EDA_Report_for_1st_Candidate.ipynb

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  "metadata": {
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4hgJXDhRSe4i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing Libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "A4eqwLFXZAzw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "QlB3oOC-YzTH"
      },
      "outputs": [],
      "source": [
        "# Load the dataset for\n",
        "file_path1 = '/content/1 (1).csv'\n",
        "data_trans = pd.read_csv(file_path1)\n",
        "file_path2 = '/content/emotion (1).csv'\n",
        "data_emot = pd.read_csv(file_path2)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Basic Information\n",
        "\n",
        "# Display the first few(5) rows of the dataset\n",
        "print(data_trans.head())\n",
        "\n",
        "# Display summary of the dataset\n",
        "print(data_trans.info())\n",
        "\n",
        "# Show basic statistics\n",
        "print(data_trans.describe(include='all'))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xc7Zbo7CTmuP",
        "outputId": "4bd54281-0cf2-4527-fd52-c07cd332e97f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   id  seek  start    end                                               text  \\\n",
            "0   0     0   0.00   5.56   Hello, I am Jeffrey Shepherd and I am current...   \n",
            "1   1     0   5.56   9.60   IIM Coikode. I have completed my B.Tech in Bi...   \n",
            "2   2     0   9.60  14.48   Technology Kolkata, followed by my M.Tech fro...   \n",
            "3   3     0  14.48  18.48   of three years in the regulatory affairs doma...   \n",
            "4   4     0  18.48  23.28   as a medical writer in Ciro Klein Farm, Mumba...   \n",
            "\n",
            "                                              tokens  temperature  \\\n",
            "0  [50364, 2425, 11, 286, 669, 1587, 84, 2938, 49...          0.0   \n",
            "1  [50642, 286, 6324, 3066, 1035, 1429, 13, 286, ...          0.0   \n",
            "2  [50844, 15037, 26137, 74, 3274, 11, 6263, 538,...          0.0   \n",
            "3  [51088, 295, 1045, 924, 294, 264, 18260, 17478...          0.0   \n",
            "4  [51288, 382, 257, 4625, 9936, 294, 383, 5182, ...          0.0   \n",
            "\n",
            "   avg_logprob  compression_ratio  no_speech_prob  positive  negative  \\\n",
            "0    -0.447179           1.651235         0.63588  0.580265  0.152281   \n",
            "1    -0.447179           1.651235         0.63588  0.550327  0.189263   \n",
            "2    -0.447179           1.651235         0.63588  0.639860  0.111150   \n",
            "3    -0.447179           1.651235         0.63588  0.441894  0.399186   \n",
            "4    -0.447179           1.651235         0.63588  0.236254  0.532010   \n",
            "\n",
            "    neutral  confident  hesitant   concise  enthusiastic  speech_speed  \n",
            "0  0.267454   0.846701  0.845698  0.635805      0.647783      2.517986  \n",
            "1  0.260410   0.679283  0.733701  0.544145      0.417390      3.217822  \n",
            "2  0.248990   0.902729  0.834620  0.715861      0.700062      2.868852  \n",
            "3  0.158919   0.774308  0.813044  0.522462      0.279916      3.750000  \n",
            "4  0.231735   0.286049  0.561375  0.334381      0.197305      3.541667  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 18 entries, 0 to 17\n",
            "Data columns (total 18 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 18 non-null     int64  \n",
            " 1   seek               18 non-null     int64  \n",
            " 2   start              18 non-null     float64\n",
            " 3   end                18 non-null     float64\n",
            " 4   text               18 non-null     object \n",
            " 5   tokens             18 non-null     object \n",
            " 6   temperature        18 non-null     float64\n",
            " 7   avg_logprob        18 non-null     float64\n",
            " 8   compression_ratio  18 non-null     float64\n",
            " 9   no_speech_prob     18 non-null     float64\n",
            " 10  positive           18 non-null     float64\n",
            " 11  negative           18 non-null     float64\n",
            " 12  neutral            18 non-null     float64\n",
            " 13  confident          18 non-null     float64\n",
            " 14  hesitant           18 non-null     float64\n",
            " 15  concise            18 non-null     float64\n",
            " 16  enthusiastic       18 non-null     float64\n",
            " 17  speech_speed       18 non-null     float64\n",
            "dtypes: float64(14), int64(2), object(2)\n",
            "memory usage: 2.7+ KB\n",
            "None\n",
            "               id         seek      start        end  \\\n",
            "count   18.000000    18.000000  18.000000  18.000000   \n",
            "unique        NaN          NaN        NaN        NaN   \n",
            "top           NaN          NaN        NaN        NaN   \n",
            "freq          NaN          NaN        NaN        NaN   \n",
            "mean     8.500000  3009.333333  41.002222  45.931111   \n",
            "std      5.338539  2598.468689  26.117009  26.294913   \n",
            "min      0.000000     0.000000   0.000000   5.560000   \n",
            "25%      4.250000     0.000000  19.680000  24.400000   \n",
            "50%      8.500000  2776.000000  40.560000  46.640000   \n",
            "75%     12.750000  5336.000000  62.420000  66.660000   \n",
            "max     17.000000  8272.000000  82.720000  88.720000   \n",
            "\n",
            "                                                     text  \\\n",
            "count                                                  18   \n",
            "unique                                                 18   \n",
            "top      Hello, I am Jeffrey Shepherd and I am current...   \n",
            "freq                                                    1   \n",
            "mean                                                  NaN   \n",
            "std                                                   NaN   \n",
            "min                                                   NaN   \n",
            "25%                                                   NaN   \n",
            "50%                                                   NaN   \n",
            "75%                                                   NaN   \n",
            "max                                                   NaN   \n",
            "\n",
            "                                                   tokens  temperature  \\\n",
            "count                                                  18         18.0   \n",
            "unique                                                 18          NaN   \n",
            "top     [50364, 2425, 11, 286, 669, 1587, 84, 2938, 49...          NaN   \n",
            "freq                                                    1          NaN   \n",
            "mean                                                  NaN          0.0   \n",
            "std                                                   NaN          0.0   \n",
            "min                                                   NaN          0.0   \n",
            "25%                                                   NaN          0.0   \n",
            "50%                                                   NaN          0.0   \n",
            "75%                                                   NaN          0.0   \n",
            "max                                                   NaN          0.0   \n",
            "\n",
            "        avg_logprob  compression_ratio  no_speech_prob   positive   negative  \\\n",
            "count     18.000000          18.000000       18.000000  18.000000  18.000000   \n",
            "unique          NaN                NaN             NaN        NaN        NaN   \n",
            "top             NaN                NaN             NaN        NaN        NaN   \n",
            "freq            NaN                NaN             NaN        NaN        NaN   \n",
            "mean      -0.306962           1.666544        0.748891   0.709199   0.141214   \n",
            "std        0.107591           0.148591        0.213651   0.207253   0.154933   \n",
            "min       -0.447179           1.129870        0.036593   0.236254   0.004977   \n",
            "25%       -0.447179           1.649819        0.635880   0.587941   0.043334   \n",
            "50%       -0.278873           1.651235        0.833173   0.739705   0.080390   \n",
            "75%       -0.196253           1.785235        0.910383   0.870056   0.160178   \n",
            "max       -0.196253           1.785235        0.910383   0.980389   0.532010   \n",
            "\n",
            "          neutral  confident   hesitant    concise  enthusiastic  speech_speed  \n",
            "count   18.000000  18.000000  18.000000  18.000000     18.000000     18.000000  \n",
            "unique        NaN        NaN        NaN        NaN           NaN           NaN  \n",
            "top           NaN        NaN        NaN        NaN           NaN           NaN  \n",
            "freq          NaN        NaN        NaN        NaN           NaN           NaN  \n",
            "mean     0.149586   0.733828   0.485172   0.429418      0.466497      3.113771  \n",
            "std      0.080974   0.208330   0.260785   0.272635      0.286292      0.599958  \n",
            "min      0.014633   0.286049   0.008425   0.012767      0.088580      2.034884  \n",
            "25%      0.082926   0.576869   0.342871   0.280775      0.211429      2.605702  \n",
            "50%      0.155741   0.789854   0.407792   0.441477      0.418900      3.134206  \n",
            "75%      0.224622   0.898628   0.710799   0.612890      0.686992      3.589744  \n",
            "max      0.267454   0.980931   0.845698   0.919735      0.990310      4.166667  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few(5) rows of the dataset\n",
        "print(data_emot.head())\n",
        "\n",
        "# Display summary of the dataset\n",
        "print(data_emot.info())\n",
        "\n",
        "# Show basic statistics\n",
        "print(data_emot.describe(include='all'))\n",
        "\n",
        "# Statistical Summary:\n",
        "\n",
        "# The mean values of emotions like anger (14.45), fear (18.38), and neutral (38.36) show that neutral and fear have relatively higher probabilities,\n",
        "#indicating they might be more common emotions across the dataset.Maximum values show that some emotions like surprise (97.83) and fear (94.98) reach extremely high probabilities in certain images.\n",
        "#Standard deviation is quite high for emotions like neutral (33.47), fear (25.07), and surprise (19.62), indicating high variability across the dataset."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bt7oMl14WoCL",
        "outputId": "c4e2b478-3cee-4a2e-a7da-40cd581e4377"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                               movie_id  image_seq     angry   disgust  \\\n",
            "0  93663f94-bf0a-4ce8-a29a-a5236cc7fe6a          0   4.31735  0.000594   \n",
            "1  93663f94-bf0a-4ce8-a29a-a5236cc7fe6a          1  53.22530  2.981640   \n",
            "2  93663f94-bf0a-4ce8-a29a-a5236cc7fe6a          2   8.79651  0.029468   \n",
            "3  93663f94-bf0a-4ce8-a29a-a5236cc7fe6a          3   9.45303  0.106778   \n",
            "4  93663f94-bf0a-4ce8-a29a-a5236cc7fe6a          4  56.00020  0.000004   \n",
            "\n",
            "        fear     happy        sad   surprise   neutral dominant_emotion  \n",
            "0   2.879790   1.65035   2.779980   0.600814  87.77110          neutral  \n",
            "1  12.736800   1.52347   1.051320  27.216800   1.26462            angry  \n",
            "2   2.968160  16.83150  39.884600   0.279335  31.21050              sad  \n",
            "3   1.553080  20.93010   3.503870   0.909426  63.54370          neutral  \n",
            "4   0.162231   5.58358   0.197026  12.807600  25.24940            angry  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 87 entries, 0 to 86\n",
            "Data columns (total 10 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   movie_id          87 non-null     object \n",
            " 1   image_seq         87 non-null     int64  \n",
            " 2   angry             87 non-null     float64\n",
            " 3   disgust           87 non-null     float64\n",
            " 4   fear              87 non-null     float64\n",
            " 5   happy             87 non-null     float64\n",
            " 6   sad               87 non-null     float64\n",
            " 7   surprise          87 non-null     float64\n",
            " 8   neutral           87 non-null     float64\n",
            " 9   dominant_emotion  87 non-null     object \n",
            "dtypes: float64(7), int64(1), object(2)\n",
            "memory usage: 6.9+ KB\n",
            "None\n",
            "                                    movie_id  image_seq      angry  \\\n",
            "count                                     87  87.000000  87.000000   \n",
            "unique                                     1        NaN        NaN   \n",
            "top     93663f94-bf0a-4ce8-a29a-a5236cc7fe6a        NaN        NaN   \n",
            "freq                                      87        NaN        NaN   \n",
            "mean                                     NaN  45.390805  14.451059   \n",
            "std                                      NaN  27.587643  18.544205   \n",
            "min                                      NaN   0.000000   0.164384   \n",
            "25%                                      NaN  22.500000   1.867450   \n",
            "50%                                      NaN  44.000000   6.412790   \n",
            "75%                                      NaN  68.500000  18.765500   \n",
            "max                                      NaN  94.000000  71.172500   \n",
            "\n",
            "             disgust       fear      happy        sad   surprise    neutral  \\\n",
            "count   8.700000e+01  87.000000  87.000000  87.000000  87.000000  87.000000   \n",
            "unique           NaN        NaN        NaN        NaN        NaN        NaN   \n",
            "top              NaN        NaN        NaN        NaN        NaN        NaN   \n",
            "freq             NaN        NaN        NaN        NaN        NaN        NaN   \n",
            "mean    6.168965e-01  18.382797   5.865318  13.575324   8.744969  38.363648   \n",
            "std     2.679399e+00  25.073562  11.237819  19.787221  19.621163  33.468718   \n",
            "min     2.400910e-10   0.079219   0.000005   0.000073   0.000008   0.000117   \n",
            "25%     8.769180e-05   1.862945   0.143215   1.845405   0.210637   8.035385   \n",
            "50%     3.443590e-03   6.366870   1.476330   5.578010   0.970922  28.221400   \n",
            "75%     6.486260e-02  21.010100   5.569355  14.056700   6.524355  71.227450   \n",
            "max     2.150890e+01  94.981800  66.222300  91.563600  97.834400  97.823000   \n",
            "\n",
            "       dominant_emotion  \n",
            "count                87  \n",
            "unique                6  \n",
            "top             neutral  \n",
            "freq                 37  \n",
            "mean                NaN  \n",
            "std                 NaN  \n",
            "min                 NaN  \n",
            "25%                 NaN  \n",
            "50%                 NaN  \n",
            "75%                 NaN  \n",
            "max                 NaN  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Missing Values: There are no missing values in any of the columns, which means the dataset is complete and ready for analysis.\n",
        "\n",
        " Statistical Summary:\n",
        "\n",
        " The mean values of emotions like anger (14.45), fear (18.38), and neutral (38.36) show that neutral and fear have relatively higher probabilities,\n",
        "indicating they might be more common emotions across the dataset.Maximum values show that some emotions like surprise (97.83) and fear (94.98) reach extremely high probabilities in certain images.\n",
        "Standard deviation is quite high for emotions like neutral (33.47), fear (25.07), and surprise (19.62), indicating high variability across the dataset."
      ],
      "metadata": {
        "id": "Edyqeyt4apCm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(data_emot.isnull().sum())\n",
        "\n",
        "# Visualize missing values if necessary\n",
        "sns.heatmap(data_emot.isnull(), cbar=False, cmap='viridis')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "wQvucnA6VEpZ",
        "outputId": "7a9430b6-6f1a-4d55-d163-862820d8e90f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "movie_id            0\n",
            "image_seq           0\n",
            "angry               0\n",
            "disgust             0\n",
            "fear                0\n",
            "happy               0\n",
            "sad                 0\n",
            "surprise            0\n",
            "neutral             0\n",
            "dominant_emotion    0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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REekujYqPkydPIiQkBGfPnsXhw4dRUFCALl26IC8vT3XN0KFDkZKSgr179yIxMRH9+vXDwIEDER8fX+bhiYiISPdotLfLvz18+BB2dnY4efIk2rVrBwCwsLDA8uXLERAQoLrO1tYWc+fOxeeff16s+3JvFyIiIt1T3L1dSjXmIzs7GwBgY2OjavP19cXPP/+MzMxMKJVKbNu2Dc+fP3/jrrZERERUuWi0vPo/KZVKjBs3Dq1bt4a3t7eqffv27fj4449ha2sLIyMjVKlSBbt374abm1uR95HL5ZDL5er3FhQwkBiWNBoRERFVYCXu+QgJCUFSUhK2bdum1j5lyhRkZWXhyJEjiI2NRVhYGAYOHIjExMQi7xMREQGZTKZ2pCK5pLGIiIiogivRmI+vvvoKv/76K37//Xe4uLio2q9fvw43NzckJSXBy8tL1e7n5wc3NzesWLGi0L2K6vnoK/uMPR9EREQ6prhjPjTe1Xb06NHYvXs3Tpw4oVZ4AEB+fj4AwMBAvUPF0NAQSqWyyHtKpVJIpVK1NhYeRERE+kuj4iMkJARbt27Fr7/+CktLS6SnpwMAZDIZzMzM4O7uDjc3N4wYMQLz58+Hra0t9uzZg8OHD2P//v3l8gGIiIhIt2j02EUikRTZvn79enz22WcAgKtXr2Ly5Mk4deoUcnNz4ebmhgkTJqhNvX0XTrUlIiLSPcV97FKqdT7KC4sPIiIi3aOVdT6IiIiINMXig4iIiLSKxQcRERFpFYsPIiIi0ioWH0RERKRVLD6IiIhIqzQqPpYvX44GDRrAysoKVlZWaNWqFQ4cOAAAyMzMxOjRo1G/fn2YmZnByckJY8aMUe18S0RERARouMJpzZo1MWfOHNStWxeCICAqKgq9e/dGfHw8BEHAvXv3MH/+fHh6euLWrVsYOXIk7t27hx07dpRXfiIiItIxpV5kzMbGBj/88AOGDx9e6Nwvv/yCIUOGIC8vD0ZGxa9zuMgYERGR7imXjeX+SaFQ4JdffkFeXh5atWpV5DXZ2dmwsrLSqPAgIiIi/aZxVZCYmIhWrVrh+fPnsLCwwO7du+Hp6VnoukePHmHmzJkIDg5+6/3kcjnkcrlam1JQcGdbIiIiPaXxbJf69evj0qVLOHfuHEaNGoXAwED8/fffatfk5OTA398fnp6emD59+lvvFxERAZlMpnakIlnTWERERKQjSj3mw8/PD66urli5ciUA4OnTp+jatSuqVKmC/fv3w9TU9K2vL6rno6/sM/Z8EBER6ZhyH/PxmlKpVBUPOTk56Nq1K6RSKfbu3fvOwgMApFIppFKpWhsLDyIiIv2lUfERHh6O7t27w8nJCU+fPsXWrVtx4sQJREdHIycnB126dEF+fj42b96MnJwc5OTkAACqVasGQ0MWFERERKRh8ZGRkYGhQ4fi/v37kMlkaNCgAaKjo9G5c2ecOHEC586dAwC4ubmpvS41NRXOzs5lFpqIiIh0V6nHfJQHrvNBRESke4o75oN7uxAREZFWsfggIiIirWLxQURERFrF4oOIiIi0isUHERERaRWLDyIiItIqjYqP5cuXo0GDBrCysoKVlRVatWqFAwcOqF0TExODjh07wtzcHFZWVmjXrh2ePXtWpqGJiIhId2lUfNSsWRNz5szBxYsXERsbi44dO6J3797466+/ALwqPLp164YuXbrg/PnzuHDhAr766isYGLCDhYiIiF4p9SJjNjY2+OGHHzB8+HC0bNkSnTt3xsyZM0sViouMERER6Z5yX2RMoVBg27ZtyMvLQ6tWrZCRkYFz587Bzs4Ovr6+sLe3xwcffIBTp06V9C2IiIhID2lcfCQmJsLCwgJSqRQjR47E7t274enpiRs3bgAApk+fji+++AIHDx5EkyZN0KlTJ1y9evWN95PL5apN6F4fSkFR8k9EREREFZrGxUf9+vVx6dIlnDt3DqNGjUJgYCD+/vtvKJVKAMCIESMQFBSExo0bY+HChahfvz7WrVv3xvtFRERAJpOpHalILvknIiIiogpN4+LDxMQEbm5uaNq0KSIiItCwYUMsXrwY1atXBwB4enqqXe/h4YG0tLQ33i88PBzZ2dlqhwvcNY1FREREOsKotDdQKpWQy+VwdnaGo6MjUlJS1M5fuXIF3bt3f+PrpVIppFKpWpuBxLC0sYiIiKiC0qj4CA8PR/fu3eHk5ISnT59i69atOHHiBKKjoyGRSDBx4kRMmzYNDRs2RKNGjRAVFYXk5GTs2LGjvPITERGRjtGo+MjIyMDQoUNx//59yGQyNGjQANHR0ejcuTMAYNy4cXj+/DlCQ0ORmZmJhg0b4vDhw3B1dS2X8ERERKR7Sr3OR3ngOh9ERES6p9zX+SAiIiIqCRYfREREpFUsPoiIiEirWHwQERGRVrH4ICIiIq1i8UFERERaVariY86cOZBIJBg3bpyq7fnz5wgJCYGtrS0sLCzQv39/PHjwoLQ5iYiISE+UuPi4cOECVq5ciQYNGqi1h4aGYt++ffjll19w8uRJ3Lt3D/369St1UCIiItIPJSo+cnNzMXjwYKxevRpVq1ZVtWdnZ2Pt2rWIjIxEx44d0bRpU6xfvx5nzpzB2bNnyyw0ERER6a4SFR8hISHw9/eHn5+fWvvFixdRUFCg1u7u7g4nJyfExMSULikRERHpBY13td22bRvi4uJw4cKFQufS09NhYmICa2trtXZ7e3ukp6cXeT+5XA65XK7WphQU3NmWiIhIT2nU83H79m2MHTsWW7ZsgampaZkEiIiIgEwmUztSkVwm9yYiIqKKR6Pi4+LFi8jIyECTJk1gZGQEIyMjnDx5EkuWLIGRkRHs7e3x4sULZGVlqb3uwYMHcHBwKPKe4eHhyM7OVjtc4F7iD0REREQVm0aPXTp16oTExES1tqCgILi7u2PSpEmoVasWjI2NcfToUfTv3x8AkJKSgrS0NLRq1arIe0qlUkilUrU2PnIhIiLSXxoVH5aWlvD29lZrMzc3h62trap9+PDhCAsLg42NDaysrDB69Gi0atUKLVu2LLvUREREpLM0HnD6LgsXLoSBgQH69+8PuVyOrl274r///W9Zvw0RERHpKIkgCILYIf6ts8EAsSMQERGRhg4rfynWddzbhYiIiLSKxQcRERFpFYsPIiIi0ioWH0RERKRVLD6IiIhIq1h8EBERkVaVqviYM2cOJBIJxo0bV+icIAjo3r07JBIJ9uzZU5q3ISIiIj1S4uLjwoULWLlyJRo0aFDk+UWLFkEikZQ4GBEREemnEhUfubm5GDx4MFavXo2qVasWOn/p0iUsWLAA69atK3VAIiIi0i8lKj5CQkLg7+8PPz+/Qufy8/Px6aefYtmyZW/cyZaIiIgqL433dtm2bRvi4uJw4cKFIs+HhobC19cXvXv3Ltb95HI55HK5WptSUHBnWyIiIj2lUc/H7du3MXbsWGzZsgWmpqaFzu/duxfHjh3DokWLin3PiIgIyGQytSMVyZrEIiIiIh2i0cZye/bsQd++fWFo+L9eCYVCAYlEAgMDA4waNQrLli2DgYGB2nkDAwO0bdsWJ06cKHTPono++so+Y88HERGRjinuxnIaFR9Pnz7FrVu31NqCgoLg7u6OSZMm4b333sOjR4/Uzvv4+GDx4sXo2bMnXFxcivU+3NWWiIhI9xS3+NBozIelpSW8vb3V2szNzWFra6tqL2qQqZOTU7ELDyIiItJvXOGUiIiItErj2S7/VtQ4jn/S4KkOERERVQLs+SAiIiKtYvFBREREWsXig4iIiLSKxQcRERFpFYsPIiIi0ioWH0RERKRVpSo+5syZA4lEgnHjxqna0tPTERAQAAcHB5ibm6NJkybYuXNnaXMSERGRnihx8XHhwgWsXLkSDRo0UGsfOnQoUlJSsHfvXiQmJqJfv34YOHAg4uPjSx2WiIiIdF+Jio/c3FwMHjwYq1evRtWqVdXOnTlzBqNHj8b777+POnXq4Ntvv4W1tTUuXrxYJoGJiIhIt5Wo+AgJCYG/vz/8/PwKnfP19cXPP/+MzMxMKJVKbNu2Dc+fP0f79u1Lm5WIiIj0gMbLq2/btg1xcXG4cOFCkee3b9+Ojz/+GLa2tjAyMkKVKlWwe/duuLm5FXm9XC6HXC5Xa1MKChhIDDWNRkRERDpAo56P27dvY+zYsdiyZQtMTU2LvGbKlCnIysrCkSNHEBsbi7CwMAwcOBCJiYlFXh8REQGZTKZ2pCJZ809CREREOkEiaLDz2549e9C3b18YGv6vV0KhUEAikcDAwAApKSlwc3NDUlISvLy8VNf4+fnBzc0NK1asKHTPono++so+Y88HERGRjjms/KVY12n02KVTp06FejCCgoLg7u6OSZMmIT8/HwBgYKDeoWJoaAilUlnkPaVSKaRSqVobCw8iIiL9pVHxYWlpCW9vb7U2c3Nz2NrawtvbGwUFBXBzc8OIESMwf/582NraYs+ePTh8+DD2799fpsGJiIhIN5XpCqfGxsb4v//7P1SrVg09e/ZEgwYNsHHjRkRFRaFHjx5l+VZERESkozQa86EtnQ0GiB2BiIiINFTcMR/c24WIiIi0isUHERERaRWLDyIiItIqFh9ERESkVSw+iIiISKtYfBAREZFWaVR8TJ8+HRKJRO1wd3cHAGRmZmL06NGoX78+zMzM4OTkhDFjxiA7O7tcghMREZFu0nhXWy8vLxw5cuR/NzB6dYt79+7h3r17mD9/Pjw9PXHr1i2MHDkS9+7dw44dO8ouMREREek0jYsPIyMjODg4FGr39vbGzp07VV+7urpi1qxZGDJkCF6+fKkqUoiIiKhy03jMx9WrV+Ho6Ig6depg8ODBSEtLe+O12dnZsLKyYuFBREREKhpVBS1atMCGDRtQv3593L9/HzNmzEDbtm2RlJQES0tLtWsfPXqEmTNnIjg4+K33lMvlkMvlam1KQcGdbYmIiPRUqfZ2ycrKQu3atREZGYnhw4er2nNyctC5c2fY2Nhg7969MDY2fuM9pk+fjhkzZqi1ucADrhKvksYiIiIiEWhlbxdra2vUq1cP165dU7U9ffoU3bp1g6WlJXbv3v3WwgMAwsPDkZ2drXa4wL00sYiIiKgCK1XxkZubi+vXr6N69eoAXvV4dOnSBSYmJti7dy9MTU3feQ+pVAorKyu1g49ciIiI9JdGxceECRNw8uRJ3Lx5E2fOnEHfvn1haGiIQYMGqQqPvLw8rF27Fjk5OUhPT0d6ejoUCkV55SciIiIdo9GA0zt37mDQoEF4/PgxqlWrhjZt2uDs2bOoVq0aTpw4gXPnzgEA3Nzc1F6XmpoKZ2fnMgtNREREuqtUA07LS2eDAWJHICIiIg1pZcApERERkaZYfBAREZFWsfggIiIirWLxQURERFrF4oOIiIi0isUHERERaZVGxcf06dMhkUjUDnd39aXQY2Ji0LFjR5ibm8PKygrt2rXDs2fPyjQ0ERER6S6N97r38vLCkSNH/ncDo//dIiYmBt26dUN4eDiWLl0KIyMjJCQkwMCAHSxERET0isbFh5GRERwcHIo8FxoaijFjxmDy5Mmqtvr165c8HREREekdjbskrl69CkdHR9SpUweDBw9GWloaACAjIwPnzp2DnZ0dfH19YW9vjw8++ACnTp0q89BERESkuzQqPlq0aIENGzbg4MGDWL58OVJTU9G2bVs8ffoUN27cAPBqXMgXX3yBgwcPokmTJujUqROuXr36xnvK5XLk5OSoHUqBG9ERERHpq1Lt7ZKVlYXatWsjMjISHh4eaN26NcLDwzF79mzVNQ0aNIC/vz8iIiKKvMf06dMxY8YMtTYXeMBV4lXSWERERCQCreztYm1tjXr16uHatWuoXr06AMDT01PtGg8PD9WjmaKEh4cjOztb7XCB+xuvJyIiIt1WquIjNzcX169fR/Xq1eHs7AxHR0ekpKSoXXPlyhXUrl37jfeQSqWwsrJSOwwkhqWJRURERBWYRrNdJkyYgJ49e6J27dq4d+8epk2bBkNDQwwaNAgSiQQTJ07EtGnT0LBhQzRq1AhRUVFITk7Gjh07yis/ERER6RiNio87d+5g0KBBePz4MapVq4Y2bdrg7NmzqFatGgBg3LhxeP78OUJDQ5GZmYmGDRvi8OHDcHV1LZfwREREpHtKNeC0vHQ2GCB2BCIiItKQVgacEhEREWmKxQcRERFpFYsPIiIi0ioWH0RERKRVLD6IiIhIq1h8EBERkVZpXHzcvXsXQ4YMga2tLczMzODj44PY2FjVeUEQMHXqVFSvXh1mZmbw8/N768ZyREREVLloVHw8efIErVu3hrGxMQ4cOIC///4bCxYsQNWqVVXXzJs3D0uWLMGKFStw7tw5mJubo2vXrnj+/HmZhyciIiLdo9EiY5MnT8bp06fxxx9/FHleEAQ4Ojpi/PjxmDBhAgAgOzsb9vb22LBhAz755JNivQ8XGSMiItI95bLI2N69e9GsWTMMGDAAdnZ2aNy4MVavXq06n5qaivT0dPj5+anaZDIZWrRogZiYGE3eioiIiPSURsXHjRs3sHz5ctStWxfR0dEYNWoUxowZg6ioKABAeno6AMDe3l7tdfb29qpz/yaXy5GTk6N2KAVFST4LERER6QCNig+lUokmTZpg9uzZaNy4MYKDg/HFF19gxYoVJQ4QEREBmUymdqQiucT3IyIioopNo+KjevXq8PT0VGvz8PBAWloaAMDBwQEA8ODBA7VrHjx4oDr3b+Hh4cjOzlY7XOCuSSwiIiLSIRoVH61bt0ZKSopa25UrV1C7dm0AgIuLCxwcHHD06FHV+ZycHJw7dw6tWrUq8p5SqRRWVlZqh4HEUNPPQURERDrCSJOLQ0ND4evri9mzZ2PgwIE4f/48Vq1ahVWrVgEAJBIJxo0bh++//x5169aFi4sLpkyZAkdHR/Tp06c88hMREZGO0aj4aN68OXbv3o3w8HB89913cHFxwaJFizB48GDVNV9//TXy8vIQHByMrKwstGnTBgcPHoSpqWmZhyciIiLdo9E6H9rCdT6IiIh0T7ms80FERERUWiw+iIiISKtYfBAREZFWsfggIiIirWLxQURERFrF4oOIiIi0SuPi4+7duxgyZAhsbW1hZmYGHx8fxMbGFnntyJEjIZFIsGjRotLmJCIiIj2h0SJjT548QevWrdGhQwccOHAA1apVw9WrV1G1atVC1+7evRtnz56Fo6NjmYUlIiIi3adR8TF37lzUqlUL69evV7W5uLgUuu7u3bsYPXo0oqOj4e/vX/qUREREpDc0euyyd+9eNGvWDAMGDICdnR0aN26M1atXq12jVCoREBCAiRMnwsvLq0zDEhERke7TqPi4ceMGli9fjrp16yI6OhqjRo3CmDFjEBUVpbpm7ty5MDIywpgxY4p1T7lcjpycHLVDKSg0+xRERESkMzR67KJUKtGsWTPMnj0bANC4cWMkJSVhxYoVCAwMxMWLF7F48WLExcVBIpEU654RERGYMWOGWpsLPOAK9poQERHpI416PqpXrw5PT0+1Ng8PD6SlpQEA/vjjD2RkZMDJyQlGRkYwMjLCrVu3MH78eDg7Oxd5z/DwcGRnZ6sdLnAv2achIiKiCk+jno/WrVsjJSVFre3KlSuoXbs2ACAgIAB+fn5q57t27YqAgAAEBQUVeU+pVAqpVKrWZiAx1CQWERER6RCNio/Q0FD4+vpi9uzZGDhwIM6fP49Vq1Zh1apVAABbW1vY2tqqvcbY2BgODg6oX79+2aUmIiIinaXRY5fmzZtj9+7d+Omnn+Dt7Y2ZM2di0aJFGDx4cHnlIyIiIj0jEQRBEDvEv3U2GCB2BCIiItLQYeUvxbqOe7sQERGRVrH4ICIiIq1i8UFERERaxeKDiIiItIrFBxEREWkViw8iIiLSKo2Lj7t372LIkCGwtbWFmZkZfHx8EBsbqzqfm5uLr776CjVr1oSZmRk8PT2xYsWKMg1NREREukujFU6fPHmC1q1bo0OHDjhw4ACqVauGq1evomrVqqprwsLCcOzYMWzevBnOzs44dOgQvvzySzg6OqJXr15l/gGIiIhIt2hUfMydOxe1atXC+vXrVW0uLi5q15w5cwaBgYFo3749ACA4OBgrV67E+fPnWXwQERGRZo9d9u7di2bNmmHAgAGws7ND48aNsXr1arVrfH19sXfvXty9exeCIOD48eO4cuUKunTpUqbBiYiISDdpVHzcuHEDy5cvR926dREdHY1Ro0ZhzJgxiIqKUl2zdOlSeHp6ombNmjAxMUG3bt2wbNkytGvXrsh7yuVy5OTkqB1KQVG6T0VEREQVlkaPXZRKJZo1a4bZs2cDABo3boykpCSsWLECgYGBAF4VH2fPnsXevXtRu3Zt/P777wgJCYGjoyP8/PwK3TMiIgIzZsxQa3OBB1zhVdLPRERERBWYRhvL1a5dG507d8aaNWtUbcuXL8f333+Pu3fv4tmzZ5DJZNi9ezf8/f1V13z++ee4c+cODh48WOiecrkccrlcra2v7DMYSAxL8nmIiIhIJMXdWE6jno/WrVsjJSVFre3KlSuoXbs2AKCgoAAFBQUwMFB/mmNoaAilUlnkPaVSKaRSqVobCw8iIiL9pVHxERoaCl9fX8yePRsDBw7E+fPnsWrVKqxatQoAYGVlhQ8++AATJ06EmZkZateujZMnT2Ljxo2IjIwslw9AREREukWjxy4AsH//foSHh+Pq1atwcXFBWFgYvvjiC9X59PR0hIeH49ChQ8jMzETt2rURHByM0NBQSCSSYr1HZ4MBmn0KIiIiEl1xH7toXHxoA4sPIiIi3VPc4oN7uxAREZFWsfggIiIirWLxQURERFrF4oOIiIi0isUHERERaRWLDyIiItIqjYoPZ2dnSCSSQkdISAgyMzMxevRo1K9fH2ZmZnBycsKYMWOQnZ1dXtmJiIhIB2m0wumFCxegUPxvx9mkpCR07twZAwYMwL1793Dv3j3Mnz8fnp6euHXrFkaOHIl79+5hx44dZR6ciIiIdFOpFhkbN24c9u/fj6tXrxa5eukvv/yCIUOGIC8vD0ZGxa9zuMgYERGR7in3RcZevHiBzZs3Y9iwYW9cNj07OxtWVlYaFR5ERESk30pcFezZswdZWVn47LPPijz/6NEjzJw5E8HBwW+9j1wuh1wuV2tTCgrubEtERKSnStzzsXbtWnTv3h2Ojo6FzuXk5MDf3x+enp6YPn36W+8TEREBmUymdqQiuaSxiIiIqIIr0ZiPW7duoU6dOti1axd69+6tdu7p06fo2rUrqlSpgv3798PU1PSt9yqq56Ov7DP2fBAREemY4o75KNFjl/Xr18POzg7+/v5q7Tk5OejatSukUin27t37zsIDAKRSKaRSqVobCw8iIiL9pXHxoVQqsX79egQGBqoNJM3JyUGXLl2Qn5+PzZs3IycnBzk5OQCAatWqwdCQBQURERGVoPg4cuQI0tLSMGzYMLX2uLg4nDt3DgDg5uamdi41NRXOzs4lT0lERER6o1TrfJQXrvNBRESke8p9nQ8iIiKikmDxQURERFrF4oOIiIi0isUHERERaRWLDyIiItIqFh9ERESkVRoVH87OzpBIJIWOkJAQ1TUxMTHo2LEjzM3NYWVlhXbt2uHZs2dlHpyIiIh0k0aLjF24cAEKhUL1dVJSEjp37owBA16tyxETE4Nu3bohPDwcS5cuhZGRERISEmBgwA4WIiIieqVUi4yNGzcO+/fvx9WrVyGRSNCyZUt07twZM2fOLFUoLjJGRESke8p9kbEXL15g8+bNGDZsGCQSCTIyMnDu3DnY2dnB19cX9vb2+OCDD3Dq1KmSvgURERHpoRIXH3v27EFWVhY+++wzAMCNGzcAANOnT8cXX3yBgwcPokmTJujUqROuXr36xvvI5XLVJnSvD6WgeOP1REREpNtKXHysXbsW3bt3h6OjI4BXu90CwIgRIxAUFITGjRtj4cKFqF+/PtatW/fG+0REREAmk6kdqUguaSwiIiKq4EpUfNy6dQtHjhzB559/rmqrXr06AMDT01PtWg8PD6Slpb3xXuHh4cjOzlY7XOBeklhERESkAzSa7fLa+vXrYWdnB39/f1Wbs7MzHB0dkZKSonbtlStX0L179zfeSyqVQiqVqrUZSAxLEouIiIh0gMbFh1KpxPr16xEYGAgjo/+9XCKRYOLEiZg2bRoaNmyIRo0aISoqCsnJydixY0eZhiYiIiLdpXHxceTIEaSlpWHYsGGFzo0bNw7Pnz9HaGgoMjMz0bBhQxw+fBiurq5lEpaIiIh0X6nW+SgvXOeDiIhI95T7Oh9EREREJcHig4iIiLSKxQcRERFpFYsPIiIi0ioWH0RERKRVLD6IiIhIqzQqPhQKBaZMmQIXFxeYmZnB1dUVM2fOxD9n6wqCgKlTp6J69eowMzODn5/fWzeWIyIiospFo+Jj7ty5WL58OX788UdcvnwZc+fOxbx587B06VLVNfPmzcOSJUuwYsUKnDt3Dubm5ujatSueP39e5uGJiIhI92i0yNiHH34Ie3t7rF27VtXWv39/mJmZYfPmzRAEAY6Ojhg/fjwmTJgAAMjOzoa9vT02bNiATz75pFjvw0XGiIiIdE+5LDLm6+uLo0eP4sqVKwCAhIQEnDp1SrVxXGpqKtLT0+Hn56d6jUwmQ4sWLRATE6PJWxEREZGe0mhvl8mTJyMnJwfu7u4wNDSEQqHArFmzMHjwYABAeno6AMDe3l7tdfb29qpz/yaXyyGXy9XalIKCO9sSERHpKY16PrZv344tW7Zg69atiIuLQ1RUFObPn4+oqKgSB4iIiIBMJlM7UpFc4vsRERFRxaZR8TFx4kRMnjwZn3zyCXx8fBAQEIDQ0FBEREQAABwcHAAADx48UHvdgwcPVOf+LTw8HNnZ2WqHC9xL8lmIiIhIB2hUfOTn58PAQP0lhoaGUCqVAAAXFxc4ODjg6NGjqvM5OTk4d+4cWrVqVeQ9pVIprKys1A4+ciEiItJfGo356NmzJ2bNmgUnJyd4eXkhPj4ekZGRGDZsGABAIpFg3Lhx+P7771G3bl24uLhgypQpcHR0RJ8+fcojPxEREekYjYqPpUuXYsqUKfjyyy+RkZEBR0dHjBgxAlOnTlVd8/XXXyMvLw/BwcHIyspCmzZtcPDgQZiampZ5eCIiItI9Gq3zoS1c54OIiEj3lMs6H0RERESlxeKDiIiItIrFBxEREWkViw8iIiLSKhYfREREpFUsPoiIiEirNCo+FAoFpkyZAhcXF5iZmcHV1RUzZ87Em2brjhw5EhKJBIsWLSqLrERERKQHNFpkbO7cuVi+fDmioqLg5eWF2NhYBAUFQSaTYcyYMWrX7t69G2fPnoWjo2OZBiYiIiLdplHxcebMGfTu3Rv+/v4AAGdnZ/z00084f/682nV3797F6NGjER0drbqWiIiICNDwsYuvry+OHj2KK1euAAASEhJw6tQpdO/eXXWNUqlEQEAAJk6cCC8vr7JNS0RERDpPo56PyZMnIycnB+7u7jA0NIRCocCsWbMwePBg1TVz586FkZFRoccwbyKXyyGXy9XalIKCO9sSERHpKY16PrZv344tW7Zg69atiIuLQ1RUFObPn4+oqCgAwMWLF7F48WJs2LABEomkWPeMiIiATCZTO1KRrPknISIiIp2g0cZytWrVwuTJkxESEqJq+/7777F582YkJydj0aJFCAsLg4HB/2oahUIBAwMD1KpVCzdv3ix0z6J6PvrKPmPPBxERkY4p7sZyGj12yc/PVyssAMDQ0BBKpRIAEBAQAD8/P7XzXbt2RUBAAIKCgoq8p1QqhVQqVWtj4UFERKS/NCo+evbsiVmzZsHJyQleXl6Ij49HZGQkhg0bBgCwtbWFra2t2muMjY3h4OCA+vXrl11qIiIi0lkaFR9Lly7FlClT8OWXXyIjIwOOjo4YMWIEpk6dWl75iIiISM9oNOZDWzobDBA7AhEREWmouGM+uLcLERERaRWLDyIiItIqFh9ERESkVSw+iIiISKtYfBAREZF2CZXQ8+fPhWnTpgnPnz8XO0qp6MPn0IfPIAj8HBWJPnwGQdCPz6EPn0EQ+DnKQ4WcalvecnJyIJPJkJ2dDSsrK7HjlJg+fA59+AwAP0dFog+fAdCPz6EPnwHg5ygPfOxCREREWsXig4iIiLSKxQcRERFpVaUsPqRSKaZNm1ZoN11dow+fQx8+A8DPUZHow2cA9ONz6MNnAPg5ykOlHHBKRERE4qmUPR9EREQkHhYfREREpFUsPoiIiEirWHwQERGRVrH4ICIiIq1i8UFaFxgYiN9//13sGKXWsWNHZGVlFWrPyclBx44dtR9IQwUFBXB1dcXly5fFjkJ66vnz52JHoArKSOwAVPlkZ2fDz88PtWvXRlBQEAIDA1GjRg2xY2nsxIkTePHiRaH258+f448//hAhkWaMjY35y6EC2Lt3b7Gv7dWrVzkmKRtKpRKzZs3CihUr8ODBA1y5cgV16tTBlClT4OzsjOHDh4sdsVLJysrC+fPnkZGRAaVSqXZu6NChIqWqBOt85OTkFPtasTfaKY6wsLBiXxsZGVmOSUrn4cOH2LRpE6KiovD333/Dz88Pw4cPR+/evWFsbCx2vLf6888/AQCNGjXCsWPHYGNjozqnUChw8OBBrFy5Ejdv3hQpYfHNnj0bV65cwZo1a2BkpNv/Fvnggw8wfPhwDBgwAGZmZmLHKTYDA/UOaIlEgn/+tSyRSFR/VigUWstVUt999x2ioqLw3Xff4YsvvkBSUhLq1KmDn3/+GYsWLUJMTIzYEd9oyZIlxb52zJgx5ZikbOzbtw+DBw9Gbm4urKys1L6XJBIJMjMzRcum98WHgYGB2n/wt9GFH+wOHTogPj4eBQUFqF+/PgDgypUrMDQ0RJMmTVTXSSQSHDt2TKyYGomLi8P69euxZs0aWFhYYMiQIfjyyy9Rt25dsaMV6Z/fU0X9+JiZmWHp0qUYNmyYtqNprG/fvjh69CgsLCzg4+MDc3NztfO7du0SKZnmxo0bh61bt0Iul2PgwIEYPnw4WrZsKXYsjRw5cgSTJk3C7Nmz0apVKwBATEwMvv32W8yePRudO3cWOeG7ubm5YeXKlejUqRMsLS2RkJCAOnXqIDk5Ga1atcKTJ0/EjvhGLi4uxbpOIpHgxo0b5Zym9OrVq4cePXpg9uzZqFKlithx1Oj2P3WK4fjx46o/37x5E5MnT8Znn32m9oMdFRWFiIgIsSJqpGfPnrC0tERUVBSqVq0KAHjy5AmCgoLQtm1bjB8/XuSEmrl//z4OHz6Mw4cPw9DQED169EBiYiI8PT0xb948hIaGih2xkNTUVAiCgDp16uD8+fOoVq2a6pyJiQns7OxgaGgoYsLis7a2Rv/+/cWOUSYWLVqE+fPnY+/evYiKikK7du3g5uaGYcOGISAgAPb29mJHfKdx48ZhxYoVaNOmjaqta9euqFKlCoKDg3VifM7du3fh5uZWqF2pVKKgoECERMWXmpoqdoQydffuXYwZM6bCFR4AAKES6dixo7B169ZC7Vu2bBE++OAD7QcqAUdHRyEpKalQe2JiolC9enUREmnuxYsXwo4dOwR/f3/B2NhYaNq0qbB8+XIhOztbdc2uXbsEa2trEVOSrnvw4IEwc+ZMwdTUVDA2NhZ69+4tHD16VOxYb2VqaiokJiYWak9ISBBMTU1FSKS5Jk2aCJs2bRIEQRAsLCyE69evC4IgCDNmzBDatGkjZrRKp2/fvsLPP/8sdowi6X3Pxz/FxMRgxYoVhdqbNWuGzz//XIREmsvJycHDhw8LtT98+BBPnz4VIZHmqlevDqVSiUGDBuH8+fNo1KhRoWs6dOgAa2trrWfTRFRUFN577z34+/sDAL7++musWrUKnp6e+Omnn1C7dm2RE1Ze58+fx/r167Ft2zbY2dnhs88+w927d/Hhhx/iyy+/xPz588WOWKTmzZsjLCwMmzZtUvXUPHjwABMnTsT7778vcrrimTp1KgIDA3H37l0olUrs2rULKSkp2LhxI/bv3y92PI3cuXMHe/fuRVpaWqHB5RV5TN1r/v7+mDhxIv7++2/4+PgUGk8n6gBmsasfbapXr54wceLEQu0TJ04U6tWrJ0IizQUEBAjOzs7Czp07hdu3bwu3b98WduzYIbi4uAhDhw4VO16xbNy4UXj27JnYMUqtXr16qn9JnzlzRjAzMxNWrlwp9OzZU+jbt6/I6Yrvl19+EQYMGCC0aNFCaNy4sdqhSx48eCDMnz9f8PLyEkxMTIT+/fsLBw4cEJRKpeqaP/74QzA3Nxcx5dtdvXpV8Pb2FkxMTARXV1fB1dVVMDExEby8vISrV6+KHa/Yfv/9d8HPz0+oVq2aYGZmJrRu3VqIjo4WO5ZGjhw5IlSpUkXw9vYWjIyMhEaNGgnW1taCTCYTOnToIHa8YpFIJG88DAwMRM1WqYqP3377TTA1NRW8vb2F4cOHC8OHDxd8fHwEU1NT4bfffhM7XrHk5eUJo0aNEqRSqWBgYCAYGBgIJiYmwqhRo4Tc3Fyx473TixcvBENDwyK7lnWNmZmZcOvWLUEQBOHrr78WAgICBEEQhKSkJOG9994TM1qxLV68WLCwsBC++uorwcTERBgxYoTg5+cnyGQy4ZtvvhE7nkaMjY0Fd3d3Yd68eUJGRkaR12RnZwvt27fXcjLNKJVKITo6Wli8eLGwePFi4dChQ2oFFGlH8+bNhalTpwqC8L/HR0+fPhV69eol/Pe//xU5ne7T+9ku/3b79m0sX74cycnJAAAPDw+MHDkStWrVEjmZZvLy8nD9+nUAgKura6FZChVZnTp1sHv3bjRs2FDsKKViZ2eH6OhoNG7cGI0bN0ZYWBgCAgJw/fp1NGzYELm5uWJHfCd3d3dMmzYNgwYNUpuZMHXqVGRmZuLHH38UO2Kx/fHHH2jbtq3YMSq927dvQyKRoGbNmgBePQLbunUrPD09ERwcLHK64rO0tMSlS5fg6uqKqlWr4tSpU/Dy8kJCQgJ69+6tE1PpK7JKNeYDAGrVqoXZs2eLHaPU7t+/j/v376Ndu3YwMzODIAjFnlIstv/85z/45ptvsGnTJrU1MnRN586d8fnnn6Nx48a4cuUKevToAQD466+/4OzsLG64YkpLS4Ovry+AV1OEX48bCggIQMuWLXWq+HhdeGRkZCAlJQUAUL9+fdjZ2YkZS2N5eXk4efJkkeMMdGFtiU8//RTBwcEICAhAeno6/Pz84O3tjS1btiA9PR1Tp04VO2KxmJubq/77V69eHdevX4eXlxcA4NGjR2JG08jJkycxf/581UwpT09PTJw4UfRCXe+Ljz///BPe3t4wMDBQLQ71Jg0aNNBSqpJ7/PgxBg4ciOPHj0MikeDq1auoU6cOhg8fjqpVq2LBggViR3ynH3/8EdeuXYOjoyNq165dqNcmLi5OpGSaWbZsGb799lvcvn0bO3fuhK2tLQDg4sWLGDRokMjpisfBwQGZmZmoXbs2nJyccPbsWTRs2FA1nViXPH36FF9++SW2bdumWrPH0NAQH3/8MZYtWwaZTCZywneLj49Hjx49kJ+fj7y8PNjY2ODRo0eoUqUK7OzsdKL4SEpKUg2O3b59O3x8fHD69GkcOnQII0eO1Jnio2XLljh16hQ8PDzQo0cPjB8/HomJidi1a5fOrB+zefNmBAUFoV+/fqrvndOnT6NTp07YsGEDPv30U9Gy6f1jFwMDA6Snp8POzk61OFRRH1kikejEImNDhw5FRkYG1qxZAw8PD1U3eXR0NMLCwvDXX3+JHfGdZsyY8dbz06ZN01IS+vzzz1GrVi1MmzYNy5Ytw8SJE9G6dWvExsaiX79+WLt2rdgRi+3jjz9GfHw8li5dqraOz9ixY9GoUSNs27ZN5ITv1r59e9SrVw8rVqyATCZDQkICjI2NMWTIEIwdOxb9+vUTO+I7WVhYICkpCc7OzujVqxdat26NSZMmIS0tDfXr18ezZ8/EjlgsN27cQG5uLho0aIC8vDyMHz8eZ86cQd26dREZGakTs9k8PDwQHBxcaL2kyMhIrF69WtR1Y/S++Lh16xacnJwgkUhw69att16rC99MDg4OiI6ORsOGDdWe0d+4cQMNGjTQiXEG+uJdm+O1a9dOS0lKTqlUQqlUqpZW37Ztm+ov2BEjRsDExETkhMVnbm6O6OhotQW6gFdjQbp164a8vDyRkhWftbU1zp07h/r168Pa2hoxMTHw8PDAuXPnEBgYqBqrVpG1aNECHTp0gL+/P7p06aLqTTt79iw++ugj3LlzR+yI76RQKHD69Gk0aNCgwk/5fxupVIq//vqr0KJv165dg7e3t6h7O+n9Y5d/FhTFLS78/f2xZs0aVK9evbxilVheXl6Rq9VlZmZCKpWKkKjyat++faE2XduHw8DAQG1vkU8++QSffPKJiIlKztbWtshHKzKZTLUacEVnbGys+v9hZ2eHtLQ0eHh4QCaT4fbt2yKnK565c+eib9+++OGHHxAYGKgaWL53716dWavE0NAQXbp0weXLl3W6+KhVqxaOHj1aqPg4cuSI6JMs9L74KInff/+9wnYNtm3bFhs3bsTMmTMBvPplp1QqMW/ePHTo0EHkdMVTtWrVIgfHSiQSmJqaws3NDZ999hmCgoJESFd8/96joqCgAPHx8ZgyZQpmzZolUirN/fHHH1i5ciWuX7+OHTt2oEaNGti0aRNcXFwK9SJUZN9++61qgS4HBwcAQHp6OiZOnIgpU6aInK54GjdujAsXLqBu3br44IMPMHXqVDx69AibNm2Ct7e32PGKpX379nj06BFycnLUir7g4OCKucz3G3h7e+PGjRvF3u+lIho/fjzGjBmDS5cuqQaWnz59Ghs2bMDixYvFDSfaJN8K7J9LAlc0iYmJgp2dndCtWzfBxMRE+OijjwQPDw/B3t5euHbtmtjxiiUyMlKwtbUVhgwZIixZskRYsmSJMGTIEOG9994TZs2aJXz++eeCVCoVVq1aJXbUEjlx4oTQpEkTsWMUy44dOwQzMzPVf/PX3/dLly4VunfvLnI6zTRq1EiwsLAQjI2NVQt0GRsbCxYWFjqzeNqFCxeEY8eOCYLwatG0rl27CpaWlkLTpk2F+Ph4ccNVMgcOHBAaNWok7Nu3T7h3756QnZ2tduiKXbt2Ca1btxZsbGwEGxsboXXr1sKePXvEjlX51vkojn+OpaiIsrOz8eOPPyIhIQG5ublo0qQJQkJCKuRjoqL0798fnTt3xsiRI9XaV65ciUOHDmHnzp1YunQpVq1ahcTERJFSllxycjKaNWumE+NvGjdujNDQUAwdOlTt+z4+Ph7du3dHenq62BGL7V0Dmf+pog5qfvbsGQRBUPUQ3Lx5E7t374anpye6du0qcro3a9KkCY4ePYqqVauicePGb532ryuz2f75OPKfn0f4/8sa6MJj1YqMj110kEwmw3/+8x+xY5RYdHQ05s6dW6i9U6dOql15e/TogcmTJ2s7mkb+PXVbEATcv38fc+bMKXK/moooJSWlyIGxMpkMWVlZ2g9UChW1oNBE79690a9fP4wcORJZWVlo2bIljI2N8ejRI0RGRmLUqFFiRyxS7969VWPO+vTpI26YMvLPHdGp7LH40DEHDx6EhYWF6ln8smXLsHr1anh6emLZsmU6MbDOxsYG+/btKzT9a9++fapFx/Ly8mBpaSlGvGJr1KhRkVO3W7ZsiXXr1omUSjMODg64du1aoUXRTp06VWF7/t4lNjZWbUGlpk2bipyo+OLi4rBw4UIAwI4dO2Bvb4/4+Hjs3LkTU6dOrbDFx+vCT6FQoEOHDjo/SwQAXFxcUKtWrUK9OIIgVOjBvzY2Nrhy5Qree++9N46vey0zM1OLydSx+NAxEydOVPUaJCYmIiwsDOPHj8fx48cRFhaG9evXi5zw3aZMmYJRo0bh+PHjqtHvFy5cwP/93/+pdh0+fPgwPvjgAzFjvlNqaqra1wYGBqhWrRpMTU1FSqS5L774AmPHjsW6desgkUhw7949xMTEYMKECTozSPO1O3fuYNCgQTh9+rTqF19WVhZ8fX2xbds21XLfFVl+fr6q6D506BD69esHAwMDtGzZ8p1LBVQE+jJLBHhVfNy/f7/QCrmZmZlwcXGpsI9dFi5cqPoeWrhwYcVd+VrE8SYV1uzZs4UnT56IHaNI5ubmQmpqqiAIgjBt2jShf//+giAIwsWLFwV7e3sRk2nm1KlTwieffKIaAPjJJ58Ip0+fFjtWpZCQkCAoFArV199//71gbm6u2u3S1NRU+Pbbb0VMWDJdu3YVWrRoISQnJ6vakpOThVatWgldu3YVMVnx+fj4CIsXLxbS0tIEKysr4cyZM4IgCEJsbKzO/Hw3bdpUOHLkiNgxSk0ikRS5QeHNmzeFKlWqiJBIv1S6AaebNm3CihUrkJqaipiYGNSuXRuLFi2Ci4sLevfuLXa8d7KxscGpU6fg6emJNm3aYOjQoQgODsbNmzfh6emJ/Px8sSNWGkuWLCmy/Z9Thtu1awdDQ0MtJ3s7Q0ND1b/o6tSpgwsXLsDS0hLXrl1Dbm4uPD09YWFhIXZMjZmZmeHMmTNo3LixWvvFixfRtm1bnfjZ2LFjBz799FMoFAp06tQJhw4dAgBERETg999/x4EDB0RO+G4HDx5EeHg4Zs6ciaZNmxbaPsHKykqkZMUTFhYGAFi8eDG++OILtenBCoUC586dg6GhIU6fPi1WxGL758/6Pz1+/Bh2dnai9t5Uqscuy5cvx9SpUzFu3DjMmjVL9R/e2toaixYt0onio02bNggLC0Pr1q1x/vx5/PzzzwCAK1eu6ES38mtKpRLXrl1DRkYGlEql2jldWBkUeNWl+fDhQ+Tn56vG2jx58gRVqlSBhYUFMjIyUKdOHRw/flz0BX3+ydraGqmpqbCzs8PNmzehVCphYmICT09PsaOVSq1atVBQUFCoXaFQwNHRUYREmvvoo4/Qpk0b3L9/X23X506dOqFv374iJiu+1xss9urVSydnicTHxwN4lTcxMVFtlV8TExM0bNgQEyZMECueRt7UtyCXy0VfvbhS9Xx4enpi9uzZ6NOnj9q0wqSkJNXCOBVdWloavvzyS9y+fRtjxozB8OHDAQChoaFQKBRv/Nd4RXL27Fl8+umnuHXrVqEfDl34y+m1n376CatWrcKaNWvg6uoK4NWyxSNGjEBwcDBat26NTz75BA4ODtixY4fIaf8nODgYGzduRPXq1ZGWloaaNWu+sXfmxo0bWk5Xcr/++itmz56NZcuWoVmzZgBeDT4dPXo0Jk2apDezMCq6kydPvvV8RR/L9VpQUBAWL15c4XtqivL690BoaChmzpyp1pOpUCjw+++/4+bNm6pCSwyVqvgwMzNDcnIyateurVZ8XL16FQ0aNKiwq5qWxJw5czBy5MgKOeirUaNGqFevHmbMmIHq1asXGhClC7uPAoCrqyt27txZaFptfHw8+vfvjxs3buDMmTPo378/7t+/L07INzh48CCuXbuGMWPG4LvvvnvjzKKxY8dqOVnJVa1aFfn5+Xj58qVqr5rXf/5317+Yo/yJytvrVVlv3bpV6B8XJiYmcHZ2xnfffYcWLVqIFbFyPXZxcXHBpUuXCu3xcvDgQXh4eIiUqnzMnj0bAwcOrJDFx9WrV7Fjx45C+w3omvv37+Ply5eF2l++fKlanMvR0RFPnz7VdrR36tatG4BX4yHGjh1b4ac1F8eiRYvEjkD/35MnT7B27Vq1Kc9BQUGqqfS6oGPHjm89f+zYMS0l0dzrmXgdOnTArl27KuQSDJWq+AgLC0NISAieP38OQRBw/vx5/PTTT4iIiMCaNWvEjlemKnKHVosWLXDt2jWdLz46dOiAESNGYM2aNapBjvHx8Rg1apTqL67ExMQKvTeELkzNLq7AwECxIxBe7Y3Vs2dPyGQy1eOvJUuW4LvvvsO+fft0ZkzXP8fcAK/2brp06RKSkpJ05nvtnwulvf6dUGGm3ooyx0ZEmzdvFtzc3FTTCmvUqCGsWbNG7FhlriLvT7Nr1y7B09NTWL9+vRAbGyskJCSoHbri/v37gp+fnyCRSAQTExPBxMREMDAwEDp37iykp6cLgiAIx44dE6Kjo0VOWvk8e/ZMZ/fi0HXe3t7CF198Ibx8+VLV9vLlSyE4OFjw9vYWMVnZmDZtmjB+/HixYxRbVFSU4O3tLUilUkEqlQo+Pj7Cxo0bxY5V+abavpafn4/c3NxCU5D0RUXen+afeyb8my4NOH0tJSUFKSkpAID69eujfv36IieqnPLy8jBp0iRs374djx8/LnRe176vdJWZmRkuXbpU6OcgJSUFjRo10vmxddeuXcP777+vE+OGIiMjMWXKFHz11Vdo3bo1gFerFy9btgzff/99oVWmtalSPXb5pypVqujU9s765N8rg+q61wWHQqFAYmIinjx5UiGfseq7r7/+GsePH8fy5csREBCAZcuW4e7du1i5ciXmzJkjdrxKo0mTJrh8+XKh4uPy5cuFHmXoopiYGJ1ZxXjp0qVYvnw5hg4dqmrr1asXvLy8MH36dBYf5Ukfd1vUda8H/P79999IS0vDixcvVOckEkmhAcEV1bhx4+Dj44Phw4dDoVDggw8+wJkzZ1ClShXs378f7du3FztipbJv3z5s3LgR7du3R1BQENq2bQs3NzfUrl0bW7ZsweDBg8WOWCmMGTMGY8eOxbVr19CyZUsAr6bXL1u2DHPmzFHbkLFBgwZixXynfv36qX0t/P+NI2NjY3Vm64H79+/D19e3ULuvr6/oM/D0vvj4526LvXv3rjiDbcpZ27ZtYWZmJnaMIt24cQN9+/ZFYmKi2sZsr//f6Er3+I4dOzBkyBAAr37x3bhxA8nJydi0aRP+85//6MQKiPokMzNT9ZjRyspK1S3epk2bCrshmz4aNGgQgFc9UUWde/0zX9Efsf57yr+BgQHq16+P7777Dl26dBEplWbc3Nywfft2fPPNN2rtP//8M+rWrStSqlcq7ZgPXXb9+nWsX78e169fx+LFi2FnZ4cDBw7AyckJXl5eYsd7p549e8LQ0BBr1qyBi4sLzp07h8zMTIwfPx7z589H27ZtxY5YLKamprh27Rpq1qyJ4OBgVKlSBYsWLUJqaioaNmyInJwcsSNWKg0aNMDSpUvxwQcfwM/PD40aNcL8+fOxZMkSzJs3D3fu3BE7YqWgyQZ4utLLqat27tyJjz/+GH5+fqoxH6dPn8bRo0exfft2cVfNFXGwq9YNHz5cOH78uNgxSuXEiROCmZmZ4OfnJ5iYmKhmtERERKg2mavobG1tVbNarKysVBuBHT16VGjUqJGY0TTi5OQkREdHCy9fvhRq1aol7N+/XxAEQUhKShKsra1FTlf5REZGCosXLxYEQRAOHz4smJqaClKpVDAwMBAWLVokcrrK4cWLF0JQUJBw48YNsaOUiSdPngirV68WJk+eLDx+/FgQhFebeN65c0fkZMUXGxsrDB48WGjSpInQpEkTYfDgwUJcXJzYsYRKVXz06tVLkEqlQs2aNYUJEyYIly5dEjuSxlq2bCksWLBAEAT16bTnzp0TatSoIWa0YrO2tlb95VSnTh3h2LFjgiAIwrVr1wQzMzMxo2lk2rRpgkwmE9zd3QUnJyfh+fPngiAIwtq1a4WWLVuKnI5u3rwp7Ny5U6emb+sDKysrvSg+EhIShPfee09wc3MTjIyMVH/X/uc//xECAgJETqf79H7Mxz/9+uuvePLkCX755Rds3boVkZGRcHd3x+DBg/Hpp5/C2dlZ7IjvlJiYiK1btxZqt7Oz04m9aQDA29sbCQkJcHFxQYsWLTBv3jyYmJhg1apVFXJq8JtMnz4d3t7euH37NgYMGKAaW2RoaIjJkyeLnK5yOnr0KI4ePVrkhoXr1q0TKVXl0qdPH+zZs0fUmRRlISwsDEFBQZg3b57aCsA9evTAp59+KmIyzWVkZBT5MyHmgN9KVXwAr/Z/CA4ORnBwMO7cuYOffvoJ69atw9SpU4tcKruisba2xv379wutmhkfH48aNWqIlEoz3377LfLy8gAA3333HT788EO0bdsWtra2ql16dcVHH31UqE1XVj/UNzNmzMB3332HZs2aFblnEGlH3bp18d133+H06dNo2rRpoX11xowZI1IyzVy4cAErV64s1F6jRg3V9gkV3cWLFxEYGIjLly9XuE08K13x8VpBQQFiY2Nx7tw53Lx5E/b29mJHKpZPPvkEkyZNwi+//AKJRAKlUonTp09jwoQJanO5K7KuXbuq/uzm5obk5GRkZmaiatWqFf4XxpIlSxAcHAxTU9N37iCsK3/J6osVK1Zgw4YNCAgIEDtKpbZ27VpYW1vj4sWLuHjxoto5iUSiMz8XUqm0yEHjV65cQbVq1URIpLlhw4ahXr16WLt2Lezt7SvU36+VbrbL8ePHsXXrVuzcuRNKpRL9+vXD4MGD0bFjxwr1P+ZNXrx4gZCQEGzYsAEKhQJGRkZQKBT49NNPsWHDhjdujU5lw8XFBbGxsbC1tX3rni0SiUSntqPXB7a2tjh//jxcXV3FjkJ64PPPP8fjx4+xfft22NjY4M8//4ShoSH69OmDdu3a6cRGhpaWloiPj6+Q+2hVquKjRo0ayMzMRLdu3TB48GD07NlT9Zxe16SlpSEpKQm5ublo3Lix6HO2icQ2adIkWFhY6MwCUFSxZWdn46OPPkJsbCyePn0KR0dHpKeno2XLljhw4EChx0kVUZ8+fRAQEID+/fuLHaWQSlV8rF69GgMGDKiQ28yTbggLCyvWdRKJBAsWLCjnNPTP/x9KpRJRUVFo0KABGjRoAGNjY7VrIyMjtR2vUho2bNhbz+vawN/Tp08jISEBubm5aNKkCfz8/MSOVGyPHj1CYGAg3n//fXh7exf6mejVq5dIySpZ8fFPrxccqlmzpshJNPOmX34SiQSmpqZwc3ND7969YWNjo+VklUOHDh3Uvo6Li8PLly9V+1hcuXIFhoaGaNq0KY4dOyZGxErl3/8/3kQikfD/h5b8e+GqgoICJCUlISsrCx07dsSuXbtESqY5XZ89tW/fPgQEBBQ5dkXsAaeVqvhQKpX4/vvvsWDBAuTm5gJ49Uxs/Pjx+M9//vPW3VYrig4dOiAuLg4KhaLQLzx3d3ekpKRAIpHg1KlT8PT0FDmtfouMjMSJEycQFRWl2kjuyZMnqn1Fxo8fL3JCoopBqVRi1KhRcHV1LXLZ9YroXbOndu/eLVKy4nN2dsaHH36IKVOmVLxJFSKtLyKKyZMnC9WqVRP++9//CgkJCUJCQoKwbNkyoVq1asI333wjdrxiWbhwodCvXz8hOztb1ZaVlSV89NFHwqJFi4S8vDyhd+/eQpcuXURMWTk4OjoKSUlJhdoTExOF6tWri5CIqOJKTk4WHBwcxI5RbA4ODsLGjRvFjlEqFhYWwrVr18SOUaRKVXxUr15d+PXXXwu179mzR3B0dBQhkeYcHR2Fv/76q1B7UlKS6jNcvHhRsLW11Xa0SsfCwqLI5fqPHTsmWFhYaD8QUQX222+/Ce+9957YMYrNxsamwv7iLq6hQ4cKq1evFjtGkSrVOh+ZmZlwd3cv1O7u7q7aAbOiy87ORkZGRqFHKg8fPlQ917O2tlbbpp7KR9++fREUFIQFCxbg/fffBwCcO3cOEydOLLQdN1Fl8e9xacL/34r+t99+06kF+D7//HNs3bpVp2dP1atXD+Hh4Th16hR8fHwKDTgVc82VSjXmo0WLFmjRokWhxaFGjx6NCxcu4OzZsyIlK77BgwcjJiYGCxYsQPPmzQG8WolvwoQJ8PX1xaZNm7Bt2zbMnz8fsbGxIqfVb/n5+ZgwYQLWrVuHgoICAICRkRGGDx+OH374QSem4hGVtX8PAjYwMEC1atXQsWNHDBs2DEZGuvFv3rFjx2Ljxo06PXuqIq9FVKmKj5MnT8Lf3x9OTk5o1aoVACAmJgZpaWk4cOCATmzlnpubi9DQUGzcuFG1HLyRkRECAwOxcOFCmJub49KlSwCARo0aiRe0EsnLy8P169cBAK6uriw6qFLLz8+HIAiqn4ObN29iz5498PDwUFvduKJ720wqzp4qvUpVfADA3bt3sXz5cly+fBkA4OHhgS+//BKOjo4iJ9NMbm6uqmqtU6cOLCwsRE5ERAR06dIF/fr1w8iRI5GVlQV3d3cYGxvj0aNHiIyMxKhRo8SOWOm8ePECqampcHV1rTA9T5Wu+Hj+/Dn+/PPPIudti7ngChGRPnjvvfdw8uRJeHl5Yc2aNVi6dCni4+Oxc+dOTJ06VfUPPyp/+fn5GD16NKKiogC8WpahTp06GD16NGrUqCHq7tsVowTSkoMHD2Lo0KF4/PhxhdvhTxOxsbHYvn070tLSCg0s1aUFfIhI/+Tn56u2oD906BD69esHAwMDtGzZErdu3RI5XeUSHh6OhIQEnDhxAt26dVO1+/n5Yfr06aIWHxV/Va0yNHr0aAwYMAD37t2DUqlUO3Sl8Ni2bRt8fX1x+fJl7N69GwUFBfjrr79w7NgxyGQyseMRUSXn5uaGPXv24Pbt24iOjkaXLl0AABkZGbCyshI5XeWyZ88e/Pjjj2jTpo3aImleXl6qcWpiqVTFx4MHDxAWFlbxVnrTwOzZs7Fw4ULs27cPJiYmWLx4MZKTkzFw4EA4OTmJHY+IKrmpU6diwoQJcHZ2RosWLVSD+w8dOoTGjRuLnK5yefjwIezs7Aq15+Xlib6Le6UqPj766COcOHFC7Bilcv36dfj7+wMATExMVN9EoaGhWLVqlcjpiKiy++ijj5CWlobY2FgcPHhQ1d6pUycsXLhQxGSVT7NmzfDbb7+pvn5dcKxZs0ZVFIqlUo35+PHHHzFgwAD88ccfFW7BleKqWrUqnj59CgCoUaMGkpKS4OPjg6ysLOTn54ucjogIcHBwgIODg1rb64X4SHtmz56N7t274++//8bLly+xePFi/P333zhz5gxOnjwparZKVXz89NNPOHToEExNTXHixAm1bieJRKITxUe7du1w+PBh+Pj4YMCAARg7diyOHTuGw4cPo1OnTmLHIyKiCqJNmza4dOkS5syZAx8fHxw6dAhNmjRBTEwMfHx8RM1WqabaOjg4YMyYMZg8ebJO7GBblMzMTDx//hyOjo5QKpWYN28ezpw5g7p16+Lbb79V7a5KRERUHHPmzMHIkSNhbW2ttfesVMWHjY0NLly4AFdXV7GjEBERVQhWVla4dOkS6tSpo7X3rFSPXQIDA/Hzzz/jm2++ETtKqWVkZBS5UFqDBg1ESkRERLpIjD6ISlV8KBQKzJs3D9HR0Tq7UdDFixcRGBiIy5cv6/RCaUREVHlVquIjMTFRNc88KSlJ7ZzYc56La9iwYahXrx7Wrl0Le3t7nclNRET0WqUa86EPLC0tER8fDzc3N7GjEBGRHrC0tERCQoJWx3zo5pSPSqxTp05ISEgQOwYREVGJVarHLvpgzZo1CAwMRFJSEry9vQuNW+HOvEREpIm2bdvCzMxMq+/Jxy46Zt++fQgICEBOTk6hcxxwSkRErxkaGuL+/fuF9nd5/Pgx7OzsRP19wccuOmb06NEYMmQI7t+/r7M78xIRUfl7U9+CXC6HiYmJltOo42MXHfP48WOEhobq9M68RERUfpYsWQLgVW/4mjVrYGFhoTqnUCjw+++/w93dXax4AFh86Jx+/frh+PHjXKWViIiK9Hr3YEEQsGLFChgaGqrOmZiYwNnZGStWrBArHgAWHzqnXr16CA8Px6lTp3R2Z14iIio/qampAIAOHTpg165dFXLPLw441TEuLi5vPCeRSHDjxg0tpiEiItIciw8iIiI9pFAosGHDBhw9erTIvcCOHTsmUjI+dtEJYWFhmDlzJszNzREWFvbG6yQSCRYsWKDFZEREVFGNHTsWGzZsgL+/P7y9vSvUdhwsPnRAfHw8CgoKVH9+k4r0jUVEROLatm0btm/fjh49eogdpRA+diEiItJDjo6OOHHiBOrVqyd2lEK4yBgREZEeGj9+PBYvXvzGxcbExJ4PIiIiPdS3b18cP34cNjY28PLyKrQ0w65du0RKxjEfREREesna2hp9+/YVO0aR2PNBREREWsUxH0RERKRVfOxCRESkp3bs2IHt27cjLS0NL168UDsXFxcnUir2fBAREemlJUuWICgoCPb29oiPj8f7778PW1tb3LhxA927dxc1G8d8EBER6SF3d3dMmzYNgwYNgqWlJRISElCnTh1MnToVmZmZ+PHHH0XLxp4PIiIiPZSWlgZfX18AgJmZGZ4+fQoACAgIwE8//SRmNBYfRERE+sjBwQGZmZkAACcnJ5w9exYAkJqaKvrCYyw+iIiI9FDHjh2xd+9eAEBQUBBCQ0PRuXNnfPzxx6Kv/8ExH0RERHpIqVRCqVTCyOjVxNZt27bhzJkzqFu3LkaMGAETExPRsrH4ICIiIq3iOh9ERER6KisrC+fPn0dGRgaUSqXauaFDh4qUij0fREREemnfvn0YPHgwcnNzYWVlBYlEojonkUhUg1HFwOKDiIhID9WrVw89evTA7NmzUaVKFbHjqGHxQUREpIfMzc2RmJiIOnXqiB2lEE61JSIi0kNdu3ZFbGys2DGKxAGnREREesjf3x8TJ07E33//DR8fHxgbG6ud79Wrl0jJ+NiFiIhILxkYvPnhhkQigUKh0GKaf70/iw8iIiLSJo75ICIiIq3imA8iIiI9sWTJEgQHB8PU1BRLlix567VjxozRUqrC+NiFiIhIT7i4uCA2Nha2trZwcXF543USiQQ3btzQYrJ/vT+LDyIiItImjvkgIiIireKYDyIiIj0kCAJ27NiB48ePF7mx3K5du0RKxuKDiIhIL40bNw4rV65Ehw4dYG9vr7axnNg45oOIiEgP2djYYPPmzejRo4fYUQrhmA8iIiI9JJPJKuSmcgCLDyIiIr00ffp0zJgxA8+ePRM7SiF87EJERKSHnj17hr59++L06dNwdnYutLFcXFycSMk44JSIiEgvBQYG4uLFixgyZAgHnBIREVH5Mzc3R3R0NNq0aSN2lEI45oOIiEgP1apVC1ZWVmLHKBKLDyIiIj20YMECfP3117h586bYUQrhYxciIiI9VLVqVeTn5+Ply5eoUqVKoQGnmZmZIiXjgFMiIiK9tGjRIrEjvBF7PoiIiEir2PNBRESkpxQKBfbs2YPLly8DALy8vNCrVy8YGhqKmos9H0RERHro2rVr6NGjB+7evYv69esDAFJSUlCrVi389ttvcHV1FS0biw8iIiI91KNHDwiCgC1btsDGxgYA8PjxYwwZMgQGBgb47bffRMvG4oOIiEgPmZub4+zZs/Dx8VFrT0hIQOvWrZGbmytSMq7zQUREpJekUimePn1aqD03NxcmJiYiJPofFh9ERER66MMPP0RwcDDOnTsHQRAgCALOnj2LkSNHolevXqJm42MXIiIiPZSVlYXAwEDs27dPtcBYQUEBevfujfXr18Pa2lq0bCw+iIiI9Ni1a9dUU209PDzg5uYmciIWH0RERHojLCys2NdGRkaWY5K34yJjREREeiI+Pl7t67i4OLx8+VK1zseVK1dgaGiIpk2bihFPhcUHERGRnjh+/Ljqz5GRkbC0tERUVBSqVq0KAHjy5AmCgoLQtm1bsSIC4GMXIiIivVSjRg0cOnQIXl5eau1JSUno0qUL7t27J1IyTrUlIiLSSzk5OXj48GGh9ocPHxa5/oc2sfggIiLSQ3379kVQUBB27dqFO3fu4M6dO9i5cyeGDx+Ofv36iZqNj12IiIj0UH5+PiZMmIB169ahoKAAAGBkZIThw4fjhx9+gLm5uWjZWHwQERHpsby8PFy/fh0A4OrqKmrR8RqLDyIiItIqjvkgIiIirWLxQURERFrF4oOIiIi0isUHERERaRWLDyIiItIqFh9ERESkVSw+iIiISKv+H3krjS3axYgoAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Distribution and Visualization\n",
        "\n",
        "# Distribution of numerical features\n",
        "data_emot.hist(bins=30, figsize=(10, 7))\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Box plots for numerical features\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.boxplot(data=data_emot)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()\n",
        "\n",
        "# Distribution of categorical features\n",
        "for column in data_emot.select_dtypes(include='object'):\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    sns.countplot(y=column, data=data_emot)\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "p2jgxf3dV1xN",
        "outputId": "4197e187-7296-407f-8ed8-65d0a1e7c441"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Emotion Ranges:\n",
        "\n",
        "Angry: Ranges from 0.16% to 71.17%.\n",
        "Disgust: Small values from nearly 0 to 21.51%.\n",
        "Fear: Has a wide range from 0.07% to 94.98%.\n",
        "Happy: Ranges from 0.000005% to 66.22%.\n",
        "Sad: From 0.000073% to 91.56%.\n",
        "Neutral: From 0.0001% to 97.82%."
      ],
      "metadata": {
        "id": "cj7CtTglaVlg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyzing the distribution of emotions across the dataset.\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Set up the figure size and style\n",
        "plt.figure(figsize=(14, 8))\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Plot the distribution of each emotion\n",
        "emotion_columns = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']\n",
        "data_emot[emotion_columns].plot(kind='box', subplots=True, layout=(3, 3), figsize=(14,10), sharex=False, sharey=False)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yN6Tonpmaf8-",
        "outputId": "8decb2ff-56c4-4e6b-97a8-ec53596a69f9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 9 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Box Plots: This will generate box plots for each emotion to show the distribution, outliers, and variability. It gives an overview of how each emotion is distributed across the dataset."
      ],
      "metadata": {
        "id": "EhPsqiFsb3xV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Count the occurrences of each dominant emotion\n",
        "dominant_emotion_counts = data_emot['dominant_emotion'].value_counts()\n",
        "\n",
        "# Plotting the frequency of dominant emotions\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=dominant_emotion_counts.index, y=dominant_emotion_counts.values, palette=\"viridis\")\n",
        "plt.title('Dominant Emotion Frequency', fontsize=16)\n",
        "plt.xlabel('Emotion', fontsize=12)\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "Finfaya7clfQ",
        "outputId": "1942c47f-525a-4771-f114-ba20f1f94508"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-cda89470677c>:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=dominant_emotion_counts.index, y=dominant_emotion_counts.values, palette=\"viridis\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A bar chart displaying how often each emotion is the dominant one across the dataset."
      ],
      "metadata": {
        "id": "ACPXYBX7c4AG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyze Correlations between Emotions:\n",
        "\n",
        "# Select emotion columns to explore correlations\n",
        "emotion_columns = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']\n",
        "\n",
        "# Compute the correlation matrix\n",
        "correlation_matrix = data[emotion_columns].corr()\n",
        "\n",
        "# Plot the heatmap of the correlation matrix\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
        "plt.title('Correlation Matrix of Emotions', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "S-XH8_xrc5UA",
        "outputId": "85b4ff29-8275-4499-9945-c01e85340803"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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AQABdunThs88+w9nZGXNzcwwMDPD19U2y9im1kjonUuvRo0ea0X7UajXXr19PtIldfpTceZMd51ROSuv+FSxYkFWrVnH9+nVOnTrFlStXuHLlCjdv3mTVqlV8/vnnTJ48OYtKK4T4L5CgQuQbTZs25ZdffiEsLIxjx47RsmXLVOdNmJQqJCSEiIiIRGsrEmoTsmICqyJFivDo0SMGDBhAxYoVM7y9gwcPAjB37txEm1Y8evQow9/xvoRjknCMEpOVxy81WrdurRlFC0jVSDcXL14kICCA8uXLM2PGDJ3ljx8/zvRypkVsbCwjRowgMjKSjz/+mMOHDzNr1iyqVq2apvPIwcEBiB8+Nyk5/f+XlaytrTE2NkahUPD06dNEfzOZvf859ZupVKmSplbizZs3HD16lDFjxrBhwwZat25NnTp1Mu27hBD/Lfn7VY74TylRogTt2rUD4JdffiEkJCTZ9QMDA/Hx8QGgcOHCmqYGCePuv0/9dk4DIMWOvemR0DY6IRjIqITx7RNrznLq1CmCg4MTzZfQnCIuLi5N31ezZk309fXx9vbmzp07Ostfv37NqVOngKw5fqlRsGBBOnfujLW1NcWKFaNFixYp5kk4jknVaOzZsyfJvAnHMi3zi6TVTz/9hLe3N7Vr12bWrFmMHTsWpVLJiBEj0jSxY8WKFTE1NSUkJARPT0+d5TExMZqZvXPq/y8rGRoaUr16dQDN7/xDCfM4fLj/efk3Y2hoSJs2bWjQoAFAouUQQojUkqBC5CsTJ07E2dmZZ8+e8fnnn/Pvv//qrKNQKNi2bRudOnXSBBWAZrSchQsXat1c1Wo1CxcuxNvbG0tLSz755JNML3f//v2xtLRk9erVrFy5Uqu9d4KnT5+ye/fuVG0vYZ6AdevWaaX7+Pgk28Qh4Y3o/fv3U1t0IL5te5s2bVCr1UyaNEkraImKimLSpEnExsZStWpVnQ6n2WnChAmcP38eT09PnZGxEpMwm/XZs2d58OCB1rLNmzdrHrQTk3AsP8yXWfbu3cvmzZspVKgQc+bMQV9fny+++ILWrVvz7Nkzxo8fn+ptmZiYaPrzzJw5U6vGQqlU8tNPP+Hv70+xYsVSNSBBXpQwatTGjRt1RobasWMHx44dw8jIiJ49e2otyyu/mb/++kvrepfA39+fmzdvasokhBDpJc2fRL5iZWXFxo0bGTFiBBcuXOCLL76gWLFieHh4ULBgQQICArh+/TpRUVGYm5trmn3Au34Gu3fvpmvXrtSsWVMz+Z2vry8FChRg9uzZqZ5MLC0KFy7MwoULGT58ODNnzmT58uW4ublhb29PREQEDx8+5MmTJ1SuXJmOHTumuL1hw4bx9ddf88cff3Dw4EHc3NwIDAzk0qVLVK9eHQcHh0Rn3W3RogULFixg3bp13L9/n8KFC6Ovr0+zZs1SHC530qRJ+Pj4cO3aNVq2bEnt2rUxMDDg4sWLBAUFUaxYMWbPnp3uY5QTypUrR/PmzfH09KRTp07Url0bKysrvL298fX1ZdCgQSxevDjRvK1atWLlypX07t2bOnXqaDoIf/vtt1rDh6aHj48PkyZNQl9fn9mzZ2uNivXTTz9x+/Ztjhw5wpo1a+jVq1eqtvn1119z8+ZNzp49y0cffUTt2rUxMzPj6tWrvHjxAmtra/74449UBWNptXXrVi5cuJDk8vr162f6iFMfaty4MUOGDGHRokX06dOHatWqUaRIEXx9fbl16xYGBgZMmTIFNzc3rXx55TezZcsWfvzxR4oVK4abmxvm5uYEBwfz77//EhMTQ506ddI1aagQQiSQoELkO3Z2dqxbt46TJ0+yf/9+rly5wtmzZ1EqlVhbW1O1alUaN25Mx44dtTo96+npMWvWLBo1asTmzZu5desW0dHRFCpUiC5dujBgwIAsnSm4Zs2a7N+/n/Xr13PixAlu3LiBQqHAzs6OIkWK0KFDB1q1apWqbbVq1Yr169fz559/cufOHZ4+fUrx4sUZNmwYffv2pV+/fonmK1OmDPPnz2fFihVcu3aNs2fPolarKVy4cIoPSDY2NmzatIl169Zx4MABvLy8UKlUFCtWjE8++YS+ffumeVSi3OCPP/5g7dq17Nq1i0uXLmFiYkKFChWYMGECzs7OSQYVI0aMQF9fnyNHjnD06FHNnAFDhgzJUFARExPDN998Q1RUFMOGDaNu3bpayy0sLPj999/57LPP+PXXX6latWqqRvYxNjZm+fLlbNmyhd27d/Pvv/+iUCgoUqQIX331FQMGDMiy/hSXL19Oci4GiN+nrA4qIP7/rFq1aqxfv55r165x7do1bGxsaNOmDf369Uv0OOaV38zIkSP5559/NPsVHh6OnZ0dlSpVomvXrrRr1y7R0bKEECK19NS5ZfgXIYQQQgghRJ4kfSqEEEIIIYQQGSJBhRBCCCGEENnk8ePHTJo0iY4dO1KuXDnat2+fqnxqtZqlS5fSpEkTKlWqxKeffsrVq1d11vPz82P48OFUrVqVWrVq8cMPP2TaZLfJkaBCCCGEEEKIbHL//n1OnDiBs7OzZpTB1Fi2bBnz5s2jd+/eLFmyBHt7e/r27as1341SqaR///48evSIOXPmMGXKFE6fPs3o0aOzYle0SK8sIYQQQgghskmzZs00cyWNHTtWM6xzcmJjY1myZAl9+/ald+/eAFSvXp02bdqwYsUKpkyZAsDhw4e5f/8+Bw4c0AwuY2lpSb9+/bh+/XqqBu5IL6mpEEIIIYQQIpvo66f98fvy5ctERETQtm1bTZqxsTEtW7bk5MmTmrSTJ0/i4eGhNVpl/fr1sba25sSJExkreAokqBBCCCGEECIXS5i88sOh7UuVKsWLFy+IiYnRrPfhOnp6epQsWTLRCTAzkzR/EkIIIYQQIg1SmofG09MzU78vLCwMY2NjTExMtNItLS1Rq9WEhoZSoEABwsLCsLCw0MlvZWVFaGhoppbpQ7k2qNhv5JHTRcj33O4cyeki5GtPIrNmojDxTjEz/5wuQr4WGWea00XI9xx5kdNFyPfOBZfL6SLka91q595GL1n6LNmoWNZtO4/KtUGFEEIIIYQQuVFm10SkxNLSEoVCQWxsrFZtRVhYGHp6elhZWWnWS2z42NDQUIoUKZKlZcy94aUQQgghhBDppGekl2Wf7JbQT8LX11cr3cfHh6JFi1KgQAHNeh/2nVCr1fj6+ur0tchsElQIIYQQQoh8R99QL8s+2a1atWqYm5tz8OBBTZpSqeTvv/+mUaNGmrRGjRpx584dHj16pEk7e/YsISEhNG7cOEvLKM2fhBBCCCGEyCbR0dGa4V2fP39OREQEhw4dAqBWrVrY2trSq1cvXrx4wZEj8f1fTUxMGDRoEPPnz8fW1hZ3d3c2btxISEgI/fr102y7devWLFmyhOHDhzNq1Ciio6OZNWuWZhburCRBhRBCCCGEyHf0jHJng5zAwEC++eYbrbSEv9euXUvt2rVRqVTExcVprTNgwADUajUrV64kKCiIsmXLsmLFCooXL65Zx8jIiOXLlzN9+nRGjRqFoaEhLVu2ZPz48Vm+X3pqtVqd5d+SDjL6U9aT0Z+yloz+lPVk9KesJaM/ZT0Z/SnryehPWSs3j/502K58lm27deCtLNt2XiU1FUIIIYQQIt/Jib4P/2W5N7wUQgghhBBC5AlSUyGEEEIIIfKdnBj69b9MaiqEEEIIIYQQGSI1FUIIIYQQIt+RPhXZS4IKIYQQQgiR70jzp+yVruZPs2fP1pqpTwghhBBCCPHfla6gYvfu3bRt25bPP/+cnTt3Eh0dndnlEkIIIYQQIt30DfWy7CN0pSuoOHHiBIsWLaJQoUJMnDiRBg0aMHHiRK5cuZLZ5RNCCCGEEELkcunqU6Gvr0+TJk1o0qQJwcHB7N69m507d7Jt2zZcXV3p2rUrHTt2xM7OLrPLK4QQQgghRIr0DKRGITtleEhZGxsbevfuzcyZM6lRowYPHz5k1qxZNG7cmDFjxhAUFJQZ5RRCCCGEEELkUhkKKsLDw9mwYQNdunShc+fOREREMGnSJE6dOsWUKVP4999/GTlyZGaVVQghhBBCiFTRN9DLso/Qla7mT2fPnmXbtm14enpiYGBAu3bt+PHHH6lQoYJmnW7dulGkSBEGDx6caYUVQgghhBBC5D7pCir69OlD5cqVmTBhAu3ataNgwYKJrufi4kL79u0zVEAhhBBCCCHSSk9fahSyU5qDCrVazfr166lYsSImJibJruvk5MSMGTPSXTghhBBCCCHSQ88gw12HRRqk+WgrlUp69uzJ2bNns6I8QgghhBBCiDwmzTUVxsbGFC5cmLi4uKwojxBCCCGEEBkmHaqzV7rqhT7//HNWr15NbGxsZpdHCCGEEEIIkcekq6P2y5cv8fX1pUmTJtSqVYtChQqhp6cdDU6YMCFTCpibGZiZ4jq6H9a1KmNdsyLGttZc6zeWZ2t35nTRcrWIiAhWr1zG2TNexMbG4u7hQd/+gyhd2i1V+Z8+eczyZYu5fesmhoZG1KhZi/4DB2NlZa1ZJzAwgNUrl3P/3l2CggLR19enqFMx2rXvQLPmLXXO1/wmKjKMXevmcu3CMRSKaJxLV6RLz9GUcC2XbD6VSsX5E3u5dv4oTx/dISoiFDsHJ6rXb0uLj3thZPyuH5UiNoYtK2bw6MF1ggP8UKnisC9cnLpNO9Go9acYGBpl9W5mi4iICNasXMq5M6eJjY3FzcODvv0HU6q0e6ryP33ymBXLFuF968bb87U2fQcO0TpfP/TP8aPM/XUGBQoUYPOO/Zp0lUrFcc+/OXvmND4PHxARHo5j4cI0bNSUTl0/wdjYOKO7m2tERoSzYfUC/j17AkVsDKXcy/FF368pWdojxbwP7t3i5NEDPLh3i6ePHhAXF8eGvbpNdgP9/fjn6D6uXPTi1Ytn6OvrU9zZlU6f9qZilVpZsVvZSqFUsvqvTRw9foLwiEhcXZzp8+Vn1KhaOcW8/oGBLFq2in+vXkOtUlOlUgWG9O9N0cKFk8xz45Y3I8bG3/t3rF+FlZWl1vJjJ0+zefsuHj99hmnBgtStXYOBvb7SWS+/iI4M49Dm2dy+dBRlbAzFSlWk7Wff4+RSPsW8F49v4eqZvfi/9CUmKgxLawdKlq1Fs05DsbF30qwXEviSSyd3cPfaCQJfPUZfXx/HYm406TCY0hXqZeXu5QnSUTt76anVanVaMzVr1iz5jerp4enpme5CAew3SvnGkdMKOjvR7MExoh4/J9r3GXZNauepoMLtzpFs/06VSsXY70bi6+tDl66fYGlpyf79ewnw9+f3eQso6lQs2fwBAf58M2wIZmZmtO/QiZiYaHZu34a9gz1z5v6JkVH8g6yvrw9LFy+gbLny2Ns7EBf3hqtXLnP+3Fm6f9KDnr37Zfm+Pol0zPLvSIxKpWLupN48e3SXFh17Y25hw8nDmwkJfMWYmZtwKOKcZN6Y6ChG96xDSbdKVKjeCAsrW3zuXef8P3soXa4630xergnIIsNDWTjjf5QuWx07h6Lo6enjc/cqF0/tp3q9NvQZMTPL97WYmX+Wbl+lUjHuuxE88n1I566fYmFpycH9ewjw9+e3eYtSdb6OHDYIUzMz2nfoTExMNLu2b8XewYFf5y7QnK/vi46O5n8DexEVGQmgFVRER0fTo2t7PMqUpUatulhbW3PH+zbHPf+mXIWKTJ8xJ1MD5sg400zbVlqoVCp+HDuYx74PaN/lCywsrTiyfwdBAX5M/301RYoWTzb/tg3L2b11DSVcShMTHcXL508SDSoO79vKxlULqFGnEe7lKhEXF8epYwd59PAuA7/5gSYtsn70QkdeZNm2p//6Gye9ztG1QzucihbhsOc/3L3/gDk/TaVi+bJJ5ouOjmbQiO+IjIqie6cOGBoYsH33PtSoWfLHHKwsLXTyqFQqhoz8nmcvXhITE6MTVOw5cIg/Fi2jWuWKNKhbB//AQHbu2U/RIoVZMOeXLA2IzwUn/zIlK6hUKpb99CWvntylwUd9MbOw5rznRkIDX/G/H7dRqLBLsvl3r56KUhFD4eLuFDC1JNj/Gf+e2IZKFcfw6buwtHEA4OyRvzi8eTZlqzXH2b0qqrg4rnjt5sWj23Tp/xPVG3XJ8n3tVjv3doY+X7d2lm279tnzWbbtvCpdQUV2yAtBhb6xEUY2VsT6BWBVvQINzm2XoCIFp06eYNYv0xk7fiL1GzQCIDQ0hEED+lC9ek2+GzM+2fwLF8zD8+jfLFqyEgeH+Ivq1SuXmfjDGIYOH0Gbtu2Szf/jlIncuH6VTVt3YWBgkDk7lYScCiounTnMyrnf0W/UbKrVbQVAeGgQU7/5mPJVGiT7sP9GqeSJzy1cPapopR/Yupj9WxYyfOJSylSqk+z3b1kxgxOHNvLz0mNY2RTK8P4kJ6uDitMn/+HXX6bx/fhJ1G/QGIg/X4cM6EX16rUYPeaHZPMvXvAHnkcPs3DJKuwd4s+Hq1cuMfmH7/nf8JG0bqv70Lpm1TLOn/WitJs75896aQUVSqWSB/fvUbac9pvOTRvWsnH9Gqb+NIsqVatndLc1ciqoOHfqKPNmTeSbsT9Ru378S6yw0GBGDfqUKtXrMOy7H5PNHxocREFTU4xNCrBq8WyO7N+eaFDx7LEPlta2WL5Xa6RUKhj3dU9iYqL5c9XuTN2vxGRVUHHn3n2Gjh7LoD49+aRLRwAUCgX9ho3E2sqK+b/+nGTeTdt3sWz1OhbMmUkZ99IAPHn6jH7DRvJp10707/mFTp69Bw+zct1GWjRtxI49+7WCCqVSSbev+uHq4sxvM37UBL5nL/zLhGkzGDawH50//iizD4FGTgQVN84fZNOCUXw27Hcq1GoNQGRYEL993xb3Sg359H+z07zN5763WDi5G626j6LxxwMA8Ht2H3OrQphZ2GjWe6NU8OeEzihio/j+9+OZs0PJyM1BxcUGyd+vMqLm6XNZtu28KveeCXmASqEk1i8gp4uRp3idPom1jQ116zXQpFlZWdOgYWPOnzuLUqlINv9Zr1PUrFlbE1AAVKlaDSenYpw+dSLF73d0dCQ2NpY3b96kfydyuSvnjmBhZUeV2i00aRZWtlSr25rr/x5P9hgbGhnpBBQAlWvFP9i9eu6T4vfbOhQFIDoqPI0lz33OaM7Xhpq0d+frmRTP1zNeJ6lZs44moACoUrU6RZM4X188f8aendvpO2BIokGvkZGRTkABUKdu/O/p2dMnqd633Oy813GsrG2pWbeJJs3SyoY6DZpx6fypFI+7lY0txiYFUvyeYs6uWgEFgJGRMVVq1CMo4DXRUZHpKX6ucMLrLPr6+rRr01KTZmxsTNuWzbl95y6v/ZO+d530OouHW2lNQAFQongxqlWuyInTZ3TWDwsPZ+W6jfT+ogfmZmY6y30fPyUiMpImDetr1aTVrVWDggULcPzU6fTuZq518+LfmFsVolyNd8ffzNKWirXb4H35GG9SOIcTY1Mo/toaExWmSXMs5qYVUAAYGhnjXrkRoUGviI3Ou+ewyHvSFVTs2rUryc/u3bs5duwYL15kXZWuyLt8fB5QqlRp9PW1Tz13dw9iY2N4/ux5knkDAwIICQnBzU23Lbubuwc+Dx/opMfGxhIaGoqf3ys8j/7N0SOHKVOmbIpzrORlz3zvUNy1rM4xdildAUVsDK9fPErzNsNCAgEwt7DWWfZGqSQiLJjggFdcPe+J55412NoXxb5w8k1U8oL489VN51i6uZd5e74+SzJvYIA/oSEhlE7kfHV3L4Pvw/s66cuXLqRi5SrUqJm2KvuQ4CAALC2t0pQvt3rkcw+XUh46x72UezliY2N4+Txrg6eQ4EBMTApgkorAJLd64ONLMaeimJlq1zYlBAoPfH0TzadSqfB59BiP0qV0lpVxd+PFy1dERUVrpa9avwlbG2vavxfAvE/5RgmAiYluEycTY2MePPRFpVKlvFN5yMvHtynqrHsdLuZaEaUimoBXj1K1najwYCLCAnnmc5Pty+NrRkuVT/nte0RoAEbGBTHKw+dwZtAz0Muyj9CVro7aY8eO1bxteL/11Ptpenp6tGjRglmzZiU547b47wkOCqJChUo66ba2tgAEBQXgUrJkonmDguIfbG3erqud347w8HCUSgVGRu9uXHt272Tt6hWavytXqco3I7/N0D7kdqHB/pQuq9sExtLGXrPcyTl1nYwTHN29igIFzSlXtYHOsqsXjrLq9zGav0uUKs+XQ6ZiYJCuy0uuEhwUSPkKFXXSbWztgPhz0qWka6J5g4KCtNbVzm+rc77+e+EcVy//y+8Llqa5nDu2bcbU1IxqNfJ+52KIf6gvW6GKTrq1bXxzuuCgAEq4lNZZnhlevXjKxbP/ULt+M/SzuIlkVgoKCsbOxkYn3fZtWmBgcKL5wsMjUCqV2NomkzcoCFPT+M7CD30fse/Q38yY/EOSTUqLFSmCnp4eN2/foU2Ld30ynz57Tkho/Fv38IjIRPtq5FXhIQG4eNTQSbewjr8Ohwe/pnDxlK/DM0c00dRqmJpb0/7LHyhdoX6yeQL9HnPr3yNUqNUaff28ew5nBj19aZCTndJ119+5cycjRoygU6dONG/eHDs7OwIDAzly5Ai7d+9m6tSpPHv2jF9++YU5c+b8J0aCEqmjUCgwTKRzasKDVWxs0lXCCoVCa12t/MZGmvzvL2/cuClubu6EhoZw8cJ5QkKCUSTzHfmBUhGb7DFWKtI2FPShHcu4c+Mcn/b/AVMz3VFa3MvXYvjEpURFhnH3xnmeP76HIjY6kS3lPQqFItHzzfhtmiKZYbUVb49zYp2xjYzfne9GRsYolUpWLF1Im48+pkQJlzSVcevmv7h29TKDh36Dubl5mvLmVgpFLIaG6TvuGREbE8MfM3/A2NiEz3r9L0u+I7vEKhQYGene4o3fXisTrqeJ5YPEz9uEvLHv5V2wdCW1qlelRrUqSZbFysqSxg3q8fexfyhRvBgN6tYiIDCIP5eswNDQkDdv3iRZnrxKqYjBIJFz2NAovpZcqYxJ1XZ6jV6KUhmL/wsfrp7Zk+K1VREbzcb5IzEyLkDrT0anveBCZEC6gorZs2fTvXt3+vfvr0mzs7PD3d0dY2NjFi9ezJo1awgODmb9+vUSVPwHKZVKIsK129RbWllhbGzMG6UykfXjbyiJVY8nSBgdJLH21EpF4tXrDo6OODjGt2dv3KQZf86by4Qfvmfx0lV5vgnUG6WSyIhQrTQLSxuMjE2SPcbvDwubkkteh9i36U/qNetMo9afJrqOpbUdltbxb+Or1W3FoR3LmD9tEJPn7cvyjtqZJbnzNbHzTfE2zTiZc8jYOOHhIZH/C4X2+b5n1zbCwsL47MteaSr3qRPH+WvtKlq0akvbdh3SlDc3eKNUEhERppVmaWmNsbEJb96k77inlyoujvm/TuT5k0eMmfIbNnb2mf4d2cnE2BilUrfvmOLttTKp0ZZMNNdZ3fM2IW/COsdPeXHrzl2W/zk3xfKMHDoIhULBkpVrWLJyDQAtmjSiaGFHTp09T8ECebOZzps3CqI/uA6bWdpiZFyAuETO4TfKhJcNqdtf13LxTSE9KjeibLVmzBvfAeMCptRtqdtZXqWKY/PC0bx+8YBe3y7VjBD1XyZDymavdAUVly5dom/fvokuK1euHAsWLACgUqVKmiYA4r/ljvdtxo/Vbma0fNU6bGxtNc2Y3pdwntjaJv0Qavu2GUlwIudUUFAgFhYWib5Vfl+9Bg05fOgAt25ep1r1minuR27mc+8qf0zRHhr3xwUHsbKxJzREd1SksOD4NCub1D0seV87y9o/f6B8tYb0GDgx1eWqWqclezfO5/q/x2nYsnuq8+WkO963mDBW+63e0lV/YWNrl+j5Fvz2HLZNpGlTgoQmfcGJnO/BQUGa8zUyMoItm/7io3YdiIqKIioqCogf1lOtBj+/V5iYmGBtrd0c5erlf/l9zkxq1KzN/4aPTNsO5xL37txg+vihWml/LN+BtY1dosctJCi+c7FNMteJ9Fr25wyuXPRi6OgplK+s22wlr7G1tSEgMJFrZXB8syc7O93mTQAWFuYYGRkRFKTbPEqT9+25vXTlWhrXr4uRoSGv/F4DEPF2KOTXAQEo37yhkF38uuZmZkybMBa/1/74vX6No4M9jg4ODP9uPNZWlpib63bwzgue3L/KihnaLwO+nXMUC+tChCdyHU5Is0jHA7+dYwmKOpfl2pm9iQYVO1dM4u7Vf+g++FdKlcu6UY+ESEq6ggpbW1sOHz5M/fq67foOHTqkuZlGRkZiaZk/J7URyStZ0pVpP2kPXWpjY4uraylu3bqJSqXS6sB27+4dTEwK4FTM6cNNadgVKoSVlTX379/TWXb/3l1Kuup2LPxQQtOnyMio1O5KrlXM2YPhE7Xb31taF6KYiwcPvC/rHONH929gbFIAh6IuKW7b9/51lv46ghKlytNv5Ow09Y9IaF4Vk4dGfypZshRTf5qllWZjY0tJ11LcvnUjmfM16Xkq7ArZY2VlzYNEztd79+5Q0jW+T0BERAQx0dHs2LaZHds266w7sM8X1K5Tj/GTpmnS7t7xZsb0yZR2c+e7cZOyfHjkrFKiZGnGTftDK83KxhZnVzfu3rqmc9wf3LuNiUkBijiVyNRy/LVyPieO7uerASOo17hVpm47p5Qu6cLV6zeJjIrS6qztfff+2+WJ913T19fH1bkEdx881Fnmffc+RQo7Ymoa30/ydUAAnidO4XnilM66g0d8R6mSLiydN0crPT6YiH+xERERyf0HD2lYL+8+ABcp4UGf71dopZlbFaJIibI8undJ5xx++vA6RsYFU5ynIilKRWyitXgHN/7K5VM7aPfFOCrXTX5o9f8SfelQna3SFVQMHDiQKVOm8OzZM5o2bYqtrS1BQUF4enpy7tw5pk6dCsC5c+eoVEm3U67I/8wtLKhStZpOev36jfA6fYqzZ06/N09FKKdPn6RW7TpaNQ0vX8aPIFakSFFNWr36DfD0PIK//2vs7ePf9Fy7epnnz5/RsdO7SX5CQ0MSnbH4yN8H0dPTo1TprOnkmZ1MzS0TnTOiap2WXDl3hKvnj2rmqYgIC+byub+pWL2J1jH2f/UUQGukplfPfFg0Yxh2DkUZMvbPJIfmjAgLxszCWmeytTOeOwAo4ZryrLG5Rfz5qtu5vV79Rpw5fZKzZ05p5qkICw3F6/QJaqbifK1bvyHHPP/WOV9fPH9Gh07dALC2smbchKk6371vz07u3rnN6O9/0Ors/fTJY6ZNGY+DY2EmTvkpTzfjMze3THTm6tr1m3HB67imwzRAWGgI508fo2qtBlrH3e9l/AhcjkWSn4gwKXt3rGf/zg107N6Lth0Sb+KXFzWqX5ctO/ew/9CRd/NUKJUcPnqMsh5uONjH1/b4vfYnNjaWEsXfHb+G9euyfM167t5/gIdb/LXy6bPnXLl+g086v2tmN3X89zrfe/yUF/+c8mLsyOEUKpR0TR7A8rXriVOp6Nrx4wzvb04paGaV6MzV5Wu24ubFw9x+22EaIDI8mJsXDlOmahMM3zuHA/3iRzOzc4wPluPi3qCIiaSgmfZobk8fXsfv2T0qfRA0nNq/gtMHV9L440HUa90zM3dPiDRJV1DRo0cP7O3tWbx4MTNnzuTNmzcYGhpStmxZFi5cqJlxe9iwYRga5v0RYJLj/L8vMLKypEDR+AcGh3ZNKeBUGIBHC9bxJiwiJ4uX69Rr0BCP3WX5Y+5snjx5jKWlFQf270UVp+LzL7UvhhPGxd+wVqxer0nr/ulnnD59kh/GfsfHHTvHv+HdvhUXl5K0aNVas96WTRu4ffsW1avXxN7BgfDwMM54neb+vbu079CJokWTrhHJ66rWaUlJt0qsXziJV898MLe05uThzahVKtp9MkRr3Xk/xk+gNG3hIQBioiP5c/pgoiLCaNGhNzcvn9Rav5BjcVw9KgNw4dQ+Tv+9lUo1m1HIsRgxMZF4Xz3DnetnqVi9MR4Vs24m0+xSr0EjPHaXZd7cX3n69nw9uH8PqjgVn33ZW2vdSePim/stW71Bk9bt08/xOn2CCWNH83HHLsRER7Nz+xac3ztfTQoUoE493VG1zp/z4v69O1rLoqKimDJxLJEREXTu+gn/XtSefKlwkaKUKZt3grmk1K7XlIMeFVjyx088f+KLhaU1Rw7sQK2Ko9vn/bXW/WnCcADmrXg36aj/65ecPh5/Tvs+uAPAzs2rAChkX5iGzdoCcPHsP2xctYDCRYvjVNxFkydBxSq1sLLRHW0uLyjr4U7j+nVZvvYvgkNDcSpSmL+P/cOr1/58+/W7Tugz587n2s1beO7drknr+FEbDvx9lPE//swnnTtgaGDItl17sbG2pvt7QUWDRGYrfuj7CIBa1atpzai9cesOfJ88pay7GwYG+nidu8C/V67R98vPtObDyC8q1GrNmcNr2b58PK9fPMDU3IbznhtRq+Jo3mW41rorZ/YB4LvfPAFQxEQxa0QzKtZug4OTG8YmBXn17B6XT+7EpKA5TTu+u47f+vcIhzbPxs7RGYeirlz12qO17dIV6mFulTf6tmUF6VORvdL9xN+8eXOaN2+OSqUiKCgIW1tbnfGYrazyx5jpyXEd2RdTl3dveIp0aU2RLvEPC8837JGg4gMGBgZMmfoTK1csZe+eXShiFbi5uzNi5LcUK5byvAb29g7MmDmHFcsWs2bVCgyNDKlZszZ9+w/SentZo2ZtXr58yZEjhwgLDcXIyBiXkiX5ZuS3NG+RP5o3JEXfwID/jV/IznVz+OfgBpSKGJxLVeCrodNxdEq8yUOCyPAQggNfAbD7r991ltdu3EETVJQqUw2fu9f41+sg4aGB6Osb4FjUha69vqNx288yfb9ygoGBAZOmzmDViiXs27Pz7fnqwdcjv0/1+frzzLmsWLaItauWY2hkSI2atenbf3CK/X8SEx4eRoB/fNv1tauW6yxv1qJVvggq9A0M+H7KHDas/JNDe7eiVMTi6laWwSMmULSYc4r5/f1esnW9dtPAhL/LVqiqCSoe+8bPbfPqxVMW/qZbWzTh5wV5NqgAGDvqa1at38jR4ycIj4jE1cWZnyaNo1KF5M8RU9OC/Pbzjyxcvoq/Nm9HpVZRuUJ5/te/D9bpvK+XdHHm9LkLnD1/EZVKRUkXZyaNGU3jBrpv+fMDfX0Den27hIObfuXs3+tRKmIp5lqBrgNmYF8k+euwkUkBajTuio/3BW5e/Js3ilgsbOypVPcjmnYYgo39u5dir57cBeKHkd26ZIzOtvqNW/OfDipE9tJTvz/RRC6y38gjp4uQ77ndOZLTRcjXnkQ6prySyJBiZrodIUXmiYwzTXklkSGOyESxWe1ccLmcLkK+1q127p0L4vpHTbJs25UO/JNl286r0l1Tcfr0aQ4fPsyrV6+I/WDMcD09PdasWZPhwgkhhBBCCCFyv3QFFcuXL2f27Nk4OTlRqlQpLCzyzyyYQgghhBAi75M+FdkrXUHFhg0b+PLLL2VSOyGEEEIIkSvJkLLZK10N4UJCQmjevHlml0UIIYQQQgiRB6UrqGjatCmXLl3K7LIIIYQQQgiRKfT09bLsI3Slq/lT165dmTJlCrGxsdSrVy/RWbPLl8/7wxoKIYQQQgghUpauoKJv374ALFu2jGXLlmnNqKtWq9HT08Pb2ztzSiiEEEIIIUQa6enn3uFu86N0BRVr167N7HIIIYQQQggh8qh0BRW1atXK7HIIIYQQQgiRaaTvQ/ZK9+R3u3btYvPmzTx69Ehn8juAy5cvZ6hgQgghhBBCiLwhXY3Ndu/ezcSJE3FzcyM4OJi2bdvSunVrjIyMsLOz0/S5EEIIIYQQIifI6E/ZK101FatWreJ///sfAwcOZMuWLXz++eeUL1+eiIgI+vXrh5mZWWaXUwghhBBCiFSTh//sla6aisePH1OtWjUMDAwwMDAgIiICAHNzcwYMGMC6desytZBCCCGEEEKI3CtdNRXm5uYoFAoAHB0defDgAbVr1wYgLi6O4ODgzCuhEEIIIYQQaZRbh5R9+PAh06dP58qVK5iZmdGxY0dGjBiBsbFxknnOnz9Pz549E11WsmRJDh06lOx6H330EXPnzs2cHUhCuoKKChUqcPfuXRo2bEizZs1YsGABarUaQ0NDli5dSpUqVTK5mEIIIYQQQuRtoaGh9OrVCxcXF+bPn4+fnx+//PILMTExTJo0Kcl85cuXZ/PmzVppERERDBgwgEaNGumsP2PGDFxdXTV/29jYZN5OJCFdQcWgQYN48eIFAF9//TXPnz/n559/RqVSUbFiRX788cdMLaQQQgghhBBpoW+Q+/pUbNq0icjISP7880+sra2B+FY+U6dOZdCgQTg6Oiaaz9zcXOel/Y4dO1CpVLRv315nfTc3NypWrJjZxU9WuoKKKlWqaHbM0tKSRYsWoVAoUCgUmJubZ2b5hBBCCCGEyBdOnjxJ3bp1NQEFQNu2bZk8eTJeXl506dIl1dvat28fLi4uVKpUKQtKmnaZ1tjM2NhYAgohhBBCCJEr5MYhZX18fLSaJUH8C3p7e3t8fHxSvZ2AgADOnTuXaC0FwMCBAylbtiyNGjVi5syZxMTEpLvMqZXuye+EEEIIIYT4L2revHmyyz09PRNNDwsLw9LSUifdysqK0NDQVH//gQMHiIuL0wkqLCws6N+/PzVr1sTExIRz586xcuVKfHx8WLJkSaq3nx4SVAghhBBCiHwnt47+lBn27t1L+fLlKVmypFZ6uXLlKFeunObvunXr4uDgwI8//sj169eztKmUBBVCCCGEECLfycrJ75KqiUiJpaUl4eHhOumhoaFYWVmlahtPnjzh+vXrjBs3LlXrt23blh9//JGbN29maVCRf0M4IYQQQgghchFXV1edvhPh4eH4+/vr9LVIyt69e9HX1+ejjz7KiiKmmwQVQgghhBAi38mNHbUbNWrEmTNnCAsL06QdOnQIfX196tevn6pt7N+/n1q1auHg4JDq9YEsH2JWmj8JIYQQQgiRDXr06MG6desYOnQogwYNws/Pj1mzZtGjRw+tOSp69erFixcvOHLkiFb+27dv8/DhQ/r06ZPo9r/99lucnZ0pV66cpqP26tWradGihQQVQgghhBBCpFVu7KhtZWXFmjVrmDZtGkOHDsXMzIxu3boxcuRIrfVUKhVxcXE6+ffu3YuxsTGtW7dOdPtubm7s3buXlStXolQqcXJyYvDgwQwcODBL9ud9emq1Wp3l35IO+408croI+Z7bnSMpryTS7Ulk4rNiisxTzMw/p4uQr0XGmeZ0EfI9R17kdBHyvXPB5VJeSaRbt9q578E9wZPBqZ9ILq1KLN6RZdvOq6SmQgghhBBC5DtZOfqT0JVrgwp5i5717pdpmdNFyNfqXl6R00XI95SKgjldhHzN2Ng6p4uQ70VjkdNFyPcKW0TndBHyObOcLoDIJXJtUCGEEEIIIUR65cY+FfmZBBVCCCGEECL/0ZPmT9lJQjghhBBCCCFEhkhNhRBCCCGEyHeko3b2kpoKIYQQQgghRIZITYUQQgghhMh3pKN29pKjLYQQQgghhMiQdAUVZcuW5fr164kuu3nzJmXLls1QoYQQQgghhMgIPX29LPsIXekKKtRqdZLL4uLiMDAwSHeBhBBCCCGEEHlLqvtU+Pv78/r1a83fPj4+OsFDbGws27dvp2jRoplXQiGEEEIIIdJI+lRkr1QHFZs3b+bPP/9ET08PPT09xo0bp7OOWq3GwMCAyZMnZ2ohhRBCCCGESAtpppS9Uh1UdO7cmVq1aqFWq+nVqxeTJk2idOnSWusYGRnh4uKCjY1NphdUCCGEEEIIkTulOqhwcnLCyckJgLVr11K+fHnMzMyyrGBCCCGEEEKkl9RUZK90zVNRrlw5YmNjtYKKPXv28PDhQ+rUqUPdunUzrYBCCCGEEEKI3C1dPVi+++47/vjjD83ff/75J99//z2bNm2iX79+HDhwINMKKIQQQgghRJrp62fdR+hI11G5ceMGDRo0AOI7Z2/YsIFBgwZx/vx5vvrqK1asWJGphRRCCCGEEELkXukKKkJDQzWdsW/evElwcDDdunUDoFmzZvj6+mZeCYUQQgghhEijhBFLs+IjdKUrqChUqBAPHjwA4MSJEzg5OVG8eHEAoqOjMTRMV1cNIYQQQgghRB6Urqf/Nm3a8Ouvv3LmzBlOnjxJ//79Nctu376Ns7NzphVQCCGEEEKItJLJ77JXuoKK0aNHY2Zmxs2bN+nbty+DBg3SLLt16xZt27bNtAIKIYQQQgiRVjKkbPZKV1BhaGjIsGHDEl22YMGCDBVICCGEEEIIkbdI5wchhBBCCJH/SPOnbJWuoKJMmTIp9nz39vZOV4GEEEIIIYQQeUu6goqxY8fqBBVhYWF4eXnx+vVrevbsmSmFE0IIIYQQIj2kT0X2SldQ0bt370TThw8fzvfff09oaGhGypTjIiIiWL1yGWfPeBEbG4u7hwd9+w+idGm3VOV/+uQxy5ct5vatmxgaGlGjZi36DxyMlZW1Zp3AwABWr1zO/Xt3CQoKRF9fn6JOxWjXvgPNmreUMZDfMjAzxXV0P6xrVca6ZkWMba251m8sz9buzOmi5ToKpZJlm3Zx6ORZwiKjKF2iGIM+60ytyuWTzff4+St2/v0Pt+77cM/3MQrlG3YsnEkRh0LJ5nv26jVfjJyIQvmGlb9MpGxpl0zcm5ynUCpZsWEbf/9zivDISEo5l6D/F59Qs0rFFPP6Bwbx54p1XLx6A5VaTdWK5Rje90uKFnbUWi8oJJQlazdy9tJVoqKjcS7mxJddO9C0fp1ktz9q8s/8e+0mnT9qyciBfTK0n9lBoVSybt06jh07RkREBCVdXOjZsyfVqlVLMW9AQABLly7l8pUrqFQqKleuzMABAyhSpIjOuocPH2b7jh28evUKe3t7OnToQMcOHZLd/vjx47ly9Soft2/P//73P016bGwsCxct4u7du/j7+6NSqShSpAitWrakffv2eWro9IiICFatXMbZM2eIjY3B3aMM/foPTMM97QnL3run1axZi/4DB2nd054+fcKRvw9z5colXr18SYECBSlVujRffNETN3d3re09e/aUgwf2cffuXR4+uI9SqWTFqrU4OhbOzN3OcVGR4Wxd8zuXzx9HERtDSbcKfNp7JM6lyqaY1+feTbyO78X33g2ePX5AXNwbVuy8nOi6oSGBbF83j+v/niYmJooixUryUZc+1KzfMrN3SYhkZXpjsw4dOrB58+bM3my2UalU/Dj5B078c4z2H3ekT9/+hISEMH7Mt7x4/izF/AEB/oz9fjQvX7zgq1596dy1G/9evMDEH8agVCo164WFhREQ4E+9Bg3p028gX/bsja2tLb//9ivr1qzMyl3MU4wL2eA+cRjmZVwJv343p4uTq03/cyUb9x2hVcM6jOzzGfr6+oz6+Q+ued9PNt/New/YevAoUTExODvpPqgl5Y/VmzAwMMhosXOtGfMWs2XPAVo2rs/X/Xqir6/P99Nmcf32nWTzRUXH8M3E6Vy95c2X3TrS97Ou3Pd5xPAfphEaFq5ZLzIqiqHjpnDi7EU6tGrG/3p/gWnBAkz+dR5HTngluf0TZy9w627y/6e5zW+//cbOnTtp2rQpgwYNQt/AgEmTJ3Pz1q1k80VHRzN27Fhu3LzJp598wldffsnDhw/5fswYwsLCtNY9cOAAv//xByVKlGDIkCGULVOGxYsXs2Xr1iS37+XlhfedxP8/FQoFjx8/pmaNGvTp3Zv+/frhWrIkS5ctY86cOWk/CDlEpVIxdfIETvxznPYfd6BP3wGEhoQwbsx3PH/+PMX8AQH+jHl7T+vZqw9dunbj4sULTPhhrNY97e/Dhzh8+CBupd3p138gnTp34fmzZ4we9TVXr2g/DN/x9mbvnt1ER0VRvHiJTN/n3EClUvHH9K85f+oQzT76lO49vyE8NIhZEwfi9+JJivlvXD7NqaM7QU+PQo5OSa4XHRXBL+P7cunsMRq37sonvUZQoIApi2eP4dzJg5m5S3mSnp5+ln2Erkx/1eLr64tKpcrszWYbr9On8Pa+zdjxE6nfoBEADRo1ZtCAPvy1fi3fjRmfbP4tmzcSExvD3HkLcXBwAMDdvQwTfxiD59G/adO2HQAlS7oyY6b2jan9x534ccpE9u7ZxRdf9c7XD2ypFfvyNUeL1SfWLwCr6hVocG57ThcpV7p134cjXhcY9lV3vujYBoC2jevxxahJ/LluK8t+Tvq8bVCjCn+vmY9ZwYL8tfsQ9x89TfH7zl29yfmrt/iiYxtWb9+XafuRW9y+9wDPU2cZ0vtzPuvUHoDWTRvS++sxLFqzkUUzpyaZd9fBIzx78Yolv06jrFspAGpXq0zvr8ewefd+Bn7VA4A9hz15/tKPuT/+QPVK8bVJndq0YPCYSSxY/RdN6tXGyEj7Eh2rULBg1V983vljVmzclhW7nunu3r3LiRMn6NevH926dgWgRfPmDB4yhJUrV/JbMg/o+/bt4/mLF/z+++94vH3bXaNGDQYPGcKOHTs0teaxsbGsWbuWWjVrMuGHHwBo26YNKrWajRs30rZNGywsLLS2rVAoWLZ8Od27dWPd+vU6321hYcHvc+dqpbVr1w5TMzP27t3LgAEDsLW1TfdxyS7v7mkTaPD2ntawUSMGDujLhvVr+W7MuGTzb9m8kdjYGH6ft+C9e5oHE34Yq3VPa9y4CZ9/8RUFCxbU5G3Zqg1DBvVjw1/rqFL1Xa1U7Tp12FR/B6ampuzYvhUfn4eZvds57tLZozy4c40h382iRr0WANSs34rxQzuxe9NiBo76Odn8Tdp0p23n3hibFOCvpb/g9+JxouudOLyd1y+f8u3UxZStVEuT9+cxvdiyei416rbA0Mgoc3dOiCSkK9RatWqVzmfp0qWMGTOG2bNn07x588wuZ7bxOn0Saxsb6tZroEmzsrKmQcPGnD93FqVSkWz+s16nqFmztubiC1ClajWcnIpx+tSJFL/f0dGR2NhY3rx5k/6dyEdUCiWxfgE5XYxc7/i5Sxjo69OpZWNNmomxER83a8DNew/xCwhKMq+VhTlm7z0IpOTNmzfMXbmRT9q1oFhh+wyVO7c6ceYCBvr6dGjVTJNmYmxMuxZNuHX3Pn7+gUnm/efsecq4uWoCCgDnYk5Uq1Se417nNWnXbt/F2spSE1AA6Ovr07R+HYKCQ7h6S3ewi40796FWq+nxNtDJC06fPo2+vr7W/EXGxsa0btUKb29v/P39k87r5YW7u7smoAAoXrw4VapU4eSpU5q0a9evExYWRrv22sfl4/btiYmJ4eLFizrb3rptG2qViq5vA53UcnSMb8IWGRmZpnw5xev0KaxtbKj3wT2tYcNGnDt3JsV72hmv00ne006dOqlJK+3mrhVQAFhaWlK+fEWePtV+M29hYYmpqWlGdivX+/eMJ5bWdlSr8+4aYmFlQ836Lbly4Z8Uj7uVtR3GJgVS/J573lewsLTRBBQQfx2pUb8locEB3L11Kf07kR/o62XdR+hIV1Axc+ZMnc+ff/7JlStX6NmzJxMmTMjscmYbH58HlCpVGv0PhiFzd/cgNjaG58+Sri4ODAggJCQENzd3nWVu7h74PHygkx4bG0toaCh+fq/wPPo3R48cpkyZspiYmGR8Z8R/xj3fJxQv6oiZqfZNvVzpkvHLH6Vc3Z5am/YfJTwyij5d886DbVrd931EsaJFMPvgwSchUHjg+yjRfCqVCp9HTylTylVnWVm3Ujx/5UdUdDQASqUSE2NjnfUKGMf/9u8+9NVK9/MP4K/texjcswcmJrr5cquHDx/i5OSkcyzdPTzil/v4JJpPpVLh6+uLm5tuu38Pd3devnxJVFSU5jsA3D9Yt3Tp+Gt5wvIEr1+/ZuvWrfTp2zfFa61SqSQ0NBR/f3+8zpxh+/btODg4ULRo0WTz5RYPk72nxSZ7Twt4e08rncg9zT2Je9qHgoODsLS0SnvB87gnvndwdi2jc9xLulVAERuTZM1DWr1RKjBK5BxOCEgeP/xvj8Spp6+fZR+hK13Nn+4k0QY1PwgOCqJChUo66QnV3EFBAbiULJlo3qCg+LeXNolUidva2hEeHo5SqcDI6N0DwZ7dO1m7eoXm78pVqvLNyG8ztA/ivycwOAQ7a90bdyEbawACgkIy6XtCWbVtL8N7fqITwOQngcEh2L09du+zs41PCwgOSTRfWEQECqUy8bw2NvF5g4Ip4VSQEk5FuXT9Jq9e+1PY4V2Nz3Xv+OtrQKB27dKCVetxc3WhecN6ad+hHBQUHJxoMyHNNTUw8Vqf+OulEtu3xy3RvEFBmJqaEhwUhL6+PtbW1lrrGRkZYWFhQWCQ9rFctnw5pVxdadK4MSnxOnOGmTNnav52c3Nj5IgReaZ5avw9TXdwARtbOwACgwKTvKcFvz1uif3/2djaJnpPe9/Nmze4c8ebT3t8nt7i51mhwQG4l9MdiMDKJn4AjJAgf4o5p66jfHIKO7lw+/oFAl6/oJDDu0D3/u0rAAQHvc7wdwiRWnln+IpsolAoEm1/mHDRjI1NuspSoVBorauV39hIk//95Y0bN8XNzZ3Q0BAuXjhPSEgwimS+Q4jExCqUGCdy3honnHcKpc6y9FiwfhtOjvZ0aN4wU7aXW8X/TnUvjwnHOKnrQGxs/HE2Su7/4m3e9i2bsvvwUSb/Oo9h/b7C1sqK417nOHXu3/j1FO++4/KNW5w4e5HFs37MwF7ljNjY2MSPR8KxVCR+LN9dTxO5HhsnXI9jNdtIbD2Ib2qleLsewLVr1/Dy8mLuB/0lklK5UiV+/uknIiIjuXr1Kr6+vsS8t73cTpHEsUk4/opk9iVWEb8s8fP53T0xsXteSEgws2fNwNGxMF27fZKusudlCkUshok9C7xNUygy5xxq2KIT/xzexuLZY+nRdzSWVrZcPHOEy+ePA6DMpO/Jq2RI2eyVrqBi165dSS7T09PDwsKCMmXK5OrqYaVSSUR4uFaapZUVxsbGvFHqPoAltH9MrtlBwkU2sbaSyrcPdR/md3B0xOFtG93GTZrx57y5TPjhexYvXSVNoESqmRgboUjkvFUknHfGGe+od/PeQw6dPMv8yd/qVOnnNyYmxiiVuv2aEo5xUtcBE5P446xM7v/ibd5SLiWYNGoYcxatYOjYKQDY2lgzvN9XzFm8koIF4psvvImL449la2nVpIFWP428wsTEJPHjkXAsE2kCBu9fTxO5HisSrscmmm0kth7EP1Qbv10vLi6OxYsX06xZM61+GsmxsbHB5m1tScMGDdi0eTM//PADy5cty1UdtZO7pyV3/I2Tuc+YvG2Kl/j5nPQ9MSYmmqlTJhEdHc3MX3/S6WuRn7xRKomM0B5G38LSBmNjE94k9izwNs3YOHPu78Vd3Bk48mfWLf6ZGePih5e2sinEZ32/Zd2SnzEpkH+Pvch9Mjz5nVqt1qS/n6anp0eLFi2YNWtWrryg3PG+zfix2s2Mlq9ah42traYZ0/uCNNXASY/db/u2Ojk4SLdTbFBQIBYWFklWEyeo16Ahhw8d4NbN61SrXjPF/RACwM7GGv+gYJ30hGY6hd4228mIBeu2UrmsG0UdCvHydXzn+ZCwiPjvCQnhlX8ghe3tMvw9uYGdjTX+gbrHM/BtM7JCiTRvArA0N8fYyIjARJpHBQbHb6+Q7bvmPE3q1aZ+zeo8ePQYlUqFu2tJrty8DUDxovHD+x4+foqnL17w7f/68dJPu1NzVHQML/38sbG2pEAufQlha2NDQCJNnDTXVLvEz5n466URQcG6/w9BHzTLsbG1RaVSERISotUESqlUEh4ejt3b9Y56evLs+XOGDx+On5+f1jajoqPx8/PDysqKAgWS7iDboH591qxZw7lz5/joo4+S2fPs5e19m/Fjv9NKW7Fq7dt7mu49Kfjtfc7ONunfrM17zcx08wclek9TKpX8NP1HHvn68OP0Gbi4JN60Kr94cPcav04cqJU2c8k+rGwKERqsO8hIQpq1beYNclGjXguq1GzM00f3UKnicHYty51b8TWejkWdM+178iQZ+jVbpSuo2LlzJyNGjKBTp040b94cOzs7AgMDOXLkCLt372bq1Kk8e/aMX375hTlz5uTKjtslS7oy7aeZWmk2Nra4upbi1q2bqFQqrbex9+7ewcSkAE7Fkh4v2q5QIaysrLl//57Osvv37lLSNeW3jAlNnyIjo1K7K0Lg5lKcyzfvEBkVrdXX4db9+M6+7i4ZHwv+VUAQr/wD6fK/MTrLvv9lPuamBTmy9s8Mf09uULqkM1du3CYyKkqrg/Htew/eLndJNJ++vj6uzsW581C38/Htew8p6uiA6QcvWYyMDLVqIC5duwlAjcoVgPgO2m/exGlqM953+PgpDh8/xU9jR9KwTu58CeFaqhTXrl/XOZZ378bPO1PKVbdTO8QfSxcXF+7f152T487duxQuXFgzglDCNu7dv0+tmu+Ow/3791GpVLi+Xe7/+jVv3rxh9Le6/dY8PT3x9PRk4oQJ1KuXdL+VhDf0uW30J9eSrkz/6RettOTuaXfv3sHExCTZe1qhQoWwsrLiQSL3tHuJ3NNUKhW/zZnFtatXGDtuAhUr6vZPzG+Ku7gzesoirTQrazuKu3hw3/uKznH3uXcDY5MCmf6wb2hkREm3dyPJeV+LH2muXKXamfo9QiQnXUHF7Nmz6d69O/3799ek2dnZ4e7ujrGxMYsXL2bNmjUEBwezfv36XBlUmFtYaI2bnaB+/UZ4nT7F2TOnNfNUhIaGcvr0SWrVrqP1VublyxcAFCnyrplXvfoN8PQ8gr//a+zt44fgu3b1Ms+fP6Njpy6a9UJDQ7RmI01w5O+D6OnpUap06UzZT/Hf0KxODTbsOcyuIyc081QolEr2Hz9NeTdXHAvFv3F85R9IjEKBSxomuUswdlBPYj5o/37pxh22HvRkeM9PcHbKP7PhNqlXm0279rPn72OaeSoUSiUHjp2gnHtpHN/WyPj5BxATG4vzew9mjevVYsnaTdx54EOZ0vEPs0+ev+DKjVt82qldst/79MVLdh/2pF6NqhR/+3/UvGFd3ErqPoD88Mtc6lSvwsctm1LWPfdeLxrUr8/27ds5ePCgZp4KhVLJkSNH8PDwwN4+/o3t69eviY2NpXjx4u/yNmjAqlWruHfvHu5vmys9e/aMa9euaQ0FW7lyZSwsLNi/f79WULF//35MTEyoVSt+uM3GjRtrAoz3TZs+nZo1a9KmdWvKlCkDxF/3LS0tNTXwCQ4dPgyQ6KhUOSnpe1pDvE6f4syZ05p5KuLvaadSeU9ryLEP7mlXr17RuacBLFm0gFMnTzBs+DfUq9+A/wIzc0vKVdZ9cK9RrzmXzh7l8rljmnkqwsOC+ffMUSrXbKR13F+/jJ8byKFIcZ3tpIffiyf8c3g7lWs0pLDTf7umQvpUZK90BRWXLl2ib9++iS4rV64cCxYsAKBSpUqJVpvmZvUaNMRjd1n+mDubJ08eY2lpxYH9e1HFqfj8y55a604Y9z0AK1a/mzip+6efcfr0SX4Y+x0fd+xMTHQ0O7ZvxcWlJC1atdast2XTBm7fvkX16jWxd3AgPDyMM16nuX/vLu07dKJo0aTfHv3XOP/vC4ysLClQNP6G5tCuKQXePsA+WrCON2+b4PyXlXd3pVndGizasIPgsHCKFXbgwD9neOkfyPj/9das9+P8FVy5fZez296NOBYRGcXWg54AXL8T/yZ+60FPLMxMMTczpXvb+HlnalepoPO9EW9r1KqW86BsaZcs2rvsV869NE3r1Wbpus2EhIThVMSRQ8dP8ep1AGOGvWvq8NPvi7h6y5uTuzZo0jq3bcm+v48zZtosenRqh4GBIVv2HMDG2ooeHbWDiq+GfUfT+rVxKGTHy9f+7D50FEtzM0YP6adZx7mYk1bQ8r4ijva5toYiQZkyZWjYoAGrV68mNCSEIkWL4nn0KH5+foz45hvNerPnzOHGjRscPHBAk9a+XTsOHTrE5ClT6NqlCwaGhuzcuRMbGxu6dHn3QGtiYkLPr75iwcKF/PTzz1SvVo2bt25x7PhxevXqpZn4rnjx4lpBy/sKOzpq1VAcO36cAwcOULduXQoXLkx0dDSXLl3iypUr1K5dmypVqmTykcoa9TX3tDk8ffIES0tL9r+9p33xwT3th3HxtZArV6/TpH3yaQ+8Tp9k/Njv6dCxE9HR0ezYvg0Xl5K0bNVKs97uXTvYv38vZcqWw8TEhOPHjmptu269+hR4274/MjKSvXt2AeB9O35W9X1792BmZoaZuTkff9wx049DdqtRtwVH3Dewcv4UXjz1wdzSmuMHt6JSqejUY7DWurMnx/89a+l+TVrA6xecPRH/W3j0ML5J5N6tywGwsy9MvSbvhvSeMLwrNeq1wM6+CP5+z/nn0DbMzC35avAPWbqPQnwoXUGFra0thw8fpn79+jrLDh06pGnnGhkZiaWlZcZKmM0MDAyYMvUnVq5Yyt49u1DEKnBzd2fEyG8pVizltwj29g7MmDmHFcsWs2bVCgyNDKlZszZ9+w/SejNRo2ZtXr58yZEjhwgLDcXIyBiXkiX5ZuS3NG/RKplv+O9xHdkXU5dimr+LdGlNkS7xAdrzDXskqHhr0vD+LN20k0MnzhIeGUkp5+LMHvc1Vct5JJsvPDKKpZt2aaVt3Ps3AIXt7TRBxX/N+BFDcNywlcMnThMREYmrS3FmTviWKuXLJpvPtGBB/pg+gT9XrmPt1l2oVGqqVijLsH5fYW2lfT0sXbIEBzxPEBwSipWlBU3r16Zvj27YJDI8cF727bffsnbdOjyPHSMiIoKSJUsydcoUKlbUHer0faampsycOZOlS5eycdMm1Go1FStWZNDAgVhbaR+j9u3bY2BoyI4dOzh37hz29vYMHDiQTh3T94Bavlw5vG/f5sQ//xAcEoKBgQHFihVj4IABdOjQIV3bzAkGBgZMnTqdlSuWsXfPLmJjY3Fz92DkyO/ScE+bzfJlS1i9agWGRkbUrFmLfh/c0xJmxb7jfZs73rd1trNi1VpNUBEREc76dWu0lu/cET9DvIODY74IKvQNDBgxcT5b1/yO5/5NKBQxlCxdnn5fT6Wwk0uK+QNev2DXhoVaaQl/e5SvrhVUFHdxx+vYXsJCAjG3tKZm/ZZ07DEYS+vcM5BAjsnng4rkNnrq93tap9KmTZuYMmUK9erVo2nTpti+7Qjm6enJuXPnmDp1Kp9++ik//fQTT58+ZfHixWku2L2HmTdZl0jc/TItc7oI+VrdyytSXklkiNIw9w0CkZ9EGlvndBHyvTgZ2T3L+cUmPcCKyLgG5cxyughJCv9jdJZt2+KbOVm27bwqXVezHj16YG9vz+LFi5k5cyZv3rzB0NCQsmXLsnDhQpo1i5+WftiwYRgaygVTCCGEEEKI/CzdT/zNmzenefPmqFQqgoKCsLW11Rm73soqf1XhCyGEEEKIPEKaP2WrDB9tfX19ChUqRGxsLI8fPyYdramEEEIIIYQQeVi6gooVK1bw55/vxqP/999/adSoEW3atKFVq1Y8eSL9IYQQQgghRM7R09fLso/Qla6gYuvWrTg6Omr+njFjBqVLl2bhwoXY2Njw22+/ZVoBhRBCCCGEELlbuvpUvHr1Cmfn+AlV/Pz8uHXrFuvXr6dGjRrExcUxZcqUzCyjEEIIIYQQaaOXO/tUPHz4kOnTp3PlyhXMzMzo2LEjI0aMwNjYONl8zZo14/nz5zrp169fx8TERPO3n58f06dP5/Tp0xgZGdGyZUvGjRuHubl5pu/L+9IVVJiYmBARET83wNmzZzE1NaVq1aoAWFhYEB4ennklFEIIIYQQIh8IDQ2lV69euLi4MH/+fPz8/Pjll1+IiYlh0qRJKeZv3bq1zgTU7wcjSqWS/v37AzBnzhxiYmKYOXMmo0ePZsmSJZm7Mx9IV1BRqVIlli5dir6+PitWrKBRo0YYGBgA8OTJE62mUUIIIYQQQmS7XNj3YdOmTURGRvLnn39ibW0NQFxcHFOnTmXQoEEpPkMXKlSIKlWqJLn88OHD3L9/nwMHDuDq6gqApaUl/fr14/r161SqVCmzdkVHuuqFxowZg7+/P4MHDyYyMpKRI0dqlh08eFBTayGEEEIIIYSId/LkSerWrasJKADatm2LSqXCy8srU7bv4eGhCSgA6tevj7W1NSdOnMjw9pOTrpqK0qVL4+npSXBwMDY2NlrLxowZg729faYUTgghhBBCiPTQy8I+Fc2bN092uaenZ6LpPj4+dO3aVSvN0tISe3t7fHx8UvzevXv3smXLFoyMjKhRowbffvstHh4eWtt/P6AA0NPTo2TJkqnafkZkaLrrDwMKQGvHhBBCCCGEyBG5sPlTWFgYlpaWOulWVlaEhoYmm7dZs2ZUqlSJokWL8vTpUxYvXsznn3/Orl27KF68uGb7FhYW6dp+RqU6qJg+fTp9+/alaNGiTJ8+PcX1J0yYkKGCCSGEEEIIkRslVRORld5/tq5Rowb169enbdu2rFixIleMvJrqoOLYsWN069aNokWLcuzYsWTX1dPTk6BCCCGEEELkGD393DekrKWlZaKjpIaGhmJlZZWmbTk4OFC9enVu3bqltf2EEVo/3H6RIkXSXuA0SFNQkdi/hRBCCCGEEClzdXXV6dsQHh6Ov7+/Tl+I9G7/3r17WmlqtRpfX1/q16+f4e0nJ9VBxa5du9K04U6dOqWxKEIIIYQQQmQSvdzXp6JRo0YsXrxYq2/FoUOH0NfXT/NDv5+fH5cuXaJjx45a29+zZw+PHj3CxcUFiJ9TLiQkhMaNG2fafiQm1UHF2LFjtf7We/sfpVarddJAggohhBBCCCHe16NHD9atW8fQoUMZNGgQfn5+zJo1ix49emjNUdGrVy9evHjBkSNHANi3bx/Hjx+ncePGODg48PTpU5YuXYqBgQF9+vTR5GvdujVLlixh+PDhjBo1iujoaGbNmkWTJk2ydI4KSENQcfHiRc2/Hz9+zDfffEPHjh1p3bo1hQoVIiAggEOHDrFnzx5+//33rCirEEIIIYQQqZML+1RYWVmxZs0apk2bxtChQzEzM6Nbt25ac74BqFQq4uLiNH8XK1aM169f8/PPPxMeHo6FhQV16tTh66+/1oz8BGBkZMTy5cuZPn06o0aNwtDQkJYtWzJ+/Pgs3zc99ftVDanUp08f6taty8CBA3WWLVmyhDNnzrBmzZoMFezewycZyi9Sdr9My5wuQr5W9/KKnC5Cvqc0LJjTRcjXIo2tc7oI+V5cxkZ2F6ngF1sop4uQrzUoZ5bTRUhS1OqpWbZt096Ts2zbeVW6QrgrV65Qvnz5RJeVL1+ea9euZahQQgghhBBCZIieXtZ9hI50BRW2trYcOHAg0WX79+/H1tY2Q4USQgghhBAiI/T09bPsI3Slq9518ODBTJo0iSdPntCiRQvs7OwIDAzk6NGjXLx4kR9//DGzyymEEEIIIYTIpdIVVHzyySfY29uzePFifv31V968eYOhoSHlypVj4cKFNGvWLLPLKYQQQgghROrpSY1Cdkp3D7GmTZvStGlTVCoVQUFB2Nraoi/VQUIIIYQQQvznZHjYCX19fQoVkpEVhBBCCCFELqIvHaqzk1QtCCGEEEIIITJEBsgWQgghhBD5jp70qchWcrSFEEIIIYQQGZJrayqeRDrmdBHyPZnxOWudrdYvp4uQ77neOZbTRcjXQpXmOV2EfM/KKCKni5DvXbhvmtNFyNcalMvpEiRD+lRkq1wbVAghhBBCCJFu0vwpW8nRFkIIIYQQQmSI1FQIIYQQQoj8R0+aP2UnqakQQgghhBBCZIjUVAghhBBCiPxHX96dZyc52kIIIYQQQogMkZoKIYQQQgiR/8joT9lKjrYQQgghhBAiQ6SmQgghhBBC5D8y+V22kqBCCCGEEELkP9L8KVvJ0RZCCCGEEEJkiNRUCCGEEEKI/Ecmv8tWUlMhhBBCCCGEyBCpqRBCCCGEEPmPTH6XreRoCyGEEEIIITJEaiqEEEIIIUT+I30qspXUVAghhBBCCCEyRGoqhBBCCCFE/iPzVGQrCSqEEEIIIUT+Ix21s1WajnZsbCzTp0/n+vXrWVUeIYQQQgghRB6TpqDCxMSE7du3ExMTk1XlEUIIIYQQIuP09LLuI3SkuV6oatWqXL16NQuKIoQQQgghhMiL0tyn4uuvv+bbb7/FwMCAxo0bY2dnh94HEZu1tXVmlU8IIYQQQoi0k47a2SrNQUWPHj0A+PXXX5k9e3ai63h7e2esVEIIIYQQQog8I81Bxc8//6xTM5HfREWGsWvdXK5dOIZCEY1z6Yp06TmaEq7lks2nUqk4f2Iv184f5emjO0RFhGLn4ET1+m1p8XEvjIxNNOsqYmPYsmIGjx5cJzjAD5UqDvvCxanbtBONWn+KgaFRVu9mtlMolSzbtItDJ88SFhlF6RLFGPRZZ2pVLp9svsfPX7Hz73+4dd+He76PUSjfsGPhTIo4FEo237NXr/li5EQUyjes/GUiZUu7ZOLe5A8GZqa4ju6Hda3KWNesiLGtNdf6jeXZ2p05XbRcIyIigjUrl3D+zGliY2Nx8yhDn/6DKVXaPVX5nz55zMplC/G+dQNDQyOq16xN34H/w8rKWrOOn98rBvX5PNH8o8dMoGHjZkD8Nea459+cO3MKn4cPiAgPx7FwYRo0akqnrp9ibGyc4f3NLSIjwtm8Zj6Xz/1DbGwMrm7l+azvN7iUKpNi3of3bnH62D4e3rvFs0f3iYuLY83uC4mu26tjrUTTu381lPbdemVoH3KT3HQef+jNmzeMGDqAZ08f07vfIDp1/TTN+5cbxUaHcW7/rzy6dZQ3ihgcilekTvsx2BdL/p73obg4JdvmdiLk9UPqtPuOyo37aS0PDXjM+YNzeP7gHKo3Cgo5laNGq69xKl0nM3cnb8rnz6u5TZqDii5dumRFOXINlUrFohnDePboLi069sbcwoaThzfzx5R+jJm5CYcizknmVcTGsH7hREq6VaJhy+5YWNnic+86+zcv5O6N83wzebkmIFMqYnn57AHlqzbEzqEoenr6+Ny9yvY1v/Lo/g36jJiZXbucbab/uZJj5y7xabsWFC/iyP7jXoz6+Q8WTPmOymXdksx3894Dth48ikuxojg7FeH+o6ep+r4/Vm/CwMAAlG8yaxfyHeNCNrhPHEbU4+eEX7+LXZPaOV2kXEWlUjF98jge+T6kU9dPsbS04uD+3UwYM4o58xZT1KlYsvkDAvz54fsRmJqZ8WWv/kTHRLN7+xYeP/bl17kLMTLSfnnQsHEzqtfU/j/wKPPuZUZsbCzz587Co0w52nz0MVbW1tz1vs2mv9Zw/doVps2Yky9e+qhUKn6bNpKnj+7TtvOXWFhY43lwGzN+GMLU39ZQuGiJZPNfv+TFiSO7Ke5cGntHJ169eJLs+uWr1KZ+04+00pxdU/ewnRfktvP4Q/v37CTA3y/9O5gLqVUqDq4cRODLu1Ru3JcCZjbcPruRvUt60vXr7VjZu6R6W7e81hMR8jLRZREhL9m1oAd6egZUbtwXI2NT7l7cwYHl/Wk3cBVFXWtm0h4JkTKZp+IDV84dwefuVfqNmk21uq0AqFa3FVO/+Zj9mxcm+7BvaGjE6OlrcfWookmr36IbdvZF2b8lPrAoUyn+zYGZhRXf/fyXVv6GrT6hoKkFJw5tpEuv77CySf5NfF5y674PR7wuMOyr7nzRsQ0AbRvX44tRk/hz3VaW/Tw+ybwNalTh7zXzMStYkL92H0pVUHHu6k3OX73FFx3bsHr7vkzbj/wm9uVrjharT6xfAFbVK9Dg3PacLlKucub0Se543+L78ZOp16AxAPUbNeF/A3qycf1qRo+ZkGz+bZv/IiY2hjnzFmPv4AiAu3sZJv/wHceOHqZ12/Za65cq7UaTZi2T3J6hoSG/zJ5HmXIVNGmt2rTHwbEwG9ev5vrVy1SuWj29u5trXDzjyYM71xn2/Qxq1m8OQK0GLRgzpBs7Ny5lyOjpyeZv1rYr7br0xNikAGuX/JpiUFG4aAnqN2mbaeXPbXLbefy+kJBgNm9cS+dun7Fx/ap07F3u5HPjMH6Pr9Dyy99xrRR/zytVqS2bfm3Dv0fm0/zzOanaTnREIJeOLqRKk/78+/c8neVXji9DER1O91F7sHZwBaBMre5smf0RZ/fOoOs3OzJvp/IimaciW6XraF+8eJFvv/2Wbt268fHHH+t88rIr545gYWVHldotNGkWVrZUq9ua6/8eR6lUJJnX0MhIK6BIULlWfJXvq+c+KX6/rUNRAKKjwtNY8tzt+LlLGOjr06llY02aibERHzdrwM17D/ELCEoyr5WFOWYFC6b6u968ecPclRv5pF0LihW2z1C58zuVQkmsX0BOFyPXOnP6BNY2NtSp11CTZmVlTf2GTbhw7kyy1wOAs16nqFGzjuZBDKBy1eoUdSqG16l/Es0TExONUqlMdJmRkZFWQJGgdt0GADx9+jiFPcobLp45hpW1LdXrNtWkWVrZUKtBCy6fP5nicbeytsPYpECavlMRG4NCEZuu8uZ2ue08ft+6VctwcipOk2YtUlw3L/G5cZiC5oUoWaGVJq2guS2lKrXh0a1jxL1J/pgnOH9gDtb2JXGr1iHR5a98/8XOqawmoAAwMi6Ic7mmBDy/Taj/owztR16n1tPLso/Qleag4tSpU/Tq1Yvg4GBu3rxJkSJFsLGxwdfXl+joaCpU0L3h5SXPfO9Q3LUs+h9Ety6lK6CIjeH1i0dp3mZYSCAA5hbWOsveKJVEhAUTHPCKq+c98dyzBlv7otgXLp6e4uda93yfULyoI2am2sFBudIl45c/Sv5NYlps2n+U8Mgo+nRtn/LKQiTD1+cBrqXcdK4Hbu5liI2N4fmzZ0nmDQzwJzQkmNJuHjrL3NzL4PvwgU76pg1r6dGlHZ90asO33wzhyuWLqSpnSHB8UG5paZWq9XO7Jz73cC5VRue4u7qVQxEbw6vnmXe9ADh9bB8DP23MgO4NGTf0U86eOJSp289pufU8vnfXm+Oef9Nv0NB81/Y94Lk3hZzKoffBMXcoXok3ymhC/H1T3MbrJ9e5d2kXdTuMI6mjE/dGgaGhiU66oVH8vdb/+a00l12I9EpzUDF//nx69erF0qVLAfjmm29Yu3Ythw8fxtDQkDp18nbHoNBgf6ysdd9uW9rYa5an1dHdqyhQ0JxyVRvoLLt64Shj+jVmwpBWLJs9Ems7RwaPmYeBQf5qmRYYHIKdte4DTyEbawACgkIy6XtCWbVtLwN7dNIJYIRIq+CgQGxs7XTSbWxtNcuTzhukta52fjvCw8M0b4j19fSoUq0GvfsOYvzk6fQd8D9CQ4KZNmkc/144l2I5d27bjKmpGdVq5I8+MSHBAVjb6B5367dNQkOC0n4dTkrpMpXo9uUQvh73K70Gj0HfQJ/Fv03C8+C2TPuOnJYbz2O1Ws2yRfOp37AJZcqmreNyXhAV7o+ppe6zhKlFfFpU2Otk86vVarx2T6dU5bYUdq6a5HrW9iUJfHUPRUyEVvqrR5cAiAzNX31V0kxPP+s+Qkean1wfPnzIyJEj0dfXR09Pj+joaACcnJwYPnw48+fPp2PHjple0OyiVMRiaKQ78pKRkbFmeVoc2rGMOzfO8Wn/HzA1s9RZ7l6+FsMnLiUqMoy7N87z/PE9FLHR6St8LharUGKcyHE1NjbSLM8MC9Zvw8nRng7NG6a8shApUCgUOp1QAYzfXg9iY5O+HsS+vVYkXDu08hsn5FdgZGSMvYMjU6bP0lqnSfOWDB/ch1XLF1GjVtIva7Zu/otrVy8xaOg3mJubp7xTeYBCEYthIsfN6O1xy8xmShNnLtf6u1GLDkwe3ZNt6xbRsFn7NDejyo1y43l87MghHj/25fsfpqR5f/KCOGUMBoa6x8zAKL5W4Y0y+XP47r87CHp1j5Zf/ZHseuXqfsZj7+Mc/WsUtdqMwNC4ILfObsT/WXwNRdyb/NmkT+ROaQ61TExMUKlU6OnpYW9vz5Mn76qhzczMePXqVaYWMKu8USoJDQ7Q+qji4jAyNuFNIu1AE97EvD8sbEoueR1i36Y/qdesM41aJz5EnqW1HWUq1aFa3VZ8NnAiFao3Yv60QYQG56927ibGRigSOa6Kt8GEiXHGh9C9ee8hh06e5ZvePXSq+YVIjlKpJDgoSOsTFxeHsbFxou3CFW+vByYmSV8PTN5eKxJrr65QJORPeghYCwtLmrdow/NnTwkISPzN/OkTx9mwdiUtWn1E23Z572XOG6WSkOAArY8qLg5jYxPeJHLclG+Pm3EarsNpZWhkRIuPuhMVGc6jh3ey7HuyQl45j6OiIlm3Zjmdu36Kvb1D6ncwF4p7oyAq3F/ro1LFYWBUINF+E3FvgwlDo6SPuSImggsH51KpcV/MrYsk+/0lyjSifscJvPT9l+1/dGHzr2154n2Cmq1HxH+PsWn6dy4/kJqKbJXmmooyZcrg6+tL/fr1qVu3LosXL8bGxgZDQ0N+//133N3zxjB8Pveu8scU7bGef1xwECsbe0JDdG/gYW+bPVnZpK7jr/e1s6z98wfKV2tIj4ETU12uqnVasnfjfK7/e5yGLbunOl9uZ2djjX9QsE56QHAIAIVsrTP8HQvWbaVyWTeKOhTi5ev4oCwkLL5KOCAkhFf+gRS2120CIMQd71tMHDtKK23Jqg3Y2Nol2jTkXZOQpM+nd01LdAchCA4KxMLCMtG3v++ze/vAFREeRqFC2teeq5f/5fc5v1C9Zm2GDB+Z7HZyq/t3rvPLhCFaabOX7sLaphAhwbrHPeTtyxZr26wdgMG2UHyH5IjwsCz9nsyWV87jXdu38Eb5hgaNmuDnF/8iMvBtwBEREYGf3ytsbe0SrV3JbfweX2HvEu35TD4fexRTC3uiwnSfJaLC49NMLZMOpq6dWIkqTknpyh8RHhTf3yXibTOm2OgwwoOeYWrpoKkJqVD/SzxqdiHw5V0MDIywK1qWOxfim+9ZF3LJ8D4KkVppDip69erFs7edukaNGsXgwYMZMiT+plC4cGH+/PPPzC1hFinm7MHwiUu10iytC1HMxYMH3pdRqVRab7sf3b+BsUkBHIq6pLht3/vXWfrrCEqUKk+/kbPT1D8ioXlVTD4b/cnNpTiXb94hMipaq6/DrfvxndXcXZIfdz41XgUE8co/kC7/G6Oz7Ptf5mNuWpAja/PG+SmyV8mSpZj6069aaTY2tpR0LcXtWzd0rgf37npjYlIAp2JJj+9vV8geKytrHty/q7Ps/r07uLiWSrFcfq9eAGD53gRjAPfuePPL9EmUdnPnu3GT4+djyYNKlHTj+6nav0krGztKlHTj3u2rOsf94b1bGJsUoLBTxq8XyXnt9xzQPe65XV45j/39XxMREc7wwX111t22+S+2bf6L3+YvxbVU6RS3ndPsipSh3YCVWmkFLewpVLQML30voVaptDpr+z25hqFRQaztSya5zYiQF8RGh7Jlju5gI1eOLeHKsSV0HbGTQkXLatKNjE21+l48f3AWQ6MCOLpUy8ju5XkySlP2SnNQ0bjxuyFBHR0d2bFjB48fPyYmJgZXV9c8M6OrqbmlZs6I91Wt05Ir545w9fxRzTwVEWHBXD73NxWrN9F6I+P/Kn6+hPdHanr1zIdFM4Zh51CUIWP/TLI9bkRYMGYW1joTVZ3xjB9TuoRr/uq41qxODTbsOcyuIyc081QolEr2Hz9NeTdXHAvFvw175R9IjEKBi1PyVb6JGTuoJzEK7ermSzfusPWgJ8N7foKzU+GM74jIl8wtLBKd36Fe/cacOX2Sc2dOacb3DwsN5czpE9SsXVfrevDyZfyDaJEiTpq0uvUbcszzb/z9X2uaeVy7epkXz5/RoVM3zXqhoSFaMxND/Jtbz78P4VLSFdv33iQ/ffKYaVPG4eBYmAlTfk626UpuZ2ZuSfkqujNa16zXnItnjnHp7HHNPBXhYSFc9PKkas2GWsfd72X8Sy7HIslP4JaYsNBgLK1stNKioyL5e88mLCytcSlVNomcuVNeOY/bd+hM7Tr1tdYLDQ1h0fzfaNaiNbXq1MexcN64XpuYWlHMrZ5OesmKrfG5cRjfm39r5qmIjgzG58ZhnMs11epvERoY34zcyi4+WK5Q/ytcymsPsRsdEcipHZNxr9EZl3LNsbBJ+nx/9egyvjePUK5OD0wKWmR4H4VIrQwPMaSnp4eLi0smFCV3qFqnJSXdKrF+4SRePfPB3NKak4c3o1apaPeJdjX9vB8HADBtYfzwgzHRkfw5fTBREWG06NCbm5dPaq1fyLE4rh6VAbhwah+n/95KpZrNKORYjJiYSLyvnuHO9bNUrN4Yj4r5YxSXBOXdXWlWtwaLNuwgOCycYoUdOPDPGV76BzL+f7016/04fwVXbt/l7LYVmrSIyCi2HvQE4Pqd+OELtx70xMLMFHMzU7q3jX/oqF1FdzjjiMgoAKqW86BsaZcs2ru8zfl/X2BkZUmBovEPCw7tmlLgbQD2aME63oRFJJc9X6vboBEeu8sxb+4snj55jIWlFYf270YVp+KzL7WbPEwa9y0Ay1Zv1KR1/fQLvE6fYOLYUbTv2IWY6Bh2bd+Ms4srzVu10ay3ZsUSXr18QaUq1bC1s+O1nx+HD+4lJiaGfoOGadaLjopi6sQxREZE0Lnrp/x7UXtEncJFiuaLkXRq1mtGKY8KLJ83jedPfbGwjJ9RW6VS0fmzgVrrzpo0FIA5y3Zr0gJev8TrnwMAPHrgDcDuLfHXlEL2RTSzZ3se2Mrl8yeoUrMhdvaFCQkK4JTnXgL9XzFwxNREB+3Ii3LbeVyqtDulSms3lU5oBlXC2YU69XRHSsxrXCu15sbpyvyzZTzBfg8pYGbDrbMbUaviqNFymNa6+5b2BuCLcccAsC9WHvti2r/jhGZQto6lKVnhXcARHvyco+tH4lyuGaYWhQjyu4/3uc3YFfagVpu82SwyU+XSvg8PHz5k+vTpXLlyBTMzMzp27MiIESOSfTH/+vVrVq9ejZeXF0+ePMHCwoKaNWsyatQonJzevQQ4f/48PXv21Mn/0UcfMXfu3CzZnwTpCiru37/PwoULuXHjBq9evWLz5s2UL1+euXPnUq1aNa3ajLxG38CA/41fyM51c/jn4AaUihicS1Xgq6HTcXRKuroSIDI8hODA+Avj7r9+11leu3EHTVBRqkw1fO5e41+vg4SHBqKvb4BjURe69vqOxm0/y/T9yg0mDe/P0k07OXTiLOGRkZRyLs7scV9TtZzu+OfvC4+MYummXVppG/f+DUBheztNUCHSx3VkX0xd3r31KtKlNUW6tAbg+YY9/+mgwsDAgIlTZ7B6xRL27dmBIlZBaXcPvh45BqdiKTfBsbd34KeZc1m5bBHrVi3H0MiQGjXr0Kf/YK23w1Wq1eDwgb0c3LebiIhwzMzMKV+hEt17fKn18BUWHkaAf/xQlGtXLdP5vqYtWueLoELfwIDRk35n06p5HNm3GYUiFle3cgz4ehJFijmnmN/f7wU7/lqilZbwd5kK1TRBhVvZyty/c4MTR3YTER6KiUlBXN3K0W/4BMpVqpn5O5ZDctt5/F+gr2/AR32Xcm7/r9z0WscbZSz2xSvQ9JOftSaqyyhjE3NMLe25deYvYqJCMLNypEL9r6jabBDGBfLHaHAZkgubP4WGhtKrVy9cXFyYP38+fn5+/PLLL8TExDBp0qQk8926dYsjR47QtWtXKleuTHBwMIsWLaJ79+7s27cP2w+GfZ4xYwauru/ONRsbmw83men01Gq1Oi0ZvLy8GDRoEOXLl6devXosWrSI7du3U758eebPn8+tW7dYvHhxhgt29LoMg5bVqumlbmItkT5nq/VLeSWRIa53juV0EfK1UKU8lGQ1K6P/btCeXQ7eLJrTRcjXRnXMfQ/uCaJObc2ybZs2TN9gOkuWLGHx4sUcP34ca2trADZv3szUqVM5fvw4jo6OieYLCwvD1NQUQ8N39QGvXr2iSZMmfP/99/TtG99HKaGmYtu2bVSsWDFdZUyvNNcLzZkzh48++ojNmzczdOhQrWVly5bl9u3bmVY4IYQQQggh0kVfP+s+6XTy5Enq1q2rCSgA2rZti0qlwsvLK8l8lpaWWgEFxA+QZGtry+vXyU+mmF3S3Pzp/v37jB49GkCnk7GlpSXBwbrDhgohhBBCCJFfNG+efNNrT0/PRNN9fHzo2rWrVpqlpSX29vb4+PikqQy+vr4EBgZSqpTuKGwDBw4kJCQEe3t72rVrxzfffEOBAlk7mWeagworK6skI6JHjx5hb5+144cLIYQQQgiRktw4pGxYWBiWlpY66VZWVoSGhqZ6O2q1munTp+Pg4EC7du006RYWFvTv35+aNWtiYmLCuXPnWLlyJT4+PixZsiSZLWZcmoOKFi1aMH/+fCpXroyzc3yHOT09Pfz9/VmxYgWtW7fO9EIKIYQQQgiRWyRVE5Fd5s+fz7lz51i+fDmmpu9mTi9XrhzlypXT/F23bl0cHBz48ccfuX79OpUqVcqyMqW5Udjo0aOxsbGhQ4cOdO8e30ll/PjxtGnTBgsLC4YNG5bCFoQQQgghhMhievpZ90knS0tLwsN1JzgODQ3FysoqVdvYsmULCxYsYOrUqdStWzfF9du2bQvAzZs301bYNEpVTcWwYcP47rvvcHZ2xtPTk6VLl/LPP/9w5swZrK2tsbKy4osvvqBjx455ZvI7IYQQQgghspOrq6tO34nw8HD8/f21hoBNypEjR5gyZQpff/013bp1S3H97JSqoOLYsWMMGDAAZ2dnxo0bx+bNm+natatORxMhhBBCCCFyA3UunPyuUaNGLF68WKtvxaFDh9DX16d+/frJ5j1//jyjRo2ie/fuOiOwJmf//v0AWT7EbKqCCkdHR44dO4adnR1qtRp/f39evHiR5PpFi8qY0EIIIYQQIgflwo7aPXr0YN26dQwdOpRBgwbh5+fHrFmz6NGjh9YcFb169eLFixccOXIEiJ+Fe+jQobi4uNCxY0euXr2qWdfW1pYSJeInsvz2229xdnamXLlymo7aq1evpkWLFrkjqOjVqxczZ85k6dKl6OnpJdlvQq1Wo6enh7e3d6YWUgghhBBCiLzOysqKNWvWMG3aNIYOHYqZmRndunVj5MiRWuupVCri4uI0f1+7do3w8HDCw8P57LPPtNbt3Lkzv/zyCwBubm7s3buXlStXolQqcXJyYvDgwQwcODDL9y3VM2o/fvwYHx8fhgwZwrfffouLi0uS67Zo0SLDBZMZtbOezKidtWRG7awnM2pnLZlRO+vJjNpZT2bUzlq5eUbt8Av7s2zbFrXapbzSf0yqh5R1dnbG2dmZzp0707p1a4oXL56V5RJCCCGEEELkEWmep2LGjBlZUQ4hhBBCCCEyTy7sU5Gf5b5u8UIIIYQQQog8Jc01FUIIIYQQQuR6uXBI2fxMjrYQQgghhBAiQ6SmQgghhBBC5Dtq6VORrSSoEEIIIYQQ+Y80f8pWcrSFEEIIIYQQGSI1FUIIIYQQIt9RI82fspPUVAghhBBCCCEyRGoqhBBCCCFEvqOWPhXZSo62EEIIIYQQIkOkpkIIIYQQQuQ/UlORreRoCyGEEEIIITJEaiqEEEIIIUS+I5PfZS+pqRBCCCGEEEJkiNRUCCGEEEKIfEdGf8peuTaoKGbmn9NFyPeUioI5XYR8zfXOsZwuQr7nU6ZZThchXyvpfTyniyBEhlV1jcnpIuRzufhZQpo/ZSsJ4YQQQgghhBAZkmtrKoQQQgghhEgvaf6UveRoCyGEEEIIITJEaiqEEEIIIUS+o0b6VGQnqakQQgghhBBCZIjUVAghhBBCiHxH+lRkLznaQgghhBBCiAyRmgohhBBCCJH/yDwV2UqCCiGEEEIIke+opUFOtpKjLYQQQgghhMgQqakQQgghhBD5jlqaP2WrdNVUqNXqzC6HEEIIIYQQIo9KV1DRuHFj5s6dy5MnTzK7PEIIIYQQQmSYWk8/yz5CV7qOyscff8yOHTto3bo1X331Fbt27SImJiazyyaEEEIIIYTIA9IVVHz33XecOHGCBQsWYG1tzYQJE2jQoAGTJk3i+vXrmV1GIYQQQggh0kSNXpZ9hK50d9TW19enWbNmNGvWjKCgIPbs2cO2bdvYunUrpUuXpmvXrnTp0gVLS8vMLK8QQgghhBAil8mURmEBAQG8fPmSwMBAjIyMcHBwYN68eTRr1gxPT8/M+AohhBBCCCFSTfpUZK9011RERESwb98+tm/fzs2bNyldujRDhgyhY8eOWFlZERERwbRp0/jpp59o3rx5ZpZZCCGEEEKIZMmQstkrXUHFd999x9GjRwFo27YtP/zwA1WqVNFax9zcnM8//5zdu3dnuJBCCCGEEEKI3CtdQcXDhw8ZM2YM7du3x9zcPMn1Spcuzdq1a9NdOCGEEEIIIdJDOlRnr3QFFTt27EjVemZmZtSqVSs9XyGEEEIIIYTII9LdpwLAy8uLa9eu4e/vj729PVWqVKFevXqZVTYhhBBCCCHSRTpUZ690BRX+/v4MHz6cq1evYmVlhZ2dHYGBgcybN48qVaowf/587O3tM7usQgghhBBCiFwoXSHc5MmTefbsGatXr+b8+fMcOHCA8+fPs2rVKp49e8aUKVMyuZhCCCGEEEKknkx+l73SVVNx5swZpkyZQp06dbTS69aty7fffpungoqIiAjWrFzKuTOniY2Nxc3Dg779B1OqtHuq8j998pgVyxbhfesGhoZG1KhZm74Dh2BlZZ1knn+OH2XurzMoUKAAm3fs16SrVCqOe/7N2TOn8Xn4gIjwcBwLF6Zho6Z06voJxsbGGd3dbKNQKlmxYRt//3OK8MhISjmXoP8Xn1CzSsUU8/oHBvHninVcvHoDlVpN1YrlGN73S4oWdtRaLygklCVrN3L20lWioqNxLubEl1070LR+nSS2HG/U5J/599pNOn/UkpED+2RoP3OD+HN4Cec153AZ+qTxHF65bKHmHK5eszZ9B/5P6xz283vFoD6fJ5p/9JgJNGzcDHh3Dp87c0rrHG7QqCmdun6ap87hzGJgZorr6H5Y16qMdc2KGNtac63fWJ6t3ZnTRcs1IiMiWLNyMefPvj2H3cvQu/+QNJ3Dq5YtwPt2wjlchz4DtM/h136vGNT3s0Tzj/p+ouYcTuB16jh7dm7l2bMn6OsbUMLZhc5de1CjVt1072dOyo7rxIdOHD/K3F9/pkCBAmzacUBn+f69Ozm4bzevXr7E0sqSBg2b8nnPPhQoUDC9u5mrREWGsWPd71y9cBxFbDQupSvQrddoSriWTTafSqXi3Im9XD1/jKe+d4iMCKWQgxM16rehZYeeGBmbaNYNCnjFmWO7uHH5FP4vn6Cnb0DR4qX4qNsAylZK/l4oRGZLV1BhaWmJlZVVksssLCwyVKjsolKpmDZ5PI98H9K566dYWFpycP8efhgzmt/mLaKoU7Fk8wcE+DP++5GYmpnxZa9+xMREs2v7Vh4/9uXXuQswMjLSyRMdHc2alUspUKCAzrLY2Fjmzf0VjzJlafPRx1hbW3PH+zYb/1rDtWuXmT5jDnp5ZMzlGfMW88+ZC3T/uA3FihTm4LGTfD9tFn9M+4FK5cokmS8qOoZvJk4nMjKKL7t1xNDQgC17DjL8h2msnDsDK8v4cysyKoqh46YQHBJGt/atsbWx5rjXOSb/Oo83b+Jo2bh+ots/cfYCt+7ez5J9zgkqlYrpk8fxyPchnbp+iqWlFQf372bCmFHMmbc4VefwD9+PeHsO9yc6Jprd27e8PYcX6pzDDRs3o3rN2lppHmXKaf4dGxvL/Lmz8ChTjjYffYyVtTV3vW+z6a81XL92hWl56BzOLMaFbHCfOIyox88Jv34Xuya1U870H6JSqZg+Zezbc7gHFpaWHNq/m4ljRzL7jyWpOocnjPkGUzMzvujVn5joaHbv2MLjRz7Mmrso0XO4Wg3thy2PsuW0/t6/ZwfLl8ynes06fNV7IEqFgmNHD/HT1PF8P34qdes3ypydzybZfZ2AhHvdkkTvdQBrVi5l57ZN1GvQiPYduvD06WP2793JkyePmDJ9Vqbsd05SqVT8+fNwnj++R8sOvTC3sObE4S38Nrk/42ZtwLGIc5J5FbExrF0wmZLulWjYqhsWlrb43rvO3i2LuHPjPCOnLNNcR69d/IfDu1ZTpVYT6jb+mDhVHOdP7OOPHwfT839TqNesUzbtce4kfSqyV7qCil69erF06VJq1aqFmZmZJj0iIoJly5bRs2fPTCtgVjpz+iR3vG/x/fhJ1G/QGIAGjZowZEAvNq5fw+gxPySbf9vmDcTExvDbvEXYO8S/RXdzL8PkH77n2NHDtG7bXifPlk3rKVjQlIqVqnD+rJfWMkNDQ36ZPY+y5cpr0lq1aYeDoyMb16/h2tXLVKlaPaO7neVu33uA56mzDOn9OZ91ij8GrZs2pPfXY1i0ZiOLZk5NMu+ug0d49uIVS36dRlm3UgDUrlaZ3l+PYfPu/Qz8qgcAew578vylH3N//IHqleKPV6c2LRg8ZhILVv9Fk3q1MTLSPr1jFQoWrPqLzzt/zIqN27Ji17Pdu3N4MvXensP1GzXhfwN6snH9akaPmZBs/m2b/yImNoY58xZrzmF39zJM/uG7RM/hUqXdaNKsZZLbSziHy5SroElr1aY9Do6F2bh+NdevXqZyHjiHM1Psy9ccLVafWL8ArKpXoMG57TldpFzlrNcJ7njf4rtxU96dww2bMnTAV2z6axWjvp+YbP7tm9cTExvD7D+WvHcdLsuUCd9y/OghWrX9WGt911LuyZ7DEP8GvbR7GX6Y/LPm4a15q7b069md456H81xQkd3XCYCtm9a9vddV5fzZ01rLgoIC2bNzK02atWTEt+M06UWdirFs0XwunD9Drdp5e9CXy+eO4HP3GgNG/0r1uvHnW/V6rZj8dUf2bV5EvxG/JJnX0NCI76avplSZKpq0hi27YudQlL2b4wOLhFoIjwo1mLH4IOaWNpp1G7Xqzk/ffsrezYskqMilzZQePnzI9OnTuXLlCmZmZnTs2JERI0akWJuvVqtZtmwZGzZsICgoiLJlyzJu3DidueL8/PyYPn06p0+fxsjIiJYtWzJu3Lhkp4HIDOkK4V68eMHz589p3LgxQ4cOZdKkSQwdOpQmTZrw4sULzc4kfHKrM6dPYm1jQ916DTVpVlbWNGjYmPPnzqBUKpLP73WSmjXraC6yAFWqVqeoUzFOnzqhs/6L58/Ys3M7fQcMwcDAQGe5kZGRVkCRoE7dBgA8e/ok1fuWk06cuYCBvj4dWr1rTmBibEy7Fk24dfc+fv6BSeb95+x5yri5agIKAOdiTlSrVJ7jXuc1addu38XaylITUADo6+vTtH4dgoJDuHrLW2fbG3fuQ61W06OT7g0wrzpz+gTWNjbU+eAcrt+wCRdScQ6f9TpFjQ/O4cpvz2GvU/8kmicmJhqlUpnoMiMjI62AIkHtt+fw06ePU9ij/EelUBLrF5DTxci1zpw+gbV1Bs7hM0mdw8XTdQ4DREdFYmVlrVWrZmpqRsECBTExMUkyX26V3deJhHtdnwFD0E/kXnfX+xZxcXE0aNxUK71ho/h7xukTx9Owd7nT5bNHsbS2o2rt5po0CytbqtdrxbWL/yR7zA2NjLQCigRVasUfn1fPfDVpRYuX1gooAIyMjKlQrQHBgX7EREdmcE9EZgsNDaVXr14olUrmz5/PyJEj2bJlC7/8knSgmWDZsmXMmzeP3r17s2TJEuzt7enbty9Pnz7VrKNUKunfvz+PHj1izpw5TJkyhdOnTzN69Ois3C0gnUHF8ePHMTQ0xNLSEm9vb06fPo23tzeWlpYYGBhw7Ngxzef48dx7cfDxeUCpUm7o62sfBjf3MsTGxvD82bMk8wYG+BMaEkJpN932qO7uZfB9qNvEZvnShVSsXIUaNdPW/CEkOAgAS8vEm5zlNvd9H1GsaBHMTE210hMChQe+jxLNp1L9n737Dm+q6gM4/u0G2qabUVZpS8sue5W9QUCmigsBmQKC6CsgWwQURBSUvVU2InsVZFNBNpTZsqG0tHS3SZu8f4QGYjrTXX+f58mjOfecm3MPpzc59yw1gXcfUMnD3eBY5YoePHoaTGxcHKD9o7FKoUVf5OVY0xt3gvTCg0NC+W3LdoZ8+A5WVoVnXH9Q4G3cs1SHw/Gs6G1wrKJXJYLu3DYIX//7Gt7p8QZvdevA558O5fy5MxnKZ0GrwyL3BAXext3TK9U6/PiR8XU4MNCwDm9Yt5o+PTvxdvf2fDFqCBdSqMPVatTk/D9/s2v7Vp4FP+Xhg/ss/mUeMbExvNG1pxFXmbdy+z6xfMnPL7/rUh7Tn9ygs7LUb6AlN9ju3L6Z9gUVAA+CrlO2QiWDMnfzrIYyIZ5njzP/gCXihfbhhI3CPv244aFYWhXB0jLl4Wf/FRoT0xx7GWv9+vXExMSwYMECmjZtSq9evfjiiy9Yv349wcHBqaZLSEhg8eLF9O/fn48++ohGjRoxd+5c7O3tWb58uS7evn37uHXrFj/++COtWrWiU6dOfPPNN/z1119cunTJ6HxnhFHDnw4dOpTd+cgT4WHPqVrNcOKwg6MToO2idatg+ANXeyxML65+ekeioqJQqZRYWGh/wJ79+zQXzp1l3s9LMp3PrZs3UKyYNbXrFoyNBJ+Hv8DJwd4g3MlRGxYa/iLFdJHR0ShVqpTTOmifxISGhVOudFHKlXbln0tXePoshJLFXy1ffCngujbe8zC99D+v/JWK7m60blqwu9T/LTzsOVWq1TAId3B01B1PrQ6H6+qwYwrpnYiKitTVYVMTE2rWrkvDRk1wdHYm+MkTtv+xia8njWP8pOnUrZ/2hMA/dHVY5hMIfeFhz6lSNaU6/PI+/DyU8m6p1OHwtO/D0a/VYZOXdbhBo6Y4OTnz9Oljdvyxma8nj2XcxOl6E7AHDB5BZEQEyxbPZ9ni+YC2QTz1m++pVNmwNzm/y637BLz+Xbc01fyULlMWgIBrV6juU0sXfu3qZQCePy/4PXuRL0KpWMVwqKfCwRmAF+EhlC5fMVPn3P/nKooUs6FqrZTnDCZ79uQ+F/4+RO2GbVPsKRJ56+jRozRq1Ah7e3tdWMeOHZk8eTInTpygR48eKaY7d+4c0dHRdOzYURdmaWlJ27ZtOXDggN75vb29cXd/9Tft6+uLvb09R44coUYNw3tBdsnS5ncFnVL56kb4OsuXYcqEhDTSao+lNEHN4uUT9IQE7flVKhXLl/xCh05dKFfOLVN53LThNy5eOMeQTz7N8bFw2UV73YZVy/JlWSUkpNztm5CgfXqVUplaWuqn7dy2JX/uO8jk2T8xfMAHONrZcfjEaY6dPquNp3z1GecuX+XIqTMs+m5aFq4qf9LW4RTKyyK5DqZehxN0dTiFv4F/1WGX4iUMJk+2aN2WEUP6sXLZwjQbFdo6/A+DC1AdFrkntTqcfB9VKlMfJpJ8j04xve4+/qoOT/56tl6cFq3aMXLIR6xavlCvUWFlVQTXMmVxcnahbv1GxMXFsmPbZr77ZhLffPcTpVxLZ/5C81Bu3SdUKhUrlvxC+05dKJvGd52Hpxde3pXZunk9Tk7OVPOpxcP791j08zzMzc3T/O4tKJTKBMxTrJfa3hiVMj5T59uzZRnXL/nTZ+B4ilkrUv/chDiWfv8FFpZWdH9/ZOYyXQjlxzkVgYGB9Oyp3+OpUChwcXEhMDAwzXSAXmMBwMPDg9WrVxMfH0+RIkUIDAw0iGNiYkKFChXSPH92MLpRERYWxurVq/V21Pbx8aFv3744pvBEIy+pVCqio6L0whR2dlhaWqY4rlH5MswyjbGzli+7bVMal6t6+SWYPMxm+7bNREZG0uf9vpnK97Ejh/ltzUratOtIxze6ZiptXrKyskSlSjQIVyZ3eacy/MjKSnsDTqlMlUr9tB5u5Zj02XC+X7icT8ZOAcDRwZ4RAz7g+0UrKPpyxZHEpCR+XLqGdi2a6M3TKGjSrsMplJcquQ6mXoetdHU4hb+Bf9XhlNjaKmjdpgNbNq0jNDQEZ2fDDS+PHznM72tW0KZdJzq+8Waq5xKFX2brcPJ9NK2Ji8n36BTT6+7jadfhVm07snXT73p1ePbMKZiZmfHV5Bm6uPUb+jJs4Af8tmYZn4+dnOo581Je3yd2bNtMZGQEfd7/KN28fvnVFObM+pr587QNPVNTU7p2783Vyxd59OhBOqnzj0SVipjoCL0wW4UDlpZWJKZYL1820jIxLOnsiX1sX/8zvq2707z9W6nGUyclseyHsTx5GMjwr37G3rF4hj9DZF7r1q3TPO7n55dieGRkJAqFYcPQzs6OiIiIFFK8SmdpaWnw96pQKNBoNERERFCkSBEiIyNTXIU1vfNnB6MaFRcvXuTjjz9GrVbTuHFj3NzceP78Ob/++iu//vorK1aswMfHJ7vzarTrAVeZMFZ/gsqSlb/h4Oik69p9XXiYdiKxYwpd6skcX+s2Nkwfhq2tLRYWlsTERLNx/W90eqMrsbGxxMbGAtrl9jQa7fr/VlZW2NvrT7S6cO4s877/lrr1GjBsxOjMXXAec3KwJ+R5uEH487AXADinMLwJQGFjg6WFBc9TGB71PFx7PmfHV+XUonEDfOvV4fbde6jVarzcK3D+yjUAyrqWAmDf4WM8ePyYz4cN4ElwiN45Y+PieRIcgoO9giL5fPLl9YCrTBz7mV7Y4pW/v6zDKddBSHlYSLJXQx9S/huwtVWk+HTydU4u2i+t6KhIg0aFtg7Pok69BgwtYHVYZL8bAVeZOE6/HixesU5bh8PTuA87Oad6TgeHtO/DNhmow8n1NrkOP33ymPP//M3QEfrfGba2CipXqcb1a1fSPF9eysv7hPa77lc6vvGm3nddfCrfdU7OLsyc8xOPHz0kPDwMV9cyODg60u/93pROZ4nb/OTOjQv8MGWgXtj0X3ahsHcmIjzEIH5kuHZol72D4UOYlFy7eIpV8ydQrXZT3h2U9oqUvy6axuV/jtL/0xlUql4whkvnNM1/bAnzvGZUo2Lq1Kl4enqydOlSveEMUVFRDBw4kGnTprFlS/5ZNrFCBQ+mfqM/dMPBwZEK7h5cu3oZtVqtN5nq5o3rWFkVoXSZ1G9sTs4u2NnZc/uW4YSymzevU8HdE9AusxsfF8fWzRvYunmDQdxB/d6jQcPGjJ/0tS7sxvUAZk6fjGdFL74YNynFlaLyM88K5Tl/+RoxsbF6k7Wv3bz98rhbiulMTU1xL1+W63cMu+eu3byDa4niFCuqvymShYW5Xg/EPxe1X/h1fbQrEAWHhJKYmKTrzXjdvsPH2Hf4GN+MHU3ThvUydY25TVuH9YdupF2HAzJRh28YHLt18zpu7un37AQ/fQyA4l8bYN28HsCs6ZNe1uHJBa4Oi+znVsGDKdPn6IXZOzjiVsGTgKuXUq3Dae2h4OTsgiKNOlwhQ3X4CfCqDr94oX2AoVarDeImJSWRlJSU7jnzSl7eJ5K/6/7YvJ4/Nq83iDu437vUb+ir910H2mVkk/+NH9y/S3jYc1q1aZ/xi85jZdy8+XTSIr0wO3tnyrp5c/v6eYMyD7p1GUurIhR3TX2fCl3cm5dZPPszynlUYeBn32FmlvpPti1r5nLy8J/07vcF9Zp0TDWeyD6p9USkR6FQEPWvHkXQrgqV2h5wyemUSiUJCQl6vRWRkZGYmJjo0ioUCqKjo1M8f6lSpYzKc0YZ1ai4ffs2P/74o8H4aFtbWwYOHMjo0fnrqaSNrW2K+zs09m3GyeNHOXXymG6fisiICE4cP0K9Bg31nnA9eaL98VSqlKsurJFvUw757Sck5BkuL5/YXrxwjsePHtK1Wy8A7O3sGTfBcF+Gndv/4Mb1a4z531d6T4ke3L/H11PGU7xESSZO+aZALl/YonED1m/bxfb9h3T7VChVKnYfOkIVL09KuGivNzgklPiEBMqXeTU+uXnj+ixes57rtwOp5KkdE3j/0WPOX77K293eSPNzHzx+wp/7/GhctxZlS2v/cFo3bUTFCoY3769m/UDDOjXp0rYllb08s+W6c5KNrW2K+zs09m3OyeNHOX3ymG79+ciICE4eP0K9Bo3+VYcfAVCq1KvyzkgdBoiIeGGwc+7z0BD89u/FrYK7Xq+etg6Po3iJkkyYMqNA1mGR/VKtw02acerEkSzU4WYc9ttHaMgznF/W4UsX/uHxowd0yUgdPrBHrw6Xci2NqakpJ44epn3HLrplZUNDQ7h29RKVqxgu7pFf5OV9wt7OnrETDOet7dq+lRvXr/HZ/yak2fuvVqtZvXwJVlZF6NCpS6rx8htrG0WKO1fXbtSWc6cPct7fT7dPRXRkOOdOHaBGneZ6ZR7yVDvcy6VkWV3Yk4eBLJg5AicXVz4ZNx9Lq9SHS+3/cxUHtq+hQ48BtH7jvey6tEJBo8l/PRXu7u4GcxuioqIICQkxmAvx73QAQUFBVKr0ahPhwMBAXF1ddRtNuru7c/Om/gNvjUZDUFAQvr5pT/LPKqMaFeXLlycyMjLFY1FRUZQtWzbFY/lN4ybN8P6zMj/9MJsH9++93GV0O+oktcGY0EnjPgdg6arfdWG93n6XE8ePMGHsGLq82UP7lGbLRsq7VaBNO+2TFqsiRWjYuInBZ/ufPsGtm9f1jsXGxjJl4lhioqPp3vMtzp45rZemZCnXArHySBUvT1o2bsCStRt48SKS0qVKsPfwMZ4+C+XL4YN08b6Zt5ALVwM4uu1VmXbv2Jad+w/z5dff8U63NzAzM2fj9t042Nvxzpv6jYoPhn9BS98GFHd24smzEP7cexCFjTVjhg7QxSlfprReo+V1pUq45PseivQ0atIM7z+r8NMP3/Hg/j1sFXbs3fXnyzqsP4fnVR1epwvr+fZ7nDh+hIljP6Pzmz2Ij4tn25YNlHdzp3W7Drp4q5cv5umTx9SoWRtHJyeeBQezb88O4uPjGTB4uC5eXGwsUyd++bIOv11g63B2Kz/sPSzsFBRx1f4gK/5GS4qULgnA3Z/Xkhhp+FTpv6KRb3O8Km1h/rxveXD/rvY+vPtP1Go177z3kV7cyeO1Q5KWrHz1JLzXW+9x8vhfTBw3ms5dexIfH/eqDrd9VYfXrHhVhx0cnXgW/JT9e1/W4UEjdPHs7Oxp1bYjB/ftYtL4MTRq3JS4uFj27PoTZUICPd96N2cLJAfkxn0iM991AMsWLUCpUlLB3YOkxCSO/uXHrZvXGfnZl3r7YRRUtRu2oYJXDdb8PJmnDwOxtrXn6L6NqNVqOr89VC/uD1O134szFu4BID4uhp+mDyM2JpJ2Xfty5dwxvfguJcrg7q0dYn7e/xBb186jeKlylCrjjv/RXXpxK9doiMI+9cZcYacxbueEHNWsWTMWLVqkN7di7969mJqapvmjv3bt2tjY2LBnzx5do0KlUrF//36aNXu1IWezZs3Yvn07d+/exc3NDYBTp07x4sULmjdvnnMXhpGNii+++IJp06ZRqlQp6td/NW7P39+fBQsWMHFi2jug5hdmZmZMmjqTlcsXs3P7HygTlFT08mbk6P9Rpkz6DSMXl+LM+PYHli9dyJqVyzC3MKduvQb0/3hIuuN4UxIVFUloyDMA1qxcZnC8VZt2BeYH2fhRQynx+yb2HTlOdHQM7m5l+XbC59SsWjnNdMWKFuXH6RNYsGItazZtQ63WUKtaZYYP+AB7O/2JTZ4VyrHb7wjhLyKwU9jS0rcB/d/phYP9f2cvBDMzMyZOncmq5YvZuX0rygQlnl7ejBz9JaXLlEs3vYtLcb759gdWLF3IWl0dbki/f9XhmrXrsm/3Dvbs/JPo6CisrW2oWq0Gvd95Hw/PV3u1ROrVYcMlJVu2aV9g6nB2ch/dn2Jur4aYlOrRnlI9tA8eHv2+/T/dqDAzM2PilFmsXrGIXTter8NjM1SHnV2KM33WPFYu+4W1q5Zibm5OnXoN6ffxUP06XKsue59uZ/fObcS8rMNVqvoY1GGAIZ+MpkIFDw7u383a1dp67FmxEp9+No6q1fLPfMGMyq37RGZU8PBkx59bOHr4ICYmplT0rsS0GXP0lpgtyEzNzBg+fgFb1/7Aod3rUCnjKe9Rlb6fTKNkabc000ZHvSA89CkAf/z2o8Hxhi266BoVD+9qh6U9e3KflT8ZzrkYPWXpf7pRkR+98847rF27lk8++YTBgwcTHBzMd999xzvvvEOJEq8a1H379uXx48e65WKtrKwYPHgw8+fPx9HRES8vL9atW8eLFy8YMODVw9T27duzePFiRowYwWeffUZcXBzfffcdLVq0yNHlZAFMNBqNJrOJunTpwrNnz3QzzB0cHAgPDycqKgqFQkHx4q9WHDAxMWH79u2Zztj1O6lvxiOyh4My9U1WRNaFWZbM6ywUeoGVWqUfSRitQkD+3by0sDAxyfRXsMikp7H5a0XKwqZl9aLpR8ojN+/cz7Fze3mk3yBPzZ07d/j66685f/481tbWvPnmm4wePVpvtbsPPviAR48e6e0Np9FoWLJkCb///jthYWFUrlyZcePGUauWfmM8ODiY6dOnc/z4cczNzWnbti3jx4/P8WXdjWpUjB07VjfONCNmzpyZ2Y+QRkUukEZFzpJGRc6TRkXOkkZFzpNGRc6TRkXOkkaFSGbU8KdZs2Zldz6EEEIIIYTINvlx87vCLP/NYBFCCCGEEEIUKEbvqB0ZGcm+ffsICgrS7ar5ugkTJmQpY0IIIYQQQhhLeipyl1GNirt37/LOO++gVCqJi4vD0dGRiIgIEhMTsbOzw8bGRhoVQgghhBBC/EcYNfxp1qxZ+Pj4cPLkSd1M9IsXLzJ79mysra358UfDJdCEEEIIIYTILRpMcuwlDBnVqLh06RLvvPOObukrlUqFmZkZXbp04aOPPmL69OnZmkkhhBBCCCEyQ6MxybGXMGRUo0KpVGJjY4OpqSl2dnY8e/ZMd6xixYpcv3492zIohBBCCCGEyN+MalS4ubnx6NEjAKpUqcLvv/9OdHQ08fHxbNiwQW/zOyGEEEIIIXKbDH/KXUZN1H7jjTd0vRGffvopAwYMoH79+piYmKDRaGQfCyGEEEIIIf5DjGpU9OvXT/f/NWvWZOfOnRw7doz4+HgaNmyIl5dXtmVQCCGEEEKIzJIehdxl9D4VSUlJXLx4kadPn6JUKrG0tMTS0pJr165x7do1unXrlo3ZFEIIIYQQQuRXRjUqrl69yogRI3jy5AkajcbguImJiTQqhBBCCCFEnpGeitxlVKNiypQp2NjYsHr1ajw9PbGwsMjufAkhhBBCCCEKCKMaFbdv32bevHnUr18/u/MjhBBCCCFElsl+ErnLqEaFm5sbMTEx2Z0XIYQQQgghsoVahj/lKqP2qRg3bhyLFy/mzp072Z0fIYQQQgghRAGT4Z6KLl266L0PCQmhS5cuFC9eHFtbW71jJiYmbN++PXtyKIQQQgghRCbJRO3cleFGRdWqVTExkX8cIYQQQgghhL4MNypkl2whhBBCCFFQyETt3GXUnAohhBBCCCGESGb0jtpCCCGEEELkVzKnIndJT4UQQgghhBAiS6SnQgghhBBCFDoypyJ3SaNCCCGEEEIUOjL8KXfJ8CchhBBCCCFElkhPhRBCCCGEKHRk+FPuyreNipikYnmdhULP0tI+r7NQqEWobPI6C4VehYDDeZ2FQi2ocsu8zkKhp/G/mtdZKPSKWiTmdRaE+E/It40KIYQQQgghjKXO6wz8x8icCiGEEEIIIUSWSE+FEEIIIYQodGRORe6SngohhBBCCCFElkhPhRBCCCGEKHRkn4rcJT0VQgghhBBCiCyRngohhBBCCFHoyJyK3CWNCiGEEEIIUejI8KfcJcOfhBBCCCGEEFkiPRVCCCGEEKLQUWvyOgf/LdJTIYQQQgghhMgS6akQQgghhBCFjsypyF3SUyGEEEIIIYTIEumpEEIIIYQQhY4sKZu7pKdCCCGEEEIIkSXSUyGEEEIIIQodjaz+lKukUSGEEEIIIQodtUzUzlUy/EkIIYQQQgiRJdKoEEIIIYQQhY5GY5Jjr9x26NAhunbtSvXq1Wnfvj1btmxJN82lS5cYN24cbdu2xcfHh3bt2vH9998TGxurF2/+/Pl4e3sbvNatW5epPMrwJyGEEEIIIfKps2fPMnz4cHr16sX48eM5ffo0X331FdbW1nTo0CHVdHv27OHevXt8/PHHuLm5cfv2bX766ScuXrzImjVr9OIWKVKE1atX64WVLVs2U/mURoUQQgghhCh0CstE7YULF1KjRg2mTZsGQMOGDXnw4AE//fRTmo2KgQMH4ujoqHvfoEEDFAoFn3/+OVeuXKFatWq6Y6amptSsWTNL+ZThT0IIIYQQQuRDSqUSf39/g8ZDp06duHPnDg8fPkw17esNimRVqlQB4NmzZ9mbUTLRU/Hhhx9m6sT/7lYRQgghhBAit2gKwepP9+/fR6VS4e7urhfu4eEBQGBgIGXKlMnw+f755x8Ag/PFx8fTsGFDIiMjcXNz46OPPuKtt97KVF4z3KiwsbHBxOTVP87ly5cJDQ2lUqVKODk58fz5c65fv46LiwvVq1fPVCaEEEIIIYQoKFq3bp3mcT8/v2z5nIiICAAUCoVeePL75OMZERYWxvz582ndujVubm668HLlyvH5559TpUoVEhIS2LFjBxMnTiQqKooBAwZk+PwZblT88ssvuv/ftm0bQUFB/Prrr5QrV04Xfu/ePYYOHZpuQed3MdFR/L7qZ86eOoIyIR4Pryq8138kFTy90017++ZVjh7cze2bV3lw9zZJSUn8vuOUQbznIcH8dXAn58+c4Onjh5iamlK2vDvd3v6I6jXr58Rl5QilSsXatWs5dOgQ0dHRVHBz48MPP6R27drppg0NDWXJkiWcO38etVqNj48PgwYOpFSpUgZx9+3bx5atW3n69CkuLi507dqVN7t2TfP848eP5/yFC3Tp3Jlhw4bpwhMSEvhl4UJu3LhBSEgIarWaUqVK0a5tWzp37oy5ecGfahQTHcWG1fM5d/ovEhLica9YlT79P8XNo1K6ae/cvMrxQzu5c/MqD+/eIikpidV//p1i3L5vplxXe3/wCZ179c3SNeQXMdHRrF6xCP9Tx0lISKCiVyU++ngoHp5eGUr/4P49Vi79mYBrlzE3t6BOvYb0GzgMOzt7XZxnwU8Z3L9Piuk/+99EmjZvpRd24thhtv+xiYcP72Nqaka58m507/kOdes3Mvo6Cyoz62K4jxmAfX0f7OtVx9LRnosDxvJwzR95nbV8Ly4mkp2/f8/lM36olPGU9ahG1/f/R5kKVdJMp1arOXvsTy7/fZBHd68TFxOBo0tpajbqSIvO/bCwtNL/nNgoDv6xmCtn/XjxPBhbO0cqVmtEu55DcXB2zclLzFOxMZH8sXYeF/8+hDIhjvKe1ejZ93PKuVdOM51arcb/yA4u+PvxIOg6sdEROBUvTV3fDrTp2levfJUJ8WxYPpO7ty4T/jwYjToJ5xJladSqG83bv4WZuUVOX2a+ps6ncyqioqIyNPwosxOl06JSqfjss88AmDJlit6xN998U+99ixYtUKlULFy4kA8//BALi4zVI6N+Pf3yyy+MGTNGr0EBUL58eUaMGMHcuXPp3r27MafOc2q1mtnTxnAv6Dade7yHrcKOA7u2Mn38MKbPW0Up17T/gS+cPcXhA9sp5+ZJ8ZKlefLoforxzvofZcfmtdRt2IxmrTuRlJTEsUN7mDnxUwZ9+hUt2nTOicvLdnPnzuX48eN069YNV1dXDh48yKTJk5k1axbVqlZNNV1cXBxjx44lJjaWt996C3Nzc/7Yto3/ffklPy9YoNci3717N/MXLMDX15fu3btz9coVFi1aREJCAm/17p3i+U+cOEHA9espHlMqldy7d496detSokQJTExMCAgIYMnSpdy4cYMvv/wya4WSx9RqNXO/Hs2Du7fo2P19bG3t8duzmZlfDWXq3NWUdC2XZvpL/5zgyIE/KVveE5cSpXn6OOU6nKxqzQb4tuykF1bePWM/uPM7tVrN9CljuRt0h24938FWoWDvrj+ZOHY0c35cjGvptLucQ0NDmPDlpxSztua9vh8THxfHn1s3cu9uIN/9sNDgRt20eStq122oF+ZdWf8H3q7tW1m2eD516jXkg48GoVIqOXRwL99MHc//xk+lkW+z7Ln4AsLS2QGvicOJvfeIqEs3cGrRIK+zVCCo1WqWfTeUx/du0KJzf6wV9pzcv55fvv6I0d9swqVU+VTTqpRxbFg0gfIVfWjc5i1sFI7cvXWRfZt/5tbV0wydsFI3skGtVrN4xscEP7xD47bv4FLKjdDg+5w8sJ4bl07wvzk7KFLUOrcuO9eo1Wp+mTGCR/du0KbrR9jY2nN03wbmTR7A2O/WUTyN8lUmxLP250lU8KpB03a9sVU4EnTzIjs3LuT6ZX9GTVmmK1+VMoEnD+5QtXZTnFxcMTExIfDGRbasms3dW5fpP2pWbl1yvpSTS79mpSdi7969TJgwId14u3fvxs7ODtA2RF4XGRkJoDueFo1Gw/jx47l06RK///47xYsXTzdNx44d2bdvH/fv39cNtUqPUY2Kp0+f6g2Fep2JiQnBwcHGnDZf+PvEIW4GXObTsd/QwFf7dLBhk9Z8Nvhttvy2lOFfTEszfduOPeja830srYqwctGcVBsVVavX4acV21C89rSyTcfujBv5IZt/W1ogGhU3btzgyJEjDBgwgF49ewLQpnVrhgwdyooVK5j7/feppt25cyePHj9m3rx5eHtpf4DWrVuXIUOHsnXrVj766CNA26uwes0a6terx4SvvgKgY4cOqDUa1q1bR8cOHbC1tdU7t1KpZOmyZfTu1Yu1v/5q8Nm2trbM++EHvbA33niDYtbW7Nixw2C1hILmzEk/bl+/xPD/zaSer7bXsH6TNnw5tBd/rFvC0DHT00zfqmNP3ujxIZZWRVizeHa6jYqSruXwbdEx2/Kfn5w6cYTrAVf5YtwUGjdpDoBv05Z8MvAD1v+2ks/+NzHN9Fs2/Ep8QjxzflyMS/ESAFT0qsyUCZ9z+OBe2nXsohff3cOLFq3apnnOXTv+wNOrEl9NnqG7D7du15EBH/bmsN++/1yjIuHJMw6W8SUhOBS7OtVocjr9tdsFXPLfz92bF/hw1Fx8GrQHoGbDDsz67A32bV7A+yNmp5rWzNyC4VN/pYJXLV1Yw9a9cXRx1TYsrpzGq7q21+ze7Ys8uHOF7v2+okm7d3Xxi5eqwIbFE7h15RTV67XJoavMO+dPHyDwxgU+HjOH2o20f9O1G7djysiu7NywMM0f++bmFoyZvhqPSjV1YU3a9sSpuCs7NyzkxmV/KtXQPnywtrXjfzP1v+eatX+LotY2HNmznp59P8fOwTn7L1BkSe/evemdykPRf1MqlVhYWBAYGEjTpk114YGBgYDh3IiUfPvtt+zZs4elS5dSqVL6IxaMZdTqTzVq1GDevHk8ePBAL/zBgwf8+OOP+Pj4ZEvm8oL/icPY2TtSr1ELXZjCzoGGTVrxj/8xVCplmuntHByxtCqS7ueUKe+u16AAsLCwpGbdxoSFPiMuNsaY7Oeq48ePY2pqSseOr35QWlpa0r5dOwICAggJCUk97YkTeHl56RoUoO3mq1mzJkePHdOFXbx0icjISN7orN/I6tK5M/Hx8Zw5c8bg3Js2b0ajVtPzZUMno0qU0P7oi4nJ/2WfljMnD2Fn70idRi11YQo7B+o3acM5/6Pp12F7pwzV4dcpE+JRKhOMym9+dvL4EeztHWjY+NWN3M7OHt+mLfj79Ml0y/LUyWPUrddQ16AA8KlVB9fSZTlx7K8U08THx6FSqVI9Z1xsDHZ29noPdooVs6ZokaJYWVmlmq6wUitVJASH5nU2CpxL/vuxtXOier1XjVgbhSM+Ddtz9Z/DJKZRt83NLfUaFMmSGwfBjwJ1YQmx0QDY2jnpxVXYa3/oWlgUzjp7/tRBFPZO1Gzwaji4rZ0jdRq349KZw2neO8wtLPQaFMl86mvP9eRhoMGxf3Ny0Q4ri4uNSidm4abR5Nwrt1haWtKgQQP27dunF7579248PDzSnaS9ZMkSVq1axaxZs2jUKONDZHfv3o1CoTAYlZQWoxoVU6dOJSYmhg4dOtCtWzcGDBhAt27d6NChAzExMQZjtQqSu4E3cfPwxtRUv2g8vKqQkBCfas9DdnkR/hwrqyJYZfJHXV64c+cOpUuXxrpYMb1wL2/t3JM7gSnf+NRqNUFBQVSsWNHgmLeXF0+ePNHt9njnzh3tOf8V19PTE1NTU93xZM+ePWPTpk30698/3R9YKpWKiIgIQkJCOHHyJFu2bKF48eK4uhbsMb73A29S3qOSQR12r1gFZUI8T7O5Dh8/tJNBbzdnYO+mjPvkbU4d2Zut589LQYG3cff0MijLil6VSEiI5/Gj1Jfyex4aQsSLcDwrGs7FquhVicDA2wbhG9atpk/PTrzdvT1fjBrChXOGjeZqNWpy/p+/2bV9K8+Cn/LwwX0W/zKPmNgY3uiauYa0+O96dC+A0hWqGNTtch7VUSbEEfLkbqbPGflC27iztrXXhZVxr4alVVH2blzArSuniQgL5s61M+z8fS5lPapRsXrhnAf0IOg6ZStUNijf8p7VUCbE8+zxvUyfM7l8bRQOBscSVSqiI8MJC33KBX8/Dm5fg6OLKy4ls29Mvsg7Q4cO5cKFC0yZMgV/f39++ukndu7cyYgRI/TiValShfHjx+ve79ixg++//54uXbpQpkwZLly4oHuFhYXp4vXo0YM1a9Zw/PhxDh48yKeffsr+/fsZPnx4hudTgJHDnzw8PDhw4ABbtmzh0qVLhISEULlyZd5++2169OhRoJ+WvQh/TuVqNQ3C7R21T1XCw0Ip5+aZI5/99PEDzpz6iwa+rTA1M8uRz8hOYeHhKQ4TSg4Le/48xXRRUVGoVCocHQxvjLq0YWEUK1aM8LAwTE1Nsbe314tnYWGBra0tz1/7owBYumwZHu7utGjePN38nzh5km+//Vb3vmLFioweNQqzAlD2aXkRHop31ZoG4fYvu8BfhIVQNpvqsGelGjRo0gbn4q68CAvBb89mFs2dRGxsNK079sqWz8hL4WHPqVK1hkG4g6P2qWvY81DKu6Xc9RweHqYXVz+9I9FRkahUSiwsLDExMaFm7bo0aNQUJydnnj59zI4/NvP15LGMmzhdbwL2gMEjiIyIYNni+SxbPB8AhcKOqd98T6XKqc9jEuJ1keEhuFeqaxCusHcBICL8GaXKZW5u1OEdKyhS1IbKNV/17NkoHPhg5PdsWjqZRd+8WkXGu4YvfUf/gJlZwV8YIyWRL0LwrGK4YImdw6vyLV3e8MFaWg78uYoixWyoWsvX4NgF/4OsmDdW976cR1U+GDa10JZvRqkLwZKyoB0ePn/+fObNm8fmzZtxdXVl+vTpeiNFAJKSklCr1br3J06cAGD79u1s375dL+7MmTPp0aMHoF39adWqVYSGhmJiYoKXlxezZ8+mazoL4vyb0bXN0tKSPn360KdPyiuWFFRKZQLm5pYG4ZYW2jBlQs4M8UiIj+fHb7/C0tKKPn2HpZ8gH0hISEixBWv5MixBmXL3rvJleEppLSwtdedOPkdqrWRLS0u9f4+LFy9y4sQJfvjXfInU+NSowYxvviE6JoYLFy4QFBREfA79++YmpTIBcwvDOpxcttk5TGnit8v03jdr05XJYz5k89qFNG3VOdPDqPIbZSr171VZpj6EIblupphedz/RNipcipdg8tf6Y9hbtGrHyCEfsWr5Qr1GhZVVEVzLlMXJ2YW69RsRFxfLjm2b+e6bSXzz3U+Uci2d+QsV/zkqZQLmKawMlHzvUGXyPnFw2xJuXTlFz/4TKWqtv/SljcKB0m6V8fV+l5JlPHl09zp/7VzB+kUT6DsqY/frgibV+7CFcffhvVuWcf3Sad4Z+BXF/lW+AF7V6jNy0mJiY6K4cdmfh3dvokyIMy7zIl9q3bp1uqur3rhxQ+/9rFmzmDUr/cn68+bNy0rWdP6zTdhElYro6Ei9MIXCHktLKxITDX8oKF+Of7TMgV4YdVIS82dP5NH9u3w5ZS4OTi7Z/hk5wcrKKsWx38qXYVaWhjdU0DYGgBTTql7+SEvu7bKytEx1fLlSqdT9eyQlJbFo0SJatWqlN08jLQ4ODji87C1p2qQJ6zds4KuvvmLZ0qUFYqK2tg7rr0+tUDho63AK43WTy9bSMud6Es0tLGjTqTerFs7i7p3reFWpmWOflZ1UKhXR/1pZQ2Fnh2Uq9e9VWaZcx+HVvSLF9Lr7SerpbW0VtGrbka2bfic0NARnZ+19YfbMKZiZmfHV5Bm6uPUb+jJs4Af8tmYZn4+dnOo5xX9PYqKS2H/dJ2wUjlhYWpGYaFg3k+8d/14WNi3nT+1h78afqN+yJ43bvqN37HnwAxZO70+foTOo0aAdANXqtsLRxZX1i74i4MIxvZ6NgiZRpSLmX+Vrm9Z9WJX5+/DZE3vZsX4BjVt3p1n7lDcjU9g7obDX9orWbtSWvVuW8dO0wUyZv+M/PVE7N+c+iCw0KrZt28aGDRu4e/eu7qny686dO5eljOW0m9cvM338J3phPy7bir2DE+FhhsN2XoRpxzI6OGb/H+fSBTM5f+YEn4yZQlUfw+7o/MrRwYHQFIY4JY/Tc3QyHPYB2tWXLCwsCAsPTz3tyx/1Do6OqNVqXrx4oTcESqVSERUVhdPLeAf9/Hj46BEjRowwWH0sNi6O4OBg7OzsKFIk9SfnTXx9Wb16NadPn6ZTp06pxssvbl2/xKwJQ/XC5izZhr2DMy/CU6jD4do6bO+Ys41WR2ftpOToqMh0YuYfNwKuMnHcaL2wxSvW4eDopBvG9Lrke4SjU+r3AwcHR724+unDsLFV6J5apia5IREdFYmzswtPnzzm/D9/M3TEGL14trYKKlepxvVrV9I8n/jvuXvzAgu/7qcX9tVP+1E4uBD5wnAxjeQwO4f0l5wEuHHpJOt+GUflWs3oNWCSwfEzR7ehUiZQpXYLvfCqdbQLSdy9ca5ANyoCb1xg3pSP9cK+/mU3CnsXIsMNFxCICM9c+QZcPMWa+ROoWrspfQalvwRpslqN2rB93XwunTlM03YZW2VIiKwyqlHx559/MnHiRLp378758+fp2bMnarWaQ4cOoVAoDDbRyI/KVfBk3Nc/6oXZOThS3r0iN65eRK1W602wun3zGlZWRShVOuOz4DPitxXzOXJwFx8MHEXj5u2y9dw5zd3Dg4uXLhETG6s3WTu5+80jlWXOTE1NcXNz49atWwbHrt+4QcmSJSn28nzJ57h56xb169XTxbt16xZqtVq3lFrIs2ckJiYy5vPPDc7p5+eHn58fEydMoHHjxqleT/JQloKy+lO5ChX539QFemF2Dk6Uq1CRm9cuGNThOzevYmlVhJLZXIf/7VnwIwCD1c3yM7cKHkyZPkcvzN7BEbcKngRcvWRQljdvBGiHIaWxT4WTswsKO3tu37phcOzWzetUcE9/3e/gp0+AV2X54oW2If76mNlkSUlJJCUlpXtO8d/iWs6bweP1hyna2jnjWr4SQdf/Majb929fwtKqKC6l3NI9973bl1g1dyRl3avy4adzUxy/HxXxHNCgVuvXzaSkRO1/1QW7zpZx82bkpMV6YQp7Z8q4eXPn+jmD8r176wqWVkUo7pr6PhXJgm5eYsns0ZTzqMrHn83O1PyI5OFrcS9X3/qvysl9KoQhoxoVK1euZNiwYQwaNIiNGzfy7rvvUrVqVaKjoxkwYADW1vl/IxsbG0WKO1c38G3F3ycO6yZMA0RGvMD/+CFq1W+i92Qx+Il25ZcSpdJezis1O7b+yq4/fufN3n3p2PVto86Rl5r4+rJlyxb27Nmj26dCqVJx4MABvL29cXHRPmV99uwZCQkJejtDNmnShJUrV3Lz5k28Xg5XevjwIRcvXtRbCtbHxwdbW1t27dql16jYtWsXVlZW1K+v/Tds3rx5ims1fz19OvXq1aND+/a6tZkjIiJQKBQGe63sfblcW0qrUuVH1jYKqqZQh+s1bs2Zk4f459Rh3T4VUZEvOHPCj1r1mmZbHY6MCEdhpz/ZPi42hv3b12OrsMfNI+1dY/MTG1tbfGrVMQhv3KQZp04c4fTJY7p9KiIjIjh5/Aj1GjTSK8snT7SNqVKlXs1paOTbjMN++wgNeYazi/bJ5KUL//D40QO6dHs1kT0i4oXeDtugXT3K78Ae3Cq44/hysncp19KYmppy4uhh2nfsoqvDoaEhXLt6icpVqmdDaYjCpJiNnW7PiNfVaNCOS/77uXzmgG6fiujIcC7676dK7RZ68wFCg7UrxjmXePVAIvjRHZZ9OxRHl9IM+OIXLCxT7gV2KemGRqPhwum91G/+alPc8yd3A1DareDcJ1JSzEah2zPidbUateH86QNc8PfT7VMRHRnOuVP7qV6nud69I+Spdnn+11dqevIwkF9mjsDRxZVh4+anOj8tOjIca1t7g++zE35bASjvkfbu6IVdft1Ru7AyqlFx7949ateujZmZGWZmZkRHa1vCNjY2DBw4kBkzZtCvX790zpI/NWjckj3e1Vj84zc8uh+ErcKeA7u3olEn0etd/S7ObyZol/L6afkfurCQZ084fli7pGbQbe2Ozn9sWAmAs0tJmrbSztQ/c+ov1q38mZKuZSld1k2XJln1mvWxc8jf4/orVapE0yZNWLVqFREvXlDK1RW/gwcJDg5m1Kef6uLN+f57Ll++zJ7du3Vhnd94g7179zJ5yhR69uiBmbk5f/zxBw4ODrrVCEA7t+LDDz7g519+4ZsZM6hTuzZXrl7l0OHD9O3bV7fxXdmyZVPdzr5kiRJ6PRSHDh9m9+7dNGrUiJIlSxIXF8c///zD+fPnadCgATVr1szmkspd9Rq3wsO7Gst++ppHD7R12G/PZtRqNd37DNKL+90k7RDA75f+qQsLffaEE39p/63u3g4A4M+NywFwdiml2z3bb/cmzvkfoWa9pji5lORFWCjH/HbwPOQpg0ZNxTwTy9DlV418m+NVaQvz533Lg/t3USjs2LP7T9RqNe+895Fe3MnjtUOSlqxcrwvr9dZ7nDz+FxPHjaZz157Ex8exbcsGyru507ptB128NSsW8/TJY2rUrI2DoxPPgp+yf+8O4uPjGTDo1ZKBdnb2tGrbkYP7djFp/BgaNW5KXFwse3b9iTIhgZ5vvdpc7L+k/LD3sLBTUMRV23Ar/kZLipQuCcDdn9eSGPnfflqbEp8G7ThW0YcNiyYQ/PAO1rYOnDywHrU6ifa99IcGL5quXbVpwvwDAMTHxbBk5iDiYiJp2aUfAeeP6sV3KlEWN6+aANRr3o2/dq1k87KpPL57nRJlPHgUFID/4S2ULONJ9XppTzwtqGo3bMthr99Y+/Mknr4s36P7NqBRq+n8tv5iLD9O1d6Xpy/cA2jLd8H0ocTGRNK2a18un9MvX5cSZXH31u4H9vfRXRzbvwmf+i1xLlGG+LgYrl04yfVLp6letzne1WWHeZF7jGpU2NjY6IaKlChRgtu3b9OggbbiJiUlEZ7CWPmCwtTMjP9N+Z7fVyxg745NqJQJuFeszJBRE3Atk353ZUjwEzb9ukQvLPl95Wq1dI2Ke0HaNeqfPn7AL3OnGpxnwoyf832jAuDzzz9nzdq1+B06RHR0NBUqVGDqlClUr572E9NixYrx7bffsmTJEtatX49Go6F69eoMHjQI+39tOd+5c2fMzM3ZunUrp0+fxsXFhUGDBtHNyGF2VatUIeDaNY789RfhL15gZmZGmTJlGDRwYKaXT8uPTM3MGDNpHutX/sSBnRtQKhNwr1iFgSMnUSpDdfgxW3/T785Pfl+pWm1do6JiZR9uXb/MkQN/Eh0VgZVVUdwrVmHAiAlUqVHP4LwFkZmZGROnzGL1ikXs2rEVZYISTy9vRo4eS+ky6Q8jc3YpzvRZ81i57BfWrlqKubk5deo1pN/HQ/WeVNasVZe9T7eze+c2YqKjsLa2oUpVH3q/8z4envoLDwz5ZDQVKnhwcP9u1q5eCoBnxUp8+tk4qlYruBuPZoX76P4Uc3vV21aqR3tK9dA+fX/0+3ZpVKTA1NSMj/+3kB2/f8+xvb+RqEqgrHs13hnyDcVdK6SZNjb6BS+ePwVg1zrD1ZvqNntT16iwtrVn9Dcb2btpAVfP/cXJgxuwtrGnfovudHx7VIqrLRYGpmZmfDL+Z7auncvh3etQKeMp71GNDz/5mhKl3dJMGxP1gvBQbflu++1Hg+MNW3TVNSo8KtUi8MZFzh7fS2TEc8zMzCju6kbPvp/TolPhWp3TGDJRO3eZaDSZL/KhQ4dSp04dPv74Y6ZPn87u3bsZNmwY5ubmLFmyhPLly7Ny5cosZeyfm4aTI0X2cjAruI2/guCZ6r+74kZuUZgXjPkvBVVQ5ZbpRxJZovG/mtdZKPSKWiTmdRYKtdbV8+/S4X/8nXNzdrrXL9h7WuUEo3oqBg8ezOPHjwEYOXIkjx49YsaMGajVaqpXr860adOyNZNCCCGEEEJkhqaQbH5XUBjVqKhZs6Zu3LlCoWDhwoXExsby4MEDvLy8DCYMCSGEEEIIIQov0/SjGFq+fDkLFrxayvLs2bM0b96cbt260a5dO+7fv59tGRRCCCGEECKz1JqcewlDRjUqNm3aRIkSJXTvZ86ciaenJ7/88gsODg7MnTs32zIohBBCCCGEyN+MGv709OlTypfXriITHBzM1atX+fXXX6lbty5JSUlMmTIlO/MohBBCCCFEpsjqT7nLqEaFlZWVbm+KU6dOUaxYMWrVqgWAra0tUVFR2ZdDIYQQQgghMkkaFbnLqEZFjRo1WLJkCaampixfvpxmzZphZqZdWuv+/ft6Q6OEEEIIIYQQhZtRcyq+/PJLQkJCGDJkCDExMYwePVp3bM+ePbpeCyGEEEIIIfKCWmOSYy9hyKieCk9PT/z8/AgPD8fBwUHv2JdffomLi0u2ZE4IIYQQQgiR/xnVqEj27wYFgLe3d1ZOKYQQQgghRJbJnIrcZdTwJyGEEEIIIYRIlqWeCiGEEEIIIfIj6anIXdJTIYQQQgghhMgS6akQQgghhBCFjlp6KnKVNCqEEEIIIUSho5GlX3OVDH8SQgghhBBCZIn0VAghhBBCiEJHJmrnLumpEEIIIYQQQmSJ9FQIIYQQQohCRyZq5y7pqRBCCCGEEEJkifRUCCGEEEKIQkfmVOQu6akQQgghhBBCZIn0VAghhBBCiEJHeipylzQqhBBCCCFEoSMTtXOXDH8SQgghhBBCZIn0VAghhBBCiEJHhj/lLumpEEIIIYQQQmRJvu2pKMHjvM5CoReHbV5noVCzs4jO6ywIkSUa/6t5nYVCz6RB1bzOQqH34MCNvM6CyCNqdV7n4L9FeiqEEEIIIYQQWZJveyqEEEIIIYQwlsypyF3SUyGEEEIIIYTIEumpEEIIIYQQhY70VOQuo3sqbt26xejRo2nTpg3VqlXj6lXthL4ffviBI0eOZFsGhRBCCCGEyCy1JudewpBRjYoTJ07QvXt3Hj9+TJcuXUhMTNQdMzc3Z926ddmWQSGEEEIIIUT+ZlSj4vvvv6dTp05s2LCBTz75RO9Y5cqVuXbtWrZkTgghhBBCCGNoNJocewlDRjUqbt26xZtvvgmAiYmJ3jGFQkF4eHjWcyaEEEIIIYQoEIyaqG1nZ8ezZ89SPHb37l1cXFyylCkhhBBCCCGyQjoUcpdRPRVt2rRh/vz5BAYG6sJMTEwICQlh+fLltG/fPtsyKIQQQgghhMjfjOqpGDNmDJcvX6Zr1654eXkBMH78eB48eECFChUYPnx4tmZSCCGEEEKIzFCr8zoH2efQoUPMmzePoKAgXF1dGTRoED179kwzzcOHD2ndurVBuI+PDxs3btQLO3fuHN9++y0BAQE4OTnRp08fBg4caDDNIS1GNSpsbW1Zv34927dv5+TJk9jb22NnZ8d7773Hm2++iaWlpTGnFUIIIYQQQrzm7NmzDB8+nF69ejF+/HhOnz7NV199hbW1NR06dEg3/WeffUaDBg10762trfWO37t3jwEDBuDr68uoUaO4ceMGc+bMwczMjAEDBmQ4n0ZvfmdhYUHPnj3TbSUJIYQQQgiR2wrLnIqFCxdSo0YNpk2bBkDDhg158OABP/30U4YaFeXLl6dmzZqpHl++fDkODg7MnTsXS0tLGjVqRFhYGIsWLeKDDz7IcGeBUXMqoqOjCQsL0wvbvn07P/zwA6dOnTLmlEIIIYQQQojXKJVK/P39DRoPnTp14s6dOzx8+DDLn3H06FFat26t13jo1KkTkZGRnD9/PsPnMaqn4osvvqB48eJMnToVgAULFrBgwQLs7OxYunQpc+bMoVOnTsacWgghhBBCiCzLyZ2vU5qr8Do/P79s+Zz79++jUqlwd3fXC/fw8AAgMDCQMmXKpHmOKVOmMHr0aOzt7WndujWff/459vb2AMTGxvLkyROD87u7u2NiYkJgYKDe0Km0GNVTcfnyZZo0aQJoNxb5/fffGTx4MP7+/nzwwQcsX77cmNMKIYQQQgiRLTSanHvlloiICEC7D9zrkt8nH0+JpaUlffr0Yfr06axevZr+/fuzZ88ePvroI1QqFQBRUVEpnt/S0pKiRYumef5/M6qnIiIiAgcHBwCuXLlCeHg4vXr1AqBVq1Zs2rTJmNMKIYQQQgiR72WlJyIqKirV/d5eV7ZsWaM/A6B48eJMmTJF975+/fpUrFiRwYMHc+DAgWwfVWRUo8LZ2Znbt29Tt25djhw5QunSpXUXHhcXh7m50fO/hRBCCCGEyDJNTo5/IuNLrf7b3r17mTBhQrrxdu/ejZ2dHfCqRyFZZGQkgO54RjVv3pxixYpx9epVOnXqhK2tbYrnVyqVxMXFZer8Rv3679ChA7Nnz+bkyZMcPXqUjz/+WHfs2rVrlC9f3pjTCiGEEEIIUaj17t2b3r17ZyiuUqnEwsKCwMBAmjZtqgtP3oD633MhMqtYsWKUKlVKb0NrgKCgIDQaTabOb9ScijFjxtCvXz+USiX9+/dn8ODBumNXr16lY8eOxpxWCCGEEEKIbKHW5Nwrt1haWtKgQQP27dunF7579248PDzSnaT9b4cPHyY2Npbq1avrwpo1a4afn59unkXy+RUKBbVq1crwuY3qqTA3N0911+yff/7ZmFMKIYQQQggh/mXo0KF8+OGHTJkyhY4dO+Lv78/OnTv54Ycf9OJVqVKFbt26MWPGDABmzZqFiYkJNWvWRKFQcOnSJRYvXky1atVo06aNLt2AAQPYsWMHY8aMoU+fPty8eZPly5czevToTG1oLZMfhBBCCCFEoVNYNr+rW7cu8+fPZ968eWzevBlXV1emT59uMDIoKSkJtVqte+/h4cG6devYuHEj8fHxlChRgl69ejFy5Ei9+c/ly5dn+fLlzJo1i0GDBuHo6MjIkSPp379/pvJpotFkrMhr167NmjVrqFatGrVq1cLEJO0JKufOnctURv7t4c0rWUov0hdnZpvXWSjUEqXNLgq4OxEl8joLhZ5Jg6p5nYVCL+TAjbzOQqH2UYu8zkHqvt2sTj+Skb7sZdQMgkItw796+vfvj4uLi+7/02tUCCGEEEIIkVfUuTn5QWS8UZE8h0Kj0fDBBx9QrFixTI2zyk+UKhWrflvPwcNHiIqOwd2tPP3e70PdWj7ppg15/pyFS1dy9sJFNGoNNWtUY+jHH+FasmSqaS5fDWDUWO3SYVt/XYmdnf4GI4eOHmfDlm3ce/CQYkWL0qhBXQb1/cAgXkEUHR3NyhVLOXXyJAkJ8Xh5V2LAx4Pw9KyYofQP7t9n6dJFXLt6BXNzC+rVq8/HgwZjZ2f/Ks6D+xzYv4/z5//h6ZMnFClSFA9PT95770Mqennpne/hwwfs2b2TGzducOf2LVQqFctXrqFEidT//fK76OhoVq9YjP/J4yQkJFDRuxL9Ph6Ch6dX+omBB/fvsWLpLwRcvYy5uQV16jWg/6BhemUcHPyUwf3eTTH9mC8n0LR5qxSPJSYmMuqTgTx8cI+PBgymW8+3M319eS03yvffjhw+yA+zZ1CkSBHWb91tcHzXjj/Ys/NPnj55gsJOQZOmLXn3w34UKVLU2MvMd+JiItn5+/dcPuOHShlPWY9qdH3/f5SpUCXNdGq1mrPH/uTy3wd5dPc6cTEROLqUpmajjrTo3A8LSyv9z4mN4uAfi7ly1o8Xz4OxtXOkYrVGtOs5FAdn15y8xALDzLoY7mMGYF/fB/t61bF0tOfigLE8XPNHXmctX4uPjeTwltncuHCARGU8pdyq07r3WEqWS7936sKxjVzx387zp4EkxEViY1eccl4NaNL5E+yd9SfmxkSGcnjr99y58hfK+BicSnrQqOMgKteRRXMKy/CngiLTfTcqlYrGjRtz8uTJnMhPrvhu3nw2b9tB6+ZN+WRgP0xNTRk/9RsuXw1IM11cXBxjxk/m4tVrvNu7J33ffZvbd4L4bNwkIiKjUkyjVqtZsGQ5RYoUSfH49t17+Wb2DyhsbRg64CM6tW/DX0dP8PmEKSiVyixfa15Sq9VMnTyBI38dpnOXrvTrP5CIFy8Y9+UXPHr0KN30oaEhfPm/MTx5/JgP+/ajR89enDnzNxO+Gqu3QsH+fXvZt28PFT29GPDxILp178Gjhw8Z89lILpzXH4Z3PSCAHdv/JC42lrJly2X7Nec2tVrN9MnjOPaXH526dKNv/0FEvAhnwpef8fjRw3TTh4aG8NX/RvHk8SPe7/sxb/Z8i3/O+DP5qy/0yjhZ0+atGPX5OL2Xd6XUf+Tt2v4HoSHBWbrGvJTb5Qva+8zqFYtTvWesXrGEpQvnU668Gx8P/oRGvs3YteMPZk2fnKVrzU/UajXLvhvKuRO78G33Lm+8+xnREWH88vVHhDy5l2ZalTKODYsmEBMVTuM2b/HmB19S1qM6+zb/zNJvB/P6iF+1Ws3iGR9z8sB6qtVtTfePxlOzcScu+u9j/uT3iY+LyelLLRAsnR3wmjgcm0ruRF2SoUQZoVGr2bhgEFfP7KROi/dp2eMLYqPC+O37DwgLvptu+qcPrmHvXIaG7T+m/btTqNqgK4FXj7JqZi+iXry6pybERbN29rvcOL+Pmk3fplXPL7EsYs22JaO4+veOHLxCIQxletC3paUlJUuWJCkpKSfyk+Ou37zF4aMnGNzvQ97q8SYA7Vq1YMDw0SxZtZb5s2ekmvbP3ft49PgJP3//LZW8PAGoX6cWA4aPZtO27Xz84XsGaXbtO8CzkFA6tWvN1u279I6pVCqWr/mdGlWr8N3Xk3VDyqpW8mbC1zPZte8g3btk726HuenE8WMEBFxj7PgJNGnSDICmzZoxaGB/fv91DV98OS7N9Bs3rCMhIZ55P/1M8eLFAfDy8mbCV2PxO7ifDh3fAKB58xa8+94HFC366ilt23YdGDp4AL//tpaatWrrwhs0bMh6360UK1aMrVs2ERh4J7svO1edPH6U6wFX+d/4yTRu0hwA32YtGDbwQ9b9uooxX6a9uc7mDb8RnxDP9z8twqW4dvy8l1clJn/1BYcO7qN9x8568T08K9KiVdsM5e3Fi3A2rFtD9159WPfrSiOuLu/ldvkCbFq/lqJFi1G9Ri38Tx3XOxYW9pztf2yiRau2jPr81d+Pa+kyLF04n7/9T1K/QeOsXnaeu+S/n7s3L/DhqLn4NGgPQM2GHZj12Rvs27yA90fMTjWtmbkFw6f+SgWvV8sgNmzdG0cXV/Zt/plbV07jVb0RAPduX+TBnSt07/cVTdq96okrXqoCGxZP4NaVU1Sv18bgM/5rEp4842AZXxKCQ7GrU40mp7fkdZbyvevn9vLoznm6D/qRSnU6AFC5bkcWT2rPsR3zefPj79NM3+HdKQZhXjXbsGpGT66c/pNGHQYBcP7oesKf3aPP6FW4VdLW69rN+7D627fw2/QtlWq3x8y8YI4qyQ7SU5G7jJpl8u6777Jq1SoSEhKyOz857siJU5iamvJGh1c/jCwtLenYtjXXrt/gWUhoqmmPnjiFd0VPXYMCoFzZMtT2qc6R44Y9N5FRUaxYu46P3nsHG2trg+NB9x4QHRNDi6a+enNUGtWvS9GiRTh87LhBmoLkxPFj2Ds40LhxE12YnZ09TZs24/Tpk6hUaffEnDxxnHr1GugaFAA1a9WmdOkyHDt2VBfmWdFLr0EBoFAoqFq1Og8e3NcLt7VVUKxYsaxcVr5y8vgR7B0caNj41YY4dnb2+DZtwd8ZKONTJ45Rt15D3Q9eAJ9adXAtXYYTx/5KMU18fFyqT9lft3blUkqXLkuLVgX3R1lul+/jRw/Z/scW+g0ciqmZmcHxGwFXSUpKoknzlnrhTZtph58dP3I4E1eXf13y34+tnRPV6726T9soHPFp2J6r/xwmMY1yNze31GtQJEtuHAQ/erXBU0JsNAC2dk56cRX2zgBYWOgPlfqvUitVJASn/t0oDF0/tw9rhTPetdrpworZOlKpTkduXfRLsw6nxs6pNKAdVpXswe2zFLN11DUoAExMTalcpyMxkSHcv3kmC1chROYY1ah48uQJQUFBtGjRgk8//ZSvv/6a6dOn673yq9uBQZQp7Yr1v35YJjcUbgcFpZhOrVYTePce3p4eBscqeVXk8ZOnxMbG6YWv/HU9jg72dO6Q8pNdVaL2h5mVleFTBCtLS27fCdJbGqyguRN4Gw8PT0xN9auZl5c3CQkJPHqY+hCo0NBQXrx4gWdFw3HrXl7eBN65ne7nh4eHoVBkbvv6giYo8DbuHhUNyriiVyUSEuJ59DD1ITrPQ0OIeBGOZ0Vvg2MVvSoRlEIZr/99De/0eIO3unXg80+Hcv5cyl9YN28EcNhvPwMGfwIFeFGH3C7f5Ut+prpPTerWa5jiOZMbc1b/mhdgZaV9f+f2zbQvqIB4dC+A0hWqGJR7OY/qKBPiCHlyN9PnjHyh/VFsbWuvCyvjXg1Lq6Ls3biAW1dOExEWzJ1rZ9j5+1zKelSjYvVGqZxNiLQFPwigRLkqmPyrDru6VUeljCPsWcq/Nf4tNjqcmMjnPLl7mV2rtb2TrzcgkhJVmFsYDpU0t9SGPb1/1dhLKBTUGk2OvYQhoxoVhw8fxtLSkqJFi3L58mUOHz7MoUOHdK/Dh/Pv07KwsHCcHBwMwh1fhj1/Hp5iuqioaFQqFY6OaaQNC9OF3Qm6y869+xk64CPMUnjiCFCmVClMTEy4cu26XviDh494ERFJglJJVHTBHdMbHhaGo6OjQbiDo/ap4POw52mmBVJJ70hUVFSaT4mvXLnM9esBNG3WPLPZLlDCw57ryvN1Di/LLTwDZeyQyr9RVFSkroxNTUyoWbsuH/UfzPjJ0+k/cBgRL8L5etI4zv59Wi+tRqNh6cL5+DZtQaXKBXu5zNwqX4Czf5/mwrmz9B84NNVzli5TFoCAa/pLbl+7ehmA588Lx9PkyPAQFPYuBuHJYRHhzzJ9zsM7VlCkqA2Va77qdbJROPDByO+Jj41i0TcDmPZJK375+iMUDi4MnbACMzNZFloYJzoiBBs7wzpsbafteY9+kbE6vODLZvz0RWNWzezFozvnafv2BCpU8dUddyxRgajwp0Q8139I9+D2PwBEhRfcOW2i4DHqjnno0KHszkeuSVAqsbAwvGxLSwuAVCdHJ7wMt7CwSDVtwmtpf16ygvp1alG3ds1U82Jnp6B5k8bsP/QX5cqWoUmj+oQ+D2PB4uWYm5uTmJhYoCdrK5XKlMvrZZgyjeFzCUrtsZTLW9uzk5CgxMLCsJfnxYtw5nw3kxIlStKz11tG5b2gSL2Mk8soI2VsWIb/LmOX4iWYMv07vTgtWrdlxJB+rFy2kLr1Xz1ZP3RgL/fuBfG/r6Zk+nrym9wqX5VKxYolv9C+UxfKlnNL9Zwenl54eVdm6+b1ODk5U82nFg/v32PRz/MwNzdP82+qIFEpEzA3Nyx385dlqVJm7joPblvCrSun6Nl/IkWt9VfVs1E4UNqtMr7e71KyjCeP7l7nr50rWL9oAn1H/ZDKGYVIW6IyPsW5DMl1OFGVsTr89silJKoSeP4kkCv+21Ep9UdE1GzSi/NH1/PHklG0eWsc1gpnAs7u4eb5Ay8/Jz6LV1KwaQruYI8C6T/3GMbK0hKVKtEgXKnUDitIbZlcq5fhKY0lT06bHOfwsRNcvX6DZQvS/0Ia/clglEoli1esZvGK1QC0adEM15IlOHbKn6KprACTn6hUKqKj9Fe/UtjZYWlpmXJ5vQyztEp9vHLy8I6Uy1vb0Epp2Fh8fBxTp0wiLi6Ob2d/YzDXoqDKfBknl1FGytiw4ZpWGSeztVXQuk0HtmxaR2hoCM7OLsTGxrB29TK693wbF5fiqabNb/K6fHds20xkZAR93v8o3bx++dUU5sz6mvnztJOVTU1N6dq9N1cvX+TRowfpps9PEhOVxEZH6IXZKByxsLQiMdGw3JPHof97Wdi0nD+1h70bf6J+y540bvuO3rHnwQ9YOL0/fYbOoEYD7dj3anVb4ejiyvpFXxFw4Zhez4YQ/5aUqCQuRr8OF7N1xNyyCEmJhn/7yXXYPIPzdcp7ax/YeFRrTsWarVk2tTMWVsWo2/J9AIqXqcSbA+aw97fJrP2uDwDWChfavDWefb9PwcKq8MwhFPmf0Y2KsLAwVq9ezcWLFwkJCcHFxQUfHx/69u2b4pCV/MLR0YHQ52EG4WHh2mFPTk6Gw5sAbG1tsLCwICzMcHiULu3L616yYg3NfRthYW7O02BtF2d0jHYY07PQUFSJiTg7aePaWFvz9YSxBD8LIfjZM0oUd6FE8eKM+GI89nYKbGwMJ3jnNwEB1xg/9gu9sOUr1+Dg6EhYmGFZJw8ZcUphWEmy5CEjKacPw9bW1uAJsEql4pvp07gbFMi06TNxc6uQ6WvJr64HXGXi2M/0whav/B0HR6cUh+C8GnqTfhmHp/JvZGurSPEp++ucXjYcoqMicXZ2YduWjSSqEmnSrAXBwU8B7dwC0O73EBz8FEdHpxSf/uelvCzfmJhoNq7/lY5vvElsbCyxsbEAxMfFodFo9wixsrLC3l57b3JydmHmnJ94/Ogh4eFhuLqWwcHRkX7v96Z06TIGn5Wf3b15gYVf99ML++qn/SgcXIh8EWIQPznMziFjDdYbl06y7pdxVK7VjF4DJhkcP3N0GyplAlVqt9ALr1pHOxH+7o1z0qgQaXp45zy/z/1QL2zoN37Y2LkQHWFYh2MitL8JbOwz/9DFwaUcJcpW4ar/Dl2jAqBSnQ5U9GlF8MPraNRqSparwr2bfwPgWMIt059TmGhk7kOuMqpRcfHiRT7++GPUajWNGzfGzc2N58+f8+uvv/Lrr7+yYsUKfHzS30guL3hWcOPCpSvExMbqTdYOuHHr5fGUf4iampriXr4cN24bLkEacOMWpUqWoFgx7VPxZ6Gh+B05ht+RYwZxh4z6Ao8Kbiz5SX85OW1jQjv+Mjo6hlu379C0ccqTNfMb9wruTP9mll6Yg4Mj7u4eXL16BbVarTfh8saN61hZWVG6TOlUz+ns7IydnR23bxlOPL158wYV3PUnzKvVauZ+/x0XL5xn7LgJVK9eI4tXlb9UqODB1G/0l9F0cHCkgrsH165eNijjmzcCsLIqQukyqf/IdHJ2wc7Ontu3DNedv3XzOm7uhosS/Fvw08cAKF5u5BYS8ozo6ChGDOlvEHfzht/YvOE35s5fgruHp8HxvJSX5RsdHU18XBx/bF7PH5vXG8Qd3O9d6jf0Zfykr/XCXUuXwfVlI+LB/buEhz2nVZv2Gb/ofMC1nDeDxy/TC7O1c8a1fCWCrv9jUO73b1/C0qooLqXc0j33vduXWDV3JGXdq/Lhp3NTnB8RFfEc0KBW6y+RnpSk7c1OUhfMpdNF7ilephLvjNJfMtvGzoUSZSrx4PY/aNRqvcnaj4MuYWFZFMfixj30SlTFk5hCD4iZuSWubq++9+4GaFekdKtc8JeYzooCvNZNgWRUo2Lq1Kl4enqydOlSbGxsdOFRUVEMHDiQadOmsWVL/lzHuplvIzb+sZ1dew/o9qlQqlTsO3iIyt4VKe6iXUow+FkICQkJlCv76kdDU99GLFv9Kzdu3ca7ovZH0YOHjzh/6TJvde+qizd1/P8MPvfwsRP8dewEY0ePwNk59aebAMvW/EqSWk3PN7tk+Xpzg42trd5eEMl8fZty4vgxTp48rtunIiIiguPHj1G/QUO9p+BPnmh/nJYq9WoH28a+TTnkd4CQkGe6oTQXLpzn0aOHvNmth95nLV74M8eOHmH4iE9p7NuEwsbG1hafWnUMwhv7Nufk8aOcPnlMt49CZEQEJ48foV6DRv8qY+1EvlKlXjXmGvk25ZDffr0yvnjhHI8fPaRrt166eBERLwx2gH4eGoLf/r24VXDH8eUT+85du9Ogoa9evIiIFyycP5dWbdpTv6EvJdLYfT6v5GX52tvZM3bCNIPP3rV9KzeuX+Oz/03QlW9K1Go1q5cvwcqqCB06FYx7RrJiNna6PSNeV6NBOy757+fymQO6fSqiI8O56L+fKrVb6MalA4QGa5eNdi7xajPL4Ed3WPbtUBxdSjPgi1+wsEx5GKlLSTc0Gg0XTu+lfvPuuvDzJ7W7mJd2q5z1ixSFWlFrOyqk8MPdu3YHrp/bx43z+3X7VMRGh3H93F48a7TUq8PhIdo67OCircPqpEQS4mMoaq2/euHjoEs8e3STqvUN97d5XVjwXc4fXY9n9ZY4lSg8PfYi/zOqUXH79m1+/PFHvQYFgK2tLQMHDmT06NHZkrmcUNnbi+a+jVi25jfCIyIoXaok+w/9xdNnIXw+cpgu3rc/zOfilav47XjVOHqzUwd27z/I+GkzeKt7V8zNzNm8bQcO9vb0fq1R0aRRA4PPvRN0F4D6dWpjZ/dqouC6TVsJuv+Ayl4VMTMz5cTpvzl7/iL93++jtx9GQeTbpCnef1bmxx++58H9+ygUCnbt2oE6Sc177+t3F3817ksAVqxaqwt76+13OHH8KOPH/o+ub3YjLi6OrVs24+ZWgbbtXq39/ee2rezatYNKlatgZWXF4UMH9c7dqLEvRYpoe5FiYmLYsX0bAAHXtEvt7dyxHWtra6xtbOjS5c1sL4ec1KhJM7z/rMJPP3zHg/v3sFXYsXfXn6iT1PR5v69e3EnjPgdg6ap1urCeb7/HieNHmDj2Mzq/2YP4uHi2bdlAeTd3WrfroIu3evlinj55TI2atXF0cuJZcDD79uwgPj6eAYOH6+J5eHrh4am/DHDyMKhy5d1o2LhgNfhyo3ytihRJsVz8T5/g1s3rBseWLVqAUqWkgrsHSYlJHP3Lj1s3rzPysy/19sMoyHwatONYRR82LJpA8MM7WNs6cPLAetTqJNr3+kQv7qLpAwCYMF87MTU+LoYlMwcRFxNJyy79CDh/VC++U4myuHnVBKBe8278tWslm5dN5fHd65Qo48GjoAD8D2+hZBlPqtdrnfMXW0CUH/YeFnYKirhqG8fF32hJkdLaBwR3f15LYmR0XmYv36lUpz1n/Gqya/U4Qp/cpqiNA+eOrEOtTqJplxF6cdf98BEAw2ZoF8FRJsTy87gWVK7bEZdSFbGwKsqzRze5fHIrVkVt8e00TC/9kimdqFS7A3aOpXgR+pBzR9dT1NqeDu9NzZVrzc9k+FPuMqpRUb58eSIjI1M8FhUVRdmyZbOUqZw29rORrPx1HQcPHyEqOgZ3t/J8M2kcNaqlvfxlsWJFmTtjGr8sW8lvG7ag1qjxqVaVYR/3w97OuP0QKriV5/jpvznlfwa1Wk0Ft/JM+nIMzZsU/C5LMzMzpk6dzorlS9mxfRsJCQlU9PJm9OgvKFMm/Tri4lKcmd/OYdnSxaxauRxzCwvq1avPgI8H6z0hTt4V+3rANa4HXDM4z/KVa3SNiujoKH5du1rv+B9bNwNQvHiJAteoMDMzY+LUmaxavpid27eiTFDi6eXNyNFfUrpMuXTTu7gU55tvf2DF0oWsXbkMcwtz6tZrSL+Ph+iVcc3addm3ewd7dv5JdHQU1tY2VK1Wg97vvG/QiChMcqt8M6OChyc7/tzC0cMHMTExpaJ3JabNmEN1H8MN3woqU1MzPv7fQnb8/j3H9v5GoiqBsu7VeGfINxR3TfvJa2z0C1481zZkd60zXCyjbrM3dY0Ka1t7Rn+zkb2bFnD13F+cPLgBaxt76rfoTse3R2H+H96J+N/cR/enmNurnvtSPdpTqoe2F+nR79ulUfEvpqZmvDViCYe3fMfZQ2tJVCVQyq06nfvOxKmke5ppLSyL4OPbi/s3/bnxzz5UqgRs7YtTpd4bNO40FHtn/WGXxctU4vLJrcREhVLUxoHKdTrQtMtIrBVpj4oQIruZaIxoxh09epRp06YxY8YM6tevrwv39/fnq6++YuLEiTRvnrX9AR7evJJ+JJElcWa2eZ2FQi3xv7e4mihk7kQUjp6P/MykQcHey6UgCDlgOK9JZJ+PWuR1DlI3YVXOLcs//SN56PBvRv3qmT17NlFRUfTt2xdbW1scHBwIDw8nKioKhULBnDlzmDNnDgAmJiZs3749WzMthBBCCCGEyD+MalRUrVqVatWqZXdehBBCCCGEyBYatcypyE1GNSpmzZqVfiQhhBBCCCHEf4Jp+lH0JSQkUKdOHQ4dOpQT+RFCCCGEECLLNJqcewlDme6psLKyomjRopiZmeVEfoQQQgghhMgytQx/ylWZ7qkA6NatG5s3b87uvAghhBBCCCEKIKPmVCgUCi5cuECXLl1o2rQpzs7OmJiY6I6bmJjw0UcfZVcehRBCCCGEyBTZ/C53GdWomDt3LgAhISHcunXL4Lg0KoQQQgghhPjvMKpRcf369ezOhxBCCCGEENlGo87rHPy3GDWnQgghhBBCCCGSGdVTcebMmXTj1KtXz5hTCyGEEEIIkWVqmVORq4xqVHzwwQeYmJjoTYB5faI2QEBAQNZyJoQQQgghhCgQjGpUbNu2zSAsIiKC48ePs3//fqZOnZrVfAkhhBBCCGE0Wf0pdxnVqKhUqVKK4Q0aNKBIkSJs2LCBhg0bZiljQgghhBBCGEs2v8td2T5Ru3bt2hw5ciS7TyuEEEIIIYTIp4zqqUjLwYMHsbe3z+7TCiGEEEIIkWEy+il3GdWoGDJkiEGYSqUiKCiIJ0+e8MUXX2Q5Y0IIIYQQQoiCwahGRUxMjEGYlZUVjRs3pn379jRt2jTLGRNCCCGEEMJYGplTkauMalSsXbvWICwuLo5nz55Rrly5LGdKCCGEEEIIUXAYNVF7+fLlLFiwQPf+7NmzNGvWjA4dOtCuXTvu37+fbRkUQgghhBAis9QaTY69hCGjGhWbNm2iRIkSuvczZ87E09OTX375BQcHB+bOnZttGRRCCCGEEELkb0YNf3r69Cnly5cHIDg4mKtXr/Lrr79St25dkpKSmDJlSnbmUQghhBBCiEyRORW5y6hGhZWVFdHR0QCcOnWKYsWKUatWLQBsbW2JiorKvhwKIYQQQgiRSdKoyF1GNSpq1KjBkiVLMDU1Zfny5TRr1gwzMzMA7t+/rzc0SgghhBBCCFG4GTWn4ssvvyQkJIQhQ4YQExPD6NGjdcf27Nmj67UQQgghhBAiL6g1OfcShozqqfD09MTPz4/w8HAcHBz0jn355Ze4uLhkS+aEEEIIIYQQ+Z9RjYpk/25QAHh7e2fllEIIIYQQQmSZzKnIXVlqVOSk0+FV8joLhV5J27i8zkKh9vetYnmdhUKvlnt8XmehUCtqkZjXWSj0Hhy4kddZKPRc2srDzhylkjostPJto0IIIYQQQghjaWSTulxl1ERtIYQQQgghhEgmjQohhBBCCFHoqNWaHHvltkOHDtG1a1eqV69O+/bt2bJlS7pp5s+fj7e3d4qvSZMmpRtv3bp1mcqjDH8SQgghhBCFTmEZ/nT27FmGDx9Or169GD9+PKdPn+arr77C2tqaDh06pJqud+/eNG3aVC/szJkzzJkzh2bNmumFFylShNWrV+uFlS1bNlP5lEaFEEIIIYQQ+dTChQupUaMG06ZNA6Bhw4Y8ePCAn376Kc1GRcmSJSlZsqRe2Pr167GzszNoVJiamlKzZs0s5VOGPwkhhBBCiEJHo9bk2Cu3KJVK/P39DRoPnTp14s6dOzx8+DDD50pISODAgQO0b98eS0vL7M6q9FQIIYQQQgiRGa1bt07zuJ+fX7Z8zv3791GpVLi7u+uFe3h4ABAYGEiZMmUydK7Dhw8THR1N586dDY7Fx8fTsGFDIiMjcXNz46OPPuKtt97KVF6lUSGEEEIIIQqdwrD5XUREBAAKhUIvPPl98vGM2LlzJyVKlKBevXp64eXKlePzzz+nSpUqJCQksGPHDiZOnEhUVBQDBgzI8PmlUSGEEEIIIUQmZKUnIioqimfPnqUbL7MTpdMSGRnJkSNHeP/99zE11Z/98Oabb+q9b9GiBSqVioULF/Lhhx9iYWGRoc/IcKNi//79GY0KQLt27TIVXwghhBBCiOyizqerP+3du5cJEyakG2/37t3Y2dkB2obI6yIjIwF0x9Ozb98+lEolXbp0yVD8jh07sm/fPu7fv68bapWeDDcqRo4cmdGomJiYEBAQkOH4QgghhBBC/Bf07t2b3r17ZyiuUqnEwsKCwMBAveVhAwMDAQzmWqRm586duLu7U6VKlcxnOIMy3KjIrgknQgghhBBC5LTCMKfC0tKSBg0asG/fPvr27asL3717Nx4eHhmapP3s2TP+/vtvhg8fnuHP3b17NwqFgnLlymU4TYYbFaVLl87wSYUQQgghhMhLhWXzu6FDh/Lhhx8yZcoUOnbsiL+/Pzt37uSHH37Qi1elShW6devGjBkz9MJ3796NWq1OdehTjx496NatG+7u7sTHx7Njxw7279/P+PHjMzyfArJhonZcXBwJCQkG4fb29lk9tRBCCCGEEP9pdevWZf78+cybN4/Nmzfj6urK9OnT6dixo168pKQk1Gq1QfodO3ZQo0aNVHsdypUrx6pVqwgNDcXExAQvLy9mz55N165dM5VPoxoVGo2GX375hQ0bNhASEpJiHJlTIYQQQggh8oq6EAx/Sta6det098a4ceNGiuFbtmxJM928efOMzZYeo3bUXrVqFatWreK9995Do9EwZMgQPvnkE9zc3ChdujRff/11tmROCCGEEEIIkf8Z1ajYvHkzI0aM4OOPPwagTZs2DB8+nF27duHh4cH9+/ezNZNCCCGEEEJkhkatybGXMGRUo+LRo0dUrlwZMzMzzM3NdWvlmpqa8u6777J169ZszaQQQgghhBAi/zKqUWFvb09sbCwArq6uXLt2TXcsPDyc+Pj47MmdEEIIIYQQRtBoNDn2EoaMmqhdu3ZtLl++TPPmzencuTMLFiwgNDQUc3NzNm7cSKNGjbI7n0IIIYQQQoh8yqhGxfDhwwkODgZgyJAhREZGsnPnThISEmjcuDETJ07M1kwKIYQQQgiRGZoUllcVOSfTjQqNRoOjo6NuBz9LS0smTJjAhAkTsj1zQgghhBBCGKMwLSlbEGR6ToVKpaJx48acPHkyJ/IjhBBCCCGEKGAy3VNhaWlJyZIlSUpKyon8CCGEEEIIkWUyoTp3GTWn4t1332XVqlU0adIEKyur7M5TnouLiWTvhjlc++cgqoR4ynhUp2Of/1HarWq6ac8c3siFkzsIeRJEfGwkCvviVKhcn1bdPsHBpbQu3ovnT/jn6FZuXDzC86f3MDU1pUSZirToOgTPao1z8vLyXGxMFJtWz+Oc/2GUCfFUqFiNtz8aTXmPyummDbx5hROHdxB08zIP790mKSmR5X+cSzFuxIvnbFn7E5fOHic+PpZSZSrQqUc/6vm2ze5LyncS4iI5vWs2d68eJFEZT/Gy1WnY+UtcyqRfh1+XlKRi8w/dePHsDg3f+AKf5gP0jkeE3sN/z/c8un0adaIS59JVqNtuJKU9G2bn5eRLsTGRbF07jwt/H0aZEIebZzV69R1DOfe067Fareb0kR1c8D/Eg6DrxERH4Fy8NHV9O9C264dYWL66p4aFPuXkoW1cPneMkCf3MTE1w7WsB516DaRyjcJdxrExkfyxdh4X/z6EMiGO8p7V6Nn38wyVr/+RHVzw9+NB0HVioyNwelm+bbr21StfZUI8G5bP5O6ty4Q/D0ajTsK5RFkatepG8/ZvYWZukdOXmafiYyM5vGU2Ny4cIFEZTym36rTuPZaS5dK/T1w4tpEr/tt5/jSQhLhIbOyKU86rAU06f4K9cxm9uDGRoRze+j13rvyFMj4Gp5IeNOo4iMp1OubUpRUoZtbFcB8zAPv6PtjXq46loz0XB4zl4Zo/8jprQmSKUY2KJ0+eEBQURIsWLahfvz7Ozs6YmJjoxSmocyzUajVr5g7h6f0bNOnUH2tbe/z91rF8Rl+GTduMc0m3NNM/vheAg0sZKtduRZFiCsJDHnL2yGauX/iLEdO3oXAoDkDAuUMc27WMyrVbU7vJm6iTkjh/4k9WfjeAHh9/Q51mPXLhanOfWq3mx+kjeXD3Ju27fYitrT2H927iu4mDmDTnN0q4lksz/eVzxzl28A/KlK+Ic4nSBD++l2K8uNhoZo3vT+SLMNp07oOdvRNnThxg0ZwvSUpKpGGzwvtlplGr2bNiMM+f3MCneX+KWDtw7dQ6diz+kJ4jt2Dn4pbhc1098SvRL56keCz6xRO2/fwOJiZm+DTvj4VlMW6c2cruZR/zxqCVuLrXy6Yryn/UajULZozg0b2btO3aFxtbe47s28jcyR8z7rvfKVGqfKpplQnxrPl5MhW8atC0XS9sFY4E3bzEjo0LuX7Zn9FTlurupxfP/MW+bauoWb8FjZp3IUmdhP+Rnfw4bQgfDptC41bdcumKc5dareaXGSN4dO8Gbbp+hI2tPUf3bWDe5AGM/W4dxdMp37U/T3pZvr1flu9Fdr4s31FTlunKV6VM4MmDO1St3RQnF1dMTEwIvHGRLatmc/fWZfqPmpVbl5zrNGo1GxcM4tnDGzRoO4BiNg6cO/I7v33/Af3Gb8WxhFua6Z8+uIa9cxkq+mi/616EPuTi8U3cvnyYARP/xNa+BAAJcdGsnf0uMZGh1G31ITYKFwL+2cO2JaNQD0ikav0uuXC1+ZulswNeE4cTe+8RUZdu4NSiQV5nqdCQTepyl4nGiL6hVq1apX1SExP8/PyMzhTAZv+8mbF/2X8P63/+jD7D51GtfnsAYiLDmPu/jnjVaMrbw+Zk+pyPgq7yy+RetOv9Gc27DAQg+OEtbOycsbZ10MVLVClZMKE7yoRY/jfvcPZcUBpK2sbl+Gf825kT+1k0ZyxDv/iOuo3bABAVEc74T7pRvbYvgz6bkWb6iBfPKVrUGkurIvy2ZBaH9mxMsadi7x+r2bTmRz6fuojKNeoD2h8qM77sS9jzYL5bvAtzi5x9Cvn3rWI5ev7U3Lm4h4O/jabt+/Nwr9EBgLjoMNbP7kA576a0fvf7DJ0nLvo567/rQI1m/Ti7/yeDnopjf0zjuv9Gen+2Hfvi7gColHFsnNOJItYO9Pw05zfBrOWeN3vinD25j2Vzv2TgmNnUaaTt+YqKCGPyyDepWsuXAWn8GE1Uqbh35yoelWrqhe/atJgdGxby6aRFul6Ixw9uo7Bzwkbx6j6hUin55vO3SYiPZebifdl/ca9RY5J+pBzwz8l9LJ/7Pz4eM4far5XvlJFdqVqrSZo/9lMr392bFrFzw0JGTlpMpXR6eTYsn8mRPeuZudQPOwfnLF9PWh48L5Kj509NwNndbFs6mu6DfqRSHe19IjYqjMWT2uNetRlvfpyx+8Trnty7wqoZPWnRfQyNOgwC4PS+ZRzeOps+o1fhVkm73LxGrWb1t28RGfaUT2YewszcMvsuLAUubb1z9PxZZWppgYWDHQnBodjVqUaT01sKVE/FG6obeZ2FVPUeHZRj5970Q4UcO3dBZdTmd4cOHUrzldUGRV66cmY/NnbOVKn7aoiMtcKR6g06EHDuEIkqZabP6eDsCmi7mpOVKFNRr0EBYG5hiZdPMyLCnpIQF2PkFeRvZ0/6obB3onbDVw1TWzsH6vm25fzff6FKp3zt7J2wtEr/S/hmwHlsFQ66BgVod3yv69uWiPBQblz9x/iLyOcCL++jqI0zFaq104UVtXHEo0YH7l49RFJixuqw/+7vsXepQMXaXVM8/jToLE6lK+saFAAWlkUpX6UloY+uERFyN0vXkZ+dO3UQhb0TtRq01oXZ2jlSp3E7Lp5Jux6bW1gY/OAFqFlf+zfx9OGrL0HXsp56DQoACwtLqtVuQvjzYOIL6X3i/MvyrZlC+V46c9io8vWprz3Xk4eB6X6+k4v2nh0XG5XJnBcc18/tw1rhjHetV/eJYraOVKrTkVsX/Yz6rrNz0g7xff277sHtsxSzddQ1KABMTE2pXKcjMZEh3L95JgtXUTiolSoSgkPzOhuFkkatybGXMGRUo2Lbtm2Eh4eneOzFixds27YtK3nKU0/uXcO1fGVMTfWLpox7dVTKOEKf3s3QeWKjwomOfM7DwCtsWfYVAB5V0x8DHR0RioVlUSwy8MO5ILofdJ3y7pUMyrdCxWooE+JTHc6UWYkqJRYpzPdJbpDcuxOQLZ+TH4U+CsC5dBVM/lXGxcvWIFEVx4uQ9J/cPLt/iZv/bKNR13GpPqtOSlRibm5YxuYWRQEIeXQ103kvKB4EXadsBcN67OaprcfPjKjHES+0PypsFPbpxw0PxdKqCJaWhfM+oS1fw/tw+SyUb6SufB0MjiWqVERHhhMW+pQL/n4c3L4GRxdXXEqWNe4CCoDgBwGUKGd4n3B1037XhT3L2BPe2OhwYiKf8+TuZXatHgeg14BISlRhbmFYT81f1t2n9wvvfUKI/xqj5lSMGzeODRs24OBgeHN++PAh48aNo1u3blnNW56IehGKm3ddg3Bbexft8fBnlCzrle55vh3VQvekp5iNPZ3f/wrPar5ppnkefI+rZw9QrX57TE3NjMh9/hcRHopXldoG4clDDF6EhVCmfMUsf07J0m5cu/Q3oc8e41zcVRd+69p5AMLDnmX5M/Kr2KgQSrkb1uFitto6HBv5DKdSqQ8H0Gg0nPhzOh4+HSlZvhZRYQ9TjGfvUoEnd/9BGR+NZREbXfjTu9peoJiI4KxcRr4W+SKUilXqGIQrkutxeAilM1mP9/+5iiLFbKhaK+37xLMn97nw9yFqN2yLqVnhvE9EvgjBM8X7hLYOR4Q/y3T5HkijfC/4H2TFvLG69+U8qvLBsKmYmRn1FVkgREeEULai4X3C2k477y/6xTOKl05/2NCCL5vpej+LWtvT9u0JVKjyqowdS1TgbsBJIp4/0vVkADy4rb1PRIUX3vuEyHtqjWx+l5uMumOmNQ0jMjISa2trozOU11TK+BTHd5pbaJ/IqlQZG8Pdd8wSVKoEQh4HcuHkdpQJac9fUCbEsW7+aCwsi9D+rTGZz3gBoVQmYG5hWL4WL8OUyoRs+Zymbbrx177NLJozlnf6j0Fh58iZkwc456+dq6LKps/Jj5JUKddhs5d1OFGV9rXfOLuVsKc3afvBj2nGq9KoD/cCDnPwt8+o32EU5pZFuXpqHSEPtU8ekxILbxlr67HhnByL5PuEMnNzPfZsWcb1S/70GTieYtaK1D83IY6l33+BhaUV3d8fmblMFyDZfZ/Yu2UZ1y+d5p2BX6VYvl7V6jNy0mJiY6K4cdmfh3dvpnvPLugSU/2u04ald59I9vbIpSSqEnj+JJAr/ttRKfXLrWaTXpw/up4/loyizVvjsFY4E3B2DzfPH3j5OXkzL0oIkf0y3Kg4cuQIx44d071fsWIFzs76E9gSEhI4ffo0lSunvzRoXktMVBIXHaEXZq1wxMKySIpjzpNvsBYpdOOmxL2KdvUGb59mVK7dip/Gd8WySDEatX3PIK5ancSGX8bw7PFt+n6+RLdCVEGWqFIR86/ytVU4YGlpleJY3eQx0paW2bNEcVk3LwaNnsHaRTOYOa4foO0N6dP/c9YunoFVkaLZ8jl5KSlRSUKcfhkXsXbEzCLlOpz0sg4nN5BTooyP5u89P1CjeX9s7Eul+fnlKjXD980J+O+Zy5YftauVKZzKU6/9KPx3z8bcMm8mqmentOuxyiC+Kvk+kYlhSWdP7GP7+p/xbd2d5u3fSjWeOimJZT+M5cnDQIZ/9TP2jnKfyIizJ/ayY/0CGrfuTrNUyldh74TC3gmA2o3asnfLMn6aNpgp83fk+ETtnJaUqCQuRr+Mi9k6Yp7qd502LK37xOvKe2uH9XpUa07Fmq1ZNrUzFlbFqNvyfQCKl6nEmwPmsPe3yaz9rg8A1goX2rw1nn2/T8HCquDfJ0T+JXMfcleGGxV3797l0KFDgHZ1p7Nnz2Jpqf+Uw8LCgooVK/LZZ59lby5zwP1bF1g+s69e2OffH8TW3pmoFyEG8ZPDbI34we9Uohyu5Stz8eSOFBsVfyyfxI0Lf9F7yGw8qhSOtedv37jI7ImD9MK+XbwTOwdnIsINJ6Qlh9k7umRbHuo2bkPNes15cPcmanUS5d0rc/3qWQBKuKa+JGVBEXzvPDsW69fhd8cepJitC7GRhnU4NkobVkyReh2+eGQF6iQVnj6ddMOeol8OY0qIiyQq7CHFFMV1Tzir+b6Pd70ePH9yAzMzC5xcK3P9780A2Du7Zfka89qdGxf4YcpAvbDpv+xCYe9MRLhhGUcm12OHjNXjaxdPsWr+BKrVbsq7g75KM+6vi6Zx+Z+j9P90BpWq108zbkEReOMC86Z8rBf29S+7Udi76MrydcllbpfB+3DAxVOsmT+BqrWb0mdQxpc5r9WoDdvXzefSmcM0bdc7w+nyo4d3zvP73A/1woZ+44eNnQvREYZ1OCZCOzTUxj7z33UOLuUoUbYKV/136BoVAJXqdKCiTyuCH15Ho1ZTslwV7t38GyDdpWuFyAppVOSuDDcq+vbtS9++2h8wrVq14pdffqFSpUo5lrGcVqqcN/3+t1wvzMbOmVLlKnP35j+o1Wq9SYIP7lzCwrJouvtUpEalTCAxhadCe9bN5tyxrbzx3jh8Gr1h1Lnzo7JuXoyZslAvzM7eibJu3twKOG9QvoE3L2NpVSTbf+ybW1hQoeKrjZwCLvoDUKVGwV8H3KlUJd4YuEIvrKitC86ulXgS9A8atVpvEmbw/YuYWxTF3iX1ZfCiXzwmIS6Cjd93Njh2/tBizh9aTM9Rf+Ds+qo30sKyGCXL19K9f3T7FOYWRSjhZjgmvqAp4+bNp5MW6YXZ2TtT1s2b29cN63HQLW09Lp6Behx08zKLZ39GOY8qDPzsuzTH729ZM5eTh/+kd78vqNek8OyxUsbNm5GTFuuFKeydKePmzZ3r5wzK9+6tK5ko30ssmT2ach5V+fiz2ZmaH5E8PDIuNjrDafKr4mUq8c6olXphNnYulChTiQe3De8Tj4O033WOxY1bLjNRFZ/id52ZuSWubjV07+8GnATArXLh3uxViP8So+ZUJPdYFGRFre1S3Lm6ar12XDmzj2svJ0wDxESFc+XvfVSq1UJvnO/z4PuAticCICkpEWV8DEWt7fTO+eDOJYIf3qTGvxoNx3Yt5/ieFTTvMpjG7fWfJBV01jYKqvgY/nCv27g1/5w6yLnTh17tUxEZztmTB/Gp10w3Zhrg2ZMHABQvlT0rsAQ/vs9f+7bgU7cpJUsX/J4Kq2J2lKloWIcrVG9P4OV9BF3Z/2qfiphwAi/vo3yVlnrjqCOea+uwnZO2Dlfz/QC3qm30zhcX/ZxjWyfjVbc7blVaY+ugv1vu657ePUfQlQNUafgOVkVts3yNec3aRpHiztW1G7Xl3OmDnPf30+1TER0ZzrlTB6hRp7lePQ55qq3Hr68k9ORhIAtmjsDJxZVPxs1Pc5nk/X+u4sD2NXToMYDWbxj2dBZkxWwUKe4ZUatRG86fPsAFfz/dPhXa8t1P9QyW7y8zR+Do4sqwNMo3OjIca1t7g81bT/hp91gp71ElaxeYDxS1tqNCCj/cvWt34Pq5fdw4v//VPhXRYVw/txfPGi31vuvCQ7T3CQcX7X1CnZRIQgrfdY+DLvHs0U2q1jd8KPG6sOC7nD+6Hs/qLXEqIWv9i5xjxFZsIguMalRkZMnYgrr6U7X67Tm5bw1blo3n2ePbFLNxwN9vHRp1Eq17jNCLu+Jb7Vj9L+Zq9+VQxsfy3ahWVG/QgeKlK2JpVZSnD29y7ugfWBW1oeWbQ3Vpr549wN4Nc3AqUZ7iru5cOLFd79ye1RpjY1ewx/KmpG6jNhzw+p0V86fw+EEgNgp7Du/ZhFqtpts7Q/Tizpmsff/dkl26sNBnjzl1ZDcA0PHAGwAAHldJREFUd+9cA2DHpmUAOLmUpHGLV19mE0b0pG7jNji5lCIk+BF/7d2MtY2CD4akPcykoHOv0Z7Lx334a+N4woPvUMTagauntHW4btvhenF3LvkIgPfGaR8UuJSpikuZqnpxkodBOZbwpEK1Vw2OqPBHHPx1NOWrtKKYrTNhwbcIOL0Bp5Le1O8wOgevMO/VbtiGCl41WPPzZJ4+DMTa1p6j+zaiVqvp/PZQvbg/TNUOA5yxcA8A8XEx/DR9GLExkbTr2pcr547pxXcpUQZ3bx8AzvsfYuvaeRQvVY5SZdzxP7pLL27lGg11cwEKk9oN23LY6zfW/jyJpw/vYG3rwNF9G9Co1XR+e5he3B9flu/018p3wfShxMZE0rZrXy6fO6oX36VEWV35/n10F8f2b8KnfkucS5QhPi6GaxdOcv3SaarXbY539YLfo5maSnXac8avJrtWjyP0yW2K2jhw7sg61OokmnbR/65b98NHAAybob1PKBNi+XlcCyrX7YhLqYpYWBXl2aObXD65Fauitvh20v83WjKlE5Vqd8DOsRQvQh9y7uh6ilrb0+G9qblyrQVB+WHvYWGnoIirdthZ8TdaUqR0SQDu/ryWxMiC32smCj+jGhVjx45NMfz1pz0FtVFhampG388Xs2f9bE7t/xWVMoEy7tXoOXAmLqXSfqJiYVWEus17EhjwN1fO7CdRmYCtgws1GnWiZdehOLi8Wk7v6X3tDpTPg++xafGXBucaMG51oWxUmJqZMWrifDatnoffrvUolfFU8KzKgJFTKVnaLd30oc8es+33X/TCkt97V62j16go6+bFiUM7iHzxHBuFPfV82/LmO0NQ2Dtm6zXlN6amZnTqv4TTu2Zz5cRaElUJuJStRsu3ZuhtVJdVllY2FFO4cPXkb8THvsDargTVfD+gVqvBekvMFkamZmYMH7+ArWt/4NDudaiU8ZT3qErfT6alW4+jo14QHvoUgD9+M1xhq2GLLrofvQ/vau8Tz57cZ+VPho3h0VOWFspGhamZGZ+M/5mta+dyWFe+1fjwk68pkU75xrxWvttSLN+uuvL1qFSLwBsXOXt8L5ERzzEzM6O4qxs9+35Oi059sv268hNTUzPeGrGEw1u+4+wh7X2ilFt1OvediVPJtO8TFpZF8PHtxf2b/tz4Zx8qVQK29sWpUu8NGncair2zfm9m8TKVuHxyKzFRoRS1caBynQ407TISa0Xhq7vGch/dn2Jur8qtVI/2lOqhHS3x6Pft0qgwklotS8rmJhONEX1DUVGGu4xGRERw/PhxfvvtN+bMmYO3d/rrW6dls79UhJxW0rZwL5mY1/6+Jaua5LRa7rIcZU5Sp7r1ocguD54Xzg0M8xOXtln7PSLS9obqRl5nIVVdBufcRrc7Fuf/lU5zm1E9Fba2hmOlbW1teeedd0hISGD27NksW7Ysy5kTQgghhBDCGLL6U+4yTT9K5lSsWJF//vknu08rhBBCCCGEyKeM6qlITVxcHBs3bqR48YK/KZMQQgghhCi4NBoZSp+bjGpUdOnSxSBMpVIRHBxMfHw83377bZYzJoQQQgghhLFk+FPuMqpRUbVqVYN1vS0tLSlZsiTt2rXDw8MjWzInhBBCCCGEyP+MalTMmjUru/MhhBBCCCFEtpGeityV5YnaT5484dy5c8TGxmZHfoQQQgghhBAFjNGNig0bNtC0aVNatmzJe++9R1BQEACffPIJq1evzrYMCiGEEEIIkVlqjTrHXsKQUY2KVatW8fXXX9OtWzdWrFjB6/vn1a9fn71792ZbBoUQQgghhBD5m1FzKn799VeGDRvGsGHDSEpK0jtWoUIFXa+FEEIIIYQQeUHmVOQuo3oqgoODqVWrVorHLCwsZH6FEEIIIYQQ/yFGNSpcXV25fPlyiscuXryIm5tbVvIkhBBCCCFElmjU6hx7CUNGDX966623WLBgAQ4ODrRr1w6AxMRE/vrrL5YvX86oUaOyM49CCCGEEEJkigx/yl1GNSoGDBjAkydPmDRpEpMnTwagT58+ALz77ru899572ZdDIYQQQgghRL5mVKMCYMKECfTt25eTJ08SHh6OnZ0djRo1kqFPQgghhBAiz2lk6ddcZXSjIikpiZCQEGxtbbGysgLgwoULXLhwAYBu3bplR/6EEEIIIYQQ+ZxRjYqrV68yYsQInjx5ordHRTITExNpVAghhBBCiDyjljkVucqoRsWUKVOwsbFh9erVeHp6YmFhkd35EkIIIYQQQhQQRjUqbt++zbx586hfv35250cIIYQQQogsk6Vfc5dR+1S4ubkRExOT3XkRQgghhBBCFEBGNSrGjRvH4sWLuXPnTnbnRwghhBBCiCzTqDU59hKGjBr+9PXXXxMSEkKXLl0oXrw4tra2esdNTEzYvn17tmRQCCGEEEKIzCosS8qeOHGCrVu3cvHiRR48eMB7773HpEmTMpQ2KiqKmTNncvDgQVQqFU2bNmXChAkUL15cL965c+f49ttvCQgIwMnJiT59+jBw4EBMTEwynE+jGhVVq1bN1IcIIYQQQgghMu/YsWNcv36devXqERERkam0o0aN4vbt20yZMgUrKyvmzZvHwIED2bJlC+bm2mbAvXv3GDBgAL6+vowaNYobN24wZ84czMzMGDBgQIY/y6hGxaxZs4xJJoQQQgghRK4oLMOU/ve//zF27FgA/P39M5zu/PnzHD9+nOXLl9OkSRMAKlSoQKdOndi/fz+dOnUCYPny5Tg4ODB37lwsLS1p1KgRYWFhLFq0iA8++ABLS8sMfZ5RcyqEEEIIIYQQOc/U1Lif60ePHkWhUODr66sLc3d3p3Llyhw9elQvXuvWrfUaD506dSIyMpLz589nPJ9G5VIIIYQQQoh8TKNW59irIAgMDKRChQoGUxbc3d0JDAwEIDY2lidPnuDu7m4Qx8TERBcvI4wa/iSEEEIIIcR/VevWrdM87ufnl0s5SV1kZKTBYkoAdnZ2XLlyBdBO5AZQKBR6cSwtLSlatGim5nDk20ZFrwbSiZLzrPM6A4Vakyp5nYP/gqJ5nQEhRH6nupHXORB55PiO5jl27nTaFGmKiori2bNn6cYrW7Zshucz5Af5tlEhhBBCCCFEfpSVnoi9e/cyYcKEdOPt3r0bDw8Poz9HoVDw9OlTg/CIiAjs7OwAdD0ZyT0WyZRKJXFxcbp4GSGNCiGEEEIIIXJJ79696d27d45/jru7O6dOnUKj0ejNqwgKCsLLywuAYsWKUapUKYO5E0FBQWg0GoO5FmmRMUZCCCGEEEIUMs2aNSMiIoJTp07pwoKCgrh27RrNmjXTi+fn54dKpdKF7d69G4VCQa1atTL8edKoEEIIIYQQIp969OgRe/fuZe/evcTFxXH//n3d+9dVqVKF8ePH697XqlWLJk2aMH78ePbs2cOhQ4cYOXIk3t7etGvXThdvwIABhIWFMWbMGE6dOsXq1atZvnw5Q4YMydScDhONRlM4dgYRQgghhBCikNm6dSvjxo1L8diNG68WIvD29qZ79+56m1RHRUUxc+ZMDhw4QGJiIk2aNGHChAmUKFFC7zznzp1j1qxZBAQE4OjoyHvvvcfAgQMNlqNNizQqhBBCCCGEEFkiw5+EEEIIIYQQWSKNCiGEEEIIIUSWSKNCCCGEEEIIkSXSqBBCCCGEEEJkiTQqhBBCCCGEEFkijQohhBBCCCFElkijQgghhBBCCJEl0qgQOSIyMhJvb2+2bt0KQKtWrZg2bVoe5yplDx8+ZP78+QQHB+d1VrLVqlWraNGiBZUrV2bYsGF5nZ0CZezYsXTu3DmvsyFy0LBhw/jggw/yOhuF1vz586lVq1ZeZ6NAW7VqFUeOHMnWcz58+BBvb2+DnZiFyA7meZ0B8d+wYMECFApFXmcjRY8ePWLBggW0aNHCYIfJguru3bvMmjWLgQMH0rJlSxwcHPI6S0KI/5DevXvTvHnzvM5GgbZmzRpatGgh5SgKDGlU5BGlUom5uTmmpv+NzqIqVarkdRb+U4KCgtBoNLz11luULVs2xz8vPj6eIkWK5PjnCCFyVlb/lpO/20qWLEnJkiWzMWciNf+13xMi//rP1sDz588zZMgQmjRpQs2aNXnzzTfZtm2b7ri/vz/e3t6cOHGCMWPGUKtWLVq2bMnSpUsNzrV+/XpatmyJj48P/fr149q1a3pDf+DV8J+lS5fSsmVLatSowblz53Sf8bqkpCSaNm3Kd999l2PXn902btxIq1at8PHxoW/fvty7d0/v+L+HP926dYuBAwfSoEEDfHx8aN++vUHZZqRcvb29Wb58uV66VatW4e3trXuvUqn49ttvadGiBdWqVaNJkyYMGTKEqKgo/P39+fDDDwHo1asX3t7eemkLorFjxzJkyBAA2rRpoyuzyMhIpkyZQpMmTahWrRo9evTg+PHjemn/+usv+vXrR6NGjahduza9e/fm6NGjenG2bt2Kt7c358+fp1+/ftSsWbNA1dXM8Pf3p1u3btSsWZNevXpx5coV3bEVK1bQs2dP6tSpQ6NGjRg8eDBBQUF66ZOHUR05coTOnTtTvXp1evTowYULF/TiJf99LFu2jKZNm+Lj48PQoUN59uyZLk6PHj0YM2aMQR5nz55NkyZNSEpKyt6LzyfSulekdx9PdufOHd5//32qV69OmzZt+OOPP3L5KnJGWmXzwQcfMHjwYL34AQEBeHt74+/vrwvz9vZmyZIlzJ49G19fXxo1agRkvu6+/t324sULg+FPad2Hk2XkHpWXksskrfuCRqNh+fLltG/fnmrVqtG6dWtWrVqV4nlel9KQ4UePHvHbb7/pvpf+PZz432V+584dRo8eTfPmzfHx8aFTp06sWLECtVqdswUjxEv/2Z6Kx48fU7t2bfr06YOlpSXnzp1jwoQJaDQaunfvros3efJk3nzzTX7++WcOHjzInDlz8Pb2plmzZgD4+fkxefJkevfuTfv27QkICGDUqFEpfub+/fspX748X331FaamplSrVg0fHx+2bNmCr6+vLt6xY8d49uwZPXv2zNEyyC6HDx9m4sSJ9OjRg06dOnH16lU+/fTTNNMMGTIEZ2dnvvnmG2xsbLh//z5Pnz7VHc9MuaZn8eLFrF+/ns8//5yKFSsSHh7OiRMnUCqVVK1alUmTJjFt2jRmzpyJu7u7UZ+RnwwbNgwPDw/mzJnDggULcHFxoUyZMvTr14/nz58zatQoSpQowfbt2xk8eLCukQDa8bYtW7akf//+mJqacvToUQYNGsTq1atp0KCB3ueMGTOGt99+m8GDB1O0aNG8uNQcFRISwvTp0xk0aBC2trZ8//33DB8+nAMHDmBhYcHTp095//33cXV1JTo6mvXr1/POO++wb98+7O3t9c4zdepURowYgUKhYOnSpQwYMID9+/fj5OSki3fgwAFKly7NlClTiIyMZM6cOYwYMYINGzYA2uEks2bNIioqCltbW0D7AOLPP/+ke/fumJmZ5Wr55Ja07hUZuY8nJCTQv39/ihYtqmv8/vTTT0RHR+Pm5pZXl5Ut0ruPZtSaNWvw8fHhm2++ITExURee0br77++2YsWKGXxGWvdh0D5tz8g9Kq+ld1/45ptv2LRpE0OGDMHHx4dz584xZ84crKys6NOnT4Y/Z8GCBQwaNIjatWvTv39/AMqVK6c7nlKZ37hxgwoVKtClSxesra0JCAhg/vz5xMbGMnz48GwvCyEMaIRGrVZrVCqVZuLEiZq3335bo9FoNKdPn9Z4eXlpvv32W714LVu21IwfP14X1rNnT82HH36od76ff/5Z4+XlpdmyZYsurGXLlpr69etrYmJi9OJu3LhRU716dc2LFy90YcOHD9floyDo3bu35t1339ULmzdvnl4ZtGzZUjN16lSNRqPRPH/+XOPl5aXx8/NL9ZwZLVcvLy/NsmXL9OKtXLlS4+XlpXs/aNAgzfDhw1P9rOR/60uXLqVzpQXHgQMHNF5eXpoHDx5oNBqNZvPmzZoqVapobt26pRevd+/empEjR6Z4jqSkJI1KpdL0799f89lnn+nCt2zZovHy8tIsXrw45y4gj3355Zcab29vzc2bN3VhyfXkzJkzBvETExM1cXFxmpo1a2rWr1+vdx4vLy/NyZMndWGRkZGaWrVqaebMmaMLa9mypaZWrVqayMhIXdjJkyc1Xl5emqNHj2o0Go0mKipK4+Pjo/ntt990cfz8/DReXl6aoKCgbLnu/CYj94pkKd3HNRqN5vfff9dUqlRJr4zu3r2rqVSpkub999/PiWznivTK5v3339cMGjRIL+zatWsaLy8vzenTp3VhXl5emk6dOmnUarVe3MzU3ZS+23766SdNzZo1de/Tuw8bc4/KbendF+7du6fx9vbWuwdoNBrN7NmzNb6+vpqkpCTded544w29OBERESn+bkj+3nxdamX+uuS/h4ULF2p8fX114Q8ePNB4eXlp9uzZk7mLFyID/rPDnyIiIpg+fTotW7akatWqVK1alQ0bNhgMX2jSpInu/01MTPDw8NA9CUpKSiIgIIBWrVrppWndunWKn9mgQQODJzhvvPEG5ubm7Ny5E4CwsDAOHz5Mr169snyNuSEpKYmrV6/Stm1bvfD27dunmsbB4f/t3X9M1PUfwPHnBcciIA+SC4MG56Di8LpMU7sN5kJSXBvmhrpYVDRFjmGbaUERv6ypiVrHDyFTV2uUA2SRjlpR9MNf/0RpMhyzSVSShKDgTQLx+wdfLs47uLv4IcjrsfHHfXh/Pvf5vO59r8+9f9z7fAkMDGTXrl1UVVXZ9Ky5GldHtFot3377LQUFBZw6dWpaDgUfPXqUBx54gJCQEPr6+ix/BoOB06dPW8q1trby6quvEhkZiVarJSIigh9++MHmfQGwePHiCbyCiadWqwkLC7M8Dg0NBbCsEvbTTz/xwgsvsHDhQrRaLXq9HrPZzPnz562O4+PjY5lSMvjYYDDw888/W5VbuHChZQQC4PHHH0elUlnKeXt7ExsbS2VlpaXMoUOHmD9//pTvcR+Oo1zhTB4/deoUYWFhVjEKDg7moYcemqjLGBeOYuOKqKgoFAqFzXZX6q690YmhHOVhZ3PUrTZSXjh27BgATz75pM01tLW1ceHChTE7D3sx7+npwWQyERMTg06nIyIigt27d9PW1sbVq1fH7LmFGM60nf6Unp5OfX09qamphIaG4u3tzccff0xNTY1VuaE3eQClUmmZA3rp0iX6+vrw8/OzKjN0WNjR9rvuuounnnqKiooKEhISqK6uRqlUEhsbO5rLmzDDxWDmzJnD7qNQKNi3bx+7d+8mLy8Ps9lMREQEGRkZPPbYYy7H1ZGUlBTuuOMOqqqqKCwsxM/Pj4SEBFJTU+3eSG9HHR0dNDQ0EBERYfO/wWkz/f39pKSk0NXVxYYNGwgODsbT0xOTyWT3ZjjSa3w7uHm1MqVSCQzcuP/880+SkpKYM2cOubm5qNVqlEolycnJ9PT0WO13cz2Ggbp87tw5m2038/Pzo62tzfJ41apVrFmzhsbGRtRqNXV1dZN2qeax4ChXOJPHL168aDe299xzj81rNZU4io0rhsuto6m7N3OUh53JUZPBSHmho6ODGzdusGjRIrv7XrhwgcDAwDE5D3sx37FjB+Xl5aSmpjJnzhx8fHyora1lz5499PT04OXlNSbPLcRwpmWjoqenh7q6OtLT063WKS8rK3PpOH5+fri7u3Pp0iWr7e3t7XbLD/cBNj4+noMHD9LY2MihQ4eIjY2dMm/+4WLw999/j7ifRqPBZDLR29tLfX09u3btYv369Xz33XcuxdXDw4Pe3l6rbVeuXLEpk5aWRlpaGs3NzVRWVlJQUEBQUBArVqxw4WqnrhkzZvDggw/y1ltvDVumubmZhoYGioqKWLJkiWX7tWvXJuIUp5Tvv/8es9lstVRyX18fly9ftil7cz2Ggbrs7+9vs83evkPLzZ07l7CwMCorK7nvvvvw8PBg2bJlo72cSW2kXOFMHler1Zw5c8bmuO3t7Xh7e4/7+Y+nkWJjLzfaq58w/L3J2brrTOeMozzsTI6a7GbMmIFCoaCsrMzS2BhKo9EA9u9bw702w7EX888//5zVq1ezbt06y7ax/p0LIUYyLac//fPPP/T391u96bu7u/n6669dOo6bmxvh4eHU1tZabf/qq69cOo5OpyM8PJw333yTs2fPTpkvaMNADLRaLV9++aXV9i+++MKp/ZVKJQsWLGDdunV0d3dz8eJFl+IaEBBg02s2OARtT3BwMBs3bkSlUvHrr79azgGY0r2WjhgMBlpaWlCr1eh0Ops/+Pf6h74v/vjjD+rr62/JOU9m165dQ6FQ4O7+b79MTU2N1ZdcB3V1dXH8+HGrx8eOHUOv11uVO3nypNVKOMePH6ezs9OmXHx8PJ999hkVFRUsX77c4bST28XNuaKlpcWpPK7T6WhqarJaka65uZnGxsYJO/fxZi+PBgQEWJaWHnTzSoOOOFt3XWUvDzuToya7walinZ2ddq9hsBEbEBBAa2ur1ZQke6+NUql06b7U09Nj9X64fv06R44c+a+XI4TLpuVIhY+PDzqdjr1791p6xd977z28vb3t9syMJCUlBaPRSGZmJsuWLaOhocGypKEra0bHx8eTl5eHRqNh3rx5Lp3DrbZ+/XqMRiMZGRmW1Z8+/fTTYcs3Njayfft2li9fzv333093dzelpaUEBgZaVrdwNq5Lly7lgw8+QKfTodFoqK6utvllbKPRSEREBFqtFk9PT7755hsuX75sGaIOCQnBzc2NyspK3N3dcXNzmzI3MWetWLGCTz75hMTERJKSkggJCaGrq4uGhgZ6e3t5+eWXmT17NgEBAezcuZP+/n7MZjMmkwm1Wn2rT3/SGaw7GRkZrFmzhqamJg4cOGD3Bx5VKhWvv/46GzZswMfHh71793Ljxg2ee+45q3JeXl6sXbuWtWvX0tXVRX5+Pg8//DCRkZFW5eLi4sjPz6ejo2NK9+o6Y6RcERYW5lQeX7lyJXv27CE5OdmyKp3JZJry0/cc5dGlS5dSUVHBli1bWLJkCT/++KPTnT2DnK27znCUh53JUZOdRqMhISGBV155hRdffBG9Xk9vby/nz5/n5MmTFBcXAwPfuTCZTLz22musWrWKpqYmKioqbI43e/ZsTpw4wdGjR7n77rsJCgoa8YdMDQYD5eXlhIaG4uvrS1lZmWV1LSEmwrRsVADs3LmTrKws0tPTUalUPPvss5jNZvbv3+/ScaKjo8nJyaG0tJTq6mr0ej05OTkkJSW5NLQeExNDXl7elBqlGBQdHU1ubi4lJSUcOXIEvV7PO++8Q3x8vN3y/v7+zJw5k9LSUv766y98fHyYP38+O3bssMyddTauRqOR9vZ2ioqKUCgUrF69msTERLZt22Yp8+ijj1JTU8OBAwe4fv06Go2G/Px8DAYDMDCFKysri/fff5/q6mr6+vo4e/bsOEZs4nl4ePDhhx9SUFBASUkJbW1tqFQqtFotzzzzjKVMQUEBeXl5vPTSS8yaNYuUlBROnDhhtQ67GFjbf+vWrRQWFpKcnEx4eDjvvvuu3WWP/f392bRpE2+//Ta//fYbYWFh7Nu3z+ZDbUxMDAEBAWRnZ3PlyhUMBgO5ubk2x1OpVCxYsIDW1lYeeeSRcbrCycFRrnAmj995553s37+fnJwcNm/ezL333ovRaKS2ttZqZGiqcRSbqKgoNm/ezEcffURVVRVRUVHk5uby/PPPu/QcztRdZzjKw87kqKkgMzMTjUbDwYMHKSoqwsvLC41GYzVNMTQ0lG3btlFcXIzRaGTevHnk5+cTFxdndayNGzeSk5NDWloaV69eZevWraxcuXLY537jjTfIzs5my5YteHp68vTTTxMTE0NmZua4Xa8QQyluDB0bFWOivLyczMxMamtrCQoKcmqfiooKsrOzqaurs5mvKgb8l7gKcSulp6fzyy+/WFZ3G84TTzzB4sWLycrKcnjM7u5uIiMjSUtLs6xfL8RYc7buCiHEoGk7UjFWOjs7KSwsZNGiRXh5eXH69GlKSkqIjo526oPv77//TnNzM8XFxcTGxkqD4v9GG1chbjfd3d2cO3eOsrIyFArFiD2WQgghxESTRsUoubu709LSwuHDh+nq6sLX15e4uDg2bdrk1P6FhYUcPnyYuXPnkp6ePs5nO3WMNq5C3G7OnDlDYmIis2bNYvv27Va/2i2EEELcajL9SQghhBBCCDEq03JJWSGEEEIIIcTYkUaFEEIIIYQQYlSkUSGEEEIIIYQYFWlUCCGEEEIIIUZFGhVCCCGEEEKIUZFGhRBCCCGEEGJUpFEhhBBCCCGEGBVpVAghhBBCCCFG5X9416xRHH3bSwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "JQkniMaYfElY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heatmap: A heatmap will visually represent these correlations, where:\n",
        "A value close to 1 indicates a strong positive correlation.\n",
        "A value close to -1 indicates a strong negative correlation.\n",
        "A value near 0 means no correlation."
      ],
      "metadata": {
        "id": "H5Hlni72dYsV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Objective for performing all this EDA on our dataset :\n",
        "\n",
        "Target Variable Definition:\n",
        "\n",
        " we can Create a hired column based on emotions, which we could further Define a logical rule for creating a target variable based on the emotions. For instance, you might assume candidates with high \"happy\" or \"neutral\" values are more likely to be hired, while those with higher \"angry\" or \"sad\" values are less likely..\n",
        "\n",
        "EDA:\n",
        "\n",
        " Analyze the relationship between emotions and the target (hired/not_hired).\n",
        "Feature Engineering: Create new features like dominant emotions or aggregate positive/negative emotions.\n",
        "\n",
        "Predictive Modeling:\n",
        "\n",
        " By Using machine learning algorithms (e.g., Logistic Regression, Decision Trees) we can predict the hiring outcome based on emotions."
      ],
      "metadata": {
        "id": "wY0jA2mKfLvP"
      }
    }
  ]
}