{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0Vw7hWngjPR"
      },
      "source": [
        "# **═══ Import Libraries ═══**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "OlUkYDBJgHrp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "#for Neural Network\n",
        "from keras import layers\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout, BatchNormalization\n",
        "from keras.initializers import HeNormal\n",
        "from keras import regularizers\n",
        "from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l5gnOr_mgnNm"
      },
      "source": [
        "# **═══ Exploratory Data Analysis ═══**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aZu8opZIgaNm",
        "outputId": "c5f14d4e-af0e-4f59-ec13-991b8ff5363f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fault</th>\n",
              "      <th>MAP</th>\n",
              "      <th>TPS</th>\n",
              "      <th>Force</th>\n",
              "      <th>Power</th>\n",
              "      <th>RPM</th>\n",
              "      <th>Consumption L/H</th>\n",
              "      <th>Consumption L/100KM</th>\n",
              "      <th>Speed</th>\n",
              "      <th>CO</th>\n",
              "      <th>HC</th>\n",
              "      <th>CO2</th>\n",
              "      <th>O2</th>\n",
              "      <th>Lambda</th>\n",
              "      <th>AFR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3.549</td>\n",
              "      <td>1.889</td>\n",
              "      <td>7.428</td>\n",
              "      <td>5.227</td>\n",
              "      <td>1192.769</td>\n",
              "      <td>3.057</td>\n",
              "      <td>11.720</td>\n",
              "      <td>24.901</td>\n",
              "      <td>0.460</td>\n",
              "      <td>196.089</td>\n",
              "      <td>14.356</td>\n",
              "      <td>1.080</td>\n",
              "      <td>1.047</td>\n",
              "      <td>15.385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>3.559</td>\n",
              "      <td>1.899</td>\n",
              "      <td>7.646</td>\n",
              "      <td>5.327</td>\n",
              "      <td>1145.191</td>\n",
              "      <td>2.914</td>\n",
              "      <td>11.276</td>\n",
              "      <td>25.030</td>\n",
              "      <td>0.478</td>\n",
              "      <td>196.283</td>\n",
              "      <td>14.492</td>\n",
              "      <td>1.107</td>\n",
              "      <td>1.083</td>\n",
              "      <td>15.926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>3.539</td>\n",
              "      <td>1.899</td>\n",
              "      <td>7.578</td>\n",
              "      <td>5.100</td>\n",
              "      <td>1208.505</td>\n",
              "      <td>2.976</td>\n",
              "      <td>11.926</td>\n",
              "      <td>24.213</td>\n",
              "      <td>0.464</td>\n",
              "      <td>200.367</td>\n",
              "      <td>14.443</td>\n",
              "      <td>1.099</td>\n",
              "      <td>1.072</td>\n",
              "      <td>15.754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3.519</td>\n",
              "      <td>1.889</td>\n",
              "      <td>7.371</td>\n",
              "      <td>5.255</td>\n",
              "      <td>1164.127</td>\n",
              "      <td>2.908</td>\n",
              "      <td>11.786</td>\n",
              "      <td>24.407</td>\n",
              "      <td>0.471</td>\n",
              "      <td>195.345</td>\n",
              "      <td>13.853</td>\n",
              "      <td>1.076</td>\n",
              "      <td>1.006</td>\n",
              "      <td>14.787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3.549</td>\n",
              "      <td>1.879</td>\n",
              "      <td>7.405</td>\n",
              "      <td>5.177</td>\n",
              "      <td>1172.032</td>\n",
              "      <td>2.898</td>\n",
              "      <td>12.169</td>\n",
              "      <td>24.875</td>\n",
              "      <td>0.468</td>\n",
              "      <td>194.979</td>\n",
              "      <td>13.752</td>\n",
              "      <td>1.060</td>\n",
              "      <td>0.984</td>\n",
              "      <td>14.470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55994</th>\n",
              "      <td>3</td>\n",
              "      <td>1.971</td>\n",
              "      <td>1.278</td>\n",
              "      <td>92.665</td>\n",
              "      <td>3.589</td>\n",
              "      <td>4584.034</td>\n",
              "      <td>5.423</td>\n",
              "      <td>5.623</td>\n",
              "      <td>95.090</td>\n",
              "      <td>0.902</td>\n",
              "      <td>162.065</td>\n",
              "      <td>13.123</td>\n",
              "      <td>0.568</td>\n",
              "      <td>0.966</td>\n",
              "      <td>14.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55995</th>\n",
              "      <td>3</td>\n",
              "      <td>1.879</td>\n",
              "      <td>1.258</td>\n",
              "      <td>96.644</td>\n",
              "      <td>3.579</td>\n",
              "      <td>4662.496</td>\n",
              "      <td>5.655</td>\n",
              "      <td>5.578</td>\n",
              "      <td>100.032</td>\n",
              "      <td>0.926</td>\n",
              "      <td>161.237</td>\n",
              "      <td>13.591</td>\n",
              "      <td>0.585</td>\n",
              "      <td>1.030</td>\n",
              "      <td>15.147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55996</th>\n",
              "      <td>3</td>\n",
              "      <td>1.960</td>\n",
              "      <td>1.288</td>\n",
              "      <td>93.961</td>\n",
              "      <td>3.493</td>\n",
              "      <td>4792.675</td>\n",
              "      <td>5.542</td>\n",
              "      <td>5.572</td>\n",
              "      <td>98.595</td>\n",
              "      <td>0.925</td>\n",
              "      <td>160.030</td>\n",
              "      <td>12.999</td>\n",
              "      <td>0.579</td>\n",
              "      <td>0.974</td>\n",
              "      <td>14.324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55997</th>\n",
              "      <td>3</td>\n",
              "      <td>1.930</td>\n",
              "      <td>1.299</td>\n",
              "      <td>95.379</td>\n",
              "      <td>3.618</td>\n",
              "      <td>4563.732</td>\n",
              "      <td>5.525</td>\n",
              "      <td>5.568</td>\n",
              "      <td>98.575</td>\n",
              "      <td>0.942</td>\n",
              "      <td>159.800</td>\n",
              "      <td>13.187</td>\n",
              "      <td>0.579</td>\n",
              "      <td>0.990</td>\n",
              "      <td>14.550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55998</th>\n",
              "      <td>3</td>\n",
              "      <td>1.930</td>\n",
              "      <td>1.299</td>\n",
              "      <td>95.379</td>\n",
              "      <td>3.618</td>\n",
              "      <td>4563.732</td>\n",
              "      <td>5.525</td>\n",
              "      <td>5.568</td>\n",
              "      <td>98.575</td>\n",
              "      <td>0.942</td>\n",
              "      <td>159.800</td>\n",
              "      <td>13.187</td>\n",
              "      <td>0.579</td>\n",
              "      <td>0.990</td>\n",
              "      <td>14.550</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>55999 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Fault    MAP    TPS   Force  Power       RPM  Consumption L/H  \\\n",
              "0          0  3.549  1.889   7.428  5.227  1192.769            3.057   \n",
              "1          0  3.559  1.899   7.646  5.327  1145.191            2.914   \n",
              "2          0  3.539  1.899   7.578  5.100  1208.505            2.976   \n",
              "3          0  3.519  1.889   7.371  5.255  1164.127            2.908   \n",
              "4          0  3.549  1.879   7.405  5.177  1172.032            2.898   \n",
              "...      ...    ...    ...     ...    ...       ...              ...   \n",
              "55994      3  1.971  1.278  92.665  3.589  4584.034            5.423   \n",
              "55995      3  1.879  1.258  96.644  3.579  4662.496            5.655   \n",
              "55996      3  1.960  1.288  93.961  3.493  4792.675            5.542   \n",
              "55997      3  1.930  1.299  95.379  3.618  4563.732            5.525   \n",
              "55998      3  1.930  1.299  95.379  3.618  4563.732            5.525   \n",
              "\n",
              "       Consumption L/100KM    Speed     CO       HC     CO2     O2  Lambda  \\\n",
              "0                   11.720   24.901  0.460  196.089  14.356  1.080   1.047   \n",
              "1                   11.276   25.030  0.478  196.283  14.492  1.107   1.083   \n",
              "2                   11.926   24.213  0.464  200.367  14.443  1.099   1.072   \n",
              "3                   11.786   24.407  0.471  195.345  13.853  1.076   1.006   \n",
              "4                   12.169   24.875  0.468  194.979  13.752  1.060   0.984   \n",
              "...                    ...      ...    ...      ...     ...    ...     ...   \n",
              "55994                5.623   95.090  0.902  162.065  13.123  0.568   0.966   \n",
              "55995                5.578  100.032  0.926  161.237  13.591  0.585   1.030   \n",
              "55996                5.572   98.595  0.925  160.030  12.999  0.579   0.974   \n",
              "55997                5.568   98.575  0.942  159.800  13.187  0.579   0.990   \n",
              "55998                5.568   98.575  0.942  159.800  13.187  0.579   0.990   \n",
              "\n",
              "          AFR  \n",
              "0      15.385  \n",
              "1      15.926  \n",
              "2      15.754  \n",
              "3      14.787  \n",
              "4      14.470  \n",
              "...       ...  \n",
              "55994  14.200  \n",
              "55995  15.147  \n",
              "55996  14.324  \n",
              "55997  14.550  \n",
              "55998  14.550  \n",
              "\n",
              "[55999 rows x 15 columns]"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"EngineFaultDB_Final.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "twJYSfyvhUJZ",
        "outputId": "5f2d3b8c-171c-461a-9eab-ccc089898d92"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(55999, 15)"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BqsTYAYvgrjo",
        "outputId": "8ad14ae9-98fd-4fbf-ad40-033e25faad02"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 55999 entries, 0 to 55998\n",
            "Data columns (total 15 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Fault                55999 non-null  int64  \n",
            " 1   MAP                  55999 non-null  float64\n",
            " 2   TPS                  55999 non-null  float64\n",
            " 3   Force                55999 non-null  float64\n",
            " 4   Power                55999 non-null  float64\n",
            " 5   RPM                  55999 non-null  float64\n",
            " 6   Consumption L/H      55999 non-null  float64\n",
            " 7   Consumption L/100KM  55999 non-null  float64\n",
            " 8   Speed                55999 non-null  float64\n",
            " 9   CO                   55999 non-null  float64\n",
            " 10  HC                   55999 non-null  float64\n",
            " 11  CO2                  55999 non-null  float64\n",
            " 12  O2                   55999 non-null  float64\n",
            " 13  Lambda               55999 non-null  float64\n",
            " 14  AFR                  55999 non-null  float64\n",
            "dtypes: float64(14), int64(1)\n",
            "memory usage: 6.4 MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "AkfOhmDmghus",
        "outputId": "8e9cd6aa-9230-4868-d7e6-d966b64a475c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Fault                  0\n",
              "MAP                    0\n",
              "TPS                    0\n",
              "Force                  0\n",
              "Power                  0\n",
              "RPM                    0\n",
              "Consumption L/H        0\n",
              "Consumption L/100KM    0\n",
              "Speed                  0\n",
              "CO                     0\n",
              "HC                     0\n",
              "CO2                    0\n",
              "O2                     0\n",
              "Lambda                 0\n",
              "AFR                    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "aCB9-9fzhEbk",
        "outputId": "847824a5-bf26-4f4d-e0fa-2ba7403f0dc7"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fault</th>\n",
              "      <th>MAP</th>\n",
              "      <th>TPS</th>\n",
              "      <th>Force</th>\n",
              "      <th>Power</th>\n",
              "      <th>RPM</th>\n",
              "      <th>Consumption L/H</th>\n",
              "      <th>Consumption L/100KM</th>\n",
              "      <th>Speed</th>\n",
              "      <th>CO</th>\n",
              "      <th>HC</th>\n",
              "      <th>CO2</th>\n",
              "      <th>O2</th>\n",
              "      <th>Lambda</th>\n",
              "      <th>AFR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fault</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.072753</td>\n",
              "      <td>-0.025270</td>\n",
              "      <td>-0.049601</td>\n",
              "      <td>0.117879</td>\n",
              "      <td>0.205279</td>\n",
              "      <td>0.056363</td>\n",
              "      <td>-0.128618</td>\n",
              "      <td>0.203806</td>\n",
              "      <td>-0.224785</td>\n",
              "      <td>-0.172228</td>\n",
              "      <td>-0.078492</td>\n",
              "      <td>-0.068178</td>\n",
              "      <td>0.187571</td>\n",
              "      <td>0.187584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MAP</th>\n",
              "      <td>-0.072753</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.884219</td>\n",
              "      <td>0.880061</td>\n",
              "      <td>0.629456</td>\n",
              "      <td>0.201352</td>\n",
              "      <td>0.697801</td>\n",
              "      <td>0.739555</td>\n",
              "      <td>0.201107</td>\n",
              "      <td>0.134665</td>\n",
              "      <td>0.044645</td>\n",
              "      <td>-0.049704</td>\n",
              "      <td>-0.157730</td>\n",
              "      <td>-0.144828</td>\n",
              "      <td>-0.144827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TPS</th>\n",
              "      <td>-0.025270</td>\n",
              "      <td>0.884219</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.891460</td>\n",
              "      <td>0.582596</td>\n",
              "      <td>0.092894</td>\n",
              "      <td>0.647843</td>\n",
              "      <td>0.806927</td>\n",
              "      <td>0.092682</td>\n",
              "      <td>0.253781</td>\n",
              "      <td>-0.098425</td>\n",
              "      <td>-0.163978</td>\n",
              "      <td>-0.330770</td>\n",
              "      <td>-0.261230</td>\n",
              "      <td>-0.261220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Force</th>\n",
              "      <td>-0.049601</td>\n",
              "      <td>0.880061</td>\n",
              "      <td>0.891460</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.621402</td>\n",
              "      <td>0.072542</td>\n",
              "      <td>0.719759</td>\n",
              "      <td>0.848365</td>\n",
              "      <td>0.071914</td>\n",
              "      <td>0.216722</td>\n",
              "      <td>-0.002452</td>\n",
              "      <td>-0.125891</td>\n",
              "      <td>-0.274184</td>\n",
              "      <td>-0.231231</td>\n",
              "      <td>-0.231229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Power</th>\n",
              "      <td>0.117879</td>\n",
              "      <td>0.629456</td>\n",
              "      <td>0.582596</td>\n",
              "      <td>0.621402</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.403647</td>\n",
              "      <td>0.695179</td>\n",
              "      <td>0.473038</td>\n",
              "      <td>0.405901</td>\n",
              "      <td>0.060640</td>\n",
              "      <td>0.101330</td>\n",
              "      <td>-0.041272</td>\n",
              "      <td>-0.161577</td>\n",
              "      <td>-0.078817</td>\n",
              "      <td>-0.078808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RPM</th>\n",
              "      <td>0.205279</td>\n",
              "      <td>0.201352</td>\n",
              "      <td>0.092894</td>\n",
              "      <td>0.072542</td>\n",
              "      <td>0.403647</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.603519</td>\n",
              "      <td>-0.197747</td>\n",
              "      <td>0.996894</td>\n",
              "      <td>-0.020227</td>\n",
              "      <td>0.236539</td>\n",
              "      <td>-0.015877</td>\n",
              "      <td>-0.080016</td>\n",
              "      <td>-0.004557</td>\n",
              "      <td>-0.004525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumption L/H</th>\n",
              "      <td>0.056363</td>\n",
              "      <td>0.697801</td>\n",
              "      <td>0.647843</td>\n",
              "      <td>0.719759</td>\n",
              "      <td>0.695179</td>\n",
              "      <td>0.603519</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.614128</td>\n",
              "      <td>0.603262</td>\n",
              "      <td>0.358751</td>\n",
              "      <td>0.375354</td>\n",
              "      <td>-0.313437</td>\n",
              "      <td>-0.251838</td>\n",
              "      <td>-0.347567</td>\n",
              "      <td>-0.347545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumption L/100KM</th>\n",
              "      <td>-0.128618</td>\n",
              "      <td>0.739555</td>\n",
              "      <td>0.806927</td>\n",
              "      <td>0.848365</td>\n",
              "      <td>0.473038</td>\n",
              "      <td>-0.197747</td>\n",
              "      <td>0.614128</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.197161</td>\n",
              "      <td>0.441402</td>\n",
              "      <td>0.027346</td>\n",
              "      <td>-0.349849</td>\n",
              "      <td>-0.290361</td>\n",
              "      <td>-0.412699</td>\n",
              "      <td>-0.412697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Speed</th>\n",
              "      <td>0.203806</td>\n",
              "      <td>0.201107</td>\n",
              "      <td>0.092682</td>\n",
              "      <td>0.071914</td>\n",
              "      <td>0.405901</td>\n",
              "      <td>0.996894</td>\n",
              "      <td>0.603262</td>\n",
              "      <td>-0.197161</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.019955</td>\n",
              "      <td>0.236040</td>\n",
              "      <td>-0.016246</td>\n",
              "      <td>-0.080890</td>\n",
              "      <td>-0.005199</td>\n",
              "      <td>-0.005167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CO</th>\n",
              "      <td>-0.224785</td>\n",
              "      <td>0.134665</td>\n",
              "      <td>0.253781</td>\n",
              "      <td>0.216722</td>\n",
              "      <td>0.060640</td>\n",
              "      <td>-0.020227</td>\n",
              "      <td>0.358751</td>\n",
              "      <td>0.441402</td>\n",
              "      <td>-0.019955</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.445796</td>\n",
              "      <td>-0.868488</td>\n",
              "      <td>-0.435150</td>\n",
              "      <td>-0.899408</td>\n",
              "      <td>-0.899417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HC</th>\n",
              "      <td>-0.172228</td>\n",
              "      <td>0.044645</td>\n",
              "      <td>-0.098425</td>\n",
              "      <td>-0.002452</td>\n",
              "      <td>0.101330</td>\n",
              "      <td>0.236539</td>\n",
              "      <td>0.375354</td>\n",
              "      <td>0.027346</td>\n",
              "      <td>0.236040</td>\n",
              "      <td>0.445796</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.413162</td>\n",
              "      <td>0.199745</td>\n",
              "      <td>-0.352851</td>\n",
              "      <td>-0.352841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CO2</th>\n",
              "      <td>-0.078492</td>\n",
              "      <td>-0.049704</td>\n",
              "      <td>-0.163978</td>\n",
              "      <td>-0.125891</td>\n",
              "      <td>-0.041272</td>\n",
              "      <td>-0.015877</td>\n",
              "      <td>-0.313437</td>\n",
              "      <td>-0.349849</td>\n",
              "      <td>-0.016246</td>\n",
              "      <td>-0.868488</td>\n",
              "      <td>-0.413162</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.360441</td>\n",
              "      <td>0.852599</td>\n",
              "      <td>0.852599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>O2</th>\n",
              "      <td>-0.068178</td>\n",
              "      <td>-0.157730</td>\n",
              "      <td>-0.330770</td>\n",
              "      <td>-0.274184</td>\n",
              "      <td>-0.161577</td>\n",
              "      <td>-0.080016</td>\n",
              "      <td>-0.251838</td>\n",
              "      <td>-0.290361</td>\n",
              "      <td>-0.080890</td>\n",
              "      <td>-0.435150</td>\n",
              "      <td>0.199745</td>\n",
              "      <td>0.360441</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.519821</td>\n",
              "      <td>0.519842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lambda</th>\n",
              "      <td>0.187571</td>\n",
              "      <td>-0.144828</td>\n",
              "      <td>-0.261230</td>\n",
              "      <td>-0.231231</td>\n",
              "      <td>-0.078817</td>\n",
              "      <td>-0.004557</td>\n",
              "      <td>-0.347567</td>\n",
              "      <td>-0.412699</td>\n",
              "      <td>-0.005199</td>\n",
              "      <td>-0.899408</td>\n",
              "      <td>-0.352851</td>\n",
              "      <td>0.852599</td>\n",
              "      <td>0.519821</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AFR</th>\n",
              "      <td>0.187584</td>\n",
              "      <td>-0.144827</td>\n",
              "      <td>-0.261220</td>\n",
              "      <td>-0.231229</td>\n",
              "      <td>-0.078808</td>\n",
              "      <td>-0.004525</td>\n",
              "      <td>-0.347545</td>\n",
              "      <td>-0.412697</td>\n",
              "      <td>-0.005167</td>\n",
              "      <td>-0.899417</td>\n",
              "      <td>-0.352841</td>\n",
              "      <td>0.852599</td>\n",
              "      <td>0.519842</td>\n",
              "      <td>0.999990</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Fault       MAP       TPS     Force     Power  \\\n",
              "Fault                1.000000 -0.072753 -0.025270 -0.049601  0.117879   \n",
              "MAP                 -0.072753  1.000000  0.884219  0.880061  0.629456   \n",
              "TPS                 -0.025270  0.884219  1.000000  0.891460  0.582596   \n",
              "Force               -0.049601  0.880061  0.891460  1.000000  0.621402   \n",
              "Power                0.117879  0.629456  0.582596  0.621402  1.000000   \n",
              "RPM                  0.205279  0.201352  0.092894  0.072542  0.403647   \n",
              "Consumption L/H      0.056363  0.697801  0.647843  0.719759  0.695179   \n",
              "Consumption L/100KM -0.128618  0.739555  0.806927  0.848365  0.473038   \n",
              "Speed                0.203806  0.201107  0.092682  0.071914  0.405901   \n",
              "CO                  -0.224785  0.134665  0.253781  0.216722  0.060640   \n",
              "HC                  -0.172228  0.044645 -0.098425 -0.002452  0.101330   \n",
              "CO2                 -0.078492 -0.049704 -0.163978 -0.125891 -0.041272   \n",
              "O2                  -0.068178 -0.157730 -0.330770 -0.274184 -0.161577   \n",
              "Lambda               0.187571 -0.144828 -0.261230 -0.231231 -0.078817   \n",
              "AFR                  0.187584 -0.144827 -0.261220 -0.231229 -0.078808   \n",
              "\n",
              "                          RPM  Consumption L/H  Consumption L/100KM     Speed  \\\n",
              "Fault                0.205279         0.056363            -0.128618  0.203806   \n",
              "MAP                  0.201352         0.697801             0.739555  0.201107   \n",
              "TPS                  0.092894         0.647843             0.806927  0.092682   \n",
              "Force                0.072542         0.719759             0.848365  0.071914   \n",
              "Power                0.403647         0.695179             0.473038  0.405901   \n",
              "RPM                  1.000000         0.603519            -0.197747  0.996894   \n",
              "Consumption L/H      0.603519         1.000000             0.614128  0.603262   \n",
              "Consumption L/100KM -0.197747         0.614128             1.000000 -0.197161   \n",
              "Speed                0.996894         0.603262            -0.197161  1.000000   \n",
              "CO                  -0.020227         0.358751             0.441402 -0.019955   \n",
              "HC                   0.236539         0.375354             0.027346  0.236040   \n",
              "CO2                 -0.015877        -0.313437            -0.349849 -0.016246   \n",
              "O2                  -0.080016        -0.251838            -0.290361 -0.080890   \n",
              "Lambda              -0.004557        -0.347567            -0.412699 -0.005199   \n",
              "AFR                 -0.004525        -0.347545            -0.412697 -0.005167   \n",
              "\n",
              "                           CO        HC       CO2        O2    Lambda  \\\n",
              "Fault               -0.224785 -0.172228 -0.078492 -0.068178  0.187571   \n",
              "MAP                  0.134665  0.044645 -0.049704 -0.157730 -0.144828   \n",
              "TPS                  0.253781 -0.098425 -0.163978 -0.330770 -0.261230   \n",
              "Force                0.216722 -0.002452 -0.125891 -0.274184 -0.231231   \n",
              "Power                0.060640  0.101330 -0.041272 -0.161577 -0.078817   \n",
              "RPM                 -0.020227  0.236539 -0.015877 -0.080016 -0.004557   \n",
              "Consumption L/H      0.358751  0.375354 -0.313437 -0.251838 -0.347567   \n",
              "Consumption L/100KM  0.441402  0.027346 -0.349849 -0.290361 -0.412699   \n",
              "Speed               -0.019955  0.236040 -0.016246 -0.080890 -0.005199   \n",
              "CO                   1.000000  0.445796 -0.868488 -0.435150 -0.899408   \n",
              "HC                   0.445796  1.000000 -0.413162  0.199745 -0.352851   \n",
              "CO2                 -0.868488 -0.413162  1.000000  0.360441  0.852599   \n",
              "O2                  -0.435150  0.199745  0.360441  1.000000  0.519821   \n",
              "Lambda              -0.899408 -0.352851  0.852599  0.519821  1.000000   \n",
              "AFR                 -0.899417 -0.352841  0.852599  0.519842  0.999990   \n",
              "\n",
              "                          AFR  \n",
              "Fault                0.187584  \n",
              "MAP                 -0.144827  \n",
              "TPS                 -0.261220  \n",
              "Force               -0.231229  \n",
              "Power               -0.078808  \n",
              "RPM                 -0.004525  \n",
              "Consumption L/H     -0.347545  \n",
              "Consumption L/100KM -0.412697  \n",
              "Speed               -0.005167  \n",
              "CO                  -0.899417  \n",
              "HC                  -0.352841  \n",
              "CO2                  0.852599  \n",
              "O2                   0.519842  \n",
              "Lambda               0.999990  \n",
              "AFR                  1.000000  "
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {},
      "outputs": [],
      "source": [
        "dr = df.copy() # Membuat copy dari dataset awal\n",
        "dr.drop([\"Fault\"], axis = 1, inplace = True) # Drop kolom label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983
        },
        "id": "sTBmRdq2wlha",
        "outputId": "2967b9a9-0e16-4c4e-a5db-8c8d0ac7f512"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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mOsUIBY1Qi2qy/FfUKqH/o58CKQBo6AchhBBCCCGEEEI0BmVUEEIIIYQQQggp1LR0Cl4G8H8ZZVQQQgghhBBCCCFEY1CgghBCCCGEEEIIIRqDhn4QQgghhBBCCCnUaNYPzUIZFYQQQgghhBBCCNEYFKgghBBCCCGEEEKIxqChH4QQQgghhBBCCjWa9UOzUEYFIYQQQgghhBBCNAYFKgghhBBCCCGEEKIxaOgHIYQQQgghhJBCjWb90CyUUUEIIYQQQgghhBCNQYEKQgghhBBCCCGEaAwa+kEIIYQQQgghpFDTEtLQD01CGRWEEEIIIYQQQgjRGBSoIIQQQgghhBBCiMagoR+EEEIIIYQQQgo1AQ390CiUUUEIIYQQQgghhBCNQYEKQgghhBBCCCGEaAwa+kEIIYQQQgghpFDTEtDQD01CgQryWXr27ImNGzdiwIABWLFihdJjQ4YMwbJly9CjRw9s2LBBvv3atWuoXr06GjVqhCNHjijtExgYCDc3N/m6hYUFypUrh9mzZ6NMmTLQBBbVy8P91z4wLVsCIgcb3G47GOEHz0BT2bRpC7tOXaFjYYGkgFd49888JD57mmd72/YdYN26DfRsbZERK0b0+bMIXrkc0rQ0WQOBAI69+8KyQSPoWFogLTISkUePIGzj+u/WJ+tWbWHXsYu8T0EL5yPxed59smnXATYtW0PX1g4Z4ljEnD+H4NXKfXLoyfrUEDoWlkiLjEDU8aMI2/T9+qRXvhb0qzaAwMgUGeHBSDq2HRmhgXm219LTh0GdVtD1LQstfQNIxNFIPLET6a8ey45XriZE5WtCYGbJ1zMjQpF88Yj88R9l14lz2HroFKJixfAq4oRfe3VEcc/s33lF+89cwtGL1/E6KJSv+7q5YFCnVnm2/1EMqtWHUZ3mEBqbIj30HcR7NyD9XYDKtpZDJkHPs1iu7SlP7yF69Rz8KEY1G8GkQSsITcyQFhyImJ1rkBb4SmVbm1F/QORdItf25Ed3ELH0r1zbzTsPgHGNhoj5dx3izx7Gj1IQ3nt7j57Ejn2HER0rhoerC0b064Fi3p4q2755F4y123bhRcAbvI+IxNDe3fBzi8ZKbfYfO4X9x0/j/YdIvu7m4ogeP7dB5XJ+auvDoUOHsGf3bsTExMDN3R2DBg2Cj49Pnu0vXbqEzZs2ITw8HA6OjujdqxcqVKyosu3ixYtx7OhR9O/fH61at5Zv79mjBz58+KDUtmevXvj555+hLlKpFLu2rsHZE4eQmBgPn6Kl0GfwaNg7On9yvxOH9+DQ3m0Qx0TDxc0TvQaMhKdP9t+MaeOG4tnje0r71GvUEn2HjlVbXxT7dHjnMlw5vRfJSfFw9/FDp/4TYGNfJM99Xj69g1MHNiDo9TOIYyLQf+w/8KtYJ1e7sODX2L9lAW8vycyAnZMH+o+eBwtre7X36dCO5bj0sU8ePn7o3P932Drk3acXT+7g5IGNePexT4PGzodfpdx9yrJ15Z+4eHI32vcajXrNukLdCmKfCFFEQz/IZ3N2dsaOHTuQnJws35aSkoJt27bBxcUlV/u1a9di2LBhuHjxIkJDZV8Cczp9+jTCwsJw4sQJJCQkoHHjxoiNjYUmEBoaIO6hPx4PnwZNZ1GnHpyHjkDo+jV40qcHkl69hPf8BdA2M1fdvn4DOA0cjND1a/GoSye8mfUXLOrWg1P/QfI29l26wbpVG7z9Zy5vE7x8Key7dIVNO/V94VNkXrsunIcMR+jGtXjaryeSA17Ca+4/efepXgP+/EM3rsPj7h0ROHsGzOvUhWO/gfI2dp27wbpla7xbMI+3CVm5DHadusCmbfvv0ifdYuVh2KA9ki8chnjVn8h8HwTjLiOgZWCsegeBECZdR0JgZoX43SsQu3QyEg5vhiQ++3dEEh+DpDN7IV79F1/S3/jDuMNgCNX8pe9TTl29hYWbdqNP26bYOGsCPIs4YcSMRYgWx6lsf/fJCzSoWgHLJo/Cmum/wcbSHMP/WogP0THQFCK/yjBt1Q3xJ/YgYt7vSA99C8sB4yAwMlHZPnr9fLyfPFC+fJg9BtLMTCTfv44fxaBcNZi36wXx4X8RNmM00oMDYTNsMgTGpirbR66Yg+CxveVL2LQRvA9Jd6/maqvvVwl6bt7IiI3Cj1QQ3ntnLl/D0nVb0LNjG6yZ/xc8XV0wetosxMSKVbZPSU2Fg50NBnTvCAtzM5VtrC0tMKBbR6ye9ydWz/0TZUsWx+8z5/EghzpcuHABq1etQucuXXhQwd3NDZMmTszz8/3p06eYPWsWGjRsiMVLlqBKlSqYPn06v6mR09UrV+D//DksLWXB2Zy6duuGLVu3ypcWLVpAnQ7u2Yrjh3aj75Ax+HPeauiJRJg5eRTS0lLz3OfqxdPYvGYx2nXqjZkL16GImyffRxyr/L6r07AFVmw+KF869x6C7+HU/vU4f3Q7OvWfiDEztkBPTx+Lpw9C+if6lJaSDCdXH3ToOz7PNhHvgzB/Yk/YOrph5NQ1mDBvN5q06w8dXV2o24n9G3D26DZ0GTAB42Zuhp5IH4umD/50n1JZn7zRqV/efcpy78ZZvH7xEGYW1vheCmKfCFFEgQry2cqWLcuDFXv37pVvY/9nQYqcWRAs6LBz505+B6Vp06ZKmRaK2BcNOzs7lC9fHnPnzuV3Um7cuAFNEHHiIl5MWYDwA6eh6Ww7dkLEoQM84yElMBBv/54NSUoKrJo1U9neqERJJDx6iOhTJ5H2Pgxxt24i+vQpGBYrptQm9vJFiK9d5W1YdoL45k0YFS32ffr0cydEHj6IqGNHkPI2EG/nzYEkJRVWTfLoU/GSSHj8CNGnWZ/eI+72TUSfOQVDX4U+FS+J2CuXIL7O+vQeMRfO8b4rtlEnUZX6SL17GakPriIzMgyJR7YC6WnQK1NNZXu2XUvfEPE7lyEjKAAScRQy3r5AZnj2xUX6i4c8e0IS/YEvyef2Q5qWCm1Hd/wo24+cRsu61dG8djW4OzlgXN8uEOnq4tC53Be4zB/D+6Bdw1rwdnWGq6MdJgzsDolUituPnkNTGNVqiqRrZ5F88wIywkMg3rWWZ+oYVKqlsr00KRGSeLF80fMuCWl6KlIe/Li/b8b1miPhyikkXjuLjLBgRG9bCUl6Koyqqr6bJklKgCQuVr6Iipbm762kO8rnUWhmAfMOfRG1bgGQmYkfqSC89/49cBTNGtRGk7q14OrshF8H9YFITw9HzlxQ2b6olwcG9+yCuj9Vha626kTZahXLoUr5MnB2sIezoz36de0AfZEIT/xfqqUP+/btQ6PGjdGgQQO4FCmCocOGQU9PDydPnlTZ/sCBAyhXvjzatWvHv1N0794dHh4ePCtDUWRkJJYvX44xY8dCKBSqPJaBvj7P0sxaRCIR1HlH+9iBf9G6Qw+Ur/wTDzgMGTUJMdGRuH3tUp77Hdm/E3UaNket+k3h5OLGgxy6eno4f0o5E4m9ZmbmlvLFwMBQbX1R7NPZI1vRqG0/lK5Ym1/U9hj2J7/7/uDm2Tz3K162Olp0Ggq/SnXzbHNw22Lerk23kXB2LwprO2eUqlALxqaqg0752aczh7eiSbt+8PvYp17DpiM2JgL3b57Lc78SZaujVeehKPOJjAMmJiocO9bMQp8RMyAUfp9k9YLYJ02gJRRo7FIYFc5ek6/Wu3dvrF+fnSa/bt069OrVK1e7f//9F76+vjzNs2vXrrwd+6P6Kfr6+vzftKw0ffJZtLS1Yejtg7jbt7I3SqV8nV2Yq8Iu6A18fGH4Meig5+AA08pVeVBCsY1JuQrQc5alr+p7esK4VGnEXr/2/fp0J0ef7tyCYfHcqej8+T55BANvH3nQQdf+Y59uXFNqY1K2PPScPvbJwxNGJUsrtVEbgRDa9i5Ie/NMYaOUr+s4qQ4q6HqXRkZwAAwbd4L5qLkwHTgF+tUbA1p5jKHU0oJu8QrQ0tFFRvBr/AjpGRl4/vodKpYsKt8mEAhQoaQvHr38vOeUkpqGzIxMmBip/0v5ZxEKoePkhtQXCsNppFKkvnwMnSJen3UIFtBIvneNX+j/EEJt6Lp4IOXZw+xtUilf13XPOx1fkWG1uki6fVm5D1pasOw5AvGn9iM9LAg/UkF476WnZ/AhHOVLlVDqQ7nSJfItqJCZKcGZS1eRkpKKEr6f9/79Eunp6Xj18iX8/PyU+sDWnz9T/PuXjW0vo9CeYcNBFdtLJBJ+Q6Ntu3YoUiTv1PZdu3ahw88/Y+iQIdi9ezcy1Rg8+xAeitiYKJT0Ky/fZmBoxIdwvHiuevhdRno63rzyR0m/CkqvDztGzn0unz+Ffp2bYPTgrti+YTlSU1KgblEfQhAXGwnfUpXk2/QNjeHqVZLfXf9a7Pw9vnuJDx9ZPH0gxvauhTnjuuD+J4If+SUyXNanojn65Mb65P/gm47N+rV+0UQ0aNkDDi6qh2epQ0HsEyE5FZ4QGckXLOgwfvx4vH37lq9fuXKFDwc5f/58rmEfrC3DalSIxWKeClqrluq7jywdlKV5GhkZoWIeY1KJatqmZvzCPj06Wml7enQMREVcVe7DMinYfr7LVvILDYG2Nj7s24uwzRvlbcK2bILQ0BAlt+6EVCKBlkCAkFUrEH3qxPfrU4xynzJioiFyUf0FlWVSaJuawmfJiuw+HdiL91uy+/R+6yYIDQxQYvOO7D6tWcn3VTctAyNoCYSQJiqnoEsT46FlpXqYhtDcGgI3X6Q+uoG47YsgNLeBYZPOPOiRfDH7zpvQxhGmvX8DtHX4RWT8v8t5xsaPEBuXgEyJBBamysNZLExN8Db0/WcdY+nWvbCyMEUFhQvOH0lgaAItoRCZ8cqp9yxTQtfG4f/ur+PiAR0HF8TuXIUfRWhkLOtDnHLqPRtGpGPn+H/313X1hK5jEURvXqq03aRBa0glmYg/q1yH6EcoCO89cXw874O5mfJwHAtTU7wLVj2E8nMFBL7D4HFTkJaWzrMp/hw3kmds5Le4uDh+oWNurjxMz8zcHEHBqoeasDoWZiras+2KAQihQICWLVvm+bNbtGwJTxZUNzbmw0k2btiA6OhoXstCHWI/fkaZmlkobWfrsXkMg4pjGUqSTJX7hAS/k69Xq1Uf1tZ2MLe0wrs3r7Btw3KEhrzDrxNmQp3EMbI6JiYf6x5lMTG15BfGXyteHI3UlCSc3L8OzTsORauuv+Dp/StY/fcojJi6Bt7Fs4M9+S3reefukwXE3zhc7cT+9RAIhajTtDO+p4LYJ0JyokAF+SLW1tbyoRwsQ4L938rKSqmNv78/bt68yVM/GW1tbXTo0IEHL3IGKqpWrcrvJCQmJsLd3Z0PF7G1tVX5s1NTU/miKF0qgY4WJQZ9KeMyZeHQrQfezvsbiU+fQM/JCS4jRsI+spe8WKZFnbqwrN8Qr6dNRvKbNzDw8oLL8JG8qCYrQKlpjP3KwL5LD7z7529eRFTP0QnOw36Bffde8mKZrO4F79P0KUgJfAN9Ty+4DP0F6axPJzSvTyzgIkmMR+Lhzfzud2bYOwhMzKBfpaFSoCIz8j1iV06HlkgfekXLwahlL8RtnPvDghXfYuP+47zOwLIpv0JPVwcFAcumYMU38yq8+V9gWLUeL76pWHhTx8UdxnWa4v2M0SgICuJ7T5GLowPW/jMTiYlJOH/tJmYsWoHFf01SS7Aiv718+RIHDxzAosWLoZVXRhmANm3ayP/PCnbraGvzGhm9evbMlzoIl8+dwOqlf8vXf5uS/f/8xgpnZnFx9YCZhRX+nDAc78OCYWeff+fs5sUj2L5qunx90PglUAepVML/LVWhNuo278b/7+zmy+/+Xz65K18DFTcuHuFFILMM/X0x1OFtwFOcPbINE/7e/sn3ZX4oiH3SRAJh4euzJqNABfmq4R9Dhw7l/1+6VPnuGsMCEhkZGXBwyL7TyIIabKzlkiVLYGqafaeIBSaKFSvGa1WYmakuApZl5syZmDZNubBlJy0LdBEqB0oKGza7hTQjg8+MoUjHwhzpUaqj6o59+yPyxDFeA4JJfh0AoUgfRcaOQ9imDfyi2HnwMIRt3YToM6flbXTt7GHfrbvaAxXyPpkr90nb3ALp0ar75NCnP6JOHkfkkUPy5ysQiVBk9DiEbf7Yp0FDEbZ1M2LOZvdJz9YOdl26qz1QIU1K4HeetQyViy9qGRpDmqC6SJ4kQcyLF7LnrhiU4MUPBUJA8jGlWZIJSUwE/29S2DsIHVwhqlQXiUe24HszMzHidz2jxfFK21kxQ4scd4lz2nLoJDYdOI4lE3/hszVoCkliHD8PfLYPhe3sPOTMUMhJS1cP+mWqIv74LvxImQnxsj6YKP+dFRibfVYfDCtUg/jQDqXtIs9i/DVwmJGdKcKyNsza9YBx3WYInZBdyPZ7KAjvPVNjY96HnIUzo8XiPAtlfi4dHW042dvx//t4uuP5ywDsOnQcYwb3RX4yMTHhNyAUsyGY2JgYWOTImsjCsi9iVbTPysp48vgxz7zs0b27/HGWtbFmzRrs378fGzZmZ84p8vH15UM/wj98gJPTt5/XcpWqw9OnuHw9PV02VFUcGw1zi+zvImy9iJvqYTUmJmYQCIS8jSK2bpbjM09R1owg4aEh+RqoYDUi2LCOLBkZsj7FxUbB1Dy7iGKcOIoXy/xaRsbmEAi1YZ9jqKOdoxsCnt9HfipdoRYfApElIz2vPkXD2dX7q3/Oy2d3eabI+AHZs+ywbJndG+fj7OGtmLHiGPJLQewTIf8PBSrIF2NDOVgdCRZpbdiwodJjLECxadMmzJs3jxfRUtSqVSts374dAwdmf3llxTlZwazPwYacjBo1SmnbWYtyKOzYBX3iC39eTyL20kXZRi0tvh6+V/XFEbuAV7z45cfJuuhlEXSplLeRSnLUFcnM5MMlvlefjMuV5wU9s54Xqy/xYd9ulfsI9ETyOzZyEolyn/RYv5XbyIaAfIcIuiQTGWHvoOPmi3T/rC9lWtBxK4qUW6oLX6UHvYJeCTYUij0/2bkQWtjIZv3IOl8q8LsgP6j4FbuD6evugluPnqFmBT/5BcWtx8/RvmHtPPfbfOAE1u87ioW/j0BRD9VDln6YzEykB7+BrncJpDy+LdumpQU9r+JIvPzpYUOi0pX4MCZW2+GHysxA2rsAiHxLIfnBTdk2LS2+nnD+00E6g3JVoaWtg8QbysUcE2+cR8pz5THr1sMnIfH6BV6w83srCO89Fkzw9nDDnYdP8FPlCvI+3H34BK2bKH+mfitWNJTVxMhvOjo68PTywoP793nWJP9ZEgnu37+P5nnMwOFbtCh/XHGq0Xv37vHtTJ26deGXo2g3m0WkTp06qJ/ju4ai1wEBPGiieIPkW+gbGPJF8SYMK3L5+P4duLrLLg6TkhLxyv8p6jfO7osibR0duHn64PGD26hQpUZ2/YYHd9CwWds8f/bb17IaJWYW+Vt4UqRvyBfFPpmYWcH/0Q2e8cAkJyUg8OUj1Gjw9TNksX4X8SiO8BzTcX8Ie5vvU5Pm1afnj24q9enNy0eo2fDr+1S5ZjMULVVZadui6YNQqUYzVK2T9xClr1EQ+0TI/0OBCvLFWKXtZx8LXOWsun348GF+F6VPnz65vhi0bduWZ1soBiq+BMvIYIsidQ77YNOTGnpmT7tq4OYEk9K+SIsWIyVIs1Lqw3dsh9uESUh8/owPe7D9uQME+iJEHpGNG3ebOBnpEREIXrmcr8deuQy7Dp2Q9MIfCU+fQOTozLMsxFcuyy/uWRuH7j2RFv5eNvTD2xu2HToh8ujh79Onf7fDbfwkJD1/jsTnT2DbrqOsT8dkP9/1d1mfQlbL+iS+epnPFJL08gUfziJycoJD7/58u7xPVy/DvivrUziSA1/DwMsHtj93/G59Srl2CkateiEz9C0yQt9AVKkeL3yZev8Kf5wN2WBBiKSzsmFTqbcvQFShNgwadUDKzbMQWtpCv3oT/v8sBnVaI43N+iGOhpaeiAc2tF29kbx1IX6UTk3r4Y9lG/hFXzEPV+w4eoYXKWxWS3bRMnXJelhbmGFIZ9kXeXYne9W/h/gMDA42loj6eDdZX6QHAzVW7P8SCeePwLzzIKQHvUb621cwrNmYZxokfbx4N+s8CJniGMQfUc46MKhcGymPbvOMmh8t/vQhWPYchrS3r5Aa+BLGdZpDoKuHhKuy95Nlz+F8elHx/q1K+xlWrYuk+zchSVTuA1vPuY0FddgMIRnh31ZPoTC/935u2QQzF67gWQ9sRo9dh44hOSUFTerW5I//tWAZrD5ON8qwYENgULC8oGhkdDRevg6Evr5InkGxcvMOVCpbGrZWVkhKTsbpS1dx//EzzJ0yTi19aN26NebPmwcvLy94+/jgwP79fOhm/fr1+eOsKCbLpMwqxs3qTvw2diz27tmDChUr8ppWbLjHsOHD5VkabFHEvn+wjIusTAn2vYRNW1qqdGlenJsV4ly1ahVq167Na1aoAwsKN275M/bt3Ag7RyfY2Drg3y2reXZF+So/ydtN/304D0o0at6Orzdt1QHL//kL7l6+8PQuhqMH/uWFMmvWa8ofZ8M7rpw/hTIVqsDI2BTvAl9h0+pFKFrCj88sok6sT3WadsGxPat54UtLG0cc2rGU37UvXTF7poiFU/uhdKU6qNW4E19PSU5CxPvsGhtR4SEIevMchkam8kBE/ZY9sPafsfAsWg7eJSrwGhWPbl/EL9PWqL1PdZt1wdHdrE8usLJxxIHtS2Fmbs1nzMgyf2p/lKlYB7WbdFTZp8gPyn0yMjbjiyI2Q4aJuSXsHNUb9CyIfdIE3+XGFflsFKggXyXnF4YsLBBRr149lXcvWKBizpw5ePjwYZ77axLTciVQ5cxm+Xqxub/zf4M27cXDPv9//unvKfrsaWibmcGxbz/oWFgi6dVLvPh1JC8+yeja2rHbZ/L2oawOhVQKx34DoGttjfTYWB6YYMUys7z9Zx4c+/VHkV/HQMfcnNemiDi4H6Hr136XPsWcOwNtM3M49O4r79PLMaxPsvRgPRvb7IwJ1qfNsropjn2y+hQD8dUrCFmT3ad3C+fDsU9/uIwczYeVpEVG8D6FbVz3XfqU9vQ2kgyNoV+rBQRGJsgID0b8tkW8oCYjMLVQmh1HEheD+K0LYdDgZ5gNnMIvAFNunkHyleNKQ0dY8ENgZAppajKfOpPtk/5adXX976F+1Qq8sOGqfw8iKjYO3q5OWDB+OCzNZL/34VHRECh8Gdh76iK/wBo/f6XScfq2a4Z+7ZtDE6Tcvw6xkQmMG7XjwyfSQ94iauUsPjyHEZpb5cpSElrbQ8/dF1HLZ0ATJN25AoGxCUybd+J9SAt+gw+Lp/OioIzQwipXVpK2rQNEXsXwYaHysDtNVRDee3WrV0GsOA7rtu9GdEwsPN2K8IBC1vCV8IgoaCkE6SOjY9BnlOzzidmx/whf/IoXxaK/JvFtMbFxmLFgOaJiYmFoaACPIs78mBX8VM8M9a1q1qyJOLEYm7dsQUx0NNw9PPDH9OnyoRwRHz5AoDD+nQ0BHfvbb9i0cSOvgeXo6IhJkybB1dX1izI5WIBj69atfOYRVu+KZWi0UcjSUIcWbbsgNSUZqxfPQVJiAnyKlcK4P+ZBVzf7xkr4+xDEx2UP56laox7ixLHYtWUNL8hZxN2L75M19ENbW4dnXBw7KAtgWFrZoFLVWmjdsSe+h/qteiE1NRnbVv6BpMR4ePiWwdCJy6Cj0KeI8GAkKAwbexfwBAumZg8j2rNxLv+3cq0W6D5UVgODTV3aqd9EnNi3DrvWz4atgyv6jZ4Hz6Jl1d6nhq16Ii0lGVtWTOd98vQtg+GTlPsU+T4ICfHZQ5DeBjzB/Cn95Ou7Nszj/1ap1Rw9h2XX9fhRCmKfCFGkJf1/c0YSosGO6Hz9eElNZVNJ9Rje/zKtAlicyK2uer7g/0jCFgWvwnfSRuUL0IIiI/kHTXOqRiYDZXfPC5JUPfXcyf+REvQK3mdUXKbm3zz5UtEpRihohFp0yfJfUauEPv6LblTJnu5V01S6dgOFDWVUEEIIIYQQQggp1GjWD81C8zoSQgghhBBCCCFEY1CgghBCCCGEEEIIIRqDhn4QQgghhBBCCCnUCmJNtf8yyqgghBBCCCGEEEKIxqBABSGEEEIIIYQQQjQGDf0ghBBCCCGEEFKoaQnoHr4mobNBCCGEEEIIIYQQjUGBCkIIIYQQQgghhGgMGvpBCCGEEEIIIaRQ0xLQrB+ahDIqCCGEEEIIIYQQojEoUEEIIYQQQgghhBCNQUM/CCGEEEIIIYQUagIhDf3QJJRRQQghhBBCCCGEEI1BgQpCCCGEEEIIIYRoDBr6QQghhBBCCCGkUKNZPzQLZVQQQgghhBBCCCFEY1CgghBCCCGEEEIIIRqDhn4QQgghhBBCCCnUtAR0D1+T0NkghBBCCCGEEEKIxqBABSGEEEIIIYQQQjQGDf0g/2k2lcxR0Hy4EYOCxqq8GQoaHSMDFDRiQxsUNPrGBe88MXHBUShotArg+88s5g0KmtfwQkGTlKGLguZdpAgFjbGB5Ec/BVLA0awfmoUyKgghhBBCCCGEEKIxKFBBCCGEEEIIIYQQjUFDPwghhBBCCCGEFGoCIQ390CSUUUEIIYQQQgghhBCNQYEKQgghhBBCCCGEaAwa+kEIIYQQQgghpFCjWT80C2VUEEIIIYQQQgghRGNQoIIQQgghhBBCCCEag4Z+EEIIIYQQQggp1LQEdA9fk9DZIIQQQgghhBBCiMagQAUhhBBCCCGEEEI0Bg39IIQQQgghhBBSqNGsH5qFMioIIYQQQgghhBCiMShQQQghhBBCCCGEEI1BQz8IIYQQQgghhBRqNPRDs1BGBSGEEEIIIYQQQjQGBSoIIYQQQgghhBCiMWjoByGEEEIIIYSQQo2GfmgWyqgghBBCCCGEEEKIxqBABSGEEEIIIYQQQjQGDf0gX0RL69MpUVOmTEHPnj3h5uYm32ZhYYFy5cph9uzZKFOmDN/25s0bTJgwAefPn0d0dDSsrKzkbXx9fdXeD5s2bWHXqSt0LCyQFPAK7/6Zh8RnT/Nsb9u+A6xbt4GerS0yYsWIPn8WwSuXQ5qWJmsgEMCxd19YNmgEHUsLpEVGIvLoEYRtXA9NY1G9PNx/7QPTsiUgcrDB7baDEX7wDDSRbdt2sO/SBToWlkh69RKB8+ch8Wne58muQ0fYsPNkZ4t0dp7OnUXQ8mXy8yQwMIBT/wGwqFETOhbmSHzxAm//mY/EZ8++W590SleHXvk60DI0hiQiFMnn9kDy/p3qtsUqQr9RZ6Vt0ox0xC8ao9BIF6KfmkPboyS09A0gEUcj7d5FpD+8qrY+HDh8FP/u3Y/omFh4uLli6IC+8PXxzrP9hctXsGHLdrwP/wBHB3v069kdlSqUkz8eExOL1Rs24c69+0hITETJ4sX5MZ0cHeRt/lmyHHfvP0BUdAz0RSIUK+rDj+Pi7KS2fooq1oZ+tUYQGJkiIzwIiUe2ISPkTZ7ttUT6MKjbBnrFykJL3xCS2CgkHNuB9JeP+OMGtVvAoHZLpX0yIsIQu3givhezhs1h2aI9hGYWSH37GuHrliLllX+e7c2btIZZw2bQsbJBZlwc4q9fQsS2tZCmp/PH9YuW5MfTc/fiv6fBc6Yi4Ra9977Vv6cuY/PRc4gSx8PL2QFjurdGCY8iKtvuO3cNRy7fRkDwe75e1M0Jg9s3UWo/deV2HL58S2m/KiV9sHjsALX1QSqVYt/2lbhwaj+SEhPg5VsK3QeOg52Dyyf3O330XxzbtwXi2Ci4uHqha78xcPcuLn/8Q1gwdmxYiJfP7iM9PR0ly1RB1/6jYWpmKW/zPuQtdm5chJfPHiAjIwPOrp5o03kgipYsn+99PLxzGa6c3ovkpHi4+/ihU/8JsLFXfa6Yl0/v4NSBDQh6/QzimAj0H/sP/CrWydUuLPg19m9ZwNtLMjNg5+SB/qPnwcLaHurG+nXp0CI8uLwLqclxcPQoi4adpsLC1jXPfe5e2IZ7F7dDHBXC163svVCt6WB4lKgpb3N862QEPruKBPEH6OgZwNG9DGq3GQ1LO4/v0qczexfj1vldSEmKRxGvMmjRcwqs7PLu04VDq/Dk9ilEhL2Gjo4ILl5l0LDDr7C2z/6Oy7x7eQ+ndi9EUMBDCAQC2BfxRc8xa6CjK6I+EfINKKOCfJGwsDD5smDBApiYmChtGz16tLzt6dOn+bYTJ04gISEBjRs3RmxsLP9iUb9+fYjFYuzduxf+/v7YuXMnSpYsyR9XN4s69eA8dARC16/Bkz49+AWw9/wF0DYzV92+fgM4DRyM0PVr8ahLJ7yZ9Rcs6taDU/9B8jb2XbrBulUbvP1nLm8TvHwp7Lt0hU27n6FphIYGiHvoj8fDp0GTsdfYZfgIBK9di8c9eyDp5Sv4/rMQ2uaqz5NlgwZwHjQYIevW4EHHjngz4y9Y1q0H54HZ58l9/O8wrVARAX9MxcOuXSC+cQO+i5ZAx9r6u/RJ27sMRDVbIfX6cSRumYvMiBAYthkILX2jPPeRpiYjfsUk+ZKwRvm8seNpu/oi+dgWJGyYhbS7FyCq0xba7tlf7PPTuYuXsWLNenTr1AErFs6Du5srxk3+AzF5/O4+efYcf82Zj0b162LFonmoVrkSpvw1C28C38r6J5Vi8p8zEfY+HNMmjseKhfNha2ONsROnIjklRX4cL08PjPllGNYtX4xZf0wGpMBvk6chMzNTLf3ULVEBho06IOn8QcSumIbM90Ew6T6SB5hUEgph0uNXCM2tELdzOWIWTUD8wY2QxMUoNcsID0HUnJHyRbx2Fr4X46o1YdNjACJ3bUHgb4N5oMJ5wgwITcxUtjepXhvWXfogatcWvPmlL8KWz+fHsO7cW95GoCdCCgt4rF2i9udfWN57J6/fwz/bDqBf64bYMn0UvF0cMGzOKkSL41W2v/MsAA2rlMWK3wdj/ZThsLUww9A5K/EhWvl1qVrKF8cXT5Uvfw3pBnU6um8TTh3eiR4Dx2PynPXQE+lj3rRhSEtLzXOfG5dPYse6BWjVsS+mzd8MZ1cvzJ02DHGx0fzx1JRk/D11KNg9k7F/LMeEmWuQkZGOBX+NgkQikR/nn79G8fPz2/TlmDpvEz/OP3+ORGxMZL728dT+9Th/dDs69Z+IMTO2QE9PH4unD0L6J/qYlpIMJ1cfdOg7Ps82Ee+DMH9iT9g6umHk1DWYMG83mrTrDx1dXXwPN06uxp1zm9Gw81R0/+1f6OjqY+fiPshIz7tfxuZ2qNVqNHqO34ue4/egiE9l7Fk+BBGhL+Vt7FyKo0mPmeg75Sg6DF/Lfguxc2EfSCTq+V1SdOnIGlw7tQUte07FoCk7eaBkw9/9Pnmu3jy/hcr1OmPg5B3o9dtaZGamY8OcPkhLTVK6oN8wtz88S1TDoKk7MWjaLlSu1wVaWuq/xCqIffrRtAQCjV0Ko8LZa/LV7Ozs5IupqSnPsFDcZmSUfcFlaWnJt5UvXx5z585FeHg4bty4gSdPniAgIADLli1D5cqVUaRIEVSrVg1//vknX1c3246dEHHoAM94SAkMxNu/Z0OSkgKrZs1UtjcqURIJjx4i+tRJpL0PQ9ytm4g+fQqGxYoptYm9fBHia1d5m5jz5yC+eRNGRbPbaIqIExfxYsoChB84DU1m36kTPhw8gMgjh5Ec+AZv5syCJDUF1s2aq2xvVLIU4h89RNRJ2XkS37yBqFMnYVRMdsGupacHi1q1EbR0CeLv30dqcDBC1q7h/9q2bvNd+qRXrhbSH19D+pObkESHI+X0Lkgz0qBTolLeO0kBaVK8wpKg9LDQwQ1pT24hM/gVpHHRSH90jWdqCO3yvqP3LfbsP4gmDevzi78iLs74ZchA6Onp4fgp1Vk5ew8eRoVyZdChbWsUcXZGr26d4enhzu+MMyGhoXjm/wIjBg+Ar7cXnJ0c+f/Zxcy5C5fkx2nWqAFKlSgOO1sbfuHIjhMREYnwDx/U0k/9qg2QcuciUu9dQWZEGBIObYY0PQ2istVVtheVqQ6BviHiti1BxrtXPJsiI/AFMsODlRtKMiFNiMtecpxPdbJo1hbiM8cgPn8SacHv8H7VQkjSUmFap6HK9vo+xZDs/wRxl88hPSIcSQ/vIP7KOYg8feRtEu/fQuSODUi4eUXtz7+wvPe2HruAVrUqo0WNinB3tMP4Xu0g0tPBwYs3Vbb/c3BXtK9XDT5FHOHqYIuJfTtAKpHi5tPsC0RGR1sbVmYm8sXE0ADqwoJAJw9tR4ufe6NspZo8UNBvxDTEREfi7o0Lee534sA21GzQCj/VbQFHZ3f0GDQeunoiXDxzkD/OMiQiI8LQd/gUniXBln4jpiLw1TM8eyTLGImPi0V46Ds0bdOD/1yWwdG++1CkpaYg5F1Avvbx7JGtaNS2H0pXrA0nV2/0GPYnz5J4cPNsnvsVL1sdLToNhV+lunm2ObhtMW/XpttIOLsXhbWdM0pVqAVj0+ysEXVh/bp1ZhOqNh4Eb796sHHyRbNec5AQ+wEv7uf9vcGrVB14lKzJsy4sbN1Qs9VI6OoZIPTNfXkbv586wMWrAsysnHjQokaLXxAXEybPwlBnn66c2IRaLQaiWLm6sHPxQfsBsxAf+wHP7ubdp55jVqPsT61h6+QFexdftOs3E7FRYQh580Te5ui2WahSvytqNu/H27HMhJKVGkNbR71BpYLYJ0JyokAF+S709fX5v2lpabC2tuZpZLt371bbHam8aGlrw9DbB3G3FVJgpVK+blS8pMp9Eh4/goGPLww/Bh30HBxgWrkqD0ootjEpVwF6zs58Xd/TE8alSiP2+jV1d6lA4ufJx5cHheSkUohv3YJxiTzO06OHfJ+sABI7T2ZVqyL2muwCSkso5MdlF2aKJKmpMC5dGmonEEJg64SMty8UNkr5utA+7zRN6OrCqO9kGPWbAv0WfSCwtFN6ODP0DXQ8SkDLyJSvC509ITC3Rsbb5/neBZYN9eJVAMr6Zb9e7He5rF8pPH2uevgA267YnqlQ1g9Pn8teh7T0jI/d1FE6po6ODh4/VT0kh93tPn76LOxsbWFtZYV8JxRC274I0gMUfr5UivSAp9B2Up2irOvrh/SgABg16wKLsfNhNuQP6NdowsbLKR/a0hbmo+fB/JdZMGrbDwJTC3wX2toQuXsh8eG97G1SKZIe3oO+d1GVuyT7P+X7ZAUmdGzsYFimIhLvqr5gVqfC8t5Lz8jA88BgVCrurfScKhb3xsNXgZ91jJTUNGRkZsI0RyDizvNXqD94MtqMmYmZ63cjNj4R6hIRHgJxTBSKlaoo32ZgaAQP7+II8H+ocp+M9HQEBjxX2of1vXjpigjwlw2fSk9Pgxa0lC6YWJYBu9P74ukDvm5kbAo7xyK4cv4Iz8DIzMzA+RN7YWJqAVcP1e/1rxH1IQRxsZHwLZUdaNY3NIarV0m8fqG6j5+DZYY8vnuJDx9ZPH0gxvauhTnjuuD+J4If+UkcGYzEuAi4Fq0q3ybSN4aDW2mEvFb4+/EJLEPi6a0jSE9LgqObbMhvTuwO/sOre2Fq5QQTc+XPtfwWExGMBHEkPIpXkW8TGRjDyb0U3r2SvW8+R0qyLKvJ4OPnbUJcFB8aYWRiiZV/dMKModWx+q9uCPS/A3UriH0iJCeqUUHUjg3nmD59Os+2qFixImxtbbFo0SKMHTsW06ZN4xkXtWvXRpcuXeDu7q7W56JtasYvVtOjZWmkWdKjYyAqovpikWVSsP18l63kFx0CbW182LcXYZs3ytuEbdkEoaEhSm7dCalEwlO0QlatQPSpE2rtT0GlbZbXeYqGfhHVmQIsk4Kdp2IrVsnPU/jePQjdKDtPkqQknnHh2Ks3kgMD+bEs6zeAUYkSSAnOcddbDVjNAi2BkGdFKGLrQgtblftIYj4g5cQOZEaGQktPBN1ytWHYcQQSNs6CNEHM26Sc2wNRvQ4w7j8NUhb4k0qRcmonMkNe53sfxHHx/Eu0uZnsC00WczMzBAWrviPGagCwxxWZmZkhOlY2JMLFyRE21tZYs3ELRg4dBJGeHvYcOISIyCheE0DRgSPHsHr9JqSkpPC733P+nMIvKvObwMCYB7YkiXFK29m6Th7jw1lwSMetKFIfXod480IILW1g1KwrINBG8nnZ3eD04NfI2LcOmZHvITA2hUGtFjDtMw6xSyZDmpY91EAdtI1NeJ8yxDmGoohjYOAoC7DmxDIphMamKDJ9PnsH89/JmJOHELVvB763wvLeY8GDTIkEFqbKQ4wsTIwRGPp5GRyLdx6GlbkpD25kqVLKF7UrlISjtQWCw6OwdNdRDJ+7CuunjIBQDSnFrL4Eo1g3gjExteQBDFXi42P5Ba6pmXLwjgUYwoJlQRoPn5LQE4nw78bFaNdtCP979++mJXw/8cdhHSzTc+y0pVg0cwwGdqrJgxgmpub4dcoiGBqZ5F8fP/48ExV9ZAGMrxUvjkZqShJO7l+H5h2HolXXX/D0/hWs/nsURkxdA+/i+VtnI6eEuAj+r6GJcr8MjS2RGPfpfn0I8cfmOR35EBGWTdFmwFJYOXgqtbl7fivO7ZuL9NQknnnRccR6CLXVe6c+Xix73kY5MlKMTK2QECvr7//D/v4c2TITRbzKwtZJ9rsV/SGI/3tm3xI07jSWZyjcu3IA62b3wvAZBz9ZK+JbFcQ+aQKBkKYn1SQUqCBqU7VqVX43JDExkQcgWB0KFqRghgwZgu7du/NimtevX8euXbswY8YMHDx4kNevUCU1NZUvitIkEuiqedyWcZmycOjWA2/n/Y3Ep0+g5+QElxEjYR/ZS14s06JOXVjWb4jX0yYj+c0bGHh5wWX4SF5UM+q4LM2YQP3nqUdPBP49BwlPn0Dk5IQiv4yCQ6/eCF2/jrcJmDYV7hMmouyhI5BmZCDxhT8fHmL4HQq4fo3MsEC+ZEkOfQOjnuOhW6oqUq8e49t0/WrwjIyk/ashiYuG0MkDorptIUkUI/OdYvaGZtLW1sbUCb9h3sIlaN2x28e75KVRsVxZSNm4FwV1a9VAOb/SiI6Jwa69BzB91lws/HsmdL/TuO1PYRdHLJCRcHAjv3jKDHuLJBNzGFRrmB2oePlY3p4NCYkLfg3zUXOgW6I8Uu9ehqYxKFYKlm064v3qxUh59Rw6do6w7TUIGW2jEbVnK/7rCsp7T9GGQ2d4jYuVvw+BnkKmSMMq2Xe1PZ0d4OnigFa//oU7z14pBTS+1tULx7Bx+Uz5+siJ/0AdWMBhyJhZ2LhiFk4f2cmDEJV+aoAi7r7yMdwsJX7zqjm87e8zVkNHVw8XT+3ndSym/L0RZhZflwlz8+IRbF81Xb4+aLx66rJIpbJaG6Uq1Ebd5rI6Is5uvnjt/wCXT+7K90DFkxsHcXzbFPl6+yErv/pYlrZu6D1hP1KT4/H87gkc3vgbuozaohSsKFapBVyLVuMBkZun1mL/6l/Qbcx2aOvoIb/cv3oIB9ZPla93/3X5Nx/z0KY/EB7yEv0nZv/tY+81pmKdDihXQzaE1MG1GAKeXsedi3vR8OdRyC8FsU+E/D8UqCBqwwITxYoV47Uq2F2snIyNjdG8eXO+sPoUDRs25P/mFaiYOXMmz8BQ1NfZEf1dPr/yeoY4ll+gstk+FLEZINKjVN/lcezbH5EnjiHysOxiI/l1AIQifRQZOw5hmzbwixLnwcMQtnUTos+clrfRtbOHfbfuFKj4ChmxeZ0nC6RHKWdZZGGzeUQeP4aIQx/PU0AABCJ9uI0bj9AN6/l5Sg0JwbPBgyAQiXgGDDvnntP/REpIqNr7JE1OhFSSCS0D5TulbD3nnfs8SSTI/BACgdnH4p/aOtCr3hTJB9ch441sNhRJZBiE1o7QK18bSfkcqDA1MeYXczGxsmyOLKyYobm56oKMbHvOYocsy8pCoXitt6cHVi7+h8+6wCr0m5maYuiosfD2Uh5mYWRoyBc2I0NRH29+cXn52g3UqflTvvZTwmqBZGZCYKh895WtS+LFqvdJEMszWrJkRoRCYGzGh5JAxTA3KUtLjwqH0MIG6pYRH8efn7apcjFatp7xsVBhTlYde0B88QzEZ4/z9dR3gbx4pt2AEYjau02pr+pWWN57ZsaGPMMhZ+HM6Lh4WJrlUcj1o81HzmHD4TNY9tsgeLlkz1qiipONJf9ZQeGR+RKoKFOxBjy8S8jXM9LT5JkVioGBOHEUXNxU/zxjYzMIBEKIc7wf48TRMDXPvmtcokxl/L1yP69FwdobGhljeM+GsLZtwB9/9vAW7t++jGVbzkDfQFY3y9VjHJ48uInL5w6jWdueX9VHViOCDeuQ9zFD1se42CiYmlsr9ZEVy/xaRsbmEAi1Ye+knGFq5+iGgOfZ9R7yi2fpOujtVjpXvxLjomBkmv23KTE+iter+BSWGWFuI8t6tCtSAmFvH+H2uU1o1OUPpWEkbGG1LBzdSmPBqIp4cf8UilVQXSfsaxQtUwfOHqVyvR8TxFEwMcvuExs6YV/k/w8HOrhpOvzvX0DfCZthapE9TMX442exjYPy3wsbe3eIo8KQnwpinwj5f6hGBVEbZ2dneHh4qAxSqLobyaYlZdkXeRk/fjyfKURx6en06S9jOWXdRWf1JBR+OF9PeCIbA5sTu6jN+YWcXXBm7ZvVhhUvU5KZWWir9H4rfp78n8OkvPJ5Mi1fAfGPVZ8nITtPClXfuaz1HHUCWPFUFqQQGhvDtFJlxFy6CLWTZEISHgxtFy+FjVrQdvFWypr4JDakxco+O7DBKkELtXNfMPL1/E9fZKnu7MLu7oOHSqmj9x48QjFf1V/M2fZ795XHa9+59wDFfHNfrLALQXahGBwSyusRVK2Ud5FR1kN215vVLsh3mZnICHsLHXeFL3taWnw9I1h1Mb70d69kAQeF95rQ0g6ZcbEqgxScrh6E5jZ5Bj/yVUYGUl6/hGFJv+xtWlowKOmH5Beq6zGwoETO36mcf/u+l8Ly3mMFL31dnZQKYbJ+3nryEqU880653nj4LNYcOIXFY/qjmLvqoTyKwqNjIU5I4kU184O+viFs7Z3li4OzOw8uPH2YXQ8qOSkBAS+ewMMn+2JLkbaODlw9fJX2YX1n62zIR07GJmY8SMEejxfHoExFWdAoNVU2jCrnDAXse0auz+kvINI3hI29i3yxd/KAiZkV/B/dUOpj4MtHcPdW3cfPwV6HIh7FER6q/LnwIeytWqYm1RMZ8eBC1mJl7wlDE2sEPs+usZWanIDQNw/4dKJfmh2SdUGt+nHZHfxPtfkaevqGsLQtIl9sHD35kIjXT6/L26QkJyD49UO4eOZdo4o9N3ZB//TOafQetx4W1so3xsytHGFsboOIMOVpqyPfv4WZ1Zd9Py2MfdJEWgItjV0KI8qoIN/d/fv3MWXKFHTr1o1nXLDU2QsXLmDdunX47bff8tyPVXdni6KvGfYRvmM73CZMQuLzZ0h89hS2P3eAQF+EyCNH+ONuEycjPSICwStlaXWxVy7DrkMnJL3wlw0pcHTmWRbiK5flX+JZG4fuPZEW/l429MPbG7YdOiHy6GFo4vSkhp7Z89gbuDnBpLQv0qLFSAnSnGh52Pbt8Jg0mZ+nhCdPYdexIw8IRRyWvabuk6fw8xS0fBlfj7l8CfadOiPxxQskPnkMPSdnOPXvj9jLl+TnyZRdfGhpIeXtW/64y9Bh/P+Rhw99lz6l3jkP/UadkRkehMz376Bbtia0dHSR/kT2RVfUqAuvPZF6WdZH3coNeRBDEhsJLT196JWvA4GJOZ/Zg0tLRUbQK+jVaAFpRjof+qHt5AmdYuWRcv6AWvrQtlULzPlnEXy8PODj7YW9Bw7zcfuN6skq2M+atxBWlhbo21OWstymRTOMGjeRp8tXqlCOTzHJLgRZTYAsFy5fgamJKWxsrPjUkctWrUXVyhVRvqzsojr0/Xucv3iFr5uamCAyKgo7du3lfzsqli+rln4mXz0J49Z9kBEaiIzgNxBVqQctXT2k3JUVZzVq04dPPZp0ei9fT7l5DqKKdWDYuBOSb5zhRTMNajRB8vXsGSkMGv6MNP/7fEYQlmlhUKclu/JHqsKFjjpFH94D+yFjkBzwkg/lMG/ahgcjxOdktXTsh45BRnQUIrbJhkol3L4O82ZtkPIm4OPQDwdYd+yBhDvX5b9TWiIRdO2yv7yygpt6ru7ITIhHRuTnjZP+XIXlvdelcU1MXbUdxdycUdzdBdtOXEByahqa15AVmZy8YhtszE0wtIPsDjTLoli55zif/cPeygKRsbJApoFIjy9JKalYve8E6lQoBUtTEwR/iMSiHYfhbGuFKiXVM+yNBQUaNO+EQ7vWwc7BGVY2jti7bQXMLaz4LCBZZk8ahHKVa6NeU9lU3g1bdsbqhdPg5lkU7l7F+cwhrCDmT3WzZ3u6dOYg7J3cYGJijlf+D7F17Xz+s+wdZYEcT99SMDQ0xuqFU9GyQ1/o6urh/Kn9iPgQitLlq+VrH+s07YJje1bzwpeWNo44tGMpz64oXbGOvN3Cqf1QulId1Grcia+nJCch4v07+eNR4SEIevMchkam8kBE/ZY9sPafsfAsWg7eJSrwGhWPbl/EL9PW5Nvz/1S/KtTtjqvHlsPCpggvdnnp4EIYmdnwWUCybP+nB7z96qNc7a58/fy+eXAvUQMm5vZIS03E05uH8e7FTXQYxqYhBWIjgvDszlG4Fa0GfWMLxMe8x/UTq6CtK4JHiZpq71O1ht1x7sAKfpFvbu2E03sWwdjMBkXLZvdp7axeKFauHqrU78LXD278Aw+vH0HXX5ZAT2SI+I+1H1jRSh1dET/uT41783oOrJaDfRFf3L20HxFhr9Fp2ALqEyHfiAIV5LtzcnKCq6srH8YRGBjI/yhmrY8cOVLtPz/67GlerNGxbz/oWFgi6dVLvPh1JDJiZOmmurZ2gMJdl1BWh0IqhWO/AdC1tkZ6bCwPTLBimVne/jMPjv36o8ivY6Bjbs5rU0Qc3I/Q9bIPaE1iWq4EqpzZLF8vNvd3/m/Qpr142Cfved2/NzaMRsfcDE59+0PH0hJJL1/g+chf5OdJj9U7UbjbG/JxeIfzgI/nKUZ2noJWZI/jFBoZwXngYOja2CAjLg7R588heMVyWcr+d5Dx4h5SDAyhV7UxtAxMIIkIQdLelfIpKgXG5pAoZEew4IR+/Q68rTQ1iQc4Ercv5FObZkk+shF61ZtBv0lXaIkM+MVz6uWjSH+onukia9eoDrE4Dhu27EBMTAw83N0w84/J8vT7DxEREChE/osX9cXvY0Zi/eZtWLdpCxwd7DFtwji4uWYXRY2OjsGKNet5Wr+FuTnq16mFrh3byx/X1dHF4ydPsffgISQkJPKCiiWLF8eiv2flKpaYX9Ie30KigTEM6rSCwMgEGe+DELf5H0g/ZrMI2WwdCueKve7sccNGHWA+eBok8TFIvn4ayZdktUT4PibmMG43AAIDQ0gS4/k0prGr/vpuU5TGX70AoYkprDt0h9DMHKmBrxH01wRkimXDI3SsbJT6FLlnK7/7Zt2pB7QtrJAZJ+bBi4jtsto8jL67N1ymzZWv2/YcyP9lU6CGLc3enh8Ky3uvQeUyiIlPwIo9xxEljoO3iyPPlLD8WGDzfVQMBAoZLXvOXEV6RiZ+W5Rd4Jnp17oBBrRpxF+Tl0FhOHzpNuKTkmFtboLKJXwwsF1j6Oqo72tgk9bdeZBh/bIZSEpMgHfR0vh18iIeOMjy4X0IH8KRpVL1BogXx2Lf9pW86CYbJsKKYCoW5QwLeYtdm5ciMSEOVjYOaN6uFxq26KyUacH22bNlOWZPHozMjAw4urhjxPi5eQ47+Vr1W/VCamoytq38A0mJ8fDwLYOhE5fxuhhZIsKDkaDQx3cBT7Bgal/5+p6Nst+TyrVaoPtQWQ0MNnVpp34TcWLfOuxaPxu2Dq7oN3oePIuqJziWU6UG/ZCWmozjWycjJSkOTp7l0GHYGqU6EjERQUhKyC46mxQfhcPrf0Ni3Afo6RvD2tGHByncismCQ0IdXQS9vI1bZzbyY7Jinc6e5Xl9ipyFO9Xhp6Z9eZ/2r5/Cfz4rINlz9CqlcxX94R2S4rP7dPOsrHDwmhk9lI7Vtt8MPsUnU61RD54Rwqb0TEoQw97FB73GroWlbfYNIeoTIV9HS5pVNYWQ/6Bb1SujoPlwQ7nafEFgVV49X+h/pKLts6cEKyjEzfujoNHfPA8FUeQzWWX2gsRwRsG7W2cWo5w+XRA8Ns7fuhyaIClDs4qi5od3kSIUNMYGOYZ3Eo3VrtJ/c+jzm94toKnc1slqsH2upUuX4u+//8b79+9RunRpLF68mM++qEqtWrV4dntOTZo0wZGPGec9e/bExo8z6WVh9QWPH5fVsVIHyqgghBBCCCGEEEIKyIQGo0aNwooVK1CpUiUsWLCABxX8/f1hY5O7gPfevXuRlpZdKyYqKooHN9q3z84wZBo1aoT167OzKnMOyc9v/81wFyGEEEIIIYQQQpTMnz8f/fr1Q69evXg9QBawMDAw4PUAVbGwsICdnZ18OXXqFG+fM1DBAhOK7czNlWcSy28UqCCEEEIIIYQQUqgVhFk/0tLScOfOHdSrl11UlU3vzdavXcuezedT1q5di44dO8LQ0FBp+/nz53lGho+PDwYNGsQzL9SJhn4QQgghhBBCCCEaKjU1lS//b0bEyMhIZGZmwpYVnVfA1p8/f/5/f87Nmzfx+PFjHqzIOeyjTZs2cHNzQ0BAAH7//Xc0btyYBz+EQiHUgTIqCCGEEEIIIYQQDTVz5kyYmpoqLWxbfmMBipIlS+YqvMkyLFq0aMEfa9WqFQ4fPoxbt27xLAt1oYwKQgghhBBCCCGF2pcMsfjexo8fzwtkKlJVzNLKyopnOISHZ09lz7B1VlfiUxITE7Fjxw788ccf//f5uLu785/16tUr1K1bF+pAGRWEEEIIIYQQQoiG0tPTg4mJidKiKlChq6uLcuXK4cyZM/JtEomEr1epUuWTP2PXrl18eEnXrl3/7/MJDg7mNSrs7e2hLhSoIIQQQgghhBBCCoBRo0Zh9erV2LhxI549e8YLX7JsCTYLCNO9e3eeoaFq2Acb1mFpaam0PSEhAWPGjMH169cRGBjIgx4tW7aEp6cnn/ZUXWjoByGEEEIIIYSQQk1LUDDu4Xfo0AERERGYPHky3r9/Dz8/Pxw/flxeYPPdu3d8JhBF/v7+uHz5Mk6ePJnreGwoycOHD3ngIzY2Fg4ODmjQoAGmT5+uMqsjv1CgghBCCCGEEEIIKSCGDh3KF1VUFcBkU45KpVKV7fX19XHixAl8bwUjbEQIIYQQQgghhJACgTIqCCGEEEIIIYQUapo860dhRBkVhBBCCCGEEEII0RgUqCCEEEIIIYQQQojGoKEfhBBCCCGEEEIKtYIy60dBQWeDEEIIIYQQQgghGoMCFYQQQgghhBBCCNEYNPSDEEIIIYQQQkjhpkWzfmgSyqgghBBCCCGEEEKIxqBABSGEEEIIIYQQQjQGDf0ghBBCCCGEEFKoaQlo6IcmoYwKQgghhBBCCCGEaAzKqCD/aVrCghf5tCpvhoIm8nYsCprMZqkoaDK1Ct5HQkp0HAqi1PgUFDSiAvj+k2jroaAx1UlAQZMpNUVBYyiSoKDRFUp/9FMghHxHBe9bASGEEEIIIYQQ8gW0BDTYQJPQ2SCEEEIIIYQQQojGoEAFIYQQQgghhBBCNAYN/SCEEEIIIYQQUqjRrB+ahTIqCCGEEEIIIYQQojEoUEEIIYQQQgghhBCNQUM/CCGEEEIIIYQUajTrh2ahs0EIIYQQQgghhBCNQYEKQgghhBBCCCGEaAwa+kEIIYQQQgghpFCjWT80C2VUEEIIIYQQQgghRGNQoIIQQgghhBBCCCEag4Z+EEIIIYQQQggp1Gjoh2ahjApCCCGEEEIIIYRoDApUEEIIIYQQQgghRGPQ0A9CCCGEEEIIIYWbgO7haxI6G4QQQgghhBBCCNEYFKgghBBCCCGEEEKIxqChH4QQQgghhBBCCjUtLZr1Q5NQoIJwPXv2xMaNG3Ntf/nyJTw9PVHQWLdqC7uOXaBjYYGkgFcIWjgfic+f5tnepl0H2LRsDV1bO2SIYxFz/hyCVy+HNC1N1kAggEPPvrBs0BA6FpZIi4xA1PGjCNu0/rv1ybZtO9h3YX2yRNKrlwicPw+JT/Puk12HjrBp3QZ6drZIjxUj+txZBC1fJu+TwMAATv0HwKJGTehYmCPxxQu8/Wc+Ep89g6axqF4e7r/2gWnZEhA52OB228EIP3gGmkivXE3oVa4PgZEJMsODkXRyJzJD3+bZXktPH6JaLaHr6wctkQEk4mgkndqFjIAn/HFR1YbQ8fGD0NIO0ox0ZAQHIPnsfkiiw9XWh4OHj2D3nr2IjomBu5sbBg8cAF8f7zzbX7x0GRu3bEF4+Ac4OjigT6+eqFihvPzxhk2bq9yvb+9eaN+2Df//lGnTEfDmNWJjxTA2MkIZv9L8OJaWllAXwxoNYVy3BYQmZkgPeYuYXeuQ/vaVyrbWI6ZCz6t4ru3Jj+8iasVMQCCEafOOEBUvC6GlDaQpSUh5/gjig1shEcfge7Fs3ho27TpC29wCya8DELJsIZJf5P07bdWqPSybtYSutS0y4sQQXzqPsPWrIE2X/Z2w7doLdl17Ke2TEvQW/v26FYj33vvwcGzbvhP3Hz5ATEwsLC0sUKd2LXTq8DN0dHSgLrtOXsCWQ6cRJY6Dl4sjRvf8GcU9XVW23X/mCo5cuoHXwaF83dfNBYM7tJC3z8jIxPJ/D+Hq/ScI+RAJI319VCjpg6EdW8LawizfnrNUKsX2Letx+sQRJCYmwLdoCQwYMhIOjk6f3O/o4X3Yv2cnYmOi4ermgb4Dh8Pbp6j88bS0NKxfswyXL55DRnoa/MpWwIDBv8DM3II//ub1K+zdtR3Pnj5CfJwY1jZ2aNikOZq3bCc/xuOH9zFp/MhcP3vd5j0wt7D45n4f2L4CF0/vQ1JiPDx9S6PbgN9h6+CS5z7+T+7gxP5NCAx4BnFMJIaMm4eylWortTmwYwVuXj6J6Mj30NbWQRGPomjTZQjcvUt+0/P93D6d3bcYty/sQkpSPFy8yqBF9ymwtFP9HmQuHF6FZ3dOISLsNXR0RHD2LIMGP/8Ka3s3/nhMRAjmj6mnct8Og/9BiYqNoO4+ndyzBDfO7UJyYjxcvcugTe/JsP5En84eWIVHt08jIvQ1tHVFcPXyQ5OOv8LGQdYnZvfaKXj5+DriYj5AT2SAIl5+aNqJtXFXa38Kap8IUUSBCiLXqFEjrF+vfGFtbW39xcdhXyp0dXWhqcxr14XzkOF4O38OEp8+gW37DvCa+w8ed+2IjNjcFwwW9RrAqf8gBM6ZgYTHDyFycoHr+ImQQorgpYt4G7vO3WDdsjUCZ05HcuBrGPoUheu4CchMTMCHPbvU3ieLuvXgMnwE3syZjcQnT3gQwvefhXjQ8WdkxOTuk2WDBnAeNBivZ/yJ+IePoO/iAveJk9inHt4tWsjbuI//HfruHgj4YyrSIiNh1bARfBctwcPOHZEeEQFNIjQ0QNxDfwRt2IPyu5dCU+kULQf9em2RdGw7MkLfQFSxDow6DkfciqmQJsXn3kEghFHn4fyxhD2rII2PhcDUkl/kZtF28ULqnQvICH0LLYEA+rVbwqjzMMSt/AP4eDGZn85fvIRVq9dg2NAh/AJx3/6DmDBpMtauWgEzs9wXPU+ePsPMOX+jd88eqFShAs5duIBpf/6FpQsXwNW1CG+zffMmpX1u3bmDfxYuQvWqVeXbSpcqiY4d2sPCwgKRkVFYvXYdps+YhQXz/oY66JetCrPWPRCzcxXSAl/BqHZTWA+ZgPd/jIAkIS5X+8jVc6ElzP5IFRgawXb8XCTfu8bXtXT1oOPsjrhju3nQQ2BgCLN2vWA14Dd8mDMO34NZjTpw6DcEwYvnIcn/KaxbtYf7X3Ph37cLD8Dmal+rHux790fQ/NlIfPYYeo7OcPl1PPt6jNBV2b9n7G/e6/Gj5OvSzEy1PP8f8d4LCgqGRCrBiKFD4GDvgMC3b7Fg8RKkpKSgf98+aunnqWt3sGDzXozr05EHG3YcO4fhs5Zg17wpsDA1ztX+zrMXaFi1PEp5u0FXRwebDp3CsJlLsOPvibCxMENKWhr83wShd+tG8C7ihLjEJMzfuAu/zl2JTTN+y7fnvW/3Dhw5tBfDR46DrZ09tm1ehz8mjcWiFRvy/E5w+eJZrF+9HAOHjuTBiUP7d/N9lqzaBDMzc95m3eqluHPrOsaMnwJDA0OsWrEIs/+ajJlzl/DHA169gKmZGX4Z/TusrGzw/NkTLF8yD0KBEE2at1b6eey4BvqG8nW237c6tm8jTh/Zjj7D/4CVrQP2b1uO+X8MwZ+LdkNHV0/lPmkpKXBy9Ub1ui2xdPZolW1sHYqgS7/fYG3riLS0VJw6tBXzpw3BzGUHYGwqe23U5dLRNbh+agva9JsJc2snnNm7CBvn9cOwvw7n2afA57dQsU5nOLqXgCQzE6d3/4ONc/tg+IzD0NUzgKmlHcYuuKi0z+0L/+LysXXwKvUT1O384bW4fGILOgyYAQsbJ5zYtQhrZvXH6DmH8uxTwPPbqFqvE5w9ZH069u8CrJ7VF2PmHIKuyIC3cXIrjrJVm8PMyh5JCWKc2ruUtxm/4BQEAiH1iZBvQDUqiJyenh7s7OyUFqFQiAsXLqBixYr8cXt7e4wbNw4ZGRny/WrVqoWhQ4fil19+gZWVFRo2bMi3P3nyBM2aNYOJiQmMjY3x008/ISAgQL7fmjVrULRoUYhEIvj6+mLZsmXfpZ+2P3dC5OGDiDp2BClvA/F23hxIUlJh1aSZyvZGxUsi4fEjRJ8+ibT37xF3+yaiz5yCoW8xpTaxVy5BfP0qbxNz4Rzibt1UaqNO9p064cPBA4g8chjJgW/wZs4sSFJTYN1M9d1Co5KlEP/oIaJOsj6FQXzzBqJOnYRRMdkdYS09PVjUqo2gpUsQf/8+UoODEbJ2Df/XtrXsDrcmiThxES+mLED4gdPQZKJKdZF6/wrSHl6DJPI9ko5uBzLSoFu6isr2un5VoaVviIRdK5AZ/JpnU2S8e4nMDyHyNgk7liDt4XVIIsP49sRDmyA0tYS2Xd53877F3n370ahRQzSsXw9FXFwwfOhg6In0cOLkKZXt9x88iPLlyvK70y4uzujRrSs8PTxw4PBheRsLC3Ol5dr16zwwYW9vJ2/TpnUrFPX1ha2NDYoXK4oO7dvhub+/0t+i/GRcpxkSr55B0vXzyHgfjNgdq3i2kWGVOirbS5MSIImPlS8i31KQpqXKAxUsuBS5ZDpfz/gQirTAl4j5dy10XTwgNLfC92DV5mdEHz+MmFPHkPruLQ9YSFNTYNGwqcr2hsVKIPHJY8SeP4308PdIuHsLMefPwEDhbjeXmYmMmGj5khknLjDvvQrly2H0yF9QrmxZvq1K5Upo16Y1rlyVnVd12HbkDFrVqYrmtarA3cmeByxEuro4dF71z5w+tBfaNagBb1dnuDraYUL/LvxO663H/vxxIwN9LJkwDPWrlEMRB1uU9HLDmF4d8PzNO7yPjM6X58x+3uEDu9G+QzdUqlKdZ0aM+HU8oqMjcePa5Tz3O7hvF+o3aoq69RvD2cUVA4eOgp5IhDMnj/HHWWbGmZNH0avvYJQqXRYeXj4Y9stvPBjh/zELsl6DJug7YBhKlPSDnb0DatWpjzr1GuHa1Uu5fp6ZqTnPoMhaBN9Y4Z/1+/ThbWjWvi/KVKoFZ1dv9BnxB2KjI3D3xvk89ytZrhrPjihbWfXfE6ZyjcYoVroSrO2c4OjigQ69RiE5KQFBb19AnVifrp3chJotBqJo2bqwc/ZB236zEB/zAc/u5v0Z22P0apT9qTVsHb1g7+KLNn1nQhwVhtBAWfYfu8A1NrNWWp7eOYMSFRpBT2So9j5dOr4JdVsNQInydeHg4oOOg2YhLvYDntzJO/uy32+rUKFma9g5ecGhiC8PCMRGhSH4TXa2auU6P8O9aHlYWDvCya0YGrYfjtio94iOyP6cpj79d7AbPpq6FEaFs9fks4WEhKBJkyaoUKECHjx4gOXLl2Pt2rX4888/ldqxYSPsjsmVK1ewYsUKvl+NGjV4cOPs2bO4c+cOevfuLb+o2Lp1KyZPnoy//voLz549w4wZMzBp0iSVw0/yk5a2Ngy9fRB351b2RqmUrxsWL6Fyn4Qnj2Dg7SMPOujaO8C0clWIb1xTamNStjz0nJz5ur6HJ4xKllZqo9Y++fjywIicVArxrVswLqE6RTTh0UO+j2ExWZ/0HBxgVrUqYq9dkR1TKOTHlaSlKu0nSU2FcenS6uxOwSUQQmjvgow3zxU2SpH+5jm0nVSnU+p6lUJG8GsYNOoI0xGzYdJvEkRVG7FBlJ8cKsJIFLIu8kt6ejpevnqFsn7Z7wH2Rb+Mnx+ePpddFOX07Plz/riicmXL8O2qxMTE4Oat22jYoH6ezyMuPh5nz59HsaK+0NZWQ2KgUJtnP6T4P8zeJpXydV23vIcZKDKsWhdJd6/yYEVeBPoGkEokkCQnQt3Y77OBlzfi793O3iiVIv7eHRgUzT1khUl8+pjvo+8tC0zo2tnDpEJlxN28rtRO19EJxbbuhe/6HXAZOwk61jYF9r3HJCYm8uC7OqRnZOD5myBUKOGr1E+2/ujl6886RkpqGh/uYWIkuzuqSkJSMh+LzYIY+SH8fRhiYqJR2q+cfJuhoRG8fIrC/7nsQlXVOWXZEIr7sL6W8isr34c9zr43KLZxcnaBtbUt/J+pPi6TlMc5GjmsL3p3bYupE0bzoSLfKjI8hA/dYAGFLAaGxnD3KoEAxb8f3ygjPR0XTu6FvoERD4aoU0xEMBLEkfAolh1AFxkYw8mjFIICHnz2cVKSZVmC+oamKh8PCXyC9++eoVyN7CE66hIdEYz42Eh4Fc/uk76BMVw8SuHty/uffRw2DIYxMFLdp7SUJNy+sA8W1k4ws8wOtKtDQewTITnR0A8id/jwYRgZGcnXGzduDG9vbzg7O2PJkiX8Sw3LfAgNDcVvv/3GAw1ZdyO8vLwwZ84c+b6///47TE1NsWPHDvk4XnasLFOmTMG8efPQpo3s7rybmxuePn2KlStXokePHmrro7apGf/Cnh6jfBeJ3QUUuchSgXNimRTapqbwWbKCXyAKtLXx4cBevN+SHVR5v3UThAYGKLF5B7/wYJHPkDUr+b7qpm32sU/Ryn1i6/pFVPeJZVKw16LYilXyPoXv3YPQj4EiSVISz7hw7NUbyYGB/FiW9RvAqEQJpAQHq71PBZGWgRG0BEJIEpWHDUgT4yC0tFW5j8DMCtquPkh7fBMJO5dCYG7NgxYQCpFy6YiqnwL9+u2REfQKkgjZePX8FBcXB4lEIk/JzmJuZsZT5FVh4/rZ4znbs+2qnDpzFvr6+krDPrKsWbcBBw8fRmpqKor6+uCPKZOhDgIjYx6sk8QrZwZI4sTQsXX8v/vrFPGEjoMLorcuz7uRtg5MW3ZF8p0rkKYkQ92EJqZ8aErO4W0ZsdHQc1adfcMyKdjfPs95sr//7O9M5OH9+LBzi7xN0vOnCJo3E6nB73h9HNsuveA5dwn8B/aAJDm5wLz3soSEhuLAocPo16c31CE2LgGZEkmuIR5s/W3o+886xpJt+2FlboqKCsEORalp6ViyfT8aVC2Xb4EKVl+CMTVXPj/sfGU9lhOrJ8HOqalZ7n1Cgt7Jj8vqMxgqfDfJ+jl5Hff508e4cukcJk6dKd/GsifY8BJPTx8eIDl18ggmjRuJ2fOXwcPz6y/8xbFR/F8TU+U6FyZmloiLjcS3enDrIlbOH4+01BSYmlvh16nLYWyi3mEfLEjBGJkq1/8xNLFCgvjzhn2y83p020y4eJWFrZPq1/fuxd2wdvDg9S/UjV3QM8amytlrrI9Zj31Onw5ungVX77Kwc/ZSeuzqqe04sn0u0lKTeU2OfuPXQFtbvUOgC2KfCMmJAhVErnbt2jxjIouhoSGGDBmCKlWqKFXBrVatGhISEhAcHAwXF9kX3HLlsu92MPfv3+dDPVQVG2N3o9gQkD59+qBfv37y7eyuCQtu5IVdnLBFUZpEAl01p0MZ+5WBfZceePfP30h89hR6jk5wHvYL7Lv3khfLZHUvLOs3xOvpU5AS+Ab6nl5wGfoL0iMjEXXiKDSNcZmycOjRE4F/z0HC0ycQOTmhyC+j4NCrN0LXr+NtAqZNhfuEiSh76AikGRlIfOHPh4cY+qr+8kvUQEsL0sR4JB3dyu9+Z75/hxRjM4iq1FcZqGBBDKG1A+I3zcV/1YlTp1CnVi2VY9rbt22NRg3rI/zDB2zdth1/z/sHf0ydrHFVutnwkLSQt3kW3mTZNZZ9RrG4EmJ2roamMizlB5sOXRGydD6Snj+DroMjHAcOR3rn7viwTVbbIf72DXn7lDevkfj8GYpt+pfXw4g+oSqY9t987zGsNsqEyVNRo3o1NGkkG+KoaTYeOMlrXCyf9Av0dHN//rJMi98XrmV/TvBb745f/XMunDuFFUvmy9cnKAQFfqS3gW8wc/pEdOjcgxfdzOLo5MKXLL7FSuB9WCivicFqW3yu6xeOYtOKv+TrIybI6lSpi2/JCpgyfzsS4mJx8dQ+rJj7GybM3gQTs28rAKrowdVDOLhxqny968hPBFg/0+HNf+BD8Ev0nbBV5ePpaSl4eO0IarUYBHW4e+UQ9qzN7lPvMSu++Zj7NkzH++CXGDw5O1CbpUy1ZvAqWQXxMZG4cHQ9tiwahSFTtuZZJ+JrFMQ+aSItgWZ9nyjsKFBBlAITXzvDB9tXEbsrlRcW5GBWr16NSpWy0yUZVhMjLzNnzsS0adOUtvVzcUR/V9lwi8/BCsaxi26dj9XCs7AK+OnRsjsjOTn06Y+ok8cReeQQX2eV8gUiEYqMHoewzRv4BaTzoKEI27oZMWdPy9vo2drBrkt3tQcqMmI/9ilH5XK2nh6l+o4Tm80j8vgxRBw6KHu+AaxP+nAbNx6hG9bzPqWGhODZ4EG8r0JDQ6RHRcFz+p9ICcn/O/WFAathIJVkQmBoAsVSg1qGJrmyLLJIEsTslgc/H1kyo95DwFI0WUErSfaR9Bt2gI5XCcRvms+LbqoDqzfDsqhic9yVj4mNhXmOO6lZzM3N+OO52+cuYvfo8RMEB4fg999UF/hjgUy2ODk6wsXZGV179MKz5/58CEh+kiTE84KQAmPlwKnAxBSZcZ9+bVnRTINy1RB3ZOcngxSsLkXk4mnfJZuCYXUjpJkZ0M5x91rbzIJnlKli170PYs6eRPRxWcAhJfA1/3vgPHwMPmzfrPS+zCJJTEBqSBAPahSk915UVBTGjv+dv9dGDBsKdTEzMYJQIEC0WLm4Llu3NDP55L5bDp/GxoMnseT3YfAq4qgySDF+4VqERUZj2cTh35RNUbFSNXj7ZNdgSv9YuFccEwMLi+w78ex8ubmr/l5hbGLKz6k4xzll+2TN6MH+zchIR2JCglJWBfs5WW2yBL0LxJQJv6J+o2Zo3/H/zzrj5V30i4d/lK5YE1O8SygNyWDixNEws8guPh4XGwVnNx98Kz2RPmztXfji4VMK4we3xKUz+9G0bf5l9PiWqcOHdWTJyJCdywRxFIzNsodxJcZFws4lR30aFQ5vng7/BxfQd/xmmFqoHirw5NYJHqzwq9YS6lCsbB0+BCJnn+LFkTAxzz5PrI+sTsP/s2/Dn3h27wIGT9qkcvgDG3LBFjbbhotXKUzuXwWPb59Gmaqq6/9Qnwj5PFSjgnwSK3Z57do1XrQnC6tDwcZ+OjnlPeVYqVKlcOnSJZ5imZOtrS0cHBzw+vVrHhhRXNgQkLyMHz8eYrFYaenp8mVfhrMyA4zLlVe6a83qS7CicaoI9ESQSiXKG9nF48d9s9ogRxvZEBD1R2Z5n/yfw6R89t0j9rxMy1dA/GPVX8KEIlF2H/Lok3xzSgoPUgiNjWFaqTJiLilX7SafSZKJzLB3fChHNi3ouPrwOhSqsO1suAe/9f6R0MKGF2vMGaTQ9fFD/JYFkIhVB9zyA8uQ8vL0xL37D5VSR+/ff4Bivqq/lLMCmPcfKI9rvnvvPt+e04mTJ/nxPdzz/jug+PvFqPob880yM5Ae9BoiH4UaL1pa0PMuibQ3ny5kp1+mCh8ikXTrYp5BCm1rO15Yk13Ufy/s70TSyxcwVhjrz/pk5FcWSXmM9ed/1yTSz/o7Id9HpA9de0dk5BH4/S++91gmxZhxv/PHf/1lxDcXYPwUHW1t+Lo5ywthZvXz9hN/lPTKe2rATQdPYe3eY1g4bgiKeRTJM0gR9P4Dlk4YBjNj5aEUX0rfwAD2Do7yhRXCNDe3wMMHd+VtkpIS8dL/GXx8i+d5Ttmwi4f37yr19dH9u/J92OOsDs3DB3fkbUKC3yEiIhw+CrVV3r19g0njR6F23Qbo2qPvZ/Uh8PUrmJt/2fTG+vqG8sABWxyc3fmQjGcPs2tEsYKXr18+5oGF/CaVSPkUrflJT98QlrZF5IuNgyeMTK3w+ml2LZqU5AQEBzyEs0fpTxdU3TwdT++cRu+x6/lsIXm5c3EPfMrUhqFJ/mWGKBLpG8LKroh8sXX0hLGZFV49UehTUgLeBTzkU29+qk/sgp5doA+YsI7PrPF/sT+Z0vw/TwWxT4T8P5RRQT5p8ODBWLBgAYYNG8Zn9vD39+f1JUaNGvXJL2us7eLFi9GxY0ceYGB3Qa9fv85nD/Hx8eGZEcOHD+fb2bSobEjH7du3eTEzdmxVWGFOtij6mmEf4f9uh9v4SUh6/hyJz5/Atl1HCPRFiDwmqwTv+vtkPv1myGpZ+qP46mU+Uwj7kp/4cZiEQ+/+fHvWl/bYq5dh37Un0sLD+VR9Bl4+sP25IyKPZleXV6ew7dvhMWkyT7tOePIUdh078jufER+r27tPnsL7FLRcNrNKzOVLsO/UGYkvXvAADSsC6tS/P2IvX5L3yZRlu2hpIeXtW/64y9Bh/P+Rh2WZJZo2PamhZ3Zar4GbE0xK+yItWoyUoDBoipQbZ2DYogcPWGSEBvLpSaGjx2cBYQya9+BBiJTzB/h66p2LEJWvCf0G7ZF6+zwEFja8mGbq7XPyY+o36gjd4hWQuGsFL9zIMjQYaWoyu92X731gs2/Mnf8PvL084ePtjX0HDvCpGhvUr8cfnzNvPqwsLfmUkEyrFi0wZtx47N67DxUrlMeFi5d4UcRfctyVTkxKwsXLV1RO+fj8uT/8X75EiWLFYGRshLCwMGzcvJXPQlQ0n7MpssSfPQyLbkOQ9i5APj2pQE8Piddlr715t6HIFEcj7uC2XMM+kh/eyh2EYEGKvr9Cx9kNUStmsfxSCIxld/YlSQk8OKJukXv/hfPo8Uh66Y8k/2ewbt2eBxaiT8qyvpxH/470qEi8X7+Kr8fduArr1j8jOeCFfOgHy7Jg27P+Ttj3HYy4G1eQ9iEcOhZWsOvWC8iUIOb86QLx3uNBivHjYWNtw+tSiMXZ2U9slhB16Ny0LqYt34Si7i4fpyc9i+TUVDSrWZk/PmXZRtiYm2FIJ9ndaJZFsWrXEUwf2hP21haIjJXVVjEQ6cFAJOJBinELVvMinfPHDuI1MLLamBoZ8uDIt2LDr5q1bIddOzbzwEXW9KQWFlZ8FpAsk38fhcpVfpJPG9qidXssmj8LHl7ePMOBzRzCzmnd+o3kBTnrNmjCpzA1MjKBgYEBVq9YzAMZPh8LXLPhHuy4ZcpWQItWPyPmY70mgVAAU1PZ7xgb4mFjaweXIm58CvXTJ4/g0cN7mDJ9zjf3u16zzji8aw0PXLDpSfdtW86zK8pWqiVv9/fkAShbuTbqNpENt0lJTsKH90FKRTnfvfGHoZEJLK3tkZqSjMO718CvQk0eCEmIj8XZo/8iJvoDylf9dLHXb8X6VKVBd5w/tAIWdkVgbiWbntTY3AZFy8p+15j1s3uhaLl6qFyvi3y4BxvO0XnEEuiKDBEfGyEvxKmjK5LvFxX+Fm9f3Ea3kSvV2o+cffqpUXec2b+SX+SzwpAndi+CiZkNiperK2+3ckYvlChfD9UadJEPjbh39Qh6jlrCZyaJ+9gn/Y99ivoQhAfXjsG7VDUYGptDHB2Oc4fW8OERRf1qUJ/+iwrp7BqaigIV5JMcHR1x9OhRjBkzBqVLl4aFhQWvLTFx4sRP7mdpacln+2D71axZkw/p8PPz4/UtmL59+/IvHH///Tdvw4aOlCxZkk9xqm4x587w9GeH3n158bekVy/xcsxIZMTI0k/1bGyVsg1CN2/gEWjHPgOga22N9NgYiK9eQcia7PGB7xbOh2Of/nAZOZoPK0mLjEDEwf0I2yir96Bu0WdOQ8fcDE59+0PH0pIHVZ6P/EWe0q1nq9ynkI/DO5wHfOxTTCxir1xG0IrssalCIyM4DxwMXRsbZMTFIfr8OQSvWM5T4jWNabkSqHJms3y92FzZmOOgTXvxsM94aIr0Z3eQbGgEUc1msiEg4cFI2LGY16FgBKwgm0L2kjQ+BvHbF8Ogfnvo9ZvIgxipt84h5doJeRtRuZr8X+NuygG+xEMb+bSl+a1WjZ94NtOmLVt5YNHd3R1//TFNnn4fEREBgcLddjaV6Lgxo7Fx8xZs2LgJDo4OmDJxAlxdle/6XrjAMhCkqF0z9xchNgUlmw5y89Zt/CKGXSCWL1cOEzp0gK6KOjj5IfnuVcQamcCkaQcIjc2QHhKIyKV/yQtsaltY5Rr6oG3jAD3PoohYMj3X8YRmFtAvJct6sh2vXEMkYuEUpL7MnhpOXWIvnoXQ1Ax23Xrz4W7Jr1/hzcTR8gKbuuxvn0KfwlkdCqkUdj36QsfSmg+dY0GKsA3ZdTV0rKxRZNwUCI1N+OOJTx7h5ciByBSLC8R77+69ewgNDeNLlx49lR478XE4YH5j04jGxMVj1e7DiIqNh3cRR54pkTX0IzwyRqmfe09d4rOFjFuwRuk4fds2Qf92TfEhJhYX78iy67qOU64lsXzSCJQrlj+zSLRu1xEpKclYvngen1a0aLGSmDR9tlLND1YXIk5h+trqNeogTizGji0b+Kwhbu4emPzHbKVhHb37DeEXZXNmTOEZVKz2xIDB2d8Vrl25gDhxLK+bwZYs1ja2WLV+B/8/Gz6yYe1yREdFQldPBFdXd0z9cy5Klv72Qo6NW/dAWkoyNi7/E0mJ8fAq6oeRk5YojeWPeB/M60xkCQx4ir8n9Zev71wvq/dRtXZz9Bk+jd8Ieh8ciGXnDvP9DI1N4eZZHOP+WsunKlW3n5r0RXpqMg6un4KUpDi4eJdF919XKfUp+sM7JMVnD9u5eVb2Wq+bpVwQvXWfGXza0ix3L+2FibkdPErIvg9+L7Wa9eGFIXevZX2K5wUk+/6m3Keo8CAkKvTp2mlZn1b8qdynn/v/xaf41NbRwxv/O7h0fDOSE8U8E8XdtxyGTNmWqxgp9YmQL6clVczpJ+Q/5nbN7GmZCorMtBxDMgqAyNvqqZnwI1WdWgcFjbjDSBQ02gsmoCCKehWOgsZ0ifKFdkFgHiebvaIgCTHJrk1RUESn5V3I+78qVJw/M7poEpEOXbL8V7Qon3fNOU0W9Ud2AFHTWE6WZTsWJpRRQQghhBBCCCGkUKNZPzQLDcQhhBBCCCGEEEKIxqBABSGEEEIIIYQQQjQGDf0ghBBCCCGEEFKoaWnRPXxNQmeDEEIIIYQQQgghGoMCFYQQQgghhBBCCNEYNPSDEEIIIYQQQkjhRrN+aBTKqCCEEEIIIYQQQojGoEAFIYQQQgghhBBCNAYN/SCEEEIIIYQQUqhpCegeviahs0EIIYQQQgghhBCNQYEKQgghhBBCCCGEaAwa+kEIIYQQQgghpFDTolk/NAplVBBCCCGEEEIIIURjUKCCEEIIIYQQQgghGoOGfhBCCCGEEEIIKdy06B6+JqGzQQghhBBCCCGEEI1BgQpCCCGEEEIIIYRoDBr6QQghhBBCCCGkUKNZPzQLZVQQQgghhBBCCCFEY1CgghBCCCGEEEIIIRqDhn4QQgghhBBCCCncBHQPX5NQoIL8p7nVLYmCRsfIAAVNZrNUFDRXp55FQVO3ZAkUNDF6OiiIvLs3RkGj9fQUCpqw4g1R0JhkRqOgSdMpeH8nLI2EKGhMdFN+9FMgn83sRz8BUgBQ2IgQQgghhBBCCCEagzIqCCGEEEIIIYQUalpaNOuHJqGMCkIIIYQQQgghhGgMClQQQgghhBBCCCFEY9DQD0IIIYQQQgghhRvN+qFR6GwQQgghhBBCCCFEY1CgghBCCCGEEEIIIRqDhn4QQgghhBBCCCnUtAQ064cmoYwKQgghhBBCCCGEaAwKVBBCCCGEEEIIIURj0NAPQgghhBBCCCGFmxbdw9ckdDYIIYQQQgghhBCiMShQQQghhBBCCCGEEI1BQz8IIYQQQgghhBRuNOuHRqGMCkIIIYQQQgghhGgMClQQQgghhBBCCCFEY9DQD0IIIYQQQgghhZoWzfqhUehsEEIIIYQQQgghRGNQoIIQQgghhBBCCCEag4Z+FEI9e/bExo0b+f91dHTg4uKC7t274/fff4e2duF4S+iVrwX9qg0gMDJFRngwko5tR0ZoYJ7ttfT0YVCnFXR9y0JL3wAScTQST+xE+qvHsuOVqwlR+ZoQmFny9cyIUCRfPCJ//HvQKV0deuXrQMvQGBL288/tgeT9O9Vti1WEfqPOStukGemIXzRGoZEuRD81h7ZHSXmf0+5dRPrDq/he2OuqV7k+BEYmyGTn6eROZIa+/eR5EtVqCV1fP2iJZM856dQuZAQ84Y+LqjaEjo8fhJZ2vL8ZwQFIPrsfkuhwaBqL6uXh/msfmJYtAZGDDW63HYzwg2fwX7HjygNsvHAXkfFJ8La3wrhWNVHSxe7/7nfs/guM23octYu7Y0HPZtAkBtXqw7BWMwiNTZEe+g5x+zYiPShAZVuLQROh51ks1/aUp/cQs/Zv/Cg7bj7DxquPEZWQDG87C/zWuBJKOlr/3/2OP36NcXsuopaPMxZ0rCvf7jdtg8r2v9Qrj57VSuC/+F47/egVdl17hGchERAnpWDnL53g+xmv0dc6eOgwdu/Zg5iYGLi7uWHwoIHw8fHJs/3FS5ewafMWhIeHw9HBAb1790LFChWU2rx79w5r16/Ho0ePkZmZyT/nJ034HTY2Nvzxo8eO4dz5Cwh49QpJycnY/e9OGBkZ4Xvaf+QY/t17ANExsfBwc8WwAX3g6+2lsm3g23fYsHUHXgS8RviHCAzu2wttW37/vw9SqRS7tq7FmROHkJgYD5+iJdF38GjYOzp/cr8Th/fg0N7tiI2JRhE3D/QaMBKePsVUHn/W1NG4f+cGRk+YgQpVasgfW79yAfyfPkTQ2zdwdC6COYtV/+7lB/Y8Du9chiun9yI5KR7uPn7o1H8CbOyL5LnPy6d3cOrABgS9fgZxTAT6j/0HfhXr5GoXFvwa+7cs4O0lmRmwc/JA/9HzYGFtn6/Pf8+2VTh38gCSEhPgXbQUeg0aCzsHl0/ud+rILhzZtxXimCi4uHmhe/9f4eFdnD8WER6Kkf1aq9xv2NgZqFRd9nfx8YNb2LN1JYLeBkBPT4Sf6jRF+24DIRR++/frgtovjUKzfmiUAvbuIp+rUaNGWL9+PVJTU3H06FEMGTKEBy3Gjx//Q59XWloadHV11fozdIuVh2GD9kg8shUZIW8gqlQXxl1GIHbpZEiT4nPvIBDCpOtISJLiEb97BSRxsTwgIU1JkjeRxMcg6cxeZEZ/4Ot6pavCuMNgiFdNR2ZEGNRN27sMRDVbIeXMv8gMewvdsjVh2GYgEtbPgDQ5QeU+0tRk/rjCFqXH2fG0XbyQfGwLJHHR0C7iA1HddpAmiJHxWnbhr046RctBv17bj0GkNxBVrAOjjsMRt2JqnufJqPNw/ljCnlWQxsdCYKp8nlh/Uu9cQEboW2gJBNCv3RJGnYchbuUfQHoaNInQ0ABxD/0RtGEPyu9eiv+S4/dfYO6hS5jYtg5Kuthi66X7GLTmAA6M7QZLI4M89wuJjsP8w5dQ1s0BmkbkVxkmLbpCvHsd0t+9guFPjWHRfxwiZv8KSUJcrvYxG/6BlkLgV2BgBKtfZyHl4Q38KCcev8G8k7cwoWkVlHSyxtbrTzF4yykcGNoaFob6ee4XEhuP+Sdvo6yLba7HTv/6s9L65ZchmHbwCuoVy/uCRtPfa8lp6Sjj5oCGpb0wbfdZtT7/CxcuYvXq1Rg2dCh8fH2wf/9+TJg0CWtWrYKZmVmu9k+fPsWs2XPQq2dPVKpYgQcb/pj+J5YsWghXV1feJjQsDL+OGYuGDRqgW9euMDAwwNu3b5U+W9lnf/lyZfmyfoPsxsX3dO7SFaxYswG/DBnAgxN7Dx7Gb5OnY8OKxTA3M83VPiU1DfZ2tqhRvSqWr1mPH+Xgnq04dmg3Bo+cABtbe/y7ZQ1mTB6Fecu3QFdXT+U+Vy+ewaY1S9B3yGh4+RTD0QP/8n3+WbkdpmbmSm3ZY0DeF0q16zfFS/+neBeoOkCaX07tX4/zR7ej+9DpsLRxxOEdS7F4+iBMXrAPOnn0My0lGU6uPqhapxVW/T1KZZuI90GYP7EnqtRtjWY/D4LIwAhhQQHQyefvfYf3bsbJw/9iwIjJsLZ1wO6tKzF7ygjMXrojz/N0/dIpbF27EL0G/wZP7+I4fnAH3+fv5f/C1MwClla2WLLxqNI+507s4wGA0uWq8PW3b15g7rSRaPlzTwz4ZQpioiOwftlsSCSZ6Nx7BPWLkC9EQz8KKT09PdjZ2aFIkSIYNGgQ6tWrh4MHD/I7Oiy7wtzcnH+5ady4MV6+fCmP5FpbW2P37t3y4/j5+cHePjsKfvnyZX7spCTZxWFsbCz69u3L9zMxMUGdOnXw4MEDefupU6fyY6xZswZubm4QiURq77uoSn2k3r2M1AdXkRkZxgMW7CJVr0w1le3Zdi19Q8TvXIaMoABIxFHIePuC3+HPkv7iIc+ekER/4Evyuf2QpqVC29Fd7f3hz7FcLaQ/vob0Jzd5dkDK6V2QZqRBp0SlvHeSgl/UZy/KAQ2hgxvSntxCZvArSOOikf7oGs/UENp9nwsQFkBKvX8FaQ+vQRL5HklHtwMZadAtLfvgzEnXryo/Twm7ViAz+DXPpsh49xKZH0LkbRJ2LEHaw+uQRIbx7YmHNkFoagltu0/fjfgRIk5cxIspCxB+4DT+azZfvIc2lUqgVYVi8LC1xMQ2dSDS0cb+m0/z3CdTIsHv205gUIPKcLLIfaHyoxnWaIKk6+eQfOsCMsJDIN6zFtL0VOhXrKmyvTQ5EZJ4sXzR9S7J26c8+HGBis3Xn6BNWW+0KuMFD2szTGxWRXZe7sn+xud5XvZewqBafnA0z33H3crIQGk57/8OFdzs4WRujP/qe615uaIYWL8SKnmp/+/C3n37+I2DBg3qo4iLCw9YsLuVJ06eVNl+/4GDKF+uHNq3a8uzJHp07wZPDw+elZFl48ZNqFC+PPr26c0fc7C3R5XKlZUCH61btUKHn3+Gr68vfoTd+w+hScN6aFSvDlxdnPHL4AH8u8PxU6qzxny9PTGgdw/UqVGd31T5Edh3oKMHdqFNh+6oUPknFHHzxJBRExETHYVb1y7lud+R/TtQt2FzHmRwcnFD3yFjoKsnwrlT2eeMCXz9Eof37cCgX1TfMOo14Bc0bNYWtnYOau/n2SNb0ahtP5SuWBtOrt7oMexPniXx4GbegbviZaujRaeh8KuUnXGV08Fti3m7Nt1Gwtm9KKztnFGqQi0Ym1rm6/NnF+Mtf+6FcpVr8gyCgSOnIjY6EneuX8hzv2MHtqN2g5aoWa85HF3c0WvwOP67eOH0If64QCiEmbml0nL72gVUqlYXIn1ZUPT6pdNwdvVE6459YefgjKIlyqJjz6E4dXQPkpMSqV+EfCEKVBBOX1+fZzOwYSG3b9/mQYtr167xP4xNmjRBeno6tLS0UKNGDZw/f57vw4Iaz549Q3JyMp4/f863XbhwARUqVOBBDqZ9+/b48OEDjh07hjt37qBs2bKoW7cuoqOj5T/71atX2LNnD/bu3Yv79++rt6MCIbTtXZD25pnCRilf13FSHVTQ9S7NhwgYNu4E81FzYTpwCvSrN2algVX/DC0t6BavAC0dXWQEv4baCYQQ2Drx4Ek2KV8X2svusKmkqwujvpNh1G8K9Fv0gcBSOVU6M/QNdDxKQMtI9kVe6OwJgbk1Mt7KzrVaCYQQ2rsg443iz5Ii/c1zaOd1nrxK8dfboFFHmI6YDZN+kyCq2ijv8/RxqAgjUci6IN8mPSMTz0I+oLJXdiq0QKDF1x++zTu7aOWpmzA30kebirJ0VI0iFELHyQ2pLxWGckmlSH3xGLpFVKeq52RQqRZS7l3nAcwfIT0zE89Co1DJPTuwLNDS4usPgyPy3G/lhQewMBShdVnv//sz2HCSyy+DeSDke/ivv9fY5+rLV69Qxs9Pvk0gEPD1Zx8/U3Ni28uUyW7PlCtXVt5eIpHg5q1bcHR0xO8TJ6FDp84Y8ctIXL16DZqC9fvFqwCULV1Kqd9l/Urhqb/i55hm+RAeitiYKJT0yx5mY2BoxIdwvHyuephnRno6Xr96gZJ+5ZX6ytZfPs/OTExNScGiv6eh96BR/ELxR4r6EIK42Ej4lsq+0aFvaAxXr5J4/eLhVx+XvTcf373Eh48snj4QY3vXwpxxXXD/E8GPr8GGMrAhDiVKV1Q6T2yow0v/R3mepzevnqO4X0Wl81S8dAW8eq56nzevnvFMg5r1WygdJ2d2CMt0SE9LxZuAb/vuVFD7pWlYtq2mLoVR4ew1kWOBiNOnT+PEiRP87gwLULDshp9++gmlS5fG1q1bERISwtNRmVq1askDFRcvXkSZMmWUtrF/a9asKc+uuHnzJnbt2oXy5cvDy8sLc+fO5Xd1FLMyWIBk06ZN/FilSmV/cVEHLQMjaAmEkCYqp2pLE+PlF+Q5Cc2toVusHPvrjrjti3jtCVHl+tD/qalyOxtHWIxbBIsJy2DYtAvi/13OMzbUjWUR8D7lGA7B1gWGJir3kcR8QMqJHUg6sJYP7WAX84YdRyi9Binn9iAz6j2M+0+D8Yh5MGg9ECln9iAzRP3Bl6zzJMl1nuLy7JPAzAq6RcuyTxkk7FyK5MtHoVepLkTVm+T1U6Bfvz0ygl7xTBGSP2ISk5EpkeZKu2frrIaAKnffhGLfrSeY0j7vO3E/ksDQGFpCIc+MUCRJEENgnDs9PycdZw/o2Lsg6cY5/CgxSanIlEphmWOIB1uPTEhWuc+9d+E822Jy86qf9TMOPngFA10d1C36fTKU/uvvtbi4OH7xZmau/B5in5Ex0TEq92E3CHIOCeHtY2LkWYzs5sG/7HO3XFnM+HM6qlatgul//YWHj1RfmHxv4rh43m/zHP1mQz5YvQpNxepLMDmHa7D12Njsmy+K4uLEPD2epdgr72PBgx5ZNq5ZBO+iJXimxo8mjonk/5p8rLmVxcTUkgcwvla8OBqpKUk4uX8divlVw7BJK1C6Uh2s/nsUXjy5jfyS9bqa5HjN2br44znM9dziYvM8T+I8zu35U4fg4OzK60RkKVW2El4+f4SrF05AkpmJ6KgP2Ldjrex5RX/9a1eQ+0XIp1CNikLq8OHDvHAWu7PBvjB07twZbdq04dsrVcqOoltaWvKiXixzgmFBiBEjRiAiIoJnT7AgBRtCwgIUffr0wdWrVzF27Fjelg3xSEhI4MdQxL5EBQRkj69kw0/Y0JD/h42pZYvStoxM6GkLoVZaWpAkxiPx8GZ+FzUz7B0EJmbQr9IQyRezUzczI98jduV0aIn0oVe0HIxa9kLcxrnfJVjxpTLDAvmSJTn0DYx6joduqapIvXqMb9P1q8EzMpL2r+Y1KoROHhDVbQtJohiZ7zTwrpeWFg84JR3dKjtP798hxdiMD/VJuXQkV3OWeSG0dkD8prk/5OkSmcSUNEzYfhJT2tWF+SfqJPyX6VeqxYtv5lV4UxMlpqZjwr5LPEhhbvB5Q/IO3HuJJiXdoaehRZkLw3uN3Xxg2FCPNq1lBfI8PDzw9NkzHDl6FKVKlvzBz/C/49K5k1i9NLvw7bgpc9Tyc27fuIwnD+5i9qJ1+BFuXjyC7aumy9cHjV+ilp8jlUr4v6Uq1Ebd5t34/53dfPHa/wEun9wF7+LZWSdf4sr541i3bJZ8ffTk+VC3tNQUXLt4Aq1+7q20vWSZyujUcxjWL5+NFf9M48OUWnboDf+n93kmw5coqP0i5Eto5rcJona1a9fG8uXLeXEtBwcHPtsHy6b4f0qWLAkLCwsepGDLX3/9xQMVs2fPxq1bt3jgo2pV2d03FqRg9Suysi0UKd4RMjQ0/KznPHPmTEybNk1p25haZfFb7XL4XKwOg1SSCa0cd+XZTBmsSKQq7K6pNDOTX/wqBiUExqZ8iAIkmR8bZkISI0uhTgp7B6GDK6+zkHhkC9SJjYXnfTJQHhPO1nNmJORJIuE1GwRmHwNG2jrQq94UyQfXIeONbKw3q+sgtHaEXvnaSFJzoCLrPLHsiY+vLsfOW159YueJ9UPpPEW95zO7KJ0nduHYsAN0vEogftN8XnST5B92ASgUaCEqQfmONlu3Ms5d3DAoSozQmDgMXy8bL8tIPp7Dsr8txoEx3eBs9f+zFtSJBSrZ3wD+O6+Avbck/+f9o6WrB32/Kog/kZ1F9iOYG+hBqKWFqETl7Am2bmWU+6I9KCYOobEJGLH9TK7zUu6Pjdg/tDWcLbL/jt59G47AqDjMblcL38t//b3G6jaxL/mxObIIWFaEuYXyXfssrH4UezxXe3Nz+TGFQiHPkFTk4uyMJ0/yrtvxPZmaGPN+x+Tod0ysGBY5six+pPKVqvPil1nSPxZcFsfGwNzCSr6drbu6eao8homJKQQCYa6712w9a4jH4wd3EP4+BL06NFZqM2/mRBQtVgpTZqkncJCF1YhgwzqyZGTI+hkXGwVT8+ybSHHiKF4s82sZGZtDINSGfY7hm3aObgh4/vXDfstW/Ek+gwWTkZEue76x0Urnia27uKselmZsYpbnecqZjcDcvHoWqakpqF4nd8Zmk1ad0bhlJ55pYGhkjIgPYfh30zJY2zlSv/4LPjFc+L9m6dKl+Pvvv/H+/XueJb948WJUrJg9DEjRhg0b0KtXL6VtrG5QSkqKUiB8ypQpvAA0+9ypVq0av5ZkGfPqQoGKQooFBzw9lT9YixYtioyMDNy4cUMebIiKioK/vz+KFZN9WLM6FWxYyIEDB/DkyRNUr16d16NgmQ4rV67kQzyyAg+sHgX75WBBkKxq5N+CzUgyapRyJemEuSO/7CCSTGSEvYOOmy/S/bM+GLWg41YUKbdUp2WnB72CXgn2i83+eMm+1AotbGQXKAoXvzmx1wrfY9omFiAJD+YzWmQEZKX2akHbxRtp9/Mu8KVESwsCK3tkZNXuYOPh2HNXuOjn+Pp3+CMuyeSZK9quPkh/kVV8VQs6rj5IuZ078MWw+hSsNsj/O08sSKHr44f4zfN5YVSSv3S0hSjqaIMbr4JQp4QH3yaRSPl6x6qlc7V3szHH7l+7KG1bevwaElPTMLZlTdiZfZ+ijJ+UmYn04DfQ8yqO1McfU5S1tPh64hXVRQ+ziEpX4rN/JN+5jB9JRyhEUQdL3Hwdhjq+ReQX6Wy9Y8XcBRXdrEyxe1BLpW1Lzt5FUloGxjaqCDtT5QDzvnsvUMzeEj52ub/8qst//b3G7kp6eXri/oP7fHgGwzIcWa2m5s1VT71Z1NcX9+8/4MUws9y9d49vzzqmt7cXgoOziz0zISGh8qlJfzT+HD09cO/hI1SvUkne73sPHqJVU+WL9R9J38CAL4pf0llw4dH923D9eGGYlJSIV/5PUb9x9vlQpK2jA3dPbzx6cEc+1Siv1fDgDho2a8PXW7XvijoNmivtN2Zod/ToOwzlKqou8p2fRPqGfFHsp4mZFfwf3eAZD0xyUgICXz5CjQbtv/rnsNeiiEdxhOeYCv5D2NtvmppU38CQL4rP39TcEk8e3EIRd1ltnaSkBAS8eIK6jdvk+dzcPH35PuUr15SfpycPb6F+0/Yqh0ewQIKJqXme3//MLWVBnmsXT/KZNdzcvyzIU1D7Rb6PnTt38mumFStW8Ez5BQsWoGHDhvyaLq/PAhboZo8rXccomDNnDhYtWoSNGzfyCRAmTZrEj8lmo1LXZAiUr0PkWESsZcuW6NevH68vwYZudO3alRflYtuzsOEe27dv57N1sOEj7M4IK7LJ6llk1adg2EwiVapUQatWrXDy5EkEBgbyoSETJkzgBTu/FIvssV8ixeVrhn2kXDsFUdmfoFeqCoRWdryeBCt8yWaYYNiQDYM62XNKp96+wOtAGDTqAIGFDXS8SkK/ehOk3Mq+YGbtWaCATYfJalXwdVdvpD3+PhX+U++ch07JKtApVgECC1uI6rXnfUp/Ivv5okZdoFc9+4uvbuWGEBbxgZapJQQ2TtBv3A0CE3M+sweXlsprN+jVaAGhkye0TCygU6widIqVR/qr7zPOOeXGGeiVqQ7dkpV5oU+Dxp0AHT0+Cwhj0LwHRLWy35epdy5CoG8A/Qbt+XnS9izBi2my6Uiz6DfqCN0SFZG4fx0vasgyNHh2jfaPqSL//6YnNSntyxfGwM2J/1/knH9zzatLtxplsPfGExy8/Qyvw6Px595zSE7L4DMzMCz9fuFR2e+bno42vOwslRZjkR4M9XT5/9nFqCZIvHgUBpVqQ7/8T9C2cYBJ297Q0hUh+abs/WXaaRCMm3TItZ9BxVpIeXwn16w6P0K3ysWx9+4LHLz/Cq8jYvHX4WtITs9ASz/ZRdfEfZew6PQd/n82fMPTxlxpMRbpwkBXtp0FPrIkpKbh1NO3aF3Wq0C818RJKXgeEsGPxwRGxPD1yLj8r27PhmccO34Cp06fxrt377B46VKkpKagQf36/PG/587DuvUb5O1btWyB23fuYM/evQgKCsLmLVvx8uUrtFAIbLRr2xYXL13CsePHERoaioOHDuH6jRto1iy7rhIraM2GYIaGyoYmss9nth4fr2LqZzVo16o5jpw4jRNnzuFtUDAWLFuFlJRUNKxXhz8+a/4irNmYnY3IsjVfvX7DF3ZDJTIqiv8/5OPz/x7Yl/YmLdtj386NfLgGmyJ06fw/YW5hiQpVsmtLTP99BI4f2iNfb9qqI86eOIQLZ44hOCgQa5bNRWpKMmrVk50PFvxwcXVXWhgra1vYKMzw8T40mM8MwmplpKWl8v+zhRU6zO9+1mnaBcf2rMbDW+cR8vYlNi6eyLMrSleUnR9m4dR+OH9su3w9JTkJQW+e84WJCg/h/49WmKK9fsseuHP1BC6f2oMPYe/4/o9uX0SNhj/n6/Nv1KIj9v+7HnduXERQ4Cus/GcazCys+GwZWWZMHIKTh3fJ11m2wPmTB3DxzBGEBL3hwxxYkdOadZWDhu9Dg+D/5B5q1VcO5CpOIcp+ZvC717yOw6E9m9Ct/698dg3qF/le5s+fz6/nWJYEu9nMAhbsxvK6des++R5jWfJZi62trVKgjAU7Jk6cyK8JWU1BVl+QfcZk1TFUB8qoIErWr1/Pa1A0a9aMF7lkAYijR48qTQfGghGZmZk8YJGF/Z9lWShuY294ti8LTLBfFFbXgr3x2TEV3/zfW9rT20gyNIZ+rRYQGJkgIzwY8dsW8foGjMDU4n/s3QV4U1cbB/B/3V2oUlpKi7u7wwZjuG3AkA39hg13GTAGbLgNd3d3d3d3r7v3e84JSZOSMhi0hPb/e5487U1ubnJybuS+9z3vUY3zFZLCghG+bDLMazaFbadhSAoLQczpfYg+tlNj6Ihl/bYyFTw5NlpOXyjuE39ffXaR9JNw+wJizC1gUvYb6JlbI+nNM0Stn606ONK3slOlOMvna2IGsxrN5LrJsVFIfPUEkSsmy6lNlaK3LZLBDbNvf4Seqbl8HWKPbkf8ZcWP/vQWf+Mcoi0sYVqprmIIyKuniFg5VaOf1DM+ksODEb5iKsxrNIHJz4NlJkXsmQOIObFLtY5pMcWXuVUrzcycyC2L5LSlusSmWH6U2bdEtZx3wkD598ni9bjcXvv0dbqidmE/Wehwxq6TCAiPhL+bE2Z0+B4Ob9PxX4aEyxknviYxF08izMIalrUaw8DaFvHPHiFo7jgkRSiGIhmIwnNvx2ArGTi5wtgnNwJnj4EuqJXfG8FRMZh58IIsoCmyH2b8UAMOb4d+vAiN+E9ZrzuvPpDvxdr5M2Y65vTe1w5eu4+hq1OmBe63TPFZ36lGSTml6edUqVJFhIaFYsmSpbIgpo+PD0aPHKkayvH6zRvo6ac8f/GDs1/fPli0eAkWLlwEN3d3DB0yWCNrsVzZsvhft65YtXoNZs6aDQ8PdwwZNBD586WkkW/bvgPLli9XLf/Wt5/826tnD1WQJD1VqVAOoaGhWLhspRwCktPHG+NGDFYN/Xj9JkDjbF5gUDA6dv9Ntbx6w2Z5KZQ/HyaNHYmMUq/RD/Igb87U8YiKjIB/3gIYMHKinAFBSQzjEEUMlcpWrIaw0BCsXvqPDDLk8PGV97G1+7jso9lTxuH61ZQhEv1+VaRpT523Bs7ZPm8Au0b9toiNjcby2SMRFRmOnLmLoNvgGTBSa+cb8Z2s1s7H967h7+EdVMvrFinqP5WuXA+tuylqYIipS1v8PBi7NszHmgV/IJtbDvz820T4ikLYn1Hdhq1kMGj+9LGyn/zyFkLf4ZM1+ul1qn4qXaGG7Kd1y+fI2TVE1kLf4X/LLAZ1YlpPewdnFCiiffr3y+dOYPOahTK4lj2HL3oN+hOFin1YQeKs2i6dosM1N2K11OoTJ3HFRZ04fhMzLYpMdCVxUlmcQBYzOqZFDNkXdQNF1o3Iih8zZgzyvf3eePDggcySF9tQsrGxkdkaYpvNmzdHetBLVj8iI/rKBI78BZmNUaoK9plBYvSXmZIxPR0f/nmnVNMF1db9iswm+FDGBNYymm2xzFcUUc9S+4w+X7MX+WohszFKTBmznFkE6H25kyfpJTBGB4bNfWbWxplv38usSvjrTr2ZjxG1ULMWni4Z/zD5nVp9ombE8OHDNa4TWQ4iG15ksYvMdiUx2YGoLyiG+Kcmgg137tyRmRIiiCxmaRSzO4ph/h4eHnJboiaF2LaoP6jUtGlTGVQWQ03SAzMqiIiIiIiIiHTUAC21+lJnU/xXIqChHtQQtQpF7UJRf3DUqJRZgTIaAxVERERERESUtenwsFQTLcM8tHF0dJSzP716lTKcWxDLYgj+hxBD/osUKYK7d+/KZeX9xDbUMyrEsqhZmF50dyAOEREREREREX0QY2NjFCtWDPv2qU0vnpQkl9WzJt5H1CK8cuWKKighZvkQwQr1bYaFhclhJB+6zf+CGRVEREREREREmUCvXr3Qpk0bFC9eHCVLlpQzdkRGRsrJDYTWrVvLOhZjx46VyyNHjkTp0qXh6+uLkJAQ/Pnnn3j06BE6dFAUyBV1KHr06IHRo0fLWSKV05O6ubnJ2R3TCwMVRERERERElKXp6fCsHx+jWbNmcrbFoUOHytk6xPCMnTt3qmZdFNNhi5lAlMSsU2I6U7GumHVKZGSIAppipin1Ypwi2PHLL7/IYEb58uXlNk1NTZFeOOsHfdU468fXgbN+fB0468fXg7N+fB0468fXgbN+fB0468fX42ud9SN6yWjoKrNWg5HVZI6wERERERERERFlChz6QURERERERFmbHs/h6xL2BhERERERERHpDAYqiIiIiIiIiEhncOgHERERERERZW36el/6GZAaZlQQERERERERkc5goIKIiIiIiIiIdAaHfhAREREREVGWpsdZP3QKe4OIiIiIiIiIdAYDFURERERERESkMzj0g4iIiIiIiLI2zvqhU5hRQUREREREREQ6g4EKIiIiIiIiItIZHPpBREREREREWRtn/dAp7A0iIiIiIiIi0hkMVBARERERERGRzuDQD/qqGdRricwm1MIZmU2iXub7qKlWID8ym32NpiCzKTe6BjKjg3l+Q2aTVDLzvae8bjRCZuMVdheZTaitAzIba+MYZDYWBlFf+inQB7PFV0mPs37oEmZUEBEREREREZHOYKCCiIiIiIiIiHRG5svHJiIiIiIiIvoY+jyHr0vYG0RERERERESkMxioICIiIiIiIiKdwaEfRERERERElLXp8Ry+LmFvEBEREREREZHOYKCCiIiIiIiIiHQGh34QERERERFR1qav96WfAalhRgURERERERER6QwGKoiIiIiIiIhIZ3DoBxEREREREWVtnPVDp7A3iIiIiIiIiEhnMFBBRERERERERDqDQz+IiIiIiIgoa9PjrB+6hBkVRERERERERKQzGKggIiIiIiIiIp3BoR9ERERERESUtenzHL4uYW8QERERERERkc5gRgX9q59++gmLFi2S/xsaGsLDwwNNmjTByJEjYWpqKq/XUys+Y21tjfz582PUqFGoWrWqxjY6duyIWbNmaWy/a9eumDFjBtq0aYOFCxfiS1iz6wCWbdmDwJBQ5PLyQO+2zZHP11vruhv3HcH2wydx/8lzuZzbOzs6t6if5vrpZdPW7Vi9fiOCgkOQ0zsHunXsgNz+fmmuf+joMSxcugIvX72Gu5srfv6pNUqVKKa6PTg4BHMXLsa5CxcRERmJAvnyyW16uLup1vlr2kycv3gJgUHBMDM1Rd48/nI72T09PkubNm/dhrXr1iMoOBg+3t7o0qnje9t0+MhRLFq6FK9km9zQvu1PKFmiuOr2WnW+03q/Du3aokmjhvL/YSNG4d6D+wgJCYWVpSWKFC4kt+Pg4ICMsvLYJSw6dB4B4VHwc3VE//qVUCC7y7/eb8fF2+i/bCeq5PPB3z/Vha6zL18cPr3bw6Zofpi6OeNsoy54tXkfvhbGRSrCtFQ16FlYI/H1M0TvXYPEF4/SXF/PxAymFb+DkV8h6JmaIyksGNH71iLh/nXoiuTkZOxYMx0n969FdGQ4vP2LoEn7IXBy9UrzPns2zsXl03vx+vkDGBmbIodfYXzXsieyuWXsZ2Bm3vdEv6xcOh97d21FVGQE/PMUwC9de8HN/f2ftTu2bsCmdSsREhyEHN450b5Td+TyzyNvCw8Pw6ql83HpwlkEvHkFaxtblCxdHs1btYeFhWW6t2n17iNYsm0/AkPDkCu7O/q0aYT8ObXvZxv2H8e2o2dw78kLuZzH2xNdmtVNc/0x81Zh/f7j6PVjA7T8pnI698sC7HnbL7nz5P/gftmo6hdfdOj0q6pfhN07tuDIob24f/cOoqOjsGTVFlhYWr2znbOnT2DNisV49PAejIyMka9AIfQf8nu6tHPd8jk4sHuTbKdfnoJo27kvXNyyv/d+e7atwbYNyxAaHIjs3rnQ+pfeyOmXT3V7SHAgViyYgqsXTyMmOgou7l74vulPKFm26ic/3xVLF2Dvrm2IfNsvHbv2/Nd+2S77ZZXq/SL6xU+tX+Li4rDgnxk4evgAEuLjULhoCXTs0gO2dvbvbCssLBS9unVAYGAAlsr+S3lPxcfHYdXyxTh8YC+Cg4NgZ2+Ppi1ao3rNb7/KNl29fBFDBvR8Z/35S9bJthF9LsyooA9Su3ZtvHjxAvfv38dff/2F2bNnY9iwYRrrLFiwQK5z7NgxODo6om7dunJ9JU9PT6xcuRLR0dGq62JiYrB8+XJkz/7+L7/0tOf4GUxevBbtG9XBonGD4Ovlge5jpiAoNEzr+uev3UbNsiUwY2gv/DOqH5wd7PDr75PxOig4w57zgcNHMeufBWjVohlmTZ4IH+8c6D90JIJDQrSuf+3GTfw+fhJq16iGWVMmolzpUhj2+zg8ePhI9YU4dPRYvHj5CiMGD8CsyZOQzdkJfQcPR3RMjGo7uXxzok+P/2H+zKkYN3IokAz0GzoCiYmJn9ymg4ePYM7cf/BDyxaYPuVvGagYNGQoQtJq0/UbGDv+T9SuWRMzpkxG2TKlMWL073j4tk3CiiWLNS69enSXQbXyZcuq1ilUsAAG9e+HeXNmYfDAAXj+4iVGjRmHjLLz4m1M2HIEHWuUwsoezeHv5ojO/2xCYETUe+/3LCgMk7YeQVHvlECSrjOwMEfY5Vu4+usIfG2McheFWdUGiDm2A+EL/5CBCoumXaFnnsYBnr4BLJp1g76NPSI3zkP43FGI2rkcSeGh0CX7Ns/H4Z3L0KTDUPQcvRzGJmaYNbYj4uNi07zPvRtnUb5mC/QYtRydB81BUmI8Zo35BbEx799nv6Svbd/buHYFtm9Zj45de2PspFnypMCoIb8h7j39cuzwfiycOx1NW7bBn1Pmwss7p7xPaIjiuyk4MABBQYFo3b4z/pqxEN16DsCFc6cxY/L4dG/P7hPn8deyDfi5YS0sHd0Hftnd8L9xMxEUGq51/XM37qJWmaKYNagbFozoiWwOtug2biZeB737fXDgzCVcvfsITnY26d6ODWtXYNuWdejUtRfGTZoJE1MzjBrS5739cvTwfiyYOwNNW/6ECVPmyoPHkUP6IORtvwixsTEoUrQkGjX9Ic3tnDh2CFMmjkHVGrUxado8jJkwDRUqV0d62Lp+CXZvXY12nfthxJ/zYGJiij+GdX9vO08e2YNl8yajQfP2GP3XImTP4SvvExoSpFpn1l/D8eLZY/QaPAFjpy5HiTKVMXX8IDy8d+uTnu+GtSuxTb5feuKPSTNgYmqKkUP6yoPy9/fLTDRr2QYTp8x52y99Nfpl/tzpMjjUZ8AwjB73t3z//PH7UK3bmz75T/me0+bPsSNw5dJ5dO3eB9PnLEavvkPg7uH5VbdJmDZnsQxOKC82trb46okTr7p6yYIYqKAPYmJiAhcXFxlsqF+/PqpXr449e/ZorGNrayvXEdkUM2fOlAEJ9XWKFi0q779+/XrVdeJ/EaQoUqQIvpQV2/bi+2rl8V2VcvDxcEP/Dj/A1NgYWw4c17r+yF/bo3GtyvDL4Ykc7i4Y1Kk1kpKTcfbKzQx7zus2bsa3tWrIwINXdk/06NpJ9tHOPdrPEK7fvBUlihVBs0YN4OXpibatWsI3p4/MyhCePX+OG7duo3uXjsjtlwueHu7yf/Gj5MChI6rt1K1dEwXz54NLNmcZtBDbefMmAK9ev/7kNq3fsBG1a9dCrRrV4ZU9O37t1gUmpibYtVtzP1PauHkzihcrKjMjsmf3RJtWP8I3Z05s2rpVtY69vZ3G5cTJkzIw4eqakq3QsEF95MmdG9mcnZEvbx40a9IYN2/dQkJCAjLCksMX0LBUftQvkRc5szlgcMOqMDUyxMbTaZ91T0xKwsDlu9C5Zml42Kf/j/PP5c2uw7g97G+82rQXXxuTElURd+k44q6cRFLgS0TvWgnEx8G4QBmt6xsXLCOzKCLXz0His/tICgtC4pO7SHrzDLpCBCgP71iCmg1+QYHiVeHm5Y8fuo5BaPBrXDmbdrZBpwGzUapyfbh6+sLdKzdadv4dwQEv8PSB7mSKfM37nuiXrZvWoHGzVihZprw82Phf74EIDgrE6RNH07zflg2rUb12XVSt8S08s+dAx2695cHNvt2Kz/nsOXzQd9AolChVDi6u7ihQqChatu6As6eOIzExfT/vlu04iPpVyqJepdLw8XDBgHZNYWpijM2HTmpdf3TX1mhSowL8c3ggh1s2DP65BZKTknD62m2N9UTg4s9F6zCqaysYGhhkQL+s1eiXX3sPQFBQwL/0yxrUqF0H1Wp887Zfesl+2f+2X4Tv6jdBw6Y/wC93Xq3bEP0zb/ZUtG7XCbW+/R5u7p5yW+UqVEmXdu7cvBLfN22LYqUrycyITj2HIyQoAOdOHkrzfjs2rUCVmt+jUvXv4J7dB2279JcBjkN7t6jWuXPzCmrWbSKzLJxd3FG/WTuZzfPg3s1P7pcmzVqh1Nt+6f62X069p182p+qXTm/7Zd/uHfJ2kcUg3jttO3RBwUJFkTOXP/7Xox9u3riGWzc1P+t2btsk1/++YdN3Huf82dO4dvUSBo8Yh0JFisE5mwty58mHPHkLfLVtUrK1sZMZFMqLPus70GfGPYo+2tWrV3H8+HEYGxunuY6ZmZn8mzry265dO5l5oTR//ny0bdsWX0p8QgJu3n+MkgVS0uLEB22JArlx5U5KNsj7xMTGITEhEdaWFsgI8fHxuH33HooWLqTxnIsWLojrN7WflRDXq68vlChaGNdvKn70xcUrfqQaGxtpbNPIyAhXr9/Quk2RabFz7364ZMsGJ0fHT27Tnbt332lTkcKF02zTjZs35e3qihUtIq/XJjg4GKfPnEWtmjXSfB5h4eHYf/Ag8ubJLYc5pbf4hETcePYapXOlnFnR19eTy5cfKVKetZm95zTsLM3QsGRKSi2lI30DGLh4IuGR+r6YjISHt2Dorn24g5FvASQ+fwCzGs1g3W0MrNoNhEnpmjp1ViTw9VOEhQTATy3YYmZuBS/fgnh4+9IHbyc6KkL+Nbf8eoJmuuzVyxcybbtg4ZSheeJgTgwVuHXzWpqfoffu3ta4j/gMFcu307iPEBUVCXNzcxgYGKbv9+yDJyiV30/juZXM74fLdx5+8PdsQmISbCzMVdclJSVh6MylaFW3KnJ6uCKj+qXQO/2S952DPM1+uaW1X9K6jzZiSEhQYAD09PXR+38d0O7Hhhg1tC8ePfyw3ykf482r53LoRv5CJVXXmVtYyuDCnVtXtN4nIT4eD+7eRL7CJTXama9QCdy9mXKfXLkL4OSRvYgID5X9d+LwbsTHxSFP/qKf1C/BWvvl398vhd7pl6Kq+4jbxQkL9XU8PLPDySkbbt1I2e6Txw+xesVidO81APp67x5WnTl1DL6+/jJDon3rJujycyss/GcmYmNjv9o2KfWU+2IjDB/0G25c175vEH0KBirog2zduhWWlpYy/bRAgQJ4/fo1+vTpo3XdqKgoDB48GAYGBqhUqZLGbT/++COOHj2KR48eyYsYJiKu+1JCwiLk2Wl7G82xoPY21ggK+bAU7enL1sPR3gYl1IId6Sk0LFx+wdvZah4U2NnayjoT2ojrxe2pM2CC3qYDZvdwh7OTE/5ZtBThERHyC2/l2vV4ExAo61Go27RtB+o2boHvGrfAmXPnMX70MBnQ+BRhYWGyTba2dlraFPzBbXrfa7Bn334ZQFMf9qH0z/yFqNewMZo0FxkibzB8yGBkhODIaCQmJcPBMuXHtyCWRb0Kbc4/eI4NZ65hWJNqGfIcCXJ4h56+AZIiNdPUk6LCZL0KbfRtHWDkXwTQ10PkmpmIOb4TJiWrwaRsbeiK8JAA+dfKRrMei1gWAYwPId63GxaNk7UtXD1zpcvzzGrEwbCQesy4ja2d6rbUwsPEgV/iO5+h77tPWGiIrHdQvbb2Wj6fS0h4pPbvWWsrBKYx9CO1qSs3w9HOGiXz+6uuW7RlHwz09dG8lubvjPSifB1tUvWLeM3FQWXa/SK+2969T1r9os2rl4qaWKuWLUTj5q0waNhYWFpaYeiAHrL2yOck6kgI1qmes1gOTbOdIXL/s0l1H7GsPvTjf33HyOyQTj/URNtG5TF/xjj0GPgHXNw8P0O/2H3wa6zsF/H+SOs+4q+hoZFGrQnl4yjXEbUnJo0fJTNdnJyzpRl0EAfxjx89QL9BI9H+l644fuwQ5sz4+6ttk8ie6NStJ/oNHIG+A0fAwckJQ/r3lIGQr54IzOjqJQtiMU36IFWqVJHDOSIjI2WNCnG2uVGjRhrrtGjRQgYnxJAPJycnzJs3DwULFtRYR1xfp04dWTRTpLaJ/0U9iw8hos+pI9CxcXEweU9mR3pbtHGnrHExY1hvmKhlI3xtRH8OH9QPEydPQ4Pmrd5maBRCyWJFkSwKUaipVrkiihUuJAterlm/CaPGTcDkP8e+N8NGF+zaswdVK1fW+jybNGqA2rVqyCEsy5avwJ8T/8LI4UM1isTqgsiYOAxasRvDGleDnYUia4l0lJ4+kqPCEb1zhcjjReKrJ9C3spXBithjijTcjHb26FasnptSo+GXfjM+eZtr54/Giyd30X3E4k/eVlZ1+MAezJ42UbU8cHj618gRmRRjhveHZ3YvNPvhy2U1foiFm/dg94kLmD24m+p79saDJ1i56xCW/t4n3T6nD6Xql0EZ0C9pEcNLhcbNfkSZcorATLee/fBz6yY4fvQgan1T7z9v+9jBnTJgoPTb0ElIL2uXzZbFOfuPmgYraxucO3lY1qgYMnY2PHP4fnC/zJqW8hwHDR+LL2XJwrnw8PRC5ao13tt3Yh/t2WeQqmitGHrx59jh+KVLDzls92trk7tHdnlRyp03P16+eI4tG9eix28DM+iZUlbAQAV9EAsLC/j6+qqGaxQqVEgGItq3b69aRwQwRO0KGxsbGZBIixj+0a1bN/n/9OnTP/g5jB07FiNGaBZC69exDfp3+gn/la21pTwjk7qglyikaZ8qYyG1pVt2Y/GmnZg2uIecKSSj2FhbyUBCcKqMD1FI085OeyEjcX3qQpuiSKW9WuTdzzcnZk/9S874IVIDbW1s0K1XX/jl0iykZGlhIS9iNpA8/n4ysHH0xClUrVThP7dJzBQj2qRe8CmlTXYf3Ka0XoMrV6/h6dNnGNivn9ZtiX1WXDzc3ZHd0xM/tmmLGzdvySEg6UkEGwz09d4pnCmWHa00syyEJ4GheB4chl8XbHnnB2zRflOxqU8reDpmgmJWOiY5KgLJSYnQt7CCetlYfXNrJEdqP5uZHKE4wy2CFEqJgS+hL4ZH6BsA4rYMlr9YFTmsQ0lUfBfCQwNhY5fymS2W3b1SzlynZe3833H9/CH8b/gi2Dr8+yw1pJ2oGaE+A4TIaBPEGU47+5RsF1EUM4eP9oM5cdCnr2/wzmeouE/qzIzoqCiMHtIHpmbm6Dt4dLoPc7O1stD+PRsWDodUWRapiVlCFm7ZhxkDusiZQpQu3LyHoLAI1P11uOo6kbXx97KNWLHzELZM1iz2/V+ULFVOY7YEZb+IrAJ7tX4Rr7n3e/tFfLdpngUX99E2y0Ja7OwUjycCS0pi1o9sLm4I+MQaUUVLVtCYmSMhQdHOsBCx/6WcSBLL2X20Z01ZWdvK/U89e0IQy8osi1cvnspZQcZNWwGP7D7yOi9vP9y6fhF7tq9Fuy79P6JfUmp5iAwA+VjBwR/dL8pCs9r6RfwVr0VkRIRGBoJ4HOU6Vy5dkJkSx49qZji2bvG9DCq1+LGtzD6wd3DUmFlHBALEybrAgDdyFo+vrU3a5PLLw+Ef9NkxUEEfTXwQDhw4EL169ULLli1V9ShEIU1lMOPfZhARtStEhLlWrVof/LgDBgyQj6ku+qb2QlwfysjQELl9suPMlRuoVEJR70Ckzp25ehNNaqVdpGrJpl1YsGE7Jg/sjjw5cyAjiWEWIqhw/tJllCtTSvWcL1y6gu/rfqP1Pnlz++PCxcto9H1Kiu+5C5eQN/e7U3+KIITw9NlzWQvjpx9bpvlcxCGYyLhQ/oD7lDbl8vWVz7FsmTKqNl28eAn16tbReh9RAPPipUtoWP971XXnL1yU16e2a/duuf2cPv8+faIo2CZ8aps+hJGhAfK4O+PU3Seoml8REEpKSpbLzctq1hQRvJ3tsLa3ZlX46TtPIDI2Dn2/rwQX2/f/6Kf/KCkRiS+fwNDLH/F3Lr+9Ug+GOfwQe+6w1rskPLsP47xiqlxxtlcRrNC3c1bM+vEFghSCqZmFvCiJH8rWto64c/UkPHIo3jcxURF4dPcyytVo+v6pCxeMwZUz+9Bt6AI4OGdcoDYzMjM3lxf111ccNIhZArxz5lJlQNy5dUMWUkzrMzSnrx+uXDyHUmUqqD5DL188j2/qNlCtJ7YjZgIRB7kDho6BsbFJurdPfs96e8pCmJWLF1T7nr2NpjXTDnCLoR3zN+3GtH6dkddHc2awb8uXkDUu1P3vj1n4tnxxfFdR8b2YXv1y+Z1+uY7a39Z7T7/4y37Q7Jdz+FatX/5Nzlx+clvPnj5BnnyK11CcUHj9+mWa6fkf3k4LeVFvp42dA65dOgMvH8VrHBUVgXu3r6HaN4ppvVMzNDKCt29ueZ/ipSup2nnt8hnUqNNELsfFKmYQS50BI35TKr93/2u/2Kn6xVfj/VL7X94vin4pr3q+V9TeL+J2EcS7fOmcKovl2dPHePPmFfzzKAI7fQeNQFxsSi22u3duYtrf4/H7+ClwcVXMyJUnT34cP3pIZhsrfy8/f/ZEttvB0emrbJM2D+/fVQXUvmosCKpTGKig/6RJkyayRoXIiPjtt98+6r5ieMiNGzdU/38okR4nLuqSPsNwgxZ1qmPkjIUy4JA3Zw6s3L5PFu6qW1lRy2D4tAVwsrdF15aKD3qRRTFn9RY5+4ebswMC32Y2mJmawNzUFBmhUf16GP/XFPjnygl/v1xYv2mrnOq1dnVFBHzcxMlwdLBHh59ayeWG9eqiV//BcqhGqRLF5PSmIgjRs1tn1TYPHT0GG2sbODs7ymlLZ8yZh7KlS6J4UUUA5/nLlzh4+JhctrG2RkBgIFauWS+HUpQs/t8LYanPvjFh0l/wy+ULfz8/bNi0SbapZg3F9GvjJ06Co4MD2v3URi7Xr1cPffoPwNr1G1CyRHEcOnxEFuTs8T9Fto5SZFQUDh89hl86pGT/KN28eQu37txB/rx5YWllKafXXbRkGVxdXZEnnbMplFpVLIIhq/Ygn0c25PfMhqVHLiI6LkHOAiKIoR7ONhbo/m05mBgZIpdLqnoCpor3ROrrdXWKSAvflAMOc28PWBfKjbigUMQ8Sbt4qC6IPbMf5nVaIeHlYyS+eAiT4lUAIxM5C4ggbhNBiJjDmxXrXzgCk6IVYVa9MWLPHYK+nRNMy9SU/+sKccBQ8ZtW2L1hDpxcvGDv7I7tq6fBxs4ZBYqnnE2bPqo9Cpaohgq1W6qGe5w7th0dfpsCEzMLVT0LU3NLGBtnzGdgZt73RL/U/b4J1q5cDFc3Dzi7uGDFkvkyu0LMNqE0fGBPlCxTAd9+pzh4/K5BU0ydNBY5c+VGLr/cctaA2JhoVK3xjepAZ+Tg3+RUmN1/GyyXxUWwtrH9qO/jj/XDN5UxfPYy5PXOjnw5s2P5zkOIjo3Dd5UUQQVRFNPZzgbdmiuC6Qu37MXstdvl7B+uTvYICFFkLpnL71kTmaUhLurErB8ONtZylpD065fGWLtyieyXbC6uWLFkHuztHTX6ZdjAXvJAMaVfmsh+8c3lL886b5H9EqPqF0HM6CIyaF68UMwK9OjhA3lQ6+icDVZW1jA3t0DNb+th5bIFcHRylsGJjetWynXLlq/82dtZu15zbFy9ANncPOGczU0O2bC1d5SzgCiNGdwVxUtXlrN4CN983wKz/x4Jb988yOmXV84cItpZqVpdeburRw5kc/XA/Onj0LLdr7C0EkM/DuHqxdPoPWTiJ/fLGtkv7rJfli+ZL/tFecAuDB3YC6Xl+0XxW65egyaYMmmcDAKJfhHvF/Gbo1oNRR0hkQFRrea3crpPS0vRB+aYO2sq/HPng//b2VlcXVOyfJR1IgRPTy9VxoKYQnb1yiWY+tcfaPHjTwgLC8Wi+bNl/6f+Tfu1tEkM8RCzl2T38pYnHvfu3oYrly9g2Kj0n+qYshYGKug/ERFZMXxj/Pjx6Nw55WD3Y1L9dUWNsiVkUc05qzcjMCQMfjk88PeAX+Fgq3iOrwKD5EwMSuv3HJZVzAdMmq2xnQ6N6+LnJulblEypSsXyCA0Nw8KlK2WxSZEpMHbkUNWwh9dv3mg853x5cmNgn55YsGQ55i9eCnc3V4wY1B/eOVLSSIOCgjHrnwVySIm9nR1qVK2MH5srfoAIxkbGuHrtOtZv3oKIiEhZzLNAvnyY8ue4d4pa/heVK1ZAaGgoFi9dJtvk4+OD30eOUA39EEUu9dXOxIipRPv3+Q2LlizFwkWL4ebuhmGDByGHWpuEQ4fEGe9kVKlU8Z3HFNOfHjt+AkuWLZdf5mIK0+LFimFQs2Yw/sQCoR+qdmE/WVRzxq6TCAiPhL+bE2Z0+B4Ob4d+vAwJ12j318ymWH6U2bdEtZx3gmIs65PF63G5/QDosvib5xFtbgmz8nWgJ4aAvH6GyNXTZR0KQd/aXmOYR3J4CCJWz4BZtYawajcASeEhiD17ELGntE+3+6VUq9cOcbHRWDV3OKKjwuHjXxQd+8+CkdqZ9oBXTxARnpJOfGzPKvl32kjNFOAWnUbLaUt10de279Vv3AIxMdGYNXWCnCIwd94CGDLqT40MCDEmXHkQIZSrWBWhoSFYuXS+POgVKeKDR/6pSum+f/e2PPsvdO2gmSk3c/5KOGdLv5kzapYpiuDwCMxaux2BoWHw8/LA1H6dZGBBtiUwWONzbt3eY3JWpH6TU2YJE35uWBsdG2nPHMwIDRq3kAffyn4RU0wOGTU+Vb88kwejSuUrVpWFS1csXaDqlyEjx2sM/di1YzNWL1+kWh7c71f5t1uPfqqARpt2nWGgb4DJE8cgLjZWDhcaMWYSLK0+fyZd3YatZJBr/vSxsqaEX95C6Dt8skY7X798JotoKpWuUEO2c93yOXLWEJGN0Xf43zI7Q/m7sc+wv7Bq0XRMHNVbbl8ELjr2GIrCxct90vNt0Li5fL/MnDpRrV/+0KhJJd4v7/ZLKFYuXSiLoXr75MTQkX9o9Eu7n7vKoMH4McNklmXhoiXQsUuPj3puIuA0fPQE/DNrCn7r0UkGnspVqIyWrdp/tW0Sw0cWzpspZ6IxNjFFjhw+so0FChX5qO0Q/Ru9ZJFfRPSVCrl4EJlNuIUzMptEvcwXE3W5rlsHnJ/DvkZTkNmUG512QbCv2YmafyGzSSqZH5mN1w3dyaD5XLxCLiKzeWz77lC7r11UYuYruGxhoH02LNI9eX3THiaiy2J2zYOuMq31/uBWZsSBOERERERERESkMxioICIiIiIiIiKdkfnysYmIiIiIiIg+hh7P4esS9gYRERERERER6QwGKoiIiIiIiIhIZ3DoBxEREREREWVtmWQ6+MyCGRVEREREREREpDMYqCAiIiIiIiIincGhH0RERERERJS16fMcvi5hbxARERERERGRzmCggoiIiIiIiIh0Bod+EBERERERUZaWzFk/dAozKoiIiIiIiIhIZzBQQUREREREREQ6g0M/iIiIiIiIKGvT4zl8XcLeICIiIiIiIiKdwUAFEREREREREekMDv0gIiIiIiKirI1DP3QKe4OIiIiIiIiIdAYDFURERERERESkMzj0g4iIiIiIiLK0ZD29L/0USA0zKoiIiIiIiIhIZzCjgr5qUYtmI7MxszJHZhMTFIbMJtjECJlNudE1kNkcG7wHmVHZpO7IbPQy4f5nen8HMp2kRGQ25yJrI7OxNEtCZhMUbvulnwJ9oLy+X/oZUGbAQAURERERERFlbZz1Q6ewN4iIiIiIiIhIZzBQQUREREREREQ6g0M/iIiIiIiIKGvjrB86hRkVRERERERERKQzGKggIiIiIiIiIp3BoR9ERERERESUtenzHL4uYW8QERERERERkc5goIKIiIiIiIiIdAaHfhAREREREVGWlsxZP3QKMyqIiIiIiIiISGcwUEFEREREREREOoNDP4iIiIiIiChr0+M5fF3C3iAiIiIiIiIincFABRERERERERHpDA79ICIiIiIioiwtmUM/dAp7g4iIiIiIiIh0BgMVRERERERERKQzOPSDiIiIiIiIsjY9vS/9DEgNMyoyMT09PWzcuPFLPw0iIiIiIiKirJVR8fLlS/z+++/Ytm0bnj17BmdnZxQuXBg9evRAtWrVkNkNHz5cBiQuXryocf2LFy9gZ2eXro+9cOFC+TqHhIS8d722bdvC3d0do0ePlgGUDRs2oH79+hrr/PTTT3I7XyK4Yl6uBiyrfgcDKxvEP3+M0PULEf/4ntZ1HboOgYlv3neuj7l+AUFzx+NLMS1ZBWblakPf0gYJr54gcttyJDx7kOb6eqZmMK/WECZ5i0LPzAJJIYGI2LES8XeuyNvNq9SDeZXvNe6T8OYFQqYORkaxqFgLVtXqwcDaFvHPHiF4zXzEP7qrdV2n7sNhkivfO9dHXz2PwFljAX0D2HzXHKb5isLAwRnJMVGIuXkFoZuXISk0GF+S2P8sKtdV7X9hGxYh/on2/c++8+A097/geX9CVxgXqQjTUtWgZ2GNxNfPEL13DRJfPEpzfT0TM5hW/A5GfoWgZ2qOpLBgRO9bi4T716HL7MsXh0/v9rApmh+mbs4426gLXm3eB11lUrQiTErVgL6l6JeniNq9+t/7pVI9GPsXftsvQYjauxYJ967J2w09feX2DF08oW9li4i1sxF/5xK+pMyw7608fA6L9p1CQFgk/Nyd0b9xDRTI4aZ13b0Xb2He7hN4EhCM+MQkeDnZoVXVkviuZH6N9e6/DMDfmw7i3N0nSEhKQk4XB0xs3wCu9jYZ06Yj57Fo/5mUNjWqhgJertrbdOk25u05iScBIW/bZItWVUrguxIpn/FRsXH4e8thHLh8B6FRMXC3t0GLikXRtHxhZKTk5GQc3jwFF46sQWx0GDxyFsU3PwyHfbYcad7n3MHlOH9oBUICn8llJ7dcKF+nC3wLVFKtc/7wKlw7vRUvH19DXEwkev99Bqbm1hnWpr3rp+LMgTWIjgqHl18R1P9pGBxd0m7Twc1zcPXsHrx5cR9GRqbwylUEtZv3hpOrt2qdOb+3xoObZzTuV7JqMzRoOxwZ0aZjW6fg8jFFP7n5FEXNFsNh55x2my4cXo6Lh1cgLEjRTw6uuVD22y7wyZfSTyv/aoUnd05r3K9Q+Wao2XIk0ltmbBN9PtOnT8eff/4pj5MLFSqEqVOnomTJklrXnTt3LhYvXoyrV6/K5WLFimHMmDEa64vjtEWLFmncr1atWti5c2e6teGrD1Q8fPgQ5cqVg62treyMAgUKID4+Hrt27ULXrl1x8+ZNZFUuLi7QBYmJidi6dasMJOki08KlYVO/FULWzJMHwRaVvoFDx/54PbY3kiLC3lk/aMEk6BmkvHX0Lazg9Ns4RF88iS/FOH8JWNRuhogtS5Dw9D7MytSAdeueCJ4yCMmR4e/ewcAA1m16y9vCVs2UP8z1bR2QHB2lsVrCq2cIXTQh5YqkJGQUs6JlYdugDYJXzUHcw7uwrFIHTl0H4eXI7lr7JWDuhFT9YolsAyYg+sIJuaxnbAIjTx+E7Vgrgx765hawbdwWjh374fX4/viS+591vR8RunY+4h/fhUWFb2D/S3+8+UP7/he88C/oGaq109wSjr3HIebyKegKo9xFYVa1AaJ3r0LC84cwKV4FFk27InzuSCRHRbx7B30DWDTrhuSocERunIfk8BDo2dgjOSYaus7Awhxhl2/hycJ1KL52OnSZUZ5iMKvWCFE7V8h+MS1RFZbN/oewOcPT7BfLFr/Kz4mI9XORHBECfWsHJMeqfU4YGcuAR9zl47Bs1BFfWmbY93aeu4EJG/ZjcLNaKODlhmUHz6DzjFXYNOQXOFhZvLO+jYUpOtQqA+9sDjAyMMDha3cxbNk22FuZo1weH7nOkzfB+OmvpWhQphA6f1selqYmuPcyAMZGGfMzcOf5m5iw4SAGNxUBF1csO3gOnWeuwaZB7bW3ydwUHWqUVrTJUB+Hr97HsOU7YG8p2qQ48J2w4QBO33mMMa3qwM3eBiduPcSYNXvgbGOJygV8kVFO7JqLM/uX4Lu242Dr6IFDmyZjxeT26DhiOwyNTLTex8rOBVUa/gZ7Zy8kIxmXj2/Emhld0WHIBhm0EBLiopEzXwV5ObBhIjLS4W3/4PjupWjyy1jYOXlgz7opmD/+Z/QctxVGxtrbdP/mGZSp3hIePvmRlJiIXWv+wvw/2sv7GJuaq9YrUbkJajT6n2rZyMQsQ9p0es9cnD+4BN+0HgcbBw8c2zoZa6a2R7uh7+knWxdUqv8b7EQ/JSfj2smN2DCrK9oM2ADHt/0kFCzXFOXq/prSJmO26WuVWWb9WLVqFXr16oVZs2ahVKlS+Pvvv2VQ4datW/KEfmoHDx5EixYtULZsWZiamuKPP/5AzZo1ce3aNXmiWal27dpYsGCBatnERPt+9rl89b3RpUsXeYb+9OnTaNSoEfz8/JAvXz7ZOSdPphw4Pn78GN9//z0sLS1hbW2Npk2b4tWrVxpZCSILY8mSJciRIwdsbGzQvHlzhIenHOStXbtWBkLMzMzg4OCA6tWrIzIyUt5WuXJlmVmgTmQMiOiTktiuyCho3bq1fB5eXl7YvHkz3rx5o3puBQsWxNmzZzUyFkQQRmQZ5MqVS+48Ykd78uSJ6vYRI0bg0qVL8nUQF3GdtqEfV65cQdWqVVXP/5dffkFERMoPN/FcxXOeMGECXF1d5Toi2CMCP5/i+PHjMDIyQokSJaCLLCvXQdSJ/Yg+fUhxYL5mHpLj4mBeqrLW9ZOjIpEUHqq6mPgVQHJ8LGIufbkDRbOyNRFz7jBiLxxD4psXMmCRHB8H06Llta5vWqQ89M0sELZ8GhIe35XZFAkPbyPx1VPNFZMSkRwRlnLR9kM/nVhVrYvI4/sQdfIgEl4+RcjKObJfLMpU1bq+eG5J4SGqi2nugkiOi1UFKkQGRcC0UXI54fVzxD28g+DV82CcPScM7BzxpVhU/BZRJw8g+szb/W/dPLk/mZVMObuhLjlac/8z1oH9LzWTElURd+k44q6cRFLgS0TvWgnEx8G4QBmt6xsXLCPPZEeun4PEZ/flWfvEJ3eR9EZxxkeXvdl1GLeH/Y1Xm/ZC15mWrIrYS8dU/SICFkiIg3HBslrXNy5UVvZLxLpZin4JDULCkzsyS0FJZB3EHN6C+NtfNosiM+17Sw6cRsMyhVC/dEHkdHXE4Ga1YWpshI0nLmtdv0QuL1Qr5A8fF0d4Otnhh8olkMvNGRfupXyeT916GOXz5UTP+lWQx9NFrle5QC6tQYJ0adPBs2hYtiDqly6AnC6OGNy0pqJNJ6+m0absqFbIDz4uDvB0FG0qhlxuTrhwP6VfLj54ju9K5pPrujvYoHHZQvBzc8bVxy+QUcSB3um9i1G+Tmf4F66ObB65Ua/teISHvMatC2l/JvgVqiqzJ0TWhUM2b1Rp0BPGJuZ4dj8lM7Zk9Z9Q9ptf4O5TCBlJnqXfuRhV6nVC3mLV4JrdH007jpNtun4u7Ta16zsXxSo2QDaPXHD1yo3Gv4xFSOALPHuoyL5SMjIxhZWtk+piamaZIW06t38xStfujFyFqsPZIze+bTMeEaGvcedS2m3yLVgVPvkryQwF+2zeqPC9op+eP9DMYDYyNoWljZPqYsI20Rc2adIk/PzzzzKjPW/evDJgYW5ujvnz52tdf9myZfKYWhwL586dG//88w+SkpKwb59mhqgITIgT4cpLemfuf9WBiqCgIJluIg6mLSze/bIVB/iCeKFFIECsf+jQIezZswf3799Hs2bNNNa/d++ePLAXZ//FRaw7btw41TAKEWlq164dbty4ISNPDRs2lB8UH+Ovv/6SGSAXLlxAnTp10KpVKxm4+PHHH3H+/HnkzJlTLqtvNyoqSg5tESk5x44dk8MjRBBFEG3o3bu3DM6I5yguqdsliICKCHCIHerMmTNYs2YN9u7di27dummsd+DAAfk6iL8ivUcEPZSBj/9KBGO+++47GTjROQYGMPLwRuxttR9LycmIvXMVRl4pkeX3EQENcfArDoq/CAMDGLp6If7ejZTrkpMRf+86DD1yar2Lce7CcmiBZd0fYN93Emy7joRZxW/fKSJk4JANdr9NhF2PcbBs9DP0bezTuzVvH9hQZj/E3FL7YZ6cLJeNvf0+aBMWZash6vzx9/aLvpk5kpOSkBStCDh+sf3vTqr97/ZVGH/E/hdz4eSX2/9S0zeAgYsnEh7dUrsyGQkPb8HQPSUFWJ2RbwEkPn8AsxrNYN1tDKzaDYRJ6ZosavXZ+yU7Eh5o9kv8w5tp9otxrgJy+Jh5zeaw+XUcrDsMhmmZWrrbL5lg34tPSMSNJy9R2j8ldVtfX08uX37478ET8dvh1K2HePg6CMV8PeV1SUnJOHLtHryc7dFp+ipUHjAFP0xYhP2XbiMjqNrk56XZJj8vXH74/APb9AgPXwejWE4P1fWFvd1w6Mo9vAoJVwQM7jzGozdBKKP22qW3kICniAx7gxx5UoJ9puZWcPcuhGf3L3zQNpKSEnHt9DbEx0XB3acIvrTgN08RHhoA3/xlNNrk6VMQj+9+eEAyJlpxos/MQnNo0aXjWzGqcxn83f877Fw1CXGx6Z+9FBqo6Cev3Cn9ZGJmBdcchfD8I/rpxllFP7ml6qfrZ7ZgWp9SWDCqLg5vnIj4OLaJPr/Y2FiEhYVpXMR1qcXFxeHcuXPyhLqSvr6+XD5xQnHy7t+IY09xotreXvN3vzj+FRkZ/v7+6Ny5MwIDA5GevuqhH3fv3pVfTiLy8z4iGiSyCR48eABPT8UXtzjoFwf34qBdeaZfBDTEQbmVlZVcFkEEcV8RJBABgISEBBmcEJkQgsiu+FjffvstOnZUpMcOHToUM2fOlI/fpEkTeV2/fv1QpkwZme2hHLohdpRp06bJ1B1BBBDy5Mkjs0jE2CGRiWFoaPjeoR7Lly9HTEyMbLcyqCO2KQIIIr0nW7Zs8joRyBDXGxgYyNdVBFPEayCicv/Vpk2bZIBGnQj6iMdQJ95s4vEykr6FNfQMDJAYHqpxvTxT7ax9PLA6o+w5YeSWHSGr5uBL0Te3km1IitQcJiCWjZy0j//Vt3OCkXcexF4+idAlk2XNBsu6PwL6hog+uFmuE//0PhI2zEdiwEvoW9nAvHI92LTvj5BpQ5EcF5O+bbJ826bU/RIWCqNsKSloaTHy8pX9ErRsZtorGRrB5vsfEX3u2BdL8xbDhrS2MyIUhh+y/3nmhJFrdoSumgtdoWduCT19sT9qDjlKigqDoYPicyY1MexI38YPcdfPIHLNTLl/mtVsJgM5scd2ZNAzz9xU/RKl+TkhhnWIgKQ2+raOMPRyQNy1M4hYPR36ds4wr6Xol5ij26FrMsO+FxwZhcSkZDhYa558EZkPD16l/YMwPDoGNQZPl0EBEQQY2LQmyuRWBGeCIiJlPYf5e06iW50K6PF9ZRy7fh+95q3HP/9rieK5sqdzm6IVbbIyT9Umczx4HfSeNsWixtCZKW1qUgNlcqcEIfo3roaRK3ej5rBZMNTXlydDhjWvqQrQZARxoChYWDloXG9h7YCIsID33vf101tY+EdzJMTHyjPajTtPh5Nbxg1ZSUt4iOJ5W9potsnSxhHhoYr2/hvxe3rr0rHw8isKF8+UkwuFy9SFraMbrO2c8eLxLexcNREBLx/gx+5TkZ4i3z5v0S/qxHLkv/TTm2e3sGxCSj/V/2U6HF1T+ilPibqwtneDpY2zXPfQxgkIevUA9TtOQ3rKjG3SCboaiAcwduxYmUWvbtiwYXJUgLqAgAA57F55bKcklj+0JII4HnVzc9MIdohhH+I42NvbW57UHjhwIL755hsZ/Eh9TPe5fNWBig/NZhAZECJAoQxSCCINRmRciNuUgQoxNEMZpBDE8IfXr1/L/0URElGYUwQnRGaCGLfTuHHjj055EUM7lJQ7kHrAQ3mdeFxl4EEEIdSHTYgAgvK5p1UURdtrINqgnnkiMjvEl4kYr6R8XBG8Ud/ZxGsggjz/lXjc58+fv1PUVAQu1Hd+5ZtCvLHSIgIZqSOHsQmJMDFMnzfHh57NFsUP0yq8qavEDzoRyIjYvEiewReF5qKs7WBerlZKoELtLL8YEhL29D7seo2Hcf7iiD1/FLpMDA+Je/YozcKb4syrQ/tegB4QrEMH+R/LTLn/pVF486uhpy9rBESLoQhif3z1RBZmNClZjYGKL0lPTwYyonYsU/TLyyeIsbSBaekaOhmoyMr7noWJCVb3bycDEiKjYuKG/fBwtJXDQpLe/laqUiCXLLIp5PbIhksPnmHN0QvpHqj4ryxMjLG6bxtFm24/xsSNB+DhYCOHeggrDp/H5UfPMfnnBnCzs8a5e08xZu1eONlYamSkfE5XT23G9qXDVMvNus3+z9tycPFGhyEbERsdjpvndmHLgn748belGR6suHBsCzYuSDnQadP7PQH+D7R50Ui8enoHnYYs07i+ZNWmqv9FAMPa1gn/jGuLwFeP4ZDt8+2H109vxu4VKf3UqPN/7ycxPKLNgI2IjQnH7fO7sH1xPzTvuVR1YC+KTCo5ufvDwsYJqyf/hOA3j2HnxDbR5zNgwABZ2kBdetSIEKMJVq5cKbMnRMkBJWU2v/LYVRzTipEAYr30mrziqw5UiJoN4oDrcxXMFHUU3jmYe1s8UBy8iyEjot7C7t27ZeXUQYMG4dSpUzKyJFJqUgdOtNV2UH8M5VAIbdcpHzejve81+K/DPmrUqKGxowsiCOPrq/llLIJE75s9RFsksVepfOhd5uMzW5TEwXpyYqJitgW160UGQWLY+2cyEcUZzYqURfjONfiSkqLCZRtEdog6sZz6TL3qPhGh8j7ih7lS4pvn8ge6OJMILQEjkXWQGPgKBvbvFuH53JIi3rbJSjNlVN/6w/rFvFg5hG1b9d4ghahLETB1xBctmifO/Gptp6WNrLPxr/tf4TII37UWukTUCklOEvujFdT3In1zaySnyvpR3SciVKagauyPgS/l6yD6S9RKoc/UL+bWGv2iZ2GltWirIK9P/Hr6JTPse3YW5jDQ10NgmOZwtMDwSDimyrJQJzIOsjvZqYIQIvti3u6TMlAhtikyDkS9B3XeLg64qFbHIr3YWZgp2hSuWaxZLDtafWSb9p6SgYqYuHhM2XoEf7Wvj4r5FEMcxUwit569ljOLpFegIlehqujgnVIzIjEhTv6NDA+ElW3Kd2NkWCCyeb4/29fA0FgW0xRcvfLj+cMrOLNvMb5tlbEzK+QtWhWevikn0RLjFW2KCA2EtVqbIkID4OqV51+3t2nRKNy8eAi/DFoCG/v3F3X3zKl43M8dqBB1GMQQiHf6KSxQZgkoiWVR2+Hf+kkUnhRcsufHi0dXcO7AYtRKYwYM5eOGvHn0WQ/qM2Ob6OOYmJh8UGDC0dFRHreq12IU1LP10yLqFIpAhSgPoH5yXRsfHx/5WGKEQ3oFKr7qGhVi3IzIbhDTryiLWqpTHvSKYRKi+KSyAKVw/fp1ebvIrPhQ4qBdZCGIg2VRY8LY2FhOsyk4OTnJ4SFKIjNAOcXLpxJDTtQLbIoMCPHcRbsE8Tzel4kgiHVFwU3110nUuxABFjHOKL2IYR+iPsjniiSGhoZqXLqV+PD+0yoxEfFPH8DYT20aNz09Oc1l/KM7772raaFScvaFqLNfOLsgMREJLx7ByEftB4SenlxOeKr9TLuYXUIGHNRS3AwcXBRBgLT2JWMTGNg5pxn8+KwSExD/5D5M/Qto9otfAcQ9eP+4arMiZRT9cuZwmkEKQycXWVgzKTLjioO+b//TmFb17f4X94H7X/Q5HctuSUqUZ94NvdQ/V/RgmMMvzelyE57dh4Gdk1xPSV+5r+nYwfBXS/bLYxjm0OwXIy//tPvl6T05FEK9XwzssymCaLrYL5lg3zMyNJDFLk/dfqi6TtSYOHX7EQrm+Pdhb6r7iDpFCQmqbebzcpV1K9Q9eh2UIVOTprTpkZY2/fsQN802KfpETK+akJgE/VRp2iK4ocwgSQ8mppYyuKC8iDPQFtZOeHgjZdx3bHQEnj249NH1JpKTk1QHnxnJxMwCjtm8VBdnd19Y2Tji3rWUgvQx0RF4cv8ysvumXdhTnKwTQQpRcLPDgAWwd06pJ5KW548VJxpFUc3PydjUUh6IKy8Ob/vp8S3Nfnrx8NI7tRn+1b/00+uninph4vE+p8zYJp0kZv3Q1csHEseFYnpR9UKYysKYorxAWsaPH49Ro0bJ+o/Fixf/18d5+vSprFEhsu/Ty1cdqBBEkEIcpIshEOvWrcOdO3fkcIMpU6aoOkMMMRApKj/88IMsWClqO4iClZUqVfqgjhBE5oSYT1YEDMQMIuvXr5ezdSiDBWI2DTH9priIDA9RYOR92QEfm+Xwv//9Tz4HURxFzM5RunRp1bAPMWRF1N+4ePGiHJekrbCKaLvIamjTpo0MoIhimWKbog5H6jFMH0u8/uKx1S+iD8TwFfF61a1bF5+DiCKKGVvUL59j2EfEwW2wKF0FZiUqyroANo3bybPVUacOydttW3aGVZ2UdCcl89JVEHPlbIbOhJGW6OO7YVqsIkwKl4WBoyss6v4o2xBz/pi83bJhe5hXb6haP+b0AeiZWcDimxbQd8gGI7+CMK/4LWJO71etY16rqfyBL8ZwG3rmhHWLrvLLLPZKxswuEb5/qyyIaV6qEgyzucO22c/QNzFB5MkD8na7Vt1gXa+l1mEf0ZfPvBuEEEGKDr1hlN0HQYumyA99kUGiyCL5csllkYe3w7xUFZgVryD3P+tGYv8zlbPQCDYtOsPq23cL5JqXrIyYq+d0Yv9LLfbMfjljhFH+UnL/MhN1DYxM5EwMgnmdVjCtWC9l/QtH5MwLZtUby4NEQ598MC1TE7EXtASbdHB6UutCueVFMPf2kP+beqbfF/d/Jd7fJoXLwbiA6BcXmNduruiXy4ofuuZ128C0UkpgOfb8EVlw1qxGE+jbO8MwZ36Ylq2F2PNq/WJkAgNnD3kRxOeF+F/POn0rgWfmfa9VlZJYf/wSNp+6gvsvAzB69S5Ex8bJWUCEQYu3YPLmg6r15+0+gRM3H+BpQIhcf9G+U9h2+hrqlEgJwLepVhK7zt/AumMX8fhNMFYcOofDV++iaYWMKd7YqnJxrD9xGZtPX8X9l4EYvWY3ouPiUb+U4jkOWroNk7ekvObz9pzEiZsP37YpUGZJbDtzHXWKK05OiOlVi/t6YtKmQzhz5zGeBoZg06mr2HrmOqoV/LBCxJ+DOIFVsnprHNs+E7cv7pN1JzbP7yuzK/yLpAxvXTapDc7sX6paPrB+Ih7fPiOLcYr7iOVHt08jX6nvVOtEhL7Byyc3EPT6sVx+/ey2XI6ODEn3NpWr3Rr7N83C9fP78fLJbayZ1V+2KW+xlDb9M7Ytju9JGdqxadFIXDy+Bc06/wkTUwuEh7yRl/i3Na1E1sS+jTPw7ME1BL95Jre9ZnZ/ePsXlzOLpHebilVtjRM7ZuLu5X2y7sL2RX1lJoKYMUNp1eQ2OH8wpZ9EEcknd87IwpXiPmL58Z3TyFtC0U9iKMTx7dPx8vFVuY7Y9vZF/eDhW+JfsxrYJkpPvXr1wty5c2VdQ3FMJo5LxclqMQuIII6DxQlgJVGvcMiQIXJWEHFc+fLlS3lRzg4p/vbp00fOqPnw4UMZ9BAnokV2vEgaSC9f9dAPZdqJCD6Igpdi9guR1SCyG0QkSRSqVL6ZxZl9cWBesWJFmUUgCoKI4RsfShwUHz58WM5DK6qsioKaEydOlEVEBDEbiMhYEB0vakr07NkTVapU+SxtFNPJiPoNLVu2xLNnz1ChQgXMmzdPdbuYllUETsTjieCImN9WfVpU5TZ27dqF7t27y3oXYlncT0xf86nEzlukiOaPHTFmSbwBRDBFpAXpspiLJxFqaQ2r2o1hYG2L+GePEDh7nBweIcipK1OdoTFwcoWJT24EzhwDXRB39Qwiza1gXrU+9C2tkfDyCcKW/KVKdzYQs3WotSEpLFjeblG7Gey6jEBSeDCiT+5F9JGUMdkG1nawatwR+uYWcoiCmMY0ZM7vGXZgHH3+OEIsrWFdpxkMrES/PETA9N9VGR2G9u/2izjQN/HNgzfTRr2zPQNbe5gVVNR6yTZggsZtbyYPQ+yd6/hS+1+YhTUsa6Xsf0Fzxf73tu9sHWSAKPX+Zyz2v9m6sf+lFn/zPKLNLWFWvo4cWiCms4xcPV3WAhD0rTX3x+TwEESsngGzag1h1W6APGMfe/YgYk/tga6zKZYfZfYtUS3nnTBQ/n2yeD0ut0/5EaAL4m+ck/1iWqGuHBqW+PopIlZPU+sXO419LTk8GOGrpsG8WmOYtB+k6JczBxBzcrdqHUPX7LD6oadq2bx6Y/k39vIJRG1LeV0ySmbY92oXy4PgiCjM2HYEAeGR8Hd3xowuzVQFNl8Gh2lkEogD/jGrd8vZL0yMDOGdzQG/t/5ObkdJTF86uFktWVDzj3V7kcPZHhPbN0DRnBlTeLJ20dyKNm0/hoCwSPh7OGNGp8ZqbQp/t01r9uBVaISiTc72+L1VHbkdpT/a1MXkLUcwYMk2hEXFwNXOGt3qlEeTcoWRkcrU+hnxsdHYvnQoYqLC4OlbDM27/wNDo5QU7eA3TxAdEaxaFkNFNi/oJ6eSFDM1OLv7o0X3efDJW061zvlDK3Fka0rxwiV//iD/1v1pLAqVTTnxkB4q1ukgZ+PYMH+YbJMoitm2zxwYGae0KfD1Y0SFp7Tp1L6V8u/cMW00ttX45zFy2lIDQyPcu3oCx3Ytlq+XGBaSv3gNVKnfGRmhZA1FP+1aPhSxUWFwz1kMjbtp9lNIqn6KCg+UB+mRYa9hYmoFR3d/NOk2DznyKPrJwMAIj26ekMMm4mOjYGXnCr/CNVHmmy5sE31RzZo1kyfUxcQNIuAgph0VmRLKk9PipLs4HlYSx8xithBRf1FbsU4xlOTy5csy8CGONUWhTVGvUWRgpEedDCW95I+dX5MylJiFpEePHp8tOyMj1atXD+XLl0ffvn3T7TGe92yBzMY4VWX0zCAmSPv48K+ZgYlmPZfMwMw5g6afzUDHBut+sOO/KDsyfcaDfkl6aj+aMgvTIsWQ6ejisJ9PtNqsAzIbS7MvU+ssPQWFZ77PiMyqw1f6FRV2bhd0lXWx9Mtc0FV8x1O6EUEKMQ0pERERERERUZYZ+kG6Kz0zKYiIiIiIiChzYkaFjhO1Jr7GYR9ERERERERfjUww60dmkjVbTUREREREREQ6iYEKIiIiIiIiItIZrFFBREREREREWVoyUqZJpi+PGRVEREREREREpDMYqCAiIiIiIiIincGhH0RERERERJSlJWfR2TV0FXuDiIiIiIiIiHQGAxVEREREREREpDM49IOIiIiIiIiyNg790CnsDSIiIiIiIiLSGQxUEBEREREREZHO4NAPIiIiIiIiytKS9fS+9FMgNcyoICIiIiIiIiKdwUAFEREREREREekMDv0gIiIiIiKiLC2Zs37oFPYGEREREREREekMBiqIiIiIiIiISGdw6AcRERERERFlbZz1Q6cwUEFftYToWGQ2YU8DkdnEhscgs/Fr/Q0ym4N5fkNmUzapOzKj40P3IbPRP30VmU2Vu1OQ2Twu2gSZTdxDZDqx8ZnvgCsiMvlLPwX6YJlv/6OMx6EfRERERERERKQzmFFBREREREREWRpn/dAt7A0iIiIiIiIi0hkMVBARERERERGRzuDQDyIiIiIiIsrSklkEVKcwo4KIiIiIiIiIdAYDFURERERERESkMzj0g4iIiIiIiLI0zvqhW9gbRERERERERKQzGKggIiIiIiIiIp3BoR9ERERERESUtelx1g9dwowKIiIiIiIiItIZDFQQERERERERkc7g0A8iIiIiIiLK0pJ5Dl+nsDeIiIiIiIiISGcwUEFEREREREREOoNDP4iIiIiIiChLS+asHzqFGRVEREREREREpDMYqCAiIiIiIiIincFAxWeip6eHjRs3fumnQURERERERB8pWU9fZy9Z0UfXqHj58iV+//13bNu2Dc+ePYOzszMKFy6MHj16oFq1asjshg8fLgMSFy9e1Lj+xYsXsLOzS9fHXrhwoXydQ0JC3rte27Zt4e7ujtGjR6v6SjxfY2Njrfd9/PgxOnfujAMHDsDS0hJt2rTB2LFjYWiYsnscPHgQvXr1wrVr1+Dp6YnBgwfjp59+Ut0u/hfbVg/WrF27Fj/++KN8Dr1795brLFq0CB07dsSsWbM0nkPXrl0xY8YM+diinenNslJtWNesDwNrW8Q9fYjgVf8g7uFdres69xoJU7/871wffeUc3kz//Z3r7Vp2hFXFWghePR/h+7cio9jW+g4O9ZrAwNYesY/u49X86Yi5eyvN9e2+bQDbWnVh5OiMxLAwhJ88gjfL5yE5Pl7ebpangNyeiU8uGNk74On44Yg4cxwZyeG7BnBu3ByGdvaIvn8Pz2ZMRvTtG2mu71i/CRzqfg9jp2xICAtF6JGDeLFgDpLj4+Tt2X5sC5cf22rcJ+bJI9z6uRUyysrTN7Do+FUERkTDz8Ue/b4phQLuTv96v51X76P/usOo7O+Jv5unfNYWHqH9/dKjenH8VO7d/TajJCcnY8ea6Ti5fy2iI8Ph7V8ETdoPgZOrV5r32bNxLi6f3ovXzx/AyNgUOfwK47uWPZHNzRsZzaRoRZiUqgF9S2skvn6KqN2rkfjiUZrr65mYwbRSPRj7F4aeqTmSwoIQtXctEu5dk7cbevrK7Rm6eELfyhYRa2cj/s4l6CL78sXh07s9bIrmh6mbM8426oJXm/fha/K1738rT17DoqOXEPD2c6J/3XIo4OH8r/fbcfku+q/ejyp5vPD3D7W0rjNq0xGsPXMDfb4tgx/LFkB62bZlE9avW4Pg4CB4e+dEx85d4eefO831jx45hKVLFuH1q5dwc3PHT+06oHiJUvK2hIQELF28AGfPnJa/Qy0szFGocFG0adseDg6Ocp0rly9hYP/ftG574t/T4Ofnn2772rGtU3D52BrERofBzacoarYYDjvnHGne58Lh5bh4eAXCgp7JZQfXXCj7bRf45KukWmflX63w5M5pjfsVKt8MNVuORHoTbTqwcSrOH16DmKgwePoWRd3Ww+CQLe02Hdk2GzfO7UHAi/swNDaFp28R1GjcG46uPqp1gl4/xu5V4/H4zjkkJMTBN38FfPvDYFjaOGZIm87snoobpxT95JKjKCo2HAZbp7TbpO78/jk4tWMSCpRvjfLfD1Rdf/3kKty5sBVvnl1HfGwk2o08DRMza2SEzNgmInUfFZ55+PAhihUrhv379+PPP//ElStXsHPnTlSpUkUeaGZlLi4uMDEx+dJPA4mJidi6dSvq1asnl+Pi4tCkSRMZiEhr/Tp16sj1jh8/LgMJIlAwdOhQ1ToPHjyQ64h+FgEPESzp0KEDdu3alebz+Oeff/DDDz9g5syZMkihJIIcK1euRHR0tOq6mJgYLF++HNmzZ0dGMC9WDnaN2yJ062q8GPMb4p8+hPP/hkLfykbr+gGzxuNp33aqy4sR3ZGcmIio8+8etJsVLgUTbz8khAQiI1mVrQTnNh0RsGYpHvbrIgMVnoPGyECMNtblq8Dph/YIXLMUD3p0wIuZk+Q2nFq2U62jb2KKGBHwmDcNX4Jtxapw+7krXi5diNvdOiDm/l34/D4Bhjba22RbuTpc2/2CV0sX4uYvrfDkrz9gW6kqXNv+rLFe9MP7uNaivupyt3e3DGoRsOvqA0zcfQYdKxXGio714JfNHl2W7kFQZMr7QZtnIeGYtPssimbP9s5te3s31bgMr1cOohRU9bxpH5BlhH2b5+PwzmVo0mEoeo5eDmMTM8wa2xHxcbFp3ufejbMoX7MFeoxajs6D5iApMR6zxvyC2JioDH3uRnmKwaxaI8Qc3Yaw+WOR+OoZLJv9D3rmltrvoG8Ayxa/wsDGARHr5yJszghEbV+O5HC1wLCR8duAxyroOgMLc4RdvoWrv47A1+pr3v92XrmHCTtOoGOVYljZpSH8XRzQeeF2Gdx8n2fB4Zi08xSKermkuc6+6w9w5clrOFmZIz0dOXQQ/8ydjRYtf8TfU2fC28cHQ4cMQEhIsNb1b1y/hj//GIOaNWtj8tSZKF2mHH4fNRyPHj6Qt8fGxuLe3bto1kJsbwYGDB6GZ0+fYvSIlN8qufPkxeKlqzQuNWt9g2wuLsiVyy/d2np6z1ycP7gENVoMxw99Vst9bc3U9kiIT3tfs7J1QaX6v6F1//Vo1W8dvPxKY8Osrgh4fkdjvYLlmqLz2KOqS6UGfZERju34B6f2LkHd1sPRYbCiTUsmdkD8e9r08NYZlKjaEh0Gr0Lr3vORlJiAJZM6IC5W8f4Rf5dMbC9SkNGm70K0H7gciYnxWD6lM5KSktK9TRcP/oMrR5egYsPhaPS/1TAyNsPWfzq8t5+UXj+5Ig/eHVzfDXbFx8fA078CilbtiIyWGdtE9J8DFV26dJFDHE6fPo1GjRrBz88P+fLlk2faT548qXGG/vvvv5dn562trdG0aVO8evVKIytBZGEsWbIEOXLkgI2NDZo3b47w8HCNs/EFChSAmZkZHBwcUL16dURGRsrbKleuLA+W1dWvX1/jDL/YrsgoaN26tXweXl5e2Lx5M968eaN6bgULFsTZs2dV9xEH6La2tjIrIFeuXDA1NUWtWrXw5MkT1e0jRozApUuX5OsgLsqz/6mHfoggTtWqVVXP/5dffkFERITqdvFcxXOeMGECXF1d5Toi2BP/9mz2fyWCDUZGRihRooRcFs+3Z8+e8rXUZvfu3bh+/TqWLl0q++Sbb77BqFGjMH36dBm8EET2g7e3NyZOnIg8efKgW7duaNy4Mf766y+t2xw/fjz+97//yYCEyO5QV7RoURmsWL9+veo68b8IUhQpUgQZwar6d4g4tgeRJ/Yj4cVTBC2fjaT4WFiWrap1/aSoCCSFhagupnkKITkuFlHnNAMVIpPBrlkHBM7/W0SAkJHs6zZC6L4dCD24G3FPH+PlnMlIiouFTVXtZ9TM/PMi+tY1hB09gPg3rxB1+RzCjx2AqW/KF1bkxTMIWLkQEaeP4UtwbNgUQTu3InjPDsQ+foSnUyciOTYG9rXqaF3fIm9+RF67ipCDexH/6iUizp9B8MF9MPfPo7liYiISgoNUl8Sw0IxpEIAlJ6+hYVE/1C+SCzmdbDG4bhmYGhli4wXNH6caTzcpCQPXH0HnyoXhbvfugbKjpbnG5eCtxyjh7QoPOyt8KeIsz+EdS1CzwS8oULwq3Lz88UPXMQgNfo0rZ9M+M99pwGyUqlwfrp6+cPfKjZadf0dwwAs8fXA9Q5+/acmqiL10DHFXTiIp8CWidq4AEuJgXLCs1vWNC5WVWRQR62Yh8dl9JIUGIeHJHSS+VpwtFRLuX0fM4S2Iv62bWRTq3uw6jNvD/sarTXvxNfra978lxy6jYfHcqF/MHzmd7TC4XgXF58S5W+//nFizH52rFoOHvfYzn6/CIjFu63GMaVIFRgbpm0a8ccM61Kr9DarXrI3s2b3QpVt3eTJnz27tJzg2b9qAosVKoGHjpvDM7oUfW/+EnDl9sXXLJnm7hYUFRo35AxUqVoKHhydy586Ljl264e7dO3j9+rVcR/z2sbO3V12srK1x6uQJVK9eS/5GS6997dz+xShduzNyFaoOZ4/c+LbNeESEvsadS2m/f3wLVoVP/koy68I+mzcqfN8TxibmeP5AM1tXZPZY2jipLiZmaQRLP3ObTu5ZjIrfdULuItXg4umPBh3+QHjIa9w8n3abWvX6B0XKN4Szey64ZM+N+u3GIjTwOZ4/VGSVPb5zHiEBz1C//Vhk8/CXlwbtx+H5w6t4cONkurfp8pHFKFatE7zzV4ODmz+qNv8DUWGv8eDa+z/nREbB3uW/oXLjUVqzCgpVaIOiVX9BNq9CyEiZsU26IBl6OnvJij74myooKEhmT4iDafGFkZo4wBdEVFQEAsT6hw4dwp49e3D//n00a9ZMY/179+7JA3tx9l9cxLrjxo1TDaNo0aIF2rVrhxs3bshhBw0bNpRvyo8hDqTLlSuHCxcuyIyAVq1aycCFGI5w/vx55MyZUy6rbzcqKkoOVVi8eDGOHTsmhzOIIIog2iCyA0RwRjxHcUndLkEEVESAQwwFOXPmDNasWYO9e/fKA3x1YqiFeB3EX2Umw6cOexDBmO++++6Dv5RPnDghgxjZsqWcqRXPPSwsTA7zUK4jAkXqxDri+tT69esnAx2iTxs0aKD1MUW/LliwQLU8f/78dwIa6cbAEMbZcyLmxuWU65KT5bKxz4elhVqUq4aos0dlsEJFTw8OP3VH+J6NiH+hCGxlGENDmPrkQuTlCynXJScj6vIFmPmlOkh/K/rWdXkfZWDCyNkFFkVKIvK8Zprpl6JnaAjzXH4Iv3BWo03hF87BPE8+rfeJvH5V3kfZZmMXV1iXKI2w05o/gIzdPZB32XrkXrAS2fsOgZHTv6dTfw7xiYm48TwQpXxcVdfp6+nJ5ctP36R5v9mHLsHewhQNiv77GUFxxvXonacyEPIlBb5+irCQAPgVKKO6zszcCl6+BfHwIw7Uo6MUwV1zS+3ZTulC3wAGLtmR8ED9oDAZ8Q9vwtBd+xAA41wFkPDsAcxrNofNr+Ng3WEwTMvUkp8LlPG+5v0vPkF8TgSgdE4P1XX6+noondMdl5+knPBJbfaB87CzMJMBDm2SkpIxaM0B/FS+IHyz2SM9iRMud+/elkMzlPT19VG4cFHcuqk96HPz5nUULpKyvlCkWHHcvJn2UL+oyEj5W8fS8t3fpIIIUoSHh6F6Te0B+88hNPApIsPewCt3ShDTxMwKrjkK4fl9te/k90hKSsSNs9sQHxcFNx/NEzbXz2zBtD6lsGBUXRzeOBHxce/Pqvkcgt88RUToG/jkTWmTqbkVPHwK4uk9zUDK+8REK04+mlko3j+JCXHyM9HQ0Fi1jqGRCfT09OVQkPQUHvQUUeFv4JFLs5+csxfEq0fvb9PhDSPhlacyPPy0B6q/lMzYJqL/XKPi7t278oA+d+60xxcK+/btk9kEYriAOHMuiIN+cXAvDtqVZ/pFQEMclFtZKc76iSCCuK8IEogAgBiPKIITIhNCSCsj4H2+/fZbWQ9BEEMZxDAE8fhiKITyoLpMmTIy20MM3VB+wU6bNg2lSinGRYoAgsgiEFkkJUuWlJkYonaDcn1txDAGMZxBtFsZ1BHbFAGEP/74QxUUEIEMcb2BgYF8XUUwRbwGP/+smar+MTZt2pRmpoM2YqynepBCUC6L2963jghmiCEcImtE2LFjh3x80QaRTZIWESgaMGAAHj1SjPcWASGRfSECUunNwNIKegYGSAzTrNWRFB4CIxf3f72/cQ5fGLt7IWjJdI3rrWs2QHJSIsL3b0NGM7Sylm1KCNVMqRXL5u6K92BqIpPCwMoGXqMmibCADAwE796CwA0roQsMrG2gZ2CIhFRpwgkhQTDx1D5ESGRSGNrYwHfiNEXGk6EhArZuxOtVS1XrRN28jicTxyL26WNZdyPbD23hO2EabnVqgyS14UjpITgqFonJyXCwULxflMTywwDtWR0XHr+S2RarOimGcv2bzZfuwtzYCNXyZMwwqrSEhwTIv1Y2DhrXi2VxAPkhxHfEhkXjZG0BV8+MC7yI4R16+gZIigrTuD45MhwGDu8OvRH0bR1h6OWAuGtnELF6OvTtnGFeqxlgYICYo9sz6JlTZtj/gqNikJiUDAfLVJ8TlmZ4EKC9PtX5hy+x4dwtrO7aKM3tLjhyEQb6emhZJv3r1oSFhcrXL3XdLltbOzx9m6GaWkhwsOqEl/r6IcFBWtcXGZ8LF/yDipWqwNxce6Biz+4dKFK0GBwd/70G0H8VGaoIMltYa+5rYjky7P372ptnt7BsQnOZpi+yKer/Mh2Orr6q2/OUqAtrezdY2jjLdQ9tnICgVw9Qv2P6DseMCFO0yfKdNjkiIvTD3z87V4yRtS2yeSiC7B4+heUQkj1rJqBao54yALx37UT520kERtKTOKAXzKw022Ru6Yio8LTbdOfiNgQ8u45Gv66FrsmMbSL6z4GKD81mEBkQIkChDFIIefPmlV9A4jZloEIMzVAGKQQx/EGZvleoUCFZmFMEJ8SZ+5o1a8qhBh9brFIM7VBSHmirBzyU14nHVQYeRBBC+RwFEUBQPncRqPjQ10C0QT3zRGR2iA/uW7duqR5XBG9EkEL9NRBBnv9KPO7z58+/WFFT8XoHBARg2LBhqqCONk5OTjIoIwJVYr8S/zs6/nshJTFGVVw0rktMhInaa5jeLMpWl8U31QtvGmX3gVXVOng5RnsRL11knrcgHBo2x8u5UxFz96YM0mRr2xkJjYIQuG4ZvkYWBQvDudmPeDZ9EqJu3oCxmzvcO/2K+Jat8Xr5YrlO+NlTqvVjHtxH5M0byLt4tayHEbQr44NM7xMZG49BG45g6HdlYWdu+kH32XThDr4t4AMTtUK4GeHs0a1YPTelnsEv/WZ88jbXzh+NF0/uovsIRd/pND09GciI2rFMZv4kvnyCGEsbmJauwUBFBsjK+19kbBwGrT2AYfUrwM5C++fE9WdvsOzEVVnvIr2GQGQkcSLrj7Gj5O+HLt1+1bpOQMAbXDh/Dn37D/6sj3399GbsXjFMtdyo8+z/vC0x5KPNgI2IjQnH7fO7sH1xPzTvuVQVrBCFM5Wc3P1hYeOE1ZN/QvCbx7Bz+nzB6MsntmDL4pQ2/dBDs9j5f7F96Ui8fnYH7QYsV11nYW2PJp3/xrYlI3Bq3xKZSVGgVB24euWV/39Ot89vwaF1KW2q0+7j2xQR8gLHNo3Bdz/Pl5kfX1pmbJMuyqqza+iqD/41K2o2iC+4mzdvfpYHFmMJ1YltK4vpiIN3MWRE1FsQNRSmTp2KQYMG4dSpU7JWgkghTB040VbbQf0xlF/O2q7LiCI+H/sa/NdhHzVq1JC1NT6UCNCIbBF1ynoiyuCN+KteY0S5jqg/osymEMRMI6K2iCi6Wbt2bZlhoR6MSj38QzkURtTD+BBiJhJRc0Ndj2K50bO49uEN2iRGhMtCmKmLTIoq/KmzLFLTMzaBRYlyCN2imXVg6ptXFuJ0GzMnZV0DA9g2bgOranXxfFAnpKeE8DDZJkMbzUCeWBYZCNo4Nm+D0MP7ELp/p1yOffxQFs906dgdgeuXy4OtL0nUjUhOTIChbao22drLuhLauLRuj+D9uxG0UxFwiHl4H/qmpvD8tQ9er1iitU1JkRGIffZEBjXSm525CQz09BCYqnCmWHZMdfZUeBIchuchEei+ImVMfdLbNhQbuQgbuzWAp9pY9POPXuFhYBj+aFwZGS1/sSoyrV4p4e0sK+GhgbCxSzmbKZbdvf59iNXa+b/j+vlD+N/wRbB1SDt7LT0kR0XIM3z65tZQrzSjZ2GFpAjNLAsleb2oS6O2jyUGvoS+GDKgbwAkZWzNmqwmM+1/IigpMh9SF84Uy6IGTWpPgsTnRDh+Xbrrnc+JokPnYlP3Zjj/6KUs2Ft7QspBo8jamLjjJJYdv4Idv7X8rG2wtraRv9OCgzUz4kQhTTt77SecbO3s3pmVTKxva2evJUgxWp5g+n3sn2lmU+zdvQtWVtYoVTpl+M/nIGpLiGEdSnI4gwgYhQXKzAclsSzqVbyPgaEx7JwVWcMu2fPjxaMrOHdgMWqlMauH8nFD3jz6rIEK/8JV4O5T8J02RYQFwspWvU0BcMn+77+3ti0diduXDqJt/6Wwsdd8//jmL4/uf+xBZHgw9A0MYGZujT97lEf+ktqzP/+rHHmrIFv2d9sUHR4IC+uUNkVFBMDRTXub3jy9huiIQKyZ3FB1nfhueP7gLK4eX4Zfxl6Gvvh8zyCZsU1Eny1QYW9vL7MbxEHlr7/++k6dCvEFIzIPxDAJUXxSXJRZFaJYo7hdZFZ8KHHQLrIQxEUM2xBDQDZs2CALd4oz8mJ4iPrMFVevXpUHyJ9KfAmKApvK7AmRASGeu2iXIKb4FI/3PmJdkS0galUoXycxvEF8cfv7p8/0WIIYdiGKdn4MMfRFDLcRX/piqllBBIlEEELZX2Kd7ds1zwqKdcT1qYl+EvVGlMEKUddEW7BC3CZSN0U/i/3qQ4jhIqL/1b3q/ZHTSiYmIO7xPZjmLojoS28DNHp6cjni4PvPfJoXKws9QyNEnjqkcX3kqYOIualW80Kc/fh1CCJPHpIFO9NdQgJi7t+BRYHCKdOH6unBvEBhBO/crPUuIiiBVEEx8WWlvO+XDlQkJyQg6s5tWBUuhrATR1XPy7JwUQRu2fCeNqV63so2ptEmfVMzGLu6I2HfbqQ3IwMD5HFzwOn7L1A1t5fqgEIsNy/57g9ab0cbrO38vcZ10/afR1RcAvrWLgkXG83P4A0XbiOvqwP8XdJ3/Lk2pmYW8qIkAsnWto64c/UkPHIo2hYTFYFHdy+jXI2maW5H3G/dgjG4cmYfug1dAAfnlHH6GSYpEYkvH8Mwh7/a9KF6MPLyR8w5zfe+UsLTezDOKzLxRPBbsZ8Z2GeTQ8oYpEh/mWn/MzIUnxOOOHX/GarmzaGqL3Hq/nM0L/VufR5vR1us/V9jjeum7z0jM7L61ikrPyfqFs6FUjk1g7FiFhFxff2i/ulyEsbX1w+XL11AmbLl3rYhCZcuXkCd7zQ/05REcUxx+/f1Uw6gLl44j9y587wTpHj+/BnGjPtT/k5Jqx/37t2FKtWqa0yz/jkYm1rKi/pjWVg74fGtE8jmqXiusdERePHwEgpXbPFxG09OUh18avP6qaJeh3i8z0kU6FQv0inaJAp3Prh+Aq5vAxMx0RF4ev8yildJu03iftuXjZIFN3/qtxh2Tmm/fyysFAGr+zdOIjI8UAZL0rufzK2c8PTuCTi6K9oUFxOB148vI18Z7W1y9y2Npr01f0MdWDUQds4+KFylQ4Yf0GfGNhH9m4/6BBdBChE4EAfxI0eOlKn+4otDHLSK+g9i6IEouiiGV4ipKf/++295u5gtpFKlSihevPgHPY7InBB1DsSQD3HwLJbFbB3KYIGofyAOWLdt2yYLYk6aNOmdSPynfMGKGSumTJkiv+DEWf/SpUurAhdiyIqovyGm6fTw8JAH4amnJRVtF8Mf2rRpI2c4Ec9dbFPU4Uhd6+FjiSCJeGx14vHFrCEiwCKyKtSJGVhEYVPxV/2+vr6+cmiGeI1FQEI8NzFbh6hHMXjwYFk0VdmuTp06yVoaffv2lZkQYnra1atXy9dfGxGgEvUmRLBCBCFEsCL1DwqRNSP2F+X/H0I8n9Svdch/GPYRvncLHH76H+Ie3UXswzuwqvod9I1NEHFcEVRw+OlXOb1o6EbNIRAWZash6uJpeRZenVhOfZ04uypmCEl49RwZIWjrOrh27YPoe3fkUA67Og3lgXvoAcVZNtdufZAQFIg3y+fL5YizJ2FXtyFiHtx7O/TDDU7N2yDi3EnVwb2eqSmMXdxUjyEKbprk8JFZKQkB6TueVAhYvxqevw1A1J1biLp1A04NmsjAQtBuRUDJ87eBiA8MwMsFikyWsFPH4dSgKaLv3VYN/RBZFuJ6ZZtcO3RB2KljiHv9Ckb2jnBp1VacWkTwwYyZ3aBV6XwYsvEI8ro5Ir+7I5advI7o+AR8X1gxBn7whiNwtjLHr9WLyeEbvs6aZx+tTBVFyFJfHxEbhz3XH6F3zQ/7jE1vIgBZ8ZtW2L1hDpxcvGDv7I7tq6fBxs4ZBYqnDE2bPqo9Cpaohgq1W6rS7c8d244Ov02BiZmFqp6AqbkljI0/PFPsU8Wc3g+Luq2R+PIREp4/gmmJKoCRCeIuKwoIm9dtI4MQMYcUMxLEnj8C02KVYFajCWLPHZQ1KkzL1kLsWbW6O0YmMFA7u69v6wADZw8kxUQiOUz7lI1fcnpSC9+UM7bm3h6wLpQbcUGhiHmScpJAV33t+1+rcgUxZN1B5HNzQn4PJyw9fgXRcfGoX0wx1l8M9XC2tkD3miVhYmSIXKmKY1qZKr4nldfbGhrANtXwMTHrh6OVOXI4aZ/u+VPVb9AIf00aD99cfvDz88emTRsQExuD6jUUJyYmTfgDDg6OaNO2vVyu930DDOjXGxvWr0HxEqXk9KZ379xGt/8pZngTvyXHjRkppygdOnwUksTndpAiu87SykojQ1UESF69fCmnJs2Ifa1Y1dY4sWOmzI6wcfDA0S2TZXaFmAVEadXkNshVqAaKVv5RLovCmN75KsLa3hVxMZG4cWYrHt85jSbd5snbxfCOG2e2yJlBzCxsZY2K/WvHwsO3xL9manyONpWu0RqHt86CfbYcsHNyx/4NU2R2Re6iKW1a9OdPcrlUtR9VmRRXTm5Fi1+nw9jUAuFv606YmlnJ2UuEC0fWwdEtJyys7PHk3kXsXP47ytRoA0dXn3RvU8EKrXFu3yzYOOaAtb07Tu+aAnNrZ3jnS2nT5tk/wTt/dRQo96MMCji4aBaxFtN/mpjbalwfFfZG1oQIDXgslwNf3IaxiQUs7Vxhap4+76/M2iZdkJwJhsdl2UCFj4+PnC1DnIEXs1+IrAaR3VCsWDEZqFC+ccSZfXFgXrFiRZlFIM6ei+EbH0oc1B4+fFgGOkTBRnGWXkyNKabOFMTBspgiVMzYIYIJYvrNz5FNIZibm8simy1btsSzZ89QoUIFzJun+OIQxLSsYjpN8XgiOCJmr1CfFlW5jV27dqF79+6y3oVYFvcTAZVPJaY4TT2NpwjWiGwDEUxJXetBZKOIgkhkbFcAAQAASURBVKBKyvuKmUbENK8iSCBm6OjcubPMkBAZICLAIgJRSmK4jQhKiNd58uTJMkDzzz//vDcTQqyjHqwQr0dqaZ0NSW9R545B38oaNt+1kENA4p4+wOupo5AUrihoaGDviORkzWwDw2xuMM2VF68naw490RXhxw/JApROzVrDwNYOsQ/v48nvg5AYqgjgGTk6a2QUBKxbJqPxTi3awNDeUQ61EMGLNytSZmMx8/FD9hETVMvZflIMYRFToL6YnnJ9egk5vB8GNrZwadUOhnb2iL5/Fw8G/6YqsGnsnE2jTa9EHYrkZLi06QAjByckhIbIIMWLhXNV6xg5OsGr/zAYWFnL2yOvXcGdnp2QGJoxU5TWyu8ti+XNPHgBARHRMvthxg81VIXzXoRG/KeJInZefSDbXjt/+v7Y+xjV6rVDXGw0Vs0djuiocPj4F0XH/rNgZJwSbAx49QQR4SkH6cf2rJJ/p43UnAWoRafRctrIjBJ/4xyizS1hWqEu9C2skfj6KSJWT0NylKKKvb61nTz7qZQcHozwVdNgXq0xTNoPkkGM2DMHEHMyJVPH0DU7rH4QBeQUzKsrzoLHXj6BqG1LoEtsiuVHmX0pzynvhIHy75PF63G5/QB8Db7m/a92gZwIjozGjH1nERARBX9XB8xo8y0c3g79eBkSIWcM0mUVKlVGaFgIli1ZJIeA+PjkxIiRY1S1xt68eQ09/ZQ25MmbD7/1HYClixdi8cIFcHN3x6Ahw+GVQzHTTmBggJzFQ/i1m+ZwyjHjJqBAwZThGLt37USePHnhmUbh5c+tZI2fER8bjV3LhyI2KgzuOYuhcbd/NGoAhLx5guiIlH0tKjwQ2xf1Q2TYa5iYWsHR3V8GKXLkUWSgGBgY4dHNE3IoSHxsFKzsXOFXuCbKfNMlQ9pU7psO8v2zZdFQxESFIXuuYvix11wYqbUp6PVjRKm9f84eWCH/Lvyjtca2vm83Rk5bKgS8fIi96/5CdGQobB3dUKFuJ5SpqfkbOr0UrtxBzppyaO1QxMWEwSVHMdTtMFejn8ICHyMm8uMCx9dOrsTZPSlDmDfNVARuqjQdg9wlUjKE0kNmbBOROr3kj53zMxMTwzV69Ojx2bIzMlK9evVQvnx5mfWQlTzulPk+MKMCU2VnZAKx4THIbPxap//Zuox2MM/XUxD2Q5Xe2R2Z0fGhKbVLMgv901eR2VS5OwWZzeOiipnTMpPDD7/sTEnpwcI08/28fxX4pZ8Bfage9XQ7mJqWZ7f/+6QG6c3d7+NnwPzaZWxpeEo3IkjRosVHjockIiIiIiIiJMtaU6QrGKjIJLJaJgURERERERFlTpwsVo2oNfE1DvsgIiIiIiIiyiyYUUFERERERERZWrIez+HrEvYGEREREREREekMBiqIiIiIiIiISGdw6AcRERERERFlaZz1Q7cwo4KIiIiIiIiIdAYDFURERERERESkMzj0g4iIiIiIiLI0zvqhW9gbRERERERERKQzGKggIiIiIiIiIp3BoR9ERERERESUpXHWD93CjAoiIiIiIiIi0hkMVBARERERERGRzuDQDyIiIiIiIsrSOOuHbmFvEBEREREREZHOYKCCiIiIiIiIiHQGh34QERERERFRlsZZP3QLMyqIiIiIiIiISGcwUEFEREREREREOoNDP+irZt3pV2Q2ehbOyGxM9TLfR43e9T3IbJJK5kdmoze6BjIj/dNXkdlkxv3v7o1DyGy8gi8isynjZYTMJirRDJlNoWxRX/op0Adzw9coWY9DP3QJMyqIiIiIiIiISGcwUEFEREREREREOiPz5WMTERERERERfYTkZA790CXMqCAiIiIiIiIincFABRERERERERHpDA79ICIiIiIioiwtmefwdQp7g4iIiIiIiIh0BgMVRERERERERKQzOPSDiIiIiIiIsrRkcNYPXcKMCiIiIiIiIiLSGQxUEBEREREREZHO4NAPIiIiIiIiytI49EO3MKOCiIiIiIiIiHQGAxVEREREREREpDM49IOIiIiIiIiyNA790C3MqCAiIiIiIiLKJKZPn44cOXLA1NQUpUqVwunTp9+7/po1a5A7d265foECBbB9+3aN25OTkzF06FC4urrCzMwM1atXx507d9K1DQxUEBEREREREWUCq1atQq9evTBs2DCcP38ehQoVQq1atfD69Wut6x8/fhwtWrRA+/btceHCBdSvX19erl69qlpn/PjxmDJlCmbNmoVTp07BwsJCbjMmJibd2sFABRERERERESGrD/3Q1cvHmDRpEn7++We0bdsWefPmlcEFc3NzzJ8/X+v6kydPRu3atdGnTx/kyZMHo0aNQtGiRTFt2jRVNsXff/+NwYMH4/vvv0fBggWxePFiPH/+HBs3bkR6YaCCiIiIiIiI6CsXFxeHc+fOyaEZSvr6+nL5xIkTWu8jrldfXxDZEsr1Hzx4gJcvX2qsY2NjI4eUpLXNz4GBCtIJDx8+hJ6eHi5evPilnwoREREREZHOiI2NRVhYmMZFXJdaQEAAEhMTkS1bNo3rxbIINmgjrn/f+sq/H7PNz4GzfmQxb968kYVQtm3bhlevXsHOzk6OWxLXlStXDlnVml0HsGzLHgSGhCKXlwd6t22OfL7eWtfduO8Ith8+iftPnsvl3N7Z0blF/TTXTy+btm7H6vUbERQcgpzeOdCtYwfk9vdLc/1DR49h4dIVePnqNdzdXPHzT61RqkQx1e3BwSGYu3Axzl24iIjISBTIl09u08PdTbXOX9Nm4vzFSwgMCoaZqSny5vGX28nu6fFZ2rR56zasXbceQcHB8PH2RpdOHd/bpsNHjmLR0qV4JdvkhvZtf0LJEsVVt9eq853W+3Vo1xZNGjXEy1evsHzFKly8fEm238HeHlWrVEaLZk1hZGSE9LDy2CUsOnQeAeFR8HN1RP/6lVAgu8u/3m/Hxdvov2wnquTzwd8/1VVdv/fKXaw5cQU3nr1BaFQMVvVogdzuTtBF9uWLw6d3e9gUzQ9TN2ecbdQFrzbvw9fCuEhFmJaqBj0LayS+fobovWuQ+OJRmuvrmZjBtOJ3MPIrBD1TcySFBSN631ok3L8OXSHSOXesmY6T+9ciOjIc3v5F0KT9EDi5eqV5nz0b5+Ly6b14/fwBjIxNkcOvML5r2RPZ3DL2MzAz73uiX1YunY+9u7YiKjIC/nkK4JeuveDm/v7P2h1bN2DTupUICQ5CDu+caN+pO3L555G3hYeHYdXS+bh04SwC3ryCtY0tSpYuj+at2sPCwjLd27R69xEs2bYfgaFhyJXdHX3aNEL+nNr3sw37j2Pb0TO49+SFXM7j7Ykuzeqmuf6Yeauwfv9x9PqxAVp+Uzmd+2UB9rztl9x58n9wv2xU9YsvOnT6VdUvwu4dW3Dk0F7cv3sH0dFRWLJqCywsrd7ZztnTJ7BmxWI8engPRkbGyFegEPoP+f2T27Ru+Rwc2L1JtskvT0G07dwXLm7Z33u/PdvWYNuGZQgNDkR271xo/Utv5PTLp7o9JDgQKxZMwdWLpxETHQUXdy983/QnlCxbVbXOptULcPHsMTy6fxuGRkaYs2Lff27DiqULsHfXNkS+7ZeOXXv+a79sl/2ySvV+Ef3ip9Yv4sz0gn9m4OjhA0iIj0PhoiXQsUsP2NrZq9ZpUKfKO9vt1XcIKlRStPPEscPYtX0zHty/i/j4eHh65UDzlm1QpFjJLNemr0Fysu7O+jF27FiMGDFC4zpRg2L48OHIrJhRkcU0atRIFklZtGgRbt++jc2bN6Ny5coIDAxEVrXn+BlMXrwW7RvVwaJxg+Dr5YHuY6YgKDRM6/rnr91GzbIlMGNoL/wzqh+cHezw6++T8TooOMOe84HDRzHrnwVo1aIZZk2eCB/vHOg/dCSCQ0K0rn/txk38Pn4SateohllTJqJc6VIY9vs4PHj4KKWS7+ixePHyFUYMHoBZkychm7MT+g4ejmi1Ijm5fHOiT4//Yf7MqRg3cqgYzId+Q0fIyO2nOnj4CObM/Qc/tGyB6VP+loGKQUOGIiStNl2/gbHj/0TtmjUxY8pklC1TGiNG/46Hb9skrFiyWOPSq0d3mblTvmxZefuTJ0+RlJyE7t26Ys6M6ej4cwds27ETCxYtRnrYefE2Jmw5go41SmFlj+bwd3NE5382ITAi6r33exYUhklbj6Cod0rQSCk6Lh5FvN3Q41tFm3SZgYU5wi7fwtVfNb9ovwZGuYvCrGoDxBzbgfCFf8hAhUXTrtAzT+MAT98AFs26Qd/GHpEb5yF87ihE7VyOpPBQ6JJ9m+fj8M5laNJhKHqOXg5jEzPMGtsR8XHvnqVRunfjLMrXbIEeo5aj86A5SEqMx6wxvyA25v378Zf0te17G9euwPYt69Gxa2+MnTRLVmEfNeQ3xL2nX44d3o+Fc6ejacs2+HPKXHh555T3CQ1RfDcFBwYgKCgQrdt3xl8zFqJbzwG4cO40Zkwen+7t2X3iPP5atgE/N6yFpaP7wC+7G/43biaCQsO1rn/uxl3UKlMUswZ1w4IRPZHNwRbdxs3E66B3vw8OnLmEq3cfwcnOJt3bsWHtCmzbsg6duvbCuEkzYWJqhlFD+ry3X44e3o8Fc2egacufMGHKXHnwOHJIH4S87RchNjYGRYqWRKOmP6S5nRPHDmHKxDGoWqM2Jk2bhzETpqFCZc1U7f9i6/ol2L11Ndp17ocRf86DiYkp/hjW/b1tOnlkD5bNm4wGzdtj9F+LkD2Hr7xPaEiQap1Zfw3Hi2eP0WvwBIyduhwlylTG1PGD8PDeLdU6CQnxKFmuGqp90+iT2rBh7Upsk++Xnvhj0gyYmJpi5JC+8qD8/f0yE81atsHEKXPe9ktfjX6ZP3e6DA71GTAMo8f9Ld8/f/w+9J1t/a9HP8xfsk51KVWmvOq269cuo1CRYhg8YhwmTJ6NAgULY8zIQbh/706WaxN9mgEDBiA0NFTjIq5LzdHREQYGBvKEtDqx7OKi/cSYuP596yv/fsw2PwcGKrIQccB35MgR/PHHH6hSpQq8vLxQsmRJuZPXq1dPriMO4mbOnIlvvvlGTj3j4+ODtWvXamznyZMnaNq0KWxtbWFvby+LqoihG+r++ecfWYxF/LgSU93MmDFD43YxRU6RIkXk7cWLF5fBky9lxba9+L5aeXxXpRx8PNzQv8MPMDU2xpYDx7WuP/LX9mhcqzL8cngih7sLBnVqjaTkZJy9cjPDnvO6jZvxba0aMvDgld0TPbp2gomJCXbu0X42Yv3mrShRrAiaNWoAL09PtG3VEr45fWRWhvDs+XPcuHUb3bt0RG6/XPD0cJf/ix8qBw4dUW2nbu2aKJg/H1yyOcughdjOmzcBeJVGFeGPsX7DRtSuXQu1alSHV/bs+LVbF5iYmmDX7j1a19+4eTOKFysqMyOyZ/dEm1Y/wjdnTmzaulW1jr29ncblxMmTKFSwAFxdFR+qJYoXw289e6BY0aLyujKlS6FxwwY4djx9xtstOXwBDUvlR/0SeZEzmwMGN6wKUyNDbDyd9hn2xKQkDFy+C51rloaH/bs/xL8rlgedapRCqVzvP/ulC97sOozbw/7Gq0178bUxKVEVcZeOI+7KSSQFvkT0rpVAfByMC5TRur5xwTIyiyJy/RwkPruPpLAgJD65i6Q3z6ArRIDy8I4lqNngFxQoXhVuXv74oesYhAa/xpWzaZ/Z7DRgNkpVrg9XT1+4e+VGy86/IzjgBZ4+0J1Mka953xP9snXTGjRu1goly5SXBxv/6z0QwUGBOH3iaJr327JhNarXrouqNb6FZ/Yc6Nittzy42bdb8TmfPYcP+g4ahRKlysHF1R0FChVFy9YdcPbUcSQmJqRrm5btOIj6VcqiXqXS8PFwwYB2TWFqYozNh05qXX9019ZoUqMC/HN4IIdbNgz+uQWSk5Jw+tptjfVE4OLPReswqmsrGBoYZEC/rNXol197D0BQUMC/9Msa1KhdB9VqfPO2X3rJftn/tl+E7+o3QcOmP8Avd16t2xD9M2/2VLRu1wm1vv0ebu6eclvlKlT55Dbt3LwS3zdti2KlK8nMiE49hyMkKADnTh5K8347Nq1AlZrfo1L17+Ce3Qdtu/SXAY5De7eo1rlz8wpq1m0isyycXdxRv1k7mbnz4F7Kb6VGLX/BN9+3gGeOnJ/cL02atZIH06Jfur/tl1Pv6ZfNqfql09t+2bd7h7xdZDGI907bDl1QsFBR5MzlLw/eb964hls3NT/rLCwtYWdvr7oYGxurbmv/Szc0aNwCufxyy2yIH9v8DFc3d5w5dTxLtYk+nYmJCaytrTUu4rrURF8VK1YM+/alfI8nJSXJ5TJltP9mEderry/s2bNHtb63t7cMSKivI4aeiNk/0trm58BARRZiaWkpL6I6q7YxTUpDhgyRmReXLl3CDz/8gObNm+PGjRvyNpHiJYqrWFlZyaDHsWPH5DZFpVhllHfZsmVyKMnvv/8u7zdmzBi5TZHFIURERKBu3bqyCq0o9iJSln777Td8CfEJCbh5/zFKFsijUXCmRIHcuHLn/gdtIyY2DokJibC2tEBGEH1w++49FC1cSOM5Fy1cENdvppypUCeuV19fKFG0MK7fVPzoi4tX/Eg1NjbS2KYY/nD1uqLvUxOZFjv37odLtmxwcnT85DbduXv3nTYVKVw4zTbduHlT3q6uWNEi8nptgoODcfrMWdSqWeO9zyUyMlLu359bfEIibjx7jdK5PFXX6evryeXLjxTpzdrM3nMadpZmaFgyJaWWMpi+AQxcPJHwSH1fTEbCw1swdNc+3MHItwASnz+AWY1msO42BlbtBsKkdE0RDYauCHz9FGEhAfBTC7aYmVvBy7cgHt6+9MHbiY6KkH/NLdP/jHZW8OrlC5m2XbBwytA8cYAnhgrcunktzc/Qe3dva9xHfIaK5dtp3EeIioqUleANDAzT93v2wROUyu+n8dxK5vfD5TuaJzne9z2bkJgEGwtzjR/eQ2cuRau6VZHTwxUZ1S+F3umXvO8c5Gn2yy2t/ZLWfbQRQ0KCAgOgp6+P3v/rgHY/NsSooX3x6OGH/U5Jy5tXz+XQjfyFUlL2zS0sZXDhzq0rWu+TEB+PB3dvIl/hkhptyleoBO7eTLlPrtwFcPLIXkSEh8q+OnF4N+Lj4pAnf1F87n4J1tov//5+KfROvxRV3UfcnpCQoLGOh2d2ODllw60bmtudM3MyWrf4Hn16dsbe3dtloCEt4rWIjo6GlZV1lmrT1yKzzPrRq1cvzJ07Vx57iWOxzp07y9+3YhYQoXXr1hrZGN27d8fOnTsxceJE3Lx5Ux6bnT17Ft26dVOdyO7RowdGjx4ts/GvXLkit+Hm5ianMU0vrFGRhRgaGmLhwoVyuhoxTY2YdqZSpUoyECGmmVFq0qQJOnToIP8X09OIiNrUqVNlVoSYl1d8IImMCbHTCgsWLJDZFQcPHkTNmjXleCmxozds2FAVhbt+/Tpmz56NNm3aYPny5XIb8+bNkxkV+fLlw9OnT+WbKKOFhEXIM9b2NpoHpvY21nj0/MOKw0xfth6O9jYooRbsSE+hYeHy9bOz1TwosLO1xZOn2s/WivoL4nZ1os+C3qYDZvdwh7OTE/5ZtBQ9u3WGqYkJ1m3agjcBgbIehbpN23Zg7oLFct5kkXkxfvSwT67nIKKyok22tnbvtunJ0w9uk1gW12uzZ99+mSWkHPahjcgs2bRlK35u3w6fW3BkNBKTkuFgmfJDWxDLD15rHzZ0/sFzbDhzDat7tvzsz4c+nBjeoadvgKRIzTT1pKgwGDpoFpZS0rd1gL6NH+Kun0HkmpnQt3OCWc1mgIEBYo8pzm59aeEhAfKvlY2DxvViWQQwPoR4325YNE7WtnD1zJUuzzOrEQfDgvqYccHG1k51W2rhYeJgMPGdz1Bxn2dPHmu9T1hoiKx3UL229lo+n0tIeKT271lrKzx8/mHZeFNXboajnTVK5vdXXbdoyz4Y6Oujea1KyAjK194mVb+I11wcVKbdL+K77d37pNUv2rx6qaiJtWrZQrT9uQucnV2wecNqDB3QA9PmLP3PB4iijoRgner5ieXQNNsUIvc1m1T3EcsvnqUMvfxf3zGY9ucgdPqhpkxDNzYxRY+Bf8DFLSVY/3n7xe6d1/j975ck+f5Iq1/EfQ0NjWRmgTrxOOrbbfFjWxQoVERmlFw8fxZzZvyNmJho1K2nfTjLpvWrEBMdjbIVKmepNlHGatasmaouoSh2WbhwYRmIUBbDfPz4sQxkKZUtW1Yen4npRwcOHIhcuXLJE9v58+dXrdO3b18Z7Pjll19kln758uXlNsWxXHphoCKLEZkSderUkdkQJ0+exI4dOzB+/HgZePjpp5/kOqlTeMSycjYOkWVx9+7dd844i4PWe/fuyR1Y/G3fvr0MiCiJCK6YxkYQkT0RGFHfsT8kbUhkgaTOBImNi4OJWjpaRlu0caescTFjWG+YqGUjfI1BrOGD+mHi5Glo0LzV2wyNQihZrCiSRSEKNdUqV0SxwoVkwcs16zdh1LgJmPznWI20QF20a88eVK1cOc3nGRAQiEFDh6Ni+XL4tnYtfGmRMXEYtGI3hjWuBjsLsy/9dOhj6ekjOSoc0TtXiDxeJL56An0rW5iUrPbFAhVnj27F6rkpNRp+6ac5JO+/WDt/NF48uYvuI9KnrktWcPjAHsyeNlG1PHD4uHR/TJFJMWZ4f3hm90KzHxRn2HTVws17sPvEBcwe3E31PXvjwROs3HUIS3/vozpp8rkdStUvgzKgX9IihpcKjZv9iDLlFIGZbj374efWTXD86EHU+kYxfPffHDu4E/NnpLTjt6GT0ukZA2uXzZbFOfuPmgYraxucO3lY1qgYMnY2PHP4flK/zJqW8rwHDR+LL6lpi9aq/31y5pIH9KKYpbaD+sMH92LV8sUYMGS0RlAxM7aJvrxu3bqpMiJSEyeXUxMnqsUlLeKzduTIkfKSURioyIJEgKBGjRryIoZkiOwJkQWhDFS8jxi2IcY9ieEdqTk5OcnbBZFuJObWVSci6p+72m2/jm3Qv9O/P++02FpbyjMyqQt6iUKa9qkyFlJbumU3Fm/aiWmDe8iZQjKKjbWVDCQEh2gW5ROFNO3sNDMMlMT1qQttimiovdqXip9vTsye+pec8UMElmxtbNCtV1/45dIcO2ppYSEvYjaQPP5+MrBx9MQpVK1U4T+3SYyzE21SL/iU0ia7D25TWq/BlavX8PTpMwzs10/rtkQx2b4DBiJvntzo/j/tH+qfSgQbDPT13imcKZYdrTSzLIQngaF4HhyGXxdseefHatF+U7GpTyt4Omrvb/q8kqMikJyUCH0LK6iXjdU3t0ZypPaiu8kRijPcIkihlBj4EvpieIS+ASBuy2D5i1WRwzqURMV3ITw0EDZ2KTPFiGV3r5Qz12lZO/93XD9/CP8bvgi2DulXTCuzEzUj1GeAECncgjjDaWefku0iimLm8NF+gCcOBPX1Dd75DBX3SZ2ZER0VhdFD+sDUzBx9B4+Wger0ZGtlof17NiwcDqmyLFITs4Qs3LIPMwZ0kTOFKF24eQ9BYRGo+2tKtXuRtfH3so1YsfMQtkwe9snPu2SpchqzJSj7RWQa2Kv1i3jNvd/bL+K7TfMseIiWfnkfOzvF44nAkpKY9SObixsCPqJGVNGSFTRm5hDFLIWwELGvpQzhFMvZfbRnSFlZ28p9Tb1wpiCWlVkWr148lbOCjJu2Ah7ZfeR1Xt5+uHX9IvZsX4t2Xfrj0/olpZZH/NvPsdDg4I/uF2WhWW39Iv6K1ycyIkIjA0E8zvv6Tuwza1Yukc9L9JHSkUP7MX3KBPTpP0wWoszsbfpafewQC0pfrFFBslaEyIRQEpkW6sSyKIwpiOEid+7cgbOzM3x9fTUuImNCpBSJ8Ur3799/53YxBEQQ27p8+bLMwkjrMT+02m3Pdp+WEm9kaIjcPtlx5kpKHQaROnfm6k0UyKX4ctVmyaZdmL9uG/4e8Cvy5MyBjCSGWYigwvlLlzWe84VLV5A3t/aDC3H9hYsp6wvnLlxC3tzvTv0pghAiSPH02XNZC6NsqoCTOnEIJjIulD/gPqVNuXx9NZ6jaNPFi5fSbFOe3Llx8ZLmOPrzFy7K61PbtXu33H5OH2+tmRR9+g+Ut/fu0V0jFe5zMjI0QB53Z5y6+0R1XVJSslwu6PXu+GpvZzus7f0DVvVsqbpUzuuDEjk95P8utp+/jgalISkRiS+fwFDj4F0Phjn8kPDsgda7JDy7DwN58J/yo0ffzlkx68cXCFIIpmYWcHLJrrq4eOSEta0j7lxN+fyNiYrAo7uXkcNPs6aNOjFWWQQprpzZh65D5sPBOeMCtZmRmbk5XN08VBdRBE8cNFy5dF4jA+LOrRvwz50vzc/QnL5+uHLxnMZn6OWL5+Gndh+xnZFDesupIAcMHQNj43cLsX1u8nvW21OjEKbie/Y2CuZK+/tTDO34Z8MuTO3bCXl9NIsFf1u+BFaM7YtlY/qoLmLWD1GvYmq/TunaL5ff6Zfr8E+jCKaiX/xlP6i3/fLFc2neR5ucufzktp49Tfn+ECcUXr9+CSfnbB/RJgs59EJ5cff0ho2dA65dOqPWpgjcu30NufwLaN2G2He8fXNr3Ee06drlM/DNrbhPXKzi913qbBfx/SqKon56v7irLqJf7LT2y7+/X1L3y5WL51X3EbeLIN7lSynvqWdPH+PNm1fwz5N2zagH9+/B0tJK84D+4D5M+/sP9OozGMVLlskSbSL6HJhRkYWIs8Yipaddu3Zy6IUYviEKpYihH2LmDqU1a9bImTjE2COROSFm6BD1JARRXPPPP/+U64vUHw8PDzx69Ajr16+XY5fEssh6+PXXX2XgQhTZFMM1xOOIYoaiuEvLli0xaNAgOTREBB/EjCETJkz41+cvKtumrm6b9BmGG7SoUx0jZyyUAYe8OXNg5fZ9snBX3cqKWgbDpy2Ak70turZsIJdFFsWc1Vvk7B9uzg4IfJvZYGZqAvN0HKelrlH9ehj/1xT458oJf79cWL9pqwz81K5eTd4+buJkODrYo8NPreRyw3p10av/YDlUo1SJYnJ6UxGEEPUolA4dPQYbaxs4OzvKaUtnzJmHsqVLonhRRcHK5y9f4uDhY3LZxtoaAYGBWLlmvRxKUbL4pxfHatigPiZM+gt+uXzh7+eHDZs2yTbVrKGYfm38xElwdHBAu5/ayOX69eqhT/8BWLt+A0qWKI5Dh4/Igpw9UmVEREZF4fDRY/ilQ3vtQYoBA+Ds5CzrUoSqTUkrZgn53FpVLIIhq/Ygn0c25PfMhqVHLiI6LkHOAiKIoR7ONhbo/m05mBgZIpdLqtoBpor9X/360KgYvAgOx5swRbDx4RvF2RSRpeFonTEFXj9mikgL35QDDnNvD1gXyo24oFDEPEm7oKguiD2zH+Z1WiHh5WMkvngIk+JVACMTOQuIIG4TQYiYw5sV6184ApOiFWFWvTFizx2SNSpMy9SU/+sKcRBR8ZtW2L1hDpxcvGDv7I7tq6fBxs4ZBYorPkuE6aPao2CJaqhQu6VquMe5Y9vR4bcpMDGzUNWzMDW3hLFxxnwGZuZ9T/RL3e+bYO3KxfIA2dnFBSuWzJfZFWK2CaXhA3uiZJkK+PY7RT2o7xo0xdRJY5EzV25ZjV/MGhAbE42qNb5JCVIM/k1Ohdn9t8FyWVwEaxvbT856fJ8fvqmM4bOXIa93duTLmR3Ldx5CdGwcvqukCISLopjOdjbo1lxRL2Phlr2YvXa7nP3D1ckeASGKz2Zz+T1rIrM0xEWdmPXDwcZazhKSfv3SGGtXLpH9ks3FFSuWzIO9vaNGvwwb2EvO1JDSL01kv/jm8kcuvzzYIvslRtUvgpjRRWTQvHihqDP16OEDWVPJ0TmbrD9hbm6Bmt/Ww8plC+Do5CyDExvXrZTrli1f+ZPaVLtec2xcvQDZ3DzhnM1NDtmwtXeUs4AojRncFcVLV5azeAhipo7Zf4+Et28e5PTLK2cOEW2qVK2uvN3VIweyuXpg/vRxaNnuV1haiaEfh3D14mn0HpIynCbgzUtEhoch8M1LeVD96L4imCXuKzJ+PqZfxBl/cZAv+mX5kvmyX9Sn1Bw6sBdKy/eL4rdcvQZNMGXSOBkEEv0i3i/iN0e1GrVVxSur1fxWTvdpaSn6wBxzZ02VB/3KIJOY5UJkLIhsCPFb6NKFs1i3ehm+b9hUY2iEeBwxU4ZYLzhIkYlibGIsHyOrtInov2CgIgsRs3OI4Rh//fWXrCMhzoJ7enrKgIEonKIkAg0rV65Ely5d4OrqihUrVsisC0F8qB0+fBj9+vWTxTLDw8Ph7u6OatWqyfR9QQwlEeuJgEafPn1gYWGBAgUKyGqxyuexZcsWdOrUSU5RKrYtpkwV9TO+hBplS8iimnNWb0ZgSBj8cnjITAkHW0V7XgUGydkZlNbvOSyrmA+YNFtjOx0a18XPTdK3KJlSlYrl5UH1wqUrZQBIZAqMHTlUNezh9Zs3Gs85X57cGNinJxYsWY75i5fC3c0VIwb1h3eOlDTSoKBgzPpngRxSYm9nhxpVK+PH5ilj1YyNjHH12nWs37wFERGRsphngXz5MOXPce8UtfwvKlesILNkFi9dJtskpsb9feQI1dAPURRIX+3sTL68edC/z29YtGQpFi5aDDd3NwwbPAg51NokHDp0WOZ9VKlU8Z3HPH/hAp4/fyEvP7TRHEK0a1vKkIvPpXZhP1lUc8aukwgIj4S/mxNmdPgeDm+HfrwMCddo44c4eO0+hq5OmXKx37Kd8m+nGiXllKa6xKZYfpTZt0S1nHeC4nPnyeL1uNz+3bnAdUn8zfOINreEWfk60BNDQF4/Q+Tq6bIOhaBvba8xzCM5PAQRq2fArFpDWLUbgKTwEMSePYjYU9qn2/1SqtVrh7jYaKyaOxzRUeHw8S+Kjv1nwUjtTHvAqyeICE9JJz62Z5X8O22kZm2DFp1Gy2lLddHXtu/Vb9xCjgufNXWCnFIwd94CGDLqT40MiJcvnssCekrlKlZFaGgIVi6dLw96RYr44JF/qlK679+9Lc/+C107aGYjzpy/Es7Z0m/mjJpliiI4PAKz1m5HYGgY/Lw8ZOaDCCzItgQGa3z2rdt7TM6U1G/yAo3t/NywNjo2SjnAz2hiSkZxQK7slzyyX8an6pdnCFPrl/IVq8rCpSuWLlD1y5CR4zVS7Xft2IzVyxUzowmD+/0q/3br0U8V0GjTrjMM9A0weeIYxMXGyuFCI8ZMguUnzlJVt2ErGdCaP32srCnhl7cQ+g6frNGm1y+fySKaSqUr1JBtWrd8jpw1xMvHD32H/y2zMwRx1r7PsL+watF0TBzVW25fBB869hiKwsXLqbazbtkcHNm/TbU8qIfi5MrA32cgb4EPH0rQoHFz+X6ZOXWiWr/8oVGTSrxf3u2XUKxculAWQ/X2yYmhI//Q6Jd2P3eVQYPxY4bJ38yFi5ZAxy6K37KCmC1nx9aNmD93uvz8F9P+tv25M2rUUgRshN07tyIxMVHOoiEuSlWq1cKvvfpnqTZ9DTj0Q7foJb9vvhnKcsSH14YNG9J1qpnPKeTiu8VgvnbhFs7IbBL1Ml9M1OW6bh1wfg77Gk1BZlNu9Puno/1anaj5FzKbpJIp1cUzC68bupNB87l4hSiKa2cmj23THur0tYpKzHxFmC0MNGs8ke7K6+uGr9GVu6+gqwr4pk+2mC5jjQoiIiIiIiIi0hmZ7zQnERERERER0UdITubQD13CQAVp4EggIiIiIiIi+pI49IOIiIiIiIiIdAYzKoiIiIiIiChLS+KsHzqFGRVEREREREREpDMYqCAiIiIiIiIincGhH0RERERERJSlJXPoh05hRgURERERERER6QwGKoiIiIiIiIhIZ3DoBxEREREREWVpyckc+qFLmFFBRERERERERDqDgQoiIiIiIiIi0hkc+kFERERERERZGmf90C3MqCAiIiIiIiIincFABRERERERERHpDA79ICIiIiIioiyNs37oFmZUEBEREREREZHOYKCCiIiIiIiIiHQGh34QERERERFRlsZZP3QLMyqIiIiIiIiISGcwo4K+arEmVshsbIMfILNJMjRBZvMiXy1kNl43GiGzMb2/A5lRlbtTkNncvXEImc2jPJWQ2fgu+xmZTdfVNshs6rYshcxm44JLX/op0Ac6usXtSz8FygQYqCAiIiIiIqIsjbN+6BYO/SAiIiIiIiIincFABRERERERERHpDA79ICIiIiIioiwt6Us/AdLAjAoiIiIiIiIi0hkMVBARERERERGRzuDQDyIiIiIiIsrSOOuHbmFGBRERERERERHpDAYqiIiIiIiIiEhncOgHERERERERZWnJ4NAPXcKMCiIiIiIiIiLSGQxUEBEREREREZHO4NAPIiIiIiIiytI464duYUYFEREREREREekMBiqIiIiIiIiISGdw6AcRERERERFlaZz1Q7cwo4KIiIiIiIiIdAYDFURERERERESkMzj0g4iIiIiIiLK0pOQv/QxIHTMqiIiIiIiIiEhnMKOCPquXL1/i999/x7Zt2/Ds2TM4OzujcOHC6NGjB6pVqybXOX78OEaPHo0TJ04gOjoauXLlQtu2bdG9e3cYGBhkyPNcv303Vm7YiqCQUOTMkR3df26DvH6+Wtd98Pgp5i1fg9v3HuDlmwB0a9cKTet9o7HOxh17sHHnXrx8HSCXvbO7o03ThihdrDAyyuo9R7Fk+wEEhoYjl6cb+rRugPw5vbSuu+HACWw7ehb3nr6Uy3m8PdClybca6w+fvQJbj57RuF+ZAv6Y2rcjMsqa3YewdMteBIaGIVd2d/z2U1Pk882hdd2N+45h25FTuP/0uVzO7Z0dXZrVU62fkJCImau34PjFa3j2OgCWZmYoUcAf3Zp/Dyd723Rrw+YtW7F23ToEBwfDx9sbXTp3gr+/f5rrHz5yBIuXLMWrV6/g7uaGdu3aomSJEhrrPH78GPMWLMCVK1eRmJiI7NmzY8iggfL9JmzfsQMHDh7Cvbt3ERUdjbWrV8HS0hLpKTk5GSuXzsfeXVsRFRkB/zwF8EvXXnBz93jv/XZs3YBN61YiJDgIObxzon2n7sjln0feFh4ehlVL5+PShbMIePMK1ja2KFm6PJq3ag8Li/Rtj7Dy8Dks2ncKAWGR8HN3Rv/GNVAgh9v/2bsLsKiyNg7gf6W7uzsEUbAwsbu7u7t7dXWtb9W1u7sxsMCOtTtAMVBElO4Gv+ecWWYYnMFYwFl4f88zD96Zc4c5zrnD3Pe+5z2Q5NzDF9jsfwOhUbHIzM6BlYEOetargpZV3MTavfkUhWXHLuHeq1Bk5eTAzlgPS/q3hYmuVpH3h/fp5jNsv/YIUUmpcDTWxZQWNeBuLhg3BTn9+BWmHLiAui5WWNa9scQ2c49dxaE7gZjYzBs9qrujuJTEsfe9dGtWgu34/tDydIOyqSHuth+Gz8fPQxbtu8XG3mPR2Gte/TvH3mtMOXgBdZ3Z2GskvH/mkUs4/iBYrG11e3Os7S3+97k49O1sgRYNDKGuKo+nLxKwdMNbhH1Kk9q+bFmgTycLNKylD11tRUTFZuDMpQjsPBQmbHPpkLfEfdfueIf9xwV/54rymLp3biWC7hxERmoijKwqomabWdDSl/z3N7+HlzbiztmlcKveE94tp0l8/jPbBuPDy6to2GMlrMs1wK/Qv7s1WjYyhoaaPJ4EJmDxmmB8CE+V2l5FRQ4Du1ujtrc+dLQU8PJNEpZvfI2g4ETIipLYJ1J6UUYFKTQhISHw8vLChQsX8Oeff+LJkyc4c+YM6tati+HDh/M2vr6+qFOnDszNzXHx4kUEBQXxAAULXHTp0oX/8Spq56/dwOotu9CnSztsWjoP9taWmPD7QsTGxUtsn5aeDlNjQwzu1QW6OpJPaA30dDG4ZxdsXPIHNi7+A57u5TBtwRIe5CgO/jcf4K89xzCwbWPsmjsOjpamGPm/DYiJl/yH5l7gazT29sS6acOwddYoGOlqY8T/1iMiJk6sXfXyzjizcrbwNm94TxSXgBv3sGznEQxo3ww75k+Bg5U5Ri1cVUCfXqJx9UpYO2M0Nv8+AUZ6Ohi5YJWwT2kZGXjxNhT92jbBzvlTsGjcQLz/+BnjF68vsj5cvnwFGzduRI9u3bBq5QrY2tpg+syZiIsT/3/O9fz5cyxc9D80btQIq1eugLe3N+bM/YMfW7k+hodj/MRJsDC3wP8WLcTaNavRrWsXKCoqCtukp6ejkpcnOnfuhOJy9NBenDpxBIOHj8eCpeugrKyMuTMnICMjXeo+169cwLaNq9GpW2/8uWIjrGzs+D7xcbH88djoKMTERKNX/6H4a802jBg7FQ/u3caa5f8r8v6cuReIxb4XMLhpTeyb1BdOZoYYumY/ohOTJbbXUlPGgMbe2DGuJw5N6YfW1dwxa/dJXA98I2wTGhmLPn/tgo2RHjaN6srbDWpSA4oKxXPN4MyT11h8+gYG1/XCvmHt4GSsh6HbTiE6SfqXWCYsNhFLz9yCp5Wx1Dbnn7/Fk9AIGGiooriVtLH3I+TUVJHw+AWejvodskww9m5icF1P7BvaVjD2tp/+vrF3VvrYq+FgjvOTugtvizrVQ3Hr2sYU7ZsZY+mGNxg67QlS03Pw50wXKCpIXz2gaxsztG5khOWb36L3mIfYsOsdurY2Q7tmon62G3BX7LZw9Svk5HzBlZvRRd6nR1c24dnfu1CzzWy0HrYfCoqqOL1lILIypR9TuSJDnyDw9n7oGksPyD+9vv2Xr63Qvb0FOrQw4yfygyY8QGpaNpbOcS/wfZsy0hGVK+pg7tIg9Bp5F3cexGLZ3PLQ1xX9/f2VSmKffsWqH7J6K40oUEEKzbBhw1CmTBncvn0b7du3h6OjI8qVK4dx48bh5s2bSE5OxsCBA9GqVSts2LCBZ1pYW1tjwIAB2L59Ow4dOoQDBw4U+es8cOwUWjSqi2b1fWBtYY7xQ/tDWUkJJ89fltjexcEOw/p0R/1a1aEoL/mEokYVL3hXqggLUxNYmJlgYI/OUFFWxrMX4ld7isru05fRxqcaWtWuAlszY0zt2wHKSgo4fuW2xPZ/DOuBjg1qwMnKDNamRpgxoDO+5HzB7efir1dBXh762prCm6Za8Z2E7Dl5Hm3qVUdLH2/YmptgSv8uUFZUxIlLNyS2nzuiLzo0qg1HawtYmxlj+qDuPPB15+kL/ri6qgpWTR+Jht5esDI1gruDDSb27Yygt+/xKSqmSPpwxNcXTZo0QaNGDWFlaYmRI0ZASUkZZ/39JbY/euw4Knl5oWOH9jxLonevnrC3s+NZGbm2b9+BypUqYUD/fvwxUxMTeFerBm1tURCtbZs26NypE5ydnVEc2P+z37GD6NC5J6p41+RXp0eOn4bYmGjcvnFN6n4nfA+gQZMWqNewGSwsrTF4xHgoKSvjvP8p/riltS0mTZ+LylVrwNjEDO4enujWawDu3vob2dlZRdqnnRdvo523B9pUKw87E33M6NwEyooKOHrjscT2lR2sUN/DCbbG+rAw0EF3n8pwMDXEg9eiYOVKvyuoWc4OY9vUhYuFMW/n4+4APQ21Iu2LsE/XH6NdJWe08XKCnaEOZrSqBWUFeRy9JzhGJMnOycG0gxcwtJ4XzHU1Jbb5nJCMhX5/Y37HulCQK96vFSVx7P2IyLNX8HLWMnw+dg6ybOffTwRjz/OfsdeypmDs3f/G2Dt0EUPrecJcV0NiG0U5OehrqApvmipKKG4dmptg5+EPuH4nFm/epWDBylfQ11FEzSq6Uvdxc9LAtTuxuHk/Dp8i03H5ZgzuPIqDi70oWycmLlPsVrOyLh48S0B4xLeDBf/2mHp6fQcq1h0Ca9f60DNxgk+nhUhJjMC75wWPs8z0ZFzYPxG1282Bkorkz4voj4F4cnUbaneYh1+pYysz7DjwDtduReN1SDL++CsIerpKqFVNX2J7RcWyqFPdAGu2vsGjZ/EIC0/Dlr3vEBaeirbNJGfaFbeS2CdSulGgghSKmJgYnj3BMifU1L7+0s1Oovz9/REdHY0JEyZ89XjLli15YGPv3r1F+jozM7P4FI5K5UXp2GXLloWXh1uhBRWys3Nw/urfSEtLh5uzA4paZlYWgkI+oGo5R7E+VSnniMevRFfiC5KWnoGs7Gxo5QtE3At6hYbDfkO7iQuwYOshxEm5mlwkfXobispuzmJ9YttPgkVXqL/Zp6xsaKpLD64kpaTy4BoLYhS2zMxMBL96hYoVKoj1gW0HBgVJ3IfdX7Gi+HQhLy9PYfucnBzcvnMHZmZmmDZjJjp37YbRY8bi778lB2+Ky+dP4Tx9vnwFL+F9LD2epdG/CHom9f/n9auXYvuw/x+2/VLKPkxKSjJUVVUhJ1d0WQiZWdkIDP2Eak6iNOeyZcvw7cchotTsgr7o33oRgpCIGHjZW/D72JXQq89ew8pQF0NW74fP1BXovng7Ljx6ieLA+/QxCtXszMX7ZGeGx6Gfpe63/uJ96Kip8JNMSVi/ph+8iD41y8PeSPqJWVEpaWOvJBKOPVszCWMvQup+6y8+gI6aMtp5SQ+43g0Jh8/CnWi17AD+OH4NcSnSp1sUBRNDJejpKOLeY1FGZnJKNp4HJ8HVUXJwhXn6IhFe7powN1Hm23ZWqnB31sCtB5Kz7VhKfjVPbZw6L/3/q7Akxn5AamIUzOxFU08UlTVgYFEen98/KnDf68fmwtK5Dszsq0t8PCsjlQcyqreeCVUNA/wqpkbK0NdVwp2Hggwq4fv2MgFuzpIDLHJyZSAvVwYZGTli96dn5KC8a/FM3SttfSKE/tqSQvHq1Sv+5bygK7gvXwq+kLu4COYA58f2zW1TVOITE/lVGh1t8Q9gXS0tvP+ntsHPeh3yHsOmzEJGRibPpvhjyliesVHUWPCA9UlXS/xLka6mBkI+ft+XmpX7/aCvo8WDG7m8yzujbmV3mBno4sPnaKw+eAqjFm/A1lmjIccm2BahuIQkyX3S0sC7j4K6Gt+yas9RQZ/yBDvySs/IxKq9R9GouleRBCoSEhJ4YEE733QhFrQLDQ2VuA+rY5E3MyK3PbufYVNGWF2XAwcP8myL/n374O69e5g7bx4WLVyA8u7FVxcgL3aiyF+rjviJqpa2jvCx/BIT4pGTkw1tbZ2v9gkLfS9xn4T4OBzcuwMNmrREUYpNTkF2zhfoaYoHXVnmw9vP0tOuE1PT0HDGan5ixk7EpnVqBG9nG/5YTFIyUtIzsCXgJkY0r4UxrX1w/fkbjNt8BJtGdkMlB8ui7VNKmqBP6uJjnW2/jZJ8cnQ/5BN8773AgeHtpT7v1qsPIVe2DLp5i9fiKC4lbeyVRD819t59gu/9FzgwrJ3U561ub4H6LjYw09FAaEwCVp67g2E7zmDnoFZF/jcql66OAv/JMh7yio3PgK624DFJ9viGQU1FDjuWV+DBPvZ5sWnve5y7KqhzlV9jHwOkpObg6q2in/bBghSMirqe2P0q6vpITYyUut/rRycR9fE52gw/KLXNjZMLYWRZgWdq/Eq6OoJpDbH537e4DOFj+aWmZuNJYDz6dLFCyIcU3rZBbUOUc9LkGQi/Wkns06/w5UvpnGIhqyhQQQrFj9SW+Nk6FGzePbuJ3ZeRAaU8c/N/JUszU2z+awGSk1Nw6cZtzF+xDivnzSyWYMW/se3EeV7jYv204VBSFH2xauxdUfhvewtT2Fuaos34ebgX+EosoCGLth/z5zUu1s4cI9anXCzTYtryzWBDcXK/LvivyD122FSPdm3b8n/b2dnheWAgTp46VWyBiisXA7B+1RLh9rTZC4v8d7Kr2fNnT4GFpRU6d+8LWaSmpIQDU/rxgATLqFjiewHm+tp8WkjOP+9dXXcHXmSTcTY3wqO3YTh47UGRByp+VHJ6BqYfuohZbWrxq9qSPA+LxO4bT3m9C5aZVBxo7JV8wrHXWvrYY5qWtxP+28FYlxfobP7Xftx9G46qdqLsjcLUoJY+xg+yFW5PWSA5O+5b6lbX48/1x/JgvA1Nhb21Kkb0tUZ0TCbOXv46GNCsniHOXY1ERmbh1/F69eAErh6dLdxu0nvtDz9HUlw4bvgtQNN+myGvIHn6zbvnF/Dx9U20G3kExa1hHUNMHC763jJpzpOfeh5Wx2HqaCcc2+6NrOwvePk6EeeuRMApz5Sd4lIS+0RIfhSoIIWCrdzBvqiy4pjSsKkdTGBgIKpX/zotkN3v6uoqdf8FCxbg99/FC4aNHzYQE0d8/yoUWhoa/EpL/sKZMfHxUgtlfi8FBXmYmwgKYTnZ2yIo+DUOnjiDicMGoChpa6jxPuUvMhmTkAg9bempp8zOkxexze881kweCgfLgucjmhvq8d8V+jmqyAMV2prqkvsUz/okOYUx1y6/c9h+3B+rpo2Eg5WZxCDF1OWbER4VgzUzRhVJNgWjqanJ08njYsWvGLKsCB1d8Su5uXR0dL4qtMnb6+gIn5OtjMPqV+RlaWGBZ8+eo7iwefu5qyPkptLz1xobAx1d0VU4VpjQ2lbyajoamlooW1YOcf8UL8y7T/6r46kpKfhj5kQoq6hi0ow/IC+lVkxh0VFT5VkC0QniU51YIU39fFkWebGropYGOsIgBMu+2Ox/kwcq2HPKly0LW2Pxq5Q2xnp4mKeORVHRUVUW9Clf8UK2rS9hehS7Qv0xLhGjdp0V3pcbbPH8bSOOje7Mr3rHJKeiyeI9wjbsyvmS0zex++8nOD2hW6H3o6SPvZLox8deIj7GJWHUbgljb9YmHBvdCRYS6qWwGirsd72PSSiyQMX1OzEIDE4SbivICwJ0LHsib1aFjpYiXoVInyo5pKcV9hwNw4XrggyJt+9TYGyghO7tzL4KVLi7aMDSTAW/Ly2ajFNL13poZ1FeuJ2dncF/piZFQ1VTtCpLalIU9EwkZ8RGhT3j7X1XibKvvuRkIzzkLp7d3IN+cx/xIEVCTCi2z6kqtu+53aNhbO2FFoN2oKhcux2N5y/vCrcVFQQZNzraCoiOFfRXsK2IV29E729+Hz+lYeTUR1BWKgs1VXm+7++TXPj9xa0k9omQ/OgvLikUurq6aNy4MVavXo1Ro0Z9VaeCnWw1atSIt1uyZMlXgYrjx48jODgYc+fOlfo7pk6dygtzij3vW+nziaUFExztbHDv8TPUqiZY8pGl599//Axtm4mWPSsM7IsVq4lR1FjBS2drc14I06eSu7BPd54Fo1PDmlL32+53AVuOn8OqSYPgaiuYR1+QzzFxiE9K4UU1i6VPNha8EKZPZQ9hn+4+e4GOjepI3W/H8QBsPXoGK6aOgKuEpVlzgxShnyKwduZoaGsU3RUDBQUFONjb4+Gjh6he3VvYh4cPH6JlyxYS93FxdsbDh494Mcxc9x884PfnPqejowM+fBA/sQ0L+yhcmrQ4qKiq8lveTA92gvfk0X3Y2DkIr0IHvwhE42atJT4H64udvSOePLyHqt61hP8/jx/eR9MWgmyR3OdhqzEoKChi6m/zoahY9MXyFOTleLHLWy9DUM9DEJRj6dm3Xr5Dl1qeP/YZkJUlfM5yVia8bkVe7yJiimVpUt4nU33cehOGeq7Woj69+YguVct91d5GXxuHRnYQu2/1uTtITs/EpObVYaylhhYVHL46IWSriLD7WdHEolDSx15JVPDY+/rihI2+Fg6NEJ9utPrcXSRnZGJSM28YSwkWfo5PQlxqGgwKqEv0b6Wm5Xy17Cg7sfN018KrkBS+raoiB1cHdRz3lz5NUUmpLHLEywLwIJ+kxKTm9Qzx4nUSXr8TPH9hU1RS47e8x5SKhj7CXt+EnqkgMJGRloTI0MdwrSo5A9HU3hvtRx8Tu+/yoenQNrCBR50BPDDo4TMQTpXFP1MOL2+Nas2nwNKlLooSm+IQlpotdl9UTDoqeejg1dtk0fvmqImjp749DTgtPYfXwWJLgFapqIu1276vdlZhKol9kgXFsPgg+QEUqCCFhgUpatSogSpVqmDOnDkoX748srKyEBAQgLVr1/KMifXr1/NlSAcNGoQRI0bwK8Tnz5/HxIkT0aFDB3TqJH05RSUlJX7LK/Unpn10at0MC5av41kPbEWPgydOIzUtDc3qC06A5y1bA/1/lhtlWLAhJFRwYshOOqJiYhD8JgQqKsrCDIr1O/ehqqcHjPT1kZKainNX/8bDp4FYPGsKikP3pnUwe8NeuNpYoJytJfacvYzU9Ay0rC1IMf9t3R4Y6mhiRGfBCTLLolh/+Axf/cNEXxdRcQn8flVlJX5LSUvHRt+zqFe5PPS0NPEhIgor9vnBwkgf3u7Fs5JEt+b18fvaHXCxtUQ5e2vsO30BqenpaFGnGn981prtMNTRxvCugpMRlkWx4eBJzB3RByYGrE/xefqkzIMUU5Zt5EU6l04aymtg5LbRUlfjwZHCxqZnLF66lGccOTk6wvfYMaSlp6FRw4b88T8XL4Genh769e3Dt9u0boWJk6fg8JEjqFK5Mi5dvoLg4FcYPXKk8Dk7tG+PBQsXwd3dDR7ly/MaFTdv3eJLleYtbsvqWnz8GM632fKmKioqPJihoVFwls3PYNlULVp3xKF9O2Biag5DY2Ps3bmFX+FmKzHkmj1tLKp410KzloJ55y3bdsLKpQtg5+AMB0dn+B07hPS0VNRr2FR4ojhnxgSkp6dh9IQZfJvdGE0tbZ5dUlR61q2Cmbv8UM7SBG5WJth16S4/ptgqIMz0HSdgqK2B0a18+PZm/xtwtTSGhb4OMrKyeOHMk7efYXrnxsLn7F2/CiZtPQYvOwtUdrTiNSquPH2FTaMKP/NAYp9qlMfMw5dQztQAbuYG2PX3E6RmZKKNlyAYw9LtDTXVMLpRFSgpyMMhX3FMDWXB52/u/dryctBWFU/NZ6t+sNUXrA3+XYZaaR57P7o8qZq9KMNK1cYcmh7OyIiJR1qo4PiXBT2ru2PmkcsoZ2YANzMD7LrxVDD2PL9z7KkI/tbn3p+Snol1F++jQTlr6Kmr4kNMAv7yv80zLao7FO90y0Mnw9GzvTk+hKfxFTn6d7FAVGwGrt0WBSWXzHLFtVsx8D0jCF7cuBuLnu3NEBGVjhA29cNGDZ1amOLURfG6UuxEs463HtbueFds/WHHlFuNXnhwYR209KygoWuOuwEroKphCCvXBsJ2Jzf1hbVrA5Sr3p0HOnSNxTMtFRRVoKyqLbyfFc+UVEBTXdsEmrrFP0X24PEw9O5sidCPqQj/nIYBPdjUm3RcvSmqE7Lsj/K4ciMKR04KTvSrVNThwaT3YakwM1HB8L62eP8hBSfPfV/trKJWEvtESjcKVJBCY2tri/v372PevHkYP348wsPDYWBgAC8vLx6oYFgw4uLFi7xNrVq1kJaWxk/ipk+fjjFjxhTLPOf6Nb0RF5+ALXsPISY2DvY2VjygoPtPgc3PkdEoU0ZUiCsqJhb9x00Tbu87epLfKpRzwYp5M/l9sXEJmL9sLaJj46Cmpgo7Kwv+nJUrFE/NgEbVKiI2MQnrDp9BdHwCHC3NsHLiIOj9U4zyU3Qsyub5vz18/m9e8G/yiu1izzOwbSMMbteEp7AHh4bD7+pdJKakwkBHE9XcnDCkQ1MoKhTPxwZbRjQ2IREbDvkhOi4RjlZmWD5luHDqx+co8T4dCbjKA0lTlm0Se54B7ZthUIfmiIiNw5V7gjmcPaYsEGvDsiu8XAt/OkudOrURnxCPnTt38cABO0b+mDNHOJUjIjISZcqK+sCmPk2eNBHbd+zEtm3bYWpmht9mzuDL+OaqUb06Ro4Yjv0HDmLtuvUwNzfDzOnT4FZOdFX85KnT2L1HlI4/YdJk/nPc2DHCIElha9OhK9LSUrFu5WIkJyfB2dUdM+f+KXYV+lP4R17IUNiX2vUQHx+Hfbu28NR9G1t7zJjzpzD9/s2rlwh+IZjSMnyA+Mn82i37YGhkgqLSxMsFsUkpWHPyKqISk+FkZog1wzoLC2x+ik0QG3/spGv+AX98jkvkJ1o2RnqY16slf55cbPnSGZ0b84Kaiw6fg7WhLpb0bwtPu29nNBVKn9ztEJucijXn7yIqKQVOJnpY07sZP9HjfYpLEuvTf0VJG3s/QsvLDd7ndwq3XRcL/laF7jiCx/2nQlYIxl4a1py/Jxp7vZqKxl58Mv+7871Y25efo3H84UskpmXAUEMV3vbmGF7fC4ryxRtE2nv0I5SV5DBhsC3U1eTxJCgBk/4IFKsnYWakBC1N0d/O5Zvfon8XS4wZaAsdTQUe2DgR8BnbD4lny9WrocdPIs9fk1xks6h41B7AV+i46jsLGWkJMLLyRJO+G8TqTyREv0daivj0qf+S3YdDoawsh0kjHAXv2/N4jJ/1RPx9M1aBtqaozhVrN7iXDQz0lZCQmInLf0dhw863yM6WjcvwJbFPpHQr8+VnKxsSIgM+B95DSaOaWPKi2DnyJS9lOkZLFDwoKZK/lLziWfZvTqNEipe8osV/2auK3VHSvHORPlXtv6r+7oEoaZocqIGSpkU38VoQJcHRrVd/9Usg3+naif/mZ9/5J7Jbm6O+u/TiwiVV8azfRAghhBBCCCGEEPIdKFBBCCGEEEIIIYQQmUGBCkIIIYQQQgghhMgMKqZJCCGEEEIIIaRU+/Llv1dQuiSjjApCCCGEEEIIIYTIDApUEEIIIYQQQgghRGbQ1A9CCCGEEEIIIaXaly+/+hWQvCijghBCCCGEEEIIITKDAhWEEEIIIYQQQgiRGTT1gxBCCCGEEEJIqfYFtOqHLKGMCkIIIYQQQgghhMgMClQQQgghhBBCCCFEZtDUD0IIIYQQQgghpVoOrfohUyijghBCCCGEEEIIITKDAhWEEEIIIYQQQgiRGTT1gxBCCCGEEEJIqfblC636IUsoo4IQQgghhBBCCCEygwIVhBBCCCGEEEIIkRk09YMQQgghhBBCSKn2hVb9kCmUUUEIIYQQQgghhBCZQYEKQgghhBBCCCGEyAya+kEIIYQQQgghpFTLAa36IUsoUEH+05KUdFDSvIEDShothSSUNJrZMShprBJeocTJyUZJ9N6zI0oaq9iHKGnsdw9ESXO++0aUNG0Oj0dJ08fyMkoardE+v/olEEKKEU39IIQQQgghhBBCiMygjApCCCGEEEIIIaUarfohWyijghBCCCGEEEIIITKDAhWEEEIIIYQQQgiRGTT1gxBCCCGEEEJIqfblC636IUsoo4IQQgghhBBCCCEygwIVhBBCCCGEEEIIkRk09YMQQgghhBBCSKmWQ6t+yBTKqCCEEEIIIYQQQojMoEAFIYQQQgghhBBCZAYFKgghhBBCCCGElGpfvsjurajExMSge/fu0NTUhLa2Nvr374+kpKQC248cORJOTk5QUVGBpaUlRo0ahfj4eLF2ZcqU+eq2b9++H3ptVKOCEEIIIYQQQggpZbp3747w8HAEBAQgMzMTffv2xaBBg7Bnzx6J7T9+/MhvixcvhqurK969e4chQ4bw+w4dOiTWduvWrWjSpIlwmwVCfgQFKgghhBBCCCGEkFIkMDAQZ86cwZ07d1CpUiV+38qVK9GsWTMeiDA1Nf1qHzc3Nxw+fFi4bWdnh3nz5qFHjx7IysqCvLy8WGDC2Nj4p18fTf0ghBBCCCGEEFKqfUEZmb2lp6cjISFB7Mbu+zdu3LjBgwm5QQqmQYMGKFu2LG7duvXdz8OmfbCpI3mDFMzw4cOhr6+PKlWqYMuWLfjyg3NYKFBBCCGEEEIIIYTIqAULFkBLS0vsxu77Nz59+gRDQ0Ox+1iwQVdXlz/2PaKiojB37lw+XSSvOXPm4MCBA3xKSfv27TFs2DCerfEjaOoHIYQQQgghhBAio6ZOnYpx48aJ3aekpCSx7ZQpU7Bo0aJvTvv4t1hWR/PmzXmtitmzZ4s9NnPmTOG/K1asiOTkZPz555+88Ob3okAFIYQQQgghhJBSLacIV9f4t5SUlKQGJvIbP348+vTpU2AbW1tbXj8iIiJC7H5WZ4Kt7PGt2hKJiYm8UKaGhgZ8fX2hoKBQYPuqVavyzAs2XeV7+0GBCkIIIYQQQgghpAQwMDDgt2/x9vZGXFwc7t27By8vL37fhQsXkJOTwwMLBWVSNG7cmAccjh8/DmVl5W/+rocPH0JHR+e7gxQMBSoIIYQQQgghhJBSxMXFhWdFDBw4EOvWrePLk44YMQJdunQRrvgRFhaG+vXrY8eOHbwoJgtSNGrUCCkpKdi1a5ewsCfDgiNycnI4ceIEPn/+jGrVqvEgBqtTMX/+fEyYMOGHXh8FKkihYSlGLCp39OhRsfsvXbqEunXrIjY2lleWZRVfN27ciM2bN+PZs2e8aIu9vT1f1oYVYlFVVS3U18UOlsOHDvHfb2Nri6FDh8LJyUlq+6tXr2Lnjh38ADM1M0O/vn1RuUoViW1ZUZjTp07x192mbVvh/X169/4qlapP377o1KlTofWL/T/67l2PywFHkZKcBAfn8ug1ZAqMTS0L3O/cqQM47bsL8XHRsLR2QI+BE2HrWE74eET4B+zbthzBgQ/5B5Z7RW/0GDQBWtp6wjafwt5h//YVCA58xFPELKzt0a7bELi4V/rhPuzdtRXnzp5EcnISnF3cMHj4WJiamRe43yk/Xxw9vB9xsTGwtrHDgCGj4OjkInw8IyMDWzetwbUrF5GVmYEKnpUxeNgYaOvo8sffvnmFIwf3IvD5EyQmxMPA0BiNm7VEy9YdhM/x9PFDzJw69qvfvWXnYejoCp6nsBw9eRoHjhxDTGwc7GysMXJwfzg7OkhsG/LuPbbt3oeXr9/gc0Qkhg3oi/atW+BXOuB/FTtPXkB0fAIcLM0wsXd7uNlZSWzre+FvnLx2B69Dw/m2i40FhnVuIbX9/M37ceTC3xjXoy26NfVBcdp39T62X7iDqIRkOJoZYkr7+nC3MpHY9tyjl9gccBOhUXHIzM6BlYE2etatjJaVRcdWSnoGlp24gouPgxGfkgYzXS10re2JTjUrFMnrP3niGI4cPojY2BjY2Nhh8NDhcHRyltr+2tXL2LVzOyI+f4KpqRn69BuASpUFV1TYcb5rx1bcvXObF9hSU1OFRwVP9O7bH3p6+rzNk8ePMG2K5C8hS5atgqOj9M/df6Mkjr99t55h+7XHiEpKhaOxLqY0rw53c/GCZ5KcfvwaUw5eQF1nKyzr3kh4/8wjl3D8QbBY2+r25ljbuylkjW7NSrAd3x9anm5QNjXE3fbD8Pn4ecgq9nfsjv9KBN46iPTUBBhbe6J2u1nQNrD+rv3vX9iAW6eXwr1mL9RsPU14//Ob+xH8wA+RYc+RmZ6MfnNuQ0lFE7/CwbMXsftEAKLj4uFgZY7xfbugnL2NxLZHz1/FqSs38Sb0I992trHE0K5tpLYvzvfp+skVeHJd8D6Z2nqiYZfZ0DGU/j49vLIHD6/uRUJMGN/WM3GAd9NhsC1XR9hm37Ke+BB8W2w/j5qd0bDrHBS1ktinX+0HF6UoEXbv3s2DEywYwVb7YIUvV6xYIXycnQu8ePGCByaY+/fvC1cEYedveb19+xbW1tZ8Gsjq1asxduxYPk5Zu6VLl/KAyI+gQAUpdj179sSRI0cwY8YMrFq1ikffHj16hGXLlvHB3aZNm0L7XZcvX8bGDRswYuRIODs58SDKzBkzsGHjRh40ye/58+dYtHAhDyqwqCELsrD5VCtWruSvLa+/r1/Hi6Ag6OmJTuDz6tGzJ49S5irsAMwp3x0I8NuPgaNnw8DIFEf2rMOS30di3soDUFSUnFZ165o/9m1Zht5Dp8DW0Q3+x/di8e8jsXD1IWhq6yI9LRV/zh4BSxsHTJqzlu/DnnfZvHGYuWgr/wBj/po3DkYmFpg8dy0UFJXgf2Iv/vpjLP63zhfaOoKTlu/he2gfTp44glFjp8DI2AR7dm7BnJmTsGLdNigqKkrc59qVC9i6cS2GjBjLgxMnjh7i+6zasAPa2jq8zZaNq3Hvzk1MnDoLaqpq2LBuBRbN+w0LFq/ij79+9RJa2toYM2Ea9PUNERT4DGtXLYFcWTk0aykKODHseVVV1ITbbL/CdPHqdazbtA1jhg/mwYkjx/0w+be52LZuJXS0tb5qn5aeARNjI9SuWR1rN23Fr+Z/4z7+2u2Lqf06wc3OGnvPXMLIhWtxePF06GppfNX+XuArNPb2RPleNlBSVMD2E+cwYuFaHFg0BYa64v+3F+88wtNX72Cg8/X/Q1E7cz8Ii30vYUanhnC3NsHuS/cwdO1BHJveH3oaovGQS0tVGQMaVoONkR4U5MviytM3mLXnNHTVVVHDRfAFfbHvRdwOfo/5PZvDVFcLN16EYP7BABhqqcPHXfyP/b919fIlbNq4HsNHjIKjswuOHz2C32ZOxboNW4THSV6Bz5/hz0Xz0btPf1SuUhWXL13EvLmzsWzFGlhZ2/A5pa9fvULnrj14wDcpKREb163FH7//hr9WrOHP4eziih279os9766d2/Do0QM4ODiiKJTE8XfmyWssPn0TM1rV5MGJ3TeeYuj20zg2uhP01FWk7hcWm4ilZ2/B00ryvOIaDuaY01Z0MqIoLwdZJKemioTHLxC67TAqHVoNWffw0iY8ubYT9TovhKauOW6fXQ6/TQPQZcJJyCsUnOIcEfqEByT0TL4O4mVmpsHCqRa/sUDGrxLw9x0s33EIkwd0QzkHG+w7dR6j56/Agb9+h67W14GT+89eolH1yijvZAdFBQXsOHYGo+Ytx94ls2Co+/VnT3G5HbARDy7tRNOeC6Glb45rJ5bj0Kr+6DvzlNT3SUPHGLVbT4COoRU/2Xp26yiOrh+OXlN8oW8quphQvkYn1GguKhAoryj9OKU+EVmjq6uLPXv2SH2cnf/kXVbUx8fnm8uMsvOfvOdAP4uWJyXFii1TwyJ3e/fuxbRp01C5cmV+ALRu3ZrPiWKZF4WJFXdp0rQpT1GytLLiAQs2N8rf319i+2PHjsGrUiV06NABlpaW6NWrF+zs7HhWRv6leNauXYuJkybxFCdJVFVU+MGfe/ue+Vvfi31AsOBAq0794Fm1DiysHTBw9O+IjYnC/VuXpe539tge1GnUBrXqt4KZhS16D50KRSVlXDl/nD/OMiSiIsMxYNQsniXBbiwQEvIqEIFP7vA2iQlx+PzxPZq3681/L8vg6NhrBDLS0xD2/vUP9cHv2CF07NwTVb1r8syI0eOnIiYmCrduXJO633Hfg2jYpDnqN2wKC0trDBkxDkrKyjjvf5o/zjIzzvufQt8Bw1DewxN2Dk4YOWYyD0a8CHrO2zRo1AwDBo+Em3sFGJuYwqdeQ9Rr0AQ3/r761e/T1tLhGRS5t9xgTWE5dPQEmjVugCYN6sHa0gJjhg3mY/RMgOQriM6O9hjcrzfq1a75zcJFxWH36UtoU7c6WtWpBltzY37CqKykiOOXb0ps/8fwXujYsBacrM1hbWqEGQO74ktODm4/eynWLiImDn9uP4y5w3tCXsoxVpR2XrqLdtXLo001d9gZ62NGp0ZQVlTA0ZtPJbav7GCJ+h6OsDXWg4W+Drr7eMHB1AAP3giuWjEP335EyyrleFszPS10qO4BR1NDPH0vuLpfmI76HkbjJk3RoFETWFpaYdiI0XxcBfifldj++DFfeHpVRrsOnWBhaYUevfrAzs4efieO8cfV1NQwd/4i1KpdB+bmFnB2dsXgYSPw6lWwMHuMjce8x4qGpiZu3byBBg0ao0yZMigKJXH87fz7CdpVckYbTyfYGepgRsuaUFaQx9H7L6Tuk52Tg2mHLmJoPU+Y634doGEU5eSgr6EqvGmqfP884eIUefYKXs5ahs/HzkHWsb9jj6/ugFf9IbBxqw89UyfU67IIKQkRePus4NfPsiTO7ZkAnw5zJWZKeNTqDc96g2Bk5YFfae/Jc2hdvyZa1q0BW3NTTBnQHcqKijhx8W+J7eeM6o8OjX3gaG0BazNjTB/SCzlfvuDukyD8yvfp/sUdqNZkKOw9GsDAzBnNev8PSfERePVI+vtk514Ptm51eIaCrpENarUaC0UlVYSHPBRrJ6+oDDUtA+FNSUWd+kRIIaBABSlWLEjBpl2wwER+7IssWxO4sLBUpVfBwahQQZRWzU4y2XaQlCV52P0V87RnWHGZvO1ZgZnFixejfYcOsLKSnC7MHDx4EJ07dcKI4cNx6NAhZGdno7BEfg5DfGw0XMuLpqSoqqnDzrEcXr94LHGfrMxMhLwOEtuH/X+U86iC1y+e8O3MzAyUQRnIK4iyGRQUFVGmTFm8fP6Ib6traMHYzArXL53kGRjZ2Vm4dPYINLV0YW0nmn7xLZ8/hfOUdI8KguI9jJqaOhycXPAi6JnU95RlQ+Tdh/WhfAVP4T7scZamnreNuYUlDAyM8CJQ8vMyKcnJvHJxfmNHDkC/Hu0xe/oEPlWkMLH+vHz1Gp4e5cX641mhPJ6/ED9xkkWZWVkIehuKqm6OYq+/ipsjHgeHfNdzsAyRrOwcaKmpih1jv63dhZ4t6sHOXPJUi6KUmZWNwNBPqOYoOr7Lli3Dtx+HCNKZv/UF8taLdwiJiIWXnWgaUwUbU1x+8hqf4xJ5G5Zd8S4yBt5O1oX7+tlnHz9OPPN99nkKg3X5BQU9R4WKovZMRa9KCAoKLPCYYZ/b6upfZ5gwLEiRmJiABo0aoyiUxPHHx97HKFSzNRMfe3ZmeBwqPp0wr/UXH0BHTRntvKRP7bkbEg6fhTvRatkB/HH8GuJS0gr99Zc2iTEfkJIYCXOH6sL7lFQ0YGhZHp/fiZ/45XfFdw6sXHxg7ijaV9bwY+zNe1RxdxE7xiq7O+NJ8JvvPsays7KhKeVzojjER39AckIkrJzE3ycTaw98fPvgu54jJycbQXdPIjMjBSY2FcUeC7xzAqsnVcXWP1rgyrElyMxIRVEriX2SBSxRQFZvpRFN/SCFys/PD+rq4lHXvCfowcHBBdaHKEyssAv7wskqzOalraOD0A8fJO7D62hIaM/uzxuAkCtbVmKwJVer1q35fCx24sumk2zfto0v9cNqWRQGVl+CyVs3gtHU0uMBDEkSE+P4HyUtbfH6CizAEP5B8KXezsmdZycc2L4SHXoO55+MB3as4vvFx0bxNuzEZNLvq7FiwUQM6VqHBzE0tXQwftYKqKl///xZVl+C9yH//7e2jvCxr/qQEM/fUy3tr/cJC30vfF55eQWo5RuH7PdIe96g509x/epFzJi9QHgfuyLMppfY2zvxE78A/5OYOWUsFi1dAzv7wkljj09I/GeMiqecsykfoR9EV+JlVVxiMr+Smz/FXldTAyEfpZ9U5bVy33Ho62iiipvoc2H7ifP8GOvSWJSmXpxik1ORnfMFehri07XY9tsIyWOISUxNR8Pf1vKTTXZyOa1jQ3g7i4IQUzrUx5x9/mg0ax3ky5blx9KsLo3gZW9RqK8/4Z/j5KvPPm0dfAgNlbhP3D81hPK3l3bMsDow27ZuQu06daGqKvkEJMD/NCp6ekFf/9uVx39GSRx/sSlpgrGXb4oH234bFSdxn/vvPsH3/gscGNZO6vNWt7dAfRcbmOloIDQmASvP3cGwHWewc1Ar3lfyc1iQglHREP9brKquj5REwd9MSYIfnkRU2HO0H3UIsiwuIUnyMaaliXcfP33Xc6zefQT6ulqonCfYUdzYCT2jqpnvfdLQQ3KC9PeJiQx7gT2LuyArK51nHrQeuBr6JqKpei6VWkBT1xTqWoa87ZVjixH7+S1aDxJMNS0qJbFPhORHgQpSqNjUDTYlIi9WcIUVymS+NaepIGyONLvlv+9Hlrn5t1ig5fixY7xmRUGpzO3aib4w2tjYQEFenhfe7NunD89Q+FF/Xz6N7WtFJ9FjZ/yFosACDsMnLsT2dQtx7uR+HoSoWqsRrGydUeafL7PsPdy54X+87bT5G3mNiisBR3kdi1l/boe2ruQaFZcvBmDdKtE82+l5ggK/0ruQt1gwdwY6d+vNi27mMjO35Ldczq5u+BT+kdfEYLUtyL+37XgA/G88wPoZI3i9ACbwbSj2nb2MXfMmFtl0gaKipqSIA5N686KZt16+x5KjF2Gup8WnejB7r9zH43cfsXxgW5jqaOLe6w+Yf+gcDLTUUa2QsyqKEstYWrRgLv8sGDZCNIc5r6ioSDy4fw+TpsyArCoJ4y85PQPTD13ErNa1eEaFNE3L2wn/7WCsywt0Nv9rP+6+DUdVO1H2BinYy/sncPnwLOF2837rfvg5kuLCcf3YfLQcuOWbNSz+67YfPcNrXKyZNV54jBWH57ePI2Cv6H1qN2z9Tz8Xmx7Ra+pRpKcl4uWDszi9czI6j9klPLFnRSZzGZg5QV3LAAdW9EFc5HtoGxRc3Ly094mQb6FABSlUbB5z/gqwH/JkLzg6OiIo6OfmKS5YsAC///672H0jR43C6NGjJbbX1NTkKYp5syFyrxzq5rvSmItdgYyT0D73yuSzp0/5yia9e/USPs6uXG7atIkX6ty2fbvE53VyduaZJZ8jImBuXvCKFpJUrFIbdo5uwm22kkVuZkXewEBCfDQsbSRf7dfQ0EbZsnKIjxO/QpoQHwMtHVFE3q1iNfy5/iivRcHaq6lrYFSfxjAwElSQD3x8Bw/vXsOaXeehoirIWrC2m4Jnj27j2kU/tGjfR+Lvr1K1BhydXIXbmbl9YO+Hruj3x8Wx1VkkFxbU0NTi72l8XL73KI5lwggyRdjPrKxMJCcliWVVsN+T2yZX6PsQzJo+Hg2btEDHLj3xLQ6OLoU6/UNLU+OfMSp+pTQ2Lh66+bIsZJG2hhq/GhsTnyh2f0xCIvQkFDLMi63SsO3EeayZOoyv1JDrQdBrxCQkocWo2cL72BW9ZbuPYu+ZyzixXPRFrajoqKlArmwZRCcKKlznYtv6Egpp5mJZFJYGgs8KZ3MjvP0cjc3nbvFARVpGJlb4XcVf/dugdjnBSSNbSeRFWARfWaQwAxWa/xwnX332xcVCR0oxO5Y5xj7b8rfPf8wIghR/8LoU8xb8KTWb4pz/WWhoaKJqNW8UlZI4/nRUlQVjL0k8zZpt66t/XZA5NCYRH+OSMGq3qPYIqwfAeM7axAtwWuh+nelmrqvJf9f7mAQKVPwAa9e6MLIUTdXLzhL8HUtNjIaapmhVlpSkKOibSs4giPzwDKlJ0Ti4XHRB40tONj6+vYunf+/GoAWP+d9eWaCtqS75GItPgK6EYs957Trhzwtprpoxhq8UUpzsy9fjUyDyv08pCdE8SyBXSmI0DM2lT5di5OQVeeFJxtjSDZ/ePeG1IRp1k7wChvE/vzc28l2hntSXxD7Jopwvsh+gLk0oUEGKVbdu3fjavKxoZf6pE+zqHJuuIa1OxdSpUzFu3Dix+z6ESU+PZ4Xd7B0c8OjhQ1SvXl0YVHj48CFatmolcR9nFxf+eN6lRh88eMDvZ+rVr48KFcXn8bFVROrVq4eGjURLweX35vVrfuLwszU4VFTU+C3v/xULLjx/fAdWtoKU5dSUJLx++Qx1m4iW2MxLXkEB1nbOfB+vaj7C/w+2Xb9Zx6/aa2gKTpTZ44nxsahYpRbfTk8XzGtm2RZ5sauPX3KkZ8yoqKryW94+6Ojo4vGj+7CxEwQmUlKSEfwiEE2atZb6nrJpF48f3ucFOHP78OThfTRtIXjP2ONsydvHj+7Bu4YgdTvsw3tERn6Gk4toqcj3797it2njUbd+I/ToPQDfI+TNK+jkCer8W6w/jvZ2ePD4CWp6VxX258Gjx2jTXPaWDcyPZQo521jwQoQ+lcoLX/+dpy/RqZFgvEjCUuu3HPPHqslD4Wor/qWnWc3KvMZAXiMXrUOzmpXQsrbg/6ioKcjLwcXCGLdevkO98oIq6Dk5X/h2l1ridRwKwk4Y2TQQJisnh9dCKJvvKj0LbuSeWBba62effew4efQA3tVr/PP6c/Do4QM0byn52GLFMdnjrduITp4ePrgPZ2eXr4IUHz+GYf7CP3kwWBJ2bJ87dxZ16zfgx2JRKYnjj489U33cehOGeq7WorH35iO6VBUFenPZ6Gvh0Ij2YvetPncXyRmZmNTMG8aakgNJn+OTEJeaBgMJwQ8inaKyOr/lHeuqGgb48OoG9M0Ex0pGWhIi3j9GOe+uEp/DzL4aOo0XFLDOdXH/NOgY2qJC3QEyE6QQHmO2lrjzJBB1KlfIc4wFoWNj6cXPdx47i62+p7B82mi42FnLxPukpmmAdy9uwNBC8D6lpyYhPOQRKtSS/D5J8+VLjjBIIEnkB0FdH5aFUJhKYp8I+RYKVJBi1alTJ74SR9euXfnypGw1DrY86ZMnT/DXX39h5MiRUpcnZVM88k/zUIoqeB5e27ZtsXTJEjg4OMDRyQnHjh7l00UaNmzIH2dFMdnyon379uXbLHgyedIkHDl8GJWrVOHLm7LpHixzg2FfzPN/OWerfrCMi9xMicDAQL5saXkPD6ioqPBCnBs2bODTYiQVa/wZLCjQqGVXnDi4BcamFtA3NOPLiOro6vNVQHItmjkUXtXqokHzTny7cetu2Lj8d9jYu8DWoRxfOYQVxKxVv6Vwn6vnj8PE3Aaamjp49eIxdm9eyn+XiZngy4a9c3moqWlg4/LZaN15AF8K9VLAUURGfIRHpRo/1IcWrTvg4L6dMDE1Ey5PqqurLwxCML9NG4dq3rWEy4a2atsRK5YuhJ2DI89wYCuHpKWloX7DJsKCnPUbNeNLmKqra/JlYTeuWwkn53JwcnYVTvdgz1vRszJatemE2BhBlklZORZMEgRo2BQPQyNjWFrZ8Pn45/xP4snjB5g1938oTB3atMSiv1bygAVbnvTwMT+kpaWjcYN6/PGFS1dAX08XA3oLpk+xehnvQj8ITxyjoqPx6s1bqCgrw8y0+AtPdm/qg9nrd8PVxhLl7Cyx58xlpKZnoGUdwUkdK0poqKOFEV0EY2zbiXNYf+gUX33BxEAXUXEJ/H5VZSV+Y1fJ2S0vtuqCnpYmX6WhuPT0qYSZu0+hnKUx3CxNsOvyXaRmZKJNVUFm0/RdJ2GopYHRLWvz7c0BN+FqYQwLfW1kZGXj6vM3OHnnOaZ3EnzWqCsroZK9BZYeuwwlBXmY6Gri3qsP8LvzHBPaCAKHhalN2/b4a+n/YO/gCEdHJxw75ou09DQ0aCgobLl08SLo6emjd9/+fLtV67aYOnk8fI8cRKXKVfnypq+CX2LEyDHCsbZw/hy+ROlvs+ciJztHeNyoa2iIrUDDAiSfP31Co8ZFH2wrieOvZ3V3zDxyGeXMDOBmZoBdN54Kxp6nIIDCpnoYaqphdKMqfCw5GIlnvWioCKYX5t6fkp6JdRfvo0E5a+ipq+JDTAL+8r/NMy2qOxTvle7vXZ5UzV4UQFK1MYemhzMyYuKRFlr4K+T8G+zvWPlavXDv/Dpo6VtDU9cMt8+ugKqmIWzKNRC2O76+D2zcGsC9Rg9+sqlnLB4MU1BUgZKqttj9KQmRvM5FfJSg/lJ0+EsoKqlBXccEyqrFl3HXtXkDzFmzjQccXO2s+fKkrEBmCx/BBaDZq7bCQFcbw7sJ/kazLIoNB07w1T9MDfUQHRfP71fhx1jhrX72o++TZ91euHlmLc8k0NIzx3W/5TwTga2YkevA8t6w92gITx/B31tWRNLGtTY0dU2QkZaMwLt+CA2+jQ7DN/PH2VSIwLsnYFOuDlTUtHk9h4uHF8DcvjJfhYP6RMi/Q4EKUqzYBytbq5eduG/ZsgXz5s3jV9xYIIEtBdq4ceFWh69Tpw4S4uOxc9cu/qXa1s4Oc+bOFU7liIyIELvC6erqikmTJ2PH9u3Ytm0bzMzMMHPmTL6E6vdiX9hZgIOtcMJOKo2MjHiGRrs8WRqFoVnbXjzIsHXNfKQkJ8HRxQPjf1vBAwe5Ij6F8SkcuarWbITE+Dj47l3Pi26yaSKsCGbeopzhYe9wcOdqJCclQN/QFC079EXjVt3EMi3YPod3rcWi34YhOysLZpa2GD11sdRpJ9K07dAFaWmpWLtyCV9W1MXVHTPnLoJinjoerC4EKw6Yq2btevw93bdrG181xMbWDr/NWSSWot5v4HA+1v43fxZ/D1jticHDBCdczI3rl5EQH8frZrBbLgNDI2zYuo//m00f2bZ5LWKio/gSrtbWtpj9x2K4e4hn1PxbdWvVQHx8PLbt3sengNjZ2mDh7zOEUz8iIqPE5spHx8Ri8OgJwu0Dvsf5zcOtHJYukJy2WZQaeXsiNjEJ6w6dQnR8AhytzLFy8hB+Ysd8io4VO8YOn7vOswwmL98q9jwD2zXB4Payk0XSxNMZsUkpWHPqOqISkuFkbog1QzpA758r1J9iE8X6xU4k5x8M4Feq2cmjjaEu5vVszp8n16LeLbD8xFVM3XkSCSlpMNHRxIjmNdGxhvhKQ4WhVh0fxCfEYffO7XwKiK2tHX6fM1/02RcZgTJlRa/fxbUcJkyail07tmHHtq0wNTPD9JmzYWVtwx+Pjo7iq3gwo0YMEftd8xcuhnt5UUqy/9kzcHFxhYVF0acIl8Tx18TdDrHJaVhz/h6iklLgZKKHNb2a8iAD8yk+mWfifC/W9uXnaBx/+BKJaRkw1FCFt705htf3gqK87Fy9z6Xl5Qbv8zuF266LBTWBQnccweP+UyFrKvgM4CsiXD70GzLSEmBs7YUWAzaK1Z9IiH6PtGTxqVjf8uzmPtwNWC3cPrZWcKJZt9N8OFeWXji1sDWsXpkX1dxw4Dii4xLgaG2OZVNHQU9bcIx9jo4RG49HAq7w1UKmLhWvoTCgQwsM7Ci6KFLcqjQcyN8n/z2/IT01AWZ2Xmg/fJPY+xQXFYrUPO8Tm0ZxesdkJCdEQFFZg9drYCf01i6CizJl5RXwLugG7l3cgcz0FGjomMCxQiNUazKM+vQfVVpX15BVZb78m+qGhPxir9983/JY/yUR6ZKLUf6XaSkkoaTRzJG++sN/lVaC5NVw/ssUokpen5j3DtKnmv1XmcRKXjr1v0zhzVOUNOe7b0RJE3z452pnybI+lpdR0hyMKvzsM1I0BoqSOv5T9l6X3dPirjVKX/0MWpOKEEIIIYQQQgghMoOmfhBCCCGEEEIIKdVonoFsoYwKQgghhBBCCCGEyAwKVBBCCCGEEEIIIURm0NQPQgghhBBCCCGlWg5N/ZAplFFBCCGEEEIIIYQQmUGBCkIIIYQQQgghhMgMmvpBCCGEEEIIIaRU+/KlzK9+CSQPyqgghBBCCCGEEEKIzKBABSGEEEIIIYQQQmQGTf0ghBBCCCGEEFKqfaFVP2QKZVQQQgghhBBCCCFEZlCgghBCCCGEEEIIITKDpn4QQgghhBBCCCnVcmjqh0yhjApCCCGEEEIIIYTIDApUEEIIIYQQQgghRGbQ1A9CCCGEEEIIIaUarfohWyijghBCCCGEEEIIITKDAhWEEEIIIYQQQgiRGTT1gxBCCCGEEEJIqUZTP2QLZVQQQgghhBBCCCFEZlCgghBCCCGEEEIIITKDpn6Q/7SEbE2UNClZiihpsr9ooaTJUFBASROvrYeS5l5yE5REGSEocbytSt4xNfxAyfvsa3N4PEoah/bOKGmUT05BSVN58cxf/RLI92pwFf9FOTT1Q6ZQRgUhhBBCCCGEEEJkBgUqCCGEEEIIIYQQIjNo6gchhBBCCCGEkFKNVv2QLZRRQQghhBBCCCGEEJlBgQpCCCGEEEIIIYTIDJr6QQghhBBCCCGkVMvJ+dWvgORFGRWEEEIIIYQQQgiRGRSoIIQQQgghhBBCiMygqR+EEEIIIYQQQko1WvVDtlBGBSGEEEIIIYQQQmQGBSoIIYQQQgghhBAiM2jqByGEEEIIIYSQUo2mfsgWyqgghBBCCCGEEEKIzKBABSGEEEIIIYQQQmQGTf0ghBBCCCGEEFKq5dDUD5lCGRWEEEIIIYQQQgiRGRSoIIQQQgghhBBCiMygqR+EEEIIIYQQQkq1LzK97EcZlDaUUUEIIYQQQgghhBCZQRkV5Lt9+vQJ8+bNw8mTJxEWFgZDQ0NUqFABY8aMQf369Xmbv//+G3/88Qdu3LiB1NRUODg4oG/fvhg9ejTk5OR4m5CQEMydOxcXLlzgz2lqaooePXpg+vTpUFRULLaI6cHdm3Dh7AkkJyfCyaU8+g+bABMziwL3O+t3GCeO7EF8bAwsbezRd/BY2Du5Ch//fcoIBD59ILZPgyatMWDEJBRHn/z2r8H1c0eQmpIIW6cK6DpoOgxNrKTuE/z8HgKObUPom0DEx0Zi0KS/UKFKva/ahX94g6O7lvH2OdlZMDa3w6AJS6BrYFLkfTq2dx2unPNFSnIi7J090HPwNBiZWkrd58Wzezh7dAdCXrM+RWH4lCXwrFpXrM2xfetw+5o/YqI+QV5eAVZ2LmjXfThsHd2LpA8Hd2/GeeFYc8eA7x5rexEXGwMrG7uvxlre5184ewIe3ruFCdPno7J3beFjW9cvw4vnjxH67i3MLKzwv5XbCq1P+3ZtRcBZP6QkJ8HZxQ2Dho+DqZl5gfud9vPF0cP7eJ+sbewxYMgoODi5CB/3P30CVy+fw5tXwUhNTcHO/Segpq7x1fPcvX0DB/fuwLuQ11BQUEQ5dw9MmTmvUPqWv59Xjq/Ag6sHkZ6aAHM7TzTtPhu6RtZS97l3aQ/uX96LuOgwvm1g6oCazYfB3r2OsM39K/vx7LYfPr1/hoy0ZIxfdgfKqpqF/vql9em63wo8vi7ok6mtJxp1nQ0dQ+l9enBlDx5e2YuEGEGf9EwcUL3ZMNiWE/Vp3189ERp8W2w/j5qd0ajbnCLpQ2kYf307W6BFA0Ooq8rj6YsELN3wFmGf0qS2L1sW6NPJAg1r6UNXWxFRsRk4cykCOw8J3jfm0iFvifuu3fEO+49/RFFi79sd/5UIvCUYe8bWnqjdbha0DaSPvbzuX9iAW6eXwr1mL9RsPU14//Ob+xH8wA+RYc+RmZ6MfnNuQ0mleI6n76VbsxJsx/eHlqcblE0Ncbf9MHw+fh6yaN+Ve9h+/haiEpLhaGaIKR0awt3aVGLbcw9fYLP/DYRGxSIzOwdWBjroWa8KWlZxE2v35lMUlh27hHuvQpGVkwM7Yz0s6d8WJrpaxdQrQL9lWxh27Ap5XV2kvnmNsNXLkPIiUGp7g7YdodeiDRQNjZCVEIe4q5cRvnk9vmRm8MeNe/aFcc9+Yvukhb5DUP8eKC4lsU+E5EWBCvJdWHChRo0a0NbWxp9//gl3d3dkZmbi7NmzGD58OIKCguDr64tOnTrxwMTFixd523PnzmHSpEk8cHHgwAGUKVOGt83JycH69ethb2+Pp0+fYuDAgUhOTsbixYuLpT/HD+/GmROHMGzsDBgYmeDAro1Y8Ns4LF67C4qKShL3+fvKOezctBIDhk/kJ4ynjh3g+yxdvxda2jrCdvUat0KnHgOE24pKysXSp4CjW3Hp1F70GjEXeoZm8Nu3GivnDsVvy3yhIKVPGWmpMLd2QvV6bbDhz3ES20R+CsXSGX3gXb8tWnQaCmVVdYSHvoZCMQSVTvtux7mTe9F/1BzoG5ni6J61WDpnOP5YcaiAPqXB3NoRNeu3xupFEyS2MTK1QveBk2FgZIaMjHQEnNiNpb8Px4I1x6ChJXovC2usneZjbToM+VjbhPm/jcOSAsfaeezYtAoDhk+Awz9jje3zV76xxrDHCkoHrNuwOYJfPMf7kNeF1iffQ3tx8sRhjBo7FYbGJti7cwvmzpyI5eu2Se3TtSsXsHXjGgweMQ6OTi7wO3oIc2ZOxMoNO6H9T5/S09NQ0bMKv+3avlHi89y4fhlrVyxG994D4O7hiezsbLx/97bQ+ib2u85uxJ0LO9Gy70Jo65vj8rHl2Lu8Pwb/fgryCpL7qaFjjLrtJkDX0Apf8AWP/z6Kg2uGY8BMXx60YLIyUmFXrha/XfRdguJ0O2Aj7l/aiaa9FkJLzxzX/Zbj4Mr+6PdbAX3SNkadNhOgw/r05Que3TwK33XD0XuqL/T/6RNTvkYn1GgxSritoKhSJH0oDeOvaxtTtG9mjAWrXiE8Ih39uljgz5ku6DPmITIyJacmd21jhtaNjPg+IaGpcLJTw+Th9khOycaRU594m3YD7ortU6WiNiYNtcOVm9Eoag8vbcKTaztRr/NCaOqa4/bZ5fDbNABdJpyUOvZyRYQ+4QEJPROnrx7LzEyDhVMtfmOBDFkkp6aKhMcvELrtMCodWg1ZdeZeIBb7XsCMzo3hbmWK3ZfuYOia/Tg2cxD0NNS+aq+lpowBjb1hY6QHBTk5XHn2CrN2n4SuhipquNjyNqGRsejz1y609fbA0GY1oa6shNefoqCoUHynINp16sF08Ah8WLEEyUHPYdCuI2znL0FQ/27Iiov7un3dBjDpPxjvlyxEyvOnUDK3gOWEaSzaho/rVwnbpYa8wevJY4XbX7KzqU//cTI986MUoqkf5LsMGzaMBxlu376N9u3bw9HREeXKlcO4ceNw8+ZNHmRgwYZWrVphw4YNPNPC2toaAwYMwPbt23Ho0CEeqGCaNGmCrVu3olGjRrC1teX7TJgwAUeOHCmWvrAv2qePHUDbzr1RqVotWNnYY/i4mYiNicLdG1el7nfy6H7Ua9wSPg2bw9zShgcsFJWUcCnAT6ydkpIStHX0hDdVVbVi6dOFk7vRpP1AeFSpy0/Ue4/8g2dJPLp9Qep+5TxrolXXEahQVZARI8nxPSt5u3Y9x8LC1gUGxhYoX9kHGlp6KOo+nfPbgxYdB6BiVR9YWDui/+g5iIuJxP1bl6Tu5+5Vg2dHeFb7OjMkV7XaTeHqURUGxuYws7RD577jkJqShNB3Lwu9D6eOHUS7zr1QWTjWZiA2Jhp3Chxr+1C/cUseZBCNNWVczDfWQt4Ew893H4aOmSrxefoOHoPGLdrDyNi0UPvkd+wQOnTuiSreNWFtY4dR46ciJiYKt29ck7rfCd+DaNikOeo3bAoLS2t+wqikrIwL/qeEbVq26Yh2nbrD0fnrzBEmOzsLm9evRK9+Q9C4WWuYmlnw56pRSzxjprD6efvcDtRsPhROFRrAyNwZrfr+D4lxEXjx4JzU/Rw96vHsCZZ1oWdkg7ptx0JRSRVhbx4K21Rp0AfVmw6Cma0HihPr070LO1CtyVA4eDSAobkzmvX+H5LiIxD8SHqf7MvXg61bHZ51oWtkg1qtBX36+FbUJ0ZBURnqWgbCm5KKepH0oTSMvw7NTbDz8AdcvxOLN+9SsGDlK+jrKKJmFV2p+7g5aeDanVjcvB+HT5HpuHwzBncexcHFXvQ+xMRlit1qVtbFg2cJPBhSlNj79vjqDnjVHwIbt/rQM3VCvS6LkJIQgbfPpI89hmVJnNszAT4d5krMlPCo1Rue9QbByKp4j6cfEXn2Cl7OWobPxwru66+28+JttPP2QJtq5WFnoo8ZnZtAWVEBR288lti+soMV6ns4wdZYHxYGOujuUxkOpoZ48PqDsM1KvyuoWc4OY9vUhYuFMW/n4+4gMfBRVAzad0b06ROI8T+F9Pch+LB8MXLS06DbuLnE9mqubkh+9hRxF88h4/MnJN67g9iL56CaJwOLy85GVmyM8JadEF88HSqhfSIkPwpUkG+KiYnBmTNneOaEmtrXf1hY5oS/vz+io6N5wCG/li1b8sDG3r17pf6O+Ph46OpK/wJWmCI+f0RcbDTcK1QS3qeqps6zJF4GPZW4T1ZmJt6+egH3CpWF95UtW5Y/R/59rl0KwMBuzTBhWA/s3bYW6WnSU3ULS3REGBLiouBcvqrwPhU1DVg7uOPNS8lfML4Hy3x5ev8qnz6ycu4QTOrng/9N6Y6HBQQ/CkvU5zA+dYMFFHKpqmnA1sENr1/8fJ8kvbeX/Y9ARVWdB0OKZqxV/mqsBRcw1t68eik2PnPHWnDQM+F9bFyt+PN39Bs6jgfEisvnT+E8dd6jgpfwPjU1dZ758SLoucR9WPbV61cvUD7PPqxPbFvaPpKwlPyY6CiUKVsW40cOQL8e7TD3t0l4F/IGhS0u6gOSEyJh7VJdeJ+yqgbMbDwQ9kZ8epc0OTnZeHb7JDIzUmBmWxG/Wny0oE9WzqI+KalowMTaAx9/oE+BdwV9Ms3Xp+d3TmDVxKrYOrcFrhxdgsyM1ELvQ2kYfyaGStDTUcS9x6ITBJYV8Tw4Ca6OX09FyfX0RSK83DVhbiLI4rOzUoW7swZuPfj66iqjo6WAap7aOHU+AkUtMeYDUhIjYe4gPvYMLcvj8zvxgFd+V3znwMrFB+aOon1J4cvMykZg6CdUcxJNxSlbtgzffhwimj5UUDDq1osQhETEwMteMLUxJ+cLrj57DStDXQxZvR8+U1eg++LtuPCocC8KFKSMvDxUHRyR9OBe3heLpAd3oeZSTuI+yc+f8n1yT+IVjU2gWaUaEm7fFGunaGaOcnt94bJ9PyynzISCgSGKQ0nsEyGS0NQP8k2vXr3if4CcnZ2ltnn5UvBHx8UlX2T2H2zf3DaSnn/lypXFNu2DfclltLTFAyNsOy5OcvprQkIc/4IuaZ+wD++F2zV8GsLAwBg6evp4//YV9mxbi49h7zF++gIUJXZCz2hqi5+wamrp8QDGz0qMj0F6Wgr8j25Byy4j0KbHGDx/eB0b/xyH0bM3wbGc6GS6sMX/815oaon/n7M+/ps+5Xp05wrWL52KjPQ0aOnoY/zstdDQ1CmisSb+vGw7Lk7wWH4JCfFSx9rHD++E29s3rYCjixvP1ChOwj7piL8+lj4f+89j+SXyPuVAW/vrfcJCRcfPt3z+JJhDv3/3NvQdOAyGhsY47nsAv00dg1UbdkFDo/DmpbMTekZNQ/yYUtPUQ1JCweMv4sMLbFvUBVmZ6TzzoMPQ1TAwtcevlhz/T580v+5T8jf6FBn2ArsXi/rUZtBq6JuI+uRSuQU0dU2hrmXI214+uhgxn9+izWBRSnFhKA3jT1dHgf9kGQ95xcZnQFdb8Jgke3zDoKYihx3LK/ATRHaSuWnve5y7Kvm9bexjgJTUHFy9VfTTPliQglHJdzypqusjJVH62At+eBJRYc/RftShIn+NpV1scgqyc75AT1P8ghTLfHj7WfoYSUxNQ8MZq3mgg425aZ0awdvZhj8Wk5SMlPQMbAm4iRHNa2FMax9cf/4G4zYfwaaR3VDJQXq9qcIip6mFMnLyyMz3+ZAZGwslC8k1vFjWgbyWFuyXrubZxCwwEHXiKCL27RS2YdMtUv+cj/QPoVDQ1YNxjz5wWLoaQYN6ISe18IO0Jb1PsiIn51e/ApIXBSpIoS7V86PL+rCinGwqSMeOHfnUkYKkp6fzW16svoC0Ocm5rl08i42r/xRuT54l+ndhY4Uzc1la20FbVx9/TB+FT+EfYGxScKG3H3H7ykns3TBXuD10auGeDOT68kXwiV2+cl3Ub9mT/9vCxhlvXjzCNf+DhRqouHn5FHasExWkGz19BYqSs3tlzFq6F0kJcbgS4It1iydj+qId0Mx3MvMjrl70FxtrU2b9D0Xh7q1rePboPhat2IKidvliANavEtVRmD57IX6VnH8+Xzp07gHvGoJCjiPGTsbAXh3x97VLaNy01U8/99Nbx3Fq1yzhducR63/6ufSMbTBg5lGkpyYi6N5ZnNg6GT0m7Cr2YMXz28fhv1fUp/ZDf75PbMpH76lHkZ6WiJf3z+LUjsnoMnaXMFjBCmfmMjBzgpqWAQ4s74PYyPfQMfj5k5HSMP4a1NLH+EGC+fzMlAVBP/U8davr8ef6Y3kw3oamwt5aFSP6WiM6JhNnLwsCBXk1q2eIc1cjpda8+Dde3j+By4dFY695v3U//BxJceG4fmw+Wg7c8s0aFuTXUVNSwoEp/XhAgmVULPG9AHN9bT4tJPeYqevuwItsMs7mRnj0NgwHrz0olkDFz1AvXwFGXXriw8qlSAl6DiUzM5gNHQ2jmN74vHs7b5N455awfdrb17yd666DvHZEzJmTkDUlsU+k5KNABfkmtnJHbhFMadjUDiYwMBDVq3+dnsnud3UVn/f78eNH1K1bl7dndS2+ZcGCBfj999/F7hs0YiKGjCp4RQ2vqjVh7yRKhcv8p7pxfFwMdHT1hfezbSsbUWG4vDQ1tVG2rBxvkxfb1s53ZS+v3FUaPn8MK9RABasRwaZ15MrKEvQpIS4aWjoGwvsT4qN5scyfpa6hg7Jy8jAxF32JZozNbPA6qOB03R/lUaUOZjm6iU2BYBLiY6Ctm6dPcdGwsPn5PuVSUlaBkYklv9k5lcfUYa1x9fxRNG8vXvH6R1SqWpOnoH891mLzjbVYvuqAJJqaWgWMNcHVyKeP7uHzpzD07dxUrM2SBTPg4loesxYWXuCqStUavPigqE+C94WtfKOrK7o6GhcXCxtbyX3S4H0q+1UWCdunoOMnP51/+m9hKbpixFZdYDU4oiL+Xfq6g0c9DLARzXHP/ueYSk6Mhoa2KPU1OSEaRhbSs8sYOXlFXkyTMbFyw8eQJ7hzfgea9Sz8FTAKwmpLsGkdX/UpIZpnPuRi26xexbf6xIppMsaWbgh/9wT3Lu5AYymreuT+3rjId/8qUFEaxt/1OzEIDE4SPae8oDguy57Im1Who6WIVyHJUp9nSE8r7DkahgvXBVe/375PgbGBErq3M/sqUOHuogFLMxX8vrRoUvCtXevCyLL8V2MvNTEaapqisZeSFAV9U8mZmJEfniE1KRoHl7cT3vclJxsf397F0793Y9CCx/yzkhQOHTVVyJUtg+gE8TEWnZgM/XxZFnmxLApLAx1hEIJlX2z2v8kDFew55cuWha2xeCaNjbEeHuapY1GUWI2FL9lZUMh3rCvo6CArRnKmiHHvAYg974+YM4K6UGkhb1BWWQUWoyfi854dEisuZicn8UwEJdPC+65XmvpEiCRUo4J8E6sd0bhxY6xevZoXzcwvLi6OF8Zk7ZYs+bqC/fHjxxEcHIyuXbuKZVL4+PjAy8uLF9ZkXyK/ZerUqbyWRd5bvyGjv7mfiqoajE3NhTdWnJCd8D19KJrbl5KSjFcvnsPRWXxJrVzyCgqwsXfC00d3xes3PLondR/m3Ztg/lM7zxfqwqCsogZDE0vhzcTcDpra+njxRBQNZ8UhQ4KfwNZR9GXxR7F+W9mVw+ePIWL3R4S/K/SlSVVU1ISBA3YztbDlUzICH98W69Ob4Kc8sFDYvuR8QdY/gYWfpaKqKnGsPXl496ux5lDAWLO1d8STR/e+GmsOzoKAW5uOPfC/lduxaMVW4Y3pPWAkho4RLdtXGFifTEzNhTdWPJCd3D1+dF+sT2xlEScpRQgVFBRgZ++Exw/vi/Xp8cN7UveRxM7BkT9X2IdQ4X1ZWVmIiPgEA0Mj/BtKyuo8uJB7Y5kCapoGCAm8IWyTnpqEsLePfrjeBMtMyj1RK06Kyuo8uJB70/unT+9fiPcpPOTRV/UmvukbfYr4IFgij/2+f6M0jL/UtBy+7GjuLeRDKqJjM+DpLlq6UVVFDq4O6nj+MlHq8ygplf0qbZml8peRsChQ83qGePE6Ca/fpaCoxp6WvpXwpmNkD1UNA3x4JRp7GWlJiHj/GEZWFSQ+h5l9NXQafxwdx/oKbwbmbnCs2JL/m4IUhUtBXo4Xu7z1UvT3nk0huvXyHcpbm33387AsisysLOFzlrMy4XUr8noXEVNsS5N+ycpCSvBLqOepUcMOCradHCiq+5RXWWVlfMl3MAlXv5B0QPF9VKBoYobMmH8/NbU09klWsHiNrN5KI8qoIN+FBSnY8qRVqlTBnDlzUL58ef4FLSAgAGvXruUZE2y50S5dumDQoEEYMWIENDU1cf78eUycOBEdOnTgS5fmDVJYWVnxuhSRkaIrPcbGxlJfA1tNg93yUlT88S//LDukaetO8N2/HcZm5jA0MuXLk7Ir3pW8RfP9504bhcretdGkZQe+3bxNZ6z9ax5sHZxh7yhYMpIVNKzTQFBhmU3vuH4pABUre0NdQwvvQ15hx8YVcHGrwFd7KEqsT/Wad8fpwxt54Uu2POmJfat5doVHFdHqF8tnD4RH1XrwaSoIGqWlpiDyk2iOdvTnMIS+DYKaupYwENGwdW9s/msS7F284OhWmdeoeHL3Csb8vqnI+9SgRTf4HdzEAxdseVLfPWt5doVnVR9huz9/GwzPanVRv1kXYZ8iPoWKFeV8//YF1NQ1oWdggvS0VPgd2oQKlevwQEhSYhwunDqA2JgIVKresND70Kx1Rz7WTMws+PKk+3dtgo6uHiqLjbXR/4y19ny7eZsuWPPXPNg5OMPO0eWfsZYKn3/GWu6KMvnpGxjBMM8KH58+fkBaWiqf18+mSbFVQhhzC2seEPnZPrVo3QGH9u3kJ45GfHnIzdDV1eerMOSaNW0cqnrXRLOWgquhLdt2xMqlC2Dv4AQHRxecOHaIHz/1GoqyQthqKOy1hocLCre9C3kLFRUV6Bsa8fn/bAWdRs1aYd/urdA3MOQnh0cP7+Ntq9cUjYnCwPpZpUEvXD+1lgcucpcnZdkVThUbCNvtXtobjhUaonI9wTrzF48sgZ1bbWjqmiAjLRnPbvvh3cvb6Dp6s3CfpPhIXuciJkJw7EWEvYSishq0dE2goqZdqP3I3yever1w4/RaHrhgy5NeO7GcZ1ewVUBy7V/eGw4eDeHpI+gTK4xpU07Up8A7fngffBsdRwj6xKZ3BN45wVcGYa+f1ai4cGgBzO0rfzNT42f6UBrG36GT4ejZ3hwfwtP4ihz9u1ggKjYD126LTviWzHLFtVsx8D0jWHr0xt1Y9GxvhoiodL48qb2NGjq1MMWpi+LZHizoUcdbD2t3iGreFDX2vpWv1Qv3zq+Dlr41NHXNcPvsCqhqGsKmnGjsHV/fBzZuDeBeowcPdugZixc4ZkveKqlqi92fkhDJ61zERwmOp+jwl1BUUoO6jgmUVYvuePrR5UnV7EWZRao25tD0cEZGTDzSQsMhK3rWrYKZu/xQztIEblYm2HXpLlLTM/gqIMz0HSdgqK2B0a0E432z/w24WhrDQl8HGVlZvHDmydvPML1zY+Fz9q5fBZO2HoOXnQUqO1rxGhVXnr7CplHdiq1fkYf3w3LiNKQEByElKJAv5clOwmPOClb9sZw4HZnRUQjfIpgel3DzOgzadUbq62A+/UHR1AwmvQcg/uZ1YRED04HDEH/zb2RGfIK8nj5MevXjj8VePE99IqSQUKCCfBe2jOj9+/cxb948jB8/HuHh4TAwMOAZESxQwbBgxMWLF3mbWrVqIS0tjU8bmT59OsaMGcO/qDAsuMEKaLKbubn5v6px8bNate/OT/w2rvwfUpKT4ORaHlPmLBGrd8FS61kRtlzVazdAQnwcDu7axL/MWtk68H1yU4fl5RV4xsXp44IAhp6+IapW90HbLn2KpU8N2/RFenoq9qyfg5TkRNg5V8SIGWugkKdPkZ8/8JoMud6/foZlswcItw9vFxQ0rebTCr1GCGpgsKVLuw6cgbO+W3Bw6yIYmVpj4IQlsHfxLPI+NW3bGxlpqdi+9g/eJweXChg7c5V4nz6J9ynk9XP8OXOQcHv/1qX8Z/W6LdF/1O88e+fThxCsuejH91PT0IKNfTlMmbeZL1VaNGMtDRuEY80dUyWONVEfqteuz8fagX/GmrWtPd/nR9LUmfUrFuL5U9EUncmj+vKfKzcf5EGTn9W2Q1fep3UrFyM5OQkuru6YOfd/Yn36FB7GC4Pmqlm7Hu/T3l1beZ9Ymv7MOf8T69PZ08dxYI9griwzY/Io/nPEmMnCE8re/YZCrqwcli+Zj4z0dDg4ueD3+UuhriF9NYSf5d14IDLTU3Fq129IS0mAhb0XuozeJDZfPjYyFKlJscJtNlXk+NbJfMlPvqqBmRMPUti61hC2uX95H676iabn7PyzO//Zos8CeFQXpbkXhSoNBX06u+c3pKckwMzOCx1GiPcpLl+fUhKjcWr7ZCQnREBJWQP6Zk48SGHtIuiTnJwC3gXd4FNBMtNToKFjAscKjeDddFiR9KE0jL+9Rz9CWUkOEwbbQl1NHk+CEjDpj0CxehJmRkrQ0hR9jVu++S36d7HEmIG20NFU4IGNEwGfsf2QeIp9vRp6/ALq+WvFe5W0gs8AvhLM5UO/ISMtAcbWXmgxYKPY2EuIfo+0ZNHY+x7Pbu7D3YDVwu1jawUBtrqd5sO5ctEeT99Ly8sN3udFRQtdFwsy30J3HMHj/pKXl/4Vmni5IDYpBWtOXkVUYjKczAyxZlhnYYHNT7EJKJvn6ntqRibmH/DH57hEKCnIw8ZID/N6teTPk4stXzqjc2NeUHPR4XOwNtTFkv5t4WknWBmkOMRdvgB5LW2Y9OoPeR1dpL55hTfTJyArTjDWFFlGVJ7vn5927+DfR9mJvIK+AbLi4/gJ/aetG4Vt2GoY1tNmQU5Dkz+e/OwJXo4ejOx4yavsUJ8I+XFlvhTXmSEhReBBcMlLR4tJE615X1Ioyf2TXliCqCsUTcr0r6RQRpCuW5LcCyvcKUqyIqPkvVXwtpKdK8uFZfgU8WlzJUGbXtVQ0ji0L9zMH1lQ/+QUlDRBi4u+gDQpHBX8r+K/aOkx2T0tHtda8hSdkoxqVBBCCCGEEEIIIURmUKCCEEIIIYQQQgghMoNqVBBCCCGEEEIIKdWoIIJsoYwKQgghhBBCCCGEyAwKVBBCCCGEEEIIIURm0NQPQgghhBBCCCGl2pccWZ77UQalDWVUEEIIIYQQQgghRGZQoIIQQgghhBBCCCEyg6Z+EEIIIYQQQggp1WR65kcpRBkVhBBCCCGEEEIIkRkUqCCEEEIIIYQQQojMoKkfhBBCCCGEEEJKtS809UOmUEYFIYQQQgghhBBCZAYFKgghhBBCCCGEECIzaOoHIYQQQgghhJBSLYeW/ZAplFFBCCGEEEIIIYQQmUGBCkIIIYQQQgghhMgMmvpBCCGEEEIIIaRUo1U/ZAtlVBBCCCGEEEIIIURmUKCCEEIIIYQQQgghMoOmfhBCCCGEEEIIKdVo6odsoYwKQgghhBBCCCGEyAzKqCD/aTFp6ihp3kcpo6RRU85BSaOnLoeSRlMxDSWNukrJG3tMemYZlDQp2SooaVp0q4qSpo/lZZQ0yienoKQ533whShqfNR1/9UsghBQjClQQQgghhBBCCCnVcmjuh0yhqR+EEEIIIYQQQgiRGRSoIIQQQgghhBBCiMygqR+EEEIIIYQQQkq1LyWzrNV/FmVUEEIIIYQQQgghRGZQoIIQQgghhBBCCCllYmJi0L17d2hqakJbWxv9+/dHUlJSgfv4+PigTJkyYrchQ4aItXn//j2aN28OVVVVGBoaYuLEicjKyvqh10ZTPwghhBBCCCGElGpfSuGqH927d0d4eDgCAgKQmZmJvn37YtCgQdizZ0+B+w0cOBBz5swRbrOARK7s7GwepDA2Nsbff//Nn79Xr15QUFDA/Pnzv/u1UaCCEEIIIYQQQggpRQIDA3HmzBncuXMHlSpV4vetXLkSzZo1w+LFi2Fqaip1XxaYYIEISfz9/fH8+XOcO3cORkZGqFChAubOnYvJkydj9uzZUFRU/K7XR1M/CCGEEEIIIYQQGZWeno6EhASxG7vv37hx4waf7pEbpGAaNGiAsmXL4tatWwXuu3v3bujr68PNzQ1Tp05FSkqK2PO6u7vzIEWuxo0b89f87Nmz7359FKgghBBCCCGEEFKq5eTI7m3BggXQ0tISu7H7/o1Pnz7x+hF5ycvLQ1dXlz8mTbdu3bBr1y5cvHiRByl27tyJHj16iD1v3iAFk7td0PPmR1M/CCGEEEIIIYQQGTV16lSMGzdO7D4lJSWJbadMmYJFixZ9c9rHz2I1LHKxzAkTExPUr18fr1+/hp2dHQoLBSoIIYQQQgghhBAZpaSkJDUwkd/48ePRp0+fAtvY2tryGhMRERFi97OVOdhKINLqT0hStWpV/vPVq1c8UMH2vX37tlibz58/858/8rwUqCCEEEIIIYQQUqqVlFU/DAwM+O1bvL29ERcXh3v37sHLy4vfd+HCBeTk5AiDD9/j4cOH/CfLrMh93nnz5vEgSO7UEraqCFsC1dXV9bufl2pUEEIIIYQQQgghpYiLiwuaNGnClxplGRDXr1/HiBEj0KVLF+GKH2FhYXB2dhZmSLDpHWwFDxbcCAkJwfHjx/nSo7Vr10b58uV5m0aNGvGARM+ePfHo0SOcPXsWM2bMwPDhw787K4ShQAUhhBBCCCGEEFLK7N69mwciWI0JtixpzZo1sWHDBuHjmZmZePHihXBVD7a0KFt2lAUj2H5smkn79u1x4sQJ4T5ycnLw8/PjP1l2BSu0yYIZc+bM+aHXRlM/CCGEEEIIIYSUajklY+bHD2ErfOzZs0fq49bW1mJTYiwsLHD58uVvPq+VlRVOnTqFf4MyKgghhBBCCCGEECIzKFBBCCGEEEIIIYQQmUFTPwghhBBCCCGElGpfSuPcDxlGgQpS6EJDQzFr1iycOXMGUVFRfKmaNm3a4LfffoOenh4vysIqv7J5S2/evIGWlhYaNGiAhQsXCivMFjU218pv/xpcP3cEqSmJsHWqgK6DpsPQxErqPsHP7yHg2DaEvglEfGwkBk36CxWq1PuqXfiHNzi6axlvn5OdBWNzOwyasAS6BiZF3qerJ1bg0bWDSE9NgJmdJxp3nQ1dI2up+9y/vAcPruxFfHQY39Y3cUCN5sNg51ZH2ObM7t8QEvg3kuIjoKCkCjPbiqjbbgL0jO2KtD+5fbrguxJ3Lx9EWkoiLB0qolWvWdAzlt6ny34bEHgvAJHhb6CgoAwL+4po1Gk8DExs+OOxkWFYOrGBxH07D/sLblWaoCiVxLGX26/Dezbgov8xpCQnwdGlPPoOnQRjU8sC9ws4eRAnfXcjPjYaljYO6DVoPOwcywkfj4uNxt6tK/D04W2kpabA2MwKrTv1QZXq9YqlT+eOrMSdiwf5e2XlWBFt+syCfgHj79LxDXh6VzT+rBwqokkX0fhjNszrhbdBd8T2q1KvM9r2nY3i6NPFoytx/wo7phJgYe+JFuyYKuBz4urJ9fyYigp/A3lFwTHVsMN46JvYCtvERLyH//7/4X3wPWRlZcDerRaadZ8BdS39EjXWjh3Yiod3r+Pdm5eQV1DAhr3nC71/uX28d24lgu4cREZqIoysKqJmm1nQ0pf+PuX18NJG3Dm7FG7Ve8K75TSJz39m22B8eHkVDXushHU5yZ+JRe3g2YvYfSIA0XHxcLAyx/i+XVDOXnSs5HX0/FWcunITb0I/8m1nG0sM7dpGavvisO/KPWw/fwtRCclwNDPElA4N4W4t+XvMuYcvsNn/BkKjYpGZnQMrAx30rFcFLau4ibV78ykKy45dwr1XocjKyYGdsR6W9G8LE10tyBLdmpVgO74/tDzdoGxqiLvth+Hz8aI5HgrD/gevsOPOC0Qnp8HRQBuT6leEm4muxLbHn4Zg9hnxz2hFubK4ObY9/zd7/9Zce4rrb8PxIS4Z6koKqGplhFG13WGgroLiUhL7REheNPWDFCoWeKhUqRKCg4Oxd+9evHr1CuvWrcP58+d51deYmBheNfb+/fuYOXMm/3nkyBFeTbZVq1bF9joDjm7FpVN70XXQDEycvwtKSipYOXcoMjPSpe6TkZYKc2sndB4wVWqbyE+hWDqjD4zMbDB29iZMX3IIzToMgoKiIoraLf+NuHdxJxp3m41ekw9AQVEF+1f2R1am9D5p6BjDp80E9Jl6BH2mHoaVUzUcXjsckR+DhW2MLcuhWe8FGDDrFDqP2sy+4mL/8v7Iycku8j5dPbUJNwN2oVXv2Rj8234oKqli+5KBBb5PIUF3UKVeNwyauQ+9J25GTnYmti/uj4x0QbViLT1jTFp2RexWr+0IKCqrwqF8rSLvU0kce4zfkZ3w9zuAfkMn4/c/N0NJSRmLZo1GRgH9unk1ALs3L0fbLv3xx1/bYWltz/eJj4sRtln312yEh73HuBmLsWDlHlT29sHK/01HyOsXRd6nKyc34W//XWjTdzaGzRaMvy3/K3j8vQm6A+8G3TBs1j70n7wZ2dmZ2LKoPzLSBOMvV2Wfjpi28orw1rTLBBSH66c34da5nWjRazYGzDgARSUV7FwyAJkFfE6EvLiDyvW6YcCM/eg1fgsPgu1cOkB4TLGfO5f0B8qUQe9J29B/2h7e7z0rhvK12EvSWMvKykSVGvVRv6ngy31ReXRlE579vQs128xG62H7oaCoitNbBhb4eZ4rMvQJAm/vh66xk9Q2T69vRxn8WgF/38HyHYfQv31zbF84HfZW5hg9fwVi4hMktr//7CUaVa+MNb+Nw6a5k2Gop4NR85YjIiYWv8KZe4FY7HsBg5vWxL5JfeFkZoiha/YjOjFZYnstNWUMaOyNHeN64tCUfmhdzR2zdp/E9cA3wjahkbHo89cu2BjpYdOorrzdoCY1oKgge9cV5dRUkfD4BZ6O+h2y7mxQKJZeeoRB3q7Y07MhHAy1MPzQFcQkp0ndR11RHv5DWwpvJwc1Fz6WlpWNoIhYDKjmij29GmJx6+p4F5OIMb7Xi6lHJbNPhORHgQpSqNj6uGzZGn9/f9SpUweWlpZo2rQpX8aGrcM7ffp0nkEREBCATp06wcnJCdWqVcOqVav4erzv378vnqv0J3ejSfuB8KhSF+bWjug98g9+pfrR7QtS9yvnWROtuo5Ahar1pbY5vmclb9eu51hY2LrAwNgC5Sv7QENLD0Xdpzvnd6B606FwrNAAhubOaNH3f0iKi8DLh+ek7udQvh7s3OvwrAtdIxvUaTOWn4x9fPtQ2KZCrc6wdKgMbX1zHrSo3WoMEmLDhVkYRdmnG/47UKfVELh41oexhRPaD1yIxNgIBN6X3qfeEzbCs1ZbGJk5wMTSGe0GLEB8dDg+hjzjj5ctKwcNbQOx2/N75+FWuQmUlNWKvE8lbewJr84e34fWnfrCq1odfrV6yNjZiIuJwr2b0itDnz62F3UbtUadBi1hZmmLvsOm8JPOy+dES1wFBz1BoxYd+ZVvQ2MztOncD2pq6nj7OqjI+3T9zA7UbTUErl71YWLphE6DFyIxLgLP70kff/0mbYRX7bYwMneAiZUzOgxagLjocIT9M/5yKSgpi41BZRX1Iu1Pbp9uBuxA7ZZD4FxRcEy1HbCI9ymogGOq57hNqFizHQzNHGBs6Yw2/dgx9VF4TL0Pvo+4qDC06b8ARuZO/Na2/0J8DHmKt4E3S9RYa99tEJq27goL66LLKGN9fHp9ByrWHQJr1/rQM3GCT6eFSEmMwLvn0t8nJjM9GRf2T0TtdnOgpKIpsU30x0A8uboNtTvMw6+09+Q5tK5fEy3r1oCtuSmmDOgOZUVFnLj4t8T2c0b1R4fGPnC0toC1mTGmD+mFnC9fcPdJ0X4WSLPz4m208/ZAm2rlYWeijxmdm0BZUQFHbzyW2L6ygxXqezjB1lgfFgY66O5TGQ6mhnjw+oOwzUq/K6hZzg5j29SFi4Uxb+fj7gA9jaL92/QzIs9ewctZy/D5WMFjUhbsvvsSbd1t0NrdBrb6mpje0AvKCnI49jRE+k5lykBfTVl401NTFj6koaSAtR3roJGzBax1NVDeVA+T61dE4OdYhCeIB6WpT/8tbHELWb2VRhSoIIWGZUucPXsWw4YNg4qKeJqYsbExunfvjv3794stcZMrPj4eZcqUgba2dpG/zuiIMCTERcG5fFXhfSpqGrB2cMebl5K/YHwPduXw6f2rPIV/5dwhmNTPB/+b0h0PCzgBLSzxUR+QnBAJa5fqwvuUVTRgauOBsDcPvus5WIbE8zsnkZmRAjObihLbsCunj/8+Ai19c2jqGKMoxUZ+QFJ8FOxcvYX3KatqwNyuPEJfP/ru50lLTeQ/VdQkp82yE8hP7wPhVbsDilpJHHtM5OePPJ3ezaOK8D5VNXV+whf84onEfbIyM/H2VRDKVRDtU7ZsWZTzqIxXQaJ9HJzdcfPqOSQlxvN+3rjij8yMDLi4eRb5+EuMj4K9m/j4s7Atj/ev/v34e/S3H+YO9cayKS1xZv9SZKSnoqgJjqlI2LpWFz+mbMvjw2tRcPJH+5SdlcG/AMvLi7J35BWUUKZMWT4VpDCVxLGWX2LsB6QmRsHMXjT2FJU1YGBRHp/fFzz2rh+bC0vnOjCzF73HeWVlpPJARvXWM6GqYYBfJTMrC0Fv3qOKu4vYe1LZ3RlPgkUZBgVJS89AdlY2NNWL/yQ+MysbgaGfUM1JNBWnbNkyfPtxyLeD+Ox70K0XIQiJiIGXvQW/LyfnC64+ew0rQ10MWb0fPlNXoPvi7bjw6GWR9qWkY1Ma2Mk2m8aQq2yZMqhqaYTHH6Ol7peakYVm60+i6Xo/jPW9jtdR8QX+nqSMTJ6lxE74i1pJ7BMhksheLhn5z2LTPdgfXxcX0RePvNj9sbGxiIyMhKGhofD+tLQ0TJ48GV27doWmpuQrQIUpPjaK/9TUFr/SrKmlx08if1ZifAzS01Lgf3QLWnYZgTY9xuD5w+vY+Oc4jJ69CY7lKqGoJCVE8p9qmuJ9UtPQQ3JCwX2KCHuBnf/rwlOKWTZFu8GroW9qL9bm/qXduOi7GJnpKTzzosvorZDLc1JSFFiQglHPlxGgpqnPT7a+BzvZOLVnASwdPGFk7iixzf0rh2BgasfrXxS1kjj2cuf2M5ra4nNj2XZ8bIzk15wQx4NjWvn2YdvhYe+E2yMnzceqP6djSPdGkJOTg6KSMsZMWwRjU8GX+6KSGCd5/LGaC4k/MP78di2AlaMnjC1E46+Cdwto65tCU8cQ4e9f4Mz+JYj69BY9Rq9EUcr9nFDXlHRMRX13n87snc9rW+QeU+a2FfgUkoCDi1G//Vg+PezcoSX4kpP93cdqaR5r+bEgBaOiLv4+qajrIzVR+v/n60cnEfXxOdoMPyi1zY2TC2FkWYFnavxKcQlJyM7Jga6Whtj9ulqaePfx03c9x+rdR6Cvq4XKeYIdxSU2OQXZOV+gpykeJGGZD28/Sz9RTExNQ8MZq3mggwU2pnVqBG9nQY2NmKRkpKRnYEvATYxoXgtjWvvg+vM3GLf5CDaN7IZKDgXXYCGSxaWmI/vLF+jmyR5g2HZIjCDomp+VrgZmNakEBwNtJKVn8joQffdcwMG+jWGkofpV+/SsbCy/8hhNXCx5bYeiVhL7RIgkFKgghU5SxoQ0rLAmmwLC9lm7dm2BbdPT0/ktr4yML1BUVCpwv9tXTmLvhrnC7aFTV6EofPkimItdvnJd1G/Zk//bwsYZb148wjX/g4V6svjs1nGc2TNLuN1x+Pqffi49Ixv0m34U6amJCLp/Fn7bJ6P7uF1iwQrXqq1g7VKDn+jcDtiMoxvHoOfEvfyqaWF59PcJHN8uKibYY2zB4+F7+O2cg4gPwRgwfbfExzMz0vD4xkn4tBqKolASxx5z/dIZbFmzULg94belKCqHdq/nBROnzF0FDU0t3Lt5hdcNmLlgPSysxQNq/8aD6ydwdKto/PUe/+/H3/Htc/D5QzCGzBQff1XqdRL+mwUwNLUNsGlhX0R/fg89o8I7GXl84wRO7BB9TnQfs+5fP+epXXMQERaMflP3CO9T09RFx6HLcHLn77h1fifPpHCv2hwmVq783/9GSRxr+b16cAJXj4rGXpPePz72kuLCccNvAZr22yz1c/nd8wv4+Pom2o08gv+67UfP8BoXa2aNh5Lif+ckSk1JCQem9OMBCZZRscT3Asz1tfm0EDaNhanr7sCLbDLO5kZ49DYMB689oEBFMfIw1eO3XGwaRPutZ3D40RsMq+n2VXbD5BM3WHwWUxsUb/ZVae9TUWCZTUR2UKCCFBp7e3s+fSMwMBBt27b96nF2v46ODgwMDMSCFO/evcOFCxe+mU2xYMEC/P67eNGmnkOmo/ewGQXux+bps9T6XKwiPZMQFw0tHVHqa0J8NC9Y+LPUNXRQVk4eJuaiSviMsZkNXgd9f1r197D3qId+Nh5f9Sk5IRrqWqJsleTEaF6voiAsM0LHULDihLGVG8LfPcHdizvQpPscsWkk7MZqWZjZeGDZuCp4+TAArpVbFFqfnCvW49M68vcpKT4aGtp5+pQQBWPLb19B89s5Fy8eXcaAqTuhpSt5msqzO2d5sKJCjdYoCiVx7DGeVWqJrZbACgwyCXEx0NEVrfLAti1tHSQ+h4amNq8XkreYIcO2c698fw7/wFdqWLhqL8wtBX2zsnHEi+cPEXDqEPoNm1JofXL1rAcLe9H4y84UjT/NPOOPZR6YWH17/B3bPhdBDy9j0HTp4y+XxT/jvrADFU4V6sLMNk+fco+phJ87pk7umoOXjy6h75RdX/XJ3q0mRi8KQHJiLMrKyUFFVRN/jqkJtyr/LhuhJI61/Cxd66GdRZ73KVvwPqUmRUNVU/Q+pSZFQc9E8vsUFfaMt/ddJSryyTJawkPu4tnNPeg39xEPUiTEhGL7HNHUM+bc7tEwtvZCi0E7UFy0NdUhV7YsYuLFr/6yQpq62gWvbrHrhD92HDuDVTPG8JVCfgUdNVXIlS2D6ATxwpmskKZ+viyLvFgWhaWBjjAIwbIvNvvf5IEK9pzyZcvC1lg8k8bGWA8P89SxID9GW0UJcmXKfFVkkm3nrdFQEAW5snA21EFoXNJXJ/RTTtzgNRzWd6pTbJkHJbFPhEhCgQpSaNjSow0bNsSaNWswduxYsToVnz59wu7du9GrVy8ezMgNUrDpIhcvXuT7fsvUqVMxbtw4sfuuB3878qmsosZvuVj2hqa2Pl48ucWvOjOpKUkICX6C2o064mexpeqs7Mrh80fxQkYR4e8KfXlIJWV1fsvbJzVNA4QE3YCRheCLbHpqEj6+fYSKtbv+8NX5rH9O0iQ/Lvh9BbX5GUoqavwm+j1feJr9m+c3hSeGaalJ+PD6MSrX7VLA6/uCk7v+4AUP+0/ZDh0D6V9k7105DKeKdfkV4aJQEsceo6Kqxm95+6Wlo4dnj+7AylYwHSAlJQmvXz5D/abtpL5mG3tnvk+lanWE0wqePb6Dhs0F/xcZ6YIvYewzIy82l/1LIa8mIWn8aWjp4/WzmzDNM/5C3zxG1foFj7/jOwTjb+C07dA1/PaJ1Mf3gmKArKhmYVJSUee3vK9NXcsAb5/fgIllnmPqzWNUqiv9c4Ltd2r3XF5ws8/kHQUeU2oagpOwN4E3eaCUBUv+jZI41vJTVFLjt7x9VNHQR9jrm9AzFbxPGWlJiAx9DNeqkseeqb032o8+Jnbf5UPToW1gA486A3igxsNnIJwqi9fiOby8Nao1nwJLl3/3Pv0oBXl5ONta4s6TQNSpXEH4ntx5GoSOjaW/lp3HzmKr7yksnzYaLnbft1RrUVCQl+PFLm+9DEE9D0fhldhbL9+hS63vvwLNsihYvY7c5yxnZcLrVuT1LiJG5pYm/S9hJ+QuRjq4/T4CdR3MhP/vbLtzxe/LlGLTfF5FxaOGjfFXJ/TvY5OwobMPDx4Ul5LYJ0IkoUAFKVRs9Y7q1aujcePG+OOPP2BjY4Nnz55h4sSJMDMzw7x583iQokOHDnxpUj8/P2RnZ/NABqOrq8tXDZFESUmJ3/JSVJS+DJM07ItovebdcfrwRl58UM/QDCf2reZXuD2q1BO2Wz57IDyq1oNPU8EX+LTUFER+Eq1KEv05DKFvg6CmriU8GWzYujc2/zUJ9i5ecHSrzOsEPLl7BWN+3/TDr/NH+1S5fi/8fXotdA2teLHLq8eXQ13bkK8CkmvvX73hWKEhvOr24NuXfJfA1q02NHVMkJGejOe3/fD+5W10HsmWIQXiIkMReO8UbFxqQEVDF4mxn3Dz7AbIKyrDzq1OkffJu1EvXDqxDrrGVtDRN8f5IyugoWMIF09Rn7Yu6gsXrwao1qC7cLoHm87RbfQqKCqrITEuUlg0UEFRdKUh+vM7vHt5Fz3H/vy0mZ/pU0kbe7n9atKqC44e2AojUwsYGpnyNHptXX2+MkOu+TOGo1I1H76yAsNWT1i/bA5s7F1g5+jKV3NIT0tDnfqCTB0Tc2sYmZhjy+qF6NZvFNQ1WDr+ZTx9eBvjZy4p8j7VaNILF46tg56xFXQNzBFwaAXPRHD1Eo2/TQv6wrVSA1RvKBh/x7bPwaMbJ9FzzCq+ikz+8ceyJh7e8IOzRx2oqmsjPPQFTu5eCBunSnxlkaLuU7WGvXDFbx3PjtIxMMMFX0GfnPMcU9v/7MO3q9bvIcykeHLTD11HrRYcU//UnWBZVrnH1IOrh6Fvagc1DV2Evn6IM3vmwbthb+ib2JaosRYV+QnJiQmIjvzET6zfvREUOmT7KquoFlof3Wr0woML66ClZwUNXXPcDVgBVQ1DWLmK3qeTm/rC2rUBylXvzgMdusbidXjYEtXKqtrC+1nxTEkFNNW1TaCpW/yZCV2bN8CcNdt4wMHVzhr7Tp3nBTJb+AgKgc5etRUGutoY3k2QocmyKDYcOMFX/zA11EN0nKAQoIqyElSVv+8qcmHqWbcKZu7yQzlLE7hZmWDXpbtITc/gq4Aw03ecgKG2Bka38uHbm/1vwNXSGBb6OsjIyuKFM0/efobpnRsLn7N3/SqYtPUYvOwsUNnRiteouPL0FTaN6gZZXJ5UzV6UAaZqYw5ND2dkxMQjLTQcsqR7JUfMOn0brkY6KGeiiz33gpGamYVWboJg18xTt2GoroKRtQUZkBv+fg53U11YaKsj8Z96DuEJyWjrbis8oZ90/AZfznN525q8XkTUP9kNWsqKPJBAfSr509dJ0aNABSlUDg4OuHv3LmbNmsUzJthKIGzFjzZt2vD7WCAiJCQEx48f5+0rVBBcScnFsit8fAR/1ItSwzZ9kZ6eij3r5yAlORF2zhUxYsYaKOSpdxH5+QOSEuKE2+9fP8Oy2QOE24e3L+Y/q/m0Qq8RgjoEbPnIrgNn4KzvFhzcughGptYYOGEJ7F2Kfo5f1UYD+coBZ3b/hrSUBJjbe6HzyE1i85VjI0ORkiRacz4lMRp+WycjOSECSioaMDBz4kEKG9ca/HE5BUWEBt/FnfPb+XOyYp0W9pV4fYr8hTuLQq1mA5CZnorjW2fx32/p6Ile4zeIvU8xEe+Rkijq0+0L+/jPLQt7iz1X2/7z+bKlue5fPcJXLrFzE/S1uJTEsce0aNcT6Wmp2LJ6AZ/n7+jqgUmzl4vVkIn4FMYLG+aqVqshEuLjcHjPBr6SA7tCPmn2Mn7FnJGXl8fEWX9h//bVWDJ3PH9+dkI4eMxvqFCp6N+32s0H8GPKd4tg/LGimH0nio+/6Hzj79Z5wfjbOF98/HUYOJ8vWyonr4DXT2/g+tkdfGyzKRRulRqibpuiqZOSX42mgj6d2C74nLB08EKPcRuhoCD9mLp7cS//uW1RL7Hnat1vPl+2lIn6FIJzh/9CanI8LxRaq8UQeDfqU+LG2uHdG3D1wknh9vQxgpow0+atgau7V6H10aP2AL5Cx1XfWchIS4CRlSea9N0g9nmeEP0eaSmi9+m/pmH1yryo5oYDxxEdlwBHa3MsmzoKetqCaaCfo2P4VIlcRwKu8OyDqUvFg8sDOrTAwI4ti/31N/FyQWxSCtacvIqoxGQ4mRlizbDOwgKbn2IT+EoMuVIzMjH/gD8+xyVCSUEeNkZ6mNerJX+eXGz50hmdG/OCmosOn4O1oS6W9G8LT7viLej6PbS83OB9fqdw23XxNP4zdMcRPO4/FbKksbMFYlPSsfb6M0SnpMHJQBurOtQSTpP4lJCCPEMNCekZmHv2Hm+rqaTAsxe2dq3HlwFlIpNScfn1R/7vLjsCxH7Xhk51UMlSNGWL+kTIzyvzhUJH5D/s/JMfz6iQde+jiv/KUFFTUy7a1OlfQU+9cKe+yALNn8hQknWh8UW/ktCvkJ4pPkWhJLDXL3ipvP+iy89K3vgbYHsFJY1yxFuUNOebi4rQlhQ+a35+iiQpXmoD/8B/0eQNRb9U+M9aNEg0pb60oIwKQgghhBBCCCGl2j+LqBEZUTomHBFCCCGEEEIIIeQ/gQIVhBBCCCGEEEIIkRk09YMQQgghhBBCSKnGlnklsoMyKgghhBBCCCGEECIzKFBBCCGEEEIIIYQQmUFTPwghhBBCCCGElGpfaOqHTKGMCkIIIYQQQgghhMgMClQQQgghhBBCCCFEZtDUD0IIIYQQQgghpVpODk39kCWUUUEIIYQQQgghhBCZQYEKQgghhBBCCCGEyAya+kEIIYQQQgghpFSjRT9kC2VUEEIIIYQQQgghRGZQoIIQQgghhBBCCCEyg6Z+EEIIIYQQQggp1b7Qqh8yhTIqCCGEEEIIIYQQIjMoUEEIIYQQQgghhBCZQVM/CCGEEEIIIYSUajm07IdMoYwKQgghhBBCCCGEyAwKVBBCCCGEEEIIIURm0NQPQgghhBBCCCGlGq36IVsoUEH+0+TKlLwPFA3VHJQ0inIl733SVExDSaMml4KSJiZRGyVRUnLJO6Y8jEre+Du69RFKGq3RPihpKi+eiZLGZ01HlDSXhh381S+BfKfmA//41S+BlAA09YMQQgghhBBCCCEygzIqCCGEEEIIIYSUajT1Q7ZQRgUhhBBCCCGEEEJkBgUqCCGEEEIIIYQQIjNo6gchhBBCCCGEkFKNZn7IFsqoIIQQQgghhBBCiMygQAUhhBBCCCGEEEJkBk39IIQQQgghhBBSqtGqH7KFMioIIYQQQgghhBAiMyhQQQghhBBCCCGEEJlBUz8IIYQQQgghhJRqX77Q1A9ZQhkVhBBCCCGEEEIIkRkUqCCEEEIIIYQQQojMoKkfhBBCCCGEEEJKtRxa9UOmUEYFIYQQQgghhBBCZAYFKgghhBBCCCGEECIzaOoHIYQQQgghhJBSjVb9kC2UUUEIIYQQQgghhBCZQYEKQgghhBBCCCGEyAwKVJAfYm1tjWXLlv3wfmXKlMHRo0eL5DURQgghhBBCyL/xJeeLzN5KI6pR8R/Wp08fxMXFUQDgJ+egndi3FlfPHUFqSiLsnCqg26BpMDK1krrPy2f34H9sO96/CUR8bCSGTlqKClXrSW2/e/0fuOJ/CB37TkCDFj1QHH06f2Ql7lw6iLSURFg5VESrPrOgb2wtdZ/LJzbg2d0ARIa/gYKCMiwdKqJx5/EwMLERa/c++AECDi1H6OvHKFu2LEysnNFn4iYoKCoXeZ/8D6/CrYsHkZqcCGvHimjX7zcYFNCnC8c24Mndc4j8+AbyisqwdqiAZl3Gw9BU1KdDm2ch+OlNJMRGQElZFVYOFdC8K2tjWyR9OLxnAy76H0NKchIcXcqj79BJMDa1LHC/gJMHcdJ3N+Jjo2Fp44Beg8bDzrEcfyzy80eMHdhW4n4jJ81H1Zr1+b+fPrqDw7vXI/TdaygpKaNWvebo2HMI5OTkf7gPe3dtxbmzJ5GcnARnFzcMHj4WpmbmBe53ys8XRw/vR1xsDKxt7DBgyCg4OrkIH8/IyMDWTWtw7cpFZGVmoIJnZQweNgbaOrpfPVdCQjzGjRiA6Ogo7Np/Amrq6sLHMjMzsH/PDly5eA6xsTHQ0dVFp6690KBRsx/qp6R+X/dbgcfXDyI9NQGmtp5o1HU2dAylj78HV/bg4ZW9SIgJ49t6Jg6o3mwYbMvVEbbZ91dPhAbfFtvPo2ZnNOo2B0WN9emO/0oE3hL0ydjaE7XbzYK2gfQ+5XX/wgbcOr0U7jV7oWbracL7n9/cj+AHfogMe47M9GT0m3MbSiqahfJ6f9XYa9u87lfPO27STNSqI/jcv3H9Cs6eOo63b14hMzMTFlbW6NKtNyp6VUFR6d/dGi0bGUNDTR5PAhOweE0wPoSnSm2voiKHgd2tUdtbHzpaCnj5JgnLN75GUHAiihs/nk6uwJM8x1PDLgUfTw/Z8XRV/HjybprveFrWEx8kHE8Nuxb98aTfsi0MO3aFvK4uUt+8RtjqZUh5ESi1vUHbjtBr0QaKhkbISohD3NXLCN+8Hl8yM/jjxj37wrhnP7F90kLfIah/0X+HyLX/wSvsuPMC0clpcDTQxqT6FeFm8vVnMnP8aQhmn7kjdp+iXFncHNue/zszOwdrrj3F9bfh+BCXDHUlBVS1MsKo2u4wUFeBrNGtWQm24/tDy9MNyqaGuNt+GD4fP4//spLYJ1J6UKCClEpnj27DhVN70GfkXOgbmuH4vjVYMXcYZi8/AgVFJYn7ZKSnwtzaETXqt8G6/40r8Pkf3LqANy8fQ1vXAMXl6slNuBGwC+0HLoCugTkCDq/Atj8HYvQCP6l9eht0B9UadIOZjRtycrLhf/AvbPtff4xe6AdFJVVhkGLb4kGo02IQWvScjrJy8vj0PghlyhR9QtYlv824dnYXOg+eD11Dc5w9uAKbFg7ChP+dkNqn10F3Ub1BV1jYuSEnOxunDyzDxoUDMPF/J6CoLOiTuU05eFZvCW19E6QkxSPgyGreZuqyAJQtK1eoffA7shP+fgcwePRvMDAyxaHd67Fo1mgsWr0PilL6cPNqAHZvXo6+wybD3rEczhzfx/f5c+0BaGnrQk/fCKu2nxLb5+JZXx7Y8PDy5tvv3r7E4t/HonWnPhg8ZhZiYyKxdc0i/j536zf6h/rge2gfTp44glFjp8DI2AR7dm7BnJmTsGLdNigqKkrc59qVC9i6cS2GjBjLTxBPHD3E91m1YQe0tXV4my0bV+PenZuYOHUW1FTVsGHdCiya9xsWLF711fOtXv4nrGzseKAivz8X/I74uFgMHz0RJqZmiImJxpcvOfi3bgdsxP1LO9G010Jo6Znjut9yHFzZH/1+OwV5BcnvnYa2Meq0mQAdQyt+Yvbs5lH4rhuO3lN9oW/qIGxXvkYn1GgxSritoFg8X9ofXtqEJ9d2ol7nhdDUNcfts8vht2kAukw4KbVPuSJCn/CAhJ6J01ePZWamwcKpFr+xQEZh+dVjb+SYyWKBh7wBsufPHsOjohe69x4ANTV1XDh3GvPnTMeipWtgayd6rwtL9/YW6NDCDPOWBSH8cxoGdLfG0jnu6DHsDjIyJV9tmzLSEbZWapi7NAhRMelo7GOEZXPL832iYgQnx8WFHU8P2PHUcyG09M1x7cRyHFrVH31nFnA86Rijdus8x9Otozi6fjh6TZFwPDUXHU/yxXA8adepB9PBI/BhxRIkBz2HQbuOsJ2/BEH9uyErLu7r9nUbwKT/YLxfshApz59CydwClhOmsQgOPq4XjbvUkDd4PXmscPtLdjaKy9mgUCy99AjTGnjC3UQPu++/xPBDV+Dbrwl01SRfmFBXlMeR/k2F22XyPJaWlY2giFgMqOYKR0NtJKRlYPGFhxjjex27ezaArJFTU0XC4xcI3XYYlQ6tRklQEvtESg+a+lFCLV26FO7u7lBTU4OFhQWGDRuGpKQk4ePbtm2DtrY2/Pz84OTkBFVVVXTo0AEpKSnYvn07n+Kho6ODUaNGITvfH8nExER07dqVP7eZmRlWrxb/4AsODkbt2rWhrKwMV1dXBAQEfPX6Jk+eDEdHR/57bW1tMXPmTH5FqjjwzAO/3WjWYSAqVKnLgw99R85FXGwkHt6+KHU/N8+aaNNtBCoWkEXBxEZ/xr5NC9F/9PwfvnL9r65Und0Bn1ZD4OpVH8aWTug4eCES4yIQeP+c1P36TNwIz1ptYWTuABNLZ3QYuABx0eEIe/tM2ObUnoXwbtgDdVoO5O1YtoV71aaQV1As8j5dPbMD9dsMhlul+jC1dEKXoQuREBeBZ/ekXw0YOHkDKtdpC2NzB5haOfMgB+vTh7fPhW2q1esEW5dK0DUwg7mNKxp3HIW46E+IiQwr9D6wIEPrTn3hVa0Oz4wYMnY24mKicO/mZan7nT62F3UbtUadBi1hZmmLvsOm8IyIy+dO8MfLyslBW0dP7Hb3xmVUrVEfyiqCYMzNq+dgYW2Ptl0GwNjUAi5unujSZwQCTh1GakryD/XB79ghdOzcE1W9a/Kr06PHT0VMTBRu3bgmdb/jvgfRsElz1G/YFBaW1hgyYhyUlJVx3v80f5xdHT/vfwp9BwxDeQ9P2Dk48ZPCoMBneBEkeq+YMyeP8fat23X66vfcv3sbz54+wozfF/KTRkMjYzi7lIOLq/t391Fav+9d2IFqTYbCwaMBDM2d0az3/5AUH4HgR9KPKfvy9WDrVodfJdY1skGt1mN50O/j24di7Vg2krqWgfCmpCI6AS4qrE+Pr+6AV/0hsHGrDz1TJ9TrsggpCRF4+0x6nxiWJXFuzwT4dJgrMVPCo1ZveNYbBCMrj0J9vb967LHABMvQyb3lDY70HzQCbTt0hYOjM8/w6NF7IA+U3bn1N4pCx1Zm2HHgHa7disbrkGT88VcQ9HSVUKuavsT2ioplUae6AdZsfYNHz+IRFp6GLXvfISw8FW2bmaI4sffy/kXB8WTv0QAGZqLj6VUBx5Ode77jqZXgeAoPET+eWPacmpaB8FYcx5NB+86IPn0CMf6nkP4+BB+WL0ZOehp0GzeX2F7N1Q3Jz54i7uI5ZHz+hMR7dxB78RxU82T6cNnZyIqNEd6yE+JRXHbffYm27jZo7W4DW31NTG/oBWUFORx7GiJ9pzJloK+mLLzp5QloaCgpYG3HOmjkbAFrXQ2UN9XD5PoVEfg5FuEJKZA1kWev4OWsZfh8rODPw/+SktinovSrp3fQ1A9xFKgooVh6/ooVK/Ds2TMeeLhw4QImTZok1oYFJVibffv24cyZM7h06RLatm2LU6dO8dvOnTuxfv16HDp0SGy/P//8Ex4eHnjw4AGmTJmC0aNHC4MROTk5aNeuHf8yd+vWLaxbt44HJfLT0NDgwZLnz59j+fLl2LhxI/766y8Uh6jPYUiIi4JL+arC+1TUNGDj4I43Lx79q+dm/d+6YgYate4NU0t7FJfYyA9Iio+CXTnB1XRGWVUD5rbl8f7V9/cpLVWQDqyqrsV/JiVE8+ke6pp6WD+nK+aPqImN8/7f3n2AN1V+fwD/Al1ANxRoyyibsrcMUfasDFGGyBIQQYYggsifoSjDwQ8QBFRkCIoM2RtZypC9995QOukukP9z3pI0adPSCm1ym+/nefLQ3CTtfWlueu95z3tOV1w7fxgZLTjwFh6FPkRJozHlzOWCwsUr4vpF05PU1MgyGOMxJRUXE4VDu1aqLBT3PAXwMskSDVm6Ub5S4oxsrtzOagnHxfMnzb7mcXw8rl46h3KVa5ocz+Uq1cClc+Zfc/XSWZVB8XqT1ibfxz7JjLNkcMTHxeLq5XNpHsP9e3fVcopKlasZtsnsccnS/jh/LjGgZUyCjpcvXTB5jYyhYuWqhtfI448fPzZ5TsFCheHllR/nzyZ+35s3rmHp7wsxeOhIZDeTxXPw3z0oUaK0mnnv1e1t9O/TFfN/noXY2Fi8iLCgW4gMD0SRMnUM2xxzusDbrxLuXDmapu8h2StnD61HfFwUfIpVMXnszMG1mPHJK5g3PgC7V32H+LiU0/dflkfBtxD1KBAFS5qOKV/hirh/PfVjavfKL1DEvz4Klkp8bUaz9HtP/DhrGrp1boNPhvTDti0bUm1dJ5//0dHRcHF58SUvSfnkd0JeT0ccPBZi2BYZ9QRnLoSjfBnzPy9Hjmywy5ENcXGm2UWxcU9Rsaz5z8OMYjieSps5nq6m/Xg69+x48i5qejydPbgWM4e/gnlfBmD36ow/nrLZ2SFXyVKIOGr0t1CnQ8TRQ8jtn7BEL6nIM6fUa/SBCYcC3nCtWQvhB/abPM/BtyDK/b4S/gv+QOFPR8PeKx8ygyzTkACCLM3Qy54tG14pnB8n7gSl+LrouMdoOWc9WsxZhyEr9+Dyw9QDKxFx8SrrQoIYRESp4dKPLOqjjz4yfC3ZEV9++SU++OAD/PDDDyYndLNmzULx4sXVfcmokODE/fv34ezsrLIhGjRogB07dqBjx46G19WtW1cFKIRkRezZs0cFGZo0aYJt27bh3Llz2Lx5M3x8EmZsJkyYgBYtEtMCxf/93/+Z7N+wYcNUwCRpMCUjSJBCuLrnMdnu6uaJsNCU/xinxeZV89Rsd8NW7yAzPQpLGJOzm+mYnN3yIiI0ME3fQ06y1y+aiCIlqyJ/wVJqW/CDm+rfv1bOQIvOw1XWxdE9q/HL5J4YNGFNqvUvXpQEKYSLm+lsoYxR/1haxrTm10nwK1UVBQqZpmLv3fo71v/+rVrSI1kifUb+DDu7l5slEhqS8H5ydTdd3yv3w0KCzb7mUXioOiGXJR7G5P7d29fNvmbn1rXwKeSn6l/oVaz6CjatXYK9uzaj1quNERoahJVL5ibsV/DDdIwhYT/dPBJS5vUkhT40xTGEqf97N/fkr7l984bh+9rZ2Zuk0ut/jv77Su2JKV+PR7f3PoBXvvzqwjUp2Xb2zEkVlBkx6gv1s+f8MBWPHoVj4JDkQdK0igxLOG5yu5oeU3I/Mjz1/7/A2+ex+NtOeBwfq2Z/274/E3m9EwOX/jUC4OrpA2e3fOq5u1Z9i+D7V9G2b/IlLy+TBClEThfTMeVyzouoRymP6eKx9Xh4+wzaDzINWmc0S773ROd3e6JCpSoqm+nYkUP48YepiImJRkDrhPX3Sa3+8w/EREejTr36eNk8PRI+m0JCTTMPQ0LjDI8lFR39BCfPhqFHpyK4ditKPbfxa/lQrrSryqrITBKkELmSHE+5XNJ2PP0mx9PjhOOpTZ8kx1N10+Np9+pvEXL/Ktq8n3HHUw5XN2TLYYf4JO/D+JAQOBYyX+tKMins3NxQYspMVWBcgh0P167CgyW/Gp4jS0iiv5mA2Fs3Ye+ZBwXe7YGSU2bi3Pvd8DQ6Y39nodGxeKLTJVviIfevBZuvaVLE0wVjm1dHSS93RMTGq9oWPX/bjmU9myG/S0J2n7HYx08wbfcJNPcvrOpVEBGlhoGKLEoCBhMnTlRBg/DwcDV7FBMTo7IoZLmFkH/1QQqRP39+FTSQIIXxtgcPHph879q1aye7r+8EcvbsWbXURB+kMPd88ccff6hsjsuXL6slKbJ/rq6pz0LJDGnSWVKZKUppnb/ev7vXq8KWegM++x4Z4frlM9i+/jeM+uZ3dRKSkY7tXYvV88YZ7nf7eNYLf8+1C7/A/dsX8f7/LTZs088e1mzYEdVee1N97eNXFpfP7Mfh3X+iWYfUa3Wkx5E9a7FibuKY3vtk9gt/z5Xzx+PerYvoP2ZRsseq1A1AyQq18SjkIXZtmIdF04fiw7GLU6x9kRZ7dm7CLz9MMtwfNublrdVPSVxsDPbt3oy2HUwLsFWoUgudewzEvFmTMft/n8Pe3h5tOr6H82eOqRnmlOzasRWzZyTu96hxE2Epv87/CQULFUH9hk1SfM5TnU4db0M+GaVm24Wk9H8zcRze758YsH2eMwfWYMvvYw332/eb85/3W1LUu49chdiYR7hwZDM2LByBTkMWGS6upNCfnpdvaZWqvnRaD4QE3oCHV+pFVtPjwpG12LUicUyt3kv/MRURehd7Vk/AG31+eW4NixdlTe89IQVZ9aTmhAQppECnuUDF7p3bVEHXkaO/NNTBeBFNXs+HTz5MCBiL4V+Yz6Z6HqlNMXJwaaxeUBuPn+hw4fIjbNv9AKVLZOzSCDmethodT2/2f7HjqZv+eDq6GRt/HYGOH6V8PMlSqqXTeyA08AbcX+Lx9KKcK1ZG/k5dcev7KYg6dwaOvr7w7TcY+YO74/7iBeo5jw7+a3h+zNXL6nllFy1T9TCCN62Htankk0fd9GRpR/t5m7Di+BX0f7V8soyNEWv3ATpgZOOqFthboueTcwqyHgxUZEHXrl1DQEAA+vXrh6+++gqenp74559/0KtXL1XpXB+okAsXY3Kyb26bzE69TPv27UOXLl3w+eefo1mzZnBzc1PZFN99912qr5PAi7zGWPd+n6FH/8TsDHMq1aivlnXoSYV3ER4aBDePxGKX4WHBKOSXeGKYXhfPHsGjsGCM7JuYPSIz48sXTMH2dYsxYXbC+uiXwb9KQxQqXjHZmCLCguDqnpgmKstBvIskWf9qxpqF43H+2C70HvUr3DwTlz+4uCf8/+TzSQxoqfvexRAWlHx2+0WUrdpQLevQe/w4zpAt4mr0e5IxSu2J51k5/0ucPboL/UcvNLukQ5aRyE06iBQuWRFj3q+NU4e2oUod8+uL06JqzXqGzhwJY0iY/QwPlU4UiZkhcr9wMfPF9lxc3VVBz7BQ05k6uZ80y0Ic2LsdsbExeLVh8g4XLdu+gxZtOqsMitzOLgh8cBdLF/4ArwK+KY6h5it1Uap0WcN9yWpQPz8kBJ6eiSekoaEhKFrM/PImF1c3FQyRApfG5DX6rgryr/z/REZEmMxsy8/RP+fk8aO4cf0q9v6T0MVET1Lx3+r4rprxlroBnnnyGoIUQoIbEmQLeiizuKYdbFKrLSFp6HpPnr3/IsOD1EytntyXehWpyWHnoIr/iQKFy+Pu9ZM4vGMhmqXQ1UP/c0MDr7/UQIVf2QbIX7hisjFFPwpCbtfEMUVFPEReH/OfE4G3TiM6IgjLpiUEKoXu6RPcuXoIp/YuxvsTT7y0ArTW9N4zRwpzLlvyq9ove6MaPX/v2o6Z07/FJ5+OVXVSXoZ/DgThzIVDhvsO9gnBRQ93ewSFJBbB9HB3wKUrifWnkrpzLwYDRx6Hk2N25M5lp177+XB/tT0jpXQ8RSU5nqIepf94unf9pKp3kVKXnALPfm5I4PUMC1RI3Qjdk8ewT/J+sffwwONg85mZBbr3RshfWxC8aZ26H3PtCrI75UShwZ/g/m8L1dKRZD8nMkJlVzj6pN7l5mVwz+mIHNmyITjS9L0h943rTqTGPkd2lMnngZuhEcmCFJ+u3afqUszp8DqzKYgoTRioyIIOHz6sggty4a+fOV26dOlL+/779+9Pdt/fP+EkV/69efMm7t69C29vb7PP37t3L4oUKYJRo0YZtl2/bj6l3djIkSMxdKjpDP7+S88PojjlzK1uenIB4+qeF+dOHkChogknSNFREbh68SReb/Y2/qtarwfAv2Itk23Tx/fDK68FoE7DNniZHHPmVjfjMckyjytn9sPnWWAiJjoCt66cwCuNOqXepvXXL3Hm8Db0HrlA1Wkw5pHXFy4e+RB496rJ9of3rqNUpXovdUzmfk8u7nlx6fR++Po9G1NUBG5cPoHajVMf06oFX6mgwwf/N191C3kuOT/U6QwBn/8qZ67c6ma8L24eeXD6+EEUKZYQBIuKisDlC6fRqEXihZ8xO3t7FC1RRr2meq2EFnxyPJ8+cRBNWr1tdtmHBEhc3czP4kqw0SNPQqBn3+4tqmNI0WKlUxlDLnUzHoOHhydOHD+CosUTLg6joiJx8fxZNG9p/n0tAc/iJUrhxLEjqgiifgwnjx1Bi4CEtqryuJ2dHU4cP4zadRPGefvWDQQG3kfpZ2u8h4/6HHGxib+TSxfPYcbUr/HV19NRwDsha8vfvzz2/rNL1QbImTOh0v+d2zfVZ1+evF64dg9p4uDkrG7G487t6oUb5/chf6GE919sdATuXjuOyq91Tts3NXyzp4YLNXMe3EpoZyg/72UyN6ZcLl64dWkf8vomjCkuJgIPbpxAudrmx+RbohY6fLzGZNuOPz6DR75iqNyg90vtkmNN7z1zrl65DGdnF9Mgxc6/MGPa16ptafWaybMH/ytZtnE72rSQtXTtqF7JA5euJhTDzZUzB8qWcsWqDXee+/1iYp8iJjZOtTWtWcUTs+ZfQUZK6Xi6fn4f8iU9nuql73jSPed4Cnx2PElmRUbRPX6MqIsX4Fy5GsL2/p2wMVs2df/hmj/Nvia7kxN0SSZ+DB09JAvTTKBCAhkO3r6I/2szMpoEGfzze+DAjQdoUNLXMLss9ztWSVvNrSdPdbj0MAx1ixZIFqS4ERKBHzvWVwERIqK0YKBC48LCwnDsmGkRtLx586r6E99//z3eeOMNVUNCilq+LPL9vv76a7Rt21YV0Vy2bBnWr09ISWzcuLGqW9G9e3dVdFOWnRgHJETJkiVx48YNlUVRo0YN9dqVK1c+9+c6OjqqmzEHh/Sv2ZQLt0YBXbBh+U/I511YtSdd/ftMuHt4qS4gelPGvY8qNRuiQcuEi+KY6CgE3ktY4ywePriNm1fPIbezGzy9vOHs4q5uxqTrh6tHHhTwzbhaDvox1W3WDTtWz0ae/EXg4VUQ21ZMh4t7PvhXTWwBNndST5St1hi1m3RR99cs+AIn9q/Hux/NgKNTbjx6Vs9CCnFKVwL5vvVavKdqVEh9Cu8iZXDk71UIvHsFnQdOzfAx1WveDX+tmoO8BYqoIMrm5dNVxki5aokz7HMm9ET56o1Rt2kXw3KPo3vXo8fQhDGFPxtTzmdjCnpwE8f3bUSpinWR28UDYcH3sWPtz2rJh3/l1176GJq37oRVS+chv08h5HvWntTdM6/qAqI34f8+RPVa9dE0ICEQIVkQc6Z+gaIl/FG8VFnVOSQ2JgavNwow+f737tzE+dNHMWzM/1JsjVqpam1ky54dB/fuwNoVCzFw+ARVRyU9Ywho85aaSZaOBvoWkZ6eeQ0XgmLMZ0NRq3Y9tHwj4WKwdbu3MX3KJBQvWQolS/mr7g2y/KxRk+bqccmAaNS0pWoj6ezsqjK9fpr9PUqXKYfSZRJm1b29fZPVHxCFChUxzITXq98YS5f8iu//Nxmd3+2B8PAwLPhlDho2aZHs8yI9ZNzVGnbDvo2z1GyutCeVdooyGyxdQPT+mNYdJSs1QdX676r7UhizaLnX4OrpjbiYSJw9uA43Lh7A2wMS6oPI8g4p/CedDHLmdldr6rcvn4iCJWo8d2b5RcmYKtbrhsN/zYZbXj+4evriwObpyOWaD0XLJY5pzZweKFq+MSrUfVddbOYpYJppJq1UHXO5m2yPCg9UdS7CHiZ8RgbdvQAHx9xw9vCGUy73/7y/lnrvSecOycKQDA8pDn386CGsWLrYpPOMLPeQnyPdP+R5IcEJWVAOjg4mGT4vy7I1t9G9Y2HcvBOd0J70XT8EBcfi7/2JNR6mflkRu/c9xJ/rE4IXNat4qGvgG7ej4eudEx/2LIYbt6KwflsaI3gvifwuqzbohv2bEo8nafcrx5N0AdFbOq07ShgfT6u/Q9GyRsfToXW4efEA3vrwWb0dOZ4OrUXRconH044VCceTdBbJSIEr/kDhTz5D1MVziDp3VrUnlcBC8OaE1tGFPxmF+KCHuPtLwrKX8P174PVmR0RfvqiWdDj4+MK7e2+E7d8j0TT1HJ8+/RG2fy/iH9yDXZ688O72nnosZEfKna5epi7VS2HsxgMom98D5bw98dvhi4iOf4zW5RPOYUZvOIB8zjkx8LWELNUf955BBR9PFHJ3xqNnNSruhkeiXYVihiDF8DX7VIvSae1eVTUwHj7L2HBzclDBEWtr5Zm7RGIWTq6iBeFaqQzigsMQc/PlZpFmlqw4poxkq901rBUDFRonnTqqVDGtfi1LPKQ96eTJk1UWgrQKlWUT3bolrrd9ER9//DEOHTqklmFIXQn5WbKEQ8gspgQdZB9q1qypal5ILYrmzRNOEEXr1q0xZMgQDBgwQNWcaNWqlWpPOm5cYn2CjNasbQ/ExURj0ezxiIp8hBJlqmDQ6B9M6hM8vHcTEY8S04evXz6NKWP7GO4vm5+wVKV2/TfQY+B4WFq9Vr1VYchV88YiJipcFcXsMexHkzEFP7iBKKMxHdi+RP3784TuJt+rfZ8Jqm2pqNu8u8o0kDalURFh8C5cGj2Hz0We/Bm/9rd+QC81puVzZUyPVFHM3iNMxxR0/yYijca0b1vCmGZ/aTqmDu9/pdqWyjr7q+cP4+9NvyI6MkxlohQrUw0fjv0tWTHSlyHgza6IjYnGLzMnIioyAqXKVsLwcdNMaqs8uHdbFdHUq1WvCcLDQrHitx9V1xDJxhg+bqrKzjAm7Uo98+RDhSqJHWyMnTi8D2uWzVeBy8J+JTB01DeoVC39XRvavdVJrc+f9f13qrWjtP4cPX6ySavGe3fvqCCB3quvNUR4WBiWLJqvOjcULVYcY76YbJJa/16fD9UFzNcTxqp9rFy1Bvqmo66EkCyKcV9+i59nT8ewjz5QHRfq1quPd7r2wouq2aQP4mOjsfm3MYiNCodv8Wp4a8DPJrUaQgNvIjoixCSVfcOCEYgMfwBHJxfk9S2tghR+/nXV4zly2OP6uX1qKUh8bBRcPLxRqnJT1G7RH5mhcv3eqiPCruVjEBcTjgJ+1RDQ+yeTMYUH3UBMpOnSiec5vX8JDm1NbFW9elbChWaDDhNQpob57CFrfu9JkHnjulX45aeZaqa7gLcvevbphybNEoOFWzatU+27pTOI3PQaNGqGQUMTCk6/TItX3ISTUw4MH1AKzrntcPJMGD4eexJx8Ykn1r4FcsLdNTGtXp7Xt1tReOV1RPijeOza+xA//noVT55k/sm4Op7iorFFjqfohOOp/YdJjqeHNxEdaXo8bVyYcDw5OLmoGhQSpNAfT9ntzB9PtZpn/PEUums77Nzc4d2tF+w8PBF95RKujBqGx8+WHTnky2+SJXFv8UKVWSLBCfu8XngcFqqCFPfm/WR4jnT48PtsLHK4uKrHI0+fxIXBffEkLPHvQ0ZqVqYQQqJiMWvPaQRFxaC0lztmvFXPsPTjXngUshuV4AqPjcP4zYfVc10d7VVGxrzODVVrUxEYEY1dlxOCZp0Wmraq/7HD66heOHM6mqSVW7XyqP1XYnHTst9+pv69ufBPnOg1ElqUFcdEtiObLrVeW0RWbuepzK1cnhkeRma9tEiHHFnvY8bbxXwVdC3LncP6+tq/qL3XEwv7ZiURkVnvmGpaNuvN7r0/5CKymu6DE7PBsooaX7/cpYzWoOTbWe/3tLP/MkvvAqVRq/jz0KLuYzI32yw9FnyRvN5aVseMCiIiIiIiIrJpnL+3Lta1OIyIiIiIiIiIbBoDFURERERERERkNbj0g4iIiIiIiGzaU3b9sCrMqCAiIiIiIiIiq8FABRERERERERFZDS79ICIiIiIiIpum49IPq8KMCiIiIiIiIiKyGgxUEBEREREREZHV4NIPIiIiIiIismk6HZd+WBNmVBARERERERGR1WCggoiIiIiIiIisBpd+EBERERERkU3TPX1q6V0gI8yoICIiIiIiIiKrwUAFEREREREREVkNLv0gIiIiIiIim/b0Kbt+WBNmVBARERERERGR1WCggoiIiIiIiIisBpd+EBERERERkU3T6bj0w5owo4KIiIiIiIiIrAYDFURERERERERkNbj0g4iIiIiIiGyajl0/rAozKoiIiIiIiIhsTHBwMLp06QJXV1e4u7ujV69eiIiISPH5165dQ7Zs2czeli1bZnieuceXLFmSrn1jRgURERERERGRjenSpQvu3r2LrVu3Ij4+Hj179sT777+P3377zezzCxUqpJ5v7Mcff8Q333yDFi1amGyfN28emjdvbrgvgZD0YKCCiIiIiIiIbJqtLf04e/YsNm3ahIMHD6J69epq2/fff4+WLVvi22+/hY+PT7LX5MiRAwUKFDDZtnLlSnTo0AHOzs4m2yUwkfS56cGlH0REREREREQ2ZN++fSqYoA9SiMaNGyN79uz4999/0/Q9Dh8+jGPHjqklI0l9+OGHyJs3L2rWrIlffvkl3e1fmVFBREREREREZKViY2PVzZijo6O6/Vf37t1Dvnz5TLbZ2dnB09NTPZYWc+fOhb+/P+rUqWOy/YsvvkDDhg2RK1cubNmyBf3791e1LwYNGpTm/WNGBREREREREdm0p7qnVnubOHEi3NzcTG6yzZxPP/00xYKX+tu5c+de+P8rOjpa1bIwl00xevRo1K1bF1WqVMGIESMwfPhwVcciPZhRQURERERERGSlRo4ciaFDh5psSymb4uOPP0aPHj1S/X7FihVT9SMePHhgsv3x48eqE0haakssX74cUVFR6Nat23Of+8orr2D8+PEqKyStWSAMVBARERERERFZKcd0LPPw8vJSt+epXbs2QkNDVZ2JatWqqW3bt2/H06dPVWAhLcs+WrdunaafJXUsPDw80rVUhYEKIiIiIiIismm21vXD399ftQ/t06cPZs+erdqTDhgwAJ06dTJ0/Lh9+zYaNWqEhQsXqqKYepcuXcLu3buxYcOGZN937dq1uH//PmrVqgUnJyfV+nTChAkYNmxYuvaPgQoiIiIiIiIiG7N48WIVnJBghHT7aN++PaZPn254XIIX58+fV0s8jEkXj4IFC6Jp06bJvqe9vT1mzpyJIUOGqE4fJUqUwJQpU1RAJD0YqCAiIiIiIiKyMZ6enqogZkr8/PzMthWVDAm5mSNZGnJ7UQxUEBERERERkU2ztaUf1o7tSYmIiIiIiIjIajBQQURERERERERWg0s/iIiIiIiIyKaZq8VAlsOMCiIiIiIiIiKyGgxUEBEREREREZHV4NIPIiIiIiIismlPnz619C6QEWZUEBEREREREZHVYKCCiIiIiIiIiKwGl37Qf7Jv3z68+uqraN68OdavX2/Yfu3aNRQtWjTZ87t06YJFixYle9zDwwMVKlTAl19+iXr16mVqVd+1S2bh721/IjrqEYqXrox33v8M+X2KpPiaC6cPY8vqBbhx5SzCQgLRb/gUVH6lYYrPXzznS+zeshxv9xyGxgHvIjPG9Nef3+PgzmWIiXqEIiWroHWPschbwC/F1+xa+yNOH9qKwLtXYG/vhMIlq6BZx4/h5W36O7xx8Si2Lp+Gm5dPIHv27PAuUgY9PvkZ9g5OGT6mLStm4N8dyxAd+Qh+pargzffGwCuVMW1f/SNOHtqGwDtXYOfgBL+SldGy08fI55M4puVzx+Liqf0ID3kAR6dcKFKyMlp1lucUy5AxrPjtR+zYshpRkREo5V8RPfsNRwGfwqm+buv6ZVi/cjHCQoJQuGhJdHv/YxQvVU49Fnj/Dob0aWf2dQOHT8ArrzZSX586fhArFs/BzeuX4ejohHoNW+Htrh8gRw67dI/h90XzsG3zekRGRqCMf3n0/XAIfHwLpvq6DetWYtWKPxAaEgy/osXR+4NBKFXa3/B4XFwc5v38A/7ZvQOP4+NQuWoN9O3/Edw9PJN9r/DwMAwd0BtBQQ+x6I+1yO3snDDGE8cweuSQZM//5dcV8PBM/n3SO+4966bjxJ5liI0Oh0+xqmjaeRw88qX8/ju6+zcc2/07woNvq/t5vEuiTsv+KFbudcNzlvyvK25ePGDyukqvdkTTd75ARpMxHdzyPc7+mzCmAn5V8dqbY+HulfKYjB3Z/iP+3TgFFV7thlfbfGbYfmb/H7h4dB0Cb59BfGwk3vviABxzur6U/bXUe69dqwbJvu/Q4aNR7/WEz/19e3Zj84Y1uHrlEuLj41GoiB86vdMdVarVREbp1cUPbzQtAJfcdjh5Nhzf/nARt+5Gp/j8nDlzoE8XP7xWOy883Oxx4UoEpv10GecuPkJmU8fT+uk4aXQ8NemU+vF0TI6nv02Pp9otkhxPU7vilpnjqUnnjD+e8r7RDvne7gw7T09EX7mM2zOnIur82RSf79XubeQJaAuHfPnxODwUoX/vwt25c6CLj1OPF+jaEwW6vmfympib13GuV8afQ+j9cfQSFh48j6DIGJTycsfwRlVQ3tv8Z+maU9cwbtNBk20OObJj/5D26uv4J0/xwz+nsOfqXdwKjYSzoz1eKZIfg16rAC/nnLA2nq9WR7GPe8Gtank4+eTDofb9cX/NX9CyrDimjKR7yq4f1oSBCvpP5s6di4EDB6p/79y5Ax8fH5PHt23bhnLlEi6qRM6cOc0+/vDhQ3z11VcICAjAhQsXkD9//kzZ/82r5mP7ht/QY+B45M3nizVLfsD08f0xbtqfsHdwNPuauNhoFPQrhbqN2mL210NT/f5H/92OKxdOwN3TC5nl7/U/Y9/WRWjfZyI8vQpi64rpmP9NHwyeuC7FMV09dxC1Gr8D36Ll8fTpE2xZ9j/M/7oXBk9aBwfHXIYgxfxv38frAe8joOsoZM9hh3s3ziFbtoxPyNq5bi7+2bwIHftOgGe+gti8bDp+nvQ+hn29NsUxXT53CHUad0ah4uXx9MkTbFw6FT9N6o1Pvl4LB6eEMRUsWg5V67wB97zeiIoIw9Y/Z6rnjJy6Fdmz53ipY1j356/Ysm4p+g4eA6/8Pli+eA4mjx2MyTOXwCGFMez/eysWz52Gnv1HoESpcti0Zol6zTezlsLN3RN58ubHjAUbTF6zY/NKFdioVK22un/96gV8+/kQtOnQA30/GouQ4EDM+2Gy+j2/897gdI1h5fIlWL/2Twwa8inyF/DGb7/+gi9GD8f02fPh4OBg9jX/7N6OeT/NwgcDhqgLxLWrlqvXzPhxIdzdPdRzfvlpJg4f3I9PRo5F7ly58ePs6Zj81RhM/HZGsu83c9o3KFK0uApUmCPfN1fO3Ib7bu7ueFEHtv6EIzt/RYtuk+CWpyD2rJuGZd/3wntjNsDO3vzvzsW9AF5vOwwe+YqoC7PT+1dh5ewP0X3kSuT1KWl4XsW6HVA3YJDhvr1D5py0H9v5M07+8ysadpwEV8+COLB5Gtb93Budhq1PcUx6D26eVAGJPN6lkz0WHx+DQqXrqZsEMl4WS7/3Bn40wiTwoA+QiTOnT6BSlWro0r03cud2xvZtGzHhi1GYPOUHFCue+Lt+Wbq0L4S3Anzx1dRzuHs/Br27+GHKFxXwbv+DiIs3f3L96cBSKFYkN8ZPOYeHwbFoVj8/po6vqF7zMDjh4jizyPF0VI6nrpPglrcg/lk7Dctn9ELP0akcTx4F8Fobo+Pp31VYNedDdPvUzPHUKvF4ssuE48n99Ybw6TsAt6Z/h8hzZ+D15tsoNuE7nOv1Dh6HhiZ/foPG8O7VFze+m4SoM6fgWLAQCg/7TCI4uDMn8X0Xfe0KLo9IDL7qnjxBZtl87iam7DyOzxpXRQXvPFh85AI+XL4bK99rDs/c5icmnB3s8GevFob72Ywei3n8BOcehKB3rbIolc8d4TFx+Hb7MXy0cg8Wd20Ma5Mjdy6EnziPm/NXoPrymcgKsuKYyHZw6QelW0REBP744w/069cPrVq1wvz585M9J0+ePChQoIDh5ubmZvbx8uXL47PPPkN4eDj+/fffTNl/lXmwbjFavtUHlWs2UMGHngPHIzQkEMcO7EjxdeWrvoq27wxAlVSyKERI0H0s+XkSeg2ekO6Z6xeaqdq8EPVbf4Cy1RqhQOHSeLvvJDwKfYCzR7al+Loen/yEqvXaIX/BkvAuXAZv9ZmI0KC7uH31tOE5G36bhNpN3sXrb/RRz5NsiwqvtICdvUOGj+nvTQvRqG1flK/eCD6FS6NTv0kID32A04dTng3oM+JH1Hi9HQoULAmfImVUkEPGdOvqGcNzajXsgGL+1eHp5YuCRcui2duDEBp0D8GBt1/6GCTI0KZDT1Sr9brKjPhgyDiEBj/E4f27UnzdxtW/o0HTNni98RvwLVwMPft/qjIidm1bqx7PniMH3D3ymNwO7duFV+o2glPOhGDM/r+3oZBfCbTr1BsFfArBv3xVdOoxAFs3rEB0VGS6xrBu9XK83bErXqn9qpqdHvzxSAQHP8S/+/5J8XVrVi5Dk+at0KhJCxQq7IcPBgyFo5MT/tqyUT0us+N/bdmAnr37o2KlqihesrS6KDx39jTOn0v8XYlN61er57d5s0OKP8/dzUNlUOhvkvnzImTch7cvRK3m/VCyUmPkK1gGLbt/jYiwB7h4POVjqkTFhihW/nU1S+yZvyjqtRmign53rh4zeZ5kIzm7eRlujjkTL4AziozpxN8LUa3RByhavhHy+JRGw06TERX+AFdPpzwmIVkS234bhvpvjTebKVGpXndUbfg+8hep9FL319LvPQlMGL+vjIMjvd4fgHZvdUbJUmVUhse73fvA28cXB//di4zwdmtfLFx6Hf/8G4TL1yLx5f/OIY+nI+rVymv2+Q4O2fF6HS/8MO8Kjp8Ow+27Mfjl9+u4fTca7VqaTi5kNPldHtmRcDyVqNQYXr6Jx9OlVI6n4hWSHE+tE46nu9dMjyfJnsvt5mW4Zcbx5NW+I4I2rkXwlg2IvXENt6Z9i6exMfBs1srs83OXLY/I06cQumMb4u7fw6PDBxGyYxtyGWX6KE+e4HFIsOH2JDwMmWXxoQtoV6Eo2lQoimJ5XTGqSTU42efA6lPXUn5RtmzIm9vJcMtjFNBwcbTHrLdfR9MyheDn6YKKPnkwolEVnL0fgrvhUbA2gZt348LYqbi/OvXPQy3JimMi28FABaXb0qVLUaZMGZQuXRrvvvsufvnlF3US8l9ER0dj4cKF6uuUZsdetof3byM89CH8K75i2JYztwuKlqyAK+ePv3C14HnT/w9N23SHT+ESyCwhgbcQEfYQxcslzKYLp1wuKFisIm5cSvuYYqIT0oFzOScEliLCg9RyD2fXPJjzRWdMGPAqfvqqK66dP4yMFhx4C49CH6Kk0Zhy5nJB4eIVcf2i6UlqamQZjPGYkoqLicKhXStVFop7ngJ4mWSJhizdKF8pcUY2V25ntYTj4vmTZl/zOD4eVy+dQ7nKia+Ri+5ylWrg0jnzr7l66azKoHi9SWuT72Of5JiSDI74uFhcvXwuzWO4f+8uQkKCUalyNcM2mT0uWdof588lBrSMSRr85UsXTF4jY6hYuarhNfL448ePTZ5TsFBheHnlx/mzid/35o1rWPr7QgweOhLZU8niGTKwN957tz3GjRqGs2fM/z+lR1jQLUSGB6JImTqGbY45XeDtVwl3rhxN0/eQ7JWzh9YjPi4KPsWqmDx25uBazPjkFcwbH4Ddq75DfFzK6fsvy6PgW4h6FIiCJU3HlK9wRdy/nvoxtXvlFyjiXx8FSyW+NqNZ+r0nfpw1Dd06t8EnQ/ph25YNqf6tk89/+Zvm4vLiS16S8snvhLyejjh4LMSwLTLqCc5cCEf5MuZ/Xo4c2WCXIxvi4kyr2MfGPUXFsuY/DzOK4XgqbeZ4upr24+ncs+PJu6jp8XT24FrMHP4K5n0ZgN2rM/54ymZnh1wlSyHiqNHfQp0OEUcPIbd/Yjapscgzp9Rr9IEJhwLecK1ZC+EH9ps8z8G3IMr9vhL+C/5A4U9Hw94rHzKDLNOQAIIszdDLni0bXimcHyfuBKX4uui4x2g5Zz1azFmHISv34PLD1AMrEXHxKutCghhE1kane2q1N1vEpR+UbrLcQwIUQmpUhIWFYdeuXahfv77hOXXq1DGZ0fz7779RpUqVZI9HRUWpE79q1aqhUaOEdfUZTYIUwtU9j8l2VzdPhIWm/Mc4LTavmqdmuxu2egeZ6VFYwpic3UzH5OyWFxGhgWn6HnKSvX7RRBQpWRX5C5ZS24If3FT//rVyBlp0Hq6yLo7uWY1fJvfEoAlrUq1/8aIkSCFc3ExnC2WM+sfSMqY1v06CX6mqKFDINBV779bfsf73b9WSHskS6TPyZ9jZvdxgWWhIwvvJ1d10fa/cDwsJNvuaR+Gh6oRclngYk/t3b183+5qdW9fCp5Cfqn+hV7HqK9i0dgn27tqMWq82RmhoEFYumZuwX8EP0zGGhP1080hImdeTFPrQFMcQpv7v3dyTv+b2zRuG72tnZ2+SSq//OfrvGx8fhylfj0e39z6AV7786sI1KZnllhT/EiVKq4vUrVvWY/SnQ1T6ffESCe/j/yIyLOG4ye1qekzJ/cjw1P//Am+fx+JvO+FxfKya/W37/kzk9U4MXPrXCICrpw+c3fKp5+5a9S2C719F277Jl7y8TBKkEDldTMeUyzkvoh6lPKaLx9bj4e0zaD9oOTKTJd97ovO7PVGhUhWVzXTsyCH8+MNUxMREI6B1wvr7pFb/+QdioqNRp17i38KXxdMj4bMpJDTeZHtIaJzhsaSio5/g5Nkw9OhUBNduRannNn4tH8qVdlVZFZlJghQiV5LjKZdL2o6n3+R4epxwPLXpk+R4qm56PO1e/S1C7l9Fm/cz7njK4eqGbDnsEJ/kfRgfEgLHQuZrXUkmhZ2bG0pMmYls2bKpYMfDtavwYMmvhufIEpLobyYg9tZN2HvmQYF3e6DklJk49343PI3O2N9ZaHQsnuh0yZZ4yP1rweZrmhTxdMHY5tVR0ssdEbHxqrZFz9+2Y1nPZsjvkpDdZyz28RNM230Czf0Lq3oVRESpYaCC0uX8+fM4cOAAVq5cqe7b2dmhY8eOKnhhHKiQpSH+/onpjIUKFTL5PvK4ZGWcOnUKw4cPV8tH7O1T/6MVGxurbsZkpiildf56/+5erwpb6g347HtkhOuXz2D7+t8w6pvf1UlIRjq2dy1WzxtnuN/t41kv/D3XLvwC929fxPv/t9iwTT97WLNhR1R77U31tY9fWVw+sx+Hd/+JZh1Sr9WRHkf2rMWKuYljeu+T2S/8PVfOH497ty6i/5hFyR6rUjcAJSvUxqOQh9i1YR4WTR+KD8cuTrH2RVrs2bkJv/wwyXB/2JiXt1Y/JXGxMdi3ezPadjAtwFahSi107jEQ82ZNxuz/fa6OrzYd38P5M8dSXRaxa8dWzJ6RuN+jxk2Epfw6/ycULFQE9Rs2SfE5vgULq5tembLlce/uHVWX4CNZ/51GZw6swZbfxxrut+835z/vt6Sodx+5CrExj3DhyGZsWDgCnYYsMlxcSaE/PS/f0ipVfem0HggJvAEPr9SLrKbHhSNrsWtF4phavZf+Yyoi9C72rJ6AN/r88twaFi/Kmt57okPnboavpeaEBCmkQKe5QMXundvwx28LMXL0l4Y6GC+iyev58MmHiYG24V/8tywhqU0xcnBprF5QG4+f6HDh8iNs2/0ApUtk7NIIOZ62Gh1Pb/Z/seOpm/54OroZG38dgY4fpXw8yVKqpdN7IDTwBtxf4vH0opwrVkb+Tl1x6/spiDp3Bo6+vvDtNxj5g7vj/uIF6jmPDiYugY25elk9r+yiZaoeRvCmxMLl1qKSTx5105OlHe3nbcKK41fQ/9XyyTI2RqzdB+iAkY2rWmBviUhrGKigdJGAhKTMGhfPlItZR0dHzJgxwyQwUaJEyksf5PGSJUuqm3y/du3aqaCFfJ+UTJw4EZ9//rnJtu79PkOP/v+X6j5XqlFfLevQkwrvIjw0CG4eicUuw8OCUcjvv8/AXjx7BI/CgjGyb2JRKZkZX75gCravW4wJsxPWR78M/lUaolDxisnGFBEWBFf3xDRRWQ7iXSTJ+lcz1iwcj/PHdqH3qF/h5pm4/MHFPeH/J59PcZPn5/MuhrCg5LPbL6Js1YZqWYfe48dxhmwRV6Pfk4xRak88z8r5X+Ls0V3oP3qh2SUdsoxEbtJBpHDJihjzfm2cOrQNVeqYX1+cFlVr1jN05kgYQ8LsZ3hoMDw8EzND5H7hYuaL7bm4uquCnmGhpjN1cj9ploU4sHc7YmNj8GrDlskea9n2HbRo01llUOR2dkHgg7tYuvAHeBXwTXEMNV+pi1KlyxruS1aD+vkhIfD0TDwhDQ0NQdFi5o9xF1c3FQwJC01MU9e/Rt9VQf6V/5/IiAiTmW35OfrnnDx+FDeuX8Xef0yzrSQV/62O76oZb3NKlvJP9/IPqS0haeh6T569/yLDg9RMrZ7cl3oVqclh56CK/4kChcvj7vWTOLxjIZql0NVD/3NDA6+/1ECFX9kGyF+4YrIxRT8KQm7XxDFFRTxEXh/znxOBt04jOiIIy6YlBCqF7ukT3Ll6CKf2Lsb7E0+8tAK01vTeM0cKcy5b8qvaL3ujGj1/79qOmdO/xSefjlXFNV+Gfw4E4cyFQ4b7DvYJwUUPd3sEhSQWwfRwd8ClKxEpfp8792IwcORxODlmR+5cduq1nw/3V9szUkrHU1SS4ynqUfqPp3vXT6p6Fyl1ySnw7OeGBF7PsECF1I3QPXkM+yTvF3sPDzwONp+ZWaB7b4T8tQXBm9ap+zHXriC7U04UGvwJ7v+2UC0dSfZzIiNUdoWjT+pdbl4G95yOyJEtG4IjTd8bct+47kRq7HNkR5l8HrgZGpEsSPHp2n2qLsWcDq8zm4KsFrt+WBcGKijNJKAg9SS+++47NG3a1OSxtm3b4vfff1dLQdLrrbfewpgxY/DDDz9gyJDkbQb1Ro4ciaFDTWfw9196/potp5y51c04sOLqnhfnTh5AoaIJJ0jRURG4evEkXm/2Nv6rWq8HwL9iLZNt08f3wyuvBaBOwzZ4mRxz5lY34zHJMo8rZ/bD51lgIiY6AreunMArjTql3qb11y9x5vA29B65QNVpMOaR1xcuHvkQePeqyfaH966jVKWX207W3O/JxT0vLp3eD1+/Z2OKisCNyydQu3HqY1q14CsVdPjg/+arbiHPJX+XdDpDwOe/ypkrt7oZ74ubRx6cPn4QRYolBMGioiJw+cJpNGqReOFnzM7eHkVLlFGvqV4roQWfpLKfPnEQTVq9bXbZhwRIXN3Mz+JKdo9HnoRAz77dW1THkKLFSqcyhlzqZjwGDw9PnDh+BEWLJ1wcRkVF4uL5s2je0vz7WrI3ZNnFiWNHVBFE/RhOHjuCFgEJbVXlccnIOnH8MGrXTRjn7Vs3EBh4H6WfrfEePupzxMUm/k4uXTyHGVO/xldfT0cB75SLAV67cgkeHqYp5s/j4OSsbsbjzu3qhRvn9yF/oYT3X2x0BO5eO47Kr3VO1/eG7qnhQs2cB7cS2hnKz3uZzI0pl4sXbl3ah7y+CWOKi4nAgxsnUK62+TH5lqiFDh+vMdm244/P4JGvGCo36P1Su+RY03vPnKtXLsPZ2cU0SLHzL8yY9rVqW1q9ZmI9nRclyzZuR5t2e5CuHdUreeDS1YRiuLly5kDZUq5YteHOc79fTOxTxMTGqbamNat4Ytb8K8hIKR1P18/vQ76kx1O99B1PuuccT4HPjifJrMgousePEXXxApwrV0PY3r8TNmbLpu4/XPOn2ddkd3KC7qnpOYuho4dkYZoJVEggw8HbF/F/bUZGkyCDf34PHLjxAA1KJgSzn+p06n7HKmmrufXkqQ6XHoahbtECyYIUN0Ii8GPH+iogQkSUFgxUUJqtW7cOISEh6NWrV7IuHu3bt1fZFv8lUCEXUoMGDcK4cePQt29f5DI6UTUm2RZJMy4cHKL/089rFNAFG5b/hHzehVV70tW/z4S7h5fqAqI3Zdz7qFKzIRq0TLgojomOQuC9hDXO4uGD27h59RxyO7vB08sbzi7u6mZMun64euRBAd+Mq+WgH1PdZt2wY/Vs5MlfBB5eBbFtxXS4uOeDf9XEFmBzJ/VE2WqNUbtJF3V/zYIvcGL/erz70Qw4OuXGo2f1LKQQp3QlkO9br8V7qkaF1KfwLlIGR/5ehcC7V9B54NQMH1O95t3w16o5yFugiAqibF4+XWWMlKuWOMM+Z0JPlK/eGHWbdjEs9zi6dz16DE0YU/izMeV8NqagBzdxfN9GlKpYF7ldPBAWfB871v6slnz4V37tpY+heetOWLV0HvL7FEK+Z+1J3T3zqi4gehP+70NUr1UfTQMSAhGSBTFn6hcoWsIfxUuVVZ1DYmNi8HqjAJPvf+/OTZw/fRTDxvwvxdaolarWRrbs2XFw7w6sXbEQA4dPUHVU0jOGgDZvqZlk6WigbxHp6ZnXcCEoxnw2FLVq10PLNxIuBlu3exvTp0xC8ZKlVIaDdG+IiYlBoybNDUURGzVtqdpIOju7quP+p9nfo3SZcihdJmFW3dvbN1n9AVGoUBHDTLgs8ciXvwAKFymKuLg4bNuyHidPHMXY8V+neYwpjbtaw27Yt3GWms2V9qTSTlFmg6ULiN4f07qjZKUmqFo/oW6PFMYsWu41uHp6Iy4mEmcPrsONiwfw9oCE+iCyvEMK/0kng5y53dWa+u3LJ6JgiRrPnVl+UTKmivW64fBfs+GW1w+unr44sHk6crnmQ9FyiWNaM6cHipZvjAp131UXm3kKmGaaSStVx1zuJtujwgNVnYuwhwmfkUF3L8DBMTecPbzhlOu/tYq15HtPOndIFoZkeEih5+NHD2HF0sUmnWdkuYf8HOn+Ic8LCU7IgnJwdFA/42VbtuY2uncsjJt3ohPak77rh6DgWPy9P7HGw9QvK2L3vof4c31C8KJmFQ91DXzjdjR8vXPiw57FcONWFNZvu4fMJL/Lqg26Yf+mxONJ2v3K8SRdQPSWTuuOEsbH0+rvULSs0fF0aB1uXjyAtz58Vm9HjqdDa1G0XOLxtGNFwvEknUUyUuCKP1D4k88QdfEcos6dVe1JJbAQvDmhdXThT0YhPugh7v6SsOwlfP8eeL3ZEdGXL6olHQ4+vvDu3hth+/dINE09x6dPf4Tt34v4B/dglycvvLu9px4L2ZFyp6uXqUv1Uhi78QDK5vdAOW9P/Hb4IqLjH6N1+YRzmNEbDiCfc04MfC0hS/XHvWdQwccThdyd8ehZjYq74ZFoV6GYIUgxfM0+1aJ0WrtXVQ2Mh88yNtycHFRwxNpaeeYukZiFk6toQbhWKoO44DDE3Hy5WaSZJSuOiWwHAxWUZhKIaNy4cbIghT5Q8fXXX6s2o/9F9+7dMWrUKLV8RGpWZLRmbXsgLiYai2aPR1TkI5QoUwWDRv9gUp/g4b2biHiUmD58/fJpTBnbx3B/2fzv1L+167+BHgPHw9LqteqtCkOumjcWMVHhqihmj2E/mowp+MENRBmN6cD2Jerfnyd0N/le7ftMUG1LRd3m3VWmgbQpjYoIg3fh0ug5fC7y5M/4tb/1A3qpMS2fK2N6pIpi9h5hOqag+zcRaTSmfdsSxjT7S9MxdXj/K9W2VNbZXz1/GH9v+hXRkWEqE6VYmWr4cOxvyYqRvgwBb3ZFbEw0fpk5EVGREShVthKGj5tmUlvlwb3bqoimXq16TRAeFooVv/2ouoZINsbwcVNVdoYxaVfqmScfKlRJ7GBj7MThfVizbL4qMlnYrwSGjvoGlaqlv2tDu7c6qfX5s77/TrV29C9bAaPHTzbp1CN1IcKN2ui9+lpDhIeFYcmi+apzQ9FixTHmi8kmqfXv9flQXcB8PWGs2sfKVWugb/+P0rVvksI/f+4sBAc9hIOjE/z8imHcl9+qIogvqmaTPoiPjcbm38YgNiocvsWr4a0BP5vUaggNvInoiBCTVPYNC0YgMvwBHJ1ckNe3tApS+PnXVY/nyGGP6+f2qaUg8bFRcPHwRqnKTVG7RX9khsr1e6uOCLuWj0FcTDgK+FVDQO+fTMYUHnQDMZGmSyee5/T+JTi0dabh/upZCReaDTpMQJka5rOHrPm9J0HmjetW4ZefZqqZ7gLevujZpx+aNEsMFm7ZtA5PnjxRnUHkptegUTMMGvopXrbFK27CySkHhg8oBefcdjh5Jgwfjz2JuPjEmXjfAjnh7pqYVi/P69utKLzyOiL8UTx27X2IH3+9iidPMj+9WR1PcdHYIsdTdMLx1P7DJMfTw5uIjjQ9njYuTDieHJxcVA0KCVLoj6fsduaPp1rNM/54Ct21HXZu7vDu1gt2Hp6IvnIJV0YNw+Nny44c8uU3yZK4t3ihyiyR4IR9Xi88DgtVQYp7834yPEc6fPh9NhY5XFzV45GnT+LC4L54Epb49yEjNStTCCFRsZi15zSComJQ2ssdM96qZ1j6cS88CtmNSnCFx8Zh/ObD6rmujvYqI2Ne54aqtakIjIjGrssJQbNOC7ea/KwfO7yO6oUzp6NJWrlVK4/afyUWNy37bUKdo5sL/8SJXiOhRVlxTBmJSz+sSzbdf+0rSWQFdp7K3MrlmeFhZNZLi3TIkfU+ZrxdzFdB17LcOayvr/2L2ns95SUiWhYRmfWOqaZls97s3vtDLiKr6T44MRssq6jx9ctdymgNSr6d9X5PO/svs/QuUBq1ij8PLWrR4wSs1cb5iTWnbIV15VwRERERERERkU3j0g8iIiIiIiKyaU91zy/ST5mHGRVEREREREREZDUYqCAiIiIiIiIiq8GlH0RERERERGTT2PXDujCjgoiIiIiIiIisBgMVRERERERERGQ1uPSDiIiIiIiIbJruKbt+WBNmVBARERERERGR1WCggoiIiIiIiIisBpd+EBERERERkU1j1w/rwowKIiIiIiIiIrIaDFQQERERERERkdXg0g8iIiIiIiKyaTodu35YE2ZUEBEREREREZHVYKCCiIiIiIiIiKwGl34QERERERGRTXvKrh9WhRkVRERERERERGQ1GKggIiIiIiIiIqvBpR9ERERERERk03RP2fXDmjCjgoiIiIiIiIisRjadTseqIUSpiI2NxcSJEzFy5Eg4OjoiK8iKY8qq4+KYtIFj0gaOSTuy4rg4Jm3gmGxX/bf2wVrtXF4btoaBCqLnCA8Ph5ubG8LCwuDq6oqsICuOKauOi2PSBo5JGzgm7ciK4+KYtIFjsl2vv7kX1mrXn3Vga7j0g4iIiIiIiIisBgMVRERERERERGQ12PWDiIiIiIiIbJpOx64f1oQZFUTPIUWHxo4dm6WKD2XFMWXVcXFM2sAxaQPHpB1ZcVwckzZwTETWgcU0iYiIiIiIyKa91u4fWKvdK1+FreHSDyIiIiIiIrJpuqecv7cmXPpBRERERERERFaDgQoiIiIiIiIishpc+kFEREREREQ2TfeUXT+sCTMqiIiIiIiIiMhqMKOCKAu7cOECQkNDUbNmTcO2v/76C19++SUiIyPRtm1bfPbZZxbdRyItefPNN9P0vD///BNa8d5776Xpeb/88kuG7wsREWWcmJgYzJgxA8OGDbP0rhA9FwMVRGaEh4fj33//RVxcnLrI9/LyghaNGDECFSpUMAQqrl69ijfeeAP16tVDxYoVMXHiROTKlQsfffSRpXeVnomPj0fOnDlx7NgxlC9f3tK7Q0m4ubmZ3P/tt9/UMeXi4gKtmj9/PooUKYIqVaogq3QsP3HiRJqfK5+FWrNs2TL8/vvvKhgtSpUqhXfeeQdvvfUWtCg6Ohpbt25FgwYNkh1L8vd4586daNasGRwdHaGVz/FRo0apgKWnpyc++OADk4Dg/fv34ePjgydPnkCLbt26BXd3dzg7Oycb9759+/Daa69ZbN8ICAwMVOewDg4OaNSoEXLkyKF+Nz/88IM673v8+DEDFSn4Z+3rlt4FMpJNl1XOSoheErlAbNmypTqRkMNDTpqWLl2qTpK0plChQmrfa9eure5LJsXy5cvVGMXcuXPx/fffG+5r1a+//orZs2erQIycJMlF19SpU1G0aFG0adMGWlOsWDGsXLkSlSpVgtZl9dl6+Xw4fvy4+p1p1YcffqgueuW46dmzJ9599111caVl2bNnR7Zs2dRnuPybGi1dLD59+hSdO3dWgQoJTpQpU0ZtP3v2LC5duoS3335b/S6fN2ZrM23aNKxZs0Zl/JnTuHFjtGvXTr1XtWDcuHHqb5JcDEpWo8xgd+zYEXPmzFGPy/mFt7e3+n1qyd27d9Xf1MP049CjAAAucUlEQVSHD6v3mATH5OJXH7DQcgBG9vl///ufOme6ceOGmqgyFhwcDC34559/EBAQoAJ88juqXr065s2bpzJo7ezsMGjQIHTv3l1NiBBZO9aoIDKThSAXuPJhL3+MJRo9YMAAaNHDhw9RsGBBw/0dO3ao2V+9+vXr49q1a9CyWbNmYejQoSq4JCeE+hMkme2RYIUWyUycLMnRyonR82br5X0nv5uQkJAUb2Q5M2fOVBcgw4cPx9q1a1WAs0OHDti8ebNmMywkaHnlyhX174oVK9RnulxQHT16VN3k6+LFi6vHtHZBv23bNnVRf+7cOaxatUrdzp8/r4KbkpUgz9GaxYsXp5rZJ48tWLAAWhrPzz//rAIVMkFw6NAhbN++XQUC9ceU1oJJ4tNPP1VBQJmt37RpE86cOaOyYIw/w7X6mfH5559jypQpKqAUFhamzitkqZ+MVwJPWvF///d/6nxIsspkDAcPHlRBvgkTJqjfl2T3MEhBmiEZFUSUKE+ePLrDhw8b7oeEhOiyZcumCwsL02mNj4+P7t9//1VfP3nyROfq6qpbt26d4fEzZ86obVrm7++vW7lypfra2dlZd/nyZfX1yZMn1e9SiypXrqzG4ujoqCtVqpSuSpUqJjct6d+/v87Dw0ONadq0abqgoCBdVmL8nssqrl27phs3bpyuWLFiusKFC+sePXqk07IaNWro1q9fn2y7bKtatapOSypUqKCbO3duio///PPP6jla4+7urrt+/XqKj8tj8hytyJkzp+7q1asm227duqU+z7t06aK7ffu2Lnv27Dotn1OImJgY3RtvvKE+3+Wz/d69e5ocl5DPO/35kXyuX7p0SX0tf7c6d+6s0wpPT0/d6dOn1ddRUVHq97Fq1SpL7xbRf8IaFURJyCy2cRaCzMznzp0bQUFBcHV1hZZIxsT48ePV7KGkCkuaqWzTk+i6n58ftExmTGVtfVKyllkKhmqRpGhmpdl6maWStdqyvGPkyJFo1aoVevXqhaZNm2pyVjGrM142ocUU7qROnjypMiqSkm3yGaglFy9eVMsgUiKPaTEDUNbMy7r6woULm31cHpPnaEWBAgVw+fJlk7+vvr6+KrtMMhB69OgBLZJMAw8PD5O/s/LZLkuOZFyLFi2CVt27d0/V9BKylEXGKmQZxejRo6EVkt2SN29e9bVkTkgdMta7Iq1ioILIDDl5lT9aenLCLmuAHz16pKkCbF999ZU6cZW151JMafr06SroYlzboWHDhtAyudiQGhsyRmOSlurv7w8tGjt2LLISOZmVdfVyu379uloO0r9/f3Xhcfr06WQF2ayZpNwbk+CfrKs/deqUyfbWrVtDS2JjYw3BJP0aZ1lX37x5cxW40DL5HJACcpKKL8XlhKw/l21a+4yQCw9ZRpXSBb2sS3dycoLWlCtXTi1pqVatmtnHt2zZop6jFfJ3VQrtytJRY1K/QZaAGE8YaInU4pElBSVLljRsk7oHMhEiwQr53NAqmaCSJXBybMmyMHnPVa1aVS2d0EoRV3PnsHL+KkvDkk7caOEclohLP4iSkGUekion/ya96bdrKbUxPj5ed+zYMZVqmpRsf/jwoU7LfvrpJ52vr69uyZIluty5c+t+//133Zdffmn4WqtkyZGM7dNPPzUsl5AlSZI+rGU3btzQff7557qiRYuq35vWlhWY+1ww9zmhJf369VPLcypWrKibOnWqLjAwUJeVSKp6vnz5dF5eXrpGjRqpm3wt24zT2LWgZcuWug8++CDFx/v27atr0aKFTmvmzJmjPrPXrl2b7LE1a9aox+Q5Wlo+tWnTphQfl7/H8+fP12nN8OHDdU2bNk3xXKN169aa+/zTGzFihO6rr75SX8v5hJ2dna5EiRI6BwcH9ZhWZLVzWLJt7PpBlITM+KZF0hl8a5VVWq0+r3CZFLuSVFv9rJUUxpLlBVokM1aSCSOtMKXYqcyGyEyWFMmSauQLFy6E1mfrpahcVpitzwrkdyCziLKEKrWlOPI71CqZTZTPCSlAKSSTQjoWGGeYacHevXvVbLwsD5NCjdL1Q5/x991332H16tVqeUHdunWhNdJtRrIQZEylS5dW2+T3JS1YpbirdDMhy5IsuKioKMMyWCnYLfRLDeTx27dva+b8KDXSQUxukj1iXITc2mW1c1iybQxUEP0HkuathTV/WanValrICVRERATy5csHLZMghaScfv311ybtL+UiRS6utNSpRZZ4LFmyRHWSkFalXbp0MZzUatGYMWNUe76UUtS1SNbLp6VWiLS4I8uT7h7vv/9+sq5AUjtA2l+2b98eWiV/nySgJK1W5W+WtGCVzzwJVGiRLImQAIsEW4R+PG+99Ra0SpYeSWeqP/74w9DtQ957nTp1Uh1OpK4XEdHLwEAFURpJfQo54ZB1ztK2VAtF5iQgIRfu3377rVq3LIU1pbCcFGTLKqSYpsziGK+ZFTJGe3t7TRYLlUyKI0eOqHWyxoEKmSmRmcaYmBhoRVabrZdgy7p161StA5llk1oUsg5dX/sgq5LPES3VEklK6vHIRby0LJVZUplN/N///qeOKwk8aTEoK+1j9Z/lcgEsxWmlcJ4WSeZfWmiloLXUrpGaPBKokN+NZIkIyXyRIIzUc5DzCa0VE5bgWO3atVXWhASd9TVepCaCZMNIQFoC6sYFN7VUcyg1Wqk71K1bN1XEWs4dhJw/lC1bVp0PEWkNi2kSPcfu3bsxd+5crFixQi0pkL7a8kdACySgoi8IJST13tPTU50UauWELy2zwXLxmDRQIctdJKi0c+dOaI0U7jJ34i6zclpbuiMnTVo7GU+NHENyEbJnzx6sXbsWH330kSrA1qRJE3XBK8ta5BjTErlgHzJkSKpBWlmmI2PWolmzZqlMGPldyYyvPsgsF1NTp07VVKBCCjFKV4/9+/ejXbt2Jo9JlwIpODl79mzUq1cPWiKz8Kl9TsicmjyuhQkCMW3aNFUcVC6EkxaYlG2y9E2eI+9JLfniiy9UUFaWWebPnz/ZYxIsk3/lM0WLHbb03Y6SbhNaee9JRpJMTukDFfJZINm1EpQl0hxLF8kgskZ3797VTZw4URVSkoJrAwYMUIWV9L2ptUKKJt2/f99km/QHv3Llii6rcHFx0V28eDHZdtnm5uam06JevXrp2rZtq4uLizP8vq5fv66rUqWKbvDgwZbePUrizJkzusmTJ+vq1Kmjc3R01NWrV0/3zTffaKbwqZOTk27BggVmH5NipzKu0qVL67TK399ft3LlSvW1HE+XL19WX588eVKXJ08enZa88cYbuilTpqT4+LRp09Rnh9bs3LnTcNuxY4cuZ86cusWLF5tsl5tWVKhQQTd37twUH//555/Vc7SmSJEiqRYJ3bhxo3qOFm3dulVXtWpVNb6wsDB1k6+rV6+u27Jli06r533Gn3lEWsOMCqIkJJ1bsihatWqlZttkJlFae8oslRZllVarKZHZDuOxGM8uamUGJCkpiidrmKXWRnR0NF5//XX1O5SUW2k5qzVSU2Pr1q2qoKsUAtRSm8G0kPRnuQ0fPhyBgYEq00KKGgopeKiFZRFdu3ZVs9rG6c1SgFI+/2RMu3btgpaXh8nSI3OZS0lb9lk7SeOePHlyio/LjLbMpmqNfMYZk7+5tWrV0uwssCzJkVpDKZHHJDNGayR7LLXPb6ndZXy+oSWS3SLnea+++qrJ8llZTiU1YeS8iYgyFwMVREls3LgRgwYNQr9+/ZItJ9AiWT+fNJVRUlH1KY5aSqc157XXXsPEiRPVel85uRUyHtlmfMKhtRoVcmEvHTKkA4jUB5DlO6md+For6UAg7zcJuAg7Ozu1fEIq/GdFQUFBmDRpkqF4nhZIUEwK5Mma+vXr16tgkj5IIYV4JUjh7e0NrSpatKhKfU5a5X7Tpk2GNfZaIb+P1Naay/ElgSWyrJw5c6pjSurzmCNL+6RulNZIIWQJPBcsWDDFoKDWlr7pyXIWc4VA9d23tDpBJed50j1HziOMaXmCimwHAxVEScjFodSkkKr+chIrM41SzVqL5KQhq5OLQpmNkyKT+nXZf//9tzoRlPXcWiTFMuUkVgItWg226I0ePVrVb5A6ATImabEqmQdZNVAhrVj1bXK1pHfv3qpQntRrkGwQqelw584dFaSQ2jxaNnToUHz44YfquJKT9gMHDqjApgQzpY6Nlvj6+qquUyVKlDD7uAQ2tRxUyiok+00+8+RmjtS5kudojWQYSMcPCaQnLSAsn33yeS8BTi2qUaOG+qyQDDN9/Q0JDH7yySeqtbuWJ6iS1knR+gQV2Q52/SBKgcwoSvstmf2VE1v5UJ8yZYoq3KgvUmTtpKiVpJ5rtRJ8WskF1YwZM1RatMxkyUyBpNVqdWZHLujlxEgCMA0aNFAntDIuLZIZKqkCL1XH9d0KpJCrnADmyZMHWY28ByX7RasngZ9++im++eYb1S1HCtFKFf+sQArMjRs3zhBEkuDL559/jl69ekFLBg4cqH4vBw8eTDYjL1lL8rkhnxnTp0+HlsnfWAm6SDaMFslnnmQmSbFG+RssXT/0yy5laZ8EAyXbrG7dutCSW7duoXr16mrZlAT/jMf1ww8/qGDFoUOHNPm5Id1YpECtZMPp9//mzZsqs3bVqlUpBgetjXQHex5ZLivLdIisHQMVRGlw/vx5lWUhkXZJ55QZ4vS0tbIUWQoha0ql1kFWFB8fr2ZvZF1pVlimY5zVI3VS5IJETnil/aqcHErgQk5+5f2npfakkoJq/B40brma1WgxUCGdjIxt2LABlSpVUrP3WmwhmxoJlEkKtFY/EyXAJ+8v+WyXYKxkkglJ7ZZZennfSWvjpB0ZtPYelDovDRs2RO7cuTX7Hly5cqWqbSCZSsak24y0ym3fvj20mqnZv39/1VFMfwkhM/Tyd0kmDLRyQW+OjEeyReR4EpJVK0sus0LnKglOSCaZnMtKMElLf6PIdjFQQZQO8sG+bt06lWWhL5antYvErEbadcrFfFYKVBiTIIXMnsqJrcwKS2tMLZ1gyHtwwYIFap2vntRCkEK1xhdTWulRnxUDFdIqMS3mzZsHLR9HEviTjIp33nlHBcskE0uye5ydnaElMmMqNZQ2b95scqEoafkSrNBiFkJWfQ9KYEx+T1JcU5QqVUoVPM0KWY4hISGGcUlwQqsZjFmdTHpIcGLFihUqk0yCghIkk6UuRNaOgQqiJGRpR1pIsEILF4kyAycX81nVkCFDVBqq1KrISiT9VC6s9DdJqZXCoZJRMXjwYGiFvAefR0vrZWU2NLXZNbkglmVjWhmPLZALe8m8unHjhjqO5NiSbB45juS+Vjs6yYWipKvLaZwEauW9SdZB6iNJxsv+/ftVMCxpR6o6deqo952+rhJZh7/++gv/+9//DB0+JKNCuoForZC1TFDNnz9fBSikXleHDh3U+00C6fplmERawEAFkZkLK6kOL+3sUjo85EJFCymoMhaZyX5e2mLS1FStrdleuHChOlGXAqhJU4WlrojWSMq9rDeXoITcZMmH1N3ICumnKc06amWGUbJD0qJ79+7QKrl4FxIAzAqkToBkUMhJu9RF0S87kgBgnz59DLPCRC+LZIhJrRAJpJsjNUSkRoUsDyHrIDU2JHgpXZD0hU4l0LR8+XIVvJCaHFrwxhtvqCyKVq1aoUuXLipIK0vFpFsQAxWkNez6QZSEpNTKOj5ZhynpqNKdQMspjVIwzjjtPquRCviSai+StoTU6oW9ZMDIGlmZFZGbZMVI4EIrF/PpuSCWVPWvv/7a0ErN2mk5AJEaWZctJ+P79u1TM3BCZoLlhF0q4WttRtGYdAGS5WFJuxRIwdDbt29bbL8o65ILwsmTJ6f4uCz/+PbbbzN1nyh1EyZMUJ+BkgmjJ63qpeCpPKaVQMXGjRvVfsu5bFZdEku2g4EKoiTkwklm4SVjQpZ3jBw5UkWmpTq8nFxo7eJXWqtm5RoVMiuV1Rw7dkwVbZVZEWkP+dlnn6m+6JUrV1azdF999RW0FIyQbgv6dnbSmlRmuOXYklalMtOT0qwjZV6WiLQnlZlEOVE3bs0nBfNatmypshGkVbMWpVTXRToYaKWDE2mLHDsyg50SOzs7BAYGZuo+Uerkb6651qpy3jdixAhoqRi3fF5LhqksXZHPbTkPJNIiLv0gSsP6ZlnrJ8sLZP356dOnNVN8Lat3/TB34SEKFiyIrCIoKEilqEvxVsn00VoxTTnBk0KgMiMvs9pyci6ZSpJSKwGYt99+W71PyXKkwJ+kPKc0Yygp0RLA0OoSiY4dO6qssh9//NHQ9lKyltq0aYPChQtrrkAjWb/ixYurNqQSlDVHJkKkbemVK1cyfd/IPCmyK0t+P/nkE5PtkvkiXTKWLFkCLZFaSX/88YeaFDhw4IA6b5BJOKnDxgAtaQUDFUTPIX205URWghVxcXEqJV8rgQpb6PohF+5ffvmlOimUtoNC/gh//PHHGDVqVJqKOVobOYnVF9GUTApZevTqq68a6lVI60itkFoA0uFD1mzLMh2ptdGjRw8146O17KSsysnJSaWq61tdmmvPLNk8svxIqwFM6YghpzsSbJFWv/Jv3rx5VdZSVv58JMvVTpLPb+nYJMeXMTmOatasqbLjpFYFWY7x/78seZOghCz1MK5RsWfPHnU+IRmAWiWf4fI399dff1WZI9JKds2aNZbeLaLnYqCCKIV0df3SD0mjCwgIULPAkhaoxQvfrEyW5sgfYKnFIScYQn5nstxACuVpaZmEnlw46Tt8SGCiQoUK0CpZ7iH1XqRAqMiZM6ea3dHymLIaSRFu1KiRqhWSUlbMtm3bcPjwYWiVZMPJjKhkU0hAU+raSKE5eT8SZcTSD3mPSbaY1DzQBwFlokOWl8rs9pEjR0xaNFPmS2srXwmqZ4XsF3nfrV27Vp3bMlBBWsBABVES/fv3Vye0hQoVUilycjIrM29knaQvuLTdkhl7Y7JUQn6XLJZnWXKiLlk9+ha5+tT7tJ4gUsaTmV8Jxkr2iyzRMa5RIe365AR9/fr1KnhGRGlfNioFDTdv3mzoICYXvJLdI8EKfgYSEaWOgQqiJCRjQtYty1rF1FLTtdCe1BZIWq1c+Mo6+6yUri4zH6tWrTL0c5eWYrKmXmv1HOR4atGihaHVpczmNGzYMFkbWa0dT/L7keVgciH/4MEDtQTJ2Pbt26El165dw6xZs1Sqs74DS4ECBVQK9AcffKA6ZGiZfB58//33huNJiszJTHeZMmUsvWuUxYWEhODSpUsqWCFdGDw8PCy9S0REmsBABVESsn4+LWvnWYDNOrzyyivqlnStr6wRlvXBcuGlNXJSK50WJBtEnzIsF1qS5SMz21KoTStkyVRaaO14kotcCVRIRyBvb+9knxlSfJKsw4oVK1TVe6lNYbz2XD4fJHuuffv2lt5FIrIwuRxavny56iRmLvistWA6UVbAQAURaZq075SLRcmC0V+E7Nu3TxVB3bBhA+rVqwetkSCFfDQvXrxYFdLUd/949913VYaCBCvIsmQ5mHQCkt9VVqHvaqTPqJAAjGQepNZmUQsksCdL+L744guT7WPHjsWiRYtw+fJli+0bEVkH6XwkHaqkyKksf0safNZaMJ0oK2Cggog0SdbNyxpfOZm4c+eOaqFonNYt9SmkfoUWybIImfFNWnBSOjNIwVB9dxOyHHlvSW2HpEuOtEhmDseMGaPWzYeFhZk8Jm09JXtEitVqtZBwrly51PKwEiVKmGyXzh/SQScqKspi+0ZE1kEmBSRwmZWCz0RaZ2fpHSAi+i9kre/du3dVhwy5aJSLDglWZIUq6lLP4dGjR8m2S4BCumiQ5Um7umnTpmHGjBmab7P66aefqmUskyZNUoX+jItpbtmyBaNHj1atmSdPngwtku45f//9d7JAhXQH0mLGFRG9fBKUlYLCRGQ9mFFBRJoks7uSoi6BCuHq6opjx45liRONbt26qdZ10na1Zs2aatu///6r2q1KK0m5qCTLateunVrLLLNw5cqVS7Y8QkvrmaVo5oIFC1SQwhzpWiDvSQlcaJF0BZKMkQ4dOqBWrVpqm2QsLVu2TGWKGGdeJe0eRES2QT4DN23apFp3sm0xkXVgoIKIskSgQtpeytKIrBCoCA0NVUVdpUOGnZ2doX6AXERJkEJmfsi6i4RqaT1zSkuN9GTZRJ06dTS75CitS1YkM0a6uRCR7ZEOYRKA3rNnj+pylDT4LJMHRJS5uPSDiDRJLiqSptxrPQVfagV88803WLNmjUq1b9u2Lbp3767GJXU3kqauk+VoKRCRlqURw4YNU8VbpUiosYcPH2LEiBHqOVqVtHo/EVFS8rf28OHDqmi1uWKaRJT5mFFBRJqdJW3RooWq5yAk+6Bhw4ZqdlirKfjjx4/HuHHj0LhxY5V6Kin3nTt3VqmoZJ0CAwNV61ghrWS9vLygNdIhRwrInTt3TmVVGNeoOHnyJMqWLYt169ap9rhaIt1/pFtOQECAYZt0apFuH5GRkSoQ+P333xs+Q4jIdsm5g/zNffXVVy29K0T0DAMVRJQlU++1OPMtBUJlZrtv377q/rZt21TrVUlJ1WrHhaxKLnQHDhyoLnz1M/Y5cuRQtRzk4lc6TWiJjEFO0mUJiL49qdSukJa/TZs21eT7TwKZkgkiGSFCgi5Vq1ZVy6okQ0myl+RYk+AgEdm2MmXKYOnSpahYsaKld4WInmGggojISsjM7qVLl0xmrp2cnNS2ggULWnTfyJRc4EogSbp+SMtYfReJQYMGoUmTJpg1a5ald9HmeXt7q0yr6tWrq/ujRo3Crl271O9JSDFNya44c+aMhfeUiCxt/fr1KsgsxXelRgURWR5rVBARWQkpmCmBCWNS0Cs+Pt5i+0TmrVixAsuXLzep3SDLJ2TJjnSXyEqBCskekbXbr732GrQkJCTEpF2xBCkky0KvRo0aatkLEZHUpoiKikLx4sVVRlzSYprBwcEW2zciW8VABRGRlZAEN0lLN14zHxMTgw8++MCk9oaW6m5kVXJCa3wRrCddaOSxrEQyeho0aKC5jhjy+7l69arKUJLitFK1X9qR6j169CjZxQgR2aapU6daeheIKAkGKoiIrKjquLlZHrI+UrtBlg1IjQp9FozUEpELYXmMLE8yXD799FNMnjwZq1atUrOk9erVM2m7KrOnRETm/v4SkWWxRgUREVE6nTp1Cs2aNUNsbCwqVaqkth0/flwFLaQoZbly5aAVnp6eqT4umRQRERGay6iQ1qpvvvmmqknh7OyMBQsWoF27dobHGzVqhFq1auGrr76y6H4SkXWRTEbJwjLm6upqsf0hslUMVBAREf0HssRj8eLFqq2nkE4SXbp0UXUqtESWFfXr10+1JjXn+vXrKlNEa4EKvbCwMBWokK4sSdecy3YHBweL7RsRWU8tHukQJJ0/pK1xUlr9/CPSMgYqiIiIbJh0LZECoIMHDzb7uGSKSFtPnqgTUVb14YcfYseOHRg/fjy6du2KmTNn4vbt25gzZw4mTZqkgtBElLlYo4KIiCgN1qxZo7pGSAFG+To1rVu3hla0atUKoaGhqS4N6datW6buExFRZpJWxlJzSDo59ezZU9WzKVGiBIoUKaIy5xioIMp8zKggIiJKg+zZs+PevXuqs4d8nZJs2bIx+4CISENkGdiZM2dQuHBhFCxYUHXXqlmzpuocJMvipE4PEWWulM+0iIiIyODp06cqSKH/OqWb1oIUY8aMweHDhy29G0REFlOsWDEVlBBlypRRtSr0mRZubm4W3jsi28RABRERUTpJirB0/EhKKsXLY1py69YttaRFZhGlqObGjRuTVbwnIsrKZLmH1OMR0tZYalRIF6chQ4Zg+PDhlt49IpvEpR9ERETpJB0k7t69a8iw0JNq8bJNa1kVkgmyZ88eNXu4evVqNbYmTZqgTZs2CAgIeG4LUyKirES6HUmmWd68ebFo0SL8+OOPlt4lIpvDQAUREVE6SY2K+/fvw8vLy2S7zMg1aNBAtb7UsrNnzxqCFnKyLmu1pUBo586d4evra+ndIyLKFOx6RGQ57PpBRESURlWqVFHFMuXWqFEj2Nkl/hmVE1lZ49y8eXNonb+/v7pJynNgYKAhaCGGDRtm6d0jIiKiLI6BCiIiojRq27at+vfYsWNo1qyZqhSv5+DgAD8/P7Rv3x5ZiSxnmTRpEi5cuGDpXSEiIiIbwUAFERFRGo0dO1b9KwGJjh07qmJrWZ0UDb18+bKld4OIiIhsCAMVRERE6dS9e3f176FDh1Q9B1G2bFlUq1bNwntGRERp9eabb6b6eGhoaKbtCxGZYqCCiIgonW7fvo1OnTqpThnu7u6GE9o6depgyZIlqtUnERFZNzc3t+c+3q1bt0zbHyJKxK4fRERE6SQFMyUwsWDBApQuXVptO3/+PHr27AlXV1ds2rQJWQWr3hMREVFmY6CCiIgonXLmzIm9e/eqLiDGpJVnvXr1EBUVBa3w8PBQXUxS8vjxY0RGRjJQQURERJmGSz+IiIjSqVChQoiPj0+2XS7mfXx8oCVTp0619C4QERERmWBGBRERUTqtXr0aEyZMwMyZM1G9enVDYc2BAwdixIgRhjamRERERJR+DFQQERH9h+USsrxDlkXY2SUkJ+q/zp07t8lzg4ODLbSXRERERNrEpR9ERETpxOUSRERERBmHGRVEREREREREZDWYUUFERPQfPXjwQN2ePn1qsr1ixYoW2yciIiIirWOggoiIKJ2kDWn37t1x9uxZJE1MlFafbOVJRERE9N9x6QcREVE6VapUCcWLF1cdPvLnz6+CE8aKFCkCrZHgyvz58/HXX3+ZzRLZvn27xfaNiIiIbAszKoiIiNLpypUrWLFiBUqUKIGsYvDgwSpQ0apVK5QvXz5Z8IWIiIgoszBQQURElE6NGjXC8ePHs1SgYsmSJVi6dClatmxp6V0hIiIiG8dABRERUTr9/PPPqkbFqVOnVPaBvb29yeOtW7eG1jg4OGSpwAsRERFpF2tUEBERpdPatWvRtWtXhIeHJ3tMq8U0v/vuO7WkZcaMGVz2QURERBbFQAUREVE6+fn5ISAgAKNHj1bFNLOCdu3aYceOHfD09ES5cuWSZYn8+eefFts3IiIisi1c+kFERJROQUFBGDJkSJYJUgh3d3cVrCAiIiKyNGZUEBERpZPUp6hXrx569+5t6V0hIiIiynKYUUFERJROpUqVwsiRI/HPP/+gQoUKyZZJDBo0CFoVGBiI8+fPq69Lly4NLy8vS+8SERER2RhmVBAREaVT0aJFU3xMClFKUUqtiYyMxMCBA7Fw4UI8ffpUbcuRIwe6deuG77//Hrly5bL0LhIREZGNYKCCiIiI0LdvX2zbtk11/ahbt67aJhkjkh3SpEkTzJo1y9K7SERERDaCgQoiIiJC3rx5sXz5ctSvX99ku3QC6dChg1oSQkRERJQZWKOCiIgond57771UH//ll1+gNVFRUWa7mOTLl089RkRERJRZGKggIiJKp5CQEJP78fHxOHXqFEJDQ9GwYUNoUe3atTF27FhVo8LJyUlti46Oxueff64eIyIiIsosXPpBRET0EkgByn79+qF48eIYPnw4tEYCLc2aNUNsbCwqVaqkth0/flwFLTZv3oxy5cpZeheJiIjIRjBQQURE9JJIW0+p8XD37l1okSzxWLx4Mc6dO6fu+/v7o0uXLsiZM6eld42IiIhsCJd+EBERvSSXL1/G48ePoVXSgrRPnz6W3g0iIiKycQxUEBERpdPQoUNN7ktyomRRrF+/Ht27d4dWrFmzBi1atIC9vb36OjWtW7fOtP0iIiIi28alH0REROnUoEEDk/vZs2eHl5eXKqQpHUHs7LQxDyD7fe/ePdXZQ75OSbZs2fDkyZNM3TciIiKyXQxUEBEREREREZHVSHn6hIiIiMyStp1SeFLv+vXrmDp1KrZs2QKtkrak0vEjqbi4OPUYERERUWZhRgUREVE6NW3aFG+++SY++OADhIaGonTp0nBwcMDDhw8xZcoU1aZUa3LkyKHqbMgyEGNBQUFqG5d+EBERUWZhRgUREVE6HTlyBPXq1VNfL1++HAUKFFBZFZJ5MH36dGiRzFtILYqkbt26BTc3N4vsExEREdkmbVT7IiIisiKy7MPFxUV9Lcs9JLtCilHWqlVLBSy0pEqVKipAIbdGjRqZFAKVLIqrV6+iefPmFt1HIiIisi0MVBAREaVTiRIlsGrVKrRr1w6bN2/GkCFD1PYHDx7A1dUVWtK2bVv177Fjx9CsWTM4OzsbHpPlLH5+fmjfvr0F95CIiIhsDWtUEBERpZMs93jnnXdUxoFkIeiLaE6cOBG7d+/Gxo0boTULFixAx44d4eTkZOldISIiIhvHQAUREdF/cO/ePVV8slKlSmrZhzhw4IDKqChTpgy06tChQzh79qz6umzZsqhWrZqld4mIiIhsDAMVREREhNu3b6NTp07Ys2cP3N3d1TbpaFKnTh0sWbIEBQsWtPQuEhERkY1g1w8iIqJ0ioyMxOjRo9VFvNSrKFasmMlNi3r16oX4+HiVTREcHKxu8vXTp0/Ru3dvS+8eERER2RBmVBAREaVT586dsWvXLnTt2hXe3t7J2noOHjwYWpMzZ07s3btXdQExdvjwYdWKVTqdEBEREWUGdv0gIiJKJymWuX79etStWxdZRaFChVRGRVJSMNTHx8ci+0RERES2iUs/iIiI0snDwwOenp7ISr755hsMHDhQFdPUk68lO+Tbb7+16L4RERGRbeHSDyIionRatGgRVq9erVp65sqVC1kl+CLLOx4/fgw7u4SES/3XuXPnNnmu1K8gIiIiyigMVBAREaWT1HG4fPky5E+on58f7O3tTR4/cuQItEaCLmnVvXv3DN0XIiIism2sUUFERJRObdu2RVbD4AMRERFZC2ZUEBERkcGDBw/UTdqSGqtYsaLF9omIiIhsCzMqiIiI/iNp3Xn27Fn1dbly5ZK19tTaWCSrQsaTdA5D2q9K9w8iIiKizMCMCiIionSSjINOnTph586dcHd3V9tCQ0PRoEEDLFmyBF5eXtCaSpUqoXjx4hgxYgTy58+vghPGihQpYrF9IyIiItvCQAUREVE6dezYEVeuXMHChQvh7++vtp05c0ZlJJQoUQK///47tMbFxQVHjx5V+09ERERkSQxUEBERpZObmxu2bduGGjVqmGw/cOAAmjZtqrIrtFggtGvXrmjfvr2ld4WIiIhsHGtUEBERpZMUmkzaklTItqRFKLXi559/Vhkhp06dQvny5ZONr3Xr1hbbNyIiIrItzKggIiJKpzZt2qisCVni4ePjo7bdvn0bXbp0gYeHB1auXAmtWbt2rcqoCA8PT/YYi2kSERFRZmKggoiIKJ1u3rypMgxOnz6NQoUKGbZJJsKaNWtQsGBBaI2fnx8CAgIwevRoVUyTiIiIyFIYqCAiIvoP5M+n1Kk4d+6cui9FNRs3bgytkmKax44dU50/iIiIiCyJgQoiIiJS9Snq1auH3r17W3pXiIiIyMaxmCYREVEabd++HQMGDMD+/fvh6upq8lhYWBjq1KmD2bNnqwt+rSlVqhRGjhyJf/75BxUqVEhWTHPQoEEW2zciIiKyLcyoICIiSiOpS9GgQQMMGTLE7OPTp0/Hjh07NFlMs2jRoik+JsU0r1y5kqn7Q0RERLaLgQoiIqI0KlKkCDZt2qTqUZgj9SqaNm2KGzduZPq+EREREWUV2S29A0RERFpx//79ZEsijNnZ2SEwMDBT94mIiIgoq2GNCiIiojTy9fXFqVOnUKJECbOPnzhxAt7e3tCi9957L9XHf/nll0zbFyIiIrJtDFQQERGlUcuWLTF69Gg0b94cTk5OJo9FR0dj7NixCAgIgBaFhISY3I+Pj1dBmdDQUDRs2NBi+0VERES2hzUqiIiI0rH0o2rVqsiRI4fq/lG6dGlDbYqZM2fiyZMnOHLkCPLnz4+s4OnTp+jXrx+KFy+O4cOHW3p3iIiIyEYwUEFERJQO169fVxfvmzdvhv5PqHTFaNasmQpWpNY9Q4vOnz+P+vXr4+7du5beFSIiIrIRXPpBRESUzs4fGzZsUEslLl26pIIVJUuWhIeHB7Kiy5cv4/Hjx5beDSIiIrIhDFQQERH9BxKYqFGjBrKKoUOHmtyXAIxkUaxfvx7du3e32H4RERGR7eHSDyIiIkKDBg1M7mfPnh1eXl6qkKZ0BJHWq0RERESZgYEKIiIiIiIiIrIa2S29A0RERGR50l41KirKpGjo1KlTsWXLFovuFxEREdkeBiqIiIgIbdq0wcKFC9XXoaGhqFmzJr777ju1fdasWZbePSIiIrIhDFQQERERjhw5gnr16qmvly9fjgIFCqisCgleTJ8+3dK7R0RERDaEgQoiIiJSyz5cXFzU17Lc480331QFNWvVqqUCFkRERESZhYEKIiIiQokSJbBq1SrcvHkTmzdvRtOmTdX2Bw8ewNXV1dK7R0RERDaEgQoiIiLCmDFjMGzYMPj5+eGVV15B7dq1DdkVVapUsfTuERERkQ1he1IiIiJS7t27h7t376JSpUpq2Yc4cOCAyqgoU6aMpXePiIiIbAQDFURERERERERkNewsvQNERERkeZGRkZg0aRL++usvVZfi6dOnJo9fuXLFYvtGREREtoWBCiIiIkLv3r2xa9cudO3aFd7e3siWLZuld4mIiIhsFJd+EBEREdzd3bF+/XrUrVvX0rtCRERENo5dP4iIiAgeHh7w9PS09G4QERERMVBBREREwPjx41WL0qioKEvvChEREdk4Lv0gIiIiVKlSBZcvX4acFvj5+cHe3t7k8SNHjlhs34iIiMi2sJgmERERoW3btpbeBSIiIiKFGRVEREREREREZDWYUUFEREQGhw8fxtmzZ9XX5cqVU0tCiIiIiDITAxVERESEBw8eoFOnTti5c6dqVSpCQ0PRoEEDLFmyBF5eXpbeRSIiIrIR7PpBREREGDhwIB49eoTTp08jODhY3U6dOoXw8HAMGjTI0rtHRERENoQ1KoiIiAhubm7Ytm0batSoYbL9wIEDaNq0qcquICIiIsoMzKggIiIiPH36NFlLUiHb5DEiIiKizMJABREREaFhw4YYPHgw7ty5Y9h2+/ZtDBkyBI0aNbLovhEREZFt4dIPIiIiws2bN9G6dWtVo6JQoUKGbeXLl8eaNWtQsGBBS+8iERER2QgGKoiIiEiRUwKpU3Hu3Dl139/fH40bN7b0bhEREZGNYaCCiIiIiIiIiKwGa1QQERHZsO3bt6Ns2bKqDWlSYWFhKFeuHP7++2+L7BsRERHZJgYqiIiIbNjUqVPRp08fuLq6mm1Z2rdvX0yZMsUi+0ZERES2iYEKIiIiG3b8+HE0b948xcebNm2Kw4cPZ+o+ERERkW1joIKIiMiG3b9/H/b29ik+bmdnh8DAwEzdJyIiIrJtDFQQERHZMF9fX5w6dSrFx0+cOAFvb+9M3SciIiKybQxUEBER2bCWLVti9OjRiImJSfZYdHQ0xo4di4CAAIvsGxEREdkmticlIiKy8aUfVatWRY4cOTBgwACULl1abT937hxmzpyJJ0+e4MiRI8ifP7+ld5WIiIhsBAMVRERENu769evo168fNm/eDP1pQbZs2dCsWTMVrChatKild5GIiIhsCAMVREREpISEhODSpUsqWFGyZEl4eHhYepeIiIjIBjFQQURERERERERWg8U0iYiIiIiIiMhqMFBBRERERERERFaDgQoiIiIiIiIishoMVBARERERERGR1WCggoiIiIiIiIisBgMVRERERERERGQ1GKggIiIiIiIiIqvBQAURERERERERwVr8PynyLGWM78oYAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "numeric_dr = dr.select_dtypes(include=[np.number])\n",
        "corr = numeric_dr.corr()\n",
        "\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(corr, annot=True, cmap='coolwarm', square=True)\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1NO-8NNhXYH",
        "outputId": "564225fe-81f9-44a8-c048-7c09302591c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fault\n",
            "0    16000\n",
            "2    15000\n",
            "3    14001\n",
            "1    10998\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "class_count = df[\"Fault\"].value_counts()\n",
        "print(class_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "zFMkzFCbiYfe",
        "outputId": "0706e8fa-b1a6-4769-9aff-863003d6c7b4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "class_count = df[\"Fault\"].value_counts().plot(kind= 'bar')\n",
        "plt.xticks(rotation= 0, ha= 'right');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8puueuewizGM"
      },
      "source": [
        "# **═══ Preparing Dataset ═══**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## **═══ Original Dataset ═══**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['MAP', 'TPS', 'Force', 'Power', 'RPM', 'Consumption L/H',\n",
            "       'Consumption L/100KM', 'Speed', 'CO', 'HC', 'CO2', 'O2', 'Lambda',\n",
            "       'AFR'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "X = df.copy() # Membuat copy dari dataset awal\n",
        "X.drop([\"Fault\"], axis = 1, inplace = True) # Drop kolom label\n",
        "y = df[\"Fault\"]\n",
        "print(X.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(55999, 14) (55999,)\n"
          ]
        }
      ],
      "source": [
        "print(X.shape, y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Coba StandardScaler(-1 to 1) / MinMaxScaler(0-1)\n",
        "scaler = StandardScaler()\n",
        "#scaler = MinMaxScaler()\n",
        "X_o_scaled = scaler.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_o_scaled, y, test_size = 0.15, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(47599, 14) (8400, 14)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape, X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ni8PKfrclCGz"
      },
      "source": [
        "# **═══ Building Model ═══**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "afMkETkglBF4",
        "outputId": "7de9b48b-42d7-48aa-b9ea-fd8450386434"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_18 (Dense)            (None, 14)                210       \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 9)                 135       \n",
            "                                                                 \n",
            " dense_20 (Dense)            (None, 9)                 90        \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 4)                 40        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 475\n",
            "Trainable params: 475\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential([\n",
        "    Dense(14, activation='relu', input_shape=(X_train.shape[1],)),\n",
        "    Dense(9, activation='relu'),\n",
        "    Dense(9, activation='relu'),\n",
        "    Dense(4, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "2EZX9_nqmxUh"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=Adam(0.0005),\n",
        "    loss=\"sparse_categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7AiGaIk0ig0"
      },
      "source": [
        "# **═══ Training Model ═══**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfVlUWTinAAn",
        "outputId": "db598a5f-4db9-4785-8f1b-d67b8d8e687a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1265/1265 [==============================] - 5s 3ms/step - loss: 1.0755 - accuracy: 0.4838 - val_loss: 0.7254 - val_accuracy: 0.6403\n",
            "Epoch 2/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.5910 - accuracy: 0.6857 - val_loss: 0.4939 - val_accuracy: 0.7382\n",
            "Epoch 3/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.4516 - accuracy: 0.7433 - val_loss: 0.4130 - val_accuracy: 0.7573\n",
            "Epoch 4/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.3993 - accuracy: 0.7496 - val_loss: 0.3805 - val_accuracy: 0.7525\n",
            "Epoch 5/10\n",
            "1265/1265 [==============================] - 3s 3ms/step - loss: 0.3770 - accuracy: 0.7487 - val_loss: 0.3655 - val_accuracy: 0.7546\n",
            "Epoch 6/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.3667 - accuracy: 0.7502 - val_loss: 0.3588 - val_accuracy: 0.7587\n",
            "Epoch 7/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.3610 - accuracy: 0.7519 - val_loss: 0.3558 - val_accuracy: 0.7573\n",
            "Epoch 8/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.3581 - accuracy: 0.7485 - val_loss: 0.3515 - val_accuracy: 0.7522\n",
            "Epoch 9/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.3563 - accuracy: 0.7493 - val_loss: 0.3477 - val_accuracy: 0.7577\n",
            "Epoch 10/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.3543 - accuracy: 0.7497 - val_loss: 0.3476 - val_accuracy: 0.7543\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(X_train, y_train,\n",
        "    epochs=10,\n",
        "    batch_size=32,\n",
        "    shuffle = True,\n",
        "    validation_split=0.15,\n",
        "    verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "id": "qZpCMG2Esz-W"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training & validation loss values\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-DjeUBqeo16A"
      },
      "source": [
        "# **═══ Model Evaluation with Original Dataset ═══**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "6AagR9Vvo3LZ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "263/263 [==============================] - 0s 2ms/step - loss: 0.3541 - accuracy: 0.7488\n",
            "Test Accuracy: 0.7488\n"
          ]
        }
      ],
      "source": [
        "loss, acc = model.evaluate(X_test, y_test)\n",
        "print(f\"Test Accuracy: {acc:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "NTd8vi8MpRJB"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "263/263 [==============================] - 0s 824us/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      2379\n",
            "           1       1.00      1.00      1.00      1678\n",
            "           2       0.52      0.85      0.64      2249\n",
            "           3       0.49      0.16      0.24      2094\n",
            "\n",
            "    accuracy                           0.75      8400\n",
            "   macro avg       0.75      0.75      0.72      8400\n",
            "weighted avg       0.74      0.75      0.71      8400\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Get prediction probabilities from model\n",
        "y_pred_probs = model.predict(X_test)\n",
        "\n",
        "# Choose the class with highest probability\n",
        "y_pred_class = np.argmax(y_pred_probs, axis=1)\n",
        "\n",
        "print(classification_report(y_test, y_pred_class))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision: 0.744\n",
            "Recall: 0.749\n",
            "F1 Score: 0.714\n",
            "Accuracy: 0.749\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, precision_score, recall_score, f1_score, accuracy_score\n",
        "\n",
        "precision = round(precision_score(y_test, y_pred_class, average='weighted'), 3)\n",
        "recall = round(recall_score(y_test, y_pred_class, average='weighted'), 3)\n",
        "f1 = round(f1_score(y_test, y_pred_class, average='weighted'), 3)\n",
        "accuracy = round(accuracy_score(y_test, y_pred_class), 3)\n",
        "\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Fault\n",
              "0    2379\n",
              "2    2249\n",
              "3    2094\n",
              "1    1678\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "id": "_1GSEqdBsLJg"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x264b9737970>"
            ]
          },
          "execution_count": 143,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_class)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "disp.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# **═══ Saving Model ═══**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "metadata": {},
      "outputs": [],
      "source": [
        "model.save('Engine_Fault-small.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['scaler.pkl']"
            ]
          },
          "execution_count": 145,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import joblib\n",
        "\n",
        "joblib.dump(scaler, \"scaler.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {},
      "outputs": [],
      "source": [
        "columns = ['MAP', 'TPS', 'Force', 'Power', 'RPM', 'Consumption L/H',\n",
        "           'Consumption L/100KM', 'Speed', 'CO', 'HC', 'CO2', 'O2', 'Lambda',\n",
        "           'AFR']\n",
        "\n",
        "X_test_unscaled = scaler.inverse_transform(X_test)\n",
        "df = pd.DataFrame(X_test_unscaled, columns=columns)\n",
        "\n",
        "# Save as CSV\n",
        "df.to_csv('x_test.csv', index=False, float_format=\"%.3f\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 1D CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv1D, GlobalMaxPooling1D, Dense, Dropout, InputLayer\n",
        "\n",
        "X_train = X_train.reshape(-1, X_train.shape[1], 1)\n",
        "X_test = X_test.reshape(-1, X_test.shape[1], 1)\n",
        "\n",
        "model = Sequential([\n",
        "    InputLayer(input_shape=(X_train.shape[1], 1)),\n",
        "    Conv1D(filters=32, kernel_size=3, activation='relu'),\n",
        "    GlobalMaxPooling1D(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(4, activation='softmax')  \n",
        "])\n",
        "\n",
        "# Compile model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1265/1265 [==============================] - 5s 4ms/step - loss: 1.0240 - accuracy: 0.5229 - val_loss: 0.6922 - val_accuracy: 0.6714\n",
            "Epoch 2/10\n",
            "1265/1265 [==============================] - 4s 4ms/step - loss: 0.6432 - accuracy: 0.6752 - val_loss: 0.5182 - val_accuracy: 0.7251\n",
            "Epoch 3/10\n",
            "1265/1265 [==============================] - 4s 4ms/step - loss: 0.5352 - accuracy: 0.7058 - val_loss: 0.4649 - val_accuracy: 0.7277\n",
            "Epoch 4/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.4875 - accuracy: 0.7167 - val_loss: 0.4206 - val_accuracy: 0.7405\n",
            "Epoch 5/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.4619 - accuracy: 0.7258 - val_loss: 0.4051 - val_accuracy: 0.7424\n",
            "Epoch 6/10\n",
            "1265/1265 [==============================] - 4s 4ms/step - loss: 0.4446 - accuracy: 0.7268 - val_loss: 0.3951 - val_accuracy: 0.7434\n",
            "Epoch 7/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.4326 - accuracy: 0.7297 - val_loss: 0.3954 - val_accuracy: 0.7464\n",
            "Epoch 8/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.4246 - accuracy: 0.7317 - val_loss: 0.3922 - val_accuracy: 0.7483\n",
            "Epoch 9/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.4170 - accuracy: 0.7338 - val_loss: 0.3767 - val_accuracy: 0.7506\n",
            "Epoch 10/10\n",
            "1265/1265 [==============================] - 4s 3ms/step - loss: 0.4120 - accuracy: 0.7348 - val_loss: 0.3721 - val_accuracy: 0.7522\n",
            "263/263 [==============================] - 0s 2ms/step - loss: 0.3792 - accuracy: 0.7435\n",
            "Test Accuracy: 0.7435\n"
          ]
        }
      ],
      "source": [
        "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.15)\n",
        "\n",
        "# Evaluate\n",
        "loss, acc = model.evaluate(X_test, y_test)\n",
        "print(f\"Test Accuracy: {acc:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Precision: 0.744\n",
            "Recall: 0.749\n",
            "F1 Score: 0.714\n",
            "Accuracy: 0.749\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, precision_score, recall_score, f1_score, accuracy_score\n",
        "\n",
        "precision = round(precision_score(y_test, y_pred_class, average='weighted'), 3)\n",
        "recall = round(recall_score(y_test, y_pred_class, average='weighted'), 3)\n",
        "f1 = round(f1_score(y_test, y_pred_class, average='weighted'), 3)\n",
        "accuracy = round(accuracy_score(y_test, y_pred_class), 3)\n",
        "\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1 Score:\", f1)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2630db6ad10>"
            ]
          },
          "execution_count": 156,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, y_pred_class)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "disp.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 157,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_3 (Conv1D)           (None, 12, 32)            128       \n",
            "                                                                 \n",
            " global_max_pooling1d_3 (Glo  (None, 32)               0         \n",
            " balMaxPooling1D)                                                \n",
            "                                                                 \n",
            " dense_28 (Dense)            (None, 64)                2112      \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_29 (Dense)            (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,500\n",
            "Trainable params: 2,500\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "tf",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.16"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}