1_Assurance_prediction_corrigé.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hocine-drioueche/OpenclassroomsProject/blob/main/1_Assurance_prediction_corrig%C3%A9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxMwFcjre5Jd"
      },
      "source": [
        "<center><img src=\"https://assets-datascientest.s3-eu-west-1.amazonaws.com/train/logo_datascientest.png\" style=\"height:150px;center\"></center>\n",
        "\n",
        "<hr style=\"border-width:2px;border-color:#75DFC1\">\n",
        "<center><h1>Deep-Learning - Modules complémentaires</h1></center>\n",
        "<center><h2>Régression sur des données structurées</h2></center>\n",
        "<hr style=\"border-width:2px;border-color:#75DFC1\">\n",
        "\n",
        "\n",
        "Le but de cet exercice est de s'entraîner à faire une régression sur des données structurées à l'aide d'un réseau de neurones dense.\n",
        "\n",
        " Nous allons travailler avec la base de données **```insurance.csv```**, qui contient les informations personnelles de clients d'une assurance, ainsi que les charges qu'ils ont à payer.\n",
        "\n",
        "> La structure de l'exercice est la suivante :\n",
        ">> I - [Préparation du dataset](#preparation)\n",
        ">>\n",
        ">>\n",
        ">> II - [Régression linéaire multiple classique](#linear)\n",
        ">>\n",
        ">>\n",
        ">> III - [Régression à l'aide d'un réseau de neurones dense](#dnn)\n",
        ">>\n",
        ">>\n",
        ">> IV - [Comparaison des résultats obtenus avec les deux techniques](#comparaison)\n",
        "\n",
        "- Exécuter les cellules ci-dessous pour importer le dataset et les modules nécessaires à l'exercice."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "4cOCRU4rDoCn",
        "outputId": "96f3758e-5946-468a-b963-7f410bdb10f4"
      },
      "source": [
        "!wget https://assets-datascientest.s3-eu-west-1.amazonaws.com/datasets/insurance.zip\n",
        "!unzip insurance.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-05 14:53:45--  https://assets-datascientest.s3-eu-west-1.amazonaws.com/datasets/insurance.zip\n",
            "Resolving assets-datascientest.s3-eu-west-1.amazonaws.com (assets-datascientest.s3-eu-west-1.amazonaws.com)... 52.218.60.128\n",
            "Connecting to assets-datascientest.s3-eu-west-1.amazonaws.com (assets-datascientest.s3-eu-west-1.amazonaws.com)|52.218.60.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16429 (16K) [application/zip]\n",
            "Saving to: ‘insurance.zip’\n",
            "\n",
            "insurance.zip       100%[===================>]  16.04K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-05-05 14:53:46 (54.6 MB/s) - ‘insurance.zip’ saved [16429/16429]\n",
            "\n",
            "Archive:  insurance.zip\n",
            "  inflating: insurance.csv           \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73FmOY-YCQcu"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "from tensorflow.keras import callbacks\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "\n",
        "from sklearn.metrics import r2_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyvXSuU2CfXh"
      },
      "source": [
        "# <a name=\"preparation\"></a> I - Préparation du dataset\n",
        "\n",
        "- (a) Charger le jeu de données **```insurance.csv```** dans un DataFrame ```df```.\n",
        "\n",
        "- (b) Afficher la shape de ```df``` ainsi que ses 5 premières lignes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqzv0jPeCgVx"
      },
      "source": [
        "df = pd.read_csv('insurance.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        },
        "id": "BW1MuntLCmVm",
        "outputId": "ba878e97-2ed2-47d0-d4fb-3c7df2d939d1"
      },
      "source": [
        "print(\"Nombre de lignes dans le dataset : {}\".format(df.shape[0]))\n",
        "print(\"Nombre de colonnes dans le dataset : {}\".format(df.shape[1]))\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nombre de lignes dans le dataset : 1338\n",
            "Nombre de colonnes dans le dataset : 7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_zLD6T6thK4P"
      },
      "source": [
        "- (c) Vérifier que les colonnes de ```df``` sont du bon type, et traiter les valeurs manquantes s'il y en a."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9uDIgBiKMMb",
        "outputId": "d3bcb258-023a-4067-da40-3ec2083bdd54"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   charges   1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nU5g8hUNKXAE"
      },
      "source": [
        "df['children'] = df['children'].astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFPqQ2ZmCv0D",
        "outputId": "fc93e215-f108-4e98-84c7-6bc0bd9aec77"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "bmi         0\n",
              "children    0\n",
              "smoker      0\n",
              "region      0\n",
              "charges     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Z_-dQg6hZsE"
      },
      "source": [
        "- (d) Créer une visualisation pertinente pour chaque colonne de ```df```.\n",
        "\n",
        "- (e) Supprimer les colonnes qui ne vous semblent pas impacter les charges à payer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "B0DeNYbPDEG0",
        "outputId": "81214188-5235-4fc9-ff73-2f8033d0b537"
      },
      "source": [
        "charges_sex = df.groupby('sex').mean()['charges']\n",
        "charges_children = df.groupby('children').mean()['charges']\n",
        "charges_smoke = df.groupby('smoker').mean()['charges']\n",
        "charges_region = df.groupby('region').mean()['charges']\n",
        "\n",
        "plt.figure(figsize=(16, 10))\n",
        "\n",
        "plt.subplot(221)\n",
        "plt.bar(charges_sex.index, charges_sex.values, color='blueviolet')\n",
        "plt.title(\"Prix moyen de l'assurance en fonction du sex\")\n",
        "plt.xlabel(\"Sex\")\n",
        "plt.ylabel(\"Prix moyen de l'assurance\")\n",
        "\n",
        "plt.subplot(222)\n",
        "plt.bar(charges_children.index, charges_children.values, color='blueviolet')\n",
        "plt.title(\"Prix moyen de l'assurance en fonction du nombre d'enfants\")\n",
        "plt.xlabel(\"Nombre d'enfants\")\n",
        "plt.ylabel(\"Prix moyen de l'assurance\")\n",
        "\n",
        "plt.subplot(223)\n",
        "plt.bar(charges_smoke.index, charges_smoke.values, color='blueviolet')\n",
        "plt.title(\"Prix moyen de l'assurance pour les fumeurs et non fumeurs\")\n",
        "plt.xlabel(\"Fumeur ?\")\n",
        "plt.ylabel(\"Prix moyen de l'assurance\")\n",
        "\n",
        "plt.subplot(224)\n",
        "plt.bar(charges_region.index, charges_region.values, color='blueviolet')\n",
        "plt.title(\"Prix moyen de l'assurance en fonction de la région\")\n",
        "plt.xlabel(\"Région\")\n",
        "plt.ylabel(\"Prix moyen de l'assurance\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWMYTUKYEBHB"
      },
      "source": [
        "# Variation très faible pour la région, on peut supprimer cette colonne\n",
        "df = df.drop(labels='region', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdRTtriGGqvx"
      },
      "source": [
        "def lissage(x,y,p):\n",
        "    x_lisse=[]\n",
        "    y_lisse=[]\n",
        "    for i in range(p,len(x)-p):\n",
        "        x_lisse.append(x[i])\n",
        "    for i in range(p,len(y)-p):\n",
        "        val=0\n",
        "        for k in range(2*p):\n",
        "            val+=y[i-p+k]\n",
        "        y_lisse.append(val/2/p)\n",
        "    return x_lisse, y_lisse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "CDBbpTpkFdO9",
        "outputId": "a8b21dcf-6727-4e1b-98b2-37048434a65e"
      },
      "source": [
        "charges_age = df.groupby('age').mean()['charges']\n",
        "charges_bmi = df.groupby('bmi').mean()['charges']\n",
        "\n",
        "plt.figure(figsize=(16, 5))\n",
        "\n",
        "plt.subplot(121)\n",
        "x = charges_age.index\n",
        "y = charges_age.values\n",
        "x_lisse, y_lisse = lissage(x, y, 3)\n",
        "plt.plot(x, y, color='turquoise', linewidth=0.7, linestyle='dashed', label='actual')\n",
        "plt.plot(x_lisse, y_lisse, color='blueviolet', linewidth=2, label='tendance')\n",
        "plt.xlim(20, 60)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel(\"Prix moyen de l'assurance\")\n",
        "plt.title(\"Prix moyen de l'assurance en fonction de l'age\")\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(122)\n",
        "x = charges_bmi.index\n",
        "y = charges_bmi.values\n",
        "x_lisse, y_lisse = lissage(x, y, 8)\n",
        "plt.plot(x, y, color='turquoise', linewidth=0.7, linestyle='dashed', label='actual')\n",
        "plt.plot(x_lisse, y_lisse, color='blueviolet', linewidth=2, label='tendance')\n",
        "plt.xlim(17, 48)\n",
        "plt.xlabel('BMI')\n",
        "plt.ylabel(\"Prix moyen de l'assurance\")\n",
        "plt.title(\"Prix moyen de l'assurance en fonction du BMI\")\n",
        "plt.legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L9myMMWbY3XbbbZSXlxMMBjniiCM4/fTTufDCC3nmmWeYNWsWjY2NlJeXc/HFF1NYWMhll10GwK233ppwf4FAgPvvv5/i4mLq6+tZsmQJH/3oR218oxlSdt4aY8zoc2dLLYflFnBYIP3hIlb5NcYYM+yuv/567r//fgC2bdvGTTfdxHHHHbdn+pXy8vK09qeqXHHFFTzzzDN4PB62b99ObW0tU6ZMGfK8m4nLzltjjBl9NoSC7O/Py2hbq/waY8wE80RHE092NAFwRUU1y1pqqYl0M82Xy+dKp/Dj+q0AfCC/lAqvn3vb6gD4ctk0nu9sZU13O3ni5ftV+3J13RaOyS/hpIKypO+3fPlynnjiCVasWEF+fj7HH388CxYsYO3atQPm1efzEYvFAIjFYoRCIQDuuusu6urqWLVqFX6/n5kzZ9p0LuOcnbfGGGMA1oeCLA6EM9rWKr/GGDPBnFRQ1uui/5Ly6b2W/2TSfr1eL8or2vO8uiTAZ5i05/XVVTMHfL+WlhbKysrIz89n7dq1vPjii3R1dfHMM8+wefPmXt1Hi4qKaG1t3bPtzJkzWbVqFWeddRYPPvgg4XB4zz4nTZqE3+/nqaeeYuvWrWkdAzP22HlrjDEG4JSCMib5/BltawGvjDHGDKulS5cSiUSYM2cOl19+OUuWLKGqqoqbbrqJj3/848yfP59PfepTAHzkIx/h/vvv3xM46MILL+Tpp59m/vz5rFixgoKCAgDOOeccVq5cyaGHHsqyZcs4+OCDs/kRzThk560xxoxOb3R3MMOXm9G2oqpDnJ3RbfHixbpy5cpsZ8MYY0bM22+/zZw5c7KdjXEh0bEUkVWqujhLWRoXEpXNdt4OHTuWw+P6xhouLJ1Knseb7awYM6F86N3X+dnk/ZiXW5B0nWRls935NcYYY4wxJk33tzXQ6Y7tNsaMnANy8miNRjPadtgqvyKyj4g8JSJvicibIvI1N/3nIrJWRNaIyP0iUuqmzxSRoIisdh+/i9vXIhF5XUQ2iMj14s4JICLlIvK4iKx3/08eucIYY4wxxhhjzJhW6fXjzXCGuOG88xsBvqmqc4ElwCUiMhd4HDhEVQ8D1gHfidtmo6oucB8Xx6XfAFwIzHYfS930y4EnVXU28KT72hhjjDHGmGH1q8n7U+q12LHGjLSgxjgyrzijbYet8quqO1X13+7zNuBtYLqqPqaqEXe1F4EZ/e1HRKYCxar6ojoDlJcBZ7iLTwfucJ/fEZdujDHGGGPMsLli92baY5l1vTTGZG5FsJUVna0Dr5jAiIz5FZGZwOHAS30WfR54OO71LBF5VUSeFpFj3bTpQE3cOjVuGsBkVd3pPt8FTB7KfBtjjDHGGJNIp8aIMrECxxozGpxdPIlIhn97w175FZFC4K/Af6lqa1z6d3G6Rt/lJu0EqlX1cOAbwB9FJOX72e5d4YRHQUQuEpGVIrKyrq4uw09ijDHGjH0iUioi97rxN94WkaOTxdAQx/VuzI01IrIwbj/nu+uvF5Hz49ITxukwZjzyYqe3MSNtXaiTKu8onOdXRPw4Fd+7VPW+uPQLgA8D57iVVlS1W1Ub3OergI3AgcB2eneNnuGmAdS63aJ7ukfvTpQPVb1JVRer6uKqqqoh/ITGGGMG0tzczG9/+9sh2deWLVs45JBDhmRfE9ivgUdU9WBgPs6wpGQxND7Ie/E2LsKJwYGIlANXAUcBRwJXxQWdTBanY0yx89YM5HdTZlNg0xwZM+LaY1Gm+HIy2nY4oz0LcCvwtqr+T1z6UuDbwEdVtTMuvUpEvO7z/XAKzE1ut+ZWEVni7vM84AF3sweBntbm8+PSjTHGjBJDWYkwgyMiJcBxOOUzqhpS1WaSx9A4HVimjheBUrex+VTgcVVtVNUmnGCWSweI0zGm2HlrBnLxrvW0xiIDr2iMGTJRVdaFgrza1Z7R9sN55/f9wLnACXHTF50G/B9QBDzeZ0qj44A1IrIauBe4WFUb3WVfAW4BNuDcEe4ZJ/xT4GQRWQ+c5L42xhgzilx++eVs3LiRBQsW8K1vfYuf//znHHHEERx22GFcddVVgHNnbM6cOVx44YXMmzePU045hWAwCMCqVauYP38+8+fP5ze/+c2e/W7ZsoVjjz2WhQsXsnDhQl544QUAli9fzvHHH88nPvEJDj74YM455xzcTka88sorvO9972P+/PkceeSRtLW1EY1G+da3vrUnTzfeeOMIH6ERNQuoA37vxti4RUQKSB5DYzqwLW77nrgb/aUni9Mxpth5a4wxo49XhMsrqtFMx9ur6oR6LFq0SI0xZiJ56623svr+mzdv1nnz5qmq6qOPPqoXXnihxmIxjUaj+qEPfUiffvpp3bx5s3q9Xn311VdVVfWTn/yk/uEPf1BV1UMPPVSffvppVVW97LLL9uyro6NDg8GgqqquW7dOe37fn3rqKS0uLtZt27ZpNBrVJUuW6LPPPqvd3d06a9Ysffnll1VVtaWlRcPhsN544416zTXXqKpqV1eXLlq0SDdt2pTwsyQ6lsBKHQXlWyoPYDFOvI2j3Ne/Bq4Bmvus1+T+/w/gmLj0J919XAZcGZf+PTdtMfBEXPqxwD+S5OUiYCWwsrq6OqVjPZLG+3lrBu/Sneu1MxrJdjaMmVDqwyH99q6NujPc3e96ycpmm5zMGGMmkJ8fsm3glTLwrTf2SWm9xx57jMcee4zDDz8cgPb2dtavX091dTWzZs1iwYIFACxatIgtW7bQ3NxMc3Mzxx13HADnnnsuDz/sdP4Jh8N89atfZfXq1Xi9XtatW7fnfY488khmzHDCRSxYsIAtW7ZQUlLC1KlTOeKIIwAoLi7ek6c1a9Zw7733AtDS0sL69euZNWvWYA/LaFQD1Khqz+wL9+KM760VkamqurNPDI3tQPyX2xN3YztwfJ/05fQfp6MXVb0JuAlg8eLF/Tbh23k74c/bUenUgrKBVzLGDKmgxgir0haLMIX0x/1a5dcYY8yIUVW+853v8KUvfalX+pYtW8jNzd3z2uv17uk+msx1113H5MmTee2114jFYgQCgT3L+u4rEkk+Lk9V+d///V9OPfXUdD8Oz3e2pL1NNqnqLhHZJiIHqeo7wInAW+7jfJzhQ/ExNB4Evioif8IJbtXiVpAfBa6NC3J1CvAdVW0UkVYRWYIzveF5wP+O2AccJuPtvDVD4xeNNRydX0weFvTKmJGSKx6KvV42h7qYnZOf9vZW+TXGmAkk1TtdQ6moqIi2tjYATj31VL73ve9xzjnnUFhYyPbt2/H7k09XUFpaSmlpKc899xzHHHMMd911155lLS0tzJgxA4/Hwx133EE0Gu03HwcddBA7d+7klVde4YgjjqCtrY28vDxOPfVUbrjhBk444QT8fj/r1q1j+vTpFBQU9Lu/mCoPtjWkcSRGjUuBu0QkB9gEfA4nBsifReQLwFbgLHfdfwKn4cTc6HTXxa3kXgO84q73Q+0dp+N2IA8nRkdPnI6M2Xk7dOetMcaMZbkiHJyTn/EM21b5NcYYM6wqKip4//vfzyGHHMIHP/hBzj77bI4++mgACgsLufPOO/F6k985+f3vf8/nP/95RIRTTjllT/pXvvIVzjzzTJYtW8bSpUsHvOjPycnhnnvu4dJLLyUYDJKXl8cTTzzBF7/4RbZs2cLChQtRVaqqqvjb3/424OfaFO5iVk5gwPVGG1VdjTM2t68TE6yrwCVJ9nMbcFuC9JXAmJ/XZ7yet2boXFY+g1KPXUobM5LWhYIIcEJBaUbbi1OuTRyLFy/WlStXZjsbxhgzYt5++23mzJmT7WyMC/HHMqpKl8Yo9PpWqWqiyqRJUaKy2c7boWPHcnhcums9P6qaRYnXKsDGjJTnOlt4qqOZ4wtKOTa/JOl6IpKwbB7OqY6MMcaYces3TTvwi2Q7G8aYLHmju5Noxp0vjTGZOCgnnzm5+bRGM5tj2yq/xhhjTJpiqtSEu8kRK0aNmci8WAOYMSNpV6SbXPFQmmGPCyu1jTHGmDS1xqIsySvKdjaMMVl0w5TZFHos0rMxI+ntUJBKr5+FgcKMtrfKrzHGTAATLb7DcIg/hsFYlA8VVmQxNxODnbeDZ8dw+Hx513qaMux6aYzJTHcsxtuhTh7raMpoe6v8GmPMOBcIBGhoaLCL4EFQVRoaGvbMyXpT805CGstyrsY3O28Hr+95a4aenZ3GjKyzSyYxx6Y6MsYYk8yMGTOoqamhrq4u21kZ0wKBADNmzEBVaY9FKbIIr8PKztuh0XPemqFX6PGS77H7SMaMpDtbapkfKGSONz+j7a3kNsaYcc7v9zNr1qxsZ2PciKryxdKp2c7GuGfnrRntrq2aaV0ojRlhtZEw+eLBl+FsC/Y3a4wxxqTh5WAbeXa3x5gJ7z9rN9IWi2Y7G8ZMKNP9OdRFw/y7qz2j7a30NsYYY9LwREcTJR7rOGWMMcaMtFMKygmIJ+Mxv1b5NcYYY9JUYuN9jZnwPlFUSZn9Fhgzoq5rrGFObj4nFZRmtL1Vfo0xxpgURVS5srI629kwxowCUZzgd8aYkVUbCfFGd2dG21rl1xhjjEnRI+2NPNPZku1sGGNGgfvbGui2qbiMGVGnFZbTGotSE+7OaHur/BpjjDEpWtPdwaGBgmxnwxgzSmQWb9YYMxh+EQKSWTXWKr/GGGNMihYHCin3+rOdDWPMKPDryfsz2ZeT7WwYM6E81N7A3NwCPlZcmdH2Vvk1xhhjUtAQCVPtD2Q7G8aYUeKK3ZvZmWHXS2NMZgThre4O7mypzWh7q/waY4wxKXipq5UdEbvQNcY4OjSW8XQrxpjMfKN8BgCxDP/4rPJrjDHGpGBNVwfzcwuznQ1jzCiS57FLaWNGSncsxsPtjZR5fOyfk1lPLJuczBhjjEnBl8um2fy+xpg9bpl6IHnizXY2jJkwghpjR6SbYq+PmWRW+bXmKmOMMWYAuyIhHmpvyHY2jDGjyBd3rqMhGs52NoyZMCKqTPHlsD3czaMdTRntY9gqvyKyj4g8JSJvicibIvI1N71cRB4XkfXu/2VuuojI9SKyQUTWiMjCuH2d766/XkTOj0tfJCKvu9tcLyIWcd4YY8yQW93VziSL6mqM6cPG/Bozcsq9Pj5bMhmPCJn+9Q3nnd8I8E1VnQssAS4RkbnA5cCTqjobeNJ9DfBBYLb7uAi4AZzKMnAVcBRwJHBVT4XZXefCuO2WDuPnMcYYM0GFNGbjfY0xvRwVKKLchkIYM2LWhjq5q2U3U3w5fCC/bOANEhi2yq+q7lTVf7vP24C3genA6cAd7mp3AGe4z08HlqnjRaBURKYCpwKPq2qjqjYBjwNL3WXFqvqiqiqwLG5fxhhjzJBZECikymfz+xpj3nNMfgndGst2NoyZMLpiMQIeoTsWY1cklNE+RmTMr4jMBA4HXgImq+pOd9EuYLL7fDqwLW6zGjetv/SaBOnGGGPMkKmLhPhTS122s2GMGWV+2VhDWyya7WwYM2FU+PzMycmnNRbl9e72jPYx7JVfESkE/gr8l6q2xi9z79gO+3AJEblIRFaKyMq6OruAMcYYk7rVXR3MDxRkOxvGGGPMhJYrHib7chAg0z4Xw1r5FRE/TsX3LlW9z02udbss4/6/203fDuwTt/kMN62/9BkJ0veiqjep6mJVXVxVVTW4D2WMMWZCOTg3nyV5xdnOhjFmlLmyspoZvtxsZ8OYCePfXW2sDXVS7c/lwtKpGe1jOKM9C3Ar8Laq/k/cogeBnojN5wMPxKWf50Z9XgK0uN2jHwVOEZEyN9DVKcCj7rJWEVnivtd5cfsyxhhjhsRLwVab39cYs5d/tDWwM8Nxh8aY9AVjMQLiYXc0zJ9adw+8QQLDWZq/HzgXeF1EVrtpVwA/Bf4sIl8AtgJnucv+CZwGbAA6gc8BqGqjiFwDvOKu90NVbXSffwW4HcgDHnYfxhhjzJBoiITZEApmOxvGmFFodXcHYbXJjowZKe/PLyFfPLTGojRHIxntY9gqv6r6HJBs3t0TE6yvwCVJ9nUbcFuC9JXAIYPIpjHGGJPU+nCQBQGb4sgYk1jAMyKxY40xwDvdnczJzScgHqZlOOTA+nEZY4wxSSwOFOGTZO24xpiJ7MYpsyxES8YAACAASURBVKmwIREZ2RQKUu0P2O+rScsb3R3sl5PHVJ+fDxSUZrQPa64yxhhjkriybnO2s2CMGaW+tGs9NWEb85uJL+xcx5vdHdnOhhljIigB8VAfDXNHy66M9mGVX2OMMSaBxmiYYs/4u6sjIltE5HURWS0iK920chF5XETWu/+XuekiIteLyAYRWSMiC+P2c767/noROT8ufZG7/w3utnZrx4xjNuY3E9dUzWSWP5DtbJgx5mvlM6jy+fEAsQz/9Kzya4wxxiQQUuW0wvJsZ2O4fEBVF6jqYvf15cCTqjobeNJ9DfBBYLb7uAi4AZzKMnAVcBRwJHBVT4XZXefCuO2WDv/HMWbkTfPlUOb1ZzsbY9JD7Q00xzILWGQmrp/Wv0tElUKPl2PzSzLah1V+jTGjRkcsSq1NG2FGiW3hbg7JLch2NkbK6cAd7vM7gDPi0pep40WgVESmAqcCj6tqo6o2AY8DS91lxar6ohvIclncvowZV66orE4a2dX078VgG3WRcLazYcaY1lgUnwhC8qjKA7HKrzFm1Hizu4P72+qznQ1jAPhLax3ebGdieCjwmIisEpGL3LTJqrrTfb4LmOw+nw5si9u2xk3rL70mQbox485Xd22gLmoVuEx5bUSESdPc3HwAOmMxnupszmgfVvk1xowatZEw97TWoTZvosmylmiEYo+XcTpc9RhVXYjTpfkSETkufqF7x3bY/whF5CIRWSkiK+vq6ob77Ywxo8gd0w7i4Jz8bGfDjDHH5zsRnkVszK8xZhyYHyjgP/JL2GFdn02WFXq8/Gf5+Lxhqarb3f93A/fjjNmtdbss4/6/2119O7BP3OYz3LT+0mckSE+Uj5tUdbGqLq6qqhrsxzJmxJ1XMplqf2ZzjU50F+5cx/pQMOGyZzqb+WXDtoTLzMT2myanOCnx+Ph86ZSM9mGVX2PMqPFWdyfnlEwiIPbTZLLrrpbdRMZhFFcRKRCRop7nwCnAG8CDQE/E5vOBB9znDwLnuVGflwAtbvfoR4FTRKTMDXR1CvCou6xVRJa4UZ7Pi9uXMeNKeyxq41YzFFKlQ6MJl+WJl0oLJGb60aUxnuxoymhbu8I0xowayzubqfYFeLW7PdtZMRPcG90dlI3DaY5wxvI+JyKvAS8DD6nqI8BPgZNFZD1wkvsa4J/AJmADcDPwFQBVbQSuAV5xHz9003DXucXdZiPw8Ah8LmNG3H1t9XQmqcCZ/h2eW0ihJI6qUB8NJ70rbCa2M4ucXkJRVTaGuzLaR8olu4jkq2pnRu9ijDEpECBHhMfamzipoGzA9Y0ZDqrKgTl5KY333fpiZoXvUEm3bFbVTcD8BOkNwIkJ0hW4JMm+bgNuS5C+Ejgk1TwZM5b5LN5zRj5XOpkDcvISLssVocgzcLjBM2ve5NqqWRyUa2OHJwJVpcWdHssrQkkK50giA975FZH3ichbwFr39XwR+W1G72aMMf24pmoWIkK+x0NXLJbt7Jg0PdnRxO5xMF67W5VPFg88BvXfd7fx5wuzE6jJymZjsu83Uw5gpj+Q7WyMSf9Zu5GVXW0Jl0VUaYsNfEf90NwC8jOsAI2kv7bWsaKzNdvZGPPCKE93tgCQLx6+XDYto/2k0u35Opz5/BoAVPU14Lh+tzDGmDTtDHfzxxYnxs73Kvcl4LFRGWPN2u5Ong+O/QJ+TXd7v2OJVJVnr2/hyR83j0BM5KSsbDYmy67cvYV11j03Y8l+Pv3iIS+Fa4C5uQXkjHBE/ufdylc6/t7ekLSib1LXFYvtiQkT1Bg/rn83o/2kdHWpqn1DrtkAB2PMkNoZCRHwOIXYxlCQu1t2D7CFGU22h7tpiUV5vasj21kZtHfD3cxIEsE1FlEevaqJF29qRbyw9Jrsdc+3stmY7Gpyu2COJc90NhPS7Pes+nr5jKSRsv0ilKYQc+GGph3sHuGAY/e11ae9TYnHR8EYuEM92sXPwiBk3vacSuV3m4i8D1AR8YvIZcDbGb6fMcYk5BHhQHfOv339Ad7qHvuVqInk5a42jssv4buV1cTG+DzN83ILOCjB/JPhrhgPfL2e1+/rwBcQPvbrSg79WGEWcghY2WzMqFDiHVuB8R5vb6Itmv12sh2RbvxJxks3RiMp31Ef6RHX/+5KPyDnf5ZP57TC8mHIzcRSGwnxaLsTV9EnwuK8zMrfVCq/F+MEu5iOM1ffApIEvzDGmExN8+Uw1w1akevxcGgga5UKk4Hj80s4IlDEw+2NvBQc2927tke697qg7WqJ8ZeL6tjwVBeBYg9n3VzF/scnDtYyQqxsNibL7ph2EIVj7I7ec8FWOkfBnd97Wut4J0kFd4rPnzQYVrzPlUxhnxGeZ7kgg6kYl7XU8vIYLxdHg9ZYlBZ3LLgHYX9/ZmXwgN+gqtar6jmqOllVJ6nqZ92okMYYM2Rubd5FZ1yQq2PyimmOjr0uZRNRWGP8pbWOXI+HwwOFrBjj436faO893retNsLd59ey/d8hiiZ7+cwfJjH98JG94OrLymZjsu/8He9QE+5Oef3bmndxc9POYczRwA7LLSA/gwrccNAkHVfro2E2pnDnd3c0RPMIdz3PyeDYrQ8F2RFJ/TwxiXlFmObLASCGcldrZsPjUon2fIeIlMa9LhORvaY2MMaYwWiJRXqFrV8b6uTFMV6JGu3CQ9T6/0Z3556Im9P9uRyUQov9aBWMRXsFW2vYFOaPn91N/YYIFfv5OPvOSVTu789iDh1WNhszOqQzyKPK66ciy92k3w13Ex5kpL4PbH1t0Pm4a9rBLAwUJVw2xZvD+/NLBtzHQ+2NI96Fe7Iv/d//ebn5TPbmDENuJpZqfy4fyHeKPUHQDIdYpdJ8cZiqNve8UNUm4PCM3s0YY5I4s6iq17yqc3MLeNPG/Q6bsMb4/I53iAzB+Nz6SJglecV7Xs/NLWDXGJ3yKCAe/l/FPgDseK2bP567m9adUabNz+Ezf5hE8dRRM77PymZjsuyE/FJm+FKv1JR5fUxKY/3hsDBQmHSsbar28Q2+58t3dm9mazjxPOkbw138LYXAUrkiFHtHttv52gyie59ZVMVxKVTmTf9WdLbyr06n2PMAny2ZnNF+Uqn8ekRkTzhLESkHRk3pb4wZ+6KqtPbpujTZ6+fjRZVZytH4F1LlpIKyvY57Jubm5vcan9UYDfc7VdBo9kKwlde7O9j0TJB7vlBHV0uM/Y4LcNYtVeSVjKqxfVY2G5NlCwOFtKcwH22PV4JtvJDl+V7/1dlMhw7ubumnUpgHfSDvRrqTNpLOy83n1IKBA0SdUzyZkhSiQg+lhRnEI/l5wzYeG6Nl4mjSpe9NdSTA26HOjPaTSuX3l8AKEblGRH4EvAD8LKN3M8aYBOqjYdb0mSJHRHi1qz2tCwuTuheDrZxVXMXa7swKjx6N0TB3tNT2SjssUDBmpzxaHwrS/kyU+y6tJ9KlHHJGPmf8uhJ/3ugYIxfHymZjsuwXjTU0ptGA2KnRrJdpJxeUpTSNUH9+0Vgz6HwcmJOHP8kcvZtDXbyawry4zwdbqI+O7FRHm0OJ71b3pzUWoWuIhhk93dGccXffsW6mP8D+OQHAuUbsCSKWzrh7SC3g1TLgTKAW2AV8XFX/kGZ+jTFpao1GuDXLgTFGSm0klHAcTVh10JUzszdV5eH2RgLi4YG2hkHN+fhKsI3FfVrC/eLhisrqwWYzKzzvwprvd6BROOJzRSy9phyvf6Qn0xiYlc3GjD0nF5RxfEF2u7+uDLbRMQoalS+vqObYJF2BS7y+lKI9vxMKDsnQnXRkMrfzZ0sm75nNYrCurt86yBHbY5dPpFd09Z6AaefuWJvWflJtyl4L3Ac8CLSLyNi8qjFmDNkRCfH8BAn4NC+3gDOL9u5GdWiggJ1jdOzoaFYbDVPtz0VEWJxXxKpg+vMW9jg4N5+j48b79lgb6mR5R3OCLUavUGeM7u/FCHcoB52ax398o6TXOPRRyMpmY7LomqqZzE0wJ3gyTdEIMuIz0/ZW7PUO+nftK2XTBp2PL+9axzOdLQmXdcSiKXVp3defy5Qsj6FOhRdh2hCMkwZnSNh4sKarncfcOXtT9VxnC7sj793pv7ZqFgBfLpua1n5SifZ8KU7L8uPAP4CH3P+NMcOoORZhc5JgEOPN39sb2JGgkjs3t4CTC8oSbGEGo8rr56tl0wE4vaiCI/MSR9wcSEyVl4NtFCWIXnpATh7PBxNf2IxGqsoj32+keWOUiv18LP1h+aiu+FrZbEz2PdBWz7o0AiC9EGzln+3ZnZFsa5pdRBN5YgjGr3arEowl7nVU4fVT7QsMuI+lBeWERvjO78FpNHb0uLl5J093Dk1j8KycANFxcO/32c4W7mxJb6qiYNyYX4CfNWwD4KmO9K41Urnz+zXgIFWdp6qHqeqhqnpYWu9ijEnbkYEilmRYKRlr1oeCFHsSBxO6um4LsQk6vmW4XF23hZ5Ljhzx8N8N2+hOchHSn/WhYNK5mMu9fvb1D3zxMlr8+8523nkkCHlw+q8qySkYdWN8+7Ky2ZgsW9nVnlblax9f9u9UHpyTR+4gG/bSqfAn84XSKUxNcizaYtGkcwDHu7F5J20jPM9vJmN3c8SDd4ju+L8YHHgs9FiQIx68aZ6HHyuqZJr/vXOmxe2+vzbNwFeplO7bgLSb70XkNhHZLSJvxKXdIyKr3ccWEVntps8UkWDcst/FbbNIRF4XkQ0icr24TfEiUi4ij4vIevd/uz1kxpVfNW7n86VTsp2NEVHq8VGWZO7Dffy5vDsELdVDJTrGK+LtsSgeEXxxhc6c3PyM5lReFwpyRD8NNKcVlrN7DHRbr/l3N8t/6bTKL7y6kIr9xkS3sozKZmPM0CpM0nCbyKGBgqRz2w6kJRqhbgh+T3PFM+J3SxMp9viYlZO4gTSssZTH1o50/5wtGfTI+8mkWbw/f+/hQZnY1587ZBXpbPpwYTk/qNo3rW2e72zpdQ12UArjwhNJpfK7CVguIt8RkW/0PFLY7nZgaXyCqn5KVReo6gLgrzhjlXps7FmmqhfHpd8AXAjMdh89+7wceFJVZwNPuq+NGTfqo2Ge62ylK4M7cmPNx4sq8SRpATypoKxXRS3bPvju62wYglbvbOmIRfloYUWvtP/ILyGTQ3xKQRkL+pn2YXckzANt2e3iN5D2+igPfrOeWARmnRtg3qkF2c5SqjItm40xQ+SWqQcyJUGwxmQ2hboyjk58d+tufli/NaNt473W3THoIFH/3OeQQefj1401Scf8lnp95MnAVZQT8kuZOQZ6GP2+eRd1kaGJSh1VJTIOuj2vCwW5Oc2grmu6O/DEVfxPLXSmw3pgxry09pNK5fddnDFFOUBR3KNfqvoMkHAks3v39izg7v72ISJTgWJVfVGduN7LgDPcxacDd7jP74hLN2ZcOCqviOZohJYR7tIz0mKq/LKfaRMOyMlj2yi683tB6WSaknT1HQs2hoIc3qfCWu71U+LxEUwjAmhHLMpPGt7td50Dc/JYl+E8fCMhGlb+flkDHXUx9lmcy87PdQ8q8vUIy6hsNsYMnYt3rmd9Go2h74Q6M+plA9CtMTqGoDF8mi+HPM/ghnWctu2NvdJeDrayKo0uuQJJhzR1xmIplbOzc/J4NzKy1wdlGUwT9e+u9iFpNI+qElYlNvbrvmwJd/FmmrN5+BBy4lrqr2/cTkyV02veTHM/A1DVH6S1x9QcC9Sq6vq4tFki8irQClypqs8C04H4q+IaNw1gsqr2NBnsAiYnezMRuQi4CKC62oJhmtEvqsqBOflEVOkeOxfjGWmKRpJ2eQbwivC3tnqOHqIuQ4NRFwkxzZfLtDEQXTKRqCr3t9XzvgTTS2wLd1MfDXNiigHGXu1q7/euL4BHhEvLp/e7TjY986tmalZ2UzjJy0d+UcG1sXeHLCLncBumstkYk4Z078AtDhSxK8Ouy4sCRUwfgt+n9lh0UMN3km3759Y68j1eFqUYq+SmqQf2uosXL9/jSSkQ443NO/sdejMcDs5gyqI5OfnkpdE9PpmOWJTaEZ7XeLgUeLwcmGa35SsTdJPO5ExOJdpzlYj8XET+KSL/6nlk8F7xPkPvu747gWpVPRz4BvBHEUn5Ste9K5z086vqTaq6WFUXV1XtPZ2KMaNNXTTMI+2NnF5UmTQgxHjhE2HpABWuAo+X9lEwL+Hqrg5CGtvT2jjWrA11Mic3cbfeY/NLeDZJF7RESj0+3p9giqO+Qqr8awgigw61tY90svKOdjw++OgvKyio9HJFZfWo6mLfn2Eqm40xaTgwJ4+paVRIS72+jCpPAJN9/iGp/B6fXzqo7Ycq0vDtLbV0auJyfUckxEMpToOTSvfoobQigzv3XyybwrFD0IAf1BinF1YQGOSd+9FgaWE5X0xziqLv123p9fojfYZwpSqVo3cXzlyCs4AfAFuAVzJ6N0BEfMDHgXt60lS1W1Ub3OergI3AgcB2YEbc5jPcNIBat1t0T/fo9OJlGzOK7YyEmOrL4amO5qRjYsaL+miYsgHmrft6+XTyR7iAS2RLuIt5uQXMygmwaQxOQ3VwTj6fKU7cAFji9fGtin1S2o+qUhcNU5VCw8xUXw7L05yGYLg1bAzzyPecC6sPfKuU6YfnEtIYtzfvynLO0jKkZbMxJn2Xlk1PKSpxj9e7O1jdldm86v9sb+Tm5vTGSCbyYHsDwUH0KPMi3Dhl9l7pXyufwUWlqVdmXgm28UaSbq/zcvM5NkEPpb7OLp5EZRpjrofCrAzGGP+4/l3WdHWktc0Htr5Ga5+u3yUeH++EgnSOgpsBg7Wis5Urdm9Oa5u+Y9VDGkOAm6bufT72J5WryQpVvRUIq+rTqvp54IS03qW3k4C1qrqnO7Pbgu11n++HE9hqk9utuVVElrjjhM8DHnA3exA4331+fly6MWPefv4AJxaUUeL10jKGx5em4uVgGw0DdONpiUX5a1v9COUouc+XTmG6P5ePFFZQmsG4n2z7ecO2frterenqSGnS+ZpIiNdSvIAr8HiZ5POPmjvl3e0x/vZf9YSDypwP5XP42U7X7e3hUNrTLmTZUJfNxpg0XVq7IeEc9cnsjoTYmOHYz6H6dXpfXjEVAzQ496clFuFLu9bvlf6vjiaeT6Oxfl9/btLPtCXUzRvdA1cWt4a7Rnw2iGTT+/VnVyREY5rxWw7LLdgrEGh9NMy2cNc4CHcFbbFI2vFTFvYZavVoRxMhVS7auff52J9UKr89V6U7ReRDInI4UD7QRiJyN7ACOEhEakTkC+6iT7N3oKvjgDXu1Ef3Aheras8V2FeAW4ANOHeEH3bTfwqcLCLrcSrUP03hsxgzJqzsakOAan+AWRmGch8rdkVCA857ONnnz7i1fKi0x6JcW+8EeJrmy+GZIZqwfqTsjoQGvHg6NFCQUtfnlV1taY2zuqhsKq2joKVaVXnke400bo5QOdvPKVeV4c6eR5fGmJekS/golVHZbIzJng8XVmQcv+KMokq+U5la75z+vN3dSdsgfo/DSRoyX+/u4K00AhxeXTUz6ZCnYq+XIwMDH6fng61p3XkfCpm0kX6yuCrtQFkH5uTt1aV7VyREpc+PbxxMdVTh9XNuSdJwTQn1jTOi7r90pVL5/ZGIlADfBC7DqYh+faCNVPUzqjpVVf2qOsNtoUZVL1DV3/VZ96+qOs+d5mihqv49btlKVT1EVfdX1a+643tR1QZVPVFVZ6vqSXGVZWPGvBWdrQQ8Hso8vl6R7cajz5dOoWqAVmi/eNgvy9MZvNXdwUHuWC0R4aVg26i5m5mKpmiEkwcYW13o8bIgUIgO8Lk+WljBUSmM9+1RGwlnvUtxNKw89bNm1j0eJKdQOP26CnLy3ysCJ/v8LBnhwCmDlFHZbIwZOl8qncqcnNTH8DZGI+zvz6xBe30oyBMdg290nZUTGPA3vj+FHi8fL6pMmJ7O8KRv1G7kxSTRoZuiER7tGPiyvsTjpXIQd7FfDrbytV0b0tqmMYM7vwfm5PG+NBs97m2r32v2gW6NcVRe8VjrpZTQ0fnFlPcT7DSRW/t0+/9WxT74xcMHC9Jr9+33LHW7Is9W1RZVfUNVP6Cqi1T1wbTexRiTlghKocdLDOWvrdnv7juc/ti6O+kcv/E+UVxFWxa7gOeLt1flaHZOHjUjPMXCYERRFqZQuTu5oIx1/XTLC2mMXzRsSysw1AxfDjWR7kFdcA1Gw6Ywd51Ty6o/tCMeOO3aCspn9r5gurlp56DuhowkK5uNGR0aoxG2phH/4d9dbdyX4RCeN7s7eSnDaZJ656F9UPdKQxojN8Hv/5WV+3JZinEjANpiUXZHE3cZn+UPsDgwcHl1VnHVoD5LkcfHvDQDkGXSSHpN/bs83p5+4Me+JdL780toikbSmpZwtHqovZHr+pnmsq+o6l7Rwf/aWk8MJdeTXmNAv5VfVY3iRGY2xoygq6tmAhAQD13jeKojVU35wmF1VzvLsxj8K6gxquPuPn+hdMqQRN4cCV2xGHe1pBYT0CfCspbapMtf7+pgH396n1tEOL9kCiN9Jqsqr/6pjWVn1VL7VpiS6V4+ffskZp+w952Xpmgko/kbs8HKZmNGh7+01aU1E0Gp15fxXbtCj5dJ3sHP/rAoUEhJmnfc4tVHw9zdWrdX+lV1W/hZw7aU9/ORwnJKkvzmbos4U+8N5MG2BuoimU/94xNoT3Pu5He6gyPSkHtO8aS9KmkPtTWwepCNF6OFJqjMDuSckkm9Xm8OdxFS5W9tDWntJ5X+Cc+LyP+JyLEisrDnkda7GGNS1hWL7RlbKiJcnGYo+LEkqDFmp9gFbF5uAW+lEABjOERV+Wtb78K+W5Vr6rdmJT/perWrncMHmJO3R77HS654aEpy4RFBeX8KUTj7muLz85zbeBEND3/R3VEf5b6v1PPEj5qJdCnzPprP+fdOYcbCxBX304sq94z/HSOsbDZmFMhNY9qZ9+UVc1QKdzQT+XhRJd+omDHwigNoikYyCtrUI9mY3y6N0Z1GY/38QCGnFCYeiuNDUprCaLBz3rZEo7zalbjrdTJNaQauAvifyfszKye9oVtvdncQ6HMMWmNRziquIm8MTHV0/wA9HA7MyeeGNKI0BzXG7j4NHVXezIJpptL0s8D9/4dxaYpFlTRmWOyMhHqNg3i+s5XqkuyOdx0uPhE+m2LAg0qfn1MLsxPPZ0MouNc4rYDHQzAWI6o66sffzM3N55A0unZ9vXw6BW5U6O2vdrPixla8fijZx0dwaoxJ++XQXB2heKoXjy/5Zw8HYzRujlC/IUzthhAr3mrlnZpOWrZHmTLXz8f+r4rCquTRpzO1YXmQR7/fSGdjjECxh5O/X8bBS5N//vZYlDR7TY0GVjYbk2U3TJnN7DSCUr7Z3ZlxpOWH2htY1dXO/0zeP6Pte9RHw4O6c3hATh43Tz1wr/Sj8orwpnEn77/rt3FGUSXnle59DZDv8Qw4CwQ4UyKlW6mM16UxWjLoQqykF337720Nad/IWN3dQUcsSlHc9eBkn58dkRARVfyjvMy6tWknH0swNrxHkdfL75t37enpOJCWaITXuzs4vuC9eaovKZ+GD+E3Uw5IK28DVn5V9QNp7dEYMyge6R3OfU13O59hUj9bjF0rOlvp0CinpThReWM0TGs0QvEgumxlYqY/wNTivbubfaionKDGKJShr8ANFVXl7tbdXFw2LeVtCjxefla/jRMfKuWZX7XQt6H7LZyInh4fFE/zUVbto3QfHyUzfAQbo9RvDNOwMUJzTYT4q6wcPLS4o5h2vRnm7vN2c9atVZRMG5rvM9QZY/nPm3ntL04Pgeqjcjntx+UUTel//5tDXazrDqYVxCvbrGw2Jvuuqd/KN8pnsCjFcaAbw0FWBFs5Ockdz/40RSM0DKKLb4+IKvmDuHO4MdTFLxq2ceu0g3qlz88tJDeNgFciEEtSDfciKcWVOKWgnF2RUK8hSeko9/o4ZYBAkH1lEmDrqc5mluQVccogG/Dn5hbwQmcr3aqM9nlABppLekVnK68kCXiWSJfG9roTflXdFr5TUc23azfxj+pDU97XgFccIvL9ROmq+sNE6caYwfEhzI2bcqXU46M7Fkura9VYURsNs38ahVZTNMJboU6WjHAlZVlLLecnaJ0+PLeQreGuUT1FzqZwF7E0m/lDrYpcCcufd7opLzqvkOkLcnlpQyvhbUrBLi/N70Zoq43S/G6E5ncTdwPz+KCs2kfFAX4q9/dTeYCfolleist83HdJHbVvORXgT91WRVl15hE7AXa9EeIflzfQtCWC1w/Hfq2ExecVISnc0t0W6Up7HHO2WdlsTPbtiITSuot6WG4h70jq0wHFq/YHhmT2h4DHk/ZYy3jN0QibEsTquLl5J4Ueb8p38r5XuW+/XagPSOGO+nWNNdwwJfWus31N8eWkHVdlP38g7aM3yetH0tzq08VV+PtU9u5o3kWHRkd8eqdMDDQdYgxN6679LH+Az5VO2Ss9gtKR5neYSnN7/CC7APBh4O203sUYk7L72+o5o6iSQrfb6f+rrM5yjobP7Jw89k2j0jEvt4BXgm0jXvldHwqSk6BF2yfCPS11/HDS6K387oyEODaNKRZ2vRniwW/W46kRfIXCh6+t2BMgqupEP3niIc89N8NdMVpqojRvi9D0boSWmgiBEg+V+/upOMBH+Uw/3ri+WY3RML9r2skVldWcdcsk/vrlOna8FuLu83dz1i2TqNw//QpwJKS8dEsrL97USiwClQf4+NBPK5h0cOqBYU5Ks+V/lLCy2ZgsK/J4meRL/Xdrss9PkSezMb/H5BezK5I4OnI6jgwU0RmLUpphD6pckbSmNErmxWBr0ruubbEoK4NtfCmFn2b/IBoENoW6eKi9Ma0o1S93tRED0unv9aNJM5MG90rmn+2Ne1X2ghrju5X7UjAEx3+4vTTAXd2TCsr4j/zS4i0dpwAAIABJREFUfteJ92Z3Jzsi3b2Gvx2TV5JWV/seqXR7/mX8axH5BfBo2u9kjElJbSTUqzBd1lzL0flFzE5jLsGxoisWozyNLkQH5eRxUBrjq4ZCRyyadCqEPI+XkI7ucb/7+QNMS6GBQVVZfU87T/13M9EwTJ7rZ/9r85h9gHO8o6rc21rHRXHdp/0BD5UHeKg8ILXvsNzrpykaJqpKoNjDJ2+u4r5L6tn2Sjd/umA3n7ypislzUq+01qzq5rEfNNKwybnzvOizhRz7XyX4A+ldGNzUtJMvp9EtfDQYbNnsTpe0Etiuqh8WkVnAn4AKYBVwrqqGRCQXWAYsAhqAT6nqFncf3wG+gDMjx3+q6qNu+lLg1zjXh7eo6k8H81mNGa1umDI7reBDr3V1ZHz39o8tu3kl2MYd0w/OaPsej3Q0cXaKsTYSOTRQwAP7HLJX+vlp7vOpjmZ8CPslKNP3zwkwK4Wy/pNFVczKsMszOA3YB6d5TVHu9aV93/WK3Zv5UdUsJvlSL99aY1G6YjFyvO+dXx8urOCelt2cWVzVq/FiWXMth+TmpzSd4UgZKMjm2lAntzbtSvl8bolF9uopUO3PpdDj5Z8Jzsf+ZNJ0kA8MPtycMSahs0sm97rLmO/x0BId+3O6JfJAe3rzHYoIP6jfSnQE54sNq/KZkuRjri+vrM7oh3QktEQj3Nq8a8D1Qh0x/vHtRp74kVPxXfDpQs7+w2SeK23Zc6dhfSg4qK5yPT5ZXLUnImhOvoczb6hk1jEBgk0x7vnCbnasGXju5K6WGI9e3cjd5++mYVOEvGoPnuvghMvL0q74AtREukdt40Ua0i2bv0bvO8X/DVynqgcATTiVWtz/m9z069z1EJG5wKeBecBS4Lci4nUr1b8BPgjMBT7jrmvMuPPZHWvZ0M+86H3VRcO8kmZ04aF2cE4elYOIm/GvjmZOfnfNXukbw11pRV8u6acSuTMSYlMKxzWCsjaUWTdygBKPj1kpzjjRw0N6wa4A6tOcDxpI2CuuORahKRbZ6xpodXc7G9Pc/3B7rau93+WbQ120pRFsrMDjZUqfxoO7W+toiIY5bdsbaV0XDniVICKvi8ga9/Em8A7wq5TfwRiTMlVlbXfvH/IDc/Io8ozegEojbaovh80j+CO/rGXXXuH14zVGw/y9vXHE8pOOl4KtHDlAS3DduhDLPlXL2oc78ecLH/5ZBSdfWYYvVzi+oJTlHc2A0yPh6DS6TydzSG4Br3W912PXH/BwxvWVzD4xj+5W5c9frGPbK4m/X1Xl7X92cutHd7Lm3g48Pjj64mI8t8Epx2YWSERV94rkPRYMpmwWkRnAh4Bb3NeCEyX6XneVO4Az3Oenu69xl5/orn868CdV7VbVzcAG4Ej3sUFVN6lqCOdu8umD+7TGjA9LC8qYm5PZMJlj80u4pHzwPVR2REJpVTr6SjZO99nOlj3T2aXiusn7c1qSAFB54k26LN5A0+kMpNLrY3tk4AbXeGVef9qV32MzmB7wiEAR+X2u/Z7saKLan7tXMLBvV+zDB7M0G0YiqjpgN2+vSErfcY/ZOXkckiC+SmzP/0NY+cUZR/QR93EKME1V/y/ldzDGpKw5FuGdPq2Y++XkDWpC+v/P3nnHOVLWf/z9zKSXzfbd6wWOO3qVjghIV5SmAgKCYEGsiIqKIoIIivqzgSgIIkVABUSaUqX3dtwd12+vbC/Z9Ezm+f2R7F7KJJnJ7t7tcvN+vfZ1t5NkZlI2z3zb5zNZkVLyjUbrTSQH+0JbtPK7IZ1iRoVWpekONy/Hw1vsfKyw0O2r6Mn7zr1R/np6NwNrNJoXODnrb23seNzmFu+9PUGOzy1OC92+cRH28giFfxR5JjtcghOubWLH432kY5J7vtjL6mcLs/5DGzT+fkEvD3yrj1ifzoy9XJz993amf87FeW3TiOgZSxWYEZJS8sm6ljE9p63EWNbmXwHfYvN1QxMwKKUcUS5bD8zI/X8G0AGQu30od//R7UWPKbfdxuZ9x/GBRhZaGEnqy2jsV2NrqoSavuOK2c9bVzaANcN8l4ePB0sdGlSEpe6gi7tWlVzvjNCXSXPzUFfVfTgRlkanilmbTvJWMlr9jnksT8XRLDY+H+tv5AiL2hL3DPca+iYf5W8c1YQZ4cretdwV7ra0/4lEgmGFOp9Ph9rYzeM37dP7cKSfxUXv1bn17fiFwj5VWqyLMRP8OoBOKeVaYAFwgRDC/ISyjY2Nabq1NNMdha0uG9JJHp6klcWx0KEleb1KW4wRO7l8pCwq+42FU+taEBVaYj2KwpwxzBxNFJqUvBQfLlkkAaQueeyqAR76fj9aQrLLx318+vZWGucVXkQ4hOC+SB/LkjGuG9g4LuelCEGT6iyx7FAcguN+0shuJ/vREpJ/XtjL8sfj6JrkpT+H+fPHO1n9vwTuOsFRlzVw2s2thOar/KZ/AwB1qsrTFqoOI7yeiPBYbHBcntsWpqa1WQjxEaBbSvnqRJ+giXP5nBDiFSHEKz09PdUfYGMzydjR5TPlRzvC4mSUh6O1refPxoZ4ODJQ02Pz+U90bPvwKyoHGIhOXt4yl4uazCe0w7rGsjLB/G5uvykhwjNDbXjHIP6kCCxVHwEOqkFw8/s9q3muhiS5VhQYfq95DjcNdtJf9JlL6DqDW3A8rto1mCDrhlEpyXLLYCe/H9hoOhGTkDqeovn6NxIRVCE40t9gKfFi5hPzdyAjhNgeuAGYBdxu+gg2NjamWej2cWbRfGlIdRAuNlp9H7AunSRVQ/ZZCMGtQ+OT4UzFdJY8GOOfX+nl//Zfz6u3Fs5irUjFTbWcf6KuhaHM5HqP3klGDbPGWkryr4v7eO22CKoTjrm8gWOvaMLpNV4OdnT5uLZ/fYH39Fi5qGmmoXWXogqO+mEDe50eIJOG+77ey80ndfLUtUOk45JFx/o49/5p7H5KAKEIHooMcHSgEZdQWOTyMWDhInSEDi3JbMfUsjnKUevafBBwghBiDdmW5MPJilPVCyFGWkxmAhty/9+Q2ze520Nkha9Gtxc9ptz2EqSUN0gp95FS7tPSMiWr7zbbOD/vX0+3he8dpxD0abWtFQ4h8I/DCNRB3rqS2UkrPBsb4se960q2/6J/PbeaqNaOsK+3rmy4sjgZY1my+izvS4kwHWlrbcv5tDtcDFpcu5elYpYVhnWy4plWOCFQWl2/ZbATASV15+MCTSzYgmKg5218r2IXQhpJdyZV0as5LnVcQiFtsoq+hzvAtKLP7WuJCAMZjav6Oqr6CudjppdSl1JqQoiTgN9IKX8jhHjd9BFsbGxM87ehbvbwBFiYpy7cqDo42j95ZjnGi6TUa/ZWDakqgxmtJqsGLSlZ/UycpQ/FWPlUgnR88xfvk9cOMmMvN+07Z79gn40NsbeneovailScZal4RWGsLc17qXiJJVQyonPvV3tZ92ISl19w4m+amb1v5ar1Hp4AC1zemmaWyuEUCn8a3Mg+nmCJF6BQBIdfUo/TK3jxxmH6VmmEZqgceWkD8w7evLinpc6R/vpRcThVCC5smEFCL80OV2Jnt4+ZUzP4rWltllJeAlwCIIT4EPBNKeUZQoi7gVPIBsRnA/flHnJ/7vfnc7c/LqWUQoj7gduFEL8AppOtPr9ENum/IKcevYGsKNbp4/WkbWwmG1bCoI8Gm3g2VtuYzEnBZk4dhxGNZak4G7VUzetvWkqMpl57M2lLXVnH+BvKBmxeRWGhu3ow946JALkSGZlNFFuhN2O16Rm+3TSLRovXKympF4y8ZaRko5bixGBzSRJkltNtyXJrrOzg8uKpUHHXJWzQUsT1DMEyz3s7p5cP+kKmK/caEnfRfR2I0dkdK+NwZt6JtBDiNOAssrNFAFvuFbax2YZYpyX5sFrY6uMSCstT8YKA+P3AIb6Q4QJqhvPrp+GzEOBk0pJ1LyZY8lCM5Y/FSUU2f0lO393FomN99K5I89Y9Uf79nT7OursNp0dhZTphKqDd2e3n72MU3hhvjg80FiyQkd4Mf/9iD91L0viaFE653pytkCoEF1vwQDTLZ0JtfLd7NTu7fSWiHkIIDvlaiPrZDuIDOnueHsDlK3y/bx/qZhe3n73zgufn42GG9QwfNZhHK0eXlhqXWeatwHivzd8G7hRCXAG8DtyY234jcKsQYgXQTzaYRUq5WAhxF/AuoAFfklJmAIQQF5K1XVKBm6SUi8dwXjY2k5afts5jPwttsM/GwjXb9b0QH+bp2CA/a9uupsePYDQKY4WjA42G35kLXV5LradX93VwoLeOzzZMK7lNRfCaibGouU4PqhC8HB8uSaSaoUtLEa5B/EuTsmJVs5h16QSH+KxZQT0cHeALDdNHA+CU1Jnv9Bge985wN35F5Uctcy0do1Yk0KmlmFkmgaIKaFOdDFcIfndx+3g2Hmaaw0WDibnt/+TEvvx5DsuXt84lqetc2jzbkuWYmeD3HOALwJVSytW5bO6tpo9gY2NjmlkOt2F28Nn4EB+xcEE/FfhJ7zouaZqNuwaLGQXBw5F+Tgg2V7xfbCDDM78Z4r1H48QHN2ekW3d0suhYH4uO9hGakX290wmdDa8l6Vul8fQvhzjikgZ+2DzHlAWOW1E4rW7yVH03pJP8LdzDN3LzVwPr0tz9uR6G1meon+3g1D+0UD9r64qoeRWV7zfPKXsBIYRgt5ONW60HMmmWJGOcVeQruY83yDV9HZaC30ejAxxuUYhkkjDmtVlK+STwZO7/q8gqNRffJwGcWubxVwJXGmx/EHjQyrnY2ExF7hvuQ0Wwj8nAa106wdOxIa6rIVDrSCdHrefGwpp0Ar+FQKGYtxNRNmpJ9vYECvQwTgw2W5q/lUBEGgeejaqjpMXViI8Fm7g73MPbyQh3z9zZ9LHzOa++3dL9d6mhEHFHuIfZTg/HWJwvzq9luoTCaaFW/ji4iTbVWZDcdgoxJhEzq3RlKiuGS7Ije5VG2+4Z7qVTS3GAt44GE/mYhK6XVJt/0ruO8+vbeTo2xH7eOlwmLyerfkqllO9KKb8ipbxDCNEABKWUV5vbvY2NjRX28wYriiu9n0hK3XDu0wwNqoMno0Mlog/FPPS9ft68K0p8UKdxnoODvlTHZ//Vztl3t7PfuXWjgS9kLXeO/2kTigNeuy3Cc08PcYcF9cSk1Fk5Dkqc5VjzfIIXbgjTt6r6fNnz8fCoIEnn4hS3f7qbofUZ2nd2cvqtrVs98B2hyeHktqFuXrIoBCIQfLFxesnfSkBRR9WpzRDVM/jE1LQRs9dmG5utz/PxsKWgY6HbV7PHblBR2WkculRmOdxjEolakorypIG44E/7OvhF/3rT+zmvvr2sx26nlqJBqV4N/MPAJsszu/ns4Pbyctya73JKSlwWr9O8QrHUKi2l5NN1rTjzjrNJS/Hn3MxvMRc2zDBU4J4oUlLSXKFam9B15jg9zHWVH6vSkTSoDsw2yn+hYXpJ8Dusa0T0DE/FhizNVJvx+X1SCFEnhGgEXgP+mJvxsbGxGUc0KfnTYKfhbVe0zNvCZzPxHOqrXTReFYIvN05nWbJ8sLn62Tirnk7g8gvO/Fsb597fzoFfDJUoGufTtpOLgy7Izra+8IMwM2PmZ6JUIXhhAiyPpJS8cuswd3+uh//9eoibTujkL5/o5JW/DBPpMf6yn+v0sKcnwJrnEtx5Tjexfp25B3r45J9b8TdNrmDvU3Ut3DHUTcTkwrUpneTR6EBZhe1G1cG7Jme4vEKxpE46mbDXZpupQPc4VConMw4EDRaC2e2c3hItBrOcUtfM6eOgKzHL6aanBnHAEQSiICirlU4tVdb2SUOyyYT/bkLqHOGv57vNc2o6h/XpJG8arBevxod5zEAVW0rJe6m45SrrDdN2YJGFdncNyXPxcEGQnZYSpxCcUddGc9F87/9iQ/RvQdHNbPKk/Gugk21TXlphJvsofyNfaphuevb8X5FelKLP3c5uPyKXDrAy82sm9ROSUoaBk4C/SCn3Az5s+gg2Njam6NZStJURLPjdwAYS+paz95loUlIfs8jQPJeXhW4v9xnM2uqa5IlrsvY1+3++jvadXaYr6vueG2T67i4yvdB/rfkLhJ3cPtaNQXXSCF2T/PfKQZ64ehAkBA9UwQ9d76Z54ppBfn/EBn5y9lr+dMcmVg7G+G73ar7bvRoBrH44wd8v6CEdk+x4vI+TftdcMjc7GfAqKt9smmU6k37LUBcfqKA87REqj5i0A3kuHmbJGAVTtiL22mwz6fnkhiVb+xQmlD9N36Hs3KMRL8XDlj1iR7gz3MPlPWtremw+z8XDowFDLXy5cQa/b19Qsv1jgSY+ZCGp/XB0gL+WUYee6XCbmqk9yt9Ai8NZc+I5qmcMK/HPxsM8WmYd8QiFjMX38NKeNViRRtOkZFU6UdBaHFJV9vYEeTYeZqjI1mhNOsEDkT5L5zQWTgu18l6FTregonJKsJlYhaR2XybNX4e6TSerlxsc70O+eua6PDw0a1daLSiYm/L5FUJMAz4BPGB6zzY2NpYIKiofDRjPsGYk7yu7o43pFM/ErXuyFtOgOHgnGeXtImGMN++J0LdSIzRTZe9PW5utUhyC465qRPXCyocTLHnQ3BezSyh8q2mWacP2aiQjOv/4ci9v3BlBdcH+Pwnykd81cdbjbXz42nq2P9yLqgicryoMXKlx/4f7OeCaIJ99tx15NzzwrT50DfY5O8DxVzWiOidvO/0Mp5t7h3t5scoFjCYl27u8zKuQQZ/ldJv23XzPpJXVJMVem20mPbOmppK6aS7sXMGbFvzq+3WNhyK1+fwmpG5oX2eVNtVJU42t1wA/7+vg6r51JeGfBEvfpwqUbXkN6xlT1ekd3T7+Hu7lPzX6H7uEwoHeUieDc+vb+V7z7JLtOtCkOiynDtakE6aDPMjaWrUYtBXXqw7WpRMln4OxWFfVwk2DnQxVuCaN6BnWpZMVrbmezyWCzFpeNhm8Hr8b2MDKVJxTN7xrqZvBTPB7OVnVxhVSypeFEPOB5aaPYGNjY4qV6QRqmW/UhW6vpZaOyU5nJkXbOHxZCyH4WuNM1uZVXBNDOs/+NhtEfeib9TjMKiDkoU0H7UvZ///nigHCm8wlHu4Id7N4HCqJ4U0ad5zVzer/JfDWK3ziT608d2AYtxC0+V3seXSQE3/dzAVPTeeoHzYwcx83WkKy9OE4/7iwd7TqfehFIQ67uAGhTN7Ad4QTAs38bainYvvzE9FBTjFh9fHjlrmm/l5cQliq2kwy7LXZZtJT3J45XryRiHDBpq3/cY/o1mqAh/vqmV1mZKMaO7l8nBGyphhsxLCeITGGIHpYzxjO2d4f6eNfFqqPv2jbjiPLiA1mkBxv4HNbzP/1b0AIqFNrS2Ju7/Ky0aC9+s+DnYbzyzqSOU5PRZsfI/as0K1kREbCsYFG/HmaFOvSSV6OD1OnOEqCt0/WtfDdptJgfaJoVBwcV+H9SUqdgKIyo8L6qkvJQpfX9NjA1xtLR5QEgoyURPSMpe5IM4JXd0spd5NSXpD7fZWU8mTTR7CxsTHFy/HhshmwD/rqqRtDpnaysZPLx2FjmPnNx6+oHBdo5M+DnUgpee76IeKDOrM+4GbBEbVZSryTjLLTSV62O9RDMix56NJ+pF79Emc3t583k+arAEZ0Lk7x19O76HkvTcNcB2fc3srMvdz0ZtK0FCUMvCGV3U8NcNrNrXzu0Wl88Gshmrd34HALjvtJI/ueU9ts2dbAoyh8uXE6sszfwBuJiGHbkxHvJKPcaUKs7Bh/Y8XM9GTGXpttpgKvW6iKWqE/k2ZJauuPLOztCbC9hVnOiJ7hcH9ta998lxdZY8t0Ph8NNpnWWDBiR5ePha6xWy/+sn992bbYoYxWtiW6mDNDbfyqbfuazmFxMsorBp/RoYzGgEGAL8m2jUctJg/ODrVxqIX3PSYz3DvcW9BePWKvdGaotaTSe9NgJ38x+XqNB+0OF89X8KsWwLvJWMV29K80zmAXt990t8D3e1aXbDsx2ExQUdnHE7A0h25G8MojhPiSEOL3QoibRn5MH8HGxsYUnVqqrLT/87Fw1ZbQqcQz8SG0caxkK0IQVFTueKeb1++IgIDDvl1fs3L27h4/h/rrOfpHjXgbFNa9kOS126pfxC1y+5g+hor28sfj3PmZbqI92eD9jNtaaZjtRErJVwyynvmEpjvY77w6PvPPdr764gx2PmHqedfOc3l5Jh42XFQfjw5yhkmxl0VuH28nKreY6VLy8/6Oms5zMmCvzTZTgVq93KvhU1RCkyBx9ZlQOykLgdA7yahldfsR/h3p47Yh8w4EI9wy2FlgkfS3cE9Nxx/hQF8dHw82l7yzlzTN5rz6Us/ecnRqKUPVaIC9PEEOMmhHLubMUCuLk1Eu7lpp+rjFHGJwnEVuH7sYKGs7EOzptlbFBfhBzxoWW2h71qQkXFShX+T2cbi/nusHN9FRVK3WgaVbMBl0bKCxYjK6xeHii43TKv5t3DbUzTOxoaprdSWGdY061cEFDdNptdBlYqZufyvQDhwNPAXMBKzpgtvY2FTlu82zy1ahQqpaInAwlXklHsE3Bp9BI04ONjPwWw1dg91O8tO2qPYg9F/DfQRVB/5mlaMvy7ZlPfXLQXpXVp4pcQmFOsVh6WIINis63/vVXtJxyc4f83HqDS14Q9nPwzPxcEXhiHyEECiOyd/mXI7DfPXcHe5hOC/r3qml+GrjDEImux+cQuFwf33ZKjKQraRXsGqYAthrs82kx8i3fjzY0x3gpukLJ2TfVvhy14qCsZtqJKXknS0ssnfPcC8b8s5xX09wTIKT1w1s5Jf960tq0HeHe3jOQmC/wOUrq5GxJBVlWZVgTkrJxnSKF+PDNatXN6tOw4rhAd46Q9u8tJT0ZtJYnfoN6xl6NPPn2KQ6ObhIFXxdOsHadBJBqc7y3p4A07fgfP0fBjbhrjBOtT6d5L+RQXZ0lU/Cb9JSOIRieub3AIMkxX+jg6xNJzh/03usSiVM7QfMBb/bSykvBaJSyluA44H9qj0ol4XuFkK8k7ftMiHEBiHEG7mf4/Juu0QIsUIIsUwIcXTe9mNy21YIIb6Tt32eEOLF3Pa/CSG27LS3jc0487O+8lWo+U4vO9dgrD5ZcQiBb5yz9mueSxJ7Vkf4YM8LrWdmR4jrGVbkZTQXHOFj1xP9ZFLw7+/0kUlX/qJekoxVlPcvpljR+eAvhzj2ikKBqqdjg7RM0OzcZMOjKHyhYRrxXAIhIyXX9HZYTijs6QmytEJm2ikEx1rwBJ6E1LQ229hsSRomKMH0QjzMqevfnZB9TyRnhtrYscaW4SP9DVzbtp3lx13dOo8d8lqzV6bjbJoAC6rlqZgl9fxP1bXw5cYZhrc5EHzQV7nyqwOPxQZpUZ0120f5FNUwyP7j4CauNZj5zSCRyLL6LOU4N9SO10LCvy+TZme3n3plc/Jok5aiV8tuDxT50zeoTi41EOiaKDyKwlcajN87yM5GKwLisnzSfobTxd6eALt7qnepZXLzwYbHktlrSivT92beiZFUxaAQYhcgBJjpPbsZOMZg+y+llHvkfh4EEELsBHwK2Dn3mN8LIVQhhAr8DjgW2Ak4LXdfgKtz+9oeGAA+a+KcbGwmJTE9UzH75VcUBt9Has/faZo1rvvTNcmTP8uqPS74rJc/ik0Vq36V2KCl2MtTqBB9+HfqCc1U6V6S5rnfV1ap3sMT4I0KbTypqM7615K8dscwj1zWz80nd44qOn/kmiYO+HxdSbt2QFFpnNpVSkvs4PaxPBXn+ViYR6L9HOoP4bWYLHEKwT0V2vs2pFM0T+3XtNa12cZmizFRrZhpZFml4C3JVxpnsK/HvKPAE7FBPhqsLuRkRLeW5o8Dmyw/7gc9awpsaeY7vZZ9avM5JdhimDhscbgsBXjX9HXw92Hj72iB4N7hyuJZOpIZDhenh1o5uc7YKaMaa9IJNhgkAnSMAyQBrNdSxC1aT9arDks6J1Fd5w+DmwoUlQXgVRRmO90lr/PDkX6+OobWb6scF2jkez1ryt7uFyotqrPizP8pwRZSUppKxET0DP80sLX8TtMsZjrdfKlhhqVOLjP9KDcIIRqA7wP3AwHg0moPklI+LYSYa/I8PgbcKaVMAquFECuAfXO3rZBSrgIQQtwJfEwIsQQ4HDg9d59bgMuA60wez8ZmUhHTdfbxlq9WSuCRyAD71pjdnEykpM5PetdxWcvccdvnm/dE6F2RtTY6/pwmPCmFmNQLlBLNMs3hYrtgoRqny69w3E+auOPsbl68cRhPvUqgRcHpVXB6Rd6PwhyPm5leF1KXDHdl6FmWpntZiu6labqXphnsKE1i+BoVPv5/zczYs7RtKa5n+Hz9dMvPY6qzjyfIt7tXcXHTrJqUwetVBxE9MyoSUszjsUFODtZ2wTRJqGlttrHZkkxUx9Jcp4dj/Vu/c6NbS/FuKmZazXdDOslzsSF+1W5doOmtZKRE5OvhSD/HVOlg6ctoo500AC8nhvGMYeyoP6PhN3j81xtn4LKoglyuZXyO082uBjO3+SgITg62cFe4h//Fhrhv1i6Wjj3C+fXtJdtOCjYTNRg18giFI2oQ6/xFrk38BJOJj5HKciYvR3F4Thn7hoGNHOlvKLD8k1Bzwr9WKomveRWFHd0+1lQYCfh5/3qO9TeyQUuye5W/n7jUDRW2bwt38/FAE2mpW/Kurhr8Sin/lPvv08B803suz4VCiLOAV4CLpJQDwAzghbz7rM9tA+go2r4f0AQMSik1g/vb2Ew5HELwwQpfqF6hFCxeU5luLT2uFbcCa6OLstZGR7saeTjSzw4uL/MtKHECXNW7jkub5+AuCphm7uVmv3ODvHjjME/+bLDm81Wd0LS9k9aFLloXOWld5KRtJxcun/FFwyPRAZpVJwdXaQF7v+FWFD7XMI2InmFGjcJll7XMMQyV7yYzAAAgAElEQVR8Abq01Bb3RhxPJmBttrEZd3otzDlawS8UDvRt/WTwneEeFlloY57ldLM2bX42MR+nUJhbZJP02/4NVYPf3dx+WvPW3J1cvjHNYj8RG0QB9itKxv+gZw3NqpNvm2y//WigiXfKiEAtT8WrtrFmkDwQ6aNeddSsgb2d08NtQ12cXmQh1aOlcRmsHcN6hnVa0lKFuxZmONycG2ovWL/uH+4dnestfr6frGvhmdiWW88ejPSXvP/5bNRSvJeKc0Zd+WYkXUrcQpjqQggpKicZVPc70km6MmluC3cz1+kxba1W9tOfC1AB4lLKu03trTrXAT8m+779GLgWOHec9l0WIcTngM8BzJ695XribWzM8p/oAPOdHvb2GrdPCSH41ji3Cm8tFGDfMs+zFp77Q5610Yc3B7r7eoNc0bOOK1vnmm6Z1aUkLSXuMgvbQReG8Daq9C5Pk47rpOMy72fz7/GYDknwhJTRAHck2G2c5yyY563Gm4kI36ii9Px+Zacqmf9qDGY0bh/q5rMNpQqklzTPRq0xqN6aTNDabGMzIdQqRFSNt5NRrurr4Ik5u0/I/q1gxS5tX2+w5pbjU4LNJRZFZix3zqtvLwgKFJEd+5jnqs1vOFvxK/3uzEgY0DW+3bWKq9uq5+NUITjXoOqaf3slNClZlU5wtq+NY2rsAhjQNV5PRNClRMk73pOxQTJSltgTSbKBeVpKLCzj3DBtgSUf2mWpGFGZKbABSkiJAhwTaKSpqIDwWiLCDltYF6ZS0kfPee8+Eh3glLoWw/sc7q9nd0+APUx0TQxkNDakUyUe2e0OF5LsjLiV8lCl1M+83L/jZtImpRw1oRJC/BF4IPfrBiD/yn5mbhtltvcB9UIIR676m39/o+PeANwAsM8++2zZvgAbGxNs0pIcVKWl+c5wN19omPrtrxlgh3HwCAToX5Pm9duNrY0aVSdn1bfRk0kzQyimAp2k1A2ziyOoTsEHzq4euL+XjNGVTnGwP1Sz3dIIxwQaCb6PPJ63JO0Ol6EXaErq/HWoa6r+PY372mxjM1FMVL/SZOmD+kP7AktB5CORAXasMUj5b3SAByP9/ClP5Xp/E4nkr3St5IqWuRyU6x5an05RWjs0z/eb5xDTMyUzsYf6QwxmNB6LmuuM+nekjwcj/fykdV7Jbe0OV4E9kxE6cJC3jgO8dbySqE3ovi+j4RJKyauhABis3S4hcCJISYmVd/GWwS4uLCPuZYQmJX8L93BysGVU7HKWw02zw0lHOolXKAVJl3eTUR6K9PP7aQssnFXtfKNxJr/oX2/43gFMd7o53FfPE7Hyn4Wg4mC9luTFeJhPVKgQA3Rn0qxLJziAwuvk8+rbcQuF37RvbykJVbZuL6X8Ue7nWtN7q4IQIj/9fiIwogR9P/ApIYRbCDEPWAC8BLwMLMgpO7vIimLdL7ON7U8Ap+QefzZw33idp43NluZQXz2tVVowa22Vmmw8GumnJzM+SpNP/nyworXRHp4AjaqTi7tX8WYF4YURVqYSbOe01iZtxA5uH3PcnjEHvhvSScM5FxtzCCE4xBciXlQtWZ+zi5iKTMTabGMzUew2xu6NcuznDfLjcdSNqAUpJT/r6+AZA1/yckT1DH8e7KzpeL2ZNNGi6uELcXNBX35wl5b6mAQUbxrs5MFIf0kCYo7Tw16eoOk572ozmtVmWP1C4TB/PbeFu7mjBv/j7DnAx4JNJWdycdMsvmYQrKpCMM/lqTjvasTz8TAvWrCBalKdzCqyLprjdNOsOlmajDFUtKa5hJJVPN5Cc7/35cSnyr1HET3DkJ4hpJRP3P9juAcpszPk1UhK3XBO/ce9a1mWivGnwU46LFiOVWp7/nWlB0opv1LpdiHEHcCHgGYhxHrgh8CHhBB7kP07XAN8PrevxUKIu4B3AQ34kpRZfWwhxIXAI4AK3CSlXJw7xLeBO4UQVwCvAzdWfKY2NpOYTVqq6sD/TIebtNRxTvFgqCuTpk0d+2zKmucSrHwygcsvOPjL5edhA4rK5S1zuWFgE20OFy2qs2wV+P5IH18waJGthT8MbOIHzXPKtlCb4bl4mNnOLefd937kKH8DG7QU2+fNfselzs4TdFE+0Yx1bbax2ZIMTZBLwdp0tmK0NbUQdGCFxaT0Qb5Q1YpmOdocLo4PFrb37umu3jL6hfpp7JQXkCo1JmXfSUR5KjbIJi2FUah5y2AnSSnpMpnc/nrjTF4tU7HtzaSr2hcN6xmu6etgV7e/5hGWPTwBHhjuIyb1AvugGwc7CamOkrbsiJ5hgctr2cJrO4vaI15F4WPBJnx51w8PRvo5zF+PEKViU19vmmnJZmqsrEwnODvUVqYBPjvrv0FLcmbRLHUxAUVlmgntjUrzxZGcPWWvhRGLSr10r5reiwFSytMMNpcNUKWUVwJXGmx/EHjQYPsqNitC29hMWaSUPBkbrCpacV79NJQpW6/azCfqWgrmWGpB1yRPXJO1Ntr/c3X4myvvL6CofKNpJhkp+V7Paj4SaDK8aBrKaONmKbSL28+byciYFLqXJGMcP7W9aLc6CoI/D3ZyZV571nSHi4Xj1Hq/FRjT2mxjsyUpp+Y7VjakkzwQ6eeirayFMcvhtpygPCrQUNOxPuxv4K0iG73Xk9U7mmJSpy+THl3bjvY30JdJE7I4TqOKbLvvTIebRJlZ46TUTVXyICvgVE7nJCl1Ho8NGuo1jKAhEcBHAk3sZsIr1oi3ExEeiw2W+A1v1JIMGCRudAn/jvRzXv006i28ft9qmkXAwnXPe6k4S5Ixjstb/zUkTiE4J1Q6J/3zvg52cHmZ43Tjq8HlwiqNqoN16SQznO4Sz2HYHJxf3ru2YO3N50sNM2hQHRzsrZ7Aejw6gE9RSxIih/nqCSgqs5zusuKWRlRqe76l0o/pI9jY2FSkP6PRqFQPuP461MXq90Hr82uJ4TG1A2fSkv/9ZmjU2mjvM82LZ6lCcHnLXJYmY7yTiJa0CP2wZU7N51XMsYHGEnEGK0gp+V7zbHxjTBRs67gVBRVRYFtx3cAmQxuLqYC9NtvYZCtj3q3cBaUKwdWt8yzNGr4cH87N3G7mX8N9PBrpr/rY24a6+HV/obzNdBNVs78MddGVp7p9j4FfqhkSUmdQ1zgz1MpXG2eUBBBfbJjOBQ3TOdhkwndFOs5dZbzYd3X7S5Sti3EgOCvUhkMIfljBc7YSaSnZzxPEXfRZmuP00GQQ3NapKju5fOgW255/0LPGUsU/kyuKDOetU0f462lWndwz3FtyLRjXdd5KREsE0SaKrzXOZEkqynCZRMfObj+n1bVWfJVGnt+fBqt7Vw9lSmfMAVodLuY7vXynaVbVToF8pnb/pI3N+4BG1cHXmqoLIYRUB0MmM6qTFU3Kksy1WaSUvPefGDed0MlLN2ZbpQ67OGttZAWXUDivYRq7ePxc3dfBv4f7kFLyXGyI10zMBZulXnXwTGyIwRrfs6Wp8hcGNtb4cuOMgoubsK5ZrnrY2NhYp3Ucbe3yOdgX4tbpiyZk32bRpOTTG5eyuIxdj+Fj0HmySAToydggz5mYBzWakd1oMqDK147Y0eVjZo3jNCmp8/P+9fyoZy3FYdYriWHeTkaZYXLfxYrF+axNJ1lTJdnvU1QSUufe4V5WpmorDNSrDmY53SXB7CfrWjm/vrTqPKLY7LDYhdelpSwVL3Z2+0uCuY3pFJLs3HixYvhObh9+RSW1hWZ+L+1ZjVeoZS04l6RiPBzp55AKYwnvJmM4ct0E1WhxOGkx+Lz8PdzDK4lhLulezePRAdPnbwe/NjZbmf/Fh3jDRNC1q9tfcbGYCgxk0qM+dVboXJziznN6uO/rfQx2aDTNd3Dydc0sOGJsravfbppFTOo8nQt8Z9RwbpWY5nDxaLR6Rt+INxIRFlqcE7IxJqAo3JInMnNCoGkrno2NzbaD1VlHszweHeRrXSsmZN9m0ZGWVadPq2srCTwVjOcmi9nLE+DSlkK7zh1NjG/8sm07ds3TOOjKpGryXxZkBbacZc72pfgw/40O8DeTSduvN87k/DJWR2kp+UQZi5wR+jJpbh3qRkFY8lrOp83hoieTJlxUMb2mbx2/6S81kYlLnXrVYbkj6/hAkyWB7Y1aksP99TTkJWlfiIdJS8kMh7vk+B/wBrmwcToztqB3/Xn17cwv8/ed0HXiUq/YmRBQVLxC4UgTYwALXT7Da0eRE/lSEGQsvL528Gtjs5VZk0qamgVpcTgtKwxONlocLi5oNG8vM9yp8eB3+7j1k12sfyWJt0Hhw99v4DP/aGf+IWO/qFKF4NS6Fg711+MWCnPGWVxqf29dzZXfOkWdsqJMkw2vorI0FSOT8x6c6qJxNjZThefjYfQJqEalpWR9jcJR44WC4FN1LeztMT9681x8iDOKbF0ub5nLt5tml3nEZoKKyuNFNkJmqonXD2zkzbzZ4AO8dcRM+AMXs7Pbxx5uPx/y17OvN1gSAnsstqJf2rOGl8sk/jNI/s8g+BzhhXgYCSx0ebmwcToXNc00fdx8liZjPB0bQiv6jKalNKxISrJClJXW9bie4Se960Z/16Vkf2+QEyvYKBazIZ3igUh/gbp31ltY8AFvsKQKeuNgJ/8c7mXlFhqNO9Ab4uHoQFkXjYCi0u5wVUyEfLd5NqoQJa+9EXeHewyF1M6rb2eey8Nh/npTwlkjVP2UCiF2EEI8JoR4J/f7bkKI75s+go2NTUV8imKqGtqtpXk6NrQFzmjieDTSzzMmnkMqpvPMb4f400c6WXx/DNUJHzgnyPkPTmPPTwVQHOMv/HV+w7SaVTDLoQrBaXWtdFm8SEtLnbkuz5iUom0KOdAXoi+TZlUqznsG3r9TDXtttpkKzHK4JyRlO8vpZq8qDgkTjUJ2NtSKxUpHOsltRbY81w1s4gYTc49PxYZK1s9ywlP5LEvFCwK5f0f6LbftQjZQ9CgKIcXB9i5vyR6ubJnHT1vnc4Sv3vQ+y3W97eEJcGiFltlLulcTUFSODzTxeHSQCzqXmz5mMWfUtdLmKAwmjw80sY+BGFe7w1VVhDIudf6T14KbQXJ1XwcPmZjrHkEiWZdOkMx7f7/VPAufULh/uI9V6Xjh/SWkcsndLcF8lwddyoKZ5HxmO93sUeXv8wfda4CsinU1ElI3TKy8lLP6muN00zaewS/wR+ASIA0gpXyLrN+ujY3NOHCIL2RKNTCkqBNmG7Gl2KSlKrZu6xnJ2/+M8KfjN/H89WG0hGTh0V7OvX8aH7qoHndw6gWDYT3DzRZ9HZcmY7waH7/5Y5tsq3NaSjrSSWa9P+yj7LXZZtKjTVC30gyHi6811lbtGy/SMhvUWBEyalAdbNIKg+VOzVwbsgI1uRE0qY6C4G4/T7CmLidJtu35idggt4e7yRS9tz/sWcMPe9eYFno8wFuHq0zC+dX4MJ0VXpOTgs1IKXk9EeGdZLQgSIzrGVImK9uznR5SSLqLjuVVFBYYtPR2aik2pJP4KySmg4rKb9u3H/1dkp0Pj1kITI8KNHKUv6EgSPt9/8ay1kIn1TUz2+neYiJwtw11Z5Wey3QtvpGI8nh0gPPKtLUDpHNDA2Y6Gg/311Nn4Bn8bjLGkmSMu8I9lnyUzbxKPinlS0XbpvYVuI3NJOJX/etN3a/F4eKUYOUZmMlOu8NVdgakd2Wa287o5uFLB4j26LTv4uK0v7RywrXN1M+auuJEM5xuwnrGUkb2tUSEPWu0brApz6/61/Nhf4MlVchJjL0220x60lKfEIO+Z+Nhzt64dAL2bJ6RQES18ARPDDazW1FFbK7Tw1xX9WD0+GATVxXZxpi5iP9p6/wCR4lNWqqm9tjmXOBdLmAd1jNsSKf485C5ZO92Lg+Xtcw1vE0Vgu1d5YPoF+NhBnSN5+Nh2h0uzs6z/3k4MsCLcWP/4GLSUmd1KlGi/n/HUDd3G7TsalLSn9EqznoP6xku7Nw8j+5A8P3mOZYqkw9F+tnbE6Ql7zFdmRSKEOzrDdJetK9uLcVenmDNQma1sNDlZV6ZRIdOdg73lQp6NiPjAhc1VrcrG8hoOA0+d15FQQccQlhS4Dbzd9MrhNiO3Ki2EOIUoHp/ho2NTVXSUjfdfuQQokQlcqox3+UpUdnVNcmLfwrzl1M66XwnRaBN5fifNvLp21uZudf7okLHVxtn4LbQUn1coJEd7XnfcUUVgibVyU/61qG+D/yy2QbX5st61rAkOfVb1rclejPahFR/pTQnEjWROIXgF23bcbjfvG/vzYNdHF10/zNCrXwsUH0edGkyxsXdqwq27WJinbhg03KW5o16uBWlJv0Qn6LSrro4OdjMp+taS75H9/AE2NdgHrUctw518eOetYa3NanOigHsBi2FLrOdc5+sayWkbq5AvpWMsNzkaMsGLcUmLYlatD7rGAdIbqGwUUtZsspLScm7yWiB6Fg1YnqG3w5sLOgqGLF+yiBLxJ2eiA3yfCxsqfo5Fj4TamNZKs7DZVqW5zo97Oz2VRRzHWmLvteE9daj0X7DsbSLm2ZxiC/EZ0LtlmbvzQS/XwL+ACwSQmwAvgZ80fQRbGxsypKRcHIVRcN83rZgqTAZubGo/bd3ZZrbz+zm6V8NkUnDbif7Offednb6iB+hbO1Lm/GjRXXy2/6Npu6b1HUeiPRbMmy3MceZoTZ6tXTJhc4UZZtbm5+KDVmen7fZuhzqC6FPQOfzvt4gn28wL544EehI/jXcZ8liRSK5Mk8MCeCq3nX8wkQH2MpUvMTu8C0T1wTpolB3eSpOyKCFtBpr0gn28QZYnIyxNBUvqX4e7q/npGBzxVndYmLSOIisU1RDn918pjtd7O8NcvNgJ7/LW1+dQrGk/HtCsLmkgvn95tmcZCBQFVJV9jOYBc7HJ9SCDjcNyb8j/QVzwNWY5nARKmopPjV3rfh6IlLSai8QuIQguYWsjt5JRvEKpazVkZ47J6Nq7Qg35ubcVxbNL1vh2r4O3sq1vvdkzCuYVw1+pZSrpJQfBlqARVLKg6WUa2o+Uxsbm1G6MilTSs8j+IU1ef3JxIgcPeSqvTdmq72b3k4RbFM55Q/NHP2jxik511sNIbIL0zITVat3ktECT0ab8WO6w7VF28Imkm1xbfYLpcCv2WbyszwVr3gBXCu9mfSE7NcKaSl5MjZY4rlaib08wdI13+TTcAjBgb7CkQ0zs7tH+hvYyb3ZCmi6w2XZqmeEByL9bNSSvJoYLmkz/UXfei7sXFGgUFyJc0LtZauhq9OJijZZJwWb6dHS3BPupSeTLngJd3T5Klrs5LO7x09Mz5QkEf453GsoZLYxnaJedVTWLkFyYcOMgm2NFn3lF7p8HO5vKLgW+HlfNkGSfa6Fr/33mmezwOXdYvaIryQitDtcfKBMImBFTljyR2Xa2mGzb7UZC8/vlFFDT0pJl5ZmaSrGeynzQXTZd0MI8Y0y2wGQUv7C9FFsbGwMeTMRoVl1sr3JL6zvt8yZ4DOaOCRwdqiNvpVpHvp+P5vezmYudz3Jz2EXT00xKyt8NNjE2nSShVXu15tJs49366qYvl8RQnBJc3VLkcnMtrw2/7Z9Aa2Oqe11vq2xUUuRQY77qMGKVJw/DGzixKB5+5iJoM3hsjTL2aQ6OMJfqIZ8lL/BlP3aWaE23i1q511rQmn6IG8d/RltVCxrF7efjekkO7iteeNul6uOVkrYZ5A8FO3nW83V5zgXJ2McXKZKLMm+x+X4x3AvR/kb6M2kOSHYxNcbNwebKakzz6Tv74pUnJcTwyXt42/lRLSKW9ol8EhkgHPr28uKj0V1ne/2rOaJObsD2aTdFa3zLOl+PBjpp9XhNBSwOiHYXOK1fNNgJ5+qayE8EW0WBgiys9/l1J51JALBNb0d2bEvA4Gw03KVbDPCdbeHu/miQafHzm4fbpG12LJiqVbpry2Y+9mHbCvVjNzPF4C9TB/BxsamLBu1lCVvsh/3rCVpMqs62ehIJFj85yi3nJpX7b2+mWMuf39We4uZ7fTQojqJV1kA9/IEWGBy4bbZJtlm1+YLO5ezzEJ232ZyYOSXOlYUBFt7MsYjFK5tnW9JOfnR6ACeokAgqJhT/rg93F0yI7uziQD2st61BW2yj5pov/17uKdEMblf19jD7ef4QBM/apmLqygwOz3UyreaZrGbydnWlxPDXD9gLFOw0OUd7RSrxCfqWtjTE+CvefZR/4sP8V+TLcYRPUOz6qS5KKnWqjpLnh9kkxfbuTwVP9PF89QDusa1fR2m/GxH0JD8K9JHf14r78is+EvxMB1FiuGbtBTdmTSvJMwJfY2VK1vnkQEeK/M67++t40BfHRqyrPL2ulzi5gc9a6oeb10Zgba9PUGO8NfzhYbpo23hZihb+ZVS/ghACPE0sJeUcjj3+2XAv00fwcbGpixnh9oMv2DL4VdUwrpGi2I+YJ4M9K1K8+Al/cQWZ78Edz3Rz2Hfev9Xe4tZk06wOp3g2DI+gSMiF5VahWy2bbbltTkq9arJI5vJxQKXFzkBwe9Hg03ssZUV8ZNS5zvdqzkt1MoxVbxf8/nbUA/HBZpGf793uJcMsmwVNP94xWHEYpMCcPlh5CyHm+lVAvYHI/0cHWjElffAXi1NdybNg5F+VqTifKlxesH1S39GoyOd4DC/OZ9fQXHz7mb6MhobtfJV7QUuL9u7vKxMx7luYCPr8yrg+3vrMPuR8wmVhS4fgaKRsoubZhnqbmSQ1CmOip0MTaqTz+ZZ/OgyG5y+lYyyyGS1/UBvHRuKqvojr3WPli6pOs90uHELxbTF01i5qncd32qaRbxMMWZJMoZDCHZx+0a7kop5Nh7m+GCT4W3FTHMYf15vH+pmjtNNTOo0qA4+brITxMyVZxuQP1mdym2zsbEZI38c3GRJfGcvT4Ctr3FpjdhAhjvO6ia2WMfdKjj5umaO+fG2Ue0t5lBfiKei5RW737KoCGmzTbPNrc37eAIlavE2k5sGxWFJ18Isj0T6ucmif/p4kwE6M9YE2E4MNpdUGYHyUWAec5wevtxYOEt6gAnbtkubZ3NQXmAd1rWqLbir0gnSRYGUIgQbtRRhXWNIL3VVezjSzx3hHp6ODlU9J8hWD08uE6z0Z9KcW8Ejdn9vHa8khvnLYBcpKZmeFxy5hGJ6PGKh24tXUUrayb/Xs5p/GqgQ92U05rs8FcW4BjMaq1ObK5UeRWEvC0rEkLVLOjbQSH3ecf4V6QOgTnWU2E19OtTKbKebk7eQHeawnkEVgouajFuWezJphjIa+3rrSlq0iznQxGe4nF+wIrJVck3KEjG4Spi5+vwL8JIQ4rJcZvlF4GbTR7CxsSnLRovKpTu7fTimVuzLM78eIj6oM2MvF+f/cxrzD9kyggyTEa+i8r3m2WUrIdMdLktKmTbbNDWtzUIIjxDiJSHEm0KIxUKIkUryPCHEi0KIFUKIvwkhXLnt7tzvK3K3z83b1yW57cuEEEfnbT8mt22FEOI74/WET6trZTvntvv9MRV5KTFsadbRLFGZqeghuiVQgMN89ZYSlouTUc6oay3YdkHDdL5RJojIZze3v6DCCfC8CWubl+PDPBfbHJCeGGyuGigsdHnxFyUt9vQE2Nnto0V1sqCCTskykzZDP+1dR3+Z89CB/+vfUPaxtw51Edd1ZjrdfL5+WoH/caeWJGgy4fJaIsKjkQEyRWtyRM8YnpsuJX8L95SoLeeTlDqP59lSOhAcG2jkOAvdAS/GwyxPxQ3bq4/yN7CwaDTqp30dRPWMJUXpsbAod/y7DLyQAbxCwaeo3DvcS1eZBNFlOQ2beS5Pxe4QTUquKlJIH+G4QCMLXT62c3lotTBCaEbt+UrgHGAg93OOlPIq00ewsbExJCV1drA42/l6IsLrW3nBt0LXkhRv3hNFccCaryTx1G171d5iolLnhkHjOac3EtECU3sbm3KMYW1OAodLKXcH9gCOEULsD1wN/FJKuX1uf5/N3f+zwEBu+y9z90MIsRPwKWBn4Bjg90IIVQihAr8DjgV2Ak7L3XfMXNS9ireSU+f7zwbaVRcT0ajeprpM+8lOFB6hcEKwyVJwvzQV45l4YWX06dgQ9w33VX3svcN93FdUjaxWVQN4ODpQEETdPNRVtdDcojpLZlTfSEQIKCo7uv182N9A8aTyFS1z+cv0Rab9VsN6hidixp1QB3jrqlYEG1QHxwQaWZ6K8+XOFaPblyTj/C9mrvoMsLcnUJLAONzXwAyDtXiey8NRVXydi9ul+zNprh/YyEsWPHgzSN5ORgs+W1/JVf3/Mdxr6J8rgVUp49nY8URKyZG51vY1ZWZxD/GF2NsTyNkvGbdGj9hT3TnUXfE7IiH1kjn5EfozGk2qk13dAVMV5BFMXYlKKV+TUv5f7ud103u3sbEpS0ZKPmlhQB8gpDoYmiIzb1JKHvvJAEjY8/QA4dla2dmPbYl2h4sVqXjJbE44o1U0hLexKaaWtVlmGfmgOXM/EjgcuCe3/Rbg47n/fyz3O7nbjxDZP+SPAXdKKZNSytXACmDf3M+KnBVTCrgzd99xQUyxsY9tHZ+iMBEGfbt7Avx+2oIJ2LN5InqGi7tWVVQlLsaBwhuJQludJckY75mslipFa2jaRL+0VygFdjL7e4NVZ0+fiYdLqqFpKXkxPsyriWGuG9hYYvH0h8FNfKNrpaE/rhGLKiT/X0sMV1SyPinYzGynm1WpOI9EB0jkradn17eZtrSb7nDT5HCyqaiSu6vHbxjkbtRSxHW9pCqeT5Pq5Ndt243+rgNxqdOjmfehPbd+Wknr9iOR/rL3P8Jfj0coEzJiUIwO/G4gG7i2qcbJ+n9H+ngxPswHffW0lrlPd64i7BRKSYt9PirwQZ/xHPnL8WEejw7wn2g/d4a7De9jhF2GsbHZSrycGOapMlnPcixy+fiAxdmRrcW7D8TY8HoKX5PCQV8M2bOseZwUbGGwqKVqaez22PUAACAASURBVCq21QVcbLYNchXaN4Bu4D/ASmBQSjnyoVxPVkGa3L8dALnbh4Cm/O1Fjym3fcx8vXGGaf9Om8mBQ4iaPWUr8Uikn89uXDbu+7WCxLoCxwWN05lVFJj5FcWU1+kHfSH+0F4Y8DebmIG/tm07ZucdsyOdNBWwFyerG1QHu7n9Zf8G16WT9GbSXNPXYXh7MR/21/PdMtZzKSn5kL/8CJAmJY9FB3k+PkxIUQsC1bcT0Ypt2fl4hEJc11lbVMG8tq+DvxoEU9Fc8aGSx/QGLck3u1eN/t6kOjgl2ExQNf93cO9wL2eG2kbdQKSULM0lSHZweUtafOsUB16hcH5D+Tnp8SKDHE3CfMHAfih7n+w8blLqZTsj5udGWD4daq1o9ZUhq75thCAbjDuEUiIGVwk7+LWx2UpsSqfKKtiVQwHTGeKtSSqq89QvsoH9oV+vBz8cUaVVaFviAF8dS4pUOvfxBPlowJzyoY3NWJBSZqSUewAzyVZqF22N8xBCfE4I8YoQ4pWeHuPZsXyWp+J4y7S/2UxO3kvFGbAgRGMWnc2ByNbCLRS+0TSTIwPm17brBzZyYVHA8M2mWXzdhNdpRM9wRdHso1dUD6iu7lvHy3kWOHOcnpKqbTH7e4MlVcR2h4vLWuawr7eO4wKNJe29s51u2lRjb1ojfj2wgVvKiJY1qA7uHCr/nXB/pI+U1NnT4+ebTbNyYqBZOtJJOk1WWVek4ryVjJQETuUSG26h8HJiuOys8shj89vMo7qOSygcaeEaqCOd5K5wD905qyMdRhMkzaoTf9H34Ig41w+LrLAmAhXBCblrlct7jY833+mh3eHivVSc9WVUu0/OdQgsT8Urqvhv0pI8GzduY/98wzSOCzSynzfI/uPd9mxjYzP+7OT2MT9nGm8WRQiei5mfG9laPHd9mGiPzrTdXOx8go+V6XjFlp1tkRfi4QIrg0t71tht4TZbFCnlIPAEcABQL4QYKSPNBEbUZjYAswByt4eAvvztRY8pt93o+DdIKfeRUu7T0lJ9BOSBSH/FVkibyceH/fUlvqfjwd6eAEdbEBCaCBQBG9IpXrAwyxnXdX5cFMD+rK+DS014nb6cGC4JJIr9Xo1Ym04WWP88Fw+XBE/FuIVCuCjAezcZ5cncLG1Klr6r59dP46rW+eznrd6dNiJwtCHXbqxLyXt5CeHZDjc7uMtXb1WyHq97eoL8dmAD1/avH73NIQTLLRQJ9vfW8dEiy52Lm2YZdtnNdXpG/XbLEVQKjZDCusY9wz3cEy5Vjy5Hi+okomdGW88V4KJcguTFeNiwcr+lrh8yyNE2cxUM/YubVSchxYELUTbR8vO+7Hu2NBUjWqHtOaHreMokVO4d7mNJKkZM1y11YVQNfoUQJwkhlgshhoQQYSHEsBBi8l9929hMcgZ0zbJth08oxLaQj1ut9K9O8+qtwyDgiEvqEYpgQzpVYEVgA8cFmkaVGfsz6YozRDY2xdS6NgshWoQQ9bn/e4EjgSVkg+BTcnc7G7gv9//7c7+Tu/1xmb1yvR/4VE4Neh6wAHgJeBlYkFOPdpEVxbp/PJ4zTDWjN5sVqcSEfLelkRxkodIzEUT1DPcM9xDOmK9AL3B50YvCxoTU0UwmCHYsmpOtM/Ha7uL2sWPejO8il6/sHOYIT8WGSs4o//fHogMlQc8Pe9Zw7qZl7GhixEkHTgm2jIpKRfQM1/av52Md7wDwZjJKSCl/fXRmqI2Y1HkmNsTaVGEC4IRgE3UVHpvPLm4f9YqDB4oEx16JDxt+bt9NRtGQNFdoU3cJhRum7TD6uw4VfYGNOD2UVbYfsTQa1jP8NNdObqR7cEGum8CK6FOtpKQcHdk7zF9f8nkGeDY+xOp0giMDDexbJRniFkrFToT5Li9H+Y0TXf2ZNO8mo6xKxXmigo1kMWY+HdcAH5VSLjG9Vxsbm6o8GOkvO8RfDiEEP26ZOzEnNA5IKXn86kF0DXY9yc+0XbMB70K317T1wLbCLm4fC91eMlIS13WOsdvCbaxR69o8Dbglp8qsAHdJKR8QQrwL3CmEuAJ4Hbgxd/8bgVuFECuAfrLBLFLKxUKIu4B3AQ34kpQyAyCEuBB4hGxh4CYp5eKxPNER/jJ9ESH7e2RK0amlLM3imeX1RIS7wz3cN2uXCdi7eeoUtcCLtRp7eQIlLbO757XsVuLMUFvJY8MmWr8vapxVIAjVoDpYlY6zU5UgtdhLdp7TU1bgCDYHxzcMbOSQKpZ9gxkNVQjOCmWtydNS8l5RNbNYcCuf91JxwnqGjnSS3T1+jsnrAngk0s/Zeb6wD0f62dsTpMXA+3eTlmJlOo6vqH38v9EBJJQIg0lgWTJGVM+UVSDu0lKcv+k9npizOwDbu7x8u2kWXRnzglc/7e3ga00zRq+bNOTonPFBvjrcRZXQp2KDnBFqY67FbsJayEiJkgvAZzjc2fdJFN8nu7i8k4gSkzoHG3weRiro5eaGR1ibTpCS0tAfu93holNLoQphGISXw0zbc5cd+NrYjC+6lDVXMK7tW1/9TluJlU8mWP1MAndQcMhXN3/ZvZaI0LCVbSkmG0IIHo7083w8TJeWMn0BZGOTo6a1WUr5lpRyTynlblLKXaSUl+e2r5JS7iul3F5KeaqUMpnbnsj9vn3u9lV5+7pSSrmdlHKhlPKhvO0PSil3yN125Xg8WYDzNy1jeIqo3dtkSUid4QmY+a0ysrpFCCoOrmidxz4WRCjvCHezS1FAtZcnwHYmgpaHIv1cVtQevb+JFuNzNi1jeV5gudyE2NV+niBqUfCbknLUtuYrjTNKguNjA42cFGw2Zde3Np1geSrGVblqpltRCoS0tnN66NCS/LNMq/Bz8TApqXOkv4ETgk0FTgmbtBT351lCLU5GGdSNA8++jEZU10eFpUbwKIphgNSsOlGFICrLfw8VfzRXpuI8GOmnyUKSRENyx1D36OyyE8GeuWuE9TlhsXwW51rGb7egeFwrAUUdrTQ/HhukxyCoP9QXYr7TQ1JK+ssE/a5cAH9XuKeiANv6dLLsPo4JNHJ+wzQO99fzmZB5sS8zwe8rOYP703JtVicJIU4yfQQbG5sSBPCdJmOVw2oMlPkS39poScnjV2fbeA+6IIS/KZuxTOg6r8aHKz10m+VQXz2PRga4I9xdcqFhY1OFbW5trnQhZTM5OdLfMCGV3xPrmmteQ8eLhNS5fmAjj+bGV8wggeuLfN7/MtjFn8sIP+XTpaVK2kNfMLm26nmP05G0VwtQBcT0wneuI51kWW6W9r7hvpJzCSkOfEIxJdzYl9FozEuIp6VOt5bmB81zgGwrSVpKfj1gKBfAfKeHc+rbaXe4uKavgyfznDP299YRyTv3F+PDrCujFaAKmOV0s2uR08J17QtK5oAhGxS3VXntZjhcBZ1cSanzXirOM2W8h2MGCb1DfaGC9maHEGyfU7BepyXpLhL0qjbDPZ5E9AwP5jRcvDm17GLWaUmG9Qz1qqOs/dK/ItlW87TUDfcxgg5l93HrUBc3DnbyViI6OkZmBjOvVh0QA44CPpr7+Ui1BwkhbhJCdAsh3snb9jMhxFIhxFtCiH/mzR3NFULEhRBv5H6uz3vM3kKIt4UQK4QQv875CyKEaBRC/Cc38/QfIYTdMzgFyUjJ+m1QwOS9VJxXErUFhDu5/BXbgbYWL98cZmh9hubtHezxqc1VzNXpRFVPwW2VkOpgV4+flirzVzY2BtS0Nk9lytld2ExeOtLJEr/S8eCZ2BBLtrLzQVLXc1VU8+vxMf5Gwwt5MwmCZtXJqXWFwnD7mOgY+lz9tAK3BU1KMlVO+cX4cBmhsmxAtjadKGkzvSPczV/D3YaVwGIO9Yf4oC/EYb56Xo0Po0nJsYHGUfXgtekEH6kQRH+qrpX7h/u4P9JLOJMpUJhudjgLqsgnB5upLzMDvK+3jp1cPu4tmvn9atdK3k2Wfr7WpBLs4vYzu0KlfjCjMT/PaimgqBV9h4/veKdkW4vq5ABf3eiYR6eW5uFcwKkiKM6Vfy+XNDinvq3sccaLlNRHfZGPDTQaPrdlyRhDeoYFLi8HVJlDnuP0EKgQvB8TaGTfMvtQgKGMRkLqrEyb99uuGvxKKc8x+DnXxL5vBo4p2vYfYBcp5W7Ae8AlebetlFLukfv5Qt7264DzyYppLMjb53eAx6SUC4DHcr/bTDGGdI1ztrJX39ZgdTpRc9vzcYFGMhOgnjkWwps0XvhjNpg/4rsNqM7Nz26Ry8vpda1b69QmPR8PNvG5hmlb+zRsphhjWJunLJe1zGUX2y98SrE0FRu1OjJSha2VLi1VIlK0pREiK2C1ME+E6pd967krXN6iZ1DXOCnYXLDtxGAzX6wy9whZISdfUZDwSl67bzk8OXueEU4OttCdSVV8TKvqLBF82tsb4Eh/VqekRXWVCC+N/PZUTngoVUGc8x/hXgYzGl5F4Zvdq0hJOVoJHOHe4d6yomZ3D/fwXiqOV6icVd/Gr9q2H73tf7Gh/2fvvMPkKuv+fT/nTO/bd7PpIaGGLk3pIBaKiKJiQUUUbNgLgijyIhZsr7wiCApYUVSQIvCjd6RIS0JJ32wv0+s55/n9cWbPTjkzO7vZECBzX1euJNPnzCnPt30+HOWf0lO5MzXBUI3v+2gmzp2piaqCwoReYF2F9y+YaY5rY0Nssrlvkpih838T/db/O1Qn7w62c9QMdD3+mhjBwdQ2LUjD8sI9IdhWZevz7eENADybTTX8HrNFQVgJrX4tX7OApWC2fP+lxvHw+VbT/n2l21+3Vf4PsSFetElEgGkRucLlpUV1NCxyNvnZ6iKEWCGEuGuygiuE2FMIcd50z5NS3o8pjlF62x1Sysnhj0cxLRDqvXcPEJJSPlpUl7wWeFfx7pOAa4r/vqbk9iavI9bmsxzjj1RJ6r/RMZB1M4f1+EditGYLz/bi3h9H0bKSnY/zsvCA8u910eimMs+7JuU4hTIjwZQmTWD21+bXM58efJk1DcwrNnnt0K46LbGlYzc9O2ev26I6trvnc6vq5Iut88uEj5a7vGVVx0oeTsfp18oDsRG9wFMNdIJdGR3kutiQ9X8pZUPdEL+Y2MJESTX2t7HpW6x3dvmq2prX5NI8WQy2P9HSjbui/Pjltvn8omsZYdXB5kKO4zY9V/P1X8pnCCiqlSjodrg4xBuiXXVgSMmbPEEWOd01211fzmcIKSpvDbSgIDhvZL1136hW4OqSNvJ9PAFWuOy7z3QpaVWdvNlXHkzu5wlWzTQDrHB5OcATxKizpKl81ov5DNfHR1ifrx0w2/FgJs6EYa6N5zndlk/wnckJHq+w15pUC3+ygWRIJRlDt6ynGiGiOji12IEwqhXYYmO3dXqkm53dPlxCsU2CGFJa9pcPpmNlM9uVJA3dmg+uxK+oHOgNsZPTy2caSCBN0siZ40rMCm0BTLEMimqPW8nHgdtK/r9ECPG0EOI+IcShxdt6gVJ1n77ibQBdUsrJwYlBYNvX+pvMKbqU/CU+zNfaFuB7Ayh4bnk6x+pbU8QHpg/k3+INNyRwYUdYdRB7DSULNj2e5cXbMzg8giO+Uq5eLaUkYei4t/MipUmTNyDb6tr8mib3Grd6a1LOAqfbsnmx802dLcf5W/ll907TP3AbMqoV+OLQWp4uWbgPavm6FU+gypLlkUy8wdldWVVtHW6gxTigqARLqmIr3X6Wu2p76AKsyqfIVsxhxg2daHHtccno5jIFaYC7UlG+MbyeL7fOx68oHF3HzaLD4cSnqHiEwkHeIINanudyKc6I9KAIwYv5NH1anttrzHEe5YvwmZZ59BdyXDHRT6xkbvaslnllW2ldIVvTi7ldddLrdJGq+K6nhjr4ULg6rBjRCziFqKrAA4xpBXQp2dnt4+KOJdbtEtPrt1Zb7o87l1bd9qlIeSA3oWsMF5MmOWlUFRT2Lba/O4WY8VjcJwZe4qkZBM39Wp4rJ8zwK6CotjZFf4oNsyGfpVV1sI/NcW+AJXLlFKLuMbPU6SWs2scID2ViXBkdYHU+zXdGNjb8HRpZkfqklI9X3LZVK28hxLeKr/GH4k0DwEIp5T7Al4A/CiEaNqsqVoVr/tpCiE8KIZ4QQjwxMlK7HWVbYjQrX1U8l0uxpztAVhp8b7Txnfa1yMTGAn/+2DA3f22cXx87wBXH9XPrt8Z47h9JJjZpVVm1i8c21Xil6XmTJ1g3s/xqYmiSu75vXsgP/ESQUE95BTMrjbL2oyZNmswZc35tfq3zvlBHzUpQk9cmLoSlpDuXdnd/iA9z0ejsr6NzgURWBRr9Wr4sEKvks63zqhbyAiwbm3q8yROqsjqcLogF+F7H4jL/1zG9MK3WyphNgj2gqIRqBCEAzxVtbf4YH0ZFlHkLVzI5CvWRcBf7eIJoSHZz+/jB2GYyhk7c0PlQuPa41MnBds4b2cAT2SSqEOxVMg7xn2y8zEbyCF+kptd0t8NFu+rk4Uy5GNW5w+u5LjpU9fhRvVCzxfabI+sZ0wuszqX5U4nq8nyHi/08wZpev18ZXld12yOZOO8LdVjCZENanvXFVutuh4vWim6xSSXo73YsnrF45l7uQJXadT0MpPUeB/tCHBeo9uBNGBoSiVsoeGw+j4G0HEB2d/vLRgcqWeH21mlpFhjSnIOeyThgI8HvqBBiGcXgUgjxHsxgdVYIIT6KKcrxwWLQipQyJ6UcK/77SWAtsALYQnlr9PzibQBDxbboyfbomvreUsorpJT7Syn37+joqPWwbUbOMDh/ZEMzAK5gD7ef94ba8SoqmpRVWcZtzQu51JwJR01620YWOnAFBLEtOi/cmObf50/wm3cMcPkxA9z89TH+e32SsXUFkKbVzWzwKIrVCrO9efrPSUZfLhCer3LAx6rzVSN6oWo2pUmTJnPCnF6bXw+EFJXe10ji7/VEXhr8rgE14W3BY9kEG4qL9rvT0Wke3ThSSjZv5/Eft1B4T6iDw0s8TFOGTlKvHfz+NT7CF1rLJ/7Oae3lhxXVvz/Fqpe0YVXlbxXzk/0NbIN/Jka5uWSe9k2eYFWls5IDvcGqAGt3t8+6nh/sDVUFWcFiYNyvmdY0vyyZe63kB0WLoztSE/xqoh+3UHisWP3WAZ9Q+Nn4Ft5hE1gBfHtkAzFDYyeXl591LeP4EmXm1bl0WcvyQ5lYmSBWKc/lUjyeSVQJgBnUnlm+NTnOgE2r78v5DFlpoEmD53JTs7cFKVnh8vIBm2C+Vrvxs7kkj6Tj1rx8QUrrOy13eavG5q4qHt+/HN8y485Av6I01EEwSVBR2bsYbL+YS9vuqwudHnyKSk4a3JIcr7rfieCLxZlfA/O4qcXvooMUavwWJwbaOCnYxjyHiwMasP2apJHg9zPAr4FdhBBbgC8AZ9V/ij1CiLcBXwNOlFKmS27vEMJ0mBZCLMUUtlpXbGuOCyEOKqo8fwS4sfi0m4DTi/8+veT21xz/N9FPSFF5fJbqvm9EdCm5dGwz3mI27t3BdtJ1fNO2BVdMDDCiF0hupW/kuvszrLs/iysgOO26Tj73UC8fub6LI78eYaejvHjCCskhndW3pLnzwgmuPnGQznc5uea9g/zznFHu/uEET/4+wSv3Zhh5KU8+Vf+iNKFr/KekRcrQJHrh1UmsSCkZW1vgyesS3HD2CPddai5mjvxaCw53dTD/9/ho3ZNakyZNZs2cXZtfL1wZHWR1DeGTJrXRtmOgeLgvvE3kGff0BNhtO7sIeBSFdtXJSyVz6Cs9fuY5aydoBrR82TwqwF/iI3yrZGYVTJGfSm5PTpQFVQCpBsYAHk7Hy36DG5NjVfO6lQSEWpVkfzST4M6k2Ybc63ShVFQyv9O+iKt7VtDrcKNM8/qTStLPF79Pq+pgV5ePs1vm8YvxLRiYStYv5OwFnPyKwpG+CHu6/VwRHeAHo5ut+9xCKQu4woqjZuXXfG8nn4iUe8SeGekpU2ye5ABviOMDbbb7dEQxbX3CxaTBZGC7Wcvxz8Qof7URfpJQljyZxCtUNms5q7X8IG/QspB6KBOrOSNeKPFibpTbkuPTtuqX4kTQUazauoRCwqYYs78nSIvqwCWEreZLRhpW50a/luOFOsrtOWlWkO14LpdiXNcQgppz3XY0orKyUUp5jBDCDyhSyoYiOCHEn4AjgHYhRB9wAeZ8khu4s1j1erSo7HwYcKEQooCZBDhLSjm5534aUznaizkjPDknfAlwvRDiDGAjcGojn+vVxpCSLVqO89oX8dJ2luWvR9LQ8Qll2hPWXPFsLkWPY0oefW9PgIcysbJWlW1J2tCt2dlLxzZzfvsiQrMQHdILkrt/YAaApd62Xbu56NrNxf4fDiINyejaApufyNH3RI6NT+TIjhkMry4wvNo+2+YJK4R7VXxtKnpBomUlWs78O5c1SGUNfp7vQ8tJDA2EAgsPcLPbCX5WHOvF5Zu7Gdv0hM7GR3JsfCTLhoezJIbKg9k93uVnpyPt55f7tfxrpkW7SZM3GLO6Nr/eafZPzZycNLg7HeV8Fr2q7yulZFQvWC2LR83h9T2iqnympXf6B25DJnSNK6MDnBJsN1eomJ1+XXWs67odrrIZYYBhLU+8olpcK2U8rT+vDT0OV1lb6f6eAMumaZe+Kx3lUxUCQqXWRzfER/ly23wcYiqo/ObIeh7LJLh5wR64hOBb7bV9mCfFm1a4vJwcbGd9PotLCJ7IJHAKwR2pCdKGwcYaSZvjAq0sd3l5IZfiiUySFSXf59sdi7i4pCV+qdPDc9kUh9oEmStcXmKGxo2JMT5cYhOUlDoHe6q71h7PxMlIg3YbobH3hTpwCwW3qnBlzwrrdkOa561a3Y0dNq/17Y5F/CE2ZLVKP5wxExhvKfr/VsaTHygKUK1weWu2V9dCFaJsPT4dA1qeBzNxlrm8RFTVVrz1z/FhTg620+NwcUZFYgHMQE8pfkwnwnZueJL3hTpqxiabCzmez6VY5HRzbWyQ79vMT9vRyGr/ZSHEDcDVUsrVDb0qIKX8gM3NV9V47A3ADTXuewLYw+b2MeDoRj/P9iInDU4NdRBRHbiEQl8hV9fva3vxif4X+UC4k5MqJPi3FRlDL5sFVYXgpsQYb/GGX5UA3CEEZ0d66HG6+WRLD7clx3lfnfmSWjx5XYKJjRqtSxzs8wF7vz2hCDqWu+hY7mLX9/k4b3gDZzGPwIhCbItOrF8jvkUj1q8T69OI9+tkYwbZmEFRy8aWfPFCNJkQ2/hojo2P5vh/Fwl2OsrL7if4WHSQB8Uxs+2ZjRkMrc6z8VEz2B1aXShbcfpaFRYd7GHxIR4WH+wh0Fk7o/r51t5Zt3c3adKkLrO6Nr+eubx7eVMZfRZ4hcqBcyg21SgS04PTqyhIKWvazcyGu1JRHkjH+EPvrnP2mjNFYs7qlirRPpCJEVRV9vDYW3K9N9RR1W3W43BX7de72lSxZjvz/sOupWRKAq+MNFiVS7FyGo/gSrXjBQ4PebV2hXByjOyi0Y18rqWXv8dHOaaGvU+gpOvviugAy1weRvUCW7Q8V/fszE3JUdyKUrM9tdfh5nsjGznQG2KFy8tpJeu3i0Y28enWqcBdFaJmNTRlGCR0nY0V1kV/iQ0zqhU4s8KGMC8lUV2z1JVL+XV0gKP9Efq0POcOr+emBbvjRLCPJ0Cb6qhS+Qbzt/hbYpTPtJYncr47soELSua7k4ZuVT/3cPurgrfNWo6VBDjQG7IV46rHTi6vrbJ1LQxgci8MKQ7bDgwDs7VYEYJ70tEq8TAHgv2K56Q9PP668+H9Wp69atznU8z+g5kuMxu5iuyFqSB5lRBCAa4G/iyltJdOa1LGf7IJS+HQryjckBjhnNa6Dk/bhfeFOq0dcVujS2lr+r2zy8fL+Qw7vwqtTP9OjnOw18wCLnf5WO7ycU10kOMDbbQ5prcOAEiO6Dx8uXkYHPWNcm/bWjyeSfDhSBfLPV7ohp6V1Y+RUpIeM4j1a2TGDVSXwOERONxTf/8xO8QZXT04PALFAdm4wYu3Z1h1c4otT+VZfUua1bek8bUp7PoOH7uf4KdzV2dZICqlJN6vM7wmz/CaAsMvFhhekyfeX35hVp3Qu5/bDHYP8dC5wolQpv+uq3IpYrr+mkz2NGnyBmCHuzb/ZLyP71YI/jSZnrw0tsvYlQT6tDxbCjmWuby8MIct65LG5va2JWFF5ZttC8vWLLu7/fhF7QD15+Nb+HhFJeyUUDujFTOXT+eq1Xefyib5d2qcX3Yvt27bd5oAFuCrQ+t4Z6CVxS6zQteI1MkBnmBVq7BXUXBJ89r/oXBXVSvqAd7Q1IwqktV1uh3/mRjjAG+IC0c3skXL803VRboYoN+aHOOUYAc/GtvMcX77md9rYoPkpeQAb5BFTo81V26+t8EdyQk+Uqzkbixkbau+AH1ajlG90PC6r111MqznGdc1Wm1tpgSGlGSlgS7BKeDFfJpxXWOljUd5Le/rrDT49UQ/x/nN361FdeAr7ld5aaBXVHcfTMd5R6CNW5Lmdq0XTFZypC/Cmlyarga7ChY63URU83fRkVwVHaw6Lx/hC1vt33at64qYEmsb1PI8m02VzW2X8kA6xttrzH6/J9TBMf4WfIrC6eHqCnMtpg1+i61UVwJXCiEOB/4I/FQI8Tfge1LKVxp+tx0MKSU3JsY4tCTIiusjaFLieI1VwxY43TyUjs2q+jlTnsuleDabYs+Kk/bpkS6cM2zXmC0Pp+O8M1B+oB3pj/A/o5v4ZvuCuobbk9z/syiFtGSnIz0sefP0tkV/jY9wfKDVmnOuhRACf7uKv7324zYP53AHpy483rDK3qcG2PvUANHNGqtuSbHqX2kmNmo8eV2SJ69L0rbMwc5v9ZFLGMVgN08uUX3idbgF7cud9O7rYskhHubv58bpnfky46ls0vZk36RJk61nR7w2v5TPsKmQnVXr546MIgS7b4f5WFUIdf+L1gAAIABJREFUPhruQmKqu85lsPquQNt2Fz8rSMkdqQlW5dKcHDK75vxiaubTDonk4tFN/KLEpumy8X7WFzL8qqRV1i6oXZNPl81PSmjIomZcL1Ba91xXyNI2jT9wt8PFuF6gs2Qbr8mlyUqDXqeb53JJ5jlcZaJXe3v8uIXAIxQrUJuOyQDbKFZSL+lcwr2pKErxvttS43ytfUHV8yKKg7M75+FAcP7IeuY53LytGCDt6wmWzcQWpOSZbIoDaohvujD301J+2b2cCaO6826ew8VSp33L+C4uL62qA4GHg7xBq008qms8k02yqZDl9IrEh1sotlZHe7kDxEpmaXd2+azq7Cv5DC6hWMmMUhREWXt6I1w63se5bbVb1CsZ0zXrPFyrZbnUBssuGZQ0dG6Ij/K19gUUpGTApireCH+JDbNZy/HBUCdr8ml2afA8N+25SAihCiFOFEL8A/gZcCmwFPgXcOusPu0OQr+WZ7HTU1ZtO6994VYLLG0LbkiM8EI+TeZV+Gz3pqIc4a/OwjkRXPgqWB7pUtLjdFUpFS50evhK23xUIUhMo5bX/2yOF25MozrhiK/at/WUcnNijKiuTRv4Nkq9+YzIAgeHnBXmjJu7+dCfOtnntADeFoWxtRoP/yrOk79PsvmJHLmExNeqsPgQDwd8PMjxP2zl4zd2c85jvXz4z10c9bUWlrzFO6vAFyCu63Xl65s0aTJ7dsRrc9PmaHZIKXl+DqquD6VjjGqNq8JqUvJwJs58pxsFUbMFdjY8m0tt9/0haeg8mU2UWaykpV43OfMWb7jK1kiI6ll2u2qZR5giT6U0YnV0bKClbNu3qw5802y7m5JjdUOoZ7KpKmuZq6KD/HR8C+2qk6CiclGdLo3PFOeJD/WFubBjMX1anpOCbXxreD1BxYFTKFUV8lJOC3dybyrKY5kEo7pWFszs4wmUWeO8P9TJcI2W+4O8QY4NtFTZZv3vxBbGbdaBT2eTLHF6WGwz53qkv4WkoZOTBu8LdeIpVsa9ilLT5ktD2q57D/KG2M3tt/bxO1ITltifgKoA9zsd5jz/Qb7gjJODu7l87O+dvoNgknG9wKbiLLYQglND1S4696djVqv9eR3VWgOGnJr59QmlbsLogvbaWgUGsKGQJSsljzfklW3SyKr2ZeAk4EdSyn2klD+RUg5JKf8G/Lvhd9oB6XG4+HSFYIABXDiycc4sduaCpKHjFyp7uf2Wj9i25CORLtsBeVHMGA7OMgPUKDlpVP0uk8xzunEJhQtGNrKlhtCCNCR3XWyKXO1/epCWhfUbKKSU6Ejbof/Z0shrCSHoWenmmHNbOPvuebz7l+3sc1qAQ88Jc8qv2jn7nnl8+r55vPeKDg7/UoRd3+GnbZlzxjPCdkgp+UikC88MZ0+aNGnSMDvctfkXXcua3SSzwKuo7DkH2+1/x7fwrE07bi0MJBsKWeK66fl5fzo2/ZMa5MV8hmttFJFfTSTQpjpZVLKeuSsVrRK0KiWsOiyxp0kO9oaqZiLtRIs+29pb1c76conSdC2O8EV4saQF+SBvqGrGtZQvD63FI5Sq5MI+ngAHFu1kHEJUBceTn/hviVGG9TznjWyo+R53p8w1lEsILhvfgiElv4sNoWN24bWqDp7JJtmlRgJ9YyHHv1MTaBicGGjjux2LLXXlq6IDfLNEbOuRTJxojYLGs1nT6qjyu7ycz/Bfm99RAnelJ+i3sTr61UQ/WlHk9otDay2/5309Qd4VbOcQX3XlOaZrxG2KTldEB1jkdFvzuwVpWEmTw3wR3uwtLyBdNm7aSmUNWVc8yo4Vbi9r842v/QXl8+B2a3ZDTs3hfnt4Q9X9QUXl5KLGUJfDxQk12prBdMypxTKXhwUON04xs4p3IyvTPaWUZ0gpH668Q0r5+YbfaQfk/JENVRtYFYL9vYEZZSi2NW4h+GRLD6eEOhrKIm4Nz2STPFHnu7890LrNK+N/jY+wJlf7ghFQVL7ZvpDLJ/ptkxQv3JRm8Pk8gU6Vgz5Z38N2YyHL72PDnBRsn1Mhrx+MbZ6RNL3qFCw7wssx57bwr5PG+M3Og9zpnuDlfIZzh9dz7vB6VuVS3JQY49zh9Xx7eMNW+S73aXmujr6hLUebNNne7HDX5o8PvMSG7ezt+nokY+hlM5Gz5eyWeTWDETsUBCHFtMzJGkZZK+TW4hRixqq2c02Pw8W5bQtZVKJr8ZFwl6167yT/SoxVBZUtqqOqypi22VY/H+/j1xNT11WB2Wo7HReMbCj7/W9MjNV5tBn4vckbrAoltmg5yzLrm+0Lq2aCPxjq4vMNKnCvypuV7WujQwzpBXZ3+1np9rPA4eb/JrYwohd4MptkqU1rL5gBLcAx/hb29gSswHmSbw1PWUeN6YUqb+VJkoZOwtA5rGIm2K6yC7Czy0u36ioTECtFEVgt5pOt3PekotyVjlreyn+Pj1jFFTvhrEluT04wVAws9/UE6S3uZ8/lUjxb0RkwUpwZfz6Xsp7TKGHFUTcZUsmengCnlFR77ZJan2rpIVysvpc33ZtkpWFVjyd0jctqBLhGUTG+FoucHs5qmUevw8XFHUsa/g6NBL8RIcQ/hBAjQohhIcQNQojXnmLTa4xBLU9IUW2Vbo8PtL2mRIBW5dL0FXLoUvLtOpm6ueCeVLSuoNWengCJbRz8mnMB9S8YHQ4nF3Ys5rlcirUlmdVc0uC+n5oZy8O/FK5rKRTVNX461lfTpH1rCCsqMX1m20mXkssn+vmfziVc3LmEk0PtrHD7uLj4/93cfk4MtnFx5xK+27FoxnMjpbyQS7Fbs0LTpMm2ZIe8NsdsPCWb1EcIUXOxPhNuT03YKtbWfF9MG0NDmgFBI+JMjXJ2yzzOq9MO+WowrBe4ZGwT95Us/tcVMnRMI550Q4Xf6+3JCW5OlgekdkmGyoS0AaxpoPLrEAJHSaJggdPN0hrBHUDC0BnTClXjX4Na3qry/XSsj3TFWm1jIcvNyTHOaumhVXXWnckOFgOjXd0+ulQn6woZorrGJ1q6WVKcqVWAW0v8ektxC8H1vbsxpBW4cHQjA1rOCjZPC3WWrV7CioNLxjbZvk5IdRBRHPRUfNavtM3nozYCShlpEFBUW9/ZD4c7iSgO9vUEOLdtoaUCLpFM6AWrI+CyiX6rEj/P4eLnXcuqXquylTgnDctXOWZoVefBJcXfU4BNqFmfGxKjM3r8U5kEN0+TQLkpMWYlu+zEdFOGbnlWO0RtqyMNWbcod386xpeH1tKn5fnh2Oaaj6ukkeD3t8BNQA8wD3Oe6LcNv8MOStYwOKGGcllIdbAql5pRpmVb8lI+g8SsSisIcnNwkaxFu6O8RciOG+IjVSfVueT4QBvOGobZpQghWOL08H8T/dydMo3dH7k8TnrMYN7eLnZ9Z+0gXpcSA8k5rfMbVhGcCYf6queGpmMm+5uG5Dsjs5+/XuB0z+lCp0mTJlXscNfmI30R20Vnk/q4hKAwBw7Jj2Xi9Nm0e9aiIM0WzJUeP35FtfVGnS2XjW/hthqB0atFQUom9PLa3YCWt4IUOz4e6a7ah+0SzXZturu5/Xy2tXxk6+AaIk6lfL61l/eXiJkmDZ2JOkmk3Vw+VtkoNTuEsMRaU0blxC88lImzrpDloXScvDT4XIV9zz/iU0HWucW25Lf4wny7YxETusZbAy1cMLKRNtWBXyhc1Fm7kndB+2JO3bKK54sBVOm6clMhx/4l64/Twp0158N3c/nYxxPgLxUJiS8NreXGZHWAt66QZSeX17aAtTqXZkzXWJ1Ls1nLWnZAi5weFpQ8vvSzDGh5zhlaW/Y6UkqGtDxvDbRYgmOPZOJWwBtU1CoRqVOKgmsnBdtsrYdqoUnJ3m5/WSV3OvLIsor1+TZ+zhsLWesRe9usBUvV2r2KYrXTV6IirPZoOyaDfQWIziAx2shVpENK+VsppVb88zug8a20g9Kn5epWvnZyefl7fGbZlm1F1jCsAfmTgm1k5rA1qZSBQq6hKuj+3iD/2UZt4ZsK2RldgMOqg4s7ltDrcPP0Swn+c10CBBxzbktN/1opJT8a20xc11lSo2Vna+l2uGbU9gxma8k+DQakTqHQ4XDWnHuejmGtUMMGoEmTJnPEDndtPsIfZq9mR8ms8MxB0uAQb4iIMr1D5jmDr6BLiURyXzrGqlyKlKFzRzGJPBdkpMEzM5g/3ha4heAQX5iDShbuWwr5uonmhzNxPhrp5jslXXYfCHXyowrF30Ebgaa9PH6eKZlDFUy1/9ZjYz7H70vmo08MttmKOU0yohfY1eWrcr04zBfhqOK88i5uX82282dzSWK6xjdKWo8BfjGxxfr3+cX7HsnE+dyGV1j16TQvfiiNe0zgVRQcQnDx6Kaaa8bLi22yPQ4X/9OxmA+GOq3q9oOZGHuUnCcum+ivue57NBPn3nS06vaClIzZtNtKCdfHR8o6Aid5ovjbJAydv8ZHrW0cVFT29QR5XzHA/FRLj9VWHS928MmSyqcOPJiO0VfIWfZPeSmt77evJ8B+FQJVPypWPR/JxFk/w/ndw3yRsv1jOloUB/NLRFevjA5WjQi2O5xW8H+VzQjcvBJNJCei5npxSMtzTZ3PdpA3xAnBNvyKOiOB1UbOhmNCiA8VlSVVIcSHgPr17h2cqK5Zw/y1WObyElDUsh1+e3FauNOaJdjd7Z+T2SA7rk+MWAd6Pd7qb+FNNbJAW8uD6ZjtLE093IrCCpeXtT/Jgg7KO0GuMOeX7eZi/xgfZrnLaytDP1eszqVn7Jm4yOnhgBl4OZ8a6piVNUXa0LnP5mLSpEmTOWWHuzZfMLKRRxtY7DcpJ2sYcyJ4dYy/hZ0bmDHt1/IYSFxC4TBfmHzR93QuWeHyEdrOas+tqpM3e0NlgeTpkS5LpMiOF3Ip7k5Fy4Sqnsgm+VUdUZ9J/hAb5uGK/V/BnIusx03J0TKLpN9EB8vu/+lYHxMlgd6IXmC+011Vfb4vFeWe4tr2EG+oqvvsux2L+E3PCrxCtW29vbBE/XlyJfh4JkF4rYPoExqh9Q5Ofqqde1MxEoZO0tAZKNi32Q/peRY53ax0+7krFeVn41vK1na/qwiY3lLD53dyq5xZISJ6UrCNLpuA7HB/mEN94Zp9FB7FFAprVR3WNngqm+TeVJS7itvuv9mUdZ9DCI7xlyt461KiCsGqXJpEsZp5erjL8uF9MB23fodKxnVtRro5aUNnbSEzo27LHoeLZSXr25iukas4vj8W7rYSbgJRFev0a3n+FB+2/n99ReV9kqw08NZJ3E3oBVoUBx6hWFZXjdDI2vbjwKnAIDAAvAf4WMPvsAPyaCbeUCvK6ZEuXilMP6+xrTlvZCo75xKi5k64NehSsrmQaygg9Ckqv4kObBNF7DW5DLvOwn5n3X1Z1j+YxR0UnPXVeXQ7XAxrBb49soF/Jkati09c1zjG38K767RpzAVh1THj2bcfzWAeAkzrp0eziRknaFbn0rPaxk2aNJkRO+S1efuni1+fPJ7d+m6q62JDDVkmrXB5URCWFU5QMWuEH5hBa+V07OsJ8H0bf9RXk34tx0/H+3ixZJvcnyqvOlbiFQqPZ+Nls9OrbcbgPEKpuvZKKGuZNv2Tpz8mgoqjbMb3IG+wrBX1uVyqymf13lS0KqApIK3f9Lexwaouwf8d7+cTAy/x7Y5FLHZ6qrxzSzVl9iu+/wmBNg5WptbL8Z/pLFptFmN2dfl4ukZ1f7HTwzfaFvJyPsPd6SiqmAqwvlthsbSX28+1Ufvq4WKnh+UuL/+pUHae73DzVptg6slMElVg65N8RqQbJ4Ld3D4u6lhMe9G+x8Bs/x8pVvPvTE2wqtiuvdzlZX9PsCxZ4BKCr7eVexv/NT5SFrBV/uZv85ufNaI68M7AZSNu6AxqeWvOuhGeziZ5tKQ7c6nLg1axr148tskataicwQbIS4NUMeCu1UUJ5vc5qE4xbG0hyxXRAWKGxs/H+xr+DtNuISnlRinliVLKDillp5TyXVJK+8nxJoCZHbXzsa1ERfCr8W0T5DVK5cl1cha2MMdZWgF8ta3aqLwWQcVheZrNJed1LMSjKGh5yfCLeSY2FdBy9be/lpfc/YNitvPTYfxtZrb52EALP+xaynH+Fp7OJvna0Dp+Mt5Hl8NV92CeC1a6/VXqhPXIF2XyZ/q5RrUCq21mf+qxtyfAu7Zx8N+kyY7Ojnht/k77oiphmibT41dUSxBnaxjQ8ozXUV6d5OFM3Frwpw2dHoeLTodrTpW6r4sN8c3hdVv1GpqUW9V9J6Gq8ffFfNpSRLbj6+0LCSoqx5XYHXkUpcr+cT9PoKp6emKgje9XzMHWU5ae5IKORexSMgc6pmscs/EZNhUD7vWFbJmAphNzRlytWC90qS661erjbzKIGdHz7OX2852RDfRpOcvqx449PWaCYLHLwyK9fH42co+DiOrgqIqKaCmnhjo4e/BlBrU8/mKHweT69bLxfj5UMuPc63TX1ApwFi02K22NLp8Y4JqKCjlMCu4JW82Vv8ZHiBs6D6fjXBkdtMRbD/aG2ccToKX4Wx3kDeItzuw+nklwydjmMs/klDT4Y2yYMyLd1nG7tpCxHEMW2QiWTYprHeYLz8i1JS8l3Q7XjOaEJ2dsJzkp2F4VcBtSWrPvg3q+rPMAzORO6T7/yUiP7XsVpKzrSS2K7zXTFfe0wxtCiCXA54DFpY+XUp44w/faIcgYOpdN9POVBgI9VQgO8AZ5NBPnzTMIZOYSHTi8wjT9G20LypQB54LrYkOcNIOA6FBf2NZHbTbkUwbDawo8/3ySdS9k8a1VGVtboLRw6mtVCHarBLsdxb9Vgl0qoW4HGx7OEt2s0bbUwT7vr56Z9Soq+3mD7Ob2WWIQ25qClDyUjtcUVatEk9KaN5kJx/hbeCAdm5Fy80/H+/hiDVuBJk2azA074rX5oUycT7ntF0k7Gv/NJtmpOD41HTlpsH4OxplOj3Qxz9GYU0VemjJOT2STrMql6XK4GppPbRQJW91KfebAS3yxtZc9ZynOGFEcnBHpYS/P1PVxJ5e3pqcswPdGNnJxxxI+Nfiydds5rfOJVzznoUycgiwPQIf1PH8eGbaEoBSg0+EkLw0rmLo6OsCHwl1WMARwVXSQBQ43Z7aYx848h4usYVhtqcf6W9i5xAFjf28QTUo6KwLr+U4Xorg2fGegDXfxsx2/+XnuWbQXS51ezmzp4dzh9WQNg38kRvl8q7310dXRQb7fuZSrJgYIjzrYmwCGQ6JogrGhAlnD4LKJ/pozv5PKvru7/Rzhj5S17Y7oBdbms9a65ZbkGEfXCKTX5NNIyn1r6xFUVJ7PpjjIGySilodQk369EliXzxA3dDoxxZ8iqoO3F6uzb/GG2anYBTkZ9MqSTEpeGozpGn9LjHCgN8Qip4fOkqSDX1GrKq03JUc52Bfi1uQ4PQ4XBzbQfQqwxOXhvUoHv40NNhS3gGmvVfru18eHOcwXYaeSoPvNvrAVID+dTbKvJ4CnJGRuVZ0cVPIZH83EbTtDX8lnGNc1ltUI6I8PtLG7209YcXBioPEYY3rlAvgncBWmkuS2kwF+g/BYJjGjrMtJwTbrZLI9iBtaVcZnTC9wVzpaV2FtJuhS8lwuxemRatn4Wuzk8s5qqxi6ZPN/cgw+n2dodZ7hNQUmNpZfVFIYIKBlkQO9IEkO66THDdLjBkOrame1j/pGC6qz9qfyvorzRw4heDaXbDj4fTGfqWs7UIslLg/dDqc1gzIdhpSMaIWGHtukSZOtYoe7Nt+ZmuBQX5hDHdsnWfxa4otDa/lp1zJbJdVKClJyxBwk2D1CbWjm92hfBENKAorKWZEeJOY6wDeHSt0nBFr5bzY1/QPrsI/HT6gBAa9aCGCzlmMomeedxWvxMpcHd52204KUfGV4XVmb8aVjm1lfyPLL7uXWbUudnqrq4sPpuBVggXnQv5BLl/nbPpFJcmqoE1fJU1+puP5PWjNFdY1Oh4vDfWFuS47zwXAXupQ8konz7mA7/Vq+TNX4iWLL71H+FhIlas+HF/etyXGvvdx+29bb73ZUW1OpQqDkzA9b6JW4NwrkKOSKwd29qWjNrsGPhLsIqypfHVrHm30heh1uPIrCEqeHRzLlxYFa6taGBFXAdypapb/VvrBsW0+yvyfIizXspXocLsKqymKnm13cPmscbkMhi1GQ3KdHOaOlhztSE3Q6XCzBFC/9cfsSVA0o/kQKggVONwldtyyAJsWhANbk0qSkbmn1lCKK36lRnssmeSaXqmk1ZEdKGmWVX69QyVRsq3kOl1WpdglRVfndUMjySFH8DeDZXIr327xXVhp1xfoeSMd4LBPnM629MxKBbeSoz0opf9HwK+7gSMysTqN4FZXfRQc53BfZZsrA9Xg+lyKh62XtB/Mcbq6KDs5Z8DuuF6qqy43wj8QoHwh12h7gdmx4JMu9P4oy8lJ5AKs4oGOFk+wygz1XBliwq5uOnZ2WR6+hS9JjBvFBjcSgbv4ZMv8dH9RJDunsdKSXxYe8+r9PLfxCmVHS5N/JcT7VMruKyb+S4yxwuDnYN30mMWHoHNTA45o0abLV7HDXZgV7W5gm9RHAvekYF2zl61wTGyRpaJxWMctZSUYa+BSVtDR4OpfkCH8EBdFwsrYRRvQCh2zltWZM10jL2dsqjukF7k5NcEjJmm9QK5RVtCrZ0+PnD7FUmZhSrqRFdJKYoZmesiWWNhLKhMskZpBRGhyszqeLY2tTz1vu8nJkyRpsF5eXfElC+750zOr205EsdXr4e2K0anxJljR6P5COsY8ngEtgKfVeOt7HxZ1LOMAbotvhqgp2LxjZyD2LzM9xWsjchz4e6SauagxSwL9AQdsIjjFzdfOuYBuPZOJIKatGto7zt/JMLsmaXJqooaEgLJ/fU0Lt/Hhsav7zXcF2fjG+hQ/Z7LeTFjvfG9nI+SWf9550lENtEkZ3piZoVR2WWnMp7wq2kTMM3ELhk5Ee5hXXrgIzoJ08c72cz7A2n+FN3iBBReWGr47w4FNxDj0nzD7vCxBUVE4ItvFgOmaJPX1ndIM14y4EVUO/5xS77Va4fPhnMPObNExBqeMDjR+br+QzeITC0mIibB9PoKoK/uf4iNVR8emWeVXK4AblIwPuGgWT6SrYKUPn4UycM2UPt6cmOLxOq3wpjQS/PxdCXADcAVh9qFLKpxp6hx2IgjRoV50z9nU93BfmH4lRvtT26reKDmoFllUcxG5laqeeC2KGzvEzUGGb5M3eMI9k4rzHWb9dd/SVAvdeGmX9A2ZbV7BLZfnRXjp3ddK5q4v2ZU6Ewzzh7Gwz16CogkCnSqBThT1n/DG3C0IIvjmDGeqYrs3aeugoX4TLJ/obCn4z0uBtJbNMTZo02WbscNfmP/Tu2nB74hudj4a7Gp7jDU0jGgPmaIyAul07QUVtKPXwcCZuzYEOawU0KWlRVR7NxDmrZd40z26M+9Mx4vo4P+leNuvXeDmfsaxkZkugwr/4kUyc+9NRDquR8N/PE+C5XKqsTXc3t4+9PeWjRfMdbrKGUdbW/pW2+QxXzFyP6gVy0sBRDJKP8kWqAukvts5nTYl2x0KnhztSE1b1LiuNMl/cLVoOBQhUBFF7uP1WNa+UyfbmycTUldEBPhTu5DcTg1XbIVOsGG8qZFnp8XNSsJ3f50wxqi0deTqFE2VC0IqDZw3dVnEZTG/YfybGOCPSzV5uP6eFO60A7MdjfXyvpJL7cj5DXhq2HWyv5DO4hFIl3vVose1894qRLwN4MZdmX0+AhRXdfr+aGOBIX4QXcmn+mRjls6297Or2cUqog/FiKzbAh8KdBIvPfTqbpPCcRElK7vqfKKtvTrP3+QHu7YzywbCpHG5UJEf2cgfKZoSllNyeHOf0SDchRbWdR65FWFVxCQ8DWo6VNDbeZiBRSt4iYNOGXZqkvDExxpu8wTIv5i6HkwNKAtsLKirvkzydTdLjcJW1VJfS5nASUmqZbtWmkfTASuBM4BLg0uKfH8/wfXYIns4meSE38zacJS5vQ61E24LDfWF2tQkITwm2E6szt9IohpRcPtE/KwGo/byBuhnU1KjO7d8d53fvHmT9A1lcfsGh54Q545Zujj63hZUnB+jaxYXqFGws5CyZ+TcK55UoJ07HhZ2LZ/0+7Q4nxzWYvPh9bMgSeWjSpMk2ZYe7Nn9q4CVGtOkFl3YENCkbPtfGdI3HM/XVns8fWc+103h9fqV1QVnlUZeyps1OQUoUIUgbOpsKOTYXcmycQ8EroGwBPhsciK1KpsxzuPl8S29ZYvg9wXbqdZBeFR3kw+GuMn2QfT2BKvXgZ3Kpqi6Ha2ND/Hx8yivXIQTH+lvIlATwd6ejVe2fnx98hSdLfv/ni+vUcDFYXORw01GcKVURdKouDvOFq0SikoZutbd+vrW3qo397cV1gix+8s0Vui37egJcOtbHJWObeahk/ntdsjiPHgAZAQzYMpzjztQE+3uDtgmXGxOmq9uhvjCnhTt5MpMoOx5+WqL8+1w2xZda59sGPEN6gXGjwMqKIDdcox1+RXHeNVbHtlMi0ZDW73BDfIRX8hnCxYDXIRTLzjNvGLji5r7g71DofybPPWdHUXT4e2KETYUcOpIjfBG0vCQ1Zqozlx5LEnip2Ir932zS+ncjzHO4WeHycs8M7CmP8beUrc3X5jPWPjXJmSUCVhljStl5kkkf8Em+U2M9u7GQtbUUnWRPt58relYQURx8ZQYFxEaC3/cCS6WUh0spjyz+Oarhd9iBeCAds22TaITDfOHt4l94dypq66H1Uj7D7anxrX7953OpurL/9XAJhYczsSrPskLG4JFfx7nyHQM8+9cUCNj7/QE+cWsPB50Zwump/j7P5pKWuuAbBaPB9r9ns0nuTE5s1Xt1OZw81sD+OawV6GyqsTZp8mqww12b44ZeZcmyo/L7+HCZN2s9JExtQkeRAAAgAElEQVSrkr2/J1izujLJPxKjZeM2v5zYUuU7C/B2fytCmLY+7wy0IZEYmN6wc8XX2hbwgVDn9A+sQ5fDWXeecDomDI0/xUe4MTFq3TaiF+ius60l8LnBV8qsYn4XHeJPseGyxx3kDZZVlAEGK/b9gjS4JTlethY43BeummOu7JI1pNn6PFbcf04Ld1rfISMNNms5VARr8xluT06tA9cVsmwqBl03xEesaumPi+24k63T7wt10qm6rOrmJHu5AxwTaKkSsRKTWmxuiHSanz01bCAl3JIcr7nSuapnBWNaga8Pr2d9IWslAY7zt5R5LyvC9JFN2cyEOoXAJRTeXNHZ9qOupbYioT5FIaI6UG1yJicF2ugoik2dHGy3fKgz0mBIK1iz1rcmx6xk1H5GAKELnF7BGTf14O9QyIwYrBybWq8WpGSxy83NXx3j8qP7efmhDFsqgt+WYiJDiMbXhgD3paMN2ZeV8kg6Xmbv5VWUsgQMmEJ3k/Q6XWUCbGD6/D5TMrNfaas1iUsI/HX0dB5Ix/hI/4ukpMEN8dGaj6ukkaP+eWDmA5s7IB+LdDc8n1qJT1G5PjbyqtsePZNL2lZl93D7ZnxA2LHY6dkq2xu/UHm8eHGVhuT5G1NcdfwgD/5vjEJasuwIDx/7ezfHntdiWRDZcaA3ZPnKvVE4wBua1tweYFUuXfdi3AhtqpObipnWenw0Un8WrEmTJnPGDndt3nOGidS0oXPDNvCtf61QuaCs/TjBlmmSBgudHtrU6mrXw+kYl0/0A6YfbGmSXkqq2h3BtLyJKA7ihsbaQoZ9PUF6HS46ZzgSVo/fRgcZaTD4r8Wus0zMT5I0dDZXqGj3a3naVCejNToU3hfqICsNSxwKzEphZavyhK6xoeK15zvcnFoRkB3qC5clnB/JxIlWiDu9K9TOJ0oqcXkM1uQzljDST8b7SBZnn3UpOdQX5q50lNX5DJcUVZUr2aLlrfXHN4fXA/CvpLlGGNTyKMCFJa2supT8NjbIlkIOB6JsHng/Ya7N9or4GWwx99P8iGEJXdmtcj7XMo+zBl62VMwn7XCklLgVpWyc75zW+TiEqArQYKqK+aviPj7JFwfX8phNt8STReViOwcMhxCMagXWFbIEFZWO4u+ywOEmrE6tTzUpGS3uu2tHzc/vigjcQYWOFeZz9E3murVddTKg5blnc5RX7slgaPDyeRn0/qmtogphKTUf5A2xl7vxtW5OGngUhW+3V4uR1WJc18raxPdw+6tsqZ4u8RU/0BuqElyVlAegtT7ze4IddfWQFARRXUNKOSNF+0bOnBFgjRDidiHETZN/Gn6HHYRVuZTtgdIoqhAc5Avy3CzapreGUjGFUryKOitrnFIMKflTfLhqEL6UfNpg0+NZ1t6b4cXb0zx/Y4r//iXJE9cmeOTXcZxXC1b/JM0dF45z7alD3PatcRJDOp27Ojn1qg7e/csO2pbVv6BKKbkjOfGqqjG/GuzrCdTMlpXiURTbWeeZ4C/ONaXrtNmtyaW3q2d1kyY7GDvUtVlKySdbejiiQUETMCvFV9t4db4R+HHn0oadJZxCMG+aBOg1sUGrlbSUuKHTXwyc9/EE8JTMgcYNvSrQArPqM6jlkcCz2RQv5tMkDZ1bk1vfTTZJv5bn3hm0atpxXWxoqwJoFcFSl7dMcfvlfIarogO8d8sq2+ds0fKcGeku8wJ+d7CdL7RNWQJJKYkbOlpF2PeeUAdxXeMbRX9jBcHqXIqBktdaaGNF1aE6uSExlQSarJiXJsU/FTFnsT1C4cF0jF1cXo7zt3B9725W991R/oglMtbtcFqFk0LF57w/HWOLluOLQ2ut2yZnVP8aH+GxTJxflLRvr4mahZbbtQkyreaaJj9q8If4MMf4I7YWknemohSQhBQHn4z08PFi8UkHbkmMcWJJcuHKiYGaXrG3JMZ4JF3dvZCRBusK1e3DUkr+Fh/leRul8RsSo0gkA1qOGxKjrCqu53dz+9nTHbAE377RttA6HtePmQGbO2weV21LzPXyCy+lMKRZxU0aOuI+gSyqRGlxieu6qeMwLw0uKLYN9xVybJmBTei+niCLnG6unMF5MqyqZbPocUOvsjErTczdm4ryZIWP8q4un6WQDtQccfzJeF/NRBKYYm7H+ltwCNGQ5/UkjQheba1A4A7BfekYR89C0biU9wQ7GNc1039tBmpts0VKybfaF9a8P2HobCpkq8zXG+X5XKqmsTjAxkez/Pv8ceID088tPYN5Egl0qRz6+TC7n+BDNDjw06/lmTDeeHNidyQnONIfmbZVbU+3vyEvyOn4Qmtv3RmzRzNxa46lSZMm25wd6tpsAJ8dfIXvdSzmLQ2OF3mFMiungdcDl03088XWXlY20NGUNPSqFtRKFIRt8nJ3t98SqjnaHymbTY2oqu3M7DyHCwNz++/p8RMzzDCu0u5ka+h1uOmbwSLfjgM8QXob9C22Y4nLwwfDnWVz6F9vW0DS0DmnRsL5gXSMvSsS14NagaeyyTI7yEEtT7CiffnS8T4yhm61lApggcNTFny+UshWCR79NjpYNtN6WbHKOTmTeog3zPpCll6nC7dQkMAyl5fncyl+Mt7HhK5x96K9eDyTwCsU9vMG+UCo03xsyW/6hdapmUu7tPz57Qu5YmIAr6LSl5/67TIxAxcK81vdLJ/nZRyN8WGNAS1Hi2L6GFd2OUwKeO3m9nF7apyrJgY42t/CPKcLVQh+Mr7ZUkdOGjrvDrVbrcGlTAbl7wmWF3sO8gZtHTWO8EcY07WarcUuoeBEwSsU9OJD/pUcY2eXl4w06Ha4uC01zhnF31qPmQ/yFoPf1iXm8WVsgieyCdwixFKnh2fuNX/zPU7y8/w/U6zbOBWYSzmlnDyo5XEIYVuZtiOqa7SrzqqW+noc7A2VJSR0KXmlYs74uyVVf5dQqubQ1+czDBsFSxDtyugA3+tYjCIEhi5JjegEOtW68VA2biCc5nnJJRQ+0zrPVhncjmmDXynlfdO+ShMCQp2Rv68dihD0aTn+Hh/l/I6FOOfQE8+Ol/IZHssk+EiNVlUp4YlMYtbBb7+W5wibhUc+bXDfpVH++xczoG1Z7KBlgQOHR1h/nO6pfz8rU/QGXOzU5mOnIz04vTPbLpsKOfbzvPGCsrDqID6NKFlU1/hzfIRz6yQ5GkUHvje6kR912atr9mv5rT4GmjRp0hizvTYLIRYA1wJdmN1nV0gpfy6EaAX+AiwGNgCnSiknhLmS+DnwDiANfHRSUVoIcTpwXvGlL5JSXlO8fT/gd4AXuBU4R8qti3wmF5uNdLtMEjf0GS3qXk+sL2SZibRgLW/SSc5p7bWtsD2QjvJIJs7/di/n+vgIbw+0sqx4njekOZpUyZ5uv2W30qY68QkVQ0rOniOlZzAVc+fCT75S5XcmvJLP8OuJfvZ0B6zuqtuS43wg3MlzdTyIfzXRT+lWeyATq/JJXeT0VPsiF4+gye2oIXk6lywrMixyuquKDi6h0FVS5d3b7WcPt59RXaPD4aJddbIun2FIK9BaDBBfzKVZ5vIyXgyOwBROk8X7fza+hc+0zCOoqNy90LTJuDU5zsci3ZxfXG8cU9GlcV1smJ92LcOtKPw9PnU68CQVDEANCSKdDsbRSA8ZtChObkuN8/nWXirj0KCi8sd5u/ByIcPND41zdCqCdpKBA8HZLfP4WYng1VKXhxsToxznb63qglvg9BBSVPN4KlGDPsIXYYVNAuPFXAZ/ce63ko+Fu/EpCscGWtjZ7bV+ByklGWmwNp/hGH8L/80muT4+wlkt8zhYC3Ez43gjk8Gv+bqBvill9efWpRh6uoDqgr1ONYPfwtjU9hMC3lRc4zqF0lBL7yQPZ+LMc7hYOIORzRsTY+zi9lkdDz5FqTp3lFpH7ez2lnkPD76Q55aPjKN2CmK7awhV4M8r3OodIx+X9D2ZI5eQHP6lMHu/N2CrS5SJ6Vx/xgiF+ZI/fm6YW5bswUWjm7iwYzGNlHpm7+7dxGJMK/C2QOusFI0r2dsTIGno/Csxxru3su14Oga1PB11ZnBWevw8PBGb1WsbUrKb21cVOG9+Istt540T69NRHHDI2SEO+HgI1Vl723XmXdyRnOCE1tnNDu/p8dcdmH+9cpgvPK1S5Yv5NCvmKCB1CNN4fX0+w5KK15RS8o22BbY2CE2aNHlNoQFfllI+JYQIAk8KIe4EPgrcJaW8RAjxDeAbwNeBtwPLi38OBH4FHFgMli8A9sdclj8phLhJSjlRfMyZwGOYwe/bgNu25kOrCD7bMq9KFbfuF5VmcPBGxC1Ew9vCr6hWUFOLxzMJXEKU2ZEApAzDqmwOawXW5NIcXbSzG9MLtm3DT2eTOIVCyjBIGzp7ewII4N+prRNeLOWSsc3Mc7j4bGvv9A+uQbvDibYVwa+d5sbmQo7nsyn+GB/mzJaeqvu/3b6It29+jos6l1i3SSmrqryH+cKsLWTZR52q7B/lj7CfJ8CJfS9Ys797uH1ESp4bVhxEda1szfO51nkscLg5ftNz3LxwJXFD5/fxYUul+sbEKLu6fSQMjYii0utw8Uohi1cohBTVSji1qI6q9uGMrvP+B9fwz0N356ViNfbK6ADvDLSVFR3ius6GQpZzhtbyjbYFnFxsSzY0SSipEkWnq9XJf8YTtOHESEk+0dLNbalxS9U7Y+jW+No32xdy5MZn+OADnRx8SYg0Brf8bJyuPVykj9V55zunWmoP9oaI63pV9RGg12FWu6+JDXGYL4yKQErJD8c2s6fHz+dbyxWEN2s52lSnrXXkvekobwu08J9Mgqih8eai//NbfGE0pNXqvpc7YAWoDwyYa2wRMr9k61LzdcfXF/h4oBu3ofDvb42DhF3e5qN1UbEyXBL8qggrqD9yBmMhMDXzOxPxOANZFmC2qE4+FSnf10uTSi6hkCrx0159axojB8ZmyZrNZlLOicJqyhN0gy/kWf5BX1WSK5cw+NsnRxheU8CdEjhTAgWBWvR6bsT4aNv31u4A3Jwco38r229KeYsvzMnBdm5KjLGVyfK6hFVHXYulgKLyhdb5s/oMT2QT3FNiLVTIGtz9gwn+/LERYn06HSucfPjPXRz8qXDdwBdgmdOzVWb2tSTUX+/kpcGGfP0B/6VOj231fbacEuywnZ3eUMjxm+jAnL1PkyZNtg1SyoHJyq2UMgGsBnqBk4Brig+7BnhX8d8nAddKk0eBiBCiBzgOuFNKOV4MeO8E3la8LySlfLRY7b225LVmjWkdIqt86evR7XA27IX7euOX3curfFhrUZCyTP3Wjv9mk/w3W50oeLMvxLHFYPeUUHvZCM2ubp/taNOQXmCoOPN7bzrG/ekoL+RS3DmHwS8wo9lGO25NjhPfCmu+kOrg7YFWjg9OqRcHFLXuHPGvo/1c1r0TfyixlTq3faFVLQUzk/TH2DCFimDNr6j8ruR5KqYS7sYSoZ9VuXSZB6whJY9nEvw6OmCpHe9brNjt7JqqbL4t0MoB3hCdDhcSeGeglWP9LfymZ2dr3GkfT4DdikHWId4QLqHw5LVJDv1smId+GbOC8EGtQFTX+EGJWNbk/PKIXmBIL/CT1X386qh+Lt27j+gG8/VvEeOI4uGq5SQXjW7i3cF2K5h5x+bnrdf71vB6PhvsYeKSqf06H5dsfjjH6MUFsmt1KzlxXWyIhU53lXo2mG3oL+czqGC1/RuYnZi6zdLXKQQPpGNVbb6AJbikIVmbz1qCZQlDp9fh5mPFVuej/BFWugNIKUk9b/4mrnDR7qhNQQ2AnoAHtsT5fz+Pklsl8XYrHPX1FtwhgeoCPWV2UYIZaP4lbqqF35+OcvcMjrOzW+YRURxlv9V07OH2l4msGVJWWW/uUVI17yvkeLrk3LLlKfO4PewrYd55SSvv+H4rSy/wcPT3Ihz/w1aO+455vsnGDK6Nlc8i51MGfz1rhMEXCoTnq5x6VSdfX7YAhxAc4gvZtqrbMe2ZUwhRlQ4QQuzc0KvvIGyNnU8thDAzGP830b/NAmC/orBgmlaHK6IDbJrhBcaQkr/ERzilmJnsfybHNe8Z4snrkggFDv5UiA//pYvOXRpTIBZFlb4ts/AIHNLyZe0+bySShj6tIvd96Zitguds6XW6eTAdq8p4r8qlyi6kTZo02bbMxbVZCLEY2AezQtslpZzMYA1itkWDGRiXroz6irfVu73P5vatoiAlV0YHyixipmNc117zOgRHbXxmVtf4swZe4uUG/Txz0qiyl6nEqyi2nTsZw2C/4jZ0C4UTS0RqXslnbQO9t/tbMZC0qA5OD5u7UcowaKnhnTobjvZHmL8V87pgfs7dtkIMMqCo+BS1TMzncF+YfT0BLu9ebvucIa3Az8e3kCv5za+MDnJ+RfCw0OlmJ5eXrw6tZX3xd/5nYpQthZwlliSBDfny9vd21VnWESCB/2QSZROqfyvaGq0tvu5HIl08mI7xQDrGiF6gX8vjFQp3pCY4dcsqy7LnrlSU/xSPv1bViSLgv1eb7d2PXpmw1JkXOt1lbbCbCllaVQc/7lzKCpcXFfD/WCE5PPXJPS0Ku3f7eU+buW7MZyUOBC/kUrajDhI4wT3VDehcKXBFBMFeFVEQPHpjoiwJEFIdtt2ZErOj+ittC6xEjgDOiHSzu82+cXKwvcoTGMx1b1BRCSoq8x1uuhxO6/3/k03wQi7F1cUCwd8SIwQVlcd/m0C7U6I4YeUx5msKIfAvNrf3xj9mWXNNGqHCyT9qxxNWEELgbDe/R2rU3H5SSivo06V5rmyU62JDMzBGMvEpaplFmFKMV0o5tKTo4hLC+kz5tMHgqjwoMPT/2TvvMEmqev1/TlV1TtMTd2ZnM7DLwi6wxCWIEiWDCCggmEAFMV8jKopX1N+9V65XRBQRRUQlo+QcF3BBwuYcJ890jhXO74+q6TDdE3ZZFNZ5n2eemelQXV11qs75hvd9319k/qkB9jktwOZjCkw7zcPeJwdomWuP33yy+rwXsxZ3fKaf7teLhNtVzruplf5GvWSZtCOt2xNJGz4rhDh3+B8hxJeBuyeycSHETUKIPiHEsorHGoUQjwoh1jq/o87jQgjxMyHEOiHEG0KIRRXvudh5/VqHYzT8+IFCiDed9/xM7Iq+453A5xo7dwn3ZCTODDUzw+UlO+LCL2Ys/n5zklUPZoltMXY6OL453lsjrz8S+3oCVV5cE4EEvtU8Ha+h8MxP4/zxI33ENhk0zda44A+tHHnF+NXekVCFnaHbUQjg1GDTuK97NyLiWEmMBiklf8+ldkk7fiUylskbI1TJo6rGgt3MR3kSk3iHY6fnZuf1QeBO4AtSyiqpTqdi+7ZLtwshLhVCLBVCLO3vH9uSSFL2EZ0oYqbBM5mdo+78s6Ah6ooDjQcT6lamRsN4SstXtcysEqkZxqv5ND8a2ALAfalB7qlQhJ7t9pa4hpUoSIsOzWNbARkFprk8hBSVj0R2nRVeWNG46C1uLyNNNu9EUn0Y2/VCKSAdxtpiDrdQqtSMKzHD5WFNMVflu9xjFKsCNbCTEYOmztJ8mnhFdbrT5eE0Z01jIVGEoK2CvtZnFslXWPpYSAKKyhG+MLd0zAPgQG+QEwLRUmvqK7k0RSkxpCRnWUzTPLZ/sLO2HK6OV+7h/elBcpZFuKPcCTBc/ftwuJX9vEG+2WRXs6/oWUfcNPhm/0Yu83dwtGwgHC8nQoQbxA9glsfLG9JOJJgFyUG+EKuLubo3ooO8QYxi+ZnG/9M47vEoR3/ebjVWt5crt8cFomzR86yqUyw4zBdmttvLHcl+Ms73tLDPST2XjNuT/YRUtSbQEsAF4VZMbNuws0MtHOm0PVtSIigLvvUbOo9mYrz+F/u7tn/HjXteORybNsfedu5W+/yIj0NwYfk4u5vs12b67ee9isI5YTsR0Ka5dsjacoueRxWihp89Fp7NJmq0FA4ZodZcaR21l9tfSr51vV5EmhCYq6AFy/dzlxCl4+NrsL9rPmHx0YhdLddzFnd/doDtrxYJttmBb6RDI24Z3OdYbN2dGiA/QRrDRILf9wIfEULcLoR4BtgLOGRCW7cFL94/4rGvY/OK9gQed/6Hal7RpdicISp4RYc6n/vd4YCZMq9o+H0jP+ttx4Ppobo+d7sKp4aaWFXIckeFV+GjP4jx1H8l+Ot/DHLjyd38/Mgubr+0n2d/lmDtE7mqbNpYMJHjBu0LPQGaJ1A51PMWyW6DLcvzXP3AZrrvL3LLeb289JsUUsLBHwtx0e1TaF+wc5na/b3BGs+7iWBVMTuuGvK7FR2am8vHEBCJWwYdrl1f9T4uEOX1Ee1xHqHU5cBMYhKTeNvwXnZybhZCuLAD31ullHc5D/c6Lcs4v/ucx7cD0yre3uk8NtbjnXUer4GU8ldSyoOklAe1tIytcRFUVH7bMZd5O1ip6zHf2YJXP2mbXRPSSynHtY072h9hnmdic1tU0ThoHFXoHw9s4Tt9mwB7QVxwAii3ECWlXQPJUEWl9/V8mmfq2A09kY2TMA0K0iJmGoQUjTlu76jWRIOGzppxuphG4u7UAD8c3DLq86aU4xYGBk19h6pkIyEBN0oVD/alfIrnsglWFMvfpzIYPc/hVh5doVg+3eXhQG+otL8CODYQLXFEh5M+l0U7uDjSxg0VFCPTCVrL22+pauXWEFzdMhO/ovKRrlWAHWg8konhd9rmlxUytGku2jQXLiHoN3VcQjDV5eGUYCMHltqkfTU6Lu7WijDCGRo/HtzKy7kkz2YTJdumpGVSlJL7Th3kZ4u3o/fb+/yxe6Zw9HMRxAK7Er2t0b5eE5sMWoRGh+YuaZv8tEJsc19PANMJfvVGi6MjdieAdO48zd2u0vty0sIlRN0Kcr+pk7Us+k29FHzp0uKxTIw7kwOl1+UtiyFTR5cWcdOo8QzWkfwy3k3GMnkmG+fu1ACvOuukjzZMYY7bxxyXfb2eG25BDEkS20ykD7YeXqiykSwcUb3txHkmvgqKQ3Onvdba8GyO9IBJrMdgk6OePVXzjNvRWYlhtfMdcQSxZG3sMH+Mzr+4ZfB8zk5CbnvF3s8pi9zMcpXvX5c0tJeCdq+jfJ1LWKwoZjEKkns+P8iWlwsEmhU+dFMLDdPsuMRbUa0fTbG+HsYNfp02qIeAxdgqkL+TUk5IQUJK+QwwMt34L+UVvRVlv3p4OhNn6tsQYFRikTfINr3AQ+khtrycZ8Vfs6humH2UF3+jQj5hsemFPC/+Ksk9nxvg+mO6uP6YLu6+op8lNyQYWF+ff/LxyJS6j1ciomqowhYAyCVMXr4pyd++Nsjtl/bz+3N7uOH4Lq49eBvXHrSdG47v5s/n9RP4msoD3xxiYK1OdIbG+b9v5b1fbkDz7HwF0i0UvtY0rS4naSz8NTVYV8Fyd8FPxuBpRBSNz48Qa9gVmOrycHa4pcRHSprGLvVvnMQkJjE+dnZudjqkfgOslFL+T8VT9wHDnVUXA/dWPH6R0511GJBwPvth4AQhRNRJSJ8APOw8lxRCHOZ81kUV29ppJEyD/4ttr+FBjoV9vQEOe4e3PX+xd31NteJ1x2JmLLw/2FhlsTMWEpZZxbmrh4KUpU6ii7tWlwLVs8MtJVuW7zbPYK+KRe6ZoeZR3SBiloGCYJte4I18mpdyqZqOoWG8mk9ze2rsyn89jMXvuy7WRfc4St+bivmq9s0dxVTNzSejUzinQpz0+ECUqKqVKrumlFzavab0/I8Ht/C7jrk8WDFnfjjcyv7eYInTKoGfDW1Hl5LHpi8s8SefzMa5pmLOdyE4M9Rc8mEGuC3Zj6yolepIfjS4hYcqPi/rBG7D4mYeodCpeWjX3LRrbppUjWMDDUx3eZiqeUpt3R5FKQWUF0baCCgqxUx57O7fX+7+ktgq1gCtqgtTSo70hfHGnERKUuLyCRpnadydHuRofwMb9TydHR5yzSaFlOTBFUMc5Y+UlIIrfYNvTvQynNcKeTSGTIO0ZRLotLef2GaUAsrnsgn28QTqesmuKmQZMHU6NDfDS0WJHUhVHsdHMzG+37+ZmS4vvYZeU/msTEBIICfNUqLor6lBTClL51GXkoPX2/si95ZE3BqVctbB92i0nGsHuFNPdDPf56+yepr9Qfu8vXRjiuvf28Xvju/ltV/Z5+iNQqaub/Fo+Khzbd9Tx+N7NJweaqqhHPyugosOcHaFz7IhJRsdfZrh4Hf6gZ4qEb4HM0MlDr8nKBAKFNOSl4eS3PvFATa9kMffqHDeb1qJzigXWg7yBrmzcz4C+zryT1AHYdySnhDiMaAL2Bc7y/sbIcQzUsqvTOgTavEv5RXtyMQ5kW3t4fbV+I/taggh+FzjVDbl8jz8A5vIftglYQ7/TAQpJakek+5lRXqWFelZXqR3eZF0n8m6PpN1T+Z57v+SzFjs4cALQ8w+yotQBEnTYKOeryvlPhLPr0vQc2+Rjffm0XP1syqqC3yNKjIsaWp04Y8qNO/p5qCLgjtsTTTWcXghm2TQ1Etqk2NhyNSJ7sbVSCEE8THann8T7+G4QEONMvOuwKOZGI2KxtGBBlYUs+z9FnhTk5jEJHYcb2FuPgL4CPCmEOI157FvAj8C/iKE+ASwGRhuqX4A2+ZoHbbV0ccApJRDQoirgb87r/u+lHJ4hX0ZZaujB3mLSs9gL6CW5TOsKeaYXeeetqKQYYbLW6VyuzSX4tA6C953GkZ2j+Usq6paWA/f6NvI15qmTWgOtxfeY9NSFngCxCx7sf5frbPpdKpHf00Nlqokd6YG+GDFovaxTKxGHRpsvqSU0KxqfLxhCnlpYSE5ZgzxxR2twF4W7Sh5vdbD3akBTh+H8jTX469qGR4PQ6bOr2PdfM0Rp8pIi2ezCXQZLwURKrCX28cHnYBYQlWgJBBc3LW6yhbwx4NbsaRkZsWxXOwLc0IgyjFb3uBX7Xuyp9vPikIWieSjkekRwtYAACAASURBVDaklLyYS/GreDf/5fjZgh0MVG7HlJKUZeJRFVqd9dBmPc90zcMb+QwH+UJ8tXkaj2ViSAnTXB62G0V8QuUlZ/vDXQOv5dO0qm7aNDev5lMcG4hS7CuP08YtLtjftjhqUV34HM/gPlOnSXVxUrCR5ykHZs17uVBUO+jLSpN2zc3JwUaum78dnoHw/9N45Mohzl/QiqdOrW648jug2p0DwuNnRtiD4gErD7msRdDJffUZOttlgcNHeIRLJAp2ImeY8+sTCte0zmJbRTt7m+ZiT7eP2W5fXa69BRzli9CsujjEF2JD0VUKZ7uNIt1GkZdyKS5r7OCJbJz5/7Cv2zkH+DijoanK2rRdc9P5VTfPHZFgn30CRHzVoZq1EAInKujPSYQChZQEh1y6o6WeHw5s4YetsxBQZfU0Fv6RT3OAN4hXGb3wVymw5xUKPkXBKEq637CvhYF9DOKFTEmTJ2WapJ1khVAE3rBCLm4R+pbKhtfy+BoUzr2xhaY51dfri7kU1wxu4b5p+7KqmKVZczERVuVE1Ad+LqW8x/k7LoQ4HPjGBN43LqSUUgjxT+EVYbdS0zF71jivnjhylsX5kYnLg78VKELQd6tOfIOBb7rCIZ+wJ3QhBOF2jXC7xtzj7YtJWpLYFoOeZUW2/r3AygeybF5SYPOSAtEZGosuCOI+SaFHjJ0V7X6zwN9vTmE+Cmss+2KfebiXeSf5CDSr+BoUfFEVf1TB5Rf8NtHLh8P11YB3FS6NtvOjgS0c5Y+Mm3SIKhpfehsqn+8kzBuj1WSjnmfa26R0+j5/A9cObePoQAMLHM/ASUxiEv9U7NTcLKV8jtHXSMfWeb0ELh9lWzcBN9V5fCl2UL7LoAkxpo/4X5L9fLxhSlXwW5QWPxvazmnBpresy7Esn2FPtw/PBCsLE8WHwi01c1nCMsbUcwB7QTlRpWJNCF4fpeo6jIN9oVIA+lwuwaGEadPcJC2DZ7IJzou0sqqQ5RartxT8dWieukKHywsZLoy00aUXeCab4LONHaQsk216/TVHo6rt8BzyVDY+6vx3hlNBrefFWom93X626oUq5dqxkLZMXsiVg7chU2d1Iccsd3me7TKKzLFMPt29hnum7YvArr4O44RAlCW5ZFUSQSIZNHUGKwKGRlXjMccxYzgvEFRUDvGFuCs5wIWRNr7Zv7Fm/l1TzLFZL5T2SUGwvzfIcYEoBzljIOTYFw2LFP14YAv7egK4hYKF5Gh/hD8m+/iPJnv9tDSfrmknXVnIcrS/gXR/Ofh9YXmS406PElRUoqrG1S0zkcChvhDbjQLf6t3EyZSF16LT7fPzmWgHtyf7OT3UxN9Sg2T3tgg9A55VCvOvCiDvtF9/UqD83gsirRhDjjqzy+Y+W1LiURS8jQrZbovcoAUhuLShnQ16nnXFAoePOKfnhlsRwC9jXZweamaay0NGWtwQ62KRN1RKLu3l9vNkJs4j6RgHeIPsM2K8BhWVfb0BEpbBkGkw2+2lTbXHVYtWbsGWUuK7WSF2q32utX0Ftyb6WOwLlzjGdvu5wn6HB5nicnPt0DauqUhwCKD5u27ODbfQu6LI78/tpSXhcvbTV2rf3hFcHu2YcOC8tpiroZ+M5PM/kY1xfNAuUjWpGh+NTKH7tSJGQdI0R0OJVLcet2vuqvugt8EOfl2vKXjCgnN+3ULLXrXXqRCU1MhXFLIc6A1OqIV7Im3P94z435BSXj3ulkfHv5RXFI5E6r1kp/BQZoiVO8hT2VkkthssucG+6fZfoRNXRp8YhSJonOli/qkBTvxeI59+rIOjvxQh3K4S22zw+A/jPHLSENZ1kvi26u1IS7LuyRy3XdzHHz7cx+qHcygKzD3dx8V3tnHOr1pYcFaQ2Uf5aF/goaFTwx1QWFvMMWDob2vgC/YkfmXLDJblMyVxgtHwm3hP6aLYXXH8GBXww3zht63lu0HVONOZvP+Q6CXwNnc/TGISk6jG2zA3v6PhVxS+1DStiitZiaeziZpFX5PqokHR6vp77ij+3+BWtu5CS8NhJC2zRuzILUSJizcavtTYWfJ6HQ9BRWXvcdT4/5jo4ycOh3ZJNsmbjtDlHLevpPa7p9tXpQidkyYv5WpbLF/MpXgjn0YC/WaRLU4F7bFRLFjW67kqXZOJYEUhy12pgbrPJS2TeW4/nnHmvztTAzsUKDQoGleM8BWOqGqVZeQbhQxLckkSFWuPUMW6yKcoTNM8VWuT4wNRTg42VbWBhhWVpGWwp9tXWsx/s3k6UUVjpVPxPjPUxPJCpqoSeZgvXDXeNSF4jz/Cc9kE/+kIlx0XiNJr6qUxVpCS+Z4A+3r8NKouns4mONAbZD9PkCu3T+fEC6L8z4Pb6NQ8JWFLt1Aopi2MbIXfrNOjeU9qkBWFbKl1f2kuxdPZBJG11evD2UfZAfqz2TgmkuuGutCRRCrEnSLrtNLxq2xD3lDMU0zZ3zPkUzkr1Mx+3iC9RpFCg/246gy3v6UHnVbmWtyfHmSjnkcTZb6oISX9hs7SfFlZfqOe56FMDInk6WyipusgZupcH+uiKCWrClkeScf4myPE9IFQM7PcPo4NNDCwVqfhFuc8C1g6y/6MyjvUklySlYUsbZobn1Bq2vsXeoOc4XQ1BJrtYzPQZ3dt5KU1buKsEu9zujGezyXrcqLrwSOUGvHBDcVc1bhzvahw4ynd3P25AV66NckPl25h61L7PtC5yMMib4j9K3QIjvSHmVZxzxvm/RKAc25ooW3v+gmqNtXNDKdLxS0EE13xj7piFUI85/xOCSGSFT8pIcTEG8pr8S/lFY3VJrqj2KYXS61BbzcevyaGkZfMO8nPd94/g6w0J6zy7I0oHPLxMJc82M7p/9PE1EVuzDQM3GZw48nd3PP5Aba8nOf129P85vQe7r5igG2vFPCEBId8IsSlD7ez6SsFWueOnh3dpOdLHmb/DLiE4L8Ht415DFYXsxMS63o345ex7roLu5ips/BtrsY2qy6ezsTZqOd3uaL0JCYxifp4G+fmdzQGDJ2r+jfx2ijuA0f7IzX3wlbNxQnB6IQXRGPBqyhVi+9dhQfSQyRGePCGFa3kizoaVAHXjaIoPBJDpjHu2kerWDi2aW5y0v5vnttfCvjOCDVVJR/muf1E6syxM1weitJWGW5T3WzXC6woZEdNHsx3BzhylKRG3yi83cA4FfiYqTMwjrfxFM29Q/YovWaRnw6W2Xbz3H4uaWiv6rC6PNrBob4wpzrqtiaySn333tQgl0Tb+XtFYBV0At3hVlEJPJlJ0Ky6+GTDFJqd1uxv923kDocbnZcWSdPkC42dJCrO7SOZWJU4UtYy+WWsu6pQ82cn0TDcDbGPx09BWiQt0xE4s7sskpbJ8/+bQI0L+Kpd8etyEhlfbOpEGaye9zty5e8psavgqhD8qHU2GcvkhFftZH3kRJXUNSbzTrITMssKWT7V0E6r5maRN8iTU6sV2hNZA1OXvPJkmm2vFlj6+xQvZBL0rLADvsY9XTzleEmbgBK192t7vz12tulFZro8nFenUzNuGuhScpA3RFi1A0kVmOmu7pozpORQX4j3+CMEFbVG8MpwLlcNYSvTi7KK+/8NbWfI1BkyjSr7nhOvimKGJXPd/lKCafizVAFPZxJsN4o1Sa71xVyJk+9vVBAKyDiYuqTLKLJmghZoQCnhsqGYm7BS8iej7XSMuG7eLGSqjsn+TwWIbTZY90SOZ3+UxPMxhWd/Zp/XzoM8dBkFBio0Cx7JxEoCYQB7vs9HoF1B/QljCuVGVY3vOyr1n22cOmGl61HvHlLKI53fISlluOInJKWcEIlGCHEbsASYK4TY5nCJfgQcL4RYCxzn/A82r2gDNq/o19icIRwO0TCv6O/U8opudN6znonwiiS7zDf3KH+Eln8Cp3TtEznWP5XHHRS876sNqEJwV3KgSuRgIlA0wdwT/Jz/+za8vxbMOdWLUGDt4zn+/PF+HvlejNgmg3C7yvu+1mBXjL/YQKjNzpz3jvJ5b+TTLPQGadkB7sxbxQJvkAWewKgXuSkli7yh3T4oC6sqSbN2abc0l56wB+TOoll18ftELwd4xlYRncQkJrHrsCvm5ncjLOy206E6vrJgL8zVEdWIpbk0rapr3ArgeOjSCxzuC78lZeCxMDLQ7TIK4/raP5SOjSvoVIn4OIHgs9kEFzrBwf+0zeHTjpPA/elB7nfEkn4b76k6BssL2bo+8kf5IkRUFY8Q7OFUiy0p+eIoNKQBs1ZAaBjnbV9Z9/FvNc/gM6O4HdzVOZ9eU2d5IcOHtq0Y5Rvb1dUdOad5yypZT64tZrk50cOv4908mrGPjyklr+RTuIXgaKeiJiU1c/F3+jdV8bzvSQ3ycCZWVSHudLnZzxvka30bS+fZxA4qL4t2lBR3I2q15+q+Hn+p3Rbs60YFAorKJU6BYrrLw/nhVjY4+3WwL0S3UWSjnmdTMU9RSrqNImuLWQanlMfNS0NJYk6g/dPBbfT22Pulee3rK5W21yKXRzvY2+1nH48fXVr8PtHLHi4v2aftY7fo7BDBxWppfRZUVO5ODdCuuWlUXbw30sCR3ygnQ/rWFlnyyyQHfy/EbRf18eRP4rgfFnS/YV8jT8+Ik7FMstJiiuams9WxChqyP69Zc9Fr6PxiqGy/M4w2zU1AUQirasn606eonBtu4QsVVf6IohIQKgOmQUhRq+gVYFcdr4h20KhqnB1u5rhAtNSWP3zvejWfopixz7s8VLLw7CBfb5rOLLe3Kol0qC9congUpFVzz8s5Kupgr+v9LQpISPWaKEzco86SstQ90aBqE7ZO+/nQ9pqklE9RqgSFN6y1Ba4WfyrM3qf4UZvsHdO8gumHeNmqF+ir+F4uIdAr6t+HfjLMOQ+2MnfR2B0ra4s5Lu5aDdj3p/Hum8MYsywmhFCB5VLKeRPa2ghIKT88ylP/Ml5R2w74X40FaUnW3pplyyt5TF1iGWAa9m/LkFiGxBz+W4dAs8IJVzWOWrofDcWsxRPX2L0bR10RIdhiX3DzPH5WF7NM3cnKc88cncuumUruyxav/TnN8vsyBJpUDvxIiLkn+FC06sXC4b4wA6Zec/wMKbk53st/ts7cqf14Kzgr3Mx2R0ly4QgbhwFT55Rg4yjv3H1wUrARb50M+KpiljNCb6+/sVdRWOQNcsrb/DmTmMQkqvFW5+Z3IyKqxqnBplH5XNNcHvYawQk2kNyZGuBAX6iuMNNEcYFjEVNPaOut4paOebSOSBwLRM2cNhKqoC7fth4CijKuJWPcMtjucHK/17+ZBlXjS02dmNJOcANkLIvHM3GOqKjS1msbXppPc16klQHTYNDU+VCklZRpcFOip+5nu4QYtQJ78ijz+Hf6NjHX4+OCOl6/H3ACXrcQ9I6SLAE76Os3dWYysbHR4lQmAS7tXgvAe/0Rok7gYiLpMXQ2FPPcEOtmf2+Az0an0q652aLneTwT54poB+eGW2osGLuNYoljqwAnBBq51wlMhldji7xB9vcGuaR7DWeFmrkw0krHiPZ4BcHaYo59nfZkr1A4M9TMQm+Ar/Vt5PxIG1nL4o/JPq52Kma/jfdwuC+ChcRyPufpbIIDvEEMX/n8trzmYtnfMgy1GxS/YNH9imNLNMMgsFol67QhP5GJs9gf5rJoBxJ7/Jk9ILeCDEkC+yl8xV9OhHyzeTo/6N/MAd4gS3MpVhayfP38aaxfkqP7qSK5XovVj1S3GR+0PESXI54Un2fSoroICpW0ZWI02PtsxuzfF4RbiVtG3crm4b4wXkXhz4l+DvQFaVA1YqbOdbEupmoeLom2A7a/cq9Z5M1CmoO8IWaPuJ/4FIVX8mkO90f4ey7lBPJa+TOcBMVw8Ks7x/W2ZB8RRWMfj790zfcYRdo1N8cFGihIiyczcY6poLgFFIWmisJbuF0j01sk1WNwwNQg+9d8y/ooSlniIn9qDNvMkShIq5QoGMYV0aklSoRlSKytEoHdPer2KyzPe5iy3YPiEnYck6IqWTnP7a/ZZlBRONw/dj63cvVbkNa4HTP13lcDKaUJrBZCTJ/Q1t4FyEmLDTvhF1uJfMLijsv72X5tkQ3P5tn8YoGtSwt0vWYrLvet0hlYZxDbZJDYZpLqNelZrnPbxX1sfH7HqnFLfpkk2W3SNt/F/h8qT4aH+kJVlgM7iv28AVRhD8IjPxvhU490cOFtbex9sr8m8AVbebBBqc2VvJpPcXww+rZzfUdDo6pxc6KX/hFZqLtSAwyMMentLtAQNS1zYMvMj5SifztweePUHfKHm8QkJvHWsTvOzePBlJK5Hl/dxZAuLV7Kpara5sD2qV/oCYyrnDwRzHX7xhQY3Flc1rO2xrIoY5njVnUvi07lmMDo6snD2FjMkbTMCQl+3em01GpClJRuTwhGS62EJwUbq3Qk9nL76KrTyryqmHU4v5JX82keTA+xrJDl6Uyi5rVgV6UfHoUPPDiKnZOBZPkYmisHeYMoiCoe7Uj8Jt4zblKgEhqiJql+gDdUcp+QEtKWwZpiFk0IlhWyDJk63UaRopTETINHM3H29wa5OV5OBFzuBMTPOxY1FvBSLokuJZc2tJfWXvt7gzztiGDlpcWb+QxPZuI8XeGfnJdWVXt+n6mTskyu6t9csrs6NdRY2t4wFvvDHB+IMtft49V8mvPCLZwUbOQUT/n7hn6pMfSSwbJ7MswZ9LHqLvv4rz/DXtcKh5Gwsphlu17g8p51CMAvVB7utavjxWbJBivPlf2bStu92km2/CLWher48QpgtdfevkxWcEAdrHo1R7LbRAsIvnZAJ/t7g8xxexkwdBIRe01UdASxrh3cRv/KIpHrVH73wR42LSnHALcn+9miF1AFJc7v8N2isuL6ZiHD8kIWS9otuq8Xqu81fYZeEkOLmwab9Hzp/wZVo9Pl4eMNUyhm7a1HgvY53aIXUKjm/K4sZolZBpv1AtuNYs0aa6bLy5EV90FXq31NJrtNnrk/wYNLJ2Y96RKCTzbYwf2vYl1snmBstLfHX1N0uTc9yGrneoxvMxCGINyu4vbbr/tDso+mOa6SyNkpoSYWV3wHv6KijLhFrS7meDhd/74wjD3cPi5wvLNnu7wTti6bCCEyCiwXQrwMlMg2UsrTJ/QJ7zBIbKL8nJ3M4PauLHLvFwdIbDMhDCd9tZFAi4rqstsPFA1U57f9v0BR4dmfJVh5f5Y7LxvgxO9GWfCB8VtF+9cWWfr7FAg4/tvRkiQ8QFR18VR2YKc4x9LhN+wIhBBcO7SN/1dhMp62TGa5vBzie3t9jseCT1H5YmMnS3IpTq+oQG4s5pnZ8PYoHb+TsFG3fQorOwAMKXk0E+PifyIHexKTmMQ/HbvV3Dwehkyd64a6+GC4hdNGdJu4hMIV0Q5Ghrhxy2CRN0TzW6QnRRUNt1DYoud3Ob0nZZk1wowH+ULjCjo+m43zZiFTpQJbDz8Y2MJxgSiLx7F8mu3ylvbjQG+Q9c5CeLte4LtOhdCjCC6uqLSmLbOG+whwRrAJC2hRXZwQiFKQFt1GoYqLWokF3gDv0etzfl+q4MZWYpE3yKpRgt+FngArC1k+3tDODVP2qvsasAPk/b0T18bYZhS4emALxwSidGhupmoeFnj8rNuQ49Xr0hzyyRAXzGojomi4hGCBJ4AAjvE3sEXP82B6iIMd3+lhT1NTlzyejNFrFas46/2mzhmhZu5Ll0W9fhPvxpQQEAo5y2JVMcsB3mBVu2q/oVfxHi/qWsX7/A1VVc/rY7bb6KZinn29Ac4MNbO6kCVtmSV/YrcQPJtN8MhgjKnY64v89vI2wk+rbHSUnqcf4fgF5+3zG1RULCmZe5OPN/fO8L7TG0oeul53HcsiJB+JtLGumOOEQJRfOMJRWth+0+Y3ChRSI8aZkzvoWOTmwVyMlLDwC4UGVUNzOL9DAzp63kJcDg+uKAdRS3+XYuZie58t7Mr6iYHG0vgMKirHBqJVVkcS28Lr3HAL99XxxDWRdGpu/EKhRbNtjjKGfT3dmxokqmo8n02yR8aOPaZH7GM6RXNzuD9CsOLaMKVEQ7Bez7G/J8jFDdXdDasKWd4sZDjfuRb97XZw/I8/pel+vYjwAy/X7GINkpbB64U0U10eFMSEKQD7eAI1QaYpZcmqaHCDnXxonD36vfKB9CDtmocDnATMhmKOAdOo8g3PS6tuZ2MlUpaJyxlcB3hDVcdxLEzkVd8GTgW+D/x3xc+7Eh4nq7QzWHZPhj9e2Edim12JvfjPbex7ZoBZR3iZfoiXzkUeOhZ6aJvvpmUvN02z7SxHZKrGKdc0cugnQkgTHvpOjOevS4zJPZZS8tgP4lgG7HdOoC7h+6VcskZ+fiKIWQa376CyIkCj6qrKwv4h0VtScfxXYprLw0nBKHcny5PEJdEpVaqUuysiilYldgG2+NjOjvFJTGIS7xrsVnPzeLCwBXrqzXhrCllezqVqgtyVBbuC8lbVnr/RPJ25bt8uFcwchgtRsxB7OD1UUpo9fesytoyoyEgpWVbIoiHGrVxu0vOkLZMnK6qD9bBBz/PzKXsAtv/t+U415eV8iu84VbqH0rEqxeakZZaqnpUQwq7I5KTNke3Q3PgUdVTrwfXFfJV2xQ2xWm7mSOzvDXJly4y6z81yeclIi5dzSc7ZPjrn1yMU1u2ANoZVcay7jCKtmovbkv0svzPDuidy/PH8Pl4dSrNRz7FJz3PmtuW8kk/zRDZOq+rm4oa2Eq+3U/Ng6pIbT+5m2+cLPJ1NsDSf5tWcfd6DioouLR7LxEvjTiDwCMGl0XZA4hUK013eqpZxVVRzPk8IRHEJwRG+MH/ssFkSi31hPhvtKHXHDZo6GWmSsEz6TJ1Ozc2r+TQx00DR66+jXrvLrnx6QoJPt9nVw6Kj+vTN5unMXuFjzp0+Hv9BnLtSA0wXdlAT8WgcF4hyqK9cgNnPG+SagS3MdvtYXcyyvyPWqTTan7327hyD62uvvcZZGp1f9fBCLonA/t4LPQGO7bQ7IvJDFoPrDMQKgeYVFE617wNblxYwCva+HuIL0aS6WKvnSjo6EjvB8P5geWzPdHnZrOf5XbyXRlUrKWVf7VTUm1QX54RbcQnBkf4Ii31hTnK6BCwkprQTGsWMvQ+vKfbxu6RhCjGnOj+MCyNt7On2IYDXC2mWZEfqGFbfB5um2HXM7ted/c9CLj6+zF/KMktr+L09/gl38d2a6CM7Ijk3zeUpJQ+GNtjjqml2ub760Uh1MSZrWVXxi0soGCPu0zMqguPRMGjq3JzoBWxV74nGJGOpPXuFEF8AzgHmAc9LKZ8e/pnQ1t+B8AiFYyfQKlQJoyh55HtDPHjlEEZBsvDsAA2/0Ei0TXwiFIrgPV9s4LgrowgFXrg+yUPfjmHq9Seu5fdm2fZKAX+jwns+X39/Z7q8OyR4MYweozhhRbRKfMi5sMFWYOwyihzo27EK8tsFl1DoM4s8mo6xopAhvQva3N4NONAX5IQRi49+Q2fBpAjVJCaxW2J3nZvHw3SXh680dbKHu7ajZ8iyfTXDIxZvppSsK+ZKVcydxVf7NnBHamDCPpg7gjs659fwP9s1d8k94YRAtEbIy8IOjs4Ot5S8WkfDScFGFlcI6NTDcHLge/2bATux/UPH9siQsvS8hSyJTOYd3uhTdYLqJdkkOctWI46ZBnu4fbw/EC0pFVfiOkc8p1Is509Ocl5KOSrn96Z4D9/p38QHti6vee7e9CDHB6I1fNiRcAmxQ2uFuR4/n4uWRZCeyzpt3BU9lNnfmryaTvNQl9166haCw3whJPBUJs43HI/kixumMLCuSLLbxPWawk3tczkl2MiSXBJVCD7XOJVnstVt4h8Ot/Klpk5+OrSdZtXFx5UpRBStKmg5IdDI1ooAYMjU+VS0nZkuL+c73PWsZfLzWFephf2ZbIKAUGlUbQObjGURNw0aVY2oYX85y1s9zsQ2+735kMV/DG6wH9Nh+X0ZruzeyItLygGbADam7XGTVA1ezqeqaHsHeIJIhgXtDGKWgVcoLDhhbJrBnsf68E5RULD577PdXt4sZPiHz26EsWKQdyrGUxa6yH7JomUvF0Zesu1V+xgpCLY/X+DFDyRZ94LdSRAzDR7JxLjBqZCDfS9JmCbrHWG9YRpBRpr4hELBsrgr1U/cNPhLsp83ChnedHyVP9HQTlBRmKK5S8GvdC7Hqwe2sL6YrxJ8uyPZT5dR5IJIG62qu8YKrU1zVbl5BNprg9belePT/nQpS8mYOW4f/gm2DFtS1hSX9vUESlS7gfVO8DunnIxcMcIaql1zl7jyAPM9ft43IjbL1OEWj0TlswrUHKvRMNY3/R1wEPAmcBK7UUb5ewObJ/zaZLfBbRf18frtGVQ3nPi9KCd+r5E1IleXAzseDvhQkDP/txnNK1h2T4a7PjtQuhiGkYubPPXf9oTy3q801HAdhvGphnam7kQQO0Vzc9wY/rCjv8/Fczn7Ztygany1ado47/jn4pMN7fSZRV7IJid8Eb/bkTTNmhacBd4Ah71DkhKTmMQkdjl227l5LHQbRV7JpQnXmXeDQiVpmSwrVNsgnRVuZpE3WNXyuamY5/M963b485tVraTguytxxrblNVxl3aGugK1mOrIiowrBN50gKlFH7b8S73EsoDaMUeG0nPXiameBakl7gftmPsOXmzo5zGmZ/lJjZ2nBOpx499SZa3tNnQ16DonN/30iE+dPyf6Sd3AlVhdzuBWlavH/p6l72/sBdapeZRhS0uGqvwbKWxYeRfABx4++Hp7KJsa1TKrENr1QxYv0CIXjehpoWFoek633uJj6KQ8LLghwVq6JBlXjxZytAO1VFL7VtxGAHw9sITVQHpfTpIf1xRzHBaLclujjmoEttmBb5/xSsWKLnufb/ZuILtf446f7+PmRXbz6ZjXX/fZkf9Xyf2k+zQPpIW6IlwO54db94zwRwwAAIABJREFUSnupPd0+FnmDHOGPkJMWJwcbOcwXps20Xzt0crnY0za/HNQk/SZuTSlFEw98cwj3bxUGnisHX0d4wizL2GMr5FFJmAa3ONU6sJMtB/tCPJaJEVRUNusFDCn5a7CWu7rv2X6O+XoDxl6SAz8SYg+3j59P2bNUmMlJC9MRvMoMmmSH7OvDFVH4cLiVWY638LCA1vO5BEtvTqFvlbz52SxGQWIhUUZUVzfreTKWSYvmYnUxy8aio2bsC+NTFAZMnc2lpIMkZ1msK+awTMnjz8bYdk+BQ7aHSoJX+zWWCxRiRLV+0DSwkNyZ7Gerka8RmnUJUaWzY7bUBnxDG8cPfue4y4Jxz2YTE+qC0PMW790YwRyUVd2rb+QzpURYufJbHicvj/AD39vjp6Piew2ZRo2N3ZuFTIkeMBoWeAL8ut2mNhzhj5Ta9sfDWFf9fCnlhVLKG4APAkdNaIu7ETYtyfP7c3rpWVYk3KFy/i1tLDzbHrANilaltrYj2ON9Ps67qQV/o8Km5/Pc9tE+0v3lCeyZaxPkYhbTDvYw/7TRM199jiLdjmJNMVclWjFReIXCkxnbS+238Z53nNCRKgQXRNrYYhRqMum7K7yKwjq9+oZ1df/EkzuTmMQk3nX4t5ybM5bFklyy1A5ciWbNxZ5uX03W/6H0ENNdniq+a8oyeaNQ3yt4NFwYbuU9/gbuTg2M/+KdwEiF0plub6ki80YhU8P/zVomPxrYwppirqYleiSu7NvEq/k07x0jcC9Ki1OCjSx0OoY+FGnhmtZZCODawe2loP9v6SE+79i/DO/TSL4ywLebp2NJmOnycKkjqGMieU+dfZiiuWlUtKqq/YcceyMLWbLWGYkPh1s4wBusK3p1tD/CdqNAUUr2HcPvfpE3yDzPxEXMYpbBvWk72fzY9IVMd3lY+oMUvSt0LE2iaCAMgbZZoBQFzctduBDMdHnpMYqYsixFJYQgvr0coORTFquKOQZNnbRlElBUPtEwhbO3rSj5/z6fSxLZrLL4q2G6nreTBam/m1XV/708PvZxvtNwm/Z2vYCE0jF+LpukRXWVWti/2jSNJ+6Ic+d9AzydiZOXFhZwX2qQTWl7fCkVMchBF5WT68WQ5IJIK5XLYd8fFbIry4F9Nm9yuGa/x+1SaryhFSGYork5xBficF8YFwILWSXSpnkFj/8uxrFXRVl0QZDUL00CTSopy+SLvetYU8yyupAjrGi0R+0ASE9L4lvsYxfpVNmi59nnNHs8rHowi56zsCQUYuV9TWwzaNfcfLShraoSKYFjAw1cHJlSRa/4TbyHhGlgIjkhEKVRdbHIG2KK5sYlBC/dmKT/czqPXRXn9sv6K9Se7c882h/htGBTlQ5Pu8uNX6jkpMXe7kCNin23UaxKCgVnqIgwNO+hMeeTdnCf7h2/7XlpLsV9zj1NEwJ9AlXT5/4vyZKPp/jV+7r57/238bPF27j+mC5e/WCKDRfnueVDvfSuqm17HomH07EqC7CcZbJxxL2sYFnjClitLea4yqFl5C2LwgSpoGNttXRVSil3PdHlX4jjA9ExubLSkrz4qyR3fKqfXNxi5hFeLvpLG1P2KWcUzgg1vSVOacdCD+f/oY2G6Rp9K3VuvaCXwfU6Xa8XeOOODIoGx18ZHdOntlV1VbW3TBQvZJM71b4lhKBJdXF9rKvGdPudhO+3zJyQsuXugIBQqjoQCpaFW4jd3t94EpP4N8ZuOzePBbewlXvrzdx3JvuZpnlKwdsw+g2djcV8VWUsou540lYVgtXF7NuipXBGsInWEYn0Z7KJUjXWJ5QarrEhbUuaBkWt0XyoB5cQPO5UZfoNnftHdAttMwrcnx7iC012YLu+mMenqFwf6yIvLX4R2w7YVfMr+zYBdtC6wBPgzDpWd89kExzlj7DVKPBSLsmB3iB7un11xaV6jSKb9QIHOd1KldUkFUGb6qrL2d5qFFjkDdZdHD+dTeBXFDYUc3x/jE6/2S4vL+fqC2rVQ0gpe+p+u38T5/pbSK+xg4ynf5XgPf9bLdr15No4q4o2/1d1PI8PcjiM7/M30L+iHPwWUxYaAq9QaFJt65u/puzKZ6Vaedv66spWtKhxuK/8uQOGzqaKdeFnoh1M0dx8vqGDT94zhRV/yzDd8DDf7S8Fx7/9ew9rr87Rc6WtSn2YL8SD6SEkoBbstURrc3mMti8st5Pv0eTFLRTMMZai61J5Aqa9Tol4VQ7xhrioQjjtzFAT96YG2NcT4OlsgpBzjR5cERAaecmsDl+JAjDcDj9g6hSkXak1kUx3edgvGER4QZrQv8Y+xs3TXTyfS9K8h4v2BW6Kacnax3OcG2gmvbkcKGZjJgOGbrefV6yUD/SG8CsqH+1axTyPn4XOWE5ZdsptL7ef+R4/WWnidqy7LnS3sfT3FV0d/RDfal+vb7rspE2b5ubZbILXKu5Rw/ZZ0zQP96UH6tIbK++DMyNePvLXNi64tY22Tnt8pPrGD35z0ioVwg71htjDNX7RaHj/wT6+hZQk3WeS3WKhr5H0LCsiTdAPsvA1lO+1P2yZVbUdC9sKaRheRSnZLg3jtFAT88dJThlItjvHZ0UxM6pf+EiMFfzuJ4RIOj8pYOHw30KI0ftQ3gWY5fKWDKJHIp+0uPtzAzz7swTSgsWfDnP2L5qrTmLesvj5TlRcRyI6XeOCP7TSvp+bZJfJrR/p5YFv2Te7gz8aquqXrwchRJVowEQxaOo7XbX+StM0PhPtIPoW1TMnsWsghKhSdS5KizPHaPOaxCQm8a7Hbjs3j4WZLi9fbOzkSF+tKnDcMhBC0GtWL3yaNRdqhW0P2ArEv2rfE4Bb4r28mBv7kEkp+V2il+WF7E67RIyFuR4/oRGt3CFF5TDHBuSYQENNsl4VgoWeAEf7G6paV+vhi02dnBVqLq0VNuo5/pjsq3rNsErrxV2rAVjiHJOitDiwQnCmWXOVFt2Dps6bhQy/ruBFDuPlXIp/5NMle5+0ZdGhebg/XdvG+kYhQ8oySp62lWGuieRwf6Qu7+/ZbILfxHvqerfOdnn5RtN0Ioo25iL3pR0IfMEWqRr24pVIfvT6VkRRoLbDUbMjtC520f2BAsUGe59cSZuPOsPlYa7bR5dRLFWiI6bKpufL4zKfsljgDTDN5eW4QJRF7iDr9RwuRClAuaplBu16NY853mewsaL76wPhFuLO+jZhGdwY6+a8cCuP3RNn1Q057v/6EHuc7mPgv/QSJzpxT3k97F0rCJ2mMu9pP/tqfiJd9th84v1xlCvgrNubiHSU18PLZbZ07kZDfqXFi0mbMhdXbD7t6goOaK+hY0jJ31KDJf6uT1HpN3WEU2fxRRVODEZL3R2VI2Ke28dBvhBH+MIszaV4NpvAH7LPfO8K+54Q7Czv875n2ufgzXsyPLkmTuVtIxuzyEiTTXqeRzLl8bq2mGVDMU9GWqwu5krCSsf4G9jPG2RDMceN8R5ylsVz2QQri1muv6WLfMKifT83Lr+9xz3LiriCAmOB/T3+nOwjY5lkK8bxb+I9xE2DBd4APqHWdFnOcnmruPB9hs4/PGncAYVwm/3aiVR+m1SN6ZpdKU5ZJoMTsAcdFgo76/pmvvSPTq54fiqffqydD93bwkG/D3L+La185qkOUj+u/vyfDG6tSmwd4gsxtYKyMN3l5cIRnt0Pp4dGjdWGUXlsFMSERYBHvS9IKVUpZdj5CUkptYq/x9bMf4djTTFXIqKPRC5use2VAp6w4APXNXPkZyNVFkMAXUZhp7i29eBvVDnvxhb2OMZHISmJbTKITFVZ/KmJHeLDfOFRffBGw9VvoTKqCcE+Y7QRTeKfj/+syGxv0gvMqiMIM4lJTGL3wO48N4+FZYUMN8S763pRnhZsImUZbCpWP3dsIFrj/bi8kOWLPesBO5gbzwNYR5YWSm3qrrf1+8ngVt4c4Rk62+UtiSkNmQYLRiieuoRgoTdAr1nkgToBZSW6jSIPZWIsc/h0013ems6tvUfxL/5uy0yOD0Y5O9TCsnyGTzW0l/QkhlvHW+ushaa7PAyZOn6h4BKC5YUMj2ZidT2BD/eFOS3UVArETGSJw2dKuDc1UArm6kGrExi3aW4eygwx1+Pnx2NYQWWkWRLpGQvDY2R1MctdTqB3mCfEoZfZx6JhrsagqdOmeVj05RCey+wRc3gxXKpcbdELrCvmSsq0j1wTqwpQCimJR9i1sOt/2sV97x0k0qNyy9R5pfXmz4e6eGWTHbBrAcfbtd8s8U8Bro91lSpoBSnRkfwy1kXvk9WJoRkPeGnT3BRSFq7HytfHyx9P4cooeH+oUNxqIQuQazGZ0uTmoAtD+PZQUbTyMXcnBe6K66tlbm1hZP7zfgxH3FVx2++tTDz8PZfiI5E28tJiqsvDklySnGXSaxRpucZF094aZ/5vM/87tL3U9TA87vfzBPhowxS26HlWFrOYSFTA8Nufl9huH+PANJWrHHXweSf50TyCLS8V2PpM9ZjMxewKfGREQipumQQVlQvDreQti5gTKC7yBnkznyFtmWQtE4GTwNElxT/b+7Dxgzk8ofIxc50l+Pr0smZOi+aqavs3pd3y/VB6iCmaq0SBGMaQaVTFMBnLLFU8s832WJ1I5bdFdTPNUQrfbhQqOMujw3SCX5cHVJfAG1EITdHwzlD5x4wMUw/wEGxW+UxjR9X7MtKqondkLavU3QK2iO7/Dm2res9mvTBul2qb6ubuzn0AOCXYyIIJWpf9e6gCjcBUzT0qiTo6XeOMa5u56M9TmHN0/SxvSNE4ZicEo0aDy6dwxk+bOPCiIP5GhROuasTlm9ipWafnSkbaE4ElJT8Z3LqzuzqJdzj+mh4cVx1vEpOYxCTebZBAzNTrBr9b9QIagpHLvZviPQQUtcoXuCgtMk6V5dlsguQ4bcMKgp+0zubatjn8LV3r77krMNKuKG6Vl4lLckmWjqhQDpkGdyUHUBHjWnv8IdFLrKLbK29Z/HVE2/PKYpZLG9pLrajfd3x9O10e3MIW87midx3fH9hcSn4nnYC0nofyGaFmgopKs+ZisT+MxF7QX9rQUfPasKKyvJAlY5ls0fMkTJNLutcAdnXVgpIlTyW+1zKT88IttNbxXf6WIwb2Ui7JC7lEzfPDaFJd4/qIAlzRsxawfUeHOcbNa1wIy55r10zP8Uo+TWyYrxu1t9k1WGB1McdmvcCQaVQ5MyhP2L/dThBbTFu8mEsxuEFHuVkgMoL9Hgryoe0r2eSM+QFTJ9BvB0LTTrWDFn3QorKWcZgvxCInWTIs/CkMSfgftfzLl3NJ1j+dgwrZEKviUL90tz3u1L3s6vPPY9vpH3EuvHmFrzSVLawWnFUOPgqz7eus540iC++x9ynkVms4rIqwLbVmu300qhrz3X4Egq16gSenxznxj410LrK/73Cqavgrryrm+HrfRuKmQY+hs6fbzzyPH9+C6vMabFP5+ZDdsekNK+x5rA8kmNdXH5NczGSGU4WsHK8+odCuufEqCgFFwSUUpJT819A2io4f7Tebp9Pu8vDBUAs8CvTZvNfsYgstaO+x6oauM4r8IWF3X3yqoYN9PdW83vcGGvA741IgcI1o7U9bZtV90CXKwbq/xX5tutcc004V4OlsvCRy5RIKeo1Tei2MohP8eqv3yaco5KR9B7ak5PkRQnV2m335/1fz6apKsyYEBat6f8OKim8czu8mPc9Z22zF95dzKVYXJmZd9m8Z/O7l9nNSoL58PsCMQ700TBudqL3dKNCo7rjS81hQVMExX41y2dMdJfPtiWCe21/VPjIeYqbxb8OH/XfBcYFo6SaXMA0advHYnMQkJjGJfzVaVBf7egJ156/ncgkW+8KcNsIWx5SSQVMvcVbBbp8eFgQKK+q44oh5afFIJsYXete/Zb/geri2bU6Nl+W6Yr702LH+hiobIChXhrxCqeELj0Sr6mKWy8sWJ+E/YOo11k/ri3l+Fe/G4xzba0dUYB6v8Pb9baIHgPMjrZwabKyrrvpkJs5if5hNxTyrCznODDVxYjBKt1FkdSFLoqKS+1Q2QZ9RZIbLy7f6Nlbxqn2KyuXRjipP0WGdk+uGutik50verJW4Kd7DYl8EC3g0M7q/8QdCzayuI5g1Euuc4zVN87KHy4upS575UTmobpnrYh+Pn6RlkrZM1vgcW58hk26jSHNOY/OVeXrv0Dk/3IJRlIiMQNFg3sn2WEz1mrSpLp77j/J2n2uoDtxnubxE+uzgd9b+9joxscLk+Hh53Gctq2SRZCL5VvN0vF9WUXO1103vDUU2Pj+6YFrmVvtc9M/WOcgbqubbfxlUL+z72QCPZWJMPcmN2SrZ98wAez/gZ94lPk78jh3si60C6TSodR5m+8H+p5NgAfhc41Q26XmO9kdYXcix3SigCrg02l7l//wfTZ34hd1KPiy8NnxN+hQVryj7Xh/6nRD+WeUQJ+zS6K/obz7kEyEqi7vB+Y4Y2P8lueP7/dzU1V3laX2AN8iBviA3xns41BfmYF+InLQFmULOfWRVIUuPUeTFVILXbsw4nxPmAH8Qt9OGve8ZAYLNKtuMAlJKXswleT6brCpiSeyk2yG+cF2BP02IKpX1mS4vpzsJvjlRL5pPoOckhdTYwW9BWqXtHO4Lc+IYcdEwjLy9Tc1dPZ78QuFyxwbMAlaM6K49NtBQde+WIwTNfEIp3ZeHcX6kdVxhXaViN5KWSWqCXuz/lsGvR1FKin07g0czMSbYVr7D2FGhohbVxTk7ID5VkNa4ptGTeHdhiuYu8UWudDLek5jEJCaxO8ErFN4XaODMUDM5yyz5u5qO2M2yQoaHKharAIv9YZpUF/mKikJeWnyl0W45lFC1wK2HhGmULJTOeBv0FO5LDZYEW4ZRlFZpEe9VlBpbxaiqcVKwkQZV49Q6glOV+HxjJ/t4AkxxWrY1ITjSV90dP8yj/HXcDmyH1YX71xZJ95u4X1M46ZwoC5YEShW33yV6+Vt6iCfqBJf/yKe5P2ULJm3VCzyZjbNJz/NCLsFjmViVqmveCSCeyMZpUl1c0bOOs5zjPNzOuahC2+Qix6u239RZkk0yXfPUVLi6jSKGtAgIlaAYffH848GtE3QFtbnfLiG4JNrOU/8VR1tdXj4H9lRLVd3VhSyrneDXnRRYUtJ2m5vYYwahn6m8+N8pcjE7oPM2KHQstM/L5hfz7Pk9H0Pry4t3MyI5WWlk658KPPafMU53NVHotvc4vKD8vW48uZti1qKYtZiqlNu4t+lF/nNgC8nX7M8rhquTKIM3G6z4qx38H3PN6GrgLXu7+EOyj49FprDAqfx3fMDNtEc8/LKzm616gRlXeUj+0cQTVPi7L4XxMfhFeze6v/yZs07wYh4Nr+Uz/L/BcoLlT4l+spbFtUPbUQT4hIqC4JZEL5c3djDVac39a2qI/xnaxrJCtsQfDykqn2yYwgHeIMcHoqwqZtmk59nLG0BWxI1py2R2haBT61w3J/2gHOw1Hla+xjbfXiDzdYt1uXIV8dlsgsedsf5QeojnswlMKflYwxQ6XR6W5lLcmxrEkpJ19+dJbTWRnZL5p/g52Btm3gl+IrNVDv1kmG80T6ddc2Nh0zlUQVVV9IH0EBaSvGXV5bDOcfv4cKS19P/KYpbbHB5/wjJRnadSvaMHgv1rigR+oTJ0i86qB7O83pfmkXHuhVCu/Gqe6lhFEYI/O/tgIQmMCFpvTfRVVXrPC7cyw1Ueqx6hMHdE8DuWWN0wWlQ3hzv3s6iqVVlAjYV/y+AXYM0Esn2jYcjUd3nld2chhODFbJLCBI3avYpS8uybxO6BF7JJeowib+bTvFQnSziJSUxiEu92rNNz3BTv4b7UABnLKlngKNhCQAa1tjseYduqVCZ8VxayfKzbFnY6JdiIOU70U5QWc9w+Zru8rHwL64bR8FQ2XsXZBPhwuJX1TjviZj1f4uUNI2dZDJo6cdPgl+OIbz6YGeKB9FBJXGYvt4+5Hj9nbl1WqqJeGGkrCVtJKWm/xcUrt6S4+axefntmD/v8xY/ICrZ+q8DRTzaU9ivgVI0eTVcvms8KNWMiadVcLPQGGDQM3sxnKErJs9kEQxWL4AM8Qc4MNdOgaOzjCRC3DKJOsG9JyeOZ+P9n77/jJanr7H/8+a7UOd18J9zJTACGnDMIAygZEUQEE4rCKuoqrLqii+juqiCuiICICoIgqEQFCRIkDXmGNDncfG/37Zyq6v39o+pWd98wM+zv81Nc+vjg4dyu6u7K/X69z3md0zBBcY4rzZ6n+ylKm81mmU/2v80LxawXvzRL8/FiKcdyf4ifueZmU2GJEdwmGTDe6ztDMyhLydpKkauf7OXFWxp7tDe2Oo7ii31Bzoh28PEeZxvDWZWTI230bKip+Ub/XKWQdD7Xl1Do2ddZtv7xEtGnG8eVCwsBOA6e/a8sL92a47r/6ocU2Jok19F4rf9o315+tG8vxfMsOqWjBvCUCm5d+9JXcnzk7g4mQo3C8uNrcuVCd+NnvxzLMUsz6DMrnkz2xHArmuac/3ZNx1BUvtzuyJ+dvldJ0q5S6K6NTaNttcKk3sE8ZVf5t7Ye5ut+FhtBBqwKFpI5up8NlZJ3nb5ZKfB6ucCwVfHujxmajzHLZGUxy53ZEW8ybItZojjSOC4+LdrmOVwDzNqrdl8deGaUmYcZzNnfh79FYL4kiayuba8EQorC1Z0LiLuu80IIUlaVV8t5bGCJL4hWEVg3Ot9hfVSiaILrx/pZcnaQrlt8xGZqfG1oAx+Pd2G5ReJCI9CgQLGkREXwWjnPmbHJ52tdpchN7kQVgJQ1GXhe2ljufFh2YOq+Xykl912SZM0tRZ79UZZ7/nWUF/81t83eem/b3J5f1TeZqBuX5xtC4ZIJRIwhFCp1x/6hfIrhOr8iCfyibp/GsSOE4Ljp376ByCT2eDq8Z4vfLs34X0uYvtY2510VJVOSNuuqO6ZzfyCXnPRD28Q/N2KqSsa2WFUu/K9dvJtoookm/tHIWiaXT5NTPs6MlKRs+O3eYpb5fXaUuKJOkgDf6/a21ktzy9L2TJJ+lR5syPKcCj26n88mZnBxy6ztOkO/EzyQSyKlJKZok/KJb8sMeVLnVeWCZ341jqRdZXU5j5jAGE2FpwppLCQvuFEqfWaFn48NMEevFWTfGdnEceEW/rtjPr0vVSj9QvLIfzosVylt0/tsjZl+7T/yDKyuMFvz41MUjgrFuWJ0c8N3xlSVxUYQicPYt6gaNo7Z5jzDz8w6k6nd/WFuzQwxZpvclR3hCy0zudGVVitCsNgXaHB8fc49B6dF2/hq62zeH25xJicMPx/YsgqAj7gFw63pIb7uZoBOhRZVm7ZnOmVVvV7CuKJhCIGJpP0PtWtMSYBxOzxbyrKmUuTVUo6N1RJvaUUUA6wSXNvfR3lD7XoVY47EGSAXtYjN1LCjU59E+9nGv/13OUN2xRTIaYagwc0qhWed71viC7LICCDc+v6ynefQNc8g+ZPGvt34cg2lTj9qz2pYTEtYZ4ER4LbMkCdL/9lYP6oQHB1KcF6si739Ye7MOGZgX2vrYUW4hRPDrSTqsl77X6qwiy/IwcFoQ4/rTM3H90Y2M1P3kbFNDnEd3ddUivwln2rIMf5QtJ1lvhAt7r2+plLgd9kRz2jq8FCcvfxhwkJFPcN5T+LTGhqCnyT7GtzBI10qwWUKLYs01FZY8v0gZ9zQwU4nOgXUrs/XJgRm6z5maD7+a3QL7apOp6bzdrnAU4UMtpR05HWWfSXIL/cbxB6C9iU67z+p0WtgfOJAApePbMIQCpe29aA2hP44km5VCCSS+7OTDe0kjT4BLarGEte0TkNgzHY+Lblx6mJ2w5MlJwKqBZad7bwv87pJK9sn9cbdnicyv1AzBCzaFv850vhMODwUI1bHyg6YlYZcYWd/G3HYNrLJx5Gyqp6P0WP5sUnPyunwni1+L2qZ+b8yBiraFremh7a/4t8Ry3yhHc62GjArdP0/cqpu4t2Bo0MJ74dkopFEE0000cQ/C/rMipdHOxHLfEE+EG5llmbQoRleXNE4ezBD87H7BBbPQqIhuCUz6L22ItzCpxPdgMNGJLcT77G2UuT2zDAXDa5tGIT//4K3ygUeyCX5fXaEu2bvzOkTWpds8MynvtHW0zBIBMcFWUUQEAoHBret5FpgBDgqFKfTLRbG9/e8eJfnIpu3bW5IDXBTeoC+V7bv+Hr3l0Y4Q2ujUzV4qZTjyy2N1dKThQxhRWXQrDJkVTkyFOfCxAxuSg+QsayGvt5fpJ24op2MAHv6w1yVrPVn+4XCmdGOBlOtt9wC4vujW/lJqo/7ckkOD8T407oUB/idY/Hd0c2cFm2nR/cxtI2x0QLDz5A19fKworLA8HPEpld4vVJgxKqyrz9CT5dTuJt72iTvNBFdCqdF2pir+6lKyaBZ4YVSDjXujC99/Qr+lILiB7NdImzB7y90isSBsPPd1uza+T3gM1HmHOd8x/AT01+by6dgrA/5vFM0Pv2zDNcf3881y/tY+qEg0lUY31DqpyRt3ppf5KFbU4Te55QAW48sO32mF4M2XzD0+Qq2v7ZNC+MBytKmXdUbejD39IfZWC1x49gAjxbGPDn7dal+nitm2MUfYuTc2j507WrQb1ZYVylxbF1//jGhBIZQ0BCMWiavlHOoCM6MtrPICBB0pevXd+/EL8YGWFsp8i9uz+84OjSDn6b6eLmUY1O1TFzVOOKLcT56eyfHnp9AFwJDiAb2UQjBMb9KcPbtHZSF5HlXOScOdpav/EvWk9RX3rTJP22x1azQquq0qjpZ2+LAYJS5up+3biiQfbHGtM4+wcegdPb9jGg7CsIzsRrPek5bJleObmV9tcTquh7Ze11J97HhFu899YgqKovqxnpBRfVc1+cafg5Y5twHo2unvn6e/bmzn9YZkkO+EiPcqWKVYElBEZHbAAAgAElEQVRy+6zptorfzyQcgzALGpydwZmkq7/vW1StoW8Z4MPRRpZ7x2qV2naoQuyAZZeD92zx+6dccpsugNNhYm/OuwH7BCIcNEX24VQ4KpR410i2m/h/gxGryqulPIcHY5P6LJpoookm/lmQ3oZZyYBZoSJtFhtB+swy5/c7DryaEMzT/WyolibljX463o0hRAOv8kQhTUzRMKXkhHArD22v59c2qbqDtiva5/1vd60BjxbG+ErrbF4u5Tlr6xvcmRkG8L7HEILHCmNYUvJkIcPuvsZCZ5Hb86cKsV1DmEtbZzNoVj2XXgXBl1pm8cXBdbzissF7+sMMWBVWlwsMvTn1gFmLChac6Uf6JemtFj+5pY+qtNlilj2mdhxbzTJPF9NIJOsrRW7LDPPzsQE2V8scGIw2uHKPy53frhQ9Vu6aLmdiI22b3J9LenFBEyGBAwJRHv3iGFtPLRN+sjakfbKQpkMz2G0bsuZfp4emjEoCJztVRbC3P0yHqpOzLdZVSx7jaO0HG6olDgpEWV8p8fW2HuKqhiYEFhJf3NmWyNvO+QnNVDG7GwuCXMw5Eos7avt34GeiZD5rwTbG/ZtXlHizXEBvq237gn/xM3N3502F123GNjv3kpmsfedqpYiFM5HUktA4/4fdXPTUTMzDneVf+PgsPvWHbt5MFGk9uDbhsFYr8kwxy+dbZjHLleAfFIjxWGGMNZUihlCwpONBA07P+KBZ5YejW3kmkaXwZ4vXLsxzyL/EGLVM+s0yV9dNcvw6PUi7pvNoYQwFx7gI4Kb0IEeGEoTdAvBT/W8zaFVJWibfcvtBuzUfP+1a5LGKA2aFvG1hCIW0sOhcZvCLzCBlaXNUKOGZuo3jztwIZUXSquqMugqD0C4Kwg/KgKCclVSLNvd9OMnfLsrgGxW8WMqxqpxniS/I7v4wuWGLt35bRIrasQ4kFJ5wWcgBs0KrpnvF4S6+EEcGE1SlxEIioKFUXOOa2D6SH5vkBA94Blvj2FAt8bRbz2yslHim01FHjExR/Pa+VGbryjK+iOBz584goqi0LXDO292vbdsLybbkNovf8R5dFdh1wjPrtXK+wZzuxHCbd72MY+MEI77b3efitjBLNzyvm3m6n7n6jhkGv2eL35m6j77qOy9kS7bN0h3UlP898c1tSHvqMWBWmm7P/8dQlc6M5S/Sg9tfuYkmmmjiXYq9/REv43UiBswKjxccw6RxB1hbOn2By30hVMEk+fBf8mMoQvCxeKf32tZqmdszQ+Rti4q0JwgOJ8MvFHb3hzkv1vmOYwL7ppHUfizmmOTMNXyUpe31Kp+0ZTWvlXKcGmln2KxSkTbDVmVSHNPaSpGH82NUpPSk3dPhP0Y283wxy8muidTegQhvu+zpOBOzuz/M2dEOElJj87POALRnv5o0efahPh64OYn9L3Di95zP8d2lsLZcYl2l6BlkVaWNlJJLW2djAwuNAKdE2pA4PZ47+0LcMDZAsa43e7k/xCyX4Vnucwy1fl33W1awrYb+xvNcRnxFKIEAxq6vYjzlDGWr1zjn/32hBCNWFRX4WLxr2mNzQCDKYdPI3k0kr5cLLPUFiSoqAhg0Kzybc1iz46ItRBQVQyi8VM7xo2QvbarOfCPAp+LdRMJOMVa+3jnGlU6b7Mcaz+M+XY6R13qzNuhXNMGbwQLVo2ocVt8JZdR5dc64h6rkpMXM6wz2uDjMEz9O84djRwm2bHsipCfiQwGeKmaIqhoPFlL4YwqfTnSj4BRbXx/aQFzR0A+sfV9Zs5mt+bgtM8TbbpRMqI6RXOYLMlM3+Jyb7TrP8BNSVALuOgvCAaInqwTjKrqYHN2jCMEBgSgHBKLeZIWNpFszWFnKNigbjwrGJxVN3xje4GXUBhWFgOI4Qv853ygZ9glBdUIxaeMUQhFF5cuts7gnO8qyQAh/1NnGQtLimiNqffU3sJj0V01Wf7LAkzemWflfWf54zCjSBG0fwYKL/QztVWHJ0UHvefR4Ic2IWeVK1+TrpvQgbZqOIpxCeE9/mEODNQIroTj7V5Vyyp7fAbPCfXWmvbaU3nNMCMCdoxtZV51kBvfcjU5hvMeZYW6oOPdV60KHEKtucNZ98TdZ7rxgmD/9e5In/yfNy7fnWPdYkTf/VMA2wT9DoOrTPzc1Idh9Qt7uRNb9urH+SaqMdxLbOo6UZXK3+wwM7UA00jjes8XvXN3f0POyo5il+96VhlF+RWmIA5gKtpT8dRpJWRP/vIgrGm+U8yxpSp6baKKJf2Jcmdw6rfuyIgS62xemCsFRwTgSuCMzzFazTIdqsN+E3+Y3XAbl1VJNUhhRVBKqTlna/Cmf5JLW2dvcpp2MIMt9YXrNCunt/MbWQ0rJJUMbplx22chGAE4It3J4KMYMt/g7MhQnomh8f3QLLaqODUQUjZdLjQZLOTdSR4HtCrFHrCoWkrtcVvyJQpp7cqN8tXW2J529cWyAHt3H1zf3kB+x8c9WOOOGdv511Ww+ekcnz38pS1WVvFjKcc/yUYKtCmq/4LhTEpzf380Cdyz1sRvf4tfnDPJ0f4ZjQgn6qhXWVYvs5gsxV/dzTCjBgYFow3kKCZV7c0n8QuGW9BARReVpdxAcEAonRlrJu8ZT5ZzN367OkOk3GfxFhbaTNObdWvvdK3Q660UVjbii8VghzaXTnAOALk2f9nqLuX2+K4s51lZLzNadgk63nEH/E+U058W6eLKYxhCCjG3xWjlPSCg8VUwz+KozsK+MOmeo2GFz7IEtBL9UG3ZvCDhFb/rjJsEWhWMuS3jbH51bU+gtPzlE+5za3ysWJQgJlY6ZBgd+LMrpe7QjVUmwbfKQXtTdEt+dMQ+fUFhiBPhAuIWsO4n0eCGNBOboPl4p55mpG2QPNCEMqcUmw1aVQ4IxXq2T5j6YTzFX93NurJNZuo8WVedbbr/+8eEW3h9u4bOJGXwm3s0b5YLXD7p3IMJx4RZOCNf6YfcPRLk1PURYUXm7XKBN1REIgkLhjXLBu8Zjisp8w8+u/hD7+Z2Jg36zwohlorq12GHBOEuMYIMvzwmRVgyh8EwxOymn+IxoOwFFRQjBj5O9/LUwxqBZRXVzeV+/t0AlV7vLvvb0BrJPWuRetVh/dYniHXVS3kU6wQ+prPx2jrBfnZS+UT859/vMCAqCvfwRtlTLnqM8wJfc3ORes8xdmUY1yzjq7/uFRoBD3OI5LFQWdwYIxBUqOUlusPbMGl5TYe2jJTSfYM+PRBi2qgghPOa3dbPGhieLPHzFGOufKPHaXXmevjbDQ99OcdeFI9z3VWcyYWyfqZ+DhwZjSCnJ2ha/ncDaHhduYbmvVhDbUm7XO+n8ePc2lwNUpPSuyzWVIi9NeFZOh/ds8RtXtUnyhx3Bz1J9k2Zh3w34QLi1QU8/FZKW2TRE+j+IGbqPr7XN8RzvmmiiiSb+GTFiVdk4jSHjIcEYH4t3sW8gwiIjwPvCCYrSJmWZJFRHvDqR1RmXBNdnZZ4caeOChCP3EwhuTg9NYkfq8WhhjL8V0zyUT7H4HUwwDlpVtpjb7p+9MtnLilALe7kD+f0DUSSOe+5p0Tb8QuGsaHsDYwJuj5+q4xMKH98GswkOQ3pRYib7u3FBVWnTqeoMm1WPmZZI/ntwC7+91hmwpo6segPTzqWGZ5ikIUhSJdLpHFdhCl6/ssDp0XaO2/wau10ZZvDlKsmrTN4oF8jZFknLxHxTssvmIL8YGyBjm7xWN9B/IO/EugSEwh7+MBnbIu7K0iUgEJzgxjn96RtJFt4e4GdH97Pp2jLmhHCDlqLGvw9t5K7sMCvCCXp03zYHuYNmdUpZKTjMY1BRma37OC7UwspSlj39YZbrzgC+rNr89+gWNAR7+sMsNgJYUvJsMcszxSwtCxvHWp3S4K7sCCedVIvL2tDuXOtzF/r53OMz2e1050B/o62HvVrrZKOzILO+VnBcYWxmoeFnP/d6CSoKBwVj+CN1eyvgsevHMHatjXMvGlhL1rYoSpsrk720a842jvebdmlObMzqcoHZMT9L/hhgp2v9fKFlFn/OJ5mv++ms68PUhOCX6UFeKGZ5tpjxNBTXpvq5OT3IG+UCd2SHeaaY4RGXeHmumOHe7CjVuu5MnxAIAS+Uslg45lIqzkTVUaG41896Rcc8rh8b4LlihgV192KLqjFb8xESCleObuV115X9klan+ExZJhKJb4LjMMDblYLXyz9sVXmhlGOrWcYKuq+91chOxtbWtQ2e1rCINUbRe+bkbIvrxvoB53z6FYVd3OLv7FgHqnBijp4ojJG2HZn4OL7hKjn3DUTYbQKDCtCuGbzPjdYCp4+/172XQ4rKTr4Qre71N7Ku9rnP3ejcMLueGiLUqjLPnbRqc9dNv2Zy/785Be6eZ4c5+t8THPCZKLueGmLewX7aFum0zNOofGDqWqNH91FFYkkm6WlWlwsNBf7egciklo1v12U/W7LWg70t6HV1nIrYYV+G92zxC3B7dvt68omot+F/N2GREZgy7L0erao2ySSgiX9+SCk5p+/NSVKgJppoool/JhwQiBJQlCkLksfyYzycHyNvW/ytmOY/R7YwbFY4NBQjpmhkbIunJjh9/pvLvKh1Q7EfJXt5vZwnbVt8pXU2I1a1wQF2IsrS9oyhVtSZ9GwPm6sl9vVHplx2cJ1Hx3dGNnObm495+cgmni9lWWaEuC7Vz4BZ4eH8GGdPkD7O0/3sE4ggwesrnA6HBGP8Kj3Ia6VagXNhy0x+lx3mdZcZPyvUzkEXxmh71ZVbHtB4/N8fbuWarkV8u2MuC/UAS0+otX4F31D42ao+RF3iUuQllbWVIroQlJMWj5+T5p4zk5SyNp2qY3r0ujsQ3tkX5MRwGynbtcix4KiH4nzvxc3kbIsnC2mPcNj0zNQTI29/2dkPZY0glXcG+3dkhlnuC/Eh10zspC2rPKfocbxRLnis+0T0VsucEmnl0rYelvtDlG2bJwtpUmVnWzp9juPvCZFWLOmYNi32BdnJF2Bvf5iTf9BKeucaURI6zCm8fmL28umHutn4xSJjezjLT5mQH/3VoQ08PFpT6Q35qpjvc87JxtNK2Do8X8ryYD5Fybb5SaqP+bq/oeLwL1AozLBZE60lgVSlxEYybFaZrfnY270+x71CdCE41d2W5b4QSxMhIj6NHyS3MGqZfCYxw2NYz4l18pZbZBpCwQYOqpuAt3AY5VHLbMjHtnEY0AdyNUnyU4UMhwfjJC2TiKLyUimHDWyulmlTdS9y6nMDa739GL9nFhkBvtE2h6K0yUsnAm28tW88+/aJQhpTSk6KtDrHqQ4rizkvS3eRHqBH82FLieYyvyNrnXNk9Dh/d6x17hH/UoUvXjaLfX5dm6TIBxxFBjinYlyuff1YP4YQXsvk+kqJj8e7KUmbsKLiFwr+KYytniikJxXrzv7bDNaN94esKpvdibZRq8ofsyO0LXDqlN4XK7z4myw3ntjP6/cUECrsfa5z3k+LOuel1WV+S32SQtKmZ18fR3wlzu5nhDn4whjHfruF069t52O/7+IT93Tz2b1nTNomgD/lUqTdc/iBcGP+eN62GKlj3VtUDX1CiXxVspb9XJS2d31tC+2qzp2zlgG4TuI7RgK9p4tfA+Udxx19INz6roo5GocqhGczPx2eKKZ3WBLQxD8PhBDM0X3vyuuyiSaaaGJHsdAI8M32uWhTPMvK0nbyzMtOXmxC1cjZNmXbRhUCFZgoxvu6K3m9vKNmVFWRNn2mkxP6QC7JfMNPeRvM705GgCVGkBu7d+JHdYOz7SFlmdOaUfW4pkHvD7egugZJAEGhUrAtL0e0Im2K0vZ62saxspTl0bxTHNW7xE6FK0Y2029WWOwOvDtUg1vSQ/iE4hUVG1NlQlucbU2eVuWi/RoHtz4h+OzAGr4zsokTI23selaIws0WW48qI2zBgZ+MccQna72z6pig8H0b62HJTn+tMVeHvhhj9UN5Xn05xw0pp99wkRHwImA6NZ19Vocp/tDGd65CteyYAf0ln6KctSlnG8+TOBEy95n4jqkNZcXVgk+pjlzymWKWhKqTtUwytjWpv7tbMzwDp4moSMn1YwN8bWgDC40AM3UfW64rIR9wli8OhGhTdV4q5Xi+lOXHqT4ylklIqOziC5GYo3P5b+fR8qBG/1Vl1u1Z4tBgDIEg2q3RfYKPT7c62/n9ZGMvuY1E1og9Bq0qylmQ+alJ/LMan3bloAIYTyi6JT3kFV4AVptzrOzW2jE7JdJGQChYSA4IRrnb7Ru9tK0HVQjGLJMvD60noWgMmFVuSPXzP6k+FugB9vCHuXRovVfQPVvMeL3qe/jD7OePeNf1UaE4y3zBBpb4SDe2pk3VmaU1HnMFR/Wx2AjQruq0uaaso1aV54pZUnataPrX1lnMqHv/VrPMxYPrPJOsFaEEre77N00wUFpdznuO1OCkt9jU+mVXhFsYtCocE26hO+ps+9gWp/gN7+bcH74N7rUWk1yZ3Io1v3Z8D9SinqFsSFG91sp+s0LOtjwDp6eLGV4sZZmr+9nNH2ZXX4hj65jccZWGXyhTTs5kbYtX6u57iWMyBXjmWePM79M/y/DwFWOMrnf2o2eFj/gsZxu/7/Yg+8IKkS7nEwJxheO/24KiTj+efHCaVoGAolCUNhVpNxS6ADFVw1/Xj3tvNkleNj6xR+veU7Qtz1xuWxg0K5y29XXAYe7XVHYs9vU9XfxekOh+R3FHRdt615pFhRW14cE3FTZXy4RE0w34/yJumrHkH70JTTTRRBP/a1Sl5MFcip+l+hieQsXUqRl0ajoSmK35uLhlFot9Af7kmtq0awbnxjob3jM+tX3FyGZP2rxfIEpUURm1qiStKqdH2hvMeyZCQxBUVD7e//Y72p8V4RYvq3cibnUHwTbwmUS3l2d5aVsPJ0Va+XMuyR7+MDFVY29/hF6zzDPXZ7j7SyOUMjamlKhCoACB7bg9+xVnAP2iO/H9bDHD25UC/9O10DODGjdxUuPwzMez3DvBKOg+l6XL2zY/TG5BVxU+tXM3Cw+fXgZe+qPNfV9JsuqHtUH65p+W2fOKCOKzjqGUlJI3y06f3j7+CE8WMhSeqB2zzFMmx21oQdiCzIAzeC+11Jarp0PKZ7J/qNbYOvshH3ceNkLuAouxZ01eL+d5u1KkU9UbIpPA6QV9ujC1yc4yX5A9/GFK0iauOLEsqRtrY6xnKxn29EfYVC15vYzrqiV8ivAylQFeUnJYuzhFGjj9sOCwn/dMY1a2byDKYR+IUT7H5m/fz/DRWCfndXQRWaoRVBUK0mKO7mcnI0BM0bimaxE7+0KkbBM75k6kHKxwQCDKqcc6zHegS2GhEUDi3Gsl2/Yyor8xtKGhBzOqOsZU4610WdvikGAMC7xCcUO1xJGhOOfHu8naFpvNslfcqQg6VJ1PxLv4Xsc8XinlvM/qUHV6dD//Wtdrf0q0jTWVInv5I2w1yyw1nON5RCjOiFVlfG7qQ9F27skmmaEZnhqinmmdrfnIS5uge090u0Xyl1pn4RcKKctsKMqO37KKb7bN8Vog/3t0Czv7QjyYS1KZ33jv7rdfo5/AvLYAo1aVsFFTguajNfM6S0rOinZgS+nJi+uxplJkfaWIAF4p5xucjcednD8QbmWZb7LseSL2D0Q50WXXE6rGseEEM/fwOQdFwJwDfCw/PUTgSIXnzs54Eul69OzrQ6hw7OUtRDq3rW59szI1I3tKxHFwztmW97wZx3JfiAPqYtlsZIMiB2CBXnuetKk6n22ZmmGuRz3pM2aZnvHZ9vCeLn77zApv7ACtPo7N1bIn1Xk34pK2bRt3hITKDL2Z8dtEE0000cS7C1UpmaEbSKAwRdE4R/fxvlCCUyNtBBSFAbPSYARVsC3+lGss2vZyDZ1K0vZyJzs0nTOi7ezuD9OtGfw+O+L1CE6FxwtpRqwqMzSDE8Nt0643EVcnexmzpvYHke623J8bpc+sUHAZ2E3VEg/mUhjCKR6kdLY3kdN44kdp3vpzkauO3orvOcFiI4AihCftng7nxDo5PdruZcDbODLme3NJLhla76zk1nR51dmOtRP6rnUh6NYMdnbZ47xt8Wwxy4Ntjcd7OqjumNccdvZb2IJ5JR/P/zLL8HMVdCF4pT/HrFsN5txfKxTu/WKSu88b5aQnWskPO9uWm2Xx7OUZCl+weKO7iCklLarG2P2Nxzr9qkX3JQYLNScG67sd8yexaPUu0hOxrlLC7/Yhb6qWuO+bjYWqogmuTvUSVlS3+GjFlpIXirmGnmYVQZuqc16si1XlvDfc7zcrnuv28RMkonv4Q/h0hbHzTPJLLV4q5bgnN0pQcbJwf50eQuD0fpelzfdHt3BwMEpC0ZhznY/Yl1U+cU4Xp0XaKC2wOeTmGA9emeLaVB9522L/QJSVpSx7BcKUbOfeUISgRdX4QstMNlXLLDD8fDTexQ3dO3FGtJ2rk70cHUp4hVxEUXmhmOO6sX5SVpU15aK3bw/nUzyYT3FnZoRLhjawvlriKVdy/kalwFPFdINS8cVSjmGzym8yQygIvt0xF1UIHiukOSfW6fX3WlLyZqXAi6Ucb7mu00GhcHQowXzdz2zdx9XJXm/y7JPxLqSU3nmOqlpDwbV/IMKVyd6GTNoXSznytk37STpSr72+aXHjPbFpfomFeoC9/RHOubOTlgs0nj2wNpFiSsl/jm6hKG3OjnUSVlROdM/zt9vnElJUnitmyds2CjTk096cduTai31Bry+7HrM0H5+o6/V/rpjhoVzK+96+aoXOJQYfvaOT8//czRnXd/C+bybo/3qFr+48h4CikLFMVtSxzcdc1kL0dnWbE1rjiE7T+rmpWmLArDYw0eNYVc43nPMPxzomRXOeXCePvzM7MsnobyqEhOIpBXShsKNNqe/p4lcCb00zgzEVes1yg9zi3YbV5cI2L5YDgtEdkhE00UQTTTTRxN8TPkVwZDDBTkZgyuzVJwsZ7s8lebQwxjPFDGurRQbMCl9xGSSbxpxI25Qsd81idvWFsNxx7G8zwzxZzLC+UuKESCthRfXYmqlQljY+oXDLzKWYSK8/cHsYMCtc3jF3ymWfqHMxfSSf4lHXDOipQpq3KkXOjnXyQD7JxmqJNZUiuz5bY3/UvOCpizMU1jvD5X8f2rjN7cjYFt8d2ewN7/f0h1nuD7GhUiSuaqytFPmXkOMFYumSkFA86eY4PhXv5vRIG4cF47SqOqdvfZ0N1RIts7c9nlB90HKehnHL5PN54F9i/PX7afb7WpR97opwxMfi9Pxq6gSOJ36eJj/inKPODp3W/XRCp6ikLJM1buxTnza158ku1TArQgk+3v/WJElkRFHZfZoc4GGrykIjwD7+CIYUZO9uvEZ2DgbZPxDhI7FOzop1ULJtjg4n2C8Q4cPRWo/2ZxMz8AmFl0o5Rs2qx6J3qIYni51YKNycHuJ3mWGW+UIcEYqjC8Hr5QJ3Zkd4rpSlVdV4q1zktbIz3ltfLTFkVSlJyZ2xUW49YpjrMgMkLZMH8ykenpUiG7XQhcDGiZQZLwKztun1HBdtm7fKRX43cxkSeLNc4Iaxfv4n5WTyLveFPLfkj8e7KUubr7bORhMCRcBH65QXkhrb/ZHo5Lieevlxb7XM51tmElVUT7IMsMQIsMUss9ll836XHUFDUJE2L7hmSHN0P3v6w/SZFS8qZ1yh+cPRreSlzVazjBCCgwNR9gvU+vDn6w57O64APTvWwZ7+MB2aTleHD3tRbXtlXUZzYoFG6kSTEyOtPFVM89LMHMGPqHT4DXyutHeckXwsP8bKYhZLSnrd4/Gj5FYuSMwgoChEFJVWVWeBsePpM0NWld/VMcUVKb0WzqK0Pda1c4lBbIYbYyQluhC0a45R3jeHNzY4X2uGYGuiskOtoJdOM+E2aFYYMCt0aQafnODUbIjGNtMXSrlJ3g4/qJP/b6yWmDlNP/7Ez/1a2xwAdvWHONXt8d8e3tPF7yzdt12pcD0OCsQ4PDh1Jty7AXFFbXBTm4ir3kG/UhNNNNFEE038vZC3bQRO5mxMnTx/b+MM8EZNk7It6VINUpbJY3nH8MkQwnvfG/fn+fHBvdx2hTNAPDQY83ojpfu/R/JjPJBLsswXnNb0COCD0XY6XfblsfzYDg0OK9JmV1+IOybEfVw4sAbAm6T+UstsVIQ3CAwqKmFF5XHXxKoi3Z7X+5zi+PDLYiQO15AmPHBmkse+P4aZbdyevmqZu+q+997sKBbSM8gZtao8lEuhCsEpkTaKts3N9zi5ul0zDBb7glzcMqvhM/9aGOPHqT6uSfXRo/tY7AsQVVQ2i5rEUJnCS/Pgr8fY9XMh/PHJ0uwnH6kZdT39owxq1TlBwTaFP/0hSXrXGpMrt8Jrdzljm63RCnv7I3y5dTYnhFt5XyjBHv4wH452UGybPJ772w8y3OzmBk+ctmjVdNLTpHfoQnB/bpQf9G0h+Obk7ZfCyaF92O1/fDCf8iKD6j/zurF+VpXzrCxlubi1dlzbNI3z3PzpeycoFsYzUff1R3gwn6JgWx5L1qEanvGUQHiF3hvlAsNWhVmaQULRqErJo4UxDCEQCHo0H19tm02rqrPECHBhywyeLWZ4q1JkH9f4qiJtz307Z1vcmxtlZTFHq6pzSqSVHyS3epGaN6T6GbQqLPMF2dUX4oPRdo/x/kS8m8OCMXb1hZiv+0nbFoe5RkTjbGm9BD2sqPwqPej1DI9jZSnXIJkGOD/Rzc6+kCe/XlXO85+jWzzm9JczFnvmn0FFpWjbXo/xM8Us97p9zlJKfpMZYrbu86ba1lVKDJrOPs3RfXSZtedCh6/277YFOp0+gx8n+1AQ2FLySD7FR+qM6UJC4X2hOGurRRYaAapS8kzRKdhHLZMbUv2cHGmjRdWIKmoDsTaxfWMiLCm9bG1wpM5d7jNMIKb0OzaE4MvuRG7HU+sAACAASURBVGFIUbm0rWfSs2yu4fNUKNNhXaXI16aJDwsrKlUcb4Y/5hqVEu2q3iDhXlXKT3JmVhDe5OICPTCpTWEq5GyLiwfXAc5kzW0ua749/N2LXyHEYiHEy3X/ZYQQXxBCXCaE6K17/fi691wqhFgrhHhLCLGi7vVj3dfWCiEueafb0qUZnLOdi6weN6cHd9hG+x+B8RtsKlhSvqP+5iaaaKKJJpr4eyFvW/gVhacKGV6ZQsG0hz/MLr4QLarG0eEER4cTLPOFKLimKUFF5ePxLl6+Pce9X0lSyUn8v1OQtuSOzDDDbpTIp+LddKoGfkUhIFQSrrnUdHjcdYoFZwC5LXMscDJzP9r7Jkt8wUn9ZwGXFRpnZh7Kp/j39jleCsOlbT1ckOim1yxzaqSdXf0hDqrEUF8CRYO2w3UW7u/IEqUJz9+UJXGSzrq/Ooxmybb5+dgAP07VrJfDisqKUAs2kE+arNqQJ2ObnGG005P10fd8GftnzrqRo1VeLOX42nDj4HZ9nQz6wVyKUyJtfDLRzb7+CMV5zvFfeHKjXLJlZw1rb8nGaplPJrpp36VxIGu/OvXxW3prkLlBP/Ou8bPTMbXP3LLSOZZjcZPfZYf51vBG7s+NsqqcZ57uZ8SqYl4l2Xpmmd47yhx9bRzbL1n1hzxb3yrz4WiHVyCM44hgfJLEexwHBaJc170Tx9/cyl0fdeSawXYF8weSOaf42LpHmZxte6Zh4JicFWx7Ur/jIcEYYUXl/lySi91zfXa0c9p+4w9G2vli6yzPYO3gYIxPxbvZ2x9mv0CEvxUy7O4Pc2Agio7g7lk7M1f3U5WSXf0hVCFYajgS9a+09nBWtJ2vts3mb4UMY5bJm5Uit2WGsSXcmxv1WMrxMaKC8Ji6bs1gmS/ouQ6PR8sI4EPRDnp0P+urJX6bHvbytB8rjPG5gbUcH27ho7FO7suNeqqMvLQYs0wurOvnPC3aTlBRiCtagwrjo7FOWuqMkv6nayF3ZoYpSZt/b3fYvvG7MSAUvtAyk0/2ve1t6wGBKIYQ3ji//v4dX+eIYBzF3ae/5FNUpeS+XJI1lSLDxRozukAPEJ/vTED07O/jUPecztQM5hl+1lZLnoEY4Dk5HxCI0q0ZhBSVvLtvC3Q/NpKrk70oQjBgVfhz3QTIG9tRpBpCwRDCcy/v0XwscqXhrarGRVP0ymZsiz9ka7LjDs3gwxPqn3NjXZ6z/VTI2Raf7H972uflkaEEhwbjVKRNekLLR0hRiNZ9ti7EpJqk3iF6mS+4Qyau9atI2G7k6zj+7sWvlPItKeXuUsrdgb2AAvB7d/GV48uklPcDCCGWAWcCOwPHAtcIIVQhhAr8BDgOWAac5a77jvD14alnMKbCmmrR+/F6NyIwhb34OCwkp0V2vF+piSaaaKKJJv6eCCmq5xg6EVnbokszODIU57VSns3VEj9J9TY4x172+kYe+/5Yw/tG1lbxCcUbFK0s5ejRfRwRjLPQ8DNsVh3GTkpSm6uMbTHJjVhUS876q+v6ND/j5gNvC3nb4oLEDG7PDHmM8ThWukXRuJt1n1nmB2u28puHBpFScnN6kG8Ob0JFsLqcZ22lyEsPZRG2YM7+fl4wcliLJg897/rcCPkRiwGzwiOFsQbZ+BdbZxFQFKSU/PK0QXo/WOHUUhv3HZTkp4f388J1Dhu1+4dCvLYiPyUL3qbpnBVt59RIG1vNMv8xspkbx/rZWC2x6rI86z5fJP0hk7l/9LPbtSHWPFQkc42F0aYwZFb4aaqP8uU2pU9bHP/fU8dFbfxAiYdvSWFHnSinfQMRln0vSPDaxjFXOWHjFwoKgjm6n5ii0WtW+EN2hHSXxb4XRdBbBJW9JMrOznFoz+gYQni91uP4z9Etk14bxx9zo7xVLrLmtzWp9NxP+bl0RQ87fz2E5ld4tpjxMmj/tXUWCVVj0KrQX2fYFlc0NlVLHB9uYdisco9bIGmixtB9fkIEZb9Z4d7sKFFVY4kRZH2lxBUjm1lZyvFgPsUThTRrK0U2VEtI4MStqzk0GGOxESRjWfToPhYaAf6trYfbMkO8UMrxuYG1vFLOU5Y2u/lCIJ2om/oCpFXTuXXmUmKqRruq86sZS7i2exEdqsF9uSRfbZ3NfLfIWuwLcnN6kC3VMpaUZGzTu67HC92rklu5KtnLbTOXehNBA2aFNdUi33SzbAFuTQ8xZlk8OSGK6lfpQVaEWjyvmsuGNzFoVXm9nOcq16l4tu7j8va5xFWNR/JjVJFedFmnpmPh9N6CQ3iN9yxrQvDDzgXc4GbxArSqOkOWk/2sCUG1o/Ycuj+XZJdrQrx4SZZdTg3xk1Qfx4QTRBWNmNsDu75S8phxgeCu7AgZ20IRDkP/TbdgPynSxk5G0FOedqgGQ3US5OeK2863TagaHZrB90ac/VpZyvGMe+yytsWvxgYnvadoWyQnODBPxO+zI5MmbupRtm1+0DGffQJTR7i9Usrx++wIhlAmTTRlbKsh3uoSN/u4HqrAm2i8KT19P349worKWa7UOa6ozJ3CXGwq/KMruaOAdVLKTdtY5yTgNillWUq5AVgL7Ov+t1ZKuV5KWQFuc9d9R1DqJEfbwxzd/66Pk7lxrJ8Rc/IFPmxWJzWXN9FEE0008d6DEOJGIcSQEGJV3WstQoiHhBBr3P9PuK8LIcTVrsLqVSHEnnXvOdddf40Q4ty61/cSQrzmvudqsQM/nN3uYGkXX4glRnDS8tfLBZ4qZLg9M8zrlTyGUOjR/A25jqGfqFQLkoVHBlj2Aecztjxf5tBgjFZXQvdyKceGbIm/PJAkYmqEFZX02ya/+cgQNxw/wPXH9fPTw/u4ev9eNjxVxCcUDHfS+5HC2KQIj4lYaDixMN/tmM+n440MTI/t48H/TrL75SFSm6p0agbKjyD/ZZunr82wdbSMguNO+0a5wNZqmS1/cYqGnY4O0GuWWbpHgNR3qsw5pVEi+suzBzwJbr1L6r3ZUX6THuKIVNwzjXrzC7WCTn/J2bee/fyoCIq2I9mux7mxTp4rZtlcLXkstS2d3sPhNpPNK8oYqsIHF7TzvoMSfDrejYVkoRHgkGCMirTZFC3TerbB77tr7NOGE50iKTZf5fULClw0fyZtqs4r5Ty/y46wpVpGXyqoLq+N0Q5dGKNd1dk7EOH5UpY3KgVSVhXhXiMri1kWGgE2VcsEg86+zXsqwK8GB+md4CK+tz/CMaGE5wReD1NKlDxedu6jN4wRPVnjipHNLDICnBxpY47uZ4lbDN6eGeaAQJR5up9/a631RJ4QaWWW7mNlMYsuhMc0+1wzLai5aY9jVTnPM8UMK0IJ3qwU0IVgi+m8LygUFvsCDJtVBs2Kx1o+XczwajnHxmqJV0o5bs8M8cuxATZXS7zqypHbVB0Fx13Ypwj+VsjwxTqJe9KqclbvG1TduJoLB9Zybt9b3JsbJaFoPJBLetmrC3S/OwnhFCALjIDXCxpXNPYPRFBccXK7ZjSwflPhnFgHy32hBoOnnX1Bni5meNs1txqxqiz3hahK6RWOtnQyg0etqref4wX9ylKW10p5L3Isrmre549aVX6THmyQD38m0c0iI8Be/gjdmkHnlww6luicfHUbFWnT3mFw44cXo6uCvG0REAprqkVWuv3HBwWjRNxCWBFOL/ObdWZ6P046vdM/TG5lqSsXB4gqqpevvCMYNiv8amyQlCuvl3VxTTZyymeUEGK7nkVxVZvWpA+cHu47ssMsm+L5DA6bm7MsOjSdI0KNLaJGnXs4wHdGNk2qvR7Lp8lL6x0pVU0pGXG3Oa5qXqTb9vCPLn7PBG6t+/tC98f1xvEfXmAmUB+CttV9bbrX3xHeF0rsEE1elTZnT9G0/27DYl9wShOv18sF+swdswBvookmmmji/zRuwlFS1eMS4GEp5SLgYfdvcNRVi9z/zgd+Ck6xDHwT2A9nMvqbdb/bPwU+Vfe+id81CfVs2VQMhUSiUJM5JhSNddWiZ2K0/okixuMKekBw1KVxZu/jDPQ2P1+mUrF55Xc5/njxCOFrFVZdkcf/TZUHvjLK4B8qWJ+R9L1SwRcVxGap+CIC24S3/lzkEjcDFWDErG7XJ2SZL+hNNH9rZKP3ui0l5WdsXvllnnUPl3j93gKn0Eb0CWew/NRPMsRO1jhgJMq3hjehCYGVlmRWWkhFMnxAlTfLRTp1H0/vnmWnrwX59EPdKIc4n5/vtXl6dYbDgzEvNxecSBU7J9l6be33v7SuccxTOcJm0VEOU7inP+z1TI7j2lQ/66ol9g5E2Fgpc2FiBguNAPN1PwlF4+xYB+8Pt/BwPsWTRYeVPD7cQtIyGbWqLDaCtKk6H4l1UO6qfbd/N0HPXT62/o9z7u/OjbLECDJiVWlVNSpSskcwQvYKkz0vCOP7muC22cOcEGllkRHgU/FuDg/GWGAE+FzLTL7XMY+dfEEWGQE0IVg92xkLvX1XkYMujlIes3j25xlyw845nKkbHL9lFZ93ewbrMU/3o60RIKFrZ52Ld5lFVFEp2TbHb36N54tZjgjGPRfjjdUSd2ZGiCoaTxVr/cw/Tvbyl3yKxwppPt8yyyvEdCE8lmrtBCMun1DwCYUBl8jwC4W93b5cCzg/3k1U1TxzpUVGgIq0SVoO+2q76201y/S45+icWAdfbJlFl2awfyDCBYkZbDHLDVLYcVg4zOj6aomqtLGBY8MJXq1TQTxaGMMvFMKKSpums8wX9NjYj8e76FANTo608WG3D3a80N/LH+ED4RYODtQmrebqfn4+NoACnjQYnB7OPrPSwM3v5g9xTDjBzm7huNks81Qx4xVLt8xY4hW4fuG4YR/lFmLDZtVjH0esKi+UchxaN3n201QfO7nHUiB4/7JWzv1dF+cveJsDXDft47c484VLfUHeqhQ9p2a/UFhkBLzsWh3BinCLF1s0/p3jeCifYi+XQRVCNPj1fKd97qRzUg8hBAFF4dvueouNIEvdok8XCrOnyK5uU3XeH55adTGORUZgm4kwd2SG0YXCNXVtFfVoVXVm6gbrKiXunOB3EFE0zqrriS64Ltf10IXwWjc/uoMtqVUpecid9BswK9w9xfU8Ff5hxa8QwgBOBO5wX/opsADYHegHfvD/8LvOF0KsFEKsHB5uPCFLfcFtOj2OY12lxJ07eFD/kTggEJ1ydmfcga2JJppooon3NqSUjwMTc2pOAn7p/vuXwMl1r/9KOngGiAshuoEVwENSyqSUMgU8BBzrLotKKZ+RDqX2q7rPmhbjvYRjlslLU0jvTgy3cVgoznJfiM+3zKJV09EQJFyTq2euc4qQ/T4bIdqt0bOvU1hseqbEX04d49n/yPL2Q0X02xU23edKMB+r8vC3xrArsPz0EJ9+cAbn/2kGp1/ryOj6Xi7zjTpzl3ZN3+6g6eepgSm9QWxg/saaJM8sS352xuRBpHjOYTAuapnJ/Of8SAvMPSVW1Il5GS/E/UIh2q1x6o/a2eVMZ+C71wNh/lpIc787wB81q3RoBkf9Is6MJ6ZmfeJXaeT/zUZRBTeNDfBwYYwbJkQA2UgWGQF6dD9/zifZapYJK47b8jHhBGFFZXWlgCUlY5bF6nKBYbPKiFVl2Kyyiy/EhS0z+O7oZhYHasxMe8DgzuAIll+ysy/I25Uiq8t5Dg/GOD7cylzdzyzN4Mi2BN2fNPj9gaMg4KpkL3dmhrlurJ+KlLSrOg/mkuzqDxFVVK5PDXBKpI3YuSo7He0UH5HNGm9cWOTxK9Pccb4zDtRRCAqlIZpoHB2aQe5VZ2zYtYvB44UxlvqC7OIPYQEpq8pd2WHvTHeoBhaSNyoFXinVPm++4efsWCeHBWOc3fuGN7FjSsmtGcecJz4hOuZj8S6u696JP7t5yzFV49BgnA9HOzgh3MJ1Y/3s4Q9zuFvUfTDSTo/up13VOSvawaHBGCdHWpml+fhgtJ2zYx0Mm1WuSm5lU7XMkcEEGoKworK+znXZJ5wSUsXJt15qBHl/uJWLEjN41DWWi7v3m4pgjuH3YsduTQ95EWU/Svayqpxnpm54kuC/uiZur5cLPJZP01MnT+3WDGKqxoZqqYEN3M0fZk9/2GsfuHnGEm5OD/G3QobDQk7RqiHcIlzhosQMzu5702MvDwlGWWQEPNXHuJEYOD2/c3W/9zfAlmqZB3OO+3raNrnaZWojitrAiAohyLnM72IjyEGBKA/07MrKYs4zabJwvHaOqmNAx3Nsjw+3oCL43shmb9mLpRyWdLKv/2u0ntebjLCicmFiJn/zjPFs77gFhDIpOgvg7UqB32W2XcO0qU57wFSQUlKVkv38kWm9hVpUjcVGELuOiR6HAJ6tk3Pv4gt5qoVxnBvvpFXVSVrVBifqbaH+eay4Ez/v9H1/bxwHvCilHASQUg5KKS0ppQ1cjzOTDNAL1AfYznJfm+71SZBSXiel3FtKuXd7e6MN9qZqaVrTgXpsNcsNvUXvVnRqBm9PwfyeHnV6DJpoookmmmhiCnRKKccb4AaA8an3d6q+mun+e+Lr20RQOIzYdD2/jxbG6DcrBCoK31u/mYq02WyWSagaQ29W6H25AjrMOMWZ5I3NUol0qVRyErNfEpivcNDnokjFdVY+WEGooPkEua+arLisBV/YGRJ1LDVQDRhdb6LfLKgUnO15f7ilYdA+FarYXs/toXXpENKUaE/VBnvP3ZjFqh+xuItW31ug/XKdH5y9hUfudvqXDzguyoHBmBcD8+ic3VhSl7nbtsIZcOf+aCNtxwAH4Lujm/lq62w2Pjq96mv98iIrog5hP2hV2csfbmCrAPYNRPmkmysqcPofb0kPsos/xEdinVyV7CVlmXRpBglVQxXwVDGDwOn1+21miMuGN6EgODQY4/Rft7HoAj8P7pJiv0CEkyKtrC4XaFM1DCE4OdKGJSWPF8ZYVS7QqenkXWOpvf1h1zAIt2Au8EIpy5PFDK+V8pwd62Su4ePe7ChLQkFOurINbYXL3L/pDKhH1lQZs0z+kk/RompTGp8+mEuy7kmHke3Z38+TxQxrK0UvKieuauhC8WTPFyRmsIc/jCEEM+vYszm6n78V0szWfVSRvOBO7CjCkY4DfLm10V37/lySM3pfZ47u5w+zdmZXX4j/Gt3CbzJD/DjVx9Zqmb/kUzziMl5XjG5mqRFkpu7jj7lRnitm2VApcW68ixvHBrhpbJA/5VNYSF4q54ioKt2awYejHQ3RQj6h8MsZSwgoKkIIvt7Ww+7+MA/nxxiwKnyrfY6najgwGOPFYpa0ZaHiOEovrrtu5ul+HsylGnpqAYrSImubXuEPjmR7luYjbVsNRdOqcp4uzfBimT7S9yYSJyZnnMFdZAT4fud8BIJrUn2cH+/2vHlGrCr35JKsdqXHrarOCW5huLs/zI0zFjdELh0RihNWVAq23dA3f2lrD08U0lTrJrVUBEt9QcZsk03VMt8Zcbo3t7i9zQK4KT3YkCt8atS5rsdd7XeqO16dmsGIVcWG7RJyCvBgPsmTxQw522KN2/8NTt75VMxsRcppC9txFGyLu7Oj0y6/tK2H/QNRz6V88vttbk4P0q4aHtNfv80vlmrF74HB6KT3P1PMMGRW6DUr3nHcHiKqxk0zFgNO8X7kDiby/COL37Ookzy7s8XjOAUY70W6GzhTCOETQszDkVA9BzwPLBJCzHNZ5DPddd8RZmq+HZID92g+dvWHtrveuwGP5Mcm9bDcMDbgmRE00UQTTTTRxHRwGdv/v0cb1KuycklnMDtL800Z9TFqVdlcLfHAeUnEOVAZsWlTNXKbLIfJk+A/WlAIyPHPJnemRecynSMvi/OJO7s44DNRcpdb7PWJMHd9cYSP3NXBJ+7twndcYz+iZgjmHeQUuYEbVe787AiVgs092dFtGsIAnBnt8LxB6s2jXvtDnshGp9Bond/I9OW+bvKp+7uRikR7S2A+IrFfgcpKiVChd/8KD+SSXuF9xKZXvN7LAavC6MwaKxVdp3JmrIPV9+VJnKTxvc9vBnfMmf2Pxn6+yAyVqKJ5Pc3zdD+WlCyb0DfXoeqeidGXW2fz1dbZfLt9Lk8U0gyZFTQEQaEw3wiwqy/El1tnY0vJ3oEIp0bavAtpV1+I32aG6drNIHaeDqpzvsel5B+OdtKt+djNzW1dVS4QVBTuyY0yX/cz393/eYafkyKtrKuUCLk5qfN0P7oQ3J4ZYrkv7EyOuANo7WOTz9PlvZuYb/gpSJtfpycbBJU22Iy+bKIaMHd/P/r/x955h0lWlun7/k6qnDpO9/TkADNDzhmRaABFzIqsuLKrqCuLrqsrioIBd3+uK2YUZcEEiwoKwqKgIoiCCIgDAzPDMLFzqK6ufM73++OcOlOpq3tmepiB/e7r4qKnusKpU6G/93uf93lw3WmvG9vBAiPAKeEk7brhd8E+Pfw8lhB06GZNJGZQaMQ0g4dyaU4I7Vzw6wg/GufjVeZP4Hby0rbNG+Kd3JUZRReCcW++84RQnHOibUhZ+wF9KJfmrswoSc1gmRXkgVyazw5vZqrK6OjwYJQvj27j2tFt7CgX+crYdn+GG9yNlHdsf9rvIr5t+9P8v5EtbCjlWBMIc/XQZn88YdwuM+aU0YXbmT49kuRET0J8SDDCO5LdSHbO3x7lFUMxzfA7sRUEkNB0jgvFfFk4uNLhR/MZPy8Y4O+T8wgI4Rfhm0sFPjG0iYJ0sIG3JLoIeEZKk7bN4/kMiar7XOt1+e+YHOETQ5v4Q5XJ1j+kenlVtI0LE910G5ZfKH9s6Dly0iEsNK7rWenet2OTdsqM2mWeK+WZchxeF++gy+tSa8B/9x5Y09387ng/Y3aZOzKjZB3bP18A70/10m1YSPA3tqYj49j8JjvB0cEYOcfGYWcxJ4QbvVRPl27OWMMkdIMJu3nh/bvsBJtKeR7ITfCzaQrkysal5v1cjRDCl+kDfKNJgT5QLvnnNak3xt01I+vY/N32dYC7efO/U2PcOD7AxrpRgnr2SfErhIgAZwI/qbr4C55BxhPAacBlAFLKvwE3A2uBu4BLvQ5xGXgfcDfwFHCzd91doseweM0sBs0H7VLDHMz+SrdhMVAnGVDzvgqFQqFowUBlE9r7f6U1s6vqq23ez/WXN1Ctykq1uwvNonT4QZOsxpRmYI4K7GdBmxD88bpJrnGWcvPfD5EddVh0XIDXf7LTn3ezpWTsvDLvuHke1qs1Hi5N4gCrT41w6geTOAHoWmoR7zH4WMfChsc774sdnH9tB8FOja2PFLj38+N+/EuF54p5ButMlKpnl39U1d3a8Du3eJy4vMzFt/fwys+1sfD0APN/GuDYV8dJLjBYfGUQM167ST3vKIu1oSwCanxHKpvZcU0nG925YD11S4J7psa4+xOjWBkN616vEFhU5veHptEPdq8Xvlzjwlu6+WjHQt+B+LWxdtp1k4fr3GZ/NTXGLybdzYlfT41xTChOTDc4J5IiKDS+2L2ME8MJ/pqf4qeTw3xvvJ/L2/t4NDfJ73MTvDLaxomhOHFdxxKCnHT4Uy7NJckeMp4LbEho3Dgx4HenTCE4wou3MhA8XshwQijOI/kMcc1gUynPSivESitMl25xTjRFjxFAINhYyjHfDPgGSD8zGxfr9lOSJWbQNzyy6wqG+Dfcgung8yMEYhrvTvX4XdItZbfzemYkVdM5/M3UBCnddXeu8EhukkHbLeT/MbXTiEwTgr95Kr16x+lew43S+d54vx/NU3nth+0SRwdjLLeCvknce1O9hDUdB9cx98lC1r/PE0JxjgrF+Nq8FawJRDgoEHaNqoQAJJ+oKry1uv+D+z4zEBwaiNZ0Pp8rufnAUU1HA0btMjd4cvmYpnPTxCBnR1N8pmsxgF+sLjWDHB2KcbGnJABX3vxsMUdJSkpVn69OwyLr2P6jnhxOsK6Q44J4J0d4xfSEV4BqQHfdGj2q6RwXivuztQAPe93HtGMT13QuTdWa0l2cnMdDuTRTjk0Jxy8k35+ajykE797xDABf6F7K6kAEDbd4XxMI80Q+48uohRANs7cxTWfS28R4suBGjlV4KDfJ/2ZGEcBZkRStELgNubcluggKjVPDCY71NlYsIXh5pLH7qQnhm3FNhyU03jlNV/fRfIZu3aRNN2s2DKoxcVUbW8oFHmniWH3lDLPMKd1AE65i5pwZzkEzDCH4x1QvBwcjbC0XGmbpq9knxa+UckpK2S6lnKi67EIp5cFSykOklOdVya+QUn5GSrlMSnmAlPKXVZffKaVc6f3uM7tzLLoQ/DY7PuP1fpEZ8Wdt9nfe19ZbM98rpWTRLO2/FQqFQvF/ktuBimPzRcBtVZe/w3N9Pg6Y8P4+3w2cJYRIeUZXZwF3e79LCyGO81ye31F1X9Ni+kWP5ncZq3l5JMni53f+HXv8lgz/e/EYk/02vYdZvPbLHaQN2zfAkuBnXxalw6BnHnRyOIEuBIcHo35X5uNVc70VdFOw/LQQA58vgoC1P59iQTrgFxwAHx3cWBPfAW6HpJ7+cpHtT7sb0NlV7uJ+zbkRuj9rsS6Z9Ueqlr0iRPcvLXKv3VnMrjorTEho9JkB/+/6Civkz1IeGYzx2lgHay9xC71Nf83z2FQGWdfAMd4r6LRMrE8LVt8YJnOujRkXXDm0yS84vjq6nfuy4zVzoOAuKgPeufpNdoL7vef4h1yagnT41vh2HsxNYAjXLbooJdd7hVtJSraXiyw0A/xmasI3Oso4Nt8a38EThSkcXOnvmFMm7XWBjwvF6TMDFKTD2xPdZByHm7zNhF9NjXHf1DjrilkM3AijW9LDBIRgqRnkr/kpVlgh/7U6Khlj8Ym1hUjv5QF+9+udr1XNpsYDOfofdN8vR7/TLSp+lB4koum83otV0RFcM7LFl+ifHE5gI/lddqLmPdBumKy0wlyU6KZTN2vclSubDMeFaiWgBwcjnBtrZ8QusdAroOK6s6mXBgAAIABJREFUwftTvfQYFl8e3UZI0/3n97vsOEutIMeGYrw8nOJt8S6u7FjMyyNJllkh1gQivLf/WW6fHGF7ucgnOxcRFhoHB2qlqSFN55Rwwv9cXJyYx4faF3B11xJ+kB5kqbmz4NYRbCjlcSTkHIffZMf9292SHiLvOGwq5bnN6xJWZn6fKExxZ2aUx6oUFIPlIpOOzZ/zmZqZzc2lPOdE23w59anhBPfnJripSk4c03TWBMKENb2hkbUyEKbPDPgmWlaVoVKfGeDoYIwH6j6v3xzfwe+zEzhI7sq4UvG/S3Tzw/RgwxwruIX7G+KdnBpOsqGU9z8nzTgpnCChG1zWNp/t5WKNiqRNN9heLpJ17IaZ+3pSusl72npZX8xxX3acP+Um2Vhyv/c0RFOV59OFrN/1bsV0MUsRzY0vOigQadhkqCCEoN8zKNOanIarhnYG+7yxiYHwedF2lpkhbk4PsaHJ34Bm1D9XQwgOC0Y5JZxsGN+oZl+7Pe8XrC1kG3b9XswMl0v+DhxACcnbZ+mcplAoFIqXNkKIHwJ/AA4QQmwVQrwL+DxwphDiWeAM798AdwIbcWMGrwPeCyClHAWuwh1Behj4tHcZ3nW+7d1mA+BvWs+EIURTc8YfjA7w6AM7F4t2CSb7bbpWmVzwtU6ssEZWOjzjyYEFcF7M7SYndIOIppN2ytzkSVyr5+00RE3xU0FKSXmJZPlpIewSDPykSFDULpuKLdYO706601zPr89T3CEhDqcdsFPquMgMcKAV5keeM+qtk8OsL+Y44oNReBnI+ZL5ZwS4onMRvYZFm9d5fGdini8PHbZL3DgxwClHuvebeMbgqG0xnDLQA6mDDNLHlvnrIVOUpOT4vgQHHxRhk5fPaiD8jqQmBH1GwJ8ZrvDqaDuf7VoCuNLlygL/0GCUkKbzZCGLLV3Z9LGhGKsCYbaWC7TpBvONAL+ZGucH6UEK0qGEJKEZvNVbk5wRSfHxjoUcGYyxwgr5nd8NxRxfHNlKSUr+6uXTfqitj6/NW8FRwZjfEZVIHO88jDtlFptBUrrJ77ITXOAVQ1FNZ9P5jeq3pTcHOTOS4q3xLp4oTDHhdYEf+Jnb2TJCgmSfe85Hbbcwr3T6l1hBTgwlfCnzfMPizfFOllshzqjqWh0filOQDiN2iZJ0+OLoznH4yrK93o/lT7k0HxzYwMGBCId6UtWvj23n2rHt/DY7QUDTeDg36RtrPVnIEhIaW0uue/P304NcO7aNdt3kR+lBrvPmbk0hGLXL3DQxSEjT6DMtTqxyXdah5rMX1DQezWf4N29z6J3JeeS82etzom0YCMpINOHKu8/2nndcMzghHGddIccfp+kSVpvajdglTo8kubVvNYkquetSM8i2UoHt3jm/2jOI+nM+4xsiLfVcv8tS8q26+eLhcokvjGypkQRf7XUfFxgBDgpE/M2WCiaCmK5jep/zrOMwaJfYVMpjCtHQkXyqkOXW9BBfG9tOQGiMtIgKCgudjGPz88wox4finFl1Xz2GRaduUkYyUyhp1rH5xOAmTCEYKLvmUJXXpSylb3hXTVE6/nhDKx4rNI51FKXD6+OdCCFIaDrfn79q2tv/LjvBAVao5rlVqGwUSSkbFDMAt02O8Eh+kv5ygfAsjhVcw7Pv9x44q+tWo4pfYKEZaJltJaXkX9sbZVH7K92Gxbqqdv9ThSw/bzHErlAoFIr/O0gp3yKl7JFSmlLKPinld6SUI1LK06WUK6SUZ1QKWc/l+VJPYXWwlPKRqvu5Xkq53Pvvu1WXPyKlPMi7zftksyDVFjSLuShuhB3fdxdMmQ+XeeuNXZxzVYo3fbuLYNxdyqQ0g7y3wJpybL4x5i6Gl5hBXhZOeKYv7nV/XBXFcVw41nQD3AEuSszjyLe7HbK1t2T5U3qSrd4s6RvjnZxTFx/yiY5F/s+PeAv8tb9yC/LtxxRY7+zsaCyxQjXxH+B2pwb0EnwKCjdJ4imdK4c2cWt62F+of2zoOT82JSQ0tpWL3NThdkXjmwxW3+oWU1tXF9h8bYGNn8qjCUGXYeIAG4t5ni5mEQiOCsW4O+MaJ50RSfLGeCfHVMlEAZ4qZv2i7bRI0pdz9hkBLCHoNSw6dMMvRA8LRlloBplvBlgdCPvdvHOibawv5phybG72urimENwwPsD2cpHjQ3HafLdcSU46RDWd50p5jgpGeUW0jQ8PbOCwYJTPdC6mS7d4T6qXE0NxDglEMBE8kp/ktbF2BPiv/73ZcUJHC7SjwVwi6DrXfYy2p0zMlwkeuybDvw4+x9pCFulItj/qvr6v/M7O17bXsDARfgczoelENZ1nvLXWj9JD/D6XJq7pNSZSj+YzbCzmuG9qvEY2DPCZriV8dngzH6tTHlTeoyeHE9wzVatMPDIY5XWxDgKitg+5vVzkgVyaRWaQo4JRtpUK3D45zGVtfXzck/W/MtrGmkCYxz157o8nhvy4JXCLw5urPhdfG9vO/dkJ8tLhFZE2rhja5Mt2/5hL81AujY5b7F6cnOebkp0dTbHAdN8blU2aSqRQn2GxJhCpMYLKSoc/5zP+a19hfSnPplK+Zn3+/lQvK6yQf79P5DN8cGBDU4OCoDd3Gq+S+17jOSnfNDHAU8Vsg8phdSDCe1O9RITG5W19bCsXuDMz6o891heQjvc+BVcy3Wo88vlSnr8Vsqwv5jg6FOP0qgIxrhus9iTpZ80QSVSSkhKubP9ob066cj400dys4dhQnCPrTKiaERZ6w3fhU4Wsr3ARQsyogt1WLvB0oVFyXMk1Tjt2U0d/w3PjnmcEfBf/mSgjedv2p2d13WpU8Ys75N7qRG8o5afdvdof0YVgTdXA/I5y0beKVygUCoVif+azI5sbLlsTDWMmBMYyOOeMNuYfHuDg86MEEzuXMYutIBd5s4RlpO/Yur1c5LvjA8Q03S9Wf1jVvTgkEMUBbhjvrzGL7C8XmXJsFhwdoHOlSWlUMv7rMl8Y2UJROvSZgZp8Tqg1cvmLN1+Yu98tWjtONdnWxH/jXd4x/2Oqh4uT8xi0S5wTSXFBvIOA0ChK6RvuVKjEKUU0nV7DwgkC3p/9oV+5hfHUsTY68LJIkkG7xLpijp9MDlGUDpemeglqGqsDYX82TiB4MJfme3UGUJtLeTYU3SLh/iqZ6LtTPbTpJld2LmKJFWLULnNHZoTbJ4fdbmo+w++yE1zRsZB3JXv8aBYbSUjofKl7GRuLeTaUcgzZRf57YsA3W4ppBh9q6yOhG4SFxvcmBtzCWTo8Ucjwn6Pb2FIuYAqBEK7zbodhYgrBl0e3cXgwSrjKdOeIUIye/7SYd5PF0f8Ww2rbuYBf/Isgryu2s8QKsv6+HGJAEOnWWL56Z2H48nCSmL6zJ/d0MUdE02rmNp8t5Mg6O422wM13nm8EmJIOOqKm2Lxq6Hk2l/LEtNpe33IrxGVt87ly6Hm/C3hlxyLOiaQQQF46HB6M+dmuN89fjY4gpRm06waP5DMsMoNoCBK6QZtu8o15K9hWKpK2bboNi4DQyEuHH6Qb5+uriWs6BwUiTPjP0z1veenwgbb5BL05+JvTQ/4s6K+mxnk0l+HtiW6+6RlEpb0CVhcCSwg+7c0CA5wWTjZdo54cThAUmi9tvbytj9/n0rw13uWfx4qMWRc0dGVDQuPSVK9vgAX4zyMnHZJe/nE1C80AXxrZRgnJTyaHfQOov0vOw0Zye6a2kRTXDPqMAK+LdXDTxEDLHHB35temzVOiROte92+O78CGmrinZuhCcHI4wXwzwKhd4uWRnRJfA9EwxwzuBtbgLOKDruhc1FDcri1kWR2YneHvFR0LGbXL9NuNnd0TPYfnIbvkR0JVs8wKMs+wOCkUJ6TN1P920ZpI0Wd3OwV/yKb5RROZQIXnivkXzbxvhbMiKSa9L5slZtAPBFcoFAqF4sVG5zKLv7+/h8tuW8BkovkC05aST3tzZSGh+cYvASEoSIec45D1FqfV8s7fZMfZWMzx2+wEd3vxMQDrizmG7BJCCI54q9s1kT9xZbglKfnOeD9/qyt+B6oWfZbQkFKS3+h2hoYOLjU1YanM/j2QTfONsR30l4tMOLa/9jgsGOX8WAdhb0F4aarXd8zVheAVlU5RVcrhof8cYfEZQd4U7+LYkCspPj2cJO84HBAI82AuTVE63JkZpeB1ru6bGmd9MVfjUg3uAn+p5RYbf8lneK5Y2y27ZMez/K0whSEEea/I+/zIZr8D9c2xHTyWz6AJwTkRN+N0e7nABwc2IISbkVuRk+veUqvHsPzZ2YuS8+jWLYKaRp9hkXcc1hWzLDeDvoPsj9NDlKTk4ECEbsMi6zj+8zgkEOHYUJyTIgm6DJNLR9Zz6p0Jtp+6s0idusTh4T9P8uy97uvzyKsyTImdUvib0oNICf/mdVElkt9kx6nUKX+fnEdI01hbmGJtYecLYXqZsJemeolrOudWdfVy0uHaecsbDNd04NlijoVmwDd2+mF6kC3lAhuKee7OjLGlnPcLrTduW8tyK8grom0kdIOjgzHelujinVWmUv/Y/ywbSjm2lAtc1bkYQ4imEU/XePJ2gG/3rOSqzsW8Kd7Jg7k0707OY7HX9W/TTW6aGKDsvcobSjm/DHkkN4mD5Jb0EO/0nHgf8z4nTxWyfGtsR03G7f3ZiZpzVn35ebF2DvXOwb1T4/wln+HDgxv9MYWlVoh/SPZgNJl11YXg1rps2wM9iflb4l0sMgP8uq6z/kAuzTPFLLpnnHZIMMJtfWv4r9Gmnn106Ia/OWAiGHOmV5GuCUR4RSTFZW19TX+vIxi1Szw0Q7MtqRscHoziSLhnaowfTAz63ysSuCsz1nCb54v5loV5he+O9zd8vg8NRvxIr5m4dXKYMbvcVLZcUWIsNYO8o4mxVqduEdN0vtLECXo6NHY6ie8KqvjF/ZJt5YZcRtbs1r0YeCw/xUPe4PqoXfZnhRQKhUKh2J85vcm82L1TYzyRz/CdsR3cN41JpS4EOc/pyWanGUpM0zkyFGXQLvmZn9VENJ11xRyf7lzMAVbY7/4O2iXf3GXVq8JYCcHwk2U6HzbREQSFViODtKVkqblzkfipzsWUchI7B44lOaUrwaWpxsjjigTwiUKGTsPkbYkuHshO8IC3CD42FHMX5N7zeX2803fPBfjS6FY+1NbH6JluZyf4aY3AmzQ0TbClVODzntRzxC5xWXsfOsI3v7GExj95cTeGEHQaJofVLSbPjqR4r9dNOjOSaoiq+Uj7AlZaIRaZQT7Y1sep3qbDMivEIcEIA7Zr7rOhmOOxQoaopvMqL0ZmlRXmn9rmc0woztmRlN+tHywX+ZPXOf/xxCC6gKRm8OnOxSw0g3QbFhnHaej7VKTSzxRzHO91mj7XtQRdCDp101/LLQmFeOYjOfqvL1Luk+hDgqffleVvt7nvj7FVtUXMe1O9GELwnfF+unSTQwNRjg3F/TzT+6bGuTg5jyNDMV4f32m8dGo4yUHBCI/mM26xW7Wwl0h+mRlriLYZskv8wpsLrRSX64o5/lbIIgQkdJ2NxTzbquYmJxybn0wOs6GY4+H8JP81uo376gq7itS4UpCuLWQb3H0/WWVKdO3oNq4Z2eLLsjeXCv5jHmCF6NItNFwZ7AorxLtT7oz7CeE4x3izzvUFV7/3ulZfntQN3tLEAOn4UJy7psZ8Y7C/FDIc4jVyqmNzKnP3zZpY9R3IipPyplIeB/x54gomgmNDcTTcTZlHcpN8avh50k4ZA8G13ctrrr+5VOAXmRF+PTXOcivUsg+ZlTZ3TY1xRV20VYW3J7opS1kjCW/GuF3my6Pb0MROv4LqBt26YuN3nCZEg19BM8JC82O1wB37fK6Yn3UnNiQ02nSDs1tIt+/Njjfd7Hi6mOWR3GSD+3krNCH49+5ls76+f7tdvsVLkF7TalncnhCKt3QN2x85IBDiae8DcHtmuCawW6FQKBSK/ZWEZjRk1Tu4stz6ucl6Dg+686pjdtl3crWERlwzPNOXxr+Fx4fibCkVmHDKtOsGnxnejJSS18c6/ELQDGkceLFbpBzyzQgl2+Hi5DzeXycxfKPnBgxwzfBmNv7R7cjkkw4/z4zyjfHGrkalSA8JnYjQ/RncCjeOD3DvVOtUiv8Y3Ur3P5tEb9BYfXaYnHS4Z2rML6yfLeYYtkv8bHKEUbuE8HJrz4gk+fKY29W6KNHNv3ct5cPtC2ru+56pMT7qFUBLzCCddRLVc6JtpHSTMbvMrekhMo7NmZEURelQlA6LzSA67vw0uDN/N04McHgwSkBo/GP/s6wrZNEQfkayWbVQl7jd57Cmc/GOZzgyGOUTHYv4Yd8q//qvirZhCsFQucTBgQg5x+baUfdcVzrmW0oF7vAKpIimI4FHuzPc+/9qz61MSCKrNN+BHOAN8U504XasB+0S28oFUprhxyndlB5kR7lI2JMTV7hlcogfTQzyh1y6YRV2YihRE+1TofK4Wen47uUVNARvjXeR0GtXdTnH4fF8hpRu0KGbBITGn/M7nXuPDsZ4S6KL96R6/BnVP+XTPJGvVS5UH/vjhSn6y0Vs4BWRNu6eGvMluY/lp9hYyqHhFoxvindxh+ctc2QwRlAIllhBTvJmfSszv5Ws6lVVBf+UV7jX84dcmrKUNcXQMaEYp3qO7eBK8m+aGPDfB/Xct+jQmn9XNh8eyE1QlLKhCDo+HOeUcAIhBJe29bKhlCNtlzk1nMQB/mlgfc31NYE/077UCrYsME2h8XSToq9CSTo44Oc/z4SB4KMdCzkkGPUbXBquVL2eN8Q7Z8wPBjggEK7p2g7aJb+WmA2rAmF+m51ocMEHfAO6zaU8oSbnyRTu9/v5s4if3VNU8Yv7h7Gvzt2wms83mT/a3+kzAr7joIRpvxgUCoVCodifuCMz0uCi/IZ4J4vNACusEB+vMpWqpyKBK0vpF5UCuDk9xJpAhNc2WVgVpcO92XFWWCHiusECM8C92XG+MLKlptRe85YwohcCOzRufdcwvzl/nM9euhm75F5r3C77zroAxQcd7rzcXQQmzzTcjXSzcSP9054D7Sc6F7HMCpLQdd7XNp8rO93naXpGMNNxrtdF7U0EuKtrjGuGt3BmJMVSM4gm4INt85mnWxSk5MGsG0l0bCjm57NWDIXuy47zb026UgUpfSfkOzIj9DdxagV3lvexfIa1hSnXYbmU55lCjjfFu7iqawnfHN/ub8T3GBbduskThQxpu4wlBL+c2rlgjmoa7/E2FpZaQX46OewXQXdNjXFRnclNUUpMbz70B+lBXhZJ+k7MFQwh/EWvzk732QWpAM71O8/voa+M8oHu+U3dcRcYbvE2VC7xbDHny+h/ueBgllshni3mWFdl9rMmEOG8WDvduokuhD/fDfCd8X56m6w9F5tBbpm/mp9MDvtdwI+0L+C9qV5OCMX57PBmvjfez/He87uuZyWWEBwXirM6ECGiaZwSTtBVJV9faYX4+OBzPJyb9IsQcJ2Tp6NbNzkoEOE9qV4/ErRSmOvCNdASQiCBjw0+50v+Pz+ymY2lPKeEk/xzuyvxrXRvhYDXRNt5WXhnFu0KK8Qyq7EBtcoKs9QM+iZSV3Uu5m+FLAdVjfEdH4rzkwVrAHh31bmdjupCOtDEWX7ULvEVbzPoyqHnfVO4bsP0Dd2q6dItTgzF+Wj7Av5WyLaUFkeE5mUPx5r+ftQuc9/UeM1cfTMCQvCR9gVoQvD77AQj5ZJfdAsh+EiT3PLrxnaweRbxQcutEMkqpei64uznfcF9zRaZwabttnGnjC0lMc2gu4mj/yorzGGBqD/HvTdRxa/H99MDfph1NbtoUrnfoAnB016E07/U7eIqFAqFQrG/EvWMYap5JDfpyytvrDNkqua74/2UpWS+afldWE24WbBPFqZ4ON+YY5lxbF4eTvrFzlsTXbTrJmNOuUZO2BcOctIb3A7W0KMlilsk1u80fnW126ktSMeXY/7xO2mCH9Oxi3DgG0M8eeEUZWRTl9OrPanplUOb+H02Tc5x+N54P1/xOpfnxTr4YvfSaZ9zSUqWmkEOCkSYcmw6DJM7M6O8NtbBZW19PFXIkpE2BwbCBDWN5VYIB7dY3VzK0+fNcW4rF5omX6y0QrzOO5fvTvZMq4QzESR1Ax3BbZMjONItdr4wsoXPDm/m4ECEQ4JuVFJJSu6aGmOJGeS8WHtNxA24TYnrvBnBiKZzZDCKKTTeGu/idbEOvzCucI83q31QIMLRwRh/LUw1dKi7DIuVVtgv2lKawRmRJJ/sXMSOhUWEl/pzx0kjfGTwOX8WuppKV64y35zyCrOKs3DasetuJ9lYyvOySBIDUZNvu8AMcEiwsbBI2zZv2LaWw4NRv2N6zcgWvja2ndszIwzYJdKO7Xed373jGeabAeKaTlzTGSiXWGgGuahqpvf76UGODcU5NBhlkdd9/WznkoYi4O6FB/s/3zj/QC6Id5B1bLLS4RMdi3yjooVmkP/1zrnmbSp06m5Bc33PAZwcTtTcb2Wj4bhQnBVWiMsGNvi/e7Iw1TQi6KlilgMCYV8FcMXQJv6US/PVKum4VuUo/aMqp+rpqGQBX9W5hJimM79u82F7uchw1bG8I9HN+bEOfjE52jTn10GScWxunBgkJLSmct4KlW7ydJ+feYbFxlLOd3KfDlNo/HBip1HZt8f7ayKb6t3DwT3/5iwUoFtKBe6q6tqeHEr4UvHZ8qOJQd8RvpoHs24u+MvCyZoCu0LGsXkkP1nzGdlbqOLXo103m374HPBnU15sDNpFHs5PziiXUigUCoVif+HCRDdxvVa6t66YZVu5yM3pIYbL0y8OE7pB2ikzUC753SaA96R6GbPL5Jp0ZqKaXuVm6y4ux+xyg/HTpF3mwdN3dmUKZ7gL+idvm8IpSyKazknhBIVJh4euc+d1D397lAUfDjCBTVhoO82pqsh6hUFRShaYAY4LxdlaLjDkddLG7BKfHZ5egXZnZpQVVognC1McHIjwlngXg+UiD+QmuGFigF9mRlloBFhphfh2zwGM2WW/u2QJjSVeMdShm/5MZTXzDItl3nVOjSQbImkqpHSDT3Yu5rWxDoJC49BghNO8Dt+kU2ZDMc/agju7HPKKxYRusMwKEdcM3l4397nIW0AvMoN8wJtLtpGkdKMhYqrC1nKBvHQYt8s4db0LR0p2lIv+zPJPFqzhoECEgNB4vDCF/jV4/Q0dbFnqnvdmpcLnRjZzRDDKcaE47brZ4Ni7JhDmhPDOjnO7bjJPt/hLPoONrOm0tukGj+QaN2OCmsYKK8RxobhfTFVmiw8LREh60T2V9/KH2/sYKhe5KT1IUncdn/+Qm+D9/bUS3XNj7aQ0w3dTv2ZkC1dXGVxBbZTPBVvX8pGB57hufAfdusnXxrb5M7I6okYVcV6sw5erzjcDDV3zpVWjhRFN93N6wXX2bqaISGkGY3aZ26ok0W+oGiuop37DrBllKRm1S3zbU2jUb4YZCH/T7MRQnI8PPcd/Twww7pQxhOBXCw+puf64bfPn/CRbywWWWSE/l3k6Pjm0iZsmmjtsr7RCvCvZ42+oTEfOsdns+RQtNAOUpDPjaONKK1TjEzAdSU2vmfn99PDzu1QoCiHoMEy69cbOruUpWL44uqXpbdOOzZ9yk00L47lGFb8eb4l3EdUaT8eQXWqQRbxYONAK84dsekbbdIVCoVAo9heeKebYVqqV1gaFhoFrKrWwhUfHa2MdhIQbQVMd7fFANk1QaA1mTeB2sa70pMcVJpyy38mqYAqNyZjN+OfK9J1t8YFPzSfUreGUYXhDCRuJNgr3XjNGMSPhMDj8Q1G2lYtIJOuLOX5eF5UCcJA3i3dY0JWs/q2Qpc8I+OZZTxSmGvJI64/17qkxnivmOTIUY9Qu0W6YrLLCxL1IlbVFN6vzvC1PIoAFRgAdwZmRFM959/26WAfnxxtl4Q/nJ33TrFaM2GU+0L/edQZO9bChmOfxwhRnR1KssiKsCYSJajo5x2HILvF3iW7yjsPVw5spSMefxwXXvOzbvQcA7uZExXDnx+mhpkVORcrrSLcIcpA8WlfYZKXDplKeU72u5LpCli+NbmO7F0108UE9hA51F/9/n5xXM3dczaP5DL/PTnBkKOobrFU4NhTn5NDOrudThSwP5dI8U8w1xLJMOnZTaXVE0/lWz0p+mRn1ZbRf7F6GBmwuF/jqvOV8qK3PVxm8MtpOh25ya99qilKyrVwkXFfEXOW9v6sdkcfsMte02FTRcDcbBHBEMMawXfYjtvrLxZoCxxKC71RJ/uup7gSutEKcVCVJN4VgSxPT2X/pWEC3YTLgbXZFhEZE0/nXPVAzPphNs6NcpCglwjNBq+bUcML/jlhoBvzZ+H9I9mBLyRmbn6i5fmXmt9ewkEhGmzTRZovm5Sy/coac32oOtMJ8oXspiarNwvqc7sr1ZlP8thsmZ0Xcx3dn9uUuj00uN0MNnz2AD7TNbzqPXMH0lD2vUzO/Lxw56fBkE7nC4/nMrHTy+yOnRpIcF4qzuMkshUKhUCgU+yMjdqkmMgjg0rb5LLNCnBfr4IImBVqFnGMzZJfQEcS1nR2EJwoZVgfCfgdtJo5ocj1TCA6wwkSO11l+dZjry/2kj3AXu8/ck+PXXxrnnleP8eTPsiCg62KTjaU8QsBRwRhdhtW0+D7Z644eHYyTcWxy0vblluAu1JY16YxVeJ/nIH1gIMx3xvu5PTPCKeEEb0908/p4J1/vWcHJ4QSHBCLkpENQaH5n/fF8hj963ccbJga4YnBTw/2/MtrGf/ceOOM5E0DaKTPpzfZ9b6IfR0pims5J4TiP5ad8M53hcomfTA4z4G1QBITWMiammmbL5994M6lRTWNTKc+hgWiDa7WOu1GQc2rlzEFN452JedycHvK7qY/np5r20k6PpDjOi4764cQgm4q1RVtE04lVda6WWkGOC8U5Ohigp5WcAAAgAElEQVRDAB+ocvveWiqwpMX6bFMpX9NZvjTVy1IzyNu2P01OOjXvUSEEbbpJSTq8LJzg6GDM75YD/Lu3eXF2tI0vee64BwRCLWdUXx1t56JEN9f3HsAvp0ZJaYbfddbFzqxpgB9MDDaVBVf4bdUca68Z4KqqjvN07ZnjQnEsofFWT2p+SaqHx/OZaeXN3+pZMe3jV4hoGlOOzXLvvNc//8cLU3zdk1X/KD3EMaEYp4QTPDWN6dN8I8CFiW7emZxHQUoGp5mHr3ByOFEjR6/nt9lxftrE/KuakKbzsXZ3rreM5NL+9TUqh1VWo7HVDRP9fjxUKyyhsdEzWcs4NqdFkjPcopF23e3Y1/PzzCgD5SLnRZt/fy+3Qqy0Qk3j4OYaVfxW0Uxnvq1caJgJeDFxS3qIY4LNh+sVCoVCodjfSGlGg2LpP0a2MGqXeSKf4Ybx6Wd+t5eLbCjmWWmFeHVVByUs9AYDq1YsNIO8pc51VReCE8Jxf7500C5hnuwuo/7wjTSbvptHFmH5aUHeckMXrziljfmGxSuj7SyzQoTqnIArVBbb143voCzdTueXRrdx9bA7C3xhopv3tzVGJFW4Z2qMT3cu5vRIim7dpMew+Orodn+x+53xfu7PThDVdE4OJYhoOkXpWv84wBlVC1y7yRmyhNYwP9uMsKZxWtiVRc83A3xl3gpOCCf4tWceFtE0wppORNNZboVIOzarrBCf8XJnZ8M3561o2vmvjK216W7He10xR0+daq9dN3l1tJ0NJXdxndANrupczDIzxFPFKdYVs2wuFTgpFOfh/GRNcVchJDRModFrBjgsGOWAQOskEA1BTjq06QaaEL6zNrgz4q3kqieHEhxcJaO9dmy7P3OrVzljV+NI+E12gvlmoMbcrTIT+mB2gh+mXdnt2ZE2DmjhAHxEMOrKVEfcKK3L2/t8t/RlZqimkx3VdI4I7XreKrhS5FZxnM8Wc0w5Nv85uo2BconMNJskK5oUffVclJzHKivMKd6GU1/dbKpd9b0TFBoLjAATTtl3aV5e994btUv8MjPKZ4Y3ExJa05n+apaYIR6vywav5gAr3HRzp/4YK9L1inKg+p36vYn+htsUpWyqMmjG73PuRkXathtM42bD2xLdTRUkE3aZ7eUikSYqW3DjzT41/HzLTZS5QhW/HvMNq2bmp8IFsU4/3P3FyF8KmZpBeIVCoVAo9mdOjST9iJQKBemQdWweyqWbxhVVSOoGE06ZR/KT3Da5U2J8RecibGSNgdXu8I2xHRzv5bsut4Kc/7IOjODO+zzyPVHOv7aTviMCdBkWf8pP8vPJEZ7IT7GtXCAyzQK0Yq6Z1N3uWlzXmfD+dv8wPcSnhjdNe0xPF7O06wZRTWexGeQfk73kq8y3KkfXbVi8JtZOwYvQEbhzuiNld+1zRDDKhS26UjNhCMHxXpez5nIEZW/xXZIOJenwcH6SNt1gwrH5mfc6fX3ezJ27ywc3Nu2uVYrICadMWNMYLBd9Q6gKOelww0S/XwTENJ1bvGimh3KT/Dmf4crh5znRe+/Vy5QBbk0P+wtnDdHQRa5n0rEZtkt+Z7qaU8NJRlqYGx0ejDY0ZT7SsZDXRNubuiODuwHxsrrPTjU2+J3+GycGOLJFc+QrY9v51vgOHi9M8apYOx8f2uSbMY3apZpu6CWpHtq06QvYU1ocU9axm3YKK5SkG+ME8HfJbm7pWzPtdWfiT7k0N6eH+O1URSlQW2qmdINPem7yFya6+XF6iPuzEwjcgulVsVoPoJI3Rw5uxNBlnrv1dNyVGeXuJjFAFS5KdPuGZNNRrtuUMREzbh69yYvq2hVunBjYLefloKY1LbR7DIvNpQLrpunsCm+OvGMWG217iip+PUKazuVtjW/aH6YHp537eDHwq4WHvCDD4wqFQqFQzAVrC1P8oM7R+aBABFNoJHSDo6aJCgFXXnxutJ2yrC10Pzm0iVfPkXnlYjPAPVNjXBDrZMIss/g1AYQGqaMMoq+qXUw/lE2TkzavirXxL+0L+PK85Q33V5lXfWO8k3OiKV4b6+CkUMK/fLhcqulI1VOUkse9vNailNyeGeGcaBua9/wP9Dpi90yN8aHBjRhCcEQw6s7+mgG/G53UjD2KGck6DpcPbuSBXG1Uy9sSXbw71UNJOlhCoywlJ4XifLtnJVFN902HZpNDmnHshrgZgP+a50p5TQR/zmdoN8ymmySLzSCne53uHV6BXLm/KzsW8ZnOxZwRSXFr3+qmC2RLCC72InUSujGjkkDDnZ0seOe1+ohuz4z4GxTN+PLYtpr7rzgoHxuK18QYNT5m4/P+n/mrAdfUrLJRIIBrR7c1XLfCf3Qv5X2pXuZXPVblXOWlrFEM3Ds1zn1NCvwKrd5XPabVMm50TSDC1nKBNYFwjRPz7pBxHJ4vF/yi94ueBLzCfCPgz+U/W8wx6di06SbvSfXiAP9Vd74sodGuG5wWTiKE4Kfp1pLlqKYz1aIh9WAuXWMG1gyBq46p8L+LDqkpft+RqI18eqqQbbm5UE8lem3SsRtc2PeE0yNJDCF89UI9IU3jokQ33TMYfs0FL96qbi9wxdCmmmgjKSWbXqTzvhX2dJdboVAoFIoXkrBojDo6JBBlgRngkmQPiRamKWN2mf+eGKDbsGocZreWCg3mNrvDBbEO1hfz7CgXuX68ny3lAj0fCvDPj/bRfa1Fuat2ke/OdAq6dIuQpjctWtKOjY1rImQJjYDmFvmVQuPAQIjTW8SNXJLs8Qu9vxQyPFPM1izuTo0kOT/W7ndkDQQjdgkJbCsV/Q7e/dkJbmoRIzUTldXGoYFa+euj+Qwbijn+X/cyeg0LIQS/z6VJaIY/A7wrNCsYK1mkAW1nV7d+kW0KwaZS3ndQrhxvSNM4I5LkuxP9fGFkC3dnRrlg69qmj31COMGQZ8D0q6kxniu1nk/s0F3n24qJz6VVEU2rrTDzzemL2JRm1JhKVYqCjw0919RQyH0uOld0NuZgt3vdtEVmgBvnu/Pbp0WSLQ2I2nSTl0eSfL7Ljdk6L9ru57NaQtQkiTycm2wpVv1DLj3t7/KO02AcVs0BgTAXxDoRsMcePJ26yWorzMpp4oY2l/M86nWZN5RyvC7WwcvDyRoztmradYPzYx20e0XiTDFF58ba+Y5n5NaMXsMiPcPse0hoXJLqmfb3z1R15IvS4brxHQ3z7634zng/k3aZ97R4jN3hjswoKd3w86nrCQutZjZ8b6KK3yqide54WemwooXJhEKhUCgUirklpRu+03GF7030M1Qucs3IlpZzdRFNY3MpT1zT6a6Sz+lCsLnU6Ci7q4zaZbaUCnToBgGh+ZJezRDoolFGeUmyh9PCyZpjqef+7AQl6fDbqs7Z4/kMX/G6TAcFIn73thm3TA5xtjfffGQwynIz1LBYDwmdmKbzkfYFaELwfKmAwC1izveknEKIpnOusyWi6bwn1esXWhWeL+X5RWaUuG4gvFzW40IxNCFYZoX4+YKDZv0YN/UeSKpFN8pAcEgggpQ05KmaCF4eTvmz0N2GxTVdS4hqOv/U1sfzpQITju130ZrN1P7n6FZWePf72lgHh8/gqXJSOME/t/f5BfNXqjJq1xazLWXTx4fjNe+n/6kyQmrW3Z0NTxWyvGXbU4DrXv7yGQyN/pzP8O4dzwDuzHTeO95ew+LoKgVGn2lxXguX3l+0eI3PirZxSV1ucz0fH3oOgeCS5J4VZMeEYiy3Qr57eD1mVVkUFBpPFDLckRlh1C6hAZfVzd6P2CW+PrbDf21majj1l4t8oC6CqpoL4p28sS7yq56sdLimhfv6H6vis/rLRd4c79ylDq4tJb/LTsz5yKQlNH6cHmzqewCu38CmUn6X3aV3B1X8VnFSOFEjzTAQXJjc/fkXhUKhUCgUu0ZM01ls1cogHSlxgC2lAuEW3aqg0OgzAjyWn+KvVTOfS81gS2fd2XLP1BjzDJPlVogTw3FeFW3z55NfEWnjkLoOS9qxeeO2tS1VZLpwZzHrn0fJK0SvH+/nxhYd2bWFKdZ53UwNwaVtjYXEU4UsbbpZk48rhCClG/wh6y6Wz4u286XuRln2bDHEzozUakJCrzF2qszYgpvffO6WJ2f9GG/f/nRLCacmBF/sXsbjhQz/08QV+LsT/SQ8yWjaLvNxz906qun8euEhnBNJtfR5ub7nAOJeIbHYDLQ0agK3GMpLh/tzjR2tU8IJX5rejJKU9DeZb76qczFH7kInr/54KjyRz7Bhhk5qUTp+sfLdiX7fG2fALtUUWR9qX8DaFkZOs4nZaUW7bpJ1bP/c7y5/LUzx4cGN00qP23WDD3kjkG+OdzHp2Ew6NovMIEKIJgW+u2FUOasXzBDTM2qXWsaWzYag0PhcXT5zNRKJlJKnClmGyyWO2UXTqpVWmIdyaUJzPPK5KhBiwranNbyaKat4LlHFbxUHByI1DpP3Zsd5MDu9VEOhUCgUCsXcInALvmreGO8kpGm8Mto2rWwO3ILu3Fg7ZSR61WLqklQPy2YwkpkNhhBckurlrswYYU3jgVyaBzyp3vXj/TxbF4lS6bi06tT9U1sfVw8/zyc6dspVTwkn+HbPSgAub+/jXzumzzZdbu2MrHk4P8kVQ5v4YF2Hanu5gF51CId4MmFDCD9W6q+FKa5rkdW6u1yY6OJrdTE0x3pdw111dn1Xcp4f0zQdujfTXG/mJITg0ECEZV7ndswp+xsM4BbOx4XiJDVz2min6g2UY0LxGc2JAOyqZnp1vu3vshMtzdvumRqrMTd6v9cdPSmcoLPFzG8rFhgBjqoqnO/JjLW8/nwjwFlVkvtKV1xH8KqqjZSPDT7Hc8W9NyZ4eiTF2/fAjK1CpaCbrhjXhPDneu/IuEZ1i80ga6aZRw9rGseG4vyDJxGeTh5dIakZM26YzITuvU+n4zOdS9z548nhlpno03FCOI4uBEunkYbvLjHNoMewpvVREkLw+RZF/Vyiit8q+stF7pra+UWwtVRosEFXKBQKhUKx92gme9taLpLQDHpNi9EZzFv+c3QrRwWjNTExbbo5J3K6D7bN57qxHTxbzPJEfoqidMh6UtCClE3nUc+LttPVQvb8s8lh/pibrCk8Y7rhF2kp3STVYl55hRWqkTUWpfTjmCocEYyxoGo9U/Q2+qXcGZOytVSYMapld6hInSsYCL9r2K4bvox4Nrw90T2ryJac4zR1wB2yS34X3hKi4bGvHH6eDaUc79j+9KyPaSZMITjHKyB7qoydjgpGWz6XNYEwC6qu/709mMeupmL8dmYkNWPu9QGBMB/tcDNlP9GxiHneDHJY02oKvSnHnnWUzu5woBXiwRZzw7OlyzD5SPuCaWed847jb4iENZ0Pts/ngECYZ6dxKDaFIK7pvpJhe7n1aMWhwShfm4Wr+Z7wzfEdpO0yOvizyLvCn/OTHBqIzjp+bLasL+aa5qdXc+xuRCvtDqr4raLXsNhWNRN0UCBS43KnUCgUCoVi71MfuXN/doJRu8S/j2ylNM3MWIWQ0Hm2mGNiD51hm/GtsR3EdZ0TwwmCQqNTN/1OzoGBkC+preay9j7aWhSvG7yF9dZSo8R1NmwrFf1O7morzJpAuEEa3K4blKs6kE97HerqAjCm676h0d7EFMJfWwWExntnmPfcHYpSNl28B4Xmb1D0GAHfubnm+BB+l3UuiGs6A54R0i2TO6XYq2Zwt15hhShUvdfrTeB2hzGnzJVefvSOcrFlB7GeGyb6fUOngNBqDMVeHWvfq2OCuhANGzq7Q0TT+WVmdFq5ebUbcVIzuGlikOvGdvhxRvVkHYfbJkd8yfdM53NHucibvZnrvYVrIObwofYFu7XhFxQad0217mDvDhrwjb2gLNkdVPFbRUw3eHOidl5luqF4hUKhUCgUe4f1TSWU7kIuNsPf5fNi7Wwo5hnZC8XvkF1isRkkqRmcEk6wzAr5xkoLzeBuzTZqCE4IxVm0m0qzh/OTfjHbphssMYMNqrXfZieausgKIfi419k7N9q+17tSAHnp+MXE+mKOywY2zPljbCrl+VOu0RF5UylP0nuNnilk+ejgczW//2THIlZaIV7XZHZ5d8k4tt9Rr0QOAVw8g3nTQ9lJpqpckO9bdOgeH0v1u3PQLvHdif5pr1vP86WCP/+bl06Ns/ECI8D356gz3QxLaNy98OA9vh9H0pD/XE1U0/259YVmgDbN4MBAeFq3ZA1wkL6cOt4i6xjczvM/7KFp10ystEJouCZhu8MxoTjf2AvfA92G1TLv+YVEFb91/MXLypNSclumdV6XQqFQKBSKueehXLomevATHYuIaK7RS88MRWJU0xm0i3Mu2wO3W3dMKMb3JtyYoycKU9zruTTfNDFQ4xsyW96T6sUQws9x3VWODcb8bubvc2l+k53gvLpM42/2rOCYqhnYajltZS5QF2KvnLNmVGTQxT3IFW7FieE4p4UbnYwDXpQUwMpAmM921s4Y3pUZnZMOazWaEES816feCbsV/XaxZl54LlhgBrncM3QKC22X8l8DQvhFng41eaz/Nbptt977u8JcyKoDM7y/JfA1z5F7fTHH5nKBPiNAcJrHjmg6Fyfn8S/t7kz+TOfTEhpvTrR2c95TTgon0IUgJHa/ebc3HJdDQuPEF0jWPBOq+K3j2WKWtF1m1C7Tpu39oGWFQqFQKBS1WEJQqFpMf2NsOxqCLwxvmXGRvbVUYE0gskvZlrPlZeEkP58cYcQuM+U4GAhs73iKUrY0MJqODw1uYGupwKc8OequUkLWmOj8JZ/hT3U5sCFNr1nQduxCETbXVJ+jZVaQf/aKsblkoFzyTcCqWWgG/PdPVNM5Ply7GP9jfrIm8nIusITgqFDrSKRmnBVJ0TPHMvRxu+xHaq0JRHj9DO7E1Xy0fSFJ731mCQ27yowrrutz4qa+txFC8L0WObs6+G7PCd3g6GCMEbs0rc9AWUr+c2Qrd3uS7CdbdJVfKO7KjPJkYWq/6bJW6DUDnFVlkrYvUcVvHQuNIIN2iYRu8O5U4yyIQqFQKBSKvcsVHYsIVkViDNolstJmzCnP2J3sNEy+Prad9dOY1OwJXxvbThnJB9rmE9V0FpkBv8i+MNHdMrpmOo4Ixjg31k6XvnuFzvOlvC+3XmGFOCIYJTWD/HJvGFvNlrKUfhevy7A4N9Y+wy12nVG71HROc10xN2MHsTTHHUwdwZ9343ybQtTM/M4FNpJHvGMZd8rTZq4248rh5xnypM4aMFxVEL4j0c1qKzLNLfcvWjl0CyF4lfd+DAuN9cUcv5oaY7RK4l2Ng+SxwhQHezP3b93LXd3ZENI00rbNyjl2a34psc+KXyHEJiHEX4UQjwkhHvEuaxNC3COEeNb7f8q7XAghviyEWC+EeEIIcUTV/VzkXf9ZIcRFe3pcFya7WW6FeCiXnnbAXaFQKBQKxd7j5vQQm6vyMHdlHnalFeLMSArJ3Msw5xkWxwTjrC/mWBUIIxDkPbfnZ+pijmbLB9rmc3gwygm7KQkcscv+MYAr7Z03Q8dwX5p5hjSd77bovs0FEpoWjguMQMvNk18tPMQ3D5srbGTTLvRM3JEZJTfHxW+1G3lROvxihmieeiobA0KIGifhh3KTPNgky/jFjCYEWelwfqyjxjm+mkpUV6/3edq+H9QNhwajjNll/lbYve+j/wvs687vaVLKw6SUR3n//lfg11LKFcCvvX8DvAJY4f13CfB1cItl4JPAscAxwCcrBfPu8kwhy00TA6wtTDWNLFAoFAqFQrF3iWs6GzzTq6J0eE2sg7hmcFXn4hlvayD41dQYsRm6n7vDEcEoYU3jzswom0t5yuyMFXpoD6JYtpUKrNvN4vnUcMJfrzxbzPFEPlMTfdQMfRfzdeea6F42E51nWDXRThW2zBBFowsx5/OOOoJVVmtn52bcPH8VC425jdtMaDrX97gbD6sDEX6x4KBZ3/aoYLTGzbzaUO6eqbHdMnvbn1liBvlc1xKOCsWmlZ8HNY37Fh26V2ZkdxcDwZZyYZ+ONuzv7G/V3WuAG7yfbwBeW3X5f0uXh4CkEKIHOBu4R0o5KqUcA+4BztmTA+g2LLaUCjjs3MlRKBQKhULxwnF0KEbR63r9aGKIdYUseelwxdCmGW8rhKDLsGrmYOeKKcf23WKDQqNbNxnwuj1iDwpKQwge2M3i+dRw0p+jXWwGG+ZYmzEyjYzzpUJY02hvEi913G7M3u4pGpDajfdip2HNeVE16dhcvGOd/299F+7/9EiKUNUoQvW5XGQGfOnvSwVdCOYZFocFoy1ztqvp3w86v9vLRRKarmTPLdiXxa8E/lcI8WchxCXeZd1SykoIVD9QCQ2bD2ypuu1W77LpLt9t4prOCivEhYluFXOkUCgUCsU+oMuwWGoFGS6XWFfMcko4sUtuss8WcwyX577Aq4xE/X1yHlHPROpjXlTQ1bPoSk/HYjO427Ln0yJJv0gyheDfOhbNeJuzonskktvv2Vwq8FQT2eeuuBvPFUIIHtwDVcBcsivFbj3XjGzxN3rANZar8HcJ5ZEDrgpjXxMSGvdlx5V6tQX78sycJKU8AlfSfKkQ4pTqX0o342BOBnaEEJcIIR4RQjwyNDQ003U5OBDhqt10XVQoFAqFQrHn/DLjGs18rGMhQgg0YE1gdvLR08PJ3TKfmokjgjFODye5NT3sx5/cNTVKf7nIv49smeHW09Omm3ygbY/27gG36J8NH9gLDsv7E2+Nd3F5e+Nz/EbPyn1wNPsPlhAYu6lQ+G7PASyuMou6cf4q/+dPDT+/W3PNLzWaSe1faFYGQvQY1gsWW/ZiZJ8Vv1LKbd7/B4Gf4s7sDnhyZrz/D3pX3wYsqLp5n3fZdJfXP9a3pJRHSSmP6uycObj8q2PbCe9BPpZCoVAoFIo94+hgjP+ZHPZnCeO6wVfmrZjVbU8MJ0juhZnfXsOihKQgHb+ECAmdv+QzjO1BRI4hBN1zMGr1rqTqwIFrVrQnXc65Zn/oCAKYQuOeRYfs1m0XW8GW5zSs1JIsNIN8dd7yfXoMk7bNk8rsqiX7pPgVQkSEELHKz8BZwJPA7UDFsfki4Dbv59uBd3iuz8cBE548+m7gLCFEyjO6Osu7bI+Yb1gcOMvdZYVCoVAoFHPP8eG4LyneVU6LJGuikuaKxwsZHstnuGPhwb7UeFUgzPpijnfuB9LPtye6Z76S4gXnyj2QxL8YOC/avldm7F+MrN7Hs89CwCv3kzzd/ZV99U7tBn7q/eEwgB9IKe8SQjwM3CyEeBfwPPBG7/p3Aq8E1gNZ4J0AUspRIcRVwMPe9T4tpdw13/YmXJjofsm51ikUCoVCodgzXh/rbJjHWm6FeP8cSJYVihcrlzWRmCv2DSGhMbEPZttfTOyT4ldKuRE4tMnlI8DpTS6XwKXT3Nf1wPVzeXy9+4FmX6FQKBSKFytCiHOA/wJ04NtSys/v40OaE9T6QKFQ7M+ENJ2ru5bs68PYr1FWYAqFQqFQKOYMIYQOfBXX0HI18BYhxOp9e1QKhUKhUKjiV6FQKBQKxdxyDLBeSrlRSlkEfgS8Zh8fk0KhUCgUqvhVKBQKhUIxp8wHqnN/tnqXKRQKhUKxT1HFr0KhUCgUihccIcQlQohHhBCPDA0N7evDUSgUCsX/AVTxq1AoFAqFYi7ZBiyo+nefd1kNUspvSSmPklIe1dnZ+YIdnEKhUCj+76KKX4VCoVAoFHPJw8AKIcQSIYQFvBm4fR8fk0KhUCgU+yznV6FQKBQKxUsQKWVZCPE+4G7cqKPrpZR/28eHpVAoFAqFKn4VCoVCoVDMLVLKO4E79/VxKBQKhUJRjZI9KxQKhUKhUCgUCoXiJY8qfhUKhUKhUCgUCoVC8ZJHFb8KhUKhUCgUCoVCoXjJI6SU+/oYXlCEEJPAun19HHNEBzC8rw9iDnipPA9Qz2V/5KXyPEA9l/2VA6SUsX19EC9mhPj/7d17jFxlGcfx789uK4QqtVCbaglt1FBrTZeLhlpCtEaDYijGKm3UNNpETdRgglrrXxggyh8IVSOJcrECptRio2lItYHGxMSAbVnEXvBGCa29hYuKEpD28Y/zTjLZ3dnunmn2zPvO75Nsds45s8nznGfyzvPOvOesjgNPNx1HByW9Vrvh8+BzAD4HLT4PvX8Ozo+IEf9Hrx9vePVkRFzSdBCng6SdJeRSSh7gXHpRKXmAc+lVknY2HUPuRmtQekVJr9Vu+Dz4HIDPQYvPQ77nwMuezczMzMzMrHie/JqZmZmZmVnx+nHy+6OmAziNSsmllDzAufSiUvIA59KrSsrFRnJ9Kz4PPgfgc9Di85DpOei7G16ZmZmZmZlZ/+nHb37NzMzMzMyszxQ7+ZV0nqQdkvZK2iPp2rR/pqTtkv6Sfr+h6VhPZYxcrpd0SNJQ+vlw07GeiqQzJD0q6fGUy7fS/vmSHpH0V0n3S5rWdKxjGSOPn0h6qq0mg03HOl6Spkh6TNLWtJ1VTdqNkkuWdZF0QNITKeadaV+OY9hoeWQ3fgFImiFps6T9kvZJWpJjTWx0JfUOdZXUc9RVSq/SrRJ7nbpK6pHqKqW3KnbyC7wKXBcRC4FLgS9KWgh8A3goIt4GPJS2e12nXABujYjB9PNgcyGO28vAsohYDAwCV0i6FLiZKpe3As8DaxqMcTw65QHwtbaaDDUX4oRdC+xr286tJu2G5wL51uV9KebWvxPIcQyDkXlAfuMXwHpgW0QsABZTvc5yrYmNVFLvUFdJPUddpfQq3Sqx16mrpB6priJ6q2InvxFxOCJ2p8f/pirWm4HlwIb0tA3A1c1EOH5j5JKdqLyYNqemnwCWAZvT/p6vyxh5ZEnSXOBK4I60LTKrScvwXAqU3RhWCklnA5cDdwJExCsR8QKuSTFK6h3qKqnnqKuUXqVbpfU6dZXUI9VVUm9V7OS3naR5wIXAI8DsiDicDlGisMMAAAWrSURBVB0BZjcUVi3DcgH4kqQ/Srorl2VYadnEEHAM2A78DXghIl5NTzlIBm+0w/OIiFZNbko1uVXSaxsMcSJuA74OnEzb55BhTZLhubTkWJcAfiNpl6TPpX05jmGj5QH5jV/zgePA3Wnp1x2SziLPmtgplNQ71FVCz1FXKb1KtwrrdeoqqUeqq5jeqvjJr6TpwAPAVyLiX+3HorrVdTafYI2Sy+3AW6iWohwGbmkwvHGLiBMRMQjMBd4NLGg4pFqG5yFpEbCOKp93ATOBtQ2GOC6SPgIci4hdTcfSrTFyya4uyWURcRHwIaqlh5e3H8xoDBstjxzHrwHgIuD2iLgQ+A/Dlr9mVBMbQ0m9Q12l9Bx1ldKrdKuUXqeuknqkukrrrYqe/EqaSjVw3xcRv0i7j0qak47Pofokq+eNlktEHE2D0kngx1SDczbScsEdwBJghqSBdGgucKixwCaoLY8r0nKxiIiXgbvJoyZLgaskHQA2Ui3lWU+eNRmRi6R7M60LEXEo/T4GbKGKO7sxbLQ8Mh2/DgIH27752Ew1Gc6uJtZZSb1DXSX2HHWV0qt0q4Bep66SeqS6iuqtip38pvX4dwL7IuK7bYd+BaxOj1cDv5zs2CaqUy6tN+Lko8CfJju2iZI0S9KM9PhM4ANU1xPtAFakp/V8XTrksb+tORLV9R89X5OIWBcRcyNiHrASeDgiPklmNYGOuXwqx7pIOkvS61qPgQ9SxZ3VGNYpjxzHr4g4Ajwj6YK06/3AXjKriXVWUu9QV0k9R12l9CrdKqnXqaukHqmuknorqJZwlWop8GngiXStAsA3ge8AmyStAZ4GPtFQfBPRKZdVqm4rHsAB4PPNhDchc4ANkqZQffiyKSK2StoLbJR0I/AY6YYyPaxTHg9LmgUIGAK+0GSQXVpLXjUZy30Z1mU2sKV6T2EA+FlEbJP0B/IawzrlcU+G4xfAl6leT9OAvwOfIY0BGdXEOiupd6irpJ6jrlJ6lW71Q69TV0k9Ul059laounTFzMzMzMzMrFzFLns2MzMzMzMza/Hk18zMzMzMzIrnya+ZmZmZmZkVz5NfMzMzMzMzK54nv2ZmZmZmZlY8T37N+pSkqyWFpAVNx2JmZmYg6YSkIUmPS9ot6T1p/7z0nn1j23PPlfQ/ST9I29dL+mpTsZvlwJNfs/61Cvhd+m1mZmbNeykiBiNiMbAO+HbbsaeAK9u2Pw7smczgzHLnya9ZH5I0HbgMWAOsTPteI+mHkvZL2i7pQUkr0rGLJf1W0i5Jv5Y0p8HwzczM+sHrgefbtv8L7JN0Sdq+Btg06VGZZWyg6QDMrBHLgW0R8WdJz0q6GJgPzAMWAm8E9gF3SZoKfB9YHhHHJV0D3AR8tpnQzczMinWmpCHgDGAOsGzY8Y3ASklHgRPAP4A3TW6IZvny5NesP60C1qfHG9P2APDziDgJHJG0Ix2/AFgEbJcEMAU4PLnhmpmZ9YWXImIQQNIS4KeSFrUd3wbcABwF7m8gPrOsefJr1mckzaT6JPmdkoJqMhvAlk5/AuyJiCWTFKKZmVnfi4jfSzoXmNW27xVJu4DrqFZqXdVUfGY58jW/Zv1nBXBPRJwfEfMi4jyqm2g8B3wsXfs7G3hvev6TwKz0CTSSpkp6RxOBm5mZ9Yv03ximAM8OO3QLsDYinpv8qMzy5m9+zfrPKuDmYfseAN4OHAT2As8Au4F/pk+ZVwDfk3Q21bhxG77DpJmZ2enWuuYXqpVXqyPiRLrsCICI2IPfg81qUUQ0HYOZ9QhJ0yPiRUnnAI8CSyPiSNNxmZmZmZl1y9/8mlm7rZJmANOAGzzxNTMzM7NS+JtfMzMzMzMzK55veGVmZmZmZmbF8+TXzMzMzMzMiufJr5mZmZmZmRXPk18zMzMzMzMrnie/ZmZmZmZmVjxPfs3MzMzMzKx4/wfR7rdejb4SfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzqTQjegJloh"
      },
      "source": [
        "- (f) Finir de préparer les données.\n",
        "> Cette partie regroupe :\n",
        ">> - L'encodage des variables\n",
        ">>\n",
        ">>\n",
        ">> - la séparation des variables explicatives de la variable cible\n",
        ">>\n",
        ">>\n",
        ">> - La création d'un jeu de test et d'entraînement\n",
        ">>\n",
        ">>\n",
        ">> - Le scaling de ```X_train```, ```X_test``` **et de ```y_train```, ```y_test```**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yX0FL4s0JlDn"
      },
      "source": [
        "df['sex'] = df['sex'].replace({'female' : 0, 'male' : 1})\n",
        "df['smoker'] = df['smoker'].replace({'no' : 0, 'yes' : 1})\n",
        "df = pd.get_dummies(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "oR3IIFBNJ2YS",
        "outputId": "c901f9df-4e35-4d17-cbb4-6cee709ca671"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoker</th>\n",
              "      <th>charges</th>\n",
              "      <th>children_0</th>\n",
              "      <th>children_1</th>\n",
              "      <th>children_2</th>\n",
              "      <th>children_3</th>\n",
              "      <th>children_4</th>\n",
              "      <th>children_5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>27.900</td>\n",
              "      <td>1</td>\n",
              "      <td>16884.92400</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>33.770</td>\n",
              "      <td>0</td>\n",
              "      <td>1725.55230</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>33.000</td>\n",
              "      <td>0</td>\n",
              "      <td>4449.46200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>21984.47061</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>3866.85520</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex     bmi  smoker  ...  children_2  children_3  children_4  children_5\n",
              "0   19    0  27.900       1  ...           0           0           0           0\n",
              "1   18    1  33.770       0  ...           0           0           0           0\n",
              "2   28    1  33.000       0  ...           0           1           0           0\n",
              "3   33    1  22.705       0  ...           0           0           0           0\n",
              "4   32    1  28.880       0  ...           0           0           0           0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3aYZ5ceKjKF"
      },
      "source": [
        "X = df.drop(labels='charges', axis=1)\n",
        "y = df['charges'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1vzWV9VKqIV"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OL5n4rMvK6mp"
      },
      "source": [
        "scaler_X = MinMaxScaler()\n",
        "\n",
        "scaler_X.fit(X_train)\n",
        "X_train_scaled = pd.DataFrame(scaler_X.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
        "X_test_scaled = pd.DataFrame(scaler_X.transform(X_test), index=X_test.index, columns=X_test.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJ-kUAy_WZvq"
      },
      "source": [
        "scaler_y = MinMaxScaler()\n",
        "\n",
        "y_train=np.reshape(y_train, (-1,1))\n",
        "y_test=np.reshape(y_test, (-1,1))\n",
        "\n",
        "scaler_y.fit(y_train)\n",
        "y_train_scaled = scaler_y.transform(y_train)\n",
        "y_test_scaled = scaler_y.transform(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZjn_KOfLEF_"
      },
      "source": [
        "# II - <a name=\"linear\"></a> Régression linéaire multiple classique\n",
        "\n",
        "- (a) Instancier un modèle de régression linéaire.\n",
        "\n",
        "- (b) Fit le modèle aux données d'entraînement.\n",
        "\n",
        "- (c) Effectuer une prédiction à partir des données de test (attention au scaling)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rTh22A8BLAf4",
        "outputId": "60d35507-3f2e-4d32-d8d5-3839e3d5118c"
      },
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(X_train_scaled, y_train_scaled)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frLfujjFTU8X"
      },
      "source": [
        "y_pred_lr = scaler_y.inverse_transform(lr.predict(X_test_scaled))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIy6-A3_LHSZ"
      },
      "source": [
        "# III - <a name=\"dnn\"></a> Régression à l'aide d'un réseau de neurones dense\n",
        "\n",
        "- (a) Instancier un réseau de neurones dense de l'architecture de votre choix (attention à la couche de sortie, il s'agit d'un problème de régression).\n",
        "\n",
        "- (b) Compiler le modèle avec une métrique appropriée.\n",
        "\n",
        "- (c) Entraîner le modèle avec les paramètres de votre choix. On pourra également définir des callbacks.\n",
        "\n",
        "- (d) Effectuer une prédiction à partir des données de test (attention au scaling)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtSsVD4aLY3T"
      },
      "source": [
        "input_shape = (X_train_scaled.shape[1],)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOrXjVt9LY-n"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(units=10, input_shape=input_shape, activation='relu'))\n",
        "model.add(Dense(units=128, activation='relu'))\n",
        "model.add(Dense(units=2048, activation='relu'))\n",
        "model.add(Dropout(rate=0.2))\n",
        "model.add(Dense(units=256, activation='relu'))\n",
        "model.add(Dense(units=256, activation='relu'))\n",
        "model.add(Dropout(rate=0.2))\n",
        "model.add(Dense(units=256, activation='relu'))\n",
        "model.add(Dense(units=128, activation='relu'))\n",
        "model.add(Dense(units=1, activation='linear'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88RU0CCTLZFn"
      },
      "source": [
        "early_stopping = callbacks.EarlyStopping(monitor = 'val_loss',\n",
        "                        patience = 20,\n",
        "                        mode = 'min',\n",
        "                        restore_best_weights = True)\n",
        "\n",
        "lr_plateau = callbacks.ReduceLROnPlateau(monitor = 'val_loss',\n",
        "                            patience=5,\n",
        "                            factor=0.8,\n",
        "                            verbose=2,\n",
        "                            mode='min')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBGr18leMFfs"
      },
      "source": [
        "model.compile(loss='mae', optimizer='adam', metrics=['mse','mae'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zbd00iJTMKFW",
        "outputId": "d9f2da06-c0b0-4557-8115-80972d05a29c"
      },
      "source": [
        "history = model.fit(X_train_scaled, y_train_scaled, epochs = 200, batch_size = 30, validation_split = 0.2, callbacks = [early_stopping,lr_plateau])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "29/29 [==============================] - 2s 32ms/step - loss: 0.1593 - mse: 0.0522 - mae: 0.1593 - val_loss: 0.1002 - val_mse: 0.0263 - val_mae: 0.1002\n",
            "Epoch 2/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0824 - mse: 0.0194 - mae: 0.0824 - val_loss: 0.0765 - val_mse: 0.0154 - val_mae: 0.0765\n",
            "Epoch 3/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0690 - mse: 0.0146 - mae: 0.0690 - val_loss: 0.0654 - val_mse: 0.0134 - val_mae: 0.0654\n",
            "Epoch 4/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0650 - mse: 0.0130 - mae: 0.0650 - val_loss: 0.0599 - val_mse: 0.0127 - val_mae: 0.0599\n",
            "Epoch 5/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0610 - mse: 0.0124 - mae: 0.0610 - val_loss: 0.0581 - val_mse: 0.0118 - val_mae: 0.0581\n",
            "Epoch 6/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0640 - mse: 0.0122 - mae: 0.0640 - val_loss: 0.0561 - val_mse: 0.0105 - val_mae: 0.0561\n",
            "Epoch 7/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0616 - mse: 0.0119 - mae: 0.0616 - val_loss: 0.0630 - val_mse: 0.0116 - val_mae: 0.0630\n",
            "Epoch 8/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0602 - mse: 0.0110 - mae: 0.0602 - val_loss: 0.0814 - val_mse: 0.0149 - val_mae: 0.0814\n",
            "Epoch 9/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0592 - mse: 0.0109 - mae: 0.0592 - val_loss: 0.0502 - val_mse: 0.0095 - val_mae: 0.0502\n",
            "Epoch 10/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0485 - mse: 0.0093 - mae: 0.0485 - val_loss: 0.0487 - val_mse: 0.0097 - val_mae: 0.0487\n",
            "Epoch 11/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0462 - mse: 0.0090 - mae: 0.0462 - val_loss: 0.0485 - val_mse: 0.0092 - val_mae: 0.0485\n",
            "Epoch 12/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0464 - mse: 0.0090 - mae: 0.0464 - val_loss: 0.0513 - val_mse: 0.0096 - val_mae: 0.0513\n",
            "Epoch 13/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0506 - mse: 0.0096 - mae: 0.0506 - val_loss: 0.0473 - val_mse: 0.0090 - val_mae: 0.0473\n",
            "Epoch 14/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0411 - mse: 0.0073 - mae: 0.0411 - val_loss: 0.0490 - val_mse: 0.0097 - val_mae: 0.0490\n",
            "Epoch 15/200\n",
            "29/29 [==============================] - 0s 12ms/step - loss: 0.0411 - mse: 0.0072 - mae: 0.0411 - val_loss: 0.0500 - val_mse: 0.0098 - val_mae: 0.0500\n",
            "Epoch 16/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0466 - mse: 0.0086 - mae: 0.0466 - val_loss: 0.0479 - val_mse: 0.0095 - val_mae: 0.0479\n",
            "Epoch 17/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0463 - mse: 0.0085 - mae: 0.0463 - val_loss: 0.0436 - val_mse: 0.0082 - val_mae: 0.0436\n",
            "Epoch 18/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0417 - mse: 0.0074 - mae: 0.0417 - val_loss: 0.0484 - val_mse: 0.0096 - val_mae: 0.0484\n",
            "Epoch 19/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0454 - mse: 0.0082 - mae: 0.0454 - val_loss: 0.0511 - val_mse: 0.0090 - val_mae: 0.0511\n",
            "Epoch 20/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0447 - mse: 0.0076 - mae: 0.0447 - val_loss: 0.0462 - val_mse: 0.0087 - val_mae: 0.0462\n",
            "Epoch 21/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0426 - mse: 0.0076 - mae: 0.0426 - val_loss: 0.0449 - val_mse: 0.0082 - val_mae: 0.0449\n",
            "Epoch 22/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0477 - mse: 0.0086 - mae: 0.0477 - val_loss: 0.0433 - val_mse: 0.0079 - val_mae: 0.0433\n",
            "Epoch 23/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0369 - mse: 0.0058 - mae: 0.0369 - val_loss: 0.0437 - val_mse: 0.0081 - val_mae: 0.0437\n",
            "Epoch 24/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0481 - mse: 0.0095 - mae: 0.0481 - val_loss: 0.0453 - val_mse: 0.0084 - val_mae: 0.0453\n",
            "Epoch 25/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0366 - mse: 0.0063 - mae: 0.0366 - val_loss: 0.0485 - val_mse: 0.0095 - val_mae: 0.0485\n",
            "Epoch 26/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0419 - mse: 0.0078 - mae: 0.0419 - val_loss: 0.0511 - val_mse: 0.0103 - val_mae: 0.0511\n",
            "Epoch 27/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0438 - mse: 0.0079 - mae: 0.0438 - val_loss: 0.0451 - val_mse: 0.0083 - val_mae: 0.0451\n",
            "\n",
            "Epoch 00027: ReduceLROnPlateau reducing learning rate to 0.000800000037997961.\n",
            "Epoch 28/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0408 - mse: 0.0078 - mae: 0.0408 - val_loss: 0.0495 - val_mse: 0.0099 - val_mae: 0.0495\n",
            "Epoch 29/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0404 - mse: 0.0073 - mae: 0.0404 - val_loss: 0.0418 - val_mse: 0.0080 - val_mae: 0.0418\n",
            "Epoch 30/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0356 - mse: 0.0061 - mae: 0.0356 - val_loss: 0.0411 - val_mse: 0.0079 - val_mae: 0.0411\n",
            "Epoch 31/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0358 - mse: 0.0064 - mae: 0.0358 - val_loss: 0.0447 - val_mse: 0.0081 - val_mae: 0.0447\n",
            "Epoch 32/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0326 - mse: 0.0052 - mae: 0.0326 - val_loss: 0.0447 - val_mse: 0.0080 - val_mae: 0.0447\n",
            "Epoch 33/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0392 - mse: 0.0070 - mae: 0.0392 - val_loss: 0.0421 - val_mse: 0.0081 - val_mae: 0.0421\n",
            "Epoch 34/200\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0347 - mse: 0.0061 - mae: 0.0347 - val_loss: 0.0509 - val_mse: 0.0102 - val_mae: 0.0509\n",
            "Epoch 35/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0396 - mse: 0.0066 - mae: 0.0396 - val_loss: 0.0416 - val_mse: 0.0075 - val_mae: 0.0416\n",
            "\n",
            "Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.0006400000303983689.\n",
            "Epoch 36/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0339 - mse: 0.0053 - mae: 0.0339 - val_loss: 0.0495 - val_mse: 0.0103 - val_mae: 0.0495\n",
            "Epoch 37/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0389 - mse: 0.0069 - mae: 0.0389 - val_loss: 0.0415 - val_mse: 0.0081 - val_mae: 0.0415\n",
            "Epoch 38/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0367 - mse: 0.0065 - mae: 0.0367 - val_loss: 0.0415 - val_mse: 0.0080 - val_mae: 0.0415\n",
            "Epoch 39/200\n",
            "29/29 [==============================] - 0s 15ms/step - loss: 0.0350 - mse: 0.0067 - mae: 0.0350 - val_loss: 0.0395 - val_mse: 0.0078 - val_mae: 0.0395\n",
            "Epoch 40/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0305 - mse: 0.0054 - mae: 0.0305 - val_loss: 0.0462 - val_mse: 0.0098 - val_mae: 0.0462\n",
            "Epoch 41/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0374 - mse: 0.0067 - mae: 0.0374 - val_loss: 0.0415 - val_mse: 0.0077 - val_mae: 0.0415\n",
            "Epoch 42/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0371 - mse: 0.0069 - mae: 0.0371 - val_loss: 0.0412 - val_mse: 0.0082 - val_mae: 0.0412\n",
            "Epoch 43/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0371 - mse: 0.0073 - mae: 0.0371 - val_loss: 0.0460 - val_mse: 0.0091 - val_mae: 0.0460\n",
            "Epoch 44/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0386 - mse: 0.0073 - mae: 0.0386 - val_loss: 0.0418 - val_mse: 0.0075 - val_mae: 0.0418\n",
            "\n",
            "Epoch 00044: ReduceLROnPlateau reducing learning rate to 0.0005120000336319208.\n",
            "Epoch 45/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0375 - mse: 0.0072 - mae: 0.0375 - val_loss: 0.0418 - val_mse: 0.0079 - val_mae: 0.0418\n",
            "Epoch 46/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0343 - mse: 0.0065 - mae: 0.0343 - val_loss: 0.0408 - val_mse: 0.0080 - val_mae: 0.0408\n",
            "Epoch 47/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0404 - mse: 0.0080 - mae: 0.0404 - val_loss: 0.0399 - val_mse: 0.0078 - val_mae: 0.0399\n",
            "Epoch 48/200\n",
            "29/29 [==============================] - 0s 12ms/step - loss: 0.0348 - mse: 0.0063 - mae: 0.0348 - val_loss: 0.0452 - val_mse: 0.0086 - val_mae: 0.0452\n",
            "Epoch 49/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0321 - mse: 0.0055 - mae: 0.0321 - val_loss: 0.0422 - val_mse: 0.0083 - val_mae: 0.0422\n",
            "\n",
            "Epoch 00049: ReduceLROnPlateau reducing learning rate to 0.00040960004553198815.\n",
            "Epoch 50/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0304 - mse: 0.0056 - mae: 0.0304 - val_loss: 0.0398 - val_mse: 0.0078 - val_mae: 0.0398\n",
            "Epoch 51/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0374 - mse: 0.0070 - mae: 0.0374 - val_loss: 0.0417 - val_mse: 0.0082 - val_mae: 0.0417\n",
            "Epoch 52/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0358 - mse: 0.0075 - mae: 0.0358 - val_loss: 0.0397 - val_mse: 0.0077 - val_mae: 0.0397\n",
            "Epoch 53/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0369 - mse: 0.0075 - mae: 0.0369 - val_loss: 0.0411 - val_mse: 0.0082 - val_mae: 0.0411\n",
            "Epoch 54/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0349 - mse: 0.0068 - mae: 0.0349 - val_loss: 0.0412 - val_mse: 0.0082 - val_mae: 0.0412\n",
            "\n",
            "Epoch 00054: ReduceLROnPlateau reducing learning rate to 0.00032768002711236477.\n",
            "Epoch 55/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0341 - mse: 0.0065 - mae: 0.0341 - val_loss: 0.0395 - val_mse: 0.0078 - val_mae: 0.0395\n",
            "Epoch 56/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0337 - mse: 0.0065 - mae: 0.0337 - val_loss: 0.0406 - val_mse: 0.0080 - val_mae: 0.0406\n",
            "Epoch 57/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0344 - mse: 0.0066 - mae: 0.0344 - val_loss: 0.0411 - val_mse: 0.0081 - val_mae: 0.0411\n",
            "Epoch 58/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0333 - mse: 0.0064 - mae: 0.0333 - val_loss: 0.0408 - val_mse: 0.0077 - val_mae: 0.0408\n",
            "Epoch 59/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0311 - mse: 0.0054 - mae: 0.0311 - val_loss: 0.0425 - val_mse: 0.0083 - val_mae: 0.0425\n",
            "\n",
            "Epoch 00059: ReduceLROnPlateau reducing learning rate to 0.0002621440216898918.\n",
            "Epoch 60/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0330 - mse: 0.0061 - mae: 0.0330 - val_loss: 0.0398 - val_mse: 0.0078 - val_mae: 0.0398\n",
            "Epoch 61/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0316 - mse: 0.0061 - mae: 0.0316 - val_loss: 0.0428 - val_mse: 0.0082 - val_mae: 0.0428\n",
            "Epoch 62/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0326 - mse: 0.0058 - mae: 0.0326 - val_loss: 0.0421 - val_mse: 0.0082 - val_mae: 0.0421\n",
            "Epoch 63/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0388 - mse: 0.0075 - mae: 0.0388 - val_loss: 0.0405 - val_mse: 0.0080 - val_mae: 0.0405\n",
            "Epoch 64/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0328 - mse: 0.0061 - mae: 0.0328 - val_loss: 0.0418 - val_mse: 0.0082 - val_mae: 0.0418\n",
            "\n",
            "Epoch 00064: ReduceLROnPlateau reducing learning rate to 0.00020971521735191345.\n",
            "Epoch 65/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0289 - mse: 0.0052 - mae: 0.0289 - val_loss: 0.0408 - val_mse: 0.0081 - val_mae: 0.0408\n",
            "Epoch 66/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0330 - mse: 0.0062 - mae: 0.0330 - val_loss: 0.0410 - val_mse: 0.0080 - val_mae: 0.0410\n",
            "Epoch 67/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0337 - mse: 0.0067 - mae: 0.0337 - val_loss: 0.0403 - val_mse: 0.0079 - val_mae: 0.0403\n",
            "Epoch 68/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0361 - mse: 0.0071 - mae: 0.0361 - val_loss: 0.0407 - val_mse: 0.0081 - val_mae: 0.0407\n",
            "Epoch 69/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0357 - mse: 0.0073 - mae: 0.0357 - val_loss: 0.0417 - val_mse: 0.0082 - val_mae: 0.0417\n",
            "\n",
            "Epoch 00069: ReduceLROnPlateau reducing learning rate to 0.00016777217388153076.\n",
            "Epoch 70/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0311 - mse: 0.0059 - mae: 0.0311 - val_loss: 0.0404 - val_mse: 0.0080 - val_mae: 0.0404\n",
            "Epoch 71/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0338 - mse: 0.0068 - mae: 0.0338 - val_loss: 0.0396 - val_mse: 0.0078 - val_mae: 0.0396\n",
            "Epoch 72/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0283 - mse: 0.0050 - mae: 0.0283 - val_loss: 0.0402 - val_mse: 0.0079 - val_mae: 0.0402\n",
            "Epoch 73/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0300 - mse: 0.0058 - mae: 0.0300 - val_loss: 0.0407 - val_mse: 0.0080 - val_mae: 0.0407\n",
            "Epoch 74/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0322 - mse: 0.0064 - mae: 0.0322 - val_loss: 0.0425 - val_mse: 0.0084 - val_mae: 0.0425\n",
            "\n",
            "Epoch 00074: ReduceLROnPlateau reducing learning rate to 0.00013421773910522462.\n",
            "Epoch 75/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0304 - mse: 0.0056 - mae: 0.0304 - val_loss: 0.0393 - val_mse: 0.0077 - val_mae: 0.0393\n",
            "Epoch 76/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0295 - mse: 0.0057 - mae: 0.0295 - val_loss: 0.0397 - val_mse: 0.0078 - val_mae: 0.0397\n",
            "Epoch 77/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0336 - mse: 0.0067 - mae: 0.0336 - val_loss: 0.0402 - val_mse: 0.0079 - val_mae: 0.0402\n",
            "Epoch 78/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0299 - mse: 0.0056 - mae: 0.0299 - val_loss: 0.0403 - val_mse: 0.0080 - val_mae: 0.0403\n",
            "Epoch 79/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0272 - mse: 0.0052 - mae: 0.0272 - val_loss: 0.0398 - val_mse: 0.0078 - val_mae: 0.0398\n",
            "Epoch 80/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0304 - mse: 0.0057 - mae: 0.0304 - val_loss: 0.0421 - val_mse: 0.0082 - val_mae: 0.0421\n",
            "\n",
            "Epoch 00080: ReduceLROnPlateau reducing learning rate to 0.00010737419361248613.\n",
            "Epoch 81/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0307 - mse: 0.0065 - mae: 0.0307 - val_loss: 0.0398 - val_mse: 0.0077 - val_mae: 0.0398\n",
            "Epoch 82/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0338 - mse: 0.0066 - mae: 0.0338 - val_loss: 0.0400 - val_mse: 0.0079 - val_mae: 0.0400\n",
            "Epoch 83/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0303 - mse: 0.0060 - mae: 0.0303 - val_loss: 0.0399 - val_mse: 0.0078 - val_mae: 0.0399\n",
            "Epoch 84/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0376 - mse: 0.0080 - mae: 0.0376 - val_loss: 0.0404 - val_mse: 0.0079 - val_mae: 0.0404\n",
            "Epoch 85/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0291 - mse: 0.0056 - mae: 0.0291 - val_loss: 0.0406 - val_mse: 0.0080 - val_mae: 0.0406\n",
            "\n",
            "Epoch 00085: ReduceLROnPlateau reducing learning rate to 8.589935605414213e-05.\n",
            "Epoch 86/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0350 - mse: 0.0074 - mae: 0.0350 - val_loss: 0.0407 - val_mse: 0.0080 - val_mae: 0.0407\n",
            "Epoch 87/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0294 - mse: 0.0055 - mae: 0.0294 - val_loss: 0.0403 - val_mse: 0.0080 - val_mae: 0.0403\n",
            "Epoch 88/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0299 - mse: 0.0058 - mae: 0.0299 - val_loss: 0.0399 - val_mse: 0.0079 - val_mae: 0.0399\n",
            "Epoch 89/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0338 - mse: 0.0067 - mae: 0.0338 - val_loss: 0.0404 - val_mse: 0.0079 - val_mae: 0.0404\n",
            "Epoch 90/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0322 - mse: 0.0066 - mae: 0.0322 - val_loss: 0.0398 - val_mse: 0.0078 - val_mae: 0.0398\n",
            "\n",
            "Epoch 00090: ReduceLROnPlateau reducing learning rate to 6.871948717162013e-05.\n",
            "Epoch 91/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0347 - mse: 0.0073 - mae: 0.0347 - val_loss: 0.0413 - val_mse: 0.0082 - val_mae: 0.0413\n",
            "Epoch 92/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0327 - mse: 0.0067 - mae: 0.0327 - val_loss: 0.0409 - val_mse: 0.0080 - val_mae: 0.0409\n",
            "Epoch 93/200\n",
            "29/29 [==============================] - 0s 14ms/step - loss: 0.0324 - mse: 0.0066 - mae: 0.0324 - val_loss: 0.0417 - val_mse: 0.0082 - val_mae: 0.0417\n",
            "Epoch 94/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0323 - mse: 0.0065 - mae: 0.0323 - val_loss: 0.0407 - val_mse: 0.0081 - val_mae: 0.0407\n",
            "Epoch 95/200\n",
            "29/29 [==============================] - 0s 13ms/step - loss: 0.0249 - mse: 0.0045 - mae: 0.0249 - val_loss: 0.0406 - val_mse: 0.0079 - val_mae: 0.0406\n",
            "\n",
            "Epoch 00095: ReduceLROnPlateau reducing learning rate to 5.497558740898967e-05.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0iSLuJtGM337",
        "outputId": "81fa6a22-0847-4517-a63d-7beb1dc414f3"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(121)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Perte du modèle par epoch')\n",
        "plt.ylabel('perte')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='right')\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(history.history['mse'])\n",
        "plt.plot(history.history['val_mse'])\n",
        "plt.title('Erreur du modèle par epoch')\n",
        "plt.ylabel('Erreur quadratique moyenne')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='right');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcGVsgWUNMOz"
      },
      "source": [
        "y_pred_dnn = scaler_y.inverse_transform(model.predict(X_test_scaled))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isWoT8v-ZtBM"
      },
      "source": [
        "# IV - <a name=\"comparaison\"></a> Comparaison des résultats obtenus avec les deux techniques\n",
        "\n",
        "- (a) Comparer les résultats obtenus avec les deux modèles (utiliser le ```r2_score``` par exemple)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "jGmHsGtNY9MK",
        "outputId": "27444d68-392e-4adf-c148-e243967b7390"
      },
      "source": [
        "result = pd.DataFrame()\n",
        "result['y_test'] = [y_test[i][0] for i in range(len(y_test))]\n",
        "result['y_pred_lr'] = [y_pred_lr[i][0] for i in range(len(y_pred_lr))]\n",
        "result['y_pred_dnn'] = [y_pred_dnn[i][0] for i in range(len(y_pred_dnn))]\n",
        "result.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>y_test</th>\n",
              "      <th>y_pred_lr</th>\n",
              "      <th>y_pred_dnn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9800.88820</td>\n",
              "      <td>16017.830362</td>\n",
              "      <td>10050.867188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4667.60765</td>\n",
              "      <td>9018.133067</td>\n",
              "      <td>4450.803711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34838.87300</td>\n",
              "      <td>28488.850833</td>\n",
              "      <td>28641.277344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5125.21570</td>\n",
              "      <td>5468.261480</td>\n",
              "      <td>4895.868164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12142.57860</td>\n",
              "      <td>11538.509563</td>\n",
              "      <td>12267.756836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10702.64240</td>\n",
              "      <td>10667.203309</td>\n",
              "      <td>11019.179688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4827.90495</td>\n",
              "      <td>1655.588452</td>\n",
              "      <td>5010.414062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4766.02200</td>\n",
              "      <td>3191.485491</td>\n",
              "      <td>5425.725098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1674.63230</td>\n",
              "      <td>4409.223795</td>\n",
              "      <td>2005.378296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9361.32680</td>\n",
              "      <td>8141.631455</td>\n",
              "      <td>9173.564453</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        y_test     y_pred_lr    y_pred_dnn\n",
              "0   9800.88820  16017.830362  10050.867188\n",
              "1   4667.60765   9018.133067   4450.803711\n",
              "2  34838.87300  28488.850833  28641.277344\n",
              "3   5125.21570   5468.261480   4895.868164\n",
              "4  12142.57860  11538.509563  12267.756836\n",
              "5  10702.64240  10667.203309  11019.179688\n",
              "6   4827.90495   1655.588452   5010.414062\n",
              "7   4766.02200   3191.485491   5425.725098\n",
              "8   1674.63230   4409.223795   2005.378296\n",
              "9   9361.32680   8141.631455   9173.564453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNSSDzesZixT",
        "outputId": "90de0662-ca11-44d3-c40e-871f885e656a"
      },
      "source": [
        "print(\"r2_score du modèle lr : {}\".format(r2_score(y_test, y_pred_lr)))\n",
        "print(\"r2_score du modèle dnn : {}\".format(r2_score(y_test, y_pred_dnn)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "r2_score du modèle lr : 0.7963958782947849\n",
            "r2_score du modèle dnn : 0.8692014876399028\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}