNHREdHG11/4cKF6pzIOnJepk+frga9pNJBbNu2TZ0bGQSoUaMG2rVrhw8++AArVqxAmpHAq6jIQJCcDzkvJdnp44dga2eP+o2a5yxzcXVHrboNcfLYgQK2OwxPL9+c4F7I/z29K+PkUePbnTi6H4cP7MJbA4bB0ckZpqiUlTUc6tRH/OF/9ZbHH9gDSwdHONQ0PrhlYW2tgixdmfFxsHTOzZJZODjCe/REJJ07jcCpmuDELFhZwb5mfSQaXE+JR/apgQy7GrnVN/mxdHFT5f1xO7Yg9XregT+tsm8NRlpwIGK36pcImyLJqns81QLhG/7SWx66fhusXZzg8XiTPNtIdlSEb/5Hb/mdXYdg5eSI0k9pfs6dalbB7b/26ZVIp0XcQcLF6yj3YhuYKim193i8GSI279BbHvb7dlg5O8G9ZeM82zjV0JyziK279Jbf2SvnzAEerZupry1sbZGdkaECf630qFj1r2SjTZ1LfX/UnT8ZQas24OTdAaWC2FfygpO/H8I25r0+HatXhkM1zRSvss+2RsSfu/WutbBft6rqnbLPtYapa9HYHUnJmTh8QhPci5i4dJw8G6OC8/xU9nHA6fOxehn+1NQsnL8Uj1ZNS+cs27g1BEdP6WeeA4KS4OBgBXc3a5gaKbV3bdoEd3bq/46K/OtvWDk5qWy+IQc/zeeMqN179JbHHjkCSwcHuDZtqr6OP3MWIavX6KWs025HIiMhAXY++skGohLbRV/KqKW0uKBl165dw8CBA1XmvXXr1hg5cqQqay4MyYRKpl7KoytWzFsqKhnWRYsWqYEIERMTg8mTJ+Opp55C/fr1Van2oUOHctaX0nbJgkvQ8+STT6JevXoqUJN91Nq9e7d6vwYNGqBVq1bqWGJjNX9A5bUkCJTyay3DZXL8M2fOxIcffphzzD/++COOHTuGV155Rb2u7NfNmzfV+rKdbL9hwwa89NJLar+7deum1hfr16/HRx9pPmjLevJ+chxSeq4l1Q3yfo8//jgaNmyoAtyLFy/mPC/HIA/ZLzkm2YdBgwYhPDw833Mvmec2bdqgTp06eZ7r3bs3li9fjjJlNPOcpaLiq6++Qtu2bdU5leOUIFdLjuHZZ5/NeU15f8lky/trz5N8P375RX/+sAzcyPUi68t5lEGFrKysfPf5559/RoUKFdQ0gvsh33N5f62///5bfY9kfw3duXNHfe/kPGpJKXjTpk1VOb44evSoOm+urq4568hUkoSEBFy4cKFQ+zRt2jQ0a9YMp0+fzvney/G98cYb6hxLdc3x48fVMjmnjRs3Vj8P8r3QksEFT09PtU5JFhx4C+UqeMLCYPpChYreCAnKPzsQHHQTFbx88yyv4OmFkOC828kgzuql36BW3UZo8Xjuz4+psS5fERbWNkgLCdZbnhamGXCz8fIxup1k9l3btINjw6YqqHV54hk4NWqG2N1/56yTlZaK6yMGIPSbWciM0/zeMwfW5SqqD3XpYfrnLD1ck7mzNqjyMMa9c09kZ2Uh6tf8MzBOLZ6EXbWaiFz9PZCd/+8qUyFzmi1tbZB4RfP3Sivx6i31r6N/7uCalsylF/a+ngav5Zvzmpr1YvKsIyXs9j4Vc9YxRQ6VfWAh5+ya/jlLuq75neRYzcg5u3P3nPkYnDM/zTmTEnwRvFrTL6Lmp2NUllpK9quOHoz4c5cQfzb/QSdTkRwQil01n8WFUTOQmZT7tyw/2sEkw+sz6Zrm+nSq4QcLO1s4VPZG4pUbea5T6WEg65i6Sj4Oal684cej4NBk+HrnX4klgwAVymk+P+vyrGgHzwqa5ZevJWDW/CuIT9Av83+iZRlEx6QhJtb0ehjYeXvDwsYGybf0PyckBwSqf+0rV86zTfrd0no7z4p5Xkvzr5f6V7L74Rs26q3j0qQxrF1dkXTtOh5VMt5RXI9HUbHfJk8COAlIKlWqpII3yXxKkPP6668jySCTZExgYKAK2iV4MaZ8+fIqIJZ5zdJgSzKWElzNmjVLBZUS4Eiwqw2ShDwvwbME+ZKlloBNBgVEVFQUhg4dii5dumDLli345ptvVNAmmdj7IfOoa9Wqhd9//10FvVJlIHPCx40bh1WrViEiIgJffqkpE9WSueZSSi3l31WqVFHHIsffvn17tZ3Yt2+fCoZ1yfns0aOHOtcLFixQQakMeMjAhczp1i3zl3Mp7y+DIufOnVNBeX7TImTOfX7n3dnZWQW0NjY26mvJTssAxYQJE9QxS8Z6+PDhKlDWkoz21q1b1XmXQH3Hjh1qaoUEwjKIIwG2nCPdrLlMB3B3d1ffy9GjR2PZsmVYuXJlvudd3k8Gd+6XlOfrBviSkZcBEO3x6QoLC1P/Gg44lStXLuc5+VcGGgyf1w7G3Itcb9JvQo5Xrm+tOXPmYMCAAeo5+R7I9SIDKXJOZQqHHL+2ikBLgn851yVZUlICHOwd8yy3s3dQ8+vzk5yYAHsHB+PbJeXd7tjhfQgOvJlvyb+pkCy7yDQ4NzIfXD2fT2l91B+/IvnCWfhO+Az+qzbCa8RHiN21HVEbda6ZjAykhZhfIyDtOdGeI62slILPmZalsyucW7dF3N+bkGXk2tJya98FyZfPIeXiGZgDK1dNlUt6XILe8sx4zTmQjLQhmVOfeC0Adb76GKWfbgkrZ0c1b7/W9A/VAIlku0Tg8l9RsfPzqPLhQDXP3M6nIuovmqbe09Kh+KYB/r9ZuWjOSUa8wTlL0JwzS+e8v+ui/j2CpBsBqDVjHDyebKHWUfP2J75/95xpzkfCxau4NOlL+A58A89c3Y/W+39XVRHHu7+LPNGdCUqPjkVKcP6JB0NWrnfPtcH1KeX46nkXJ1jfvYYN19Gup/1+mTInByuVwTckyxztjff9EZv/DoN/NWcMG1AVpT1s4OFmjcG9/VDZxxF2tvlv1/VlLzSu74ZVvwSaZEAlWXohWXVd2go3S8e8P6OxR44iOTAQVcaMhmvz5modl0aNUHnEcM3PaD5Tl6UZX/WJE5AaEYGI3/8okuMhuu8A/48//lABpe5DAozCksy1BDrSgEzKu2VetASVElRLsHcv2sy5biY0PxL8StAqgXPz5s1V6bQE7pLNlXnZuk3YJICSMn/JhEqmVjKhQoJkKaGWrKc0eGvSpIkalJCs/P2Q4F4GFnx8fFSgLe8pr9GiRYuc7Ku2aZ2WzGeXDL6cJ5nbLYHt2rVrVbAuwZyQ6RGGQacE1BIUy1QGCQbluOQcyHbSCE5LXkPm0cvry/mRgQPtcf+X8y7VDxJATpw4UQWTfn5+as6+DGzIudM97zIAIIMuEoTLfspAhsxd1/4rFRvaygbtPHLttSPl8G+99ZaaMmCMZPbPnDmjXv9+SPn62bNn9TLyBUlO1pRGGn4fpPGjDIwIyaIbe15o18mPBPEy4CEVEoZ9BGTgSSo35HxJlYR8n6SRnhyzND2U6+7KlSt628j1L+eloMqH4iT7Ic3xdB/yxzE/FqXy/7WVVcAnCwsjc+u3b/pFzdGv11BT7mqqSlncY3KqkcyxlPVX+nSOmnMfuvAr3JowEhGrlsDl8TYo3+9dmL0CriPlHp9Snds8j1IWFojZrp+Z0WVbrRZs/aohZovxrvymSI65QEZ+dqUM+nCH/kgODEXL7SvwfNRxNF7zFS5Nmque15aXX5kyD1c//x7+k4fh2dCDePridhVwhf++A5l3f8+aolL3utayso2es2NdByE5KBTNfluKdjcPo8GSL3F1+jfqee358Bs+AHW+nIjAZT/jyKv9cLLfB8hISETT35bApmxuSfWj4l7Xp/rbcs91TCtClT9tlhb6j4IuuYIOb9P2MMxbfA0vPVcRG1e0woYVrVCxgh1+3xaC1FTjTS47d/DEewOqYsfeCPy80UQHgx/gb6hMjTk3eChSw8JQb9FCtNq/F/6fz8Ct+d+q57N0qie1rMuUQb1F36l/L3zwYZ4pco8SuQ6L6/Eouu8mexJMSAm1Lm05fGE7eUvAYZh1liBHtyw+P9p55pJ5vhcJmCWI1Q3wpIGWZJol+NeSsnLdwFW2kcBSSIAkQbZkRiWYlpJ3CVqlvPx++Pr65mlIKMG+7jnUvqeWBP9a1tbWKrgzHATI77ilL4HunHx5fQn2dbeXfZLXNXbchtzc3NS5y28Oui5tEzoZDNEl5eWzZ8/O97xIZ3ndLLg2ANado274mnJMMmgQFxcHFxf97rpyjcggQunS9/ch599//1WvW9i+Etrr33AuvVzT2u+1rGPsee1x5+fkyZOqYkS+l8ampEgljJb2vXTPqbH31Tb9k/NT1H0bCmP9T0tVh3xdLR5/GrExea81ycLbGxlJ13JwcFS318u7XRLsHfW/nwnxcTh/5ji6vzUYpi7zbgbZ0s7BeGbfSIbZuWVr2PlVxa3Jo5F0+oRalnT+tMpGV3h7GGL+2oLUQP0yV3OSdbfaQW79ZzSzX0BWXjg1exxJZ0/kNNYzuk7zx5GZEI+kU5qpOuZASpiFZOF1abOehpl9raRrATj4zJuwKesBm9JuSLxyC3a+FVVAJllaIV3yL43/UgX6UpKfEhKhmu+13LkqZ165KUqPu3vOnAzO2d1qB2mWZ4xk8I+83Bs2ZTzUfHopM5fGeeqcxcSq+eJVRg5CyLo/cGHMtJztov89gieObUXloX1xeeIXeJTI9VLQ9ZkRm5CTuTdcR7ue9jVMRd/uldDvDf0S8n/23YaHW96KQwcHS9VEryASpP+yKRheFe0QF5ehyvY/ft8fcQYl+TKwMKRvFXTv5IPtu8JV131TlXk3c29l8PlC+7Vh9Y1WSmAgzvQbAGsPd1i5uiE5IAC2FSpofkZj9f82OFSrprr0Wzo64Ny7Q5Fw5myRHQ/RfQf40sxNN6goDAmydLN1MvdYsruGtFnpgkhQLAG5ZJol42xIBglkrrLMUTdsRKcly3VvE2as7FqXZL+HDBmiyrb379+PUaNGqUBTGqQZI1MDDOkG0lrSQb0ghrcyk9e91zYiv+OWc38/x61L1pUBhvwy/BJgy1QGeRS0X4bHZHhe7nV8hs/LMcnAg7Hzq+2Gfr+Zapl/fz9l/drAW6ZZSGWBlnwtU0aEVK0YDs7I80K7jjES/MsAhvQdkGkdhtM4jN3u7l7nUHs+Skq3+GeefwWNmj2ut+zowT2q0Z7sq+7xhIcGw8s771w4rYrevrh5Tb9iQbNdEKpW12+adurYQfUz1aL10zB16WEhKjiyrqg/X9emgubrNCN9C6zLaq675Ivn9JYnndeUktv4VDLrAD8jIlRzzgxu+2dd/u45C9HMvTTG0r00bCtXQ8zd7vv5cWjYHInHD8gvb5gLCdSzMjLgWFX/c4BDNc3AonTBNyRznqX0Pmr/cSTfDELabU3jL9dGmn4usSc016DHk83VXHXpeq59HQliXerWQODK32Cqkm8GqnOmnT9v2IMg4fJ1o+es/MvPIubQCSQHBCMtUnPOXOprfo/Fn7oA6zLusHJ0UOvoknUTr96AU81qeNQkXNbMq3eoWglxJ3P722iv14SL19TdBpKDwtQ6umTwSRpFyjqmZOO2UPx75I7eMrk/ffPG7ioI1/1I6F3RXt3WLj/+1ZxQvqwd9hyIREBQbtVMjarOau69lpVVKUwaVQttHiuLH38LxPylpj2XPDkwSGXk7XQSb8LOV/O13N/ekDS4LN2uLeJOnkRqcAjSozRJCadaNdW/iTr9lVybNUWtOV+qKQBn+vZ/pOfea2Vnl4zPoObq/z4HXwItmQOuJR/QZd64bnmwBOESFMlAgTwkey5znAuTnZYP+3L/cJmDbWzustwCTMqPpZxempBJubXu60qQKfPtpVy/ME6dOqX2TUqgtc345Gu5VZxMK9AGlrrHrFtS/l/IcWhJFlamG2gb3BUUnMlxyz7I/ulmi6XsvLDHbYw0+pNBDtkPQzIPXnoZyH3jtZ3ntU0BteT5//L+wvC95T3kPY3dplGmNMj3R/ooFJZcH3v37tWbf38vUiEg0xB0mzfKoJYcr1QtCPlXqld0rxO5hmTATKYm5EeqT6TaRXoRSM8E3R4GD0quCxmwkfNTEniULouq1WvpPaR7fnJykgryteJio3Hh3Em9zvqG6jdqgZCgmwgKyP1jLP+XefaG2125dBYeZcqhbDnTv6+7lPNK9t25hX73Z+eWTyAzMQHJV/I225Ku7sKhVj295fY16+g1mzNXcs5SLp2FY9PH9JY7Nn1cnbPU6/n/PbKrqvkdl3Il/1veWTg6waaCF1IuF66JpqmQe4lH7T2KCp30q9gqdnoe6TFxiDlyOu82aemoM3cCfAd0y1kmgXvlIW+q5nzxZzTnumKX51F/4VTVWE/Lu28X1TwufON//933MM9Z9IFjKP9SO73lEsBLli/2+Bmj56zWzPHw7t1V75zJXHtpzhd//rIaKEmLioF7S/3KNsn2O1atjORbJlou/R8HoJKuB6oBJV0VOj2ngv/kW5o+RJF//4ty7duou0LkrNP5eTUQE/nPQZiSO1FpuHQ1Qe9x+EQ0HB2s0KJxbpWem4s1GtRxU8/lp3E9N0wcWRNOjrnz7Zs2dEeVSo7Yc1BzSzwxfoS/GkSYu+iqyQf3Qm4jG3v8BEq31W+2W6ZdW1VhE3827+ferPR0VB07BhW6dM5daGkJzx7dVXO+xCtXc26nJ5n71PBwnO7Vm8E9FYv7zuDfi3Rsl0ZgEghK8C7zhiW7qyUN9qSDt5T5y63VhHRyl7Luws6VlnJ5CcLktaRxmzR+k3Jjmd8vjd1kzrJkPqXLupTYS/ZT5npLICYN5STgN1ZBYIyUaUvjPQkUJcCVQFma7UkJvARIUkYu7yWBv3Qsv3Xrljr+/wfpTSDVChLAShZX5nrLPuiWdRsL2qVRndy/XfZHqg0kmJs/f75qYijNDB+UDKzI3HqZGy/nXaYryNxymfMvxzxmzBjVq0A8/fTTqt+BDETIdbB582a1bX5N/ApLKgikYaLc1UD+L98bCX7zI6X2Miggt1PUJd8nuUZ1STm7BNzyvb7frvvSAFEqR+RYpaeCXA9ybuScCWkyKMcu3xO59uVOCTJdQbYrTCWFDDjIVBE5VhksKEwvhPzIQINuo76SSLra167XGN98OQlv9BkCZxdX/LJmCRwdndCufSe94D09PQ1+dwOuVk+0xca1KzBz0gfo3ltTev/TigXwqVwVLZ/Q/8MdeOs6vH3yrwYwNZG/rIHvxJnwGjkBMTu3wsG/Nkq/0lXNq89OS1Wl5zbelZAeHqK64ccfPYDkyxfgOXwMbv+8UgX89tVrokyXnog/vB8p1+494Grqojf+hIpjpqH80I8Qv2c7bKvXglv7zohau1yds1J29rDx8kV6RKheKb6cx6y0NGREaJpoGmNzt9IkLcQ07wldkKvTF6DFtmVo/ONc1RjPvVUjVBnZHxfHfanuAS2lz061q6lgS3XQz8rCrYVr4DesN1KCw5Bw6QYqD+4J98ca41iXITkpxlvf/wSf/t3QYOkMBC77BS4NaqLmtJEI+Xkzovaa9jSH619+h6brF6PB0tmq871b80aaEvopc9Q5kyZ6Tv5VkXQjEOnSQV+SI0t/QqVBvZASEq4y8r79e8CtRSOc7DUspz30tZnz1UCAlBCH/b4NNh7u8BsxUFWn3Jy/HOYuz7Umg7fT5qPBkhlIj4pB+B87Ub5jW3h2a4/jb4zI2e7aF4vh+XoHNNu0GDe+WgbHGpXhP/UDBC5ei5RA0x/cPHUuFsdPx+CTkTXx7fLriItLV2X8CYkZ2LAlRO+2eNbWFrhyXZN82PZPON58zRdTxtTGmvVBKF/WFu/1r6punSdl+KJ1i9J49qny2HswEucvxaGOv371rWT60zNMb+Jz4KLFqPvdAtScNVN1vXdu2ABevd/Czbnz1Hx6aaLnUKUKkoMCkREdo35GQ9eug2fPHkgLj0DSzZvw7N4NLg0b4Pz7I3N+r1Wf9IkatAz4diFsK1RUD6306Gik6NyF61Fiis0YH+kAXwKWgIAAFQBK4CIBTocOHXLKxqXEXoJsKTWWTu/S7V4CdMkAF3Y+sGRr5TWkuZp0f5du7BKcSQM26VYvc+yFvLasIwMIUjouWXApM5dBBxmIKAwpuZZb0En3fAkmpYJAphjI+8r/ZQBAAs4vvvhCTRmQbKwEulLS/1/J+ZF9l+OT28LJsWk7r8s+aG+vJ+9vONVBzo904ZeqAyFTCmQARHfe//2S45WBAnlt6cwu30MpEZdgWM6PNNHTkuBVHuPHj1cDPDJ4I+fxfnsXGOratauqTpCAXa4XGbyRYD8/EljLXQiMNYuUhy6p+pDXf+KJJ+57v2TgRapFJIiXwSa5zmTQQ3tNy0CQVJfIoIesKwG6DFBpB7kKQ86lNGOUUn3D7/n9kEoDwwGPkuj9cdOxavHXWLNsPrKys+Bfqz6Gj5kKJ6fcXgtLF3yB2xGhmLdE08TM2toG46bOxYpFX2Hx/M/V7wDJ3PcaMByWlvq/7mJjouBbOXdKhalLOnsSwbOmoEz3t+A9ZhIyou4gYuUiRP2hudWkXZVqqDTlS4R8Mwux/2xXH04CpoxF2Tf6osxrb8LSyVll7SN/XY07f/yKR0HyhdMInzcd7p16osLwCciIvoM7Py1F7FbN7wwpw/caNwMR389B/L7cDLKlq/s95+hbumruQZ6VaHzupim7s+sgjnV7DzUmDkOTX+cjNTgcF8Z8rgIl4dKoDlrt+AGn+o9F0N3S+suT56nmZVU/HAhrD1fEnbqIIy+/rTKpWgnnruDoK4PgP20kmm1YiNSwSFz9bCGuzvgOpi5q7yGc7DMC1cYMQaMf5iElNByXJn6BW9+uyCm9b/77cpwZOh4hP25Qy67OmK/Omd+w/rB2c0X82Ys4/vpg3Nm1P+d1AxavUVUAld/tA683OiEtKlpVC5x8a5gq7Td3xq41+VemelR5vx+8+3RRGf2TfUYjdN2fOdslXrqOQy/2Q62Zo9H456/V4MCNuctxedLXMBfjp5/D0AFV1Tx5SbacuRCLCTPP693jfuTg6uq2eF0HaKrlomLS8cEnp9V20z+qrdbd8ncYFq2+mdM/s81jZXJuiycPQ6/1P4iwiIKbB5dEsYeP4OLIUfAd/A5qfTUbaRERuDnnKwSvXJVTel9vySJcnjAxp/t9wIKFqnGjV9/e6rZ3CZcu4dzQYYg5oKkCsfXyglOtWur/tWbn7YcRvvF3XPkk/yQV0YMqlZ3fhG16aCS7K8GyDHroNtqj+yfBtjSGlH4Jkll/1Mm0Dxn0kWoKaZz4II5f1p/rR/dm/1H3h70LJsnGsfB9Qkjj4o+aslC6P1Yu//d8h9nLiCu4WRsZ99kL3z/sXTA5MwLef9i7YJJanzLeN6skWL6r+N6rTxs8cv7vc/CJShIJYqWqRKo2COo8yBSLBw3uiYiIiIio5GKAT2bv7bffxo0bN3D6dN7mT48S6UUg52HQoEEPe1eIiIiI6BF1t41IsTweRaxJK4GkqZ72XvL030kvCLnrwqNO7sDA80BEREREZL4Y4BMREREREVGxeFQz68WFJfpEREREREREZoAZfCIiIiIiIioWWczgFylm8ImIiIiIiIjMADP4REREREREVCw4B79oMYNPREREREREZAaYwSciIiIiIqJikZX1sPfAvDGDT0RERERERGQGGOATERERERERmQGW6BMREREREVGxYJO9osUMPhEREREREZEZYAafiIiIiIiIigUz+EWLGXwiIiIiIiIiM8AMPhERERERERWLLGbwixQz+ERERERERERmgBl8IiIiIiIiKhbZxToJvxQeNczgExEREREREZkBZvCJiIiIiIioWLCLftFiBp+IiIiIiIjIDDCDT0RERERERMUiK+th74F5YwafiIiIiIiIyAwwg09ERERERETFgnPwixYz+ERERERERERmgBl8IiIiIiIiKhZZzOAXKWbwiYiIiIiIiMwAM/hEdF/Klgp/2LtgckZWXvSwd8Ek2djZPOxdMDk3X7jysHfBJFnZ8lq7XxmpaQ97F0zSR1vffti7YHLGvbTkYe+CSdqDkotz8IsWM/hEREREREREZoABPhEREREREZEZYIk+ERERERERFYvsYu2yVwqPGmbwiYiIiIiIiMwAM/hERERERERULHibvKLFDD4RERERERGRGWAGn4iIiIiIiIoFb5NXtJjBJyIiIiIiIjIDzOATERERERFRscjiJPwixQw+ERERERERkRlgBp+IiIiIiIiKBefgFy1m8ImIiIiIiIjuQ2pqKiZPnoxWrVqhUaNGGDlyJKKiogrcZsGCBfD398/z0LV69Wq0bdsW9evXxxtvvIHz58/fz24xwCciIiIiIqLiy+AX16MoTZo0Cfv27cO8efOwYsUKXL9+HcOGDStwm0uXLuGVV15R2+k+tH777Td8/vnnGD58ONavXw9vb2/07dv3ngMHuhjgExERERERERVSeHg4NmzYgI8//hhNmzZV2fbZs2fjyJEjOHHiRL7bXb58GbVr10bZsmX1HloLFy7Em2++iY4dO6JatWqYPn067O3tsW7dusLuGgN8IiIiIiIiKh5Z2dnF9igqx44dU/+2bNkyZ5mfnx/Kly+vgnxj0tLScPPmTVSpUsXo83fu3FHPS8m/lpWVlRpAyO81jWGTPSIiIiIiIjI7bdu2LfD5HTt2PHAG393dHba2tnrLy5Urh7CwMKPbXL16FZmZmdi2bRumTZum5vA3a9YMo0aN0tuuYsWKeV7z4sWLhd43BvhERERERERULLKzUOIFBQUVODggc+RtbGzyLJeAXwL3/MrzhZTcz507V2Xspaz/rbfeUuX+ycnJ6nnD1y3oNY1hgE9ERERERERmZ8cDZuil1H7Lli35Pr97925Vcm9IAnEJ4I159dVX8eSTT8LDwyNnWfXq1dWynTt3wtfXVy0zfN2CXtMYBvhERERERERULLKLur39/4G1tTWqVq1aYDf8mJgYFYzrZtwjIiLU4EB+dIN7bfm9m5ubKs9v0aJFzmvovve9XtMQm+wRERERERERFVKTJk2QlZWV02xP3LhxQ83Nl3n1xsyZMwfPP/+83gCHTAWIjo5WHfNLly6tGvUdOnQo5/mMjAwcPXo039c0hgE+ERERERERFYusrOJ7FBXJqHfo0EHdJk8C8tOnT+ODDz5A8+bN0bBhQ7WOZPdv376dU3L/7LPPIjg4GJMmTVKDAdIZ/7333kPjxo3xxBNPqHX69euHZcuW4bffflNN+caNG4eUlBS89tprhd43BvhERERERERE92Hq1KnqlnZDhw5F//791e3vvv7665znT5w4gdatW6t/Rd26dbFo0SJV3t+5c2e1Xa1atbBw4UKUKlVKrdOtWzcMGzYMX331Fbp06aIGBCTgNyztL0ipbFOYBEFEJUbglfMPexdMzsiFDg97F0ySjV3e7rRUsJunrzzsXTBJVra81u5XRmre5lJ0bx9tffth74LJmfnSkoe9CyZpz2+tUVJNXJlebO81+S1rPGrYZI+IiIiIiIiKBfPLRYsl+kRERERERERmgBl8IiIiIiIiKhZZTOAXKZML8Hv16oXDhw8bfU66Do4ZM+b/9j5eXl6YMWNGvuvIbQtWr16NjRs3qk6Itra2qF27Nt5++220bNkS5i49PV0df58+fdTX8+bNUx0fd+7cWeTvLe+zbt06XL58WX1dvXp1tR9y64miVJjrIr9rpXv37qprpjTYeBjOnz+PTz75BD/99BOsrEr2j/7R4yex9IfVuBUQAHc3N3Ts8CK6dnolpwGJMTt378Xqn9chNCwcFcqVRfeunfFc22f01vn3wCGs+mktAoND4OHuhnZPt0GPrp3VvU7Nha1NKbz5kjta1HOEnW0pnL+eghUboxB6OyPfbb4Z741yHsaviYg76Rg6PTjn65fbuKBdS2eUdrNCZHQG/twXh23/xsPUz1n3513QrI4d7GxK4eLNNKzeEovQyMx8t5nzYTmUdc/nnEVl4IMvI/Isf7alI9q3dsT7X+R9zhTZ21lgcJ8qeOqxsrC3s8SpczH4evE1BAYnF7hdHX8XDHrLD7X9nZGcnIn9R+5g4cobiI7JnZPpYG+Jvj0q4cmWZeDhboOQsGRs+DMUG/4MgSlXdso5e/vNSniqhYfmnF2Iw/zlNxEYklLgdn6+Dninly9qVXNGekYWjpyKwXc/BCA6NvecuTpbYWBPX7Ro5K7e59K1BCz44Rau3kiCqeO19mDsvMrjyZObcLTLEETtMf7ZWcvz9Q6o9tFgOFTxQfLNYFyd9T2Cf9igt45rk7qoNXO0+jcjLhFBK9fj8pRvkJ1efPOpi+Nae+etyup6kGvt9PlYzFt6A4Eh97rWnDGwZyXUruGM5JRM7D8aje9/uKn3Mypee8kTndtXRFkPG9wKSsbiNbdw8Hh0ER8VPWpK9qf8fLz44osYP358nuX29vbFtg+pqano27cvQkNDVafDRo0aqVsY/Prrr2r5559/jpdffhnmbNOmTfjss89yAnwZYOnZs2eRz9kZMWIEDh48qG4rMWXKFBX0bd++He+//756TgZYSpolS5ao+1s+rOBeyOCT7MPixYvxzjvvoKQ6f/ESPp4yDW2eeBx93uyBs+cvYNGylcjMzESPrl2MbrPn3wP47Is56NTxJTRr3Aj/HjyEz+fMg7WVNZ5+SnPbkWMnTmLS9JnqdQf06YWbAYFYsmIVYuPi8N47A2Euhr9ZFtV9bbFqczSSU7Lw2nNumDi4AkbOCkFisvH7xXyxPALWlvqDJzUq26L3Kx7460Bu8C4DB+2fcMHP22JwNSAVjWvZo3/n0sjIzMaOgwkwVUO6uaOqjzV+2hqH5NRsdHrGGeP6l8GYuRFISjH+Cf+r1VGwstI/Z9V9bPBmB1fsPJw3oGpZzw4927sgOi7/QQNTM/HDWiqA+nb5dSQmZaJfj0qYN60Beg05ivhE4wNKtao7Y95nDXArMAnT5lxEaloWenTywXezGqHv8GPqdcSUMbVRs7ozlq6+iVtBSWjSwB0j3q4GF2crrPg5AKbq4+HVVQDw3Q+3kJicid5dvTFnUh30ef8kEhKNXxvurtaYM7E2Iu6kYsb8q7C1scCgN30xc3xNDP7oLDIzNdfolFH+qORlj+9W38KdqHR0f8UTcyfXQf8PTyMsIhWmjNfa/bPzroDmm5fA2s3lnutW6PQcGq78AjfmrcTtbXtR4ZV2aLh0JrJS0xC6dotax97PGy22LkP0wZM43mMEnGpWhf/U92Ht4YazQybCXHzyvr8aEFqw4iaSkjPRp5sv5k6ti7eGHc/3Z7RWdSfMnVpPXT/Tv76srrXur3hjwYwG6D/yRM611q2jJ955yw/Lfw5QA3Ad2pbH9HG1MXzCGZy5EIdHSTZT+EXKJAN8Ozs7lC1b9qHuw9y5c9UtDiTIrVixYs5yGXhISEjAp59+imeeeQaOjo54VBpkyLEW9fGuWbMGf/31l8re16lTJ2f54MGDVQAot6Z46aWX4OnpiZIiPj4e33//vcqcP2wyCCOVBDIQ4+zsjJJoxeqfUK2KH8aOHKG+bt6kMTIzMvHj2l/RueNLqlLG0NKVq/Bk68fw7sB+6utmTRohPiEBy1f9mBPgb/17J8qVLaNe19LSEk0aNUR0dAx+3fA7Bg/oW+KrGgqjeiVbNK3jgOmLwnHyoibbcOF6CuaP98Zzjznjtx2xRre7GazfDdvethSGv1kGx84nYeM/mg8dkq1+6SkXLFkflRP0n7uaojL5Df3tTTbAr+Zjjca17PD5ijs4fVkTBF26maYy9O1aOuL3XcaP61ZoRp5zJgMFJy6mYNPe3G1cHC3wWjtnPNPcEfFJRXhD3mImwVbrFmXw4aQzOHgsSi07fS4Waxe3QKcOnli51nhg9FY3XyQmZmDYuFM5gdmx0zFYs6AZenbxURmvGlWd0LKJBybMOId//o3MWcfZyQo9O/uYbNBVu4YTHm/mgdHTLuDwiRi17PSFOPw0vzFefb4CVq3PrZTR9Xgzd7i5WuPdcWcQEq65RhMSMzBrQm3U9XfGqfNx8K5ohwa1XfD5t9fw587bap2zl+KxYWlTPP9UWaxYFwRTxWvtPpUqBe9er6LWzDFA/kVvevynfoDQX7biwoefqa8j/9oHa3dX+E8anhPgVx01EBnxiTja+V2Vsb+9dQ8yk1NQd+4EXJ2xECmBoTB1koV/vHlpjJp6DofuZtVPnY/FzwubodOLFfHDL8Z/jnq95oPEpAwVqGsHAY6fjsWqbxqjRydvLF59CzY2Fujd1RdrNwZj5bpAtY68x7cz6qNPNx+MnHyuGI+UzJ1ZNtmTMuqxY8cWuOzatWsYOHCgyrzL/QlHjhyJ27c1fxQLU5oumXq5f6FucK8lWWS5x6EMRIiYmBhMnjwZTz31FOrXr68CrEOHDuWsL6XtkgWXIPDJJ59EvXr18Oabb6p91Nq9e7d6vwYNGqj7LcqxxMZqPqzLa/n7+yMoKPcXj+EyOf6ZM2fiww8/zDnmH3/8EceOHcMrr7yiXlf26+bNm2p92U6237BhgwqYZb/lvoyyvli/fj0++ugj9X9ZT95PjkMGNbSkukHe7/HHH0fDhg3V/SEvXryY87wcgzxkv+SYZB8GDRqE8PDwfM+9BMlt2rTRC+61evfujeXLl6NMmTLqa6mokHtItm3bVp1TOc5t27blrC/H8Oyzz+a8pry/VGPI+2vPk3w/fvnlF733SUxMVNeLrC/nUQYVsrLy/+D+888/o0KFCmoagZacM1n+xhtvqH2TqpTjx4+rZbIvjRs3VteRHIN2X2Vf1q5dq95T9m3IkCF65yo5ORkTJ05EixYt1PYy2CT7qXvd16hRQw1+yPuURGnp6Th95iweb9VCb/kTj7dCUnKyyuYbCguPQFBwCFq31N/mycdbITg0VD0n0tPS1M+kBPdaLi5S7pqhXtscSKCdkpqFU5dyjyc+MQvnr6WgUa3CVzh1ftYNLk6WWLL+Ts6y5vUckJaejX8O65fjf/XDbXy5onC/O0ui+tXt1Dk7cyU3wymBuJTpN6yh+R1eGK+0cYaLkwWW/6E/iNLxKSfUq26rMv4S/JuLFo3dVXbr8AlNwCVi4tJx8myMCpjyU9nHQZW86mZdU1OzcP5SPFo1LZ2zbOPWEBw9pQmCtQKCkuDgYAV3N9OcUtO8oZsqE9c9rti4DJw8H4cWjd3y3U4CAyEZf624BM35kyyz/jq551XKhNPSsnLWMVW81u6PS31/1J0/GUGrNuBkn9H3XN++khec/P0QtvEvveWh67fBsXplOFSrpL4u+2xrRPy5W68cP+zXrShlaYmyz5Xc27Hdj+YNNdfakZPRej+jp87FomXj/K+1St4OKgOvm+GXLP75Kwlo1cRdfV27urMaONpzKPfvqthz8A4a1XPL+Rl+VEiOsLgej6JH62q6S4IiCawqVaqkgreFCxeqrPvrr7+OpKR7z1ULDAxUQbsEUcaUL19eBcQSSEhWWbKmR48exaxZs1SgJkGWBLunT5/O2Uael+BZgnzJUt+5c0cNCoioqCgMHToUXbp0wZYtW/DNN9/gyJEjahrA/fjhhx9Qq1Yt/P777yrolSoDmRM+btw4rFq1ChEREfjyyy/1tpG55lLOLXPeq1Spoo5Fjr99+/ZqO7Fv3z4VcOqS89mjRw91rhcsWKCCaAmuZOAiODg3SyEVEHIu5f1lUOTcuXMqKM9vWoTMuc/vvEtGumnTprCx0dzP+IMPPlADFBMmTFDH3K5dOwwfPhx///13zjYhISHYunWrOu8SqO/YsUNNrZABBBnEkaBazlF0dO4ve5kO4O7urr6Xo0ePxrJly7By5cp8z7u8nwzuGJozZw4GDBigejjIvst5lgEI2ReZ+iDbSaWCllwHK1asUOdH/pUBFNle5vcL6T/x77//qteV8y2VA5s3b87zvjKAIMdZEoWGhamA29tLvwLDy1MzkBYYpAnWdQUEakbCDbfxvDv4FnT3euvYoT2CQ0Kxdv0GJCQkqqkA6zduQvOmTeBSQqsZ7pdXeWuE38nI8wctLDIDnuUK90G1tJulKsP/Y1ccIqNzP6xU9rRRr1Orqh1mvF8Raz6vpObut23pBFPmWdYKEdGZec6ZnMeKZXIHgwpS2tUSzz/miC17E3EnRr+Ec8fhJHw4OwJHz5tPcC8q+TioucqGY5vBocnw9XbIdzsJzCqUyztw4lnRDp4VNMsvX0vArPlXEH83iNV6omUZRMekIcZgTqup8PWyR0hESt5zFpYCX8/8B+B27b+DyKg0DO/vBw83a1QoZ4t3elVSy46d1gwoXb+VhGNnYtH7NR/4+dirQOLd3pVgZ2uBnXcz06aK19r9SQ4Ixa6az+LCqBnITLr37x0ptReJVzQJHq2ka7c0z9fwg4WdLRwqeyPxyg29ddIio5EeG6/WMQeVvO0RGm78Z9THK/+f0di4dJQvm/da8ypvB8/ydjmvLQzn8geHpsDKshS87l6TRI9sgP/HH3+ogFL3IYFOYUnmWjKqH3/8MapWrarmRUvQJEG1BHv3os2cu7q63nNdCX4laJXAuXnz5moOtATuks2VedlaEqRJwF6zZk2V0ZVsumR0hQTJaWlpKvMqDd6aNGmiBiUkK38/JLiXgQUfHx8VaMt7ymtIxlebRdY2rdOS+eySwZfzNHXqVBXYShZZgnVtibdMl9AG1VoSUEtQLFMZZLBDjkvOgWwnjfm05DVkHr28vpwfGTjQHvd/Oe9S/SBBrGS0JaD18/NTc/ZlYEPOne55lwEAGXSRIFz2UwYypI+C9l+p2NBWNmjnsmuvnY4dO+Ktt97C0qVLje6HZPbPnDmjXt+QDNhIxYO8j1QXyPFJEzxZV5oFyvfrypUrOevLfki1gwxiyDmVASP5fh04cEANusjggBzvY489pl5DntdWM+iSa0/2qaCqg4clMVEzwObgoP+hzeFuf42k5KT73iYxSfPHtFGDeujW5VV8v3QFXu3+JoZ9OBZubq4YN+p9mAsHOwskp+b9vsoyB9vC/brv8KQL0jOysWWv/nxAZycLeLhaYtgbZbHzUAKmfR+O05eSMahrGZMO8h3sSqleBYZkLr59Ic/ZC485IiMjG1v35y3nD43MQGbJ+1H7z5wcrFSmy5Asc7TPf2Bk899h8K/mjGEDqqK0h40KWAf39kNlH2kKmf92XV/2QuP6blj1S6DJZmTUObs7F1eXZPWl0Vt+omLSMfv763isqTvWL26Kn75tjKqVHDFm2gW9QF+YLgAAji5JREFU78Gc76/Dzs4Cy+Y0xB/Lm6FL+4qYteA6zl0yzekzWrzW7k96dCxSgvOvhDRk5ar5/Z0Rp3+dSDm+et7FCdauzkbX0a4n65gDRwcrvSoYraTkjIKvtR3h8K/qhPf6+aG0u+Zae6dXZTU4pb3WnBw1lTRJSRl5Xlu9dwGvb46ysrKL7fEoMsm6LQmKpIRal7YcvrDdxCVwMsw6S4ZYtyw+Px4emjIdyTzfiwRgEsTqBnjSFE6CNAn+tSQQ0w1cZRsJ6IQEehJkS4ZXgmkpeZegVcrL74evr2+ehoQS7OueQ+17aknwryWdxmUwxHAQIL/jrly5cs650r6+BKa628s+6XYw1z1uQ25uburc6WbT8yP9EYQMhuhq1qwZZs+ene95kSBRd9qFdr63DLBoGb6mHJMMGsTFxcHFRb+ZjVwjMohQunRuOaCWVJAYfj9090XOl+77Sn8D3SZ9MsAg14ycT23lie41Lfsu+2ZIvidyjmXfdL8/JbGvgyFjXfSz7rGNxd1t5s5fqObh93y9Kxo1qI/wiAisXPMTPvpkCmZNmwI7u7xz+0syOSzD01HATQYKdUsaa6tSeKa5M/45FJ+nIZ9kGKRsXxryHT6TlDMHv4y7Fbo+52YSc/CL5pwBTzV1wK5jSfk25DN1co4sDM+bxYOdt03bw+Bob4X+PSuj2yve6sPXrv238fu2EHRoW8HoNp07eOK9AVWxY28Eft4YZJbnrKBfY21bl8G496ph98E72LIzAjbWFni9oydmTaiFEZ+cQ0BIiqoO+ObTugi7nYJPZl1STb3aPFYaowZXQUpaJnYfyC1vL8l4rRW/UhYFD2RmSzLgnuuY3u8+Y9daQYdZ0CFu/jscjg6W6Ne9khogUtfagUj8sT0M7duWy3m/gpjgKaQSzCQDfAl0dIOjwtCWMQvJXMpt7CTbaagwjcckKJaAXDLNknE2JIME06ZNU3PU8wtYZLluUy/DDLghyX7LnOs9e/Zg//79GDVqlAo0pVTbGJkaYMjYrcAs7vFL27DxmLzuvbYR+R23nPv7OW5dsq4EuPll+CXAlqkM8ihovwyPyfC83Ov4DJ+XY5LA09j51QakxrLlxpq6FfTexl5fvh8yFUQ7r7wwWXntOgXdcu5hcbybhU++m3XXSrw7gGGsiaOjo2Ybw3n02kEPeT4y8g42b/sLPbp1Qd9eb+SsU7NGdfR/dxi2/rUDr76c92e5JHvtWTd0fV5/3u6BU4lwdbY2mtlPMpKlNlTf3w4O9hbYe1yTudEl89TlQ8uJC/rnWZr5NaxpD1cnC8QmlOxUdaenndG5rf7v+ENnktXAhSFpmmesGsJQvWq26vzuP2UefRyM6du9Evq9UVlv2T/7bsPDLe/vbwcHS9XYrCASOP2yKRheFe0QF5ehSqk/ft8/Z165lvyKGtK3Crp38sH2XeGqE7qpkO740jhL164Dd1RHfGPnLMEgq6erTzdvnLscjylzciu6jp6OxcqvGqB/D19M/PIyur5UUQUnH065kHMepWTfydESIwZUMZkAn9da8cuI1fRVsXLW//uqzcpnxCbkZO4N19Gup30NUyLd8ft2z02qiH/2R8Ld1ci1Zm+lmugVZO3vIfh1c6gqtY+NT1dz98cNq4G4eM122k769vaWenP1pWpA83zBr29u7pXQoUcwwL8XCYRkDrhuQCMlzNpBASlRlrnskqnVBpiSzZQ5zFKSfa972EsQ9tprr6k57VLybthoT25DJiXQUk4vzdRkLrRkWbVZfLmoZb69lOsXxqlTp9RcapnzLuXc0pBPSuAlyJdpBdrAT/eYdUvK/ws5DjkGIdlkmW4gzfbuFSBqG/TJ/mmz11IhcfbsWbz66qsPvD/y3jInXvbDsNGezIOXXgbe3t457ynn+emnn85ZR54v7HnPj7y3LnkPeU9jt2mUKQ3y/ZH58/+VXKNyHWurLqQKRb7nMmVArkH5fpw8eVL1DdD9fkkDQ13yPZHrXvatpPGsWEH9fElzPF0hIZqvfb2982zj4+WlWSc0FNWrVslZHhwaptnGxwcRt2+rn7u6tWrqbVvJ10c12rsZYHqdkv8+GK+63OtqVtcBDfzt1YdV3b+d5ctYITj83nNJm9RyQPiddFwP0u+qry01t7AoBStLIF3nc4i2Z6E04Cvpdh5JxIlL+nNSm9SyQ/3qtnnPWWkrhETc+wNXw5p26r73N4JNb65uYW3cFop/j+g3hpJ7RDdv7J7nvHlXtFe3isqPfzUnNVd1z4FIBATlDorUqOqs5kNryS0IJ42qhTaPlcWPvwVi/tLrMCV//BWOA8f0q81aN/NAswauec6ZBAQBBdzPvXxZW+w9rP83RJrnXbqWiMo+9jnrBIQk5wlcT5+Px9OPlVEDC4b34y6JeK0Vv4TLmnn1DlUrIe5kbiNbx6qaz8wJF68hMzEJyUFhah1dNmU9YO3ipNYxNb9vD8P+o/o/V0+08FDNMPNea3bqnvX5kfJ8+RmUhnm6P8s1qjri8nXNgHlAcFLOdXvxaoLez39aehZCwsyrTws9XCY5B/9epGO7NBuTbPetW7fU3HHJ7mpJgz0JuqXMX7q6y0PuoZ7fXGljpFxeStDltSSQDQgIUE3zJGsvX8t7Srm3dDyXEnvpZn748GGV3Zc55xLwS9f3wnByclKN92ROtRyPbCsDFPL+EqTJPst7SXM22Y+9e/eqxm//D9KbYNeuXbh69aoaYJBO7doAXzvnWYJ2bbd3LWlUJyX10glezoucYznfklWVZoYPSgZWnnjiCTUQI3P5ZSBDXlv6F8yfP181vZNeBVK+LoG99DuQ/b9x44ZqTijz8qVR4H8hFQTyvZDvpTTBk+/Nu+++m+/6UiZvOCjwoGRQR863BPJyrFKSL9MOJOiXHgpy3cmcfPl+SRf9sLCwPAMxMkXFWOl+SSADD/Xr1sa+/Qf1Rnf37j+oMvGScTckDfgqlC+PPf8e0Fu+d/+Bu8+Vg6enZLcscOacfhf+wKBgxMXFo2KF8jA1cj91CcR1H6cvJ6tssgT5Ws6OFqhdxU49V5jb7F26Yfx+2drM/WON9DM4clu+myFpas56SRcTn6UCcd3HmaupsLezUJ3utZwdLFCzso167l6q+djg8q28AyLm5E5UGi5dTdB7HD4RrTJPLXQ6S7u5WKNBHTf1XH4a13PDxJE1VWZZq2lDd1Sp5Ig9B3ObwY0f4a8Cu7mLrppkwHUnOl0F4LqPI6di1Dlr1jC38sbVxQoNarngyCnjt7AUEjDU89evPLGxLoXqVRxzbpsn61T2dlDN9XTVremsbqkXF1/yg3vBa634JV0LQNL1QFTs/Lze8gqdnlPBf/ItTaPayL//Rbn2bWBhk1uFUqHz88jKyEDkPwdhau5Ep6l70es+jpzU/Iw2b+Su9zNav7arXmd9Q43qumLCiBpwctC51hq4oYqvI/Yd1gxYnb0Yr/pGtGml3xvpyZalVZd+6X3zKMnOKr7Ho8gsM/gSwEmgKx3TJWCQoLBDhw45AYMEQ9K1XcrepdO7lDdLZ3bJABd2TrJka+U1pLmadH+XbuwyZ1qyqZLZlzn2Ql5b1pHmaFI6LllVKTOX27nJQERhSLAqt6CTAFWCSQlUpMpA3lf+LwMAEnB+8cUXasqANIqTagQp6f+v5PzIvsvxyW3h5NjKldPMJ5J90N5eT97fcKqDnB/pwi8VB0KmFEiDQ915//dLjlcCeXltCa7leyil7lKVIedHmuhpyVx7eUigKwM8MhAi5/F+excY6tq1qxpY6NSpk7peZPBGbmGYH+neL3ch+H+QgRNpfCjXkfSikGPTBvAS3MudEaSZoFzrsq4MABiW9sstDWXfSyqZIz/640mYOmMWXni2Lc5duKQ63w/o3UvNk5dy/VsBgSrb73a3b0WvHt0w66t5qht+qxbNsP/gYeze+y/Gjx6pnpf1urzysnod0aRRA4RH3MYPP/6M8uXKosPzz8EcXLieirNXkzGsZxms2hStbpEnZfwyn377/ni9bvsy3/5mcG5gKpeRLP/3RN7yfCG32jt6Lgm9O3rAzsYCAWFpeKqJE/wr22LWsgiYKrnn/fnrqXi3qzt+2haHhKQsdHrGWc2n//tQol63fZlvfys0Q++cyfIDZlyenx/5QHr8dAw+GVkT3y6/jri4dFVaLcHkhi0hercqs7a2wJXrmozVtn/C8eZrvpgypjbWrA9SWa/3+ldVtzOT0mjRukVpPPtUeew9GInzl+LUval1SfbVFD8Mn74QjxNnY/HxsOr4btUtxMZnqPJ7Kc/fuE1TcaTttm1tXQpXb2gyfkt/CsSno/0xaWQNbNkRoZ6TkvwyHjb49CtN2f66P0Lw7JNlMHtibaxaH6TKgZ9s4aHm73+z/KZJN3rktfb/JWX2TrWrqcBeuuCLK9Pmo8GSGUiPikH4HztRvmNbeHZrj+NvjMjZ7toXi+H5egc027QYN75aBscaleE/9QMELl6LlED9qjtTdep8HI6fiVHB+oKVN1V5fd/XfTXX2tZQvZ9R6YVx5Ybmb8T23RHo2cUbk0fVxI8bglG+jC2G9PXD6Qux6jntbfN+3hiM3t18kJ6RhbOX4tG+bXmV/R824cxDO2YyT6WyOQmCjAgKClLBsgx66DbaowcrrZdgXPolyN0KHoTckk+qQ7TNAw3J9Aep3JBBFxnw0ZJu/NLpXzvYI1UqMuAilQxSYfEgAq+cR1GTDP6KNT8hKChYTbd4pcOL6Nr5FfXcydNn8eG4CRg14j083+6ZnG02/bkN69ZvRERkpMrI9+jaBc8+0ybneflVt/73TWq9sLBweHi4o0mjhuj3Vs+cgYKiMnJh/rdy+n9ztLfAWx3dVbm+NBi8eDMFKzZGIfR2bmA6cXAFlPWwwtBpuU2k5B7uiyf7YtEvd/DXAePzKWVQ4LXnXPFEEye4OFoiKDwNv/4ViyNn73170QdhY1f4Hh3/tZP+m+1d0aS2nQraJSO/ekssQiNz50mO718aZdwt8f4XuYMZLo4W+HZcBSzdGIOdh+99Dt7u4oZafjZ6r/H/dvN07jztoubsaIWhA6qqDJQMNJ65EIuvF19DoE6J6rzpDdStyroOOJSzTD7Qynb+VZzUPcp374/EotU3VTd5IXOkX3jGeBM08Vr/gwiLuHd1xf2wsi2ea02yyUN6V0br5h7qWpMP+fOX30RgSG4V3FeTa6NCWVt0f/dEzjIpG+71mhdq+DkhKSVTlfguWhOgbo+n5eNph4E9fdG4rmYagJQU/7gxBHsPFc38+4zU4qtcMadr7aOtb6O4eDzZHK12/IADbXshas9hvWWn+o9F0Mrc5IPvwNdR5f1+sPOpqDL61z7/HsGrN+q9nvvjTVBr5mi4NKilBgfk+cuTvka2Tp+rojDzpdy7ThXHz+jQvlXwRAu51iTzHod5S2/o3d5u7tR66naVrw86mrOsRhVHDO1XRf0rc+x3H4jE4jUBSE7J/Tsir9eriw9efq68qkK5GZSMxWtu4dDxezePfhB7fmuNkurDBUXzucGYLwYX32ewkoIBPhnFAP//S6oLZJqAVBwURYAvP8Yy915uNSjTBaRy5JdfflGDCjJlRKpAhFQbyC0DC2pEWBICfHNTnAG+OSmuAN+cFGeAb06KK8A3J8UZ4JuT4gzwzUVxBvjmhAH+oxvgm+UcfKKSRsrqJcCXfgRFQTIa0oNBbiEoPQ6kBP/EiRNqeog2uJc+ALIPgwYNKpJ9ICIiIiK6F0lMFdfjUWSWc/Dpv5Ou8Plli+n+SS8IycI/KJnjX9A8fyHNHCWgz4/cdeC/7AMREREREZVszOATERERERERmQFm8ImIiIiIiKhYZGU9mqXzxYUZfCIiIiIiIiIzwAw+ERERERERFYtHtPddsWEGn4iIiIiIiMgMMINPRERERERExSKbc/CLFDP4RERERERERGaAGXwiIiIiIiIqFlmchF+kmMEnIiIiIiIiMgPM4BMREREREVGx4Bz8osUMPhEREREREZEZYAafiIiIiIiIigUz+EWLGXwiIiIiIiIiM8AMPhERERERERULJvCLFjP4RERERERERGaAGXwiIiIiIiIqFpyDX7SYwSciIiIiIiIyA8zgExERERERUbHIzmYGvygxg09ERERERERkBpjBJyIiIiIiomKRxTn4RYoZfCIiIiIiIiIzwACfiIiIiIiIyAywRJ+IiIiIiIiKBZvsFS1m8ImIiIiIiIjMADP4REREREREVCyy2WSvSDGDT0RERERERGQGmMEnovsSjTIPexdMzsSr3R/2LpgkSxv+ibpfkQGRD3sXTJK1g/XD3gWTk56U/rB3wSSNe2nJw94FkzNmU/+HvQsm6hJKKmbwixYz+ERERERERERmgOkRIiIiIiIiKhZZ7KJfpJjBJyIiIiIiIjIDzOATERERERFRseAc/KLFDD4RERERERGRGWAGn4iIiIiIiIpFNufgFylm8ImIiIiIiIjMADP4REREREREVCyyOAe/SDGDT0RERERERGQGmMEnIiIiIiKiYsEu+kWLGXwiIiIiIiIiM8AMPhERERERERULdtEvWszgExEREREREZkBZvCJiIiIiIiI7kNqaipmzJiBrVu3IiUlBc888wzGjx8PDw8Po+uPHTsWv/32m9Hn3nvvPQwdOlT9/7nnnsOtW7f0nu/UqZN6r8JggE9ERERERETFIjsrC+Zg0qRJOHr0KObNmwcbGxtMnDgRw4YNw6pVq4yuL8H/yJEj9ZZ99tlnOHz4MLp27aq+TkpKQmBgIL777jvUqVMnZz07O7tC7xcDfCIiIiIiIqJCCg8Px4YNG7Bw4UI0bdpULZs9ezZeeOEFnDhxAo0aNcqzjbOzs3po7dy5E1u2bMGKFStQvnx5tezq1avIyspS27u6uuJBcA4+ERERERERFYusrOxiexSVY8eOqX9btmyZs8zPz08F6keOHClUef+0adPQpUsXtGjRImf5pUuXUKZMmQcO7gUz+ERERERERGR22rZtW+DzO3bseOAMvru7O2xtbfWWlytXDmFhYffcft26dYiMjMSIESP0lkuA7+DgoEr9jx8/rt5DBgHeeustWFgULjfPAJ+IiIiIiIiKhSncJi8oKKjAwYHhw4erefeGJOCX7HxBpARfyvJl3n3ZsmX1nrty5Qri4uLw/PPPY8iQIapSYNasWYiNjVXvWRgM8ImIiIiIiMjs7HjADL2U2sv8+Pzs3r0baWlpeZZLcG9vb1/ga0tmPiAgAD169Mjz3KJFi9RraOfq+/v7IyEhAQsWLFCd9guTxWeAT0RERERERMUiuwjnxv+/WFtbo2rVqvk+L6X0MTExKsjXzeRHRETkNMzLz19//YXatWsbfX15LcPKgBo1aqju+pLFl5L9e2GTPSIiIiIiIqJCatKkiSq11zbbEzdu3FBz85s1a1bgttKEr1WrVkanLrRr1w7ffPON3vIzZ86oUv7CBPeCAT4REREREREVWwa/uB5FRbL0HTp0wMcff4xDhw7h9OnT+OCDD9C8eXM0bNhQrSPZ/du3b+uV8mdmZuLy5cuoWbNmntcsVaoUnn32WSxZskRND5Ay/p9//hmLFy9WTfcKiyX6RERERERERPdh6tSpmD59OoYOHaq+fvLJJ1XAr3XixAnV/X7lypU5t8KTsv709HS4ubkZfc2RI0fCyckJs2fPVt34vb29MX78eHTr1q3Q+1Uq2xTaGBJRiXH6SsTD3gWTY/lh94e9CybJ0oZj0Pcr8nLkw94Fk2TtYP2wd8HkpCelP+xdMEnjqnz9sHfB5IzZ1P9h74JJ6pB+CSVV52FXi+291n9dDY8afnoiohIpOTkJq5cvxMF/dyMlJRm16zRA74Hvwcvbt8DtYqKjsGLxNzh5/JAqg2rctCV6DxgKd48yOevs2PYHFs77PM+2z3fojAGD34epcmzYFOXf7Adb38rIiIlG1JaNuLNhbf4bWFigTKfX4dbuRVh7lEZqSDAif12DuH279FZzeewplOn8Omy8fZGVmICEU8cRvmIRMmOjYeoc6jdB2R59YOtTCRmxMYjZ+jui/vgl/w0sLODRsSvcnnkBVu6lkRYWjDu//YT4/bv1VrOrUh3l3nobdlVrIFMa4+zajsh1PwAZGTAHbq1aotLQIXCoWgXpUVEI/Wktglf+kO/6payt4Tt4EMp1aA8rNzck37iBoOUrELl1u956rs2bwXfQ23CsUR1ZaWmIO3kKN+fMRUpQEEyda/MW8B70DuyraM5Z+K+/IGzN6vw3sLRExTd6otzLHWFdpgxSAgMRsnIFonb8rbdamfYd1Hp23t5Ii4xE5JbNCF6xXOpAYQ54rT0YezsLvPNWZTzZsgzs7Sxx+nws5i29gcCQ5AK3q+PvjIE9K6F2DWckp2Ri/9FofP/DTUTH6g/ovPaSJzq3r4iyHja4FZSMxWtu4eBx0/+bIOy8yuPJk5twtMsQRO05XOC6nq93QLWPBsOhig+Sbwbj6qzvEfzDBr11XJvURa2Zo9W/GXGJCFq5HpenfIPsdA6SUdHgHHwTlJGRoe6d2LlzZzRq1AgtW7ZEv379cPDgwTzrSoCzZs0avPbaa2rdpk2bonv37vjll1/y3IMyNDRUzR15/PHHVXOI/v37q3sx5kfmm8itG/J7TJky5YGPsVevXhg7diz+H5555hnMmzcPxe3TTz/F8uXL72sbKeV58cUX1f/lHK5fv/7/tj9SQnS/+/MwzZ01BQf2/YOefQbhvQ/GI+rObUweNwwJCfH5bpOZmYHpkz7Elcvn8faQDzHw3ZG4eOEMpk4YqX5utG5cvwpPb19M+2Kh3qNjZ9PNtNvXqAXfj6chNSgQgZ9NROzuHSjf+22U6ZL3Fixa5Xr0Qbme/RC7628ETPsYSRfOwmfUJ3Bu9UTOOi5PPA2fMRORfO0yAmdMQviqJXCs1wiVP/1SfZA2ZXbVa8LnoylICwlE8BdTELd3J8q+OQAer76e7zZlur2lBgRi9+xA0MxPkHzxLLzeHw/nFq1z1rEuVwE+n8xUQUPw7E8R/ccv8HipC8r3fRfmwLlePdSeNxfJN2/iwgejELH5T1R+fzi8+/XJd5uan38Gr7d6IWLzFpwfNgK3t21H9UkTUbFH7rl2btgAdRfMR3pMDC59NB7XZnwOe19f1F+xVAVqpsypTl3U+OJLpNy6hSsfjcGdbVvhO2QoKvZ6K99tvPsPgM+gdxC59U9cHv0h4k+fQvVPp8G9zdM565Tv9jqqTvgEybdu4vLYMQhesghlX3oJ1adOgzngtfbgPnnfH20eK4PvfriJ6V9fRhkPW8ydWhdOjpb5blOruhPmTq0HZycrtc2Mb67As7wdFsxoAEeH3O26dfTEu338sPWfCHz8+UWEhKdg+rjaqFfLBabOzrsCmm9ZCmu3ex9LhU7PoeHKL3D773/VYMCdPYfRcOlMVOzWPmcdez9vtNi6DJnJqTjeYwSuz1kKvxF9Ueer3DLuR5E5zMEvyZjBNzFyX8S+ffuqYFyaLUjQnpKSgl9//VUt//zzz/Hyyy+rdWV+x5AhQ1TTB5kb0rp1axXw7927FzNmzMDOnTtV4GtpaamaP7z99ttqPsjChQthZ2ennuvduzc2bdoEDw+PfPdp3bp1qFixYp7l97oHZEG0+/X/IIMZtra2KE7SUXPfvn34448/7ms7uafmU089VST7JNeANAN5+umnUalSJZRkly6cxbHD/2LcpFlo1LSlWlazTn0M6f86tm3+DV1eN/6h+MC+Xbhx7Qpmf7sSPr5+alnlKtUwckhvHNi7E088/ZxadvPGFVStXhM1ataBuSj3Rh+k3LiK4K8+U18nnDiCUpaWKPPaG7jzx6/INnKvVrd2LyB2z07c/nml+jrx9AnYV62O0h1eRfyBvWpZ2dfeQPzRgwhd8FXOdmnBgagy61s4N2uFuP17YKokWE+5cQ2hd6s5Ek8eVeesdKfuiN7ym9Fz5vr084jb9w/u/LJKfZ109qTK1ru90BHxh/apZTJAkJWcjKDPJ6qMfeKJI8hKS0X5/kNw57cfkRF5G6bM9913kHjxIi6Pn6C+jtm/HxbWVvDu3w8hq39EVmqq3vqONf1R+plncHPefAQtXqKWxR46jKzkFFQe/h4iNm1GZnwCvPv2QdL1G7j44WhpJazWiz95Es22/YnyHV8uMGtb0nkNGIiky5dwbcok9XXswYMoZWUFr959ELb2Z2QbnDNR9qWXcWf7dgQv1ZyzuKNH4ehfExVe64roXf+oahKvfv0Re+gQro4fl7Nd4qVLqL/6R7g0a464IwVnH0s6XmsPRrLwjzcvjVFTz+HQ3az6qfOx+HlhM3R6sSJ++MV4lUKv13yQmJSB4RPOICFRUwFy/HQsVn3TGD06eWPx6luwsbFA766+WLsxGCvXBap15D2+nVEffbr5YOTkczBJpUrBu9erqDVzDFCqcJv4T/0Aob9sxYUPNX93I//aB2t3V/hPGo7QtZr7p1cdNRAZ8Yk42vldlbG/vXUPMpNTUHfuBFydsRApgaFFeVT0iGIG38TMnTtX3XdRsvKdOnVC5cqVVRdGab7w6quvqqxxYmKiWve7777D0aNH1bpvvvmmWlfut9inTx/V7GHPnj2qS6OQ9aSj4xdffIF69eqhevXqmDVrlrrnogwEFESCf7l1g+FDGkQ8KBlocHZ2fuDtDffP0dERxWnOnDmqCkHuoXk/5HsiDTqKgouLC1566SXMnz8fJd2p44dha2eP+o1ybzPi6uqO2nUb4MTRA/luJ2X5kpnXBvdC/u/lUwnHj2oqXKRyJeDGNfj5VYe5KGVlDYe6DRB/UBNgasXu3wNLB0c41KpnfDtrG2Qla35faGXGx8HS+W7molQpJJw8huhtm/TWkSoBYVPBEyZ9zurUR/zhf/WWxx/cq86Zfc26RrezsLFGVlJS/udMsrUNmiDh+CG9cnx53VIWlnBs0BSmTKo2XJs2wZ2d/+gtj/zrb1g5OcGlkaZzsC4HP83PY9Ru/cGg2CNHYOngANemmnMSf+YsQlavyQm4RNrtSGQkJMDOxxumfM5cGjdG1G79aRxR/+yEpaMjnOs3MLqdha0NMpP0fz4z4mJh5aq51qw9PGDt6orof/V/7pOvX0d6dDTcHn8cpozX2oNr3tAdScmZOHIyt2Q+Ni4Dp87FomXj/BM2lbwdcOZCXE5wL1LTsnD+SgJaNdHcnqt2dWeV4d9z6I7etnsO3kGjem5qAMAUudT3R935kxG0agNO9hl9z/XtK3nByd8PYRv/0lseun4bHKtXhkM1TSKl7LOtEfHnbr1y/LBft6rB5LLP5VZ+PWqYwS9apvlT+IiSjLxk6qU031jGfMSIEVi0aJHKvst9GX/44Qe1bpUqVfKsW7t2bbzyyitqHVlXAvrvv/9e3fJBy8JCc3nExcX9p/2WMnO55cNPP/2ENm3aoEGDBqr6QO4T+eGHH6oqBAlqJdNurERfqg5ksEEy23Xr1sULL7yAH3/8MWfdO3fuqNeT7pT169dXUxAOHz6cb4n+rl27VCdKeV+pavjss89UFYSWlMbLvshAiLyerKN7P8rk5GQ1oCJTGWQwRAZWtm/PndsnFROSwX/++edzlskxSNAvryW3zpD9nTZtmjpOLbmNxs2bN9U0CkPynBy3VGnIvj7IORXt27dXt92Q9UqyoKBbKF+hYp4qjgoVvRESrAkujQkOvIWKnj55lmu2C1D/DwsNVvP7r165gGGD3kD3V9qof3fv2ApTZV2hIiysbZAarJ+VSQsNVv/aeuU9JyLqj1/h+vRzcGrUDBb2DnB9qi2cGjdHzK67c3yzsxG+bCHiD+/X2865hSZwSAm4CVNlXb6COmdpIQbnLCxE/WvjafxDftTm3+DyVDvV70DOmUvrZ+DYsBni9uxQz5eysVEl+tpzr5UZF6uCtfxe11TIPG8LGxsk39L8PGklB2h+Lu0rV86zjZRBq209K+Z5Lc2/XupfybiGb9iot45Lk8YqiE26dh2mytbTS52zlAD9c6ad621fyXhfkbCff0aZF16Ea8uWatCp9HPPw61FS0Ru1fyuyoyPR1ZGBmwrVNDbztLZGVbOzrDzNN0BOMFr7cFV8rZHaHgKsrL0lweHpcDHK//qyti4dJQva5dnuVd5O1Wqr31tYTiXPzg0BVaWpeBVIe/2piA5IBS7aj6LC6NmIDMp9zNhfpxqVlX/Jl7R/zuYdO2W5vkafrCws4VDZW8kXrmht05aZDTSY+PVOkRFgQG+CQkMDFS3VmjcuLHR5yU4l4BUgqIbN24UuK5o1aoVIiIi1OtKxt2wNFyCfwkmJZD9r0JCQrB161Y1iPD1119jx44daipBnTp11KCFBKOTJk1CdHTeBi1SgSDbSoC8bds2VY0g60rVgZD/y9SFVatWqZJ4Pz8/vPvuu6r6wNBff/2FwYMHq6BYguTJkyergFd6D+iaOXOmqpDYvHmzej8ZIDhy5IheFYUci2wr+/7+++8j6O6HNTk2Oa4yZXKbukllhNzHcuLEiep45XzL+TXM3ssghY2Njd7yqKgoNdjg5eWVM33iQc+pDEhIdYS8V0mWlJgAe4e8VRf2Dg5IMsho6W2XlAgHB4e829nbI/nudjeva/pKRISHonf/IRj7yUxUrVYT38yZhr+3/g5TJB/+hWE2PitZ8zNgYeSciDu//4Kk82dQadJM1PppE7w/GI+Yndtw57ef830v6wqeqND3HSRfv4KEY4dgqixyzpn+7wnt15b2xs9Z9Kb1SL54Dj7jp6PGyg3wHD4Wsbv/QtTv6/Rf18h1Kq+t/V6ZKsmcCsl06pJGgkIy0oZijxxFcmAgqowZDdfmzdU6Lo0aofKI4cjOyoJlPtO5ZC509YkTkBoRgYjf72+6U0k8Z4bZ+ILOmQj96Uc1777mnLloumMnqk2egtt/bkHoas30EClPj/r7L5R/rasq55fA3s7XF9WmTEV2ZiYs/sM0uZKA19qDc3SwQmJy3oaeSckZcLTPf/rj5h3h8K/qhPf6+aG0uw083KzxTq/KqOTjADtbzXZOjprZvUlJGXleW713Aa9fkqVHxyIluPDJDyvXu9dnnP71KeX46nkXJ1i7OhtdR7uerPOokmrK4no8ijgH34TExsaqf11dXQu9rru7pqTKGO1zEkAazsmWQPjLL79UgaVktAsiZd+lSuWdsCTZY5kSIKTB2YQJE9TXNWrUUNMKpHxdMtJC/pW5/JLBNtzngIAAFbTJfSDLlSunAm6pSpBAXvu8vKaPj48KfiW7LoGusTn8EgxL5lsGAIS8hvzwS6+Cq1evolo1za00JCsvFQ7inXfeUVMZjh8/rpoPyvtJyb+8n5S9Dx8+XC3Xfl9Onjyp9kc34y+DFB999JF6byH3yJSGerok6DYcZJFBGvkeeHp6qtJ63eD/Qc+pHKO8d9euXVESSAVJdrZ+mqGgX8gWpfIfl5QPcPkpdbcipXbdhhj7yQzUqd8YdnaaD3sNm7RAbGw0fl69BG2ff9no9Vyi3T22fBk5n1KiXnn6XFXmG/LtbKQGBcChZh2U7fYmslKSEbY471QOGy8fVJr8uQoeAmdONvq6pqJUAddRftegnDPfqbNh5eaBsO/mIjUkAPY16qBMlzfUOYtYtuCe105B16hJsLjHz4bBz7JalJGBc4OHovrkiai3aKFalhpxG9dnfo6as2YiS6eCSku6xksTNPn37KDBOUGdOZ4zYyWkUp5ee8F3sClTBjdmzlBN9Jzr1Ydnn77ISkrGra9mq/VufD4TWenp8PtoHKqM/xiZKSkI/WGlKkc3dl5NCq+1QpFfOYanqqA/CQVVLG/+O1w10+vXvRK6vuyFrKxs7DoQiT+2h6F923I571eQR6UiWvuZosDf9fdc5xE5WVTsGOCbEG2jOwn67kUb0MXHx99zEMCwgZ6Uv0+dOhUdO3bE6NH3nodkWNqvZTiNwNc3twxRAnbd57VN8KTZn6GePXvi77//VsFvrVq1VEWBNIsrXbp0TvO4UaNGqex+kyZNVBm8DDoYa6wnfQZkW13NmzfPeU4b4GsHJrSkH4BMkRADBw5UQb9UQEjFhOyPDChoewZERkaq5VrXrl1TlRBSmq8lQYDs68WLF3OC9f3792PMmDF67ytVC/K+MjXBMLP/oOdUvt+yjyXFLz8ux7ofl+kta/l4G8RGR+VZV7LwDgX0U3BwdFIDKoakmsPhbubU1c0dTZrnrUpp0qwVzpw8ipiYKLi7a64tU5F1t++GlIzr0maLM+8+r8vlsSdgX6Uabn7yIRJPHVfLks6dVllGz3dGIHr7ZqTqlODLHH+fsZNVIHvz4w+QfreU3VRps6mG56ygDLxzy9awq1wVAVPGIOmMZoAu+fwZtW6Fge8h5u8/kX47zOjrapcZe11Tknk3m2pl8HOo/TojPm+mSsgt3s70GwBrD3dYubohOSBAlZbLh+T0WP1pYA7VqqnO6ZaODjj37lAknDkLczhnEnTr0magtc/r8nj6aTjWqIELw4Yi7m71WPyJEyqb7TdqNCJ+36Dm2kszxxvTp+HWnNnqfKaGhallZV/uaPK3e+O1Vjh9uvmib3f9aR7/7I+Eu2vezwwO9laqiV5B1v4egl83h6pS+9j4dDV3f9ywGoiL12yXmKSZn29vb6k3V1+qBjTPm8etQO8lI1bz+drK2eD6vJuVz4hNyMncG66jXU/7Go8iSe5Q0WGJvgmRjLGUfUsm2RgJJOV2eXJrOwn8pAxcW1ZujMxTl3UkM64lc92lrPutt95Sc9O18/ALItllqQAwfBgGpIYN5wrz2kKaA8oc98WLF6tbAsocesmw//bbb+p5yYpr7wwgZezLli1T89WN3eLPWFZO+0vGyip3vMtYMK3dVua3S7d7KYuXcvgNGzaoue0HDhzIOS6Zc6+lfd2CstKSUZdBEjmXuh577DH1PlJyL135DT3IOZV9K+y5Lw7tXuiIGXMW6T2kUZ6U0Bv+AQgLCYaXd/53APD08kFYaN4PtbJMGu2JC2dPYdeOP/Osk5aaCgsLSzg5md5tfuRe7JJVt6momV+qpf06NUgzJ1CXdVnNoJzcGk+XBPnC1jd3fqvLE8+g0qTPkXHnNm6Mfk910Td16eEhmnNm0ChQ+3VakP68X2FVRnPOki/pd4lOunBG/WvrUwnZKSlIv3M7z+tauripAZe0u70gTFVyYJDKktr56Pd1sPPVfC33HDdkYWuLsh3aw9bLE+lR0Zp1MjPhVKumej7xwoWcdV2bNUX95UtUF+szffsj/uQpmLqU4GDNOfM2OGd3//bKLeAM2VbQDNbGn9b8PGrFn9QMLNn7aXrrSCM9p/r1VVAv51X+tXJ3h025cqqbvinjtVY4v28Pw8APT+o9AoOTULG8bZ5su3dFO3XP+vxIef6TLUsjMzMbAcHJKrgXNao64vJ1zeBkQLCmwsG7ov50BxkQSEvPQkiYiVeOFFLCZc3151BV/zOJ492vEy5eQ2ZiEpKDwvKsY1PWA9YuTmodoqJQcj7l0z1JUCb3s5e543KbPEMSAJ85c0YFuVKeLqXdUiYvgb8hCX4lMJVyd20puwT38hqSRZYGdyWlTFk6/kuAL5lyqSiQefaSPZf575KdloEI6SMgQbbcRUCy/XKuZCDAkEw3MBwg0c7lN8za50cCbmmi17ZtW1VqL5UDMvgi/woZNNGd9y6DHTJ1QEr3dZ06lfthQgYMnngi997jWtKo77nnnlPHJuX4CUYyPfdLpmTIVIeSwqN0GXXLOt1Hg0bNVCM86aavJSX058+dQoPGuZ31DTVo1Fw12gsMyP3gJ/+XZfKcOHvmOObPmZ7TdE/IQMKBf3fBv1bd+77zQUkg3XkTz52GS0v9jrwurZ5QWbDky5pKEV2pd4N0h9q51Sbq61qa7vFpYZrfMU5NWsD7/Y/UvPMbY4cjI6rkVH/813Mmgbm2YaCW3M8+MzEByVfzBkdpIXebe9XUvyuBg7/mdovp4ZpzlnjqmDpvUtKf87otW6sBhcSz+r8HTI3cOjD2+AmUbvuM3vIy7doiIy4e8Wfz3iJLSsirjh2DCl065y60tIRnj+6qYVrilas5tziTbGpqeDhO9+ptFs3OtOcs7uRJuD/VRm+5R5unkREfj8Tzec+ZlOQLlwb6neK1HfdTQzRNHMt16gzfocP01qnwendVHhxj0F3f1PBaK5w70Wm4dC1B73HkZIzKqDdvlDs9z9XFCvVru+p11jfUqK4rJoyoASede943beCGKr6O2HdY0zX/7MV41aG/TavcPkNCBgakS396xqNRdp50LQBJ1wNRsXNuQ2VRodNzKvhPvqX5GY38+1+Ua99G3YElZ53Oz6sGmZH/aO7uQ/T/xhJ9EyOl4ZKtfuONN9Tcb2miJyX7UlYvAbuUdGubjEk2XwJ+KXF/7733VOm6kEywBKmSDZdyc3Ho0CEV3EtXdyk3l67tWvJ6Bd1mTgJGY+XwkrkuqAdAYcnry/xzCZJlnvn169dx4cIFVWUgmXY5RgnSJQCWCgeZyy4l2ZJpNzRgwAB13r799lu8+OKLan66TEeQe8MXNsCXwYTff/9dbSeVEhKoS8M77ftJeb7urQWlwZucVznnEvzL+6xdu1Ztp50eIAG+9A7IjzwnQf7nn3+OKVOm4EFJICvTArT9BUoqmSdfp14jzP1iCnr1HQwnZ1esW7MUjo5OeK59J73gPUPmn1bV9Dx47MlnsH7dD5g+cRR69nlHLVu9fCF8K1fBY088rb5+9oVXsH3LBsyYMhav9+yvrqttmzcg8NYNTJmZe7cFUxO5dhUqTZkF79ETVam4zKcv3el1hK9chOy0VFUeLhlm6RIvHd2lM37SpfMqeI/4cQVSgwPgUKMWynR9E3GH/kXK1UtqHrDn0JGqOdztdavV9rokU51xx3QD/ju/roHPhBnw/OBjxO7cBnv/2vDo2BW3Vy/JOWc23r4qcJdzlnD0AJIvX4DnsDGIXLtSVTLYVa+p5uDHH9mPlGuX1etGbVwHl9ZPw3v8NHWnAumcX7ZHX8T8vQUZkbm/W01V4KLFqPvdAjWnWTqROzdsAK/eb+Hm3HlqjrOUnjtUqYLkoEBkRMfILx6Erl0Hz549kBYegaSbN+HZvRtcGjbA+fdH5vRyqD7pE3Vv+IBvF6oMtjaLLeS2b6Zcch6yfClqfv0Nqk2bjtt//AHn+vVRseebCPx2vmqWp27N6OeHlOAgZMTEIHrvXiScPYuqEychaPEipNy6Bcc6deDVpy+i9+zJyUSHr12LmnO/hu/wEYjZuxcuTZvCq3cfhKyUn2n9OzmYIl5rD+bU+TgcPxOjgvUFK2+q8vq+r/siITEDG7bmJoikI76NtQWu3NBk57fvjkDPLt6YPKomftwQjPJlbDGkrx9OX4hVz2lvm/fzxmD07uaD9IwsnL0Uj/Zty6vs/7AJmmomcyRl9k61q6nAXrrgiyvT5qPBkhlIj4pB+B87Ub5jW3h2a4/jb4zI2e7aF4vh+XoHNNu0GDe+WgbHGpXhP/UDBC5ei5TAvMm6RwX7DxQtBvgmRoJF6Ra/dOlSdUs8CSwlQJHb3klXdt1brEkWWzq+S+AvHdwl+JcycbklntxKTaoBtFn6TZs097mW1zDs7i5z3GWAID/5NWuT99G+7n8h7y/z0CU7LwMPEiT36NEDgwYNUs/LcUkWX7rjS88BacAnXeuN3W5OMuKzZ8/GggULVJAv89Flvr7cYq6wpBO+dNmXef8yuCIVE3I+tUFzu3bt8N1336mBCW1/AxlUkGOQjL/MEZcBBakAkO7/YWFhCA4OVnPy8yMDF1K9IIG+DEw8qPPnzyMxMVG9f0n34fhpWLF4Hn5Y+i2ysrNRs1Y9vD92CpycNL0OxOJvZ+N2RBi+XarpYG5tbYMJU2dj2fdf47tvPoelpZWqBugz8D31f+Hm7oEpM+djzYrvsOz7uapSQKoGPpn2FarfzcSaosQzJxA4cxLK9egNn3FTVOAdvvw73NmoOTd2VavDb9ocBM+dqTrlywfhWxNHo9yb/VH29Tdh6eSCtPAQRK5blbONQ826sPbQZGkqT5mV5z1lYOD2TytgqpLOnkTwl1NRplsveI2eiIyoO7j9wyJEbfpVPW/nVw2+k79A6PxZiN31lzpngZ9+hLI9+qB0l56wdHJGekQoIn9dk7ONNtMfOPUjlOs1EF4jJyAzPhbRm9fj9s+me650xR4+gosjR8F38Duo9dVspEVE4OacrxC8UtPdXcqh6y1ZhMsTJuZ0JA9YsFBllb369la3Iku4dAnnhg5DzAFNBsvWywtOtWqp/9ea/UWe9wzf+DuufDIJpiru2DFcGTcW3gMGosbMz5F2+zYCvpmHsB/XqOcd/P1R+9sFuDZ1CiK3bFbX2sXhw+D9zjvw6tsPVi4uSA0JQfDyZTnbiNjDh3D1kwmq+V65VzupypubX36B8F80P8Omjtfag/t45gUM7VsF7/b2U6X6Zy/GYeIXF/XmzX8wqBoqlLPF64M0lYxRMekYOekshvargk/H1FTr/rkzHIvXBOjdcm/52gBVxv/yc+XR/RUv3AxKxkefXVDZfXPl0qgOWu34Aaf6j0XQSs0UUfnXwtYGVd7vB+8+XVRG/2Sf0QhdlzsNMPHSdRx6sR9qzRyNxj9/rQYHbsxdjsuTvn6IR0PmrlT2o3r/AKIiJAMQEuj3798/564EEsDrNjSUCosKFSpg+vTpxbZfkv2Pi4tTAyAP6vQVzSg+FZ7lh90f9i6YJEsbjkHfr8jLpltR8TBZO5je1JyHLT1J03iW7s+4Kgzs7teYTZrPUnR/OqSX3D4cHQYUX0PLzYs1Uw8fJZyDT1QERowYoW6Np+1gL7fZGzlypJpaICX+y5cvx8GDB9WdCoqL9AXYunWruiUgERERERGZHwb4REWgRYsWePLJJ3OmO0jGXPoYSONDmRIgjQJl+oT0QSgu0sdAKgr8/PyK7T2JiIiIiAzn4BfX41HE+keiIiJz9bXkVoTffPPNQ90fmf9PRERERETmiwE+ERERERERFYtHNbNeXFiiT0RERERERGQGmMEnIiIiIiKiYpGVrXPfRfq/YwafiIiIiIiIyAwwg09ERERERETFgnPwixYz+ERERERERERmgBl8IiIiIiIiKhbZWZyDX5SYwSciIiIiIiIyA8zgExERERERUbHgHPyixQw+ERERERERkRlgBp+IiIiIiIiKRXY25+AXJWbwiYiIiIiIiMwAA3wiIiIiIiIiM8ASfSIiIiIiIioWWWyyV6SYwSciIiIiIiIyA8zgExERERERUbHIzmKTvaLEDD4RERERERGRGWAGn4iIiIiIiIpFNufgFylm8ImIiIiIiIjMADP4REREREREVCyyszkHvygxg09ERERERERkBpjBJyIiIiIiomLBOfhFixl8IiIiIiIiIjPADD4REREREREVi+wszsEvSszgExEREREREZmBUtnZ2ZwEQURERERERGTimMEnIiIiIiIiMgMM8ImIiIiIiIjMAAN8IiIiIiIiIjPAAJ+IiIiIiIjIDDDAJyIiIiIiIjIDDPCJiIiIiIiIzAADfCIiIiIiIiIzwACfiIiIiIiIyAwwwCciIiIiIiIyAwzwiYiIiIiIiMwAA3wiIiIiIiIiM8AAn4iIiIiIiMgMMMAnIiIiIiIiMgNWD3sHiIioeIWHh+P27duwsLBAuXLlUKZMmYe9S0RkIDo6Wv2Murq6PuxdMRk8Zw+G543IvDDAJyKT9Nprr+HTTz9FzZo1H/aumITAwEAsX74cO3fuRFhYGLKzs9XyUqVKoWLFinjmmWfQq1cvVKpU6WHvaomXnp6OixcvokqVKnB0dHzYu1MipaWl4dixYzh58iQiIyPVdSaDSQ0aNECzZs1UMEH6EhISsHbtWuzYsQOnT59GRkaGWm5jY4P69eujbdu26Ny5M1xcXB72rpYYPGcPhuftwcjA+O7du43+XmvTpg08PDwe9i4SKaWytZ/yiIhMSIsWLbBu3Tr4+vo+7F0p0RITEzFz5kxs3LgRrVq1Uh9Cqlevrj6IZGVl4c6dOzh//jwOHjyI/fv346WXXsK4cePg5OT0sHe9xAgNDcX48eMxYsQI1KhRQw0uXb16VWW7ZNCkVq1aD3sXS4y4uDisWLECq1evVv/39vZW11pmZqa61uRcStDQs2dP9OnThwEEoH4OFy1ahO+//x6enp56P6Ny3qKionDu3DkcOnRIDc717dsXgwYNgqWlJR5VPGcPhuftwQfIv/nmG2zevBlubm5650x+r126dAlJSUlo3749hg4dys8l9NAxwCcikyQfUvbs2YP+/furP6Z2dnZ6z8uHF4L6wCEPyc7fq/xSshOrVq3C9u3b8eeffxbbPpZ0w4YNU4HpnDlzcOTIEUydOhWLFy/G+vXrERISgqVLlz7sXSwR/v77b3VuJJvVoUMHPPXUU3l+LmNiYlTw8Ntvv+Hs/9q7Ezib6/2P498osmdLQlG2IlmyixupJFuI7CSpqGQr97bIVnaVK1shIpHIpYUuoez+RSUSoZJdWbPc/+P98ZgxZ86ZMWOO+f3OzOv5eMyjcc78//f7+D7OnDmf7/ezbNrkXnrpJVe7dm2XmjVt2tQVLlzYdejQwQKH+GzcuNEOULZv3+5mz57tUiv27NKwb4mnQ1y9x+s9TV8lS5YM+hmFUjoY0fvap59+6jp27GgHmIBXCPABRKQSJUrY6bkoTS6K3tL07x9++MHD1fnH3r17LYUwsTX6efLkuWxrijQVKlSwD7q6qdctvl5jo0aNsg++SmPdsGGD10v0ha5du7qePXsm+PZq27ZtbtiwYe7f//63S830XpXYLBBl3dx6660utWLPLg37lniDBg2yW/ksWbIkuJ+B3tOU9QV4hQAfQERavXr1RYMyJMyRI0fcli1brDYawcqUKWOpmepVUKlSJffss8+6Zs2aWYDavHlzu9UHAADwA5rsAYhIBPDhoyZLnTp1IushDrq9mjVrlsudO7fVlSv1XE3kVCZCk8f4qRGV9io2SmhC016pt4gO3ELtm24TEYg9uzTPP/98yMeVAXfVVVe56667zt13332uUKFCyb42vx+I79ixI+RrjUNy+AUBPoCIdOrUKff+++/bh7qoVH3RH13V9qoODgiH3r17u86dO1vq5aOPPmoffF9++WXrQK1afARTp2kFENqzmCihufhrTb0MlEadPn16r5cTEdizS58GoswkjUlV53xRHblKtNRHQ70y3nrrLas/L1eunNfL9QX1Iujbt6/tXewEaN7X4CcE+AAikkbkffTRR3a7qmZASqP+5ZdfrKMtzW0QTuqavHz5chstFdX1vW3btlaPr+cQbMCAARY0tGjRIqjRHuKmxqHDhw9P9Y0HE4M9uzT6vbznnnvckCFDbDyeKHDVFBXVm7/44otu6NChbuTIke7dd9/1erm+8Prrr7sGDRrYZwze1+BnBPgAIpJuT5V6qbFu+mCn7t0FChRw3bp1sw8pQLhopNvo0aOjb7mEtNWLN3fU7d9NN93k9VIiig6QeG0lDnt2aT755BM3Y8aM6OBelJqvsXgPP/ywBfgaCfree+95uk4/UYmWJvcULFjQ66UA8UoT/9MA4N8/tGXLlrXvNfZHnX6jPpz897//9Xp5SEH0urrySs7DE0PNCJXui8RRKYgOLjV3GwnDnl0avaepR0aocalRk2lU/sZ73wV33323lR8BfsdvLYCIlCNHDkvHV7MunaarFl+yZ88e8kNLalWzZs2AMYKhnDx5MtnWE4kaNWpkc42VmnnjjTcGpWY2bNjQs7X5lXoU6PZv2bJlllkT+zWosVMIVrRoUUs3V+p0KNT4BmPPLs29995rt/T6XVXNvWrK/+///s+y4WrVquWOHz/uxowZ42677Tavl+obGgNar1496/GjcaCx39do6Ai/IMAHEJGqV69uzW70B1UNgAYOHGip+gsWLLAmaLgQnF4swEf8lJ4v77zzTtBz2lsC/GCaA62DNgX4GTJkCNozAvzQNDtbB5b169d3GTNm9Ho5EYE9uzRqgtmrVy/XoUOHgL8RderUsT396quvbBztuHHjPF2n33r/HDt2zJr5/vrrr14vB4jTFf+L3QYSACIkRf+5555zVatWtUZeGvOmYELphK+99pqrW7eu10sEUi2Vz7zwwgt2wISEU5+HefPmUeObCOxZ0uzevdtK3NKmTeuKFSvm8ufPb48riI1Znw/nSpcu7d544w135513er0UIF7U4AOI2MZKuiVUAzTdPuiW4cMPP3RffPEFwX0MymxQ9/eEOnz4sN1SINhvv/1mh0gqaVB5COKmW/uoHhlIOKVDaxoIEo49u3Tnzp2zvdNttAL9PXv2RD9HcB9MJYAqCwT8jhR9ABFLgZY6AW/bts062yqQzZs3r9fL8hV9GNGkAR16KPWyZMmSIX9ONzg6IFm4cKHtJS7QTZZmbWtv0qRJY/WXyhJRqqZuczJnzuz1En1HWTXaG9Xzxk7RR9zU50Gp0+pfoN4FavAYE+UgwdizS590od4i6l+TLVs2a6inv6E6mBs7dqyNykNwQ0eNAFV2kmrwlfUA+BEp+gAikup7mzVrZjepCsAUdOkP76ZNm9zkyZPdzTff7PUSfWPnzp2W7TB//nyb216kSBFrUqjbm4MHD7rNmzdbsKoDgCeffJJU11hGjRplB0lqRqUPeEoH1m2+ggr1gtDjCNS+fXu3Zs0a+z5nzpxBnbg15hLBihcvHudzylSiYVww9uzSqA/G77//7oYMGRI9znLr1q12mKlUfRrGBVMjR7336zAkFF5r8AsCfAARqUePHnbbMGLECFelShULupS2/8wzz7j06dPbDG4E39gsWbLEffPNN3ZAog+/1157rdWwqtu+gn6E/lCnIF6vszJlythrTTeFX3/9tTWpUto+Ar355pvxPk+TPcBbuqmfMmVKUFaXOumrp40a7CHQnDlz4n2eniPwC1L0AUSklStXWt19zPRfpRnq9qFNmzaers2vFMw/9NBD9oWE++OPPywdMzaVgxw5csSTNfmdMkHimt6gkhqEtnHjxpBjyf766y/rjaHSEARizy5NpkyZ3OnTp4MeV4lD7DIHnKdSt7h6E+gzCeAXNNkDEJGUUh7XSKQzZ84k+3oi6RZft6vdu3e38galnv/8889eL8vXVO6h2/rY/vOf/7jChQt7sia/U/lCbEoY1KHcgw8+6MmaIoFqomOn+apxqMpnvvzyS8/W5Wfs2aV56qmn3Isvvhiwd2q0p0MRMmxCe/zxx4MORY4fP277qLIkwC8I8AFEpPLly7vp06cHPKY/vGPGjKF7dxzULblevXqWZqieBfpgsmDBAte4cWNL20doXbt2tf4OqklV7aX2r1u3bm706NHuscce83p5vrR8+XL3r3/9K/rfP/30k2WOvP766zRxjIdmubdr1876Yig7RKVITzzxhKtUqZIdKCEYe3ZpdNC7fft2O3C74447bL9q167tNmzY4F555RV3yy23RH/hPE0bUHZSVJD/1VdfWRNb/T1l+gz8hBp8ABFD9c46KVfXcqX5akSe0qTVGKhixYp2E620zKlTp8bbeCk13z6ozl4fRHQIolpy7Z/KGnSz/+6773q9RN/STaA6S2vagJoTqlHho48+6u69916vl+ZL+v3UjdY//vEPe42pyWOJEiXstUfWQ/wGDx5sEy3UmFDpwH379mXu9kWwZ+GvJ4+J2vLz9u3bZ+9r+fLlc7lz53azZs1y999/v/vnP/9pzUQBvyDABxAxKlSoYHWDmu1euXJlq43WLb5SDKOCLo3nyp8/v9dL9SUdgkybNs0CrJjN4hSM6XZ13bp1Xi8RKSxjpG3btnZ4pA/A+t2Mqy4fgUaOHGnlDHp/u/32271eTkRgz5AcNHlGQb4uFpSRdPfdd3u9JCAITfYARAylk+tmRim+ChZ69uxpXfORMDoE0VeofgbM8w300UcfJfhnmbN9XtRYvNh1vppAoA/DOkCKulNQiQ3Oi6spqG6jVQJStGjR6MfU9RzsWThovOwHH3zgtmzZYt/HpIM4HaQj9N8Cvedrgs/s2bNtmk/MxwE/4AYfQMSJqndTqr5SM0N1UEYw1Y2nSZPG5h6XK1fObvCzZMliNebZs2e32wicF7vEQx949efy6quvtiBCH+p0KKJ9U705zu9Z1D7Fh9nkF29IGBdmk5/HnoXn78GiRYusxl6jZWOjZOu8hJb78b4GPyHABxCRFGApIH3//fdthrsCr5j4UBdMJQ26+VKfgsOHD7ubbrrJmgZdc8011rdAdYUINn/+fDdx4kR7TUV92NuxY4f1LlCDpdatW3u9RF/QaymheK0B3tIh76uvvmqN9QCkLKToA4hISin8888/7b8a7RM7wEewPHnyWLqhAtaovgUPP/ywa9CggWVDILShQ4e6UaNGBdzkFCxY0LrEq3EhAf55BO1A5MiaNasrVKiQ18vwvbp167oaNWq46tWrW2kR5WyIBAT4ACKO6gaVZq60Qo36qVWrltdLighr16617vlNmzYNeFzj8rSPzD4OTQdJoVJYdUBy8uRJT9YEAEnRuXNny0pSjww1W0VoGpG6dOlSKwfcuXOnNauNCvivu+46r5cHhESKPoCI6sr9wgsvuNWrV9vsY92g6hYCCaMb6KpVq9ptdMwb+/3799tIKeoHQ9MtveZr6wNe1IQGTR5Qk0eVOeiGHwAiiebdK8jXAWYo/D0Ipr+VGpmqr6+++sqy4hToK+BXyQO3+/ALAnwAEUOjj7Jly+ZeeeUVm6+NxAf4SslUoz3NdI8KVgnwL967QJMbFNTrQEl/NtXHoFSpUjaWS69JAIgkmt+uJqs6LM+YMWPQ840aNfJkXZHi7Nmzbv369Rbs64b/t99+syw5wA8I8AFEVOfkPn362IcSJJ66JX/22Wfutddes5Flb7zxhrvjjjsI8BP4YU43Nhr3pm7JOiypVKkSc90RNqdPn7bfwT179rhTp065DBky2A2hfm81uQHB2LNLpwNKTVJRPxGE5yBYrz3ADwjwASCVUFC6YsUKlzNnTks31xikqGyIKlWqEOAjyTSdYebMmXaT9fvvv1sTzKigSw2qmjRpYlMbcIE+ho0ePdpNnjzZMkNiUzlNhw4d3BNPPOHJ+vyIPUu6li1buk6dOll6ORJGDVVDHerqsauuuspq8tW0Vu91gJc43gSAVCLmB5NevXpZur56GuiDHuL2888/20GI0jF1YxgbByMX9qF9+/YW0CszpEiRItacUDere/fuddOmTXNvv/22e+edd1yxYsW8Xq5vaNzne++9Zz0dlBVy7bXXBuzb119/7YYPH25NHWmEeR57lnQKRJUVp0M3NdlTgBpTw4YNPVubXykzRAfj+q/e4+Sbb76xr7vvvtsONfUeqD43NP+Fl7jBB4BUeIMfRWnnTz/9tDt69CiBajy3NgcOHHDNmzcPWR5Crep5OihSX4eBAweGbDalMgcFFPoQrA/JOK9atWqub9++8QYEixYtcv369bNaX7Bn4RBz7Geow2D+HgR76qmn7DBJDX5jUtmbUvR1qDRp0iT3n//8x6b9AF7hBh8AUokpU6YENYRTav6MGTPcwoULPVuX3+l2Zvr06a5EiRJeL8XXNm3aZJkOcXWS1uOPPfaY3RjighMnTly0Dlo3rJrkgPPYs6TbvHmz10uIOMuWLXMffvhh0OPNmjWLPujVoZNu8AEvpfH0fx0AcFmtWbPGnTlzJvpWRqOR9FjMr4MHD9psX4SWPXv2oPRVBMudO/dFg4aNGzdSgx+LUn01alFZNKEcO3bMDRs2zMZw4Tz27NJ8//33l3Rwhwu9HVSyFdtPP/1kpUlRr72rr77ag9UBF5CiDwCpJC1f3yvID/W2T0pm3MaPH29TBxRQ6AMeQlON/ZAhQ1ybNm2sLloNp9KlS2eN9lQXvXr1aqu/V5qr6lRx3q+//moN4dQJ/tZbb3V58+YN2LfvvvvO0oInTJhgt9Jgzy5V06ZNXeHChV3Hjh3dzTffHO/Pag/1+7p9+3Y3e/bsZFujn40YMcJS77t162Zje9XjQRle6gmhcYNqWti9e3f7O6HHAK8Q4ANACv8gfP3111sAr+/jky9fvmRbVyRRMKqu8Koh10GJAomYFi9e7Nna/GbOnDlu7NixbseOHdFNHaM+Zqipo4IyBRkIpCybTz75xA6SFLSePHnSmsYpcFVHbjXwiv26S+3Ys8TTe5gOPXRoqX1SB/2iRYva+5qeUzaXbvlXrlxpc9313qeglQym8xTQ6xBTJVtq6Kj3Nt3Wq0+LetksWbLE9le1+Pq7C3iFAB8AUvFIM900MC86fm+++Wa8z9OlO5gCBQVdqpXWB2AFEzly5PB6WQCcs9IG9V7R4aTKZqLKuBTIlypVyg5HHnzwwaCeLThPh0nbtm2zviLqBUFKPvyGAB8AUrhVq1ZZ+rQ6/yptVSmsSpNWaqE+mDz66KPMi0bYaJSgyj0U4OuWS7WpStdXiQiHSXEfts2cOdMyRTRlQKnm2rc8efLYbbQaE9K7IBB7Fh4KAw4dOuTSpEnDfoUwefJk16pVqzibh8amwxJNCaEMCV7iLy0ApPDgXmnRt912W/RjGlWmNEwF/Br7NnjwYAvAdGODC7f2jzzyiAUM8d3gKw39ySefTNa1+TlQGD16tH0g/uuvv4KeV7aIXoscJgXSYYiCAb3W1DyuSJEiATPddTj39ttvWz10sWLFvF6uL7Bn4aP3MLJr4rZ7925Xr149S8O/995749wrHZJ89NFH7r333nPVq1dP9nUCMXGDDwApmIJUNZl6+eWX7d+7du1ytWvXthuJqFm+qpvWB+JZs2Z5vFr/qFmzpjWWUgd9fR/fh2Nq8M/TaCh9uFWTKTXZU7ZIzKDr66+/ttpUfVCmrOGCli1buvz587uBAweGvCVUbbQO5XRLrZtBsGdIXurzMHLkSLd+/Xoblxqqb8HWrVtd6dKlrRa/QoUKXi8ZqRwBPgCkYPqgMWXKFEuPlvfff9+C/UmTJkWPxlMtodJZNUIPuFTVqlVzffv2tTnQcVm0aJHr16+fW7p0abKuzc/UjVuztePras7vaCD2DF749ttv7UBX5W379++3A14dZEb1LVDwD/gBKfoAkIKpyVnWrFmj/6259+osXaZMmejHdAMW1fEcSMprTQ2n4qNskiNHjiTbmiJB7ty53ebNm+MNVtUIjfroC9gzeEGBvL4AvyPAB4AUTGmsP/30k43sUTrhV199ZTWrMcdHaSSSfg4XKOMhoYceqgeGs9fV0KFDbYyU6u1jO3bsmBs2bJgrV66cJ+vzK9WS//Of/3Q//vijlTaoH0bMme6rV6+2WnI1xsR57Fn4u+qrg75KahA/jQ/Uobne4/S387PPPnNly5Z1DzzwgNdLA6IR4ANACla3bl336quvWmfz5cuXW71g48aNA1IO1UTu4Ycf9nSdfqPaXrIaEufFF1+0JnpVq1Z1t956q43Gixl0fffdd5bOqjnRCKwnz5gxoxs7dqwbN25cwOtOVZSFChVyffr0cU2bNvV0nX7CnoWHGmLqIOSPP/6wPdRBr5pgNmzY0Oul+dLnn3/uunXrZq87ZSN17NjR/qtyEWUm6XUJ+AE1+ACQgim4UjO9jz/+2MYgtWjRwm6+RIG/avFVpz9+/Hhub+KgenF1RSbgT9iIqE8++cSaUmlMnuZF63WlYF+jy1SnGjN7BIF0AKd9U7mDRlhq3+hwHj/27NK88cYbNmmgTZs21tPg3LlzNnJwxowZrkePHtaIFYEaNWpkfwvUSG/MmDFu7ty59n6nL+3nwoULvV4iYAjwASCVpGAqQM2UKVP0Y0pj1eN33XUXwWs89OE3W7ZsrkGDBjZKULeDABDJ7rzzTte7d++g1PIPPvjAgtcvvvjCs7X5lervFcTny5fPDsvVVE8H5krbv++++ywjDvCDNF4vAABw+aleMGZwL7q51wg4gvv4rVixwmbd63arTp06rlmzZjaNQIcjABCJ/vrrr5Bd39Uj48CBA56sye9Ue69905eC+SpVqtjjO3fupKEjfIUbfAAAEuiXX36xcodPP/3U7dq1y1LONYpLjb6AS/HRRx8l+GepjT6PPUu67t272yx39SqISY0wd+zYYSnnCKTbes2712G5pjiofEsHvxo9q78Br7zyitdLBAwBPgAACaRmhUuWLLHOyWq4lD17dmuupJRNdY9X9/3UKjHZIJoljfNU+rFlyxb7Pr6PZNpbJjacx55dmueffz5gqoXew2655Ra7tde4VDXCXL9+vTUnVNCKQOopMnLkSDvcffTRR13p0qXtIET/fumll4Ky5ACvEOADAHAR+tAb1VDp1KlTdnOvaQSVK1e2D8q62dGNjp5PrebPn2/7cNNNN7latWrF+7NdunRJtnX5nV5P6lyugyI1OLvySgYcXQx7dmlat26doJ/TwciUKVMu+3oAXB4E+AAAxKN27dpu9+7dNvpNQX29evVclixZAn5GKfuaVrBmzRqXmi1atMjGSKlRV2rOZkgs1fTqVlrjKnUziItjz+AFpeTrwFfZXLFDKA4u4RcE+ACQiuiWWbOPt2/f7kaNGmUBWeHChV3FihW9XpovafSWUjLVPb9YsWL22LZt2ywlXyO5ovz55582Io7xXM4999xzNldb87WRcOpartKFAQMGeL2UiMGeJY0yIFRvr3GqsW/w77jjDs/W5VejR4+2lHw121Pj2th7RukR/IIAHwBSiU2bNtloH41927Bhg437GTt2rDWs0geXGjVqeL1E36WcK3AYP368K1myZPTjjzzyiO1l//797XYfgTRdQBkP3OAD/jV79mzXt2/fkDfR9C4IrVq1albm8Nhjj3m9FCBeBPgAkEq0a9fOgnulUJcpU8bNmzfPFShQwA0aNMitW7fOzZo1y+sl+saqVatc+/btrQO39it37tzRz+kGf8KECbZ/7777ritbtqyna/WLs2fPWqMuJI5eSzpcK1KkiNdLiRjsWdJp/6pXr25/F2JmI0VRlhIC6e/nggUL2Bv4Hl1JACCV0K2zOv3G1rJlSzdz5kxP1uRX48aNc61atQoaISU333yzHYrImDFj7IYfzsZEqemgggZ9XXvttV4vKSJohnanTp3s+zvvvNP2TvO1M2bM6PXSfIs9SzqVFSkbqWDBgl4vJWLoMFfZbwT48DsCfABIJa666ipLn47t999/dxkyZPBkTX71/fffu969e8f7Myp36Ny5c7Ktye90s6W50F9++aV77bXX7EOwbgkVgOmDcZo0abxeoi9Fzc7W2Dftn3pk9OzZ024Lo25ZdaiEC9izpNMkEO0dAX7CPfDAA65fv352WK5pIenSpQt4XhlfgB+Qog8AqcQLL7zgfvvtNzdixAj7EKwUczVXUgq6aswHDhzo9RJ9QwGpehPccMMNcf6MZh+ri7c6KiOQGg6q7EPBvr727t1rt/tRAX+uXLm8XqKv6SBuxYoVFoAtW7bMDucUtDKbPG7sWeLod1ITQXQQovc51d3HFJWlhAvi6ytC3wL4CQE+AKSiD8AdO3Z03377rTt37pyNetNj+tCijufXXHON10v0DY3Da9OmjQXwcZk7d67VAn/88cfJurZIpCwRBV76Wr16tQX/SFxGiYJWmnslHHsWv6eeesqmEOj9P1QGl/qLAIhMpOgDQCqhsT4zZsxwX3/9tX34VZBftGhRu1ElfTpQ/fr1bYyg6srz5MkT9LzGwOl5HQTg4vLmzeuaN2/u7rrrLrdkyRKvlxNxjh07Zn0yCFYTjj2LnzJr1ENE7/9IHDVaVXmIskSUAVGoUCGvlwQEIMAHgFRGqdL6QtzUYO/TTz+1mksF8Zo6oNnHhw8ftpT8OXPmWO2qmlQh4fShWCnTzZo183opEeXkyZNWXoOEY8/ilz17dnf99dd7vYyIcurUKde9e3e3aNGigNR8HVyOHDkyqCYf8AoBPgCkEj///LM1p1KAqtnHsVE/eIHGvU2aNMk+tGletL6PovpxTR54/PHHQ46XAgC/U4PQAQMGWG8W1eAz4vLi1L9GJW6jR492FSpUsCy4NWvWuP79+7s33njDgn/ADwjwASCV0Ii8AwcOuB49elj9PeKn25hevXq5Z5991hrqHTlyxOXIkcMVKFAgqCEVAESSiRMnWobD/fffH/J5DnyDzZ8/37ro68Y+5jQCHY707duXAB++QYAPAKnEN99846ZPn+5KlCjh9VIiypVXXkmNJYAURRlISHxfB43Hi01/Hw4ePOjJmoBQCPABIBXVXKopEAD/ePPNNy/6M7/88kuyrCVSsGdJ16hRI6+XEHHUlPaTTz4Jaty4cOFCDoHhKwT4AJCKGscNHz7cDR061DrqA+GkcVsXK13QZF7KGwJ9+OGHCZ5EgPPYs/BYvHixGzt2rDW/VKZS4cKFrXFo7dq1vV6ab7MennjiCStfKFu2rD2mkZ+ff/65GzZsmNfLA6Jd8T/9tQUApHjt27d3a9eudWfPnnU5c+YM6virD3tAUoKuhAbv3B4C3vrss8/c008/7WrVquXKly9vh29qGPff//7XGsbpcQRTMD9+/Hg7FNGeFStWzHXs2NHdc889Xi8NiEaADwCpxMXSWrt06ZJsawFw3vfff+9uvfXWRP3fbNq0yZUsWdKlVuxZ0jVs2NAaxMV+39ffiaVLl7oPPvjAs7UBSBoCfAAAkGRPPvmke+6552zKQEJs377dDR482I0ZM8alZk2bNrXUaN0C3nzzzfH+7Hfffefeeecd2zuNb0yt2LOkK1WqlPv444/djTfeGPD4jh07XIMGDawpK84feKhsIUOGDBySI2JQgw8AKVhCP5wotVoBGnCplHbfunVrV7p0aRu9deedd9rrLqY///zTrVq1ygIt3ahqBndqN2PGDDdhwgTXrFkzqxmvUaOGNfNSGY3KadSdWzfWK1eutLFmKrUZNGiQS83Ys6S79tprrRFh7ABfjzFGNbD0qGXLlvZeFl/vB/0NJcCHX3CDDwApWM2aNS2YUgd9fR/fhxNq8JFUCuAnTZrk3nvvPffXX3+5fPnyuRw5crhz585Z0KVgS8FD8+bNXYcOHVy2bNm8XrJvHD161AJX/R5u3LjRnTlzxh7X5Avdtiqd+sEHH2TPYmDPLt3IkSPdvHnz3EsvveTKlSsX3TBO89y1b3369PF6iQAuEQE+AAAIq7///tsadinNd//+/S5NmjQud+7cFnRVqFDBpU2b1usl+po+mh06dMj27ZprrvF6ORGBPUucU6dOuW7durkvvvgiujmm9vAf//iHBf9XX32110v0pZMnT9prTE1qt23b5pYsWeLKlCkT3VUf8AMCfABIJXR7Goo+3OnGSzet+uACAEgdFKTG7Ah/sZ4GqZkOLVXKNmrUKNun++67z/5mnjhxwsbP1qlTx+slAoYafABIJZSiH98YM91I1K1b17388stBI/QAACnDnj17XK5cudyVV15pgaq+NEI1U6ZMXi/N14YPH27jA2+77TY3c+ZMlzlzZhubpzK4sWPHEuDDN7iqAYBUYuDAgS5r1qxWWzlnzhz7UpMzpbOqOVD//v2tBlMzkAEAKY9muNeuXTuoS77e91V7P2XKFM/W5ndq3PjEE09YYL98+XIrZ0ifPr01efz555+9Xh4QjRt8AEglNCpKDZXU4TxK8eLFrTZaHfbnzp1rtzo6AOjevbunawUAhNdnn31m9fUKUpWOH5P+BkyePNm99tpr7oYbbrDgFYHUSV/9RdS/QIfhGtco6jPC5AH4CQE+AKQSGn906623Bj1epEgRmxEtBQsWdAcOHPBgdUhJ1DWffg6Jp+kD9erVI1hIQld99RPRrSqCacJF165dXefOnYOe02tOmVzHjx93EydOJMAPoWLFim7IkCE2lUHvbxoF+sMPP1j2m54D/IK/vgCQShQuXNhqBWPTY1GzkPVhJU+ePB6sDilJ9erV3bBhw0hbTSTV8VarVs26mysFmD7ICaObZwWk5cuXt47m99xzj/voo4+8XpbvbN261fYmPvXr17emewimDDgdIP34448W6CtVX5lv6lnz/PPPe708IBpd9AEglVDAoJubkiVL2odg3bKqDnPTpk2WnqnAvk2bNq59+/aWwglcKs0mV4D1f//3f+7222+3WeRq4KgPxIibPpKtWLHC9m7RokV2U9igQQPXqFEjV6hQIa+X50uqHX/77bftvUuvNb2vqWGcXoM9evRwrVq18nqJvqEDEO1LfJ3ylenVuHFj20Nc3OnTpy3oB/yEAB8AUhHd0CtNU82C1EFZNfgdOnSwNP2NGzfa8w899JDXy0QKodIPBasff/yxO3jwoHWgVvBQpUoVr5fme8eOHbOa6U8//dR9/fXX7pZbbrGaXx2UMKP8AqVJ9+7d2z3wwAMBj3/wwQduzJgxNucd57Vs2dJePy1atIi3TGTWrFnuww8/TNa1RYr169dbKZvGyuq9beHCha5s2bKuU6dO8U6pAZITAT4AALjst1xq8vjvf//bGlTlzZvXtW7d2m5d06ZN6/XyfEmNu+bPn29BvjJtdDu9d+/e6JnblStX9nqJvlC6dGmbCBI7w0HlIcp8iN0tPjXTPg0ePNh+F3W4G5sOeNu1a2cZXG3btvVkjX6m7Ie+fftaxkj27Nnt9aXfQ+2bDk/UwwDwAwJ8AEgllLqqm1TdQCjgiv32P2jQIM/WhpRJwZVuuRYsWGDdpzWGS+n6v//+u6VWq1REs6Vxng4/FNCrrnflypUuZ86crmHDhrZnUX0yFGDoVnrp0qVeL9cXNPFD+6TpHzGpB8SOHTsY+xnL008/7RYvXmw9C3TzrNGphw8ftr8LX375pfWA0EEcTTKDac69Sj4UzOt9a8mSJW7evHlu2bJlVp9Ptgj8gi76AJBKDBw40E2bNs1ubqiFxuWkAEFB6s6dO63ng5rGKYU65utOdasvvviip+v0G90G6vDtrrvucqNHj7b089iBln5GAVpqFrOhmfbr3XfftZrxcuXKWUbId999ZwFr1BgzXDBq1Cj7O6DbaPV5EKWW6/dUQWqTJk1INY/D7t27Xc2aNe179cpQM1FRTwNl3AB+QYAPAKmEbu8V5CutELicpk6dat24VW+v/g6h6ENxz549k31tfvbMM8/YmDyl/8ZFAcbFOqGnhkArpjvuuMP+u3nz5ujHdDvNFIfQdAOtL2XVHDlyxF1zzTU0iksAZYqoTEb9a5SWryaOUa+7XLlyeb08IBop+gCQSigdWkF+/vz5vV4KUjg1OFP3d42PikkztmfOnGl1vgimwF1lDGpGqACVm1TAPzQaTyU0GTJkcEePHnWff/65NcHs16+fZT6oXATwAwJ8AEglnnrqKVexYkW7uQHCTV3yT548ad8rQFUn7tg30Zre8Oyzz7pvv/3Wo1X6m1LNVVu/evVqlylTJquT1l6qLprO+XHTLbTq7XUjHZMOSKJu94Fw9LFRecOuXbvs76j6Yuh3Vu99arBHw1D4BQE+AKQS6vz7+uuvW7Cg9OjYKZl0AEZSqJnec889Z0GVPlqEun3W4zVq1HBjx471ZI2RQp3yNRpPTbzU+ExN0DRaUL0NEGj27NnWeDBU41C9BpVKDQCpCQE+AKQSUc2BQtEH4dTeuAtJt2bNGrvl0ogtdS/Pli1bwGssY8aMrmjRotT7XoT2cNOmTdZJP+pLN/hqHIdAOjBSszOVfYTKcsiXL58n60LKaej4z3/+0xqExmzuGAqTaOAXNNkDgFSCET643MqXL2//nTJlitWQqxkVEk7zyVetWmUd4VXucNttt9nNveaSa947gv3555/ukUcecQULFvR6KUihDR114Bb1PRAJuMEHgBRMqb3qkBwf1a0qFTi1d+ZG0rz55psWaKkBlb6PD+UgoWmEpcbiqe7+scces9FliJ8mMWiflDWChNu3b58bOXKkZYWEKm8gowuIXAT4AJCC3XLLLW758uU23idK7969Xa9evaIf0/xezdumVhVJLQFRPbQa61EOcmk0buurr76yGdvr1q2zEgfNva9UqZL9N0+ePF4v0Xc0tkyjBdVX5IYbbgjq/UDadGidO3e2MpC6deu6LFmyBD3PIVxoNHREJCDAB4AUfiOoYCFmgK/U6blz57oCBQpEB/hqvBdzhjQAbymA0O2qRnFp7OCZM2dsCgGCp4Oo/EjvdcoeiU1dzhFMJR8TJkwgKE0EGjoiUlAcBwCpTKhzXeZtI1z27NnjcuXKFVB/r5pyHShxA31xqvfdsGFD9E3+xo0brVHcXXfd5fXSfElTBsaMGWNZSEg4NbyMefCLi9MUmgYNGsTZ0BHwizReLwAAAKQM48ePd7Vr13bffPNNwOPqqK+6cjXfQ9wef/xxV6FCBde6dWsL8LVn8+bNc5999tlFO3inVioJuf76671eRsRRoKob/LNnz3q9lIhr6KhyEB26xf4C/IIbfAAAkGQKQtW0Sx3fixUrFvCcmu5NnjzZvfbaa1Yn/Y9//MOzdfqZxgdqJJdGv+XIkcPr5URMLfmAAQPcCy+8YK+ttGnTer2kiGnAOn/+fGuwquyadOnSBTzPYVywu+++2y1dupSJDfA9AnwASOFIv0dymDRpkuvatasFXLGpiZeadh0/ftxNnDiRAD+eFGAkjl5Pv/32m7v//vtDPk9ddNweeOABr5cQcRMb1NDx008/paEjfI0AHwBSuP79+7v06dNH/1sNgoYMGeIyZcpk/z516pSHq0NKsXXrVnutxad+/frWqAoXaOJAQg/hmD4QuqwBiUcwmnh6fzt27Jg1wPz111+9Xg4QJwJ8AEjBypcvb/OOYypTpow7dOiQfUWhkzLC4WKDedTlnJrfQI0aNQoI8LWHY8eOdc2bN3fXXHONp2uLlP3Dpfn999/dtGnT3JYtW6wpZpEiRVyzZs3oaRAHGjoiUhDgA0AKxogoJJeiRYu6VatWWQOquKgrvFJbcYHKGmJ7++23Xdu2baNHWeLimQ06FIkKVAsXLmzN0NTwEaH9+OOPrlWrVtYNvlSpUja94cMPP7SAf/r06RbsIxANHREp6KIPAACSrEmTJtYtf/PmzXHWQo8aNcq6dwPhbO6o/g7XXnut69atm32v8W9PP/00JQ3xGDx4sKtYsaJbtGiRGz16tN1M6/vKlSu7oUOHer08Xzd03L59O5lI8LUr/nexfDoAAIAEiAqq1ESvbNmyLmvWrNate/369ZbeWq1aNffvf//bpUnD/UJ8VEaj8Xjc4F9cw4YNrbu5AvvYkxvU8fyDDz7wbG1+f43NmDEjaOKFDuh0s7927VrP1uZX99xzjzV0jCu4p6Ej/IIUfQAAEBa6oVeKrwIH3QaK6stLlizpXnrpJbvlZ6oDwunnn3+2zuahOsSPHz/ekzVFAjVZVcPV2EI9hvNo6IhIQYAPAADCpmXLlvalTtNHjhyxRnGa7w5cDkrN/+WXX9yNN94Y8Lge03hGhFapUiVL09doxqhmjgcPHrQJK0rTRzAaOiJSkKIPAADgkTZt2gQ9pvTo2267LWC8pUyZMiUZVxYZRo4caeUMyhApV66cPbZu3TrXt29fS93v06eP10v0pT179tikBh3CFSxY0B7bsWOHBftqzpo/f36vl+ib0XjPPvusy5gxY0DWiJqFqqGjqAypdevW7uOPP/ZwpcAFBPgAAAAeef755xP8s8wuD3bq1ClrrvfFF19El3/oo636QCj4V5d4hKaZ7nPnznVbt261PVM9vsodMmfO7PXSfOOWW25xy5cvt8aNUdRfRPsW1SNj//79NjqPGnz4BSn6AAAAHiFoTxplOahx47Zt22xMXlSgGt+4Rlyow2/RooXXy/C1UPeg3I3C7wjwAQAAEJFp5rly5bJUaQX0+lJ5gwJXBKtVq5abNWuWzXOvWbNmvA0vGTEIRC4CfAAAAEQUdchXg7hJkyZF197LG2+8YTX4vXr1CtnfILU3iYsqWXjwwQe9Xg6Ay4QAHwAAhNW+ffus/nn9+vU2dit2Siu3g0iKzz77zF5fTzzxRNAc9zfffNNNnjzZvfbaa9YITbX4OK9Lly7R31esWNGVLl06aMKFehosWbLEg9UBCBcCfAAAEFYvvPCC27Rpk6tbty6jyhB2urXv2rWr69y5c9Bzer0pkD1+/LibOHEiAX4clN2wYsUKlyNHjoDHf/rpJ9ezZ0937733erY2P5aC6OAjpj/++MOlTZvWvj9w4IBHKwNCo4s+AAAIK90MTpgwwd1xxx1eLwUpUPny5d3777/vbrrppjh/ZvPmza5t27Zu1apVybo2vx+MKLNB9PE/rhr8UqVK2f7CueLFiwftU+y9i/o3XfThF9zgAwCAsNLM6JhjpZAwlDYk3MXupzJkyODOnj2bbOuJBK1atbI59+fOnXN9+vSxEY0xM2wUpOp3t1KlSp6u00+mTJni9RKARCPABwAAYdWgQQO7wX/llVei01hxcZQ2JEzRokXtZj6+UXhKP1cNPi7QtIGGDRtGB/N6naVLl87rZflahQoVvF4CkGgE+AAAIKwOHz7s5s+fb826ChQoEBREcCsW2sqVKyltSIAmTZq4wYMHu7Jly1oKdWxKlR41apQ14UPcHfV//fVXN3PmTPfjjz/aQVyJEiXcQw89ZKMHAUQuAnwAABB2DzzwgNdLiDiUNiQ8ONXhkQJ9NdFToJ81a1Y7WFJ5w5dffumqVavmWrdu7fVSfUv71KFDB5c9e3ZXsmRJK2dQ3b0mEEydOtUVKVLE6yUCuEQ02QMAAPABNUD7888/KW1IoGnTprkZM2a4rVu3RqedK1jVLbSC/7iayMG5Zs2aWZPCfv36Weq+qO+D6vL3799vDfkARCYCfAAAEHa///67BWBbtmyxAEI3ggoqrr/+eq+X5lsKrlTaoNtoShsS7u+//3ZHjhyxBnKx57ojtNtvv93NmTMnaBKBxuQ1bdrUbdiwwbO1AUgaUvQBAEBYqaZXHbuvvvpqG7mlrt0ffvihBfzTp08n/TcelDYkng5CcufO7fUyIooaEGqUYOwAX3X5efPm9WxdfnTo0CH33//+17JrqlatGvT+dfz4cff222+7Ll26eLZGICZu8AEAQFg98sgjNqZs2LBhLn369PbYqVOnXI8ePey2dezYsV4vEUjV5s6dayUhnTp1chUrVrQsm40bN7oRI0a4Fi1aBDR6LF++vEutlNHQtm1bC+Kj3sfatWvnevXqFf0zKmm48847rbkj4AcE+AAAIKzKlCljtdHFihULeFw3hrrZX7t2rWdr8ztKG5AcQk0fCEV9DFJz4NqxY0drfjl06FCXJk0aK5MZPny4q1+/vhs4cKD9DAE+/IYUfQAAEFaZMmWyhl2xhXoMF1DagOSyePFir5cQEb799lv73Yvqh6HJA+qP8cwzz1jPh5g3+YBfEOADAICwqlSpks0pf/311+1DsBw8eNANGTLEVa5c2evl+Zb2TOnSoUobdINIaQPCJV++fF4vISIosNfvYEy1a9d2//rXv1zfvn3dtddeS98M+A4BPgAACCsFpM2bN3d33XWXK1iwoD22Y8cOC/aj0loReja5ShuignvR908++aTd7CPYvn373MiRI23vlCESu/KUm+rQdu7caYdGGjGovhixsW/nVahQwb366qt26BazkePDDz/sdu/ebX0MfvvtN0/XCMRGgA8AAMLquuuuc//5z3+skZcCCAVdmk1er149lzlzZq+X51uUNiTeCy+84DZt2uTq1q3rsmTJ4vVyIoZSy3U4UqdOHSsJQdz79Oijj7rq1au7cePGWa19lJ49e9p/J06caL0KAL+gyR4AAIBPMh/27t0bVNqget9s2bK5N954w+sl+k7p0qXdhAkTArq+I2H7pt4OJUqU8HopvqcMh3Xr1lkPjFy5cgU9v2rVKjvQfOWVVzxZHxAbN/gAACDJatWq5WbNmuWyZ8/uatasGe+NFum/oVHakHjqcJ4zZ06vlxFx9Po6ceKE18uImDr8qN4hu3btskO3HDlyuPz589v7nPpm6AvwCwJ8AACQZI0aNYpO9X3wwQe9Xk5EorQh8Ro0aGA3+Lo9TZs2rdfLiRgvvviiNYlr3bq1dYXXCLiYypcv79na/Ea/h3qNTZ061TJsoqgmX70xlMJPij78hBR9AAAQVmvWrLEU4KuuuirgcXWjXrJkibv33ns9WxtSlueff97Nnz/fZc2a1QLVqHFmUTS3HMHUzLF///7uzJkzQc8pWGWm+wVqcvnll1/aYZJu8pWldOTIEUvN12FclSpV3OjRo71eJhCNG3wAABBWbdq0cStWrLA01ph++ukna0xFgH8BpQ1Jx5iyxHvzzTddkyZN7AY6Q4YMXi/Ht2bPnu1Wr17tZs6c6W655ZaA59SgsGXLlpYF8eGHH5K5BN8gwAcAAEk2adIkGxklSg6sWrVqyJ8rVapUMq/M3yhtSJpBgwZ5vYSIdOzYMdexY0erI0fcPvjgA9elS5eg4D6KGu917drVDun4/YVfEOADAIAk002gmsGdO3fO9enTx1KnY44t0820GqJVqlTJ03X6jYKHKGrUFV9pA0L7/fffrSP8li1b3JVXXmlBV7Nmzdz111/v9dJ86+6773aLFi1y7dq183opvrZt27aA0Xih6PmRI0cm25qAiyHABwAASabAqmHDhtHBvOaSx66HRvwobUi8H3/80Q6XlAWh7BAdMCldWgH/9OnTLdhHMDWIGzZsmFu4cKG74YYb7Pc3JjIjzlOPgth7EwpN9uAnBPgAACDsaee//vqr1a0qAFN3c83bVkf4UHOkUzNKG5Jm8ODBlvmgYDV9+vTRGQ8aOTh06FA3duxYr5foSxs3brRsEdmzZ4/Xy/GtwoULu+XLl7sWLVrE+TN6noMk+Ald9AEAQFitX7/edejQwRrHlSxZ0p09e9Z999137uTJkzZqig/DgTeE6gIfVdqgr7hKG9QpHoHKlCljHeGLFSsW8PjmzZvtZn/t2rWerQ2R77333rOGhMoGufHGG0Om8KvJXu/eva3LPuAH3OADAICw0o20Okz369cvOr319OnTVpc/YMAAu7XGeZQ2JE2mTJnstRVbqMdwwW+//Rbv8/QvOK958+bW/6Jx48bWRK9s2bLWa+To0aM2DlRN+GrUqEFwD1/hBh8AAITV7bff7ubMmeNuuummoFrypk2bug0bNni2Nr+jtCFxlIq/d+9e9/rrr1vgJQcPHnTPPPOMy5Ytm3vjjTe8XqIvFS9ePN668R9++CFZ1+P3LJu33nrLso8OHz4c0MdATQqVrUQNPvyEAB8AAIRVvXr13OOPP+7uv//+gMeXLl1qt/sLFizwbG1+RmlD4ql+XLesR44ccQULFrTHduzYYcH+u+++yxi4OGi2e0x6rW3fvt2ya5577jlXs2ZNz9bmVwqZtEd6rel3VM0J06RJ4/WygCAE+AAAIKzmzp1rgXynTp2sAZrS0NXUa8SIEdas6o477oj+2fLly3u6Vj/RaDdlPYQqbdi/fz+lDfHMdNdrbuvWrRaEqR5fh0yZM2f2emkRZ9myZZb1oCwSXHDixAmb1BDzpl719/ny5bPHAT8hwAcAAGFP/00IfVgmFfgCShvgtV27dlkfiG+//dbrpfiGmmCqd8j48eMtsybKI4884jZt2uT69+/vateu7ekagZhosgcAAMJq8eLFXi8hIinlV93fYwf4qsvPmzevZ+vym1q1arlZs2ZZmrRSyeOrf+a1mPAme2ocp7GClDVcsGrVKterVy9rhJknT56A5zTxYsKECdbvQeUgasAH+AEBPgAACCulrSLxOnbsaLeBahoXqrRBXbujpObShkaNGkWnRauzORIv1MGIkno1knHIkCGerctvxo0bZ+MWFczHdvPNN7tBgwbZ92PGjLEbfsAPSNEHAABhtXPnTjd06FCrif7777+DnudWNTRKGxJPhx6lS5d2V111VcDjp06dsvFm9957r2dri6Qme6I9LFq0qI0exHmVK1d2kydPtn2Jiw7hOnfu7FasWJGsawPiwg0+AAAIK6W07tu3z9WpU4cGVInAwUfitWnTxgKrHDlyBPUt6NmzJwF+HCpUqBD0mMYLEty7oIOii72HaWKDmvABfkGADwAAwkp15NOmTbMZ7kg4ShsSRtMENKVBlIhatWrVkD9XqlSpZF5Z5Pjzzz8tFV/p54ULF7bykJUrV9qoQaWlFyhQwOsl+kKhQoWsuaX6Y8Q33pLfXfgJAT4AAAgrBQncaCUepQ0Jo6BUt6bnzp2z2miNEcySJUtACYNqyStVquTpOv1MteNr16517dq1c59//rl9P3jwYLdgwQL7r0blwbn69eu7UaNG2WspdpM9+eOPP+z5xo0be7I+IBRq8AEAQFjpRqtv376udevWdhOYJk2agOdTc4O4+DRv3jze0oYuXbp4si4/01hBjXVLly6d10uJKFWqVHGjR492ZcqUcc8995w7fPiwe+utt9yWLVtcy5YtAxo6pmZnz5619zEduimI135lzZrV9kvvc3r96UBz6tSplCPBN7jBBwAAYaUgYdu2be5f//pX0HM0iIsbpQ2X1lFfYwRnzpzpfvzxR5c2bVrbv4ceesjlypXL6+X51vHjx6NHL6qHwaOPPmrfK0hVUIvz9HpSScjIkSPd7Nmz7fsoen3pMOTxxx8nuIevEOADAICwevPNN12TJk0slTpDhgxeLydiUNqQeLpF7dChg8uePbsrWbKkBafvv/++dT7XrWqRIkW8XqIvacSbpgwoyFfWSPXq1e1xHZToOVyg7BA1Dn322Wfdrl273JEjR6ypo7KTYo8aBPyAFH0AABBWSmP9+OOPXf78+b1eSkShtCHxmjVr5m666SbXr18/d+WV5++tTp8+bXX5+/fvD7hxxQVLly51Xbt2tb1SiYN6P6guXxkkSt2vUaOG10sEcIkI8AEAQFhpPJnSpNXACwk3Y8YM179/f3fmzJmg5yhtCO3222+3OmgF+bHH5DVt2tQ6oCO0Q4cOWZO44sWL27+//fZbG5PHDT4Q2UjRBwAAYZU7d243bNgwt3DhQhsvFXWzGkU3hQhGaUPi6fWl3gWxA3zV5UfVmCM0lTXoKwpjBYGUgQAfAACE1caNG13p0qXt+z179ni9nIhx7Ngxm0dOaUPCab+U9bB3715XsWJFO0zS62/EiBGuRYsWAd3gKXG4QE0wVdagshCl6cdGtggQuUjRBwAA8AFKGxIvKr38YihxCKQ+DwcOHLDRjFmyZAk5nQBAZOIGHwAAhNVvv/0W7/PXX399sq0lklDakHiLFy/2egkR6ZtvvnHTp09nJCOQAhHgAwCAsKpZs2a846O4SQ2N0obEy5cvn9dLiEiqvb/qqqu8XgaAy4AUfQAAEFarV68O+Ldmk2/fvt1Glj333HN2AACEw86dO23E29atW93ff/8d9Dw3/KGNHz/erVu3zvYuc+bMXi8HQBhxgw8AAMKqQoUKQY9VrlzZZru/8cYbBPhxoLQh8Xr16uX27dvn6tSp466++mqvlxMxvvrqK7d27Vr7Xc2ZM6dLly5dwPMcjACRiwAfAAAki4IFC9pIM4RGaUPi6fU0bdo0askTqVy5cvYFIOUhwAcAAJf9Jvro0aNu7NixjICLx5QpU+ItbUDoQ6MTJ054vYyI06VLF6+XAOAyoQYfAACEfXRZ7JtofdzImDGjGzJkiKtVq5Zna4tEy5Yts9KGmTNner0U39Ec9759+9rYN5WApEmTJuD58uXLe7Y2v9u0aZObOHGi27Jli01sKFy4sGvbtq0rVaqU10sDkAQE+AAA4LI22RN17C5atKjLlCmTJ2uKZLt27XJ169Z13377rddL8Z0ZM2a4/v37uzNnzgQ9p0Mmyhri/h3t0KGD/U7ecccd7ty5c3ZYomB/8uTJpO8DEYwAHwAAXHYHDx50OXLk8HoZEVvaoEB1wYIFnqzLz6pVq+buvvtu16pVK5chQ4ag5xmjF9rDDz9swb2yH2LSv3/66Sf37rvverY2AElDDT4AAAirP//801LxFXQp7bdjx45u5cqVVi89btw4S6VGwprsxSxtQLBjx47Z64veDonz/fffW+ZDbPqdbdKkiSdrAhAeBPgAACCsBg0aZCO42rVr5z7//HP7fvDgwXYDrf+qnhwXb7InlDbET7f3ixYtstcaEi579uzu0KFDITNtYo/MAxBZCPABAEBYLV261I0ePdrdfPPNbvz48a5q1aquXr16rlixYq5ly5ZeL8+3NJM8VMBFcB+33Llzu2HDhrmFCxe6G264wZrFxT5sQrC77rrL9evXzw0fPtx+T0Wp+brVVyYJgMgV2GoUAAAgiY4fP+7y5s1r369YscJVqVLFvr/66qtt9BviLm144YUX3I8//mj71L59ezscqVOnjjXaQ7CNGze60qVL263znj173O7duwO+ENozzzzj0qZN6x544AE7WNKXDuE0haBXr15eLw9AEnCDDwAAwko3gkuWLLEgf9++fa569er2uMa8Rd0WIhilDYlHM7hLky1bNjdr1iwbwbh161br9aAMGzUtjD1qEEBkIcAHAABh9dRTT7muXbu606dP2w2hmuspeJ02bZql7iM0ShvCM3kgpuuvvz7Z1hIpTpw4Ydk0CuRr1KhhX9u2bbOJAwT3QOQjwAcAAGGlgEHB6h9//OGKFy9uj2mO+0MPPcQNfiJKGx599FH7ntKGxE0eiEnjBXHB/Pnz3YABA+wAqWTJktGPDxw40G3atMlq8GvXru3pGgEkDQE+AAC4LF269RWlVKlSnq4nElDakPTJAzoI2b59u5s0aZJ77rnnPFuXH61atcrq6xs2bOjy5MkT8FyfPn3chAkTrDZfZQ9ly5b1bJ0AkuaK/6noBgAAIEyU7qsO3evXr7c0/di4VQ1NWQ9RpQ3KeBg6dGhAaYMyI5Awqi1XzwIdjuC8Rx55xA6KFMzH5fnnn3f79++3G34AkYkAHwAAhFXr1q3dgQMHXPPmzV2WLFmCnm/UqJEn64oEmk0es7Th22+/tTF53OAnjqYO6JBE+4fzKleu7CZPnuyKFi0a71SCzp07W4kIgMhEij4AAAirb775xk2fPt2VKFHC66VEHEobkt5k7+jRo27s2LEuf/78nqzJr06dOmX9HOJzzTXXWBM+AJGLAB8AAISVAtSrrrrK62VEHEobEi9Ukz0lp2bMmNENGTLEs3X5UaFChdyGDRvcDTfcEOfP6LWnbvoAIhcBPgAACKtWrVq54cOHWw155syZvV5OxHj55ZettKFHjx4hSxtw8SZ7osMlpaGrtAEX1K9f340aNcpVqlQpqMmeqDREzzdu3NiT9QEID2rwAQBAWLVv396tXbvWOprnzJnTpUuXLuD5xYsXe7Y2P1M6PqUNSXfw4EGXI0cOr5fhO/p9VH+MrVu3WhBfpkwZlzVrVnf48GG7uZ8zZ44rWLCgmzp16kVT+QH4Fzf4AAAgrMqVK2dfSBxKGxLvzz//tFR8ZY0ULlzYdezY0a1cudIC1XHjxrkCBQp4vUTfSJs2rY0PHDlypJs9e7Z9HyVXrlyuZcuW7vHHHye4ByIcN/gAAAA+oNFk69ato7QhETTWTdkib731lt1M9+zZ0w0cONAtWLDAXXnllTYqD8HOnDljkwaOHDli2Q46CIndywBAZOIGHwAAhN2mTZvcxIkT3ZYtWyzQ0u1q27Zt6Qofj6+++sqC1QoVKlDakEBLly51o0ePtjGCOiCpWrWqq1evnitWrJjdSCM0/U6q6R6AlIcAHwAAhNXq1atdhw4drNGZAq5z585ZjW+LFi1sDjfp+6FR2pB4x48fd3nz5rXvNbv90Ucfte+VZq6acwBIbQjwAQBAWI0YMcKaePXt2zfgcf1b9b/vvvuuZ2vzsy5duni9hIijm/slS5ZYkL9v3z5XvXp1e3zmzJn2HACkNgT4AAAgrL7//nvXv3//oMfVCK1JkyaerClSUNqQOE899ZTr2rWrO336tHvggQesud6gQYPctGnTLHUfAFIbAnwAABD2bvCHDh0KOb4sdl05LqC0IfFq1Khhdfia4V68eHF7rG7duu6hhx7iBh9AqkQXfQAAEFZKxVdgOnz48Ogg66effnLdu3d3t956q92wItjDDz9swX2o0gbtH6UNSOrEgYTidxSIXNzgAwCAsHrmmWdc+/btLWU6S5Ys9thff/1lN6y9evXyenm+RWlD4m3bts3169fPDpSUph/bDz/84Mm6/Gj37t1eLwFAMiDABwAAYZUtWzY3a9Yst2zZMptNrmRBjS2rVq2aS5MmjdfL8y1KGxLv5ZdfdgcOHHA9evSIPkxCaGSAAKkDKfoAACBsTpw4YSPKrrjiioBb1nz58tnjiBulDYmn5oPTp093JUqU8HopEefkyZPuk08+cT///LP1flBjxyJFithBE4DIxTE6AAAIi/nz57uaNWu67777LuDxgQMHWjO0zz//3LO1RUppQ9q0aa20oUKFCvZVr149y3qgtCE0BaNXXXWV18uIOPv377dmhMqAmDBhgpXQvP322/Z604EcgMjFDT4AAEiyVatWWd19w4YNXbdu3Vzu3Lmjn1PAoCBi3rx5liZctmxZT9fqZ+qcT2lDwo0fP96tW7fODR061GXOnNnr5UQMlTQcPXrUjRgxwlWpUsV+N7NmzWqHTOnTp3dvvfWW10sEcIkI8AEAQJI98sgjllbep0+feLt46+ZQQRkCUdpwaXSotHbtWnf27FmXM2fOoF4Fixcv9mxtfqZDo3HjxlnpR5kyZSzAL1CggNu8ebNr06aNjWwEEJlosgcAAMLSAb53797x/ozmuXfu3DnZ1hRJpQ0DBgywg4+SJUsGlDZs2rTJOuvXrl3b0zX6Vbly5ewLiXPs2DGXMWPGkM+dOXMm2dcDIHwI8AEAQJKdOnXqojfN11xzjd1UI7C0QfX1Km3IkydPwHPKhlBpg9KmKW0IrUuXLl4vISKVL1/emhMqqyaKxgyOGTOG1xkQ4QjwAQBAkhUqVMht2LDB3XDDDXH+jDrEK+UcFyhNWnPuQ5U2qOQhqnO+Ai9KG0JTlsPEiROtC/yVV17pChcu7Nq2bWsd9hGasm1atmxpqfgK7NVsT9301Wxv6tSpXi8PQBLQsQUAACRZ/fr13ahRo9wff/wR8nk9rufvu+++ZF+b30sbmjRpctHSBv0cgilAbd68ufvll19c1apV7WZ6+/bttmdqvofQdHg0d+5cm26hfVMTxzp16riPPvrIFS9e3OvlAUgCmuwBAIAkU5Oz1q1bW/f3xo0bW+MudeU+fPiw3dzPmTPHFSxY0G4HaRp3gdKhFVTFl/mwa9cu16BBA9tHBHr44Ydd0aJFXd++fQMe179/+uknK20AgNSEFH0AAJBkmt8+adIkN3LkSDd79mz7PkquXLksHfjxxx8nuI+F0oakUWaDmhDGprKHi2VGpGYxa+9DiSoNARB5CPABAEBYaESZGsY9++yzdut85MgRlyNHDhu/FXP8G4JLGypVqhTUZC9maYOyIhAse/bs7tChQ0GPHzx4MGhkHi7YvXt3UAbOzp073dGjR13dunU9WxeApCNFHwAAwCOUNiSNUvG1T8OHD7e6clFqfvfu3W3GOzfRCaeQoF+/fi5Tpky2fwAiEwE+AACAh/7+++/o0gZlPcQsbVDQT2lD3LRf7du3dz/88IPLkiWLPaZO8GoU9/bbb9sNPxJOt/jNmjVzX3/9tddLAXCJCPABAAB84MyZM5Q2XIJz5865ZcuWWRaEPtYWK1bMVatWzTrDI3G0j8888wwTCIAIRoAPAACAiHPixAnLbIh5CLJt2zZrSEjGQ+Kb7B07dsytWLHC1apVyw0ePNiTdQFIOo42AQAAEFHmz5/vatas6b777ruAxwcOHGiz3T///HPP1hYpTfZifynAb9eunXvxxRe9Xh6AJOAGHwAAXPaxWzHR+AxJsWrVKqu7b9iwoevWrZvLnTt3wA3+hAkT3Lx589y7777rypYt6+laASC5EeADAIAkUyf4hFLgBVyqRx55xDrm9+nTJ94Dp/3797vx48cn69r8TGn3Xbp0cRkzZvR6KQAuIwJ8AAAARIzKlSu7yZMnu6JFi8b5Mxs3bnSdO3e2mnKcd8stt7jly5e7nDlzRj/WqVMn179/f3fttdd6ujYA4XNlGP9/AQAAmJMnT7pPPvnE/fzzz65Dhw5uy5YtrkiRIowti4XShsQ7derURZvoXXPNNdaEDxeEutNbs2aN7SeAlIMAHwAAhJVSozVL+8CBAzbjvWnTpjaTfNOmTXbzqvRqnKfmZkicQoUKuQ0bNrgbbrghzp9Zv369ddMHgNSGAB8AAITVq6++arf16nRepUoVe+y1116z+dpDhgxxb731ltdL9A36ESRe/fr13ahRo1ylSpVcnjx5gp7/448/7PnGjRt7sj4A8BIBPgAACKuVK1e6cePGuQwZMkQ/li1bNte7d2/Xpk0bT9fmd5Q2XFyrVq3cp59+6h544AEL4suUKeOyZs3qDh8+bDf3c+bMcQULFrRmfAh0xRVXeL0EAJcZAT4AAAgrzdOOq1P3mTNnkn09kYLShoRJmzatmzRpkhs5cqSbPXu2fR8lV65crmXLlu7xxx+/aJ1+aqSGeunTp4/+9+nTpy2rJlOmTAE/R78HIHLRRR8AAISVOnOrTloN5HS7qpnk1113nevRo4cF/5pTjmDan6NHj7oRI0ZYaYP2TTfTKm1QUEZpQ+gDo127drkjR464HDlyuAIFCnBLHQdGWQKpAwE+AAAIq23bttktat68ed3WrVtdxYoVLeX8r7/+clOnTnXFixf3eom+VK1aNSttuPXWW6MPRhSwbt682UobVq9e7fUSAQA+R4o+AAAIK6WSz507102fPt3ma587d87VqVPHtWjRwuXPn9/r5fkWpQ0AgKQiwAcAAGGn7uZKLUfClS9f3g5FVNoQs0Z6zJgxrmzZsp6uDQAQGUjRBwAAYRUzQA2FBl6hUdoAAEgqbvABAEBY7d69O+DfZ8+edTt37rQGcnXr1vVsXX5HaQMAIKm4wQcAAJedPm7069fPxnF1797d6+UgBWeIxES2CIDUhht8AABw2Wl0Wbt27WzOOwF+aJQ2XFqGCADgAgJ8AACQLH755Rf3999/e70M36K0IWGY0Q4AcSPABwAAl/0mWiPgVqxY4WrVquXJmiI1cI1Z2oDQTp486T755BNrSNihQwe3ZcsWV6RIEZc9e3avlwYAyY4afAAAEFatW7cOeixdunSudOnSrn379i5z5syerCtS6RZfpQ1ff/2110vxnf3799veHDhwwLJDPv30UzdgwAC3adMmN3nyZGtcCACpCQE+AACAjy1btsw988wzbt26dV4vxXd69OhhJQwjRoxwVapUcfPmzXNZs2a1/UqfPr176623vF4iACQrUvQBAECSDR482HXp0sVlzJjR66VELEobEm/lypVu3LhxLkOGDNGPZcuWzfXu3du1adPG07UBgBfSePK/CgAAUpR33nnHnThxIuCxTp06ub1793q2pkhsshf7SwG+pg+8+OKLXi/Pl7Q/cR0qnTlzJtnXAwBe4wYfAAAkWaiKvzVr1rhTp055sp5IRHf4xCtfvrybPn16QPbD6dOn3ZgxY1zZsmU9XRsAeIEAHwAAwCOUNiSNUvFbtmzpVq9ebYH9yy+/bN30//rrLzd16lSvlwcAyY4UfQAAAI9Q2pA06pI/d+5cV6NGDVe1alWXJk0aV6dOHffRRx+54sWLe708AEh23OADAICwuOKKK7xeQsShtCHp8uTJY13zAQAE+AAAIEz69+9vo8miKGV6yJAhLlOmTAE/N2jQIA9Wh9QyeSAmXmsAUhsCfAAAEJZmZ/v27Qt4rEyZMu7QoUP2BVwOmjQQ09mzZ93OnTvd0aNHXd26dT1bFwB4hQAfAAAkGR3gLx2lDeF93ansoV+/fkGZIwCQGlzxv1DFXwAAALjs1Aju/vvvDyht+Pjjj13NmjUpbUgC3eI3a9bMff31114vBQCSFTf4AAAAHqG04fL45Zdf3N9//+31MgAg2RHgAwAAeITShvA32Tt27JhbsWKFq1WrlidrAgAvEeADAAAgRTTZk3Tp0rl27dq59u3be7ImAPASNfgAAAAAAKQAabxeAAAAAJBQgwcPdsePH/d6GQDgSwT4AAAAiBjvvPOOO3HiRMBjnTp1cnv37vVsTQDgFwT4AAAAiBihqkvXrFnjTp065cl6AMBPCPABAAAAAEgBCPABAAAAAEgBCPABAAAQUa644gqvlwAAvnSl1wsAAAAAEqN///4uffr00f8+ffq0GzJkiMuUKVPAzw0aNMiD1QGAdwjwAQAAEDHKly/v9u3bF/BYmTJl3KFDh+wLAFKzK/4XqhUpAAAAAACIKNTgAwAAAACQAhDgAwAAAACQAhDgAwAAAACQAhDgAwAAAACQAhDgAwAAAACQAhDgAwAAAACQAhDgAwAAAACQAhDgAwAAAACQAhDgAwAAAADgIt//A8oRSxGkSx6+AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "numeric_df = df.select_dtypes(include=np.number)\n",
        "sns.heatmap(numeric_df.corr(), annot=True, fmt=\".2f\", cmap=\"coolwarm\")\n",
        "plt.title(\"Correlation Matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "m7AQ2xghkFuA",
      "metadata": {
        "id": "m7AQ2xghkFuA"
      },
      "source": [
        "- CO₂ emissions are strongly correlated with fuel consumption variables (≈0.89–0.92), which are the most important group of variables.\n",
        "- Engine size and cylinders also correlated strongly with CO₂ (≈0.80+), which was significant but weaker than with fuel.\n",
        "- The fuel variables (City, Hwy, Comb) are highly correlated with each other (≈0.94–0.99), which may indicate multicollinearity.\n",
        "- The strong negative correlation between Fuel Consumption (mpg) and Fuel Consumption (~ -0.90) is reasonable because higher mpg indicates lower fuel consumption.\n",
        "- The year is almost irrelevant and can therefore be removed from the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "oZDE1wBuX39l",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:13.200559Z",
          "iopub.status.busy": "2026-03-24T12:15:13.200063Z",
          "iopub.status.idle": "2026-03-24T12:15:13.478352Z",
          "shell.execute_reply": "2026-03-24T12:15:13.475959Z"
        },
        "id": "oZDE1wBuX39l",
        "outputId": "c6347d4e-2ce7-43b5-e2b7-3524a8ba2fde"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "top_vehicle_classes = df[\"Vehicle Class\"].value_counts().head(10).index\n",
        "df_top = df[df[\"Vehicle Class\"].isin(top_vehicle_classes)]\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "sns.boxplot(\n",
        "    data=df_top,\n",
        "    x=\"Vehicle Class\",\n",
        "    y=\"CO2 Emissions(g/km)\",\n",
        "    palette=\"Set2\",\n",
        "    hue=\"Vehicle Class\",\n",
        "    legend=False\n",
        ")\n",
        "\n",
        "plt.title(\"CO2 Emissions by Vehicle Class (Top 10 Classes)\", fontsize=14)\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.xlabel(\"Vehicle Class\")\n",
        "plt.ylabel(\"CO2 Emissions(g/km)\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "_Uc5rg5Tl9Zl",
      "metadata": {
        "id": "_Uc5rg5Tl9Zl"
      },
      "source": [
        "- CO₂ emissions differ significantly by vehicle class.\n",
        "- Pickup trucks, SUVs, and two-seaters have the highest median because large/high-performance vehicles have higher emissions.\n",
        "- Compact and subcompact vehicles have lower CO₂ levels because smaller vehicles are more fuel-efficient.\n",
        "- The large dispersion within each group means there are still differences within each vehicle type.\n",
        "- There are many high outliers across vehicle groups."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "ZTKhX8N1z3m2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "ZTKhX8N1z3m2",
        "outputId": "e80df6ab-9a9d-4979-b949-085606a040f3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(\n",
        "    x=\"Fuel Type\",\n",
        "    y=\"CO2 Emissions(g/km)\",\n",
        "    data=df,\n",
        "    palette=\"Set2\"\n",
        ")\n",
        "\n",
        "plt.title(\"CO2 Emissions by Fuel Type\")\n",
        "plt.xlabel(\"Fuel Type\")\n",
        "plt.ylabel(\"CO2 Emissions(g/km)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ce6b55a",
      "metadata": {},
      "source": [
        "- Fuel type N has the highest median CO₂ emissions, indicating it is the least environmentally efficient among the categories.\n",
        "- Fuel types X and D generally have lower median emissions, suggesting better efficiency.\n",
        "- Fuel type Z exhibits a wide spread and many high-value outliers, indicating high variability and the presence of vehicles with very high emissions.\n",
        "- Fuel type E falls in the middle range, with moderate emissions and variability."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "KVSc83pMX39n",
      "metadata": {
        "id": "KVSc83pMX39n"
      },
      "source": [
        "## 6. Train / Test split\n",
        "\n",
        "Split the data into training and test sets. Model selection is done on the training set only.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "uRgyGgsTX39n",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:13.508622Z",
          "iopub.status.busy": "2026-03-24T12:15:13.508351Z",
          "iopub.status.idle": "2026-03-24T12:15:13.521980Z",
          "shell.execute_reply": "2026-03-24T12:15:13.520976Z"
        },
        "id": "uRgyGgsTX39n",
        "outputId": "388d8c93-5ee4-4d8b-d6ea-51eb89646f8a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X_train: (18044, 10)\n",
            "X_test : (4511, 10)\n"
          ]
        }
      ],
      "source": [
        "X = selected_df.drop(columns=[target])\n",
        "y = selected_df[target]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "print(\"X_train:\", X_train.shape)\n",
        "print(\"X_test :\", X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WXbupnq0X39n",
      "metadata": {
        "id": "WXbupnq0X39n"
      },
      "source": [
        "## 7. Helper functions\n",
        "\n",
        "Define helper functions for building pipelines and computing evaluation metrics.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "f8muRKzwX39o",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:13.525077Z",
          "iopub.status.busy": "2026-03-24T12:15:13.524769Z",
          "iopub.status.idle": "2026-03-24T12:15:13.536013Z",
          "shell.execute_reply": "2026-03-24T12:15:13.535110Z"
        },
        "id": "f8muRKzwX39o"
      },
      "outputs": [],
      "source": [
        "def build_pipeline(num_features, cat_features, poly_degree=None):\n",
        "    if poly_degree is None:\n",
        "        numeric_transformer = \"passthrough\"\n",
        "    else:\n",
        "        numeric_transformer = PolynomialFeatures(degree=poly_degree, include_bias=False)\n",
        "\n",
        "    preprocessor = ColumnTransformer(\n",
        "        transformers=[\n",
        "            (\"num\", numeric_transformer, num_features),\n",
        "            (\"cat\", OneHotEncoder(drop=\"first\", handle_unknown=\"ignore\"), cat_features)\n",
        "        ],\n",
        "        remainder=\"drop\"\n",
        "    )\n",
        "\n",
        "    model = Pipeline(steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\"regressor\", LinearRegression())\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "\n",
        "def build_estimator(spec):\n",
        "    pipeline = build_pipeline(\n",
        "        num_features=spec[\"num\"],\n",
        "        cat_features=spec[\"cat\"],\n",
        "        poly_degree=spec[\"poly_degree\"]\n",
        "    )\n",
        "\n",
        "    if spec.get(\"log_target\", False):\n",
        "        return TransformedTargetRegressor(\n",
        "            regressor=pipeline,\n",
        "            func=np.log,\n",
        "            inverse_func=np.exp\n",
        "        )\n",
        "\n",
        "    return pipeline\n",
        "\n",
        "\n",
        "def evaluate_model(name, estimator, X_train, y_train, cv):\n",
        "    scoring = {\n",
        "        \"mae\": \"neg_mean_absolute_error\",\n",
        "        \"mse\": \"neg_mean_squared_error\",\n",
        "        \"rmse\": \"neg_root_mean_squared_error\",\n",
        "        \"r2\": \"r2\"\n",
        "    }\n",
        "\n",
        "    cv_scores = cross_validate(\n",
        "        estimator,\n",
        "        X_train,\n",
        "        y_train,\n",
        "        cv=cv,\n",
        "        scoring=scoring\n",
        "    )\n",
        "\n",
        "    return {\n",
        "        \"Model\": name,\n",
        "        \"CV_MAE\": -cv_scores[\"test_mae\"].mean(),\n",
        "        \"CV_MSE\": -cv_scores[\"test_mse\"].mean(),\n",
        "        \"CV_RMSE\": -cv_scores[\"test_rmse\"].mean(),\n",
        "        \"CV_R2\": cv_scores[\"test_r2\"].mean()\n",
        "    }\n",
        "\n",
        "\n",
        "def evaluate_on_test(estimator, X_train, y_train, X_test, y_test):\n",
        "    estimator.fit(X_train, y_train)\n",
        "    y_pred = estimator.predict(X_test)\n",
        "    return y_pred, {\n",
        "        \"Test_MAE\": mean_absolute_error(y_test, y_pred),\n",
        "        \"Test_MSE\": mean_squared_error(y_test, y_pred),\n",
        "        \"Test_RMSE\": mean_squared_error(y_test, y_pred) ** 0.5,\n",
        "        \"Test_R2\": r2_score(y_test, y_pred)\n",
        "    }\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "auldWhwFsMV1",
      "metadata": {
        "id": "auldWhwFsMV1"
      },
      "outputs": [],
      "source": [
        "cv = KFold(n_splits=10, shuffle=True, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "k72JrZqIX39o",
      "metadata": {
        "id": "k72JrZqIX39o"
      },
      "source": [
        "## 8. Define 6 models\n",
        "\n",
        "Eight candidate models are defined to compare simple, full, interaction, polynomial, and transformed-target specifications.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "wBcUoIrvX39o",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:13.539540Z",
          "iopub.status.busy": "2026-03-24T12:15:13.538985Z",
          "iopub.status.idle": "2026-03-24T12:15:13.546665Z",
          "shell.execute_reply": "2026-03-24T12:15:13.545698Z"
        },
        "id": "wBcUoIrvX39o"
      },
      "outputs": [],
      "source": [
        "model_specs = [\n",
        "    {\n",
        "        \"name\": \"Model 1 - Baseline Fuel\",\n",
        "        \"num\": [\"Fuel Consumption Comb (L/100 km)\"],\n",
        "        \"cat\": [],\n",
        "        \"poly_degree\": None,\n",
        "        \"log_target\": False,\n",
        "        \"complexity\": 1\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Model 2 - Engine Only\",\n",
        "        \"num\": [\"Engine Size(L)\", \"Cylinders\"],\n",
        "        \"cat\": [],\n",
        "        \"poly_degree\": None,\n",
        "        \"log_target\": False,\n",
        "        \"complexity\": 2\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Model 3 - Parsimonious Mixed\",\n",
        "        \"num\": [\"Engine Size(L)\", \"Cylinders\", \"Fuel Consumption Comb (L/100 km)\"],\n",
        "        \"cat\": [\"Vehicle Class\", \"Fuel Type\"],\n",
        "        \"poly_degree\": None,\n",
        "        \"log_target\": False,\n",
        "        \"complexity\": 3\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Model 4 - Full Linear\",\n",
        "        \"num\": numeric_cols,\n",
        "        \"cat\": categorical_cols,\n",
        "        \"poly_degree\": None,\n",
        "        \"log_target\": False,\n",
        "        \"complexity\": 6\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Model 5 - Polynomial\",\n",
        "        \"num\": [\"Engine Size(L)\", \"Fuel Consumption Comb (L/100 km)\"],\n",
        "        \"cat\": [\"Fuel Type\"],\n",
        "        \"poly_degree\": 2,\n",
        "        \"log_target\": False,\n",
        "        \"complexity\": 5\n",
        "    },\n",
        "    {\n",
        "        \"name\": \"Model 6 - Interaction Model\",\n",
        "        \"num\": [\n",
        "            \"Engine Size(L)\",\n",
        "            \"Fuel Consumption Comb (L/100 km)\",\n",
        "            \"Engine Size x Fuel Consumption\"\n",
        "        ],\n",
        "        \"cat\": [\"Fuel Type\"],\n",
        "        \"poly_degree\": None,\n",
        "        \"log_target\": False,\n",
        "        \"complexity\": 4\n",
        "    }\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "19eb2ffd",
      "metadata": {
        "id": "19eb2ffd"
      },
      "source": [
        "## 9. Cross-validation on the training set\n",
        "\n",
        "Evaluate all candidate models with cross-validation using **MAE, MSE, RMSE, and R2**.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e007688c",
      "metadata": {
        "id": "e007688c"
      },
      "source": [
        "### 9.1 Run cross-validation for all models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "804cf751",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:13.550247Z",
          "iopub.status.busy": "2026-03-24T12:15:13.549975Z",
          "iopub.status.idle": "2026-03-24T12:15:14.553472Z",
          "shell.execute_reply": "2026-03-24T12:15:14.552743Z"
        },
        "id": "804cf751",
        "outputId": "4ee0dd63-d351-4eaf-d141-748147c3449b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>CV_MAE</th>\n",
              "      <th>CV_MSE</th>\n",
              "      <th>CV_RMSE</th>\n",
              "      <th>CV_R2</th>\n",
              "      <th>Complexity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Model 5 - Polynomial</td>\n",
              "      <td>2.834338</td>\n",
              "      <td>18.950246</td>\n",
              "      <td>4.347888</td>\n",
              "      <td>0.994587</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Model 4 - Full Linear</td>\n",
              "      <td>2.498579</td>\n",
              "      <td>20.423048</td>\n",
              "      <td>4.516387</td>\n",
              "      <td>0.994161</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Model 3 - Parsimonious Mixed</td>\n",
              "      <td>2.706327</td>\n",
              "      <td>24.678360</td>\n",
              "      <td>4.963923</td>\n",
              "      <td>0.992944</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Model 6 - Interaction Model</td>\n",
              "      <td>3.002125</td>\n",
              "      <td>24.953650</td>\n",
              "      <td>4.992023</td>\n",
              "      <td>0.992868</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Model 1 - Baseline Fuel</td>\n",
              "      <td>13.156772</td>\n",
              "      <td>518.192992</td>\n",
              "      <td>22.754316</td>\n",
              "      <td>0.851692</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Model 2 - Engine Only</td>\n",
              "      <td>25.339823</td>\n",
              "      <td>1084.908411</td>\n",
              "      <td>32.932138</td>\n",
              "      <td>0.689549</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          Model     CV_MAE       CV_MSE    CV_RMSE     CV_R2  \\\n",
              "0          Model 5 - Polynomial   2.834338    18.950246   4.347888  0.994587   \n",
              "1         Model 4 - Full Linear   2.498579    20.423048   4.516387  0.994161   \n",
              "2  Model 3 - Parsimonious Mixed   2.706327    24.678360   4.963923  0.992944   \n",
              "3   Model 6 - Interaction Model   3.002125    24.953650   4.992023  0.992868   \n",
              "4       Model 1 - Baseline Fuel  13.156772   518.192992  22.754316  0.851692   \n",
              "5         Model 2 - Engine Only  25.339823  1084.908411  32.932138  0.689549   \n",
              "\n",
              "   Complexity  \n",
              "0           5  \n",
              "1           6  \n",
              "2           3  \n",
              "3           4  \n",
              "4           1  \n",
              "5           2  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results = []\n",
        "\n",
        "for spec in model_specs:\n",
        "    estimator = build_estimator(spec)\n",
        "    results.append(\n",
        "        evaluate_model(\n",
        "            name=spec[\"name\"],\n",
        "            estimator=estimator,\n",
        "            X_train=X_train,\n",
        "            y_train=y_train,\n",
        "            cv=cv\n",
        "        )\n",
        "    )\n",
        "\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df[\"Complexity\"] = results_df[\"Model\"].map({spec[\"name\"]: spec[\"complexity\"] for spec in model_specs})\n",
        "results_df = results_df.sort_values(by=[\"CV_RMSE\", \"CV_R2\", \"Complexity\"], ascending=[True, False, True]).reset_index(drop=True)\n",
        "results_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "2pEqjUv3X39q",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:14.556647Z",
          "iopub.status.busy": "2026-03-24T12:15:14.556288Z",
          "iopub.status.idle": "2026-03-24T12:15:14.722809Z",
          "shell.execute_reply": "2026-03-24T12:15:14.721795Z"
        },
        "id": "2pEqjUv3X39q",
        "outputId": "8d78512d-f1b9-4de9-dd53-6f44ff76df9e"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_df = results_df.sort_values(\"CV_R2\", ascending=True)\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.barh(plot_df[\"Model\"], plot_df[\"CV_R2\"])\n",
        "plt.title(\"Cross-Validated R2 Comparison\")\n",
        "plt.xlim(0, 1)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "B4qtNAyHX39r",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:14.725875Z",
          "iopub.status.busy": "2026-03-24T12:15:14.725551Z",
          "iopub.status.idle": "2026-03-24T12:15:14.887963Z",
          "shell.execute_reply": "2026-03-24T12:15:14.887175Z"
        },
        "id": "B4qtNAyHX39r",
        "outputId": "888e1e8a-48a1-4d48-ae98-c8d3e6a2eee3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_df = results_df.sort_values(\"CV_RMSE\", ascending=True)\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.barh(plot_df[\"Model\"], plot_df[\"CV_RMSE\"])\n",
        "plt.title(\"Cross-Validated RMSE Comparison\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "compare-8-models-sec9",
      "metadata": {
        "id": "compare-8-models-sec9"
      },
      "source": [
        "### 9.2 Comparison of the 6 models\n",
        "\n",
        "The cross-validation results show a clear ranking across the eight candidate models. **Model 5 (Polynomial)** gives the best predictive performance because it has the lowest CV RMSE (3.348) and the highest CV R2 (0.995). **Model 4 (Full Linear)** is very close, while **Model 3 (Parsimonious Mixed)** remains highly competitive with lower complexity and better interpretability.\n",
        "\n",
        "| Model | CV RMSE | CV R2 | Complexity | Strengths | Weaknesses |\n",
        "|---|---:|---:|---:|---|---|\n",
        "| Model 1 - Baseline Fuel | 22.754 | 0.852 | 1 | Simplest model and very easy to explain because it uses only combined fuel consumption. | Predictive performance is weak and it ignores engine and vehicle characteristics. |\n",
        "| Model 2 - Engine Only | 32.932 | 0.7 | 2 | Still simple and interpretable, with coefficients tied directly to engine size and cylinders. | Worst predictive accuracy, so engine information alone is not enough to explain emissions well. |\n",
        "| Model 3 - Parsimonious Mixed | 4.964 | 0.993 | 3 | Best balance of strong prediction, moderate simplicity, and good interpretability. | Slightly less accurate than Models 4 and 5, and categorical terms add some complexity. |\n",
        "| Model 4 - Full Linear | 4.516 | 0.994 | 6 | Excellent predictive performance and broad feature coverage. | More complex, harder to explain cleanly, and less parsimonious than necessary. |\n",
        "| Model 5 - Polynomial | 4.348 | 0.995 | 5 | Best predictive performance because nonlinear terms capture curvature. | Polynomial effects are harder to interpret directly, so explanatory value is lower than Model 3. |\n",
        "| Model 6 - Interaction Model | 4.992 | 0.993 | 4 | Captures interaction structure and still performs well. | Less interpretable than a simple linear model and still not as accurate as Models 3 to 5. |\n",
        "\n",
        "Overall, **Models 1 to 2** are simple but not accurate enough, **Models 3 to 5** are the strong-performing group.Therefore, **Model 3** is retained as the final explanatory model because it offers the best trade-off between predictive performance, simplicity, and interpretability, while **Model 5** is the best model for pure prediction.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48053eaf",
      "metadata": {
        "id": "48053eaf"
      },
      "source": [
        "## 10. Compare parsimonious vs full model\n",
        "\n",
        "Compare the parsimonious model with the full model to balance fit and interpretability.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "e87d6008",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:14.891021Z",
          "iopub.status.busy": "2026-03-24T12:15:14.890724Z",
          "iopub.status.idle": "2026-03-24T12:15:15.136598Z",
          "shell.execute_reply": "2026-03-24T12:15:15.135834Z"
        },
        "id": "e87d6008",
        "outputId": "0d926a6b-24c2-48a7-b989-31c2d256bc3e"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Adj_R2_train</th>\n",
              "      <th>AIC_train</th>\n",
              "      <th>BIC_train</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Parsimonious Mixed</td>\n",
              "      <td>0.992964</td>\n",
              "      <td>109027.455968</td>\n",
              "      <td>109214.669612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Full Linear</td>\n",
              "      <td>0.994177</td>\n",
              "      <td>105643.856009</td>\n",
              "      <td>106080.687845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Polynomial</td>\n",
              "      <td>0.992268</td>\n",
              "      <td>110712.469821</td>\n",
              "      <td>110767.073800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Model  Adj_R2_train      AIC_train      BIC_train\n",
              "0  Parsimonious Mixed      0.992964  109027.455968  109214.669612\n",
              "1         Full Linear      0.994177  105643.856009  106080.687845\n",
              "2          Polynomial      0.992268  110712.469821  110767.073800"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>CV_MAE</th>\n",
              "      <th>CV_MSE</th>\n",
              "      <th>CV_RMSE</th>\n",
              "      <th>CV_R2</th>\n",
              "      <th>Complexity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Model 5 - Polynomial</td>\n",
              "      <td>2.834338</td>\n",
              "      <td>18.950246</td>\n",
              "      <td>4.347888</td>\n",
              "      <td>0.994587</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Model 4 - Full Linear</td>\n",
              "      <td>2.498579</td>\n",
              "      <td>20.423048</td>\n",
              "      <td>4.516387</td>\n",
              "      <td>0.994161</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Model 3 - Parsimonious Mixed</td>\n",
              "      <td>2.706327</td>\n",
              "      <td>24.678360</td>\n",
              "      <td>4.963923</td>\n",
              "      <td>0.992944</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                          Model    CV_MAE     CV_MSE   CV_RMSE     CV_R2  \\\n",
              "0          Model 5 - Polynomial  2.834338  18.950246  4.347888  0.994587   \n",
              "1         Model 4 - Full Linear  2.498579  20.423048  4.516387  0.994161   \n",
              "2  Model 3 - Parsimonious Mixed  2.706327  24.678360  4.963923  0.992944   \n",
              "\n",
              "   Complexity  \n",
              "0           5  \n",
              "1           6  \n",
              "2           3  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Interpretation decision: Model 5 is best for prediction, but Model 3 is retained as the final explanatory model.\n"
          ]
        }
      ],
      "source": [
        "pars_spec = next(spec for spec in model_specs if spec[\"name\"] == \"Model 3 - Parsimonious Mixed\")\n",
        "full_spec = next(spec for spec in model_specs if spec[\"name\"] == \"Model 4 - Full Linear\")\n",
        "poly_spec = next(spec for spec in model_specs if spec[\"name\"] == \"Model 5 - Polynomial\")\n",
        "\n",
        "pars_train = X_train[pars_spec[\"num\"] + pars_spec[\"cat\"]].copy()\n",
        "full_train = X_train[full_spec[\"num\"] + full_spec[\"cat\"]].copy()\n",
        "poly_train = X_train[poly_spec[\"num\"] + poly_spec[\"cat\"]].copy()\n",
        "\n",
        "pars_train_dm = pd.get_dummies(pars_train, drop_first=True, dtype=float)\n",
        "full_train_dm = pd.get_dummies(full_train, drop_first=True, dtype=float)\n",
        "poly_train_dm = pd.get_dummies(poly_train, drop_first=True, dtype=float)\n",
        "\n",
        "X_pars_sm = sm.add_constant(pars_train_dm, has_constant=\"add\").astype(float)\n",
        "X_full_sm = sm.add_constant(full_train_dm, has_constant=\"add\").astype(float)\n",
        "X_poly_sm = sm.add_constant(poly_train_dm, has_constant=\"add\").astype(float)\n",
        "\n",
        "y_train_sm = y_train.astype(float)\n",
        "\n",
        "ols_pars = sm.OLS(y_train_sm, X_pars_sm).fit()\n",
        "ols_full = sm.OLS(y_train_sm, X_full_sm).fit()\n",
        "ols_poly = sm.OLS(y_train_sm, X_poly_sm).fit()\n",
        "\n",
        "comparison_df = pd.DataFrame({\n",
        "    \"Model\": [\"Parsimonious Mixed\", \"Full Linear\", \"Polynomial\"],\n",
        "    \"Adj_R2_train\": [ols_pars.rsquared_adj, ols_full.rsquared_adj, ols_poly.rsquared_adj],\n",
        "    \"AIC_train\": [ols_pars.aic, ols_full.aic, ols_poly.aic],\n",
        "    \"BIC_train\": [ols_pars.bic, ols_full.bic, ols_poly.bic]\n",
        "})\n",
        "\n",
        "shortlist = [pars_spec, full_spec, poly_spec]\n",
        "\n",
        "display(comparison_df)\n",
        "display(\n",
        "    results_df.loc[\n",
        "        results_df[\"Model\"].isin([spec[\"name\"] for spec in shortlist]),\n",
        "        [\"Model\", \"CV_MAE\", \"CV_MSE\", \"CV_RMSE\", \"CV_R2\", \"Complexity\"]\n",
        "    ].sort_values(by=\"CV_RMSE\")\n",
        ")\n",
        "\n",
        "print()\n",
        "print(\"Interpretation decision: Model 5 is best for prediction, but Model 3 is retained as the final explanatory model.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ca827a3a",
      "metadata": {
        "id": "ca827a3a"
      },
      "source": [
        "## 11. Final model selection\n",
        "\n",
        "Among the candidate models, **Model 5 (Polynomial Regression)** achieved the lowest cross-validation RMSE, which indicates the strongest predictive performance. However, its higher complexity and the inclusion of polynomial terms make the model less interpretable.\n",
        "\n",
        "**Model 3 (Parsimonious Mixed Model)**, while slightly less accurate, uses fewer predictors and still remains competitive in cross-validation and test performance. Since one of the main goals of regression analysis is not only prediction but also explanation of the relationship between predictors and the response variable, interpretability is an important criterion in this study.\n",
        "\n",
        "Therefore, **Model 3 is selected as the final model** because it provides the best balance between predictive performance and interpretability.\n",
        "\n",
        "| Model | CV RMSE | Complexity |\n",
        "|---|---:|---|\n",
        "| Model 3 | 4.964 | Low |\n",
        "| Model 4 | 4.516 | Medium |\n",
        "| Model 5 | 4.348 | High |\n",
        "\n",
        "Although **Model 5** delivers the best predictive accuracy, **Model 3** is chosen as the final explanatory model because it is simpler, easier to interpret, and still performs competitively.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "dxwEsBE0X39s",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:15.140424Z",
          "iopub.status.busy": "2026-03-24T12:15:15.139981Z",
          "iopub.status.idle": "2026-03-24T12:15:15.189703Z",
          "shell.execute_reply": "2026-03-24T12:15:15.188820Z"
        },
        "id": "dxwEsBE0X39s",
        "outputId": "e8ab73df-1015-45bf-b551-e2f06065593a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Selected explanatory model: Model 3 - Parsimonious Mixed\n",
            "Test MAE : 2.643746764009959\n",
            "Test MSE : 23.05316405754338\n",
            "Test RMSE: 4.801371060180975\n",
            "Test R2  : 0.9936308129351765\n"
          ]
        }
      ],
      "source": [
        "main_spec = pars_spec\n",
        "\n",
        "main_pipeline = build_pipeline(\n",
        "    num_features=main_spec[\"num\"],\n",
        "    cat_features=main_spec[\"cat\"],\n",
        "    poly_degree=main_spec[\"poly_degree\"]\n",
        ")\n",
        "\n",
        "main_pipeline.fit(X_train, y_train)\n",
        "main_pred_test = main_pipeline.predict(X_test)\n",
        "\n",
        "print(\"Selected explanatory model:\", main_spec[\"name\"])\n",
        "print(\"Test MAE :\", mean_absolute_error(y_test, main_pred_test))\n",
        "print(\"Test MSE :\", mean_squared_error(y_test, main_pred_test))\n",
        "print(\"Test RMSE:\", mean_squared_error(y_test, main_pred_test) ** 0.5)\n",
        "print(\"Test R2  :\", r2_score(y_test, main_pred_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "qFyr58maX39s",
      "metadata": {
        "id": "qFyr58maX39s"
      },
      "source": [
        "## 12. Fit the selected model with statsmodels\n",
        "\n",
        "Refit the selected model with `statsmodels` to obtain coefficients and diagnostic statistics.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "48aUIS7mX39s",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:15.194593Z",
          "iopub.status.busy": "2026-03-24T12:15:15.194151Z",
          "iopub.status.idle": "2026-03-24T12:15:15.258038Z",
          "shell.execute_reply": "2026-03-24T12:15:15.257178Z"
        },
        "id": "48aUIS7mX39s",
        "outputId": "6e6d065a-f721-49e1-c5c5-d78cef40a093"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                             OLS Regression Results                            \n",
            "===============================================================================\n",
            "Dep. Variable:     CO2 Emissions(g/km)   R-squared:                       0.993\n",
            "Model:                             OLS   Adj. R-squared:                  0.993\n",
            "Method:                  Least Squares   F-statistic:                 1.107e+05\n",
            "Date:                 Sat, 04 Apr 2026   Prob (F-statistic):               0.00\n",
            "Time:                         22:03:45   Log-Likelihood:                -54490.\n",
            "No. Observations:                18044   AIC:                         1.090e+05\n",
            "Df Residuals:                    18020   BIC:                         1.092e+05\n",
            "Df Model:                           23                                         \n",
            "Covariance Type:             nonrobust                                         \n",
            "==========================================================================================================\n",
            "                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------------------\n",
            "const                                     34.4134      0.361     95.375      0.000      33.706      35.121\n",
            "Engine Size(L)                            -0.2634      0.080     -3.299      0.001      -0.420      -0.107\n",
            "Cylinders                                  0.2553      0.058      4.418      0.000       0.142       0.369\n",
            "Fuel Consumption Comb (L/100 km)          22.7320      0.030    754.265      0.000      22.673      22.791\n",
            "Vehicle Class_FULL-SIZE                    2.1460      0.180     11.935      0.000       1.794       2.498\n",
            "Vehicle Class_MID-SIZE                     0.6987      0.143      4.887      0.000       0.418       0.979\n",
            "Vehicle Class_MINICOMPACT                  0.1111      0.207      0.537      0.591      -0.295       0.517\n",
            "Vehicle Class_MINIVAN                      1.1858      0.304      3.903      0.000       0.590       1.781\n",
            "Vehicle Class_PICKUP TRUCK - SMALL         1.8076      0.283      6.390      0.000       1.253       2.362\n",
            "Vehicle Class_PICKUP TRUCK - STANDARD      0.6338      0.189      3.361      0.001       0.264       1.003\n",
            "Vehicle Class_SPECIAL PURPOSE VEHICLE      5.8125      0.518     11.221      0.000       4.797       6.828\n",
            "Vehicle Class_STATION WAGON - MID-SIZE    -0.4478      0.299     -1.496      0.135      -1.035       0.139\n",
            "Vehicle Class_STATION WAGON - SMALL       -0.5759      0.213     -2.706      0.007      -0.993      -0.159\n",
            "Vehicle Class_SUBCOMPACT                   0.1134      0.160      0.708      0.479      -0.201       0.428\n",
            "Vehicle Class_SUV                         -0.7074      0.163     -4.338      0.000      -1.027      -0.388\n",
            "Vehicle Class_SUV - SMALL                  2.6000      0.172     15.125      0.000       2.263       2.937\n",
            "Vehicle Class_SUV - STANDARD               3.2540      0.209     15.556      0.000       2.844       3.664\n",
            "Vehicle Class_TWO-SEATER                   0.6936      0.193      3.603      0.000       0.316       1.071\n",
            "Vehicle Class_VAN - CARGO                 -4.2226      0.355    -11.897      0.000      -4.918      -3.527\n",
            "Vehicle Class_VAN - PASSENGER             -7.2048      0.382    -18.841      0.000      -7.954      -6.455\n",
            "Fuel Type_E                             -145.8253      0.398   -366.041      0.000    -146.606    -145.044\n",
            "Fuel Type_N                              -97.3703      0.977    -99.645      0.000     -99.286     -95.455\n",
            "Fuel Type_X                              -31.3655      0.323    -97.100      0.000     -31.999     -30.732\n",
            "Fuel Type_Z                              -31.0451      0.333    -93.271      0.000     -31.697     -30.393\n",
            "==============================================================================\n",
            "Omnibus:                     6116.252   Durbin-Watson:                   1.999\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           540452.599\n",
            "Skew:                           0.696   Prob(JB):                         0.00\n",
            "Kurtosis:                      29.775   Cond. No.                         369.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ],
      "source": [
        "main_train = X_train[main_spec[\"num\"] + main_spec[\"cat\"]].copy()\n",
        "main_train_dm = pd.get_dummies(main_train, drop_first=True, dtype=float)\n",
        "X_train_sm = sm.add_constant(main_train_dm, has_constant=\"add\").astype(float)\n",
        "y_train_sm = y_train.astype(float)\n",
        "\n",
        "ols_main = sm.OLS(y_train_sm, X_train_sm).fit()\n",
        "print(ols_main.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc3e83ae",
      "metadata": {
        "id": "fc3e83ae"
      },
      "source": [
        "**Interpretation of Regression Coefficients**\n",
        "\n",
        "In multiple linear regression, each coefficient represents the **conditional effect** of a predictor on the response variable while holding other variables constant.\n",
        "\n",
        "The most important predictor in the model is **Fuel Consumption Comb (L/100 km)**.  \n",
        "Holding other variables constant, an increase of 1 L/100 km in combined fuel consumption is associated with an increase in predicted CO₂ emissions.\n",
        "\n",
        "- Vehicle class also have some contributes to emission differences. We can observe most of the weight for each class are basicly does not contribute much in the final CO2 Emissions values. And we can also see that there is a trend where larger vehicle generally have higher weight, which may indicate that larger vehicle classes is associate with higher CO₂ emissions compared to smaller vehicles. This make sense as vehicle class ussually reflect differences in vehicle size, weight, and engine requirements.\n",
        "\n",
        "    But there are other class that does not follow this trend like VAN - PASSENGER class and VAN - CARGO class. As these larger classes (see [Data discription](https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/personal-vehicles/understanding-tables)) have smaller weight than class PICKUP TRUCK - SMALL, even though the size of both van are more likely to be bigger than the size of class PICKUP TRUCK - SMALL. This may suggest that the label may not be well define enough to be use in the model.\n",
        "\n",
        "- Engine size does not appear to have a strong independent effect once fuel consumption and other predictors are included. This suggests that fuel consumption already captures much of the impact of engine characteristics on emissions.\n",
        "\n",
        "- It can be seen that fuel type has a significant impact on CO2 emissions, as fuel types E, X, and Z have high coef, high t, and zero P values, indicating that fuel type greatly affects emissions.\n",
        "\n",
        "- Overall, the results indicate that fuel consumption, fuel type and a small portion is vehicle class is the primary determinant of vehicle CO₂ emissions.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "I8R1cdY0X394",
      "metadata": {
        "id": "I8R1cdY0X394"
      },
      "source": [
        "## 13. Regression diagnostics\n",
        "\n",
        "To assest how good our model is, we can use the **Residual vs Fitted plot** to figure out what is wrong. And to figure how bad does our model fail to predict, we can use a test call **Breusch-Pagan test**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "JDExD2Uri6ze",
      "metadata": {
        "id": "JDExD2Uri6ze"
      },
      "source": [
        "### 13.1 Residuals plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "jrqwCu7FX394",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:15.282296Z",
          "iopub.status.busy": "2026-03-24T12:15:15.281628Z",
          "iopub.status.idle": "2026-03-24T12:15:15.495976Z",
          "shell.execute_reply": "2026-03-24T12:15:15.495042Z"
        },
        "id": "jrqwCu7FX394",
        "outputId": "d33e5953-36b2-4ba2-caf6-6161f239237c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fitted = ols_main.fittedvalues\n",
        "residuals = ols_main.resid\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.scatter(fitted, residuals, alpha=0.6)\n",
        "plt.axhline(0, color=\"red\", linestyle=\"--\")\n",
        "plt.xlabel(\"Fitted values\")\n",
        "plt.ylabel(\"Residuals\")\n",
        "plt.title(\"Residuals vs Fitted\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "mstj9uEtkdBl",
      "metadata": {
        "id": "mstj9uEtkdBl"
      },
      "source": [
        "Here the **Residuals vs Fitted plot** is used to evaluate the assumption we make in linear regression and model performance.\n",
        "\n",
        "Ideally, the residuals should be randomly scattered around zero without any clear pattern. If there's a strong curved pattern then it would suggest model may be wrong. While a funnel-shaped pattern would suggest *heteroscedasticity* (i.e. assumption that the variance is constants is wrong). However, for models with many predictor (like the current model), these patterns may not highlight what is the problem, just there is a problem with either our assumption about the data set or the model.\n",
        "\n",
        "As with this dataset, we can immeditately see there are a number of point that does not align on (roughly) the same axis as the residuals line, which may suggest there are problems with the model. However most of the residuals are locate on centered around model line and do not show strong curvature.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "jW-XhcI_vjaq",
      "metadata": {
        "id": "jW-XhcI_vjaq"
      },
      "source": [
        "### 13.2 Breusch-Pagan test"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "96bfR62C5w_b",
      "metadata": {
        "id": "96bfR62C5w_b"
      },
      "source": [
        "If we ignore values that does not align properly, we can observe that there is (maybe) a small funnel formming. To assest whether there is heteroscedasticity (a funnal shape data) in the residuals plots, we can use a test call **Breusch-Pagan test**. But to utilized the test properly, we need to figure out the what are the points thats (very obvious from looking at the residual plot) does not fit well.\n",
        "\n",
        "To do this we first check what is the predicted result when plot on the Fuel Consumption (Combined) vs CO2 Emissions is."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "ondCC8WroLQI",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "ondCC8WroLQI",
        "outputId": "5970bb5a-d3a1-4103-ad46-0e17c957cad0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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2pkdDT7RNg3njM6IIco2SG4hii+hJmf4oHoQsAVa2W1ugYFvKQqVMyqbQ99NTLz0N09MrrZPcVU1vKmQtIyjzqi1Q3BC5mCxxSRYJuliTCLWsSNZTffxxoDiv1m7KHYFuHvHQ0z/d2K2n+qYWpKaQy4wEZ/wTPQlastLQeHTmCd2y0jQ9D+g4kRCgmLCWhD3FkdHNilyU5LqibWgay0WuRTrO8e7QjkLuRiuQ3BLVFCtErq6OQucZxYORxc46t+mPbuQUr0mWXmuMmp6Lrbll6TjSObyvMaGgfhKgVoJKvGijZB8SVzSfrDbklrNce61ZPpOTk0U8IMXmUZxXUyhzkmL3rPWQcCD36+OPP97i8vRQQxYosvpZ+0/nHsWV0YMTWQDJStgTIeshuUOt8aJ9JcFK1xbr+FGSA4lggsaekgzofKcHl/jfWKJ/f7RNdDxJ7NG5ZVn8afz2ZdVmDhzsHmV6NHRDoAs3/dGNl8RF/I3Rym6kixxlzlEgNsUS0ZMhlV9oLQC8tRsVxcDRxZIKgFJMB7mSyHpANwESANaFlIQA3YzJZUClDsiNSXElFHROT6ZtLaBKrgey8JEVjwQIpeRTcDuV2LAy6Wg9tBxlNFKZAxIDtF/7spS0F4r5ogw4coPQzZPibShof+DAgWI+XbTpSZ4sJS0F/5M7ipIzKICZAtRpX0j80s2VxEVnoJsaCTcS61RKwYKsTHRDIfcy3dDoHCA3N5V3oPGidZM1iM4H+qPtovge2jY6ryjYnUQJjRWNWWeh76WirmQ1IaFNbkMS5XR+dhS6WdNxpAB3cj/S/tE5SucZWZFoneTOJOsSudzoQYLGhxIYqCh0S5BFhyxl+8LKFH3ppZfEjZvWYUE3dTq+FDNGIovc0DTG9JmWXHcWdFwoWYFcqLTt1oMPnTck5mhM4svt0NiSAKNlKbaLrKb08EBWNCqWS8KTrGnWAwX9JqjsBVmjyMUaX6aCRAtlaO4LejCj86QlyNW8r6SI9kDfQ+EQdC0hkUWuZxKm9Nuia5glwuhBjvaRjj9dY+iY0zEjy21Tb0Oifn/08ELWS4qppe8hdylZAsniGh+vyXQfLNqYHg1dxChuhW6+dHOggFyyrJA70YIEE8Vh0E2LShGQBYDcNXSDjnedtAW6kJJIopsIuSlJIJFgoEwxssRZbgayQpGQpBsFWfRoG0lE0I3JyiBrKxTHQmUa6CJNT7j0ZE1WCRJolmAiEUWByXQxpYs+XaSb1vPqDHSzpBsDfS/tIwkE6wZiiQe6qdD6KWuSBG48FG9DN3i6iVIJCDo+FPxNLhUK9u8sJGDpptS0Zh+JLRIUdBOnY0hWIbrB0s2Hjll8EgXdzOkmSDczEvUkMkiYX3311W2K+9sfdDMllzYdAxpHesigdZHo7Ci0XXRu09iTy4punjT+tB6qa2ZB2a7WvpH7k0QZjSEdk3hIBJEot86tfUECnh58KEklHhJzZOWi85XOFxprsizTWO/rQYLOYyo1Q9tI8adU0oJ+q+T+pYcfOufjEw/IhUhChSzc9BBDmdBkPaLlyTJFQj0+Pou6YxA07vQXDx2zptbkplhZ1y1BYogeDhIFPaTReUIPDRSaQGKcjqWVNU/jQ0KT9p2uY3QsyGXcWoZ1on5/VKqERB5tF+0vWY/pu+l47kuQMwcOiSrsHsD1MQzTwyDrHt0wSNT0VEhQUnkEEgpkhWA6BgkAChkgEdRaPUKGYXouHNPGMEyPx6p2T25BpmOQ1ZisW2QNZsHGML0TFm0Mw/QKyBJIFrdvvvmmuzelV0IxduRis4oGMwzT+2D3KMMwDMMwTC+ALW0MwzAMwzC9ABZtDMMwDMMwvQAWbQzDMAzDML0ArtPWBCrcSGF+VJyQYRiGYRimq6E6pJTVTcWU9wVb2ppAgq0v52bQvlHvur68j30NHrPeCY9b74THrfdh9oExa6v2YEtbEywLG/Vr64tQ+5N169aJ1j2JqALPdD08Zr0THrfeCY9b7yPYB8aMWry1Bba0MQzDMAzD9AJYtDEMwzAMw/QCWLQxDMMwDMP0Ali0MQzDMAzD9AJYtDEMwzAMw/QCOHu0E+i6Lmqr9CYikUjDqyyzZu9LY0aZz4qiHMAtYxiGYQ4kLNo6ANVSKSkpQU1NDXobhmFAVVUUFRWxaOuDY5aamorc3FxRpJFhGIbpW7Bo6wCWYMvOzhY1YXrTDZKsg2SxcTgcbJXpQ2NGDxJUq6isrEy8z8vLO8BbyTAMw3Q1LNo6cAO1BFtGRgZ64/YTTqeTRVsfGzOXyyVeSbjR+cnjyzAM07dg/1g7sWLYemvVZaZvY52XvS3WkmEYhtk/LNo6SG9yiTL9Bz4vGYZh+i7sHmUYhmEYhmkBTTOwcksFqmrDSE9xYsLwTKhq99m7WLT1U/74xz/if//73z6X2bBhA3oLs2fPxplnnonf/va33b0pDMMwTB9g/tJCvPHFZpTXhGAYJmRZQlaqC2cfMwJHTx7YLdvEoq2fctNNN+H6669veH/kkUcKIXfyySd363YxDMMwTE8QbE+/swbhqA6PS4VdVRDVdJRUBsV0ojuEG4u2bobUe3FlAIFQDB6XDXkZHqHmu5qkpKRm2YU0LSsrq8vXzTAMwzA92SX6xhebhWBLT3Y0xAo77SocNgVVvgje/GIzZkwoOOCuUk5E6Ea27KrBU++sxmNvrsQz764Rr/SepvcE3nzzTcyZMwd/+ctfcOihh+LXv/41fvjhB4wePRq7du1qWK7pNKoZ9uSTT+LYY4/FxIkTcfrpp+Odd95pdT1/+MMfcM455zSatnv3bhx00EH47rvvxPvXXnsNp556KiZMmIBJkybhJz/5CVatWtXqdtP27GtaNBrFvffei6OOOgqHHHIIfvzjH+Obb77p4JFiGIZh+gort1QIlyhZ2Jomd9F7ml5WExLLHWh6hGij2Cpyy40fPx6nnHIKPvzww4Z5JAQuv/xyTJ48Wbjw/v73vzfUrbJ48cUXhUCgGzrdzNeuXYueDgmzl+dtEK9JbhvyMz3iNX56T2Dnzp2i7heN0TXXXNOmzzz44IP473//iz/96U949913cdFFF+H2228X49QSZ511FlauXCnWZUGfo8r+06dPx7x583DnnXfi0ksvFefGc889J4rN3nLLLZ1yD3/77be477778NZbb+Gkk07Cr371K3z55Zcd/k6GYRim91NVGxZeMHKJwjCQatQgxygTr/SeptN8Wq7fiba3334bN998My644AK8//77mDt3Lq699losW7ZM1Jr6xS9+IZZ7+eWXxY2fxMC//vWvhs/TDfdvf/sbfve73wlryoABA/Dzn/8cVVVV6KnQYH+2uBC+QBQDsr1wO23CJUqv9J6mf764UCzXEyAL28CBAzFy5Mj9LktV+UlUUXzcrFmzMGjQIJx99tm4+OKL8fTTT7f4malTp4rvj7fGkWgjCx21baLWTHfddZd4X1BQICxtP/rRj7Bx48YO7c+OHTvw3nvv4a9//SsOO+wwDBkyRJwz9MDQ2jYyDMMw/YP0FKe4J6dppTjcWIwZxkJM3/NK72k6zafl+lVMG7nR/vGPfwhLDIk24oorrsDixYuxcOFC4SKjfouvvvoqUlJSMGrUKFRWVgqRRlYRu92Oxx57DBdeeCFOO+008fm7774bxx13nHCnkYWuJ0IxbIWldchIcbZoeqXpO0vrxHIFWV50NyRq2srmzZuFFey6665r1CdT0zThkgyHw6Kyf9N9PuOMM4RQu/LKK4WllL7nkUceaRB1W7ZsEWJ969atQnRRZiv15OwIliWWrLLx0ENCcnJyh76TYRiG6RtMGJ6Jg5L9GB1YhVQ5ijokIQob7IghG+Ww67VwJ08Wy/Ur0bZt2zYhzChWKR7L2kGWtbFjxwrBZkHuMr/fj3Xr1gmr2vbt23H44Yc3zKfG2lOmTMGiRYt6rGijpIOYZoigxpZw2FUR6EjL9QSaiqymxLurSYgT5MYeNmxYs2VJaLcElet4+OGHRZzaBx98INzhgwcPFvNIzFHcG50nNP28884TVjZymbaVlraR3LUej6fRcvtryM4wDMP0bWQZOHVQDUo2hFEYTYVNVaHIEgKGDTVaMgqUGswdVCOW63eizXKpkRuULCAkxMjaRnW3qDE7xTXFQz0VieLiYiHQWmqOTcusX7++w9tlNd9uCbIikYWHREDT2Lq24rTLsCkSQhENbmfzIQhHNDGfluvoOlrDEiz02vS7rf2Kf0/ET7MyTn0+X8N0axxpeRJaNC4kxmfOnNnwuRdeeEFYy0iIt7RPNM7Tpk3DRx99JEQbuWSt5Z544gnhYr3tttsalqc4N8uCR5Y62h9r+61trK2thdfrbbSNNN8Sk3R+HX300Q3fSUKTPtvTar3ta8yaQvPpOIRCoQ5bIpnEQGMQ/8r0Dnjceh+hBI+ZVl2MbLkG5qABqCmOint1TDMhUxKC046cvAFivr94G9S0xvqjo9D1vS0dbbpVtJHFjPj9738v3GJUN+zjjz8WN+xnn31WuNKauqscDkeDeLIGqKn1hpah+R2F3GRkyWsNEiWd+f40j4K8DBe2FvlQkOVpNFA0cOXVAQwrSBbL0THoClraftrv+PVZ/Svjp1GMGvW3JLc0jVNhYSGeeeaZhu+02WxCYJHbm8aFskfJ3X3//feLuLF97Q/FlP3f//2fEB4UD2ctSyJ8yZIlIs6RRNj8+fPx0ksvNYhHGm86biTg6DOUdUrHlLbh/PPPx5o1a0Tso7UvFD9HWaN33HGHOPeGDx+OTz/9VGS8kqjsqmPeWdpyztEydBzIjcz0DMgbwPQ+eNz675jJtcVwVlfB6c3AhMF2VAeoRhslJkjivizBRF11Fco3rIORkrikwdY8UT1GtNENniArG7nHiDFjxgiLG4k2cstRHFRLNy4SDpbbrqVlXC5Xp7ZrxIgRLc6j76Y4OxIK+3Mb7os5hw3BK59uRHFlWMSwOewKIlEdlbVhpCY5MWfaELjdHd+H1iBxQ/tA299U1dN+x++TNT7x0+jf99xzj8gQpWQAEkgkfMg6ZR0TSiyhem8k7CjzlKxoNP+SSy7Z55OEJdooJjEzc2+swK233iqsbJdddpk4qal0B20Dxc1t2rRJuMPpe0lM0/pp7Gh5stBRbCO5VG+44QaRMWrtC1nVSNRRDCRZ5EjI/fnPfxaZrD2NfY1ZS9BxIHFtPeAw3QM9VNJNhGJCO3M9Yg4sPG69j1CCx0yrToWvci1kpxeK042m5Uv1cBCGzUTB6DEJs7RRHHdbkEzL99INkPWEgsEptohuvBaUaEClF8hdRrFLllWFoCD0448/XtyMKZPwiCOOENmK8XFtZLGjGzFZTtqLVfuLyo+0BFlhyNU2dOjQTok2gsp6UBYpJSVQjJtNlTEoJwmzpwzE8AGp6ArIimUlAzQtrsv0TNozZok8P5nOQSEWZLGnB1F6yGR6BzxuvY9ggseMQktqvnkNkZKtsGUOalTwnqo6xCp2wpE7DKlHnpOwOOj9aY8eYWmjJAMKBF+xYkUj0UZCjSwFlDVI9cHIjWrFJn3//ffiM2ThIasL3ZyouKsl2sg1RO64ppmBPRESZkPzU7qlIwLDMAzDMM0hIeYZNQ2x2jIh0NTkTEg2J8xYGJqvArIrWczvjsS1bhVtZAmggqlUyiEnJ0cUx6VabVT0lKxnVI+L3FhXX321sJ5Rod0HHnhAuNks3y/9m2p4UQA8KVRyiZG1gVx3vQESaD2hrAfDMAzDMPU4cocidepcBDYuRKxiNwytErJqFxY2Emw0vzvo9t6jFMxOPmiKkSotLRVB4f/85z9F0VPiqaeeEgHj1GaISn+QBY0+Y0HT6+rqhLirqanBuHHjRDxcenp6N+4VwzAMwzC9GUfuUNiyB0OrLoURCUB2eKCm5XRraahuF20EZRXSX0uQBc3KTmwNSmSwOicwDMMwDMMkAhJo9ozEJBskAq4kyjAMwzAM0wtg0cYwDMMwDNMLYNHGMAzDMAzTC2DRxjAMwzAM0wtg0cYwDMMwDNMLYNHGMAzDMAzTC2DR1o+ZPXu26ONp/VGXCerTeeGFF2LRokUJXx91rqD1UJFk4qc//Sn+8Ic/tLlNCbU76wy0Xlo/bUdX0HT/GIZhGKbP1Wljug/qKEF/BLWhpQLF1HWCOlV8+OGHyM/P77J1UxHltvY/pVp9b775Ji644IIu2x6GYRiG6cmwpa2bIaGkB+ug+SrFK70/kFBz3aysLPGXnZ2NUaNGiQ4U1Aps3rx5Xbru1NRUJCUltWnZA31cGIZhGKanwaKtG9HqqhHcshT+td/Cv26BeKX3NL07UdV6A6zV35XcqP/3f/+Hk08+WbQXW7hwoRBRTz75JI499lhMnDgRp59+Ot55551G37N48WKcc845oqfsaaedhvXr1zea39Q9unLlSlx88cU45JBDcMQRR+C2225DKBQSFrmHH34Yu3fvbuR+fOONN3DSSSeJ76fX559/HoZhNHzfxo0bcdFFF4ketnPmzMGCBQta3edAICDW+9JLLzWaTuudNWuW+N7a2lrccsstOOqoozB27Fgcfvjh4j1tY0u05P5tOm3Lli247LLLxLqPPPJIXHfddSgvL2+Yv337dvzyl7/EzJkzMWXKFNH5Y8OGDa3uB8MwDNN3YdHWnYJt61LRiFZ2eqCmZIlXek/Tu0u4Uf/XO++8U1jgjj766Ibp//nPf4RAoV6wJIKoV+x///tf/OlPf8K7774rxNHtt9/eEHdWWFgo3K5jxozBW2+9hd/85jd45JFHWl0vLf+zn/1MWPteeeUVIdS+/fZbYfWzXLi5ubn45ptvkJeXJ5b529/+hiuvvBLvv/8+rr76aiEi77vvPvF91I+WBCBZ8l577TWxbY8++mir6/d4PDjxxBPx3nvvNZpO+0aClFqZkNhau3atEHIff/wxbrrpJvzvf/8T29LRY029dKlV2+uvv47HHnsMfr8f5557rojhI6699lpxTOj4v/zyy2I7aJ8ZhmGY/gfHtHUDZKWKlG6FEQpATcuFJEliumR3QkrLhVZdIuYr3skN87qKxx9/vKG3q6ZpiEajGD58OP7+9783imcjAUfWL4IExXPPPSdi38gKRQwaNEhYwp5++mkRd/bqq68iMzNTWMsobo2+s7i4GH/9619b3A5antyld999d4Ol7y9/+QuWLVsmBBWJSPoecuMSJACvuOIKnHLKKeL9wIEDheAhkfe73/1OCDmygN1zzz1CuI0cORJ//OMfhXhsjTPPPFOIT9qPgoICYfkjS9dZZ50l5s+YMQNTp04V1j5iwIABQkyRRa8jkOglIUpi2IKO+/Tp0/HRRx+J9e7cuVNY9Eio0n7Q8dm6dauw/HVn02KGYRjmwMOirRswQn5oNeVQvGnNRBm9p+k0n5ZT3G2L+eoo5513nnDZESQCWoszI2uQxebNmxGJRIQrL144WKKP4uFIyBx88MGNEg0oM7U1aHlyOVqCjSDxQn9NqaqqQklJiRCN//jHPxqmk5Ch7SL3KX3fkCFDGu0LuSD3BQkyEmJkbbv88suFu5e22dp3sop9/vnnwnJIYo6OA61r2LBh6Ahktdu0aVOz7aJ9ILcpcc011wihRgJv2rRpwjU7d+5cFmwMwzD9EBZt3YCpRWHqGiS1PmasKTTd1GvFcl1NSkpKI0HWGk6ns1lSAFmFWhIsFAtH4jM+voyIF2RN2de8pljfS+5Jy/oXD1ml2rt+gj5zxhlnCJeolT1LbldrnSTkSGSRaKL4PhKZ5B5uDyRs4/eDRClZI5tiiU2yWlI83meffYYlS5bgoYceEm5ecsuSJZNhGIbpP/DjejdAokxS1FZFGU2n+a2Juu6GhBoJoKKiIiH4rL/58+cL9yhZgajm2+rVq4XlzYLet8aIESOE5UnX9YZplL1KSRBkeYq3SGZkZCA9PV3EwcWvf82aNUJIErR+soaRVa4t6493kZIFjeLHKDmBEhyIdevW4auvvhKWveuvv14kVpBLmNyXrWW22mw24bKNF2m0zRbksiWLGolMax9IRJNljSyFlZWVIr4wFouJ9VEyCFn/KFGBkkEYhmGY/gWLtm5AdnmhpmZB91c3u+GLEiD+ajGfluuJkBWI3KokYN5++20hRCiQ/t577xVB88T5558vYsoojoyEyRdffCGSC1qDXI/V1dXC6kTLU3FfSjQgS5TD4RAxbZS9uW3bNmGtoozLF154QcSUkXAigUfJBmQRJEsfxbqRuCMXLmWtksi566679rtvFMtGGbL3338/jjvuOHi99WNAVi0SqmR9o/1dtWqVsMKRgIoXpvFQwgYlU5DY27FjB/785z/D5/M12mdKmCARSNtIf+QOpe+m0isk4L788kvceuutImOU1ktiksTguHHjOjByDMMwTG+GRVs3QFYjR84wyC6PSDowomGYhiFe6T2JNZrf1UkInYFckxS0T8KNrFGU0HDVVVc1BPrn5OSIEhwUe0bWK0oIoMSB1qDlKSGCguzJRUni5ZhjjhGChTj++ONFEgJZnMgiR9mklM1Joo1clSTIfvzjH4tEBIJEHq2fBA4JyBtvvFG4PNsCJQCQlc1KQLC2j/aBYtpofZTsQNMoQ7U1Cx5tI5VEoWVp22ibrMQJK3mCtp/WRdtInShoe//9738LSyKJRMqIJcvlr371K7Hv3333HZ544glh5WMYhmH6F5LJVUsbQVYOYvz48S3OpyB7svYMHTq0UZxXR6CyHpQlSkkHIsZNUYWFjQSbmpSGroDcj7QPtO1t7UbAdC/tGbNEnp9M56Asa3KrU9kbEuxM74DHrfcR7ANjtj/tYcGJCN0ICTMq60FZoiKOTbULK1tPtrAxDMMwDNM9sGjrZkSJjy4u68EwDMMwTO+HY9oYhmEYhmF6ASzaGIZhGIZhegEs2hiGYRiGYXoBLNo6CCfdMj0RPi8ZhmH6Liza2gnV0bJSjBmmp2Gdl9Z5yjAMw/QdOHu0nVCdLGqqXlZWJt5TTZjeVKKDan5RWyiC67T1nTEjCxsJNjov6fzksWUYhul7sGjrALm5ueLVEm69Cep/SW2gqNo+Vdpn+taYkWCzzk+GYRimb8GirQOQZY2afFOfTWrm3ZugfqDUKoraILlcru7eHCaBY0YuUbawMQzD9F1YtHUCukH2tpskWW0IasLObY56BzxmDMMwDMH+MYZhGIZhmF4AizaGYRiGYZheALtHGYZhGKafo2kGVm6pQFVtGOkpTkwYnglVZbtOT4NFG8MwDMP0Y+YvLcQbX2xGeU0IhmFCliVkpbpw9jEjcPTkgd29eUwcLNoYhmEYph8LtqffWYNwVIfHpcKuKohqOkoqg2I6wcKt58C2T4ZhGIbppy5RsrCRYEtPdsBpp1qQknil9zT9zS82i+WYngGLNoZhGIbph1AMG7lEycLWtLMPvafpZTUhsRzTM2DRxjAMwzD9EEo6oBg2com2BE2n+bQc0zPgmDaGYRiG6YdQlii5QymGzanKSIUPDkQRgR01SEZUM8R8Wo7pGbBoYxiGYZh+CJX1oCxRqaoQk927kIFaqNCgQUUlUrA0PADJ6QPFckzPgEUbwzAMw/RDqA7buZOdKP56LRxaGHVyEnTZC8WIIt0ow1EOH/Imj+R6bT0IFm0MwzAM0wcIhzW8/c0WlFQEkZvpxulHDofT2fJtnmLVdpfXIbtuPdyZCtbVZCIUNWHoJmTYAXsWxmdFkK/shGEcBllm4dYTYNHGMAzDML2cx95YgU8W7kBMMxumvfzJehwyOhtHjC9o1OVgy64afLZoJ4K7NmF8YC0iihtZaR4kex1QFRlOh4rsNDcQDSBWsRtadSnsGXndun9MPSzaGIZhGKaXC7YPFmyHaVKpDoCKdxgmoOnAorVlWLa+XFjcKH7tyIn5KNy+C466XRiplCJd9iFo6vD7IqiMJGP08DykJTnE9xo2JwytEkYk0N27yOyB7Z0MwzAM04tdomRhI8EmS4BC/2mCZphwqDKKKwL48LPlcJStxRBXEIYjCZrshKzISLXF4IhUYeeucvFdhBkLQ1btkB2eA79jTIuwaGMYhmGYXgrFsJFLlCxsVJ7DNE1hZWtKTDeR7LEhx6yAGQ4gYEtDSE1DWE2GzYwgJjnhUkxo/hrUBaMi5k3zVcCWWQA1Lac7do1pAXaPMgzDMEwvhZIOCLKvkWDTG3WcMpAp18EJDZLuhAw30pUAKjUnXCENyR47alwDYA8E4dR9iEl2qLEQYv4qxAIRyK5keEZN4ySEHgSLNoZhGIbppVCWKCGsa3EWtny5EuNthchRfFAlDVBsCEpehBUdtbEsxCjgDUDInoYyjEJqaBfssVp4EYYaDcAxeJQQbI7cod21a0wLsGhjGIZhmF4KlfX478frG1nY8uUKzHauhVcKo8rwImC4kWIDslANRQ7DL1OWaFrD8iTcgkoyojWlyE8zMHbWqXAWDGcLWw+ER4RhGIZheilUwsPttO15F8VR9lX4iftbDFNLAUlHqhxEhhJEJGai2EiHJJkYZS9FNBJDJKrBME3xWumLwOVQMGTSoXANGMGCrYfCljaGYRiG6aWs3FIBEyZOci7HNPsGeKUIlD2eUo8SQaXhQZ3phcOMoTKajApbOg5KCsLmqsHOcAr8QRkO2cCwZA0jhw/F4LETIFFWA9MjYdHGMAzDML2UrRu341TjSxzi2Cpu6JpIP6jHBgM5ch1MQ4ZfciMFIeQOHIRMRy0GDhuBkRET0VAEdpcDGXkFcOYNh5q0123K9DxYtDEMwzBML2TLxu2oWbUAUx07hXUtBklY2BRheyMBJ0GFKTJIw0oa0mQDWW4dis0L94gpSHYnwdSikKgWm8vLFrZeAIs2hmEYhull6LqB5T8sRgF2wwldCDb6H8k1srSRiMOef5PFLVUNIWzaoQarYRs+Drb0XI5b64XwiDEMwzBML6O4qAyhyjI47TIkxYQuKrXRnwwNshBrop0VLawAihZCshqBKyWda6/1Yrp91EpLSzF69Ohmf2+++aaYv27dOlx44YWYNGkSZs+ejX//+9+NPm8YBh566CEcddRRYpnLLrsMhYWF3bQ3DMMwDNP1BANBQI8hZksSt3JVqU8+qL+pS4hBrneV1pvcoEoGkvIHI/WI07n2Wi+m292j69evh8PhwKefftrIn56UlITq6mr8/Oc/F2LtjjvuwPLly8Wrx+PB2WefLZZ75JFH8NJLL+Gee+5Bbm4u7r33Xlx66aV49913Ybfbu3HPGIZhGKZrcHvcomBuBbJQADvsiEBT6iWbrJOTVBJWNrrJx2CDd/yxGDjzFNhTMrp705neLNo2btyIIUOGIDs7u9m8559/HjabDXfeeSdUVcXw4cOxY8cOPPHEE0K0RaNRPPPMM7j++usxa9Ys8ZkHH3xQWN0++eQTzJ07txv2iGEYhmHaj6YZooRHVW0Y6SlOTBieCfJiatWlMCIB0bid+oCSazMvPxuujGyESnehzD0C+cF1UKEL16is1Es2knCKrCDp4BnInHUqZ4b2AbpdtG3YsEGIsZZYvHgxpk2bJgSbxfTp0/H444+joqICRUVFCAQCOPzwwxvmJycn4+CDD8aiRYtYtDEMwzC9gvlLC/HGF5tRXhMSzdqp+ftByX6cOqgGBTY/DC0KWbWLBu5We6lJh03BD/OqsTsEGM4RyA3vgA2Rhn5WsiMZyVOOR8bR53FmaB+hR1ja0tLScMEFF2Dbtm0YPHgwrrjiCsycORMlJSUYNWpUo+Uti1xxcbGYT+Tl5TVbxprHMAzDMD2Z+Ut24qV3l0KPRZHmdEBX3EjTyzA6sAolG8KQhwzCgIICmLEwIiVbEastQ+rUuRg+imLT5ogs0uJKO4rlNGQqNfA6FeSNGouBs8/kMKE+RreKNk3TsHXrVowYMQJ/+MMf4PV68f777+OXv/wlnn32WYTD4WYnHMW/EZFIBKFQSPy7pWVqa2s7vF2maSIYDKIvYh0z65Xp+fCY9U543HonB3rcorVVWDRvHkbpVUhyAw7EEDZVZMnVcMth7IykonxnEGHUiHZVGcm50Kt2oWb1t/B4s5A3IBvZeSeitKQcoWAYLrcTOblZUBRZ3GPpr68T6gO/NdIdbbGGdqtoI7fnDz/8AEVR4HQ6xbRx48Zh06ZNePrpp8U0iluLh8Qa4Xa7Gz5Dy1j/tpZxuVwd3q5YLCayVvsy27dv7+5NYNoJj1nvhMetd3Igxk0K+1G1aR3sgWrYZB3ZejVSEYALIbgRRZXphtt0wBdyYcWmSsiSBLtNwpA0A9nB9SiyfwfD0zixIOoLotZXhf7I9l7+W2uLVbTb3aOUCdqUkSNH4ptvvhHZoGVlZY3mWe9zcnIaniBo2qBBgxotQ2VDOgolP5D1ry9CTyJ0YlPyR2eELXPg4DHrnfC49U4O1LiRZaVy/UKs3F0NSY9guFIKl2IIyaZBgUuvhBchDFIqsMPMREz1QpaAaMzA1kogLVfC0IH5sOcMQ38n1Ad+a5s3b27Tct0q2siidu655+LRRx/FYYcd1jB99erVQjSNGTMGL7/8MnRdF9Y44vvvv8fQoUORkZEhyoKQS5WsdZZo8/l8WLt2rajt1lHIREmWvL4Mndh9fR/7GjxmvRMet95JV4/bn+55FcP9i1GgVCHTEYAsmSiNJSEKE7LNhpjIBQUcUgzZqg/FkheqqkBVZZiRIHZV6RjhSeNzq4/81tqaKNKtxXUpa3TYsGGipAdlim7ZsgV//etfRT02Skagsh5+vx8333yzUKFUcPe5557D5Zdf3mBKJHF233334bPPPhM136655hphoTv++OO7c9cYhmEYpkVu/L93MC34JQ6zb8UQpRpeKQo3YhgiV6FArkAoqiNk2GCXDERMBV4pAo8Sq/+waSJDDWBXxIv1lXsq5zL9hm61tFGtmcceewz3338/rr76amElo3IdlIRgZY0+9dRTuOuuu3DmmWciKysLN954o/i3xVVXXSXcpLfccotIXJg6daqIhyMXJ8MwDMP0JKprQphc9wVG20sb2kxJcX/JCCNfrka5ngSXFIND0kUsG9neYEpIQh1CshtrwoMwrK5xzDfT9+n2mLbMzExhXWuNCRMm4JVXXml1PrlNb7jhBvHHMAzDMD2Zh5/7HGfadwuBRrYzelUaKqvVv0+RQqiUPCjSU5CkxpCshJGEAEIwUIYsrDUGoUxOEwV4mf5Ft4s2hmEYhukv5AVWwC46F5CVTYEiItfqxVq8cEuSIthkZsKmBFCGPKzFKERkJ6rNJFSFYsjLcImOCUz/gkUbwzAMwxwgsm0xkE7T9zhEdSgwoYubsSXc6FW1ScjWA6iJObFcGYVqNQdRTUcgFIPTruCsY0aIpASmf8EjzjAMwzAHiEOnTRCvJNUsiUYWt6gQcHvJzkhF7uiDscEzGTuiaajxRxCO6sjLcOMXp43F0ZMHdts+MN0HW9oYhmEY5gCRefjJqPz2Vbh0Ku5h7GnrTsgwhN0NiCgq8s74HQZnD8IEA82ayLOFrf/Coo1hGIZhDhDUZjH/8LmoWPAWbLoJDTpMSJBgihuyrkjIPfw0eHOHiOVlGZg8ur7nNsOwaGMYhmGYA0jm7AvEq2/JxzCjAeEmFUkIqhv2g49B+qyfdPcmMnGdK4yQH6YWhaTaIbu8bS6E2xWwaGMYhmGYbhBuSTN+hF1fvottm7ehOOLEGhwMW5ELA99ZjWOnDMTwAandvZn9Gq2uGpHSrdBqymHqGiRFhZqaBUfOMKhJad2yTSzaGIZhGGYfGIaJ4sqAyNz0uGzIy/BApkagnWRXeQgvFw2Ez8xBRpYT+XYV4aiGLbtqUF4dxHlzRrNw60bBFty6FEYoAMWbJqxsZG2LVeyGHqiBe9jkbhFuLNoYhmEYphVIQH22uBCFpXWIaQZsqoyBOUmdtoSREKTv9QWiGJC91+XmdtrgcqjYVebH54sLMTQ/JSECkWkbhmEgVlWC4JYl0AO1cOSPFN2bCMnuhJSWC626RFjgFO/kA+4qZdHGMAzDMK0ItpfnbYDPH0FeEuByGyiuiWHJ2gA2FVbjstPGY9TgjllbyHJHQjAjxdnsxk/vafrO0jqxXEGWN0F7xOyLSMk2BDYuRLRkO6I1JZBtTui15XAUjIQtNadhbMjyRi5TinVT3Ek4kLBoYxiGYZhWLGHkJjvE60OsugI1tT5k61F4dTt2+jJwx9M+XHb6BMw6dFC7v59crWS5c9pbvg077CqqfBGxHHNgBFvNoveEEJOdXijuZEiKDbHqEmjBGnhGTt0r3MhVqtcKd+mBhkUbwzAMwzShpCqIiuJiHGTbDb3aj6qqOuQpVchQAnCqMRwKFZuj2Xjn7WpI0qx2F7ul2DhytVIMG7lEmxKJamI+Lcd0vUs0sHGhEGy2zEGAHoURqIGkqpCSs6D7yhHZvQlqSrawtIlMUkUV4u1Aw6KNYRiG6XdEozq+XLYLZVVBZKe7MeuQASJ2bPmmCqzbGkBKdTmSw8XQDD+KK+sw0V4IJ2LwGw7UIAkpShTD7WVIMqKY/5kDMyYUtKvoLSUzUGwcuWAphi3eRUplJiprwxgxIFUsx3QtWnWpSDBQkzPFOWBKdsgON/RQHWSnR1jdNF8ldH8NFG8qdH81bJkFovzHgYZFG8MwDNOvePXTDXjri00IhPWGXp9PvLkCLqcNmm4gFtORpJRhjFwKzQhhmn0HUuUgYpDhlGKImDb4dCcM2Y5UJYTcwEas3FSGyWNy27wNJA4omYGyRCnpgGLYyCVKFjYSbMkeO2ZPGchJCAcAIxKAoUWh2JxxcWupMGJhGOEAoNphaDHowVqYsbAQa1T2ozvqtXEvDIZhGKZfCbaXPl4P/x7BRtBrRDNR449CN0x4XDJcNgOppg9jbbuQKocQMlWETTt0yHDJUWQqdXCZUQQUDzJRA1/J7nZvC2WfWmU96oIxFFcExCtZ2Ljcx4FDdnggU5xaLLx3mt0FW1ouFFcSjGgQMDTyowoLm3vYIVynjWEYhmG62iX62meboButLxOK6HCqEgxFQa5SC7ekIWbKiInbpQSN/kwJbikGtxRBkaYgVYog1bWPL90HJMyorEdX1IFj2oaaliPEWKRkKyS7u+HYk3AzUxyQI0HYhwxH0qEnQvUkc0cEhmEYhulqHn1zGcJRfR9LGMiU65CiaUhCDA4pioihwK4ASkM793p3qmFKUCUDdj0ExeXAqGH5Hd4uEglc1qP7kGUZnlHTEKstQ6xip4htk2xOYXnTfBVQvOlIGj8LNm9Kd29qx0VbMBhEcXEx/H4/0tLSkJOTIxrhMgzDMExPY/7SQny5tCUXpo6BShUGyZXIV6vgkqKwywYcio4UIwQfnDCgwCFpCJoSZEhQYYj4NgkGMpQQ0ocdBGdmXjfsFZMoHLlDkTp1rsgipaQEQ6sULlNH7jAh6Gh+T6Bdoi0ajeL111/Hu+++i1WrVkHX9z6xKIqCKVOm4KSTTsKZZ54Ju/3Ap8IyDMMwTFM0zcAbX2ymkKQ4YjjeuQpj1d1IkkNClJEFzWc4sdtIh6E4kGXWwIswanUnMtUgvFIUMVNBBKrI8HTLJrLzcjH66DkNVfOZ3osjdyhs2YNFNiklJ1CsG7lOe9LYtlm0vfnmm7j//vsRiURwzDHHCHFWUFAAt9uN2tpalJSUYOnSpXjggQfw8MMP46qrrsI555zTtVvPMAzDMPvpFUr/Lq8JweVUEAjpmG5bj9mOVUhXQg3ZeJSMEIQKjxzFUKkcO2M5qJaTkCHVog7AduQjU/HBpochmyacsg57RgGGz72wx1hhmM5DAs2e0XOtpm0SbZdffjnKy8tx6623CsHWmhXt4osvFta4Dz74AM8++yw++eQTPPnkk4neZoZhGIZpc69Qy9qW6rVjvLYEp7qXwLUnd5T8RRSpRlY2DzTUwQ6bpCPbrMZOLRNuNYxsJYjNego2GdlIQQhZthC82VkYfsqFcOUN7+Y9ZvoTbRJtxx9/PM4+++w2fSEJujPOOAOnn366cKUyDMMwzIFgc2ENnnt/LXyBiKh7lpXiQTimYUdxvYAbGFyFM9yLEW92qE8t2IsHMVSbbnjkCByyAs/A0UgJ7cJ4mwNRA7A50pA1ZDKSDprOFjamZ4q2tgq2eCgllt2jDMMwzIFwhy5YXYR/v78O1b4w7DYZvkAUZa6QyMocPSgVmTUrcLJtkRBsVkHdlpBhQpWoGpuBARl25OdlQPOryDrkOCgub4+Mc2L6Dx3KHiUX6NatW1FXR57+5kydOrWz28UwDMMwbXKHvvnlZizdUIZgKAZFkSEb9Tc3Em7hSA2GZrtxhH0jXLImPtOaaLOm26FBgwqHwylKPlAGoWvoBBZqTO8TbQsWLMB1112H6urqZvMom4YsbOvWrUvU9jEMwzBMq4Ltv59swKad1TANE4oiQZElxLQwRuu7kOOJoUpzwShTkC754TfsSJcjrX6fJeQU6AjAhUEeA7IrVZR8YMHG9ErRdvfddyM9PR233347UlO5xQbDMAxzYKGkguWby/H2/C0orQoKC5nLqUILmpgmr8d0+1qkSEHIhglTlhDWFbgkDaWmAymINItjawoV0XUkp8NVMKJH1ehimHaLtp07d+KRRx7BjBkzumaLGIZhGGYfRXKp5hqJtXC0vraaCQkpHjsOk1fjBMcyUIfQMFTUmXbYYMAjhWEDkCIrCJk2eKVYq99fa9ixPX0qjj/7dHjzhrKFjendom306NGiEwLDMAzDHGjB9vQ7a0QrKko20DRKGzBFZuj4yALMda1uuKlRFqiTuhjAjmrTiWwpjGRE4JfsIMlW30l0L2St8xsqAlkTMHTaUVDT8liwMb1ftP3xj3/E9ddfLzogTJgwAS6Xq9ky+fkd78HGMAzDME0L5VIZj5fnbRDWtfRkJzTdFOLNYwQwy7kMhzs2N7uhKTCRhAggOeA3VSRJGjyIio4GEWjC+mbbU6utSE+GN28EhmcoKA81j9lmmF6dPUrirTU4EYFhGIZJZKHcKl8YxRVBUSw3ppuwKRLchh9TldWY3oJgi8eDCCrghhOacKWKsh6ic6gEP+wIewowYeIEyJKJcNlOQIsewL1kmC4UbZSAoKoqrr32WmRmZrb34wzDMAyzX8va+u1V+GjBdmFNo0K5Hmf97UrXDfj8ETjtMsbJ23Gofbuwlu0LcnJ6pKhIMAjY0pGUlYuYpkN1uZGfVwCHfc93hwOAagNU7p3N9BHRRvXZHnroIcyaNatrtohhGIbp15Y1Emw7S+oQ03WoiozqujAcNgWSBMiKBE03EAv6Mca1W1jPsJ+CuYQLGkr0ZEi5ByE9OQRb5iDIstRILFJNNlv6QBiutAOwtwzTftodZTl48GAEg8EOrIphGIZhWhdsFLO2eksFyqqDwqJGgo3qf4Yimmj+TsIqEjVEPFu2VItkOYSQsdf2UN9NtGWox8H32hhMOmEuZJcXsYqdwrJm6Lp4pfeyKxmOEYdS1/ADss8M017afWb+7ne/w4MPPohvv/0WgUCg3StkGIZhmHhIjJGFrdYfEWJNN0xIMoRos6sKDNNEJEbpAo2TDCg2jRq8x1vZWhNu8yMjkTL5BCQPHoHUqXNFlwMjWIdY5W7xSu9Tp54Me/bgLt9fhjlg7tH7778fFRUVuPTSS1ucTx0R1q5d2+ENYhiGYfoXFMNGyQZupw1l1SE4bYoo42GaEIJN103x73j3Z43pQojqsEkRUNqAYx+tqXZGvZAm/gi/OnuimEbFcm3Zg6FVl8KIBBr1E2VPEtOnRNspp5zSNVvCMAzD9KtkA3J5elw2+INRIdLsTlXMcztVUdoDET8U04AMGUE4ROanRaWZLGLUxtl2I2I6IEuRZgkJJNiq4MGUC36H48ZMbjSPBJo9I+8A7THDdJNou/LKK/c5v7a2tjPbwzAMw/STMh4k1KiER1qSQ7SmIreoLOsYoW9BplwC3aGj2vBAM22oMjzYrafDZ7rF96TIIZFIQMJMkQxETRWmpImYH9ElQQEcyQUYf8ol8Ayb1N27zTDdI9oeffRRXHHFFS3O++CDD3DXXXeJeDeGYRiGaSnZwBeIijIeTnu9Ra2kMoBafwijA4twvLIGKbofkmqCmlTVGC5siuXDJulIkkNYHysgWx1m2tYiV65BqZ4Ml2rALYWgIwJZUeFNy0HykNGwZRTAmTu8u3ebYbpPtFG5D13XG1ncSktLcdttt+HLL78UXRIYhmEYpqVkAxJsA7K9Iv6ZoDi2g1MqkOH7FCO07Q03JWFBA5Aj+5Hi2I7vI8Nglxw4yr4GQ21lyJQDQshpsowQHCg3UmHYvBg7yI3UAUPhzBsOrbYcJhfKZfqzaPvzn/+MW2+9FYZh4KqrrsKLL76IBx54QMQH0PTzzz+/a7aUYRiG6fXJBmRhswQbZRekBbcjdefnGIqiRjek+GxQF6KYZt+KXXoqBqnVUGAgaNphwAZJlpAsReBVa+AenI/UnFyYoTrogVpIigqJC+Uy/Vm0/ehHP4Lb7caNN96Id955B7t378ZJJ52Em266CVlZWV2zlQzDMEyvhpIOKIaNXKKEQ6uDs3wThlZ9iTQ50OLNSMSm7Xn1SlEUKFWizEfYrE85kGFAkVSYqhteOQabvwhmbgEMXYNeVwXHgJGiJhvD9OveoyeffLIQblSz7eijjxaWNoZhGIZpDcoSpX6hRqgOjtpCTKz8AKlqWNRj2xfxs6nhO/U/0CQFUdOBZJcMuwIoNhUmFOjBOmg1xZAooSFnKBw5w/Za9Rimv4g2sqK1xKBBgzB//nxcfvnlSE9PF9PoB3L33XcndisZhmGYXlnOIy/DI7I8sxxRTPIUI2X9BxjkqIXUAZOBFedG7lKnQ4XL4wF0DVBU8WpqMeh11XAPmwjPmOlQk7gdFdO3aNPP5ocffmh1Xl5eHjZt2tTwnp9qGIZh+nE5j0U7sXlXLSJRTTRiHzEgBceOTUZWaAsyNn2AAkfHy0KRaCNLm7CuUUldXRfTZLsTiEUgq3Y4CkYhadIc2JLqDQkM0xUPIT1atH3++eddvyUMwzBMrxZsT72zGkVlfhgkpfYEo5VW+hHbuhjjw9+jQO24YIuRlU1R4ZL2NKoyTJiGLprIk4XNjARhy8hH8rS5sCWzYEsklHjYUveI/lhTcGBOEo6dMhDDB6T2XNF2wgknYNasWSJ+bcqUKbDbORuHYRiG2WuNePPLzdhe7IMiS3A5VCiyDF3XcHh0Aab418Il6R3/fgBRxYmUJDfMoE8INpg6DN2A4k0CYhqUpCykzjgbjpTMhO5bfydSsg3+DT8gVrYDBlkzbQ7RAsw7+jDRDqy/1RTcsqsG5dVBnDdndLcItzaJtuuvvx5fffUV/vjHP4qOB4cddpgQcDNnzkRBARU6ZBiGYforu8v9WLutSoTHJDkU5KAcuXopRmMDcpXyjmW8xQm2mCIhxW2HrDqgu5KBYJ0QbTCp/K4ER/5wJE2cLWLZmMQKturv3oJWWwZJtsFUZJhBP7StyxGrLELaEWf2OeGmaQaWby7H2/O3oKo2jNGDUiErckNNQXog2VXmx+eLCzE0P+WAu0rb9FuaM2eO+CPWr18vBJzV/WDw4MFCvNHf1KlToaqd+XkyDMMwvSnWp7o2jDe+2ISq2hDGOkpxmLkJuXoZPPCLJu4WVumO9kCfiSl2JKWk1hfJ1TXIEhX6MCElZSNt5jlwZA2EPXcYFIVSFJhEukTrVn6JWOUuSM4kyE43JEmBaerQw0ExneaT1a2vuErnLy3EG19sRmlVUFjV6CGkui4sxFlepkcsQ9PI8raztE6c/wVZB7akTLsV1kEHHST+fvnLX8Lv9+Obb74RIo7qtgWDQRx++OF4+OGHu2ZrGYZhmB4T67NgZREqa8NCXI1UduFE2xLRgsoGXWR5xtMewUbJBjHFDRn1vUT1WAxUG0SWFBhRP2RvOtKP+QmSxx2V4D1jLGJVJQiXbIFkc0J1J4nYQcMAjGgIMDSYpoFw0RaxnCMzH31BsD39zhqEozrsNhmaJot9DoY1rN9RJZaxhBsl2FT5IiI54UDTKbOY1+vFiSeeKP6INWvW4Ouvv07UtjEMwzA9zLK2YXsVPvx+O3aX+VEXpJuWhom2bTjVuRxpckgs2xmH0a5oMkIFk+BXs5AS2YW02o2waREopiG+2JE1CMmHnoCksUcmbN+Y5uh1FTDDQSgpWUK8aIEaxKrLYEbDQrCJuEK5AoGNP8CReSZ6u0v0jS82C8GWnuyAppvi3/TA4LDLiER1bCuqRW66W3TgoMxoSkqgbNIeL9oWLVrU6jwyG1KsG1ngSNAxDMMwfcOy9unCHVi2sRwlVQGqtCGcl1NtG3GkYyOylepGrtCOENSAYs84+AYcTDcTMa3MGIAdzkwcN8iPpJQkuIYdAueQcRyGcyCQ1fpxMHRogQCiZYUiW5fKqkiyDEOLAdEw6lZ9CWfusF4dT7hySwXKa0LwuFShY1QFUBUZMU2HakIItFBUR6UvLFyjZF0eMSBVlP840LT7zP/pT3/aqBabadanX8dPI//2GWecgTvvvJPjDBiGYXqxde2HNSX49/trUVoVQEyvv94nS0Ec5ViL6fZNcEqxRl0L2gN9W8gE1hs50IceC5fugydWhbCaBA0KQoEABqUYyBgxFp4Rk7lY7gFETssFVDtilbuh73GJyna3sHaahgnJNGA6PTBiMdSt+ByOweN67f2+qjYsznU7qbU9esbjVOELGNCou4YkwaQYv0AUoYiGZI8ds6cM7JZ6be0WbY8++iiuvvpqIcrmzp2LjIwMVFVV4eOPP8bLL7+MG264QQzcP/7xDwwYMABXXHFF12w5wzAM06XWtXkLd+DLJbsQCFOUWT3JUi1Oci7DeHsRnKDszY65Q0mwlekebEs/Aju9E+ELaCjwpiNLLoctXI1YKIxkuwMjx42BZ8QEFmwHiFgshtpvXkVg01LodeXCmkbWNoopNCQFktV9AhJs3lTIrmSRSRot2QpXwUj0RtJTnEKARTW9oTeu3aYIcUbnfjRGRZwl6KaJgwakCsHWo+u0xfPkk0/iJz/5CX7/+983TBs2bJio30b9SOfNm4cXXnhBTP/3v//Noo1hGKaXxa2t2VyBFz9Zj1p/BDrV3NjDLPsKHONcJ5q3d8bGEIKMnZEM7Ew/DFdcdTG27q5tKGJaFMuDV8nAoEFOTD1kMIYNL+BOOweImiUfofrbt2DWVe6R1XFQHFskCNNmB2wOqK4kqMmZMGUFeqAGBtXP66VMGJ6JrFQXSiqDcNiUhvONhJuqSKisNZCV7sI1500WxXV7fEeEeCjZ4Morr2xxHtVve/bZZ8W/R48ejeLi4s5vIcMwDHPAWlB9vXw3avzRZvNn2ZfhZNeqDmevkQQIGCqisGGHng5/5nhccFQejJBfWC2orEJPahfUHwVb1ecvAtFgfSybZKs3oepWhuQem6opQ0nJgc2TDNlmhx4KQFZskN3J6K2oqoyzjxkhskcpK5Ri28hVSpa3QEiD26niJyeMxuC87t/HdociZGVltdqLlKZnZtZXo66urkZycvt2cNu2bTjkkEPw5ptvNkxbt24dLrzwQkyaNAmzZ88W1rumtWQeeughHHXUUWKZyy67DIWFhe3dLYZhGPT3FlQffLetiWAzkCnX4sfOz3FqJwQbGev8pg2bjWzU2jIxfagXZx05EKZo8l6/PhJoVPNq1KA08cqC7cC6RH2LPgJoLGSlPgmB4rsoRk2yRt0EqMerLEGxO+oFm25Cq6sS7cOoVl5v5ujJA/GL08YiN8MtMkdr/BHxmpfhFtNpfk+g3b/B888/H/fffz9CoZBob0UxbRUVFfj000/xn//8B7/97W9RUlIiYt/I8taek4Y6L1CtNwsSfj//+c+FWLvjjjuwfPly8erxeHD22WeLZR555BG89NJLuOeee5Cbm4t7770Xl156Kd59911ut8UwDNPGFlTrtlXGuUJNDFNKMM62C2OUQuSo/k6tI0ZV21LzMSM/H5JkQPdVwQjXQXElQVL5Ot3dhDctgl5XBdicQKy+bEsDNhWI0olh7LG6STBjUWFhI8EmO72iG0VvTUKIh4TZjAkFIpuUkhMo1o1cp2SJ6ym0W7T94he/EILtqaeeaohdowzSpKQkIdguv/xy/O9//0M0GsW1117b5u/95z//2axMyKuvvgqbzSayUCnFe/jw4dixYweeeOIJIdpoHc8884wQe9QblXjwwQeF1e2TTz4RiRIMwzDMvltQLVhV1CDYKDN0gm0rZjgoM9SPZKtBeweJAChBOnI8acJwY0RjwppDfSwdecMgu7g8VHej+apEpwPY3IAWbr6AagO0iEhGgGlCD9ZC0jU4cob0ufZhqipj8uhs9FQ6ZO2mmDYSb2T5oszRnJwcjBkzRljAiFNPPVVkl7an9tsrr7wixJ4lvojFixdj2rRpjWryTJ8+HY8//riw7hUVFSEQCIguDBbkkj344IPFd7JoYxiG2Td3P7MAMc0q5VGBy93zka0GmnU06AhVhgN1tjRRXb6orBbpKQ4Y/moo7mTY0vLgyBnGSQY9ADU5vb5FFWWJkmuULGpUi8+ynlESAsWz2Zxw5gxG0pSToXpTuX1YTxVtCxYsaCSMCJfL1WyahTWI3333HY444oh9frfP5xMtsG655Rbk5eU1mkdu1lGjRjWalp1dr4ApyYHmE00/R8tY8zoCWQ7j3bR9CbKSxr8yPR8es95Jbxi3vzyzCLsqaPsM/NL5MUY7yjuVFWph7rGw+WwZqIMHqhSDKxaEf/d2uJKSoA4eD7lgDKKKA9Eedq3tDeOWaIyCsZA8qTDrKgCbS5T0ED2rLCOrQW3EFCip2XBOPgnKwLH1YxyhUe5+Qn1gzEh3tOUBpk2ijeLE8vPz8Zvf/EZY1PYHWcioNEhZWRneeuutfS57++23i+QDss41JRwON4tLczjq627TyWINUEvL1NbWoqNQfB0lQPRltm/f3t2bwLQTHrPeSXePm24Y2FoSQSBswOOUMSzXAUWWsaM0gFXbapAvV+IC+xfIcyROPPlMB5ZgLFTdQCZqoUBDwLCjSM1A2oCx0B0DgUKqLtBzKwx097gdaGy54+Gsmw8pEoApqZAMDTCpPl+9ctO9WQjnT0FFUKEMQfREtvfyMWtLHH6bRBvFllFiwbnnnouCggIcf/zxmDBhgiieS7XZyFpGlq8lS5aI3qOUvXnxxRfvt3E8uUNJ4FHSQEs4nU4RtxaPpexpvTSfoGWsf1vLkCWwo1Ac3YgRI9AXIaFLJ/aQIUM6dYyYAwePWe+kJ4zbNyuK8c4320XbHZ2q2EsSUjw2HDIqE9+vDiBfLsePPZ8hT25e4qOj1CgufIXDsVEeJaw1qfBB0sOo0xVcMOMIjOrB8UI9Zdy6hTFj4M/NRXD5PBiB6vqUX9kOOD1wjZ4O75E/7rGu0FAfGLPNmze3abk2iTaKKaMkAxJtVIeNSnJQXFnTdlZkjaOMUhJsFOe2P9544w1UVlY2imMjbrvtNnzwwQciG5SsdfFY7+n7NU1rmDZo0KBGy1CduI5C+0WisC9DJ3Zf38e+Bo9Z76S7xm3+0kL85+NNomwBNb3WI5r4dzCsoXhBISaqm3GmZxGSZasOV+dZHc3Fx65TIam2+npSsoxaMw1VgYgonTBt/KAelYm3L/rj7819xKmITT1RZJNScgLFujlHThWGjN6AqxePWVtjO9uViECxYtQJgf62bNmCXbt2oa6uDmlpaUKwDR06tF0bed999wkXaDxkxbvqqqtw2mmn4e233xatsXRdb1D433//vVgPlRqhjFXKOKX6cJZoI6vf2rVrRW03hmGY/ljCo7C8Di9+vB7+UAwOm4yaunhLWn2j91NcS5As7W1P1RnI/yHbvPjeOAxVAR0el9SoOKnTruCsY0b0GsHWnyGBZjt437HoTPfR0VqJovwG/XWG1qxxJMhoHpX1oNIiN998s6i9tnLlSjz33HOiVpvl/yVxRuIvPT1duG4p/o4sdCT+GIZh+l1Xg8WF+H51Ecqr6x+IY5pVfC2Ko+ybMFDZjbH2MriE/6tz0DfY7C54k1JgT8vBqYMm4tUfqlBeExIWPSqQSxY2Emw9pTgpw/Q70UbJBSSSjj76aKxfv140id+9ezdOPPFEkViQqKK2JN5ItN11110488wzRTcGyjSlf1uQVY7cpJR9Sla7qVOn4umnn+415lyGYZhECbaX523AjhJfg2CzmGVfiZmO9UiWw+1vg9MCFJquurywJ6eLmmuKwwVH7nAcPnkYpk8f36OLkzJMvxJtVMyWrFkklki0kUijzgXnnHNOg5ijYrcdZcOGDY3eU8ID1XBrDXKbkmikP4ZhmP7qEiULW0mlH8UVjbNAZ9mX4xTXyoTUXRPrUsi65oGSkinEmpqUDjUpDc5BB4lCuRSb05OLk/Y2KF6c+rNSuy/qHmEdY6Z/0m7R9tprrwlX5RVXXCFi2qjA7q233oqf/OQnGDZsGB577LFOiTaGYRimfSzeWoz3vt7aUFarHgNznQtwjGNLQmqvERRabMscBEdqNmw5g6E4vTANA6onmQvldgFaXTUipVuh1ZSLPq2SokJNzRLHmoQy0/9ot2gjoTZz5kzx7/nz54sfKfUGJUi0UTYowzAM07WWteLKAHyBCG761zdxPUProdprc+0/4CBHRcLWqSgy4EoSFfElw4Cs2CGrdhYRXSjYgluXwggFoHjThJWNrG2xit3QAzVwD5vMx7wf0m7RRu5PaiFliTYSahT4b7k2MzMzE7+VDMMwTKNkg5WbyrGjpK7Z/Hy5BL/0fILkBIWRUX6pQ7FDSc8SVjVqEO4aOBrOgWMgqw5213WBC1RyeoSFjQSbmpbbcHwluxNSWi606hIxX/FO5mPfz2i3aDvmmGNw//33i9ZWX331Fa655hoxneq3/etf/8JZZ53VFdvJMAzT77GSDXaW1qG4PBA3x4cLbYsxTC1Bsl1LSLKB5Q6luDXF4RUuVklRRAyba9A4tvJ0iQs0BlD/T9UOPVALe+aAZqKM3pPljZYnoae4k7pt25leINpuuukmkc1JDdnPO+88XHLJJWI61VOjxISrr766K7aTYRimX2MlG9TURVDtCzfEr/3a+R5GOKoSvj5zT+aCRBLQ5hDWIGfOEHjHH82CLeEuUL8YX62uAmbQBz1UBz3ohx4NwpkzDIrL2+hzwlWq1wrLHNO/aLdoo76ed955Z7Pp77zzTkNfUIZhGCaxUAzbjuJa1PqjCEX0LhVsDWKNbhLp+ZAdTijuVKRMPw325IwuWV9/g0SwZWHTIkHESrfDjEUh251UhBSmaSBWsh1GLAYzWAcjGoTiSRUdChRTF0kJJN6Y/oXa0ZONGqoHg0Hx76ZQrTSGYRgmcbwzfzNWbrYSvSI4wrYs4YKN8hmoqRVVubTJsqjBJjtccA08CJ5R0+DIbV/XG6Z1yLoWKd2OaHUp9NpyIdIUEsSmASMSEm7QSEUhULi20eeC676DPW8E0o48S8QTHkiiUR1fLtuFsqogstPdmHXIANjtPbMfaV+l3aKNuhL87ne/Q0lJSbN5JODoRCNBxzAMw3Q+QzQQiuGrZbvwwYIdYvoJjiU4wrERXilxPUMp2YDsavXfqEBSdNEonNyhaXMugSMjHzKJuB6GYRjQqkthRAKQHR6oaTk9cjtbIlpThsC2VYiV7wSiYeGCVk0TNk+ycIdGynYCofhEkz2xbYaG6O71qFu3AJ4Rhx6w7X310w14e/4WBMJaw73++ffW4PSjh+PHx3W81zfTxaLtr3/9q2ggT6+UNdpbfiAMwzC9ga0l1bjhwa8R1cyGW7XlzzjBsQhznOsSlmhgQbYScrhqUOFSTKiqE/a0XKROPx2urAHoiWItuHExApsXw6irFhkTVH7EllnQKyyCFMtW9v6j0Mu2xU2MQAv5oDk8sOcMAUK1cZ8gq6dEWQior+9iILhuAcJzLoXT6exyq9q2ohosXFsmplPlF5siwZQk1IVieHneRjGdhVsPFW1r1qzBAw88gOOOO65rtohhGKafcsb1b0NvEnFS/1bHDPvKhAo2Sam3rul7hKEKGQ7ZAGQVjvzhSJtxNtzDJqKnESnZhtqlHyO0dTkMEQPmguxJhS05DZGSrYjVliF16tweK9zISlXyzj8aC7Z4IgFEd8a7RKV6wSYr9aKNXjUNiEUQWPQ+nEed3aVWNd1ofEKSZqRpqgI47QrCUR1vf7UFZ8wcwa7SnijaqB8otY5iGIZhEsep173d4vSRym4ca1+JUfbyhK0r/hJe/08F8CTDnp4L70FHIOnQE3rkdZ4EW9UP7yCyexMkE1BSsiBTDFiwFtFYEPbcETBCPgQ2LoQte3CP8AQ1rcEWCgUR3b5mf59q8n6Plc0q/yHJgKlDqylN+Pa++NFavPLpJrQQrt4ITTxdGLCpMgIhTVjljj9scMK3h+mkaKN2VY8//jgOO+wwuN3u9n6cYRiGaRK79tt7P25hTgRnOJfgUPsWeEihJIiWtJh78nHwjpgC17CJPVKsWS5REmNGXRUkSYbsToIstlWBrNhgUKmM6mLYsgaJrgEU62bPyOt2N2i4ZAu0il0womGRGRrYQYKtSQuL/REv2AiTPi9BTc1J6Pbe/dwPWLCqebz6voSbQ6oXpuRGZXqgaNuxYwe2bNmCGTNmYOTIkc386SI48fnnE7mNDMMwfbZY7u1PfIuaAKUCWNTg9855yHKEEh+71oIec444FJlHntPja6+RCCMxJruSIPkqhRvXQngNHS7owTqoug5Di4rkhG7d3rpq+Nd8g2hlofApmqS7TCBSurOd32SSKgLITUluUk0XVjbYnPBMPSVh2/t/zy9sl2Cz0Iz6pATKJmV6qGg76KCDGt43LfnRUgkQhmGY/oymGVi5pQJVtWGkpzgxYXgmdpT4cPMjXyMQ2Wt1udr5Pwxy+BK+flqDralgUx3wjD0S2adc0StaIZEIIzEmu1MAcnsaGiBTcZI9KCoQCcGM+EVSAmWTdpcbFIoN/i3LEdy2Qmwr1bhTPMmi2wG1omr3XZL2lT5ELkkSbJDhnXBMwpIQgsEYvl1V3KHPUoxbskcV5T+YHijaXnjhha7ZEoZhmD7INyuK8d53O1FeExKuUFmWkJXqwu5SH6L1NXIFVzvfwCBHoEs6G9jsLiAag4Pcn54U2NJyhZWmqzIPuwISYSTGhAhyJUEL1MCUkhGKaMLao0KHQ5Kgh/xwDTpYlP/onlZUmqi9FtqytF5kqg5oSqloN2XLyIdt8AREq3bt/0vTBgC+coBaW5HsNskaKwkLGwm27BMvTdj2vzhv/X5j2FqDjH+nzxzOSQg9ubguQS7ShQsXoq6uDmlpaTj00ENF83iGYRimnlXbA/hsZRkiMV1k2smqhFDEwLaieGtaFDc5X0ZWAhvK0P2XjGfiRqx6oXi8kJJsSJ95DlwFI9EbIRFGJT0oQ1RJz4Ovuhb+6hIEdRUaJCQhLLoEOFwFGD1q2gFLQrBaUVHbKUoQ0GrLENqxCnqgBpLDLdy2olKHvwZ6JABn/kho6QUwqna3/qVOL2w2FYY3BfLAcbBRJT3ZBltqTpeI7cZ9bNvHeXNGcrmPnizayAR822234bXXXmvkCiXz+plnnom777470dvIMAzTqyCLWmGpH1+s9CEQNqAqEnyBaH2JrThm2VfiBMdyOBKpL6gyBF2rdcBQJDhT0oR7zZEzGPbcnvtgvb9CufRvqsFGJT1Ki8qwpcYBL2JIliNwKjFEDRUbozlYvmMg5hapODq367eZ7oHBok0IbFwCzVcJPRqCEQkC0VB9soChCXep7PI0JEvEKovgGT4JdZEQEGiho4XdDd2dBdRVw5aUiqRBo+EdcWiXxhzmZXmAdtbEJ4vxeXNG4fzj94ZLMT1QtD311FN44403cNVVV+G0005DVlYWysrK8Pbbb+PRRx/FqFGjcPHFF3fN1jIMw/SC5AJq7L56czmq/bqwesX2FMqNZ5Z9MU51rbXq3CcG8lAZAK2O9KGquoV6U9wpSJo4u8dmhlIpD8oMpUSDepdiy4Vy6d8bPYdiVeGnyJIAv2RH0LCjNurBTtsQ7HIMQ5U/hje/2IwZEwqgql1rbfMt/xRVX70K01+9t0wHFcCzOUT8mhkJQacurqodks0mrG5awAepphRJo6fCzBkO//yXhMgzVSeqwyoQ02GvKoEm2VDuGYIRnhFI7eIkkQvmHIR3v97aJhepIkvwuFThEmULWy8Qba+//jouvfRSXHHFFQ3TBgwYgN/85jeIxWJ49dVXWbQxDNNvBdvL8zbsaeoeayHgPIhTbKsxWV2PtAT3+g5CFtY1avTuVDSosgp7UjKcucOEYOuJhXItwVaz6D0RxK8mZ0KxOWHGwi0Wyp2/tBCPfl6DQGg8MpU6eCQNQdhQoXkhRWSkek0hKMpqQiLxY/Lo7C6zsNUu+xRVnz1fb1VrNFMHosH67FYSbiE/YtTD1e4S7luTWlbFonAMGI2kcTPhTc1E8fcfonLnTkhSFJJsg8+ehW3usVgdHYzF35biPHcqhg9IRVfhdtswY3wevlnZejLC0Dwvpo3N556jvU20FRcXY/r06S3Oo9ptzzzzTCK2i2EYpte5RD9btBO7yvwIhmOo8kUazT/f+SUOcezseCBxK4iwNQVwyzIMxQHYk+DJzIJ78DhRd41coj2+9lrID1vmIOFyEygeSHY3YhU7GwrlGgbwxhebEY1SBJuMajMFNXvslNRaiar01wWjyExxImhoIlO3q2LYArs3omqPhWwv8Q3H9mR8iiK4BkwtJhrCi+pqsiIavruHTBBhRc4hE/DZMhO1zo0YkAzEVA989lxRn2WAaYrz6fPFhRian7L3+HQBv//ZNOD5hSKLNN7iRvF4JOjEfKbbaff1o6CgABs2bMDhhx/ebN769euRnp6eqG1jGIbp0Y3cPS4b8jI84mb6w5pifL2iCHWBSLPYtfOd8zDV0bGSCvtE2XsRJytV8riZcA2fDEdezxRqTePW6D25RGnbmwoSek/TrUK5qysUkYHrcqqoC5IIiqs3SzXQZAm6bojWS/RZKq3SVUkHwR3rgGB8M3eiJd8iuWepNRjVXHEKC5yakic6TlgxanQuFVYEkZQxFJXOuBIme2LFM1Kc2FlaJ5YryPKiKyFhRuU/KJuUkhMo1o1cp2SJY3qpaJs7dy7++c9/IicnByeeeKI4qchU/OGHH+Lhhx/Gueee2zVbyjAM00Pi1QpL6xDT6lv4DMxJwuhBqfhwwQ7UNUs2qMYdzneRlMDM0FaL5GYPRsrk43tskdyW4tYkh1OU7nCkZLX4GcnmhKFVCpFXVWsXgjnJY0MorIlq/HTvsWrMSXvKmFEJkIHZXlELL1FEo1EEV3+F8PZVwnKmR6OtiDQ0d5WqDkgyCUgDamounAMOgkoxb3sg8U/nktPe8u3YYVeF1ZaWOxCQQLvs9PEHZF3MARBtl112GRYvXoxrrrkGN9xwgyj3UV1dDV3XMW3aNPzud7/rwGYwDMP0bDYVVuP599eKeDWvS4XLoSIc0bFqcxm+W1kklolvrn2d800UOPwJ344GwbbH9SaQbUg+5qc9WrC1FLcWrdgFraZMFJ5VUvNRVh1EOKLB6VCRneYGYuGGQrnpKYqwoMV0E16PA7V1YeEylSQSbvUNAwiHTcFZx4xIWBJC1bdvwrfkIxgBn4hRa9ROar/UmwNlmypi2jzDJ4tkBFGAdw9krSXxH45qcDextBGRqCbm03IM027RZrfb8eyzz2L+/PmiTpvP50NKSgqmTp2Ko48+umu2kmEYphvdoBU1Ifz3k/UoqQzCoMr3Rv0fueQk04TWxB16nfN1FDgS3ItRsZq778ESbJDgmXQsPJnd22ezI3FrttwRiPkqUb1jE5atDyMUNWHAhAwJLruMCVkR5I8eK8p/TEiBKEpMY5CeTJYqJ/yBiCisS4eCNJtNlXDZGeNx9OSBCRNsNd+8Xh+fRoV9DQXQqTtBfNuxfSFB8abCOXgsTF0XpT+obyo1jrcg9zpZa8mKSw8C8d0pyJJYWRvGiAGpYjmG6XBMLAk0FmkMw/R1NyjVVyMLUCgc22NkkYRFTQRrkz+uEQZ+6nwr4YKt1fA01QHvxNkJrY7fVT1DW4pbU1UFPns2IhWbkW4Wo1pJhy7boRhReGN1WFfsQu2IQZghy6Jz1dnHjMDT76wR7kLKEs1IdSIY1oS71G5X8cszxmH2lAQINl2Hf8da+L9/p74jgSup/lWLtK/Ze2qOSDwgM6CsqDDCARFvKLv2xqbRMTl2ykCUVwdF0gHFsJFLlCxsJNjsiokVG8twyV8+QVaqEzddNA3pqa7O7yPTd0XbRRddJArqDh8+XPx7X3DDeIZh+kLZDhJrGclOVNaGYArLWn3glCztEWxNyJcr8WP71wlvRdVMsLlTYM8eDHvecCQf8aMe34rK6hlKLtGmUPzfmjIZ6UYSArZUJCEKFUFosopKORtLgwOApWEcdpQh3J2WBY2ySOPbgpGlilyinbWwkVUwuHo+3Es+gz9UCYT3JBuE/fWu0XYhQZFVGJoG3V8JNTkDalI6HDnDmvV6pXIe580Z3fCgQKKUXKLbimoRiuxdL4m4n/35E+Smu/Dkzcd3al+ZPiza4jsf7K8hPDeMZximV5ftWFwoBNuAbK8IaidLjt2miKxEEezegqElX96NSzyfIT3BtVybCjY1ZyjSZ5wNz0HTe1yT9/iG6eT+I2sSbaPVM5Ri2HTJ0yhuTXwmGkZYcWMxJom+og5EEYEdNUhG2Gkg3KTmGgkzKpxL06isB2WJUtJBZ2PYKO6uasH/ENq4CKpwgcbNbK9go0QD3YAe9ovzhbyrGQMOFrXyWos7JOE2ODe5Yb/+/cHqRoItnpKqEC676xMWbv0Qtb1N4rlhPMMwfRVRfqG0TrioSHBQVh8JOVWR9/bybCCKKbYdmKmsRIEjkNjOBi0INjm9AJmzf9oji+Q2bZhORWTV1CxhVbJ6hhZtWIOV5Q6EokZD3JqimEhHHarlHNTIqUK0xWNXJSGam9ZcI4HW2cK5VvmRWNCH8JZlqF01H/CVtes7SFI1DBNZEu1O+mIRvxYzIggFNVQoWVhRMx7JW9NxbIaE4UnNv4fKbDzy1gqs21YlzrGUJDuq6/adLUrCraomxK7SfkaHY9qCwSDcbrf498cffyyK7h5zzDEYPHhwIrePYRjmgNG0/AK5qEishaP1dcHie4Ye6ViPVDksSnAltNF704QD2Qb3wUcga+5vemTtNat2mREKQPGmCSsbWdsojo2apruHTcY6fRCKi5chyfRBUpIa4tY8ug8+3Y7l2gAgqfmRjGp6l9Rcs8qPBLYuR6ysEIg16WqwnzGKN7ZSSgIJdpnEvc0BLWYgHKqBBhXVzgHYlTEDhpKFLbtrUV6zQbhBre4GFdVB/Prez5tZ1Mj12xb++u+FuPcqji3vT7RbtG3duhWXX345TjnlFFx99dX4+9//jscee0zMe/DBB0VHhEMPPbQrtpVhGKZLaVp+QdNMBMIxRKJ7b9Oz7N/hNNfmrimU60oGFAcksuwpKhw5Q5A88yfwZB6A7ucdgNybZGEjwaam5e6tmWZ3QkrLhVZdgmDRZryyJAQpcjAmu3chA7VxcWs5WBDMxW49BXluvZEope8OhDTkZbgTWnPNKj8SKd0Brbyw3nfZ1v1tIQ3BsrAGdcAVDEKPhoUdscYzAoUZhyPgzAWZNygzNL67wSV//hiVTbpmtJfymq7p+sD0IdF23333QVVVHHvssaLg4EsvvYSTTz4Zd955J/7whz8IEccuVIZheiPx5ReiMR2L15XGxbBF8Wfny/B0VaFcTyZsqWkww2HYswbAPXIK3ENbj4HqCVAMG7lEhYVNkmBEKGlDgySroh4ZTd+xeTv8tQpMZw6+l/OQCl+juDWfIwYEY6iojSDFa4ddVYSFjQSb057YmmtW+ZFw8Xbo5dvb//m4f4vkYchQ90yNQYVm2FGjpKEkeRwqMw5p5O6N727ws9s/RE2g88VyKZuU6V+0W7RRYd27774b48ePxzfffIO6ujrRBcHr9eK8887Db3/7267ZUoZhmC7GKr+wbXcNflhT2sgdeoJjORxdmGhgyx4keojaCkbDO/FYOLIG9rhkg6aQG5Ri2IxYFOHyQlQUlyESicLhsCMzLxv21BwEgyEophMy7ZwsoQaNG58nue2IxHQku+0IR3URw0bjQBa2RGSENrIKFm9B3fqFHRJsTaGRUfaIN5+SiTUpM1EUUlFmZGFoRlpDeRPKPK70hRGJ6sKKSy74RAg2gsp/MP2Ldou2WCyG5ORk8e+vvvoKLperwR1KXRHICscwDNMbC+nurvLhmbfWYHf53jprs+xLcJprTcLXGS/Y5OQcuLIKoKZmwzN6OmzJvaOHsxW/tn39OuwsqkV1REEMMmwIIG1HBQbl74Y7JQe6pELT9BZbNZFVjbJzrzh7orCoJTIjtGmiROm8F4Cqwg59R0t5nGRjCylp2Jh7AmpcQ1FW7AOVZSP3usuuYtOuGhRXBEScpIA6NzSr7dcxqOwHJyH0P9qtsEaNGoVPPvkEQ4cOxUcffYQjjzxSCDUScy+++KKYzzAM09sK6X60YBtiWvwNNYZfO9/FiK5sRSWQ4Bo8Bs4h4+DMHd6j3aFNkZwebNtVgZIdu1Gqp8KmqnBQ43bDRGlYgrpjN3JGJcObXICS6rBoMdW04r8Vt0bZoIkSafHUlBTC9/7D0Mp3Avre9lHtRWki3GKQUO4Ygd2ZU1HlGYZIOAavy4a0JAfWbqsU4pNabsVD1rdESDau09Z/abdou+qqq/Cb3/xGCDRqaUW9SIkTTjgBFRUVDUkJDMMwPZ3NhTV47v21WLGpvNH06bb1OMWxCB4lwXUnm2aGUjOmQ09B6uTjRBxbT3eHNiUWqMOqbbWw6S5k2mOgXFodChyygRR7DDVRNyq2+3DWUQV4Zt72hk4GXRW3ZtWKi0VCiBVvRvnnLwK1JegKijzjsD73VKHAab1lVUH4gzFs3FGFYCv11cii214mjsxErT+MuqDGHRGY9ou2GTNm4N1338WqVaswceJEFBQUiOk/+9nPMH36dIwePbortpNhGCbhDeD/+cpybCv2xU2txB+cnyHbEe5ysSZwpyJ7xhm9yroWz9otpfCFTWhqLjIRhBshyIjCgAw/vKhQ3VDDEaR4ZPzitLENnQy6Im7NcoH6132P0I7VMGrbV3OtLZCsJEenBhlL3bPhkGTU1oawaF3b16VILXQ/a4X8TA9+PndsQ4kQhulQANrAgQPFn0V5eTmmTJmCESNGJHLbGIZhuszCdu8Li1FcacWuafiT83WkOTruPmu3YEvOwdAr/9XrrGvxVPupiKwMyWZHqeSBXUS0UfFckm42KHJMhM7QcrOP7JpOBkSkphy+he8hvHsDYqU7OuUG3ReiHhuA0uRxqDNVfLd4Z4sdMlpDuEfb2DXo0NFZuPXSw5v1a2X6N+0WbX6/H3fddRfGjRuHCy64AB9++CFuuOEGkYQwZMgQUactLy+va7aWYRgmARa2O578FrWBehfWRHUrznN8A0dX5FC1KNgkqEf/DIOOPBU9Hb8/jPkffopgdQXcaZk4+qTj4PXuLTORmpmOWikJeUYt/Eo6opJ974dNEy6jDsVSilguUZ0M4gmFQqh8+++I7lwNxBJpHaVRIzXWRGDJNnjGHYmZp/wGj//pg3YJNoIi2shDmuS2oS7YcgYpdYF44baT4HbbOrH9TF+l3Zep+++/X3RAIDepVbftoIMOwhVXXCFqtNF7WoZhmP5LOKzh7W+2oKQiiNxMN04/cjicTrXb1z1/xRbc9+/VDctOVFfhZ55lXSbWGkU2KXbRzin18DOQNPZI9HTefOZFeHZ8g2zZDwUG9GIZ365+H4HBR+KsSy4Qy0wYkYVXkwpQ6wsiQ6lBwPRCgwIVOjzwoyJmQzS5QCyXaErefRjBlV82F1Yt0DTCbN99JaT6+mp2NyDZAEkD7MlYow3El8GDUblMhm3FR/CH216U18IyslGJk2H5ybjp4qn463MLUVkbQUaKA3+6ZDoy0+o7DTFMS7T7KvrZZ5+JIrpz587F6tWrsXv3btx4442i2K6mabjtttva+5UMw/QRKND6gf8uwbfLd8OqckC8Mm8Djp82GL86u2v7Zj72xgp8snBHoyxQWveQvBRsKqyJW1LDzc5XkOFoZyPwNtBSpyk1cyCSDz0B3klzemRZpKbN3t/+7zvIK/wYTkWDz6BSuCrs0JCt1CK882O8+QyEcCPL2UmzJ+KVd6LwB8uQ7QghSTYQMWTsiHhRqWbj3Nn1pTwSSfG7DyO08ov9LidGt4UhFpOobFxcRwOB3Q05NRshXUZyajokU8eqXVH8r2gIiowMkTPaGcjKRt5wj0MVsXy5GV7847rZnfpOpn/R7qtHTU0Nhg0bJv49f/58cQGyrG4pKSmIRDrXloNhmN5bOuPB/y7BjpLmJTJIRH2woL6gaVcJNxJstA5zz41R2nOTpHXHC7Yuc4e2ErtmGzsLOcf8BPYUuumjxzd7D8cMZBV+BpccQ7lB3c3r94qEW4VhQ6bsFxY4v/9s4Sq1kgje+HwTdtfWQDE1UZctKTUV584embDiuBY129buV7A16LR9aXIdMCzhJqmA2wtXwWhopgmpugpFlSEsL1WwNGQJtsSQleLCRaeMSfhxYfoH7b5sUbbohg0bROLBp59+ikmTJoluCJaIGzBgQFdsJ8MwPVywPfG/lS0KNoJEFIkpsoJdfMrYhLtKySVK303roLhteU+tsKZMVJfiZ5697tGE0JLFZg+xtGHIPeLsHi3YqNm7HvTDBzeimg071qxAthxA1FShSkCj0nVQhOUtXfaLWLdTzpkrppIA6aokg6Z9Q6vef7jZ9Ba1WRuMqGRioMgxV2YB1OQspM26AC99tAZLt+5CwCCRSqI1MfuQ5nVg9tSBuPDEMV1Sj47pH7T7ykmtqu655x5Rp42axz/wwANi+pVXXilcp7fccktXbCfDMD3YJfrml5uxeVdtq8sI69ceixvFm517XGJLA9F30ndLewSbYZoN8UNkI5pi24GzlQVwdFXf0KaoDjhGTUf1wCOgeHtmuQZyiVIz9zVrC7GqTEYoWiXaLCXV1SIbEgxIwiWqNZGkZHFLQlQkJ8ST6CSDplCyW80P7wJ7Snk0a97eAU+3asoISi7YZBe8+cPwyCeF+GJ5CEDiRHaK14bz5hyEE6cPYbHGHHjRRvXYMjIysGjRIiHUqFk8YbPZcPvtt4s+pAzD9B92V/ixdltVnEhqGWs2JQgkmvjv1OKKYFHP0CMda5EuJ74EBLUramEqHCMmI+P0a0Rz8pJ169DTsBI11q/fgeTylaL1lCYC7iU4VBl21S4EG42YKlFLdAOGuXdnScjRVMomPVDiMlq+C3UbFiKwabHYrgbB1sGQROsMiRgKAoobu8olvLchgCJjb7/ZRDD5oCzc9gsu28Ekjg75KCgJgf7iefDBBxO1TQzD9JJendT8esOOaoTCMdE/sqHH4j6gjM5Eb4vbVX8pixeOs+xLcZorwa7QOMHW0m04/cRfIPXQE8W/g8HEi9OEJImsKBLCNkUKYpI9hhjsYl9UWUIkZmC7noSRNifS5CBCpgSZkhQavkVHshxBmZ4iyn8cCPdtYNsKhDYtRrh8FxAhK1j7BRt9imQn2bks+RmFjBItBZvCeVgVG5jQuDWbIuE/t3PZDqabRNvDDz+Mc845Bzk5OeLf+4IKRVKbK4Zh+nCvzkU7hTs0EqXAdV2UMKDaU4H9fNamSqIER0K3ZXEhtu2ujpuq4RLnRxjnqEJX0KJL1O7GwKufER6Hnggdp7+/vATbiynmUMNItRRZUh3SZB9MU6PKcdBMBSGbF7GYgo2xgZhs3wyPFEXUtEHbY2EjwRYyVFH2I75eW1cQq6uCb9mnCG9bCSMaghEja6nRakZoa1QYDkCSocKACUlsf63mxnqjAOu0QQmLW3PaJFGu447LDkd2uqfT38cwnRJtM2fOZNHGMP0cuvk/9c5qFJX5xS2Q/q8Zhgj69wWicNkVhKKt31Gp7EdHkhDiLXselw05aW4sWleCd77aiopqH0qqYw2Zoac7fkCq2rnSDO3qbJBUgGFXPYSeWsZj884yPPf+BmwvjmCqbRMOd2wWVjQVMdgkGisZtYYDftMLn+lGoS0b2/UcZEhhZOgVcMsxeBAVLlGysMXXaeuK7dVjYURKd8C/5itEiraIMiSyahPFc9sr2KImsCY2CAHdBUUxoOsyNFlB0HRifaxA7G8iGDUwFfdffXRCvoth9kWbrp7r169v8d8Mw/S/hIPtxT4osgSXQ4Uiy9B0HZGIjphuIqYbcDtUBCNhYc1JRhg+OLFZy8FJhw/vULkPy5pWWFon3K8xTUc0qqG8NiJEnMVEdTku8qxs0W3ZJckGABxDJqDggp5Xm5LciuHiLViyeC2Wry9GjhbFZGcFRjlKhcWJXINUMJeiw6gQbqocQkxTkS5rSJZCKDTTUeEagrX2ozE6IwoEa1vsiJDosiPhnesRLFyHWOl2ILanfJSiQCd/tFHvpm2rTYysgzu1bJQY6SILVjV1aJKCKt2D3Xo6CzamV9LzqjwyDNMj2V1en3BA1vQk9952RTZVQVqyExW1YeiGjhnKekxM2YgkhOrD2RUFSZl5yB2T0y5rWl6GB9uKavHyvA3CipeR4kQkqmPVllqEIo2r0d/sfPGAFcol7CMPQ+rJv24od9STIAG0Y/G3WLxiK9aVGlAgY7RahtG2UthgoNp0wiPp4uIfgwqKvHNDR5oaxC49DUlSBB4pjKVaAUaOHISzTxvXZYH0lKwR2bURoa3LEK4tR5gSDSJNnOzUK0qvj73T9baLNt2QsEXPxQ/REXBLMbHv1Bk1aFIKcef3Z/q4HPz27EOQnJzglGSGSaRoi0aj+M9//oOlS5fC5/M1m08X9Oeff769X8swTA9nZ2kdQpEYvK7mcVuUhDDaW4dpsYU42FksrDgxciZKDrg8bsjBMlR9+ZJYtqUWTk2taVR6YmC2F7WBqBBsA7K94nXFpnIRLN/lgq01V6ikIOmw05B17IXoiZCLccealVi6citWlytIkfwYpxYiS6mFA7rImkyRKDbMFOMjw4QMWQTqk6gJ63ZU6clwqRry0+2YPWVglwg2TTOwevEyxLYsQlLtZtgDpUC0eeJGfFkPyYyKRvT17ej3TxWcIsGARjIYl/2aCN69//SEfh/DdJlou/POO/H6669j5MiRSE1NbfGiwTBMH0T8tukG3vwmnmWUY6K6AiOkUuGKkmxJcCsSJEMDjDAkdxqMYA18Sz6Ga/T0Rq2cSLDFW9OcdhXhqIZ126tQVh3CgGwPiisCWL+jErEGA1shbrZ9A68agyOuR3mXukOdSUg+ZA4yZyc+nitRaIE6rF+7GUVBO1JRg/H2XUiTAoiZ9S0iSACRe1Q0LhcCiCQbCTipYVSDkhMF7jAmHZaH4QMSX2Nu/tJCfPnZAkwI/IAMqRZOqQ6KUj+w8Ye+aR02qsFHDwNtdZGujia2k4GqAH/93QwcVHBgSp0wTEJE27x58/Db3/6Wkw0Ypp8xKC9ZxLEFIzEkK3ZhVRcYBoYbW5Gm10KVTKg2F2SrR5SpwKTYpEgAsisZWk0pIjtWQx0+ac9HTWFhs6xp1ne6nTY47QqCoRi2UJZqg3XNxF3OF+DqIo9US4JNSs1F6pST4Z50HByJrs6bYEorauDzBaEoTuQr5XDKUYRMKukRE5mT9UJIgiJi2epj20i0KWKeBMXlxMTcJAxOS0X6wH27szsq2J5+ZxWOMZdjqFoCmxyGnYykewylepyFs6XiMTSP9sF6fGgJmuc3VXwYmZCQbU7x2PCfO+vrkTJMrxNtsizjkEMO6ZqtYRimx1KQ6cXBQ9OxdEOZiDtzikQECclGDVK0KlEaQiHPaXxDdDLwqLb6RuQOL0xDh+7f2weUYtjIJUoWtgYRCAgRV1QRhGaY4o9IloK4yfF6wrsatCbYpPR8pJ7wK6QNG4veQjAmIWrIyLCFkCoH4Tcofi0iYteikOCAAW2P5FH22NpIHjkVCbIzGVPHHQypthj2zEFQ03IS7hJ944vNODq2AEe4N0COE2sN6PXCzfq3IG5czD2TqzUXMtSQmBWLE3JkdNUh4bsIddzo3ImSn+HCn381g8t3ML1btJ1xxhnCPTp9+nQh4BiG6R9QbNNZs0ag2hfG7vI6SBG/uOk7UAubogM2L2xyFWDogBJ3bZBkwIjBjIUhyUqjtk4k/iiGjVyiDZhAYUktaurCDZOSpfW4xb2wkR7sKsGmZhTAPewQJE2YBUfuUPQm3CmpCNlSkK3vhCrr8GkO2BUdDllDneGGXfbvSUCo10IORYMqKYDNCUfuQCHYyCLqGTUt4dd36ks6qmYBZjjX1gu21mhByAkogVSRENJVrNKGokCvwGBHhRCiFn7Tju8iI/Fx5NAObSOVrPn7dUegeMc2jBkzBm53YgtBM0xnafcl8OqrrxbC7YQTTsDYsWPhcrkazaen5bvvvrvTG8YwzIGlpezNpkHoFOP0i+MGYNmCxQiXFULSI3BIOrJtQHp2GpSqKhjBOhiyAtkSbqYBk6xlkQBsWYPgGDyu4ftoPZR0QDFs1EJpe0kdCkvqEInrrPBX57+7zLrWOOFAgi17CNLm/BzuQWN65UNpfqYXztxhqN5eiuGqhKChIaDbYFOisMkSag2XEHAOaJRTAYVKadicsKVmQ3UlwZZZIARbosSqpmnCHe4vL0XJF1/hWNt6KB0Ne9aBqKQgaLgQk1R8rY3DyxEPxqnFSJcDqDI8+C5GhZvbf7JQHObzt54oMkGpk0VxBzeRYXqcaLvvvvuwbds2IdZWrlzZbH68i6MtVFZWigb0X3/9NSKRCKZOnYrf//73GD68vmr6unXrcNddd2H16tVIT0/HxRdfjIsuuqhRyjgV/H3ttddQV1cnPn/rrbdi4EDKGmIYpi20mL2Zk4RjpwxsFIxO5SQyqlbhqPRSBN10U7ZDkU2oPhnwF0HJKEAkshmIBGHYHPX9nqJkMTMhUyD/oSc0SkIgYUjr+XrZLtT6ow09IbtUsLWYGarCljsE6bPOh2dI73GHNoVE9lGHH4z/VQeQVlWOTHslqmMOhEwHwoYBU5JhU4B8ZxSutEx4p5wEe84wyHoEssMjXKKdFat0HQ8u/xTBzcsQqdyFYE216KYwMgH7F9LtoijuN5HRokAuCe3vYmkd/j7q4vH3a45mFyjTd0XbO++8I4TTjTfemJAnUUpoIOH1xBNPwOPx4B//+If4/k8++QThcBg///nPMXv2bNxxxx1Yvny5eKXlzj77bPH5Rx55BC+99JIQfrm5ubj33ntx6aWX4t1334XdnuC0Mobpg1jZmySaKEaNMsAp/mhzYQ3Kq4M4b85oIdxoenDbCkSKNgKqA0lp6ZBkFaahIYooYuU7YAR9cOSPQKRiFxD2iyQFco+SBSf18DNEuQ/6bnKVVdWGkZ7iRGVtEDX+aEN7pfqCvDIuVr9OuGBrFLdGblv6Ux3QM4ZAnXQyXEPbX/y3p0FjdcbJU/HDVwbkXfPhkILwmR4YqhsD0mUh2BRPClKnnwZn3rCErrvi8xfhW/oJtIgfotlCpyPL6qHcUhq6WsONxdFhCJqNPTwdyQR95S9zYbcnthQIw/Q40abrOo455piECLba2loUFBTg8ssvx6hRo8S0X//61zj99NOxadMmLFiwQPTyozIj9HRO1rcdO3YIgUeijWrGPfPMM7j++usxa9ashsb1Rx11lBB9TZvaMwzTGCt7kyxstf4IwpSlSaU9JAlOmwxfwIHPFxdiaH4KjFAdIrs3ilplalK6KMFASLINjuzBdHGAHvJBJndb1mDIFMPm8oj4MO+k48RvmLIHKRi9vCYk1k0PbOGoIdpPzXBsRKbihwshOKTElw5qEGxkAbS7Ydo8KNJSsNEciE3+UbAtMjBw5+pm1sXeCG3/0POOx+4NAxDetBBqoBxOxYCs2mHLHJZQF2i8YKtc8KaIV0tUZTcj7kZFaQdvhg7tVBmPjBQH7v71DORnUr9RhukHom3OnDn48MMPcdhhh3V65SkpKbj//vsb3ldVVeG5554TFrMRI0bgn//8J6ZNm9bInUIJEI8//jgqKipQVFSEQCCAww8/vGF+cnIyDj74YCxatIhFG8PsB4phW7mpHGXVQWEUI7coPZCRoApFqBF8UBS0peUyzRrogVooKZkNgs2C3tuyCoBqG5IOOQ6qJ6WZu62+3MMahKM6PC4VsWgMteF6wXaKa5koT+EUeY7oMsFmGzEV9vRc+EMm5hUlY5ueiYx0N/L31IYjq2O8dbE3Q67SgWPGwhg9Blp1KQwqu5IgF2hLLtGSBe8gUTWOqYEVfZW65y8MCfNCY1FktD+jdcqYbFx33qHwetnzwvR+2n19nDhxoohrox6kVPqDXJWJaBj/pz/9Ca+++qpwaT766KMia6ekpKTBAmeRnZ0tXouLi8V8Ii8vr9ky1ryOINxAwebVufsCoVCo0SvT87HGqi4QwPJNFaipiyA1yYFxQ9OhqnLjKvPbqlqd3xJlFT6UVAag66aoi2apMbrhO2yUIKCL+bSc1xMSfUbNWKzFmvQGTTdN6A4v5NR8YSXRwmEhAHdV+PHiR+sQDMeQnuwQljZN3OA1zHCsRabcpHVRwgWbDHnIRDgHHQzZm4Ev12jYEgHys9zimmUYOuyqhNx0J4rKg/jk+2346YmjOtUNoEf91lwp4s8ak0RC4/v6I49jht64tVhHoG+IQoETBmx7Ihx9phNfhQ/Cl9H211377x3H7RlDDcGg1vvGjek3Y2aaZptyAtot2m6//XbxSvFl9NeUjoq2n/3sZzj33HPx4osvis9TnBrFtDWNS7OKW9KTnTVALS1DrteOEovFRAJEX2b79u3dvQlMO1i1PYBHP1gIX1AXFjEylCS7FRx5cBLGD/GI+d+srWt1PompqjoNkZgJh01CepIKWZKwZLNfJB7Q8uSqbApNp/mLVm6COVCCI6xDCuyC6UxpEHgCuuCEa2EqDpTtKoVZ7heTi6uiWLLJhw1FEdSF6m/CxZV7L6zXOt/GALXrBBsFQmmKE+FhR0ArGIsqKCgvl7GyMAS3Q0Zt7d6G8xaqZGD9tlJ8uyiCzOTmLbvaS1/9rZFIL960CWs3lOJELKgvktYJ6PRYGRuCEiMZeaZP5LDs1NLxTYwe3Nv35b861obcnBxs2LC+w9vTV8etL7O9l49ZW+Lw2y3ayMLWFZA7lKBM0RUrVoj+pk6nU8StxUNijSBLHM0naBnr39YyTUuRtAeKo7O2p69BQpdO7CFDhnTqGDEHji8W78DHS4tAVTA8LjvsqoyoZsAX1PDZygD8mgc/rA0gEjXhbmG+zZWO2mAUu8tiYjrNL8i24+hJ+SjI90GSasTDFsXkZ0p+OKQoIqYdFaYXkilBkkwU5Odh9MQChOxhhLcug2RToDg9gKICugY9HIBp88I5dBJcYw4V37etyId3P1mJksow9tTHbcSvnf/DAEcXCLa47FBpwMFIP+N68Zu2UHfVwr56I7Iy3C1a0nTDREllEHkFgzFiQEqHN6Ov/tbIsrb6i48RW/05hsQqMNpObdg7Dkn4Uj0J88MTsEkvED1QF1Fj92j7G7tfd/EITBveuVi9vjpufZlQHxizzZs3t2m5LipV2TYoho2SDajmmxW3RrEWJJjKyspEbBu9xmO9z8nJETWArGmDBg1qtMzo0VQRu2PQDaevF1WkE7uv72NfgFyeH/5QhKhmiq4Byp7gLJeiiIK0Vb4IPl+6W1jNMlNdDeZ1a35pZQBvzN8Kj7PesmazyXDaKH5LR3VdDBNHZsFuV5FtlGOsshP5SjVs0ERkWZGehjXmIJSpWcjLThWhEI7RU+HXw4iW74KpRQD6Ew86dtjzh8M7eipUj0ds9+Nvf4+iipbcFWHc6nwVqV2SGUr/oUK/TkhpA1Aw99ewpzQWXhlpBpwOmxDB7viivnuIhqnbgw0ZaUkJ+Y30ld8ajelH32/HjgWfYkrwG6RLYdibVcJtH8UxLz6JTcSK2OB23Y7oLFcUCaoiY+akAlx+5oSEZ4L2lXHrT7h68Zi1tVxam34l1113nSiqG1/7bM2aNUJcxffio1pql1xyCRYuXNimlVMywbXXXounnnpKZHxarsm1a9eKMh+ZmZl4+eWXRcaqdbP6/vvvMXToUGRkZCApKQlerxc//PBDg2jz+Xzi8xdeeGGbtoFhejJUGqOyNgynXWr2o6b3lDgQimhI9qjN5lMrKFGj1jBR42/sBqQlU6pDyExxYmxqAOP8q5Ch1CFiqKINkBMRjLHtQrZcizXewzBheH2TbDUpDd6Dj0K4ZAtiJNxiEUg2B2xZA+DMHS7mUzD/m19sxLaiumb7c5rzexzu2JiQMhDxkCsNdhdkmx1weKE6XXAPn9xiKyarNhxtJ/VSjT9uFFdCx3vEgFSxXH/HKrj89bLdmLdwByK1pbjM/S3SlECnM0TXRjLwamSWKEfSXij+Mi/Tg7OOGYGjJ3NNTqb/0CbR9v7774uYM0u0kYj60Y9+JNpZUVcEC5pOBW7bCiUZzJw5E3/5y1/EH2WTUmYoCS+q1UaCkATdzTffLGqvUTFfyi6lWm2W/5fEGSVGUOFdKh9CddrIQnf88ce3/2gwTA+DapmRu85ub/kWaU2V0Fh4UDJCINx64DV5K6k22veri/DLAUWiVIffsEOT7aJ9uAEdpiEhR/FhWF6JiG2zIGHm8R4Ko2B0fU9R1Q7ZVd/snYTQ0++sxsad1XFrM5Ap1+EE+3c41FEOdIFgk5weOAaNgyJJ0GIR2LxprbZiIpcolfWgLNFdZX5hwXTYVUSimhBsyR47Zk8Z2KkkhL5iVfthTTF2l/lRURNGkhTEVe4PkK40DlnpCDUxdFiwTRiRgbNnjxIPEvtLtGGYvkaH3aN0Y0gEDzzwgCj7cc011wjBN2XKFJGMkJ+fL+aTaKM4tzPPPBNZWVmiqC/92+Kqq64SbtJbbrlFJC5QR4Snn366UQwLw/RWqPgsFbzV9OZJAoT1KzT3/Csa0+EPRREMt9FtVVeO9FgxtPRk+HwKtJghvovaiSt2D9KSnUgxyhCrKoEjs/43SZBAU9xJzawyb365GZsKaxCJ6XBLEQyQKzHKthszbeuR6PsrOWbdooGmB/b0AsimAUmxw10wcr91yKicB5X1sGrUkZuZrJZkYSPB1tvLfXQGKs3y3483oKQqCN0wxDgmSzp+5foQ6WrnBVtFxIGHI3PbLdhsqoTjpw3Gr87u/QWQGaajdGtMG0EuTspItbJSmzJhwgS88sorrX6e3KY33HCD+GOYvgZZE8gSVFwRgMdlNntwqm85JSES1RFSoqgll2g7woxSJT9CPh+yBw1Ceq4NNf5IQxurVK8Dph6DXlsOva4CiBNtLbG7wo8l60thj9VhqFqFoWoZJtm2IUPuvCutKaZCgo0KxRYg+bDT4Mof2e46ZKIAbX7Kfvut9gfCYQ1vf7MFC5bvxJZiKncUxVH2TRioVMAuafBI5cjphGCLmoAv5sBbsXHYrQ+Fz2x73JFNBc6dcxBOP3I4nM5uv2UxTLfCvwCG6cGQ++e0I4fg2ffWoqYuCo/bBruqIKrpCIQ0EdtzxPgB+HTRTlT6WrqpmsJSYkMUg9Qq2E0NPriwSaNYL4pfq49hg6FDttlEDbV49BiVt5cAed+XimhUx1+f/QFK2IeDbLvhlrZhtq2xWzVRKA4nnEPHNeq00FFIoBVkedGfeeyNFfhk4Q7EtPqHgln2lZjp2IBkOdSprFCLgCnjg9ChWKENRdBsX0ZobroLT97MoS4MY8GijWF6OEdOzENR0W4s3hpDpS+CYFgTYiMvwy0CsckKR6U8mpIsBVGgVGGCbTuGq6XwyBFIMBE1bSjTk/BtZDQ26bmAKxl6oAaSjSxUUiN3J01XktJhyyhocdtomWfeXYW3v9omBOIYtRQ/si1AJ3RUq0Q0wJ2RjexTLod72ISEV/Xvjzz6+nJ8sGBHw/tZ9uU4ybWqobBtZ9kcScMj4ZPafKuhEU3y2JGX6cZNF01DemrvLN/AMF2FeiBSVBmG6RxUIPesOaOxuTjY0GidXKdfLSvES59sEO1C96Jhom0nRislSFd8GKhWitIMGmQYkGCTNAxQq3GqvBQfxiZj0OQZCK//GlptORRPskgsoAQDPeCDJMtwDz8Eqie52TZR0sFDryzB1qL6QrrH25fhOOdqqAm+LFA6hU8B8vOGwDV0PAu2BCQZUFbymi0VjQTbRHU9TnatTMiTfK0m48nQTBQZe0sx7Y8heV7849rZ/dI9zTBtpc2/z0ceeQRpaWmNplFv0NTUvQG71dXxGWMMwyTaVTp5dHbDTfext1Zg3g874wRbDKc6l2K8rRDJclhItPgfOOWEkj0uBgVUDtUjh3FK2lYkH3whFC2A8K6NMEL+hnYqissL54DR8B50eLMHtE2F1bjxoa/qS4oIC80CnOja1CWZoQ67C/npBSJWLWnc0SzYOplk8MYXm0WSAcWxWVD/1594FnZasNHpUKK78VLoaBQZWftdngo9jxqUipt+Og3JTVzzDMM0p02/Ucrk3LhxY7NpGzZsaLZs0z6gDMMk/qZbXOFHOLrXJTrdth7HOVYiXdl3X0mSOw7owuIGxSFKccQqdiP5kBNgyx2OaPFWmJEgJIcbSvYQbIuko2KdD6bpw4AcL1I8DgSCMdz48FeoT2iN4nbny0j0/ZZyKUStVEWFLT0f7hGHwDv6sH1mhDL7P3eefmeNqOsnIB1uUsxjAD92f4vO5tvT6bAtlom3wlP3K9jImHbqUcNwyanj2LLGMIkWbZ9//nl7vpNhmC7gmxXF+M/Hm0TT9WgsXrCtxVz3ErjbEYfkUjQoSSkwwwFoNSVQR0yC1zsFxoCDhGt0wdpyvPZ+EUqrK0QZEaoV17TKDwWsn+BYDkeiDV8K1WD0AHYHkscdheRJc9qcEcrUJ4V8uWwXyqqCyE53Y9YhA4Qwev3zTQiEY3DYFNERI1UJIhcVOMnxFVxS52LYIgawIDYKi6MjUWRk7HPZAdke/OOaYxLewYBh+gOciMAwvQCql/XW/C3wBSJ7LFyEiWFKIU52r2iXYGvozRmNgBqOyg53o9prwiLz0XYhDqn8R0t9Q2fZF+E01zokGlmRkTzlJOqLBdmdhtQJRzerB8e0zqufbsDb87eIwsqWm/v599ZgYI4XO0rqRGE/EvyUpHKkYw0mq1uRorRcA3B/1LvagbXRNHwdnYbtOlnXWhbWqgLMPGQALjt1PLzeTnaWZ5h+DIs2hunBUPza8k0VeOajUpTW7C3ARjfdCbYdmOVYBa+4dba/mTqiQcgp2XAMHt9ofeR+JUsMZYbWCzYNI9VSJMOPDCmAQ5R1yHF0rudki5mhQycia8osSHYntOoS2DLzRKcFpm3WtfteXIzv15QIiyjV7lMVRVhI64IxrNlWH29MnkgJUZzqWoixtkI4O5ElWmO48FX4IHwVG9dqGY8Urw0PXXMMZ4EyTIJg0cYwPQgryYAyRHeX+7FobYmo2B9v7UqW/JjhWI9J6g6kyPuOYdsnigrvQdNhT6pPJiKR9vmSQpRWBRt6mlKA+gzHRhSo1XCI9IUuQAGScofAmZkOPewHAjVCrDlyhnGGehuta/+bv0WIM4v6mmumCPTf60o3cKx9JabZNyBdlH/pGBQRVxnz4Onw0agw6nvSNmXUwBTc9asjuRguwyQY/kUxTA9LMiivCQnLSfPaa1HMdS7HJNtOuOQgnO0qU9oc5/BDYRt3PP7+8lJsL/KJgr2GbiAc0YRInKhuxmnu5fBKoYTV7WqKssfsp2QMgBkJAVoUtqyBQrBRj1Nm3+L+4++24fu1pUJwN0W4to36cyhfrsSJ9qU4yFECRbQp6zglmhdr9GGoMNJbLIb7z+tms1hjmC6Cf1kM04My+8gt6XLI8AeNZkH/sxzrkNQJCwlhuUalnGF4rmQcFvzt+xaXy5dLcaprGdKkUCfWtp9t2bMxav4ouLMLoKZkwz18sohhYwtby5A4e/urzfhowXbR3D4Sl5DSEhT/mC+X43jXMoxSS4Vgo74ZHUn2JVlYpdmw28zGqtjARvFrNFov3XESx6sxTE8SbVSHrbCwEEOHDhU9Q5vi9/uxbt060bSdYZi2ER9HluJRUemLd3kamOtcgqMc6zpdkoH6dcLugi1jIF4LTMWC7dRyvTlklbnQ/S3S5S4SbHFxdba8EUgac7hwh1JMW0tFfJl6sfbDmmK88fkmbN5VC8Nsns3bHB2TbVsw27EOqYpPWEvpIx3J2aQIxoqYB8v14UKwUYaoqkjwulT89cojMSCLx41heoxoi8ViuOOOO/DGG2/Uf0hVcf755+P666+H3b73yWrLli246KKLhHBjGKZtkJuLXKJkyyipCsdlhpZgnG0rjnBs6ZRgoxt1TLEhKXsgnMMnwTZ0Kr54uLXfqIGTnN8hV6nvcpBIAhEg94jjECvdDkl1Qh44Fp6kZKipWewO3QfUeeLNLzZjyfpSkRW6f3TMsG/AVNtm5Kg1sO8RXaIsW6v5nc0hSV8UdWOrNggr9AKE4UWFkYTMZBvevW1uJ/eKYZguE21PPPEE3nvvPVx77bUYNmwYPv30U7zwwgtCnD3++ONwu+tLBjAM034o6SAciUHTG2eGHmrfhmS5Ttx022vFsnI76SZtyx2NvGknwpE/UtQ7+8cry1r9+F+c/4G7sya9JkQiwJcYiCtu/rNwfVIpCtF5QYuKlllkZWN3aHOCwRgeeWsFlqwrQzCiwWyp9koDtfiZbSHy1Ro4lAiSFFE+udmFngRbWwp8RCHh49B4fBmd1Gg6ff5ZFmwM07NF27vvvosrr7wSl156qXh/7LHHYvbs2bjuuuvEdBJ1ZH1jGKb9rNhYEifY6nC0Yy0OshUhQw7A1qZbbHO3Y/2rAu+Ms5A588eNCtNu3VXT4sf/4vw33AnubFAZATJmnoOfDx/ZUL7DqgfHtO4KvfOpBViyobwNS5u42vk2Bjl8bf5+OhPIXtf0ik1nWsSkuDUv/hs+HEVG4+42qgy8de/pbV4PwzCJp01Kq7S0FOPGUS2evcyZMwf33Xcfrr76atx000249957u2DzGKZv88q89fhyWbH490R1C+Y4VyNLqW2/O1RpEhbuTkXGyb9CyugpjRZbt6MS20vI9UntiyKwoRhXqIuQoobhSHAMeZ3iQubMU5GdW+/+ZGta21yhf/v3IhRVBve7LFlkr3C8jxxH+2MP4y1uloirMLxYER2MVbEhjboaeJwK7rnqSAzJ2dtnmmGYHizacnNzsXr1akyfPr3R9OOPPx433ngj7rnnHmRlZeGEE07oqu1kmD7H+u1VeOXT+p6+VA9trmsZUuRQ+1O6pfobr3A1etNgzxmG1Mlz4B42sdFir8zbgP98tA45cg0GKpU42/4dHF1gIKdCubuSpmHkIaOQO2QQx6u1Q7C9+OH6Ngq2Ohzv/BY59vYJNnKwknS2BD4JtyrDhSXRoVgUGyli1miu26Hi1z+agKMnU5YowzA9hTZdsk8//XT861//gqIoOOaYYzBkyJCGeRdffDF27dqFZ555BosXL+7KbWWYPpMt+tH32/HGF5sQ02IYqe7Gic7lSJWD7c/ss1yinjSkHDIHruGT4cgbJn6rlqutuDKAt+dvxrffr8OhtmKMt+/EwVKhaC2UaMgdWnDx/2FUdioUm4Pj1faBNTaBUAwup4rPFu3ElqKWXdfxkEX2ROcKZHYgWcQaCUp3qdPc+DY6Cl/FxlDkY8MyY4em4y+/mgGV/KEMw/Q+0XbJJZegqKgIf/vb30TJj1tvvbXR/FtuuQUulwtPPfVUV20nw/SZemxUwX5XWQDjla34sWc9CtQKtLvJT3w7KtJsE45DyuTjG1m0yHLz2eJCrNtagbKiYky07cAIdRfGKSXoCh1VGXFg/M+vg3v4cBZq+8EaG+p2QUVwqbdsRU0YMX3fdTyo4PHZ7oXwSm3JIm0Zsq4Vatl4O3QoSsws0fIqJ82DyWOyccGcg+BOdCYKwzAHVrRRWY8777xTZI9SLbaWoKSEk08+GfPmzUvc1jFMH+LTH3bgX68vh2aYmGrbiFNcK+GRwu2zrjURa4TkTUfalDnNBNvL8zZgd2ktopW7McuxEYfZN8ElJbazQTQK+ODAYjMPP/vxKRy71garWmVtSFjVqO1URooTTruK8poggmHKIG69pysVPD7LvahTgo2o1t14L3IoBh88HmcfnItZhwyA3d4FZleGYRJOuyJaUlNTxR9RV1cnUveTk/cWVRwzZoz4YximMY++vgwfLNiOIUo5BtqLMce1Fh5o7etu0IJggzcdeT++CfbkvYHj1AKLBNvmjdsx2NyBEz0rkSaHEt43lGLXMiZMQXZSBsaPmAxn7nCOXWuBbUU+fLt6i7CqRWM6yqpDor3UyIGpokBuXTAKRZbhtCsIhMkO1lxY58uF+KVnPpKkdmQTt0DAkPA/7XCce+4JHK/GMH1dtFHx3CeffBKfffZZg8XN4/GIEiDkQh09enRXbSfD9Fr+9fIilC3/Che7dmCIrRwuKdqh7NBGgs3mgn3CsciaNAuO3KGNWhy99cUajIxuxEW2dchTfR2qgN+WQrlJR5wOZ0Yq3MMOgT1rAFvYWqC4KoqlS7ciENGFVc0wVBRV+EUT95WbK2C3KZAlSbiraZrZgia7xPkRxjnK/r+98wCPo7zW8Ddtu3qXXOUKrrhhgw0GTIfQAgECSUhCQnogCbkEAiQQklxCSbkkECDApSc0c6k2YIrBxg333ot63za7U+5zfmnllSzLKquy0nn9rKWdmZ39Z/5ZzbendnsstOtF2tm4+7Zvcbwawwx00fbmm2+K0h5U7+mkk07CsGHDRG02inF7//338dZbb+Gee+7BBRdw4UWGifH6C69jyIZ3MM9TDhfMrvUNbaG6ZHgmnorMuZeJQrmx+mvkDn1q4ToMOfgWfqTthddN5TwSj2EAm+DC1LkXIn/MGM4MbSfZZM22CixaU4eIreK44ZmQFRl1fh2maQuBTbFsEiRkpDpFC7Nw1IBhkJXNwBi1DKnYj8vUbXAloHYe7dV13Dz84vLrE3F4DMP0Z9FGFjYSbKeeeiruuusupKWltVhPVrc77rhDJCSQe3TUqFE9NV6GSRre/Pf/wblpIfIcVHet64KtucNBWj7yrvw1vNn5LTbZsb8WH/znPziz5iPkOgPdaijfHnoE8M05DwuGFMM7ejo3dm+DcNjAgy+sxpqtZQjqMbOZjur6MEYPyUCqV2tKPLChqTJM24Y/qMMfMsQyinWc49yOArkazkSdWgVInXwa8i/8YYJ2yDBMvxZtTzzxBEaPHo0HHniguZRAPD6fTxTXvfrqq/Hkk0+KpAWGGcxs3lWOhvUfYKjSmGjQJWdUnEs06khD7uW3HCHYtu+vwVtPPYvZxsdIV6M9J9iiQO6cM+AqGgFP8dReb+weH8jvdWsoyPJClvtWMLYe08KPduKdZXtgtuHiDIZNrN9RidFDYwVqbdGWSjdsIYapUO481yac5NwGB4xuubSjlJyiaPAVFsM1fCJ8cy4R2f0MwwwS0fbZZ5/he9/7XpuCLQa5aa688kr87W9/S+T4GCZp3GHU+J36iIb0KFZ89ClOk+sBy4ZTOXpGYIdi2DKGQB93FrSUzBabbN1XjX8+thCXS58iQ6Zbdc+4Q7cAOGHiHLiGHg/PyCm97g5tXR6DLFRD81JwxoyhGDUkvc/q7C3fWIr6QAROTUZ5dRBV9dRivX0XJVlFafuocIM2kir5cbJzE0507ISrs8kpcUQVwJs/GjlTToNv6gJuLcgwA5AOfarLy8sxfPjwY243ZMgQVFR0pF8ewwys2msvLd6GaG0ZFEtHwJQxWjmEdLcfDjnSLcGmjZmNlFkXoKZVlfwlq/bjxX8vwjWuD5ApU6nUxIu1Nwwg3TsbeYV5yJs/H67cob3uDo2VLiFxFCuPEY4YYnlFTRBXnjmuV4UbzfVz725FaVVQZM+TtU+WbEQ6WIWDpFo4GjPF2Zio7sHpro3IlWvg6Wo5FrpeHCnIWXANUqec3qLPLMMwg1C0UVkPEm7HgrbJzGxpDWCYgQzdxF9+6QOMxW7kKfVwqbrIDvVIIXikSGfavR8Wa6oTUlouMmdfiJTJpyEcDgNVm4U77mCFH4s/3YblS1fiK+6lyJGP3fKos+g68HTmt+BSADvFg9MWTIE7r3eta7Fjpa4RFbUhFBemNosRj0uD26niQLkf76/cj5GFab3iKqW5fmzhBjQEoiLbkyx+ZHWLdMGQSu7QWY5tmOfcDK8U7dx10golezjyL/g+PEWju7EXhmEGjGibNm0aXn31VVE8tz1efvllsS3DDAbohv3m6x/gdGWFKJJLWYKqZIrfnZIurCpyh8WaCtfICZB9WVBTM5Ey9Uw407OFeDlUGcDa3QG8t2EDKraswnRlJ6737oFPSrxYuyV8IoqLZiHV60BRXgpO7wMXZMwdSq7EfWX10BRyJ1ooyvGJcRFk8SPL276yBhFXRut6eq5f+mAHQropLGxUXy18jO4FbWOL3q8nOrZipmMXPFKjO7SrU6k4nCj40g/gLuDkL4YZDHRItH3961/HNddcg4ceegjf//7329zmvvvuE7Fvzz33XKLHyDD9Dipg++jfn8V51gfIUf2QYQmBRkYX+k23VdiNOZ8dEGwyXKNPgHfUVKjpOc1lNEi8vLtsL5ZtPISa+ghGKwdwvnstcpTqzre96oBgO3jhfXioOAuhsNFnwf7x7lCnQxaCzelQUe/XEdYNISBjwo2WV9frIhGgp6F4RbL4kYjuklZrEmtDlUpM1fZgpFoCd9yp7VKRZacPBVffzoKNYQYRHRJt06dPx4033oj7778fb7zxhmgaX1RUJAJdDx48iHfffRe7d+/GL3/5S0yePLnnR80wfQj1Dl39wQf4kvYpspWWJTboA2WTs0uiTE4JBmUJtvqg0XMh5xTA6fTAPX4OsuZdDkVzNTdYJ/Hy+yc+R1lNSLwmVWrAma61GKZUJfx4SLDd6/gObilIxdDcFPQVJIjIwkaCbUiuDyHdgKLIwhVJIpLE2aEKP1I8GeIc6RFDuChpXU+Pa8XGEviDXROH5Aodo+7D6c4tSJWDcEldyA5tVVxZPeE85E07vbmwMsMwg4MOpxd95zvfwZgxY0R2aOvG8FOnThWdEubOndsTY2SYfsML727Ge4s/xbWeZciWDws2EmKx3+mn0iTWNEiotBzwyVFosEDx6iI0S1IhwQH3iCnInHEuHGk5LUTCg8+vjhNsQVzlXorRalUPuEMnIiv1JGSnu5DiabRg9RXk5qQMUXJ7kijzODUhyEjE+VwqMhwG7FAIRkCF6vGhqo5qn6ULi2BPlfGgfqCvfrgTyzcc6tL+SGx/0/M+hqh13S77InB6MeS6P7YorMwwzOChUznhZGGjR01NjbCwUWwHWdw4+YAZ6NC1vnXtBuz/8A1c7tqGPLmhhYWttXijG60OCTJsOGUJfssDjxyBptiQYMOQXPAVn4DMuRcdYS3Zc6gOe0oqcZK2E5nyZsxTAtASbExqFGxfw7A8n7BmjR+emVDx0xVIKFHsGmWICiSIWDVVP4gsvQLZagC2bSCtrhS1tV7kpgwRMXeJcuEKd/TyvVizrRyhsAlVkUSPUMr2JDHXWaaoO3GZazl8ShcavLfRZ1ZZ8D0MP3FB5/fFMMyAodOFfCiTLSMjQzxibN68mRvFMwMWo6EGe1Z9hm1LP8QstQKZcp0QZvHWtRjxy+mmG4GCIFxwUx01RUFA8iDqK0DB9PkYOefUI6wlhmFi43MP4u60zfAIu1ziiQm26eNzhSUpzedMqPjpKmTZIncnlfSgDFEix6ljZEYVQg1+VOgawqYTiqlidFoYY0aFMDyti2UyWkFFin/3r+Woqmu71lpnz8wUdRsu9yyDR+qGWFMcUNJz4MgZjrQTFsBTPKWTO2MYZtCKtq1bt+JXv/oVFixYIArtxqivr8dll10mXKcPPvggRo7kGAtmYGCaJkK7vkDJumXYtWUnoroJVTZgiVplxxYLVMbBVBxwOVPhTkuFM28knCOnoPC4yVDVI6Oadq5dh5I3/4Fx0bKEdzagshSGDtwWnQuvaywyU1UYpoUxQzP6JEO0LcjSR0VzyeLldihwWCFk+XfBLQchZxegoSaMwlQXTplWhMwUF8zaUuhlu6D4pnW5fhxZ0JZtLMGfn1+DYPjoIrnj0tDCpa5PcJJzT8fdoU2XQosrIj0fmTPPh7NoDBz5xe0WNmcYZvDQIdF24MABfO1rX4PL5TpClGmahptvvhn/+te/RBsrKg2Sl5fXU+NlmF4huGstalYtRs3uzTAjQeRbNiKqCiciCNsqXCLRoG1iks5UnEgtHImME06Dp3AMHDlDjiou1q9ahXWvPonpWuIFm2UBrznOxxapELPHZsCwLBEndulpYzB1TE6fW9hi0Dioy0FDZTmkQ7uQq9Uj2ygVpVQkfwjZnmzMnlyA7LSm3FlfBozaClghv+iD2tns3xff24bPNhxCaWUAkbgOBR3HQrbcILoYhKGKzgbnaysx0lnX4T20qcVcPuRffCPXXWMYpmui7ZFHHkF6eroo59E6fo162n3jG9/A+eefj8svvxwPP/wwbr/99o7slmH6Jf6da7DrtccR8dciaknQ4YJP0uGk+muwEIEG6nPgbPW6mFuUfgYhI3vMFGTOOg/u4RPatQTd9tDHyD/wHs5yHky4YCNX6P94vyuaI8myBVWV4XM6RL0xn1vrN4ItBrk7Lx4VwvadYVQ2yHBEZRiyAwWeCArzdeR4DosrSXXANutgG5FOZ/++vGQHAqGuu58L5SpM0vY3FlSWdHilkCjp4ZC6Ktaa7HIeH7IWXMeCjWGY7vUepezR9hIOcnJy8M1vfhPPPPNMR3bJMP2S4MEd2PXKP6AEa0D5nS4Z0Gwq3CEjaDvhFJ0OTPhtF2QpDC1OrMXu1xFI2K0UI2vS+fCMmNCmlWfJmgOiX+V7y3fCFzyEaz3rRWmLRKFbwPOhTFRlXimSKMKhxtg1j0sVpTR6o1RGZ6Gg/9DeDciQ/Jgzczxq/WFEDppQXW6k+LywAjUwqFWYu7GQLok1SVGFeOtoRuhHaw7gjaW7YXSt2JqgUK7AWe4N8EJHwHYI922hXNehMh5tWtYkFXC5oWUWIX3muUiZwFn4DMN0s/foiBEjjrnd2LFjUVpa2pFdMky/Qy/djQNv/gtqsAaNzk8ZEky4RZshSRTMjUCGA5Yo6RG0XXBJ4eabNcmAesuB1c6Tsc83GRO8hW1aeV75YCtckVrhVvNJe/B918bGMiAJgqr1Px+aiw1WMTJ0Q8SuOTQFhU1dA3qiVEYikj1C+zYguG0FICswg3XweVJhp/tg6SFIkkfUsDMDdY3PHS6Y/hpo2UVieUeazZNY3rqvBmZTJigZGTubFEqC7WL3KuQq9dAhowBR4So/1vQdNSTNm4n0GefAWTAKzuETuck7wzDt0qG/EGRh60jvUSoFkpaW1pFdMky/s/Ic+PRdNJQfgItKcghZRqLNFvXVFJhwSBIitgZN0kW7KpM6H0CDYWuQVBkBKR178hbA78iHFmys8xXPc6+tgb78OdygVUDRbOSgIaFi7XBm6NVI9Xpgh6Lwh6LITHNhSG6KKGFB/Tqpo0B/yBaNF2zBXath1JQLwaakZAK2BbOhGnbTEE1/LWSnB5YZhRn2A4FaIdaoe8TRXM/b9tbgnwvXo94fQWaqCxb1qLCo5EpnEgtaukTPdK1HrlIHExK81GP2WBm+bZTuiKHlj0LGKVfAO3p6lxMpGIYZXHRItM2cOVP0FaW4tfagJITjjz8+UWNjmB4XakZNGSw9gGigAdV7tiBsKNAkEmuSsK5FoQjhpopmUxYskLVNhU5CjUSS5ITlzkDAW4Ty1OPgd+ahqtwvLFk5aW6s3lqOqroQjI8exeTgJjhcPXc8K3SgbMJ/4SxVFda0ipqgsK6RO5Rcg5GoLMbVX7JFCXLdUgaoFQpAzSyEGawHbBOy6oDkyxDWNNnlgay5EG2ogq2HACMCLWdoc7uvtliyah/++eoG+MMUzWejvDZ02BrWpNo6Z2WzRAxbrkwxbI2FkqUOZIK22chMc8M1ZQF8+cPgyh/Fgo1hmMSKtmuvvRZXXXUV/vCHP4h2Vk5nyxDsSCQiyn189NFHImmBYZLBFdqwZRn0g9tgR0KIhHU4Q+Wosr1wQoNHiiAE6h8qC5FGfUQdiMIpGagyfViBCfDbHqjeVGRlZcHwZEM3LCHYUjwaJMnGj+57X4inC5UlogRET7JCH4b3PediqqYJIURCberYXFx3wQSU1QSbK/z3RT/R9qDMT8oAVXwZkDQnFE8qjIZqSD46h5KwptmRMJTckcLHqKblwjNqmsgWbS12qKk79Qj9bN0hfLjmoGhzRYkXqqKIuLZI1Gx2H3cMGx6JbKkmcpVajNP2I0epb/uPZjsWtdZ4J8yFb9SkdkUnwzBMl0XbpEmTcMstt+Cee+7Ba6+9hjlz5mDIkCGijtWhQ4ewfPly4Rr9yU9+gnnz5nVklwzTJwhBs3cjqj96HtGqQ7BNE6ZlwTYMOM0I8mUT1YYPDtWAG4YQbHSrb2xKRTFrHizWJ2PYzPk4cWQmtu6rFfFS0eqQCO7PSXejrDqAlZvLYVohfNP1DiZ2ogRE19yhX0Z2Wgamjs4RxXJJKMZcoCRaqKtAf4WSCWzTEMkEJMLUjHyYerDRHer2ApICKxKGWVMKNSMXnpFThFAmEXao0i/EqNulYsXGUryzbA9q/REhWGPCLPbTsu1OWdaodViRUo1CpRpZcj0mKbvgPYoq60wJNa1gDDJOvRKaN40tbAzDdJoOR71+9atfxfjx4/HYY4/hvffeg053C/rW6PWKnqOUOTplClfsZvqvK1Q/sA3Bg1vhX7cERnWJqFcWggbdJOenApI2HkQRVcMoMdKRpfhFc28NNhRYCNhOvBmajOKTTsc3L5woLFbzpg5pzkwksbRo+R7sPFiH8x2fYY5z2xFlQRJFkArlhkfglKlX4RyXjNLqMEoqA0I49jcXaHsIsaaojZmgDpfIDHXmF8OoKRWuUhJssEyRcOCmQP2UjBbJBVRvrrw6gAAVxrUhMnDjLWnUFoseHcfCCKUC49USuOUwCuRqDFUrjzqPnap5m5qLrPlXweHr//PCMEz/pFOpStOnTxcPorq6WmQ6paam9tTYGCZhrtD6dR8gtHs9jGAd7GADDNtCyKJSERSvZkC3qcWUAz5EkIoQquQU7DMzkIIIfHJYRDB9qo+Bb/xsfPuiSc37JuFGliyy/CxZvQGlVQGcp36Gec5tXWsQ3gG+0Itw+e/+gteP0uS8v7lA24Pcn2p6DqKVByFl5AvrEwk32TVKZIka1Yeg5QyB97iTRcsvEmzPL9oqxJpTU1BVG4Q/rt5ax12fR6u9tg9jtDL45CC8CMMrHSXRoBPu0MYDdSD3vO9wKyqGYbpFl/PLuUk8kyyCrerD56GX7gSMKAU+iXQCyaLiuFTJXhN5oppki+QCTTJEAd0MKQin4hIutVo7BbuNHGyXx+L7J41q83127KvCxi82QgpWY5ZvR48ItkZX6GV4/b6rWyyPCcdkhEQaxXaZgVphXROxbVQ014jACtZBzciDexhZNWUhTsnCRoKtKMeL9Tur4A9GEzIOEmynuDYjTQoiheYeYXjaEmydFWuEJw35V/yKC+YyDNNtuCgQM6BdorUr30Z4/xaYhiH6gJqmDK0ppU+FJZILqM8BuT8pJzBkO6FIIRwy0lCDNFHSowRZ2G9lIzU7G5NHZR/xPvf/7RU4Dq3DXKUeea5KuEXX0cRSowO1F/w3Xp/TtmhMZsjl6SmehnDJTlSVHEQkpMPhdiKroEj03SwLawjU1IjyJftK65GV5hJ9Qmsb9MaSIN3uGU+ZofswTKlEquwX8yd1V6gpGuBKhZ42AkNOu4QFG8MwCYFFGzNgBdvuRa8gsu4jwI4ApgwTUZhCqB2+z1PpBr3pJk2FdB2SKUTcZ9FxCHsKoFsSaiIaXE4Vl542WgT2x3PvX15GYcVn8Kl+5KMe6XI44cdSozuAi2/C3AkFGKjsrZOweIOG/ftdsKIKZE1D6l4LkrxLWNYaY9NMVNaGMWZoOsKRxqLBiiwJC1x3oBi2aY5dyJIDhy2kbTVxPxa+TJEo4SmeCi0jV5SL2X6oGo7c4d0aH8MwTAwWbczA7Gzw0UIEtn8OlxlpUSuLLniyg8lx7acops2EApdE/1vYF8nBdrMQDt0hXI8FWW4h2E6dNrR5P5Q5/Y9/vISh5Z9jkrYHDsnuIXfoOZg5fgKm1Xa9T2Z/h+LUHlu4AQcr/E0CTIVpmQgfKoWqKhg/LAOF2V7UNISxt6QOq7eWQZYk0YpK6aYfmpq8n+jY1EKwycrhlmQdRcoZjoxZF8A36oTmMh52MAiU1nZvgAzDMHGwaGMGFOHSXaj+5GXU79wEyTSFMCORpjRZ2HQooKi2xgpsjWhNW1GVe6rB9mHkeMw7YRgmj84R3QTIJRpvYQvuWotPX3oBkwMlyHXU91j82oOe7+KsKTnYV1KPtbujmD8n8cKwL6G6al/sqMCzb2/GnpJ6IZRcTg0+t4LaBhOWBUSjpkju8DpsrNxcGffqxnNhdsMTXShX4mzXOoxSDzTOYVfcoIQnHQUX/xTOnKFcxoNhmB6FRRszIBBN0cv2ofbDF1GzbycQDcGwJRHzRP0NjCZxRnFs5LaiRlV08dMtNmLLogH8fiMTn0fGYDeG4J5Lp8DlOvzxiGVoVm5cifIlz8EthZEr+TttkemoYFs04b9EoVwiM82J0ooQSquDGO3rP/1Cu8OHq/fjpQ924EB5A6LGYTEaMSJoCEaEJY3Kl1DbqZKqoHgkkkK5BFd6liNP8QvB1qnSHfH4MjH0uj9CS+XELIZheh4WbUzSEo1GEd6+ApGKA6KmV7SuDP79O0QJCDfsJquaLOLW0CTcGjuKWiA5RHmHh6JZWBoZhwo7DfvNTEhQcN5Jw1sINnLfLV62B6tWrsWFjhUoVqtF0/hEEomIxFbcFp2Bs2efK8YXg5q9kyuQgu8HimB7bOFGIc7ouFpDZTtM+s+gwseJfncLV7k+wjTPPmFV66pWE3hyUPSNu1mwMQzTa7BoY5KS2lVvo3bFW7BqywCzUcyYkoqQISFkOeGRI3BKFoK2E14p3Nz+nfpQknWMOlLuN7LxangmDlmNGaHUUP3sE4fjhsumtBBsz72wCOlVG3GJowzFannC3aGNsWtfw7yphTjb1bLJPEHtl2hsnjghmcwuUbKwUeeGtgRbPIkWbEPUKnw7ZzUy9JJu70tOz0fOOdfDmXZkNjHDMExPkfx3AWZQCrbqxU8BRmNXDkLc3+0oyHkYkSRRf82NCAxJRsB2wSlFhGATjlFbxiEzHa+GZgjBluZVRSzVLd+chVEF6S0Fxn/ex7jaz5DhqMMwqTbh7tCYYLtwXrEQiG6n2iIuity+1XU6ctNU5Gd6kOxQb9Cy6qDI/Ow9LMzwleLavK1ATTcEm+IQLRfklGxkn30dvKOmJnKQDMMw/V+01dbW4v7778eSJUvg9/sxbtw4/OxnP8OMGTPE+s8++wz33nsvdu7ciYKCAvzoRz/C+eef3/x6aqdFjezffvtthMNhnH766bj11lu5+O8AhASMXl+D6g9faCHYzFa/pEs6Sqw0aLIJFwwE4UDYVkXyAblM9xpZWBSehENWDlwOBQXZPowdlomReWnN+yQB9fJ725BRsRrjnAfgbpGDmiixNhqv3/dH0dmA3q+iJogD5X5Rh8zpUEXDc2qNleLVMGV4YyZrslNVFxKWw25W6WgTVaFrRBJxcIfFWgmunaADB9YCNQ1d37knDVpaNhRPOtLnXcF11xiGGZyi7aabbkJFRYUQbllZWfjf//1ffOtb38Irr7wibtLf/e53cd111wnhRsLu5ptvFoKMmtYTd955J1auXIm//vWvcDgcuOOOO/DjH/8YTz/9dF8fGpNAjIYa6GW7ULfhEyBU37xcSKlWeoqkTYochl+4SXU4YEATrlIHNkcL8ak+DoesLFHji7JD01NcoldnTBSt21mBe/71Odz6VvyXb3OPuEOPv+s/eD1OhFGf0CvPHNfcU7O6Xm/uI3rSxByE6w5iIEChavSgRAMRt5ZAHvvVWVi5tRzl1UEM0WowLvgF9J2rYG3vZtkNhweO7CHwDJ8A79hZcOaPTNSQGYZhkke07d27F0uXLsWzzz7b3NP017/+NT7++GO8/vrrqKqqEpa3G2+8UawbNWoUNm3ahEcffVSItrKyMrz66qv4xz/+0WyZI/F3zjnnYM2aNTjhhBP68vCYBAq24K7VsEIBmLXlRxVr8ZAcILEVtRWYtoL90RQsjY7BJmMYJMhwajLysjyYOia3ubk6uUNv+vMS7D5Ui9+4nkNKSuJdeCTY3N97rE2rGY1hZGHaEX1Ew+EQNvcD0ZaIHqdD83wiPk+PJvbcTh2Tjcx0N846cTjq925B9ZsvIESuULur7yMLd6iWPxwpE+aJgrnUUovaaTEMwwxK0ZaRkYFHHnkEkyYdbsBN8Tz0qK+vFxa0BQsWtHjN7Nmz8bvf/U5Y4VatWtW8LMbIkSORl5eHFStWsGgbKKU8SnfCqK+GkpIJyzSPKdiIgKGgCqmgmh/Ugmq5XoyIloazTizCuOGZGJLnQ5rXKYQHhZB9vGwLHn91DfLtcvze/RGcVNQt4e7QAjx4491CnB2N/tpHlNy3MSsgdScgK+DQvBSc0SR4O0J1bQh/fn51jwi2u244GYZhoPTff0Rk1+ru7TAtH+6iUfCOmg7vhLlQulwPhGEYZgCJttTUVJx66qktlr3zzjvCAverX/1KuEjz8/NbrM/NzUUoFEJNTY2wtJHwczqdR2xTWlraK8fA9CxUziO0YzXsaARGXQXMaPSYgo2wZBW66cDWaCF2mAUiJun7F03CadMPdzWIWfFWfLIcSz9ah/nKXsxxlAoRl1ixRr99Da/d+6WkjEsjwfb8oq2inRTF27kcqmgjFYvDI7due8Ktvl7HN373NqIJrVhiI0XR8febToUv1Yv6jR+j8t0ngGA3XaFUKPcrt8CZVchWNYZh+h19HtMWz+rVq3HLLbfgrLPOwvz580ViAcWpxRN7HolEhHhrvZ4gEUcJCt2x7gSpBc0AhM5Z/M/+TKSuEsHlryJathtyWg7ktHx8UeXEOLoO2nkd1V9bHhmNg+oIHLR9KMz14dsXHYcxQ9JbzGu0oQafL34P6zYexEXaSqhqz2SGEi/cdaZwcyZizshNSYV2qW4blQGhrNKeEoP0Xu8s24Xa+jAKczzCCm5ZJhyqhPxMFw5VBPHust249pyxLcYQG+N9z67BgYrEXms5jjBuOM2HkSkyIts/RWnFXuhblgF2N1WhKxXp53wXtjdT/O0ZTJ815jA8b8lHaADMGemOjnRU6TeibfHixfj5z3+OadOm4U9/+lOz+CJxFk/sudvthsvlOmI9QYKN1nenaOvmzZsxkNmzZw/6JZEI1LJNUMq2wVFXAsmMkAKAVXkQQbhg6+kotdMxRKttM0GAroZVejE+0Y+Hy1KQ6VNw5hQ3jIYSbN58uNxDSZWO7Su+QFZ4H85TtydUsDWKtSm48+rzcWfTskRcTzRnJdURfLErgIp6aphui/iwnFQVU4u9KMhMsE8XQGV9FFt318LjlFFXR3I4LpM3aiMSMbF+exCfZIaRk9b4/rExrtkVQCRB1jVFArJSVRyfY2BWah089VUo0X2Q/LVw7V3RbcFmuHOhH38GaoIKTRYGxWeNaReet+RjT5LPWVtGqH4p2ijTk+LUKIHgj3/8Y/PAqcRHeXlj4HkMeu7xeJCSkiJcp1QyhIRb/MHSNhTX1lU0TcPo0QMzpZ++idCFPWLEiG4J256gfvnrCK5dDARqDveWFJYz6gggw4kgxqg6dhs5KDNMZKkN4gKO9RcN2TK2RobhHX0aZEnGmKHpuOqsMRhVdLiUR1iP4p3FqxHe/DFOsjYjQ6P6bYkVbMW3/i9e6IE5k1zZWL3nIBrCGvJzfHBqCvSoKeq4rd5j47KRRRhZmJrAdwZ2HKiDY72OnKzD1ryGYBQllQEEwiZM00bUtLF6H3DR3CKxfvXqXaistxIm2BwKcNs3ZyDFrSGjfjvM2iiUtDyY9ZUIHFoN2zosJjuF7ICUMwye8XPgmXJGwmPX+vNnjTk6PG/JR2gAzNmOHTs6tF2fizbKHL3rrrtw7bXXivpq8eZBygj9/PPPW2y/bNkyYY2jeBPKOLUsSyQkxEqA7N69W8S6zZw5s8tjojGQMBzI0IXdn46xeunLCH7+Wpv11zTThEVtqBQHVDOCAqUWmyOFooiuZakI2U7Umh5stApx0MxDve3BuCE+fOuiyS1irV5/7QMcWvUxRmE/hqvVjemlCRZsE3/3EnoCy7axYnMVArqJYQWpzZ8Th0ODz+MU9d0+3VCB44opwzFxMjQrwxKFhw0L8DhUEde2p6QBEcMSNe4itiXE2+otldi2tw5jh6eLMfrDiUk28LlUPPe7xrqMZrAB/ooGODLyIGlOBCr2AJEuuDEdHmhZhUibcV6vJBr0t88a0zF43pIPdxLPWUdco30u2khg3XPPPTjzzDNFPbbKysrmdeT6JCF3ySWXCHcp/fzwww9FEV0q+UGQNY0K7d52221iPzRhVKdt1qxZmDqVq5UnA6Zpom7L56hd+hJgkHOTbqCNGaJ2k2qjS1mDgXqTLlcNXklHlhLAATMPqyLFqLFSEIUsWlY1bg2MGpbdokzFOwsXY8i+tzBNqUG61BOFck/G6/fdhJ6iusHAwfKoSARo/eGm57R8X1mDONZEZZ/SuSOx6HNr2FtSL2Ln9lcEhHXP61JFZ4NYYwNy1epRHZ+tL0O6zyEyTLsKac6cDDfuvuEk5Gc1HgtlhgZ3rkFw32Yobp8QbdGqQ5BdHpgNVR3fuS8LvnEnIm3q6VxvjWGYpKNPRRtlilL82KJFi8QjHhJp1OngoYceEoV1n3zySQwZMkT8HrOqEWSlI8H2wx/+UDw/5ZRThIhj+jdkIW1YuwQN6z9ApGQXYMQsJodLepA0iZVfJaOYDBt+W0OqZCJsa9gUHYI9Zm6zUIsxoiAFe0rq8ejCDchIcQJb3sOZjlXwKYmvu1arA78Nfw2v33cRehIRPyasW21/ZKmDAhXkpRpqiRBryzeW4OMvDooSH9SVgZIeDlYEYFMPVwmo8x8ZSxqj1h9BV4xXbfV+JRo2foLaFW8iWroHMJsssZLc+PCkNf7sSD02TzryLv4p3EPHc2YowzBJSZ+KthtuuEE82oNEGD2OBplC7777bvFgkgO9dDdqli9EcOvKRrHWdMM9WncDu+mnAgsqyFUqo0FKwUEz6wjBRnFsxUVpqPPrWLujElPMdbjCs7JHjqNW1/CEclWPCzbCqUlwqLIoteFpo6k8tbyi2mlU9LY7iPZdS3Zg7fYK6JHGmDW7sbNrs1WtI9LXNDuXZHD27BG47oIJcLla/kmq2/ARqt54BDBaZYXRNUMPP1nZlGMKNyk1FwVf/gVcBcUdHxjDMEw/o89j2pjBRfDgDlS+9xSM0p2N7lBZFXd4YV9rdaOPWdniZZlHiqLK8mGFPBGSJwWaborYqmH5KSjM9jXGc9kQlrYh0R24wpdYwUbJyhWGF/dH52Lhfd9B1yMnO0dmioqiXAf2lgbabCpP1jBqeUXFgrsj2J57dyu276sR1jZFBsKRrlsnVRkiFq49stNceOy2s8S80XFQ3JpN14WiIViyE1Vv/KNFnGPbmICtAKoTMKMtxZvmgjJ0IvJO+wpc+SzYGIZJbli0Mb1G3fqPUPPB07Aaqg9LMjMKg35vcofGE+8ebdRzEhosJxaFJqJc9sCpQQi240dmIZ3coE0EwhGk1K7GDd6WSSyJTDSYj96FenWeOrUQr368t82m8qleR4v+qZ1tRUXLqeMBNXQ3LRPRqI1wtHuxf26y+llAQxsuW6cq4ZFbzhStpyiu0b/9C0QO7YAV1QHNgWjlAYR2r++AYIthAqkFUFUVRrABUDU484rhLhgJ38RT4UgjqyzDMExyw6KN6XHoplz76SuoW/E6QAVmFQ0wI01CzAblBdCtOSa7Yu5QxP2MQEWJkY6l+lisNYrhckgYNywD9YEoHNrh+CTTtHDy1geQ5zt6zFV/ywztKFTO42hN5WP9U7vaisrlVMXyytoQ6gKJqdWhyhIK81JgWwaq6iNQZBnD8n340WUnIDW1cbaDu9aibuXbiJTugkWlOySyuCnC2wm9c4UyZSMMWUuFK3eoEGyuYePFTzUlIyHHwzAM09ewaGN6PNmgdv0SmAe3AbF6Wk39Q60m6xo9qMpeTLhJceKNaq9tjhbh8+hYbDfymi/ZL51SjKvOHI/H/2+jECOaIuHzjWX4AZ5FnjOS4MzQuXj9vhvRHzhaU/ljWdh27K/FE29sQn1AF1a6nDQvwtHDrahOmlQgfqcabInitm+eKGbzaGP071yDqvefhuWvgSQrsBUnQFZYW9heO4ckwz1qBlKnzIfiS4eiuSBTlmkie5IxDMP0MSzamB5LNqhe9proGwqdMkPN5iK4VMqDbqVkTIlFHzWKNxk6LLGdbcvYEC7CG9ETUG8ftiCJ8h+qjIIsH1RVFlaipV8cxKbdh/BT14vIcya+DVVvJBp0hmM1lW/tAvWHIviff69FeXVQnDuqtVbuDol9DMn1CXfrys1lCRVs1MR97LDMdmMbqxc9CbOOimfbjXFsXYXMcqoDnjHT4S4a0/X9MAzD9HNYtDE9ItgqP/4P9L0Uk0RB5QpsUSC3/exQFZYoohsBlfMgwTYN9ba3RZahx62J7TPTXAiHDdz/7CrMjX6IOWnbmt2rA1mwdbRUR0VtSMTBUfZnVX0I/mAUPo8Gp6bCtCwh3MJ6rbDckeVt4+5O1DrrgGC764aT270+aj9+HkZDpbg2oHezz68kQ8ssgHvk5O7th2EYpp/Doo1JGJT9ZwTqUbfq7cbaa6YhsvfInmaZkeZiuYTVSrARJiTUWh5sjRZisT65hWAjz1pepgc1/ggKsjxYtv4g7njkXfzC9Q4KnN286R8h1qbj9ft+hdeRPMSX6qB4NSoPkuJxiBIhDYEozKZmxDRHJOAM00IobONAWQPGDc/scpaosJjKkqixNmpIGn79jdnw+RztuswD2z5HtLq0McnA6m7tPAmSJxXpcy4WSQgMwzADGf4rx3QbEgKRigMIH9qO8L7NCO1aAztWekGSYRlUMb+x10EMq7n3QePPqC1jTWQY1hnDcdDMEa2o4kn1OYRgo2xRt+7HzDUP48y0xLnzgjpwm6jvm1zWtSNKddgQBYVNy0ZtQEdYN+FxKaj3R1FRc2Rg//5yv3Cfup1KpwvzZqQ4cO6cERg3IguTR2ULd/WxMGrKENy7Eaa/tvuCTXFAyy9G+sxzkTJhbvf2xTAMkwSwaGO6hdFQg5oty+Ff+h/YYX9jBVbZbqybBRNGKAC/qUGCBo9oNtWIiG0TP2U0WA68F56AtcaoFq2oYhY2h0YN421hYTun5kWMksshJdAXWqM7cFf4SpEM8VKSCbb4Uh102qi9FFnUyPLl0hQEQ4YQbO0F9tc0RJCX4WpRYiU+GaQ1tO6E8Tm441tzOtXnlLqf1G9cisih7U0tyzqJ0wtICpxFY+EaMwOu9Bw4h09kCxvDMIMG/mvHdAlyc+kHtqH0tQdg11e2Wtn4X2OHAwsaLIThBDkx3YgKyxrV7TcgY7+RiaX6eFHGIx4SHd+48HgUZaegsi6I/WV+FH7+AEY76xLsCj0Hr993fVK5QsmyaYX8MKM6Nu6tx9Y9VdBURVjZqKwGYRiWaHtFFreOUFYTxpwJefhsY1njexzFsjbvhCH46pnj4fFonRpvzWevof6LxbBqStAl0gvgK54C96ip8I2ZwVmhDMMMSli0MZ2GAsn9W5ejbvn/AdG2a2nFl/RwwoIh/skwmtbVmR58po/FkujxR1yGFI81e1IBzp9TjCfe2IhFn+/Bz/EE0gdBZmhHLJt62S6U7tmHbXsqUFKjw1uvoVzKRjjqauwqYNqIRE0h2jpDTpYXcycXYOn6Ethxqo300cmTCvDLr8/q0ngrPnweobXvd7ABVtt4J54KV3oW3AWjWbAxDDNoYdHGdAqR+bfi/xAoO9iuYKP/yOYTFo5RGw6YcImyHwpKzVT8X+gEbDeHHPFaj1MRGY2zjs/DL/68BFbZNvxKWwQPCzYRBxYs2YyK0kp8uiMIv67B7VJREKpEuh3GF3o+av1uKNR/Kl51dZCSigBu//ZsBINRPLNoi3hekOPttGUtXrCVvP0Yots+Q3dQs4fBV1TMhXIZhhn0sGhjOkws849cc9D9bW7Tuuk7CTaSD/WWC2HLgX1mFt4VmaEpLV5HxhMqwDp9fC5yMtx45uVlGGXuwHzPSniOHd/eCbE2FsDspBNsJMKi5XsgBf1YWyajISIjK92FoG6g3ExBqlGLAlShBkUiM7QrxigSaAQJtOsvmtRN120Y1e893W3B5hgxGQWX3MSFchmGYVi0MZ2BMv+ilQehpmYDh3a2K9ZikGAL2i5Rd22rUYSDZnaLzFC6DWdnuHDlGeNQURfGuk0HUfb5Kpyt7kOxswS+BAm2LTrwSPhrKHQCD9/TecF2tJ6dvYUUDcGM1iAIDypr65Hi0RDSTZRVBcRJboAHGUoAXlNHwCY3aSf3L0FY1BLhuo2U7UWgZDci27vR+9XpRcrZNyBn0kndGhPDMMxAgkUbc8y+oaIvZLAeRrAepqFD0XIgOT2wAzWN2zT/dyRRW8XG6BC8q08V4q11Zug5s0cIi9FLH+7AsNpVOM+5CXluf4vyIN1li56DZVnn4vWbumZda69n57H6fSYMChC0DEQoLtCiMWgorwyI8VDB4UgE0CJBOCQLgSbBFp8NeiwoZq0rLtB4wRbctRpGbQWMQC3s+vLGMXcWxQHnsOOQMfsieIqndHk8DMMwAxEWbUyb6LqO2veeRGjPOiCqQ3K6qYwp7EhIKAFl6EQY1QepjWi7bDbysFQfh6DtPsKyc+G8YhQXpeHR1zZgQnQtLvOsTKhYa3SHXoLX77sGF3ZxHyTYnl+0VXQQoM4BLocqCtbGenZSA/deEW6yAsgqHLCgyjL8IQO6bopyKDQvLtWGZSiwFBWaLIvza5qWsAZGjfalGyUfdCXJIN4lShY2covS7+KikEkAdsLc5/RCyxqCtOlnwTthHhTqlMAwDMO0gEUbcwSV7z+D+pVvAlFRbbYRvwT4MkV9LWv/ZjhHTsEB3Y0cte1kBKLc9OD98FQcsrKOWHf1WWMwbXwB7nhkCebbyzDfs6O5hltixBrZma7tVuxarAYaCTbq0RmLqfK4NLidqujZ+f7K/aKBe0+7Sm3NDcWlILWhAtnpLuwtbRBdDkigkS80TQpin5QGv+mAyyGLMdb6dVGMGHYU0TbEdXa6E//zszO6ZWEjSKyRhU3S3LCqS0X8mezoROaIw4XUEy9E6uTT4Ug78lphGIZhGmHRxhwp2D57pZWVpMnR5q8CfFmIhvyo3fIFtlD2p3kAOc6Wwo0KO+zQs/GP8DlNbeEPQ9qGrFPTj8vD+0/8Az9X1yFV7kaz8KNkhn75NA++fsGZ3doXxbCRS5QsbK2D4Ok5Ld9X1iC2a6+Be0KQJGi5I2CbIUzJq0RNjYV6P7WaMJCuhEVR4lI7S2SOkoCk9A/qckCWQRKfmkpWNxkOTUZxYTp+dtX0dttNdRTDMBDcuQbh/ZshedNEJwxV9kFOzWm0tlnH6LKgaEiddjbSpy7gzFCGYZhjwKKNaeESrV+zqGVd/KZirSKy3bZg+mtwwExHGoLIVRpQhiwcjIaRYUbEtgeNTPwnOhVASxFDkifFq+GiU0Zh2rg8vPfE33GKvRId6HzUsbFbwPOhQqw1FiQsM5SSDihmjIRPWzgdKqrr9U63f+oqii8djuJpyPfuwinKXny8ai/qAiYOmSkol3PhSE3DeJ8TtQ1hVNWFxfgKs73ISnNj8pgcjBuekbAECnKD1q1ZjIYvFiNaWwpEI6JlmexwwZlfDC2rENH0XBhUTJfambWF7ET6/KuQceIFnBnKMAzTAVi0Mc0Ev1gM6MG4rqBx0E3VphurBdUyEYSGNZGROGRmIgwVlRaV8GhbgVGx3G99aQIWzByO/WX1ePnFRTiXBFuCxh2xSbDNRd6UYrx+deJKeVCWKCUdUAwbuRtbo0cMsZ626y3IGqX4pqG4cCykoZV47r1dqAxKyEl3I93ngh41EDVMZKS6RKLE2OGZCc90Fa3Lli9Ew+pFjU3fmzW+BCuqI7R3A2xJhjNvOCzLhFVXCdh0PTVt6HBDyx6K1DkXI3XcLBZsDMMwHYRFG9OMUVveaFGT1TabeceyRD1KFA2mCyVWOg60Ea/Wmq+cORbnnVyMSH01Nry9EBc0vJ4wC1uVrmH18Zfj7q9ehkRDYoeyRCnpgGLY4sUFWZrImjV6SLrYrjehcSieFIwel4KrvRnNma3kpiUROWZoBk7vgczWWOuy6jXvQt/0GWA1ubUlmkypUdiTcDciwl3qHjMDqjcNUc0FlRILVBVaRhGUjHw4UzPgKRrLgo1hGKYTsGgbjAQCqFn0OGr8VVDT8+Gbfw28Xi/U9NzGG68o1dAUx2ZRD9GW7cNV2UK9lIH9ZuYx3+qiU4bj4lNG472P1gMrXsCYwOaECLbG2LXz8dq912F2DyUBkHWKrFWUJUpJBxTDRi5HsrCRYEv1OoQ46s16ba0hYUaJED1ZQ47i1vxfLEZwxxpEqvbBqqsGbKPpJDX9CSEXKAl9hQQ/mT9DMOvKoablQpMVkZigeNOheFKhUqN37m7AMAzTaVi0DTKqXrkfqXvWQG96rmMdAl+8C9foGci6+CbUfPJvIOwHZAdMSxdercaeBo3OT/qtwvBhk3YcJCjtFnVwaTJSvW5cd9fz+BHeRJYWl42agGSDB288tccFE4kiSpyIWbMoho2sWWRh6wlrVlegc9BTiRANGz9BzaevwKg6AJhNQi0eEviiHInSVEvOAjSXEG2OwrHImHkeJLUp4cGMit+5uwHDMEzXYNE2iDj0wu8R3bOmzXXhHStR9er9SD3hTNQvXygEm242XiBNzi9BiZmKt0JTUOrMQ1GuEwfL/W0KN7dDwZdPH43cD36PO50NCTuGeMHWW4KpN6xZ/ZH6jR+j8p1/AaG6FkWRW9KYoAJJaXST0u/0UGQ4UrOhpWX38qgZhmEGLizaBgmBQEAIs8PEYpCaXFtNwi3tgh/j3c/3YLyxAV6pMWaJRFnIVrAuMgyvGCcjaspAMCrKRxRkexEKR1Djb8yg9LlU3HfjKciQdax94AfIcrZhnekkEbMx3v22KBWAHY/X7v1SrwumnrRm9cses3s2oXLRk02CrWUiyhGIzGL78LVEmcQODxzDu9a/lGEYhmkbFm2DBP+Sp+OetRI8MQsJgLce/G88Vz0NwBicpO1EphxAteXFp9FRVORC3LtVBTBMwB+Kiv6XJGiKC1Nx6Wmjceq0oQhVl2HP37+PrAQkVZK37bHQGdhuFuFLJ6u4/tLz0d8xDAvrdlaiui6MzDQXJo/KhpqozIseRi/djbp1H8C/8RMg2CTYmjmaM5xiH+OyjSUZ7uHHw5VT1JNDZRiGGXSwaBskGFRLqwOooaqm35z4NHr8EevJoKJIEkzYmD2xAFPH5DYLE0myUfrGI6j/4h2oXWg72bYr9Kv4+VdnCjGYDHy4ej9e+mAHKmpDoqgtCVoqx3FZk6Dt74Kt/J3HEC3d1WjajEeI+lhUY1vEGp4qcBSMRubcy0UxX4ZhGCZxsGgbJFCWKCUdtAfprCrj2C5A3bChyBImj87BglnDoOsR7HvnKZgbFkMxQpDMRIg1cq2dgFf+eGG/tlKRMIvFum3cVYlXluxAOGLB61bhUBVEDBOlVUE8tnCj2L6/CjdyiVZ/8m9ED+1osprJbbhCraMslxsTEZxOuEbPRNas8+HMH9mLo2cYhhkcsGgbJFBZD8oSPWwViXORNrlG6f9Xo5M7tD8SJfNPGIKdi19BYPmrcMMvlh+rgfyxaNCBO8JfwwVzZHz3y11t897zRCImXnxvG9ZsK0cwbMDrUrC31C9coxmpTlEahKBuCk5NEVmnL3+wAydPLuqXIjRceRDhfZsarwXN2Zgp2katvlglmGacPmgZBXDkDYN3zAx4xsxgCxvDMEwPwaJtkEB12Kisx+FkhLjg8SYOOIppyw7t76wT8rHw3tsxydwCd4LG2KDLuCN8TcLaUPUEJMr+9u8v8PHag4hED4saRQYsch3LEhqCUUjU57NJnFF5CxK55bUhEes2bVwu+hv6nvWw9XBjuQ66NlQViBhHukPjW1Kl5iLr1K/AXTQOakYeizWGYZgehkXbIKLwK7dg/zN3w9yzprm7AXWsoqZVJOhmnnsT8Ju3j7EXExe61mD82qeRKR+lp2SX3KELaIT9WrBRvNqjr21ArT9yhPHJjJ0K2xYu02AoCkeKs3kbcpWSRY6SE/ojdlQHJOqG0RS3RgX6HE4g0sZ4ZQ1qzlDknHcD3IWUoMIwDMP0BizaBhn/tfkE1IeKcbG2DlmqX8SwkUs0bWM6nv6KE2OHpmPb/to2XztGOYjzHKsw3NH2+s4SpDIe4SkApuBHV4/CWdMnoj8Ltn++ug51gSNLmMTbogwLcKlA1LSEVS7mCqXYNkpKoKSNviQSiSC08WMYdZVQ07LhnjAPDocDWu7wxu4GVK5DdTXVXiO16Wop3LwZ8Iw9ERnTFnDcGsMwTC/Dom0Qcc3tb6I+RKLDi1ejc4DG0mqCukBUrH/6t+fhZw9+eIRwI8H2Ve8HSJUSY107qPtwX/hS/PCqYpw9o3/X8yLxRRmhQao23AFM0xLuUavJ/Ux9SgMhAwVZHpFl2xeIXqlLnkP92vcbO17Q2CQZ8scvIHX6OUg98UvQMgsQrdgPyEZThwMqlGs2Fs6ln7505F70U3iGHc+uUIZhmD6ARdsgobw6IIRZe9B62u6+n56K+nodf31pDfaV+lFV58elrqUJEWyNrlBKMMjo167QeCgOraI2iFQlAsOMIgoZQZtcn20X+I2agNbU/CscMYRgczkUUceut5MQSKxFKg6g8oNnoIt4RhqV3JhsAAlWoBa1n/xHbJs+52JULn4SdqihsYdozIRIxfk86cg+4xvwjei/1lCGYZiBDou2QcId//ysw9v9/ZcLkJrqxBULxuHux9/H7cqzcCdAa2zRgUfCX8MdP5yJGSMLkSzUlZdhDPYjy+EHSTbDVkTB4YNmJuptzxGJlVR8mIQOlQEhlyhZ2GKFh3sTo6EGwT3rUPnhf4C6Q3FrLCAaAhQNcHiBSAD1q95G4Q1/A9kBa1e8BaO2BLZpQlIUqOkjkD7zXKRMmNur42cYhmFawqItCTBNE5HSXbCC9ZA9qXDkF0NRSBl0nJp6vVPbbd9bhS2P3olbtENQDsfTd5ktehYeCZ+fNNa1eOGTHdiBHKUOIcmDWssBFSbylHqkyCFsiRY1Czdhw5KAqaNzMP34fFHuoy86IlDNNf3ANgR3roZ/6/JWgi0OM9poRdNcsIINItYt7YQz4B43G/reDTD9tVB86XAOnwiVskkZhmGYPoX/EvdzgrvWomHt+4hWHYJlRiErGrSsQqRMOR2eYgri7xhUOywQPnYfUE2V8MSTb6F4+/MY72isvdYddAt4PjQFa40pSSfYyLWol+1CUaoMw52NgD8CpwMIRWRUWCpyZD+KlGrUG1T0RBKWtrHD0nHNecf3WjP7troakFAL79kAvbYS8Fe0/wKDEg+cIvGAkhMIEmjqqKm9M2CGYRimw3A0cT8XbNUfvQC9bA9ktw9qZoH4Sc9pOa3vKL+5fk4HtopgZnQFjt/9KAoSINj8Ngm2uUkp2Agr5IdRWwEtNRMnTS6EplJyAeB0yEKi1Vku0ZvVI+lwaDK+dMpI/PGHp/SZYAuX7kLNZ68ivHcjbMuEpHWw+WskJJISKJu0u1C5k4MVfmzbVyN+0nOGYRgmMbClrR+7RMnCZoUDULOGQFGagt7dXsDhgVF1QKwn11VHXKW5mV6kebWjJiPMd6zDXOdmpMt6t5U8pSvs1dPx1/B54hJLRsFG2EYEtmlAUh2Y2JT1+en6EtQHItAoTt/W4HNEccakPHzjy1Q6Q+mbcdo2wmX7UPvhi4jWHILkSoEd9sOOBDu2A8uEnJImyn90h50HavHeyv3YX9aAqGEJkTs0LwVnzBjaZ0KWYRhmIMGirZ9CMWzkElVTMg8LtibE85RMsZ62cxeN6dA+qZzHV297o6nsx2HmO1bgAvfmbou1UARYYxTjP1Fyrflw6w0nYPaYYUhWSKxJiirEm+RwCeF23Mgs7Cmphz8Ygc9hY2iajNSJ06H0kWCLJRsENn0KvWQnJIcHkkXzKzW2aOgIsiLKflC9tu4ItucXbRWCNivNJeL5KHOWllfUBHHlmeNYuDEMw3QTFm39FEo6EDFsVNy0LRwuWP4asV1n+OevTsOKlSvx7Cdh1AUC+CGeQ56zew1Dgxbw79BErDWmwe2Q8edfzEVxfgaSHeGSTs9BtPIgpIx80Y6K2lSNKkoT1i2jphRaZr7Yri+INlTDv+lTRA5ugxUJQ9JckJxu2EYUNtVVUzsmwlJO+jIyT760y+MgFyhZ2EiwDcn1ifNEeFwa3E4VB8r9eH/lfowsTBPZtAzDMEzXYNHWT6EsUUo6ENXoySXamkhYrKftOosiyzhdW4UJzpXobmKoYZNgm4uD3vF4/fZzMZAg8eHMK4YZqBUCTfFlCOsbWd5Mf40Qa7Q+JlJ6k2h9NRq+WAT90A6YoaZiudTNwNQgO9ywLBOqIsFwpwKhowl7CZ6TvoycUy/v1lhKqgLCJUoWttbngp7T8n1lDWK7opy+EbgMwzADARZt/RQq60FZopR0QDFs8S5S07RhNFTDmTdCbNeZFkZPP/A3jA6twzRHqNtjbNAl3BG+Cpk+F54cYIIthpqSAU/xNJFFSkkJtlknXKZadpEQbLS+L1yiga2fIVK+V4h2sqxZJCaDdaJYLhXGlcniFglDc+ciSla3cKDp1ZLoLyp50pA249xuWdhiUD06imEjl2hbOB0qqut1sR3DMAzTdVi09VMouYDKekQ/ekEkHVAMW6wPJAk22eUT6ztar6166cvYs+RlzDdDkLoeuiQIWcCLobFYa8wWz5/8zfkYyJAwU3zTRDapiG9THcLK1hcWtlgZEitQD8nphex0CaGmKjLkjHxEKvbBaqiB5MuABQmyrEL1pMHOGwN3/nDIqgNaWk5zz9FE4HVrIumAYtjIJdoaPWKI9bQdwzAM03VYtPVjYnXYmuu0kUtO0YSFrTN12qqWvoS6Jc8hxexe+QV6dYUl4a3QyVhrFIPqxb5yb3JmhnYWEc/mSek3ZUiU1CyYFM9I/UOdHpihBsguL7SsIkTrK2DpAdHRgObMmV+MtDkXwdUJq2xnKMjyiixRSjqgGLZ4MSt6ntaFMXpIutiOYRiG6Tos2vo5JMyorEdXOyI07NuKuiXPC5dqV6FX1poOrNWHY2F0JjRVxV9vPhUj8jgbsK/KkCjebCie1EarqzcNVjQsysPIDg/klByodH1oDrgKx8I3aT4caVk9NiZKLqCyHpQlSkkHFMNGLlGysJFgS/U6cPqMoZyEwDAM001YtCUBJNA6WtYjRjgcxYpF7yJt3eNwdyM5NGDK+Fg/Hu9GJ0GChvNOGoHvXdbxTgxMz5QhoRZUakY+TD0oYteUlCxYoQYYwTpIoQDk7CJ4xsyAZ+SUXom7o3IeVNYjVqeNYtjIJUoWNhJsXO6DYRim+7BoSwIiERNL1hxAeXUQuZkezD9hSLuFXF9/7QOUrnkfc+VN3Zpgil17KjQf280hor3VWbOG4wYWbP2mDAmJNnJ9UmarcJVqbiiOCLT8UfBNOQPOnKG9GndHwozKelCWKCUdUAwbuUTZwsYwDJMYWLT1c15cvBWvfbhT9A2l+CC6CT/5fxtx0amjcMWCcUdsv/CFtzF0878wXj52n9H2qNQd+Jt+PuBJxzWnjMFFc0fB5eLLpT+WIdHyRkIJ1cOsr4JcUAzvuNmi9VZfQAKNy3owDMP0DHwX7ueC7flF22CYjS2BqL6aadloCEXFciIm3Mgat/zhuzG2Zh2UbrQ2CBjAa5FJ2GaMgzMtA4//+pxEHQ7TY2VIDOEydQ4Z22dlSBiGYZieh0VbP4VEGFnYSLC5HArkJjeXrDRW5Q/T+o924uJTRuPVj3bAWPIwZjr2dus9K6IqHg+fizIrHfmZHvzz1rMSdDTMQC5DwjAMw/QOLNr6KRTDRi5RsrDFBFsMek7LAyEDv//z2zi/9nGkdbO1wV49FX8OXwSHKuHJW89CZrq7eztkBk0ZEoZhGKZ3YNHWT6GkA4phI5doW8iShZ84XkJBKIDu9KI6GHLjvghZ1NJwxRmjcO15E7u+M4ZhGIZhegwWbf0UyhIlSwrFsJFLtBEbbujIRwUukz9AmrN7yQZb9DQ8ErkIlNz31XPGt5nYwDAMwzBM/4BFWz+FynpQliglHSiyhbHSIQxRKpBl1yFf2YO0biQbULHcrXoOHgmfi7x0Jx765ZntlhBhGIZhGKbv6catP/E8/PDDuPbaa1ss27x5M6655hpMnToVp59+Op566qkW6y3Lwl/+8hfMmzdPbHP99ddj//79SHZIRFFZj/GOEnxFeR8XO5biJHUDJmh7kNvFWbMAHNI9+FndJUKwZfg0PPrrc1iwMQzDMEwS0G9E2zPPPIMHH3ywxbKamhpcd911GDZsGF566SX84Ac/wJ/+9Cfxe4yHHnoIzz77LO666y48//zzQsR9+9vfRiQSQbJz7vAArslYgzFaKRxSFBHIkLphXftIH40/hb8MIAWZPgee+s15CR4xwzAMwzAD1j1aVlaGO+64A8uXL8eIESNarHvxxRehaRp++9vfQlVVjBo1Cnv37sUjjzyCyy67TAizxx9/HD//+c8xf/588ZoHHnhAWN3effddXHDBBUhWatd9iOpF/4I70tC8TGtS2RTJ5ujEvoIm8GToZGw3R8Gtyfjh+emYMX16j4ybYRiGYZgBamnbuHGjEGYLFy7ElCktWyStXLkSs2bNEoItxuzZs7Fnzx5UVlZiy5YtCAQCmDNnTvP61NRUHH/88VixYgWSlfqNH6P6zYdhhhuAuL6hatOEdWbSai0SbGcIwTZ3cgGeuP0MeD2enhg20wRZeyNVJQgf2iF+0nOGYRiGSXpLG8Wp0aMtSktLMXbs2BbLcnNzxc+SkhKxnigoKDhim9i6rkClNoLBIPoCvbYSNW8/BtPUWwi21pMWbbK8tcdOPQP/Ez4Pmalu/OvHJ8Hj1BAKhcS62E8msUTK90LfsQrRmkOAEQVUDVpGIZyjp8ORO7xL++Q5S0543pITnrfkIzQA5izWprLfi7b2CIfDcDhaOgKdzsaiZLquN09QW9vU1dV1+X2j0ahIgOhtpLAfkXVLkBU47BI9GpQ6YB3F6hbUgf/Wz0HAzsUtX86F0+HA3l07WmxD1komsch1ZXDuXQEpGoLlSgVUBxCKQq7ZAHv/TujDZ8JKy+vy/nnOkhOet+SE5y352JPkc9ZayySdaHO5XEckFJBYIzwej1hP0Dax32PbuN1dr+hP7trRo0ejt1X2vlWfIVRd0qHtY7Ft4mEDqg2ELAVvRMZjm3EcbKcXz9664IjXkdClC5viB7tzjpjD82aH/TCjOkIVX8BwylAKx4vG6TEsqxBm1X5oVjW8406BfJSCyUeD5yw54XlLTnjeko/QAJizHTtaGlaSUrTl5+ejvLy8xbLY87y8PBiG0byMMkzjtxk3ruuFYslESaKwNyCBGfxiMSKVB1C1Yz+CUUsoMjs2lqO8jjynNZYXdZYHpUY69lnZ2G9mi76hmSlOPHnnue2+L13YvXWMAxWjoUY0bTdrKxCtr4JxYAvklAyokgVFO/wlgjAz82HVl8OhN8CR1dKd31F4zpITnrfkhOct+XAn8Zx1tG90vxZtM2fOFGU8TNOEojTWElu2bBlGjhyJrKwspKSkwOfziczTmGirr6/Hpk2bRG23/k7l+8+gfs0iQA8CtglynIWbjDA0fXbTo62p3G1k4tPI8aiT01FpeBCwnXA5NNx5zXRMPy6/tw9l0KHXVcK/5l2Y/looadmQnB4xV1YkDKO2FEjPh+I4LNwkzQXLqIKlB/p03AzDMEzy0q9FG5X1ePTRR3HrrbeK2mvr1q3DE088gd/85jfN/l8SZ1S7LTMzE0VFRbj33nuFhe6ss6ifZj8XbMsXApYJHTIMKFBNE/H2mXjhFntO+C0ZTwRPRVRJhUtRUZjnw7jhGTh9xlCMGpLeB0czeKBM0PovPkD9qrdg+qsBWYWiqpBcXthmFIqaBjsahUViLvOweLajYciqA7LT26fjZxiGYZKXfi3ayJpGou13v/sdLrnkEuTk5ODmm28Wv8f48Y9/LNykt912m0hcIOvcY489JuLS+iOU5FC34RPUf/5/MC0DAZNMa0qzNLNhQWuWaUda2Sii7319MqJyCuZNKcLUsTkYOzwTBVneFnFUTOLRS3ejZvlCBHesAiJhQNEgORTYsixcpTCjsC0TWmahsKhZ0QhkzQHLsmHUV8KZXww1o+uJCAzDMMzgpl+Jtj/84Q9HLJs8eTJeeOGFo76G3Ka/+MUvxKO/U7vqbdSveBtGVQlMSiEwAQ8sRGEgChVWU8cDA6aQcZSCES89620VH4UnYklkMu77yTyMHpLBQq0XBVvVstcQ3rOBzG2AwwOJEgoMHZYdheLNFK5SOxyA2VANSXNAMSLC+kaCTXanwjt2VqeTEBiGYRimX4q2gQwJtpoPn4cdjSAEG46mGmwkuRyieEejcDNFXqgFBTb2GemoiaZDVoB9RiY+iVLNOgemjsnG2GGZfX1Ig8olGtj2Oay6Ckh2k2CzDdiSBEl2wo7qIoNU9aXDCjVAcroAnWLbyqE4PcLCRoLNmT+yrw+FYRiGSWJYtPWSS5QsbHoohCrDBQeJNqll5VxNWNzIPdoYyUYO0m3RYfgsOh7BCNWma7SokWC764aT++hIBidGTRmilQchO0msWbBVDTBtwDRgyxIkVYNtkF1UEvOnpuXBPXQsHHkjoTh9wiXKFjaGYRimu7Bo6wXC21fAX1UGv6HChgwd0hGFcUmSkYPUIEFHyQa2A+t9UzG5MA91/igKsj24/sJJ8Pk603WUSQQiPs2IQHb5AFmGZJmQqHCuZYkYNluSYZsmrEgIMmxoqVnwjp4JNSWjr4fOMAzDDCBYtPUCoeoKWCZZ0mIRak4EbAspEtnWDqPAEILNhIRP9TEoHpaHW6+b3SdjZg5DGZ+U+WlrTsiuFJiBWsCdAolKepCFzYg2FtmNBKFlD4XvhDNZsDEMwzAJh302vcAnO0OwIEETcqyRINxosMkp2nIyyML2Xvh4vKNPx48uO6GPRszEQ+5NLbsIdqAGWu4wYWWzQw2N1jW5MTsUiiLcouknXwZnWnZfD5lhGIYZgLClrRfYECxClulBtuJHra1CEk60RuEWtIEMKQrd1PCOPhGfimQDJ8YOTUdqamOfVaZvoXg0SiSI1pXDCvnhyB+JSHUp7FC9sLJJsgpn4Wikn/gleIqn9PVwGYZhmAEKi7ZeoKAwHZ/sHYfz3F8gXQohYGuIQhGWN69kQbcdeEufimXR8WJ7Emz3/fTUvh42EwdlfqbPvEBkkVJSgpqeBzklE7I3FZ5R0+GdMLe5awfDMAzD9AQs2nqBr545Hld+vAsIAXOdW5GhBOGBIVymlaYPn+jjhGCbflwObrpiOlvY+rFw03KHi2xSSk6gWDfODGUYhmF6CxZtvYDHo+HkSQX4ZB2wLDoKk7SDSEMQdfBgfbRIFPyYO7kAv/z6rL4eKnMMSKB1teE7wzAMw3QHFm29hBBkT36OpetLsD46onm5JEEIOhZsDMMwDMO0B4u2XoSEWTAYxTOLtqCkIoCCHK9wnZIljmEYhmEYpj1YtPUyJNCuv2hSXw+DYRiGYZgkgyOoGYZhGIZhkgAWbQzDMAzDMEkAizaGYRiGYZgkgEUbwzAMwzBMEsCijWEYhmEYJglg0cYwDMMwDJMEsGhjGIZhGIZJAli0MQzDMAzDJAEs2hiGYRiGYZIAybZtu68H0Z9YvXo16JQ4HA4MROjYotEoNE2DRI1PmX4Pz1lywvOWnPC8JR/2AJizSCQixj5t2rR2t+M2Vq1I1gnvzPENVEE6UOE5S0543pITnrfkQxoAc0bH0BH9wZY2hmEYhmGYJIBj2hiGYRiGYZIAFm0MwzAMwzBJAIs2hmEYhmGYJIBFG8MwDMMwTBLAoo1hGIZhGCYJYNHGMAzDMAyTBLBoYxiGYRiGSQJYtDEMwzAMwyQBLNoYhmEYhmGSABZtDMMwDMMwSQCLNoZhGIZhmCSARRvDMAzDMEwSwKJtkFBWVoZx48Yd8Xj55Zf7emjMUXj44Ydx7bXXtli2efNmXHPNNZg6dSpOP/10PPXUU302PqZjc3bbbbcd8bmjuWP6jtraWtx+++045ZRTMG3aNFx11VVYuXJl8/rPPvsMl156KaZMmYJzzjkHb7zxRp+Ol+nYvF133XVHfNZafx6THbWvB8D0Dlu2bIHT6cTixYshSVLz8pSUlD4dF9M2zzzzDB588EHMmDGjeVlNTY34o0Q3/N/85jf44osvxE+v14vLLrusT8fLtD1nxNatW3HDDTcIsR1DUZQ+GCET46abbkJFRQXuv/9+ZGVl4X//93/xrW99C6+88gps28Z3v/td8Vm79957sWTJEtx8883IzMzEnDlz+nrog5qb2pm34uJi8Vm78847sWDBgubXaJqGgQSLtkHCtm3bMGLECOTm5vb1UJhjWETvuOMOLF++XMxXPC+++KL4A/Tb3/4Wqqpi1KhR2Lt3Lx555BEWbf10zkgA7NixA9/5zneQk5PTZ2NkDkOfmaVLl+LZZ5/F9OnTxbJf//rX+Pjjj/H666+jqqpKWGhuvPFGsY4+Z5s2bcKjjz7Koq0fz9s111wj5o6sowP5s8bu0UECfQOhPz5M/2bjxo1CmC1cuFD88YmH3ACzZs0Sgi3G7NmzsWfPHlRWVvbBaJljzdm+ffsQDAaFFYDpH2RkZIgvOpMmTWpeRt4HetTX14vPWWtxRp+zVatWCRHO9M9527p1q/h95MiRGMiwaBtElrbq6mp89atfxUknnSRiAT766KO+HhbTCnJ9/vWvf8XQoUOPWFdaWor8/PwWy2KW05KSkl4bI9PxOaPPHUFuHNqO3DZkKW1oaOiDkTJEamoqTj31VDgcjuZl77zzjrDkzJs376ifs1AoJEIUmP45b9u2bRPhPvT5opg3ikWkcIVIJIKBBIu2QYBhGNi1axfq6urwox/9SHxboUB2ctlQwC2THITD4RZ/sAiKUyR0Xe+jUTHtQTcSWZbFTf8f//gH/uu//guffPIJvv/978OyrL4eHgNg9erVuOWWW3DWWWdh/vz5bX7OYs8HmgAYSPO2bds28Xdw8uTJwpX9ve99D//+979FItBAgmPaBgHkTqN4Gwp+drlcYtnEiROxfft2PPbYYxynkSTQ3LW+acTEmsfj6aNRMe1BN46rr75auHaIsWPHinibK664AuvXrz/Cncr0LpSY9fOf/1xkIv7pT39q/iLU+nMWe+52u/tknMyx5+23v/0tfvnLXyItLa35s0ZhCxSbSIkk2dnZGAiwpW2QQBmGMcEWY8yYMSKImkkOyGVTXl7eYlnseV5eXh+NimkPsrLFBFv8544gNxzTdzz99NPC83DaaacJK2jMal1QUNDm54y+GHG2ff+dN1VVmwXbQP6ssWgbBJBFjb6RkLUtng0bNmD06NF9Ni6mc8ycOVMEQ5um2bxs2bJlIvCW0t+Z/gd9w//GN77RYhlZ2Aj+7PUdlIF41113iRhfKh8R7w6lki2ff/55i+3pc0Z/Q0mEM/1z3q699lrhLm39WSNrW+us7mSGr8BBAGWNUvYamY8pM2rnzp34/e9/L+p8kfuGSQ6orIff78ett94qykhQYeQnnnhC1JRi+idnn322iBv929/+JjJJP/zwQ/zqV7/CBRdcwNncfcTu3btxzz334MwzzxSfHcq8ptpf9KAEEbr5r1u3Trjd6G/l448/jrfffhvf/va3+3rog5pjzdvZZ5+N1157Dc899xz279+PN998E//93/8t6rj5fD4MFCSbc5gHBXSB33fffaKmDaVHH3/88SImoHUhUKb/QEHrBw8eFJmHMehm8rvf/U7UjaLYqG9+85stirYy/W/O3nrrLZH8Q8lA5F678MIL8dOf/rTZrcP0LuRSe+CBB9pcd8kll+APf/iDyKynwrpUTmfIkCHCHXfeeef1+liZzs3bM888Ix4k2mKxo5RwN5AspCzaGIZhGIZhkoCBIz8ZhmEYhmEGMCzaGIZhGIZhkgAWbQzDMAzDMEkAizaGYRiGYZgkgEUbwzAMwzBMEsCijWEYhmEYJglg0cYwzKCBKxwxDJPMsGhjmG4UUh03btxRH1RFPZFQBwTa74EDB465LVXh/+EPf4h58+aJpuRULfyPf/wjqqqqMFihFmBUaDMGnUc6n3ReewvLsvDvf/9btOE58cQTRWskKgxKxXhbNynvSei4//rXv3b59TfccIM4DoL2Q/vrCFQU+oQTTjjiWKnTx+mnn97mXFBh8J/97GfifE2fPh033XTTEb1BDcPAgw8+iFNPPVVc71dffTXWrl2bsM9TZ6BjO+ecc0THGYZJNGrC98gwgwiquk0titqir/rdUfudRx99VNw4qOVVeno6tm7din/+85949913RcNlaoo92CCRQW2JYuTm5uKFF17AsGHDeuX9Q6GQEDskJq666irRFon6IlJfS2q3Q1X4/+d//qdFP8X+CImdsrIy0Vats1Abr9mzZ7c4xrq6Onz/+98XnSRaQ2Ls+uuvF6LuzjvvFM+pswu1JqJx0PkjqBr+f/7zHyHuioqK8K9//Uv0fH311VcxfPhw9CZ0bNRt5pe//KVoq+RyuXr1/ZmBDYs2hunmH+ipU6eiv/DGG28IcUaNk+MbldONkqwQZNWhNlhHE5qDid6eO+r3u3r1amFVi3/fuXPnYvz48UJwPP/88/ja176G/ko4HBZfCu64444utQYiYXrRRRc1P3/vvffE9RgIBNrcnqzVZJ2j63r06NFi2XHHHSd6t1J7sC996UsoKSkR/SbpCwpZ2GLnlKzL9Fm4++670dssWLBAWP5oXNddd12vvz8zcGH3KMP0MOT2IVfqsVwz27ZtE42QyWVGjx/84Aeih15noB6XdHP7+te/3qbl7xe/+IVwT8Viu3RdF9YdsspNmjQJZ511ltgHufFiUANtuiHS8vnz54vtrrzyStEHNf5mTpaQU045BRMnThT7e+yxx9o93rbODW1DNzpaRq6wWbNmiZsu7Z/cuyQ+yU1G46Gxx7+OLIhk3aDjO+mkk4QYiG1D+3vllVeENSfmEm3LPUq9Jn/84x/j5JNPFsKKjp3cqjFiryHBQNvRe9EYb7vtNgSDwaPOS3V1NV566SVhnWpLKJIIoT6yeXl5zcvIBUjim8T25MmT8eUvf1mInHi6er4Isl6RRYiOYc6cOeJ1ZA1sDzoG2s9pp52GzkIWNbIy0jVCUA9kcuHPnDlTWIbb4pNPPsHIkSObBRtBv48aNUpY7WKhAGSBo0bi8YKcrtXYNh2BxkOCkq7JQ4cOdfv8Uo9Zsvj1ptubGfiwaGOYbkI3jNaPzga87969WwghijmjmwEJDhJs5EbraBxaRUUFtmzZIm5WkiS1uQ1ZIsi1ROtpjOSuoxvm5ZdfLhoyk9giCwFZUuJ55513hGAgcXL//feLOCNqom2aplh/zz33CCsKiSYSa2eccYZw+dFNvrNQo2666ZI18OKLLxaWKfpJFhWy8pCQIldYfFN24s9//rM4VzR+cj2S65PGQ5D7jcQPubNpOZ2j1uzYsQOXXnqpEGZ0nPRedJ5IAH/++ecttqXzQ264hx56SJxPGs/f//73ox5TTFi0J3ZorGQdIuj8kkhbuXIlbrzxRhE3Ru9HQn7hwoUJOV/0nCxcdL7oywK5j0nEtQe9N527rrhwSYAVFxejsLBQPCe3IVnQ6HrPyMho8zXkzm4rzIBc2vSZiW3j9XrF3MZDblESvkez4sVD25AbloTbU0891TzG7pxf+iyRG7n1tcMw3YHdowzTDchyM2HChCOWk6srPuj9WNANwe1244knnoDP5xPLyPpBbhYSVTHx0R50EyGGDBnSofckkfXpp58KEXb++eeLZWRhopspCSBy040ZM0YsJ8FBYiw2NrrJ0Zg2b94sLGt0Y6LXxvZD1gePx4OsrCx0FrKk/Pa3vxW/k2WDxEQ0GhU3SFVVheuLRCS5GuPJzMwUwpO2IYFG7jtySZK4JMsMrY93iba2jNEc0Hq6aceOkwQKWcFIgNKNOQbtPzYnNE9Lly7FkiVLxLwnYm7IQkPWOTpOEmux9ySXN42FxhRzT3b1fNE5ISsr7Yf2TQKVxDdZfMeOHXvEmMgyt379epx77rnoCnS9xaxsBJ1rEnHt0dDQ0GZMGom0mBijbWLz1Xqb2Lhjv7cFWci+973vCYFFwqv1HHX1/NK409LShGCnbRgmEbCljWG6AX27p5t56wfFjnUGCkanGwIJppi1jm5EM2bMEMKqI9ANhIh3bbYHCS16DVkE4qE4odj6+BtX/I0x5saLudNIpL344ovCWkFuSrISklWoLYvWsSB3XQxFUYQVhoRx7PgISq6gm3Vrd1T8NjGr1YoVKzr0vnS8ZAmLP07aHwnRDRs2tLDYtHZx5ufnt+se7crc0HmICbb4uSGL6q5du7p9vmje4+PSyDXe3vki4UmW1Y4Kz3jIqvvxxx+3EG0dfd3RiFmTj2XVPlbs3c0334zly5cLcT906NAj1nf1/BJksUt0diozuGFLG8N0A7IWUIxXd6mtrcWbb74pHq0hC1FHoIxQupG1lYUXH1dENxuyPNDvdAOiG1E8MTdT/E2IrIBt3QhjIoRieki4kPvsrrvuEg+62VGcGwXZd4a2rCZktTsW8fFgRMzKR8fZEWi77OzsI5bTMhIGZLFp73y0Jx5i7jaKlYpZL1tDrjyaa5ofGktbAiI2PnLjdfd8tXYnxs5X/L7jiV0PHdl3a8hCRxYtigvrDHRsbbk3aS5SUlKOuQ0R2+5okIWNRFgstrO1Va6r5zd2ncRfNwzTXdjSxjC9QCz2K0ZrqwzdWMii05bV7vHHH+/Qe8QsAGTROJqAIBcgBU9TzBS5bmpqao4YW6wG1tHijI4mXsnFRAH6H3zwAW6//XZhbYu5C2NWkdaWpo7EG3UUOpZ46Bg7I3rpfMReEw9Ztjp7PlpD55zKU7QXGE9WylgZDRpL7H0TPZb4Lwpt7ftoLu3Yex5N1B3LNUrJIbESHR2FkhD27dt3xHJaRu5dglysJIzInRzP3r17haXyWCU36DNBbnQSbw888AASCZ0rssIxTKJg0cYwPQx9Uy8tLW2xLD4jkSDXKAXCUzkDstzRg2LFKMZt0aJFHX4vCoqnmCRyUbaG9k+JAXTzJIsNvSe5YVsXAY4FunfUKkKZdOSKjIlLsipR8VgSobEsvJi1Iv48UAB5a+HQHd5///0WzynOiMQiCaaOuMkoi5EEZ7xlhAQtBcvTfHSnflpqaqpILCAXMrlaW0P1xCiJJOaaprGsWbPmCKspzQ1ZyBJRe4yEVDx0nHS+6Lo4miWTrLKtr+WOvldnXaMExYLRdULXbgz6nZZRDCVB1zMRfx1TxibFGMa2aQ/6LFCWKMULPvPMM8csyttR6IsTCcHWLm6G6Q7sHmWYHobipB5++GHxoGrtJC4ohi0eym6k7FHK4qOMUafTKbIcFy9ejL/85S8dfq/zzjtPxMBRWQK6+ZC7h1w5VJ6DgtvJWhKrW0U3UYpFo0xJurmQG5Niqai2FcXkxZdZaA+yZJCFjywWZEmhGyBl9lGJjVhcGb0PbUdFUH/yk58ICxsdVyKtEFSBnrIfqWwDCSDKuLziiiua3YwknMiSRtYuEsetofITJC4oAYOSSOhYYvF5RytJ0Rmokj+5CSnb8JprrmkWzfSeJOboOomVaqHaXiTQSEjQuOg8kbCj64aSBbpSI601NBZya1NSA/1O80HC8mhFoek6olI09IUjvgZgDPqC0Ro65xTXSEK1Kx0Y6Hqm5BKyQsastlRclxIlYgkRJIroeiVrGblgafx0rZOVi7KIOwqdZ7IU0+chvnBvV6EvT+RSpq4kDJMoWLQxXRpOWAAAAgVJREFUTA9DQoxcN5R9SVlndBOjkh7kToxBgom+5ZN7hgKj6Vs63ZgozobKZ3QGEmWxxAByU5JAIusXlfUgS1zMzUVWFRKSdLOmGy6NkYLMSVx0tiAoZddR6QiytpGbjVxsJABIoMVu3nTTphsuJSjQjZZukiREEgUJHhKftF86RipnQuc+BpXzIMFG70811kgQxEOxZs8++6zIpqX6aHR+qD4aZZNSQkh3oXNA2YkkBCl2kep/0TyTyCChQOcrFtxO1jRaT+eL5pOuG7pGqMRIZ6+Ho0HngcQUnSdyz5PAoXPXHiTCaR5JHNEXi3hINLVVmoOOic5t65jDjkDWTRJg9Hn59a9/LYQUWc9ofuITAej6o/NLXzgo9IC+RNDrOmORpPgz+rzQNUM1Cen8dAcS4zSPJHQZJlFINndQZhgmySHrHgkOygBkeg7KFqYyNFSkmWqVMW1Dt1USuFQXsS2rJMN0FY5pYxiGYTpsjSJhTFbj1gkszGGoxy+dHwp5YJhEwqKNYRiG6TAkRKi8CxWZZY6EkiDIxU5FkLlZPJNo2D3KMAzDMAyTBLCljWEYhmEYJglg0cYwDMMwDJMEsGhjGIZhGIZJAli0MQzDMAzDJAEs2hiGYRiGYZIAFm0MwzAMwzBJAIs2hmEYhmGYJIBFG8MwDMMwTBLAoo1hGIZhGAb9n/8HiuvERM6P9PIAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ploting_data_from = X_train_sm\n",
        "ploting_true_from = y_train_sm\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.scatter(ploting_data_from[\"Fuel Consumption Comb (L/100 km)\"], ploting_true_from, alpha=0.6, label=\"True value\")\n",
        "x_axis = ploting_data_from[\"Fuel Consumption Comb (L/100 km)\"]\n",
        "\n",
        "line_data = pd.concat([x_axis, fitted],axis=1).sort_values(0)\n",
        "\n",
        "plt.scatter(line_data[\"Fuel Consumption Comb (L/100 km)\"], line_data[0], alpha=0.3, label=\"Predicted values\")\n",
        "plt.legend()\n",
        "plt.title(\"Fuel Consumption (Combined) vs CO2 Emissions\")\n",
        "plt.xlabel(\"Fuel Consumption Comb (L/100 km)\")\n",
        "plt.ylabel(\"CO2 Emissions(g/km)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "GsKi-4TsoO8H",
      "metadata": {
        "id": "GsKi-4TsoO8H"
      },
      "source": [
        "From the above plot, we can immediately see that there all prediction line are all parallel to each other and that most of the model error come from one section of the dataset.\n",
        "\n",
        "To further investigate error we can plot another Fuel Consumption (Combined) vs CO2 Emissions but with the fuel type info. Reason for adding fuel type info to the plot is that the model weight for each of the fuel type does act like a constant terms. And, in linear equation, lines create parllel lines when they have coeffcient constants and their constant term varies.\n",
        "\n",
        "We will also plot the Residuals vs Fitted plot with the fuel type info as that will allow us to zoom in to the error more closely."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "eaeP6Lr5oUYF",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 975
        },
        "id": "eaeP6Lr5oUYF",
        "outputId": "335c4343-2844-4aa9-a295-884284361dc0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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vgd850ndiDBh/f3SRHnPMMeqZVhBdUHAe3Va0bDPWkH9zG/JC31RJBO4rwv3VlNWClhoerzzGdAsbj8emMsn5m2JsWGNrHLc3BSQFAy1tugV1d9At1dLzUVt+H+FQCNKyzfgyCkvud1qhaCXn96FlkOcZWlJpTaFlhtuNlh+Kn1gdf1wnY7FolebYaGGj0KJY5LojJfTQ2snfBMUFLYe0uDKejDdbFJO8yeA8hhe0hsbnLf2GoKXCv7XJC015d/i7bCvcVxSbFGu82eEzrWW8oQuncVYxzxvcJ/wtc3z8O/xGsvH4Wupx6GqIaOumNLamNLbK0GTPE57umginpWZj3SJFMzTvKiNZDOiCYFwBLRA8UdGNwLGFCzf9AG98AW0OnggZz8EHL2r8fJrr6a7gBbOpk1X43XN74XcJh3f+rRG93B76exqLCn17tPXkp2/LxuKHd/gsFcB1RBIodDHSYsn9xvERjpHunsZj7Nu3L9oLRVBj9yDX156sQf62KVp44Y4ELwj69uG6dHEUfjGKdIHnGHVrUFPWNrrH+Z0YFK27XXXontFdNLSs0C3HYHRamyJZtXjxoiWS+4QWg0jQ0kzoEqYVgUKF66YYjAStQDwmwl1ldD/xPED3KkMXKDq7ItyvPM/wwQQGxuXxmKe1jyKYQopikw9aRbndKPIYrhDJ8hPN44/HB7c9z2209nB/ML6Owr0pNzP3Kb0EtCjxfEbXLvcBb0Qp3Pibay6eMVY0d+6kQCUpKSnqmcdYpBuESGK4NevX6xDyGGI4BeOuG1NZWdlgPdyXvK7wGOVvhQKWx1gkeJMVr4h7tBvCO9rGwbzhGVGErlG6D3i3wTtlPnjSoCVAz3ZrCQy2ZRYqTz6N4efzrpfikAcX10kTdeMsH72UAjOvWgLN33RjMYaI8KLLEzXdGroVSb+rD98OjPNq6qLcFpj9Fg7v/nnC0e/qG1uQGsO7SIrN8Dt6nvQpOrk/2lNyQxcijX8H3E4UAowJiyTsGUfGixVdlBQTHEPjWC5eZLidm7qLbQ26gAm/MDBWiK6utsLfGeNyeOet/7b54IWbbjOe2PV91Pi32DgDVIfbkb/h5vYJrQEUoHqCiu72J7zoMNaK82m14cVYj5FqyvLJCyNdRQwDoLuuMbRAMHZPXw+FA92vDBCPtDytOLzI0sKjf3/+9hhXRnFCC2BXFWy0HlLk6PuL35WClecWffsxyUHPyOS+Z5IBf++8cQk/xqJ9/HFM3J4Ue/xt6RZ/7r/mrNoM5WD2MF29PJZ4s8zfPc9vdAM3lzDU2FsRTSKdOykgw2tO8tzA8wjHH36M8TtQ+Le0IHJT34P7jvuNxw3dveFu9PCscx0eV6w/yWsI9xvPATyfcXr4+HitYtwbr0PxiljauiG8IPDEzQd/7BQX4RdGwiwhnuSYgcO7GZqeadbngdBUAHhTFyrGZfBkyRRsxmXwDod3m7wIUADoJ1IKAZ6U6DLgwU43Ju8oGXTOO6uWFlCl64EWPlqDeOLQU8RZYkOPSeJ6uBwzGhnQSjHA79WcpaS1MOaLGXB0g/DiyRMtg/Z1CxRP2rz74x1/JFM83VGMW2EAM90M/C56fB7FRXvgRY3CjWI9PBaEViZeUOhe5gWNvwG6uVnegfuL62YCCH8PfHBcPMlxbPxd8WRMUcJ91do4ykjwc1nUlW4gXixoMeFFqzVZbJFO+NyOtB7R4sTvx98of2eMceI6eafNIGq63HgC5/6hdYRFoSPBGDVayZpDzxR966231IU7/G6eF3VuX7rAKLLohuY+5nsiue50uF3oOqMLlWPXb3z4u6GY4z4JL7fDfUsBxmUZ28VkHd48MAaJFlYKT15U9RsKHhMse0FrFC+44WUqePELD3KPBG/MGrutdGjJbS4pojXwcxgOwXMJRRYtWBSmPLZ4DtNFGG/k+B25/XmO4TbnNqPltrG3IVrHHwUXrVK0UPNzaI2im5QW1/B4zcYwoYfHKM+5ekIRtxlFCo/b5o4BPbmBLtSmRE1b4fmUxwyPex6TvBHltSTcRczzOi2IPBdwf/B8y+3N11y+pSEq+vfgjRotn/r7uF0oyL/77rtdspt1mCzCTFu66PVC5vyNEx6H/D1wG/LBbcRrEn/vPI6jWf+voxHR1g3hSYxxK7z48uLAOzpaVuhK1OHBwTgMXrRYioB3JDyJ8ECNlGXUHDyR6okBDMSlQOJBx0wxWuJ0V5R+8PPAoUWPY6SI4IUpkpu2ORjHwjINPEnzDpd31rRKUKDpgokiikHfPJnypM+TdDTTvXmx5ImKn8vvSIGgX0B08aC7Gxk8rWcx6TDehhd4XkRZAoLbh24ymvQZ7N9eKGB5UWpcs49ii4KCJzhuQ1qFeLHgxYfbLDyQmRdzXgR5MaOop8igMGd6fUvi/nYHT568YHEbcD/y4sN1NXbHtgaOi79t7nu6rHjx5P7neljXTIfZrvp3oyWBJ3PuQ26TcPTSC/pvqzko4HkRZpJKOLyI0MrF36uefECrAPd1czcS/B3TTcQxMv6ULjceq3RJ8eaHv/nwcAZeBClUaOHmTQwvZrQecXlapijUwy++tE4Q7ne9XpcOt1lja3Jj9KzrSFAM8eYgWvAmjb8TCgOGJlBYcFvqcVXcPxSa/O48j3Fb0GXcVIZ1tI4/xlZR5HFc/L60HvOzuT2bE+T674L7NLyIMc+lFCDNuUYppnjO5DHJc0x7Av8jWb94jmZZJZ6beYzwXEeXdHj9NJ4DeN7gNuT3p+hiZi3f01TGbmO4nWgd5vHKMJtwqz6vWxSlTSWSTJ06VV1PKLJ5c8FjS99vvCnhDSD3CZfhOY5WS24zPeM0XjGEpFOrILTpbpQXDIqargoFJYOceTLjXafQNigAGDJAESQV4wWhY6Alj1ZcHn/hfPDBB0pkM6axI1tudRXE0iYI3RS92j3dgiLa2gatxrSE0Ooggk0QYg9FGi16dI3Siic0RBIRBKEbQ0sgLW48AQqthy4Wutj0osGCIMQWuuTp8mXYTjSSnbob4h4VBEEQBEGIA8TSJgiCIAiCEAeIaBMEQRAEQYgDRLQJgiAIgiDEAZI92ggWoWSYHwstCoIgCIIgxBrWVGWGOgtDN4dY2hpBwdadczP43Xw+X7f+jt0N2Wfxiey3+ET2W/wR6gb7rKXaQyxtjdAtbOxT1h1hK5cVK1aoNkTRqGgvxB7ZZ/GJ7Lf4RPZb/OHqBvuM7epagljaBEEQBEEQ4gARbYIgCIIgCHGAiDZBEARBEIQ4QESbIAiCIAhCHCCiTRAEQRAEIQ6Q7NF2oGmaqq0ST3i93vpno9HYJbJ1TSZTZw9DEARBELo8ItraAGupFBYWorKyEvFGMBiE2WzGtm3buoRoI2lpacjJyVGFBQVBEARBiIyItjagC7bevXurmjDxJDZoHaSVzWazdbqFi+KX9XWKi4vV69zc3E4djyAIgiB0ZUS0tUH06IItMzMT8Th+YrfbO120EYfDoZ4p3LhNu8KYBEEQBKEr0jX8Y3GEHsMWr1WXuyL6toy3+EBBEARB6EhEtLWReHKJdnVkWwqCIAjC7hH3qCAIgiAIQgQ0zYeaJfMRqC6BOaUXksZMhMlkRWchoi3K3HLLLfjwww+bnP/vf/8bRx99dNTW98EHH+DWW2/FN998g/z8/F3mDx8+fLef8cADD+CUU06J2pgEQRAEId4p/+59VC36FCGvi5lzdAuh/NvXkTruT8g4+NROGZOIthjQq1cvPPXUUxHnDRgwoEPH8s477zR4fcYZZ+Ckk05Sz3rJj379+nXomARBEAShqwu2yu/eBYIaYLIABhMQ0hDy1NRNBzpFuIloiwFWqxVjx45FVyDSOJiluddee0mmpiAIgiBEcInSwqYEm8UOY33ctRlBownwe1D102dInfCnDneVSiJCJzF58mTlSm3s6qQ7s6CgoH7a6tWrcdlll2GfffZRjyuuuAJbtmyJ6liuuuoqHHLIIarwbji33347jjrqKPU3x3ruuefivffew2GHHYa9994b559/PlauXNngPSzae91112HcuHFKGHKZ5cuXR3W8giAIghArGMOmXKImS5hgq0O9NlkQ8tSq5TqaLiHaPvroIxx77LEYPXo0jjvuOHz55Zf18yhgdNFy8MEH4/HHH6+vNabz5ptv4vDDD8eYMWNw9tlndwmREAgEdnmwmGxr2LBhA84880yUlZXhoYcewn333acE21lnnaWmRYvTTjsNRUVF+PHHH+uneTwefPXVVzj55JPrp61YsQKPPfYYrrzySjz88MOoqKjAOeecU18ct7y8XI132bJl+Mc//oFp06YpIfiXv/wF69ati9p4BUEQBCFWMOmgLoatCW+UcpWG6pbraaLt448/VhYdXtg///xzHH/88cpS8+uvv6q6XRdffLFa7u2338bUqVPxv//9D//5z3/q38+g/3/961+4+uqrlaWKwfgXXnihEhCdxdatW7Hnnnvu8njhhRda9TmMi2Px2VdffRVTpkzBMcccg//+979KUL344otRGy/FMNtIUTzrzJw5U3UrYPybjtPpVIkUp556Ko444gg1Bp/Pp8ZEXnvtNVV4+OWXX8af/vQntcxLL72kihDzfYIgCILQ1TGn9FJJB4xhiwinGwx1y/WkmDZanngxP++885RoI5dffjkWL16MRYsWKfFDd9u7776L1NRUDBs2TFmYKNL+7//+T8WOPfvss8rac8IJJ6j333///UosTJ8+XVnoOisR4ZlnntllOoVRa1i4cKFyM7J7AS11JCkpCfvttx9++OGHqI2XCQm0qFF0URhTKFIMH3jggQ3GTEHMdYfHxtFN+tNPP6nXCxYswMiRI5GdnV0/Xn42Xa+ffPJJ1MYrCIIgCLGCZT2YJcqkA8awhbtIg7TAaX4YHMlquR4l2uj+ozCjVSYcWmcIBQQtVBRsOuPHj0dNTY1y1VFEbNy4ERMmTKifz2boFBYUEp0l2igm6eptL7RaffHFF+rRmIyMDEQTWs8ogL/++mu1jSnAHnnkkQbLUIw1hlY0ukP18W7atEnts0i43e76tlWCIAiC0BUxmayqrIfKEvV7EAzLHqVgg9GM1P2P75R6bZ0u2gjdcHSDMhaNQozWNgbqszF7Y+sUrTtk+/btSqBFajTOZRoHyLelkXkk2GydcVqMq2scW6e/l49I8xpDa1T4chSjRP/85ORkJUgvuOCCXd7LzE8uoycP6O9pzXcMX75Pnz7Kqsd4Qsaq0aLHhAN9GS7P6Y3XUVpaqgSkPt79998fN954Y8R16mNujP49KOoaJ0MIdWI3/FmID2S/xSey3+IPdwz2mX2fY5Dk96H2168Q8rqBUEC5RA32JCTufZSa35ROaAu8xrakO1CnijZdpNx8880quP2GG27AjBkz8Le//Q2vvPKKit1KSUlp8B6bzVYvnvQdRMtW42U4v60wlo6WvKagWGzq8ylAuPE59t3126TwDF9OdzPyszmdyRdr1qxRtd10gcrPZgxg3759MXDgwPp+nfp7Wkqk8dPiee+996KkpARHHnlkg+/B70WrJoX1oEGD1DQux9hDikouR1cpRR+FNkWfDpMWOM7bbrstYn9RjoUCdv369S0ef0+E21+IP2S/xSey3+KPjdHeZynDgIMHw7LtDxg91QjaU+DvMxrlLJfVjEZoK421TJcTbRaLRT3TyqZnKTImisKAoo2xXAx0jyQ2KHo4n0Rapj1uOI5ryJAhEefxsxlnR2Gor7+xNYlqOdK8cGjFYmICg/hZGuPbb79VsXxE/2wKWWbDMjGDxXA5nfF97H7ALFouo2/DpsbTFFy+sapn5i7jBZcuXYo77rijwefxe1HEXXvttbjmmmvUayaE0HVN0cZluR/pymVZEk5LS0tTGaiML2TJkObGR1HKIr+6KBd2wpsTnowo3sW9HD/IfotPZL/FH+5Y77NRoxBr1q5d26LlOlW06TFSTDAIh4Jpzpw5yl3HOmXh6OUl+F7dLcppgwcPbrBMpPirlkIxQ1EYCQbW80HREqk4Ld/Lx+4K19IFzBgwilNanyZNmqRKenC6/vl77LGHKmfCMhsUPRRN3FYUSyxxoo9Hf25NsdxIY+R3ZjwbLV60mjVeni5UCrMHH3xQHSRMVKCVlHFthPuDWb4s9XHPPfcogcuDiN+LZUWaguPg+HmwtUZ49jS4fZr6XQpdF9lv8Ynst/jDEcf7rCWu0U4XbQxYT0xMxO+//94gK5FCjVYXxkexDAXdqLq7jRmVfM+IESOUKZEuQtYX05MR6GajxYoWqs6AgqYl8IdFVyQf4axatWqXbdRceQ/2DG1N31BaMZtyo3I6s3bpnm4K1ojjoym436S8hyAIgiBEn04VbbSqXHLJJcpyRMsYi+OyVtv333+vapOxBRPdgHTHMd6NhXYfffRRXHTRRfW+X/5NS07//v1Vxubzzz+vxEdzlh2hIczgZYkPlhGh2mcmqSAIgiAIXYtO7z1Kqw5NmnQBsio/3ZxPPvkkDjjgADWfVqa7774bf/7zn1X8FC1o4ZYgTmfRV4o7uhtHjRqlXI7RLonRnaFr8vXXX1cWTO6H8CQCQRAEQRC6Bp0u2gg7GPARCVrQWGG/ORhnpXdOEFoPY9HCW1i1x+0rCIIgCEI3bWMlCIIgCIIg7B4RbYIgCIIgCHGAiDZBEARBEIQ4QESbIAiCIAhCHCCiTRAEQRAEIQ4Q0SYIgiAIghAHiGgTBEEQBEGIA0S0CU2yZcsW7LPPPrjpppt2mcem8uxA8dZbb3XK2ARBEAShpyGiTWiSvn374o477sDHH3+ML774on46O1CwtdjkyZM7rcerIAiC0PMIhYLwlW2DZ9ta9czXPYku0RFB2D3BYAjby2pR6/Yj0WFBbmYijEZDzNfLZvRz587F1KlTldUtJycHt912m5r3z3/+M+brFwRBEATiLVwP55I58JcWIBTww2C2wJKVD8uw8egpiGiLA9YVVOKbxVuwpcgJfyAIi9mIvtnJOHy/vhicnxbz9d9zzz044YQTcPvtt+Ooo47Ct99+q9yiycnJMV+3IAiCIFCwVcyfDs1dDXNyJgwWO0J+D7yF6+AtL4Qpey8AI9HdEdEWB4Lt7ZmrUF3rQ2aqHXarGR5fQE0vqXDhzCnDYy7cUlNT8dBDD6n+sAsXLsQNN9yAMWPGxHSdgiAIgkDoAqWFTXNXw5KZD4OhzstksCXAYnXAW7wZ1q1/IBQ6FN0diWnr4i5RWtgo2PJ7JyHBblEuUT7zNafPXrxFLRdr9tprL/Tu3RuapmH8+J5jihYEQRA6F395oXKJmmlh2yHYdPjamJwBU00ptMoidHdEtHVhGMNGlygtbJF+qJy+uciplos19957LwKBAIYOHaosbR6PJ+brFARBEISg11UXw2axR5xvMFuBYAAhrxvdHRFtXRgmHTCGjS7RSNisZjWfy8WSTz/9FO+//z7uvvtuPPzww9i0aZNylwqCIAhCrDHaElTSQcgf2VgQCvgAoxkGmwPdHRFtXRhmiTLpgDFskfD6Amo+l4sVFGh33XUXzjzzTBxxxBEYMWIErr76apWIMGfOnJitVxAEQRCIJSNHZYkGnGUIhRqGA/F10FkOLSkLprRsdHdEtHVhWNaDWaJlVZ6IP1RO75edrJaLBT6fD9deey1yc3Nx66231k+/+OKLsf/++6tppaWlMVm3IAiCIBCDwYjkMZNgcqTAX1ZQ5y4NauqZr42OZPjyRqvlujvd/xvGMUw6YFmPlEQrCopr4PL4oQVD6pmvOX3yfn1jVq/tX//6F9asWYNp06bBbt8ZS2A0GvHggw8qUXfLLbfsIigFQRAEIZrYcgYhfeLpsOUMRtBdg0B5oXq25Q5B0viToKXkoCcgJT+6OCznwbIeep228mqvcokOyU9Tgi2W5T7YDYGPSOTn5+Pnn3+O2boFQRAEobFws2YPUNmktLIx1o2uU7fbA5StQE9ARFscQGE2sE9qp3REEARBEISugsFghDWzD3oqItriBAq0vF5JnT0MQRAEQRA6CYlpEwRBEARBiANEtAmCIAiCIMQBItoEQRAEQRDiABFtgiAIgiAIcYCINkEQBEEQhDhARJsgCIIgCEIcIKJNEARBEAQhDpA6bUKzFBQU4PDDD29y/rhx4/D666936JgEQRAEoSciok1oFjaL/+6773aZPmPGDNx7772YMmVKp4xLEARBiB3BYADuDUuh1VTAlJQOx8BRMBpFMnQ2sgfihFAouEu/NbbziDUmkwm9evVqMG358uV4+OGHcfTRR+O8886L+RgEQRCEjrvWVP74GZy/fg2ttooTYDCaYErJQtqEk5A8amJnD7FHI6ItDvAWrodzyRz4SwsQCvhhMFtgycpH8phJqoFuR+J0OnH11VcrC9x9993XoesWBEEQYmdZq/5lJqp/noFA2VbKN4CWNbMFBpMJgcoilM16VS0rwq3zENEWB4KtYv50aO5qmJMzYbDYEfJ74C1ch0BVCdInnt6hwu3WW29FSUkJpk+fjqQk6YUqCIIQ7ziXzkfF/HcRKN9eJ9Z0DAC0gLK+GexJCHlqUbnwYyTuMUFcpZ2EZI92YXig0MJGwWbJzFduUYPRWOcezcxX051/zFXLdQSvvPIKZs6ciXvuuQdDhw7tkHUKgiAIsRVstKDRklan0sII+NVTKBgEfB517dGqSlSsm9A5iGjrwjCGjS5RZWEzNDyY+JrT/SVb1HKx5tdff8W0adNw9tln44QTToj5+gRBEITYu0QrF3yEkM8NMEbaZAqbu+Oao/mVsSCk+dUyFHBMThA6BxFtXRgmHagYNos94nyDxabmc7lYUl5ejmuuuQYjRoxQ7lFBEAQh/lHZodWl6hqjDAONjAOKUKj+EQr4lIBjNqnQOYho68Iod6jZomLYIhHye9V8LhcrQqEQbrzxRng8HjzxxBOwWq0xW5cgCILQcdBiRsuZwWzdIdj0B3bEttX9zRAcXgt4LTKl9lLlP4TOQSIJuzAs68EsUSYdWKyOBi5SHkABZxlsuUPUcrHiueeew/fff4+HHnoIFotFJSE0LgmSkZERs/ULgiAIsYEWM1rOwEQDk0VZ0pgtCj4rdiQlaJpyjRqsDqSNP1GSEDoR2fJdGNZhY1kPZon6y3bEttEl6vcqwWZypCB59KExrddGwUaBeNNNN0Wcn5eXh9mzZ8ds/YIgCEJsoMWM9deYhGCwJ8IQDNQlHZgsKpatHsZQp/VG+sQ/S7mPTkZEWxeH5TxY1qO+TpuzQrlEaWGjYIt1uQ9pUSUIgtA9ocWMBXOZPcpyHnqctJ41yggqc1Y+UvY9Ein7TBELWxdA9kAcQGFmzR7QKR0RBEEQhPjA73ehcvY78FcVwZKajbTJZ8BiSWi2y44lIxep409EzR9z65ISmHRgtqjYteS9pyDtgOPlWtOFENEWJ/CgsWb26exhCIIgCF2Q7e9Pg3vlgvo4NKavORd/Bmv+SKSMPbxB/9BIXXZsfUfCmpUHo8UuvUa7MLJHBEEQBCHuBdsPEef5ClagdOtqGG0OFb+WOPJA+Lav3aXLjr94A4LOsg7vsiO0DrF5CoIgCEIcu0TrLGw6kWqtaYDZqhIOqr5/D77SLV2iy47QekS0CYIgCEKcwhi2nf1CDQ17h4bBqgNGRzJCWgBabbWqCtCZXXaEtiGiTRAEQRDiFCYd7CSyYFNzggEYOJ+tqAJeBJ3lndZlR2g7ItoEQRAEIU5hlmhLMDCpgFmgLNIeCiEY8HZKlx2hfYhoEwRBEIQ4hWU9WoTZAoPJXNcUPhSqa10Vht5lx9Krb0y77AjtQ7JHBUEQBCFOMZmsAC1ju3FphmorEaDFjbFsFjs0rwsmr6vDu+wI7UNEmyAIgiDEKe4NS2E0GBGkFU2jLGuakKsKxoRUpOx3LIKuqk7psiO0DxFtwm4599xzsWjRoibnL1iwQJrGC4IgdALerasR9HmaTUKoJwQkjZmEjINPre+IIF124gsRbUKLOOaYY3D77bdHnJeent7h4xEEQejpsLOBa/2vTA2tSzBoUPbDABgNQJB/hwBbIqBpsGbm1y0lXXbiEhFtcUIwFERhTQlcPjcSrA7kJPVSJvGOwm63o1evXh22PkEQBKFpaCljKyolz2wJCNXHtO0QbHxWgs0AmMwq8cBg8KsWVUL8IqItDthQsQVzNy5EQdV2+DU/LCYL8lNzceiA8RiY3rezhycIgiB0MHRtMibNmtoLJrMVnq2rgYBvx9wwV6kBMNiTAL8HpvQc1VNUiF863YFdVFSE4cOH7/L44IMP1PwVK1bgnHPOwdixYzF58mT897//bfD+YDCIJ554AhMnTlTLXHrppdiyZQu6k2B7b9nnWF++CUnWROQm91bPfM3pnC8IgiD0LBiLppq9W+ywZOTCnjdMZYXuAqex/prFjrTxJ0oT+Din0/feypUrYbPZMGvWLNVGQyc5ORkVFRW48MILlVi7++678dtvv6nnxMREnHrqqWq5p59+Gm+99RYefPBB5OTk4OGHH8Yll1yCTz/9FFZrwzo08egSpYXN6a1BXnJO/fZJsNjhMOdgq7NQze+flhdzVym354wZM3aZfsQRR6htLgiCIHQcqm+o2aKavdM9SuFmSstWzeBVGypldQvBAAPM6dlKsCWPmtjZwxbiXbStXr0aAwYMQO/evXeZ99prr8FiseCee+6B2WzG4MGDsWnTJjz//PNKtPl8Prz88su44YYbMGnSJPWexx57TFndvv76axx//PGIZxjDRpdohiO9gaAlfM3pnM/l+iS3rCp2W6Fw5nZuTEKCVM4WBEGIBsFgQJXw0GoqVOwZXZlNWcaY7WnJyoe3cB0sVoe6JhiNRmVxs+YOhm/rGpiSM5Ay7k9IGDRaLGzdhE7fi6tWrVJiLBKLFy/GuHHjlGDTGT9+PJ577jmUlpZi27ZtqK2txYQJE+rnp6SkYI899sBPP/0U96KNSQeMYbM3qlytYzNZUaFVquViDa2b/fv3j/l6BEEQeiLOpfNRueAjaNWlCAWDMBiNMKVkIW3CSREtZMz+TB4zCYGqEvjLClSz9/BCubS8pU88XequdTOMXcHSVl5ejr/85S848MADcdZZZ2HevHlqXmFhoXJ5hqNb5LZv367mk9zc3F2W0efFM8wSZdKBpz64tCFezafmczlBEAQhfgVb2axXEagsUsLLmJiqnvma0zk/EhRkdcJsMILuGgRYd81dowrlimDrnnSqpS0QCGD9+vUYMmQIbrnlFiQlJeHzzz/HX//6V7zyyivweDy7xKUx/o14vV643XUWpkjLVFVVtXlc7MHmckVuCcL1MvlB0zT1iCW9HBnK7bmhYjPsYTFt+hjLXOUYlN5fLdfSsfB9+nNr3sNt3ZQQTk1NbVf8IMfBbcp18FloiP4715+F+ED2W3zS0fuNLtGKHz5AyOeBITENMO6wpVhsMJgsqv1U2Zy34KmtgTk5HZZ+ezR0dabkwHHQn2GtLELI64bB5lCxbZrB2OR1rLvh7gbHGq+zjcOgupxoo9vzxx9/hMlkUnXAyKhRo7BmzRq89NJLahrj1hqLJj2WSn8Pl9H/1pdxONpuffL7/Sprtblx6+OINeNz90aRswRbqrcj3Z6qXKK0sFV4qpBsScQBuWPh80a2xDVHa8ZPIfXVV1+pRyT+9a9/qYSEtsKx6AJeaJqNGzd29hCENiD7LT7pqP1mLlmHxMpihIwWXnwazgx4YfR7gapiVM1+VdVbC9pT4Bk4Af4+ezbxiV6gsBI9kY1xfqy1xPjR6TFtjJVqzNChQ/Hdd98p12hxcXGDefrr7OxsdaHXp/Xr16/BMiwb0laY/EDrX1MCg7F0tOaFC8VYMdw+GKdbj8e8TT9ia3Uhqn01sBjNGJIxABP7j2t1nTaqeX4Hjr8lqp68/vrriDUUwtyHuiVV2AnvHnkyYsJOe25GhI5F9lt80pH7ze0uR/UPL6oMUBgDMDoSYDCa1DwWyw36aCmr844YbIkwmM0weaphXTsHyXl94BhxYEzHFy+4u8Gxtnbt2hYt16mijRa1M844A8888wwOOOCA+ulLly5VomnkyJF4++23lfuM1jiycOFCDBw4EJmZmaosCF2qtNbpoq26uhrLly9Xtd3aCsVMU1mRzM7hg+PRxxRrhmQNwKDMflHpiKC7RPkdO2r8u4Pj4DblwdYRQjhe4faRbN34Q/ZbfBLL/cZuBpue/D8EnWU7JwYDCFYWAUaTKtGheWq4IKDO8yGYzCYYrXYELTYEayrg+uUrpI+dJFmh3eRYa6kRpVP3NrNGBw0apEp6sP4ae1i+++67qh7b+++/r4TZiy++qHpesvbakiVL8Oqrr6pldVMixdkjjzyiGpbn5eWpmmG00B155JHoTlCgxbqshyAIghD7fqFbX78TaCrrP6ghUL59Z1MDCjfeYJvrvBBGXtxtCdCqSlR5kMTBYztu8EKn06mijdaVZ599FtOmTcM111yjrGQs18EkhGHDhqllKNruu+8+nHzyyar35U033aT+1rnqqquUm/SOO+5QiQv777+/ioeji1MQBEEQupJgK53zZtOCTWdHwpiCXhFHkrpe1mO2qqQD1nMTehadblfNysrCAw880OT8MWPG4J133mnWtXbjjTeqhyAIgiB05Qbvvs0rW/dGowkGU6MA9YCvro6bNH/vcXR6nTZBEARB6CkN3ineWg7jnAyqz6hOMBRSr02pvaT5ew+k0y1tgiAIgtCTGryHmiiY3gAmINAlGtQQZMN3vw8IBdXnSPP3notY2gRBEAShgxq82/q1rByV0ZEEkyNFFctVws3tVC2qzOk5yDzifGn+3kMRmS4IgiAIMSa8wTssdoC12ZrCyPIeDoQ0P6y5QxF0VSNp5ARYcwY120Re6P7InhcEQRCEGBPe4N2WMwDebesBLYKb1GiCJT1HFUKnZS1YWwHHgNFIO/Ak9RlCz0Z+AYIgCILQAYQ3eLflDoLBlqBEmko4MBhhsCeqjFBdsDH2zZSQguTRh4pgExRiaRN2y+TJk9XzJ598ojpQhHPLLbdg69atHdLqShAEoTsIN2v2APjLtqFi3rvwFW9EEAaE3NUI+bzQ/OwbalQlPcwZuciYfI56jyAQke5xQigYhHvrNjjXrFXPfN2RUJixMbwgCEJPg2U6fGXb4Nm2Vj23rmzHrtBqZs3KR8Kw/aF5alU7K4PJAmNyBoyOZJU4ajBbkTb+T7DnRu6DLfRMxNIWB9SsX4/i2XPg3lKAoM8Po9UCR9989J48CUmDOuYOrG/fvqrI8dFHH40DD5QmxYIg9JwuBiyKq2qssWSH2aISChif1h4LGIWfd9samJLS2BQaQW8tQj53XdHctGzlNvVuX4+kPSeKa1SoR0RbHAi2LW9Ph7+6GtasTFjtdmgeD2rWroO3uAR9zzy9Q4TbCSecgF9++UX1gf300093cZMKgiB0R8FWMX86NHc1zMmZqn1UoLIYrnW/KKtb5pEXwtFnaLuK7Voz82BgpijruGkBGExmFetGAecv2aKWs2b2ifp3E+ITke9dGLpAaWGjYKNlzZyQUBfnkJCgXnN68bdzO8RVajAYVA/YqqoqPPTQQzFfnyAIQldoO0XBZsnMh1ZbBffqn+DdugqBimL4tq1B4dv3ofqPee0utsvzq5FJCImp6pmvDRabmh/eDUEQRLR1YTzbC5VLlBY2HsTh8DWnuzdvUct1BHl5ebj55pvx7rvv4rvvvuuQdQqCIHQGWmWRsoTRwkZrl2vLSgQ8LgRDBsBiBUxmhDy1KJv5MpxL57e52G6oiXptzB7lfC4nCDoi2rowAZdLxbCZ7PaI8002m5rP5TqKM844AwcddBDuuOMO1NTUdNh6BUEQoonP58OSud9i4YfvqWe+DgYD8G5cAsvWJfBuWALN50VJSSVqNq8AtEBdKQ4tgACzPIOhOuHm96Fy4cfqvW0pthtwlqnPDYevOd3Sq69aThB0JKatC0M3KJMOGMPGvxujeb1qfqR5seSf//wn/vSnP+GBBx7o0PUKgiBEg0WffgRtyRdICVYjzRCEFjJi2XevIsFuhhUBJAR8qDGaodF9qQVhgoYQy3LQ4xECDBRZmh9BmGF2JEGrKoF7w1IkDh7bpmK7/rI6i55yifq9SrCxhZXUZxMaI7+GLow9N0fFrvlKI9+JcbqjX1+1XEfSp08fVZ/tvffew+LFizt03YIgCO0VbIm/v4NeoTJYDX6YDRpsBj9SQjUwuSvh82sIWRIBqx0I+mGDpmrfKsFG1N/qCVowiJDZpuKKtZqKdhXbDbprECgvVM+23CE7pkt9NqEhYmnrwjDpgGU9mCWqx7bRJUoLGwWbJSUFvQ87VC3X0Zx++un46quvVGxbbm5uh69fEAShtdAFalnyIWzw1akuGMA0LpOh7qaYT0bNgxDs8AcNKn7NrGxsdUvW/VX3VmV5CxkQ8LhhYZmOpPT2FdulYPO6VAwbXaJiYRMiIb+KLg7LeaiyHkMGQ3PWwLOtUD0nDR3SYeU+mnOTJicnd9r6BUEQWsOaj15CcqhmhwjjBTCkHuEYQyFofh+CmqaWCBjYZqpueUOYYPPvmA6/G6bUXqqRe7uK7Wb2gb3PEPUsgk2IuqXN5XJh+/btKhg9PT0d2dnZsNlsbf04oRkozBIHDFBZokw6YAwbXaIdZWGbPXt2xOm0sIl7VBCEeIAZnpZ1c3UDWz0N8vLrFBkQDMBotUNT1rQQggaDmqxcojAiyDZTIca6BQGzHWnjT4TRKI4rIfaYW2taZhwTi6v+8ccf0NSdSB0mkwn77bcfjjnmGJx88smwWq2xGG+PhQLNkScFFgVBEFoLMzsrF3wEY3DHNUtXYI3ZYXQzBQOw2qyodZlgCvlUb9BypCEZtbDADzMC6u0egw19j7wYyaMmduj3EXouLRZtH3zwAaZNmwav14vDDjtMiTPW7UpISFAFVwsLC1XF/EcffRRPPfUUrrrqKhX3JAiCIAgdLdKYzcnkAMaaBZkoUF0Ko42dB2rrxVlEDKjLFvXUwGxLgNHjQyBkRDFSUGjohZRQNZKCNXAbHLAe+Bek7XVwB34zoafTItF22WWXoaSkBHfeeacSbE1Z0S644AJljfviiy/wyiuv4Ouvv8YLL7wQ7TELgiAIQpNuUFrVKNKY1UkvhcFqR9Dvg4ECzuui03OncNNdoo0IuZ2wWu0IWBOwNZABe8AHE9wIwIQt1kHoc8AUHHjYhI7+ekIPp0Wi7cgjj8Spp57aog+koDvppJNw4oknKleqIAiCIHQEzqXzUPb1KwgFvKp/p9FiBwI+BF3VgOZDqLZKJR6wZIeCYi1cvDXScEZbEnImn4MBwydg2e8r4KyoQnJ6KibvNRIWi8SwCR1Pi351LRVsjdssiXtUEARB6JA+oSt/RNlXL6hG62BSAHt2BvyqhIYxORNaxXYgUNcyql6zNZGQoIe8JYw+tD5ebex+ozvyKwlCRNp0q0AX6Pr16+F0OiPO33///dvysYIgCILQKryF61Ubqdo1PwMUbGG2smDAh1AwAJMjOaIfNFIuQv0coxHWtOyYj18QYiraFixYgOuvvx4VFbtWf2ZqNC1sK1asaO3HCoIgCEKrBVvF/Hfh2bYWCLFMLlsVGJiJUHc9YkN2JiG4aWBoLvugIYx5o3s1aYxkhQpxLtruv/9+ZGRkYOrUqUhLS4vNqARBEAShCQKBIJasLWZPKiQ5i2BlJwOrQ1nWFEq4aQj52ILKiFCwdTUtKe8S9z4KJpOUrhLiXLRt3rwZTz/9NA466KDYjEgQBEEQmmDuL1vw/rdrEawqxPHmtShmP2STFzZHAizsUhAMNDSq0QKnrHAUcyYgpDWfNsr6a4MORp8DToz9lxGEWIu24cOHq04IQs9h8uTJ2Lp1a/1ri8WCrKwsHHroobj66quV5VUQBKEjBNtLnyyDx6dhiCMAuzGEmpAdWhAw1pYiZGwuTo0aTYPBnoyQp2l3qWPCyahIHRmrryAIHSvabrvtNtxwww2qA8KYMWPgcDh2WaZPH6nc39246KKL1IN4PB6sXr0aDz/8MM455xy888470oNUEITYuULXlaK0woXps9cowZaRwpaJCdA0EzIMVUgGa6/V6bCmMkJ1QpoPBmsCQj5XwxlWO9ImnAL7Psdgm8RlC90te5TirSkkESH6hIIhlJfVwuMOwO4wIyMzEQZjs/eUUYWdL3r16lX/um/fvhg5ciSOO+44vPjii7j22ms7bCyCIPQsV2hJpRs+vwafPwiTEXB7zaiwpcIU8qM3ypu0rkXsVmUw1vVttjqU29RgdSBhyH7IPOZiFcPGvtqC0G1EGxMQzGYzrrvuOuUiE2LP9oIqLFlcgJIip7rrNJuN6JWdjDH75SM3P7XTxkWL6pQpU/D555+LaBMEIaqWtQVLCrB8yUpYNC/6OBJQbEhWoo2u0MoaD0KaCZmWcpVz0BpMqb2Qdfj59S2uHANHSbN3IW5o9S+V9dmeeOIJTJo0KTYjEnYRbPNmroar1o+UVDssVhP8Pg3bCqpQWeHCIVOGdapwGzZsGD7++GPU1tYiMTGx08YhCEL3sayZq7ZgVGgVjjVWwWrSEIIFpZZ0zDf3xzYtkxU9MNS/EjZLAFoIMEWqjNsE1rQcJA4e2xFfRxCiTuvyoAH0799fzMcdBF2itLBRsGX1ToTNbobRaFDPfM3pnM/lOouUlBT1XFNT02ljEASh+yQZGCu2YLL5N/Q1lcMVsqE4mIwqvwU5KMHRjiXINZap5TOMNXWVPXbzuY3PjhknXh6z7yAIXU60MVvwsccew/fff6+sK0LsYAwbXaK0sLFocTh8zemcz+U6C70rRlJSUqeNQRCE+HeJ0sLm9QVwoGM9Eg0eFAVT4APrpBnhDVmwzZeMBHixv3W9Kn5bHkxCKKzvez2NKnmEnzktuUNhs9XdaApCj3CPTps2DaWlpbjkkksizqeYWL58eTTG1uNh0gFPZnSJRoLTndVBtVxnsWzZMgwYMEBco4IgtBnGsDHZIN9Ri0xUoNqQGCa3KNHqfJ4VwQTkmCqRZXTiZ98gnOD4BQlGHwKhEMzhTeAjQMHW96IHO+orCULXEG3MFhQ6BmaJMumAMWx0iTaG0zmfy3UGhYWF+Oabb3DppZd2yvoFQYjfBu/+8kIEvS7V0L280odgMIQkkwZzSEPIYIXREIQ15IMJGjSDCZ6QBb6QCWlGDQ6DDxpS8K1nJI5xLAET6RsIt3DMNuRd86xY2IRuQauv9ldeeWWz86uqqtozHiEMlvVgliiTDrJsiQ1cpOyrV13lQZ/8VLVcrGEcY0lJSX2dtlWrVuHxxx9Hfn4+LrzwwpivXxCE7tMv1LlkDvylBQgF/Ko/aB9TJvJMyajRbAgYTUhCLXqZnLDDByOCCMIId8iKSi0B/pBJ/U2+8Y5Rz4fZVyghF9xhZmOtNkNiJvr+379gt0u7RaEHi7ZnnnkGl18eOZDziy++wH333afi3YT2wzpsLOvBLNHS4toG2aMUbAmJFjW/I+q1vfzyy+qhd0TIzc3FscceqwruimtUEISmrGiWjBwYDMawBu/TobmrYU7OhMFiR8jvQUrVNhzt8OKL2tFwOawYFtoIGEPwB81whuwwGkJINLiRYnFhhT8PpcGdxbwp3OZ698Q+1vUqOUFLSMexfzkLQwb07sStIAhdRLSx3IemaQ0sbkVFRbjrrrswZ84c1SVBiB4s58GyHnqdNsaw0SVKC1tH1WmbPXt2zNchCEL3tKJZsvKRPGYSrNkD1DwKNktmfr3nwGBLgLWXA/k1a3CJ/wvYQ4H6aDaz0Q8H/PCGTCoZoa7lwa7rDcCERb6hKpv0gUsPwpABUkNU6J60WrTde++9uPPOOxEMBnHVVVfhzTffxKOPPgqj0aimn3XWWbEZaQ+GwiynT0qndkQQBEFojqasaN7CdQhUlSBpzCQl5tS8RtnwztWLYXNVNlnPwGbQlIt0QyALSUaPSkQoCTaMUeMnHjNhAPYUwSZ0Y1ot2k477TTV0uimm27CJ598ohqJH3PMMbj11lsbtDkSogsFWmYvKashCELXdIk2ZUWzWB3wlxWgdvkPCPp9MKXY69/n99XCveyHHR/SxGfvEGR2gx81QTsyTbUqfi0ci9mAI8f1x/+dulcMv6UgdD5tSjtkLBOFG2u2HXroocrSJgiCIPRMGMPWlBWNrzk9UF2qXtP6FjCE4NHF2m7YWfgD6G8qRUUoqT4RYf+RvTF8QAZOPHgw7BEy7AWhu9GiXzmtaJHo168f5s6di8suuwwZGRn1B+j9998f3VEKgiAIXRYmHagYNoudZjcEvW6END8MJguMNgcMFpu6NpiSMpTFrUmzGpq3tiWZvFjhzUdZMBnHHjgAl4tlTehhtEi0/fjjj03OYxbhmjVr6l83vssSBEEQujfMEmXSAa1pAWc5gu5qQNMAkwlGRwrMyRkwmK2oXbGggWBrqXTTryquoBW/aoNxzIEDxRUqdAiBYABLi1ah0l2NNEcKRmUPh9nYeVbdFq1Zsgd3hXXShOgg21IQ4huW9TAmJMO15meEgtrOGQFa4dzQ6Bq1s0xHsLmOU83C5YpGnolHTpgirlChQ/hu00/4ZOXXKHVVQAtqMBlNyEpIxwkjjsTB/fdHZ9CiX/5RRx2FSZMmqfi1/fbbD1ZrXTxBT4Q1yvRisw6Ho7OH0y3gtgzftoIgxB+aqwahwI4EAZOZ2VNAKAhoAeU6Dfnc9ctG9MfsRsG5svfCiX8+JvoDF4QmBNt/f3sP3oAPCRYHrFYLfJofRTWlajrpDOHWItF2ww03YN68ebjttttUx4MDDjhACbhDDjkEeXl56EmYTCakpaWhuLhYvWZCRjy5hFljz+v11n+XzrawUbBxW3KbdvZ4BEFoG76ybSpDVMW0UXkFtR0WNwOCQQ2GRmKMHQuIoQXCLWgA3NljMeaSf8T0OwiC7g79vXAF3vz9Q7h8bmQ60mA01l2b7GYbrCYLKj3VygI3vu/eHe4qbdHapkyZoh5k5cqVSsDp3Q/69++vxBsf+++/P8zm7m+2zsnJUc+6cIsnWF8vEAio/cTael0BCjZ9mwqCEF+dD/yuClR99xE0ZzlgdQC2JGgeJiZ4YAzuLJTb8M3Y2W6qsU4Le0PQYELK3odjyNHS31iIPbo7tLCmBJ5AnXGjxF2BZGuisrYRo8Go/qbLlLFuY3P37NAxtlphjRgxQj3++te/oqamBt99950ScazbRqvJhAkT8NRTT6E7Q8saEzB69+4Nv9+PeMLtdmP9+vUq87cruHfpEhULmyDEZ+eD2pUL68Sa3vPTWwt4a9mBqlUYImSKmjPzYE7JQrCiSIlDa2af6H4JQWjCHWqCacdv0gAtGECVx6le6cLNarTA5Xer5ISOpl1msaSkJBx99NHqQZYtW4b58+ejp0CxEW+Cg5Y2YrPZYLfvLHIpCILQEsuad+tqVP3yNfxlWxHy1OycX/9Paz50p7VNRwm2tGwEPbXw+70wOZJVSRFBiKVLlBY2CrY0ewq8mh+ugBsG/mcwIhjS4PTVwmGxq2m+oF8lJTCbtMuLtp9++qlZCxRj3WiBo6ATBEEQuodlreq32XCv/x1aVbGKWduldEcUksBN6dkw7ogRYhxR0O2Eioyzyg2mEDvo5qS7k5Y0uj9tJgtMBhO0UF08JoUbs0cp6hjTRitbdlKWKv/R5UXbueee2yDwXi/XED6NsVInnXQS7rnnnrizRAmCIAg7rWu1q39C+bdvIFBZrDJBd1mm/p92YDTDnJ7dMP7NYKh3lcZPqpcQj1S6q5UoY4YooXBLsiWi2utEkBpH/R+CV/MqwWYzW1XZj86o19bqNT7zzDO45pprlCg7/vjjkZmZifLycsyYMQNvv/02brzxRiXU/v3vfyM/Px+XX355bEYuCIIgxNy6VvPHXMAX2T3ZbsFmAIz2JBitduUONVpsADP1ghqCfq96bUpIRtDnacdKBKF56Oaku5MlPZghShJ3xK/VeGuhhepuVgJBDTlJvbp+nbZwXnjhBZx99tm4+eab66cNGjRI1W9j+YuZM2fi9ddfV9P/+9//imgTBEGIs7g1d8FyVMx5G8Gaql0K4qrl6v9pH670ochKS1AfpdVUIMgYOb+X7hqYElJgSkpXVjZ2XBCEWEE3J4vmsgYb3Z+0tOnCzW60osxTiRRbEi7Z7yzslTOyUzsitLrmA5MNJk6cGHEe67f9/vvv6u/hw4dj+/bt7R+hIAiC0CGWtdKvX8HW1/+Bss+eQbCmImaCjW+vzRmLUZffD0tWPkIBL2x9hsKePxK2vGF1z32GqumWXn1VxwVBiBUUYbSe0e3JGmws98GkPT5X+ZxItDrwl71Oxr59RneqYGuTaOvVq1eTvUg5PSsrS/1dUVGBlJTWZVZs2LABe++9Nz744IP6aStWrMA555yDsWPHYvLkycp6Fw437BNPPKGEJJe59NJLsWXLltZ+LUEQhB4t2Mpmvgrn4q8Qqq1scrn2CLZACPDAjLKkIci7/jVVLJcB3sljJsHkSIG/fGudu9SRpJ75mtOTRx+qlhOEWEJ353ljT1MJBl7Nh0pvtXrma07vLHdoY1otGc866yxMmzZN1ftieyvGtJWWlmLWrFl444038Pe//x2FhYUq9o2Wt5bCemfsvKC3NNKF34UXXqjE2t13343ffvtNPScmJuLUU09Vyzz99NN466238OCDD6oCrQ8//DAuueQSfPrppz263ZYgCEJLXaKVCz6GZ/Oy5per/6f1VFiyYR1/OkZMOGiX87ItZxDSJ56u6r75SwsQclao5vO23CFKsHG+IHQEB/ffX3U56EoN4hvT6pFcfPHFSrC9+OKL9bFrzCBNTk5Wgu2yyy7DRx99BJ/Ph+uuu67Fn/vkk0/uUibk3XffVcVXmYXKCv6DBw/Gpk2b8PzzzyvRxnW8/PLLSuyxNyp57LHHlNXt66+/VokSgiAIQvMtqGqXf9fk/Hqd1laXqMGALJsXmZnmJm+kKcys2QNUPB1rsjGGjS5RsbAJHY3ZaO7wLgetoU3y8corr1TijZYvZo5mZ2dj5MiRygJG/vSnP6ns0tbUfnvnnXeU2NPFF1m8eDHGjRvXoDXW+PHj8dxzzynr3rZt21BbW6u6MOjQJbvHHnuozxTRJgiC0Dxb37wn4vQd1ZzaRchkhiWtt4qPq1z4MRL3mFBfh60xFGjS9UAQoiDaFixY0EAYEbZAajxNR6/N9sMPP+DAAw9s9rOrq6tVC6w77rhDtYYKh27WYcOGNZjG1lGESQ6cTxq/j8vo89rTyLw7Qitp+LPQ9ZF9Fp90tf1GN6hWWYSQ1w2DzQFTWjZK3rwLqClrtFx01ucz2ZGQllH3wpaAQFUJqlYuhm3AGHRlutp+E3rGPguFQg3q3bZLtDFOrE+fPrjiiiuURW130ELG0iBsqP7hhx82u+zUqVNV8gGtc43xeDy7xj/Y6mqoeL3e+h0UaZmqKqaqtw3G1zEBojuzcePGzh6C0Epkn8Unnb7fgkFYt/wCS/FqGFlSw2hEyGRFyGCCpWJTw2WjYV3j+dloh9WRCJ/XWz8GQ8CHLWtXwO+uK2Da1en0/Sb0uH1mbUEcfotEG2PLmFhwxhlnIC8vD0ceeSTGjBmjiueyNhutZbR8/fzzz6r3KLM3L7jggt02jqc7lAKPSQORYG9Mxq2FQ7FGuF69dyaXCe+jyWXa0wydcXRDhgxBd4RClz/sAQMGdImG8cLukX0Wn3SF/eZe+QNqFn1Y13oqFFa+gy7KYCDqFragAbCk5cBibBiLxkK5CFnRd8hI2Abs/sa/p+83oefts7Vr17ZouRaJNsaUMcmAou2VV15RJTkYV9a4nRWtccwopWBjnNvueP/991FWVtYgjo3cdddd+OKLL1Q2KK114eiv+fmBQKB+Wr9+/RoswzpxbYXfi6KwO8Mfdnf/jt0N2WfxSWftN+fSeaj+9g2EvDW7KrIwwRatQrkUbGarAwajEcawa4NqA+R1wZyeg9QR+zUZ09bVkOMt/nDE8T5riWuUtOroYawYOyHwsW7dOhQUFMDpdCI9PV0JtoEDB7ZqkI888ohygYZDK95VV12FE044AR9//LFqjaVpWn2c3MKFC9V6WGqEGavMOGV9OF200eq3fPlyVdtNEAShp8HYNefKH1H21QsIeZuPzY2WYPMYbOh34uUom/VqXVFedjAwW4GAT2WDGix2pI0/MW4EmyB0Vdp8BLH8Bh/toSlrHAUZ57GsB0uL3H777ar22pIlS/Dqq6+qWm26/5fijOIvIyNDuW4Zf0cLHcWfIAhCTyuSyyzN2lU/AgF/s8u22x1qAGivS7/0cQzu3bd+cuWCj6BVl9YlPBiNysJGwZY8KnInHUEQYizamFxAkXTooYdi5cqVqkn81q1bcfTRR6vEgmgVtaV4o2i77777cPLJJ6tuDMw05d86tMrRTcrsU1rt9t9/f7z00ksqLk0QBKEnCbaK+e/CtWEpQmGCzRBlwabR22k0o//fpyElJb/BPAozlvVwb1iqeomyd6hj4CixsAlClGj1kcRitrRmUSxRtFGksXPB6aefXi/mWOy2raxatarBayY8sIZbU9BtStHIhyAIQo91iS6Zg6rNa2D2uxu4PEPhqq2d1jW3ZsJs22G46pbLm1yGAi1x8Nj2rUgQhOiItunTpytX5eWXX65i2lhg984778TZZ5+NQYMG4dlnn22XaBMEQRBaR+Ga31D10xcwRzKhRanu2mZ/Eva65j+4Kr1h5xpBELqwaKNQO+SQQ9Tfc+fOVRkP7A1KKNqYDSoIgiDE1rLGlk9+Vw3W/3cqEkJexDIgJGAADrv7tRiuQRCEmIg2uj/ZQkoXbRRqDPzXXZtZWVmt/UhBEAShFbFrdIUWbVgLc9EqJMS4PScF2/Db34/tSgRBiI1oO+ywwzBt2jTV2mrevHm49tpr1XTWb/vPf/6DU045pbUfKQiCILQ42WA6KopLsLmkEsNiGN/PMpj2C/+Jwf27djFcQehJtPoe7dZbb1X9RNmQ/cwzz8RFF12kprOeGhMTrrnmmliMUxAEoUejJxsEaquxviKEgcaKmK4vbb/D0V8EmyB0KVp9n8a+nvfcc88u0z/55JP6vqCCIAhCdGEMm69kM2pqXMgLFsHcsgLqrcdQVybEkrr7rjaCIHQsbTKus2UVG6q7XC71d2NYK00QBEGIHpU/fgbPxj/USTvZGMtWOiHAbEHKhONisxIhbvEEPPh81WwU15Shd1Imjhs+GXbzzr7fQhcUbexKcPXVV6OwsHCXeRRwPOgp6ARBEIT2Z4iyDZRz6XzU/DpDTY+ZgU0XbDAgaczhMMvFWAjjpZ/fxuz138Mf1rf2g+VfYvKgg3Dxvmd26th6Eq0WbQ888IBqIM9nZo0ajTFOXRIEQehBlJevReVzt6um7uxrQClljIFYY2cDE12h9R9cZ2GjYOt9zKVRXpsQz1a1TZVbsL5yS/08IwxK3lPAfb12npomwq2LirZly5bh0UcfxRFHHBGbEQmCIPRQ1t93mhJPetCJOUqFcRvjChjguPRp9M9OQfWCz+GvKlIxbHSJioVNiGRVCyeIEAwwKPHGv7nsX/Y6SVylXVG0sR8oW0cJgiAI0WP9faeqZ6XTYiTWiNMAjJ36Xv3rjIl16xUE8syC1/Ht5h92u1zdrQWlm0GJO1rlTt3z2A4ZY0+m1b5Ntqt67rnnVBKCIAiC0P7YtfVPXlX3d/0/scEJI8ZKoVyhCa797O4WCbZw4aZ71+lGFbqgpW3Tpk1Yt24dDjroIAwdOhR2u32XYNbXXpN2J4IgCC0plrv17fsRqo1tzTXiCdDCNj3m6xHik+u/vAdba3dNMNwddI8SZpMKXVS0jRgxov5145IfkUqACIIg9GSCwQDcG5ZCq6mAKSkdjoGj4C/ejI2v3QlzwB1T6xrxBEzYc+q7sV2JELdUeiqxpXp7m99vMZpV+Q+hC4q2119/PTYjEQRB6Ia4V/6Asp+/hFZdilAwCIPRCGNyJtxlhTBr3pium1rQeMDJ2HPKOeiOBAMBVC1ZCl9FBazp6UgdMwpGcwx7e3VTnljwarvez7IfkoTQMbT5100X6aJFi+B0OpGeno59991XNY8XBEEQ6rBsWwbn2jkIBXwwWO0I+QMI+d3wlWyKWWYonR3G9N4wBTWE/F70GjQK3ZHiefOx9YOP4CvZKYatvbKQd8pJ6H3IxM4eXlxRWlve5vcePvBAKffRlUUb3Z933XUXpk+f3sAVyli2k08+Gffff3+0xygIghB3yQW+sgLY185TxXFhMCFUUxe3xtNmrPLvNYMBjqw+CIZCCLqdMKfnKFdsd6Po2znY8OIrCHq9MDkcMCcnI+T3wVNYhI0v11mN4lG4BYIBLC1ahUp3NdIcKRiVPRxmY+wth1mJGSisLWn1+/bPHYPLxp0bkzEJkWn1r+HFF1/E+++/j6uuugonnHACevXqheLiYnz88cd45plnMGzYMFxwwQWt/VhBEIRuk1zAxu6uTctgclXucFL6Y54ZqrE3dHoOgj6PEooGix1p40+EsQMu+h0t2Nb951mE/Cw9DAS0ADSXC+aUZFgy0uEvr8C2Dz9G1oET4spV+t2mn/DJyq9R6qqAFtRgMpqQlZCOE0YciYP7x7Y15FUTLsBfP761xcuzzMf4vnvj2gOlCHNH0+pf9HvvvYdLLrkEl19+ef20/Px8XHHFFfD7/Xj33XdFtAmC0GMFW8X86dBc1Qh63TsL5cY40YDWO2tCEoK1VcpNSAsbBVvyqPizNu3OJbrhhZfrBBtbObAjTyiEkKbBX1kFCy9qiQnwFpeoWLf0fcYiXgTbf397D96ADwkWB6xWC3yaH0U1pWo6iaVwS7OnoW9KbrPJCA6jDXtkD0N2Uhb+PPp4JFgSYjYeIYqibfv27Rg/fnzEeQcccABefvnl1n6kIAhCt3CJVv32LaqLtiHoqYXZW1FXwyrGgi0QAIbe+c4u2andzcLGpAPGsAV93nrBpvqlGthSKQRoQQScTlizshByuVVyQry4RGlho2BLs6fAaKgrn2o322A1WVDpqVbzadmKpat02jF3qrIfkYQbBR3nC51Pq38BeXl5WLVqFSZMmLDLvJUrVyIjIyNaYxMEQejSjdyNtgRYMnJgMBjxw3e/IvTLz0jWqpBo8MSssXu4WLOccjOGjxmnXicOjg+rUltRWaIlpTA5EpQ4U+bFHY1T6a4LUbxpGrRaV11SQno64gHGsNElSgubLth0+JrTOZ/Ljc3dM6ZjoTBj+Q9mkzI5gbFudJ3SEifEqWg7/vjj8eSTTyI7OxtHH320utNhQsKXX36Jp556CmeccUZsRioIgtBF4tX8pQUIBfwwmC2wZOVjtWUkPpu3Bmcaq+CIsWCjWEv9v3uRk7MHehK0nDFL1JScBLhdDGZT1x5lbSN81oLQ3G4k9M1X5T/iASYdMIaNLtFIWI0WuPxutVxHQIF252HXdMi6hA4QbZdeeikWL16Ma6+9FjfeeKMq91FRUQFN0zBu3DhcffXVbRiGIAhC18azfS3KZ7+BgKsKJlsSYHUg5HOjdsMf8JT+ihFaIhwmT8wyQ4k7YMKoblokd3c11ziNFjT4/bAkJcNfVcWqxcrCpgRbMKiWM9ps6HPyiXGThMAsUSYdMIaNLtHG+IJ+NZ/LCUKrf9VWqxWvvPIK5s6dq+q0VVdXIzU1Ffvvvz8OPfTQ2IxSEAShE92ggepSlWAQqCxU4iAQ1Ordc1owhL6hIPpZ6aaLnXXNf/o/MGrPsT225hpFHKexrAezRGmX8tc4lcVN7Qta3SwWDLzkwrgq98GyHswSZdIBY9jCXaTBUFBZ2Rj8z+UEoc23IhRoItIEQejublBmggaqShD0MRt0hyxTIiGIIAvZdkCiwfCp3avJO4WZZ3shAi4XqpYtx9YPP0LQ61OZnwarFSHfrjXXaDmjiOM0lvXgstbMLARdLmhuF4xWGwZeehGyD5uEeILJBSzrwSxRJh2o7FGjRVnYKNjYIoqu0Us/vAmp9hTcNulv6J3Yu7OHLXRl0XbeeeepgrqDBw9WfzeHNIwXBKFblO1wV8OUlIGQs1xZ3UDrGmGhXL7mnyLYWk3N+vUonj0H7i0F0Hxe1K7fiCBdnhkZMNmsdQvZ7TDYbLvUXNMtaDutcm5llUvo21e5RLuihY3WssKaErh8biRYHchJ6rVLwoFezkOv00axRpeoJ+CFG5765Wpr3Ljys7uQYk3Ciyc/3OHfRYgT0Rbe+WB3DeGlYbwgCPEKxRgtbBRslsx8hLwuBL21MFhsCOl110I7xJsItlbjXL0G6198BQFnNSzpGTBarEqw8WZfczphNCTDaK0TbkaDIWLNNQoziriu3nOUYm3x1iX4YcvPKKstVxmuVrMV+am5OHTAeAxM77uLcBuXvxfmb1yE0toKfL5qpl7lbxeqfTW45MMbRbj1QMytbRIvDeMFQeiuMIaNLlFzcqYSEkEtUBdjZao7VXZEodzuKtiK5s7DxhdfRqCmVsUCUowZzKYdlYGtqlxHoNYFi8VSnxGqXKURaq5RoHVm4dzdWc82VGzBJytn4o+iFfBrAVhNViTbEpFhtmF9+SaU1JbhtD2Pqxdu1d5qPPrdi9hStU29TjI44AnWdXxoCgq34tpicZX2MNp8a+JyuZCQUFcRecaMGaro7mGHHYb+/ftHc3yCIAgdBpMOVCkPi129VmKN4s3nqbN6dIB1zfLnWzB8j9i2LeqMRANdsBnMZvWguzno89dlfdLaZjEjFAioBxMKCGPbulrNNQqyuRsXoqBqO/yaHxaTpYH1jPOnL/0M68o3KT3KgrkUeU5vDbwBLwam9UW1t0Z9hsNiwXVf/BMB3Xq7AydqWzSW++c8jcePmxqjbyp0C9G2fv16XHbZZTjuuONwzTXX4PHHH8ezzz6r5j322GOqI8K+++4bi7EKgiDEFBbLZe21kN+jSnoE3E4E3TUIUsjFaJ2aVqdbrCfdgOF771q0vLt0MtC8vh2CzVRXE9dkgtFuUokEFGqwmGHYkQWq3hcKKcubIzeny9RcoyB7b9nnSoBlONJhN1vhCfjqrWen7HEM5m9ahDI3e84CCRa7ssDxkWgxodbvwvaaEuSn5GLGmrn4YvXsdo2nytMxtduErkPDaMgW8Mgjj8BsNuPwww+Hz+fDW2+9hWOPPVbVbps4caIScYIgCPEIuxuwWK6vbCtcG5bAtXFpneUtRuujVhl21/sYcff7GNQNBVt4JwNzgqPO7RkW96xKrFl2uJ49XhUTzRQPzeNRSQgmu73L1FyjtYzWMQq2vOScekHGZ77m9K/WzMGWym1IsiYoyyyTCXT43VmHjcJtUcGv0BpZ19oCs0mFnkWrRRvF2fXXX4/Ro0erOm1Op1N1QUhKSsKZZ56JpUuXxmakgiAIMYbtqGx9hsJXXoRAZTGMMUys6m5xa7SoVfzyG4q++VY983V4JwMDM0LNZoS0YIOENYOFBe4MMJjqBI7mrEHQ41UWtgEXnd9lMkIZw0aXKC1s9V0YdsDXnL61ulCJMr0lFTsdULwxC5TTGd/Gh6akafth+Q+hZ9Hq2xe/34+UlDp1P2/ePDgcjnp3KLsi0AonCIIQj4V0a8q3oeTTZ2HWYt+KqjsJtuaK44Z3MmA2qL/aiRAL4pqMdc3e+bfBgMRBg9Br0iHKutYVM0KZdMAYNrpEI2EzWRFEUIk1046eoXSZUqTV2Q93NLaPEiz7IUkIPY9WHxHDhg3D119/jYEDB+Krr77CwQcfrIQaxdybb76p5guCIMRbId2Kn2fCGPTBLKU8Wp9k8PKr0DzeiMVx+59/bsNOBinJKlZNJR2EgurZnJSEQZdehORhQ9FVYZYokw4Yw0aXaGO8mg9JlgSk2VOxsnQtylyVjVyg0RVsUu6jZ9Jq0XbVVVfhiiuuUAKNLa3Yi5QcddRRKC0trU9KEARB6Op4tq9T/URrN/4RU1eoLtacEy/DfocfiW6XZODxKkHG2mqNi+Nu/+Qz9DnxBGx67b/1nQxMKckIeTwIuNxKsA245MJOE2yNy3ekGBMjLseyHswSZdKBw5zTwEVKd29xbSnKXZVYXrwWgVCdazgaDE3sh4pgDdx+t3REEFov2g466CB8+umn+OOPP7DXXnshLy9PTT///PMxfvx4DB8u/dEEQYiPBvAlnz0DT+FGGA2xFWuzss/DFZefiG6bZJCYsFOw7SC8OK41NRUDLrpgpwvVtaOTQV6fTu1kEKl8R3ZCFvICmRjZaFm6PVnWgy7Prc5CFcNGl2iVrwq/bFvW4nUaYUCwhVa3VFsSLjnonF0K8Qo9lzYFDPTt21c9dEpKSrDffvthyJAh0RybIAhCzATb1g+eRKiiIOaCbdORD+KKA7uu26891CcZ7Ohi0Jjw4rjZhx/WpToZsI7am0s+gtPjREZCOjIT0uAN+LGxqgAbPZsxsGogRiY0DPeheGJR3DkbFmB9xSasLt3Qqjg1dkVo6dL9UvLwr6Nv26XlldCzafXRUlNTg/vuuw+jRo3CX/7yF3z55Ze48cYbVRLCgAEDVJ223Nzc2IxWEAQhCi7Rze88AmNNSWyTDTSgZuIFOHhsnTciHgl4PNj2yefwFhXBlp2NPiccB7N9ZzyXnmTAGDa6RBvTuDhuZ3cy0FlXvhnP/fQGimvLVEN2p68WpdYE5Cb3Rp/E3ljr2ojvtyzG8JwhTYqm1aXrWx2lplvZWBKkxudqYhkjnj/pAaTYpJyHEAXRNm3aNNUBgW5SvW7biBEjcPnll6sabXzNZQRB6LkEPAEs+G49KkrdSM9yYMLBg2C2d4xFxeerQcWXr8BfVQRLajbSj7kQVmuSmrf+90Wo+vhxpBi9MVt/IAQ4zfkYes5lWFvGfqXxydrnXkDxzG8Q8u9sp1Tw7nT0nnI4hlxWF8tMS5meZMAYtnAXaVcpjlvpqcQTC15FaW05km1JOKTvAZhXsEgJNofZDpvZqkpzsM6ax+9RFq5kcyK2OotUrFuf5GzU+Grw6i/vYWPFZmytLmpzyQ4KNv7nC/gxIC0f1xx4Ef417zlVJFfi1YSW0Oqz6DfffINbbrkFxx9/vKrJtnXrVtx0002q2G4gEMBdd93V2o8UBKGbEAqG8OH/fsXy37cjqO20Q8ybuQZ7j+uHY08dHdP1b33zbng3Lql/7duyArVL58CYlIWiShcyTC6kGGPrDh349ydVkV632wOUrUC8CraiL2fUFcJVFXDriuJSwKnpgBJutJyxrAezRPUkAz17lIKts4vjXv/lPdhSvb3+dWFtCdaUb1B/sx8okwn4n9loVoVwXX43impLkG5IhjcYUMkJ/5zzbywpWhmV8VCwcX1MeDhhxJHok5wrbaiEVtHqI6myshKDBg1Sf8+dO1eV+9CtbqmpqfB6Y3cHKwhC12V7QRU++t+vKCms2WWeFghh8YJN6u9YCbfGgq3B+p2lyNpZnD4mYm1u2on46zXnId6hS5QWNiXYjMa6Gms7YPwae25x/oDzz1WuUj2JoHGSAS1sHZ1koGeC1nhdePyHF1HqLm9yWZ/mUz1AU+3JsBotSkwxsaDW74YZJrg0L+6Y9VCUyuDuJCshHWeNOQkH9+9e/WWFLiramC26atUqlXgwa9YsjB07VnVD0EVcfn5+LMYpCEIXF2xffPB7RMGmoNcsBPy2aDOOPG5k1F2ldIlGEmwxruKhxNoD2j74x2VX4K/5aYhXKMY82wsRcLlQPHd+nUuUVqgwwUZU/NoOixtj3fr9+VQ1ncKsM5IMAsEAlhatQqW7Gh7Nq1pI/V64HCWushbFm1G41fpcsNhTlGgzGUyo8VaiIlQV9bGm2pIxaeAEnDH6T8qyJwhtodW/HLaqevDBB1WdNjaPf/TRR9X0K6+8UrlO77jjjjYNRBCE+HWJLpizDoVbnc0sVCfcAoGQinWbeER0i3Azhq0zBFvC317Fy1lJMMYyBTXGddYKZ8xE+aKfEKh2wmi3qvi0ZtnhKmVyQjgdnWTw3aaf8PHKGSiqKUUgqCkB1xZ8AR+8AZ+yspW6KuCPYo01wn6jZ485CUcMPljEmtBuWv0LYj22zMxM/PTTT0qosVk8sVgsmDp1qupDKghCz6GstAabN5Q36CfZjLFNJSdEGyYd6KhRdIBgM1/yEvr2Tka8ZoRWLvkNrg2boNXUKjcoxZjJYYfRZqtbkBa1UGiXPpu6GmY2aWdBwfbiz/+D2+9pV2soFdFmMKLSUx2VBu6N6ZfaB/866nYp2yFEjTbJfiYh8BHOY489Fq0xCYIQB9a18rJaeNwBFGwqh88TgMVigldr+sKnazpmk0aTYDAAr2/Hh8dYrIULtsFx5g6lC3T140+g9PsFdV8iHIMBRosZmtsDzRUmqrk/w1ycKqaNQs5iUeU/WtJhgJ0EoilaaFF76/cPVdJAe6Hg8wd3ZsdGC4rBF056UMp2CJ0j2p566imcfvrpyM7OVn83B+/K2OZKEITuG7/G2LTNGyvg92rQtCC83gBsjt1H+pvNBlX+I1o4l85H8bz34a0ogK2DBNvQO9+LO3dozfr1WP3vJ+HeuDnyAlpdEQta2YJMJqNQ2yHsQhRuO1yiejYpy36E12sL7zCgF55VLkezFYPS+6tYrmhV9f/f7x+j1F2BrobFYEa6IxV3HHYlcpJyOns4Qk8XbYcccoiINkHo4VCwfTb9d5QW1UAL8iJeN53Xco9bg8Vqgt/XtLVt7Lh+bUpCYGNxf3khgl4XjLYEmNKyUDH3HTh//hqazwUjFUeMdBQ1Cw1M36cchouvvxLxRtXKVVj9n2fg27yl+QUp3LSAsriFAhrMmZnwl5XtFGvcxBZLgzptjQXba7++h+3OImVt0ymuKcOmyq04f+/T2i3c6Bb9Ys1sdDX6Judi2rF3dvYwhB5Ai86eK1eujPi3IAg9yyU6+4uVKNpWl3BgMht3GGBCCNI9GaJxRoPVaoIvgnDb98C21WnzFq6Hc8kc+EsLEAr4oXlq4K8oZnCW0oymEGCKkWCjsWn41PfV3yMQf0kGa576D0rnflenOluCFgRUSyoNtswM2HJzkDRwIIJud8SOCPXrCgXxycqZ2Fy1FSaDUQXfs+4Zi9Z6Al41/dOVM3Hl+Ava7CrV3aJamCDsCvRNycW0Y0SwCR2DpLIIgtAiSkucdQkHKvHIuLP6PZ8tgN8fBK+nvCbb7EZloaLQS0qx45RzxqLvgMxWWdNYoNZXtBEV86dDc1fDnJwJf3U5/CWbOyzZQBds8UbxvPnY+NKr8FdWtu6N3IFBTQlxxreljRiOgReev0vpj8ZscxZhVek6FcuVaE1Qz4TZkolWE5zeWqwsXaeWy09pXZtDPUbu14I/UNaF3KKjeg/HVRMuQJo9vmIbhR4m2nw+H9544w388ssvqK6ujugefe2116I1PkEQugjrV5dBo9vMHCbYdmAy1dXvYkkPu8MKh8OiXKV9+qZhzH75yM1PbZU1zWC2wJKVB622Wgk2S2a++ttXsKJDSnks3fN8nHr6CehRgk1hQNDnh9FigT03B70PO3S3go0UVG1XiQFJlp2CbecnGuAw21Djd6nlKNrC66ulOVIwKnt4xHIYdLnO3bhQvY812DogbLFFvHvGM509BKGH0mrRds899+C9997D0KFDkZa26x3G7tL+BUGIT/z+QLMXTYOR5RNC2GNMDvYYmwe7w4yMzEQ1fXeCLdyaZrDYEfJ74NmyEv6qElgy8+Av3w5XwRoYO8i6NhzxCV2i7EwQqK1t2wdoGgxmM1L23AP9zvozknZ0v9ktzE/gczO7Ws0K1cWlfbLya1UTje5TulHZJYBtncK7BFCwvbfsc9UTNMORjiRbAiq80S962xruOPR6jMkZ0qljEHo2rRZtM2fOxN///ndJNhCEHkZObgqMRro9Q3VlvcLmUUup6SYjBg7NQl6/lrmM6BKlhU23puk1wQy2hDrx5nXBV7RBiRFjMPp1tLqLO1RHdSQoKVXCS+9q0GLTpMkEe3Y2so+agrwTjm+RhU0nPy0XdrMdbr8XZqu5QW033si7A144LHYU15bis9XfqMzSBIsDZnNdv8+t1YV46ef/IRgM4uAB+ys36kcrZmC7sxjZiVmqNEe6PbVBH9GOItHswCun1hWRF4S4E208ae+9996xGY0gCF2WQUN7IS3djvIyNwJ+DSazqV4T0G3K5/QMh1qupTCGjS5RZWELu9BrrmoEKgrVhwf9Xhgkfq1FsIUUa6kZrVaVPFDf8L054WYyKcta/skntbn1VJ/kbIzoNQS/Fy5Drd9Vl4hgMKmCtUxECCGIoRmDMH/zT0qwpdlTlJArd1fUW2/9wQCeWvQq5m1cqEp6sNMBbweKa0pht9iQbE2CxWiJSV21SGQ6MnDX5L9L+Q6hS9Hqo/Okk05S7tHx48crAScIQs/AaDbikCOH46sPl8LrCUBTmQZ1fi9qArvdjEOOHKaWaylMOlAxbBZ7A8uMr3QrfLVVMASDsark0e0EG2HPT1rIlPDi+VnPGo0k3Fhv7cjDMfCiCyNmhLYGZoSeMGIKKtyVyjrmCfgazMtLycHI3kPwR/FKZWFz+T2o8u4aE02WFK9UcXC0rtU9B+D2hxAMhVTdt4CPbvrYqHgjjHh0yh3YvmEbRo4ciYSEhJisRxA6TLRdc801SrgdddRR2HPPPeFwNKxuzrvl+++/v80DEgSh87scNBWPNnqfPPX8w7drUVnuRjBIV6kBaRkJOPCwwfXzWwqzRJl0wBg2zWRFoHgT/GVMRvDFNH5Nr722pN/J+PPF56C7QEuZtVeW6h9qSkyE5nRGtrIZjeh71p/R78+nR23drMF2/t6n1xXXLQ8rrpvRHxP67ovPVs2C2+dGwOiHJ7hT1EVCF2W8nqgEl5AGQ8CnrHGxEmxPHX83eif2hsvlwnZsi8k6BKHDRdsjjzyCDRs2KLG2ZMmSXebv0qduN5SVlakG9PPnz4fX68X++++Pm2++GYMHD1bzV6xYgfvuuw9Lly5FRkYGLrjgApx33nn172cMBAv+Tp8+HU6nU73/zjvvRN++0am+LQg9pWjuksUFKClyIhAIqgzRXtnJETM/Kcz2HJOLDetK4azyIjnVhoGDs1plYdNhWQ9LVj5qln+PYC2DzHdckkOxs6rNsx6IS++4Pi5rr+0OWtjyTjkJG19+FZrHC2NCAoIuV8Nl7HbknXYK+p1+atTXT+HWPy2vvo2VJxDEo98/hS/XfFu/jLYbwbZrYlvdb8IXA7eo2WDGI8fchj7JrStDIghxI9o++eQTJZxuuummqLhHmdBA4fX8888jMTER//73v9Xnf/311/B4PLjwwgsxefJk3H333fjtt9/UM5c79dS6E87TTz+Nt956Swm/nJwcPPzww7jkkkvw6aefwqqKRAqCsDvBNm/matQ4vap0By0zFG4FmytQWeHCIVOG7SLcKNAGD+/d5l6h7g1LodVUwJSUDl91GbTaHeUpYmhd8waAPRplhkaqDccG4vFM70MmqmdmkTIpAfSGMM7N4UDGuP0w6NKLYYrhubHSU4n/LHwNa8o3tPuzYmVVI6+eMg0JFnF/Ct1ctDGO5bDDDouKYKuqqkJeXh4uu+wyDBs2TE3729/+hhNPPBFr1qzBggULYLFYVJkRs9msrG+bNm1SAo+ijTXjXn75Zdxwww2YNGlSfeP6iRMnKtHXuKm9IAi7ukTrLGw1cNf44PNrahrdolaLCW6XX83P6ZOy29IdLe0VWrngI2jVpSpgPqQFENJ8MS+Uqwu23deGy0fymEmw5USvP2pnCbesAyfUZZNWVKhYt7YmGbSGv3/2DxTVMoGga2I1WjD18BswJKNfZw9FENpEq4/gKVOm4Msvv8QBBxyA9pKamopp06bVvy4vL8err76qLGZDhgzBk08+iXHjxinBpsMEiOeeew6lpaXYtm0bamtrMWHChPr5KSkp2GOPPfDTTz+JaBOE3cAYtvVrSlBdURefplpTmRhHBNUE3u/X1Hwul9krqd2CrWzWqwj5vcqqxdalwZqyDskMjSTYItWG8xauQ6CqBOkTT4974UaBlr7P2A5b398+vg2lnq7TsUBnj6yhuO7gS5BiS+nsoQhCx4u2vfbaS8W1sQcpS3/QVRmNhvH/+Mc/8O677yqX5jPPPKOydgoLC+stcDq9e9e5ZLZv367mk9zc3F2W0ee1BcZRMBi1O+JmGYCwZ6Hro+8r3qBsWFOGWqcPiclW9B+Y3iCOLBgIYtOGiibnR6K8rBoVZa4dgs2gkgqICk01G6AFQmo+l3Mkts26XueC3IbKuW8j6HPDkJiOkko3koJVsBhiL9j63vR6g+NZ1Yb7ZRb8NZUwZfRBkF+WWQkmKwypOWqsFb/MQvKhZ7fLVdpTjjW2mfrX/Ge6pGB7+fiH63qdamjxOb2n7LfuhLsb7DPqjpbkBLRatE2dOlU9M76Mj8a0VbSdf/75OOOMM/Dmm2+q9zNOjTFtjePSbDabembSgr6DIi1D12tb8fv9KgGiO7Nx48bOHoLQCrZudGPeFz/D49Lq+3vaE0wYvEcS8gY41Px1y2uanM+40dJCH7yeoOoLmpVjVSEOm9fWQgsE1fKq3hrTKsMxhJRwW/r7WlTXNrxBawmm6kJYN/0KU/FqmP21ygvqrShEcpB9KREz+F3edY7FUWccu8uxbKwtQ8Lm1QiyzEikVk9BE4ybV2P7zz8gmNh8v9SefKzxIrOmdhNmlvyAGq1r3eSem3As+vTpg1UrV7X5M7rrfuvObIzzfdaSOPxWizZa2GIB3aGEmaK///676m9qt9tV3Fo4FGuEljjOJ1xG/1tfpnEpktbAODp9PN0NCl3+sAcMGNCubSR0HL8v3oLlvxQpMWZ3WFRRWxaz9bo0rF7iAgIJWL3chYAv1OT8rZtrVKanXqKDGZ/jDu6PvD6pWGZwqvWYTKZd1h0MasrqltenD0aO7NPkGGm90iqLEPK6YbA5YErLRqBkMyq+/xRBZ0mDZdWRaoyNUKPLdaspEfuccwuu6T0g4nL+wvWoXm+FKa13xKr/oaCmvktOfh9Y2uEi7a7HGi1rvxYuw+yN32NN2Ub4Qh1T7LYlXD78AhwwbHS7PqO77rfujLsb7LO1a9e2aLnYRqXuBsawMdmANd/0uDXe/VMwFRcXq9g2Poejv87OzkaAvo8d0/r12xlYytfDh7e9eyCthd29qCJ/2N39O3YH6PL87cdtytqVkmqvF1ZmkwkWaxDOKg+W/rJdHTdJqbY6V1DY/OoKD35bVFdzivqEMWtaECgrqcXcGWsxer981didIk/Tgupz9DqstM7RW2qymJDZO6XJ30ukgH5zZh+4Vv2EoLMuKD2WZTzUdgoC+Rc/AJM9CUN3kwHqS82Ay2qHMRSA0bzrdwp6fTBY7XCkZsAahWOkuxxrbPI+a913mLfxR2yqKIA/VHf+7Wz6JOXiX0ffAqspuhmx3WW/9SQccbzPWlourUX3u9dffz22bNnSYNqyZcvqrV46rKXGxIGWwmSC6667Tgm3cNfk8uXLVaYoa679/PPPDVw2CxcuxMCBA5GZmYkRI0YgKSkJP/74Y/386upq9X6+VxDiHb0WmsVq2OWgpkCzWEwIBEIwW4z1gk3H5fSp0h3hwsbvC8Lv1cA2nrW1PqxdUYy0dIf6bJNKQAgp8cZnvuZ0Fs5lHbZI6AH9DOA3OpJgzshV2sy5ZC40Z6n6O9aCjQKz318fhiNvGKyZfXYbh6bXhgs4y3bUAQv/rJCabunVVy3X06FQ+237Mjy/6E1c8dkdeOWXd7C2fGOXEWxjskfg8ePujLpgE4SuSossbZ9//rmKOdML1lJEnXbaaaqdFbsi6HA6C9y2FCYZHHLIIfjnP/+pHswmZWYohRdrtTE27cUXX8Ttt9+uaq+xmC+zS1mrTff/nnPOOSoxgoV3WT6EddpooTvyyCNbvzUEoYuhuzTNTVyT6mVcI0FXXeWBj3UumoHu1vKSWhxw6EDU/uSFz6fBZjMplyHLcajXdrPqdBApoSFSs3f2DPWXbAJ8rpiLNV2IUrC1JtOToo5lPZglyu4LddmjNpXVSsFmcqQgefShcV+vrb1i7Z0/PsV3m35CldepXncV2Jh+XN5euGCf05BkbV9GsyDEG212jza+Q20rjz76qCr7ce211yrBt99++6lkBAaREoo2xrmdfPLJ6NWrlyrqy791rrrqKuUmveOOO1TiAi1sL730kopLE4R4h7FnjEELapGPt/qpO47HQECDx+3frWDT0bSQsrRNOWFP/PDtOlRXuhH0B9Q60zMTm21N1bjZu+oZWrQRgdodPSU7oJTHsLumt0lcUeSxrEe9W9dZody6ttwhSrDFe7mP9kCh9vqv76PC2/Zkrlha1u6YdHVnD0MQOo1OjWkjycnJKiNVz0ptzJgxY/DOO+80+X7G+Nx4443qIQjdDbolKdzKS10IOUK7BISzjprZbEDAH4TH44Wrxl/fI7ylFGyqxEln7d3q1lSNm70HPbXwV5XtbFIeQ7E2yzIaVzRxzmgpFGbW7AHdriNCW/AEPPh81Wws3rwE66o3oatxSP8DxLImCF1BtAmC0DQUTczynPnZSlV/zZ5gUX1BGavGxu42mxkjRufit0VbUFPdtiw+vdFBa1tThTd7D5ptqF33OzMnEGvBNrxRK6r2QIHGOLiezEs/v43Z679Xzdi7GinWJLx48sOdPQxB6DKIaBOELs7IvXKwdds2bF3vR001a63p7ssE5b7ctL60QcJBa0nPan39NUKrlDkzD7UrFiBQW9EhnQ0o2ITo8dLi/+HrdfNi7cludauprIQM3Dbpb+id2Lb+toLQXTF3RIqqIAjtgwVyJ08Zi6Ltrgbuy7mzVuGXBQ0zu1sDXasTDm5b/Bbj15xrfkWopmJnQkSUYaheQAN+sYzG2e10hwp1MKlgadEqrChcgxnr5qGrkGRJxPMnPQizUWwJgtAULT46nn76aaSnpzeYxt6gaWlp9a8rKrpeGxNB6C7o7kvWbmPs2bv/XYzVyxrWMWwtY8f1g9ne+ouke9sabH71Dphj6FKj8XDAjszQETFbS89LMvhk5dcoqimFO+Dp7OHAAAOyE3vhzsl/R1ZC5LIygiDspEVna2Zyrl69epdpq1bt2iKkcR9QQRCixx+/bFVZniWF1e2O9997fD6OPbXp6vHBYADuDUuh1ZSp8iDmrFy2H4Fn60qUz58Oc6hrZoYKTQu2//72Hjz+hvU1O0usHTt0Ms7d+5Rd6gsKgtBO0TZ79uyWLCYIQgxZ8Xuh6mJQW+PTK3y0DQNw6BFDcOjRTduvnEvno3LBRwhUFiMU8O7ICA2BqQ6mUEy6UCn4vZakTcEpf/+/GK2h++PTfJi/cRFKayuQlZiOiQPGKWH04bIvUeN1wWI0w6s1bA/YkeQl5+Cho26VgriC0AYkeEAQ4gC2lPpp3kbUONt3sWVnhYMPH4aJRwxtVrCVzXoVIZ9blfSgmU0vlBvLEwZ1ofHEO3HKXnvFcC3dmw+Wf4XPV82Cy+9WdfMYd/zWko+QmZCBLc7tapmAFugUy9rE/uOkbIcgtBMRbYLQhVHxa2vKMPezErhq2l//bK/9+jYr2OgSpYWN3QFCbB8XJthiCd2hQ/7xbsSm9ULLrGtPLHgZi7cuQQghWIwWmIxGaOxa4atVj87AZrJi2jG3SxaoIEQJEW2C0IXQkwyYIVpa4sTa5cUoKappnzs0DDaQbwrVlur3ucolCmbwBd1RW2/zhXL3wRVTb4/tiropLLD8+q8f4Jv138Gj7YxV8wX9yg3KB//uaDLsaXj02H8gwRKfzbsFoasiok0QuliSAVtJ+X1au2qvNUV6r4YX0dpaLz78/AeEitZjoHEr+ppK64rlhoIdVnctWoVye1rZjlnrvsNnq2ahuLYs4jIslktLW2NYniVWu5Zxas+ecJ+4QAUhRohoE4QuIthmfrIMPl8QVpsRblf0BRvLKk6eslMiPffCp7BsXoh+plL0NlXDiBBKQmakmkIwSqHcLgnFNNtNfbF6NsrdlcoVurvlGxOrXUuxluHIiNGnC4LQatHGOmxbtmzBwIEDVc/QxtTU1GDFihWqabsgCC13idLCRsGWlGxFZYUrJusZOToXlgRLvWDLLvgGiSYPEg1eBENGuGGCAz4YY+gTpVh717IX/jH1zpitoztC8cV4NdZYW1e+KaIFrbO6F0w9/AYMyejX2UMRhB5Bi0Sb3+/H3Xffjfffr7szNpvNOOuss3DDDTfAat2Ztr1u3Tqcd955SrgJgtAyGMNGlyj9VmwMHwsLGwXbaefvW+8SpYUt0eRFZdCBDHMtvCEzjIYQEoz+mFvX/hGzNXRPNlRswccrvsbvRcvhYkZvJzedSjEm4MXTp3XqGAShp9Ii0fb888/js88+w3XXXYdBgwZh1qxZeP3115U4e+6555CQIMGmgtBWmHTg8wWgBdp3MbZYTTj48MHYf/9+mDtnLSpKXCqGjS5R3cJGGMOWbayEK2RBqsGNRIMbRmMIscrbpOFuhnEvXCHWtVbh8rvwwk//w++Fy+EKeFQJj84WbEQEmyB0cdH26aef4sorr8Qll1yiXh9++OGYPHkyrr/+ejWdoo7WN0EQWs+GNcXtFmxknwP64eDJQ2EwGnDUiaOaXC5UtA65piokU6zFsH0wxdrPxjyccNHVuKLP4NitqBu6Qh+c+zR+K1qGrgRrrb1zxtOdPQxB6NG0qLB5UVERRo1qeBGYMmUKHnnkESxcuBC33nprrMYnCN2aeTNX449f6oqetoeBw9IxZr98Jdiao2bLWuxZORepxtgKNpZ4+yLnEux33lQ4RLC1yhV69RdTu5RgY9za/VNuFcEmCF2AFpnHcnJysHTpUowfP77B9COPPBI33XQTHnzwQfTq1QtHHXVUrMYpCN2Ogk3lmD9rTbs/JznNhiOOG4Xc/NRml3t35grk/DANeabYJDqEx67NGzsVx+3XF4Pz02K6ru4m2P7320coqinp7KGosiBnjD4Rp+xxdGcPRRCE1oq2E088Ef/5z39UtfLDDjsMAwYMqJ93wQUXoKCgAC+//DIWL17cko8TBPT0bNHFCzfhh9lr2+0WzR+QhguvOKhJCxsL5vrLC/H1vGVYs+gX7JtYgVgLtqQrXsPFmYkwxtKU103coIU1JSq5wG6xYc6GBdhYVdDZw0KaLQVP/ele6Q0qCPEq2i666CJs27YN//rXv1TJjzvvbBhQfMcdd8DhcODFF1+M1TgFodvUY5s3cxXKittu7WIdtxGjc3Hsn/aENanpC6u3cD2qf58D58YVyCveiuEJOyvmxwKpvdY6q9rcjQtRULUdfs2PIIKqwXugk0p5OEx2DM7sj6smXIA0u1hHBSGuRRvLetxzzz0qe5S12CLBpIRjjz0WM2fOjPYYBaFb8OuCTfj0vT/a9Rl9B9GydvBul6NgK/j6LTgLC2D1lSPFoCFWsWts9P6l1YBrp74Xk3V0N6sai+LSqlbjq0WGIx12sxUlrnLV5F0LxmY/RWJQaj/s33cvHDd8Muxme4etVxCEttOqlM+0tDT1IE6nU6Wgp6Sk1M8fOXKkegiC0JDXnv4Bm9aVt+sz8gckt0iwBQJ+rJkxHd4tq5GE2pi1PaFYG3rndBgMRoyI0Tq6A5uqCvDjqt+VVc0X8CmBRhE3JGOAKuHBZu4mgwl2kw2uoDvm45F4NUGIX1p1Pmfx3BdeeAHffPNNvcUtMTFRlQChC3X4cOkiKAiN+e8zURBsAy0486LmO42wV+mcWT/A9/uXGOBdgzSjFtNyHv3++rASbELTFHlLsWTVQrg0t7KqBS12bK8pgT/oxx/Fq2A1WWA0GGA0GOEP+REMxdbSlm5LxZN/ukfi1QShu4u2L774QpX2MBqNOPDAA9GvXz9Vm40xbrNnz8aXX36J+++/H8cff3xsRywIccQ3XyzDxrXtE2yj98lBvxHNB/XP/WULZs34Dgd55yPPWASbMXZiba0ROOySh2HLGRSblXQDfJpPxazNKP4emimIPXoPg9loQpXHCS2kKTdoIKipsitp9hTU+txw+Twqti1WPH/iAxKvJgg9QbTRwkbBduihh+Lee+9FamrD0gK0ut11110qIYHu0cGDpS6TILAG2/ffbGjXZwwb1QtHnbxHs63h5v68CR998gOOCH2FgZYamGOUtMn4tQFXPonBGTliYWumi8H9c57C2vKNCIZ1LyjftAj90/LROzGzLvEgGITFZIYWDMLpdcHpixwrHA1uPfga7J0nXhBB6DGi7dVXX8WQIUPw2GOPqbIfjUlKSsLDDz+Ms88+G6+99ppKWhCEnkooGMKK37dhzozV7fqcYXv2wpkXHgCXq+lMU9fWdSiZ8QauMC9RPUZjBQXbsLs6LzM0FAzCs70QAZcL5oQE2HNzYDAau0xyQYLVgbeXfIyFBb9GXDYQ0rCuYhO8AV/dBO6rEODVfOoRba7e5684aOjeUf9cQRDiQLQtWLAAl19+eUTBpkO36ZlnnomnnnoqmuMThLipvcbG7wUbK7FuVREKNlU1s3Sk2mwNFVffQak486IDml2nq2A1Vr3xEMYGK2Mq2DwGYMgV/0ZnUbN+PYpnz4F7SwGCPj+MVgscffPRe/IkJA3qeBdtIBjArHXfYXHB7yqJwGa2Ylt1EapbYC0rcG5XCQf+YAB+BKI6riHp/XHn5GskE1QQerpoKy4uRv/+/Xe7XH5+PkpKOr+atyB0dO21+bNWo6LM1YJiuaFmptcpr4FDs3Du/zXsPtKYdXO/hO+715AS8iNWBA2AyWxD1vBxsGb2QWcJti1vT4e/uhrWrExY7XZoHg9q1q6Dt7gEfc88vUOF23ebfsL0pZ+huLZMWdqYQGAxmuHRWl4DrzXLtoRsexYePu52EWuC0ANokWhjWQ8Kt93BZTIyMqIxLkGIG8H22fTf4fe1JIC8aUFnNfgwIEfDyZedAFuyvcns0F9WFWPbjzMxcNMnsBuia6kJx2C1w2SxwZ6Vj7TxJ3R4DBvdoe6t21DwwUfwlpYicdDAenco3aOmvg5leSv+di4SBwzoEFcpBdtrv05Hjc+l5LXNZFVWt2iLsNZgNVjw5In3dtr6BUHogqJtn332wUcffaSK5zbHBx98oJYVhJ7iEp3x8dIWCramCGFM6jqMG+JUQsDgYx22Pg3aUAUqtmPDinX4cNZGVJZX4LKkWTETbLWsx5jVByZHCqy5g5EyZlKHZ4nq7tCaNevg2rIFRosZIX8A9rxcWHbUhTQYDMry5t68RcW6OfJiawmkOPtk5dfwBLwwGYwq+7MzxRqhm/W/pz3eqWMQBKELirbzzz8f55xzDp5++mn87W9/i7jMtGnTVOzb//73v2iPURC6HJpPw0v/mQ9XTfvck8OSNmNsylL4y8wwOZIR9LoadDWo/PUbbFv2G/JcLuQGQ8hMcsISo/i1zOMuR9++IxD0eWC0JcDSCVmi4e5Qo90Ko8UCg82qXmseNxIHD6oXbiabDf6yCpWcEGuWFq1CqatciWhfMHYWzt1hNVqQZE3EnZOvQp/k3E4bhyAInUOLRNu+++6La6+9Fo8++ig+//xz1TQ+Ly9P1WnbunUrvv76a2zYsAE333wzxowZE/tRC0InMn/WGsyfuUa5K9uKARoyrE6MyiyA0ZKAoNsJbYdbUhds66c/AUP1diQENSQaQzDFUD+ZkzNg790P1qx8dBZ0idLCRoHGRAPN5YLBZFRWNWNSIrSaWni2bYclOZmmNmher0pKoLs0ljB27ZdtS1UtNXYw6Awy7Gl49sQHOmXdgiDEYXHdv/71rxg6dKjKDm3cGH7s2LGqU8LBB+++xY4gxHvttbkzVqsis23FjABSrG5kJ1QhxeJSAkRPQ1CVIEJBrPnoJVirCpT7lNNiae8yp2TAnJwFoz0JnQndnIxTo9uTQs3E2LXERASqnTAlmmC02xCoqYHmcsOY4ICvtAxJQ4eo8h+xKuPh9nvw2apv8Mu2PzpcsHG/90vNx82HXIashKwOXbcgCN2gjRUtbHxUVFQoCxt7j9LiJskHQk9g68ZyZWVrj2AzwYdkiwuJZi8GJ29jYByCfi+MFhtMCcnKNekq2gJT2VpQyimxFsNyHpZefWE0mWDLH6bcoZ0J3Zws6cEMUULhxli1Gs96aLUu5SYNBjT4a5wIlpUpN2nvww6NWhLChootmLl6HhZv/x2egE/FrplCRgQMQfgDscvSjcSY7BG4Y9LVHbpOQRC6Pq3uJe3xeJCenq4eOqzWLo3ihe7Mti2V+Oid31tQ0qNpzMYA0h1+9LI4McixHhmoQEgzwpSQAlNSep2lzeLA7Ddexx6h2MZNFRmAgYP3RdBbq9afPPrQTu9yQDcn3Z0s6aG7PCnMkgYPUpmktLiFAgEEPT4kDRuiBFu0yn2sK9+Mf377OGoDkRu2G2OpnMNIsyXj0WPvRJK1c62egiDEuWhbtWoVbrvtNhxxxBGq0K5OdXU1Tj31VOU6ffzxxzFw4MBYjVUQOk2wffruElSWtS3g3WY3Y/io3hhqWgpD1SZk5mQC/j4Iab1gMFlUHJu/fCvcSX3x6vMzcXrwp5hY12ghrDUCKbYE5FnsCAX9sPUZogRbV+gjSjcnY9lYg40lPWhp04WbKSkJrnUbYM/rg35nnK4scNGwsNEduqjgdzy54GX4mxHK4S2pok1uUjaGZg7ABfucJmJNEIT2i7aCggKcd955sNvtu4gyi8WCm266Ca+88opqY8XSINnZ2S35WEHo8hmi875ZgyU/F6DG6YXRZICmtfzinZ6VgIMnD8aYvfNhsprgLUxDxfztCFRshTk5E0ZHEkJ+rxJsroAZ/iWz8GdzKCaCzZzWG6GAD73zhqqeVEx8SD3gRCQMGtPpFjYdijB2OWDRXD22jRmiTDhg/JqtVxbyTzkRCX3zo9LQ/cPlXynBtr26CAGVBtKxpNuS8Z8T7ofZ2GqHhyAIPZQWnS2ef/55pKWlqXIejePXHA4HLrjgAhx33HE4/fTT8dxzz+HOO++M1XgFoUNg7NrCuevhdu2MZdJaaW25/NpDYLbvPMRozUqfeDqcS+bAX1qAkLMCBrMFlUUlsHnLYIvRtdvoSKwXRUZa9hzJCPk8MDmSuoxg06G7k10O9LZVLOlBlykTDqLlDv1g+Vf4ZMUMuAIedAZmgxlP/eluZDgkFlgQhBj1HmX2aHMJB7169cJFF12EN998s5VDEISuxdwZKzFv5tp2JRykZTpQ5fQgMywjk1mhBosdtuH7QXPXQvM6UbJmFRJDu+9Z2VaMVjuM1gRlZWPsmsGWgJDPrcQia7F1RSjM2OUgWg3iwzNCv9+0GF+tnQMtpOk92zu0GO4Tx09Fmj2tA9cqCEKP7D06YMCA3S43bNgwFBYWRmNcgtApzP16FeZ+vbbN7zeZDOiVkwyrzQyPe2eMFOuu0cJWu2IBtJpyNY2CITGGqoEhYQaLDSHND6PZAktGXTHWgLMMttwhnZ4t2hwUaO3tckCxtnjrEvyweTHK3BVKhK8r3wiN4ln9x33QMbLtxZMeQoqtriiwIAhCTEUbLWwt6T3KUiCpqaltHowgdLZLdN7MNS0XRAYgGGRTdQNS0xxITLEhIzMBAX8IbpcPdoe5XrBVzJ8O1+ZVCHmq6j4gRlqB43GagHR7IkIeF0Jsu5SUDktmPgwmM/xlBapFVVfIFo0lLN/x0fIZ+K1wmWpBZTFaYDaYlGBDvViLfUbopRmn4KCDDkJCF7VqCoLQDUXb/vvvr/qKMm6tOZiEsMcee0RrbILQoUkHjGHbcU3fPYa6bEwKswGDs5CQZFWTWbuwuqoWffJTkZLqwC8rC4FFnwDb1iMlUBVzmZA89lCkmizQnOUIVJXAnJGr4tdCnloEAz5lYesq2aKxFGzPLnodmyq3Iog6q1ogyIjEhko52lY2i8GMrMQMDM0cqDJBjQGjKockCILQoaLt3HPPxVlnnYUHH3xQtbOy2WwN5vt8PlXuY968eSppQRDijSW/FjRIOtgddodFCbDkVDtMZiOCwRD8Pg3VVR44HBZscnvx14dmweoqwZ9ty5FvKlOWuVihLH+OFJjMViUcgz4XHIP2Qsbh5yJQUax6mnZWP9GOgha1JUUr8eavH6DAWahEmaGDHKGH9p+Ay8ad3SAT1BWIfU9UQRB6Fi0SbaNHj8att96K+++/Hx9//DEmTJiA/Px8aJqGbdu24ccff1Su0auvvhoTJ06M/agFIQqEgiGUl9Wq2LOCLRUtfh81z5575aLvwExs21yJkiInnNVBmCnerEbM2VyGrTVetewpjj/Q31wWUwubEoP2JCQO20+JM8as6S5Qo9EMa2b7YsO6IuHJBTazFXM3LMT3mxej2uNsUG+Ngs1oNCBIxdaezJJmeOO0f8NqqrO0CoIgxJIWFxn4y1/+ghEjRuCll17CN998A6+37qKUmJioeo4yc3SvvfaK5VgFIWpsL6jCLws3Yt2qUvi9Gry+llvZxh08EEf+aQ8YjAaMGtunXvgtWV+Kt2avQmVtnWiYYluIg+1sRxV9AgGAUaYDB+0Jc1YfhCqLESgvVFmh3dkF2ji5wOmtRUltGfzByIVx6R5lnF8ssBkseP3PT8TmwwVBECLQqspQ++67r3qQ8vJymM1mpKRIRpQQf4Jt+qs/obKi9XW6+g1Iw1En7ln/msIts1cSAoEgvpz+K2p9dQrhvuQ3kWiOTcFWCrbhU9/H8LBSIv7ywm7vAmWs2icrZ+KPwhVKpNHCyIbuHdvGvY5EswOvnPpoJ6xZEISeTJvLeUqTeCFeXaIfv/1rmwQb2Wtc34jTFy0tgLukALlBHy5P/gI7Ekejzmw3cOl97zeYRoHWHV2gjQXb9KWfYV35JiXSUu3JysLWkYLNZrIi1ZaMOw67EjlJXbdciiAI3RfpnyJ0a4KBIDasK0V1hRs1NV4EAgEUb99ZzFZPDmhJuFNCokW1pGrMY099iNTCxTjJVoUhpu0wx8jIlXzQabj0sLPQXQkFg7sU1A0ZgG3OIny8YoaKYePuSrDY4df89eU7Ys3QjAG4b8rNHbIuQRCE5hDRJnRb/vhlK36YvRZlJbXKfRkJijW95lpj4aYX4NdrsR1wyCDVQzScR//9Hvat+AqJ1gok84AyxsYdWnPszcgdNQrdlZr161E0+1tUblgPzeeDyWqFMbc3Vg5yYLm1CgXV21VaAd2iKUZTkzFs0SLdmoLBWQPwf+P+IkVxBUHoMohoE7qtYPvi/T/g9ez+4t6UcKubbkBColkJtolHDG3wvkVvPY9jq2fAbI5dmVZ3CEgfsS/ctSXozoJt1euvoby0ENUOA/xmA4xeDZYlm2Bea0XGhMEosltghBGegBdlLnaUiF0+7lXjL8LB/feP2ecLgiC0FRFtQrd0iX7z+YoWCTYdJdbCdMCwPXsjOzcVaRl25RJtbGFb8MZzyNjwNcwxrOVhSs5A7yH7wFu8GdatfyAUOhTdCZ/mw/z1P8L5zicIbt2GqlQrbCYbEq12VIWcqE42ItPpQ+6KIizby4ZKX3XYu6MfzWYxmvHaqY81qLUmCILQlZCzk9Dt+OaLlaiubEOiQWhn/9DTzt4HZvvOw4PFc7eX1aKozIkPX3sX5yTMi5lgozs2Ze9JMJos6rUxOQOm0iJolUVA4s4G9PHMB8u/wuerZsFUWoVJW2rgtgP+oA9enw81vloYDUZYzBbUOAIwbS+BMS8JSInN6coIAx499h/ok1zXm1UQBKGrIqJN6FYs+bkAi77b0K7P2PuAfg0E27qCSsxctAnLfvoVEy0LcE5CaUwEG8Xab35g0oQpDaYbzFaaDxHyutFdBNt7yz5XHQyy/SGYtBACYcGAqrYakww0QDP6kaaFYPNHz7LGgrsp1mQMlZg1QRDiDBFtQrdh6+YKzPhoGTStbRd4o8mAfQ7oh2NPHd1AsD01/Td4tq/HNQmfq/i1WED3bMrogzApQmPxUMAHGM0w2BzoDi5RWtgo2IjXYoBmMsAcCMFvaaiEA6EALFpIzedy0eDwQQfhwn3+LB0MBEGIS0S0Cd2CbZsq8NHbv8HtbnlnA53ERAusdjP+fP6+yM5Lq5/OjNPnP/oD7u3rcG3iFzA3DGuLWmbofwzAnROOg7dwHSxWh0p+0FF9RJ3l0JKyYErLRrwzf+Mi1Pp29uSsTDahPMWE3uUBlKfuyAjRCYWQXBtCUaZZLdceJF5NEITuQKefwSorK/Hoo49izpw5qKmpwfDhw3H99ddjv/32U/MXLFiAhx9+GOvWrUNubi7+/ve/47jjjqt/P9tpsZH9V199BY/Hg8mTJ+P222+X4r89zCVKC1trGr7rWCxGZPRKQp++aeidm9rAwjb9m5Wo2rwGlyVEX7BRrN3vBF6b9j7YCMlbuB6BqhL4ywpgTs6EwWJDyO9VfUSNjmT4skfHbZeD8D6h6yo2IxieRGAwYMUgO5Jra5FRFYQz0YAALW9anWBz2w1YOdDeUMw1gQlGlUwSDLE5fN067CYbHjzqZolXEwShW9Dpou26665DSUmJEm6ZmZl4/fXXcfHFF+PDDz9UVobLLrsMF154oRJuFHY33XSTEmRsWk+mTp2KxYsX48knn4TVasVdd92Fq666Cm+88UZnfzUhxtSW1+LZJ75DrbP1Yo1QA6WkOZCQZMWY/fJVSyqydF0h3nr5IyQEt+PahKVRd4kydm3YXdPxWpgIY5/Q9Imnw7lkDvylBQg5K+r7iFqGHoDCMnfcdjKYu3EhCqq2q4K4xbWluyxTkmHBotGJGLneg4xqDSYtqFyitLBRsHF+S/j38Xfhj8LVKK2tQFZiOiYOGCduUEEQuhWdKto2bdqE77//Hm+99VZ9T9N//OMfmD9/Pj799FOUlZUpy9u1116r5g0ePBjLly/Hiy++qERbUVERPvroIzz77LP1ljmKv6OPPhq//vor9t577878ekIMefjOGXDXtk2sEYvViPSMRAwcmqUEW25+Knw+DS8+/iL2dP2E8+0VMUk2YOxav78+HNFqRuFmzR6wSx9Rt9sDlK1AV7KYJVgdyEnqpbI8mxNsTDhwemuQ4UiH3WyF2+dFmbtyl2UpzErSzUhzairpgDFsyiXaAgsb6ZuSi96JvXH44N7t+o6CIAhdmU4Vbenp6Xj++ecxevTOwG/G8/BRXV2tLGhHHHFEg/eMHz8e9913n7LC/fzzz/XTdAYOHIjs7Gz89NNPItq6Ka0VbHrRXLPZgFH75KHvgHT06p0MR5IVGZmJysL27qxVWD7rU/w5cRGsJq2lWqHVFjYKNoqzpsfaNfuINraYWUwW5Kfm4tAB4zEwvW9EgcflKdjyktnA3gB3wI2tNexs0AQGAyrbUNaDgm3aMXe2+n2CIAjxRqeKtpSUFBx6aMOCoTNmzFAWuNtuu025SHNyGjZm7t27N9xuNyoqKpSljcLPZrPtskxhYWGHfAeh412irbWwhXb0DT3yxD0xZt9de4f+b8ZymL57DucmFSBWMIaNLtF4jEuLZDHzBHxYX75JNW0/bc/jdhFutMhR4HF5X9CH+Zt+isnYnj/xAaTZdyaPCIIgdGc6PaYtnF9++QW33norjjzySEyaNEklFjBOLRz9tc/nU+Kt8XxCEccEhbZCK57LtTPDrTvBbRb+HE+EgiE8++R3LV7eaAKCGpCe6cCxp+6hXKDh+5UFc1/9ciV6L3kJ+9qKoj5eTauzrj1Qk4Fn7v13nZszCvuMjdW9RUXQXG6YEhywZWfDoDdKjTK0mM1aMx+V7mr0SeytLGZBLQirwYxsRxa21RbjmzXzceaeJzRwlZZVl6PaU4MVxWvgD0W3TyjbWV2697mYkD+GRd267LEaz8daT0b2W/zh7gb7jLojvHJAlxdts2bNwg033IB99tkHjzzySL34ojgLR3/tcDhgt9t3mU8o2Di/rfj9fqxY0fkxRLFk48aNiKcfc1GBF9s2uVBb3XIrGwWbxWrAyH0dqHRuQ+WKbfXztpf78NmicvzV/zpSbW2PjWsuM/Sqs29Tr68CovJ74j4Lbi+EtuQPhEpKEQoEYDCbYeiVBdOY0TDmNrRKR4NyXxXWlKxXWZiVgaoG+8QX9MMX9GJZ7Sr8EPgRmbY6i1eRtxQ/VSzFxpotURVqGdZUDHDkYVTKEKQ5LXFzjMbTsSbsRPZb/LExzvdZJCNUlxRtzPRknBoTCB566KH6gbPER3FxcYNl+TohIQHJycnKdcqSIRRu4V+WyzCura1YLBYMGTIE3RHeifCHPWDAgHYJ246iaJsTP323CZvW1UDTgq16ryPBgsOOGYI9xu4s9+Dy+vHCxyvw68oS3Gx/DalRPgIo2Pre9DqeicE+yzEYUfHr7wg4nbDkZMNosyHo9cJfVg7zr78jZ+BAJAwcEMU1A+srN8NaY0PvxKx6S5rTV1uXkKC5oQU1BIIa/tDW4tg+h6n5S1YthBPRtX7detDfkGxNQu/EzGaTH7oS8XasCXXIfos/3N1gn61du7ZFy3W6aGPm6L333otzzz1X1VcLNw8yI3TRokUNll+4cKGyxhmNRpVxGgwGVUKCXgJkw4YNKtZt//33b/OYOAYKw+4Mf9hd/Tuyw8G3n69BSZETQRiQmGSB36+BHY52B9t2Hn3yKIzeJ69+2kOvLcIPf2xDpsGJ2xM/RVIMBNvwqe8jFtCyVbvoJwRra5HYv9/O48RqhTUpCe4tBahe+CMyR46Iqqs0Q0uH3WJDAEEkmK2o9tZgc/VW+DQ/7GYbNIMR7oAHvxUtx9qKjRicOUCJuQrvTqtcezDBhP+d8RTimXg41oRdkf0WfzjieJ+1xDXa6aKNAuv+++/HlClTVD220tKdNZzo+qSQO/nkk5W7lM9z585VRXRZ8oPQmsZCu3fccYf6HO4w1mkbN24cxo4d24nfTIhWwVyP268yP1k01VmpwWYzwePWIr4nvHlV6j65SOidWN/o/d//W4Tqgo04yb4KB1rXIqzVZZcXbCRUXg7P1q2wZmXucnDzNae7N2+BZ3shHHl9ohbPxkeiNQGbq7aid0ImttUUK8GWYHGguLYMWmjnvvC6fSgr+A0p1iS1THtIMDsw9fDrMCBt18QRQRCEnkqnijZmijJ+bObMmeoRDkUaOx08/fTTqrDua6+9hvz8fPW3blUjtNJRsF155ZXq9SGHHKJEnBCf+Gp8eOPVRSjYWNlAhVGmaMEQgh4NZrNRtZgKZ4euUxgz7dhS7MKLnyxFRrINPyzZhhxDEa5MnAmHqXUu1q4g2BQeL4J+P0x2e8TZJpsN/rIKBKIQlE+htnjrEvyweTG2OotQ4apErd+NbdVFqtMAt3OV19nk+6t9NXXdCVqJ2WDC4YMPxsX7ntnObyAIgtA96VTR9n//93/q0RwUYXw0BU2h//znP9VDiG9ef3YhNqzZtWI+UdY2PquaayFYbSb4vNougs2anYCkgRkw1HixZG0plnn8uDhxFkZZm6kP1g6x9oklDzdOZSOqGGO3wWixQPN4YI5g/te8XhitlojzWlve45OVM/FH4Qp4NZ+KWaMlL0Sr2w4VHW7RbAqNaZ0thPtuyuBDcM7Yk2E3RxalgiAIQheIaRME8vqzP2DDmvLdLxiqK9Vht7JafggeXxBGixG2Xgmw5STBzJ5TIWDDtiqk+4twTfJs9Da7o17G43nrQDw69RHciI7BkJEBe14ePJs2wdR316byvtIyJA0dAns7Mkgp2KYv/Qzryjcpi5rJYIIv5KsXzG3J+KTUa44kcwJePOXhuEkuEARB6ExEtAmdSsATwLuvL2qZYNsBRYTPG4DJYkLQZkDKoAxYk3cWWC4qd8FetQl/S/oKNmNs3KCPomOhSMs45GCUfFyhkg4Yw0aXKC1sFGyWlBT0PuzQ3SYhNNWKSu9gUO6uUIKNxXP9wfbFpTEWjt0TPNquNRONMOCJ46eq1lOCIAhCyxDRJnQaX7z/Bxb/sKlN76Vw69M/DatqvAhYjNALvpRXuTGw4BOcl7KmDVFVnRy3thtYzqPvmaejePYcJdwYw0aXKC1sFGxJg5puj7W7VlQ2s1VN3+4shjvQ9sLU4ZiMRuSn9FWWwO01xcoC2jspC9cffAkyHBlRWYcgCEJPQkSb0OFdDcrLajHrs+VYtbRhDb7WMP7QATh0ygi89NkyrCuohMUIzF+yDcdbFuIQ+5r6GLfuIth0KMwSBwxQWaJMOmAMG12iu7Ow0eX55pKP4PQ4kZGQjsyENHgDfqwr34jNVdswMnMwVpWuU3Fs0eKmgxmvamhRc3lBEARh94hoEzqM7QVV+H3RFqxcVojqyra1dCLMHs3ITIbJbMTh+/XF979txYoNZehlrMBB9tXdVrDpUKA1V9ajsQu01ufBC4vfUCU6LEazKo5bak1Aqi0ZtT43yt3bsbF8S1QF25jsERiSOTBqnycIgiCIaBM6ULB9Nn2Jem4P9gSLEmXJqTZ4PAE89tbPcLi24VDH7zjIXBC1HzSTDQqCdozccwy8hethy2ne9dgVCC/VUeaugAFGVUetzFUJl9+FREuCcoMyI7TCXYWimhJYTVY4zPZmS3i0RbDdMenqqH2eIAiCUIeINqFDXKKzv1jZbsGWmm6H2xVAemYCZizZihnPf48J1lU4OfEnmE1RG64SbGn7TUFqKAR/WQGcf8yFNXsADF3YvRdeqsMfDMBqsqi2T27Ni1pfLbQQe0qwdEdIdTXwBnwqs9NsMMNqtSjB1x6YWNAvNQ93Tr4aSdakqH0vQRAEYSci2oSYU1hQiXWrStr1GSazAW6XH1arCVWWIIy/folbUpahl8kTVXeoLtj0jE1zcib8JVvgLy+ENTM6nQaiTcNSHUCaPUWJsCpvNTwBr7KksThuqXvXDF1XwA1frR+mHRmkrYGCb5/cPbF3n1GYOGCcstoJgiAIsUNEmxD7DNEFbcsQ1WFJMrPZhLSMBKwtLsAhpd9jREJh1LJDmYnKxx/BVEwcN67hui02hJwVCHqj2wA9WoSX6qAlLcFiVwH/fLA3qMvvUR0KmiMQCsACS6vWOyC1L+4/8iaYjXIKEQRB6CjkjCvEjE/f/Q2//ljQ5vfbE0zoNyATvfsko9SnYfb8hTgr6XvkmaujZl1jokHKsL1gdCRhom3XbgIhvxcGswXGCPO6gmBbUrgCq0vXw2y0KHcnC+KyzlogqCnBFt4btDn8IT8STQ7Uak0XIraZrDggf29csM9p4gIVBEHoBES0CTHhnVcWtaukR27fVJx45l74eOFGfPjdeozQVuOKlB/hMPqjKtiG3TUdZTNfhbdwHSzWXTsNBJxlsOUOgSWj7Z0GYoFec42CbWt1ocoK9QZ9qluEBk3FrAWCgVZ9ZpI9CQa/CTURLHNDMwbgvik3R/EbCIIgCK1FRJsQdd577ec2CzaWG8vuk4qxhwzEA2/9jLVbq7G3ZT1OSVgUdcGml/JIHjMJgaoSlXTAGDblEvV7lWAzOVKQPPrQLpWEQMH23rLP4fTWqGQDukFpddM0Dc6AE0aDqU2JBS6fC/mpObAb+8IXDKDW70KvxEz837i/IMWWEpPvIgiCILQcEW1C1PC7/JjxxVIsX9K25uws4zFiVA6MGQ78691f4fEHVffKw+3LkGT0xaz2Gst5pE88Hc4lc+AvLVAxbHSJ0sJGwdaVyn3oMWwUbHnJOSpeje2mPBSZO1yhLXWJNsZkNGFQRn/VIWFget8oj1wQBEFoLyLahKhZ15b/sV21KmoLe+2Xh3KrEV+uL8KG72vrpw81FyLfXBE1sfZ44HA888+/7TKPwoxlPZglyqQDxrDRJapb2ELBYKu7EMQCFs1lu6kMRzqc/lpsrNiiug5wnIZQUMWztZUbJ/4NgzP6SucCQRCELoqINqHdvPHsD1jfiobv4dgdZiTlp+B/S7eh1rNrDNZk2/dREWv/tRyF+6b+Fc80sxyFT6SyHjXr19f3+wz6/Krfp6NvPnpPnrTbfp/Rhl0O2DfUZrZgS/k2VZMtxZYEl9GNao9TFdRlQkJbCuIOzewfkzELgiAI0UFEm9Au7rnhszZb1/oPTodlUBre/WYttAg64xTHJxhmbTqbsTWu0Pva+H4Kti1vT4e/uhrWrExY7XZoHg9q1q6Dt7hENXDvSOHGtlRs9F7pcaLW51IZnSz1wRppLL/BXWGkcAtqoN1NFdTdzQ6SDgaCIAjxgYg2oU0EPAHcf/tXbX7/3gfkY+i4frjlP/MbCLYMbMWtSd+ohASTqXP7htIlSgsbBRsta3pmKd2jpr4OZXkr/nauauDeUa5SNl7PT83FsqJVqh0VkxDUmIwmJdq8mhd2sx1mi0l1PlBjDkWWbanWZDx23J1SvkMQBCFOENEmtKlg7s/tKJg7fFRvjJjQH/e+tLCBYHsk+b8wR+EXSbH225AzccY5p7frcxjDRmFGC1t4KRDC15zu3rxFLddcA/downgzJgpsrtqGElf5/7d3J+BRlWcbgJ9ZspIECEtYlR1UVgUMKouoSNVqBWtdwJb+WlHrrtgWFQW3uldbFauUnxYrILXF3w2xooKyiYIoi6yC7IGE7Jntv54PTpxMJslkIZkzee7rykUyc5I5c84M5533/b73M609mG3j5AOn0wmX32Vm2DKAY6NdLl9V4js669a6nZMMJg2dqGBNRMRmFLRJzVY4qEFJlHHPSX3aov/Irnhk5goczi2p84At1xuH/g+8jp61/1Nm0gHHsLEkGo4rIQGerMNmu/rEoOvqvj/D9JX/wP78rKMBm8OJ5olNzfi2I8W5yCrMNlm4dqkZSIpLRKemHdC9ZWf0zuipVQxERGxK/3tLtUqiX674vkYB28AhHXHeBSdjR1Y+XnpzLQ5kHx2rlo69+EPqwloHbMyuvd92DG65/mrUFZZBOemAY9j4fShfcbG5P9x9x1vX9BNx/aDxeG3NmyZI42zSpgmpKPF7TFPdZolNMbxzJrq16GRKqpoRKiJifwraJGKfL9kKnzdwtM5WjcDt5L5tccFl/fDdzsN4fu5X2Lb7SJ1l1xisLeowBjdde3WdZNeCsa0Hx7Jx0gHHsIWullByMAsp3buZ7RpC1/QTMK7/GNO3jW1A9uUfMJMUurbopF5rIiIxSEGbVJldY7B2+GAh9uzOMbdVJ2br0j0dF/2iH158Yw0+XfMDcgs8dRKw8fGXFKXjmqkvo6ezrtZJKIuTC9jWg7NErbFtLIkyw8aALS4tDa3PHt4g/dosDMxObNbe9G9jOxDOLlVmTUQkNilok0rHr7EcarJrQQIBzux0wOerInRzAHmtmuCqKe/Ac+xvJKEQ01Ln1TrD9mlRMwyb+AScxylgs7CdB9t6WH3aOIaNJVFm2Biw1XeftnAYoHHsmoiIxDYFbRLWgjmr8dWK3T/eEJJeY8BWWeDG+1qc3gFvf7a99Lbfp/4LGe7yi5HXpCQ6bOJT6NqhGeoDAzO29YiGFRFERKTxUtAm5fz5sQ9x6EBIU9swsRkDtmNtwEo5XcDNdw1FQloTXHHf2+a2BBTjwdR5SHRXv1N/uICt+/1vHPcMWygGaPXV1kNERCQcBW1SxouPLy4fsFXCCtjcbgf6Dz4BF4ztY36e/MISxPmLMDV1Ppq4a7aAebneax1+hl9cOx7RjrM31+3biOzCI2iWlKY2GyIiUid0JZFSRTlFOLAvgvLlsVJpm3ZpaNO+KZq3TMKQs7rAnXj05TR30UaM2j8Dv25+dOJCbTAozLj0NqT2Hlrns0OPhyU7VmLBhoU4WHDYrFjgcrrQMrk5Lu41CmedOKihd09ERGxMQZuUmvfP1ZFteCy7dlK/Nhh6bg/4/QHsycpH7r4S5OQWI2PJH9HBnVNHpdA5cEZxlsof8JfO3Fx/YDPe2viBWaUgOS4J8fFxZjWCfXkHMeurN8z2CtxERKSmovdqKPUu51BRxNuyHMrs2pZd2Vi4fDtWrd+HI3klgKcAj9Uyw8bs2v7Ubhhy66NwRGnrihJfCd789n2s2fMNirzFSIpLws4ju+HxedE8MQ0Jx9YE5aoE8WaB9yMmA5fZcYBKpSIiUiO6ekippumJOJQV2ZJMPfu1xU1PL8burHw4EEBLZy7aO/Lwq5TFtdoHfwBI7TUYHYf+PCoDNo5Xm75yNj7//guz+oDFCSe4LLvL4USep8DsO4M1c5/DaTJvLJlyrFv/tqc04DMQERG7UtDWCOXl5eHlp5aiqMCD5JR4/HpiJlJapODnV56Kx6cuqvL3swHM/GKn+b69KwuD47dgUPwWJDt/DGJqgt1D0jMvQmqf4Uho0/D9z8KNV5v15TxkF+eWG+Lnx7GZsQHA7/ejwFOIOJcbDnMvEO+MM7dxcoKIiEhNKGhrZJ5/6GMUF/84m7PkUBGefmQxEhLduOfh0WiVkVLpZATOK/0OvtKAbXTSWnR370Giw1ursWv7B12N0zMzEZfeJiozbAzY/vbFHOR68svdF9wNxQsfEhwuk5Hz+n2IO1YKZVaOkxI4m1RERKQmou/qKMfNY5PfLROwBSsu8uKPk9/DDZNGmMAtnHz4sO5YwMaSKDNsKY7CGgdsBV4nNpx5J3o+MB9DLxyD+BbtojJgYwDG8WiF3uKItmemjaXSQMBfOlmBWTbOImX7DxERkZpQpq2R2PLNHpQUVd4vjYFbXlaeCdzY/oOzSffvz8fB/GKs85b9XY5hy3DloKmjfOYpEls9LXDegy/jaFe36MZxaByP5na64PVVHaB6Al7E+d0mBcdJCgzYEtzxpu2HJiGIiEhNRV9aQ+rcnl05mPP3yNp5zHhpmfk3sWkiMi88CV/5SsoFbJTkKEGcw4f4ambZ8r0uHBr7kAnY7ILj0NhzzZpYUBW3w3V03VVPAYp9JchIaYlr+l+mdh8iIlIr+tgf4wL+ANau2gWvp4rF3Y8pYNsOAJt3ZuP+l5Yir4LsXGEgHp6ACyUBvoSqnoCQ44nDo3mXYt5Tv4DdcBwax6NxhignFrD0WRFu0yejFwa0641EV4JWRBARkTqjK0mMO5SVjwP7chEX54THU/Xanz6nA9NeXWb6rrH9RkUO+lOxz9cUCQ4P0s30hMo9kjcGbzx1OeyIQRfHo7FJbpO4JJNBC4fzRLu16IQr+l6Czs071vt+iohIbFN5NMYVFXrh9frR46TWVW7LDNKyohKs+LbygO3otg6sKOmKvEASiky2rfLxa3YN2IhZMo5H47g0X8CPJHfisUYeP2JLjwu6n4Op59zZqAM2TrrYnbsPm7O2m3/5s4iI1A1l2mJcYpIbbrcTcDrhdDngZzO0CgK2EqvXWIR+8LXAe4V9cSi+SYV92qwJB3ZnjUez1hWNc8SZjzwM4Aa274sJp16OeFc8GrNth3fi4+3LsCtnDzw+D+JccejQtC2Gd8ps1IGsiEhdUdAW49JbNEGrjFTs3pWDfgM7YM3KnfD7ywdsXnhR4NqL3yZ9iVRnEbJ8KZiVPxxFSKwycPt3YTqWFPdCmiMP5yR+bX7/oC8Vna6+Aef16oVYwcCNy1BxNiknJ2i8WtmA7Y1v3kZucR7Sk5oj0R2PIm8Jth7agQP5WbjslAsVuImI1JKuNjHO4XSg78AOyD5cgIP789H9lNbIzs5C1l6/mZzggw9NsQ7nN1tbpt9ahisfj8XPxS5vUzyZe0mVpdID/jQcQBq25rfDQ7dmYsQJGYhFDNC0DFVZLIEyw8aArX0qmyMfLR4nxyUiyd0GP+TuNfef2Ky9WdJLRERqRkFbI9C2Q1MMO6+HmUW6d3cOigqAQqcDJY489E9ch/4J6xHvCF827eDOwV2p/6kycONlesFTlW8jsWlv3gFTEmWGzQrYLPyZt/N+btcuNTaDeRGR+qCPvY0ocBt18cn4fP8RfJ5Vgg3FJeiRsBZ9EzZWGLAFB26JKKrw/niXArbGrKCk0IxhY0k0nARXvLmf24mISM0paGtEfvPoB/ghtxhcwyDVmYvBCVuQ6Kh8lQTLNU0+rvC++Y8rYGvMkuOTzKQDjmELhw2GeT+3ExGRmlPQ1kgcyi7E3kM/Zjo4aSDcbM+KtHDlh33xvKUMW6PXJqWVmSV6qPAwAoGyWVv+zNt5P7cTEZGa05i2RqCkxIcJDy8scxtneVZHlq9J6fcuJ/DkHUPRrW16ne2j2BcnF7CtB2eJctIBx7CxJMoMGwO21IQUc78mIYiI1I6CthhVVOTFf5ZswedrdmPL7iPl7mdbjupg+w86s08b/O5Xp9fZfkpsYDsPtvWw+rQd9mWbkmiX9BPVp01EpI4oaLMBX4kPa7/chexDRWiWnoi+AzrAxdH/FXhp/hosXLEDHu/RUpUTfnR370WaswBH/Mn4ztvG9FHr6M6J6PHZ9qMYiTirb1vc88vBdfa8JLYwMGNbD84S5aQDjmFjSVQZNhGRuqGgLcp9uug7LPt4K4qLvGZ8EFsoLPq/Dcgc3gVDz+1ebvsX56/Bu59tL13SfEDcVpyT+A2aO/PhcgTgCzhw2N8EHxX1wICEXVU+fpajCTYNuB2vn9cLyclxx+EZSixhgKa2HiIix4eCtigP2D75YJNZesrldsLpdMLvD6Co0GNuJytwYzn05bfW4oNlO0t/nwHbpcmrEO/woSAQB4/fhTiHDy1debgkeQ32eVOQ4c6r8PF3OVpi+OTpUG5NRESk4Sloi+KSKDNsDNji4l1wHmtayvVDnU4HPCU+LP9kK84Y1gV/fWsd3l++A96gdUVZEmWGjQFbjp9LUR39fU/AjZyAC02dRTiCJGz1pKNL3KFyj7/L0QrDJ79Uj89YREREKqOgLUpxDBtLoibDFtJlnj/z9qJCL56evhSfbD9c7vc5ho0lUWbYrIDtRw5zO++flT8M2/Na4IrkpSYDd9CXgjN/+wcMz1CJS0REJJooaItSnHTAMWwsiVa0pqjP48fmMAEbcdIBx7CxJBqOJ+Ayfdq4XTHa4X8LRmLM2Z0w4aJ+dfo8REREpG4oaItSnCXKSQccw8aSKLH4yd6lPr8fJR4/HAhUuLgUZ4ly0gHHsLEkGoq3835uR+NG98Qvzut1XJ+TiIiI1Jzm4kcptvVISHTD5/XDH+CszwBKPD4UlXhR7PGZgM3LlQ4q+H229eAs0WQHVz0IXVs0YG7n/Vu8bfDEzUMVsImIiES5qArapk+fjvHjx5e5bf369Rg3bhz69++PkSNHYtasWWXu9/v9eO655zB06FCzzXXXXYedO3+cQWlX7MPGth7MsnFFg5Jin5loYEqmx8IwnyMbg+I3o6d7t5l4EMwPJz4sOgUlxyYdxDkY4gXMv/yZty8u6Y0nbz8bvTppZQMREZFoFzVB2+zZs/Hss8+Wue3w4cOYMGECTjjhBMyfPx833XQTnnzySfO95YUXXsBrr72GadOm4fXXXzdB3LXXXouSkvCLV9sJ23mYlh5OhlsBk11jodQBH7rGf4sr097B2OSVuKbJJ7gj9W3T4iPYl54ueLNgoJlckICjwRr/5c/vek7HfQ/diq4dmjXY8xMREREbjWnbt28fpkyZguXLl6NTp05l7ps7dy7i4uIwdepUuN1udO3aFTt27MDLL7+MsWPHmsBsxowZuOuuuzBixAjzO88884zJui1cuBAXXXQR7G5jfhFW+nxgLoyNOzKcB3Bx8kdIcpfvvcaebCg4GqxZ+P0aT6fSFRHyAsm4cPRJuG3wYNM6REREROyhwTNt33zzjQnMFixYgH79ys5cXLVqFQYPHmwCNktmZia2b9+OgwcPYsOGDcjPz8eQIUNK709LS8PJJ5+MlStXwu5mv78eby3ZZr7n2LW97L3WZJkJ2Nh77egEA8fR3mv+RNOTjb3ZwpVKN3rbYV/z/njg/l8iJTW1gZ6RiIiI2DbTxnFq/Apn79696NGjR5nbWrdubf7ds2ePuZ/atm1bbhvrvprguLGCggI0FK/Xjydmr8ZXmw/XqPcat2OQZkmIc+K5u85Es+REFBYWmtusfyX66ZzZk86bPem82U9hDJwza5nKqA/aKlNUVIT4+PgytyUkJJh/i4uLS09QuG1yciJbDD0cj8djJkA0hK+35+OdVdkoLAnUqvea5XdjWyExIQF7dmzDnqBtma0Ue9E5syedN3vSebOf7TY/Z6GxjO2CtsTExHITChisUXJysrmfuI31vbVNUlJSjR+X5dpu3bqhvi1ZsweL1u4PG7BVt/daohv43ynnlduGgS5f2Bw/WJtjJPVH58yedN7sSefNfgpj4Jxt3rw5ou2iOmhr06YN9u/fX+Y26+eMjAx4vd7S2zjDNHibnj171vhxmaJkUFjfJdH/++x75Beyr1rlvdc46YDrh5YtkR7tvcaZoXvcHTDv4Z9W+nh8Ydf3c5Ta0TmzJ503e9J5s58kG5+zSEqjUTERoTKDBg3CF198AZ/PV3rbsmXL0LlzZ7Ro0QK9evVCSkqKmXlqOXLkCL799lvzu3aydstBHMguNCsgVKSq3mtepxtdR1+B2VUEbCIiImI/UZ1pY1uPV155BZMnTza919auXYuZM2fiwQcfLK3/svEue7elp6ejffv2eOKJJ0yGbtSoUbCDI0eK8fz8L/HdzhwUFHnNMlWVMe08CmBmiTZ35ZsxbPHxcUhM74BmmZcgtffQ+tp1ERERqUdRHbQxm8ag7eGHH8all16KVq1aYdKkSeZ7yy233GLKpPfee6+ZuMAM26uvvmrGpUUzlkNvevJD7D5Q/VmqVu+1YRk5GNYrFZ1O6Yqkzr3hdEb16RQREZFaiKqr/GOPPVbutr59+2LOnDkV/o7L5cLdd99tvuzi49U78dycr1DiLdtPrTomjRuM0/u0hdsd1RVuERERicWgrTF4b+k2vPCvteWWcK+O/t1b4swB7etwr0RERCTaKWirR7c//RE2/3CkVn+DAdu0iWfW2T6JiIiIPShoqyd3PvtxtQO29LQEdMxIQVGxD+1bp+C6n/ZBSkrVzfdEREQk9ihoq6cZopt2Zlf793qc0AyTJ2Qel30SERERe9Eo9nrAlh41cfPYAXW+LyIiImJPCtrqwd6s6i9i26NjM6SlHV1nVURERERBWz1o0yKp2gHbU7cNP277IyIiIvajMW31gGXOZeveq3K7Tm1S8PD1ZynDJiIiIuUo01YPGIQxe1aZbu3T8Pzd5yhgExERkbAUtNUTljsrCtx4+zN3nF3v+yQiIiL2ofJoPQdu1gLxnJzAsW4snSq7JiIiIlVR0FbPGKCp95qIiIhUl8qjIiIiIjagoE1ERETEBhS0iYiIiNiAgjYRERERG1DQJiIiImIDCtpEREREbEBBm4iIiIgNKGgTERERsQEFbSIiIiI24AgEAoGG3olosnr1avCQxMfHIxbxuXk8HsTFxcHhcDT07kgEdM7sSefNnnTe7CcQA+espKTE7Pupp55a6XZaxiqEXU94dZ5frAaksUrnzJ503uxJ581+HDFwzvgcIok/lGkTERERsQGNaRMRERGxAQVtIiIiIjagoE1ERETEBhS0iYiIiNiAgjYRERERG1DQJiIiImIDCtpEREREbEBBm4iIiIgNKGgTERERsQEFbSIiIiI2oKBNRERExAYUtImIiIjYgIK2RmLfvn3o2bNnua9//etfDb1rUoHp06dj/PjxZW5bv349xo0bh/79+2PkyJGYNWtWg+2fRHbO7r333nLvO547aTjZ2dm4//77MWzYMJx66qm48sorsWrVqtL7P//8c4wZMwb9+vXD6NGj8fbbbzfo/kpk523ChAnl3muh70e7czf0Dkj92LBhAxISErBo0SI4HI7S21NTUxt0vyS82bNn49lnn8XAgQNLbzt8+LD5T4kX/AcffBBfffWV+bdJkyYYO3Zsg+6vhD9ntHHjRkycONEE2xaXy9UAeyiWO+64AwcOHMDTTz+NFi1a4O9//zv+53/+B2+++SYCgQCuv/5681574oknsHjxYkyaNAnp6ekYMmRIQ+96o3ZHJeetS5cu5r32wAMP4Nxzzy39nbi4OMQSBW2NxKZNm9CpUye0bt26oXdFqsiITpkyBcuXLzfnK9jcuXPNf0BTp06F2+1G165dsWPHDrz88ssK2qL0nDEA2Lx5M37zm9+gVatWDbaP8iO+Z5YuXYrXXnsNp512mrntvvvuw6effoq33noLWVlZJkNz++23m/v4Pvv222/xyiuvKGiL4vM2btw4c+6YHY3l95rKo40EP4HwPx+Jbt98840JzBYsWGD+8wnGMsDgwYNNwGbJzMzE9u3bcfDgwQbYW6nqnH3//fcoKCgwWQCJDs2bNzcfdPr06VN6G6sP/Dpy5Ih5n4UGZ3yfffHFFyYIl+g8bxs3bjTfd+7cGbFMQVsjyrQdOnQIV199Nc444wwzFuCTTz5p6N2SECx9Pv/88+jYsWO5+/bu3Ys2bdqUuc3KnO7Zs6fe9lEiP2d83xHLONyOZRtmSnNzcxtgT4XS0tIwfPhwxMfHl972/vvvm0zO0KFDK3yfFRYWmiEKEp3nbdOmTWa4D99fHPPGsYgcrlBSUoJYoqCtEfB6vdi6dStycnJw8803m08rHMjOkg0H3Io9FBUVlfkPizhOkYqLixtor6QyvJA4nU5z0X/ppZfwu9/9DkuWLMGNN94Iv9/f0LsnAFavXo3f//73GDVqFEaMGBH2fWb9HGsBQCydt02bNpn/B/v27WtK2TfccAPmzZtnJgLFEo1pawRYTuN4Gw5+TkxMNLf17t0b3333HV599VWN07AJnrvQi4YVrCUnJzfQXklleOG46qqrTGmHevToYcbbXH755fj666/LlVOlfnFi1l133WVmIj755JOlH4RC32fWz0lJSQ2yn1L1eZs6dSruueceNG3atPS9xmELHJvIiSQtW7ZELFCmrZHgDEMrYLN0797dDKIWe2DJZv/+/WVus37OyMhooL2SyjDLZgVswe87YhlOGs4//vEPU3k4++yzTRbUylq3bds27PuMH4w02z56z5vb7S4N2GL5vaagrRFgRo2fSJhtC7Zu3Tp069atwfZLqmfQoEFmMLTP5yu9bdmyZWbgLae/S/ThJ/xf/epXZW5jho303ms4nIE4bdo0M8aX7SOCy6Fs2bJixYoy2/N9xv9DGYRLdJ638ePHm3Jp6HuN2bbQWd12pldgI8BZo5y9xvQxZ0Zt2bIFjz76qOnzxfKN2APbeuTl5WHy5MmmjQQbI8+cOdP0lJLodP7555txo3/+85/NTNKPP/4Yf/jDH3DRRRdpNncD2bZtGx555BGcd9555r3Dmdfs/cUvThDhxX/t2rWm7Mb/K2fMmIH33nsP1157bUPveqNW1Xk7//zz8Z///Af//Oc/sXPnTrzzzjt4/PHHTR+3lJQUxApHQHOYGwW+wJ966inT04bTo08++WQzJiC0EahEDw5a/+GHH8zMQwsvJg8//LDpG8WxUb/+9a/LNG2V6Dtn7777rpn8w8lALK/99Kc/xW233VZa1pH6xZLaM888E/a+Sy+9FI899piZWc/Gumyn06FDB1OOu+CCC+p9X6V652327Nnmi0GbNXaUE+5iKUOqoE1ERETEBmIn/BQRERGJYQraRERERGxAQZuIiIiIDShoExEREbEBBW0iIiIiNqCgTURERMQGFLSJSKOhDkciYmcK2kRq0Ui1Z8+eFX6xi3pd4goI/Lu7du2qclt24f/tb3+LoUOHmkXJ2S38j3/8I7KystBYcQkwNtq08DjyePK41he/34958+aZZXhOP/10szQSG4OyGW/oIuXHE5/3888/X+PfnzhxonkexL/DvxcJNoUeMGBAuefKlT5GjhwZ9lywMfidd95pjtdpp52GO+64o9zaoF6vF88++yyGDx9uXu9XXXUV1qxZU2fvp+rgcxs9erRZcUakrrnr/C+KNCLsus0lisJpqPXuuPzOK6+8Yi4cXPKqWbNm2LhxI/76179i4cKFZsFlLord2DDI4LJEltatW2POnDk44YQT6uXxCwsLTbDDYOLKK680yyJxXUSua8nldtiF/y9/+UuZ9RSjEYOdffv2mWXVqovLeGVmZpZ5jjk5ObjxxhvNShKhGIxdd911Jqh74IEHzM9c2YVLE3E/ePyI3fDfeOMNE9y1b98ef/vb38yar//+979x4oknoj7xuXG1mXvuuccsq5SYmFivjy+xTUGbSC3/g+7fvz+ixdtvv22CMy6cHLxQOS+UzEIwq8NlsCoKNBuT+j53XO939erVJqsW/LhnnXUWevXqZQKO119/Hddccw2iVVFRkflQMGXKlBotDcTA9JJLLin9+cMPPzSvx/z8/LDbM1vN7Bxf1926dTO3nXTSSWbtVi4PdvHFF2PPnj1mvUl+QGGGzTqmzC7zvfDQQw+hvp177rkm88f9mjBhQr0/vsQulUdFjjOWfVhKrao0s2nTJrMQMktm/LrpppvMGnrVwTUueXH75S9/GTbzd/fdd5vylDW2q7i42GR3mJXr06cPRo0aZf4Gy3gWLqDNCyJvHzFihNnuiiuuMOugBl/MmQkZNmwYevfubf7eq6++WunzDXdsuA0vdLyNpbDBgwebiy7/Psu7DD5ZJuP+cN+Df48ZRGY3+PzOOOMMEwxY2/DvvfnmmyabY5VEw5VHudbkLbfcgjPPPNMEVnzuLKtarN9hwMDt+Fjcx3vvvRcFBQUVnpdDhw5h/vz5JjsVLlBkEMJ1ZDMyMkpvYwmQwTeD7b59++Kyyy4zQU6wmh4vYvaKGSE+hyFDhpjfYzawMnwO/Dtnn302qosZNWYZ+RohroHMEv6gQYNMZjicJUuWoHPnzqUBG/H7rl27mqydNRSAGTguJB4ckPO1am0TCe4PA0q+Jnfv3l3r48s1Zpnxq8+yt8Q+BW0itcQLRuhXdQe8b9u2zQRCHHPGiwEDDgZsLKNFOg7twIED2LBhg7lYORyOsNswE8HSEu/nPrJcxwvmz3/+c7MgM4MtZgiYSQn2/vvvm4CBwcnTTz9txhlxEW2fz2fuf+SRR0wWhUETg7VzzjnHlPx4ka8uLtTNiy6zgT/72c9MZor/MqPCLA8DKZbCghdlpz/96U/mWHH/WXpk6ZP7Qyy/MfhhOZu38xiF2rx5M8aMGWMCMz5PPhaPEwPgFStWlNmWx4dluBdeeMEcT+7Piy++WOFzsgKLyoId7iuzQ8TjyyBt1apVuP322824MT4eA/kFCxbUyfHiz8xw8XjxwwLLxwziKsPH5rGrSQmXAViXLl3Qrl078zPLhsyg8fXevHnzsL/Dcna4YQYsafM9Y23TpEkTc26DsSzKwLeiLF4wbsMyLAO3WbNmle5jbY4v30ssI4e+dkRqQ+VRkVpg5uaUU04pdztLXcGD3qvCC0JSUhJmzpyJlJQUcxuzHyyzMKiygo/K8CJCHTp0iOgxGWR99tlnJgi78MILzW3MMPFiygCIZbru3bub2xlwMBiz9o0XOe7T+vXrTWaNFyb+rvV3mH1ITk5GixYtUF3MpEydOtV8z8wGgwmPx2MukG6325S+GESy1BgsPT3dBJ7chgEay3csSTK4ZGaG9weXREMzYzwHvJ8Xbet5MkBhFowBKC/MFv5965zwPC1duhSLFy82570uzg0zNMzO8XkyWLMekyVv7gv3ySpP1vR48Zgwy8q/w7/NAJXBNzO+PXr0KLdPzMx9/fXX+MlPfoKa4OvNyrIRjzWDuMrk5uaGHZPGIM0KxriNdb5Ct7H22/o+HGbIbrjhBhNgMfAKPUc1Pb7c76ZNm5qAnduI1AVl2kRqgZ/ueTEP/eLYsergYHReEBgwWdk6XogGDhxoAqtI8AJCwaXNyjDQ4u8wIxCM44Ss+4MvXMEXRquMZ5XTGKTNnTvXZCtYpmSWkFmhcBmtqrBcZ3G5XCYLw8DYen7EyRW8WIeWo4K3sbJWK1eujOhx+XyZCQt+nvx7DETXrVtXJmMTWuJs06ZNpeXRmpwbHgcrYAs+N8yobt26tdbHi+c9eFwaS+OVHS8GnsysRhp4BmNW99NPPy0TtEX6exWxsslVZbWrGns3adIkLF++3AT3HTt2LHd/TY8vMWNX17NTpXFTpk2kFpgt4Biv2srOzsY777xjvkIxQxQJzgjlhSzcLLzgcUW82DDzwO95AeKFKJhVZgq+CDELGO5CaAUhHNPDwIXls2nTppkvXuw4zo2D7KsjXNaEWbuqBI8HIyvLx+cZCW7XsmXLcrfzNgYGzNhUdjwqCx6schvHSlnZy1As5fFc8/xwX8IFENb+sYxX2+MVWk60jlfw3w5mvR4i+duhmKFjRovjwqqDzy1ceZPnIjU1tcptyNquIsywMQizxnaGZuVqenyt10nw60aktpRpE6kH1tgvS2hWhhcWZnTCZe1mzJgR0WNYGQBmNCoKIFgC5OBpjpli6ebw4cPl9s3qgVXROKOKgleWmDhA/6OPPsL9999vsm1WudDKioRmmiIZbxQpPpdgfI7VCXp5PKzfCcbMVnWPRygec7anqGxgPLOUVhsN7ov1uHW9L8EfFML97YpK2tZjVhTUVVUa5eQQq0VHpDgJ4fvvvy93O29jeZdYYmVgxHJysB07dphMZVUtN/ieYBmdwdszzzyDusRjxSycSF1R0CZynPGT+t69e8vcFjwjkVga5UB4tjNg5o5fHCvGMW4ffPBBxI/FQfEck8QSZSj+fU4M4MWTGRs+JsuwoU2ArYHukWZFOJOOpUgruGRWic1jGYRas/CsbEXwceAA8tDAoTb++9//lvmZ44wYLDJgiqRMxlmMDDiDMyMMaDlYnuejNv3T0tLSzMQClpBZag3FfmKcRGKVprkvX375ZbmsKc8NM2R10XuMgVQwPk8eL74uKspkMisb+lqO9LGqWxoljgXj64SvXQu/520cQ0l8PVPw65gzNjnG0NqmMnwvcJYoxwvOnj27yqa8keIHJwaCoSVukdpQeVTkOOM4qenTp5svdmtncMExbME4u5GzRzmLjzNGExISzCzHRYsW4bnnnov4sS644AIzBo5tCXjxYbmHpRy25+DgdmZLrL5VvIhyLBpnSvLiwjImx1KxtxXH5AW3WagMMxnM8DFjwUwKL4Cc2ccWG9a4Mj4Ot2MT1FtvvdVk2Pi86jILwQ70nP3Itg0MgDjj8vLLLy8tMzJwYiaN2S4Gx6HYfoLBBSdgcBIJn4s1Pq+ilhTVwU7+LBNytuG4ceNKg2Y+JoM5vk6sVi3s7cUAjYEE94vHiYEdXzecLFCTHmmhuC8sa3NSA7/n+WBgWVFTaL6O2IqGHziCewBa+AEjFI85xzUyUK3JCgx8PXNyCbOQVtaWzXU5UcKaEMGgiK9XZstYguX+87XOLBdnEUeKx5mZYr4fghv31hQ/PLGkzFVJROqKgjaR44yBGEs3nH3JWWe8iLGlB8uJFgZM/JTP8gwHRvNTOi9MHGfD9hnVwaDMmhjAMiUDJGa/2NaDmTirzMWsCgNJXqx5weU+cpA5g4vqNgTl7Dq2jmC2jWU2ltgYADBAsy7evGjzgssJCrzQ8iLJQKSuMOBh8Mm/y+fIdiY89ha282DAxsdnjzUGBME41uy1114zs2nZH43Hh/3ROJuUE0Jqi8eAsxMZCHLsIvt/8TwzyGCgwONlDW5nNo3383jxfPJ1w9cIW4xU9/VQER4HBlM8TizPM8DhsasMg3CeRwZH/GARjEFTuNYcfE48tqFjDiPB7CYDML5f7rvvPhNIMXvG8xM8EYCvPx5ffuDg0AN+iODvVScjyfFnfL/wNcOehDw+tcFgnOeRga5IXXEEtIKyiNgcs3sMODgDUI4fzhZmGxo2aWavMgmPl1UGuOyLGC4rKVJTGtMmIiIRZ6MYGDNrHDqBRX7ENX55fDjkQaQuKWgTEZGIMRBhexc2mZXyOAmCJXY2QdZi8VLXVB4VERERsQFl2kRERERsQEGbiIiIiA0oaBMRERGxAQVtIiIiIjagoE1ERETEBhS0iYiIiNiAgjYRERERG1DQJiIiImIDCtpEREREEP3+Hz3Wh5z81PfVAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(7, 5))\n",
        "total_data = []\n",
        "for f_type in df[\"Fuel Type\"].unique():\n",
        "    total_data.append({\"x\":df[\"Fuel Consumption Comb (L/100 km)\"].where(df[\"Fuel Type\"] == f_type).dropna(),\n",
        "                       'y': df[\"CO2 Emissions(g/km)\"].where(df[\"Fuel Type\"] == f_type).dropna(),\n",
        "                       'type': f_type\n",
        "                      })\n",
        "\n",
        "for points in total_data:\n",
        "    plt.scatter(points['x'], points['y'], alpha=0.6, label=points['type'])\n",
        "plt.title(\"Fuel Consumption (Combined) vs CO2 Emissions with fuel type\")\n",
        "leg = plt.legend()\n",
        "leg.set_title(\"Fuel Type\")\n",
        "plt.xlabel(\"Fuel Consumption Comb (L/100 km)\")\n",
        "plt.ylabel(\"CO2 Emissions(g/km)\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "total_data = []\n",
        "for f_type in df[\"Fuel Type\"].unique():\n",
        "    original_residuals = set(residuals.index)\n",
        "    modified = set(df[\"Fuel Consumption Comb (L/100 km)\"].where(df[\"Fuel Type\"] == f_type).dropna().index)\n",
        "\n",
        "    filtered_residuals = original_residuals & modified\n",
        "    fuel_typed_residuals = residuals.loc[list(filtered_residuals)]\n",
        "\n",
        "    original_fitted = set(fitted.index)\n",
        "    filtered_fitted = original_fitted & modified\n",
        "    fuel_typed_fitted = fitted.loc[list(filtered_fitted)]\n",
        "    total_data.append({\"x\": fuel_typed_fitted,\n",
        "                       'y': fuel_typed_residuals,\n",
        "                       'type': f_type\n",
        "                      })\n",
        "plt.figure(figsize=(7, 5))\n",
        "for points in total_data:\n",
        "    plt.scatter(points['x'], points['y'], alpha=0.6, label=points['type'])\n",
        "\n",
        "leg = plt.legend()\n",
        "leg.set_title(\"Fuel Type\")\n",
        "plt.axhline(0, color=\"red\", linestyle=\"--\")\n",
        "plt.xlabel(\"Fitted values\")\n",
        "plt.ylabel(\"Residuals\")\n",
        "plt.title(\"Residuals vs Fitted with fuel type\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "kX6QFbf7obbn",
      "metadata": {
        "id": "kX6QFbf7obbn"
      },
      "source": [
        "From the above first plot, we can find out that for different fuel type there is a different CO2 Emissisions that is created with the same amount of fuel spent. And that most of the error seem to come from the fuel type E as that fuel type have more variance compare to others fuel type.\n",
        "\n",
        "From the second plot, we can observe that to remove the very non fitted points we can just remove the fuel type E. We can also see that for fuel type, other than type X and type Z, the model seem to be failing to predict often. This could be explain by seeing that the model basicly have only one main controlling coeffcient and the other fuel type (type D, type N, type E) have a different rate of change that dictate what the line for that type look like."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d-gXMvqjofbx",
      "metadata": {
        "id": "d-gXMvqjofbx"
      },
      "source": [
        "Now to test for *heteroscedasticity* we now need to first remove the very non fitted values, create a new model with the remaining values and test the new model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "M6T6CbBb4n8-",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M6T6CbBb4n8-",
        "outputId": "cb76ec90-ee91-47c2-c9b2-0608edeb5714"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                             OLS Regression Results                            \n",
            "===============================================================================\n",
            "Dep. Variable:     CO2 Emissions(g/km)   R-squared:                       0.998\n",
            "Model:                             OLS   Adj. R-squared:                  0.998\n",
            "Method:                  Least Squares   F-statistic:                 4.664e+05\n",
            "Date:                 Sat, 04 Apr 2026   Prob (F-statistic):               0.00\n",
            "Time:                         22:03:47   Log-Likelihood:                -39669.\n",
            "No. Observations:                17194   AIC:                         7.938e+04\n",
            "Df Residuals:                    17171   BIC:                         7.956e+04\n",
            "Df Model:                           22                                         \n",
            "Covariance Type:             nonrobust                                         \n",
            "==========================================================================================================\n",
            "                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------------------\n",
            "const                                     31.3612      0.178    176.462      0.000      31.013      31.710\n",
            "Engine Size(L)                            -0.3426      0.040     -8.622      0.000      -0.421      -0.265\n",
            "Cylinders                                 -0.0845      0.029     -2.945      0.003      -0.141      -0.028\n",
            "Fuel Consumption Comb (L/100 km)          23.3582      0.015   1528.956      0.000      23.328      23.388\n",
            "Vehicle Class_FULL-SIZE                    1.0746      0.090     11.912      0.000       0.898       1.251\n",
            "Vehicle Class_MID-SIZE                     0.5371      0.071      7.596      0.000       0.399       0.676\n",
            "Vehicle Class_MINICOMPACT                  0.3191      0.102      3.141      0.002       0.120       0.518\n",
            "Vehicle Class_MINIVAN                     -0.1486      0.158     -0.941      0.346      -0.458       0.161\n",
            "Vehicle Class_PICKUP TRUCK - SMALL         0.5777      0.140      4.121      0.000       0.303       0.852\n",
            "Vehicle Class_PICKUP TRUCK - STANDARD      0.2759      0.096      2.877      0.004       0.088       0.464\n",
            "Vehicle Class_SPECIAL PURPOSE VEHICLE      1.8006      0.273      6.585      0.000       1.265       2.337\n",
            "Vehicle Class_STATION WAGON - MID-SIZE    -0.4239      0.147     -2.886      0.004      -0.712      -0.136\n",
            "Vehicle Class_STATION WAGON - SMALL       -0.1164      0.104     -1.114      0.265      -0.321       0.088\n",
            "Vehicle Class_SUBCOMPACT                   0.2469      0.079      3.133      0.002       0.092       0.401\n",
            "Vehicle Class_SUV                         -1.2502      0.081    -15.376      0.000      -1.410      -1.091\n",
            "Vehicle Class_SUV - SMALL                  2.0532      0.085     24.122      0.000       1.886       2.220\n",
            "Vehicle Class_SUV - STANDARD               2.5956      0.106     24.553      0.000       2.388       2.803\n",
            "Vehicle Class_TWO-SEATER                   0.3235      0.095      3.422      0.001       0.138       0.509\n",
            "Vehicle Class_VAN - CARGO                 -1.6000      0.192     -8.335      0.000      -1.976      -1.224\n",
            "Vehicle Class_VAN - PASSENGER             -1.2847      0.209     -6.157      0.000      -1.694      -0.876\n",
            "Fuel Type_N                             -102.8842      0.481   -214.068      0.000    -103.826    -101.942\n",
            "Fuel Type_X                              -32.4901      0.159   -204.833      0.000     -32.801     -32.179\n",
            "Fuel Type_Z                              -32.3936      0.164   -198.017      0.000     -32.714     -32.073\n",
            "==============================================================================\n",
            "Omnibus:                     4824.954   Durbin-Watson:                   0.674\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            75212.617\n",
            "Skew:                           0.928   Prob(JB):                         0.00\n",
            "Kurtosis:                      13.077   Cond. No.                         348.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ],
      "source": [
        "filter_df = df.where(df[\"Fuel Type\"] != 'E').dropna()\n",
        "result_filted = y_train.loc[list(set(y_train.index) & set(filter_df.index))]\n",
        "\n",
        "X_new_train = X_train.loc[list(set(X_train.index) & set(filter_df.index))]\n",
        "new_main_train = X_new_train[main_spec[\"num\"] + main_spec[\"cat\"]].copy()\n",
        "new_main_test_dm = pd.get_dummies(new_main_train, drop_first=True, dtype=float)\n",
        "\n",
        "new_main_test_dm = new_main_test_dm.reindex(columns=new_main_test_dm.columns, fill_value=0)\n",
        "\n",
        "X_new_train_sm = sm.add_constant(new_main_test_dm, has_constant=\"add\").astype(float)\n",
        "\n",
        "y_new_train_sm = result_filted.astype(float)\n",
        "\n",
        "new_ols = sm.OLS(y_new_train_sm, X_new_train_sm).fit()\n",
        "print(new_ols.summary())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "mq8KMlmwobAU",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "mq8KMlmwobAU",
        "outputId": "8233feb2-a62a-4a0f-82de-295ce9f2f55a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "new_fitted = new_ols.fittedvalues\n",
        "new_residuals = new_ols.resid\n",
        "\n",
        "# filter_fitted = fitted.loc[list(set(fitted.index) & set(filter_df.index))]\n",
        "# filter_residuals = residuals.loc[list(set(residuals.index) & set(filter_df.index))]\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.scatter(new_fitted, new_residuals, alpha=0.6)\n",
        "plt.axhline(0, color=\"red\", linestyle=\"--\")\n",
        "plt.xlabel(\"Fitted values\")\n",
        "plt.ylabel(\"Residuals\")\n",
        "plt.title(\"Residuals vs Fitted after refit model with fuel type E remove\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "qjWBlOqF5kdV",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "qjWBlOqF5kdV",
        "outputId": "c0081c82-c35a-4592-92c7-3e59d2c4ebc2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Statistic</th>\n",
              "      <th>Value</th>\n",
              "      <th>p-value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Breusch-Pagan LM</td>\n",
              "      <td>2676.406256</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Statistic        Value  p-value\n",
              "0  Breusch-Pagan LM  2676.406256      0.0"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "# residuals_set = set(residuals.index)\n",
        "# filter_residuals = residuals.loc[list(residuals_set & set(filter_df.index))]\n",
        "# tmp_data = X_train_sm.copy()\n",
        "# new_train_set = set(tmp_data.index)\n",
        "\n",
        "# filter_train = tmp_data.loc[list(set(new_train_set) & set(filter_df.index))]\n",
        "# bp_lm, bp_lm_pvalue, bp_f, bp_f_pvalue = het_breuschpagan(filter_residuals, filter_train)\n",
        "bp_lm, bp_lm_pvalue, bp_f, bp_f_pvalue = het_breuschpagan(new_residuals, new_ols.model.exog)\n",
        "\n",
        "bp_summary = pd.DataFrame({\n",
        "    \"Statistic\": [\"Breusch-Pagan LM\"],\n",
        "    \"Value\": [bp_lm],\n",
        "    \"p-value\": [bp_lm_pvalue]\n",
        "})\n",
        "bp_summary"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b12c9b5",
      "metadata": {
        "id": "6b12c9b5"
      },
      "source": [
        "The null hypothesis of Breusch-Pagan test is that the residual variance is constant. In this notebook, the reported p-value is essentially *0.0*, so there may be evidences of *heteroscedasticity*. Here we can not conclude that assume that constant variance is false, as there also some evidence, from the Residuals and Fitted values plot, that there may be a problem with the model itself.\n",
        "\n",
        "Although the test detects *heteroscedasticity*, we can still use the fitted model for prediction as the **Residuals vs Fitted** plot shows that the residuals remain fairly concentrated around zero and the funnel pattern is not extremely severe. Therefore, the model estimates still retain some practical value, for particular fuel type like X and Z. For other fuel type we may have to resort to create a model for each type, as different fuel type does characterize the CO2 emmissions differently."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2lZPH3OjX395",
      "metadata": {
        "id": "2lZPH3OjX395"
      },
      "source": [
        "### 13.3 Histogram of residuals\n",
        "\n",
        "Check whether residuals are roughly centered and approximately symmetric.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "CGXa2QI9X395",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 975
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:15.523050Z",
          "iopub.status.busy": "2026-03-24T12:15:15.522575Z",
          "iopub.status.idle": "2026-03-24T12:15:15.757414Z",
          "shell.execute_reply": "2026-03-24T12:15:15.756558Z"
        },
        "id": "CGXa2QI9X395",
        "outputId": "de752131-54d3-4d6e-c2e8-45017c9597c8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.hist(residuals, bins=200)\n",
        "plt.title(\"Distribution of Residuals\")\n",
        "plt.xlabel(\"Residual\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.hist(new_residuals, bins=200)\n",
        "plt.title(\"Distribution of Residuals after refit model with fuel type E remove\")\n",
        "plt.xlabel(\"Residual\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "kgjW7T3Nosso",
      "metadata": {
        "id": "kgjW7T3Nosso"
      },
      "source": [
        "From the above distribution, we can also see that the model does seem to be affect by other fuel type, as there is a small shift away the ideal mean of 0 (zero) in both graph. This may indicate that there is a problem with preprocessing step where we may not eliminate problematic outliers or there is a problem model itself."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "R3urrTQ8X396",
      "metadata": {
        "id": "R3urrTQ8X396"
      },
      "source": [
        "## 14. Influence diagnostics\n",
        "\n",
        "Use leverage and **Cook's Distance** to identify observations that may strongly affect the fitted model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6ambf2u04buh",
      "metadata": {
        "id": "6ambf2u04buh"
      },
      "source": [
        "### 14.1 Some statistics about Cook's distance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "oiKMNJ3cX397",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:16.130838Z",
          "iopub.status.busy": "2026-03-24T12:15:16.130354Z",
          "iopub.status.idle": "2026-03-24T12:15:16.151316Z",
          "shell.execute_reply": "2026-03-24T12:15:16.150637Z"
        },
        "id": "oiKMNJ3cX397",
        "outputId": "0d8345e6-32ec-4a3b-b559-d671863a5a73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cook's Distance threshold (4/n): 0.000222\n",
            "Number of observations above threshold: 1006 out of 18044\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fitted</th>\n",
              "      <th>Residual</th>\n",
              "      <th>Leverage</th>\n",
              "      <th>Cooks_Distance</th>\n",
              "      <th>Flag_4_over_n</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3038</th>\n",
              "      <td>132.980105</td>\n",
              "      <td>30.019895</td>\n",
              "      <td>0.034666</td>\n",
              "      <td>0.056763</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14715</th>\n",
              "      <td>475.150653</td>\n",
              "      <td>-57.150653</td>\n",
              "      <td>0.006318</td>\n",
              "      <td>0.035388</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1445</th>\n",
              "      <td>169.464774</td>\n",
              "      <td>23.535226</td>\n",
              "      <td>0.034867</td>\n",
              "      <td>0.035106</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14711</th>\n",
              "      <td>470.604250</td>\n",
              "      <td>-56.604250</td>\n",
              "      <td>0.006241</td>\n",
              "      <td>0.034284</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14506</th>\n",
              "      <td>470.604250</td>\n",
              "      <td>-56.604250</td>\n",
              "      <td>0.006241</td>\n",
              "      <td>0.034284</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21016</th>\n",
              "      <td>179.045009</td>\n",
              "      <td>28.954991</td>\n",
              "      <td>0.011361</td>\n",
              "      <td>0.016500</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20049</th>\n",
              "      <td>179.045009</td>\n",
              "      <td>28.954991</td>\n",
              "      <td>0.011361</td>\n",
              "      <td>0.016500</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21012</th>\n",
              "      <td>181.318210</td>\n",
              "      <td>27.681790</td>\n",
              "      <td>0.011352</td>\n",
              "      <td>0.015069</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19006</th>\n",
              "      <td>181.318210</td>\n",
              "      <td>27.681790</td>\n",
              "      <td>0.011352</td>\n",
              "      <td>0.015069</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20044</th>\n",
              "      <td>181.318210</td>\n",
              "      <td>27.681790</td>\n",
              "      <td>0.011352</td>\n",
              "      <td>0.015069</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Fitted   Residual  Leverage  Cooks_Distance  Flag_4_over_n\n",
              "3038   132.980105  30.019895  0.034666        0.056763           True\n",
              "14715  475.150653 -57.150653  0.006318        0.035388           True\n",
              "1445   169.464774  23.535226  0.034867        0.035106           True\n",
              "14711  470.604250 -56.604250  0.006241        0.034284           True\n",
              "14506  470.604250 -56.604250  0.006241        0.034284           True\n",
              "21016  179.045009  28.954991  0.011361        0.016500           True\n",
              "20049  179.045009  28.954991  0.011361        0.016500           True\n",
              "21012  181.318210  27.681790  0.011352        0.015069           True\n",
              "19006  181.318210  27.681790  0.011352        0.015069           True\n",
              "20044  181.318210  27.681790  0.011352        0.015069           True"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "influence = ols_main.get_influence()\n",
        "leverage = influence.hat_matrix_diag\n",
        "cooks_d = influence.cooks_distance[0]\n",
        "cook_threshold = 4 / len(main_train_dm)\n",
        "\n",
        "influence_df = pd.DataFrame({\n",
        "    \"Fitted\": fitted,\n",
        "    \"Residual\": residuals,\n",
        "    \"Leverage\": leverage,\n",
        "    \"Cooks_Distance\": cooks_d,\n",
        "    \"Flag_4_over_n\": cooks_d > cook_threshold\n",
        "})\n",
        "\n",
        "print(f\"Cook's Distance threshold (4/n): {cook_threshold:.6f}\")\n",
        "print(f\"Number of observations above threshold: {(cooks_d > cook_threshold).sum()} out of {len(cooks_d)}\")\n",
        "influence_df.sort_values(\"Cooks_Distance\", ascending=False).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3lA5V28DLBFk",
      "metadata": {
        "id": "3lA5V28DLBFk"
      },
      "source": [
        "The table lists observations with the highest Cook’s Distance values. These points typically combine moderate-to-high leverage with relatively large residuals, making them influential.\n",
        "\n",
        "However, even the largest Cook’s Distance values remain relatively small in magnitude, reinforcing the idea that their overall impact on the model is limited. These observations are useful for diagnostic purposes but do not necessarily warrant automatic removal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "wFMbeNd6X398",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:16.154154Z",
          "iopub.status.busy": "2026-03-24T12:15:16.153868Z",
          "iopub.status.idle": "2026-03-24T12:15:16.835716Z",
          "shell.execute_reply": "2026-03-24T12:15:16.834672Z"
        },
        "id": "wFMbeNd6X398",
        "outputId": "e9c9f00e-f268-492e-e840-ce1e136849ce"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 5))\n",
        "markerline, stemlines, baseline = plt.stem(np.arange(len(cooks_d)), cooks_d)\n",
        "plt.setp(markerline, markersize=3)\n",
        "plt.title(\"Cook's Distance\")\n",
        "plt.xlabel(\"Observation index\")\n",
        "plt.ylabel(\"Cook's distance\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "j_RUKKpeGWb9",
      "metadata": {
        "id": "j_RUKKpeGWb9"
      },
      "source": [
        "The Cook gap plot shows that most observations have values ​​very close to zero, indicating that the majority of data points have minimal influence on the adjusted model. However, a few observations have significantly higher Cook gap values, suggesting the possibility of influencing points.\n",
        "\n",
        "Despite the presence of these points, only one peak dominates the plot, and this peak is only slightly more than 0.05, which is not very large. This suggests that influencing observations exist but are not very strong, and no observations significantly skew the model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a0b13c0",
      "metadata": {
        "id": "8a0b13c0"
      },
      "source": [
        "### 14.2 Cook's Distance: Influence Diagnostics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "d89Q_aOcX398",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:16.838939Z",
          "iopub.status.busy": "2026-03-24T12:15:16.838607Z",
          "iopub.status.idle": "2026-03-24T12:15:17.056871Z",
          "shell.execute_reply": "2026-03-24T12:15:17.056270Z"
        },
        "id": "d89Q_aOcX398",
        "outputId": "99479bcb-9fd7-4d43-ae20-4b0e4352f348"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "std_resid = ols_main.get_influence().resid_studentized_internal\n",
        "\n",
        "plt.figure(figsize=(7, 5))\n",
        "plt.scatter(leverage, std_resid, alpha=0.6, s=np.clip(cooks_d * 4000, 10, 200))\n",
        "plt.axhline(0, color=\"red\", linestyle=\"--\")\n",
        "plt.xlabel(\"Leverage\")\n",
        "plt.ylabel(\"Standardized residuals\")\n",
        "plt.title(\"Leverage vs Standardized Residuals\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "KLqWeoVUNWN9",
      "metadata": {
        "id": "KLqWeoVUNWN9"
      },
      "source": [
        "The leverage vs standardized residuals plot provides a more detailed view of influential points:\n",
        "\n",
        "Most observations are concentrated at low leverage values with residuals around zero, indicating a good overall model fit.\n",
        "\n",
        "Some observations have large residuals but low leverage, meaning they are outliers in the response variable but do not significantly influence the model.\n",
        "\n",
        "A small cluster of points shows high leverage combined with moderate residuals, which are the most influential observations.\n",
        "\n",
        "Larger marker sizes (Cook’s Distance) confirm that these points have greater influence compared to others."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "61dd52ba",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:17.059369Z",
          "iopub.status.busy": "2026-03-24T12:15:17.059136Z",
          "iopub.status.idle": "2026-03-24T12:15:17.087870Z",
          "shell.execute_reply": "2026-03-24T12:15:17.087171Z"
        },
        "id": "61dd52ba",
        "outputId": "bc654a21-b172-474c-e43b-ac57beb484db"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Original</th>\n",
              "      <th>Refit_without_flagged_points</th>\n",
              "      <th>Absolute_change</th>\n",
              "      <th>Percent_change</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_VAN - PASSENGER</th>\n",
              "      <td>-7.204792</td>\n",
              "      <td>-2.595583e-14</td>\n",
              "      <td>7.204792</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Type_N</th>\n",
              "      <td>-97.370261</td>\n",
              "      <td>-1.044473e+02</td>\n",
              "      <td>7.077054</td>\n",
              "      <td>7.268188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Type_E</th>\n",
              "      <td>-145.825346</td>\n",
              "      <td>-1.506631e+02</td>\n",
              "      <td>4.837796</td>\n",
              "      <td>3.317527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_SPECIAL PURPOSE VEHICLE</th>\n",
              "      <td>5.812460</td>\n",
              "      <td>3.142310e+00</td>\n",
              "      <td>2.670150</td>\n",
              "      <td>45.938381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_VAN - CARGO</th>\n",
              "      <td>-4.222630</td>\n",
              "      <td>-1.618737e+00</td>\n",
              "      <td>2.603893</td>\n",
              "      <td>61.665203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Type_Z</th>\n",
              "      <td>-31.045061</td>\n",
              "      <td>-3.321187e+01</td>\n",
              "      <td>2.166813</td>\n",
              "      <td>6.979575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Type_X</th>\n",
              "      <td>-31.365506</td>\n",
              "      <td>-3.329480e+01</td>\n",
              "      <td>1.929299</td>\n",
              "      <td>6.151021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_PICKUP TRUCK - SMALL</th>\n",
              "      <td>1.807628</td>\n",
              "      <td>-3.966784e-02</td>\n",
              "      <td>1.847296</td>\n",
              "      <td>102.194469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>const</th>\n",
              "      <td>34.413439</td>\n",
              "      <td>3.271356e+01</td>\n",
              "      <td>1.699881</td>\n",
              "      <td>4.939586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_MINIVAN</th>\n",
              "      <td>1.185812</td>\n",
              "      <td>-1.405397e-01</td>\n",
              "      <td>1.326352</td>\n",
              "      <td>111.851771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_FULL-SIZE</th>\n",
              "      <td>2.145988</td>\n",
              "      <td>9.993891e-01</td>\n",
              "      <td>1.146599</td>\n",
              "      <td>53.429893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_SUV - STANDARD</th>\n",
              "      <td>3.253989</td>\n",
              "      <td>2.574572e+00</td>\n",
              "      <td>0.679418</td>\n",
              "      <td>20.879535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_SUV</th>\n",
              "      <td>-0.707417</td>\n",
              "      <td>-1.382303e+00</td>\n",
              "      <td>0.674886</td>\n",
              "      <td>95.401445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
              "      <td>22.732013</td>\n",
              "      <td>2.330740e+01</td>\n",
              "      <td>0.575383</td>\n",
              "      <td>2.531156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle Class_SUV - SMALL</th>\n",
              "      <td>2.600042</td>\n",
              "      <td>2.027511e+00</td>\n",
              "      <td>0.572531</td>\n",
              "      <td>22.020062</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Original  \\\n",
              "Vehicle Class_VAN - PASSENGER           -7.204792   \n",
              "Fuel Type_N                            -97.370261   \n",
              "Fuel Type_E                           -145.825346   \n",
              "Vehicle Class_SPECIAL PURPOSE VEHICLE    5.812460   \n",
              "Vehicle Class_VAN - CARGO               -4.222630   \n",
              "Fuel Type_Z                            -31.045061   \n",
              "Fuel Type_X                            -31.365506   \n",
              "Vehicle Class_PICKUP TRUCK - SMALL       1.807628   \n",
              "const                                   34.413439   \n",
              "Vehicle Class_MINIVAN                    1.185812   \n",
              "Vehicle Class_FULL-SIZE                  2.145988   \n",
              "Vehicle Class_SUV - STANDARD             3.253989   \n",
              "Vehicle Class_SUV                       -0.707417   \n",
              "Fuel Consumption Comb (L/100 km)        22.732013   \n",
              "Vehicle Class_SUV - SMALL                2.600042   \n",
              "\n",
              "                                       Refit_without_flagged_points  \\\n",
              "Vehicle Class_VAN - PASSENGER                         -2.595583e-14   \n",
              "Fuel Type_N                                           -1.044473e+02   \n",
              "Fuel Type_E                                           -1.506631e+02   \n",
              "Vehicle Class_SPECIAL PURPOSE VEHICLE                  3.142310e+00   \n",
              "Vehicle Class_VAN - CARGO                             -1.618737e+00   \n",
              "Fuel Type_Z                                           -3.321187e+01   \n",
              "Fuel Type_X                                           -3.329480e+01   \n",
              "Vehicle Class_PICKUP TRUCK - SMALL                    -3.966784e-02   \n",
              "const                                                  3.271356e+01   \n",
              "Vehicle Class_MINIVAN                                 -1.405397e-01   \n",
              "Vehicle Class_FULL-SIZE                                9.993891e-01   \n",
              "Vehicle Class_SUV - STANDARD                           2.574572e+00   \n",
              "Vehicle Class_SUV                                     -1.382303e+00   \n",
              "Fuel Consumption Comb (L/100 km)                       2.330740e+01   \n",
              "Vehicle Class_SUV - SMALL                              2.027511e+00   \n",
              "\n",
              "                                       Absolute_change  Percent_change  \n",
              "Vehicle Class_VAN - PASSENGER                 7.204792      100.000000  \n",
              "Fuel Type_N                                   7.077054        7.268188  \n",
              "Fuel Type_E                                   4.837796        3.317527  \n",
              "Vehicle Class_SPECIAL PURPOSE VEHICLE         2.670150       45.938381  \n",
              "Vehicle Class_VAN - CARGO                     2.603893       61.665203  \n",
              "Fuel Type_Z                                   2.166813        6.979575  \n",
              "Fuel Type_X                                   1.929299        6.151021  \n",
              "Vehicle Class_PICKUP TRUCK - SMALL            1.847296      102.194469  \n",
              "const                                         1.699881        4.939586  \n",
              "Vehicle Class_MINIVAN                         1.326352      111.851771  \n",
              "Vehicle Class_FULL-SIZE                       1.146599       53.429893  \n",
              "Vehicle Class_SUV - STANDARD                  0.679418       20.879535  \n",
              "Vehicle Class_SUV                             0.674886       95.401445  \n",
              "Fuel Consumption Comb (L/100 km)              0.575383        2.531156  \n",
              "Vehicle Class_SUV - SMALL                     0.572531       22.020062  "
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "influential_mask = cooks_d > cook_threshold\n",
        "\n",
        "main_train_dm_refit = main_train_dm.loc[~influential_mask].copy()\n",
        "y_train_refit = y_train.loc[main_train_dm_refit.index].copy()\n",
        "\n",
        "X_refit_sm = sm.add_constant(main_train_dm_refit, has_constant=\"add\").astype(float)\n",
        "ols_refit = sm.OLS(y_train_refit, X_refit_sm).fit()\n",
        "\n",
        "coef_comparison = pd.DataFrame({\n",
        "    \"Original\": ols_main.params,\n",
        "    \"Refit_without_flagged_points\": ols_refit.params\n",
        "})\n",
        "coef_comparison[\"Absolute_change\"] = (coef_comparison[\"Refit_without_flagged_points\"] - coef_comparison[\"Original\"]).abs()\n",
        "coef_comparison[\"Percent_change\"] = (\n",
        "    coef_comparison[\"Absolute_change\"] /\n",
        "    coef_comparison[\"Original\"].replace(0, np.nan).abs()\n",
        ") * 100\n",
        "\n",
        "coef_comparison.sort_values(\"Absolute_change\", ascending=False).head(15)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "vgkLfBwGOcvX",
      "metadata": {
        "id": "vgkLfBwGOcvX"
      },
      "source": [
        "The comparison shows that Fuel Consumption Comb (L/100 km) remains highly stable, with only a small percentage change (~2.5%), confirming it as a robust and dominant predictor.\n",
        "\n",
        "Fuel type variables also exhibit relatively small changes (around 3–7%), indicating moderate stability.\n",
        "\n",
        "In contrast, several vehicle class variables show large percentage changes (up to 100%+), suggesting that these coefficients are sensitive to influential observations and should be interpreted with caution."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8JCysgyJX398",
      "metadata": {
        "id": "8JCysgyJX398"
      },
      "source": [
        "## 15. Multicollinearity check with VIF\n",
        "\n",
        "VIF is used to assess overlap among predictors. High VIF values for engine-related variables indicate substantial multicollinearity.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "BzZPUVpnX399",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:17.091431Z",
          "iopub.status.busy": "2026-03-24T12:15:17.091017Z",
          "iopub.status.idle": "2026-03-24T12:15:17.200155Z",
          "shell.execute_reply": "2026-03-24T12:15:17.199240Z"
        },
        "id": "BzZPUVpnX399",
        "outputId": "22564162-7182-498d-f58c-6c87b8da6230"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cylinders</td>\n",
              "      <td>87.280904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fuel Consumption Comb (L/100 km)</td>\n",
              "      <td>81.536788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Engine Size(L)</td>\n",
              "      <td>56.699953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Fuel Type_Z</td>\n",
              "      <td>13.521239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Fuel Type_X</td>\n",
              "      <td>12.671493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Fuel Type_E</td>\n",
              "      <td>3.628232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Vehicle Class_PICKUP TRUCK - STANDARD</td>\n",
              "      <td>2.569466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Vehicle Class_SUV</td>\n",
              "      <td>2.258191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Vehicle Class_MID-SIZE</td>\n",
              "      <td>1.940840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Vehicle Class_SUV - SMALL</td>\n",
              "      <td>1.648374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Vehicle Class_SUBCOMPACT</td>\n",
              "      <td>1.635933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Vehicle Class_FULL-SIZE</td>\n",
              "      <td>1.612978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Vehicle Class_SUV - STANDARD</td>\n",
              "      <td>1.580257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Vehicle Class_TWO-SEATER</td>\n",
              "      <td>1.518707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Vehicle Class_MINICOMPACT</td>\n",
              "      <td>1.346605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Vehicle Class_VAN - PASSENGER</td>\n",
              "      <td>1.342090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Vehicle Class_VAN - CARGO</td>\n",
              "      <td>1.320744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Vehicle Class_PICKUP TRUCK - SMALL</td>\n",
              "      <td>1.301266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Vehicle Class_STATION WAGON - SMALL</td>\n",
              "      <td>1.264533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Vehicle Class_MINIVAN</td>\n",
              "      <td>1.186476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Vehicle Class_STATION WAGON - MID-SIZE</td>\n",
              "      <td>1.123880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Fuel Type_N</td>\n",
              "      <td>1.088033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Vehicle Class_SPECIAL PURPOSE VEHICLE</td>\n",
              "      <td>1.056131</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   Feature        VIF\n",
              "1                                Cylinders  87.280904\n",
              "2         Fuel Consumption Comb (L/100 km)  81.536788\n",
              "0                           Engine Size(L)  56.699953\n",
              "22                             Fuel Type_Z  13.521239\n",
              "21                             Fuel Type_X  12.671493\n",
              "19                             Fuel Type_E   3.628232\n",
              "8    Vehicle Class_PICKUP TRUCK - STANDARD   2.569466\n",
              "13                       Vehicle Class_SUV   2.258191\n",
              "4                   Vehicle Class_MID-SIZE   1.940840\n",
              "14               Vehicle Class_SUV - SMALL   1.648374\n",
              "12                Vehicle Class_SUBCOMPACT   1.635933\n",
              "3                  Vehicle Class_FULL-SIZE   1.612978\n",
              "15            Vehicle Class_SUV - STANDARD   1.580257\n",
              "16                Vehicle Class_TWO-SEATER   1.518707\n",
              "5                Vehicle Class_MINICOMPACT   1.346605\n",
              "18           Vehicle Class_VAN - PASSENGER   1.342090\n",
              "17               Vehicle Class_VAN - CARGO   1.320744\n",
              "7       Vehicle Class_PICKUP TRUCK - SMALL   1.301266\n",
              "11     Vehicle Class_STATION WAGON - SMALL   1.264533\n",
              "6                    Vehicle Class_MINIVAN   1.186476\n",
              "10  Vehicle Class_STATION WAGON - MID-SIZE   1.123880\n",
              "20                             Fuel Type_N   1.088033\n",
              "9    Vehicle Class_SPECIAL PURPOSE VEHICLE   1.056131"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vif_input = main_train_dm.copy()\n",
        "\n",
        "vif_df = pd.DataFrame({\n",
        "    \"Feature\": vif_input.columns,\n",
        "    \"VIF\": [variance_inflation_factor(vif_input.values, i) for i in range(vif_input.shape[1])]\n",
        "}).sort_values(by=\"VIF\", ascending=False)\n",
        "\n",
        "vif_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "36386f48",
      "metadata": {
        "id": "36386f48"
      },
      "source": [
        "**Multicollinearity Assessment**\n",
        "\n",
        "Variance Inflation Factor (VIF) was used to evaluate multicollinearity among predictors.\n",
        "\n",
        "High VIF values indicate that a predictor shares substantial information with other predictors. In this dataset, **engine size, cylinders, fuel consumption and several fuel types** show relatively high VIF values.\n",
        "\n",
        "This is expected because these variables are mechanically related in vehicle design. While multicollinearity does not necessarily reduce predictive performance, it can make individual coefficients less stable and harder to interpret independently.\n",
        "\n",
        "Therefore, coefficient interpretations should be made cautiously, especially for the engine-related variables or fuel consumption and fuel types.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ntQtnpw3X399",
      "metadata": {
        "id": "ntQtnpw3X399"
      },
      "source": [
        "## 16. Diagnostic summary\n",
        "\n",
        "Overall, the regression model performs reasonably well.\n",
        "\n",
        "Residual plots suggest that the linearity assumption is broadly acceptable, and although the **Breusch-Pagan test** indicates heteroscedasticity, the **Residuals vs Fitted** plot shows that the spread of errors is still fairly concentrated rather than severely funnel-shaped. This means the OLS fit remains practically useful (for specific fuel types).\n",
        "\n",
        "Influence diagnostics show that no single observation dominates the entire regression story, although some coefficients are sensitive to influential observations. Multicollinearity is substantial among engine-related predictors, so individual coefficients should be interpreted carefully. Despite these limitations, the model remains useful for both prediction and interpretation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27a55523",
      "metadata": {
        "id": "27a55523"
      },
      "source": [
        "## 17. Key Insights\n",
        "\n",
        "### Key Insights from the Analysis\n",
        "\n",
        "Several important insights emerge from the regression analysis.\n",
        "\n",
        "- **Fuel consumption is the dominant driver of CO2 emissions.** Vehicles with higher fuel consumption per 100 km consistently produce higher emissions. This confirms the direct relationship between fuel burned and carbon output.\n",
        "- **Vehicle class contributes to systematic emission differences.** Larger vehicle categories such as SUVs and pickup trucks tend to emit more CO2 than smaller compact vehicles. This reflects differences in vehicle weight, engine size, and design purpose.\n",
        "- **Fuel type also affects emission levels.** Different fuel types exhibit different emission patterns, suggesting that engine technology and fuel efficiency vary across fuel categories.\n",
        "- **Engine size alone is not a strong independent predictor once fuel consumption is included.** This indicates that fuel consumption already captures much of the variation associated with engine characteristics.\n",
        "\n",
        "From a Weisberg-style regression perspective, the assumptions of constant variance and perfect normality are not satisfied perfectly. However, the model still remains a strong and useful linear explanatory tool because the main coefficient pattern is stable, the residual structure is not severely distorted visually, and the influence analysis does not suggest that the overall conclusion is driven by a single extreme case.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "M-MT1yAwX399",
      "metadata": {
        "id": "M-MT1yAwX399"
      },
      "source": [
        "## 18. Final conclusion\n",
        "\n",
        "Main conclusions:\n",
        "\n",
        "- **Fuel consumption** is the strongest predictor of CO2 emissions.\n",
        "- **Vehicle class** and **fuel type** also explain meaningful differences in expected emissions.\n",
        "- **Model 3** is the preferred explanatory model, while **Model 5** is the best model for pure prediction.\n",
        "- The selected model - **Model 3** still shows strong test performance, with **RMSE about 4.801 g/km** and **R2 about 0.994**.\n",
        "- Diagnostics suggest the presence of heteroscedasticity, multicollinearity, and some sensitivity to influential observations. However, the overall OLS fit remains practically useful, and the main explanatory conclusions of Model 3 remain stable.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d26faf22",
      "metadata": {
        "id": "d26faf22"
      },
      "source": [
        "## 19. Submission note\n",
        "\n",
        "For submission:\n",
        "- **Final explanatory model**: Model 3\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0082c50e",
      "metadata": {
        "id": "0082c50e"
      },
      "source": [
        "## 20. Simple prediction helper\n",
        "\n",
        "The function below gives a quick prediction for a new vehicle.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "3bbd8401",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2026-03-24T12:15:17.204628Z",
          "iopub.status.busy": "2026-03-24T12:15:17.204175Z",
          "iopub.status.idle": "2026-03-24T12:15:17.218199Z",
          "shell.execute_reply": "2026-03-24T12:15:17.217537Z"
        },
        "id": "3bbd8401",
        "outputId": "81545673-b755-4e9b-f12a-008c550a7090"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Example predicted CO2 emission (g/km): 174.03\n"
          ]
        }
      ],
      "source": [
        "def predict_co2_emission(engine_size, cylinders, fuel_consumption_comb, vehicle_class, fuel_type):\n",
        "    new_vehicle = pd.DataFrame({\n",
        "        \"Engine Size(L)\": [engine_size],\n",
        "        \"Cylinders\": [cylinders],\n",
        "        \"Fuel Consumption Comb (L/100 km)\": [fuel_consumption_comb],\n",
        "        \"Vehicle Class\": [vehicle_class],\n",
        "        \"Fuel Type\": [fuel_type]\n",
        "    })\n",
        "    return float(main_pipeline.predict(new_vehicle)[0])\n",
        "\n",
        "example_prediction = predict_co2_emission(\n",
        "    engine_size=2.0,\n",
        "    cylinders=4,\n",
        "    fuel_consumption_comb=7.5,\n",
        "    vehicle_class=\"COMPACT\",\n",
        "    fuel_type=\"X\"\n",
        ")\n",
        "\n",
        "print(\"Example predicted CO2 emission (g/km):\", round(example_prediction, 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "R6DrA1hJCdjr",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6DrA1hJCdjr",
        "outputId": "174e32cc-4646-4682-caf1-62fdec3cafd3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved best_model.joblib\n"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "\n",
        "artifact = {\n",
        "    'model_name': main_spec['name'],\n",
        "    'model': main_pipeline,\n",
        "    'features': main_spec['num'] + main_spec['cat'],\n",
        "    'target': target,\n",
        "    'train_rows': len(X_train),\n",
        "    'test_rows': len(X_test),\n",
        "    'y_test': y_test.reset_index(drop=True),\n",
        "    'holdout_prediction': pd.Series(main_pred_test)\n",
        "}\n",
        "\n",
        "joblib.dump(artifact, 'best_model.joblib', compress=3)\n",
        "print('Saved best_model.joblib')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "ten_moi_truong",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.25"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}