Clustering.ipynb

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    "colab": {
      "name": "Clustering spinoff.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbBoBDSF56PV"
      },
      "source": [
        "## Load Libraries and Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQ_ZxOVmGgH7"
      },
      "source": [
        "!pip install -v scikit-learn==0.23.0."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdcW5JcvHFC2"
      },
      "source": [
        "pip install -v yellowbrick==1.3.post1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ei1H5FbA1Exs"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNDqoK6-3wRf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1py4YZ61OvE"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/ocean-modelB/Data/final_big_five.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCpoLZG21mau",
        "outputId": "3c5423c5-08fd-457c-efd6-d175c7ac5483"
      },
      "source": [
        "columns = df.columns\n",
        "print(columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Unnamed: 0', 'EXT1', 'EXT2', 'EXT3', 'EXT4', 'EXT5', 'EXT6', 'EXT7',\n",
            "       'EXT8', 'EXT9', 'EXT10', 'EST1', 'EST2', 'EST3', 'EST4', 'EST5', 'EST6',\n",
            "       'EST7', 'EST8', 'EST9', 'EST10', 'AGR1', 'AGR2', 'AGR3', 'AGR4', 'AGR5',\n",
            "       'AGR6', 'AGR7', 'AGR8', 'AGR9', 'AGR10', 'CSN1', 'CSN2', 'CSN3', 'CSN4',\n",
            "       'CSN5', 'CSN6', 'CSN7', 'CSN8', 'CSN9', 'CSN10', 'OPN1', 'OPN2', 'OPN3',\n",
            "       'OPN4', 'OPN5', 'OPN6', 'OPN7', 'OPN8', 'OPN9', 'OPN10'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "hwqzOskZ1nqe"
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      "source": [
        "df.drop(['Unnamed: 0'], axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "id": "hgpL9PIW1psn",
        "outputId": "bbd47c1b-14ad-4675-a034-574c488f64de"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  EST2  \\\n",
              "0     4     1     5     2     5     1     5     2     4      1     1     4   \n",
              "1     3     5     3     4     3     3     2     5     1      5     2     3   \n",
              "2     2     3     4     4     3     2     1     3     2      5     4     4   \n",
              "3     2     2     2     3     4     2     2     4     1      4     3     3   \n",
              "4     3     3     3     3     5     3     3     5     3      4     1     5   \n",
              "\n",
              "   EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  AGR3  AGR4  \\\n",
              "0     4     2     2     2     2     2     3      2     2     5     2     4   \n",
              "1     4     1     3     1     2     1     3      1     1     4     1     5   \n",
              "2     4     2     2     2     2     2     1      3     1     4     1     4   \n",
              "3     3     2     3     2     2     2     4      3     2     4     3     4   \n",
              "4     5     3     1     1     1     1     3      2     1     5     1     5   \n",
              "\n",
              "   AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  CSN4  CSN5  CSN6  \\\n",
              "0     2     3     2     4     3      4     3     4     3     2     2     4   \n",
              "1     1     5     3     4     5      3     3     2     5     3     3     1   \n",
              "2     2     4     1     4     4      3     4     2     2     2     3     3   \n",
              "3     2     4     2     4     3      4     2     4     4     4     1     2   \n",
              "4     1     3     1     5     5      3     5     1     5     1     3     1   \n",
              "\n",
              "   CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  \\\n",
              "0     4     2     4      4     5     1     4     1     4     1     5     3   \n",
              "1     3     3     5      3     1     2     4     2     3     1     4     2   \n",
              "2     4     2     4      2     5     1     2     1     4     2     5     3   \n",
              "3     2     3     1      4     4     2     5     2     3     1     4     4   \n",
              "4     5     1     5      5     5     1     5     1     5     1     5     3   \n",
              "\n",
              "   OPN9  OPN10  \n",
              "0     4      5  \n",
              "1     5      3  \n",
              "2     4      4  \n",
              "3     3      3  \n",
              "4     5      5  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
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        "def minMax(x):\n",
        "    return pd.Series(index=['min','max'],data=[x.min(),x.max()])\n",
        "df.apply(minMax)"
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      "execution_count": null,
      "outputs": [
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              "      <th>AGR9</th>\n",
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              "      <th>CSN1</th>\n",
              "      <th>CSN2</th>\n",
              "      <th>CSN3</th>\n",
              "      <th>CSN4</th>\n",
              "      <th>CSN5</th>\n",
              "      <th>CSN6</th>\n",
              "      <th>CSN7</th>\n",
              "      <th>CSN8</th>\n",
              "      <th>CSN9</th>\n",
              "      <th>CSN10</th>\n",
              "      <th>OPN1</th>\n",
              "      <th>OPN2</th>\n",
              "      <th>OPN3</th>\n",
              "      <th>OPN4</th>\n",
              "      <th>OPN5</th>\n",
              "      <th>OPN6</th>\n",
              "      <th>OPN7</th>\n",
              "      <th>OPN8</th>\n",
              "      <th>OPN9</th>\n",
              "      <th>OPN10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     EXT1  EXT2  EXT3  EXT4  EXT5  EXT6  EXT7  EXT8  EXT9  EXT10  EST1  EST2  \\\n",
              "min     1     1     1     1     1     1     1     1     1      1     1     1   \n",
              "max     5     5     5     5     5     5     5     5     5      5     5     5   \n",
              "\n",
              "     EST3  EST4  EST5  EST6  EST7  EST8  EST9  EST10  AGR1  AGR2  AGR3  AGR4  \\\n",
              "min     1     1     1     1     1     1     1      1     1     1     1     1   \n",
              "max     5     5     5     5     5     5     5      5     5     5     5     5   \n",
              "\n",
              "     AGR5  AGR6  AGR7  AGR8  AGR9  AGR10  CSN1  CSN2  CSN3  CSN4  CSN5  CSN6  \\\n",
              "min     1     1     1     1     1      1     1     1     1     1     1     1   \n",
              "max     5     5     5     5     5      5     5     5     5     5     5     5   \n",
              "\n",
              "     CSN7  CSN8  CSN9  CSN10  OPN1  OPN2  OPN3  OPN4  OPN5  OPN6  OPN7  OPN8  \\\n",
              "min     1     1     1      1     1     1     1     1     1     1     1     1   \n",
              "max     5     5     5      5     5     5     5     5     5     5     5     5   \n",
              "\n",
              "     OPN9  OPN10  \n",
              "min     1      1  \n",
              "max     5      5  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "9N1sc4cp4lOw",
        "outputId": "a028fea1-6b27-4a29-a68f-3465b1824116"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>EXT1</th>\n",
              "      <th>EXT2</th>\n",
              "      <th>EXT3</th>\n",
              "      <th>EXT4</th>\n",
              "      <th>EXT5</th>\n",
              "      <th>EXT6</th>\n",
              "      <th>EXT7</th>\n",
              "      <th>EXT8</th>\n",
              "      <th>EXT9</th>\n",
              "      <th>EXT10</th>\n",
              "      <th>EST1</th>\n",
              "      <th>EST2</th>\n",
              "      <th>EST3</th>\n",
              "      <th>EST4</th>\n",
              "      <th>EST5</th>\n",
              "      <th>EST6</th>\n",
              "      <th>EST7</th>\n",
              "      <th>EST8</th>\n",
              "      <th>EST9</th>\n",
              "      <th>EST10</th>\n",
              "      <th>AGR1</th>\n",
              "      <th>AGR2</th>\n",
              "      <th>AGR3</th>\n",
              "      <th>AGR4</th>\n",
              "      <th>AGR5</th>\n",
              "      <th>AGR6</th>\n",
              "      <th>AGR7</th>\n",
              "      <th>AGR8</th>\n",
              "      <th>AGR9</th>\n",
              "      <th>AGR10</th>\n",
              "      <th>CSN1</th>\n",
              "      <th>CSN2</th>\n",
              "      <th>CSN3</th>\n",
              "      <th>CSN4</th>\n",
              "      <th>CSN5</th>\n",
              "      <th>CSN6</th>\n",
              "      <th>CSN7</th>\n",
              "      <th>CSN8</th>\n",
              "      <th>CSN9</th>\n",
              "      <th>CSN10</th>\n",
              "      <th>OPN1</th>\n",
              "      <th>OPN2</th>\n",
              "      <th>OPN3</th>\n",
              "      <th>OPN4</th>\n",
              "      <th>OPN5</th>\n",
              "      <th>OPN6</th>\n",
              "      <th>OPN7</th>\n",
              "      <th>OPN8</th>\n",
              "      <th>OPN9</th>\n",
              "      <th>OPN10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.00000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "      <td>874434.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.645693</td>\n",
              "      <td>2.795889</td>\n",
              "      <td>3.298964</td>\n",
              "      <td>3.167806</td>\n",
              "      <td>3.297512</td>\n",
              "      <td>2.411223</td>\n",
              "      <td>2.780479</td>\n",
              "      <td>3.443987</td>\n",
              "      <td>2.979535</td>\n",
              "      <td>3.588282</td>\n",
              "      <td>3.310360</td>\n",
              "      <td>3.197204</td>\n",
              "      <td>3.867576</td>\n",
              "      <td>2.689094</td>\n",
              "      <td>2.852700</td>\n",
              "      <td>2.866246</td>\n",
              "      <td>3.063028</td>\n",
              "      <td>2.694104</td>\n",
              "      <td>3.103318</td>\n",
              "      <td>2.795166</td>\n",
              "      <td>2.247902</td>\n",
              "      <td>3.867886</td>\n",
              "      <td>2.270176</td>\n",
              "      <td>3.956921</td>\n",
              "      <td>2.285038</td>\n",
              "      <td>3.778318</td>\n",
              "      <td>2.209351</td>\n",
              "      <td>3.719934</td>\n",
              "      <td>3.807989</td>\n",
              "      <td>3.617462</td>\n",
              "      <td>3.341473</td>\n",
              "      <td>2.949740</td>\n",
              "      <td>4.004439</td>\n",
              "      <td>2.630877</td>\n",
              "      <td>2.634131</td>\n",
              "      <td>2.841238</td>\n",
              "      <td>3.733087</td>\n",
              "      <td>2.480876</td>\n",
              "      <td>3.222857</td>\n",
              "      <td>3.627431</td>\n",
              "      <td>3.703026</td>\n",
              "      <td>2.07053</td>\n",
              "      <td>4.035008</td>\n",
              "      <td>1.995491</td>\n",
              "      <td>3.824307</td>\n",
              "      <td>1.901202</td>\n",
              "      <td>4.022261</td>\n",
              "      <td>3.221340</td>\n",
              "      <td>4.166739</td>\n",
              "      <td>3.983939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.250871</td>\n",
              "      <td>1.306720</td>\n",
              "      <td>1.188259</td>\n",
              "      <td>1.210432</td>\n",
              "      <td>1.239669</td>\n",
              "      <td>1.208842</td>\n",
              "      <td>1.380444</td>\n",
              "      <td>1.242385</td>\n",
              "      <td>1.325278</td>\n",
              "      <td>1.274354</td>\n",
              "      <td>1.318007</td>\n",
              "      <td>1.191387</td>\n",
              "      <td>1.129266</td>\n",
              "      <td>1.227439</td>\n",
              "      <td>1.254425</td>\n",
              "      <td>1.296906</td>\n",
              "      <td>1.270450</td>\n",
              "      <td>1.324289</td>\n",
              "      <td>1.274190</td>\n",
              "      <td>1.305114</td>\n",
              "      <td>1.321227</td>\n",
              "      <td>1.080140</td>\n",
              "      <td>1.266212</td>\n",
              "      <td>1.075868</td>\n",
              "      <td>1.155366</td>\n",
              "      <td>1.165019</td>\n",
              "      <td>1.105146</td>\n",
              "      <td>1.041956</td>\n",
              "      <td>1.131329</td>\n",
              "      <td>1.030953</td>\n",
              "      <td>1.122325</td>\n",
              "      <td>1.372541</td>\n",
              "      <td>0.995169</td>\n",
              "      <td>1.232578</td>\n",
              "      <td>1.254199</td>\n",
              "      <td>1.399420</td>\n",
              "      <td>1.079109</td>\n",
              "      <td>1.120525</td>\n",
              "      <td>1.248199</td>\n",
              "      <td>0.998013</td>\n",
              "      <td>1.100078</td>\n",
              "      <td>1.09506</td>\n",
              "      <td>1.040036</td>\n",
              "      <td>1.071258</td>\n",
              "      <td>0.933451</td>\n",
              "      <td>1.084797</td>\n",
              "      <td>0.935574</td>\n",
              "      <td>1.222737</td>\n",
              "      <td>0.971584</td>\n",
              "      <td>0.986196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                EXT1           EXT2           EXT3           EXT4  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        2.645693       2.795889       3.298964       3.167806   \n",
              "std         1.250871       1.306720       1.188259       1.210432   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         2.000000       2.000000       2.000000       2.000000   \n",
              "50%         3.000000       3.000000       3.000000       3.000000   \n",
              "75%         4.000000       4.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                EXT5           EXT6           EXT7           EXT8  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        3.297512       2.411223       2.780479       3.443987   \n",
              "std         1.239669       1.208842       1.380444       1.242385   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         2.000000       1.000000       2.000000       2.000000   \n",
              "50%         3.000000       2.000000       3.000000       4.000000   \n",
              "75%         4.000000       3.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                EXT9          EXT10           EST1           EST2  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        2.979535       3.588282       3.310360       3.197204   \n",
              "std         1.325278       1.274354       1.318007       1.191387   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         2.000000       3.000000       2.000000       2.000000   \n",
              "50%         3.000000       4.000000       3.000000       3.000000   \n",
              "75%         4.000000       5.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                EST3           EST4           EST5           EST6  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        3.867576       2.689094       2.852700       2.866246   \n",
              "std         1.129266       1.227439       1.254425       1.296906   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         3.000000       2.000000       2.000000       2.000000   \n",
              "50%         4.000000       3.000000       3.000000       3.000000   \n",
              "75%         5.000000       4.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                EST7           EST8           EST9          EST10  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        3.063028       2.694104       3.103318       2.795166   \n",
              "std         1.270450       1.324289       1.274190       1.305114   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         2.000000       2.000000       2.000000       2.000000   \n",
              "50%         3.000000       3.000000       3.000000       3.000000   \n",
              "75%         4.000000       4.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                AGR1           AGR2           AGR3           AGR4  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        2.247902       3.867886       2.270176       3.956921   \n",
              "std         1.321227       1.080140       1.266212       1.075868   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         1.000000       3.000000       1.000000       3.000000   \n",
              "50%         2.000000       4.000000       2.000000       4.000000   \n",
              "75%         3.000000       5.000000       3.000000       5.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                AGR5           AGR6           AGR7           AGR8  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        2.285038       3.778318       2.209351       3.719934   \n",
              "std         1.155366       1.165019       1.105146       1.041956   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         1.000000       3.000000       1.000000       3.000000   \n",
              "50%         2.000000       4.000000       2.000000       4.000000   \n",
              "75%         3.000000       5.000000       3.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                AGR9          AGR10           CSN1           CSN2  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        3.807989       3.617462       3.341473       2.949740   \n",
              "std         1.131329       1.030953       1.122325       1.372541   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         3.000000       3.000000       3.000000       2.000000   \n",
              "50%         4.000000       4.000000       3.000000       3.000000   \n",
              "75%         5.000000       4.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                CSN3           CSN4           CSN5           CSN6  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        4.004439       2.630877       2.634131       2.841238   \n",
              "std         0.995169       1.232578       1.254199       1.399420   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         3.000000       2.000000       2.000000       2.000000   \n",
              "50%         4.000000       2.000000       2.000000       3.000000   \n",
              "75%         5.000000       4.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                CSN7           CSN8           CSN9          CSN10  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        3.733087       2.480876       3.222857       3.627431   \n",
              "std         1.079109       1.120525       1.248199       0.998013   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         3.000000       2.000000       2.000000       3.000000   \n",
              "50%         4.000000       2.000000       3.000000       4.000000   \n",
              "75%         5.000000       3.000000       4.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                OPN1          OPN2           OPN3           OPN4  \\\n",
              "count  874434.000000  874434.00000  874434.000000  874434.000000   \n",
              "mean        3.703026       2.07053       4.035008       1.995491   \n",
              "std         1.100078       1.09506       1.040036       1.071258   \n",
              "min         1.000000       1.00000       1.000000       1.000000   \n",
              "25%         3.000000       1.00000       3.000000       1.000000   \n",
              "50%         4.000000       2.00000       4.000000       2.000000   \n",
              "75%         5.000000       3.00000       5.000000       3.000000   \n",
              "max         5.000000       5.00000       5.000000       5.000000   \n",
              "\n",
              "                OPN5           OPN6           OPN7           OPN8  \\\n",
              "count  874434.000000  874434.000000  874434.000000  874434.000000   \n",
              "mean        3.824307       1.901202       4.022261       3.221340   \n",
              "std         0.933451       1.084797       0.935574       1.222737   \n",
              "min         1.000000       1.000000       1.000000       1.000000   \n",
              "25%         3.000000       1.000000       4.000000       2.000000   \n",
              "50%         4.000000       2.000000       4.000000       3.000000   \n",
              "75%         5.000000       2.000000       5.000000       4.000000   \n",
              "max         5.000000       5.000000       5.000000       5.000000   \n",
              "\n",
              "                OPN9          OPN10  \n",
              "count  874434.000000  874434.000000  \n",
              "mean        4.166739       3.983939  \n",
              "std         0.971584       0.986196  \n",
              "min         1.000000       1.000000  \n",
              "25%         4.000000       3.000000  \n",
              "50%         4.000000       4.000000  \n",
              "75%         5.000000       5.000000  \n",
              "max         5.000000       5.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9ptnEAGE6qg"
      },
      "source": [
        "## Combining cloumns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTihtGK04Ta7"
      },
      "source": [
        "df['E'] = 20 + df['EXT1'] - df['EXT2'] + df['EXT3'] - df['EXT4'] + df['EXT5'] - df['EXT6'] + df['EXT7'] - df['EXT8'] + df['EXT9'] - df['EXT10']\n",
        "df['A'] = 14 - df['AGR1'] + df['AGR2'] - df['AGR3'] + df['AGR4'] - df['AGR5'] + df['AGR6'] - df['AGR7'] + df['AGR8'] + df['AGR9'] + df['AGR10']\n",
        "df['C'] = 14 + df['CSN1'] - df['CSN2'] + df['CSN3'] - df['CSN4'] + df['CSN5'] - df['CSN6'] + df['CSN7'] - df['CSN8'] + df['CSN9'] + df['CSN10']\n",
        "df['N'] = 38 - df['EST1'] + df['EST2'] - df['EST3'] + df['EST4'] - df['EST5'] - df['EST6'] - df['EST7'] - df['EST8'] - df['EST9'] - df['EST10']\n",
        "df['O'] = 8 + df['OPN1'] - df['OPN2'] + df['OPN3'] - df['OPN4'] + df['OPN5'] - df['OPN6'] + df['OPN7'] + df['OPN8'] + df['OPN9'] + df['OPN10']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZkQXQzW_jwE"
      },
      "source": [
        "columns = ['O', 'C', 'E', 'A', 'N']\n",
        "df = df[columns]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "7nMbjOoT9Srr",
        "outputId": "fcf0c4af-6217-4e3f-bca8-62e153228842"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O</th>\n",
              "      <th>C</th>\n",
              "      <th>E</th>\n",
              "      <th>A</th>\n",
              "      <th>N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35</td>\n",
              "      <td>22</td>\n",
              "      <td>36</td>\n",
              "      <td>29</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25</td>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>34</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31</td>\n",
              "      <td>24</td>\n",
              "      <td>15</td>\n",
              "      <td>32</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>29</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>28</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>19</td>\n",
              "      <td>36</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    O   C   E   A   N\n",
              "0  35  22  36  29  26\n",
              "1  25  27  10  34  25\n",
              "2  31  24  15  32  24\n",
              "3  29  15  16  28  21\n",
              "4  38  38  19  36  31"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "Qpb9S3sk_wdd",
        "outputId": "eed9a27a-254d-4fb1-aed1-e4e98e5f390a"
      },
      "source": [
        "df.apply(minMax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O</th>\n",
              "      <th>C</th>\n",
              "      <th>E</th>\n",
              "      <th>A</th>\n",
              "      <th>N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      O   C   E   A   N\n",
              "min   0   0   0   0   0\n",
              "max  40  40  40  40  40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRi950kpE_o3"
      },
      "source": [
        "## Deteminimg optimum number of clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "MSL0lPyHJF6u",
        "outputId": "7de547d8-3b58-408a-ffde-06b83844848a"
      },
      "source": [
        "from yellowbrick.cluster import KElbowVisualizer\n",
        "\n",
        "model = KMeans()\n",
        "visualizer = KElbowVisualizer(model, k=(2,15))\n",
        "\n",
        "visualizer.fit(df)        \n",
        "visualizer.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b57b751d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "El-5pTk7FHXJ"
      },
      "source": [
        "## KMeans Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utn-wN9D_2Ez"
      },
      "source": [
        "kmeans = KMeans(n_clusters = 6)\n",
        "k_fit = kmeans.fit(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "pLZfnn96_-Mw",
        "outputId": "77972a17-6133-4f83-f199-84c26fb41dbe"
      },
      "source": [
        "predictions = k_fit.labels_\n",
        "df['Clusters'] = predictions\n",
        "df.head(15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O</th>\n",
              "      <th>C</th>\n",
              "      <th>E</th>\n",
              "      <th>A</th>\n",
              "      <th>N</th>\n",
              "      <th>Clusters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35</td>\n",
              "      <td>22</td>\n",
              "      <td>36</td>\n",
              "      <td>29</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25</td>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>34</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31</td>\n",
              "      <td>24</td>\n",
              "      <td>15</td>\n",
              "      <td>32</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>29</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>28</td>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>19</td>\n",
              "      <td>36</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>34</td>\n",
              "      <td>27</td>\n",
              "      <td>22</td>\n",
              "      <td>28</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>33</td>\n",
              "      <td>23</td>\n",
              "      <td>25</td>\n",
              "      <td>31</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>33</td>\n",
              "      <td>35</td>\n",
              "      <td>32</td>\n",
              "      <td>28</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>25</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>40</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>27</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>33</td>\n",
              "      <td>26</td>\n",
              "      <td>30</td>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>39</td>\n",
              "      <td>12</td>\n",
              "      <td>34</td>\n",
              "      <td>38</td>\n",
              "      <td>26</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>30</td>\n",
              "      <td>23</td>\n",
              "      <td>6</td>\n",
              "      <td>34</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>32</td>\n",
              "      <td>25</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>28</td>\n",
              "      <td>18</td>\n",
              "      <td>10</td>\n",
              "      <td>24</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     O   C   E   A   N  Clusters\n",
              "0   35  22  36  29  26         1\n",
              "1   25  27  10  34  25         5\n",
              "2   31  24  15  32  24         5\n",
              "3   29  15  16  28  21         2\n",
              "4   38  38  19  36  31         1\n",
              "5   34  27  22  28  28         1\n",
              "6   33  23  25  31  20         3\n",
              "7   33  35  32  28  34         1\n",
              "8   25   7  12   9  19         0\n",
              "9   40  14  19  27  11         3\n",
              "10  33  26  30  29   4         3\n",
              "11  39  12  34  38  26         3\n",
              "12  30  23   6  34  10         0\n",
              "13  32  25  21  24  13         3\n",
              "14  28  18  10  24  13         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4oQTCGY7dMA"
      },
      "source": [
        "counts = df.Clusters.value_counts()\n",
        "counts = pd.DataFrame(counts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "4smGCEfwFQEq",
        "outputId": "0dfc560f-0085-40a8-d22f-1ea3e01cbd17"
      },
      "source": [
        "counts.plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8b57d92250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "xvu9QoVNdtDp",
        "outputId": "4620b2ed-9253-4100-9a66-8d481ed8bea3"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O</th>\n",
              "      <th>C</th>\n",
              "      <th>E</th>\n",
              "      <th>A</th>\n",
              "      <th>N</th>\n",
              "      <th>Clusters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35</td>\n",
              "      <td>22</td>\n",
              "      <td>36</td>\n",
              "      <td>29</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25</td>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>34</td>\n",
              "      <td>25</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31</td>\n",
              "      <td>24</td>\n",
              "      <td>15</td>\n",
              "      <td>32</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>29</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>28</td>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>19</td>\n",
              "      <td>36</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    O   C   E   A   N  Clusters\n",
              "0  35  22  36  29  26         1\n",
              "1  25  27  10  34  25         5\n",
              "2  31  24  15  32  24         5\n",
              "3  29  15  16  28  21         2\n",
              "4  38  38  19  36  31         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Nyz4G9Md9ye"
      },
      "source": [
        "columns = df.columns[0:-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "aCuUvwX2CQNv",
        "outputId": "116c430f-c49d-43ab-f2df-c61c61cc14bb"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=3)\n",
        "pca_fit = pca.fit_transform(df)\n",
        "\n",
        "df_pca = pd.DataFrame(data = pca_fit, columns=['PCA1', 'PCA2', 'PCA3'])\n",
        "df_pca['Clusters'] = predictions\n",
        "df_pca.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PCA1</th>\n",
              "      <th>PCA2</th>\n",
              "      <th>PCA3</th>\n",
              "      <th>Clusters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-16.310238</td>\n",
              "      <td>-4.762240</td>\n",
              "      <td>6.959984</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.667711</td>\n",
              "      <td>8.311921</td>\n",
              "      <td>-6.366763</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.977089</td>\n",
              "      <td>4.275881</td>\n",
              "      <td>-2.567444</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.874158</td>\n",
              "      <td>-0.670665</td>\n",
              "      <td>5.561069</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-13.607433</td>\n",
              "      <td>10.991538</td>\n",
              "      <td>-11.384092</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PCA1       PCA2       PCA3  Clusters\n",
              "0 -16.310238  -4.762240   6.959984         1\n",
              "1   1.667711   8.311921  -6.366763         5\n",
              "2  -0.977089   4.275881  -2.567444         5\n",
              "3   3.874158  -0.670665   5.561069         2\n",
              "4 -13.607433  10.991538 -11.384092         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "QYyrr7PbgQnE",
        "outputId": "1960d99e-f23a-494a-efda-7889cf5fbdd4"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.scatter3D(df_pca['PCA1'], df_pca['PCA2'], df_pca['PCA3'], c=df_pca['Clusters'], cmap='viridis', alpha=0.3)\n",
        "plt.title('Personality Clusters after PCA')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.92, 'Personality Clusters after PCA')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/OvTxj3+Y3/zm11x77TuIRPTpGqC5ESCBQCBY7oiUlkDQZmiajKbJmOb8s1Rs28JxgrGuqJ4Ck8pSYLIQQAKBYFmwUEpLRHgEgjahGNXRNBlVVRcUPMGuWy0C5AEOnlf0pRICSCAQLG+E4BEI2oDywmTPW7hGp9nMJ4A8z54+LwlZVpAkGT84JASQQCBof4TgEQhCRlHCFTiLMSNm/P97nkc0agAemUyWYnF0eQSo8vcEAoEgfITgEQhCopjCmr+Krj2p7AKTZ0WApLLHKUIACQSCtkEIHoEgBMpTWMtdB1SLAPn/d5Akp+yRyvSk6WJEaJk/cYFAsKwQgkcgaCG1tZt7FMXDAkcK5oSWiOd58wqWav/uiyAH1y0XQAsPQxQIBIIgEYJHIGgR1SYmVyPsouVmMP8sILcspScEkEAgaB5C8AgELSDowuTlrgWEABIIBK1GCB6BoIks18LkVlOLAJJldTr6JQSQQCCoHyF4BIImUR7VEXtzfVQTQB0dcbLZLKZZdEkXESCBQFA7QvAIBAEThA+W2Lsr8VvhYcYQVaTABAJBfQjBIxAESK2FyQvhb+Zis16I2muAJCRJQQgggUAgBI9AEBDRqIptOyzgxytoEgs5ws/1AxMCSCBYjQjBIxA0SDGFFYnoZLN5HGd1CJ521gvCEFUgEMxGCB6BoAEq281XTypquQWxhAASCARC8AgES6BaYXKQAwOXx167LE6yKrU4wgtDVIFgZSEEj0BQJ0EUJi/EcouerASq+4F5dHbGKBRM8vkCQgAJBMsbIXgEgjpQW/aJafeNdGWrsrkCCPwIUKUhqnCEFwiWD0LwCAQ1UEtUJ7iU1soWE8uR6ikw4QgvECwnhOARCBahdh+s1VO0vFqY730XjvACwfJDCB6BYB6CmJjcyNqC9qDWmiphiCoQtDdC8AgEVZDl8Ew/a1lT7JPtjxBAAkF7IQSPQFBGeVSnXrETZFv6QsiyTCwWx7JsTNPCNK3mLypoGCGABIJwEYJHIJim2e3mQaDrGtGoTi5XACCRiKGqKrY9I35aJYDEXtwY8wkgTVOIRDRSqQxCAAkEwSEEj0BAPYXJCxFU0fLc40gSxGIRZFkmlcph2xaWZU//TELTVHRdmyOACgWz9LggWT2zglonMIpiRpZlFEWdfo1FBEggCAoheASrmmJUp5028NmpMUVRiMcjWJZNJpOt8nivIrJTLoCSyTiqqmJZM9GfZgiglU0YF4cnHOEFgoARgkewapFliMcNXNejUDAbPl4zangiER3D0Mhm81iWs/gvIARQkEhS+4hh4QgvEDSGEDyCVUdlYbLXtrUoiUQUgKmpbGnQ3VJYXAApFQXQtQugNn3hljm1iixhiCoQ1IcQPIJVxXIoTNY0/2NpWU4gkafZBCOA2iTssSKRWMrrKwSQQLAwQvAIVg3BFCYvRONFy7GYgTpt2NUMsVON5kWABGEiHOEFgkqE4BGseIoprGppAs8DWQ7mBt9IDY+iyMTjEWzbZWoqQ3d3MpBzWgq1CCDXdXFdD01ThQAKmGbpjfkc4X0BJBGJGNi2jeMgBJBgRSIEj2BFU57Cqn7fDt//yjA0IhF/to5p+uKhkZqdoKkmgBKJKLquiwhQk2jF2z9bABmGged5WJY5jyO8MEQVLG+E4BGsSML0waoVSZKmZ+tIpFJZXLd9RM5CeJ6HbTvIss3kZLoUATIMjY6OOIqycgRQOJt7OIKimOYqPmfhCC9YaQjBI1hx1FOYHHQrea3HUlV/to5pWmQyranVaRbVIkC67qfAVoIACifaFsaalcXSwhFesNIQgkewolhaYXJQNTze9E1/YYqzdTKZPLZd22yddmMhDeB5HoWCRaGwMgVQswlr9k8t6wo/MMFyRggewYpgocLkhWndziLLEvF4FM/zapqt005D7xpBCKCVixBAguWEEDyCZU95VKfee2mrHM41TSUWM8jnzdLGv9h5rVSWKoBWz0YZVg2P1HD6TgggQTsjBI9g2dJuhcnziadYLIKqyqTTORzHrfVoLDyAbuVsEvMLIJ2OjgSKImNZNrIso6pKy9zgw8KP7K0MxVuLAJoxShUCSNBchOARLEuCnZjcnBusP1snim3bTE3NNf0UVKe6APKjP7FYlEQi1rIU2EpJK9ZCK55rNQHU0REnk8limsXifREBEjQHIXgEy45YTMVx3DqiJfMTrJfWzEyf4mydbLawgmtSWrMR+QLIxHGipNNZLMtG17VpETQTAWqOAFpNm+3SLC0aWlGSplNp/vrVU2DCEFUQDELwCJYNxaiOqvo3viAETzNIJKJIUmOzdYrpsfaNLoR3YkUBVLTeKEaAmieAWi8CViv1+YEJASSoDyF4BMuC5vtgNY4sy+i6Sj5vks8v79k67Uq12pbWC6DmE0YNT3jt8PMXSwtDVEGQCMEjaGuqFSYH2VnlH6vxg0WjBrquYtvOqhE77bqvrEQB1Bpan9KqFyGABI0gBI+gbZHlpc7WqYfGDl6creO6LtlsAV0P6iO1WJdWuLRvqm0uQgDVRjtGeGr53XKEI7xgIYTgEbQd5VGd6g7nXlvcsHRdJRo1yOVMTNNC0/yaglbR3jU+7Us9AiiMy0ySwA2lPC08D6+gruPFHOFnHqcIAbQKEYJH0FYE226+OEtJj0kS06afMqlUDnd6dwo+1RbMsQQLs5AAUhSFnp4uLMtqYQRIwu9Uai3hzf9pXiSzugDyDVFjMQ3P88jnC8IRfpUgBI+gbQivMLn2RYuzdSzLJpMJb7aOuB83j3IBZBgaExNpFEUWKbAm0cpIZbmQURQF1/Xb34Uj/OpACB5B6NQ7MdnzvOmbURDUfqctmn5ms3ksa3mafgrqRcLzXAoFu2U1QOHtre1bM9YMiiKmeh2QcIRfiQjBIwiV1hQmL8xi9y5JkojHIwA1mX4KVjatKIJuV7f05qzbuIfX0tat/nyFH9jKRQgeQSgsVpi8EEHXyixE0fSzULAWbTcPvpha3ESXA8ELoNUZaQln3cVfZyGAVg5C8AhaTqsLk5dKLGagqmqdpp/BEKzlRbNo+xMMBdEGXx/hRUyXFlkSAmj5IgSPoKUEU5g841nVOHOjMrIsk0hEcByXqalMQOusNFZHBCKINE8tAsg0ZwTQaho3EOZzDWptIYCWD0LwCFpCMYUVxA2mmSmtoulnLlfANOv75t3KVvIZ08XwdkZxz14aCwmgzs4EqqqgqiqyLGOaFrbd/AhQeMIjvPRdsz4/tQggXdexLEf4gbUYIXgETUdRIJmMkk7n2naTrJyts3TTz1ZQrCsCcBx3OjJgYpp2ywTQaolAtILZAqizM4nrOiiKTGfn3AhQcwRQOMJjNUSzqgmg7u4OBgdHhCFqixGCR9A0yguTfYfzoAgypeXfkDo64phmo7N1gj2vahTrilKpLPl8HlVV0XWNWCxKZ6eGbZfXhlgrfjNZiUgSmGb1NvjWCaBWsfIiPLWsO/0nQPiBtRIheARNoZmFyUGmjiIRHc/zyGTy2Hb7zNaZ/RyLdUW27dcVFW/UlmVPD0HMAZQ2xkQihqqqFQLINK0wnsoyJ/yOqdkRIFmW0LRgBVB4Lelh1vBIbdGGLwxRW4cQPILACW9icu0UTT+LN54gxE6wNTwz0SJd14hGa6srKhc2kkRpY/QFkCK6g1YArluPADJrvLbDEnZhC8pwIjwLRZaWYogqBFBtCMEjCIwgC5MXwhcWS/+AF2tg8nmTQsGiqysR4NkFSzw+17OrVjxvtgCS0HUVXdfntEcXCss9NdIcwolA1CcCFhZAyZoE0OqN8LR/3dLChqjFfxeO8LUgBI8gEMpTWPN9zoK7uS39ILFYBFVVKmbrFCMz7VTv4hdRG1iWTSoVjGeXnxrxxY2/RrXaEKssNdI+Kb7VRKPX4tIEUFibY5g1PMtzsrRwhF86QvAIGqJWHyz/Qxneza1o+mnbdpXZOkGdWzBFy4ahoaoq+by56HTnRqhWG1IUQLFYFFmWSuKnULBwHCGAliO1CCDL8qMFqqq0VOiGHeFpx5TWUo43/Seg0hFeGKJWIgSPYMmENTG53lqZ4mydbLbQ1LqVRmt4ylvjTdOuO4XVKK7rlYmsDLIslwRQPB4FpFJUoJ2iYYL6qCaAIhEDVY3R1ZUszf9pRaQv7FlSYUV4Wn38ckNURVHo6elibGz1DVUVgkewJNQ6r5xg00a1RVKKpp+SJC04W6cdUlqKopBIREqt8bFYJLyTmcZ1XfL5Avl8AfCjZEUBZBg6kiTR2ZkoRYBaLdBWKq0WAa7rYZo20ajD6OjkrEhfpKUCqJWEKbZavW65CJJlGVlefdEdEIJHUCe1prDmElxKq5ZIiqoqxOORmkw/wyYS0TEMrUprfHvdlBzHJZcrkMsVSp1fpmmj6zqJRBzP88o2RrOthzfWTnu9B82iXPBXRvpmpzqDFUBhtYaHSdhRrbDXDxMheAQ100i7eSttF6JRA11X65itE5wYqydcXYxAAUxNZWfdhNr7hlQ811wuTy6XB3yRqet++rCjI47ruqXoj2lay/gmu1zPOxiaL4BW1+sbtuBYjSKziBA8gkVZelSnWVRPaRVn67hucThfjUcL1JurtkWXUwSqVmzbwbYdstlyAaQTjUbo7EyU2WAsHwEURqozjFrSejbhIAXQcu2UamztcNPnYQuuMBGCR7AgQRUme95cV/KlH2vupqDrKtGoQS5ntv1E4flTWDO0MiLWLHwBlCOb9adAa5pa2hR9AeSUIkCW1TofsPZneW1IjQmg1efhFbbgCHv9MBGCRzAv9RYmh0U8HkFRljacr5VIkkQiEcHzqqWwVj6zbTCKAkjYYIRPkOK6HgEUXnpl9Xl4zazfXjPHWsky2dIErUSWIZHwoyVBfTCDjlhIklSareNvoksfzhdk9Gk+NE0hFouQz1ulduDVTq0+YIWCKWwwmk7zNuGFBJBhGMiyVDHzqRVdYKu5jiWs+UPtgBA8ggqKhcmqqgT8TSRYJ3HP80gkok2frVMv1Vrco1EDTVMrpjsL5jKfD1hHRxxluogsGjUA2uo9D5qV/g28XADFYg6qqmCaVkvb4P3XeLVGeERKS7DKmV2Y3K41JAt3NrUDMx1f5UXUqVTtRdSVx6nOSp+jUe4Dlk7773tPTyeSJFX4gPkdYLUaZNZPONNoW/8NPMzi4bkRIHna8615AijMKIf/nMP74rOao1tC8AiQ5eabfgYhoIqmn4WCNf2Nvz3TbTDXoDTocyoaqLaf4GsOnufheR7ZbH7aBmEmLdLq6cArk/ZJc/gDL2cLoOqWJ42816u1aFmWV899YzZC8KxiyqM6s6//4OtaGktpzU4LGYZGcDfpYNNt0agxx6BUECzlPmCpVHldiF7aFIvzf0xz6T5gYblph7Fmu7aHz574HYQAWu1t6W3c29FUhOBZpbTaB2upURRZlkkkIjiOW8X0s72QZRlJkpAk2v5cG6XdUp7V0yIahqGRSEQBKjbFdheird8Q2+jNXIQgBJBoS2/v679ZCMGzCqllYnIxZRImuq4RjerkcgVMs7JINUj/qyA27+K5ep5HLldo/KSWBe27SS7kA5ZIxAGvIgLUXuMMWv+6+td/OM7hjb72SxNAYabwwk0fhi24wkQInlVEMYVV27Ue7AeinhRZuWv4/KafwdlBNEr5HKBEIkJwNhUNH6KJLK9UT7kPGPhmrZU2GEUfMHNaAIV9XbV+/ZWyB9YigIpRH1VVWl7vFX5Ka+W81/UiBM8qoTyFVcvGEVbKomi5UHQNn49gz29pNTzFOUC2bTM1lQ30vNohwtaOBHWjdhyHXM6p4gNm0NExY4MBrf9GHM6GFNbE4+a/ttUEUCIRnS547wisCLpWwo6wrOb7ihA8K5zG3c2DwfMWb6WuxXKhGSxFpBiGHx1o3hygYF9/wcJU8wEzDB2A3t7uZekDVg/hFS23fl3XdbEs//4yNZVpWhfYfLSD4Flp12+tCMGzgmmkMDn4CM/8G3hxXo3neW06W2eG2tJtguWObTs4Tp54PMrQ0NgcHzDbdkobomVZqzZF0Djhe2ktlgKTJAnLCk4AhZ1SEoJHsOKopTB5cYKNMFQ7n6XOq2mFHcRsFMVPtzVqZSFYfmMx2E8AACAASURBVFTzATMMvSk+YGFsSGFtgu3YDt9sARS24PDXX53RYyF4Vhj1FSbPjy8ogjkn/3gwW0DFYgaqGr7lQi3iqZhu8wffzX+DC1KIreJUe9tTFEBFZvuAlW+IK9kGo3HCucjr+YwGLYDC9rIKW3CFiRA8K4h6C5Nby8wHbKbY12loXk0rnmNYVhar9H60bKn0AZOmI0BahQ1GOwugcGt4wrnYl7puLQKocubTbAEUnrVDUegJwSNYtkgSRKPqnFk1jeB5HnKAkwmLNUFBFfv6H9jmKp5ix1ihYJUG2gnCpO1UfFU8zysTQNkKG4yiADJNu9QCPzsiEJZ/VxjD6MLqGArSz2ohARSPzxVA4RqX+v9fpXpHCJ7lTjGqE4tFMM102KczL0UH9nYs9vU7yCr/LayOsbKzop03+PAmLbfPdVMr5TYYQEkAGUZ1c8zV9u17pdUOLSaAADo7kw3bnjTCarvGigjBs4wJpjC5OkFuaKqqEI0aeB6kUsEU+wY/h8dXPJIkkUhE8LylpbBaJQTi8QiqmijbKJvnGL5aadamOFsAzU6JQPmGaLakvm01taVPr0yrxHO5AJIkid7ebgoFc94IUDMF0ExErT2GtrYaIXiWIUEVJi9MMBGGaNRA11XyeRNNC/JyCz4ComkKsViEfN4qbUb1E9x5VRNOxfony7IZG5tAUZTpSEHHLMPM1myUgsaZHRHo61uDaZrTRdCt8gELawMMvy09jHVn255omlb2fjdPAIkaHsGyojyqU74hFruDgrqQG41UFGfruK7L1FS25GbdrqiqUuHGHjbV3sbZ9U+u62HbxUjBzAC12YaZRREUvF9U+6bcgqH1m3FxQ6y0wShGgHQSiXhFjVBQ72vYAqD164bVhj/3mnIcF8eZz/ctWAG0mqcsgxA8y4ZaJiYHe/NYeqRC11Wi0dmzdYL9oAWVOpJlqTRRN5XKtG0xXzy++LDD2ZECF9g7NMZAKkMiZnDeSRvpNIyAakXa9IVa9lTfEGf7gBXFr+8D5s4SQMvpvVldkSW/OHzhdee+38EJIL9YezldH8EiBM8yoJaJyTNzbsKN8MRiEVTVN9Is/+YZXpHr/BSHHvrdYsG0igb9PMv9uuqtf3rk6ACHxiZZE48yNZXmrof2cPnZZ7CmM05np+8XVSiYYlrwMsNxHLLZShuMaj5g9QjbsGbDrL7IUv3rBimAwp4BFDZC8LQ5ao3vUNCDAuulmpFmJUF7czU24K986KGfQ2+3j4JXMjlcagt//0SKnlgEWZKIahqZQpaB8Qlk178hVpsWPCOA2m9WTKsJ4/O0lDXn+oCppQ6worCdcYK35xVAK714uGLVEFNaja7biAAKuiX+ttu+wm23fYXbb/8eW7eeymOPPcpNN32CQqHAhg0b+OAHP0p3d09g6zVKu93lBdM04oMVBPUIipkpxAtvzO0Q4fFFRATbdktDD4OcNxSUsDMMHUWRmZrKLrlGI6KpFGyHmO4/P9fz0Mqea/m0YEmiVDhZPizPr/9ZvR1gYYiARte0bRvbrrTBKHaAdXZqFTYYM5G9sObhLJ9ISzDrBi+0ahFAd955J48++ijnn38+O3bsoNiV2gh79+7h8ccfY/36DYCfUv/oR6/nfe/7ENu3n8M3v/k1vvjFf+R97/v7htcKCiF42pCltJs3w1tqscPVM4W4HdIlxdqiXK4Q6JDG2TRW7O0LMsfxsG2noYLUczav53cHjpAzbVzPY1NnkjWJWNXHet5MJ1A6XTksr6srWZoVUyyAXs1h8eYSfMRjtg/YbBsM27aRZRlFaf03rPBsDsKMLDV3jWoCyPPg0Ucf4Y47vovrumzb9izOPfc8zj33PM4+ezuxWPX7wnyYpsk//MMNfOhDH+faa98CwN69T6LrOtu3nwPAq1/9Gq688lVC8AiqU0thcqtYbJJxsYW79inEwae06onMzFdbFPS5NXIzKxdkrusRiegNnUtPPMrFp29hKl9AVRTWxKPINaqx8lkxqRRVOsAkJAkiEaNJHWCCZlFpg+FH9jo6EsRiERKJWNvbYARBuGaprV3XcVx27LiAHTsuQFHgySef5De/+T0PPfQA3/ve/0OSJL7xje+yZcvJNR/za1/7EpdddjkbNmws/dvg4EAp2gPQ1dU13aU7SUdHZ6DPaakIwdMmNJrCCjrCs1DxbTRq1N3CHVbR8uK1Re1RUD1bkKnq4sq3lnOOGzpxozHhBHM7wDRNpbu7o6xTyMM0zVIEaLXO+WiUVqdaipE913VJpTLYtoOuq+i6PscHrFCwsO2VIYDCTGmFGR2NRqPs2LGDM8/0ozD5fJ6DB59m8+YTaj7GY489wt69T/IXf3Fts06zaQjBEzJBRXVasWnLskw8HindHMPc02p5vkH5djWTypqiYKZQtwLHcfE8j4mJFFDsFNKJRssLZX0BtHw7wFZfR4sf2bNK4yTKU5udnUUfsNqdwduX9m1Lb+rqs1JqkUiEM898dl3HeOihB3nmmYNceeUVAAwPD/Gud13La17zWgYGjpceNzExgSzLbRPdASF4QiXYwuTmdkHpukY0qje9/iUIJImSR1GrfbvqibQtVFPkC7o2qPKuEb9TKEc2W1koW14n4kcJzLYVn+1BeO3h1ZhrgzEjgGKxKLI80xFUKNQ/EyZME83VVCxdRJblhl/zq6++hquvvqb099e85lXceOMtbNmylX//9x/w8MO72b79HH74w3/l4otf1uAZB4sQPCERtA9WsyI8leKhWv1LWFQXeOXWC5lMbRGTZhR8L4bfFq8s8Jou78jCfIWyHR1xFEVZFmmSMDanML2latkIXdcjnzen6/YyVYwxiwLIbKINRuMs57b0Rtdv1i1clmWuv/4j3HTTJzBNk/Xr/bb0dkIInhbjCwgN1/Wa8E03+E07mYzXJR4WoijKmjXgb6Y9Po9ltWeofbmmsBplpgOsMk3S1ZWocAuvN0ogCJfZtV2VLdF+50/5e1su7sON8ITVhh+24Am+aPpf//U/Sn8+++zt3H77PwV6/CARgqeFyLIf2fG7i4KV2UEPHix2CGWz+QDz9MWoTLAfuHra48OkONk5lzNLXTLNIGdZpAsWmizTFYs0bZ2lCuy5HWASuq6XRQnm3yQFzSGoLyLVZ8LoVX3AHCe8yF6YQitMa4ewi6bDRgieFlBemOx5zUuhBOUtFY9Hps+TwL9tBxey98WTqirE4/W0x1c5UoDpQM+rXpdVPtm5mWH+sUyO3x88iuu6uJ7HKb09PHtDbxOut+Bumn6apFqUQCeZnOkAkyRpWdU1LY2V5S3lC6A8udxsGwwdXY8jSRIdHfGWdveFXTsUpuAIO8IUNkLwNJn5CpODvm/7IqqxCuhiBKJo+tnZGSdof66g8LxivU6ETKbRKFSQBd8e5VNMK13jM7UfZYki7MEjA0RUhagewfU89g+PsbEzSc905GQ5MDtKMLNJSnR3d+I4Tk1WCcuRlV5MW26DoaoKnZ1JbNud1d1Xnw9YvYRbOBy24Gj+4MN2RgieJjJfYXIQ4mTuMRsTUdUiEEHW3PgEk9KSJIlo1ECSlkcKq9I1vnl4nkfOMumJ+eJGliRkScJa5jUxxU0yGo0wOZkq1QBVs0oINlW4usP/rSKbndvdN+MD5pRSm5YVlLgN730NO8Iiy2ELrnARgqcJFFNY811XzemoWlqUohglKfeWmnvM4CI8jT7v4oRn07RRVSWQD28z3o+lDGecdVYs/n5W/lySJPqSCYZTGbrjUQq2jSRJJAIYPFh19ZCySwtbJSiBTQoOu4W4lYRR21Ft85/93s433qARcRvm+xr+NSUiPIIAKU9hzb8hBDszB5a2aS82mK8dJhCXMyMi8oBX0zTi1iOh6yq27dSVwgqK7Zv7ePDwAMPpDLqqsOOkjYFMWp5NO900K60SpLIW+OU3KC/MCEA7vadFavEBKxTMusRt2N1hYbeliwiPoGHqmZgcdoRHkqTp2TpSSwfzLbVYu7wOpjjhOVijw2AEqKapRKM6juOQyeQbP61FqPZSGqrKBVs347gusiQxlTf5/bSB6MauJKet60EJ1B2+vZh/UJ5OLBZFkqTSjJh2nhPTelr/zWYpm281H7Bq850WFkCrPaUV2vKhIwRPANQ7MbkZXVq1iqjyrqZMZuGupjAG8s1mvjqYoDurGj1WMfqUzxeQ5cYjT42ekyLL5CyL3z19BFn2hdDewREcz+XZG9Y1fH7leJ5HumDiuB4JQ0cNwXV7PioH5c2eE1PeJm1Oe0qtzt0gDFPLRin6gFWb71TuA+Z3gJml6N7qTmmFv36YCMHTIEFPTF4qtVgRRKP+rJPGu5qWRr2b+MKt3MGnBZfC7OiTpqkB2oU0xngmT/9kiqxlIQEbOpM8MzIRqODxPI9Hjw1xcHQCWZaIaSovOHkzcUMnZ1nkLZuIphLVtMDWLMd2XI5NTpG3bNbEY6ydHnY3H/N3gBl0dFR2CXle66M/7XAvaRVBRztmR/cqB1wmSwMuw43qhRtdguUnbINECJ4lslhh8kI0J3Iy/4nMbMpeXV1NwZ9nbSKlchpx9TqYdqgvqhZ9aofzKjKYynB0YoqNnUk8PJ4eGuekNcEa+R2fTPHIsUHWxKMkIwapQoHH+oc4saeTBw4fL70e5524gQ2dyUDXdlyX+545xmg6g6rIPDkwwnknbuSE7o6aj1HeJg2VXUK6ruF5HolErAkdYPMRTsohjEhus6Md1QdcakSjETRNZd26ntL8H18INf9LYPgzgITgEdRJbYXJ89OMTXG+YxYNKlvVGr0QtTzvhQw124nGu7Bqob6LxLQdxrN5ZAl64lEsx2FtIkq6UECSJBxc1iaCm7yct2x+9/QRDo5OMJzOEtc1Tu3tYSybYySdI2Ho6KqCaTs8eHiAS8+MoQdYaD6WyTGaztKbjAP+83+8f6guwTOb8iJZXddITh+7WCRrWVYgHWAL0/oNKZxNsLXRjmJ6szj8dWoqg65rGIZGIlE54btZkaDZbuWtZEbUrt5xC0Lw1EE9hckL04x0zNxjxmIRVFVe8qbcnGjF/Acsnm9tJqXhpLQkSSKR8KNlxQLq5lDfgTMFk98dOELOsvE8WJOIoisK3dEohqYgSxKm47KpK7gIz77jw9ieR9zQSBgaWdPi6ZFxnr2hl9FMriRudFUhlTcxHSdQweN6XsUVoMgSjucGGpl0XZd02vc881MkKrqul3WA2aX6n3bvAJuPsCKS4Q1Z9EXHwj5gccCriAAFYXESZq3UzGdidYodEIKnZuotTF6IZkd4io7htm03aFAZtKio/kFbyvmGkToqFnzn81apTmDueQWz2db7/PYOjmI5bqmG5enhcUzbZiybJ29ZbOxMsnVtNyev7Zr3GDnLImfaGKpSUyt71rLpjkXQVYXDY5MUputoztm8nl89fYisaRHTfSGkqTIRNdjbTVcsQkRXmcjmiWgqk7kCZ/StaVp6xk+RWKVI6UwHmFbWAdaYU3g4IiCsNEs4kYb5RMdcH7AZG4yOjqLFSWMF7mG3xIMoWhYsQsD36abV8EiShGHoRCLavLN16jpiwKKi2vEWmwXUKhabKl0s+E6n823p5p01LYzp6InreRyfTLGpM8mOLZuYzOWZzJk8Z3MftuMiMTfSMjiV5r8P9eN6IOGxfXMfJ/bML47AY31HkoetI/QmYvTEIgync7xgyyZihsaOLZv470P9jGZyGKrCji2bAu/eMlSVF249gb2DI2RNm7M29rJ1bXegayxEZQdYBlmWMYz5IgRmjRvk6kk3hNexVNtr7DgOuZxTxQdsboF7q3zAGiHslvh2QAieBQguhdUKfCWhaUrAdgtBCzP/eJLE9CwgecmzgJpTUF15Hn4KyzdTbWcbi/UdCR47PlSqmcnbDmuSMVRFZk0iRt6y+c3+w8iSjCRJbN+8jhO6/fSW7bjcf/h4qebGdlwePjZEbzK+YHfVlt4uzlrfy96hUQDO2dTH5un6me5YlEvOOBnLcdEUuWmzfxKGznknbmzKsevFdedGCIqCvqMj3rYbZLsLj8BXXeLznV3g7gsgvcwGY+H3N+xmhuU4eiBohOCZh3ZpN6+Fot0CQDqdC+y4fiQqsMOVjldMYfnFoUtPuTX7wxuEE3szkWUZ8KNNW3u7yVs2B0bHUWSJM/vWlB6Xt2yOjE9xxvq1dMciWI7DQ0cG6I5FSRg6luPguG4p6qMqMngepu3MK3iKYxBO61vDqet6gLkCVJHnFzqOZzFuD2J5BRJyN0m1Z9Hnu1w+j0UcxyGbLd8gVQyj0ieqvEYEwnqOy0t4NL5uMJEOXwDN7wNm205ZgbtFeKlDHxHhEYKnKrLc/BtPUMac5XYLyWT7O2Krqv+tN5vNY1ntkxqa/X5EIjqGUf/MolrmIdVK+XEc160QD7GY/74DFAomDx/q55nxSTwPTl7TzebuDh441M+B4XFs1yWma3RFfVGsKQoSkDOtUlQnoqnsGxwla1q4nse6RaI7851nLTiezTOFR8m4U6iSypB9iBO8M+nRNiz6u8v5fm3bNrZd6RNlGHqFTUIQhbH1EqbwCINmrVvNB2z2+wu+PUZrRhxUEmaHWLsgBE8VWnNRVE+h1Ios+6af5XYLxc02KBXved50FKFxikPApAAdzoMSjeVIkkQ87guDpZ1nsBfP0FSa+w/1U7Bt1nckOH/LJnq6kriuy/j4JEdGJ9l9bJADIxNsP2kDHdEIR8Ym6UrG2djVwUgmR6eq8sTxYZ4cGOFZ69diOS7D6Sx7BkYYzeTYurabNbEYDx0ZQJVlZAlsx2Msk6UnHmwrOUDWnSLjTpJU/KiO7tkM2AdqEjyL4XouQ9YzDFlH0GWdTdrpJNU1i/9iFZotBIobpL+WhKapxOPR0oyYldABthjh+YY1f93y9xeYTm0mWjziYAYR4RGCJzQa2az94Vk6uZw565tCe17MxdSQ47g4jhvgh64x0Tj7WIqiEIsZbZPCSuXy/O7AEZKGTkfUYDSbY8/IGOfH/DTbsYkU9x08ymg6S65Q4L/3H+bsTX1EFIWRTI7RbI7TN/XhuS6JqMEfDhyhf1JnJJ1DVxRyts2+oTFGMlkyBZMdWzb6nUa2w4NHjvPjJx3WxmOcc8J6NnXNPzQwZ1kcHBknZ9n0JRNs6kou+C3am37fLMfFchwUGTxpaZENyyuQsscASCjdHLf2sy//39NXhMNx6wDnRl9Gl9a3hKO3LgJRtLhQFAXHcUilsqUZMbFYFFmWKgqgg5wRE4ZTur8uhBDQQpKkUCJptu3gOC5jY5MlgWsYlTYYzRRAQvAIwRMaM51atV+AlYW+c2fVBB3xCKJLqzw1JMsyqtomvguz8A1VjdBsN6oxlSsAHoamYugaG6MGBwdG2ba2B1mSODQyQVzXcGJRUgUTWZIYSWeIaRpdhs7UVIaYLKNpKms6E5x36omcd9JGfrvvGdbGo7iOS8KAkXQWTZaxXY+opvDE8AieC2sTURKGxoNHjtMVNVBkGVWWK7qtTNvhdweOki1YGJrCkbEp8ta6Ul1PNWJyB7kCPD5yADwV5ALP7T277tfHdHPsLzyE7U1bCVgSKWcUGZW44gu0tDvBMfupJQoeaLUQKH5+K20S/A6w2UPygpwRE17R8uqx7ygXHDMebhaQreoDFnSETxQtC8FTlXa8JhTFj5IsVOi7FBG1MEufw1MtNaTr0pKPV/XsAhB4M+cpkcnkGr6xBNXKbzkOE/k8UzmTRCzKVDrLU8eHGJrKcGRskvUdCWzXIaoorEvGmMjlOD6ZJqqprO9IsG39WnK2zeBkmmREI2vaJHSNDk1FVxRi0QiqomDbDpOmybPXr+WRI4NM5QuMpLNs6krSGY0gSxK27fLbpw+Ttx0kSeKsDb2cPN3+PZ7Nkc6b9Cb9+T8RVeWp4bEFBY/ryAwdW0syCrLigLWBA0dVTkm4dbWuj9r9uDgkFf9cppxRsu4UEuUpOCkUT6ygmTskb/aMGHd6RpDJsewBRqwjSEj0aSfTra5f5Ohh1dKEJ7TC2PgXjHrO4wNWLHIv+oAV/1vKfUrU8AjBExr1zOJZagFtoyx18y52jVVLDQX77aqxlFZ5F1aw4d7GnmTOtPjt/kPYksSRiSl+f+AItuNg2g4buhJkCiZT+TyyLDGVzdPjRlkbj9EVjfKCLZtY35lAkWWee8IG9g6OMprJsqkzybb1a1E82JRM8FT/MDFDo+C4uBIMZQuctL6XtTHDF9eaiixJWI7DsUnfj2t9ZwLbdXn42CCd0QgzMROv7E+Lv4Y5ywZPp0c6BVywcRm3c5iOU5fgcTwLpUzc6FKETqWXMfs4adfB8zxUSWW9urXmY4ZPbddO9RkxOll1jJR+jJ5oD6Zp0m/uRZN0Esr8AjS8b/6rqzsMan+dZwugYoRv7pDL2lOcIqUlBE9o1CIm6i2gDcvss5zyrrHZA/qC/qw1Ek2ZLSI1TQnotWv8Se4fGsP0PJIRA0OR6YlHKFi+GDAth65YhNFMjjP61hDvUFibjKNIMhu7ksT0mc6qkUyW45MpLNdhbSKGNi0mztywloShMZ7NcXhsipF0hrHJNIqicMKaTnaefQYPHT1O3rSwPVjfmSQR0ZnIFjBUBRmJjOmLxO6Ybxo6msmhKzJZ0+aczQunj6LTYiprmhybSDOYSuO6Hmeu7+X0vtoLjDuUXkadfhTPv42ZXoHTjOeT1SY4bh1AkVQ269tYozY+p8fzPFLOKAPWQQpehrjSzUbtVCJyvOFjl7MU8ZFzU5iFPJppMGg9g+VKFDQbTdOJR5IoMZceo5NCwWxZgWwtLPe29FauOzvCVznksjYfsKD2hr/7u3fT39+PLEtEozHe+c73cNppZ3D48CE+/vEPMTk5SWdnJx/4wIc54YQTA1kzKITgCY2FxcSME/f8NgbNph5BMePI7i7gMRW0VUX9zCci2+WLjySBI4GmKhyfmCKdN4lrOo5TIGlo9OcL2K6HhITpOGzp7uTUdT2YtoMswSNHBzk8PonluKTyBU7o6SAqqzw9PIYqy2xbvxZZkjhpTRebuzu47+AxjkxMYlouLh5Hx6d4dt9anrdhHZbnEo9G+cmep3n4yCAxTcF2HBKGTqTMJ+tFp5zAwZEJ8pZFbzLBxs7Egs9RVxWef9JGfrh7L4PpNB0Rg9PW9fDEwDDd8Si90/YYi9GhruEkns2QfQSAk/Rn0aX20kUvG43TGnsjqKyn6bf2sz//AFl3Ck0y6HJz5J00p0XPR5OMhtdaKsPWEY5b+5GQ8XCRPRVJBseJ+MXPzhRxK0s6lq2oD7Ese7oGyAzxm39YU6XDa4cP6nWeO+SyGAHSSSTiFTVCQ0PDRKPR6WLtxtd///s/TCLhf8Z//etf8MlPfoTbbvsON9/8Sf7kT65k586X8+Mf/4ibbvoEt976pYbXCxIheEJiITERixmoav1O3M2wrKjlcDPibGFH9jD8r8ppxSDBRp5jcSBjIqLzHw89gWFoDKUyeJ7H1t4ehlMZMnmT3YePs7W3m55YlHXJGL966hCpgsnAZBpDVdja282xiRRHxifZ0JnAUFU6oxH6J1NsW7+2tJ7jehwcGccFEhEdx3U5PDbB0FSGdck4KjL5XJ7xqTQ6Hpbt+NuTLHPy5j4sx/ENX02r4ri10JuM0xOPMJXPY7suQ6ksnRGDVL5Qs+AB6FL76FL9iJLrOVheARUNSQquOD7vZRixjuB6Dl1KLy4uGXccXY6Qd9NoSjiCx/ZMBqwDxOVOZEnB9RymnFE0zyDljAMeESlBt7KhtPml05UFsl1dSWRZxvMgGo1MRwdakzYPL8ITTgqvmc+3mg9Yccr3X/7lmxkZGeHcc5/L+eefz1lnncvpp29DXaJnUlHsAKTTaSRJZnx8jH379nDLLZ8H4GUv28ktt9zI+Pg43d2ts3tZDCF45qH5m/NcceJveBFs22VqKrOkowY9GXmxb0NLFWdBEHQdVJhirDhq4MDxEfYeGyJhGGi6wsbOBJP5AhIeMUPjRaefSEzTsV2XdckYjxwbImdZrIlHeWZknKl8gRO6O4jrGp7nu6hnChb7h8eI6hqn9/bQFY9iqAqS5Ndb5Qs2+emhdzFNwykr8rUcB0NR2b65j7xlI0sSE7k8P7zvEbSogee6XHjGFjau6ylFDGoxzkwXTPqn0siyREfUYDyTZTKXZ8fJm5b0+qXsMY6YT+DgoEsRTjLOCizd5HoOsqSgyAouLoqkUPBcXM+ZVSDdOPV8YXE8PzUlS0rp/6qks8U4GwcbkEjInShS5QDJ8vqQVMr/bMTjMXRdrUiPFLvAmtXCvRxTS8tl3fIp31/84ld45JGHefzxR7nzzv/k1ls/SzweZ/v253LJJZeyc+fL6z7+pz71Uf7wh/sAuPnmWxkcHGTt2nUo015MiqKwdm0vQ0ODQvAI5ir9ohrP5QqY5tJy7P4xg++CqoYsyyQS/mydWsVZc0xTF6aYwpIkacE6qFrEXbOIxSIoisT/99+P8Ys9BxnNZLEdl5eceTLnbFrPVC6P6TjgSayd7obqn0hx12P7yZk2Z/T5BakxXSObzlKw/TqfuKFxeGySg6OTuJ5HR8Tgo3f9is3dHXRFI7z82adyZl8v+4ZGMRQF13OJJFTWxGciLBFNRVEkRtJZ1sRj5G2bgckMuqKyqW8N+48N8O1fPcBLTj+ZrX09dMSjuIrKM4Mj5Aom6xNROg2j9Lo7rsuxiRSHxiZI6Bp5SyKVN3EAXZbp65j59mh7FlP2MA4OCaUbqG5mansmh8zH0eUIUUmn4GY5VHiM0yPPazDS46dcDDmGKmnEpS4m3CEKdhbbMzGkKONWPxE5hiot7i5fK7VuipoUwZCi5NwUESlO3suiSxFickdJBIH/OrqejSoZyFVeD8/zN8jJyTRQTI/os1zCzZIAWu2Fr0slLKGVTHbwohe9mCuu9otFuQAAIABJREFUeBX5vEV//yAPPfQADz54P/ff/4clCZ6//dvrAbj77jv5whc+y5ve9BdBn3ZTEIInJIqbvz//JYIsS0s20aw8ZoAnOU/Nja6rRKNGlcGH7YWiKCQSEUzTLoV6m0/twqm87mnf0SF++sTT2K6LB0zm8vz+6SNcuu0UcpbN1rVdHBydAGA8k2Pv4DCdkQi48OTAKLIssam7g6PjUwxMpUkWdF5w8mb2DY3Smc2zLhlnz+CI33aeyiEh8U8PPMHrnn82juMymS+gqzLPWt/LhukanJxl8dv9R9g/NE4qn0eRZc7asI7ejhhrElEGJlLsHxpjMlfg2/ftpisa4dR1PeQtm5iho+sqz4xO8NJnnUJfR4JcvsAvnzzA00Oj2I7L4fEptvWtoTseJW/ZxHUNefoCtj2LA/mHyHtZZCT2Tjo8k0ojFzQ2dyaJGzMCw/TyeLho06JDlhQGCoc5PupSyHSwNtLNszb21myVMRtV0jjZ2M4xcx+yLTPuDZFQuuhS+phwRzALBbYa2wNNo9WCLMlsMc7mqLWXrJMipiTZrJ1RIXaGrSMMWAcAMKQYW4yz0eXIgsf10yP5OR1g0Wi5SaYvgCzLWnKaZjVEWmavGybF593Ts4ZLLrmMSy65rOFj7tr1Cm688ROsW7eOkZEhHMcpDc8cGRlm3bqlzr9qDkLwhIgsS3R0xDBNi0wm/Mm+s6kW4YnFIqhq9cGHSzleY+c3f8SoGDFrtWdXrc9xdt3TVK7ASCZLwtDpnjaCHU5lKFg2zz95Mxs6EwykMvz6qUMMTqXBg+2b+pCRePjoAPsGRjkyMcVUrsDRiRRdUYPnbdnE4ESGQ6OTPHF8iHTB9L/heykcz8FxIVOwuPK8ZzGey6PKMmviURRZpmDb/OTJA+w+OoDrekzk8qyJRcg7Nl2RCDnT4uBEiqiucmwizcauBI7n0T+RZjST5YWnbEaRZbKmxX/tOcBLtm1lJJvjvmf6MTQV2/GjUHsHRzl7Ux+6qnDuCTPWEil7lLyXIan0MDiZ4aH+p9FjB+iwN/H08MlcfNopRKc70lR0kPwUj4vDkcJeBlITkHOJ6CqF9CmkDpq8+NQTl+zaHpHjnBI5l4KbZV/+v0kofrRJQyftjGN5BXRp6V52xXP3PH8mVK3ocpStxjlVf5Z1pzhu7S/V+OTcNMesfZxsPKficYvVlsxnklnuEeWnwMy26QBbiOXQlt4MgpjDk81mSaWm6OvzZzv95je/oqOjg+7uHk499XTuvffH7Nz5cu6998ecdtoZbZXOAiF4QkNVFVRVIZ1ufNhdkWamjIoFtbZtMzW1VIfz5qeN/GnU0bojZq0sqNYMjQPDYzzz5BhxTePMDb3EdBXPA3c6LalIMhs6O7jwjJNYl0hQsGwcx2VLTxdJQ2dgKs3gVJpTe3vYurabh48NkjNtumMRRjN5RtJZnh4e5enhcUanB1UWHA9w8Dw/ipSzHfYMDHPy2i6OjE0ykSvQE49y1sZeRtJZJnMFXNd3W9cUmeF0jqmDR7nmBedwYHSCiVwex7RJGL4x6Ug6y0gmw8BUmuMTKTZ3d4LnF0dncgX+66lDpM0RtM4J0vYIx7IeVmEtfbk4F512Cms7k0jetP0ILo4D+4ZSPDC0G1dP06OoZPQpsrlR+id7OaXXb2HX5QibtDM4Zu1l0hqh4OYhu5FuowubLHp8gqmpKOmCRWd08QLjtDPBqH0Mw9PZqGwBZiJDEjLg4XkukiSXXstGojtj9gDHzH2AS7e0hi2R5yz6O7VguXkkpFLEx5BiZJzJKo+sr1tqtknmzITgOIqilCwSCgWrZJhZjXAjLeGsG4alxcz6jQuufD7H9df/Lfl8DllW6Ojo4IYbbkGSJN7znvfxsY/9Pd/4xtdIJpNcf/2HAzrz4BCCp8UU0xhA25sCFgXUTLSk0NA3uOAjPJXHK4oy/2ZcbwqrFWLMrye6/+BRnjw6SDKiM57JMZbJ8qJTT2L7pj72Do0wmrZIRnTO3bKRpOFHe1L5ArbrcsKaTvo6E7h4HBmfIqnreJ6H53kkIzqm7RJVZAqOw3A6hzRtBaHJMpbjYrou9rQI6TAMHjpynJRpYtkuGzsTDE1luN+0OaGng4imkjHN6QhPFkvKkNA8ftV/P1c+6wJOWLeGxw8dY2AqzVgmx3A6y7pEDNf1eHJglOFMjvFsjpiu8fDRAabMFFLnIYayJmO5Aq7sYEpjPD64j5HJHENnnMzl52wjpmuMH8/wk90pxjM5nFgew4NCrICh6WSN/Yx4BzmFmZk9Pep6EnIX/fJTjJsjpLwcrueCJON4Lq5nMuDs4VguQ1ROsEk7DVXSSbvjuJ5LVE5gyDEyzgQHCrvRJB3b1dibvp9N3rOJyR2AL67WqicwbB9CQsHFYb22Zcmt6Tk3zTFzD1E5iSKpFLw8h3KPs0l61ry/43g2ppdDQUWX548qaXIEDw/Xc5ElmYKXJabM9URrNOIx0wE2MyHY7wBLVEwILhRa1wG2MKsrlVa5fmPH6OlZw1e+8s2qPzvppC189avfamyBJiMEzzw04xt/sfYlnzf9KbBLbAucjyDdzWHm+eu61nB9UXOYESlBibIgqBZlK7bEZ7J5njw6yNpEDEmSiGgaw6kMWdPiVedso/fAERzPRZUUdpx+EnFDx3VdVEXB9cD1PHRV4Yx1vXRGDDZ3d3BgZJzRTI6pyRzqhEPBcnDx2HBGgnHHAQ90TaVDkhjP5IioCr3JGLqqMZrJoYxMsiYe4bH+Ibas6WQkncHFYzyTw1AUnh4fx5GydCdUNnQZ5E2bR8cf4YITLyaXyaKrCvsGR9FkmeNTGTpjBnnbYveR45zYlWQq5+C5kGGSiJXH8RxkVyWfjZCMQUQpYJsmg2OT7N53iPVdHfzqqX4ku5vuBBzN6hgRCzOvU5AUJFnF1YewvEKF0NDlCH3aFlLOGOu6NPrH06AU0PJrWbtuGEtWiUpxcu4UBwq7UTHIeBPIyEjIbI2cw7g9gCbpROQ4uqxRkLKMmwPEjI7SOuu1rSSULkw3h4uHJhlk3amSKKoHv/4IFMm/F8TkJFPWGJ5SPVqbdzMcLDyM7VmAR5+2lXVa9eFuMbmD9dopDFgHkJDQpQibtTPqPsd6mN0BJssSuq6j61rFFz3TDHq6ee2E2w7f+nVn1hfWEkLwtIhi7UuxfVvT1MAFVZAirRgtkSQ/NRQMzUm5xeNFQ9Wli7IgX7vZN3HD0IlE/Jb4gmkhIeF6Hsr0gp4HsiSxubuTy54dIVsw0VSFgudx7xP7UWWJMzes45TeHp4eHsN1XCazOU5e081Tw2OkCiZJXeP4wCi256LpCgYyqf408V6VtGIxlc1jea4fVvc8CraN40JC10noKqmCyVgqy4HRcQq2y+bhcXKmxWQuR8YsEDEAD/KWRVfPJLvHn2HcTuOlN7J17Xo2dSb5we499CVjJCMGDx09Tqpg0p/KMp7N0xUxSCbBlSxyTh5ZjhCNqKiKhO34wjWiqViuQ//EJFlzgpQ1QkSCpK7hOgqK7hCJOZzYsYWeaCcmBZ4ZHSbnZOmMySSNGB1qL1uNc+hQjtJj5FDNHjp7k/QrfwASONhE5SQj1lFkSaZnegpzwc1x3NxPRErglRla+lHOmS8ROTeN41lE5ASWW+C4tRcJCQ+P9f8/e2/yY1eeX/l9ftOd3hAvIkhGkExmMrMqa1CNrZbKLUg92DujYbkbbXnTC2+96G7DbnQvBEMWIMBAQwsZ6Ib/AG28aghwe2PYsKwqyRpapZorh2ImmZxijnjjnX6TF/fFkAOZJDOYkVLxAAFGMOLd+d173vf7PeeY17hiXnmqa6UbtP5gFabPMZn30TFxu1jaroJlbyEQ9NWIEAPb9h0GapVcfnSa/RXzMqt6Ax8dicjeN9B8iufX4gkhfiAD7NQgTynJ2tqpA3TTfDoKsJ+3YenjdXf4+WY8LwjPc8ajZl+ez7zN+bRljh/QZdmcuBKfB87/vS7I84S2fXSg6kXi7DzRsSReSclXrl3hh/e3MbpzLr486DFvWhZty3qvYGPY53u3HvDmzgFrgxyrBX986z3+sy++ShEl//sf/Yjbu0f8aXuLcepZW++RIsiVolWSl0ZDXr+yxnvbR/zCjevs1yV/fvshOgaUhjxLGGadOmx9kLPWK/jB/R2EgKpxFKkhBE/jHIvWspImiKQhMwbT3+WoDlweFgyKhEo84CdbkWnlOCxrDsuaVCvqpbVCL9EczAP75RyRBooqZWVthk7nTI5yDvYvQWO4einHx8jmcMDuYsw47LAxyNidWZy9hFQtL40MLxUbrCebgOL/vP1dDsoJPnmInqR87crLrCYjPpd9k1W1ic3ew6f7HIb32LcPyEKOEJIN/Qo+OrQ49enRwuBiy1pyjaN6mzJMsV6jlGJNd8PU95o3eGhvIYUiIcfGhpG+ghKdpH/H3makNj5WBXUWXRXmNbbaW7Q0GKkwMePH7jsUcoijpQklSmi2w7vUoeSyeQnoVFoCgY0NOR9NeACMSB/bcvs0Kw9nDfKSZI3xeI4xiizLGA6PFWDHfk7ub5QE/qJmh87ib9LxfBa8IDzPEZ92m+WTVilOPWtOYxfOI5H8g+s4D3Q5Mpq2teckOT/fGZ7HzRO9vrHOIE85WlQoKXh3/4j/eOd+13bQkjVn+P9+eJs8TRnHCa+8fhlhYHs84yc/vM+DoymDfkY9cdj9hgfSsZoVKCHJNMwby/3DCRHYXZS8vX9IkSbUrqsufXnzEqMi48qgz92DCamWXOoXNN6RaUlpHbuzBTZ0519pDRFmbs6qLklln6FcI7oUG2a8c7CHs6aTlUvBwaI6GXTen1fEGLDREbzBlxscVivIdErbJGyaDS4PLuND5Esb66z1coKek5jA2E9RmccYy+vXc75+5XVsE0hiwc5hxe5hyWBtjpQDKufYLz1FUfOnuz/i0N7lUjain0fu27cYqU3qOMdFywP7M15Lv8nMH9CGCiUSyjBhXd9g7HaRQuNCyxV9g2u9m9gF7NsH/KT+DgkFCNBo6lCytkwil0KCEEvDv+UVFSNNLIkEEpHjomXX3SYEz1BfZqQ2ujwyvcm+vUcTKvbbbQox4Ip8jSO3zTQc8Gr6NQCcaJmEPeo4JxcDfHRE+ETqsA4Xl5bunMNaS1l2EvhjBVhR5KysmBMF2PHXX3dcFN84ve9ePOm6SLwgPM8BZ4nDo9osz09R9WzLfHTswjEROJ83yXl8wjhuYTVNe243kPNtaUG/nz+W6G4O+2wO+7y9vU/dOlaLglld81d3tjjYnZFqxVBKrpqct9/YJn+54HJaMK9ahJZY78kzwyA17LSWNvPENYnbtxjvWNCyen3A/ekMiCgpyI2mai1b4zk3VlfQoqs2lW1Lnmg+vzLiu3e32J9XLJpuzqxIElrnQUgu52sYUbGoDHeakvvjB1R+zIPDKxhtyIzAeYkAWufpJ4awNHTsZZJBLlk0kV5W0NaaIgwozArfuLHJvGkZ5hk+Oiwz1q/fI19k1L7EpTuU0fEX70HEcmOwyUEzYRESot9DeY0MOQeTGdNyRqb6aK3ZmTt0ZuglfbRQXOPzNL7kKGwx92NcbNl39xmpDS7rm7ShZBL2yWSPIDyLMCGRKW2suNv8GIletptg4WcE4ViEKT05pIklhoREZMtrIPDA/oxDt4VA4INlz93D49FCM7SXuJl9nSvmZbbaW9SxJJdDrCyXGViH5KrPvrtPiBEpBBLFSF0hETlzf4RAciP58id2lL64tPQP41EKsE4Cf6oAe9YQ1ItuK110S+uzcp4vCi8IzznjSfOansdQ9LMaDz4uduG8KzyfBO+vmpRk2fm5254XiuKY6J5GbTTOMakapBCM8pT9RcWkrOglCZVzuOD5wf1t9mYzHhxMSRvB65fWuHU0ZhYqZoclNy5d4b3JhJlt8W3AycC8bLB4EtP55qSpIrmuGaYZX395k83VPn986x6rRcrOrORo3jBpWmbNIWVreWV9iFGGYZ4wzAxbkzlE2bXehKAJgUXbIogYrbmUr6MWijS7R+sbDsuW3e0ewdWM2xnWgZDQNgoZFaVzCCFQSnJtOCQyZ2EdxghikEgSCqNZNJ15XZEo7jQ/ZOoOmNUtD2ZHCF1jQsvufo9e/w4xBnZnM1ZHDQuV4BcJMt/Dusi14hqtL1jnGpXfpXEtb23PGV2ekOd9Lg3WebiYkbqUkbpECIG5P2JNXyMTPd5q/4xcDkhESk8NacKcqR1zp3mDXfcecz/GB4uWnffONfN5+mrEwo/J5YCXki+eDB/PwiEH7gEDuUYk8Gb7F7ShYsO8gostizBhu32XGAPvNj/ExrqLhjASRYrHQswRiI78yB51XLBpXuWKfgWHRaJO1vfXEU9CAM5Wds4qwI5DUM9Wfz7Lile4aMLT/ftZuI9fJP76vlueM57lwsjzFGP0Y/OazqyB51FKfhrC86jk8PfjvCs8z0agjrOmPgsqrI/Csd2A94EY40lVb9G0fOfWe9StI8SADQEBZMbQOs8oz7i9d8RhWbE9nVN5R2ygah2vrI949+4eX7x5ha9/7mWkFFSLFrPQ3N0/YO4csqdItEYECErgvCcmks9trFE7xxeurPPe0ZgYI5Xz5EazkmXsL0q2ZgtW85RX1ldZKzJ25yVH1YJhnjIYDXh794DGeoQQSBl5MJkQ5iV11LjgaapVbKPoDUqqKqO1ASkiQnpcgNhEbq6uoLUmERopDS8NW0AgQ0aiDA/HU4wOrOQZ/8cbP6C3tsVAXmH30NDQYqRjOtEEHzHGE4PC2Yq26rOxccTB7ipplAwHNY4ZzSJlJj11WjGL+6TSIKNhWk3YclsYXbBuBhRZjlISYyWzepd9fxcbGwiCMkzZ1K8hVGSreZcqLLhkbhBCZNffIw05iUyRQrGmrn3IyA+gDTUKhRACFxwqKtpYsbdMdo8xkqs+2/Y2q2qDA/8AFTU2VDShRkXDhHdZUZeZ+B3q0OO19Jtc1i8hhMSQ4qNj7o8ATmTtz4KzD+ImlOzY27SxYUVd4tJyfc8DT1vhPqsAg2MFWDcAXRSdwOI4y+1ReW4XXWW5yJbWz3t1B14QnnOBlF3oZwiB2WzxRBf186rwPCmJurhK1NMTqEepsM5Thh9jdwN9Fhy7Jh9HbaysnLYYfrK1h/OeS4MC5wP/z1vv8o3rm6z1cmKM7M07Wff+rMS6gFEKORDcO5pgEsXaSsHXv3oDrbrKy05T8q1v3sS/I5k3lrJpmbUNs8bSB7SUbI/n/NW9bb6wsc6vf+OLvLW9z//2lz+ilyVsDnqMywrrAy4ErAu8vXPAap5yVNUn/78vJY3zaCkoljEO++02K4OIiB7feGonaazBLSSKiJQRgULgiRFaF3hvPGZzTTIvFa+MLvG1q69wZ7zHWzsHOO8IouXdt/cwRjAqElaGNenKn1OMDjF6ASIg2wwfM1y0SCQBT0vFMFV889UVmmiYzRT3H/bZ2Ve8wS2KxLC5eoWvvrbOlWxEHRbcTL+CC4536+/hm0CicqwoCcqymq8TkpaHi3dwwbLrJV8yv9zlVMmCnD6VnjJtD1iX13gp+yISyb6/x4r+cFJ8Jnt4PCF6lNA4WnywILpWdhUXiCCRRtITKwQ8E79HIHI9+SJlO2dVbrCqN4kxMguHJDI7IR8uWu40P6QMU0CQiR6vZl9/Zj8gABsb3mm+DwS0SHhobxHwbJhXn3mZj8MnfQB3CrD25P51qgAz9Ps9YoxnBqAtIcQLrVZfZOtQyheEB14Qnk+M48rD0+ZKPY8ZniclJ3mekiSa+bz+1I3AnqbCc1bhdn7S+EduGc9ScTuu6p1Niz+7j4umJVtmOEW6WQy3dFsVQiARGKWIRK6u9Hk4nnJ3MqNIDTdX1gky8O7+Ia9cWuVgXtJaT+0d88bSSw2zqqatHT4E6toilKKfG6rW8vdff4V+mvBLN6/xJ+/e5e7RlHfrltZ7XAhIIfDBM29aDsuKlaxLWHfOY3H4CKlWXBn0aKzFFhVr2WW2ZzOqSpOkDoLGOkB1D3apIHqF0ZIQAsq0TJqIjAnvjh/y+ksJ6eCQZmeGSBc0jcbHFOkENtZsz2aspCVxIZGmQAICi3XgmoLE+K7Ckc+ooufQ7hNC5P7WFVoqrq9dorauy/PqNyQaJKo7+jEyC/sswoS9cA9tE24kXyYPA7arB0zDPhJDpEYlgs+vfoW7i7fZtveIXmBEzlCvcdncYOo7R+eRvoyPjirMEXSVFikUfbXKNfM62/Y2kcCmeQ2JBhEIRF7RX8XILm0+KM9IXyHESK2O0FIxDXvIqBiodbQwKKFpY31y3R24B1RhzkB1wbFzP2bfPuBq8tozvwPKMMPFloHq4gCU1Oy7B8+F8DwP4nFWAQbHGWCGLEtPFGDHzs8XUfH4ea0ufZbwgvA8IzrJ8XHl4elzpZ4PHv/QPg2rjEynT1qJ+vQTzuEskXx0evynGQfxQZxtBz6uqrc57PPTrT0SrTo35LQbAq6to2wtqVakSrGSZyxaS91apJQM8xQlu/W8vbPPGzv7bI9nLFrL3mzOYVlxZ/8A77szHgCkQEcopMaFgBaC/aM5f37nPu/uHeG87yo4x9saI4dnqntl6zBakRpD7RwpASUErff46MkThRSK1Ci08kgpugdXMKikxaSeujaEqBikCV5UJGlkkBmujXpsHVX85b3bXB70iXj6uaRpBAiPjWBdQCaa+aRHXeYEL1Hak2ee9bWKPldoa0+SVNAqpOsxTvZoreMwOBIdEbokN2toPcalNVuuYtZscSP5EvMw5lbzV5iYsio30SIhEz0uJzd4s/ozDtx9mlghkNS24bvq/yXVOffsm0gJUYH1DT+t/hgbWySKKryCCy1aJggihRxxM/0aSmgumxus6auEGKjDHCU0hRzQhJodd5s6pIzUZWZ+jBaaJi64mr2K8l0lZ9e+h/OW6+kX8XhyeZokb0ONFqeRF0YktLF65ms5hID8QOU1EJZk8Xng+auFugww/z4FWJalSCm4fHkV5/ynrAC7OIXUi5ZWhxeE5xmgVNcOOh6e/azgcQTgg2GVF4djAvXoN98nCSh95q16CvL08e3A07bd6xvrNM5zZ/8IJQX/5Te+TGUd+/NFR3yC5aDsMo9CCBitMM7hQuDB4YRA5HBWgQCzTCE+mJekRmF97ETQHqQCFzwhBOp5y+pmwh//2S3euLvHDx/ssBtqGEikEIgYid3LEIBcft+GiG0dwywhUYrVImfRLNUwynNjZcT+fEZZB5yXSC/wVjEYLhgOWlaGnro02OlVBAlNbEmKkl4umNuSSysKnbaM1ubkh45pGbtql1AEH7BeIFqJjIasKEEJECDsED27SV70KcsJO+4BiU559ZU5WkisrkAM8NHik11ac4hurhDaAaksyGWfgbrEnfaHtKHmwWKPua3Qoc/ro5KRvsLCT5jGAwAMKYsw4fuzP+SLvV8mWNAiYSO9yY/ddyjjjIEZ4YJj271LHed8If0WE7/Hrr1HiJHPZ3+LgOdhe4ux30Wh6cs15uGQHXeHXAy4aj6HXw4fv5Z9k7vNTzFSsls9gGXcRR1n3Ldv8NX8759Uc6Dz1jl0WxRiSKFWaGLFFXnzma9/gEKu0JMrzPwhSmh8tLycfOUTLfNRuIjW0vHsnzGaw8PJuSvAPg4X2057QXjgBeF5ahwrms4jhfv8FVAfXeEpihSt3992eeIlnnOF53H7KqWk389wLjxhQOl5Dn4/2bIep2j7IKrW8nA8pUgM/+mXXmWYpSfHcne24Ds/u8PmaEA/S/nzumF7MifQkYDDeYUQXeaVjXEZwulOPIAr6zvK6LtNDw6ChyxG+oXhBz+5z1tii2KYUgpHUkVqHZCFQgRPqiStDwQg+IhyEU9cRkC0XB8NsCEyKhJU74DB6pxxiEysY7pYIQZB4zTaNKysVGihmC0MeepY31hwI3mZ2/MFC9nQBE9dt6iYsV4UyGtH3Hx1yk9/1gfpkVKSakNP9kgKRVJYYmIRokH6ghEbDONVHu5PiPmsG04tc/YOGlY399Dasbk5ZjpNESIgdUkvP+RG/wapVvTkKk2YI1Hcmz9ksoikJuL0Q350OKdmwiTsIlEYDBZLS432hu3qAbZNCDLyIN6iCgsgsrBTMtHDR0fJmB35M3I9QFvNjrvFmtugjiVHfou+XMVjqcKUTfMqkUhfjqjDnCYsOHDb+GjpiRX2m/vcqX+MCIJU9biZfg0XLcMzZKcMU3bdXQSKHXcH7TVfzn+V1aUn0NPi+B6khOZm+vX3OTv3lqnw54+LC/A8fvB/WAGmSZLkjALMncz/fNYVYB+HF4SnwwvC8wh88Np4MkXTU6+F56GAOsbZYerpdHEu6zgffJhYHOeMPa6F9UF8mi2tj3JNftx21dbyR2/fprIeIeCNbcHfe/0VVovOJK5s2s5bRQgyoxgVGbO2xrmAkYpp06IkqDOrOUtVY4jLi1SAEIgAMoAIUAtP21gmviENLa0PeMA7z/EHVy0EXi7Jjl2qyvTyalwE9tSCNgQG/ZZRus/ukSEC5SIjBIFtUgKgTY3JK4JXECQRcOaI727dRmczkiSlpSTvgfKCob7JwV6Nyw/53KsTYpT0lSBvbnBQTRlXntKCsOtoY3FBMliFo6OKvUnAVEMWHlAthWuWR0XQ7wXSfIoPASUFfZmxEFvk/jpKKzLRZ+GPmM0VeeoIZgzCg4aHtsEJu2zfyOWx9tSh5vb0LrbKUDGj6JW4pMFh0RHKOCMQiVYw5Yg8XUFJxWb/JZyZYNuaFbVG9AIRE7qJLY1AsmVvU4c5O+42IXoCXbZUjxWUMEihUOjOGFHqZUp7hz17Fy0MG+ZlNvRNFmGMEdkn+GByeg9SQrNmrj3jcp5ijZ8Rq4tjdAowe1L9PlWAmTMKsNMB6Kf94HjRhOOzdrwvCi/6YNiDAAAgAElEQVQIzxPgtB1kTySR54Hn4XFzfNM7JRBPN0z9QZw3qfgor6CLaGF9eLsevZ/PksJ+73BCbT2XBwUAu0dz/sO3f8z1rE8+yhhdKmidp2ot3377Dm/u7DOtmq7KlRp6UXfxDFoiXDilxD6STAMxRKIW+CwQtSZqkBEyJyi9pxEB0Tga5xCye7aLRKFll9uljcK1EUTozPCKgFIeESXtXFG2HpTAy5YQwHtBCNA2GqmOz5GntQnVIkGowGhlRjFsaeY99KgmRIippy8VWRZJk5Ld+wVNXTEYDFhZmwGO3sohs0PwjWFn6gg4lDCEqFlfr5mZdzgKG+zPUvI4R5lAs8iwTY4UCSjb+dZIj1rygpaaEB0r+psIFLea77Jr72HShoAD6cFltCGShBqvLAk53dGwnezbpSRaIIoxvh1w0I5ZTTIEjooFCs2q2EQKydjts/ATlDCUdcVLxRcZJAOitgx6K4QQaKsZI7PKzK/ysL1FFSaEGBipjc7dObbstXcpxIhFHGNDxZ5TXDWv83b9H4HIdfM6TajYs/dw0aKEoi9X35f/9bS4mIfhxVd4Hof3K8AWXRUyPVWAQUeQjknQx2X4XTThuGjC9VnBC8LzMfgk7aCPx8fPszz1EmM8ZwLxfPyC4FlaWB/E89u2Yzyr/48NHikEB/OS/emCd9/dY5SnvDs+Yv9nFZdXe7xybZ3v393i9sER3nfBnsRI1Vhyo2mkJ5EKqaFxgRAhmQSkj7hEECTIEnzfQ5S4JnIEuKpByUiqILQRFSEMFXnP4OFEjt74QPSRQdZgBg22TVDaoaRg1haAwFqNlAGICATGOOaz/OTxGrzk4GDA9Zf20ElgfNBnNumTZ5GNawcc7Oc0AYSISAnjdkae1Yh8zGQhMFpyb99TVfts37tKaw3FSkmRlxS6x6VVTd5zsLpgESa0zuK9QieOjAFXk5SJ36FkenrOyElEjoySEB132x+TigIfPHnumNc1eIH3DYka0FMpNZGIW74TBZe4wdQlSDNDyIBWliQYBqwTZI+jsEXOiCvJy0zcLi622GhpY8MivImvWn5J/OfsufeYLCZIJVjPrtLoKWPzgKqdYGmRUdJSYUiY+zGlH2NUTioLJKozWnS3CXgEkm17mx4jDsID4jJ09Eju8Pn0b5/XJf+p4KIIwLOuN4T3K8CUUifRQcNh70wGWPf1QXJx0YTjhUqrwwvC8whIKRgO8+XD+Pm0g877AlTHH2+Jz0ggPoznU+ER51KBOv+W1vsXdkocnz6F/dpohT968zZ37x8wuTfnYH+GWzfk/YS1XsHt3SP2mpp+bpBCMMxT2uCpm7YjNy7SSwyr/ZxEKvYPZ4x9i3QRnwqihiABLU74shSRNpddlycG5v3lLyJkHhaNxS1f0kbARwQOM2xpZgYnFMJF8n6NihneS6oy5ehwwGhtTlObTkbuj28bCqUcMUomkz5KRxazHBC4VtC0kiRrSdISW2eMZ5rWRso6YbRpiUFTu8BilmOMJ+3NSYIG2dVZQnKIMxbd74aT+6GHVJ6jwyGuyqj8nCN/QEKOJqXFAYKAR8eEgGfX3iNTOS62ICMJipWioPIVUbdoVbGgRCBQ6C4riyljdvDG4EPEiASLBV3iZEEiE3pxjQ39Mpf0dZpQ4YLFiE4t5XC4YNmxd/hi8S3q5fzQrDnivcUtSleCFygUXgnqOMcIQ82cRGRYGmLoJO5BBJpYMlSXUGim4YAtd4u+WEfIgEDgomfmDxBSEgkUcviUfjwXUW25uArPeazXe09ZnirAtNakqaEoMlZW+h9SgF10cOhFr/+zgheE5xEIIVJV7XN19T3PgeDjTxsxdtt9vjjfKkqaGqSUz6lq9mw422qTUtDv589ceYoxdiGYM0/13gI3s51EfFEzXtTsyBk6kWT9hPXeCu/sHZFIRaEN07IhWJA+EJqWEonVip5Ou8e5blmIrsUlBeAhmC640iVy+Z8Q9PIPBNBGrPMwE0hBR4ok4AIIiFLgtcR50X0SVBIhjm+OgvHhgMm4QCDpCoadVFniESIQnKQqE/KXLE0daJuISlsODwqKXkPb6K7lIlvaVmJ95HC/k1iHoBBEspUa5ySRSJ5bpLJkuUMXJTZ6IGWxSIh0BowoRxtaSldTUtJaiEKhjQflqJiTiQwpBDv2LnYp2Y5AT65QqB4Tv0fD8fmNKBI8jpQeFTOUil0YaHOZRLa4dMos7JOEBBAUesSh22ES9nGxJRMFdVzgsITo2Hd3caVl391DC0NKgRCKQ7/V7Xv0DMIaVlhqV9ITQ9CREC09MyIEy9hPsKFi4acooXDRUfoJicm4rG6g0Ez8PnftG/TDFiDQGF7LvtllgsXAkds+CRxd1Rsfck6+iGrLxbV4nk+lxTmHc6cZYMZo0jRZKsA03neO5cboC3GK76wHPvXVfubwgvA8Bp/FCIMP4v1+QCX9fnGuN5Nnzef6KEgp0FoTgj+Xqtnz8Aj6oGvy06BxjnndYkVksr9g751DCmVY3yz43sMtnPOEdCm1bgN3tg/58rXLfOP6Jj+4v83htATb0QklAC9wrSNThutJweUi41Z7AIuAcN3DO3TOfOB912aycDJ7uyREZhFxy6qQrkGHgOt1laCoJeUiRSWeLDiQ0NQJzp29NShiOPVjSZWg9RFUQKqA94K2Sbjz7gZr61OSLDKbFdSVYjSsMakjEpnNcurGIERgfNhnZXVB0avxAeoqoSwTBisLiuGc4aBGqIgAykWKc4q8aHFWk+WOyWEfbVomMwFCdkPTaIwJ9HstVlVsiJdpfb301okIukiGMozJxQpqWRuyVB1JwWMJWFoUip7s08iaUSoRZCz8Kk0sKeSATAy51/yUQg3ZVDc5iA/ZC3dRaDQJiSjYd/eY+D0EmjouqMKUhIw1fZVazIkxMjJXSWXOg+YW83iAdJI2VEziAaPkMsN0ha16jPU7ODxDuc5V8zrTsMeh26Kv1lAiAeKJbH3i9vlx+Ues65eowgIbK9pYMfUHrKjLfKX4NXI5OHN+P30zq4tq8XxaROs4BLVbpyDPU3q9/MIUYN3xfsF4XhCex+B5q4A+6QP7g2Gay6VykQZXj8IxkfDeP7EK69OGlJI8T5+p8jStar5z6y7Wew4PF/ixRbaB8dGCmRKUwYMB5JKkqIipA/cOxkzqhr7RTAg0Yqm2ElCaiHGOJHjmxjIb1zQ2YnM6OXqgIzehq9ScnPaw/Do22JGnP0fFScsL3b2mqROEDSjVVY68N3zUQ/Ck5iM9BI/AkyQW5yRCQLVIeTC/gvOazCh0UlIt+kwnkiQr0dozGC6oSkPdaPxBHzNrKXotKysNg5WSNHUkuqscdW9ACEEilMeYFpNYqipBqYBQMD7KWb8yQ+vOVahaJGSJJFGOKkypKGkoScjI4wAvLCBJZIbzLS0VankgHC1xuZcSTYwSFxv2wn0kComkr1bZ1K9x6B8C4KPjID4k4CnEEEOOjRWLeETtK7qI1JS+WqWNFSVTWlvTU6tLs8Ca2i04WlZnclGQkGNpycIQ27Zc0S9z5LbRBFayVb42/E+4M/8pczdlRV3uiJzo4j/aWHPgHgCCNjRs23dYV9fx0pGInEP/kLerv+BL+a+Qym6g/iIjDz5tXATRijGemCAeHU1PQlC7Flin/Dw7AP08qt4XPUP0WcELwnOB+CSEKk0Tssx8aJj2eczcfNK8qrOD30liLswd+VE4/gQmhGA6nT/TJ8Dv3d1CChhlGVvjQxoV+NzrG0gpeW/7iKAEuu3OT9TgXcQ1jjs/2CYYGFzrE6REEAgSajohkZCwsA2zRZd/1cgAtlNkhWNe4jh9Jx/bLqvl9xqc7qo7xCX/SeCUBXWIXuK8BHXWWbcjz0J6lAo4u2yXSUteOAICaxVChOUxCzhv0Nqj05Ysc8QgwcPm1SlIy3RSoIwjSRuIiuAlIgiqytC2Ch8EK6sRqeJSBp/QNgIhugqPtQlGe1ZW5/gQkVFRlylJaomxk+jX1qGzlj3uoTAQIlv7mmacEWXK5mqPz2++zO3wQ9rYIBB0Fo6nJ95RcRS3cT4slWmQ6oinxcYWIlRxSkuFFIYqTtCkSFHTUNHQ+fV0vNRR+jkaRUZvGRWhuKJfpoolh+4hgUDAUTlPIVYxMsOIhCaWpLHfeflEh3YFTeNYT67xhdHf5pX+FxiXR/x0+he0sWK3vc++vY+WBitGeBwP3Nts6FfQymBICXQp8ceE5yLw8zZEe5ZwvD8EtVOAHROgfr+zrTglQPZclKs/b8f7UXhBeC4UT68yOvYD6h7OH/aDuagoiI/CaZTF8xn8Pg9yd+ya3LYOrZ+93D2rWwZ5QvDx5JRO65ppbFEDTZ6lLMoWUXmoQRFRUSDmHk9kK05pexFCQDoQEWSiSBNFshAsZhYZBUZHrIxEQTejI7uyjp7GjsJocLkAoThu2sdUINqILAMkAq9UN7lMR0ZOyj8nvPbYhxl6g5JLV6YIBNYKpI6srs6pa41tDd4p9naGNHWGSVtU7KpN1gokGiEjeeqJRGyjKcuU4WhBmlqE9JSzAucli6OUyxtH6MRzuL+CNnvEKJEiUi1S5vOM4UqNdxohA/1BRVq0xKCYT1K0ccQgqCtFr++6zVd0hn+1QEqLLMZoUg4O1njYm2KyBI3G4whLP5/T/e+iLqbjDGMCoVG00uP7C1qqzi8HQ8kMEQUBh8NSR9HN/ZwhTx67XKrGkJCIlFnYJw0FE7vDLB7haJcttRoXPSOu4KKl8nMWYsKKvMRIbRDxzNojVvVVVuub/Gz2BvfcT5nFA6bukBA8RhtEVDSiJJE5rW+YxkNE1PTksDsvZ3x9Lu5+8dmVpZ//eh99bwkhUNcNdX2qAOsywDoFWAjhfQToWbb/56mK9zi8IDwXiKd9YD9pwvl54llJxaOjLM6TkH0yWfpZ1+QYI1o/e27QlWGPrcmMUZ6xUJ4f3d5CNIERCZcHPUZFn5+YI0QRcC4Qxx7pHSJReOkJpQXVtYaihBAFAqhdoGoC0QhCBC8iwnSDxggBbSCpItJ1rzMNSAvtOh2PaQEFMRF4IbrSUHLm8EFXRnpfUnwnQzfGsbo+o640AsVgZUZ/2GCMwzpFXpQQuxvp9kNN8JKIwFtBkBGIXNqYUAwqesMFMUisVRR5jUk9zsqOqAjNSm9KljmUjtQeYtQkacNiWjCbFWRFjTaONLP0BjUmaRBI0qyhKEq87x7gRb9C624eW9Ptu1JQ9Bu0UrRlQqMO2W4rsmzOaS/w7AHpUM4zlOpm2KQMeK9oKo3qWyrmiCVZigQ0GZ52WdP5aAQ8FQuS0EdhuK5f58A9IMTIaf9xuR0RDt0WPbWGFhoXWoLyJGTM/ZhN8xrb7c/4s8V/YBGmuNjQ0OWADdQIKSQDPSJVPS7Jq+xXD8llj1T0yGTBUK+/b9tijLSh5shvE6JnqC7RUytPfP0/LS5Oli6eWnF5Xut9UoLnvaeqPFV1rABTJElyogDz3p8QIGvtEx3HFxWeDi8IzwXiaaoxTxppcP4VnqcnFR+VIH66fee4Zc9IxoSAXi9HiFPXbCnlJ6oWfeOlTaz3/NWdh2zN5yRNZG4d857iCy+t4msHK4bDwwX721Paw5aQCprg8aKrLggfiVJ01RsTcdbjJIgUhAXhIyHtAjuVhShjN+RcgU9AeEEMAVlGxEpnTkgiTis3ko4ANV2lh+Mbf1x+Sbk8oN0QUAQO91YIHqIIJ+Z2Wd5VoIKX5L0ak3jyoqVcpEgZ0dphEkevX7IymmISjzaBPC/Jioqj/RHlQmGtQQiPMQ1SRtpWE5vuvCoZEaJrsykZCE5xuL+CSS2TcY/VtSnrV+aIAIkGrwMxgHMKayNad+7S0YHzkrpKiMER1AIleyRZi8cikI98DIUgkNKjje9ac/iTv3W0CMIJwXHUy0NsCHz0sLtEYKnZ5w6GjPfan6CjIeLwS58dg0EgWTCmr0bksoeRmoUIhODx0mPjjO+X/xczf8TYdzEQx9shgOADUmqqpkLpjErP+fzw67w6+AUMOX3WiU68byjfhoZ3mu/hYosSij13l1eTbzDQax+5L58cFzlneBHrffbKUjf/U1GWH60Ac87RNO1jM8A+aWVrMhnzO7/zWzx4cB9jDC+99DL/6l/9Jqurq/z4xz/id3/3f6ZpGq5evcpv/dbvsLr6vK6bTwb127/927/9qF+W5adTRfis4uT+/5xwXFF4HIERopNIH8u4P66fmyQa78O5ORZLKdFaPpFirZNzd3MBi0X1kZ+knmSfnxRCCNLUPFUYqlKSfr/AOc9iUZ9ZFiRJ8szBqlpJXhqt8Jc/vYusI4eHC2zw1NGjlCSLilgo9nYmTGihBe8CzkZCCLi8++QpIsQEiEt/naXaKqpu9gcjkC3L4WcBGpJ5RC5fK2I3+2MLAUa8n6uK5Y7a2LEKJ07/H07XR0CbiIoeAtRNgncpdZ3greqGLvOGJLVYqzk66GMbg9IOrSP9YU2eN+R5iw+SxSIlOHVSncnyGilgMcsYjspuPiYNFL2GybiH0Z71yxNiEOxsrzCbZQSvMYklTT1SBQ52R7SNxnpFklraVrO9NWJ8MOTooEcIkqJoltWZiBTdl5CRot+QFX5JEo7Z3ochhIUokCIShUcpgRIOfRJS/uH32OMcjyUGjcaQ42ipw5w5s2XLqyOZgY5YBnx3DdCprhbhiCouSITBiJwjt00ZZ7RU7xu2VmgUhoFahxjpqRG9uErwUFeWa+YLpCZFJgFdRLI0JTEJO4v7PKzf6ZYTu3DSmgVr+urjL/xnhDEaKdW5Otc/CbKsE0582tlYSaIRQp5LKnsInclhVTWUZfehsqv+5wwGBUnS2X7EGGlbuzR4LWga+8zVrbZtuH79Bv/8n//3/ON//Bu8+eYb/MmffJtf/dW/y7/4F/8t//pf/4/8s3/237G9vcUf/uH/zd/9u//gE+/ns6LXe7QH1YsKz2Pwaai0HjcQ/CwtrPPf5ier8DxpGvt5DEE/7bYd41ldkz8OXX6TZFE2uNIyC5bLlweMj0oOmprdvSnNyNHOYIeaWkfsFYGuFKoNCCtwpvvEaxYBLwUYEEEQJd279Lg9FQHE8T9ECTbtWllIiELgs4j0H/E4XhIaEVkO+HL6rA90F4/0CBsxuyCCJghJkkI7jHirWMwLdOLJipZgOmdlISJFv8I5gzENMUamkx5Nk3QDzUEwHO3T1CkxwmBYYm3LcDSj12uxrUYoyPKGG6/ssrs9Yn93SJJV2Fpx+coM7zXGOOrScHQ4RBuHEJG2Vhzu90B00vg8b7EWpkc9sswyWKkQgEk8JvFUi0jRd49pPJ0izUBKhw+gJRA9JvnYlz0SjhqHBEoECkvLh89SPB4Vp2JOEyo8joQcYsOR3yPzJUamVG6+/OvlrBYCRUJPrrCi1hFKEYSjYU4VZpRhxs36q+yUd7nfvsUijPHC8uW1X8aqhrnaJRN95r5FBsMV8eqz7+zH4LM4S/N81/t89jfG0xDU+bw8UYAliWEyOeTXf/3Xef311/mVX/kVvva1v8VXv/pN8jx/6vUMhyv84i/+0snPX/nKV/mDP/j3vPXWGyRJwje+8U0A/tE/+q/4jd/4L/jN3/yfzm0fzxMvCM8F4nHkJM9TkkQ/USr3+5d5/unmH7e4x7WwnieehtwVRYZSj3ZNfpplxRjZ3p7w7u197t07oNdLuHRpwGuvXeF6MeReuSDJNNko43qleeXmOiFTvPlgD40k+i6nyvXAZRKiQEhBbCOyDUgjkK7LygouEtWyLZV1GyhEPCU+LYRM0piAWPK/IB+xI0tfnqgABVIF+oMSnQQW85SmTpEV6DJ2HkCmW69qY9c260na1rC3PWRve0ivX5FmgSStCUGT5w1Kdx47IQiU8oQgcbYLFq0rg0k6Z2ajA6P1bgbIeY1tNNbmZJnDtpq7tzcIAT7/pS1m0xTbSIyOZL0WNfVc3piQ5Q1Cdv461oJSHucF5TxF60jbaILvKrXH51iqbmTp0ee2m/U+ru6apHMT+PAfdsp+sZz9fnIck5NHvaePw0sjEkVLTafzUlh856tDQ0aBxyNRpBS0NMebxab5HDeSL/Cj+tuM1BWMTPDBMw2HzMOEB/ZnLMKUOpbIqPjJ5E/wLQhh8CqS6ZxZGHNt9TrDpE/bdh9i/iYMvV4c0fp01ntWAdbrrfBv/+3/yve+912+972/4vd///cB+MpXvsYv/uIv8Q//4a+zufn0FbwQAn/wB/+eX/u1v8fOzvb7ljEajZYilQnD4fObAXtWvCA8F4oPVyhOlU2R6XTxmRg0exQROLuts9mTbeunmXAO78/rms3OJ27j3r1DfvCje9y/f0QIgbJKSBLDW29t89pLa2wdjLl17xB8YNTPmG4v2J3MORIN7czilT8dHFYAEWxENp3CPJ1GbE8QVEd6ToeLu398CqIFaePy54hoJYSIaiKikPj0Awc5gGwjsomErJv/2XjpkDRzOCcQOiJmkbpJIS69guTJS5GOM49ohZCBxbxPXQeUKkhMixCOS6MSpS22TvFBcLA/RMmAc4qiX1PVCUpbpBA0lSHNWpR0tCgSE2gaQ563SAXzccpsmpFljvXLh3gvWMxz8qJGSc9i2sOkDik9Wd4wPshI8walI1WZMVxdnJAbISF6yPIPV/aclVjbuUGnaff7446w+og5dn+Wq0RQ/vg8Ph5hSZAef/0vvZDweBQSsaQ+uku8p8Vh6alVYoyE6FBoUnIQklW1SRSeI79DwHHgHzCMl0AIVtUVAg4fLXWcU8jO7TpKyzge8ZL+IlWcE62nHy+jmgInHVmWMRz28T6czIp80tbMxfnCXIwi7aKGpX/hF77CV7/6Nf7lv/wfePBgj+9///v85V/+BX/6p39ClmX803/63zz1Mn/v936Xosj5J//kv+bb3/7D57DVzw8vCM8F4oMP/ydtCz1+medd4fnotpExiqLInmFbP5my6oN43L4+vWvyk23X7ff2KfIUKQVpmjCdVmxsDKmrli99aZONd3bR1yU7R1O2Hk7Yjw3zNFDr2KmXlDg1BhSyuxBsQIiAEqBsxAcIYjnAfFZIBCAF0YA/dlPWgig7aTqJwKWcVh0iy4T1bm5HAGYqEOstqWqpy4wI2FZT9BvqRYZvBUkFXkWUCqSZI4wi0hiadtkWCJ3Rj3eS4MG5hKxocbar5CymOU1jcFZjIxzu9hkMFyhtSRJPjKCNZDopGKxUrKRTFrM+RjkwAiEbSm3o9dqTtphSgctXjqhLQwhyaZIo6fVKlPFo7ajLjHKRkOUt5SLDNiVJ1pEYqU4JzDFpqauEna21juQFwWhtxur6/ORQew9KwuMKl57Hk54QwLvOQJHYeQw9WWts6Z6NoGa+/E6iUNR+Tk+tUrmShBQjM5RUjPQVqjhlXV1jk9fYs/fIZL9zh5Y9BmoNicJH11121PRUj4EaYWkY6cvY2KLQmFBQlvVJXpQxepkW3g3LWnsqlf7r4EoPFyfP/izIwtM041vf+jt861t/55mX8e/+3f/C/ft3+Tf/5veQUrKxscn29tbJ78fjMVLKz2R1B14QngvFWXLyfFPZnx0fVZE5brc9y7Y+D2PEj8LTttmeJEJj0bQ8nMzYmc5xIfDmwQHRB9K6UzxtXBvx3nsHpInhcy9f5sfvbbMVa2Z5wJQBDbhMEH0nMyeKpR+OwDQeWUeSOfhcdPM7wMmIxpmHqaiWlR9AuEj0gBS4QkJ+ZieWr00PPWbR+fG4VIAPZPch+JQoNKx7pPHUVQoC/EDiiJg2sDJaIPqeeMmzllnGhz0mRwPOlp1iVEQvODosmM0y8rwFAcp40qybsHZBsb/TZ7ReEUPH9ryXNLWGWDBcXVD0S/Z3V/FeIqIgy1vyXoXWEak6pZSUkcsbRzinutZY4sgLS1tLRmszqiqhaQekeTc43doujDTLwsn5PSY7McLe9gitHUp35onjwwFFvz6p9MAjyM4HePvjijyhM4FGqm4d3gMtTzEPFM985ymZUIYpl+INJJGSGUORcEV/Di00TTTEGFjXL52orkZ6g2vm8yQy55Xkqxy4h+y5uxRyhQ19jY30dRJyZvGQXAzZMDfRwiyPU8TR4pbEZrGoTgKAkyQ5E5fQkZ+m+f/Ze7Mfybb0uu/37b3PFGNOlTXcse/QzbZJ90SAki3alECrZT4S0D/gJ73pwS8SIAGyAYmAmwBtgAIkvxig9EAPMAQIsE2gIYMmKdKc1AO7b/e9fceqW3MOkRFxxj34YZ/IzJqnrMpqMj+gKqeIM0ScOGed9a1vrQ7nHt6G/8veWrrPmjmtqbSTOt/+y3/5z/nxj9/jW9/6H0nTePB+6Utfpmkavvvd7/CVr3yVf/Nv/nf+5t/85ZNZ4XOoM8DzkHpRn4vxeHBi5nwnKwqG42f21cRYCOG+pocvQ6228UnabHXdsVxG1e8KhH505TY//OwGc9vyxsUNtgYFf/bJNYIEboWS7314HbGW+bLGucD+lYpZXZOnCR98eIOrB3Nu78xpEouZR28c0aBcQJcBVwTqbR2vhk1Aao9qhG4I3kictrLgTOhB0VHpph+wMlF8rG3Au0DI73Nm6wKyuhr7QFKCb6HKElo1ItEd6W4gfyUyIv2LSDcGnVn8lsUGA42CoBiOGvZ3R9wrXFE4azDGIQpsoxkOG/JBE0elg2I0bSjLhP29AV2TYDvNK2/skBhLU2eMxwuKQU1dZhTDmsk0TnAp5ZAAzgtJCvmgYzGLjs7OKmZ7GaNJ3WuSaja3ljSNoW00dWWoSoPaKEkzf8fJP4IPRZKtMo+iPsq7R39+noSndF4IPtoJiIqg51k+Or5vLt4Ol5mqLUzIGciEys8xZJRhRu0rjGSM9EYcL9frBMCGlpv2EzbNRQo3wCO8OfhZUqYImm3euHPbQ8dn7Xss3C4BOJ+8wbZ5E4huwCt2VykhTYsZ6DIAACAASURBVGNrdzi82y24PZV2zstUpxntsLqpfpbVf/TRh/yrf/U/89prr/P3/t5/DcDFi5f4tV/7df7xP/7v+Na3/hlt23LhQhxLf1nrDPCcYhmjMUZTls2JjCuu6mQZlLi81cRYXXfPNEr6vETVITzdNu7tL/nTP/sE7wN5nrK9PWavqfi/f++H7JYVShTvTa9SW8dwlLOW5Ryojs2tAZ/v76ARdKEpDzp2liX7accnsuDDcg+VRPBCG8DH6SgyUD5AI6gmoLqAdBBSBdbTjRWqAb8KAF0Jlg93uO9i1R5X9MnhtvfkWY2yH7uZFCW0Y4Xa8yhPvPBkQjsRqA3WaBrnCTczWn+McjCBZGgZjBuqpeCdwi0Ff5CS3QQSS1toQq4PV6b7FpgQCEEQieGiaeKY7Y9wTtPWOW2tuX1rjYuv7rC/M2K6scC2gjBgf28UR8+UZ21j2Wuz405L9EPEmMBgWDM/KABhMKpJk0CXtWSZI3gh8R6lFXWZMRw1OKfw3t+hyVEK8qKlrRPSvMP1mWBJcsTuaH2XZqevxxe493qgfvrdhxXwefaLnyfuqxbNnrtBsA6LZU2f5/P2fQZqws8V/wV1WPJp9Rdxn9HY0DIxW0zMFnv2Bu/N/wjdDhmqKa+lXyaRo7He690nLNweI72OD57r3UcM1CSOvR/fFh/ucQvOsiO3YOf8IfhpW9trWl48k/1XbTosrvvZ9/mtt97m93//T+/7t5/7ua/wW7/1vzzT8l9UnQGeU6rBIMcYfeipcFL1IM3Ns9QqzuJJJ8ZeTMX9zfPksYwZ767vf/8KeZaQ5wmDQc6HP7nBH3zwGfuhwSfCWpFy/aM9ileGDDVMxjkfXd7DBsdB2WJaT+MCpg7sJh2fvv9jFrMGaT1eQ+gCdqywFggB00VSx44F6QKmjgJhryOjo5eBZlPHVpWWO/skve6ny2PrS+871DLQbAoU+h5ghI9mhSFANxH0ImAHxGkvG0jLuM4QNF2jYe2o36gkoILH1QYdPPa2gaWhu5FCHoW0+SzQiMNnEfR4H9kMkziQOrJPKuCcIh/UKAl0raaqcpzTaBWomwR1UFBXGcvSsrczInh45c2K5bzAezCpZTJpSFNL0yoUgjKO9a2oa1Eq/huNbGSTfECbmM6+mBfcujllNKm59OotirvaSFvn97l9Y426TEm058KlXUzyOC3Q/lN2l5XR/SpJAs6uwHlEoydDwgYOuI0Jcac8Do1m6fc4n7xJqjIWbpel22cgEwJwrfuIXAombGJDx669ygbnmOp1SnfAZ817vJ1/9XANSz8j7zO3lCi0JFR+eQ/gubucc5Slu0f/MxgUTKex5bZKFH+R+p+/bGPpL/u6X7Y6AzwvuFYJ59ZaFovy0KjvpOokNTIroAO8tC0s4Km30ftAWTYYo/n88z2yLOHyzV325iXZWkbnA7vLinnT4CvDcs8ROk+5X3Pb1nRtR6cAp3DiWWqFXzbkcxenoUzAeHCVRwUhaFCVR5L4/aG3nInJRi6L/ivoSHLg42MIsQ0mNgaGmjKQLCCpYqZWld/n6ilHy1ciWCO49YAXIFNkt1yc8tIKrwNJA6EOh9NdEjwqwPJ6hmoUndb4haFLNCoIzsf4CNUJPosrDEGjpKFrddT16AYIjCclVZVy5ZNztJ0meIX3UbA8mpS0TUKWtXhvaJqkZ4ngYDZkOl2AMxzM4j5aqwk+MF2fR9Bw7HhffVUayrmhXmZM10p8UOzvTtjYnJOmdf/eQ/CCUZ4LF3fxFtR9QuKduz/LI9KPr/d2RurYY6LZ4V3bpFb+OiGOxj/2Ufrw8rgYYIocDry3oSEQaELJLNzCe8dNd7k3FfR0qmIjXGTpDij9nEv6dXxnGagxC7eHDw4lEWkXMmLmbmJ0SggBF+xThY6ugM1yGd2C19YmiMgx/Y/t2Z/uud5UPUnEw0mv93QBz6ms+qWrM8DzAmtF8a6M70TkhY5oP0kdNz00Rp/Yh/UkW1paq0NqvCybJ35+Zx03b815//3rgDCdDtgrK8ZZStd6bPDsLypIhKSCdlbx/dkuA23YzBP2JWGhHOIC1VgRjKBKhw8e14uHdR3QPgIJ5cCnkV2RGsJY8A68Dqi256o6jzkA1XjQcbxcdYFgQBxoD9nyCPwEBRKEYDlKSFeAjUggaHAmLiNZxLaQTzyiA12xYpDi36UHV4jgnEHE4zOFW2rG5xZ4b1juDml9giCoAPaQgYo9m6ZJAcX65gEm8cxmw9752NA2CXVj8M6gtGNvZ4woz/rGAqUdVy+vg1d4L3SNYTSpyAqHD4FykVFVhmLQYQzU5ZDJ+pLg7w/ynU37SAhhPKlJEsfurSmLgyFtoymGDfjAsGgYFE2cVGtAD3qwclyQ/IDrrzoWLt914J2gVDgcvFv9TWsi0Dz+3KePbbur/LH/hYDH0uBCB2hyRvyw+wMSUkQUnW/ZVJdofMUN+zE+WPabW4Ruj03zClrSO4JFL6RfoGmWLNw+gcCmeYWJevbYgBA8dd1R182hY3rMiyoQkUPvn5NKCz+qs5bWX+U6AzwvoERiC0upO43vnkey+Uks8+7crjx/BnvZ51Qr8BiThh+vJbhf1szrhiwxnBsN+OijmwyHKXmeEghUVcuFV6csrCWbpNy6Paf0HW++vc3lD25jK4s2CpMa2OvYGqbUbUnX60XoPGKJ4MUEVAcoMHUcW7ZpBC3OgJSBwvWAZQle+nZXCtmBR1toRh5E4VdiZBUwC8AJ6NhTCRqkC4TD6aweuMwdYWrACGLjWLo3cbpLtQHx8aLrezGz0OuNQj8yj7C7PybPOrbOHVAYSz0FUzmohLYzuFx6RuiYsB3I8pbxpKbrNEZ7ZgdDRKCus/7eOqCVY7oefZHiFJRiMKyY7Q9ROoAEikGDc0LwCmM83huytMFaoe5SJizja9BPPh2PglHa0bUpWdHSVSoGgRqPksD+7pj9vRHbG7t0iaZsUvKsJTjoKrDBMBjZ+96MaH0vwHJuZWoYW1UhEimH7UiR+/v5rJ57GNTeL1c99Vk54OjwBEo/52LyNgkZ0rtqV35O7RY0fokWwznzBhhPq+fsu12UGH42/8U7zh+JZLyVfY02VAhCKsUJnbOOmJYQAnXdHrrJa61I04QsO7m08MO1nhrwOM0prTPAs6ozwPOQOoljZNXCinTuyRjfPa96ES2sk2i5DYdH4HEVAvqourI3448//rxvRQTeOrdOd1AxHhWcOzdmNMpwNpAOEtxiwetvb3HxCxuYz28w36uwlSMXRZpoTKJRAyAQhccKrAokNVgCKgSkBGwEMD6JX5UnTkrVcdJKeiFIl4EYCAZspqIgWcXHOAPax1BRt/LyCESgYaP5oCo9PtcoH0jnodftCK0NeCURAEk4TET3qaB6cKWayBB1w/jznTogTV0KasuxrDK0F8xmS9MpmroHSIcvfrwLTxJHlluaVqMktr6U9nirSLMO7yQmrY8a8kHF+saC8bTCWk1WtGxsHZBmljTrsJ3GWoW1CV2ro7mh9gSrERVYLFKUCAezgq2tOSo7omKytKXNEmybUJVHkRYHswEQTQ8vf3qBzQt7DMdtBDwtSAquE+paKIr7H//3O97uaWM9xjHpjkV5+Tr+c20ER9mFRz//zjp64wwJuQzwWD5o/gTnHaI0zls6WnzwfNL+BWvqPF8svsHFwaukzRXOmdfvGxaqRJHL8Ek36KH1MODhnKeqGqoqsrbGaLLsKC3cWncIfp5U//hXbxz+dNmll63OAM9zrLtbWC+inpbheVhu1/FJqBPYQp5WwfC0rsk+BL7z2XXWhzmJju25j3f2+cJojN1dMhnn7M9KvIet3PC3f+FL5BsZv/vjTwhLy08+u4VVlmGj2cwytIPhWoEAaVdjVLzcOHHoBtoUtFGxfaQjKMlmHu9jhIQuA0oiuxMEjIWuz8sKEkXMxoEL8W+6AVsEkhBZonIblBNMA22msEVUKCezyBSt2CRCBEVhpa4N/SSXBNBCOwIZ9X4/KzHK8de7DehO6K4WeCW4bYuYwETPWVzZWi149WggoLOWEALLXoic5i2DQU1dZxA8ZZeRJpbRqGYwatg6f4BS4JylbRTDdc9st4geO0mHc4aqNpw7N2N/Z8rBvmIwbMmykix1BISi6Lh2dZ3tCzvkRfRT8iEmuVurGE8XXP98i85qmirDB0gzSyuK2WzCaHILZSDkUcejXP/Gce9n9kGAPayMJJ+k+o9CcCBJZNxUBs0V0EMw48dbTEKGoHF0pBRsmEu01Pyk/g+01LFF5SKNZEhYM+exoWPhb/N58xOqao25W/JG+rN83n7Awu2QqSEXk7efSq/zePX454BVWvhK/7PKiooGiJqusz0D1L6EQxWxTl/Dc4Z44AzwPJdaMSUi8sDspudVT8Og3N3Cus9SOSlK9mkZniPX5Ia2fTLw6H2g845ER/ZKRFACl15Zx/TtG4DXX9/irS9sceHClO9dvs61z3bZn1WczwfsUNM5y17VkKP46lffoBbHrGtpJdDsHSBtQHtwqQLb3/V34JOYiYWOmVTa9jf2WS9ODpHJoQPV+pibreLvXApWxS0URdTlpEInYPOetTGCdD5OdmVC8AEThK72MFYEJdhhwCwloigR2nXAqDvfUX/4UkS9UAMugbJLmeQ1YQbm1Yb5LD8Gdo4ArFIeJQqloGk1040F6+sLPr+yhbMKbQLFoCZJLPmgJcssSkHXKbo2ZTKte03WENeBMY7RpGRzqyVJHcUgpq8bbfFB40PAOk2S2Pg3l2JtQ2KI+p0giPIY5VjfnHPzxhouSK+vcSBxumzz3EHU1AyIAawISXr/C+fxnK1wLKBVP+2ZtMeg2Pj+Sw7ppR5APaQUmnikBBwejSJjSKFGJGTctld6MbPp9euxxzZQU3IZ0lJjfcOBu83QDcjViPfrP2agpgz1lNIf8FHzXb6Y/zxa7psm9kz1LM7Dx5mdVVhmliUMBhOUkkPw07bdS2PietoantNqp71sdQZ4TrieNOF8xcicHAJ/fAblcVtYJ8vwPHk9zDX5cRgtoxUXpmNuzBasD3PKtiPRhrVBzvmffY2f+dIlRGBjY8KtW/u8/8EN/p8/eo+PPr1JWXbgYTDRzI1Dd5CK8OfvX2Z7bcgoMXw6n6Nq4kXLSAQnEltZqutbWbbXEucgFvC96FjAZkQ9zAh0G/ep62MlxEPIBVUFvOq7Ug5MC14LLotXzJAIdhT1Q8oFpAqgwU1i28kOFE4HpCGColXWVgBcQC8CxkY9ULOu+riKuKyqzfGNJrEWW+csygFk0p89joBPmjVo7TmYDciyjtGwpixzutaQZjZ68uQtbZ0i4iiKtgcQAeeELAXrIMtaQBgMS4bD5nDsPDoVe7wHWytcULhOo5RnOKxJkjZqZSR6AkU00cdPjJa8kliuX10nBMEHGAxrNs4dkGS95Lc/xgcD+0DNDRzlbD2ojmuJHloSgU1o4zEhaf+zjQzPw8ofSzbzeHLSKFf2JY0v6ajweBI0WjQheBJyIFCFAxQGIxlfLv4625Nt9vb3+aD+M9b0ebQYChmxcHvUfslQrz3GzpxOHQ/LhCVKqV4AnTAaDQkhHAqgu87+lRQOn01pHdUZ4DnBejRTcm+dNJh4XAblyYDZSTI8j99yO3JN9o/tmnx3LcuG2azitXyEFuHmfMkkz/jqaxfIknj4J0m8upVlwx/+0Yd89NEtdq/NmS8aVKqxjaXcaUgrsDawEzrcsuL6p3tgFDYLGBcQLYdGeRGM9DoZCfErgikDbhpbVjYDlwgqCL4QfCLYNJCUAdNEQbJuAqH0mBLsGJpMEbQQ0hAJGdefSI3gJeqJ3FDQdaAbqaPxIAvBRHYoUkgctmDMEnQV0C5gKlDWUW8Jpha8ie99e2CoBgm+UXG8eRnZo7iMGLpZFC15YREJLJcpn1/ZpBg0nL+0S5I4ktSyd3tEExTBK4pBhRBou4TgYW9vzO6tIZO1CqVgPi/IMkdWWOg7btLLhvKi4+CgQKmAMR5tXBT7htWxE78mKSTBg0CaNbyW3GJvd0xdG6bTiunGkbv5atnPUg8DSneXkvj2qAL8Ir5Hvos/6+JJ1hrjJqacR6HoaHAENAmegAsNoNnQF7mQfoEyzBnJGoiQ6ryPMIk7Ho4JiQMcjqefdD0vAOD9vfqfNE0oipzp1CACo9HgRAJQf1pKqbOW1qrOAM8J1LOJfU8OTNy5vAfXkwKzF51wDsddk58+SHVvf8mf/OnHeOfZn1UkieYr/8lrvPHGFom590R+5couXedIUk2hDeuNYW/Z0jlLUjqy9ZQOh/MBuoATkNLR9ePh0UpZ+uiIGP6pXUC1gbwCr+J4uReo1gTdCaKgLWLbCxdQdSD0LTDTxucCKAeqBhkSs7i0xImrhYcstrTSRXycSwW/ygZbedWE/ggTwIdeniKIdSRVbLVJiI83FUgTsLmQLCDg6cY6tt08oCUKo30AFSiGDWnWUVc5SMNgVOOdMNsbMBg0MRbCKro2JxtYPEvefOcaaWZZLgZo7RkMG2b7Q27fWKPrEhLjUGZIeTDitbdvUBRHx8BK7DscVoSgERwmBWv7LtXddezYzQrHuQv7hBDvMk7TFkJUDCYF8JPYItPq6ae0WkoGasJUtvDuBlGiHLVuhoxMF3xt+LdJJI6o3+g+4Yb9GG09yzDjtfRn6EKDcxYbWjbMJXIZndDe3lkvijGO+p9ogKiUYnMzhloe1/+s8r+sfT46y9MWDcc28emt/2WqM8DziHrUxf5JW1hPuvyTXN7LYiT4KIbnSQDZwxijH/3oGlmaMDuo2N8vaWqLc57d3ZJvfOMNlouGqu7I84RikLK3LHEEJuOc+bzCV44NnSBJyr5UqE6oQgQ7iCAqGv+ls6hx1S6CGjFADUkXs5vE94BIxyks8QFxQrPRi3P6k5GqA0njCSq2qEIXcHkEw7qJrsjZrscNFC4LqCa2y6TrAYuPjJKE6OAsykfxslGElQyjDpgyTpKZZXz/kxqkH6H3OjJLpoUuEZox+FT1oKk3TAwQEgUqIiSjA0Z7fOrIiwrvNc5pQNM0KTeuFSRZy2hUkQ86JtOSvLA4K9RlgnOa2f6I3Z0R3hsIQpJblID1sHNzwvlLO1irqJY5f/zv3yVLHOcu7vPWF6+R5/GYTwyP7ObGHCuF9BkdbaMwiX/8NtQJlnc9q6QOh7ViS6sD/RSymYolYzbRklDImHnYIaMgkQwjGd4Hdt1VLiRvAbBt3mBoJiRZYJK0TM15ln6XT+of0IWGmb3FjvqcreTVE93vWC9eV7ICHotF2f8shwLo6fQoAHWlATo5/c/pamgim3aGeOAM8DxTrVLDnyVy4Xl48dyvtNaMRjlt21FVTwbMTj7/6kHjvicLyJrWkiSa69f2GY9zRBqmk4Ld3QU/eu8ql6/sISIctA3lIDBeG/Djmzd4a22N0Sjj86v7DFNDmhu07Qg2+tWIg6CieaC2xLHiBKyJGppmI7I3aQ8oojlg1AtHw5b+4rryaukHgtI6siuEgOvZIF0HdK//8SqCGt3GbYjOzIALiIlj6+I86SwCFqXALXzMzeqdm6WL00jWxSyvpIkTYqvra/BgJ9ANNd4ApgdlLkS/n9U0V8LhmHvXChtbNUh8z6rSUC2zfnmKJOuwbULTWAajmq5J+Ml7r5JmlnKZUNc5ohxGx9aU88JwWGFSS5pEVLBza8Tljy9x/fNNQOhqqKoBs50xf/2X3ju8g37UURrvdP3hA9PURxlT76j8LIf547azjhsZhkDvpn20bl8CBegntr/ylP6AVDIGeoJ1HbnK0aS9gh5cOGIxRISp3mK9mHJ7uQfAgdsDgXPmNXxwXO0+IFcjRies4zkN1uNu8e6d+p84BboSQI9GxwNQ294A8ek2+LSnpE57/S9TnQGepyilhOEwJnK/zJELq8qylDx/8pypF1laK0aj4okB2cMYrUsX1/jggxt47+namLM0GGQcHFS8/5MbXLywhijh/av7VFcbklYYWMX3P7rGO69v4L3HJYpP9/Yx05Sm7dBBCA4oI4iR1VsfIsgIAkkFQcc/OAV+ICRlOGwtKSu0GUdjzB6kDb3uIi4nsWCT+Hjd9dPkHbhRBFtuIPjewVfXvUg5F9IlhCSu1/Xsk80CPuunuRIIXczp8kZwTYAstsCUBUzseImLkz+HKexK8HpFRckhK2VSy7mLc7Rx5EWNSR27t8Y9wxN1ONO1JWnWobWnnGcsFkPSvGUzn1FXQxYHA7RxlMuUYlixsbkkBGFQdCjjWM4zrn1+kaq6s13rrGF3Z51PP9zkzXd3HgusqGOMij8a1MKYZ7sAP4l258Eb138ZxPbW8brb7fn+69NYWraTNyn9jMovUJKSSMq+u4kLHfv2Jgk5M38TTcrF/E3WmRwuYeF2KVRUTCvRCIrGL08c8JwW6/Gw9ziamN4vADVjMhkdBqA2TUvXdY99vJx+S+t01/8y1RngecJajUc/i7bkeD0ft+UjEDAYFCglzwTMngfDc/yu48ivqKbrnhSQPViz9Pbb24QAs4OK3d0lX3jzHFXdUhQpSgtaKzrnsM5zcLNkplOWdcutnTlpA6Nxync+uYFrHCKC1mAwGK0ImcPXR7Myq1FzgGwnsjNhNers43h3NLwV2jHxrt4RL3JtdEcWiULnpIrtFnQfp9UDJXERLNmh4HSMqlCeCF5yIan6iAotuDwuVzvI9j12onF5ZKIg+gF5E8G7M+BN3AcJ0fvHO7CZHLI7qglkC/Dax4mvXvw8WGtQylMuCpbznIBgktX7IXinOdgfkKSWIBnb53exncL5nE8/jKaRXacJeJLUMp2WZFmD7k0E08QShoJIQOR+RjeB+cEA73ceC3SIivu2+n7FrMRW1wPAc69/Wjko3z2C/iRg54GdBbnz+7udnFfbQa+zWjFSd29ozZIf1L/Hln6didqi8gfc8ldIJeMV8zPc7j7nB9XvMVXb5HrIPNxirTsCPLkasfT7FJIQQsDjSFR294qeuU6L4XmSc+CDAlCj/sdgraVpIvvzMJ+102ZYXkQH4aelzgDPE9TDxqOftp6PIDigtWYwyHuH5yfPmXpRddw1+aT9irRSfOmLF3j3nfN8dnmH3d0ly2XDTz66yWef7rCxMeBrX3sdW1vqruPjxQEfXL2Jt57yesN4mDHeHlLuVrTWUS8bGudRNpD0o+V9BihwdM3S9BdVD6EBnwVoY0J6OwQJClEh+ua4HkzaOGllM6HMA9mt2HZxGYQUkt5nMWSCWEirmIUlIkgVAZbNo2jZ60CQPrtLwBUxK8vjYaQhjdNkdiox2LQB08TnexMBU1ILtohMkPjIULk0RDbKRzNFX0ikgxwEF2hdQrCCZxVG1QuudWA4qXrWQtN2Ca5W2NZEuCoBH4jj4l6zfu6APO8ICMHFvDmtPHl+v4uKkCYl1iq0fvBn0nvwLm6XMS6+tm4FQh8xobXCfQ66xpDKnWPr9wcfD1iUAh6B6d0CFj8AWijehfQiR2h61QqVY07N0qdY9PvfUHPdfciIDSJk6RipC8zDDrftZRyONBzgXIcSxW57nSHR2vli8jYfN9/ts7M8m+YVxieQnXVvnYaG59mAx/EAVBFIklX8xZH+Z6UBcscouZcB8LzsXYgXVWeA5zFKKcVwmOO95+Bg+egnPFE9eqrqaWo4zJ+SMbm3noewWmvFYJBhrXsi1+Sn2TalhDff2GI6LfjX//oPSRLFpUtrfPzxLaz9hPOvTPled5XdK3vYxpIoTeUti4MOKRWFNlRdRyU+il199IuREC80QfX/XK/jpW91BdA1MbYh6e/uRaFaT77vUTbqcuoxuDVD1wZ0C04Ldixku5Fa6ApIep2Q1xGUrMoncbY56ZPO22EgqeI4u1dQbyh8IigXUEg03F0Jdrp4pexyQFbLii9ocB6sRghIP42Flnix9gGTBCTrcJ0iOE+aWppFSppb9ndXJjJRsZ0VFaNRSZo5qmVGntfMZ5P4IgVFmra9XseyeX6HLLO0rcaYgHXCwX5BVjT4kIGUEApWV/7zF29ispS6UqSpv++x4Bzs7Yz7tpqjbTSDUXsIUto2hn4+TMMTAhgNIX18L5fgYXXjr1V8aZNHiJFdCbvfjo7LCOz/vzD9zyG7uFpo/OcqDiMsVBpbmOJWoaQeh2fOLkKMmthx1xj7ljLMycjxOOZhh7nbYW2xwTlfsm3eIFU57+TfoAklQoyUeB4MwU97myWEOw0QlVoJoFOGw6j/WYGfEMIZ4HlJ6gzwPKKSxDAcZlRV+1x8G04STIjEkNLo8FzdcZfxbHWyoEwEhsOCqqqf2DX5Werq1X0659jciqO2X/7yJa5dn3HQNJhcE3YtygZIAssETOXJ54H50FKLQ7lAsuQw/kEDbRYnmWwu6DagXPS0MVXECMpDOo8tLZsElPOkZRQ/ByVxrHsOy1GvqylA+gwsb0I8PnrQ45I+Q8vH8E+XEN+WIhrpKQVuoPAJ6LmHPjNLuRABme7HnrQgHug87TBmd0HUEdkcFCGKlXWIlIQNqCC4XnBdOMt4Y0ky7tC5xQoc7Oc0bcr8IMW5Xm0rnmJQImiGo5rxZMlOWOtBR4Z1seUVEJoyJ12bs5yPaNZihMB8lvCTH79GU2f4oEiTjvWNhvF0hywPZHmLdxrwNPWAvS6gE0tR1JikZ5cUVMssfsaCR+sY8VHXmixz7O+mGBNV5Vo7isH9Q0NXF2itjzKzHlUrElgkfr8KONX6Tk3OcXao3YnxFrqfBg8WmsuQbUcwNP8euFmMnRh+BcwKW7q+hXqsPB0Kg6VDcNwOlwEhEGj8lcj0UJCQcbP7FBcsr6RfRIthIBOeZ52G++/zvPB7f78A1BhMnKYR5Y7HwxMJQH3Sivt91taCM8DzyNJaxRHl52RkcFL6mOMhpbHddnIfqJMEZUWRISIsl/WJ5Is9e++1QgAAIABJREFUyeuXGB29Y/pqW8uyaagrYZClqKGmxPX5AWAWMbjTXLfRo1bHdk6QyLLYIfgMrIlOxkF5dBvohpDvg9iYlJ7U8Tlpo6h7QOOSeHsuQQjWx8mvjJ5BAZdDOzmazApZFEOL97ih9ILm6MZMJnRJZHXw4HMheEERdTamjQxUMw7o2pMexKu3OAgEXAbLLUVSrTLBwBY9INNx3S7xZJ1DdGC4tkStWzqnaWsFBpqDjLpNwfQTYeIZDCsS49FpdFlWOnrl3L41oa7SyHYpR/CBjXMzNs8dUBQNbZViMssH771JXacY7REJVGUGeF77wg0ImrrKyAd1r8sRgrEQAouDAeO1EumNGEXFPDWTxGNFaQhO45wjyWKCuoRAWWY0jWF9o77rIONQV/OgQ83FLlnPsKyOzR4chfj748DmQS2wAHgbybRkPQIfM40t0v3fBd/G39WfRQC0/ktxm7yNhoV3l+/zwDQRhKZk/e8DgcBQjdlz1zmv3mXXXudS8u4L03y8eA3Pi1tnDECtqar6MPjUe39PAGoUQD/fm74zhueozgDPI6qq2se+ozutujukdDwunsNJ69mWdzTZ5nHOPzcA+bB67fV18iLhBz/4nCxLKPKE1945x85sQZEmSKaj8Z8OSBOg89gusimaOL7tUqI2p0839zpgxxJbVr27sikjkBAf3ZRN75dj8SRlP6ZO35pqfGyLWQ8DHTtKqaCXPk5gNWB6jxybEBmoWaA9p6KaORNWHjne9D45KuCVjqyF6y/GNqaoD25D6JkjN1IRzDpiSOgQyPsJrMBhdmY+6BhvlQSnsJ0i2W6wbYIPCu8E36joZkyLGXiazoBYbGfwTiNdws1rm6TJDQTdsyQO5xQeSBLBe2H39oQLr+yiTGR4nI0fPB+EtjWkaUfeuzkPRwcIMYn91s110tySGhcnsHygaw2J6QgalO4Qn6AkHnMhCNrEHK80sfhgCA6S1OLtfZDIYx76q8mv46Bn1Xa6u7y7/y2J2YgATRnwTWyTZq9AaKMeLDsXjz8/PQI9KgUeOlIveByahJQBLTUDGdKGmkRybtfX2EheR4t+YWDndIS0pzMZFo3/PMvlvQGok8kQrVcBqLGLcJKTtKvX+QzwxDoDPKdcIQTUMyCq+4l+T/rYDs/oSHu3a/LzAWQPL+8DP/jBVd58fYu1yYCDeUWaGlSAg92aNBXSDoxTDJVhPq+xOhDq/jVllV/VAxYXL0zaQr4XsANPO4zxEdpG9sSlghsKnQNVe0zdEzg6mgzqlfldDmGo+/OxR7eQzQNJ2Wt81iCk/cXfCGYWhdN+0IMe5CitOwCtID565vjgSeqYi1Xc7KfHBNq1GNUeNHEZWtC1j0LhXpO0ap2NJxVNmWAx2EaRXWtYXy+pmgybQWVzGjTrF0qcE7T2qKSjqnKy3JLnNUnWcfmTC33rzbO+tYy6mrRlOKrJBx3lIuPm9TVee7OhSFs2tmfcurZO1xmKQYNRniyvcVaTJB7bKZQKTCYlVZXgk8gYJanHWUErTdsGssyj1iqCj+s2KhxOWikFKjh8316MavSnr+MfvQd9rB/WadZDWP9bEcgEG1kelcefxfRC6xqy85H5IQBJn/L+QOF0QJGgEDrqyEQFRSIpc7fLQEZcDu/xtcF/+TS7/FR1Ghfg08qUuh+ztGptLRYcGiDGANR4blx5/zRN90w3h6vT7BneiXUGeE65nrZddLyFtVzeKfqNy3w5erYr1+TFoj7UFJ3kh+9xAWNVtdzeWbC9PWF7e8LlK7t8+OFNXhmsk2rNlat76FzIvEAVcOKhH25bXZ+8xB8kRFdlAayO4lTVBfJZwKUqBnkOAj4VpI3sivIRvNhMCAkECdjBihXqM6kcqE7QM4dqoxDVFn3+VYgtNW+g3VTQHRsR6zwKiSPqEtmiMIhXP+UUqvO4NK7P9KJoEUF3Eev4RCIbZIG5J6jYqpMyEBKoQoIrNViBRlO2Y3Qt5OstphZ25imkMaSztYYki2no+cCSDRuKosO2ivn+kDR3dE71x4Bjc/uAfNCCE/K8ZefWmFvXNhivLZhOlzRVyu2b6xDAZC3bFw5IUodSgSSLX7OipVxkHCwLjPEYY9GmQZmA8gEv4TD9fKXDWZXSxBgPEzDmZMX5d2t1Hvt5oyMNz6rMELLXYPEjKC6B3Yfs1QiKxEWTwtAfk9wBfCKr4Wgjm0aBACULhjJhqCe8PvwZXAfCi6GyT6vFEoHHaQGtB6/37gDUqP+JAujxeIj3RwGoT6v/OWN4Yp0BnlOvJxcE393Cuv8yT66ehoV6WWIsjjaICBp6zU9ZtlRVR7lo2Rjk7HoFVqhSx95uRbCBvHdKXhHhqwms4Hs9T3+xMS5g+/aTMh5fCF0RQYzpPRRDr/8NcCgGcYUQpAcbi6gbUg2EXLAmamhcSmQfCCgf3ZG1C0gdsN7hCk2ycCQluCK2tgDqLAqMdemxWTgSKodeK2Qj04Tvt60L2BRECcoG0h0fzQ0R6kWOqMhGmeAICSw+GzPfdwQU6UZDGwzKdOAVIQSyoqauc2Z7Y6qyYVB0rG8dxNZU1jDbG1MMOoxxuE4jAkZbphslWnXUZc65izu8knXUtWJtY8HatMYkjizr4ktIwGhw1pEXDVlu0aYjzzuslRh34TQewTWKtjUo7UiSgEncUQr7EzsaP175k+tMEAKMvw5qSJz+G/SJ6jZKzu5Y1R1j8qtjWBAC29krTJNzXCs/JhBYM+cZmimN7XC8uAGC06jTEEqv1vtk/j/3C0BN79H/PE4A6tGN7+ns+8tWZ4DnEfW8r9NPyvA8jm/NaYR9Hq+Va/KD8sVO0sjwcfd1UKRcvBhH0RfLms8+3WVnZ47SMF/UlG3HQKfI0lLuObBHMPTuV1mII8bKRSGxSyCNelm6NY3rnfxpevdi4vRWCJAtAt5E5+PQxdehmMWQ0CD9NNdIRRgcgBbCGHCCd4GkCSgbDQZNF2hbS7aMOVvGQmcCfqTAe7J5DBw1JgIlm8THBA9BBfBC6FmNdM9jh/FnLLHF1QnKQlMoFGC8w1tN0B6deewwnj4kyXBtYLksCKKxLsH7KBo2xjIctEzWFoynS7rW8NnH5ymXCePpEq09TW3QCagg5HmD0oG2NjhrcE5TDCzlfERiYHu6TzGsERUFyYE4AVUuMwbjhsGwIwRPmgnaeEg8baNZLApco9i6MAcJUWvzHAiNPm3j0czOqkvxAI3P/SoAySaEDswo/sJr0Pl9HnisBEXoc0z27S2myRbrxSY36svcdlfYWXzOmC3eyb9+3/UuZyU//IMfM99ZMN2e8B/9Z1+iGN290sev02otwem0dp71XBcDUCvK8k79z70BqO09+p+T0PD85m/+D/zu7/47rl27ym/91m/z1lvvAPDZZ5/yT//pP2E2mzGdTvlH/+i/5bXXXn/q9byIesnluH/563Ev/lorJpMhIYTHMOk72THyJ2mRrXJoyrJ+qjDV51Uiwhff3UYphRLNsmm4emvGn//wMu99fotriyWfXt/j9mwZmZuHLMvD4a2C+D7J3EK71rsXW6ADlwvtKBB0L3aWHpjUsV2lvJDv+mg+6OLvszmI84gWfCr4FMwikNQeZX0UH/duedJCsR8vet04CqbdKAqojVd9cnrMwBIfwU47jJEUsrJvDoEgITosl/Gy6JP4uLYANwbVmwp1KNpU02hDM9LYIFijaVuN9SnLckDTGrpOMdsbkmaOS6/ucPH120zWSpo6RevAZG3B1vaCIutAYjtJi6MoGrK87t2MHYGYxH7ptR22L+0y2y+iC7MOd4R9pmkgyWrA03UgEgNBdT8ZpXRgf2fMbDbixvU1nAPzFOGcj1M+PGYbS2I7KjwhqWLWe32PBwzoMXe1sO6tgMfNhPoD4dqf7LF/fc5BPSOhYGAmjPSUYFrG05zRaHA4Rg1gO8d3vv19qkXN9NyE+e6C7/67vzgBXcnLz7ScZJ3keqP2p2R3d8atW3uHSfBra2O2tzeYTsc0TcX3vvcd2vbZz8G/+Iu/xG/+5v/EhQsX7/j9r//6r/Grv/p3+e3f/j/41V/9u3zrW//smdf1vOuM4fkpqDRNKIqHtbDurJNneB7vw/q4rsknu32PB+60VvigyPOEvcWSD3f3sENwHTRDQTXgdJxqMtx5DelzN4/W2IMN4UjXEzS4QkUNjY7treCj8WC+F/U7bhhjHAhRfBxUQNvoq+M1MSHdB5KlYNOARvA6jnmbA49PY66WTwETW0+qA3seVAfdQMWYCBUBjdMBlBC6mOIOxOWZgJkHXCF9OrpQr6uY9J4qvIQ4Xk5A8PieI1DGYesEBGyqQYMxDvEKi4ASXGdwCvb3NVnRsX1+FxEolwXOQzHsEFEELzgx7N6ekmUted6xc3vCoKho24QQhLZNo8B5c8765pLZ/oiuMSwO8ujlNKnI8w5jYPtChfdgOyGgCF6B8ngPy4MhSeIwA0/XGsqyIMuqkzn8nrCChfLH4OYgCQy/3H8W+jOxq6C9Fie0std6JudYiUDxVmR5ZOXDdHcd/52DbgaL73qyNGUwGXD5OzcZft1ycfomIzVGK8VudZPb8x1eHU3vYA5uX9ulrTqm29GXZ7I5Zu/6Hk3ZPhPLc1ri4VMYDj2c0noedVz/M58fBaB+5zv/gX/4D/8Bxhi+/vVv8JWvfJ2f//lf4N13v4h+wuC3r3zlq/f8bm9vl/ff/xG/8Rv/HIBf/uVv8hu/8d+zt7fH+vr6iezb86gzwHPK9SiGZzDIey+gx49eOPnsq4cDFKViC8ta+5iuySfHQD0OeEpTQ1FkfHZ5l/d+dJUPrtymm3d0uWDz2EJSJuC1kO3ee2IS7gU9q1UqFwEGbQ8ycokmfalHechnsZ2lHPguRLCiFG4gBK3oEosOUc8DPTvUBowVbB8wqpce5YROILGhn8AifnotUEc2yesIaIKAaj3aqwjGBMTEUXeXBKwI9YZCt6HXNYHPFaSr6ax+70RwoZ8OswGdCpJBsEd6AO8VIn3su+MQKQavuX51jSTpuPDqHrZTDMcVSjm8UyhtKRcD0rxjfjBEZIHtEmoJLBcFOvHoxjMYVcx2R4wmJdvnb3Pj2iZau9iKPCi4+OouWR41PdEpOQDu0GjNu8i65EWL98JwYHGd3HPcOAdVmTAYds/VhqK9GrVcZh1cHWMkGAEd2Fl8vyUBP4P2Mqh34gQWAdw+2AaSyWPojtzRftWfgULhG0GPA6MsRw4cN0Y/YVYPMTpBSKFTLBbx87uaHMoHGUWRMxoNCcFRlQ1KKUzy9Gmpp8e0CHd+il/QWl/g/q4CUH/hF/5T/u2//b94770f8J3v/Dm/8zv/J//iX/wmk8mUr3/95/n7f/+/4dy57adez40bN9ja2j4ET1prtrbOcfPmjTPAc1YPrgddsCOIyJ85euFk6sEAagUmqqp5bNfkF6kxMkZz7doBu7tzrlzdBYihrz5AE7BTCD767ph+DPtwOznitlZsjhcQHaezTN1LMPrpKLMIuEKB9egOVLXS8PT6iz7JvMs8ulHYXGg2NWkZnZlFwCYSfXjKgPYRgCSLCKTCWFGnxNZWFyeo8NHnp0tBH+p94hZ7ORJN2xT8SGjXFNJPjAUDooWQSDwTqGM722+1rEC2CrjVH9TRg7yXoyeuroE+oLqA0Z75XsZoVDCelASgqTOWyxTnDEpHfY02jmLYsH87i+0xaxBle32OJtMWQTBGk6QOH4Sm1vigmO0XbG51rHJFRcD1uil0DPrU2pKkMa09tsui07V3UNcJttOY1JJlFmefn4gZQLLebXkAzKDbB19BfSWyOmYtbpcZxs9JaCIr0V2PrJBeA1/Hf2ZyjHt1cdnHfoxMYgXdLaG9AYNti+ssg3qD0bmU3fAxlo6J2qSQKXVYsHQZt5orNMuWDX2RrfXzXPjiNh/++SfRxkEp/tp/9Q0uXDr3VMnhp1mnOx32wlfLcDjkb/yNX+Tv/J1vsrdXcvv2bf7sz/6Y73//eyfS6vpprDPA84h6/gfqvWzH04CIO5b4jN4+9y7v/r8fDDKMMc/VifrRdX+2aHWH+r/+b/8ff/hHP8F5T1k2DIcp9awmUYpGHEEUaukxVVyU9C+3vWupnhjkafM4Jh4UuDa2hpAITCRE8a9pPdL0omMdlxv6TV1pdSTE8e+YfxXPiLrrnxNiLpbLBbEBW0Q9DzZEb58svrfpnoutMgFnBNKA9OJniKwSKmp8XBZ1RBJ6kOYgDFQUKd/9EsrRN4e5XbpX1+pjD1y9SMdv9n0g24+ARynIDzRXPjvPYKNGb1rIPcWgI0lKqmXCcj5Eq4b5bEgx6v10PNRVEsfcO4NfU9hO01lFXaaUZYYST+cMWgU2tw4iSEgio2GtIkn6uAyBybTC2qgtyoqONI2i5bLMcFbRNAnLWxMuvLpDlp3gaNV9ShdReEyIYuN2B+bfh+G7IGkEKO4GmFcA1Wt8ekzpOvC7sc3lKmhvga0h24gOzKIjc8SqzdrB/h+ByUDlAdcE6hsBP/QM1guCvcRytkQvtpBRxv7Fm1ytPuLaD29SLRu8/Cnv5F/nq3/ta6xfXKMpW/Jhhh4Ydnf2GQyL+whnO6x9+DnrrxrwOE3t0PFR/K2tLb75zV/hm9/8lWde7vnz57l9+ybOObSOruW3b99ie/v8My/7edYZ4DnlupvtGAxyjHm+cRZPWndv43HX5KcJU33eU1oro8M/+dOP+fd/+AHTtYKmsVy7tsfNm3PSVCNKSLoIWtQSEPCpoKt4crgbAxxF0QR0E9PGXSaoFKQP/Qw6CoB1FVkf+tH11aku9J+2oATdBHTlcUaDEtoioAuiXsYKuguks/jMbiLYVBAXBdARXB1NdYVcobuAHQoEME1AGk8wKg4aC9GPxhPbYYAOgu0NB+/Bi3fvvO4Xgrpz/nnF/hwDQbqKKfAuE2SpWTRDuhGU8wwqIaSOdNSRpR2TaUXq6n6yKfRZcI6Lr1wneGG5zNi7PWQ8XmBSh24sojxb52cEr1jM00jU1Qn54IhlSFJ/Z4yDAW0s2THJiXcaYxzGWNKso8jbvjf5HACPA9dEA0E9JgLg/vgwU8guxPaWP4jHjS76xw5AekCabEYX5vYGtLP4OHsA6YW4bF0QHZj79UDvwpyAFAE9ARYaQWi+cJ0PPv+ccrnAXN0gP9+ws3uT5qBBpVCVDekWWAl8+vH7XPj4FS69c4G27vjh77/H7c/3UQre/vpbvPEfv9rfXBjSNGVtbYRSqveMaZ/ZOO9k66djLP2k1/08an19g3fe+SLf/vbv8M1v/grf/vbv8O67X3qp21lwBnhegurzfQ5bWJ6Dg2drYZ288eDRFfBu1+SXrVYeRctlzc2bs0MXkt3dJcZo6trhPbS1RQF5F3A6AhrdxWklODItFnoNzDHWZBUNsfo+6eIDk2Vkb/wqc6s3KTysEPO5wihgM/CFQjfgTIBcxVHp9oilWb2D0kLIQVVA3hsXeqE+F1mfvAKaKJa2eTypmyB456Mo2kWPHtLI0EjPOJkqYNXhsFZMURc56uVJ/3PfIov7EKCN7TPdRrG1XZPDa8lxgCcuapbExtdDtI8XcKeoy5zBoGX7/D5tm/7/7L1ZrGX5ed33+w97OuOdauqq6ond7BaHpkyRbLVIK0YimbZkyLIcwUj8EORBT44BvggBAhuJAVsGIgMyDAROYiFOEMWQjDwEUESHMq0hmiiKpJrNuUf2XMOdzryH/5CHb587VXX3rapbdVthfUCh+557zn/vc/beZ6+7vvWthW8MSeoouiVlldItapnwurBJkgWs8QxW5gQvTE0AzpwbM9rtUNdWdDxH8qretbRMxpVlimssZWlYWZud4GyjVGgvZTeCxLRtJyU6HtVqsJQCt9UyNGfEXydZ3jfa46AsuCWASYT50S3IMR1hfMIY0sty3sUIZiACZzzYhyHd6eI2Izvl26jEiEHm+S3eWFRsnDvLzrcXqEdn7JirhOtg+pHORsl0MgXgpa+9zNZbu6xdWME7z/NffpHhRp+Vc0OqStidpXA2y5aj0909Ye3SN+YHjeE5zTqJz/qf//Nf5vd//3fZ3t7ic5/7ewwGQ37t1/4tv/iL/w3/+B//t/zrf/2r9Pt9/uE//EcntNd3r+4DnlOu5bnY7xe33cK6cc07i4K4cT35sriZa/Lt1knt30G2aDkltjQ6vHhxSIyR8XiBazx17Q+NMwN7guJIGyGlhTVRAVSzD3j0UivTxi7Eto2lW7CzNCLcXy+iWh0Npo2TcAIusolsO1TSWkJL+CNejAdVDOigcalkZ9kK9KZkdkUd9+kmIwAmTAPGC0MFLYAxwiKlY9Gr1EqJc7OWg6maQFQKO42YSgJHg2uB0VGF9uEjB40wWSFVEj8xb2GlEZIkcQpvAy4DXStCzzNYKVk0GT5qjHGce2CXolOS5Q3jnT7KQKIanLNoYGVtgk0cdZWQpJ6mhrwT6PTmLGYdlG6IQZPlNXlHNDzL4+qWtFZkLzX9aGkdqHxKkjairwrS2ur2jgHi2yVjkPf8bs9TRp6erglQYRkBgrScQinMTahFk+Nn0HmCA2h3fzljQa8DVhyi3UjW1x15TLURE8tjoYFkRXRApFCnE7iExGdYT2gUaRFxL2YUg1XmZ+eMwjXKWUWWZzS7ntFohv5R+Uy23x7RX+u1+2LQRjMbz1k5Nzz0tkO40ThvGaA5HPbwPqCUIknsXQ/OPFinB7ROl+G5021/7nO/yOc+94s3PP7QQw/zr/7V/3ZHa9/rug94Trk6HVEavp9aWEdLKfmrzVpzIq7JAlJOdhRmMOjcIPB+5OGz/NiPPcbnf+s5yrLBNR6bmJtOuyna6IdCRr+BvegG3R4WFeV3vhWUJvOILQVQxHY0PdKCpagIQNVvZS5V62wMWN9mcrWtJjuPhEZclY2L2CnoGPBZ2+KoIVtAXUXqor2zt5IaIrgOBLcPrnyqiXiyqg0otUCI2GkkDIThsdNI1YsklcZlLXhpIna+ZInYv9m2OiTp44GOEaWF+dEezDzSDJTogqymLhy5F21RGEZiXzGbFwQVURoyHZhNOoSgmM9zyiohz+VDNyYQliaOjWU06tLplBTdBu+g26tJ08BsmpIWNasbNdZGaREBdWlZLBKSRNLce73ypqyPayT41HtLCBqbODmA71VL5q4N64yuBT3vAOCX2q3QtOzTkU002+15kINZb/Oxjq7lZZsYATVLw2C7yl6chNICbrQ6DMJM/8B5dzaA9XJcykicGZrEkT00odxuWHt0Bf+2mDvV8wa91SfpWXprXQB6a112rwroiSESXCArMt6rlsZ5y+DMbregKDIGgx7G6D3mp6pqvL9734GnFbdz+hqeU9n0+7LuA55TKq1VO8otF/hJgp2T1MgYY+i1fhvT6el4l7xbWWswRjOflzdlx+azmtW1DrNZw2g0x3tPkljSDGbTw5MK0bSi3raNEJWAIH3g0JgG8s1WEHukXaVCJKTSIjOVwuUKHSV3K7RBoy4D5pBOQCNTQ+TS/sl3WxbIyXaSedsea0dudBDn42DbnpRDbqBKwNLezVTtmyMGJaApKWXNGCKuJzueNIALKCvgU5mWsUrY7+k1sf1ZGBNVRtIK8BHfiah2JJ4g21VlFO3QmZrhxhxiJHqY7HZwLqVjS3qdBS5oXGMZroyJXjOf54x2eng3I8s800lBWaV4pxgMPdFHZmXW+u7IVFeWeZyD8U5GWeZEJAW915+SppCG5h1bXN4bvLOkhUPhqBtDDP4dJwhjwz6zE4/8P2176mgtQakG046WH9RHRS+TV5F2PD1AevHIdp2AomZTjk96Tlpey+0t/6uXPx94v74Rxig6CDMIHY8ZCjMVZhBsIFaG7kaB3o2EMuKjJ7+oKVwHHlTYzR59uwbABz/5Af7833+DnSu7xBC5/KGLrF+8dc2G956m8YxGE7RWpGlKmiZ0uwXAIf3PSQOFHzSGR2t9+rE+76O6D3iOUSc9Rp0klk4nY7GQvnaSdE/1oninOpjZtWSiTqJO6vMsiow0tYQQbgp2dkYznvvGG6wMu6yvKV5qakYjR5aJEnTWMjgHm3PLKaYIe62qg+2dphvwvdYxeVehvQiYo464oRgBYqEaSNZmNpX3q4EmaUEGAoL0bis61u3PpTAmqt0PouR3RQPlUIkQ2AgSU01ERxlhNk2kyRXUEZJ27zMNtSc5MDqPAtuAmoLPZdxZG0Ww0oDDIaaDSx3P8vNopEUXiYKpAugQJODUQugaYT6CiLediWycG1FOc6rKglYkWaAJoK3CeU2SOqaLnP7Q0DjDYpHhas329SHGOjr9BdZEksRRFBW9Xk3whqqMhAQxEJwprIpsbg5IU4fSkcloQJY1pEm1l4h+tGKAJJF+ZVMZYlR7o/HvdF7usTjtcdl7WmhlTnNYvAzNdRETFw9C9hAkawcWOdDOAmF1skdg9EeydnZRWl8HK5QiSFbr8vpmE0zWHh4tzM7BDBTf5mjFCM1W2+bKReMzfxmKy+AW4vETHRgdGDdTUj1BvZUSg8K8NoSOw+uahx/4AN1C2lhFL+dTf+PjzMdzjNF0hp3b/MNqXzwcQqQsK8pS2l/GmPZ7J2cw6OG93wvNfK/cqPfc6g+gdkhMD09n2+/Hug947nEtR7mn08UefbsEACd1UZwEw3PUNVmp23dVvbGOjgHdWh0MJp1O53S7nZs+b7S7oJMnVJVjPF6wWDi8j3gXCPEI0kFAzJ5TcYBoxSl5Wc0gUF4E5SLBglqLdF5V6FrTDCLkEJWSkFAtSd2q1nITVQJe9IHvbEXbPpu1QCccunftga6mCz5Vog8qIzFvAQyRbBJwRqEzJannWhii6GQM3S5kbZfTjrmDbiLNAU8e02qJQtZmay0Ctgp7ERs+1wQlrGT0EboeZxXRQ/AKuwj45fnmA7GA3Dh2q4SAxqoo+P8WAAAgAElEQVSAUR4VA94pTBlwRhgeYwNGe7zTOGfQxqM0uCZB4/Ba451itNuhqhIGKzOMDczakfK6slgTqeuENGvoD+fMJjn9QXVTYB0CXHlrhU5Ro60nSQJJ5tA6vLvh4IHfqeXP7Y0kepg+B/PnoXxBNDn5RZh/W/Q4yZkDa8SDi4g4ef2n2ZvqO1Qt+I2tXkd3ZFrLL9oJrqNDR+1Jszyt3QSSoexfsiIs1fz7sl/5I5D0IX6/wL00xDxVkKwY8rLg0uUHMalBFYFza4fHjG1iGKz33+WDeu96t+867z3zuWc+lwsvSSxZlh4af18KoI/mRt3Jdu9mnX5L6/31h/Rp1n3Ac4/q3Ue59+aBTmx7t4t3jNF0u7fimnzrdScMz9FgUqXUO67V7+f0hwVbW1Mm05IYYTDI0QrK+Y1flioKwHFJ24lYHIZl9ZmILgV8BKMgj1RnFOmmiMSjl7+4YwRVS7CoCtINahIwyxskCDA5oA2KHLqn7m036CggKkLTUaAF+BDFrNDOgTzCNNJ05bkxAqmi3DBo7wm1ohm0rE0ibbK6TdqOmcKFiG7NE9U8MngryPZEuoHviK/T0sk5JAatRFTic8RzqI54q/CrGqzDl5Y8L5kueniv9nRAVWNxqwadOWxwVIsUpRVZ6tAqEqK0x0Jj0EVFp1fR6dZsbQ0IzrJ5dYXZzBJcwtkLOyitCBH6AwkUVUQcahlIf0NVc0tdpuSFI09rmsaSEG/dXblVuSsD9TbYfjtO3oNkA/wYzBDqawcAz8EDe8CVWiluBDvtc/1C2lahbpkkJayRsi2+qYXJsUOZCLN9UJmI4E2nXWNXflap6H7mL0D5JthC0TEG18xI52+h64fYSa6iJjW9fo/RizWVTTn3mZqt17bZfHObvJNx+cOXyO+I8T3+d13TuD1h89JbK8sSOp0Cpdjz/qnr+hhO9Cf7HXv8Oq3tLnVL9wHPsu4DnntQR1tYR+ukW2aC6G99wTs1PLzbtZ8pVtI07/3X3fpaj0994hH+91/7Y6bTCu89aaKYzhuaRr4EDq8iY+lJffPPTjXyuHEK00DTQzKhciSbaghRifAXL2xOMOKCHDOoMkVQMnVFq62Jel9UutQPHRzQUS4QUk3T13stJKxC15KajoKYamKIpGMoVxVRSzr6kllqBi171N6k655CaTFPRCFtsFLaarYK0LZEooKYKOwcqhWIWrVtPkVAWnkkihADQWv5NlFAZdi+1mdlY0rIK5QKLMYZziXYbkU6LAlO473h2pUhWolZYIiRprZ7IlxjPf3BlBA1nU7Ny8+vs5gX9HpzBqszbOJxPpKl4L1GxYDRkW7vRqC+vMYmkw5p7tAqEILGmIBrNNYeky04yNBombTzW1C9JSBEmXYUfQl8b/YNu0S373WZamGAmuvtRNYZSJL91ytEDO3GouuxwxYEzRGPn/5+0Ghsj32YweI16D4GbhrZuTZHqxrz4ILN10vO/3jB9uQ6X/mt53Dbiv/we8/xW2d+j0///NMMNwZcLx3X39jmkz/1wyTZ7SWw3i7TcjA3CmYYo0nTlDxPGQy6eB/2xM83+549PYbndFta9xme/boPeO5yFUVGkhxuYR2te519dbOSVpu5J9Nit/N+9w0Zj2aKvfNdI8bIm2/uMJksaBp5T6PxzSzVD6hQ4R3Xy66DG4LzMm2UbEO+JcjAOIXeFUZGxXY0PWphfApF3RPX4mgVbh7Jx1F87hS4HoSosGUkmR/YugKfGcxMYhpcqiW2YRFJRpFsHql7oEIkXUQIkMdA3RXjQuVlX1wujJOJcm6EQsu73XNRRhLWS/EA0rWAI11DtSotlbg0KGyN8KJSsqAXs0Q0IqJOADTjec7ilRRjA40xeCV36CKrKTJHTCOzaYeqTGmcodurhGWKGiIMVqZ0iprEBmK0VIsEk3g63YV49SwyZlPPmbPbdPs1dZmgdMAmnrw4fLOLsY2bUDLObbXD2EhdW1xtGK7egnnmEqi0/8JMPpMwbxnCHsy/A2YVugPILx94rW9PreW/ZQX57IJvTQMP/s5Ccg4GLatzaGIuys/Jegu0GmGCYilsjrL7+h2TCRO183vi8Ky7UL4q58jwQwZXedKLDWfzJ3j2N79Fua3pPmRZfbzD21+7yotffYW//PPP0BnA9ts7jK5P2Lh0RHB07DoZ1sH7wGJRsljst7/E++eo+3ONc/4H8uavlozv/QLuA567VnfqRnzndTxAcXg/37mFddI6o+PWcv+8v/n+vRu4cy7wxd/5FnXzXgDu6JsSENVKeVl+lmYhUQ82kXFvM12Kc6TVozwYr1iKOxTS5nGZEu2OgSYDVShsFbEzCBl4Dfk0SoupNaOL7Yh80BBSjfLQ2fRUicKoKCLoXGFqGY+PiIdO1JFsHOVzSUSbk06gHoCL8nM0YOaeZFvM91xH4Tq6TWiXbaum9R6aRsp1JcLlDLlpL2/6QbVvtW2X+dCCHgVG4zuOLG+wNMxnKQFLd7BgsCI6m263pNutqCoBPJPdDknqGAxnZLkYHM1nBf3hlMHqApt5XJVSVoZyZvBe0R8uSJKAIogmx8o5MZ8ZstxTTQ1NbVmMLWlP0RuO0RicNyg0Wb/BJscA+Adxtdp/qNmF6k3x0MEACRSPQf6guCDrzv7rY4BYSctJLdmwlnmJLWD0EzEMPFSaQzlZy9cF3577tYSK6kw0SrGWqbDmurSwdOsbFeYQEzjzN8W0cOUZCLVn9aylXnjcrCQmnsWswlihHbXVaG3YuTra/ygCxDsALHfre2TZ/prNFnvtrzRNWFnpo7VGKUWep/fU/fm0QdZpb//9VvcBzzHqVhmTZQvruG7EJ8/wHM94MEkMnc5xXZNPTmd0XCfoO3V19sEzndZtkrbeswA4WO2gVlv7kuGmG6gvCKixo4hdCLjRjYLmiOSiDeP0qYSF6rD8Ez6KwZ9VNH1FyEGFgB2JYCRmopMxSkCI8uwJYUOr0dCubXm1uVtaR9xAETqimymugS4VIRHxsa7YE1zXOfiegDSiEu1RAnoRyEYR4wSzpDsRO/U0GaROUfflfSRTAT6gMVXAWy03qj1vn/b9H2A9FCKMNoUj7zYMB1NMGuhXCVubPQiat99YJSscziUkSUVeeJSOaBPxXrOY54SocHWCtZ4zFwQwLaY5aeZYW1/wdpnS6y+wNqA05B05ik0jn7u1gWqW4GpFYiM+jQSncXVKb1CTRr93yJta0zQGbSJZ5m68dqK0rqq3Wx1MLkyOL+W4JevilByB4gPQ+SA39eZRy4T7o2SibZ8ahaHBsffN7EaweEFCRU2/XTcKYNlzbe6IKWF9vT2ne8LsJAOoN0VTtMw9u/C3wU1h+qwM9Q0/AX7aECuDXYnsnHmZzgOW6faMsMiYvSHz+P3VHptvbvPdP3me6e6crbe2eeZnP8FDHz5IYcFisuDlZ7/PfFKycWmdhz58CW0OC6TuhR/OwfbX0v35zJlV0jSl3+8SQjg0/XW3QMFp++DcZ3gO133Ac8J1nBbW0bqX6eHL2ndNPt5+nizD896L3Iqr8zt9gRqjGfRztndmNwU7sidH21kQTKS6LOAhJOC6sfXG2QdEcrffhz3RBKpzEFJIJpDsyPNVA3hpE0Ujwg9dRUIIMmIelIiFW8nU3jtptTzBihmhbjU4ykRCjLDQJFPQPhJMRDktXSULMZXX2RLqDEJXE3Sbim7AtIxXaG/KS+2QbgkbpbSMfveD5IzpKO7SgT2djsJhk0iDkTu5B4wiGtEG6RTms4zGiz7GeUPUsFhkTCcZvcGCGCN5UaNtZDBYkCQ1m1fXQEUW85z5NGO4MiEGRZ5X9FcnGAVp6jl7fheFYzLJGQ5l9t414BpBYqbV0OgMQiMnb1yyUgc+aO8U5TylrBKSxBM9FN19/VqoBTRUr0AzknFulUD2MHSfkCmouoRqR06JwSdvottZ6mca8duJQYTMNztrQy0REnYgz9393X1GaPGK6HQ6jyEJ69+D7oekZbV4XabEMMLoZA9C98OttqfXTmxtSCsrG0B0UF8Rxqmc17hNy6pao3A9Hv/LZ/mzf/k829+qCbOG9cur/PR/9RP8wb/5EuW05NKTDzDZnvIH//ZP6P5Ch41L6wA0VcPXfvs5XO1Ji4QXv/oy9aLmiacfu/G6u8d34SWjMxqJzflR92fn/J725yTdn0+bYTnt7b/f6j7gOaE62BqaTGa3CAxuT2R8O3VwpPvWXJNPjuGR/Xjnx5cGZMfdv3d6TmItf/UnP8Jvff5ZRuPqht8HIvFQhoKsE60AhmaAtLB2IJksQc5+y+rg6+oNaFKFigpXBOphIN3SxERhZuKWHKfiCKyqSOgqmeSaRhkHj608o+0OxRBwhSYGsLWIh6kjsc2m6k6i5Ck1+7setTAAVXcpvI4oFQlKEXREz1vmLwiDY2qkHaeFqQipJtbit6ODtOhCDsluoDlr2m+LiE5lzLw3WFA1OfNZtgd4lvoUBTg0vmx7MUpYpibzDFZmWAXd/pS6TpjtChPTH5Zcevgq5SJjPk2wg5rVjRnaiPjl3LkxdaWYLzrMJjk+aK5fW+ehR66wtjGjqhImow5Ft8Yaz+5WQZIFktRTNZb5pKC/Pj942JhOchZT0RqlWYPzWvyJ2hzR2AhoCCWSIXYGaKB+C3pPCKBKNwQUhWnLuhy9nGMLdhYCWtI1iH1QxeHnqERYnGU8RH0N3DYUH5TH7XyfOYqVAKLpt4QF2v5tmd7qPSXgaP48ZJdFuxNqYX1sD9xM9llZ6H1U2lzNxDN4vIPJGmZvNeiX+zx+4aP4yzXKRuLEcuXF65jUcOnJiyRZQlak7Ly9y86V3T3AM9maspiWrJwb0lSOwcaAN773Fo9/8lH0gTE4pbjn3jBH/1g76v68nP466P68nP66E/fn056Sus/wHK77gOcE6lZbWEfrbjE8Ry/yZYtoOdJ9K3WS+yj7dONiy5H4pnF7OTx3Wn/rZz/OfF7x777wTarq8F9uQeS2N5aCZg3sNuSbLfhZAXP1KBsUiGh8DvXAoNsMp2AV4AmtA3FagZpKIGg0oqOoM4W2wiCZVqgctfwuIhNTTWjvnBFiHfGFFlSkI3UWSZD4ij0/mASaAmKucTrseQh5G9FRxtmjBaXVnqCZRr4Emly0PGjRBIUy7GmAomwSqojuehSBOqaMRwqTKqzxuNCKZ4y859AyKUpHYojyxlEkVmyno/UUnRprPbNJTsAy2umhVCQvSpqmS1Y4xjs9mtpy9vwuPijS3PPmaz2MieTdkugVr7x0gTR7DW2lLaYUTKcFLljcNPLmzlmamWFlY3rYeTlC4zTBGxLjaWqL1sLyLMNbTYvX0rNQG2i+L+2i/LJMQCkL1TXRzHSeattWS1y8LC2viV60PTpFAOJBI8Iov1e2ZYiCsDB6KG20pSOzMlBvQfmipKRnWcv6fEDEzTh5vc5aEbSRdXUmeqOwkLX8VNaN25pkRVHVJbEJhKHHX88IXUXeTwHNaDbizdfe5O0XrlCXDeuX1li7sEqMkaK3j9q00dSLmue//CLVoiGGyOr54U0Y2Jv90XC3692Bx76x4XzP/TnLEnq9QvJyD0x/3RpjcnqAY/mx32d49us+4LnDup0W1tE6aQ3Pcs2DF/nBFPFbNew6+brxAlyOxM/n1S1Tyu/Wbnv55es89803brjoI5L0bW7yUagIZiYalri8QixErVHhMCOk8ARj0HUr4zQKl0V0o1FVa/xXs8d8eCOPZXXrvjwA14VsW1bUra7GFxrtwCURnynJ1fIBM4faKugrYh5IpmDHak/kjIJsRxwMmxxcpoSdMmI8qAMko0hTQKOE5VFB9svMWz/lADYDU3uavpF2XKKgTZOPaBQinFbIxJr8oCD18laNg5BIDlsCvlGS2m28jJBrqKpUMsnSgFUOk0NTG0Y7Q7LckWcNNm0Y73YZjyrWrCc4YVMHq2NpB/ZgeiXnpRcucPHBaxRFzWJhqStD3mnY3emzsjqGNU23t2A2zVhZa1keDVnWMN3pkuYl1nqM9eLQvLwctfjrmBWIb8P8u5BdEOCh+6Lfyc6L0Fhn+69pT4/9dYL80ynYroCRg93U6AXgLMGOb9mc7Lzoh0IpbFNs3ZaDh7iA8jXofRz6H5eJMd1tx+IjpKviv+M9VC/KvsYGZt+SkXXJ9wq4KwprDcFoqrymOKPZ+fqCTtHBu8B8p+aN8CohKqajOZPtKddf2+LTP/c0ddnwlX/3LFkn4+GPXma8NWV0fUpnkFPXDlc75qM53ZXu/vV1CrqWW2ntHHV/ttaQpsmh9tcSIL2X+/PpGv/JyXcf7+zXfcBzm6WUZGGFEG+jhXX36yAj0+kcdk2+vfVODpQdZYuWoPH2R+Lfud32xd/9DpNJRa+Xsb29384IRBH3ckP3gYBCz8RkUDeKaKIwIzf97AwqSup4yKBJJV6iuCrp6qppWZJUbmq2dS9WSoCGmUgYad2BdCpTXG6oiQp8HmmGso6dKdARH0A5aTdlOyIQ9lbhLTQd0Q1VeYQgaen5juiPXA9U2YKmWt6oa8NJtRe9T3SBZA6+D9EGzCzS1CKWxkdIFC4YFJ6MhszWRAzjKj/wTSLio4AkqQavwce9mTe/UOQrDhehXAggUtqjtGI+TUlSh3MGmzaEEEkTR6dbYnRAqQB4eoM5zdzQNClp6llZmTEedXjpuw/y4MNXSTsNk6tdOt0F5Y4lqIAuoD/0N7CU/X6NVdewWcvoHAQ7SzYoCOgJc0gfkHaMKQSI6BRIJcvKj4UZsl1aAHhgQ0oAjEradk7bMtsDR6E1F2xBDYhxYd6FOJNMLDMUM8HRl2H+zX0iqdu21ppNea1dhZUf3183GcD494V9MkPJ45p/D/IHFAwiuhPxpadxHj2xNKOGpGvZfmNCaMSBej4pefQjj3OhdOy8vcPahVXOPbLBS197he5ql9loztZbWww2+mxcWqUpHd2VLk1Zs5hWhwDPSbbGj1t3ArKk/bXv/ryc/lqOv9e1o65v7v582i7LcJ/hOVj3Ac8x6uj5cqfTQzeuHw/1uE+mIlobOp2sHdW8O67Jt1/qAGi8e6P7k/GCqqypbmKkqKO0lGIUZiUCTc+gPCTXAm4NkjHS5tm6ce2IkptkFXEdjV5EbCPARQdhkFRQ2AjUEdPeAV0ha4a2xaRbN+a6ByHfPw9cpoQxSiKhByGRKTHVaCKSqq6IxFThE9B1SxkEibdwfY2qRdhM68uTzSTE1NSQzuQG3XTbx+ZtS2YBJGJQaEqobSSZBlxX4w0kNaRdYXImk1QEL8vBNBREhVIOrSMxaFSjiEnEKkecG3pndiXRXWlsUrNYdPBe473GLVK63RlJEuivzIhR0e1W5J0FPkScy+l2F+yUfdFKB8NgOKWaW3QqxoLr+YjBygT/msf/qaJJPCZ3xHWF7d7Yyi267N+Db3IZxiB6muyiTGGFBkKUaAaVy8/j58TRWPcFgNiVG9epWoZoCUzWPivGgsrKWHlciAYoGXAohsJPBGjFCGEsr/ULYc7SSy0D1EDvYxJroW3LEClhc+q32imzq6LjUalMlQ2ejlRvgDkLbuzRE2Ce4K6k7F7ZITSBcqelIivN89df4skffZy8mzMfz/n673yLx37kEfJuRt7N2L6yi00MWSdn7UIH7zx1WZN3D7syn87k0smBrIPMzr77c3pT9+fTBTxLxH26OqL3U90HPLdYRSHJvseZHjpu3R0Nj4iTxZX4zqcOTpbhEZ3FYNC5LT3Rjeu985foxkaPsnLEeIQ5UpFqA5p1uYFkV8DMJI2cCMlMk0ximyCuUP7ge5fjvtf18NJGagpp+5g2sBMjRn4Qxf04iI5H14qQKjEeDG0bK5FIi5gI+AiJePqEufw+mQWaXOE7oGLELiJ1X6FQ2EVEGVkzGQcZR7fikKyCACfrwS5ErOxSec9JJdsDyfRSQf7ZGkig6SuajuwLraO0cmC2FNNBIdldy28QRcuIGKIPqLkm9wHd1DTK0GQaFTVl6LD1ncjgyTFrD+yCMoRgmU1Sur0SiOTZAu9zrr29hkkC3e6MM/2G6SQHImlRs74xYjbvgIqkqaM7rzDGk2aeNG3o2xnXXzUMHvLETkrecYS3HOmFI3+g7AE1xAdn3kYyHGhLVW9Dc1Xyp4hQvwrFw5A9IMdv54vSjrIDSTb341aLo/fbUaERwbJKZJy9fEWAzOpPyO/m397fpeGPSxbX0p9HpeB25LX1rgiZ67fArkNvQ7x+bCbb10b2Bd+eSwMY/98ta6Rg8RIUj8Lw6Rb8nIHpdyAdJtS7sPX/LOjbKcomxNigMQxXhtTThvnunO996QW00Tz5o48xG5W88JWX+NCnnyTJEqIPPPH0Y7z+nTfZubILwJM/+ji91YPszvJD/4vD8LxbHXZ/5gb35xijMJVpcsfhp7day+/r+wzPft0HPMes259uOk6d7JRWp5OhtWI2Oxmwc9KVtZb0J6cneueW1mMfOMeffvkVdnenhx53vXZfroCqkCuh/ataOyCAiu1Y+Q213+uIB46dcZGkDCikJSVPk33SQegDFSPGR8xCWkXlCjLKzdJvJ+ITRVRtPERQciNOFUkZoZb900rhckhmXqIq6kgNOCMtsBBkgitqSUj3BmyQbehlK0W3QmnTgh0vAm0S0I0m2YU4gGAU9VCQQTQRl0i0BLW0r/ZuJloE1naGBKommnSuSELAjsCvyPMdFmUj41Gf2SwXKxobcLVmNs3Y9kPOXBhhbEDFSIiWcpFSLjKytGE+z6grKzeTCDFYEXqnAaUDWe5gBEnm6T66IFKyeAl0HvHXwZ4V8HDw9KmuCNAJc9G56LRtE40QYfe6AInl1JayAkSaTTEYNLl8DsFDdb39bB2oQl7jd9rHGiR2pJCJqWYXJn8mo+bL02ryR5D8Ndm+G0l0BBrmX4fJ16UllZyH+ips/Tas/SfQ/aichqGREXS3I6+ffFW2Wb7egrTz7RlpZV0dDEUvx88ck+cCcTdjZmekeYo2CtUYOt0OvaEm62a42nPpyQforfZYv7jG83/2CldeuUZ32GHl3JD+Wo+oFK7xfPBTH+DSEw/ccPW83zU8d1JH3Z97veKG8NOD7s93s+6F39FftLoPeI5R1hoGg9ubbjpOnRTDc3A03jl/ohf4Se3jMoUdOLEL/t32bWOjh2vCXnYWgCfi2/Fh5VhOTANxj+k4Xul2TipKj8OIABkXMK02RrXqlZiAbkS8EYjS0goBFTTaCwNkaqCJuK5CJRFTRexuEE+crgAJkojvSO7UXuRDC7uSufjwVAOFMgozjzIO34K4ugfZvB0OqlpLmhbk+UT+P6QR5UUj5DL5bJJ5xCWeUFhQipBFlI2YWUQtogAprXA9RFwdlOybizSJxiwU5BGTOnFrzgNNbRnPcvJOJYxVElhZm2LTyGhHkyQeAtRNilae+SzH2EBdW5QzVHWCqxKStCHtlvQHC2l/9eekWcB35ZywaWT6XdE9xRhFQD1tW04HmB3bY2+6iSiAwfQh1ki2WJDXaAvxivwOL+xNegaqN9pzMJGJrKUzssmgWYh42PYhuShj7Fd+XVpY06+JPkdnsg2lxLDaT+Wfm8hkVTNqR+MdVK+1p5xHXJbHAmqqTfEFmn8TFi/LNufPIyPsM5h9V95Leg7CDnQesCgMdErydUvqepSxwTUOpRXGGspZxdVXr3Pu4TNknYzBRkbeSdm5OiJJLZeePM+lJx7g3CNnKPo5/9evfJ7FtMRYwwt/9hJ/4+/9VR7+6IOHrprTaPOc1s3f+0jTOMbj2Q3uz0rptj1W3xX35/sePDfWfcBzjAoh3OXppjtvFx0dje92ixNuk90ZC6W1ptfLcc4zmcxZWemd3K69S129NmY2X+z9LM47QYI4+yLkXeZAaQ8qLAOPaKey3msLMmJbt5lTeIVGWlthmeSu494nFwnUq0aei8GOA2jJrDIuoiPkW4HQCopVRNpRE2k1NQ+IgNhEMCOImcInrX1i0gKgAHYSCIXCdWUqCyJlT0wOs0UkpCKU9u12mgxiLu27dApVX2ax8IroI0q14aC+bbc1IvrOp2JKGLriVRSDElaqCWil2v1S1F0FeUqMCl81dIOiKg1FEekP51gT2N2WEXRtFJtXB6yuT8lNRV2ljEcFRSGu2UnmqMpMJoycwXtDnlckaSAv2tZCFwafFnAQm4jtRPKH2BMHx0YYGgDftALkyJ7PjUpEJ0MUwGg6bYCnkSktnUH5FtD+/RMqOVahgsHjLTtUC4NjCmAgGprydQEovafAjwT8lK9JzpVJZd9oR9NjFAYJLS21ZEOYoTBrmacaig/B4BnxAKpfgckU3K6As+olAUzlqwKaiLJfdZCWWLIuDtf1t6HUMHthSpoUGGWIBNYeWMWmlsnWBFc7YoS8n1EtamyWcOWV6zz+8Uf48GeeJO9mfOXzzzIfLzj3yFlAfHn+7PPP3gB4TqtO4+Z/0AfnZu7PWZbcNffn050Qe3/WfcBzjAoh3lX68U7PyZuPxp9sm+xOGJ53Sou/F9T21WvjvW0IFxOkmzNG9LWtzleLg35bkWBFvLz/iEwatXvOwc82WHDrMnGlXKuLaUDHKO2qVJLVVQVRCejQTcsqJeC7QVooHmwEXSmiU/hCPiNdSbvJG3m9qaVFpl2bRxnEvDAYJTqfHNJZJFnImzZVpOkrYqJpOoqmEQAWWp2KmQdcV0ERJWTUty4pVrK2QiFj8fJkQGmij4RcXIGNEn1QVGCJYJWM+wcB8nU34nOzZ71SOst43MVmkbJM6A0Uo1GBdynGOiKO6aSguWrxzjBYGVMUJc4llIuMotNQlilaB4x15EVNmjuC1yi9f0JlF0QUbFaEDbFtAKfp7re0QmhFvkFAQWhEB0MmzEj5ipB32gqQSM8K6NAWbAdCJq0luyK+OL1HZDRct27TbiIePTqHwf7DGvcAACAASURBVFPQjEVInF+G8g1Zp/MkpEPR9vgFDH5E/t/2xDHZjdp9RsCTXZXTr/cjsq+2kFalyWH+Coy/BMlZqF5vnZ0dEi2RtePzGpRV1G9r3C40VxRJrimGKaoxGAxnHzrPD/3YB3nluVfZfmuHN56/QpJZrr26xer5AWcf2sDVDd55nv0P3+DjP/kU3vlDwxcm0bibtNRPi+E5HcDzzqAjhMBiUe15jp20+/N9hufGug94jlF3+5y5XUHwQV3RZHJYV3S3hNC3WkWRkiQ3Rlgc9Qm6k3q3z+/CueEeVSxb2t9mMjr83LD328NrRaK0cXwU/QuwnB0OGbiOtJySTSR0s1GgZEorJIHqEoRU/HiaDuRvRPBaGIEclNPYkWwpZBHKiG0U5oDsqE7Ar4iuJ2ZKjP0U6EZ0PaGv8InCd3TbmoLogpgMGiRyoJYwU6zCG6CSHKp0EkmqiFbS2hI5jjhB6wB1LjlcpgWHeNA6YpOGpBuh1oRa4xMRYnsDPpdsh6ClvaaCtO1IAGuYTHokVU1/GESTU6f0egt6gxmLMsMmDUYFlPGoLUfzbI2f1cz7hvwjkV4+xWNQRtPUiogiyxsODjvGVouVbIC72upoEmk37T2tdUCOjYA3FSRkVXdg/LqlfkORqob8srSklJb2UGhNHWMt15mbymuWABot27WrwtqYQh5POxLpEJq2Paag92EBPbERcBO9aImaCtIL0jLLH5CQ0ryBWMp2kzMQR+xNbc2/I6zT9JsCsJIzsm/FgzJlFmoBcTEDO4j4SSA2Gj9VuHmNXjOwqBhePMPOlR3+6P/8EkmRsvn6NmmR0D+/gnOOa69dJ8lSHvv4w1z6oYuMN8e8+q03eORjD/K1L36D3asjbGaZbk34j/7up9/12r1XdVqZVrcCOm7u/pzetvvzfcBzY90HPO+TulVw8t6uySfN8BwvkHRZ7wbG7mV9+sce41/+6u9QL1qtzbuArP1J4NYVuX2/IZdx80g7saQjNODaVGtTiY+N74DdiigCymkIEdeLIhietNqgHOr1SP52xOUK11EUm60nUCrtKTrA6CY758THJzaBkLZC50zhBwZXKHQdZRweRdNT5DsQTaTpi4uyt6B8ICglTE0GupRprKSCoGTQPppIk0ZiIaZ1oRBwtlcmkpmaSMRnhs7C4aymShKcVVijUCHQFBLTHTVY60gzh0oiVW1xTUK3W0EMzCYFnU5JXSfMZwXrZ3YxZx2zaYGvDIsdy2xtSKQk/+6E6SjFPpxgsogfaPLzYLQjSQ8Aai8sibLC5tjzMj1FBYtvg1kDLIQJjP6w1dA00P8RsBfATzTZuoK1jOp7mvhyxfBT0tIyPQE4yUorQkbaVdOvwFCJwd8S9CjbevtEBFF7sOdEqBwXAkrSC/K4sjJqHi1UbwnQiVFerwaQBJkCCxoWr8kYfP8ZYZRmfyg6IteKrmOE5oq4MPtK2B+7Jj+bjjwWxprmOgQbSFJDMwn0ez2ijzS1Y7ozZ7DRRSlF1kkppxXnHz3DfHfO+gNDHv3YQ2ijSYuMxWTB4594lJ/5+5/lq5//c66+tkldO77y+a9jjOHDn3ly79iczo34dEazlVK37X12cPz9sPtzhxjjnvbnndpfdyNW4rXXXuWf/JP/jtFoxHA45B/8g3/E5cvvj5blceo+4Hkf1D7bcbzKspQ8T95VV3QagaTLWkZE1PU7i7xPMoz03daYzWrqKtwkFf3mL9pzrgiBYLWIQy2EWgI8oxIWJ2rwqYxzRyUxFL4nOg5dR6L2e/hKewEmZhGxs0hIBXxEDdmutIZoBDCRyFoirTbtPgWsVzglGh3tQJUSNWGqtlPUFZ8eEV1HQhpZDDWmNVeMBvJJRDWRpqdpUqATwURCoYjX4l4Lz3cEpDSplpbYotU8pQpSIAnUyhAqTWI8VUcTEiOxBglo77EtVdJkwupk3YrYaJzTdDoVVR0IQVFXBcP1iUxvOc3O9T5pWrFxboy1kOuK7iMlaa9he5Kj8hpT1lBZ6qsBdUZTo9g2Q4Zrczrdau8gSnAolBONG0G9m5Keh6RX4WeRWAsrEr20q5Kzwo70VjX1dbBDR/dsIPnhlOpPwLU6GNURNkd3BFz4iYyE2057Vnnx1THFgWmwNjw0lNK+2vipfS8d1QKiULZtrbHodFwroLaPCTOjooApP4Z0IGA03ZBR82YTMNBcE3CDEnCTPdS6Q19s9z2F7JIkul/5DU8zNaSFRVcGV0V85hlvjumtdQk+cubSGcppzXx3QdGHnatjhmeGXPjAeRGwh8BisuChj1xitDnmd3/tD3npz1/hje+9Rd7NeD15k2/83rf4L//pf8ZT//FHjnlFn3ydHsNzMjqa47o/V1W91/66G8Dyn/2zf8rP/dzP89nP/hRf+MLn+eVf/iX+xb/4H090G3ez7gOe90EdF5wote+a/N6j8ScbV3Fcc8Q0TSiK9BgRESfX0nq39/r8C1cgtNmWEgRxrG1qH9FeBMy2EWHuMntKKdHUJLNWsxPFtM8X7CWAyoi3BhdxRSBmkWZNnl+8EEl3A7HNqgqZiJZ1gOR6RFcyPqWIoGTbEPE9TayUADC9P12mHSKetRHVZmO51tHZVlHeeyn7qaMimIhxijiNaCean5hHkm2FTyOmVmJy6CHZ8eTbUA8E/PkAaENIwacarxTOR6z26DlkVcT1FSVG2JXckVonI9taBNdVZUkzYXC0hp2tPusbkmJd1RalJK7CO4MKAeUCnX7DuJORhhllOgAN6UZg48Et0gEoIrNJRpZVEsNQyrFI+qALxfWX1mnGmjRTJKZhNW7hRxE3FcCxNAPUqbA1YRxJL2ixB0gjvadEi2N7ApCSNQErPgjwyS/uh3MugUxs2D/FWyG0VuyFg+pUfH902j4niu5o9AbgBbhEtz9i7kYyaRUW8vT8kngEJRvtOPwVESiTyD6FWkTbybpMZvmpRFCAtOfWfkwxnlvCVkpwnrRISTopRSfDJpbKNuxc2cUkGh88KPlj5hN//WM8/slHeeuFK8QID3/0Qc5/4Cz/09//X7n26ia7V0Y0ZYPWmgsX15luT/jC//J7e4DnB0vDc3eYpXdyfx4MuvzSL/0Sf/qnX+aZZ36UZ555hg9+8CmKoniPFd+7dna2ef757/Irv/I/APATP/FZfuVX/nt2dnZYXV294/XvRd0HPO+Lem+G52Cw5nFck0/jr5lOJ8fa40VY3CsG6tlnXwf2nXNuHWRFETcnSNvB7b9ctUJn3epairdFpwMKlylCC2hAdD6mltebinaSq/W9iZCO5OamnaHJxDtHL8dUg6LpKkwd2+MasQsEqGhpT5k6SnTFIoqvUBGxXqFcFAEtEixKAyaIEFc1cgB0A9G2QCuKwNoVCl2CipqgA3YRJVU91YQKolUCTKK4PMeQQAfSeSA2kcw4amPQpaZ3tiIqjS4qktSzmKYUnQqjA7WTttd0nJNmnjxvCFERnJLcKCxWN9RTSydMUbFh+NAu3miy9Qg9RTrUaOVRhZPO30QAgk7ksy2bDqYHWV4TS5jvpqi6YOX8HGUE7BSPiTan2YKkCKgNheqCj4ZmR0I6/UI0V+lZ0chUb0rrya5C+ZJoc/wcxs9C/2NQXES+YaM8R7VxE2Emj+usHYNvDShDLR4+yar47ZRXRN+T0hoXPi/bm35dXlf8kLyu2RRAM32ufe+F+O10PgyLF2X9UIoAmiCTZM0WJCal+0Mw/VIk1JGmrBlfGxFWetRVQ3+jR9768Tz45CUeePQcj/3II0xGc7Q2/JW/+xk5/43m6vevce37m5x5aIPR5hiTWFzjqedVq7Pa/+PndJjn02lpwb0BWsv213QKP/uzf5uVlVW++tWv8Bu/8RvEGPnIR57ik598mk9/+sd57LHHb2sbV69eZWPjLKZN4DXGsLFxhmvXrt4HPP9/q7t5g36vtY/PmhxeU+uTntK6+XoH/X/G43sfYfFun9/ly2tHn338dUFGhFtTPvRhaGoqYVhim36t97p3gWgkSML15HNLtxR2BmYWW2GtfAHrNhcrQjsBJdtcrIJdaOxczAfrFQU6kl+X6Sy8CJabXtxrremJ3DtV8Gij8QmEVATU6RbYubSldBMlG0uBdhGtorBgmQSO1h3kjRnxBHJdi93xGBfwWoFWNFELmYVMjC2Tv2MErIapIvY0uttQu4wQFEniMSbSHZQsJhmXHrpG4w2zcUFdJ1jtmJcJL3znMsa+Tr9XUteGJAtoU9H9ZANPaqrvBfTEox82JGchzTzRR9LMSctoHZppy5wEcI0h7UdYSKtJm4hZ1eSXpZVVXxHQoBPo/pC8qfxBWEwt1BF9pSJels/cbQogWbwBoz9qhcHtcet/Uvxvdv9fSHtA3baSlIAM24U4l5Fy17bB0taXLzoBVMtzK10VYGXaNln5kkx1uR15jtKw+A4MPyP77XaheETYqd4PQ++j4K/LuukFyM4IE5SeE3Zq9AeaMA74CopBhsYRnCd48M7TX+tTdDOUVgy6feazBS9/81W2r+6Sd3Os1nziJz6Gc46qasRpmUjwnvUH1hhdmwCRcl7iKs8nf/qHD19b9xh7nG5L695u89y58/ydv/Of8wu/8AuUZcUf//GX+fKXv8Tv/M6/59d//f/gN3/zt7H2B/PW/4P5rv8C1T5rcqvBmicrWpa20Y2P3m6u2MkmxN/8vXY6GX/zZz7O//yrv3/HqxP3tTscOAwqAtXhrder4Nsrq16XlpWeynOWmV1SAdqQTZ/te+IIY6MIScR328mnVNpBrhBgZRrx5bG7oiVqEnArogNq1hBgVAJa9Dy+E4nGgGpHz51ojjAClOyOIuQQOrDMxgqJQleI03NPYyYOOwvS3kJJq0zJvnol50ewCm8VVTdBLRx0DElaMp/laBOYjDoYE9DKk2SOVNVoHWkqQ9FdcP0bD9M0Cd957mHOnd8myWp6/Rl1laGVgqoBP2di+/BtTXo1cPETO/SGDq2hrgXo2I60kGIJeVYxmxSoOqAKhckVqw9VJAMoPijiY7sqQuS4gM4jYGKkoysWVwWHutYzJzYCOqbfFqBUfFCYmWZTRsS1hcHTcqPzM4mM6H8Cmh0BH81VmaTSBqbfEC1N/y+1PkBW9EG2I6+3qzJGngzbYNgh+O9AerHV/TjZtu3J2mEhYC82Yj7oplA8Af2PyPP9TJifegswgaAik+cV2cJhrEFrTfCB/noPrTVnH9zge3/6IiYzzHZnlNOasw+uU/Q76ETz0nOv8IGnHqHbLVhbG/Cpv/aX+PLn/xydGlbODnCNp78x4NN/61P85H/xV/avmR+wltZpZmkVRcHTTz/D008/A8go/O3mNp47d47NzWt47zHG4L1nc/M6Z8+eO8ndvqt1H/C8T2vJmnh/e6zJSTNSN7tm81ymBu6uKeOt18FQ0mvXdskzaLV+y2dwHKYnGtHl6FL0NSGVse1k2r66Hes6OMEUkZ/NIhBSjZoLiNA24HOFnkA6h+UQfETWLNcUvqsxZUSXUSIVOoFqBYJSBAV2rvBNOxrf+iPuQb0k0pwBbzQxirbGuJad0opYeMqVCFph5ori7QhTYWtoJA2+yUDVkI+jZGhZATCqBXnVULciHtWKwGU0vili26+J+I6StomGZGTQSUD3I7mtKMtMzkkjawSnSfOAVpHVtRlrdkFY22Zr1mV7XnDlpRWCNnS7cy4/cZ088+z8oWeen6W/NiP6wOKqZfPNNVb6V9A64FG4bS3BrBGyDej1F4yfVYx3uphupH9+xu5uj8ZUZGcqskxS1KOG5LIwOGEu7anJVwRArP91aWNRiIDYdlt/nhQZTTciOKZ9bNlOW7wGekXWaa6L4WD+INSbMnG1FyuxlJYlsl6yLqyN6cv27Yq0pfRQ2Ebb3Z/uyh+C6hrkHxB2RxvJ3Jp/T7Q88zeBifgFlS+BHoivz/ZvK8w4pwmOSE12XpFlFrsKkzfmfP8brzHZmbZhoSXRBybbUx7+6IMsJiWvfPM1zjx8BpD2xn/6X/8Mj338UV75xquMt2dcePgs+TDnQ5954tYv4hMupcRz6d5v93QBz1FpwZ2EVK+urvHYYx/ki1/8Ap/97E/xxS9+gccff+IvTDsL7gOe900tGY8Y4zsa9d3iipw8wyPriXhaBKd3kit2UoDsILjbZ5xkYqHTyQ6BHQmDON43n/Ytc9JjD9ik16XbU6/LiLqqWgDTetbIG2tdk/HEuSJ5Oe45LqdbBwGSZG41hSJmCj2PqCgZW3YuU1OqVMTcoFQUDVFQewuodp8aE6kuRpoVIHpU0HL0nSSbByVxE9kI/P/H3psFW3ad932/tdYez3jnoecRIECCIEACpASRpiZHqUR2Uk7F8VPykidWKUPFLy67Ile59J740amyE8u2/JCyyrFiDdYUWRQoUSAJEDO6Gz3evvM94x7XWnn49r3daEw93EaDpf6qurr7nH32Xuecfc7+n+/7D1oJ0brJ0QKFjcDGnjpWaNdwg5SQl+u0GduFQFsLkvEeaxxUimAkoEjXXrg8CdISKUBHjk6SkW/FhL0KE3iCoKbVydHaMRmnqGCCCWq+NLfJsdaI/iDE+E1+cHGRi8UceI/fsfiBp5oF4zwWhS8VBJCENWkJPV2hnKdtPFt5CNpgR+B6gsWCm1OOLE8p44jdyTxRVhCnmqzqMbc8ImrXRPvnUzM1Ni1onW3M+mJ5X/dl7skpcU0urol83PQac8KhqKdaK+KgHK2C3W4IykPY/E3oPN/weBqwUo8aJ+epdJswck4HCMhxkYyi2l+SDlGwLKCMRqVFIEGmfiLEZW8FMLXOSQdq9KciSY9WYPSqAK9oHnSoqIqaqrBEHUW2obB9y9XtawQ+RpcGE2jiVoitRHVpAkPcipkMpg3IlbLWYq3l/E+d4/qlNWYCTdQKiYKAS99/n5/+L1/AOkdZVo9Qlv7ZI56HIQ2/t2Mf7sH/7t/9e/yjf/S/8k//6f9Bt9vlH/yDf3io+3/Y9RjwfE5q/6KdJB/lmnz/+zvM9cGd5Onikx/0ifs7bEAmoaRJEn2g4xQEH/xFo+7ymPtfE95CvC036AZ71in4oOHwaCgWQBcQ7cr9OhfQ4EJwLU+0AWYizZTbG0tO33Jixsn9yorTsTIKcoVRovRykXCF/MQ37snirusVFEte1pc3bQrlCStwbZGYK+dJNiVslNjjQkUVN8dxIg+yRsCO101XSPlbnCKFtDPKxr9HK7AKM3CYqUe1NE41XZ5mJEeoQDnC2uLHhqqIcEbjZxR5FgMOWxuSVsF8Z8xSnLE9CSnKmryM+dr5m6y9neKmipiS6VXD3LzFJwq3B4UG5TSh93RxTHcSKB0LcUbUzbjhOtQDUTFhIFkSMFKupARYVO4wzlKMNaMiIjxVoxshi1JAJGT0cEVGXN7Je+QrATcqkm7K8GXx5ek8IyOn/AoUl8UjJzkCwaJI36stIRO7Cvb+SB6TnpT95NcRSX8kxzYt6SJVAxlnTd4S8nL3WTm+yxGwdFyATH5BxlWmD/5a8xw0hMvy1pl+47x8Q3yH7AhKC0pZKm2J+xEzpztMrhfkWwX9E22Wn+lTXgi5/s46trJEaUCZV8TtmMHGABMF7NzY5p/9vd8gDA1nv3qar/3SV7hxYZ23v3eRVi/h+oUN+gtdZpd7DHbGzC316XTE16fbbVOWZeMhc1cfyQeqR+m0/GjI0g/nOZ88eYp/8k/+z0Pd52dZjwHP56Y87XaK957RaPLAXwKHy5HZJ0FrOp2ULCsoy7sjT38W5b0nikTre6dC7Hvf+94Ht70X0nIknBu1T0ZuAIuy0gXwEVDJ6EtVwtWxkfyJd+T2cCzbKG4Doc0SXOKxkadqgwqk0xIOHD5SOGtRVhOMPUG433UREnJVeQIPOOk4uKABYHXza7L2mEJB3XTkLBjrcT1wgai4TK4p2x5TQ5iBVh7vFcqBVeBjBaU8TiFAyNFgGq0wocj8CUXNpWso57iFYTVUYUC+lRDEFd2oJPMh5SCgrMGEnnY3xzlNVYYUk5j1ax0SVRDGNYktWO7vYtKc/IrGLIdo7YmfCpi7uMuk6FBWATPHYLV3g3rkqGvPzjChvzwlCCyFM2jxfwQj3Jm8stgFGRmXWUDU86jMUq6JZ83+8uvJrfwqGguAAydlpOPTOg2TH0C5Ka+LmZFR1uzPy/ucXRNicT0UonAwC8kZ4QPt/RkECwJytv+dEJTTU1A2MvOgK2B7+pYAlvGPoGyUfHHT1Sk2BejYTEBT2nB7lG7I1Bp2fleATudZ6TB533B+vPB/lBcyfLZbEKQGU2mWvjDD3Nku072QwewIExpMENCZ7TCz1Ke/2AOlePUP32A6kqDQ9fc32dsYknYS4jSk1U3RgWFvfY8oDbHeMRiIdfjy8jzOOVqtlH6/+5kkiD8q0jKfkUrrI4/8CLtLn9d6DHjush6mSitoCINFUR3kqnzeKo5DtFaMRtMH6jzt12G9nkop4jjEOc9o9GGu09e//nXg+4BY+d3try0FUEJw+1Pd73KVAmRcBARQ9uX5lAEEmXjeqEZJlZ9oeEAFJGtgmhxTr+X2ek6cAfVUbqs7QCidExdCWEMVIfyfqccHHrvgmPYh2QCHRk+EsOydE3CmkIRIZVA1BGOPDxTBroxdnFdEzTqMbeIkDAS5jNSwQKDEbDCWkRhKob1ccJWRzpRqMprqBIjkOfv41svllKGMDSpw1Dhqo6lLg3eOJCzAQ56FbGaKzbpFN65J2jlh7bh5KSTd2aG1bNmuY/qnK0abCXFSM/OiY6W/Ac5ghyHJ7ISpDjCFpz2pKUaapKgYF5o6VIR9UN0mefxSxlRp8icjpqOIMKrp7EwoMunStL4g52W9BcF8Aw5KASZoeW3qEU3EhKf9jKLaFjBarQnHpkUz9lqF8ioHCeXTt4Qb1H4aZn9RlFPDv4CwJ10dZeRvNwWXCuFad2DwXZHA7yvBBt+D9nnh97gCdn5fQFJ6CuZ+qUloH8HkR5C/L4Bv+OfCCfIVxMdldJZdavLFQkc5rXDTkngppA5Kpu9C3IpYPr3EkXPLaK2ZW51l5ewStqq59tYaV9+8zvyRWca7E7Jxxvf//Q955ttPc/JLx7n+9hoAxbTgzFdOEiVh83mVc2MykQgFpfY9ZCJmZrrN9+AtB+HDSxB/NLL0R83heRwt8cF6DHgece0Tf6119xUQ93F1WB2efQKw9x7n3KGAHakHH2nt83WsdZ/4y3C2DzuDfbBzm0YqFG6KKriVkXVbKZoL/EcdO4daC9gxOWQnRPZdLkEwaC6As/InaDKxshOQXhGgYyP524yhNQWvbJPBBXXL42OwsaNCY2Ph5LhEgXbC80lg9AVFsOdxVsHUQ0++1K2GalETDT268phKUsxNrrBKlFwOiDJZp/LAVDhItukW1Rp0ovChuLwqDaoSoIMHXUi2mI+F1Oy9Rtk7UKxHFFtxgIkqAu/Qqsb0CrRxtNoFvX5GNg55+eIyp3sTTicFa6MWFydtormKaN6RvqiJ2zXOFtSVEWMaH2LCmmChZMtHdIOaMPJMVEj2juXUqSkqUmxXMaYlcu1oGVrnLPH1Cbt/mMN5gx+X7Fx19L8J2eXGqXgR0iNC/t3vmNQjuU8n4o9TrYFpKxmZ0YSUdpqxVQbGCOjpPCtjrMmPm26RFbARLwqw0UYIxEFXtpv+GFpnhJtTb8Poh7LP7nMiSy+vAhqyq42TM7IfZUTmrtNmdHelAV0nJbG93hFwgwc3hgrpRgULCjtCyOyhcG5Gb1om5S6tbs7f+B/+U575a09RFzVaa6I04q2X3zkIwS2mBYONIWk3wXvPYGOA1ppzXzvNZJCh8Jx97vQHPlV3Zv4VhWRE3Z4gniQRvV4ba90HIhTut/4qydJvHftxh+fOegx4HlHdIv4qhsPpQe7U56luz+sqy4pOp3Vo+37QDs/tfB1j9CeqD46fPMruq81P5CZgoupAcQx0Jl2XcPujQc9+vtZHgp6p3FEuQLwhwIeG01MsCYFX5c3Iy0pHqFgUgGVbYLvy+GDSjM68p46afc4hgEJ7KL0Ed+JlX06jXbOmWlTktlZE1xSua1GJkviKyFF3BNREo4ZUbMS52dSSXq4UWC1Gh15oN+jc42dF6YQCYo2vnJBptccUshZanlp7nDEElRcTxcavaJ/7E85XxEFNGNaYAmy3wkSeMg8Y7LQJwoq0VbKXJLwx6nP1ZorRFtKSuacm6AD6g4x8khD3HHZqCCPPYJAShDXdbs5Uh+xsJSTaEWU5ybJn6EOCzOIGnuE1RXlNLv5uBKq0tFcsphaeTLQMQSJgYfIW9BdFuh0H0nFBNxyeSMCECcC3RRlVjzx4hR2KxDxaAN1uOn4b0lGZXhDX5GhJujb1ULot8VFoBwJIdFuyr1Qsaxp8V0BKcVPIx8oIMbm4LqOs1mkotmDvu0AggCl7H9Lzsl4dg9uC6Tu3srTiVQiPQNWQrZ0TrlHcd3S+UUJeU1wMGLxV0l2Q0N2/+H9e4Z2X32P1/ApnvnKSo+dXOfbkUa6/fZNWJ+HGhXW0khHs2a+e5dgTq2gjJ8DckRmefPEcaefWd9unAYA7E8TDMCCKJD8qCAxVVR8kiN/L+Ouvpiydxx2eO+ox4HkE9VHE38Pn3DzY/u4kACulHlk2153VaiUYc8vR2ZhPllrW1f4Xo/ztNRRHBOyEQyH/2i7ovVuPcZGAFBvLw0zJBwM0ESCjcwkG9abpCDUGfGb6QU2IVzKuKpZuGRQGo8YwsAFFHoWpPQ7pPtkeqKEimgiB1hu5uAaZ5HHpqezDGghD8NZTdqX7glc4o1CNVNwVHp+I0queAZc5om2NTeR5KivjKB9LVyPILHVfowtQuUd7cJEjGjhJiJ83WKdQuXR8ao10Xm5Lk9ehx3QsRWkII03cK/BAJ6yY+Ji4VZBlLTwl3gQUOsQPAuYXZISqGgAAIABJREFU9jj/5IheWIL3VCm8ux4ytW209oyGMRs35gkCy3RmytLSDonO6dQVlJ6F+ZzZumCwnLB3vWZnrw3aUu1BvGjxl2RU1XkGJq9DNRSwUG0L2FChAAidin+OTmA/+LMeyt+2FkJztalkVGTFRDBckdfPToXAnJ6S0dZBdpwV9+NyR0jLXolUfXpBOD1xuxmnzTRRFQNAyUjNN8KoePEWb0hFAqqCOeH/VNvQ/2kZrQ3+byguydrdDPRfgu5XoDgKe38C2iiKGx5lYPiX0nlKT9eUA4UvPOWkZGdtrxk9lrz6B69z/oWzfONvfJVv/BdfY+XsIv/2f/ttdtcHtHotVk5LPPvJLx7j9LMnP+bTeG+jpaqqm+/JTAw8o5A4Dmm1UpTiID28KD46QPPgqJ+XL6/PsB53eD5cjwHPZ1wf55r8MFRV97u/dlvyuu4mIuJ+634A2e3eRLfzdT7tuXa7H+yeeYMQURtEovZva8qFzfBLN9L0HqhhA1T8rW6Pso0KZgxVX0CSV6KgCnclzyo/JtvoWMZnTstalZEujvcQbipspA/IweBQlYcYbGLJj+mDNYZbjmgMdaII9gSsmVpAjy4gGAqownqoPCqGaENMDHUt2+sCqnlPMPRUqSLIGk5RCPWMxymPQZP3PGoKplI464lyR2Ahm1d471BaSQfJgylljWhPEFgcmjQuSaKKdreiHRREYU0/qKmKgC+fXqeXlPxoPMcwS5hMU4KgRpcVC+8MWByO2fAp4WpJZ1ozm+dsTGIGvsOg6NIKp9grjnzHsT6d49jRm5DA8pGCIxS0rhTsbkfMtyrGmyXVeUV901NuiouxjRvOzLzwXMxK0w2Zwug1mPuWnCO+ee9wQmTGy+jJdGDymoAanTbuy5egsynyb1/LCCw5ihCmLwrYiZdgegmSCtbflOPVpXRcZr4l+6v3pBulAuHp1MMGFNdCiI6Pydonr8ta8AJWnJOu0M5/ADcU8KbnwA1EpVXeFC8e70TivvM7iiCVc0SXimpHRqskMLwxIG2n9Ff77Nzc5UhvlfZsmyIr+NHv/5gXf/mrbF7ZIe3K5zGfFPzFb73Csz/7JVbOLH3sZ/FBRjze+4bfUwITjNFNkHJMr9fBWtuMx8qPpAd89maHj26cJcd/DHjurMeA5zOsT86aOnyZ9r2W1sLXqWv3IQLwYXeg7rXu19EZ4MiRvvyj+QZSjfLKxSLxVa6ReNMMvRIE2LgGTJWShRVY6bQ413R7mh+rpoLkGlQL0jUKpkAtPB/V+NrsK5zCQowInZKuUrAFZUcTNOMwiYMwhJs1ZgLljBL11P4P48ihp43D8RhxSq6Qjkql8XsKG3vKBSBQBJueYNfjOtLxqVMPxoMGF3swmioVsrKLBcToXGFb0qUS5KcgtvhSDBCV9Sg0KI3xYBMvJGmn6YQZ3U7FOE8Ig5qZVkaQ1FArFroTvn3iCq9fXaJEoY3naDDl0uYSCkW94cl/b8L0iOfi9YTOaY9tGbL5kHbXE+w68vdgomLKTSsGhrsR8Q3LbGHQJwPK2rA5Crj+HxLWLsdEJzXhMYtZNkTPCZk3SgQolGuIQeGX5HVMzwu5d/KX4PZg9pdkdEQtCq2wydMiFLn41gVQSeOxM2k8eBrJuPeQnJbHmX6juHpXuEDRAgxfacaJNRQXJdl897dlHeFcQzAuZaQWHRWFl05Eiq4aAJacEoJycRMm7wlIyi/LNtUA/KQhQzuwOzDJhVvUeR7Iodr0MA+R8eiux20LcAsXYLJpMZFm9/oecTtm8/I2JtCceOooVVmzc3OXG++uMXd0lsWTC0x2Jwx3xiwcnyftflJQ5eGRh611TKf5QYBmGAbEsXB/jDEH+VJFUT5C759HR1iGxyOtO+sx4LnLepDz5nYg8XGuyYff4bk3gHI4Zod3X/eS9fVR/jp37I1PAov7YXdey4VHe0ivQX5USMVmAuEIpscaxVEiF4loA8LpLZDjEsQvpfmD5yBqwlRg1j78HNN1cEZTdzzjRU+4J6OJegb0NgSF7NfRSM8j8ecJtxVeKdxS4++jxA3ZpQqcItlrAFlDMgonAFZe1zVRbikrZoPaexyebElk6NqKlL3qKKzxuEBB5NFeo3KHqhWVacwSvSLatdQdhRpDOPZUFog8NKBR5wozVrjYo1NFqQw6sORlxHySMXIBkyJmNslRRcBiN2N92Ga6lzKjLQthzo1hG/fqhEqFVO2IVitj70LIgrIsrJa8tTZLftkwWw7ZVXO4OEBHCps5Cl+z+Vua6G8rslxz9U9brP2JwVcK+2OojabzLYjOKOZ+xosL8sBytJOTzFt2ehFrezEqVJgOqFRGRD5vFFlGeFLTd8S0z7RgsiGk4eImsCRGf35LiMNoiYcw5xt35EC6MkFPAJGrQL0hYys1kNGlq+TcTI5KB8ZNZJzW+qIAHaUFAO3HZVR7MHlDQFt+Cep1SUY3PVFu5Rfk2HYqajOtRRIfnxHApFtArag2wJ+U7wsdS0cgmETEK54kMBTrJUVWMNmbEgSGxZMLrJxeIk5CtDHYvCLtpIQrIdZa2v1P5vk9zK7H/vhrPOZAvRlFEe12itaadrtFnheN98/DBwKfD5XUoz7+56seA56HXHcLJB5lByVJIqIofGCzw3uru+to7Y/XPsnR+dPAYhzJaa4aTxmQ8VTrfRkxFUtQNQRiuyD36QzKo+C2Jclc5lICFMxtzgHeffhZ2LSRqgdI+GjuMAPx5qn6jbImhNZl2cYbT9VSkAgXxhTguh6beFyqqa1Dl2IgKBwZh/IGf5vnDU3H0MVGIhLypkOjG/JyKo7QZR/KsHlIIvwbFTvyGUW8JyRnMxGStHOeYAta25p6IEBJ54rWBRidAFItY70KXOgx2tJanGA0hGHFcKfN7m4HAsfx9pByFDMMUnanLZ6a36SoIn64tkLPVPhwyDWroR1yfdxjoTsh9I4gCbi43sHnnoV+iZtM2Egz6gqItSTWZ1ZCOkrYLCJ23/DiXhwoiqmirALikSMcw94fQue8Y3G1Jt51+F3Hyd4Y3XZcG7Uo12Hm202IqGqSyOdh/IoopsoNATBmAaJjoLvgC8nX6nxVOiQqAJx0fYrrAmxMSwCIL2WbYE7UWdll4d3YqbyPQa8ZeZ6CdE5GYHYoPjwzPysdJjdpyNCvy8jL9ITro1qS22oHzdq2pONjh9D5aeg9L87P+5leCsAqpm9CuOIxscZtKeh5wnZA2g0pNyqOPrFKUYiT9aUfXObFX36e2dVZzj5/iu//9g/Jxzm2qlk8sciZr3wcd2e/Ppuuh/eePC/JcyHMLSzMYq2l1Uro9zvUtT0gPx+mOvb2etQuy82/eAx6btVjwPMQK00PxzX5YZVS6kAdNhrdf0TE/R//4+/7OL7O/dSPXr36gf+7APCNKqvh8+haSMWmAJQorvYtQOqOgIcgk5ETRn6NK6T7c3t50/BhugJ8lJJf6D6FeE324zWwKDyScAzFvKNqa/RUYQpQ2srIrQXhTTEMtB1Ra4UjyM6B8o74ukd5Rd3yBDnoCQd5X9QQWekclX0DRrg5Zc+TbjfbaIvWmhp5zkXXoGqHCUTRZXYhKA1V2HSGCnmtlFUEU43PPS5WB5yoVi8nzxJJX48US0sj8kHI0faEyGnaylFazcneAF8b5sMh31gpWJ/MEB+x/NvXltnOQ9rLFe+POwzzPmERc2QyIa0sT/bW6c5NGWRDbm7FVNc9esviQo2bOiY/hO5Pa4Y3RXlmInk9tLfCY+kJryYKHCqz3Hg5pLoYQuFZfb5i+Cok5yR0s7gJrVNAIWAjvyLve3xU1E7ZlaZ70hPAohqvnfyyjLJ8JiBItwSk1DsCNFQgIKl1XhRhFA0pOhCJerUuo7XsLekgTd8V2Xl8XHx8qi0hO+fXhOhc7Uqnrx4155+Tx0UL0ukJl+S4rbPNdLIUdZmKIFjyVBsKX2nS01DvOmwJxciT0qLfWqLq7aC04uyzJ5nsiV344tF5tNY8/9e/zPyRWW5cuEm73+aJF87Q7rc/8bP4KOXh02l+4Osj5OeIXq+DMfoD5OfD8v55lCqpxyOtj67HgOch1K3wyrt3TfbeP1Cw273WvlKsLKuDX0GfVgdKk0P4DMk+Phrx3Dtf51NGWgevq/za8cEtpRRNEKcqgUAAiyqFtuIbHo9pzARtIxn3jezaGgFISkRRlAsyIvNORlbBQICPKqFuQ+wg2moeX0I1L92ReAucdrh9vkUXXC1S8fCmpx5qCKDqyAuvS/DaMz0H4Y48n3xFfHQYIfOLwlEXnqovNsHKK1GB7TpU7gmH4BMo5qBqKUyh0FZeR1tpgrE6+G1Ydb34tljhJ1kjQCIoQRVe+E+BxzdEo5kkZ7kzJnOGo7MT3t2d4Ytzu6A9b2/Ps9wdc7I1YDRO2BxEbBUJ3XbJ89/c4QfXelB56pkWvt9BB4r5ZA+0p4gTnprZ4MzFHbb0PMo5ytgQjjPqbcvmv4XB9+VCb8tb/Ksghbjn5H1OtYz4rnnGb2tSY4n7jp2rIe2nHPlFjS+FEGyH0F6xLKzWZEPYeDUg/RmDCiA9LuTjoC1y8fYXbvngUAifxzsZQamgGeG2BRjZCcSn5X47ELPD/BJUU+m8xSdFFl9eaZLVIwE3eNmfngd7E1RHJPJ2DGRQG2iflvPRlTKaM7E8D9WEntqyAWotMH3hs9mpZ/RnmvCYI+oo1DBiYfUIWEt5fIv1eB07HNGrVzj51HFMoHn9T95ktDNldnWGb/zy1wiiu7uUPKou9p3jpX1+D9zy/omiiG63jXPuNvLz/UdfPGrTQXi0pOnPYz0GPIdct3vX3C2Q2K/D/i64PZD09vo4pdhd7JHDa5F+9D5EdXFvCeyfNtL67/7bn+V/+l9+/eCY3kiXR5eitAoyUUzpAun4eOGmqBrCHRk7uUAuJMW8AIVgCGaHA6l6uSCkZTWVzk610HRRUsneUgHUN0RtU3eFqxPeFO6Q0lDOCpfYpkB4y9AwXgelnHjmhOLZY1vyTIqVxtfHIuGeGSQTTx1b3IKArv3oCJXL/n2kMZm0flQJNnRYbdCFJ5wiJowVVLHDtrUYDTqRxjulcEFDeq6hjpovVA1eK7TytKOc1d6IcRFQ+IDjM0N6UUEvrdgYd1DKM8ojtnyLlinZrmeYupCd3RbHV3aotkJUbMhsiHGW2Ne0dU0QVhQWXE/xzXNXuLYXcn29TTSZot8e4SzMLVZ86fkM03L86P9rU1SB5InVirDr8BND+8tQ9QwTFzH/jRp9wRKmnsubEXZP0XvB4wsFNdjMMzPOOaIqrlwLWX0W9rYUlde0npDuyej7opyiARmmIzydfTfm4hrSMewJj8dbGW0lKzKaCjqN6mtWoiPsAEbflduKtYbYjIyrlJeuETcEFIWzAqzik0KitpWMztLTMHkFqhsSSNp9Vt6j+KgktJtEtrWuWe8EXKbw7ye0V+cYbA25Mb7JzM/WxE5hs4BJd4M0iJg98gx//BvfJe2mtGfaXHvrGsUk55lvP33XYObRgICP/9660/snCAxxHH3A++d+oi8ee/B8/uox4DnE2ndNvpeL9X49jDDNj+rItFoxQWDuS3J+2B2eO78f74avcz914kSfc2cXePvCJuBE4hsBjSGgbowHTSEgp24LgPBBc8GJGxVXAiihy5SzEISgr8qv8roHeiwXpbAQlY3tARUEtezftiE/wkE+V9ACsoakGsnxg0KOa7V0f2winRtVNaORFtLt6co6dS5dF5UDHoKJZHH5UuNTRV1r6tQTbzhMrVE5RNsOl0p2F84T78lr4QwETuGNJ8zAR448FQdBXTqCUuEDOU9dCIF1mI6j9ho3VeS7ETOtnKLSVHXA8c6A4/MD2J7BAO2gxDrN5qBL2q7IlKZWmrLSLMQT1nZbaOuxtSP0NcVUkwWecLZiGoQ83d/mRCun7Cu+cGmN0VpPLuoFxG3H0bMlynmCCH7+v97jtZ0ew70AV0FceWZtRlQoijBkNDaoGU34oqMYafS8oT3nUUaRXZcxU9pyTPZC1v7Sk9WGdt/T0Y7MabwVR+T8moy0dNqM0Grh+NgRTN8WUDK9AjNfF0WYaoCrt1Bel9GYar6F7S7C/SnEi2c/NZ1CFFc0BHmQTo0aCqhSGvo/K3yh6qYYHOo2dL4Fw+/C6AdiUjj7bRmZ2RHka1CtaaoSyCFsKWZXZgnTEBMGBDOWcMaQ1rOY+YDB+h716TE7a7tc/vFVlk4tMbPcZ3Zllo0r21R5RZTu581/fD0qXsu9fG/VtaWusw94/0RRyMxMD6XUbc7P5Sd+hz5K6x+tPw+E6c9fPQY8d1mfdO4oBe22GGHd78X64WR13erI7HNinPt4pdi97O9wSp7wJ8nh72pVd0H4fuL8Ku9d2MIioxufCWG5XpTRkkdGVvEe0v0o5OK0/5RtR8YRwUg4PXoCxSyEm7JtMAZso4AKwYwg2mluq+RPPSP3h3syAlNVoxIbQLQnnaCkSWaPtmkyrWQ9GOEAjZ5puEFawA6qkdcD0SYQglMakytcoASkVYq6rfEFRAOHUhpdOJRTVJmSMYsRRZoPPHXLYSxor4gKjbeWfMlLmroCCs9cVPHNU5fplhXKKr5/YYWr2zMUJmImqTFTRVnGvOlXuDLqszo74ERvSFmHtIOKq7sdyjLg7PKIhaWMKxsd3rvRZWVlj8lQM35fwZUxGSmvb0T8/C+v8/TKBrsYAg3BDTh6PGcvDSk0RDiSjqMsFfWuIo40T30r441X2vhLllNPFYzKgLUfRSQXHPlUUwwM8ao5yMmaPVej92A70WS5odYQzYDNNUEMelYRaFFjeSeGheGcEIgJIDzWJJ1b6aDoWPg36QlJLqcSQGQzOfNtIedUnUnHR7eb7K4KygrIgXbz/itQiXCDaBRbLmycoGnk543j8uQVITHHR6H7pHSc8psiuY9mhOjsc2Ao+1IRVFPLuBhiEkd6LqD3nEbNFmTTber1gKKoWYj79Ptd0l6L0faIfJwTpRFa07gr3/Un9h62fbR1u/fPaCTeP1EUfSD6Yp/8/GFRyuMcrc9bPQY8D1i3uyY/WPDnw+vwPIiHzUft73DW5j+wtocth7fOyi/vWuOUlYtGBOUi6ClUK9K18XEDHGIZOZlCukCqlhFSOStjpCASQFQuSHdnX3kVbskYKdmRxzjkOK4l8ndlZXTmTTNSy6WDE4xkfOYS6fbgZNxlEw3GHajBdC7RFSIflvXWIUQjUZ3putlmVmEc1IEXAFeDyRxB8xIrK52b1iYUfd/wlzzlokejqD34yFNTi6tzrERRpmV/P7V8BV3C9qiNji1f++IaO6+k7AxabF8wzMxmdIOcYM+SZXNs6g5FHbI7TdgZt3lqaZOVmQl/du04salJg5LdSRf7ruOZ3vu8faPP6smKcG5CPlWM36m4sZAS9RU6c1ig17cwY6giTzKumW4pxu+FtGcsy6sF1/84xr9vme+UZJuKwmuKPQWlonPKQUfjK/GmOf1Szvm5MXZRMUXx5o02k26I3oLkjMftedxIYU9o4rgxkvRCDs6uCA8nmBH+EB7SYxDNibIr6XEAWmzegI1AYiFUBL0nhKejtPCC6m253VtgIttihIMjyFxGXmFHRmXJSRi/JhL16EhzbvRu+QxpIwnr3gl/pxoIwLIDT72t8KXsfzqaEnnN3C96/HzF9JrCdEGvVJhQ058cJTmWMLc6y9U3rrG3MSBKQs6/cO4eODw/2bwSax1ZlpNlH/T+uTP6oiiqR87h+Ul+nR9WPQY8D1D3z4X5cD2sNHZpxwb3NWZ72CVdp4TxOMfah7u2qnLNNEDCN73m4Ox3qXBzdCHgJd6S7gux8BxcLGMtH0K+LMAkHMs2db8xAAR8Wy5aZtikohvIluVvKvHTqfqNgktBOr2tk9QGvAAhDEKOjsClnrot27kZAS6qOVatJRqDEHqvQjjY5x6Jass26e1Ucoxo2vB9kOeirJfrZ89Tdz0YsH3Q66CtwnmPm/P4UMZcqpaEdJM6iBzTQYRRHudk9tJul4w3Qt7b67FIRlokmKrmmefWeC07QqAdi0mGrSAOala7I2aSDI+iG1ecXd5B75ScjScsHhuzFvaYZIa5sGS8Z5iUEeOpp8o0CyslW1dahH1LNVDs7oWsnshYPOEocs3VtxOuD2NG64aiA6r01KGhCjzJk4raKkxbCL9BWXM+HJFbQ1Frqgi+9MUJf/r9WVTPoFJL75c9WzuGYlsRLsgIyzsZEVU7oqDqviiqKTcUdZYywpdxXuTn9a6Mm0ws20Vd4d/UO+BT4QS1FAw2ACcdH7cH1M05ETbnSRP6mp4XaXu1Iy7KRPJ3MCePt2NkDJbcUn7ZXMau1UBJQOlm8wEJBMTpUDN8o6b9vGW65THrhmgJ6jfb7M6VZEdyOjMtnnrpCc4+d4r+Qo+5I7P38En87GXSDxN47Hv/7B9nX/0lHX+Fc444jj4z75/9etzh+eh6DHjus+7Mc3rQOmwfHqUEUIShOTROzGGuMUkitNYMBpMHXtvdgMUzpxb4gz9svmobvsp+7pMNmv+3AAfTU8LHSRpibzXbkE0D6f6EI+H9uFCAxL40XWXCvwmyZl0Wgr2Go9HwN8xIeDmBakZToTxuv7lncqABK3qqCCpFsKeoe556wZMfla6OmQpxOr4ujs7hvk/OFFzkYAeyJY3OFLpuYiUC6QD5AMquIxopivlmHZWAvaoj73O4JSM+5Ro+kwLlPbpQYBXZKGA2KcnymOXOkC+d2CTRlsuzKT+4NM/OtE1/knNsueDyeh/XUuhM0UoqnjsyZFimnJnbZTbJ2Z6mhKElPVayeTOhn5dcfzMhzyA8VTMdapJTmnERMJ9ltDPHaEFz9KslOz8wTMqQYqJ4/XtdotBiQkWZK1oLlmS2otjULCyVVNajc021bdArwsOp1z2dE463fj+lsJq505UEidaesOvxfcPNIiXRHrUMcdn46+wIgBn+JbgMOk/LOWha0lHxXnhcZlbGWpN3wG4IqAl6YOZg6b+S8db4VeHVuEpcjl0pgCnoQzkSwnJyStRWLoP4nAAlDGSvy5jMl7KPsC8jsWCmGbeFkCyJ03K4CPn1hlwdeYJFCG56AUZGQQS2stQZoD3aK1ymMHVEta2x7Qq859SXT3Di6WNESXjPn9VH0eH5rI75wegL6HRSwjAkTW95/+yTnx+W989+PQY8H12PAc891oPyTT6L0lrT6aR4D1lWfK5O/P3Xz1qHte6Q1vbp48Dl5T4ej281PjwKcBCtSd4VkVw0gobbQyhGgV4LQNBNl8RMGmBRcZC97pR8qbpA7neGA94NCVgvSvHayvjK62aUVkonKFQCrIp5AS+mAFM2x0ZJcKh3lAuytmBHiMr7Joi+UWnVYQO+CoicI7js8Ea6MzaGqqco+hDlHh9CGYPte3F47orvkHYNX6n2eNUQrHc9rqVQmZbXojb8+N0Vfv78ZZaO7PDEwg6vXV5ibdrl2LEdvC358ZVFllcnLC9nnDgxIozAFgFrVYv3dueprOHNjSW+MneN+XyPNK4Je4aVToRZmzI86fDXNZM6JDoF+qTm7UGXo9uO1lrF+l7E7mZEVFime4bRthgujrMApT3tnmVxvqIzUzFNDOEpxdGlnBLN2xttqhty4U+XHNmGpne2Ro8ta38eM3u8ZnxC4xPJnqo2wU0UycnGo8gZFOJ2XO0Kb0YZARomBtJmHGVBh9J1KS5Jx8W0Id+F3qqcY2FPAJArpDvoChlvBT3h/nSeEVBcb0LwhIAoNEx+KMCrbsjqOpTHRosyDsvebbpJbVFyTd6HzpOw8DfBhAKOyl2IFj2+lPGayxReeyHRV6AjhUk141cVURlx5PwKK2eWOPf86fv+pD6KC/Gjuvh7T+P8LNeJffLzvvePgB8hPx+2T9tP+ujwYdVjwHMP9TDjFw6re3JrjQVhuH9lP5x60DXeztepa0unk3z6g+5qXZ/e4RlPM+rENynoCu+8qJqm0HlDujX7UnBfCRBxCIAJBgJiwh2oZgQEuIbrYw0UxxtQVAiJmSZ01AYiTa8WZP+6hviK8H6iDTC2IU53GjJ0T7ZN16TD5FNPuWgpZqFqCbcnaUYQ4agBWl5GajaUfzsHtKCKZU3xulzAdCUdmmIZ8i7EG5LPVYdQrHIQl+G8jO/qBYh3ZdynJqAyLfEGxmPGMCxSfveVMzz91RvUrmajlTDfHZPbkNVTOaNym5/7yjXmeiXPHd9hlCe8srbMSu0ZFgmnF9ZpZ1P+zb9YZdHvEYaeE09kfPHZXfyc56//zZytOuPtsks3tbz1TpvifcWGSdh5s01voWLiA7Z3IvIcylyjIohmPVHXs3De8cTJCe+/nbC1EzMTWUY2oL9U8+QXMraXU7begH5aEQaeagzpjmc5Kdi9HvL6j9pCZs88KlYSIeEVyQmPjptQz1fECdmHEhw6fQvCVWifF7m3K0WC7ioxJDRdgebhksjVkxNy7qZPyHisXIPBf5T3VYfN49fEHbn9DIxegfGbDTHegx/S7FA6P2hRrPWelE5R9mPI/lw6O8kxwIiKq/XXAAf5v4NqqDCRp/eCZ/gfDboN9Zpi558nhG2FzaEVdZg9NkPSie9KifX5q0fjNnznd+U+sXk8nqK1IopE1dvptA66Q/vbPChAe9zh+eh6DHjuspSSMczDck0+DA7Pnc7OQRA8Umnk7bUv2d9fm+RofXaLe+utDblghw0/x0v+lQ0kFLTzrhCGfQCjs02nY1ZuM0OgDVVbgEaw13B0mgytcFMe52Ioj4C+1ACMuuHE1I1KK4XpUYiG0sHBSRPI0RgTbstazQTyRchPIy0kL/ydcAPQDQ+oI/83Qzm2quW57MvpnQbXFQKzmcL0tKjEqCG9KeCqToQ47l0DbJq1qFyoH1YjoygNZiAmddqBziRGfJIYLiddnkloY8USAAAgAElEQVQ28S1Lbg2r6YTNOua5n9vlC4tDemHNoE6IQstcOGaSz7IYDOm7KZf+3KA0HD1S0O+W7G2GlBNNOmMJUs/oIui+R2eWL58e8sObLa5MOmhlcZmmm1hs6lGBob9aUwWGtGNxkaL9ouf61YTZOcfYOqLAcuVHMQvHFPa6Yua5jNPPO6KdmtFVQ7GjePuNFkEMVQWbbxrio57e12ly36C4riA0mI6oqbrPyahq9/cg/gXh0hTXhNfT/iIUVxuwcxXcSECJOwlmFdJzwtGpNwUI5Tdg79/LNmZOZPHldfC1KK6qXTEnDDrgxvIe+QRowky1AdWB2W/IY1mH3jflfPG1cHdCLcqweF44RXaicDuK2ntaTylmn40wNqTMS1RLM7lYESUJM8dmWD2zwsrpZVbPLj/Q5/DRdHgeXbfj456rc548L8jzD3r/3Bl9URQVdX3v46/HgOej6zHgucvyXiTnDw9A3L9KS5ydE5zjDmfnw1V+3S8oE38d9SEu0eG9lp/+PNM0RFtFHXmM9ZhmJESjpjUZVHO3dVF8w2eZlQ5PuAO1gtZWE9TZ3D85DZQQFTJesi1wTRgkulFjNeK9YMwBaHBt4QFV3SZuooZ6Xv5dpRJs6kLpAqEamXoAZg+JI7ANf6fpDLlYjmET4fjYUMBY3YZ8tVGJTQHb+AxZj7HCH+qMxR+o7srIUXsFhSIYeYJ1GW/Zjhw32lOSJm889VHPKIt4f9jn/MI2xVCjLuW8+b2IF07n7PxMTN4OqI3hRH/E+fldVvoZf3llhWvXU7Y3obvsaKWVnAvKUztPu21BKRZmazYHjt0oIHbQOgLF73myXcP8Qk4xDUSKHXoqFEHLE51WpOc9vfOeYFUx93pJb7VkOg2JQ08dggoUS5OCsFDotiffg8E1w871kM6SZeVkwYU3G1XTRFGPIXuvOU86jiNP1PgNx7gOGGwa6l1FPZTX1nsJ8USJ/46fiEtynQlYmbwqMRE6kbGSMjJKLdYOLHZE5m6QkM+ZRhrfAFty6Rr5WvhCNpfzj674ArlSABaVdObS8xJHYVrSUUpOyDlZbYIvffOZ1pRXPUxqwnZEZ6bL3JMd0qcWGN0c85Vf+DLP/ydfZnZ5hiA0D/ZRfQT1qC7++6Tlu6nbvX/gVvTFzEwHre89+uJROVp/3usx4Pmc1P2CiU9ydj585de9ASjhEiVUlWUyyT+0tkNb1V08z5d+6jy/+Ts/INpzlL3mxqqRdivp5OhSLipmKqOrauEWOdmbxlcnFnmwrsUkMNmSzo330s3xXsZcQdQAHdcYAzbxFVVfujK23YChphPkZ6DqNeOtaQNOGk7Q/rbKNentpSi+VKPk8pGMoZQVkFNFQmJW+y2bAIKJPgBBXjnq2aZLpTQuUOihADYXObRT4CHekb+DPYUdKeqWok6hH+foOUvQzzi5NOBId8iSGfPKH6VMLmhm8inXBgHUbZ7+dsb5IyPm05wxIW1fcGZpQGJSBldapIMxLMPelsY7RW/REsaOKxdjfvRHPfZ2DX7OkMzUBFlNZ1BRtSOi0HPqqSHFyOBPOnzocSdCbhYpQeI5slJiC9h9O2KwEdDWlsEgQMcw42tUB3IbYB2svlSxcKNidytkOAmopob4OLSeUuQN/8aXomKajBxZWdM66gm3oVtbxvPxgSePBnwGg5el21NvCgihFvAcrgrYya7KPv1E/Hx846mEkW5QcbHhATkoIgFfqlFSeSv7cxWQyO2ubMZaDXcIZMSJE16Rd3Jumo4kqW//FoQr0F51lBcDxq872ssh9DxBoglbmvnWDGmY4q1j4ejcoVxE/yp1Hh7kue6PtkYjPhR9Ya07ID9/HLVCwNaDrP6D9Tu/8//yL//l/8X771/iV37lf+Zv/a2/fXBfnuf82q/9Q95++02MMXznO/8jL730zcM7+CHWY8DzE1xxHJIk0SdIzh9dh+fT+U6H7zv0SbW03KU9G8OooLCWYK/ptAD74aH7Rn5FX7otuiEoqxzqWYjH0kFRZeNvYkFNZZxQLN3y2innm+gJC8kNka67GKJabtdOzAY90lXKj0KdCKjxMeQz0g3SmXRwTAZFtxmZdRqVWMP/2Y+2iLckF8sHIpkPB0AtIGY4J631IFNUsUcr0AMIMoUPFcWCbBuMNT40JO2CqLKUYYArDHVPi0rLyRqNdXz56E06rZLaW870B6y/H2DWK555OiNqQ7dVUZaarbzHsWLCngqZFjGhrjk3v8OJrmJj+zSvv36cG+sVP33yKj/z4hYzSzWBge/+9gwb78ckLcvosmFnI+brvzBE7TgW5jO+8tyQK68ldFs1vgdlppg9XXKsW7KTRtRjjdEeN2/Qm3BpM6UXVMzOVqzfjInylKVpwcWXE5yL6LQs0YIjCBSDbUN6Hrpf9az/C7lwBLOQHvOokeLmhZh4y6HHjnRBPJJG35XRFFqUUDpsOjGTW6e5HwMGdv9IVF3VnnR9TF8Iy+GMjMOgcV1uMtuogVKArRs2tyXQ+lJjajjXKLDeF8JzuCydo6AjtwWLso3pQbklx/E1TF4DuwzFe4DX+LTEB57xtie5lFDO1wSRIWpFP9Edg0fX4TmcH3Z3Rl+EYUAUhXd4/0gHaP86IM/58BDP+fNP8Ku/+mv8+q//sw/d96/+1T+n3W7zr//1b3L16hW+853/nt/4jX9Dq9U6tOMfVj0GPD+htR/D8Emy+Ifj7fPpO7yTr/NZrO3TvpC1UsQmZBgWBLYZAVVyAbeAVo0kXEF9VAwGQycXBtUQi5kVJ2TVrN1F0iGqu3JbuCP7c12RdEMDThpjN9WkjVe9BrAEUHYFIKnyVhdHTYBaSMN4ea2isYzUqgXpJFFD3BCYddM8CwqoAnkeqhauknLQvgzjc2CNx8ae2oEOOMjwcm3QIwDPmfkdXlheY7CRMAgTfry7SJ5HmIYj5DqeURrwjunzn89e4FxvD2U9x46MWX9Rs3CkYncv5sqFhG5ccnZmgM4tJnbMdzMC7YhMzR/cOEtn3rN4tma1l/HG+hGCl2ueenqP2QXHlXdjTKCYST2+b9m8FvHeGwlTazjdz7j4ZzHKemZP1JjIcfNyzOiK4ujXa3ResjMJyToB83MVp3+qhm3NxqsBF/4gJexC8b5ldENTxYbWqmN7L+LaWkSwoAj64AZQbymSozJOSo4KQDW5p9ryVFuaVFsmWyEY8eWpJwIm4hNQrSGjpv3TMpRzIHtXOoq+hv7XpEszfFnAkelIZIQrgNsFoE7OG2qgyWXTGtqnZF+mKwowFUNxQ9R9rbMCnkwK3edh/IbcN/cLjZrrmERR1BOFqxS+0BTXPAaFG1v2BiV+4Tpf+8Vneek/+xq9Xru5qD4YofZexjyHVY9ypPUwjrvv/XN79EUch7RaPf7O3/lvCIKAl176GV544UVOnjyPMQ8+hjxz5hzARwZc//7v/x5//+//KgDHj5/gC194ipdf/i4/93O/8MDHPex6DHjuoR6WOeCt/X902OftdS+y+H2y5eGu7+PvVwparQeL2HiQtX1cRVFAbT0dHbIzhcCL94xzDfGThrgL4GV0FY6kM0MgF5VgdBvI2efCLDRKrJ6MvVwkIENX4DvC21CNPN1F4Dq3xlquJZ486SX5NV4HUM/JWpIb0l0yhYyqgkpUV+n1Zj8hTSgmVF66Uwf5X42/kEtvSeVVAe0LiroD2aIiqD3FUY/e8IRjuY76StHplzx/6iaboxa2ralLmItz1vJQdC4dx9LchF5U8vTCGjP9nAzFrK7pp5Z6qSZNPFVaoVxMf8Vx5UaXvlcc+WpGry2/PrMy4PqVmOJmhRlkvLaeUMYJ589GrF9KMKpgaalisBVSThV5pslLUWj1nnbMnLTsvBsyWldELYg7jt2NELvlmVty7BnN9dciypFCrSvCJ2ueWh7RPhNSvNZmbqVmNAq5mcbESjhB+UATzYkarx4L78UVEhfhC8nLcoUi39LEkcMknlxrRkWA7koXRUUCRoI54CnI34XxK0As5xG2GS0VgoXyKzB5U7qFJhRCsVYQnIDyIhItsc9qD8Sbx42lU6T6DY+nL4CsHolvT3JCjq8sZFsyNhu9Ij4+OmoCSQNJd7c7UG9oKBVhHFDuVagwJWmFREmIs54jTx+hvSgk2g8Tau8tTPOvWn0WQOuD3j8TvvOdX+FP/uSP+d3f/R3+8T/+3+n3+3ztay/ywgvf4KWXvsXs7L2YRN5dra/fZHl59eD/S0srbGzcPPTjHEY9Bjyfo/q0cM57l8V7Dli5D7n2vX8ePGLjcGtfuTYYZGitSIyhrh166qHJs/JGeDimIYMmG4gKKWyyrppRlMkEfJRdGXkVi6K2KuY42Ne+87GyiJFbJJwdVQhAqlMBT94Lmdgnt4CWSyG5JiDF9WRcppxI0rFIl8jJOosFAU9oAUP5MrQvNoAHqBIozkjXhwr0yKGcIRqCjR3RhozPfOWItjVlD6JORWOaTCuqCE6WlNuKmzqlRvH86TXOze7R7eScW9hl4CIKF9KPxmAUx56uKQYepyTnanamZK5dc7We473dmqfjPZLQsXdTs/EDT5zC6Kql2KxY/LonCmrGw/+fvTeLtSs77zt/a609n+HOA6dLUqy5SlVSqTTGki27bUuGnTjdD0GngTwE/dT9kJfA6KSBhoHED35IGmgEQZ7SaMB5aaCN2DGcwY5jS5atqSTVyJo5Xt55OtMe11r98O1LslhkFavqVrEs8QMIkvecs9e+5+x99n9/338wDHZCPvfVAT/6do+TnyrpTls++6tDzCOKYsew9oOA5QdKXBNT1opmoGmAUw/WrP3IcOH5hNOPFmwOYkJj2bgc8/CpCZ85WbPwqxV/8e/naLY10aehyBVTUcN4HQKrqUOFnUhkhHfQ7HjC4wo7lvfV9TSjDYXpekwa0LReSsGsx5YKPxbfHZ1A8imRmlfbMuIKZkRx5WsBHU0u3Bs/kVBYrQVsqXWRlddbtGhUHJvRCFJCzAaVgnIdoiUJKY2WxHPHOwE/2SNCkra5qMDsgcjnw55sM12KqNdSalcRBiG1bsjHBVNLU8wuT7N1ZYdLL13l8Z97hKaxTCbSSrxBqO0J4bkl05Zl9Z4X+J8lH557MQZ85JFHeeSRR/kn/+R/4/LlVf7yL/+K73//u/ybf/Ov+Pa3/5zf/d3/87av+4f/8H9iY+P2IOUP//C/HEmX6JNQ9wHPJ6oOeS3vPDnvZkz0jq0dcUfKe3/blubN3j9V9dE6iN6pbgWLh4GuIMq1INTMzfYYjyoG40oM/AohgKpIujIuhXxBzPviNQEzTctdqeeBPUk9DzUUKwJmdCPdGDslnSHtIbraOiwf4k0t3J98Sbo20arwbNw+MIEwlLEYl2XNarElPI9biXgkI7g6bUdhsYAmYiFI1zPSMRo+KuvaGOkMgHQVQihPQrRjCYcCdJxpn5d5rLKY0lCOAwk39Q6vYVYXcNZizSwLtuDx5S2GoabqwcWyy8lsxPp+Rm+upBtVXBn1iXsVM2HF3r6j9obXXpji5NkxCs3qoMfp2QMuvxbz+Moub47nUP0Qux1wst4maWqcU+xuBDz89Igzj+ZUhSGKHd0lh401u7shuy9NU4eKsqu48EqH4brh0V/MGfuA3X3DeBiwteHYvRZw5omGwb6h3FDsbEVkfUvUseSFptuF4WUY7mlM6EjnHKt/HWKmFG4s3ROtFMZ4fKBk3LkLdqLwtSJbtoR9qCeaet3TFArdkffe13IWBwvQTFq+1rYAEZNJXpadkZGTVUIstiOEq9MIiT4+DuUmqBCCxZZr05YbiUNz5wkBTzgBT3YI9RDCJyA51/KE+tLlMa2UvvOgIr42z9zsHC+d36IzHaADTeIj6twSRAHFpKQ7ndGUDVVeE2c3/HduJtS+M0zTXgc/t3MT/ml2Wr5d3cssrbm5Bb75zV/nm9/8dZxz7zpK/Lf/9t994LWWlpbZ2Fi73j3a3Fzn6aef+cDb+yjrPuD5BNXtAMrNF+73Pyb66InBSRIRRe8PiB3We3W03l/dAIu36zaFgeGLnz/DG29toFC4xONaUm+tWl+aVIihaiROyCgZLx2OiXzM9ZgI3UZFuESM/4Ic0mstCKqkK4QFnwpPxrYeP8EmNO21Qxft+KuVrodtoKmqBKRUfQi9gKFoS0CNNxBtywirnoFiRjoDviW4uiko09bosJW024483hySsqdbHyCASrpRNoBRGfG9l0/whYdWCeOasQv58foScVjw0Iktzizv8eJkDvBMVMBukzCfFUShZWxDMB7tNXlpWC9SdtYzTi0OKfcgn9cYE5KXAR7oRSVPJte4WmQMN7t0yjFX3wjZXI04frbkzGMTOl3H9JwliDyB8QSBJZjyxHHDcD/CzxkKZygDw5WthEkY4LYdtoJe3DAyBp17GHl8CU1P406A+vmQ4HVNs2cxc5rqQDM9V9PsKMK+FxJ7VxH0PEpD0PGMz4tE3be8KAfU657ePPhPWXZ+FOAqiOY89VDhMgG8vpIYCLvDYX4o4YLwa6JlMOda8vC4/QxDOZT9EGh9dpQSpRi3TI8mr0J+EQiEv9P/PNfHX8MXIP0UTN6AelOMCmMtgKhZM9j9nKCc4HNFlAao9nuiKSekvZgkS+jN9ehMZZh3kaLfKUzz7W7CN8upP34TwJ82Ds8HWVtrfdub1aOor3/9l/iDP/h9HnnkMa5cucz58y/z27/9Ox/JWh+27gOeT1Dd6mR8I4m9Js+rd3nlnbZ31B2eG23at3dQPihf584drQ9a79ZtOvfAMqOyFHVTIwAHhGTcZFAsy94EYwEceDHfC1qTQBAX3KbP9YtPsQhqXro7RSWp5YAQokOYnBOTQNd+1/ig5eykoNvxl41FrUUtai19CJY0jB+Q/Qg3Ib2MePuEQny2sfCCXC2dJR8AOaRXoFmAKpPRVzASIIUS40NTCvna7whwQgORxZSwv53x5/okzaJjWMco77GpQoWWgoBT0YCJDTjWnbBeZIwaw5Vxj9BYQuWBhm5c8dDygFcPPGnqOBiFlIWn0SG2D8fP5nzvv0wRJ45y2/Gpc2NcDfnIMLtYkySOb/3hDF/+lQPxv9oP6XQt557IKWrNg58v+OEfBbgQVj5bUow1g9WA8aZhrtfw1N8+YLgVMzNfYxvFw58ZMzGGhWM1q4MYpxTZp2Hvjw3NvqJ72jJ41TDcNoSz0DnZoOYMxWUIY0dxFVwegILQeBqv2pGUwtcWNVLo0OH2Nc2e4jAstillvBRNIR0jL5+ldwJGOg+AmvbgFeVVeU5xRY4JlJCMsSJTB67zgK6fLgris+DbMNHisozCkpMSKbHzxwKKTCZy9uKyqL+K4x6/X7O2to0qQ7wt6M33qaoc3YXtjS3OPnKWhVOzPPLlB9+X984hofZWN+FDObVS0DQf93jk3ijM7jXgOcr6kz/5T/zrf/1/MRwO+Pa3/4Lf+73/h3/5L/8VZ89+ir//9/8Bv/M7v83f+3u/idaa3/qtf0qWdY50/aOq+4DnE1pHk8R+tIGksr2j5escVYfHe0iS8F27Ta+8uY71cgd/KN+2iYCM4njrxVO08m7TdluUjBp0JXf2xWkZKalSzP2I5S4+GIDPJJ8qqYVUPFlpt1EDHVC5gCnsjWuWbQNIaaMdTN6aBZ5oX9uaA1YL0g3Srg37VC1gOsZ1N11Vyu9QLkGwJ8RWm4HtyWjMzYvaS1vIT4ihIgpGpyU3LModpt9QxYqZvIFIYSvDINBcyKfo71ecTMbMdwsmLuRq0aOnS67lGZ+b22a3SpiOPbY0pIHFbpYEfUu3D7t7CVO2YbShGO1pnv7agNF+SBg7+tOWq28mrfmapm4Ugx3Df/p3szzz9RFzx2pGB5rLr8VML5QEO47jSyWZcYznQ2bnrSjZKseZeEgn8vgmJ+149rYChvsG7xRvXYi5splQO40qoNgBaphsaEyp2lGfIl/T9LxlbtlSXVXsrYbYNnW+8oog9ZhIBAQm8DSlkH9N4vBeUw9FEh4ttIGfY/G8qVdb4vBlmP0G6DlH90FFmMLeCHwHVA/y861SyyHgB1AJInE3QISYT/bfLl83c+LubPdkO9WhASGgu+IJpFdk3Da+3KALRdYJaPAcDHcJ56A3GxPNOPZHOxyvlj9UlMQ73YQDpqa6pGnc3sg117s/1n505Gf5jvmbK0v/YGurI137l3/5G/zyL3/jto+laco//+e/e3SLfYR1H/C8j/qoD97DDk+WxQSBYTjMP5SE86j397DD0+ulR8LXOcr901oRBMG7jv2sdxCAjT3KCdhxgaixmgRIRX6u6zYktBDAUU9DuCdjIKehnOFGCGklAKLuQjQG2oT10UPSMQr2Bcg4DW4OJh6StTakNEIIxbWADx/ItpppAWNeCz+omZYEd5/JPutGXudSWqe79j2wLfcnENl5vCbKrzoVkOZiiPdbib0WZZgeAQnkZxxq11EloKzhzHTOqXjAJDI8X89w9aDLMhM2bEZaN4xshKphpT9ifZwx6IbXPY324pRuWnDsZM2plZKirDHhiIXFksGOYn87pspheaXmgScaLr+eMhlrbKNRHvJhgNIw3De8+nzKo4HHGMf5Z1PSTswrz3bY2QoZ7gX0jjcs/w8lC1M1Jm+ocsOVixHLZyq0rinHmue+22HmrOP8n3cZ72qOP5MzGRqCOMAaTTlSBA6oFLby5LVBJYqdVw3VDtS5hps0ArZU6LElfdATndDokSfUFptoUWklIlNPToj3Tf6qEIZJJGVdGeGO+ULjrUdn0P8KDP9aiOreI8CmNR7Ey/MJEBIzoKblcVsJiMILOVq14616TWTyOpbsLmfFhiCaBbeucJUnMoEkuzcOHUEaZuhCUx1YZj7doAPNc3/2El/49c8yNd/nw1bTNDjnGI9z6rq5Tn7udCTo+JD8fBRZUjfXz9pI6/Am92fF4PH91H3A8wmrNI2x1jIYfPgk9qMKJD2sMAxaIDY5ojyxDz/SOpTpe++ZTIp3PcmfePg4fkFTR5ZgV/g1eiyEZOUkhfyQx4OTO7SmJ8or3Qj4aWbakVQknRcQMFMdh2oio63ycSAWQFQvtMRoLQCnnBffnUPZuSkFbJlaCNHJugCfut92kfqtVHlXRlg+Fjm9itpxFG3nZyRgR7cGdWgBbXUf4YIYeZsP4zMO7z5NC8DMSBMWEMw5umGFnVHspoZRkdIdKA6aiB/sH+NL/SvMhXnbldJc3p9iOElIGs1ib0hfl3SoaJzisacneGB3I2S4GxDgePXHMTvrMVvXIk4/VFBOFCfOlXzmKwOe/24PW2vWr4QEIUSpmCR+/8/6LJ8q2bwaE0QNO1sB29dCetMWlSumdiuOLxX84JUuT3wxR58paSrFS9/r4oE4Utg9OH68ZNLXpHiGq5pebNndUbhSUSmFzjxRm3Q+ueApdrUQvG49alsAUm95rlwLaEqFG0Lc2gqoLphIfG6KK6K2srmot/xEOjOTt4TD450iWhRisTHixzN5/SaAAwJ8Wi8nEDAbzQihOei2ZGcjY00XQfMmuNY6wXdbU8IrQATVRTkQfCHdQGWMpHqPLGpRkczGhD2FV56km4CHwdbwSADP29/Dm+XU4hgfRbfLkvqbK32X7957CXg+9qU/8XUf8HxCKggMYRhQVc11+ecnpQ75OkopmsYeWXjqh+UYHcZqFEVFFIXv+fzTJ+d5+hfO8BfPvYnad3gr3R1fizfPIW9GKQE3zgj5Fy9AwsXyf5u0kRCFdF9UDZQtQXkRkY8LNeM6plN1G/TZkpebeWC/NS1UYvxXLQrfxrThpFVLTPZaLlzKygjMHY7HQwjXId2QNbwS1ZYeCViqp0SJlpwXJVB5UgJQ4632d0lapRagK09oHGHhWZwfoNc1YbchrBs+s3iFi0Wfsgw57SesdAc8l8coDcudMW6ieXF9ns/kNetFzHS3Yn0z5uS8Qlc1QejwGLo9y+yi5a2XArJuw/S8ZWvNkPUanvhSwdJKzflnu2yvByyerBnuGS68kuAtjA80vRnL6CBkvB8QRnD2sYLN1RATQtMomtrw2k8ydtZCwsSxcKxm7VJMktV86tGK6bmGycjw5ssxB1dicIosc4wacZ90laLYgyAEj0YnSuIb3nHgQp0b9q8ZVAdopMuiKk+woPBWxln1FoyebV25Q6RL5AX0TM5DeQmC/1kASbMnx2G9B2EXqhbEoNo/GnSvBTKa6748CklTd5VI0t2uHIscOjgHrVJrRsZkzYFI2uMzkDUhLrdobVh+cpZwRoG3NMoyE5+kP9djb+OAIDq6y8Sduh6HWVKTSY5SEIYfXPp+uzU/brPDw7qXoON+h+eddR/wfAIqjiOSJKSumyOdZR8FafkGcbqhqhqyLD6anfuQdWusxt0AHoD/5es/z+vbO2xf2RVw095JBwPpjFgvUnQTyN2yqaVrY4ZQnJTnRzvC8Sn6CHdiIt2VuieABiedmMNYAJXLNqId6SQ1fahmWyATtnL2Ssis1SJ0Ww6HNvIcF7cdqAlct1ZyoCfQe02eE5QySrMGxisCqMI9AU4ubYHUZRm11VPys0NitteeaOih74i8pR9ZzsW71MbzVmBIAsuXZjZ4bn+O1VGPzxxf51g0ZqfO+OHF4+R5RDX0nIx3iU2f9V1HMKp4qZilqyvm1IinvjxmMtbsbQX0ZhuCAF79ScyjT+dEsWJvS1FMFI9/YczVN2OKsSJKPUsnKxZOVDir6PYs1y7FbF0LmJ5p2NuQkUxTa3Y3A46fLjn/bIelUxXGeIyBqbma5ZWa119IsE6RJI6n/9aYK68mjAcBtlYECfjGo1NwVuEUqFph2q4cDddJ62+rljejck+Yedwe5AfSsZm8AvVEwj2DnnTkgtY4sB4ABVgH4xfbTWkkKb0FwNd5tgHXAQ9O/HZ0BtmDMHlNEtQ5aJ97SK5XwEj4QwQQPAv27jcAACAASURBVCyy96ALdKST1DmW0YtS9p61xCTMpnO4OCeZDpjtLbLQP87+xj6zyzMsrMzf/Yn5HnU3vBYZb31w6fsHWfOnqf4Gp4B85HUf8NzjOoyIGAwmJEkrzziy+nCy9EPF0yFx2pijlTV+0JFbliUY8/ZYjbvdVs/EPDyeZbOzj7UOMxGZN4Hc+QYDIYiGu8LdSVYh2pAxVrIJvoB6ScZSYS4ydN+OElQ7TtIjcLMCSIK99lMohbDsWm5GsgHFFEI49kAh2VvOCMDxXtRj0SZUx+Rxe9PFT1UQbrexFUoeR4spYXQg3B3jZCRmBvI8G4jaq2yJzjgwuafueVzmmbUlDs1annGmM+TJxQ2K9YjleExiGhIa+knJK+NZXj6Y4QdvnKGbFvziicvM1DmfXtzjlVe7mNISWMOxYxM2mj6PP7hD1rVcGyS8eaXL+e9kzC9VhMazea3BhI7VC1PkI4MJPWm3YWHZESWOqlRcfiNmdBBgjOeZr4+Ynm+oCs30YsOV1yMOdjSzi56FYwX9GcveVsjF12KS1DHYM0yGmv6Mo66h03OEsaM345iMPE2pBDB2BXTo0KNTT9QBFXkGr2sc6rrh382lu9K5s2MlHbVa4XPIR0BHRpGmIzwaPwSdesI5iDIYbyrIJWqi+6R0eHRLgK932wUOg0DbcaSzchyFXdCddlTaQRSBzU3EZs/19qJqx5u+Bj0F9Z4nWlHoCOqxYzIZo2rD6os1nX6HR3/zSX7pH3yN4e4YrTUzy1OY4L1VVZNBznB3RBAaZo5Nv4sE+v2PeW6VvkeRiBPuLH2/ZcV7MFr6JCi0fpZA3t3WfcBzj+pmpdN4LHydj0ZG/sFee+hQfLPi6aOO1nivUkr4Os69d6zGnWptbZ8XL6ziIi/jqXYk5ULphuhc/HDiqxAlSBo1LVBpwYRyyN32PhSnhNfjVfvcwy5MLV0h270x8nIaqiUkaqISwFT0ZcyknDgmKwVjI6Tm0UlIdyC9KHwd20cylrQsEQxkLXPI4Ri36ykZZdEmtLueXCzD/ZYb1Mj4LYhq1BxEtULPNqR7FhNVaG/pnBlzYnqPx1xAV1uUgq+dWCVKK17K54RD1Ld8dfkqUdhgE0eoLf15z/pOQoBlpZ9T7odkPc/mOOGP/2CJ8RosnqvZvJKQJTWTUcDUbM7WNcnhwsP2Wogyjt5se9xR8sL3Nb/4dwf0ZixKw95mwOPPjJmaqdm4GhHGnsuvRSQZXHk9Jus4Nq4EzCxapuYbnvjChNHAMBlpirGm22tYcyG2UjggChz9RUvlNNlZCDqO0bWAYPFwVHTLzUN7Pa+35d+VVfKhGIhOgc/lPU/PQZw5Jq+CaxQmguikl7iJjmLymgCX5JSQjscvSafneqREKCRktIzC1BxYKw7KbgLZGYm9sLl0CP2kfR0KPe2FHD1p1YAGdMcTzWsm1wqqjZh6bOktxDQTz+7GHj/5ry9y9qnTPPaVh9/1BuLmG4y99X1+/CfPC0i3juWzizz+tUduC3qOotty2P25k/T9nUni98rd+WNd8pa176Od29V9wPM+6qiOoTt5xdzJyfiD1gfpoCil6HQkYGo4HN/yOx+tkeH7AVDGCEAsivo60fHWbd1N/fi1y+wnJa4HwY7COU8z3ToXIwZtdhrKFeny6FELNiK5Yy9bQ7mgkBFTuCcAQrolkK+IFFxXN0ZGBGL+p52Mv1zcgqgYfKd1S1YyBovatOxiQdZTVsjH5QnZltIQ7AiICVuisim5/tHU/Za4msqoK9mQC14wgWToiHYsB84wecDTDWsm/RAqx2ww4UtPXaL2hipUrPQGpMrz+MI2pTN0TY1XnsoHvJrP8ko+gzew0BvRCSsaZ/CxZ2qxJJp2LHRz8jrieHRAUziuXYs4no1InvA0TlMEhmJVM9oz/MUfTlPmitULjhNnC/KJ4cTZkqWVgq2rIf1ZxS/8xoCllZp8DL1pRxRXbFyNGOwanHNsr0dMRoapuZKFUzVxalk6VZFmjvFIEcaOheOO3c2AH3+ry6kHS8Yjw1bk0BqWVipMALVRzD9kKUzAZtfDckR3LaeZ1kSpY+dayGAvEDsCEFDZksBdO1LytiWWA9VVUMc1pusIe554XkjGyYyjbAy9p2DwPchf4+3Nj9Y0EiX8Hx2Lg7Ivhc8VzIJKxT1ZBfL34Eft69oNuRaYq1i8eHwFQU8TBTGmE1AWnqgXku9XGB0yszxNZyrj8otXWTq9wPzJuXecP8463nj2AquvrWEiw0OfP8dbP75I0k1JOjLuXr+4yYmHjjF7/Ha5TUfbbblV+n67JHGtNVX18UTsHNa9ksLL2vcBz53qPuD5mOt2nZNPSh3ydaqqpihuDyqOtsNzdwDq0JPokK9zp23dDbjbaXJ8ptAjTxP76/ybqOVVjB6XTsvUywIYqgUBC6aW7CwfSffHFK0PTygqrrrbXp9KCBrpxhxmXaGlg+OtEIZ9INutesgFqZBxl82FBB1dA3MgnaFw1ErH6xvb0hohSQfCBXLtWexbelW5KK7M2oiE3pSwkh7wxafW8V3Plo/50c4iMw+MODM7YMHnLKqCN8spsqzmTDLg8f4u62WCcrDTpBw0IR3TsBxNCFXDiWzEqA5ZnXR4aKpio0qxao6Hp/aZ9iWv709REPDU0hbagWo81mm2tjQmADNw2LHhzZcTqlwzu1SzkJa88Nc9Fk9WoDxNETAaBMSJRxvN5tWAU+cqBruOTs9zsBvwgz/rcfrhik6vwhjH/nbAQ09N0BpW34p49bmUheM1z3+ny8Gu4cS5gjiBwZ5hczXikc+OaSx0Op7+iYadrYi9Nw3nnsrppAFvvezQFUR9y85GiDYQhDdGYVgZc3ojnwlGAIjdbw+4tqMRTwlxOH3MU/1EcrR8A8GNzMV34oBQxlSuav12wnbkeU1GV8lZMDOi9pqcB3/QLnjYfhqCmfNQO6JFWlNF6J2G/Hsh2bmMrZcGeCBKI4IwwDWeK+dXSboxX/67XyC5hbN38YXLXHrxCjPHp7G15YW/OE+VlyydXbz+HK0UzR24NR81n+Z2SeL9fodeL2u/2z4a6futda9HWvfxzu3rPuD5mOrdOydSRy0jfz8VRQFpGn9Io8P3V3cDoA4B4s18nQ9TQawx45aHmnL9TloVsj/1lNydu0x+7kOJhoh25PFwDwInFyttoS5Esp5syEUvu9pK0VuQo0vx77Eh5GcgnAiAqRPh2KihKLQs0rVRSjpMKBlRVR2ICyE2V4utMiyT6IpyFjDQPCa/mxm1Hj2+JWNXcvnr6ZKvnLjGziijqjWzK0O+rNc41j8gSRqMdnxz7hJjF7BadDmTDqi9Aa0Y2ojtOiOiZi4quVp2sV7zc7PXCLzlR/kCwzBgNiy4WvWYLUsOXMwV12M6LNmqE57owMMPDNm5GnH5jZjtqwHFnuL0AxPixOO9540XMoIwpphoTj9UsrxSsbkaUeaa+eWKuoSXf5ARx57FExUHOxHf+g89xsMQpUue/06XB57MOdgDEziG+wFrl2NGe4as61k4VlHkmsFOxKlzOd/9kynyseJgJ2CwF/DgF3PCHiyomvW3QnY3DJNdLR0VPHWhCUJHY7Wcuw5oA0VpfZHkoG6dkV37s1D4Ocpr7NATKocbKMYHhvQBGUe9rQ4VWKmMqKIFUXMB6GkBOioR8GzHEFoY/7jtEN26qa6QlaMlRf/TCl8oAhtRvmlIwoRHvvoEb9prXPjJFYpRztzxGaYWe3jnmRzkPP9nL/HMNz+Dvom7t3V5h95cV6IKYo0JNN2pDvsbB0wt9KmLGmU0vdnuHc7Aj49Pcyh9b5qE8Vg8zT4+6fu9kaTD/Q7Pu9V9wPMxlDGGbjehLG/fOTmse8WRuQEq3t3o8KMBZLff3t0AxLfv23u/d01jCQeKbBBQzFTgJbE83BUOT521IKh1O677QCBjKBvJOKnpy8gq3JeLTjCR55RLwtHJ1iRSIEB4OL5VVYW5OCTbTjsC06LS8QkwDXof4eHk0rFRcD1iolqULo2y4hnkUtBr4HttZIUTeXlzvOUg7cj+6VxMBrOZkiZR5FpLh8pbFqb26cQlQeA4mx2Agn5YMx9tEmtP4QwvD2f41v5JHu3s0Qk9DriQTzEXFWjl+fmZa/xRHTEfTPhMusUr4xk26w6ZqemYmqIyRIlj36YsdXO+/is7bH02YuNSwHjPcPrhkr3NiGuXQ6bnLfkAki6k/YYrr6dUtRwHB7sBdQHnHi/pL9ZgNWuXIoJQoRW89VLC7kbEq89lzCzUPPD4hMe+MGF7LSLrWQFULyd0Oo7BrmZ2EZZXSsZDg/MeYzw2MriiYfWNiI0LEZNBQD5WFAeKk+cKNq6ENJUm6zqcu+VAu+nYVEocjW/+mS+Fn4NSRA8bguOK7JjkWo2eu/XABxoB3dEcdJ4UA0E7kdGW70jgqBCTYfST1lH5bTsjnVOvofOwIjrhCE94klmN3XEULzvKgxEvPvcctox54Jmz2Noy2h0TBAHHHlhiYWWevfV98lFBZyq7vvWkG3OwNbjuwGxry0NfOcdwb8zmhU3iLObpX3mStJfe9hy8N+GhAgDuLH3vo5Rquz/SAfqwgOHeuyzfBzy3q/uA5yOuW+XT715Hy5GBGyDldifA+wUVR1+3X/C9Rmt33ta7v3fWeUJrCNBE2x6npFPjQwEG1SwwkeTzfAXibblY4WDwMIS1dHhcB4qOAArtJG/LpkIYtrsy7gr2ZUxVLgmROV2X3K1mSgCPC4SDAfJvPyX8nXhbuktVIkqwQ4J0cQzsTZSK6rhES5hWfeUTRGmWQRm1PJ8Z4QmNVUgz41EbYjg3DkNYsJwIBhzUEatFnxeGcyxFBZ2gJNOW7x4s8dxgkY6pWSsztuuEflDxULbPYpLTMxWDOuK7g2XOJAMiLE/0tvnTg9N8ZmqH2SDHaM8Pd+a5sJ/x9ePXONcf4iuPK0VGXlea+eUGhUIbT201CzM1F17MiFJLFHmsg1d+lDG3XPHUV8ZUE8OV1xPqWvHVXxvwR783zU++M0XWa5hbrEkyT55rDrZDPvvVAVdeTxkeGPY2QjZKhbOKzdUI5+DsYzn/9f+b5thKxfobAYMYrryc0pm2kCqG1wKaMVx+NaEuRUG2txXgD+8JjHBn3Ei6MXiPb245BtvPDw0mEaPM/W/LGMzldzgFHFCINcHen7ZEZC3kaVLp/oQzMgIlFlA0fv6Wc0GJMiuYd1RrinIVgsJgFhrs0DC73Kfa9KhjBUGpeebXPsuz//EnnHv6LJ3pTG58PJjg7dyXB54+y7P/+Tl21/bBe+ZOzHLs3BInA8OjX3rwXc8/qU+GYurt0vfxTdL3mH5fuj+H5OcP0vG+P9L6ZNZ9wPMR1u3k0+9WH1WH53Z3Gx8MVBxt3e73vVUKf5QVhYba1ZSqJqgMFBbVyBgLJcollwGVAJjJSZF0B9sQFUJodlFrRhhI16WcQhQ1bdJ1NSVAp54S0jFaHm9i4eNUi5BPt5yP1gdIV+2IzAl5ugbUdNtVolWIBbwd0zkBXi5CspUc14MlVdmOvbzEY2zrlJ/Us3x2eZs3qmleZ5FqpNA9x6P9PTzQlAm/f+UcxjgKHRLQEJuGXlhzIZ9iOsjZKDP+1swa+3WM9ZphE/ALc1fRjWUxzvGx5jeTtzDaszbMeHayRDTO+ZUHV+lFNRcvZ6y/bhjsBowHARfPx5z79ITjZ0vKPOLhp0rSjuXyaylmoohiT5LKxXtqrqE3U1MVhsGeoakUaskzv2xRylMVhrpyjA4U1sXYRnPyXEHSaXj5hwk7GyErDxUUY0NVaozxOKupcsP65RB3MaBRSqIaEjEfbHK5Yagqg7MKa286WNsgT1fJ564duFrJuXZrkzSR7kz6sDgp2z3eec0P5DgxfVFTOVrl1Ui2T4AcGLmsW62Bq8VcEA90uDFiU4DyKK1oxkCtKd7wuF1LnHviKCGhTzzt8ElDZ5QRJxELp+YZ7g2pigpnHWefOk3S3hAdVme6wxd/4xmGu0O01kwvTb1t5PVe9Un1xPmw0vdb694CnntHmP6k133A8z7rbkCJ1opOJ8Xa9yef/ihGRrc77u8FX+fOdeP3TZLoXcM/363u5nNRSpElCZw0uMs1NhKF1OS0kJFpuO6S7FppOKGY+Gnfuh/PtgGgTkBNNdsSnmsk22gaiiUBQ9qKF089A64rJGWbybhCG+neqBbEBLm8xmdtGnsg6iwVyh+zB35POkS2215UFdfjBrBy0cULmIoOboChVFeEwMvVDFsqpWwCvrF8kbmoYK+KmY9zSme4WPU4nQ3pGLEE3ikTBnVMjSLV4fV784V4zNjGnOkMOdc5YLXoohQkuuZ4MODVS31eeD6jszDmaw9vEBlLUyu2rkTYWhyQD3YMvZmGKHXMzDd0+5bRgUYpxfJKKR0V49jbDvFOYbTHNpq6hAefzNm8GtJUkI81Siu0djz8mTELx+t25ORJuw63AZ96vKSYaKpc0ZtpxGww9Dz3V12cVeSjQ9NKD0bhJmCNQzmF1v5tQCdKHFHs6S80jPY1g/0QFUMyb2m2FR5FXagbn4uTKIhgRtRZox/zDrCje2DmodmUro/OgFy6QIcTKqVuvCxYhGZDujx+3HZ+rjtCa0G6CvzEQ6WIV8AEiqCrJB5lzzCqJpjMs/fyEB/32b66w9O//GnmV+bJRzlpJ2Hm2PRtz6M4i4izdyq47qbuBUfxg4CP20vfo5uk7zfIz7df896OtO4Vf+iTXvcBzxFXGBqyLCHPqzueDB9n3QBRcgLcLV/nzts7upNZ9k3+LdEVMBzeOfzzKCpLI7JxwDguqaZknGUTsDOtKmpPAIOyCJdiSsZGh546ttc+t1VPaSfjJ5PKO2wz4fx4JUBKN8L1ESmM/N8UUM/J/2lTzn0fojXpKJkONB7CQsjN3kuURbgNzVDGVPkJAUu+El6HyoVjdKgc84cp2zU8M79B11e8Us4yiQIW0glPzWxzueyyUUVMbMTxZMJUVJGZmstFj1hZJlbARjeo8MDJZEjhArpBSeMsBzYkUQ2nkyGlC9gqYta2M2ZsyXSZs/VszDhzlA8FZKklDCyjQoiu+1sB3iuU97zxQkradfSmGoLY8uSXB2ytRaxfjClzWDxVUhSa1TdDllcs2jimZi1vvZJw4eUUb+GzXx/Rn7Xs7wbMLdacebTkub/qYULLtQsxe1shrz4XkSSOtOcY7QfsrN/qzq1IUseZh3OCwNPUiu31kO01eV53pmFq1jLaNzSl4tSDNW+d1zQzGj9WoDx1rQlPyjjJVxAvSzZWdenOx6Segma3HZ+29gSu5HpQKLVs7/rzHcRLEkOhQznWbhgjejnWGgDN/l/C9Oeh9xQoa1CX+kTHLb6oSPU0Jx55ABrNg8+c4+xTK+13xe2BzlHU38TU8veSvldVc53/c3ijdn+k9cms+4DnCCtJxADrg0rOP0qV1iFfx3sYDMbv/YI71uFc5WjOKKUU/X5GXVvyvPzge3WX712UGVwDxmlc6GRc1Kpuwj1xXW76MrbyqYyaXDuyoAYKASjxvjxeN0DaqmdsO4JyoLrCzXE9UVaZUbuOFr6NrkAdtGDFtTyiY1BbUWepOSEke0SRpXLZR5dI5yiYCMk6HIuM3ndbCxaNKLxGbU5Yz9PPKvIiYiopOLA9Khe0VwBPV9dUXlGjCY1jWIdoC0MV06DFXwjIdMOLw3meYAvnFZfzLqF2/HB/nv9u4RqDKqApNEvhkNn5mieKARezDDXypIlFN5bTp0pG1zr8t9+f4pUfdXjiy2OuvJlgK82JBwoeeDxnPNAkHXj8mZyF5Yas59ABHFup6M9YxgPFZBiyfjXk2/9hisFeiA4dC8drNldj5pdLejMNnX5DmcPFH3eJU8fKQyVRAns7hkuvJi1KeHsFoeexz40JI09dQpQqHj024bs7U+AdDz2Zs7sRoI2RvKzUkqaOvQNDvatAe4l+0AKa01MW39MkZxTFG7cc95mMzlxrFnjo6YNp87La3K3b1fgliE8IkLaNdHveVu2BoGJPesrjGk3zSocTp09SDh3FVs7C8kkS2+HYyjK2sUwv9j/y7stPS2r57aTvt6a+S90b1KH1fdLyneo+4DmCOgzXBBgMPniH4qPg8HjvW5VY9J4qsbvb3tF1eJTSxHHAZFJ+bN2wEoteDgi3DTZ24mPTxkKEA/CBgBFdiheOsUCbf2UNdC9AvCNdoWJZwIfzwv1RVl4X7AOpkIx1KRcm396JK4RXE1SiBHPxjS5QE8t4Q5cysmq6rbT9AMIDBMzksi01lniBepEbZ/HhZ+I8zbxDRWCVZqNKWY5y9gPNXDFh4gKull1OpSP5HJDj5Jf6l5iJK85PZvir/ZPEypIEln5Qcqno8dmpTRyagzpi2IQMbcBzg0U+1RlxPBqz0JnQVJrdYUwQwOPnBnSrimhYivjIaq68FfHmyzH5xFBMFOVE05u2DHcMZam5/EbK8TMl+VAx2DckqWNppaYqFZOBpjdrWbscsXYxJe1aFk+WrL4ZU+aKtFNTFIa0Z4ljz8lzFf1Zy/rlEG08/RlDPtR0+o7xwTvjEtKeJQg8VaXwXvg8D34lZ2stoK4Mo6FiMtZiCKlg50pIWSj83uH7rojmPd2zTsCpAacVNLwd8ARiJOhK8dExHXDrcuypWED2u14rG0lil5birQ8euhV6ep9RpP2EeMVC2dCcOmB2bpm6jLDbjngm4NJrlzj96Ardmc47lrlf713vlvpujCYIgo899V3CUj+Wpf7G1X3A8yHr5nDND9OhkDp6lZZSquXr5NT1UZxwR9PhieOIKAradvDHN/rrdhJG2wWjpYYibbk7gSipVCNgRBXSmYkHMFkBlHRo0nUhM7tIUs6j3TazaiTjLt168gSVAKKgBU317I0RU7AnF8K6Ix0iDDjVAqce5FHbATocqTmIKgE+TZvPpAfCE3Kmfd0habolq6IcwYLFWA+14bl8juH0gMw0pB3LTp7wH7dO8XeWLzATlgxtSEdV/OLSKpt1ylRcExvHX+8fI7eGiQlZjHK+2F9j4iJeH/fYrRNW0jGf6h5wKe+TmYZGKUa5pthXjIeKJ5cndGca0sRTV/Cj73UY7AbgAuaWKjauRMSRpzdlmVlu2FkLaWrP5mrIsdOO42dqxrMN2+sRw92AtOtYXqnoTTvykebUuZK1y1CMDc//dcZXf+OA/rRjdt5x+Y2Yrashnb7lqa+MWbsc058u2d8xRLFj3JJsothz7GxJkjqaSlEWirRjmYxCTp4ryPqOtOs5Nlsws1jz2gsZ5cTRNLB+KSYfvZ2wO76i0H1DuOhh7DGpo1r18kFr4d9Ex6FaBT+EZq114Y5bxVcjoy1b3MXBfMjhsjf/QL4/TBc8jrIoiDBEvYjOQzA+v8XJlbPQ7TDoX4XM0v3aIuNwj4ilD3Fm3cXu3qNRy8fZWTqUvisl69Z1TRR9NNL3O5X8vvcRz+3qPuD5EHXU5N+j7vBkWYwxut2/o7m7OIp9PAxMLYryyAJJ7zaWYzbLKKYbRpnH95ALRiN4wRvILgoXJndQpwI+8AJgwu2WCNy6HnsEKPlAujsuEvKxM+KV46383LQjC93e9deL3JArt+ocl8l+oIBE1DymklHV5FPiCRQNpEtUzd7oDAGi3Om2r8WRBDWPd3Y5kYxZKzOeG87z8miWKHA0wmelcCH/79pDfHZqm54u+frcKkp7JjZgs4zZb1IC5cm9JreGz/d3+Mv9E1wru+zWCdNBwVyY8wuzawxqw8Qaskix2e1Qjz1PnNwlm3WkPcV4R5F0HFlmWbuUECWW2aWG/Z2AvZ0Arzw7G4apecfTXxviPSwcr7n6ZkzScRw7XVEVmmNnSrKecGzefCnlxNmSTt9x8lzJ/rbhwksp88cq3ngxZWc95OyjOWceE2XW1rWItGN54gsTxsOAg50ApWHhREWZK3ozluk5R3e6Yf5YjW0q5pcbzv8wodO3ZFOO3qylO22xVrG1HlPkmnfcpExg9CrMdy3Kaewlh9pXJFOO2mjsEKpNSTsfO7DbAk7iY1BvtHi1BcLX1Vy3uy7a9thp3Z7fcaNkhENEB+xOCHOaqaUO+aUUDmKChyfMhVOsPHCWTjflavUKme4R6+zWlY6s5HvjZ2PUcgiyJNX9o5G+v9fa9+ud9fEGjPwU1OFxlGUxSRIxHOafAKXT20spRa+XtXcU79Uf//jqcL+8v5mc/PGqNrY2hri+xvTU9btjpaTTU81CfhKsEpKwzYSvoxoBF7bTApZZhEga3kRitm0MxLjtzlSAacNCPcR7rWKry03dGG5wNRRCYtbtzyLpHhECjZCRi3n5u2kDTbE3qbXaC6Nxjs93NjmdDMHBw909zqZDQFE1kvyt8RRO45XCWs/TU1tExqKBR7oHOAzae2JtibWjq2u26w5DFzEblqS65lrZ41iS0zEVe03KdFixUyVgNP0Vz6kHCugrbABpz+O9Ym874MrrMWWhqEpNf7oRjq1THD9T45zn6psxtlHkEzEV3F4NsY1iZqEmDD2jgTgdP/3VIZdfi9ndCAhCePRzE7Ke5bXnOzgr5OM4sUQxbK6G9KcbgsDTm5YZpdaeKPGYwOOdYrQfkHQcP/939vnKrw5ZeTBnsGsY7oWAQml5XjmRXK6qVKjD2JBbvkX9GOrzkJ/3lKsyHst6TkBvLOPTaBHik0AAYR/COdCRfN46hvQBBBDf6dRteWfXic23AC97IMdfekIx/emAxS93OBk+xOf+9uOsPH6C7FjAE09/mqn5PkYFeDyVz9/v6fS+62flOnw70HEofd/fH7K5uctoJArefr/L4uIs09M90jT+0HmKn1Tp/yeh7nd43mdprej1JLF7MPhgid0fZR2GbB7ydbIs5ihBxQclVgeBodNJKApp5x7WUXW07rbz1MliQgxh46i0FT+ctrPiYshPebAWJQAAIABJREFUSaemnBZicLzRil56oroKxwKAvBe5udcChlQrB7cWIg/xAbhdMTCsF6BeQkDQ4XfZzWOow4uXRvxU2h/hgYl0mbyTTlLdZmP5AvzhzfhhSOkYZpOS0/GQjVFHulOV51y6z2vD6UP/XSyalIqtPOZS0Of1yYhuULEcW/CwHE9YKzNWsJztDGm84rnBIhcnHebimvmgYCGckDtDaiwPZgfEuuGZ/jX+aPMBVocZP7o6RegsSeLoBxFpXlAXns/+3IBXn+vQlIo488wtNvzcr+2Rdj2D3QClPUsnKqrcgJJQz4OdgMuvx+xvhajAU5Vw+qGK6YWGxz6f09Qi83/oqYKtazGXX084drpieBBw9c2IrbUQrRRxamlqjbOaTz1RMNqTnK5iovBWMdgL2Loa8fx3YuKu5dQjBUuna/KhgLU3X0h45dkMbxTWaZTyhJEXj56bJggmdninCCNPWQlAs40AJFfI51xcEJNKalFodZ8EfwL6MzXpgmPzuZBwQeEDJeaYNbgxrUnTTcfM9WNe8bbz3CkmL2imOn3UaUdUdfDGMzMzy6e/8SXOH/wQrxyuVJSVpM8G6u25WUdd96LzcO+I0u8NOt4ufdfEcUgU3b30/c5r31dp3anuA573WVkWt23Ij4Z38m7OyO9VtwvZvFdxFe+1X3Bv7kLOnJ7j5HM9LtohzcRSdASoqJbL7dUN/xo1FLdjj0Q3KCdmhCoSABJMpJvjvYytlBWOT5G2aq1Q7v71SNZwLV8IEPCTwGGUAO/k0Mqoiva1sZCpiSWYlNZ9l4AbnSLAuQCvFJF3+BJUoOjqGj322K4mwNEzJd4rCh/inHjHfP9gmY5pmA0LFqIxmnlOxBMSZVkrM3aqhPWiw1ZVcTIdMROUpKZBK8XABcSqJtSKn+tf5lvfXeAnwyl24i77qwHTyxWnmz0e7uzy9f9+h1/5H3fZXI2pS8W5JyYoFCaQ9zRMPCb0ZD2HMrC/FXL1zYhLryTsbwUEMWxvhEzNHHD64ZJiAtoowshRVYrFUyWv/LDD1bciNlcD1i6HnHmkwmgxJ3zt+YhzjxesXYqpa6hKRW+6YeFkha0Vf/h/LzAaapZO1WxeLghCONgzuEbx2nMZ+Sigd7whVTWj7QBrFd4eolUpmytM3xJoTzxrUXh2t0NsIanlyYqMO8tVeX69CZNXof+ohUjhM03StcQPacrUoGIxGjQ9GHy3XUQwiiDum6+uh5yeAIwKGL8CarfD5/7XJ3l08dNMuwX2dybM67NsmteoTInuKB6JPsO0mn+btPqnoe5Vt+P9euE458jz8joP9G6k70e19s9S3Qc877NGo4IP2XF8z/ogJ2mWxQSBuYOr873r8Lz7fh3lSOvutrW0NM0//fVv8I//87+nqCpU4PBRS/x1oAKo5sX/Jt4E2gwsIgiHrWQ9BQLJwtKljL28EoDjYnB9ef6hN4qqpTtDu4bZlfgKbooouLlUI68N96HuyWt9iFzIDl13O1x339VF2wEKYX8v5mJ/mrPZPtYpAuP53u4S+sDgtSfpN4TKUfiATNf0g4rKGvpRzXqREmtLqBzPTK3x/GiRV0YzdIIK7Sz9oGImLtksUwZNhMEzRcnuQMjGF/YyKq/RA4s7FaPP15zoFyzNV0QnEs6Ympm0YWrWs3yqpi413b6lbhRb1yKSzJFkEmmwu2G4+FrCeBDwxospW6sRceJJwhpbw4vf7RLGnoXjNWWuOdjS7O8ErF5IyCeasgBrA2yj+M4fxQSRoioUM4sNBzsh3V5N1ofRrmFm0bJ1JWI41Ax3QqLUsvpmzN6Goak0daPpT1vSrkNtAbmnqAPCEHzl6c5YOv2KfKLZ3YqkuzLUnH64wBjYuBJSDOVDtjnkr7exEjdNwscvePwamFQx85glijT6jKe8IjJ1ZcTTJ1qAer+9nJU32W63x74OhOiOg6iv6c93OfXISZamTjGfHb++XuhSPj37c1zbWsO4kG7QI4iD69LqsqyoqqpNFX/P0+qu6l51eO7Nxf/D/a63St8Puz83S9/vlPp+1O/zv/gXv8uzz36fMIzIspR/9I/+MY88IonFu7s7/LN/9n+wtrZGHMf81m/97zz++BNHtvZR133A8wmrG7yWuztglVJ0u3cesd1s7vdx1g3fH3/H0d9Rdp/uZluHTs6mq/mlpx7hW396nkvliGIKSCG7IoorZSGYlngG1QjocJ32ZvqQYxOCj0Wqrgrh+1zHW62BIQaaVEZPagxMWk8dL5Jk30ZL+JCWNS35XOnVVo4+AJYlNwskagLTrtOCnfDmtO0RROuKF64cY3uuR7BQsRvErPsMk4KqFKMqoggDImXRKLbqjJ39lBDH12cdJ9Ixa2WXy0WPUR2wXvUY5YYxER1TshwO2SGlH1S8vDNNvyh55VKPldKSbySEZwzj3GMrKHTA1EOavSTCVZqBD1meKVBKCEfmJk5OnFiCnqM75akKaBoFXhGnjtG+IckcQWRZfSuhyDXTj5S89bJ4+JjAsr8doTwY7ZmasXj+f/beLEayLC/z/J1z7mq77+7h4bFmRlREbpWZtS9QDDAsAzQ90rwM84BaPLQ0I/HQao1GahBDP/QjQgIGNVK/tZCgmWloRk1RMFA0lSSZVZWZlfsSe4Tvi+13PefMwzFz94iMPTwyEoi/5AoLN7d7r5nd5bvf//t/n/PVkZ5ldrHk8rkKfqCJKoY8FXiBZWqmwBauXZX03fos4HmCMociV1hrqLdK19YsJV5gSfoK6UG1bqg2LEUuKArFiz/SY/NawLm33Da+//2bjHrnozgKQCqnbbIGMIJsS9JcKOl+5DF5umSw4aFHYMebZg/XCPAXoTjPyAIcKNx5Q0gByuL7itpMzPxzk3hSsrG5Sl8foiobiJHoyFcBkXCp5jeOVjtfmQrNpqIoyt3JIq0/mdHqg6tH6f1zMMuy1pKm+a6lyP7R92azRlGUpGnGa6+9xtLSUYSYwt7EZ+p+60tf+gq//Mv/Cs/z+M53/hu/+qv/B3/wB38MwO/+7m/x3HPP8xu/8du88cbr/Nt/+yv8/u//34/EUftu6jHgucd62MfOvYCAu01hP8h9726279OQ03VjjSfDer0hnSzlo8EW696AMnSREeFVN3ZeTOzFN5gAsHtZVkVz3wLHbahgxN6M94v+iMgJAOGysWyKi35IIdhwieeehlI40EPgnqeAaNktz3hOoBxtQj4BhOy1wHDrE6mb7rLSMUzRyghABYK15Tpi2VI7knBkqUtY12ylEVsyokChrcBYzaWkTmoVIRpPlRihuJA0WQz7XExbBKJkLsjoac1WHnI4HjITpgxLn5ooQAkuiRZKWmZqHfS2IS8Ug3XJcqvFZLaNDAVCW7a8Cn7YIQghiCBPLOsrls1Vn4kpS2PCYrFIz+lf8kQy7As+9yM9Ln8Y8f5rMUIZKnWn9zEGzhWChaM5W2seCIE1gqOfSdlY9l07UTpQ4fmaY6cyTr84IPAtfmSoNQ1pX7J8KWD1ss8bL9UxBvLcjasHoeGFH+4ShJbLH0ZceDekOZnT2fFRHkzMFFRqhrWrPvXJksA3vPAjXdJU0poqOP92xNqVW+tijL7+QNJaoAtJtVnSvuJRNBS1Z0FnTr+ju6795cDuCPmMwBKAHyviqZC4HpINS+LJiKAl2LiyzFzd53z2fZpqlqXgLPJGpfX+XXs0Wn07Yz3HLuT3dD58NAzPo2pp8dDe642p70HgMxgM+Nf/+l8xHA55/vnnefHFL/D5z3+ZJ5889cAi6K9+9eu7j59++hk2NtYxxiCl5K/+6i/4wz/8LwA899xn8X2f9957hzNnnnqgdT6segx4PnV1dy2ju01hdwzPQfbgbt86upfwz4N1lr75dt0s16w0mtdXr9I75NLD42XcJFTo8quy6dHS9GiEHJxmYgRiKIE++LkTLBdNB1Dwgdh5qeA7JsdfBn/oQJPQ7voUXYN8wU2CFROjztYI+KiBa4tZ4ZZRjoNBrduG8fi8DUbTYKPXyuHo7yIHmMoJqPdLZhZ6rJgKHoYT9Q5CazZNBQMk+LtXg0AWrKVVrqZ1YlmiraA0EqU0Na8gEJpMKwJKPtvaYaB9grLk1eUp6vWSV7cXeD4paDQMwSnL6ocxvYWQrB6yEPQ4ITtMzRQEoXsrvW3QpSTpe2wuR7QmhkgP1q+GFBnUmobTLw4591ZIXDU8+7WCk08P+X9+b5qk7xEEmrhuKDJBv6vod0Y/XUmeBvihJQwtVgsufegz7HosPdkFLVhZDnjmy32i2PDhGxX6PcHXfrrDxnLA8sUIowVhqFk6lXHq+SHL50KCyDC7WNDd8YirGgFo49atPEFrsmSQePzg5ZAMgYwtX/yxHt/6Q488lQi5d+HVxa33+c66opco97ee0+74TdcCSy6O9ksPipV9ImUFylco3yNqBeRpgbGaNEu48vYyh5+Z5/QXT+Erj7Zep2Xmaarpuzr2bmesN2YXPs3szz8Wd+dblWs/FnhewB/90R/zzjtv8cYbr/Hnf/5n/O7v/jYTE5N84Qtf5Bd+4Rc5ceLkA6/vj/7oD/jyl7+GlJJOp421llZrL4pkbm6e9fW1x4Dncd1d3c0xci8p7ActWr7d8h40p+ugt2vMgN04GVZaQyo0QjiAYZUDH9KMIiUEqGI0Qj5e5njYbYSrwp4bQzchUDghcdnEmRiOTONk6YTNogQxmqTyh850UHVAzzqgYuo4U7rtUZbWKJjUVp1AWjK6ifecq7NM3Dba0HkD2cDphIR1eiOX5WVJjho+8htEWjv/nkBS0yWd1F2wQ1lgrCCxPoWVdHXEd7YP8UJzFV9quoXH5rDOQuyMhA5Hfb4wsUHdy/mT9WPM+glpHKATwUD7fBjP8PXpZV5dnqfMBVEvZ+eq4PSRjLhSEo4+89UrHknXx1qDFIKpuQJtJFFcMHsoZ3PNZ2PFY3axIIgEZaEoh4Lp+ZJjp3Pe/Z5He8vn2a+0CSJLe9NpdTaXA4yF9qZPWQiq9ZKyhN62Y2C0hqJ04N/zIc8knm+o1SXJ0KM1WdJvl85xWcHisYQffKdOnkq21316bY9+V1GpaspSkPQFzYmSEy8O6Gc+k4dLOh95pH3J6sUQ/5glijVag+dZ/AgqVc3aleBj7M7ejjzS94RAB7IuZPtxhA+2c+NEFighmFycpDlXQU7nCCUwmz5lPOTE1xbwI3eqlyi0KZxPz31ckK9nF8bsj7/rND8GPzdjf/4p+cM8ivcaBAEvvPAiP/mT/z2/9Ev/K6urq7zyyt/x6qt/z7VrV24JeP7Fv/gF1tZWb/rcn/zJn6OU05/9xV98k29968/47d/+vYf2Hh52PQY8n7K6HeuxX69z9ynsB+118/Hl7Y/W6PUGd00hP8wJsrEp5M0YsKoKiCOftG8oPQvajZ2LIVSuQt6EbMb57uwavDF6LJy+xh+6thfCTXj5W84JGekYHgUu0NOO9D2jFHYYAakQtOfMBUU6YnQ09M4480KRO0ZoOL+n+8GMBnNGoaMmdlNkXt+NrpsAgi1DkEPWcL5CUkoSBUYFzMk+iZHMhSk1keErw1YRk+WKofXwtKVA8Odbx6iIjNADJSyldkzZs7UNJv0EJSwehs12SL4Nq+2I5+tr5IdClnt1klRSXtbYfsF7YZNz201Olxv8DwsXmX3Oo7vj43sWz4PGVEGtJei1Pa6d96jULcpzbkGDrsL3DWmiQFqKVDCzmPHRWzEnzqT4kSVLJBvLAYOeotIs6XeUi4dIJb2Ox8LRlK/8RI8gMhx5MmXYU2SpYGdDMXsoJ4wNjYmSucMFz35tQKVh6HcUUgFCkieKMNZOFG6g31akQ4kxLnpi0FVoLShrEq/uRNA6l9jAsnrNJ0vdsoIIfN/g+ZZ6ywmnryvpvt/dKav+LXZsDYxNl3zhHH2NwAt8puZafOGnXqS302Pt/AZd0WewNuRvf+8NfCIOf2EWiyVWtfs+rvbX9ezP4BbsjwM/ZakfmZbw0zqW/jDLWsvs7Bw/8zM/z8/8zM/f9m//w3/4j3dc3re//Vf8+3//O/zmb/5fTE5OAdBsOman3W7vsjxra6vMzj5cx+4HqceA5x9I3crH5k71sBkeKZ3vz/1FaxwcGNsPFO/ENC1VWzw9tcBrXKOQOaY/EivjUsjLCEwN17oae9yMF6M/vsnCd4yNKB1bROzEzCZyXitiX+p1suAytrR0Ymad4lpl2cidWUAROJGzGuO0kYBZN3AOz3b0GtwyispoA5UhmChJc0VqJUIoKqIg8AxCWC4kTRpezul4i8R4rBc16jJjkwiLZC7oE0lDpiWpVUx4OQaYClK+1Fyh6hd40jIoFT9bO8ffXpvFiyOeiHd4+7UG9mk4X7ToDhRqJmeyTFmoJuTG4zNrqyyv+yxOBjSnNNW6xmjo7Si0ttQnNDp3+5ZSoAvF1hpMHyoIo5yykCRDwfS85tjphLhusFqSpZInnk4YdCTp0KfMFVnmTAuD2HD6uZT2lkcQGZ581tDZUvTbig+2K2yt+kzNF8wsFrz+UoW0rzhxNuWtV6ucezNi/kiGAdo7Hn4A7S2X8J6nI6ExkA4Vw56ksxWwvRXgZwZfGvAsvR2fqOLcm6fmStauBEzP5Wwu3wRwjF23R1jmthWArFhUYDGpRGSSky8c5ciZJQbtAW9/+z382MeUhnrUQsQF7/7NOeLDHmeOfJZY1h8KELg1+9PYfR4+WRDyKOMsHsV02N7N8sGt/zvf+W/81m/9Br/xG7/NwsKh6577kR/5Mf7zf/5P/OIv/hJvvPE6WZZx+vSZA1nvw6jHgOdTVjdjeO5Wr3OLJXKwDM9e+b6iUolIkmzk6Hxv9TAYnlrtzkxTxQv4+aWzrGc91rwBvWMJ5rybqNK+AxqyN2JNRrqY3XPHKAm7aLhAz3GsRFEbZWpVXCsK5VpOw8NQvQwYpw3KRoGjwoAtgHGkRAD0XVaWsBD0HQMkc7cdos8eAAt2P8F9/7o2xyDyINo76fXwXUvLwuGwx/+04FIsmyrjg0GDv2kfQWJZitt8fWKFjwZNlHAAaSePsUJQWMWVrMFpb5tBKampnEurFSZlxjDxaFQLKvWCziDg6eoG67FH+VQV3bWYzPK59CqTh3OuvO3T2fJ46c8bnDyb4oclOvM49UKC1U5ILARcei/ijZdqVOqar/5Ul2Qg0CUIYTny5JBhX3D5/ZDFEwmDrkd32+PkMykv/3nAxEyO8h3LprUYsVMwMV1y9XxIZ8MnGwquXQj57l/XmDtc8NTnB7S3fNKhJBlIlDL0ex5/+18bnDiTcu1CRK+tSPvODFFKi5ACFRhngVRKVGZIEkWGotaA7obbj9SIyepsevTaip2NCsngFpq6Xdfs2xyzCtRIMqFGLdQgDNm61sZoy5f+2ecIKiHXPlhmYrbF5OEJwkrIRKXJoeQsTW/25ss94LqR/XHt5Zgg8JmZmaAoyt1YhYcfqvkPbyz9vtc6OqEe5Lr/3b/7P/E8n3/zb/733d/95m/+Ds1mi3/5L/83fv3Xf5U/+7N/ThiG/Mqv/PoDi6QfZj0GPPdYn/SU1r3ode5meQ++fS6zKooCwtCn308+FUZlUgqUkmRZcUemqTSG728vo63FE26U1x9ajAfRVcgXIWg7TY5MobRAzN41SDjgoivOh8eEI/3ODdIKANOCXgVU6kTRhLh2hcCJjVPwNt3rjQ/KulaYVY7RUTlg3Wg8lRuXL9ijnsa/cUaC4/Ix1FVBpAr+x/lzpEbhScu1vMbR6oBqr2DSS3ixscFKXqVEsplXOF3dorSKbummjJaiPqeqbTQKX5X4sWXRH9DKc9a7IeVQ8MXmCo3DUEt8FhrbmKpg522B3tLUjhk8X1BtlDxxNqHSMEwvaAYdy7vfjznzwpBKYBh2Ai68F4GweJ4LEhVCc/xMxsZyiC4lM4cKTp5NWDqdoAvBB69Xsdbjqz/d5tL7Me0tRaNlOHQio97QWOvAeDZUrK/4BKFlaqFgYzmgveW5YafRNbdSs6xc8rClu/FI+pI8danuYJ3GRguCikZagTaC3paHUFCJSybmXPL7IPFQ1hJWDZc/iCiL8fd1m7qbwygEqwXBHMRHQUWG7l/mpIOc2mSNsBLx/I89zbCboDxJpRajAo+oGhE34r395BNuuWityfMSay293oAgcOcPF6rJKHMqv6mvzIPUP7WW1sN4v3/6p39xy+empqb5zd/8nQNd38Osx4DnU1fu7m48XXRvep2bLM3yMcboQcv3vV1/nQc7uA5mSsvzFJVKjLX2rttq392+yrDMKawB36JrDuyUIa6FZJ2guWhxPTuc4VgWBeX4udvVaPpKR3uvN5FjhVCOSVLZaMo9dbogPYqMEKUTT9sSlO8yvm6+glEfRAuE0kypnNxKEiM5GvexVlD3cmKvJNE+hRYE0lBRmtwIQs8SCI22glhpBEOMlRwK+kx5CUtxnx+avArCogQMtGR6Nqd92aMV5AxTOFrtUpu0JKtw6vkBlVzjDQsaJyTZFNQmLMfODImrhrJQdLckQWA5fCJHKUvSl2yuRBgjmZgpOHbaBYMKoZlZKNi4FrC56lFrWZd7VTHkiaQ1Y/jMC0M8z/navPTNOu2NgDwTtKY1xlgOnyjQGi69H9LZ9Kg2NElfEUaGQVdx9VzA9KGS6WrOoCOJqpoznxtw4qkMa+D0Z1M++EHsgBUQV0qUD+lAUuQO5UoJeSo4eSalt+nheZa4WlKpGnptbzRGfrd1s791O6FfASKL9KDYEASfAVNYTFkihEB5iuZsk2d+6DNsXNkibsY0pusce3qJqcWJjy3vk6wxCBgbG+5nf8LQ39X+lKXeff5B2Z9/TMDj7tf7ia/2H0w9BjyfsrIWlBJEUeWe9Tq3WOKBbBc4FiUMXT/lQUDYuA7iwBy3+4bDhDiO7vyC3XVbBmWOFSClopjQBBuAGjkcT+NGifdrS8feO2OMAXfXLRwzvCPvHDvS6pC6NpipOGDDiNkxkWN8gk3397o1cnu+Qwngc7VNnqi3R/pX13K5mDYYlD47RUQgSi6mTRpexmYWUhiBNZa3+5M829iiX3pMRCnHKn06pYcPHK+2GWiPlp9jrCIrXdJ4NuMxbFsypXj+mS7X+jVS7ZNkgqnJIX5T0KtHzJUpZrVk6YQL7Fw4mpKliu11RTKMyBJoTpW8//0G6RAOP5Hhh4bVyx5pEjA5VzoGz7dc+TCg3/ZQvqUxWeAHJcO+x7AvCGPL3OGC2cMlSlhWLgdcfD8iGSgmZwrSoWTheMa5Nyt0tnx83wGf1/6mwdR8wezhnNnFkqNP5hw/m7J62efahQiL5dRzKd1tB5ZmDhUMupIsUQx7ipUrPkXqgtG213yyRGIMKKmoNc2u3udm1ZwuUJ4gjEpWLrr9VyrrcrcsLmoiYyRWdp5LXgAyEKgItr4FGIEf+kwvTfHeyx+Q9FJqrSo/98s/ybCbUmQFk/Ot61oNj8YX7uYgS2vNcKgZDtNdX5kgCA6E/fmnFrPwT2kS7n7qMeD5lJVSCt9X9PvJgfS2D6qlNRZNF0V5oIzRgyxrf2zFvbxPT0pON2ZYy3oIC8JYbOwYG11z7IvX2XNYJmUv3BOuc7y9o0RqPzM00vWotnustt1oe1nf+5cRwLIhZAvsJqffDbBaiAY8WWuzkrv+23ww5Hilw3pRwReaD/sNjlX71FRBYT2+357kStrEV4b1QYVAlCyECRfTJucGLb48sUK7jHi5fYilsE/NS2h6BUvxgG4RYAOFsA5caAPv/HXE6bND+lIwvFqQ5pLtwmKPK2bUkA8+iJHSZVk1pzPml6C97VNklvPvVCkKS5YqNq75fPCDmFpDU6trXv2rBttrinrLuRpXappr5wJOnh0w7Hq0txQX3ot54umEMhcMeh5PPjvk2kWXtL65EpANFZNzGXkKh45nZIkkzyRhbJiYKWlNFxgtKXOBVBBGhkrVEldKikzh+ZZTzyW0pnMmZt3E0cV3I7Y3FNVmOcoD01x4L8bzDcoDECxfCPECgxWSMrv+S5TKkiWKKDYcfiLb1SMZLVB1qByBcNEB7853oGwLylXQVUG5BbYQkEpmjkxSacVU6jGeUkwfmsQPPV76o1eJ6xFe4HHxB5d54sUTHH/2yN6u+QlfF++GbRn7ymRZQa83QClJGAYPxP588u/z0bEsjwHP7esx4PkUVbUa4XlOh3JwQr4HFy3vF0276YuD223u5+C8WWzFvQKn/2XxBX7w0VWWVY/CA1uDdGk0reW7GKx8hl0H5OtAx1g6Y7l56Oe4DE6vM56qkuyKnlUKehKK0AEsM4qZMMG+10uc5ueO5TYo9gqssLsTzltFRJiWtLwMzxrOJS3eWJnGCok2UBgXMTHvDfA9zUeDJt/tzRMISyBKruZ1FsM+nrBcSWtYK/hcfZVIappBQWEL8ilFIzG89kYTczwkXhzSXRd8b2OKp+d3mBYp2TXFy6+2aMYltSnN+XcqnHmxT9zSzEY5y5ccKzI5WzI9X2KsJYgts4dy6i3N5qrP23/fIIwtx59KiWPNkdOJGxsvLNcuRrSmS8LY8u73KqSJazUdP51jTcrxp1KkgKsf+Rw+6WIr2tMeO1se1brb/ybnSgYdRVEKihz6HUVruqC9JSkLS1TRLB4v6Wx5GK2pNTXVhiaqajqbPmvXAhpVy9ScZu2qT9ER+KHFCvA8DViOPlHQ2Vb0ux5aOwAwJiDWLgd7TJCCymkoO5CvQzALza9Ytv4rYBV2CEoECCGpHaogpGBqcZILb1zmK//880glyZOc9189x/M//ixxLcJow/nXL7J4aoEg8nk0La17Pw9pbRgOU4ZDZ241dn2+W/bnUbk7PypW6WE6PP9jqMeA51NQ+92AkyTD8w4SUDwYw1OphCi1F/7peeqR5qSMx+A/Hltx98CuLDXn317jiN8iSQs6RUYRWWzDhYaqn02YAAAgAElEQVRaDWjw2g6c2Hg0rQWOqRm5IDO+boxXv2+SC9hjhQp2g0OxjjUyLfc73XRaIdQoK2s/4Llt7X+/FpB0Sp9AGJQwWCuZ8DPe7E3xZn+alkxJtUdh3aRRKEs6NkRg0FkNnStS62irST8h1YqVrMpGFtPyMqxwYujltEagPmCzHVPzCqomx9qSlWqDzy1uk27B6vmAFz7f5cr7FXqrirBaUosKFk8WeB5EFcu5tyocfnLI9IwBIZDSEsaGILQUpcXz4cRTCSAw2qK1wo8KTp5N2Vj2aIUWreHyBxG6hCKHjWUfoyXpQJIkgjSDybkCzzdu+k2KkROzwgBFKkgHirCqmV7IGHYrTM3lxFXLh29FLB7LmT2cs3Ip5OK7IYefyKk2NUXh8ruOfCalt6XcGL1xRoTDvguV1VoQSIspwEhJY8JQaWiKAjzffT8AWSIJIpcFZsY7kIKyDZSjMNAB+DMgRu0tIQUTs02COKBSrzC50MKPA1Y+WiVPc6JqRFmUgMDzHSKXSo4E1y6B9lFpWx70YpznLjDzbtmfR8N4PDqW5dOaYfVpqceA5z7qICefbvTX8X3vgPvr9ycMvj+Tw3uv8Wd5N+eH28VW3Mt3MhhkXOt1SFTJZFxBGUm/LBjK0hkRAhinnbHSjZAz/hkDkjGzsxdUPdoQdrGIyEZQRIFJcUdbOPpXsBtCqvdndHHDssZM0nVH6tgMaN9YuoLNIuL77WmeauwgBVxOa/ygN01uFZsmxsNgEQQYOjrEIrAo+kgmRYqHorCSdhlQlS7iPTWKgfWZUimBNMSq4J3+NL4o6SUeJ9tDllpD0s8EdFZ9BusQ6tJNoEUCUxpOHxm4bKvxW7KQDARKQLcrscZy7GzCzppPLxNEkeXkU0PSoaQcZUbNLWUcOpYzv5TTmCzYXA2o1jNOnEl56ZsNLn1Q5enPD2lMFjQm4MnnEgY9gefDsCdZerJg/WrJd79dZ+V8RLftgbBML+TsbAT84O/qnHxqQFwxbK/7bK97fO+v6jSnCxoTht6WolIzHD+bUpaglKWzrVh6MscLYGM5oNdxGVt2JFA2GoLIMjlX0Jou2VoLMKULLk2GMDVXEoaGYV/S3fFJh8rtWIXFDAz+vECGIGPrwmZzQRD7hNUQ6UnmT8/QnKpTqVRI+hm1iSrt1TZBJUQXmqXPHGLYTag0YNAe0JpzIOlRlRD7bhwOoG7P/gjyPEcIcceIm4OuR9lWetzSun09BjyPsG7tr3NwiOd+9n2lHItys1DSg5/6GjMVt9/QgxyDl0pSlBp8iKXPjkkxWMrxNuhRzMQI4NhRbIOwo4iH/T/79TzjGj22I3GzGTNBJa5XNhZCj3Q9ogQ5cJ47xQS7YaL+9iieogPZJJQzo+UobvF5KT4YTnJh2MQKSKyPHm2kh6FAYlDkuy91FNSkSpkNE/JSsFlWKK1kq4wI0W6kXRhyqxAGQiWoqpSfmrlExRToimV5p8KMP6C9FLORhZBp4rKDvSY4frjgiWf6bK00eed7MfWmJowtJ5/uc/mjkI3lgKWTOX5k+MbP77CzLmlMGaSEzrZHEBr0rOTLP95lcj6nMVUyHASc/dyAxoTGTMBXf7pLa6bk8ocB0/OG+kTBubdjjLUsnUypN0suvhdSZB4LSzndbWdO6PmWIpdMHco4eSbl+Jmc7bWAfk/x1itVykyyveaPcrAEq1dD2tseSyczNpc9/EDwysUQP7C0pkuGfcmgE1KOxul0KZiYKanWDMsXIgfgSkF9oiSMLGuXAwY9idHi+qgJKyi6gsYXR/8tBO2/FSAFXuBhMOQipXLK0tnZYLhTYbo1y1NfO80LP/EsRVYQVSOiesRH371Ab6vPwsk5Tr5wYle4/KiYj7ubvb+/ujn7ExOGAWEYHNjk153qUbosPwY8t6/HgOcR1Ti9+8bRbmc8+Oi2axzJMBymFMXNTgwHezDdDcNzq8/qxrpbIFarhjxxeIb3P9xkEBV4pSALyz1bfwUk7AETf0TcjFtTJddPae33/9vvuRWP/rbAmRdKB5qsZo8JSsHbGU2DSfe4nATVcx4/AufA7PdHjFNgbtu9S1GUCJS1iBGgmVMD+jqk3Cc4CigJpaE0glO1Hbp5gBEBgdDMhkN6ZUAoSj5bXeO9dAasZSZMeLG+xoutdY5VethSkBaCzWs5O4UPczB10hCcMGxthjx5ZsDUQsGw43HsbILnW9JUcvLpITMLJZsrESeeyuhsK2IheP+1CmdeSJDKIhUcPlFQJGCF5vkf6nHlfECeSg4dS6nUoMgEnW2far3kGz/Xob2hyArJu9+tcOGdGLAsHs259FGFnTWfYV8SVy0CQVkKgtBQFoKJKcPUrOb8OyGDnsepZ4a0JkuufBRy/u2ILFEICcOe4rt/1eCdV53bc5ZIpG8ZdBXVhmbtckB3e2+srywEa1d9NledEDmsWMJQo0uBUpbujsKaG79IDUhMV7D5pyBDMEOwpfu7bJjhVRRP/fQpTn3xBFffXKcUKTUV88xXzzJ/bG50jLid8rkfObN7bBkzPr88mhPMJwkExuyP5ymKQqO1HrE/dYSQo8R3x6ofNEB4tAzPowNb/xDqMeD5hOtm6d3762H45txt3U3458PMv7qx7rWtdrcnGSEEX3vuSdbTPu9dXaUMNNtBisSQCetCOivOFHCcfI7CHS1jV+QxdhgDnJtETsDoNd7eYztkj6WpQLjmtDvGx2VkGbcOWYAoRmPqPtixtggxmlfmFoJpQYliTOJH5HR1jCcMnjUEGCqqQCOpy5Q4KFEWEuORaIUUcKrSxlq3mnXd4Nn6OleSpoubCBKOxl1y46E8SxyWLMxkvLy1xFw2pOZlbOz4vLl5iH82e4GNZcvV8xGnn0s5+UyKHxiyBDZWfJSvOXQsI4x8Vi8FJD0fbQdYIxBi7z1ur3isXAmo1gxBpNnZCOh1LHkm6e0oDh1zeVtpX3DunYjJ2ZKddU1vR3H+vZgiAYRAWMgzQXMq5/P/XZcsdVNZWSZJM0GRKWYP5yw9kdGaLqnWNQtHC176Zh1dCIS0eMp9LqWGXtdDKUutabh2LmLY+/gXYo2gzN2OkfQEue8meEx5486yX/g1MoIqQWdqd8fyIw8/8pk4XWXiUIvpky1aizXavW1OzZxlcX5xd//er7AXwo7sLhwQGh8nj4bheTTr3GN/HIMdBAFRFNFo7Nf+FJTlg7e/HiXgkVI+ZnhuU48BzydYd5eHdfA76xhE3epAuLfwzwef+rpxeTfzyrhdW+0ganW1TaWtONGa4t1si7Iw+IFCy5JSA8G+ian9m3erI+Z201r7q3L9f7V067HjKa4RS1Q2cQGhFTeijmE0sbVvHn7MRt1QHpqmlxOLgp4JKTX0reurKQxWgNbQtSFGSCJtSIzHwPicjDskxqewgobKeDLaYcpPqSpDYhTH4y5Vr2SgPQJhEIGlNqvxU+eTo5SgEWji0NDuKaKB5eq5iNaEoTFZUGlako5Pkkh6Ox7n3lIsnUp551Wfp784YNBRJEPJxIzGGMPWasDffauJNda1enLB4ZNDWjOGuGIJ5koE0NmUWCPY2QiotVJa0yXaWK6dC5icLslzQViBxmTJ9KEcz4e4NNSbmmpTk6WCc2+GHD9bkGWS9eWQ9eWQo6cSFo+FNKcLutsengc76x69HQ+jLWlfUZYWa9w4u9EQ1xyQq9QNK5d9Nq/tjdrpWxoQCnbNdgDPg7hmXERDEmMtxLWIM185Re2Uor3R4f2/vMhTP3+cqZkGM/HNIyPG4Gd8kzJmfyqVCGvNLrAc3988zJutR8E+3GxE3A2HpCTJjdqf2j72x01/3d8U6aNtaR2kTuofW6lf+7Vf+7VbPTkcHvyF5h9DSXnvLEcUBcRxSL+f3lZEJwSjfvODGg7uX7d/y+VJKanXK5Sl3hX/3b7EbZd3rxUEPkVRXndi8X2PajViOMzI87tfTxgG5Pmd91ljLK+/cZmun/KOt8llOvRtTimNu+TIG34e4o2pVU4vBK7dhWBX2GwkCOWYJh0yEkzvHwWz17fQ3BJpypST1TazYcLhsEfLz+kVITkCi0RYUAIaXkrTy5n2EzplSGEkUkKoSr7Q2iA3kqoqEAK28pC1rIKUlqNxn9CWDEufRPtcHDR4vTtNGBhiCvomoBJpTog20lp6Ox79rmR2qWTlSkS1ZihzqFQt6VDQ3vSoVEoufxTw1ssNBj2P9prCjwXvvxZjDbSmNVkm6Hd81lfc8nptj15bkiWSeks7EbYRhIHllb9osLESYLWgNllSqVnyRPDhmxFf+akevm8RUjLsK6bnCi5/EFFvWWYO5XS2FVurwYi90RS5YGvNo7vl05wqOHIq5/w7Eb1OgNGCpOdjjMVaB/riiqEoJTMLOWeeT1i55Fpxt65xL3VUAvzA7QozhwVFUaMsSjzfQ/mKUFTot/usX9okqHs8e/KLVL1bKd+vLyEEjUYNKSW93mAXEEnpzmuwd7E+aPATRSFal59AdtZexXFIUZRofet1am3I84LhMCXL3BhmFAU0GlXCMNjVPd2K9b6xPM/D99VDuVG7U8VxhDGWovini3qq1Vt7eTxmeO6j7rWtc7calPtZ9t3UrXQy46mn+w3/PLjae8NRFBAE/m3bareuOwugnYbBYIzmgm2TUrCgavSLjBSNwd781funsiTXXZ8epOwosFRoB352QRZAzd15myr7PqLb7xwCS8+EtIuIC0WEwlKVOUfDNu+kM8RkTAcZdVWwXsQk2vCDdIaOCdEIGjrnbGWbXAum/JROGbKW+Zwbtric1vGEQQLPVdYxUlGmcG0tZi7tEUUlWRQw10x5vrpGkGve/W7MybMDdEXybnOW1y43eTLt8dzUBmqgOfd2nX5HufdpnJB42POYWcy4dilm/Zpi9nBJe0uR9BTdrqJW17SmDN/8j5NYIThxNuH42RTPAz8wvPv9Cl5gsQOw1vDWy1XCWKN8gS6gOVESVaDW1Gwu+6SJ5OVv1TFIDp9IWDhaUK2XtKYsO5seg66itxMQVzRFruhsCQSCSq3EU9BtK0zhvjRrBFkqsdY4MDRQRBVLb+dW35ja9692hoWjrmVc06QDD11oJ0KuhiT9lEE7Ielq6tNNOt+x2Gd9uItMUCGgXq+N/KtcoJsQ4gbvFotSAGb3dwfF/jwqhude7lZuzv74NBq1UVbfndmfR63heVy3rseA5yGWlE6DUpb6Hke7D3av3RMq7h2E9zv1dNCix/0i7Wo1Qggxck5+GK09i7XuRH7ixAzffusSuWeI8VmQdS7R5Y5I5mEEAXtgb3Uk3nR9o2mXm7SzLJKqGHKm2mbCz7EY3uhOs5HFVGVJS2Uk2kcKS1oquraGHbe6UGwWMSWKupdSUQW+MPRLD2MFVZnRLiMoNH/TXuJkpUNFFXiTis9FWxypDwl1zuZFn0EPylBgSjj5TMYrxQKl8DhUTbh6OWTq+Sb6g4ReW9KaLelsKDZXAhaO5khlWb0SMhxIrDEMuh5PPpPiB5o8l5x8JmF9JWRiruCDH1SZWcz58AcV+m2BUoLTLwyoNTVXz/lceK+CQJAlHs2pnB/+uTZ+qImr7jOsT2he/maDfttnYr7g2vmI8+9UOHoqJRlI3vy7Gi/+cB8hEtqbAVFsQRi8wHkGDQceZp/w2FrIUoHWkmvnQibnewy6t9ppbvwCJVhDGBsaE4aikGwse0QVmF6YoDXXYmetzeaVbU48d4TmbJONK1usnFujNXt7hmfM7JSlZjD4+LlofEzvpW3v1/6Y0e/sA4GfRwUEHmSVY+0PDJHSTX6N2Z+y1LuJ7/tZ+8dOy5/eegx4HlKN9TpJkt9TW+aTmNJyjJM4gPDPgyl3Mq6MTsZ301a7ed1+4msP7AAcXpzk69lJvrX8IV2RUfFDmmlEt0jIMQ8+PLt/dP3AGfxbM1kCy9ONHRpeznoeoRE829zi9fY0DVuwmtcQ1tJJayN7H03oWTItKKxEAy9tz/Ozc+fZKWuUWjAdpLw/gCdqHUKpeS+bocwFvdLjs41tVooKA6k4KgcAbK95ZF3BxqrHE08lNKdKiiRkJh/SDWJ+7Gs7bAxCLpyr0Gxm2AKMlviBZXvDp94sKApJv62YnCt5/itDLn4UkQ4kz3y5x+RcyTd/fwZjXATEtfMhp59L8X1Bmgo8D9av+eSZojGlKTOJEC62ojlZkgwF3S2PnY0ALzS8/UqF/kBSXAmZnCvYXA7obinqLUO9pXnn+xW+9KMdKjWDtYLJmYL2RoDA0pzUXHg/ROd7oMYax/4gBG+/XLupmPnm5YBSOoQ887FCEtdjmlN1jj13hKSXAIbpxQkmZ1oMuwlB6FNmbgfr7wy49uEKpjQsPDFPa7YBuLZ1o1Ejz/O7bFtfr/2xdm9fuxn782k2uztIAGDMx9mfIPg4+yPlo/bheSSr/gdRjwHPQ6iD9Iw5iBqzMtczTvcPLA66KpWINM0OKCj140DAWoMx9mO/n56qcUg3IOlyYdhmMnCjzKXR7Ojs9kT4DbKLj9X+oZsDr9u07BBMhhkbZYxG4GMRFnxpyLXCtwVWCL7eWiWxPlWv5PKwxoatkGhBjsfVrMZ/Wn2So1GXupdhEcyEQ3KjiKTm/LBJzaQciXsk2sNIhRCSTu6TXPa48G7M9GxOral5//UKjUlNdaokLzxmZ3OGfcHGdogucrrDgNlFFxbq+YaJ2Zyk7xMEmqc+n7B4vOCZr/R58rNDum2PPIU8VXiem9KKKobOlsc734tZOJozv5Tx0Zsxlz8MmZwtaU1oLCVhbCgyOQIfEqUEtXqJCgTHziZsrATkqaAs4OTTQ7JEEdcMUcXQ70jee71GWcD0QkFzUjN/NOPq+QgKmJopWb+2z9DPWpK+R699s2ms/TXegfYDIoHRCqNBKIsf+NSn6qx8tIaxFisk3e0e7373I6I4JIgDTK7xheIHf/k2Zamx1nLl/WU+/9OfZfrQJI1GnSRJSdPsvva2jwufb2R/3OM7sT+PKubhYa1yzP70+2P2xx+d+wOMGQnOb2B/HnY9ZnhuX48BzwHXveh1PslSSt4X4/QwKwh8PE8dUCr8x/VPxliEsDfVAu3kCe9113m6ucCzrUO83Vnj77Yuj8yN7/J7G4OefWnoH2tBHbjgeb/C+ea1k0fMhUN2yogciwB6pc9yVkdg+aGJqyxGA17pzOMJy9G4x3peIaJgIeijkUSiYC2PmQlSEqtQGAIBCstipc+C30MDM+GQQJW0ggxPWnaWPYyBzWWfiakCXUguvh9Tne/xne4iUhtO1jvYSzmf/1KHi++HTM5pai3Nl368x+xSRpEZdOFRlNBoWaLIIDA0pwzba5IrH8VMTJesXA4pSzcZ1Wt75Jng2sUAP4CwUtKaKVm7ElBvlWDAWMvGSsD88QLlW/xI0GtLgtAwMVtSZIJDx3IOHc/otxVPPD1k/mjGGy9VeOOlBkZL5pZyZx7Y0jz75T5FatneCK4DPEKBdgQPQjpQUBb33gu11oK0zJ2YpcxK8kHG5GKL829cotKsoLOCYS/hr//wJbY3Owhg9vA0nlJ0t3psnN/k5JmjDAYJWXZwAtq7YX/cj/gUsD+fzCi8Y38ykiSjVqsgpXvfe+zPOPMrH918PZx6zPDcvh4DngOq+9frfLzGjMxBASYhBFEUMhgcbAL7g2ye8/xRFEV5H+LkO9eeOPnmG5kZl3AtRo9PVCZYTbtgLecGW3e/outvzj9eB3q+t6Du/KG/1pvhG/5VZoMhAstbgwm2ypBYFvjCMhekbOUhM/6QgQkQ1lLxCg6FPapK8xOzl1iK+jS9jKtJlXeHE3yvs0BFFkRSc7a6RWJ9plWHpUoX0joKQ03lBAaSdkh9QpOngksfRGx1A8rXFBXVhid85uMeE03LzELOF3+8Q5FKslQxMescHdtbkrUrkuZ0ydplj0sf1KhPGJ58OuHYZ3KWL4WEFc3UfEGRQbWuwUqkchNhJ84648Cnv9Tn8gcRlz+M8APDzKIm6SsuvBXTmCrYWvMII8Pi8YKjT2zT6yriasnKpZDGpObEUwmNSU13p+C971t8X+N5UGtpujsKzzNUJi1v/f31UyFWC4xyBofWgtYulT1L7hL0SAgin5nDUzRnGvS3e3Q2+tRaVfo7A4q0pL/TxxSGyYUWUS3i0ltXmJhvUZ+qAVBoN9VlraVajfF9j6IoyPPyQG/Ebsf+jHNRxuzPowi2fFSsktaGwSD5GPvTaFTR2uy6Ph80+/OY4bl9PQY891Efn3ZSVCqfLvZkXJWKY5zSNHsICez351Gx5/kzJI5DHoZIe79e52ZVVQGlNbzZXaVf5gzLgkNRHR9Fr0jpljk9c0B3xXdqf91tybsDhgPt883No9RVTmklPR1QFTlNLyMxPhfSBjNBQkWVPFPfwJNu7DzXkp899CHTQUqsSkKpOVvfYTLIOR61yY3H2XqbDwYtJkRBpDV/vHqcEp+Gl3EhafDciWVqH5ZIARsrITKCmSdL2lcsRgvyCyAOw9R8gfQABFHV0JrVJH2JLiXWSnbWPd7+XoTOPOaP5KxeUKxfCXjxG12efCahu+3zzncFReHx9Bd6dHcC1q8GzB1JieOSftcJfmeXMprTJXkCCIk3ykpbuRDhh5qTZ1PCqqW97pGmgslZjRQZM4s5xjg9jTHQaGnyQiKU5a2XKywcywliw9sv11i57N/0ezCja5m1gLBIz96hxQVSCWaWppk7NsOhJ+dpr3WYWpqk2qhSm6zRWevQ7wyZaUxRmYrpd4aEccjh0wv0tvt0t3r4vg/aMnVkkp2dLlKKkd4koFqtorXebcfcblz7fupW7I/vS5RSjC3GPymD1UfjiSOwdu9Y3c/+gJuODcPgOvZn7Pz8oOzPY4bn9vUY8DxgPQy9zp2MAu+m9rsUO5+bA9m0fdt37yeScdJ5UZS7B/9BjuGPBd93AjsAdT9kyo95ZfsymdEoBNvZECVASYkxhgBJflAinP1M0K2uMeOR9xtfZ0fPIbmTKEhgHZiRJTtFSE+7i3FhJV3tvHbe7E7yU7OXmQ/7CCHZzAMaKuNovcfJaodUe0z5GX3jEwpNXWV8caJHYSTdImDCz9HakkuP6SBDiozZcEBqAnb8Ol/6iS2EMXS3PV7620k2ByG6MHjWkiVgClg6nTI5o9El+KEDFVtrirVrIRffCTj8RIEpFFPzOdWGRtRh9VrAlY9CTjyVcvLskCjWvP1KzPLFCksnU8pCUmSSSl1z5sUBUwslZQGv/EXE1lpArZXTmrLoEnrbHnNHC8pCEtqSILLMHs6oNzXzR9OR2zOcfzeive4jPcPKRxFb6x7dTUlZSuaPZq41FkCeuvYVgLvWid2vSykH9tQNgCeInfFhvwNZovBCj2PPHKExXaPWqrKz2ubkC8eJKiHzx2bZXt7BjwKOnllESMmgM6TaqtCca6A8xed/6rPY0uL7PhOLTRozTrRsjCVN811fGN/3CAKfer2KlGIX/BRFcaDniTH4UUrSbNYYDlOstSNfm7vX/jzgVvBJuzvf6dxdFCVFUV7H/oRhQL3+YOzP9eaSj+tm9RjwPEBVqzFCcOB6nQed1Bq7FKep6xvHcXjAk1/3zvDsTa09PM8frfUufb83Tnrr2i4S6l7Ekh+xnvW5OuySmZKBzskwu7NaB37KvNkCb2dgPY6iuAsm7IlKh5OVNlN+wmyQ8EG/yf+7dRyNYHzTGUjLm90Wi3HEZh4irOF/PvQBM0FCXRXM+AlSgMUy4yeUusJHwzrbeQWB4Ym4y3JZJUdhhWAu7BNKyAzgwdzhnGIosFoSyRKjfIoJRb5mqE0bTp0cMDWrCSIoMqfBuXoh4K1X6pQFpIni778VUxaC7XWPzzyfUqlrhHUxDv22Ik0UZSn4zOcS1q8Yzr0T8cLX+0zO5swulgx6ClNasHDqsyk76wXLFwO+/+0aRSkIAsulDyM8v830fMniiZTOlmTtashEllNtWPodj86Wx9ULIW+/UqPb9pieLzjxdMr8Uk6RCZ75kktYf/X/q+/LxbJYLZDKgieoNkvSoSRP9lBvtaGpNTXTCyVSFCxfDqhNLzF3fJqZw5PUJurMH5/lmW+c4fVvvUW/PeD4c0eJzq9RlgXPfeMpLr59he3lNkIIFk8t8NSXTxPHEZ1O/7Zt4vEFFxKklASBTxSF1GpVyrIctb6KA7mBU0rRbNZ2dURCyDtofzhQ9ufReP/c/TrvxP6Mx96zrLhj63/PUuCBNv8fdT0GPPdRSkkajeuZioOv+zvgg8Anjm+WwH5wda+szLh/fXMW7MF9fcbi5PEd040n8Dx3rcYb6eJUl3hCEXk+Xi4ZljkIS6417BtNH0+X79/ym/3urmt//tb4/3e9oP39MYvCEIkST1oMsBT1eKLS4WjcoybdHf1GscFr3Ul8SiaDgk4ZsVHUaPiarg55prbJTJhSGMX7vQpnGw4wxVaylsYMjMff7SxSGsXRSpe2TqkFOTUK2mVAT0cMtSW1is9X28QhSCwyNHz5R9u896bmXKfG2S8M+NxXujRrJYi9C4O18Nq3a1x414V3PvFMQmvagYTejselDwOm5koqNYM2lgvvuqiFC+/GHP9MQnOq4PRzhi/8aJc8gzC2NKdKjBYMB5LW/8/em//YdeZnfp/3fc9+17q1s6pIFjeJFEXtS6sXS63uTtsDx/bYiB0Hk0kcxL94MDGMYIAJHMOZGQ8SOH/AIMAY8BiGPfYgXjJGxna7291yS61WS2qpW62FEndWFWu/29nP++aHU7e4iFsVq0h6wucX1q0qnntu3XPe+7zf7/N9nuGM4fGUSxfc0pHZlBEY/Z5k7ozL0HBOEknyvDQ1/ODtCmhBmkKlbjjzvkfYl6SxpNdWHDkRcem8Q9yXLM0LxmZSakMFndXBclpez4ME9M6qfRXBtb2CeitH54KVBYtqs+DJF10qY8fptaAdzpoAACAASURBVPs0Rpt85b9/EanKC+XES4/w/msfsTq/zsTsGHuPzbB0domRqWEOPraPhz9zhKnZCRzHZn29u6XNl9aaOE42J7hs28ZxLOr1UguUppcJ0FZhWYp6vUqvF37q/29F+3P597eOe6Ph2f5zXl39Ebiug+NsrfqzU6/3d3/33/L1r//1Rj4X/KN/9N/x8stfASCOY/71v/7f+PDD91FK8Su/8qt89rOf35Hn3U08IDzbgBAQx7tXqdhuhWcgBO52w6s+3O9lQnIQeCh146m1O703rxUnX7mACzFYwG2CoGzvDSo/eV5wqDbMQtylncZIIYh1jtGa1GgUcrPGo/m0Nnlw2nckz7nyoNcjPbewb3EoaNgp0kDFSsm04lh1hdmgQ8PKeKM9RlJYPFxdZSlxuZhUiFKLDEWSKkadiP1em0mnT2EkPe3Q0R4f9eocqhguRhXe6w1zPqkRqAIpNVkh6OQuhRFUVM5LrQus5S793GHG6zDsRCxeVCxddPj4g4DCwLNf7PJSdYU8ldhKw0aMgdbl7v7iGYe3/65GEklsG370RpXZYyH7H46ZPhAzd8ZD2gXLc4qP/rBFHCo6bcm+QxEn3w0Yn0k4+lRIUNNUm5oklChbs7po43qa8emCPNPMHo04cCzm/EmXd1+r4LgQthVvf7tK9JeC1njO8ef6FJnEr2rcQDN32i39emyw7PJdj7qKpXmLYuCuXNcbFMegLEORX9OXNNdcJaastuWFxJICZSs6qz7G6SGUwrLVVfsdr+LyxJceveqQaVTqPdzA2YiKELTb3Tu+n7KsbG31+9FGyOa1m4dsY/Nwc4Zu2xa1WoVut39brZkbT34NND/bq/7ci3Vvp0iW1mZL1Z/Lr3VnatI/+7M/zz/+x/8DAMvLS/ziL/4czzzzPPV6nT/4g9+jUqnw7//9n3L+/Dl+5Vf+R/7wD/+EIAhucdR7iweEZxvIMn2VKG2nsZ2bulLxMKZsr+02bodAbTXpfLvncTO9jjFc1dqyLIXjOFSrpXbhOWeW1TxmLenTy1L2BHVW05BOlCIRm/zDReJJm7YuFx4LQUFZUalLByUky8Ud+hpttq1uHw07Iy0UORK0Ic8Fa5nDk1aCRpBphRCGOLeoWjlZUj6BxJAgORXWUBTERfn9Ca9PXaXUnJzX1iZ5ZW0PVStjKa2gDYw6MQtRQNpRfGX4DAdr61RUTstJUECiJf3QZuGCTVFAbaygu27z3dcaPPlynyCPcDywVKl30QXkOVw46TB9IGFt0QYJnRVFb13y7EsRE/typmYz5s7YRB2Lj98N2HMoYWhY8/F7NWr1lBMv5EzsS/B8jeVApaIRElwvpbeuSBNDvQUnng85875Lv+cRR5J+V9Bd91hZtAHNl362zZkfBQyN5TheQWNYc+kC1Fspe4/knPnQJ0sknTXB8edC4p7CsjUfvePTbZep5te/EovN91ipsvITRQovgMOPV3CcmItnXNbX5zjx0nGOfubIZn7TjeD45Rh8vV5GRbTbva1dPLeBMmah/MC9evPgYYy5QvtzNaFxHJtqNaDT6W2ryny9wNNByOmV9/vtVH/uhZ5lt0jWtdUfxyk9f95//z1+7dd+jSeffIqXXnqRY8eeZGRk5I6fr1qtbn4dhuGmPhLgb/7mr/n1X/9NAGZm9vLww0f5znde5Ytf/NIdP+9u4gHhuS9x+zdp6a/jk6Y3ThW/LBS8O7idcxpg++d260msa5HnBXkeEYaldqHi2PzU7HFWsogz7WWWspB+nmJLRa4LXCSa8vxCnV4Rc2U2yVBXZzf8mNtdGFpWxIrxyQpFP3fAGP7j0iwHgw4PVds4Imc+rZJqSSe3MRtj+OXZSrra4/1+i2E7YSVxma102eP3WW2P8XZ7HFdmHKx0WEyrLGYeC3EAUlARMXu8EF9lhMamHQXsD7pYqqDdtzh9zsP4FrY0UJX0lhULWYXZao5lpaVHjYS4D9/9W4+P3w04d3Kj3VTAnoMxT3y2w0NPxKyvSqJ+6Xr80BMRwxMLvP1KnSSWjE0lHH405OCjferNAlEmMyCtsgorBShL4PoCxzVlwKcQ+IEmzwRht9QBZbHEqxlqjRy3WqapSalZmrdIIwiqhiIvXZrrrYyleZdeVzG6J8NxYX1ZbbavbjaFJeXlXnCWSDxfsLwQUGtNMfPIPvy6x4kXj2K7Fmmc4XjXn/6Cy1ERRVHQ692NTc7Vmwel1GblVCm1WRkCQRCUOqKdmgAbEKCNR1eYHt68+nMvR7R3+3lLIXpZyZ6e3s8/+Sf/lDfeeJ3f/u3fZm1tjcOHj/D885/l+ec/y4kTj22bhP3pn/4H/uiP/oDFxUv883/+GzQaTQAuXVpgfHxy8/fGxiZYXFzYkde2m3hAeO5D3G6FZxD+GYbJLXq6OzcJVR7vxhWe2z2nO3v+6zsnbwWD1hfAkLBYEBYImKyWN/T5fhtPWbjSombZLEY9YorNys7muVzz+O5BsJL5OCKnh0NTRWRGsZQH/J+nn+IfjJ5myu3Tzy1OR3XOJ3UGpW69Qdt8kfFc4xIzfpcwt7kYVwm1Q6rLn3dyj25m07RCLsQBqbGom4QvjCww6YbkRrGc+FhS084dqjIlVwoRSVZxCFxNvyMIqpqGk3LmEw+nr9h3uHRW7q4q0sjlkx/6dNfKpcgPDPOnXJ5+UeD4UG9qemsOXqApCoMXGI4+GfH+mz6rSzaTexN0AYtzNiAYHs9wA4MlodBQa+XYDrRXJHOnPbygYM/+FAxU6jlrSzZal27JtldGRUhhGBrTvPlNm6V5G8ctKHJJd1VhO5rDj8ac/8Tl9I9sbEczMnn1dS6VKfO1TPm1wWAKidYl4ZRSIIRFlhSc/VFMrdVltLuA5SoWzyyjlGTP4Qm++ssvU6l/ukUgpaBer20kfEe7eI3dGEVREEUFURQjhMC2LXzfw7IUWmtc19loHe+8z8ztVn/uzUj63SdalmXxxS9+ia9+9cep1yt8+9tv8J3vvMp3vvMqv//7v8u/+Te/w7Fjxz/1/37pl/4bLl26Pkn58z//K5RS/PRP/xw//dM/xyeffMy/+Be/ztNPP7tJev4+4gHhuQ9xOxqeQar47Y3D32wEaOewnRH9rZCxmzkn3wmMAZNr9rsNvtE+xVzULZ8HTawzTA4JBVxTI4ErJsbvAZZzn7pMqKiEAsVKXvobhdrmjy8dJpD5xuMygtumwKagQJKimPU7gGAxrZDkULNLX+blzOczQ/PkRjKXVOjkLjWVYVkJzzXneKZxidKPWjDu9nGlJjeC93tDKNtw/PNdXv2mw9qaYs+emH0nUtI+HJru0V5UfPxDl6BakCWKr/9JnYmpjD37U6KuYnQqY8/+mPkzNhfPKEyhqA1lSAmXLtjMnXUxhWDPbIJfKwj7gu/8VYPZozETe1NWl2warQwlIM8FWmtsCy6esjEYslTgeobR6ZS4p1iak0hp2PdQxMI5h8XzDm41Z2i5oLsuaa/YpLFEKUOWCQoty/HyjYks1zd01y73IqUy2K4hjcu6nxFgW4K0AL+qSWOJFAbHzTFIhJQkUcbaYhtdGJ740nEcz2bu4wXe/E/v8IX/6jNXveflwESVOE52cWBiazDGYFkKIQRra+3NwYFKxUcpSZZd1v7sNBG4WfXHsiR32/dncE73ppVWWn8cO3acY8eO80u/9MukaYrjONf9/d/5nd+/7WMfPHiI4eFR3n77TV588WXGxye4dGmeoaEhABYXF3jyyad35HXsJh4QnvsWN745B+Pwt5sqvvMVnk8fb/uRGrdHxm7lnHynGPeqLKYhAqjbDv08Iy1yNGUrpGa7rGXxp2Inbofs7KYTSEe7NzgJQWoUrixQGAoEltAYJK4osHSBpwqU1Cg0w05OZhSZBl+WU19PVOb5q9X9NFVKvZpysl8jMTb7gw41q/RscaVmLq7w9dUpLiVVfJlzoLLGz/zCOZZ6HsKXLM47TFd7eK7BGs+wbEHUg+99o0rYsamfSDAGXvjxDtWaxq9qznzocOmsy/qSYvpgShxJ3nm1SlHA0Sf7jE5laA1/8XstLp13+eFrVSYPJOx/KGJq1kIP2maRZHQypb1iUeSSohB8/9tVuqs2xmj27EtYW7SQyjBzKCGNJWtLVilejywq9YLmSE6eSfo9C5PB8rzF6ET5+tNE8P6bAVKWSeK6KMsKrbGModGMJJZcPOUCijSSBLUU2zEUhUVnRSLtlErTJ4tLLYzRZQvVr3iszq1d9Y4Opp52OiriTjGwgihF0watiw3tTln9GYRsViqfHhzYSVxZ/ZFy4P1Tiq+3qv25s/O4fypLNyI7t4PTp08xO3sAgLm5i5w8+SH795ePX3rpZf7sz/5vHn74GOfPn+P993/Eb/7mb23/5O8SHhCe+xA3qvAM4iuyrNjG7m4nb+7LJGUgTi6K7YmTb4eM3Y5z8p2i5QRM+zU+6SxjCUXLsciKguW0T2pysry4IWmRQMvyqFkuc3Fvoxp0+WceinBHI9NvPRs24fT5XGsehSbWim+tTmEKzR6vT6ItCiRNK2Gf3yHKLSa8PpNuyNdXpokKi2PBCmNuTKByBNC0EzwZULcy1jKXmpUhBUhh+NbaFN3cZcSJcETOuajBj5whHm2uYmmQOsMuSqpY5IpzJx06qzaXznkoWWpkKrWcoFrQHM2wbcPwuEEpycl3AtprNkpBczgjzyXKAtfXvPG1GnliEVQ1QUWzdNEliwX9XspTn+sQhZJ+T/H639TZ/1DMW6/UcFxNEkuyXNBbd3joiT5eJSfqKZRlcH3N2FRG2JXMnXVI+oLmeI5S4PoFq0sWP/xuhaGxHMsuqztpIgmqBa3JjHMfBdSGcj73Ex2UZbAsw8WzLq//ZZ0steiuuQirJAGGFF0UeIGHLjRJnBJHKW7Fpbce8uiPHd18PwdTT9cb8b6XqFYDlFK0270bTGGazVFqKNsvA1GzlHLT8HAnIy/KKliNMIxIkgwhJDfS/uwG+bl3FZ6dfd7f+Z3/i9OnT2FZFlJKfvVX/2f2758F4Bd/8b/lt37rN/n5n/9ppJT8s3/2vxAElR177t3CA8KzTex01eTaYw/C5wa4bNy39fiKOzUyvN75CXGlweHOhH/e4Nl2newMMFtp0W2lvLJ8hihL6RYp+Ub4potiUEqRsPlIIRl2fUb8Go8OTfBRe5n3VufIMNhCUlEOAkGUhztc5Rm0UYqNcyowiLKCI3M+35qjV9gk2qKiMj7fmuMvF6epWAUH3S6ZKb01KiLlUK3Naubxt8t7uJQFjNp99gcdNNBQKYMaVb9wOOC1ORB0sKWhl0nCwuVk2KSX2xyvrWBLQ5gquplL0hP0U4XtFhS5QOcQh1BtaPpdw8ierGwT5bD/aExQ1WAgDhUzhzNWLim0NnzvGzUQgvHpjH0PRZz6kY/AoLVkZE/GhY/dDRFzShoJ8liCkMR9xfK8RZoo0lRSqRZUGjmNVobOodeWTO1POPRIwdqyw8qCIo0VeQFh12JoJCetSvYdiyliiesZGq2ULC1T13VhqNYL8lyTJpJ+12J0MuHxz/XQ2jA2nhP2JUeOh4DgrVeGSPtguTbKssjiDGlJgnqAV3VQtuKTt07jVVwee/EYT3zlMeDKqaf+jmti7gS1WgUhynH420We5+R5ThiyK5EXl40Ow6vWpKvH3gd3ornK9BB2pvpzbwnPzh3vX/7L//2GP/N9n3/1r/6PnXuyu4QHhOe+xNVtHtd18DybXi/e9kKw007LSik8bycMDm8sgN4JcfJWcKI5SS9P6RUpryydxhc2RhgKNEJAICyMAUcqHGUR5inTQYOnWlN81F/lo84yVcvhcHOMdhpR6NLEcDUryY4F7JKMG4eCFAuBoaKycvRcl7d3v7AZc0JqKufDbpMLcZW6neCTM+Z66MjwYTgMSKLcxrYKlhKfx+rL/MLkh7y6vodTUZ0vj5zh88MXcWVZNQqsgm7iMuV2OasbLCQVPuo3qKicnxw5xfn5gJUPFY5dMLE3I00Erl8wNJLzyXsuUmmGJ1PiSBCHknorZ2isQIhy4uqjd2zaqzZR3wIDUV+zPGczPJnTGM6ZeSjivderTB2ICXsCy4HxfTGuC6tLiiwWdFZt9h4Omdqf4TiGpTmLfQ8lDE9mtEY1jZEcreHjdw1ry5IPvx+wdNFh9ljE3sMxyoK9hyPqLU29lbFw2qPWKnj3tRphR6Ls8k5dWbDxg4I4UoxMpTz8RIzONwz1lGR8n2TfwxHf+jObPFPEoYOuuQxPDvHYS4+wMrdGY7TBkacPkOUFnucipcDzHHzf39GppzuFEFCrlePwnc72x+F3OvLiZkaHV5+/uOrfK00Pd6L6c688zx4Eh94aDwjPfYgrBXbb18ZcebydFS0rpVBqYHB4Z5Ld672k3RIn3wq+snlx7ADPtKaZ8up80Fni7fWLm6ZemvJ9mfIbBJbNahLyszPHmWw0mVpv8tbKRYbtAKUFe/0GZ6N1Pu6Uyeul08tlDEbcSyn0nUABOSkWVZkTaYusKG0TbZGTGUldZiijsURBF4tOVmExC6jKjNQoHqmtsscLGXEi9vodllOfM3Gdg5V1pICXh8/zEiCMRm+MAGdGkmrJG+0xhuwEYdqcikqy80RtAY1k8azLt/+kysR0wrmTOZZr2P9QzPBEQpHJ0j25VmCMIKhpmiP5hj+PoL8uOX/S58AjIePTGe+8WqPfFRx5LMP3NSMTGUNjOVmsOPOBi+0IxvemnHi2T9AsuPCxg0Ixeyxk5mCMZcGhEykHHxX01y3aK4qglhP1BL2OQkgIQ8X+h2Mq9TLIdGgs58kvdBmbyokjQb8tyQrDkRMRP3y9ghEKNNiVgtHpFLVhmjc6oZl9KKXXtUHYjO7RxLGiOX2MOEs5/c4CaSYZnn6Ckb3DXDqzhOs7PPXjTxHUffI8Z/H8MiY3G1ER3bt+L9wIuzkOfyeRF1s1OrwSn67+DEwPYTvVn3tFOu5FGv3fNzwgPPchBi2oWi3Ytjbm6uPtXIWnWvU3eu/prizCuy1OvhWUkNRtj8eakwghmPQqfGftAp0sYcj2mfSqNByfuMgZcys4nkua5ngoHm9O8nhzkt878zaetBhSPsoIEgyOUGhT7tAHLbKUAgtJdgPp8+0nTggcclp2yGrmk2rF99pjvDg0R9/YGAOvrE6ymgdkCASizNYSgsUsoB5lHK2ukWmLhaTCfBLwldGzeKpAI8lMSaAsCdpIYmPQCAoNI05KRfUZasScDJusph5Ha+skueL90xWUpemHFqxKem1Bd83iF/5pxAs/0WbxvMvinEOlmjNzMKbeMuRZma+1csli/oyDF9gceTzi2S+1uXDSZd9DMQcejlhftTj3kcv4dMK+wyG2C1JqGsMabQR7D6W8/7bD6KTBaIlUBY4DYR+yTHDo0YTVRUVQM/hBTnvZYmomY3XZoj5UkMaCejNjdKoca5fSkOeaakPz1jdrKCXYdyim2ig48EjI269U+eCtKn5V0O/ZtNdcRqds3OooabSOLgxR3+PxF8c49myD+nCVS4tPAlAbqlLkmiTPIASBwHVdGq0aRaFxHOu60Sh3G0IIGo27Mw7/6ciLsvpzvciLAdnZiZbfzSIvlNIb37tx9edeVlkeJKXfGg8Iz30IKSW2bRFFyQ5pY+68wnOlYDrL8h0zMrzS0+duiJNvFwerw/SLlDNC8IXRA4w5Psfq4/SKlB+sLzBebdA1Ga/Pn2HGb7AQd5nya7y5dpGG4xLmGd0sIbBssqxAG4MaOONBmZO0sVMNUESUomgbiScVdctjNQuJzO20MRSGjJadUlE5q6lHmFt8vzPCybhJUQh8WfBcc56lxGchrZAZi35RtuyWMp8TcpFOYfP6+jhaw1wUcLhi48sUhECbjSkvIejlDgZYTrxy3FoKDAJXapp2ik2Baxu6vo9y+qSxwHUFSWwxtifC8wyT+3ImZnKk7FNkEIaSSxcEtqNJI4myNEHdcOZ9h+ZogR8UHHw04uEnQvwKRJFm75EEEHgVjeOWE1PddYXnFyjLUG9CczRDKY1SCssx1CyD72e01yVCGPygIEkElbrm4/c8hkaKTXF1ayInz2F5XmHbhvaKhdGwtqx48gtdHBeSRLBw3mPvkYQzHwQMTxj6bcmFUy6L89OM7dXsmW3hOA8x+9gR3ErA6e9/j856aSEQ9xPcwOXwU7O8/+pJupSancd+7BhxmmLp4obRKHcTg6mnOE6Jojt0Fd8GBtWfayMvarVSKBtFya5swG4ceQGXA0/NBjES95R0PGhp3RoPCM99hvJGdjaC4nZGCHynFZ5rBdOua++CYPv+ITsAlpQ8OTTFicYEILA2CN5fzH3A3uYwnu1gRzFHasMYAw/XRmm5AdoYlpM+81kXz7JRQuIrh1TneNIhLBKUkNhCklK+L66yMQWkpkAJiZISA0gEaiPG4lZI8DgV1WjZMZNeSGYU74XDhNqjJiM+05pnNmjz3bVx1nIPpQ1KaeLCopfbvLI8RV1FzHhdciP59toeDlS61KyUcSfEFoZEKwot+KDf4NXVKbqFixCCZ5tzjDp9WnZMoiRCaNYzj5mpPt1mRp4JklgwPJ7w/FfbFBp0BsqGLIUP3wmQAs596LC2bLP3SER9SLNnb8L8WYv3Xq9w/Lkuz/8XHYJayRkdR9McKl2RhaQ0F1wVpLHEbpbth4mZhGqz3I3bTjGwZGF9TXPpvMcHb1aJQsHwRMbUvpTuioXJJXEsOPxoxNBIStRTfP9b9U1x9fBkXjouD+ecfd8nTQRJJHn+K33e/laB5UjeeX2YJOpz9Nk2nbVx1rqf5eHnjlMT72OI2XPkBN/9mk8UruO4No+//CjNsTpDk0NYRuIGLsZig+Doa6JRLk847aa/zZUYTD1FUbxZcbmXGEReaF1qCaMoxrIUjUYNuHHkxZ3idgJPpTSbJOhua3nulXbo7xMeEJ5tYjemtILAxbIU/X6M72/fP+HT2H6FZ5B0fqU4eSc1QYNoCdu2SNP0vivJWvLqgKtaJSA3huVOp3TNRRDqlHGvLLVLIWi5PuejNqtRj1QXSKBqOYBBGUHddrGlIioycqOJi4xhJyDThkIUaLMRrknp5Gwhya+YELvRPna9qNItPBZEQWoU+cbtnWnBeuYyJBNeaM6zmnms5j6BjNnrpuRG0LBTHql1WIgD0kLybn+E/3dpHw9V1jlaWWHSC7FEuaBHucP5pMYeL6RhJXwcDjHqxBytrLOYuCgJ416PA0cTlgKHudM2zZGMmUMpQUVjWRBFkCZw6YJTTlwVMLKnHAf/wWt1xmcSlG2o1jW2G2N7hvaKTWssxQhojRk669BecQh7EscrGJnI8IKS2Ng2eMHGbtyCogDLgiSEuVMeH75V5a1vVlhbtnE9Q3045Ys/s870gdInZ3gyo9rUuC4cfbrH8oKDNhB1JUtzDmHPpr2iiHoW1UaOGxgeebaP7Vq88Tc+f/v/zHDq41Ge/gdP0RwbplDHKMxBoMBp+rzwD81mfIRUsmyfTo/dVBtTRqMUhGGMlGIz26pSCXZkwul6uF+9fwZi7k6ne5Wm50aRF7vRErxe9adarZLnpY3BtdWf3YYQ4p63Pe93PCA89wFKLxsPrQ2dTohSO5t7tV1yNiBg16avw86QPa3Lqk6v199YpOoUhd6VhftOUdr5Vzmih/m3H7xOogsKYzhUaXGoOsxqFtFyAgqjOdNfZzZoERUZQZ7STiMCZRMomyHj080z4jxFmQ0iY6BXpLScAI1FJ43p5SmWlBSFRAqJNmVyuwQcJGU86KfJT4FFaK6+rQ2Sbm6jLM1Br8tPydOcCuv0cxshDGfjBkeCNS5GPheSGnNJlWE7pJs7rGcOi1nAiBsTaUFhBEoZ9nldCgR1K6UwkrhQJEZwqNqmMAAC3YQsMUzuj3Bdw6ULNq0xWFuSvPm3NaYPRnz8gwpGGw4+muBXCiwrYWi8YO6Mw9qSTbWZMzGVMjZdVjmyBCynJDCeD23KuInVBZtT73lM7kuZns2otsoA0SIryaNllSPxvbYg7EuSGGwX9sym6ALWlmy+/3dVjj97ieGJHMsyGATtNYNfMUzuy4j7glVtgdBEXYnralw/o9rIWThvkyWSJHZ56ot9fvB6DeUG9NsZR57ZCHIU7uXrSQm8invFtbU1bYzWV/vbXNa4VACx+SF/J54996v3j++7eJ57XTH39SIvLrcEzRUtwZ2v/tRqVYSATqePEOpT1R9jdtf08EFL69Z4QHjuMQZeNklyOWhzN2zQdzJ9fSfuqSvFyVeW7a9duNM03ZXy9FaglNqw8485t77G/koLJcRGorpmb6VJ1tcsJX2MMQzbPsca4wBYSJYsm6O1UTSGbpbgJn0W05C6qxh3K3TzlDDPUEKQFgWTfo1A2awmEetZSIahYjzCIqOgbONUpYOS5e5yPUtu2vRKsUiMQ7+wORx08VXGpbjCfF5jIQsIM4Xb1FgItIZAZrw0PIcrcwbNtUJD3S4fryYZShomnIjMCMadPhNuH1fmCDMo9YPtG4ZGcuJu6VsztifHceH0By7rK5LxGcXoVEIWW3TXFMo2dNYspg8mGA2Xzrt4QYEbQJ6ZDYdigSMNeQ5oiCNF2JP4VbDWBPPnXIbHc2qtkuyUO+2SIEV9WFt2+OQHHivzDpVGQZ5IKrWcxlBGY0O/kxeGsKvorFlUGwVnP3KZmk3IUsXodMbhR1MWztkkkSTsKZLIobtukecK27FJE5dKNcKreBx74QjTD+25ybW1M1ER12pcbPvqCafBB/3t6lwGrbPtTD3tJnzfw3WdDbJz84XoykR3uNwS3I3Ii0FqfafTB25X+7Oza/0DwnNrPCA89xCOY+H71wva3FmjQLj9cUUpSwJ2q/T1O7lJbyZOvp44sSxP3x3NQqoLlBClwJjLC/9gl7uWhkx4NZyNVtdS0qcwRM/QcwAAIABJREFUhhdHDxAVGZaQfGPxFP085WC1xVzU5kLU4VR/lf1BkyeHprgUd7kYtVFCMuZVOdtbY8508ZTFw7VRhJAsxV0QgmdaewmLlHfa8wSWg9ggWonJNuZQIZAWsc43K0AFZcOxvLklGliIR/jdCzbf9tscCNqcjJrYUmOo0NjQ6EwHXVZzl5mgRyAzOrmzSWj2eH1cCd3cIrAyhu2YTmEhjGHYiRl1YlyRkWqBRpLmULUL/AAqFY0QulzcgdZYxpOfL6g2CqK+ZH0JFi/aKAv8asbynM3CBQtTGE7/KGB1IWN4XHHgeLrZprIsaK9KOmuKsGNRrefEsWRoJGf+jMPYdIRll22zIqMMGL3g8NpfBfQ7NiN7MtYWFauhQ3vV4pFnQh55uo/OFec/svCrBqnKBMogKG0JLBsqVc3jn+vy9f8wzLmTHpZdthGkzDBC0l5VRKFg5pFDNPccIA2TGwr8d6tdVBSaoignnIRgs/UVBGUVeVD9uRGRcd2yTdbp9O66OPpmCAIfx7kcYbFVDFqCOx15MSA73W7/uj+/He3PTlR/Hkxp3RoPCM89gu+72LZFtxt9ate1GxftoK11s2PbtiIIPKIoIU13K+n89sXJA3FiFCXXLFADzUJ6Q1+OrSIuct5YPc9i0kcJwVND0xxujeH73lUL/7ATcD5qM+JUKIwmNwUVy0EKQcUqdVfPDc/wd8tnONldJzOaz4/sw1EWNcvhqxOHOdtf59WVcywmfd5rL5LrgseaE0RFjkAQ5ym5MUwHdQLbwbMshu0KtlIsJyExOVGeMWIHGCUZcStcCNukJqcibVxlExU5lijF1lGeE2tYLxr08io/7E2SFIJxL2bYTXBlyof9MUacNomx2Gt10Eawmrkc9DscqrRJtI0tMrqFTa+w+bHhC3RzG1cUHKi0kQIwAksaClNgS7DlBuEwJUGRVnn9DY3lLF4IGMijhsZS9j6UkKWGLJHYDihLszTvMHMkxq9o9j0cMzScI0RJXvpdyfmPPeJ+WZUqtCDqCYZGQSi5+eGiLFies+isSd59rUZ9yDA6EdPrWswcDKk0c8anMiwb3vtehW5XonNJkcHkvoyLpwRj0xnKMrTG8zIJvbA48UKXTttHiIKRiZjpgxGfvNdk4bykPgzDU0dp7mkx9/EiB588sNm+GuButYuM4VNVDtu+8SbistFhd0fuq51CpeJjWdYNIyy2ip2KvKjXq2itt+RJdOPqz2XTw+1Ufx5UeG6NB4TnLkOI8uYF6Hb71yUgu6PwHwiNr39DXHZzvnXS+XbLsHfinHztAnW59bUzkxnfX59jOQ0Zc6tkuuD7vQVmRkZIr9EJHG9M0MtTlpIexpSPx9yrM2SGHJ8vjx3ij7Mfcrg2vEmEFpMe77TnOdldoV+kdNKIuuXybGuKSb/B+91FPu4u82hjklhnnAvXebg2St12WYh6LCY9AtsGY6OLsv2yx6/Rsn0Wky55LjECqo7LhFVnttbiM6P7iKKIf3fmbRaTPpZ00MZQCE0792jYayR5QNOJebszRaYNJihDR18ePsfhSgclNe/1WqWjclSn5SbYGEadmLNhDaPbG2SmXKgtyipTXpTTU2lcfq0o9TSeD7NHY1YXLZbmbEanEowuqA/B2qIk6kv2Hs5oTaxgWYKp2QQ3gCQqJ7R6XcGp91wWztv02ha6gPlzAQeOJVz42GPqQIguIOwJFi9aGKMIuwplwfh0TpLC6pJg4ZzHQyMhfsWgbMP0bMrKJYuzHzhYDnzjz+usXfKYmo35hf9pEWWBFxTYLngVwxf+y4iLpz2SsIzDGBpLePe1BsuXRpg4qmiaAmEWsPQbSD2OFgdAqHvaLhpUOQYalyurHINNUbcb3ldkp8zrknQ63V2rYFwbeVFWxW4ceSEEGwaMWyM71+LT1Z/SdLX8emumhw8qPLfGA8KzTWznwhq0i7Isv6N+/XZwswpPEHgbC8rtujlv7cXvhnPy1a0vdVXra6uW9AALcY+WHSAENKoV4qjL3Moye/wGSZGT6Bxf2XjK4gujs4RFhiUEnrKvezxXWdQtF/eKKa/luMd3Vs7RcnwEgsmgxnoaM+bVEEIQ5hlTQYPZaot2FnMubNPOYpq2z6PNCb69ehZXG/pZyuHqCJ08pqJs5uMuFeUSKBdB2eIasyv88uwz7GsO4zg2bVHwx6e/T2E0YVbu5ke9JiNOyFySMu3lHAnWWE4UT9RXORMFNJ2MzEjamUM793i42sHEisWkwkeZjSczmlaOJSHXYIuyQC8MLM8pzn/kU6kVTB9MqDc1iMti93pLU22mTM2mSFV+XypgrCBNNLZjmD60sdhvdBg8ryQ8K/MOb36rwZETfRw3Z23JIk0EzdGU6QMp9VbK+rKg17WwlMAIje1Kor6i3y0Ie4qgrskzwfhUxtxpj6PP9vH8mKBm01lTfO2Phuis2fi1gvUli/MnXUYnU1wH/CBDCs3c2T61Roowkiy2sCyboALLSzlZmrE+9zYzBzW+X0WYeQraWN4LBMH9ERVx5SYiCHxc1yZJMioVHymDbd1HO43LeV3bj7DYKm4kCL8y8sKyrI31Z2fdpgcEaOPRdSMvblT9eVDhuTUeEJ67hLvRLro5Pl3hGSSda701N+etTH3dDefkKyczBoGEWxVs1iyXUGeMVxtkeU4/inFqFhfDDm+snacwBlcqXhjZR8sJNsbMbwwhBMfr47y5PocnLRKdsZyFNCyPISfAGMNKGqKNZjHuU7EdCgyjdlktatgeU36dTBes5REv7j3CkeYYZzurjLgBjlC8uz7PWh7TzmL2Bg3CPMNSZTL0cyMzjDoVwjCm2w8ZVS6zlSHm4h69PCWwHLp5QmIbnq/3mfK7zEUFrmWznFbo5R6vrU7yeGOZbm5jS4MnDTNen6+t7OWjsEHTynh5+CwCcCQbY/rlJFSWKapDBeRw9kOPwyfK1tSVkKqs+ACbAfB+pZyKAi5fsmLjn43E1rgn2H8kJkkU7WWLPBU8+bk+tYZmYiYliSVGw/mTLhQSt5Lj+KUubnXRQheCPBMceSwiTQWWYwg7Cs83ZKkkzyVf/vlVOmsOWSK5dMHmwsceeSqxnYjuus/8eYvWqCbqai6e8gELaStG91lMn5hlfG+F2SOK6YcPg5QYU8O357B9c19FRQBUKgGWpVhfH2hjth7tsBuo1ysbgxN3j+xcD1dGXpQDDOU96rrORltwd/4uW6/+7I4c4j8nPCA8dwGe5+C6t9cuGuB2NDdbwbUk5XrTYVs4Grfjw3MvnJOvDSQclOwHgs2B7udaYeJzozN8tzPPhe46aZpypDZCoGxeWTpD3fZwpCLMM15dPsePTx7ZFDXfDAc32llLSR9HSnKjWYh7xEWGp2wKY3ioPsaR+ggGwyP1MT7oLrGU9NDGcLwxzufGZxlq1An7ERP4RGlCP88ITcaXJw7TzRP+9MKPaOcxe4I6SVEwn3RZTUK+t3qBA9UW3125wHudRWrK5aBv0bJ9DIbC9DlU7XC81sWTPUadlB92JVUr4Sl/lZXEJyochuwER2o+Cuscq67zpdZZCj3LPr/HmBOSAhU2fP0MZInEaIOS4FQ1S/M2/Y78FOHh2j/hIIJ+cLlsXGJCQJFTZlUBQhla4xlrSzZFUf4sSSRvfqPGnv0RupC4gabRyvjkRxXqiWFoouDAsRA30IQdC2UVaA1nPnSZmMkIqgXGSLJYMD6VYjmlw7LWmmdf7nDxtMvCecX6co0iFzi+4K1v2ji+pjVekKZl6OjTXx4l4RBPf/UENX8Ns/EiPc9GyVKDoo3L/YJBBeXadtG10Q6OY2PbNvW6x26a+w1wKyHwvUA5eh6QJJftA24WebEbz3+j6o8QD4wHbwcPCM8uY/vhnzfX3GwVV+qCbNsiCK43HXa7x7p1hed+iYm43lhqtXp1GjPA3mqDiu2yGvaxpKRhe6ymZcr5YCIrsGwWk4RUF/i36ZU04deY8GsALCXl8RbjPktxD0dKfnziCKMbpoUAe/w661mMFII99Qa1SmVTNF21HF4eO0i/yFBCUrUcwjxjMenTz1I+7q2wnHZ4ujnFpF/j75bO8NcLJzlQGeZAZYgfZDHzaZ/DQZNeMY8vVzjoLWHLMTqFx6h1nmO1FeoqoqJiRp0cjY/WEldmrKQei0mVquPw9FCH+biBpk2qXQKZIBSIAtorZTSq6xrSVLJyyePhpzJQmlumg137Zy2uaHeZktzUhnJ6HUUcCqQExzdgoFLPSSIbIQ01x3DoREJ7xebcRz5ry4ZHnukxsT+h35G88h+bdNdsdCFwvZCFMzl5IZm/YDM8XlCt5zz5+Q5L8w5xKClSweHHYi5d8LBdQ3fdYn3FIc8kQdOmNVnDqyR0e/t55qeexw0qGDOJNHM4XhMp1unFU2hx/5Cdy6PUt66gDO6Vfn93zf12M5z0TnA5RywlDC9Ha9wo8mK7dgBbOZ9B9UcIaDRqG/qs+6dyeD/iAeHZJUgpqFT8bYd/7nSFZwDPc3CcrVWbro8bM547ESfvJq52qpWbYs2BTb9n2Yz6lc1FO1A2Akh0jistwjzFl/YmAdoqnm5NY61LfGXjK4vnWnsZcvyrfsdVFuOquqmpWF+/uv1hSUXjiucPLJsfG53lnfUFVtKQluMzW23xw84l+nnGUtxDCMEj9XEeaUywlq6xP3iLQ1Ubl1XSImQ17dApYI+dMutDX0sy0+STqElBk8drH9DPFbH2ERIUCXu9AlsdYzXfywH/P10mwAqaY5rvfaP8MM1Sl8MnIuqtLWrWrpG3bLa+BFi2Zt9DEUG14NxHLr22xYdveRw4nvC3f9bk8ImQsamMJJTMHE4Zn0np9wVxJDn1XkDYk9SHNCN7QnzfkMSCk+9VWLpgMXfGpTlacOKFDiPjmsn9XdaXbepDOfsfTpk76xP1JLqQSKXJozppPsnITIXFi+M89LkvUR8uCW7Gs9Sr82DadPs+Wuzf2t9gl3Cngttrzf0cx9qc/LqT8e6SVFQ3CcT9goExZJLcPEfs6qnSy3YAvr97VbEB2TEG2u0+O+WA/58rHhCeXcAgeyqO023nYV2uyOxchcf33Y0y8VarTdc7t09/fzfEybsFrTWWpTDGsL7e2di1OpskNU0zVKp4bniGN1bP0zExrrT4zMje22pnXQ+esnh+eO8tf68UR8orNBU3R9PxmQkavN+9xIWww2oWoo1BA1XbQSCZj7scrLR4qhlStzM+7DlgLD43ZGGrYfxcU5EXKIxPzYJMG4btlJw+ltAsZyNM+BpbGixiPFXhUX8FzTCSKWAOKD8MWuMFn//JDt3OGI7r0mx12BTp3A6K0jvnyj+zVOUhigjqLUMWC1qjGUFV8+Y3q0hboKwyQDTqKRYvONSGCqQ0KKkIAs2FTxz8oMDzC6xxmD9tc/KcR3045f23KuSZYGQ8Z+pAxNwpn5GJPpW6Zm0ZHF/iVQyHjme88fUqnXWbWqtOY/wY49MWa4sx4/sMe/aVH2ZCCOqNIYqiXpKKnTVP3zZ2mlSUwudsc527Nu/ryhbPza7lG1VQ7jWkLM9rq8aQ19oB7EZV7GqyE26MuD/AzfCA8GwTN7p3r5c9tX3szAUspcCyrI0y8e7snO6GOHmnUJbNy0rO+noXYJPkwNVuz4/Uq+wbGqYbx1j609laO39epa9Hu9297f+3nka8tX6RQ5VhJJLvrZ5jJY2Z8Rs0HJ/1NERgOM0aM67mYHWK9cziVFewXhj2eBmTpNgGlvNpMIqGfZa67eLZj3Ip8jjcyBmSC9hkCFxsexLLaqDzOQxNNBmCZSBFYPArCqfiIOkBA42YC9z6Q8NoyjwxAF06JHsVsDZEzt01heWAGxjqwxkzhxLSSNEaz9l7OMZxIc8FKwuKJFLU6prOksW5kw5f+Mk2UhnefsVDa4HlaFYuOaSxZO/hhIef6FMbMsyfMUjLYnUROmsee48UaO3RHBU888WUkRkPv7EPWXuBPPwAyznMyKSLY/2A3BpD2eNkWbpr99t2cDnCYvdIxXbyvi6TinuTxH4jDBLioyi549DUnY68eEB2tocHhGcHMRjvvl721FZxoyrKVjGoNg1KzbuB+0WvczsYLGJXCg+vxfX68uON5q66PV8+r61/GPWLFInAVTaz1Rbnw3W0EUwHDQywpgt+YvIhNIblaA1XnMIWdWwl6OUeZ8QLOHxChTVcpQELR43h2s+B/Q/ZW3mds6tfo1t0CVTBWv44e+0WJn8TxXkU+opE9xolwSmQrFMuMRLIud0Kz6AbmoQQxWAKCOrlz2wP/GpBv6eoVDVGw8RMxtkPNJgCIQxrS5Ix1zA8njN3xmL5ksU7r1ZZXrD4zl81mdwbMbEv4cz7Pn5NE4cQVAomZxOaI4aoL6k2NXsPh2AU4zOCfs/h0vmAIg85+JigNr6XWPzXWOIM1CdAeCQp5FlOUF+h0KM4jkOzaW27xbOTGFxfdxphsRXcKu9r8AE/mF69H5LYB9hJsnMt7jTy4gHZ2T4eEJ4dwHbHu2+G7QZ+Xomyf1xWmxzH2hUV/98nsmNZFvV6ZUtW/jd3e86v2LFuv423nYiBqMhIdUFF2XiynPgqNrRTlpQ80hin5fhoDGNuhf3VFmGe8kF3nFZmaFpnaNh9zkazqKxBO8l5oj7FqDIUJiVngop1lOpQg37vBYaDSXrpMka+zax7FsWPEMxhEOTYiE2SIzFiHGOWEeRoSqYiiIBrCeZg6uRqvZdSZUhorwtGK2oNvTmOKyQEtZKgDKbCZh7OgB7rK4o8kwyPF3iBxnI1Bx4NOfe+R62pmdjXw3HA8QXD4wXHnlwhjiRRJLEtw0fvVlhesHArmme+2MX3CgojiCJNpSY48oRAy3GUexzNFFLEGOMhWcXgImWK7ySEoSEuupvv7bUOvgMdx926ZwZZcGF4bxPPr91IuK5DtRoA5VoF7IrAd6so881qd+3vdavIiyiKee211zh48DDNZuMB2bkDPCA8d4g7G+++GW5v9PtGuBxdUVabbHtn3+pyrR7ode5/suO6pT7nTtxtb8ftOUm2lsS8Hdfdj7vLvNNeQCDwlOKzI/t5tDHOe51FtDF40mJfpcmoW6WTxSgp8ZVF1XJ4qjnNux0LE08y6dV4vFrlbH+NXn6UuuOD6KPJOZ84TE3M0O10yHKDb+3Ft/ZiFR0wIYLzG/qdDoaAspXlkROAGQbGkXIN2/sZMu1TxH+BYAnJGhAiEBhcBCHl9XNF9UOB60NDgpIF0gJxxeWrJBTyMl0Cwd6jKeOhYulCFb9aIKWg2rCxnJzcWLTGc2pDhkotxxiQQuD6YDmaarP8/c98uY3jSizfYHIwKAQKxynQOkfrYRx/mJwWWoxj8R45jwJLOOJ7uHKZNArBXEKKL6PlQ59q8dxtb5sBmb7fEs9B4Hnuxnmln8r7Ghge3m0n6rtNdq7F9SIver0Ov/Eb/yvtdpsTJ07w+c9/nieeeJZDhx5CygeEZysQ5ibbjKWl29cR/P8RQTAI/4zJsp0tVweBtzHWuLUb/sroin4/2tQaeV5plLcTpExrQ70eIMTlNPP7KWTwWgRBmbDc6fR27YNlIEp0XfuanfyNXWp938XzvI3zur2/XzuL+dqlkww7FZSQdPMERyq+NH6Ifp6SG02hC95an99wafZ4tjVDzb48Dq2NQRv9/7H35nFy1HX+/7Ouvo9MrkmAIISEgKBhEUWEKGgEF11lES/8rauu7KJZ8QJR7hAIYtAAC6wHIB67uqwCsuqSJXIFWEQQsvIFEiCQICEzOWZ6jj7r+P1RXd09M90zfVR11SSf5+Ph44GZIf2hp6fqVe/j9arMI20dHeCPe15jXiRCWNqFab2EJcucNCuMbs6mJB0Hkv0Erpn3IVkDhKwHgRwyr2GRQGIEiRIms7CYBRQxiSNJPVjKG9B4BcXYg2G9BGSxW1zh8j9bTLmu7rzPgFGJRtUBFYskEnkswlhmxH7qlU1kaRgJiy3/T+LZJ8LMma+TmmFQKkkk0iXm7ldEVSVKhu0bZBoS6Vk6kiKhF2yfE0myMHSVwT0zCMVmEO+Zi8l+6PLbkaw9GNJiVFUlrmygkH8N04ohkcckQVH+AKa8qOF/i+NtY1c33N/icfK6gpZ4PpUIc6pimqZ1LTQY7N/hdDqI4hAMQ+eVV7bw4IMP8cADD/DSSy8ya9Ysjj32Hfz93/8D++9/gN9HDAxz5iQbfk1UeNpEkiTC4VDd8E83aCdPS5YlEomYp9EVjttnJjPc0NcmSBeLVjee2qVVt+d4PIamKWQyQy3Ne+WMEhLVNPekGqa/bFQYr3F/Xt67CMMy626UyZKELFWHr3sjCeKqxs5igbiao2jKvDU9F90MIVt9KLyMIR0KgMkBqPRhMAeFrVjEykJnNiXeCoRR2IJFCpM0ivUKsn4fJiYWu5EoASpyWayYzMDe7qpnMGfXcCyiSFjI5DHs8AqcPHiT2Rjsj0I/MruwZA2LxZjEkHgGiSwHLDbo/4vEztcjDA+YHHxEngMO0SnkZUolO39IUS10XUJS7BabLFsUcipqyMQ0JQzDIhItIrMVk5nlEmcBLdRDLJIll1HACmNJCSxLA2RUXqJIY8HT2Num85u8UzkcGhptqeLoNc2IsMlaPIZhVqpibj5kBbcSZj/Ezpo1k5kzezjkkMP5zGfOpq9vB4899ih//OMf2L79NSF4mkRUeDpAUTqfs2lENBquDP01gzOc3Ci6wqk8dCKEJpvXcXxtwmENRVErFyWvn8oaUbvx5Ldbq3PBDoU0TNNEkiRM02orDHGolOfevhfp0aJoskKmlCemarx77iFT/8uWhT1QrIztEWELqR1mFqO0kVlkmBuZAYBkZTCk/TDko8t/h4liPY9qPYtsbcUkhMlBlKR3gDIL2XwZ1XwIhQGgiMLm8vZWEruVNVhuZRWRMLEHmg0UShiMvdFYpAG1LG9KGNi76TJZLCJYhMrttDgG+6Py/4AkOgsxSRDmAWzBlMQ0+hkaCINlkuwpoKmjlEoRhgcMVNlEVkwiyTCqagAlLFOmVJAp5mRMSyOeKiBrESTA4AhKHI4afRuh+AkMDz6Hqv83EruwmInMIAZzMeU3UJLf09oPGMbc5DVNrWwQjt9uaoTTvg1CXlctjtjpRIRpmlqpitU+ZHWS9xV0sZNOp7AsS8zsNMlkFR4heDrAS8HTSgvKWYUfGck3vMB1KnhaGU6uf8Eudi2Hx+7Dt7fx5CX2WrDjqiy1XRXbOjrAnwa3Y1kWCTXMO2YfSEKdwsHXyqNZjyOzEwsZnb/ClA+yTyJBMmkbBY5mNqJaT2Ex1z4zfZSkYzHlN0zydxuADJKEZA0RMu9CsV7DJIHGH4BSueVVxHYHUpAoYSFhkUAmg92ekrEnZ5ytrxSWcioSWykZCiaLkHkRha2YzMFkDjJ7kBjE4EAgjISOxB4kJGA3EgXsapBiCx/iyPQBBRRGsdDLc0RaWYhJ5Ym0EiAjoWBhIKFjEcLgAHTeixaOoyb+P4aGRzANA8V8krB1DxIFTGZiSAegS+/ElOc1/XNthDMrZre+pMoNvt5nJhIJE41GGBoaDlTiebXiNOJaZcZ5yAqFNFRVbWsmyllkCFrbD2wLhlRKiJ1WES2taYhl1bjLTkKzq/DttMiq/25rzsn1h3tDpFKRmpXMoidzP85TZCsbT93A2ZTJ5/MV0TkxoNEeep6qKvaGeA/7RVMUTYOIojZlhKhaG5Gs3ZjSXLB0VOkJSlYaSZlJKlVjRCctxGAEhZcACZ0jMKUGZolmHtX6I4r1AhI5dJaiK8ei81eobCq3rwpI5JEYxZ6+MbFIYhEBItgVmXkovIAteGLoGICEwv5ImoKlvAelOAT6FmRrR/nF7f0wgzmo7EZhNxYpLCwsIhgcWhF3KpsxCZVf63hUnsDCAF5HZjegYxHGFl15JNRyq65QFjsaFj1YhDE5hLC2C0npITM0Yv+cJBlDeStZ81Bk6y9IyJhyL5Y0s4lPxtTUbjfVC/V0BLOd2RcKXDhpOBzyJCG+07yvoM44gRA7XiEET2CxmMye1V6FtzcamlmFb3fN3Y2YiOoFu/Garhul5EjEvrAGdW5hfMm89oLtmJE57Yip2hiarKC1YIIo049Fj/1/JBXJklDVLIn0QWNEGJKMIR2FYR1Z+d56SNYAIfM/0HgeyGMykzAvIhvbKEnvxaAXhRewiCAxXP70xLBbTPsBw1jMwiKMwUFI7EFmtFwFCiEzA0OKk8vvj6mdStT6MZq8HUWah2FJWEacIm9D41EseoEcEjuRUCnxdkzpECRrsCykskAPJU7EQrVnfdCxSGCgIBNFwnGDVjCJYzEfiAIDWKTK55pBKNQDVh8juROxlHG/E3Iak3TTP5N2GPuZocbYz15UyOUKyLJEUPROJBIiGo12peI0Vd6XUxlztlY7ba95hRA73iEET0CZTKAoikw8HqVYdHsVvvb1bZHj9pNivTyrVisc9YjHo2jaxOwpv3EEzFSl/PFmZGNN2pjyaXUqLNJIDNlzMZaJokjEk7MYzWbrx580EDr2X6ajWY+WV9JtCaPwMpBA4zFkaycmi7GkJJr1RyyGMZlfNiK054hMepEZKbe0chgcQ5FeNJ4gJOUwpFkUzf2QpRyGsRnT3IHOYiQ5iiIZKGqGZDiJkc9hmQUsq4iJve0kUUKyXgcKWMxGZ0l5qPlVZHZhMAs73vQ1LAqYzEcihsECFPqxiANFdP4K0JHZjckBRCJgGbsolJZgKEe29XNwEye+IBTSyr9TOTRN9dXzpxZ7CzHsS8VpYt6X3V6Px6NYloUsy4yMZIXY2ccQgqcD3DAHbPx3129BtZt03kpLq1tmguMrHKGQOiHPqlgsTvlkmEzGkSSJTMbbTaxWcdbh2xFh9dyeO9ng0aWj0HgYmZ0oqkQkvpTh0TglvfXKmkQeKGBJaSTr5fIMjIpyu/wMAAAgAElEQVSFhkUMhW0Y8pFYLADrCexKSRiLueWWUy8F3oXKa9hx6AqGtQhZSRBS+inpUDQPxpLnIFkDSOYAFiOo/AXTTKMzB5NR8vomwmxDooQip8AC08oCe5B5wf7v5mhMeX9MawCsPBJ7gBgmCrAACwmDWSi8BiQpcTAKOwADS+oBSabIe0nGXsLQ8+SNAygpx44N+vKR2s8+2J+bsZ4/IRKJuGsmmc0SjUZq2mv+/k7Wttid6nKhUCQaDVceHO0KkD/C0EGW7QFl0xRixyuE4JlG2D16N5LOG+OXc/L4EMJmTP2cIWBdN3zfxBqPm+vwbrg9W1KKIsuJRkqEI3EyI3Lb8xQWIezWz36YbEHldSw0TOZgMRsYRLJGsaQeTOYhoQJRLBQsVPLSRzCVIzEsHYkRQCIkv0A8/BqF3Awssx9F2o5uzUJCR2Zn2Y9ZQWYXMtsp8SYUXiyfZRTD3F0ehJ6Bquko6lswSs8hSQlKpgKWhEkvKs9iMhukEIYVRWYPBfmzKNa28paXjGEZdj6YdACWchipGQeQzx1GrjRi+xF59ZTTIqmUPWg+NDQy4WumaZHPFysV4No5Oq+Sux1isSihkBq4BxBH7NTOEjnC0H7QijfM+/IaJ09MiB1vEVtaHSDLzQ0Wt4OqKuXNK9uOPx6PlMuwubYuIooiE4tFJp33CWpMRD1TP103iEYjY+dPAkBtMGk3RJhzIwuFtDFtsXqlervtpzI0NNLxU7dsbkflD0hmHpU/YBHFYCEW9jo7koIkgWT2AQqmdACQwyJOSX43SFXPoHBIJi7/hmw+hWHkUK3nkOjHkBajcyQKrwAxZOtFZPoBMDgAhZ3YK+3PIzEAGJj0YrIQSCPLOrKaQA0djGGFKVpvhNw6JKu/PFNkYZEir3wOJA3J2oVEFos4ljSrMmiey+U8ax23g2O5YIcBtx5l4/w+hULuG/vF41FU1f6MBek60uyqfisbcW7hiB3DsMWOW6HR+ypiS2uaIknVnC7D6Cyna6r2mxvDyV4x3tQvGo1WhjQ1rXqj97t03kwwqdvUDoQrikI4rJFIRCe4PScStW2/zl/XlPejaP01kpKlYJ2MxnNI7MGih5L0V9j+OTksOYxMPzI7sIhhSIvHiJ1oNEwkHCI7UMIwSyBF0K03IUuvUpJOxJQOQLFetc38pDhYMSRGUHgZkzkovI699l4AQuXNqhQSBiVzLkZpCaZxEIo2x76RxfcH9kPXTXQ9g2HOq7hIW9LsyqffWVcOmjeLJEmk0zVbdW1Qb76l1tiv3QpHIhFDUeS2/KW8pLolNvXgdL1W8mQGop0ixE53ERWeDvCywuNUZGRZIp8vdbxi7bgwDw2NrTp4NZzsFY7PyPCw/aTmbKmEQlpLcz9u45iXZbPBqAbUluo1TcU0LXK5fCDCGR2cQfOhoWHQt6DyBFgKoGNKb0CX3gqSjGy+gmY9gGq9aIsmegHKG1i5sugplAejBzA5AIhQlJZRkpePGcCWrD2EpCfRlDyKNh8lehwlPTSmwtFOvlk3qCaeF8nlvPGXciocmqYhSdIYA9HJcGaJ6rXX/MStLbHajTjbQLTzvC8hdrxBGA96hJeCJxwOlZPOc67kdNll8BiZTFXwBLWF1Qg7jsFpyUy8eI0tR3e+2dQsQb5BplKJiudRrdtztfXljxNvtRowWvn8SdYuJCsDUhSTeWMGg2XzeTTzISxpLpaVQGETCpvKG1RxO72cPcgMoLOQkvRudPl4kBqs7lsmSPI4k0z7vZFlKXCVHSfUMpfLV3xnvKaesV+9CkcqFceyCNwcnf1wFCaTqX+96IRO876qYsckk8khxI57CMHjEZJkuy27jZN0DkyoyLSLJNlDjpmM/QQ2ncSO4wQMMDw80lS5fPycghsW9PVoJwC0G6iqQjKZqHuDtC/WoQn2/N24wde6Ord0g7TyhKx7wdRReAqF7ZgciEkElZcw2R87gNSgIJ1mR2G0OFzsrFGXSnrl969bonkynFBLP800G1U47Oqh//Et46muxLsvdsbTKAqkVKr/QCHEjrcIweMRXgieeDyKJEE2myeZHFuR6ZQZMxIMDo5MK7HjVClKpVLbMwu17Z2xT6rFjuZ+3BwCdpNGRof1GP8U72UGWnX+xGB0tPV5NMncQ8T8GSp/xmAuFknsINI9GMy3A0ul+ZTkE8fMCTWDvVlkt9ecn6WXw73NEtRZIk1TSCQSFU3pt+dPLdFohEjEv5X42ragLEuMjmb5zW9+w+LFh3LwwQczY0ZKiB0PEUPL0wB7xiZKqWRUto7ajYJohCRJgR5OHs9kVYpWqF3RrX1SjcVSmKZZMTtspULjZGK5NQTsFs0aHTo054XU+YquK/MnkoIh7w+mikUepBiSNYjOQnRpKZY0F1Oa37LYsdtryoQ16sbDvbHy+rK3+XBBjT6QJIl4PEaxWCzHXfjr+VNLLBYhFAq5YgfRLs7gM9hRINnsKD/96U946aWX2H///Vm27J285S3HcvTRxxAOR3w5476KqPB0gFsVnmrSeXHMU5xTkXEDy7JIJh0H1qLvZfqpqM7FZCmVvHuyVVWVcLj5uZ9a7592VoK9xDE6dKuM79ZMlGvr3dYIIWsdlhlD5XkkCkiMkJc+hCEfM3ULqzy3U0u7w7Zez4t5EbbpBnaVLkmx2DiYt/rehPDa86cWR+wEzf8H7OvGwMBuHnzwQe677wGefvpPyLLMW97yNs4662wWL17i9xH3GkRLyyPcEDyhkFYeTs5PuLC5JXhqW1jOqmUoFPJ0tqUT7JJ0uOtzMfZ7ExrTwigUipUyvZPC7uWWTLs4VQqv/E/qvTfNtHdaaa81dQ7z/1DZhGVaSNIoBkeiK2+d/F+ysmjWk0j0Y5G0t7/kHlKphCt+Se2+N41w1qiDNhfmzJ608vkf2xZUPGuZOu3lTCZY/j9Qf2Ynm83yxBOP84c/PMpJJy3nmGPe5vrr3nrrD7j11h/wk5/8goULF/HMM39mzZrVFAoF5s+fzyWXrKKnx52A2yAhBI9HdCp47OFkhZGRXN1eczqdKN/A2n+NyeZ16s+2FCkUujejMJ5EIoaqKr7PxTgtjHBYQ1U1TNNAluXApbBXB7ot1wbcp37NsZtNTgujUBi7vdNqe60pLAuZPrCyWFIcS+qd8vs1634ka8SOirBGkaUS0Z4PU9KVtufCGtFogLVZq4RuDtu2gtOSzOUKbbeXG783nbVMg2p2CP4NKG/a9Dw/+MFNbN36Mt/+9loOOmghn/jE6VxwwWUsXXoUt912M9u3v8YFF1zalfN0k8kETzACYaYp7f5uSRIVc7ihoewkN3Y7mrFdphpOdmZbhoZGGBgYJJ8voKoqPT0p0ukk0WgERenOR8QZaJUkqZw9FYz8naGhUUZHs8iyTKlkEI9HmTEjSSwWQfFiRa8FnPaCYZhdEztQfW+Gh0fZs2ewnN2kkE4nmTEjRSxmG0M6Zm+utmQkCVOahykvnFrsAFBEZsAWO4CkJIhFFQr5AdfFDox/bzJks7lKG7SnJ1WpRNTDbkn6E7Y5GYoik04nyWY7m6Wr995IkkQyGaenJ00iEau0CJvFfkBSy2aHQuwAFItFvvvdqzn33G9U/mzTpucIhUIsXXoUAKeddgb337++K+cJEmJoucvIslweTi6Ry01eKXDckdv5PW51ONlJXp6Y1m1nWdmDvUVP5gn8cChulupcTNW4zFnrtpPMJV9mooLUXqt1e7bn0ewqnWVZRKMRn1umKhYqWEVkJUwsGqJYKJE3zK7cf5oNgY3FoqjqxMFpv/FyJd55b2xR2LqrcVCdncHf1fObb/4eJ5/818yfv1/lz/r6djBv3vzK/58xYwamaTI0lCGVSnftbH4jBE8X0TSFWCxCLlegWJz65lhNOG/+t9kt5+SxF2onssAZerbbF24MEztrt0FrFUF1Lmb8xoeuG+h6jmzWnSTzVgnyexaJhAHYsyeDJEEoFKpzE+vMDqAlJAXdOoaw8keikSL5fJ6CcRiW3Ljs7RWNQmATiRiWZZHPF5Dl9kNd3cZxD+/GSvzYbcHaTcpIxfOnNiPOuRY5vmJBwhY7KQzD6LrYeeaZ/2PTpuf4/Oe/2LXXnE4IwdMlwuEQkcj0TDo3DINs1ii3LpwbfBhFiXc0hOjMeARx7dYJAM1kJp9jmyzJ3IvsnaC6OtcaCjrvmWXR4CaW6qrbsxJ+A9H4AkaG+yiZKpbc4+nrNYPT3gmHtXK1t1CuqMYB/w0P/VyJH19tdlyNnTEA+zpnCbFTh6ee+hOvvPIyH/nIBwHYubOfr371i5xxxsfYseP1yvcNDg6WPc72neoOiKHljlGbkIz2vEfrSeeJRJR8vrk2kh9mguOHV1u5wTutInsTJTjzCtU4hs7ba857U3WmLXZ0g3dyxIK2veOkd+t684aCtW7PTmaTW1XDWjwZnHYBx/m83pbY2E1K7zabGuGInaGh0UpFJSgkk3EURcYwzDED8354/ozHETu6bjA0FAxTwTPO+JvK0PLHP/63XHjhyn16aFlUeDpkshRyJ+nctl5v3bNlqoTz6vf545zsPKU6bRXn5m6Xoc1K62v8zTmZjCPLsq/mYPWoBoC6k1dU/ynV9n1xWjvNPj07QZuZzJDvA921VOevGvuy1KO2Lehm1bAWRyA2k5LdTaYSiI2rhu6aQdYjqP4/YAtEy7IYHKw+iFdnDW0jUL8qY0EUO7XIsszFF1/OmjWrKRaLzJtnr6Xva4gKT4coSn1Roij2cHKhUGrbbC0ej1As6pP+8gbVOdl5gg+Ha43Z9IoYClr2TreMDsG++Dhmh4oydZyDLRClMUGbQcA1Q8EaOl3rdnBM6GqjIoKAvbGVbLuC6KXhoVMNy2SCVUGEqtiZ7LrhV2VMUSRSqeCKnX0N4cPjIfUEj6apxGJhstlCRxeiWCxSbhNN/DvcGk7uBooiE4mEKwOthYKz1RQMs0M/W0WTedpYllWZJQqaQHTbUHCy12n1Bu+1CWO7VOM1CpX4mE6o3uCbE86TEYmEiEajDA0FqxoGTuvPbMnZvJ5wdt4fNytXQuwEDyF4PGS84IlE7LmE0dHOh5Oj0XAl66mW6RT+CdWb4+hojlKpNMHs0BFAfvz3OK2ioFQC7Bu8XRmTJKnS9ghSe8GvuZhmHI2dqIjh4c4MO93GthFIuloNq6UTUz9b8AfP7NCec0piGJ3HuKhqVTjLslR5bzoR60LsBBMheDykVvDE4xFkWWJkJO/Kzbue4JluYicScWzyJw5AVjd3QoRCzkXa20DGWoJ7c7RbRfZslEUoFHLtIt0p1WqYv5WAepUxSZIxTaOrJozN0M31boexlbHGXlHRqLM8EAzB71Cdc9JdN4h0ZsZCIa3GYb61bUohdoKLEDweoij2h98eKDTIZjsvVTtEInbqs/NEON3ETrV60tyTY23ooGU53hvumx06F1M3nhzdplGraPxFutubOwCxWJRQKDjVMAfHcdr5Zy8/O60ShMTzRq0vJ+cqaGaHXoqdia9VtUuoblOO9fwZj6LYA8qlkhA7QUQIHg8JhRSSyQj5fHFC66lTIpEQkgS5XDGww8mNqFZP2hu0dbaa7BK0Y3bY/FZTI4LkUDyeZltF9dsX3lbGgjwXY9sIVLfE6n12/JgZq248BWe92/nsOLExhmGUXdS92fpq53zpdIJiUffFdX3iZ0cnkxnk1Vf/whvecBCqKpfFjs7QUB4hdoKHEDwekkpFPDNPC4ftfnM2mw9Ub30ynNwgXXeveuJUN8LhzoYza2eJguZQbCfEt+dLVFsZq8aANH5CbYVaQ8GgDU5X52Ia2wg0bl946/YcVP8fqI1kGBkz22K3vlqzS3ATp1JXK179RJYlNE3jgQfu49xzz6W3t5cTTzyRE05YxuGHLyUSifp9REEdhODxEFm2/+cFdi5RpOx1484NzEuc2ZN8Pu/KFko92jU7DKqrM1QT4jOZzqsnTgzI+OpGO7MjQW79OXMxrYjXie0Lb9yegzLnVA+n8jo0NNGl2M2tr1Zx1vULheBVXgH6+/v405+e4P777+fxxx8HJN7ylmM4/vhlfOADp6E240Ar6ApC8HiIV4LHmdep9Wxxs7XjNk75vpuDmc7rjr+BjTc7DKqrM1AxTPNicHpidaNUqf5MdQNr11CwG7g1F1Nb3WjHDLIeTqUuaBtPAKlUHMuiqUqd/WBhbwy2uvXVKk7Qplvr+m7jpMU7baxcLs+TT/6RRx/dwP/939NcfvlVLFy4yLXX++Y3v8b27duRZYloNMZXvnIeixcvYdu2rVx55WVkMhnS6TQXXbSSBQsOdO119xaE4PEQLwRPo+Hksa0dpbyW66x0u3uGVnCeaIeH/S3fOzewWrNDRZGRJDlwsyfdrp5IkoSmqYTD429gEw39vDAUdItw2M4pc7tV1KoZZD2qbtjBGgKG5oz7JmPi1ld1LqoTHGGdyxVccTd3m/FipxszOyMjIyQS9oPQhg0P8KMf/ZBbb/03zjnnbN7//g9yyimnsm7d7/jtb+/m+uu/5/l5phsiWmIaMdlw8thEYal8cw8RjztJ1MVya6d7F9t4PIamKWQyw74/0eq6jq7bw46qqpBI2A7F9jmjvq90O/hRPaluLtn//Y0s+YGuGAq2g5dREabZTJxDY7dnZ6g7aGLHLS+bUsl2fB8dzVXE4fgokFKptWuP/Xsw+QyWn/ghdoCK2AFb/EiSzMDAHjZvfp61a28EYPnyU1i79tsMDAzQ0+N/GO50QQieDnHr2taqc3LjHKtoZWunUHAvpXs8zjArUL7Ie/IybSHLMomEfSG2L9D2DSwSCZNIxH01O2xn9sQLam9gznpyIhFDluVKS0eS3Pt8d4rTluyGsB7/uzVVXpMzF+OkxAcFr9a7x4tDxyyzlawvZ+A8mw3eAgFUxU6xqDM8XKDb21jf+tYqHn/8MQCuueZ6+vr6mD17LoqilM+nMHv2HPr7+4TgaQEheAKAG/469Z7e0+lk+cm+s5Tu8TirwI6gCBKqqpBMJsY8NZqmRT5fJJ8vVgZXnSHmbpodVvO6gjU4bRgGlqUC9k1bVdUx4tCPymEtThXRr7DZeuIwFouiKDKWZQuAekPAflLdeGovs6tZxlcOnbayIwKdyk9ttXD6iZ3u841vXAzAPff8lptuuo7Pfe7zvpxjb0MIHp/xwkyw9gI9MaW7vuNqs9QTFEGhmcFpy6ovDlOpZM3F233Dumo7JnjBjI6hoJPErutGuW1a3WqqVg6769lSWz0JQrXJMAxyOfv9sSs+FqZp0dOTbsux1wuqQ8Dd33iqbSs7M4dV8VyiVDKIRsOBbJlCMMROLe973/v59rdXM3fuXHbt6scwDBRFwTAMdu3aydy5vX4fcVohBI+PdMM5WdcNdN0gm81Xsoicp9NWV5a7mSjeKrUBoK2IlUbi0IlycGMjrioo/J9zGs9ksyeNxWEccDepezy1/j/BrJ4kKp8bh2pbOVLj2Ntdt+cgDQGPnzl0YmYsy25RqqrimYdZOwRB7GSzWYaHh+jtnQfAww8/RCqVoqdnJosWHcr69es45ZRTWb9+HYsXLxHtrBYRW1odIkl2vESr+O2cbM+1hJqOKrDXbcO+JIpPRasRFs0wdmvH2Yhr3ZMkqA7F0Nnmzvggz2JRL690d74xGGT/H6d6MtXAeSO3Zy+rGkFuFY3PExtvCVB7/fGDIIgdgD17dvONb3yNfD6HLCukUilWrPgyS5Ycxtatr3DFFZcyPDxMMpnk4otXcuCBB/l21qAi1tI9pFXB0+pwcjeoF8To+LWYphnom3anERbN0NjssLFbryRJJJPxQDoUuy0onKHwUCjUsZuxY0AXFLfdWpxYklarJ/Xdnt2di1IUhXTa/2H4eqiqSirV2Depnl9UtTXo/fUmKGJH4A5C8HhMsyab0yX8s9bMT5IkDMNgeDgbqMqOn1WA8WaH47OInJt2EIe6u7ES34wZZD2aiYrwC7cERSO352ben0b4kcbeLK2aRFa3vrx1w3YQYmfvQwgej2lG8EwXsePg3BhLJR3LssqGY1TmWvzsuwcpAFRV1UrrC+yZIE3TPI3XaBc/DAXHvj+Nh+aDsq5fDy8FRaduz0FIY2+Ec7ZOwlO9bH0JsbN3IgSPx0wleKab2HFK0ONvPvVymrodcxHkAFBn1d2yrJqbVzDMDp33zc8qQDWrKYSiyBWncNO0yjft4A3Dd1NQtJplFWyxo5FMuuuI7WZrUFXlcutUiJ29DSF4PGYyweP3cHKrNBuy6UfMRZADQMefbfxQeCs5Vm7jbNd18qTtNs5cVCQSRlUVdN2omEEGZb6t+r51PzJl7NyYOsEvys+zTUU3ztZJEKwQO3s3QvB4TD3BE8Th5KloN2SzGnOhoaqaJ0OZTihjEANAo9FwzQbbxLPVz7Hy1gnbIcjJ3bVWArXVDa/nNpohHLZXqIOylVg71yJJEpIkMTKSDVyV0xE73fabsrfiQuXqc+PqqhA7ez9C8HjMeMEz3VpYYG87yXLnIZvjn7zcuLkHeUvMXolXyyvxzZ3NseIPhTRPnLAdHP+foaFh31ySGzFZqnjtzcutFPPWzmYL2CAmnkciIaLRKMViEVVVK61Bp7Xs5++HU+X021yztvUlSRJf+tI57Lff/hx//Ds4/vjjMQyLkZFgCUWBewjB4zGKYt/oYfqJneq2k8nIiPvr09Un01DLN/cgr3ZD7Ur8SNttvKpfSwhZlsptr85v7tNBJGYyU59t7NyP0laKeSvEYhFCoVAgRaJdERsrxGRZKkel1FZXu+/2XK2IBa2SaHHXXXexfv3/sHHjRhKJBMce+w6OP34Zxx77DpLJxjdHwfRECB6PcQTPdBM7zrZTt1K7a2/uU8VcBDmvy6uVeLfMDjsxFPQau5IolYVYa//uVH5RndKKEOs21YrY5EJsrCWAd9XDWpyqUxCdxKHaxtqxo5/f//4BHnlkA3/4w/9SKOT55Cf/nn/8xy+49lqZzCCrVl3Ca6/9BU3TOOCAAznvvAvo6enhmWf+zJo1qykUCsyfP59LLllFT89M115bYCMEj8fYxoPTazjZ722niU691Sf3IOd1dcPHBto3OwyqQzG4L8ScyoYbN3e7Iia3JcS8pjaapBUh1g2353pVpyBhi50UhUJxTBurVCrx9NN/Ih6P88Y3Huna6w0NZXjxxRc4+uhjALjxxusYGspw/vkX8YlPnM4FF1zG0qVHcdttN7N9+2tccMGlrr22wEYIHg/RNIVEIkyp5L8/TbM4mTZB2fCo3WjSNHsgKp8vkM3mA/WkXRVi3fOxcZja7LA7QqwduiHExrcGHTO/ZtbcndZk0DK7wK46qaracWvSC7fnIM86geOflJwgdrrJAw/8njvv/BVnn72C1atX8tOf3g7A4OAgH/nI33DvvRt8OdfezGSCR4SHdkipZJDPlwiHVSKRcE1oYIFSyX8xMR4nd2pwMDjlZ9O0KpUcVVXI5fKoqkpPT9r1tkW7OL4ifvnY1D6ZO2Z+ToinY3bohxCbimpURIls1rvWZG1IrnNzj8XCKEq84dxPkANKoXYOq/Ok+LFBntXFglgsWldAT0WzLTa/CILYMU2TO+/8FSec8E76+nYwb978ytdmzJiBaZoMDWVIpdK+nG9fRAgeF8jlSuRyJSQJwmGVcFgllUqWh3RLlScpkHw9p/Mk22ppvBtMFGK2ABqbQN36hdkNglYR03UdXbeTumvNDmMxuxoQFLPDqiN2oauu0+NTup3PUDwewzCc1qBOIhELbPuv9nfVbSxrooAOhRwBPfVWnDPYPTgYvOsIBEPsAKxdu4ZYLMqHP/xRHnroft/OIagiBI+LWBbk8zr5vI4kWYRCtvhJJhMAY8RPN68T9lN2Al03Aj3IWk+ITVbZ6EbMheNNFKSKmIN9E49WDAWd1mAkEiaRiPtqdhiUqAjLsigUipUzOH5IM2bYIrFU0ivGh0EhmbQ/392qOjkCOpvNVbbiYrFIXbdnW+y0Pk/ULYIidm644Vr+8pdtXH31WmRZprd3Hjt2vF75+uDgYHkxQ1R3uokQPB5hWRKFgkGhYAD5iviJx2MkElLFmbhQKHoqfpz8pCBmO9XOdmQyUwux2sqGE3ORSMQ8i7lwWgpBfJJ1TPsymeoasNMarFY2bL+feDzaVbPDIMRYNMIwTDRNJZvNUyqVKkZ5Xg31torfG3aGYZLL2RW5sdWxaPk6ZZ8taL8PYIuddDpJPu+v2Pn+929k06bnWLPmOkKhEABLlhxOoVBg48anWbr0KO6665ecdNJy3864ryKGln1A02TCYbXiCqrrRkX8uNkPdy7mQbzxuLkS73bMRe1sRxArYlVDweaHRR2zw3DY23XlIMZYOEyWxj5+qNdrv5/xSBKkUsnAtticlrOu2/NiYFXenyDEvARF7GzZ8hKf+tTHWLDgQMLhCADz5+/HVVddw5//vJE1a1ZTLBaZN89eS585c5ZvZ91bEVtaAUZV5crcj6Io6LpeyRTqxMDLqQAMDwdj7qSWRuGkbtBpzIXT/iuVDEZHg3fjccNQcKIfkjtmh44fS1DiGGpppcU2VY6V2ziVTqd6GTTqDU8rSnXl3baV0CufIT/ap0EQO4JgIATPNEFRpLL40SpzBfaFtoiuN3+hjcdjaJrSUtxBt+hmAGirMRd+Ddk2SyqVACyGhtyrOlXNDscmmBeLrd24JouK8JtOW2y1buFgVeai3KhgSZJEOu39Flu7NONP5KfbsxA7gvEIwTMNkWUIhzXCYXXcU2ax4bq704oBOoo78Aq/A0Ani7nwsurUKd0yFKxfHZvc7BCC7VDstNjcEtjO7JgbZn6yLFVu1rlcsLyTgMpsU6vD091ye9Y0pdyiLDI6GqzfWYF/CMEzzZFlKkPPmqbWXXd/5plneOmlFzjzzDMDWxZX1eBUncYb1UmSxOhobi2oK4cAACAASURBVJ91dq5H7Y3LFtz2Z65WrFYrAMEbZHWqiV7ZCdQaZjpmfk47eqr3wvm55nKFwH3mwD0zxnqGkG4MhguxI2iEMB6c5pjm+HV3bcy6+5133skll1zCJz/5ycCJnWrVyWJwMDgC2jGqM02LaDRCoVAoByBGArGtA9UNO78iNmrfA6c6ZrfVKEeAqFiWSSYTPNO+eltsbjN2K85pn9ZuxU0UiFAdns5mg1dNBHfX4usZQlZtE9pzexZiR9AuosIzjbEskx//+BZuvfWHnHfeeXz605+mVKrO/fj9wO247AYxABSqmye1ydjjn9q7va3jEOTVbjtiw64AAIERiA5BmCeqtk81oPoemaZFOu2/P1EjurUWX3++bmpTUSF2BFMhWlp7IYVCgW99axUbNjzApZdewbJlJ1bW3cNhDUmSxqxnd7uN5Hd1YiqcG/ZkniJep3M3Isir3eOjIvxe5x5PdWU/OJEHzkZTOFwdDM/nC5RK3TUgnQo/PYDGCkSpct2qnbsSYkfQDKKltRfyxBOPs3HjU9x4480sWXIYAKWSSalkbytomkwopBCNRkkk4jXip4hheHuV9Tt3ajKcAWA7x2bykv14l95uxFx0oxXTLs7cSe0WW+MYhyiGYVRCPLtRaXHWp4PmAmwYBrouEY2GGR4eRZaljts6buP8TvjlAVQq6ZRKjqmo4/Yc5cwzz6RYLPLOdy7j5JNPZuHCxWSzwbqmCKYPosIzTXF+bE5bYTJUVSoPPXe27t4MkUiYWCwSmNypWmwr94QrK8BOzEVty6LTmIt2DAW7hVOxa2XupFtmh1BbsQvedqLTnhy/KdZuW8dNgm54uHXrKzz44P1s2LCBZ599ljlz5vKOd5zACSe8k6OPfivhcNjvIwoChmhpCSooiiN+nHV3o1yp6DyTajrcsL1osdVbVW415sINQ0GvcGOeyN7WsWej3DQ7BP/jGCaj2p6c+gGg0dyPV35VQTc8hLFtrC1btvHoow/z8MMP8ac//ZGFCxdxyy0/dfX1brjhWh588D5ef307P/nJL1i4cBEA27Zt5corLyOTyZBOp7noopUsWHCgq68tcAcheAR1qbfu7oifVi+yTgBoENeTuzkA3E7MhReGgm7hto8NUGlZ1Jod2gKxtZkWuzqRwDD8a8VMhrMWn8m07jxtv0ehipNx9T1yx8nYNjxMVNpIQWSymZ1sdpShoSHmzZvv6mtu3Pg08+bNY8WKs/j2t9dWBM8555zN+9//QU455VTWrfsdv/3t3Vx//fdcfW2BO4gZHkFdxq67U873UstPzM4TZqHi9VOPqileMNeTvfZiGU+9mRb7DBPnNbplKNgu9pp+ezfsyagXUBmJhGpmWqY2Owz6Dbv63rU3i2W/R3lyuXzNexQe9x61NxvlvHfFoh5Id2ewH1LsimyB0dGJDymxWJxYLO766y5detSEPxsY2MPmzc+zdu2NACxffgpr136bgYEBenp6XD+DwDuE4BEAYFnjvX5s8ZNIJJAkKjeh2krFc889SyqVYPHiQ7tuitcM1fVkfwaAa4eea+c1YrEopmkiyzKFQjGQN+xoNEwkEiaTGfa0Pdl4MDzV0MumuinWfTPGZrAHz91776YanncGw5sRpdUoi2C+dzC12Ok2fX19zJ49F0VRALuFPXv2HPr7+4TgmWYIwSOYgGVJFAoGhYIB5MviRyEej5FI2Ovud9zxK1atWsVXv/o19t8/eL1sZyZmcDAYGzvVilmpMk9kGAahUAhN0zwd6G0Vx5/IvmF3972byuywVNKJx6OBzTurFdlevXe175GqOu9RnEbr3A6OUCwUghllAVWxk80WxDaWwHWE4BFMgUSxaFAsGoyMFFFViVtu+QG33PJDLrzwQj72sY+XAzknb0F0E+cGmckEbwat3jyRM9CbSMTLA72Nb1peE6TV7rGrygqRSIhkMo5lVb1tgmR74Aztd1Nk67qOrtutqdp17moQrC2OJIlyblcwhSIEV+z09vaya1c/hmGgKAqGYbBr107mzu31+2iCFhGCR9A0xWKRVatW8eCD97N69TWceOK7KJV0wuEw8XisMltQKEy00+8GzmyCrgdzJqaRoaBtv5+ruWmFKjetbroYO1WCIApFe8YsxPDwKLpujJlp8dvsEOyqmKqqvgrFerNRzmcOqLS+gkhQxQ5AT89MFi06lPXr13HKKaeyfv06Fi9eItpZ0xCxpSVomltu+T53330HV199LYcddviYrymKVBl6dnvdvRnsfKJEYJOnHUNBOym+ufejWzEX9vB0PLDbTo18bMA/N+xaqpYCw4HzALLNIu2ZHee9cuZ+gtJCDZLYufbaNTz44P3s2bObdHoGqVSan/3sdrZufYUrrriU4eFhkskkF1+8kgMPPMjXswrqI9bSBa6QyQyWb47pSb9Plin70qhomtLRunszqKo9ExPUfCI3/Im8urEHfdupFR8bsG+e4XCofGP31uwQ3EsV94JGIaVeeiK1SpDEjmDvQAgegW846+61Xj+1G1+N1t2bxQufGDfxylDQET/OE3s7MRfVqIhgVsU68bGBejd2d2ejbLFDIP2Tmk1kl2W5YpqpKN3NQguFVJJJIXYE7iIEjyAQOOvuduur1k12cmO+RrTTJuom3TIUbCfmop2oiG7irHbbP9vOW1NumR06BNndWVEU0ulEy0ab3WwPCrEj8AoheAQBpFb8qGPS3QuF4pQ3oSDHWPhpKNhMzIWqqqRS3XGebodYLEI47N1qd60hpKqqLQV4Bj17ymnvuvGz9ao9KMSOwEuE4BEEHk2TK5UfWZYqAafj192z2SzhsMbMmTMDmTtVDSj139itXsyFYZhEIqHAtgBtDyCVTKY7P9vGAZ4TNw2Dnj3lptip93c7FTJZlmh3e1CIHYHXCMEjmFaoqlyZ+1EUBV232w9btmzhK1/5MsuWLWPFinP8PuYEvAwo7RRJkojHo4TDIYAxGV9B8U8KQoBqNcAzBFhjZqPsbadSIOMYnKpdN4TsWCHd/NyPEDuCbiAEj2Da4qy7b9r0LF/4whc48sgj+da3ri63IoLTyupmQGk7OFERTgtwYlWjSKHQvVXu8QRx26naHrTnfnTdIJfLB+7nO9navteMnftRK58lWyRWP0tC7Ai6hRA8gmnNww8/yKWXXsDJJ7+PCy+8mEQiOu7iWqRU8m9oOeibYtV5p/ozMbVVDXuLrrsxF0EeAK7dZLMsy3NPpFZxxM54M0s/zxMKhTBNg9NP/1sOOWQR73rXOzn55JMJhaJC7Ag8RwgewbTl0Ucf5hvf+Cqf/vTn+MxnzkKS7DV2WaYy9OzFunuzBH1TrNU2kder3LUEPS3eWe3O5XLk89VNtiCYHYI9e5RMNu9R1G3+938f4f7772PDhg2Mjo7ypjctZdmyd3HCCe9i//0P8Pt4gr0UIXimKT/+8S3cd9+9yLKMZcHf/d2nec97TgYgn8+zevVKNm16DkVRWLHiyxx//DKfT+w+mzc/T1/fDpYtO7Hh99jr7lpl4ws6W3dvllgsQigUCuSmGHTeJnJiLmyPFndjLoJueOisdjdjZqlpWmUzrtX08nZp1ZDRD5w21uDgCI8//gQbNjzIww8/yOuvb+dDHzqd8867wNPX37ZtK1deeRmZTIZ0Os1FF61kwYLgBR0L3EUInmnKyMgIiYQdhLlr107OPPMMfvnL/yKVSvGjH/2Q/v4+zj//Il59dRsrVpzFL35xJ7FYzOdT+81Yrx9JgmJRr7S+3BI/QRiwbYS9Op3AMCxGRtxpE7kZcxF0w8NOtp2cClk4rDFVenm7OGKnXUPGbhAKaSSTcUZHC+Ry1ffQsiy2bHkJSZJYuPAQT89wzjln8/73f5BTTjmVdet+x29/ezfXX/89T19T4D+TCR65i+cQtIgjdsBex5YksCy7kvD739/Lhz50OgALFhzIYYcdzmOPPerLOYOFne4+PFxg9+4RMpkspmkSj0eZOXMGqVSCSMRerW2XVCqBLEuBSBQfj105SZYDVN2biTFNi3y+wNDQCAMDGYrFEqGQRk9PmnQ6QSQSRpanvpwoip3tZIdcBk/sOFEHw8OjbVWydN0gm80xMDBUnpkyicWizJyZJpGIV0R4u9ju0zEymeFpJ3bA/nwecsgiz8XOwMAeNm9+nuXLTwFg+fJT2Lz5eQYGBjx9XUGwEWnpAeeuu37J7bf/nP7+Pr75zUtIp2cA0Ne3g97e+ZXvmzt3Hv39O/w6ZmAplUxKpSIjI0U0TSYUUohGo+WU7WrlxzCmFi5BnznpVuXEzkYrVlo9zjxLLBaZNOailTaRH7i97VSbXm5XyKoJ762YHTqEwyFisShDQ8OuuE97wWRip5v09fUxe/ZcFEUB7M/e7Nlz6O/vEynn+zBC8PjIZz/7Sfr66ouUu+/+HxRF4bTTzuC0087gpZde5PLLL+KYY95WET2C1rDFj8noaAlVlcqtrzDxeKxidGinu0+8mQTJULAeVQ+gsQO23aB2rseJuUil4pWv2eJGCrS7s9czMXaFrEg+XxxjdhiLRSc1O3SIREJEo9Gy+7QQOwJBOwjB4yO33vpvTX/vIYcsYtasOTz11JOceOJ76O2dR1/f65Wnlf7+HRx99DFeHXWvQ9ctdL1ENltCURzx49yAjEq6u64bPPLIw9xxxy+54YabAlmZ8NJht1V0Xa84ETs+NslkHFmWy7lVwWoBQuchpa1iWWNFomMLkEolqZodVrPQnFyxIIudcFgjkQiO2Ont7WXXrn4Mw0BRFAzDYNeuncyd2+v30QQ+ImZ4AszLL2+p/PP27a/xwgubOOighQCcdNJ7+PWv7wDg1Ve38dxzz/L2tx/nyzmnO4ZhkcuVGBzMsXv3MNlsEVVVSKeTrF+/jm9+83yOPvotgRQ7mqZ1NHPiJYZhoOsGkiQxPDxCsagTi0WYOTNNMtn5PIsbOG0iP2dinE21gYFMOWjWIpGIMXNmmnQ6WRY7wdwEhOCJHYCenpksWnQo69evA2D9+nUsXrxEtLP2ccSWVoC5+OJv8PLLW1BVFVmW+eQnP1VZS8/lclx55WW88MImZFnmC184Z9LVbUFrWJbFbbfdzG233cyFF17MmWd+vObJvEipVMSy/L1bO5WJoK4mO22Y8TMn1fBODVXV2ppnced84UCLiVjMjgIxTbOchRYMs8Nagih2HLZufYUrrriU4eFhkskkF1+8kgMPPMjvYwk8RqylCwQtYFkW11xzFf/9379l5corWbbsxLLXj1r+n7Pu7r3XTyOcm7VteBi8m7UTZTGVmKgf3ul9zEU0GiES8S6RvVMcjydnEzAoZoe1BFnsCPZdJhM8YoZHMIbvfOdqnnzycTTNLvV/6UvncthhbwRgz57drFp1Ca+//jrhcJivf/1CjjjiSJ9P7D7Dw8M8++wzXHvtjbz5zUcBYFkShYJBoWAA+bLwUYjHYyQS0piNL6/vP7U3w6DfrKc6X/15lhDpdMSzmAvnfIODwbMVACcKRB1jezDVZlw3zA5rccTOyEiefN7/SAuBoBlEhUcwhkce2cCxxx6Hqqo88sgGrrvuGm6//dcArF69kv32259Pf/pzbNz4NFddtZKf//yOStzDvoq97q6W06PlivgpFIquC5IgGx4CxOMxVNWd83kRcxGPR1FVNcDvn50Tl8k0f75umB3WIsSOIMiICo+gaWrjKY488k3s3NmPaZrIssz996/nP//zvwBYuvQoNE3j+eef5fDDj/DruIHA8foZHS2iqna6e3XdXa+sZnfaenKiIjKZYD6IJBJxFEViaGjYlRafrhvoeo5sNleJuYjFom3HXFTFojvncxtHjLUidqC598nekOv8jELsCKYzQvAIGvKrX93OccedgCzLZDKDWJbFjBlVD6De3nn09/ft84KnFmfdfXTUXnd3Ii7qrbs3iyRBMmknirebi+U1ju9OJuPN+WwTvzy5XL4Sc+GY+DUzzBt8seiOGJv4Pk00OywU2ht6FmJHMN0RgmcfoxmzQ7DXOO+99x5uvPGH3TzeXoVhWGSzttePLFMOmFSJRsPlmQxn46vxzSPoIZvV3C6za+7TTsxFPl8YM8wbj8fqDvPaYofAikVb7Miui7H6Zoehps0OaxFiR7A3IATPPkYzZocPPng/P/jBTVx33b8yc+YsgIq78+DgYKXK09e3Qxh5NYlpQi5XIpcrIUmU2152bpM9nFvd+AJ7JmrLlpeIxSJEo4cE0t3ZidpwjAb9YKphXpAwTTPAYidezmXz9nytmh3WIsSOYG9BGA8KxvDIIxu44Ya1fPe7NzB//n5jvnbSScu5665fArBx49MUCgWWLDncj2NOaywL8nmdTCbP7t3DjIzkkSSJZDLBrFk9JJNxnnjicc466x946KENgRQ7smyHlJZKpUBVnorFEiMjWQYGMuU/sVAUmZ6eVHlGRvH1fLXYDtSSL2JsMrPDLVte4M4772DHjtcJh0NC7Aj2GsSWlmAMH/jAclRVY8aMqiPpddfdRDo9g927d3H55ZfQ12evpZ977jd505uW+njavQ3b6+fee/+bSy+9hLPOOovPf/4LlEr20HNQBm2rIaV2MGbQqFd5cmIuQiENWZYrg+RebTJNRTJpzzwND7uXaO8Gsizz8MMb+M531rBjxw4OP/xwli17F8cdt4xFiw7d5zcyBcFHGA8KBNOE22//d2644Vq++MWvcuaZZ1aGnmVZqgScerHu3iyKIpNKJclmg5l47sw8FYs62Wz9ypMsyxWnZ9vBWC+Ln+4YSDptzKCJnVpCIY1t217hnnvWcd999/PSSy8wb958TjjhXXzuc2eTSCQ8e+11637Hv//7T3jllZc555yv8uEPf6zytXw+z+rVK9m06TkURWHFii+P2SwVCITgEQimAb///f9w+eUXc+GFKzn55PeN+ZqqypW5H0VR0HX7Jl0slrrmtBykkNJ6yLJEKpWkUCiSyzXXBux2zEUqlcA0LUZGgit2IpEQ8XhsTBtr+/bXePjhh3jyycc5++wvcvDBCz17/S1bXkSSZH72s9t44xuPGCN4fvSjH9Lf38f551/Eq69uY8WKs/jFL+4kFot5dh7B9EL48AgCjXiis3nzm4/ihz/8MYceetiEr+m6ia7bXj/Ourvj9WOvuxfLQ6feiB9NU0km4wwPj/rWBpqMdttstUPPtTEX1U0m92IubLHTvW22dqgndgD2229/PvrRT/DRj37C8zMsXLgIsH+m4/n97+/loosuA2DBggM57LDDeeyxR3n3u5d7fi7B9EcIHoHvLF58KJddtpqf/ey2CV/7+c9/Sjwe5z/+4669/oluzpy5zJkzd8rvq7fuHg6PvUnb6+7uxAyEQhqJRIyhoVF0PXhix2mz5XI58vn222xexVzYq/tJDMOYlmInSPT17aC3d37l/8+dO4/+/vo2GwLBeITgEfiOeKJrn9p1d1mmHHGhkkolG667t4KTyJ7JjHQtp6kVFEUhnU4wOur+TFGppJe3marxDYlEvKWYiyCs7jeDI3aGh/MUCt6KnWa9wAQCtxGCRxBoxBNd85imve6ez+vldHeNcFglmbQHTFtNd7cT2SNkMsOBTGTv5kxROzEXQTeNdOim2IHmvMAa0ds7j76+1+npsbdI+/t3cPTRx7h1NMFejhA8As8RT3Tdx05318s3MKtS+YnH4yQSVDaTisX66+7RaIRIxEk8D6LYUUml/JkpaibmolTSSaXik26LBYFui51OOemk9/DrX9/BYYe9kVdf3cZzzz3LZZdd6fexBNMEIXgEniOe6PxGolg0KBYNoICmyWXxEyWRiFXS3YvFEtlsjjVrvsWxxx7LKaecGshE8SANUDeKuUgkYpimWQneDaJoDKrYuffee7jppusZHh5iw4YH+dnPfsx3v3sDBx+8kDPP/BRXXnkZH/vYaciyzNe/fgGxWNzvIwumCULwCAKNeKJzHyfdfWSkiKbJhEIK0WgUyzL5+te/xvbt2znrrH8MpNipDlCPtDw87DWWZVEqlYhGI2SzeQzDGBNz4cQ3BKE9GFSxA/De976P9773fXW/Fo1GueKKq7t8IsHegvDhEfhO7ROdqmpEo9HKE10ul+PKKy/jhRc2IcsyX/jCOSxbdqLfR97rGBgY4Nxzv0ihkOfmm29hwYIDKkaHXq67t0LQB6gnW41XVbXi9AxMml3lNUEWOwJBpwjjQYFA0JCdO/v58pe/QDgc5jvfuYGenh4URarM/WiaWvb6KVEqubfu3grhsD0kPDQUzAFqR+zkcnZ7azL8jLmoip0chULwRKNA0CnCeFAgqMO2bVu58srLyGQypNNpLrpoJQsWHOj3sbrOY489yrx5+7Fy5epKZIBhWHXX3aPRZNmsr1T2+mlv3b0V7G2xcGAHqG2x07wPkGEYZLMG2Wy+EnMRi0U8j7mIRMLE41EhdgT7LKLCI9hnOeecs3n/+z/IKaecyrp1v+O3v72b66//nt/HCjSSRDnfy/5f1ayvSKlUxLLcFT9jt8WCN1PkZrZYo5iLQqHU8TxVNBomFhNiR7D3M1mFZ6LTm0CwDzAwsIfNm59n+fJTAFi+/BQ2b36egYEBn08WbCzL9voZGsqze/cwIyP2ynUiEWfmzB6SyTjhsIYbodqxWIRwOMTg4N4vdqAaczE0NMrAwCD5fAFV1ejpSZFOJ4lGw3XNOadCiB2BwEa0tAT7JH19fcyePbfiAaQoCrNnz6G/v6+yAi+YHNvrxyjfRPPlqo9CPB4jkZDGrLu3Klji8SiappLJDAdyW8xxePbK9NCtmAshdgSCKkLwCAQCF6h6/VTX3VWi0WjZkM9pzxSnFD+JRAxFUcpip0vHbwE/UuPbibkQYkcgGIsQPIJ9kt7eXnbt6scwDBRFwTAMdu3aydy5vX4fba/A8foZHS2iqmPT3e3ZFHszqXbjanR0FF0vkkolyGSCOT/oiJ3h4Wx5YLv7NIq5eOKJP/K9732PY499O+9+97tZsuRQhoZyZcNJgUAgZngE+yQ9PTNZtOhQ1q9fB8D69etYvHiJaGd5gK5bjI6WGBjIsmfPCIWCjqZp9PSk6elJldfNM/zzP3+etWu/y9DQiN9HrosdZ5EoOzz7I3bG48RcZDLD9PbOZ9Gixdxxx6/40Ic+yAc+8DfcdNONbN78fCDbggJBtxFbWoJ9lq1bX+GKKy5leHiYZDLJxRev5MADD/L7WPsMsgzhsMbAwG7OOusfiMVi3HjjvxKLxXyPjBhPkOIsJiMatQNfn3rqz9x333089ND9bN68id7eeZx33gW8/e3v8PT1v/Odq3nyycfRNLvq9KUvncthh70RgD17drNq1SW8/vrrhMNhvv71CzniiCM9PY9g30MYDwoEgkCyfftrfOlLX2DOnDlcf/2/MGtWD5qmlgdzq+nuXnv9TIYjdoaGRtH1IIudCLFYZEIba8eO13nkkQ28+c1HsXjxoZ6e4ZFHNnDsscehqiqPPLKB6667httv/zUAq1evZL/99ufTn/4cGzc+zVVXreTnP78DyY2VPoGgjDAeFAgEgWNgYIAVK87iDW84iKuu+g6qGiWTySNJ1XT3ZNI2QqwVP93szgQ5u6uWRmIHYN68+Xz4wx/tyjmOP35Z5Z+PPPJN7NzZXwlQvf/+9fznf/4XAEuXHoWmaTz//LMcfvgRXTmbQCBmeAQCD7nhhmv5yEc+yAknHMOWLS9W/nzbtq380z99ho9//HT+6Z8+w6uvbvPxlP5gWSYf+MCHuPrqtUSj0Zo/t9fdh4YK7No1zNBQDsuCeDzGzJkzSKUSRCIhV7x+JmNvEDt+8qtf3c5xx52ALMtkMoNYlsWMGTMqX+/tnUd/f5+PJxTsa4gKj0DgIcuWnchHPvJxVqw4a8yfX3PNVZx++kcqLs9r1qze51yeZ86cxT/8wz9N8V3VdXcooGlyOeKi9XX3VnDETlCDSh26LXY++9lP0te3o+7X7r77fyq+VuvXr+Pee+/hxht/6PmZBIJmEYJHIPCQpUuPmvBnjsvz2rU3ArbL89q132ZgYEBsiU2Bs+4ORVTVFj92RlTjdfdWcYJKM5lgBpU6+FHZufXWf5vyex588H5+8IObuO66f2XmzFkApNN2ZWdwcLBS5enr2yFsIARdRbS0BIIuM5nLs6B5dN1kdLTInj3VdfdQKFSz7h5BVZWW/s6gp7I7BLWN9cgjG7jhhrV897s3MH/+fmO+dtJJy7nrrl8CsHHj0xQKBZYsOdyPYwr2UUSFRyAQTHsMwyKbLZHNlirr7uGwRiwWxTDM8sBzkVKpsTiIREJEo9HAprI7BFXsAFx11UpUVeOii86v/Nl1191EOj2Ds8/+Zy6//BLuuedvCYfDXHzx5W1lgwkE7SIEj0DQZYTLs7eYJuRyJXI5W/w4G1+pVLLuurtpmvzmN3dz6ql/TbGoC7HTAb/5zfqGX5s1azbXXXdTF08jEIxFyGuBoMsIl+fuYZp2unsm46S7F5AkiWQywaxZPcRiEdas+RY33ngDO3fuEWJHINiLEcaDAoGHXHvtGh588H727NlNOj2DVCrNz352u3B59h0LWYZVqy7h0Ucf5eabb+awww6nULBbX0FLYhBiRyBoDuG0LBAIBDXous5ll13AU089ybXX3sQb33h4OeBUQ5Kkyrp7sVhydd29HWKxCNGoEDsCQTMIp2WBYB8jkxlk1apLeO21v6BpGgcccCDnnXcBPT09PPPMn1mzZjWFQoH58+dzySWr6OmZ6feRu8r3vncDGzc+zb/8y/dZuHBRZd19ZMRZd1eIRiNjvH6KxSKG0V3x44idTCZLqRTcdptAMB0QFR6BYC9kaCjDiy++wNFHHwPAjTdex9BQhvPPv4hPfOJ0LrjgMpYuPYrbbruZ7dtf44ILLvX5xN1ly5YXicXizJs3f9LvU1WpPPSsoaoKum5UxI+uUOfhTgAABtFJREFUeytAYrEo0WhYiB2BoAUmq/CIoWWBYC8klUpXxA7AEUccyY4dO9i06TlCoVDFEPG0087g/vsbb9bsrSxcuGhKsQOg6/a6+8CA7fWTz5fQNI0ZMxyvnyia1prXTzMIsSMQuI9oaQkEezmmaXLnnb/ihBPeSV/fjjE3+hkzZmCaJkNDGVKptI+nDD6GYdVdd49G7XX3QqFU9vrpLN1diB2BwBtEhUcg2MtZu3YNsVi0a4nZ+wJj191HGBkpIMsyqVSCmTN7SCTihEIaktTazI8QOwKBd4gKj0CwF3PDDdfyl79s4+qr1yLLMr2989ix4/XK1wcHB8s3alHdaRfLgkJBp1DQkSSLUEglFFJJJOJIUnyM0eFk6+5C7AgE3iIEj0Cwl/L979/Ipk3PsWbNdYRCIQCWLDmcQqHAxo1Ps3TpUdx11y856aTlPp9078GyJAoFg0LBAPJl8aMQj8dIJBqvu/stdn7841u47757kWUZy4K/+7tP8573nAxAPp9n9eqVbNr0HIqisGLFlzn++GVdP6NA0CliS0sg2AvZsuUlPvWpj7FgwYGEwxEA5s/fj6uuuoY//3kja9asplgsMm+evZbupFoLvEPT5MrGl6LIFfEjywqRSMjXys7IyAiJRAKAXbt2cuaZZ/DLX/4XqVSKH/3oh/T393H++Rfx6qvbWLHiLH7xizuJxWK+nFUgmAzhwyMQ7GMsXHgIDz/8RN2vvelNS/nJT/6jyycSOF4/o6NFVFUqGx2GURTZ9zaWI3YAstkskgSWZZ/n97+/l4suugyABQsO5LDDDuexxx7l3e8WlUHB9EIIHoFAIOgyum6h6yVGR0t+H6XCXXf9kttv/zn9/X1885uXkE7PAKCvbwe9vdXNvrlz59Hfv8OvYwoEbSMEj0AgcJVvfvNrbN++HVmWiEZjfOUr57F48RK2bdvKlVdeRiaTIZ1Oc9FFK1mw4EC/j7tP8NnPfpK+vvoi5e67/wdFUTjttDM47bQzeOmlF7n88os45pi3VUSPQLA3IASPQCBwlQsvXFlpkWzY8ABXXXU5t976b1xzzVWcfvpHOOWUU1m37nesWbOa66//ns+n3Te49dZ/a/p7DzlkEbNmzeGpp57kxBPfQ2/vPPr6XqenpweA/v4dY0wtBYLpgvDhEQgErlI7DzIyMoIkyQwM7GHz5udZvvwUAJYvP4XNm59nYGDAr2MKanj55S2Vf96+/TVeeGETBx20EICTTnoPv/71HQC8+uo2nnvuWd7+9uN8OadA0AmiwiMQCFznW99axeOPPwbANddcT19fH7Nnz0VR7BgGRVGYPXsO/f19lcqBwD9uvfUHvPzyFlRVRZZlvvzlcznooIMBOPPMT3HllZfxsY+dhizLfP3rFxCLxX0+sUDQOkLwCAQC1/nGNy4G4J57fstNN13H5z73eZ9PJJiMVau+1fBr0WiUK664uounEQi8QbS0BAKBZ7zvfe/nT396krlz57JrVz+GYQBgGAa7du1k7txen08oEAj2FYTgEQgErpHNZsdsAz388EOkUil6emayaNGhrF+/DoD169exePES0c4SCARdQzgtCwQC19izZzff+MbXyOdzyLJCKpVixYovs2TJYWzd+gpXXHEpw8PDJJNJLr54JQceeJDfRxYIBHsRkzktC8EjEAgEAoFgr2AywSNaWgKB4P9v745ZqgoDMI4/3MHRHLVJ+gLXoNFFCKGhELmSDaJ7NHgRatEhIUjBhgYjxNkhSpwDv0FmXbELYW5pQprjHS4NwR0bPfH2+23ncIZn/HOG9wUonuABirO5+Tqjo7dydPQ1SdJqfc7s7INMT09mfv5hzs9/VrwQuGqCByhKu/0lBwetDA7+uf+p2+1meXkxzebjbG29Tb1+M+vrLyteCVw1wQMUo9PpZG3teRYWnvTetduH6evrS70+kiSZmGhkd/d9VROBiggeoBgbG68yPn4nQ0PXe+9OT096f3uSZGBgIN1uN5eXv6qYCFRE8ABFaLU+pd0+zOTkVNVTgH+QqyWAIuztfcjx8bdMTd1Lkpyd/Uiz+SiNxv2cnHzvfXdxcZFarZb+/mtVTQUqIHiAIszMzGVmZq733GjczcrKiwwP38jOzrvs739MvT6S7e03GRu7Xd1QoBKCByharVbL4uLTrK4+S6fTyeDgUJaWlqueBVwxJy0DAEVw0jIA8F8TPABA8QQPAFA8wQMAFE/wAADFEzwAQPEEDwBQPMEDABRP8AAAxRM8AEDxBA8AUDzBAwAUT/AAAMX7623pAAAl8IcHACie4AEAiid4AIDiCR4AoHiCBwAonuABAIr3G5jKVHKuCbTPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "o57qGctrBvDb",
        "outputId": "5d11df6b-f1aa-4803-92ed-4bac616e5f97"
      },
      "source": [
        "pd.options.display.max_columns = 150\n",
        "df.groupby('Clusters').mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O</th>\n",
              "      <th>C</th>\n",
              "      <th>E</th>\n",
              "      <th>A</th>\n",
              "      <th>N</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clusters</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26.905562</td>\n",
              "      <td>18.222254</td>\n",
              "      <td>11.052087</td>\n",
              "      <td>24.749665</td>\n",
              "      <td>10.558674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.630442</td>\n",
              "      <td>29.330763</td>\n",
              "      <td>28.439245</td>\n",
              "      <td>32.318483</td>\n",
              "      <td>28.291003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25.991454</td>\n",
              "      <td>18.780905</td>\n",
              "      <td>22.875412</td>\n",
              "      <td>25.991826</td>\n",
              "      <td>23.957009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31.198617</td>\n",
              "      <td>21.181732</td>\n",
              "      <td>27.427912</td>\n",
              "      <td>30.954623</td>\n",
              "      <td>12.655377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30.107129</td>\n",
              "      <td>25.349711</td>\n",
              "      <td>11.739110</td>\n",
              "      <td>17.444404</td>\n",
              "      <td>24.684395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>28.394932</td>\n",
              "      <td>29.120431</td>\n",
              "      <td>14.235054</td>\n",
              "      <td>31.133519</td>\n",
              "      <td>18.299658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  O          C          E          A          N\n",
              "Clusters                                                       \n",
              "0         26.905562  18.222254  11.052087  24.749665  10.558674\n",
              "1         31.630442  29.330763  28.439245  32.318483  28.291003\n",
              "2         25.991454  18.780905  22.875412  25.991826  23.957009\n",
              "3         31.198617  21.181732  27.427912  30.954623  12.655377\n",
              "4         30.107129  25.349711  11.739110  17.444404  24.684395\n",
              "5         28.394932  29.120431  14.235054  31.133519  18.299658"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I3GRgBu9FhXl"
      },
      "source": [
        "## Visualizing scores for clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLCoxCBrJ-pP"
      },
      "source": [
        "dataclusters = df.groupby('Clusters').mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "id": "6sf86KuRL5mg",
        "outputId": "d5a23754-cf06-4e22-ec94-a72b0ac3283c"
      },
      "source": [
        "plt.figure(figsize=(22,3))\n",
        "for i in range(0, 6):\n",
        "    plt.subplot(1,6,i+1)\n",
        "    plt.bar(dataclusters.columns, dataclusters.iloc[i, :], color='black', alpha=0.2)\n",
        "    plt.plot(dataclusters.columns, dataclusters.iloc[i, :], color='red')\n",
        "    plt.title('Cluster ' + str(i))\n",
        "    plt.xticks(rotation=45)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ibC79TpZPebTW1si2bCKxYkWdzmXsoyL+a968eQCEhoayePFixo4dq/dzxMWlotG8elpZYmI6CQlpuh3wnXeI9PWj5rzZVBn6AXcHDuJ+734gf311JgeHdCBFt3Plws3NnthY/RzL2HOYYga5XPbazmhB0JfSv57BMjmJ+HbtcD56FLf/+5XYJk2ljlVsTLqXS+XkxI3RH1F77izK7dzBgz79pI4kCEbH4d0+cPY37Nt1RBnUjaxWrcHaWupYJssyIZ6K32zG69B+VLa23PpwBA+DuqG1tPx3I5mMmGYteNKwMT47t1N++3e4nvs/HvTux/1efdGY8PVJSEhg0qRJhIeHY2lpiY+PD7Nnz8bZ2Rk/Pz+qVKmC/J8PJIsXL8bPz0/ixP9S29tzdfosGowdQa35s7m0cCn8M4pD0DOtFvdTJ6j01XpsYqKJbdyE20OHkV7O56VNVfb2POocxKPOQdg8foTHiWN4HjtCjcULqLryM2LeakZk2/bE160nwQMpOsZc3D980iQU5y9QY8lCLqz8Em0+yxAIr/e6URH2d27hu2kDrhfOk+niyvWPxvO4fSfqfr4Eh31h/P7Oey/ep/Jg7KMictO1a1dCQkLw8PAgOjoatVqNmZkZarWamJgYPD09pY74gmS/apz7cgPVPltKpU0bcLpymT8nTyXLSf9ThAXhdYz5niRIz+vgfjI8PLg/axZWf/5Fxa+/IrZxkzy/pDAVJv8uKKZ5K6KbncZ3yyZi32xCuk95qSMJgqR0rU+QPmospd1csDxwAOsfd6BW2JHWug2pHTqR3qw5WqtXdyCZ2rfwRU2uVFJu907Kb/8OuVLJw6Bu3BvwHtkOpXLdR2Nlxb3+7/G4XQcqb1hLxW+/pszhg9z6cDgxzVvpNOzcWMhkMoYMGUKjRo2AnALlS5cuZf78+QBs374dhUIhZcTXSq1UmRujx1Fj2SJ8v9nE34OGSh3J5JS69j+qrFtNqRvXSfGtxO8TJpPgn78OuYwyXtwbMJB7/d+j1J/X8Dx+BPefTuF54hiZLq6kd+sBA95HXa16ET+Kordo0aJntcCOHz/OlClT2LNnz7Pi/sHBwYSFhRESEsKWLVskTvsitaMjN8Z8TJ3Z0/H5YRv33xkgdSSTZRMRge83X+Fx+iTZ9vbcHjqMh8Hd0VhZARDfqRMuhw/jev4ssU2bS5y2+KWlpZGcnPyss+7kyZOUKlUKFxcXqlWrxv79+wkODmb//v1Uq1atSOrpFZZaYce1qTNI8K9HlTVf0OjDwfw5eSrxJXxKtVC8jPmeJEjL5vEjnK9c4u+Bg9FaWHD33feptWAO7qdPEh3QRup4xcLkO/WQybg5+iOc/rhCjaWLuLBilRgZIZRoOtcCs1Xw5OOPSRwwGKfLl3D/+RRup07isDcUla0tsY2bENOiFXH1G6CxtHq2m6l+C693/xlRFNOkKXeGDiPdu2y+D6Es7c61qTOICOyK3+qV1J47i4TaYdwcMZpU30pFGL74OTo6PuvQA6hbty7btm2TMJHuIjt0wvHPa1TYtpWk6jV40riJ1JFMgk3kYyptWIv7mZ9QOrvw54TJRLZpV7B7vkxGUs1aJNWsxa0Ro3E9+394HjuC69dfIdu4juyatVH26kNmt55o3d31/2CKgSEU9y+M2GbNiWoZQMWtXxPbpImYhqtnVk9iqbD1G8ocOojW0oJ7/QbwoGdvVHYv1ttKbtgQpZMznsePlshOvYyMDMaOHUtGRgZyuZxSpUqxdu1aZDIZM2fOZPLkyaxZswYHBwcWLVokddzcyWQ86hxEYvUa1Jo7C//JE7jftz933x0oyhcJxcLY70mCdMocOoBWLudxuw7YAtEtA6iwbSsVt2wmpkXLEvEaZvqPEMhycubmyDHUWjCHcrt3Et6zj9SRBMHoaM3NiW/QkPgGDbkxdvw/HXyncfv1DJ4nj//bwde8JXENGkod1yhY/36RBrOm/zuiaOJkEgoxxS+xdh3OrVmP16ED+G7eSKMRQ4noHMjd9wYBL087NHYajYZt27YREBDw7HcDBgxArVbTvHlzRo8ejaUO08H+6/V1YDJJTtZ9RKCTU84+0dM+xeneHWouns/1rVvJ8vLKdR9HR1u9F642hELYesuQmIhixWJ8N2xAa2bG4w8+IHrAADQ2NuRVtfDp9Xg9BdnBnQkP7ozM1hbX48ex2LIFi5Ap2M2aDu3awbvvQnAw2Oj+RYaU16I4ivtD0bWlqGlTcOl5mdrLF3Pj669fuxq4aEf5kUlaeDYe33xD6R07QK3mSc+3iRw0CJWLC7mdKbFTR9x++AFXWTbqfK6iairXw9XVlR07drzyb76+vuzcubOYExVOWgVfzq9ah9+alVT4/luc/rjC/6aE5Lp4kCDok9QLzhSG6d0PDCFDPt4fqFR4HztM0ltvYVslZ2FUJxd7okaNpNKECfj+epq44OA8z2Ts96QS0akHEN2qNe4/n8L366940rgJ6WXLSR1JEIzWix18H+N05TLuP536t4PPxob0Nu3Q9uhNVkAbUYPvP+T376GYOxO3vXvIdHEt3Iii/zIz41GXIKJbtKLilk147w3D4/RJEj6aAMNHv/ZDr7GZM2cOtra29O/fH4DTp0/j6elJamoqEydOZPXq1YwbN67Ax9d3UXInJ8UL+1yeOpOGI4biM2EiF1esemGk6/P0WZQcTKgYd3Y21ls2oViyAJuEBCLbdeDvgYNRurpBpgYyX399/ns98sPBwRVtn4HQZyBmt25ivXM7Vju3Y9a3Lxp7B5RBXVH27EN2Puu4SF2UvDiK+0NRtiULro8eR+3ZIZRau/G103BFO8pDaiqylcuouX4tZhnpRLZpx90B75P5tAZcLtfIyUnBvaatcP/uO6z2HuBRYNd8nc7Qr0dJLvCvsbHh+vhPiPevR7UVy2g0bDB/TZjMkyZvSR1NMHFSLzhTUCZ3PzCQDPl5f+D26xks4uK436YjCQlpz94fJNSuj5tfVdzXrefvxs3zrPlq7PekPN9xJiQkMHToUNq3b09gYCCjRo0iPj4eAD8/PwIDAwkODiY4OJibN2/qLbjeyWTcGPMxGisrqi9dlLOipyAUsxEjRhAUFETXrl3p168f169fB+DevXv07t2b9u3b07t3b+7fvy9tUB1ozc2Jr9+A6+MncWbHHi4tWEp0ywAUZ36m1MB+uFSriP2wQVge2Ac61PIzRbKkRBQzp+HctAFWx48QN/ojftu8lcj2HfVeFkBlb8+tkWM5t3YjyZWqUHrODJxaN8XizE96PY9UFi1axIMHD1ixYsWzhTGe1jSys7OjZ8+eXLp0ScqIecrwLMOfk6bgcPsWVVavlDqO8dBqsTxyCKcWjbH/dCKqGrUI37OfvyZMzunQKybqKn6kTZ1B/KU/SfxxH1mdA7EK3Y1j1044N6iN7cI5mP19u9jyFEbXrl05d+7cC8X9AYMt7v+8mGYtnk3DVdy7K3Uc46NUYr1xLS4N6+C6Yhnxdf05u24Tf02a8m+HXh5SfSuRWr4CnsePFnFYoThFB7Tl3JqNZLq7U3fGFKqs+QJZVpbUsYQSwJjvSULx8jq4n0wXV+KeK80DgEzG3wOHYBMTjdfB/dKEK0Z5duo9LUp+5MgR9u3bR9myZVm6dOmzv2/fvp2wsDDCwsIMapXBV8lyduHmiDE4/nWNcqE/Sh1HKIEWLVrE3r17CQ0NZdCgQUyZMgXgWRHYI0eO0K9fP0JCQiROWjDPOvg+nsTdX8+TuCMUZbceWJ4+San338Glum/J7ODLzsb6q/U4N6qLzZdfoOzek/j/u0T86I/QFGC6ni7SKlTk8qJlPF61FllaGo49AnEYNAB5uA51FQ3M8uXLuXbtGqtXr342vTYpKYnMzEwAVCoVR44coVq1alLGzJcnb77FvT7v4H1wP55HDkkdx+CZ/+8PSvUIpNSA3gAkffsDSbv2klW9hnSh5HKym7UgZeWXxF27TfKaDagrVcZ2xTKc33wDx44BWG/agCw+TrqM/5GWlkZkZOSzf7+quD9g0MX9n3dz5FhUdnbUWLIQmUoldRzjoFZj9cP3ODd5A/spk1D5VeXhjt1cnTWPtAoVdTuWTEZk2/Y4/vUnNhERRZNXkESGtzcXPl9DeNfulNuziwYfjcLiwX2pYwkmxtTuScUiMxP5vbtY/PYLVj/ugHv3pE5U7KxiYnC5eJ7I9h1fWTcv/o36JNSqQ4Vt3yL/5zOCqcpzHpYpFCV/XlTrtrj/dArfzRuJbdSEDG9vqSMJJUiJKgJrYUF2ywCyWwaQumg5Fr+ewWpfKFYH9mK9excahR1Z7dqjDOqeM0W3iDu3JKHVYnnsMIqZ0zC/c5usps1JmzUPVa06OX9PjC+eHDIZae06EB/UHdsvv8D282U4Hz9C+ogxpI8eBwa8Wux/3b59m3Xr1lG+fHn69Mmpj+rt7c2QIUMICQlBJpOhUqnw9/cvsqmE+nZ34CBK3fiLqiuXk1KpssktbqIP8sjHKBbMweqH79E6OZGyYAmZ7w4CCwupo71IoUD5dm+Ub/dGHhWJ1Y87sd6xDfvJ47GbPpmsNu3J7NWXrDbtwOrV062Lg8kU9/9H9j+r4daeHYLPjm3c7ydWw82VVovloQMoFs7B/MZ1suv4k7L0c7JbBpCZlAC6LKT1nKiANlTauA7PE0f/qeMqmAqtpSW3Ro4loW49qi9bRNmuXUhbvhJlt7eljiaYCFO7JxVWVvwTsu/dxTwqCvPoSMyjorCIisQsOhrzqJx/mye8+Bki29+fxO27QSbL93lsbGyMelHDMkcOItNoeNSh86s3kMn4+/3B1P94DGX37uFBr77FG7AY6VRcyZiLkj8vcsZ0nHr1ovbnS7i1fv1LdW+MvVCiyGDYGaQvAqu/tvQ6L7WjnsE5PyoVnD6NfMcOrHfvxnrPj2BnB126QM+e0LFjrh18Ul87nTL88QeMHw8nTkCVKhAWhmVgIJYv3GwLdi2goNfDDebPhhEfwKRJKJYvRrHje1iyBHr31umNAEhzPSpXrpxrqYd9+/YVcxr90JqZc21KCI2GDaH27BDOrVmPWlEyazm9JC0N29WfY7tmJahUZAwfTfq4CWhL5a8Yv5Q0Hp5kjBxDxsgxmF37H9Y7tmH94w6sDu1H4+iIsmsPGP8RuBf/IjamVtwf/pmG26IVFb/9mtg339J9tFkJYPHLzyjmzcTi94uofCuR9NUWsroE6/za/ypKVzfi/evhcfwod999Xy/HFAxL7FvNOFu5CvWXLcLhw0FknPmJ1LmLwNZW6miCkTPFe9IrabXIkhKRP36MWeQj5I8fI3/8CHnkY8wiHyOPfJzzu+Skl3bNKlUKpasbKW5uKH0rkelaGqWrK0o3N0r9eQ3fLZtJPLifpJq18x3Hx8fHeDv11Gq8Dh0grl7915aKSKxVhyf1G+Kz/XsiOgehNqKBDLrQqVPP2IuSP2Nui9mwUdRYsgC7r7/l4X++aTL0QonGmEExZwa2t68T+80Pkr7RK4rnwRALkxdbW3qN17ajOo1yfmYtyhnBtzcUq4N7kW/fjtZWgbJ9B5SB3chq3fZZB5/U/w/nN4M8OgrbBXOw3rYVraMjafMXk/ne4JwRRU9SX9i2INcC9HA9rErB5+sw7zsQu6mTsOjbl6zPvyB13mLUtfL3ZkAUJdevLCdnrk6fxRsTxlJjyQKuzphbsj8Uq9VY7diGYsEczKIiyQzqRtq0mWjKV5A6Wa6Uygwycisr4O0FH0+AMR9h+9svOITuRrFtK6rz/0di6EGdzmPs36wXpZujPsL5j8vUWLKQC1+seeV0nJLI/MolFPNmYfnTKdRlvEj5bBWZvfvpfeGkyDbtqbl4PqX+/J9OHywF46Es7U7Et9vxWr8W28+XYXHxPMnrv0Zd1fDLXQhCkdJokMXFvdBZZxb5b6fd0447WXr6C7tpZTI0pd3RlCmDumIlspo2J93JiUhzc5Qubijd3FC6uua6mBpAYrUa+IT+iM+uHVwtIa+9Lr9fwDo2hlsfjshz278HDqbRqA8pt3sn9wYMLPpwEsj33fxpUfK1a9fmWpT86fRBYxDZtj3up09SadMGnjR6k4wyXlJHMiqv/fDyH/KkJOe3rAwAACAASURBVFw2fAmZmWScOY2ydp18n8eUP7x07dqVkJCQF4rAmpmZlawisObmZLdoRXaLVqQuWvZCB5/1nh9zOvjatUcZ2A165W9FPcmkp+dMbf1iBWRnkfHhSNI/nojW0UnqZLlSNX6TxKOnsf7+WxTzZ+HUtjmZ/QeS9ul0tC4uUscrcZJq1uLO0GFUWbsan53bTXqawOtYnPkJxYypWFy7SvYb9Une8A2qRo2ljpWnjIwMHuRn6qJPBRg7HrMhw3Cys+SJjtMdjfqb9SKW7ejIjdHjqD1nBj4/iGm4ZrdvoVg4F6t9oWicnUmdNZ+M94cU2Yr0sU2boV5pjefxo6JTz5RZWJA2dQZZTZriMPIDnNq3JHX+EjL7DSjZX0YJJZL98CHw+3lcHz9+aSEZrbk5Gg9PNJ5lUNWsTVbbDjmdd2W80HiUQVOmDBp3j5dKiSQmxhOtw3sDjY0NsT164PH119g8iiDDy/TLi3kd3E9WqVLEvpn3qtwpflWJeasZPrt+ICK4G9kOpYohYfHKV6fe06Lk69evf6EouZWVFdbW1kZVlPwZmYzr4ybw5pCBVF+2mN+XfPbSNFwhd/n+8AKU2/UD8sxMNBYWmG1cx4Pxn+T7PKb04SUtLY3k5ORnnXWvKgIbHBxccovA/reD77dfcjr4DoRhHbobBoOLW2nU5XxQ+/igLlceTTmfnH+X80Hj5S1NfS2NBqud21HMn41Z5GOUXYJJnT4LjbFM/TIzI3PAQJSBwdguXYjNV+uxCttN+qRPyRg4xPBqlpm48O49c6ZRfLWBpKrVSdThSxBjZ3bnNopZ07A6cgi1d1mS123KmaJqoh8S1QoFakcFFGC0rpC7mOYtS/w0XHnEQ2yXLsR6+3dobWxJmzCZjOGj0No7FOl51Ta2xLzVDPefTnFrxOjXjiwRjF92q9bEn/oNhxFDsR83Coszp0ldsqLI/z8ThKKiy6CVpyxsbeGNN0hv2wGVhycqDw9U7h6oPDxRu7jAP+WV/kvfA1die/XC/dtvKbdnFzdHfaS34xoiy/g4XM/+xsNub6PNZ+m3vwcOxu23X/D5YRt3hg4r4oTFL89OPVMsSv6U0q00t4aPpPqyxXjvDyMiqJvUkUyPRoP3vjASatZCVbkSHgcPcvvDEajspK+NVtxEEdh/5eumWbt2zs/kKdicP4fTzWuobt/FIuIhFufPYRW2B9k/S9wDaM3MUHl4kO1dlmzvsqi8yyKr6IusYmU0Pj5oSrvrvePe4tczOSOKrl4h278eyes2o2r8pl7PUVy0jk6kzV1E5oD3sZv2CXZTP8F6y2ZS5y4iu0UrqeOVHDIZf43/hIb37lJr3kzOrdkIPsVfc604yeLiUCxdgPU3m9Ba25A6bSYZQ4eb5uI5QrF4Og23+tKFXFxZcqbhyp48wfbzZdh8vRG0WjKGDiN97AS0rq7FliGybXs8TxzD9ez/EdO8ZbGdV5CG1t2dpB17sF25HNtF87C49DvJG75GVcdf6miCoDNdBq089eDd919dFicjA16zGri+B65ku7kR1aoNZY4c4u93B6FyMN3Odc+jR5Cr1TzqmMsCGa+QVr4CUQFtKBu2m/AePclyNq0ZSXm+yzHFouTPe9y+E6V/Ok2lDet40qDxawstCrpzuXge28eP+Pv9IZhXrYTbnj14Hj/Kw649pI5W7EpMEdh80Pmm6eWNU02/F26YMrUKq9hYbKIisYmKxDoqKue/IyOxOXUSq/i4Fw6htbZG7V32udF95VH7+OT826e8TtNkze7eQTErBKtD+1F7eZO8ZgPK7j1NYrSv2q8qSTtCsTx8ELuQT3HsGYyyUyCps+ah8SkvdbwSQa1QcDVkNg3HDKfWvFk8+eFHqSMVDaUSm43rsP1sCbLUFDLffZ+0iVPQurlJnUwwci9Mw92xnft9+0sdqUjJUlOw+XIVNmu+QJaRTmbvfqRPmIymbLlizxJftx6ZLq54Hj8qOvVKCjMz0sdNJOvNpjgMG4RjpzakzZiT8+WMiY60FgRDFN6jJ2WOHcbr4D4e9HlH6jhFQ6vF69B+EmrVJr2cbl963x0wEPdTJym/bSu3RhrXYLS8lIyvLl/n6TTcoQOpvnwxlxYvlzqRSSkbtgelszMxbzXDsbQjSVX88Nq/l4fB3cWNXigUrZk5mR6eZHp4kvCKv8uVSiqayXFKTET+4AFm4Tk/8vAHWF26iDwx8YXtNQ6lcqbxPu30e9rhV6486rLlclZ3i49HMW0aNps2oLWyJm1KCOkfjjS9EUUyGVkdOxPfqjW2a1dhu2Ipzk2Pkj5iNOljxoOJrhxlSNIqVOT62PHUXDQP7fIlqOcvkTqS/mi1sGsXzuMnYhZ+H2XrtqTNmCsKrQt6FdO8JdHNW+ZMw23cxDRHvGZmYrNuNbYrliKPi0PZJZi0ydNQV/GTLpOZGVEBbSi3eycWSYlkG8FK1YJ+qBq/ScLJX7AfOwK7aZOx+OVnUj5fg9aphJWTEQSJpPpWIq7eG5QN3U14j15oTbCEjtMfl7F9/Ii7/d/Ted8ML28i23fA+8A+HvTsg7K0exEklIbxDyvRA2Vpd259OALnK5fwOmD8ow8Nhc3jR7hcOMejzkHPXlQedQnG7sF9Sv35P4nTCaZOY2VFtm8lslq3I3PQUNJmziV507ckHv+ZuFvhPLkdTsKJMyRt2krqzHko3+6FxsMDszu3sPnmK+w/nUipd3rh3KwhbuU9cKlRCSpUwGbjOjL7DiD+7GXSP5pgeh16z7O2Jv2jCcT/3yWUXYJRfLYU5yZvYLV7Z07HjFCkotq042FgMM4b12F5cL/UcfRC/jCcUl07Qc+eaBUKEn/YQ/K2H0WHnlAkboweh8rWlupLF4JKJXUcvbI4eRyqVMFu+qeoatQm4cgpkjd9K22H3j8i27ZDrlbjfuqk1FGEYqZ1diF5y3ZS5yzA8sQxnAKaYn7urNSxBKHECO/RC+u4J7ifNs3XX6+D+8m2syvwSPCnnYEVv/1Gj6mkJ0bq/eNxxy64/3SKyuvX8LBrN3AU3yoVlve+MLRyORGdAp/9LqplAFXWrsZ7/16xMpogqUxrKzLKloWyZYGmL/5Rq8XsSWxO/b6ICMwjHmLx8CFWci3RAwaR5Vc1Z7vE+Hydy9hXcdZ4liHly41kvD8Uu6mTcBg2GOytoW1g3jsLhXJr2Cjc7t/HfvQwEqr+hKair9SRXqBLUWnFkcO4T50EGi0py5YR1alrTgHpEtKOhOL3bBru3Jlkb1yHdvJ0qSPlSpe2ZLd/L66TPkZVuTJRX39HRpN/Vv8zkLaUVsGXlIqV8DxxlIiu3YvsPIKBksnI+HAk2Y3exOGD93Hs2pH0T6aSPuZjkyhTIgiGLK5BI1J9yuOzawdRbdqZ1Mw4i+QkSv/yM486dUFjVbCFmJSl3YnoHIT33lDu9+5HhrdprBQsOvWeksm4Pm4ijT94n9LTPiV9936TagTFTZ6ZSZkjB4lp2pys5wo0a2xsiGzbDq+D+7k1fJSYliFIJl91/Zxdc35q1wX4txCujkV0DXkVZ51W+qpSmSfbd2H76xms3niDxHx+gHxKdMroTmtpSeTK1fh0D6TUoAEkHDyeMxXcQOSnHcmzlFReu4Yy+0JJ8qvKtakzsK5emYTXFJB+FUNuR4LhimnRiuifT+P2xeckdn3bYEeF5rfWrNeBvXh8vpzEmrW4/8VK4rJlBnlPimzTjirr12D7MJx0CWr7CdJT1a1Hwokz2E0Yi2L+bCx+OUPy6vVo3U1nypsgGByZjPDuPan+2RKcrlwmwb+e1In0xuP4UeTZ2Tzq2KVQx7nf9x28Dh+g4reb+fNTw/2yTxfi65LnZHp4cnvocBS/nsH6uy1SxzFqHqdOYJGSQkTwyysKR3QOQp6djefRwxIkEwTheU8/SOb7JyKC6z4VeJCaqtt+Dx7kv/NQeIHKy5vkLzdidv1P7CePN6qpz7bhD2gwejhl94Vyv2cfLn62igzPMlLHEkqYG6M+QmNnh/2YYUY9Ddfnh++ptmIZcQ0acXn+EjR2dlJHylVUQBu0cjmex49KHUWQkNbegZS1m0hZ/gUWF87iHPAWFiY6LVAQDEVUm7ZkOTris+sHqaPoj1aL16EDJPlVJdW3UqEOleXswsPg7nicOoHi3l09BZSW6NT7j0edA0lv/CaKkCnIIx5KHcc4abWUDdtNSoWKJL5iim1ahYok1qiF9/69oNFIEFAQBMG4ZAe0Jf3jSVhv/85ovnTyPHqYRiM/wCruCZfnLuLOB8NNsmizYPiynZyInTkHiyuXsVmzUuo4utNq8f1qPZU3riOqZQB/zJyLxtpa6lSvleXiQly9+nicOCbe65V0MhmZ/d8j4fApNM7OlOrdDdv5s426g10QDJnG0oqHQd1wPX8W2wf3pY6jF6Wu/4nd/Xs86lS4UXpP3e/VF7WNLb7ffKWX40lNdOr9l1xO9LzFyDQa7MePMaoREYai1F9/Yv/3nZxRerlMYY7oEoTt40c4XblczOkEQRCMU/qEyWS1DMDu0wmYX70idZxcmaWnU2PRPGosWUCSX1XOrf2KuEaNpY4llHCpHTujDOyKYvF8zG5clzpO/mk0+H3xGRW2f0dE50CuTZ5mNJ3jUW3aYRMdheM1010cLSEhgaFDh9K+fXsCAwMZNWoU8fE5pSn8/PwIDAwkODiY4OBgbt68KXFaaamrVSfhyGky+w1AsWIpjl07QXi41LEEwSRFBHZFbWFJud07pY6iF14H96OytiG6ZWu9HE/l4MCDt3tR+tdfcLhpRO8JciE69V5BVbYsqdNnYnnqBFbbv5M6jtEpG7abbIUdkQFtc90mpnkLsuwd8N4fVozJBEEQjJiZGclffoXG1Q2HQQOQJSZInegl9ndu0XDEUDxOHufv9wZxadFylK5uUscSBABSFi5Da2+P/djhRjFKSKZSUWPRPMruC+N+r77cGDs+Z3EZIxHTpCkqGxs8jx2ROkqRkclkDBkyhCNHjrBv3z7Kli3L0qVLn/19+/bthIWFERYWhp+f9CsTS87WltTPVpG89ivM/rwG/v7Iw3WrCSkIQt6yHR2JbNsOz2NHsUhMlDpOoZilpeH+0ymiWwWg1mNd6fDuPclyKIXvZuMfrSc69XKR+f5Qst58C7vpnyKPfCx1HKNhGR9H6TM/Edm+Ixqb3IswayytiGzfEbfffsEyLq4YEwqCIBgvrYsLyRu/QR75GPtRHxrOtDatlrKhP9JgzAjMlEp+X7KCe/3fM6oOCMH0ad3cSF24DIvLlwx+Gq5cqaT2rOl4njzO7cEfcGfoMKNbwE1jY0NM0xa4/3wauVIpdZwi4ejoSKNGjZ79u27dujx+XDI/NyiVGSQmxufrJzqgNeG7QtGmpmK2fFG+93v6o1SKGr2CkJfw7j0xy87Ce1+o1FEKxePkccwyM3nUKVCvx1UrFNzv0w+X3y9gfeGcXo9d3MTqt7mRy0lZsRrnlm9iN2EsyVt3GN2bKSl4HdyPXKXiYWDXPLd91DkQn10/UObwQe6/M6AY0gmCIBg/1RsNSJ29APtPJ2C7cjnpH02QNI95cjLVly2i9G+/ENv4Tf6aMFmsbC4YLGVwd5R7Q1Esnk9W+06o/apKHeklZunp1An5FKerf3B9zDge5eM9laGKbNOOMscO43r2V2JaBEgdp0hpNBq2bdtGQMC/j3PAgAGo1WqaN2/O6NGjsbS0lDBh0crvCs7PmFug6NwZ5507uBrcnSxnl3zvKlZDF4S8pfuU50nDxpTdu4cHvfuisbSSOlKBeB3aT0pFX5KL4H4dEdgVn107cP1sGaltOhhtf4/o1HsNTYWKpE2dgd20yVjt2Iaydz+pIxk0mUqF14F9PKnfkAxv7zy3T/cuS3zdengd2s/9Pv3EiA5BEIR8yhw0FIsLZ7FdOJfsevXJbt5SkhzWFy/QeOxILBPiuTl8FA+7vW20b4iEkiNl4TKcfzuD/ZhhJB44DuaG83bYIimRulMmYX/nDn9+MpWo1rmXMjEGCXXqkunmhuexoybfqTdnzhxsbW3p378/AKdPn8bT05PU1FQmTpzI6tWrGTduXIGP7+LyutWOM0lOVuh8TCcn3fdxdLTFzc1eLxmi330X17AwqhwI5dGYMXrIUHD6Pp7RZbh5E+4m4vbcyFPB+D14uzdvTBqHx/FjPNbTIhPFyf72LRxu3+LmyDFF8v5SY23NvX4DqLpqBcpTJ8gOaKP3cxQHw3kXY6AyhgzDal8YdtMmk92iFRoPT6kjGSy3337B+kksN8bk/w1LRJcgas+dicvFC6KQuiAIQn7JZKQsXYn5tf/hMGwQCSd+QeNZpvjOr1Zju3I5rovnk+HuwYUVq0kxwBFPQu4SEhKYNGkS4eHhWFpa4uPjw+zZs3F2dsbPz48qVaogl+dUaVm8eLFJ1QN7Og3XYehAbNZ8QYYO71uKktWTWPwnT8Dm8WOuzpzDkzffkjpS4ZmZERXQlnI7f8AiIYFsJyepExWJRYsW8eDBA9auXfus3Xh65nxmsLOzo2fPnmzevLlQ54iLS0WjefUCfomJ6SQkpOl0PCcnhc77ADg4pAMp+slQtizRzVviumsXN7r1QmWXv06t3DIUlJubPbGx+jueIWRQKjPIyMjfNGXL27fw7vs2qsaNCV+1Xqfz2NjY5DpqUi6X5dEZnaMk34+KWkJdf1IqVqLcjzt43LGz0X3xWubQftSWlkS2bldk53jUqQu+e3aiWDiHxFatje45AlFTL29yOSmfr0amzMRu4kdiNdzX8N67hwwPD540zH/nXGyTpiidnMWCGYIgCLqysyN583eQkYnDkPcgO7tYTiuPjqJUr24oFswhtWNnzq3ZIDr0jFBJL/CvDOqGskswisXzMLt5Q+o4mIeH88a40VjHRHNlwWLT6ND7R2Sbdsg1ajxOn5A6SpFYvnw5165dY/Xq1c+m1yYlJZGZmQmASqXiyJEjVKtWTcqYBut+736Yp6fjHWbcdb8MzdPp0Hn9RP1+EfeB/VGZm3Nr1Kh87fP8T347Dl+npN+PipRMxoO3e2EX/gAXI6sbJ0tPx/PEcWKatUBlX3SjWLUWFsSPGovFlctYHtxfZOcpSqJTLx/UFSuR9mkIVkcOYfXjDqnjGCTFvbs4/3GFiC7BOk2j1VpY8LhDJ1zPn8UqJroIEwqCIJgedeUqpK5YhcWFcyhmTy/y81mcPI5Tq7ewuHiOlBWriVr2OWqF7tO3BOmV+AL/Mhkpi5ajtbOTfDVcs+t/Ubbf25inpXFpyWck1PGXLEtRSCtfgeTKVfA8dlTqKHp3+/Zt1q1bR0xMDH369CE4OJiRI0dy9+5devbsSVBQEEFBQZibmzN27Fip4xqk1EqVedKgEeX27ET+T0eoUDzM0lKpO2US5mmpXJ63mKwyxTji/zkl/n5UxKJbBpDp4ko5I+vHsDt8APP0NL0vkPEqycHdUVWqjGLRXFCri/x8+iam3+ZTxgfDsdoXit2UiWQ1a4nW3V3qSAbFe18oakvLnGG9OnrUqQvlt3+H18H93B04uAjSCYIgmC5lcHfSz5/Fdt0ashs0Iiuom/5Pkp2NYsEcbFetQFWtOsl7DuQsMJAYr/9zCcWuqAv8G0ItsFfW4HKzhzVrkPfpg9uW9fDJJzodUy/1r86fh64dUVtacnvjBsx8fdFlgqrUNdHymyEpsAtlly/HMzGGzAoVTKYmWuXKlbl58+Yr/7Zv375iTmO87vd9h/ofj8Hr8AEedu0hdZwSQZaVRZ2Z01CEP+DK/MWkVqqs02tPUTHG+xHo6Z5UyByvy/Ckbx+8V62iTOwjMqpUyUeGgtHn8bJ/3EGmjw+y5m/ipMOU2ALdF10dMJ87B/r0we3kQehX+LUUivOeJDr18svMjJSVX+LUqgn2k8aR/PV3RjnfuiiYpaXieewo0S0DyHYopfP+mR6exNVviNehA9zr/x5aAypYrU+iXoQgCEUlbcZcLC5fwn7sSBKr1UBduYreji0Pf4DDh4Ow+P0CGe8OInXOArARqw6akqIu8G8ItcByrcHVqiMOXYKxDAkh4a2AfK+Gq4/6VxZnfsLh3b5oXV0J/2oLkVot6PC4DKImWj4zpDVuhpf8c2x/DCVy8AcGXxMtv7XABP1IrFWHxBq1KLfzByK6BJvsZwGDodFQY+lCnK9c5tonU4mvV1/qRM8Y2/0I9HxPKmCOvDKktGqP58aNOG7ewuNJn+aZoSD0+TpsduM6zhcucO+D4SQkpud7v0LdF1t2wKl6TZg2nYSWHcDCQufjPFXc9yQx/VYH6kqVSftkGlaH9mMV+qPUcQxGmWNHMM/M4GFw9wIfI6JLMFbxcbie/U2PyQyLqBchCIWXkJDA0KFDad++PYGBgYwaNYr4+JzRYleuXCEoKIj27dszaNAg4uLiJE5bjCwtSd74DVhb4TB4AKTp/obmlYfdF4ZTQFPMbt0kaeM3pC5dITr0TMzTAv8rVqzItcD/pUuXpIxYtGQyUhYuK/ZpuJaHDlCq39toypYlcf9RVOXKFct5pZLl5Ex8/QZ4njgGGo3UcQQDdK/vO9jERONx8rjUUUxepY3r8Dh1gtuDPySqTdEtQKCrEn8/KkIqBwcet++Ix6njWD55InWcPFl/9w1aCwsi23YovpPK5aRNnob5vbtY//B98Z1XD0Snno4yho8i+4362H06AVlsrNRxpKfV4r03lKSq1UmpUvCOqLhGjch0c8N7/149hjMsol6EIBRebp3jGo2GiRMnEhISwpEjR6hfv/4LneYlgaaMF8lrN2F28wb248cUbmGnjAzsJo6j1OABqCtVIuHkL0UzrVeQlCjwn0NbujSpC5dhcel3bL5cVeTns9q5HYdB/VHVqEli6EE07h5Ffk5DENmmHdaxMThdvSJ1FMEAxTVsTEpFX3x++F50/Bahsrt3UX7ndh4Gd+NB775Sx3lG3I+KXni3t5Gp1ZTdu0fqKK+XmYn1jm2ktmlHtqNjsZ46q31Hsuu9ge2yRaBUFuu5C0OMbdaVmRkpK9bg1Lop9pPHk/zVFqkTScr58u8oHoZz7ZOphTqO1sycRx274LtlMzaPH5FRxktPCQ2TMdaLMPRaEYaa4fU5DKF2hyFk0OUYL3eOb9u2jWvXrmFlZUX9+jlTSPr06UPr1q1ZsGBBoc5nbLJbtCJ98jQUC+aQ3bAxmYOG6nwMs1s3cfjgfcz/ukb6iDGkTQmBQrweCYbpaYH/8uXL06dPHwC8vb0ZMmQIISEhyGQyVCoV/v7+JaLAvzK4O8qwPSgWzyOrfUfUhfii8nWsv1qP/acTyGrWguRvvkdrV/x14KQS26QpKltbPI8dIS1YfEkg/IdMxv0+71Br/mzcfvuF2KbNpU5kckr/dIoqa1cR81Yzbg4fbTClpMT9qHhkeHkT26Qp3vvDuNe3v9RxcmV1aD/yhASSe/Yu/pPLZKR9GoJjz2Csv91M5pBhxZ+hAESnXgGo/aqSNmkKdnNnYrl3T4keveAdtocsR0eim7cs9LEed+xMha1b8DqwjztDjaMBFZSx1YswhloRhprhdTkMoXaHIWSAgtUver5zPDIykjLPrdrm7OyMRqMhMTERxwJ+y2cIneNQgM7QuTPh6iXsp0/GvlVTaNjwpU1eeTytFr7+GkaNAltbOHgQ244dsX3tyQylU7jkfFGgr8LWosD/f/yzGq5z84bYjxlG4v5joM+6XlottiuWolgwB2WHziSv3wzW1vo7vhHQWFkR3bwl7j+d4n5GBhTvAAzBCMQ0b0H6116U3/4dsW81M5hOJ1PgePUPai6aR1L1mlz7dDqYmUkd6RlxPyo+D97uRelfz1Dm2GGomr8assXNeus3qMv5kN6kKTx8WOznz27ekqy3mqH4bCmZfQeAomDvc4tTnu9WXlfc/8qVK4SEhKBUKvHy8mLJkiW4uLgUR27JZYwYg9X+MOwnjye+STO0rq5SRyp21tFRuJ39jfu9+6HVwygOpasbT958kzJHD/H3e4P0ckxD9LRexNq1a3OtF7F582YpIwqCUXi+c/zYsWN6P74hdI5DwQoZy5avxqltC+jxNgnHfkb73L35VcV7Zakp2E0ch/WPO8hq2pyUNRvQeHhCHkV+DaVTuCR9UaDvznHhX9rSpUldsBSHDwdh8+UqMkZ/pKcDa1HMmo7tmpVkvt2blM/XFKoAtzGLatMOr8MHUZw4Cv3flzqOYGC0ZuY86NWHaiuW4XT5Egn13pA6kklQ3L9HnRlTyPDw5Mrs+WisrKSOJEgkqUYtkqpWo9yPO3k0YrTUcV4iv3cXyzM/kTZ5GsglqhQnk5E2eTpOge2w+Wo9GWMKPtCmuOT5TIn6RbkwNyfl8y+RJSVhN2WC1Gkk4fVP/buILsF6O2ZE52AsExMp/esZvR3TkIh6EYKgH/8tpuzp6flCjcr4+HjkcnmBR+kZO62TM8lfbUEeE43DiCGgVue6rfnVKzi2bobVnl2kfTKVpJ1hOR16glACKbv2QNk5CMXieZjdevXIEZ2o1diNH4PtmpVkDBpKyqp1JbZDDyChVh0ySrvjsGe31FEEA/W4bQeUzi5U2LZV6igmwSo2Bv8pE9FYWnF5wRJUDg5SRxKkJJMR3qMXto8foTh1Quo0L7H5bgtauZxMiacHqxo1Rtm6LbarPkOWnCRplvzIs1Mvt+L+r6pfdPjw4aJLaoDU1aqTPmEy1qG7sTThBR5eRabMxOvgfmLffAtl6dJ6O278G/VJ9/A0yQUzntaLiImJoU+fPgQHBzNy5Eju3r1Lz549CQoKIigoCHNzc1EvQhBe41Wd4zVr1iQzM5OLFy8COatJd+hQjCtmGSBVHX9S5y/B8tQJbJcvfnkDrRabDV/i2KkNMqWSpNCDpI//xKCm5AhCsftnGq5WochZDfc1HeJ5ysrCfthgbLZ+Q9rHRZ3HBgAAIABJREFUE0ldsFS6kQeGQi4nqnVbbH89gyw6Wuo0ggHSWlry4O1eOF+5hMON61LHMWpmaanUnfoJ5mlpXJ63iMwSsiiP8HoxzZqT4e6B06aNUkd5UXY21tu2ktW2PRrPMnlvX8TSP52OPDERm7WrpY6SJ52KhYj6Ra8wazocOUCpyR9DUAfIZfqxPurfFJY+M1j9cAzL5CQS+/fT6TnOz7bxb/fAe9UqPBNjcKxTTe/PnVTXQtSLEITCy62Y8urVq1m8eDEzZsx4oSRESZc5YCAW589iu3Qh2W80IDugDQCy+DjsPxqJ1eGDKNt3JOXzNWidS0b5DEHIy0vTcEcV4Iu29HRKDeqP5cnjpM6cR4YBTnOSSmSbdlTYthXrPTvJGDZK6jiCAXrUOYgK27ZSfvtWrs6cJ3UcoyTLUlJn5jQUD8O5PG8xqZUqSx1JMBBaM3MedutBlbWrybz8Oyp/w5jmbnn0MPLYGDL7D5Q6CgCq2nVRdgnGZu1qMoZ8aNDvk3Xq1BP1i17NbNkqnNq1QPnBcFLWfvXS319Vv6i46TuD/fqNpJbzIdy3GuTzOc7v9Uht3oYya9div20HiXUaoGstqdcpimshahgJQvF5Xed4vXr1RAf5f8lkpCz+DPP/XcVh+GASTvwCN+Jw6tMXeWwMqXMXkjF0uChGLgj/oezaI2c13EVzyWrXQafVcGXJSZR6pxfm58+SsvwLMvu/V4RJjU96OR8ya9bGasd20aknvJLa1paHQd2o+N0WFA/uk+ZTXupIxkWjwf2TCdhfucy1ydNEbULhJY86dMZ36xZs1q4iZZ1h1HK33vo1ag9Pslq3lTrKM2mfTMXpwF5sv1hB2ow5UsfJVb7nAIj6RblT16hJ+riJWO/eieWhA1LHKXLmly5i/b8/iAjqViQfBLOdnIhp2hzPo4eQ/VNrThAEQSggW1uSN38LKjWOwR2hZUu0VlYkHjxOxgcjRIeeILzKPx3iWltbnabhymJjKdWtC+aXLpKyfrPo0MtFctfuWFy7itn1v6SOIhioh916oLa2xueH76WOYnQUs6Zjf2Aft4d8SJQBdZAIhkOtUJDcqw9We0ORRxT/CrP/JX8UgeXJ42T2fUe/K88XktqvKsq3e2OzaT3y6Cip4+QqX516on5R3tLHjie7Zm3sJn6ELCFe6jhFymbTBjQKBZFt2hXZOR51CcIiNRW7g/uL7ByCIAjGTKnMIDExPl8/cc7ORC1YjPxRBJldu/LgxzCe+Pjke3+lMkPqhysIxe7pNFyL3y9i8+WqPLeXP4rAMbgD5ndukfTtdpTB3YshpXFK7RKI1twc653bpY4iGKjsUo486hSIx8njWBvwh2lDY7NuNbZffkHigIE86NVX6jiCAUt8dyAANhvWShsEsP7+W2RaLZn93pU6ykvSJkyG7GxsPzPcsj55doOK+kX5ZGFByudrcGrfErtpk0n5//buPC7Kcm8D+DUzMAsIgoBG7nu8paXS5jlaR0sxUTQVEdy3ck+zjqdIXFBDTdPc0nI3NdRE1MQ6LWq+luUp9VhpHrVS1ET22WDmef/gqPmqwKz38wzX9/M5fxyGee5Lmh8P85t7WbpSdCKPUF27Bl3GDuT37gtboOP7FVZWbstHUFy3Hqpv2YSioSM9Ng4RkVKZTCZcuHCh8k9oHoXT23chqE4t5OZcB3Iq/wFU/fr1odMZnEhJpGyWnr1h2bWzbBlu5y6wNW121+/TnD2D6n16QJWfj7ytO1H6xJNeTqosthphsHZ4BrptW1H8egoP6KG7utA7HnV2fYT66Vvw89iXRMeRPV3GDgROfQ2Wrt3xx2tvAL//LjoSyVjp/bVh6d4D+o3rYJz8d0hBgk5Gttmg/2ADrE/9DXYZLrW3N2wEc78B0G9YC+OYCbDXrSc60h0qnKl3Y/+irKwsZGRkICMjA0uXlp0AcmP/ov3792PNmjUIDw/3eGA5s7VoCeOEl6FP3wLt/o9Fx/EI/Qfry05JTBrg2YFUKvwe2x2G7/8FzckTnh2LiKiKKK0WxOW2RI64cRpuQACCxt99Ga7m5AmEdIuBymxC/ke72dCrJHN8P2guZ8P/0AHRUUimLBE1kf1MJ9z/8R745+aKjiNr/ocPIWjMSJQ+9gQKlq1io5wqxfTiWKgLC6DftF5YBu0X/4Tm4u8wDRgsLENFjC+/CqjVCHgrTXSUu6r0nnpUOcaJr6A06kFUm/wSVHk+dvOx2WBY+z6s7Z6C1QsnKGU/0xl2nQ6G9as9PhYRERHR3Ui1av13Ge5RGFYsve0xv2++RkiP5yBptcjblYXSlo8ISqk81k5dYA+uziW4VK4L8f2gLilBvY+2iY4iW5ofTyF4YD/YGjRE/vrNgIEz66lySlu1gfWJtmVLcEtLhWTQb1gHe3g4rDFdhYxfGfb7a8M0eBj0Wz+A5uwZ0XHuwKaeu2m1KFy8DOo/rqLa1NdEp3Er7f590Pz+G0xDRnhlvNLgYBQ9Fwtd+laoisSeHkxERERVl6Vnb1i6xCLwzZnQnDkNAPD//J8IiY+DPTwceZlZsHnhA0+fotfD0r0HdLt3AcXFotOQTBnr1sPVv7ZHnV07oSkuEh1HdtSXLqJ6v16QDAbkb9kBKbSG6EikMKYXx0Lz26/Q7dnl9bFVV65Au/9jmOMTgf+e3SBXxvEvAzo9AubOFh3lDmzqeUDpw61gHD8R+i2boP3nftFx3MaweiVs99eGNeY5r42Zn5AIdXERdDv46RwREREJ8ufTcMePAj78ENX7x8PWsDHydmXJco8dJbDE94PKWAzd3kzRUUjGzvfrD//iItTJzBAdRVZU+Xmo3q8XVAUFyN+8HfY6dUVHIgWydu6C0oaNYFj+DiBJXh1bv3UTVKWlijgpXoqIgHHkKOg/2g7Nv0+KjnMbNvU8xDjp7yh9IArVJo0H8vNFx3GZ5pcz0H75OcyDhnr1mGnzI61R+j8PQb9utdd/yRARERHdINWqhaLZ8+D/3VGgb1+UPtIaeTv3QKpZU3Q0xSp57AnY6tVX7BLc3NxcjBgxAp07d0a3bt0wduxYXL9edgjR999/j+7du6Nz584YOnQocnJyBKdVrsKmzZDT5lHU254OtcUiOo48WCwIHpwEzS9nULB2E2wPtRCdiJRKo4Fp5Gj4H/sOft987b1xJQmGjetgfaKtYma6m0aPgz24OgLTZomOchs29RxgsZiQl3e9cv8zFeNS6ptQX70CS1IS8nJzKv/cvOuwWEyi/7m30a9ZBUmrhan/YO8OrFLBNGgo/E/8AL9/fefdsYmIiMjnOfL33ZUOHZEf3w+Wbt1wYdUa5Ep2xf5tJwtqNcy94+F/4AuoL2eLTuMwlUqF4cOHIysrC5mZmahbty7mz58Pu92OV155BVOnTkVWVhaio6Mxf/580XEV7Xy/JOjycnF/1l7RUcSz2xE07gVovzqIwsXLUdL+adGJSOHMCUmwh4QgYMUSr43p/9VBaM6fU8QsvRukkFCYRo+Dbt8e+B37VnScm7w35coHmEwmXLhwofJPqB4C8/AX0OzdZVBFROK8AyfG1q9fHzqdTDY5LSqCfssHsHTrASkiwuvDW3rHo9r0N6BfvwZFraO9Pj4RERH5Lkf/vrsw4kWEhgYi948/HBpHVn/byYilTwICF8yDbns6TGPGi47jkJCQEDz++OM3//8jjzyCzZs34+TJk9DpdIiOLvu7NSEhAR07dsScOXNERVW83JaPIC/qf1A/fQt+HzVWdByhAqclQ79zB4qmzoSlV7zoOOQLAgNhHjQMhsULoD73H9gbNvL4kPqNa2GvHgJLtx4eH8udTCNHwbBqOQLnzER+ujy2BGBTz8N+7RWPsF/PofG691HYpAlyHn9SdCSH6bdthbqwAKZhI4WMLwUFw9wrHvptW1A8fRak6iFCchARERGRe9kaN0VJm2jo07corqn3Z3a7HZs3b0aHDh2QnZ2N+++//+ZjNWrUgN1uR15eHkJCnPs7NiysWjmPmlFQEOjwNUNDHX9OSEgAIiKChGS4NmI4mkyahFqfZyF49GiHxyrP3f9N3lWpDAsXAiuWAOPHo9q011FNpbrHN3rnNXHv1wMpjWnYSBiWLYZh1XIUz57n0bFU13Og270L5gGDFXdas1QtCMbxL6NaymvwP3wIJW3/KjoSm3oep1Lhwuuvw//ML2gxeya+WfoujEraxFSSYFi9EiUtH0Fpm0eFxTAPGgLDhjXQpW+BefiLwnIQEREpXW5uLl599VX8+uuv0Gq1qF+/PmbMmIEaNWrg+++/x9SpU2GxWFC7dm3MmzcPYWFhoiOTjzP3TkDQPyZDc/KEYvcGmzlzJgICAtC/f3988sknbr9+Tk4R7Pa77y+dl2dEbq5jJwiHhgY6/BwACA42AigUkiH3wVa4r0FD6N5ehD96JQFq9+wkFRERhD/+uPPf5E2VyaDL2IHgSZNgiY1DwT+mA9fufRqwt14T93o9AIBaraqgGV2G9yR5sN8XCUvP3jB8sBHGV1+DFBLqsbH06Vugslq9v7WXm5gGD4Nh+TsInD0DeZlZwD2b697BPfW8QNLrcXxaKuz+fmiZ8jo0xY7fQEXxP3wIfj/9WDZLT+CLtbTlIyhp1RoGHphBRETkEu4DRnJj6dELkr+/Yg/MSEtLw4ULF/D2229DrVYjMjISly5duvn49evXoVarnZ6lR/+lVuN8QhJ0Z05Du3+f6DRe5X/4EILGjIT1ibYoWLYK0GhER3Ib3pPkw/jiWKiMxdCvX+u5QSQJ+o3rUNK6DWwPPuS5cTzJYIBx0qvw/+YI/D//VHQaNvW8xVzrPpx4YzoCfv8dD86dDdjtoiNVimH1KthDQ2Hp0Ut0FJgHDYPfzz/B7+sjoqMQEREp1t32Abt06dJd9wHbt69qvXEmMaSwMFg7doJu+4dAaanoOA5ZsGABTp48iaVLl0Kr1QIAHnroIZjNZnz7bdlG6lu2bEFMTIzImD7jytN/Q0mdughY9FaV+aBf8+MpBA/sB1vDRihYvxnQ60VHcivek+TD9lALWNs9DcN7KwCr1SNj+B39Bn4//wSzQmfp3WBOHABbvfoInJMq/HcRl996Ue7DrXDmxdFovuwdNNy0HucGDBYdqVzqSxeh3ZsJ04tjZbHW3Rz3PAKnvgbDuvdR+ITy9ibk1HIiIpIbT+8DBshjL7Dy932qOhnKzyE4w/AhQO89iDj+DdC5s8PXFLGv15kzZ/Duu++iQYMGSEhIAADUqVMHS5cuxdy5c5GSknLb33bkOknjh9zhI1Fz2htl+1n9pZ3oSB6lvnQR1ROehxQQgPzN2z26JFIOlLg3JaDM+8E9M/zjVeC55xDx+cdA//4OXbNSv4e3fwBUq4agEYMRVE383weAC3tDzpgOzeDBiDj0KfD887c95M17Ept6XvZbj14IOnMajdevQWHjprjW9i+iI92Tfv0awG6HafAw0VHKBAbCHJ8Aw/o1KEpNg6SwpteNqeU3PolKS0vD/PnzkZqaildeeQVz5sxBdHQ0li1bhvnz5/OENCIi8jhP7wMGyGMvsPL2fapKGcrLITzD408hrHoIrKtWo7B1W4eu5+490Sq7F1jTpk3x888/3/Wx1q1bIzMz022Z6JaCXn0QvvQdBCx6C/k+3NRT5eeher9eUBUVIW/XPtiVtC+7k5S2NyWg3PvBPTO0bovQZs0hpc1DXqfuld6CqzK/h1WFBQjbuhXm5/ugyCQBpnt/v/B7UmV0jkNo02bAa68jt22Hm8vivX1P4vJbb1Op8NOEl1HQtDkeSktFwK8XRCe6O6sVhg1rYX22M+z1G4hOc5N54FCorFbot2wSHcVhnFpORERywn3ASFZ0Oljinofu491QFYk9tIDkTdLpYXxhDLRffAa/H/4lOo5nWCwIHpQIzS9nULB2k3L3HnMA70kyoVbD9OJY+J88Dv+vDrr10rrt6VAZjTD3H+TW6wqj0aD476/D7+efoNuRLiwGm3oC2HU6/DAtFXatFg+nvA5N8b1PLhJFtzsD6j+uwjR0pOgot7E9EIWSx5+Efv1qxexLeDeVnVpORETkCdwHjOTI3CcBKqMR2t27REchmTMPGQZ7cHUELFogOor72e0IGvsCtIcPofCdFShp95ToRB7He5K8mHv3hT08HIbl77j1uvqN61D6Pw+htFUbt15XJGtsHEoeaonAeXOAkhIhGbj8VhBLzZo4/sYMtH51Ih56MxU/TJ/ttmPZ3cHw/kqUNmyEkqc7iI5yB9OgoQgePQL+hw6gpP3TouM4xdNTy7l/kXwylJ9DDnt3yCEDEXkT9wEjuSp97HHY6jeAPn0rLAlJouOQjElBwTANHYGARW9Bc+Y0bE2biY7kNoHTkqHP2IGilFRYnu8jOo7H8Z4kQ3o9TENGIHDeHLfVl9/x7+F//HsUzplX6SW9iqBWwzjldVTv3xf6LZtgFnBuApt6AuW1fBinR43DA0veRqMNa/GfQUNFRwIA+J34Af5Hv0bRzDmyajTeYImNgz357zCsW63Ipt6NqeUrVqzw2NRy7l8knwzl5ZDD3h1yyABUfv8iInId9wEj2VKpYO6TgIC30qC+dBH2+2uLTkQyZhoxCgHvLoVhydsoWrRMdBy3MCxfgoAVS2AcOQqm0eNEx/EK3pPkyTR4OAIWL4BhxVIUvbXI5evpN6yDpNfD0iveDenkxfpsDEraPIqAt9Jg7pMAwLuTGOTXsalifu/eA5c6d0GjjesQceiA6DgAAP3qVZACAmCW6yekej3MCf2h/Xg31Fcui07jEE4tJyIiIro3c+++UEkSdNs+FB2FZE6KiIApaSD06Vugvvi76Dgu0+3cjmopr8HSrQeKZ8zxrdlMpDhSRATM8f2gT98M1bVrrl2suBi67R/C0q2Hb57grFKh+LWp0Fy6CMP61V4fnk090VQq/DR+IvIfiMKDc2cj8MJ5sXFyr0O/Ix3mXn0hVZfvJqTmgYOhKi2F/oMNoqNU2o2p5VevXkVCQgLi4uIwZswYqNVqzJ07F9OnT0enTp1w9OhRvPzyy6LjEslSWloaOnTogObNm+P06dM3v96hQwfExMQgLi4OcXFxOHjQvRv7EhGRd9gbNUbJo49Dv20LIN191QHRDabR4wHA7Xt/ed0XXyBo7AuwPtEWBUtXynK1FFU9phfGQGU2w7D2PZeuo8vcCXVRoZClqd5S0u4pWNs9hYC33wKKHV8F5Qouv5UBu1aH4ykz8djokWiZ8jqOLlkhLIt+8yaoTCaYho4QlqEybI2awNruaeg3rIVx/KSbx0fLGaeWE7muY8eOGDhwIJKS7pxJvHjxYjRr5jt76hARVVXmPgkIenUi/E4eR2mLh0XHIRmz16kLS694GDaug3Hiq5DCwkRHcpjm1L+BHj1ga9gIBes3A3q96EhEAABbs+awPNMJhtWrYBz7ktOvTcOGtSht0hQljz/p5oSeZbGYYDKZKv39pvETUXv4IFw/fhx54fdX/IQ/MRgM0OkMjkYEwKaebFjCI3B86nS0mfwSHpo9E7nrP/B+CLsdhjWrYH2irSKOTTcNHorqwwZC+/mnsD7TWXQcIvKC6Oho0RGIiMjDLHE9US3579B9uJlNPaqQcdxE6D7cDMOq5TBOSRYdxyF+J35AcL/eQGAg8rfs8M2liaRophfHQte7O/TbP4Q5aaDDz9f89GPZfv0pqYpbUm4ymXDhwoXKPyEkFGe27kCIVotcR54HoH79+p5t6qWlpSErKwsXL15EZmbmzZkQHTp0gFarhU6nAwBMnjwZ7dq1cyoIAfkPtcTPYyYgavECqBctgG3GHK+Or/3sE2gunEfx6yleHddZ1piusNWsBf261WzqEREmT54MSZLQpk0bTJo0CcHBwS5dTw6nSANyP7lYDhmcz+F7GYh8gxRaA9ZnY6DfsQ3FKamAH+ch0L3ZmjWHtUssDO+vhGnsBEjVxP6OrOzsnoCDXyJs/GjYqocg/8OtuB4YAORdr/Q4rszsIaqsknZPofTBFjCsWAJz4gCHG3P6Tesg+fvD3DfRQwnlRdJ4/35VqRG53Ml7LsZ2R/CZn1F7xVLkP/o4rN16eG1s/epVsNW6D5bnunltTJf4+8OcNAABixZA/ftvsNepKzoREQmyadMmREZGwmq1YtasWZgxYwbmz5/v0jXlcIo0IO+Ti+WQwdkcvpiBp0iTLzH3SYBuzy5ov/wM1o6dRMchmTNOmITQvZnQr1sD05jxQrNUZnZPZNbHaLxwHorrN8S/ZqUhUK/36sweokpTqWB8cQyCx70I/88/RUmHZyv/XLMZ+g83w9IlFlJ4uOcyVnGV2oEzOjoakZGRns5CQNnBGWNfgumRVggeNwqaH095ZVj1f85C+89Pyjav/O+prEpg7j8YkCToN64THYWIBLpxj9JqtUhMTMSxY8cEJyIiIldYn+kEe2godB9uFh2FFKC0VRtY2z0Nw4olgNksOs69SRIablqPB+e/idyHW+HbBYthZbODZM7Sszdste5DwPIlDj1PtzcT6txcmPsP8lAyAtywp547lztxqdMNgTCuXwdDp06oMSQROHoUqFHDqXH/rNzlOm9uADQaBE4ch8BKLeuRyTKjiAeBLl0QuHkDAt9MBfz9K7wmly0R+Raj0QibzYagoCBIkoS9e/ciKipKdCwiInKFVgtL3PPQb9mEosICSEGubalAvs84YRJCeneHfusHMA8aKjrOHVS2UjR/523U2ZOJ7Gc64dSkVyFV4r0LkXBaLUzDX0C1WdOh+ffJSu+/r9+4DrZ6DVDS/mnP5qviXGrquXu5E5c6/SlD/XCo39+AkB7PoaRXH+Rv3u7SCa8REUH444+7L9eB0Yiw91fD2rU7Cv2DgHt935/IYZnRDdp+g1B9717kb0qHtWv5S4fL/Tk4icudiLwnNTUV+/fvx7Vr1zBkyBCEhIRgxYoVGDduHGw2G+x2Oxo3boyUFGXsDUpERPdmju8Hw9r3od29C5Z+/UXHIZkrafcUSlq3QcCSt8s29JfRXoxqkwktZs9AxJHDOJeQhLNDRyju0ACq2swDhyBw4TwEvLsUhYuXV/j96v+chfbQART/4w1AXakFouQkl366XO7kWaWPPo6iN9+C9ovPEDh7hsfG0e9Ihzo/D+ZhIz02hidZn+kEW+06MKx7X3QUIvKw5ORkHDhwAKdOncJXX32FPXv2oG7duti5cycyMzOxZ88eLF68GDVr1hQdlYiIXFTa5lGUNmoMPZfgUmWoVDCOfxmaC+eh2/WR6DQ3+efloc2rExH+zRH8OH4izg4byYYeKY4UWgPmhCTotn8I9ZXLFX6/4YMNkNRqmBPuPJeB3Mvppp7RaERhYdmMJy538hzzgMEwDRyKgHcWQpexw/0DSBL0q1ehNOpBlDz+pPuv7w0aDcz9B0H7xWdQn/uP6DRERERE5A4qFSy9+0L71UGof/9NdBpSAGvMcyht/gACFi8EpLuvAPMmw6WLeHTCaFQ79x8cT5mJi148BJHI3YwjRwOlpdCvXln+N5aUQL95I6zPdoY98n7vhKvCKtXUS01NRfv27XH58mUMGTIEXbt2RU5ODgYMGIBu3bohNjYW586d43InDymaPRcljz2BoAmjoTl5wq3X9vvma/ifPA6Twj8xMicNhKTRwLBhregoREREROQm5t59AQC67R8KTkKKoFbDOPYl+J06Ce2nWUKjBP/0Ix6dMBp+RUX4bu5C/NH2r0LzELnK3qgxrDFdYVj7PlB87221tPv3Qf3H1bJDLcnjKtXU43InwbRa5L+/Afbg6qg+OAmq6zluu7RhzUrYg6vD3CvebdcUwX5fJKydn4N+y0bAYhEdh4iIiIjcwN6gIUoef7JsCa4MZl6R/Fme7wNb3XoIWLRAWIbwI4fR5pWXUKo34NtFS1HwPw8Ky0LkTsZR46DOzS13WwT9xrWw3RcJa8dnvZis6uKOhQoh1aqFgjUbob58CcEjhwKlpS5fU3XlCnSZGTD3SwICnTvVV05Mg4ZCfe0adHszRUchIiIiIjcx90mA35nT8PvhX6Kj3CYtLQ0dOnRA8+bNcfr06Ztf79ChA2JiYhAXF4e4uDgcPHhQYMoqyN8fxtHj4P/NEfgfOez14YO3bsbDKa+jqF59fLt4GYx16no9A5GnlD7+BEpatYbh3aWA3X7H4+rff4P2s09hTuwvq8NqfBmbegpS2uZRFM1dCO2BzxGYOs3l6xk2roWqpATmIcNdvpYclDz1N9jqN4B+3WrRUYiIiIjITSzde0DS6aBL3yI6ym06duyITZs2oXbt2nc8tnjxYmRkZCAjIwPt2rUTkK5qM/cbAHt4OAyL3vLeoJKEgDdTUeuNfyAn+jEcm/82rKE1vDc+kTeoVDCNGge//5yFdv++Ox7Wf7ABAGBOHOjtZFUWm3oKY04cANPQEQhYtti1vUVKSqBftxrWv3WErVET9wUUSa2GaeBQaA8fgub0z6LTEBEREZEbSCGhsHbqAv1H24CSEtFxboqOjkZkZKToGHQ3AQEwjRwN3T8/gebEcc+PV1KCoAmjEbhgLvJ798UPM2bBZgjw/Lg+grNelcUSGwdbnbowLH/n9gdsNug3b0TJU3+DvV59MeGqIM6HVKCimW9Cc+rfCJo0DrZmzVHa4mGHr6Hdtweay9komv+2BxKKY+7XH4FvzoR+/WoUp6aJjkNERCRLaWlpyMrKwsWLF5GZmYlmzZoBKHsDpdVqodPpAACTJ0/mLCOSBXOfBOgyd0L7+aewduoiOk6FJk+eDEmS0KZNG0yaNAnBwcEuXS8srFo5j5pRUOD4VjqhoY4/JyQkABERQTLN8P+8MhF4ZyFqrHwH2FL+LM9KXe9eCguBAYlAVhYwbRqso0Yh5DfHT2t29GdR/s/BO/89Kv3fogIdO3bEwIEDkZSUdMdjixcvvnmPIpnw84O75VB6AAAcfUlEQVRpxChUS3kNft8fQ+kjrQEA2i/+Cc3F31E0fZbYfFUMm3pK5O+PgvfWI7TTUwgenITc/V9CCgtz6BKG91fCVq8+rB07eSikGFJ4OCyx3aHfuhnFr08DDAbRkYiIiGSHb6BIaawdnoE9LAy69K2yb+pt2rQJkZGRsFqtmDVrFmbMmIH58+e7dM2cnCLY7Xc/KCQvz4jc3HufRHk3oaGBDj8HAIKDjQAKZZnhThoEDhkBw5K3kTvx7/dcnRQREYQ//qjM9e6kunIF1ZP6wO/fJ1C0cAnMSQORl3fdKz+L8n4O3vrvUV4GtVpVQTP6lujoaIfGJfHMSQMQMG8ODCuWoHBF2fZX+g3rYA8PhzWmq+B0VQuX3yqUVLMmCtZugvrqFQSPGOTQwRmaH09Be/gQTIOHAxqNB1OKYR40DOr8POgydoiOchtOKyciIrngskFSHK0Wlh69oNu3B6r8PNFpynWjtrRaLRITE3Hs2DHBiaou48jRgFYLw5JFbr+25pczCO36DPx+OY2CDVtgTuIeYp4wefJkdOvWDdOmTUNBQYHoOPRfUnB1mPsPgi7jI6gv/g5cvgzt/o9hjk8EtFrR8aoUztRTsNJHWqNw3tsIHj8KgdOTUTzzzUo9z7B6FSS9vuxEGh9U8uRfUNq0GQzrVsOScOcMBFE4K4KIiJTAF5cNyn+ZmhyWLsohQwVGDgPeX4nwL7KA4bcf9OaOJYDuYDQaYbPZEBQUBEmSsHfvXkRFRYmOVWVJNWvC3K8/9BvXwfjKP2CPvN8t1/X7+giqD+wLaPyQt3PvzeWH5F7unvXqifsRoMz7gVuWTk+ZDKxajrBNq4GwMKhKSxEwfjQC3PL7WA73JLm8JsrHpp7CWRKSYDzxAwLeXYbSFg/DEt+v3O9XFeRDn74F5p69IdVwbMmuYqhUMA8aimrJU6A5eQK2h1qITgSA08qJiEj+fHXZoNyXqclh6aIcMlSowQMIbdIU9tVrkR/X9+aXXVk+eTeVXTaYmpqK/fv349q1axgyZAhCQkKwYsUKjBs3DjabDXa7HY0bN0ZKSorbspHjjGMmQL9+DQzLl6B4xmyXr6fdk4ngUcNgu7828rfsgL1BQzekpLv5/7NeR40a5dL13H0/ApR7P3D69/CfBdRAULc4aN9dCXVYDVif/Avya9wPuOH3sRzuSXJ5TVR0T2JTzwcUT5sFv3+fRNDkCbA1fwClD7e65/fqt34AlbEY5qEjvJjQ+8zx/RCYOg2G9atRNHeh6DgVcvesCCIiIme4+w0UkVupVLD0SUDgnJlQ/3pB+OmKycnJSE5OvuPrO3fuFJCG7sVerz4sPXvDsH4NjC+97NLEBv37K1HttVdQ2joa+Rs/dHhfc6o8znpVBtOLY6HfuQMoLID5lddEx6mS2NTzBXc7OCMi4s7vs9uhX70KJW0eLbfx5wukkFBY4p6HLn0riqfOgFRNHksy7sYTsyK41Ek+GcrPIYcp3XLIQERywDdQpATmXvEInDMT+m1bYZz0qug4pBDG8ZOg37YVhvdXwvjKPxy/gN2OwFnTEfDOQlhiuqJgxftAQID7g1ZRnPWqXKWto1Hy+JPwP/0TLLFxouNUSWzq+QgpPBwFazchJLYTgkcMQn56BuDvf9v3+B/4An5nf0HB0pWCUnqXadBQ6Ld+AN2ObTAPHCI6zj15YlYElzrJJ0N5OeQwpVsOGQDHTkgjItfxDRQplb1efVjb/hW69C0wTnwFUKlERyIFsD0QBUvMczC8twLGUeOAag78zWG1ImjCaOi3fwjT4GEomjPfJw8bFImzXuXFYjHBZDJV+vsL5y1EiN2MPIsJsFT+eQaDATqdwZmI9Cds6vmQ0paPoPCtxQgeMxKBKa+hePa82x43rF4Je3g4LN17CkroXaVtHkXpgy2gX7ca5gGDZflHH2dFEBGRCHwDRUpm6ZOAoIlj4XfsW5S2eVR0HFII4/hJCH3uGRg2roXpxbGVeo6qIB/BQ/pDe/BLFCVPg2ncRFm+pyByJ5PJhAsXLjj0nNDQash18Dn169dnU88N1KIDkHtZ+iTA+MIYBLz3LnRbNt164Px5aPfvg6n/YECnE5bPq1QqmAYNhf+JH+D3r+9Ep0Fqairat2+Py5cvY8iQIejatStycnIwYMAAdOvWDbGxsTh37hxnRRARERGVw9ItDpJeD336FtFRSEFKox+D9S/tYFi+BLBYKvx+dfYlhHTvAv///QoFS96FafwkNvSISHY4U88HFafMhN+pkwh65aWygzNatQFWrAAAmAcNFZzOuyy94xE4/Q3o169BUWuxp89yVgQRERGR66Tg6rDEPAfdR9tQNGOO6DikIMYJLyMkvgf027bCnDTwnt+n+elHVE94HqqCAuR/sA0lT3fwYkoiosrjTD1f5OeHgpVrYa9ZC8GDk6D+7VfgvfdgjekKe+06otN5lVQtCJZe8dB/tA2q/DzRcYiIiIjIDSx9EqDOzYX2n5+IjkIKUvLU31DycCsY3lkI2Gx3/R7/w4cQEtsJsNmQl/ExG3pEJGts6vkoKSwM+Ws/gDovF6GdnwZycmAaNlJ0LCHMg4ZAZTJBxyUaRERERD7B+nRH2MPDuQSXHKNSwTh+Evz+cxa63Rl3PKzbuR3V43vAft99yNv7KWwtWgoISURUeWzq+TBbi5YoXLgE6mvXgKgolPy1vehIQpS2eBglrdvAsG41IN39RFgiIiIiUhB/f5if7wPt/o+B3FzRaUhBrF27obRJUxgWLbjtvYFh+RIEjxyCktbRyMvMgr1uPYEpiYgqh3vqKZBDR0x36IjiBYugfygKefmO/cHjS0dMmwYNQ/CE0cChQ8ADj4iOQ0REREQussT3Q8DK5cBXXwGPPyU6DimFWg3juIkInjAa/p9/CvTpgcA3piDg3WWwdOuBgqUrAb1edEoiokphU0+BHD5iusUjCA0NrNJHTFvinof9jX9A/e67wMLlouMQkZPS0tKQlZWFixcvIjMzE82aNQMAnDt3DlOmTEFeXh5CQkKQlpaGBg0aiA1LREQOceiDawCoVw9FH+6AvmVL5OVdd2gsX/rwmhxn6RUPW9osBC6YB2zfgoD0dBhHjkLxjDmAmovZiEg52NSjqiEgAOa+/RCweSOwYBmPoydSqI4dO2LgwIFISkq67espKSlITExEXFwcMjIyMHXqVKxfv15QSiIicobDH1wDQGgYQv/4A7m5xQ49zZc+vCYnaLUwjR6HaslTgG+OoGj6bJhGjRWdiojIYRU29TgrguTK0U9z88eMR/izHRxehgzw01wiuYiOjr7jazk5OTh16hTWrFkDAIiNjcXMmTNx/fp11KhRw9sRiYiISACH3xvEdketQwcg9eqJK089Czgw25PvDYhILips6nFWBMmVM5/m5jdq5PAyZICf5hLJWXZ2NmrVqgWNRgMA0Gg0qFmzJrKzs11q6oWFVSvnUTMKCgIdvmZoqOPPCQkJQEREkNsyOJND3hmcz+F7GYiIqi5n3hucf/nvVX6bIiJStgqbepwVQUREVVFOThHs9rufmJ2XZ3R4qVdoaKDDzwGA4GAjgEK3ZHA2h5wzOJvDFzOo1aoKmtFERERE5Euc2lOPsyIc53uzAapOhopzEJFIkZGRuHLlCmw2GzQaDWw2G65evYrIyEjR0YiIiIiIiDxGVgdlcFZExRmczcEMzmeoKAdnRhCJFRYWhqioKOzevRtxcXHYvXs3oqKiOHOciIiIiIh8mlPndf95VgQAzoogIiKvSE1NRfv27XH58mUMGTIEXbt2BQBMmzYNGzduROfOnbFx40ZMnz5dcFIiIiIiIiLPcmqmHmdFEBGRCMnJyUhOTr7j640bN0Z6erqAREREVFWlpaUhKysLFy9eRGZmJpo1awYAOHfuHKZMmYK8vDyEhIQgLS0NDRo0EBuWiIh8UoUz9TgrgoiIiMi3pKWloUOHDmjevDlOnz598+vnzp1D37590blzZ/Tt2xfnz58XF5JI5jp27IhNmzahdu3at309JSUFiYmJyMrKQmJiIqZOnSooIZEy8J5E5LwKm3rJyck4cOAATp06ha+++gp79uwBcGtWRFZWFtLT09GoUSOPhyUiIiIi17EZQeS66OjoO7YfysnJwalTpxAbGwsAiI2NxalTp3D9+nUREYkUgfckIufJ6qAMIl/GJRpERCQX0dHRd3ztRjNizZo1AMqaETNnzsT169e5xQpRJWVnZ6NWrVrQaDQAAI1Gg5o1ayI7O9ulOir/UDYzCgoCHb5maKjjzwkJCUBERJBPZXAmx70zOJ/D9zJUHu9JRM5jU4/ISzp27IiBAwciKSnptq/f+AQqLi4OGRkZmDp1KtavXy8oJRERVVWeakYQketycopgt0t3fSwvz4jc3GKHrhcaGujwcwAgONgIoNBnMjib414ZnM3hixnUalUFzejy8Z5EVDls6hF5CT+BIiKiqkgOM4zkP6NFubOc5JDBmRzummH0/0VGRuLKlSuw2WzQaDSw2Wy4evXqHct0icj7PHE/ApR5P2CGyuSQy2uifGzqEQnET6CIiEguPNWMkMMMI7nPaFHqLCc5ZHA2h6dmGIWFhSEqKgq7d+9GXFwcdu/ejaioKP5dR+QgT9yT3H0/ApR7P2CGinPI5TVR0T2JTT0iH8RZEfLJUH4OOXz6I4cMRCQHbEYQOSY1NRX79+/HtWvXMGTIEISEhGDPnj2YNm0apkyZgmXLliE4OBhpaWmioxIpDu9JRJXDph6RQJwV4fsZysshh09/5JABcH3fFSJyDJsRRK5LTk5GcnLyHV9v3Lgx0tPTBSQiUibek4icx6YekUD8BIqIiERgM4KIiOSC9yQi57GpR+Ql/ASKiIiIiIiIiNyFTT0iL+EnUERERERERETkLmrRAYiIiIiIiIiIiMgxbOoREREREREREREpDJt6RERERERERERECsOmHhERERERERERkcKwqUdERERERERERKQwbOoREREREREREREpDJt6RERERERERERECsOmHhERERERERERkcKwqUdERERERERERKQwfqIDEBERuUuHDh2g1Wqh0+kAAJMnT0a7du0EpyIiIiIiInI/l5t6fANFRERysnjxYjRr1kx0DCIiIiIiIo9yy0w9voEiIiIiIiIiIiLyHi6/JSIinzJ58mRIkoQ2bdpg0qRJCA4OFh2JiIiIiIjI7dzS1OMbKCIikoNNmzYhMjISVqsVs2bNwowZMzB//nynrhUWVq2cR80oKAh0+JqhoY4/JyQkABERQW7L4EwOeWdwPofvZSAiOeE2RURE5GkuN/X4BsozOeT/xqHqZKg4BxHJRWRkJABAq9UiMTERo0aNcvpaOTlFsNuluz6Wl2dEbm6xQ9cLDQ10+DkAEBxsBFDolgzO5pBzBmdz+GIGtVpVwd9SlcNGBJH7cJsiItfwnkRUPpebenwD5Zkccn/jUJUyVJSDb6KI5MFoNMJmsyEoKAiSJGHv3r2IiooSHYtIkdiIICIiueA9iejeXGrq8Q0UkXvxhkXkvJycHIwbNw42mw12ux2NGzdGSkqK6FhERFSFcZsiIiLyJJeaenwDRUREclG3bl3s3LlTdAwin8BGBJHruE2RMjI4k0MOWwTJP4P78J5EdG8uNfX4BorIvXjDIiIi0dzZiADk0YyQ/5tf5TZE5JDBmRzeaEZwmyL5Z3A2hxy2CJJ7BndtUST35jigzPsBM1Qmh1xeE+Vzy+m3ROQ6ud+wlPhLWg4Zys8hhxuFHDIQkZy4sxEByKMZIfc3v0ptiMghg7M5PN2M4DZFRO4h5+Y4oNz7ATNUnEMur4mK7kls6hHJhJxvWEr9JS2HDOXlkMONQg4ZAPd9mktErmEjgsg9uE0Rket4TyKqGJt6RDLAGxYREckBGxFE7sFtiohcx3sSUcXY1COSAd6wiIhIDtiIICIiueA9iahibOoRyQBvWERERERERETkCLXoAEREREREREREROQYNvWIiIiIiIiIiIgUhk09IiIiIiIiIiIihWFTj4iIiIiIiIiISGHY1CMiIiIiIiIiIlIYNvWIiIiIiIiIiIgUhk09IiIiIiIiIiIihWFTj4iIiIiIiIiISGHY1CMiIiIiIiIiIlIYNvWIiIiIiIiIiIgUhk09IiIiIiIiIiIihWFTj4iIiIiIiIiISGHY1CMiIiIiIiIiIlIYNvWIiIiIiIiIiIgUhk09IiIiIiIiIiIihXG5qXfu3Dn07dsXnTt3Rt++fXH+/Hk3xCKqWlhHRO7BWiJyHeuIyD1YS0SuYx0Rlc/lpl5KSgoSExORlZWFxMRETJ061R25iKoU1hGRe7CWiFzHOiJyD9YSketYR0Tlc6mpl5OTg1OnTiE2NhYAEBsbi1OnTuH69etuCUdUFbCOiNyDtUTkOtYRkXuwlohcxzoiqpifK0/Ozs5GrVq1oNFoAAAajQY1a9ZEdnY2atSo4fD11GrVPR/z89NAr9c5dD2tVgu9vtThHH5+mrtmcSaDsznulcHZHMzgfIaKcpT3uq0Md9dRRZmq0n87OWQoL4ccfqfIIQPgeh0BvCd5MoecMzibwxczyLGOKsolh59bVcpQXg6lZnA2h9JqSQ51dGMsvn7kUc9yz1AV6gjwzf92VSlDeTnk8pqoqJZcauq5W2ho4D0fCwurhoYN63gxjTwzyCUHM8gba4kZlJRBrlhHysgglxxyyCBXrCVmUFIGuWIdKSODXHLIIYMcyb2O5JKDGeSToTJcWn4bGRmJK1euwGazAQBsNhuuXr2KyMhIt4QjqgpYR0TuwVoich3riMg9WEtErmMdEVXMpaZeWFgYoqKisHv3bgDA7t27ERUV5fTyDKKqiHVE5B6sJSLXsY6I3IO1ROQ61hFRxVSSJEmuXODs2bOYMmUKCgoKEBwcjLS0NDRq1Mhd+YiqBNYRkXuwlohcxzoicg/WEpHrWEdE5XO5qUdERERERERERETe5dLyWyIiIiIiIiIiIvI+NvWIiIiIiIiIiIgUhk09IiIiIiIiIiIihWFTj4iIiIiIiIiISGHY1CMiIiIiIiIiIlIYNvWIiIiIiIiIiIgUhk09IiIiIiIiIiIihWFTj4iIiIiIiIiISGHY1POgoqIi0RGIFI91ROQerCUi17GOiNyDtUTkOtYRAYBm2rRp00SH8EWHDx/G6tWrERAQgLp16wrLUVpaCrVafO/WZrPJIgcpC+vodqwjchZr6XasJXIG6+h2rCNylhxqiXVESieHOgJYS3Lgc//qb7/9Funp6Vi1ahW++OILIRm+/PJLpKWl4ZlnnkFoaKiQDADw2WefYcmSJbDZbELG//HHH3HkyBEAgEajAQDY7XYhWb799lscOnRIyNhKJbqWWEdlWEfKJrqOANbSDawl5WId3cI6uoV15DjWUhnW0S2sI8exjm5hLd0ispZ8aqbe559/jhkzZqBhw4Y4duwYvv/+e6Snp6Nnz55QqVReyfDjjz/i9ddfx4wZM9C2bVuEh4cDACRJ8loGADh06BAWLFiAPn36oEGDBl4bFyj7t+bk5KBLly7IyMjAlStXYLfbER4eDr1ef9v3eetnsm/fPsybNw8PPPCA0E8ylEJ0LbGOWEe+QHQdAawlgLWkdKyjW1hHt2MdOYa1VIZ1dDvWkWNYR7ewlm4nspZ8pql34sQJJCcnIy0tDbGxsYiJicFjjz2GL7/8Ejt37kSPHj28kuP48eOwWCzo37//bVNAvVlgBw4cwMKFC/Haa6/hySefxG+//Ybdu3ejZcuWXhlfpVIhICAAkiShZcuWUKlUOH78ONauXYv77rsPKpUKISEhXv2ZtGrVCpIkYdmyZWjYsCHq1avntbGVRg61xDpiHSmdHOoIYC0BrCUlYx3dwjq6E+uo8lhLZVhHd2IdVR7r6BbW0p1E1pLPNPW+++472O12JCUlwWq1wt/fH0FBQWjdujUOHjyIyMhI1K5d2+M5jhw5gp9//hmxsbFQq9WQJAlA2Qvv+++/x7lz5zzauS0sLETfvn0xaNAgdO3aFdnZ2Zg0aRLq1auH1q1be2zcuzl//jyOHTuG2bNnIyYmBt988w3WrFmDo0eP4uzZs2jatCmqVavmsfG//fZb/PLLLwgICEBgYCBat24NtVqNt99+G40aNeJN6x7kUEuso1tYR8okhzoCWEt/xlpSHtZRGdbRLawj57CWWEd/xjpyDuuoDGvpFrnUkp9XRvGC4uJiFBYWAgD8/f1vTrWsVasWzGYzzp49i0cffdTjOVq2bIn58+dj//796NSp022PnTp1CmfPnkWbNm2g1Wo9Mn5QUBCWLVuGBQsWIDIyEmvXrkX37t2RlJR083uKioo8+uK+IT4+Hnv27MG2bdvQuHFjnDx5Em+88Qbq1KmDefPmeXTswsJCDBw4EHa7Hc8++yxMJhMSEhLQtWtXAMDcuXOh1Wrx2GOPeTSHEsmhllhHt7COlEkOdQSwlv6MtaQ8rKMyrKMyrCPnsZZYRzewjpzHOirDWiojp1rymZl6arUaixYtQoMGDdCkSRNIkgS73Q5/f3+cOXMG9erVQ9OmTT2eo2bNmlCpVNi2bRuCg4PRpEkTqFQq7Nq1C+vWrcPkyZMRERHh0Qx169ZFgwYN8PLLL+Ppp5/GmDFjbj62fft2ZGVl4bHHHru5maQn3Pglp9frkZmZie3bt+PVV19FTEwMatWqhbi4OAQFBXlsfJ1Oh7Zt2+LgwYMYNmwY/P39cfr0abz55pto0qQJ9u/fj08++QQtWrRAnTp1PJZDieRQS6yjMqwj5ZJDHQGspRtYS8rEOrqFdcQ6cgVrqQzriHXkCtbRLawlmdWS5EPWrVsnxcfHSx9//PHNr2VmZkoxMTHS+fPnvZYjPz9feu+996RWrVpJQ4cOlSZNmiQ999xz0s8//+y1DJIkSUePHpW6desmfffdd5Iklf0sevbsKf30009ey5CdnS09++yz0ty5cyVJkiS73X7zf97wv//7v1LPnj2lH374QZIkSTpx4oR0+PBhafLkyVLPnj2ls2fPeiWH0sihllhHt7COlEkOdSRJrKU/Yy0pD+vodqwj1pGzWEu3sI5YR85iHd2OtSSPWvKppl5hYaG0fv16qXXr1tLw4cOlCRMmSJ06dfLqi+rPfvrpJ2n37t3S4cOHpUuXLgnJcONF9tZbb0m9evWSzpw54/UMe/bskcaOHStdvXrV62NLkiQdPnxY6tKli3T06NGbX7Pb7ZLRaBSSRwnkVEusozKsI+WRUx1JEmvpBtaSsrCO7sQ6Yh05g7V0O9YR68gZrKM7sZbE15JPNfVuOHPmjLRv3z7p888/ly5evCg6jnCHDx+W2rdvL6TAJEmSfv31V+mFF16QCgoKhIwvSZJ05MgRqVu3btLXX38tLIMSsZZuYR2xjpzFOroda4m15AzW0e1YR6wjZ7GWbmEdsY6cxTq6HWtJbC2pJOm/x6WQTzObzdDr9VV2fAA4dOgQlixZgrVr1wrPQsok+nUsenyAdUTuIfq1LHp8gLVErhP9OhY9PsA6IteJfh2LHh9gHZF7iH4tix4fEFdLbOpRlWIymWAwGETHIFI01hGRe7CWiFzHOiJyHeuIyD1E1BKbekRERERERERERAqjFh2AiIiIiIiIiIiIHMOmHhERERERERERkcKwqUdERERERERERKQwbOoREREREREREREpDJt6RERERERERERECsOmHhERERERERERkcL8HyC9PvQU1SMjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1584x216 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1XSu-D0SfmW"
      },
      "source": [
        "import pickle\n",
        "pickle.dump(kmeans, open(\"/content/drive/MyDrive/ocean-modelB/Data/model.pkl\", \"wb\")) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "md0sB8Sxg3im"
      },
      "source": [
        "dataclusters = pd.DataFrame(dataclusters)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hvy6Vfkji8lo"
      },
      "source": [
        "dataclusters = dataclusters.round(2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "yfl9imA4hrw4",
        "outputId": "a05f7010-b0e3-43b9-ee70-dbe88ab8e84e"
      },
      "source": [
        "dataclusters = dataclusters.apply(lambda x: np.round(x*100/40, 0))\n",
        "dataclusters.astype('int')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>O</th>\n",
              "      <th>C</th>\n",
              "      <th>E</th>\n",
              "      <th>A</th>\n",
              "      <th>N</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clusters</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>46</td>\n",
              "      <td>28</td>\n",
              "      <td>62</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79</td>\n",
              "      <td>73</td>\n",
              "      <td>71</td>\n",
              "      <td>81</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65</td>\n",
              "      <td>47</td>\n",
              "      <td>57</td>\n",
              "      <td>65</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>78</td>\n",
              "      <td>53</td>\n",
              "      <td>69</td>\n",
              "      <td>77</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75</td>\n",
              "      <td>63</td>\n",
              "      <td>29</td>\n",
              "      <td>44</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>71</td>\n",
              "      <td>73</td>\n",
              "      <td>36</td>\n",
              "      <td>78</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           O   C   E   A   N\n",
              "Clusters                    \n",
              "0         67  46  28  62  26\n",
              "1         79  73  71  81  71\n",
              "2         65  47  57  65  60\n",
              "3         78  53  69  77  32\n",
              "4         75  63  29  44  62\n",
              "5         71  73  36  78  46"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atoOR8ysheuk"
      },
      "source": [
        "dataclusters.to_csv('/content/drive/MyDrive/ocean-modelB/Data/clusters.csv')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}