Ridge_Reg.ipynb

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    "colab": {
      "name": "Ridge_Reg.ipynb",
      "provenance": [],
      "collapsed_sections": []
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QzaCubJZ_dt"
      },
      "source": [
        "***Problem Statement:***In the mushroom data set,there exists multicollinearity among the features.\n",
        "\n",
        "Now,using Ridge Regression,I will try to overcome the existat multi-collinearity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WzARuJgaa8fP"
      },
      "source": [
        "#***Loading Important Libraries***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_deEDt8bBSD"
      },
      "source": [
        "import numpy as np # for linear algebric operations\n",
        "import pandas as pd #To craet data frame for easy to use the data set.\n",
        "import matplotlib.pyplot as plt # for data visualisation\n",
        "import seaborn as sns # for visualisation of advanced statistical realtionships "
      ],
      "execution_count": 589,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKCAU1xSOfgd"
      },
      "source": [
        ""
      ],
      "execution_count": 589,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cw-BIxMAboVg"
      },
      "source": [
        "# **Unzipping the Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AnAknShRA7J9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "25b7123b-57e2-42fa-aac4-d1e6a968b4b5"
      },
      "source": [
        "\n",
        "import zipfile as z\n",
        "zip_ref = z.ZipFile(\"/content/train.csv.zip\", \"r\") #the source path is given. 'r'<= to read only file\n",
        "zip_ref.extractall(\"/content/\") #the destination part is given\n",
        "\n",
        "zip_ref.close() #to close the zip(i.e archive file)\n",
        "\n",
        "'''\n",
        "After Using 'with open(path name):'.....not required to close file after processing\n",
        "'''"
      ],
      "execution_count": 590,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nAfter Using 'with open(path name):'.....not required to close file after processing\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 590
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uA6a1C1FMf8p"
      },
      "source": [
        "\n",
        "\n",
        "*   **Importance of  '.close()' :** \n",
        "*This function will close the zip file\n",
        "*If we don't use .close() function, the zip fill will still open even after reading it,then :\n",
        "  * It will consume the memory and the efficiency of the local host will get reduced.\n",
        "  *   The zip file may be over-written and changes the values of the data points\n",
        "\n",
        "***So, it is advisable to use .close() function after extract the data***\n",
        "\n",
        "**Note:** There is another funtion called \"with open()\",after using \"with open()\" there is no need to close the file,because,after reading the file,the file wil automatically being closed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7CHA4vYbuPU"
      },
      "source": [
        "\n",
        "\n",
        "# **Importing important libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC3D9RggDmmi"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns # seaborn is a module of python built upon matplotlib to handle statistical data plots\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 591,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gte5HdTUQP0i"
      },
      "source": [
        "* \"%matplot inline\" takes care of the plots are being displayed in the same tab without opening new tab\n",
        "* But, updated version of pyplot takes care of the issue of opening new tab without explicitly mentioning '%matplotlib inline'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0CD5pwjEIL1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "6f3dd8de-e3d8-4bbe-da02-6eb8c1e324bb"
      },
      "source": [
        "df=pd.read_csv(\"/content/train.csv\") \n",
        "df #df is te data frame made out of \"train.csv\""
      ],
      "execution_count": 592,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>07/08/2006</td>\n",
              "      <td>Kayseri</td>\n",
              "      <td>Other</td>\n",
              "      <td>FC</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2544857.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>135</td>\n",
              "      <td>10/29/2010</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7217634.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>136</td>\n",
              "      <td>09/01/2009</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6363241.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 43 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id   Open Date        City  City Group  ... P35  P36  P37    revenue\n",
              "0      0  07/17/1999    İstanbul  Big Cities  ...   4    3    4  5653753.0\n",
              "1      1  02/14/2008      Ankara  Big Cities  ...   0    0    0  6923131.0\n",
              "2      2  03/09/2013  Diyarbakır       Other  ...   0    0    0  2055379.0\n",
              "3      3  02/02/2012       Tokat       Other  ...  12   12    6  2675511.0\n",
              "4      4  05/09/2009   Gaziantep       Other  ...   4    3    3  4316715.0\n",
              "..   ...         ...         ...         ...  ...  ..  ...  ...        ...\n",
              "132  132  06/25/2008     Trabzon       Other  ...   0    0    0  5787594.0\n",
              "133  133  10/12/2006       İzmir  Big Cities  ...   0    0    0  9262754.0\n",
              "134  134  07/08/2006     Kayseri       Other  ...   0    0    0  2544857.0\n",
              "135  135  10/29/2010    İstanbul  Big Cities  ...   0    0    0  7217634.0\n",
              "136  136  09/01/2009    İstanbul  Big Cities  ...   0    0    0  6363241.0\n",
              "\n",
              "[137 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 592
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qZ0QZulUAHx"
      },
      "source": [
        "* Id : Restaurant id. \n",
        "* Open Date : opening date for a restaurant.\n",
        "* City : City that the restaurant is in. Note that there are unicode in the names. \n",
        "* City Group: Type of the city. Big cities, or Other. \n",
        "* Type: Type of the restaurant. FC: Food Court, IL: Inline, DT: Drive Thru, MB: Mobile.\n",
        "* P1, P2 - P37: There are three categories of these obfuscated data. Demographic data are gathered from third party providers with GIS systems. These include population in any given area, age and gender distribution, development scales. Real estate data mainly relate to the m2 of the location, front facade of the location, car park availability. Commercial data mainly include the existence of points of interest including schools, banks, other QSR operators.\n",
        "*Revenue: The revenue column indicates a (transformed) revenue of the restaurant in a given year and is the target of predictive analysis. Please note that the values are transformed so they don't mean real dollar values. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZIkeAQ3m0NC"
      },
      "source": [
        "# **Data Analysis & Data Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILH3I-fGETXo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55a1dfdf-a85a-4c94-ed79-546e4599a0c8"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 593,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(137, 43)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 593
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clvAAqogcAwo"
      },
      "source": [
        "This says that the dataset has 137 rows and 43 columns. Simply meaning, there are 137 data points in the datatset and there are 43 features, one of them is target feature.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTWlQ4fLExop",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ece7c627-a36d-4978-e258-27534379778e"
      },
      "source": [
        "df.columns # \"df.columns\" returns a list of names of fatures(viz. 'Id','Open Date',etc.)."
      ],
      "execution_count": 594,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'Open Date', 'City', 'City Group', 'Type', 'P1', 'P2', 'P3', 'P4',\n",
              "       'P5', 'P6', 'P7', 'P8', 'P9', 'P10', 'P11', 'P12', 'P13', 'P14', 'P15',\n",
              "       'P16', 'P17', 'P18', 'P19', 'P20', 'P21', 'P22', 'P23', 'P24', 'P25',\n",
              "       'P26', 'P27', 'P28', 'P29', 'P30', 'P31', 'P32', 'P33', 'P34', 'P35',\n",
              "       'P36', 'P37', 'revenue'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 594
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jk2lcu8cEhld",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20e3595a-51d8-47a8-98c4-1a252e5aa427"
      },
      "source": [
        "\n",
        "df.isna().sum() "
      ],
      "execution_count": 595,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id            0\n",
              "Open Date     0\n",
              "City          0\n",
              "City Group    0\n",
              "Type          0\n",
              "P1            0\n",
              "P2            0\n",
              "P3            0\n",
              "P4            0\n",
              "P5            0\n",
              "P6            0\n",
              "P7            0\n",
              "P8            0\n",
              "P9            0\n",
              "P10           0\n",
              "P11           0\n",
              "P12           0\n",
              "P13           0\n",
              "P14           0\n",
              "P15           0\n",
              "P16           0\n",
              "P17           0\n",
              "P18           0\n",
              "P19           0\n",
              "P20           0\n",
              "P21           0\n",
              "P22           0\n",
              "P23           0\n",
              "P24           0\n",
              "P25           0\n",
              "P26           0\n",
              "P27           0\n",
              "P28           0\n",
              "P29           0\n",
              "P30           0\n",
              "P31           0\n",
              "P32           0\n",
              "P33           0\n",
              "P34           0\n",
              "P35           0\n",
              "P36           0\n",
              "P37           0\n",
              "revenue       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 595
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ITaRHK4WFbx"
      },
      "source": [
        "\n",
        "* 'isna()' returns boolean dtype(i.e. True or False) for each data point,\n",
        "* 'sum()':go through column-wise and returns how many nan value is present in that specific column.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMYoT2s1cYbe"
      },
      "source": [
        "This says there are no Missing Values. Good for us."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5_JPlFsFR8X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9681b57b-3460-40c7-e966-b7595c278608"
      },
      "source": [
        "\n",
        "df.info()"
      ],
      "execution_count": 596,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 137 entries, 0 to 136\n",
            "Data columns (total 43 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Id          137 non-null    int64  \n",
            " 1   Open Date   137 non-null    object \n",
            " 2   City        137 non-null    object \n",
            " 3   City Group  137 non-null    object \n",
            " 4   Type        137 non-null    object \n",
            " 5   P1          137 non-null    int64  \n",
            " 6   P2          137 non-null    float64\n",
            " 7   P3          137 non-null    float64\n",
            " 8   P4          137 non-null    float64\n",
            " 9   P5          137 non-null    int64  \n",
            " 10  P6          137 non-null    int64  \n",
            " 11  P7          137 non-null    int64  \n",
            " 12  P8          137 non-null    int64  \n",
            " 13  P9          137 non-null    int64  \n",
            " 14  P10         137 non-null    int64  \n",
            " 15  P11         137 non-null    int64  \n",
            " 16  P12         137 non-null    int64  \n",
            " 17  P13         137 non-null    float64\n",
            " 18  P14         137 non-null    int64  \n",
            " 19  P15         137 non-null    int64  \n",
            " 20  P16         137 non-null    int64  \n",
            " 21  P17         137 non-null    int64  \n",
            " 22  P18         137 non-null    int64  \n",
            " 23  P19         137 non-null    int64  \n",
            " 24  P20         137 non-null    int64  \n",
            " 25  P21         137 non-null    int64  \n",
            " 26  P22         137 non-null    int64  \n",
            " 27  P23         137 non-null    int64  \n",
            " 28  P24         137 non-null    int64  \n",
            " 29  P25         137 non-null    int64  \n",
            " 30  P26         137 non-null    float64\n",
            " 31  P27         137 non-null    float64\n",
            " 32  P28         137 non-null    float64\n",
            " 33  P29         137 non-null    float64\n",
            " 34  P30         137 non-null    int64  \n",
            " 35  P31         137 non-null    int64  \n",
            " 36  P32         137 non-null    int64  \n",
            " 37  P33         137 non-null    int64  \n",
            " 38  P34         137 non-null    int64  \n",
            " 39  P35         137 non-null    int64  \n",
            " 40  P36         137 non-null    int64  \n",
            " 41  P37         137 non-null    int64  \n",
            " 42  revenue     137 non-null    float64\n",
            "dtypes: float64(9), int64(30), object(4)\n",
            "memory usage: 46.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYWWFLO4cfq6"
      },
      "source": [
        "From here we see that **4 of the features are object type** which the model can not understand. So they need to be **encoded**. \n",
        "\n",
        "Apart from that, Another observation is that we don't need to use the feature 'ID' as it's anyway **not going to give me any insight of the revenue**. So we will simply drop it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qpk0yfZyLPRp",
        "outputId": "7d9d0eb4-b05a-4f06-adc4-fb0c32c4816b"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 597,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'Open Date', 'City', 'City Group', 'Type', 'P1', 'P2', 'P3', 'P4',\n",
              "       'P5', 'P6', 'P7', 'P8', 'P9', 'P10', 'P11', 'P12', 'P13', 'P14', 'P15',\n",
              "       'P16', 'P17', 'P18', 'P19', 'P20', 'P21', 'P22', 'P23', 'P24', 'P25',\n",
              "       'P26', 'P27', 'P28', 'P29', 'P30', 'P31', 'P32', 'P33', 'P34', 'P35',\n",
              "       'P36', 'P37', 'revenue'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 597
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUUJ-1T-ErHE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "ac3a6d19-0d6f-4c62-9477-0278d20373e5"
      },
      "source": [
        "#The ID column is irrelevant so we will drop them.\n",
        "df=df.drop('Id',axis=1) #'axis=1' =>selecting the whole column while traversing through all rows\n",
        "df"
      ],
      "execution_count": 598,
      "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>Open Date</th>\n",
              "      <th>City</th>\n",
              "      <th>City Group</th>\n",
              "      <th>Type</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>P9</th>\n",
              "      <th>P10</th>\n",
              "      <th>P11</th>\n",
              "      <th>P12</th>\n",
              "      <th>P13</th>\n",
              "      <th>P14</th>\n",
              "      <th>P15</th>\n",
              "      <th>P16</th>\n",
              "      <th>P17</th>\n",
              "      <th>P18</th>\n",
              "      <th>P19</th>\n",
              "      <th>P20</th>\n",
              "      <th>P21</th>\n",
              "      <th>P22</th>\n",
              "      <th>P23</th>\n",
              "      <th>P24</th>\n",
              "      <th>P25</th>\n",
              "      <th>P26</th>\n",
              "      <th>P27</th>\n",
              "      <th>P28</th>\n",
              "      <th>P29</th>\n",
              "      <th>P30</th>\n",
              "      <th>P31</th>\n",
              "      <th>P32</th>\n",
              "      <th>P33</th>\n",
              "      <th>P34</th>\n",
              "      <th>P35</th>\n",
              "      <th>P36</th>\n",
              "      <th>P37</th>\n",
              "      <th>revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>07/17/1999</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>IL</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5653753.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>02/14/2008</td>\n",
              "      <td>Ankara</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6923131.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>03/09/2013</td>\n",
              "      <td>Diyarbakır</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2055379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>02/02/2012</td>\n",
              "      <td>Tokat</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7.5</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>2675511.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>05/09/2009</td>\n",
              "      <td>Gaziantep</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4316715.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>06/25/2008</td>\n",
              "      <td>Trabzon</td>\n",
              "      <td>Other</td>\n",
              "      <td>FC</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5787594.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>10/12/2006</td>\n",
              "      <td>İzmir</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9262754.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>07/08/2006</td>\n",
              "      <td>Kayseri</td>\n",
              "      <td>Other</td>\n",
              "      <td>FC</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2544857.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>10/29/2010</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7217634.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>09/01/2009</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6363241.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 42 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Open Date        City  City Group Type  P1  ...  P34  P35  P36  P37    revenue\n",
              "0    07/17/1999    İstanbul  Big Cities   IL   4  ...    5    4    3    4  5653753.0\n",
              "1    02/14/2008      Ankara  Big Cities   FC   4  ...    0    0    0    0  6923131.0\n",
              "2    03/09/2013  Diyarbakır       Other   IL   2  ...    0    0    0    0  2055379.0\n",
              "3    02/02/2012       Tokat       Other   IL   6  ...   18   12   12    6  2675511.0\n",
              "4    05/09/2009   Gaziantep       Other   IL   3  ...    3    4    3    3  4316715.0\n",
              "..          ...         ...         ...  ...  ..  ...  ...  ...  ...  ...        ...\n",
              "132  06/25/2008     Trabzon       Other   FC   2  ...    0    0    0    0  5787594.0\n",
              "133  10/12/2006       İzmir  Big Cities   FC   4  ...    0    0    0    0  9262754.0\n",
              "134  07/08/2006     Kayseri       Other   FC   3  ...    0    0    0    0  2544857.0\n",
              "135  10/29/2010    İstanbul  Big Cities   FC   4  ...    0    0    0    0  7217634.0\n",
              "136  09/01/2009    İstanbul  Big Cities   FC   4  ...    0    0    0    0  6363241.0\n",
              "\n",
              "[137 rows x 42 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 598
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeo4eoTfglLd"
      },
      "source": [
        "Now I am going to convert the 'Open Date' feature in **datetime format** so that I can extract the month and year from it. I want to do this because the date doesn't give me any insight of the revenue. But the **month and year surely does.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eG9WRuxcFDE_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "6ef92b0e-a77b-4411-ec21-eed12f40965d"
      },
      "source": [
        "df['Open Date'] = pd.to_datetime(df['Open Date'])\n",
        "df"
      ],
      "execution_count": 599,
      "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>Open Date</th>\n",
              "      <th>City</th>\n",
              "      <th>City Group</th>\n",
              "      <th>Type</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>P9</th>\n",
              "      <th>P10</th>\n",
              "      <th>P11</th>\n",
              "      <th>P12</th>\n",
              "      <th>P13</th>\n",
              "      <th>P14</th>\n",
              "      <th>P15</th>\n",
              "      <th>P16</th>\n",
              "      <th>P17</th>\n",
              "      <th>P18</th>\n",
              "      <th>P19</th>\n",
              "      <th>P20</th>\n",
              "      <th>P21</th>\n",
              "      <th>P22</th>\n",
              "      <th>P23</th>\n",
              "      <th>P24</th>\n",
              "      <th>P25</th>\n",
              "      <th>P26</th>\n",
              "      <th>P27</th>\n",
              "      <th>P28</th>\n",
              "      <th>P29</th>\n",
              "      <th>P30</th>\n",
              "      <th>P31</th>\n",
              "      <th>P32</th>\n",
              "      <th>P33</th>\n",
              "      <th>P34</th>\n",
              "      <th>P35</th>\n",
              "      <th>P36</th>\n",
              "      <th>P37</th>\n",
              "      <th>revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1999-07-17</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>IL</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5653753.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-02-14</td>\n",
              "      <td>Ankara</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6923131.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-03-09</td>\n",
              "      <td>Diyarbakır</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2055379.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-02-02</td>\n",
              "      <td>Tokat</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7.5</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>2675511.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-05-09</td>\n",
              "      <td>Gaziantep</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4316715.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>2008-06-25</td>\n",
              "      <td>Trabzon</td>\n",
              "      <td>Other</td>\n",
              "      <td>FC</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5787594.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2006-10-12</td>\n",
              "      <td>İzmir</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9262754.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>2006-07-08</td>\n",
              "      <td>Kayseri</td>\n",
              "      <td>Other</td>\n",
              "      <td>FC</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2544857.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>2010-10-29</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7217634.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>2009-09-01</td>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6363241.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 42 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Open Date        City  City Group Type  P1  ...  P34  P35  P36  P37    revenue\n",
              "0   1999-07-17    İstanbul  Big Cities   IL   4  ...    5    4    3    4  5653753.0\n",
              "1   2008-02-14      Ankara  Big Cities   FC   4  ...    0    0    0    0  6923131.0\n",
              "2   2013-03-09  Diyarbakır       Other   IL   2  ...    0    0    0    0  2055379.0\n",
              "3   2012-02-02       Tokat       Other   IL   6  ...   18   12   12    6  2675511.0\n",
              "4   2009-05-09   Gaziantep       Other   IL   3  ...    3    4    3    3  4316715.0\n",
              "..         ...         ...         ...  ...  ..  ...  ...  ...  ...  ...        ...\n",
              "132 2008-06-25     Trabzon       Other   FC   2  ...    0    0    0    0  5787594.0\n",
              "133 2006-10-12       İzmir  Big Cities   FC   4  ...    0    0    0    0  9262754.0\n",
              "134 2006-07-08     Kayseri       Other   FC   3  ...    0    0    0    0  2544857.0\n",
              "135 2010-10-29    İstanbul  Big Cities   FC   4  ...    0    0    0    0  7217634.0\n",
              "136 2009-09-01    İstanbul  Big Cities   FC   4  ...    0    0    0    0  6363241.0\n",
              "\n",
              "[137 rows x 42 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 599
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aViODuORh7dn"
      },
      "source": [
        "Here I am extracting the **month** from the feature **'Open Date'**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSo_oj_aF3mj"
      },
      "source": [
        "df['month']=[x.month for x in df['Open Date']]"
      ],
      "execution_count": 600,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwLSlQsoiU3o"
      },
      "source": [
        "Here I am extracting the **year** from the feature **'Open Date'**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EleiuLuyGJfo"
      },
      "source": [
        "df['year']=[x.year for x in df['Open Date']]"
      ],
      "execution_count": 601,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkC26SmDLcb1",
        "outputId": "a3657713-b1a2-4598-8685-1457a976db71"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 602,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Open Date', 'City', 'City Group', 'Type', 'P1', 'P2', 'P3', 'P4', 'P5',\n",
              "       'P6', 'P7', 'P8', 'P9', 'P10', 'P11', 'P12', 'P13', 'P14', 'P15', 'P16',\n",
              "       'P17', 'P18', 'P19', 'P20', 'P21', 'P22', 'P23', 'P24', 'P25', 'P26',\n",
              "       'P27', 'P28', 'P29', 'P30', 'P31', 'P32', 'P33', 'P34', 'P35', 'P36',\n",
              "       'P37', 'revenue', 'month', 'year'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 602
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSsfdg5UYH56",
        "outputId": "6dfe2226-4713-406e-91d8-e442118b1d76"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 603,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Open Date', 'City', 'City Group', 'Type', 'P1', 'P2', 'P3', 'P4', 'P5',\n",
              "       'P6', 'P7', 'P8', 'P9', 'P10', 'P11', 'P12', 'P13', 'P14', 'P15', 'P16',\n",
              "       'P17', 'P18', 'P19', 'P20', 'P21', 'P22', 'P23', 'P24', 'P25', 'P26',\n",
              "       'P27', 'P28', 'P29', 'P30', 'P31', 'P32', 'P33', 'P34', 'P35', 'P36',\n",
              "       'P37', 'revenue', 'month', 'year'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 603
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Wzl8W15iW_c"
      },
      "source": [
        "* Look,there are two features added named **'month '** and **'year'** besides existing features.\n",
        "* Now we will **drop 'Open Date'** as well as we have extracted all the information from it and now its of no use to me."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZu5c9baGJ_J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "549d0614-df1b-442b-daa4-81bf2efc5277"
      },
      "source": [
        "df=df.drop(['Open Date'],axis=1) # 'drop()' function dprops the feature mentioned in argument from data set\n",
        "df"
      ],
      "execution_count": 604,
      "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>City</th>\n",
              "      <th>City Group</th>\n",
              "      <th>Type</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>P9</th>\n",
              "      <th>P10</th>\n",
              "      <th>P11</th>\n",
              "      <th>P12</th>\n",
              "      <th>P13</th>\n",
              "      <th>P14</th>\n",
              "      <th>P15</th>\n",
              "      <th>P16</th>\n",
              "      <th>P17</th>\n",
              "      <th>P18</th>\n",
              "      <th>P19</th>\n",
              "      <th>P20</th>\n",
              "      <th>P21</th>\n",
              "      <th>P22</th>\n",
              "      <th>P23</th>\n",
              "      <th>P24</th>\n",
              "      <th>P25</th>\n",
              "      <th>P26</th>\n",
              "      <th>P27</th>\n",
              "      <th>P28</th>\n",
              "      <th>P29</th>\n",
              "      <th>P30</th>\n",
              "      <th>P31</th>\n",
              "      <th>P32</th>\n",
              "      <th>P33</th>\n",
              "      <th>P34</th>\n",
              "      <th>P35</th>\n",
              "      <th>P36</th>\n",
              "      <th>P37</th>\n",
              "      <th>revenue</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>IL</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5653753.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ankara</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6923131.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Diyarbakır</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2055379.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tokat</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7.5</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>2675511.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gaziantep</td>\n",
              "      <td>Other</td>\n",
              "      <td>IL</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4316715.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Trabzon</td>\n",
              "      <td>Other</td>\n",
              "      <td>FC</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5787594.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>İzmir</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9262754.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Kayseri</td>\n",
              "      <td>Other</td>\n",
              "      <td>FC</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2544857.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7217634.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>FC</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6363241.0</td>\n",
              "      <td>9</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 43 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           City  City Group Type  P1   P2  ...  P36  P37    revenue  month  year\n",
              "0      İstanbul  Big Cities   IL   4  5.0  ...    3    4  5653753.0      7  1999\n",
              "1        Ankara  Big Cities   FC   4  5.0  ...    0    0  6923131.0      2  2008\n",
              "2    Diyarbakır       Other   IL   2  4.0  ...    0    0  2055379.0      3  2013\n",
              "3         Tokat       Other   IL   6  4.5  ...   12    6  2675511.0      2  2012\n",
              "4     Gaziantep       Other   IL   3  4.0  ...    3    3  4316715.0      5  2009\n",
              "..          ...         ...  ...  ..  ...  ...  ...  ...        ...    ...   ...\n",
              "132     Trabzon       Other   FC   2  3.0  ...    0    0  5787594.0      6  2008\n",
              "133       İzmir  Big Cities   FC   4  5.0  ...    0    0  9262754.0     10  2006\n",
              "134     Kayseri       Other   FC   3  4.0  ...    0    0  2544857.0      7  2006\n",
              "135    İstanbul  Big Cities   FC   4  5.0  ...    0    0  7217634.0     10  2010\n",
              "136    İstanbul  Big Cities   FC   4  5.0  ...    0    0  6363241.0      9  2009\n",
              "\n",
              "[137 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 604
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "id": "8_WDjuZbZRco",
        "outputId": "b7d92165-a963-4eb5-d483-a3282bd0769d"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "f,ax = plt.subplots(figsize=(15,15)) #Add a subplot to the current figure.\n",
        "sns.heatmap(df.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)"
      ],
      "execution_count": 605,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5eeaebe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 605
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X6olRFXB9z+5V1P4GYJf07NqJAH+/G5bv2LOfkcOHIAgCXTq2o6SklFyj7Z1ZJ68YaBwWSKPQQNz0Oob1aEPycfVVB0EQKKuoAqDUVElYgI+qXIxohlyUg1xsBMmK5dwhdC06cyN0sb2wnDtsk49qgVyQg1yUq8inHkDXquuN5dvdhuXMAdV3ft1aYbpsoCItB9lsIfenvYQMU199tpaabDq8PQD1rpQnM/NpHOxDoyAf3HQiw9o3IvncjZ+t23TqGsM7NFLruJxJ47AgGoUFKf7s1Y7kY+qrXpcyjfRu2wyAXm2bknzMduX5ZEY+jYN8aRTki5tOx7AOTUg+m6mSF4CySmUKWVphJszPS21DRj6Ng/1oFOyr2NCxCcln1Ve8BEGgrNKi6Kh0oeNaLo1D/WkU4q/o6NKC5FMOJ2ICtrioqCLM31tVrmvZFik7Ayk3C6wWqvZvx61HP5e+dMXJ7CIaB3rTKMBb6Y/WkSRfcr5qdp3NZw0Mj41scB36Nu2wZmYgGbLAYqEyeTvufW//w+1wa6vIW7MU+Yrt2/Ho/8flweZLudqX5v07bsqXAB6d2mBOy8SSbgCLhbJNO/EZ7KwjeOZkCj9di1xV5VSmb9sOS0YGUnVbKndsx6PfzbXlZJpDbHVtSfIph5MgoKzyes5Rx9bJq9nVOStAke8eS/KJS87yNbFZSZi/Q86KbomUn41cqOQc66n96GN73NBmfYe+WE7tU33n2TkWc1om5nQDmC2UJO7Ed0gfJ9nQWZPI/+R7J3+ezC6mcYAXjQK8qmMzguRLxhvasPl8NsNbR6i+c+/YBkt6JpYMpT/KtiTjNai/k2zgY1MoXrUWudK5TxsiPvWt22HNykDKro6LXdtxu+2P6wjo3oryy9mYruYgm60YfkohbHgvVR2n/C075O8rWTQOD6JRWPWxrFdbkn9zPJY5xEWgr6pcbBaLZMxEzjOA1YLl6C70ndUrBdz7Dce8679gKgVALi1S+6Ke+aIhxpiuhUO+OLADt+7qse4e9zcqt62H8up2lKgvAPp2a0XFlSwq07KRzRbyf95D0LDeqjq/1ye65m2QcjKRjYo/zYeT0XdR+9Pt9hFUJW+ws8PmT/f2bbGkZ2DNVHxR/st2PAc65yz/aQmUfL3GZc7Stai24bovDibj1k09RtwH3kXl9p9v6AslPg2q+Awf/jvnFw6bXjfu2grjVQP513Kwmq0c37CPDvFqHQXpRgypaciy847ZLbq2Iueqgdxr2VjNFg5u2EO3ePUY6R7fm70/JANwOHEf7fp1ctLTL74vW39QtpVP/TUVH39fgsODneql/ppKfk5t71fV+KtxS95pcmA30BmUNwALgtCsoX8gOzePyHDblbaI8FCyc41EV690yyksJTLINumKCPLjxBX1RGH63/oyY/EPrEn+FVOlmRWz1e9EEnyDkEsKaj7LpYWIkeolUDV1/YIRA0KRrtmuugq+gcgltsEplxQgRrVwJY7gH6LIp6mvmHpEBVOZmVfzuTIrH7/urZ3ko6YOo9GjdyO66Tl+3zxVWU5JBZF2k4cIfy9OZLhOGplF5WQWltG7WbhaR2EpkXZL7SKC/DhxWT3piW0czrajZ5lwZy+2/3qOsooqCkvL8QJySkxEBthOEF3ZMD2uAzNW72LNwQuYzBZWPKherpNTbCLS30FHuoOOQR2Z8VUyaw6cU3RMUi9HyykqJ9JuchwR4M2Ja+rJxvSh3ZnxyWbWpJzGVGVhxSPqd9eIwaFIeTk1n6V8I/qW6qWGAG69BqBv2wmrIR3TV0ttNpRWEuHrYbPB14OT2cVO8gCZxSYyi030aqRO/g2hQwwJRcq1a4cxF31b53Z49I/DrWMXrBnXKFuxBClX8ZcYGoqUYyefm4tbOxfyA+Nw69wFa/o1Sj6yyQMIQaHI+bbPUn4uuhv5sk1nJEM6ptVLVTL68FAsBttnS3YuHp3bquTd27VCHxmGafdBAqc6v/tMF+rgi9xc9K7aMiAO985dsKRfo3Spui05xWVE2p2sRgT4cCItRyU/Pb4HM1YmsmbvaUxVZlZMs70/J6fQQT7QlxNXDWr5EbcxY+nPrNl1XInNx0erygW/IORi+5yTjxjd0qkdAEJACEJgONKVU6rv9RGhmLPs/Gkw4tlFvZzXo31L3KJCKdt5iOCH7lOV5ZRVEOHnaWtHHWJTHxaKxWDznTUnF/eODn3athW6iHBMew7gP2msk+6GiE8xJBTJaKcjLxe3WGcd7v3i0HfogjXzGuWfLEGqXl7mEemQvzPz8O/eykm+0dR4mk7/G6KbniNj3lCV5RQ4HMsC/Thx2eFYdnd/ZnzwPWt2HFXiarbaH2JACFKBbeIqFRjRNVP3qRAegwh4P7UIRJHKxNVYzxxR+6Ie+aIhxtgfyRdipHLBz33uByCIVP70JZYTh2rK3SNDqLLrk6qsPHxcHFMjpgwnctpIBHc9Z/6hfkegEBiCVGCzQy4womuujk8xQrHDe857ij//+7WtLCwUa459fBtx76Buh1tsa3ThYVSmHMBvgvP7vhRf2B+HctG1dLDhui9eeh9EneKLkzZfeEYGU2Hni4rMfAJcxGdju/g87BCfARFBFNnpKMrKo3FXZx03IigimPxMW2zmZ+XTsqu6PwLt6khWCVNJOb5B6ovqIZEh5Gba+sSYlUtIZIg2QdK4Ze80ASAIgh4YgbJU75Zm8+FURvbpQNLbj7Lk8b8zd1UiklS3d4fpYntiOX/U6YrUH5Zv21u5S1VH+azPt3CozxNcenM1TZ+q+8vitpy6xp3tYtCJN/+ut6f/cQdHzqVx//zPOHwujfBAP0Txj4fr5pNpjOzSjKSn7mHJuAHM/ekg0k36Y/OJq4zs2pykZ0axZEIcc3/cf9N9uvnYRUb2aE3Sy+NYkhDP3G933rQO89F9FD85npIXH8Fy4gje01+4KfnrbDlnYEiriDr1R0PoqNqfQv7k+ymckYD56GF8n33ppuQr96VgHHc/+Q8nUHXkMAEv3Jw8gOXXfZQ8NYHSlx/BcvII3o8+f3MKBIGQOY+St6h+79ir3JdC3oT7yX9EaYv/8zffls2/XmBkz1iS5o5nScJw5q5JRpL++JKLzUfOMfK2tiS9kcCS6fcw96ukOucsffu+WFMP3nzOEQTCX5hGzrsr6/S79mw5n82QluE3H5uCQNDTMyh4z/l515uhIeKz6mAKBQ/dT9GsBMzHDuP75M3rSP88ib23zeb8m9/Q/Kmbf/nq5kNnGNm3I0nvzGDJzDHM/fzmj2WCTocQHk35B89jWvUunuNngZfP7wva0RD5or5jDJ0OMTKGsgVPU77sLbymPg3eN9cOgOxVmzne7zGuvfUVMbPv+30BR0QdYngM5f+ag+mTBXg96LxU7IYIAgGzZ1D04bKb/11HGyJiKHv3GcqXv4XX1Kduuk8Brn2exJ7bZnPuzW9o8ZT2HJDGn4tbddLkJQjCMeAwkAb84QWjgiBMEwThsCAIhz/++I+d2ESEhWDIsV2dyM4xEhFmu/MUHuiLocC2Tj67oITwAPWShXUpJ4nvrqyt79IimkqzlcIy261oubQAwc/2PILgG4hcWoAr9LE9sZ47pPpOLi1E8LNdQRX8gm4s37Y3VoeleaDcWfKItj2H5REVTFVWnlO96+T+tJeQ4eqlBuF+nhhKbO3KLjYR7rBs7TqbT6czvENjp+/DA30x5NuuFmcXlBAe6OdQx4/3HhvD2lcTeGK0cpfI39uz2gYvDEXltdqw7thl4tsrv92lcSiVFiuF5ZU2/f5eGIoddPg76Pj1EvEdatER4I2hqMymo6iccIclTusOnSO+i3JHsUvTiGodFTXlUr4RMcR2J04MDlVddQSQS4vBoiw1rNqRiL657cpZuK8H2aU2m7JLKwnz8cAVW85lM7xNpNP3DaFDyjMihtm1IzQMyaheRiWXFINZaUfF5o3oW9ueRZGMRsRwO/mwMKyO8sU2edPGjehjY9XlBUaE4DCbjuCwmg0faurY+zI5EV0z9VVIS44RfaRNhz4iDGu2bYwIPl64t2pG1Gf/pPHmL/Ho3I7IxfNxb2/TYzU6+CLMhS/s2lKRqPYFQLi/D4bC0prP2UVlhAc4xtZZ4rsod5u7NFNiq6BAyQnhgQ7yhaXOOWv/aeK7KXZ3aR6lxKZ9ziopQPC3zznBqrvl9ug69HFamgdgyTbiFmXnz8hQLHb+FH28cG/dlCZfLqTFtlV4dmlLo6Wv4dFRsSvcx5PsEtt4qTU2z2czPDbC6XtLrhF9pK0/dOFhWHPs+9Qbt5bNiFz5L2L++zUendoR9v583Ns1bHxKeUbEUDsdIWFY81yMker4rEzaiK6VTUelwSF/R4dQaXDdHwCGdSmEjVAvTQoPcjiWFZYQHuQQF3tPEN9DuXPUpUUMlRYLhaW2XCkV5SEG2Y6PYlAocpH6OCIVGrGcOACSFTkvGyknAzHMtolOffNFQ4yxP5IvpPxczL+mgNWKbDQgGdLRRdiWm1cZ8nC36xP3qBDMWTe+G5H30x6CHI6pcmEeYpDNDiEoFKnQoS0FRiy/7bfzp+0ZHSnXiC7cPr5DsdrfUfP2Rt+iOaFL/03Ej9/g3qE9IQvfxK1trEq/EGx/HApDLnDo04JczMf2qX0RafNFhSEfTztfeEYHU2m4sS9cxWdRdgEBdjoCokIozr5xjDtSkJ1PsN3zecFRwRRkq9tRaFdH1Il4+XlTajcmlm3+iPycfMKibX0SGhVGnuHG50p/SiTp1v67RblVJ03Xn2nqKsvyE7IsOy/CvQGyLH8sy3JPWZZ7Tps27Q/JDLq9D+s3b0OWZY6fPIOvrw9hobaThQ5NI0nLKSTDWITZYmXLkbPEdVYvU4kK8uPAWeV5lktZeVRZLAT52k7CpeyrCIHhCP4hIOrQx/bCeuk3J1uEoAjw9EHKUj9/IGVdRgiKQAgIVeTb3ob1wjFn+eBIRT7zolNZybELeLWIwrNJOIKbnrDR/clLOqyq49ncdkIcfGd3TA5LNzpEB5GWX0pGYRlmq8SW0+nExap3pQO4bCyhuMJMlxjndcAdmkWTllNARm6h4s9DZ4jroj55LSgpr7m6+emmfYy+3fb8V4eYYMWGglLMVitbTqUR57CrXZS/NwcuZwNwKbeYKouVIG/byVaH6GDS8koUHRYrW06mEdcmRq0jwIcDl67rKFJ02J2wdWgURpqxmIz8EkXH8UvEtVdvvhEV6MuBC8rSw0vZhVSZrQT52JYbWS+lIkbGIIZFgk6Pe587MB9Rn3wKgTYfuvXoh9Xu4dwOEf6kFZaTUWRS+uO8gUEtwnDkcn4ZxZVmukQGOJU1hA7L2VR0MY0QIyJBr8dj0B1U7d+rbkewrR3uffqrHvo2p1bLRyrynnfcQWWKWl60k/fo1x+Lw0Pj1kup6CJjEKp96dZnMOajKWobAmw69N37qnwJUHnyLG5NY9DHKHb4jIijLNnWH3JpOVcH/oNrwydxbfgkKn87g+GJV1W751lSU9HbtcVjcO1tce/b3+kB+A6Nr8dWsRJbxy66jq3z12OrgCqLleBqvR2aRJCWW0hGXnXOOnqOuE7q5cBRQb4cqN4E5pIhX4lN+5yVeQkxOBIhMAxEnTIxOncUR4SQKARPH6T0805lFSfO4dY0GreYCHDT43dXHKXb99t+o7Sci30f4NKQKVwaMoWK46mkPzavZve8DhF+pBWVk1F8PTazGdQ81Ol3LheUUVxpcRmbVafOom8cgz66uk+HDcK00xYXcmkZ6UPGkHH3g2Tc/SCVJ86Q++Srqt3zGiI+LedT0UXbjZGBd2A+6DBGguziond/1SYRxb9exLtFJJ5NwhDcdESO7kfuFnX+9rbL36FDu2G65JC/m0Ypudd4PfemEtdZvfwpKtifA6l2xzKzhSA/21Jm6eo5xLBohJAI0OnRdx+onNDbt/X4PvStlWdFBB9/xPAYpDzb8tD65ouGGGPWy6noImIQQqvzxW2DlQmSfTuO7kXfVnl2WPD1R4xshJRj82npsQt4No/Co7FyTA0edTsFSeoLnh7NbcfHwDt7UOFwTLVeOYsYHlPjT7eeg7AcV/vTfDwFXWxnO3/aTdzOpKJvHIMuSvGF9xZvOgYAACAASURBVJ13ULHbLmeVlWEYcS/Zfx9P9t/HU3XqNHnPzVXtnme9fBZduJ0veg9y4YsU9G273NAX1+PTyy4+c7YcUemwj8+wod0od4jP9OMXCW0WSVCjMHRuOrrc05fTW9U6auPy8QuEN4sitFE4Ojc9ve+5nV+3qsfIr1sP0X/MIAB63tWXMyknVeUzhj9OypZ9DB0zBIC23dpSVlKmLc3TAP4czzTVmzmvvcOhX3+jsLCYIaMf5LGHJmKxKA/433/v3xjYtxe79x1ixNgEvDw9eeOlp1Tyep3IC/ffwYwlPyBJEqP6dqRVdChLN+ylfdMIBnVuxdNjBjF/dRKrtx8FAeZNHI4g2C0TkSWqktfiMXqWsn3v6RTk/Czc+tyDlH0V62VlAqWP7eV0l6lG/pev8bjvaRBFLCf2IOdl4tZ/NJLhCtaLygRK3/Y2ZZmMK6wSF176lI5rXla2HF+zg/Kz6TR97n5Kjl0kP+kwMQkjCBzYCdlsxVJUytlZS9S+EEVeGNaVGWv2Ikkyo7o0pVWYP0t3nqZ9VCCDqicvm09fY3j7Rmof2Ptz/FBmvP8tkiwzqn9nWsWEsfTnXbRvGsWgrq05fC6ND39MRgB6xDbhxfHxahtGdGfG6l2KfNfmtAoPYOmOk7SPDmJQmxieju/C/A2HWX3gHCAwb1RvlS16ncgLd/Vgxlc7kWSJUd1aKDq2n6B9dDCD2sbwdHxX5m84xOr9ysFl3ujbnHWM6suMTzYrvugVS6vIIJZuOUL7RqEM6tCUp+/uzfz/7GH1buVZj3n3D1D7RJIwrVqMz/PvgqijaucmpIwreI6ZguXyOSxHU/AY9nflAWWrFamsmPLl7+K/6IsaXzw/qA2P/axsnT6qQzQtQ3xZuv8C7cP9GdRCuXq45ZyBYbGRrvujAXQgWSn96H0C3laeYahISsR69QrekxKwnEulan8KXqPG4N63v9KOkhJK//WOSr7kw/cJWlgtvykR65Ur+ExNwHI2lcqUFLz/PgaP/v2RrVbk4hKK33nHwQYJ05eL8Znzbs22uVLGVTz+PgXr5bNYft2H+7B7cevWT7liW1qC6WP1NshYJYxvLyFy+dsIOpGSdVswX7xK0OOTqDx1jvJk9cmMSyQrJYvfJ/DdRQiiiGmT4gufKQmYz6ZStS8Fr3vH4NGvui0lJRQvVLdFrxN5YXQ/ZqzcpPRJ7za0igxm6ZbDtG8UpsTWPX2Y//1uVu9WVi/PGxtX0zd6ncgL98UxY+l6JWf1aU+rqBCWbtxP+ybhDOrUgqdHD2D+t9tZveNXEATmTbjTOWdt/gLPcc8pOefYTmRjBm5xY5AyL2M9r0yglA0gbuAXq0TOG8to9OmbIOoo+iGJqgtphDwxkYqT5yjb4XxHXOUHUeT5gW147OdfkWQY1T5Kic0DF5XYbK5M7recy2ZY6wjXsWmVyH93MeEfvQOiSOn6zZgvXSVg+mSqTp/DtMv5DpnLPq13fFopW/4+/vOqn/P5JRFr2hW8JiRgOZ+K+WAKXveMwe02ZYzIJSWUfmDTIVslzr74Gd2/fQlBJ5K5Jpmys+m0fO4fFB+/RO6WIzR+aBjBAzohW6yYi8o4OWupygTlWHYnMz78jxIX/Topx7L1e2jfNJJBXaqPZV9vYfW2w8qxbPIIp5xV8d0yvB9/EwQR8/4kJEMa7n97EGvaeawnDmA9cwR9u+54v7wcZInKnz6FMrtdDRsiX9RzjCFJmL5ykS/unYL1ipIvLCcOoe/YE9+3PwPJSsXaj5HL7J6ps0pcefkT2nzzKoJOJPfbbZjOXSNmzgOUHb9IYdIhIqeOwH9AZ2SLFWthKZdmL3ayo+Lbj/Ce/TaCKFK1Nwkp6yoe90zCevUclt/2Yz11GH377vi89jHIEhU/rMT7kZdqbCj812JC31eOIWX/3YTl8hX8HpmC+cw5KvaoJz+u41vCtHoxPs8oY8S8ezNS5lU8Rk/GeuUclmP7sJw8hL5jD3zf/FSxwcEXslUi9cXPa+IzY80Ol/EZMqAjksWKpaiMk7PUSwYlq8TPr67i4S9fRNSJHPoumezz6cQ/dR/pJy5z+pcjNOrcgkkrnsY7wId2Q7oz9Kl/qORXv/oJz3z5CqJOZPd328k8f43RTz3AlRMXOPbLYXZ9t41p783ineQllBWWsvyJfzu54+D2g/S+oxer9nymbDn+jG13vWWbP2LGcOX1Pw+/9BCDRw/Cw8uD1Qe/+n0/a/zpEVztQPK/RhCEUlmWfV18X/MGYCAbeE2W5dqW7slm46VaimvHLVRZ+mLaVvfnF7yGTKP8g+m/X7EWvGcvp/yfCXWXn/MZALsinR9a/6MMNCjv4TB9+WKddXhNWoBp16o6ywN4DZyCafUrdZev3urVtOa136lZi45x8zD9vPD3K9amY9RzFE64o87ygau3A1D+0czfqXljvB9f0iA6jMPifr9iLYRu2Un24LrriNixE4CiiUPqrCPgK2WnpEud4n+n5o1pcSKJnCH180X4tp2Y1rt+NcIfwWukshW9acuS36lZi45hSjyUvflgnXX4zP2as21H/H7FWmiTuonyxY/VWd77CWXCcLX7nXXW0fSosj1yfeMz7576xUXIhp1sjXB+gP+PMjRbedeYaccnddbhNfhhAEpm3vU7NW+M35LEBskX9Rln4duq88XkeuSLL5R8cSD65p8Zu85tmT9S/OiwOsv7r1De85jRt+7HkZh9ynGkaGrdx0jA57+QFPFAneUB4rO/5blmtb6rtFYWXlHeSzW1Wd2fwf78yg+KLY1veoPmGpKubQZlY9JbnqrMU7feyb8d7tEdbkk/3pJ3mlxNmKq/r/uo0tDQ0NDQ0NDQ0NDQqAO36jNNGhoaGhoaGhoaGhoatwS35J0mDQ0NDQ0NDQ0NDY3/B9zCO9Tdymh3mjQ0NDQ0NDQ0NDQ0NGpBmzRpaGhoaGhoaGhoaGjUwi25e14D8pdunIaGhoaGhoaGxi3DLbnrmyNV147f0ufH7o273JJ+1O40aWhoaGhoaGhoaGho1MJffiOI+r5jCaC+73qqzzs+QHnPR0O80+fLmLq/d2VSxtcAlL87tc46vJ//HNPaeXWWB/C6/7V6vfMkZIPyjo76vucj72/1fO/Kxp2k31b3Pm10QOnThngHTX3fTXSi+T11lgfodHkDvzWru47OVzYAcK5d3d+vEXtmMwCHYu6ts45eGev4tcmoOssDdEv7mZJZd9dZ3u/D/wL1H6cABWMG1VlH0A/JDZL38u+tu47gdcpYP9Z0ZJ11dL26HoDTLf9WZx3tL26s1xgDZZx9Xo/8PbU6f5s+fbbOOrweUt4ftr8e7ybqk/kjhxuNrrM8QM/0nxqkT+vzHrE2qZsA+E/UhDrruC9rdYPkrL2R99VZR3/DfwBIja37u7fanktkSeO6xybAzGtf82mjuut4KF2J77ua1L0diWmJAHSO7FtnHb8Z/sCLsjX+1PzlJ00aGhoaGhoaGhoaGtVI1v+1BX9KtOV5GhoaGhoaGhoaGhoataBNmjQ0NDQ0NDQ0NDQ0NGpBmzRpaGhoaGhoaGhoaGjUwv+JZ5r2nrrMwu93IMky9/brSMKw21TlWfnFvPLFZkpMFUiSzKzRAxjQsUVN+dy332PX3oMEBwXy09fLnfTLssyC95eze98hPD09eOvlZ2jfppVTPfdevfGb+QToREwbN1K+5htVueew4fhNn4HVmAuAad06TIkba8r1nXvhNXEmiCJVyYlUblij1j9wGJ7jHkUuMAJQmfQTVcmJqjrRgzrTa/5EBFHkwppkTn60QVXebtoIWo8bhGyxUpFfQsrTH1OWkaeqIzbviPuQ8SCKWI7vwnJA/RtudzyArkm76g/uCN7+mD54XFVn7/lMFiYeUfqke0sSBnZQlWcVlvHKj/soqTAjyTKzhnZhQGyM7Te698bnkSdAFKnYupGK/6h96TFkON5TZyDlKb6s2LiOyqSNqjruvXrj+3i1jsSNlH/r3B++0+z64+d1VCSqdbj16I3PtGodSRup+N7BjjuH451gZ8cGZzs8+vQi8OmZCKJI2fpESr5U9+t1vAYPIOSdeWRPnq62s18vgp99DHQipes2UbzqW5fy3ncMIGzRa2RNeIyqM+fUuvv3JOT5GQg6keIfN1P06VqXOnzuvJ2If79K+v2PU3X6vKrMd2B3ol97BESRgrVbyV3+H5c6/If3o+myF7kw8ilMJy6odcR1J+bVR0Ankr92K7nLbqyj2fIXOX/PU+o23t6D8JdmgChS9J/NFHzynUt536H9if7wFa7e9wSVp9Tt8B/UjSbzH0IQRXLX/ILhox9V5WEThxE+eQRIEtayCq48t5SK8+mqOn5x3Wj0+iMIOpG8b7eSvfQHVXnIg8MJmzQC2SohlVdw7YWlVJy/VlOua9cdz79PA1HEvC+Jql+c/aDvdjvuI8aDLCNlXKbiy0Wq8oYYp/quvfFOmAmijsptG6lcp45v98HD8Zo4HSm/OudsWkfVNudxVp+859atN94PKWOs8peNVPzobIP35BlI+Yp8ZeI6Kn9R2+AX152Y1x5G0OnI+zaJnGXq/rhOwIi+NF/+ImfvftopNn0G9iDylWkIOpGCtUnkrfjepQ6/Yf1ovPRlLo2eTYWDjvqOs5hBnbmtOn+fW5PMCYf83WHaCGLHDUKqzt97XOTvvZdyWLjtpJJ7OzchoU9rVfk/t53k0DVFpsJsJb+8kj2z1ZsmBAzqRrM3EhBEkZw1v5C5ZJ2qPHxiPJFTRiBXj5HLc5Zhchgj/oO60WTew6ATMa7Z6jzOHhxG2JS7wCphLTNx9Xn1OGuIPvW+vQcRL0+vyRf5K133qW98f2I+nMuV+2ZReVKdLyIGd6br/IkIOpHL3yRzdom6T1o/OoLm4wcjWaxU5RVz+OmVlKcbney42bxlT+DgrrR4YyroRLJXbyNjyU+q8shJ8UROHabkm7IKLsxZgemcuj98BvQg/OVHEXQihd9vIf/jG8R3fH9ilrzMlb/PpsLBF00GdWbA64ovTq9J5uhStS+6PjKC9g8MQrJaMeWVsP3ZjylxiM+YQZ3pM28iok7k7JpkfnOI8Y6PKDEuW61U5JWw+xnnzb4enfcovQb3otJUyXvPvMfFkxed6kyaM4khY4bgG+DLmHZjnMqff/MpBgzpR4Wpgldmv8GZE+pjpqeXB4tWvkXjpo2wSlZ2Ju1x6a9bFln6X1vwp+SWnDQJgmAFTqDYdwaYDIQAXwIRKO9f+liW5Q9+T5dVkliwdhvLZ91HRKAfE95dTVznVrSMCqmps3LTfuJ7xDJ2YFcuZuUx86Mf2fSmbdI0+q6hjB8zkpfeWOTqJ9i97xBp6Zkkrv2U306l8saiJaxZ+b66kijiN/tJCuc8gzU3l+DlK6hM2Yv16lVVtYod2yn50EWzBBGvKbMpWzAHKT8XvzeWYT6agpShljfvT8b0xYcu7RREgdvemszWce9QnpXPXYnzuZZ0hKLzmTV18k9eYeOIV7BWVBE7aQg95o5j14wldkoE3IdOpHLtIuSSfDwnv4r1wjHkPJsO8/ZvMVf/r+8+BDGiqcoOqySx4L+HWT75DiL8vZiwYgtxbRvRMjygps7KnSeJ79iUsb1bczGniJlfJ7Pp6ZgaX/pMf5LiV55Byssl4L0VmA/sxXpN7Yuq3dspW3GDEBFF/GY9ScFzzyDl5hK0dAWV+1z0R/J2ShffWIfPjCcpnvsMkjGXgH+vwLzfhR27tlO2/MY6gubMJveJOVhzcglftQzT7hQsl9U6BG8vfO8fQ+XJ007ywc8/Qc5jz2PJziXq648w7UzBfDnNSd5v/L1Unjjj0obQl2eSNe0FLAYjMd8upnzHPsyXnHX4P3gvFcdd64ieP53LE1/BYsij5c/vUfzLASovXFNX8/EidOo9lP+a6lJHzPzpXH7wFcyGPFqtf4/irTfWUeaoQxQJf+VxMh56CXO2kabffUjZjv1UXXRuR+Ck0Zhu0I6mb03j3LjXqcrKo33iQgqTDqpO1vLW7SL3qy0ABA7tRZPXpnLuwTdUOhq/+SgXJryGOSuPNhsWUbT1oGpSVPDTTvK+Vna/8h/am5hXErg4qXpnSUHE8x8zKP9oLnJhHt7P/hvLyQNIBpu8EBaN+9B/UP7vOWAqQ/C1jR2lQv3HKaKI9yOzKZ3/LFJeLn7vLsd8aC9SukN8p+zA9Ekt46w+eU8U8Z72JCWvK2Pdf+EKqg66sGHvdspX3tiGRm88ysUJr2I25BG7/l8U/XKQyvPOcRU2dSRlR8+61BH1+gyuTp6L2WCkxbp/U7JtP1UuYjN4yqgbxnd9xpkgCvR5azJbqvP3PYnzSXPI33knr7C+On+3mTSEXnPHkWyXv62SzIJfTrB8bB8i/LyY8OVu4lpF0jLUr6bOnCEda/5fc+QyqTlFTu1o/vYjnHlgHlVZeXRMXEjBlkOqSVHeut3kfJUEQFB8L5q+PpXUCeox0uTNRzk3Xhkj7Tb+03mc/bSL3K+VcRYwtBeNX0vg/IPza+Qbok8jXn2c9ITqfPH9B5RuP+CcL3y8CJo4CtMxV30q0O3tKey+fwHlWfkM2fQGmUlHKTmXUVOl8MRVtg2fi9VURYtJQ+g0dxwHpi9W2VGvvCWKtFjwMKfGzqcqK58um98hP+mwalKU++NuDF8q/REc35Pmr0/m9Pi31L547TGuTX0Zs8FIsx/ep3TbfqouOvszaLJrXwiiQNybk/l5/DuUZuUz9r/zubz1CAV28Zl78grf/e0VLBVVdJw4hH4vj2PLY0tUOvq9OZnN49+hLCufkRuVGC+0j/FTV/j5LiXG204cQq+Xx6ns6Dm4JzHNYnh44MO06daGmW/N5KlR6otrAAd+OcCGLzbwyc5PnMpuH9KXpi0ac3fff9C5ewfmvvscE+562KneF8u+4dDeo+jd9Hzy/WKnco2/Hrfq8jyTLMtdZVnuCFQB0wEL8Iwsy+2BPsDjgiC0/z1FJ68YaBwWSKPQQNz0Oob1aEPycfXVJkEQKKuoAqDUVElYgI+qvGfXTgT4+3EjduzZz8jhQxAEgS4d21FSUkquMV9Vx61tO6yZGVizssBioWL7djz63/4HXKGga9kWKTsDKTcLrBaq9m/HrUe/PywPENKtJSVXsilNy0UyW7ny834aD+uhqpOdcgZrtS+MRy7gHRWsKhejWiAX5iAX5YJkxXLmILrW3W5sd/s+WM7sV313Mj2PxsG+NAr2VfqkU1OSU9VXvQRBoKxSOaUrragizM+rpkzfuh3WrAykbMWXlbu243bbH/clgL5tOywZGUjV/VG5Yzse/W5SR6zSp5LBzo4+N6fDvX1bLOkZWDMVHaat2/Ea6Nyv/o8mUPLVGqisUst3bIMlPRNLhiJftiUZr0H9neQDH5tC8aq1yA7yAB6d2mBOy8SSblB0bNqJz2BnG4JnTqbw07XIVc46vLu0pupqFuZr2chmC0UbduE/9DanehFPTyB3+Q9IlWZnHV0VHVXVOgo37MI/3oWOZxQdsoMOz85tMKdlYU43gNlCceJOfO5w3j42dPYkCj753kkewKdbayqvZFGZptiQ//Megob1VtWRSk01/4veHji+H9y7a2sqrxioqtZRsGE3AfE31qHz8sBeidg0Fik3CzkvG6wWLEd3oe/URyXv3ncY5t0bwVQGgFyqPrFtiHGqa9UWyWAbZ+Y923Hv5RxbtVHfvKdv3Q7JbqxX7dmOe++bG2NKf9jiqmDDbgJcxGbUMxPIWf6DyzHi1SWWqquZmK8psVX031343dnHqV7YUw+St+I//0/GWahD/r70836aOORvg13+znWRv09mFdA40IdGgT646USGtYsm+YLBhdcUNp3JYHi7GNV3vt1aUWE3RvJcjBGrwxhxHCQ+1/vEbpwFOox11Rjx9lTpaIg+9ewcizktsyZflCTuxHeIc5+GzppE/iffu8x7wd1aUnolm7K0XGSzlWs/7yfaoU9yU05jNSmy+Ucv4OXQJ/XNW37dWlFx2UBlWg6y2ULuT3sJHtZLVcepP1z4wj6+izfuwvdOVzZMJG/l9y79GdG1JUVXsimujs/z6/fTIl7ti4x9Z7BUx6fh6AV8I9W+COvakuIr2ZTYx7iDjiz7GD96AR8Hf/aJ78O2H7YBcPbXs/j4+xAUHuRk79lfz1KQU+D0PcDgYQPZ8J2yrfxvR0/h5+9LaHiIqk6FqZJDe48CYDFbOHPCxcRc4y/HrTppsmc30EqW5SxZlo8CyLJcgnIHKqZWSSCnsJTIINuEJyLIj5yiUlWd6X/ry8aDZ4h/aQUzP/qRF+4fclMGZufmERkeavuN8FCyc9W338XQUKScnJrPUm4uutBQHPEYGEfwJ58R8Po8xLAwm3xwKFKenXy+ETEozEnerdcA/BasxHv2awjB6nLvyCDKMm2TufKsfLwjnZPJdVqNiyNjx3HVd4JfEHKxTYdcko/g61qH4B+CGBCKdFV9ZSynxESk3cQ0wt+bnOJyVZ3pgzux8fhl4hetY+bXybzwt541ZWJIKJLRzhd5uehCnH3p3i+OgA8/w/eFeYihal/oQkORctX9IbrqjwFxBK/8DP/X1P3h0g7jDezoH0fAks/wfdGFHeGhWLNtOqw5RnQOv+PWpjW6iDAq9h5w0q0PC8VisJfPReeQ3N3btkIXEY5pj7M8gD48FIsht+azJTsXXYSDjnat0EeGYdp90LWOyBDMWbaYNxvycItU6/Ds0BK3qDBKdhx2qcMtIgRzpp2OrDzcHOzw6tAS9xvo0IeHOLTD6CTv0V5pR9lO1+1wjwymys6GqizndgCETx5Bp73LaDx3Mmmvqq9UukeGOOuIcNYROuku2u9eTvRLU0h/bWXN92JgCFKhrR1SoREhQC0vhEcjhsXg/eRCvJ9ehK5dd3V5A4xTMTgMyWhnR34uQohzznHvMxC/9z7F59l5TuX1zXtCcChWh7EuuhpjfeLw//dn+M6Zh+hgg5tjbGYZnfrUq2ML3KJDKd7uOjb1EWodFoNzbF2P79LkQ6511HOcucrfPrXk71gX+TuntIJIuwtQEX6e5JRUuJTPLCons6ic3k3U/lbi27akqiorD3eHE1eAiCnD6ZqylCZzJ3HllU/VOqKCqbLzZ5XBtY6wySPouGc5jV5Wj7OG6dNQzFl2/WEwonfKFy1xiwqlbKfrPvWKDMZkt7zMlJWPVy190mzcIAwOfVLfvOUe5ZyzPFz4MnLqcLrvX0KzVyZy6WV1f7hFhGAx1B7fii/CKLtBfPtEBlFiF5+lvxOf7R+I42qy2hfeUUGUZdnFuCEfn6jaYzzdwZ+hkaHk2vWr0WAkNNI5Z9RGeFQYhszsms/ZWbmERznnvuv4+fsSF39zF3P+50jSrf13i3JLT5oEQdADI1CW6tl/3wzoBjidBQqCME0QhMOCIBz++OM/9mLbzYdTGdmnA0lvP8qSx//O3FWJSJL8+4INTOW+FIzj7if/4QSqjhwm4IWXbkrefHQfxU+Op+TFR7CcOIL39BfqbEvzv/cnpEsLTi3b+PuVb4Cu3W1Yzh52usr4R9j82xVGdmtB0rP3suTBQcz9IeWm+qTqYAoFD91P0awEzMcO4/vkzfkSlP7Im3A/+Y8o/eH//M3rqDqQQsHU+ymamYD518P4Pn2TOgSBwNkzKPpg2U3/9nX5oKdnUPCe87N4N6MjZM6j5C2q+4uiEQSi5j5E1luf/n7d2nS88hCZddUhCIQ9P43cd1f+ft3fIeeLTZzoP4Nrb31J9Ox/1EmH8ctETg+YTuaCL4iYNfamZAVRhxAWTfmHL2Ja9U88H3gCvHx+X9AF9Rmn5kMpFE1/gJKnH8J8/DA+T7x40zrqnfcOp1D46P0UP5Wg2DD75sdYzNyHyHzzs5uTc9AR8dLDZL/tvNTnZnTUe5xV06I6f5+oR/7ekprJnW2i0IlCneSzV23mWL/HSHvrK2Jm1+0FrLlfbOLk7dNJf/tLombdxDhroD4Nf2EaOQ2QLwCajOlPUJcWnFv635u2oyHyluHzzRztM5Mrb35N46dusj8EgYgXHyHnnYbxRey9/Qnv3IKjy+seny3/3p/Qzi34rR46GgKdTse7y+fzzSeunwHT+Gtxq06avARBOAYcBtKAmrMkQRB8gR+AJ2VZLnYUlGX5Y1mWe8qy3HPatGmEB/piKCipKc8uKCE8wFclsy7lJPHdYwHo0iKaSrOVwjITf5SIsBAMObarNNk5RiLC1Fc2JKMRMTy85rMYFobVqL4bJRcXg1m59W7auBF9bKxNPt+IGGInHxyKVJCrli8tBosiX7UjEX1z9QO+5YYCfKJtV6C8o4IpNzjfno4a0IFOs0ayY8p7SFUW9W+UFCD423QIfsHIpa5vcevb9cZ6xvnuRrifF4aisprP2cXlhPt7q+qsO3qJ+I5NAOjSJIxKi5XC8koApDwjYqidL0LCsOY5+LLE5ovKpI3oWsWqyq1GI2KYuj+kWvqjInEj+tZqHU52hNbBjhwjugibDl14KNZcW78K3t7oWzYnbOm/iVz3De4d2xO66M2ackuuEX2kvXwY1hzblU/Bxxu3ls2IXPkvYv77NR6d2hH2/nzc29nssOQY0UfarqLpI8KwZtvr8MK9VTOiPvsnjTd/iUfndkQuno97e1t8WQx5uEXZYt4tMgSzwaZD9PXCM7YpLb59mza7P8G7WxuarpyLVyfbhinm7Dzcou10RIVgznbW0fLbt2m7R9HR7JO5du3Ic2hHqFrexwuP1k1p/OVCmv/yBZ5d2hKz9HU8OtjaUWXIx93OBvcodTscyf95D4EOS5OqDHnOOrJvrKNg/W7V0iSpMA8x0O4uc2AocpFaXirMw3LyAEhW5PxspJxMxLDomvKGGKdSfq7qzqgYHIacV0vO2bYRfQuHMVLPvCfnG9E5jHWptjH2y0Z0DjaY8SRvcQAAIABJREFUHWMzKtQ5Nts0pdW3b9F+z0q8u7Whxacvq2LTkq3WoY90EVuxTWn6zTu02vkZXt3a0njFq3ja66jnOHOVv8tukL+7zBrJNhf5O9zXE0OJ7diWXVJBuJ+nkw6AzS6W5sH1+LbdhXCPCqEqK9+p3nXyftpD0HCHMZKVj7udP90ja9eR//NuAu02cGqYPjXiZnf3QB8ZisWhT91bN6XJlwtpsW0Vnl3a0mjpa3h0tOULkyEfrxibL7yigjG56JPwAR1oO3sUKZP/5dQndc1b16nKcs5ZlbX40vjTXoKHq5fvmbPz0EfWHt/usU1p8tW7tNz+OZ5d2xKz7FU87XxRZijAzy4+fW8Qn41u70DPJ0ayMcE5PsuzClTL7bwjgynLctYRfXsHuj4xkq1T1ToWb1pMfk4+YXb9GhoZitFgdNJxI7775QuM2UYioyNqvouICiMnK9dl/VcXvcDVS9f4eqXrTV00/lrcqpOm6880dZVl+QlZlqsABEFwQ5kwrZZl+cfaVSh0aBpJWk4hGcYizBYrW46cJa5zS1WdqCA/DpxVHrq8lJVHlcVCkK+XK3UuGXR7H9Zv3oYsyxw/eQZfXx/CQtW3x82pqehiGiFGRoJej+cdd1CZsldVRwy2yXj0648lzfaws/VSKmJkDGJYJOj0uPe5A/ORfSp5IdAm79ajH9ZM9YOkeccu4dc8Et/GYYhuOpqN6sO1pKOqOsEdmtLnnQR2TH2PijynOSlS1mWEoHCEgFAQdcoJ14VfneoJwZHg6YOUccGprENMCGn5JWQUlCp9cuIqcW3VB+eoAG8OXFJuj1/KLaLKIhHko6zFtpxPRRfdCDFC8aXHwDswH1T7Ugiy+cK9d3+nzRksqano7frDY3Dt/eHetz/WNAcd56r71N6OA7XYcZuzHVVnUtE3jkEXpejwGnoHpl22fpXLysgadi+Ge8djuHc8VSdPY3zWNlGoOnUWfeMY9NGKvM+wQZh2ptjkS8tIHzKGjLsfJOPuB6k8ceb/Y++8w6Oo9v//mtn0kN4h9BaK9CBSQ5GmKFzuBekQGyKIBSz0pqAioiBg4VoRsSuXGgkBJID0noSWhJDek80m2d2Z3x8Tszu7QXST7+/y9Tvv5+F5yJxz3vs5n3bmTPkMOc8uUlXPq7iQiHPjBjg1qOIY1g99nJUMpWWk9P0XN4dO5ubQyVScu0zmrEWqql5l567g2qQ+zuEhCM5O+IzoS/EvlkdJpJIyLnedQGKfx0js8xhlpxNJeXyFqppV2dkruFhx+I7oS3GMmuNSlwkk9H6MhN4KR/Jjlg1k+flEnBvXx6lBCDg74T28H/r9lvd0pNIyrvUcy41BU7gxaArlZxO4NWOJqnqe/swVXJuG4dIwGMHZCf+He1OwV/04imvTsOr/+wzqSsWNDFV72Vk1h9+IPhTFqB+rcW1i4fAe2I2KZAuHlJqEGFQfwT8EdE44demL6bx6U2M6fwSnFvcAIHh6IwbXR8q1vJtSF3FqvpqIGBaOGKz4hXPvAVSeiFePt8453XpivqXOObXNe6YrCSoZXHoPwHj89jHmHNnLrkiEYo/6uDQMqbZHcYxFn1JJGRc6T+RS78e51Ptxyk4ncv3RV1W+aTiXhEuTBjiHK77l82BfSvdZcZSWkRQ5nqv9ornaLxrD6QRuPrlMVT2vtnGWe+Y63lb5u9lt8nfPVdHsu03+bhfmS2qBnluFZRjNEnsup9OvRahdvxt5JRSXG+lY3/7RqNIzV3FrGoZrlX8H1BAjblYx4juoK+U2MaI/ewU3mzgrtI0R6zgb2E0VZ3Vh0/LzSTg3ro9zVb7wGt6P0libfHHfI1wfOJXrA6dSfjaBtBlLVdXzCs5cp17TUDwaBiE462j4cA8y9pxUzcO3fWO6vPEo8VPeoqIGmziat35HyZmruDcLw7WRosugkb3It7OHxcZ+g7pQfkP9Hlv5+aTq3IuzE94P9KV0n1qGq/eO49qAaVwbMI3yMwncemqZqnpe1tnr+DQJxavKP1s+1IMbMWr/DGzXmP6rotkRvQZDDbrIOWvv46k2HAHtGtNrVTQx0fY+PmvYLI7sOcLA0corFq07t0Zfor/tu0s1YcygKcTuPsiIMUrFyA5d2lFSoic32/7C18yXnsDLy5M3Fq61a7vbIcvSXf3vbsVdWT2vJgiCIKDccbosy/KaPzvOSSfy8tgBPLX+OyRJ4uH72tOifiAbth+mbeMQojq04PnRUSzbspctsadAgKWThqL8nIK5i1dx/PQ5CguLGThyIjMenYTJpFzdGDvqAfreF8mhI8cZNiYadzc3ls+zr9SCZKbk3bX4vbFaKU+9ayfm5GQ8p0VjSkygIj4ej3+MxrVXL2SzGbm4hOJVq6zGSxg+WYfnS6+DqKPywC6kW8m4jZ6K6UYSplPxuA75B85deoLZjKQvpmzT6yoRZLPEbws+ZdCXLyolx7cdoCjpFh3njCbv7A3SYk7RdeE4nDzd6Pf+MwDob+Wxf5qVumWJypgtuI55AQQR0/lDyLnpOPceiZSZjPnqGUXvbe6t8ep1tU0e6MZTn+1HkmQe7tKMFsG+bNh3jrYN/ImKCOf5oV1Y9tMxtsQnKDYZ1cNiE8mMftNavJeuripDvBNzajLuE6IxXUnA+Fs87iNG43xvLzCbkUtKKH1nlVoIyUzJurX4vr4aQRQx7NqJOSUZz6nRGBMTqDwSj/uo0bj2rLJHSQnFb9hz6DeuxXt5lRwxVXJMrJLjWDzuD1nJUVpC6ds2HGaJwtXrCHz3dQRRh377Lkw3kvF+YiqVl5MoP6Q+SbWDWSL/9XUEv7cKRJHSn3djvJ6Cz/QpVF5KUm3A/ogj97X1hG56DUEnUvLDHozXUvB7ejIVF5Moizv6pzjSF2+i6WdLlZLj3/xCxZVUgp+bgOH8FUp+qfkdIjuORZto9tlS0IkUfK1whFRxFN+JwyyRs2ID4R+9CqJI8fd7qbyaQsCsSZRfuKI6EfkjjtQFH9L6y8UgiuRu20d50k3qzxlH2dmrFMYcJ2TqcLz7dEA2mTEVlXL92XftONIWfkDzz5coJcerOEKfH0/Z+asUx/xG4NQH8OrdEdlowlykJ+V5q0VXkij/dhMeM5YpJcePxiBlpuIyfALm1CuYL/yG+fIpnCK64DFvA0gSFT99DGWWO+p1EadIZso+eod6C99UPnMQuwvpZjJuj0zDfDUR44l4XB8YjUtkTyVGSkvQr68hzmqV98yUfbgWr8VVMbZvJ+abybiPi8Z0NQHj8XjcHhiNc6RVjK2zj7G0Re/T7DPFHvlf/0L5lSp7nLt6Z7+q4shcupFGnyxHEEUKv42h4koqQc9OxHD+imoD9UcctYkz2SxxdMGnDK7K31e2HaAw6Rad54wm9+wNbsacInLhOJw93Yiyyt/7rPK3kyjy8qD2PPXNUSRZ5uF7GtIi0IsNhxJoG+pLVEvl5Hr35XSGtmmgWget55E8/yMivlyEoBPJ/mofhqSbhM99BP3ZaxTsPU7ItGH4/B4jhaVcm73OjiN14Ye02rIYRB15236pjjP92asUxRwneOpwvHt3rI6zG8+9oxpfFzbNXr6R8M0rQNRR9N1eKq+mVuWLJPT772xT2SxxZt4n9Nn6EoJOJPmrAxQn3aLt3NEUnL1Bxt5T3LNwPE6ebvT4YDYAZbdyiZ9qtabWNm+ZJa7P+4h2WxeATiR7ayyGxDQavTiW0jPXyN97grDoYfj27YBUlW+SnrG3R9ayjTTcvAJ0IkXfKroIfGYi5ReuUBr753RxcOGnPPzFi0rJ8W0HyE+6RfcXRpN97gbJMafoNX8czh5uDN2k+Gdpeh47oteoOI4s/JShWxQfT6ry8S5VPp4ac4rIBYqPD/idw6Zk+fHY40T2j2Tzoc1UGCp4e87b1W3rdq1j1jClXHv0vGiiHo7C1d2Vz459puI49Es8fQb2ZMfRbyg3VLDwWcvFua9/+ZQxg6YQEhbEE89N43pSMttiPrmjfjT8PSDIDjzH/j8NQRBKZVmuZ3OsN0pRiPPA79vQebIs77QdbwXZsM/x58PdBz4BgDH3usMczoHNyOrfz+HxACH7D1A4YYDD4323xALwWYOJDnNMvvUFAGWvT3OYw+OljzFsW+rweAD3sYvJG+G4PgO2HwAge6DjHMH7DpD3QO1sGrDjAGn3Om7T8GOKTVO6DHKYo/GpXwC4fs9ghzmand/L+aYjHB4PcM+N7Zxr4jhHh2TlOx5JbYY6zNHqslL++3iDUQ5zRN76gdONHnZ4PEDn1J8oeeZBh8d7vau8L1HbOAUoGB3lMIffd3F1kvfyRznO4f+DEutnGj/kMEenlJ8BuNT8AYc52l7bUasYAyXOPq5F/p5Wlb8Nm+c4zOH+qPLJjaP1/+EwR4/07zkRPtLh8QDd0n6sE5smRgy7Q8/bo3WCUlnt27AJDnP8M2NLneSsw6GOvS8G0CtT+f5bQqvhDnNEJO1kfUPHfRNg5s0v2BzuOMejaYp/D2/k+Dx2piqnkh1C7asG/lmcyzwC4NhLgP+fUXHt6N138m8F1+Y97ko93pV3mmw3TFXHfuV/iTNq0KBBgwYNGjRo0HBX4i6uUHc34259p0mDBg0aNGjQoEGDBg0a7gpomyYNGjRo0KBBgwYNGjRo+APclY/nadCgQYMGDRo0aNCg4X8Ad3GFursZ2p0mDRo0aNCgQYMGDRo0aPgD3JXV8+oQf+vJadCgQYMGDRo0aLhr8L+iYFlF0q939fmxa6ved6Ue//aP55W9M93hsR6zNwHUqnRuyP4DtSpZDkrZ8rooT72hFmVBZ9xUSnoWPznEYQ7v9/egX+Z4iVYAz0VbKFvzuMPjPZ7/EADDpy87zOE+ZRWGrYsdHg/gPm4pNzre7/D4pmdjAOpEF7WNkdqU7gWlfO/llo6Xim1zRSkVmxUV5TBHSFwcAMdqUU753vTva1UCHpQy8LWJEc9FWwAoW/2Ywxwecz4CoGia43Px+fgXfgwd7/B4gJGZX1L6vOOlpeutUUpL16bEdbe0HwFqXRK/LmJke+g4h8ePyNwKgH6J4xyeSxSO2q4BdVGK/lp7x2VofmEPAJl9oxzmCD0YB9S+3HdtPl0RsEMpqV8Xn53Iud9xOYJiDhATMtbh8QD3Z21jXGPH43RrihKnM5qMcZhjQ/LXAExs7Pga8EXK9w6P/f8OyfzfluB/JbTH8zRo0KBBgwYNGjRo0KDhD6BtmjRo0KBBgwYNGjRo0KDhD6BtmjRo0KBBgwYNGjRo0KDhD/C3f6dJgwYNGjRo0KBBgwYNVdBKjjuE/xObJrFxW1z6jQFBxHTxMKYTe1Ttzn3/hS68lfKHkwuChxeGTc+r+rhEdsdr5izQiRh27KBs65eqdrchQ/Ga/hTm3BwADD/8gGHnjur2Ba+t4eDh3/D38+XHLzbZySjLMivXbuLQkeO4ubny6vwXaNu6haqPa49IfJ+fiSCK6H/eSclnW2ucr3v/PgSsWkrWlOkYE5JUbQ2jOtB7ySREncilrXGc3rBd1d7x8WG0eSQK2WzGkFdC7JwPKL2Vp+qja9cNtzHTEUQdlb/uonLP13YyOHXti+uDSuEJKe06hs2r1BzNO+AyZBKIIqbTcRgPq+VwGTwRsUlbAARnFwRPb8reeKK6XWzSDpeoR5Tx5w9hOr5bNd653xh0DSOq/nBBcPfCsGG2qs/ha1m8EXMOSZYZ1bEx0T1bq9rfjDnH8ZRcAMpNJvL1lfz6woNqjisZvLH7FJIkM6pLM6L7tFW1ZxTqWfjjMUrKK5EkmWcGdaRPq/qqPu49u+H/0gwEUaTkh10U/XubnT4BPAb2JmTNYm6Ne1p1vC50URcx4tG7KyHzp4MoUvTtbvI//KbGedQb3IsG7y4g+Z/PUHHhiqrNs09XQhY8iaATKfx6D3kf1MzhNaQX4evnc2OUeh4u3bvjNXMm6HRKnH5pE6dDh+I1fTrmXMWuhh9+wLBjh6qPT1RnGi+PRhBFsrf+Qsb6H1TtwZMGEzJ1GLIkIenLuTF3I4Yraerf6RmJ/5wZoBMp/WEXxZ98VeM8PAb0IWj1YjImzKDysiVW7xQfALq29+LSbzTIMlJWKhU/vKdqF5u0w2XAOMWm5w9h+m2Xqt05aiy6RlU+7+SC4OGNYf0zqj5O7SNxGz8DRBHjwV1U7LSfh3NkP1wfngzImG9ex/D+a2p99e/APcsnI+hEUrbs58p69VyaPzmcxhOikE0SlXnFnHruAwxpuZZ5RnTBdeRjIOowHt2LMfY7OxmcOvbCZcg4ZEBKv0HFF2+p2r2jOtNo6WOgE8ndGkPme+oXtoMmDiFo6nAwS5j1BlJe2kC5jU3r9etCg0WPg04kf1sMORu/tZMDwHtoT5pseoUrI57DcP6qqq22MRLUvyPtq3SZumU/V9f/rBrXePIgmky7H9ksYdaXc3buR5Qm3VL10bXoiMvQyYpvndqP8Vc1B4CuXQ9cokaDDFJWChXfrbdpr/0aUNs11b1XNwJfno6g01H83S4KN9vLAOA5qDehaxeSNnYmFRfV+cale3e8n5kJopIv9FvUMrgPHYrXjOmYcxR/LPvePl/49u9Es+XTQCeStWUft9b/qGoPnTyY0GlDkM1Kvrg6930MSWrfcu7aHc8nZoEoUr53B+XfqOVwHTQUj+inkPIUXZRvV+ek2uYbAOdu3ak3YxaCKGLYtQPDNhsZBg+l3uMWGQw//UD5LrUuAvp3pPWKqQg6kVtbYkle95OqPXzyIMKjh4BZwqQv5/KcD9Db+CfAlCWP0al/VyoNFWyc8y7JF+yLaTVt35zpbz2Di5uL6njbfh3516JpCDqR+G372LtRLYOTixNT1sykYftm6AtL2DxzLflpOXb8k5Y8Sqf+XagwVPDBnPU1ytCkfTOefGsWLm4unNl/yq5dw98Pd+WmSRAEM3AeRb7LwBRAAg4CrlXHv5Vl+c4lzAQBl6hxVPzwDnJpAW6PvIL5+jnk/IzqLsaD32Cs+r9TxyjEoIZqDlHEa/azFM59AXNODv6b3qci/jDmlBRVt/L9sZS8+06NYowcfj/jRz/EvOWra2w/dOQ4qWnp7Ny2mXMXE1i+ej1bP1yrksFv7mxyZs3FnJ1D8CcbMRyKx3RDLYPg4U69saOpuHDJXhWiQN8VU9g+fhWlGfn88z/LSI45ScGV9Oo+OReSufjAQkzllbSbNJCe88exd8Z6axLcxz2Nfu0ryAW5eL6yDtO5o0gZqRZRg+vjOnQs+jefh7JSBC8fG0EEXIZNpfyLlcjF+bg9thxT4inkXEvyrNz7RfX/nSIHI4Y2Vo8fMJ6K795GLinAbcJ8zNfOqm164GuLTTsNQAxW29Qsyazcc5ZN43oR4u3OhI/3069lGM2DvKv7zL2/Q/X/tx6/RkJWoQ2HxMqdJ9g0qb/C8WEM/Vo3oHmwZb4fHrzI4HYNGRPZkmvZRczccoBdrawqgokiAfNmkfnkS5iycqn/5XrK4o5gvJ6q+i3Bwx2fCaMoP3fZXpe11EVdxUjIoqdJi56HMSuXxt+8Q2nsMSqv2czD0x2/SQ9jOJOAHUSR0CUzSJ06H2NmLk2/W0tJ7FEqr95Ud/N0x39KDRyiiNfs2RTOmVMVp5uoOFxTnO6n5J2a4xRRpMlrj5PwyFIqM/Jot/MNCvccV22K8n44RPbnewHwHRxJoyXTSJywXMXh/9Issme8hCkrh7Av3sNwIB7jDXubeo0fRcX5Gmx6h/gQ/ENw7vUQho+XQHkZeHjbcwyaQMU3axS/mLgA87UzyHlWNo3bZrFp5wGIwY1sOETcJs1Cv/ol5Pwc6i16D+OZeKR0q1gPaYDrA+MofW12Vaz72uhToOPKaRwesxJDRh5Ru1eQufcUJVYnSkUXkjkwZAFmQyVNpgyi3cJxnHhyXbUMrv94EsOmRchFebg/9xami78hZ1l8QggMw3ngvyhb9xIY9Aj1bPKNKNJoxZMkjV+MMSOPNjvepHDvb6pNUd6PB8n5QrlQ4HN/JA0XR3Nl4jIVR4Nl07kxcSHGzDxa/LyG4phjVNTgm4HTRqA/XbN/1ypGRIF7Vk7j6JjXMGTk0Wf3q2TuPanaFN36/jApnykV0UIGd6XdkkkcG2+1WREEXIZPo/zz15CL83B7/FVMiSeRc6x9KxTn3g9j2LwEyvXgaetbdbAG1HZNFUWCFjxN+uOvYMrMJXzbOvT7j9acNyeOpPzs5Ro5vJ+bTcHzSr4I+GAT5b/ay2CI3U/J2tvni2YrH+PimGVUZuTTcfcq8veeUG2Kcr4/ROZnSr7wH9yNpkumcGn8qyoOz6eepXjBC0i5Ofi8/T7Go4cx31TLUXkwFv0mixz1Zr9YPb5W+aaKw2vWsxS+pMjgt/59Ko8cxpyqlqHiQCyl62+nC4GIVdGcGvMq5el53LtnJTl7Tqg2RRnfHyatyj+DhnSl1dLJnB63UkXTqX9XQpuG8Vy/p2jRuRWPrpjOwpEv2v1c9KtP8uHL73H1dFJ19TxBFBi77FHenbiCwsw8Xvp5JediTpB51SJDzzEDKCvSsyTqGbqO6MmolyeweeZaFXfH/l0IbRrGC/2epnnnVkxd8QRLRtpX3J326pN89PJGrp1OYu6nC2rWi4a/Fe7Wd5oMsix3kmW5PVAJTAcqgAGyLHcEOgFDBUHocSciMaQJclE2cnEuSGZMScfRNetw2/66VpGYkk6ojjlHtMGcfgtzRgaYTJTHxuLaq/dfmlC3Tvfg4+112/b9vx7loaEDEQSBju3bUFJSSk5ufnW7S9sITGm3MKcrMhhiYnHv29OOx/vJaEo+3woVlXZtwZ2aU5ScRXFqDpLRzNWfj9J0cFdVn/QjlzGVK2OzTl3FM9Rf1a5r2hopOx05NxPMJown4nDqeJ+qj3PvYVTGbYeyUgDkkiJVu9igOVJBFnJhDkhmzBeP4tRaLYc1nNrfh+niEcv40KbIhTnIRVU2TTiOrnmn247XRURiSvhNdexCej4N/TwJ9/PEWScypG04cVcybsMAuy6lMbSdeqNw4VY+Df29CPevh7OTjiHtGxGXqL5qJggC+goTAKUVRoK83FXtru1bY7yZjulWJphM6HfH4RFlb1e/p6dS+PE2ZBu71oUu6iJG3Dq0wpiajjEtE4wmSnYeoN5A+/AMfGYy+R99g1xp75/uHVpRmZKO8abCUbzjIF4D77PrF/TsJPI++AbJRhfOERGYb9nGaa/bzqMm1OvcgvLkDCpSs5CNJvJ/+hW/Id1VfcylBosuPFzB5lt3Lu1bY0pLx3RLkUO/Jw73KHs5fGdMpfiTGmz6J+LDqcsATCdilA0TQFmxmiO0KXJBtpVf/HYHv+hu5xe6ZlWxnpOhxPpvcTh3Vs/Dpe9wKmJ/sop19YUFv84tKL2RRVlqNrLRTNqPRwgdop5L7uFLmA2KDgpOXsE9zJJzxEYtkXIzkPOzwGzCdPoQTu3vVY137jEE4+EdYNArMpSq841np5ZUJGdQaWVT38FqDkllUzc7m3p0akllSgaVNxWOwu0H8bbhAAh5YQI5m75DrjDatdU2Rvw6t0B/I7Nal+k/HiF0SDdVH5Otb9p8qlBs0AIpPxO5IBvMZswXjuDUWs3h1HUApuN7lQ0TgF7tW3WxBtR2TXW9pzXG1HRMaUreLN0Vh+cA+1zhP2sKhf/+usZ849zGJl/si8Wt91/LF16dW1B+I5OK1Gxko4mcHw/jPyRS1cc6X4gernYcTq0UXUiZihwVB2Nx7vHndVHbfAPg1FotQ3lcLC49/9o5jk+XFpTdyMKQovhn5o/xBA29vS5qyp0AXe/vzqHv4gC4ejoJD29PfIP91HMJ9sO9ngdXT6vvljXp1IKclEzybmZjNpo5uT2ejoPVMnQY3I2jVfyndx6ldc/2Ncrwa1Wfa6eT8LytDO5cq5Lh9/7/ayBJd/e/uxR366bJGoeAFrKC0qpjzlX/7vhxLqGeH3JJQfXfcmkhQj2/mvt6+SP6BCLdVF/hEwMDkbKzq/+WcnLQBQbajXft2w//j/6Nz5KliEFBd5yYNbJy8ggNtnCGBAeSlWP1iEpwIOYsiwzm7Fx0Nr/h3LolupAgyg8fq/E3PEP9KE23bMRKM/LxDK1ZFwBtHulHatxZ1THBNwCpwHIrWy7IRfRV60IMCUcMaYDH3DV4vLQWXTv1oix4+SMXWR75k4vzEbxuYxOfQATfIKQbFy3H6vkil1jmIZcW2F/htvot0dveptkl5YR6WzYwIV7uZJeU18iRXlRGeqGe7o3V+s4uNhDq7WHh8HYnu9ig6jM9qj07ziUz+K2fmLnlAC8PV58w6oIDMWda9GnOzsUpRK1Pl4gWOIUGYTikPqmFutFFXcSIU0ggxgzLPEyZuTiFBKj6uLZtjnNYIPoDx2vkdgoNwJRh8XljDRxubZvjFBZEaZw9hxgUhJRjkUHKybGLEQDXvn3x37wZn6X2ceoSGkBlusU3KzPycA7zt6UgZOpQOsZvoOGCySQv3KyeR1AgpkzrWM1BF6yeh0tEC3QhwRh+tY/VPxMfon8oQkAYbtMW4xa9FF3zDjYctjYtuH2MeVfZNFV9BVrwC0TOt8p7+TkIfup5iKHh6ELC8Zy3Fs8F63Bqrz5BcQ/zw2Clz/KMfNWmyBaNx/cnK9aScwSfAORCi0/IhbkIPjYyBNVHDGqA+6zXcZ/9JrqILqp2lzB/Kq38qjIzD5caZAiaMoz2v24ifP4UUhd9pGpzDgnAmG7lmxl5ONv4pnu75riEBVGyX31B4XfUNkbc7HSZh1uYvU2bTLufAUfX0nbheC7M/1TVJnj7IRdb+1YegreNbwVU+Vb0EtweW4auRUc1Rx2sAbVdU52CAzBZ5U1TVi5OwTZ5s42SN8sO2udNRYYgzNlWuTcnp8Z1261fXwI+3ozvsqWIwTb5IsyfSiu/qMzIw7UG3wpT3bT7AAAgAElEQVSdNpQuR9fTZOEkrs9X5wsxIBAp10oXuTnoAux14dKrHz7r/029V5YiBlrpopb5BhR7mHNsZKjBHi69++H3/r/xXmifO11D/amw8s+K9Dxcazi/CJ82mF7H3qHlwgkkzv/Ert0/1J88K53mZ+bhH6LWqX+IP/mZebZD8Q3xp8BKhoKMPHxsxlr3kcwShpIyPP3UF7T9apDBz4bHz0aG/Ax7eTT8/XBXb5oEQXAChqE8qocgCDpBEM4A2UCMLMt2GUAQhCcEQTghCMKJDz744C/9nq5VN0xXTtV49eNOqDgST+64seQ/Fk3lyRP4vDzvL3PUCoKA7+ynKHpnY53QtRrVi6AOzTi9acedO9tC1CEGN6DsrbkYPlqJ+8Rnwd3TITmc2vXAfPk3h2wCVVfQHbTp79hzKY1BEQ3QiX/9A9W7z6fwUKem7H3hYdZP6MeC748iSX9BFkHAf8508t96/y//ti3qQhcOx4ggEPzyE2S//qHDv40gEDzvcbJXOs5RER9P7iOPkP/oo1SeOIHPK684xJP1yW7O9pzBzVc/p8Hsv/iBS0HA7/mnKFhj/27jn4aoQ/QPofzTFVR8vx6XBx8DV487j6sBuojumJJOOuYXog4xpAH611+gbNOruE97zuFYDx/dC9+OTbm64T9/XYagMAzvzaP889W4/utpcPvrMuR8uosLvaeT9tpnhD3zr782WBAIW/go6a9uvnPfP+CodYwAyR/HENvjWS6v+JKWz4366wSiDtE/lPJPllPx7TpcRjwObn/Rt+pgDajVmioIBL74BHlv/rVzAFuUx8eTM+YR8qY9SsWJE/jMcyxfZH68m1M9ZpK84gsaPvfXP4hbeSyegmljKZoZjfH0Ceo9/9d0Uet8A1QeiSd/0lgKnoym8tQJvOY6do6T9vFeDt87mysrvqTpc45/SFaDhv8G7tZNk3vV5ugEkApsBpBl2SzLcicgHOguCILdfVVZlj+QZbmbLMvdnnjiCbsrrEI9X+TSAtthADi16oY5yf4Kn5SbixgcXP23GBRU/SJ59e8WF4NReSTDsGMHTq1a/aUJhwQFkJlt4czKziUkyHKlx5ydiy7EIoMuOBCz1VV1wcMDp+ZNCdrwNqE/fIlL+7YErl6Bc4RFDn1mAfXqW66W1AvzR59pr4vw3u3oOushdkWvQao0qedZmIfoZ7nCJPgFIhXa6KIgF9O5oyCZkfOykLLTEIMbWNpL8lVXiwVvf9VVcWvo2t2H6cIR1TG5tBDByzIP5U5Joe1QAJxaR2JOsL/SGOzlRqbVXaGsEgPBXm41cuy+lMbQduH2HN7uZBaXWTiKDQR7qx+/++H0dQZXPdbXsWEgFSYzhWUV1e3m7Fx0oRZ96oIDMWVZ9Cl4uuPSogmhH60mfOfnuHZoQ8g7lnct6kIXdREjpqxcnMOsrn6GBmLKslx5Ez3dcWnZmEafvUGzfZ/g1jGC8A2LcW3f0sKRmYdTmMXnnWvgcG3ZmEZfvE7z/R/j3imC8E2LqtslmyvFYlCQKkbgznFamZmHS32Lb7qEBWDMyOd2yPvxV/yGqh/fM+Xk4hRqHatBmLMt8xA8PXBu3oTQD9+iwX++wPWeNgStXYZLG0WWPxMfcnE+5sRTSowV5iDnZyAGhFraS2xt6nfbGHNq3b1mvyjIRfC3ynv+QcgF6qupUkEOxjNHwGxGzs1EykxDF2qJFUNGAe5W+nQL88dQgz6D+rSn9eyRHJ3ylirnyEV5CFZ3MQTfQNVdOKVPLqYLvym6yM9CyklHDAqrbq/MyMfFyq9cQgOo/AOb5v90CN8h6kfvjFl5ONe38s2wAIzWvlnPHbdWjWn+1WtE/PoRHp1b0+SjBbjfYynmU9sYKbfTZQDlGTXbFODWj0cIHaq+wyMXFyB4W/tWAHJxTb510uJbeRmI/la+VQdrQG3XVFN2Hk5WedMpJBCT1fopVuXN+h+/QaM9n+LaoQ2h65bi2s6Sb6TcHHRWd450Nneq7WT4zw6cbfNFRj4uVn7hEhZAxR/4Vu6Ph/G3eWRNystFDLTSRWAQ5jwbXZQUg0mRo2LvDnQtrHRRy3yj6CIXXZCNDLb2KLHoonyXfe6syMzH1co/XesHUFHD+cXvyPwhnqBhFl2s3Pk2K3e+TWF2AQFWOvUPDSA/S63T/Kx8/EPVd9MACrPy8bOSwS8sgCKbsdZ9RJ2Iu5cH+oKS6vZXd75VowwFNjwFNjL4h9nLc1dDlu7uf3cp7tZN0+/vNHWSZXmWLMuqh3BlWS4E9gND70QkZaUg+AYrC4Wow6lVJObr5+z6CX4h4OaJlGFfIcWYkICuQThiaCg4OeE2YAAV8YdVfUR/y4mra89emGxeoLwTonr34Ofd+5BlmbMXLlOvnidBgRbOyssJODVsgC5MkcH9/gEYDlo2E7JeT8aQUWSOGk/mqPFUXrhE7pwFqup52Wev49MkFK+GQYjOOlo81IMbMeqKL4HtGtNvVTQ7o9dgyFM/yw5gTk5EDG6AEBACOiecu0VhOntUra+z8ehaKY8LCZ7eiMHhyLmW94WkW9eVx4t8g0DUoWvXQ7nSbQMhIAzB3RMpTV3xSMpMrrJpoGLTiEjM18/aj/cLBVcPpIxrdm3t6vuRWlDKrUI9RrPEnktp9GsZZtfvRm4JxeVGOjawf+SiXX1/UvNKuFVQitFkZs+FVPq1bqDqE+bjybHrWQBczymi0mTGz9PyXHvFxUScGzXAqYFiV8+hUZQdsLJraRmpUf8kbfgk0oZPouLcZbJmW20U6kAXdREj5eeTcG5cH+cGIeDshNfwfpTGWvxCKi3j2n2PcH3gVK4PnEr52QTSZixVVQYznE/CpUl9nMMVDu8H+lKyT81x5d5xXOs/jWv9p2E4k0DadMsG0piYiC7cNk7jVXKq47QnplT1y9KlZ67i1jQM14bBCM5O+D/cm4K96k2ia1OLn/gO6kr5DfW7cJUXE3Fq2ACn+lU2HRKF4YBFDrlUT9rA0dx6cCK3HpxIxfnL5Dy7qLqa1Z+JD3PiCcQmbZQ/3Osh+IchFVg9XpOZjOAXguDzu190x3ytBr/wDwU3D6R0e78w30hEF9wAITBUifXuURhPq/VpOhWPU4Ty+JZQzxsxNBwp26KPwjPXqNcsFI9GQQjOOsJH3kfmXvVcfNo3ptObj3J0yltU5qpzjnTzCmJQfQR/Jd84de6D+YL6AQPThWPoWtyj/OHphRhUHykvq7pdf/YKbk3DcLGyaWGMepNobVOfgd2osLFp2dkr1b4pODvhO6IvxVYcUkkZl7pMIKH3YyT0foyy04kkP7ZCVT2vtjFSeOYans1Cca/SZf0adOnZ1LK5CRnUGf2NTLU+068pj9/5BoFOh679fZgSbXwr4UR15VI8vBAC1L5VF2tAbdfUigu/580QcHKi3rAo9PvVukzuM4bUIVNIHTKFinOXyZy1WFU9z5ig5Ivf11S3gQOoOGyTLwKsZOjVE1OKOl+UnLmKe7MwXBspvhU0shf5NvnCzcomfoO6UG5jE1NSlS5CFDlc+w7AeEytC8HPIofLvb1URSJqm28ATIk29ogaQOWR29vD5b5edkUiik9fw6NZKG5V/hk6sic5e9SPqnpY6SLw/s4Yrlt84pXhz/HK8Oc4sfcYfUZHAdCicyvKSvQUZqs3X4XZBRhKy2jRWb1xSzl7jeAmYQSEB6Fz1tF1RE/OxahlOBdzkh5V/J2H9yAx/qKqff7wFzi59zd6V/Vp3rkVZSVlt5HBQPMqGX7vr+Hvjbuyel5NEAQhCDDKslwoCII7cD/w+h0HyhKVcdtwHfmMUnr3UjxyfgbOPUYgZaVgvqGcHDq1iqzxCjoAkpmSd9fi98ZqpSTorp2Yk5PxnBaNKTGBivh4PP4xGtdevZDNZuTiEopXqcurzl28iuOnz1FYWMzAkROZ8egkTCbliurYUQ/Q975IDh05zrAx0bi7ubF83nNqGcwShavXEfju6wiiDv32XZhuJOP9xFQqLydRfkid7GtUhVni0MJPGfHFiwg6kYRtByhIukXkC6PJOXeD5JhT3Dd/HM4ebgzZpJQeLknPY1f0GitdSJR/9R4es19DEEUqD+9FykjBdcRkzClJmM4dxXzxBE5tu+C5+AOQJcq/+xBZX2IliETlrk9wm/CSYpMzB5BzbuEcNRop/QbmJGUjZ1sAQjV+/5e4jn4WBAHThcPIeek493wIKTOletPgFBGJOfE278+IIi8P7shTXx1GkuDhjo1pEeTNhgOXaBvmR1Qr5SRq96U0hrZtgCDYP5rnpBN5eXhXnvr8AJIs8XDnZrQI9mFD7Hna1vcnKqIBzw/uxLLtx9lyVFmglo68V81llshbuZ7QjStBFCn5cQ/Gayn4zphC5cUk1QaqZqPWXhd1EiNmiezlGwnfvAJEHUXf7aXyaioBsyZRfiEJ/f6an6W35chcupGG/16hlBz/VuEInD2R8vNXKI29A4fZTMk77+D35ptVcbqrKk6nYUpMVOJ09Ghce/ZU4rTEPk4xSyTP/4jWXy5C0InkfLUPQ9JNGsx9BP3ZaxTuPU7otGF49+mAbDJjLizl+ux1dhz5r68j+L1VIIqU/rwb4/UUfKZPofJSkupiR434E/FhvnYOXfN7cH/qDZAkKn/5Egylao59VX4hipjOV/lFr4eRMpOrN1BOEd0xJ9wu70kYtqzD8wVlHsZDu5HSU3AdOQVzchKmM0cwXTiOU/uu1FuxWYn1bR8gWxUOkM0S5+Z9Qs+tLyslx7fGUZJ4i4gX/0nhmetk7j1Fu0UT0Hm60f1DJeeU3crj2JS3qmWo+P593J9Yosjw2y9IWTdxGToe882rmC/+hjnhFLpWnfB4cT2yLFG5/RMos8o3ZonUhR/SastiEHXkbfuF8qSb1J8zDv3ZqxTFHCd46nC8e3dENpkxFZVy4zmbCmFmifRFm2j22VLQiRR8/QsVV1IJeW4ChvNXKP6l5vdmbDlqEyOyWeLCvE/osfUVBJ3Iza1xlCam0frFf1J45gZZe0/SJHowQX3vQTKaMBbpOf2MzePakkTlzk9wm/SK4lun45Bz0nDu/0/FtxJPYr56VvGtp99U+sdsUftWXawBtV1TzRK5r71H2PuvIehEin/Yi/FaCn5PT6biYhJlcepNXM32MFO89h38Viv5wrBzF6bkZOpFT8OYmEjF4ap80asnmM1IxSUUrbTPF9fnfUS7rQtAJ5K9NRZDYhqNXhxL6Zlr5O89QVj0MHz7dkAymjAX6Ul6xiZfSGb0G9fivVzRRUXMTsypybhPjMZ0JQHjsXjcHxqN8729lDu6pSWUvr0Kv/c/r5ahVvmmSobS9WvxWbkaQRQp37MTc0oyHlOiMSUlUHkkHveRo3G5T5FBKimh5E21LmSzROIr/6bLV/MQdCLpW+PQJ6bR/MV/UXz2Ojl7TtLw0SH497kH2WTGWKTnwjMb7EQ5HXuSTv27svbgJioMFbw/593qtpU73+aV4cq50ccL3q8qOW65CCmZJbYt+jczP5uPqBM58vV+Mq6k8eBzY0g5f43zv5wk/utYpq6ZyZK4dykrLGXzrLV2MpyJPUnH/l146+AGKqtKjv+OV3e+xfzhLwDwyYIPeKKq5PjZOK3k+P8FCHIt3m/4n4IgCKWyLNezOdYB+BTQodwh+1qW5WU1jbeCXPbOdIfl8JitPAOc1b+fwxwh+w9gzLW/Mv9X4BzYjLR7Bzg8PvxYLAAbGk50mGPGTaUEePGTQxzm8H5/D/plExweD+C5aAtlax53eLzH88r7AoZP7cuH/lm4T1mFYeudq93/Ice4pdzoeL/D45uejQGoE13UNkYSI4Y5PB6gdcIuLrcc7vD4Nld2ApAVFeUwR0hcHADH6jv+jP296d+T0mWQw+MBGp/6pVYx4rloCwBlqx9zmMNjjlIEoWia43Px+fgXfgwd7/B4gJGZX1L6/EN37ngb1FujfH/oRPhIhzm6pSmljM81GeEwR4fk7XUSI9tDxzk8fkSm8k0//RLHOTyXKBy1XQNqs56CsqZea++4DM0vKKXlM/tGOcwRejAOgMOhf/39pN/RK/Nb8h5wXBcBOw4A1CrnND6llP/Oud9xOYJiDhATMtbh8QD3Z21jXGPH4/T3kuMzmoxxmGNDsvKdr4mNHV8Dvkj5HuCvv/z8X0DFuT1338m/FVw7DLkr9XhX3mmy3TBVHTsHdP4viKNBgwYNGjRo0KBBg4b/w7hb32nSoEGDBg0aNGjQoEGDhrsCd+WdJg0aNGjQoEGDBg0aNNQ9ZNn83xbhfyW0O00aNGjQoEGDBg0aNGjQ8AfQNk0aNGjQoEGDBg0aNGjQ8Ae4K6vn1SH+1pPToEGDBg0aNGjQcNfgrqz6Zovyszvv6vNjt47D70o9/u3faSp7M9rhsR5z/w1A4QTHy337bomtVblwUEqG16ZsuXNgMwDKNs12mMNjuvLdkpJnHnSYw+vd/1C2bobD4wE8Zm1Av8Lx0umeC5TS6bX1i9qU+gal3HfJDMfLEHtt2AXU3h5Q+xLCpxs97PB4gM6pP7GvFiVrB2ZtA6iTsuXfhjle7vufGVtqVZoalPLUho+ed3i8+2PKN9VKpt/xu9+3hdem3QAUTRroMIfP5/t4qYnj5a0BXk/eWuuy5wBb6jueLyakK/mitv5Zm/G/cyxv7LhvLkxRStHrFz3iMIfnsq8ASO/Z32GO+vH7SWjleJwCRCTt5HiDUQ6Pj7z1A0CdfMbjw3DHfevxtC9I7eZ4jDU6sQ+AvSGO23RwlmLT6/cMdpij2fm9vF8LPQA8mfZFnZQLf66J47p4O1nRxeNN/uUwx4fJ3zg89v87ZOm/LcH/SmiP52nQoEGDBg0aNGjQoEHDH0DbNGnQoEGDBg0aNGjQoEHDH+Bv/3ieBg0aNGjQoEGDBg0aqiBpj+c5Au1OkwYNGjRo0KBBgwYNGjT8AbRNkwYNGjRo0KBBgwYNGjT8Af5PPJ4nNmmPy8DxIAiYzh3C9NtOVbtz/0fQNYpQ/nByQfDwxrBupqqPU4dI3CfNBFGkMm4nFdu3qtpd+g7BbdyTyAW5AFTs/ZHKOPXvuPaIxPf5mQiiiP7nnZR8pub4He79+xCwailZU6ZjTEiqPr7gtTUcPPwb/n6+/PjFJrtxsiyzcu0mDh05jpubK6/Of4G2rVuo+hxOzuHNuMtIEoxsH05092aq9tVxlzmelg9AudFMvqGSQzPUFax0bbrg9o8nQBQxHtlL5S/f2sni1Lk3LsPGgywj3bpB+Wer1XKk5PHmoSQkWWZk2/pEd22iluNQEsdvFVjJYeTQE/0sMjTrgMuQSSCImM7EYYzfrhrvcv8ExMZtARCcXRA8vSlb/aSqT134hdikHS5Rj4AoYjp/CNPx3WqOfmPQNazicHZBcPfCsEFdxVDXtitu/5oOgogxfjeVe+0r8Dh16YPLAxOr9KmupFgX9tC164bbmOkIoo7KX3dRuedre46ufXF9UKmSJKVdx7B5lardq19nwpc8jqATyfsqhqwN36naAyYOJWjyMGSzhFRWzs2XN1B+5aaqj3//jrRaMRVBJ5K+JZaUdT+p2htMHkR49BBks4RZX07CnA9U7Z59uhKy4EkEnUjh13vI+6DmakZeQ3oRvn4+N0bNpvzCFVVbSP8OdFo2CUEncuPLOBLXq32r5ZPDaDq+P5LJTGVeMSee/5CytFxVn3r9utBg0eOgE8nfFkPORnubAHgP7UmTTa9wZcRzGM5frT5++EY2b+y7hCTLjOrQkOh71XH8ZuwljqfmAVBuMpNfVsGvz6irIOradsVtzFOKXxzefRub9sHlwYkgKzYt//fr6vZ7InGb9LTCEbeTiv98pWp37jMEt0eesOS9mJ8wHlDHUat+HXlo0WQEncjxbfuJ2/izqr1p9whGLJpMaEQjts56l/O7flPL0D4St/EzFBkO7qJip1oGAOfIfrg+PBmQMd+8juH911TtYVEd6LZ8EoIocnVrHJdsbBrxxDBajI9CMpmpyCvh6PMfoL+Vp+rjiG/qk27VKUfzfh0YsljxzdNfxRG/UT2PRt0jGLx4IiERjfh+1nou71TrEkDXoiMuw6coufNULMZDP9v3adcDl/7/BGSkzFQqvl2nane9NxKfZ2eCTkfZ9h2Ufq5ey9yHD8H76elIOYpf6L/7gbLtar/w7NOV4PlVsfrNHvJvF6uDe9Fg/XyS/6GOVe+ozjRa9iiCKJKz9Rcy3/teNS5o0hCCpwwDSdFl8osbKL+Spp5HLddkgPCoDty3VLFJ4tY4zr6ntsk9jw+j9bgoJLOZ8rwSDr7wAaU2vuV2XyR+c5Q40/+4k+JP7X0cwH1AH4LeWELmpKdUxwP6dyRixRQEnUjalliS16ltGj55EA2jB1f71aU5H9r5pnuvbgS89BSCTqT4+90Ubd5Wowyeg3oT8vYi0sY+TeUlde5sGNWBnlW6SNgax5kadNHGShdxNeiibb+O/GvRNASdSPy2fezdqI4RJxcnpqyZScP2zdAXlrB55lpVe0S/joxapOji2LZY9tnkm2bdIxi1aAphEY34fNa7nN11zG6O7fp14pFF0xB1Ioe27WP3xh/tZIheM4vG7ZtRWljCBzPfJi8tp0Z93bXQquc5hLty0yQIghk4jyLfZWCKLMtlVW064ARwS5blO9dbFgRc7p9IxddvIZfk4zZpEeZrZ5Dz0qu7GPd/hbHq/06dByKGNLLhEHGfOhv9yrlI+Tl4Ld+I8VQ80q0UVTfj0TgMn75bsxyiiN/c2eTMmos5O4fgTzZiOBSP6YaaQ/Bwp97Y0VRcuGRHMXL4/Ywf/RDzlq+2awM4dOQ4qWnp7Ny2mXMXE1i+ej1bP7QkFLMksyr2Ehv/EUmIlxsTvjxCv+bBNA+oV91nTlSb6v9vPZ1CYk6xnS7c/vUUZe8tQC7Mw2PO25guHEPKtJz8CkH1cbn/X5S9PRcMeoR6PioKsySz6kAiGx/uTEg9VyZ8fZx+TQNp7m8lR59WFjnO3iQxt8RKBgGXYVMo37IKuTgft0eXYUo6iZxrsWllzJbq/zt1ux8xtInNPOrCLwRcBoyn4ru3kUsKcJswH/O1s8j5GRaOA19bODoNQAxuaK/PsU9T9u485MJcPF56B9O5Y0iZqWp9DhlL2eoXwFCKUM8H58j+dWYPBBH3cU+jX/sKckEunq+sw3TuKFKGRQYxuD6uQ8eif/N5KCtF8LLhEEUarniSqxMWY8zIo/X21RTF/KbaFBX8eIC8L5RNpff93WmwMJprk5dacQi0XhXN6TGvUpGeR+SeleTuOaFa3DO/P8ytz5RS0oFDutJy6WSVDKFLZpA6dT7GzFyafreWktijVF5Vb8xET3f8pzyM4UwCdhAFOr82lUNjV1KWkc/AXctJ33uKEisZCs+nsG/oAsyGSppNHsg9C8ZxbLrVSaUo0mDZdG5MXIgxM48WP6+hOOYYFTXIEThtBPrTajnMkszKmItsGnOvEqef/0q/5iE0D/Sq7jN3QNvq/289dYOErBridNzTlL0zD7kgF49X3rWzqRBc5VdvvlCzTQURtynPoH/9ReT8HOot24Dx1BGkdJu8dyyO8s/UJ9UWCoGRy6bx0cTXKMrMY+bPr3Ip5iTZV630mZ7L13M20ffxB2oiwG3SLPSrX1JkWPQexjPxSOlWvhnSANcHxlH62uyqefjayRD52hRiH1lFWUY+Q3cuI23PSYqvWGK94EIyu4YtxGyopOXkgXReOI5fp6+3kDjom2fGrawzDkEUGLp8KlsmrKQ4M5/Hfl5O0i+nyL1iGV+UnsvPL7zPfU/UoEtQctaD0ZR/+ipycR5uT76GKeEkco6FQ/APxbnvwxg+WgzlevD0VnOIIj5zZpM3W1nLgjZvovxQPKZktV+U79tP0Zrbr4chi2dwc5oSq02+W0vpvqNUXrOPEb+aYlUUafzqEySNW0JlRh5td75B4d7fVJuivB8OkvP5HgB874+k0eJpJE1cruKo7ZosiAK9Vkxh5/hV6DPyGbljGSl7T1Jo5Vu5F5O5NHwh5vJK2kwaSPf544idYe1bIn4vPUP20y9izsoh9LMNlB08UqMcXo/8g4rzNnKIAm1WRXNyzKuUp+fRY89r5Ow5qfKrjO8Pk1blV0FDutJ66SROjbO66CWKBM6fScYTL2PKzKXBV+so238E4/VU1U8JHu54TxxF+dnLt9XFjipd/GPHMpJtdJF3MZnvhy/EVF5J20kD6TF/HL9Y6UIQBcYue5R3J66gMDOPl35eybmYE2Ra5YueYwZQVqRnSdQzdB3Rk1EvT1CNH70smk0TX6UwM4/nfn6NCzEnybIaX5Cex5dzNtL/8ZpPHwVRZPyyR3l74nIKMvOZ//NKzsacIOOqxbd6jxlAWVEp86NmETmiJ6NfnsgHM9+ukU/D3wt36+N5BlmWO8my3B6oBKZbtc1G2Uj9KYhhzZALspGLckAyY0o4hq5Fp9v217W5F9Nl9ZUHXfMIpKxbSDkZYDZReTQW5649/9KEXNpGYEq7hTk9A0wmDDGxuPe15/B+MpqSz7dCRaVdW7dO9+Dj7WV3/Hfs//UoDw0diCAIdGzfhpKSUnJy86vbL2QW0tDXg3BfD5x1IkNahxJ3Leu2fLsTMxjaOkx1TGzcCiknAzkvC8wmTKcO4nRPD/Vc7xuC8dAOMOgBkEuLVO0Xsopp6ONOuI+7IkfLEOKuq6/Uq+S4ksXQliEWGeo3R8rPQi5UbGq+eBSnVl1vO96p3X2YLh5Rz6MO/EIMbYpcmINclFvFcRxd8z/giIjElKC+8is2aYWUk46cl6no8+QBnDra6LP3UIwHtoOhFFDrsy7soWvaGik7HTlXkcF4Ig6njvep+jj3HkZl3HYoq5KhRM3h0aklFcmZVKZmIRtNFGw/hM/g7qo+UqnB8pvurmDzYW3vLi0w3MiiPCBCmfoAACAASURBVCUb2Wgm68d4AodGqvqYrTk8XLH+OLd7h1ZUpqRjvJkJRhPFOw7iNVA9D4CgZyeR98E3SDXEmH/n5pQmZ6FPzUE2mrn501HqD1H7Vk78JcwGZWz+qau4h/nb6aIyJYPKm4ouCrcfxHvwvXa/FfLCBHI2fYdcYVQdv5BRSEM/qziNqE/c1dvH6a7L6QxtU191TGzSGik7o9qmpuMHcOqg1oVL72EYD/zntjb9Pe/JVXnPeHT/X857DTu1IC8lk/yb2ZiNZs5uP0Lbwd1UfQrScslMSKWmD63rmlX55u8y/BaHc+de6nn0HU5F7E9W8yhUtQd0bk5JchalqTlIRjMpPx2loY1Ns+IvV9s099RVPGxsWlvfrAuO+p2aU5CcReFNZR4Xtx+l9f3qeRSl5ZKdcBNZqvm7lWJ4C6T8TOSCbDCbMZ+PxylCbQ+nbgMwHdurbJgA9OoNuXPbCExp6Za17JdY3PqobXInuNUQq/UG2cdq4OxJ5H34DbJNrHp2bklFcgYVVfkm/6df8Rty+3wjerjapps6WZODOjWnODmLkirfuvbTURoPVtskI/4y5nJlbPapq3ja+JZLuwhMN29hvqXIUbZ3Px797OXwmT6N4k+/Qq5Uy+HTpQVlNzIxVPlV5o/xBA9V29TWr7DRhes9rTGmpmNKywSTCf2uA3j2t5fBf+YUCjdvs5MBINhGF1d/OkoTG12kx1/GVKWLrBp00aRTC3JSMsmryhcnt8fTcbA6RjoM7sbR7+IAOL3zKK17tq9ua9SpBblW409vj6e9Xb7JIeM2+QagaZUMuTezMRtNHN9+mE42HJ0GRxL/3QEATu48SoSVDBr+3rhbN03WOAS0ABAEIRx4APjozw4W6vkil1g2DnJJAUI9v5r7egcg+gQipar3ZKJ/IFJedvXfUn4uol+Q3XjnyD54rfwQj9mLEfzV7brgQMxZFg5zdi66IHUf59Yt0YUEUX7Y/nbxn0FWTh6hwYHVf4cEB5KVY9mMZJdWEOLlbmmv50ZOaUWNXOnFBtKLDEQ2DFAdF30DkAott6GlwlwEH3UfIbg+YlADPJ59A4/nV6Nr00XVnq0vJ8TLzUoOV3L0fyBHsYHIcEtyFbz8kIutbZqP4HUbm/oEIPgGIyVfVB+vA7+w4ygtsLvKbZHZH9E7EOmm+oqp6BuIVGClz4Ka9NkAMaQBHi+sxmPu2+jadrUaX3t7CL4BKhnkglxE30BVHzEkXJFh7ho8XlqLrp16EXEJDaAy3eJrlRl5OIeo5QAInDyctoc2UX/eVNIWf6hqcwv1pzzd8qhGRXoerqH2NgmfNpj7jr1Di4UTSJr/SfVxp9AATBkWGYyZuTjZyODWtjlOYUGUxh234wVwD/XHYPW4iCEjH/caZPgdTcZFkbn/rOqYc0gARitdGGvQhXu75riEBVGy/4QdZ3ZpOaHWcerlRnZpeY2/n15URnqRge6NbOzlp7apVJiL4Hcbv5r7Fh4vqv0KQPALRM634sjPQfBT/w4oea/eqx/iMcs+7/mE+FFoZdOijDx8Qm6vT1soMljn3hy7eYih4ehCwvGctxbPBetwaq8+yXIP9aMs3RKnZRn5uIfdXobm4/qRHqu2aW19sy44vEP9Kc6wjC/OyMfrD3yzJghe/shFFg65OB/BW33iKgaEIQSG4fbYUtweX46uRUdVuy7IZi3LyUEXZO8XblF9CfrsI/xeXYIYbLPWhQRgyrTEiCkz1y5GXNs2xzksCH0NseoS6m+fb0Lt803wlGHcc3gjDRdMIXWR+rShLtZkzzA/SjMsvvX/2Hvv8Kiqrv3/c2bSQxLSJ6GF0Iv03hJ6UUHERocgiEgxVKWJCKKo2HgQUER5kGZFBaQloQWkSQAhJJQkpPdeZ+b8/pghM2dmAjLJ+3vz+j33dXkZZu91z9prr7X22Wf2WacoNRvnh/hWi7FBJJrkC50ehjhTp2eg9JHa07ZFM2xUlvUw9avS5GzsVR5m/RpMHUKfPz+l+YrxRJv4po2PF+pUIx3SMlCazIddq6bYqLwpOWV+5BPA6TFt0XJsEAkmtqjr60GO0VhyUrJw8/Woso9Wo6WkoFjSJs032Wbyj0JdXw+yJTpkU9fEFjodMiU61HGv+oZ2rYRWU7v/q6Wo1ZsmQRBsgOHojuoBfAIsBqo8jCkIwgxBEC4KgnBx69atVXWzCGXLbqhjLprdAf8nqLh8lvzXx1Hw5nTU1y7hNPONxyMQBOrOe5W8T7947O/+n8DhWykMbO6LUiE8tqygUCJ4+1P82ZuUfPMBDi/NAUdn6/SITWNgEx+r9ACwad0TTfR5q+b0AarjFxKO2MtWcejsWY/ij5dQ8vV7OIyf92ghM/lqzodCicKnHsUfLaLkq3U4TnjdqjnN3HGQG31nkrzuW3znWvcG+MTtRzjbfR631+yiceiz/1xQEPBZOp30dV8+uu8/QMMxvXFvH0jMpt8fT1AQ8FsxjeS126qtw+HoFAY1V1kfpz7+FH+0mJJt7+FgxZyq/zpLQeh4CpdNR339Ek6vLHlsPaoNhRKFbz2K3l9A8ea1OE4NtTrfBDzbG892gdz44oBV8lb7Zg1zVAsKJQoPFaVfr6bs+8+wGzUDHJwei6L09FnSxowlY9LLlJ2/hPuKx18Pfd+cTvp71YvV9G8Pca33q9xfuwP/ec8/tg41uSY3fbY3Xu0Cidr8mL4lCLjPn0nOx+bPMT8O7m8/wunu84hZs4vA0NGPrYPnolfI+vDxrqmqQrNne+NtjS1kyPhfRm3dNDkKgnAF3bNLCcA2QRCeAtJFUbz0MEFRFLeKothFFMUuM2bMQCzMRXAx+ZWiMMeirE3Lbmhumt/J0WZnovD0qfy3wkP66wCAWJgPat0xm/Lwg9g0biZp16RnovQ1cCh9vNBkGDgEJydsmjTGe9PHqH7ehV3b1nh9uAbbls35p/D19iQ13XD3LS09E1+jO4A+dexJKzD8TJ9WWIp3HXuLXIctHM0D0OZmoahruBunqOsluXP5oI/6+p+g1SBmp6FNT0bhbTg+5OPsQFqB4c55WmEZ3s5V6BGbxrDmvpLPxIIcyd1RwcUDscDynCrb9DA7mgfUiF+YcdRxNzsaVMnRoqtu82YCba70V0uFuyV7ZqK+ek5nz6w0tGlJRm3Vnw8xN0uig+DuhTZXelxSzDHRIT0RhU+9yvby1Czs/A2+ZufnSUWaVA9j5Px6iromR9ZKU7Nx8Dfc0bP396Qs1fKcAKT9HIn3cMOvCurULGz8DDrYqrxQG+mgcHbEvlkjGu58nybh23Hs0JL6m1fi0NYQqyWp2TjWM+jg6OdBiQUdfPq2oeW8UURO/ghtuVrSVpGWha2RLWxNbKGo44hD80Y02fMuLU9/hVPHFgR8tRzHJ3TFHnzqOJBqHKcFpfjUMfwya4w/os2P5gFoc6RzqqjrhZhjwa+iqp5TMSdT8suRwsO7suBDZR/jvBdxEGWANO/lpeVQ12hO3fw8yUurek5NodPBOPd6m48jJ4OKK2dBo0HMTEWbmohSVb+yvSQ1Byd/Q5w6+XlQkmKug6pvG9rOG0nElA1mc1pd36wJjvzUbFz9DPKufh4UPETeEsSCbMkv0YKrh+RXewAxPwvNrUs6v8jNQMxKQeGhqmzXZJisZd7eaDJM/CI/Hyp0flH82wFsW0jXsYq0LGxUhhixUXlJY8TZEbvmjWj43/dpErYdhw4tqfeFIVbLU7PN801q1fkme/9p6poc36uJNbkoJYc6RkfMnFUeFFnwLf8+begwZyRHppr7lk4PQ5zZ+HijMVrHBScnbJs0xnfLBvx//Q77tq3x2mB4NsvUrxz8PShLlc6pMVIt+KY6PRMblZEOvt5ojOZDcHbErmkAfl9/QIM/dmDfrhWqz1dj19oQ78X/0Bb1+rSh45yR/GHBFrlp2bgbjcXdz5O8tOwq+yiUChxdnCRt0nzjYSb/KOSmZeMh0cGDXJO1TKeDl0SHwpwCZPz7UVs3TQ+eaeogiuIcURTLgd7ASEEQ4oA9wABBEHY+ikibcg/B3RfBzQsUSmxadkdz+4pZP8FDBQ7OaJPvmLVp7kajUNVD4a0CpQ12PQZQcUl6IS7UNSQL28690CRLH6AsvxmNTYN6KP1UYGOD4+ABlJw0cIhFRaQMHU3q6HGkjh5H+fUbZC5cblap52EI7tODX/84jiiKRF2/SZ06znh7GfRqo3IjIaeYpLxiKjRaDt9KJTjQx4znXnYh+WUVtPczP2qmTYhB4e2P4OELShtsOvVDfU26oVBfO4tN0yd0dnF2ReHjjzYz1aCHrwsJecUk5Zfo9IhNI7ix+fGOezlF5Jepaa+SPqCuTb6LwkOFUNcbFErdxijmspm84OmH4OCMNjHWrK0m/EKbGodQ1wfB9QFHVzR3o8w53FVg74Q2xQJHfAwKH38ET709OwfpNidGUEedxaZ5Ox2XsysKX8OFbU3MhybuFgqfepU62HYJ1l1MG6EiKhKlsQ4+9REzDQUviqNisW/sh10DHwRbG9yf7kveUekm0T7AsAl3HdiFsrgUSXvBX3dwClTh0NAbwVaJ7zO9yDwsPb7m2Nhw8eY1uCPFdw0cJddisAvwx7a+L9ja4PpkPwqOG8ahLSwmtvtY7vSfyp3+Uym5Ek3izNWSilw5V+5Sp7EKpwY6HRqM6kHKYel9mrptG9Fp/TQiJ39EWZZJAQa9LR7oIdjaUPfpfuQb2UJbUMyNTuOJ7vMy0X1epvivW8S9vKayel4bPzcScopIytXHaXQyQU19zb7nXlYh+aUVtPc3PwKjjb8l9auuFvzqSqTUr3zqozWaU83daJSqegj6vGfboz8VlyMlHIKbIb/YdOpplvcSo+7gGaDCvb43Slsl7Z/uyc2jD73vJYHm3i2UPvUQvPQ6dAum4i+pDurLkdi01B0hE+q4olDVR5tuGEfWlbu4NFbh3MAbha2SRqN6kHhEmi/c2zai2/shnJiyweKcVtc3a4IjOeouHo1V1NWPo83TPYh5DFsCaJPuGHKnUonyiV6oo6UcmpsXUQToC404uSB4+qHNMRxjq7gZjU19o7Vs0ABKT0vnROFp8AuHPr1Qx0n9otRCrBaaxOrt7mO5M2AqdwZMpfRKNEmvGmK16Io033iM6kPOEekxPvvGhnzjNqgzZfek81ETa3JG1F1cG6tw0c9Jk1E9SDgq9S3PNo3o+14IR0I2UGrBt8pvRGPboB5Kf50eTkP6U3LSYE+xqIikQc+SPHI8ySPHU3b9BpnzV1S25+v9ylHvV6pnepFukrOcjPzK24Jvll2/hW2jetjU0+ngPDyIoggjWxQWE9/vee4Pm8T9YZMou3qT1DkrJdXz0qPu4mZki6ajehBfhS3+qMIW8VF38Anww1OfLzo/3YurR6UxcvXoJXqMCQag44ge3Io0HL2/H3UH7wAVHnr5jk/34u/HjJG4qNv4BPjhVd8Hpa0NXZ/uTZSJDleOXqTXGF01384jenAr8vpjfUetgKit3f/VUtTK6nmWIIrim8CbAIIgBAMLRVGc8GhBLeXHdmL/3Hx9WejTiFnJ2PZ+Bm1qHJo7ugtlm5bdLf4SAIBWS8k3n+O85H1QKCk/cQhtUhwOY6agvheD+nIk9kOfxbZTL9Bo0BblU7xZWroXjZbcDz/H67P3ERRKin47hPpeHK4zplB+M4bSU5GWv9sIi956jwt/XSU3N5+Bz0xg1rSJqNW6OzUvjn6Sfj27cursBYa/EIKjgwPvLA2VyNsoFCwZ0JpZP11EK4qMalOfJl4ubIqMpbWvG8FNdBuow7dSGNrcD0GwcORHq6X0h804zVqtKwF87ija1ATsRoxHkxCL5vp5NDcvY9OyE05LN4FWS9n+7VBcINWjXwtm7f8LrQijWvvRxLMOm/68Q2sfV4Ib6+54HY5JY2gzX3M9RC3lf3yLw9jFujm9cgIxMwnboDFok++hidUlal0BCOmFooSjun4haikP34X9mNd1Zcuvn9Fx9BqJNjW+cgNl07IrmluWn6FBq6V07xc4zV4DCiUVZ4+gTUnA7qmJaOJj0Fz7E82NS9i06oTTii2g1VD20zYcp71RY/OBVkvpnv/gNO9dBIWC8jNH0KbEY//0JDTxMaivnkPz90VsWnfC+a2tIGop/fFLxCIjDo2WxBVbafLfVbqS43uPUxpzH9X8cRRfu03+0fN4TXkSlz7tESvUaPKKiJ8vLRUrarTcevNrOu5ZCkoFKbsjKLqVSODi58mPukvm4Us0mDYU975PIKo1qPOKuDF3Ez1OflSpQ+rbX9Dg6zW6MsY/HKH8dgJe8yZQei2WwrBHPysoarRcWfoNfXcvQVAqiNtzgvyYJFovGkNO1D1SjlzmiRXjsHF2oMdW3THJ4qRMIqdskNgieeVmAne8DUoFOfuOURabgG/oeEquxZJ/rAp/0sNGoeCNQW159YfzaLUio56oT1MvFzadvkVrVV2C9RuoP6KTGdbSv+o43bsJp7lrdX4RqZtTu6cnoomPRXP1nM6vWnfG6a0tOr/46SsokvpFyY7PcV70fmW5b21SPPbPTkFz7xbqv85iN3Q0th176X6VKCygZOt6qRoaLftXfsO0HW+iUCq4sC+CtNhEBoc+R+K1e9w8don67QKZtGU+jm7OtBrYicGhz7NhyCKDDt99jvOC93Q6nPoDbXI89s9MRhMXg/rKWdTXL2DTtjN11mzT+eberYhGxQtEjZaLy75lwK7FCEoFd/acIC8miXaLxpAVdY+kI5fpuGIsNs4O9Nk6Vz+nWZwwmlNrfdPUt6rDIWq0/LHyG8bt0Plm1L4TZMQmETR/DClX7xFz7DJ+7QJ5YWsoDm5ONBvUkaDQMWwebHRkUqul/MB2HCYt1eW9y+GIGYnYDngebdJdNLcuobkdhbJpOxxnf6jLcYd3VhaheeDfeRs+w/Pj9aBUUPy7bi1zeXkq5dG3KDsdifPzz+LQp7duPczPJ3et9PUEaLSkrf6CBtvWgFJB3oNYnTuB0uv/IFY1WhKWf0mLXW+BQkGmPt/4LxxLcdRtco9ewHfKCFz7ttPbspC7r39mxlHdNVnUaIlc8S3Dv1uMoFBwa+8JcmKS6LxwDBlR90g4epnuy3W+NWizzrcKk7I4EiLNF9kffI7P5++DUkHRr4eouBuP2ytTKL95S7KRq0qH6De302nPUgSlgqTd4RTdSqSJ3q8y9H7l2bctWr1fXZ9rcuRQoyXz3Y2oNr+LoFRQ8PNhKu7E4/7aJMr+jqE4oop11ESP0yu+ZYSJLbrobRF/9DI9lo/F1tmBwUa2OGxkC61Gy96VXzN7xzIUSgVn94WTEpvIU6EvEH/tDteOXSJyXxhTNsxmVcRnFOcWsm3OJ3R+qmel/I8rt/PKjqUolAr+3BdOamwiw0Kf5/61u/x97BIN2gUSsmUBjm7OtBnYiWGhz/H+g3yj59i1chuv71iGoFRwZl84ybGJjAx9kfhrd4g6dpHT+8KYtmEOayM+pyi3kK1z5Mp5/69AqKqCyP8mBEEoFEWxzkPag9Ftmh5Vclws/iDEaj2cFn0NQO74AVZz1P0ujMTu1ssD1P8zjIrMu4/uWAVsvXTvYire/HjPwhjDaeanABTMfXSV96rg8tnvFH8+y2p5AKc5myha8+i9clVwXq77cbK6flG8YbrV8gBO87+kYNZwq+VdNh0Cqj8fAPmvDH1Ez6rhuuUwfzUcZbU8QMeE/Rz3fdFq+YFpuveJ3Gw2wmqOVrG6d8j84Df+ET2rxnMp33E14Gmr5QHaxf1GyVfzrZZ3fFl3AVIwc5jVHC6bdWXh8yYOtJrD7b/HWRIw1mp5gPfjdpM3ddCjO1alw3ZdieXv/K3PF+OTdfmiuv5ZHfkHHO80st43V8TrXsFQtPIlqzmcV+veHZTcq7/VHP6R4UQ3tz5OAVrGHORCvcd8JscIXZN+BqjWulz/zzAAvqxvvW9NT9xJQhfrY6zhxeMAHPG1fk6HpOnm9O4TQ6zmCLx2hC3VsAPAK4k7mRVg3TOuAJvidO+gCw2w3hYfx+lsMT3gMZ99M8KXcd8DWPcA9v/PKD3/fe27+DeCQ7fna6Uda+UvTQ/bMOnbI4CI/1+UkSFDhgwZMmTIkCHj3wJt7T0CV5tRW59pkiFDhgwZMmTIkCFDhoxaAXnTJEOGDBkyZMiQIUOGDBkPgbxpkiFDhgwZMmTIkCFDhoyHoFYWgqhB/KsHJ0OGDBkyZMiQIaPWoFYWMDBF6dndtfr62KHn2FppR/mXJhkyZMiQIUOGDBkyZMh4CGpl9byaxEmV9eUj+6V+D8COetaX05yUtJNNDapXjnPW/Z01Ui68JsqWn/N/1mqOHsk/kdSzeuXX650NqxZHvbO6UrGp/YKt5lCdjKiW/AOOmig3e6et9eXCm1w/DFS/VPebAeOslgdYF7eLuQHWl2T+LE5XcnxjNeJs9n1daenZ1dBjY9xetlcjVwBMTdpJ9qggq+U99p8A4HID68vAd7q/v0Y4mnl3tloeIDbjElcajbRavkP8r0CNlBCutn9WpxQy6MohhwQ8Z7X813E/ANXP3wCnVNbr0Tf1B35RVS9fPJO6q0bKr9dEKfrF1Sirvz5uNz9VwxbPpu4CYHM18t5Mfd6rTt6amlS9cuGgKxn+YqNnrJbfG/8LABMaWe/fO+N1/r22Gr61TO9bMv69+NdvmmTIkCFDhgwZMmTIkKGHXHLcKsjH82TIkCFDhgwZMmTIkCHjIZA3TTJkyJAhQ4YMGTJkyJDxEMjH82TIkCFDhgwZMmTI+H8F8vE8q/D/xKbJvX8HmrwzFUGpIPW749zf+Iuk3W/SYPynDkPUaNEUlRK7aAvFMYmSPv7B7ei6eiKCQsHt3RFc/89vkvZWM4bTbGwwolpDaXYBkfO3UpSUJenTILgdfVZNRKFUcGN3BH9tknK0nz6cVi8FI2o0lGQVELZwK4VGHGfiMvgg4iZaLTzTtj4h3QIl8h9G3ORCYjYApRUaskvKOTVrkKTP8nc3cPLMeTzc6/LLzs1mthJFkXWfbObU2Qs4ONizdtkCWrdoKunjFtyRgHdCEBQK0ncfI3njz5J2n4lDUE0ZjqjV2fPeoi8oiZXa075HV9xen42gVFD060EK/7vbTBcAh+C+eK57m/SpM6mIjqkxeQC7bt1wnTsbFEpKDhyg6LtdknbHYcNwmTUTTUYmAMU//UzJgQM1zuHQsyvuC18DhYKiXw6S/+0ei2NxHNAX7/WrSJ34qvTz3l3wemMmglJJ/o+HyN22z6K886A+qD5ZQeKLsyn7O1ba1rczvstfQVAqyN13mKyt31vkcBnam/obl3Fv9DxKr0s5mge146mVk1AoFVzYG86JL6T+HdCtJU+tnIiqZUP2zPmc64fOm/G3CmrPsyunoFAqOLs3jGNf7Je029jZMGHDazRoG0hRbgHfzP5U0t4wuB19V01E0MfYZZMY6zB9OK1fCkZrFGMFJnHaKqg9z+l1iNwbxlELOkzc8BoN9Tp8PftTshMzJH3qBbejuz5fxOyO4JpJvmgzYzjNxwaj1eeL0yb5wrZjN5ymzwGFgrKjByj9UepXdgOG4TTlVbRZuu8tO/gzZUelfuUa3JH6q6aDUkHW7qOkbfpR0u41YRjek4cjarRoi0pJeGMTpbH3a5wDYMW7iwga1JuS4lKWzF3FjavRknYHRwc+3/Y+DQLqo9VoCDtyqrLNJagT9d56GUGpJGvPEdK/+NGUHgC34T1pvPlNbj01n5Jrt6X2DurASyunolAqOLX3OH98IV0DbOxsCNkwh0ZtAynMLWDr7I/JMplTa3zT1C9aBrVn9MrJCEoFf+4N4/gXv0raA7u1ZPTKyfi1bMh/53xG1KE/Je1tgzowbqVuLTu19zgHLYzjZf04inIL+WL2BrNx1ET+du/fgUCjNTXRZE1VTRqC/9ShlWvqbQtrqk//djzxziQEpYL478KJ3SiNkSavjKDR+GBEtZbyrHwuh26lJDHT0B7UjqFv6WL9rz0RRJrkm4bdWjLkrQn4tmzIT3M2cvOgeb7xC25Hl3cM6/oNEx1azhhO03G6OC3LKuCchXW9eVB7Rq3UjeP83nAiTOa0cbeWjFw5CVXLhuya8xnXLOQ93/7taKe3Rdx34cSY6NH0lREE6G1RlpXPpdCtkvYGwe3orc97N3dHcMUk77WbPpyWRtcWESbXFlD9nAXQOqg9z+v9M3LvcY5YiJHJG2ZXxsi22Z+YxQjAlFUv07F/Z8pKyvhi4Wfcu25exKpx2ybM+mgudg52Zm0TV02jQ/9OlJWUsXXhRuIsyAe0DeSVj+Zg52DHlfDLkrbAoHYM0fvWlT0RnDXxrQZ63/Jp2ZCf52wk2oJvyfh3olYezxMEQSMIwhVBEK4LgvC9IAhO+s/jBEG4pm+7+I/IFAqarpvG9XFrudgvFO/RvXFqXl/SJf2n01zqv4DLgxZx/z/7CVw1WaqPQqD72skcn7CeX/svJuCZHrg185f0yb4ex4HhK/ht8FLiD5yn8/KxZhz91kzmwKT17B6wmGajeuBuwpFxPY4fnlzB3iFLuXPwPL2WGTg0WpH3wm6w8Zku/Di5D3/cSuFOVqFEfmFwK/ZO6M3eCb15qUMjBjb1NTPHMyMGs3nDmirNdersBRISkzm4dxurFs/lnQ83mtmz8bvTiR6/hqjgeXiO6otjM6k9s34+xdWBoVwbvICUTb/QaNVUM466C+aRNf8N0sZOxWnwAGwCGpnpIjg5UueFMZRfv1Gz8noO19B55CxaQuakyTgMHICykTlHSVg4WdNeJmvay2abnZricF8yl/S5b5LyfAhOQwdg09jyWFxeepaya+a28F7+GimvLidh5HTqjOiPbWBDi/JuE56hNOqmRVuoVs3i/ssruTN8Jq5PBWHXtIF5N2dHPCaPouRKtFmboBAYuXoq26es9kpYSgAAIABJREFU5+PBi2g/shc+TetJ+uQmZ/LDws1E7Y8010HP8fzqEDZPWce7g+fTeWRvVCYcPV4YQHFeEe8EzyNi20FGvjFOIh+0ZjK/TVrPrgGLaV5FjO17cgV7LMTYA44XVoewaco61lShQ88XBlCSV8TbwfMI33aQUW+MM+PosXYyRyas5+f+iwm0kC+yrsfx6/AV7B+8lLgD5+lqnC8UCpxeeZ2CtxeTN3sydn0Homhg7hPlp8PID32Z/NCXzTZMKBQ0WPMKtye9zc0Bs3Ef1ReHZtI5zf7lBDcHzyN6WChpm3+m3sqQmucAggb1plFgAwZ1e4YVC9awev2bZn0AvvrPfxnWawyjBoyjU7f2lTrUf+cV7k5+m+hBr+E+sh/2zSz7pvfUkRRdvmXWJigUjFs9jU+nrGXl4FC6jeyNX1NpzurzwgCK8wpZFjyHY9t+Z8wbE0w4quebDzjGrA5h65T3eH/wAjqO7I2vCUdOcha7Fn7B5f1nLI5jwuqX+XjKWpYPDqX7yD74m4yj7wsDKcor4s3gORzZ9jvPm4yjpvJ3k3Uv8/e4tVzqF4r36D5ma2rGT6e43H8Bfw1aROJ/9tPYZE1FIdB+3VTOjlvP8X6LqD+6Fy7NpbbIux7HiaHLCR/wBkm/n6fNCkOMCAqBYe9MYdfk9XwxaDFtR/bEq5mJfHImvy7YwvWH5Juu704mfPx6fg9eTMCoHriaxGnO9TgODV/BwUFLSThwno4rzPPF6NVT2TblfT4avJAOVeS9vQs3c8XCnBrb4sy49Rytwha51+MIH7qc43pbPGFiiz76a4u9AxbT1ELey7wex09PruD7IUu5e/A8PSzkvWrlLD3Hi6unsXHKu7wzOJQuFmKklz5GVgXPJWzbAUa/YV6prkP/zqga+zEv6FW+fHMT09bMtGi2l9e+wtY3/sO8IOlNxPb9O6Fq7MeCoNfY9uZmpqyZYVF+6tpX+OqNL1gQ9Bqqxn6ScQx7Zwp7Jq9ny6DFtLHgW/nJmfz2EN+S8e9Frdw0ASWiKHYQRbEtUA4YR01/fVuXf0Lk0rEpJfdSKU1IR6xQk/HLGTyHSkU1hSWVfyud7DF9J65nxyYUxKVRmJCBtkJD3P5zNBgqLaebFnkTTWk5AJmXbuPk5yFp9+nQhLy4NPL1HLd/PUfjIVKO5LM3Ues50i7fxlll4LiemkuDuk7Ur+uErVLB0BYqIu6kVTnuP26lMKyFn9nnXTo8gZurS5Vy4afPMXLYQARBoH3bVhQUFJKRmV3ZXqdjU0rjUihLSEOsUJO1/zTuQ7tJOIztqXCyB5MXKNu1bok6MQlNcgqo1RQfC8OhXy8zXVxnhFCwczdieXmNygPYtmqJJikJTYqOo/R4GA59eldpF0uoCQ67Ni1R309Ck6Qfy5FwnILMx+I2cyr53+4xG4v9Ey2oSEhGnZgKajWFhyJwHtDTTN5jzmRyv95n0RaO7ZpTHp9Mxf1UqFCTf+AkLgPNObxfn0jW1u/RlplzNOjQlKz4NHLup6Op0BD121lamfh3bmImqdH3EUXLxwIadWhKRnwaWXqOy79F8sSQrpI+TwzpwvkfdWW1rxw8R/NebSvbfE1iLPbXcwSa6JBkFGOpl29TRyWN04AOTck00aGdiQ7thnThT70Ofx08RwsjHQC8TPLF3f3naGiSL1KN8kWGSb6wadYKbWoS2jSdT5SfCsOuWx+LNqsKzh2aURaXSrk+TnN+PYXbEGmcah8RpzXBATBoWBC/7NVt6q5cuo6LWx28fb0kfUpLSvnzjO4+WEWFuvKXKKcOzSiLS6H8vl6H307hNri72Xf4LRhP+uYfES34ZuMOTcmITyXzfjqaCjUXfjtDhyHSNaDDkK5E6uf00sFztDSZ0+r6JkDDDk3JjE+t5Pjrt0jamuiRk5hBSnQCll46H9ihKenxqWTox/Hnb2foYKJDxyFdifwxAoCLB8/SqtcTkvaayN8uHZtSarKmegztWiWHbk2Vwr1jUwrvpVGckI5YoSHxl7OoTGIk88wNNCW6+cy5FIujUYz4d2hCTlwaufd1Mfb3b+doMVgqn5eYSXr0fUSt5Xd4mq7r8VWt63odMi+br+sN9HOabZT32pjNaSapVcwpgEfHphSZ2MLvIbbINrGFT4cm5MelUaAfx51fzxHwiGsL07xX3ZwFutyZYeTfl36LpL2F3HlO75+WcidA18HdOKnvE/tXDM6uztT1cZf0qevjjmMdJ2L/ijGT7zy4G6f18nceKu/IHb38g/6g861sI9+68ds5mj+mb/1fgChqavV/tRW1ddNkjFNA00f2qgL2fh6UJRt+Qi5LycbOz9Osn9/UoXQ99zmBKyZwe9nXkjYnlTtFyYaNQ3FKNk4qd1OKSjQdG0RSeJTkM2eVO4VGHIUp2Tg/hKPVS0EkRBg40gvL8HVxrPy3bx0HMgrLLMom55eQnFdC1wbm43wU0jKyUPkYLmh8fbxIyzAcibBTeVJuZM/ylCzsTJIngO+UYXSI3ETD5ZOIW7FN0qbw9kKTnl75b016Jkpvb0kf2+bNUPp4UxYpPZ5SE/IACi9vNOmGYwGajAwUJhwADkH98Ny+jbqr30bhI22vCQ6ljxeaNAOHOj0DpY/0gtK2RTNsVN6UnjEfi42PJ+pUI/m0TGxM5O1aNcVG5U3xSctHCGxUnqhTDHNckZqJja/UdxxaN8HGz5vCiAsWOVx93ckz8ov8lGzcfM394mGo6+tBrhFHbkoWbr7SGHEz6qPVaCktKK5sc1a5U/AYMdb6pSDiI6Rx6ubrQY6RDjlV6JBjpENJQTHO7oYbEZbyxcP0aG6SLwRPLzSZBv/WZmWg8PQyk7PrGYTrp19TZ8nbKLxM/F/lSXmy0ZymZGGrMs8HXpNH0Ob0ZuotnULiyi9rnAPA18+HlGTDDZ7U5HR8VeZx8gAurnUYMKRvpQ4Vxr6Zkmmmg2PbQGz9vcgPs3z4oK6vB9mSOc2mrol/1/X1IEc/1gdzWsdoTq31TeeHcOQ9ZozoxmGwRU5KFu4m8sZ9LI2jJvK3bk016FGekoW9BQ6/qcPocm4jjVdM5M4yKYejnzslRnqUpmRLNgKmaDSuP2lhhhhxVXmQnyLNNy4PiTFLcFS5U2wSp45+VXM0GRtEcphpvpDmvbyULFx9H08PBxNblDzCFgHj+pNqpEd1ry2g+jkLHsSQae4098+H5U4Ad5UHWUb+lZWahYcJj4evB9mp0qOBVclnp5rHibuJfLaRL7moPCiopm/J+PeiVm+aBEGwAYYD1/QficARQRAuCYJg8TdXQRBmCIJwURCEi1u3brXUxSJSth/mQo853F3zHY1Cx1itc+Nne+PZPpC/vzjw6M5VoPno3ni3C+SvzdZxHL6VwsDmvigVgtU6VBdp3/zBlV6zSFj7X+rNe8yXIQoCbvNeJe+zL6z78urK61EaGUnGCy+RNXUaZRcv4rbU8rGi/1EOQcB9/kxyPjZ//uyfynstnkHWB/88Fixx+CydTvo68wvi/6toPro3Pu0CuWxljNUUAvX54tpj5ouKC5HkTn+R/HkhVFy5iPO8pVZ9f+a3B/m7z0yS1n2Laq51L6isCY4HUCqVfLz1XXZ8Zfm5PjMIAvWWTyN5zdeP7ivjH6Na+VuPlO1/cLHHbO6t2UnDUOtfiFt/TG/qtm/M7U2/W81RXQQ82xvPdoHcqMa6XhNoMKY37u0bE2ulLZrpry2uVCPvWZuzZMj4N6C2bpocBUG4AlwEEoAHt6n6iKLYCd1G6jVBEPqZCoqiuFUUxS6iKHaZMWMGZSnZ2Psb7ira+3lQnmL5DgWgO743THpcoTg1B2d/w50KJz8PilNzzGT9+rbhibkjCZ+yAW25WtJWlJpDHSOOOn4eFFngqN+nDZ3njORQiJTDp449aQWGIw9phaV41zE/9gC6TZOlo3n/BL7enqSmG+7SpKVn4uttuMtdnpqFnZE97fw8KU/Jpipk/XIadxN7ajMyUfr4VP5b6eOFJsPwa4ng5IRNYGO8Nn2M70+7sGvTGs/1a7Bt2bxG5AG0mRkojX71UXp7o82QPpAq5udDRQUAJb8fwLZ5c0l7TXBo0jNR+ho4bHy80RjZX3BywrZJY3y3bMD/1++wb9sarw3vVLar07OwMbprb+PrhdpIXuHsiF3TAPy3r6fh4W+xb9cK1edvY9+mmYEjNQsbP8Mc26q8UKdlSTjsmzWi4c73aRK+HccOLam/eSUObQ0c+Wk5uBn5haufB3lpVfuFJeSmZVPXiKOunyd5adIYyTPqo1AqcHBxqmwrSs3B5R/GWJc5IzkQYh6neWnZuBvp4F6FDu5GOji6OFGUU1DZbilfWNLDr28b2s8dyXGTfCFmZaL0Mvi3wtMbbVamRFYsyAe1zq/Kjh5A2UTqVxWpWdj5G82pnycVVdyZBcjZf4q6Q6XH3qrL8Wv4Ln4N30V6WiZ+/obnK1X+PqSlmj/8DbBmwzLi797nmy27K3WwNfZNPy+JDoo6jji0aETTPWtpffpLnDq2IHDbMhyfMBxOyE3LxkMypx7kpknHkZuWjbt+rA/mtNBoTq31zaKHcLg9ZozoxmGwhbufJzkm8sZ9LI2jJvK3bk016GHn50nZQzh0a6r0mFZJSg6ORno4+HlQYoHDu29bWsx7hnOTP5LESH5qNq5+0nxTYCHGHoaS1BycTOK0JMWcQ9W3DW3njSTCwrqeZ5L33Pw8yU97PD1KTWzh+AhbnDWxxT+9tqjXpw2dLFxbQPVzFjyIIdPcae6fVeXO9w9+zPsHPyY3PQdPI//yVHmSbcKTnZaNh4Vfvdce/MhM3kNlHic5JvIeRr5UkJqNSzV96/8EtNra/V8tRW3dND14pqmDKIpzRFEsBxBFMUn//3TgZ6Dbw0gACq7cxjHQD4eGPgi2Nng/05usI9JjHA6NVZV/ewzqRMm9FEl71pW7uDRWUaeBNwpbJQGjenD/iLTaikebRvR4L4TwqRsozco30yM96i5uASpc9BxNR/bg3lEph1ebRgS9F8LBkA2UmHC0UbmRkFNMUl4xFRoth2+lEhzogynuZReSX1ZBe7+6jzKNRQT36cGvfxxHFEWirt+kTh1nvL0MybTwym0cGvth30BnT89Rfcg5Ij2y5WD0UGXdQZ0pNbFn+c1obBrUQ+mnAhsbnAYNoPTU2cp2saiI1OGjSXt2HGnPjqP87xtkLV5eWf2uuvIAFdG3UNavX8nhMHAAZWekD3UqPA3jtu/dC3V8gqS9JjjKb0Rj26AeSn/9WIb0p+SkgUMsKiJp0LMkjxxP8sjxlF2/Qeb8FZXtZddvYduwHjb1fMHGhjrDgykKP1fZri0sJq7vCyQMnUzC0MmUXb1J6py3JNXzSq7FYBfgj219X7C1wfXJfhQcl3LEdh/Lnf5TudN/KiVXokmcuVpSPS8x6g5eASrc63ujtFXS/ume3Dx6icdBQtQdvANUeOg5Oj3di2tHpbF6/ehFuo0JAqDDiB7ERv5d2ZZmEmPNqoix/u+FcMBCjAHE63XwNNLhqokO145epLteh44jehBjpANA5pW7uBrli8Aq8kWv90I4biFfqGOjUfjVR+Gj8wm7vgOoOC99iFxwN/iVbbfeaBPjJe1FUbHYB/hhp49T95F9yTsqPZ5pH2CIU7eBXSiNS6lRjpH9xzGy/ziOHYrgmRefBKBD57YU5BeSkSbdBAKEvvkqLq51WLPsw8rPiqNisW/sj10DX50OT/cl/6jhmKq2oJjrHSdwo890bvSZTvFft7g7ba2kel5c1G18Avzwqu+D0taGrk/3JspkTq8cvUgv/Zx2HtGDW5HXJe3V9U2A+yYcHZ/uxd+PESP3om7jazSO7k/35spRae7VjSMYgC4jehJtMo6ayN8FV27jEOiHvdGamm3GYbqmpkrac6/coU6gCqeG3gi2Suo/05PUI1JbuLVtRIcPpnFu8keUZ0pjJDnqLh6NVdTVx1ibp3sQ85j55sG67qznaDSqB4kmcerethHd3g/hxJQNlFnIF5by3o3H1CPHgi1SLNii4wfTODv5I8pMbGF6bdFkZA/iTPKeZ5tG9HsvhD9CLF+fVDdngS53+gT4VebOzhZy59Wjl+ih98+OI3pwyyhGlowIZcmIUC4c+ZN++j7NOjanuKCI3HTppiU3PYeSwmKadZTeLFo2YgGXjpynj16+ScfmFBcUVyFfQhO9/IP+YPAtN70tWlvhWzL+vfg/U3JcEARnQCGKYoH+7yHA6kcKarTcXrqNtruX6cqj7g6n+FYijRa/SMGVO2QfuUi9kOHU7fcEYoUGdV4ht+ZKK8aJGi3nl3/LoF2LdaVJ954gLyaJ9gvHkBV1j8Sjl+m8Yiw2zg4EbZkLQFFSFuFTN0g4Tq34lqd3LkZQKojee4KcmCS6LhhDxtV7xB29TM9lY7F1cmDoZh1HQXIWh0J0HDYKBUsGtGbWTxfRiiKj2tSniZcLmyJjae3rRnAT3Qbq8K0Uhjb3QxAsH81b9NZ7XPjrKrm5+Qx8ZgKzpk1ErdbdMXpx9JP069mVU2cvMPyFEBwdHHhnaaiZPeOWfUXLXSsRlArS9xynJOY+9Re9RFHUHXKOXMB36nDc+rZDVGtQ5xZyZ97nZhy5H32O1yfvg0JJ0e+HUN+Lw2X6FCpuxlB6+hEVaaorD6DRkP/Jp7h/+AEoFJQcPIQ6Lo46IVOpuHWLsjOROI0Zg33vXqDRoM0vIG/de/8DHFqyP/gcn8/fB6WCol8PUXE3HrdXplB+8xYlJ8/yUGi0ZL77H/y2vIugVJD/8xEq7sTj/tokyv6OoTji3MPl9Rypb39Bg6/X6EqO/3CE8tsJeM2bQOm1WArDLD8XZgytRsuvK78hZMcbCEoFF/dFkB6bxKDQ50i6dpebxy5Tv10gE7aE4ujmTKuBnRgU+hyfDFks4fhh5dfM2rEUhVLBuX0RpMYmMiL0eRKu3eX6sUuc3RfOxA2zWRHxKcW5hXwz51M6PqUrWiFqtJxc8S2j9DF2Y+8JsmOS6LZgDOn6GOutj7Fh+hgrTM7iQMgGiQ77Vn7NazuWIhjp8KReh2vHLhG5L5xJG2bzVsSnFOUWsn2OtOy5qNFybvm3DNHni9i9J8iNSaLjwjFkRt3j/tHLdF0xFltnB4KN8sXxB/lCq6F46ye4rPpQV3L8+EE09+NwHBeC+nY0FecjcXhqDLbdeoNGg1hYQOGn5n51f8VWmu5chaBUkLX3OKUx9/FbMI7iq7fJO3oe7ylP4tKnPaJajSaviPjQT2qeA4g4epqgQb05fn4/JSWlvDF3VWXbr+G7GNl/HCo/H2bNf5k7MffYH/adRIfElVsI3KHTIXvfMUpj76Oar9Mh/9ijS/1qNVp2rdzG6zt0a8CZfeEkxyYyMvRF4q/dIerYRU7vC2PahjmsjficotxCts752IzDGt805fhx5XZe0XP8uS+c1NhEhoU+z/1rd/n72CUatAskZMsCHN2caTOwE8NCn+P9IYsq5Xeu/Ir5O5ajUCo4vS+M5NhEngl9kbhrd7hy7CIn9x1n+oa5rNOPY4vJOGoqf99Z+hVtdy9HUCpI2x1mtqb6hwynbr92iBVq1HlFxMyVcogaLVeXfkOv3bp8Eb87goJbSbRc/By5V+6SeuQybVaOR+nsQLcvdTFSnJTFn5M/qpT/Y+U3jNuxBEGpIGrfCTJikwiaP4aUq/eIOXYZv3aBvLA1FAc3J5oN6khQ6Bg2D14i0eHism8ZsEuXL+7s0a3r7Rbp1vWkI5fpqF/X+2w16HBiijRf7F/5DS/veFP3qoV9EaTFJjIk9DkSr93jxrFL1G8XyKQt83HS573Boc+zQT+nD/S4svQbepvYopXeFilHLvPEyvHYODvQXW+LEqNS36JGy+kV3/KkPu/d0l9bdNFfW8QbXVsMNsp7f4RIr0+qlbP0tti78mtm71imK8u/L5yU2ESeCn2B+Gt39LkzjCkbZrMq4jOKcwvZNsc8X/wVdomO/Tvz6cnNlOtLjj/A+wc/ZskI3TXJtuVbmPXRXGwdpCduroRdon3/Tnx0chPl+pLjD7D24EcsG7EAgG+Wb2WGvuR4VIRhgyhqtBxe+Q1jdyxBofetzNgk+ul9K1bvW88Z+Va/0DFsNfItGf9eCFVVdPnfhCAIhaIo1jH5LBDdr0ug2+ztEkVx7SOoxJOq563Wo1+q7l01O+pNeETPqjEpaSebGlgvDzDr/k6KN8+zWt5ppm7xrsg0f1fBP4Wtl+6dUOf8n7Wao0fyTyT1HGC1PEC9s2HV4qh3NgyA1H7BVnOoTkZUS/4BR0KXgVbLN7x4HIA7bYdazdHk+mEAbjYbYTVHq9iDvBkw7tEdH4J1cbuYG/Ci1fKfxe0FYGM14mz2/Z26/1dDj41xe9lejVwBMDVpJ9mjgqyW99ivq952ucEoqzk63d9fIxzNvDs/uuNDEJtxiSuNRlot3yFe976c6QHWrwFfxunWgOr6Z2jAS1bLA3wct4eQAOufC/o67geg+vkb4JTKej36pv7AL6rq5YtnUnfxTiPzUtX/FCvidZvy7/ytj9Xxybp8sThg7CN6Vo31cbv5qRq2eDZV9+62zdXIezP1ea86eWtq0k5mBVTvWcZNcft4sdEzVsvvjde9J2xCI+v9e2e8zr/XVsO3lul863/vYfLHQEnE17Xv4t8IjsEhtdKOtfKXJtMNk/6zu0D7/wV1ZMiQIUOGDBkyZMj4d6CKV3/IeDhq6zNNMmTIkCFDhgwZMmTIkFErIG+aZMiQIUOGDBkyZMiQIeMhqJXH82TIkCFDhgwZMmTIkPE/gFpc1rs2Q/6lSYYMGTJkyJAhQ4YMGTIeglpZPa8G8a8enAwZMmTIkCFDhoxag1pZ9c0UJce31urrY8eBM2qlHf/1x/NKdrxptazjpHUAFL8/1WoOpyXbyX/F+rLQAK5bDlMw9ymr5V0++x2omXKz1S1bXrJ98aM7PgSOU9fXSPn14g9CrOdY9DVFa6pXWtp5+c4aKTletNL6UsbOq/cAkDd1kNUcbtuPEddhsNXyAAFXjhLfyXodGl0+BkCO0QsKHxfuP0YA1S+/ntyrv9XyAP6R4eS+aD1H3b3hABStm2w1h/Ob3wJUe06q86oG0L2uIWOw9eXXvY/qyq9X9/UCQLX1yHneeh0A3L+P4EK90VbLd03Sva2j5Kv5VnM4vqx7J0/+NOvj3XXbUVL6VC9G/E6Hkz7Q+vnwOa7zi5rIFxG+1pezD077vkZ0uPvEEKs5Aq8dAaiWf7p/H8Gf1bi2AOie/BMfNrQ+XyxM0JVOH9XQ+uuk/Qm666SWPl2t5ohOv/DoTrUFcvU8qyAfz5MhQ4YMGTJkyJAhQ4aMh0DeNMmQIUOGDBkyZMiQIUPGQ/CvP54nQ4YMGTJkyJAhQ4YMPeTqeVZB/qVJhgwZMmTIkCFDhgwZMh6C/yd+aTpzJ5X1R66iFUVGdwggpFcLSfsHR69yIS4DgFK1huyiMk4vfFrSR9G4LXYDx4FCgTrqJOo/D0rabQe8hLJhK/0/7BCcXCn59DVJH2WbLji8MBNBoaT89CHKD+8z09Wmcz/sn9I9EKlNvEvJtvcM8q064fDsDFAoqDh7hPJjP5jLd+yD3fBxIIpok+5RuuNDSbtbcEcC3glBUChI332M5I0/S9p9Jg5BNWU4olaLpqiUe4u+oCQ2sbJ9+bsbOHnmPB7udfll52az7xdFkXWfbObU2Qs4ONizdtkCWrdoatbvzN001h+7hlYLo9s3JKRnc0n7B8eucSEhE4DSCg3ZxWWcDn3SIB+XwQcRN9Fq4Zm29QnpFiiR/zDiJhcSsw3yJeWcmiV9sF0RoJ9TQUB99RTq8yZz2v8llA1b6g2rn9PPZ0v6KAPbYTd0IggK1FciqIj8TdJuN3g8ikatARBs7RCcXSn+8BVJH4eeXXFf+BooFBT9cpD8b/eY2QvAcUBfvNevInXiq1IdmrbHbsRknQ6Xw6g49auZrLJND+z6PweIaFMTKPvhc0m7TduuOIybpfOtk4coO2iug23XIOxHTQJENPfvUrLlXal+vbrgsVjHUfjzIfK277U4DqeBffD56C2Sx71G+Y0YqS16dcVj4SxQ6jjyv7FsC6cBffH+8C1Sxs+SjqNDN5xCZoNCSdnxA5T9vEvSbtd/GI4TZ6LN1vlW2aGfKT9+QNLHuW9nfJe/gqBUkLvvMFlbv7eog8vQ3tTfuIx7o+dRej1W0mbfvStur88GpZLi3w5Q+N/dUluNGIrrazPRZuj0KPrxZ4p/M/ifTfuuOE7RjaM87ABl+6XydkFDcZgwE/HBOA7/THmY1H+VgU9gN2i8LmddOUHFOek47QaOQ9FI59+CrT2CkwvFH0vtac18lN+Uzql/cDu6rp6IoFBwe3cE1/8jjZFWM4bTbGwwolpDaXYBkfO3UpSUVdlu26UbdWbNQVAoKDl0gJK90jm1HzKMOtNfRZuly+El+3+m9JDJWLt1w3Wuzp4lBw5Q9J2Uw3HYMFxmzUSjn4/in36m5ICUoyb0sOnQDaepRv75i4l/BlvwzzADh2twRxqunoagUJCx+xip//lJIu89cSg+k4eDPn/HLd5EqVH+BjhzL531x2/o1sN2DQjpLs3PH4Td4EKCzv6lan3unSstZqRs2wWHsbMQBAXlpw5Rfsg81m269NPlC1FEe/8uJV+uk9qre1dc5+lsUfz7AYp2msTI8KG4zJqJNtMQIyW/G3zcrms36rw2BxQKSg8eoHiP1JYOQ4dRZ8araDKN5uOghfmoZr7w6N+BpmumIigVpHx3nITPf5G0+08ajH/IMNDo5uTWwi0Ux0jnxBo9JLbq3QXPJa8iKBXk//QHedss517nQX3w/XgliS++RvkNac6qrm+C7vqikdH1RYqF6wtf/fWF1sL1BUBAUDsGrJqIoFRwbU8E5zdJ80X9bi3o/9YJ3wG1AAAgAElEQVREvFs14PfZG4k5aF58YfrbM+jcvwtlJWV8uuAT7l6/Y9ZnwqKJ9B8zAGe3OrzUyrygx7K1C+g3qDelJaW8Oedtbly7JWl3cLTnk6/eo2FAfTQaLeFHTplxyPj3oVZumgRB0ADX0Ol3E5gsimKxIAh1ga+AtujKiYeIonj2YVwarci6P6LYPK4Pvq6OjP86nKBmfjTxdq3ss2hwu8q/d1+4Q3RqrqlC2A2eSNneDxELsnGYvBLN7SuIWcmVXSrC9lCh/9um00AUvo1MOBQ4jn2Nok/eRMzJxPnNz1FfPYc2JaGyi8LHH/thL1L0wXwoLkRwcZPIOzz/KsX/WY6Ym4XTwo9RX/8Tbep9Qxdvf+wGP0/xx4ugpAihjhsSKBQ0fnc6N196m/KULNoeXE/O4QuSpJX18ynS/6urqOM+pCuNVk0levw7le3PjBjMuDEjWfqOdDP2AKfOXiAhMZmDe7dx9e9o3vlwI7u//ETSR6MVWXfkKptf6oWviyPjvzlBUDMVTbyM5mTQE5V/7754l+i0PIn8e2E3+OLZrvi6ODB+11mCmvjQxLNOZZ+Fwa0M8n/Fcysj32Q+BOwGT6Bs30e6OZ24Es0dkzkNN5rTjgNR+DY05xg+mdLv3kPMz8Zh2mrUMZcQMw0c5Ue/q/zbpstgFKoAKYdCgfuSuaS/thhNWgaqHZsoPnkW9b146Vc5OeLy0rOUXbthrsNTIZR+uxYxPwuHV95FHX0JMSPJ0MVDhW2/UZR89RaUFoGzqwmHAoeJcyj6cAlidgZ1Vv6HiiuRaJONfNO3HvZPjqXw3Xl636xrNg6PN+eQNnMJ6rRM/L/bSPGJs1TcTZB+lZMjruNGU3b1JmZQKPBYMof0WUtQp2Xgt/M/lJyIpOKeOYfLuNGUXbtpJu80fR6FqxeizcrA5f3NVFw4gzZRasvyyHBKvvrU/Pv1HKpVs0iYsoyK1Ewa//gJBWHnKL99X9rN2RGPyaMouRJtkcNt4Tyy5i1Ck56B97bNlJ6KRB0n1aP0eDh5Gz4zlxcUOIbMo2jtIt041m2m4mIk2iSpfEVkOCXbLciDzi+GTKJ0z3qdb05ZhTr2L4l/lx83XBTZdB5knrOqOx+AoBDovnYyR8e+R3FKNiMOrub+kUvkxRr0yL4ex4HhK9CUltN80kA6Lx/LyVc3VurgMud1cpcsQJuZgfvGLZSfPYMmQWqLshNhFG6sek5dQ+eRM38hmowMPLdupvT0GTTxUo6SsHAKPqmaoyb0cJo2j8J3FqLNfjCvVfjnNgscCgWN1s4gZuwqylOyaH1wPblHzks2RVk/nyTjv4cBqDu4Kw3fmkrMBEP+1mhF1h39m80vdNflzv+eJqiJL028XCr7LBrQuvLv3ZfvEZ1mmjsVOI6fQ9FHS3Rr2YqNqK+cNVnLdPmiaN3rVeYL1/nzyA7VxYjXV5spO20hRsLCyf/Ygo8rFLjMfZ2cxQvQZmTgvmkLZWfN57Q0IozCzx8yHzWQL5q9N42oF96hLDmbzofXkXn4omRTlPbTaZJ3HAXAc2gXmr49matj11ZbD+dZiyrlvZbNJmXGG6hTM6m353OKw6vIvRNGUxplOfdWyzf1HAHvTidaf33R5uB6ch9yfVF3SFcarprKLaPrC0EhMGjNZL4f/x4FKdlM+G01d45eIssoX+QnZ3FowRa6vmK50mnn/l3wC/BnZr8ZNO/YglfXzmLRqAVm/c4fO8+Bb3/nixNbzdr6DexFo8CGDO3+LO07t+Wt9W/w4nDzKsrbN+3kzzOXsLW1YfuPmyzbpbZCrp5nFWrr8bwSURQ7iKLYFigHZuo//xT4QxTFlkB7dBuqh+J6cjYNPJyp7+6MrVLB0Nb1iYhJqbL/ob/vM6xNfclnCr9AxNx0xLwM0GpQ3zyPslnHKjmUrXugvnlO+lnjFmjTkxEzU0GjpuJiBDbte0r62PYZTnnEb1BcCIBYYNgoKBo1R5uRgpiVBho16ssnsXmih0TerudQKk4dgJIinXxhnqS9TsemlMalUJaQhlihJmv/adyHdpP00RSWGL7TyR5M3uPVpcMTuLm6UBXCT59j5LCBCIJA+7atKCgoJCMzW9LnekoODdydqV/3wZzUIyI2tUrOQzcTGda6nkE+NZcGdZ2oX9dJJ99CRcSdtCrl/7iVwrAWfpLPFH6BiDlGcxr9J8qmHarkULbqjvrmn1IO/yZos9MQc3Ucmr/PYdO8c5UcNm16ov5buse3a9MS9f0kNEkpoFZTfCQcp6BeZrJuM6eS/+0exPJyqQ71m6LNTkXMSQeNBs21SGxadpF+b5cBqP88otswARRJL4KUgXrfzEjR+eb5CGw79pbq2W8EZWH7jXxTemPBvm0L1PeTUSelglpN0eEInILNx+H+2hTyvtlrNg4Au7YtUCcmo9bbouhwBI7Bvc361Z01hfxv9iKWSTmUTVuiTU1Cm6aTrzgdhl1Xc/mHwbFdc8rjk6m4nwoVavIPnMRlYE+zft6vTyRr6/doy8zHYdu6JerEZDTJOj1KjoXh0Pef66Fs2hJtWjLadN18lEeGYfuY41D4B6LNMfLNm39i07xTlf1tWvdAfUOas6o7HwCeHZtQEJdGYUIG2goNcfvP0WCoNEbSIm+iKdXJZl66jZOfh0GvFq3QJCehTdXpUBoRhl2vPo9lC9tWLdEkJaFJ0XMcD8Ohz+PZsyb0qPTPdL1/ngnDrss/18O5YzPKjPJ3toX8rTXJ36avYbyekksDd6Pc2dKfiNtV585DN5MZ1spfOo5Ak7XsfAQ2HaWxbttvOOVhv1aZL2xbtURjEiP2jzEnNi1boU5KQquf07LwMOytnY9q5AvXTk0puZdKaXw6YoWa9F/O4DVMmn+N11Slkz2m78asrh72T7SgIiEZdaI+9x46gXN/89zrMXsyudss597q+iaYX19Y8k9TW5g6qKpDE3Li0sjT54vo387RZIg0X+QnZpIZfR9Ra/k1Q92GdCf8xzAAYv66hbOrM+4+7mb9Yv66RU56jkWOgcOD2L9P9yta1KXruLq54O3jKelTWlLGn2cuAVBRoebG1VtmPDL+faitmyZjnAKaCoLgBvQDtgGIolguimLuQyWB9IJSVC6Olf/2dXUk/f9j77zDo6i+xv+Z2U3vZdPpvXekhybFhsJXUEGqBRBEsCOgYkERsaAUxQ4iNlSk9xZaQJCWhB7Se98kuzvz+2MCu5PdgGzyvr/4fefzPDwP2XvvmTPnnDlzZ+bOmUKjw74p+SWk5BXTtX6I6nfBJwC5wDrxlwtzELztD0IAwTcI0S8Y6ar6ek7wD0LKzbTKyM1C9A9W9RFDoxBDI/F8fjGeL36IrpU1+Yr+QUh51vFSXhaCn/ogFkIiEA2ReD6zEM9Zi9C1UE+SXMOCKE+xLnspT83G1WaCcp3Q8UNoH7OUunPGcmXuFw73syrSM7MJC7HuV2hIMOkVS16uY+cTHw8yCksdylN8UkLXegbr+KIyQm3He7uTWVTmeHyBkZR8I13qVLKVtz9yoa1Pc2/t08RKPnUUFz5VyPALQvAPQbpyRvW7LiQYS7rVr+aMTHQh6rhwadYEfZiB0gPqizZFh0DkfKtP5YIcBF+1T8WgcITgcNwfex33x99A17idWkZAMHJOxo2/pZxMhAC1vcSwKHShUXjN/hCvOUvQt1Z/y0IXEow5zWY/0rPs9sO1eWN0oQaM+47Y7QeA3hCMOc2qhyUjE12lE5UiIwTjfntbiIEGpCybYyQnEyHIYNfPtVsffBZ/gddzr9u168OCMKda49WUloU+VK2De8tG6MMNFO12/E0OnSEYS7rNfmRmojME2/Vz79sHw7crCXjrNcQQqx5iYDBSto0/sjMRA+zHu9zRB5+FK/Gc+ZrdfgjetxGbvkEI/gakq+qnmNX1B4BnWADFKVY9SlJz8AxzrAdA44ejSd518sbfYnAwlkwbW2Rlogu2t4Vrr2gCVnyJ79zXEQ1qW4jBBiwZ1riwZGba9QFwj+5D0Fdf4D//dZU/akyPQMONpXtwk/i8ow8+i77A61l1fLqGBVKeYo3N8tRsXMKC7MaHjBtKmwPLqDNnHInzVqraMooq5153MopuknvzjXStq95PwT8YKecW57KwinPZSx/iOftjdK3VFxI6QzCWDBt7VnWMRPch+OuV+L+hPkZ0wcFImerxogN/uPWOJvDzL/F9tQp/VDNfuIUFUmZzTi1LycHNgU8iJgzmjsNLaDh3DBde+bJG9dDb5d5MdJVylmuLxujDqs691Y1NcDy/cKliftEuZil1HMwvfMICKLTJF0WpOfiEVp0vHBEUFkSWTQ7PSssmyIFPbkZomIHUFOvNhLSUDELDQ6rs7+PrTb/BvW9rGxr/Tmr1RZMgCHpgKMpSvQZAJvCVIAh/CYKwUhAEr5rc3pYz1xjYIhKd6PyHiHUt7sAcH2t3B+UfIeoQQyIpef95jCsX4DHmGfD457soiDoEQwQlH7+M8ev3cH9o+m2Nv07615s50WMqiW99R+SM/9z2+Jpky7lkBjaLcNonW+JTGdA0tHo+bd4Vc4KTPq1A37I7lrgjty9DEAiYNZncD+zfH/vHiDrEwDBKv5xP2U8f4zrsCXD3vH0ZoZEUv/ssJcvfwmPCzNuLLUEg8LnJ5C5ecXvbrSQjYNYUchc7bwvT0RjyJz9E4axJmE7G4jX9Nj9+LQiEzH6cjAWfO60DQOn+g6SPeJjMsY9RduQYAXNfuq3xpmMHKZj2MIUvPIb51DE8p97eeFv0Le/AEnfUydisnj9saTC8J0HtGnJm2YZbd7ah/GAMOY+OIvfJiZQfj8Xn+dm3ve3SmBgyRz5E9oRJlMXG4jf79j+KXhN6mGJjyJ/6EIXPVcTntNvXI+ObTZzqOYVrb31LxAznP766JS6VgU3DnMudFfmi5L1nMX72Nh7jbjNfAKUHDpLx4MNkjX+M8thj+L9yezFedjCG7NGjyHl8IuXHYvF90Ql/VDdfVJDy1RYO3zGdS2+upt7MEf+7eggCQc8/SfYi+2Vot6VDDcQmKPOLkz2mcq0WzC9qAp1Ox/sr3uK7zx2/R6bx30VtvWjyEAThBBALJKI8XdIDHYFlsix3AIoBuywqCMITgiDECoIQ+9lnnxHi406azZOl9AIjITZ32mzZfDaJIa3q2P0uF+aq7t4LPoHIRY4f6+pbdMVyzv6uq5yXjRhgvTMjBAQj5amfwMi5WZj/PgSSBTk7HSkjCTFEWZYm5WUj+tvcjfYPVj1huN7HfPqwMj4nHSkjBdFgXVpRnpaNa4T1jotreBDlqeqlc7Zk/7afgCFdq2x3RKghiLQM636lZ2QRWukOop1PCo2E+Lg7lLf5bDJDWqqXS4Z4u5FuO76oFIO3m8PxWxwszQOQi/IQfGx9GlC1T5tX4VNHcVHoWIauVTe7pXkAlowsdKG2dw0NWGzsJ3h64tKoAaErFhPxx2rcWrckeLF1DbhcmKN64ij4BqqeMADIBdlY4o8pcZGXiZydihgYZm3PzUIItN5FEwMNyLmVYis3E9OJg2CxIGelIaUloQuz+sWSkYU+zGY/QoPV++HlgUuj+oStXETUxu9wa9OCkA/n49rSWgDEnJmFPsyqhy7EgCUj20aGpyLj8/eJ/HMVbm1aYPhwvlXHnEzEYNsnNgZkm7unAHJRAZiVN9XKd2xA31BdgMSclo0+3BqvLmHBmNOtOoheHrg1qUfdVe/SaNdXeLRvTtTyebi3bmK1RWYWulCb/TAYbhQYuKFHQQGYFD1K1m/ApZlVDyknCzHIxh9BBqTcSuNvsR9y0W3EZgv7pXngvD9cW1h1KUnLxSvCqodneCAlafZ6hPduRZun72PX+MVI5WarLbKy0BlsbBFswJJVyRaFVluWbtqAvqnaFlJWJjrbpxQGA1Jmpbiw8Yfxzw242MmoAT1yMhGDbiM+d6r9Wp6Wg2uENTZdw4MwpamPU1tyft+Pf6XlUSHelXNvKSHeVeTeOPuleQByXhZi4D84l504ZM0X6cmIodYl1pbMLHQhNva8zWPEkpWFaFCPlyr7w2Z86cYN6Js48Ec180VZWg5uNudUt4hAym7ik4x1BwgeWmlJZTX1MNvlXgOWdNvj1APXxvUJ//I96mz+Fre2LQhbMh/XltacVd3YBMfzC9Ntzi8K03LxsckX3uGBFKY7zluO+GDTx+Rm5BJsk8ODw4LIvolPKrNu52oy0rMJjwi98VtYRAjpqRkO+89/fzZXLyXy7WdrHLbXWiSpdv+rpdTWi6br7zS1l2V5uizL5UASkCTL8vXZ688oF1EqZFn+TJblzrIsd37iiSdoFRFAYk4RyXnFmCwSW84mEd3UfhJ9OauQglIT7SLtHydLqZcRAkIQ/IJB1CkXRhf+susnBIaBuxdS8gW7NsuVeMSQSISgUNDpcencF/NJ9UTFdDIGXVOlKIXg5YsYEoWcpbx/JSUmIBoiEAKV8fqOfTCfUk/kzacOom/cxmZ8BFKW9V2hohMXcG8QjludEAQXPUHDepG7Vb3EyL2B1Tb+AztRernq978c0bdXN/7YvANZljl5+hze3l4YgtU2bRXuT2JOsY1PkoluHGYn63J2IQWl5bSLVD+ebxXmR2JuCcn5Jcr4+DT6NrR/dH45p4iCMhPtwv3t2hSfhlp92vwOLBdO2PW74dMU++o7UsolxMAwBH8DiDrlwijhuL2MoHAEdy+kpPN2beVn43CpE4kuIgz0ejwH9cO4N+ZGu1xcTPLA4aTcN5qU+0ZTdvosWbPmWnVIvmjVQadD16YH5rhjqm1YzsUi1q94udvTByEoHCnXZtnV5Xh0IZEIwWFKbHbti+mvGJUM8/EY9M2VZX2Cty9iWJSy/r2CsjPx6OtGoq/YD6/BfSnZY71IlItKuNbvPyTd9ShJdz1K2alzZDwzT1U9r/xMPPo6ahnGPTa2KComacAIku8ZQ/I9Yyg7dY7MZ+ZZ9+NCPGJ4FGKIMt6lV3/KY9X7IfhbY9Glcw8syeqXpY2nEnCtH4FLVCi46PG9uw+FO6zHqVRUwvk7HuZivwlc7DcB44k4kibPV1XPM52LQx8ViS5c0cNjYH9K96v1EIOserj36oH5ilUPy8U4xLBIRIPiD9ce/THd5n5IKZcRbeJb1+IOzOcd5axwBHdPhznLWX/YVs/LPnEJnwZheNcxILroqD+sG9e2qo+RwFb16PbORHZNWExptvp9O3N8HLrIKMQwRQf3vv0pP3hAbctAqy1cu/e0K85giotHFxV1wx/uA/pTdqBqf7j17IH5qtqeNaGHXXz2/AfxmWTVo/jEedwahONakb8DHeRvN5v87TewE2WV8nercD8Sc4tJzqvInXEpRDcOpTKXs4uU82GE/dIoy+V4xFB1vjCfUN8QMv11AF2zinOZty9iaKTyzuT19rg4dHXUx8hNfdJL7RNzXBx6G3+49etPWUw1/eFEvij86wIeDcNxr6v4JOT+nmRtiVX18WhgPbcF3dkR4yW1T6qrR9npeFzqRaKPrDhOh0ZTvFude6/2eZBrQ8ZybchYyv4+R9r0earqedWNTbCfX9wqPh3NL9JOXiKgQRh+Ffmi+b3duLjN/pxaFTOHPs2hLQfpN6I/AE07NKO4sKTKd5cc8UD/0ezYtJthI5Vqve06taawoIjMDPsLrxkvTcbH15u35yz+x/I1/t3Uyup5jpBlOU0QhGuCIDSTZTkeGACcvdU4vSjy0uD2TFlzAEmSGdauHo0Nvizdc5aW4f70barcSdt89hpDWkYhCA6WIsgS5dtW4zbyWaWs86l9yFkpuPS6Hyntyo3Jtr7FHQ6fSAAgSZT+8CmeM95GEEXKD2xFSr2K271jsVxNwPz3ISxnYtG37IjXq5+BLFH6y+fIxYXW8T8vx3PqfKUs9KFtSGmJuN41GkvieSynj2A5dxx98454zl4KkkTZ719BSaFVB4vElVdW0vz7eQg6kYwfdmBMuEbU8w9RfPIiuVuPEjphKH692yKbLZjzirg4Q12a+vlX3+HoX3+Tl1fAgPvHMHXSo5jNyp3hUQ/cTZ/uXdh38ChDR07Ew92dN2bPdOyTQW2ZsvYgkiwzrG1dxSd7zyk+aRJe4ZNkhrSMtPOJXhR5sX9Lpv4aq4xvFUWjYB+WxpynZagffRspF1Bb4lMZ3DS8ap9uX4Xbf2YpJZlP7UfOTsGlZ4VPL1b4tPkdyrI6R8gS5Zu/wf3hF26UdZazknGJHoGUchnLeSXZKwUg7O/kX/dJzntLCFnyLuhEiv/YhOnSVfyeHE/5uXiMe29aHBIkifINX+E+draiw/FdyJlJuPR/ECn5Epb4Y1gunETXuC0e0xYpOm9ZBcYilQzj6iV4PfuOElv7NiOlXMXt/nFYriRgPnEQ8+mj6Ft3wvvNL5TYXPsZsm1BCYtEzjufELpsgVJy/PctmC5exX/KOMrOJmDcc4v9uC7j3SWEfKroUfTHZsUWk8dRfjbhH9jCQsnKj/Ce+x6IIuU7NyFdu4L7QxOwXIjHFBuD290jcO3SA9liQS4qpPiTd9QyLBJpry+jzpdvKiXHf95K+YVEgmeMofTUeYp2VnF8V5KRv/hjgj5YCDqRkj83Yb58BZ/HJlAeF0/Z/hi8HhyuFCOwWJAKCsh7y0YPScL45cd4zV6o7MfuTUhJV3B/cALmS/GYj8XgNnQ4Lp16gmRBKiqgZGml/ZAlyrd9h/tDzys56++9Smz2fgAp9cqNmz76lvYFTmrMH4BskTgy5xsGfv+CUnJ87R7yE5Jp99wIsk9eJmnbcTrNfRi9lzvRK54GoDg5m10TFt/wadEnH+K3YBGCKFK6ZSOWq1fwHDcRc0Ic5Qdj8Lh/BK7dK2xZWEjhe5V9aqHgw48IWKTEhXHjJsxXruA9cQKm+HjKDsTgOWIEbj17VPijkPwFlWTUhB6ShZIvPsL7lYr43FXh11ETsFysiM+7RuDa2SY+P7WRYZFInPM5zb5/FUSRrLU7KE24RsRzD1Ny8gJ5244SOv4ufK/n7/wiLj2jrjynF0VeGtiaKT8fUc6HbaJoHOzD0v3xtAzzp2/FBdTmuBSGNI9wnDslidLVn+A5c4FyLtu/RckXwyryxcmDWE7Hom/VCa83Vir9f7I5l1XsS8HijwlcrMS4cYNyjHhPmoApTvGJ13+GK8UhHB4jFgqXfIj/u4sqSsAr/vAaPxFTfIU/HhiBW4+eii0LCylY6MAf1cwXskXi/Mtf0PaHV5SS42t2URKfRP0XRlF48iLZW2KJnDSUgN5tkM0WTPlFnHv6kxrRw+/jb2/YMuvtTwhb/jaCTqRwnZJ7A54aS9mZBEp2V3HuqcnYrNDjyisraVYxv8ismF9EVswv8rYeJWzC0Bvxackr4lKl+YVskdgx9xtGfPcCok7k1No9ZCck03PWCNJOXebituOEtW3IsM+fwd3Pk0YDO9Bjlnq547GdsXTu15nl+z6nzFjGkues1Xs/2PQxM4cqeWbc7An0GRaNm4cbXxz+WiVjz/YD9BnYk61H1lFaUsrsGdYVDet2ruaB/qMJDQ9hyqxJXEy4zK87Vt3axhr/FQiVK7nUBgRBKJJl2dvB7+1RSo67ApeACbIs3+wWgmz81rl1twAeY5XvSpS8a19q8p/i+eJXFDw5+NYdb4Lvii0UPn2P0+N9Pv4TgEMRw52W0S1F+R6IKeuS0zJcghti/OoFp8cDeExYSMnyGU6P95yslEsteW+i8zKe/5LiN8c4PR7Aa84qEjsPcHp83dgdABTPe8h5HeYr39zJnzDwFj2rxu+r7Vxpf6fT4wHqn9jG1Y7O61Dv+HYAckf0dVpGwC+7ATjXxHEZ239Ci/MbSenRz+nxABExu8gb5bwM/7W7ACheMM5pGV4vfwNQbZ98G1m9Y2Rs8ioy74x2erxh2x4A0vr0dVpG2N7dANXWI/dB53UACPhpN0cjH3B6fJdk5Xs5xpWznJbh8ZhyIVswyfnj3feLbaT2qt4xEr5/FxkDnPdHyA4lLmoiX+wOdf6dsb7pP9WIDpfaDHJaRsNTSvnv6sRnwE+7OVyNuQXAHSm/sqiu8/niuUTlomVYXefnSb8nKvOk5iFdbtGzauIyjgI4/wL1/yLGDR/Wvsm/DR53P1Mr7VgrnzQ5umCq+P0E0NlRm4aGhoaGhoaGhoaGxv8EtfWdJg0NDQ0NDQ0NDQ0NjVpBrXzSpKGhoaGhoaGhoaHxP4BceyvU1Wa0J00aGhoaGhoaGhoaGv8aBEEYIghCvCAIFwRBcPQJorqCIOyq+Lbr34IgOP/ScgXaRZOGhoaGhoaGhoaGxr8CQRB0wKfAUKAl8LAgCC0rdZsD/FjxbdeHgKXV3m5trJ5Xg/xX75yGhoaGhoaGhkatoVZWfauM8Y9FtXp+7HHfcze1oyAI3YHXZFkeXPH3ywCyLC+w6bMCuCTL8rsV/d+XZblHdfTSnjRpaGhoaGhoaGhoaNQKBEF4QhCEWJt/T1TqEglcs/k7qeI3W14DxgiCkARsBKZXV6//+kIQxr1fOz3Wo894Rcba152XMepViuePdno8gNe81ZQsmer0eM/pyhPJ5O79nZYReXAnQLW+s+QxYWG1vvMEFd96qoFvbxl/nH+LnjeRMXJetcZfl5F9r/PfGglar3xrxPjFc87rMGkRQLVjqya+u1IT32mqie8bnWpwr9My2lxeT9ZQ530KELxpD8bVc50e7zH6DQCMvy90XsYw5RgvmnWf0zK8F//B5lDnvyEGMCT9h2rlTq95qwE42+hup2W0vLgBgIutnf/WXqPTW7jWxflvsgHUObqDrdWw56B05Zts1c3fAIVThzotw2fpJnKGVe8YCfx9T7W+hxYRoxzrNZEvYqPud1pG56Tfqv3NK6Ba+Td8f4UtRjs/N/BfvZN9Yf9xejxA77SfmVHf+fj+6IoS34/Uc/5bZt9fVeUCSVIAACAASURBVL5lNrG+8/vy5ZWfnR6roUaW5c+Az6op5mHga1mW36940vSdIAitZdn5Khj/9RdNGhoaGhoaGhoaGhoV/Pur5yUDdWz+jqr4zZZJwBAAWZYPCoLgDgQDGc5uVFuep6GhoaGhoaGhoaHxb+Eo0EQQhAaCILiiFHr4o1KfRGAAgCAILQB3ILM6G9UumjQ0NDQ0NDQ0NDQ0/hXIsmwGpgFbgHMoVfLOCIIwXxCE62vLnwUeFwThJLAGGC9Xs/qdtjxPQ0NDQ0NDQ0ND4/8K0r9+eR6yLG9EKfBg+9s8m/+fBXrW5Db/T1w0HTh9kYU/bEeSJB7o3Z6JQ7ur2lOy83nt6w3kFpbg6+XB25PuJTTQVy3jfAoLNx5DkmUe6NiIiX1aqdpT84qZ++tBCktNSLLM03e2o3dTdSEPXaO2uA5+FEQR81+7MR1Yr2p3HTQGsb5SZl5wcUXw8qVkobVgyIGr2by3LwFJlrm/ZQQTO9VXjV+0L4GjybkAlJos5BhN7HtC/dKtW7cu+D0zDUEnUvzHRoq+W+PQZu59exO04HUyJkzGFJegtsWldBZuP4UkwQPt6jKxe1NV+3vbT3E0McuqR0kZ+2daX8ae8/Zi9h44QmCAP7+tWm63bVmWWfDhcvYdPIq7uxtvvfIsLZs1VutwMY2FW/9W/NG+PhN7NFO1p+aXMHd9rNUf/VrTu3GYWsb5FBZuiFVkdGpctU+N5YqMQe3tfFoTMlw6dsXr8ekgipRu20Dpz9+r2t0GDMFzwhSkbOWpcumGdWodLmWwcMdpRYe2dZnYrYmq/b0dpzl6LVsZe90fM9Qvc9dIbN3RBd8Z00DUUfLnBopXqWPLY+hgfKZORspSYqP4l3UY/1TlO9x7dCHwuamgEylat4mCr3/AEZ79e2NY9Cqpo9VFLPTtuuAxXtGhfOcGyn5X6+AaPRj3MZORcxQdyraso3ynWgfvPh2JePVxEEVy124jc7njl3t9h/Sg3rKXuXDfTIynLqjaXDp1xWvydARRpHTzBow/VfLpwCF4PTYFKUvxqXH9Osq2bFD1OXAhlYVbTiBJMg90aMDEXi1U7an5xcz97QiFZSYkSebpAW0ZZDs+PomFvx9CkiUe6NqMif3aqcfnFjF37V4KS8uU8UO70LtFHVUfXfOOuN3/GIg6TIe2Ytr5i50d9O164jr4YWRASrlM2ar3Ve3B/drR4s1xoBNJWr2Ty0vUKyjqjB1I3YmDkC0SluJSTj/3OcUJ1qXp1c2bAF59OhE29wkEnUju2q1kr/jJbj8AfAb3oM7SV7h0/wxKK/nUo2dngl+ajKDTUfDLJvK++NGhDK+BvQj7cC5Jo6ZRdua8qs29exf8n30KRJHi3zdS+I3j+Pbo15vgha+RNnYKpnPW/BvUrx3N3xyHUGHLK5VsGTV2IHVsbHm2ki2h+vkbQNeyE+4PTgZBxBSzmfKt9vbUd+yN691jQJaRki9R+pW6OIlLh654VuS9sm0bKP1FfYy49h+C53hr3ivbuI6ybdZjxO0O5VyGTkfJ+g125zKPuwbj+9RkpExrvilZrz7WayJf+PbtQN3XHwOdSNaabaR9+quq3TBmMIbxd4FFwlJs5OqLSyk9n6S2Z+vOuD88FUEQKd+3ifJNa+3t2bkPbsPGKva8pi6oVBO5V9+2Cx6PTgNRpHz3RsrWV7JFn8G4P/wkcm6FLbb+RvlutYyAfu1p+MYEBJ1I2uodJH3ym6o9bOwgIiYMvhGfF55fQUmC2hbNo9sxfN44RJ3IobU72b5MHeONujbngXnjiGhel2+mf8zJTYftbDX2tUm079eJcmMZy59bwpXT9gWoGrRuyJPvP42ruysndh1TtbWObs8j85T92Ld2BxuXqfdD76rnscXTqde6IcV5RSybtpjspGqt+tL4l1ArL5oEQbAAp1D0OweMQ3nhyzaTNATmybL84c1kWSSJBd9vZfnMhwgN8GX0W18T3a4JjSKCb/RZ/NNO7unemvt6tOXIuSt8vG43b026Ty3jz1iWj+tPqK8Ho1dsIbp5FI1C/G70+XzPaQa1rsfIrk24mJHPtFW72TTLZnIsCLgOHU/pqgXIBTm4P/YG5vjjyFnWE1r51lU3/q/vMggxrJ6NDjLv7Iln2bAOhHq7MfrHo0Q3CKZRoPeNPs/1tp781py8RnxWodoYooj/szPImvE8loxMQr5cRum+GMxXrqq6CZ4eeI8cQfnpsw7sKbNg698sf6gHoT4ejP56D9FNwmgUbL3IfH5gG6sesZeIS89Xybj/rjt5ZMR9zH5jkZ18gH0Hj5KYlMLGtV/w95k43lj0CWs+t7rZIsks2HyS5Y/0Uvzx5S6im4TTyGDV4fP9cQxqEcXITg25mFnAtLUxbJo2xEaGxIL1R1k+vj+hvp6MXr65Cp/WZWTXpopPv9vFpmcja1QGoojX5GcomPssUnYmfotXYDp8AMs1tU/K9+2keMVHN/72nv6C1RbbT7F8ZDfFH9/uI7pxGI2Cfaz+GNDa6o9jl4nLUPujpmLLd9YMcmYqsRW8cjll++1jq3TnLgo++BiHiCKBL04nY+qLmNMzCV/1KcY9MZguJ6q6CZ4e+DzyAGWnzqnHCyIeE2dQ/NbzSNmZ+CxYjik2BilZrYMpZhfGr6rWIWL+ZC4/OhdzWjaNfl9MwfbDlF24pu7m5UHwhHsp+SvOoQzvp54hf/azSFmZ+H+0gvLDB7AkqvUo27OT4mUf2Y+nIrY2HWf5mGglxlduJ7pZBI0MNrG17xyDWtVhZOfGXMzMZ9r3+xj0ms34dTEsf3wIoX5ejF7yB9Et69IoNMA6fscJBrVrwMjuLbiYnsu0L7eyqcUolT3dhj+Jcfk85PxsPGa+j/nMEeR0qy2E4HBcBjxIyZIXwViM4G3VT7GFQMt3JnJ05FuUpmTTfcvbZGw5pprIp/x6gGvfKhUQDYM70fz1Rzn28DsVG6he3rzuj/DXpnB13BxMaVk0XPcBhTsOUe7Ap4Hjh1XpU8Ocp0h5/GXMaVlErV1C8a5DmC7Zx6bfmPspPXnOoYyAF54mY9oLWNIzCf1mKca9BzFfdpB/HxpO2alK+VcUaPHORI5V2LLblrfJrGTL1F8PkGRjy2avP8rx67akZvI3goj7qKco+Xg2cl4Wni9+hPnvw0hpVlsIhghcB4+iZNGzYCxyEBcink8+Q+GrSt7zXbSC8iMHkCrnvf07KfnMwTEiivg9N4PsinOZ4YvlDs9lpTt2kb+4imO9hvJF3TefJOGRVzGlZtNiw3vkbT2iuijK/m0vmau2AOB3ZxfqvDqR82Nsqq8KIh6jp1P8/ovIuVl4zf0E84mDSKlWe4ohkbjd/TDFC56BkiIEH39cuvW/oUO1c68g4jF+BsULnkfKycTnjWWYjjuwxaHdGL+p2haNFjzG6ZHzKUvNof3md8jZGqu6KMr8dR9p324FIHBQZxq8No4zj7xlo4bAg/MnsnTMW+SlZfPsH29zatsx0i9YYzw3JZvvn1tGv8fvcahG+34dCWsQwazoqTTu0JSJbz7JvPtftOs38a3JrHxpKRf+SuCFb6wVSwVRZMz8x3h/zHxy0nKY98c7nNgWS8oF6370HjmA4vxiXu47na739uTBl8awfNoHju2i8V9FbX2nySjLcntZllsD5cBkWZbjK35rD3QCSoB1N5UCnL6cQh1DAFGGAFz0OgZ3acHuE+onJ5dSsujavD4AXZrXY/cJ9d3B00nZ1An0JirQW5HRph6749R3RwRBoLjMBEBRaTkGHw9VuxjZCCk3HTkvEyQLljOH0DfrVKXe+tbdMZ85aNUhvYA6fh5E+XngohMZ3CSU3Zeyqhy/+Xw6Q5qEqn5zbdkcc1IylpRUMJsp2b4T9z723/nyfWIihavWIJeX27WdTs2lToAXUf5eih4tI9l9Pq1KPTadS2JIS/WTlc7t2+Dn61PFCNi1/xD3DRmAIAi0a92CwsIiMrNyrDqk5FAn0IuogOs6RLE7IVUlQxCw+qPMhMHbXb0fSdnUCfIhKtDH6tNz6kmUABSXVu3TmpChb9ICS2oyUrrik7K9O3G5o1eVtqnM6dRc6vjb+KNFBLsv3MwfyQxpofZHTcSWS4vmWJJSbsSWcftO3Hrd3lNx19bNMCelYE5WZBRv2Y1HX3sZ/lPHU/D1WuQydXzqGjdHSk9BykgFi5nymJ24dLk9HTzbNaH8aiqma+nIJjP56/fie+cddv1CZ40mc/kvSBUxZou+aQssKclIaRU+3bMT127/3KcAp5NzqBPgTVSANy46HYNb1WV3fIqqj4BNjJeaVLF1+lomdYJ9iQryVWKzXUN2n6k0wReguLS8Ynw5Bl9PVbtYtwlSVipyTjpYzJj/2oe+tdoWLt0GYzqwAYzFAMhF6gm2f8fGlFxOw3g1A9lkIe23GEKHdFb1sRQZb/xf7+mm+iR5dfMmgEe7ppRfTcF0LQ1MZvL/3IvPwG52Yw0zx5C94me7uAJwa9MMU2IK5qQ0MJsp2rQbr/7d7foFTh9H3pc/Osydrq2aY7qWjKUivku27cIj2j7/+k2eQOG3P9jJ8HNgy5Cb2FJXyZZQM/lbrN8UKTMFOTtNiYtje9C3U9vTtdcQTHvWg7EIsI8LfZMWSGnWvFe+byeuXf/5MeLSsjnmSvnGvfftHes1kS+82jeh7Eoq5YlKvsj5fT/+g9THiKTyiTtUeqVC17AZUkYKcpZiT9OR3eg7qOPCpc9Qynf+ASUV9izMs7bVQO7VNWqOlJ6MlFlhi0M7cel0e98A9enQmNLLaZQmZiCbzGT+doDAwV1UfezisxL12jcm82oa2dcysJgsHF8fQ5tB6hjPScokJS6Rql5N6XRnV/b9opRSv/BXAp6+XviHBKj6+IcE4OHtwYW/lLng9f4ADds3JuNqGpnXMrCYzBxef4D2g9T70WFQF2J+2Q1A7MaDtOjRBo3/G9TKJ02V2Ae0rfTbAOCiLMtXHfRXkZFXRJjNUrvQAB9OXVZPPprWCWHH8XhGD+zCzr8SKC4tJ6+ohOtTkIxCI2F+XlYZvp6cSlJPKif3a8OUb3ay5nA8xnIzK8arv8sh+AQi52ff+FsuyEGMbORQZ8EvGMHfgHT5jHU/iksJ9bFO/EO93TidXuBwfEqBkZQCI12iAlW/i4ZgLBnWSouWjCxcW6mX/Lg0bYIuxEBZzGF8Ro+iMhmFpYTZTM5CfTw4lZLrWI/8ElLySuhaz+CwvSrSM7MJC7E+CQwNCSY9MwtDcKBjHXw9OJWco5IxuXcLpqw5wJrYixhNFlY8oj4hZxQYCfOzThJD/Tw5lZStltG/LVO+3lHhUwsrxvevcRliUDBSltUnUnYmLk3VPgFw7RGNvlU7LCnXKFn5iVWHosr+cOdUSp7deKjwR34JXesGq36vidjSVYotKTMTl5b2++Ee3QfXdm0xX0uiYMmnSBnWJQ16QzDmNNv4zMS1dXO1HZo3RhcagnH/YXzHjlS1iYHBSNlqW+ob2+vgckcf9C3aYklNwvjtp8jZNjqEBWFKtR7bprRsPNurly+5t2qES7iBwl2xBD8x3E6+GByMlGmjR1Ym+mb2erj1isalTTssydcoXvHJjaV6cD3n2MSWoxiPbsWU1XtZc+QCRpOZFWOsyyUz8kvUOcvPk1PX1MtHJt/ZkSkrN7Mm5qySsx5XL9kU/IKQ86y2kPOyEOupl8GKhggAPKa/qyzr2bIGS9xx6z6GBWJMsR4TpSk5+HVUL7UFqDthEPUn343goufoiDesOlQzbwLoQ9U+Nadl4dFOvR/XfVq0+yhBj9v7VB8ShDnNaj9zehbubSrFZovG6MMMlOw9gv8E+2+96AzBWNKtMizpmbi2rpR/mzVBF2qg9MBhfB5Vx7d7WCCl/8CWdSYMot7kuxFd9MTa2BJqJn+L/sFIudb9kHKz0NVX21MIiUQEPJ9dBKKOsg2rsJy1LoESgoKxVMp7ekd5r7uS96SUa5R8YT1GFFva5IrMTFwd5Zu+fXBtr+Sb/I/U+aYm8oVreCDlNrFVnpaNd4cmdjIM44YS+vgwRFc98aPU32ET/IORcqwy5dwsdA3UsSWGRQHg+dKHynLGP7690VYTudfOFjlZ6Bs5sEWX3uibt8GSloTxu6XINnq7hQdSlmJji9RsfDra2yJ8whAin7wH0UXP3/95TdXmFxpInk2M56XmUK+9fYzfjICwIHJsZOSkZRMQGkhehjXOA0IDyUmz6ZNq/b9/aCA5NvuRm5pNw/bq/bDtI1kkjIUleAdUfSO4VvLvLzn+/4Xa+qQJAEEQ9MBQlKV6tjyEUgnD0ZgbXxH+7LN/9l2sWQ/251hCIqPmf0lsQiIh/j6I4u2ZZvPfV7ivQ0O2PvcAn4zpy5xfYpAk54p06Ft1w3LuiN0dqX/KlvPpDGgUgk4Ubm+gIOA3Ywr5Hy9zart2epxLZmCziNvXowbYfDaJ+9rWY+vTd/HJqB7M+UN57+i2ZPx9hfs6NmLr88P55FHnfFoTMsqPxJA7aRT5T0/EdCIW72dm39b462yJS2Fgs/Bq+cPp2AJKDxwk48GHyRr/GOWxx/B/5aXbEyAIBMyaQu5i+/fg/immYwcpmPYwhS88hvnUMTyn3r4O4XMmkfrWF07rAFB+OIac8aPImzoR0/FYvJ+9fZ9uPp3Ife3qs3XmvXzycG/m/HYE6TZe7t184iL3dWrC1lce5pOJg5jzw57bz1miDtEQjvHT2ZR+twi3B58Cd69bj6tE4ldb2XvHDBLe/J5GM537QKXTeVMQCJ39GOlvr3Rqu9dlBL/wBNnvVeNbjIKA/8zJ5H3ofHwDXPtqK/srbNnQSVtC9fK3IOoQDJGUfPAixi/fwX30DPC4vbgwHY0h7/FRFMxQ8p7XjNs7Rkr3HyR9xMNkjn2MsiPHCJh7m8c6NZAvKsj8ZhOne00m6e1vCX/6wdsXIOoQQyMpee9ZjJ+9jce4mbc1vNq5FzAdP0jBM49Q+PLjii0mO2eL1K82E9ttGpffXEXdmdX7KK6Gxv82tfWiyUMQhBNALEqd9RszlIp67PcBDt/klWX5M1mWO8uy3PmJJ54gxN+btBzrXfP03EJC/NV3BEL8fVg8dQRr501k+v3K3VpfT+ud9xAfD9Lyi60yCkoIqbSUZd3xSwxqXReAdnUNlJkt5JWUWfUqzEHwC7rxt+AbiFzo+A6frlV3zKfVS0xCvNxJLyy16lBUhsHL/vE2KBPbIU1D7X6XMrPQhYRYtxMSjCXTeqdI8PRE37ABwUs/IPTX73Ft1ZKghW/i0tx6pz3Ex520Qusj9vRCIyE+6qVv19l8NpkhLaMctt2MUEMQaRnWOz3pGVmEGqxPR+x0KDASUmnZ27oTVxhUsaykXVSQnT9CfD1Iyy+xysgvsZdx7GIln0o1LkPKzkIMtvpEDDJgyVY/xZQLC8CsLMMq27oBXWMbf3hX9kdp1f5wsDQPaia2LJViSzQYsGRW2o+CAjAp+1GyfgMuzdRPcMyZWejDbOPTgCXDegdQ8PLEpVF9wj5/n8g/V+HWpgWGD63vBkg5WYhBaltKuZV0KLLasnzHBvQNK+mQlo1LuDXWXMKCMNnckRS9PXBvWo+GP7xNs30r8ezQjHqfz8GjjfVuqJSVhWiw0SPYgOTIpxW2KN2yAX0TtR5KzrGJLYcxfplBLZXCDe3qBFNmtpCbq+SUED9Pdc7KLyHEVz1pXXc0gUHtGijj64VWHCPWOJDzsxH8rbYQ/INVT32UPlmYTx8ByYKck46UmYJoCL/RXpaWg0eENe+5RwRSlqZ+YmZL6roYQoZal8JUN28CmNPVPtWHBWNKt/GplwduTetR7/t3aLznSzw6NKfOinm42/jUnJGNPsz6xEUfGozZJkeJXh64Nq5PxFcLqbvlG9zatiBsyeu4tbLeobZkZqELtcrQhaqPEcHTE5dGDQhZvpjw31fj1rolhvffwKWFEhulaTm434Yt09bFYBiqXlZUE/lbystCDLDuhxhgHxdSXhbmvw8pcZGdjpSejBhizT1ydha6SnnP4TFyPe9t24CukfUYUWxpkyucyDc1kS/KU3NwtYkt17AgylOr9knO7/vwH6xevifnZSEGWu0pBAQj5VXSIzcL84lDYLEgZ6UhpVvf8amJ3Gtni0D100SoZItdG9E3UD99KUvNwc3mXXHX8CDKbmKLzN8OEDREHZ/56Tn428S4f3gg+elVy6jM2xsXk5eRS6CNjMCwIHIrychNzyEwzKZPuPX/eek5BNrsR0C4/XjbPqJOxMPHk6LcSu/5avxXUlsvmq6/09ReluXpsizbLu4eChyXZTn9nwhqVT+CxIxckjPzMJktbDl6juh26oM9t7Dkxh3WLzYd5P5e6tWArSKDSMwpJDm3SJFx6irRzdWTz3A/Tw5fUlS6lJlPuVkiwGbiKSVfQgwMQ/A3gKhD16ob5gR1xRYAISgcwcMLKUn9XlWrUB8S80tILjBiskhsOZ9O3wbBduMv5xZTUGamXZifXVv5uTj0dSLRhYeBXo/nwP6U7rNOMuTiYtKGPkD68EdIH/4I5WfOkv3CHFX1vFbh/iTmFJOcV6zocTaZ6EpV6QAuZxdSUFpOu8gAu7Zb0bdXN/7YvANZljl5+hze3l43luYBtIoIIDGnyEaHJKKbhqtkhPt6cviykvQvZRUo/rBZQ90qMojE7Mo+VU8Qwv09OXxRWe9/KSOfcrNF5dOakGE+H4cuIgoxVPGJW5/+mI4cUMkQAqz77tq1p6pIRKtwfxJzi0nOK1FscS7lJv4w0S7C3h81EVumuDh0NrHlMbA/ZQdiVH3EIOt+uPXqgfmq+h2b8jPx6OtEoo9QZHgN7otxj1WGXFRM0oARJN8zhuR7xlB26hyZz9yoLorlYhxiWCSiIQx0elx79McUq9ZB8Lfq4NK5B5ZktQ4lf5/HrX4ELlGhCC56/O7tQ8H2IzfapcISznUaTXzvx4jv/Rglf8Vz9fE3VdXzzAmVfBrdn/JDN/Fpt552hT9aRQYqMZ5bhMliYcuZRKKbRqj6KDF+PecUUG62EBioyG0VZSAxq4DknEIlNk9eIrplXfV4f28OX1CWKl9Kz6PcZCHAyzqBlq6dRzREIASGgk6PvkNvLKfVlarMpw+ja1yxnt/LB9EQgZRtTc35f13Es2EYHnUNCC46wu7vQcYWdd7zbGCNV8OdHSi5ZH0/sbp5E8D4dwKu9SNxiQoFFz1+9/ShaId1P6SiEhK6PMKF6IlciJ6I8a84rj05X1U9r+x0PC51I9FHhoJej/fQvhTvOqSScaX3SBIHjyNx8DjK/j5H2vRXVdXzys/G4VI3El1FfHve2Q/jXpv4Li4m5c7hpA4bTeqw0ZSdPkvms3NvVM8rqKYtoWbyt3Q1ATEkAiGoIi46RSsXSDaYTx5E31Q5jwpevoihkUhZVl3M5+MQw6MQQxRbuPa+ed5z6doTKcl6jJjOxaGPUueb0v1V5xv3Xj0wX1Ef6zWRL4pPnse9QTiudUIQXPQEDutF3rYjqj5uDaznJr8BnSm7rPaJ5XI8YmgkQrCih0vXvphPqC/+TX8dQNeswp7eij1vtNVA7rVcqmSLbv0xHVProLJFpx5YUtQyCk9cwL1hOG51FVsY7u9Jztajqj7uNvEZOLAjxsvq9+kST17EUD+MwCgDOhcdHe/twelt9sd7Vcy+axaxWw/Te0Q/ABp3aIqxsES1NA8gLyMXY5GRxh2Ui8fr/QEun7xAaP1wgqNC0LnouePenpzYpt6PE9ti6TGiLwCd7+pOXMzpf6xjrUGSave/Wsq/4Z2myjxMFUvzHKHXibz0yJ1M+fAHJFlmWM+2NI40sPT3vbSsF07f9k2ITUjk4193IwCdmtbl5UcG2cu4uzNTvt2FJMkM69iQxiH+LN3xNy0jA+nbPIpZQzoy//fDrI6JAwFef6AbgmCzrEGWKN/0Ne6jXwRBxHxiD3JmMi59RyClXMaSoLwH4OhFZgC9KPJin2ZM/f0vJBmGtQynUZA3Sw9fpGWIL30bKHeqtiSkM7hJqHrb17FI5L2/hOAP3wVRR/GfmzBfvoLP4+MxnUuwO+k4tKco8tKgtkxZe1CxZ9u6NDb4snTvOVqG+9O3iXKCUO5SRjrU4/lX3+HoX3+Tl1fAgPvHMHXSo5jNZgBGPXA3fbp3Yd/BowwdOREPd3femD3TXofB7Zmy5oDij3b1FB32nFV0aBrBrIFtmL/xOKuPKBOf1+/tpNJFrxN56Z7OTPlmZ4VPG9E41J+lO07SMiKIvi2imDWkE/N/P1ThU4HXh3evcRlIFoqXf4jv64uUterbN2JJvILH6ImYz8dhOhKDx70jcLmjp3KXsbCQoo/eIWDZd1ZbDGzNlJ8OKf5oU4fGwT4s3RdHyzB/+jZRTlKbz6UwpIVjf9RUbBUs/pjAxQtBFDFuUGLLe9IETHHxlB2Iwes/w5UXlC0WpIIC8t56x05GzrtLCPn0HRBFiv7YjOnSVfwmj6P8bALGvfbHhQpJwvjlx3jNXlhRNncTUtIV3B+cgPlSPOZjMbgNHY5Lp54gWZCKCihZaq9DyqvLafDt60rJ8Z+2U3Y+kZCZozGeOk/h9iOOt63Sw0LRsg/xe3MR6ERKtyo+9Xx0IuaEOMoPx+AxbASu3SpsUVhI0ftqPfSiyEtDOzJl9V7Fr+0b0DjEj6W7TtMyIoC+zSKZNagd89fHsvpwAiDw+rCuN3yj14m8NKw7U1ZuVmKzS1MahwWwdMsxWkYF07dVPWbd05X5P+9n9T7lHaDXR/WuFJsSZb+uwOOJ10AUMR3ZjpR+Ddchj2C5dgHLmSNY4o6ja9oezxc+QZYlytd/DSXWO66yReLsy1/R+YfZSpnsNbsoik+i8QsPkn/yEplbjlF30mCCerdGNlsw5Rdz6mmb5cHVzJvXfZr2+jLqfv0GgiiS9/M2J6o16QAAIABJREFUys4nYnhmDMZT51UXUFVikch6+1PCV7yNoBMpWLcV08WrBDw1lrIzCZTsPvSPZOQuXILh43cRdCJFf2zCfOkqvk+Op/xcPKW3iG/ZIhH38ld0rLBl8ppdFMcn0eiFBymosGWdCltKZgvm/GJOP61eal0T+RtJonTtMjynvamUoj+4FSk1Edd7HsVyNQHLqcNYzh5D36IjnnNXgGSh7NcvoNjmTrxkoeSzD/F5rSLv7diI5doVPB6ZiPmCkvfc7xmBS9eKvFek5D1bW+Yv/pigDxaCTqTk+rnssQmUx8VTtj8GrweH436zfFND+SJx7uc0Xf0qiDqy126nNOEaEc89TPHJC+RvO0rI+Lvw7dUO2WzBnF/E5Zkf2elRuvoTPGcuQBBFyvdvQUq5ituwcViuJGA+eRDL6Vj0rTrh9cZKpf9Pn+P55Cs3dKh27pUkjF8vwetFZW5QvmcTUvIV3EeMx3w5AfPxGNwGD8elYw9FRnEBJcvftbPFxdkrab1mDoJOJH3NTkrik6j3wigKT1wkZ2ssEROH4t+nLbLJjDm/mISnl6jVsEj8Mu8rpnw7Wyk5/uMu0s4nMXTmg1w7dYnT249Rt21DJq14Fg8/L1oP6MjQSkv8Tuw8Rvt+nfhg7zLKjGWseM66jbc3Lmb2XbMA+HLOCiZXlBw/ufu4SodV81Yy69s5iDqR/T/uJOV8EvfPHMWVUxc5sT2WvT/u4PHFT7Ng9xKK84pYMV2rnPd/BaGaH8f9H0EQhCJZlr0d/O6FslyvoSzL+fYj7ZCNe792Wg+PPuMBMK593XkZo16leP5op8cDeM1bTcmSqbfuWAWe05cCkNy9/y16Vk3kwZ0AGL96wWkZHhMWYsqy/17C7eAS3BDjty87r8PYBQAYf5x/i543kTFyXrXGX5eRfW/0rTtWQdD6PQAYv3jOeR0mKSXfqxtbqb363brjTQjfv4urHQc6Pb7ecaXEct4o5/XwX6tUTzrV4F6nZbS5vJ6soc77FCB40x6Mq+feumMVeIxWXvw3/r7wFj1vImOYcowXzbrvFj2rxnvxH2wOfcjp8QBD0n+oVu70mrcagLON7r5Fz6ppeVH5LtDF1oOdltHo9BaudRlw6443oc7RHWythj0HpSvfg6pu/gYonDr0Fj2rxmfpJnKGVe8YCfx9Dyk9nD/WI2KUY70m8kVs1P1Oy+ic9BsFk+50erzvF9sAqpV/w/dX2GK083MD/9U72RdWvXeTeqf9zIz6zsf3R1eU+H6knvPv8H1/VSnGPLG+8/vy5ZWfQSlsWusx/vxm7Zv82+Dxnzm10o618kmTowumit+LgSBHbRoaGhoaGhoaGhoat6AWL4GrzdTWd5o0NDQ0NDQ0NDQ0NDRqBdpFk4aGhoaGhoaGhoaGxk2olcvzNDQ0NDQ0NDQ0NDT+B6iF9Qz+DdTKQhA1yH/1zmloaGhoaGhoaNQaamUBg8oY175eq+fHHqNerZV21JbnaWhoaGhoaGhoaGho3IT/+uV5NVG+t7qloUsWP+70eADPWZ9T/OYYp8d7zVkF1EzJ8ZLlM5yW4Tn5o2qVCwelZHh1ypa7BDcEoOzMDqdluLUaQOnRX5weD+DeZQQ/hTtfTvnBVKWccumx35zXoZNSLte44UOnZXjc/Qx/1R3m9HiADom/szfsQafH90n7CaiZ0rs7Q0c6LaN/+o8kdq5eaem6sTsoXjDO6fFeL38DQNHzzpfe9X5PKb17pb3z5ZDrn9jG0/VHOT0e4OMra6tVnjrwd6Us/6GI4U7L6JbyKwB/13e+FH3bK+tJaDHE6fEATc9tJiZ8hNPje6Qq+ar4FeePM6+3lOPMuOoVp2V4jHmrWuXCQSkZXt3YBMh5oBqxtU6JrfVhDzst4960NeQ+2Nfp8QE/7QaqXw4fIPvuasxxNuypkZLjg+o4f4xsvbYZgGnVyDmfXFlbYzL+FWjV85xCe9KkoaGhoaGhoaGhoaFxE7SLJg0NDQ0NDQ0NDQ0NjZugXTRpaGhoaGhoaGhoaGjchP/6d5o0NDQ0NDQ0NDQ0NCrQ3mlyiv8TF00HLqSycMsJJEnmgQ4NmNirhao9Nb+Yub8dobDMhCTJPD2gLb2bhKv6uHTsitfj00EUKd22gdKfv1e1uw0YgueEKUjZmQCUblhH2dYNqj5i/Va49n0IRBHzqX2Yj25WbyN6JLo6zSv+cEXw8MG41Fp4QdewLa6DHwVBxHxiN6aY9arxrneORqzXEgDBxRXBy5eSRU+q9ezWBb9npiHoRIr/2EjRd2sc2sy9b2+CFrxOxoTJmOIS1Pa8ksl7u88hSXB/6ygmdm2oal+0+xxHk3IUO5gs5BjL2Td1oFrGxTQWbv0bSZZ5oH19JvZopmpPzS9h7vpYCktNSLLM0/1a07tx2I32OW8vZu+BIwQG+PPbquV2+suyzIIPl7Pv4FHc3d1465VnadmssarP/uNnePfLn5AkmeEDezBpuPqF2pSMbOZ9uorcgkL8vL14e8Z4woID1PtxMoF3v/sTSZJ4oG8XJt2nfqE2JSuXVz/7hdzCEvy8PHh7ykhCg/xUfUL7taXD/EcRdCKXvt9N/CdqvzZ5cigNH+mHZLZQll1A7KzPK+kQz7vf/qHEd78uTLpP/aJ1SmYur372E7kFxfh5e/L21FGEBvmrZZxLZOFv+xUZ3VowcUBHVXtqbiFzv99JYWk5kiTx9N3d6N2ynqqPT3QHol57HEEnkv3DNtKXOi6W4Te0Ow1XvETcPc9i/PuCqi2gX3savTEBQSeStnoH1z5RF7oIH3snEROGIFskLMWlnH9+hard7Y4u+M6YBqKOkj83ULxKHd8eQwfjM3UyUlYWAMW/rMP450ZVn8B+7WjypqJD6uodXF3yu6o9YuydRE0cfEOHuOdWUJKQrOrj3r0LAc89BaJI8W8bKfjmB4e28OjfG8PC10h7dArl56zHma5hG1wHjlZyxYk9mA6pc4nrgEcQ6ym5QnBxQ/D0oeSDqao+umYdcLtvEogipiPbMe361W77+rY9cB30ELIsI6Veoez7D9T69ehM4AtTQRQpWreJ/K8cv+jsOaAXIe+/SsojT1F+Vp0vWkS3Y/i88Yg6kYNrd7J9mdqeelc9YxY/RZ3WDSnOK+TraR+Rk5R5o92lQ1c8K3Jv2bYNlP6izr2u/YfgOd6ae8s2rqNsm9pefn07UP+NiQiiSMaa7aR8sk7VHvLoIMLGD0WWFJ9efn4ZxvNJqj7e0R2JnPc46ERy1m4jc9nPDm3hO6QH9Ze/zPl7Z2I8pY5vz16dCJk9BUSR/J83k7vyR4cyvO/sScTHc7n6n+mUnTl/43f/fu1pMH8i6EQyvt9BcqX9CB07iLDxQ8AiYSkp5eLzyzEmqPdD16Q9rndPUGIrdgemvfbFZHStu+M6YCTIMlLaVcp+/EjVfuBCmnJOlSvOqT2bq9pT80uY+/tRa/7u39runOp2h3IuQqejZP0Gu3ORx12D8X1qMlKm9VgtWW89VmsiNl06dMVzUkVsbd9A6a+VYqvfEDzHTUHKsYmt7erYMvRrR+s3xiLoRBJX7+LCJ3+o2uuNHUj9CXfeyBcnn19JUaV8oW/fFc8JSt4q27GBst8q6dF3CB6PTkbKUWxRtkntd4+enQl+aTKCTkfBL5vI+8JxXHkN7EXYh3NJGjVNFVcALp264vVExRxn6wZKf6o0xxk4BM+JNnOc9fZznIB+7Wlok7+TKuXvsLGDiJhgzZ0Xnl9BSaX4BJj6+hS69O9CmbGMRbPe58LpC3Z9xr8wjjtHDMTbz1v1e4vodvynIt/ErN3JNgf55tHFT1G3It98WSnf1JQMjf9OauVFkyAIFuAUin7ngHGyLJcIgjATeAzl+0ungAmyLJfeTJZFkliw6TjLx0QT6uvB6JXbiW4WQSODdeL6+b5zDGpVh5GdG3MxM59p3+9j04x7rEJEEa/Jz1Aw91mk7Ez8Fq/AdPgAlmtXVdsq37eT4hXqk4vNTuHa/xHKfvkAuTAX99GvYLl4Ejkn9UYX054fMVX8X9++P2JIHfX4oeMoXf0OckEO7pPmY044hpyVYt3+ttU3/q/vfCdiWH21DqKI/7MzyJrxPJaMTEK+XEbpvhjMV9T7IXh64D1yBOWnzzqwp8w7O8+ybHgXQn3cGf39QaIbhdAoyJq4nutrvShd89dV4jML7GQs2HyS5Y/0Unzy5S6im4TTyOB7o8/n++MY1CKKkZ0acjGzgGlr/x977x0fVbH//z/P2U3v2SSbRg8tIL23hGJAvRTlXhAQEVAUpdgLRQUB6xUpIopYL6LczwXLpfcWSoJ0CCWUEJJN2U2vu3vO748Ts3t2EzCB7/3xuZ99PR48HmRn5n3e824zc2bmfRLZPM2WXWfEg/czduQwZr3zkbOsgf2HkkhLz2DTT6s5dTaFdz5aztpVtixxVqvEolU/8cVbM9DrAhnz6vvEd21Hswa2gf3v365naHx3hvfvwZHTF1i65hcWzXzCrh8Si779lc9fn4Q+2J+xb64gvnMrmkXpq+t8/MNmhvbpxLB+nThyNpUl67ayaKpdhjZRoNOiJ9g3+l1KM00M2vwOGdt+p8huUM0/fZ0dQ+ZgLauk6eMDaTdnjJqHr3/m8zeeRK8LYOyc5cR3iqVZtB0PazYytG9nhvXrzJGzl1ny0xYWPfuoisa76/ez8pmh6AN8GLf4X8S1aUyz8GCbPrYfI6FDM0b1bkuqwcS0VZvYbL9oEkUaLHiay+PewpxppOVvH1Gw/Sjll26o9CL6eBE2aSglv19wVpooEvPuZE6PeoeKTBMdt7yLcVuyalDNXn+AzO+U7FfBCV1o+vYEVXv/F2diekGx75AvV1JxwNm+y3ftpnDxUufnV+mj5XuTOT5qARUZRrpsfZecrcmqRVHW+gNkVPEQMrgzzedN4OSYRSo+gl6bQfZzr2LNyiH8uxWU7juE5aqzn/k9+ggVpx38TBBwT3ic8h8/UHz9ibexXDqObLTz9Z22CY228yBEfSMHGiIeD0+h7Iu3kQuMeM34AMvZo8jZNlkKIRG4DRhJ6advQFkJgo96MY8oEvzGdLKeeQ1LVi6Ra5ZTuvcQ5itpTv3wH/swFafOO4lTEAX+Nn8Snz62kHyDkZd/fZcz25MxXLbJs8eoAZQWlPBO/Ew6De3FsNfH8s20JdU8eD/9PEVvKbHX/6PPqTx6EMkx9h7YRekXtcReUaTJoqc4/+g8KjONtN30AXlbk1SLIuOG/WR/vw2AoISuNHp7IilV2VP/oBE1/xmuPjYXs8FIzK8fU7j9CBWXne07ZOJQSo6n1MhH2NznuDl5FuasXBqtW0rJ7sNUpjrLM/DxEZSdPO/Uvumipzg7ej6VmUbabX4f07Yk1aIod/1+sr77ox9daPz2E5wfu8BeIbgPnUz51+8otjX1XSznk5Fz7OxCF45b3MOUfT4HykvAxxaX4Y/4fZyV4/qi9/dm3Jc7iWsRqY7f+8+TEBvNqC7NlPi99gCb7RdNokjAyzMxVo1FoatX1jgWle/cTcHHNfjqXbBNRBHvKc9T9HaVbX1QZVvpDrZ1cBelq2qzLYH73p3I4VGLKMs00nfLQgzbjqkWRTfXH+T6dzsA0Cd0ps3b4zky9j01H5NnUvzOy0imHPzeXYk5uQY+EndTttrGh8/UV6rbh855joyn3sBiyCX6p2WU7D5coywCHhtBuaNdVdHwmfo8hXNeQsrNIWDx55gP1zDH2beLkpW1+1mzd5/kzKj5VGSa6LDlPUwO8Ttn/X4MVfYZnNCFJm9P4OzYhSoyXft3JapJJBP7TqJVx1bMWDSNGcOed3rc4e1H+PWb3/h632pbH0WBUfMnsbwq3rzy67ucdog3PUcNoKyghHnxM+k8tBfDXx/L19OW3FUaLvz34l6901Qmy3IHWZbbApXAM4IgRAEzgC5Vv2uAR29FBODMTRMNgnyJDvLFTaNhcJuG7LmQoaojACUVynKluNxMqJ+XqlzbvDXWzJtIWZlgsVCxbxdu3fvUqUNieBPk/BzkglyQrFhSktA061BrfU2rrlhSjtraRzZDMmUh5+eAZMV69jDaFp1rba9t0xPL2UOq39xjW2FJv4k1Q+lH6Y5dePbr5dTWf8okiv6xFrmy0qnsjCGfBoHeRAd646YRGdwynD2pWbXyseVCJkNaqt8wnskw0SDYh+ggH4VGbDR7Lmaq6giCnU4qzIT6eqrKu3S4jwB/v1qfu/vAYYYNGYggCLRv25qiomJyck02Hi5fo2FEKNHhIbi5aRnSpzO7j55U0biSbqD7fS0A6Na2BbuPnlL3IzWdBnod0WHBuGm1DOnRjj3H1ANS6s1surVRduK6xTZ1Kg/u2Izia1mUpOUgm63c+OUwUYPVes1JPIe1TNGF6ffLeEXYFjNnLt9QeNDrFB56tmfPMfUkPPVmFt3aNKvioZlT+Zm0bBqEBBCt88dNq2Fwxxj2nLmmqiMgUFL+h49UEhrgrSr37tCcimsGKtOykM0W8n7bT0BCNxwR8fJYsj77F1KFs235dYyh7KqB8rRsZLOFnJ8PohvcRVXHWlxW/X+Ntwf23692a90Ka3pGtX2X7diFR5/eTs+5Ffw7xVB61UD59Wxks5XsnxMJHdL1Fjx4On1Z3b1NKyw3bmK9WeVn23bjHefsZwHPTKTw2x+d/EyMbIqUZ+fr54+gbdHJqf0f0Mb2wHLusJpGw+ZIuZnIpiywWrCcOIC2jVofbt3vx5y4GcpKAJBLClTlHm1bYrmRgeWmASwWSrbuwTveuR9Bzz1BwTc/1RgvGnWIIed6FsYb2VjNVn7/LZH7EtTyvC+hC0f/paRwPrHpMC16tbX1rXlrJIMt9lbu34V7t7rFXt+OMZRfy6SiyjaNvxwgaLBaFvY6Fb09nHTq3aE5ldczqbyh0Mj/bR/+Cd2dnqV/aRw5K/+FXGF2KvNs1xJzWibmdAOYLRRu2ovPgJ5O9UJmPk7el/90ouHbMYaya4bqfuT+coDgwXWzTTE6BslkQM7LBqsF66mDaFurfUzbZRCWI1uUBRNAifql15kM+zFVZHCbBs5jqmP89lPHb7fYVlgcfNWz75/31bthm9rmrZHsxvXKA3W3raCOMZRcNVCapsSLjJ8PEe4Qsyy3iFkAmphWio1nK3yYD+7CvUsdZHFfS8xpGVjSFVkUb95To10FT59A/lfrapZFi9ZYM24iGezmOD3qJgu/jjGUO8TvW9unR410eiX0ZPu/lE+CpBxPwcffl+CwYKd6KcdTMGWbVL817hBDrkO8aecQb9oldOFIVbw5vukwLe3izd2i8b8CsnRv/7tHca8umuyxH/jjXJUW8BIEQQt4Axm1tqpCdlEZ4XYTPL2/F9lFZao6z8S1YePpNBIW/8a0tft5fUhHVbmoC0HKza7+WzLmoNGFOD3LvVccAUu/wvf1eYghoaoywTcQucjm4HJxHoJfoCMJpa5fMKJ/CNKNFLvfgpAL7doXmRD8gmpqjhCgQwgMQ7p2Vt2P0BCs2bZ+WLNz0YSq+XRr0RxNWCgViUdqpJ1dXIHeblGp9/Ukp7iixroZhWVkFJTRtYFOTaOonHB7GjXppG9rNp65QcLSTUz7KZHXB7ev8Rm1ISvHSHiYTUf6sBCyqo55AGQZ89HrbPLT64LINqknjS0aR7Hj8AkAdh45QUlZOflFxbZ+5BUQHmx7Ox8WHEBWnnqC0bJhODuTFD3sTD5LSXkF+UWl1eVe4cGU3jRW/12aacIrvGa9AjQZE49ht21xl51XQLjdUbuw4ACyHPrRslEkO4+eUXhIOktJWQX5RSU2GgUlhAf62GQR6EN2QYmKxjNDurDx2EUS5n3HtFUbef3hvqpy93AdlRk2+VZmGnHTq/Xu1bYp7hEhFO46VmPfPCKCqciwyaIi04R7hM6pXsTEwXQ9vIymcx/j8uyvqn/XONi3lJODJtTZTz3j+hHyzZcEvvM2Ypja/j3CHXjIMOIR7jxgR00cTM8jS2k2dxwXZ3+tKtOEhWDNsh3VsGTnoAlT8+HWsjna8FDKDzr7meBbB1/31yEEhiJdP+fwezByvk0fcoERIUAtSzEkEjE0Eq/nFuE17T00LdVxTxMWgsVg14+sXKd+uLeKQaMPpWz/UWpCoD6YfDt55mcaCdCr+xJgV0eySpQXleITpLwQEXQhWB1ir1hT7O0Zh/+Sr/B9zTn2KrZp46Ey04h7hLNO9U8MoUPiChrOeZxrc1erytz0Osx29m2uyb7bNMM9IpSi3ck1ykIbpnOSpyMNj9gYtOGhlOx1lqdHeDCVN+19zIR7uLN/hD8xhE6HPqXRnPFcnfOVqkzwD0YusMlCLjTVYBcRCLpIPKe8g+fTC9E0V7/cyy4sI9z/NvG7X6wypn6ykWlrDziNqZrQEKxZdmNRbb4a34/Q774kaKHaV++GbQrBf9K2esThv/grfF+Zh6hT25ZnRBBldrZVnmnEM8LZVxtPvJ8Bhz8hdu5Yzsz+VlUmBodWH3kDkEw5CA7PAXDv3g+/j1bj89I8VXlNdqV1lEVrxa5K99UsC6c5Tm4tc5zecQQs/wrfN5z9TInf6jHAowY/i5g4hC6Hl9Nk7nhSZ692KteF68jJsPUnNzMHXQ12XhMC9MHk2ekjr5Z4k2cXb8rs4s3douHCfy/u6UVT1eLoAeC0LMs3gY+ANCATKJBleVsNbaYIgpAsCELyF1988aees+VMGsPaN2bbC0NZPqYvc34+iuTwhu52qDyaSN7k0RTMmIT5RDK+z8+qU3t7aFp1w3Lpd6e3hH8W2tieWFOO1r29IBAwcyoFSz+r13MdsfVCJgNb6NGIQp3bbjmXzrB2jdg240GWj+7FnF+T66yTO8VLEx7h2NlLjHppEclnLxEWHIgo1s1lXhz7IMkpVxk1exnHzl8lLMgfsR7yAGg4sjdB7ZtyYcW/68bDuIdITrnCqDeWcOz8FcKC/evcjy2/X2ZYt5Zse+txlj/1EHN+2Ikk1UEfgkDU3EncXPD17eveBplfbyWpx3SuLFhDoxfq9sHP8oOHyP7bGHKfeJLK5GMEzn69Xjzc/Horh7rPIHXBGhrXkQcEgaAXnyFvsfNdvLpCG9sda0pS/WKFqEEMiaDss7mUr/kYj78+C57et2/3BwSB4JefIe/jz29f9/8hzEmJ5D81msKZSuz1mVm/2Jv1zRZO9HqWtIXfEzWzjh/rFAQi5k4mY6HzJLAuNEJfm0LO+6tuX/cWMHyzhd97Psf1hd8T/Xw9PohbZRflX75NxboluI94um52AWw5e0MZU59/iOVj+tRrTC0/cIiskWPIefxJKo4eI2huHXz1LtmmOTmR/KdHU/jCJMwn629b177ezq4ez3N+wQ80f6HuH542JydS8OyjFL08WeFjWh0+Ei8IhLw6BeOHf24+VBsqjySSN3E0BdMmYT6ejO+L9ZNF5tdbSO4xjasL/kHDF+7so7guuPCfxr26aPISBOEEkIyySFotCEIQMBxoAkQCPoIgPObYUJblL2RZ7iLLcpcpU6YQ5ueFocD2Zj+rsIwwh+N3G05cJSFWuT/UvkEIFRYr+aW23RPJmIsYElb9t6gLxWrMVdGQiwrBohxHqNi2EU1MC3V5cT6Cn+2ti+AbhFyUX2PntS27KoseFf08BH+79n7ByEV5NbbXtOnhdDQPQMrJRRNm64cmLARrju2NjuDtjbZpE0JWLEa//gfc28Si+2ABbq1sfQnz9SDL7q1iVnE5ob41b7NvreFoHkCYnycGexo16uQaCbFRALSP1jnp5HbQh+owZNvtLGXnord7k6nXBZJltMkvy5hHWLD6TkdYcCCLX3uadX+fxYyxwwDw97FNHsKCAjDY7epkmwrQB6nP/4cF+bP4+cdYt3A600clVNGw9bXMYMI7yvYWzTsimDKDs17D+rah9czhHJzwd6RKi5oHo82Osk0F6B37EeTP4hceZ927M5k+erATD2EBPhjybTtLWfklhAX4qGhsOHKehPbKhm/7xuFUmC3kl9h0WGkw4h5pk697hA5zlu1tnejrhVfLRsT8tIDYg1/g07ElzVbPxqudLTlHRaYJj0ibLDwigqnMtNFwRM7PB9ENsR2zsjrYtxgaijXHwU8LC8Gs+Gnpbxtxa6n20wqDAw+ROioM6iMg9sjakEjoAw5HULJz0ejt3wSHYrWzRcHbG7dmTdB//jGRv67Bo20sIR+/g3trhRe5uA6+3tr5aJ7STxNCoE0fQoBOtcMAyu6T5WwSSFbkvGyknAzEkEhVP7Thdv3Qh6j74eOFW7PGhH/5EdGbvsfjvtaEfTIf91ibTPOzTATayTMwQkdBlrovBXZ1RI2Ip583JXlFCo/GXDQOsVe6VezdvhFNM7VOFdu08eAeoaMys3adGn8+QNAQ9fE9c5YRNzv7dqvBvj1bNKLZj4todeBLvDu2pPGXc/C6z2bflmyjkzxVNHy88GjeiAbffUCTHd/i2b4VUSvexqNNc6VvBhPuUfY+FkyloXb/yP35IMEO/XDcWXLceVLqGLGet9mFbMxE1NnieJi/F4bC28Tv49dIiI0G/ojfkip+W3Ny0ejtxqI6+urdsE3ZVEfb2rERTVO1bZVn5uFlZ1ueETrKM2v2VYCbPx8ifIj6+J5kylHtYInBochGdVIBudjGR+WujWjt+KjJrix2shB9vHCPaUzk1x/QcOu3eLRrTfiyedV2BTXMcULqPsdR4rd6DKi4hZ8p8dsWOz/b8imfbfkUU7aJ0Ehbf0IiQjHews7tUZBlIshOH0G1xJsgu3jjZRdv7haN/xWQpHv73z2Ke3XR9Medpg6yLE+XZbkSGARclWU5R5ZlM7AecD7E7IA2UcGkmYq5mVeM2Wpl69k04lpEqupE+Htz5KpyL+dKTiGVFitBdudtLZdS0ERGI+rDQavFo98AzEcPqmgIQba97UsFAAAgAElEQVRJjnu33k4XKCXDNYTAMAT/EBA1aFt1xXpFfYdGoRMOHt5Imanq9hlXEIPDEQJDQdQoC6OLvzu310UgePogpV9yKqs8n4K2QRSaCKUf3oMGUL7ftriSS0owPPAwWY+MJeuRsVSePYfx1Tmq7HltwgNIyyvlZkEpZqvE1gsG4puGOT3rqqmYwgoz7SOcjyC2iQxSdJJfotA4l05cC/XiStGJMnBcyS2k0iKpdHI7xPfpwa9bdiLLMifPnMfX14fQEJuO2sQ04npmNulZuZjNFrYcOEZ813YqGnmFxUhVzvvl+q08PFB9TrxN0yjSDLmkZ5swWyxsOXyKuE7qzIx5RSXVNFb/upcRcer7SnknruDbJBzvBqEIbhoaDO9Bxlb18bXAto3o/MFkDk74OxVG9fG/Ns2iSTMYbTwcOklcZwceCu14+GU3I+LUk/w2DcJIy8nnprEQs8XK1uOXiWvbWFUnIsiXI1UX569k5Sk+4mubKJWevIRHkwjcG4QhuGkJGtqXgu22hb9UVMrpDuM513sK53pPoeT4BVInL1Rlzys6cRmvphF4NlRohI7ojXGb+qiTZxNbBsXgQZ0ou2qXSCUlBY2dfXsNGkDFwURVe1FnswGPPr2wXFdfli46nop30wg8Gyr6CBvRi9ytah687HjQ3d+J0ivq+3iV51JwaxCFJrLKzxL6U7bPxodcUsLNQY+QMWwcGcPGUXHmHLkvzq3OnidlXEUM0iMEKLFC07o7lkvHcYQQHIHg6Y100zmzlHTjknLMKigMNFq0HfpgPZekqmM5ewRNs6qz+N5+iKGRSCbb/cSKsxfQNoxCW9UPn8HxlO61ixfFpdzo/1fSHxxP+oPjqTh9nuzn31RlKEs7mUpo43CCo0PRuGnoNLQXp7er5XlmezLdRipZJzs82INLibZjxZZLKYgR0YhhCg/ufW8de9269Xa6QF984jKeTSLwqLJN3fA+5G1Ty8KziS3+BA7qTPlVtU5LT17CvXEkbtF6BDctgUP7Uehg3+c6jSOlz5Ok9HmS0uMXuPbkAlX2vPLTF3BrFIk2Sg9uWvwfjKNkt23BKxWXktprNFcHTeDqoAmUn0zh5rNvV2c5Kz5xGS+7foQM74Npq6N/2PoRVEM/pJuXEXU2u9C0640lRU3Dei4JsUkb5Q9vPwRdhMouquN3XlX8PnvDOX4HeHHkmnLcq6Yx1Xw+BW202lfLD9Tuq559emG5ZvPVu2GbTrbVZwDmpFvYVldn28o/kYpP03C8quJF5IieGLap47ePXbzQD+pIyVWDqtx6+YKKD7feA6hMVstCCLTjo0svrOl2sjhzAbeGUYpdabX4PhDvZFfX+o4ibfAE0gZPoOLUeQzT31Jlz7NcTEET5TDHOXKLOU535zlO0YnLeDaNwMMufpuc/MwxfttkMXXIc0wd8hyJWw9x/8iBALTq2IqSohKnu0u14XpVvNHZxZtTDvHm9PZkulfFm44P9uBi4tm7TsOF/17ck9nzakEa0EMQBG+gDBiIshN1S2hFkdcf6MTUNfuQZJnhHZoQExbAit1niI0MIr5lFC8mtGf+b8msOXIREJg3vBuCYHeESrJSsvIT/Od9VJWadBPWtGt4jZuE5VIK5qOJeA0diVv33mC1IhcVUbzkPTUjskTl7h/wGPk8CAKWMweRjRm49RqGZLhevYDStuqK9YI60FS33/ItnmNerU5DLOfexC1uJFLGVayXlAWUkgDC+c0zAFaJ/L8vI+ST90HUUPLvzViuXsPvqScwn7/oNGjVJs/XBsTy7HrluNzwNtE0C/FjReIlYvUBxDdTFlBbL2QyuEWEWo72OhncgalrDyJJMsPbNyIm1J8Ve88RGxFIfItIXhx0H/M3/c6ao8qkY97Qzipar7z1HknHT5GfX8jAEY/x7OTxWCzKDszohx+iX8+u7D+UxAOjJuHl6ck7s15Q86DRMOvJ0UydvxyrJDFiYE9iGkby6drfiG3WiP7d2pF05iJL1/yCgECn2BhmTxntROONCcOY+sHXSJLMiLjOxETr+fR/ttOmSTTxnVuTfP4KS3/aBgJ0btmEWU8MU6vVKnF81jf0W/sagkbk6o97Kbx4kzavjMR08iqZ236n3dyxaH086fmFkn6+1O5eg1aj4Y0nhjP1vdVIksSI+K7ERIfz6T+30aZpNPGdY0k+n8rSH7eAINC5VRNmTRzh0A+R1x/py9Qv/q3oo1srYsKDWbH5KLENQolv24QXh/Vi/rq9rNl7CgSYN2aAWrdWifS5X9Ds+7eVlOM/7aT84g3CXxxL6enLqglmrbBKXJ61mrZrZyspa9fupvRCOo1eHU3RiVRM25KJmvQAgf3uQzZbsRQUc2HGcrrsXVzdvvDjpQR//AGIImUbFfv2nTwRc8oFKg4m4vPXR5TkEFYrUmEh+QvVfipbJS6+8RUdflR4yFi7m5IL6TR5dRRFJ1PJ3XqM6MlDCOp7H7JF4eH8jE+d+mH6cBlhy94HjUjJr5sxX7lOwNNPUHn+AmX7nHeB1UxIVG7/Hs9HX1E+L3Bqn+LrfR9GyryG9bKygNLGdsdyvua7h0gSFT+vwuupt6pSju9EyrqBe8IYrOmXsZ5LwnrhOJoWHfB+eSmyJFH572+h1O5tqVXC9N5y9J+9q6R1/mUr5tTrBE6dQMW5i5TtvU0/UM77/8+bX/Hsd7MQNSKH1+3BcCmdB1/4G2mnr3BmxzEOrdvN+I+nMXfPEkrzi/lm+hJ7ApR+8Ql+b1fF3p2bsN64htfYSVguK7HX8y8jcetWFXuLa4i9Volrs7+k1Q9vImhEsn/cSdnFG0S/8iglJ1PJ25aEfuIDBPRtp+g0v5jUmcucaGS8uZKm380DjUjeuh1UXEpD/8I4yk5fonDHn7PvnAUriP5yIYgiheu3UXn5Orrp4yk/c0k10a2t/ZVZXxK7di6CRiTrx12UXbxBg1cepfjkZfK2JRM+6QEC+7ZDNluwFJRwacZyB4VIVP62Gs8nZiu29ftu5Ox03AaORrqZijUlGeulE2hi2uM1c7FSf8v3UGa7y6kVRV4f0oGpP+xXxoD2jZUxdc9ZYiOCiG8ZyYv3t2f+v4+x5vAlJV4M6+IULwo+Xopu8QegESn9Yyx6ciKVKReoOJCIz98ewbM2X70LtolkpXTVJ/i95WBbY6psKykRz4dG4tbVzraWOceLM7O+ocfaNxA0IjfW7qH4QjotX/0r+SeukrXtGI0nJRDa7z4kswVzQQnHZ3zmzMfqJfjO/hBEkcrdm5HSr+E5eiLW1AuYkxPxeHAk7l16IVfxUfLpewQs+a5aFrmLPiXi80UIGpHCDdswp14n6LnHqTh7kdI9t7GrKh5KPvsE/3eqZLG9ao7zWNUc50giXsPs5jjFRRQvdvaz1Flf0nbtHMU+1+5yit+Rkx4gsJ/NPi/OWObEytFdR+k2oCvfHPhKSTn+0sfVZZ9t+ZSpQ54D4MlZk+k/Ih4PL9tiXLJKrHvzK577bhaCXbx5qCrenN5xjMR1u3n842m8tWcJJfnFfD1dnfXubtBw4b8XgvwfvifyZyAIQrEsy741/D4PGA1YgOPAk7Is3+rMlly2Zm69+fCqSjlrHBp3m5q1Q/fbXko/fqre7QG8X1xFyQKnk4h/Gj5z/gHAzZ4D6k0j6tAuAEpXzrxNzdrh/cwSyr6rw1nsGuD1+LuYc6/Uu71biJLJruLsznrT8GgzkPKkmr9B9Gfh2XUk/4wYV+/2f8tU0suXH3P+xsqf5qGzsngq2/jJbWrWDq+Hnud4w+H1bg/QMe0X9oX/rd7t+xn+CUBmn/63qVk7Ig7sBmCXftRtataOAVnrSOsysN7tARom76Tk3Qm3r1gLfN5QLpkXv1L3exN/wPdD5Rsw1zrcX28ajU9sZ0bj0beveAssvfYTpuH1j73BvyjZrQ5HPlJvGj0ylG9bnWo8tN402l37jYuth9y+4i3Q4vwWEiPqcT+pCr0ylXhVMrv+fuazUPGzsn/MrjcNr8cWktGr/n4KEJm4+45tE8D08B3Y1gbFtn4LH3ObmrVjqGEteX+Lr3f7oH/uASC17eBbV7wFmp3ZCoDxoTuY42zcy/7wO7ub1NfwPyQ0qL+PbLuhfPNy2h3EnOXXfrpbNOp3afk/jLJvX7/3Jv928Jrw3j0px3typ6mmBVPV728Bb/2H2XHBBRdccMEFF1xwwQUX/g/jXr3T5IILLrjgggsuuOCCCy64cE/gntxpcsEFF1xwwQUXXHDBBRf+H+AezlB3L8O10+SCCy644IILLrjgggsuuHALuBZNLrjgggsuuOCCCy644IILt8A9mT3vLuK/unMuuOCCCy644IILLtwzuCezvjmibPXL9/T82GvyR/ekHP/r7zSVra1/sj2vMfMAyB5Y/3ScYTv3Uvbt6/VuD+A14T1KP5xU7/ber3wFgKFffL1phO/bA3DHfJStm1/v9gBeo96843ThwB2nLa+87vxh4brAvVGnO0pD3OK8kmK1Mv10/XmIvg+A8pOb6k3Ds/2DXLkvod7tAZqe3sb55g/Wu33rSwr/dyOFcFJU/VN1d725gdzB9ecBIGTr3jtO6w9Q+tn0+tOYqnw7pWDioHrTCPh6By80frTe7QEWX/uR4tfqny7c930lXXhy9Ijb1KwdXdKVlP53ap93w0fuJK1zX8P/AFAyt/4p9X3eWQdA2Yb3blOzdng9/PodpbcGJcV1Vnx8vdvr9+wBoGB8/T8PEPC9MgYduYN09t0z1lP07AP1bu+3YjPA3ZHFhDuQxbc770gOoMji4Yb1T+u/Ie03ACY0rn9a/m+vKWn5X2tc/zTy719bW++2LvzvgOt4ngsuuOCCCy644IILLrjgwi3gWjS54IILLrjgggsuuOCCCy7cAv/1x/NccMEFF1xwwQUXXHDBhSrIrpTj9YFrp8kFF1xwwQUXXHDBBRdccOEW+D+x03TwUiYfbPkdSZJ5uFNTJvWNVZVn5pcw9+cjFJVXIkkyMwa1p2+LSFUd967d8H1uOogi5Zs2UvrjD6pyz8FD8J0yFWtuDgBlv2ygfNNGNR+pWXyw/RSSLPNw+0ZM6tVSVf7h9lMkXc8FoNxiwVRSyYGX/lJdLjZui/vAsSAIWE7tx3JUfYHfrf+jaBq2Uv7QuiN4+1O2bJq6H9264T9jGogayjZupGSNuh9eQ4bg9+wzWHMUPkrXb6Bso7ofd4OPg5cy+GBjsiKLzjFM6tdGVZ6ZX8Lc9YcoKqtEkmVmJHSgb4uo6vIDv5/l/a/+iSTJPDKoF5MfGaxqn5Ft5M1P/0FeYREBvj4smvkE4SFBqjpzFn3MvoNHCQ4K5Od/rMQRsizz7icr2X8oCU9PDxbOfonYljHV5XP/vpJ9h48THOjPhlUf1tj+vRXfsj/pBJ4e7ix4eSqxzZs41fPu05mwWVNBFCn4ny3kfbnOqQ6A7/29iVw6l+t/VV/yn/vhp+w7fIzgwAA2rF5cMx+ffsX+I8cVPl6dRmyLpqo6B0+c5/2vNyg+MrA7k0eokwBk5Jh467MfySssJsDXm0XTH0OvC1TV8erdBd1rUxE0IoXrt1Cw+qca++EzqA/6xW+SPvo5Ks9dUpf17Yx+ztMIGpH8dVsxfvHPGmn4De5N9PLZXH1YnTTBrWM3vCcrflqxYyPl69X27d5/CN4TpiKZFD+t2LSBih1q+/aP70jD+ZMRRJGctTswfLpeVR46fjBhEx4AScJaUs61V1dQfildzUeXbvg8Mx1BI1K+eSNl69R8eNw/BJ8npyIZq+LFrxuo2GLj4+C1HD7ccx5JghFto5nUTa2vj/acJyndBEC52YqprJL9z6p1dvBaLh/uTUGSZIVGV7XtfbQ3haQbeQoNixVTaSX7nx2gqqNt2xXPsc+CKGLet5mKTT/iCLeucXgMfxyQsd64Qtnni1TlreLa8/CbExA0Ikd+2sXOz35VlTft1oqH35xARKuGfD99KSc3H1GVa1p0xGPYJBBEzEk7MO/Z4MSDtl0v3AeNRkZGyrhGxY+fqMr94zvScN6ToBHJXbvdWaePDSb0iQfBKmEtKeP6a846rY9tlp9R2/ed+khQ/w40fWcigkbEsGYn6ct/VrULfzyByImDka2KbV5+5XNKL6r7oYlpj/tDE0EQsRzbiXn/L07P17TtiXv/vwEykuE6Ff9cqio/eCGdD347osTvri2YFN9OVZ6ZX8zcdftt8XtIZ/q2aqCq49a5Gz5TqsbUbRsp/6eDjwwagvckm4+U/7aBim02H3Hv1g2/adNAo4xlpT84jMlDhuD3zDNYc5WxrGyD81imva8rnuOfU+x7zyYq/q22b7e+g/F8dApynkKjYvsvmPeqx7uA+I40emcSgiiSvXYHmcvV9hk2PgH9Ew8gSxJSSTlXX/mMMgfb0sR2xvNvzyg2nriFym3OtqXt1Bf3hx4DWUa6qU5kdNdkMa5KFns3UbGxBl/vFofHiAmAjDUtlbKVal+/G7IAmDxvCp37d6airIJlLy3hyplUpzrjXhlP/Mj++AT4Ope9NYn2/TtRWVbJqpeXcf3sVac6jds25cmPpuHu6c7J3erETi3i2jPszccRNCJJP+1mj0PMatKtFUPffJzwVg1ZO30ppzcfdaLvwn8n7slFkyAIVuA0Cn/ngQmyLJcKgjATeAolpeMqWZY/uQUZAKySxLubklk5vj96fy/GrdpOXMsomoUFVNdZte8sCW0aMKprc1KzC5i2Zi+bWwyzERFF/GY8T96rLyHl5BC04nMqDh3Eev266lnle3ZRvGxJLXzIvLv1JCvH9Fb4+Ho3cc0jaBbqX13nlfttA8/apFRSsvLthYL7/Y9Rse7vyEUmPMe/iTX1BLIxo7qKefePmKv+r+04EFHfUM2EKOL/wkzyXnwZa04Oui9WUn7AuR9lu3ZT9EnN/bgbfFgliXd/S2LlEwPQ+3szbuUW4lpFq3Wy9wwJbRsyqlsLRSff72bzS8qiyWqVWLTqJ754awZ6XSBjXn2f+K7taNYgorr9379dz9D47gzv34Mjpy+wdM0vLJr5hIqPEQ/ez9iRw5j1zkc1dnX/oSTS0jPY9NNqTp1N4Z2PlrN2lc3kht8fx5hhg5n9wYqa2yed4PpNAxu/XsyplMssWLqaH5YtUFcSRcLmPsfNybMwZ+XSaN1SSnYfpjI1TS12by8CHx9B2cnzTs8ZPrg/Y4Y/wOz3l9XMx9HjXE/PZON3yzh1/hILlnzBD5/aMmBZJYlFq//F53OeQa8LZOwbi4nv0pZm0eHVdT7+/leG9uvCsPhuHDlziSU//JtF0x9T9SNk9jQyp7yOxZBL1I/LKN19CPMV5374P/Yw5TX0A1Ek/O1nSXtiNmZDLk3+9QlFuw5TefmGupqPF8EThlN2IsWpvfeU5yl6+yUkYw7+H3xO5dGDSOlq+648uIvSVbXYtyjSaOEULo55m8pMI7GbPiB/21HVBNq4YR85328FIPD+rjR8ayIXH3tHRcP3uecpeOMlpNwcApd9TuXhg1jT1HxU7NtFyafOfFglmfd2neOzR7qi9/Nk3A+HiGsWRjOdbXLwcnzr6v+vPX6dCzmFzjR2n+ezRzqj9/Vk3NrDxDUNVdOIa2WjcSKNC9lqGgginuOnU/LRa8imHHzf/BTziUSkDJtORX0UHg+NoXjRTCgtRvALdCAhMHL+JFY+tpB8g5EXfl3Eme3HyLp8s7pOXoaRH17+jP5P/QUnCCIeI56i7Mt5yAVGvKZ9gOVcEnK2TR+CLgK3+Eco/WwWlJUg+ASoaYgiDRc8zcWxb2HONNJ644fOOv15Hzn/UHQacH9XGrw1iUuPzVfRuCPbrKJxRz4iijR790nOjJpPRaaJDlvew7QtWbUoylm/H8N32wAITuhCk7cncHbsQjviAu5DJ1P+zQLkQiOez7yLJSUZOcemDyE4HLd+IyhbNRfKS8DHNkZBVfz+5TArJw9GH+DNuOW/Ede6Ic30Nt2v2nWShHZNGNWjFalZ+Uz7ejubX7dbNIkiPlOfp3CO4iMBiz/HfPgg1hsOvrpvFyUra/BVUcRv5kzyX1bGsuCVK6k4WMOYvHs3RUtqG8tEPCfMoOT9VxX7nr8C8++HkDLUNMxH9lD+Xc2xFVGk8aKnSHl0HpWZRtps+oD8rUmqhYBxw36yv1d0EpjQlYZvT+TCOLt4IYh4jn6O0qWzkPNz8X5tCZZTR5AMNrsQQiNxHzya0o9egrJiBN8A3Lr2v7uyeHwGJR9UyeLtFZiPq2Uh6qPw+MsYihfMqNHX74osgE79OxPZOJJn+z1Ni44teXrhVF4b/rITy0k7jrLp23/z6d7PVb+3i+9EeJMIXo2fRrOOzZmwcArzR7zh1H7Cgil8/cZnpB6/xEvfzLYThcCI+RP58rFFFBiMTPt1Iee2HyPbLmblZ+Sy7uWV9HvqoZrl+b8AsnRPZxy/Z3GvHs8rk2W5gyzLbYFK4BlBENqiLJi6Ae2BvwiCEHMrIgBnbppoEOxHdLAvbloNg9s2ZM+Fm6o6giBQUmEBoLjCTKifl6pc26o1lps3kTIzwWKhYvcuPHr1qVOHzmSYaBDkQ3SQD24akcGx0ey5lFlr/c3n0hnSxjbIiBFNkfOykQtyQLJiSTmCJqZDre01rbtjOa9+Y+vWuhXWmzexVvWjfOcuPPv0rlM/7gYfZ9KNNND5ER3sp+jkvkbsOa+efAhASbmy9Cour1Tp5MzlazSMCCU6PAQ3Ny1D+nRm99GTqvZX0g10v68FAN3atmD30VNOvHXpcB8B/n618r77wGGGDRmIIAi0b9uaoqJicnJNtvbtWhPg5/yWq7p94jGG3d9Xad+6OUUlpeQY81R1PNu1xJyWiTndAGYLhZv24jOgpxOtkJmPk/flP5ErzE5lXdrFEuB/Cz4OJjEsIV7hI7YFRcVqPs5cTqNBeAjR+hDctFqG9OrInqQzKhqp6Qa6tW0OQLc2MexJVpd73NcSc1oGlnQDWCyUbN6LT/9eTrwET5tA/uqfkCsrncq82rWg8noG5htVsti4D7+BzrIIfX48xi/+iVShpqFt3hop8yZSlmLflQd24d6tbn7q07E5FdcyqUjLQjZbMP1ygKDB3VR1pOKy6v+L3h44fupO27I11oybSIaqeLFnF+49/zwfZwz5NAj0JjrQW4kVLcPZk5pVa/0tFzIZ0jJC9dsZQwENAryJDqii0SKcPanZdaKhadoSKTsDOScTrBbMR/fg1lEdL9z7PUjFrl+gtBgAuShfVd6wQwy51w0Yb2RjNVs5/lsibRO6qOrkpeeQmZJGTd8MFBvEIBkzkU1ZYLVgOXkAbaxaH27dBmE+tAXKShQeSgpU5T4dFJ1W2uk0MKG7qo69TjXenjgq9U5tE+7cR/w6xlB+1UB5Wjay2ULOzwcJHtxVVceq6oeHE20xOgbJaEDOywarFevpRLSt1TS0XQZiObJVWTABlKgX02du5CrxW1cVv9s3Zc85h4UfUFKu8F9cXkmov8OY2sLBR/btwq3Hn/cRt1YOY9muXXj0rttYpmnWCinrps2+D+/GrbOzPm4F344xlN8mXjjpxMG2xMYtkHIykI0GxcaP7UXbvoeqjnufIZj3/gZlVX5WbLPxuyKLpg6yOLIbt05qWbjHPUTFzl9r9fW7IQuAbgk92P2vXQBcPH4BH38fgsKCnOpdPH6BvOw8p987JXTl4HrlExKpxy/h7edDQKh6gRcQGoinnzepx5Vd3D/qAzToEIPxugFTVcw6+dshYp1iVi6GWmKWC//duFcXTfbYD8QArYEjsiyXyrJsAfYCt/04QHZhGeH+3tV/6/29yC4sU9V5Jr4tG09dI+HvvzBtzV5ef7CzqlwTEoKUY5twSDk5iCEhTs/y6BtH8Kqv8H9rHmJoqJqPonLC7QYOvZ8X2UXlNfKcUVBKRn4J3RrZaAi+gchFtgm7XJSH4OscSAAEfx1iQAhSmvpNpRgSijU7p/pva06OE58AnnH90H29msD58xDD1OV3g4/swjLCA+x0EuBNdpGDTga0Y+PJqyR8uJ5p3+/h9YdsQSvLmI9eZ3umXhdEtkk9UWrROIodh08AsPPICUrKyskvKq6Rz9qQlWMkPMymZ31YCFlVxxb/DLKNJsJDdbb2IcFkG02qOtowHRaDTSeWrFzc9DpVHY/YGLThoZTsrd8RgOxco5qP0GCyc422clM+4XZH7cJ0AWQ5yLNloyh2Vi08dx49TUlZBflFJXb9CHHoRw4ah364t1b6Uba/5n5ow3VYMm3yNRty0TrQ8IxthjYilOI9SU7theAQrLl2fmrMQdQ5+6l7jzj8F3+F7yvzEHVq+3YPD6Yyw8ZDZaYRt3CdIwnCJjzAfQc/o8GcCaS9+aWqTNQ5xIvcWuJF7zgCP/sKvznqeJFdXIHe7iWB3teTnOIKp/YAGYVlZBSU0bWBmsfsknL0fp42Gn6e5JTcjkaw6nchKATZZNcPUw5CkPo5Yng0Gn00PrM+wWfOMrRt1RPwQH0w+Rk2WyvINBGgVz/nVhACdMj5tvZygREhQN1eDI1EDInAa+oivJ57D02Ljqpy94hgKu3sqtJgxD3CmYfQCQ/Q9sBKomc76/RObRPu3Ec8IoKpcLBNjxr6ETFxCF0OL6fJ3PGkzl6tKhP8g5ELHOTp5yDPkEgEXQSeT87Hc8oCNDHtVeXZhaWEB/hU/60P8Ca7sERV55lBHdl4PJWERT8x7evtvD5MvQgQdSFIuWof0dTkq73jCFj+Fb5vzEMMsfmIGBqKlGOTpZSTg6aGscyjXz+CV68mYJ7zmKzYtx0NUw5CkDMPbl374rtwFd7T30IIdowXOirt7Lsy04hbDTrRPzGE9okraDDnca7NVetEDAxByrPjIy8XIUBtF0JYFKI+Cu+XPsL7lcVoYm1zlP+ULKp9fc4SfOYuQ3uf2tfvhiwAdFmR4e8AACAASURBVOE6jHa+ZjQYCa4hBteGIH0wRjs/MRmMBDm0DwrXkZdp49Vk9/8AfZBDzDISoK95juPC/z3c04smQRC0wAMoR/XOAH0FQdAJguANPAg0uFX7P4stp68zrEMTtr00nOXj4piz/jBSHbcuKw4lYhw3GtNTk6g8loz/a7Pqzc/Wc+kMahWFRqzfB5E1rbphuZhc41uc26E8MZGcUY9inDiZiuRkAmY5b2v/J/jYcuoawzo1Y9srj7B8fDxz/pVYJ528NOERjp29xKiXFpF89hJhwYGI4j1t7jVDEAh9bQo576/6/5WNF8cPI/lcKqNe/Yhj5y4TFhxQN3kKArpXnsb40Rf1Z0IQCJv1FNnv1l8W5uRE8p8eTeELkzCfTMZnZv38NPvbzZzuPZUbC78jcubf6ty+8nAipgmjyZ86CfPvyfi+XD8+tl7IZGALfb1jhULDwMDm9aQhahD1UZS8/xKlKxfiNfEF8PK5fbu7CVGDGBJJ2edzKf/hYzxGTgVP79u3c0DOt5s50+cZ0hd9R8SMOur0LtjmXfERIPPrLST3mMbVBf+g4Qv1+CCuKCLqIij/ah4V65bgPuLpOstzy8krDOvcnG2zRrN84v3MWbevzmNq5ZFE8iaOpmDaJMzHk/F9sW4+UpGYSO6jj2KaPJnK5GQC3qj7WGY5foiiF8ZRPPspLGeO4f30a3WmAZD1zRZO9nqWGwu/J2pm3XUiiBqE0ChKF79G2Vfv4Tmubh/AvhuyQKNBDI+i5N0XKf1sIV4TXwTvuvv6ncrChbsESbq3/92juFdnkV6CIJwAkoE0YLUsy+eB94FtwBbgBGB1bCgIwhRBEJIFQUj+4osvCPP3wlBYWl2eVVhGmMNRgQ3Hr5BQdRSufYMQKixW8kttb2WtubmIoWHVf4uhoUi56h0HubAQzMrRqfJNG9E2b6EqD/PzxGC3w5VVVEaY3Ztge2w5l86QNtFq+sX5qjeCgl8QcrHz1jSAtlU3rA5H4qDqbZ7dzpHG4Q2VYz/K/r0RtxbqftwNPsL8vTAU2OmkoJQwhyORG46lktBWuQvVvmEoFRapWid6XSBZdsfLsox5hAWr7zGEBQey+LWnWff3WcwYq9xP8/ep28CvD9VhyLbpOSs7F32o85vI2hCmC8aQY3tjlZVrIkynfvNmyTaiDbfpRKsPwZxlayP6eOHRvBENvvuAJju+xbN9K6JWvF2nfoSF6NR85JgIC7G9eQsLDsRgtB21yDYWoHeSZwCLX57Eug9eZvoY5Ry3v49NZ5bsXId+hGK164fg44V7TGMivvqQBlu+w6Nda8KXzcc9trmNhsGINsImX7fwECw1yKLhP96n2e6v8erQiuiVb1aXy6ZcNCF2fqoLRTI6+GlRIVgU+67YsRFNU7V9VxpMuEfaeHCP0GE2GKkNpl8OEOh4fM/oEC9CaogXRXbxYos6XoT5epBlt/OaVVxOqK/zUStQFk2Ox+oAwnw8ybLbyc4qKifUpxYaFw010pDzchGC7foRHIqcp5aFlJeD+cQhsFqRcw1IhnQ04bbYlZ9lIjDSZmsBEcEUZKl3W28FucCIEGhrLwTokAtMTnUs55NAsiLnZSPlZiCG2BL5VGaacLezK/dwHZWZtfNg+mU/gYPVx/fqa5uebe3s+w59pCLThIeDbVbcoh85Px9EN0S9GyAXmlS7GEKATnVyAEAuMGFNSVbkmZ+DnJuJqLPZR5i/N4YC285SVkEpYf7qyfOGpEsktGsMQPtGYVVjqs0eJWMuYojaR6y38tVtG9HE2HxEcjglIYaGYr3VWLZxI1rHsSwvV7VzpNi3Aw/FNh4q92xC07i5qrzSYMTdzr7dI3SYb6ET488HCBriEC/ycxGD7PgIClHtBv5Rx3LqsKITYxZSlu2KwX9KFpIpB/PxRLWv622+fqey+HjzEj7evIS8bBM6O1/Thesw3SIG22P+po/Iz85DZ+cnweE68hza5xmMBEXYeA22+39BVp5DzNJRkFXzHMeF/3u4VxdNf9xp6iDL8nRZlisBZFleLctyZ1mW+wF5wEXHhrIsfyHLchdZlrtMmTKFNpHBpBmLuJlXjNliZeuZNOJaRqnaRAT4cOSKcmfgSk4BlRYrQXYTDEtKCtqoaMTwcNBq8eg/gIrEgyoaYrBtMuzes7fTpe82kUGk5RVzM78Es1Vi67l04po7T1Su5hZRWG6mfZR6ci1lXkUI0iMEhICoQduqO9bLJ5zaC8Hh4OmDlOGcbcaccgFNdDSaCKUfngMHUHEwUd0Pu0m9R+9eWK6rz6rfDT7aROnUOjl9nbhW6kViRKA3R1INAFzJVuukTUwjrmdmk56Vi9lsYcuBY8R3VWdvyissRqp6W/Hl+q08XMP9g9shvk8Pft2yE1mWOXnmPL6+PoSG/PmjRf17duLX7fuV9ucv4evjTahOvc1ffvoCbo0i0UbpwU2L/4NxlOw+XF0uFZeS2ms0VwdN4OqgCZSfTOHms2/XqR/9e3Xh1217FD7OXXTio02zBqRl5pCebcRssbAl8ThxXdTZDO3luXrDDkb0V08qK85cwK1RFNooxbZ8HoijZM+h6nK5uJTr/f7GjSGPc2PI41ScOo9h+puqzGBlpy/i3jgSt+gqWTzUj6Kdallc6j6G1P4TSe0/kbITKaQ/Y7usb7mUghgRjRim8ODeZwDmJLWfCkE2/bl17e2UJKLkxCU8mkTg3iAMwU1L8PA+5G1TH7fyaGLz24BBnam4qr6baLmQgiYqGlFfFS/iB1B52IEP+3jRQx0v2oQHkJZXys2CUiVWXDAQ3zQMR1w1FVNYYaZ9RKBTWZtwf9Ly7WhcNBDfrCYaJUq8iQhwKrNevYAmLAohJBw0Wty6xSuTJvu+/p6ItpVyfEvw9UcMj0bKtsnjxslUQhuHExwdisZNQ8ehvTi7/ZjTs2qDlH4ZUReBEBQGGi3a9n2wnlfrw3L2KJqmVfbq7YcYEolkMlSXl5y8hKeDTvO3q4+/qXQ6sIuTTutrm/bZ8+7UR4pOXMazaQQeDZV+hI7ojcnBNj2b2JK3BA/qRNlVg6pcupmqyDMwFDQaNPf1wpKSrKpjPX8UsYlNnkJIBJLJdqeuTXQIacZCbpqKlPh98gpxsepDHxGBPhy5rMjwSnY+lWYrQT62l4SWiw4+0m8A5iO1+6p7996qJBHmC8pY9seY7DlgABWJDmOZnY959OqFJU09llmvpKAJj0IIrbLvHv0x/66mYX8UVNupJ9YMNY3iE5fxbBKBx5+MF4GDOlPuYFvS9YuIYZEIOr1i453jlAWSHSwnD6FtoYxxgo8/ot42h7krsriagkZv5+vd+9fg6wfRtlLuL9fk63cqixcfmMmLD8zkyNbD9B+pZPFs0bElpUWlNd5dqglvPvgyv287Su9H4gBo1rE5ZUWlFOSo718V5ORTXlRKs47KIviP+gDpJ1PRNQ4nqCpmtR/ak/N1iFku/HfjnsyeVxsEQQiTZTlbEISGKPeZetyujVYj8vqDnZn6/V4kWWJ4x6bEhAWwYtdpYiODiW8VxYsJHZj/WxJrDitrsHkjuiMIdkdVJCtFyz4h8P2PEESRss2bsF6/hs8TkzBfSKHyUCJeD4/Eo1dvZKsVuaiIwg/eU/Mhirye0J6pPx5EkmB4+0bEhPqzYu85YiOCiG+hBJMt59IZEhulfj6ALFG54x94/PVFEEUspw8gGzNw6z0CyXANa6qycNG26o41pZa7L1YrhZ8sIeijD0EUKdu0Gcu1a/hOmoj5wgUqDibiPXIkHr17gdWKVFhEwbvqftwNPrQakdf/0oWp3+5CkmSGd2pGjD6QFTtPEhupI751NC8O6cz8Xw6zJjEFBIF5j/SslolWo2HWk6OZOn85VklixMCexDSM5NO1vxHbrBH9u7Uj6cxFlq75BQGBTrExzJ4y2omPV956j6Tjp8jPL2TgiMd4dvJ4LBYlIcjohx+iX8+u7D+UxAOjJuHl6ck7s15QtX910VKSTp0nv6CIgWOf47nxf8ViVdqP+sv99O3WkX1HT/DgE8/j6eHBgpefrkEnEjkLVhD95UIQRQrXb6Py8nV008dTfuaSagFVG15dsJikk2cVPkZP4bkJo218DB1M3+6d2Hfkdx4cPw1PTw8WvPKsgz40vDFpJFMXfo4kSYzo352YBhF8+tNm2jRrQHyXtiSfu8zSHzaCINC5dVNmTXY4VmGVyF20nPCVixA0IkUbtmJOvU7Qc49TcfYipXtu3w+sEoZ5n9HgqwVKWuf/2Ubl5TRCZj5G+elLFO9y3rVUQbJSuuoT/N76SEk5vnMT1hvX8BozCcvlFMxJiXg+NBK3rr2Vt6XFRRQvc7Bvq0TanFW0/OEtEEVyf9pJ+cUbRL48htKTl8nfnoT+iQfx79sO2WLFUlDMleeXOvFR/OknBCz6qCqdshIvvB+fhOViCpWHE/EaPhL3ngofUlERxX+38aEVRV4bEMuz65WU/MPbRNMsxI8ViZeI1QdUL362XshkcIsI51jxB43+rXh2w+9VNKJopvNlxaHLxIb5q2m0DK+RBpJE2Zpl+Lz0npKGeP8WpIzreIyYgPXaRSwnDmE5k4S2bWd8F6wGWaL8py+Q7RIHSFaJf735NU9/NwtRI3Jk3W4Ml9IZ8sLfuHH6Cmd3HKNBu6ZM+vwlvAJ8aDOwE0Ne+CvvJ7xSzUPFL1/iNflNhYeknUhZN3C//1Gs6alYzydhvXgcTYv2eL+4BFmSqNz0bfVl9Wqdzl1FizVvgajB+NOOap2WnLxMwfYkwp54EP8+7at1evUFhyxjd2qbVTTuyEesEqmzvqTt2jkIGpGstbsovZBOo1dHU3QiFdO2ZCInPUBgv3bIZguWghIuznDI+iZJVP77KzwnzFbi9++7kbPTcRswCikjFWvKMayXT6KJaY/X9I+VeL/1H9UJCKAqfg/rwdSvtinxu0tzYvRBrNj2O7HRIcTHNuTFh7oxf/1B1hw4q8Tvv/V1GlNLPvsE/3eqfHX7Jqxp1/B6bBKWSymYjyTiNWwkbt3tfHWxna9arRQtWULQh8pYVr55M9Zr1/CZOJH/j73zDo+qWB//55xN770BoUMoEjpIL9K8iqBXFOkgAioqKKIgKIhgbyCCoNgQQaV56dKlhw4CoZOe7G56Npst5/fHCdk92Q1lk9+V6/d8noeH7M7Me2beed93ds6ZM2O+cAHj/tKxrEMH25j8bjlft1oxfD8f3ynvlW2pb025juejI7FcvYD5+AE8+gzEvUUH+QlPQT6Gr9536JNr05fS8KeZCBqRrJ+3Y0hMotqUJyk8eZmcrUeIGtWvLF5Ycgq48qJjnxSv/BKf5+eAqMF0YCvWtBt4PDQMy/VELKcPYfnrKG6NWuIzYzFYLRhXf433mNeqVhc/ONHFwJFYrsm6MJ8+glvT1vjN/QasFgdfrxJdAEd3JNCqe2u+3PuVvOX4KzZf/HjTZ0zuJy9NHD5tJJ0f6Yqnt/IJ+smdx2jWvSUf7P4Co8HI0ilflKXN3vghMx+Ud+L7bsYSxpZuOX5q13E707Sybua3jPn+dUSNyJFVu8i4mEyvSf8m+fRVzv1xlOrN6jB88WS8A31p1LMlvSY9zsc3Y9b/Curhti4h3Iu7fwiCUCBJksN2YIIg7AVCARMwWZKk7bcRJRlWvOlyPbwHzwIgs2fX2+SsmIjtuzF895rL5QG8R7xL0QejXS7vM+UbANK7dHNZRtSeXQCVrodh1ezbZ7wF3oNmYjx7u26vGM8mPQEwaa/cJmfFuIfVoeT6sdtnvAUeNVuS2Kivy+UbnNsMQEnyadfrUP0+AIpPbrxNzorxin+QK/f1drk8QJ3TWzlX/0GXyze6KNdfP9B1Pw1ZI++edKTaQJdltElZg7aP63UACNuym6JFd/e+gj0+4+UfGEVfTrxNzlvImCD/kMkd9cBtclZM4LI/mFTrSZfLA3xy7WcKpt52r58K8XtPPospofoAl2W0TpbPQaqsfVaFj+yNcv39j87pvwJQOGOQyzJ835bPjzOsefc2OSvGe+Br6P5VOR8J3bCbjG7dXC4fuWsXALnDerosI/AHeQw6FOO6fbZLXU3+s/1cLu+/cBNA1ehiRCV08d32SukBZF0MjH3Y5fJrbvwOwIhaj7ks47trvwEwtdZgl2W8d20FyJtH3vMUfTnx3vvxb4fPhPn3pB7vySdNziZMpd93/m/XRUVFRUVFRUVFRUXl/zb35KRJRUVFRUVFRUVFReX/A+rhti5xr24EoaKioqKioqKioqKick+gTppUVFRUVFRUVFRUVFRugTppUlFRUVFRUVFRUVFRuQX35O55Vcg/unEqKioqKioqKir3DPfkrm/lKZr/7D39+9hn4sJ7Uo/qkyYVFRUVFRUVFRUVFZVb8I/fPc+w7v3bZ6oA70deBajUuRKhG3ZTmbOiQD4vqujjsS6X95m8BKiac5oK5wx1WYbvGz9WyTlNxUd+c7m8Vxv5HIfKnLPkUbNlpc55Avmsp8qe/wVgPL/bZRmecfL1S5JOuizDo0Y815r3crk8QK0T26rkzKqcwd1dlhG0YicABytx3kj71NVVcgaNYfkMl8t7D3kbAMPSya7LePpjAAped/3ME795vzGz1hCXywPMvracos/Gu1ze58VFQNWc03SmzkMuy2h65T9cja+cj9Q+uY1dkY+7XL5bxi8AFL03ymUZPlOXAZU/p6ky56mBfKZaVYxlVXFO0/5o132kQ9pvFEzu73J5v4/XA5DVy3V9hm+Tx4+8sa6fIxawZCv7KnGGGEDH9F/pV8P1M6s2JclnVk2uxNlwH1/7GaiSc5pU/sH84ydNKioqKioqKioqKiqlWK1/dw3+J1GX56moqKioqKioqKioqNwCddKkoqKioqKioqKioqJyC9TleSoqKioqKioqKir/V/hn75z9/43/E5OmfReSeX/dQaySlYFtGzK6e7wiPS27gBkr95BfbMRqlXihXxs6N6qhyOPeqi2+z0wEUaR46waKf/lJke75QF98Rk/AqssCoPj3NRi3blDW42Ia728+htUqMbBlHUZ3bqysR04hM9YeIr+4RK7HA/F0bhBTli7WaoJHtydBFDGf3ov5yGZlHbsOQlMjrvSDB4K3P4aFLyryeLRtS8ALz4OowbBhA4XLle3w7tsX/2fHY8nSAlC0eg2GDcp2aOo0w6PPMBBEzCd2Ydr/u/IavYYg1pTbJrh7IPgGUPThuHK6SOX9DQlYJYmBreoxuksTR12sPkC+oQSrJPFC7+Z0blDNVv5kIu/98B+sVisDu7VhTH/ly7Cp2mze/Oo3svOLCPT1Zu6EQUSGBiryzPhoEXsOHickKIA1Sz6gPJIk8e7C79h75ARenh7MeWUCjevXLkt/Y+7H7Nl3mJDgINb+uMhp+XmfLmLvgSN4eXnyzvSXadywnkM+jzZt8Xuu1LY2bqDoZ2WfePXpi98zE7BoZdsyrFujSP/z2BneW7ISq9XKo706MebfyhdqUzN1zJz/Hdm5+QT6+zJ30hiiwoKVuvhgIXsOHSMkKJA1Sz9yrosvlrH38HG8PD2Z8+qzNK5fR5HHu0NrQl59FkSRgjWbyF220kEOgE/PTkR89CapTz1HyV+JyrROrYiYNgFEkdxfN5O9dJVTGX69OhLz+Qyu/3ui4nu3+DZ4D5ftu2TnBozrlS/menTpg9eQ8Uh62b6NW9dQsnOjIk9gtxbUens0giiSueIPUhco9R0xrDdRI/shWa1YCou5OuVLDBeTFXmqJF5cSuP9LSfkeNGiNqM7NVKkp+UWMmPtYfKNJjle9GyG/evc+65m8v72v2Qfa1aD0e2UtvfBjr84ckMnX99sQV9k5M8X+ijyaBo0x/Oh0SCKmI5sx7RbqQsAt/s64NFzEBJgTbuGceWnivR6XZvx4MxhCBqRYyt3sfdLZbyo2TaOfjOHEhkXyy8TF/DXpsOKdLFmYzy6DpLjzdl9mBO2KNLduzyOpnqD0sp4IPj4Y1ik3AgjoFsLYmc9DRoR7YptpH+xWpEePrQP4SMfBIsVS6GB61MXUlyuT/26tCR65jMgimSv2op20a8OugAI6NuB2IXTuPTISxSfvqRI8+7QmpCpzyKIIvlrNpH7TcU+Evnxm6QMVvpISPfm1JszCkEjkrZ8Ozfmr1WUixnei5jRfUvbUcyFVxZTlKhsh1i7KR49n5LHkZN7MB9S2r97jyfRxJbamrsHgk8Ahs+eU+TZdyGZ938/JNtWmwaM7tZMkZ6WU8CMVXtt8btvKzrHlRtTW7TFZ4zsI8Y/NlC8WukjHt374jNiAla97CPGjWsw/mHzkaoYy9zua4PXsOdk+961EeN/flbWsXMfvJ58Bim7NF5sW4dpt1JfQd2bU3v2aNCIZP60nZRy8SJyeG+iRpb2SVExl6cswlCuTzRxLfEc8DSIGkwHt2La4bjZkVt8Rzz6DJb9LPWqsp6t2+L37EQEUcSwaQOGleXiTe+++I21xRvDujUUbyo3rjdpjdeTExBEkZK9mynZ7Gibbq274PnwMEDCmnQFw1LlpiBB3ZtT5+1RoBHJWL6dlAVK+4wa3puoUX2QLFashcVcmrLYQRcA42eNp02PNhgNRj6a/BGXz1x2yDPi1RH0fKwnfoF+iu/jusYzYOYIRI3IwZU72PHlekV6nbZxDJg5gui4WH6Y+DmnNh1ykN2gazz9Zw5H0IgcWbmTXeVk1G4bx8MzhxMVF8uKiZ9zulzMUvnn8rdNmgRBsACnS+twDhghSVKRIAjfAA8BmZIkNbXLHwKsBGoB14BBkiRl3+46FquVeWv2s2hsXyIDfRkyfz1dG8dSN9L2o3HJ9hP0jq/NoPsbcTkjm+e/2cqmRk/YhIgivhNeIu+Nl7Fqswj8ZDGmg/uwJF1XXKtkzw4KF31WcT02JrBoWHciA7wZsmQbXRtWo26E7Yf8kj1n6d2kBoPa1OdyZi7PL9/Npgb9byoAjx5PYfztE6T8bLyGTMdy+SSSPq2svGn3Kkylf7s174EYoRykEEUCJr1I9uRXsGRlEfrVIor/3IflurIdhh07yf/UeTsQBDz6jaB4+btIeXq8xszGnHgUSZtq08O25WV/u7XuhRhVy1EXvx9h0cgeRAb4MGTRZrrGVVfqYvcZejeNZVDbBrIuftjJpperlZWf+916Fr82msiQAJ6auZBureKoWy2yrPzHP23i4U4t6d+lJYfOXuazVVuYO2GQoh6P9OrK4P59mP7+QqdN3XvkBNdT0tmw7BNOnb/EnM+/5qf5c8rSBzzYi6ce68+0tz90Xv7AEW4kp7Jx5decOnuetz9cwIolyh+UiCL+L7xE9qsvY83KInjhYowHHPukeNcOCubb+iTgZXlnR4vFytzFP/HVrElEhgYz+JW5dGsbT91Y22T7o2W/8HD39jzSowOHTp3n8x9WM3fSGKUu+nRj8IC+TH/vC+dtOXxc1sV3n3Pq3EXmfLaUnxbMVbQj5PWJZIyfijlDS8zyBRTtPoDpyg2FHMHHm4CnBmI8dc7xIqJIxIznSBkzDVOGlpqrPqdw50FKLjvKCBo+AMPJcjIEEe9RL1I4dwpWXRb+7yzCdHQ/1hSlLk0HdmL49nOn7UQUqT13LOeenEVJmo6mG98ne8sRxaRIt2YvmT9sBSC4dxtqvjWK86W71t2UUSXxYtMxFg3tKseLpX/QtWEMdcPtfGTvOTletK7H5axcnv9pL73fulleYt62sywa1I5Ify+G/PAnXetGUjfMv6z8lB62mzYrjl3lfEaegz49+4/F8PVspDwd3s+9h/ncEaRMmy6E0Gjcuw2kaNF0KC5E8A0oJ0Lgodkj+W7oPPLS9Yxb/zbntx0j61JKWZ7cVC1rXllMx7H/clSEIODRbTDGNZ8hFWTj9eTrWK6cUsa9Pb/Y4l58N8Rwx7gXO2cciU+9iSlNR6MNH5Cz9bBiUqRbu4esH+XJWGCvNtR4czQXh85WyIiZNYGrw9/AnK6jztpPyP/jEMZLScpL+XoTOrI/RcfPO7ZFFAmdNpH0caU+8tMCinY595HAIQMpLu8jokj9d8dwctDbGFP1tNoyD+2WBMWkKGP1n6R+vw2A0D6tqTdrBKcGv6PUZ69hGFd+iJSvx2vETCyXTiDpbPHbtONnmz5b9kSMrKmohsVqZd66gywa04fIQB+GLPidro1iqRsZVJZnyY6T9G5Wm0Ht47ickcPzy7ax6TW7fhFFfJ55ify3XsaqyyLg/cWUHN6HNbmcj+zbQdESJz5SJWOZiNeIFyh871UkfRZ+sxdiOnYAa2q5eHFoF8Xfz3cuQxSpM3csZ5+YTUmajmab3kO/9YhiIqBdvZeM72/Gi9bUemsk556yjSMIIp6PjsOwaCZSrg7vSR9hPnsYKcNmW0JYNO49H6do/lQwFCL4BeLesmtZHfwnvkTOVDneBC9YTMmBfVhuKNth3L2DggUV68L7qecp/OQ1pGwtvtPnYz55AGuazTbFiBg8+z1J4XuToKgAwT9IKUMUqTPvac4Omk1Jmp74ze+i35qg0EXW6r2kl+oipHdrar81gr+eekchpk33NsTUjmFM5zHEtYjj+bnPM6n/JIcqH9p2iPXfrufrPV/bNUPg0dmjWTT0HXLTdUxaP5ez246SYRdvslN1rHjlS7qNdb4jpiAKDJg9iqVD55KbruP59e/w17ajZNrJyEnVsuqVRXRxFrNU/tH8ne80GSRJal46MSoBbu4t+y3gbP/h14DtkiTVB7aXfr4tZ5KyqBEWQPXQANzdNPSJr8Ous+UGKQEKi0sAKCguITzAR5Hu1qARltQUrOlpYDZj3LMD9/ad7rylwJkUPTVC/Kke4ifXo2ksuy6kKPIIgkCh0SzXw2gi3N+7LE2Mqo2Uk4WUqwWrBfP5I2jqNq/wepq4NpjPK+9+uDeKw5KSgiVNbkfx9h14llE92wAAIABJREFUdep4V+0QY+pi1Wcg5WSB1YLl7EHcGrSqML9bk/sxnz2g+O5Mso4aof5UD/GXdXFfTXadU/74EIDCYnnoLiguUejizOVkakSGUj0iBHc3N/q2b8auo8ofGJdTMmnbRH4S0rZxHYd0gNbNGhHo7+fw/U127j9K/16dEQSB+Eb1yS8sIktnm6e3bn4fgQH+FZf/8yD9+/aUyzdtRH5+AVlavVI/cY0wp6RgLe0T484deHa4c9s6c/EqsVERVI8Kx93djb6d27DzsHIL8StJabS7T34C2fa+huw85LjFeOtmjW+jiwT69+oit6VxA/ILChW68GzaEHNSKuaUdDCbKdyyC59uHRzkBD83ktxvVyKVlDikeTVriOlGGqbkdDCZydu4G98e9zvkC3txONlLf0EymhTfa+rFYU1PxZqZBhYzJQd24N767uzbr0U9iq+lYbyRgWQyo1v3J8F92iryWAoMZX+LPp4OyxyqLF4E+1E92A93jYY+TWLZdSFVkUcACo03fUQZL86k5VAj2IfqQT64a0T6xMWw61JGhdfbdC6Vvo1iFN+JNeph1aUjZWeAxYz55J+4NWqjyOPe5gFMBzZDcSEAUqFy4lW9eV301zPITsrCYrJw+veDxPVWxoucZC0Z55NwdtC6GFkLKTcTKa807iUeQVOnmUO+m2gatMGcmKD4zrd5fYzX0igp7VP9uj8J6t1Okcdq16caHy+HPvWOb4DxehqmJFlG7n/24N+rvcP1IyYPJWvxrw62CbKPmOx9ZHPFPpKzbCWSUekjAS3rYbiaTvH1TCSTmcy1+wjr21qRx6Joh6eDTsXoOkg5mUi5cvw2nzuMpn4LhzqUyWjcHvO5g4rvziRp5fgd6m8bU/8qN6ZSfkz1VqS71W+ENS0Fa4bsIyV/7sCj7Z37SFWMZZq6cVgzUpCy5HhhOrgT91aO/XEr/FrUw3AtvSxeaNf9SUgfpY9YbmNbYmx9rNo0JH2pnx3fi1tTpX26t++Dad8GMJT6WUFuWZpbQ2W8Kd61A4+7GEMANLUbYs1KRdKmy7o4shu35kpduHd+kJKd66GoQK5Dfo4i3b9FPYqvpmO8Idtn1tp9t9SF6OPptC7te7dn+2/ylu7nj5/HL8CP4Ihgh3znj58nO1N53zy2eT2019PRJ2ViMVk4/vt+mvZW+kh2chZp5284jTcANZrXQ2cn4+TvB2jsIENL+i1k/E9gtd7b/+5R7pXleXuBZgCSJO0RBKGWkzyPAN1K//4O2AVMvZ3gzNwiogJ9yz5HBvpwOilLkWd8r5ZMWLqZFfv/wlBiZvFY5fImMTQMqzaz7LNVm4V7Q+VSGQCPjl1xaxqPJSWJoiULsGpt18nMMxBlNxmLDPDmdLLyB/T4bk2Z8MMuVhxKxGAys3i47dwZwS8IKd+WXyrIRoyujTME/xDEgDCsScq7nWJYOJZMW50sWVm4N25cvjheXbvgEd8MS1IyeQsWYLUrI/gHI+XZ1SNfjxhT13k9AkMRgiKwXjur+D4zz0BUoJ0uAn04naxT6qJHMyZ8u50Vhy5gKLGweGQPW/nsXKJCbHfcI0ICOX1ZOelqGBvF9iNnGdK3I9sTzlJYbCQnv4gopzV1TqZOT1R4qK2eYSFk6vRUu0UZezKydERFhNnKR4SRkaUlPCyk7DtNWBjWLDvbysrCrZGjbXl27opHs3jMyUkULFxgu4Yuh0g7eZGhQZxOVC7daFC7Bn8cPM7Qh3uy/eBxCg3F5OQVEMmdk6nVExVu15bwUDK1Nl1oIsIwp9vsxJyhxbN0onYTj7h6aCLDMew9TOAI5VM/ALeIUAcZ3s0aKvJ4Nq6HW1Q4hbsPEzxaeTaIGByGVWenS10WbvUcdenetgtujZphSUvG8P0XSHrbNT2iQilJtdliSZoOv5b1HWREjuxL9DP9ETzcOPe48hy2KokX+eV8JMCb0ynl4kXXJkxYvocVhy/J8WKobYlqZkExUXaTqEh/L06nKX/k3CQ1t4jUXANtY8MU3wsBIfJNmlKkPD1iDaUuxDB5ouU97h0QRUq2r8SSeKIs3T8yhFw7feal6ane3Hm8cIbgF4yUb/thJBXkIEbdIu4FOsY9j+gQStJs7ShJ1+HXwrFPw0f0I3LsI4geblx4QnlWlntUKKY0O9tM0+LdXGmbXk3q4h4dRsHOBMLHOp7fo4kIw2Jn35ZM5z7iFuXcRzyjQjDa6dKYqifAiW3GjOpDjfEPIbi7cfKxWYo0p/E7uoL4HRAq6/O68oZTZt4djKkPtGDC11tYsf+cPKY+rVz2KYSEYdGW89UGTnykfVfcGsdjTU2i6JsFZcvLqmQsCw5T+L5Vn4WmrpN40aYzbg2bYU1PxrB8oaKMZ1QIJSl2tpWmd2pbUSP7EjPuYQR3N84+/pZSF4GhSDl2fpajRayptC0xvNTPJr4n+9kW27JjMSwMS1a5eBPnRJeduhJ8XzyW5CQKFi3AmmWni6CwsmWQAFJ2FpraStsUI6sD4DP1E3lJ5fofsJy13aDwiA6hJNVeFzr8ndhn1Ki+xIx7CNHdjTP/fsshPTQqFK2dHG2alrCoMIcJkjMCI0PIsfORnDQ9NZs7Lom/tYxghYzcNB2xdylD5Z/L3757niAIbkA/5KV6tyJSkqSbazLSwflvPkEQnhEEIUEQhISvvvrqjuqw+cRl+reqz9bpg1kwujdv/Lwbq/Xu7iCUHNpP9qgnyH1+NKbjCfhNnnZX5QE2n75O/+a12fryIywY0pU3Vh+863oAaOLaYr54zKUX/Yr37ydr0JPoRo3BmJBA4LTX71rGTdwa34/l/GGX6rH51DX6t6zL1imPsmBYN974bf9d6WLyUw+ScP4qg6bP5+i5q0QEByCKwl3X417AeGA/uiFPoB87mpKjCQRMvTvbennkvzl6JpFBL71NwplEIkKDEMX/susLAiGvjCf748WVkhE+9Rmy3lvisgjTsQPkvTCY/KlPYz59FJ9n7+iBtQMZ327mRIdnufHOD1R78e4PdqySeHHmBv3ja7F10sMsGNyZN9YexurCHbot59N4oEEUGlf8QyMihkVjWDKT4p8/wXPgBPDyuX25/w9oGrR2Oe4BZH23iTOdxpM893uiX7jLg2QFgejpT5P+zte3z3sLGSGvjEf/USV8BEhdtoVD7SZyZc5yak5y/fBVTaN2mC8kuBa/T16Rx9RpT7BgVC/eWLXnrscyU8J+csY9Qd6k0ZhOJuD74t35SFWMZebjB8ifNISC6WMxnzmKz7jb3qd1Svq3mzl2/3Ncf+cHqr/kQp+IGsTwaAxfTKP4hw/xfPy525exo+TAfvTDniB73GhKjiXgP+Xu4w0aETGyGkUfvoJhyTy8h08Cb9/blytH+rLNHGv/PNfm/EiNSZU7FFdF5b/N3zlp8hYE4QSQANwA7ni0keRnok4jsCRJX0mS1FqSpNbPPPMMEYE+pOcWlqVn5BYREaB09DVHEukdL9+9jK8ZidFsIaeouCzdqtMihkWUfRbDwrHotAoZUn4emOUlGcatG9DUa6BIjwjwJj2vyFaPPAMR5ZYsrDl+hd5N5HXf8TXCSuthlOUX5CD4254oyHdgnd85dmvYRp6slMOqzUITEV72WRMerrjbBCDl5YFJbofhPxtwb6Bsh5SfjRBgVw//EMWdYHs0Tdo7LM2DUl3k2ukit4gI/3K6OHqZ3k1jAYiPDcdotpbpIiI4kHS9bXlCpj6XyGDluxQRwQF88tJQVr0zkYmD5NfjA3yV17gdEaEhpGfZ7jhlaPVEhIbcooSSyPBQ0jNtdpKRqSUyXHk336LVIobb2VZ4OFZtOduy65PijRtwq2/rk8jQIDLslvxl6HKICFUuZYgIDeKT1yew6tMZvDB0AAABfnf3wzYiLIT0LLu2ZOmIsHvCZcnU4hZlsy23yDAsdm0XfL1xr1uLqKUfUn3jD3je14iIT2fj0djWFnOmzkGGKcOmf9HXG8/6Nanx/fvU/uM7vOLjqLbwrbJ0a7YWMdROl6HhWLPL6bLA5qclOzbgVltp3yXpOjxibE8XPaJDKUlTPuGxR7f2T4L7KpfvVUm88C/nI3kGRx85cZXejZXxIjtb9sUIPy/S821LYTLyi4nw83Lahs3nHZfmgfxkSQi02av85En5RFjK1WE+dwSsFqTsTKzaVMSw6LL0/Aw9gXb6DIgOIS/j9neMy+QXZCP42+xZ8AtCKnBe3q1BayyJRxy+L0nT4xFta4dH1K37VL9uL0F9lMujTOk63KPtbDO6nG36eePZIJbaK+bRYM/XeLdoSM2vZuB1n+0OtSVTi8bOvjURYZgzlD7iUc/OR5o1IvIzm48Y0/V42unSMyYEY7qyP+zJXLOPsH5K23QavyvSZ6O2WM45viQfEXAnY+pFejerBUB8zQiHMVXSa9GElfPVW/nIHxvQ1LH5SJWMZdlahBCbDDEkvGzDh7I89vFi10Y0tZRPTozpejyq2dlWdAglt+gT7dp9hJSLF1KuDiHIzs+Cwpz4mRbzmcOyn+kzsGbZlupatVo04eXiTfkxJN9uDNm0AbfyusjRItrpQggOx5pTrg7ZWswnDoDFgqRNx5qRjBhpW3dRkqbHI8ZeF6EYb+Fnsi5sy/cWbF7Ags0L0GfqCbOTExYdhjZd60yEA7kZeoLsfCQoOoTcjIrr4FxGtkJGYHQouXcRs/5nsEr39r97lHvhnabmkiRNlCTJ8SUHJRmCIEQDlP6feZv8ADSpHs4NbR4p+nxMZgtbTl6ha+NYRZ7oID8OXZKD0JWMHEpMFoJ9bT8wzInn0VSrjhgZBW5ueHbpgenQPoUMIdg2EHm06+jw0neTmBBu6PJJyS6Q63HmBl0bKhd6RQf6cuiK/N7BlaxcSswWgn3ldb/W9GsIQREIAWEganCLa4PliuO7KUJwFHj6YE1z3G3GdP4CmurV0UTL7fDq2QPjvv2KPKLdpMCzYwfM15Vr1a2pVxBDohCCwkHUyBOjxGOO9QiNRvDyxZp80SGtSbVQpS5OX6drXHWlLoJ8OHQ5XdZFplIXTepU40a6luRMPSazmc0HT9G1pXI5QnZ+Ydld96/X72ZA14rfu6qI7ve3ZP22vUiSxMlzF/Hz9SE81HFtdUV069Se9Zu3y+XPnMPPz1exNA/AfP48btWqI0aV2lb3Hhj3K21LDLGzrfs7Kl7wbVK/FtfTMknO0GIymdm89wjd2ip3h8zOyy/TxdJfNzGw592t/Qfofn9r1m/bI7flr0QHXRjPXsAtthpuMXI7fPt0o2i3bcIsFRSR1P3fJD84jOQHh2E8fY7Ml2YqdgYrPn0B95oxuFWLBHc3Ah7sSuFO2/sU1oIiLnd4gqsPjODqAyMoPnmelGffKku3XD6PGFUNMTwKNG543N8D01GlfQtBNl26t+qAJUVp3wUnLuFVOxrPGhEI7m6EPtKJ7K3KH+JetW2TgqAHWlF8NU2RXiXxoloIN/QFso9YLGw5e4OuDZQTm+gAHw5dvRkv8igxWwgptZUm0YHcyC4kJacIk8XKlvOpdK3n+HD+qq6AvGIT8TGOdm1NvoQYFo0QHAEaN9ziO2E5p3xfyPzXYTR1Sne+9PFHDIvBqre9O5Vy8gohtaIIqh6Oxl3DfQ+35/y2ow7XqghrxvXSuBcqx70GbbBcOeWQTwiOBC9frGlXHNIKT17Eq3Y0HqV9GvJIJ3K2KW8qedr1aWDP1hjL9anhVCKetWJwrx6J4O5G4ENdyP/DNqGw5hdxvvUQEruMIbHLGAzHL3D9mbcVu+cZz17APbYabtVKfaSvo4/c6GbnI6fOkfGizUfyj1/Cu040XrFyOyIGdES7Rdkf3rVtC5BDe7XEcEXZDmvaVYTgCHkyLGrkidGl4476DImS9ZlyySGtSfUwbujKj6nKzTeig3w5dEm+9pVMJ2PqxfOI0dURI2RdeHTqgelIxT7i3qajYpOIqhjLLFfOo4mqhlAaL9zbd8d0rFy8CLTJcGt5P5ZUx3jhbRcvwh7phL5cn9jHi2An8cKadBExPAYhJFL2sxadsZxRTlbNZw6hqXef/MHXv2y5HoD5Qmm8KR1DvLr1oOTAnY8hAJZrFxAjqiGEleqiTVfMJ5U3PE3H96NpKI8tgl8AYmR1+X2wUvJPyPbpWWqf4QM6oneInTb7DH6gJcVX08s+P9/3eZ7v+zwHthyg52M9AYhrEUdhfuEdLc0DSDp5mfBaUYSUxpsWD3fgzF3EG4Dkk5cJrRVFcKmM+Ifv59xdylD553KvvNN0J6wHRgDvlv6/7k4KuWlEXnvkfiYs3YzVKvFImwbUiwpm4ZajNK4eRrcmNZn8UFtm//ony/fK797MekJ++b8Mq4XCLz8l4O0P5bW82zZiuXEN76GjMV88j+nQfrz7P4Z7u47yXZiCfAo+edexHg+2YsIPu7FKVh5pUYd6EYEs3HGaxjEhdIurxuTezZn9+xGWH5QHyVkD2tnqIVkp2fkTno+9BIKA+cw+JF0q7h36Y02/XjaBcotrg+WC491WACwW8j79jOAPPwBRxLBxE+Zr1/AbPQrThQsY9+3H57HH8OzYASwWrHn55M5TtgPJSsnm7/Aa/Kq8Ze2J3UjaFNy7PoY19SqWi/IESt4A4qCTSpTq4qHWTPhuh9wnLetSLzKIhdtP0jgmlG6NqjO5bytmrzvI8v3nQRCY9ej9Zbpw02h4fUR/Jry/DKtVYkDXVtSrHskXv26jSe3qdGvViIRzV/h85VYQoFXD2kwb2d+hHq/O/Zwjp86Rk5tPz6ee47lh/8ZskTfiGPRQLzq3bcGewyd4cORL8jbbryi3TZ/y5rscOX6KnJw8eg4YyrNjhmE2y+WfGPgvutzfhr0HjtBv0Gi8vbx4e5rjDkBYLeTP/5Sg9z4s3S52I5br1/AdORrThfOUHNiP98DH8OzQEcliQcrPJ+/9dwn99ocyXUx7ZjAT3voUi9XKgJ4dqRcbwxfL19G4Xk26t2vOkdOJfP7DGgQBWjZuwPTxgx118c6nHDn5l6yLJ8fz3IhBZW0Z9HBvOrdrwZ7Dx3hw+Avy9utTni1nW1b07y4g8st58pbj67ZgunydoAkjMP6ViGG34xNHByxWsuYspPpS+f2YvNVbKbl0ndCJwyg+c1ExgXKK1Yrh28/xff19ed3/rk1Yk6/h9e9RmK9ewHx0P559H8W9leyn1oI8ihaVs2+LlWvTlxL300wEjUjmz9sxJCZRfcqTFJ68TPbWI0SO6kdg52ZIZgvmnAIuv1huZ62qiBeiyGv9WjJh+R6sksQjzWvL8WLnGRrHBNOtYTUm945n9u8JLD+UCAjMeqStzUdEkdceaMqEXw/LPnZfdeqF+bPwzws0jgqiW+kEavP5VPrGxSjjnZ0+jeuX4j16BggipoQdWDOT8HjgSSwpl7CcS8CSeAJN/eb4vPQpkmSlZNP3ZS+Ky6qwsmHmtwz/fiqiRuTYqt1kXUyhx6THSDl9lQt/HCOmWR0GL56Ed6APDXu2oMekx1jQu3QZlGSlZNdKPAe8IG85/td+JH0a7u0fxppxHctVeQLl1qCN06dMN/v0xowlNFj+JogadCv/oDgxiZhXBlN48hK5244QMfJBAjrFy32aW8DVSZ85yEh9axG1vpuNIIpk/7IN48UbRLw0BMPpi+Rvv4Mthy1WdPMWEFXqI/lrS33k2RGUnE1UTKCcIVmsXHz9a5r9PF3ecnzFToouJFPr1SfIP3kZ3ZYEqo3pR3Dn+5DMFky5BZx7YUE5IVZKti3Hc9DLsj5P70XSpuLeaQDW9GtYLsnvo7k1auf0KROUxu/+7ZnwzVbZtlrXp15kMAu3HpPH1MaxTP5XW2av3sfyP8/K8ftxxzG1aMmn+L9Z6iPbN2JJuob34NGYL53HdGQ/Xv96DPc2dj4y385HqmIss1oxfD8f3ynye0KmPZuwplzH89GRWK5ewHz8AB59BuLeooP8hKcgH8NX7zv06ZVpS2m8YgaCRiTj5x0YEpOoMeVJCk5eIntrAlGj+xHUuRmSyYw5t5CL5fvEasW4ejHez7wl1+PwH1gzkvDo+xSWpEtYzh7Gcv4YmgbN8Xl1gexnv3+L1/ApZbosWPApgfPkMaR4izyG+IwYjTmxdAwZ8Bge95fGvfx88j9w1EXxTwvweWkugiBSsm8L1tTrePYfjuV6IuaTB7GcTcCtSSt8Zy2R8/+6BKkw30EXTVa8IW+/vmIHhgvJxL76BAUnLqPfmkD06H4EdWmG1WTGkltI4guOuxIe2XGENj3a8M2f31BsKOaTlz8pS1uweQHP930egNHTRtN9QHc8vW0bSlgtVlbPXMYz309D1IgcXrWTjIvJ9J30OEmnr3D2j6PUaFaHUYtfxjvQlyY9W9J30r95v/cUhYx1M79lzPevI2pEjqzaRcbFZHpN+jfJp69y7o+jVG9Wh+GLJ+Md6Eujni3pNelxPraTofLPRfi7dv8QBKFAkiSH7boEQViBvOFDGJABvClJ0teCIIQCq4BY4DryluO3e+4qGda9f5ssFeP9iLyts+5fXW+Ts2JCN+zGsOLN22e8VT0Gz6Lo47Eul/eZLL8Dkt6lm8syovbsAqBwzlCXZfi+8SOGVbNvn/EWeA+aSfERxzMs7hSvNvJ68pLrjk/H7hSPmi0xaR3vaN8N7mF1yOzpul1FbN8NgPH8bpdleMbJ1y9Jcnxiead41IjnWvNeLpcHqHViG4mNnG2YeWc0OCefV5YzuPttclZM0IqdAByMedRlGe1TV1cqVkBpvFg+4/YZK8C7dOtzw9LJt8l5CxlPfwxAweuuvw/jN+83ZtYa4nJ5gNnXllP02fjbZ6wAnxfls9MSqg9wWUbrZPmcmTN1nG9PfCc0vfIfrsZXzkdqn9zGrsi7fM/Kjm4ZvwBQ9N4ol2X4TF0GgGHNu7fJWTHeA19DP7ByPhKyZneVjGW5w3q6LCPwB3l3t/3RrvtIh7TfKJjseDPvTvH7WD47KKuX6/oM3yaPH3lje98mZ8UELNnKvqjKvZvUMf1X+tXod/uMFbApaRMAk2s96bKMj6/JZ3RNreV4U/FOee/aCpA3j7znKfrw6Xt3DRzg88rSe1KPf9uTJmcTptLvnVqsJEk6wPUop6KioqKioqKiovJ/Hene3db7XuZv3z1PRUVFRUVFRUVFRUXlXkadNKmoqKioqKioqKioqNyC/6WNIFRUVFRUVFRUVFRUKsM9vK33vYz6pElFRUVFRUVFRUVFReUW/G275/2X+Ec3TkVFRUVFRUVF5Z7hntz1rTxF7426p38f+0xddk/q8R+/PC9nSA+XywYt3wFAcjvXZVQ/tKNKtpvNf9b17Tj9F8rbcd5o7frmg7EJ26tEhu7hSm7J/Ptufol2fSvjx9OWA1R6i+vKbBcO8pbhldm23D2sDkCltnrtmP4rUPntlK/c5/p2tQB1Tm/ldO2HXS5/39XfATjf4EGXZcQlbgRgc6TrW9b2zfi5Uttbg7zFdVVsQ1z0wWiXZfhM+UaW8em42+S8hYyXFvN9NdePJwAYnvIjhbNd93XfmbKvV0W82B75hMsyemasZEfkIJfLA/TIWMWkSmyn/EnpdspVYVuVaUuPjFVVEi+qYvv1U7VcjznNrskxZ23UUy7LGJD+E5ca93G5fL2/tgCV7w+Ac/Vdj52NLm5kfo3K+frEpB+ZXst1Xb5z7ScABsa63qdrbsh92r2667/XdiZvc7nsfxvJqu6e5wrq8jwVFRUVFRUVFRUVFZVboE6aVFRUVFRUVFRUVFRUbsE/fnmeioqKioqKioqKikop6u55LqE+aVJRUVFRUVFRUVFRUbkF/yeeNLk1a4P3sOdBFCnZtRHj7ysU6R5d+uA1eBxSthYA49a1lOzaqMjj2b4NQZOfRxBFCtdvJP97pYybeHfvTOi7s8gYMR7T+URlWofWhEx9FkEUyV+zidxvVjqV4dOzE5Efv0nK4Oco+csmQ9O4FV6PjwdBxLR/MyVbf3Fsa8vOePxrKEgS1pQrFC97X5HudX8bgl95DkSRwrUbyfvuZ+ft6NGZ8PffIn3YBErOJVa5DPeWbfEdOxFEkeJtGyj+9SdFumfPvviMmoBVlwVA8YY1GLduKEuP7N6MFrOHIWhErvy0iwsLfleUrz+uH3We6o7VbMGoyyNh8hKKkrWKPD6dWhExbQKIIrm/biZ76Sqn7fDr1ZGYz2dw/d8TMZ69qEjzaNMWv+dK27FxA0U/K9vh1acvfs9MwKKV22FYt4bijbZ2vDH3Y/bsO0xIcBBrf1zkcG1Jkpj36SL2HjiCl5cn70x/mcYN6ynyBHVvTp23R4FGJGP5dlIWrFWkRw3vTdSoPkgWK9bCYi5NWYwhMVnZxi4tiZ75DIgi2au2ol30q1NdBPTtQOzCaVx65CWKT19SpHl3bE3o1AkIGpG81ZvJ/dq5ffs+0InIT2aS/MRzlPyl1Kdfl5bEvDlWrsfKbWTdoh41v3ydS/0nKWV3bkXE9HEIGpGcX7ag/8rRRwD8e3ek2oLpXHv0RYrPKOsQ1j2eRnNGgEYkefkOrs5fr0ivMfwBYkf3RrJYsRQWc+aVJRQmpijr160FsbOeBo2IdsU20r9YrUgPH9qH8JEPgsWKpdDA9akLKb5o6xNNXEs8BzwNogbTwa2Ydvzm0Aa3+I549BmMBFhTr2L88SNFulirKR49nwJBwHxqL+bDypjm3v1JNLFxpcI8EHwCMMx/XimjZhM8ug4CUcR85k/MCVuUMro8jqZGQzsZ/hi+VPZJTLdmtJk9DEEUubRiF2e+UPpqo2f6UX9wNySzhWJ9Pvsnf0Vhis6mi7rN8OgzTK7D8V2Y9inLA2gat8Oj62Ny3Mu4gXHNF4r0qogXId3jaTBnJIJGJHX5Dq7PX6dIrzb8AaqP7lNmF+df+crBLkK6x1N/zigEjUja8u0OMmKG9yonYzEmkXT8AAAgAElEQVRFdjLiusYzcOYIBI3IoZU72P6l0jbrtI1j4MwRRMfF8sPEzzm56ZCjrqrAtirbDqh8vAjp3px6dnW4MV8Z92KG9yJmdN9SHyvmwiuLKSof97q2pNrMsaAR0a/cRtaXFcebWote5+LDkzCUi3sR3Ztx39vDETQi15fv5GI526o77kFqDumGZLZSosvj2KSvMDiMRa0Je308aDTk/bqJnArGIt9enYj+bAZJjyv9tCr6w7dzKyLfKI2dq7agqyh29ulI9QXTuTrQMXbGdmtGl7dkP/trxS6OLlTqovnYfjR5shtWiwWDLp/tr3xFvp2vA9Tv2ox/zRyOqBFJWLmTPV8qZdRqG8e/Zg4jMi6WlRPnc3bTYYc6jpn1DK26t8JoMDL/5c+4cuayQ54699XlhY9ewsPLg6M7jzqkT5z9LO16tKXYYOS9SR9w8cwlhzxjXh1F738/gH+gPw82dH2TFZX/Hf62SZMgCBbgdGkdzgEjJEkqEgThG+AhIFOSpKZ2+R8H3gIaAW0lSUq4swuJeI98kcJ5U7Dqs/B/+0tMx/ZjTbmuyGY6uAvDd587lyGKBE95kayJU7BkZhHx7ZcY9u7HfFUpQ/Dxxu+JxzCe+cupjNBpE0kfNxVzhpaYnxZQtOsApis3HGQEDhlI8alzDu3weuI5ij6fhpSjxWfqZ5hPHcKabisvhMfg0ecJij58GQwFCH6Bju2Y+gKZz72KJSOLqO8XUrTngNN2+D/5KMbTzttRFTJ8x79E3oyXseqyCPx4MaZD+7AkKWWU7N1B4eLPnJQXaDl3JHuemEdRmp4HNr1N6tZj5NsNAjmnr/NH3zewGEqoM7wnzd4YzMHx8xV1iJjxHCljpmHK0FJz1ecU7jxIyWXH/ggaPgDDyXL9USrD/4WXyH71ZaxZWQQvXIzxwD4s15XtKN61g4L5TtoBDHiwF0891p9pb3/oNH3vgSPcSE5l48qvOXX2PG9/uIAVSz5V1KHOvKc5O2g2JWl64je/i35rgmJSlLV6L+nfbwUgpHdrar81gr+eekchI2bWBK4OfwNzuo46az8h/49DGC8lKZvr603oyP4UHT/vVBdh058n7ZnXMKdrqfbzfIp2OrfvgKEDKa5AnzGzx3N12AzM6TrqrvuYvArqETbqYcd6iCKRbz5L0qjpmNK11PrtUwq2H6TksmP54BGPYDjhrB0Cjd8dzZFB71CcquP+LXPJ3HJU8eM3dfU+kr7/A4DwPq2ImzWMo4PfVdQjds44Ep96E1OajkYbPiBn62HFpEi3dg9ZP8oTkMBebajx5mguDp1dqiQRz0fHYVg0EylXh/ekjzCfPYyUYWuHEBaNe8/HKZo/FQyFjr4uCHj0Gopx1UdI+Xq8hs3EcvkEki61LItp58+YSv92a9ETMTLWUUb3wRhXf4pUkI3X4NexXDmFpE+zydjzi01GfHfEiBpKEaJAu3dGsG3wuxSl6Xlw42ySth4l96KtHvoz19jQbwaW4hIaDO9JqzcGs2fCAlsd+o2k+Md5SHl6vJ5+G/OFY0haW38IIZG4d+yPYdlbUFwEPgHKdlRJvBBo+O5ojg96B2OqjjZb5qHdkqCwi/TV+0gptYuwPq2oP2s4JwbPKydjDMcHzcGYqqP1lnlkbUlQ/HjNWP0nqd9vs5MxgpOD55bp8rHZo1k09B1y0nVMWj+XM9uOknHJVj47VcdPr3xJ97EV7IhZFbZVyXbIMioZL0SR+u+O4eSgtzGm6mlV2h/2kyL7OoT2aU29WSM4NVgZ96rNHs/VoTMwpeuot/5j8rZVHG8KncY9gfh5o9g3aB6GNB3dNs8hvZxt5Z65xu4+sm3VGvEATWYMJmGcciwKf+M5Up5+HXOGlhor51O48yAmZ2PRsAFOdFE1/RH11rPcGCnHztq/fUr+joOUONFFSAWxUxAFus0Zwdqn3qUgTc8T/5nNlW1Hybbz9awz11j5rxmYi0toOqwnHacPZvOzCxQyHp49imVD55GXrmPC+jmc23aMLDsbz0nV8usri+hcgY237N6KmFoxPNtlHA1aNGTcOxOY+sgrDvnGv/MsC6cuIPH4BWZ895YirV2PtlSrXY2hnUbSqGUjJs17gWcffsFBxv4/DrLm23X8uPdbp3W5p5HU3fNc4e9cnmeQJKl56cSoBBhf+v23gLP9oM8AjwJ77uYimrpxWDNSsGalgcVMycEduLfqcFcV9Wgchzk5BUtqGpjNGLbtwLuLo4yAcaPJ/2EFGEsc0jybNsSUlIo5JR3MZgo378Knm6OM4OdGkrNsJVI5GWKtBlizUpF06WAxYz66G7f49sp6duqLaffvYCgAQCrIVaY3icOclIIlRW5H0dad+HR1rEPg+FHkffczUoljO6pChlv9RljSUrBmyDKMe3bg3q6TQ76KCGlRl4JrGRTeyEIyWUhad5BqfVop8mTt/wuLQb62/tglvKNDFOlezRpiupGGKTkdTGbyNu7Gt8f9DtcKe3E42Ut/QTKaHNLc4hphTknBmlbajp078Oxw5+0AaN38PgID/CtM3/nnQfr37YkgCMQ3bUR+fgFZWn1Zun+LehRfTcd4IxPJZCZr7T5C+rRRyLAUGMr+Fn08Ha7hHd8A4/U0TEkZSCYzuf/Zg3+v9g75IiYPJWvxr0514XlfQ0w3UjEnl9r3pt34dne0i5DnR5Dz9UqnduETX58S+3r8voeAXu0c8kVOHkLWot+wlquHV7MGlFxPxZRU2qcb9uD3gLM+HYZuyS8OPgYQ1LIeRVfTMVzPRDJZSF+7n8i+rRV57PXp5uPpcBqcb/P6GK+lUXJDbod+3Z8E9Va2w2onQ+PjBXbn5Ymx9bFq05D0GbKvH9+LW1Nleff2fTDt2wCGQsDR18XoOkjZmUi5WWC1YD5/CE295g7tLatDo3aYzymfSohRtZFyM5HytLKMxAQ0deMrltGwDeYLRxTfhbaoS/61DApuZGE1Wbi27iA1yvlqxv5zWIrlvtAevYSPna+K1epizc5AypHbYTl7ELeGyvJuLXtgTtgmT5gAivIU6VURLwJa1sNwNYPiUrvIWLufsL4V+5nGx5PyZyAGlNrWTRmZa/cTfksZSruIbV4P7fV0dEmZWEwWjv++n6a9lbaZnZxF2vkbDte+SVXYVmXbAZWPF3J/3KyDmcy1+wi7hZ866w+f5nK8KSmNNzm/7yGgt5N487Icb5zFveAW9Si4mkHRDVkXyWsPEFXOtrT7bLaVffSi41hkrwuTmYJNu/BzMhaFvjCC7KWrHOJWVfSHt5PY6d/TsQ7hLw1D99UvWJ3Ezsjmdcm5lkFeqa8nrj9Ind5KXaQcOIe51NfTj13CN0qpi+rN66G/nkF2qY2f+v0AjcrJyEnWknE+CamCH/1te7dn52/ycTGJxy/gG+BLcESwIk9wRDDefj4kHr8AUJb/Jh1738/WX+UbIOeOncM3wI+QCGVdb6bpM/UO36v8c7lX3mnaC9QDkCRpD+BghZIknZMk6cLdChZDwrDqMss+W/VaxOBwh3zubTrjP28JPi++iRCiTNdEhGHJsMmwZGrRhCvzuDesjyYynOJ9jsshymSkZylkuEWGKfJ4xNXDLSocw17Hx81iUBjWbFt5a7YWITBUkUeIqIYYWQ2flz/EZ8onaBorg43cDpsMc2YWmghlHdwb1sct6jbtqKQMMTQMq9auT3RZaELDHPJ5dOhK4Off4PfaLMQwm769o0IosnukX5Smxzsq2KH8TWoP7kb6zpOK79wiQjHb9Yc5Q4t7pFKfno3l/ijc7dgfAJqwMKxZdu3IykIMc2yHZ+euhCz5hoA3ZyGGO9rercjI0hFlp9/IiDAysmxLOzyiQyhJtX0uSdPhGe0Y3KNG9aXlwQXUmjGMK9O/VqS5R4ViSrPTRZqjLrya1MU9OoyCnc4f8LpFhJXTZxaacjI8GlVs3wBuUaGY0mxtMaXrcI9yVo9w8p3Uwz0yFHO6rbw53VmfyuULdx0pX1xOjwrBkGqzreJUPZ5RjvqMHdWbLoc+o8GMIZyb/q2yndEhlNi1oyRdh4eTPgkf0Y+mfy6i+vQR3Ji5tOx7ITAUKcdWXspx9HUxPAYxvBreE9/D+8UP0MS1VKQLfkFI+bYwKuVnI/g59xEhIBQxMAzrDeUdbME3CCk/WynDN8i5DP8QWUaS8g60T1Qwham2ehSl6fG5ha/WG9yVFDtfFfxDkHJt/SHl6RH8leXFkCiE0Gi8Rr2J1+hZaOo2U6RXRbzwigqh2M4ujKk6PJ3IqD6qN/cf+ox6M4aQWM4uPKNCMDrIcLSLaqP6cP+hz6k7YwiJ05eVfR8UGUKOXfncND2BkY7lb0VV2FZl2wGVjxeOddDjWS5WAMSM6kO7Q/OpM2Mol6Z/o0hzjwzFZBc7TWk6h3jh3aQuHhXEGwDv6GBlvEjTO0yK7Kn5VHcydihtSxMZisleF+lahzHVs1QXRXvuRBcu9EdUKGZF7NXiVn4MaFwXt+hwCiqInb5RwRTY+XpBmh6/W/hZkye7cn2XUhcBkcHk2rUlzwUbD40KRWfXFl26jpBythESFYou3T6PcrlkWFQYmam2sV2bpiUsynFsV/m/x98+aRIEwQ3oh7xU72/BdOwAeS89Rf7rYzGfPorP+NfuToAgEPTiBHI/+9L1SggCIa+MR//RYtdFiBqE8GoUfTIVwzfv4jXkRfD2vas6BE8eT/Ynju/W/FdlACWH95M95glyXxiN6UQCfi9Nc0lO7GMdCY6vw4WF/7m7goJA+NRnyHpviUvXvYnxwH50Q55AP3Y0JUcTCJjqWjsqS/qyzRxr/zzX5vxIjUl3eSCuIBA9/WnS3/n69nlvISN0yjh0H35VKRnRb4whzdV6CAKRr48l893K9SnAjWVb2dPuRRLn/ETdSQNdkpH13SbOdBpP8tzviX7hLg/rFDWI4dEYvphG8Q8f4vn4c+B1F75uhyauLebEBIe7z3clo2EbzBePVUpG7Uc7Ehpfh7Nfbrh9ZntEDWJIJMXfzcG4egEeDz0Nnj4u1cHleFFK8rKtHGj3Ipfm/ETtSY+6JCNl2RYOtHuBy3OWU2vSYy7JqBRVZFuVakdVxAsgddkWDrWbyJU5y6npQh2iZ4whtTJxz47qj3UkKL42l1wYi8KmPoP2/crporL9ETFtLJnzKh87ARoO7EhEszocW3SXvq5SdVile/vfPcrfOWnyFgThBJAA3ACqJDIJgvCMIAgJgiAkfPXVV/KTpdCIsnQxRPnEBkAqyAOz/Oi9ZOdG3GrXV6RbMrVoIm0yNBFhWLJsMgQfH9zq1iZ84SdErfkJj6aNCftwDu5xDZQyosIVMswZtrsbgq83HvVqEbX0Q6pv/AHPZo2I/Gw2Ho1lGdYc5RMyMThMcQf2Zh7zqYNgtSDpMrBmpCBGVCvXDpsMt4hwLJl2dfDxwb1ubSIXf0zM+uV4Nm1M2Mdv49GoQZXKsOq0iGF2fRIajkWnvNMj5dv6xLh1A5p6tvKGdD0+1Wx3jnyiQzCkZ1OeiM5NaPTiI+wb8RHWErMizZypw82uP9wiwzBl2PQp+nrjWb8mNb5/n9p/fIdXfBzVFr6FZxObbVi0WsRwu3aEh2PVlmtHXh6Y5HYUb9yAW/0G3A2R4aGk2+k3I1NLZLjtjldJmh6PGNtnj+hQjGkVLxfQrt1HSLmlG6Z0He7RdrqILqcLP288G8RSe8U8Guz5Gu8WDan51Qy87rNtSGHO1JbTZzgWOxk37Tv6mw+osfl7PJs1Imr+bDwa2/RpTtfhHm1ri3tUKKZ0ZT28GtSkzs9zabh3KT4tGlJzyRu2dmTocLO7G+gW5dinHg1qEvvDe9TdsQyv5nFU+3ImXk1tdTCm6/GOsdmWV0wIxvSK9Zm2Zj8R/ZT6LEnT42HXDo+oUEpu0Sf6dXsJ6mNbFiTl6hCCbOWFIEdfl3K1mM8cln1dn4E1KxUxPNqWXpCD4G+7Qyv4ByMVOPoIgFtcWyznHJ8KS4U5iqc6gn8wUmGOcxkNWmO54HgXvCg9G98YWz18okMocuKr0Z2bcN8L/dk58mOFr0r5esWTECEgRPH0C+SnT5YLx2Rd5GQh6dMQQ6PK0qsiXhSn6/GyswvPmFCMTmTcJGPN/2PvvMOrqLa//5k56b03Qu+9hSI1gDSvCFwUBaQLAlIEEQWkSEe9qBQFBdsVKV5FUXovoUgNNQkQCKSc9JB+knNm3j8m5pzJCSAnvD/13vk+T54nZ/bea9Zae621Z8/svfYJ/MvYhUGfgaMVjQfbRVkaWckZeFm09wz24X7y4y0NehK2VVE5oOLxwpoHHwx6tRyWSNkWgV/v1qprxcnp2FvETvtgX6u451SnKjU3L6HecSXeVFv/Ds4Wca8gKVMdL4J9KCjH1/07NqLulH6cKse2TMnp2FvqIshPNaaKrs441K5Gpa/fo+q+r3FsWp/gNe+Wlj+R/tCnY6eKvX4YyxkPq3y7nJqHvsS5WT1C16pjZ54+EzcLX3cL9iG3HB+p3KEhYZOe49dRK6x0kZ2ciaeFLB6PaeMrdn1MZkoGvhay+Ab5klHGNjL06fgGWdYx///5nrWkp2QQEGIe2/2C/Ugr8zVKw/8m/gp7mprJsjxJlmXrRbI2QJblz2RZDpNlOWzs2LGYYqMQgyoh+geBzg6Htl0pPndS1UbwMju6fct2mBLVGzCLrkdhV7kSuuAgsLPDuXtXCo6aach5eST17I++/2D0/QdTdOUaadPfUWXPM1yNxr5KJewqKTRce4WTf8SCRm4+d8OfJ/6ZocQ/MxTDpeskT5lbmj1PiotBDAhB8A0EnR12LTsrEyQLGCNPYldHWZoiuHogBlZCSjNv2i66FoV95UroQhQeXHp0oeDoCZUcCU//k8TnhpD43BAMV66RNm2OKvPdk6BhvBGFLiQUMVCh4dipK8W/Raj7xNvcJw6t26uSRGRejMWtehAulf0R7HVU7tuWxD3q7DdejarS8r3RRAz/F4Z09R4HgMLL0dhXDcGuUiDY2+HxTGfyDpn1KeXmc6vdi9x+eji3nx5OYWQUCRPmq7LnGaOisKsUihhUIkeXrhhOqOUQfSzkeKo9prvqJBGPQniHtmzffQBZlom8ch03N1f8/cw0cy7exLlGMI5VAhDs7fDv156MverlE07VzQ+Q3k+3oPC2XlVecCkGx2oh2IcGItjb4flsJ3L2mx+ipZx8osKGENNpNDGdRlNwIZq4sQtV2fMMV6Kxr2ph3707k3dYbd9xnV7gXq9h3Os1DMOl6+gnzVVlw8q/dEPNR59OZO//TcXH9ZZDiO74CtEdXyH/QjRxYxaVlhdejsGhpD32dnj8oxO5B9R9erPNIG51HcmtriMpvBhFwvgFqgxQ9y/cwqVGEM5VFNsK6teOlDK25WKhT//uzcmPTVKV50XewKl6MA6VlT7x6duBrH3qCYVjdfNDqGe3MAy3zTSkezcQ/UMQfEp8vXlHTFfUkxrjldPoajVWfri6I/qHIKUnm2kk3UbwDkTw9ANRh129NphuXqQsBJ8gcHJFSrTOLiXp7yB4BSB4+Co06oRhuhVpTcM7EJxckJJircrSL8biXj0It8r+iPY6qvVty72951V1fBpWpe2yURwauYLCMr4qJcQqy++8/EHUoWvYFmOMuj9M0WcRq9VXfji7IfgEI2Wal9Y8iXiRU2IXTiV2EdivHWl71Eu2nC3swq8cu1BoBJfSCHgEDd/uLVQ07kXewr9aED6h/ujsdTTv046r+6yzfj0MT8K2KioHVDxe5FxQ4p5TSdwL6Nf+kTwUlOEhP/JGabwQ7O3w6tOJ7H3qeHOtxRCiOrxCVAcl3tx5ZZEqe17WxVu41QjCpUQXof2eQr9X3SeejarS7P3RnBr+L4rSyhmLSnWhxC233uFWY9Ht9gOJ6z6cuO7DMUReJ+m1eU+0PwrKiZ05ZWLnjTaDuNVlJLe6jKTgYhTx49SxMzkyFq9qQXiU+Hqd59pye5/a1/0aVqXLslH8OmoFBeX4WULkLXyrBeFdYuNN+jxF1GPY+LTeUzi95xRdBnQFoE7zuuTn5JOZop68ZaZkUpCbT53mSubP3+sDjOk5jojdEfR4/mkA6reoT15OnrZ3SQPwv5ByXJIo+GoVrm8tB1FH0ZFdSAl3cBowAuPtGIznT+DY85/Yt2gHJhNSXjb5a5eraZgksj5Yhd/K5QiijrxfdmG8fQePsSMouh5D4bET5d+7DI30pasJ+nQpiCI5P+2h+FYcXhOGU3Q1RjWBepAchVs+xWXiIiVV7Mm9SEl3cXh2KKa4GEyXT2O6dg67+i1wmbMOJBOGHzdAXo6Kh4z3VxGwajnoRPK276I4Ng7PV0dQdD1aNRF8mBwVpiGZyFv7ER7vfgCiiGH/Tkx37+A8ZBTGG1EU/3YC5z4DsG/THkwm5Jwccj82ZyeTTRIXZn1Fp01vIehEbm8+QnZMAg3fHEBG5G2S9p6nyZzB2Lk68dRnUwDIT0gjYsQKlRypiz4hdP1iEEWyf9xL0c04fCcNpfDKDdWg9TA5clZ9hNfyDxBEkYJdOzHF3cF1xCiKo6MoOnkC5/4DcGzXHrlEjuz3lqlIvDlvGWcuXCIrK5tu/V5mwuihGI3K27cX+/+DTk+14tjJM/QeOApnJycWzlKnc8YkETtrPQ03vQM6kZRNBymIjqfKjBfJvXiLjL1nCR7VG69OTZCKjZju5xEzeZUVjcT5a6n29QIEUSTz+30Ybtwl4PUhFFy+Qc6B8vcglaWRtmQ1QWuXIOhEcrYp9u392jAMV2PIP/wH9GmSSJy3lurfvKukHP9+v8LH1BI+9j+CD5NE8oJPqbxhEehE7v9nL0U37+I3+WUKr9wg92D5e+wsIZskrs38krDNsxB0IvGbDpEbHU+tGS9wPzKW1D3nqDK6J74dGyEbTRTfz+Py5DLLck0Sd+d8Tp2N80DUkb5lP4Ux9wiZPoi8yJvc33eGgBHP4NGhKbLRhPF+LrenWmRXlCQMP67Deex8EEWKf9uPlHwPh16DMd27ienqb5iizqOr0wyXGauRZYmiX76CfAtflyWK9n+L4/PTlFTdl48jpydi374fkv4OplvKBMquXhtMUQ/QqyxRdGgzjv2ngCBivBqBnJGEfds+SClxmGIvKTTqtsIUXf6eD9kk8ds7X/P0dzOUlONbjnA/JoGm0weQHnmb+H3naTlnEHauTnRep2SnyktI59DIFWYedn2F05C3FB4uHkFOTcA+fABS4m1MMecx3bqErmZjnMe/B5JE0f7vSpPh/M5DReOFbJKInvkFzTfPAp1I0qbD5EXHU2PGC2RHxpK25xyVR/fEu2Pjkj7N49rkT6x0ETPzC5ptnq2kLd90iLzoeKrPGEhO5C3S9pwjdHQvCxq5XJ9sTp0umSR+mPslr34zC1EncnrrIfQ34uk19QXuXY7l6v5zVG5Sg1Hr3sDZ05WG3VrQa+rzLO/x5hO1rYrKAVQ4XsgmiRszN9CkhIekTYfIj46n2owXyYm8Rfqes1Qa3buUh+L7uVyfvFpNxCSROHctNb55F3QimVuVeBNYEm+yHxVvSvi4NOsr2m16W0k5vukwOdEJ1JvxPFkXY9HvPU/DuUPQuTrR+vPJJbaVzunhFincTRKpi9cQ8vkSBFEke5syFvlMHEbh1RjyHzEWPan+0L/7KZW/WKSkHP89dk55mcLLfzx2HpnzNc99OwNRJ3JtyxEyYhJo88YAUi7d5va+83SYPQh7Fyd6r1V0kZOYzo5RZj+TTBK/zP2KEd8o+jy/9TApNxLoNvV5Ei7HErX/PJWa1GDIuqk4e7pSr1sLupVZbn7u4Fladgnj02OfKSnHp5tj64pdHzOtt+Lj6975tDTl+PkyKcdPHfyNNl3b8O3xrzEUGlg+zZzd9vM9axnTU8lb9ursV+jWryuOzo5sPaM+bkTDfyeEB2XZ+f9+Y0HIlWXZrZzrm4BwwA9IBubJsrxBEIT+wCrAH8gCLsqy3PMRt5GzhnR9RJUHw2ujklElvo3tNEJPH+R20+42tweoHrmPnAm9bW7v/skuAO6GdbOZRpWzB54IjfQ+nW1uD+D7yxG+Dx5ic/sXkjYCEFO/vASNfwx1ru8mpVvF5Ag4cITiNOs3838U9n41AIgIesz9SRZor1fOJLlS4wHpif8AGsX+SmzjHja3B6hxeS+Xq/exuX3j28o5HlF1nrGZRr0Y5Qyj3YEv2UyjV/Jmzob2s7k9QFj8T+ROs/28D7cVyrk9+e+PspmGy5vKhvn8j161ncbr6/im0ss2twcYlvAteQts93XXuYqvP4l4cSDwRZtpdEvewsHAgTa3B+iavJWp1Wy3zQ/vKGfpPQnbqogsXZO3PpF4cTjwMfcAWiA8WTl/6FI122NOkztKzPkpaLDNNPrpv+Nmg0c9wjwYta4pRxZUtD8Arte2PXbWv7GTVZUr5uuT7n3L7Gq263LxHWXC0r+K7X267a7Sp11CbX9eOxS/D0CwmcD/IfLmD/rrbhwCXOdv+kvq8U/70lTehKnk+qAHXN8GbPv/ypQGDRo0aNCgQYMGDRo0lMGfnj1PgwYNGjRo0KBBgwYNGv7K+O/f06RBgwYNGjRo0KBBgwYFf+G03n9laF+aNGjQoEGDBg0aNGjQoOEh0CZNGjRo0KBBgwYNGjRo0PAQ/GnZ8/6P8F8tnAYNGjRo0KBBg4a/DP6SWd/KIm/OwL/087Hrwq1/ST1qX5o0aNCgQYMGDRo0aNCg4SH4r08Ekb9mos1tXV5TDsOLa/G0zTSqnt9P/ooxNrcHcJn2OTmTbT9Lx33lrwDcamT7mRA1ryhnQuTNtf2sENcFmynYMN3m9gDOoz+g8NxPNrd3aqmco1MUf9lmGg6hjTFEHbG5PYBjvc5P5IylJ3HWU+HFX22m4dTsWS5Wtf3sF4Bmcds5U6m/ze1bJSgnET6ub5UAACAASURBVCR16GIzjeDjh4CKn8dTkXPMQDnLrKI+BpA3v9yTG/4YjfmbAEgOD7eZRuDhw4yvVrGziT69s5Wccbafp+a+djfAEznT50TwAJtptEv64Yn4yJM4jyd7tO1n0Hhs2AdAwc/v2UzDue+MJ+IjT+J8o+Qutp+1F3hIif/7KhAvuidvIXNAuM3tvX84DFT8+QQgraftuvDbc4SjQbb7GEAn/fc8W+UfNrf/9e4OAEZWs91Pv7zzAwALq9p+rtucuI02t9Xw98B//aRJgwYNGjRo0KBBgwYNJdCy59kEbXmeBg0aNGjQoEGDBg0aNDwE2qRJgwYNGjRo0KBBgwYNGh4CbXmeBg0aNGjQoEGDBg3/I5Al6c9m4W+J/4lJU8SdNN4/Go0ky/RrWIlRYdVV5R8cjeZMfAYAhUaJjPwijo1Tbyp3atcKn+kTQCeSu20X2V9tLvdeLl074v/BPJKGTKDoeoyqTKzWEIfwl0AUMV4+hvHMblW5feeB6CrXK/nhgODsTsEnU0rLdfVb4PTPsSCKFJ/cS9H+/1jd3655Bxx6DwZZRkq4TeE3H6jKnduH4ff2OASdjuwfdpG1YWu5crg+3YGgj+YQ/+JEDFdvqMp0tZri8MxwEESM5w9SfGy7VXtdw7Y4dHkekJH0dzH8Z5WqPCI2hfcOXEGSZfo3qcKotrVV5e8fuMKZe+kAFBabyMg3cHxKb3P7yGiWf7MdSZLp36UVo59T91diaibzPvuezOw8PN1cWDLhRQJ9vVR15ry/hqOnzuHj5cm2DR9aySDLMsvWfMGx0xdwcnRg0YyJNKhTQ1Xn+PkrLP98C5Ik8c/uHRj9fG9VeWJKOnNXfU3m/Rw83V1ZMnU0QX7eqjpeXZpRY+FI0IkkbzxAwmp1oougYT0IGtkT2SQh5RVy8811qvJ3lqzgaMRv+Hh78dO3a8uVY+lHazl28gxOTo4snv0GDerWUtWJuBjF8q9+QpIk+ndtw+h+6s3aiakZzFu7xazPiYOt9OneuQWV5r2CoNORvnkvKZ/+YMULgGfvp6i+dibRz06j4PJNVZlHeHOqLBiNIIqkbtqPfs2PqnL/oT0JGN4bJAlTXiF3ZnyiKnds0wqPKRNB1JH/6w7yvt2kKnfu3RP3CeOQ0tIAyPthGwW/7lTV8enSlDqLRiDoRBI3HiRu1c+q8krDniZ0lNIfprxCoqZ/Rl5MgqqO01Ot8J7+GogieT/tJPvr8uOFc9eO+L83H/3Q8ap48SR8TFerKQ69hinx5vwhio8/gEb4AJBBSo7D8MNqVblD69a4T5wIOh0FO3aQ/913ajl79cJ93DhMJfos2LaNgh07VHUadG7KwLkjEXQiEVsOsPdTtT7tHOwYvmIiVRrVIC8rh/UTPyIjPtXMY4OWOA0cr8S9iN0U7bGOWXYtO+Lw7MuKHPGxFH6xXFXu06UZtRYpPCRtPMDdVWofCxnWnZBRvaCkT6OnryM/Jl5Vx6tLM6ovGAU6kZTvDpCwepuqPHBYD4JGlNDIL+TWm2spKEOjoj7i06UptS3kKGubIcO6l7HNdeSXsU1dozCcBk1AEESKju2iaNcWa32GdcKx7zBlHLkXS8HnS1XlEdHxvPfzKSRZon/ruozq0lRVnpSZy5wtR8kpNCBJMpN7t6Jj/cqqOhX1EZcOYfjNHAc6Hdn/2UXW+geMZd07EPzxHO69YD2WObRqjfvESaATFfveVMa+e/bCfdx4TGmKPRZs20bBTrV9+3ZpSt2SeJGw8SB3yvRJaEm8wCRhzCvkejnxwq5Za1xGKXHLcGAHhm1qPhy69MJ56DikDMXPDLvUtvcknk/sw1rjOm4Sgk6kcNcOCraqeXDs3gvXV8YjpZfoYvs2DLvVuvDu0oyaCxX71G88wL0yY1nwsO6EjOxVap833rT2M4Cx775KWJcwDAUGPnrjQ25duWVVZ+ibw+g6oCtunm6q6406N2Pw3FGIOpGjWw6w81O1ruwc7BizYjJVG9UgNyuHTyeuIN0i3gDU7NyEnvOGIuhELmw+zIlPf1GVV2ldjx7zXiawXhV+nLSa6zt/s+JPw38n/rRJkyAIJuByCQ/XgeGyLOcLgvAF8CyQIstyI4v67wN9gCLgFjBSluWsR93HJMksOxzFp/1bEOjmxJAtp+lc3Z+avmZHm96pbun/myLvEp2aoyYiivi8NYmUCW9hTE4l+Ns1FBw5QfHtu2qZXJxxH9wfw+Xr5QmMQ9fBGH74EDknE6chszHdikTOSCqtUnxkK8Ul/9s164oYYDHICCJOL4wnf807yFnpuEz/EOOV00j6e+Yq/iE4dH+B/A/fhII8BDdPKzn833mNxDEzMerTCN2yirxDpyiOtZbD8+V+FEY+QI5nR1H49WLk7HScXl2CMeoccqp5EBB8grDv1JeC9fOgMA9cPVQkTJLM0v2XWTuwLYHuzgz55hidawVR08+9tM6b3Uq7nk3nbhOVct+ivcSSL39i3cxXCPT1ZPA7qwlv0YCaoYGldVZs3EGfji15rlNLTl+9ycdbdrNkgjorWd+eXRjUtzezl6sfNn/Hsd8uEBefxI5vVnHp+g0WffwZ361ZZubDJLFk3Xd89u5UAn29GTR9CeGtm1KzSkhpnX99+T19urSlb9d2nL4Uxcp//8iSqaNVfVJj6StcHbiAoqQMmu5eRsbes6qHrdQfj6H/Zi8APj3CqD5/uIrPfs90Z/CA55i1UD1BLpXj5Bnuxieyc8sGLl2NYuEHq9n0+UdqfX7xI+tmv6roc+ZHhIc1pGZokFmf//6FPp3CeK5zK05fucHHm3ayZOJglRyhC1/l1pC5FOvTqbP9X9zf/xuGG/csWUF0dcZ/5HPknY+2ZlQUqbp4LDGD5lOUlE6Dne+Rtfc3Cm+YdZG+7Sip/1ayX3l1b0WVeSNV7T2mTSFj6puYUlLxW78Ww/ETGO/EqW5TePAQ2R+uLFdXiAJ1l43iwsDFGBLTabVnKWl7zqoecvQ/RpDwjZJ1yq9nS2q/O4yLgyweKkUR77cmk/LaDEzJqQR98wn5R09ivK3mQ3Bxxv2lf2K4fE3NwxPwMQQBh2dGUvjvJQqNMYsxRpdDo0NfCjbML5+GKOI+ZQpZ06djSk3FZ+1aDBERmOLK6PPQIXI+/rhcdQqiwEsLRrPy5UVk6tN5e/tSLu07i/6mmY92A7uSfz+PeeGTCevTjv5vD2HDxI9+J4DToNfI/3gWcmYaLjNXYrx0CinJHLOEgBAcer5I/vtvQH4ugrt13Ku9bDSRAxdiSMygZUmfWj6sJf94nMRvlOxwvj3DqPXucC4NWqyiUWPJGK6+uICipHSa7FpOxt4zKj9N+/EYySV+6t0jjGrzR3B98CIVjQr5iChQd9loLgxchCExnbA9S0ndc1Y1KbKUQ7HN4UQOWmLZITgPmUTev95CzkzDdc5qjBdPqvQpBlTC8R+DyFv6eok+1S9HTJLE0m0nWDumF4GergxZtZ3ODapQM9D8MujzAxfp0bQ6A5+qz63kTCZ+sZdd9S0yzVXUR0rGsoRXZmJMTqPy72PZLeuxzGvoA8YyUcR9yutkvflGiX2vw3CiPPs+SM7K8u0bUaDeslGcH7iYwsR02pT0iWW8SPoxgviSeOHfsyV13h3GhTLxwmXMFHIXTEdKT8V9+VqKz0Qgxav5KDpxiIL1Zj5cJ7xZ2r7CzyeiiNtrr3N/5htIaal4rVpH0akITHfVPBiOHiRvzYN0IVJr6WguD1yIISmD5ruXkr5X7WcpPx4nqcQ+fXqEUWP+cK4MXqwiE9YljJBqIYztNIa6zesyYfFrvNF3mtXtftt/ml+//oXPjnxullEUGbpgDB+8vIAMfTpzty/n4r4zJN4089BxYDfy7ufydvhEWvdpz8C3h/LpxBUWNAR6LRzBxiFLydZn8Mr2hcTsP0/aDXOf3k9MY/sb63hqrO0Z/zT8PfFn7mkqkGW5WcnEqAgYV3L9K6C8PLP7gEayLDcBYoCZf+QmV5LvU9nLhVBPF+x1Ij1rB3E4NvWB9XdH6+lVJ0h1zaFRXYzxiRgTksBoJG/PYZzD21u19ZowguyvtiAbiqzKxKDqyFmpyPfTQDJhjDqDrmazB/Khq9cKY5T57YVYtQ5SahJyejKYjBjPH8WucVs1n0/1pPjYDijIA0DOva8qd2xcl+K7iRjj9WA0krvrMK5dn7K6t8+k4WR9sRW5qBw5QmshZeiRM1PAZMJ0+QR29cJUdezCumI8vVd5EAPIy1aVX0nKpLKXK6Ferkqf1A/h8E39A3Wx63oCvepXMre/eY/Kgb6EBvpib2dHr6eacvicelC9lZBM64Y1AWjdoKZVOUBYkwZ4erhZXf8dhyLO8FyPcARBoGmDOuTk5pOanmnm48ZtqgQFEBrkj729Hb06tuLQb5EqGrH3kmjTWPl62LpxXQ6dVpe7N69F4W09hrspyMVGUn+KwKdnK1UdU25B6f+ii6O1HM0a4+nhbnW9VI7jp3iuVzdFjkb1ycnJJTUtwyzHzbtqfbZrzuEzV1U0FH0qX6daN6zF4bNXVOUuzWpjuJNE0b1k5GIjmb8cw7N7Gytegt8YQsraH8r1EdfmCg3DXYVGxs/H8e7ZWlVHKqMLy7O57evXwxSfiClR8dOC/Qdx7GDtpw+DR4taFNxOpjAuBbnYRPJPJ/Dr9eD+0Lk4UvaAcIeG9TDeS8BUEi/y9x7CpXM7q3t5jhtJ9tebrfzsSfiYWKkMjSsnsatbhkbLrhjPPJiGfb16mBISMCUpchQePIhj+8fTZ7VmtUiN05N2LwVTsYmzv5ygaQ+1Ppv2CONUSfrk8ztPUa+d+YWJWK0uUkoScppeiXtnjmDXRB2zHDr0pvjIr5CfC4Cco457Sp/qS/rUSMpPEfj1UuviUX3q1rwWBXf0pbaZ9vPxh/qpzsUJytCoqI94tKhFfqkcJlJ+OoH/Q23TmgddjbpIKYml+iz+7TB2zdW2ad+pN0UHt1voU/1u8sq9VCr7eRDq64G9nY6eTWtw+GqZB3QB8goV/nMLi/D3cFGVV9RHnCzHsmJlLHMrZyzznTyczPVby4039vXqY0osa98drOo9DJ4tapF/O5mCkj7RP7JPHK37pFY9JH0CUrLCR/Hxgzi0+uN+9iSeT+zqKrqQ9AoNw+GDODz1eLpwb17iZxZjmW/Ph/sZWGdwa9OjLQd/OAhA9IVoXD1c8Q7wtqoXfSGazJRM1bUazWqREqcn9V4ypmIjv/1ynOZl4k2LHq2JKIk3Z3eepH67xqrykGY1ybyTTNa9VKRiE1d/OUXd7i1Vde7Hp5ESdQ9Zy0D3P4e/SiKIY0AtAFmWjwIZZSvIsrxXlmVjyc9TQOgfIZySayDQzfygGejmSGqeody6idkFJGYX0CrUR3Xdzt8Poz6l9LcpJRVdgK+qjkO9WugCAyg4frpc2oKbF3KOWSw5N9PqDV5pXXcfRA8/pHtRpddEL1+kLPNkT8pKQ/BU8yAEhCD6V8Ll9fdwmfYBuvot1HIE+GLUm2kYk9OwC/BTy1G/FnZB/uQfLf9zs+Dug3w/3SxHdgaCh1pfom8wgl8wTq+8i9OYhehqqZdupOQWEuTuXPo70N2JlJzCcu+XeD+fxPv5tK5i5jMl8z5BFkvDAnw8Sc5QPyjVrRrCgd+UB/sDZ66SV2AgKyev3Hs8CClp6QT5m3Uc6O9DSppZ9uT0LAL9zLIH+nqRkq4O4nWqV2b/qQsKH6cukFdQSFZ2bmm5Q7APRYlppb+LktJxDFbrEyBoZC9anFpNtTlDiZ294bHkSE5NJ8iinwMD/EhONd8zJaOMPn09Sc4sT5/K2VYHfrtspU/7IF+Kk8w0i5PSsA9S26dzoxrYh/iRffBsuXw6BFnroiwNgIDhvWkc8SmV3xnO3bnrS6/r/P0wpZj9VEpNRefvZ9XeqXMn/L5aj9fC+YgB/uqyIB8KE819bEhMxzHIesAOHdmDp05/TK05Q4iZ/ZWqTBfghynZws9SUtGV8TP7urWxC/KnMMI6XjwJHxM8vJGzLWmkI3h4l6ERhOAbjNOo+Ti9ssCKhujvj5RqEXNSU9H5q/UF4NipEz4bNuD57ruIZcq9An3ItNBnZlI6XoE+D6wjmSQKcvJx9VZeAojevkiZZeKed9m4VwkxsBIub/4LlxkfomugfsBxDPLBoOrTDBzLsauQkT1pc3oVNea8zM3ZX1jRKEqwtM0MHMqhETSiFy1OrqHqO0O5/Y6aRkV9xFqOdByDrGNFpZE9eer0SmrOGULM7C9VZYKXH1KGWZ9yZhqil9o2xaBQRZ9vf4TLrJXoGqkffFPu5xPk6Vr6O9DThZRsdWwd170FOy7cosfiTUz8Yi9v91VPaCrqI7pAX4otxzJ9mlV7x0eMZaKfH1LZeOFnHS8cO3XGZ/0XeM63tu/y+6T8eNH+9MfUnjOE6DLxQvTxR0qzsPGMVARfaz9zaNsJ9xUbcJ3+rqr8STyfiL5+SKkWukhLRSxPF+074/XpF7i/U44ugsvoIikDh2BrHwke2ZNWp8r3MwDfIF/Sksz6SNen4VuOr5UH70AfMizGkIykDLwD1W29LOr8Hm/cvM0vHT2CfMhOMsuRnZSBezl9+reHJP+1//6i+NMnTYIg2AG9UZbq/VGMAnY9gN5YQRDOCoJw9rPPPnssXvbE6OlWKxCdKDxWOwQB72njyVxhvZ/EFujqtcZ447zVG6lHsiHqEPxDyF85k4Kv3sfppUng7ProhqUEBPxmjCX9/cfTmxVEHaJPEIVfLMDw/Uoc+o4FJ5dHtysHe6ISebpu8GP3ybQh/+BsVCwDZ37MueuxBPh4IIr/9+b+xojnOXclhoGvL+TslRgCfL1s4kP/5W7Ot53InUXfUnmq7Yfi2oppL/fh7LVYBr71rxJ9ej6eHIJApXdGk7jIepB8XKR8vYvL7cdzb/E3hEx5vEMVCyNOkvLCINJGvELR2XN4zX7bJh7iv9zLyTZTuLnoO6pP/efjNRYEvKeNI/PDCsSLJ+Fjv9P4aiGG/6zCoc+Yx6ZhOHGCtJdeImP0aIrOnsVz5h9aAPBEIYg6hIAQ8v81g4INy3B6+fXHi3slSPxyD6fbTCJ20UaqTrXtkEz9V7s5/9RrxC3+N6GvPyaNJ+QjCV/u4WSbydxatJFqtsgh6hADK5H//hsUfLYE5+FTH1ufuy/e4rmWtdk7exCrR/Xgnc1HkB7nQaiiPiII+L01lrT3KjaWGU6eIG3Qi2S8Moqic2fxfHuWTXTiv9xLRJsp3LAlXgDFZ05wf9xL5EwbTXHkWVwnPYafPaHnk6JTJ8gY/iJZ40dRfP4sbtNt00XSl3s407ZifqZBw5+FPzMRhLMgCBdL/j8G/KHX54IgzAaMQLlHL8uy/Bnwe6SUT75zlORc85el5FwD/q7WS5wA9sQk83aXelbXjalp2AUFlP7WBfhjSjG/iRBcXbCvWY2gz/+llPv64P/RAlJfn2vmKzcLwd38VlBw87Za9vA77Oq2ouiAehOmlJWOvZf5zY7o5ad6G/17HVNcNEgm5IxkpJRERH/z/hpjSjp2QRZvqAL9MKaY38qIrs441KpGyJfKie86Px+CVr2LftI8sxw5GaovXIKHD3K2+sOgnJ2OFH9T4SMrFTk9CdHHvOQxwM0JfY75M31yTiEB7k7l6mL39QRmdld/Pg/w9kSfbtZdSsZ9An08y9Tx4MOpwwDILzSw/8xlPFydeRwE+PmiT7X4spSaQYCfxZcnXy+SLZa5JadnEeCrfiMV4OvFhzPHK3wUFLL/5Hk83MwPpkVJGTiEmN/oOQT7Ykiy+tBairSfIqi5fMxjyRHo74veop+TU9IItPgCE+BTRp/p9wn0LqNPH08+nD5CkaPQwP7Tl1T6LNanYx9spmkf7Eex3qw70c0Zp7pVqbVZWb9u5+9NjQ2ziR1tXs9epLfWhSWNssj4+ThVl75a+tuUmoYuwOynor8/JosvagBytnkJWv4vO3AfP1ZVXqjPwCnE3MeOIb4Y9Oqvh5ZI3naCestfUV0zpaShC7TwswB/TBb6F1xcsK9ZncB1yjp6na8PfisWkjZtjsLjE/AxOTsTwcOShi9ydmYZGhkPpSGlpqreJiv6TC1Dw6zPgh07cHv1VVV5VnIG3hb69A72JSs5o9w6WfoMRJ2Is7sLeZnKvlIpMx177zJxL7Ns3EvDdDtKkSM9GSklHjHAvJzXoM/AUdWnPhgeYlcp2yKos3wMsEZFw6GSpW36UPQQGmk/RVBjmdq2Kuoj1nL4YtA/OFYkbztB3eVjsNzBImelIfqY9Sl4+yFllfGRzDRMsVFgMiGn6ZGSExADzfoM8HRBf9/8ZSn5fj4BHupJ1bYzMXwyuicATasGYjCayMov5PdaFfURU3I69pZjWZCfqr3o6oxD7WpU+to8lgWveZek18xjmZSWhlg2XqQ9OF4U7NiB29hxqvLy++TB8UJfTryQMlIR/Sxs3McfOb2Mn+Wa+Sg6sAOXoWY/exLPJ1J6GqK/hS78/EuT5ZTykGPmoXD3DlxeKaOLpDK6CPahKOnBPpL6UwS1Lfxs5S5lb/GNSzH4BZv14RvkR/pDfM0SmckZ+FiMIT7BPmQmq9tmldTJtIg3uZnmfezZ+gw8LL6QeQT7kPOQPtXwv4W/wp6mZrIsT5Jl2XqhbRkIgjACJUnEELnsovMHoGGgB3ez8km4X0CxSWLPDT3hNaw/fd/OyCPbUEzTIE+rsqKr0dhVroRdSBDY2eHaM5yCIydKy+XcPOK7DSDh2ZdJePZlDJevk/r6XFV2Gkl/B8ErAMHDD0QddvVaYYqNtLqX4B0Eji5ISepsMdLdGET/EASfQNDZYdeiE8bL6k/txssnsaulTDAEVw/EgBCkNPNeIcOVaOyrVMKuUiDY2eHWO5y8Q6fM98jN507HgdztOZy7PYdjuHQd/aR5qoxDUsItRJ8gBC9/0OnQNW6HMeqcig/T9bOI1RooP1zcEXyDkTLNn/4bBntxNzOPhKx8pU+uJ9K5lnofGcDt9ByyC4tpGqKeiDSsGcpdfTrxKRkUG43sPhlJ55b1VXUys/OQSlJqbvj5EP06q9c1/xF0aRfG9r2HkWWZyGsxuLm64G8xKWpYuxpxSSnEJ6dRXGxk97EzhLdWL3HKzM4p5WP9f3bRv5t6rXnOxZs41wjGsUoAgr0d/v3ak7H3jKqOU3WzbryfbkHh7Qfv/yoP4R3asn33AUWOK9dxc3PF32JZYcOalbmrTyM+JV3R54kLdA5rWEaOXLM+fzpAvy7qvUb5kTdwrB6CQ+VABHs7vPt0JHuf2T6lnHyuNH+Zax3GcK3DGPIvRBM7erEqM1jexRs4Vg/GobKiC5++HcgsowvH6sGl/3s+3RLDbYtEKlFR6CpXQhes+Knz010xRJxQtRd9zXI7dmiHMU69FyPnwi1cagThVMUfwV5HYL92pO1RL5VytugPv+7NyY9NUpUXXYvCvnIldCXxwqVHFwqOWsSLvDwSnv4nic8NIfG5IRiuXCNt2pzSePEkfExKvKUsv/udRqOnMEaXoRH1cBrF0dHoQkMRgxQ5nLp2xXCijD59LPTZrh3Gu2p9xkXeIqBaML6h/ujsdYT1acelfWp9Xtp3jrYDwgFo8Uxbok+Y99NJcdGIASEIviVxr1VnjJdOqdobL57Ark4T4Pe4F4qUZu6TnAuKjzmV+FhAv/YP7VPf7i0oKNOnuRdv4lw9GMcS2/Tr24GMMjScLGzT++mWFN5W06iojyi2GVxqmwGPsE3f7i2sbNN0OxoxsBKCXxDo7LBvHY7x4klVneILEejqlujTzQMxsBJyqplOw1B/7qZlk5CRQ7HRxJ7IWDo3qKKiEezlxumbiQDEJmdRVGzC29X8YqyiPlJ4JRr7qiVjmX35Y9nt9gOJ6z6cuO7DMUReJ+k19VhWHBWFrlJZ+45QyaG27/YYyyRGyC4TL4L6tSO1TJ+4lIkXZW3LdDMaMTgUMUDhw75DV4rOqv1M8DLzYR/WDlOC2c+exPOJMbpEF4EKDcfwrhSdUutCsNCFQ9v2Vkkifh/LnCzGsvS9ZX3ErAufp1tQYOEjk3tPYnLvSZzcc4quA7oCULd5XfJz8qz2Lj0ItyNvElAtGL/QAHT2drTu04ELZeLNhX1naF8Sb8KeeYrrJ9T7cxMjY/GpHoRXZX9Eex0N+7QlZp86dv5X4M9efvc3XZ73t0k5LghCL2AG0FmW5fw/2s5OFHkrvC4Tfj6PJMn0bRhCTV83Pjl1kwYBHoTXUN6u7InR07NOEIJQzjIwk0TG8lUErFkGokju9t0Ux8bhOW44RddiKDh60rpNWcgSRYe+w3HA6yAIGK9EIKcnYt/uOSR9XOkEyq5eK0zRZ6zbSxKF/1mLy4QFSurdU/uQ9HdxeGYIprs3MF35DdP189jVa4HLrE9AkjD8/CXkW2QCNEmkLVlD8LolCDqR7G17Kb4Vh/drwzBcjSH/8Cnr+5bDR9GOL3EaNqs0lbGcGo991xeQEmIxRZ/DdDMSXa0mOE/8QJF7z7dQYN7HYyeKvP10I8Z/fwpJlunbuDK1/Nz55FgUDYK8CK+tBNbd1xPpVb+SVZ/Y6XTMHNGX8cs2IEkS/cJbUSs0iDXf76VhjVDCWzbg7PVbrNy8GwSBlvWqM2tkPytRZiz6kDORV8m6n0O3F8fy2vAXMZqUbXMD+/SkY5sWHD19nmeGTsTJyZFFb06w4mPW2EGMn/8RJkmiX7f21KoSwpqNP9OgVlW6tGnGmcsxrPz3NgQBWjSow+xxg9RMmCRiZ62n4aZ3lFTGmw5SEB1PlRkvknvxFhl7zxI8qjdenZogFRsx3c8jZvIqWhwxZ797c94yzly4RFZWNt36vcyE0UMxGhU5Xuz/Dzo91YpjJ8/Qe+AonJ2cWDhrqrU+R/2T8Us+Q5Jk+oW3plblINZsOhlCkwAAIABJREFU3a3oM6wRZ6/dYuWmnSBAy3o1mDW6zLIKk0T83HXU+GY+gk4kY+t+Cm/cI2jaYPIv3SR7/x9IyWqSuPvO59T9bh6IImlbDlAYc4+Q6YPIj7xJ1r4zBI54Bo+OTZCNJoz3c4l9fSWND60sbZ+9YiU+K94DUaRgxy6Mt+/gNnokxVHRGCJO4Pr8P5XkECYTUnY2WYuXqViQTRLRM7+g+eZZoBNJ2nSYvOh4asx4gezIWNL2nKPy6J54d2xcwkMe1yZ/YiVHxvurCFi1HHQiedt3KfHi1REUXY9+dLx4Aj6GJFG08yuchs5U0pZfOKzQ6PI8UuJtM42ajXF+7X2l/r6NahomEzkff4z3+++DKFK4axemO3dwHTkSY3Q0hhMncBkwAMd27ZBNJuScHLKXqfUpmSQ2z/2CSd/MRtSJnNh6iKQb8Tw7dSB3L9/i0v5zRGw9yIgVE3n38Erys3LZMOkjlRyFWz7BZfJiJe6d2IuUFIdDn6GY4m5gunQK07Vz2DVoicu8dUrc+3E95JnjnmySuDFzA002z1ZSdW86RH50PNVmvEhO5C3S95yl0ujepX1afD+X65PVqdd/99MGm+Yg6ESSNx+kIOYeld98idzIm2TuPUvQqN54dWyCXGzEeD+PG+XQqIiPyCaJmJlf0KxEjsRNh8iLjqf6jIHkRN4ibc85Qkf3srDNXK5PXqMmIkkUblyNy9SlCKJI0fE9SIlxOPYdjulODMbIk5iunMWuYUtcF65X6n//ObKFPu10Im/3fYrx63crY2qrOtQK8uaTPedoEOpHeMOqTHu2NQv+c5yNx5QJ8LsvdlTH8Yr6iEkidfEaQj5fgiAqY1nRzTh8Jg6j8GoM+Yf+yFhmImflR3i/90GJfe8sse9RGKOjFPv+5wAc27dX7Dvb2r5/jxctNs8q6RMlXtQsiRepJfHCp9S28rhSNl5IJvLXf4zbHMXPig7uQrp3B6eXRmK6GU3x2RM4/mMADq1K/Cw3h7zVy/Bc+Y1ZlxV9PpFM5K75CM8lJbrYuxNT3B1cho3CGBNF0akTOPcdgMNTJbEzJ4fcf6l1gUni5qwNNNqk2Ke+xM+qzniRnJKxrNKo3nh1aoxcrNhndFkfAc4ePENYlzA+P7ZeSTk+3XwcyMpdq5jcexIAI2eNpHPfcBydzSuHJJPExrnreeObOYg6kWNbD5J44x79pr7Encs3ubj/LEe3HmDsisksO7yavKxc1k5SHzcimyR2z/2Kwd+8haATidx6hNQbCXSeNoCkS7eJ2X+e4CY1GPjZVJw8Xaj9dHM6Tx3A2u5vPVrPGv72EP7gB5snf2NByJVl2Sp1mSAIm4BwwA9IBubJsrxBEISbgCPw+7fWU7Isjyvbvgzk/DUTbebR5TXFoeNaPG0zjarn95O/4vGWU1nxMe1zciY/a3N795W/AnCrUU+badS8oqR5zpv70iNqPhiuCzZTsGG6ze0BnEd/QOG5nx5d8QFwaqlMnoriH2cLnRoOoY0xRB2xuT2AY73ORATZvjepvV45o6s4LdZmGvZ+yplThRd/tZmGU7NnuVj1OZvbAzSL286ZSv1tbt8qQTmHI6lDl0fUfDCCjx8C4EDgi4+o+WB0S97C3bBuj674EFQ5e6DCPgaQN3/QI2o+hMZ85Wyr5PBwm2kEHj7M+GoDbW4P8OmdreSMKy+R6h+D+1rlHLzDgY+3780S4cnfA3Ai2Pa9F+2SfngiPnIw0HZ9dk1WzjDKHt3dZhoeG5RU0QU/v2czDee+M56Ij9xsYPtYVuuaMpYld+lsM43AQ0r831eBeNE9eQuZJV88bIF3SQa4ij6fAKT1tF0XfnuOcDTIdh8D6KT/nmer2J6++9e7ynlRI6vZ7qdf3lHOTFtYdYjNNObEbQR4zE3xfw5y3+z/1/2cA7i9v+0vqcc/7UtTeROmkuvljvayLNcq77oGDRo0aNCgQYMGDRr+IGTpz+bgb4k/PXueBg0aNGjQoEGDBg0aNPyVoU2aNGjQoEGDBg0aNGjQoOEh0CZNGjRo0KBBgwYNGjT8r+DPzo73BLLnCYLQSxCEaEEQbgqCUO6hi4IgDBQE4ZogCFcFQfiuvDqPgz8tEcT/Ef6rhdOgQYMGDRo0aNDwl8FfMoFBWeROe+4v/XzstmL7Q/UoCIIOiAG6A/HAGWCQLMvXLOrUBrYCXWVZzhQEIUCW5ZRyCf5BaF+aNGjQoEGDBg0aNGjQ8HdBa+CmLMuxJee8bgb6lqkzBlgjy3ImQEUnTPA3OqfJVjyJlOOxjXvYTKPG5b3kf/yozOiP4GPKWrJftT3Fqsc6JcXq9drP2Eyj/o2dANwfaXt6U88v95O/asKjKz4ELpM+oWDHR4+u+AA4/+N1AAojd9pMw6npMxTdsz6Y+HHgULkpV2rYnka+UaySJryi6cKh4mnLKyIHKLJcrt7H5vaNb/8CQFrvCqTN3aWkEK5oynF9p3Cb2wMEHT1M/vKRNrd3eetL4MmkHM8eY3vc8/h8L3Or2Z66F2DBnY1PJP36sQqk9u9Yktr/VMg/babRNvFHrtW0PZ0yQINbO55IyvEnkcK94MsZNtNwHvneE/GRJ5FmO/0ftscL3x1KvKhIqu1O+u8rPJ7CkzlqoaKpzyuS1h+U1P7hobbr4nC8oosnkC6cZVVftpnG23Hf2tz2/xryX/gAWQBBEMYCYy0ufSbL8mcWvysB9yx+xwNtypCpU0IrAtAB82VZ3l0Rvv7rJ00aNGjQoEGDBg0aNGj4e6BkgvTZIys+HHZAbZSzX0OBo4IgNJZlOctWgtryPA0aNGjQoEGDBg0aNPxdkABUtvgdWnLNEvHAdlmWi2VZvo2yB6p2RW6qTZo0aNCgQYMGDRo0aPhfwZ+dHa/i2fPOALUFQaguCIID8BKwvUydn1C+MiEIgh/Kcj3b9yOgTZo0aNCgQYMGDRo0aNDwN4Esy0ZgIrAHuA5slWX5qiAICwRBeK6k2h4gXRCEa8Ah4E1ZltMrct//iT1NEXfSeP9oNJIs069hJUaFVVeVf3A0mjPxGQAUGiUy8os4Nk69udK5fRi+b41H0Ilk/7ib+xu2lHsv16c7EPjhXOJffI2iazdUZWLVBjh0HgiCiPFqBMaze1Tl9p1eQBdaR/lh54Dg4k7B2mml5bqGYTgNHIcg6ig6vouiPVut7m/XshOOzyobGaX4WAo2LFPz17Elge+8iqATydq6h/TPvi9XDvee7QldPZvb/adQeEUth12jVjgNngCiSPHRXRh2brZqb9+qM459hwEypnuxFKxboiqPiEvn/WMxSp80CGFUy2qq8g+OxXAmIROAwmITGQXFHBtr3rgbcf0u7/10HEmS6d+2PqO6tVC1T8rMYc53B8kpLEKSJCb/oy0dG1RV83DxOsu/3KbQ6NaG0f3UG1ETUzOY9+lmMrNz8XRzYcmklwn09Sotn/P+Jxw9fR4fL0+2rf+XlQ5kWWbZmi859tsFnBwdWTRjAg1q17Cq59apBcFzx4Iokrl1L2lr/2NVB8CjVzuqfDKLm31fLyNHFMu/+glJkujftQ2j+3WzlmPtFjKz8xQ5Jg5WyQHwzpIVHI34DR9vL376dm25siz9aC3HTp7BycmRxbPfoEHdWhWWo/DyTSsaIfPGKDS27CP1ITSqfjqTm89NVV23b9ka13GTEESRwt07KPhefSyD49O9cH1lPFJaKgAFv2zDsGeHqo5Pl6bUWTQCQSeSuPEgcat+VpVXGvY0oaN6IpskTHmFRE3/jLwY9aoAh9at8Zg8EUQdBTt2kLdRzYdzr164TxiHKTUNgPwft1Gww8yHWL0RDt0GgyhijDyK8bQ6cYl915fQValf8sMBwcWDgo9fU9XR1WqKQ69hCo3zhyg+XvYlHOgatsUhfADIICXHYfhhdZnyMJxeGo8gihQd203Rbuu4ZxfWCcc+QwEZ6V4sBevVMadW5yY8M3cogk7k/JbDHPv0F1V51db16D33ZQLrVeH7Sau5tus3azmeGa7EzfMHKT72ADm6PK/woL+L4T+rVOXeXZpRY+FIBJ2IfuMB4lf/pCoPGtaDkJHmPr355jryY+JVdTzDm1Nt4SgEUSRl034SV29TlQcM7UHQiN7IkkLj9pufUnBDTcO1U0uC5oxF0IlkbtlL+roHxd92VP5kNrH9pqh8xKdLU2ovUuRI2njAyjZDhnUvY5vryC9jm7oGLXEaOF6J3xG7HzCOdMTh2ZcVu4iPpfCL5aryiNhk3tt/GUmC/k2rMOqpOqry9/df5sxdxbYLi01k5Bs4PlWdDKOiPuLUrhU+0yeATiR32y6yv7IehwBcunbE/4N5JA2ZQNH1GFWZfcvWuI6dBKJI4d4dFJYTL1xGjUdKV+JF4S/bMOxVxwvvLs2oaWFb98rYVvCw7oSM7FXaJzfKsS1bxlQVn21a4TFF0WX+rzvI+3aTWpe9e+I+YRxSmqLLvB+2UfCrOqbYNWuNyyiFhuHADgzb1Lpw6NIL56HjkDIUGoZd2yg6UDZ2NqOWhX3eXaXWRciw7oSM6gUluoiebq0LgEkLXqNt19YUFhhYNvU9bly5aVVn9IyR9Hy+O+6e7qrrNTs3oec8Jd5c2HyYE2XiTZXW9egxT4k3P05azfWd6ngDUL1zE56eNxRRJxK5+TCnytCo3Lou3eYNJaBeZX6etJronWesaGj4/w9ZlncCO8tcm2vxvwxMK/l7IvjTJk2CIJiAyyU8XAeGy7KcLwjCF8CzQIosy40s6i9ESScoASnACFmWEx91H5Mks+xwFJ/2b0GgmxNDtpymc3V/avq6ldaZ3qlu6f+bIu8SnZqjJiKK+M2eSNLYtzHq06i0eRX5h05SHHtXLZOLMx4v96cw8np5AuMQPgjDto+RczNxemkmpthLyBlJpVWKj35Pccn/dk3DEf0tlmsKIs6DXiPvo5nImWm4zlyF8dIppCQzD2JACI69XiTv/WmQn4vg7mklR9D8CdwdMZtifRrVf/iInIOnKLp5T13N1Rmf4X0puBhVjhwiTkMnkffBW8gZqbjNXUPxxRNIiRZ8BFbC8R+DyF0ypYQP9QO6SZJZdiSaT/s2J9DNkSFbz9C5uh81fSz6pKN5EN4UeY/otByL9hJLfzzG2nF9CPR0ZciHP9C5YTVqBvmU1vl83zl6NKvJwPaNuKXPYOLnO9llMWkySRJLNvzAunfGEejrxeCZHxIe1oiaoUGldVb8ezt9OoXxXHhrTl+5wcff/cqSSebMOn17hjOoXy9mL19jrSfg2G8XiEvQs+PrlVy6foNFH6/nu9XqySOiSMi747k97B2M+nRq/PQhOftPYyinT3xHPEf+BXWfmCSJJV/8yLrZrxLo68ngmR8RHtawjBy/KHJ0bqXIsWknSyYOVtHp90x3Bg94jlkLPyhflpNnuBufyM4tG7h0NYqFH6xm0+cWWQwrKEcpjQXjuD10DkZ9OjV/XkH2A2j4jexjTUMUcXvtde7PegMpLRWvj9dRdDoC0904VTXDkYPkffpxuXIiCtRdNooLAxdjSEyn1Z6lpO05q5oU6X+MIOEbJVuTX8+W1H53GBcHLVXx4TF1CpnTpmNKTcX3s7UUHo/AFKfmo+DgIXI+KocPQcCh+1AMWz5AzsnAafhcTDcvIqebw13xwc3mWNGiG2JgVWsaz4yk8N9LkLPTcRqzGGP0OeRUsxyCTxD2HfpSsGE+FOaBq0cZGiLOgyeS9+HbSsyZvQpj5EnrmNP7JfKWTy3X1wVR4NkFI/j65aVk6zN4dftCovadJ/WmmY/7iWlsm76O9mPKyTAnCDg8O4rCrxcrcry6BGNUOXJ06kvB+nnlyyGK1Fz6ClcGLsCQlEGz3cvI2HtW9bCW+uMx9N/sBcCnRxjV5w/n6uDFKhrVl4zh+kvvUpSUTqOd75G554xqUpS+7Rgp/1ZoePdoRdX5I4kaslBFI3j+eOKGv0OxPo0a2z4k58AD4u+IvuXYt0DdZaO5MHARhsR0wvYsJXXPWdWkKPnH4yR+sw/43TaHEznIIuYIIk6DXiP/41nImWm4zFxpNY4IASE49HyR/PffKHccMUkyS/deYu1L7Qh0d2bIV0foXDuImn5mvb/5dOPS/zedjSUq+b5Vn1TIR0QRn7cmkTLhLYzJqQR/u4aCIycovm09JrsP7o/hcjljsijiOv51st9R4oXnh+soPhWB6Z6ah6KjB8lb+6B4IVJr6WguD1yIISmD5ruXkl7GtlJ+PE5SSZ/49AijxvzhXLG0LRvHVIe2Xc26nDaFjKlvYkpJxW/9WgzHT2C8o5aj8OAhsj9c+UA5XMZMIXfBdKT0VNyXr6X4TARSfBldnDhEwfoH66L2stFEDlyIITGDliWx01IXlvbp2zOMWu8O59KgxSoybbq2JrR6JYZ0GE6DFvWZunQKE/pMsrrdyf2n2PbVz2w89rWFKgV6LRzBxiFKvHll+0Ji9p8n7YY63mx/Yx1PjS0/o6UgCvRYOJzNQ5aRo89gxPYF3Nh/jvQb5vibnZjOjjfW0Was7RmJNfw98WcuzyuQZblZycSoCPg9L/dXQHl5Ud+XZbmJLMvNgF+BueXUscKV5PtU9nIh1NMFe51Iz9pBHI5NfWD93dF6etUJUl1zbFyX4ruJGOP1YDSSt+sIrl3aWbX1mTicrA1bkIuKrMrEwGrI91OQs9NAMmGMOYOuRpMH8qGr0wpjzFnz7+p1kVISkdP0YDJSfPYwdk2fUrWx79CbosO/QH4uAHKOeqByblKHorhEiu/podhI9o6juHdT0wDwf30o6Z99j2SwlkNXo4SP1CSFj98OY9+8vaqOQ6dnMBz82YIPdaKSK8nZVPZ0JtTTuaRPAjkcm/ZAXey+kUyv2oHm9ndTqOznSaivB/Z2Ono2r8XhK3dUbQQE8gqVx8rcwiL8PV3UPNy8S+UgP0ID/bC3s6NXu+YcPnNFVedWvJ7WjZQ9g60b1uLwWXV5WJMGeLq78SAcOnGW57p3QhAEmjaoQ05uHqnpmao6zk3rYIhLovheMnKxkf/H3nlHR1G9//+1u+m97W5CAoQQeq8iLaGDSlEUlCZNBAUUbHQEBdSPoiIiCCgfFREs2OgtgPTQEZKQAIGUTe99d+f3x4Tszu6GsuF85eNvXudwDtl773ufe+8zz+zM3Hlu3p+H8OzTyUpLM3MUGWt+QiirkHx+Kf4mtbX+hGj9zfrxt7QfyWl0bBZebT8A2rdugbeXp9XnVX356ziD+vcS+9K8CQUFhWRkZj+wfgC4tWpAubnGH4fw6mOZQRS0M0eSsfpnjBYaDg2bYEhJxqhLBb2esoP7cerUtdo+2cKrbTgl19MoTUxHqDCQ9utRAvp3kNQxFJZU/V/l5ozlBuGOTRpjSE7GkCraUbpvPy5dpcfInVAGhSHkpiPkZYix4spJVA3aVFtf1bQT+ivHpRrB4RizdQg56WAwYLh0DIdG7SV1HNr1RH9qt3ihAVCUL9Wt1whjhlnMOXUQh9bSuOfY7THKD/xe7bEe0ro+2Ylp5NzKwFBh4OIfx2nct52kTm5SJmkxt6zGEUAZYtGPi0dxaGzRj/Y90Z+ovh+ebcIpva6j9GY6QoWejF+P4NfvznNqiUebcEpvpFJ2U/TNrN/+wrdfx2o1lG7OYNEf11bS+Jv35yE8e1sfI+oZo8ha8xOCRfz1ahtO8XVdlW+m/3oU9R1908XKBmVoI4zpqVVzqj91EIeW0nOAU9cBVBz8s9rzyKXUHGr7uhPi4y7G76bBRF3VWfXjNjuuJNG/abDks5oeI07NG6FPSkGfLLYv2hWFa6R1e5+XxpK/YbPVWIKNeHFoP473GS8824RTYuFb/v2k/mntW9I5qek51bFJYwxJKRhSxH6U7N2P832MJYAqvDFGXTLGNFGj4q/9OHW4Pw0xdt72Tz3pvx4hoP+dx8LWMd+lb2d2/SReWF0+cwUPLw/8NH5W9S6fuUJ2erbks1qt65NzI43cWxkYKwz8/cdxGvWRxpu8pEzSY25Vm3I7qFIjr1Lj8h/HaWBDI+MOGv8TGI0P97+HlIflnabDQDiAIAiHgGzLCoIgmJ8J3bGMPNWQXliG1sN0EtR6OJNRVGazbkp+CSn5JXQIkR6gDpoA9DrThZY+LQOV1l9Sx6lJOA6BakoOWz/qBVB4+CIUmH4wC4W5KDx8bdf19EPpHYDxlulOo8LHH2OOyQYhJxOlT4CknVIbglIbjNsby3F76xNUzSx+XAT6o081XZxU6DJxsOiHS9P6OASpKYyy/bhZ4RuAkG3aH8yYnYHCV6qhDAxBpQ3Bfc4nuM/7DIfm0hN7elEpWk+Xqr/vd07S84oI9HE3tfdxJz2vSNJucv/2bDsdR99F3zB17TZmPdlNakN2LoFmS9Q0/t6kZUt/HDSqG8y+kxcA2HfyIkUlZeQWSL/nTqRnZhOoNs2RVu1PeqbUtR0D/alINfOt1EwcLeekWX0cgwIoPBCNJenZedb9yLHsRy32nbxodz8A0jKyCNSY9UUTQFqGyZdq2g8Q/bNC4p9ZOAba0lBTYENDGRCAMcPMNzMzUPoHWNVz7hqBz6qv8Jy7CGWAWqof6EdpimnJc1lKFs6B1sdpyLi+PHriU8LnjyRu7gYLO9QY0k1jYcjIQKlWY4lLRHf8v16Pz+JFKDWmcoWnL0K+yU+EguzqY4WXvxgrEq9YfO6LkG/qh5CfhcJLqqH0D0ThH4TL+LdxmbgYVXgrqYZPAMZs85iTgdLH4ljXhqDUhuD21se4zf7UKuZ4av3IMxvP/NRsvLS2+2Kzf55+CHnm/chG4SWNz0r/IBQBQbhMXITLC+9Y9cM5yI+yFJNfladm4Rxk/SMsaFx/2h9fSb35o0mYu15S5hToT7lZP8pTs3CyoaEd25/WR1dRZ94YbsyXajhopf6t11V3jNiOv86BfpRZ+aa1DcHj+vHoiRXUnz+SuLlfS8qUvtLziDE30yp+KzTBleeRj3B782NUTaU/GNMLSgn0dDX12dOV9IJSKzsAUvKKScktpmNdqf/X9BhxUAeg15mOdUN6BiqNxTm5cTgqrYaSv07YtE3pH4AxUxovVDbihVOXCLxXfoXHbOt4IfqW2ZykZuMU5G8pQdC4fnQ4/hlh80cRP/crSVlNz6kqdQCGdLP2GRmo1Nb9cInoTsCGdfi887ZkLAGUfuqqJctVNvhbz4dTp+54Ll+P++uLrMqt/TMb50Drsag1rh+PnLA9FgDqwAAyUky2ZKRmoA607o8tvAL9yE+VxhtPG/H7TngG+lKQaoq/BXZoyPx7+ccvmhQKhQMwAHGp3t3qLlEoFLeAkVTzpEmhUExSKBTRCoUi+ssv7y/F+644Hb3CtaiUivtqh0KB/xsvkvVhTVPKi6gatkd/9YzVXcK7olSh1ART/NEblKxbhuuoV8HV/e7tbqNQoJnzAunL1t7f99qyQxtM0fuvUbx6Ca7jZtyfHWbsuppGr/qa+56TnWfiGdSxEbsXjmHlC48z7/t9GO/zrtDM0YOIvpzAsDc/5PTleDR+3iiV/8eHjEJB0NyJ6Jasv3vdapg5aiDRl68x7K2POH3l2v9sP1AoCJo3gdQaaJSfOEr22OHkvjSeijPReLw2xy6dpK93c+yRV4h/93vqzbj/jU9Ljx4lY9izZI2bQFl0NN5zZttlh6rJI+hjo+8/VoB4nPoFUrrhHcp++gyngS+Ai9vd20kMUKLUBlP84euUrF2G6xj7j3W7ud2PrxZT9uMKnAZPuv9+AKlf7yS601Suv/sddWbYtyFu2oadnOv8EjeXfEvwK/epoVCgnTORtKXr7Pru2yR/vYtjj0wn4d2NhM4Yet/tFUoVCk0tij96k5L17+Fyv+cRM3ZdSaZ3o1r3f06lhseIQoHvzCnkLLd+P/N+KD9xlJxxw8mbOp6Ks9F4zLQvXqR+vYtTnaZx7d2N1LVjTmyeU++D0iPHSH/mOTLHTqQ8+jQ+c2fdtwkVp46SN/lZCmZOoOJ8NO7T7ItZKV/v4sQjNRgLGZl/kH/yoslVoVCcA6KBm8BdfwkJgjBXEITawEbErBm26nwpCEJ7QRDaT5o0CY2HM2mFpqcYaYVlqN2tl18A7IpLo3+jQKvP9emZOASa3eXSqjGkme5mKNxdcQoPJeir/1B75zc4t2xC4GeLcWpqSgcvFOag8DTdrVB4+CAUSpdqVek3bI8hTnqnUcjNQulrdjfaNwBjrnRJm5CTif7CcTAaELLSMKYnodSYlkXodVk4BJnu2DgGBqA364fS3RXnBnWp89371D/wNa6tGxOyegEuzRtIvkPhpzG18VMj5EiTkRhzMqg4dwwMBoRMHUZdEqrAkKpyjbsLaWZ3Ju84J1fT6N9QK/lM4+2OLtf0pCQttwiNt/SkvvXEFfq2EpektQoNpKxCT26RaWmAxs8HXZZpiUN6Vh5aP+nafY2fNx+/Pp4tH7zOtOfE9c9e7q7cK5oAP3RmT2PSMrLQBEjvDFfosnAMMvOtoAAqzOfEwxXnhnWot2kZDQ+tx7VNI+p+OV9io1U/fG31Yyxb3n+Nac8OuO9+gPiUTJdu1pf0TLRmdzPt7YdLC1MyCb0uC0eJf/pToZNquDSsS9gPS2l0eB1ubRpRd+28qnJjZiZKtZlvBqgxZlkcIwX5UCEu6yvdtQ2HBtIX2Et12bjUMt0dda7lT5nO9nEKkLb1KOoB0iepxswMVGZ3clVqNcYM6ZJgId9kR8mf23BsaLJDKMiRPE1RePpVHyuadMRwxfpOupCfg8LL1A+Flz9Cfo5FnWwMsafFeJGbgZCVitLPFP+E3EyUfuYxR40xV3qsCzmZ6M2P9bQklFpTzClIy8bbbDy9gvzIT6t+PK36UZCNwtu8H36Sp3BiP7Lu2I+y1Gyca5n8yinIn7JUq8UMVWT8egR/i2Vv5bosnMz64RTkT/kdNLJ+/Qvf/tLle/oNuF0SAAAgAElEQVQ0qX87BFocI+6uODesS93v3yP84Fe4tmlM7TULqo6RMl02zla+Wb0NNn0zR3oeUfoEWMfv3Ez056s/j2g8XdAVmGJpWkEJGrOVA+bsvJxM/6YhVp/X9BjRZ2TiEGg61lUaNYZ083OyG471Qwlc+xHBf36Hc4smqD9ZjFMTk4YxKxNlgDReGGzFC71oQ9nubajCpfFC9C2zOQnyozy1+uRcom9J/cLec+ptDBmZqDRm7dXqquQZVd9hNpbFf2zDsZG0H8bsDMlTNKWfGiHLYj4KTWNRvm8bDmEWY2Hln36U6aofi/StRwgYYBqLdbtWs27XarLSs1HXMtmiDlKToat++b45+bpsvIKk8abgDvHbFgW6HDzNniJ72qHxP8E/nVK85inH/xEehneaWguCME0QBOtFx9WzEbinWxTNtF7czC0mOa+ECoORXVd1RIZZP3a+nl1EflkFrQK9rcrKLsXiWDcYh+BAcHDAfUAERVHHqsqFwmISuz/Drf5juNV/DGUXrqCbtkCSPc+YlojCRyP+kFGqcGjYAcO1C1bfpfDVgos7xlRpdhzDjViUmmAU/lpQOeDYPlI8sZlRcf4oqobie1IKdy+UmhCETFOiiZKLcTiF1sIxRAuODng93p2CfSYNY2ExVx95joQe40joMY6SczEkTV4syZ5nuB6LShOMIiBQtKNjJBVnj0rs0J85ikNjcYmMwsMLZWAIxnSTHc20ntzMKyY5//acpBFZz/rx+/WcIvLL9FZz0qy2hpsZuSRn5VOhN7DrbDwRzUMldYJ8PThR+ZL2tbQcyvUGfD1MFwrN6tfmZmoGSelZVOj17Dx6loj2zSQaOfmFGCvX1q7fupchPazfr7kTPR5tz+97DiEIAucvx+Hh7obaX/qYv+RCHM6Vc6JwdMD7ie4U7DX9CDYWFBPTfiRx3ScQ130CJWdjSZxkerm8Wf3a3NRl3ns/ft3HkB7Sk/a9ENm1E7/v3Cf25dIVPDzcUZtdANrbD/PMYMUXrko1BnYnf+9JicaVdiOJ7TaR2G4TKT4bS+IL71aV6+NiUNUKQakVj1PniJ6UHz8i6YfC12SzU6cuVi99F5xNwC0sEJc6ahSOKrRDOpO5S7oU0LWe6Qd5QJ82FF9LlZRXxMSiCglBFSTa4dKrJ2VHpMeI0t9kh3OXzugTTS99G1Ovo/DVoPAOEGNFk44Y4s9iicIvUIwVydaZpYwpCeLyOx81qFSomj+KPva0pI4hJhplaFPxDzdPFP5BGHPMljzdjjm3j/UOEejPH5NoVJw9iqqR2bGuDRHfzagk+fw1/EID8QlRo3JU0WJgJ2L2SO24E8bkBJR+Zv1o0Rl9jEU/rty5HwXn4nEJC8K5jgaFowPqIV3I3i29KeViNqd+vdtScl36jk7huXhc6gXhXFvU8B/clRwrjaCq//v0bkfpdalflFyIwyk0uCr+ej/RncJ9ZsdIYTFxHUYQHzGe+IjxlJyN4daLi6uOEdE3g6p8U3MX3/Tv09bKN42JsSg1tarOIw4dIsQbbWbozx3FweI8YjQ7jzQL8uFmdhHJuUVi/L6cTES49c3G61kF5JeW0yrYemlTTY+R8r9jcagdjEOtynNyv0hKDpraC4VFJPUaSvITo0h+YhRlF6+Q8eoCSfY8fVwMqmCzeNG9JxUn7hAvHrERL87F4xoWhIuZb2Xtls6JtW9J58Tec6ppLGNQ1Q6uGkvX3ncZy67SsQQwxMeiDApBqRE1HLv2pDxaqqHwMWk4tu+MIVmqUXBWOhaaIV3u6p8lZv45sd9kJvabzF87j9Dv6T4ANG3bhKKCIqt3l6oj5fw1/OoF4lNbjdJRRbOBnYi7j3gDkFqp4V2p0XRgJ+L3nLkvDZl/L/8zKccVCkUDQRBu/3ofDNhIv2WNg1LJW5GNeOm3MxiNAoOb1aK+vwerjsfTVONFZJh4h2ZXnI5+DQNRKGwsIzAYyVy6ksDVS1GolBRs3UVFQiK+L4+h7O84iqOOW7exRDBSHrUZ5yHTxdS5l48iZKfi2GkgxrREDNfFCyiHhh2snjIBYDRS+sPnuL2yVEz/e2Q3xtREnAeOwZAYh/7CcQx/R+PQtC3uC78EwUjpz2sRiswyARqM6BZ9Qe2v3hVTjv+0m/L4mwS8MorSi1cp3G977belHSUbP8P9tffE9KiHd2JMScR5yPMYbsShP3cM/aVTODRvh8e760U7Nn+JYPZytoNSyVvdG/HSb2cxCjC4aZA4JycSxDmpp66ckzT6NdBazYmDSsmsp7ox5cs/xTnt2JjwQD9W7ThJ09pqIpvXY+agzizecpCNBy+AAhY911Oi46BSMXv8UKYsWYPRaGRIj0cIrx3E55t30Kx+bSLbNyf6cjwrvt8GCgXtmoQxZ4J0uc2bSz7h1PnL5OYV0OvZybz8/DD0ej0Awwb2pdsjbTh08gyPjZmOi7MT777xkvV4GoykvL2a0P8uRqFUkvPjHsqu3kTz6khKLl6lYJ/td+Sk/XiKKUu/xGgUGBLZkfDagXy+ZSfNwkIq+5HAik3bQQHtGocxZ4L1/YY3Fr7HqbMXyM3Np9eQUbw0YXRVX4Y/+TjdH+3A4WOnGDBsPK4uLrwzx2J5SA37UaWxcDX1vlkkphz/ca+oMaNSY+9dNIwGCr/4BO93PwSVktLd2zHcvIHb6PHo42IoP3EU18FDcerUBQwGjAUFFH4kTY8tGIzEzv6KNj/MAZWS1E1RFMUmEfbmM+Sfv0bmrtPUntAP324tEPQG9HlFXJ6+yqIfBvI/+RTfD/8DSiUl23egv3EDj/HjqIiNpezIUdyGDsW5S2fRjvwC8paZ2SEYKd+zEedhr4mx4uJhhMwUHLsOwai7gSH+nDj3TR6x+ZRJHAsj5ds34DJ6tqhxNgohIwnHHk9jTLmOIfY0hvjzqOq3wPXl/4j192yEkkKJRun3K3F7dSkKhZLyI7vEY31QZcw5XxlzmrXDfdFasf5P0phjNBjZtmADY755C6VKyZktB8m4mkzPGUNJvnid2L1nqNUyjOfWzMDV241GvdrQc8ZQVvZ9y9SPbV/jMmZOVep0ISMJx57PYEy+ZupHeEtcp34ojt2u76T9MBhJmLOO5pvmoVApSdu0n+LYJOq+OZyCcwlk746m1vgB+HRviVChR59XRNx0acpyDEZuzF1H4+8XoFApSf9hHyVxtwh541mKzieQs/sU2nED8O7WUvSL3EISXrHW0C36gjob3kGhVJL7k3iMqF8dRcnFq5ILKFsIBiNxs7+i9Q9zxXT4mw5QFJtEvTeHUXA+gcxdpwmZ0N/MNwu5Mt0is6fRSOnmVbhNXyLG76PiecRp4GgMiVcxXDiO4fJpHJq2w23hGjAaKftlHZjNqYNSyay+LZmy+RhGQWBwyzqEq71YdegKTYN8iGwgXjyKT5mCqzmn1vAYMRjJfv8zNJ+L56HC33dScS0R78nPU345jpJDx6y/0xKjgaIvPsHrnQ9BqaRsjxgvXEeNR381hooTR3EdNBTHR8R4IRQWUPixNF5gMBI/Zz3NN4lzott0wMq3gscPwKd7C4QKcU5ip6+0sMO+c6rblHlVNuQvX4Hf8g/Esdy2A/31G3hMGEdFjDiW7k8/JSaHMBgw5ueTu8SiH0YDxes+xWO+OB/l+3dgvHUDl2fHYYiPpSL6KM6PD8WpQ2eEyrEoWmkdO6/OXk/LSv9MrRyL0DeHU3A+gaxd0QRPGFDlnxV5hVyxHAvg+P4TPNKzIxv/+oay0jLen/mfqrJ1u1YzsZ+YM+zFuS/Qe0hPnF2dJTbsXLCBEd+8hUKl5HxlvImYOZTUC9eJ23uGoJZhDPtyBi7ebjTo3YaIGUNZ3ecticbuBf9l+DdvolApubDlIJlXk+lWqRG/9wyBLcN46stXcfF2I7x3G7rOGMr6Pve/5FHmfw+Frewl/ydfrFAUCoJglXpMoVBsQtzBNwBIAxYKgrBeoVD8DDQCjEAiMFkQhGTL9hYIxZ/bXMV3T7i9LB7Q11r0tVsj7OJuij+dfPeKd7LjldXkv9jP7vZea8T9oK40sD89ZpOrYir8vHG971Kzery/3kvxZzYuHO4Dt2mrKNn2yd0rVoPr4+IeR6Xnt9+lZvW4tHqM8lvn7W4P4FS7FZfCnrC7ffNrfwJQeu5PuzVcWovfX5Fp/wbZjgFhNeoHiH25WG+g3e1bXBf30MgcEHGXmtUTsOMgAPu0w+3W6JW2GV33SLvbAwQeiqL4/XF2t3d7S3zxv+jt5+zWcH9b3OMl/wX7457X2t0sCB1pd3uAxTc2UrTgWbvbuy8W97o5HGjf+0kA3XTiPmHHa93/e2u36ZTyC5fr205vfK80TdjGfu0wu9v3TBP3YiqYbCsx7b3huXonACVfv2m3huu4Dx7IMZLY1v7zUN0z4nYBWY/bHy/8t4nx4lDgM3ZrdNf9WOPzKUBq1x53qVk9QX8dACBnaKTdGr4/RxGltX8cACLTfiQyxP6xiEoSx+KduvbHnPmJGwF4r+6ou9SsnlmJ3wHc/wt8/wAFk/s/vGvgAM/VOx/KcfzHnjTZumCq/Nzm2V4QBPmNQRkZGRkZGRkZGRmZ/3P+8ex5MjIyMjIyMjIyMjIyDzP/M+80ycjIyMjIyMjIyMjUjH/q1Zz/deQnTTIyMjIyMjIyMjIyMndAvmiSkZGRkZGRkZGRkZG5A/9Y9rz/I/7VnZORkZGRkZGRkXloeCizvlmS/0Lfh/r3sdfa3Q/lOP7r32nK7FeDNMS7xLSiNU2HHNt4gN3tARrF7OBsncF2t29z8zcAZoeOsFtj2Y3vAbjRuo/dGqHn9tQoPSqIKVIfxFjUNI18TcYBxLGoqQ0A5+oOslujdeLvADVOfV6TlOUgpi1/EKnTS0//ar9GuyGixskf7dfo+AwlhzbY3R7AtftY4prYnxa64RUxLXRNUwg/CI3NQTVLOT48dSMpne2PF7WOiumUS3ZZ7wVzr7j2E7esKD2+2W4Nl07DKT38rd3tAVy6jaZorv1pnd2XiH6d/GhPuzWCj+0Hap5muyZbRoC4bUTJbx/Y336wmDK95MA6+zV6TBQ1fv/Qfo1Br9coXrh2HyvasGOF/RoDpgNQeuZ3uzVc2g6iZMtiu9sDuA5bwPhQ+7cG+OqGuDXA9FD7t4xYcUM8xhuq29utEZcRffdKMv/TyMvzZGRkZGRkZGRkZGRk7oB80SQjIyMjIyMjIyMjI3MH/vXL82RkZGRkZGRkZGRkKjE+1K80PbTIT5pkZGRkZGRkZGRkZGTuwP8XT5oc23fEffI0FColpTu2UbLle0m5c5/+uE+cgjErA4CS37dStnObpI5H97bUWvgCKJXkbN5DxuqfbH6XV//O1P1iNvGDZlByMV5S5ta1Hdq5k0GpJO+nnWSvtf3SuUffLgSvmMeNp6dTdulq1eeeEW0IefsFFColWT/sIW3Vz5J2/qP6ox4zAMFgxFhcyq1Zqyi9ektSp2FES55YMAalSsmpzQc4+MUfkvLQjo15YsFoAhvX4Ydpn3Fpx0kr+1w7t8fvzZdAqaRw6w7yvrb9krRbr65oPlpIyoiXKb8cJylzfqQDXq9MBaWK4j+3UfTdJul3DOiH50uTMWZmAlD081ZK/tx+z2NxG+8BjxK2ZhYxT7xGyQXpfLh2aY//W1NQqJTk/7KTvPW2++HeuyvajxeQNPxlyi9flWo8gLGwxw5zPCPaErxwIgqViqwfdpP+RfVjUW/1bGKfmGnlmx7d2xK0YJLo31t2k3kH/66zag7xg1+l1Exj3tLlHDpyEj9fH379brVVO0EQWPbJag4fO4WLizNL5r5G00bhkjpHzsXw/oZfMRqNPNnzESYM6SUpT8nIZuHqzeTkF+Ht4cbSqSPQ+vtINc7H8v43v2M0CjzZowMTBvWw0Mhh4Zc/mjReGm6tcSGO97/dLtoR2Y4JA6XJZFIyc1i4dis5BUV4u7uydMozaP28pRqXEvjgh72iRrfWjB/wqFQjK4+3N2wjp6AYL3dXlk4YiNbPS1LHrWs7NHOmVMWLnHVbrMYVwKNPF2qtmE/i09Mknzu07ojbePEYK9u3jbKt0rjn1KM/rqMnY8wWj7GyHVsp37ftgWsE9mhJm8WjUaiUXPs+ipiV0pjT8MUBhI3ogaA3UJaVz8mZaylOyqwqd36kA96vTgWViuI/tlH4rUWseKwfXi9PxphhihXFf2yX1DlyOZEPfjkk+sWjTRnfR/qyd2p2AfO/20NBSRlGQWD6wM50axYq1bhwlfc3bhc1Itoy4YnukvKUzFwWrt9KTn4x3h6uLH1xqE2/eH/TLlGjW2smPNZFqpGVy8Kv/ySnoBhvdxeWThwi8QtVg9Y4PT4OlEr00fuoOGSd/ETV/FGceg0DQcCoS6Rsy6eScudO4ngqVEqKft9uNZ63cYnshv+yRaSPm0xFjDRm+fZoTf13xqFQKdFt3MetlVI7gsb0oda4/ggGI4aiUq6+sYbiuCTpWFy5yQe//iWORacmjO/VVlKemlPA/O/3U1BajtFoZPrjnejWtK6pfWwSH/x2HKNg5MmOjRjfo5VF+0Lmbz5EQWkZRqPA9AEd6NakttSGv6/zwZZ9og1dWjK+/yNSjex85m/YLvqF0cj0IRF0axEm1Yi5xQe/HxM1OjZifM/WNuyIoqCkXLTjsQ50a1JHqmFHvAiVjGUiH/zylzgWnZoyvnc767HcuK+yHwLTB3aiW9NQSZ0j52IqY6eRJ3t0ZMJgadKQlIwcFq7ZQk5+oRg7X37OOnZeTeGDbdEYBYEn24UzvnszqR25Rcz/5Zg4FoLA9L6t6dYwWFKneURrRiwQfevw5n1s/0LqWw5ODkxcPo26zcMoyi3ki6nLJeVNIlrx1IKxKFVKjm3ez94vfrNqP2r5y9RuHkZRbgEbpn5KdlIGlsxb+joRvbtQUlzKrOlvc/lCrKTcxdWZFevfp05oCAaDgQO7D1tpyPz7+McumhQKhQG4WGnDFeB5QRCKFQrFV8ATQLogCM1ttHsN+BBQC4KQaVluhVKJx8uvkjf7NYyZGfh8toby40cw3EyUVCs7tJ+izz+tVqPW4slcHz0fvS6L+r8tJ3/vCcripRckSndXAsYNpPhsjE0N7YKXSRo/h4q0TOr++CmF+09QnnBT2j93V3xHD6bkXIxV+9rvvkj8yIVUpGbR6I8PydtzUnJRlPPrQbK+E7NnefXpSPD88SSMWWTSVioYtHgc60ctI1+Xxcu/v8uVPWdIj0+uqpObkslPr6+m2wvVZFRTKvGbPY20yW+hT8uk1saVFB88RsU1i364ueI14knKLlyxqeE18xWyZ7yBIT2DgHWrKfvrKPob0jkp3X+A/I9tZAa6h7EAcT404wdSdCbWpkbA3KmkTpqFXpdJ8A+fUXygmn6MepLS87b78SDGokZ2KJWEvPMiCSMXUKHLouHvH5G39yRlNsZCPW5QtWNRa9EUro+Zh16XRdivH1NQjX/7jx1k07+HPNaHEUMHMecd29mkDh87xc2kFLZvXs+Fv2N458OVbFpryqJlMBpZ+tUvrJn7Ilp/b0bM/oTI9s2oHxJYVWf5t38wsHt7BkV04MSlq3y6aTtLp46Qanz9K2tmTxQ15q0ksm1T6odoTRobtzGwWzsGdW/Hib/j+XTzTpa+9KxU479/sOatcWj9vBixYDWRbZtQP1hj0vh+JwO7tmZQt7ac+DuBT7fsZunkZyQay77fzeoZz6L19WLkkg1EtGpA/VoBJo0f9/PEo80Z1LklJ6/cYMXWKJZMMMuEqFSimf8yyRMq48WWFRQdOG4dL9xc8RkzhBIbfuH2wisULn4dY1YGnu+vpuLUEYxJ0mOs/OgBStZVH/dqqqFQKmi3dCxRw5dRkppNnx3vkLL7DPlxZjHnYiJ7+s/DUFJO/TG9aDXvOY5N/qzKBu/XXyHrFTFWqNevpvSwjVix7wB5y21nETMYjSz7MYrVLw9B6+PByA83E9E8jPpBflV11u4+Rd82DRjWrQUJqdlMXfM7O5qNlWgs/eZP1rz5vOgXb68hsk1jqV/8sIuBXVozqGsbTly+xqc/7mXpi0OlGht3sGbmSLS+Xox4dz2RrRtSv5bapLFlHwMfbcGgLq04ceU6n/6yn6UTh9weTJwGTqD063cQ8rNxmbIM/ZVohAzTxYjCPxDHiCcpWTMPSovAXXohjlKJz2uvkFk5npqvvrA5ngo3VzyGDaX80mXrAVUqCV82gYvD3qEsNZs2O5eRtTtaclGU/stfpH6zBwC/vu0Je/t5Lo1YIp2TXw6zevJAtN7ujPz4ZyKahVI/0GxO9pymb+v6DOvSnARdNlPXbmdH5UWTwWhk2dajrH6hv9j+s9+JaFqH+lpfU/t95+jbqh7DHm1CQloOU7/azY4mw6U2bNrD6leGofX1ZOSyb4loWV9ynK7dfoy+7RoxLKINCSmZTF35MztavCjV2HqE1ZMeE+1Y8SsRzepa2HGWvi3DGNa5qWjH+p3sMLtosjdeLB8y3dT+p0OsnjJI9O/lPxLRvJ50LHdH07d1OMO6Vo7lmj/ZsTBUYsPSr7eyZs4kMXbOXUFku2YWsfNPMXZGtOfEpXg+/WEHS19+TtqPP06xemxPtF5ujFy9k4jGIdTXmG4crD14ib7N6zCsY0MS0vOY+u0BdrxmumhSKJWMWjyRj0YtJluXzYLf3+PcnmhS4k2+1W1YL4ryipgdOY2OA7vwzKxRZu0VPLN4PJ+PWkKuLovXf1/GpT3R6Mx+43Qa1pPivCLeiXyFtgM7M2jWCDZMlcaviN5dCA2rTZ+OT9KqXXMWfTCbZ/qPxZL1n3/LiSOncXR04L+/fGFV/jAjyMvz7OKfXJ5XIghC68oLo3JgcuXnGwCbOXcVCkVtoC9w01a5LRwaNcGQkoxRlwp6PWVR+3F6tOt9GerWqgHlialU3EpDqNCT98chvPo8YlVPO3MkGat/xlhWYVXm0rIhFTdTqEjSQYWegu0H8ejVyapewPQxZK/7EaG8XGpD6waU3dBRflO0IeePw3j37SipYywsqfq/ytUZLPbgqt06nKzENHJupWOoMHD+j2M06Su9I5WblIku5haCYLQ5Fs7NG6G/lYI+WQd6PUW7onCL7GxVz/flseRt2GzVDwDHJo0xJKVgSBHnpGTvfpy7drGqVx33MhYAQa+PIO2LnzGWWdvg3KIRFTdT0CdV9mPHQdx7WPfDb+rz5K633Y8HMRY1tUMci1TKb5mNhQ3fDHptJOmrf0awMRaurRpSZu7ffx7Cs4+1b2pmjiJjzU8INvy7fesWeHt5Wn1+mwN/HWdQ/14oFApaNW9CQUEhGZnZVeWX4m9SW+tPiNYfRwcH+nduQ9SpvyUaCclpdGwmPp3q2CycqOhLkvJL8bekGo+2Iur0ZRsa9UWNpvWtyi8lJIkaGj9Ro1MLok5LL0gSUjLo2DSsUiOMqNPSi8hL11OorfYlRO2Lo4OKfh2aEHVOeqf+WkomHRuHAtChcV2izkmfYLq0bETFzdSqeJG//SDuPaV3nwECXhlDzrofreZEFd4Yoy4ZY5p4jFX8tR+nDvd+jD0oDb829Sm4kUbRzQyMFQZu/nac4H7SmJN+9DKGEtEvs87E42Z2MePYtDF6i1jh0u3+bLiUmEZttQ8hAd7ifLRtSNRFaZp8BVBUKtpQWFqG2stdqnEtidpaP5NfPNKCqDPSeU9ITqdjk0q/aFLPqvzS9RRqa/yq/KJ/x2ZWfpGQmkHHJqGiRuNQSbkyJBxjtg4hJx0MegwXjuDQRPrEzKF9b/QndooXTABF+ZJyp6aN0SclV41n8d79uHS3jjdek8ZT8N0mmzHLs004Jdd1lN5MR6jQk/HrEfz7Se0wmJ+L3Jyx3DLx0s10agd4E+LvJc5Jm3CiLt2Q1FGgoKhU9OvC0nLU3m6m9rcyqB3gZWrfKoyovy1uKCjM57QctZebpPzSjVRqa3wJUftUHqeNibJYjSDVKEPt42HRDws7Wtcn6m+LC1CgqDLuFpbYsKOG8eJSYuVY3vbvNg2Iunjd2oZSMxu8Lfw7/ia1AwPMYmdroqIt4m9SGh2b346/9Yk6LS2/lJRFbX9PQvw8RTta1CXqivTGm2iH2Zx6ukrKw1qHk56oI+NWOoYKPSf+OELrvh0kddr07cDRym0Rorcfo0nnFlVldVuHk5GYRlblb5wzfxylhUX7Fn3bc/JncTuZc9uP07Cz1b15evWPYOtm8Wn1+dOX8PT2RK31l9QpLSnjxJHTAFRU6Pn7go2b5TL/Oh6Wd5oOA+EAgiAcArKrqfcx8Cb3sWmt0j8AY0Z61d/GzAyUAQFW9Zy7RODzxVd4zluEUq2WlDkE+lORanqoVaHLwjFQegC5NKuPY5CaggO28/Q7aAOoSDU9AtbrMnGwOAidm9bHMSiAooOnrNo7BfpTnmKyoTw1C0eL9gABYx6j6eHV1JozlqSFayVlXlpf8lKyqv7OT83GW+tnKXFHVJoA9DqzfqRlotJIx9OpcTgqrZqSw9ZL+wBU6gAM6WZzkpGBSm09Jy4R3QnYsA6fd95GqTHNyb2MhWvzMJyCAsjff9qmDQ5W/chAZaHh1CQch8A79OMBjEVN7XC09M3UTCvfdG0ehmOtAPL32/ZNUcPMhtRMq/EU/TuAwmr8+26kZWQRaDY2Wk0AaRkmu9Oz8wg0W+qh8fcmLSdPotGobi32nbwIwL6TFykqKSO3oMikkWOh4edNWrYtDfFia9+pv21o5BNotqRK4+dFWo70h2ejOoHsixYvtvZFX6aotIzcgmKTRm4hgWZLqrS+nqTnFkg0GtbWsK/yqd/+s3EUlZaTW2jScND4W/mW5Zw4NxX9ouigtV8o/dQYM03tjdkZKPzVVvWcOnXHc/l63F9fZFX+IDRcA/0oSTbFnOLUbFwDfS0lqgh7LpLUA+er/lapAzwdlToAACAASURBVDCkmWKFobpYEdkd9Tfr8F0ijRUA6blFBJr92NX6eJCeVyipM3nAI2yLjqXv/K+YuvoPZj0tXZKZnlNwb35ReRG+7/QV0S/M5jQ9p4BAX5NfaHw9ScuR+kWjEG2VX+w7EyvxC4WXH0KeaSyF/GwU3lKfUAYEofCvhcukd3B5cQmqBtKlYkqL2GtIz0Rlcb5zbNgAlUZN2dET2MI5yI8ys/NIWWo2TkHW56Kgcf3ocPwzwuaPIn7uV5Ky9LwiAn1MP9y1Pu6k5xVJ6kzu355tp+Pou+gbpq7dxqwnu5m1LybQ7Ie/1tuN9HyL9n3asu1sAn2XbGLqV7uZNVh60yE9p5BAX9ONHq2PJ+k5Fn7xRBe2nbhM31lfMHXlz8waLl0ynJ5v4VveNvrRtx3bzsTT993vmfrVTmYNkV6k2hsvcnJyKseikEBfS/+2HMuObDsdS9+FG5j65Z/MGtpNUp6ek38P8TfIFH9PXbKOnfklBJpd2Gq93UgvKJFoTO7Zkm3nr9P3P78w9dsoZj0uvdj20fqRbXZuz0nNwtfiN4p5HaPBSIlZ7PXR+pFr5pu5qVl4a6XxxtusjtFgpLSgGHdf6Q0/bZAaXYqu6u+0lDS0gRqqw9PLg559u1VbLvPv4R+/aFIoFA7AAMSleneqNxhIFgTh/J3q2UP58aNkPz+c3CnjqTgTjcfrc+5PQKEgaN4EUpest98IhQLNrEmkv7/27nXvQOY327ncbTIpy/6LdvqwGmnZhUKB3+uTyVm+pkYypUeOkf7Mc2SOnUh59Gl85s66LxuC548n+d2v7TdAocD/jRfJ+vDLGmnUeCxqaodCQfC8CaS8+9Xd695BI2juRHQ18e8HwMxRA4m+fI1hb33E6SvX0Ph5o1TeXwibOfJxomOuMWz2p5UaXvev8Vx/omNuMGze55yOuYHG1wul8v42L5/5TE9Ox91k+OKviI67icbH8/7sUChQvzWJjBrEi4pTR8mb/CwFMydQcT4a92mz/xGN29Qd2gW/VmHErLq/jY5L/zpG2tDnyBgzkbKTp/Gdfx+xopKdp+MY9Ehjdr8znpWTBzLv290Y73P5ysxn+4l+MX+VyS8U9+kXw3oTHZvIsEVrOR2XiMb3Pv1CqUIZEETpurcp2/IpTkNeBBe3u7e7jUKB9ytTyFtR86VGqV/v4lSnaVx7dyN1Zwy9ewMLdp6JZ1DHRuxeOIaVLzzOvO/33dec7DyXwKB2Ddg99zlWju/LvB8O3vec7jx1hUGPNmf3e1NYOXUo877efv8aZ+MZ1L4hu+eNYOX4/szbFHX/vmUjXqhUqnu34cxVBnVszO5FY1k56Qnmfbf3/m0Y+QTRV64xbNbHdsffnRduMKhtfXa/8RQrR0cy7+ej923Hw4ZKpeLjL5fwzTr7N8D+RzAKD/e/h5R/MhGEq0KhOFf5/8NAtb/IFAqFGzAHcWneHVEoFJOASQBr1qxhWFYmSrXpDoEyQF2VXOA2QoHpjmHpzm24TZwsKdfrsnAMMt3ddAz0p0Jnupuh9HDFpWFdwn5YCoCD2pe6a+eR+MK7Jo20TByDTHf0HAID0KeZabi74tSgLnW+EXc6VwX4ErJqIUkvie8kleuycDJb4+wU5E+FWXtLcn4/TO0lkyXrGPPTcvCuZboj6BXkR15adQ/1bGNIz8Qh0Kwf2gAM6abxVLi74lg/lMB14rstKn8/NJ8sJv3VBSaNjExUGrM5UasxZFjMSb5pTor/2IbnlElVf99tLJQerrg2qkv4ZnH8HdW+1F8/l4QJpjX1eqt+qDGYaSjcXXEKDyXoq/+I/QjwI/CzxeimmfXjAYyFvXbcpsLSN4MCrH2zUV3CfxD77qD2JWz9XK6ZjYWoYWZDUIDVeDo3rEO9TcuqNOp+OZ/ESe9wr2jV/ujMxiYtPROt2RMDjZ83uqzcqr/Ts/LQ+kpfotf4efPx62MBKC4tY++JC3i5m5Z3aHwtNLLzrF7E1/h68fGMMSaNUxctNLzQmT2dSs/OR+vrZa3xyggzjb+lGj4e6LJN/puWU4DGR3onU+PjyfKXhlZqlLPvdCxebi5V5fr0LCvfqrCIF84N6lLbLF4Er3q7qtyYnYEywNRe6adGyJK+7CwUmmws37cNt9EvSsofhEaJLhvXYFPMcQvyo0SXgyXabs1o+spg9j/5LsZyfdXnhoxMVFpTrFDdQ6zwenmSpFzj444u1/QEIS23EI23dJnV1uOXWTVFfKesVb0gyvQGcotKuP0sQ+PreW9+MV18x6O4tIy90ZctfMsTndnTqfScArS+1n7x8cvPVGqUs/dMTJVfWD5ZsnzyJNbJwnjrKhgNCDnpCFmpKP2DqsqNFrFXpQnAkGGaU4WbGw5h9QhY9bFY7ueH/wfvkvXmvKo6ZanZOJudR5yD/ChPrf5clPHrERq8/wLwuamf3u7ock1PKdJyi9BYLBnbeuIKqyaJ79a2Cg2krEJfNScabzd0Zk9T0vKK0Vgsqdx6Ko5VE/qJ7etqxTktLjWbUw90Zk/60nIL0Pha+MWRi6ya9rSoERZMmV5PbmGxScPLwrfybPTjVCyrJg6o7IcNO+yNF15elWPpgS7H0r8tx/Iyq14cKNpQL9CGf3vdW/yd+XylDWXsPWkRO71c0eWZnvqk5RWjsVh+t/V0AqueF5PztKqjpkxvJLe4rMqO3LRs/MzO7b5B/uRY/Ea5XSdHl41SpcTV001S5mPmmz5B/uSlSeNNXmWd3Mr2Lp5uFJn5wW8HNnLx7GUCawUC4j16bS0tabp0bPHO8rncuHaL/67ZxNx3X7NZR+bfw8PwTlNrQRCmCYJgvXjaRH2gHnBeoVDcAEKAMwqFItCyoiAIXwqC0F4QhPaTJk1CHxuDKjgEpTYQHBxwjuxJ+fEjkjYKP9PjX6dOXaySRBRfuIpzaC0cQ7QoHB3wHtid/L2mJTHGgmKutBtJbLeJxHabSPHZWBJfeFeSoaz0YhyOdWvhGKwFRwc8H4ugcP9xk0ZhMQmPPsu1XmO51msspedjSHppUVX2vOLzV3GuF4RTbQ0KRwd8B3Yjb490WY5zqOnk6NWrPWU3UiXlSecTCAgNxDdEjcpRRauBj3Jlj+3la9VR9ncsDnWCcagljqd7v0iKDx6rKhcKi7nV42mSHhtN0mOjKbt4hfRXF0gyxlXExKCqHYwqSNRw7d2TsiNHJd+j9DfNiXPXzugTTZd/dxsLY0ExF1uP5nKXSVzuMomis7EkTFgiyZ5XdikWx7rBOARX9mNABEVR0n4kdn+GW/3HcKv/GMouXEE3bYEke96DGAt77ZCORS2camurxiJ/j2lZjbGgmEttRnG56wtc7voCxWdjuTZhicQ3Sy7ESf37ie4U7JVqxLQfSVz3CcR1n0DJ2VgSJ70jyZ53NyK7duL3nfsQBIHzl67g4eGOOsA0x83q1+amLpOk9Cwq9Hp2Hj1LRHtp5qWc/EKMRvFdu/W/7mNID+l7bM3qh3BTl0VSeraocew8Ee2aWGgUmTR+O8CQCOl692ZhwVKN4xeJaNtYqlFgpvHHIYZESLN+NQutxc30HJIzcqnQG9h16goRrRpYaBRX3WFdv+MYQ7q2lJSXXozFsW4tHCrjhddjERQdsIgXnYdzvffzXO/9PKXnY0h+6e2qckN8LMqgEJQa0a8cu/akPFp6jCl8zN4dat8ZQ7L0nZAHoZF97hqe9QJxr61G6aiizuBOJO+Sxhyf5nVp/8EEDj//EWVZ0iVvFVdicAiRxorSv6qPFS5dO6O/IbWhWR0tNzNySc7KE+fjTBwRLepJ6gT5enCiMpHBNV025RUGfD1MP/ia1QvmZlo2SRk5ol+cuEhEmzv4xZ+HGdK9jdSO0FpmGgZ2nvybiFYNLTTM/GL7EYZ0MWWEMybHo/QPQuGrAZUDqpZd0MdIl8saLp9CWa/yuHHzROEfhDE7raq8/EoMDmax1613T0oPm8WboiJ0A54k7akRpD01gvK/L5P15jxJ9ryCc/G4hgXhUkeMv+ohXcjaLbXDpZ7p1OzXuy0l16Xnoma1NZVzki/Oydl4IpqHSuoE+Xpw4mrlnKTlUK43zUmzEDU3M/NJzi4Q25+/RkRTaUa6IB8PTsSnVLbPFefU3XRjolndIPE4zbx9nMYQ0VKa0TPIz4sTMaI/XUvNorxCj6/Zj/RmtW/bUdmPcwm27bh6247KfpjbUcN40ayOhpuZeWZjedV6LH08LfxbL/Xvqvh7O3aeI6JdU6kNkti5nyGRFrEz2J+bWQUk5xSKdlxMJKJxiIUdbpxIEJe9XUvPqxwL56ry6+fj0YYGERCiQeXowCMDu3Buj/R1hXN7ouk8NBKA9o89SsxR07utN88noA4NxK/yN07bgZ25uEfqm5f2RNNxqLj8tvVjnbh6VPpu1uAeI9m7I4onhz8GQKt2zSnMLyTDxk3qV2dPwdPLgyVzP7Iqk/l38j+RclwQhItA1e2xygun9veUPc9ooPDzT/Be+iEolZTu3o4h8QZuY8ajj4uh/PhRXAcPxenRLmAwYCwooPCj96QaBiMpC1dT75tFYkrmH/dSdvUmmhkjKbl4lYK9tt9XsdRIf+cLQta/C0oVeT/vpjz+Jv7TRlN6KY6iA7bXj5u3T5r/JfW/fVtMs715H6VxtwicOYLii/Hk7zlJwNjH8ezaCqFCjyGviMSZn0gkjAYjvy/YwPhvZqFQKYneEkX61WR6z3ia5IvXuLL3DCEtwxi1Zgau3u406dWW3jOe5pO+b0rsyH5vJdovlolptn/bRUVCIj5TnqfschwlZhcNd+pL/vIV+C3/AJRKSrbtQH/9Bh4TxlERE0vZkaO4P/2UmBzCYMCYn0/ukvck7e82FvdiQ+bSlQSuXopCpaRgq9gP35fHUPZ3HMVRx+9J40GMRY3sMBhJWrCGsG/EscjespfSq5VjcSFecnF/J42Ut1cT+t/FKJRKcn7cI/r3q5X+ve/uGm8sfI9TZy+Qm5tPryGjeGnCaPR68YnB8Ccfp/ujHTh87BQDho3H1cWFd+bMkLR3UKmYPf4ppiz9EqNRYEhkR8JrB/L5lp00Cwshsn1zoi8nsGLTdlBAu8ZhzJkw1Fpj7GCmvLceo9HIkMgOhIcE8vmPu0WNdk2JvpLAih92gkJBu8b1mDNuiLXGmCeY8p//ihrd2xEeouXzn/fSrF4wkW2bEH3lOiu27BHtaBTKnOcHWmgomTWiD1M++QGjIDC4S0vCg9Ws+u0QTesGEdm6AdFxN1nxSxQKoF3DOsweYfEQ3WAk491VhKxbAkol+b/spjw+sTJeXJVcQNnEaKB43ad4zP8PKJWU79+B8dYNXJ4dhyE+loroozg/PhSnDp0RDAaEwgKKVr73wDUEg5EzczYQsektMeX4DwfJj0um+RtDyT5/nZTdZ2g1fwQO7i50/vIVAIqTM/lr7PKqcchbvgL/jz8AlZLiP8VY4TlxHOUxsZT9dRT3Z57CpbpYcXs+no5gyioxnfLgTk0JD/Jn1bbjNK2jIbJFGDOHdGPxD/vZeOAsKBQsGtkbhdnSOgeVitmjH2fKf76p9Iu2hIdo+PyXfTQLDSaybWOiY26w4sc9gIJ2jeoyZ8wTVnbMHtGfKZ9sEjW6tCY8WM3nv0bRLLQWka0bEh2byIpf9ov+2aAOc0aa5UQyGin/Yz0uY+eCQon+zAGE9CQcew3HmJyAISYaw9VzqMJb4frKx2L9nd9Cidl7OgYjuR99RsAn74NSRdHt8XxhLBVX4qwuSG1iMBI/Zz3NN80VU45vOkBxbBJ13xxOwbkEsndHEzx+AD7dWyBUGNDnFRI7faX1nDzVjSlf/onRKDC4Y2PCA/1YteMkTWuriWxej5mDOrN4y0E2HrwAClj0XM+qOXFQKZk1+FGmrNsptu/QkPBAX1btOk3TkAAim9Vl5hMdWfzTX2w8LP4oXjS8m8WcKpk1vDdTVvwk+kXnFoTXCmDV73/RtG4gka3CmTk0ksXf7WLjvmjRhucHWGsM6cyUtTsq+9FI7MeuaJqGqEU7BnZi8Y+H2XhYfANh0bAIa40axAsHlZJZQ7sxZbW41cLgR5qI/r39hOjfzesxc0gXFm8+wMaD4pOTRSN6Wfv32CFMWba2MnZWxt8fd9GsXgiR7ZtVxs4dALRrEsaccU9az+kT7Zny3/2iHW3rE671YdW+8zSt5U9kkxBm9m/H4t+Os/FojHicPfWoxA6jwch3C9Yx85t5KFVK/tqyn5SrSQyZMZwbFxM4tzeaQ1v28cLy6SyL+oyi3ELWTPuYDk90rmr/04KveOmbOShVSo5viUJ3NYnHZjzDzYvXuLT3NMe2HGD08qnMj/qU4txCNkyzzvwZtecIEb27sPfkr5SUlDJ7uikL8W8HNjK4x0i0QRpemjmBhLjr/Lr/u2oPl4cW27m+ZO6CQhD+mbWDCoWiUBAEDxufbwIigQAgDVgoCMJ6izo3uLeLJiGzX8RdqlRPwC4xw8rFegPvUrN6Wlz/g9jGA+xuD9AoZgdn6wy2u32bm+I+BbNDR9ylZvUsuyHuz3KjdR+7NULP7SG1a4+7V7wDQX8deCBjca3FXVd6VkvYxd01GgcQx6KmNgCcqzvoLjWrp3Xi7wBcCqsmvfw90Pzan1RkXrt7xTvgGBBG6bn7e4/FHJfWov2lp633rLlnjXbixVPpSdt7p92TRsdnKDm0we72AK7dxxLXxGby0Hui4RVxy4Gcyjux9uBbmZmqphqbg0ba3R5geOpGUjrbHy9qHT0AQMmulXepWT2u/aYCUHrc/vcVXDoNp/Twt3a3B3DpNpqiuc/cvWI1uC8R/Tr50Z53qVk9wcf2A3Ao0H47uut+pGTbJ3eveAdcH3+Vkt8+sL/9YPEmYMmBdfZr9Jgoavxue4uFe9IY9HqN4oVr97GiDTtsp9q/J40BYtry0jO/263h0nYQJVsW373inewYtoDxoU/b3f6rG+K+gtNDh9+lZvWsuCEe4w3V7e9Ss3riMqJBTBL40JM3utfD++IQ4P3tvodyHP+xJ022LpgqP3/O1ucWdUIfuEEyMjIyMjIyMjIyMjI2+J9YnicjIyMjIyMjIyMjU3PkzW3t4x9POS4jIyMjIyMjIyMjI/MwI180ycjIyMjIyMjIyMjI3AH5oklGRkZGRkZGRkZGRuYO/GPZ8/6P+Fd3TkZGRkZGRkZG5qHhocz6Zknucz0e6t/HPpsOPJTjKD9pkpGRkZGRkZGRkZGRuQP/+ux5aT3s36dJe0Dcp+lCqP37NLW88QdXGjxmd3uAJle3s09r//4DvdLE/QcexB4GiW17261R98zeGrW/rVHTfUKAGs1Jk6vba7SXDoj76dR0/y+AU8FP3qVm9XRI3grUfB+ymuyxBOI+SzXZ68kxIAyAspiDdms4NxbjRMnuVXZruPZ9qUb7+YC4p090yJC7V6yG9kniXlVZj9sf9/y3ieOo6x5pt0bgoSi+qzXK7vYAo1K+q3G8AWq0R5JLt9GiRg3373oQfvEg9mlKaN7Pbo36l3YBEKW1347ItB8p+e8su9sDuD7/Xo33RwIezF5Pfy63X+OJmTXas8r18VdFGx7EPmQ13OOuZPOiu1e8kx3DFzIudOjdK1bD1zd+BmBK6DC7Nb64sQWANoFd7NY4qztid1uZ/w3+9RdNMjIyMjIyMjIyMjKVGP9pA/43kZfnycjIyMjIyMjIyMjI3AH5oklGRkZGRkZGRkZGRuYOyMvzZGRkZGRkZGRkZP4/QTA+1MnzHlr+v7hocurQEc+p00ClpGTbNoo3fS8pd+nXH8/JUzBkZgBQsnUrJdu3Sep4RLQleMELoFKSvXkPGV/8ZPO7vPp3JnT1bK4OnEHJxXhJmXu3dmjnvYhCpSR3yy6yvrT9crFnvy6ErJzL9SdfofTS1arP/Xq0ouG7Y1GolKRs3E/iZ79J2gWP6U3I+H4IBiOGolJiXv+SorhkSZ0mEa14asFYlColxzbvZ+8XUg0HJwdGLX+Z2s3DKMotYMPUT8lOypCOV+cO+L3+EqiUFG7dQf6GH2z2w61nN9QfLiR15EuUX4l7oBq+PVpT/51xKFRKdBv3cWul9EXWoDF9qDWuf9VYXH1jDcVxSZI6NZ0PALeu7dDMmQJKJXk/7SRn3RabGh59ulBrxXwSn55G2d9SDY/ubam18AVQKsnZvIeM1dX7Vt0vZhM/aIb088g21Fk8AYVSScamveg+/0VSrh7dD83zA8AojsWNN1dRelU6FvbYYOnfR87F8P6GXzEajTzZ8xEmDOklKU/JyGbh6s3k5Bfh7eHG0qkj0Pr7VJXPW7qcQ0dO4ufrw6/frbb6bkEQWPbJag4fO4WLizNL5r5G00bhkjp/nbnE+2s3YzQaeapPVyY8PUBqQ3oWCz77Lzl5BXh7urN0xgQCA3yl/bh8gw9+PojRKPDko80Y37eDpDw1O5/53+2hoKQMo9HI9EFd6NasnlTjwlXe37hd1Ihoy4QnukvtyMxl4fqt5OQX4+3hytIXh6L185aOdWQb6iyaCColmZv2WM/rqH6oxz4GBiOGohIS35ImsXBs1xH3SdP+H3vvHRbV0f7/v3YXkN6W3QXFihXsBbugJrbElqLRaGyJUWNPs8YaoykmGmPsyWOKUZOYmGBBUWzYwIoKKhZE2tI7bPv9cZDds4uF8nuSz/Pd93V5XbIz89577rnnfeacM3MvSKUUhYZQtFusezWe64fj+Mno04X5XfTXHopDxbpnFxiI6/SpIJVRGBJC/k9iDod+/XCZMgmdOg2Agt/3UBgi5vAJbkmHZaORSKXc3hHOtXV/icqbTeyP38hgDFodRem5nJm9ifyH6WXl1aE3p6LjWLXjoDAe3VszYYD4sHdiehaLvvubzNwC3JzsWfHmEFSermKOKzdZ9cM+Ib6D2zFhoDjZRmJaJos27yEzNx83JwdWTH7VYkyrGheyRq2xe2EcSKVoI8PQHLc8wC9r3hm73sPAYECffJ/iXWtE5Q5d2+M1ZxISmYyc3/aTtbV8zXJ6rhveXy0kYfhUC83y7NmahssF/U36KYz4r8V21HzjeWqO71cam0XEvmepv6fiUvj00BX0BgNDW9VlfJcmovLPDl3h/H0hroq0WjLySzj57ovG9jEP+HTvacGXgU0Y36u1qH1SZh4Ld4aTW1iCXm9g+oAOdG9WR2xDbAKf/nkGvUEvcPRsVQ7HcXKLigWO/h3o3qy2mCMmnk//iBDs6NiU8b3bmHHksnBHuKAXBgPTX+hoaceNeD7946TA0akZ43u3teT4+Qi5RSWC5rzQiT4vmLS/fp9Pfz9eqln+jH++vbh9Rq5RswwGpg/sQveAemIbLseyavtegaNnByYM6ikqT1RnsmjTbqN+Txku0m+AU7cS+XRflDCmbf0Y3yNAbEdWPgt/P01ukUaw4/lWdG9cS1SneVBrRn40HqlMyvGdYez7do+o3MbOhrdWT6du8wbkZeXy7VRxMg7/oFYM+0iIzVM7wwgtZ40zZvVU6pSucbZM/cpijQPwwfKZdO3dmaLCIhbN+JiYq2ZrGIcafLp5Ob51a6HX6zkeetKCw4r/PfxjN00SiUQHXC214QYwxmAwFEgkkm3Ai0CqwWBoblJ/MfAW8Ci65xkMhn1P/SKpFJcZM8l6/110ajWeGzZSHHEK3f37ompFR4+Qu3bNYzlqLZ3E3VEL0SSn03DvanIOnaX49gNxNScHvMYNJP9iTLkc3ounED92PprkNOr/9hW5R85QUg6H55jBFF4y45BKaLJyPBeHfUxxYjodDn5C2sFI0U1R8u+neLhdyBjl1bcdjZa8waURn5SVS6QSXl06nm9GfUxWcjrv7f2E6EORJN82cnQa1ouC7HyWBc+g7cAuDJozku+nmvhFKsXzw2mkTvkQbYoanx+/ofBYBJq78SJzJY4OuIwcSvHVG+X6okocUikNP5nA1WHLKE7KoM2BT0gPjRRdlFN/P0nS9kMAePZpT4PFY4ge+XH1jUcph3LhOzycMA9NShp1d60l/+gZSuIs++H+xhAKL5fvi5pLJ3F39EK0yen4/bmanMOPj60C89iSSqn78URujlhMSVI6/vs+JSv0nOimKH3PcdQ/CJmv3J/vQJ1F47g5aln12QDo9HpWbPudjfPfRiV3Y+TcrwhuH4Cfr3dZndU//MXAHu0ZFNSBs9G3WLNjHyumjiwrHzLgeUa+PIh5y8rPjHXi9HniExLZt3MrV67FsOzzdezYbMw+pdPpWbHxZzYtmYVK7sGI91YQHNgKvzo1y+p88d1uBvbsxOBeXTh7JYa1P/zOilkTRP34ZHc4G94Zisrdmdc/+4WgFg3w85GX1dl88Dx92jRiWPeWxCWlM3XDn+xfUl/EsWL732z8YAwqT1dGLt5IcJum+NVSGn3xy0EGdm3NoG5tOHv9Dmt2H2bF2ybZo6RS6ix/m5sjF6FJSqdZyGeW4/rHcdQ/CuPq9nwHai8aL2rvNHkmOQveRZ+mxu3LjWjOnEL3QKx7JcePkL/h8brnOmsGmbPfQ6dWI9+0gaKTltpZeOQouV+VzyGRSghcMYaw11ZSkJRB/31LSTgYRfatxLI6GdH3uNl/IbrCEhq90Zs2C0dwctK6Mhuqqjc6vZ4VP+1n4+zXUXm4MnL5VoJbN8avpqKszupdYQzs3IJBXVtx9sZd1vx+hBVvDhFz/OcvNn44ThjTjzYQ3LaZeEx/PsDAbq0Z1L0tZ6/FsWZXKCsmvSrmqEpcSKTYDZxA0XfLMORkYD/5E7Q3IjGojTEhkXtjGzSUwo0LoCgfnMQ3fkilKBa8Q+Jbc9Emp+G782vyj55Bc8fSn26jhlD0GM1qtHICl4ctozgxg3al1yJT/U35/SSJpfor79uehkvGcGWEUX91egOfHLzMhhFdUbk68Pp3Rwlq5IOfwmjv+8+3LPv/jvNxxKRkiXz5/WQgpgAAIABJREFUyZ5TbJg4AJWbE6+v/YOggLr4qYwPQDaHXaRPywYM6+JPXEomU7ceYL/JzYrAEcGGt/oJHF/vJci/jhnHJfq0qs+wzs0Ejm2h7G82XMzx+yk2vP2CwPHV7wQF1MPP24Tj8AX6tG7AsC4BxCVnMnXLPvYveN2M4wQbJg0UOL78rZTD08hxKIo+rf0Y1rU5cckZTN28jz7vm7TfHc6Gd4YImvX5ToKaN8DPx6R96CPNakFcUgZTN+5lf8BYkQ0rvvuDjXPfFPR7wTqC2/rj56sqq7P6pxAGdm/HoB7tOHvtNmt2HmDFlNfE/fg7kg1jegljuvEgQU198VMab/o3H4umT/O6DAtsRFxqNlN/DGf/bONNk0QqZfTSt/h81FIyktP5aO8qLh06T+JtY2x1H9ab/Ow85gRPJXBgV4bNGW3SXsJrSyewdtRyMpPTmbP3E66YrXG6lK5xFgVPp/3ALgyd8zpbp4qzGHbr3Zk6DXwZ3Hk4LdoGMG/Ve7wxYCLm2P7tDiJPXcDG1oaNu9dalFvxv4d/8kxTocFgaF16Y1QCTCr9/Hvgcfmcvyxt0/qZbpgA26bN0CU+RJeUBFotRUeOUKNrtwoZ6ti6ESX3kyh5kIJBoyXrr+O49uloUU/17uuoN/yGoVhjUebQsjEl9xPRPEgGjZackOO49O5sUU8xczTpm3ajLy4Rfe7atiGFd1Moup+KQaMj5Y8IvPqJn4Dr8grL/i9zrIH5DxfXbd0Q9f0U0h+kotPouPBXBC3MnqK36NOec78JKYcv7TtD4y7NReV2zZugTUhE+1DwZ/7BcByCLVN0uk8ZS873OzGY9aM6OFzaNKTwbjJF8akYNFrUf5xC3lf8ZM3cF+a/c1zV8QCwb9kETXwSmoRSjn3HcOplyeE14w0yt+wuNy4cWwmxpSmNrey/juP6fDmxNVuILb0Zh1ObRhTfS6I4Xmif8edJPPoGiuroTXwhdayB+e9ZV9UGgOjb8dRWyfFVybG1saFflzaEn78mqhP3MIXAAOHNUGBAQ8Ijo0Xl7Vu3wM3VxYL7EY6ePMOgfr2RSCS0at6M3Nw81GkZRhtu3aWOtxJfbwW2tjb0696Bo+cuizjuPEiiY4umgg0tmnD0rLg8+n4Ktb3c8PVyw9ZGRt92jQm/Kk6HLpFAfpEQD3lFJSjcnMUcdxKorfLEV+kp+KJjC8IviG804x6mEthMSJUe2Ky+RblTa2FcS0zG1d1Mc/SiGLfHdGBtGgu6p08W5ljx8SPYdqqY7tk2a4ruoYl2hh3BvlvF0vHK2/iRey+FvHg1eo2Oe3+ewbdvO1GdlIgb6AoFf6ZduI2jyWKvOvQm+m4itZWe+Co8sLWR0S8wgPBL4qfGcUlqApvVAyCwaT2L8ui4BCG+H41ppxaER4lvKOIS1QT6l46pfwPCo8RjWtW4kPo2RJ+RjCEzFXRadFdOYdNMrHs27Z9De/aAcMMEkJ8jKq/Rogma+ES0Ccmg1ZK3P7xczfKcNoasbbswlFj6U7gWJZdei7Sk/nEKr35P1l/za1F0Yga1PZzw9XDCVialr78v4beSLL7rEfZfT6BfgPENT3S8mtpervjKXYV52tqP8Gvim3kJkF8aD3mFJShcHcU2PDDjaNWA8GtmN4/mc92cIz6V2nITjjYNCb92z8wOCflFmlKOYhSuTpYcXm5ijugncZSgcDPaEX0/hdoKd6NmtS1HszDtRzk23H4g1u/OrQiPui6qI+i3HwCB/n4W5dEJ6dT2dMbX01mwo0VdwmPEbxclEgn5xSb9cHEQlTdo3ZDU+8moH6Sg02g599dJ2pitUdr2CeTUb+EARO47TbMuLcrK6rVuiPp+Mmmla5zIvyJoZda+VZ/2nCltf2HfGZqarXEAgvp24+9dBwC4euEaLq4ueCnlojpFhcVEnroAgFajJeZqrAXPvxr6f/m/fyn+LYkgTgANAQwGw3Eg48nVnx1SLy/0qallf+vVamReXhb1avQIwnPLNtwWL0GqUIjKbFVyNIlpZX9rktKxVYknkEOAH3Y+CnKPRpZrh423HG2SCUdyGjZmHPb+ftj4KMgLP2/R3t7bk6JE45aV4sR0anh7WNTzHdeHzmfX0HDh69yc/72ozF3lSZYJR1ZSOm4qMYebSR29Tk9RbgFOHsaFrI3CC22y0Z+6VDUyMzGxa9oQmUpJ4cmzFvZVB0cNH0+KTX2RlIGdj9yins+4vnQ48zUNFo7i9vxtYhuqOB4ANko52mTja31tSppFXNTwb4iNt4L8Y+fK5/CWoxHZkY6tt5kdAX7YPia27Lw9KTGJzZIky/YAyjH9aXHqW2ovGEP8R1uq1QaA1IxsvE22aijlbqRkZovqNKlbk7BzVwEIO3eV/MJisnLzy+UrDynqdLyVxrmrUnqRojbanZKehcrLuOBWyd1JTc8UcTSuX5vDZy4KNpy5SH5hEVk5ecZ+ZOXhbRLvKndnUrPyRByT+nci5HwMfRZuZeq3fzLnFfE2rdTMXLxNtlQpPV1JyRQvXpvU8SasdNERFnWD/KJisvIKysrtfDwpMRmTkuR07ExuJh5BMaY/zU9uwHe+eFylci/0aSa6l6ZGJrfUPbuuQbit24bz3CVIvcS6J/VSoEs1xrdOrbbQRgD7oB7Iv9uK+9IlSJXickdvDwoSjXJekJSBo4+lbj1CwxFBJB4x3shWh96kZubi7WF8g6H0cCElM1dUp4mvirALwqIn7EIs+UUlovFIzcx5tjGNLB3TyOvCmOaaclQtLiSunhiyjbpnyMlA4ib2hdTLB4m8JvYTl2H/9sfIGom3rJWnWTZKcVzYNRM0q+B4+ZpVw9tMfxMzqFGO5tQc15eOZ8vX39TcIrxdjQtmlYsDqblF5X5fYnYBiVn5BNY1xlZqTj7e7saHFSo3J1KzxVoyqU87Qi7cps/yn5m67QBzhnQR25BdgLebkwmHI6k5ZhzPtyXkYhx9Pt7B1G2hzBnc2ZLjaXb0bUdI1C36LP2RqVv2M2doVzOOfLzdTexwL4ejX3tCom7SZ8l2pm4OYc7Q7sb2WWa+cHcmNdtcszoSEhlLn4XbmLrhr3I0y0y/Pd1IyShPv4UHXWHnr1nod2puodifro6k5hSIOCb1bEHI5bv0+XwPU38MZ84L4pttD5UnGSbXs4ykDDzMrqnuJnX0Oj2FJnPMXeVJpklsZial467ytGifabLGKTRb4wAofRQkJxp1JyUpFaWPpfY9grOrMz36VP73naz4v4N//KZJIpHYAP0Rtuo9DVMlEskViUSyTSKRlHvllUgkEyUSSaREIonctGnTM9lQfDqCtBHDyXhzPCVRkbjNmffsHRC+FJ+FE0j8eGvF2plxKOe9ReonmyvPASR8F8rpjjO4vfxn6s96qUpclYJEgsfsyWSutjyT8l/lAJK+O8j5TtO4s/wn6s6q4A/nVcd4SCQoPpyIelXVOHwWTCCpKrEFpP5nP1e7TubBx9upOaOCP05ZTTbMHjWQyOt3GPbhF0TduIPS0w2p9L8rQe+OfYWo6JsMm7mMyOibKOXuFbbhQFQsgzr6E7psAusmD2bBD6HoK3iodvZrfYmMucewheuJirmH0sMVqURSIQ4A9X/2E91tEgkrtuMzvWLjWnI2gsxxw8meOh7NxUicZ1dQ94CiiAjUw14jfdwEiiMjcZs3t8Icj1D/pa54tmzA9W9Dnl75EapJK2YPe47I2PsMW7KZqJv3UXq4VDguZo/oJ4zpgm+MYyqt2JhWOS6kMqRePhRtWUzxrjXYDXkb7B2f3u4RJBK8PphI+mfPdu18EhK/O8jZjpXUXxMcvJ7Ac01rIaugLw9cvM2g9o0JXTCSdeP7sWBHeIXn6YFLcQxq14jQ+SNYN74PC345VnGOi3EM6tCY0I9Gse7N/izYcaTiHBduMyiwCaGL3mDdWy+w4Ocw9Ppnfxx/IOomgzo2JXTZeNZNGlg5zXr9BSJj7jBs7ppS/XatuHZeucegNg0IfW8o60YFs+C3iArb8W+DTCZj5YbF7NhS/jlgK/638E8mgnCQSCSXSv9/AnjaiuxbYBnCPqtlwBfAePNKBoNhE/BI8Q0Z70xBqjTuGZcqFOjS0sRtcoxP+wpDQnCeOElUrklJx7am8WmcrY8cTYrxaYbU2QH7xnXx+2UFADYKD+ptWcC9N5eX1dEmp2PjY8Lh7YXWlMPJgRqN6lLnx1VlHL4bPiJh0lIAipIzsK9pfOJSo6ac4mTxU3RTpOyJoOmqN0WfZaVk4G7C4e4jJztFzJFdWicrOQOpTIq9iyP5Jk9lteo0bLyN/pQpFehSjf2QODli61cP781fCOVyTxRfLUU986Nq4yhOyqCGqS98PClJMrY3h/qPUzRa9RbwjdGGKo4HgDY1HRtv49MnG5WXOC5KOWpvF355XublQa31i3k4ZbHIDluRHXI0yZax1cAktupuXlBWXpKcgZ1JbNr5iNubI+PPk9T95G3RZ5W14f5bxvhWerqRnG48c5Cano3KQ3wIXunpxpfvjQWgoKiYw2ev4Ook3p7xJKgUcpJTTd4spaahUpi8eZK7k2KyXS8lPQulXPxsRSl358u5kwUbCos4fPoCrs7GRaXS3Zlkk3hPycpD6S7efrfn9DXWTxHOu7Sq70OxRktWfiGPnrEqPVxINnlKm5qRg8pDfLZE6eHKl9NHGH0ReV3ki5KkDOxMxsTOW05J0uNfwGf8eYI6K4zjqk9PQ+plonteCnTpZrqXa9S94tAQHMeLdU+fpkZm8uZIplCgV4sPTIu08+8QXCaJY6sgORPHmsYnvY4+nhQkWeqWd/cAms8YROhLH6Mv0ZZ9Xh16o/RwIdnkjU5qZi4q8yfL7i58+Y5w01lQVMLhCzG4OtqbcLg+25jOGFnKUczh89dEY1rVuDB/s2T+5kmok47+wS3Q6zBkpmJIT0Iq9ykrL0+ztCZzSurkgF3DetT87pFmeeL99RKSpy0qq1OcbKa/NT0pfoLmpO45RWMz/VW62JOcY9zCl5JbiNLFvpzWcOB6AnP7ihM0KF2dSDZ5A5ySnY/STbzlbM/5WNa/KSSCaVVPRbFWR1ZBkXGeujmSbPJGJyW7AKWrOcdN1k/oK3DUfQzH0+w4G8P6twaU2uFNsUZHVr4phxPJWSZ2ZJXHcYP1E1804dCSmZmJXC5H6W7mi6w8lGZbhvecuc76yYOE9vV9hH6INMtMvzOyLZKYKD1c+XLWG8Cj+L4qjm8XB7E/cwpQmm1n3HPhDuvfCBbsqKMo9WdxmR2ZKRl4mlzPPH08yUwRx1ZWaZ3M0jWKg4ujqMzDJDY9fORkpWRYtPcwWeM4mK1xfjn8Pdcu3cC7plF3VD5KUpMsk0UALPj8A+LvJPDz5l28v2xGuXX+jbBmz6sc/g1nmlobDIZpBoPBcvO0CQwGQ4rBYNAZDAY9sBkIfFL9R9DExCCr5YvU2xtsbLDv1YviiFOiOlJP40W9RpeuaOPFe6MLLt/Crl5NbH1VSGxtcB/Yg5xDxq0L+twCrrd9nZhubxLT7U0KLsZy783louxihVdvlnFga4PrCz3IDTtj5Mgr4FbHEcT1HEdcz3EUXoohYdLSsmxtuRfjcGzgjX0dBRJbGaohXUg7KN4q5VDfeOje6/k2FNwR7xGPvxyHop43nr4KZLYy2g7swtVDYo7oQ5EEviy8um89oBO3IsTnUkquxWJTuxY2NQV/OvUNpvBYRFm5IS+fhN4v8/DFUTx8cRTFV2+gnvmRKJtVVTlyL93GoYEP9nWUSGxtUAzpSnqouB/2Jr7wfK4thXfFvqjqeAAUXY3Ftm5NbGqVcgwIIv+omCOuy3DuPjeGu8+NoehyDA+nLBZloiq4cosaJrHlNrAHOYfFsXWj3evEdn+T2O5CbJnerORfukWN+j7Y1RZ84Tm4G5mh4u2ENeobF01uz7Wj2MwXlbXBNL4D/GoTn5xGQmo6Gq2WAxEXCWovzpyUmZNX9nR06x9hDOn5TFO4DMHdOrH3QBgGg4HL0TdwdnZCYbIdL6BRPe4npZKQkoZGo+XAifMEB4oXW5k5uWU2bPl1P0N7i7dUBNRREa/O4mFaNhqtjoNRNwlq0UBUx8fDhbOxQpKMO8kZlGh0eDgbFw8B9WsRn5JBgjpT8MXZqwS1aSq2Izff6Iu/TzCkhzjjVv7lW9ibjWvWIfF2KdG49m4vGlftzVLdUwlzrEaPXmjOinVP4mFydqhjV4skEZqYWGS+vsh8SrWzdy+KT0WI6kjlJtrZtQva++IzIemX7uBS3xun2gqktjLqDe5EQugFUR2P5nXpuGo84WNXU5wu3q5WHXoTUK+myXjoOHDuGkGtGou+JzO3oOyJ99Z9pxjSVRw3AQ1qEZ+cTkJqhjCmZ64S1PYJY/rXcYYEiTOgVTUu9A9vI5X7IPFQgswGWcuuaGPEuqe7fh5p/dJ55+iCRO6DPiOlrLw4OhbbOrUEzbKxwbl/sIVm3es+jPi+Y4jvO4biKzdInrZIpFm5F8X6qxzS9YnXIvnzbSk0uxYF1PQgPjOPh1n5aHR6Dl5PIKiRD+a4m5ZLTpGGVrXEW6wCaiuIT8vhYUaOME8vxRHkL85I5+PuzNnShCN3UjIp0erwcDLemAX4PuLIFTgu3ymf4/Yjjixhrpty1FYSn5bNw/RSOy7eJiigrpjDw5mztx6K7XA241BniTma1yuHI0HE4Vm6binTrPRSzbpwk6AW9S3blybqKFez/HzF8X36MkHtmok4MnNMYvPPowwJEp8VCqglJz4jl4eZeYIdV+8T1FScGc/HzZGzd4R4vKPOpkSrx8OpRln53cu3UdbzwctXiczWhsCB3bhotka5eOg8XV8OBqD9gM7ciDCejb1/OQ5lPR/kpWuc9gO7cMWs/ZVDUXQqbd92QCdizdY4rz03lqMHjvPiMOFofYu2AeTl5pGWavlgYMqHb+Hi4sxnCx+TTMeK/zn8n0k5LpFIfAwGwyPlHQpEP6l+GfQ6ctd+hcennwupd/fvQ3fvHk7jxqONjaE4IgLHl16mRteuGHQ6DDm55KxcKebQ6Un8aAMNti8BmZTMXYcpvhWPatbrFF69JVpgPhY6PclLvqX2tuVCiutfQym5HY/XjFEUXb1F3pHy9+M/gkGnJ3buNtr8Mg9kUpJ2hJMfm0CDD14l5/Id0g5GUXtCXzy6t8Cg1aHNzuf6dHEKYr1Oz68fbWPK9nlIZVLO7Aon+VYCA2a9SvzVO0QfjuL0rqOMXj2VheFrKMjK4/tpZmKg05Ox6muU36wEqZS8vQfQ3LmP26QxlFy/SeHx08/kiypx6PTcnreV5jvmCynHdxylIDaBuh8MJ/dSHBmhkdQa3x/3Hi0waHRos/OInb6uWsfjEYd6+Xp8t3wMUik5v4dScvs+8mmjKYq+JVqMPIkjcdEG6m9fIqT73i3ElrI0tnKfFls6PfELNtPk50UglZK2M4yimw+o+d4ICi7fJuvQeVRjB+DavWVpXORxZ+ZaC44q2QDYyGTMHf8Sk1dsQq83MCQ4kIa1vflm1wECGvgS3L45kdfjWLtjH0igXdMGzJsg3rLz/qKVnL94haysHHoPGcWUCaPRaoW3DsOHvkCPzh04cfo8/YeNx8HenmXzZlnYMG/iCCYv/gqdXs+Q3l1pWKcm3/z0J/4N69KzY2vOX73J2h/2IJFAW//GzJ80woxDypxXg5m8/g/0BgODO/nT0EfO+pDT+NdREdyiAbOHdmfpjjB+OnoRJLBk1PNITLZQ2chkzB39ApM/245er2dIj7Y09FXyze9hBNSrRXDbpkTG3GPt7kOAhHZN6jLvjRdFdqDTE79wM41/WgRSGek7D5eNa/7l22QfOo9y7ABcu7UqG9e7s9bQ/MjXQnu9jvxvv8J1maB7xYf2oYu/h8Oo8WhvxaA5G4HDoJex7dgVdDoMebnkfWmuezpyvlqDx+efgVRK4b79aO/dw3n8ODSxsRSfisDx5Zep0bUL6HToc3LJ/kTMYdDpOT//P/T++QMkMilxvxwj++ZDWr7/MhmX75IQeoG2C0dg42RP903TASh4mE742NVlfqiq3tjIpMwd2Y/JX+0QxqNraxrWUvDNH+EE1KtJcOvGRMbeZ+3vR0AioV2jOsx7vZ8Zh4y5b7zI5M/+Uzqm7Wjoq+Kb3w4TUL8WwW2bEXnjLmt3HRLiu0k95o0ZaMlRlbjQ6yn5ayv2Y+eDRIr2wlEMqQnY9h6O/mEcuphIdLcuIWvYCocZXwr1D/wAhSbnW3R60lZ8g8/GFUhkUnL2hKKJu4/HO29QfO0mBeFP1yyDTs+tuVtp+Yugv0ml+lvvg+HkXo4j/WAktSb0L7sWabLzuGGmvzZSKXP6tGLyL6fQ62Fwq7o0VLiy/th1/H08CG4s3EAduJ5AP/9aovn1aEznDOnC5M370esNDA5sQkNvT9YfjMTfV0FwQF1mD+zE0t0n+OmEsPN/ybAgs3kqZc7gzkzeckDg6NCYht4erD8Yhb+vl8DxYiBLfz3JTyeEhfWS4d0tOV7qxuRN+wS9eGTHgfOCHc3rMXtgZ5buPsZPx6+ARMKS14LL4ejO5E1/l/alqcCx/xz+tRUEN6/P7EFdWLrrGD8duyJozoheZRw2MilzXgli8vq96PV6E806g38dpaBZQ7qz9JcjpZolYcnrz1lq1tjBTF65VYjN4A409PXmm92hgn638yfyRhxrfzkgzJGm9Zk3zphdssyOF9ozeftRoR9tG9BQ6c76sCv41/IkuKkvs/u1ZemfZ/kpIkbox9BOIjv0Oj0/fbSFd7cvRCqTcmLXERJvPWDIrNe4d/U2lw5HcnxXGBNXT2dl+Drys/LYMO1LAl/sUtb+l4+2MW37fKQyKRG7jpJ0K4EXZw0j/mocVw5HcWrXEcaunsqS8LUUZOWxdZo4cx7AycOn6da7M3vP7KKosIjFM1eUlf1y+Htee24sSh8Fb80ay52b99hx6LvHzhcr/rcgMc9q81/7Yokkz2AwOJfz+Q4gGPACUoBFBoNhq0Qi+QFojbA97x7wtslN1ONgSOkZ9JQqj4fqqJBF7kq9gU+p+Xi0vPcXNxoNqHR7gGa39hGmGv70io9B75SdAEyvV3mOtfcEjvttn6s0R90Lh6vU/hHHce8KnskxQY9k4beYqjImzW7t42azxyV4fDY0vnGAq/UrH1ct7gq/dXO+1tBKc3R4KPz+RVXtKLr0d6XbA9i3fhFN2p2nV3wMbL2EN0HFMccqzVGjqaAThaHrn1Lz8XDoM4WiMzsr3R7AvtNwIn2HPL3iY9A+Qfi9nPQXKq978hDBj8k9givN4X08nB9rjqp0e4BRiT9WWW8Aik78UGkO++5COuOic+X/htszcQS+Wi1xkT+/8rrn9LFgf1zzvpXm8IsW0tuHqypvR3DKbgr/M6fS7QEcxqykcG/5P0vwTO0HvQdA4Z+fVp5j8AcCx9+rn1LzCRwvzqYwxHLB/sztX5gp2HBw3VNqPoGj71QAiqIsf+/rWWHfbgiFO5dUuj2Aw/BFjKtX+XNv3937DYDJ9YZVmuPbe8LvlbXxrnxCh4vJp0BIVvivR8bgoH/1/jzPP4/9K/34j71pKu+GqfTzEY/5fHR5n1thhRVWWGGFFVZYYYUVVvz/iX88e54VVlhhhRVWWGGFFVZYYcW/GdabJiussMIKK6ywwgorrLDCiifg/0wiCCussMIKK6ywwgorrLCiajA8+898WWEC65smK6ywwgorrLDCCiussMKKJ+Afy573X8L/dOessMIKK6ywwgorrPjX4F+Z9c0c6QP/3dnz5H9Zs+f9I8ge3bvSbd1+CAOoUnrpxjcOkBIcXOn2AKrw8CqnyAZYV7vyKYCnPvgRgMzSH4WrDDx+CydreM9Ktwdw33mUpG6V5/A5eRSAjKGVT8nsuecYWSOq2I8dR4lpXPkxbXpTGNPq8EVa/8r7wmv/sSqlqwUhZW11pAuvjrTlVU0BXHIv8ukVnwC7eu2506JPpds3uBoKQO6kymuWy4YDAKQPrELa8r+OsaEKegMw6cGP1RLfRad3VJrDvrOQzLXo/G+V5+jwcrWk5S9YM6nS7R1nbAAgsUvl/VkzQvBnVdPAV2U8QBiTqvjTvrXw21fV8hMFVUz3XXwr4ukVH2dDI+G3iarDF5qkG5XmsPVpVqUU8CCkgX+l7qBKt//1/l4A3qz3SqU5ttz7FYDevpXX37CE0Eq3/a/Duj2vUrBuz7PCCiussMIKK6ywwgorrHgCrDdNVlhhhRVWWGGFFVZYYYUVT8D//PY8K6ywwgorrLDCCiussEKANXte5WB902SFFVZYYYUVVlhhhRVWWPEE/D/xpsmmRQfsR78DUima8H0U//2LqNy2e1/sX5uIITMNgOJDf6I5tk9Ux7FbO5TzJoNUSvavB8jcsqvc73J+vis11y7k/ivTKL52S1RmFxiIy9SpIJNRGBJCwc8/i8rt+/XDZdIkdGmCHYV79lAYElJW7tS9HaoFbyORScnadZD0TbvLtcGlb1d8183n7tAZFEWLbagT3JLui0cjkUm5viOcC+v/EpW3fqs//q8Fo9fpKEzP5ch7m8h9mC72Z+tAHMdPBamM4rAQiveI+2HXsx8Ooyehzyj15/49lISFiDladcBhrMBRciSE4j/Fh4PtgvpiP2oShkccB/dQcsQ4JjU6dsB1htC+4O8Q8n8Ut3fo3xeXKZPQl/oy/7c9FP4tHlPbNoE4TpgGUinFh0Mo+t2yH45jJqPPUAs27NtD8eFy+vFGaT+OhlC816wfPfpi/7pJP0L3UHJUbIdT93Yo55eO6+6DZDxuXPt0pda6+dx7aYbo82rxRbtAnCZNQyKVUnQghMLdYl/UeK4fTm9ORp8m+KLwrz0UHxT74tTlWFZt34teb2Bozw5MGCQ+dJ6ozmTRpt1k5uTj5uzIiinDUcndy8qhBoCeAAAgAElEQVRPXohm1ead6PV6Xnq+GxNe6S9un5rOR1//h8zsXNxcnFgxawLeXh6iOgtWrOb4qXN4erjzx48bLHxoMBj45KsNnDh9Hnv7Gnw8/138mzQU9+NGPJ/+cVLoR6dmjO/dVlSelJnLwp+PkFtUgl6vZ/oLnejuX7esfOEXmzh+9iKe7q7s2bSqXBtWfrudE+cuY29vx/J338a/UX2Leg5d2yP/cDISmZSc3w+QvXWnRR0Ap+e6ofryIxKGvyP6XObfDvthgmZpTh2g5KClZtm0647di6PAAPqEOxRtE9tr2zYQp7eEOVJ0KISiX83ionc/HMdNRp8uxEVRyB6KQ8VxUTu4JV1LNefGjnAumWlOy7f60/S1YAylmhP+3ibyTDSnOuL71JVbrPr5AHq9nqE92jLhxe6i8sS0LBZt/ZPM3HzcnBxY8fZLqDzdxByXb7Lqh78FjuAOTBgUZMaRyaJNv5GZWyBwTB6GSm7GcSmGVd//IXD06siEIeJERYnqDBZt2GmcI1NHiuaItK4/dkHDQCJFe+0U2siDova2PV5F5ttY+MPGDomjC4UbZovq1OjYAbeZwnWo4K8Q8n4w8+eAvri+Mwm92ujPgr/E/rTv0gHP96aATErenv3kfC++pj6CY6/uKD5fRNLrUyi5cVPsiyqOSVV9CdWjOaeu3+fT348LetHZn/HPtxeVJ2XksvDHQ+QWFqM3GJg+sAvdA+qJ7Yi6yqpNPwt29OnBhFdfMLMjjY++2kZmTi5uzk6seG8idRuZ2FANvliw6muOn47E092NP75fizkMBgOffL2FE2eiBO2cMx3/xn5iX8Q84NO9pwVfBDZhfK/WYl9k5rFwZzi5hSXo9QamD+hA92Z1LL5r/OK3aNOzPSWFxax77yvuRlsm+WnQ3I93vpiBnX0N0ecBQa0Z8dE4pDIpJ3aGsf9bcaIiGzsbJqyeRt3mDcjLymPj1NWkJ6gt+N9ZOoWOvTpQXFjMp7M+51b0bUs7PxjL8688j4ubMy82GWxRbsX/Hv6xmyaJRKIDrpbacAMYYzAYCiQSyTbgRSDVYDA0N2szDXgH0AEhBoPhg6d/kRT7MdPJX/UBhgw1zkvXo7lwGn3ifVE1zdlwirZ/XT6HVIpy4Ts8nDAPTUoadXetJf/oGUri4sVf5eiA+xtDKLxcTiYaqRSXGTPIeu89dGo1nhs2UHzqFLr7YjuKjh4ld82actt7L55C/Nj5aJLTqP/bV+QeOUPJ7Qfiak4OeI4ZTOGlmHJcISFo+Rj+HLmSvKQMhv29lLuHosi8lVhWRx19j10vLERbVELz0b3pMn8EB6eYZAiSSnF8awZ5S99Dn67GZdUGNOdPoU8Q96Mk4iiFW8rph2AIDuNnkP/x+wLHJxvQREagf2g2JhFHKfzOUryRSnGdPYOMWe+jS1XjtWUDxScj0N4z8+WRo+R8WU77R/2YOJPcxe+iT1fj+ulGSs6V049TRyjY/IR+jJtB/orSfny8AU1UOf04fZTCci5Cj+xQLZrCg3HCuNb77Svyws5QEmc5rh7ljWs1+cL5nZlkz3sXfZoa9zUbKTl7Cl28mKP42BHyvy3fFzq9nhXf/cHGuW+ikrsxcsE6gtv64+erKquz+qcQBnZvx6Ae7Th77TZrdh5gxZTXhPY6PSs2/symJbNQyT0Y8d4KggNb4VenZln7L77bzcCenRjcqwtnr8Sw9offWTFrgsiOIQOeZ+TLg5i3rPxsTidOnyc+IZF9O7dy5VoMyz5fx47Nxox5Or2eT34/wYZJA1G5OfH6l78RFFAPP2/PsjqbD0XRp7Ufw7o2Jy45g6mb97Hf5KZpcJ/ujBj0PPM/s7xpAzhx/jL3HyYT8t0XXIm5zfKvv+PntUvFlaRSvOZPJWniHLTJadT65WsKjp5Gc8dSc1xHDaXIXHMkUuxHvEPBmnkYMtNwnLsW7ZUz6JOM7SXKmtj1HU7BZ+9CQR4SF/ECH6kUp0kzyVkozBG31RvRnD2F7oHZHDlxhPyN5ceFRCqh2/Ix/D1yJflJGbz091Lum2lOWvQ9fi/VHP/Rvek0fwSHH2lONcS3Tq9nxQ/72Pj+aFSeroxcspngNk3wq6Usq7P6l1AGdm3FoG6tOXv9Dmt2h7Hi7ZfEHP/Zy8Y54wWOj9YT3K4pfrVM4vvn/Qzs1pZBPdpy9loca3YdZMXkYWKObb+zcf7bwhyZ+xXB7QPw8/U2cvzwFwN7tGdQUAfORt9izY59rJg6stSZEuyCR1C8Zw2GvEzsX5uL7s4VDBlJZe01x3ejKf2/TatgpIraYmdIpbi9N4P0GYI/FVs3UHSiHH+GHSV79eP1wvPDaaRO+RBtihqfH7+h8FgEmruWsekycijFVy2vh1Udkyr7kurRHJ1ezye7w9nwzhBU7s68/vlOgpo3wM/HRC9Cz9OnTSOGdW9BXFIGUzfuZX/AWLEd3/7ApuXvoZJ7MmLWUoI7tsavTi2jHVt3MrB3Fwb37sbZy9dZ+59f+aLzi9XmC4Ah/XoxcugA5q0ofy6fOBtFfEIS+376livXb7Lsyw3s+PYzsS/2nGLDxAGCdq79g6CAuvipjDeZm8Mu0qdlA4Z18ScuJZOpWw+w3+ymqU3PdvjUr8m0oLdp1KYJE5dPZu6Q9y3seevjyWyY8w23LsaWZc+TSKW8vvRNVo9aSmZyBgv2ruTSoUiSbieUtes2rDf52fnMC55Gh4FdeWXOKDZO/VLEHdirA771a/FGt3E0a9uUGZ9MZ+rA6RY2nD58hj++38v2E9+V67N/Nazb8yqFf3J7XqHBYGhdemNUAjzKp/o9YJEvVyKR9AQGA60MBkMA8Ew5LmV+TdGnPMSgTgKdFs2Zo9i261IhQ+1bNkETn4QmIRk0WnL2HcOpV2eLel4z3iBzy24MxRqLMtumTdE9fIguKQm0WoqOHKFG167PbINDy8aU3E9E86DUhpDjuPS2tEExczTpm3ajLy6xKFO19iP7Xgo58Wr0Gh239p6hQZ92ojoPT99AWyS0Tb5wG2eTxSKArGFT9MkP0acI/dCcPIJdh2fvRxlHSiL6VGFMSiKOYFsBDttmTdElJKJLFGwoPHyEGt0qZoNNo2bok4z9KDl5BLvAbhXvR7JJP04fwbZ9xeywL2dcnZ8rL7ZGk755Nwazca0WXzRuhi7xIfpkgaP42BHsOlXMF9G3H1BbJcdXJcfWxoZ+nVsRHnVdVCfuYQqBAcKTyUB/P1F59K271PFW4uutwNbWhn7dO3D03GVR+zsPkujYoqnQvkUTjp4VlwO0b90CN1eXx9p59OQZBvXrjUQioVXzZuTm5qFOyzDaEZ9KbS83fOWu2NrI6NumIeHR90QcEiTkFwlzPK+oBIWbo9iGFs1wc3F+vA2noxj0XHfBhmaNyM0vQJ2eKapTo0UTNPGJaBOSQaslf/8xnHpa6pbn1DFkbd2JoUQcF9J6TdCnJmFISwadFu35Y9i0FMeVXbf+aI79DQV5ABhys0XlNo2aoTOZI8XHj2DbsWJxoWztR869FHJLNSdu7xnqmWlOoonmpJhpTnXEd/Sdh9RWeeKr9BRis2Nzwi/GiurEJaoJbCa87QtsVp/wi+KHE9FxCUJ8P+Lo1JLwKPHNQNzDVAIDhNT1gf4NLMqjb8eL50iXNoSfv2bGkUJggPDmMzCgIeGR0WVlUlU9DNmpGHLSQK9De/M8sgYtH9tvWeMOaG+KU+Db+jdFa+ZP++4V86dd8yZoExLRPhQ48g+G4xBsyeE+ZSw53++00Cyo+phU1ZdQPZoTfT+F2gp3fL3cBL1o25jwq+K3IhIgvzS+84qKUbg6iTlu3qGOjxJfb6VgR49Ajp65aGZHIh1bNhPsaNlMVF4dvgBo3yrgybp16hyD+gYLuhXQhNy8fNTpptqppraXq1E7W/sRfk18My4B8kvjIa+wBIWrWDsBOjzfkfDfhPT2ty7G4ujqhLtS/HbPXemBo7Mjt8xipn7rhqTeTybtQSo6jZZzf52idZ8Oojqt+3Qg4rdwAKL2naZplxYWNnTt04XQXw8BcONCDM6uTngqPS3q3bgQQ0ZqhsXnVvzv4t9ypukE0BDAYDAcB8qLwsnASoPBUFxaL/VZiCUeXhgyjK9e9RlqJB5eFvVsO3TH+ePNOE5bhMRTISqzUcrRJhs5tClp2Krkojo1/Bti460g/9i5cu2QKhTo1SZ2qNXIFAqLejV69MBz61bclixBalJu4y1Hm5RW9rcmOQ0bMxvs/f2w8VGQF36+XBucvD3ITTS6Ni8pAydvj3LrAvi/FsT9cPFFQuqpKNuiBaX+lFv2w65TD1xWb8XpvSUW5VJPL/TpxuHTp6uRljcmHXvg8ukWHGctFnHIFF7oUk3aq9XIFJbt7YN64PX9FtyXLUaqFNsg8fRCl2Zmg9ySw65TEK5fbsP5/SVIzfvh8Yz9COyBy6otOM5cbBFbtio52mTjuGqTy4stP2x9FOSXM67V4Quplxd6tQlHWvm+qNEtCPf123CZvwSpl5gjNTMbb5PtHkpPN1IyxIvwJnVrEnZOuFiHnb9GfmExWbn5AKSkZ6HyMl6UVHJ3Us1uJBrXr83h0sVC2JmL5BcWkZWTZ2Hnk5CiTsdbaeybSulFitro/9TsfLzdjYsalbsTqdn5Io5J/doTEnWTPku2M3VzCHOGircVPQ2paRl4K4xjrPLytOirjdLLTHPUyMziwq6ZoDmFJyw1R+ohR59pMk+z0pB4iNtLlLWQqmrh+P4XOH7wJTJ/8c2MVO6F3myOyMqbI12CcFu7Dec5lnHh5O1BXgU0p9lrQcSbaE51xHdqZg7enq5lfys9XEnJzBHVaVJHRVjpTU5Y1A3yi0rIyisw4cjG22RrmNLTrRwOb8JKF6thkdfILyomK9eEI8NsjsjdSMksb45cFTjOXRXNEYmzB4ZcY5wY8rKQOJfvS4mLJ1I3L/QPxDd/MoUXuhSjP3WP82dwDxTbt+DxsaU/bRReaJNNOFLVyJRmsdm0ITKVksKTZ8u1r6pjUlVfQvVoTmpWPt7uxhsNlbszqdliTZrUvyMhkbH0WbiNqRv+Ys4r4m2dKemZqBQmdpSjB43r1+ZwRJRgx+ko8guLyMzMrDZfPAtS1Bl4m8SKSiEnRW2c26k5Zr5wK0c7+7Qj5MJt+iz/manbDjBniOWDILm3nPREo3ZlJKcjN9M+uUpOusl18xE8VJ5kJho/z0xKx0Pl+dg6ep2ewtwCnD3ED9q8vOWoTWxQJ6Xh5S22wYr/N/GP3zRJJBIboD/CVr0noTHQXSKRnJVIJMckEkmH8ipJJJKJEokkUiKRRG7atOmZbNBePE3urNfJm/8W2ugoHN/+sEJ9QCJB8eFE1Ks2V6ydGYojIkh77TUyJkygJDISt7lzK2SDct5bpH5SNRseofHQrihbNuDChpCnVzaD5nwE2ZNeI3f2BDSXI3GaVoF+POKIOk3O1BHkfvAm2qtROE6ZU6H2RadOk/rqCNLGvklJZBTu8yvWHkATGUHW28PJmTVe6MeMeRXnuHCanOkjyP2wcv1AIkE19y1SV1Z+XKvDFyVnI8gYO5ysKePRXIjE+d2K+2L26y8QGXOHYXPXEHXjDkpPV6TSZ5egd8e+QlT0TYbNXEZk9E2UcvcKta8uHLhwm0GBTQhd9Abr3nqBBT+Hodf/l39cXSJB/v7bpH/+bBpXLoVUhkRZk4IvPqBw60rsR80EB6enNzRBybkIMicMJ3v6eDSXInGeWfG4eIRGQ7uiaNmASxXUnOqI79nD+xAZe49hH20gKvY+Sg8XpJKK/SD97JEDiIy5y7D5XxN14y5KD1ek0gpyjBpI5PU7DPvwi9I54lapGJc1bo/21gUwVDwui06eJuXlEajfeJPic1F4LKy4ZnnMnkzm6vK3pz4rqjom1eHL6tCcA1E3GdSxKaHLxrNu0kAW/BBaYb14d/xwoqJjGTZ9EZFXY1HKPZDJZM/cvrriqqo4cPE2g9o3JnTBSNaN78eCHeH/fe20AhCy5/2b//1b8U8mgnCQSCSXSv9/Atj6lPo2gCfQCegA7JJIJA0MBvFVwWAwbAIerSQMeUumiZ7uSz0VZQkfytrkGZ9wlYTvw/61t0Tl2tR0bLxN3vqovNCkGA8qS50cqNGoLrW3fwqAzMuDWusX83DK4rI6erVa9OZIqlCgU4sPHxpyjHYUhoTg/PbbRhuS07HxMT7lsfX2QluODXV+FA5y2yg88N3wEQmTjGcl8pMzcalpfOri7ONJfrL4iRaAb7cA2k8bxJ5XP0ZfohWV6TPUoqfJUk8FhnSzfpj6MywEx9Fvi8r1GWlI5ca961K5Av2TxiQsBIfXJ5b9rVOnIVOatFco0KnN2pv4suCvEFwmTxSXZ6Qh8zKzId2MI9fIUXw4BIc3JonK9ZkV7MeREBxGiu3QpKRj420cVxtvy9iya1yXOj8I4ypTeFDr24/KyqvDF/q0NKQKEw6vJ/ui6GAIjhPEvlB6uJGcnlX2d2pGtsVBeqWHK1/OekOwo6iYw+ev4urkAAhPeVNMtsmlpGehlIufoivl7nw5d7LQvrCIw6cv4Opsub3jSVAp5CSnGvuWkpqGyuTpqdLNieQsk6fRWfko3cQ3EnvO3mD9ROE8Qat63hRrtGTlF/KstxtKL0+S1cYxTknLsOirNjXNTHMU6EziQuLkgF3DevhsE84UyLw88f7aONf1menYepjMU3cvDJnihC76rDR0d2NAr8OQnoI+NQGp0niOQp+ehtRsjuieNEdCQ3AcK46L/ORMnJ9Bc2p1C6DttEH8aaY51RHfSg9XkjOMdVIzc1B5uFrU+XKacL6uoKiYw5HXy2JTKHcj2eTNaWpGdvkcM0cZOc5fE3N4ms2R9GxUHmZzxNONL98ba+Q4e6WMw5CXicTFGCcSZ3cMeZa+BLBp3J6ScMvkDDp1GjKV0Z+yZ/Cn6ztif2rVadh4m3AoFehSTWPTEVu/enhv/kIol3ui+Gop6plG3arqmFTVl1A9mqN0dyI5y/hmKSUrD6WbeIvbnjPXWT95EACt6vtQrNWJ9EIl9xC9sSlPD5RyD76cP81oR0QUrq6u1eaLZ4FK4UmySaykqNNFb8iUrma+yC5HO8/Hsv5NIdlGq3oqwRcFRTgBn+0TzpbGXbmFvKYC4ag7eHrLSU8Ra1d6Sjpyb8s3pJkpGXjUNH7u4SMnMyWj3DqZyRlIZVIcXBzJy8wtK9948FtiL8eiqGnUT4WPF2nJYhus+H8T/4YzTa0NBsM0g8FgufFZjATgd4OAcwjH2CxnjRl0d2KQeddCovAGmQ22nXqiuRAhqiNxM058m7ad0SWKD7QWXY3Ftm5NbGqpwNYG1wFB5B89U1auzysgrstw7j43hrvPjaHocgwPpywWZc/TxMYi8/VF6u0NNjbY9+pFcYTYDqmn0Y4aXbqgjTfaUXj1Jnb1amLrW2rDCz3IDRPbcKvjCOJ6jiOu5zgKL8WQMGmpKHteyuU7uNXzxqW2AqmtjEaDOnH30AWRDV4Bdem5cjwh41dTmC7eLgGgux2L1McXqVLoh223XpREmvnT3eRMQvsu6B6K/amLi0HqXQtp6ZjYdemFpgIcmpgYZLVrIfMRbHB4rhfFp8x8KTfxZbcuaO+LbdDeihH1w65bLzTnT4lt8DCxoUNXiyQRFv3o3AtN1BP60c7SF0XljGue2bje7jiCuF7jiOs1jqJLMTycbFwcV4svbsYgq+mLVCVw1AjqRcmZx/vCrlNXi2QAAX6+xCenk5CagUar5cDpywS1ayaqk5mTj14vPELa+udRhgQZXxYHNKrH/aRUElLS0Gi0HDhxnuDAVmbtc8vab/l1P0N7V+wsBkBwt07sPRCGwWDgcvQNnJ2dUJhs0QmorSRencXD9Bw0Wh0HL94mqHk9EYePhzNnbwkHi++kZFKi1eHh/OwLkJ6d2rL38AnBhhu3cHZ0QGG2SCqOjsW2bi1saglj4tQ/iPzw02XlhrwC7vd4lQf93uBBvzcovnKD5GnGRan+fixSZU0kchXIbLDpEIT2yhnRd2gvRWDTWDgTI3FyRar0RZ9mTCqgvWUWFz16oTn3hLgItIyLVDPN8RvUiXtmmiMPqEuPleM5MH41RWaaUx3xHVC/JvEp6SSoM4XYPBtNUJsmojqZuSax+fdJhnRvI+ZoUIv45DRjfJ+5QlDbZo/n2HuMIUHi7Y4BfrVLOdIFjoiLBLUPEHPk5Bk5/ghjSM/AsjJ9yn0k7kokrnKQyrBp3AHdnSuYQ+KhAnsn9EmWGcc0N2Kw8RX7s+jk4/1p360L2ntif5Zci8Wmdi1sapbGZt9gCo8ZOQx5+ST0fpmHL47i4YujKL56A/XMj0TZ86o6JlX1JVSP5gTUUZXqRbagFxduEtRCnAnTx8OZszdL9SI5gxKNWC8CGtfnfmIqCclqwY7j5wjuKI6/zGwTO3aHMPR545bg6vDFsyC4SyB7D4YLunUtFmcnJxRyU+1UEJ+Ww8OMUu28FEeQvzjJg4+7M2dLk8CUaaeTPQDvD5jJ+wNmci70LMEvC5lXG7VpQkFuAVmp4ocDWamZFOQV0MgsZu5dvo2qng9evkpktjYEDuzK5UPibe2XD0XS5eVgANoN6ExMhPh819t9J3PqQAR9XnkegGZtm5Kfm289u2QF8H8r5fgfQE/gqEQiaQzYAZabWs2h11O4/Wuc3l8lpN49vh/9w/vUeGksuruxaC+exq7vUGzbdBGeuOblUrjpUzGHTo96+Xp8t3wMUik5v4dScvs+8mmjKYq+JbqBeix0OnLXrMHjs8+E9L3796O7dw+ncePQxsZSHBGB48svU6NLFww6HYbcXHJWrhTZkLzkW2pvWy6kpv41lJLb8XjNGEXR1VvkHSl/77gpDDo9xxf+h8E/fiCkHN95jIybDwl892VSr9zl3qELdJ0/AltHe/ptEDLF5CWmEzJ+tYk/dRRsWYPzQqEfJUf2o39wD/vXxqG7HYsmMoIaL7yMXYfSfuTlkr9updgQvZ7CbWtxmvepwBG+H33CPexfHYf2TizaqAhq9H8J23ZdQa9Dn5dDwXqxL3JWr8VztdC+MGQ/2rv3cJ4wDk1MLMWnInB65SXhwLhOhz4nh6yPzW3QUbD5K1wWfS6kHA/bh+7BPRxGjEd7OwbN+QjsX3hZSFBR2o+8r8vpx/drcZpr1o9XxqG9W9qPfqX90JX2Y4MZh05PytJvqb11OcikZD8a1+mjKIp+hnGtJl/kffsVbss/B5mUotB96OLv4Th6PNqbMZScjcBh8MvYdSrlyM0l7wsxh41Mxtyxg5m8cit6vZ4hwR1o6OvNN7tDCWjgS3A7fyJvxLH2lwMgkdCuaX3mjRsiaj9v4ggmL/4KnV7PkN5daVinJt/89Cf+DevSs2Nrzl+9ydof9iCRQFv/xsyfNMLCHe8vWsn5i1fIysqh95BRTJkwGq1WeHMxfOgL9OjcgROnz9N/2Hgc7O1ZNm+WWT+kzHmpO5M3/Y1eb2BwYFMaenuyfv85/GsrCG5en9mDurB01zF+OnYFJLBkRC8kJtuGPvhkHeev3CArO5fer0/lndGvlNkw7MXn6B7YmuPnLzFg3Gzsawgpx8sb17QV6/DesAKJTErunoNo4u7j8c4bFF+7SUH4UzRHr6do53ocpwuapYkIRZ90H7uBo9Hd///YO+/wKKqugf92k0B6z24CCQQSaoBQQ5GYIBBAX5oovDRpiqEIitiQIoigiNgQA6iIighKUaQECITeQk2EUAIkQPqmkLJpu/v9MUt2ZzehbPK+8vrN73l8Htl7z8m559x7ZubOzJmraC4cR3PxNNYtO2A/byVotZRu/gaKCox0aCiK+gzn+fo1sleYF3Yjx1NxNZHyk0ex6z8Em876NVJQQOHn4nmh02g5PGctz+hzzuUNB8i9coeOrw8h68INkvecoas+5/Q2yjm77uWcWpjf1lZWvDPqaSYt/RGtVseg0HYE1lfw1eZ9BDWqR3i75sQl3uSL32IA6NCsIbNGP2OuY8wAJi1ZI+gI60Cgr5KvfttDUCNfwju0IO7Sdb7YsBtk0KFZI2aNHWCuY/yzTFq0StARHkKgnzdfbdwlrJGOrYi7mMQX63cIOpo3ZtaEIUbO1FIWu4G6g6YJJccvHkWXk4ZNl/5oM5LR3BAuoKybdkJzper3WtFoyV/2BR6fLgErOcV/Cv50enEcZYmXKT18FIfnn8X2fvlCoyXnoy9RfPUhyOUU/rGL8uvJuESOoeziFdQHj1X9t2sxJjX2JbWTc6yt5Lz9XBiTVvyBVqtlYJeWBPp4sGL7cVo2UBDeujEzBoWy4Jd9rNt/FmQy5o/sJcoX1lZWzIocyaS5nwh29A4lsGF9vvppCy2b+NOjcztOxSfyxdrfkMlktG/VlHcnja5VXwC8seATTp1LIC//Lj2fm8Dkcf+mokIDwLCBfXmySwcOnThNv5GR2NWty/tviavJWVvJeXtQNyat3qnPnc2E3BkdR0tfL8KDGjKjfxcW/HqIdYeEtzHmDw0T+QLgzL442vfowPKDKylVl7JipqGK48c7PuONp18F4JvZUfqS43Uq27UaLT/P/YZXf5iN3ErOkY37SL16m4GvDeNmfBLn98ZxaGMMLy6bxqLYLynKK2TlK+LKeQAn9p2k81Mh/Hj4e0pKSvl4hqHu2Mror3m5j3D3ceK7L/LUoB7UtavLL6fWmemR+Och01nwzHOt/GGZrFCn05mVapHJZOuBcIS7SBnAPJ1O961MJqsDfAe0Rai2N1On0+17wJ/R5Y/u+YAu1ePyo5Cwr7QwK+b30DS9tIuM8HCL5QGUsbFcavK0xfItrgrf2FjuN8piHVNv/QRArn6HxhLcNsWSN6zHgzveB9cN+0nrbrkOn8NCVZ6cwWEP6Fk97lsOkDe8huNYv5/EppbHtPkVIaa14SXeFOcAACAASURBVIvsfpb7wnPnAUpOb31wx/tg22EQpYkHLJav21ywvzzbfGf9YbHxFKqeqbd/9oCe1WP3zKuU3Yx7cMf7UMe/I9dbR1gs3zh+NwAFkZbnLKeoXQCo+ls+Lzy2HSCqBvkGIPLWT7Uyv0uOrX9Az+qx7SqcJJec2mS5jk5DKDn3p8XyALZt/0Xx55EP7lgN9tOF94tSu1nuz3pHBX8mt+9lsY6GZ/bWKB4gxKQm/rRtKzxaWxs5Rx29/AE9q8euz1RKrx59cMfqbGgiFFGoDV+Up1XxmZSHxManBeo/HqqYcbXYDZjJcw0HPLhjNdwrOf6i/3MW6/jm5m8A9PS1PP/G3N4NQpHAx57MnmGP9ctkipgDj6Uf/7Y7TVVdMOl/N98+Fn4vA2p2FJaQkJCQkJCQkJCQkHhE/vbqeRISEhISEhISEhISEo8z/0vvNElISEhISEhISEhI1IDHuaz344x0p0lCQkJCQkJCQkJCQuI+SBdNEhISEhISEhISEhIS9+Fvq573X+IfPTgJCQkJCQkJCYnHhsey6pspGeHhj/X5sTI29rH04z/+nabaKN97qv5gi3V0urOFE/WetVgeoHPqZn7zGWmx/HNpwvcDpvoPs1jH8psbAGpc+jy+UX+L5QFa39jGPuVQi+WfytgI1Dymx2sY0y6pm9ml/LfF8n0zfgEgRml5THtmbKgVHSUnf7VYHsA25HnUu1dYLG8XMRmoeblwqHnZcvW6ORbLA9iNfL9W5ndt5L2afmphQw1yFsCwtHU1yp2dUzcDoN75xQN6Vo9dP+FbNCWHfrRYh23o6BqVpgahPHXBq5bnTqfPtgFwxm+gxTra3/odoEZxHZa2DvWaNy2WB7AbtwT1hvmWyw+bB4B644IH9LyPjqHCh6SLo6ZbrMM+8nPU38603IYJQplv9W8LLdfx3GxBRw1zp/qbGRbLA9i9uIyZ/lUWTn4olt4UythP97f8mPr5TeGY2tCjjcU6klXmH5uW+GchPZ4nISEhISEhISEhISFxH/7xd5okJCQkJCQkJCQkJASk6nmWId1pkpCQkJCQkJCQkJCQuA/SRZOEhISEhISEhISEhMR9kB7Pk5CQkJCQkJCQkPh/gk77WBane+z5f3HRZPdERzzemoTMSs7dzbvI/3ZDlf0cenVH+elcbg+bQtnFq6I25/B2NFgwAZlcTtb6vaR/tVnU7jW6D4ox/UCrRVNUws03V1By9baoj0t4Oxq+Px6ZXE7m+r2kLd8ialeMjkA5th86rRZtUQk33vgatZEOZY82tF0wGpmVnBs/x3J5+TaRfJOX+9FoRA+0FRrKVHeJm7Ga4tvZoj4twoJ5bu5Y5FZyjm7Yx56vfxe1W9exZvSyKTRo1ZiivAK+m/o5ObezxH4K7YBy9svIrOTkbYxGtarq6mlOfZ7Ad/m73Bg8nZIEsT8dn2xPvXkvgVxO7oY9ZEX9VqUO577daPj1O1wb8Brq+GuVv7v3CKbJwnHIrOSkrYsh+UvxOOq90Bvf8X3QaYR4JM5cSfGVO2LdtRRTf6OYplYRU299TDVVxBTAs0cwLRaOASs5t9ft48aXf4ja/V7oRYPxEZVjSZi5WtTu3iOYpgvHIrOSk7pun5kv6r/Qy8QXqygy8UVt6Dhy4Qof/bgDrVbL4PAOTOgfJmpPzc5l3uot5BYU4eJgx6JJz6N0dxHruHiTJZsOoNXqGNw1iPERnUTtaTl3mfPTHgrUpWi1WqYNeIIIo0JxRy6lsGTrYUG+SwvG92wvls8tYM7P+ygoKRPkn+lCaMuGle2zFy3j4JGTuLu5svWnKEzR6XQs/iyKQ8dOYWtblw/efZ2WzQLN+h25lsaS6HOCHe0aMb57C7Ed+UXM2XqSgtJytFod03q2IbSJj6iPJXPcmNrIe/bdO6CYNQnkcvJ/20XuNxur1OHY+wnqfTGH5OdeofQvsQ7vHm1op89b13+OJdEkbzV9uR+NR/RAV6GhVHWXkyZ5q6Z5E+DIpWSWbD6MVqdlcJeWjO/VQdSellvAnHUx+nmlY1r/LoS29BfrSEjio/XRQkxD2zLh6SdE7amqPOat+ZPcgmJcHGxZ9OIglO7OYh0Xk1my+aB+frdkfO+OYjtyCgzzW6djWv9uhAYZ7LBq3h7bZ18CmZzy43soizHPm9Ztu1On73DQgTb1BiU/LhW1O4e3w/e9l8BKjmr9HjJWbBK1e47qi9eYfug0gj9T3l5BydVboj41jSnAkesZLNkbj1YLg4MbML5rU1H7x3vjOZUiyJSUa8gpLuXwa88Y5K+msmTHabQ6HYPbBzD+ySCxL/OKmLP5GAUl5YIvewcT2rS+2IarqSzZHifo6BBYvQ51maAjoq25jptZfBx7Ca0WBrXyZXxIY1H70thLnLqdYxiHuoxDk3uZ+CKTJTEJgh1tGjC+SxOxL2ISOHVLJfLF6QmGuB65ckcYh1bH4I6BjA9rZT6O347o856OaX3aE9rMZBw1zJ0AR25ksiTmon4cfozvLM6NH++7yKkU/Tgq9DGd1kfUp1lYMAPnvoDcSs6JDfvZ/7X4eNg4pDkD5r6AT/MGrHvlCy7sPClqbx4WzLNzxyC3knN8wz72mshb1bFm1LIp+LVqRFFeIWurOMcBeG/xW/ToFYpaXcLMqXNIuHBJ1G5rZ8vX3y2lQSM/tBoNe6MPmOmQ+Ofxt100yWQyDRCvt+ESMEan0xXLZLLvgH8BmTqdrpVR/w1AM/0/XYE8nU7X9oF/SC7H892ppE18m4r0bOr/8iXF+49Rfj1FbI+9Hc6jBlNy/lKVOhp+MJErw9+jLE1Fyx1LyNt9UnQCrdpykKwfowXjeneiwbxxXBn1vkiH/6KXSPz3fMrSVATtWEJe9CnRwV215RCZPwrlfl0jOtHgvXFcHqnXIZfRbtFYDg1bTHFaDj13vk/q7jMUGJ245sUnE9N3Nhp1GY1f6Enr2cM5EfmlYYxyGUMXjGf5qA/IS1fxxh+Lid8TR/o1g46uQ59CnV/E/PDpdOjfjYFvj2DN1M9F4/B+bzIpY9+lPD2bRps+o2DfccquiQ+qcgc73McMRH0usUp/1lsQyY3Rc6hIVxHw+zLu7j1BaRU6PMf1p/isiQ65jGYfTuDs0IWUpqroGL2YrOg40UVRxubDpP6wBwDPPh1oMn8M54cvEtlQGzFttOglLulj2mrHEnLvE1O3iE40fG8ciSONdcho+eF4Tg39gJJUFV2jF5EZfVp0QZK6+Qi3ftgLgFefDjSfP9rEF+M5O/QDSlNVdIpeTHZ0nEg+ffMR7ujlBV+8wLnhi2tVh0arZdHabax8axxKd2dGzI0ivH0LAuorKvss+3kX/bu3ZUBoe078lcTnG3ezKPJ5kY7Fv8YSNWUwSldHRn78C2GtGxPg41HZZ3X0KSLaNWFoaBuS0lRMjfqdiNeM5DcfIiqyP0oXB0Z+uomwIH8CvN0N8ntOE9E2gKFPtCIpPYepq3ew0+jAP+jp3owYMoBZ74tPNO9x6NgpUm6nsmPDt1z4K5H3ly5n/WpxuV6NVsvinWeIGhWG0tmOkd/sJaxZPQK8DBeIqw9dIiLIj6EdA0nKymfqz4fYOf1fJjF59DlukK+dvKeYM4U7E2ZRnpFNw41fULT/OGVJ5jpcXxiEugodMrmMDovGEjtsMeq0HHrr89Zdk7y1R5+3Al7oSfDs4Ry7l7dqmjfvxeO3g0RNGiDMq2W/EtaqkXhe7I4jom0gQ7vr58XKP9k5z1+kY9G6naycMRKlmzMjFn5LeNumBNTzquyzbGMM/bu2ZsATwZy4dIPPN+9j0YuDxHb8GkvUlEGCHUs3ENaqMQE+xnbcm9+tSUrLYerKP9gZNPaeM7F9LpLir+egy1NhP2MZFQkn0GYY8qbM04c6vZ6j+PM3QV2EzFG8KYFcjt/Cl7k6Yh7laSqa/bmU/D0nRRdFOVsPkP3TLgBceodQf+54kkYbSnzXOKaARqtj8e4LRP27G0onO0Z+f4CwJt4EeBouMt/o1bry/9fHXScxI1/syz/jiBrzlLDGVkYT1tyXAIXRGjuQQESrhgwNaUJSZj5Tf4pl54z6Yh3bThE19imUzvaMjNpVjY4GDA1pKuj4cT87XzfWoePDfRf5+tlOKJ1sGfnzMcICFAR4OFb2mRlu2DBZfzaZy1l3RSHRaHUs3htP1NAugi9+OERYoDcBnk4GX/Q0XAStP32DxEwTX2w7SdS4XsI4vt5JWAtfAhSuhnHsv0BE64YM7dyMpMw8pq7dx843nhXrqGHu1Gh1LN7zF1FDOwu++PEwYQFK8TieamkYx5kbJGaIfSGTyxi8YByrRi0iP13F9D8+4OKe02QYnaPkpmazYWYUYS89gykyuYznF4xnhf4c5/U/FhFvIt91aA/U+YUsDH+Vdv270v/tEaw1PscBevTqTqPGDQnr9C/adWzDwqWzGRRhXmp/1VdrOXb4FDY21vy85Ruzdol/Hn/nO01qnU7XVn9hVAZE6n//HjD7QIhOpxum798W2ARsNu1TFXVbN6M8JZWK2+lQUUHRzgM49Ohm1s996hjyvt2ArqzMrM2hXRNKb6ZRmpKBrryCnN8P49YnRNRHW6iu/H+5fV1Mvxns2C6Qkgfo0BjpsLKvi7ES93YBFN7MoCglC125hlu/H6deH/FuadbRi2jUgv05Z65hZ3QwBvBvG0h2cgaqW5loyjWc2XaUNiY7+W0iOnJik7BjcnbHcZp1E+9Y2bVpSllyKuW30qG8grvbD+LUs6uZz7xeHY1q1a9oS839aR/chLLkNMpvCb7I33YQ596dzfopZ4wkK2oT2tJy0e/O7QMpvpFOSXImunINmVuP4tVXPA6xL20xDch/IqaqB8RUbhJTAFf9WNT6saRvPYqyb8dqdVjb1xV9stm5fSDqGxmVvsjYehTP+/qiLqYftK4NHQlJt/FTeuCrcMfG2pq+XVoTe1p8Ep2UmkVIS2EXNqRlY2JPiy+GE5Iz8PN0wdfTBRtrK/p0aEpsvPi7STIZFJUIc6qwpAwvF8PJSUJKpiDv4SzItwskNuGmWB4ZRSXlRvL2ovaObVvj4uxEdew/fJwBfXsik8kIbtWCgoJCsrJzxOO4k4OfmyO+bo7YWFnRJ6gBsZdTTeyAotJ7dpTj5WQnaq/pHK+NvGfbphnlKWmU39av9R0HcHjKfK17Tn+B3G9+RWeyTkHIWwX6vKUt15Dy+3Hqm+StTKO8pTpzDXujvFXTvAmQkJwpnlftmhAbf0PUR4bRvFKX4eXiINZxIxU/hTu+Xm7YWFvRNySI2HNXRH2S0rIIaeEPQEhzf7P2hOQM/LxcDXa0r2J+G9tRUoqXs8EOecMmaLPT0KkyQFNBxdmDWLcW5806XftQfngHqIsA0BXmi9od2jah9GY6ZXp/5v5xCJeI++c9U3/WNKYACWm5+Lk54OvqgI2VnD4t6xN7NZ3q2HnpNn1bGi5WEm6r8HN3xNfdUfBl64bEJorvLspkMqM1Vma2xhJuq/DzcMLX3cmg45J4406Ix310pOfh52qPr6u9MI5m3sQmZVQ7jl2X0+jbTHxHOSEtFz9XI1+0qEfstfv54g59W5j6wmgcbaoYh8gX5Xg5i/NebeTOhLQ8/NyMfNG8HrHXqvfFzkup9G1RT/Rbg7aBqJLTydGfo5zbdoygCPHxMPd2NmmJKWbHIICGbQPJSk4XneO0NpFvFdGRk5sOAnB+xwmadgsy09O7Xw82bRDunp6Nu4CzixMKpaeoT4m6hGOHTwFQXl5hdifqcUenfbz/e1x5XB7POwS0AdDpdAdlMpl/dR1lMpkMGAo89TCKrRWeVKQbbr1WZGRRt01zUZ86LQKx9vZCfegkruOeN1VBHW93ylINjxaUpalwaNfUrJ9iTD+UEwcgr2NNov7jdwYdHpSlqsQ62jcxVYFybF+8Jw5AVseaS8/Pq/zdztsd9R2DvDotB/d2AdWO2394OOn7z4t+c1G6k2tkQ26aCv+2gdX20Wq0qAuKcXAznERae3tQkWbwRXl6NnbBzUQ6bFsGYO3jRWHsKdxfHGJmm7W3B+UiHSrs24r9aRsUgI2PFwX74/CcKP7AZV1vd0qNxlGaqsK5Cl/WH9eHBpHPILOx5uwQ8YcM/1Mxdawmpj5VxPTeWNRGOkpSc3Bpb/64V4NxEfjrx3JqyPt49RTubth6u1Ni5gtzed9xEfhFPoPcxpozQ94XtdWGjszcu3gbPWqncHcmPkl8EtOsgTcxcRcZ2acbMXEXKSopJa+gGO97OvIK8Taaa0pXR+Jvik8eIvt1YdJXW1h/8Dzq0nJWTjV8pDgzvwhvVwcjeQfikzPF8n07MinqT9YfjkddVs7KyAFm47wfGVkqvBWGg6dS4UlGVjZenoaTwswCNd5GJxRKZzvi74gvrCLDgpi07iDrT15DXV7BylHiRxktneNde7YDaifvWSs8THRkY9dGvNbrthR0FB04idv458x0mOat4rQcPO6TtxoPDyfNKG/VNG8CZOYX4u1muLhWujoSnyw+mYvsG8KkqD9Yf+gC6rIKVk4Wz4vM3AK83Qx3QRRuTsRfF18IN/NVEnPmMiN7hRBz5jJFJWXkFRrP7yK8XU3tMJ3fnZm04ndhfpdVsHKK4U6V3MUDba4hZ2nzVFg1FOcsmaI+csB+2kcgl1O6az2axDOV7TbeHqK8V56mwr6KvOc55mmULw1AZmPD1WGzRW01jSlAZkEJ3kYXIEonO+JTc6uUT80vJjWvmJCGhrt6whozWuvO9sSbPP4X2aM1k9buY/2Jy4Ivx/YU23DXZJ262BN/WyXW8VQbJn0fo9ehYeVY8WlHZmEpSuNxONqSkC6+UK0cx101qflqOvl5iH7PLDT1hS3xqXnV+yK/mJAGhhyUebfYxBcOxN8y8cW9cRzT+2K8+PHA2sidVY4j7X7jUIvGAeCidCPPaL3npalo2Nb8WFQdLkp3E/kcM3lXk3OckgK16BwHwNtHQeodw9pMT81A6aMgM0Ps13s4OzvRq09YlW0S/yz+9up5MpnMGuiH8KjewxAKZOh0uqtVNcpksokymSxOJpPFrVq16mEMwOONl1EtfYi+DyBz7U7in5jErQ9+oN5085OQhyHj+12c7zaZWx/8SP3p5ichD0ODIU/gFtyYKyv+tEi+RshkKGa9RObi1Q/uex8dPrMnkPbBtzUy5c6aaI51nkbSwnX4v2Z+8fYw1FZMz3WbTEoNYpqyZjcHO0/nysKfCXht8IMFTLi9ZjfHOk/n2sKfafTasw8W+A/omDG8L3GJNxk6+ytOJ95E4eaMXP5oL6PuOn2ZAZ1bsvv9CSyfNJDZP+5Gq334baldZ64xIKQZu+e9wPKXnmH2zzFoteY7lv9pdiWkMCDYn92v9Wf58FBmbz2Jtoqd0wdh8Ryvjbwnk+H11kSyPqrBWjei4ZAncA9uTKIFeaumeXPXmasMCGnO7vljWT7xX8z+ae8jz4sZQ3sRdzmZofNXc/pKMgo3J+TyRzvE7jp9hQGdm7P7/fEsj+yvn98Pb4dMboXMqx7Fy2eh/mEptsOmgp3DgwVNyF67g7+6R3Jn8Vq8pw19ZPl71CSm94i+dIdezeph9ai54sJNBrRrzO6Zg1k+KpzZm44+ckx3XbjJgPYB7H7jWZaPtkzHPaIvp9GzqfKRxyHSkZhKr2Y+lvmifQC73xrC8jFPMfvXI4/ui1rMndGJafRq6l0jXzwuWFlZ8eXqj1iz6ue/2xSJ/wJ/50WTnUwmOwfEASnAw54hDwfWV9eo0+lW6XS6jjqdruPEiROpyMzG2tuwQ2Wt9EKTYdiJkDnYUSfQH5/vPsZv1w/UbdMC7y8XUKelYTezLD2HOvUMOyJ1fDwoTxfvSBmT8/thXE0eISlLV1GnnmGHqY6PB+VpOaailai2Hsatr0GHOj0Hu/oGeTsfd9Tp5jtzitAgmk8fyNExn6AtqxC15Wfk4GZkg5uPB/kZudX2kVvJsXOypyi3oLK9Il2FtY/BFzbenlQY+VPuYEfdJg1p8NNHBOxfg13b5vhGzcW2VRORDhuRDrE/5Y522DZtSONfFtHs0DfYt2tGw9WzsWst7BiVpudQ12gcdet5UJpevS8zthzFq5/40ab/VEzLHiGm98ZiZ6TDtp77fceStuUoCqOxlKTnYGvmi6p3bKFqX9SGDoWbM+k5hh3WzJy7KN2czfp8On0EGxdO4ZXnhZ1OZwfDzqTC1ZF0o7mWkVeIwmhnHmDLsb+I0N9pCG7kQ2l5Bbm5gq0KFwfS84qM5ItQmDxmteXEJSKChXkU7O9NaXkFeUVqHhallwfpmYbdxozMbJRe4t1ShZMd6fnFhj531ShMHuvZcu4GES39BDv8PCmt0JBXXFrZXtM5Xht5ryJTZaLDk/Iq1rrfD0totHcttsHNqb/iPeoGGXSY5i37avKWMjSIltMHcsgkb9U0bwIoXBxJzy00+CqvsIp5cZEI/Y50cCNvIR5G80Lh5kR6ruH9i8zcApQmu9MKVyc+nfI8G+e9xCuDewDgbG9r1O5Aep6pHSbz+/hFItoZzW8jO7T5KuRuhrkmd/VAly/OWdq8bCoSToBWgy4nA21WKnJPwyNQ5ekqUd6zeUDey/39EK59xI8A1jSmAAonW9ILDP7NKFCjcLI1VQHArot36NvS10TejvR8o7V+txiFySNnW85cJ6JVAwCCG3iZrTGFs8k6zS82X6enk0x0aMU6HOuSYTyOwhK8HOtWOY7oKh7NE3SY+qKkel+YPJonjMPexBdFKFxMx3GNiFYNjcahIa+4xKCjFnJnleNwrGYcieaP5gHkZ+TiarTeXas4R7kf+Rk5JvLu5GeI80WeyTmOrZOd6BxnR+xGMjOyqVffu/I373pKMtLEd97u8eGnc7lxPZnvVv700HZK/O/yOLzT1Fan072i0+nMH6o3QX9X6lmg6jJQVVCacBmbhvWxru8N1tY49AujKPZYZbuusJjkJ5/nVt8XuNX3BUovXCL9lbmiKlJF565St5EPdfwUyGyscR/Yndzdp0R/p24jQzJ06dWB0htpovbCc9ewbeRD3YfU4dqrAyVGOnLPXcexkTf2fl7IbKzwG9iFtOjTInnXVg1pv2QCR8d8QqlK/IIlQPL5JLz8vfHw9cLKxor2/btxYU+cqE/8njg6DxFuM7d7ugtXjv4lalfHX6GOfz1sfJVgY43zM09SEHO8sl1bWMzVzsNJ6jGOpB7jUJ9L5HbkAlH1vOILV6mr1yGzscal/5Pc3WuogKMtKOZSh5FcDn2Ry6EvUnz2MskvLaysnldwNgn7xj7YNhB8oRjUjexo8TjsGhkSnkfv9hRfF8fjPxFTjyp02N4npgD5Z5Owb+yNnX4s3oO6kWkSV3ujsXj1bicaS4Fe/p4vlA/whaeJfG3pCGpcn5R0FbczcyivqGDX8XjC2osfB8stKKq8K/TttoMMChNXZwpqoCQlK4872fmUV2iIPn2FsNbiSlQ+bk6cuCw8r389PYeycg3u7sKjcUF+CkFedVeQP3uNsFb+JvKOnNAXEbiekUtZhQY3R/EJxv0I796FP3bFoNPpOJ9wCUdHB9GjeQBB9d1JySnkTm4h5RoN0X+lENZUfILg42zPiRvCI2LXs+4KdtgbTrZqOsdrI++VxF/GpmE9rOvr1/rTYRTtF6/1pG7DuNFrDDd6jaHkfCJ3Jr8nqp6Xc+46To28cfDzQm5jRYOBXbhTRd7quGQCh6rIWzXNmwBBDRSkZOcbzYur5vPC1YkTV/TzIj2HsvIK0bwI8q9HSkYOt7NyKa/QsOvkX4QFix9ryy0ortx5/3bHEQY9EWxih35+q/Tz+8wVwlo3Etvh5mhih2F+alOuIvesh8xdCVbWWLd7kooEceWwivjjWAcKBRRkDs7IveqhVRkeMyo6f5W6/oa85zYglPw9Yh11/Y3yXs+OlNwU+7OmMQUI8nElJaeIO3lFlGu0RF+8Q1igt1m/G6oC7paUEVzfTSxf34OUnAJhjVVoiI5PJqy5+GLCx8WeE9fvrbF8yiq0uDnUFetQmeoQX5z5uNpzIknw3/XMfGGdGuvwdiElt5g7+cXCOC6nE95YgSk3cgq5W1pOsI+rWVuQjyspuUXcydPruJR6H1+UE1yvCl+oCriTUyCM40IyYc39THzhwInrpuMwXNDURu4M8nERjyMxlbBAZRXjKKxyHAC3zifh6e+Nu/4cpW3/rvy157RZv+pI0Z/juBud4ySYyCfsOU3IkCcBCH66M1dNznGeDh/K7h37GDKsPwDtOrah4G5BlY/mzZw1FSdnJ+bPWvLQNj4u6HSyx/q/x5XH5Z2mh6UXkKjT6W4/sOc9NFqyFy3HO2oRMis5BVuiKU9Kxm3KC5T+dYXi2OMPpSNl9mqa/TwP5HKyN8RQcuUW9WYOp/j8NfL2nEI59mmcQ9ugq9BQkV/I9Ve/MNNx891vaPbzXGRWcrJ+iUF95Rb13/g3ReeTyNt9Cu9x/Sp1aPIKuT7dUG1Ip9Fybtb3hK5/C5mVnJu/HODulTu0fGMIuedvkLb7DK3njMDawZYuq6YDUHwnm6Njl1Xq0Gq0bJz7HVN+mIXMSs7xjbGkX73NM689T0r8deL3nuboxv28sGwq82I/pyivkDWvfG42jvT5X+P33UKh5Phvuym7loLn9FGUxF+lcN+Jh/Jn6rwoGv0wXyg5/uteSq+moHhtJOr4qxTsPXlfcZ1Gy5V3vqPtL+8KJbLX76fo8m0avTmUgvNJZEefxndCX9xCW1fG49K0r/5jMW2uj2mmPqa++pjm7j6Fclw/XO7pyCskySim98Zy8Z01dPxFiMnt9fspvHybwDefJ//8dbKiT9NgQh88Qluhq9BQnl9E/LSv6X5waaX85Xe+o90vs8BKTtr6WIou36bxm89z9/x1sqNP4zehj5Evirg4bYWZDTXVDzKFyAAAIABJREFUYW1lxTsv/ItJH69Fq9Uy6MkOBPoq+WrTXoIa1Se8fQviLt3gi417QAYdmvkza0x/Ex1y3n4+nEkrtqLV6RjYpSWBPh6s2H6Mlg2UhLduzIzBoSxYH8O6/WdBBvNH9UZ4zVEv/2wok1b9iVarY2BIcwK93Vmx8yQt/bwIb9WIGQO6sWDjAdYduCDID3+qUh7gjXkfcursBfLy7tJz0CgmTxhNRYWwSz5s8DM82bUTh46dot/Q8djZ2vL+rNfM5qe1XM7b/dozad1BYRxtGxGocGHF/gRa1nMjvFl9ZkQEs2BbHOtOXAFkzB8YIrLD0jne+eCyyrlZG3kva+EKfL/5AORy7m7eTdm1ZDxeGU1JwlXRBVR16DRazsz6njB93rquz1ut3hhCzvkbpO4+Q7A+b3UzyluHxxrGUZO8WTkvhoQyKeoPYV50biHMqx0naNlAIcyLQU+wYMN+1h0Q3r2ZP6KnKB7WVnLeGdGXSZ+tF+b3E20JrO/FV1tjCfKvR3jbpsRdTuaLzftAJqNDkwbMGtnX3I7nwpi04g+0Wq3R/D4u2NG6MTMGhbLgl336+S1j/sheBju0Wko2RWEfKeTN8hN70aanUKffSDQpV9H8dRJN4hmsm7fD/u2vQKul9I81UGzYRUej5dacVQT+9B4yKzkqfd7zeX0ExReukb/nJF5jn8GpezC6igo0+UUkvyauDlnjmKJfIxFtmLThmLBG2jQg0MuZFQcv0dLHlXB9+X3hLlN9USwqfflMRyb9sF+IafvGBCpcWRFzgZb13Qlv7suMvu1Z8PsJ1h1NFNb64C5mMX37Xx2ZtHafXkcAgUpXVsScp2U9D8Jb+DKjbwcW/H5cr0PG/Ge7inXI5bz1VEsmbxbKlg8M8iXA04kVR6/SUulCeIBwARV9OY0+TX3MxlHpi16tmPTrcUFHaz8CPZ1YcSiRlt6uhDcRLqB2XUqlb4tqfNE/hEnfxwjy7QOFcew9R8v6HoS38GPG0x1YsOU4644IxQrmD+lm7osa5s7Kcfx2UtDR2lcYx+HLwjj0F1C7ElPp27xelb7QarRsmfs9L/3wDjIrOac2xpJx9TZ9XnuOW/E3uLj3NH5tGjNm5QzsXRxo2bM9Ea89L5LfNHcNk36YJZQc37if9Ku36ffa89yKv07C3tMc37ifUcumMDv2M4rzCln7yhdmduzbc4gevUM5GLddKDn+ypzKth2xG3k6fCje9ZS88vpErl25zvb9D72PL/E/jqyqCiT/lT8skxXqdDrHKn5fD4QDnkAGME+n032rb/seOK7T6cw/oFI1uuutIx7cqxoaxwtlbE/Vf/R3SO7R6c4WTtSz7B2Se3RO3cxvPublLh+W59LWATDVf5jFOpbfFJLCpSZPW6yjxdUdxDfq/+CO96H1jW3sU1r+jP1TGcJ3Zmoa0+M1jGmX1M3sUv7bYvm+Gb8AEKO0PKY9MzbUio6Sk1V/p+thsQ15HvXuFQ/uWA12EZMBUG//7AE976PjmVcBKM++/oCe1WPj2Rj1ujkP7ng/O0a+Xyvzuzby3pUWZkVMH5qml3axoQY5C2BY2roa5c7OqUKBVfVO85Oih8Wu3zQASg79aLEO29DRqKOXWywPYNdnKgWvWp47nT4TKoGd8RtosY72t4TvhNUkrsPS1qFe86bF8gB245ag3jD/wR2rkx8mFApRb1zwgJ730aEvClQcNd1iHfaRn6P+dqblNui/06T+baHlOp4TinzUNHeqv5lhsTyA3YvLmOk/3GL5pTeFNzam+1t+TP38pnBMbejRxmIdyaoLIBRdfOy50/Wpv+fk/yGpf2zfY+nHv+1OU1UXTPrfq105Op1u7H/MIAkJCQkJCQkJCYl/OI9zWe/Hmb+9ep6EhISEhISEhISEhMTjjHTRJCEhISEhISEhISEhcR/+1wpBSEhISEhISEhISEhYiE77WL4y9NjztxWC+C/xjx6chISEhISEhITEY8P/xNXIrU49H+vzY79TMY+lH6XH8yQkJCQkJCQkJCQkJO7DP/7xvMyeYRbLKmIOAHC2geVlWtul/E5y+14WywM0PLOXC/6Wl5ttc1MoN7um/iiLdYy7I3ztOrVbD4t11Du6n+x+lscDwHPnAVI69rRYvkFcDADZfSy3wzP6AKpnajYOj+0HiPMdZLF8x9tbAWrFF+lPhlusw/tgLOqD31ssD2D35FhKjlv+nQvbLkLJ9LKbcQ/oWT11/DsC1KhkuN3I92tUshyEsuWXm/ezWL5Z4k4Acp8Pt1iH26+xQM1z59IGlucbgJkpP3EjuLfF8o3O7wGoUblvuz5TBR2x31muI3x8jUqWg1C2vPjrVyyWt58kfLsqofG/LNbR6vqfANxsa3lM/M/tQR2zymJ5ALueEyk596fF8rZtBR+UJuyxWEfdVoIPSo6ss9yOJ0ZSemm/5Ta0EI7FtTGOsuv3/zbi/ajTOAT13of9CkzV2PWK5PmGlp9n/ZoslMMf3dDyTxT8mCx8oqCXXx+Ldey9FW2x7H+bf/ZDZv85pDtNEhISEhISEhISEhIS90G6aJKQkJCQkJCQkJCQkLgP//jH8yQkJCQkJCQkJCQkBKTqeZYh3WmSkJCQkJCQkJCQkJC4D/8v7jTV6RSC45RXQC6nZMd2in/5WdRu26cvjhMnocnOAkD9+xZKdmwX9XEKa4fvey8hs5Kj+mUPGSs2ido9RvXF64V+6DRatMUl3Hp7BSVXb4n/TrdOuM+cDFZyCrfs5O73v1Rpr/1ToXgtnUfayMmUXbpS+btjWHvqz30JrOTkbNhD1te/VSnv3Lcb/lHvcLX/a6jjr4na6oe3ofOC0cjkcq6sjyX+q22i9qCJ/Wg6PBxthYaSnAIOz1hF0R2VqE/dzp1weXUqWFlRvG07hT+uF7XbPd0H5ymRaLOyASjatIXibTtEfWw6hOAQ+QoyuZySXdtR/yqOSd1efXF4cRLaezHZtoXSaENMbLt2wm3mFJDLKdq6g7trq/al3VOheC15j/TRk0S+BLDpqLfBSk7Jzu2oN5rY0Ftvg0pvwx9bKN0lnhc2HUJwmKifW7u3U1LFOOzHG3SUbNtC6W6xDufwdjSY/yJYyclev4f0rzaL2r1G9cFr7NOg0aIpUpP81gpRe234ok5ICM7TpoLcCvX27RStE4/Drm9fnCZHotHHtHjzFtTbxeM4kpDEkl/2otVqGRzalvH9uoraU1X5vPf9dnILinF2sGPRhP4o3Z0N8heu8tG6HWi1OgaHtWfCv54Uy2fnMe/bLeTeLcbF0Y5FLw9B6e4i6jPnk1UcPHEWd1dntqz6yMwHOp2OD7/+gUMnz2NrW4eFr79MyyaNxOO4lsaS6HOCHe0aMb57C1F7Wn4Rc7aepKC0HK1Wx7SebQht4lPZPnvRMg4eOYm7mytbfzJ/QVqn07H4sygOHTuFrW1dPnj3dVo2CzTrZ9+9A8p3I0EuJ/+3XeSs/tWsD4BjxBPU/2I2N5+bJvrdum0I9uOEmJbGbKd0qzimdcL7Yjc6Em2OENPSnVso2yeOaW3kTv+wNjz13mhkVnLif4nl5ApxzvENaUaPeaPxauHHn1OXc2XHKVG7XbeOuL81GZlcTsGWneR/V3XREPue3VEum8ed4VMouyie30cuJrNk80Ehpl1bMr53R1F7Wk4Bc37aQ4G6FK1Ox7T+3QgN8hfrSLjOko0xwvzuHsz4vl1MdNxlzprtFKhLhHkxOIzQ1gEmOpL4aH20YEdoWyY8/YSoPVWVx7w1f5JbUIyLgy2LXhwkXiM3s/n4QCJarY5BrXwZ30k8d5ceSOTUrVwASio05BSXcWjyU6I+jk+2x2fuRJDLyd24m+yo6o8jDVbM4trAVykxOY7YdeuI+5uTQS4cy/LXVB8TxSfzSB1RRUz+usGSX/ej1ekY3K0V4/t0Nvfn2l0Gfw4KJbRVY4P8uUQ++n6rEI+nOjNhkLgoTmpWDvOiNpB7twgXR3sWTR2B0sNV1Ofw2Yt89N1vaLVanu3ZjQnPRoh1ZOYwd8VP5OYX4uJkz6LpY/D2cBOPI/4aH/0cjVanZXBoOyY8012sIzuPeWv+0MfUjkUvDRbFFODwmb/46JuNgh29n2DCkL4mdqiY++UP5N4tFMby2nga1vI45ixbzcGT+twZ9SGm6HQ6Poz6kUOnzmNbty4LX59Iy0B/sS/+usmS32KFmDzRivERIaL2tJy7zPkhWlhnWh3TBnYntJV4DgOMe+8l2vfoQKm6lK9mfs6NBPMCO41bBTDlk2nUsa1r1jb6vQkE92hPqbqUVTOXk1yFvH+rxkz85BXq2Nbh/P4zZu1T5k8i5KkQStUlLJnxCdcSrpn1GffmWHoP6YWTiyP9m1te2Enif4e/7aJJJpNpgHi9DZeAMTqdrlgmk30H/AvI1Ol0rYz6twWiAFugApis0+keXPJFLsdp2qvkvvk62qws3FaspPTYETTJyaJuJbH7KPzy82p1+C18mWsj51GepqLZtqXk7zkpuijK3XoA1U+7AHDuHUL9OeNJemG+SIf7W6+QOfktKjKy8PnpK9QHjlJ+I0XsF3s7nEYMpjT+kpkN9RdEcmPUHMrTVQT+sYy7e05Qek18YSZ3sMNzXH+KziaaDUMml9HlgzFED/+Q4rQc+u9YQMru0+RfTa3so0q4yR/95qApKaPZCz3pNHs4sZOMqk/J5bjMnI5q+htoMrPw+jaKkkNHqbhp4s+Y/eQv+6JafzpOeZX8Wa+jzc7C9fOVlJ04giZFrKP0wD6Kvq4iJnI5bm9NI3PKm2gysvD+YQXFB49RcUMsL7O3w+nfz1Iaf7F6G97R2/DlSsqOV2HDwX0UfVX9vHCY9Cp3Zws6XD5dSfnxI2huiXWUHdxHUVT1OhosfJkrI4S51WL7x+TtPknJ1duVXVRbD5L1k1CVx6V3J/zmja91Xzi/Np3cGTPRZGXhsSqKksPma0S9bz8Fn1U9Do1Wy+KfdxP12r9Rujkz8oPvCQtuQkA9z8o+y37dx7+6tmJAtzacvHSTL7bE8sGEAZXyi374k5VvjkHp7syI91YS3q45AfUVBvlfoun/RFsGdG/HiYvX+fzXvSx6eYjIjoERoQwf0Jt3P666mtOhU+dJvpPO9jWfcCHxGgu/XMPPXywQj2PnGaJGhaF0tmPkN3sJa1aPAC/DxdnqQ5eICPJjaMdAkrLymfrzIXZON1QlG/R0b0YMGcCs95dWbcOxU6TcTmXHhm+58Fci7y9dzvrVn4k7yeUo507h9vhZlGdk0/DXzyncd4KyJJN84WCH2+iBqM8lmsnbT5hO4fsz0eZk4bQ4ivK4I2hvm8zNo/tRf1v93Kxp7pTJZfRaOIZfR35IQVoOo7YtIGnPaVRGOeduqoqdr6+k08tPV2mDx6xXSH/5LSoysqn383KKY49Rft08b7qMHEzJhUtmKjRaLYt/jSVqyiCUro6MXLqBsFaNCfBxr+yzevcpIto1YWhoa5LScpi68g92Bo0V61i/h6hXh6F0c2Lk4rWEtQkUze/V248S0bE5Q8PakZSazdTlv7Kz9SSRjkXrdrJyxkiUbs6MWPgt4W2bElDPq7LPso0x9O/amgFPBHPi0g0+37yPRS8O0svr+HD/Jb5+tgNKR1tGrj9OWGMvAjwcK+VnhjWv/P/151K4nHnXzJ/15k/ixguzqUhX0XjrpxTsrfo44jF2AMVVHEeQy3F/5xUyIvUxWbec4gNVx8R5xGBKq4vJhhiipj2H0tWJkR+tE/zp42Hw587jRHRoytAn25KUpmLqV5vZubCxwZffbWbluy+j9HBhxDufEd4xiABfb4Mvf9xG/yc7MiCsEycSrvL5+h0smjrCYINGy6LVG1k1dypKD1eGv/Ux4Z1aE+Bn2AD55Ict9A8LYWCPLpyIv8wXP/3BouljxDH9aScrXx8l5K0F3xDethkB9Y1juof+3YINMd0Uw6KXBovtWLmeVfOno/RwY/gbiwkPaUOAXz2DHd9von+PLgx8qisnLiTyxY9b+ST02VobB8DA3vrcufQ+uTM1g+3fLuVCYhILl6/h588M5zgarZbFG/cR9cqzQkyX/ExY6wBxTHedIKJ9U4Y+GSzEdMVWdraaIPo77Xp0wKeRD6+ERdKkXVNeWjiJWYPeMLPnpQ8iiXr7K66evVJZPQ8guEd7lI18mBk2hYB2TRm3cCLvDXrbTH7sBy/z7dtfk3T2CjPXzha1hfToRP1G9RkTOo4W7ZozfdErvDJgupmO43uO8/v3f7D2oOXVNSX+t/g7H89T63S6tvoLozIgUv/790DfKvovAebrdLq2wFz9vx+IdfMWVNy5gzYtDSoqKN2/j7rduj9Y0Aj7tk0ovZlOWUoGuvIKcrcdwsVkB0VbqK78fyu7umb1HOu0akbF7VQq7gh2FEXHYhcu3mUEcJ08lrvfb0BXWmZmQ1lyGmW3BBvyth3EOaKzmbzy9ZFkRW1CV1pu1ubZLoCCmxkUpmShLddw/ffjNOjTQdQn/eglNCXC3846fQ17oxMLAJuWzam4nYomVRiHeu8+bEPNx3E/rJu2QJN6B226PiYH9lGny8PHpE5Qcypu3UGj92Xx7v3Yh3Uz6+cSOY67a39BV1Zm1mbdzMSG2H3U6fpo88JsHAf3YfMI4wBwaNuE0ptplXMr5/fDuJrEVTS37G1Fc6s2fGHTojmaO3fQ6NdIScw+bLs/WkwTbqTi5+WGr5cbNtZW9OnUgthz4p3l66nZhDT3B6BT84bEnrtqkL9+Gz+lO74Kd2ysrenbuTWxZ8QnbEl3MglpIZw0hbRoZNYO0LF1C1ycHM1+v8f+Y6cZ0CsUmUxGcIsmFBQVk6XKNdhxJwc/N0d83RyxsbKiT1ADYi+ninTIgCL9+iosKcfLyU5sQ9vWuDg7VW/D4eMM6NtTsKFVCwoKCsnKzhH1sW3TlPKUVMpvp0N5BQU7DuDYs4uZLs9pL5Dzza9mcbUKbI42/Q7aTCGm5Uf2UafjI67TWsid3m0DyL2ZQb4+5yRuO05AhDjn3L2dTXbiLXRa8xq4dVs1o/xWKhV30oW8uSsW+3Dz+e02ZSx5a8zzJkBCcgZ+Xq74eroIc7N9U2LjxTvPMqBIn/cKS0rxcnYQ67iRhp/CFV8vV0FHxxbEnr8q1iGDInWpoENdipeLo4mOVPwU7pVrpG9IkNkaSUrLIqSFPwAhzf1F7Qnp+fi52OPrYo+NlZw+Tb2JTco0G+89dl1Oo28zH9FvdsFNKU1Oo1x/HMn/8yBOvc3nlWLGKLJW/lblcaRuq2ZUGMckuvqY5H+/ocqck3AzXR8TvT87NCP2vHgnXyaTGWKiLsXLxRCThGsp+Ck98FV6CPmiWztiT/0lkk+6k0FIkHAHNyQokNi4BLEN127SwNsTX29PbGys6du9PftPXRD1uX4rjc6tmwk6WjVl/6l4sY7rd/BTuOGr0Me0cxCx5y6L7UjNFsf0rLg94epNGvgo8PX20tvRif0n7mNH62bsP3m+VscB0LF1c1ycHMx+v8f+42cY0LO7PncGUlBYTFZOnsGOqmJ6IUmkQ0b1Mb1Hp94hHNgklGK/evYKDs4OuCrEd8VcFW7YOdpz9ewVM/n2vUM4vCkWgKSzV7B3dsDFRN5F4Yadox1Jevl7/e/RLaIrezbtBeDS2UQcnR1wV4jPhe615WTmmP3+v4BOK3us/3tceVzeaToEBALodLqDQFWzUAfcu6ftAqRW0ccMK09PtFmGA4s2Kwu5p6dZv7qhYbiv/g7nefORe3mJ2up4e1CWml3577I0FTZKD1MVeL7wNC0PRVFv1lhuz1starP28qQi3WCHJjMLK4VYR53mgVgpFagPnzDTbaP0oNzIhvIqbLALCqCOjxcF+6v+Xo29txtFqQbXFqfl4ODtVmVfgKbDw7iz/7zoNysvTzQZRuPIysLKy9yftuFP4vXDN7h98B5yhdifctOYZGch96giJt3DcF3xHU7vzkfuadBhpfBEk5FV+e+KzCysFGJ5m2ZNsPb2ouSIuS8B5B5V2FDVvHgiDNevv8Nptvm8kHt4os0W67CqYhx1ngjDZfl3OL4jHgdAHR93ytKM5la6ijo+5snZa0w/Wh2OwvfdMaTM/aby91rxhacXmkyDDk1WltlYAWzDnsRjzbe4LphvFtPMvEK8jR45Ubo5kZlXIOrT1E9BzBnhhGHf2SsUlZSRV1gsyOcW4G30qJ3C3ZmMXPEuebMG3sScFu6UxZy+RFFJaaX8w5KZnYO3l2HdKD3dyTS6aMosUOPtYm9od7Yjs0At0hEZFsT2+BQiPt3G1PWHeLtvu0eyISNLhbdRjJQKTzKyskV9rJWelKcZxTU9G2uT9V63ZQA2Pp4UHRA/zgYgd/eqfCQUQJuThczDPKZ1Oj+J09JvcXh9vll7beROJ283CoxyTmFaDk7K6nOOKVYKTzTpRnMzMxtrpdiGOs0Dsfb2Qn2o6gcPMvOK8HY1XMAoXR3JzC8U9Yns15ntcZeJmPMdU6O28fZzYSY6CvB2M53fJjr6d2f7ib+IeOsrpi7/lbf/Lf6eUWauWIfCzYmMXPEaaearrFwjMWcui9dIUQlKJ1uDDU62ZBWVVjnm1LtqUvPVdPIz2fTy9hDPq7Rss+OIbZAwrwqrOY5YKTypMIpJRUa2Wc4RjmX3i0kh3m6GjQWlm5N5TJ7pyvaTl4iYtZKpX23m7WGGx+8yc/LxNnrUTuHhQkZuvki+WcN6xJwULg5iTsZTpC4lr6Cosj0jJx+lp2EuKt3dyFSJdTT1r8/e4+cEHSfOU6QuIa/AYGdmnknecnM2j6mfkpjTwgZPzJlEUUwFO3LFdni4kpmTK9LR1N+XvcfPCjqOn6NIXUJubm6tjeNhyFTl4u1pmE9KT3cyjTZ7zGLq6mi+Rp7pwvZTl4h4dzVTV2zl7aHm33109/ZAZXSuo0rPxt1kjrorPVClq0xFAXDzdifHSD4nXYW7UrwO3JXu5BjJ56SJdXl6e5KVapjjWWnZeHqbn/NJ/P/jb79okslk1kA/hEf17serwMcymewWsBR4pxp9E2UyWZxMJotbterhPqJXeuwoqpHDyHlpPGWn43B+a9YjjMBA9g87uBgaSeritSinDX00YZkMtxmTyF1m4UfiZDJ85kwg9YNvLZM3ofGzT+AR3Jj4r7c/uLMJJYePkTFkOFkvvEjpydO4zTG/Nf4gyk4cJWfsMPImj6f8TByOrz9CTGQy3GZEkvtpzT64V3b8KDljhpE3SW/DzEefF2UnjpI7bhj5U8dTfjYOxxmWza2stTtJ6B7J7UU/4DPt+YcXrCVflBw9StbQf6MaN4HSuDhcZlW5/O7LjOef4vSVFIYt+I64KykoXJ2Qyx8+Bc34dx/iEm8ydM4KTifeROHmjFz239+R2pWQwoBgf3a/1p/lw0OZvfUk2v/2lwJlMhRvTyTzo9UP7lsN5XFHyZ/8bwpmTqD8fBwOUx89prWVOy1GJsN9ZiQ5n6yskZpdp68woHNzdr8/nuWR/Zn94260Vdz5uq+OkxcZ0K01uz+awvKpzzN7zZ+PrGPG0F7EXU5m6PzVnL6SjMLt0dbIPaIvp9OziRIr+SOuD5kMn3dfJL0mxxF9THKX1TAmcYkM6BLE7kUvs3zKs8z+fscj+XPGqP7EXbzO0Lc+4fSl6yjcXR7Zl6+PGczpi9cYOvND4v66hsLd9ZF1zBjaW4jpe6s4fdmymL4+bgin/7rK0Nc+IO6vKyg8XLGysvqvjqM22BV3mQGdg9j9wUssnzyI2Wt3PfIakZD4O/k7C0HYyWSyc/r/PwQ8KEtPAl7T6XSbZDLZUH3/XqaddDrdKuDe1ZIu55XJyL0M70XIvbzQZot3dXV3DTvaJTu24/hSpKi9LF1FHaNn1+v4eFCeUfUuB0DuH4fw+yAS4ye8K7KysfY22GGl8EKTadAhc7DHJsAf79WfCO0e7nh9toCsV+cCUJ6hwsbIBhsTG+SOdtg2bUjAL4sAsPZyw/+b2dx8cWFln+L0XBzqGXZc7H3cKUoX72gB+IQGETxtADuHfIC2rELUpsnKxkppNA4vr8riAPcw9mfxtu04T5koatdmZ4tj4umFVmWio8AoJtHbsZ9giIkmMxsrpWFH21rhhSbTIC+zt8cmoBHKlcsEGz3c8Vz2Ptkz5hhsUFVhg+m8MLZh13bsXxTPC60qG7mnWIfmPuMo3b0d+/Emcysthzo+RnPL24OytOpv9+f8fogGi16u/Het+CI7CyujO0dWXl5oswy7bCCOqfrP7ThFvixqV7g6kp5j6JORW4DC1cmkjxPLJgvvIBWXlBFz+jLO9sLOucLNifQcw+5oZs5dlG7il6UVbs58Om24Xr6UvXEXcXYQPxr3IBSe7qRnGdZNRnYOCqMXohVOdqTnG+0C31WjMHn8bsu5G6wYIRSpCPbzpLRCQ15xKdU/2CJG6eVBulGMMjKzUZrcra3IyMbGxyiu3p5UGK93BzvqNGlIgx+Ep5StPN3wXTGvsl2bk4Xc6M6R3N0LncokpoWGeJXt2479aHFMNabr1ILcWZCei5NRznH0cacgwzznVIcmMxsrb/Fd5ooMo/ntYEedQH+8vxHeH7PydEf5+QIyps+t7KNwdSDdaMc7I68Qhcmjc1uOX2TFJOH9uuBGPkJMi9SVMVW4OpGeazq/TXQcucAK/WZZcEB9SssryCssNuhwE+vIzC1A6Wa+Rj6dImyKFJeUsfdMomGNONiSUVBisKGgBC8H85fgAaKvpPN2jxZmv5enq8TzysfT7DhSt2kDGq1fLLR7udFw1RySJ75f2UeTmY21UUyslZ7inONgJxzL7sXEwx3FZwvIfNU4Jo6kG92RycgtMI/J0QRWTBHe2wluXI/SckNMFO4upKsMj4ZlqvJRuomLwijcXfh05lhAny9OXBDlC6W7CxnZhrmYkZOLwsMgUPOMAAAgAElEQVRUhyufvvmSoENdyt7j53B2MNyJVria5K3cu+YxdXPi06lD9XaUsff0pcqYCna4ie1Q5aFwF9+NVbi78unbkXo7Sth77CzOzs61No6HQeHhRrrRnaX/Y++8w6sousf/2b3phZBeCIReQgkdpCW0UF4FFAWRXkRABEVQREDBgiKiVFHsioi+AqKUEHoJvbck9JDe+83NLfv7Y0Pu3dwEJOH7E3338zw8D7kzc/bMmTMzO7uzZ1LSM/GxePNk1abZ+dZ9JOoiq6datqmhtE0/2vYJANfOX8PT4l7H08+LzDL3W5kpGXiW8+bn3W0fc+P8NTwsynv4eZKZopxTM1My8bAo72Hx3dWaHauJPReLt8W3ht7+XqRX8Gbrn8r/7+d8/xYehW+aWkqS9JIkSdYbn5WMBu6GFfsVaH+PvKUYoqOxqRGI6OcHNjbYd++BLuqwIo/oYe74do91tgoGUHjuKvZ1/LGr6YNga4P7E13JiVRuO7Cvbd47Xq1nW3S3khTpxZdisKlZA5sAWQ/nPmFo90eVpkv5BcT3HEzC4yNIeHwEugtXSHt5fmmUs8JzV7GrHYBtoC+CrQ3Vn+hGroUOprxCLrceTnSXCUR3mUDhmRhuTXhXET0v/ewNqtXxw6WmN6KthroDO3JnpzJqjEfTIDp9MI7dY5dSlFHmI2JAfyUam8AaaPzlejj26kHRoShFHtHTbE+HLp0w3FJ+IGyIjUYTEIjoW9ImoT0oPqpsE8Hdok06dlYEVyi+HI1tzRpoSmzpFN4d7QELWxYUkNDrKRIHDCdxwHB0Fy+TPmOeImKcISYaTQ0LHcLK0cGjjA5l/MIQW0ZGtx7oj92jHh06WwWJKDh3FQcL3/IY2IXssr5Vx+xbbj3bortp9q2HYQt9dAyawMDSNnXo2QPd4Yrb1L5zJwy3lW3atHYAcalZJKRlozcYiThxhdCQBoo8WXmFpU8Vv9p+hEFdWpjL16lBXEom8WlZ6A0Gdhy7QGirxmXKF2AymeTyfx5kULcH2xYH0L1ja7bsOogkSZy7chUXJ0e8LRZNTWt4EJeZT0JWPnqjkYhLcYQ2DFDI8K/mxLGbKQDcSMul2GDE3an8m9fyCOvSkS07dss6XLyCi4sz3l7K7SNFF2KxDQrAtoYv2Nrg2j+U/D1HS9NN+YVcf+xZbvQcw42eYyg6F038FIuPsq/FIPoHIvrIbWrbuQfFJ5VtKlQ3X9O2bSeM8WX66UMYO5PP3cC9jh9uJWNO4yc6cj3SOlJVReguxWBbqwY2NUrGzb5hFO4/Upou5RcSF/Y08f1HEt9/JLrzV0iZPl8Rqa1pLV/i0rJJyMiRffN0LKHNlRG7/N1dOBYrB1+5kZxJsd6Iu4v5BrtpbX/Zv9NL/PvkFUJDlBEP/T2qcSxarv+NpHRZhquThYwACx83suP4JUJDGipkKPrItsMM6hxiLu9XjbjsQhJyCtEbTUTEJhNWz4ey3MwsILdIT4i/m1Wa9nws9hbziNvj3cjbZd62a8orJLrtcGK7jSe223i0Z2K4PfEdRfQ83aUYbGop57KybXKnu0WbXLhC6stl2iTIj7jUbBLSS9rkVAyhLZSRBv3dXTkWE1dizwyKDYbSNmlaryZxyenEp2bI40XUGULbNlXaMjffPF5s3s2g7spbhqb1g7idlEZ8Sjp6vYEdh04T1rZFhTK+3BjBkz2U338pxy0jO45dIrTlPdp06yEGdWmplNEgiNtJqRZ6nCCs/T30+G0HT/Y0f0P2MOrxV+jesTVbdh8qGTuv4eLshLeHeYuk3KZZyjZtXlchQ+4jJW2anCGPnSVtOqv/K8zq/wondh4ldLC8ba9Bq4YU5hWQnap80JKdmoU2v5AGrZS2ntv/VU7tPE6XwWEA1GvVkMK8QnLKlM9JzUKbr6VeSfm7+QEm9Z3C4Ygoeg+Wn8k3adWYgrzCf+y3SyoPl39SyPFEIBTYB/QArt4z911MRvJWfEr1D5cgiCLa7dsw3r6F85hx6GOiKT4SheOTg7Hv1BnJaETKyyN3cZlwm0YT8fO+oN4Pb8shxzfspij2Dn4znqPwwjVyI4/jNeY/uHYJQdIbMOYUcHvGp1YyMj9cgc+qD+QwrVt2oL9xG7dJoym+HIv2wBHuidFE4vw11P1+AWhEsn7Zhe5qHL6vDEd74Sq5u+4fSFAymjg69zvCf3oNQRS5umE/2bEJtJo5mPRzN7kTeZp284Zh6+xA2Ody+OKChAx2j12q0CNn6XI8P1kMGpHCP7djuHkL1wljKY6OQXcoCudnnpIDCRiNmHJzyX6vjD1NRvI/+xS3d5eARqRo5zaMcbdwGjkOQ2w0xceicBw4GLuOJTLy8sj/+AOFDpkfrcBnxYegESnYsl225QtjKL4Sc39b3tVh1ae4vb+kJFy47BdOo0p0OFqiw2MV6FAio+CzT6n2jixDFynXw3HEOAxXo9Efi8JxwGBsO8gypPw88j+x9q24eWtpuO4tEDVkbNhFUewdAmYOo+DcNXIiT+Azpj/VuoQgGYwYcvK5+coymu1Z8fBsYTSS++ky3Jd8BKKIdtt2DLdu4TJuLPqYGHSHo3AaPBj7zp1K2jSPnEXKethoRGY/15vJn/6MSZIY2LkF9Wt4s/r3AwQH+RPWsgEnY+NYvnEfAtCmYS3eeC7coryGN0b+h8kffY/JZGJQt9bUD/Rh1cbdNK1dg7DWjTkZfYvlv0YCAm0aBTFn1OOU5bVFKzlx/grZOXn0HD6VF0c+jcEgvy0d8ngvurZvyYETZ+k/dgYO9nLIcUU9RJHZ/Vozed0BuR4t61Dfx43Vey8SHOBOWKMazAgPYeEfJ1l3LBYQWDCwPYLFNsFZb33AiTPnyc7OpeegEUwZP7JUh6FP/oduj7Xj4JET9BsyDkcHB96Z80o5bWIi9Z3PCPzqXRA15Py2k+JrcXi+NJKii7EU7C3/+7RSTEYKv1qGy5tymxbv3Y4p/hYOQ8divB6D/mQU9v0HY9e2kzzu5edRsMrav6s6dkpGE7vnfcfgH15D1Ihc2LCfjNgEOs8YTPKFm1yPPI1fi7oMXPsyDm5O1OvVik4zBvNtr9mldshYtBK/zxaBKJK3OQL99dtUnzKa4kuxipv1irDRiMx+OpTJq7dgMpkY2DGY+v6erN56lOBaPoQ1r8uMQV1Z+PMe1u09A4LAguG9FG1qoxGZ/WxvJi/7BZNJYmDn5tQP8Gb1loMEB/kRFtKAGU/3YOGPO1i3+4TsF2P6W8l447m+TP50vezjnVtSv4Y3qzbvo2ntAMJaNuRkzG2Wb9wDgkCbBrWYM9wcE8lGFHm9e2OmbDot+2bTGtTzdGH1kWsE+1QrXUBFxCTRp5Gf4tqWfpX49hpqf7cQQRTJ+jUS3dU4fF6W55G83fefRzCayPxgJb4lbZL/e0mbTB6N7nIs2r/aJkN7MHnlb3KbPNaM+gFerP7jMMFBvoS1qM+MwWEsXLeTdXtOgwALRvYtrZONRsMb455i8vtfyOHXw9pTv6Yfq37ZQdO6gYS1bcbJy9dZvn4bCNCmcV3mjB9cRgcNcyYMYfI7qzCaJAb16Ej9Wv6sWv8nwfVr0b1dC05cusryH7cgCNA6uD5vPj/Eqh5vjOjH5KXrZD26tKR+DR9Wbdort2mrRpyMucXy/+6R9WgYxJwR/az1eH4okxcsx2g0MahXJ+rXCmDVT1sIrh9E9/YhnLgYw/IfNiMIAq2DG/DmC88+1HoAvPbBKnnszM2n54hpvDjyKQwGIwBD/tOTru1C5LFz3Ez5uIZXnrdu0yE9mLxqo9xHHmsqt+mfUQTX8iWsRT1mPNWNhT9Fsm7vaUBgwcg+Vn56es8pWnVvy4oDayjW6lg1c0Vp2kfbPmFWf3m8XDv385KQ43aK8uf2nKJl99YsObCaYq2OtTPN0X/f3fYxc/u/CsB3c79g4scvYetgx/l9ygc5x/Ycp32Pdnx/6Bt0Wh0fvfpxadqaHauZ1HcKAM/PGU+PQd2xd7Rn/fEfrWyq8u9DkP6md3SCIORLkmQV5koQhPVAGOAFpABvSZL0lSAIXYBlyAu9IuSQ46fucxkptWfofbJUjM/u/QCcqTWw0jJaxf3O7dZWuwgfiKDTuzhf+4lKl29xSz4X5ZsaIyotY2yCPCAkdrL+cPOvEhC1l/R+lW8PAK/t+4lr2/P+GSug1sndAKT3qbweXhH7yfhP1erhuXU/JwMrf65D2/jNAA/FFsndwiotw+/APrQHvq10eQDHbmMoOlr+OS9/BYeOQwEovlX+h+t/Bbva8rk92nXz7pOzYhyHv4M+3fo8kAfB1qsuMY373T9jBTSK3g5A1jNhlZbh/us+AKo6di6pVfnxBmBm3I/cDOl9/4wVUOdcJADaiJX3yVkxjn2myjL2VT6ksGPYOIoO/lDp8gAOXUdS+NlLlS7vNFm+8bxY1/pBw1+l2Y0/AbjVsvJtUvtsJNrdf+1b44pw7DmRorN/Vrq8Q0vZBrqLkZWWYd9MtkHR4XWV16PzcHRX9lZehybyXPww6lF84y8slivArm57tLuq9s2sY69JPBNU+fusuyHHRwY9VWkZP9yWNzL1qtmn0jJ23YkAORjnI8+N5uGP9Aa9uhd2PpJ2/NveNJW3YCr5fVgFvx8C2pSXpqKioqKioqKioqKi8n/F3x49T0VFRUVFRUVFRUVF5VHmn/RNk4qKioqKioqKiopKFZCkR3L32yOP+qZJRUVFRUVFRUVFRUXlHqiLJhUVFRUVFRUVFRWVfwyCIPQVBCFGEIRrgiDMvke+wYIgSIIgtK3yNf+u6Hn/n/hXV05FRUVFRUVFReWR4R+x7+1acJ9H+v64/uWIe9pREAQNEAv0BuKBE8AwSZIul8nnCmwF7ICpkiRVPswu/wPfNGm3LKl0WccBMwHIm1b5MK2uy/+kYOHwSpcHcJ6/Du2XMypd3nGCfM5S5sDKhxD2+F0Ov549tPIhx6tv2FulkM4gh3UuWDS60uWd3/gOgMI10ystw2nSsodSj/wZAypd3mXpFgAK5j97n5wV47zwZwAKPxxbaRlOr39DbJO+9894Dxpe2fFQwq/faB5+n5wVU/fCTgD2+FqfX/JX6ZHyS5XChYMcMrwqYcttveTDJLUb36+0DMen5sgyfnyz8jJGvMdO38r7JkB4ys9o935ZeR26TwBgWxX06J8i95GjAZUPZdwxcWOVyt+VUZUQ7jPj5CMjtOvfqrQMx2Hy4clVDQOfEhZW6fIAvvv2PZTQ6XfaVf64hpon5OMa9vk+U2kZYSm/VvkID4DTNSsfqrv1HTlUd1WORQk6vYsN/lW7xxmatI7JtSs/9n526xcAZtSufF9fekvu60ODKj8Xbbi9udJlVR6Y9sA1SZJuAAiC8DMwELhcJt87wIfArIdxUXV7noqKioqKioqKiorKI4EgCBMFQThp8W9imSw1gDsWf8eX/GYpozVQU5KkrQ9Lr3/9myYVFRUVFRUVFRUVlX8GkiR9AVT6JGxBEERgKTDmYekE6qJJRUVFRUVFRUVF5X8G0z8/5HgCUNPi78CS3+7iCjQD9gmCAOAHbBEEYUBVvmtSt+epqKioqKioqKioqPxTOAE0EAShjiAIdsCzwJa7iZIk5UiS5CVJUm1JkmoDR4EqLZjgf+RN0+HoOyzecgSTSeLJ9o0Y16OlIj0pK595G/aRpy3GZJKY1r8dXZvUUuTRNGmNw1MTQRTRH9lJ8a7/Wl3HplUX7Po9B5KEKeEmRd8rg1Bo6rXArs9IEEUMZ/ahP/yHlQxNcAfsQgfLMlLi0G1aZa7HzVQW776MSZJ4skVNxnWoryj70Z7LnIjLAKDIYCSzUMehaX0UeWxbtcfp+ZdAFNFFbqXot58U6XY9+uI0ZjKmjDQAdNs2oYtUbge1CWmH45ipIGoo3rMV3e/rlTJC++AwYhJSZrosI2ITxXu2KfIcvpbE4oizcpu0qsO4Lk0U6Uk5BczbfJw8nV5uk54t6NrA32ynus2x6zVctuXZ/eiPKnW06/kcYlBjAARbewQnVwo/maLU4VYaH+27gskEg5oFMq59XUX6kn1XOBGfKdtTbyRTW8zBKcoPZqtaDwBN49bYD5oAogb90Z3o9/xGWWxCOmPXZxgSYEq8qSxfPwS7/qNBEDGc3oP+4Bar8pqmHbHr/jQgYUqOQ/ffFYp0sU4z7Ho+J9vz3AEMx5TtZdvjWTS1Supma4fgVA3tshcVeZy6tMFnzmQQRXL+u4OsL3+x0gPApXdnApbP4/bTL6G7dFWRVi2sFbUWTACNSPr6SJJXbVSke4/og/eY/mA0YSzQcvv11Yp0x85t8Xx9MoJGJHfjDnK+2lCuDs69uuD7yXzih75I8WWlDh7dQ2jw7lgEjUjSut3cXvG7Ij1gVG8Cx/VBMpowFhQRPfNzCmMTFHmcurTB981JpbbIXPtr+bYI70yN5XO59fQ0dBfNesx9fykHDh/Hw706m39cY1VOkiQWfbqGg0dO4OBgz3tvvkpwI+V4cDgmgcV/Hpd9s10DxoU1V6QnZecz79fD8rgnSUzr05qujQOVMq4ly/4tlfh358ZKGTmFzPv9BHlFellGj2ZW/u3ZPYTG745G0IjEr9vDrRVK/wwc1Yua48JL7Xl55loKLOx5+NJNFv+yW65H5xaM69tBqUNmLvO+3UaeVofJZGLaoFC6Nlf2Za/uIQSX6HBn3R5ulNGh1qheBJXoYCgo4uLMteSXaVO3sFbUfmccgiiSun4XiSs3KdJ9RobjN6Yfkkmux81Zn6G9Gv9QZdQObUGPt0ciaEQu/LyP46uVc0hg+0Z0f2sk3k1q8ufUlcRuO0FZDl9NYvGO07I9W9dlXNdgpT2zC5i3+Rh5RSXzYa8QujYMUORx7NQWj9enIIgieZu2k/N1+f3MqWcXfJe+RcKwFym+HKtIs2vfHtepU0GjQbt1K4U/Kecih759cZ00CWO6PI9oN21Cu9U8zrt0a43/fHlOzvplJ+lrrOdkgGp9O1Fr9RyuDXyZogvXlNd4rB3VX30RRJGC37eR993P5cpw7N4Vr8VvkzxqMvorynp4dG9JfYvxIm6FMhhAwKjeBIzrWzJmFREz83MKY5V+Yd+hHW4vy7Yo/GMr+T8o51TH/n2o9uIkTGmyLQp+U/pNtbBWBL79PGhEMtZHkrJaOYd4jeiL9+h+SEYTpoIi4mavpujqHUUeh07t8Jg5BTQi+Zu2k/tt+bZw6tEV7yVvkTR8CsVlbOHXvQWtFsr+eeOnfUSvVPpnwxf6Ufe57kgGI7qMXI7PWEthfLoiT3BoCEPmy/Y8vGE3Oz9Tjr82djaMXjqVWs3qUpCdx5dTP1WkNw4NYdD80YgakaMb9rDnM2Vfr9u+MYPmj8a/cS1+eGk557cfK7eeY96eQKvubdBpdXw2czk3L1oH6KnTrB5TPp6GnYMdZ/aeKleOyv8NkiQZBEGYCkQAGuBrSZIuCYKwEDgpSZL1jdBD4G9bNAmCYAQulOhwBRgtSVKhIAhfA48DqZIkNbPIHwKsAVyAW8BwSZJy73cdo8nEok2HWTOxP75uzgxfvpnQpkHU83UvzbN29xnCW9RlSKdgrqdkMfWrHWy3XDQJIg7PTKZw1Vyk7AycZn6C4eIxTMnmQUfwDsCu9zMUfjILtAUILm5lK4xdvzEU/bgIKTcThwnvYIg5jZRunpgFD19sOw9A+83bUFQITtUs6iGxKPISa4Z0wNfVgeE/HCK0ni/1vFxL88zqYZ781p++SXRKGfOIIk4vvEzeW69iykij2pLPKT5+GNOd24psxYf2UPjFsvINKog4jptOwXuzMGWk4bpoDfqTUZgSlDL0UXvRfrO8XBFGk4lF20+zZkQovtUcGf7lLkIbBVDP22yztQevEN60JkPa1ud6Wg5TfzrI9umPm20ZPoqinxfLthzzNoarZ5AyEs112G2egG3a9EL0DSqjg8QHey7z2VPtZHv+dITQej7U83QpzTMzzLwAWn/mNjFpuWVkVLEeJfa0f+oFtGvmI+Vk4PjKxxguHUdKsfAtL39sez5D4YrXS33LtnWo2RaPj6Pou/eQcjNweOF9DNGnkNIs/coP224D0X75FhQVgLPZr0pl9B6JbsMSpLxMHEbPx3jtrMKe+j0/o79rz9Y9reyJKOIz70USxs9Bn5JO0C/LKdh7lOLrccpLOTlSfdQgtOeuYIUoUuvdF4h97i30SRk02foR2TuPU2Rx05ix+QBpP0YA4Na7HTXfGqco7/XmVJImzsaQnE6Nn1dQuPcI+hvWOlQb8SRF5eog0OiD8ZwZ8i66xAzaRiwiLeKkYlGUsvEQid9HAuDVpw0NFozm3DCLaHWiiO/8F4kfV2KLX5eRv+eYtS2cHXEfORDt2WgrNQb1781zgwcw553yo38ePHKCuPhEtm34ivOXonlnyUrWrzXfPBhNJhZtOcqa8eH4VnNi+KqthDapST3f6qV51u45T3jzIIZ0bMz1lGymfruL7Y2ftpAhsWjHGdYM7yrL+HI3oQ0DqOdt9p+1B68QHhzIkLb1uJ6Wy9T1h9huuWgSBZp8MI5TQ96jKDGDjhHvkxZxSrEoStp4mPjvdwHg3acNjRaM5PSwD8z1WB/JmulD8HV3ZfiiHwhtUY96AV5mHbYdIbxNI4aEtuJ6YjpTV/7G9uYvKHRo+sE4jpfo0DnifVIjTikWRYkbDxNXooNPnzY0WTCSEyU63G3TOu8/z5VnF1CclEGzbYvJijihWNBkbDpI6g9yFEb38HYEvT2W6OHvPDQZgijQ693R/Dr8A/KSMhnxx0KuR54i46q5n+YmZrD91c9p90J/ysNoMrFo20nWjOwuj1lrIwltVIN6PhZj1oFL8pjVrgHXU3OYum4/2xtaRPoURTznvETyC69jSEkn4KeVFO4rv5+5DX+SovPl93XX6dPJnjkTY1oaHmvWoDt8GONt5TxStHcvecvKmYtEkYAFk7k5ai6G5Azqbv6EvF3H0F1TLgREZ0c8xwyg8Ix1H0MUcX9tGqlTX8OYkobvd6vRHjiC4aZSB8HJEZdnn0J3oWxQLllGgw/Gc27IO+gSM2kTsYj0iJOKRZHleOHZpy31F4zm/LD3FDLcZk4nY/osjKlpeH+1hqKDURhulbHF7r3kLDXPqdXfmFVavua7L3C1ZNxs9OcSciKPKxZFmZv3k/7jDgDcerenxvxxXB+5QKGDx+svkTrldQwpafj/uArt/ij0N63b1PW5J9FdsG5TQRRo8/4Y9g1dhDYpk97b3yFx52lyLfpZ9oXbRPadi1FbTL1RPQmZO4wjk1YoZDy7cDzLR7xLVnIGs7cs4nzkSZKvmWV0GtKDwpwC3gqbRtsnOvHk7OGK8k8tHMeaEe+Rk5zBK1ve51LkKVIsymclZrB+5meEPV9xBMaW3dvgV8ef6aGTadCqIePfncTcQa9Z5Zvw3gt8MXsVV8/EMvu7qkXU/f+N9M/fnockSduAbWV+m19B3rCHcc2/c3ueVpKkliULo2JgUsnv3wLlxTD+EpgtSVJzYBN/MXzgxbg0anpVI9CzGrY2Gvq0rMe+S2UGRaBAVwxAvrYY72pOinQxqCGmtCSkjBQwGjCcPoBN846KPHaP9UF/cCtoCwCQ8nOUMmrUw5SVgpSdBiYjxktHsWnURpHHpnUPDCcj5QUTQKH5Jv1iUjY13Z0IrO6ErUakT+MA9l1LqbDe268k0reJ8umgTYMmmJITMKUkgcFA8cE92LXvUqGM8tDUb4wpJRFTahIYDRRH7cG2XecHknExIZOa7i4Eurtgq9HQp2kt9sUkKvLIbSLfpucX6fF2dSxNEwPqKm155Rg2DVtXeD2b4I4YLh9V6pCcTc3qFvZs5Me+6xXbc0dMEn0bKZ+gV7UeAGKtBpjSk5AyS3zrzEFsmimfpNt27IP+cPm+JQbWx5SZjJSVCkYjxgtR2DRWnt9m07YHhmM75QUTQIFy8Sf610XKTkXKke1puHIcTYNWFdpCE9wRwxWlPR1aNEIfl4Q+Phn0BnK37ce5x2NWZb2mjyLry1+RdHqrNOeWDdDdSqI4LgVJbyDz90NUD1fawpSvNevh5AAW58zZN2+EPi4RQ3wyGAwUbN+Pc/dOVtfxmDqa7K82IBUXW6VVa12fwpvJFN1ORdIbSd0chXffdoo8xnvoINuiIfq4xFJb5G3bj0tP5XgB4DVtFJlf/lquHm1bNsetmqvV73fZe+goA/r2RBAEQpo1IS8vn7T0zNL0i3fSqelZjUAPV3ncC6nDvivKG0pBECx803rcu5ho6d8ifZrWtPZvwcK/dXq8XR0U6W4l9tSW2DN5cxQ+fZX+qbSnveJ0vYu3kqjp406gd3W5Hu0as++88m2BIEBBUcn4XaTDu7qLIr16GR2SNkfhW0YHQxkdyh5f6NKqPkW3ktCV+GbG74dw79O+wnqITvZWflFVGX4t65F1K4WcuDRMeiPRfxylXrhyDsmNTyc9+g6SqfwjWC4mZFLTw5VADxfZns1qsS9G+UZN9gsDcLdNlWOWfbNG6O8kYkgo6Wc79uEUZt3P3F8cQ/Y3G5B01v5t27gxxoQEjEnyXFS0Zw/2nf/6POIY0hDd7ST0d2Rb5vx5ANfe1n3MZ8YI0j7/b7njjV3TxujvJGBMkHUojNyLY6h1PdwmjSXv+58rHC+0peOFgdTNh/G6j3+XPRvTNrgxhvhEjImyHtpde3Do+tdtIY+byaXjZtaWg7iFK/3KdB/ftGvWCEN8IoYSWxRE7MMxzFqH6lPGkPtt+W3q0aoeebdSKCjxz7jfj1Kjj9I/U6MuY9TKZTNOX8PJ30ORXrtlfdJuJ5N+JxWj3sjJP6IICVeOvyHhbTn62z4ATm87SuNOpc/WqdWyPum3k8ksKX/mjyiahSvbIys+jaToOKt2sKRd7/YcKLnG1TOxOFdzprqPuyJPdR93HE8wIowAACAASURBVF2cuHpGftt2N7/Kv5tHZXveQaAFgCRJBwRBqF1OnobAgZL/RyK/krvv0j41twA/i0nU182ZC3GpijyTwtswee021h++jLZYz+cTlU/pxOqemLLTSv82ZaejCWqkyCP4BCACTi8vlre+bf8J45XT5nRXD6ScjNK/pdxMxBr1lNfx8MMEOIx9CwQR/f7fMF4/L9cjvwg/i8nL19WBC0nZ5dY5MaeQxBwt7Wt5KX4XPL0wppvrbspIw6Zhk7LFsXssFJumIZgS71D41UpM6ea6ix5emDLKyKhvLcO2QzdsmrTAmBSP9vtVSBlmGal5WvzczDdovtUcuZCQqSg/KbQpk9cdYP3xa2j1Bj4fYT5jSnBxR8o155fyMhEDlLYszVvNE6G6N6bbyqeEqfk6fC3t6eLAxeScssUBSMzVkpijpV1NT6WMKtYDQHDzRMo2b0+QstMRy/iW6C0vfh1f+hBEkeII89aNcv0qULlNS/T0l/1qwgLZr/b+F+O1cxYyyrGnf8X2FN28MN1WPmm08fHEkGxuY0NKOo4tlPWwD66PjZ83BfuP4z7uacpi5+9BcZLZFsXJGbi0amCVz3t0P3yfH4hoZ0PM0Hk0P9imRAevMjqkYd9CuZ3Mromsg/bgcaqPtT5jxd7PA12i2Z66xAyqtbbWocbYPtSa9B8EWxvODF6otIWvF/okCz2S03EIKWuLetj6e1Gw/wQe461tcT9S0jLw8zH3b18fL1LS0gkoqW5qbiF+bs7m9GpOXLiTppAxqWcIk7+OZH1UNNpiA59PUJ5zlZqrxa+aRR8pz7+7BTN53UHWn7he4t/dFOkOfh4UWdizKDETt9ZK/wSoOTacoEn/QbS14eRg89uZ1Kx8/NzNi0ff6q5cuJmk1OHxzkxe9ivr956Wx+/pyvNeyuqgTcykejk6BI0Np3aJDscsdACw8/Ok2EJGcVIGLuX4he+YvvhPHIBgZ8OVZ5RnIlVVhqufO3mJZvvnJ2Xi37L8floRcpuWGbPiy7RpWDMm/7CP9cdi5TYdpTxLSOPjhdGinxlT07FvXqafNTb3M7fR1ufviN7emNIs5tS0NGyDg63y2Xfrhm2LFhjj48lbubK0jK2fp7KPJaXj2FLZxxyayn0sf+9JvJ8fbCVb4+2FMcWiHilp2DVTzmW2jRqg8fWm6PAxXEda18N6vMgsd7wIGNuHmpMeR7C14dzgBYo0WQ/znGpMS8Mu2HpOdQjrhl3LFhjuxJOzzLxt39bPk+JE87ipT8rAqVVDq/Jeo/vj+/wABFtbrg6dq0iz8fbCkGyhQ2oads2s21Tj64P20DGqjbK2haOfB9oEsy0KkzLxbFWxf9YdFkbS3nOK36r7epBlYc+spAzqtGxQYR6T0YQ2rxAXD/ntt5uvB9kW5bOTMglqad3X74e7nwcZFjbNSM7Aw9eD7NSs0t88fD3ITDZfKzMpA5V/P397IAhBEGyAfshb9e7FJeSDqwCeQRk1o0rsOHONAW0bsnPuc6wc15e56/dhquBJXUUIogbBO4DC5W+g/fYjHJ59CRyd71/QElGD6OFL0Xfvotu4ErvHJ4C90/3LlSEiOoleDf3QiA/++lV/Iors54eSO30c+rMncZ4+58FlnDpC7tRh5L02AcOFUzhNmf3AMnZcjGNASG12vvIEK4d1Ze7m45ju8WSoImyCO2CMPmH1ZO1BiIhJomdD30rZ86HUQ9QgevujXTWHoh+WYP/Mi/cvU7a8hx9FXy9E9+ty7AZOBIcH9ysATZMOGGJOPrg9BQHv1yeS9uHaSl3XkrTvtnOxyyTi3/8e/2kPcLikIOA56wUyllQ6imkpCd9EcKTDNK6/u47ar1jfkN1PD5/ZE0l9CLaoCjvO3WRAm/rsfOMZVo7pydxfDj7wuLfj0h3Zv1/+DyuHdal0P73zzU4OdZhO7Ls/UfeVJx9MhxNXGPBYM3Z+MJmVUwcz95ttD1wPgNvf7GR/h+nEvPsT9R9Qh7ukfLuDs52mEPfeD9SY/uCL4YcloyrsuHCbAS3rsPPVgawcHsrcjUcfzJ6CgMfMSWR+/HmV9NBFRZH+7LNkjh9P8cmTuL3xxgPp4P/mBJLf+6ryCggC1V+ZRPan1t8UPiiJ30RwrMNL3Hh3HUEPOl4ARYeOkDJ4GGmjJqA7fgr3eQ8+p6Z/t41LXSaRsOg7/KY94EGygoD7jMlkLa26LQCCBnfGI6Qu0av/fCjyVB4cySQ80v8eVf7ORZOjIAhngZNAHHC/0W0cMEUQhFPIoQSt3w+jPBDriy++wKeaM8nZ+aXpKTkF+LgpFzObTsQQHiJ/OBxS2xedwUh2YVFpuik7A7G6d+nfYnUvxdP9u3kMF4+ByYiUmYIpNbH0DQHIT+8FN/ObCqGaB1JelkKGlJuJMea0LCM7DSkzCdHTDwAfFweS88yv2FPyivBxUW6FucuOaOuteQBSRjoaLx9zPTy9MWUoP8KU8nLBIG9l0EVuRVNP+cTKlJmO6FlGRlYZGflmGcW7t2JTVynDx9WR5JxCc11ytfiU2QKy6exNwoPldXFITa+SNtGVyM9CqGZ+rS+4WtvyLpom1lvzAHxc7EmxtGd+Ed4u9uXKiChna97DqAeAlJOBUN38xkAox7eknHQMF4+bfSvNvEWqXL/KVT45lnIzMMacMvtVRhKih5+FjHLsmV++PW2atMd4xfrDWUNqBjZ+5j5i4+uFPsVcD9HZEfsGQdT8fjF1dn2HQ0hjaqx+G/um5qeIxUmZ2PmbbWHn50lxkrIulmT+fpDqfczb9wyp6WV08MZooYPg7Ihd/dr4f/0RNXd8j32LJvitWIhdsFkHXXIm9gFme9oHeKJLrliHlE1RePdTbh8xpKRj62+hh58XhjK2sGsQRK3vF1N397c4hDQmcPVb2DezfkJdEb7eniSnmvtdSmo6vt5m2/lUcyI5p8CcnltoPe6dvEp489oAhAT5oNMrxz2fao4k51r0kfL8+8wtwoPl4BEhgZ7oDCaFfxclZ+JgYU+HAI972jO5jD193F1Izsoz65Cdh4+7cvvdpsMXCG8jv2UIqVsDncFAdr65X5bVwfE+OiRuisK3TJsWJ2dgZyHDzv/evpmx+RDufZVbpKoqIy85C9cAcz918fcgL6X8floRcpuWGbOqlW3TG4Q3rXjMMqamo7HoZxofLwwpZl+828/8vlxC4LYfsG/RBN9lC7ELNs8DprQ0RG+LOdXbG2Oa8k2olJsLenke0W7dik1Dc3l9coayj/mXGW9cHLFvWIs66xfR8MBXOLZqRNAX83Bobn7rYExLR+NrUQ9fb4xpFvVwcsK2Xh181izF//d12DcLxvvjd7BtYtbDerzwQJdc8RuH1E2H8epXZktmWjoaX/OcqvFW6lHWFoV/bMW2kdIWdhbf+Nn6e6K/hw5ZZcZNAENaOjZ+Fjr4eGNMtRw7nbCtVxu/tR9T488fsW/eBO9PF2JnYQttciaONcy2cPL3QJts7Z++XZsSPH0gB0d/jKnYoEjLTsnE3cKe7v6eZKdkVphH1Ig4upofAOakZFLdonx1fw9yUiruY+Xx4bZPyE7NwtPCpp5+nmSWkZOZkomHn/laHv7KnSgq/04ehW+aWkqS9JIkSeUugu4iSVK0JEnhkiS1AdYD1yvI94UkSW0lSWo7ceJEmtb0Ji49l4TMXPQGIxFnrxMarIyM51/dhWMlH9PeSMmi2GDE3dm8IDHFxSJ6ByB4+ILGBpvW3TBcUN44Gi4cwaa+HJ1KcK6G6BOAKT3ZLCPhBqKHH0J1bxA1aJp2xBCrjLZijDmJWLvktbyjC4KHP6Ys+ZV5U3834rIKSMguRG80ERGdSGh9X6v638zIJ7dIT0iAu1Wa4Wo0on8goo8f2Nhg17UH+uOHFXkEd/OkbNu+M6Z45fdfxuvRiH41EL39QGODXace6E9GKWVUt5DRthPGBOXHpE1reBCXmU9CVj56o5GIS3GElonO5F/NiWM35W+MbqTlym3iJC9qTIk3Ed19Edy8ZFs26YDh6hmr+goe/ggOTpgSrlmlNfVzIy6rkIScEnvGJBNW18cq383MfHJ1ekL8q1vLqGI9AEx3rip9q1VXjBfL+NbFY2hKfAtnV8Vi3JRw3exXGg2a5p0wRJfxqysnEWuXbH1xckXwNPsVgCnpJoK7T6k9bZq0x3itPHv6gYNzufYsuhCDbVAANjV8wdaGav1DKdhrXqya8gu53mkoN3uN5mav0RSdiyZhytuK6HkF567iUMcfu5o+CLY2eAzsQnbkccV17OuYF69uPduis9iqpbsYg21QDWxqyP7t3C+Ugn1HStOl/EJud3uGO31HcafvKHTnr5D80nxF9Ly8M9dxquuPQy1vBFsNPoM6kR6hjFDqWMe84PTs3ZrCG8rtYkUXYrENCsC2xBau/UPJ31PGFo89y42eY7jRcwxF56KJn7JAET3vfoR16ciWHbuRJIlzF6/g4uKMt5e53zUN9CoZ9/Lkce/cTUKbKCPj+Vd34dh1WfcbqdlW417TAPcS/y6Q+8ilO4Q2VD488Hdz5Ngt2ZfK8+/cM9dxquuHY4k9/QZ1IjVC6Z9OFvb07t1KYc+mQf7EpWaRkJ4t1+NENKEtlNtt/D2qcSxaHmNuJGVQrDfgbnkjdeY6zhY6+A/qRMo9dPApowNA/tlrONTxx77ENz0HdiFrpzIynYOFb1bv1Yaimw9XRvK5G7jX8cOtpjeirYbGT3TkeuRpHoSmAR7EZeTJY5bBSMTFOEIb1VDk8Xdz5tiNu2NWTolfmNtUdykG21oW/axvGIX7lf0sLuxp4vuPJL7/SHTnr5Ayfb4iep4+JgZNYCCinyzDoUcPdFHKeUT0MPuzfadOGOLM84j2fCz2tQOwDfRFsLXB7fFu5O0yj5umvEKi2w4nttt4YruNR3smhtsT31FEzyu+HI1trRpoAmQdnHp3R3vArINUUEBi76dIGjicpIHD0V28TNqr8xTR8/LOXMOxrj8OteQ29RnU+b7jhbaMb+mvRGMTWAONv6yHY68eFB0qYwtPsy0cunTCcMtsi4JzV7GvbR433Qd0JafsuFlbOW4W3VLqUHwpBpuaNbApsYVznzC0+y1skV9AfM/BJDw+goTHR6C7cIW0l+croudlnr2Bax0/nEv8s9bAjiSU6WfVmwXRdvF4Do7+GF2GdRyv2+eu41PbH89AbzS2Gto+0YnzkUp7no88RcfBYQC07t+RmKhLpWl3zl3Hu7YfHiXlWz3RiYuRDxbV7vX+r3Bi5zG6lVyjQauGFOYVKLbmAWSnZqHNL6RByVbIu/lV/t08Kt803RdBEHwkSUotOeV3LnIkvftioxGZPagTk9dux2SSGNi+EfX9PFgdcZLgQG/CmgYx44mOLPz1IOsOyjsEFwwJpeQwLBmTiaL/rsFpykI55PjRSEzJcdj1H44x7irGi8cxXjmNTePWOM1ZDSYTut+/gULzE1IkE8Xbv8Vh+OtyaOiz+5HSErANG4wp8SbG2NMYr59HU685jpMXg8lE8a6fQCu/JbMRRWb3asbk/8ohhAc2D6S+lyurD8UQ7FedsJIF1I7oRPo2DlDqX1oPI4VffIrr20vk7652b8N45xaOz43DcC0a/fEoHB4fjG37zmA0IuXnkb/sgzIyTGi/Xo7zHPnbreJ92zHF38LhmbEYbsRgOBWFfb+nsG3TGUxGTPm5FK5WyrARRWb3a83kdQcwSRIDW9ahvo8bq/deJDjAnbBGNZgRHsLCP06y7lgsILBgYHtznSQTxZE/4PDsLNmW5w8gpSdg2/VJTEm3Sm/4bYI7YCjnrchdHV7vEcyUjSdlHZoGUs/LldVRVwn2dSOsnryAiohJok9D/3LtWeV6lNhTt/FzHCe+LfvW8V2YUu5g1/c5jHeuYbx0HGP0aTQNW+L02kokyUTxH9/iMGpWafnird/gMGqOHC789F6ktHhsezyDKeEGxphTGK+dQ1O/BY5Tl8i2i/ix1K/M9lyH/ZBXZXteOIiUnohtl0GYkm9hvHZWrm+TDuW+ZQLAaCLt3dUEfvkeiCK5G3dSfO02ni+NpOjiVcUCqkKMJuLmraXhurdA1JCxYRdFsXcImDmMgnPXyIk8gc+Y/lTrEoJkMGLIyefmK8totmdFafn091fit+Z9BI1I3qYI9Ndv4/7iKHSXYincd38dJKOJ2De+puXPbyJoRBLX76UgJp46rw0h79x10iNOETi+L+5dm5fqcGXaKqUQo4nUdz4j8Kt3QdSQ89tOiq/FldgiloK9FdjQgllvfcCJM+fJzs6l56ARTBk/EoNBfio79Mn/0O2xdhw8coJ+Q8bh6ODAO3NeUZS30YjMHtCByV/vwiSZGNi2AfV93VkdeYbgGp6EBddiRv+2LNwUxbpDl0GABU93VvimjSgyu29LJv90UPbvkNqyf++7RLC/O2GNApjRO4SFf55i3dGrsowBbRUyJKOJ6De+ofXPcxA0Igkl9qz32jPknrtBWsQpao7vg2fXZpgMRgw5BVyc9pmyHkN7MXn5fzGZTAzs1Jz6AV6s3nKI4CA/wkLqM2NwGAt/jGDd7pOyDqP7Welw6Y1vaP/zHNCIxK/fS35MPA1ee4acczdIjThF0Pg+eHVthmQwos8p4JyFDnfb9NabX9L4p/kIGpHUn3ejjb1D4KxnKTh3naydJ/Ad2w+3ri1kv8jO5/r0FQ9VhmQ0sXvedwz+4TVEjciFDfvJiE2g84zBJF+4yfXI0/i1qMvAtS/j4OZEvV6t6DRjMN/2Mm/lstGIzO7fhsk/7Jf9olVduU33XCA4wIOwxjWYEd6ShX+cYN1R+YZ4waAOyjHLaCJj0Ur8PlsEokjeZrmfVZ8ymuJLsYoFVIUYjeQtW4b7Rx+BKFK0fTvGW7dwHjsWQ0wMuqgonAYPxr5TJySjESkvj9wPLOYRo4nEt9dQ+7uFCKJI1q+R6K7G4fPycLQXrpK3+3jF17aQkbV4Bd7LP0TQiORv2Y7hxm2qvTCG4isxFB24fz0ko4mrb3xFi5LxImn9Xgpj4qn92lDyzl0nI+IkNcb3Kx0v9Dn5XJm20kqPnKXL8fxkMWhECv/cjuHmLVwnjKU4OgbdoSicn3kKhy7yvGzKzSX7vQ/wWf99afk7876g/o9vI2hEMjbspij2Dv6vPkfh+WvkRB7He8x/cO0SgmQwYMwp4PYrn1rpkPnhCnxWfQCiSP6WHehv3MZt0miKL8ei/Yu2OD3nW0LXvy6HHP95P7mxCTSbNZjMczdJ3HmakHnPYePsQKcvpgNQmJDOoTFLS2WYjCZ+nv81L33/JqJGJOqXvSRdjefxV4YQd+E653ed4vAvexizdCoL9i2nMDufr176lDaPP1ZafuP8b5j4/RxEjcjxX/aScjWevq88w50LN7i06xQ1W9Rl7Oev4ujmTNOeren7ytMsDlfGFTuz5xSturdh2YE1FJeEHL/Lh9s+4fX+8nj71dzPmfLxNGwd7Dm7758VcrwKXyz8TyPcK4LI/+mFBSFfkiSXcn5fD4QBXkAK8JYkSV8JgjAduPsxx0bgDen+ykvaLeWH7P0rOA6YCUDetIpDU94P1+V/UrBw+P0z3gPn+evQfjmj0uUdJ8iDUubA0PvkrBiP3/cDkD20+31yVkz1DXvRrqtaWE7H4e9QsGh0pcs7v/EdAIVrpldahtOkZQ+lHvkzBtw/YwW4LJWPICiY/2ylZTgvlM/hKPxwbKVlOL3+DbFNygt2+ddpeGUHJwMHVbp823j5XJQbzcPvk7Ni6l6Qwzzv8X3Avf4W9Ej5hZjG/SpdHqBR9Hb06dbngfxVbL3kbcbaje/fJ2fFOD4lf8eo/fHNyssY8R47fSvvmwDhKT+j3ftl5XXoPgGAbVXQo3+K3EeOBjxVaRkdEzdWqfxdGUtqjah0+ZlxPwKgXf/WfXJWjOMwOYDBzZDelZZR51wkKWFhlS4P4LtvHxfrVn5ObnZD/o7mTruelZZR88RuAPb5PsA3lWUIS/mVxE6Vn08DovYCcLrmwPvkrJjWd+QzkG637nWfnBUTdHoXG/yrdo8zNGkdk2tXfuz97JZ8JuCM2pXv60tvyX19aFDl56INtzeDHDT3kedKg/6P9LKpydVtj6Qd/7Y3TeUtmEp+H1bB78uACg4PUlFRUVFRUVFRUVFR+b/hH7M9T0VFRUVFRUVFRUWlajzKEeoeZf72kOMqKioqKioqKioqKiqPMuqiSUVFRUVFRUVFRUVF5R6o2/NUVFRUVFRUVFRU/kcwSer2vMrwt0XP+//Ev7pyKioqKioqKioqjwz/iNXIxbqPP9L3x81u/PlI2lHdnqeioqKioqKioqKionIP/vXb87QRK++fqQIc+0wFqn6OTeGSCZUuD+A080vyJlX+LBzXNTuAh3OeQ1XPSNL+vrjS5QEcB75G/qwnK13e5aNNABR+9lKlZThNXlGlc7NAPjur8KNxlddh1tcAFLxdboT+v4Tz2+sfioysKp6E7v7bPjL+U/kzxDy3ymeIPYw+UtWznrKeCat0eQD3X/c9lDOWHspZT1U8p6kq5/mAfKbPwzhn72Gc3xXfoUelZQQe2/NQziaK9B1a6fK9UzYAD+f8rownqtBX/9hfpXMPQT77sKo6AOQ+X3m/qLZW9otbLSvv47XPRpI/s/JzsssSeU6u6tmJAHlT+1dahuvKbRyr4jlkHRI3MqH205Uu/+Wt/wIP5YwlBtaqvH/+Hvdnpcuq/DP41y+aVFRUVFRUVFRUVFRkJPWbpkqhbs9TUVFRUVFRUVFRUVG5B+qiSUVFRUVFRUVFRUVF5R6o2/NUVFRUVFRUVFRU/kf4dwfO/r/jf2LRdPjybRZvPIDJJPHkY8GM691WkZ6Umce8HyPJ0+owSRLTnuhE16a1FXnEOs2w6/kciCKGcwcwHNumSLft8SyaWk1K/rBDcKqGdtmLShm1m2LXYxgIIoYLBzEc366UETYUTa1G8h82JTJWTitN1wS3wWHIZBBF9Id3UBzxi1Vdbdp0xe7xESCBKf4GRV9/qEivFtaKwLefB41IxvpIUlb/pkj3GtEX79H9kIwmTAVFxM1eTdHVO4o8mrrNses1XLbF2f3oj25VpNv1fA4xqDEAgq09gpMrhZ9MUeQ5HBPP4t+PYpJMPNm+EeO6hyjSk7LymbfhAHlFOkwmiWn92tG1SU2zDo1aYT9gvGyL47vQ791obYsWnbALfxZJkjAl3UL30ydKHW6l89H+aEwmiUHNAhnXro4ifcn+aE7cyQKgyGAks7CYg1OUH4QfvpnK4t2XMUkST7aoybgO9RXpH+25zIm4DAsZOg5N66PII9Yu8S1BwHD+IIbjZXyr+7NoajUuqZTsF5Zo6odg13eU3B6n96I/tMXKFpqmHbELGyz7RcptdL+tfOgybFq2x2ncVBA16HZvRbfpJ0W6Xfe+OI6chCkzHQDd9k0U7zb7jm2b9jhPfAlEkaKdWyn6VVnevldfnMZNxpSRJtvzj03odip972H0EcfObfF8fTKCRiR34w5yvtpgJQPAuVcXfD+ZT/zQFym+fNXaFmMtbLG5jC3CyrHFHmVdDscksPjP4/K41a4B48KaK9KTsvOZ9+th8rTF8rjVpzWWn7bPfX8pBw4fx8O9Opt/XGOlvyRJLPp0DQePnMDBwZ733nyV4EZK/z18LZnFEWdl/25Vh3GdGyt1yClk3u8nyCvSyzr0aEbXBv5Ke3Zqi8frUxBEkbxN28n5unx7OvXsgu/St0gY9iLFl2PNOkTfYfGWI7Id2jdiXI+WSh2y8pm3YZ9sB5PEtP7t6NqkllKHh9Cm9h3bUX3GVARRpGDLNvK+X1+uDMfuXfH8YAEpoyehj45VpNm1b4/r1Kmg0aDdupXCn5R+4dC3L66TJmFMl/1Cu2kT2q1mv/DsHkKjd8cgaEQS1u3h1orfFeUDR/UicFwfMJowFBRxZeYXFMQmKPJUxq+6Ng5U5LFt3R7n50v6auRWiv5bpq/27IvTWIu+urWcvtqkNQ5PTZT76pGdFO/6r5UtbVp1wa7fcyBJmBJuUvS9OTDIQ9GhaVscnp2MIIoUH9xB8Q5rv7Bp2w37J0YCEqY7N9B++YEi3bFTWzxemwKiSP6m7eR8U7F/+3z8FonPKf0bSuazgc/LtjgWiX7vb1blbUI6Yxc+TJ7PEm+WSWuH4xh5vCnesxXd70rftAvtg8OISUh3x5uITRTvUc4zmiZtcHj6BVmHqAiKI3+11qFVV+z6D5dtkXCTom+VgZ3cwloR9M44BFEkdf0uklZuUqT7jAzHd0w/JJN8f3Fz1mdor8Yr8jQNbcmw+WMRNSIHN+xm+2eblTrY2TB+6UsENatLfnY+n09daqXnmLcn0Kp7G3RaHZ/NXM7Ni9aBceo0q8eUj6dh52DHmb2nrNKfXzCRNt3botPqWPbqp9y4eN0qz4hZI+k+uAfObi482+QZq3SVfx9/26JJEAQjcKFEhyvAaMAT+B7wRT5j6QtJkpaV5PcANgC1gVvAEEmSsu53HaPJxKJf97HmxUH4Vndh+JINhDarSz1/j9I8a3eeILxVA4Z0bc71pEymfr6F7U3HWCqLXe+R6DYsQcrLxGH0fIzXziJlJJZm0e/5GX3J/21a90T0DSpbYex6DUf361KkvCwcRszFeP0sUkaSWca+DWYZrXog+lhM/IKIw7AXKVw2BykrHac3lmM4fxRTUpw5i08Adn2GUvjRq1CYj+DqptRBFKn57gtcfe4t9EkZNPpzCTmRxxWLoszN+0n/UY4k5ta7PTXmj+P6yAXKeoSPoujnxUi5mTiMeRvD1TMKWxTvNk9eNm16WdnCaDKxaFMUa57vi6+bM8NXbCE0uBb1fN3NbbL7LOEhdRjyWBOup2Qx9eudbG8ytNQW9k9ORPvF20g5GThOW4zh0nGkVPPgK3j5Y9tjMIWr3gBtEVGxbQAAIABJREFUAYKzWxkdJD7Ye4XPnmqDr4sDw9cfJbSuN/U8XUrzzAw13yCuPxtHTGqulYxFkZdYM6QDvq4ODP/hEKH1fKnn5VqaZ1aPYLOM0zeJTlHKkH1rBLpfPpZ9a+T8Er+w8K29Fr7Vqieiby1smnc1l+8/lqIf3kfKzcDh+fcwxJxCSjPfKAkefth2GYj2q7ehqACclYuuhyJDFHF6fjr5C2diykjD9cM16E8cxhR/W5GtOGov2i+XYYUo4jz5ZXLnvoopPQ23Tz5Hf/Qwxjtlyh/YQ8GacsrDQ+sjXm9OJWnibAzJ6dT4eQWFe4+gvxGnvJSTI9VGPEnRuSvl1sVp/HTy35mJKTMN10Vr0J+swBZflV8Xo8nEoi1HWTM+HN9qTgxftZXQJjWp51u9NM/aPecJbx7EkI6NuZ6SzdRvdxE+xyxjUP/ePDd4AHPeKT8C3cEjJ4iLT2Tbhq84fymad5asZP3aTy10kFi04wxrhneVdfhyN6ENA6jnbW77tQevEB4cyJC29bielsvU9YfYbrloEkU857xE8guvY0hJJ+CnlRTuK9+ebsOfpOi80p7yWHGYNRP7y2PF8s2ENg0qM1acIbxFXYZ0CpbHiq92sN1y0fSQ2tR91nTSXpqFMTUNn28/Q3swCsPN21YyXIYORnfxcrkyXKdPJ3vmTIxpaXisWYPu8GGMt5UyivbuJW9ZeX1EoPEH4zg95D2KEjPoELGItIiTikVR0sbDxH+/CwDvPm1ouGAUZ4YtUtqzEn61vbFFZDNRxHnSy+TOexVTRhpuSz9Hf6ycvnpwDwWf36OvPjOZwlVzkbIzcJr5CYaLxzAlm+ciwTsAu97PUPjJLHkMd7Hoqw9JB8fnplLwyWykrHSc31yB4dwRxXgh+gRg3+9ZCj58pWS8qP7/2Dvv+Ciq7v+/Z3bTey+E3kLvoPSiNB9BHuw0wQYoCogdUbFgexSVByl2RcXyYKMLhBZaaFJDDaQnm142ye7M/P6YJbuzmyDZ5PuT7/Odz+vFi+zee86ec+45586de+eMlocoEvrsLLKn2/x71RLKt9fiW/eOo/LPGnxLEPEa9zDmFS+q89nj72A9uR8l28EW4TF4DL2d8iVPV9vCo/sgux7THqfstSfV3LtoGZakROR0rS0sidswf/ZBrbbwvnMm5UueRyk04fvkYqzH9rqOx/A7KX93HphLteNhs0Wz1x/k9N0vU5WZR4d1b1G48YBmUZS3Zic5X6mVB4OH96LJS1NJnvCKgxgiExY+wLsTF1KQlc/8X9/gyOYkMs/ZefS/cxhlRWU8N3gWvW7tx+3PTNSI0XVID6Kbx/D4oBm07taG+1+dzvzbnnJR+YHXHmbFM//m7OEzPPPFC5q2HkN6EtMslukDH6JNt7bMeG0mT459woXH/j/2s/aL3/lo+4qa7arjvw5/5zNNZkVRuiqK0hGoAqYDVuAJRVHaAzcAjwiCcOWq8xlgi6IorYEtts9/ieOXsmkcEUxceBAeRgMjurch4Zj2roMAlFVUAVBaUUlEoJ+mXYxpgVKYg1KUC7KE9dR+DK271fqbhvY3YD21V8sjujlKQQ5KkUnlcXo/hpZda+EAhvjeWE/vt9M3a4uck4liygLJivXAdoydb9TQePYfhWX771BeCoBSUqRp9+vamsqULKouZ6NYrBT8upOg4b01feRSs/03fb1c9nDF2BbIBdkohaotpFP7MLbpXqsexvY3YD2ptcXx1FwahwcSFxaojkmXFiSccJpkBMcxqSIi0NcuQ5PWyKZMlPxs1RZHdmHsoNXDo8/NWBLXg7lMtUWZ1hbHs4poHORLXJAvHgaREW2iSTifU6seG5IzGdlWewf9eGYhjUN8iQu28YiPJeFcdq081p/KYGS7WM13YkwLm1/YfOv0PgytruIX7fpgPbXPTt+oFXJ+FkpBDkgS0vE9GNtqd1KNPYZiPbBJXewAlGkXbg3Bw9AqHjkrHTk7E6xWLLu24tmrX616OMPYph1SRjpylkpfuWMrHjf0v2Z6aJgY8erUFsvlDKxpWWC1UrZ+O35D+rr8VuijUyj8ZDVKVZVLW7Utcmy22L0Vz57XbguA46kmGocFEhcaYIuR5iSc0u74CoJAWaW6nHaOEYCeXTsRFBhAbdi2ay9jRg5DEAS6dGxHSUkpuaZ8uwwZ+TQO8ScuxF/17w6NSUjO0PAQBOwyVFqICPDWtHt1bIslNQNrus2eGxLwHexqz5BH7qPws9UolVp7Hr/slCu6tiThhNNCBSiz0ZWaXe3QEGPq2T4ea1o6UoY6pubNW/EZ6Moj8OFplHz1LVS68vCIj0dKT0fKVHlUbN2KV79r94ug7q0ov5iN+VIOikUi6+dEIkb20vSRHPK3oYb83RB+ZWzdDinTHuuVO7bi0aeOsdq0DXJuJkqeLYcf2oGx0w2aPp43jsCyc609h5faY7UhZDA0b4ucm1GdLywHtmPsqh1TjwGjqdr2q0O+KNS0e3Vsi9XRvzfW7t9Fn9fsW2KT1sh5WQ7z2c4a5rPhWHavq9EWhlbxyNkZar6RrFQlbsWjDrkXQGzWBtmUgZKXZR8P59zZdySWHb+DudRFBgD/bq2oSMmk0nZ9kf/LLkJGaPX4K/9s3rUVOZeyMKXmIFms7P9tN12Ha3286/BeJP6UAMDBdXuI76vdKe11c2922NrPHj6DX6AfwZEhmj7BkSH4+Pty9rC643el/xX0Ht6HbT9tBeDM4WT8Av0IceJxpa0g5y/v3V+XkBXhuv53veJ6OZ63E+isKEomkAmgKEqJIAingEbASWAsMNjW/wsgAXj6rxjnFJYRHWzfPYgK9ufYpSxNn+mj+jBj6S98u+Mo5ioryx/R1voXAkJQiu0XE0pJPmJMyxp/TwgMQwwKR76kvaMkBISglNiDSyktQIxpUQuPUJXHZTsPMSQMuSC3+rNcaMLQvK2WLrIRIuD75L9AEKn8/Wukk/ZtZ4/oMKoyTNWfLZl5+HZr4/L74VNGE/XgGAQPD87eNV/7G/412CK2dlsIwRHIl7R3XXOKyokOsi9Mo4J8OZaaq+kz/ebuzPh4A98mnlTH5MFRGvsohXY9lKI8xCZaPcRwdXHi88jrIIhUbV6NlHzYLkNZBVEOF3hRAd4cz9JOAleQUWwmo8hMr8ahmu9zSiuIDvDR8DiWWehMrvIoKiejyEzvJuGa7wX/YJQSR3tezS/CXPxCCAxBKc6z0xfnIcZpj1iJYdHIgPe0l9SjFwk/IZ072qA8xNAIZJODf+bnYmjdHmd43jAQY/vOyBlplH+2BMV2dEYMC0c22RetsikXj7btXOn7DcLYsQtSeirlK5dofrMhYsQYGY41y87Dmp2LV2ftkTTPdq0wRkdg3rmf4KmuRzLE0IjqI0FXtUWfgRjbdUbOTKP8c7stAHKKnWIksIYYGdaFGZ9u5tvE02qMPFC3985k5+YRHWn3x6jIcLJzTcTGX5HBTHSgg38H+nAsPV/DY/rA9sxYtZNvD5zHbLGyfOJATbshMhzJwZ5SjgmvTk72jLfbM2jKnZq2nGKn/B3kx7HL2psb04f3YMbKdXy7+yTmKgvLH9K+c6YhxtQQGY6Ubf9dKceEZwetf3q0bY0hKoKK3fsImOD6TiUxIgI518EvcnPxaO/qF14DB+LRuTNSWholS5ZU03hFh1KZYY/Tyow8Aru3cqGPmzqcptNvQfQwcnD8K5q2hvArl1jNy8WjTQ2x2ncQxg5dkDJSKf/YKVaDw5ALnWK1qXOsxqqxOvstEEUq13+DdOpQg8kgBIcj59s/KwW5GJpr/UKMUo8l+j79nirDr18hnUiqbje4+FbN/m2Iqtm/AYSgMO18VpiH2NRpPou4Mp+9AaJI1Sb78TsxNBw5T2sLYytXW3jY8o2UmYb5y39r8o0YFIZcYJdBLjBhaFZL7pzzjmqLdauQTtlzp2d0GFUO/lmVmYdf99YuckTdN5Loh8YgeBo5dceLmraQqFAKHK5RCjLzaNG1da19ZEnGXFJOQKh99zskOpQ8Bx55WXmERoVS6LC4CY0KJT/LLmt+pv1vgLDoMEyZdh6mrDzCosP+1y6QdDQc/vbqeYIgGIFRqEf1HL9vBnQDrtxWj7ItqgCyUI/w1cTvIUEQkgRBSFqx4tq2TDccPMOYPvFsemUaS6bfyvyvNiHL7j0lZ2jXB2tyUr2esjPE98Z65mCdeQiiASEylvJ/PYX5kzfwnjgbfPz+mtAJpi/WcaL/dNIXfUH0Y65J/lphbN8H6fQBt2yx4ch5xvRozabn72HJtOHM/2573cZENCCGx2D+6AUqVr2L1+0zwdv3r+lqwMbkLIa1jsIgun/3Y+PpTG5qE10vHqpfuOFbogExNJqKz1+h8scP8bz1wbrbogF4WA4kUjT9bkrm3o/laBJ+s56tE33VvkQKpt5F0aPTsBxOwn/uc39N5IR6x4ggEPbkw+S9U7/jGJakRIpm3k3JPJstHq2bLQA2HL3ImB6t2PTsHSy5bxjzv9+JLMv1kqvOMpxIZUyXZmyafQtL7unP/J/3I9fFPwWB0HnTyf/XcvdlOHyOMT3bsGn+vSyZNpL53ybULVc0xJgKAsGPz6Do/Y/c5wFUJiZiuvtu8u+/n6qkJIKerbtfpH22id19Hufsq9/QfE7dXzpas1/VLedU7U+k4P67KHpsGpYjSfjPdjNWI2Ip/+BZzJ+/jffds+oUqw0hAwYRMaoR5e/Mw7xyET6T59Q5X4TOm07Bu+77N2Cbz2Ixf/Q8FaveweuOR+tEbjm4h+JH76HkqQewHjuI78xrOqijgWCw5c73n8b8+Zt43/uYW9cX2Z9v4GjfmaS+9hWNHnf/hbY6dPwd+DsXTT6CIBwBkoDLwCdXGgRB8Ad+AmYrilLsTKgoioL6zJMLFEVZoShKT0VRej700ENEBvuRVVha3Z5dWEpkkL+GZs3ekwzvpt7N6NI8hkqrRGGZfRtZKSlACLTvMggBoSilNd9xMLbrjeRwfErDI8C+vSv4a3eeNDza9kZyOJoHIBfkIYZEVH8Wg8NRCrR3R+RCE9aje0GWUPKykXPSECMbVbdbsvLwjLXfWfaICcOSpeXhiIJfdhI8oo9Wj9IabFGLHoZ2rkfzACKDfMkqKqv+nF1UTqTTkcg1B84wvItamKFL0yh1TMorVBmK8xGC7XoIQWEoRVo9lKI8rCcOqLYoyEHOzajefQKI9PMmu6TCLkNJBRF+XjXqsfFMlsvRPIBIf2+ySux+kl1SQaS/t0s/gA2nXY/mASilhQgBjvYMqd234l19SykuQAgMs9MHhqEUFzj1yUdKPqjaojAXJS8TMTS6QXnI+bmI4Q7+GRqhuZOp6loMVvXYT9WWtRhb2O+mynkmxPBIO314BFKeSUtfYqev3LQWQyvt3diGiBFrjgljtJ2HMSoCKdvOQ/DzwbNVM2I+fZvGG77Eq3M7oj9ciGd7+91QOT8XMawOttiqtQVAZKBTjBSXExnkFCNJZxneqRkAXZpGUmmRKCi49juhURFhZOXYbZydYyIqwh5XkYE+ZBU7+HexmUiHnVWANYdTGN5evRvfJS6MSqtMYXlldbuUY8LgYE9DZDjWbPtvXrFn9MfvELfuK7w6tyPq/YV4tm9jk8EpfxeVudrhQDLDu6i7s12aaXMFNMyYSjkmDFF2/zREhiM57BoJvr4YWzYnYul7RK/5Bs+O7Ql/51U84h18PDcXMcLBLyIiNDwAlOJisKh+YV67FmMbO31lVj5esfY49YoNozKr9vHOWpNIxCjt0SZ3/crRni6xGuZGrBbmIQY7xWqRc6zmYT2+T43V/GzknIzqHZeGkEEpNCGG2mUQQiKQC53mkQIT1iN7QJJQTFnI2WmIUfZ8Ibn4VjhSjta/PVo6+HendkQutvs3qHOVZj4LrmU+O7nfZoscZIfnTeV8E2KY1haOu0Zw9dwLIBflIYbYZRBDahoPE9Zj+xxyZ3r1eABUZeXh6eCfnjFhWDK1O9OOyPt5FyEjtcf3CrLzCXG4RgmJCaMgO7/WPqJBxCfAfvPuzXXvUZhTQJgDj7DoMPKdeORn5xMabZc1NMb+93vrP6Agp4DwGDuP8Ogw8q5yrfS/EYoiXNf/rldcD880dVUUZZaiKFUAgiB4oC6YVimK4lgSLVsQhBhbnxig9gdQHNChSRSXcwtJzyvCYpXYeOgMgzppq6TFhPiz74z6oOGFrHyqLBIh/vaLAznzIkJIJEJQOIgGdWF07jDOEEKjwdsPOf2cS5uclYIQEmXnEd8b6fzRWnj4ImdoK7XIl5IRI2MRwqLAYMTYaxDWP7ULEuuRRIxtOqt8/AIRI+OQTfZCE2VHz+LVLAbPxpEIHkZCxgygaLN2cebVzL44CBrWk4qUTE27nHER0UEPQ7s+WM/WZIsYBG/fGm3RIS6Cy6Zi0vNL1DE5eoFB7bXVrmKC/dl3Tn1+4kJ2oTomfuqCRE49ixgegxASqdqia3+kkwe0tjixD0PLjuoH3wDEiFjkfPvzRh2iA7lcWE56UTkWSWbjmSwGt4zEGRfzyyiusNAlJsilrUNMEJcLykgvtPE4ncGgVq4boBfzSlUesa5nolXfcvSLPkjnjrj0q/YtZ7/IOI8YFo0QHAEGA4aON2JN1lYCkk4nITZrX20LISwGuSCnQXlI55IRY+IQI6PBaMSj/1CqkhK1OgTbF4cePfsipdufY7OeOY2hURxilErvNXAoln27tfQhdnrPPv1cHvpuiBipPJ6MR9NGGBupcviNGkRZwp7qdqW0nEsD7yB15GRSR06m8s9TZM1aoKm05mKLftdgizTtM30d4sKdYuQig9ppK5jFBPuz77wq+4WcQqqsEqGh2iOkV8Pg/jfw64YtKIrC0eOn8Pf3IyLcTt8hNoTL+aWkF5Sp/n0ilUFttDcPYoJ82Jei+sGF3GKqrBIhvvabD5UnkvFo4mDPkYMp36615+XBt5M2ehJpoydR+ecpsh9fUF1drEPjK7miWLXDkfM154qzV3JFgSqDn/3mRUOMadWp0xgbN8IQo/LwuXko5h0OPMrKyBwxjqxx95I17l6qjp/ENG++pnqeJTkZQ1wcYrTKw3voUCoTtX4hOoyfV9++WC/b/aL48Hl8W0Tj3SQCwcNA9G19yd2YpKH3bW6/kRF+czfMF7T5212/crSn9expDLFOsbr/KrHau4ZYvXwGMSIWIdQWq90HqhfkDrAe24Oxlfq8ihqrscimrAaTQUpJRoxshBAeDQYjHr0GYT26R9PHcjgRQ1u1sqvgH4gYFYeS65AvTiRjbNIIY6zNt0a4+nfqEAf/PnaKnNkLNNXzquez0Cvz2QCkE9p52Xp8r9N85rBwO38aMboRYoSqh2ffoVjqkHsB5Eu28QhzGA/n3Hl0D8bWjuPRCDnP/qhD6ZFzeDePwct2fRE6tj8Fm7Tzsldze/4IvqkHFRe1/ply9BxRzWIIj4vE4GGk9639OLpZy+Po5iT6jh8MQI/RN3I68Xh129Oj53Bg0z4G2tpbd2tDeUmZ5mgeQGFOAebSclrbHk+40h9gzqjH2LtxD0PGq5Vy23RrS1lJuX40Twdw/TzTBIAgCALqjtMpRVGc60j+ilph7w3b/79wDTAaRJ65fRAzlv6KLMuMvaE9rWLCWLp2L+2bRDK4Uwvm3jaAhd9tZdW2wyAIvDzhJlRRbFBkqjavwuvOJ6rLhSumDDz634aclVJ9kWts16fGXaZqHlu+wWv8bLWs87HdKHkZePQbq/KwLaCM8b3VI23OkGUqVi/F97HXbCVBNyFnXsLz1klIl84i/bkX6eRBjO174PvicpBlKv/zMZSV2HlIMqkvrKDV1y8hGETyVm+h4kwqMU/cS/mf5yjavJ+I+24hoH8XFKsVqaiMS3MWa+VQZKo2f4X33U+qtvhzB4opHY8B45AzU6oXk8b22oIFLmMy9kZmfLwBWVYY26sNraJDWLrxIO3jwhncoSlz/9GbhT/uYtXOEwC8fNcA+5jIMpU/r8TnwRdtJce3IGen4jn8HqS0c0gnDyAlH8bQpiu+8z5AkWWqfv8Cyu22MIoiTw+JZ+aaQ8iKwtgOjWgZ5s/SPedoHxlYvYDamJzJiLbRWn9w4PHMTR2Z8aNavndspzhahQewdFcy7aODGWxbQG04ncHI+NgaeaDIVP3xNV63z7X5xS6bX9h867zNt+L7uOw+XrFF1brP8Z70rDoehxNQctPwGHI7csZFpOSDSOeOYmjZCZ9H3lb7b15V/TBvw/GQKP/4ffxfeFs9c791PXJqCt53T0U6l4wlKRGvW8bj2asviiShlJZQtuQNDX3ZR4sJfMV2Xn7zOqTLKfhMnIb17Gks+xLxGTMejz791Lu+pSWUvqct/dtQMWJ6fQnRy15HMIiUrNmI5fwlQh6ZTOWJM5QnuO6cuo6JRPkn7+P/vM0W29Yjp6XgfddUpPM2W4wej2dPB1v8W6uL0SDyzJg+zPj0D2RFZmzP1rSKCmHp5sO0bxTG4PZNmDu6JwvXJLJq10kQ4OXb+2l87MkX3+DA4T8pLCxm2G0TmXn/JKxWKwB3jbuFgTf2YueeA4y6cxo+3t688twcrQyiyDMjuzLjm51qjHRpRqvIIJYmnKB9TAiD28Yy9+YuLPz9IKv2nlVlGNNT6+eSTN6iJUR/tAhEkZKfVXsGz5xC1YkzmgvMmmA0iDxzW19mrFyvxljvtrSKDmXpxiTax0WoueLWG1j4w05W7VRPeL985yAXGeo9ppJM4TsfEv7BmwiigbLf1mO9mELgQ/dRdeoMFTsTr4GHRMn77xPytuoXFevXI6Wk4Dd1KtbkZCoTE/EdPx6vvja/KCmh+A27XyiSTPKzn9L9u+cQDCIZ3yZQlpxGy6fuoPjoBXI3HqTx/SMIHdAJxSphKSrj+GNLXe1ZT79ClihbtpjAl22x+octVifYYnV/Ij63OsRqSQml79cQqz8uw3fmQjVW925GzrqM5+gJSJfPIh3fj3TqEMb47vg+t1SN1V8+s+fwhpLhmyX4zn4dQRCp2r0ROeMSXmMmI106g/XoXqQTSRg79MDv5ZU2mVeiOOWL/DeWEGXz79JfbP49YwqVJ89g/gv/viJH5ZoV+Dz4EggilgO2+WzEvUip55BO7rfNZ93wfXIJiixR9fvneE+cV01v/vQD/J5Tn/2qSrDlmzumYr2QjPVgIl6j/olHj34gS8ilxZQvrcEW33+E7yOvqjLs3aSOxy0T1fE4tg/p1EGM7brj+/wyUGQqf/7EJXemPP8xbb9ZgGAQyf1uC+YzqTR68m7Kjp6ncNMBoqeOInBAZxSrhFRYyoXHP9SKIcl8s+BjZn85H9Egsvv7rWScTWPsnLtIOXaeo38ksfP7LTzw7mO8nvAhZYWlLJ/1Hr3+YS++cXjrQboN6cH7O5ZRZSs5fgVvrnuPp0eree6T+cuZ+a/H8PD24kiC9ibhwa1J9BzSk2U7V1JpruTDefbroPfWf8CcUeqrYKY8N5WBYwfh5ePFJ/s+/+ux1vG/HoLyN73hShCEUkVR/J2+649aFOIYcOVw/nOKoqwTBCEM+B5oAlxCLTle+96vCsW8cclfdKkdPiPUc8Plb051m4fv059R/s4DbtMD+M77mJLpI92mD1imlhA/1His2zy6p6pr1LJFU9zm4ffsF5h/eeuvO14FPmOfovTJcW7T+7+tvjei/KNZbvPwnfEh5o/nuk0P4PPAu5S/Pc19GZ78FICyl+5xm4ffS982CI8Ch7t07iDkpwTybhnkNn3Y2u0ADRIjFzrVrZiCI1oc20TBHYPdpgcI+SEB839ed5ve55/qcxsWk+t7Sa4VHuHqUTfz18+7L8fE17jY5Wa36QGaH92M+deay6Vfkwxj1IvK+o4pQFqfoX/Rs3bE7dtK9uDBbtMDRCUksDnKtbjEteLmbPXdQQ3hW3m31iNWf9tOyWP/cJseIOCD3+stA0Dxg+77ReBK1S9Surrv482ObKZ0nvtzsv876pxceNcQt3kEr94GQMmjo/+iZ+0IWLKOfbF1f3bOEX0y/sMDzdx/vunjFPUdX3c1ve0vetaO1ZfU90GNbeK+f/5y+XdQC3pe9zjUeOx1/Xrb7qm/XJd2/Nt2mpwXTLbvdlGLwymKkgcM+5+WS4cOHTp06NChQ4cOHToc8bdXz9OhQ4cOHTp06NChQ4eO6xn6okmHDh06dOjQoUOHDh06roLrqhCEDh06dOjQoUOHDh06/ucgX8dlva9n/G2FIP4/4b9aOR06dOjQoUOHDh3XDf5XrEaS4m67rq+Pe6b9fF3aUT+ep0OHDh06dOjQoUOHDh1XwX/98byyVye6Tes3/2uAepVUDvkpgaKpN7lNDxD02R8UTXK/cGDQV1uAhik5fqm7+7o0PfQHpXPHuE0P4P/ur/Uu8wrUa0yCPvuD0mfHu00P4L/oJ8oXP+w2ve/s5QD1KmUclZAA1L/0bkOUHM8a6D6P6B0JQP1LIQOcaed+2fI2pzaQM8x9GQAit2yvd6lvqH+5cKh/2fJTrd0vYwzQ7uw6zD++6ja9z+3zATjW/Fa3eXS6+BsA5zuOcJtHy+Mb65U3Qc2dX8e6P5dNzFDnsvq8KsHnAfXVieui7nabx+js78i9uX4xErF5e73nIaBesRq5Rc0Xx1u4X56644Xf6/WKgpAfEgBI7eX+tUHjA+q1QX1f+bChHj4BMDL7OxY0m+A2/cKUVQA81cz912e8laK+gmNgI/ftuSN9i9u0/7+h6Mfz3IK+06RDhw4dOnTo0KFDhw4dV4G+aNKhQ4cOHTp06NChQ4eOq+C//nieDh06dOjQoUOHDh06VOjV89yDvtOkQ4cOHTp06NChQ4cOHVfB/4mdJkOLzniOmASCiPVIApbE3zTtnjdPQGzaHgDBwxPBL5Dyd7QP6Ru79sZ32qMgGqjcspbKNd9oeQzwl4oWAAAgAElEQVQZic+k6cj5JgAq16+hastaLY+OvfC+dyaIIpYd66lc952LrB69BuE1djKgIKVewLz8dTt9p154T3pEpU9YR+XvWnqPASPwvvshlAKbDJt/wbJ9naZP4OBuxL30IBhE8r7dTPbSnzTt4RNHEjFlFIokI5dVcPmZpVScTdX08e7bi9B5M8EgUrpmPcWfu+oB4Dt0ABHvvEjmhJlUnTqjaTPEd8frtgdANGDZuwnL1p9c6I1d+uE54h4UQM64SOXX/6pu8+nbk9CnVFuWrllP0Wera5ZhWH8i//UiGfc+QtVJrQz1HQ8AQ5uueP1jmsrjwBYs29e46tGpL57D7lT1yEyhcvViTbvYtAOeg+4EUcR6fBfWpI1aGQbegaFxWxszTwTfAE27Z+/eBDz6KBgMmNeupfwbrW96jxxJwPTpSCbVL8xr1mBeq/VNQ4eeeN89A0EUqdq5gaoNrvY09hyI162TAAU59QLmj9/QttczRjx79ybwMZXevHYtZau09D4jRxIwczpSrkpf/h9XPTy698bvwVkgilRsXkvFj1oeXsNG4jt1BnJeLgAVa9dQuUnLw7d/DyKfmwGiSNGPGyj4+HsXWwD439yP2A9e4NLts6g8cVara6/e+D9ik2PdWsq/cxqTESPxf2gGkkmVw/zLGirWaeXYfS6LtzYeQVYUxnVrzrR+8Zr2zKJyXvjlACUVFmRF4bGhHRleT/oBrWOq2+e//i47du8nNCSYn79e5qK/oigsWryMnXsO4O3txWvPP0H7tq1c+vkN6EHU/IcRDCKF328kb8UPNdozYEQ/4pY8z8Vxj1Nx3G7P3WfSeWttErKsMK5nK6YN6qjVo7CMF37cTUlFFbKs8NiI7gxo20jTx39gd2JffBBEkYLVm8ld9mONMgSO7EvTj57l3Jg5mI+d07T59OtJ+DPTEQwGin9aT+EnNfuF3039iV78Aml3PeriF/XNnTGDO9PrlUkIosi5bxM4sUQ7l7V7aBQt7x2MYpWoyCth79wVlKXnafrsvpjDW1tOqn7RuTHT+mjH7O2tJzlwWaWpsErkl1ey6zFtIYzwIV1o/+oUBINI6qqtXPjwV017k8k30XTacBRJxlpWwfF5Kyk9k67p49GzN/4zZyGIIub1azGvdorV4SPxf9Aeq+Zf1lCx3h4jDTEPNUSc+g/sTsyCh1Tf+n4Tpqv4VpOlz3Fu7GwqnHzL2LU3vlMdcufPTrlzsGvu1Mh5Yy+Cn1CvDcp+WUfJFzXbwmfIAMLfeomsyTOwONnCo0dv/B6y2WLTWip+cBqPm0biO80hd/7mmjvDh3Sh3atTwCCStmorF538ovHkm2hi8wvJ5hdlTn7RalBnRi+YhGAQObQ6gZ0faX28ae94Ri2YSFR8E36YtYST6/dr2tsM6sLYBZMRDCL7V28j4SOtDM17xzNmwWSi45vwzawPOOZEfwWPLXyEG4b2odJcyaI5b3Hm+FmXPg88PY2Rt9+Mf1AAI9u4XxREx/8e/G2LJkEQJOCYTYZTwBQgDPgSiEJ9x9IKRVHet/W/A3gJaAf0VhQl6Rp/CM9RU6hY9QZKcT7e9y/EeuYgiimjukvV5lXVfxt73owY3UzLQxTxffBxShfOQ87LJeDNZVgO7EZOu6TpVpW4DfPH79cih4j3pFmUvfM0Sn4u/gv+jeVIInLGZfvPRDXC65Z7KH39cSgvRQgI1tJPeYyyN59S6RcuxXJoD3KGVgbLvgQqvvywZhlEkcavPszZe1/EkplH29/foWjzfs2iKP/n7Zi+3gBA0M29abRgGucnvazhEfr0LHJmPo01O5eYr/+NeXsilouXNT8l+PoQcO84Ko+dqtEWXv98GPOyBShFefjM+RfWE/tRsu1yCOExeAy7g/IPnwZzGYJ/kFaGZ2eRPf1prNkmYlctoXz7HiwXXGUIvHcclX/WLEO9xuOKHmMexPzJQpTiPHweeRPrqQMoOWn2LmExeAweR/my56GiDMEv0ImHgOeQe6j8z2KU0gK873kW6cKfKPmZ1V0sO37AYvvb2GUIYmRjjB36Vdsi4PHHKZw3Dyk3l9Bly6jcvRvpktYvKrZto+T92n3T595HKXvvGZQCE37Pf4j16B7kTAdbRMbiNepuyt6cU7Mt6hsjokjgnMcpmKvqEbZiGRW7XPUwb91GyeJa9BBF/KbPpviFJ5Dzcgl6dzmWfbuRUp1k2LmVsuW184h84RHS738OS7aJpt9/QNm2vVSdd/Wt4Mm3YT5ag2+JIgGPzabgqSeQc3MJWbqcyj01jEnCVko/rFkOSVZYtOEwyyYMICrQlwkfb2FQm1haRtj9Z+XOUwxvH8edPVtyPreYR7/dxfCX60e/3mHRdNvom7l3/Biee+WdGmXcuecAl9MyWLf6E/48cZpX3lnCtyu1NwQQRaJfmsnl+57HkmWi+U+LKdm6l6pz2hsxop8PoVPGYj5y2skOMot+28+yqTepeny0nkHt4mgZafe/ldv+ZHinptzZpy3ncwp59IutrH/ynxoZYhdO5+KkF7Bm5dHyl3cp/mMflTXIED71VsoPa2W4wiNi/iNkPPgs1iwTcas/pGzb3hpzTtDE26ioxS/qkzsFUaD361PYcvcblGfmM2rdQtI2HqTorH0uyz+ewplRLyCZq2g9eRjdXriHXdOXONhTYdHmEyy7sw9RAd5M+GoXg1pG0TLcfiPmyaHtq//+9tBFTmcXO+kh0OGNaey/8zUqMvLot/F1cjYe1CyKMv6zm8tfqlXqIkf0oN3Lkzhwj8NNFlEkYNZsCp9+AtmUS8iS5VTt2Y10WRsjldu3Urqk5nxR73moAeIUUST25RlcnDwfa1YeLX5+j5JafCvsvjG1+pbv/Y9T+so85PxcAhYtw5JUS+78xC6H34wnq+lDnnqMnEefQsrOJeqLpZh37MF6UUsv+Prgf/c/qTx2skYZ/GbMpni+Oh5B7y3HsreG3LljK2XLarOFQPs3pnHA5hc32vyizMkvUm1+ETGiB/EvT+Kgg18IosA/Ft7HFxMXUZyVz8O/vsLpzYfIPWfnUZRhYs285fR78BYXEQRRYNzCqayc+DpFWXnM+vU1Tm4+SI4DfWGGidXzljGoBvoruGFob+Kax3Fv/8m0796OuYseZ/qtj7r0S9y8hzWf/cyqXV/Wyut6xXX9kqbrGH/n8TyzoihdFUXpCFQB0wEr8ISiKO2BG4BHBEG4ksGPA/8EdtTlR8TYlsj52SiFuSBLSCf2YmzTo9b+xg43Yj2xR/OdoVU8clY6cnYmWK1Ydm3Fs1e/uoiBoUVb5JwMlNxMkKxY9ifg0U3Lw3PgaCq3/gLlpQAoJYV2+pbxyNnpdvq92/Do0bdOMvh1bU1lShZVl7NRLFYKft1J0PDemj5yqbn6b9HXC5xefuzZsS3WtAys6aotyjYm4DPY1RbBM++j+PPVKJVVLm1ik9bIpkyU/GyQrFgP78TYsY+mj8cNI7DsXgvmMtUWpUXVbV4d22JNzcCanlUtg+9gV1uEPHIfRZ+vRqlylaG+4wEgNm6FnJeFUmDT4+gujO16afXodROWPRugwqZHmfYCRIxujlKUg1JsAlnCeiYJQ8suLvJWy922F9bkA3b+8fFI6elImep4VGzdile/Ovpm87bIuRkopizVFge2Y+yqtafHgNFUbfu1VlvUN0Y82jnpsWUr3v3rpoexdTukTLsMlTu24tGnf514eHdui+VyJpa0LLBYKV63Hb+hN7r0C398MgUf/4BSaXFpM8a3w5qejmzTpXLbVrz61k2O4xn5NA7xJy7EHw+DyIgOjUlIztD0EQQos/1+aaWFiADvBqMH6Nm1E0GB2l1NR2zbtZcxI4chCAJdOrajpKSUXFO+po9P5zZUXcrAkmqz59odBAxztWfE7EnkrfgB2SlfHE/Lo3FoAHGhAXgYDYzo3JSEU9oLUkEQ7HpUWIgI9NW0+3ZpTdWlTCypat4r+m0HgTdr8w1A1NwJ5C77CbmGMfXq1BbL5QysaWrOKV2fUKNfhM6aQuGn39eYc+qbO8O6taQkJZvSy7nIFomUX/YSN0I7l2UnnkIyq3SmQ+fwjQnVtB/PLKRxiC9xwb6qX8THknAu20WGK1h/KoOR7WK18nVvRfnFLMyXclAsEpk/JxI1sqemj9VhHjH4ejlPIxjbtkPKSEfOssV7wlY86xAjDTEPNUSc+nRpQ6Wjb/2+g4Cbb3DpFzl3IrnLf6wxX1Tnzhxb7ty9Fc+e1577PDvEY0lNR7LZonzzNnwGuc6HQdOnUvLldzX6prGNdjwqd2zF44a62cLZL7Jq8AvJwS+Mvl4uV+5xXVuSfymbgtRcJIvEsd/2Ej9c6+OFaSayT6eiODsV0LhrK0yXsshPzUGySBz9bQ8dhmtlKEgzkXX6co30V9B/RD82/rgJgJOHTuEf5E9YZKhLv5OHTpGXk+/yvY7/XlwvzzTtBFopipKpKMohAEVRSlB3oBrZPp9SFCW5royFgBCUYrtTKyX5CAEhNfcNCkMIjkROOaH5XgyNQLZtzwPI+bkIYREu9J43DCTg3U/wm/eyS7sQEo6Sn6PlERKm/Z3oOAxRcfg9txi/+R9i7NjLid5JhpBwFxk8eg3A/7WV+M56ESFUK4NHdBhVGabqz5bMPDyiw5xZED5lNB12LaPRc/eRtmClps0YEY41y66HlJOLIVLLwzO+FYaoSMy79rnwBtXOSqFdDqXQhBDkZIuIWMSIRvjMehOfx9/GEN+9us0QGY41y24La7YJQ6TWFqoMEZh31rz1Xt/xABACQ1GKHPQoznfVIzwWMTwWn4dfw2fGIgxtump5+AWjlBTYeZQUIPg57eJc6RsQihgUjpxqv1spRkQg5zr4RW4uhghX3/QaOJDQTz4h6OWXEZ3aheBwZAffUgpyEYOd9IiKQ4yKw/fp9/B99n0MHbQTUX1jRAyPQMqx00u5uS5yAngPGkjYZ58QvPBlxEhtuxgWjmxyGNO8XAxhrjHi2XcQQR98iv8zLyOGa3kYI8NcfMsjSmsLr/atMEZHULa9Zt8yhIcj5zrIkZuLGO4qh9eAQYSu/JTAF13HJKfYTHSgT/XnqEAfckrMmj7TB7Zn7bHLDF+8lke/3cUzI7s1GP21IDs3j2iHuIuKDCc716TpY4wOw5rpkHOyTBid7OndviXGmAhKEw7gjJzicqKD/Bz08COnyEmPoZ1Ze+Qiw9/8iUe/2Moz/9DGqTE6DItGBte8592hJR4xEZRsq/kAQ01+YXTOOe1UvyjfUbNf1Dd3+kaHUJ5hn8vKM/Pxjal5LgNodc8gMrYe1XyXU1pBdICDXwR4k1NaUSN9RlE5GUVmejfR6ukdHUpFhv3InzkjH69o1wvKplOHM2jf+8S/MIGTz3+uaRPDw5EcY8SUi6GGGPHsP4iQ5Z8S+II2RhpiHmqIOPWIDsOS6eAXma75QvWtcEpr8S0xNKL6yBtcJXf2GUjAO5/g94Q2dxoiwpGyHXJndi6GCK0eHm1bY4iKoGJ3zbZwyZ2mWnJnv0EELfkU/2ddc6dXdChmB7+oqMUvmkwdzsB979PmhQmccvKLgKhQihx4FGfmExhVu487IygqRENflJlXJ/orCI8OJyfDbtPczFzCo13toeP/Hv72RZMgCEZgFOpRPcfvmwHdgJqjvHZ+DwmCkCQIQtKKFSvqJIux/Y1Ip/e77K5cCywHEimafjclc+/HcjQJv1nP1pkHogExqhFlbz5B+bLX8Jk6B3z8/prOBuvhPZTMmUDp8w9iPX4Q34efrrsMgOmLdZzoP530RV8Q/diddSMWBELmzqDgXddnIOoE0YAYEYP5389R8dU7eN3xCHhfoy0EgdB50yl4d3n9ZajHeABgEBHDYzCvXEDFd+/hNW4GePv+NV1NrNr2wnr2UJ39szIxEdPdd5N///1UJSUR9KwbvmkQEaMaUf7OPMwrF+Ezue62qG+MVCQmknvn3eRNvZ/KpCSCnqu7HlX7Eym4/y6KHpuG5UgS/rOfqxsDQSDi6YfIfXPlX/e9Cir3JJI34S7yH5xG1cEkAp+uoxzAhhOpjOnSjE2zb2HJPf2Z//N+ZFmuH70bua9eEAQin3uQnEXu23PDnymM6d6STU+PZ8mUocz/YTeyXAc9BIGY+feT+donbsuAIBD+1EPkvV23OceZR4PkTqD5P/sR2rkFJz9a+9eda8HG05nc1CYag+hela1Ln21ie5/HSX71G1rNGVdn+qo9ieRPuouCh6dRdSiJgCfrECMNZMt6x6kgEPP8A2TVx7cAS1IiRTPvpmSeLXc+WofcJwgEz5lO4eL62aJqXyIFU++i6NFpWA4n4T+37jkL4PJnm9jR53HOvPoNLd3wCx0NA1kRrut/1yv+zkWTjyAIR4Ak4DJQnVUEQfAHfgJmK4pSXAt9jVAUZYWiKD0VRen50EMPqXfuA+13O4SAUM2dfUcYOtzgcjQP1Ds/jndVxNAIFIc7QwBKaTFY1a33qi1rMbZoo20vMCGERmp5FGgf0JULcrEc2QOShGLKQs5KwxAd50DvJEOB9q6uRoaEdRiatda0W7Ly8Iy13y3xiAnDkqWVwREFv+wkeIT2GIs114Qx2q6HITICKcfOQ/DzxaNlM6JX/otGv3+NV6d2RCxeiGc7uz2UojyEYLscQnA4SpFWDqXIhPX4fpAllPxs5NwMxAj1eQspx4Qx2uGuY1Q4Uo7dFoKfjyrDx+8Qt+4rvDq1I3LxQjzbO8hQz/GAKztLDnoEhtagRx7WUwdUPQpykE0ZiOH250aUskLNzqcQEIJSpj36Vq1nm55Iydq72LLTjowYEYGU6+SbxcVgUf3CvHYtxjZOvlloQnTwLSEkArnQSY8CE1ZHW2SnIUbZH7avb4zIplwMkY53T7U7aC56/L4WDyc95DwTYrjDmIZFIOU5xUiJXYbKTWsxtNLysObkufiWJdtuC9HPB6/WTWn85Vs0/+MLvLvE02jpS3h1sMeaZDIhRjjIERGBbHKSw0GXinVrMbbWyhEZ6ENWsX1HJbvYTKTDDgHAmsMpDG+v+mOXuDAqrTIFBQX1oi8sr+RaERURRpZD3GXnmIhyusNtzcrDGOOQc6LDsdZgzyZfv0nLbZ/h0zWeuGUL8O7Y2qaHL1lFZQ56lBEZ5KTHwXMM79hU1aNJBJVWicJy++6JNSsPD40M2rwn+vvg3aYpLb57nbY7P8a3W1uarpyPTyd7gYSa/MLqoLvo54Nnq2bEfvYWTTZ+gVfndkR/+LLGL+qbO8uzCvCNtc9lvjGhlGe6zmXRAzrQ8fExJNz3LnKVVdMW6e9NlsOOY3ZJBZH+3s4sANhw2vVoHkBFVj7esfbdFJ/YUCqzaj+ilLEmkahR2t0/2WTC4Bgj4RHVhWquQClxiJH12rzVEPNQQ8SpJSsPjxgHv4hxyhf+Pni1aULzbxfRZscn+HRrS9MVL+Dt4Ftyfi5iWB1y51Zt7pRyTRiiHHJnVER1sRwAwdcXj5bNiVz2LjG/rMKrY3si/vUKHg62cMmd4XXPnZVZ+fg4+IX3X/hF5ppEIp38oiQ7nyAHHoExoRRn13y9VhOKsgs09EExYXWiB/hk03LysvOIjLXbNCImAlOW6SpUOv6v4Hp4pqmroiizFEWpAhAEwQN1wbRKUZT/1PdH5IwLiKHRCMERIBrUhdGZQy79hLAYBG8/5DTXCinSuWTEmDjEyGgwGvHoP5SqpEQtfbB9MvPo2RcpXftAqnQxGUNkI4TwaDAY8eg9GMthLQ/roUSM8erzLIJ/IGJ0nHrOGZAunMYQ3QghwkZ/wxAsh5xkCLLLYOx+I1KGVoayo2fxahaDZ+NIBA8jIWMGULRZexHu1cx+QR80rCcVKZma9qoTyRgbN8IYq9rCb8RgzNvtciilZaQNG0/6PyaS/o+JVB47Re7sBZqqRXLqWcSIWITQKDAYMXYbgHRcu6FoPb4PQ6tO6ge/AMSIWOQ89ex95YlkjE20MpRv3+MgQzmpQ24nbfQk0kZPovLYKXJmL9BUz6vveADIaecQw2MQQiJVPbr0RzqlPYJhPbkfQ4sO6gffAMTwWOR8+zMEclYKQnAkQmAYiAZ1YXRee6QGQAiJAm9f5MwLmu8tyckY4uIQo1VbeA8dSmWiVg8x1O4XXn37Yr3s5JspyYiOtug1COtR7c0Dy+FEDG0dbBEVpz4PdoVHPWPEclrVwxBj02PYUCp3O+kR5qBHv75YL2n1sJ49jSE2DjFK5eE1cCiW/bud7Gjn4dm7n8uDzhXHkvFoGouxURR4GAkcPYiybXur2+XScs73vYuLN03h4k1TqDh6mvSZL2mqpFlPn8bYyD4mXkOGUpmolcNxTDxv7OfyAHyH2BAu55eSXlCGRZLZeCKVQW1iNH1ignzYl6IeqbmQW0yVVSLUxtdd+hBfL64Vg/vfwK8btqAoCkePn8Lf34+IcO1xHPOxM3g2i8UjzmbPWwZSskVrz7N97uH8kKmcHzIV85HTpE1fWF09r0OjMC7nlZCeX4LFKrHxz0sMim/spIcf+y5kqXrkFKl6+NkXAuV/nsXLJoPgYSTo1oEU/2HPe3JJOad6TCB5wAMkD3iA8sPJXHrwVU31vMrjyXg0aaT6hdGI/6jBLn6RMuBOLo+YwuURU6j88xRZs17U+EV9c2fekQsENI/Gr3EEooeBZmNvIG2Tdi4L6diUPm9OI+G+d6nMc73f2CEmiMsFZaQXlqt+cTqDQa2iXPpdzCuluMJCl1jXo01Fh8/j1yIanyYRCB4GYm7rS/bGg5o+vs2jq/+OvLkb5Re084g1+TQGhxjxHjyUqj3XHiMNMQ81RJya/zyj9a1/DKTkD/tcJpeUc7rnBM4MvJ8zA+/HfDiZSw+9oqme55I7+11D7kyz576qk6fxaNIIg80WvjcPwbzDwRZlZWTc/E8yx04gc+wEKo+fJPeJFzTV86xnbOPhmDv3XSV39nHNnUWHz+Pr4BfRt/Ul5yp+EVGDX6QfvUBos2iC4yIweBjodOsNnN6s5XE1pB09T3izaEJs9F1uvZGTdaAHuH/4w+zcuJsRt6u1SNt3b0dZcZn+7JIO4DorOS4IgoC643RKUZR3G4SpIlO14Qu873lKLel8ZDuKKR2PQeORMy4inVUnHbUAxN6aecgS5R+/j/8Lb4MoUrV1PXJqCt53T0U6l4wlKRGvW8bj2asviiShlJZQtuQNJx4y5lUf4vfEG2p56p0bkDMu4XXbFKSUM1iP7MF6/ADGjj3wf/UTUGQqVq+wFw6QZcxffojfk29Wl8iW0y/h9c/7kC4mYz28B88R4/Do1lfd1SgtwbziLa0MkkzqCyto9fVLCAaRvNVbqDiTSswT91L+5zmKNu8n4r5bCOjfBcVqRSoq49KcxS488t/8kMh/q3qU/roBy4VLBE2fQtXJM5h3uO7UudpTpvI/y/F56CVVl/1/IGen4jnyXqTUc0gn9iOdPoShTVd8n1qCoshU/fY5lJfYZXhjCVEfLVJl+GUjlvOXCJ4xhcqTZzBvvzYZ6jUeV/T49WN8pr0AgoglaStyTiqeN92NlH4O6VQS0pkjGFp3xXf2YlWP9V9WF1MAVP/c9h1e4x5XS+Kf2I2Sn4nHDbci51xCuvAnAMa2vZCSazgTL0mUvP8+IW+rvlmxfj1SSgp+U6diTU6mMjER3/Hj8epr882SEorfcPXNim+W4Dv7dQRBpGr3RtUWYyYjXTqD9ehepBNJGDv0wO/llWr/H1eilJU48KhnjEgSxYvfJ+Qdld68bj3WlBT8p03FkpxM5W6bHv36giQhF5dQtMhZD4myZYsJfPkdEEUq/1iHdDkFnwnTsJ49jWV/Ij63jsejTz91x6ykhNL3nXhIMrmvLiXu49dAFCn+zyaqzl0ibNYkKo6f1Vwo1+5bEiUfLib4zXds5ZTXIV1Kwe++aViST1O1JxGfcePx6tvPPiZvOZVvF0WeGdmVGd/sRFYUxnZpRqvIIJYmnKB9TAiD28Yy9+YuLPz9IKv2ngUBXh7TEzWF1p8e4MkX3+DA4T8pLCxm2G0TmXn/JKxWdefirnG3MPDGXuzcc4BRd07Dx9ubV56bU4N/ymS9/BGNP31VLTn+4yaqzl0m/PGJVBw7S+nWq5++NhpEnrm1NzM+36Lq0b0VraKCWfrHEdo3CmNwu8bMHd2DhWv2smq3WiHt5fF9NXogyWS8uIzmX76sloX+4Q8qz14mcs4EzMfOUvJHzc8gOethev3fxCx/HcEgUrxmE5bzlwh5ZDKVJ85QnnANflHP3KlIMgee/4Jh3zyFYBA5/912is6k0/nJ8eQfvUjapkN0f+EejH7eDFjxGADl6Xkk3GefSo2iyDM3dWTGj/uRZYWxneJoFR7A0l3JtI8OZrBtAbXhdAYj42O1dnSQ48Szn9H7u+fU0tLfbqM0OY3WT91B0dEL5Gw8SNP7RxA+oCOKVcJSVMbRxz7SMpElSpcsJmiRGiMVG9UY8Z0yDesZW4zcNh7PG9VYlUtKKHnbMV80xDxU/zhFksl4aRnNvliIIIoU/LBZ9a3ZNt/acg2+JUuUf/I+/s/bcue29chpKXjfNRXpvC13jh6PZ0+H3PnvNwh6/8tqGQre+pCID95EMIiU/roe64VLBD58H1Wnkqm4RluUfbSYwFdsuXOzLXdOtOXOfYn4jHHInaUllL6ntYUiyZx89jN6fvccgoNftLL5Re7GgzS5fwRhDn5xzMkvZElm7YLPmfzl04gGkUPfbyf3bDpD54wn/dhFkv84RGznFtyzfA4+Qb60HdaNoXPGa+h/WfA5D3z5LKJB5MD3CWSfTWP4nNtJO3aRk38cJK5zCyYvn4tvkB/thnXn5jl38O7wJzVy7N2yjxuH9uHb3V9Raa5g0dy3q9s+2bSc+4err6SZ/vxD3DRuKN4+XvyYVHOZ9+sVynV8BO56hpmnll4AACAASURBVHC1CiL/oz8sCKWKovg7fdcftSjEMeDK4fznFEVZJwjCOOBDIAIoBI4oiqJ9eYQrlLJXJ7oto9/8rwEoGD/YbR4hPyVQNPUmt+kBgj77g6JJw9yn/2oLAIcaj3WbR/fUXwC41N19XZoe+oPSuWPcpgfwf/dXUrre7DZ9syObAeo1JkGf/UHps+P/uuNV4L/oJ8oXP/zXHWuB72z1ea3swYPd5hGVkABA8YPDr97xKghcuale8QFqjGQNdJ9H9I4EAPJuHeQ2j7DftgNwpt1It3m0ObWBnGHuywAQuWU75q+fd5veZ+JrAA3Cw2K68Bc9a4dHeAtOtR7tNj1Au7PrMP/4qtv0PrfPB+BY81vd5tHpovqOmPMd/2qqqR0tj2+sV94ENXd+Hev+XDYxQ53LzB/PdZuHzwPq4mtd1N1u8xid/R25N9cvRiI2b6/3PATUK1Yjt6j54ngL99/N0/HC7xTcMdht+pAfEgBI7eX+tUHjA+q1Qd4t9cida7ezoR4+ATAy+zsWNJvgNv3CFPW1MU81u8dtHm+lfAvAwEbu23NH+haA/xWrkd3Rt1/XVcf7Zf14Xdrxb9tpcl4w2b7bRS0OpyjKGsD1zaE6dOjQoUOHDh06dOjQ8T+Iv716ng4dOnTo0KFDhw4dOnRcz7iunmnSoUOHDh06dOjQoUPH/xyu/eUUOhyh7zTp0KFDhw4dOnTo0KFDx1WgL5p06NChQ4cOHTp06NCh4yr426rn/X/Cf7VyOnTo0KFDhw4dOq4bXJdV35yxI/qO6/r6eGDWD9elHf/rn2lKjh/lNm3b0+sByB7ifjnOqG3b+Tn6XrfpAW7L+oan61FK801bKc3WET3c5nE2V31B3JeN3C97Ozn96wYpTfpYs7vcpv8gZTUAc5q5L8d7KfUrjwpqidT62hJgRrM73ebxUcr3APUu9bo6pn62uCtzVYOUU17W2H0e01NVHvXR5a7MVbzTxH0ZAOZd/ppN9YiR4dnqu0IudnG/LH/zo2pZ/vqUDG93dl29SpaDWrY8Mcb90v59M38C4Id6jOkdmasahMd/6jkH/DPrG/5VD9964rLq3/Up9x2xWS2z3TNmgNs8kjJ3srQecQowM/VrVsa5z+PBNNUW9ZFjpi1fLK+HHA+nfd0gY/phPfSYZdNjST14PJr6NQub1m8OWHBpFc83cz9GXkv5BoBpzW53m8enKT8C0CGqj9s8TmRf/b1zOv73Qz+ep0OHDh06dOjQoUOHDh1XwX/9TpMOHTp06NChQ4cOHTpUyNf14bzrF/pOkw4dOnTo0KFDhw4dOnRcBfqiSYcOHTp06NChQ4cOHTqugv8Tx/N8+/cg6vnpIIoU/biB/JU/1NjPf3g/Gn0wn5TbH6Py+FlNm2ev3gQ8OgsMIua1ayn/9htNu/eIkQRMn4FkygXAvGYN5nVrNX0ih3Sm0yuTEQwil1Zt4+yS3zTtLR8eTdMJg1GsMlV5xRyaswJzmqm6vc2gLoxZoNIfWL2NhI9+1dA37x3PrQsmEx3fhG9nfcCx9ftr1POF159k0E39MJdX8PRjL3Hyz9NaXXy8+fCTN2ncLA5Zkti6aaemPXZwZ3otnIQgipz7NoHj/9bq0e6hUbS+ZzCKVaIiv4TEuSsoS8/T9Akf0oV2r04Bg0jaqq1c/FCrS+PJN9Fk2nAUSUYqq+D4vJWUnUm3/8agLvxzwX2IBpE9q7fyx0e/aOiNnkYmvvsIjTu2oKywhM8ffZ/8tFxNn/hBXRi3YAqCQWTf6q1scbJni97xjFswhZj4Jnw16wOOrnd9yLPVoM6MXjAJwSByaHUCOz/S2qJp73hGLZhIVHwTfpi1hJM1jIk79nRE+0FduHPBVASDyO7VW9hUgy2mvPsoTWy2+PjRxS62aAg9ood0pttClceFbxI47eTfbR4eRYt7h6BYJSrzitk/dyXlDv4dM7gzvV6x2+HEElc7tLz3/7F33tFRVV3//9w76b3PBAIJIXQMHSEBEnpRAcUGiEAERRSQIiICKqAiKo8FBRQVCyI2VKS3IBCQEukkoSWQNimT3mfm/v6YITN3ZgKS8L7yPr/5rpW1Mvecve9uZ+9bzj3HaIf8Eo7YiKsmsZFEv2aQ4cKGeE5+IucROXkorR+PRdLpqMgvIX7Op5Ra8GioHgBhMZH0M8px5vt4jlrIEdK9FX1fHUdgmyb88fxKUrYes7Knf98OtF5qiM/09XtJtRgjIU8OoInZGDk/5zNZu2tUV/xemoogipRs2kbRFxutzgHg1r8XyhWvkjH6OarPp8ja3Ht3QbngGQSFSOEPO8j/1Hbu9BwcTcjKV7j64AwqzXLngjdX8Oeho/j5+vDrt6ut6CRJ4q33V3Pg8DFcXJx545XZtG0VIevj07cjzRbHgUIk57s9ZKzcJGtXPjkI1YQhoNOjK6/k8ourqUhJl/ex8GmyhU9bGH2qN/r0uA2f3ikekcYakLp+HykWPCKeGUaYsQZU5RdzwqIGhMVE0tcYV2dtxFVji7i6aCOuHLt2x2PqNARRpGLbFio2ymuZ86AheEx+Fn2+sZb9tonKbVus+MxZMoPo/j2orKjitRfeJPlMilWfqfMmM+zhwXj5eNInYrCsrUlsJL1eG4eoEDm/IZ6/LXTpMHkobczG6l6LsRoSG0nP1w22SN4QzymLvHnP5KG0Gh2LXmfIF3/Oth7rDZXhBo8ooxxJG+I5aUOONmZyxNuQo6F+bRobSR8j/fkN8ZywoO84eSjtHjfIUJFfwp45n1JiIUPT2Eh6m/FItMGjrRmPvTZ4NI+JZPCrBnv+/X08hyzqSNPurRn8qqGO/DxtJRe2WteRFjGR3LfoSUSFyPGN+/jTgkdY99bct2gcytZN2TjtI85Z1KL2MR0ZY6yHBzbuYeuqX2XtDk4OTFoxjdD24ZQVlrLq+RXkW9RDgJffmEWf/lFUVFTyyvQlXDiTLGt3cXVmxWdv0SSsMXqdnvhdB6x43M3Q/99Y5O+uw7/2pkkQBJ0gCCcFQTgrCMKPgiC4CYLQRBCEfYIgnBcE4ZwgCDPM+r8jCEKSIAinBUHYJAiCzz86kSiiXPQc6ZMXcvX+Z/C8Lxan5k2t5XF3xXfcCCpOJtnk4TnjBQrnzSV/wnhc+vdHERpq1a1y3140kyehmTzJ6oYJUaDDWxM5PGY5e/q8SMiDUXi2bCzrUnQ2lf2DF7Cv3zwy/jhKu4WmFfMEUWDk4ol8MeFtVgycQ4fhUQRFyOkLM/P4Yc5qTv52qE5zxAyIJjS8CQO6j2Th7KUsXv6yzX5rP/6GIVGjGNFvDJ27d5DJce8b49nzxHJ+7zuXsJE98G7RSEarOZvKlqEL2TxwPmlbjtJlgcXKf6JA22VxHB+zjIO9ZxP8YDTuFrbI/OUQh2LnktB/Hlc+3kzr18fJZHhkcRyrJ7zFmwNn0WV4NCoLW/R4tB/lRWUsiZ1B/OdbGT5PvjKPIAqMWhzHpxOW8fbA2XQaHo3SgkdBZj7fzVlFYh32FESB+xdP4JsJy1k5cC73DO9JoAWPosw8Ns1Zw5nfEurk0RB7CqLA44ufYuWEN1k8cCbdbNgiymiLV2Ons/fzLTw4T77S0Z3So8ubE/hz7HK2x8wldGRPvCx8WngmjV1DFrCj/8tc/+MoHSz06P7mePaOXc7m2LmEjbBth21DF7JlwHyubTlKp4XyuBJEgV5Lx7PlyeVs7DeXiBE98LXgkXc2lV/uW8iPg+ZzZetRerxizaMhetzgMWDpeH4ev5wv+8+l9fAe+FvIUZyZz7bZa7hQhz0RBdosiyNxzDIO1TFGsn45xOHYuRzpP4/UjzfTymyMIIr4z5+Geup80h+chPuQvjiG28h7bq54j32QytMXbMggonptKtcnLeLy0Cl43R+DU0QT627urviNt507Rw4byOoVS23rCBw4fIxr6Zls3fg5r82dzpJ3V1rJEP7mZM6PfYOTMS8QMLIXri1DZF3yfjnAqX6zODVwDhkf/0rYaxMseAh0fnMCB4w+bTqyp1XuLTyTxu4hC9jV/2XS/zhKpI2cdSd4dHhrIofGLGdXHTWg8Gwq+wYvYI+xBtxjUQP6Lx3PL+OXs67/XFoN74GfRVyVZOaz/aZxJeI57QWK5s9FM2k8Ln37o2hqXcuq9u+lYMokCqZMsnnDFN2vB03CQ3gwajRvvLicl5fNtnm6P3ceYvywZ6yOC6JAH+NY3dBvLi1sjNXcs6n8dN9CNg6az+WtR4l6RW6L6KXj2T5uOT/1nUvzET3wsRzr51LZNGwhvwycz9UtR+luY6w3RAZzObaOW84PfQ05x1KO/HOp/DJsIT8NnM+VLbZzTkP8KogCsUvH8/uTy1nfby4t69Bj430L2TBoPpe2HiXahgwxS8ez+cnlfHcTHj/ct5Dvb2KLoUsm8N345XwyYC7thvckoIV1Hflt9s3ryAOLJ/LVhOV8MPBFIodHWdWiwsw8fpqzmtM2bSHyxOJJ/GfCGywYOJN7h/eiUYQ8X/R+tD9lRWW8HDuNnZ//wSPzrFcO7N0/itBmTRja42Fem7OMRcvn2pR33ar1PNDrMR4eMI5O3TrY7GPHfxf+zel5FZIkdZQkqT1QDUwBtMBsSZLaAj2A5wRBaGvsvwtoL0lSJJAC2L7at4BLZEtqrmVSk54NNVpKtu7Ho38Pq34B059Es/ZHpOpqqzbH1m3QZWagy8oCrZbKvXtxju51W8r6doqg9Kqa8ms5SDU60n89jGqwfAnwvEPn0VUYzl9w4iKuwX61bU06RpCflo3meg66Gh2nNh+m7aCuMvqC9Dyyk65xs723BgyJ4deNhiJ48sRZPL09CFQGyPpUVlTy16HjANTUaGVvovw7NackVU3ptVz0NTpSfztCEws91AkX0FUa9Mg7cQk3Mz0AfDpHUH41m4o0gy2yf01AOUSui660ovZ/Bzdn2Y5boR0jyE1Tk2+0ReLmBO4Z1E1Gf8+grhz92bBE7smtR2gZ1V7W3rRjBHlp2bU8/t6cQHsre+aSdRN7hnRsjiZNTcH1XHQ1Os5sPkLrQXJbFKbnoU66XiePhtozrGMEuWnZ5Bn1OL45gQ4WtugwqCtHfo4HIHHrEVpb2OJO6OFn1KPMqMe1347Q2EKPnARTfOcnyvWwZYcQW3Yw0uclWsdVUMfmFKeqKTHyuPz7EcIs9Mg8fAGt0ZbqxEt4qOQ8GqoHgKpjcwpS1RQZeSRtPkJzCzmK0/PIS7qOVMfXuN42xkjQTcaIwmKMOLdvRc31TLQZ2aDVUrY9HrfYKKvz+D43gcIvNyJVWec918iWVKdlUnPdkDuLt/yJZ/+eVv0CXxhH/qc/orfBo2vHe/D28rSpI8C+g0cYPqQ/giDQoX0bSkpKyc3T1LZ7dIqgIjWbqmtqpBoteb8dxG+wPL7ldnABixj169ScUqNPpRod1234NNfMp5rES7Lce+d4RFBmUQOCb1IDNBY1QNWxOYVmcZW8+QgRtxlXDq0MtUyfbaxl8Xtxirq9WgYQM6QXW3/cDsDZxPN4enngH+Rv1e9s4nnyc/Ktjgd1bE5Rqppioy6Xfj9Cs1uMVXezsRpoOdZ/O0KoBX2WWd7MSbyEu4180RAZbvAwl+PSbzZyToIFDxv5oiF+VRrpb+iR8vsRwi3oM8z0yLahh9LCFhf/AQ/L3NnYmPcKrxt4nNt8hFYD5TyK0vPIuUl8hnSMMNYiQz07vfkwbeqsRXor+vCOEeSkZZN7PQddjZa/Nh+io0U97DSoGwnGenh862HaRN1jxaffkD78/qNhy5nTJ87i6eVJgEV8V1ZUcfSQYSuWmhot5y3eRNnx34m75ZumA0CEJElZkiQlAkiSVAJcABobf++UJElr7H8ECLHJyQIOygBqskyvXrXZeTgo5cHv3LY5jsEBlO23nsoAIAYEoM/Jqf2tz81FERBg1c+5Twx+a7/A+7XXEQMDZW2uwb5UZJqKR2WWxqqomiN0TF/Ue0/V/vZW+lJoRl+UlY+30rdO+rqgDA4iK1Nd+zs7MwelKrDO/p5eHvQbZNqXw03lS1mm6aKmPEuDm6puOSJGx5Cx75TsmLPKT26LTA3OKmtbNJ04iD5/fUDLhWO58Mq62uM+Sj+ZLQpt2MLbrI9ep6eypBx3X886eRRlafBW1u0PW/BU+lFkxqM4S4PXbfqkofb0UfpRYCZDQVY+PhZ6mPfR6/RUWNjiTujhqvKjwmyqRnmWBteb6BE+OpYsMz3cVL6UW9oh+OZ2yNwrjyt3lS+lZjxKszS430SGNo/HcC1ezqOhegB4qnwpsZDD8zbt6aLyo/IfjJEmEwfRyzhGkszGiCIoAF22Ke/pcvJwsHg44tQ6AgdVIBUHbE/jdVD5o80yTQ2rsZE7Xdo2xyE4kNJ427nzVlDn5qMKMsmlDApAnWs6p7PKj+oM0+/qLA1OKuuLc9WEIXQ+/DGhC8ZxdcEXsjZXlR/lt+HTZqNjyd5nHRcN5eFiUQMqblEDwsb0Jdssxj0s4qokS4PHbcaVGBCALtesluXZrmVOvWLwXfMFXgutaxlAoCqQ7EwTH3VWLkHB1nzqQkPHqnuwL6VZJvqybA3uN8kXrUbHkL7vzucLt9uUo/XoGK5ZyNFQv9rSw+MmerR7PIa0eGtbWOasm9mirQ0enio/irLkdcTzJjxswUvpa1WLbqcu+yj90GSa8kVBVj6+NurhjT436qGHr/zBTlBwINkZpuskdVYOyuCbXyfFDrr9hw//JiSEu/rvbsW/ftMkCIIDMBQ4Y3E8DOgE2NotLA7YdocEIGje0+S8/dmt+94EVYcTyBv9GJpJcVSfOI73vPn15hUyKhqfDs249MkfDZKpoVAoFPzn0zf5eu339aJv9lA0/h3CObfKenrHP8G1L3fy570zSFn6Hc1nPlgvHv9NaKg97xaEjorGr0M4SfWM72YPReMXGc75BtihxYPRBEaGc3J1/Xk0VI87getf7uSgcYyE384YEQT85kxB896a+p9cEAiaP5mctxqWO+8EstdtJ7Hnc6S98Q0hL9R/Q9ymo6Lx7RBOcgN8eid4NBkVjW+HZlz8F2Kr+nACmnGPUfBMHNWJx/F8sf617E6gpXGs/l3PsRrxUDQBkeGcasBYb6gMAC0eMvBoiBwNRasHowmKDCexgbZoKI//JigUCt5ZvYT1a3/4t0Wx438B/+ZNk6sgCCeB48A14PMbDYIgeAA/Ay9IklRsTiQIwisYpvGtt8VUEISnBUE4LgjC8U8//RStOg9HsycEDqoAtGrTkwzR3RWnFqE0/Xo54XvW4dKhNSGfvIpz+xa1ffR5eYhBQSaawEB0efIPfKXiYqipAaBiyxYcWraUtVdkFeDayPSE1CXYjwqzJ1Q3ENi7Pa1mjOTI+PfQV2trjxepC/Axo/cO9qdIXWDLBHXi933fkaPOI7iRsvaYqlEQ6mzrjyABlq54hbQr11m3ZkPtsfLsAtwbmZ7cuAX7UZ5tLUdw73bcM304+yaskOkBUJWtkduikR9V2da2uIGsTQkEDTW9Yi9Ua2S28LFhiyKzPqJCxMXTjbKCkjp5eAf7UaSuWwZbKFFr8Dbj4RXsR/Ft+qSh9ixUa/A1k8E32J9CCz3M+4gKEVcLW9wJPSqyNbg2NvFwC/ajwoYeyt7taDtjBAcs4rs8uwA3SztkWdOrerej/YzhxNuIq7LsAjzMeHgE+1FmQ4bGvdrRedpwtsVZ82ioHgAl2QV4WshRcpv2rMzW4HIbYyR7UwKBZmNEl5OHwuwNsiIoAK3alLMEd1ecIsJQrX2XkK3f4BzZBuUHi3Fqa8pb2ux8HMzeHjjayJ3OLUJp+u3bNN/3Ja4dWxOyehEuZrnzVlAG+pOdY5JLnZOHMtB0zqpsDU6NTb+dgv2ozrae7nUDeb8ewm9Id9mximwNbv/Ap0G929FmxggO2fDpneBRaVEDXG9RAw5b8Ci1iCvPYD9KbzOu9Hl5KALNalmAjVpWYqplldusa9n6XV+Ql5OPqpGJjzI4kJwsOZ+b4Z+O1ZBe7ehiY6yWZRXgYfaWzl3lR5mNfNGoVzs6ThvOzon1zxd1yQBQ/g/laNyrHZ2mDWe7DTka6ldbepTa0KNJr3Z0nTacP2zoUWYjZ9Vli67ThrPFBo+SbA3ewfI6UmKDx81QrC6wqkW3U5cL1Rr8GpnyhW+wPwU26uGNPjfqYalZPfx5zzfkqfNQNTZdJymDg1Bn2b5Oeu29l0m7ep1vPq3fw2U7/m/hbvimqaMkSdMkSaoGEATBEcMN03pJkn4xJxAEYQJwPzBWquPjCkmSPpUkqaskSV2ffvppKs+k4BjaCMfGSnB0wHNYDKV7j9T215eWc7nn41zpP4Er/SdQeSqJ9Kmvy1bPq0lKQtE4BFGlAgcHXPr1oypBvjiA6GdKOM5R0WivpcnaC09exiNchVvTQARHBSEje5K984Ssj3f7UDq+8xRHxr9HdZ7sXpH0U5fxD1PhGxKIwlFBhwd6cmGXnP5WGN53DLu3xTPysfsA6NilPSXFpeSqrYvdzJefxdPLg6WvvCs7nn/yCp7NVHg0CUR0VBA2ogfXdybK+vi1C6XHsjj2TVxBZb5cD4Civy/jFq7C1WgL1cgocnbIdXFrpqr9P3BgJ8qvZNX+vnbqMoFhKvyMtuj8QBRndh2X0Z/ddZzuo2IMeg7rwcWEc7L26xY8Oj0QxbnbtGfGqSv4hanwMfK454EeJN0mj4baM+3UZYLCgvE3ytD1gShOW9ji9K4T9BgVC0DnYT1ItrDFndBDY9TD3ahH0xE9yLDwqU/7ULouf4oD49+jykKPfAv6sBE9SLewg2/7UO59O474CSus6AFyTl3BO0yFp5FH8+E9SN0l5+HfLpQ+y+LYHmc7NhuqB0D2qSv4NlPhbeTR+oEeXLaQ41YobuAYqTqXjGPTxjg0NuQs9yGxlO8/XNsulZZzLfZh0oeNI33YOKpOX0A9Y5Fs9byKMyk4hTXCMcSQO73u60PJHnnuvHjvaC73ncjlvhOpOJlE+pTFstXzboXYXj34ffseJEni1NkLeHi4ExhgyqWlJy/h2iwY5yZBCI4OBIzohWaHPL5dmgXX/u87oAuVV7Nk7QUnr+DRTIVbE4Mtm4zoQaYNn3ZZ/hSH6vDpneFhXQOybNSATu88xeHx71FlUQOyT13Bp5kKL2NctapHXGmTLWpZbD+qD9ddy5x6RqOzqGVjB8YRv+0Awx4ZAkD7zm0pLSm1+e1SXbAcqxHDe3DVQpeAdqHELItja9wKKizsmXvqCl7NzMb6iB5cszHWey+LY2cdY72hMtTyMJMjYkQP0uqQo66c01C/qk9dwSfMRN+yDj36Lovjjzr0UFvYosVNeGypg0fGqSv4NVPhY+TR7oEepNx2TZVf50Q+0PO2atHVU5dQhgUTEBKEwtGBex+I5uQu+dThk7uOE2Wsh12H9SQp4aysfVT/cezZ9ifDHxkKQGSX9pSWlJJnI76nz3sGT08Pli34z23pacf/XdxVS44LgiBgeON0QZKkFRZtQ4C5QIwkSeX/mKlOT86SVYR8vhREBUU/76T60jX8p42j8mwKZftszf6zgF5HyYfv47v8XRBFKrdtRZeaivvEOLTJSVQlJOD20Cico6ORdDqk4hKKly2TsZB0ek7PX0fUhnmGJcc3xFOSnEHruQ9TePIK2TsTabdoLAp3F7p/Nh2A8ox8/hr/nlEEPb8tWsdTX7+MqBA59kM86ovpDJz5MOlnrnJh9wlCIsN5cs0sXL3dadO/MwNnPsKKQS/K5IjfdZCYAdHsOfobFRWVzJv+Wm3b7/u+Y3jfMaiCg5g6axKXU67y2175Cz1Jp+fogq8Y8N1cw9LQG/dTlJJBhzmjyD91lfRdiXRZOBoHdxdi1hj0KMvIZ9/EFTIe51/+kq7fzzcsp7xhH6XJ6UTMfYSiU1fI3XGCpk8Nxr93eyStjpqiMs5MX2XmDj0/LfqCqV/PR1SIHPkhnuyL6Qyb+QjXzlzh7O4THP5hH+NWPM/C+A8oLyxl3bQPLFyq5+dFX/KMkcdfP+wj+2I6Q2Y+wvUzVzi3+wRNIsOJWzMbV2932vXvzJCZD/O2mT31Oj1bFq3jya9fQlSIJP6wn9yLGfSbOYqMM1dJ3p1Io8hwRq+Ziau3G636d6LfzFGsHPRSg+1pLsP3i75g2tevICpEEn7YR9bFdO6f+SjXzlzm9O4THPphLxNWPM/r8R9SXljK59Pet7LFndAjcf46Yja8ZFiS+fv9FKdk0P7FUWhOXSVzZyIdFo7Bwd2FqE9nGOM7j4MTVtTSH3vlK/p/NxdBIXL5e4MdIo306TsT6Wy0Q+9PTeMjfoI8rg4u/Ir7vjXwSN64n4KUDLrOHkXu6auk7Uqk5yujcXRzYeBqA4/SzHy2x624Y3rc4LFn4VeM+mYuokLkzMb95KdkED1rFNlnrnJ5VyKqyHBGfPYCLt5uNB/QiahZo1g3YJ6MR9LLX9LZOEYyNuyjLDmd5nMfodg4RpoYx4heq0NbVMbZ6auI/tP4kEOnJ/+tlahWvQWiSMmvO6i5nIbP1PFUn0uR3UDVCZ2e7NdX0eSLpYYlx38y5M6AGU9QeeYipXtvnTtffHUZx/4+TWFhMf1HPsHUp8ah1RqeUD/24H306dmNA4ePMfTROFxdXFgyf6aVDFfmr6XthoUIChH193upSLlOkxcfp/TUJQp2HkcVNxSf3pFINVq0RWVcnC5fgU/S6fl7/jr6GH161ejTdkafZu1MJNLo055mPj1k4dM7wePk/HVEW9SANsYakLUzkXsWjcXB3YV7jTWgIiOfw8YaIOn07DWLq7PGuIqaNQq1Ma6UNuLqK7O4Qq+jdOX7eL/1LoIoUrljK7q0VNzGx6FNSaL6cAKuI0fh1DMadDr0JSWUvCOvZQCH9hwmun8Pfj38PZUVlbw+863atvW7vmDsV36BjQAAIABJREFUwDgApi94lsEPDsDF1YUtJ36W2eLAwq94wDhWk4xjtZtxrKaajdXBxrFakpnPtjhTvkhY+BVD1xvy5o2x3mXOKHJPXeXarkTuXWDIFwNujPWMfHZajPWGyHCDx8GFXzHMQo6uRjnSdiXSY8FoHN3Nck5GPjsseNTHr+b0+xd+xfBvDfTnN+5Hk5LBvbNHkXP6Kld3JdLLqMdQMz22WMjw58KvGGG0xQ0e3Y08UnclEm3kMcQsd1ry2LZoHWO/NoyRk8Y6EjtrFJmnr5JirCOPfjoTF283Wg7oRMzMUaweaKojep2ezYvWMeFrwxhJ/CGenIsZ9J/5MBlnrpC0O5HGkeGMXTMTV293WvfvTP+ZD8vov120lllfL0BUiBz8YS+ZF9MZOfMxUs9c5uTu4/z5wx4mr5jOW/EfUVZYyppp1jc8f+4+RJ/+UWz762cqKypZMGNJbdvPe75hVP9xKIODeGZmHJdTrvLT7q+teNztsF5Gw45/AuFmK639j55YEEolSfKwONYLw6IQZzD5dL4kSVsFQbgEOAM3rhiPSJI05RankZJbD623jK2SDJ9NqfvG1JuHct9+flWNuXXHm2Bk9ne8FDb61h3rwNuphul1LQK73KJn3biYa3ja83Vj6+U5/ymezPiW7crH600PMET9PdPDHqs3/Yephr1qZobVX47/pH7PorCxt+54EyxOXd9gWwI8G/ZovXmsSjXMwW6ILotT17MxuGG2eCxrPd82qr8tnsg02GJ1k/rzmHLdwKMhujyWtZ53m9ZfBoA5175lZwPGyCC1YYrI1Q4D682j2aldAFxoMazePNpc3EpN3pV60wM4BoSTEFz/75OisgwX6T82wKePZK2/Izx+aWANeCj7O95rQGzNvmaI79yB9a9lgbsMq5F2De59i55143jWAT5pwDgFmHr9Wz4LqT+PyekGWzREjqnGfLGmAXI8k/7tHfHpRw3QY5pRj5UN4PH89W9ZHNqwGrAobT2vhNV/jLyRathrLC7s4Vv0rBtfpP4EQDvlvfXmcU79F3AXr2Jghl3Kx/6di/9/iIHqjXelHf+1N02WN0zGYwepI+AkSYqwddwOO+ywww477LDDDjvssON/EnfV9Dw77LDDDjvssMMOO+yw438Od/Oy3ncz/vUlx+2www477LDDDjvssMMOO+5m2G+a7LDDDjvssMMOO+ywww47bgL79Dw77LDDDjvssMMOO+z4/wT21fPqh39t9bz/JfxXK2eHHXbYYYcddthhx12D/xMfC21XPn5XXx8PUX9/V9rRPj3PDjvssMMOO+ywww477LDjJvivn55X/tHUetO6TfsEAM2D9d/bwm/TfkpnDa83PYDHit8pmjig3vTeX+4G4GRo/eXomPY70PB9PsoWN2w/B/dF69GMaIA/fjPsNVL60kP15uHx9i+Uf3CrLcJuDrcZqxtkC/dFhv1jSqYMqTcPz9XbAShbVP99gdwXf09mVN960wM0SthHWuf6x3dooiG+s3rVX47gg/sA+KtR/ePi3sxfGrQ/Ehj2SKrYt7be9K59JwFQ8fu79ecxfI6Bx09L68/j4QUN2mMJDPssNWSvJ8eAcAAOqeq/d0t0tmHvliMNiIsemb80KPeCIf/eif14ylfPqDcPtymGDcIbuvdhQ/Y9BMPeh2eaPVBv+nuubgbgWtf+9ebR9PgeoOGxpY6NrTe9Mj4egEttB9ebR8T5HQBk9OxXbx6ND+9lfQP22QMYm/ntHdl/saF7DgKMDh1Zbx4b0n6tN+3/NuzT8+oH+5smO+ywww477LDDDjvssMOOm8B+02SHHXbYYYcddthhhx122HET/NdPz7PDDjvssMMOO+ywww47DLBvbls/2N802WGHHXbYYYcddthhhx123AT/X7xpOpSWzzsHUtBLEiPbNiKuS5is/d0DKRzLKACgskaHpqKGA0/LP1Z17NQdt6emgShStXsLlb98J2t36jsEt/HPotfkAlC1dRNVu7fI+ihad8Z55CQQFdQc2UnN3p+tZHXoEI3T4NFIgD7zKlXfvmdqa98NlzFTQRSp+XMbVVu/t6J37BaD84gnAQnd9StUrHlT1u4Z05nGr05CUCjI/34nOausZQDwHtqTZqtfJvn+WVScuSQ/R9fueEydhiCKVGzbQsVGuS2cBw3BY/Kz6PMNtqj4bROV2yxs0TwSp8HjQBTR/h1PzaHNsnanQU8ghrUFQHB0QnD3onz50yYZOnXHbbLRH7u2UPmzhT/6DcFtgkmGqq2bqNplIUPLTjgPjwNBpObYbmriN1nZwSEyCqcBjyEhoc9Mper792XtYmhbnGIeBUFEe+4Q2uM75Lbq8wiKkJZGZk4Ibp5UrJ51W7YAULS9F6eYUSBJ6NXXLNq64PLos4a4OLSd6h0/WOvRpTdO9z8BEujTr1D5xdtyHhEdcBo23qBH4l5qDvxuLUO7Hjj1fRiQ0Gdfo+qnj2Ttzvd2w/uF50GhoHzzFkq/2SBrdx02GK/npqDPzQOg7OdNlG/eKuvjEtUNvzlTQSFSumkbxeusYxzArV9vAt99layx8oVenO/thteM50FUUP7HFsq+tZBh6GA8p05Bn2eSoeIPuQzesZ0IXRKHIIrkbNhN1kp5XASNG4RywlAkvR59WSVXX1xFxcV0+XmiuuL30lQEUaRk0zaKvthoW4/+vVCueJWM0c9RfT5F1nbo3FWW/7AHvV7iwehI4obcK2vP0hSzcN1WSiqq0Ov1TB8ZwyCztTAOJV1n+e+HDfTdWxHXr6OcvqCUhRvjKamoRq+XmD6sG73bNJXLkJLB8i3HDTy6RhAX017Oo7CMhT8doqTSyGNwZ3q3aizr49O3I80Wx4FCJOe7PWRY2FP55CBUE4aATo+uvJLLL66mIsVkzwVvruDPQ0fx8/Xh129XW9lQkiTeen81Bw4fw8XFmTdemU3bVhFWMoQvmQgKEfX6PWSslH+wrXpyEKqJg5F0Bp9eenGNTAYwxEWYWVxk2ogLlTEudHXERUPzb9PYSPq8Ng5BIXJ+QzwnPpHnio6Th9Lu8Vj0Oh0V+SXsmfMpJRn5sj6HUnN5J/4Cej2MbB9CXPdwWfu78Rc4lq4BbtTDag5MlS/U4tarC8pXpoAoUvTTdjSf/WhTD49B0TT+cAGpD0+n6uxFWZtTt+54Pj8NFCIVW7ZQvkGew10GD8FzyrPo8ox1ZNMmKraacrhHn840enUyiCIFG3eRu/onmzJ4DYkidNXLXBo+06qWufTshu+c50AUKft1K8Vf2c43rv16E7j8NbLHPUv1Bfk4vROx5dS9O57PG3JnxZYtlH9nYYshQ/CcMgWdMW9VbJLHnluvrgS8PAUUCop/2kbhWusaAOA+sBfBHyzk+iPPU3VO7g/nHob8LShEyn7fapW/a2WJ7Y3/W6+TM3EKNUlyWwTHRtJ1yTgEUeTShnjOr5THZ+unhxIxJha9VkdVfglHZn1KmUV8tonpwEOLJiAqRA5v3MvuVb/J2h2cHHhixXM0aR9OWWEJ657/QNYeERPJsEWGMZK4MZ4Dq+QyhHZvzdBFT6Bs3ZQfp63k/LajNvUc/9okOvbtQnVFFavmfEjqWetFaZq1b86U96bj5OLEyX0nbPKx478L/9pNkyAIOuCMUYYLwHjAH/gaUGLYY+lTSZI+MPZfAozAsOhHDjBBkqTMW51Hp5dYtj+ZVSM6ofRwZuwPx4hpFkBzP4/aPnN6t6z9f8Op6yTnlciZiCJuT79AyWuz0efn4rV8DdVHD6FPT5N1qz60l/LP5APYpLCI80PPULF6EVJRPq4z30N77iiS+rqpS0Awjv0fofyjl6CiDMHDW0bvMm4aZe++hKTJxWPRx9ScTECfabqAFpWNcb5vNKVvzoDyUgRPHys9QpY8w+Wxi6jJzqfl7+9RtPsoVRevy7u5uxI4cThlicnWeogintNeoPCl2ejzcvFduYbqw4fQXZPbomr/XkpX1mULAaehE6j89i2kYg0uk5agTU5Eyssw2XLnt7X/O3QbhKgKlcng9swLlLxq9Me7Rn9ct/DHwb2Uf3oTf4ycTMXa1w3+eH452vPHkHJMxUzwD8Yx9iHKV803+MPd24KHgFPsaKo2fYBUWoDL4y+ju3IaSZNV26Xmzx+puaFHh1jEwCa3bQvBT4lj9HAqvnwNKsvBzQuHe6Jq9XAZ/RzlH8xHKsjD7eUP0Z4+gj7LFBdCUCOcBj9G+TuzjXFhQ4/746j86g2k4nxcnnkTbdIJpFxzGVQ49hlBxdpXobIM3L3kPEQR7zkzyJ/xIrqcXAI/X03lgQS0qXKfVO7ZR9GKD237RBTxe2kaOVNfQqvOJfjbj6nYn0DNVflNouDmiueYB6k6c8GK3mvWDDQzDTIErF1N1UEbMuzdR/F/6pYh7M3JJD3+OtVZ+bTbupzCHcdkF7/5mw6Q881OAHwGdaPpaxNJHrtExsN//jSyn3kJrTqPRt+tpDz+MDVXrPXwHvsglact9AB0ej1vbdjF6hmPovT1ZOxb3xAT2ZzmjQJq+3y29TCDurTi0ZhOXM7M4/mVPzNo+psm+k2HWP30MJTe7oz98Fdi2oXSXOlrot/zN4Miw3k0qi2X1QU8//l2tpndNOn0et7afJTVEweg9HJj7KptxLQJoXmQKa98tu80g+4J5dF7W3E5p5Dnv9rLthfNVpoTRcLfnMy5xxZTnZVP5La30ew8JrtozPvlAOqvDfb0HdSVsNcmcGGMaeW+kcMGMmbUcOYvsb0i4IHDx7iWnsnWjZ9z+lwSS95dyYbPzB5uiCLhb03i3KOLqc7S0GH7MjQ7j8tkyP3lANlGGfwGdaXZa+M5P+YNGY9mb07mgjEu2m9dTsFN4sJ3UDdCX5tIkkVcNCT/CqJA7NLx/DpmGaVZGh77YzFXdp2g4KKpDOaeTWXjfQvRVlbTflx/ol8ZzfapK2vbdXqJZXvPs+qhbig9XRj73WFimgfR3N+sHsa2qf1/w99pJOcWyw0uiigXPUd63Hxq1HmE/vgBpXv/ovqyRXy7u+I7bgQVJ5OwgijiOeMFCl+cjS43F7/Va6hKOIQuzWKs7ttLyYc2crgo0mjxFK6OW4g2O5/mv62gePdfVF2ytmXAxAco/9u2DL4vTSfnubno1Lmovv6E8j8Po70ql0Fwc8Xz8YeoOnPeJo87EVueM2ZQOGeO0RarqTpkyxb7KPnAZAuvF1+spQ9c8BwZk15Gq86jycaPKNt3hBpLf7i54jNuJJWnrPMNoojP7BnkGfN30BerbOZvwc0Vj0dHUX3W2haCKNDtzfHsfXwZ5VkahmxdTPqOExSbxWfB2VS2DV2IrqKaFk/2p9PC0RycslLG45HFcXz8xBsUZucz5/e3OLvrONmXTLWox6P9KC8qY0nsDDo/EMXweWNk9PcvnsBXT7xFcbaGZ35fQtKuRHLN6Isy89g0Zw3Rk++ztoMRHft2QdUsmJkxzxLRqSVPLZ3CwpFzrfrFvfEMn837mEt/p/DSVwvr5GfHfw/+zel5FZIkdZQkqT1QDUwBtMBsSZLaAj2A5wRBaGvs/44kSZGSJHUE/gAW/ZOTnFUX08TblRBvVxwVIoNbKIm/kldn/+0X1QxpoZQdc2jRBn1WBnp1Fmi1VB/ci1P3XrelrNi0Bfq8LCSNGnRatH8fwKG9/MmxY4/B1BzaAhVlAEilRbVtivBW6HMykXKzQKel5mg8jp2iZfROfYZRtfc3KC810JcUytrdOragKjWL6utqpBotBZsP4D1QLgNA8Oyx5Kz+Gamq2qrNoVUbdJkZ6LMNtqiM34tT1G3aonFz9AVqpMJc0OvQnTuCQ6sudfZ3aN8T7bnDpt8t2qDPNvPHgXr4o0kE+nwzf5w6iEPb7rI+jt0HUHN4u8kfZUVyHsowpKIcpOI80OvQphxDER5Z5zkVLbuhTTku5/EPbOHQuR/a47sMN0wA5aaLGDGsFfqcLKS8bIMex/bjENlTRu/Uayg1+/8wiwsLPUIi0GuykQpyQKdDdyYBh9Zd5TJ07Yf2r52GGyaAMvmFlGPb1mjTM9FlGnxSsXsvLr3l8XkrOLVvhTY9E22GgUfZjnhcY615+EydQPG6jVbx6dimNToLGZx73Z4MHp0iqEzNouqaYYxofjuI72B5XOhKK2r/V7g5g8UG4c7tW1FzPRNtRrZBj+3xuMVGWZ3L97kJFH5prQfA2dQsmgT5EhLog6ODgsHdWhN/Wv6UXBCgrNJAW1pZRaCP6cL37LVcmgR4EeLvZaDv2Jz4cxYXQECZ8dylFdUEernJZUjPp4mfJyF+ngYekaHEX5BflAqCQFlVjVGGGiseHp0iqEjNrrVn3m8H8RvcTdZHbk8XK3t27XgP3l6eVja6gX0HjzB8SH8EQaBD+zaUlJSSm6epbffsFEHl1WyqruUg1WjJ/fXQTWUQ3ZytzmEZF/m3iAvRRlw0NP8qOzanMFVN8bVc9DU6Un4/Qvggea7IOHwBrTEmshMv4a7yk7WfzS6kiY8bIT5uhnrYSkX8ZbWVDDewPTmLIa2CZcdcIltScy2TmvRsqNFSsnU/Hv17WNEGTH8Szdofkaqt49uxtaGO6LKMdWTvXpyj/3kOd+vQguq0LGqMtiza/CdeNmypnDWW3NU/o6+qsWpzatca7fUMdMZ8U75zH24x1uPUe8pEir/63qYedyK2HFu3RpdhaYt/nrdc7mlFzbVMtEZ/lG6Lx6NfT6t+/tPHU7D2B5v5xqlta7TpGbW5s3z3Xlz6WNvC6+k4Sr7dYNMW/p2aU5KqptQYn2m/HaHJYHl8qhMuoKsw0OYlXsItWB6foR0jyE1Tk389B12NjsTNCdwzSG7PewZ15ejPhq1DTm49Qsso09vvkI7N0aSpKbiei65Gx5nNR2htMUYK0/NQJ11Hkure27XLwO4c+DkegEt/p+Dm5Y5PkK+sj0+QL64eblz62/C27Ub//yvQC3f3392Ku+WbpgNAhCRJWZIkJQJIklSC4Q1UY+Nv86s0dwxvom6JnLJKlJ4utb+VHs7kllXZ7JtZXEFmcQXdQuQDWfALQJeXU/tbn5+L6B9gSY5Tjxi8/vMFHi++jugfKOfh7Y9UaLpZkwrzELz9ZX3EwEaIgY1xnfY2rjPeQdG6s4neNwBJYyaDJhfB14JeFYJCGYL7/PdxX/ARDu3lycZR5U9NlkmGmqw8HFVyHq7tw3FsFEDxXvnFfe05AgLQ5ZrJkZeLIsCGLXrF4LvmC7wWvo4YaGELTz+kItMrealYg+Dpa8nC0Nc7AMEnEP3Vc6Zj/v/QHz1j8PrgCzxeeh0xwJY/zGQoykfwlvtdDGyEGBCM67Nv4vrcMhQtO8l5ePgilRSYeJQWInjUoYenH6J3APrrSVbHb2UL0U+F4B+My8RXcYl7HUVz042Z6OuPviDXZIvCPKu4EIIaIyob4/bie7jN/Q+KtvIiYlMGLwtb+AcjBATjMul1XCYvQRHRQdauCAxApzb5RJebiyLQ2icusX0I/Hotvm+8hhgk94lDYADabDMeObkoguS6OLWOQKEMouLgX1a8FYEB6HLM4qIuGWL6ELBuLT5LrGVwUvlTnWmyRXVWPo4WhR1AOWEIHRI+ocmCJ0ld+LlcjqAAdNkmn+hy8nBQyuVwah2BgyqQigO2p4bkFJSi8jXdKCh9PMkpKJX1mXJ/NFv+Os+geat4fuXPzHvMtO9MTnEZKrObKKW3OzlFZXL6QV3YkniJQUu/4/kvtjNvpPwiKae4HJW3u4mHlzs5RRVyHv0i2XLyKoPe/pnnv9rLvPvlOcdZ5Ud1hinnVGdpcLLIOQCqCUPofPhjQheM4+qCL2zapC6oc/NRBZnsqwwKQJ1rOqdTsB/VmeYy5ONsw6eqiUPofGQlYQvHceUVuU9txYVTHXHRMeETmtqIi4bmX3eVL6WZppvB0iwNHirb+Qag3eMxpMWfkh3LKa1C6elqktfDhdzSm9TDogq6NZHL6KAMoCbLFN/a7DwclPI+zm2b4xgcQNn+YzZ5iwEB6C3Hqo064twnBr+1X+D9mryOOFjaMjvfypYu7ZrjGBxIyT7btUwRFIBObaZHTi6KILkMjq1a4KAKpPKQdb6BOxNbYmAg+lyzHJ6bi8KiZgI49+mD3+ef4/263BYKpT812XJ/WOrh3MaQb8r/tJ1vRIvcqcvJs5LBsWULFEGBVCXYtoWrypdys/gsz9LgGlx3fDYfHUPmXnl8+ij9KDQbZ4VZ+Xgr5Ty8zfrodXoqS8pr2zyVfhSZ0RdnafBS1i1DXfBT+ZFv5ldNdj5+Srlf/ZR+aLJN58rPkk8ztOO/E//6TZMgCA7AUAxT9cyPhwGdgL/Mjr0hCMJ1YCx1vGkSBOFpQRCOC4Jw/NNPP70tWXZcVNO/eRAK8fZvc2uOJ1D4zGMUz4yj5tRx3GfMv20eiArEwGAqPp5P5Tfv4vzIc+Difms6c3plY8renk356jdwnTgTXG+DXhBovOApMpfe3kWLJaoPJ6AZ9xgFz8RRnXgczxfrYQsjHNr1QHfhqNVT21uh5lgChZMfo3hGHDUnG+CPgEZUrFlI5XcrcB71LLi43ZrOBhQtu6K9mHjbetTK4aek8qulVP2yEqf7J90WuSAqEIIaUf7eXCo+X4bLEy/cXlzUyqCi8ovFVP34IU4jnr5tW1QePIx61Ghyn5xE1dET+C6cd3syCAK+s56lYIX1dy3/WIZDh8l5ZDR5EyZRffwEPq/cpgxGqNdt51TUVK6/8Q2NZ9zmBpeCgN+cKWjeW1Ovc9/A9mMXGN6zPTuXPcvK50ex4Mut6PX/fMvC7X9fYnjXluxcMIaVcUNYsCEevf724nP76VSGd27OzpdGsXJ8Pxb8eOi2eQBkr9tOYs/nSHvjG0JeaNimuPVF9pfbSezxPKlLv6XJzPptWqpet52TUVO5Vs+4uBP5F6DVg9EERYaTuHrLrTvXgR3JWfRvqbz9eigIBM17mpy3P6v3uQGqDieQN/oxNJPiqD5xHO95t5HDBYHgBU+R9cbnt+57Ex6+s6ZQ8J/655sbaGhsVSUkkPf442ieeorq48fxfvnlf04sCAS89DR5y2/vesiSh/eMZyn6cFX9eZgh7KFo/CPDOb+q/vFphx3/Bv7NmyZXQRBOAseBa0BtdhMEwQP4GXjB/A2TJEmvSJLUBFgPPG+LqSRJn0qS1FWSpK5PP/00Qe4uqEsqa9vVpVUEulu/IgfDTdOQlkqr45ImD0VAUO1v0T8Qfb58ip9UUgxawxSAqt1bUIS3lLcX5SP4mJ7+CD4Bsqf7hj55aM8eBb0OSaNGn5uJGGiYGiEV5CH4mcngF4hUIKfXF+RSc/Iw6HRIednos9NRqEJq22uy83EMNsngGBxAjdmTEtHDFZdWoUR8/wZtD36GW6dWhH/+Cq73mD6q1ufloQg0kyMgsPbjVJktagy2qNy2BYeWFrYo0cjesglefrI3NuZQtOuJ9uxh2TEp/zb9sWsLiua2/GEmg7c/UpHGqo/2wjGDPwpy0OdlIgY0MrWXFsjeCgkePkiltvVwaNkVXYr1U9d/YgupWIMuOdEgR2Gu7JspfUE+oq/piaDoE2AdF4V5aE8dMdDnq9HnpCMGmT7WtylDsYUtivPRJZ8wyZCfheinqm3X5eahUJp8oggMRJdr4ZNiU1yUb96CYyu5T7S5eTiozHgEBaLLMekiuLvh2DwM1Wfv0fiPb3G+pw2B7y+WyxBkFhf1kKE6Ox+nRiZbOAX7U5Mlt4U58n89iO8Qi2laOXkoVGZPgoMC0KpNcgjurjhFhKFa+y4hW7/BObINyg8W49TWJEuQrwfZBaZvK9WFJQT5mt4cAWw6dIZBXVoB0CG8MVVaLQUFhtgJ8nInu9D0ZkpdVEaQt/xGedOxZAZ1MCwC0CFMSZVWR2G5KVcGebmRbfZ2Sl1cRpC3q5zHiUsMam/43rBD00ArHlXZGpwam3KOU7Af1dl1P5HN+/UQfhb2vBWUgf5k55jsq87JQ2n2hrE6S4NTI3MZ/Km6iU8NMsjfmNmKi+rbjIuG5t+y7AI8GpmednsE+1GabZ1vmvRqR9dpw/kjbgX6aq2sLcjDGXWJ6W2hurSSQI866qGNqXkAWnUejsHmb30C0KrN9HB3xalFKE2/Xk74nnW4dGhNyCev4ty+RW0ffV4eouVYtawjZmO1You8jmgtbanyt7Zly1DCv3+TVgfW4tapFaGfLZDVMl1OHgqlmR5BgejM4khwc8OxeTOUa1bQ6Pf1OLdvS8CKJTi1MclxJ2JLn5sre3NkyFu5sj43s4VOnY+jSu4Pcz0M/gij8VfLCd31Fc4d2hD88es4tzPzh0XuVAQFyGQQ3NxwCG9GwCf/QfnLdzi1a4v/8qU4tjbJUZFdgJtZfLoF+1GRZR2fqt7taD9jOPETrOOzUK3Bx2yc+QT7U6SW8ygy6yMqRFw8TQ/vStQavM3ovYL9KFbbrsl14a2t/6EwpwB/M7/6qfzRqOV+1ag1+Jm93fQPtn6DfjdDj3BX/92tuBu+aeooSdI0SZKqAQRBcMRww7RekqRf6qBdD/yjx5HtlJ5cKyono7iCGp2eHRfVxDazngZwtaCM4iotHVTeVm3ai0mIwSGIQSpwcMCpVz9qjh2S9RF8TcnCsVu01SIR+usXEQMbIfgpQeGAQ6fe6M7KX3Nrz/6FIuIeww93T8TARujzDfPNdVeTUQQ1RghQgcIBx+6x1PydIKdPTMChtWHalODhhagKQZ9jusAuP3UR52aNcGqiRHB0wPeB3hTvMsmgLynnbKcnON9rMud7Tab872SuPPWGbMUhbXISisYhiCqDLVxi+1F9WG4L0c9kC6ee0VaLROgzrhimnPkEgqhA0a4H2hTrlWcE/2AEV3f06fJVfqz80bsfNUdv4o/uNvyRfskw5cw3yOCPDr3QXZDf1GjPHUUR3s4NS7jrAAAgAElEQVTww80TMaARek22iYc6DcEnCMHLH0QFDi27obty2loPXyW4uKPPsl5955/YQpd8HDHM+HG2qweCn+lCRp+WjBjUCMHfGFfdYtCePiLX42QCDi0NU/oEdy/EoBD0eWY3XhmXTTIoFCjuiUKbZCHDheO1qxni5ongH4y+wDSdo+ZCEg4hjVEEG3ziOqAflQfl8Sn6m3zi0isKbar8Q+Xqc8k4NGmMQyMDD/fBsVTsN/GQSstI7z+KjPufIOP+J6g6c4HcF0wvnGuSklA0kctQdahuGZx7RaFNk8tQevISLs2CcW4ShODogN+IXhTslMeFczOT/X0GdKHyapasvepcMo5NG+PQ2KjHkFjK95tu/KXScq7FPkz6sHGkDxtH1ekLqGcskq2e1y40mGs5BWTkFVKj1bHjWBIxkfIV4YL9vPgrySD/lax8qmu0+BnHXrsmgVzLKyZDU2ygP3mZmLbylfGCfTz4y/iR9hV1AdVaHb7upqnM7Rr7cy2/hAxNiYHH6TRiWssXMgn2duevK4YxcSWnyIpH6clLuJrZM2BELzQ75NOlXMzs6WvDnrdCbK8e/L59D5IkcersBTw83AkMMPm55OQlXMODcW5qkCFwZDQaC5+6NDM9APAd0JnKq9mydsu48LcRFy63iIuG5l/1qSv4hKnwahKI6Kig5fAeXN2VKDtHQLtQ+i6L44+4FVTkWyzgALRTeXOtoJyMonJDPUzOJjY8yKrfVU0pxVU1dAj2sWqrPJOCY2gjHBsrwdEBz2ExlO415Rx9aTmXez7Olf4TuNJ/ApWnkkif+rps9byaJIs60q8fVQl11xHnqGi0ZnWk/PRFnMMa4RhisKX3A30o3m2aeqYvKedCl7Ek955Ecu9JlP+dTNrkpbJaVn0+CccmjVEY843boL5U/GmWb8rKyBjwEJnDx5I5fCxVZ8+TN2uhbPW8OxFbNcnJKEIsbWGRt2S2iEJ7zZS3Ks8m4xjaGAejPzyGxlK2T+6Pq9GPkjZwPGkDx1N16gJZz70qWz2v+kISDma5021APyoPmOWssjKyhz6I+qExqB8aQ/W58+TPXSBbPS//5BU8m6lwN8Zn6IgepO+Ux6dv+1C6vx3H/gkrqLIRn9dOXSYwTIVfSCAKRwWdH4jizC55vji76zjdRxlWN+44rAcXE0xT9zNOXcEvTIWPkf6eB3qQtOv2VrV7edhMju/8i96jYgGI6NSS8pIyCnPkN1+FOQVUlJYT0clw43ijvx3/3birlhwXBEHA8MbpgiRJKyzaWkiSdGOUjwBsLIdjDQdR5KU+rZj629/oJRjRNpjm/h588tdl2gZ5EdvM8IRmR4qawS2UGESwgF5H+Wfv4/nqu4YlrvdsRXc9FdfRcWgvJVFzLAGX+0bh2C3a8JantITSj5ZZ8NBT9csaXJ9+zbA09NHd6NXXcRoyBt31S+jOHUWXlIiiZUfc5q5EkvRUb14H5SW19BXrP8J99jID/YHt6DPTcB45Hl1qCtqTh9GePYZD+y54LP0cJD2VGz9FMv9gX6cnfdEawr9+DUEhovlhN5UXr6OaNYby05dkRadO6HWUrnwf77feRRBFKndsRZeWitv4OLQpSVQfTsB15CicehpsoS8poeQdC1tIeqq3rcNl7EuGJa5P7kfKzcAxdhT6zKvoUgyJ1nIBCJk/Pn0fz9cs/DHG6I+jCbjcPwrH7mb++MCGP35bi+tTiwz2PLbH4I+Bj6NLv4zuwjF0KX+jaNkBt1kfIOn1VG/9qnYxhVo94jfiPHK6QY/zCUiaLBx7PIBenYbuquEGyqFlN5tvmf6pLXSXT6Nofg+uzy4HvZ7q3d/hMmparR6VGz/BbfobBj0SdqLPSsPpgXHo0i6iO30E3fkTOLTtgtura4xxuBbKzFaI1Oup3vIlLk/ONyx7nrgPKTcdx36PoM+4gi75BLpLp1BEROL6/LsGmXd8CxVmttDpKVrxIf7/WQ4KkfI/tqG9mornpIlUJyVTdTAB90cewqWXMS6Kiyl8w8InOj2atz8i6GNDjJf+vp2aK2l4TxlP9fkUKv60EQsW9MUrPsRvxXIQRSq2GGTweGoiNUnJVB1KwP3hhwyLQ9xEhtRX1tLqu0UICpHc7/dQkXKdxi8+TtmpyxTuPIZq4lC8ekciaXXoCku5MuMjKx75b61EteotEEVKft1BzeU0fKaOp/pciuwGqi44KETmPTaAZz/8Cb1ez4ioe4hoFMAnvx+kbaiK2A4RzBoVy+Jvd7B+z3EQ4PXxQ2vzl4NCZN7IKJ79bBt6vcSI7q2IUPnxyY7jtA0JJLZdKLMe6MHiHw+w/oBhZvTrj8bI8p+DQmTeA915dt0e9JLEiM4RRCh9+GT3Sdo29ie2TRNmDevC4k1HWH/IsCLX66Oi5DlUp+fK/LW03bAQQSGi/n4vFSnXafLi45SeukTBzuOo4obi0zsSqUaLtqiMi9NNq2kBvPjqMo79fZrCwmL6j3yCqU+NQ6s1PKF+7MH76NOzGwcOH2Poo3G4uriwZP5MK39cmb+WdhsWGJY937CXiuR0ms59jNKTl9HsPE5w3FB8+kSir9GiKyojZbq1T1NfWUtrY1zkGOMixBgXBTuPoZw4FG9jXGgLS7lsIy4akn8lnZ79C79i+LdzERUi5zfuR5OSwb2zR5Fz+ipXdyXS65XROLq5MHT1dABKMvPZEmcqpQ6iyEv92jL1l+MGn7YLoXmAJ58kXKSt0pvY5oYbqB3JWQxuGWy7Hur05CxZRcjnS0FUUPTzTqovXcN/2jgqz6ZQts/2Ny8y6HWUfPg+vssNObxy21Z0qam4T4xDm5xEVUICbg+Nwjk6GkmnQyouoXiZ2VjV6cl8dTXNvn7dsOT4j7upuniNoJljqThzkZJ/Ust0ejTvfETQR2+DQqTs922GfPPMBKovJN863xh5NDy2dJR88AG+77xjtMU2oy0mok1ONthi1Cico6IMtigx2CLg669rZch942MaffYmgihSvGkn1ZfS8Hv+SSrPpVC+74i13Db0KHzvIwLefxtEBWU38vfkCdT8P/bOOyyqa/v7nzOA9A4zA4KAYu8lxqgINkQT2/VGY4uKJZpYoomJMZZojBpvyk2ixmh6okaTqCn2hl0jdiyooCBl6Egb6pz3j0Fmzgy2gfvGe3/n+zw+j8ze63vWXmvtffbZZ5+1r143WwCrDmKFjui3v6PHhjcQrBTE/XSIu9eTaTV7CFkXbpG85yxt5w/H2tGOrmv18VmUnMWhsYb41FXo+GXB17z8/VwUVgpObo5CcyOJfjOfJ/FSPDH7znBi80FGfzSV+VGfUJRbwLfTPqHtc89UyW9f8C0vfv8mCisFZzcfIuNGMj1mDiH50i1i953Ft1V9hn8xE3tXBxr3bEuPmUNYGf6mpC3nDpyhTff2/PvwGkq0JXzxuiHb6rIdH/NWP/0Y8828LypTjttyPkpOOf5/AcKDMoj8Ry8sCAWiKDqZ/NYVfVKIS+hTiwPMFUVxhyAIvwKNK39PACaLopjMgyEWffbyQ6rcHw7TVgOQPTj0ITXvD4+thyiYNcBieQCnj37n7rheD694H7h+sw+A8wGW69EmQX9uT0Zvy23hvfcQhYtHWiwP4LhgPdkDa+CP3/RZdwre/MdDat4fTu9voeiTyRbLAzjMWFMjWzguWA9A/uQIizmc1+wCoHDBC5brsfgnUjp3f3jFB8D3+EES2lke3wFn9fGd2tVyPXyOHgTglK/lcfF0yhZute5tsTxA0IW9aA9+abG8fXf9927a36tPz/1IHANe13P8suQhNR/A8c95HPep2bdJnVN/pSzT/O3so8LGS7/18Jjasu+TALpo9Gf/nKxBXHRK2VKjsRf04+9n/qMslp92R3+EQ9GaGRZzOEzWp7uObdLXYo7G13aS1t3y8RtAdfAQl4L6Wyzf8pb+3J7EDj0fUvP+qBe9H6h5bKWFhVksr4qKAuBmsz4WcwRf0Z8rmPxMD4s56p44wHpfy2MTYGTKj0wPHGax/Ke39efgLQi0/J66+Lb+njo8YJDFHBsTtgFP8N4yI2xTj/h7Jv+PiEGaDU+kHf+2N02mD0yVvx3lPgEniuLf83WwDBkyZMiQIUOGDBky/k/jb8+eJ0OGDBkyZMiQIUOGDBlPMp6ob5pkyJAhQ4YMGTJkyJDxn8OjH04hwxjymyYZMmTIkCFDhgwZMmTIeADkhyYZMmTIkCFDhgwZMmTIeAD+tux5/5/wP904GTJkyJAhQ4YMGU8Mnsisb6b4xWfkEz0//mfq+ifSjv/z3zTVRirjmqbqjvazPIUlQIekbTVK6TkyRZ9udmLg8xZzrLv9MwCabmEWc6gPR3GlwbMWywM0i9te4/S/QI180iFpW6349Gcfy9OjPp+qT48apbLcp2Fpep8eqUHa3BDNL2h3r3x4xQfAvs9Uio/8YLG8XchoAIpPbLSc45nhAGh3fvqQmveHfd/ptWKLHSrLU8D3S/sJgPiW4RZz1L+0B6DGaZ1rEt+gj/HaSBdeG2nLS64ftZjDtlFXSu9csFgeoI5/a7Tfv2WxvP2Ly4DaSU+9TT3CYo5Bmg1ov3rdYnkA+/Ef1Kif2feZCoB26/KH1HwAx+A5eo5NiyznGLYQ7caFlssP119bu/3flnM8+6qeY98ayzl6Ta6RLUFvzwmBlvf1L2/r+3ptzHE6+IRYzBGdesRiWRn/HZC358mQIUOGDBkyZMiQIUPGA/A//6ZJhgwZMmTIkCFDhgwZejzRe/OeYMhvmmTIkCFDhgwZMmTIkCHjAZAfmmTIkCFDhgwZMmTIkCHjAfg/sT3PrvNTeLz+MlgpKNi6k7xvf6q2nkOPELw/WEjqyJcpvXpdUuYc2o66CycgWFmR9dMe0j//tVoO177PELTmLWKfm4X20k1JmUtYW+otmgBWCjI37kWzaouk3HtUH7zH9oMKHRWFWhLeXE3xjaSqcp+wVnR4dzSCQsHNjVFcWfmHRL7JpL4EjwhDV15BSVY+J2etpTA5S1KneWgbXlgwDoWVgiOb9rPr822Scus61kR+NI2AFvUpyM1n7dSPyUrKkNSp07EjLtOngsIK7fbtFK7fICm3j4jA+eXJVGRkAlC0ZSva7dsldRy7tUc9fxKClYKcTXvI+uLnau3p3Kcz/qvfJn7QDIqN7Oka1pbAdyMRFArSN+4jZeVWiZxydDjqsX0RdToqCou5NftztEa2hJr7o7Y4VN1b0XbxaAQrBfEboog18WvDl/pSf0T3Sr/mET1rnaTco3sbgpeMQ7BSkLp+P4mfSX3q+2JvfCMjKnUoJvb1Lyi6LtXBvXsb6r+r59Cs30/SSimH+sVwfMf1QazkuDnbnOPYlQRWbDmMTicy+JlmRPbuIClPzc5n/o97ydeWoBNFpvfvTEjzQIN8TBzvb9ytlw9pw/h+XSTyKVm5LPzmT3Lyi3B1tGPphEGoPFykOly8wfsbdqHT6RjcrR3jn5N+1JuSmcvCr34jJ78QV0d7lr70D1QerlKOqwms2HIUnahjcKdmRPZqL21HTj7z1+/Xt0MnMr1/J0KaBUo5amgLAK/urWm2ZAyClYI76w8Q/9nvkvJ6L/YiIDIcsUJHeWExMa9L48K+Swc835yCYKUgb8su7n61ierg2Ksrqo8XkDTsFUqv3JCUOXVrh+/CiaBQkLNpLxlrfqmWwyWiMwGfv8XNATPNxj1L4rsoKbOq3K0yNrFSkLZ+P8nVxKa6MjZ1lbGpNYnNeUs/4vCxv/Bwd2Pbj+YfvYuiyLJ/r+HIidPY2dny3tuv0axxsKTO0TOXeH/dRnQ6kX/0DmH88/0k5SnpmSz45Bty8gpwdXJk6WsTUHt5VJXP/9dqDp86i4ebK1u//LBaHZav+oYjf53DztaWJW+8TLOG9SV1jsVpWLHnIjpRZHCbQCI7N5aU/2vvRU7f1o/XxeUVZBeWcPR1aUIPh64d8HprMlhZkffLTnK/3GymC4Bj7674fDKfO89PpeSyNC6U3VvR8t0XEawUJKw/yA0TnzZ4qR8BI8MQy3WUZuVxduZatEY+BTgWn86K/TH6trSqR2SnhtK27I/h9B39/au4rILsohKOzuhrkK+FPnYsNokVf5zS6/BUIyLDWkk5cguYv/kI+dpSPUdEe0Ka+Es5bqSwYscZPUe7BkR2a27CUcj8LSfILy7Tc/RuTUijuiYcqazYdVbflnb1iQxpZs6x7RT5xaX6MadXa4xTvRy7msiKbUf18p2aEtmznVQ+J5/5Gw5UyuuY/mwnQpoFSHW4fJsVv0Tpx84uLYgM72hizzzmf7/bMO4N7EpIi6Bat2fz0DYMN5qj7KxmjjK+ao5SwBdTPzKTr+kcB+D1d2fQpWcnirUlvPPqUmIvXTer8/KcifT7Zx9c3JzpFmx5khUZ/z342940CYJQIQjCeUEQYgRB+FkQBAdBEPwFQTgoCMIVQRAuC4Iwoxq51wRBEAVB8HqkCykUeLw5jfRpc0kZMh7HiO7YBNUz18fBHucRgym5dLVaDr93XyJ+zCKu9XoF9wHdsG3ob17N0R7vcQMoPBtbLUe9JS9xffRiLnefhsfAEOwa+kmqZG07zJVeM7jSZyaaz7fivzDSoJ9C4KmlYzg4cgV/hr1B4MBOuDT0lcjnxNxmZ9/57Og1l8Ttf9F2/nBpGxUKRiwezydj32NB75l0HNAFn2CpDl2H9qDobgFvh01j31d/MmSOSdY+hQKXmTPImf0mmS+Owa5nD6wCpIMvgPbAQbLGTyBr/ASzByYUCnzemUJi5EJu9pmCa/9u1Amu3p4eYwdSdO6amXzQ0olcG7mEC2Ez8BwYgr2pLbce4WLPmVzq/Rqpq7cR8M44M46a+KP2OATaLR3LkZEr2BX6BvUGPYOzyQ0191IC+yLmsbfnWyT9+Ret5hn5VaGg4fLxXBzxHn+FzEQ5uAsOjaQ6pG05SnTYa0T3nE3iqt8IXjTGrB0Nlk3g8oj3ONNtJt6Du5pxZGw5wtnur3Gu12ySVv1G0DtSjgqdjmU/R7Fq8gC2zB3JrjPXiUvNltRZt+c04W0bsunN4SwfE8HSn6Mk8kvX72T1q8PZ+u5kdv11mbgU6Y3so8376f9MS35ZNIlJ/UP4ZMsBMx2W/rCD1bNGsnXpK+w6FUNccrqU46c99O/Sml+WvMykgaF88vN+83b8cphVLz3Hljkj2HX2BnEa03ZEE94mmE2zh7F8TDhLfz5cq7YAQCHQfHkkp0cs53DIa/gO7oKTSVykbDnGkbA3ONpzDvGr/qDpotFG8gq83p6K5uW3uTNwIk59w7CpX/245zJqMMUXqh/3fBdP5tbYd7gR/gquA7phe59+6jWuv3k/rWxHTeO7fmVsnquMTftqYvN899e40Gs2ydXEJsCgfr1Z89ESc/0qceTEaRKTUtix6SveeWM6734gzc5WUaFj6Zr1fP7OTLatepedh08Rl5giqfPh15vp36Mzv362iJde6M+n30kX1gb2CePzZXPvr8Nf50hI1rD9u09ZOHMSSz75UqqDTmTZrguseqELW17qza7LScRl5EnqzO7dis0Te7J5Yk+Gd2hAz8bSewQKBd7zXiHlpXkk9p+Ic7/u2DSoPi7cRg+6T1wItF42jhMjVrC/22z8Bnc28+ndmNsc6jOPgz3mkPznXzQ3uRdV6ESW7bvEquefZsv47uy6mkJcZr60LT1bsHlsKJvHhjK8XRA9G/kYyde8j1XodCz77SSrxoWzZeZgdp2PJy4tV8px4ALhrYLYNGMgy4eHsXTbSXOOP6NZNbo7W6Y+y65LCcSl35VyHIohvEUAm17uy/Lnu7D0z2hzjh3RrBoZypZX+rIrJtGc4/Blwpv7s2lyBMv/2Zml26Ol8luOsGrSc2x58wV2nb1pPmbtPUN4mwZseu15lo/uzdJfpVneKnQ6lm0+wKpXBrFl/hh2RccSlypdcF236xTh7Rqx6a1RLI/sx9JN5uNvTe0pKBSMXDyBf499j/m9Z9JxQNdq5ig9KbxbyNywaez96k/+aTRHqZU5DtClRyf86/sxuPNw3pu9greWv2ZWB+DwnmOM6fdStWVPOnRP+L8nFX/n9jytKIptRFFsAZQCk4Fy4DVRFJsBnYBXBEGoWnIRBMEfCAcSH/UidVo0pjwphfLkVCgvp3B3FPZhXczqub08lrxvNyGWlJqVObRpSMntVErvpCGWlZPzxxFcez9tVs/ntZGkr/m1Wg7HexyJeo7s347iFi7l0BVoq/5v5WAHRmdoebZtQP7tNAoSM9CVVZDw20n8+0hXwNOOX6VCq7925tmbOPh4SMqD2gSTkaAh8046FWXlnP7jGG3CpatzbcKf4vivhwA4s+MkTTq3kJTbNG1CRXIyFal6exbvP4BdV3N7Pgj2rRtRmpBC2R0NlJVz98/DOPfqZFbPe+Yosr74xcyeTm2DKb6dSkmlLbN+O4p7H+mqWIWRLRUOthJbQs39UVscHm0bUHA7jcLEDMSyCu78dpK6Jn7NOH6lyq/ZZ29ib+RXl3bBaG9pKE5IRywrJ33bMbwipD6tkOhgi+nZbM5tgym+paE4Uc+Rse0YHn2eeiCHKWIS0vD3dsPPyxUbayv6tGtE1CVpqmcBKCzWt6OguARvF0eD/K0U/JUe+Hm7Y2NtRUTH5kSdl67sxaVm0LFpIAAdmwSalcfEJ+Ov8sBP6YGNtTURT7cg6px0ASMuJYOOTfWrox2bBhFlMtGPSUjH38vV0I62DYm6dOv+7dCW4u3qaMJRM1sAuLULpuiWBm1COmJZBanbjqMy8Wu5mV8NZbYtG1OWmEJ5kkY/7u08hGP3zpjCY+oYcr/ahFhazbjXuiGlCamUVY57d/84jEs1455q1kgy1vyKrqTMnL+G8X0vNkseMTYV1cQmQIc2LXF1ca62DODg0ZMMiOiJIAi0btGU/PwCMjINE8+YG/HU81Hip/bGxsaaiG4dOXjqnIQjPjGVp1s1BaBjqyYcPHVeqkOrZrg6O91fh+PRDOjdTa9Ds0bkFxSSkZVj0CElG38PR/zcHbGxUtCnmR9R11Pvy7fz8h0imksnjHbGcVFWTsHOKJx6PGMm6zl9DDlfbq72XubeNpiCW2kUJepjM2nbCdQmPs08ZvBpzpkbEp8CxKTm4O/miJ9bZVua+hJ1U3P/tlxNJqKp4cGsNvpYzJ1M/D2d8fN01nO0rk/UlcQHcJTi7WIv5UjKwt/DCT8PJz1HywCirknfcgqCQGFl3ygoLsXb2YQjORt/D2cDR4t6RMUmV8NRrucoKZNwxCRWjlmeLpVjVjBRMbdN2iFQWGykg6uDVIfbmkp7uuk52jcm6mJcNRz3xr0S83GvFuwZ1CaYdKM5yl9/HKNNuLS/6+coUQCc2XGCJp1bSuRrOscBCI3oyo6fd+nbdfYKzi5OeCo9zerFnL1CVnqW2e8y/nfxpHzTdAQIFkUxVRTFswCiKOYDVwHjJayPgTd4jMQf1t5elGsMq80V6RlYmQR/nSbBWKmUaI+eqpbDRu1JWapha0FZaiY2aimHfYv62Ph6kXcg2lRcfw0fD0qNOEo1WdQxuZEAeI/pS4uja/B7ewyJCwwrjfZqd4pSDDfxotRs7H3cq70WQIPhoaQckJ4N4qbyIDvF0MFzUrNxU3ma1clJ0eupq9ChzS/Cyd0w2VB4eVORbngDUJGRgcLb2+z6dqHd8PzmK9wWL0KhlJZbq6T2LNdkYmOih13zBtj4eFMQddqMu47ak1KjdpSmVm9L1dgI2hxfTb15L3J7/ldSjhr6o7Y47NUeFBltoSxKzcZefX+/Bg0PQ3PQ4FdbtQclRrYoScnGVm0+uPuO68PTpz6j/vxR3Hz7a0mZrY8HJSlG7UjNwraadviMi6DDyZUEzR9N3NtSe6bnFqJ2M0wIVW5OpN8tkNSZ3PdptkfHEj7/a6au+YM5/ww1yOfko3Y3bLVTujuTliNdeW7sp2J/5Vvc/WdjKSwuJbegyIgjD7WHMYcLaTnSlfjG9VTsP6NfPd9/5qo5x90C1O6m7SiUtiOiI9vPxBK+8Fumrv2TOUOkWwBragsAO7UHxUZ+1aZkY6s290nAuHBCT31Ck/kjufL2t1W/Wyu9KNcY+ml5WgZWJn2sTtNgrNXeaI/8ZcYLYG067mmyzMa9e/00/2D1415N47uOjweljxCb6nERtDu5ksD5o4k3ic1HQVpGFmqlYfOCSulFWobhumlZuaiMttqpPN1Jz5KuojcK8mffiTMA7D9xlkJtMbl5Ur8/COmZ2ai9jXTw9iTd6MEtPb8YtdFkWeViT3q+luqQcreIlNxCOgYqJb9bqTwpM44LTSZWSummDdvKuCg6XH1c2Pu4ozWKzeLUbLOHImMEjOhOmsm9KL3ApC3OdqTnF9+/LXeL6FjPoGdt9LH0vCLURhN/lasD6Xkmfb1XW7afiyN86SamfrOXOQOki3vp+Voph4sD6XlFUo7uLdl+4RbhH2xl6o9RzHlWOolPz9OidnEw4rAnPU/q18lhLdh+8TbhH/7G1PWHmNPP8JCafrcQtZuRDm6O1YxZHdh+5jrhi75n6rrtzBlsOmYVoDa6z6vcnEjPNbHns53Yfvoq4W+vY+rqbcwZ2t2kHTW3p7vR/AMgJzULd5XHfevcm6PcQ23McQC81d5oUgzzxrTUDJQ+j7a5Scb/Nv72hyZBEKyBvsAlk98DgbbAqcq/BwLJoijW7JRAcwVwnzWFnI8sP9wNQaDuvPGkLPn64XUfgozvdhLTdTJJS7/HZ7plB7UF/qMLnq3qc+Xz7Q+v/B9A8fHjZAx9gaxx4ymJjsZ17mMeyigIqOZOIG3plw+v+wCkfbuL851fJvG9H6g7w7KD82rDH7XBAVBvSBfcW9cndvWfjy2b8s1uTj09jfgl6wmYOcSi66d+s4voTlO5teRH6s18fHvuOnOdAU83YWLYHKIAACAASURBVM+7kayc3J95P+xBp3v0xKezhvYiOjaBoYvWceZ6Akp3ZxSKxxvCZg0LJzr2NkMXrOFMbCWH8HgHj+86e4MBHZuwZ9FYVk56jnk/7nusdkDNbXEPCd/s4dDTM4hdsoHgmYMfXVAQ8Jz9ElkfrH3saxpz+MwbT+p7j/+QUh1qEt+ab3ZxttNUbi/5EX8LYrM28Frk85yJuc7QGe8QHROL0tP9seOztrD78h16Na2LleLxYhtBwOvNSWSuqEFcGMFvSBfcWgdx0wKf3sPuayn0auzz2G2pjT6260I8A9o3ZM/cYawc15t5mw8/PsfF2wxoW589rw9m5agw5v16/PE5LiUwoE0Qe14byMqRoczbchKd7tE3Me06e5MBHRuzZ+GLrJz4LPM27H98HaJjGfB0c/a8N5GVLw9i3ne7/hZ7yqgd6IQn+9+Tir/zocleEITzQDT67XZVd15BEJyAX4FXRVHMEwTBAZgLLHgYqSAIkwRBiBYEIXrt2rWUZ2RirTastlkpvakwep0qODpg0yAQ9boPqfvnj9i2bIr3vxdTp2mjqjplmixsjFYZbHy8KNMYOBRO9tg1DiD4p/dodnQdDm0bU/+rt7FvafiQuDQ1mzpGHHXUnpSa7ME2RvZvR3DrY9gKo9Xk4OBrWHFx8PFAm5pjJqcOaU6LGQOIGvsRutJySVluWjYevoZVF3cfD3LTsszquPvq9VRYKbB3dqDAaMVfl5mBldGbIytvb3QZ0m9PxLw8KNNvBdD+uR2bRo0k5eVpUntaq70oM9JD4WiPbaMAAjYsJ/jQ19i3bYL/Fwuwq7RnqSaLOkbtqOPzYFtmbTuKe4R0+15N/VFbHFpNNg51DW1x8PFAqzH3qzKkOU1nDOTYmA8lfi3RZGNrZAtbXw9KNPffLpC+9RhefaW2KEnNxtbXqB0+npQ8oB0Z247hGSHdMqF0c0RjtDKZlluA0lW6FWnrySuEt9V/7N06yIeS8gpyC/Urqkp3ZzRGb4XSc/JRmaz+Kd2c+fiV59m8cCLTButXOV0c7Azl7i5oso058lC5SxNFKN1d+HjaC2xePJlpQ3roORwNK95KVyc0OabtkG5D2XrqCuFtgivboZa0ozZsAVCsycbOyK/2vh6UaO7vk5Stx1H1NfikPD0Ta7Whn1qrvKlIMx737KkTHIjP1//Cf9f32LZqivqzxdRpZvgYv9x03FN7mo97jQKo/9NSGh/5Eoe2jQlYN08y7tU0vktTs6nzGLGZue0YHiax+ShQeXuiSTd6s5Seicr4rY+nG2lGb33SsnJQerpJ2+DpzsdzX2HzJ+8wffQ/AHBxkm6DehCUXh5ojN9uZWShNHq7pXS2Q2P0ZiktT4vSZKvXPey6kkREc/PvzyrSsrAxjgu1FxVG7VY42lOnYSB1v1tBwN7vsG3dFJ9Vi7BtbogLbWoO9kaxaefjgbYan3iHtKDxjEGcNPEpgNLJpC35xSid7Uwp9G0x2ZoHtdPHlC4OaIzeyKTdLUJpsoVv6+kbhLcK1HMEKPUcRYY3YkpneylHXhFKF6nPt56NJ7yF/rux1vW8KzlKjPSwR2P0diotT4vSZNva1nPxhFf6s7W/FyXlFeTk6PuR0tURTa6RDrmF1YxZVwlvXTlmBaopKSs3GbOc0Bjd59NyC1C6mdjzeAzh7fX38tb1fc05asGeOUbzDwB3H09y0qSxlVPNHOUeamOOs37v12SmZ6H2NcwbVT7epBu9dZfxfxdPwjdNbURRnCaKYimAIAg26B+Y1ouieC8VWQMgCLggCMJtwA84KwiC2pRUFMW1oih2EEWxw6RJkyi9HIu1f12sfdVgbY1jnzC0h44b6hcUktRzCMnPjSL5uVGUXLpKxqsLJNnzii7cwDbIlzr+KgQba9z7h5C317CVT5dfREzbUVzpOpErXSdSdC6W+PHvSbJIFV64gV2QD3X8lQg21ngM7EruXun2B9sgw4eurj07UHLLsF8963w8zkFqHP29UdhYETCwE0l7zkrk3VsE0PH9SA6N/YiSLOm2JIDbF26iDPTBy0+JlY01T/XvwoW90m015/dG03mIfhtD+36diD0eIykvuxaLlZ8fVj56e9r17EHJseOSOgpPw43etktnyhOk+5q1F69TJ7AuNn4qsLHG9bluFOw3smdBEdefGsHN0EhuhkaiPXeNOy8trsqeV3D+JnZBPthW2tJzYFdy9ki38dkZ2dKtV3uKb0n3/tfUH7XFkXM+HqcgNQ7+3gg2VvgP7ETK7jOSOm4tAmi/YjzHxnxo5tf8czexr++DXT29DspBXcjcLfWpfZChm3j2boc2XqpD/vmb2NX3wbaSw3tQF7LN7Gng8OjVDu0t6fcHzeupSMzIJTnrLmXlFew+e53QltLMSj7uTpyqzGoWr8mmtKwCdyf95KB5oC+JadkkZeRQVl7Brr8uE9pa+rCdk19UtSr51Y5jDOrSWqpDkC+JaVmVHOXsOhVDaNvGJhyFVSu0X/15lEEhbU3aoSQx8y7JWXn6dpy7QWiLQGk73JxN2lFe1Y7asAXA3XNxONZXY19PHxc+gzqTZhIXDkY+UfZuS5GRX0tiYrEJqIt13cpxr28ohVEnqsrFgiISuj3PnYgXuRPxIiUXr6KZtkCSPa/o4g1sA32x8dOPe679u5G3zxDfuvwirrYfSWzIBGJDJlB0LpaEiUsk416N4/u8Pr4fNTbde7Wj+Nb9v425H8K6duL3XfsRRZELMVdxcnLE2+iBpXnDIBJS0kjSZFBWVs6uw38R1rGNhCPnbn5VbH358w4G9+r6WDp0f6YDv+89rNfhynWcHB3w9jRsZWzu605idgHJuYWUVejYfSWJUKPkCPdwKzOfvOIyWtc13zJXXBUX+rHXqW8YhQcNH+PrCoq41WUoCb3HkNB7DCUXrpL6ykJJ9rzc83E41VfjUBmbfoOeQbNH6lPXFgG0+dd4To75kNJM83tRcx83EnMKSc4t0rflagqhwWa3c25lVbbFV7qlszb6WHM/LxKz8kjOztdzXIgntJn0QdPHzZFTN/X9Kj49V8/haHi4a17Xk8TsfJJzCvQclxIIbSJ9wPNxdeBUfJqeI+MupeU63B0N39419/UgMcuIIyaR0MamHI4mHBV4eOj929xfWWmLe2PWTfMxy92JU5VZW+PTcigtN7FFgJrE9BySMyvteSaW0JbSzI0+Hi6cupZYac8sc45asOftCzdRGc1ROvbvwoW90v5+YW80nYeEAdC+3zNcM5qj1MYcZ2TvSKJ2HqHf8xEAtGjXjIL8AvnbJRnAE5ZyXBAEAf0bp6uiKFblkRRF8RKgNKp3G+ggiuLDH/0rdGS//xnKVctBoaDg912UxSfgOnkMpVeuoz184qEUVOhIWvAF9b9/B8FKQfbmfRTfuIN61giKLt6UTCQexJE4fx2N1i8EhRVZm/ZRfP0Ovq8Pp/DCTe7uPY1ybD9curZGLK+g/G4Bt2Z+UiUuVuiIfvs7emx4A8FKQdxPh7h7PZlWs4eQdeEWyXvO0nb+cKwd7ei6djoARclZHBprSMepq9CxYcFXvPr92whWCo5tPkjKjSQGzBxGwqU4LuyL5ujmA4z/aBrvRX1GYW4Ba6d9bNKOCvL+/QnuH/wLFAq0O3ZSfvs2TpHjKIuNpeTYcRyGDMG2S2eoqECXl8/dZcvNbKFZ9Dn1vn0XQaEg95e9lNxIxPvVUWgv3ZA8QN3Plrff/pImGxYgWClI/2k/2ut38Jv9AoUX4sjZcxrVuL64hrTS2zK3gLgZn9WqP2qLQ6zQcW7ut3Tb+CaClYJbPx0i73oyzWcPIfvCLVL3nKXV/BFYO9rxzNoZlX7NlMjfeOsrWv2k92nqxoMUxSYR+MYw8i/EkbU7mrrj++Ie0hKxvIKyuwVcnS7NDEaFjri5X9Ji4zwEKwVpGw9QFJtEwBvDyD8fR/aeaHwj++LWrRViWTnldwu5Pl1qT2srBXP+GcqU1b+j0+kY2KkZwT6erN5+kmb1lIS1rM+sQSEs/ukA6w+eA0Fg0cheCJVb46ytFLw1IoIp/96ITqdjUJc2BNf1ZtW2KJoH+hLWphHRsQl8uuUACALtG9Zj7sgIEx2seGtUP6Z88AM6ncigkLYE11WyassBmgf5Eta2CdHXbvPpL/qMee0bBzB39LPm7RgSwpQ1v6PTiQx8uqm+HTtO6dvRIohZg7qweNNB1h/S7xReNKJnVTtqwxb3/Hr5rW/o+NNcsFKQtPEgBbFJNHzjee5eiCd99xkCxvfBK6RFpV8LuTD9c7od/qDKp5lLV6JesxTBSkH+1t2UxSXg/sqLlFy+TlGUNGtVtajQkbJwDUHfL9KnHP95HyU3ElHOHIn20g3yH2HcszS+j90btyp0xM/9kuYb54GVgvSNB9DGJlHvjWEUVMamT2Vs6srKqagmNgFmL1zO6XMXyc3No+egUbw8fjTl5fq3H8MGP0u3Z57iyInT9B0aib2dHe/OnWkSF1bMnTySKQs/pkKnY1CvrgQH1GXVj9to1jCQ7k+34XRMLJ9+9yuCINCueSPenjJSwvHGe//m9IUr5N7Np+cLk3llzNAqHYb2Dyfk6bYc/uss/V6cjp1tHZbMflmqg0LBnD5tmLLxmD42WwcQ7O3C6kNXaObjRlgjfaa8XVfuENHMTxJPxj7NeG8VvuuWIigU5G3dQ+nNBDymvkjx5esUHXx4XIgVOi7O/ZbOG+foU45vjCI/Npkmb/yT3PPxaPacpfmCkVg52tFxneFedGqMIc26tULBnF4tmPLzSXSiyMCW/gR7ObP6yDWaqd0Ia6h/gNp1NYWIpnXN2lIbfczaSsGcAZ2Y8rV+297ADg0JVrmzes9Zmvl5EdasHrOe7cjiLcdYf/SynuP5EHOOZzsw5fuDeo529QlWurF6/0Wa1fUgrIkfsyLasfi3U6w/fg0EWDS4kzlHv/ZM+eEQOlHHwLb1CVa6svrAJZr5ehDWpC6zwtuw+I/TrD+pX8xdNOhpydg55x8hTFn7p16Hjk0IVnuweudfNPP31o9ZAzqzePMh1h+6qNdheA9zHYb2YMqqLXqOZ5oT7OvF6j+P06yeirBWDZj1j24s3rCX9QfPAgKLRvepdXvq5yhf8ur381BYKTi2+QApN5IYOHMYtyvnKEc272fCR9NZWjlH+WLaxzz1XGcj+RrOcYBj+0/QpWcntp34iWJtMYtmLqsqW7/3a0b21mfBnT5vCn0G98LO3o7tZ6o/huZJhY4neA/cEwzBNJPW/7cLC0KBKIpOJr91RZ8U4hKGrINzRVHcYVLvNo/20CQmtOtlsY4BZ/cBcD5ggMUcbRJ+J9pvkMXyAB2StrHe1zwt5qNiZMqPAEwMtPx7mnW39ecoabqFWcyhPhzFlQbPPrziA9Asbjsnff9hsXynFP3Ly5r4pEPStlrx6c8+Ix9e8T54PnU9AFEqy30alqb36RG15d+AhGh+Qbt75cMrPgD2faZSfOQHi+XtQvSptotPbLSc4xl9SmTtzk8t5rDvO71WbLFD9YLF8v3S9GfQxbcMf0jN+6P+pT0AXArq/5Ca90fLW3/UKL5BH+PHahCbXTT6M6XKMuMfUvP+sPHSr7aXXD9qMYdto66U3qnZp7h1/Fuj/f4xvw01gv2L+knfzWaWnyUTfGU3ANvUIyzmGKTZgPar1y2WB7Af/0GN+pl9n6kAaLcuf0jNB3AMnqPn2LTIco5hC9FuXGi5/HD9tbXb/205x7Ov6jn2Wf5Nt32vyTWyJejtOSHQ8r7+5W19X6+NOU4Hn5CH1Lw/olOPAP8dTyPrfUc90R+TjUz58Ym049/2psn0ganyt6M8QsCJohj4n9BJhgwZMmTIkCFDhgwZMkzxRG3PkyFDhgwZMmTIkCFDxn8OT/RrpicYf3vKcRkyZMiQIUOGDBkyZMh4kiE/NMmQIUOGDBkyZMiQIUPGAyBvz5MhQ4YMGTJkyJAh4/8InuQDZJ9k/G3Z8/4/4X+6cTJkyJAhQ4YMGTKeGPxXPI58X/fJzp73YvLDs+cJghABfAJYAV+KorjcpHwWMAEoBzKASFEUE2qil7w9T4YMGTJkyJAhQ4YMGf8VEATBClgF9AWaAcMFQWhmUu0c+uOJWgG/ACtqet3/+e15ad1DLZZVHTwEUKOzhZrFbedioOXnnQC0uv0H+1XDLJbvmbYJgOmBlnN8elvPkdHbcnt67z1EXAvLzwkBaBCzu0b2bHX7DwCuNuxnMUfTGzuIqf+cxfIALeL/rBWfHvcZYjFH51T9YXw1Pfeq+OQmi+UB7DoNo/ivny2X76g/m6P4tOWHC9o9pbdjTc+L0kZ9bbE8gH1YZK2cQ5b0dA+LOfxOHQCoUV9tELO7Vs5pqg1b1PSMJaj5WU+lCWctlgeoE9AO7TdvWCxvP04/V6iNuNhVg3PEItJ+Qrt+vsXyAPYj30V7+FvL5buNBahRX7UP0x9uWtNzlmrlrKhflljO8c95ADUaw+06DavRWVGgPy/q5cChFsuvvr0ZgNEBlo8XPyTox4sAz1YWcyRkXbRYVsZjoyNwUxTFeABBEH4CBgJX7lUQRfGgUf2TgOUHnlbif/6hSYYMGTJkyJAhQ4YMGXro/m4FHgJBECYBk4x+WiuK4lqjv+sCd4z+TgKefgDleGBnTfWSH5pkyJAhQ4YMGTJkyJDxRKDyAWntQys+AgRBGAV0ACzfKlUJ+aFJhgwZMmTIkCFDhgwZ/y1IBvyN/var/E0CQRB6AW8DoaIoltT0ovJDkwwZMmTIkCFDhgwZ/0fwRKfOezScBhoKghCE/mHpBWCEcQVBENoCXwARoiim18ZF/088NNV5qiPOU6eBlQLt9u0UbdwgKbfrE4Hz5ClUZGYAoN26Fe2O7ZI6jt3ao54/CcFKQc6mPWR9Uf2H6859OuO/+m3iB82g+NJNSZlTaDvqLpgIVgqyN+0l4/NfquVwiehM4Jq3uNF/JlojDo/urWm0ZCyClYKU9QdI+Ow3iVzdF3vhF9kHsUJHRWEx115fS+F16YN309DW/GPBWBRWCk5sOsC+z6Uc1nWsGfXRK/i3qE9hbj7fTv2E7KQMSR2bDh1xenkagkKBdud2tJuk9rQNj8Bp4hR0WZX2/G0rxTul9rTv0gGvOZMRrKzI+3UnuV9trtYWjr26ov73fJKGTaXk8o1asyWAY0h7VPNeQrBSkLt5N1lr7+fTLvitfJtbg2dQHHNDUubUrR0+CyaBQkHO5j1krrm/HvVWz+XmwFfN4sISvxrDrXsbghZHgpWC9A37SV65VVKuejEc9dgIqNBRUVRM3Ow1aK8nSeq4hrUl8N1IBIWC9I37SDHhUI4ORz22L6JOr8Ot2Z+jvSHlOHbxBu+v34FOJzI4tB3jn+smKU/JzGXhV1vJySvC1cmepS8NQeXhaiR/nfd/2IFOp2NwWHvG9w81kc9h4bqt5OQX4upoz9Ipz0vkAY5duM77P/xZyfEU4wdUw7H2V3Lyiyo5hqLyNOGIieP9jbv17Qhpw/h+XaQcWbks/ObPSg47lk4YhMrDxYQjnhWb9+v16NqayIhOkvLU7Dzmf7OdfG0xOp3I9MGhhLRsUGOfGMO201O4zZqKoFBQ+PsO8r/fSHWw7x6C5/JFpI2ZTNm169KyGvZTAFX3VrRdPBrBSkH8hihiV/4hKW/4Ul/qj+iOrryCkqw8ometoygps0Z2MI3No2cu8f66jeh0Iv/oHcL456WJYFLSM1nwyTfk5BXg6uTI0tcmoPbyqCqft/QjDh/7Cw93N7b9uMas/aIosuzfazhy4jR2dra89/ZrNGscLKkz/8M1HD55Dg83F7au+1e1HMtXf8eR0+exs63Dkten0KxhkKTOsfg0Vuy7hE4Hg1vXI/KZRpLyf+27xOlEve2KyyrILirh6ExpIqPaiAuv7q1pumQMWClIWn+AW5/9Lin3f7EX9SLDq8asmNfXmd2Ljt1MZcXu8/p+1jaIyK5NJeWpdwuZv+0v8kvK9H2kZytCGvoY5GPiWPHTPn0fC2lDZN9nJPIpWXd559vt5OQX4eJoz9Lx/f8j/fTYjVRW7Dqrb0e7+kSGSBN4peYWMn/bKfKLS/UcvVoT0shXyhGbxIo/TqETRQY/1YjIsFYmHAXM33yEfG0pOlFkekR7wo3lryezYnu0XocOwUSGtjDX4ZdjBh36tCOkcV2pDjUcvwGOXU1kxbajeo5OTYns2U6qR04+8zccqNRDx/RnOxHSLEBSp1loa55fMA7BSsHxTfvZU80cZcxHU6vmKF9NNU9CMfqd8bTu3o4SbQlrX19JQox5IpfAFvWZ9OE06tjV4cJB80Qt7yx7k+69QtBqi3l96nxiLl6VlNvZ2/H51x9QL8gfXUUF+3YfMuOQ8Z+DKIrlgiBMBXajTzn+tSiKlwVBWAxEi6L4O/AvwAn4WRAEgERRFAfU5Lp/20OTIAgVwKVKHa4CYwBP4HtAhf5BeK0oip9U1n8HmIg+1zrAXFEUdzz0QgoFzjNeJXf2a1RkZOCx5gtKjh+jIkGaqr344AHyP/3kvhw+70whYcw8yjSZ1N/6Mfn7T1J68460mqM9HmMHUnTuWrUcdRdP5tao+ZRpsgj+/SPy9p6ipBoOr3H9KTTlUAg0Xh7JuaHvUZKSxVO7l5G5O1pyI9JsOUby9/sA8OrTnoaLXuT88GVV5YJC4PnFkawa9R65mixe/30ZMXuj0dw0cHQa2oOiu4W8GzaDdv07M2DOCL6damQXhQLnaa+S++Zr6DIzcF/5BaUnjlGRKLVnyaEDFKy8vz29571CysS3KNdk4rfpMwoPnqQsPlFSTXCwx3XUIIovXDWTr5EtKznU77xM4ti3KdNkEvTrv8k/cB+fjhmI9nz1HL6LpnDrxXmUa7Kov+1j8vdVr4fn2AH3iQvL/GqsQ/2lE7k8bDGlqVm02vk+2XtOSx6KMrccIe37PQC4h3cg8J2xXB2xRMIRtHQiV19YRGlqFi12rCBn92nJxDNr6xHSf7jH8RQB74zj2sh3q8ordDqWfv8nX7wxBpWHCyPe+YKwtk1oUFdZVeejn3bTv0sbBnRty6kr8Xzy8z6WvjTEIP/dH3zx5ji9/II1hLVrKpXfsIv+XdswIKQdpy7H8cnmPSyd/LxUh+9+54s5kZUcqwlr34QGdVVGHDvp37UdA7rd49jN0ilDpRzrd/LFrJGo3F0YseQrwto0ooGvt4Fj8376P9OSAV1ac+rqLT7ZcoClEwZJOJZt3MuaV4ehcndm5LLvCG0VTANfr6o667YfJ7xDE4aGtiUuJZOpK39mZ8spNfaJsbz77BlkTJtNRXoGym8/R3vkOOW3pP1UcLDHadgQSmKuYIaa9lMAhUC7pWM5PGwZRanZ9Nr5Lil7zpJvFN+5lxLYFzGPCm0p9V/sSat5wzk5+bMa2UESmxU6lq5Zz9p3X0Pl6c7wWe8S9nQbGtQzTFw//Hoz/Xt0ZmDPLpy6cJVPv/uVpa9NrCof1K83I4YMYO67H5i3EThy4jSJSSns2PQVFy9f490PVrJxnXQyN7B3KMMH9OHtFaur5zh9noRkDdu/+ZiL126y5NOv2PCZoZ9W6ESW7bnImhc6o3K2Z+S3hwhtqKaBl+FBYHavllX/3xgdz7W0uyb+qI24EGi2PJLTQ9+jOCWLZ3YvJX33GcmYlbLlGHcqxyzvPu1psmg0Z4YbMsVV6HQs23mWNaNCUbnYM/LLfYQ29qWBt2ESvu7IVcKb+zO0QzBxGXeZuuEIO2c8Z5DfsIc1M19A5e7CyPe+JbR1Q0kf++jnAzz3TAsGdG7FX1dv8+nWKN4bP0CqQw37aYVOx7Id0awZ3V3fjnV7CW1clwZKo3Ycvqxvx1MNiUu/y9T1h9jZyESP306yZnwfVK4OjFz5B6FN69FA5WbgOHCB8FZBDO3UhLi0XKZ+s5fwt4zk//iLNeN6oXJxYOTnOwlt6kcDpZH8wYuEtwxg6NONiUvPZep3B9g5+x8SHWoyflfpseUIayb3R+XqyMiPfyW0eSAN1IbFh3V7zxDepgFDu7QgTpPN1HU72Gn00CQoBIYtHs+no5aQq8nizd+XcdFkjtK5co7yTth02vfvzOA50iydrbu3QxXkw+uhr9CgbSPGLZnEO4PmYIqx773EV3M+J+7cdV7/bp6krHuvrgTVDyD0qedo26EVSz6Yx6Bw82yga1d9x4mjp7GxsWbD1i/NymX8Z1H5DLDD5LcFRv/vVdvX/DvPadKKothGFMUWQCkwGf0BVK+JotgM6AS8YpJ3/eNKmTaP9MAE2DRpSkVKMhWpqVBeTvGBA9h26fpYitq3bkRpQgpldzRQVs7dPw/j3KuTWT3vmaPI+uIXxJJSszKHNg0pTUil9E4aYlk5uX8cxiXcPNGH6rWRZKz5FbGkTPK7S7tgtLfSKE5IRyyrIG3bcbwinpLUqSjQVv3fysEW04OLA9oEk5GQRtaddCrKKjj7x3Fahks5WoZ34K9f9Ssm53ecpFFn6YqVdWO9PXWaSntGHaBO58ezp23LxpQlplCepIHycgp2RuHY4xmzeh7TxpD79WbEUqk9a2pLAPtWUp/mbT+Mc09zHbxfHU3W2p/RVeNT+9aNKElIpaxSj7t/Hsa5t3lcKGeNIuOLX6rVo6Z+dWobjPa2hpJEvQ6Zvx3Fo8+D5O3AJC6c2gZTfDu1iiPrt6O49+l4Xw6Fg60ZR0x8Ev4qD/yUHthYWxPxdEuizkofEuOS0+nYtD4AHZsGScpj4pLwV3ka5Du1JOqMdBIel5JBx2aV8s3qE3VGym/O0cqcIzmdjs2NOaTlMbdS8Fd64Oftjo21FREdmxN1XrrKHpeaQcemgXqOJoFm5TG3UvFXuuHn7YaNtRV9OjQl6oL07YsgQKFWv726QFuCt6uTpLymPqnTrAnlSclUpOj7qXbvAey7dcYULi9Fkv/DFloZfwAAIABJREFURqgmvmvaTwE82jag4HYahYkZiGUV3PntJHX7tJfUyTh+hQqtXjb77E3sfQyTrFqJzRvx1PNR4qf2xsbGmohuHTl46pykTnxiKk+30r/p6NiqCQdPnZeUd2jTElcXZ7P23cPBoycZENETQRBo3aIp+fkFZGRmSzlaNcXV2ek+DHDw+BkG9A7RczRtSH5hERlZOYZ2pObg7+6In5sjNlYK+jSrS9QNzX35dl5NIqKZ9G1CbcSFW7tgim5p0FaOWZptx1FFdJDUMfaJtYOt2V6gmORs/N2d8HN3wsbKij7N6xEVmyKpIwCFlWNmQXEZ3s72BvlbKfh7u1f10z5PNTXrh/EpmXRsEgjAU00CiDov7YO10U9jkrPx93DGz8NJz9GiHlGxySYcAoUl5XqOEmk7AGLuZOLv6Yyfp7Oeo3V9oq6YLEwAhcWllbYoxdvFyBZJWZU6VMq3CiDqqnThTq+DkS1dHKQ61HD8BohJTMffyxU/Txe9Hm2DiYq5bdIOgcLie3qU4u0q1SOwTTAZCZqqOcqZP47T2mSO0iq8Ayd/jQLg3I6TNDaZo7Tr3ZGjleVx567j4OKIq9JdUsdV6Y69kz1x5/Qxc6/+PfTu251fN+nfiJ+LvoiLqzNKlZekTrG2mBNHTwNQVlZu9ibqSYdOeLL/Pal4Ug63PQIEi6KYKoriWQBRFPPRv4Gq+0DJh0Dh5YUu3bCVUZeRgZWXl1k9226heHz5Na7vLELh7S0ps1Z5UpZq2C5SrsnERuUpqWPXvAE2Pt4URJ2uVg8blSdlKQaOstQsMw775g2o4+NN/sFoM3k7tQfFKVlVf5ekZGGrdjer5zcunGdOfULw/JFcf/tbSZmbyoNcI47c1CxcVSaDiVEdXYWO4vwiHN0NkwWFlxcVGUb2zKzennW6huL+xde4zK/GnkpPyjWGLX/laZlYK6UcdZoGY632pujwX2bcNbUlgLXak3Ijn5ZpMrE29WmzBlg/yKdqT8pSjdqRer+48KLgPnrU1K+2ag9Kkw3tKE3Npo7a00xePTaCdidWETBvNLfmSc8nqaP2pNRIh9LULOoYTVzvQTU2gjbHV1Nv3ovcnv+VpCw9Jx+10VYNpYcLaTl5kjqN66nZf0a/cr3/zFUKi0vILSiqlM97NPnoSvnoK3r5/CIjHe6acLhWz3H6ciXH5Wo48lG7G1bule7OpOXkSzn8VOw/G6vnOBtLYXFpVTsA0nOlHCp3Z9JzCyQck/t3Zfupy4S/uYqpK39mzgu9JeU19YmV0ouKNEM/rUjPxMqkH9o0boiVypviY6fMeKHm/RTAXu1BUbKhHUWp2dhXE9/3EDQ8DM3BCwb+WojNtKxcVEZb7VSe7qRn5UrqNAryZ9+JMwDsP3GWQm0xuXlSnz0IaRlZqI1so1J6kZaR+QAJc6RnZaP2NvRdlZcH6VmGB6/0/GLURhNulbM96fnF1XKl3C0iJbeIjgFSn9dGXNiqPdAa+aQ4JRtbtblP6o0Lp9upT2g0fyRXTe5F6fla1EYTZpWLPen5WkmdyaHN2X4pkfCP/2DqxiPMiWhrkM8tQO1h2sek/bSRv7Kqnx44d/0/0k/T87SoXUzakWfSjrAWbL94m/APf2Pq+kPM6dfehKMItaujgcPVgfS8QilHr7ZsPxdH+NJNTP1mL3MGdLq/vIsj6XdNdOjRiu3nbxH+/q9M/e4Ac56TPojUdPwGSL9biNrNSA83R9LvmrQjogPbz1wnfNH3TF23nTmDQyTlbioPcoxiKyc1C1eVx33r6Cp0aI3GbwB3tQfZRvODbE0WHiYcHioPsjWG62SnZknK1T5KUpINCxKalDRUPkruBxcXZ3r1qXFiNhn/BfjbH5oEQbBGf6LvJZPfA4G2gPHIPVUQhIuCIHwtCEK1d15BECYJghAtCEL02rWPlq2w5MRxMocPI3tCJKVnonGdM/dxG4Fq7gTSltbg9awg4DN/PCnvffXwug9A0jd7OPH0DG4u2UDQTMsPeqsJSk8cJ3v0MHJeiqT0bDTOsx/fnl5vTCLrXxZmm6wNWwoCyrkTSV+2rkYcPm9PQFNDn0LN/ar5dhdnn3mFhPd+wO9Vyw7ETft2F+c7v0ziez9Qd8Y/H1t+1gt9iL52m6HzV3Pm2m2U7i4ohEdfUpo1PEIvP2+VQV7xeEtSs0b0I/raLYa+/Rlnrt6yjGNoL6JjExi6aB1nriegdHdGoXi8oXTXX1cY0Lkle95/hZVTn2feN3+i0z3+p7kW+0QQcJsxhbuffP7wug/gqFE/NUG9IV1wb12f2NV/PrZsTWPztcjnORNznaEz3iE6Jhalp/tj+/RJwu6ryfRq7IvVY8Z2rcRFJRK/2cPhp2dwfckGGswc/Njyu2ISGdA6kD0z+7NyeAjztv2FTnz0PjLr+R6cuZ7IsMVfE309EaXb39NPd11KYECbIPa8NpCVI0OZt+Xk43NciGdA+4bsmTuMleN6M2/zYXS6Rz9pZ9fF2wxo14A9bw5h5ZgezPv52GPrUNPxG2DX2ZsM6NiYPQtfZOXEZ5m3Yb9F496TBCsrKz5b9z7frN3w8Moy/uvxdyaCsBcE4d4eiCNA1cxSEAQn4FfgVVEU7y13fA68i/5F/7vAh0CkKalJbncx+5WXUSgNKwQKb28qMqUrgGKeYUVFu307TpMmS8rL07Kw8TGsIlqrvShLM6xMKBztsW0UQMAG/Z5ta293/L9YwJ2XFlfVKUvLwsZor7SNj6eUw8keu0YBNPhpaRVH4JfzuD1Bv6e9WJONna9hFdLW15MSjWHrhinSth6nyfsTJL/lpmXjZsTh5uPJ3TQpx93KOrmabBRWCuycHSg0WmnXZWZi5W1kT69q7JlvsGfxzu04TjSxZ3oW1mrD6qa1yovydAOHwtGeOsGB+H6jP8XeyssD9WeL0EzTn75eU1sClGuysDbyqY3ai3JTnzYMoN6P71dx+K1ZQNJkI59qsrDxMWqHj5eZHraN6hG0cVkVR8Da+SRMMnxvUVO/lmiyqVPX0I46Ph6UarKqEwUgc9sx6i+fJPmtVJNFHSMd6vh4UpqabSpahaxtRwlaJuVQujujyTZ8P5GenYfK3cWkjgsfTx8OQFFxCfuir+DiaF9V9kjyM0YY5E9frpLXl7uacNytnuPVUQ/gcEZjtMKanpOPyl26LUvp5szHrzxfyVHKvrPXcHGwk5Qbc6Tl5KN0k27r2XrsIqun67+lat2gLiVl5eQWFHFvnbamPqlIz8RKZeinVkovKjIMb40EBwesGwThvfpjfbmnB14fLCHzdcPe/pr2UwCtJhuHuoZ2OPh4oK0mvpUhzWk6YyBRg5egKy2v+r02YlPl6Uaa0Va5tKwclJ5ukjpKT3c+nvsKAEXaYvYdP4uLk3Tr0IOg8vZEY2SbtPRMVN7mb+AfBKWnB5oMQ99Ny8xG6WlYIVc626ExehuTlq9F6WxHddh1JZm3wluZ/V4bcVGiycbeyCd2vh6UaO7vk9Stx2n2/nj0t/B7bbFHc9fwhiAtT4vSZNva1vO3WD1Cn4ygtb8XJeUV5BaV4Ago3ZzQZJv2MfN++tHL+gWiouJS9p+JrfV+qnSxR5Nn0g4Xk3aci2f1qNBq26HncEBj9EYm7W4RShdHKcfpG6yO1L/lah2gpKS8gpycHDw9Pc3l8wpRuprocOYmq8f01MvX867UodigQw3HbwClqyOaXCM9cgtRupq049RVVk/Sf5fWOlCtt2ehtkqP3LRs3I1iy93Hk7tp0ti6V+feHMXe2dBPl+z4kPiLN/Ewmh94qD3JNuHITsvGw2hHhoeP4f87ojZz8dxlfOuqq35T+6pIS60++dryjxdwKz6Br7/4kYVL36i2zpOIJ/1w2ycVT8I3TW1EUZwmimIpgCAINugfmNaLorjlXmVRFNNEUawQRVEHrAM6Vk8rRdm1a1jV9UOhVoO1NXY9elBy/JikjsLDcGOy7dyFcpOkBtqL16kTWBcbPxXYWOP6XDcK9htegOkKirj+1AhuhkZyMzQS7blr3HlpsSRLWtGFG9QJ9MXGT4VgY41b/27k7TVsadHlF3Gl3UiudZ3Ata4TKDoXy+0JS6oyvuWfi8Ohvhq7et4INlaoBnUmc7d0y5d9kKGTe/VuS1F8qqQ88UIc3oFqPPy8sbKxol3/zlzaK+WI2RtNxyH6Ab5Nv07cOH5ZUl4ea2LPsB6Unri/Pes808U8SURMLDb16vL/2DvvsCiu7/+/dilSFASEBQFBwAZGir2CUbEkGo2Jib3lY0k0RU0+xqiJ3ZiqMUaTGNOMpqmJsWHDghU7iqIgINKbtKXs7vz+GGR3dkEj8P3Gb37zeh6fR7j3vDlz7r1nd2bunDF3V4G5OQ0HhlF8+JQknok9R5DcfzzJ/cdTdjmW9JnvVlXlqmssAdRX4qo0sDDH7qleFB6U+nCz80jie08kvvdE1BevkzJtsaR6nvpyHA0M/LB/uheFB05L/LjeYTRxvSYT12sy6gs3SJqyRDIv6jquRRdvYd3cjQaeLigszGnyTA9yjeytmusrTjn0bU/pbem8KLp4CysDDadnepAXcbZGjcbVaAQ0dyc5I5eUrDwqNBr2nr5CaHBrSZ+8wuKqq6Mb/zrG0F767TYBPu4kp+eQkpkr2p+6QmjIA+x3HmVoqLQyk6iRbaBxmdCQNjVr/HmEoaHSrTIB3k0NjkPL3jNXCQ1saaRRUnV1dOPuKIZ2DzTScCM5M4+72flUaLTsi44lNFBaSc3N0Y7T18V1kZCWTXmFFgeDD/+6jkl57HXMPd0xcxPXqXW/J1EfPVnVLhQXk9Z/GOnDRpE+bBTlMdfInjNfUiWtrusUIO9iAg2bu2LjKc5vz2e6kLrvnOQ4Grf1ov2qyUSN/4iyHOmWoHqZmy2ak5SaQUp6FhUVGvYePUNYpyBJn7x7hVXz4utfdzOs76M9pxnWowt/7j2IIAhciomlYUNbnJuYbll7EL27hvDn/mOiRuxNGtra4Oyk31AR4NaY5Nxi7uYXU6HVse/aXUL9XE10bucUUlBaTqC76WaM+pgX9ypzlnVlznId2o1MozG1MchZztV8FgW4O5KcW8TdvCIqtFr2XU0m1KiinJudDadvZwCQkFVAuUaLg00D0d67qbjGsirX2NlYQgNbSOwl63TPSYb2kJ5E1sc6DWjqSHJOoXgcGi37YpIJNapK52Zvy+mE+8dxTzwO2wZ6DY8mJOcUcDe3UNS4lECov6dUo7Etp2+JMUzIzKe8Qotj5edsgLuT6MN9+8tJhLY2sre35XRCeqX9fR/0J5B1zd8AAZ4uJGflczenQPTjwi1C23pL/XBoyOnKIi4JGXmiHw31J15Jl+Jx8XbDqfI7SvvB3bhs9B3l8v5zdBkeBkDwoC7cMPiOMn/QbM5FnKFHZbtvcEtKCku4l2l0cTgzD3WRGt9gMbff7w8wKGwEEbsPMfyFweLf6NCOwoJCMjNMt9vOmTeDRnaNWDRvlUmbzL+Tx6rkuEKsCbgRiBUE4WOjNjdBEO5n3mFAzN8S1WkpXPMpDqs+BKWS0j270SYmYjtxEpob1yk7cQKbZ4fToHt3BK0WoaCQgpUrpRpaHemLvqDZt0tQKJXk/7afspvJOL8+BvWVm5ITqBrR6khduB6f7xeBmZK8Xw5QdjMZ1RujUV+5ScGB6p8JuI+g1XHj7W8I3joPzJSkbYmk+EYKPm89T8GlBLL3ncNzcn8cej6BoNGiuVfMtVelVZp0Wh2/LfyGl7+fh9JMyalfIkm/mcKgN54n+UoCMQfOcfKXw4z9eAYLIldTkl/EtzONKuDptBSt/RT7FR+iUCop3bcbbVIiNuMnoYm7TvnJE1gPHY5l1+6g1aIrLKTwA9N4Zi//HLcNy1GYKSnYHkFFfBIOr4yj7GocJZGneCB1jOV9jfRFX+D5zVKx5PhvEZTfSqbJa2MovXKTokN/c0zfW4/3d4tRKJXk/SrOC5fXRT8KDz7cj9qOa5ejH1X5kDDva/y3LEBhpiRj6yHUcXfwfPNFii7dIi8iGtdJA2ncsx1ChQbNvWJuvrrW5DgS3/ma1j8tRGGmJHPrQdRxd/B480WKL8WTF3EW1cSB2PdsJ/qQX0T8a59JJMzNzHh77FNM/+B7dDodQ3uF4OfhwufbDhLg7U5YSGuiryey5tf9gIL2rbyYN+5pqf24p5n+wXeV9u3x81Dx+e8HCGjuTlhIG6Jjb7Pml/2ggPatvJk3frCpD+OHMH3VJnQ6gaGhlRq/7SeguQdh7dsQHZvAmp8jKjWaM2/CECMNJW+PGsD0T7eIfnQPws/dmc93RBLg3ZSwoJZE30hizbZDoFDQvkUz5o0eYKIx98V+TF/9CzqdwDPdn8CvqTPr/jyGv5crYYEtmPXckyz+cS+bD54FFCyaMAiF4VaXWo5JYOTqKvv8Dz+jyZr3USjNKN65B83tROymTKA8No7SYyceOjfrvE4R5/eFed/Sa8t/UZgpub31CAVxdwl4czi5l26TFnGedgtGYW5rRdcvXwOg5G42URM+rlMcjOfFvGmjmf7uJ2h1Oob27YGflzuf/7gD/xbe9O4cxNmYG6z57ncUCgUhAS15Z7q0Utab767k7IXL5OcX0GfoGF6ePBaNRrwj9sKwp+jVtSPHTp5l4IhJWFtZsWTeGyaxeGv5Gs5ejiX/XiF9Rr3CK2OfQ6MVNUY83Y+enYI5euYigya8jlWDBiydM1V6HEolc8PbMf3nk+gEgWfaNcPP2Y51R2Pxd2tMWGU57r3X7jLA3106nwzGtK7zQtDquPb2JjpsnYfCTEnKlsMU3UjB763nuXcpgax952g2uT9OPdsiaLRU3CvmyqvS7X7mSiVzB4YwffNR8ViCmuPnYs+6wzH4N3UgrJU7s8IDWbwzms2n4wAFi57pVHVM5mZK5o7qx/RPt4r23dvh5+7Muj+O4u/lRlhQC6LjklmzLRIF0L5lM94eFS71oR7WqbmZkrmD2jP9hyPoBB3PBPuIx3HoCv5NHQlr7c6s8CAW7zzL5lPiieeioZ1NNYZ0Yfo3EaIfHVrgp3JgXcR5/D2aEObfjFlPdWLxtig2H78KCgWLnu8pjcXgTkz/9qAYixA//FSNWXfgIv7uToS18WTWoPYs3n6KzVFisYJFw7sZ+VC3/F3lx7M9mf6luIXxmU6t8XN1ZN2eM/h7OhPWtjmzhnRj8S9H2HzkMihg0cgnJX7otDp+XvgNM75/R3wtyi+HSbuZwtNvjCDpSjxXDpzjxC+HmPDxDN6LXENJfhEbZ35K+6f1BWouHTpHUO8QPjy6jnJ1GV/N0X/eLd39EfMHzQbgu/lfMuWjmVhYWXI5Ulpy/ND+Y/Tu15Oj0bvEkuMzF1S17Y78hUFhI3BtqmLm7Cnciktg1+GfTReKzL8ShXGFtf+1P6xQFAmC0NDodz0Qt+pdQX/3cJ4gCLsVCsUPQBDi9rxEYKrBSVRNCBm9a/9wnuqwWEXumu9TD+lZM/7xu7jsPfjhHR9Au8SdHFS9UGv7Phnign7Vu/YaaxJFjax+tY+n8/4jxLftX2t7AN+YfXWKZ7tEsSJObItBD+lZM21u7ibG5+mHd3wAbRP+qpcxPeFWu+eTALql/Q7Aqaa1f/atS+o2Sk/V7QPDqssLlJ6p/h1Zf8u+k7hVrvTs77XX6CjGsfTYD7XX6DkWdeQ3D+/4AKzDJtV5PABSOj9Zaw2P04cA6rRWfWP28aubaYneR+H5tM31EouyuOO11mjQUrzjVJFt+p6Xv4tFEx/Kk0zfA/MoWHqFoN5U+60/1hPFK+H1MS/2ql6stcaAjK2oNy94eMcHYD16Ceqj39bevtcEgDqtVesw8ckA9ZZ3H9LzARojF6HevvLhHWuyHyaW0Vb/tvQhPR+g8Zy47bIuOdyqywuod5m+K+mR/HjqdV72HvHwjjWwLlF8f9xYr9rnix+SxHzh5WS6rfXvkpRzGcRih489GzzGPNYPk01N+fGxjOM/dqfJ+ISp8nfHqWHCCYIw9n/cKRkZGRkZGRkZGRkZGSP+75YHkpGRkZGRkZGRkZGR+V/gsXqmSUZGRkZGRkZGRkbmfw7hsdz89vgj32mSkZGRkZGRkZGRkZF5AP9YIYj/Jf7VBycjIyMjIyMjI/PY8H/iHs56z8e7EMS0O49nIQj5TpOMjIyMjIyMjIyMjMwD+Nc/05QzuPYlsp12iiXHE54If0jPmvG5EsGN1gNrbQ/Q6vqeeilP/YZ37UvFfpK4FYC858NqreHwayR3OvaptT2A59mDxLUZ8PCONdAydi9Q9zG9Hdiv1vYAzS/t55Cq9iVWn8wQS6xe9BrykJ41E5T0J1D3kvp1KdMNYqnuupa8BSi9+FftNYLEEvLqfWsf0rNmrPvPqJdY1EeZ7YywsFprqCIjAUgK6VtrDa/zB9jmOqrW9gDPpv9UL/O7/M6lWmtYeoovLq5LyXBLr5A6lSwHsWy5+ru5tba3Hi+Wtq6PeXHM9blaa/RM/61OZbZBLLVdGrW51vZW3cVS+HV9vQDUQ8nxg1/W3r7PFNGHPWtqrzHwVQBKz+2otYZV+6F1KnsOYunz+igXPt679q/g+C5RfGVFa5eOtda4nnn24Z0eE3QP7yJTDfKdJhkZGRkZGRkZGRkZmQcgnzTJyMjIyMjIyMjIyMg8gH/99jwZGRkZGRkZGRkZGRF5e17tkO80ycjIyMjIyMjIyMjIPID/L+40WYR0wvY/M0GppHT/Lkp/+0nS3qDPAGwmTkeXkwVA6a7tlEXskvSx7t4Bp/9OR2GmpGDbXu5trP7Bddu+PVB9spCUF16h/NpNSZtNj/ao3pkGSiX3fttL7le/VqvRMLw77mvmk/jcq5TF6DUcewfScukEFGZKUjcfIumzPyR27uP64jGpP4JWh7a4lOtzvqQ47q6kT+vQQIYtHI/CTMnpnw9x8Is/Je0+nVozbOF43Fo344eZa7i057SJf+ZBnbCZOAOUZpQd3EXZDmk8LcMGYD12GrrcbADK9myn/JA0nlZdO9J49iugVFL8x24Kv9tabSyse/ekyar3SB83nYrYOEksXeZNr4pl3te/VB/Lft1pumYBSc/NpOyqdDzqY0ytu3XA8b8vo1AqKdy+h3vfVK9h06cHqo/f5e7IVyi/Fidpc+wdSIulE1GYKUnbfNBkXJuO62c0rhsk7Y1CQ3B/9yUUZmbkbI0g84vfq/XBfmBXmq9/mxtPz0J95Zb0GHu1x3XBFBRmSvJ+jiBnQ/Vzs1H/bniue4eEoa9RaqQRFRPP+1v2odMJDOsZxORB3SXtqTn5vLvpL/IKS7C3tWL5S0NROdrp7S/f5P3Nu0X70BAmP91Lap+dz7sbt5NXUIJ9Q2uWTx2OytFe6sPF67z/7Q50Oh3DnuzM5KHSwiOpWbm8u/5n8gqKsW9ow/IZo1A5NZZqXEti1bajoh9d/ZnUr4OkPS23kAU/7qdQXYZOEHh1cDd6BnjXaywA7MOC8V4yCYVSSeaWA6Su3S5pdxkbjuuEgQg6cV7cfvMLSbtlp040mjEDzMxQ79pFyU/SdWo1YACNpk1Dmy2uU/X27ah3Ga3Tbh1xnPMymCkp2r6Hgm+rX6c2T/bE+cN3SRv9MuWx0vmt6t2OdkvGoTBTkrj5MHFrd0ra/aYOwnt0GIJGR1lOAefe+BJ1SnZVe33M7wUfrOPo6fM4NrZn+9cfmdgKgsDKzzdx7MwFrBo0YOlbL+Pfwkdv/9F6jp66gGNjO7Z/9UH19uu+49jZi1g1sGTpnOn4t2gu6TN/+cccjTqDo0Njdvy4vlqNFZ+u59jJs1hZNWDZO7Pxb+Un6RMVn8Gq/ZfRCQLDAr2Y1K2VpP2D/Zc5myTGrlSjIbe4nOOzn5b0qY954dA7CJ8lYs5K33yQlLXSQgKu48JpOlGfs269uYGSuBTpsdxIYdXO0+KxdGzJpLB2kva0/CIW/HKMQnW5uM4GtKdna0+9/ZVbvP/TPnSCjmE9g5n8VA+JfWp2Pu9u+rNyjVmz/D/DTNZYfazTqJtprNp7XtQI8WFST3+j4yhmwY7TFJaWo9MJvNo3kJ4tm0o1rt5m1a+HxVh0a8uk/p2lGrkFLPhuL4XqUlFjaE/CDVJbVGwSq7YdF2PRxZ9JfdtL7fMKWbD5oJizdAKvDu5CT39vqQ+XbvD+93+Kx9G7I5OH9JbGIiuPd7/8VZ87X37BNHfG3WXVrmhRo4Mfk0Lbmsbityh9LPqH0LOVO8aMfW8ygb1DKFOX8eWctSTFmBZS8W7rw5SPZmJpZWnSNvrdSQT2DqFcXc5Xcz4j6ertau1f+nAGllaWXDpsWujlnWWz6dW3O6XqUt6euYhrV25I2q2sG/Dp1ytp5u2BVqvjcMQxEw2Zfx//2EmTQqHQAlcqfYgFxgNOwPeACvEdS18KgrDawGYm8AqgBXYJgvDWQ/+QUonttNcpWDAbXU4W9h9voOJ0FNo7SZJu5ccOUbxhdY0aTd6ZQdqUuWjSs3Hf+hklh09SkZAsPSYba+zGDKP0Umy1GqqFr5AyaR4VGdl4/bqaokOnKY830rC1xmHsM6gvXjeyV9Bq5SQujFhGWWoOHfetIHtftOSkKH1bFHe/PwBAk/7tabFoHBdHrtBrKxUMXzyJ9WOWkZ+ewxt/Lidm/zkybuk18lJz+GnOF/T+j/SD1vA4bCa/RtGSOehys2i0Yj0V0VHoUozieeIw6o01x9PhrVfJnPEW2owsVN+tQ330JJrbUg2FjTUNX3yWsivXTOxdFrzC3cmVsfxlDcWHT5nG0saaxuOGoq5hPOpjTJ3mzSR96n/RZGTT9Ke1lERWr2E/ehill6vTUNBq5WQujFhKWWoOHfatIGtfNCUG45qx7Tip3+8H7o/reIkPHkumEj96IRXpObT88yPuHThD2c070j9ja43zxCEUn5cm/vsabu9NJ2n8fCr36IQzAAAgAElEQVTSs/HZ/gmFB09RfstUw3HCM5RcuG4iodXpWL55DxtmjUblYMeopRsJC2qJb1Pnqj4f/3KQwV2fYEj3QE7H3mb1tkMsf2mo3v77v9jw1nhUjnaMem8DYcGt8XV30dtv3cfg7kEM6RHM6WsJrP71AMunDpf68M02NrwzFZWTPaPe/pSwDgH4erjqNX7YyeBeHRgS2pHTMTdZvWU3y2eMkmis+DWS9a8MRdW4IaM//JnQtj74ujlW9fkq4izhwS0Y0fMJ4tNymbHhT/YETKi3WNwfk+bL/0Psi4soT8uh7e5V5O07i/qm/otnzvZjZP4QAYBDeEe83psosW/02mvkz5mDNisLx/XrKYuKQpskXWOlhw9TuLrmder435lkvvxfNBlZuP34OeojJ6i4bTq/G40aRtmV6ud34IqJHB+xAnVaDr33LiUt4jyFBvM7PyaRw/3no1WX03x8X55YMJIzUz+r8qHO8xt4pn8YI4cO4J33P6+2/diZCyTdTWfXd2u4HHuTpau/5qe1y/X2/UIZOaQ/76xaV7392Yui/aZPuHz9FkvXbOSnz6QVxYYO6seo4UOYt+TD6jVOniU5JZXdP2/k8tXrLPlwLVu++rSqXasTWLHvEutHdkdlZ83oTYcJbeGGr7P+S/yb/fQnHlvOxnM9I1/6R+ppXviueImYEYspS8slaO9KciOiJSdFWduOkf69ODcdwzvQ/L3xXB21zOBYdKz44xTrJ/dHZW/D6LU7CW3TDF+V/kv4V4cuEd6uOSO6tCY+I58Zm/azZ65nlf3yH/ewYfYYMV8s/pqwoFb4uhuusf0M7haoX2O/H2T5f4ZJfKjrOtXqdKzYHc36sb3FMflqP6Gt3PF10V/M+eroVcIDPBnRsQXxmfeYsfkIe1oOkWr8fJD1rz6HqnEjRr+/mdB2fvi6Oek19pwivH1LRvQKIj4thxmfbyP8NQP7346yfvoQMWd9/CuhbZvj62qYs6IJD/JjRI+2xKfnMmPDX+x511sai0072PD2S2LunL+WsBB/fD1U+lhs3sXgnu0Z0qs9p6/eYvXPe1n+8osSjRU7z7B+Yl9UdjaM/mIPoW088HUxGNPDlwl/wosRnVsRn5nPjO8OsedNadW8wN4hqJq7MSf0FXyDWzJx6RTeG2paOXLCsqlsnPsF8RfiqqrnAbQLC8G1uRtvhc3AN7gF45dNYfHQt03sxy+dwqa3vyD+wk1mf/uOpK1Xn254+TSjf+dnCWzflndXzeWFgRNNNDat+5HTUeewsDBn0+/V54bHlcf6JU2PMf/k9jy1IAhBgiC0BcqBaYAGmC0Igj/QBXhFoVD4AygUit7AM0CgIAgBQPWfPEaYt2iDNu0uuow00GgoO3oIi849Hm5oQIMnWlGRnIomJR00Gor3HMG2dzeTfo4zxpO/8WeE8nKTNqt2LalITqUiJR0qNBTuPkLDPl1M+jV5dRy5X/9qomEX4of6dgalSZkIFVoydpygyQBpaUxtkbrq/2Y2DTB+cXGzID+yk9LJuZOJtkLLhZ0naBsuvYqel5JF2vVkE9sqXb/W6NLvossU41kRdQjLDt2r7VsTlgGtqbhzF+1dUaNk/2GsQ03jaT9tIoXfbzWJhVW7VlQkp1XFsmD3EWyf7Gpi3+S1ceR9/StCWYVJW32MaYO2rai4k4rmbqXG3khswkw1HF6ZQP6mnxHKTDXsQvwouZ1eNa6ZO07g/MBxtQKDsbEJakFZYhrldzIQKjTk7TyGfT/pVUoAt9mjyVz/e7U+WAe2pDwplYo7Yjzv/XWURn1N56bzG2PI2fBbtRoxt1PxdHHEw9kBC3MzBnQKIPKi9I5DfFoWndp4A9CptbekPSYhBU+VIx4ujliYmzOg8xNEnpeenMXfzaRTG/Hqf6c2zU3aY24l46lywkPlJGp0Cyby7FUjjQw6BYhX7zsF+BEZHSPVSMrA07kxHk3ssTA3o39ISyKvSK9yKoDiUjEGRaVlONvZ1mssABoG+1GamEZZsjiuOX8cx6F/J0kfw3mhtGkgmRcWrVujvXsXbZq4xkoPHaJB90dcp21boUlJRVO5Tov3RWIdZqrR+OUJFHxb/fx2DPaj+HYGJcni/E7ZcRK3/tKr4NlR19CqRdvcczexNjhBrY/5DdChnT/2jRrWeKyHT0QzpF8vFAoFgf4tKSwqJisnz8C+zUPszzGkX0/Rvk0LCotLJPYAHYKewN6uUc0ax08xZEAfUaNtGwoLi8jKzq1qj0nNxdPBFg8HWyzMlPT39yDyZlqNenuupTAgwFPyu/qYF42C/Si9nU5pciZChYasHVE49n/wZ5ExMXey8XRqhIdTI3GdBfoQec3oZBzDdVaOs5213j7hLp4uDni4VK6xzgFEXpSeMMenZkvX2AVpe32s05i7uXg6NsLDsaF4HG2bEXlDurtDoVBQXKYRj6OsAudG1lKNxPTKnNNY1GjfishLt0w17sdCXYazvT7nxCRl4tnEXp+zglsQeUV6Z0USS3W5xB4g5tYdae7sGkjkOenFSjF3+oqx8Pc1aY9JyamMReWYtvMiMlZ6cUOMhfh5XFRagbOdDcaE9OvE8d8jxb95IQ4bO1vsXRwkfexdHLBuaE38hThT+/CORG07Uml/E5tGttg7S++I2Ts3xqqRDfEXxJ0j9/vfp8/AUP74Rby7eulcDHb2jXB2cZL0KVWXcTrqHAAVFRquXa7+go3Mv4vH5ZmmY4CfIAhpgiCcBxAEoRDxDtT9e7fTgZWCIJRVtmf+HWGlUxN02fquupwszJyamPSz7BaK/ZpvaDh3EcomzpI2c5cmaNKzqn7WZGRhppIuIMs2fpi7OqM+dqZaP8xVTahIM9BIz8bcSKOBvy8Wbk0oPmJa69/K1ZHS1Jyqn8tSc2jg6mDSz2NiOF1Pr8ZvwWji3vlW0tZY5Ui+gca9tFzsVY48CkpH56ptjAC63CwUTs4m/Sw796LRhxuxnb3IpN3MuQnaDL2GNiMLM2fpmFi0aoGZypnSqGq2B7o4GY1HNhYmsRTHo/hIDeNRD2Nq5tIErYGGNjMbc5X0OCxbP1ijgasjZSbjajom7hP70/X0GnwXjCbunU1Vv7dwdaIiTb+VqSItGwtX6XFYt/XBomkTCg5FV+uDuUqqoUk3jadVgC8Wbs4URVb/HorMvEJcHfRXvF0cGpGRVyjp08pDxcHKOwEHz9+guLSc/KISvb3BVjsXRzsy8gqk9s1cOVj5QX3wXCzFpWVV9gCZufdwNdgu4uJkT0bePamGV1MOnrkiapy5QrG6jPzCYr1GfjGujfVfjlWNG5J5r0iiMW1gZ3ZF3yB8wTfMWL+Tuc9J3wVX11gAWLo6UW4wL8rTcrB0M50XqgkDCDqxjmbzx5G4YGPV75XOzuiyDNZpVhZmzqbrtEGvXjhu3Ij9okUojdrNnZugSdfnTm1mFmZGXxwsW/thpnJBfdx0nQJYuTmgNjgOdVqu5KTIGO9RvUk/pH+vUn3M779DZnYurgY5SOXsRKbBCctD7XNycXXW+6Vq4khmzt+3B8jIysHVxcAHlyZkZOmPPbOwFFeDEwdVI2syC0ur1Uq9V0JqfjGdvKRjWh/zooGbI2Wper/K03JoUM2Yuk0cQIdTa2m+YCzx72yUtGUWlOBq8MVdZW9DZkGxpM+0vsHsuhBP+PKfmbFpP3OH6C/kZOYb5QsHO9M15qni4DnxwsrB89dN1lh9rNPMAjWuBl/8VXbWZBaoJRrTwtqy63Ii4R/9wYzNR5g7SHrRIDO/CFcH/cm0yqGRac55qiu7zsQSPm8DMz7fxtwX9HvzMu8V4epgnLOMYjmgE7vO3SD83W+Z8eVfzB3eU+pDnlHudLQnI7e63CleZDp49qpp7jQeUztbMu8ZxeLJduy6eJvw939nxneHmPu06TuRHFwdyTWYX7npOTgafU9xVDmSm55jbCraqxzJMbJ3MMoZDq5O5KXp7XPTpFoqV2fSUjOqfk5PzUTl5kJNNLJrSO/+PWtsl/n38I+fNCkUCnNgIOJWPcPfewPBwP1P45ZAT4VCcVqhUBxRKBTVvoFMoVBMUSgU0QqFIvrLL//ei+PKz5wgb/IL3Ht1EhUXo2n4+rxHPQic3pxKzoe1f1EdCgUuc6eQ+f5XtdcAUjZFcLLza9xa+hPN36j9y+LqQkX0Ce69/CKFcyZTcSka2xmmt8YfiEJB4zemkf+p6b7/v2vv/N8pZNUllvU0po5zppH70YaH930Idzft42TnV4lfuhnvNx7hBX4KBe7zJ5O69Jva/3GFAtW8l8hY/nXtNYBZI/oSfSOJEYu+4lxcEi4OjVAq/34KmvVif6KvJzJiwTrOXU/ExcEOpULxaD6MGUz0tQRG/PcjzsUm4OJo/0g+AOw9F8eQzq2JWDKJtdMGM/+HCHS6R9vsUNdY3Cfj271c7PYyyct+wP21R3vpaNmJE2S/+CK5kydTHh2N/duPvk4dZk0n7+NarlMjPId3xyGwOTfXPcJLiutjfv8L2Xcthb6t3TFTPtr6gHqYF5WkbdpLdJcZ3F76I83eePQX4u69lMCQ9i2ImPcCayf2Y/4vRx9pnc0a0U9cY+99ybkbtVtj9bFO915JYkhQcyJmP8Pa0aHM33bqkfPF3ujrDOkSQMTyqax95Vnmf7sbne7v1z/be/4mQzq1JmLRBNZOeZr5Px549Jw1+imirycw4u3VlbnT7tFjcTmRISG+RPx3OGvHP8n8X6Me2Y/HDTMzMz7asIwfvqr9C9r/CXSKx/vf48o/WQjCWqFQXKz8/zGg6lKUQqFoCPwOvC4Iwv3LzeaAI+K2vY7ALwqFwkcw2kcmCMKXwP1vusK9OS+jbKK/QqB0ckabk43EplB/RbssYhc2E6ZJ2jWZ2Zi76q+2mauc0Wbor0wobK2x9PPG7RvxAWGzJo64fraY9JkL9RoZ2Vi4GWi4NkFjoKG0tcayhRfNvl9VqeGAx7p3SXl5EQCl6blYNdVfLWnQ1ImydOn2D0Mytp+g9fsvSX6Xn5FLYwMNezdH7mU82tVQXW4WSoM7R0pHZwSDO08AQpE+nuWHdmEzdqqkXZuVjZlKr2GmckZrcEVVYWODhW9zXNZ/LLY7OeL80RKyZi8AQJOZYzQeTagwimWDFl54GsTSfd173H35vao+9TGm2sxszAw0zFyaoMnINtFw/frDKg3V6sVkvKbXKEvPpYHJuNY8JhnbT9Dq/f9U/VyRnoOFm/7qtIVbEyoMrsApG1pj1coLv63i8wTmzg74bHyHhMn65ws0GVINc9dq4tnSC6+fVlZpeG5YyJ2pi6v6uDg0It3gzlBmXiEqB+lWJJfGjfjklecBKCkt58D569jZWOntDa5sZuYWoHKQPnDt4mDHJ6+OrLQv40D0Nexs9VfeXRztSc/RP8ORmXMPlYO0UISLoz2fzJmg1zh9WarR2Jb0fP1V3oz8Ilzspduytp+6xrrp4jMJgc3dKNNoyS9Wc/8aa11jAVCenoOlwbywdHOiPK3meZGz4zjNV0yp+lmXlSW5Q6B0dkabZbROC/Q+qnftouFU6TrVZGVj7qrPnWYuzmgzDdeIDRa+3rh+JRZWMHNyxPnTxWS9rp/fpWl5WBsch7WbI+pqjsO5Z1tavTaUY88uQVeuqfp9fczvv4NLE0fSDXJQRlYOLk3+/l14FydH0rP0fmVk5+Li9Gh38VXOTqRnGviQmY3K4O6XSyMr0g3uYmQUqnFpZEV17L2Wwtv9A01+Xx/zoiwtlwZN9X5ZujlR9oC5mbUjCj+DnAXgYmdDusHdkIx7JbgYbXPdfvYm6yb1AyDQy0VcZyWl2CKuH0m+yCswXWMOjfhkxgigco2di5WssfpYpy521qQX6O88ZRSocbGTbr/bfiGBdWPEu9GBnk0qj6NMny8aNyTd4A5XRl6hac45EcO6V8SLoIE+TSmr0JKXl4eTkxMu9g1JzzPOWUaxPH2NdVMHi/bNXavJWUa5M/eeSZEdFwc7PnljXGUsyjhw9oo0dxqPaUExLvZGsTh3i3Xjxbtkgc2cJWO6dLeYRxIu38LRYH45ujqRa/Q9JTcjF0eju0cAi3d/yO1Lt3Bq2oSbBvZ5Rnel8tJzcDB4ZszR4P/bD23myoVruDXVP8/l2tSFjLTqNzct/mgeSQnJfP/lFuYtnVVtH5l/D4/DM01BgiDMFAShHEChUFggnjBtFgRhm0H/FGCbIHIGscy86T47IzQ3r2PW1AOlyhXMzWnQ60kqzkRJ+igc9B9ulp26mxSJKIu5gYWXO+buoobtwFCKI09WtQtFJST1ep47A8ZxZ8A4yi7Hkj5zoaTSWumVOCy8mmLhrgILcxoNCqXo0Kmqdl1RCfFdXyShzwQS+kyg9NJ1Ul5eVFU9r/BCPDY+rlg1c0ZhYYZqaDey90m3o1g31z/w3qRfMCUJ0v3udy7F4+ztiqOHM2YWZgQP7sbV/eceFkIJ2ls3ULp5oHQRY2HR/UnKo09I49lYH0+LDt3Qpkj3qpdfu45FM3fMmooaNv16oz6q1xCKi0nt9yxpz4wm7ZnRlMVcI2v2gqrqeaVXbmDh1RTzyljaDQql+LBRLLu9wO2+47nddzyll65z9+X3JNXz6mNMy67ewKKZgcaAMEqOSDWSw54jZdBYUgaNpexyLBmvLZRUzxPH1a1qXF0eMq5O/UIk41py6SYNmjfF0lOFwsIch8E9Kdiv3yqlKywhJngM13r8h2s9/kPJhRskTF4mqS6mvhyHpbc7Fh5iPO2f7kXRQQONohLiOo7iVugkboVOQn3hOnemLpZUzwvwbkpyRi4pWXlUaLTsPXOV0MCWkuPIKyypuqq4cXcUQ7vrv9QFNHc3sNew9/QVQoNbG9kXV11d3fjXMYb2Cpa0B/h6kpyeTUpmjqhx4gKhHQKkGgVFeo0dBxnaW/qcUEAzFclZ+dzNuUeFRsu+83GEPiGtgubm0JDTlQ+9J6TnUl6hxaGh/stBXWMBUHTxFlbN3Wjg6YLCwhynZ3qQFyHdGmnV3K3q/437tqf0tn5eVNy4gZmHB0pXcW5aPfkkZSek61TpqF+nDbp1Q5NstE6v3sDc0x3zynVq2z8M9RGDdVpUTEqf4dx9egx3nx5D2ZVYsl5fKKmel3cxnoY+rthUzm+PoV1Ji5DmHPu2XgR/MJmT4z+iLFu6JbM+5vffoXfXDvy5/yiCIHDpWhwNbW1wdjLd/lyzfQh/7j8m2sfefGR7gLAeXfhz70FRIyaWhg1tcTY4cQto6kByXhF384up0OrYdy2F0BZuJjq3swspKK0g0N30pK0+5kXhxVtY+bjRoJk4N52HdifXZG7qc5Zj3xDUt9Ml7QEeTUjOKeBubqG4zi4lEOovff7KrbEtp2+JczohM19cZ7biCYs0X2jZe/oqoUEPWGO7jjO0R5DUh3pYpwFNHUnOKeRuXpF4HDHJhBpVg3Ozt+V0grjVKyHrHuUaLQ62+ue8ArxcSc7M5252Zc45d4PQdr5SDYdGnL4hjkNCWg7lGg2OleMU0MyF5Ox73M0pEO0v3CS0rbdRLBsZ5SyNNGf5epCcnkNKZq6YO09eIrR9G2ksCgzy7x+HGRoq3ewT4O4kxuL+mF5OIrS10Zja23I6QZwLCZn3YyGO6fxBs5k/aDbnIs7QY3gYAL7BLSkpLOFepvQC8b3MPNRFanyDpeO1cNAczkecofuzoZX2LVAXlnAvS1oQ5V5WPqWFJfgGtwCo6g8w7MnRHNwTyTMjngIgsH1bCguKyMo03Q742txpNLJryPL5H5u0yfw7eaxKjisUCgXiHadYQRCMZ+EOoDdwWKFQtAQsgWwehk5L8fpPsVv0ISiVlB3YjTY5EevRk9DcvE7FmRNYDx6ORefuoNUiFBZStHqlVEOrI3v5WlzXL0dhpqRw+z4q4pNweGUcZVfjKIk8Vf3fNtLIXPIFHhuXgtKMe79HUH4rGaeZYymNiaP4cPXPBNxH0Oq48fY3BG+dB2ZK0rZEUnwjBZ+3nqfgUgLZ+87hObk/Dj2fQNBo0dwr5tqr0mouOq2O3xduYur381CaKTn9y2HSb6Yw4I3nuXMlgasHzuHZzodJG2ZjbW9LQJ8QBrzxHO+HvymJZ8nG1TR85wNQKik/vAddSiJWL0xEG3+DiugTNBg0HMsO3RC0WoSiQoo/N41n3qrPcF7zPgozJUV/7kGTkITd1AmUx96g9OhJHohWR9bSdXh8vQyUSgq2RVB+K6kyljclJ1AP0qiPMc1ZsRbXL1aAUknhDlGj8cvjKb8aJzmBqglBqyPu7W8I2vqOWEp+y2GKb6TQ/K0RFF6KJ3vfOTwmDzAY1yJiX/2czkc/rvIhZeEGfL5/D4WZktxfDlB68w6us0ZRcvkWBQeqf5bK+DjSF31Bs2+XoFAqyf9tP2U3k3F+fQzqKzclJ1A1YW6m5O1RA5j+6RZ0Oh1Duwfh5+7M5zsiCfBuSlhQS6JvJLFm2yFQKGjfohnzRg8wsDfj7bFPMf2D70X7XiH4ebjw+baDBHi7ExbSmujriaz5dT+goH0rL+aNe9rIBzPenvQs05d/iU4nMDSsE36ernz+y14CfDwI69CW6GvxrNmyGxTQvrUP8yYPNzmOuc+FMn3dn+h0Op7p4o+fmxPrdp3Cv5kLYU/4MGtoTxZvPcTmwxdAoWDR6L4oDLYJ1jUW98ck8Z2vaf3TQhRmSjK3HkQddwePN1+k+FI8eRFnUU0ciH3PduK8yC8i/rXPCIysrHim1VK4ejUOH4jrtHTPHrSJidhOnIjmxg3KTpzAZvhwGnSrXKeFhRSsNF2nue9/hsvnK0GppOjPvVQkJGE/bTzl1+JQP2ydIs7vi/O+pfuWuSjMlCRtiaTwxl3avPUc+RcTSIs4zxMLR2Nua0Xnr14FQH03h5PjP6ryoc7zG3hr2aecvXSN/HuF9HlxGq+MH4FGI97RGjE4nJ6dgzl65jyDxr0qlgx/82Wp/fI1nL0cK9qPeoVXxj6HRltp/3Q/enYK5uiZiwya8LpYsnzOVBMf3nx3JWcvXCY/v4A+Q8fw8uSxVT68MOwpenXtyLGTZxk4YhLWVlYsmfeGxN5cqWRueCDTt0ah08EzgV74Odux7sg1/N0cCGspnkDtvZbCAH93yZysop7mRfy8r2m7ZT4KMyUZWw5RciMFr7deoPBiPLkR0TSdNJDGvdohVGjQ3Csm7tXPpMdipmTukC5M/0bc2vpMhxb4qRxYF3Eef48mhPk3Y9ZTnVi8LYrNx6+K6+z5nlXHZG6m5O0xA5n+8WZxrfcIws/dhc+3HxbXWHArom8ksua3Q+Jab+nFvDEDTXyo6zo1N1Myd1B7pv9wBJ2g45lgH/xc7Fl36Ar+TR0Ja+3OrPAgFu88y+ZT4sWERUM7m+SLuS88yfS1v4s5p2tb/Jo2Yd3OKPy9VIS182PW8DAWb45g86HzoIBFYwdIYjF3eE+mrxfLhT/TuY2Ys3afFnNW2+bMGtqdxT8fZvMR8XnBRaP6GPlgxtsTnmH6yo1iLMI64ufhyue/Roi5s70/0bHxrNm6V4xF6+bMm2hQ7fO+H4M7Mf3bg+gEgWdC/PBTNWbdgYv4uzsR1saTWYPas3j7KTZHiZU2Fw3vZjJPLx06R1DvED48uo5ydRlfzVlb1bZ090fMHzQbgO/mf8mUj2ZiYVRy/NLh87TrHcIHRz6nTF3G12/qK2Yu3v0hCwfNEe0XfMV/KkuOX468INE4ciCKXn27E3FmO6Ulpcx7Tb+rYvuhzQx7cjQqNxemz5pMfNxtth38kf9ryC+3rR2Kmqqk/Y//YYWiSBCEhka/64G4Ve8K+jGdJwjCboVCYQl8AwQhVtubIwjCoYf8GSFncOhDutSM006xokrCE+G11vC5EsGN1gMf3vEBtLq+h4OqF2pt3ydD3Gv7hveLD+lZM58kiu9nyXs+rNYaDr9Gcqdjn4d3fACeZw8S12bAwzvWQMvYvUDdx/R2YL9a2wM0v7SfQ6oRtbZ/MkN8L9VFryEP6VkzQUniO7qu+T5Vaw3/+F2UHvuh1vYAVj3HUnqq9vvBrbqIa6P04iM8C2OsESSegKn3rX1Iz5qx7j+jXmJxqmntn0XskirenM8IC6u1hioyEoCkkL611vA6f4BtrqMe3vEBPJv+U73M7/I7lx7Ss2YsPcU7C+VJpu9x+dsaXiFUZJu+Z+ZRsGjig/o707LLfxfr8eKJT33Mi2Ouj/580n16pv+GevvKh3d8ANbD5lIatbnW9lbdRwPUaa1a9RwLgHrLu7XWsB65CPXB2j8za91H3I6r3rOm9hoDxQsVped2PKRnzVi1H4r6t6UP7/ggP56bz1iv2ue9+yXHx3s/wvO+RnyXKL7/rbVLtY/L/y2uZ54FsVjhY88nzcY81g+TvZH842MZx3/sTpPxCVPl745Tw4Sr3L435n/aLxkZGRkZGRkZGRkZGUP+8ep5MjIyMjIyMjIyMjIyjzOP1TNNMjIyMjIyMjIyMjL/c8jPNNUO+U6TjIyMjIyMjIyMjIzMA5BPmmRkZGRkZGRkZGRkZB7AP1Y973+Jf/XBycjIyMjIyMjIPDY8llXfjPnwMa+eN0eunvfPsL8Opbr7VZbq3uRe+6J9E+/+yE7XkbW2BxicvoUlXqNrbb8gSSzPOsm79qViv0n8DYCz7sNqrdHx7nYiVLUvew4QnrGVE261LyvaLU0sK1rXsrmRqudrbQ8QlvFrvZSAr4+y5XXVKH6nbrGwXfZrnTRsl/0KQMnqabXWsHltPQCFrw+utUajT3dS8sXMWtsD2Ez/jA+b1T7fzEkW3xdSH3nvx6a192NM6o98VIfjAJid/COfedZeY+YdMRbq79+utYb1uBWixqa3aq8xcVWdyoWDWDK8LmXLLZr4APCrW+0/R55PEz9H9tYhhw/I2Erx0rrNC9v5P1LywaRa29u8+Q0AxSJPzM0AACAASURBVItrHwvbhWIs6jq3ipeNq70P73wPQMmXbzykZ83YTPlE1PjU9L1if1vj9Q2o/6rby12tn57FmDqUHP+xsuR4fZQt7+7+ZK01ou4+7C04Mv/XkbfnycjIyMjIyMjIyMjIPIB//Z0mGRkZGRkZGRkZGRkR3WO5+e3xR77TJCMjIyMjIyMjIyMj8wDkkyYZGRkZGRkZGRkZGZkH8P/F9jyn3oG0WjoBhZmSu5sPkfjZH5J2j3F98ZjUH7Q6NMWlxM75kuK4u5I+7mHt6Lx4LAqlkrgtkVz5fKekPWDKQFqODEOn0VKaW8jxWV9SfDdH0se5dyBtl4xDYaYkefNhbq39U9LuNa4v3hP7IWh1aItLufTm1xQZ+OEb2o7+745FYabkwtZITnwh9aFZp9aEvzsGVetmbJu5ltjdZ0xi0TY0iFELJ6IwU3Ls54Ps/mKHpN3c0pyXPp6JV1sfivOL+GLGx+SkZEn62IUF02zxZBRKJVlbDpD++TbpcY7tj8v4gaATjyPxrXWU3kyR9HHqHUjrpeNRmClJ2XyIxM+ksfAY1xfPSeFVsbg25yvJmDTuHUTzxZPATEnmTwe5u3a7xF41LhzXCQNAq0NbUkr8m+tRx0l9cOgdhM8SMRbpmw+SslYaC9dx4TSd2L/Kh1tvbqDESMOxdxB+S0WNtM0HSf5MqtF0XD+aTqr0o7iUG3NMNVqHBjJsoRiL0z8f4uAX0lj4dGrNsIXjcWvdjB9mruHSntNGPgTSwsCHJKP53XRcPzwm6Y/j+pwNlBjN7/rQMGsRhOVTE0GpRBN9kIqj0lgAmLXtimWfESAI6NKTKPtldb3ZAyi9/LEMHQEKJZqrUWii90naLXo9j5lHS/EHc0sUNo1Qr58l/RutQ7B69j+gUFJxaj/lB38z8cM8qAeWA0aCALrU25T+8KGkPSoxmw+OXEenExja1oNJHZtL2j88cp2zd/IAKNVoyS0p59jL+geQvUPb8eR74lq/sjWSM+uka92jUyt6vzsW5zae/DVjLXG7z5r4WB95zy2sHR2XiHnv1pZIrq6V+tFmykB8R4UhaLSU5hRyqpq85x3ajt6VxxJTzbG4Gx3LTaNjaRbWjl6V9te2RHLOyD7oPwMJeDEMnVaLOqeQg3O+pNDIh6j4dFZFXEYnCAwL8mZSt1aS9g/2X+ZsopjnSjVacovLOD5HWhQkKiGDVQeuoNPBsMBmTOraUqpx4Apnk7NFjQotuSVlHH/jKSM/Mli1v9KPQK/q/Uiq1NBoyC0u5/jsp6va5y//mKNRZ3B0aMyOH9djjCAIrPh0PcdOnsXKqgHL3pmNfys/SR9V73YELxbjmfBTJDeMxrTF1IH4jOqNTqOlLKeA6FlfUZKSLenTpHcgbZaOh8r8fdsof3uO60szg/wdY5S/Acx82mHZf6y4Vi9GUnFC6odlv9EovfwBUFhYorC1o+RDfcECpXdbLPuMAoUCzeVjaM7slthb9H4Rs2atxR/MLVHY2KH+bIbUB99KH5RKNBciqYiS+gBg5t8Zy9DhYs7JSKZs++eS9vqYW2Y+T2AZPqYyFkeoOPmXNBZ9R6H0biPGwrwBCttGUh9uZ/HB4WvoBIGhbT2Z1NlX0v7h4WucvZOj96GknGMzwiV9lF4BYu5UKtHEHK8+d3q2MohnI9RfSItQRF1PZtWOE+h0AsM6t2ZSn2BJe1peIQu2RFKoLkMnCLz6VGd6tmmGMWPfm0xQ7xDK1GV8OWctiTGmRVC82/ow9aOZWFpZVmsfaGCfVIP9lEr7S4fPm7S/vngGXZ/sTKm6lGVvrCIu5qZJnyn/ncSA58JpZN+Ifi2fMml/nJFfbls7/rGTJoVCoQWuVPoQC4wHnIDvARViufAvBUFYXdn/Z+B+NmoM5AuCEPTQP6RU0HrlJM6PWEZpag6d960ga1+0JIGnbYsi5fsDADj3b0/LReO4MHKF3lelgi7LxrNv5EpK0nIZvHsxyRHnuHcztapPTkwifw5cgLa0nFbj+tBx/kgip6+V+PHEiomcGrEcdVoOPfcuIz3inOSk6O62KJIq/VCFtyfgvbGcHrWyyocBSyawefQKCtJzeenPJcQdOE/2Tb39vdRs/py9ga5Tql+8CqWSMYtf4qMxi8lNz2Xhnyu5uD+a1Fv6L/E9R/Sh+F4xb4fNpNPg7jw/dwzrZ3xicBxKvJZNIW7ke5Sn5eC/exX5EWckJ0U524+S9YOYcBv360izdycSN2aJJBZtVk7iXOWYdNm3nKx95x44Jq0WjeX8yJVVPvgs/w9XX1hMeVoO7fa8T27EWclJUfa2Y2R8HwGAQ3gHvN+bQOyopZLj8F3xEjEjFlOWlkvQ3pXkRkRLTmiyth0jvVLDMbwDzd8bz9VRyyQaLVZO5tKIJZSl5tJ+3wqy90k1MrYdJ/X7/QA49e+A36LxXB6p11AoFQxfPIn1Y5aRn57DG38uJ2b/OTJu6WORl5rDT3O+oPd/9F+eDGPZauVkLoxYSllqDh0q57fhCY2hD036t6fFovFcGrm8fjUUSiwHT6Z00xKEglyspq9AExuNkKWPhcLJFYvQYag3zIfSYrC1qz97AIUCy7CRlG1fjVCUh9WLb6NNuIyQm1bVpeLor1RU/t88MAyls6eRhhKr56ZR8sUChPwcbGZ9jCbmNLqMO/ouTdyw7PscJavfAnUxiob2EgmtTmDl4Vi+eLY9qoZWjN5yilAfZ3ydGlb1mRPauur/Wy4mcyOzwMAFBX2XjufX0SspTMtlzM7FxO8/R45BvilIzWHP7A10nDqIaqmnvNdp+XgOvijmvYG7F5OyT5r3cmMSiRu4AK26nBbj+hC8YCTHp62VaPRZOp7fKo9l9M7F3Np/jlwDjcLUHPbO3kCHao5FoVQQtnQ8O0atpCgtlxf+WkzC/nPkGdhnxSTy81ML0JSW03ZsH7q/M5K9L+t90OoEVuy9xPpRPVDZWTP6m8OEtnDD11k/f97s104/HmfjuZ6eL/FDqxNYEXGZ9S92Q9XImtHfHiG0hSu+TQw0+j6h14hO4HrGPVONfZdYP7K76Memv+FHhtSPoYP6MWr4EOYtkZ6k3+fYybMkp6Sy++eNXL56nSUfrmXLV5/qOygVhCyfwNEXVlCSlkvfPUtIjThPocG8yL+SxIEB89Gqy/EZ14d280dyatpnEg3/lZM4Wzm3uu5bTqZR/k7dFsUdg7nVetFYzt3P3yCu1YHjKd28UlzvkxejiTuHkK0f1/L9m6v+b96hH0pXb6l9vzGU/fIRQmEuVmMXoo2/iJCjt684vFW/1oP7oFQZfTlXKLAcOIHSH1eIPry0BM2N8wjZ+uNQOKqw6D4E9ab3oLQEbKQ5pz7mFgoFlgPGUfrTKtGPSYvQ3DwvjcWBn4xi4YVFYK8qH1YevMoXz3VC1ciK0ZujCPVzwddJf2I1p7e/3ofziZJ8U+VD75GUbftUzJ0jH5Y7e6N0keZOrU7Him1RrJ/6FCp7W0Z/uo3QAG98XR2q+nx14DzhQT6M6BZAfHoeM77ezZ750iqGgb1DcG3uxuzQV/ANbsmEpVN4b6hp9cmJy6by9dwviL8QV1U97769qrkbcyrtJ9ZgP2HZVDZW2s/5br6kreuTnfFo7s4LPcYSENKGOSteZ8rgV0w0ovaf5PdNO9h6/AeTNpl/J//k9jy1IAhBgiC0BcqBaYAGmC0Igj/QBXhFoVD4AwiC8EJl/yDgd2BbTcKG2If4UXI7A3VSJkKFlvQdJ3Ae0FHSR1ukrvq/mU0DMHp3VZNgXwoTMyhKzkJXoSXhj1M0699e0if9RCza0nIAss7dwsbNUdLuEOxH8e10SpJFP1J3nMS1fwdJH42xHwavmWoa5EteYgb5d0Qfru48Rat+Uh/upWSTef0Ogq768vs+QX5kJqWTdScTbYWG0zujCAqXxiI4vCMnfo8EIHr3Sdp0e0LSbhvcgrLENMqSMxAqNOT+cRyH/p0kfXQGx6G0aWAczsoxSZeMicsAaSxMx0Tf1jDYD3ViepUP2X8cx7H/g8bUymRMGwX7UXo7ndLkTIQKDVk7oh6i0QBj7EL8UN9OpzRJ1MjcEUWThxyH8XvRmgX5kZ2UTs6dTLQVWi7sPEHbcKlGXkoWadeTTWzv+1BS5YOWzIfOb9NY1IeG0sMPXW46Ql4maDVoL0dh3kZ6HOYd+qI5vVc84QEoLqg3ewClyhvhXiZCQTbotGjizmLm046aMGvZEU1ctFTDqwW67DSEnAzQatBcOIr5E50lfSy79qfi+G5Qi34IRdIvxzHp9/C0t8HD3gYLMyX9W7oSGZ9Zox97b6QxoJVb1c+ulWv9XmW+ub7zFL7h0rVekJJN9gPWen3kPSejvJf4xyk8jPJexolYtGox72WfN817rkG+5Bscy42dp/B7hGNRVdoXVNrH/XkKHyP7uydj0VTm3vTzt7B1lfoQk5qLp6MtHg624nj4exAZl0ZN7Ll6hwEBHlKNtDw8HWzxaHxfw53Im+k1a8SmMMDf3dQPByM/bj7Aj2spDAiQfjHtEPQE9naNarCAw8dPMWRAHxQKBYFt21BYWERWdm5Vu2OwL0WJGRQnZyFUaLnzxyncjcY068S1qjHNPX8La6MxbVxN/lY9IO+ZG+VvAGVTX3S5GQj5WaDTor16CvOWUj8MMQ/oiubqSb29mw9CXibCPdFec/00Zn41X0M1a9MZTaz0Dr3S3RddnpEPraQ+mIc8iSZ6v3jCBFAizTn1MbfEWGTq/bh2CvOWIQ+IRRdJLGLS8/FsbINH48p808qNyFsZNdrvvZ7KgNZukt8pXZsb5c5ozHwDa9Qwa9URzQ3pHeGY5Ew8nezwcLLDwtyM/sF+RF5NlPRRoKC4VDz1Kiotw9nO1kS7fb9OHK/8DhJ/IQ5bO1sauzhI+jR2ccC6oTXxF+JM7EOM7G3sbLE3src3sr/f/z49+ndj72/iRcKr52NpZN8QJxfpOrjflpOZa/J7mX8vj8v2vGNAO0EQ0oA0AEEQChUKRSzgDly731GhUCiAEcDfKqbfwNWRslT9Vo2y1BzsQvxM+nlMDMdr2lMoLcw5N3yJpM3G1YHiVP3CKEnLxTnY11iiipYjQ7l7+JLkd1ZuDqgN/ChNy6FxNX54T+yHz1TRj5PP6e+M2Lk6UpCmty9Iy8X9AT5UR2OVI7mp+m0WeWk5+AS1qLGPTqtDXVhCQwf9h7SlqyPlBhrlaTnYBku3qQC4jB+IasoQlJbmXB+xUNJm5epIqWEsUnOxryYWngZjEm0wJg1cHSm/a+hDLg2DW5jYu04YQNOpg1FYmHP1+fckbQ3cHCkzOo5GIaYabhMH4D71aZQW5lx+zkjDZG7lYvf/2Dvv+Ciq7v+/Zza992wSQu9BOtIhtAD6KCCKIkpViiIIdooNER9UHlEeRCyAgtixUULvRXoREkoCIb33Tdmd+f0xIbuzuwHZ5PuVn9/5vF68XmTvPZ8599xzz52Ze+dcOxzhEwYTOfVfCM5OnB75hqrMLzSAfAuOgrRc6re3tUVNsO/ftjpETBhM/an3Ijg7cXLkm3XOIfgEIBeYOeTCXMRINYcYFIYEuE1eoGx92/k9pkun6kQeQPDyRy7KM3MU5yPq1dviqut6ByD6BiFdj1NfwzcQKc/sF1J+DroGav8WQiIQAY8Z/wZRpHzLekxx5u0dmSVlhHq7Vf8d6u3GuXT1g9UNpBYaSC0w0CXSPCF76/0psog3xWm5hLW/vbFeV3Gv1CruBXWsWY+mo/uSulMd97ys2lJ0m23x1PtTbGUL/U3iXtQjfbm2W61DZlEZem/36r9Dfdw5m2L/Rie1oJTU/BLubhhycw5vd86m5lmLW3CUcneDYFsOn9vhKLHhuBUysnLQhwSZrxESREZWNuFVC5vu+gBKLbYulqblEngTezYaHU261Vzmqg9Qz2U1xO/6E2JoWBUvjlr5luDtj1xo7gO5KBcx3L4egm8ggl8I0tU/zb95+SEXWcrnIYY1ti/vE6iM9aQLVjrYiTkRah3EAL0Scya8psScPT9iunKmurwufEvw9kcuurkelm0R/IKRrlbfEpFZbB1v3DmXlm9PXIk3hQa61A9S/S54+qljZ1HebcfOzIJS9H7m1fRQX0/OJqlfFk0d3Ilpn2xi/f5zGCoq+WSK7e4Jf30AORZzc256Dv6hAeRnmvXzDw0gNz3HRvaGfK6VfEBoAAUW8gFW8rlpVp9S6IPITDXrnpmWRbA+6B/1gHRHn2x7B+NvTwQhCIITMBRlq57l7w2BDsARK5HeQIYsy7YbTGuB5FVbOdB1Jpfe+ppGsxw/IK3xAz0JbNeYsx9vdEj+6qpt7Oz2LBfe+ppmsxw/SPbvRuaazZztOY3rC78kfKZjB5deX7WV/V1ncvGtr2nsgC3SV2/hRPenubbwK+o969iBuGmrtnCs23QS31pL/VmOHYibuiqWI12fIeGtdTSY5fjBvLVByqpYDnWdwZW31tHQQR1qzSHqEIPCKPvsdcq/W4rL8Cng5vG/J28BXfPOGC+dsFld+SsQRB1CcDily+Zg+PI93B6eDu62b0z/CmLj0xnQLBSd+Pfkf62ruNfogZ4EtG3MeQfjXl2gxYiehLRtzIkVjusQ++d1BraKqFV/xF5IYWCL8NpxnE9mYMva6VFb1B/ZE/92jYlf/vutK9tB0qqt7K2K301qMZc5te6OKe4Ph8YqgK7l3cqKsiPyog4xIJSyNW9R/tMyXP71BLg6FnPqwrecorphunDUYVvExqUyoJm+VjroWnRxOHZuOXmF+7s0Z+urj7HsiaHMW78TqYbVcg0a7kT8nQ9N7oIgnAKOAUnA5zcKBEHwQtmC96wsy1abbxkNrK+JVBCEyYIgHBME4djKlSspT8/FNTywutw1PJDydPtv9wDSNxwkeKh6G0tpeh6e4eY3wR5hAZTY4QjrHUW7GfezY/wSpAqjqqwsLQ93Cz3cwgIpS6tZj5SfD6G32PJQmJ6LT5hZ3icsgKKbtMMe8jNyCQg3v2HyDwskLyO3xjqiTsTd24PivKLq8or0XFwsOFzCAqms4Y0PQO4v+/Gz2r5Xlp6Lm6UtwgMoT6/5DY51n5Sn5+ISYalDABU30SH75wMEDFHrUJ6Wi6tVO8rTatYh6+cDBFptb7L1rQDKb6JH5oYDBA1V65GfkYufBYdvWAAFGX/9bZZ9/65ZPsOOf9cFh1yYi+Br5rBeOVLq5CgTvmRSttbkpCEGhtWJPIBcnIfgbd6GIXj5IRfbHyNOzTtjumibPEEqyEH0N/uF6Bdoo4eUn43x3BFFj9wMpKxUxKDw6vIQTzcyisqq/84oKiPY03Z7J0DsxXTV1jyAovQ8vC3ijVdYAEUZtzfW6yrueVjFvVI7MUvfO4o2M+9nt524V2zVFu+wAIpvoy0l6Xl4Wdmi2E47IntF0fmZ+/l9oq0OId5upBeZt4tlFBoIsVgdsMQWO1vi7HIUGQixeLuv5khhSOt6Nr+HeLuRXvhXOZJttnH9FYQGB5KeaX7LnpGZTWiw2Z8N6bl4RJj9wiMsAIMde4b0jqLVzGEcGPe+jT3L03PVc9kt4nfahoOEWMeLojwEH3O/Ct4BqpUOS+istqOBsooseFvK+9c81lvejemC9btXZXXLJuZY6SAX5mKKP6GM9fws5Nw0xEB9dXld+JZclIfgfXM9bkDX2tYWIV7W8cZAsFcN8SYujSEtw21+l0vy1bHT2x+5xP5qlVPzzpjibZNMhfh6kJ5fbNajoIQQX/XLpA1H4ohpp6yitWuop7zSRH6JovvCTe+zcNP75GfmEWgxNwfobe9T8jJyCdAHYo23quQDrORzreRzreQDLO6tVm9dSU5GLiHh5hXBkLBgstLVyVA0/N/EnfBNU3tZlp+RZbkCQBAEZ5QHpnWyLKu+W6palXoA+LYmUlmWV8qy3FmW5c6TJ0+m8OQVPBrrcasfjOCsQz+8B1mx6u8YPBqZg2DQoA4YEtR7krNPJeDTSI9XZDCis47Gw7pxfas620pAVAN6vDORHROWUJZj/ZwH+aeu4NlYj3uVHuHDu5O+9biqjqeFHqEDO1CSaN4zn3o6gYBGevyqdIi6rxsXt6nlb4XE05cJbRhGUL0QdM5OdL2vJ6e2qW8cT207Ro+R0QB0vqc7cQfPqcpLTl3CtVEYLpEhCM5OBAzrRd5WNYdrI/NNoO/ATpQnqu15o0/cLfokM1bdFss+CR7UgVKLPik+dRn3RmG4VukQNKwXuVZ96mahg//ATpRZ6VB06jJujcNwra9wBA/vSa5VO9wsdAgY2BFDovobhqKTl3FvHIZbFUfI8J5kW+nhbsEROKijjW9dP32F4IZ6AuoFo3PW0eG+Hvx5G/1adPIKHo3Dqv07ZHiPW+pQaqVDXXBIKZcRA8MQ/ENA54SubU+McWoO0/mjiI2ilD88vBECw5ByM+pEHkDKuIbgF4LgEwiiDqfmXTAlnMEagn8ouHkipdlmVJKSLiEGhSMEhILOCacOfTCeU98gGM8exqmp8q2f4OmDGByOlGP2jSi9D0n5paQUlFJpkoi9mE50E/WWHIDE3BIKyyppF6ZOJJF+OgH/Rnp8q8Z6y/u6cWWbbXanm6Eu4l7OqQS8G+nxrNKj4bBuJFvFPf82Dej674nsHr+EcjtxL/10An6N9PhUcbS4zbZknE7Ar6FZvvn93Ui0kg+KakC/dyby+8QlGOzoEBXuT1JuMSn5JUp/nE+mb/Mwm3qJ2UVKf0TYfrsQFeZHUm6JBUcKfZvqbTlyiigsq6BdhL8tR7g/SXlWejS7PT1uhehe3fh1yw5kWeb0uQt4eXkSHGTmyTuVgFcjPR6Ril9EDutGqlXs9WvTgE6LJ3Fg3Pt2+7SglvEbQEpNQAzQI/gFg6hTHowu2vqFEBiG4OaJlKzeWCKlJSL4hyL4BiljvWVXTJdP2coH6JWxnnrFpkxKsaeDuh2m+GPVWetw90IICEPKM2/bqgvfUmxhbouudTeMF0/WYAsPpJTLqt+j9L4k5ZeY4018GtFNQm11yCmmsLySduF+tjqkX7WKnZ0xXTltU0+JnR52Y2dUZAhJ2QWk5BRSaTQRe/IyfaMaqOqE+XtxpCp5VUJGHhVGE/5eyouDufc8x9x7nuP41j/oVXUP0qRDc0qLSlVb8wDyM/MwFBtoYvVpwLwa5Aus5Aus5G/UBxgfM5m9sfsZ8uAgpV0dW1FcWPKP2poHICHf0f/uVNwp3zQB1d8rfQ5ckGV5iZ0qA4E4WZaT7ZTZhWySiH/lCzp+MwdBJ5K6fjcl8ck0efEhCk8nkBV7nMhJgwnofRey0URlQQnnZiy34Tg8bw0xX7+IIIpc+nYP+RdT6PD8SLJPJ3J92wm6zB+Ns6cb0Z/MAKAkJYcdE5aoOM7NWU239a8g6ESur99NcXwyLV58kPxTiWRsPU7DiTEE97kLqdJIZUEJJ2d8rJLf8upqHv3yJQSdyOnv9pB1KYW+s0eSdiaRi9tPENa2MaNWzsLN14NmAzvQd9ZIVgx6qZpDMkmsffUzZn85D1Ensv+7naReSmb4rIe5evYKp7YfY+93O3hyyQwW7f6IkvxiPnnGInMegEkiad6ntPj6NRBFsr/dQdnF64Q/P5rS05fJ33aU0PH34NO7LbLRhLGgmIRnP7SxZ9wrq6r7JGX9Lrt9Eti7DZLRhLGghHMWtsAkkTDnM1qvn4+gE8n4ZieGi9eJfOERik9fJm/rMfQTh+LXuy1ypRFjQQmXZiyzaceVOZ/RZv08hWP9Tkrjk2nw4sMUnbpC7tZjhE8cil8fM8fFGR/ZtOPSK5/T9pu5Sqru9bsojU+m4YsPU3T6Cjmxx4iYNBT/at8q5oKVHpJJ4sdXVzHlyzmIOpEj3+0i/VIyQ2Y9xPWzCfy5/TiRbRsz8ZPncPf1JGpAR4ZYbBOUTRIXX/mC9lU6pFbZstGLoyg6fYXs2OPUmzSkWgdjQTEXZvzXph215UCSqPjtc9zGz1XS5p7YhZyZjPOAh5FSrmCKO4bp0il0TdvhPvM/Sv0tX4GhuG7kAWSJit3f4jp8hsJx/iBybhrO3e5DyriGKVF5gHJq3sXuKtMNPcp+XIHH1DdAFKk8sh0pPQmXoWMwJV3C9OcfmOJO4NSyAx4v/xckifJfV0GpeTXWSRR5qV9LntpwAkmWGRYVQZNAL5YfukzrEJ/qB6jY+DQGt9CjhD11f+yYv4aRX72IqBM5++0eci6m0HP2SNLPJnJl2wn0bRsz7NNncfP1oMnADvSYPZLVA19WcdRF3Ds6dw0Dvn4RQSdy5Zs9FFxMoe0LI8k9nUjy1hN0nD8aJ083eq9U4l5pSg67x6vj3k6LtpyrakuP2SPJqGpLqJ22rKlqi2yS2DN/DfevVeTPf7uH3IspdH1uJJlnEkncdoJec0fj7OHG0BWKDkWpOWycaNbBSRR5eXB7pq0/gCTJDGvXgKbBPizfc57WYX5EN1fevG85f50hrevZ9Ec1R0xbpn17SOnTtvUVjr0XFI6qhx9llSniJhztmPbNASQJKz38iW5+gyO5Ro4XXnuHoyfPkJ9fyIDhj/HUpMcxGpWVoIdH3Euf7l3Yd+goQ0dNxN3NjQVz1CmhZZPEyTmr6bNemUcSv9lD4cUUoqr6NG3rCdrOfxQnTze6r5xZ1afZHLDq0/OvrKJzlW8lr99FcXwyTV98iIIq36pfFb9v+NZZy/gNyljdsga30S8qKa5P7UHOTsG570ik1ERMl5QHKCUBxGEbOyBLVGxfi+uDsxX5s/uRc1Jx7jkcKf0qpivKA5RTy67K1j57kCUqNq/GbcxL1am+5awUnKOrdLh4AtOVM+ia3IX7tMVKzNn+tSrm1IVvIUtUxH5ZZQsB4+m9ii36PICUlojpkvIA5dS6G8bztitmTqLIS/2jeOrHPxS/alOPJkHeLD9wkdahvkQ3VR6g64MZvAAAIABJREFUYuNTGdwirGYddn2D64iZ1cc1VMfOzGvVL5+cWnTBFH/MVh5w0om8/EAvpq3cpIyRu1vQVB/A8i1HaV0vmOg2DZl9X3fe/H4P6/aeAUHgjUeibfQ5tfM47fp15P29y6moShl+Aws3vc/ce54DYPW8ldUpwy1xeudx2vfryHtV8p9ayL+16X3mVcmvqZJ3dnPhzG71A/uhHUfo3r8r3x1YS5mhjLdnL64uW711JeNjJgPw1NzJDBoxADd3VzYcq/FdvoZ/EAR7Gbn+Vy4sCMWyLHtZ/dYLJSnEWcxp5OfIsrypqnw1cFiWZdsDKuxD3hb6sMM6DspQBsGqiMcc5piQspbf9KMdlge4L309CxqMuXXFGjD/mpK6dWJDx77JAfjiqnJWzdEIx/emd0nZwNbQRxyWB4jJ+IaDYY5/F9Qj7UcA9ukdt0Xv9B/YHerYd1o3EJ3xPbMaOm6L/1z9BoCdoaMc5uif8V2dcJTMrZ0tPBd+XysOz4XfA1C6dKrDHB4zlZBS9Ox9t6hZM7w/+I3Sj59xWB7AY9pHvFff8XjzfNJaAOoi7q0Nd1yPx1LX8n4t2gHwXNJaPop0nOOZ64otDF++4jCH+1glBbth1YuOc0xYjGGNbcrj2+IY9w6V2bZv9/8qnIOUJAnfhzk+jzyUpswjW2oRw4dkfEPJW7XzC895ayl9d6LD8h4vfAFAyZuO28LzVcUWtfWtkoVjHddh7pcAlK6cdYuaNcNjsvJStPSDKbeoeROOZz/B8Lu9d9x/He7/ms1jDRz/pvJGyvHHa8HxVRVHz4i/lGPMLg6k7AT4+z5EvA0sbDDmzl3OAeZeW3dH2vFvW2myfmCq+m0/N3E4WZbH/0/qpEGDBg0aNGjQoEGDBg3WuKO252nQoEGDBg0aNGjQoOF/DtKtq2iwg7895bgGDRo0aNCgQYMGDRo03MnQHpo0aNCgQYMGDRo0aNCg4SbQtudp0KBBgwYNGjRo0PB/BHd0Fog7GH9b9rz/JfyjG6dBgwYNGjRo0KDhjsEdmfXNGm/e4dnzXr1Ds+dp2/M0aNCgQYMGDRo0aNCg4Sb4x2/PM+z6zGFZ935PKByfP+84x6T3KHm9duc0eb6+npJXHT8bw/NN5Uyfw+GOn2HQLVU5w8Dw2WyHOdyfWFKr805AOfOkLs70KZnv+NlEngu+o/TfExyWB/B4aRXFs+93WN5rya8AFE4a5DCHz+fbACiaOsRhDu8VW0jp7vi5FgARh3Zypc1gh+WbnIsFILVHP4c5wg/uAuBE5DCHOTpe/4Vzjf/lsDxAm4TfMax/zWF599FvAGD46W3HOR6Yo3DUcqxnDerrsDxA8LY9lK6Y6bC8x9SlAFxu7bhvNT2v+FZyV8d9vN6RnWRERzssDxC6e3ednLFUF2c9HanFPNI19SeudxngsDxA5NEdtRpnbRJ+ByCtl+PxImy/Ei821eLMqnsyvqlV7Iw4tBOAY/WGO8zROflnoPaxc10tznQDGJO6lqW1ONdtZtX5dDNrcfbh0qqzD7tHOG6LQym7HJb934aWPc8xaCtNGjRo0KBBgwYNGjRo0HATaA9NGjRo0KBBgwYNGjRo0HAT/OO352nQoEGDBg0aNGjQoEGBdEemWbjzoa00adCgQYMGDRo0aNCgQcNN8H9ipenAn4ks/m4HkiQzomdbJg7pqipPyy1k/upNFBnKkSSJGcP70vuuxmqOhEwW7ziHJMuMaFufid2aqcrf3XGOo9dzACirNJFbWs7+mUNVdXRN2+EyZCyIIsYTu6jc/6uNrrqobrhEjwQZpIxrlP+4TC1/zzgQRIwndlK5rwb5fg8CMlJ6EuU/fKQq943uQMMFExFEkcz120ldtkFVHvJ4DPrxQ5ElCVNJGYkvfIzhUrLaFomZLN5xvsoWkUzs2lRti53nOZpUZQtjlS1mqD/GPpCQweLtZ5EkGNGuPhO7N1dzbD/L0aRstT1n3WtuZ7P2uNw7QbHlsR1U7v3Z1hZtuuMyYBTIMlL6Ncq/W6oub9pO4RBEjMd3ULnvF/sc/R6qsuc1yr//UFUuNmqDy4BHFT1O78V4ZJOq3Ln/I+jqt6r6wwXBwwfD0qfV12jZEdfhT4Coo/LwVip3/mijh1O7nrgMHo0MSKmJVjp2xm30UwiCSMW+zVRs/tZWvnMfXIeNVWxxPQHDp4vUHK074TZqGogilQe2UBH7nS1Hp964/OsxxTeTEyj74t+qctduXfB9djqCTqTk100Uf7XehgPALbo3gYveIHPCVCrjLqrK3Ht2JujlqQg6HYU/bib/c1s9ADwH9kL/wXySH56u1qGrogM6HaW/bbTRwf2ewfg8PRUpS/Gtkh83UPqbus98ojtQ7/UnQSeSs34bGcvV/RH02BCCxw1FNklIJWUkvbycskvXVXW8+nQk7NXJIIrkfbeV7BU/2G2Hz5Ae1F8+h8vDnqXs7GVV2YFLaSzeckKJWx0bM7F3a1V5Wn4J838+QlFZBZIkM2NgO2Is5eNTWPz7H4p8l2ZMjL7LSr6Y+d8foMhQgSTLzBjckd4t66l1qIOx7tz5bryeegZBFDFs3ojh269V5a4xQ/B6chpSThYAhl82ULZ5o1mHq1m8u/sCkgTD29Rj4t3q2Pze7gscTc5VdKg0kWuoYN9TA1V1PHp1JuiVqaDTUfjDZvI/q8GvBvUibOl8rj80nfI/L6n17NYFv9nTEUTFv4u+tO/f7v16E/jOG2SMs/Vvl7vvxnu64p+GjRsp/VptC7chQ/CeOhVTtuKfhg0bMGw02yK0X1s6vPk4gk4k4evdxC/7TSXfbMpQGj/aD8loojynkGOzP6U0OVtVZ97bS9h74A8C/P34ee0KG/1lWWbRByvYd+gobm6uLJz7HK1bqPvdN7oDDSzmkTQ780ho1Twi1TCPuHXvgt9zT4MoUvLLJorWfFOjPYMWv0762GlUXjDbsy7GmGvXLvjMnA6ijtLfN1Ky1ipeDB2M91NTkbLN8cLwuzpeBPVrR+u3xiHoRK6v20nCR+p5uf7YgTSYGINskjCWlHHu+U8pvpii1sOB2KlqY3QH6r/xBOhEstdvI/2/P6nKgx8bTPD4e8AkYSoxcO2l5ZRZ9UddxM6w6LZ0XvA4gihyef1uzlv5Z8vJQ2n6aHSVfxZxePZKSlJyVHUa9G1L39cVH//zm90cW67m6PDEUKJGRyMbTRhyi9j2/Er1Nfq244FXxyHqRA5/u5PtH6v7o8ndLRnx6jjCW9ZnzTMfcnrzERs7A8x68xl69O9KmaGMBbP+zcVzl2zqTHlpEkMfjMHb15sBze+xy6Phn4W/7aFJEAQTcLZKhwvAOCAQ+BIIRTljaaUsy0ur6rcHVgBugBF4SpblP251HZMksWj9NlbMHEWovzdjFn1F37ZNaBIeVF3n002HiOnUglF9O3AlNZvpy35k811TLDhkFm0/y4pR3Qj1dmfMl/vo21RPkyDv6jovDGhT/f/1xxOJyyywbjAu90yg7Ku3kQtzcHtyIcb448hZ5uApBOhx7jUMw+evQ1kJePqo5f81kbI1CxX5KW9jjLMj32cYhs9es5UHEEUavf0kFx55g4q0HNpsWkxe7FHVZJazYR+ZX20FwD+mCw1en0DcmAVqW2z7kxWjuhLq7caYr/bTt0mo2hb9zTd3608kEpdRaNUnMou2nmHFIz0Ue67eQ99mepoEmfV9YaD5Bm/9sQTiMizsKYi43DeJslULkAtzcZu2COOFY8hZ5nYIgXqc+47A8Mk8+7YQBIVj9VuKPacuwhh3zI49h2P4dH7NHIMep/zb95CLcnEb9yqmy6eQc1Krq1Tu/IbKqv87dRyAGNrAikPE9YEpGFa8ilyQg/us9zH++QdyhvkmXAgKw3nAQ5R+9BIYShC8fHHu2Lda3n3MM5S8/xJyXjae85dhPHUIKS2pWl4MicD13tGULHoWSosRvP1sdHAb/TSlS+cg52Xj8cqHGM8cVnEIIeG4DH6Y0nefq+LwVXOIIn7PzSR75guYMrMI+eJjyvYdxHj1mvpSHu54jRpJxbnz2EAUCZ73NKlPvoIxPZt6335Eya7DVCYk2XD4PjacstMXbOR9n59JTpUOwZ+vsKtD2Y5dFCxRP/xackS+NYVLj75GZVoOLX5/j4Jtf6geinJ/3kP22i0A+A66m4hXJ3Ll8TdUHOFvTCNx7DyM6Tk0/vk/FG0/Qvll9YOV6OlO4Pj7KT0ZZ6OGSZJYtOkYKx7vR6iPO2M+3UbfFhE0CTHb/dO9fxITFcmoLs24klnA9HV7iHnNQv7Xw6yYFEOojwdj/ruRvq0iaRJq7vtPd54h5q4GjOrWkisZ+UxfvZ3NLR+00KH2Yx1RxPuZZ8l/6Tmk7Cz8l31CxaEDmJLUfVK+ZyfFy9QvNW7o8M7O83z8QBdFh68P0bdJCE0CvarrPB/dyqzDyWvEZ9nqEDzvaVKeeAVjRjaRN/zqiq1f+T1ux6+qOPxfmEnWM1X+vfpjDPsOYky0498Pj6S8Bv/2njmT/Oefx5SVRcCKFZQfOIDpmpV/7tpF0VJbWyAKdHx7PHsfXkRpWi4DNy8gdesJiixuwPPPXmP7kHmYDBU0HjuAtvNGc3iq+sXZ8HsG8ejI+5mz4D3bawD7Dh0lKTmVTd9+zpk/41jw3jLWf/qBqh0N336SuKp5JGrTYvJvMo/4xXSh/usTiLeYRxBF/F+cQeb0FzFlZBG6ZjmGvYfs2/ORByg/a2XPOhhjiCI+s2eSO0vp06DPVlC+30682LmLwv/UFC8Eot6ZyB+jFlKWmkPP2LfJjD2ueihK/ekASV9uByBkcCdavfE4R0e/o9KjVrFTFKn/1hQuVsWsVhvfJX/rH6qHopyf95K1VskE6TuoC5GvTeTSY2+qOGobOwVRoMvb49j5yDuUpuUyZNObJMcep/CSeT7MO3eVzUPnYzJU0GzsADrMH83+qctUHNFvjWPDmHcoTsvlkd/eJGHbcXItOLL+vMo3987HWFbBXY8NoNec0Sr5h96cyPLHFpKfnsNzv77N2W3Hybhs7o+81By+fv5j+j1ZcwbG7v27Etkogod6PUZUx1a8uGgWT9z3lE29/dsO8sOqDXy3f22NXHcqJO0YU4fwd27PM8iy3F6W5TZABTAV5WHoOVmWWwPdgKcFQbgxKy8G3pBluT3watXft8S5q2lEhvhTL9gPZycdg7u0ZPcZ9dsmQYCSsgoAisvKCfbzUnOk5RHp50k9P0+cdSKDW4Wz+3J6jdfcfCGFIa0iVL+JEU2RctOR8zLBZMJ07hBOLTqr6jh16o/x6FblBh2gxDz5i/Ws5M8exKmllXzn/hiP2JcH8OrQlLKraZQnZSBXGsn5ZT/+g+9W1TEVG8zX9HAFq8OPz6XlE+nvQT0/D8UWLcPZfTnjJrZIZUircCuOPCL9LezZOoLdl25mz2SGtDbbU20LI6YzB3BqZW2LgRiPbKnRFmK9pkg5VvZs1cWKYwDGI7E1c4Q1Rs7PRC7IAsmE8cIf6Jp1qLEdutbdMF44rOao3wwpOw05NwNMRown9+HURr0S6txtMJUHNoJB0UMuNj9A6hq3QMpMRc5OB5ORyj9249Shh1q+z1Aqdv4KpcWKfFG+WoeGLZAy06o5jEf34NS2u6qOS6+hVO753YJD/VLApXVLjMkpmFLTwGikdPtO3Pqo9QDwmTyRorXrkSsqbMpc72pBZVIqxuR0MBop3rwbz/7dbeoFPDOO/C++s+Fwbt0SY3JqtQ6G7Ttx693TRv5m8GzfjPKr6VRUjZG8X/fhG6MeI9Itxoh7u+aUX0uj8rrCUfD7XrwHdbO5Vsjsx8j65Afk8kqbsnMpuUQGeFMvwEuJW23qszte/XZaEARKyo0AFJdXEuztbpa/nk1koA/1ArwV+XaN2H3huh155drFZRUE+3iodaiDse7UohWm1BSkdKVPynbvxKVHrxo5bOyQnk+kn4UOLfTsvlKzDlvi0xjSIkz1m5ulX1UqfuVlx68CZ4wj77PvkMttfdPavw3bduJuz7+nTKToq/Vgh8O5ZUtMKSmY0qpssXMnrj3/un8GdGhC8dUMSpKykCtNXP/lMBGDO6nqZB08j8mgXDv3xGXcwwJseDq3vwtfH2+b329g1/7D3D9kAIIg0K5NK4qKisnKzq0ut55Hcm8xj+jsjBGXqJZUXk/BlFIVL7btwr2vrT19p06g6MtvbMZ6XYwx51YtMVnFC9detxcv/Do2pTQxHcO1TORKE2k/HyR0iHouMlrZwsoUtY6dSsxKq45Zub/sxy9GPYdIKh3cbPqjLmJnYIcmFF3NoDgpC6nSxLVfDhNp5Z8ZBy9U+2f2ict4WPlnaPsmFFzNoLCK4+Jvh2kco+ZIPnQBY9X9WvrJy3hZcDRo35Ssa+nkXM/EVGnixG8HuStG3R+5yVmkxiUhW3eEBfoM7snmH5SH/j9PXMDL15PAENux9OeJC+Rk5tr8ruGfizvlm6Z9QFNZltNkWT4BIMtyEcoK1I27ZRm48arfF0i1YbGDzLxi9P7mCSLUz5vMvGJVnan/6snGI+eJefljpi/7kZcfVp8jkVlcht7ihiTU243MojK710stKCW1oJS76wepfhd8/JELzcvQcmEOgo+/qo4YqEcIDMNt4uu4PfEmuqbtzPLeAcgFlvK5CD4BVvJhCEFhuD3xBm5PLlDJA7joA6lINXNUpOXgYmdSDR0/hPYHl1N/3liuzv/81rYovpktDDa2yCyy5nC/uT3zS7m7QXD1b4KPHVv4BqrkxKAwhMBw3CYvwG3KQnTN2qvKbTgKchC8rewZFK70xxNv4jb5LRt7Ct7+yIXmgCkX5SJ4qfvUfL1ARN8gpGvqt9iCbyByvnn7jJyfbduW4HDE4Ajcn/k37jPfRdeyo1neLwgpN8ssn5eN6Ke2t6ivhxgagcfLH+Ax50N0bdSTiOgfiJRn5pDysxH81ToIIREKxwvv4/Hif9C1Vk9kYnAQpszM6r9NmdnogoNVdZybN0MXEkz5QfvbIZxCAjGmm/UwZmTjFKJui0urpjjpgynda7vIrAsOwpRhoUNWFrrgIJt6btF9CP7yM/wXvo4YYqWjPpCKVHN/VKbl4KwPtKYgaNw9RO1fQcSc8SS/+qkNR2WaRTvSsnEOVXO4RTXBOSyI4l3HbLgBMgsN6C0eYkJ93MksNKjqTI1uw8YzV4l5/xemr9vDy/d0spAvRe/raSHvQWZBiVp+QDs2nkwgZtH3TF+9g5fvV99o1cVYF4OCMGWZ+0TKzkIXZNsnLr364v/JF/jMfwPRwm8yi8sJtdTBy42s4nL7OhQaSC0w0CVSbWtdaCCVln6Vno3Oyq9cb+JXALoQK9+y598tmqELDabsgH3/FoODkbIsxllWlg0HgGufPgR8/jm+b6ht4a4PoNRiK1NpWi7uevvxBqDR6GjSd52usbwmZGTloLewT2hIEBlZ5jFhbx5xrmEeaXdwOZF25hFlrJptYcqwHas3s2ddjDGdVcySaooXffsQtPoz/BbYxgs3fQBlFrYwpObiqre1RYMJMfQ9spSW88dwfu5qVVltY6dLWAAVaeb+qUi3P68HjxtKm/0rqDd3HEmvqs+vrIvY6a73pzTVPB+WpuXiHlazfzYZ3ZfUnWr/9NL7U2TBUZyWi1dozRxRD/flqoWP+4YGkG/RH/lpufiG2triVgjWB5GRarZHVlo2wXpbe2j4v4e//aFJEAQnYCjKVj3L3xsCHYAbUeJZ4F1BEK4D7wGv1MA3WRCEY4IgHFu5cqW9KjbYcvQC93dvw9Z3prFs+kjmrdqEJDm2dBkbl8rAFmHoRAdSk4g6xAA9ZasXUP7DR7jc9yS4edxazlr+izcp//5DXIZNvj35KmSs3sKpHk+RtPArImY+eGuBGhAbl8bA5nrHbHGD40IKA1uE3z6HqEMMCqPss9cp/24pLsOn3L4tRBExMIyyL95wnKMKulZdMcYfs3nD99f00CEGh2H47xzKvnoP14eevrWMtXxoBKXvPodh5du4j5sF7p63lrOAIOoQQsIpff9FDJ+/g9tjz94ehyDgO3MaBR9+fHu6W3EEvTiZnHf/2ri2h7L9h8gYOZqssU9Q/sdx/Oe/7BBP9ppN/NlrKimL1qCfcZsHJQsCYXOfIH3h57euexNsOXuN+9s3Yutzw1g2pi/zfjqMJP31Iwu3nE7k/k5N2frKQywbP4B53+2rRdxzfKxXHDpI7uMPkzdlIhUnjuH9whzHdIhPY0Dz0NvXQRAIemky2Ysd9ysEAb+Z0yhYWgv/BsoPHiT7kUfInTSJimPH8H3F7jR3S9Qf2RP/do2JX/57rfSpDTJWb+F0j6e47sg8Igj4zZpK/ge231z9Vfm6GGNlBw6R+dBossc/QcWx4/jNdSxeXFu1lT1dZxL/1tc0nTXi9oTrInYCWWs2c67XVJLf/pKwGbd/QHxdxU6Ahg/0JLBtY85/vPHWlWtAixE9CWnbmBOfOM6hQcPt4u98aHIXBOEUcAxIAqqjmyAIXsCPwLOyLN/YEzUNmCXLciQwy7K+JWRZXinLcmdZljtPnjyZEH8v0vOKqssz8osI8Vdvv9tw4CwxnVoA0K5xBOVGI/nFpdXlIV5upBeZ3/JmFJUR4u1mt1Fb7GzNA5AL8xB8zG/CBJ9A5MI8qzq5mOKPg2RCzs9CzklDDNArZUXq1RTBJ0C1yqHI59QoD1VvoMLNHC5hgVSk1by0nPPzfvyHqLdd2LWFVw22iLPdrgMQ4m3NYajZnudTGNJa/XG69cqS9aqRUicH04Wjii3yMhVbBIbVzOEbiFxkZc+CXExxx8z2zLbiKMpTrfYJ3gHIxeo+vQGnVndjumD7llAuyEGwWBkS/IJs21KQjfHcH4oeuRlIWeZFVjk/GzHAYhXOPwgpX/3ht5yXjfHUYTCZkLPTkTJSEEPNPirl5SD6mzlEvyDkPLUOUn42xtOHFR1yMpAykxFDLDiystGFhFT/rQsJwmTxVl3w8MCpcSOClv+H0J++xiWqNYGL38K5pTkBiDEzBye9WQ+n0CCMmea2iJ7uuDRtSPiqxdSPXYNr21boPzJ/S2TKykYXaqFDcDCmLCtbFBZCpbJVp/S3jTi3UCcgqUzPwcXie0fnsEAq09W2sETeL/vwG6xeoalMz8E5zKIdYUFUZpg5RC93XJvXp9H6RTTf+znuHVrQYOV83O4yf2wf4uNOeqE5BmUUGgjxMa+4AGw4mUBMVCQA7SKDKDeayMvLq5L3IN1iZSmjsJQQX/VD7oZjl4i5q6Ei3yCE8koT+aXmlaS6GOtSdja6YHOfiEHB1UkObkAuMvdJ2eaNODU390mIlysZljoUlxHs5WpXh1g7W/MATBk5OFv6lT4Ik7VfNWtIxJrFNNi2Btd2rQj77xu4RpmT/ZgyrXzLnn83aUTw8v+g3/A1Lm1aE/Se2r+lrCzVypEYHKziALV/GjaqbWFIz8UjwhyzPMICMKTbxpuQ3lG0mjmMA+PeR6ow2rXVzRAaHEi6hX0yMrMJtVh1sDePVN7mPKKMVbMtdKHqsSp4eODcpBEhK5YQ9ss6XNu0Jvj9BTi3UuxRF2PMZBWzRAfiRVl6Lm4WtnAPD6A8vWZbpG44SOhQ9TZwR2PnDVSk5eISZu4fF/3N5/VcOzGrLmKnIT0Pj3DzfOgRFoAhzdY/9b2jaDPzfnaPX2Ljn8XpeXhbcHiFBVCcYcsR2SuKu6ffz2+TlmCy4CjIyMXPoj/8wgIoyLi97XNrtn5KdkYOoeFmewSHBZGVnn0Tqf//IN/h/+5U3AnfNLWXZfkZWZYrAARBcEZ5YFony7JlCphxwI2/vwfUUbgGRDUIIykzj5TsfCqNJmKPxtG3rToTUFiAD0filI+CE9JyqKg04u9tXlGICvMjKa+ElPxSKk0SsRdS6dtUjzUSc4ooLKukXbjtcrKUekXZfucXDDodujbdMcYfV9UxxR1DbFj1CZeHN0JgGFKeskQspVxBDLCQv6sHxjgr+Qs1ywMUn7qMW6MwXCNDEJydCBzWi7ytR1Ucbo3MNx1+AztRlpimtmeYr9oWcan0bRpqxxbFNdoiKsyPpNwSUvJLFI7zKTexZwXtItQcUsplZSuifwjonNC17YkxTr0Fw3T+KGKjKLUtcjMsOK4oHCp7WnFc+EPNEWTFkZaI4B+C4BsEok55MLp80qYdQoAe3DyRUi7blEnXLyEGhyMEhILOCacOvTGdUz9cGc8dQde0KjGGpzdisPnm1JQYjxgagRCkB50TzndHYzx1SCVfefIAuhZtFV28fBBDI5CzzP0qXYtHDAlHCKzSoUtfjGfU314ZTx3EqXkVh6cPYkg9pGwzR8WFOJwiI9CF6cHJCY+B/SnbZ9ZDLikhfegIMh54lIwHHqXiz/PkvDhPlV2s/Fw8zvUjcIoIBScnvIZGU7LLrIdUXMrV3qNIGjyOpMHjKD9zgfRnXjO380IcTvXMOrgP7E/Z/oOqdoiB5gnZrVcPjFfVyQBKTl/CtWEYLlVjxP/+3hRsU2/Zcm1oHiO+AzpTdlU9RgxnLuLaMBzneqEIzk74/qsPRdvNfSoVlRLXeQwX+0ziYp9JGE7Gc23yAlVmr6jwAJJyikjJK1bi1rkk+rZQv4wJ8/XkSILijwlZBVQYTQQEKO2LqhdEUnYhKblFivzpRPq2Ur98CPPz4sgVRfeEzHwqjCb8Pc0PRXUx1o3xcegi6iHqlT5xi+5PxaEDqjpigLlPXLr3VCWJiNL7kpRXSkpBlQ7x6UQ3DsEaibnFFJZX0i7Mz6as7Fw8zg2q/MrZvl8l9hzFtUHjuDZoHOWnL5D29Guq7HnW/u0+qD+GvWr/Ths8gvQRj5I+4lEqzp0n+3m1f1fGx6OrZ2GL/v0pP2jlnxa2cO3RA2OS2T/zTiXg1UiPR2QwgrOOyGHdSI1VzwF+bRrQafG29JGEAAAgAElEQVQkDox7n/Icq4QYfxHRvbrx65YdyLLM6XMX8PLyJDjIrJf1PBJgZx5xvcU8UnE+Duf6EejCq+LFoH4Y9pptIZeUkDroAdKGjSFt2BjKz50n67n51dnz6mKMVcbFoYtUx4vyAzXHC9dePTBeU8eLgpNX8Gysx72+0idhw3uQYdUnHo3Mc1vIoA6UJljZwsHYeQMlpy/h1sgcswKG9SLfOmY1Usescqv+qIvYmXMqAe9GejwjgxGddTQY1o3krSdUdfzbNODuf09kz/gldv0z43QCfo30+FRxNL+vGwnb1BzBUQ3ov2giv01agsGKI+n0FYIb6gmoF4zOWUfH+3pwbpu6P26FcTFPsjf2AEMfVHKRRnVsRUlhifbtkgbgDks5LgiCgLKCdEGW5SVWxalAX2A30B+wzf9oB046kZcfHsi0D39AkiSG9biLpuFBLP91P60b6Ilu15TZI6N5c20s63YcAwHeGDcURZUqDlHk5YFtmPb9YSRZZthdkTQN8mb5vjha6/2IbqYExS0XUhnSKkIlWw1JomLTatwef0VJcX1yN3JWMs79HkRKTcQUfxzT5dPomtyF+9PvKvW3rQNDsVl+4yrcxs6pTlkuZyXj3P8hpJQEs3zTtrhPfw9kiYrYtWZ5AJPE1bmf0fLrVxF0Ipnf7MBw8Tr1XniEktNXyNt6lNAJQ/Ht3RbZaMKYX8yVmerMS9W2+EFJZTzsrnqKLfbHK7aouqnaEpfKkJbhdm3hJIq8HNOWad8eUuzZtj5Ng31YvvcCrcP8iG6mBHhllcmOPSWJit8+x2383Kr067uQM5NxHvAwUsoVTHHHMF06ha5pO9xn/kepv+UrtS0kiYrfv8Bt3FyzPTOTce4/Cin1Cqa4G/Zsh/szS+zbU1b6yHXUc4oeZ/chZ6fi3Gs4UvpVTJdPKe1t1dXuKtMNPcp/+gT3ya8r6b7/2I6UcR2XIY9iun4Z059/YIo7ga55ezxeXIYsS1T8thq3sS9Uy5etW4bHrEUIokjF/lik1Gu4DhuH6epFjKcPYTp3DKeoTngu+Eyp//2nyCVFKh3Kvl2Ox4yFig4HtyKlXcPlvscxXbuE6cxhTOeP49S6Ex6vfVKl82dgyWGSyH//I4I++DeIOkp+34wx8SreT46n8sJFmwnYLkwS2W//l7BP3kbQiRRu2ErllWv4Pz2W8j8vUrr78C3lC5Z8SOB/FoNOpPSGDk9MoCIunvL9B/F86AHcevUEkwmpsJD8he/YcFyfv5Kma19H0InkfLuDsovXCXvuUUrPXKZg2x8Ej78X717tkI1GTAUlXJv1gQ1H6usraLjmTQRRJO/7bZRfSiLk2TEYzl6iaMctk34qceueTkz7ag+SLDGsQ2OahviyfOdZWocHEN0ygtkx7Xnzt6OsO6zcSL4xvGv1WHHSibx8f1emfbFdke/cjKah/izfdpLWEYFEt67P7Hs68+aGg6zbf16Jew/2tB/3ajHWkUwUL/sA30XvIYgiZbGbMF27ise4iRgvxlFx6CDuw0fi0r2qT4qKKHr3HZUOL/VvzVM/HVNiRVQ9mgR5s/zgJVqH+hLdRHmAio1PY3DzMPs6mCSyFv6X8E/fRhAVv6q4fI2A6WMp+/Mipbtu4VdVHPnvfUTQh/9GEHWU/Kb4ls/k8VRcuEjZvr/i3yaKli7F/913QRQp27wZ09WreE6YgDE+nvKDB/EYORLXHj2QTSbkoiIK3zHbQjZJnJyzmj7rX0LQiSR+s4fCiylEvTCS3NOJpG09Qdv5j+Lk6Ub3lTMBKE3J5sB49VT6wmvvcPTkGfLzCxkw/DGemvQ4RqPytv7hEffSp3sX9h06ytBRE3F3c2PBnFk2trg69zNaVM0jWVXzSETVPJK/9Sj6CUPxqZpHTPnFJFjNI5gk8hZ/RPCH/0bQiRT/uhljwjV8poyn4kI8ZXvVL37s9UdtxxgmicIlHxKwZDGIIoaNSp96TZpAZVw85QcO4vngA0pyiBrihWyS+POVVdz9zRzQiSSv30VxfDLNXnyIgtMJZMYep8GkwQT1boNsNFFZUMLpGR/b6FGr2GmSSJr/Kc3XvQaijpxvt1N28Trhz4+m5PRlCrYdJWT8Pfj0aqfM6wXFJM5aasNR29gpmySOzV1D/69fRNCJXPlmDwUXU2j7wkhyTieSsvUEHeaPxsnTjV4rZwBQmpLDHgv/lE0Su+evYfhXCsf5b/eQezGFbrNHknE2kcRtJ+g1dzQuHm7c87HCUWTxDZNkkvjx1VVM+3KOknL8u12kX0pm6KyHuH42gXPbj1O/bWMmffIc7r6etBnQkaGzHuSdmBdUbTm44zA9+nfl+wNrKTeU89Zs8/Eaa7Z+yriYJwF4eu4UYkYMwM3dlV+O2T/GQMM/C8LNMoj8j15YEIplWfay+q0XSlKIs8CNzflzZFneVFW2FOVBrwwl5fitXiHIhl2f3aJKzXDv9wQAhs+fd5xj0nuUvD761hVvAs/X11Py6iOOy7+pnH9xOPwBhzm6pSqLfIbPZjvM4f7EEgyrXnRYHsB9wmJK5t7+fuwb8Fz4PQAl82/zOxRLjgXfUfrvCQ7LA3i8tIri2fc7LO+1RDl7onDSIIc5fD7fBkDR1CEOc3iv2EJK9/4OywNEHNrJlTaDb12xBjQ5p6TSTe3Rz2GO8IO7ADgROcxhjo7Xf+Fc45rT2P4VtEn4HcP6125dsQa4j1a2Kxp+ettxjgeUb4pqO9azBvV1WB4geNseSlfMdFjeY6pyY3i5teO+1fS84lvJXR338XpHdpIRHe2wPEDo7t18HzbGYfmH0tYBUJmd4DCHc5ByPtaRWswjXVN/4nqXAbeueBNEHt1Rq3HWJkH5ziutl+PxImy/Ei82hTo+L9+T8U2tYmfEoZ0AHKs33GGOzsnK+Ya1jZ3rwh9zWB5gTOpaltZ3nGNmkpLye2ZDx/tj6VXlPql7hOO2OJSyC8Dxj7j/F/FKw0fv5F1wLLr69R1px79tpcn6ganqt/3U4HBVZZ3slWnQoEGDBg0aNGjQoEHD/xT+9ux5GjRo0KBBgwYNGjRo0HAn4476pkmDBg0aNGjQoEGDBg3/c5Du6Bx1dy60lSYNGjRo0KBBgwYNGjRouAm0hyYNGjRo0KBBgwYNGjRouAn+tux5/0v4RzdOgwYNGjRo0KBBwx2DOzLrmzVebDj6jr4/Xnx1/R1px3/8N01F0+9xWNZ72Sag9qm6C6c4nvIWwOeT2DpJp7xP/6DDHL3TfwBqn+K66KmhDssDeC/fjGHtXIfl3R9bCIBhwzu3qHkTjhEv10r+BsfOUMfTnvfPUM6EMPyy2HEdhinp32uTBt59wmL26h1PAQ/QJ/17doc6zhGdoaSRv9ZxoMMcDU5sB+DbWqR1fjhtHVfbOz4+ABqe2kZiO8c5Gp1W0sjn3Od4uu/A3/YAtU+n3Dmst8PyAMfS9hHf0vF40SJuMwA/6x91mGN4+tcAbKmFLYZkfFOr2AtK/K2tDlD7dOFQ+7Tlpxo4ftQCQPtrv9ZJyvG6iBe/1MK3hqV/TfZgx8dpUKwyTuOaO36P0/Kico9T2/TrtYnfoMTw3hGOp6Lfl7IDgH/Vv9dhjt+TNgJ1k7Zcwz8X2vY8DRo0aNCgQYMGDRo0aLgJ/vErTRo0aNCgQYMGDRo0aFAg/d0K/H8KbaVJgwYNGjRo0KBBgwYNGm4C7aFJgwYNGjRo0KBBgwYNGm6C/xPb83StOuH24BQQRSoPxlKx7XubOk4deuNyzxhARkpJpGy1+gN73+gONFwwEUEUyVy/ndRlG1TlIY/HoB8/FFmSMJWUkfjCxxguJav1iOqM26ipCKKOiv2bqYj9zlaPTn1w/ddjAEjJCRg+NycccO3aBd9np4NOR+lvGyn+ar1K1v2ewfg8PRUpKxuAkh83UPrbJlUd/37tabxgAoJOJH3dDpKX/awq14+NIXzCYGST0o7LL3xC6UWrdrTpjNvopxAEkYp9m6nY/K1tOzr3wXXYWJBlpOsJGD5dpOZo3Qm3h6aCIFJ5cAsVW+30ScfeuNz7mMKRkkDZKnOfHLiczuLYU0iyzIgOjZjYs6VKNq2glPm/HKWorBJJlpnRvw29m4Wp6hyIT2bxb0cUji7NmRjdVs2RX8z87/ZRZKhQOIZ0onfLyDrnCOjXjmZvKX2Stm4H1z76RVUePnYQ9Saa+yTu+U9sdfjlMJIsMeLuFkzs106tQ14x87/dS1FZOZIkM2NoF3q3smpHQgaLt59FkmBEu/pM7N5cVf7u9rMcTVL8qqzSRG5pOftnqT+69e/XniYWvnXdyrfCxg4ifMKQ6nZcsuNbAf3a09TCFkkfqTnCxw4ifOIQqOKIt7KFW48uBDz/FOhEijdspnC1/Q9zPfr3Jvi910gb8xQVFy6qyvT92tLhzccRdCIJX+8mbtlvqvLmU4bS+NF+yEYT5TmF/DH7U0qTs1V13Ht0JuDFp0BU9ChYZTtGADwG9CLk/ddIffRpKs5ftOV46SkEUaRow2YKvqiZI3TJa6SMflr1u3PHu/F88hkQRcq2baTsh69V5a4DhuAxYRpSThYAZRs3UL51o6pOUL92tH5rHIJO5Pq6nSR89KuqvP7YgTSYGINskjCWlHHu+U8pvphio+PzC2bSc0A3ygzlvP7s28SfvWhT56mXn+SeBwfj4+dNn6bm5DkevToROncqiCIFP2wh91PbWAHgFdOTiA/ncfXBGZSfu6QqC+nXlrsWjEXQiVxbt4tLVn3aZMo9NBgTjWyUqMgp5MSslRis+jSoXztavTUOdCLJ63aSaGWLyLEDqV9lC1OVLUqsbFHb+FsXOvhGd6CBxVyWZmcuC62ayyQ7c9m8t5ew98AfBPj78fPaFTb9IMsyiz5Ywb5DR3Fzc2Xh3Odo3aKpTT3vvh2JeO0JBJ2OnG+2kvnxjzZ1AHyHdqfRileI/9dsDGcvV//u1acjYa9OBlEk77utZK/4wa68z5Ae1F8+h8vDnqXMQh7qJl7c8C10Ikk38S2pyrdO2vEt58534zn1GQSdSNnmjRi+sxqrg4bg+YR5rBp+VfeZZ+9OhMydgqATyf8+ltyV9seId0xPIpbN5eoDMymzGiOuXbvgM3M6iDpKf99IyVqr+4uhg/F+aipStvn+wvC7+v7CkfhtPQcAzHzzabr170q5oZy3Zy3mopWuAE++NJHBDw7C29fbpmzyG1Po3K8z5YZyPnjuP1w5d8WmzuMvjKX/yP54+XrxUCt1opaWfdvxwKvjEHUih7/dyfaP1eNM5+LEY0ueJrJNI0ryi1kzfSm5yVk219Dwz8Pf9tAkCIIJOFulwwVgHBAIfAmEoqQLXynL8tKq+u2AFYAXcBUYI8ty4a0vJOI26ilKl81Fzs/G44UPMJ49jJR+3VwlOByXmFGULnkeDMUIXr5qDlGk0dtPcuGRN6hIy6HNpsXkxR5VTSQ5G/aR+dVWAPxjutDg9QnEjVmg0sN99NOUfPAKcl42nq98hPHMYaS0JPNlQsJxHfIwJe/OhtJiBG8LPUQR3+dnkjPzBUyZWQR/voKyfQcxXr2mUrVsxy4Klnxo3xaiSJNFT3Bu1JuUp+XSfss75G49pgpaWT/tI/1LpR0BMZ1p9Po4/nx0obodY56h5P2XlHbMX4bx1CGrdkTgeu9oShY9W9UOP9s+efhpSj+co/TJS0sxnjmClG7mEILDcRn8MKXvPWfTJyZJZtGWk6wY05tQHw/GfLaDvs3DaRLsU13n030XiGldj1Gdm3Alq5Dp6/ez2eKhySRJLPrlMCsmDSbU14Mxy36jb6v6NAk16/rpztPEtG3EqG4tuZKRz/RV29j8cmSdciAKtHhnEidHvUV5ag6dYxeRFXuMUosbnYyf9pP6pZIVLWhwJ5q9MU6tw4aDrHhyCKG+noz56Ff6tq5Pk1B/sw47ThHTrhGjurfiSkYe07/YyuZWD6vtufUMKx7pQai3O2NW76FvMz1Ngsz2fGHgXdX/X38sgbiMAnWfiiJNF03i7KgFlKfl0mHLInKsfCvzp/2kVbUjIKYzjV8fxzlL3xJFmr0zidOjFlCemkun2EVkx6o5LG0ROLgzTS1sgSgS8NIzZD71EsaMLMLW/hfDnoNUJpr9CkDwcMf70RGUn72ANQRRoNPb49n98CIMabkM2ryA1K0nKLToj/yz19g2ZB4mQwVNxg6g3bzRHJr6kVqPV54hY+pLGDOyCV+3jNI9h6hMsNXD59ERlJ+x1QNRJHDOM6RPqeL4ehmlu+1z+I4ZQZk1hyjiOfVZCuc/h5SThe+ST6g8cgDTdXW8qNi3k5JPltpeH0AUiHpnIn+MWkhZag49Y98mM/a46qEo9acDJH2pZBILGdyJVm88ztHR6sySPft3I7JxPUb0GE2bjq155Z3nGH/vFJvL7d16gG+/+IkNBy1uGEWR0P/H3nmHR1V14f53ZtJ7mUmnRzoEkN4SOlgARUUERIJSlKJYPsSOiooFCyJVbIgdLKEECD200FtCJ5A+qZNkkkw5948zZObMJAQm3Cvfd+d9Hp4nnL33mneVvU7be53Xn+Fa/Fz0ORoa/foZpUn7qbpgYwdvTwLHj0B3NLVGPWLem8ieR95Dl5VP3MZ3yE48jNZKj+KTl9kxRPJp4wkDafPaGFKmfCGT0fr9eA6abdHDbIsyG1tcNdtCPeRuWr41nkPWtqhv/r1NHBrPf4pU87mszfoFFN3gXBYwuAsN35xImtW5bOQ9g3hs1HDmvv2Rva2BXXsPkn4tk/U/r+T4qVTe/mgRa5Z/auMTBVFvT+HC2NfRZ+fT/K+PKd5ygMpzV+XdvD1RTxxO2eE0u/ERb03j0uOvYsjOp+m6hWi37KfyvP344CeGU36kpriof75AIdD+vYkkm2Mr1qHYUuDzzLMUv/w8Jk0eAV8spWrfHozp8rlauTOJsi8tc9X3uZeqx4e+8TRXJ76CPltD498/pXTrPqou2NsicEJtc0SB3+xZFDwnXV+oViyhcncN1xdJ2yhZWPv1hSP5+/iYd2ViuvfvSlSTKMb0fpzWnVrx/HuzmHL/dLuf27N5L3+sWsePu7+THe/crzMRjSOY3PcpWnRswdPvPsPzI2bbjT+wZT//fPs3y3Yslx0XFAIPz4tn8bh3KcrO5/m/5nNi8yFyzlt82uORfuiKS3kn7lk63t+D++c8xrfTa8mjdyhMzi/yOIR/c3meThTFDqIotgWqgKmAAXheFMXWQHfgGUEQWpv7rwDmiKLYDlgLvHgzP6Jo3ByTJhMxPxuMBgyHd+LSvoesj1vPoeh3/gO6UgDEUvkFoU/HaCouZ1GZnoOoN5D/524Ch3SV9TGW6iy/6eUONt+/UjZpgSk3E1Ej8dCnbMclRs7Dtfcwqrb/DeVmHloLD9fWLTFcy8SYmQUGA7otSXj06XUzJqiGb8doKi5lU5Gei6g3kLduD0FDutSqh9LL3U6GsqmNHge249Kxp1yPvsOoSvrLSo8iWbuicXNMeVY+ObQDl5jusj5uvYei3/F3jT45mVlAg0AfogJ9cFUqGNKmAdvTMmXjBQHKKvUAlFbqUft6yNpPXtXQINiXqGBfXF2UDIlpyvbTNidLoKyiSpJRUYXaz/O2y/DrFE35pWwqruQi6o3krktGPfRGPvGQxdbJq3k0UPkRFexn4XDKhoNgy8FLrkdWIQ0CvYkK8Jbs2TqS7eeyqQ0bzlxjaOtI2THfjtHobGIreEjnG+jhju1n1Pw6mWVckWTkrtuDauiNZVh/Z86tbQsM1zIxZEhzpGzTdjzj7OdIwNNPUPLNz4iVVXZtQR2bob2cQ1l6Hia9kfQ/9xE55G5Zn9zk0xh10tj8w+fxCg+Stbu3bYHhaiaGjOxqHl5x8jkCEPjMExR/8zNilT0P97Yt0FvL2Fi7jKJV9rq43NUKY1YGphzJFpU7k3Dt1ttu/I0QYI5NnTk2s9YlE2rjD4OdP+zlxA7tzfpfNwJw8vBpfP18CA4Jtut38vBp8nPzZcc82jdHn56J/lo26A1o1+/AZ0B3u7GqmY9TsOLXGm0Z2DGa0ks5lKdLelxbt5cwG59q9lh8WnjoHJ42PrW1RXYNtrCOTRcvd7uvBNY3/94ODrbnsoI6zmXKGs5lnTu0w9/P/un+dWzbvY/hQwcgCAIxbVuh1ZaSpymQ9fHqcBeVl7OouirxKPx7F/6DutnJCn9+LLlLfreLb8+Y5lReyUJvHl/8z058B9nHRcjsceQt/Q3RfC6wxu3IF4Edoymziq2Mm4gtD5vYcmnRCmNmBqZs81zdnoRbj5ufqx7tm1N1JRP9VWmOlCTsxGdgD7t+qlnjyV/+a416uLZqidHm+sK9961dX9Q3f19H7yG92PibdNN++vAZfPx9CA4Jsut3+vAZ8nML7I53G9ydpN+TAEg7koa3nzeBIYF2/dKOpFGYW2h3vFGHaPKuZJN/NRej3sjhv5NpN1iuR9vBnTnw+04Ajq3fT/OebezkOPG/iTtled4uoL0oillAFoAoilpBEM4AkcBpoDmw09x/M7AJeK0uwQr/YEyFllfhpkINysYtZH2EkEgUgNdzH4FCQeX61RjPHKpudwsLpirTcjKvysrHp9Nddr8V+sRQwicPR3Bz4czDb8h/IyAYU6Hl9a1YqEHZRL6kTBEaBYDXi59IPP75AeOpFACUahXGnNzqvsa8PNxat7Lj4BHXF7cO7TFcvUbxZ19iyrX8pnt4EJWZFltUZeXjW4Me4ROHEjnlPhSuLhx/6E0bPVSYCurQI8ysx5xPJT3++g7jyRRLe4BKZosb+uT5j0ChpDLhB4ynJZ/klugIs7r5CPXz5ESGPHlO7duaaat3sebgBXR6A0vH9ZW155aUE+bvbZHh78WJq/LX61MHdmTayk2sST6DrsrA0ieH3HYZ7mFBVFrFVmVmPn41+CRy4hAaTr0XwdWFI6Pm0WNAR4lD8U1wGNSJaSs2sib5tMThKfm3b3K1FYT5WtnT15MTmfYnE4DM4nIyi8rp2kgt1yPcRo+sglpiawhR5tg69tBbddiioEZbREwcQoOp9yG4unBs1FsED5Ce2rqoVRiyreZIbh5ubeWx6dYyGmVoCLrd+/F73P47WZ5hQegyLBzKswoI7tisRlsANB0TR9a2Y7JjyhAVhmyLDww5Gtzb1cRDjW7XAfwn2PNQhqgwWskw5tYswyWsZhmKYBUmjcUWpvw8XJvb5wu3nrG4tInBmHmV8hWLMGksv+kRFkSFlT90mQUEdLJfZtVo4mAaT70XhasL+0e9bdeuDlOTnWnhkpOVR0i4yu4GqSa4hKrQZ1nZMluDR4w8V7i3boZruIqyHQcJmmT/PSTP8EB0VnpUZBUQWIMe1fo81o+cJLlP3cOC5DIyC/CvQUZDsy0EVxcO2tiivvn3dnCo6VzmXcu5LKyWc1ldyMnLJyxEZZEVoiInT4NaZbnwdQ0LRp9lsYU+S4NXR7lfPds2xTVCRUlSCiGTH5C1SeOt4iJLg2cH+XiPNlJclG5LQf3UKDuetyNfeNjElq6O2Gr4WD9ybWJLEazClGc1VzV5uLS0n6vuvWJxbRuDMeMqZUsXVR93DQ3GkG2xpSFbg2eNc0RN2faDBE+yt4VSrcKYa8UhLw/Xmq4vYvviFiNdX5R8YXN94WD+toU6TEVupkVuXlYeqjBVjTdINSE4LBiNVWzkZ2sIDguu8QapJviHBlFkpUdRVgGNOsh9GhAaRKG5j8lookKrwzuw9gcJTvzv4F8vBCEIggswDGmpnvXxxkBHYL/50ClghPnvhwH5pgzLuMmCIKQIgpCybNmym+OgVCKERFD+2X/QffMBHo/NBE/vugfaIOebjRzt+TTp735P5CwHPmaoUKIIiaT84xfRrXgPz3HP3hKPit17yRk1hrzHn6TywCECX5tz6xyArFUbSek+nUvv/EDD5xzUIzSS8g+fR7dsPp4TnrtlewoKJYI6kvKF/0H39ft4jJ11SzI2nrrK8JjGJD57L4vG9ObVdQcw1fQY/EYyjl1k+N13kTh3NIsmDuLVX3ZiMv2/lwGQsWoTe7vN5MI7q2n8nP1J74Ycjl6QOLwyhkXxg3n1px0OcQDYdCaDgS0iUCoc+1h31qpNHOw+g4vvrKbRLepxHZmrNrG/mwMyBIHA2dMo/MR+H4YjaDSqF0ExTUld/M+tDRQEgl6YSuEnS+vuW4eMgo8dl1F1IJnCSaMpnhmP/mgKPs/OdUjOlVWJ7Og2i7R3fiT6uQfqHnA7IQiEzJlM7gfL6+57E4ga1YuAmCacv1WfmpG+KpGd3WZx9p0faeagLeqbf28Hh5xvNnKs59NcdfRcVl8IApGvTiLzna8dHh/+ypNkv7uyXhxuZ76oT2xV7UumYMJoiqbFoz+cgs8LtzBXBYHQl58i9/36zZGKPXvJfXgMmieepCrlEAGvOHZ94XD+duK2QrzD/92p+DdvmjwFQTgKpADpQHV2EwTBB/gdeNZq31I88LQgCIcAX6QlfXYQRXGZKIqdRVHsPHnyZEzF+SgCLU+9FIEqxGL5E05TkQbDif1gMiLm52DKzUChjqhur8rOxy3CspzELTyYqqzan3rkr9tN4FD5kgexKB9FoOXpvBCowlQk3wwqFmowHN9nxeMaihBpGZQxT4MyNKS6r1KtxphnM76kBPTSMoTyvxNwbSHfzF+ZVYB7hMUWbuHBVN5Aj7x1ewi2WSomFmlQBN2EHkf3gdGIqMnGlJOBItSynMtUpJHZolafWNsiJ6PaFiF+nmSXWF7z55ToCPGVL3tbe+Qyg1tLb7xiooKpNJgoKq+sbg/x8yK7uMwio7icED/5Tdnag+cY3L6xJKNRCJUGI0XlFbdVRmV2Ae5WseUeEUxldu0+yVmbjHqYxSch/jfD4SyDY5qYOYTa6+HrQbbWyp5aHSE2yxmvY+PpDIaa7WoNKbas9AgPom6026kAACAASURBVCqr9jcJUmzJ54i9LYKozK5dRu7aPaiGWWQY8jS4hFnNkRA1Rqu3GYK3F67NGhO2/GMi//kB93atUH86D7dWlnmiyy7AM9LCwSs8CF22/RPK0D5taD1rBLsmfIypyiBrM+ZqcAmzxLdLqApjrmWOCN6eEo8VHxG1/nvc27Ui5NN5uLVuLpOhtJKhDFFhyJHLcIu2ktG+FaGfzatuN+VrUKgstlAEqzHm28xTbQkYpHxRmZiAMlqeLyqyC/Cw8odnRNANYzNzbTKhVrG5evPXrN78NZrcfMIiLFxCw9XkZmlqEmEHQ44G13ArW4apMORYfKrw9sTtrkY0/G4BTbd+g0dMS6IWv4F7W8sTbl1WIZ5WeniEB6GrIe+p+7SlxayR7KvBp5XZBXIZddgia20yIcPkubO++fd2cKjpXKa/xXNZXQhVB5NtFe85uRpC1SpZH312Pq7hlmOu4Sr0VnNd4eOJR4tGRP/0Lq13L8erYwuarnwFz3bRVuOt4iJchT5HPt69eUOarHmP5jtX4tmxBY2WvYZHO8sbg9uRLypsYsszPIiKWmKr+ayR7K8htkz5GhRqq7mqUlcXW7gOUWs5t1dsTMDlLgsHfU4+LmEWW7qE2djC2xO35o1o+P0HNEtahUeHlkR+9ToeVnPEmKdBGWLFwZHri3rm768Tl/J14lLyc/IJibD4Vh2uRpN9c/ni8w1fUJhbgMoqNoLDVOTfgIctinMKCLDSIyA8iOIcuU+LcgoINPdRKBV4+HpSVqi96d9w4r8Xd8Kepg6iKM4QRbEKQBAEV6QbptWiKP5xvbMoiqmiKA4WRfFuYA1gXw6lBpiunEWhjkAIDgWlCy6d+koX41YwHNuLy13SRnfB2w9FSCSmfMuejtKj5/FoEo57gxAEVxeCR/SmMPGgTIZHE0uRgYCBd1NxKUvWbrychiIkspqHa+c4DMfkPPTHklE2b2/FIwpRI8nRn0nFJSoSZXgYuLjgObA/FbuTZeMVwZblDx69e2K4LN/boj16Ho+m4bg3lPRQj+xFgZ0eYdV/Bw3shO6SfG+L8VIaitBIBFWYpEfXOAxH98r1OLIHZQuzHj5+KEIjEfMs9jBdOYsixMond8fW7BNrW4RGYjLbok1EIOkFpWQUlqE3mth06iqxzeWV8cL9Pdl/WVpucDGvhCqDkUCrPQJtolSk55eQUaBFbzCy6dhFYlvLX16GB3iz/7z0mxdzi6jSGwn09ritMrRHLuDVNByPhmoEVyUhI3ui2ZQik+Fp5ZPgQZ0ov2ixZZsoNekaWw4NbTj4sP+8tOfrYk4NeoQHkF5QRkaR2Z6nM4iNDsMWl/K1lFRUERNpvz5ce/Q8nk3D8bCKrfxEuR72sSWfI9ojchkhI3vVaQudlS2qTqXh0iASlwhpjngPiUO3wzJHxNIyrg0YRcZ948i4bxyVJ86Q9+zrsmpYBUcv4tskDO8GahSuShqO6E7GJstSXYCAto3ovGASuyZ8TGW+fS2aylNpuDSU8yjfYZkjYmk5V/s9xLV7xnPtnvFUnjhD7rOvy6rnVZ5Kw7VhJC6RZhlD7WWkx1nJOH6GnFmvV7cbzqWijIhCESqNd+/bH/2BPTKeQqAlX7h17WVXJKL4yAW8m4bhaY7N8JE9ybGxhZeVP0IGdZTF5thB8YwdFM/2Dbu45+GhALTt1JpSbelNLc0DqDhxFtdGEbhGhoKrC773xFKaZMkVptJyLvR4lIsDnuDigCeoOJbKtaffklXPKzp6AZ+mYXiZ9Yga2YPsRLke/m0b0eHDSeyb8DFVGnufFh+5gJeVLcJG9iT3BrZQ29gC6p9/bwcH23NZUA3nMvc6zmV1Ia53d/7auBVRFDl28gw+Pt6ypXkA5cfO4d4kArcGoQiuLgTe34eSzfur203ack52HMfp3k9xuvdTlB9J4+Kkd6ur5+mOn8W9cQSuUdJ4//v6ot0iH5/aeSxn+07ibN9J6I6kcWXy27LqebcjXxQdlebI9diKrCW2Yj6cxP5aYsuQlooy0mquxvWnap/NXA2ymqvde8mKRFScOIub2Ra4uuB3b19Kt8rnyPluY7jQfyIX+k+k4mgqGdPmyarn6VNTUTaQX19U7qn9+sK9d08MV2yuL+qZv+MHTyF+8BR2bdrD0IcGA9C6UytKS8puemnezGEz2LtpH/1H9QegRccWlGvLbnppHkD6sQuoG4cRFKVG6aqk0/09OblZ7tOTmw/RdZS05D/mnm6cSz510/Kd+O/GnbKnCQBBEASkN05nRFH8xKYtRBTFXEEQFMCrSJX06obJRMUvX+H1zDtSeet9iZiy03G7dxzG9HMYT+zHeOYQLq064fXKEhBNVK5bCWVWTw2MJi6/soKWP76OoFSQ+9NWdGevEvXio5Qdu0Bh4kFCJw7Dv097RIMRQ1EpF2Z9Yc/jpy/xmjUfQaGgak8ipqwruN//OMYrZzEc34fxVAourTvh/cYyEE1U/L4c8ToPo4niTz4neOECUCoo/2cDhkuX8X1yIlWpaVTuTsb74Qfx6N0LjEZMJSUUvSuvYoXRxIW5K2i75lUEpYKcNUmUp12j0Uuj0R69QEFiChHxwwjo2x5Rb8BQXMbZmTXosXoRXs+9J+mxexOmzCu4j5iA8fJZDMf2YjyZgkubu/F+e4XU/1crPa7L+PkrvKa/Awol+r2JmLLScbtvPMYrZyWfnDb75LWlYDJS+YfFJy4KBXOGdmDaj7swiSIjYhoTHeLP4u2naB0eSFyLCGYPimHeP4dYve8cCPDW8M5I4SXBRalgzvDuTPs6EZNJZETnu4gODWRx4mFaR6mIa92Q2fd2Zd4fe1i9+xQIAm893Oe2yxCNJs6+/DUdfnoFQakgc802ytKu0eSlR9Aeu4Bm0yGiJg0lsE87KbaKSzkz80u67fzEwmFED6at2Chx6NKc6LBAFm86JHFo04jZ93Vl3m+7Wb1LSuxvjbbRQ6FgzuD2TPt5r2TP9g2JVvuxeOcZWocHEGeuOii9ZYqUjbWOrfNzV9J2jaRH9pptdrEVGT+MgL7tEPWSHmkzF8lEiEYT515eSXuzLbLMMhq/NBrtsQvkb0ohctKwalvoi0s5M3MRXXcurOZQ8MEXhHz5vlTq+6+N6C9ewX/qBKpOn0W3c689bxuIRhOH535D7Jr/SCXHf9pBydkM2r44ioJjl8hMPEzMa4/h4u1Bz2WzACjP0LD7Cat0ZTRR8P4iQr96T+Lx5yb0F64QMG0ClafPottRNw+MJvLfW0SYWYZ2nVnG0xOoOnVWdgNVI0xGypZ8it9b5n2aW9ZjTL+M59h4DOdS0R9IxvP+Ubh2k/KFqNVS+pk8X4hGE6deXkXXn+ZKJa7XbKM07Rp3vfQwxccukrvpEI0mDUHVp63ZH2Ucm/mVHZU9W/fSa0B31u39iQpdBW89Z/n8wOrNXzN2UDwAM1+dxpAHBuLh6UHCod+r7ZD79ldErZRyRfHviVSdTyd4xngqTp6lbNt+u9+zhWg0cXzuN/RcM0cqOb5mO9q0DFq+9BBFRy+SnXiYNq+PRentQdflMwEoz8hn/4SPZTJOv7yKzj/NRbCyRbTZFnmbDtFw0hCCrWxxwtYW9cy/t4vD5VdW0MJ8Lsszn8sizeeyosSDhE0chp/5XGYsKuWizbnsxTfe5+CR4xQVlTBg5DienjQeg0F6ezL6gXvp26MLu/YeZNgj8Xh6ePD23OfsnWI0ce31pTT97k0EpYKCX7ZQce4qYbMfo/z4eUq2HLixU40mMt9cQuNv5yEoFBT+upnKc+mEPDsW3YlzaLfWMd4s43bki+Nzv6GHObbSbxBbXaxi64BVbGEyUvrlp/jPl+ZqReJ6jFcu4/V4PIazqVTtS8ZzxCjcepjP7VotpR+/T+CK76v1yJn3FQ1WvgNKBcW/SXNENXMcFSfPUZpU9xzBaKLkk88J+mQBKBToEqTrC59JE9GnplG5Jxnvhx6UikPUcn3haP62xd6t++nevxs/7fmeCl0F783+sLrt68SlxA+WKm9Oe2UyAx/oj4envGBKStJBOvfrzPJdK6SS4y8srG77fMMXzBw2A4CJcycSOyIOd093vtn/rZU7TPz++iqmfTdXKjn+yzayz11j2HMPc/XERU5uOcS+X7Yx7pNneHX7p5QXlfLtjFoqCt7BMP3bBP5LIdRUveT/yQ8LQqkoij42x3ojFYU4gcWnc0VRXC8Iwizg+odI/gBeFusmL2qn3+MwR99F0jcI9kU86LCM7pl/UDJlSN0dbwC/pZvI7NnP4fERydsA2BXm+Nr0PtnSNzBKJg1yWIbfys1onx5Wd8cbwHfxBnQ/vOLweM9xUnlT3dr36+h5AxkPzKnX+OsykkLtNxbfLPrnSN/40v25oI6eN+AwQipZq1v1kuMyJi5gZ9jDDo8H6Jv9K9tDHZcRlyN9k+RKp4EOy2h0WCrT/HP4WIdljM5azeUOjs8PgMZHN3MpxnEZTY5J5Xzz7491WEbw3zsAWB/6qMMy7sn5ic7hfRweD5CStYu0lo7nixapGwBYF/aYwzJGZkvlzzfWwxZDc36qV+4FKf/WlwPA/nqcy7plSgs/9JqLDstwVTXlaKPhDo8H6HDlL042vc/h8W0vSnuKbke++LMesTUi+0c0Qxyfp6pN0jxNbe74NU7Ls9I1TlZvx68vwndvq1f+BimH94kc4PD4XRlbAbiv4b119Kwd/6RL36ib1djxefbZ5Z9AKpp7x2NW40fv5K1DfHb5pzvSjv/amybbGybzsd3UEnDm7zX9dxXCd8IJJ5xwwgknnHDCCSf+63FHLc9zwgknnHDCCSeccMIJJ/7vQbyja9TdufjXS4474YQTTjjhhBNOOOGEE07cyXDeNDnhhBNOOOGEE0444YQTTtwAzuV5TjjhhBNOOOGEE0448f8JnNXzHMO/Vj3v/xH+p5VzwgknnHDCCSeccOKOwR1Z9c0W0xuPvqOvjxdd/vmOtOP//Jum21HSMyVqpMMyOl9bR04/xzkAhG7bcVvKit6O0rv1LU1aMKJ+tgj6c8dtKb+ef289SjIn7KDggXrqsXYHF9sNdnh80xOJAKR3drxMa8MUqUxrdt84h2WE7dyOLuFTh8cDeN77LLpv5zg+foJU/r1i7xqHZXj0GAPUv/y6busyh8cDeA6YTE5cnMPjQ7dvB0A70/GSzL6fSyWZ8wY5HuPqzTtY3GCcw+MBnr76Q71yZ+g2KX/rVr7gsAzPSR9JMla/5riMsW/flk8UlL3juD29X/0BgKtdHM8XDQ5K+aI+JcM7XPmrXiXLQSpbrtu2wuHxnv2eBKDi4O8Oy/DoMgqo/6crKk9tdXi8exvJlxX7fnZYhkf30ZKMY+sdlxFzT73yJki583aUC5/YeJTDMlZdluJhSAPHP3Ow6eoGh8c68d+B//mbJieccMIJJ5xwwgknnHBCgsm5EMshOAtBOOGEE0444YQTTjjhhBNO3ADOmyYnnHDCCSeccMIJJ5xwwokbwHnT5IQTTjjhhBNOOOGEE044cQM49zQ54YQTTjjhhBNOOOHE/ydw7mhyDP9f3DS5du6K99QZCEoFFRsS0P3yo6zdfdBQvJ+chik/DwDdX2up3Jgg6+MX15GGbz0JSgWaNZvJ/vIPWbt63BDUT9wDRhPGMh1X/rOYinPXZH3cunTFd/oMUCrQJSRQvkbOw2PIUHynTsOoMfNYuxbdegsP7z53E/LKFASlgqJfN1Gw7Nca9fUd3IvIRa9w+cFZVJw8J2sL6deedm8/jqBUcGX1Ns4t+lvW3mzKPTQaG4doMFGVX8Lh55ahu6aR26tbF/xmTQeFkvJ/Eij7QV61zHPYEHyfnopJI40r+30tun/k1XlcO3bF66kZoFBQuTmBit/ltnDrPxSvJyw+qVy/lsrNFlu4d+uC/7PTQamk/O8ESr+34XDPEPyemYopz8Kh/G8bDnd3xXuyxKEiMYGKX23iYuBQvOItHCr+XktlojwuXDt2xWuSWY8tCVT8YaNHv6F4TZiGqcBKjy1yGZ69OhP8n2kISgUlf2ykeGXN1ZC8B/YmdOHrXBv9jOy4R48uBL7wDCgUlK1bT8m3P9U43rN/H9QL3iR7/DSqzpyV8+zaFb+Zkk91CQmUrZbr4Tl0KL5PT8Votmf5H2vRJcj12HMmnQXrdmMyiTzQvRXxAzrJ2rMKtbz2YxLaiipMJhMz7+1On9aNLOMv5LBg83FMosgDMY2I79lCNv7Dzcc5eEX6/QqDgYKyKnY/L68St+f4OT74cSMmk4kH+nZi0n19ZO2ZmiLeWPknhdoy/L09mT/lQUKD/OUyLuawYMsJTCZ4IKYh8T2ay3lsOcHBdDMPvZGC8kp2Pyev/LTn1CUW/LpN0qVnW+KHdJPboqCE177diFZXgckkMnNkH/q0bVrd7ta1K77TpfjWJSRQ/qNNrhg6FN+pUzGa55hurb0/lK064fHgZFAo0O9NpGrLb9jCpWNv3IY9BqKIKeMSFd99JGt37dwVn6dnICgU6DYkoPvZZo4MHorPU1a588+1VGyQ82gQ157eb45HoVRwes12jiyW55yYp4bR6tE4RKMRXb6WpBeWUZqRb7FFPfMmwJ6LuSzYelLyR/uGxHe/S9b+4daTHLwq/Wa1T2fJq2jtOZ/Fgk1Hpfju2IT43q1k7VnFZby27gDaSr3k0wHt6XNXuFxG2jUW/L1f4tGlOfFx7eUyikp57ZddaHVVmESRmUPvpk/LBtXtyqbtcRsyHgQFhqPb0SfLbek2aCyKRq0BEFzdELz9KP9oitxePboQ8Lw5X/y5Hm1t+aJfH1QL3iT78WnobfKFb2wnIt94EkGpJP+nRHK/qrkSnf+wHjRZ8jJp981Gd+J89fFX53/Czj0HCAoMYN0PS+zGiaLIe58uYdfeg3h4uPPuK8/TukW03JanLrHgl62SP3q1J35oDXPsm/VodZVSvhkZS592TeUyjp3lg+//kfJFXBcmDZdXa8zUFPLGst8p1JZL+WLaI4QG2+SLevoUYPfhU3zw9a+YTCIPDuzJpAeHyHnk5vP6lz9QWKLF38eb+bOeoJFV+57j5/hg9XrJFrGdmHRfXxs9inhj5VoKS8rx9/Fk/pRR9nnv6Bk+WLVWkjGgG5NGDpTLyCvgja9+orCkFH8fL+bPGEdocIBcxm3InQCT35pC536dqdRV8unzC7lw8oJdn/EvPk7/Uf3x8feRHW8b24HHXo9HoVSw8+etrP9qrazdxc2Fpz6ZSaO2TSkt0vLV9E/Iv5ZnJ3/aW1Pp2r8LFbpKPp79Medr4PDESxMYOGoAPv4+jGz5oF27E/97+FdvmgRBMAInzDzOABOQvrm1E3A3H/9NFMU3zP2bAD8BwcAhYLwoilU3/BGFAp9nnqX45ecxafII+GIpVfv2YEy/IutWuTOJsi8/q1VGw3emcPaxN9Bn5dMq4UOKEg/Ibory1+0k74dNAPgP6kKDN+I5N26eTIbvrGcpevF5jHl5BC1ZSmXyHoxX5DwqtiWh/bwGHgoFoW88zdWJr6DP1tD4908p3bqPqgtX5d28PQmcMALd0dQaZAjEvDeRPY+8hy4rn7iN75CdeBjt2YzqLsUnL7NjyKsYdVU0njCQNq+NIWXKFzIefrNnUfDcixhz81CtWELl7mQMl230SNpGycLPa7Wn15Rn0b7xPKb8PPw+WkrVgT2YrsplVO1OonxZzbbwf2EW+bMkDuqVS6jYVQOHrdso/qR2Dt7TnqXkVSku/BcuRb9vD0ZbDjuTKFtSe1x4TX4W7ZtmPRaY9bhmI2NPEuXLa5ehemU6WZPnYMjWEPnTF5Rv24v+Yrqsm+Dlid+4B6g4dsZufOB/ZpL7zEsYc/II+24x5Tv3Yrh0xW6876MPUnnidI0c/J6bReHsFzDm5RG8bAkVu+1jU5e0De2nNethNJl4749dLJl6P6H+3oxd+DuxbRrTLCyous/yzYcY3KEZj/Rqy4XsAqYvX88G802T0STy3qZjLBnTi1A/T8au2kbsXeE0U/tVj39xkOViZM3BC6TmFNlxmP/9epa+OJ7QID8ee2s5cR1b0CwypLrPJz8lcn+vGIb37sD+0xf57NetzJ/yoJUMkfcSj7Pk0Z6E+noy9psdxN4VRjOVFY+B7Sw8Ui6SmlNsb4uft7Jk5kOEBvgy9oPVxLaPpll4sMUWG/Yx+O7mPNK3Axey8pn+5R9seKdptT98Z82i6IUXzLliCZV7asoV29B+VktcCQo8Hp5G+ZevIhbl4/XCQgwn92PKtuQLQR2B26CHKV/4IujKEHzkF1EoFPjOeJai/0hzJHDRUqr21pA7dyRRuqhmHoJCoO87E/j7sfcpzSrgoX/mcXnzIQrPZVb3yTt5mVP3voahooo24wfQ85UxJD69yMoW9cibmH265QRLHuku+fS7XcRGh9FM5Vvd58UBbav/XnPoEqm5Nfh0w2GWjIuV4nPFFmJbRNBMbbHZ8l1nGNymAY90juZCXjHTf9zFhln3yWX8uY8lk4YQ6u/F2EV/E9uqIc1CLReey5OOMbh9Ex7p3pILOUVMX7WZDXPMF9iCgNuwCVSsfh+xpACPSfMwnD2EqLHYsmrz6uq/XToPQhHWWG4MhYLAl2aSO13KF6HfLkZXS77wuUG+iHp7ChfGvo4+O5/mf31M8ZYDVJ6zPxepJw6n7HCanYiR9wzisVHDmfv2R3ZtALv2HiT9Wibrf17J8VOpvP3RItYst3zawGgy8d6azSyZ9Qihgb6Mfe97Yts3o1mEymLL9XsZfHcLHontyIVMDdMX/c6GdlNkMuZ/+xdL58RL+eL1xcTd3ZJmkaHVfT75cQP39+7E8L6d2H/qAp/9son50x6R86iPTwGj0cT85T+z7I2ZhAYHMOalD4jr0p5mDSw33B9/+wf3x3VjRL/u7D+Rxuer/+Tj2Icsenz3D0tfmiDp8eZS4jq2tMl7m7i/VweG9+5ozntbmD/FUp7baDIxf+XvLH11KqHBATz28kLiOrelWVSYRcb3f3F/384Mj+vK/pPn+OzHf5g/Y5yVjPrnToDO/ToT0TiCyX2fokXHFjz97jM8P2K2Xb8DW/bzz7d/s2zH8upjgkLB+HlP8dG4eRRk5/P6Xx9wdPNBMs9brtX6PDKAsuJS5sRNp+v9vXhkzni+mv6JTHaXfl2IbBLBxD6TaNmxJTPmT2fW8OfsOOzbvJ+/vvmLr3eutGtz4n8T//aeJp0oih1EUWwLVAFTgUqgvyiKMUAHYKggCN3N/T8AFoqiGA0UApPq+gGXFq0wZmZgys4Cg4HK7Um49eh9SyS9O9xF5eUsqtJzEPUGCv7cTcBg+VMtU6mu+m+llwfYfDTYtaXEw5gl8ahISsK9183z8GjfnKormeivZoPeQEnCTnwG9rDrp5o1nvzlvyJW2t9LBnaMpvRSDuXpuYh6I9fW7SVsyN2yPpo9pzHqpLGFh87hGR4ka3dt1RLjtUyMmZIeui1JuPfuddN6ALjc1QpTdgamHElG1a4k3LrevC1cW7fEYMPBo88tcmhuExc7k3Dtfmtx4XJXK0xZVnrsvjU9ANzbtUCfnonhWjYYDJRt2IF3v552/YKmT6Bo5c+IVXK/urVpieFqBsYMiUN54ja8Yu3H+0+dSMm3P9mNB7NPM6xic2sSHrfo05PpuTRQ+RMV7Ieri5IhHaPZfvKyrI+AQFmFHoDSiirU/l6W8ZkFNAj0JirQG1elgiGto9h+LqvW39tw+hpD28if1p68mEGD0CCiQoJwdXFhaLe2bD8iv2C7kJlH11ZNAOjaqgnbj8gfLpzMKpR4BFznEcn2c9m18zhzjaGtI+UyLmfTQB1AlCpAssXdLdh+7LysjyAIlFVIvijVVaL2965uc21p44+kJNx73Zo/FI2aY8rLQszPAaMBw+GduLTrLuvj1mMI+l0JoCsDQCyVX8DY5s6K7Um49by1+A7p0IziyzmUpOdh0hs5/9c+mgyW55zMvWcwmG2Rc/g83lY32vXNm2D2aYCVT1tFsP38jXyawdBWNj7NKKBBoA9RgT64KpUMadOQ7WmZsj4CUFZ5Pb71qH095TKuamgQ7EtUsK8UFzFN2X463V7G9bioqELtZ5GhiGiGqSAHsSgPTEaMp/bh0lxuS2u4tOmB4dRe2TG3Ni3RW+eLzdvwrCVfaL+rOV94XT8fXpXOh4V/78J/UDe7fuHPjyV3ye81nos6d2iHv5+v3fHr2LZ7H8OHDkAQBGLatkKrLSVPU1DdfvJyFg1CAolSm+dYl5ZsP247x6xtWYk6QP5G4uSFazQIDbbki+7t2X5I/lDqQkYuXdtIDzO6tm5q115fnwKcPH+ZhuFqosJUuLq6MLT33Ww7cEzW5+K1bLq1k97adG3bnG0HjlvGX7xmk/fasf2wPK9dyMilayuzHq2a2LWfPJ9OgzAVUaEqSUbPjmw/eFIu41o2XdtKb2i7tolme4q8/XbkToBug7uT9HsSAGlH0vD28yYwJNCuX9qRNApzC2XHmnaIJvdKNnlXczDqDRz4ezcdB3eR9ek0uCt7ft8OQMr6vbTq2Q5b9BjcnS2/S9/QSj2SirefD0E1cEg9kkqBDYf/FpgQ7+h/dyr+7Zsma+wCokUJpeZjruZ/oiAIAtAfuL7G5Fugzq/OKoJVmPJyq/9v0uShUKns+rn3iiXgq6/xffUtFGq1rM0tPIiqLMsStarsfNxsbiYA1BOG0Xb3EqJemUD66/KP7ylUKky5Vjzy8lDWxKNvLEErvsb/TTkP19BgDNkWDoZsDa6hwfKxrZvhGq6mbPtBO7kAnuGB6DIty14qsgrsboqs0eixfuQkyZO3Uq3CaKuH2l4Pj9i+qL5ZQcDbb6IIkdtTCFZh1FjJyM9DEWwvw61HLH6ffY3Pf95CobLIUKpVGHMs4421cYjri/q7FQS+a89BEazCpJHHMsFoggAAIABJREFUhbImDr1i8V/0NT4vyzkACEE3qUf3WPwWfo3Pi2+hCJbLcAlRYci2LA0w5OShtPGrW6toXMLU6HYdsJOtDFFhzLEan5uHMkTOwbXFXbiEqanYs99uPIBCpcaYa5FhzMuzmwMg+TR41UoC5r1lZ8/c4jLCAiwX/qEB3uQWl8n6TB3amYRDZxn81ndMX57AnAcsS+dytRWEWV1MhPp6kqutqJFvZnE5mUVldG1kw6GwhLAgy1PNkEA/cgpLZH1aNAxlq/nCZ+uhM5RVVFFUWi7n4XsrPMrteRSVEhZouSAMDfQlt7hU1mfqvT1IOHCGwXOXMv3LP5gz2vLRUYVajSnP4g9pjtn7w71vX4JWrsT/LfucpQgIxlRkJaNIg+AvjyshJAKFOhKvZxfgNfsjlK3kyykVKhVGm9xZU85y6x1L4NKv8XvNnod3WCClmZaL3dKsArzD7C8+rqPVo7Gkb7fknPrmTYDcUlufetzYp8XldG0o/41crY4wq5v8UD9PcrU6WZ+psW1IOJHO4IV/M33NLuYM7SiXUVJOmNXNcai/F7klNnNkYEcSjlxg8Pyfmb5qM3OGW250Bd9AxBKLLUVtAYJvzbYU/IMRAkIwXT4lOy7lTqu5nmOfO11b3IUytPZ84RoWjN7qfKjP0uAaJo8tz7ZNcY1QUZKUUqOMupCTl0+YVR4LDVGRk2f5zdxCmzkW4Etuoc0cu68XCftPM3jOV0xf9LtsjkkyigmzWqIWEuRfQ74IY+tByYZbU05RVlFJkdYqX9TTpwA5+UWEBlv8GBocSG6B/AFG88aRbNl3VOKx/yhlugoKCwvNemht9Kgp74Wx9ZD01lDKe5XyvFdQRJjVUruQYH9ybDi0aBTJVvPN2tYDJyjTVVKkteh6O3InQHBYMJosS4zmZ2sItomv2hAYGkRBpiVOCrIKCLQ5nwZY9TEZTei05fgEym/gVWHB5FnJ0WRpCA6zzztO/P+HO+KmSRAEF2AY0lI9BEFQCoJwFMgFNouiuB9pSV6RKIoG87BrgN1jCkEQJguCkCIIQsqyZctu6ver9iVTMGE0RdPi0R9OweeFuQ7pkfftBk72nsq1+d8RPvPhWx5fuTcZzZjRFDwZT9WhFPzn3AIPQSD05afIfX953X1vAlGjehEQ04Tzi/+55bEVe/aS+/AYNE88SVXKIQJemXPLMvQHkyl6ajQls+LRH03Be9at+aRi915yRo0h7/EnqTxwiMDXbp1D1f5kCieOpnh6PPojKfjMvvW40KckUzRlNCXPxaM/dut6IAgEvziF/I9uLpZrGh84eyqFC+33DdwKKpKTyXvkUfInTqIyJQX/uS/fsoyNh88zvGsLEt94nEVP3curP0r7EW4Vm05fY2DLSJQK4ZbHzh49mJS0yzzy+hIOpV0hJNAXhXDrcgA2nclgYIsIh3hsTEllePc2JM6fwqJnHuTVb9bfki0qk5PRPPooBZMmUZWSgv/Lt+4PQaFEUEdQ/vnL6L75EI9HZ4Cnd90DrVC1N5mC8aMpnBJP1eEUfF90LHcCNH+gF+r2TTmyJKHuzlaoV960wabUTAa2CHfMpyfTGR7TmMTn7mfRmD68uu4AJvHW4nvjsYsMv/suEueOZtHEQbz6y06H5ohL6x4YUw/YrXioE4JAwHNTKfq0HvlCEIh8dRKZ73ztuIzbgI0HzzC8R1sS35/GoumjeHXVrc0xgNmP3UNK6iUeeeULDp25REigH4pbjI3b4dPnJzzIoVPneOT5+aScOkdIUABKpfLm9Xh0CCmpl3nktcUcSr0s6XGLeW/2+OGknL7AIy99xKHT5wkJ8kehcOwSsj650wkn/k3824UgPM03RyC9aVoJIIqiEeggCEIAsFYQhLZA7e95rSCK4jLg+hWmWPTs0yjUlrW9CpW6ukBB9Rit5alMxcYEvJ6cKmuvyirALdzylMEtLJiqrAJqQ8Gfu2g4X7751qTRoAix4qFWV2/iruZRYuGhS0jAZ7KFhz4nHxerJx0uYSr0OZa3RgpvT9yaN6Lh9x8AoFQHEvnV62RMs+yr0mUV4hlheeriER6ErgY91H3a0mLWSHY9+DamKoOszZinQWmrR17tepT/nYDvtMny9nwNSpWVjGA1pvzafVK5OQHPCRZbGPM0KEMt45U3wcHvGTkHU74GhUoeF8YbcUhMwCteHhdiwS3qsSUBz8flMgy5GlzCLE/bXELVGK38Knh74hbdmPCvP5R0VQUR9oXFp8ZcDcpQq/Ehaoy5Fg6ClxeuzZoQulRas60MDkL1ydtoZr9msYUmD2WI9Zs8+ZsOsInNfxLwnSqP7xB/b7KLLE8dc4rKCPGXX4Cv3X+GxZOlPR4xjcOo1BsoKtPhDYT4epBdYnlyn6PVEeLrQU3YePoaLw+JsTseEuhHdoGFZ25hCaGBfnZ9Fs54FIDyikq2pJzGz9vydDTE14Ns7c3yyODlwe3tjocE+JBdqLXIKNQSYrNZeW3ySRY/I+2limkaQaXeWG0Lk82bPmmO3cAfCQn4TLHJN0X5uAZYyQhQIRbn2/UxXkkDkxGxIAdTbiYKdYSlXaNBaZM77XKWde7ckID3U/L4LssuxCfC8jbbJzyIsmz75SxRvdtw94zhrHv4XVnOqW/eBAjxsfVpRe0+PZPBy4Psl+uE+HqSXWx5Mp9ToiPEZvnd2qOXWPyYtAE/poGKSoORovJKrs+CED8vsq3evuYUlxPiZzNHDp5jcfwgSUajELOMCrwBUVuI4GexpeAbhKiteWmQsk13qjZ+a3dcyp1Wcz1Unjuv54uQJZZ8of74bfKet+QLfXY+rlbnQ9dwFfpsq3ORjyceLRoR/dO7ALioA2m68hUuTnq3Rq41IVQdTLZVHsvJ1RBq9UYsJNBmjhVpCQm0mWN7TrB4hrTvJ6ZpJJUGA0Wl5RZ/BPqTbfU2JbeguOZ88ay0b6e8opItB0/J80U9fQoQGhxATr7Fjzn5hYTYFGkICQpg4X+kOV6uq2DL3qP4+fmZOfra6FFL3ps5xqKHbd4LCiA737JHNDe/2K5QREiQPwtfiLfI2H/8tubOzzdIe6fPHT+LKtwSo8FhKvKz5bmrNhTmFBBkta8tKDyIwhz52CJzn8LsAhRKBZ6+XpRaxdLijYs4e+wsais5qnAV+dnyvPPfDtO/TeC/FP/2m6bre5o6iKI4w7aogyiKRcA2YCiQDwSY30oBRAEZ1AFDWirKyCgUoWHg4oJ7XH+q9u2R9RGCLCcit+697DY6lx07h0eTcNwahCC4uhA0ojdFm+VLpdybWDZt+g/oTOUl+X4MfaqZR5jEw6N/fyqT5TwUVjzce/bCYMWj4sRZ3BpH4BoVCq4u+N3bl9Kt+6rbTaXlnO82hgv9J3Kh/0QqjqaSMW2erHpe0dEL+DQNw6uhGsFVSdTIHmQnHpJx8G/biA4fTmLfhI+p0shf8Vfr0SASZbikh+fA/lTuSZbrEWylR++eGK7I13gbzqWiCI9CESLJcOvTH/0BG58EWu1r6NpLVlxBfyYVlyg5h4rdtXPw6N0Tw2UbDmdt4qJvf/T7a+fg1q2XXZEIOz1690d/8AZ6dOllVySi8mQaro0icYmUZHgPi6Vsu2UfglhazpW+D3N16ONcHfo4lcfPkD3j9er2qtOpuDaIRBkhjfca3A/dTostxLIyMgY+SObwsWQOH0vlydNoZr8mq56nT01DGRVVbU+PAXX4tJe9T9s0CCE9r4iM/BL0BiObjpwntm1jWZ/wQB/2m4unXMwppMpgJNBHOum2iQgkvbCUjKIy9EYTm05fI9am8hjAJY2Wkgo9MZH2y0rbNIkgPSefa3mF6A0GNu4/SWxHeQW+Qm0ZJpN0ulj5z25G9pEvoWoTHkB6QZkVjwxio8OwxaV8LSUVVcRE2i+PatMojPTcIjI0xZItDqUR276ZjS182Z8m2fBiVj5VBkO1LfRpkj/kucLGH7Jc0RNDutwfpvSzKNQRCEGhoHTBpVNfDCfky60MJ/biEi3dIAjefihCIjBpLM+mqnPndR5x/anaW3vOcuthnztzj13Ev3EYvg3UKFyVRA/vzqXNh2V9VG0aEft+POvjP0GXL8859c2bYPZpYRkZReWST89k3sCnemIiavBpZBDpBaVkFJaiNxrZdCqd2OYRsj7hfl7sv5QDwMW8Eim+vdwtMqJUpOeXkFGgleLi2EViW8v35YUHeLP/vHTuuJhbRJXeSKC3dOFpyryIIigMIUANCiXKNt0xnJXbEkAIDkfw8MZ07ZxdW9XpVFwbWuWLQfb5InPQg2SNGEvWCClf5D3/mqx6Xvmxc7g3icCtQSiCqwuB9/ehZLMltkzack52HMfp3k9xuvdTlB9J4+Kkd2XV8+pCXO/u/LVxK6IocuzkGXx8vFGrLH5u0yic9NxCMjRFki0PphLbXl5dLzzIj/2pVnNMbyDQ17LEsk3TSNKzNVzLLZDyxb7jxHaSV0SU5Yu/djAyVr6HrL4+BWgT3YgrWblcy9Gg1xvYuPsQcV3kNxSFJaXVPFb8sYkHBlj2M7dpEkl6ToFV3jtBbMeWtevxzy5G9rXJe80akJ6Vx7XcfElG8hFiO7eplcPKtVsY2U++j62+uXPmsBnMHDaDvZv20X9UfwBadGxBubbMbu9Sbbh07DwhjcNRRYWgdHWh6/29ObJZvkT0yOaD9BoVB0Dne3pwJlm+N+vpodNJ3rSXgaOk5ZwtO7akXFv2X7t3yYnbi3/7TZMdBEFQA3pRFIsEQfAEBgEfiKIoCoKwDXgIqYLeBODPOgWajJR++Sn+8z8yl5Zej/HKZbwej8dwNpWqfcl4jhiFW49eYDRi0mop/fh9uQyjifTXltN89RugUJL/8xYqzl4l4oUxlB07T/Hmg4Q8cQ9+vWMQDUYMxaVceu4zOx7azz8lcIGZx4b1GC9fxntiPIa0VCqTk/F6cBTuvXohGo2IJVpK3n9fxiFn3lc0WPkOKBUU/5ZI1fl0VDPHUXHyHKVJNa8/t4ZoNHF87jf0XDNHKjm+ZjvatAxavvQQRUcvkp14mDavj0Xp7UHX5TMBKM/IZ/+Ej2U8Sj75nKBPFoBCgS5hA4ZLl/GZNBF9ahqVe5LxfuhBqTiE0YippISid23saTJSvuxTfN+UbFG5dT3Gq5fxfCwew/lU9AeS8bhvFK5dJRliqZbSz+S2KP7kc4IXLgClgvJ/JA6+T06kKjWNyt3JeD/8oFTM4AYcyr76FL+3zRw2r8eYfhnPcfEYzqWi35+M5/BRuHaz4rCwBj2Wf4rvGzZ6jDHrcTAZj3tH4drFSsYX9rGlmb+IsCXzEZQKtGs3ob9whcBnHqfy1FnKt+/jhjCaKPjwC0K++ACUCsr+2oD+4hX8pzxB1Zk0dDv33ng8gNFIyaefEfjRh5JP12/AcPkyPvET0adJPvUaNQr3Xj3N9tRS/J5cDxelgjkP9mHasn8wmURGdG1JdFgQizccoHUDNXFtmzB7eE/m/bKD1TuOgwBvjemPYF4i4qJQMGdwDNN+2oPJBCNiGhGt9mPxjtO0Dg8krrl0A7XxtLR5WKhhaYmLUsnL4+5h2kffYzKJjOzTkejIEL78I4k2TSKI69iSlNTLfP6btMH37haNmDteXu5W4tGeaT/vxSSKjGjfUOKx8wytwwOIu+s6j4wb8FAwZ3R/pi36HZPJxIgebYmOULH47z20bhRKXPtoZo+KY97qRFYnHZZsMX6oRZbRiPazzwj88ENzrthgzhUTMaSlSbli1Cjce/aUcoXWJlcAmExU/LYEr6fnSSXH923GlJ2O2z1jMaafw3jyAMYzh3Fp2QmvuYvBZKLyz1VQrrWSYaR00af4v/cRgkJBxSZz7pxgzp17k/EcKc+d2g/lPESjiV2vfcv9P7yEoFSQ+vMOCs9m0OX5UeQdv8TlzYfp8coYXL08GLJEyjnazHw2xH9SzaFeefO6Twe2Zdqv+ySftmtAtMqXxbtSaR0WQNxd0oXdxjOZDG1Vi08VCuYM68S01TslGR2aEB3iz+JtJ2kdEUhci0hmD45h3t8prN5/FhB4a0RXmSwXpYI5w7sz7etEaY50vovo0EAWJx6mdZSKuNYNmX1vV+b9sYfVu0+BIPDWw30sMkQTVRu/xWPMS6BQYDi6A1GTgWvsKEyZlzCek26gpAIQteQNo4nCBV+g/vwDBKWC0r82YLh4BT9zvqi4qXxh4trrS2n63ZsISgUFv2yh4txVwmY/Rvnx85Rssd97aYsX33ifg0eOU1RUwoCR43h60ngMBukN4+gH7qVvjy7s2nuQYY/E4+nhwdtz5ZXLpDk2kGmf/ybNsZ7tpDn2125aNwojLsY8x37YxOqtKdIcmzDMxh9KXp4wnGkLVkn5IvZuoqNC+fK3zbRpEkXc3a1IOXORz39OBAHubtGEuU8Mt+dRH5+aecx9cjTT5i3CaDIxckAPohtG8OWav2ndrBH9urbn4MmzfL76TwQEOrWO5pXJo+V6jL+XaR9+h8lkYmTfTkRHhfDlH1tp0ziSuE7mvPfrZkCQ8t7j99nooeTl+FFMe3epJKNfN6IbhPPlzxto06wBcZ3bknL6PJ//mACCwN2tmjJ30kNyGbchdwKkJB2kc7/OLN+1Qio5/sLC6rbPN3zBzGEzAJg4dyKxI+Jw97Q8mDAZTax+fQXPf/caCqWCXb8kkXnuKiOfe5TLJ85zdEsKO3/ZyuRPZvL+9kWUFZWyZMZCOw4Hkg7SpX8XVu3+mkpdBR8/b+mzeOMinh46HYBJc+PpN7If7p7u/HDg+xr1ceJ/C4J4q2ueb+ePC0KpKIo+NsfaIxV5UCK9CftFFMV55ramSDdMQcARYJwoipU3+AlRMyT2Bs03hmrTDgBSouqsN1ErOl9bR04/xzkAhG7bQWrzexwe3/Ks9H2idWGPOSxjZLb0bZSs3v0clhG+exsFI+pni6A/d5DZ03EOEcnbAMi/13EewQk7KHignnqs3cHFdoMdHt/0RCIA6Z0H1NGzdjRMkW4esvvGOSwjbOd2dAmf1t3xBvC891l03976nrPq8ROki+SKvWvq6Fk7PHpIS1d0q15ynMfEBei2Orj37LqMAZPJiYtzeHzo9u0AaGfed+OON4Dv59I+xrxBjse4evMOFjcYV3fHG+Dpqz/UK3eGbpPyt27lCw7L8JwklcTWrX6tjp43kDH2bXRr36+7441kPDCHsncct6f3qz8AcLWL4/miwUEpXxxtNLyOnrWjw5W/0GsuOjwewFXVFN22FXV3rAWe/Z4EoOJgzd+Wuhl4dJHKddfHr54PzKHy1FaHx7u3kXxZsa/m7/rdDDy6SzdfFcfW19HzBjJi7qlX3gQpd97X0P57TTeLf9KlfZATG4+qo2ftWHVZiochDYbV0bN2bLq6AaSCiXc8nmz80J1bog5Ycfm3O9KO/+qbJtsbJvOx40DHGrojiuJFoOv/bV5OOOGEE0444YQTTjjhhBPX8W/vaXLCCSeccMIJJ5xwwgknnLijccftaXLCCSeccMIJJ5xwwgkn/u/AWT3PMTjfNDnhhBNOOPF/2Dvv8Ciqr49/ZjchvW52N4FQQwm9B6WFIk0FwYJKEQ1FOlJUVBABQUHFhhhEsSJiQ9HQS6QEQi8BEjoB0nbT6ya7O+8fE7I72dCS/F55f+98n4fnIXvP+c655545U+69ZxQoUKBAgQIFt8G/WgjifwH/1Z1ToECBAgUKFChQcN/gvixgUB4R93khiNX3aSEIZaZJgQIFChQoUKBAgQIF/2cgCEJ/QRASBEG4IAiCQwleQRBcBEFYV9oeKwhCvaoe879+T1Na78qXrNXtkErWVrXE6sUW/SqtDxASt4VDtYZUWr/jjfUALKw7vNIcc6+uAaruz6qUCwepZPiVNn0qrV/v+DaAKpd1rkqZbpBKdUfrn6q0fo/UXwC40KzysdXwzBYArrZ7qNIcdY9up/DPpZXWB3B77BUKN7xfef1BUknpouN/V5rDtY1Uortw3fzK2/H0vCrZcNOOuAaVLxfe4pJ0/PSBVSip/5eU96oaF6uCq1ZyfOz1HzhVf2Cl9Vte/guAwi3LK83h1k/6Hkvh7m8qz9H9eYr2ram0PoBrl+EUvBdRaX33l1cDVEtsVZWjKuXCQSoZXpWy5c4BDQAwndtbaQ6Xxl0BKPx7WaU53B6dUaW4cO0iXc+rI76rXHK8GkrqP1mF+6xfr24AoHdw5T/jseO69BmPnsGVv7/YdX1bpXX/tyH+H1+IJQiCGvgM6Vuu14FDgiBsEEXxjJ3YaCBTFMWGgiA8AywBnnZku3soM00KFChQoECBAgUKFCj4v4Iw4IIoipdEUSxG+obrY+VkHkP67ivAr0Bv4VZfVb5LKA9NChQoUKBAgQIFChQouC8gCMI4QRAO2/0bV06kFnDN7u/rpb9VKCOKohnIBjRVseu/fnmeAgUKFChQoECBAgUKJNzvJcdFUfwC+OLftqM8lJkmBQoUKFCgQIECBQoU/F/BDaC23d/Bpb9VKCMIghPgA6RX5aD/L2aaanQMw3PSFFCpKNoYRcFPP8raXfv1x3PcBCxGAwCFf66naGOUTMYrvB215o1BUKtJ/2kraZ//VuGxfAY8SP3I10h4dAaFpy7I2ty6dCBg9ngEtZqc3zaR9dXPFXJ4PNSVwI/mcv3pyZhOny/73btHW+osGI2gUmFYu52Uz36X6WlH9kM3agBYrVjyi7jyygqKzl+XyYSEt6LfvJEIahXHfoom5vO/ZO11wkLpO28E+tA6/D5lOWc3HnSwrzr86dKpIz4vTQa1moK/osj7fq3cVw/3w3vSeKwGIwD5v62n4C/bZlW3zh3wf2UiqFTkrd9E9tfrKvSle++u6D6YR9KwSRSfOSfvR1gYXpMlGwqjoij4sVw/+vfHa/x4LEbJhsL16ymMinLg8J46GVQSR/4aOYdb//54TRyPpbQfBb87cvj3bEPDt19AUKtIXrODxE//kLXXfK4PNSP6g0Ua14RZK+V97NqBgNfGg1pNzq+byPryFnHVpytBH8/l2lPyuAJw7dwR/1kTQS35M+ebnyrkcO/VDe3780gePpHis3J/7ku4ztI/D2AVrQwJa0JEz9ay9uTMPOau201ukQmrVWTqgI50a1pbzhF/jaUb9mO1ihJHrzYVcESTW1gscTzckb52+4f3HY9nyTd/YLVaGdKrE6MH95bpJxkymBe5jsycfHw83Vk8eRh6ja/chvNJLN14BKsoMqRdCBHdm8ttyMpn7u/7yS0qwSqKTO3Tmm6N5asCqsMOz+7tCHpzHKhUZP68FWPkr1QE7/6dqbPidS489pLsd+d2YXiMLT1Pt0VR9Ks8Nl1698f9hQlY06XztChqPaat8tisjrgI7tGKB+dLOSdhbTQnPpPnnJZjB9Dk2R5YLRaK0nPZPfML8m7Yrmue3dtRc95YyQ/rtmG4jR/qfv4aFwZNd8i9+85cZenvu6W4erAZEX06yNqTM3KZ+8M2cgtN0pgO7Ey35vXkHHEXWfrTdmlMu7UhYsCDsvak9Gze+iaKzNwCvD3cWDx6IHp/bznHqQss+XGLdI50a8voR7rKOYxZzPt6A5m5Bfh4uLF47BAZh6peC2r0HgaCgPnkHswH5Rv4nXs+g7pOqPSHUw0Ed28KP50sk6lMXBWV82d1cOw7fZmlP++QxqRLKyL6d5K1J2fkMPebjdKYWK1MHRxOt5YNytrnLF7G7n0H8ffz5Y8fIh2OLYoi73wUyZ79h3B1dWHRGzNp1qShTGbvkVMsWbUWq1Xk8T7dGP3Uw7L2pDQjb378NZk5efh4erB45hgCA/zl/YhPZOkfMVI/OoUS0butvB+ZucxdG22LrUc60a1pHTlHJeKirr1+dcT38bMs+Xq9xNG7E6MHywvBJBkymPf5T6W+cGfxlBGOuTPhOkv/ipVyZ8fGRPRoJbcjK4+5P++R8rcoMrV/e7qFyq8BABFvjaVtzw4UF5pYPusjLsc5Fv5o0CKESR9Mo4ari0PbpAUT6dSrI6ZCE0unv8/5uAsOMhGvPE+fJ/vg5ePJo03Kb4WBKQsm0qlXGEWFJpZMf69CjtGvvEDfJx/Cy8eLh5tUvpCFgkrhENBIEIT6SA9HzwDDyslsAEYB+4EngZ1iFb+z9K89NAmCYAFOldpwFqljVmA34FL6+6+iKM4rlZ8MvASEAFpRFI13dSCVCq+pL5H5ykysBgN+K1Zi2r8Py9WrMrGi6J3kffrxLTmCF77IxeFvUpKSTuMNH5C9/SCm89fkYh5uaF8YRP7RhAo5tHMmkTT2NcwpRoLXfUr+rgOUXEqU+8XdDZ8Rgyk6cdZBv+6icZx79i2Kk9NptnEpWVsPyh6K0tfvxvC9VBHNt09H6sx7gXMjFtq4VQL9Fz7PmuHvkJOSwZgNCzm3/SjG87aH8+wkIxtmruTBcY/8R/3pM2sa6dNexpJmQPtVJEV7YjBfKcexYxfZyz6pUN//tSmkjn8Vc6qRmmuWU/DP/gp96T1sCKaTZyvk8Jo2jaxZs7AYDPhHRmLaV0E/du0i9+Nb98N7+jQyZ0gcmi8iKdrryFG4cxe5H92ao9G7ozkxdCGmpAzab3kH45bDFJyzjWvq73tJ+k6qyqPp14GG80fJ9LVzJnFjzGuYU43UvhlXFx194Tuygrgq5fB/dQppE1/FnGog6IfPKPwnhpLLjhxew4ZgOuXIYbFaeWd9DJFj+6P38WD4pxsIb1aHEL1fmcyqHcfp27o+Qx9sysXUTCav3sqmpk+X49hH5LiHJY5P/iC8ed1yHMfo26oBQzs3kzi+2kzfV236i1f/zso3XkSv8WHYax/Ro0NzQoIDy/SXff8XA7t3YFB4R2LjzvPx2o0snjxMbsPfh4kc1Qu9txvDV24hPDSYEJ2PzYZ/4ujboi5DwxpxMS2byT9Es2lGLRlHVe1ApaLm/AlyqYc/AAAgAElEQVRcfm4O5pR0GvzxIbnbYzFdcMw5mucHUXAs3mFMPca/RM7cmVjTDfgsW0lJ7D4s1+SxWbxnJ/krbx2bVY0LQSXQ5e1RbBz2LvnJGQyOWsDVrUfIOp9UJmM8fYUzD8/FUlRM05G9CXvjWXZOXG7zw4LxXB45F3NKOiF/LiPnFn4IeGGgox8oHdNfoomcNBi9ryfD319HeIsGhATZbn5XbT1E37aNGNqtJReTM5i8cgObmj8v5/hxK5HTn0Hv583wRd8Q3roRITUDymSW/bKTRx9swaDOrTh49gqfrI9m0ehBMo7FP2xi5cwR6P29GbbgS3q0aUJILa2N4+dtDOzcmkFdWhN79jIf/7aDxWNLK6cKAjX6jMD08weIuRm4jnwTy8XjiOk2X5bs+omS0v87te2NSi+/Oa9yXFUTh8Vq5Z2124icNhS9nxfD3/me8FYhMn+u2rifvu2bMDS8LReTjExe/hubWr5Y1j744T4Me2IQry+suPLmnv2HSLyexMZ1X3HydDwL31/O2lUf2WywWFkcuYYvFs5Er/Hj2RkL6dGpDSF1apbJfLD6Zwb26sxjvbsQe+Isn3z7G4tnjpX34/d9RL74iJSzPvqd8Ob1CAm0y1nbj9K3TQOGdm7OxZRMJn+5kU1zhss4KhMXywZOsNlQDfG9+KvfWDlnPHqNL8Ne+5AeHVqUy1kbpJzVI0zKWT/+zeIpI2Qc7/x5gMjR/dD7uDN8+V+EN61DiN72YLVq5wn6tqrP0AdCuZiaxeSvt7FptvyhqW3P9gTVr8mU8Bdp1LYJ496ewGuDX3YY37GLJhA5+zPOH0soq54HENarI8H1a/Fc1xdo2i6Uae9MZfLAqQ76+7cf4I9vNvDdnq8d2jr1CqNW/VqM6Po8Tds1Zfo7U5lYAUfM9gOs/+ZPftjzjUPb/Q7r//FvtIqiaC59LtgCqIHVoiieFgRhAXBYFMUNwFfA94IgXAAykB6sqoR/c3leoSiKbURRbAEUA+MBE9BLFMXWQBugvyAID5TK7wMeAq5WyHYLOIU2xXzjBtbkZDCbMe3aiUvnrndWtIN7m0aYriRTfC0VscRM5l978OnTyUEuaOZw0iJ/QzQVO7S5tGxCSWIS5uspYDaTtykaj14POsj5TxlF1uqfEYvlHB5tJRtMiZINGX/uxa9fmEzGmldY9n+Vuwvlz4mabULIvJJK1jUD1hILp/86QJM+7WUy2deNpMVfQ7RWfEJVhz+dm4Vivp6EJUniKNy+E9duXe5a36VFE8zXkjDfkHyZvyUa9x6dHeT8Jj1P9jfrHHwJ4BwaiuXGDSyl/SjauROXLndvA4Bz03IcO3bi2vXeOLzbNaTwcgpFV9MQS8yk/bGPgP7yt4QWu3FVu7tg/6LE1T6uSqS48qwgrjRTR5H55c8VxmaNFk0wX0/CfCO5zJ9uPRz74TvxeXK+WVchR9w1A7UDvAnWeOPspKZf6wZEny53cy1AfpGkm1dUjNbbXc6RWI6jTQjRp+WnuwDklx4/r1DOEXchkdp6DcF6Dc5OTvTv3JboQ6dl+hdvpBLWXHrbHNa8IdGH4+Q2XE+ntr8nwf6ekg0t6xIdL5+tFQSBfFOJrR9ebnKOarDDrXVjTFeTKSnNOdl/78arzwOUh27GCAwrf0U0lch+d2rUFEvyDayppefp7p04d7q387Q64kLbJoScK6nkJko55+KfB6jbV55zkmPOYimNi7SjF/Cwu9lzb92IYns//LUb7wpyr37GcAyRv2Et5weAuKup1Nb6EhzgI41pu8ZEn5K/uRawj00TWm8POcflJGpr/QjW+kkcHZsSfVw+o3YpyUhYaD0AOobWJfq4fDY37tINauv8CNZJHP07NSf6uPwF28UkI2FNJY6w0HpEH7O1q4IaIGamIWYbwGrBHB+LuqF8JtYe6qadMJ+Nlf1W1biqLo64K8mSL7S+pf4MJfqk/E2+PF+Y0Pp6yto7tGmJj7fXLfu/a+8BBvXvjSAItG7RlNzcPAzGDJsN5y9RJ0hHcKAWZ2cn+ncPY1fsMRnHpcRkOrVqCkBYq1B2xR6X9yMxjdoau5zVtiHRp6/I+4FAftHNfFFBbFUxLqolvi8kUjswgGB9gF3Okueki9dTCGvRSLKhotx5zUhtjRfBGi/bNeBMuWsA5a8B8twJ0LFPJ6J/2wXA+WMJuHt74Kvzk8n46vxw93Tn/DHHF9Rd+nZm66/SS8azR+Px9PbAX+fvIHf2aDwZaRkOv0scD7L11+2lcmfx8Pa8BcfZW3Io+M9DFMWNoig2FkUxRBTFRaW/vVn6wIQoikWiKD4limJDURTDRFGs/LcKSnG/7GnaAzQUJeSV/uZc+k8EEEXxmCiKV+6VWB0QgNWQVva31WBAFRDgIOfSLRz/VavxnjcflVYra3MO1FCSbJvYKkk24hwoL8Dh1qIBzjUDyNl5uEI7nHQazCmGsr/NqUacdHI7ajRtiFOgloLdFSyJC/SnOMlmQ3FyuoMNALpRA2i573NqzxlF4pvy72F4B/qTk2xb9pKTnIFXoF95ituiOvyp1gZgSbVxWAwG1FpHDtce3dF+9yV+i95CpbNxqHUBDr5Ul/dlaEPUei2Fexx9CaDSarEabBxWgwF1OTsBXLp3x/+rr/CZ79gPVYAWS5qNw2IwOMgAuIZ3R/P1V/gumC/rB4BLoD+mJNuYmJIycKlgXGu+0I9OsZ/SYO4ILryxuux3tV5Dib0vUhx94XKbuAJw0gZgTrEbjzQDap3cBsmfOgr3xpZXByAtu4BAH9uFWO/jTlpOvkxmfJ92RB27SN9Fa5m8eiuzH5M/3KXl5BNod2Ok9/EgLbscR9/2RB29QN+3f2Ty6s3MHmx7WE7LyCbQbrmITuNDama2TL9J3ZrsOHgKgB0HT5FfaCIr13aMtNxCeT+83UnLKZDb0LMlUScu0/f99Uz+IZrZj8gfcqvDDinn2I1rshFnvXxMXJuH4BwUQN4ux5yj0gRgNdqdp+kG1BrHc6xG53B8PlmN5+z5qALksVkdceER5Edesu2mIj8lA4+gW+ecJs+Gc33XCZsN5XNvimPek/ygJbcCPwCkZZWLK19P0rLzZDLjB3Qi6nACfeeuZnLkX8x+MrwcRx6Bdsvk9H5epGXlymQa19axo3SVwc5j58gvKiYrr8COI5dAf9uMpc7Pm9RMOUeT2np2HJFmZnYcjZdxCJ6+iLk2X4q5mQieFftS8Nag8gnAmiif/atqXFUXR1pmHoF+tgceva8XaZnlxuTRLkTFnqHv7M+ZvPw3Zj/duzzNbZFqSCfQLhfqdQGkGmyxlJqehd5uqZ1e40daepaMo3H92mzffwSAHfuPkl9YRFaOzc607II756x+7Yk6cp6+C35g8pebmD1E/uKhsnGRmZlZql8N8Z2R5ZizMsrnrFrsOHhSsqGi3JlzF9eAh9pK14DF65j89TZmD3J82NYEakhPssVXRko6mnLxpdFrSE+peLFRQKAGg52+IdlIQAXX1NshIDCAtCRb7jMmGwkIdMyfCv7/4V9/aCrdnDUAaakegiCoBUE4DqQB20RRrPhqfGu+sjKFX3xxd4U3TPtjSB/+NBljIyg+chjvV1+/105Qa85okt5efWfZ23AEvDKO9PeqViwk7dtNnOoygWuLvqPmtMp/PLUqqLI/gaK9+0l94lkMz43BdPAIfnMdPvZ8awgC/rPGk7ls5Z1lbwNTTAzGZ54hY/Roig8fxue11+6ZoygmBsPQZ0h/YTSmw4fxef3eOQCSvt5CbKcpXHp7DXWnP3H3ioJAwKvjMC6tQlwJAn4zJpC5zHHfwL1g8/GLDGrfiK1vPMvyiL7M+ekfrLeY0bwlx7ELDOrQmK1zhrE8oj9z1kZjtd59HaAZIwZy+Mwlhr76AUfOXkLn74NKdW9pcPPJKwxq24Cts4awfEQP5vwWc8/9qLIdgkDQG2NIWfTVPR3XHsUHY8gc/TTZUyMoOX4Yz5fuPe9VR1zcRMPHuxDQqgEnIqPuLGxnQ9Cc0SRXwQ8Am4+cY1CnULYujGD5+IHM+X7rvY/pU704ci6Rpxes5vC5RHS+XvccWzOG9uFwwlWGvvUFRxKuovO7dw4AdWgY5nOHcVhucCdUQ1xVCwew+dBZBj3Ygq3vTmD55CeY8/XGex6TqmJmxFMciTvH0GlvcTguAZ3G797zxbGLDOrYmK1vjmD5mAHMWbvz3mOrgrhQq9V3b0N1xPfIQRw+c5Ghr7zPkTMXKpc7T1ySrgGvP83yF/ow5+fd/+tjqkCCeJ//u1/xbxaCcCt9OAJppukrAFEULUAbQRB8gfWCILQQRTHuViTlUa5MoZgxZSIqra6sXaXVYjXK31CIOTll/y/aGIXn2PGy9pKUdJyDbG8ZnIMCKEmxzQ6oPN1wbVKXhj8tAsBJ60eDr97g0uhFZTLmtHScAm1vcp30AZjTbHaoPNyo0bAeNb9eCoA6wJ/AT+eTMmUeAMUpGdSwW+9dI0gjs6E8Mv7cS913XpT9lpOSgXeQ7Y2Ld5A/uSmZt+SoCBajscr+tBiMqPU2DrVWW1YooSKOgr+i8J5kK9FvSTM6+NJi50vBww3nkHoEfimtdVdr/NF9tIC0l94sk7GWmxVSabVY7GaeyttQGBWF54tyf1qNBtT2M2DlZq8cOP6Owmu8nMOUkoFLTduYuNT0x3SbcU1bv4/GS+zW1Kem42zvi0C5L1QebtRoVI9a39riKuiz+SRPmlcmYzYYcQq0Gw+dFkuazQbBw13y56oPpHaNP9qPFmCw86fOx50UuzesqdkF6MotAVl/6BwrRvcDoHVdPSazhayCIm5K6bw9SMnKs+PIR+dTniOBFWMGSBz1JI7MzEw0Gg06fx9S7N4Up6Vno/fzkenr/H34cNbzABQUmdgeexJvD9sSEZ2Xm7wfOQXoyi0jXH/0Eiue6yHZUEdb2g+TrR/VYIeUc+zGNSiAklR5znFpXIf6a9+R2rV+1P1iblm7Nd2IKsDuPNVosaSXO8dybbFp2hqF+/Py87Q64iI/ORNPu+V2HoH+5Cc75pyaXZvTZsog/n5yEdZis82G8rk3UOOYexvXpcFPi21+WDWHq2PfLpPR+ZaLq6w8dD7ypV7rD5xhxQRp/1Hr+kHSmOYX2sbU15OUDJu/UjNz0fnKl4bpfL1YNlF6oVFQVMyOIwl4u7vK2lPs3t6nZeag9yvH4efFh5OHlnFsP3K2jEPMy0LwsvlS8PJDzKs4fzuFhlG8/QeH3ysbV1fHLaxWDp2fJyl2sympWbno/MqNyb5TrJjyJACtG9TCZDaTlVeAPCPcGnqthhS7XJiaZkRvt6JBr/El1W65Xmp6JrpyhQ10Gj8+fH0SAAWFRWyPOYq3py0f6Hzc75yzYuNZMVYqMNG6XiCmEgtZ+XZ5r7Jx4e1dql8N8e3v65iz/CvKWRGlNlSQO73v5hpwnhURfSQ76upk14D3Nkr7zS6ePI+mphZpqzv4B2pIT5VfE9NT09FUMPOzcsvnJJxIQFvTFp/aoACMt7mmlseqLZHEn0hAV1MHSMuqA4ICMN5iZkvB/y/cD3ua2oiiOKX0i75lEEUxC9gF9K/KQczx8TjVCkYVGAhOTrj07IUpZp9MRuVvuxDVeLALlkT5PoqCE+dxqV+TGrX1CM5O+A3sRs422wSYNbeAuLYjONN1LGe6jqXgWAKXRi+SVXAyxSXgXKcWTrX04OSE54Ae5O86YOPIK+BKt6Ek9htFYr9RmE6eJWXKvLIqZ/nHz+NSP4gatXUIzk74P9aVzK2HZHa61A8q+7/PQ+0xXU6WtSeduIR//UB8a2tROatpPvABzm078r/uz5Kz8TgF10IdJHG4PdSLor0xcg6NjcO1a2fMV2xro02nE3CqUwunmpK+R78eFPyzv6xdzCvgWs8nuf7wSK4/PBLTqbOkvfSmrHpeSUIC6mBbP1x79cIUU84Gu364dO6MOVG+PrskXuK42Q/X3r0w7bt1P1y6dMZ8Vc6Re+wCbg2CcK0jjatucBeMW+RLWtzq2zbjavq0o/CSbVyL4hJwrlsaV84Vx9XlLkO52mcUV/uMwnTiLMmT5smq5xWfTsCpttyfhf/Y+iHm5XO99xPceHQENx4dgenUWQwvvSmrktY8WEuiMYcbGbmUmC1sOXGJ8GbyTehBvp7EXpA2rV9KzaK4xIKfh+2msnntmxw5EsfxixVznL/JkUmx2YJ/6Tg1D6lNYoqR62nplJjNbI45RngHeeW7zJy8spmpr/7YweCe8n2BzWtpSMzI5UZmnmTDqauEh8or4wX5uBN7KVWywZBNsdmKn4etglN12FF48hwu9WriHCzlHJ9Hu5O7XZ5z4jsM51z30ZzrPprCYwmym1Lz+XjUNYNR6UvP0+69KDkoP08FP7vzNKyLY5GIaogLw4lLeNcPxKs054Q89gCJ247KjqNpXpdu70awNWIZRek5sraCk+flfhjYnZzttmWm1twCzrYfTkK3MSR0G0PBsQSujn1blnub19GTaMjiRnq2NKZHzxHesr7sOEF+nsSWFl+5lJIhxaan7Yaweb2aJKZlcsOQJXEcOkt460YyjszcgrK35l9t2s/grvLKYc3r1yIxNYPrhkxKzBY2x54mvE3jW3NE7WVwV9ueJWvyZQQ/PYJPAKjUOIV2wnJBvscGQPAPBFcPrEkXHdoqG1f2le+qg6N53SDJn8ab/ownvJW8sl2Qvzex8VK+vJScTnGJGT8v+QuM26FH1wfYsHkHoihyIu4snp4eaO2W4zVvVJ+rSalcTzFQUmJm8+6D9AiT7xHLzM4tO0+//GUjQx6S7wtsXltHojGbG+mlOevYBcKb15XJBPl5EltabOlmzvLztMt7VYyLaonvkNokJhvuPmet387gnvK9hc2DA0hML38NkBd5CPL1IPaCdP26lCa/Brz88Eu8/PBLHNwaS48negLQqG0TCnILyEqTvxzISsukIK+ARm2byH5/sd8E9m2Ooe+T0oNZ03ah5Ofm39O+o7H9xrNv8z76PvlQKUfTe+ZQ8N+L+6rkuCAIWqBEFMUsQRDcgD7AkiqRWi3kfvoRvkveR1CpKNy0EcvVK3g8H0FJQjzF+2NwG/IELp27IFosiLm55Cx9V85hsXL9zZU0+O4tBLWKjJ+3U3T+GoEzhlFw8oLsIn5LWKwYF39G0MrFCGoVOeu3UnLxKn6TnsN0+hwF0QfuqJ84ZxVNfpwHKhXGdTsoOneNmrOepeDEBbK2HUL//MN4d2uFaLZgzs7j0kvyynOixcrmN79h2HevIqhVnPj5HwznbxA+4wmST17m3PajBLVqwNAvpuPq406jh9oSPv0JIvu8Wu3+zF72CZoPl4JaRcHfmzBfvoLXmBcojk/AtDcGj6cel4oqWCxYc3LIWvSuTD/j3eXoP39HKjn+5xZKLl7Fd8IoTGfOUWj3AHVrf1rI/fhj/N57TyrJvGkTlitX8HjhBcwJCZhiYnB/4glcOne29ePd8v2wkPPRx/i9L3EUbtyE+coVPCNeoCQhAdO+Uo4unUv7kUv2O3IO0WLl/Gtf0eqnN6SS42t3UZBwnXqvPE3uiYukbzlMrdED8OvWEtFsoSQ7j7NTlxO2+8MyXxgWfUbNVYsRVFJcFV+4iv/k5yg6fY6CXXeIq5v+XPIpus/elfy5YTMll67iM34UxWfOUbj7zv50UquY/diDTPhyM1aryGMdG9Mw0I8VW47QLDiAHs3rMuPRMBb8upc1e6S3d/Of7oYgCHKOwZ2ZsGqTxBHWhIaB/qzYcphmwVqJY+ADLPhlD2v2SPuB5g8NL+NwUqt5LeJxJiz+AqtVZHCPMBrWDuSznzfTvEEwPTq04PCZi3yydiMI0D60Aa+PfsKxH490YMJ3uyQb2jWgoc6XFTtO0qyWPz1Cg5nRvx0L/oxlTUw8CDB/yAPl+lF1O7BYSXorknrfLkBQqcj8ZRum84noXhpO4anz5O64Q86xWsiP/Ajv+e+DSoVp+0YsiVdwGx6B+Xw8JQdjcBv4BM6dpHNMzM0l72PH87SqcSFarMTM/ZYBa15BUKlIWPcPmedu0H7WExhOXCZx21E6zXkWJw9XHoqUqlPl3Uhna8Qymx/mRVL/u/lSeetftkt+mF7qh7vIvU5qFbOfDGfCig1YrVYee6AZDYM0rIg6QLM6Onq0bMCMwd1Y8NNO1uw6BoLA/OEPOcbmsD5M+OgnrKLIY11a0bCWlhV/7qZZ3SB6tGnE4XOJfPJ7NALQvnEdXhvW18GO10YMYMKyNVJcdG1Dw1o6Plu/i+b1atKjbRMOJ1zhk193SnHRuC6vjxhg50wrxdt/wOXJGaBSYT61FzE9Cecug7GmXMFyUXqAcgrthCX+Fn6palxVE4eTWsXspx9iwie/SmPSuSUNawawYsNemtUNpEfrhsx4ogcLftjCmh2HpfNs1ADZmLw8710OHTtJVlYOvQePYOLokZjN0izl00MeofuDHdmz/xADhkbg5urKwtenl7NBzevjhzNh3odYrFYGP9SVhnVr8dkPf9CsUT16dmrDobgEPvn2NwRBoF3zxrwxYbhjPx7vyoQvNkpxcTNnbT4k5awW9Zgx8EEW/PIPa3aflGLrmR4OsVWVuKie+FbzWsQTTFi0EqvVyuCenWhYO4jP1m2ieUjt0px1gU9+jAJBoH3TBrw++klHXwx6gAmrpaV/j3VoREO9Hyu2HpWuAc3qMOORMBb8vo81e09LdjwlvwYAHN15mHY927N890pMhSZWzLLdx7y38SNeflj6tMKXcyJLS47XkOnH7jxIp15hfL/3G4qKTLw3w1ZdceWWz3mxn1R1cNwbY+g1uCcubi78dGiNjOPAzoN06tWJH/Z+i6nIxBI7jlVbIhnbT5qVf/GNMfQe3AsXNxd+PiT/pMP9Dut9vQju/oVQxZLllT+wIOSJouhZ7rdWwLdI5QNVwM+iKC4obZsKvAIEIu132iiK4pg7HEZM6x1+B5FbQ7fjHwCO1618/f02VzdwsUW/SusDhMRt4VCtIZXW73hjPQAL6w6/g+StMfeqlFSq6s+kzj0rrQ9QM2YXV9r0qbR+veNSVZ3UHj0qzaGPjiale+X1AQJ3RxOtr/yesx6pvwBwoVnlY6vhGak8/dV2D91B8taoe3Q7hX8urbQ+gNtjr1C4oeKywXelP2gWAEXH/640h2ubRwEoXDe/8nY8Pa9KNty0I67Bo5XWb3FJOn76wMqfp5q/pLxX1bhYFTzizoK3wdjrP3Cq/sBK67e8LH0PqnDL8kpzuPWTvnFUuPubynN0f56ifWvuLHgbuHYZTsF7EZXWd39Z2m9bHbFVVY7CXV/eWfA2cOs5hhJj5YtgOQdI33oyndtbaQ6XxtKMU+HfyyrN4fbojCrFhWsX6XpeHfFddGLjHSRvY0frhylc/+6dBW9nx5DZPFmF+6ybJcd7B/e9g+StseP6VgB6Blf+/mLX9W0gFQm87zGs7pD7+qnpx6vr70s//mszTeUfmEp/Owm0rUAcURQ/ASr4aI8CBQoUKFCgQIECBQoU/OdwXy3PU6BAgQIFChQoUKBAwX8OorI8r1L410uOK1CgQIECBQoUKFCgQMH9DOWhSYECBQoUKFCgQIECBQpuA+WhSYECBQoUKFCgQIECBQpug3+tet7/Ev6rO6dAgQIFChQoUKDgvsF9WfWtPJ6uO/i+vj9ed/WP+9KP//WFILJH9a60rs+3OwBICB1wB8lbo0n8pmopT329U69K6wfH7gRgTc3KlwAeniR9XT7ziR6V5vD7LZqsp6tWctx33S4yhlS+nLL/eqmccvbIKsTF9zuqpH+T42S9ypdTbnVFKqec2rPyvtDvknyR/kgVylNH/VMtJYSrUrbc7bFXADDF/1NpDpdQyQeFPy+ovB1D38QUt63S+gAuLfpwrWPlY6v2ISln5YytfOld71VS6d2qfl5gRe2qlRyfeO0HEjtU3hd1Dku+qEo5ZLchsyWO6NWV5+gRQdGe7yutD+DabST5Cyr/yQiPN6XS1sldK59/g/buAqpeir7o0G+V1gdw7fhEtZQLr46y5YVr5laaw234wurJWTu+qLwNvccBUHzl8B0kb40a9TpUyQ8g+eLROo9UWv/vxCiAailbXg0lxxX8F0NZnqdAgQIFChQoUKBAgQIFt8F//UyTAgUKFChQoECBAgUKJFiV3SuVgjLTpECBAgUKFChQoECBAgW3gfLQpECBAgUKFChQoECBAgW3wf+L5XlOLTviOnwSqFSU/LMRU9RPDjLOYeG4DB4FiFgSL1IYuVjW7t61Pfo3xoNKRfavm8lY9UuFx/Ls24Van8zhypNTMcWdl7XVCAvDe+pkUKkpjIoif82Psna3/v3xmjgei8EIQMHv6ymMiiprd3mgI74zJiOoVORv2Ejud2srtMGtZzc0784nddR4SuLPydqCerSiw8KRCCoVF9ZGc2b5X7L20HEDaDisB1azBVN6LgdmfEH+jXSZjFObMNwjpH6YdkRhWi/vR42e/XEbOR5rhtQP06b1FO+IknO07ojb8xJH8c4oTH/K+1IjvB+uI8Yj3uTYsp7inRvL2p3bhuE+egqoVJi2R1H0u6MN7qMmYM0wSPob12PaXs6Glh1xHVkaF9EbMf0tjwvnbv1wfWYcYmapDdv+pOSfjdXO4RnejlpvjgW1iox12zB8/isVwbt/Z+pFvsb5gdPlfe0YhtfkKaBWURgVRcFauS9c+/XHa/wELEbJF4Xr11O4Ue4L5/ZheIyT/Fm0NYqiX+QcLg/1xz1iAtZ0iaPor/WYtso59p2+zNKfd2C1igzp0oqI/p1k7ckZOcz9ZiO5hSasVitTB4fTrWUDOUfCdZb+eQCraGVIWBMieraWc2TmMXfdbnKLTFitIlMHdKTvY7b2vUfjWLJqHVarlcf7dGX0k/IiLklp6bz56bdkZvxNWcQAACAASURBVOfi4+XB4umjCQzwk9twPomlUYexiiJD2jckontzuQ1Z+cz9fT+5hcVYRZGpfdvQrXEtmczeY2dYsvpXyY7enRn9uLwwQ1JaBm+u+IHM7Dx8vNxZPG0UgRq5Ha4PdsR3phRb+X9uJPdbx7wF0vkesPQtUp6bIPtd3bwDrs9MQFCpKN6zmeLN6xx0nTp0x2XgSEDEeu0ShV/KiybU6BiG56TSuNgYRcFPjrHlOc4utv5cT1G52KrdoxVd3xqJSq3izNpojq2Q55zWYwfQ9JkeiBYLhem57Jz1BXl2Ocf1wY74zSr1wx8bybmVH3p1Q7v0LVJGTqD4rDzv7Uu4ztK/YqUx7diYiB6tZO3JWXnM/XmPbUz7t6dbaG05R9yl0vi2MqRrayL6PyDnyMhh7tdR5BYWSbE5JJxuLUPKcVxkydot0jnSrQ2jH+4ia09Kz2Le13+TmVuAj4cri8cMRu/vXdauDmlFjX4jQaXCfCyakn1yXwKom3WiRvgTIIpYUxMxrf9M1u7SqSPe06TcW/B3FPk/yHOv24B+eE0cj9Uo5az839ZT+Lc8Z7l27oj/rImgVpG3fhM531Q8Ju69uqF9fx7Jwyc6jsmJcyz5/m/Jnz06MnqQvPBIkjGTeV/8VuoLNxZPGIpe41PWvvfIKZasWovVKvJ4n26MfuphuX6akTc//prMnDx8PD1YPHMMgQH+Mpk5i5exe99B/P18+eOHSAf7RVHknY8i2bP/EK6uLix6YybNmjSU9+NCMku3HJfGtG19Iro2lbUnZ+cz94+D5JpKpLjo3YpujYJkMpXJW3XtbTh9maW/7JLiu3MLIvpVkHu/3WyLzcHd6NZCnnvnfvAFu2OP4e/rzfovllToi3c//449B0/g6lqDt2e+SLNG9avdFwDj5r9Ih54dMBWa+Gjmh1yMu+ggM/Ll5+j1RC88fTwd2iLeGkvbnh0oLjSxfNZHXI5zLPrRoEUIkz6YRg1XF47tciyEMWXBRDr1CqOo0MSS6e9xPu6Cg8zoV16g75MP4eXjxcNNKl+E4t+AqCzPqxT+tYcmQRAswKlSG84CowArsBtwKf39V1EU55XKrwE6ACXAQeBFURRL7nwgFa7PTSV/6SuIGQY831pBybH9WJOulomo9LVwefRZ8t6eCgV5CF6+cg6VCv2bk7ge8TolqUbq/vIxeTtjKb6YKD+Uhxt+Ix+j8Hi8ox0qFd7Tp5E5YxYWgwHNF5EU7d2H5epVmVjhzl3kfvRxhfp+L0/DMOVlLGkGdN98TuGeGMyX5fqCuxueTz+BKe5MBa4Q6Lh4FDufeZeC5Az6b1zA9S1HyDmfVCaTGXeFTQPmYiksptFzvWk791n2jl8us8N97DTyFszCmm7Aa0kkJYf2Yb0ut6M4ZheFX1bQD8kQ3CKmkb/oZYnjnUhKDsdgvSHnKInZReHXn1ToC/dxL5H71kys6Qa8l66k+GAFNuzbScGqW9vgOmoq+UtK42LBCkqOyuMCoCQ2mqLvPv3PcahU1Fownssj5lKSkk7DDcvI2RaL6cI1uZiHGwEvDCT/WLyDvte0l8h6eSYWgwH/yJWYYhzjqmjXTnI/uYUvVCo8JrxEzpyZWI0GfD5cScmBfViulfPn7p3kR1bMYbFaeWftNiKnDUXv58Xwd74nvFUIITUDymRWbdxP3/ZNGBrelotJRiYv/41NLV+Uc6yPIXJsf/Q+Hgz/dAPhzeoQorc9TKzacZy+resz9MGmXEzNZPLqrfSdXapvsbJ45Y98MX86eo0fz85aTI+w1oTUqVmm/8HXvzCw5wM81qszsSfj+eT731k8fbTchr8OEfl8L/Te7gyP3Ex4aDAhOtvN2qp/4ujbog5DwxpzMS2byd/vYtNM20OTxWJl8aqf+eLNyeg1vjz76nv06NiSkNq2m4MPvlvPwPAwHuv5ALGnEvjkhw0snjZKNiZ+r0wlbfIrWFIN6L9dQeHu/RWf7888julUufNdUOE2bDL5H85GzDTi8canmE/sx5psy1kqXU1cBjxD/pLpt8x7XlNfIvOVmVgNBvxWrMS0v4LYit5J3qcVx4WgEuj+9ij+GvYueckZPPn3Aq5sO0KmXc4xxF3h9CNzMRcV03xkbzq/8SxbJy63+eHVqaRNkvwQ+N0KCm7hB6+K/EDpmP55gMjR/dD7uDN8+V+EN61DiN7W31U7T9C3VX2GPhDKxdQsJn+9jU2za8s51m4j8qWnS+P7W8JbNZTHd1QMfTuE2sX3L2xqOUHGsXjNJlbOGI7ez5thb39FjzaNCampLZNZ9vMOBj7YkkFdWhN79jIf/76TxWMGl3ZSoMaA5yn64R3EnAxcxyzEnHAU0XjD5gd/Pc5dBlH49VtQVADutgeum/70njGNjOnSdSTgy0hMe2MwXyk3pjt3kfNhBbm3lMP/1SmkTXwVc6qBoB8+o/CfGEoul7seurvhNWwIplNnKxyTxd9uYOXsCPT+3gx7cwU92ocSUktv88WPmxjYtR2Durcj9vRFPv55C4snDJX0LVYWR67hi4UzpXN9xkJ6dGojP9dX/8zAXp15rHcXYk+c5ZNvf2PxzLEyOwY/3IdhTwzi9YXvV9jVPfsPkXg9iY3rvuLk6XgWvr+ctas+kvXjnU1HiRwRjt7bjeFfbie8SU1CtHb5Ys9Z+javzdAODbloyGbyj3vYNO1RG0cl89YHXQfbbFi3g8ipT6L39WL4kjVSbAZpbDZsOkDf9o0Z2r0NF5PTmfzZ72x6W/7Q9Fjfbjw7qA9vvOf48Aiw59AJrt5IIerrDzgZf4G3P/2aHz+xVRutDl8AdOjZgZr1ajKu+1iatG3CxEWTmPnYDAd7Dm6P5e9v/+KLf1bJfm/bsz1B9WsyJfxFGrVtwri3J/Da4Jcd9McumkDk7M84fyyBN76dJ2vr1CuMWvVrMaLr8zRt15Tp70xl4sCpDhwx2w+w/ps/+WHPNxX6TMF/H/7N5XmFoii2EUWxBVAMjAdMQC9RFFsDbYD+giDcfJ23BggFWgJuwJi7OYi6QSjW1BuIhmSwmCmJ3YVzu84ymRrhj2DasQEK8gAQc7Nk7a6tGlOSmETJ9RQoMZO78R88e8vfMgIETH2OjC9/QSwudmhzbhqK5cYNLMnJYDZTtGMnrl27OMjdCjWahWK+fgNLkqRfuG0nbt07O8h5vxhB7vdrweRog6ZtCLlXUslLNGAtsXD1zwPU7tdeJpMacxZLoaRrPHoB9yD5mzl1w1CsKTewpkp2lOzdSY2Od9+PMo7UJKxp0pgUx+zE+R44nBo1xZpss6F4705qhHW9NxtCysXFgV04t3f053+aw71NI4qvJlN8LRWxxEzWX7vx7tvJQU4/cziGyN8QTfL3BM6hTbEk2cXVzp24dLk3Xzg1ljisKRKHafdOnB+4N464K8nU1vkRrPXF2UlNv46hRJ+Uv5kTBMgvkmIrr8iE1lf+hjDumoHaAd4Ea7wljtYNiD6deBuOYrTe7jb985epE6gjOFCLs7MT/bt1ZNfBEzL9S9eS6dQyFICwlk3YFStvj7ueTm2NF8H+XpINLesSfVb+ACsA+UUlNhu83OQcF65QJzCA4MAAyY6u7dh16GQFdjSR7GjRmF2HTsnaazQPpeTaDSw3pDEp2LYLt3DH2PIZ/wK53/3kkHPU9ZtgNSQhGlOk2Dz0D05t5PrO3R6meNet855TaFPMN25gLY0t066duHS+t7jQtQkh+0oqOaU558KGA9TvK885SfvPYi4d09SjF/AItOWcGs1DMdv7Yesu3G/hh5xvHf0AEHfNKI2pxssWV2fKxRXl46rcmF5OprbO1xbfHZoSfUK+kkAQIL/QJHEUmtCWewMedzmJ2jp/grV+ODup6R/WnOjj8tmXi8kGwprWAyAstJ6sXVUrBGtmKmKWAawWLKcP4NRE7kundr0wH94mPTABFOTI2p2bhmK5nmS7jmzfics9XIcAarRogvl6EubSMcnfEo1bD0cO34nPk/PNOsQKrkVxF69TW68hWOePs5MT/R9oRfQR+cPVxRtphDWXbuzDmjWQtcedv0SdILtzvXsYu2KPyfQvJSbTqZU00xHWKpRdsccd7OjQpiU+3l637OuuvQcY1L83giDQukVTcnPzMBgzbHbcyKC2nyfBfp44q9X0a16H6IQkGYcA5Jtu5osSx3xRxbwVdyWF2lpfggNKY7N9E6JPlM+9gi2+C01ofTwcfdGyKT5ejrM2Zb7Yf4RBD3WTfNG0Ebn5BRjSM6vVFwCd+j7Azt+kz6QkHEvAw9sDP52fg1zCsQQy0zIdfu/YpxPRv0nl8c8fS8Dd2wPfcvq+Oj/cPd05fywBoEz+Jrr0fZCtv24H4OzRs3h4e+Kvk98L3WzLSMtw+F3Bfy/ulz1Ne4CGooS80t+cS/+JAKIobixtF5FmmoLvhljwC0AsXaIFYM0wIPgFyGRUgcGo9cF4zPkYj7mf4tSyo6zdSR9ASbKNw5xixEmvkcm4NAvBOSiA/H8OVWiHKkCLJc3GYTEYUGm1DnKu4d3RfP0Vvgvmo9LZ2tW6ACypaTb9NCPqcvrOTRqh1msp2hdboQ1ugX4UJNlO8ILkDNyCHJPRTYQ8G07STnnyVvlrsRrL+VPj2I8aD3THa9lXeMya79Cu8g/Amm7rizXdgKrcmAA4d+qO19IvcZ/+loxD8A/AYiynr3HUr/FAON4frsbz5fmoytlwN3EB4NyxG56LVuE+ZR6Cf/VzOOs1lCQZy/4uSU7HuVxsuTUPoUaQltwKlhCoAgKwptn5wmBAHeBog0v3cPy/XI3PW/Md4k6lCcBq70+jAXVF/uwSjs/y1Xi+Nh9VgJwjLTOPQD/bzYfe14u0zDyZzPhHuxAVe4a+sz9n8vLfmP20/Bs8adkFBNpdzPU+7qTl5Ms5+rQj6thF+i5ay+TVW5n92INlbanpWejtlt/oNb6kpcsvqo3r12b7AenmaseBY+QXFpGVY7MzLaeQQB/bg5jex5203EK5Db1aEXXiMn3f+53J30cz+5EOsvbUjGz0dkv+9P5+pKVny+2oV4vtB6SbuB2xJyQ7cm12qLUBWFLt8kWqAbVWPia3O98F34CypakAYqYBla88rlT6YFT6YNxf/RD31z5G3VzeD3VAAFaDPLZUFcVWt3D8V63Ge55jbHkE+pFnl3PykjPwCLx1zmn6TDiJ0bacI+U9u9ybZkCtc/SDU+Ct815azl3E1UNtpbhavI7JX29j9iD5S7G0rFwC/WyzNno/L9KyysX3wK5ExZ6m76ufMXn5L8x+Rv69l7RMOYfOz4vUzFyZTJNgPTuOSjdyO44mkF9UTFae9AAkePkjZtuWLYo5GQhecl+q/AMRNEG4vjAP14j5qEPkyxDV2gAs5fOF1nFMXcO7E/DNl/gufEt2HQJw0gZgTrG/FhlQ6+SxVSO0IWq9jsK9txiTzGwC/W0zEDp/H1Iz5Q94TeoEsuPQackXh0+TX2QiK1fyheO57kdauvyhv3H92mzff0TS33/U4Vy/G6Qa0gm0ize9LoBUgy1fp+WWyxfebo75Irw5UacS6fvhX0xeu4fZ/dvKj1HJvJWZKcmkZZXLvX5epGWXi81HHiTq4Fn6vr6SyZ/97pB77wZpxgwCtbZx1gf4y+ysDl8AaAI1GO3ut9JTjGgCNQ5yt4ImUEN6kk0/IyUdTblrqkavIT3FNo4ZyUZZe0BgAGlJthg3JhsJCHQ8T/4vw3qf/7tf8a8/NAmC4AQMQFqqhyAIakEQjgNpwDZRFGPLyTsDI4HN1WaEWo0qsBb578yg4PNFuL0wA9wd38TcEoKAbvY40pasurPsbVAUE4Nh6DOkvzAa0+HD+Lz+2j3Z4DttAtkff14lG26i3uNd0LRqwJnPo+4sXA4lh2LIHv8MuTNGU3LiMB5T7qEfNzmO7Cdn8rPkvjIG86kjuE+cfW/6h2PIevFpcqZHSDZMe/2ebTAf20/u9OHkvTEWc9wR3F989X+fQxAImjuapEVf3fOxb8K0Pwbjs0+TMSaC4iOH8Zl9774ojo0h84WnyZ4cQcmxw3jOuHeOzYfOMujBFmx9dwLLJz/BnK83YrXe27rqzccvMqh9I7a+8SzLI/oy56d/sFrvPsXOfP5JjsSdY+hLCzkcdw6dxheV6t7S4OaTVxjULoStLz/O8pE9mPNbzD33Y+aoIRw5c4Ghs97l8OkL6Pzv0Q5BwHf6eLI+qngpzV1BrUKlr0XB+7MoXPUObs9NB7d7yHtIsZU+/Gkyxkqx5f3qvcfFTTQe0gVtqwYci7yHnCMI+M0YT+aHVfADsPnEJSmuXn+a5S/0Yc7Pu+89Ng+eYVDnlmxdMonlk59iztd/3zPHjKEPcTjhKkPnr+LIuavo/LzuLS5UalT+eoq+fRvT78up8egYcHG/s54divbtJ+2pZzE+P4biw0fwfePecq80JhPIXFa1MZkx7GEOx19m6BufcuTsZXR+3qhUwl3rz4x4SjrXp73F4bgEdBq/ez7XqwOb4xIZ1LoeW6cPZPmz3Zjzx0Gs4j3miwryllqtvnsbDscz6IHmbF38IssnPc6cb+4991YHqsMXChT8m/g3C0G4lT4cgTTT9BWAKIoWoI0gCL7AekEQWoiiGGentwLYLYrinopIBUEYB4wDWLlyJcMyjbK3+yp/bdmm/JuwZhiwXDoLFguiMQVrynXUettEljnViHOQjcMpMABzqu2Nn8rDjRqN6lLnu6UAqAP8CF4xj+sT59uOYTSgtp850mqxGmxvQwDEHNubtsK/o/Aab7ffI82IWq+z6esCsNjpC+7uOIXUR7viQ6ld40/A+29jnDXHxpmSiXtN2xst9yB/CpMdp7cDuzWnxbRBbHt8EdZis4Ov7GcZVP5axPRy/ciz9aN4RxTuI1+UtVszjKg0tr6oNFqs5cakPIfb8HG2tgwj6oBy+unl9HNt+qbtUbg9N17efhdxIbMheiOuz8jXw1cHR0lqOs52+yKcgzSU2MeWpxuujesS8pNUmMRJ60e9L21jajUaUensfKHVYjGWs8E+rqKi8Bwn94U13YjK3p8BWiy38+fWKNwj5Bw6P09S7N6ap2blovOTL/VYv+8UK6Y8CUDrBrUwmc1k5RVw8zZd5+NOSrZtBiA1uwCdt/wmfv2hc6wY3U/iqKvHZLaQmZmJRqNBr/El1W7pTGp6FrpyxRV0Gl8+fE3aZ1JQWMT2/Ufx9rTdVOq83UjJLpDbUG4JyfojF1kxqqdkQx0tJrOVrAJTWT/0/j6kGm3nVWpGJjq7DewAOn9fPnxlbKkdJrYfOI63h80Oi8GIWm+XL/TasgIxIJ3vziH10UUuk9o1/mg/WFjWLmYZUdnFpuCnxZolL+giZhqxXIq35b3U66j0dnuzjEZUWnlsWW8TW0Ubo/AcK4+L/JRMPO1yjmeQP/kpjjknuGtz2k8ZxB9PyXOOlPfscq9OiyXN0Q/6lTY/BCxbiHHG3DIZnffdxNV5VkRIM0Ot6+owmS1kFRTZYtPXixS7mZDUzFx0vuXj+yQrpkp7blqH1MJUUi6+/eQcaZm56P3kS8N0vl58OOkpAAqKitl+NB5vd1cAxNwMBB/bG3PB2x8xV+5LMScD640LYLUgZhkQM5JRaQJt/jQYUZfPF4Zbj2nBX1F4TRgnazcbjDgF2l+LtFjSbLEleLjjHFKPwFUfSO0af7QfLcDw0pu2fvr5kJJhm31Ny8hG7yfff6Xz8+bDl0aU+sLE9kOn8faQzkXHcz0TnUa+J0+n8ePD1ydJ+oVFbI+Rn+t3A71WQ4pdvKWmGdHbzczpvMrli5xCx3xx/DIrhnUHoHXtgNLYsssXlc1b3pK/dL7lcm9mLrpyS0PXx8SxYtLjkg0NamIqsZCVX8i9vCLRBfiTYrCNc6oxQ2ZnVX3xySZp3+/5k+cIsLvf0gQGkJ4iz123wnsbP+LiyfNoamqRtsqDf6CG9FS5fnpqOhq7mSP/ILu9iVsiiT+RgK6mDpBmOgOCAjCmyM8TBf8/cT/saWojiuIUURRlC59FUcwCdgH9b/4mCMI8QAs47gq06X0himIHURQ7jBs3DsvleNT6WggBgaB2wrlTT0qOxch0zEf34RTaRjqGpzeqwGBpv00pik6dw7luTZxr6cHZCa+Hw8nbeaCs3ZpXwMUHn+FS7+e51Pt5ik7Ec33ifFn1vJL4BNTBwaiDAsHJCdfevTDtk9uh0thuLly6dMZ81bbuvvhsPE61a5Xpu/XpReHu/bZ+5+eT3G8IKUOGkTJkGMVxZzDOmiOrnpd+/BJe9QPxqK1F5aym7mMPcH3rUZkNfi3qErYkgn+eX4YpXb5cAsByIQFVUDAqnWSHc9deFB+W90PwtfXDuUNnLDfk+wcsF+NRBdZCpZXGpEbnXpTcA4f5fLzMhhpde1FyaJ9c389Ov2MXhyIRlkvxqANrIZTa4PxAT0qOlrPBx8bh1O5BLEmJ1c5RcOI8NerVxDlYj+DshO/A7uRsO1jWbs0t4Ey74cR3HUN81zEUHEvgypi3y9pL4uNR1wpGFVgaV716YYqR+0LlbxdXnbtgTpT7wnyulEMvcbh070VJ7K39WaNTF4ciEc3rBpGYlskNYxYlZgtbDsUT3kpeZSrI35vYeKn/l5LTKS4x4+dlu4lpHqwl0ZjDjYz/Ye+8o6K6usb9zIBSpUoVEBUbqIAFe8AGaGKLiXnVGBVTJDEajaYYTaJJNDFdja8lpqrEFGsiilGxoKLYsWAFpPc+tJn7++MiM3cGNBn8vvjLd5+1XCuZc/Zmn3322bedu2+pqOP8LYJ9vaQ67KyJvyHuk7+VXUR1jRqHuvH5tfcmJTOHtOw8ampq2XPkFCFB0up7hSWl9U+mvv41mrFDpO9i+LVyJDW/lPTCMtGGiykEd5LuBHazsyT+ZpZoQ04x1bVq7K3MtDp8WpOSmau14+gZQnpKt0kVlpRp7di6l7GDpdvBqi9fpZlXK0zcxTmxHDYI1WFtbAnl5WQMe5zM0ZPIHD2JqsTL5L6qvVBQJyehdNbJe72CqT1/XPI3as4ew6Sj6B+FtQ1KFw/x/bw6aq9exVQntswG3Tu2mvftj1ovtnLO38LW25UWdTnHZ1Qfbu+T5pyWfq0J/jCC3RGfodLLOdWXr9LMU8cPoYZ+SB/6OBmjJpExSvRD3txFkkptfh4tSc3XjytpZTw3Oyvib4hjv5UjxpW9lblWh7defCdcIdi/ofgWx38rM0/UoRvf3u6kZheQlltITa2aPScvEezfQaKjsLSi/gnAht1xjOmvjV9N+i1x+52dEyhNMPHrQ+210xJ5dVICSu+6imUW1igc3NAUarcZ1Vy9ionucWTofY5DA6THIYDqS0mYerbCtG5OrMJCUB3SmZOyctKGjCP9sadJf+xpqi5eIfeVt6Vz0rYVqVl5pOUUUFNby54TFwjuLq20VlhaXr9GNuw8xJhg7ftbfu3bkJKRTVpWrrjGDp8kJChAKl+ss9Z/2c3YoX/vfTyAkAF92LlnP4IgcD7xCtbWVjjpbKXza+VAakGZmC/UavZeSiW4g7tEh5uNJfG3swG4lVsi5gtLnXzRxLzl19qV1Jwi0vOKxdg8nURwN2nVRjf7FsQn6eTe2lrsrQ3fJ7oXg/p0Z+efR0RfXLmOtaUFTjoXTU31xazhLzNr+Msc33uCweMGA9AxsCMVpeUNvrvUEPNHvMLJmHhCxok3tdoHdqSitIIiPfminEIqyipoHyi+V3q3P8BzYTOI2xNH6BNDAejcvTPlpeX/uneXBEF4qP89rDxUJccVCoUTUCMIQpFCobAAhgEf1bU9C4QBQwRB+Ov7cTQaVD+uxGr+R2JZ6MPRaNJTMBs7FXVyErVnj1N78RSmXXpivfQb0Kip3LIOoVzn4K3WkPPef/HY8D4oTSj+LYbqG6k4vjyZysRrlB9seN+2BLWaki++xP6Tj0GpRLU7mtrkZKwjplGTlERV3DEsx43DrH8/UKvRlJRSvEyn/K9aQ9EnK2m54iMUShPKd0VTezsZm+enUn3lGpVHjjX+t+sQ1BoS3vqewZtfQ2Gi5OZPhyi+lk63+ePIP3+b9JgzBC6agKmVOQPWiZViKtLzOTT1Mx1/qqn4+kusF4njqD4QjeZOMub/mYb6RhI1Cccwe3QczXv1Q1CrEcpKKV8lLWOMRoPqmxVYLVgu6oiNRpOWjPmT06i9lUTt6WOYDX+cZj36g0aNpqyEitUfSm1Y/wUt3vlELDm+fzfqO8lYTIig9sZVak4dw/zRcWJxiTobylY2YMMPDcTF41NR3xbjonnYWJoF9hPv2paVolq3/MHrUGvIeHsNbX9YDCZKCn/+k6rrqbjMmYTq4nVK/jzJPdGoKV3xBfbLRV9URu9GnZyM1bQIapOuUnXsGJaPj8Osf39xPkpKKflQ3xdqyv/7BTbv1flz327UqclYPB1B7fWr1MQfw2LUOJr11vHn51IdpiZK3nhqKJErxDLbo/t1xce9Jat3HsW3tSsh/j7MHRfCko172bQ/ARSweMpwFAqFVMfovkR+vQeNRmB0rw74uNqzeu9pfD1aEuLXmrmPBbHk16NsOiLeAVz81MB6HaYmJix4fgKR736BWqNhzJD++Hi589WmHfj6tGZQ7wBOXbzGih+3oVBAd98OvDVjguE4HutJ5PcHRBu6t8PHxY7V+8/j6+5ISGcP5ob3YMmOE2w6dhUUChY/3ldvHCYseHY8ke99hVojMGZwH3y83Pgq6nd8fbwY1Ksbpy5dZ8XGnXV2+PDWc+MN4qJw+UqcVnyEwkRJ2c5oam+lYPPCVKqvJFF5WHoBZBgXGio3r8LylaUoFEqq4/aiyUjBbNQzqFOuUXv+BOpLCZj69cBq8Xqx/6/rEcp13rHRqCld+QV2H32CQqlEFb0bdUoyVlMjqEm6SvXxY1iMHYdZv7rYKi2lZLk0LgS1XoGDtwAAIABJREFUhiOLvmfkRjHnXN1yiMJr6fR6dRy5F26TvO8Mfd+aQDNLc8LWiDmnNCOf6IjP6v1Q8PFKnFd+BCZKyndGU3MrBds6P6ju54e7czqqD5HfxIhz2rM9Pi72rI45I8aVrxdzHw1iydY4Nh29JM7pkwMNY/M/w4j88mdRR/+u+Lg7sXrnkbr4bs/cJwazZOMeNu0/BShYPHWEgY43J4YT+UUUGo2GMf0D8GnlxFfbY/HzdickoAMJSSms2HoAFAp6tPdiwaRw7UAEDdXR32E+6XVQKKk9dwghN51mIePQZNxGfe0M6psXMGnXFYvI5aDRUP3nZlDpvN+i1lDy2QocPhNzr+oP8ThiPX0aNVfF45DVE4+LxSHUajQlJRR9oJcv1BoKPlqJ81cfglJJ2c494pzMmEL15Wt/cU5MeHPKKCKXf4tGIzAmuAc+Hi589es+/Np4ENKjMwlXbrFiSwwooEfHNiyYOkoiv2DGJCLf+Vxc60MH4NO6FV9t3I5ve29xrScmseL731AoFHT368BbkZMM7Jj/zoecOnuBoqIShox5mhenT6a2VnzS+dTYR3mkby+OHD/F8PERWJib894C6eceTJVK3hjenchNh9EIAqMD2uDjbMvqg4n4utsT0rEVc0P9WbIrgU3x18S4GB1kmC+akLfE3DuYyFW/ibm3bxcx9+6Kw7e1CyHd6nLvphg2HTgj5t7J4RIbAF5btopTF65QVFzKkEkzeWnyE/W+GP/YUAYGBXD41DlGTJuLuZlYcvxB+wIg4cApeg7qyfojX4slx+d9Xt+2Inols4a/DMC0BdMIHh2CmYWZRP7MgQS6D+rBqsNrqVJVsXqetgrkx7u/YP6IVwD4euGaupLjzTkbK72Rc+LASXoP7s3Go99TVVnFR3O11RXX713Dc2HiE/UX3nqWIWMGY2Zhxs+npJ9jkPl3ovinrugUCkWZIAjWer91A74HTBCfgv0sCMKSurZaIAW4e1TferftHgjFU/7+C493sf1+PwBJnYbfp2fjdLwaTdYjIUbLA7gejiWt92Cj5T3ixUo0m9yfNlrHpIyNABSOCzFah/1vsRQ9Nej+He+B3ZaDFIwNvn/HRnDYdgiA4slNiIsf9zdJ/q6OC94jjZbvlix+nyV7kPG+cDko+iL/UeN1OP5xCNXBr42WB7AY9CyqHcvv37Ex+dGvAVB19ZDROsw6iT5Q/Xy/lHIPO8a/TVXiPqPlAcy6DONOL+Njy/OUmLNKngu9T8/GsVkfA0DOEOPjwnn/IVZ7Gp9vAF68s5HUnsb7witB9IVq24f36dk4FmPF93lUsd8YryMkgsojPxotD2A+cDLlSwxP+v8qVm9vAiBzgPH51+2oWGEspftQo3W0PvMnlad+M1oewLzXOKquHTVa3qyD+MSpJs/w2z1/lWYtxap+qk2L7tOzcSwmvfdgctb+dcbbMETcelmdbFhk6K/S3Ltnk/wAoi8e83rUaPnfU8X3IJ9obfy3kn5N2QnAII9h9+nZOAfT9oFYJPChZ6zXyIf3cQ6wLXXXQ+nHf+xJk/4FU91vFwDDcipi20P1VExGRkZGRkZGRkZG5v8G8oWIjIyMjIyMjIyMzP8RNDzUD5oeWv7xkuMyMjIyMjIyMjIyMjIPM/JFk4yMjIyMjIyMjIyMzD2Qt+fJyMjIyMjIyMjI/B/hr5egltHlH6ue97/Ev3pwMjIyMjIyMjIyDw0PZdU3fUZ6PfZQnx/vSv39ofSjvD1PRkZGRkZGRkZGRkbmHvzrt+fFuz9utGzvjK0A/Opm/PcxnsjcRJzrE0bLA/TP+pX1HsZ/8+S5NPEbS695T7hPz8ZZnhwFQKzLk0brCMn+hQSPMUbLA/RM284uV+PHMTJLHEdT4+KY2zij5QH6Zf7GdteJRsuPyRI/pLfP5SmjdQzL3gLAYVfj5/SRrF9Q7fzk/h3vgcWoeah+/+z+HRuTf2wuAKq9q4zXETYTgIo1s43WYTnjSyrjNhktD2Def1KT1xhAcoDx3xrxPid+ayqx7WNG6+hy63fWNiFnAbyQtrFJubN/1q8AqLYsNlqHxVPviDqi3jFex4TFTZKv1/HDm8bLP7MMgN0u/zFax4jsnwDY0YS8NTprc5O+mwXit7MeSL5o4jeWoOnfenoQ36erPB5ltA7zvuKxVBW94j4972HH8FlUnvzFaHkA86AnebL1aKPlf0nZAcCE1safX0SlbAegfyvjv4kZl37AaNn/bQR5I5ZRyE+aZGRkZGRkZGRkZGRk7oF80SQjIyMjIyMjIyMjI3MP/vXb82RkZGRkZGRkZGRkROSP2xqH/KRJRkZGRkZGRkZGRkbmHvyfeNJkGxJI6/ciUCiV5ET9SeaqbZJ258mhuEwdjqDRoCmv5Pb8/6K6nibp4zKoGwFLJqMwUXJ7cyxJq3ZJ2tu/MJw2EwehqVVTnV9Cwtz1VKTlSfrYDQqg7XvTwERJ9qb9pK/aLml3fSYU12lhCGrRjhvz16K6prXDI6QbfReLNiRFxXL+K6kNXZ8bTscJIWjUairzSzn86jrK0vMlfToE+zP67WdQmCg5ueUgsf/dKWlvE9SJUW8/g2snLza/vIKL0ScN/OkwKACf96ehMFGSuWk/qSul43B/ZhjuEeGg1qAuryRp3loqrkn9aRMSiNfiZ8FESV7UPrK+2ippd3o6DKepI+p0qEh5fTWVOnPiNMifLu+J40jddJAbq6TjaP3MULynDUOos+H8/K8pu5Yu6fMg4sJuUABtlkSAiZKczftJ19Ph8kworlPrfFFRyc35ayRzCuA8qBtd68aSsukg1/Viq90LI2g9KQShVkN1fgln5qyTtDsO8qfj+1NRmChJ33SA5JU7JO0ezwzFIyIM1Bpqyyu5Mm8d5Xq+sB8UQLv3xDnN2rSfO3qx6fbMMNynhdf78/p8wzmNu3qH5TuPo9EIjA3qSMTgAEl7ZmEZi7bEUqqqRqMRmDWiFwM7e+npSGX59mOijt6diBgSqKejlEVRsZSqqtAIArMe7U2oTr2CuMspLN96WJTv60vEsJ5S+YJSFm3cp5Uf2Y+Bft5SG5Jz+Tj2ChoNjOniQURQW0n7J7FXOJVWAEBljZoCVTVHXhwq1XHxBh9t3otG0DB2YCDTHx0gac/IK+Kdb3dSWFqBrZUFS58bi4uDjaSPMetMF4t+PXF47UVQKinbFk3xt1toCMshA3D+9B0yJr5E9eVrkjbrR7rj9vbzoFRS+HMMeWt+bVCHTXg/vFYv4MboV6i8eEPS5hnSjX51eetqVCznGshbnXXyVqxe3mpq3gSIu57B8t2n0QgCY7u3I+IRP0l7ZlE5i7Yep7SyRoyLYf4M7NBKT0cmy/ecEWOre1siBvoa6tgeT2llXXwP9WdgB/cHqiPuZhbLYy6I4wjwJqJfR4n8x/sucCo5F4DKWjUF5VUcnTdS0qflIH9835+CwkTJnU0HuLVSmju9nhlK64hQhLp8kThvvUHuvJuzqMu/jeUsTV3OOjtnHSq942FcUhrLd8WLY+nVgYiQbnq+KGPRz0fEfCEIzArvwcBOnlp5I3KFQb65kcnyvedEHYFtiBjQWaqjuJxF209SWlUjzseQbgxs71bfvnDpZxyOO4mDvR3bN65BH0EQWPbFGo4cP4W5uRkfvPUqvh19DPrFJaWxfMcJMV8EdSRikL/eWMpYtOUwpZVVoh3DexGqUzch7sJ1Ptq8B41Gw9hHujP9sYES+Yy8It7ZsIPC0nIx37zwOC4OtlIbrqSwfOtR0YY+vkQM7WHoz037RX9qBGaN7MNAX2+pjgvX+OjH3aIdIT2YPjJYz45C3lm/TWtH5JMGdgBMe/c5ug/qQZWqiq/mfcntRMOCG227tOOlT2fR3NzMoG3Ku88SMKgH1aoq/jtvBckNyLfp0o4Zn86iuXlzzh08bdD+ypKZ9B3cm0pVJR/MWc61xOsGfZ5/PYLwJ0JpYduCYR0eNWiX+ffxj140KRQKNXCxzo4rwBTEb24dBszqfv9VEIR36vpvAHoi1sG/BkwVBKHsnn9EqcR76XNc/c9iqjPz8du9nKK9pyQnv/nbjpDzYwwAdqG98Hp3Gkl11XFEHQoCl07lyFPLqMgsYEj0e2TEnKFU50BSdDGF/eELUauqafvMELounED8jJUSO9oue5ZL45dQnVmA/54PKYhJkBzcc7ceIesH0Q6H0J60eXcKlyd+IPpKqaD/+1PYPfFDyjMLGPPHElJiTlN0PaNePu9SMpdHLEJdWU3nyUMIemsCB17UVhRTKBWMXTKN9U8vpTgrn5d3fsDlfafJuaEzjow8tsxbQ/BzjSQApZL2H07n/Pj3qMoooMfeZeTtTZCcQGdvPUrGD2IFLsewnvgsnsKFCR9IdHi9/wLXJr5DTWY+nf/4mKKYk5KLovzth8nduBcA22G98HwngutPL6mfj67LpnFi/FJUmfkM3PMBWTGnJQf29K1xpPzwJwAuoT3we3cy8RM/lNjQ9LhQ0nbpc1x6agnVmfl0i/6IgphTkjnN23qE7Lo5tQ/tife7U7ky8X0dHQr8l00jbvwyVJn5hOx5nyy92CpOTOZQmBhb3lOG4rdogkS+04cRnBn/AZUZ+fTeu4zcvQmSi6LMrXGk1fnCKawHHRY/w9kJyyTj8Fk2nYvj36Mqs4DAPcvIj5HOac7Wo2TWzalDaE/avjuFxInaOVVrNCzbFsea50fgYmvFpBXbCfZrTTsX+/o+6/efJbRbW8b38+VmdiEzN+whWuckRq3RsGxrHGteeFTU8cVWgv28aeeqo+PPM4QGtGV8Pz9uZhUy8+vdhM7Xkf8lljUvjcHFzppJn2whuEtb2rk5aOVjThEa2J7xA7tyM7OAmWt3Eu03VccGgQ8PXOa/j/fCpYU5kzYfJ7idM+0crev7zAvRnlhFnU0hKbcEXdQaDUs3RrP21adxcbBh4pKvCQnoSLtWTvV9Pvt5HyP7+TOqvz/xV27z5W/7WfrcWMmcGLPOdOUd3nyZ7BmvU5udh/umVVQcOk7NrVSJrQpLC2wmjqXqwhUMUCpxXxzJ7WcWUpuVT9vtn1P6ZzxVN+5Iu1lZ4Dh1FBVnrxqouJu3/qjLW4//sYRkvbyVfymZrSMWUVtZje/kIfR5awJ/3s1bTcybd+dj2e8JrJkyGBcbCyat3UtwJw/aOWtP1tYfSiS0S2vGB7XnZk4xMzfGEj23lVTH7gTWTB4k6li/j+COraQ6Dl8i1M+T8b3qdGw6RHSHUQ9Mh1ojsGzPedZMHCDKf3OQ4PZutHPSXmzPH6a98Ig6dZOrWUV6c6rA78MITtbli/57l5KzV5o7M7bGkVqXL5zDetB58WROTfhQoqPbsmkcq8tZwX8xZyW8oD0eqjUalu04wZrpYbjYWjJp1S6CO3vRzsVO64sD5wnt1obxfTpxM7uImd/uI/oNT628EbkieuEkqQ3RZ1jzdLDoz6//JLijO+2cdObjyBVxPnr6cDO3mJmbjxA9W3uXZsyIYUwcN4oF7zVcQfTI8VOkpmWwe8sGLly6ynufrCJq/ReSPmLuPMaa58LFsazcSbCvl17uPEeofxvG9+0s5s5vYgh9Qyu/9MfdrJ0/Wcw3i9cTEtiRdq2c6+U/+ymGkf39GTUggPjLt/jyl/0sfeFxqQ2/HmZN5Cgxd372C8Fd2tDOVTd3JhAa4MP4AV24mVXAzLW/E/2Ot0TH0u93sfb1aaIdb68hpHtnqR2b9zByQACjBnYn/tJNvvw5hqUzpNVCAwf1wK2NGy8Hz6B9YAeeez+SBWPmG/j2uQ9msOaNr7h+9lp99TyAgEE9cG3jxpzgSHwCOzD9/RksGvOagXzEBy+w/o2vuHH2Gq9/L62k2HdwbzzatOKpAZPx696Zecte4fmRLxnoiNt3nN++3c5PR380aHvY+Zd/o/V/jH96e55KEIQAQRC6ANXADKAKGCwIgj8QAIQrFIo+df3nCILgLwhCNyAVmHm/P2Ad6ENlciZVqdkINbUU7DiKfViQpI+6TFX/3yaWZqAXTA6B7ShLzqY8NRehRs2dHSdwD5Pehck9dhm1qhqAgjM3sNA5UQNoEehD5e0sqlJzEGpqyd0eh0NYr0btUFpK7544BbSjJDmb0tRcNDVqbu44QetQvTtBx66grhRtyDlzAys9GzwDfMhLyaLgTg7qGjXndx3HL1R6J74wLY+sq6mNLiib7j6obmdRmSKOI2d7HC3DpTr0/amvyyqgPVXJmVTrzIldaG9JH41Eh7lkTuwDfSi/nUVFag5CjZqM7cdxDZPaUKs/p3r7dx9EXFgH+qBKzqrXkbfj6D3nVH8cd8dSdju7fixp24/jqhdbeXHa2Co8fV0SW7bdfai4nY0qRZTP2n4Mp/B72WA4jhaBdXOqE5uOYfeeU31/Jqbm4tnSBg9HG5qZmhAW0I7YSymSPgqgvEocR5mqGicbSz0dOXg66ugI9CH2UrKeDgXllTWijsoqnGystPIp2Xg62eHR0laU796B2Iu39OShvG6N6MsDJGYV4WlniYedJc1MlIR1dCX2ZjaNsScpk/CObpLfEm+l4+lsj4ezPc1MTQjv7UfsuSRJn5sZeQR19gYgqJM3sWel7U1dZ2ZdOlJ7J4Pa9CyoraV8byyWIf0M7Ld/aSrF321BqK42aLPw70BVSiY1d8T4Lv79MC2G9THo5zz3aXLX/opQVWPYppe3buw4gbde3so4doXaujnJ1stbTc2bAIlp+Xg6WOPhYC3GRdfWxF6VPolSKBSUV92Nq2qcWlhIdaQX4OnQQqujixexSekN6KgVdVTVPHAdiRkFeDpY4WFvJcamrwex1zINxnuX6Et3CPfzkPxm192HittZ9fkic/sxXMLvnTv1DwVi/tXmrPS/kLPM9Y5FiXfy8HRsgYdjC9EX/m2Jvax3QY/uWq3GyUbHF03MFVA3H/bWeNhb08zEhDA/L2KTMvR0oBMXhnPaM6ArtjYtaIyDR08wKnwICoUC/y6dKS0tIzevQM8XernTvy2xl/R8odD3hTZ3Jt5Kx9PFAQ9nB5qZmhLeu4tBPrmZkUtQ5zYABHVuQ6zeDY7ElBw8W9pqc2dge2Iv3jb0RaVO/rbV8+fNNDxdHLV29OlK7GnpzZibGbkE+YpP7oN82xJ72vBGS69hQRz67SAA189ew8rGCjtne0kfO2d7LKwtuX72moF8j2FBHPktFoAbZ69heQ/5G3Xyd/vfZUBYP/b8Kt6QunTmCi1srXF0lsbw3bb8nAKD32X+vTxM2/OOAN0E8ch/9+lRs7p/AoAgCCUACoVCAVigf+bWAM1dHanO0G71qM7Mx6p7e4N+LlPDcX1+FIrmplx5UvpNDQtXB1Q620VUmQU4BLZr9G96Twgh6+B5qR1uDlRnaLcnVGfm06IBO1ynheP+wmMom5mS+MS79b9budlTlqldnOVZBTjfw4aOE4JJ07PB1sWeYh1fFGfm4xlguFXgXpi5OlClo6MqowCbBsbhPi0MzxmPoWhmyvlx0u+kNHdzoDpTxxdZ+VgHGupwmjIcl+dGo2xuStJT2jtB5m72qHRsqMzMx6674Ti8pw2j7QuPomxmyvEn3pe0PYi4MHN1oDpdd04LGhyH69Rw3F8YiaKZKZeefFfSZmEwlgLsGxjLXVpPHET2gfM4BnWst0E6H/nYNCDvMS2U1jNEX5we956kzcxNT0dmQYOx6TYtDI+62Dz/hHROc0rKcbXTPo1xsbXiYmqOpM+M0B5Ert9NVNxlVNU1rH1+hFRHccX9dYT1IHLtbqKOJoo6XtDe9c0p0rPBzpqLKVlS+eG9iVy9g6jD51FV17L2Jel3PXLKqnDROTFysTYnMavYwBcAGSUqMopV9PJ0lOooKsVVZ8uJs70NF29JT447erqw//RVJg3rzf4zVymvrKaorALXunZj15njEPFuvolzS2qzcuv71WbnYda1k0S2eScfTFycUB05ie2U8Qa6m7k6UpOpoyMzD4sA6XYwc792NHNrSdnBBJyeM/x2meXfzFudJgSTqpO3mpo3AXJKVbjqnOC52FhyUW+r2IxBXYn8/gBR8UliXEwdItVRosJV50TVxcaCi2nSk6UZIV2I/DGWqPhrqGpqWfvMoAeqI6e0Elfd2LSx4GJ6wydsGcUVZBSVE+TtLPnd3NWBSp24UmUUNJg7W08LxbsuX8Tr5Qv9/Ku6T87ymjiInAPSY1FOSYV0TmwtuXgnV9JnxtBAIjfsJerYFXFOng3TyjcxV8DduNCbDz1/zgj2I3LTYaJO3hDn42npdrP7kZ2bj6tzS+3fcG5Jdm4eTi21J+A5xX/BF8O6E/n1HqKOXRZ98dxwrXxhCa46W3vFfCO9KdDRy4X9p68wKbQP+09fMcg3OcVluNrr507pzaIZ4UFErtlJ1JELog0vjpK0i3bo5D0HGy7e1LfDlf0Jl5kU1o/9CZcpr6yiqFRrB4CDqyP5Oms+PysPBxdHinIKtX1cHMnPkr56oJV3kMgXZOXj4OKgJ+9AgY58fqZUl5NrS3IytPGUk5mLk2tL+QJJ5h9/0gSAQqEwBYYjbtVDoVCYKBSKc0AOsE8QhHidvt8CWUAnYGUDup5XKBQJCoUiYd26dfrNjZL93R7O93uROx/8SKvZxn9Q0Wtcf+z923Jt9e9GyWd9u4czfWaS/P5GPOcYZ4fP4/1p2a0t59f8YZT8gyDj273E936ZW+9vovUc4z4Em/t9NIkDZpC29AfcZv39D34mf7uPA31e4cr7m2k/Z+z9BRrgQcRF1nd7ONP3JVI++BGPV4z/KK7HuP7Y+bfhhhGxlfZtDHG9Z3P9/c20mWPch30zv93LqT7Gz+meszcY1bMDMQsnsioinIVRsWg0f2+LwJ6zNxnVqwMxbz/NqmeHszDqABqN5q/Ln77GqN6diHkvglUzRrLwx5i/bcNd9iZlMqSDCyZKxd+WnTt+GAlJKYx/dx2nk1Jwtm+BUvn307HR60yhwGHeDAo/W3v/vvfQ4fbWs2R9sMF4HTq0f7w/TkbmrabmzT0XkhkV2JaYeWNZ9XQIC3879vdj82IKowLaEPPqaFZNCmbh1hP/iA6AvZfuMLRzK6NiEyDl2xgO9Z5N0vub8TEyd0LTctae87cY1aM9MQueYtW0YSz8+fDf8kXDueJvzkdiKqP8vYmZM5JVEwaycPtJNP/AtqY9526KvnhrAqsiQln406G/lffmPhVKQlIy499eo803ir8XG3vOXGdUUCdiFk9l1fOPsXDjn3/bn3MnhJNwNZnxC7/i9NVknO1tUBoZozIy/wT/9EWTRd3FUQLidrsNAIIgqAVBCAA8gCCFQtHlroAgCNMAd8R3oJ7SVygIwjpBEHoKgtDz+eefpzorn+bu2jvBzd0cqcls/G5B/vaj2IdLt2mpsgqwaKXVYeHmgCqrUF8U54F+dJo9mmNTPkVTXStpq84soLm79o5TczdHqu5hR972OBx0tlmVZxZirbPFwcrVgfJMQxvcB/gR8PIoYqZ9ZmBDcXYhtjq+sHVzpCTbUMe9qMoqwExHh5m7A1WN3PEByNkWR8vhUn9WZxbQ3E3HF66OVN/DFwU7jmAXpt2+V5lZiIWODeZujlQ24Iu7pG8/jqveFpQHERdVWQU0b6U7pw5U38MX4pzqxZbBWBxQNWCH08AudJw9hhN6sWU4H45UNRCbd8nadgyn4dLtTVWZejrcHKjObHwcudvjcNQbh7ONFVlF2tcLs4vLcdbbvrHtVBKh/uLWDH9vF6pq1RRVVGp12FreX0f8VUL929XpcKWqRk1hoTheZzs9G4rKcLa1lsqfuExo3dNA/zZuog3l2u1IztZmZJdq/z+7rBIna8MtXyBeNOlvzRPtaEFWgfbpVE5hCS720i08zvYt+HzmeH5+93leflz8Ar2NpXl9e1PXmTonD1NX7TtUpi4tUedo774qrCxo1s4b168/wWP3j5h17YzzF0to7tuhvk9NVj7N3HR0uLWkJltrg9LaArMOXrSJWkaHwxuwCOxI63WLMO+qfepQ8RfzVqsBfgS+PIo9enmrqXkTwLmFBVnF5fX/n11SgbPe1tBtZ24R2kV8v87fy6kuNqu0OmwsyCqp0NGhwtlGulVr29lbhPqJ79z4e7Z84DqcW5iTpRubJSqc9baL3WXP5TTC/TwNfq/MKsBcJ64s3B2oymrcnxnbjuGily/086+FmwOVjeSsDrPHEN/A8dDZxlI6J8UVONvo54vrhHbzBsC/tbMkXxibK4rKdfJNCwuyivXmQ8+f287dJtS38Tm9Hy5OjmTprLvsnDxcnFpK+jjb/hVfXCPUX9xe599azJ31ec/ehqwC7XuVYr6RFpVxtrfh85f/w89LZvDyuLp8Y6Udq7OtNVmF+rlT35+XCa3bmeLfxtUwd9rbSPNeQSN2zJ7Iz++/xMtPDpXY8fHuz/l49+cU5hTiqLPmHV1bUpAtzX0F2fk4ukqf8AMs2/05RXryDq6OFGRL47MguwAHHXlHN+1/fxezjvzsApzdtU9pnd2cyM2SPp3+/x3NQ/7vYeWfvmi6+05TgCAILwuCINlYLwhCEXAQCNf7XQ38BNz31mrZuRuYt3HDzNMZRTNTHEYPoDDmlKSPWRvtiY/d0B5U3pbuEy88dwvrNq5YejqhaGaC5+g+ZO6VVlux69Ka7sunc2zKp1TlS18MByg9dwOLtm6YeYl2OI3pT4GeHeZttA+p7Yd2p/K2dmtR7vlb2LRxpYWnE8pmJrQb3YfUfWck8o5+rRn4YQQxEZ9R2YANaedv0tLbFXsPJ0yameA/si+X9xlWjbkXpWfFcZjXjcN5TH/y9iZI+ljojMNxWHdUt6T+LD9/HfM2bjTXmZOifdIqfbpzYjukJ1U6c1J07iZWbV2x8BLnw31MX7JipOOw0rHBZWgg5bel27QeRFyUnbuBhY6OlqMHUKA2DU6/AAAgAElEQVTnC3MdHfYN6Cg6dxPrtq5Y1o3Fo4Gx2HZpTcDH0zkx5VOq86TzWnL2JpZtXTGvk3cd049cPRssdXzRcligwXzcjU1zndjMj9Efh1aHw9DuqPTG4efpRGpeCekFJdTUqtl77ibBvtJKVW521sTXFQC4lV1Ida0aeytzHR3OpOYVk55fp+PsDYL9Wkt12FsTfz1dosPBQTwp9/NyITW3iPT8YlH+zDWCu7YxlK8rInArq4DqGjX21tqTBz9XW1ILK0gvrqBGrWFvUhYhbaVbnABuF5RRUlWDv5udQZtfm1akZheQlltITa2aPfGXCA7oIOlTWFpRf5d2wx9HGTNAWmmwqeus6lISpl6tMHV3BVNTrMJCqDh0vL5dKKvgzqAnSBsxmbQRk6m6eIWcV96WVM9TXbiGmbc7zTxcUDQzxfaxRyj9s/6hP5rSCq72nMS1R6Zz7ZHpqM4mkfL8e5LqeTnnb2Grk7d8RvchpZG8taeBvNXUvAng18qR1IJS0gvLxLi4mEJwJ2llPDdbS+JviduRbuUWU12rwd5Ke7Hs5+5Aar6OjsRUgjvq67DS06F+oDr83O1JLSgjvahcjM3LaQR3MLxov51XSkllDf6tDN/BKD4rzZ1uY/qRrXcs080XzsMCqbhlmLOsdHJWq0Zylv/H04lvIGcB+Hm0JDW/hPSCUtEX528R7Cu9yHOzsyL+hvi3b+UUiWu1Ll8YmyvsrXXyTSsH0Z+FZdSo1ey9lEqwXrVDNxtL4m/fnY8SUUcD7801RsiAPuzcsx9BEDifeAVrayvJ1jzRF3dzp64vGsidN+7mTtEX9XmvjTup2fl1+aaWPfGJBAdKt9EWlpbXP5na8PtRxgyUVhr089L353WCu3jr2dBCL3fWSnNn21akZuWTllMg2nHiIsHdpVuCJXbsOsyY4O71bfNHzGH+iDmcijlB8DhxW2r7wA5UlJZLttYBFOUUoiqroH2gNK++OWIOCTHxDBwXAoDPfeR96uTv9geYGvo8h/ceJfyJYeK4unemrKRc3ponAzxc7zQBoFAonIAaQRCKFAqFBTAM+KjuPaZ2giDcqPvvUYDhW4T6qDUkv/U1HTe/jcJESe5P+1Fdu0Or+f+h/PxNimJO4TptODYDuyHUqlEXlXFrtnTXn6DWcG7BdwyMeh2FiZLknw5Rci0d3/njKDx/m8yYM3RdNBFTK3P6rJsNQEV6Hsemfiax49aCr/GLWiiWp446gCopDa/XnqLs3E0KYhJwixiO3SPd0NTUoi4u59qslRIbji36nuGbXkOhVJK05RCF19LpMW8cuedvk7rvDL0XTsDUypyha2YBUJaeT0yE1gaNWsOOt7/j2R/eRGmi5NTPsWRfTyN0zhOkXbzN5T9P49GtLc+snYulrRWdh3Rn2Jwn+Sx0vsSO629uoNtPb4mlkKMOUpGUhvdrT1F6/ib5exNoNX049gO7ItSqqSku48osbQW/u75IXbSeDpveAaUJ+Vv+pPLaHdznTaD8/A2K953CeeoIbAb4I9SqqS0u4/acLyU2JC74jj5Rb4plc6NiKUtKo+NrT1B07jbZMafxjgjF6ZGuaGpqqSku5+ys/z7wuLg7p75Ri1CYKMn+6QCqa3fwnP8fys7foDAmAdeI4dgN7IZQU0ttcTnX9XwhqDVcWPAd/aLeEEuOR8VSmpROp9eeoOjcLbJizuD39iRMrMwJWj+rLrbyJfJJb35D958WoDBRkhEVS3lSGu1ee5KS87fI3Xsaz+lhONTPRzmJs1YbjOPGgg10iRLnNKtuTlu/9hSldbHZKmI4do90RagR5yNJbxymJkreGNOPyPXRaDQCo4M64uPqwOq9Cfh6OBHi15q5I/uw5JcjbDpyEYDF44NR6GwRMTVR8sbjA4hctxuNoKNjzylRRxdv5o7sy5JfDrHp8AVQKFj8n5B6HaYmSt54IpjI1TvRaDSM7uOLj5sjq/84ga+XMyFd2zJ3zECW/HSATQfPivKThkptUCp5fbAvL25NEG3w86BdyxasPnYdXxdbQtqJF1B7kzIJ6+AmkdUdx5tPDyfys01oNAJjBgTg08qZr7YdxM/bnZDAjiQkJbPi1wOggB4dWrPg6eESHcaus6DDn9fPacGHq3D57zKx5PiOvdTcTMEucgpVl6+h0rmAahS1hox31+D9/RIUSiWFv+yj6noqzq9MQnXxOqX7DT9HoI+g1nB00feM0MtbPevyVsq+M/RZOIFmVuYM08lbe+/mrSbmzfq4eLQnkT8cFGOze1t8nO1Yvf8Cvq0cCOnkwdzw7izZEc+mY1dBAYvH9jGMzRE9iPzxEBpBw+jAtvg427L6wEV83R0I6dSKuaEBLNl1ik0nxAvPxWN6P1Adpkolb4QFEBkVJ47DvzU+TjasPnQZXzc7QupO+PdcvkO4r0eDsSmoNVx681uCfloAJkrSog5SlpRG+9eepPj8LXL2nqb19DBaDuxSny/O6+XOuzmrb13OSr1Hzuqlk7NOTvlU6otRfYj8RtweO7pne3xc7FkdcwZfj5aE+Hox99EglmyNY9PRS+JafXKgdK03IVfU+3N4dyI3HRZ1BLQR5+NgIr7u9oR0bMXcUH+W7EpgU/w1QMHi0UESHfPf+ZBTZy9QVFTCkDFP8+L0ydTWik/Vnhr7KI/07cWR46cYPj4CC3Nz3lswx2BOTE2UvDG6L5Ff7xF90asDPq72rN57WvSFX2vmPhbEkl+PsunIJTEuntL1hQlvPj2CyE9+FPPNwEAx32w9gF8bd0ICO5FwNZkVv+4HoEfH1iyY/KihDeMGErlmp2hD785i7twdL+bOLm2YO6Y/S7YcZNMh8f20xROH6MW3CW8+8xiRH3+PRqNhzCM98PFw4avf/sSvTStCuncm4cptVvy8T8x7Hb1ZMEVaDh/gzIHTBA7qycrDa6hWVfHVPO16/nj358wfIfpw/cK1dSXHm0vkzx44TcCgHnxxeA1VqirWzltR37Zs9+e8WSf/7cK1dSXHzTgXK73oP74/nr6De/Nz3EYqVZUsnbu8vu27mHVMDX0egBffep5hY4dgbmHGtoSGP+kg8+9C8U+WHVQoFGWCIFjr/dYN+B4wQXwS9rMgCEsUCoUSsViEDWIhl/NA5N3iEI0gxLsb9/4GQO8M8dtBv7pNuk/PxnkicxNxrsa/IwXQP+tX1ns8bbT8c2kbAXjNe8J9ejbO8uQoAGJd/v67RXcJyf6FBI8x9+94D3qmbWeXq/HjGJkljqOpcXHMzfh3kwD6Zf7GdteJRsuPydoMwD4Xgx2qf5lh2WKSP+xq/Jw+kvULqp0Nl9v9q1iMmofq98/u37Ex+cfmAqDau+o+Pe+hI0wsxFmxZrbROixnfEll3Caj5QHM+09q8hoDSA4YZrQO73Ni1ajEto/dp2fjdLn1O2ubkLMAXkjb2KTc2T9L/KaUasvi+/RsHIunxOIvqqh37tPzHjomLG6SfL2OH940Xv4Z8RMDu13+Y7SOEdk/AbCjCXlrdNZmVNs+vH/He2Ax9o0Hky82LbpPz3voqPv0RE2e4fd//irNWrZFtWP5/Ts2ZsNosYx25fEoo3WY9xWPparoFffpeQ87hs+i8uQvRssDmAc9yZOtR9+/YyPcLTk+obXx5xdRKeL33/q3Gmy0jrj0AyCenz70hHqGP9Q1x2Pu7Hko/fiPPmnSv2Cq++0CENjA7xqg//+GXTIyMjIyMjIyMjIyMnf5p99pkpGRkZGRkZGRkZGReah56N5pkpGRkZGRkZGRkZH5n0Fz/8+cyjSA/KRJRkZGRkZGRkZGRkbmHvyjhSD+F/hXD05GRkZGRkZGRuah4aEsYKDPUM+wh/r8+M87ex9KP8rb82RkZGRkZGRkZGT+j/Avf2DyP8a//qKp5IUwo2Vt1u4F4Frn8Pv0bJwOV/aQ/2iw0fIAjn8cIrXnEKPlvRLE7zNsbUKp2MfrSlwX6nwE7u9i/1ssJdONL4UMYLNhH4VPNsGGX2IBKH1x+L073oMWq6MpmzvKaHkA6892csPX+Nj0uSzGZlPnA6B42lCjddh++yeqw98ZLQ9g8chUVH98Ybz8o68AUHX9mNE6zNr3A0C1YZ7xdkz/hKorB42WBzDrPIiMfoOMlnc/Jv79snnGl++1/kQs39vUdfapV9NKjr+aupHsEONtcImNBZpeLhxoUplsi7FvoNq/zmh5AIshz1P+wTNGy1u99QMA6X2NL6fc6vgBAPLCjD+etdx7iKpL+42WBzDzG9Kk0v7m/cVPiFRdPWS8DZ1EHzS1ZHhTS5YDTfKnmZ94XlGddtFoHc09uqL6/g2j5QEspnzIcE/jj8nRd6IBeMHb+M81rE0Wy6aHehp/vhdzZ4/RsjL/fyC/0yQjIyMjIyMjIyMjI3MP/vVPmmRkZGRkZGRkZGRkROTqecYhP2mSkZGRkZGRkZGRkZG5B/JFk4yMjIyMjIyMjIyMzD34P7E9z8SvJ+bjZ6BQmlB9NJrqvT8b9DHt8Qhmj4kvLmvSbqHaIH3x13JAD5wXRIJSSfGveyj82lAHgPWw/rivWETKEy9Tdem6pK1ZjyCsnn8ZlEoqY/6g8pfNknazoeFYRkSiyc8FoHLXNqpi/qhvN+/bC/t5L4FSSfn23ZR8/1ODNlgMHojT8nfJmhxJ9ZVrkjaXQd3o9t4zKEyUJG86yLVVuyTtPi+MwHtSCEKthqr8Ek7PWYcqLU/qq4AgLCNmgtKEqv1/ULVNOo7mg8KxmDwDTYEoVxW9jer9f0j6mHTpifmEF1EolFQfiaY6eovBOEx7PoLZ6GdAENDcuYVq/TKpDdN0bNiuZ0NIAzYc0LPBtwfmT84AhZKaY3uojvnF0IbuA2n+6NOiDem3qPxW+uKvSafumI15FpQm1JyIoebAb4Y6/PvTPGwCAqDJuE3Vxk8l7ZYDetLyzRlgYkLJr9EUNRJbVsMG4PblIu48OVOq/wHMh2mXXphPfBGUSmoOR1O12zC2mvUKFucDAfWdW6jWLpW0xyXeZPlPf6LRaBg7MICI4X0l7Rn5xbz73R8UllZgY2XB0ukjcXGwkeq4ksry7UfRaATG9ulMxJDukvbMwlIWbT5AaWU1Go2GWY/2IfRRbfvR0xf5aN1mNBoNj4c+wvQnH5XIZ+Tk8fYX31BYUoqttRVL5z2Pa0sHqQ23cli+PxGNIDC2mxcRfdpL2j/en8ipO/kAVNaoKaio4uhs6UvMR89c4qOvfxbtGNaf6eOkLxdn5OTz9sofKCwpw9bakqVzInBtaV/fbta7F7avzAQTEyp2/UHZj1ESeYsRYdi8NANNrjin5b9to2LXbkkfk46BmI1+TpzT+H3UHGwkNkMnIAiCGJubP5O2P4B15h3cjUHvTkZhoiTxp1hOrpbmnFZBHRn0zmScOnvy+8xVXN99Svo3goJoMVP0heqPP6jYLLXBPDycFjNmoM4TbVBt24bqD6kNcdczWb7njBhX3dsSMdBX0p5ZVM6i7fF1cSUwa6g/Azu4S3UkpbF8V7wYF706EBHSTU9HGYt+PkKpqhqNIDArvAcDO3lKdVy6zfJfDoo6+nUhIqy3VEdBCYu+30OpqlK0Y8xABnZpW99u0rYrzUOfBoWS2nOHqDn+u9RXQyei9O4MgMLUDIVVCyo+jZT0MesjxpbCREn5zt0GsVXv15CBOC5bTM60GdRclR5HmvUMwmrGyyhMlFRG/4HqZ71j2bBwrJ7VHstUO7dRtUc6J0fPXOKjb35BoxF4fGg/pj8uLYyTkZPP219t1K7V2VMlayTu4g0+2rwXjaBh7MBApj86QCqfV8Q73+6ksLQCWysLlj431iDfHD2TyEfrt9St0wFMf0K6jsV1+j2FxaXYtrBi6ZzpEhugLi52nBDtCOpIxCB/SXtmYRmLthymtLJKnNPhvRjYWRsXC5d+xuG4kzjY27F94xr0EQSBZV+s4cjxU5ibm/HBW6/i29HngfoSYNHHX3H4xGkc7GzZtuHzBu348KtvOBJ/FnOz5rz/2kx8O7SV9Im7mc3yfRfE+PZvTUS/jpL2j/dd4FSKuE4ra2spKK/m6KuPGfytGYtn0GtwL6pUVXw691NuJt406DPltSkMGTcEa1trye9+wQGMf3saShMlR7fsZ+9/t0vaTZubMu2zl/Hq0pbyolLWz/yc/LRcA/0vLo6st+GTuZ9yI/GGQZ+pr01h2LihWNtaM7rTWIP2hxlB3p5nFP/oRZNCoVADF+vsuAJMATTAYcCs7vdfBUF4R09uBRAhCIJ0tTT4R5RYTHiJ8i/eRCjMw+rNldReOIEmM7W+i9LZHbPwpyj/eC5UlKFoYSvVoVTivOgl0qcvoCY7j9Y/r6D84Amqb6ZK/5SlBXbPjEF1/oqhHUolVpGvULLwVTR5udh+vpaaE3Go76RIulUfPkD5mi8blLd/fRY5L72GOjsX1x9WU3H4OLW3pfIKSwta/Odxqi5ebkCHAv9l0zg6fhmqzHwG7XmfzJgzlF5Lr+9SlJjMwbCFqFXVtJkylK6LJnDyhZUSOyyfm03Zknlo8nNp8dEaak7FoUnTG8exg6i+bmAcIM7JpJcp//R1cU4WraL23HG9OWmF2aMTKF/2St2c2EltmD6bsvfmoSnIpcWyNdQkNGLDhsZtMH/qJSpWLEAoysPy9S+pvRCPJktrg8LJneZhT1HxyaugKkNhbWugw+zxF1CteRuhOB+LOZ9Se+kkQvYdbZeWbjQb8iQVK18HVbmhDqUSp4Uvkf7sm9Rm5+G5ZSXlB09Q01BsTR5DpX5sPaD5MJ/8MuWfvI5QkIv1219Rc+4Ymgyd+XAR56Ns6WzD+QDUGg3LNsewZs5/cLG3YdIH3xHs35527i3r+3z2ywEe69uFUf26cfJKMiu2xfLB9FFSHVuPsGbGSFxsrZj0+W8E+3nTzlV7UbN+32lCA9oxvn8XbmYVMHP9bkLn18mrNSz974+se38eLo4OTJizhJDeAbTzalUv/+mGLYwc0o/RQwYQf/4yK77/laWvPq9jg8CyPy+yZnwfXFpYMOmHIwT7uNKuZYv6PvOHdKn/76jTt7maUyz1hVrD0rVRrFs8GxdHeybMX0ZIUDfaeWpPwj/97jdGDurD6MF9ib9wlRU/bmfpnGn1c2o7bzb5s+ejzsnFacMaKo8cozZZOqeV+w9S/NmKRufUbOwLqNa9I8bm7E+ovdxAbA5+gopVjcdmU9eZQqlgyPtT+HXSh5RmFjBp1xJu7DtNwfWM+j6lGfnseXUtPV8YYahAqaTF7NkUzZuHOjcXhzVrqIqLQ52i54uDByn9smEb1BoNy3YnsGbyIFxsLJi0fh/BHVvRzlk73vWHLxHq58n4Xu25mVPMzE2HiO6gF5s7TrBmehgutpZMWrWL4M5etHPRroP1B84T2q0N4/t04mZ2ETO/3Uf0G55SHVv2s2bWE7jYtWDSR5sI7uZDOzdHrY7oE4T26MD4RwK4mZnPzK+2Ev1+3YmpQkHz8Geo3LwcoaQA84jF1F4/g5Cn9WX1n9qLF9Oew1C6tjbwp92rs8mriy3nb/7bYGwpLC2wHj+O6sSGjiNKrF96heI3xWOZ3cq1VJ+IQ50q1VF1+ADlXzUyJ2oNS9dvYd07s3BxtGPCax8R0qsb7Tzd6vt8+v1WRob0ZvSgPsRfTGLFph0snT213pdLN0az9tWncXGwYeKSrwkJ6Ei7Vk718p/9vI+R/fwZ1d+f+Cu3+fK3/Sx9bqzUhrWbWbd4jrhO5y0lJMifdl466/TbX+rWab+6dbqVpXOma3VoNCzbdow1z4WLOWvlToJ9vWjnor0gWb//HKH+bRjftzM3swuZ+U0M0Z2fqm8fM2IYE8eNYsF7nzToqyPHT5GalsHuLRu4cOkq732yiqj12mqjTfXlXUaHDWLC6OG89dFKGuLIybOkpGXyxw8ruXDlOu9/uY7NX2lvLqs1Asv2nmfNhP7iOvv2IMHt3WjnpL1QnT9Me6Mh6tRNrmYXGfydXoN64d7GnekDp9MpsBMzl85kzqg5Bv3i98Wz87udbDi8of43hVLJhCXT+eLp9yjMKuDNncu4sC+BzBtp9X36jx9MeXEZi0JepufIfjz+xtOsnym9SOw1qBet2rgzbWAEnQI7MWvpTGaNesXAhhP74tn53S6+1bFB5t/NP709TyUIQoAgCF2AamAGUAUMFgTBHwgAwhUKRZ+7AgqFoidg36C2BjBp0xFNTgZCXhaoa6lJiMXUX3oHvNmA4VTH7oKKMgCEUulJkHm3jtSkZlKTlgU1tZTsPoTVYKkOgJazn6Hw618QqmoM2kw7dEadkY4mKxNqa6k6fIBmfQYY9GuM5n6dqL2TjjpdlK+IOYhlcD+DfrYzplHy/U8I1dUGbQ6BPpTfzqYiNQehRk3a9uO4hfWQ9MmLu4xaJcoWnL6OhZv0LryJTyc0WeloskU7ao4eoHmv/n95HAAmbfXm5GQspoHSsTR7ZDjVB3bqzIk2udbbkFNnQ9wBmvf8ezYovTugyc1AyBdtqD19CFP/PpI+zQeEU3NoF6jqbCiTxoXSqz2avEyEgmxRx9kjmHaR3jlu1ieMmrg/QFXeoA7zrh2pSc2gti62yqJjsW4gthxnTaHw658RqqTz+kDnIzezfj6aBUp1NH9kBFUHdjQ4HwCJtzPwdLLHw8meZqYmhPXqTOw56d3pWxl5BHXyBqBXp9bEnpM+iU1MzcGzpS0ejjaijkAfYhOTJX0UKCivFNdXWWU1TraWWvlrt/Byc8bD1ZlmzUwJfySIgyfOSm24k0HvbuKd+KBunQ3aEzML8bSzwsPOimYmSsI6uxN7I6tR30VfSSe8cyvJb4nXk+vscBLtGNCLg/EX9OzIpHdX8S5sUNeOHDx5vr6tmW8natMyUGeIc6r68wDmA/9mfHu1R5OfpY3Nc0cw9QuS9GnWO5SauN2NxuaDWGeuAe0oSs6mODUXTY2apF0n8AmV5pyStDzyrt5B0Bje+WzWqRPq9HTUmaINlQcOYNb/79mQmF6Ap0MLPBysxbjq4kVsUrqkj0KhoLyqFoCyqhqcWlhIddzJw9OxBR6OLUQd/m2Jvax3YwMorxTXZ1llNU42ejqSs/B0ssOjpZ2oo0dHYs9L714rFAqtDlUVTrZW9W1K93ZoCnIQinJBo0Z9+QSmHaRPYnUx9etD7aXjkt+a+3aiNi29PrYq/jyA+SOGxxGb5yMo3RjV4HHEtKPesSz2AM37/vVjGUDijWS83JzwcG1Zt0Z6SNYAwK20LHp37QBAUJcOHDypXUOJt9LxdLbHw1nMN+G9/Yg9lySRv5mRR1Bnb1G+kzexZ6Xtiddv4+Wqs04H9jK04U4mvbt2EnV07cjBeGl74p1cPFvaaHOWf1tiL+nFhUI/Liwl7T0DumJr04LGOHj0BKPCh6BQKPDv0pnS0jJy8woemC/r7ejmi61N4/ehD8adYlRoiGiHbwdKyyrIzS/U2pFRgKe9FR72dbnT14PY65mN6ou+nEa4n6fB731C+7D/N7GM+tWzV7G2scbe2fCU7+rZqxTmFEp+axPgQ05KFnl3clDX1JKwKw7/0J6SPv6hvTjxm1hu/szuE3Tq1wV9+oX2ZZ+ODVY21jg4Oxj0u3r2KgU5BQa/y/x7+acvmnQ5AvgIImV1vzWr+ycAKBQKE+Bj4LW/qlRh54imUPvoVSjMQ2nXUtJH6eKB0uX/sXfe4VEVX+P/3N2E9J7dTQihhpYgvbeEDhaqgqiIwFeaQb6CBSkiqAgWbICAYkfFAoqE3ktoCTWUhBYCKZvesym79/fHjdm9u6FteF/5+d6Pj89DdmbOnrnnzNl778ycCcL1laW4vvYx6jD5IHPQ+lGRZpZRoc/EUecnq+MUGoJDgIaifceq1UPl548pM73qb1NmBmo/f5t6tbqF47XsK9xfX4DK3/zWTK31x6i30CE9A7VW3t6xaWMcAjQYDh2tVgfnQB9KUrKq/i5JzbZ5KLKk/lO9SNstD74qXw2mTLMepuwMBD+NdVNqde6Jx9I1uL28wKZc8PbHlH0HmwRU2mTWx7jO/hR1C7NNVL6aqmUft9WhU088PliD20xbHVTe/jK/MOVkInjJbSpogyQdZn6A6ysfoQ6V3+wJXn6Iueali2KurQyVpjYqTRAu05bgMv191M3kNzlqnR/llr6VlmljV6fmkm8V77f1rftiDx9/xGwL38zOQPCx6kdAHdS6OrjN/hi3uZ/h0KKDrDw9t5AAi6UvOh8P0nMLZHWaBGvZdUK6cdl9MoEiQxm5hcVmGXlFBHibbxJ13m6k5xXJZEwe2J6o2AT6L/iOyC+imDWsR1WZPisHncbszzp/X9Kz5D+qTRoEszM6FoBdh2MpKjGQm19YVZ5eaCDA4oZZ5+FMeoGB6kjJKyYlr5iOdeX20mfnoLNY+qLz8yY920qP+nXYWfnAtuvIKZkeao0/Rr3ZHsaMDNQa21jhHNETzXdf4vPOm6i0Vja18c2sW/hmbVxeWIzLtPdQN20jL78P48w9wIeCFPNNRUFqNu66u37nhUqjwZRhoUNGBmqNrQ5OPXviu2YNXgsWoLIqT88vIcDiRlXn6UJ6fomszuSIFkSdSaT/h38SuXYfsx5uZyWjmACLBxidlyvp+Va+2bcNUSev0H/ROiK/3sGswfKXMOm5hQT4mG+OdT4epOcVymU80oWoYxfoP3sVkcvXM2uU+Xw+wcMHscAcv8X8bASP6q+l4OmH4K3BlCifKVJp/DGmW/hWeqbN9XRs0hi1VkNpdPW/Iyo/f0wZ8t8ylb+tfzp1C8f786/wmGtrE31WLjo/yzHiQ3q2/KG9Sf0gdh45BcCuo5VjpEC6Xum5BQT4mmcKtT6e6HPk8aZpsI5dsRel9icu2sQbfVYuOouluTo/7+rjRdU4PWkbL/Luwi/6tZX84p2fiPxqO7OG2L4Uux36jCwCLAXHVV8AACAASURBVH4TdFp/9BnmsV3Ta3m3pGdmEaAxxxCdxpf0TLM/phcYCPC0jJ0ut4+duUV0rGc7lv0C/MhMMfcvMzUT/wBb/6oOb50vORb3ODmp2Xhb3at563zJrpRvMpooKSjGzUf+0OoX4EdGijnuZKZm4Bcgl6Pwf5MH4qFJEAQHYBDSUj0EQVALgnAKSAd2iKL4d/SOBDaKonjL1xeCIEwUBCFGEISY1avv8kBBlRqVNojiD1+h5Mt3cXnmv+Didud25i9F89pEMpZ8cfdtqqHsaDQ540aRFzme8pMxuM+YfU86+MyYTM5Htmui7SF4RDd8WjXg0opNd65sRfnxaPImP0nBjAmUn47Bbdrr966ASo1KF0Tx+zMpWb0Il7Ev3ZNNymOiyZv6JAUvV+oQee86CCo1giaI4o9eo+SrxTg/Pf3e/AKkfmgCKVk+G8P3H+D0xAvgfG++5f/aRDLfs/9wzPtpj6IlMyle+Q4u4+7NHgAznuhNbEISoxZ+RUxCElpvD1SqewtBW09cZnDHpmyf/yzLnn+EuT/uwmQy3XX7meNHERsXz8gX5xNzNh6tn8896/A32y6m0LdpIGqVcM9tZ44bQey5S4x86R1iziWg9fO+Jz0MBw+jHzGajGf/Q+mxWHzm2XG4pEqNyr82JZ/PwbD2A5yeiLw33+T+jLOaUhodTeaTT5I9YQJlMTF4vX7vOmw9e53BrRuwfeYQlj0dztz1RzBVM/N1WxmnrzK4XWO2zx7FsnH9mPvL/nuXEXORwZ3D2L5oEsteGM7cbzbfswyQZpmMF46DeI9tBQGv6VPI+/Tze/5OS8qORJM9dhS5U8ZTfiIG95fv4beskpljh0tjZOYiYs5dQut7b2Nkxsh+xMRfZ+Sbq4mNv47W597jzcznHic2LoGR/32LmLh7H6cAW09dkfxizmiWje/P3J/32WXTmlDTa3m/2Xb+Jn2bBdkVOxXuDyZRfKD/f1D5pxNBuFQ+HIE007QGQBRFI9BaEARvYIMgCC2AbOAJIOJ2AkVRXA38fYcpFi35Lyof89sMwccfk8UbWJBmOoyJF8FkRMzSY0q/iUprXnJTkZ6FQ4BZhoPOn3K9+W2Gys0Fp8b1CP5OShKg9vchaMWbJE99s6qOKSsTlb/W3MZfgzHLSo+C/Kp/l26PwnX85Kq/jemZqHUWOmg1GNPN7QVXVxwbNUC3StrIrfbzxX/pW2TOmFdVx5Cag0tt89sSl0BfSlJtp5Y1PVrQdPpQDgx/C1NZhazMlJ0hmwFT+WoQs+SbKMVCcz/KdkXhOmaSvDw3E5XvXdjk6kUwGhEz0zDpk1Hpgsw6+N2DDrttdTDlZuJo4RcqH3/EvCybOsZr8Wa/0CfL/ELMy0KwmCETvG1liHmZGK8nSDKy9ZgyUlBpzGvNjfosHC19K8BfZleVmwu1Gtcn6Nu/fcuXwOULzDreD3vkZCL4WvimrwYxx+pa5GTI7ZF2E3VAnapyrbc7adnm79HnFKD1lr+903p7sHTqCACKDWXsio3H09XZXO7lRlqu+S2tPrcIrZf8Jn7D0QusmChtHG5VP4DS8gpycnLw8/ND5+eDPsPsz/rMbLR+8jfxWj8fPpozTdKhxMDO6Fg83c2zEFp3Z9IKzLMQ+gIDWg9nqmPrhWRe7/eQzec6Xx/0meY31vqsXLS+Vnr4evPRrMlmPQ6frNLDmJGJWme2h1qjwZhhNT7yzde6+K8oPF+YKC+38U2/anwzC2PS376ZjikjWeab92OcFabl4FHb/DbfI9CXQr38bf7tMGVkyGYpVBoNxgwrHSyuRUlUFO6T5DpoPV1Iy7eYYcgvQWu1dG7DyauseCYcgFbB/pRWGMktLsWtSoYraRaznvq8YrSeVr55/BIrxveTZNTTVsowmGV4u5NmMRuizylAa7WJfUN0HCteGC7JaFib0nIjuUUluAFiQQ6Chzl+C56+iAXVX0t1aGfKtn5r87kpIxO11sK3tP6y6ym4uuLQsAH+K6Q9HmpfX/zee5usV+eaZWRlotLIf8tMmbf+LTNsjcL1P5Nl5To/b/RZlmMkB62vfE+d1tebj16TbCmNkVN4ukljROvtQZrFbEp6Tj46q9kCrY8HH0WOlNobytgZe0EWb3R+3ugtlrnps3KriRfefPT6FAsdTsjjhdfd+EUCKyZIiRla1dPZ+MWd0Gn8SLP4TdCnZ6KzmHmu6bW8W7T+fqRlmGOIPiMbrb/ZH7UezqTlW8bOklvHzvM3eX2APGHGsq3LAEg4nYC/xV5Y/0B/MtPk/nUrcvXZ+Fjc4/gE+pKrz7Kp41vbn9y0bFRqFS4erhRZjMvPty4n/nQCmtrmuOMfqCErTS5H4f8m//RM0997mlqLojhNFEXZAmpRFHOBPcBAoA0QAlwWBCERcBUEwTadiRXGxHhU2iAEPx2oHXBsH0HF6SOyOuWno1E3kTYoCm6eqLR1EDPNk1mGs/E41quNQ5AOHB3wfDicoj1mGabCYq50HcW1vmO51ncshtMXSZ76pix7XkXCRdRBdVDpAsDBAaeevSk/ekimh+Bjvrmo1ambLElE2fmLOAYHoa4ttXft34uS/dHma1VURHLf4aQMfpqUwU9TGneezBnzZNnzck5dwb1hAK51NQiOauoM7ULq9liZDl4t6tHm/QkcHvshpZn5WGO8HI8qsA4qraSHY/felMVEy+oI3uZ+OLbvijFZvsbbeC0elS4IwT9AsknHCCpOydfel588hLpppU3cPVHpgqQ9N9Xp0O0udLgp18F0PQGVtnaVXzi0C6fijNwvKk4fxsHSL3RBmCz8wnTjEipNbQTfShltemCMky9pqYg7ijqk8sbazQOVpjamLH1VuSEuHsd6QVW+5T4owsa3rnUbyfV+Y7nebyylpy+Q+oI5L8r9sodaK7dH+Um5jIoT0Tg0k37kBHdPVAF1pL0ulYTVr01Seg7JGbmUVxjZdvwC4a3kWedyCoqr3rCu2XKYod3l2cfCgrUkZeSSnJUvyTh5mfAW9WV1An3cOXpJ2tR7VZ9DWYURX1+pf2FNGnA9JZ2baRmUl1ewdf8xIjrJl5zl5BVUzUx9+WsUw/r1kJWHBXqTlFNEcm4x5UYT2y6kEB4SgDXXsgrIN5TTqrbt8qiwxvW4nprOTX2mpMfB40R0lPc1J7/QrMfvWxnWx7yvpPzCRRzqBKEOlGzq0rc3hoNye6j8zDZ17t6VikQr/75xCZV/oPQwrHbAoXUPjOfkyzsr4o6gblS5nt/VA5UmSOab92OcpZ2+ineDADyDNagc1TR9rDNXdpywuWa3ojw+HnWdOqgCJB2ce/emNNrqWviadXDq2pWKJLkOYbV9ScoqIDmnUPKruCTCm8r3oQV6uXH0qtT3qxl5lFUY8XFzMsuo409SVj7J2QWSjNNXCQ+V78cI9Hbj6GVpTFxNz6Ws3IiPm/mmMaxeAEnpuSRn5kkyYuMJb9lILsPHg6Pxkv5XU7Moq6jAx116wDOlXEXlq0Pw8geVGnVoZyoS5HvyAAS/QARnV0zJtj+PZRcu4hBs9i3Xvr0xHDDHXrGoiLRBw9APfwr98KcoO3eerFfnyrLnVcRb/ZZF9KbsiNVvmYVNanXuZpMkIizEeozEEtHhNmNk/TaG9TEvawtrEESSPpubGTmUVxjZevQc4a2byNtbxpuogwzt3lquQ+P6ch0OHCeio/xGPiffIl78toVhfeT76cLqaEjKtPaLurI6gd7uHL0sJeu4qrf1izsR0b0zG7fuQhRFTsddwN3dDY3FssKaXsu7pVfX9mzcvlfS43wC7m6uaCweMsNq+5CUU0hybpEUO8/fJLxxoI2ca5mVsTNIvjUgcmAkkQMjObztMH1GSMtSm7VpRlFBkc3epVuRePoy2vqB+NXRonZ0oP1j3Ti9I0ZW58yOGDqPkF6QtH24Mxej42TlUwa+QPS2w/Sz0kHZu6QA//xMkw2CIGiAclEUcwVBcAH6AUtEUYwCAizqFYqiGHIrOVWYTBh+Xo7r9EUIKhVlh7ZjSr2O02PPYryeQMWZIxjPxeAQ2ha3+atBNGH4/QvEIov10UYTGW+voM6X74BKRf767ZRdvo7ftDEY4i7JbnJvrYeRos8/xvOtD0ClonTHZoxJibg8M56KSxcpPxqNy+AROHbqJr3NLyyg8COLtOdGE9nvf4b2syWgVlG0cQvlV6/jNek5yi7EU7L/8K2/uxLRaOLU7G/o9tMsBLWK6z/tpSA+meavPk7uqaukbj/BQ288jYObM52+eBGAkuQsDo+1SJFtMlL85Se4z3sfVCrKdm/BdCMR5yfHYbwcT3lMNE6PjKBWh66Ilf0oWiZP347JhGHtMlxfeleyycFtmFKu4zRkLMbEBCpOH8YYF4NDWDvc3vpSqv+rhU1MRorXfIL7nEod9mzBdDMR51HjMF6p1OHhEdRqb6HD8mp0WPc5rpFvS+nCD2/HlJpErUfHYLyegPHsUYznY3Fo3hbXeavAZKR0/Rqw9AuTidL1q3CZ+KaU1vnYTkz6G9Qa+BTGG5cxnjuG8eIJ1E1a4/rqMkTRRNlf30CxlW+9s5zaX0j+mb9B8i3fyGcxnEug+E6+dZ/sUbL2M9xmLpb6cWCrZI+hlfY4dZiKuOM4tGiH+9trpDGybjVikfmh2kGtYtZT/Zjy8c+YRJEh3VoSEqRhxZ/7Ca0XSETrxsQkJPHp+r0IQLsmdXn9qf4yNRzUKmYN78GU1ZswmUSGdGxGSIAvK7YcIzRYQ0SLBswY3JWFv+xj7b4zIMCC0b0RBKGyvZrZk59myhsfYjSZGNqvByH1glj+wwZCG9enV6c2HD97kU+//Q1BEGjboglzpoyR66BSMatvC6b8ekTqx0PBhPh7sOLARUIDvIloLIWgrRdSGNg8qOq75f1QM/v5UUxZ8ClGo4mhfbsSUrc2y3/cSGhIPXp1bMXxuHg+/f4PSY/QxsyZ9KTMJ/KWforfR++BWkXxpi1UXEvE4z/jKLsYT+nBaNyeGI5zdylWmPLzyX3H1qalG1bj8vybUkr947sk3xxQ6Zvnj2GMP4m6SRtcX1mGaDJStsnKN+/DOBONJnbP+5YR37+KSq0ibt0+shKS6TpjBPqz17iy4wS6lg0Z8sV/cfZypVHfNnSdMYJv+1YuNzQaKfjkE3zel3QwbNmCMTERt3HjqIiPpzQ6GtcRI3DqWqlDQQH5i+U6OKhVzHq4HVO+34dJNDGkTUNCtF6s2H2W0Nq+RDQLYkb/1iz86zhrj0gPBwuGdpLZ1kGtYtbgzkz5arvkm+0bE6LzYcX2E4TW8ScitC4zHunIwvWHWHvwHAgCC57oYStjVG+mLPsdk8nEkC4tCKntz4q/DhFaT0dEyxBmjIhg4drtrN19QvLvMQPNMkQTZdu+w3n0q6ASqDi9HzEzGceewzGlXsN4SXqAcgjtTMX56vcjYTSR++Fn+H+8BFRqiv72reefo/xCgs3DebWYjBQu/xivRR9UHp+xGeP1RFyfHU9FwkXKjkTjMmQEtbpU+mdBAYUfWttEzez/jGLKwmXSWO3TRRojP/1FaKN69OrYkuNxCXy69k8EBNqGhjBn4iiL9ipef2YQU5auxWQSGdq9NSFBWpZv2ENY/dpEtGlKTHwin/62GwRo16Qes58ZZKvDxNFMefPjSh26STqs/VMap51ac/xsAp9+vwFBgLahTZgzebStbw3pwpQvt0p+0aEJIQE+rNgWK/lFWD1mPNqRhb8dZO2Bc5JvjZL7xSvzF3P85Blyc/PpM/QZpk4YQ0WFtMJj1LBH6NmlAwcOH2fQyPG4ODvz1uyXrHSo2bX8m1ff/ojjp8+Rm1dAn1ETeWHsKCqMkh4jHxtAj05t2X/0BA+PicTZ2Ym3X5kq10OlYlb/Vkz5+RAmEwxpVY8QjScr9p0nNNCHiCbSA9TW8zcZGFp97AQ4vvs4HXp34KuDX2EoMfDRTHNmu2VblxE5UDpyY/zs8fQa2gsnF/PLDZPRxM9vrGH6d3NQqVUc+mUPqZdu8thLo7h+9gpndsZw8JfdjF86jbf2fkZRbiFfTrNNr35s9zE69u7ANwe/klKOzzQfxfD51uVMGfgCAP+ZPYFeQyNwcnFi7bHvq+3Pg8qDuwDuwUYQ/8G1g5UPPu5Wn7UEvgXUSDNhv4iiuPBu2laDmD9pwB2q3BrPVdsASGg+8A41b02TC1vJeiTc7vYAflH7SGrf584Vb0HdGCkLzPqAp+yWMTxNSmWbMyLCbhk+v+8lf0I/u9sDeK7ZQc4TNdDh170AFEwddPuKt8FjxRYKZwy+c8Xb4L50I5dD7ffNkPOSb9bUHgB54/raLcPr652U7P/G7vYALj2foyTq4ztXvFX7R6RUsKWX7uKG7xY4NZZmekrWvGy/HhM+oPTCHrvbAzg170VK1152t68dLX1/4ctD7Jbh/sGfADUeZx/Wfcbu9gAzk35AH2G/Drq9ewEo+Wn+7SveBpfR0lLYkg2L71DzNjKGzaJkl/17EgFc+kyk6J1n7W7vNuc7AJK79LZbRtDh3QBkDrD/98x/2z5Kz+2yuz2AU1gfDIfW2t3eudvTAJRe3Ge/Ds2ka1Dy53t3qHlrXIa8SnnmVbvbO/pLqehrcj2dwqT7irKbZ+2WUavOQ5R8a8eeSgtcxi5mULD9v8lbbmwBYFL9J+yWsSpROqexf7D993vbb2wFKZHmA0+PoD4P9HPTgeRdD+R1/Ednmqp76BFF8QzSUrx7bqugoKCgoKCgoKCgoHC/eeCW5ykoKCgoKCgoKCgo/M9gUhbo2cU/nQhCQUFBQUFBQUFBQUHhgUZ5aFJQUFBQUFBQUFBQULgNyvI8BQUFBQUFBQUFhf8jKMvz7OMfzZ73v8C/unMKCgoKCgoKCgoPDA9k1jdrugT1eqDvjw8n73kgr+O/fqbpfqRYPRTwuN0yuqX9xvW29qd0Bqh3YifbdU/eueIt6K//GYCVwfanAJ584wcArj7U/w41b03Ds9tJ7W5/OmWAwIN7uNLC/lTdjeKkVN01TWWc0a9maeQ1O/axWzfS7va99b8A1Mi36p3YCVAjmwQe3EPJlk/tbg/gMuhFSrYts7/9AOncDsOpTXbLcG79KAAlv71tvx6Pz6U0bofd7QGcWvTjRLD96cLb3pDSheeOst+m3uuktOU3Oth/zEHw8V18VoN4AzDtxg/3JS3//UhnX1O/uB9jpHj1S3eueAtcJ0pn0cTUGWq3jPY3/wDgYpOH7ZbRLGEzhiPr7G4P4Nx51H2JFzVJA+/SZyIAhsM/2S3Ducvo+5Iu/H6kLTec3my3DOdWD9co9TpI6deH1X3M7vYbkv4C4OX6o+9Q89Z8kCjZ8n7oofDv5V//0KSgoKCgoKCgoKCgIPEvX2X2P4aSCEJBQUFBQUFBQUFBQeE2KA9NCgoKCgoKCgoKCgoKt0F5aFJQUFBQUFBQUFBQULgN/yf2NDl17oDXfyMR1CqKNm6m8PvqN286R/TA790FpI+bTPnFBFmZd6/WNHxrHKhV6NfuInnZH7LygGf7EzBuAKLRhKnIwOVXVlGScFMuv2sHfF+eCmoVhRu2kP/Nz9Xq4dq7B5oP5pP69FTKLpj18OvVimZvj0VQq7i5djeJn22UtavzbF+Cx/dHNJowFhk4//IXFCUky+oER7Sk25tjENQqLvy0l1Mr5BsXWz4/iGZPRiAajZRkFbD35dUUJmfJ6rh0a4/fa1MQ1Cry128lb031G3vd+nZH99Eb3Bz1AmXnL8nKnDp1wHN6JKjUFG+KougHuU1cBg3AY+pkTJmZABT9voGSTebNqi7d2uM/azKCWk3+71vIXfPLLXUI+HgeN0dFUnpOrkOtjh3xiIwEtZqSqCiKf/xRVu48cCAekydjrNShZMMGSqKiZHUc23fEfeo0BJWKki1RlKyTy3DqPxD356dgysqQZPy5AcMWuQzfXq1o/PY4BLWK1LW7uP7Zn7Ly2s/2o874AVV2vfjyKrmeNfQrqLk9AA5duM576w9iEk0M6xzK+L7tZOWpOQXMW7uLgpJSTCaRFx/rTI/Q+nIZ56/z3vr9mEwiw7qEMr5fe7mM7ALm/bBDkiGKvPhYV/pb5Ao4dOoiS775A5PJxLDenZgwVJ7MICUjm/kr15GTX4SXuyuLIp9C5+ct1yEhmfeiYiQd2ocwPryFXIfcIub9dogCQ5nUjwFt6dE0SFbn4MnzLPnqN0wmE8P7dGXCcHkClZT0bN5Y8QM5eYV4ebiyaPpYAvx8ZHU8I9pQ583nQa0i66cd6Ff8Liv3f2YgmrGDqmJO0qwVsnKHVh1weU6yadnuKEr/lNu0VvgAnJ+ZjJgt2bR02wbKdstt6tylA94zXwCViqI/N1PwbfW+5dKrB/7vvUnas1Mot/KtuhEt6VkZc87/tJdYq5jT+vlBhD0Zgaky5ux6eTUFFjHHtXt7/F+fDGo1+b9tIffLW4z1ft0J/GQeN56wHeuHLiTx3h8HJZt2bs74Pm1l5ak5Bcz7cXelTU28+EhneoTWk8u4D35R0zFy6FoG7+85j0kUGdoimPGdGsnaf7DnPMdvSNfOUGEku7iMA5Fy3/OMaEPdBf8BtYrMn3aQtny9rFzzzAA0zz0MRhPGohKuv7YCwyX5b5lbj3Zo50xCUKvI/XUb2at/pTo8+ncjaNkcEodPxxBnZZMzl1iydrN0PcPbMuHRnrLylMxc5q/ZQE5+MV7uLiyaNAKdr5e5vR2xokdYfVmdQ+eu8d6vezCJIsO6tmD8gE5WMvKZ9+1WCkoMkj2G9qBHi4a2/fhxqxRzerZlwqM9qunHn+QUFOHl5sKiScNl/QA4eOIcS776FZNJZHjfrkwYLk+AkpKexRvLfyAnvwAvdzcWTX8OS++cu2gp+w8dw9fHmz9+WIk1oijy7scrOXD4OM7OTrwzZyahTUPk/Th1gSVfb5CuZ59OTBgqTzKUkpHN/M9/Jie/UIqd056xjZ3xN3nvzyOSf3dsyvhereTXM6eQeev2U2Co9O9BHejRPNhG3wkLJtKuVztKS0r5bOYnXI27YlPn6VfGEDGiF25e7rLPm4a3Ysgbz6JSqzi6bg97PpffJzXs2IzBbzxLYLO6rJ32KWe2HLORbY8OTzW3P7HTP4GSctw+/rGHJkEQjMDZSh0uAGMBE7AfcKr8/DdRFOdX1v8GCAfyKkU8J4riqTt+kUqF98zpZE5/BWN6BtqvPsdwIJqKxOtyfVxdcB85grK489XKaPjufzg3ciFlqdm02rqY7O0xsoeijPUHSPtuOwC+/dvT4M2xnH/qHZkM39emkT71NSr0GQT+sJySfdGUX0uy0cPjqWGUnr1gpYNA88XjiR35DoaULDpvW0TGtljZQ1Hq+kPc/E7KiKYZ0I6mC8ZwYvRis2yVQPe3x7LpqcUUpWYzfNNCru+IJedSSlWdzLhE1j8yjwpDGaFj+tB5zmh2TrXIVKRS4T8nktSJs6hIyyTo588o3nOY8qu2/fB8ZhiG01b9qJThOWM62S9JNvH/ciWlB21tYti9h/yPqsk6pVKhmfsCKc+/TkVaJnXWfUbRniPV6uD1zNBb6uAxfTq5L7+MMSMD35UrKT10CON1Kx327KHgk09s2/8tY9p/yX1tJqbMDHyWraLs8CGMSXIZpft2U7jsVjIEmi6ewMmRb1OakkX7be+SsS2GYgu76tcfJOU7KSub/4B2NF4wVqZDjfyqUkaN7AEYTSbe/W0/K6cMRuftztNLfyW8RQMaBfhW1fliewz9W4cwsnsLrqRlE7lqE1vm15fL+HUvK18YKsn4YB3hLRrSKNBSxnH6t2nMyB4PcSU1m8hVG+k/w9x+0VfrWTVnEjo/L556/WMi2ofRqE5AVful3//FYz3bMzi8A0fjLvHJT5tZFPmUXIe/jrFyXF90nq48/fkWwpvXoZHWfHPwxZ4z9H+oHiM7NeVKei6R3+5myyvDzTKMJhZ98Qur34hE5+fN6NfeJ6LDQzQKDqyq8+F3G3gsvCNDenXm6Nl4Pv1hI4umy+0a/PYkLj01n/LULJpu+oC8HccwXLpRVSX7j31k/rAVAK9+HQl6Y7y5vaDCZfx0it55BVNWBh7vrqQ8JhpTstym5dF7KPn6FpndVCp8Xn2R9MhXMeoz0H27gpL9h6m4Vk3sfHI4pWdtY6egEoh4eyx/PLWYwtRsRm1ayFWrmJMRl8i6ypjTYkwfus0Zzda/Y07lWE/+z+tU6DMJ/nusX7H1b+8x1Y91o8nEu+sPsHLyY+i83Hj6o98JD6sv980dsfRv3YiR3Sp984vNbLF4aLovflHDMWI0iSzedY7PH++IzsOZp9ceIjxESyM/j6r2L/cKrfr3TycSiU/Pt7Fp3bcnkVDpV82j3id3+zHZQ1HWH/vJ+EHKPOjVrwPB88dz6ZmFMhm6+VO5MW4O5WmZ1P/9Ywp3HaHsyg35V7m54DN2CCWnLlZrk0XfbWLVq2PR+Xry1JuriGjTjEZB2qo6S3/exmPdWjO4exuOnr/KJ7/uZNGkEeZraUes2BL2nNwe63ax8sXH0Xl78PSStYS3DKFRoJ9ZxpYj9G/XhJE9W3MlNYvI5evZ8nZDmYxF329m1StjpH4s+IKINk2t+rGdx7q1YnD31pX92MWiSdbxYh2r578oxYtXlxDRoaU8Xny7nsciOpnjxdo/+TDcnNV36MP9eGrEYGa/9YHNtQY4cPg4STdT2LxuDWfOXeStD5bx0xfmrJJGk4lFa35n1dzJ6Py8eer1j4ho38Iqdm6UYmdERyl2/riJRdOekcl4d0M0K58fKI2zzzYSHlqXRjrzy6Avdp2if6sGjOzSnCv6HCK/2s6W5qNkurbt1Y7a9WszteckmrRpyqR3pvDakJdt+nR85zE2f7uJ5fvMLxEFlcCwheNY/cwi8tKymL7xHc7viEV/2fx7mpOSybqXVxL+/CPVO3LpkQAAIABJREFUXqua6qDw7+afXJ5XIopia1EUWwBlwGSgFOgtimIroDUwUBCEzhZtXqls0/quHpiAWqHNqLiZjDElFSoqKN65G+eeXW3qeU4cT8EPPyGWldmUebQJwXAtjdKkdMTyCjL+OITvgA6yOsbCkqp/q1ydbPVo0ZSKmylUJEt6FG3bi0tEN5t63lOfI/+bdYilcj282oZQfC2NkuvpiOVG0v6IRjtQ/mbNUge1q5PNKVXa1o3IT9RTkJSBqdzIlY1HqN9f/qYz5fAFKgzSd+tPXMbd4gcdwOmhppQnpVBxM03qx5Z9uPWyvZ6+kWPJXbOu2uvp2LwZxpspVTYp2bkbp+621+JWWOtQuGUvbr272OowbSy5X/1SvQ7NmmFMTsaYKulg2L0bp253rwOAQ9PmGFOSMaVVyti7m1pdu9+TDM9Kuxoq7Zr+RzSagbf2LbWrM1hkvampX0HN7QEQdz2dYH8v6vh74eigZkCbxuw9e01WRwCKKn2rsKQMjZeblQw9wRpvs4y2Tdh79uqtZRhK0XiaZcRdTiJY50cdnR+ODg4M7NqGvcfPydpfSdbTMUx6w9oxLIS9MXFyHW5mEezrQR1fD0mHlvXYe0F+MygIAkWl5ZU6lKPxdJXLuJxI3QB/6gT44+jowMDubdlz/IysztUbqXR6qKmkR4sm7Dl+Vlbu1roxpYlplCXpEcsryNl4AK/+HWV1TNYxx8Iv1CHNMOlTMKWngrGCsujdOHa4N5vWCmtG+Y1kjJW+VbxjDy7htmPda/I4Cr77udpxpmvdiNxEPfmVMSdh4xEaWsWcZIuYk3biMm4WMcfZcqyXS2PdvZqx7vfiWHK+/KVa/45LqvRNP89K3wxhb1yirI6AQJHhb5uWofGysun98IsajpG4tFyCvV2p4+2Ko1rFgKaB7L2st+nv32y9mMLAZoGyzyS/Sq3yq+w/D+LdXz67YrpNvAFwbtmEsusplN+QbJIftR/3vrY28Z8+hqwvfq3eJldvEqzzpY7WVxqrnR5i7wn5w9WV5HQ6NpceUDo2byArr2msAIhLTKuU4S3JaNeUvacvy2UIgoU9Sm1j1tVkq360YO/JeHk/UjLo2LyBuR8n5f2Mu5xI3UCNRbxox55jp2V1rt5Mo9NDTSQZLZqw55g8nrRv/RBenh7cij0HjzB4YB8EQaBVi+YUFBSSkZltoUMSwQH+1NH5W8ROeWy8cjONji0aSzpUFztvZBDs72keZ60asvec1csNwdImZTZjBKBj/87s+V067iXhZDxunm74aH1s6iWcjCcnPUf2Wd3WIWRdTyP7RjrGciOn/jpMWH/5fVLOzUxSLybdNntcTXRQ+HfzoOxpOgCEiBKFlZ85Vv5fozlElcYfY3p61d/G9EzUGo2sjmOTxqi1Gkqjj1Yro1agL2UpmVV/l6Vm4RToa1MvYNxA2h5ZRv15Y7g6Z42szEHjT0WapR4ZqLV+sjq1moWg1mkpOWirh3OAL4YU85IVQ0o2TgG2OgSP60/3o5/QZN7TXJzzjazMLcCHwhRzoCxMzcYtwDYQ/E3zJ8NJ2isP3g5afyrSMqr+rtBnoNZZ9aN5CA4BGkoOVD/trbayiSkjA7XG36aec3hP/L/5Eu+33kSlNdvMQetnpUMmDlp5+791KN5fvQ4qjQZThlmGpIPGpp5Tz574rlmD14IFqKzKVf7+GDMs+pGZgdrfth+1uofjs+orPOfZynAK8KXUwq6lKVnV2jVo3AC6HP2URvOeJmHO11Wf19SvoOb2AEjPKyTAx7xMQuftTnpekazO5IEdiYqNp//8b4hcvYlZI+TLWNJziwjwtpZRKJcxqBNRMfH0n/cVkSv/Ytbj5vOy0rPzCLBYLqL180Kfkydr37RebXYdkx5Qdh07S1FJKbkFZj3T84sJsLgx0nm6kZ5XIpMxuXdLok5do/+S34n8djezHpU/5Oqz89D5m8eVzteH9Cy5Hk3qB7HziPTeZ9fR0xSVGMgtMPfVMcBPFnPKU7NwDJDbFcB/7MOEHVxJ0OznuPnGF1Wfq3z9MWVZ2DQrA5WPrU0dO/XE470vcX3pTQQ/uU3VGn+MevMYMept/cKxaWPUOg2GQ9X7VnUxx/02MSfsyXCuW8Qctc6PcsuxnpaJ2mqsO91hrKfnFRHgbWFTb7dqfLM9UbEJ9F/wHZFfRDFrmJVv3ge/qOkYSS80oPNwNrf3cCGjsLTaPqfkl5CSX0KHulZxMdCXslSL37K0LGpV81umGTuIFgdXUmfOWJLe+FJW5qjzoyLNLKMiLRNHq98Ap9BGOAZqKNp7vFr90nMKCLBYoqb19USfI58Va1o3gF2x0uzlrtgLFBlKyS0sltrXMFZIMgoJ8DE/aOh8PGxlPNKFqGMX6D97FZHL1zNrlHy5b3pOPgG+nuZ++FTXDx27Yi9Y9KOsqh8A+qxcdBZLc3V+PqRn3y5enKKoxEBOzt3frOszsgiwGDc6rT/6DLMN07NzbWNntnXsDGJX5cNatbEzz2qMeLmSnm/l3/3aEnXyCv3f+YnIr7Yza0g1L0AC/Miy8NGstCx8q4l91eGl8yHX4vc0NzULL92t482tqIkO/78gPuD/Paj84w9NgiA4AIOQluohCIJaEIRTQDqwQxRFy1/jdwRBOCMIwkeCINhO59inAF7Tp5D36ec1FpX29VZOdI4k8e0fCH7pHg/EFQR8ZkwhZ6nteuR74cbX2znYaToJb/9Iw5eG2S2n8bBuaFo25NTKqDtXtkQQ8HtlElkf2H9wIIDh0GHSnxhN5nP/oSwmFu85s+5JB/9XJ5L1fs10KI2OJvPJJ8meMIGymBi8Xn/9nmWUHY4me8wociaNp+xEDB6vzLZLl+Svt3G404tceXst9V8acfcN75Nf1cgelWw9cYnBHZuxfcFzLJv4KHN/2InJdG/BcWtsAoM7NWP7W+NZNvkx5n6/HZPJdNftZzzzGDHnrzLytQ+JvXAVra8XKtW9hcGtZxIZ3LYR218bwbKxvZn766F77sfMscOIPX+ZkS8vJubcZbS+3vesB0Dmt5s5130yye9+S8CL97amvjz2MPmRoyl49T9UnI3Fdeo92lQQ8H5pMrkf18y3/qbpsG5oWzbkxL3EHEHA/7WJZL5Xs7G+9cRlBndsyvb5z7Ls+UeY++Oue/fN++AX92OMAGy7mEKfxgGoVcI9twXI+HYLcd0nc3PRdwS++MS9NRYEdK8/T/riL+5c9zbMeHIAMRcTGTlvBbEXE9H6eKIS7r4/1ceKe7RHzEUGdw5j+6JJLHthOHO/2XzPMmaM6k9MfCIj31hJbPx1tD4e99QPgJljhxN77hIjZy4i5twltL7eqNXqe5JRU2aMGUzM+SuMfPUDYs9fti92nrrC4HaN2T5nNMvG92fuz/vs8m8FhX+KfzIRhEvlwxFIM01rAERRNAKtBUHwBjYIgtBCFMU44HUgDagFrAZeAxZaCxUEYSIwEWDVqlUMy8hErTWvL1Zr/TFazDAIrq44NGyA/wrpxHS1ry9+771N1qtzq+qUpWZTq7b5LU2tQD9KU81vT63J/OMQjZY8L/usIiMThwBLPTQY081vRAQ3Vxwb1Sfgiw+lcj9fNB8vJOO/bwBgSMvGubb5TYdzbV9K026tQ9qGaJovmQCYHwaL0nJwr21+q+ge6EtRmu3bqqDuYbSdNpg/n3gHU1mFvB/pmTgEWMz66DQY9Zb9cKFWSH0Cv3pf6oe/LwGfLSRt2htVdYxWNlFpNBgt3noBiPnmt3XFf0XhMWWihQ5ZVjr4U5Fubq+q1KH21+9Z6LCAtGnzq+qYMjJksz6SDma/sNahJCoK90mTZOWmzEzUGot++GuqkkZUySgwyzBsicLt+cmy8tK0bJws7OpU2++2dtVviKaphW/V1K+g5vYA0Hq5k5Zjfkurzy1Ea7WUZcPR86yYJJ223qpBAKUVRnKLSvi7ltbbjbRcaxnyTb4bjpxnxZTBlTICKa0wkpOTg5+fH1pfL9KycqvqpmflofORb7jW+nrx0cvPSf0wlLLz6Bk83VzM5Z6upFm8/dfnF6H1cpHJ2BB7mRVjpTfOrepqpH4UG6r6ofP1Qp9pHlf67By0ftZ6ePPRq5Idi0tK2XnkFJ5u5qUq5WlZspjjGOhHeZo8IYslOX8eoO47Zt8yZWei8rOwqZ8GU46VTQvNNi3bFYXL03KbGjMyUevMY0Stk/uF4OqKY6MGaFculcr9fNF8+BYZM+dV1aku5hRWE3OCu4fRftpg1lvFHKM+C0fLsR7gj9F6rDeuT9C35rEeuHwBqS+Yx7rWy420XAub5hZV45sXWDHxUQBa1Q+gtLxC7pv3wS9qOka07s7oCwzm9gUlaNyrf3e47WIqs/qE2XxelppNrUCL37IAP8pu81uW/ecB6i6Sx71yfRYOAWYZDgH+lFv8BqjcXKjVpB51v18CgFrjQ9Dnb5A8xfxzrfXxIM1iJiM9Ox+dj3nGRqrjyUcvjgYqx2rM+aqxam+skMcbd9JyCswycgpsZUTHseIFaf9Rq4a1KS23kuHjSVq2eRyl59yiH9OerLYfADo/b/RZFvEiKwetbzXx4jXJDsUlBnYePoWnp/x7bodO40eaxbjRp2eis5g11vp628ZOGx28+Ojl8eZ+WMdOL6sxkleM1mpJ5IbjCayYICW5aFVPJxsjS7dI+34vn7mEn4WP+gX4kX2b2GdJnj4Hb4vfU+9AP/L097Z8bumWT2qkg8K/mwdhT1NrURSniaIoW/gsimIusAcYWPl3auXyvVLga6CjrUgQRXG1KIrtRVFsP3HiRMouXMQhOAh1YAA4OODatzeGA4fN9YuKSBs0DP3wp9APf4qyc+fJenWuLHtewanLuDQMxKmuFsHRAc3QbmRvly87cG5g3jDp07cthmtpsvKyc/E4BAfhUFvSw21ABCX7os16FBZxs88Ikh99huRHn6H07AUy/vtGVZaz/JNXcG0YgEtdDYKjmoChXUnfFiv7DlcLHTT92lB8NVVWnn76Kl71A/AI1qByVNNocGcSd5yQ1fELq0fPxePZOn4phiyrTcRAaVw8jvWCcAiq7MegcIr2WlzPwmKu93yCGwOf5cbAZyk9c4G0aW/IsueVX7yI2sImLn17U3ooWvY9Kj/zjZZT965UXDevjS6Ni8exbhAOQTpwcMB9UARFe45UlZsKi0nsMZKkAWNJGjC2Uof5soxa5fHxqOvUQRUg6eDcuzel0VY6+Fro0LUrFUny9dkV8RdRB1nIiOhN2eFDt5RRq0s3myQRBSev4NowEOdKu2qHdiVzW4ysjouFXf36tZXZtaZ+BTW3B0BYXS1JmXkkZ+VTXmFk28lLhLeoL6sT6O3B0crkKVfTsikrr8DH3cVCho6kjFySs/IkGScSCH+ogVyGj7uVDCO+ldc4rFEwSWmZ3EzPoryigq3RJwlvL79xzMkvrJqZWvPHLob2koeRsCA/krIKSM4ukHQ4c53wZvLsToFebhy9Ko3vq+l5lFUY8XEzL5sKC6nH9dQMbuozKS+vYOvBE0S0b3lLPb5cv41hvTvLyotOX8KpfiC1gqWY4zO4B3k75MvPnOqb96t49WmPIdHsF8YrF1EFBKHSBIDagVpde1MeI7ep4G22qWP7rhiT5TYtO38Rx7pBqCt9y7VfL0r2W/hWUREp/YaTOuRpUoc8TWnceTJmzpNlz9Ofvop3/QA8K2NOk8GduWYVc/zD6tFr8Xg2jV9KiVXMMVTFGx04Vj/Wr3UbyfV+Y7nebyylpy+Q+oJ8rIcFayv96m/fvGzrmz7uHK1MhnBVnyPZ1NI374df1HCMhAV4kZRbRHJeMeVGE9viU4lopMOaa1mF5JeW06q2t01Z0elLODcw+5XvkO7kWvtVA7lflV6T/44YziZQq35tHOtINvF8pCeFu+Q2udxpNFd6j+NK73EYTl0kecpCWfa8sAZBJOmzuZmRI43Vo2cJb9NM9j05BUXmsbrpAEN7trG4lvbFCplN6wWQlJ5LcmaljNh4wls2spLhwdF4aVxcTc2irMIqZjWoTZI+y6IfcYS3aXqbfhxkaI82snIpXqRbxItYIjrcIV70sV3Wdjsiundm49ZdiKLI6bgLuLu7ofE3j/+wRsEkpWbcfezcsJOhveR74cLqaEjKzDePkdNXCQ+tK6sT6O3O0ctSEpir+lzJJpVjZMag6cwYNJ2j247Qa0RvAJq0aUpxQfFd7xu6cfoK/vUD8K2jQe2opvVjXTi3I/bODS2oqQ7/vyCK4gP9/4PKA5VyXBAEDVAuimKuIAguQD9gSWVZoCiKqYIgCMBQIO42oswYTeR++Bn+Hy8BlZqiTVuouJaIx/PPUX4hAcPB6LuScXX2l4T9NBfUKtJ/2k1J/E3qvjqKwlNXyN4eQ+D4QXj3bImpvAJjXhEJL35mIyN7yWdoly8GlYrCjVspv3odr8ljKTufQMn+w9V/dyWi0cTF17+m7c+zEdQqkn/aQ1H8TRq9+gT5p6+SsS2W4AkD8OvRAlOFkYq8IuJe/NxGxsF53/LID68iqFXEr9tHTkIy7WeOIOPMNa7vOEGXOaNxdHWm38oXAShMyWLr+KWyfmQuWkbAykUIahUFG7ZRfuU6Pi88S+m5BIr3HuGOGE3kL/0U36XvgUpFSZRkE/cJ4yi/GE/poWjcHh8uJSMwGjHl55P7zmJZ+8xFywlcJemQv2G7HToYKfjkE3zefx9UKgxbtmBMTMRt3Dgq4uMpjY7GdcQInLp2RTQaEQsKyF+8WC7DZKRw2cd4vfsBgkqFYdtmjNcTcR07noqEi5QdjsZl6AhqdansR0EBBe/LZYhGEwmvf0Xrn+cgqFWkVNq1wasjKTh9hcxtsdSZMBCfHg8hVhipyCvkwovL6bR/adW1qIlf3Rd7AA5qFbNG9GDKyo2YTCJDOjUnJNCPFZuPElpXS0SLBswY2o2F6/awdp+0Z2XBU9LGZJmMx8OZsmIjJpOJIZ1DJRlRRyQZDzVkxtAeLPx5N2v3nARBYMHTfatkOKjVvD5+OFMWrcZkEhka0ZGQ4ACW/7KVsIZ1iGjfgpjzV/j0p80gQLtmDZk9YYRtPx7ryJRvdmESRYa0DSFE582KnacIDfIjonkwMx5ux8INR1h7SNqnsGBEV6t+qJn9n5FMeWs5RpPI0N6dCakbyPKfNhEaUpdeHVpy/NwlPv1hI4IAbUNDmPO81dI6o4kb81YT8sObCGoVWet2YUi4QeDMpyg+c5m8HcfQPPcIHt1bIVZIMef6Sx8Tuqsy65zJRMlXn+I2W7Jp2d4tmG4m4vzEOCquxlMRG43ToOE4tusGJiOmwnyKVyy20SHnvc/QfLoEQa2icOMWKq5ex3PSc5RdiMdwF74lGk3sm/ctg394FZVaxfl1+8hOSKbTzBGkn7nGtR0n6F4ZcwZVxpyClCyixpv9O+Od5dT+YhGCShrrZZev4xv5LIZzCRTvufNYd1CrmDW8B1NWb5J8s2MzQgJ8WbHlGKHBGsk3B3dl4S/7WLvvDAiwYHRvW9+ssV/UbIw4qFS81juMqb8fw2SCIS3q0MjfgxWHEgjVeRERIj1AbYtPYUDTQNl3W9o0ad4XNFk7H1RqstbtxJBwg9ovj6bo9GXydhxH+9zDeHZvVRVvrr30iY0M/cLPCV7zNqhV5P22nbLLSfi/+AyGuEsU7q5+f5vcJmpeH/MIU97/DpPJxNCebQmpo2X5+l2E1Q8iom0zYi4m8umvOwCBdk3rMfvZR+XXsgaxokrGqN5MWfa7JKNLC0Jq+7Pir0OE1tMR0TKEGSMiWLh2O2t3n5D8YsxAm7H++jMPM+WD76WY06MNIUFalq/fTViD2kS0qezHb7sApH6MkWdtk+LFKKYsXIbRZGJony6E1K3N8p/+IrRRPXp1bMnxuAQ+XfsnAoIULybKM869Mn8xx0+eITc3nz5Dn2HqhDFUVEgztqOGPULPLh04cPg4g0aOx8XZmbdmv2Rrj/EjmPLOKskevToREhzI8nVbCGsUXBk7L/Ppj1EgCLRr3pDZEx63kqFi1pAuTPlyq+TfHZoQEuDDim2xhNbxJyKsHjMe7cjC3w6y9oCUoGfBqB42fhq7O4Z2vdrz+YHVUrrvl83+t3TLJ8wYNB2AZ2c/R48h4Ti5mGdbTUYTG974hue/ex1BreL4L3vRX7rJgJce58bZa5zfGUtwy4aMXTUDVy83Qvu0pf9LT/BB/1dqrMMXR79G4d+P8E890QmCUCiKorvVZy2BbwE10izYL6IoLqws2w1okJLinAImWySNuBVicpfedusYdFjKnnIo4B73J1nQLe03rrfte+eKt6HeiZ1s1z1pd/v+eulslZXBz9yh5q2ZfOMHAK4+1P8ONW9Nw7PbSe3ey+72AIEH93ClxYA7V7wFjeKkVLr6iAi7Zej27iWjX/idK94GzY597NbZf65Db710Vk1NfKveCSk9fU1sEnhwDyVbbpGy+i5xGfQiJduW3bnirdoPiATAcGqT3TKcW0s3ZCW/vW2/Ho/PpTRuh93tAZxa9ONE8BC727e9IZ3xlTvKfpt6r9sDwI0Ofe5Q89YEH9/FZzWINwDTbvzA5VD7x3rIeWmsl0R9fIeat8blkf9KMmroF/djjBSvfunOFW+B60Rp+XlMnaF2y2h/Uzqb8GKTh+2W0SxhM4Yj1Z/rd7c4dx51X+JFyS7798G59JGWshoOV3/m493g3GU0ped22d3eKUwan+WZV+9Q89Y4+kvZCQ2nN9+h5q1xbvUwJX++Z3d7AJchrzKs7mN2t9+QJJ399nL90XbL+CBRsuV90MO+jYT/y7QP7PHgTucAMakHHsjr+I/NNFk/MFV+dgZoU011RFG0/+lHQUFBQUFBQUFBQUE53NZO/vHseQoKCgoKCgoKCgoKCg8yykOTgoKCgoKCgoKCgoLCbXigEkEoKCgoKCgoKCgoKPzP8SBnqHuQUWaaFBQUFBQUFBQUFBQUbsM/lj3vf4l/decUFBQUFBQUFBQeGB7IrG/WtAno9kDfH59MO/RAXkdleZ6CgoKCgoKCgoLC/xGU7Hn28a9/aMobZ/85Nl5fS+fY1PRcivtxpk9Nz0cC+DrI/nNTxiVL5zTlPBFhtwyfX/eS+3TNMsd7r91N1iP2X0+/qH0A5I21/wwar293kf+8/fYA8PxiOxca2+9XzS9J52pkDrD/Wvhvk65FzogIu2X4/L4Xw4mNdrcHcG47GEPsH/a3byedPVOeesFuGY6BzYGan+lTdvWY3e0BajXseF/O3iqItN+3PJZJvlXTcbashuc0Rd74gftxzl7JzpV2y3DpOxmgRmcLOXceVSP/BsnHiz+eZHd71/+uAiClq/3nd9WOls7vqum5bjU5Ewikc4Fqeq4QQFlijN0yatVvD1Cj87dcBr1I2c2z9utQ5yGg5mcsQc3PeirZtNTu9gAuj864L+c0ja9v/5maXyX+BkCvOv3slrHnZs3O6VN48FH2NCkoKCgoKCgoKCgoKNwG5aFJQUFBQUFBQUFBQUHhNvzrl+cpKCgoKCgoKCgoKEiIyp4mu1BmmhQUFBQUFBQUFBQUFG7D/4mZJocWHXB+aiqoVJTv30Lp5p9t6jh2CMdpyLOAiPHGVUpWLZKVu/Voh3bOJAS1itxft5G9+tdqv8ujfzeCls0hcfh0DHGX5N/RviPuU6chqFSUbImiZN2PsnKn/gNxf34KpqwMAEr+3IBhS1RVuUu39vi9NgVBrSJ//Vby1lS/Odmtb3d0H73BzVEvUHZerkNQREs6LRyDoFKR8NNezi7/S1YeNnEQTUZHYKowYsgu4OCM1RQlZ8nqOLTuiOu4SFCpKd0VRekf8n7UihiIy5jJmLIzASjdsoGy3VFyGS074DImElQqyvZupvSvn+Qyeg7AefQkxJxKGdv/oGyvecOrY7uOuE2cBioVhu1RGH61upZ9B+I63nwtDX9toHS7lQ4PdcD56Rckv9i3mdKoavyiYzhOQ8cCIsakK5SslPuFOqw9zk9OQVCpKDuwlbKttjZxaN8Tp8fGACKmG1cp+XKxrNytRzt0cyt965dtZN3KtwZ0o86yOVwbNl2uY/uOuE2ehqBWYdgSRckvVtei30Dc/mPhVxs3ULrV6lq07ojreAubbrCyaa9qbLpLLuPQqYss+W4jJpOJYb06MmGIfBN/SkYO81f9Qk5+IV7urix6YTQ6P29z+9Pxle1FhvXqwITBvWzbr/6VnPwiqf3UUbL2AHOXfMb+wzH4envxxze2m7RFUeTdz77kwJFYnJ2deGfWi4Q2aSTvx4Uk3vvjoKRH5+aM79NWVp6aU8C8H3dTYCjDZDLx4iOd6RFar6p83tIv2H/sJL7enmxYKbf13zosXvk9B46fxtnJibdnTiQ0pL5NPeeuHfB9eSqoVRRu2EL+N7b+CeDauweaD+aT+vRU2efq5u1wfnyS5N/R2yjbYetXDm16UOvhpwERU/I1DN+8Jyu/H+OsbkRLerw5BkGt4vxPezmxQh5zWj8/iNAnIzAZjZRkFbD75dUUWMQcp84d8PpvJIJaRdHGzRR+L48VVdcrogd+7y4gfdxkyi8myMoOnUvkvd/2Sr7ZrQXj+3eUladm5zPvu20UlJRiMom8OKQ7PVo0kMs4c4klazdLfhHelgmP9pSVp2TmMn/NBnLyi/Fyd2HRpBHofL3kMmro46p6YdQKHwkqFRVxB6mI2SZr79jzCdTBTaU/HGohuHpQ8vlLsjpOnaTriVpN8V9RNtfT5eEBeL4wGVOGNNaLft9A8V+bbWR4TpfiRfGmKIp+sJIxaAAeUydjyjTLKNkkl3Ho1AWWfL1BuhZ9OjFhqDwBSkpGNvM//9kcL6Y9I48XNWwPMO/D1ew/WjlWVy/BGlEU/x975x0eVbWF/d+ZBEjvyUxIQktoCRI6SEtooUmTaxCQFht4KYqoKKBXLXXxAAAgAElEQVSCCFguFpAqKl4BwYKNFlroLYQOoQRCSJm0Se8zc74/zpCZM5NQQr4rn9+89/G5YfZea9699rvXnLLPOixZ+T2HTp7Dzq4uC19/meCmZrq4cpuPfz2MXtQzokswUX3by9rTcgqYt2GvUVtDutAjuJGRwydfcfD4aTzcXNm67rOqOXz1DYdOnMGuXl0WvjmV4GZNajWWAHMXLeXgkZN4uLvx2w+WxVNEUWTx56s4dOyUlDvnvE5w8yA5j/gkPv7tqMSjcwui+rS1jMWmGCkWosj0wZ3p0bKBxXc9P/8l2vdqT1lJGcte/4KbFxMs+ox9YxzhI3vh6Ook+7xVWBvGvDsJwUbBoc172b5SXoTFtq4tLyydRsNWTSjKLWTl1KVkJ2da+J+24BU69+5EaUkZH732Cdcv3rDk+eYkIv7VF2dXZwY1H2rRbsU/D3/bSZMgCDrggoHDFWACoAcOAvUMn/8siuJ7hv4CsBB4BtABK0VRvH/pGkGB3bhpFH36FqImE6d3v6Li7FH0qUmVXRRKP+oNHk3hohlQXIjgLE8mKBQo33uFO5PmUKHOotEvn1O49zjlCXfk3RztcZ8wjJKz8ZY8FAqcp71K7luvo8/KxH35asqPHUGXdFvWrezAPgqXf1GlvdecqaS9NButOgu/H5dRvP8YFTeTZN0EB3tcnhtB6TnLSmKCQqDLhxPYNXoJxWkahmxfQFL0afKup1b2yb6YyB8D56ErLaf5+D50nDuamCnLZTwcnp9B4Qez0GsycV68iorYI+iT5eMoP7qfknVVjEMigv3EGRQtfkPy8cFKKuKOok+R+6g4HkPJ+iqmWKHAccqr5M+VYun62Woqjh9Bd8eMw8F9FK2qnoPd+OkUffympIv3V1Bx5hj6VKMPhdKPek+NpnDh9Kp1ISiwHzOVos9mI+Zk4ThnGdpzx9CnmWjLpz71Bj5L0UevVast1fuvkDRR0lbjXz6nYN9xym9YasujKm0pFDj9+1Xy3pZi4bZsNeXHq9DVwX0UfVVNLBQKHF6cQeGCWeizM3H+aBUVp6qZ06+r9qHT61n07VZWv/MSSk9Xxsz5kvD2IQT6Kyv7LN3wF0N6tGdoWAdOXLzBFz/uYNG/R5vY/8bqt1+Q7OcuJ7xdsJn9Nsm+Z3tOXLrBF5t3suiVZ2U8hg/ozZgRg3hnUdU8D504TVJyGts3rOT85Wt88NkqNq38RDaOxb8eYtXkIShdHRn72S+EhTQiUOVR2Wft7tNEtAkkslsrEtQapq7dzg6Tk6Zh/Xowemg/5nxaddW2Q6fOcTs1nW3rPuV8fAILl3/Lxs/nyzspFHi8NY2MV95Cm56J7w9fUXLgKBW3LNe785gRlF0wW++CArvIVyhePgcxNwuHNz5He+E4erVRV4J3fepGRFK8dBaUFCI4yQ/wa2OdCQqBsIUT+H3MEgrTNET+tYBbu0+TY5JzMi8msmXwPLSl5bQa14euc0az65XllRzcXp9B1ow30GVk4vPNSkoPHUWbKOcgONjjFDmS8ouXLTjo9HoWb9nHqmlPo3RzZuzHGwl7IpBAX8/KPmt3niCiXTMie4aSkJbN1BW/saPV8zIfi77/i9VvTkDp4cKY91cT3rYFgX4+lX2W/riLId3aMLR7W05cvskXP+1h0csj5T4eReOCQN1eoyn79XPEwhzsRr+N7uZ5RE1apX3FwZ+oMPxtG9oLhU+APBgKBa6zZpBtiKf3ulVVxrN0737yllbz86pQ4DJzBprXJB9eX6+i7HAVPvbtJ/+zqn3o9HoWrfuF1XMno/R0Y8zbnxHeoRWB/ipjLP77B0N6dmBoeCdOXLzOFxv/YtG052rF/i6GRRjW6if3WKsparZ9+x/Ox99g4bJv2fjlAtk4Fv98kFVThqJ0c2Ls0p8Ia9VYni+iY4loE0Rkd0O+WP0XO95rZOTQvxejhw1kzkfLquZw8gy3k9PY9v0yzl+5zsIv1rDxK+PFmNqKxfBB/RgzcijvfPBp1TyOnSIpOZXtm9dx/lI8H3y6nE1rjdVGpdx5hFUvD5Zy5+e/GnKnuzEWe+KIaNOEyK4hJKhzmPr1dnbMHSv7nna92lO/UX1e6fkyzdo25+UPp/DWsFkWfE7tOcn29X/x1YHVlZ8JCgXPLXiB/zy3AI1aw7t/LOHs7lhSbyRX9ukR2YeivCLeDp9GpyHdeGb2c6yaKj9Z7dy7E36N/Xiu+0RatmvJa4un88qQ6RYcju45ztbvfueHQ99VGbPHGfp/9jta/6/h79yeVyKKYhtRFFsB5cBkoAzoLYpiKNAGGCAIQhdD/4lAANBCFMWWQNWXXc1g06Q5+oxUxMw00GmpOBlDnbbdZH3q9hxE2b7fobgQALEgV9Zu17oZ5bdTqbijhgot+dsO4tT3SYvv8poxjuy1PyGWlVu02TZviS41Bb06DbRaSmP2Ubdr9wcZAgD1nmhORVIq2mQ1aLUU7TiAY6+uFv08pk4gd91mxHJLDl5tAylITKcwKRN9hY6bvx+nQX/5VTH10SvoSiXbzNM3cPD1kLXbBLVAr05BnyGNo+LIPup2kMfzfrAJbIE+PQW9YU7Kj++jTnvLsVQH22byWJYd3EedLg8eSwCbJhKHSl2c2E+ddnIOdcMGU7b3j2p1YdO4OfrMVMQsteTj1AFs28h91OkxiPL91fuwr0Jbzn0steX96jiy1/yE3kxb5roqi9lH3ScfMhZ35zTdMKeH91G348PN6cUbSQSovPBXelLH1pYBT7YhJvaSrE9CcjqdWklXJjuFBBJz+pKJ/R0ClJ4m9qHEnJYfACekpNMpRLor1Ck40KIdoENoCK7OThaf38X+IycZ2j8cQRAIDWlOQWERmdkaI4+kDAK8XPH3dKGOrQ392wYRczFR5kNAoKhUOjQtLC3H29VBzuGJFrg6O1bP4XgcQ/t0lzi0DKKgsJhMjVwXdVs1R5ucijZFmpOiXTHYh1vOidsrE8n/brNFzlE0aoY+KxUxW9KmNu4gtq3luqrbdQAVB/+CEoM2C/Nk7bWxzpRtAslLTCffkHOu/3GcJhHynJNy7ApaQ85Rx93AyeSAs25wC7TJKehSJQ7Fe/Zh19MyV7i8FEXBD5uqzHsXE9UEeLvh7+UmzWn75sScl1+5luZUsi0sKcPbVT5/F28mE6D0wN/HQ9Jn5yeIiZNfwEhIyaBTS+kOQKeWjS3aH1XjClVjxLwMxPws0OvQXovFJjDUYrx3YdO8I9qrp2Sf1QlugTY5tTKeJXv2Ydfj4dZ6nZYt0Jn5qNe9pvnCS4pF17bEnLoo65OQrKZTq6YAdAoJIib2Yq3Z30WHJ1reO18cO83Qvj0Ma7UpBUXFZGbnGHncNuQLL1dDvmhKzIVbMh8CmGir3EJbHVoH4+pyr5x1iqERhpwV3EzKF6YcaisWbZ7A1cW5eh6HjzN0QB+JR6uWFBQUkpllljs9XeS581KiWSxMc2cZ3i6WebJTRBf2/yK9LuDamas4ujji7uNu0e/amavkZOTIPmvSJoiM22oy72Sgq9By4s8jtInoKOvTNqIjR3+JASB2+zFadn3Cwne3iCeJ/ll6fcOVuCs4ujjh4eNh0e9K3BU0GRqLz6345+JxeabpEBAkSig0fFbH8N/d0+EpwAJRFPUAoihmPIhjwd0LUWPsqtdkIrh7yvooVP7YKP1xfOdzHOcuw7aVfJHVUXqiVWdV/lurzqKOUu6jXnAgdXy9KYqR/0hVfoeXF7pMEx5Zmdh4eVn0q9s9DPfV3+Aybz4Kb+/Kz219vNCqjbeQtemZ2JhxqNsyCFuVNyWHqn5XjIPKnaJU4wIvTtPgqLJMRnfRbHQYKfvPycfh4V25FQcM8fT0NjelbueeOH+6DsfX51u0Kzy80GebzkkWCndLH3U69sB58VocZryH4GFsV3h6oc8yi6VnFbHsFobr8m9wens+Ci+5f0kXZuNwl/uo1MXcL3CctwzbJ+S6ENy80Jv4EHMyUbiZaUvpj0Lpj8Nbn+Hw9hfYhHSQtduqPNGmGbVVoc7C1mxe7YIDsfX1prAKbSk8vdCb6UpRha7qdQvDbeU3OM+V6woMc5r1AHPapSfOS9fhOMtyTjNy8lGZbPfw8XQlPUd+EN68oS97T0rvJdl76iJFJWXkFhQZ7PPk9h6upGvM7euz9+RFg/0lmf2DIj1Tg8rbGB+ltyfpmcY1kZFXhMrN+EOudHMkI0/+HZMHdGDb6WtEzP+eqWu3MXtEj4fikJGdg8rL+AOs9PIgI0v+w2vr7YVWbZxXXUYmNj5m671FEDZKH0oOn7D4DoWrJ/oco670OVkIrnJ7wccPhY8fDq99isPrS7FpKT+ZqY115qhyp8Ak5xTeJ+cEPxvG7RhjzlF4e6HLMI1DFjZm+q3TrCk2Pt6UHbWMA0BGbiEqd+PBoNLNiYzcQlmfyYO7sO3UFSLmrGXqit+YHSnfNpeRU4DKZKudj4cL6Tn5sj7NG6jYazjJ2Xv6CkWlZeQWFpv4eDSNC45uiAXGg0SxIAfB0ezOtQGCswcKVy/0d+QnbjbeXujSTeKZmYmNt+Wc2oX3xPv7r3H/8H0UPvJ425jNib46H2E98frua9w+sPSRocm1zBcWsfBj78nzUixOXpDni0e0f1BkZGlQeRvXjdLLgwyTE5aMvEJU7sYTHqWbUxX5ohPbTl8l4r3vmLrmL2aPfMh8kZUt5+DtQUaWcfvq/yoW6ZnZqHxMcqePF+mZxhyTkVeMys0kFq5V5M7+7dl2+joRC35g6tc7mD3C8mTbU+VJtslvYrY6Gw+Vp0W/quCm9ECTarTNScvGXelRbR+9Tk9JQTFO7vKTRS+VFxmpRo1npWXhpbLUuBX//+FvP2kSBMEWGIi0VQ9BEGwEQTgLZAC7RVG8+0sYCIwSBCFWEIQdgiA0rcbfS4Y+sWvWrHkwEgobFEo/ij56neJVH2I/6TWwr/5KcRVfivLtF8lYsvbBbapA+bGjaMaNIuflKMrjYnF+452H4uD5xstkf/qAY74PmjzdDc/QJlxYue3+nc1QEXuUvFeepWDW81Sci8Vx6tsP7yPuGPmvjqHg7RfRXjiNw+TZD2VffuIoOZNGkTc1ioozsTjNfIhY3oWNDQqVH0WLZ1K88kPsJ80Eh4fQBYCNAoXSj+JPZ1GydjH24x9eWz7vvEjG4pprq/z4UTQTRpE7JYqKuFicZj18LCpOHSVv8rMUzDTM6bSHn9OZY58i9spNImd/xukrN/HxcEWhePAUNHPsYGLjbxL59hcGe5eHsq8t7Iy7wdBOzYl+bzzLXxzM3I170ev/x1sdBAH3mVPIWVrzF7cKNjYIPvUp/uItSr77CLsx0x9Om9TSOjOg2Yhu+LRuQtyqh8g5goDrjCnkfbmyxt8LsDP2KkM7hxD94Yssf2U4c9fvfOg5nflsf2LjE4mct4LT8Yn4uLugEISH81FLGrdp3hHt9TiowRac0sPHSB85mszxL1B28jTu8x4u9wKUHjlGxjOjyZr4AuWxp3Gb8/A+Zo4bSuzlBCLf/JTTl288fL54RPvaws646wzt1ILo+RNZ/tJTzP1hz/88Xzw2sTiTwNCOzYh+9zmWvzCQuZv2/e9zpxWAVD3vcf7f44q/sxCEveHkCKQ7TesARFHUAW0EQXADtgqC0EoUxYtIzzmViqLYQRCEp4FvAItLNqIorgHunjmIhQunIXgY950rPLwRc+SFDfQ5mehuxoNOh5ilRq9OxkblX9lekZ6NrclVBluVFxXpRh8KR3vqNmtIg/9KD5LaeLvjt/JdUqYY9z7rs7Kw8Tbh4eWNLst4RQRALDBeuSzdsQ3HFydX/lubkYWtyuTOk9IbnQkHwdGeukGN8P1Gej7DxssD1bIFqKe9W9mnWJ2DY33jVRcHXw+K1PLb2wC+PUIInT6UHSM/RF+ulcdKk4nC5C6DwsMbMVv+EKVYaBxH+b5tOIyTv8ler8lC4Wk6J17oc+7hY/927Ee/ZLTPzkLhZRbL7OpjWRa9DYeoyfL2nCz53SsP78qiE6Zj1d28IteF0qgLMTcLhYkPwd0bfa5cW2JOllxb6ckolH6V7Vp1Nra+Rm3VUXmhNdNWvaYNafCDpC1bb3f8VxnnVJ+dhcJMV/p76WrnNhxekMdCr8mU3SG475zutZxTH3cX1NnGLWYZ2Xko3eXPyPh4uPLZzAkAFJeWsefkBVwc7Q32rnJ7TZ7FQ/Q+7i589tp4o/0po/2DQuntgdrk6mh6ZjZKb+Oa8HF1RJ1rvDqanluEj9l2mq0nrrDipacACG2koqxCS25RCQ96uuHj6Y7a5M5SepYGHy/51VBtZha2KuO82vh4o8swXe8O1AlshGrtf6R2Tw+8PzfJN3nZ1DG5c6pw90LMM8t7uVnoEq+CXoeYnY4+IwWFd31jey2ssyJ1Ds4mOcepmpzj3z2EDtOGsvUZec7RZ2Zh42MaBy90mUZtCg4O2DZpjNcK6ZkEGw8PPD9eSPabcyv7+Lg5oc4pqPx3em4hPm7y7VBbj15kxdSnAQhtUt9iTn3cnVGbXL3P0OSjdHeR+fBxd+Gz6dIzesWlZeyJvSzT56NqXCzKRXA23qUTnN0Ri+TbOu/CtlkHyvdbFszQZWZhozSJp7c3ukyzOc03zmnxn9tw+fdLsnad2ZwoHsCH8xS5Dx8PN8t8YR4LD1c+mxUl+SgtY8+J88Z88Yj2DwofLw/UmcZ1k56lwcfTOAc+rk6oc4x3LdNzC6vIF5dZ8fIQAEIbqyjT6h4uX3h5yjlkavDxMt55+V/FQuntiTrDJHdmZKE0ucPo4+qA2uQObnpeVbkznhUvDgLu5k4duUWlOAJLd0jPRd44fx1Pk99ET5UnGrU8d1WH3HQNHvWNtu6+nuSka6rsk6PWoLBRYO/sQKFJfli7axXx567iU98HkLaQe/l6kaWWa9yK/z/xODzT1EYUxWmiKMo2o4uimAvsBwYYPkoGfjX8vRVo/SBfort1FRsfPwQvFdjYUqdTOBVnjsr6aOOOYttC2hsuOLmgUPlLz+wYUHrhGnUb1aeOvxLq2OIyuCeFe49XtusLi7nReTQJvSeR0HsSpWfjSZmyQFY9T3s1Hhs/fxQqFdjaYhfem/JjR2Q8FB4me/mf7CZ7mL/s4lXqNPTD1k+ydxwYRlHMMWO8Cou53fMZ7gwYz50B4yk7fwX1tHdl1fOyzt7EpbEKpwBvFHVsaDKsC3ei42QcPEIa0nVJFHsnLaU0W779BEB34yoKX38UPhKPOt16Ux4rj6fgZhxHnQ5d0SXLH17X3YxHofJD4S3NSd0uvak4fax6H+27ojMp3KG9ZoilUuJQr2dvKk7IYym4m8SyczeLh9d1t+KxUZroonOvKnRxBNsWbSR/VehCl3gVham2OoahPScfR8WZo9g0N9GW0l96jsqAkiq0VWCmreudR5PQaxIJvSZRcjae5MnGg+NKXd2NRXhvyo+bxcJUV126WRSJsJjT7g8wpynyOQ0JDCBJnUVyhoYKrZadx84S1j5Y1icnvwi9Xg/Aut/3MTy8o4m9P0nqbBP7c4S1b3kP+/0MD5Nvl3wQhHftxB+7YhBFkXOXruLk6Ii3p3FsIQE+JGXmkpKdT4VWx64zNwhr1Ujmw9fdiRPXpQeLb6bnUK7V4e704Acgvbq044+9hyUOV27g5OiAt4d8m1X5pavYBvhhW9+w3vuHU3LAOCdiYRHJfUaS8tRzpDz1HGUXrpD5qsnJ9O1rKLzrI3gqwcYW23Y90Z4/LvsO7blj2DaV9vMLji4ofPzQZ6uN7bWwztLP3cS1kQpnQ85pOrQLt3bLc45XSEN6LYliW9RSSsxyTvmVeGwD/LDxlTg49O1N6SGTvFdUhHrgCNKfHkP602Mov3SZ7DfnyqrnhTRUkZSRQ0pWnjSnp68S9oS8+pivhwsn4iVN31RnW8xpSGM/ktI1JGfmSPo8cYGwti1kPnIKTPT51yGG95RXDntUjevViQhuPggunqCwwbZZB3QJ8u3TAIK7Euwc0KfdtGiruBKPrb8xnvZ9e1N6WL7WFSbrwa57V7SJ8rVeER+PTYDcR9mR6n3U694V7e0q8kVaJskZ2VIsjp4hrEOIWSwKjbHYuofhvTrXmv2DoleXdvyx55BhrV7HycEeb5OTppAGPiRl5Znki+uW+cLNmRPXDPlCraG8Qvtw+aJrB/6INuSsy9ekfGHK4X8Ui/DuXfhj516Jx8UrODk54u1lljtlsbhBWEhDmQ8pd6ZIsajMnXYAzBw4g5kDZ3Bi13F6jZQqrzZr25zigmKLZ5eqw61zN1A28sXL3webOrZ0HtKNs7vl29rP7o6l68hwADoMepL4o/Lnu17sP5kjO48Q8S+pAmHLdi0pKiiyPrtkBfCYlRwXBMEbqBBFMVcQBHugH3C3DuhvQC/gFhAGXKvaixn0eko2LMPx9SVS6d1DO9Gn3qbe8AnoEq+hPXsM7cVT2LZqj9PCdSDqKd28BrHI5Mdbpyd9wUoC1i0EGwV5P0dTfiMJr+nPUXrxOoX7qt5LL+eho3D557gu/hRBoaB013Z0txNxmBCF9lo85ceOYj98JHWf7AY6HfqCAgo+MSlXrNOTtWg5qlWLEGwUFGzdRUXCbdz/PZ6yS9cojjle/XcbIOr0HJ+7noiNbyIoFFzffIDcaym0nTWSrHO3uLM7jo7zRlPH0Y7w1VKlmKKUbPZOWiobR/G6L3Ca84lULnz/DvTJidiNmoQu4SoVsUepN2gkdTt0RdTpEAsLKPrKrOyyXk/Jd8twfOsjUNhQfmAH+pRE7EZORHvrGtq4o9Tr/7RUmEGnQ1+UT/Gqj2QcilZ+jssHn4JCQdnu7eiSErF/Lgrt9XgqThzFfuhI6nSWYikWFlD4WRUc/rsMxzc+qixFr0+5Tb0RE9ElXkV75hjaC6ewbdUBp0XfgF5nqQu9ntKNy3F4dRGCoKD8yC5JW0PHo7t9De254+guxWIb0h7H+Wul/j+vRSwyXtVCp0c9fyUB3yyUSo7f1daM5yi98ADa0uso/OpzXBd9aigLbdDVeIOujh/FfphcV4X/MY+FjuKvv8BpnmFO9+1AfycRu2cnobthmNPBI6nb0WROl8t92NrY8PbE4UxZvBa9Xs/w8E4EBaj46qddhDT2J7xDCLFXEvjyxx0AtG/ZhHcmjTCzH8aUJesM9h0J8lfx1U/RhDTxJ7x9sMF+JwgC7Vs05p1Jwy3C8caC/3Dq7EVy8/Lp86/neWXSs2i1OgBGDRtAzy7tOXTiNAPHTsa+Xj0+eEteEcnWRsHsp3swZc1f6PUiwzq1IEjlwYodJwkO8Ca8VWNmDu3Kgi0H2HDgPAgwf3RvBJNtWG8u+YpT56+Qm19In+em8+9xT1dyiBzchx4dQzl46iyDomZJZYxfe9FyXnV6NB8tw+crKW8V/rGTipu3cZ08gfLL1yg5eMzSRjanekq3rMTh3wtBUFBxPBq9Oom6g59Dl3Qd3YUT6K6cxrZlOxzmrAJRT9lv68BUm7WwzkSdnoPz1jPshzelkuObD6C5lkKn10eScf4Wibvj6DZnNHUc7BiwSpqLwtRstkUtrYxD7n+W4fW5lCuK/tqB9lYizi9OpOLKNYsD/qpga6NgdmRvpnz1qzSnT4YQVN+LFX8dJbiBkvDWgcx8uicLNu5mw/44QGD+uP6yObW1seHtcYOZ8sn3kj57tiPI34evft1LSCM/wtu1IDY+kS9/2g0ItG/ekHfGP2XG4xE1Luop3/8j9UbMAEGB9tIRRE0adboMQZ9xG91N6ZkV2+Yd0V2NrToYOj15S7/E87OPwUZB8d14vjCJ8virlB0+iuMzT2PX3ZAv8vPJ/XCJhY/8pV/isfRjUCgo2Sb5cHp+EhXxVyk7chTHfz0tFYeoxoetjQ1vR41kyoerpVj06kxQgC9fbd5BSGAA4R1aEXv5Bl9u3CbFomUT3nn+X7VmfxdvLl4urdW8AvqMncq/x/0LrVa60xn5VF96dGojrdVJM6Vy36/L77Db2iiYPbIHU1ZJZeSHdW5JkK8nK7afILiBj5Qvhndjweb9bDggneDOH9NHni8Wfsapc5ckDqNe4t8TRqHVGTgM6U+Pzu04eCKOQeOmYmdXj4VvvGLGoXZi8cZ7Szh15jy5ufn0Gf4crzw/rjIWo0YMpueTHTl07BQDI6Owt7Pjg3deM+OhYPbT3ZmyZjt6UWRYp+ZS7tx5imB/b8JbNWLmkCdZ8NMBNhw8D4LA/GfDZbEAOL0vlva9OrDy0Bqp5PgsY2XOpTu+YOZA6ZUb49+ZSI9hYdSzr1fZrtfp+eHdr5n5/VwUNgoOb9lH6vVkhr82isQLCZzdE8vBLXt5cel0Fscsoyi3kNXTLMu8H993ks69O/PD4fWUlZbx0UxjRcG1u1bxYn/pjvrLc16gz/De1LOvx5ZTGy38PM6wVs+rGQTxbwqcIAiFoig6mX3WGlgP2CDdBdsiiuICQ5sbsAFoABQCk0VRtLzMJoeYN6nvfbpUD9dvpeop8c0G1dhHi2vbyewXVmN7AO/dB7j5RESN7ZtciAbgW7/n7tOzekxK+QGAnGfCa+zD/acYcsf2vn/He8Btwz6yB9c8np7bDgCQN6FPjX24rt9L/os1nw8Al7XRXGlac121vC698ySrf81j4bVLikWO4apbTeD+SwylcX/U2B7Art1QSk//dv+O1dm3lw4sK9Isy+w/KOr4Slf8S7Z9fp+e1cN+8KuU36y6CMuDom6TTtxuV/Oc1TBOylkFU2uuLeflkrYedZ0tD6h5vgGYeucHUp6seb7wOyZV4CrZU/Pnvuz7SgdHpcerfifeg8Cuy6hH0jdIGi/+/OX7d6wGDq9KZZlTu/a6T8/qUf/ofgDSutfch+/h/ZSe237/jveAXeigR/JhFyqtjfLEak4sHwB1G0kFfUp23P+tJ9XBfuB0ypMv1NVIeUYAACAASURBVJyDv3SXuDZiUZFleVfyQVHHqwklfy29f8d7wP6pmYxoMKTG9luTpHe/RTWyPBl8UHyT+DMAvfz71djH/uTdIBVMfOzR0qfTY33WdCXj5GMZx7/tTpP5CZPhs/NA2yq6392uN/j/Ni8rrLDCCiussMIKK6ywwgpTPFbb86ywwgorrLDCCiussMKK/3t4nCvUPc7420uOW2GFFVZYYYUVVlhhhRVWPM6wnjRZYYUVVlhhhRVWWGGFFVbcA9aTJiussMIKK6ywwgorrLDCinvgb6ue9z/CP3pwVlhhhRVWWGGFFVY8Nngsq76Zo5l3h8f6+PhaZuxjGcd/fCGIaOWzNbaNSP8R4JFK50698wO7laNqbA/QL30zq/1rzuHlZKlc+CuNImvsY0XiFgBO1H+6xj46p/7KIVXNS4IC9FD//Eg+eqilsqKPOo4jjziObuqfWfYIupp2R5rTg6pnauyjp/onAGKUNfcRnv4TJVsW3L/jPWAf+S4lm+fX3H7UewCU/PHpfXrew8fQWZKPr2fW3McLSx+pvDVIJa43+46tsf2otA3Ao+sbYOcj5M4B6T+yoGHNxwHw7u0NbKhf8zUyNlVaIyVbl9ynZ/WwHzFb8vGIpehLfl5YY3sA+3/NfaSyzvZPSbqujXg+cr749s0a2wPYT/q4duZ0w7ya+xj7AQClJ3+qsQ+7Ts9Qsn52zTlMkGJQ8vvHNfcxTJqLR9XWo5QsB6ls+ZiGI+7fsRpsvL0VgH81HFpjHz/fll6d0Vr1ZI19nFff5/15VvxPIAiCB7AZaAQkApGiKOaY9WkDrARcAB3woSiK9323hHV7nhVWWGGFFVZYYYUVVljxT8BsYK8oik2BvYZ/m6MYGC+KYggwAPjc8D7Ye+Iff6fJCiussMIKK6ywwgorrJDwDy85PgwIN/y9HogB3jLtIIriNZO/UwVByAC8gdx7ObbeabLCCiussMIKK6ywwgorHgsIgvCSIAixJv+99BDmSlEU0wx/qwHlfb6rE1AXSLifY+udJiussMIKK6ywwgorrLDisYAoimuANdW1C4KwB1BV0TTHzI8oCEK1t9UEQfAF/gtMEEVRfz9e/1+cNHn2CqXFwgkINgqSN+wjcdkfsnb/8X0JiIpA1OnRFZVyedZaiq6lyPo0CG9Nj/fHIdgouLwphrgVf8ra27w4kOBnw9HrdJRkF7Bv1hoKUrIteDRfOBHBRkHKhn0kLvvdgod/VH/Q6dEWlXJl1hoZj4Dw1nSdL3GI3xTD2a/kHJ54cSAtR0scSrMLiHl9DYVmHILDQnnm3UkINgqObt5L9Eo5B9u6tkxYOpWAVk0oyi1g3dTP0SRnyvq4hrel4QdRCAoFGZv2kLZ8q6zdZ1wEyokDEfV69EWl3HpjJSXXk2V93Hu1ockHEg/1hr0kL/9N1q4aH0H9Sf0r5+TGG6spvpZca/a1NQ43Aw9sFKRv2EtKFTxUBh56A48SMx4NwlvT00Rbp6vQVoiJtvbOkucR915tCDSJxR0zDr7j+1F/0oDKWFyvIhYevdoQtFDykbZhL0nL5D7qj+9H/agBYPBxdZaljyPXU/l4Wyx6UWRE+yCieobI2tNyi5j36zEKSsrRiyLTI9rQo5mfpY/tpyUf7QKr91FaIfnoF0qEqX38HT7+4xh6vciITs2J6t1Gbp9TyLzNMRIHvcj0QR3p0bKBnMOtDD7ee1ni0DqAqM5BsvZP9l3mVJK0rkq1OjTFZRye3l/u41IiH/8cg16vZ0S3VkRFdJLz0OQz7/tdFJSUSTyGdadHq8aV7aperWm7QNLEzY0xxC+Xa6LZywNpMqYXolZHWXY+J2eupTg5S9anNvTt1SuUlgsngCF33jLLnQHj+9LAJHderCJ3Boa1pv9741DYKDjzYwxHVsrH0qBTC/q/9xzKFg34Zdpyrmw/KWv3DW9Nhw/GISgU3NgUw2WzWLR4aSBBY8LRa3WUZRdwfOYaiszy3pGryXz85wlpTjs2Iyq8taw9LbeQeVsOGbU5oD09WgTIfVxJ4uPfDkva6tKSqD7t5D5yCpi3cR8FpeXo9XqmD+5Cj+CGch/XUqQ1ohcZ0SGIqLBWZjyKmPfzEYMPken929GjuXGNHIlP4uPfjkr2nVsQ1aetJYdNMZKuRJHpgztb6Ls24lkr+eJmOh/vuYBeDyNCGxD1ZDNZ+yd7LnAqSdJ0aYVhnb022GhfG3N6I42Pd52V4tm2MVHdW8p95BUx77eTFJRVSPPRpzU9mvrKfZy/xkf/3S6t9fD2PD8kTNaempXDe2u3klNQhKujPYumPIPSw1XuIyGdj3efl8YS2pCors3lsdh9nlO3DbHQatEUlXN6grEYxpGryXz8+3H0ol7Ke71C5ePIKWTe5oMUlBryzcCO9GhpFotH1NbcRUs5eOQkHu5u/PaDZUEcURRZ/PkqDh07hZ1dPT6c8zrBzYMs+gGMf/952vRqT3lJGatmLSPxomWhicatmvDyf6ZT166uRVvU+y/StlcHykvKWD7rc25VYd+kVSD//s8M6trV48z+WIv2txa+Ro8+XSktKWXejA+4cuGarN3Ovh6frv2QgIb+6PQ6DkQfrnIsjyv0/49XzhZFsW91bYIgpAuC4CuKYprhpCijmn4uwDZgjiiKxx/ke/+2kyZBEHTABQOHK8AEQA8cBOoZPv9ZFMX3DP0PAc4Gcx/gpCiKw+/7RQqBlkuiOB35IaWp2XTZtYjMXadlP+xpvx4h+fs9AHj3b0/z+eOIG21MSIJCIGzhBH4fs4TCNA2Rfy3g1u7T5FxPreyTeTGRLYPnoS0tp9W4PnSdM5pdryyX8WixJIo4A4/OuxaTuSv2njyazR/PmdGLKzl0WziBbWOWUJSm4eltC0iMPk2uCYfsS4n8OkjiEDyuD13mjGaPCQdBITBqwfN8+dxCctXZvPXHYs7vjkV9w8iha2RvivOKeD98Ou2HdGXE7LGsm2pSOUqhoNGiF4l/dj7ladmEbP+Y3F2nZAdb2VsPkfHfaADcIjrS4P1JXDVUG7rrI3DxC1yMXEBZmoY2O5egiY6V/ahm/noI9feSD4+IDjR+fwKXxnxYO/a1OI4mi1/gUuQCytM0hBp4lNyHx2UTHoJCIHzhBH4zaGvUXwu4WYW2Nptoq9uc0TIOQYuf50LkB5SlaWi7czHZZrHI+PUwad/vruTQ5P0JXDSLRdMlz3Mu8gPKUjW037WYrF1yH+m/HibV4MOzfweC5k/g/GijD51ez+I/T7FqYm+ULg6MXbWTsBb+BPoYDw7WHrhIRKsGRHZqRkJGHlP/u58dr/vJffwVy6oJvVG62DN29a5qfDQkslNTyccPMUTMM7HfeoRVLw1C6erI2C9/IyykIYFKd6P93jNEtG5CZNdgEtJzmLpuJztMfvh1epHFuy+xKrIzSmc7xv73MGGBSgK9nCv7vNE7uPLvTXG3iE/PxxQ6vZ7FW/axatrTKN2cGfvxRsKeCCTQ19PIY+cJIto1I7JnKAlp2Uxd8Rs7Wj1fqYn2iyYSM2oxJWka+u34gNToOPJNckXuhdvsHjAXXUk5geP7EDp3NMcmL5PN6aPrWyB4SRSnDDnryV2LyDDLnam/HuGOSc5qMX8cp81y58APJvLD2MXkqzW88McHXN0TR9Z1o4+81Cx+f301T75kPCA2te+4aAL7nl1CcZqGAdsXkLzrNPkm6yPnYiI7Bs5DV1JO0/F9aDtvNIcnG/OeTq9n8e/HWfV8f5SuDoxd/idhLRsQqDQ+87t23zkiWjcmsksLEtJzmfrtbnbMDpD7+PUQqyYPkbT12S+EhTQiUOVh9LH7NBFtAons1ooEtYapa7ezw+SkSVojJ1k1qa+0RlbuIKylP4E+Jjz2nyfiiYZEdm5OQkYuU9fvY8cbT5twOMKqlwdLHD7/1cDBRN974oho04TIriEkqHOY+vV2dsw1VjGsjXjWTr4QWRx9nlXPdkXpbM/Y7w4Q1lRFoJdLZZ83+j5R+fem2JvEp+fV/pzuiGPVc2FSvvl6D2HN6xPobZJvDl0hIiSAyA5BJGTmMXXjIXbMeErmY9H6P1n91iSUHi6MeXcV4e1aEujnU9ln6cadDOnehqE92nHiUgJfbIlm0eRnTHyILN51jlWju0k8vt1PWFNfAr1NYtHPeEK46VQC8em5JvZ6Fm89yqoXB0i6WPYHYcENzPLeWSJCGxP5ZEsp730TzY6Wo+Q+HlFbwwf1Y8zIobzzQdVVTA8dO0VScirbN6/j/KV4Pvh0OZvWWlalbNOrHarG9ZkZ9gpBbZsRtfBl3h3+lkW/qA8n8/XsFdw4c62yeh5A217t8W1cn2lhL9O0bXNeWjiFt4e/YWH/4odTWDX7K66fucqc9e/J2rr3eZKGTQJ46slnaN0uhLkfvcnYQS9Y+Fi/ciOnjsRhW8eWr39aZtFuxd+GP5DOKZYY/v938w6CINQFtgLfi6L484M6/jufaSoRRbGNKIqtgHJgMlAG9BZFMRRoAwwQBKELgCiKPQz92wDHgF8f5Etc2wVRfEtNye0MxAod6t+O4jOgg6yPrrCk8m8bh3oWb3dStgkkLzGd/KRM9BU6rv9xnCYR7WV9Uo5dQVtaDoA67gZOJj+mRh7pMh7eAzreh4eRiE+bQPIT0ykwcLjx+3EamXFIPWrkkB53A0dfOYdGbYLIvK0m+04Gugodp/88SmiEnEPriA4c/yUGgDPbj9O8q/xKqFPbIEoT0yhLSkes0KL5/TDu/eVX0e81DgDntkGU3lJTmpSBWKEl87cjePS/Tyxq0b62x1H2gDwUVfBQtgkk10Rb1x5AW44m2nJuG0SJWSw8+99H32YCd2ln8HFb8pHx2xG87rNGzN/vdjE5mwBPZ/w9nKlja0P/JxoSc+WOrI8AFJVWAFBYWo63s72lDw8n/D2cjD7i5VenBUGgqKxqHxeTMgnwcsHf00WybxNIzKXblhzKpFgWlpTj7eIg55CWS4C7A/5uDtSxUdC/RX1ibqRTHXZcSWVAy/pyH4lqArzd8Pdyk3i0b07MeflWaQGBotK7PMrwdnWsbPNoG0hBYjpFBk0k/X4cv/5yTWQcvYyuRLLPjruBg9larw19u1WRO5X30IVtFbnTr00gOYnp5N6RxnLpz+M07ycfS15yFhnxdxD1llc+PQ2xKDTE4vbvxwkwi0X60SuVsciqIhYX72RJ2vQ0aDO0CTGXk2R9JG0a5qO0HG8XM20mZRDg5WrUVtsgYi4mmvkQ5Pp2NdNWcjYBHiZrpHUVa0Sm7wqZPi8mZRDg6SLncOleHMrwdnGUtddGPGslX6TlEODuiL+bo7TOgv2Iua6mOuy4ksyAYOMFllqZ0xQNAe5O+Ls7UcfGhv4hDYi5mmrpw3Q+zHNWQjIBSk/8fTyoY2vLgC5PEHP6iqxPQmomnYKbANApuAkxp+PlPlI1Uizc78bCn5jraVSHHZeTGRBiPPm7eMcs74U2IeaSWSwE81iYabMWtNWhzRO4ujhTHfYfPs7QAX0QBIHQVi0pKCgkM0tj0a99v04c+mU/ADfOXMPBxRE3H3dZHzcfd+yd7Llx5pqFfcd+nYkx2F8/c7VaewcnB66fuQpQ2f8uevXvyZ9bdgBwPu4Szi5OePl4yvqUlpRx6kgcANoKLVcuXK127Fb8z7EE6CcIwnWgr+HfCILQQRCErw19IoGewERBEM4a/mtTtTsjHpfteYeA1qKUWQsNn9Ux/CfLtobbab2BSQ/i2E7lQWmqcWtBaaoG13aWt4QDJkXQcPJgFHVsiR35gazNUeVOQapxcRemaVC2Daz2O4OfDeN2zDnZZ/VUHpSZ8ChLzcalCh7+JjxOm/Bw8HWnMM3IoUitweceHFqMDiNpv5yDm9KDHBMOOWnZNGrTtNo+ep2ekoJiHN2NibCuypNyEx/ladk4tpP7AFBOHIDqpaEIdW258oz8Kk49Xw/KUo1bicrTsnGuwofvpAH4vfwUijq2nP/X+7VmX1vjqOvrQfkD8FBNGkB9A4+LZjwcVe4UmmlLdY95DTFoq35HaSuLFAsTXaVpqolFf/wNHM79S/5OJEttanCpwkf9Sf0JmPwUQh1bzo2U+8jIL0FlcpCodHXgQrJ8S8/k3q2Z8t1eNp24Skm5jtUTe8t9FJSgMjl5ULo4cMFsy9nkXk8wZf0+gw8tqyf2MeFQhMrNyYSDIxeS5HflJ0e0Z8ra7Ww6cpmS8gpWvzRIzqGwFJXJgZHS2Y4LaVUX00nNKyY1r4RODbzkPnILUZmsGaWbExcS5QeEkwd3YcryX9l04CwlZRWsnj6yss1e5UGJyXao4jQNnvfQRJPR4aSZrfVaWacqD0oeIHc2mBRBo8mDEerYcsosdzqrPMhLM/rIT9Pgd4+xmMNe5U6xyfooTtPg2a56+8DRYaTuk8ciI79YritXBy7ckW85nty3LVPW7WLT0SuSrl6Qb7fMyCtC5Wbiw82RC7fNtDWgA1NW/cWmwxckbU2WvzPGgoeLIxfumOn77ho5ZtB3lHEHSkZe8f313b89U1ZvZ9PhixKHl5+StddGPGslXxSYrzN7LqTmmLsADOsst5hODb2N9rUxpwVmOcvFngsp8oP4yWEhTNlwkE0nb1BSoWX1c/Ktdxk5+ahMttr5eLhwIUF+oad5AxV7Yy8ztn9X9sZepqi0jNyC4sqHMTIKSlG5PEwsiuSxyHuAWPRrx5Svd7Lp6GUpFi8OlI+jFrR1P6RnZqPyMeZKpY8X6ZlZeHvJT8rdVZ5oTPSlUWfjrvQgN8MYE3elBxq1/PflLjxVnmSnGsevUWfjqfSU2XsqPclWG9eeJk2+Dn18vVGnGi+Wpadl4uPrTVZG1d/p7OJEWET3KtseV/yTq+eJopgN9Kni81jgBcPfPwA/PKzvv716niAItsBApK16CIJgIwjCWaQ9iLtFUTxhZjIcqf56PrWIO99Gc7jzDK4t3EiT12r+krVmI7rh07oJcau21cg++dtojnSewfWFG2n8Ws1eUNn06W54t27CuRpyqA2kf7eTc11f4c6H/8VvRs1eBJv27U5iu0zl1sIfaPDaw/t4VHuonXGov91JXJepJC78gYAa8gBo/gjaSvt2F6e6TOPmwg00fG3k/Q2qQOq3uzjRueY+dp5PZGi7QKLfeJrl48KZ+4u0f/6hfbRtQvSsESx/7q6P+z67abQ/c4OhHZoRPXcMy6MGMHdTzENzuItd8Wn0babCRvHwLy7fGXuVoZ1DiP7wRZa/Mpy563fWiEfDkd3wCG1C/Iq/HtoWakffSd9Gc9CQOwMfIXc+Kho93Q3P1k24vPLh18fOczcZ2r4p0e+MYvmkfszdcvDhtRl3g6GdmhP93niWvziYuRv31kzf7QKJfmskyyf0Zu5PRx7Kx84zCQzt2Izod59j+QsDmbtpX431/SjxhEfPF3ex60oKfZvXf+h1VitzejGJoaGNiH5tCMtH92Dubycf+lmQmaMHEBufSOTcrzgdn4iPuwuKGuQMgF2Xk+nbwu/hY3E2QYrFnNEsj4pg7o8HHj4WtaitfxJsbGz4aNUCNn5d8xcdW/H/Dv7OkyZ7w8lRLJAErAMQRVFn2ILnD3QSBKGVmd1oYFN1Tk3LFK5Zs4ZStQa7+sbbqnb1PShTW94Svgv11qN4D5RvsSpS5+Bc33g1xMnXgyK15ZUg/+4hdJg2lG1RS9GXa2VtZWoN9Ux41KvvSVkVPqrjUZyWg5PJNglHlQdFaZb2ft1DaDttKDsnWXLITdfgbsLB3deTvHRNtX0UNgrsnR0oyimobC9XZ1PXxEddX08q0qqPZ/Zvh3EfIN8WVJamoV594xWnur6elN3DR+ZvR/A02cr4qPa1NY7yNA11H4JH1m9H8BhgqS0nM20VVqGLAIO2/jLTlhQLE135elCeVvXVMLgbC7P5sNCmB2XVXMUDyNh6BK+Bch8+Lvao84or/52eV4yP2VaWracTiGglPT8U2sCbMq2e3OIyow9ne9R5RUYf+cX4mG0j2Rp308yHjpycHAMHR9S5hUb7vCJ8XOVbSLaeukpEqLRVJrSRkjKtjtziUiMHJzvUBcatRekFpfg42VUZh53xllvzAHzcnFCbrJn03EJ8TK7iAmw9epGI9tLdwtAm9Smr0JJbJH1viVqDvZ9xPhx8PSipQhPKHiEEzxjGoQn/sVjrtbJO1RrsHyJ3pm09io9Z7ixQa3A1eZbLxdeDgnvkPXOUqHNwMFkfDr4elFSR91Q9Qmg1YygxEy3zno+Lg1xXecX4uJjr4joRrRsBENrQx1IXro6oc0185FahrRNXiAiV7sSFNlLJ5rRKHvlF+Liar5EbRLSSnoO6q++7PHxcHe6v7xPxRIQGmnDQkVtkHEdtxLNW8oWz+Torwce5mnV2OYUBwf5y+9qYU2eznJVfYpmzzt4iIljaChca4GXwYZKz3F1Qa4zPWmVo8lG6u8h8+Li78NmMMWxZ+G+mPSPdOXRxNH6Pj7Md6vwHjUUyA0LMYuH6ILG4RkRoY0Msqsh7taCt+0Hp7Yk6w3hHJz0jC6W38fdz0falLNq+lNyMHDxM9OWh8iTH7DglJ12Dh0q+XQ7gk+2fk5OhwbO+8U6ch8qT7HS5PrPTs/FUGb/bw9f495Y968lKz0JV31ilWunrTUaa/O7dXbz76Wxu37zDD2s3Vzt2K/45eByeaWojiuI0URTLTRtFUcwF9iO9qRcAQRC8gE5I1S6qhCiKa0RR7CCKYoeXXnqJ/DMJODRRYd/AG6GODarhXcnYdVpm49DYWLXQu19bim/K9xOnn7uJayMVzgHeKOrY0HRoF27tjpP18QppSK8lUWyLWkpJtuVNsLs87Ex4ZO6SV2wx5eHVry0lJjwyzt3EtbGRQ9CwLtw24+AZ0pAeS6LYGbWU0io43D6XgE8jXzz9vbGpY0P7IV05v1vO4fzu03QZGQ5A20FduHr0kqy98OwN7Br7Ui/AB6GOLR7DupMTfUrWp15jY3Uht77tKb0lj2fB2RvYNfGlXgPJh/fwbmjMfNiZxMKjbztKbqlrzb42x2H/EDzc+7aj1IxH+rmbuDVS4WKY12b30NZfVWjrLgc7Ew7Z0fI5tYyF2TjOyH34DO9Glpk27U18ePZrJ9MmQIifJ0nZBaTkFFKh1bHrwm3CWsh/3H3dHDiRII3/ZkYe5Vod7o715D405j7k1fV8XR04cVPaNnEzM49yrR4PD+kgMCTAm6SsfFI0+ZL92QTCgs0qh7k5ccLw0PvN9BwDB+MBSoivK0k5RaTkFlOh07MrPpWwIMtXPNzKLiS/tILQ+u4WbSENVSRl5JCSlSfxOH2VsCeayHl4uHAiXnru4KY6W+LhJB1Iac7exLmxCkeDJhoM60KKWc5ya9WQDh8/z6EJ/6GsirVeG/rOq4XcmXLuJh6NVbgZxhIypAvXdst93AvZZrFoOKwLydHy9eHeqiGdPoriwMSlVcYixN+LpOx8UjQF0nycu0lYsLxymK+bIyduSNxvZuRSXmGmiwAfkjJzSck2aOvMDcJaNZL7cHfihKHQRqW2nIwHx5Vr5C6P87cJM6vm5uvqyImb5mvEzsghK0/OIURenU/ikGLGwTiO2ohnreQLXzeSNEWk5BZJ6+xyCmFBltWDb2UXkF9aTqiffJ3Vypz6eZCkKZTyjU7HrktJhDWTXwTxdXHgxK27+SZfiqfJs6khTfxIUmeTnKGhQqtl5/ELhLVrIfORU1BUeTd83Z8HGR4mr7oYUt+dpJxCk1gkE2ZWoQ/gVlaBlHP85NvZQvzv5j3TWFSR927czXvV6PsRtXU/hHfvwh879yKKIucuXsHJyVG2Ne+dQTN5Z9BMYqNP0GNkLwCC2jajpKBYtrUOIDcjh5LCEoLayisuvjHoVU5GnyDcYN+0bXOKq7EvLiymaVupSuHd/gCRfSewb+dBhkRKWxhbtwuhoKCoyq15U996CWdnRz6eZ1nQ4nGHKOof6/8eVzwuzzQBIAiCN1AhimKuIAj2QD/gI5Mu/wL+EkXxgS9viDo98W9/S7sf35FKfW/aT9HVZALffIb8czfJ3HWagOf749mjFXqtDm1eERenr7TwcXDeeob98KZUFnrzATTXUuj0+kgyzt8icXcc3eaMpo6DHQNWTQegMDWbbVFLZT6uvv1NJY/UTTFV8vDo8QSiVkdFXhEXp6+Q2R+et55BG95EUCi4uvkAOddS6DBrJJnnbnF7dxxd5o6mjqMd/e5ySMlmlwkHvU7P5ne/Yer3c1DYKDi2ZT9p15N56rVIbl9I4MKe0xzdso+JS6fyfsyXFOcWsm6aWTLQ6Umc8zXNN76LYKMg88e9lFy7g98bz1J0LoHc6FOoJg3EpUdrRK0OXW4hN2css/CR8M7XtNo0F8FGQfqmfRRfTabhm6MoOJuAJjqW+lEDcevZGrFCizaviGvTl9WefS2O4+Y7XxOyaS7YKMjYtI+Sq8k0eHMUhQYevgYe+gotuip4iDo9B+atZ+gPb6Iw0VZng7Zu7Y6ju0FbAw3zWmCy3xudnhvvrKPVpjlSyfFN+y1i4Rc1ELeeTyBW6NDmFXJ1+nILDtffXkfrHyUfaQYfjd4cRcG5BLJ3xeL3/EDcK7VZyBUzH7Y2CmY/1YEp66UtG8PaBRKkdGPF3nME1/ckvKU/Mwe0Z8Hvx9lwNB4EgflPP4kgCHIfgzsw5fv9Bh9NCPJxY8Xe8wT7eRDewp+ZA9qx4PcTBh8wf0SXSh+2NgpmD+/KlLU7JPtOzQlSebBiVyzB/t6EhzRk5pAuLPjpEBsOXQBgfmSYnINCwey+rZjy80nJxxP+BHk5s+LwVYJVboQbTqB2xqcyoEV9ma1sHJG9mfLVr5KPJ0MIqu/Fir+OEtxASXjrQGY+3ZMFG3ezYX8cIDB/XP9KX6JOT9w73xG26S2p5PiPB8i/lkKrN0aiOXeL1Og4pbFTmAAAIABJREFUQueNwdbRjq5rZgBQnJLF4YnGtV4b+hZ1ei6//S0dDDkredN+Cq8mE/TmM+QZclYDQ+68m7MuVJE7d7z7HWO/l8ZydssBMq+nED5zJKnnb3FtTxz1Wzchcs1r2Lk60KxvW8JeG8mqfm9V2sfOWU/vjVLuTfjxAHnXUmj9xkiyz90iJTqOtvNGY+toR/c10w2xyOaASSxsbRTMHtqFKd9ES/PRoSlBSndWRMcR7O9FeHADZg7uxIJfj7Dh8CVJm8/0sNTm0z2YsuYvg7ZaSNracZLgAG/CWzVm5tCuLNhygA0HzkvaHN3b0seQTkz5bi96UWRYuyBpjew5S7CfJ+EtA5g5qD0Lth5nwxGpmMD8kV3l+n66O1PWbJfs7+p75ylJ360aMXPIkyz46QAbDp6XxvFsuIxDbcSzVvKFQsHsiNZM2XxMGkvrBgR5u7Di4BWCfd0IN5w0SHeZ/CzWWa3MqULB7IHtmLLhoMShTWOCfFxZsf8iwfXdCW/ux8yIUBb8GcuGE9ekdTqsk9mc2vD2+KeY8sl69Ho9w3u2J8hfyVe/7CGksR/h7VoSe+UWX27ZDQK0b96IdyYMqSIWoUz58Qh6PQwLbSjF4sBlgn3dCW92NxbJ1cdi2JNM+Vra4jusYzOCVO6s2HVaikVIQ2Y+1YkFPx9mwyHpIuj8UVXp+9G09cZ7Szh15jy5ufn0Gf4crzw/Dq1Wuks5asRgej7ZkUPHTjEwMgp7Ozs+eOc1qsLZfadp06s9nx1cSVlJGatnGfPSou1LeWfQTAC+mbuayVWUHI/bF0u7Xu1ZfnA1ZSVlrJj1ZWXbJ9s/541BrwLw9dxVhpLjdTkTI79wcGjPUXr06cq24z9RWlLGvFcXVrZt2bOeyL4TUPp689Jrk7h5LZHNu7+rcixW/PMgmFe1+Z99sSAUiqLoZPZZa2A9YIN0F2yLKIoLTNpjgCWiKO58wK8Ro5XP1phjRPqPACwPeK7GPqbe+YHdylH373gP9EvfzGr/mnN4OVl61u2VRpE19rEicQsAJ+rX7DkrgM6pv3JIVfNnegB6qH9+JB891FJlyUcdx5FHHEc39c8sewRdTbsjzelB1TP36Vk9eqqlPdgxypr7CE//iZItC+7f8R6wj3yXks3z79+xOvtRUgGDkj+qLnX7QD6GzpJ8fD2z5j5eWErJHsv3kzyUj76T2ew79v4d/w97Zx4XVdU+8O+FckWRTQbcAE2xTNnENREQVFK0XNpccM8FDcU1IyPJMsU93DPLSsuW18JyJfcVRC23LHADZFVQpIL5/TEXmOUCMjPv+/r6O98+fmLmnPPMc57t3Jl777kV8FLaFsD0+Ab4yYTa2SvjS6KbGT8PgKjULWxxNj5HXrulyZHCb9+vomfF1H5hlkbGj8b/klz7+Tco/Hp+1R0rkzFwLoU/xFbdsaLxfTRxbQ57mlwvPp5h9HiA2iMWmsenW94yXoa8Nf+DE8bfx1LLdxCFn8wyXgf5OU2F3y80XkY/jS9Mja2/swyfg1QdnrR349Vmxt8PWbrl+MBmoVX0rJivUzXPoGur6mS0jLPpR0Gz6eIjj6tdu0f6hrQ/s5MfSTv+18406X9hkt87C3gqdC9t7/7v1EkgEAgEAoFAIBAI9HmkLs8TCAQCgUAgEAgE/z5KHuMtx/+d/Ne3HBcIBAKBQCAQCASCRxnxpUkgEAgEAoFAIBAIKkFcnicQCAQCgUAgEPw/4b+1Cdz/Ov+13fP+QzzWkxMIBAKBQCAQPDI8kru+6dPU9tlH+vj4Ws65R9KO4vI8gUAgEAgEAoFAIKiEx/7yvBkurxg9dmHKFwBsMOEZSaNufMYrzfobPR7gi9TvzPKMpZdM0GNr6ncALGpqvC0ir33GFBfjn/0CsCzlS4Kb9DJ6/K7rmkd8vdC0bxU9K+bbazvo3aS30eMBdl7fyZsurxo9PiblcwD6NH3eaBk/XPsRgO6NexgtI+HGHka6mPbMqo0pXzPCZYDR4z9O2Q6Y5xkdkSbUi0UpXzCoWT+jxwN8lfo9403I9Tg510eb4JP1KZpnmUW5GP+cpeiULSbFN2hifLKL8c+4W56yFTCPLUytv0ObGf/cLIBPU79hiAkyPkvVPHtrmQn1e8o1zXOanmsUaLSMgzf3mlSzQFO3zJHr5qidpuT7V6nfm7SO7Ly+EzB9LTOHDFOesQSa5yyZ8qynJ+3dAMyyjjxRo5HRMv7566bRY//TiN3zjEOcaRIIBAKBQCAQCASCShBfmgQCgUAgEAgEAoGgEh77y/MEAoFAIBAIBAKBhsd8E7h/G+JMk0AgEAgEAoFAIBBUwv+LM00t/drRL2oYkqUFJ7buJyHuXzrtrr7uhEYNQ+XelM/Dl3Nu5wkDGY26t6XjO0OxsLTg0hcJnF21Q6e9zZjetHylO+riYh5k53Nw2loKbmYbyBk+bzQe/t78VVhEXORyUs4b3vzo2qY5ry+eTI1aNTiz/3TZ+0/7tWNQ1AgkSwuObN3LrrjvdcY9UeMJhsdOokkbN+7l5bNh0lJybmQayA+bNxpPf2+KZB3+rECHCbIOSVo6ALj4tSVg3lAkSwvOfZnAiY90bdHYtxX+bw/FoXUTfpi0ksvxJw3ku/u148Wo4VhYWnBs6z726Pmkua87L0QNx9m9KZ+ELyd553EDGRPeGU/7gPYUFRaxaOpifj//u+FcZwwnaEAPrKyt6OdueLPqqHfG4i3bYsW0Zfxx/qpBn9emD6X7AH/qWlvxamvDG8Jff+f1Mj0WT13MVQUZw2cMJ3BAIFbWVrzorntD91N+bXk+ahgWlhac2rqfA3G69nTxdef5qKE4ujdla/gKflWIz7HvjMPH34eiwiKWTluiqMPQ6cMIGBCAlbUVg1ob3hwfHj2RjgG+PCgs4v2IhVxRsOeoGSPoOTCIetb16N1K9+bhNn4evCrH58Gte4mP+06n/YkaTzA6Npxmbdy4l1dA3KRYsvXiUyNjJBaWFhzYupf4uG8NZIyJnUyzNm4U5OUTNynWQMeR88bg6e/DX4VFrIxcqhjfbm2aM3HxFGrUqknS/lM6ba3kemFhacHxrfvZrxebbnK9cHJvypbw5ZxV8AfAiHlj8JJja1Xkskr0mEyNWjVJ1Muzp/3aMVi25+FK8r2pnO/rJy3VaX/Gz4NXokZgIftjp4I/Rsn+KMgrYI2CP1r4tSUkSpPriVsTOKgXm8183ekdNQRH96Z8Fb6S3xRsYWp8t/Zrx4tRYVhYWnB06z72KNhhSOzEsrq3adIyg7pnDluYq/4OnTeKdv5eFBUWsTZyJakKceHSxo2xi8OpUasGyfsTDcZ7aI1XWkNc2rgxTh5/Rm98M7+2+Mn1+9cvEzilV789R/fmmVe6o/6nmMKcfHZHriVfYS2bEj2RjgEdKCos4r2IhVw+f8Wgz5iZI8vqRc+WfQzaTa1bpua6OXQA03Mdqr+O6GOOtcwcMobNG1V2jLM6ckUFxzhujFM4xpn7XiwHDp/A1qYB33222mCcWq1mwdLVHDx6klq1ahLz5jSebtVCp48xa4h+rgMsiY2md68A7hcWMmpUBElnzhv0+XHHZ6icHHniCUsOHVJeBwSPF4/9mSbJQuKF6BFsCPuAxUGReIR2pmEL3d1R8m5lsTVyNWe+P1yhjM7zh7Nr6EK2+8/ArV9HGjzlrNMn+9cUvg95i2+D5vDnjydo/6bhLlwe/t6oXJ2I8BvPutkfMWr+64qfNzJmHOtmrSLCbzwqV6cyHV6KHsXKsPd4NygCn9AuqPTm0XlwAPfv3GNe98ns2/AjL8wy3P2qVIcpVegwOmYca2etYoqWDqV69Jg/nO3DF/Jx4AzcQztip2eLu7ey2TltDRe+P6IoW7KQGBQ9kjVh77MgaBpeoV1w1JtL7q1sPo+M43QFPmnv355Grs6MeG4kS2cuY/J7kxT7Hdt9nPC+UxTbvPy9cXZxZkK3ccTNWsW4mPGK/U7uOcGM0GkV6uHs6syo50axfOZyJlWgx/Hdx5mioIdkIdE3egSfhC1kWdB02oZ2xkEhPr+OXM3ZCuzp4++Ds4szY7uNYeWsFUyImajY78Se40wNjVBs6xDgS2PXRrzWdTiLZy4hYoGyzY7uOcbrfQznKFlYMCR6NEvCYpgbFEGH0K44t2is0+e5wYHcu3OP2d3D2bXhBwbNGmIgY2j0GJaExfBm0BuVyChgVvdJ7NrwA4NnDdVp9/T3xsnVmXC/cayevYqx85V9OiZmPKtnrSLcbxxOruXxW1ov1od9wIdBkXiGdlaITU29SKogNsv1cCLc73XWzF7FmAr1eF3W43Wc9PLsZTnfo4MiaF9Jvr+tkO+ShQWvRY9maVgMbwVF4BvaFSc9W3aV/TGnezi7N/zAQAN/SPSJDuPTsIWsDJrBs6GdDGLzzq0svo1cw7lKct2U+C6tFavDFvBe0FS8FezQUbbDu92nkLAhntBZr+rJMI8tzFF/2/l74ejqRKTfRDbOXs2I+WMV7RYWM44Ns+KI9JuIo1ZctPP3QuXqxDS/iWyYvZqwCsaPiBnH+llxTPObaFC/u88fznfDF/Jp4AxahnbEVq9+Z/6awpfPv8WWnnO48uMJus4xXMs6BvjS2LUxr3QdxsKZsUyroF4c3n2Ucc8r1yNT65apuW4OHcr1MD7XwfR1xBxrmTlkePh7oXJ1ZqrfBNbPjmPk/HGK/UbGvM76WR8x1W8CKi2f9A8JYnXsfMUxAAePnuTajVvEb93AvBmTeXfRSp12c6whAL17BfBUC1fcn+7K+PEzWbVygaI+L7/6Ot4+QbTzCMDBwbZCvR9FStTqR/rfo0q1vjRJGv6nvmg18WhBVmo6OddvU/x3Mck7jvJMsI9On9wbWaRfvFbhNZ4OHs25m5JB/rVMSv4u5o/vj9E02FunT9qRCxQ/+AuAzMTfqetkmEDeQb4c3J4AwO9Jl6lTvy4NGtro9GnQ0IbaVnX4PekyQFl/F48WZKamky3P4/SOI7QLbq8ztm2wD8fk/knxx2jVuY2BDu2DfDkg97mSdJm6lehwRdahtD+AyqM5uSkZ3JFtcXHHMZrr2eLujSyyLl5HXaJsz2Z6c0nccYRn9XyScyOTW5X4pHNwJ3Zv3wvAxaSL1K1vhW1DQ5tfTLpIzu0cRRm+wR3Zv30fAJeTLlG3fl1s9GxR2pZ7O1dRRsfgjuzV0sOqvpWijItJFxVlNPZoQU5qBrmyLc7uOEprPXvm3cgi4+J11OoSRR06BHdknzyPS5XM41Il8+gS3Jmfv94NwG+JF7CqwJ6/JV5QtKebRwtup6aTef02xX//w/Edh/HQi0/P4PYckWPpVPxRWnd+tgIZGRT//Q8ndhzCU0+GV7AvhyuR0T6oAwnb9wNwJelShTlWx6oOV5IuAZT1B2jq0YJsrXpxpoJ6kVZJbGr08OWXMj0eLs9+0dKjNN+zZD1OKeR7O618T4w/hrtWvrvKtsyS/XFCwR8eWv44HX8Udz1bNvZoLsdmJsV/F3NuxzHcK4xNZVuYGt+aWpGhVyt05/FssA8ntv8CwJn4Y7TUq3vmsIW56q9XkC+H5D5X5TXAWi8urBvaUNuqNlfluDikVX+99cZXHFfK4x09mnMnJYO7cv2+vOMYbnr+uHH0Av/Ia1l60u9YKaxlXXt24aevdwFyvbC2wq6CepFdQf01tW6Zmuvm0EGjh2m5DqavI+ZYy8whQ3OMo5lb5cc4tbWOccpt4ePxLNb16ynKBth/6BihvQKRJIl2bVqTn19AZlZ5fJljDQHo27cnn27RPG7g+IlErBtYo1I1NOiXn18AwBNPPEGNGjUq1Fvw+FDlFyBJklwkSbokSdJm4DzwliRJJyVJOitJ0jtyn/clSZqoNWaeJEmR8t/TFfq7SJJ0QZKkdZIk/SpJ0i5JkmrLbQmSJPnIf9tLkpQi/20pSdKHWrKUf8LQw9rRhju3yi8tuJOWTX1Hw0JQGXWcbLiXVp6Y99NzqOtUsYyWr/hxY3+ywfu2Kluyb2WVvc5Jz8bWUXehsXW0JSe9XN/sNM3fDRxtydWaR25aNtZ6Y7X7lBSXUJh/n7o2ugXIRk+H7IfQISet/O96Khvyb5XboiAth3rVtKe1oy15WnPJS8sxmEtV2KnsyLxVfko9Ky0TO5VdtWVkp+nZwggZWVr2zErLwl5l/9Dj6+vF510jbZGVVm6L7PSsatvCQWWvY8/MtEwcqjGPBo625GjZITctGxuF+CztUxqfVlrxaaMnIyctBxtHuyplaGOnsiNbax456dnY6cmwc7QjO137c8r/tna00YvNbKyrGd8Atio7vTzLwlZPD1tHO7IVch2U873BQ+R7KTaOtuRW4Q/tPkr+qOdoaxCb1a2dpsZ3A4NaYegP7XpSUlzCA726Zw5bmLP+5lRzDdCuv/r1OyddeS4VjbdSqN9Wlfj0mZf8SFFYyxxU9tzWqxfVqXtget0yNdfNoQOYnutlepiwjphrLTNVho3KjhytPKlufFZFRmY2qobldnFsaE9GZrnOxq4hVnp52shZxY3rt8pe37yRRiNnlaJO8T9sIe1mctkXKMHjzcOeNXoK+AiIABoBvoAH4C1JUjdgK6B9cetgYKskScHyWP3+pTJXqdXqZ4A8oKqnko0C7qjV6vZAe2CMJEmu+p0kSRorSdIpSZJOrV279iGnZz6av9gF+7ZunF3943/8swUCgUAgMAetXuhCw7ZuJK4Ra5lAUBEhfV6jcVMvatb83zrTpH7E/3tUediNIFLVavUxSZIWAcFAkvy+FfCUWq3eIElSQ0mSnAEHIFetVl+XJGmKUn/gGvCnWq0+I79/GnCpQodgoK0kSaV3YVrLsv7U7qRWq9cCpd+W1CtfjMLaufyXBmsnO+5mKJ9aroj7abk6l9vVUdlyL81QhnPXZ/AID+XHgTGU/PVP2fsL4pcA8MfZK9g5l/9KYquyIydD99KFnIwcnV937Jw0f+dl5GCjNQ8bJzvu6I0t7ZOXnoOFpQW169XhXm5+WfsH8Uu4qqeD3UPoYOtU/nd+ei71nMttYeVkS3417XknI4cGWnNp4GRrMBclug4NBiDup1VcSr6Mg7NDWZu9k4POL3lVEbtzGb+fvYKdk54tHlLGyp8011JfTr6MvZY97Z3syUrPqmiYAXczcnXis/5D2qKU5TtXcOXsZeydym1hp7Kvli3W/7yai3r2dHByILMa88jLyMFWyw42TnbkKsSnrbM9uVrxWaAVn7l6MmydbMnNyK5SRikfxi+V49sBuKCRobIjW09GdkY2dirtzyn/+05Grl5s2nGnGvH9oZzrv5/9XS/P7MnR0yMnI1vnV207rTxTyve8h8j3UnIzcrCpwh+lfSryR35GjkFsVrd2mhrfeQa1wtAfpfWk1A619OqeOWxhav2dH78YgD/O/q4b4w9Rf7sNDgAgJn4xf+jFla1KeS4V1e8ChfpdoODTJl2fwXdSKF8PjqFYay0D2LhrDRfPXKKhXr142Lq3fOcKAKPq1vPDngfMk+vmqJ0fxi8xKdfNsY6YupaZQ8Z78ZoNeTTxrRV71YzPqnB0sCP9drldMm5n4ehQrrOxa4h2rp86uYtTp87QuEn5vVaNGjtx81Z6hXoVFRXxrx27ePGF5x96LoL/TR72TNM9+f8SsECtVnvI/1qo1eoNcttXwEDgJTRnnqrqX6Qlv5jyL3D/aOlVS6uPBIRryXJVq9W7qlL8RvJV7F1U2DR2wPJJS9r17cRvuw13rqmMzOQ/qO+qwqqJAxZPWuLWryPXduvuSGT3TDO6vD+S3SNjeZB9V6dtdkgEs0MiOLXrOM8N6A5AC8+W3M+/R57etcF5t3MpLLhPC8+WAGX9U5Ov0tDFCTt5Ht59O3N2t+5OQGd3n6aj3N8zpCOXjvyq0z4zJIKTu47TTe7zVBU6PCXrUNofID35D2xcVVjLtnDv25GreraoimvJV3FwUWErz8Wrb2fOP4RPDn2qcff4XhM58vNRggYEAuDu6c69/HsV3rukxNTeUzj+8zH8B2gOSFp6tuJ+/v0Kr9XWZ1KvSUzqNYmjPx8lUE+Ph5UBcDP5KnZa8dm2bycuViM+J/cO5+jPxwiQ59HKsxX3q6nD6J6vc+inw/QcGATA016tq23PP5N/x9HFCfvGDbF88gk69O3Cmd26uyae2X2KznIs+YR04uKR8wYyGmrJ8O3blSS9GE/afZIuWjIuaMmYHvIGJ3Ydp/sAfwCekn2qFN/3C+7zlGcrgLL+ANflelEamx59O/FrNfwxPSSC6SERnNx1DL8yPR4uz/y09NDPd58q8t1LL99T9Pzh27cLyXr+SNbyh7eCP24m/4Gti4oGsg7P9u1YrdjUyDAtvpVqxTk9O5zffQrfAX4AeIR05Ipe3TOHLUytv3NDpjE3ZBqnd52gq9ynuWdL7uff545eXNy5nUthQSHN5bgovSfkzQrGK8dV+fiuWvU7I/kPGriqqC/X75Z9O/KHXv12eKYZAQtGsmNULIV6axnAyOBxHPz5ML0Gan7EetqrNQV371V475I+k3uHG123ftysOetljlw3R+00NdfNsY6YupaZQ8ackKnMCZkqH+No5tbCsyWFlcRn+TGOv4G8iujetSP/+mkvarWa5PMXsLKqi4N9+Y8Apq4hAD7tg/nXv35m6Gua3+c7+Hpx985d0tNv6/SrW7dO2X1OlpaWhPQOfOh5CP53qe6W4z8D70qStEWtVhdIktQI+FutVt9G80VpHWAP+FXWv4rPSAG8gRNovoRpf/Z4SZL2qdXqvyVJagncVKvV9xRklFFSXML3UZsYvXk2FpYWnNyWQMaVGwRHDOTGuT/5bc9pGrd1Y9iaqdSxrkvrQC+CIgYRGzy9TIa6uISjb31Cry0zkCwsuLz1F/Iu38QrcgBZyX9ybXci7ee+wpN1axGwejIABTez2TNSdzvkpH2n8fD3ZumB1RQVFrEmcnlZ24L4JcwO0ezO8/HcNfKW4zU5k3C6bB5bozYyafObmq13t+0n7coN+kQMJvXcVc7tOc2RbfsIi53EvITl3M8rYEO47hbEpTp4+nuz7MDqsm3PS/kgfgkzZR02zF3DhMWTeVJLh1Jb7H3rEwZ8OgMLSwvObf2F7Ms36TJ1AOnn/uTq7kRUbd3ot+4NalnXoXkPTzpPHcCmHrN0fLI96mPGb56j2XJ8237Sr9ygd8Qgrp/7g/N7TtO0rRuj1kyjtnVd2gR60TtiIO9r+eTEvhP4BrRn06GNmi3Hp5XbOu6nVYzvpbnFbvScUfj3707N2jXZcuJTHVuc3ncKb38f4g6u1WyxGrmsrC125zKm9tbsUjRsThjP9fOjZu2arDv+sY6Mk/tO0j6gPRsPbeRB4QOWTFtS1rbyp5VM6qXZBWnknJH49/enZu2afKqlR0lxCTuiNhG2eZZmW+dtCdy+cpPAiIHcPPcHF/ck0qitG6+tiaC2dV3cA70IjNDd8vbUvpP4+Puw7uB6zba5keU6LN+5gsm9wwEYMWcEfv00tth0/BMdGcf2HadDgC9bDm2m6EERH0z9sKxt/c+rGd1Ts8viuDfH0KN/ADVr1+Srk1/ozOOzqPVM3TwXC0sLDm3bx60rN+gf8RIp565yZs8pDmzby5jYySxIWMG9vALWhC/R0aGkuIQtUeuZtvktzdbQ2/Zx68p1+ke8TMq538tkjI2dzPsJK7mXV8Dq8CX49ulcJiNx3ym8/L1ZeWANRYVFfKQV3x/GL2V6yBuaOc1dLW9DXIOkhEQdHb6N2sSYzbORtOpFz4iBXJfrRZO2bgyX68XTgV4ERwxikVZsavQ4jae/DyvkPFsVuUJLjyVMl/Ns3dw18jbENTijp8eXURsJl/P9iFa+Xzt3lbN7TnNYzvd3tPLdu0+nsvGfR63nDdkfh2V/9JP9kbznFAe37WV07GTeq8QfP0ZtYtjmmVhYWpC47Rcyr9wkIGIAN8/9yaU9iTi3deOVNRHUtq5Dq0BPAiIGsDJ4po4MY+J7efCMsvFfR21kQlmtSCD9yg1CIgZxTa4VR7ftZ2jsJN5KWMb9vAI2hS8zmIc5bGGO+pu87zQe/l4sOvARfxUWsS6yfPev+fGLmRui2ZXsk7lrGbs4nCdr1eCsVlyc2Xeadv5eLJbHr9UaHxO/mDfl8Zvk8TVq1SBZa7y6uISEtz6h/6czkCwt+G3rL+RcvknHqQPIOPcnf+5OpOubr1CjTi1C4jRrWf6tbHaM0l3Lju49TseADnx5+FMeFD5ggVa92LhrDSODNbccj39zLD1eCKBW7ZpsP/WljgxT65apuW4OHTR6mJbrYNw6oo051jJzyDgjH+MsORAnH+OU2+K9+FjmhEwFYGPZMY5ufE5/+31OJp0lL+8ugf2HMGHUUP75R3Om86UXnqdbp/YcPHqS3oNHUrtWLd6do7ujobFriD7xO/fSq1cAly4c5n5hIaNHTy1rO3VyFz7tg6lbtw7ffvMxNWvWwMLCgoQE5R1EBY8XUlVPBZYkyQX4Qa1Wt5FfTwFGy80FwBC1Wn1VbjsHZKnVan+t8Qb90ZxZ0pYZCVip1ep5kiS5A9vkPj/K8l3kXfvmA33RnHXKBPqr1eo7laivnuFiuF3qw7IwRXNQuKHxkCp6VsyoG5/xSrP+Ro8H+CL1Oya4GD4P4WH5KGUbAC+ZoMfWVM1zTRY1Nd4Wkdc+Y4rLy0aPB1iW8iXBTXoZPX7X9Z8AeKFp3yp6Vsy313bQu0lvo8cD7Ly+kzddXq26YwXEpHwOQJ+mxl8O8MM1za+23Rv3MFpGwo09jHQxfG5JddiY8jUjXKq6pbFiPk7ZDsDAZqFGy/g6VfMspkgT6sWilC8Y1Kyf0eMBvkr9nvEm5HqcnOujTfDJ+hTNrlFRLoZbZj8s0SlbTIpv0MT4ZJeXjB6/PEVzwYM5bGFq/R3a7MWqO1bCp6nfMMQEGZ+mjhZ2AAAgAElEQVSlfgPAMhPq95RrnwHwXCPjf1E/eHOvSTULNHXLHLlujtppSr5/lfq9SevIzus7AdPXMnPIeLWZ4TMQq8Pnqd/yd5bh85welift3QDMso48UaNRFT0r5p+/boLm+PSRx9Ha/dG9cQjIuHPxkbRjlWea1Gp1CtBG6/UyYFkFfQ32bqykv7bMRVp/XwTaavWbK79fAsyR/wkEAoFAIBAIBALBf4T/qWcuCQQCgUAgEAgEAsF/mure0yQQCAQCgUAgEAj+Ryl5hLf1fpQRZ5oEAoFAIBAIBAKBoBKq3Ajif5zHenICgUAgEAgEgkeGR3IDA30crFs90sfHmXcuPZJ2FJfnCQQCgUAgEAgE/094zE+Y/Nt47L80mWMLypCmIUbLiL8Wb9J2taDZsjbChK26l6Rono9hji1r+zXtY7SM76/9YJatSSeZsA3xSnkb4uEmxMUnKduZauLW6bEpX5plq1hzxPe7zYzfWvqt1C0mbQsNmq2hH5Vttk3ZEn9Zypdm2VralNiKlXPdHI8XMPVxDebYit7Ubc8BxrgMMlrGupSvAEzy66ep35hUb0BTc0zVAUyPbzB9q25TahZo6lZg42Cjx++9oXlIujm2LTflcSJfpH7HOBNic40cm6Y+JgEwKVc3pnxtki1BY09zrGXm2Lb8RRPm8o0cF4LHF3FPk0AgEAgEAoFAIBBUwmN/pkkgEAgEAoFAIBBoKBGX5xmFONMkEAgEAoFAIBAIBJUgvjQJBAKBQCAQCAQCQSX8v7g8r42fB69GjcTC0oIDW/cSH/etTvsTNZ5gTOxkmrVxoyAvn7hJsWTfyDSQM+6dcbT3b09RYRGx02K5ev6qQZ9h04cROCAQK2srBrTWvbHxab92DIoagWRpwZGte9kV972BHsNjJ9GkjRv38vLZMGkpOVp6uPu144Wo4UiWFhzfuo+9cbo3Hbr5uvNC1HCc3JvyafhyknceV7TH0Hmj8PD3oqiwiLWRK0k5b3jzpEsbN8YtDqdGrRqc2Z9o0D7mnbF4+/tQVFjEsmlL+UPBFkOmD8V/QAB1ra14ubXhDa/D5o3Cw9+bvwqLWB25QlEP1zZujFs8WdbjdNn7rf3aMTAqDAtLC45s3cduBVsOjZ1IU9mWGyct07FlKa+9PZJ2/l78VfgX6yJXkPrrn4q2GL1oEjVq1SBZzxbufu3oHzUcC0sLjm3dxz4Fn/TX8snZCnwy6p2xePt7U1RYxIppyxTt6fZscyYvfoMatWrovG+O+G7u15aebw9FsrQg6csEjsTt0Glv6utO8NtDcHRvyjfhK7kQf8JAv9Z+7XhR9snRrfvYo+CTIbETy+J7k4JPnvZrx2A5Rw5XkiOlfl0/aalO+zN+HrwSNQILSwsObt3LzrjvDMaPig2XbVHAGgVbuPu140Utn+7R86ll2TxcuZdXwCcVxNbQeaNop5VnqRXk2Vg5z7Rjy1xxFTZvNJ5yXMVFLudPxRxrzgQ5x5K0cgygpV87+kUNQ7K04MTW/STo6eHq605o1DBU7k35PHw553YaxoUmPjU+Pbh1L/EKPhkt++ReXoFBfLbwa0tIlCY2E7cmcFAvNpv5utM7ShObX4Wv5DcFHZ7x8+Blrbj4SUGHkWVxkc/aSUsU1wBTfFqKqTXHVB2qiu/m8jri7N6UTypZR8a+Mw4feQ1YOm2J4no4dPowAgYEYGVtxaDWupsNmKNuTYyeQIcAzZq8MGIRV87/bqDDyBlhBA0Mop61FX1a9TNsnzcGT38f/iosYmXkUsUccWvTnImLp1CjVk2S9p8yaB8+b3TZWhYXubyCtaw5ryusZaCJz8FyfB7aupefFeJzRGx4Wd1bN2mJTnsrOU8tLC04vnU/+xXqRWjUMJzcm7IlfDln/w15Woqp9jQmLrSZ+14sBw6fwNamAd99ttrgs9VqNQuWrubg0ZPUqlWTmDen8XSrFgb9Rs0bg5cc3ysjl/JHBfMIl+eRqBAXjzJi9zzjeGTPNEmS1ECSpAlar7tLkvRDteVYWDA0egxLwmJ4M+gNOoR2xblFY50+zw0O5N6dAmZ1n8SuDT8weNZQAzk+/j40cmnE6G6jWT5rOZNiJil+3vE9x3kj9A0FPSReih7FyrD3eDcoAp/QLqhaNNLp03lwAPfv3GNe98ns2/AjL8x6TWf8gOiRrA17nw+CpuEZ2gVHvfG5t7L5PDKOxO8PV2iPdv5eqFydmOY3kQ2zVxM2f6xivxEx41g/K45pfhNRuTrptHn7++Dk4szr3cayatZKxsdMUJRxYs8JIkOnKrZ5+HuhcnVmqt8E1s+OY+T8cYr9Rsa8zvpZHzHVbwIqV+cyWwyOHslHYQuYHzQVbwVbdhocQOGde7zTfQr7N8TTb9arBrLbdtfYYkb3SXw8J47hMcq2GD5/LB/PjmNG90k6tpAsJF7U8olXBT75ogqfePl74+zizIRu44ibtYpxMeMV+70eM4GPZq5kQrdyW5kjviULiV7vhvH58IXE9ZhBm9BO2D+lO487t7L417Q1nP/+iKJukoXEoOiRrA5bwHsV+KSjHN/vdp9CwoZ4QvV8IllIvCznSHRQBO0ryZG3FXPEgteiR7M0LIa3giLwDe2Kk54tug4O5N6de8zpHs7uDT8wcNYQxXmsCXufBRX4tNNgfwrvFDC/+xskbPiRvgqx1c7fC0dXJyL9JrJx9mpGVJBnYTHj2DArjki/iTjKsWWuuPLw90bl6sQUv/Gsm/0Ro+a/rthvdMw41s5axRS/8Qbx/UL0CDaEfcDioEg8QjvTUE+PvFtZbI1czZkK9JAsLBgSPZolYTHMDYqoJD7vMbt7OLs2/MAgLZ9IFhJ9osP4NGwhK4Nm8GxoJxxaGMbmt5FrOFdhbFrwavQoloXFEBUUgW9oF4W4COD+nQLe7B7Ong0/MEAvLsA0n5Zias0xVYeHie/SdeR0JbHl4++Ds4szY7uNYeWsFUyImajY78Se40wNjTB43xx1yzegPY1dGzGs6whiZy5lyoLJijoc3XOMiX3CFds8/b1xcnUm3G8cq2evYux85do7JmY8q2etItxvHE7yOlRKaZ5FVJFnI2PGsW7WKiIM8syCV6JHsSIshnly3dOPzy6DA7h3p4C35Ph8US9HXogewfqwD/gwKBLP0M4KPtXkadK/KU9LMdWe5oiL/iFBrI6dr/i5AAePnuTajVvEb93AvBmTeXfRSoM+XvI8JlYxj3Ex44mbtYqJCnEheDx5ZL80AQ0A5aPxauDm0YLbqelkXs+g+O9/OLHjEJ7B7XX6eAX7cnh7AgCn4o/SuvOzBnI6Bndk7/a9AFxKukTd+nWxaWhj0O9S0iVyb+cavO/i0YLM1HSyr9+m+O9iTu84Qjs9PdoG+3BM1iMp/hitOrcpa2vq0YIsrfFJO47QJthHZ3zujUzSLl6r9BcE7yBfDsmfcTXpMnXr16WB3jwaNLShtlVtriZdBijrX4pvcAf2b98HwOVKbHG5AluU6nFw+34Afk+6TJ1K9Phd1qO0v4tHC7JSM8pskbjjCG0VbHl8+y+AoS1L8Qpuz+FvfpFtcYU69epi7dBAp4+1QwNq1avD1aQrAGX9odwnOSb6xDe4Y5X2tGloQ22rOlxOuqTzvjni29mjObkpGeRdz6Tk72J+3XGMVkHeOn3u3Mji9sXrqEuU59HMowWZej55Vk+PZ4N9OCH75Ez8MVrq+aQ0R7JkGacUcqSdVo4kxh/DXUuGq2wLzfh/OLHjMB564z2C23NEHn86/ijuerZoppenmnno+rRNsA8nth8AIDn+OC07P2NgDy+9PKtTvy7Wej61riDPzBVX7YN8OSDLvFJprtfhiqzDAa1cb6KnR/KOozxjoEcW6ZXoUR6fGp8cV/CJp5ZP9OOzsUdzclIzyL2eSfHfxZzbcQz3YN3YzLuRRcbF6xXq4KoTV/9wcsdhPPTmoYkLTWye1ourUkzxaZkME2uOqTo8THzn3MjkVhWx1SG4I/vkmmXMemiOutUluDO7vt4NwIXEi1jVr4ttQ1uDz7qQeJGc2zmK82gf1IEEeV25knSpwnWojlUdrsi1t7R/KZq1TKNn5WtZHa21LKGsTb9undpxmHZ6PmkX3J5jcnzq172mHi3I1srTMxXkaWX1wtQ8LcVUe5ojLnw8nsW6fj3FeQLsP3SM0F6BSJJEuzatyc8vIDNLNz58tebxsGuyflwIHk/M8qVJkiQXSZIuSpK0SZKky5IkbZEkqYckSYclSboiSZKvJEm2kiR9J0nSWUmSjkmS1FYeO0+SpI2SJCVIkvSHJEmlPxe9DzSXJOmMJEkfyu9ZSZL0tfxZWyRJqvKJwTaOtuTcyip7nZOWg42jnU6fBlp9SopLKMy/j5WNbtLZq+zJTCs/FZ2VnoW9yv6hbdTA0ZbcW9llr3PTsrF2tK2wT6kedWU9Gjjakqc1/k5ajsH4h8FGZUu2tj3Ss7HRk2PjaEtOevln5aRl67TbqezISiuXkZWejZ1K16ZV62FHjtZ8qqOHtaIt9Q4cKrGltnwDW+jNw0ZlR26asi2s9XySZ6RP7FR2ZGvZMzs9G1s9PWxVdmSnZ+kPNUt811fZcldrXnfTcqinMjwAqgz9+MyrwCd5Wj55oOcTpRxp8BA5UoqNoy25WrbITVOOqdxKbPEwPtXX4UF+oWFsqfT8kp6NrZ4c20ri2xxxpZ/r2dXQQaOHDXd0ak429R2rHxc5Vfiksvis52iro8PdtBwjddCOqxwaKORIZXEBpvm0TIaJNcdUHcxZs7K01sPs9KxqrQHmqFv2Kjsyb5XrkJmWhX011yE7lR3ZWjJy0rOx09PDzlG39uak6dZhW4U1tSqfZGv5xLDuKcenvi1KsXa0qbL2VoWpeVqKqfY0Ni6qQ0ZmNqqG5cdujg3tycjU96kdWbe04zsbWz09bPXmkZ1muD4LHj/MeU9TC2AQMBI4CbwKdAVCgTnAdSBJrVb3lyQpANgMeMhj3QF/oB5wSZKkOGAW0EatVnuA5vI8wBN4BrgFHAa6AIe0lZAkaSwwFmDNmjVmnJ5AIBAIBAKBQPC/TQniniZjMOfleX+q1epzarW6BPgV2KvWnAs+B7ig+QL1KYBard4H2EmSVF8e+6NarS5Sq9VZwG3AsYLPOKFWq2/In3FGlquDWq1eq1arfdRqtc/YsWPJzcjB1rn8VwVbJ1tyM3R//cvT6mNhaUHtenUoyM0va1+xcwU5t3NwcHIoe89eZU+Wwi//FZGXkYONc/kvFTZOdtzJyKmwT6ke92Q98jJyaKA13trJ1mB8VcTELybvdi522vZQ2ZGrJyc3I0fnTIetU/nfS3YuJ/d2LvZO5TLsVXZkp+vatCLei4/lvfhY8m7nYqs1n+rocUfRlrqXgNypxJYA0fGLlG2hN4/c9GxsnJRtcUfPJw2q6ZPYncuI3bmM3Ns52GnZ005lp/OrJMi/2Cmc2TRHfN9Nz6G+1rzqO9mSn658WWVF6Mdngwp80kDLJ7X0fKKUI3kPkSOl5GbkYKNlCxsn5ZiyqcQWD+NTfR1q1atdNo/58YuZL+eZrV5s5ejJyakkvk2JK4AP4pcYxLddNXTQ6JGLtU7NseNuRvXjwrYKn1QWn/kZOTo61HeyNVIH7biyJU8hRyqLC1N9Gh2/yOia89zgALPoAOaJreU7V5B7Owd7rfXQTmX/0GsAGF+3fPt0AWDNz3Fk387BwblcBwcne7KqocOH8Us1tVdLhq3Kjmw9PbIzdGuvrVatXqCQZw/jEzstnxjWPeX41LdFKXcycqusvVVhap5+GL/ULPY0Ni6qg6ODHem3y4/dMm5n4ehQ/pmL5XnYO2vHtx05enrk6M1Dew0XPL6Y80tTkdbfJVqvS6j6jJb22OJK+j9svzL+TP6dhi5O2DduiOWTT+DbtytJu3V3OUnafZIuA7oD4BPSiQtHzuu0h/cO5+jPRwkcEAhAK89W3Mu/V+H9OkqkJl+loYsTdo0dsHzSEu++nTmrp8fZ3afpKOvhGdKRS0d+LWu7nnwVBxcVtvJ4z76d+XW37g48VfFmyDRO7zpBV/kzmnu25H7+ffL05pF3O5fCgkKae7YEKOsPENF7Msd+Por/AM1C3tKzFffy7z+0LeaETGVOyFRO7TrOcwP8AWjh2ZLCSvRoIetR2j9VtkWpLb0UbHlu9yk6DPADNLa8rGVLgKiQSBJ3naDLi36yLZ6iMP8+dzLzdPrdyczjQf59mns+BVDWH5R9cr4aPpnaewpTe0/h+M/HdOx5X8GeubdzKSy4T0vPVjrvmyO+byX/ga2rigZNHLB40pJn+nbkcjVj65qeLbz6duacnh7nd5/CV/aJR0hHruj5RD9HfKrIES+9HElJ/h1HHVt0IXn3SZ3xybtP0Vke7x3SiYt6tlCah75Pz+8+je+AbgC0C+mgM4+5IdOYW0Ge3dHz6Z0K8szUuAKYGRLByV3H6SbLfMqzJffz71WQY/d5Stahm1au30i+ir2LChtZj3Z9O/FbNfX4U88nHfp24YyeT85o+cRHzyc3k//A1kVFA1mHZ/t25GI1dUjRy5H2fbuQrBdXGh00sekd0pFLenFhqk+jQiKNrjkN5HueTNUBHi6+q2Jy73CO/nyMALlmtfJsxf1qrofG1q19n/4EwLie4zn80xGCBwYB0NrLnXv59yq8d0mJ6SFvcGLXcbrL68pTcu1VypH7Bfd5Sq69pf0BZodEyGuZRs8WVeRZ+VrWvaytND7tZFv4KMTn2d2n6CjHp1dIR50cuS7naalPPfp2qvaxgal5Oj3kDbPY0xzrWVV079qRf/20F7VaTfL5C1hZ1cXBvvxSxGl683jYNVl7HoLHl//kluMHgdeAd+VL7bLUavXdSm5LykdzuZ5JlBSXsCVqPdM2v6XZbnbbPm5duU7/iJdJOfc7Z/ac4sC2vYyNncz7CSu5l1fA6vAlBnJO7jtJe//2bDi4gaLCIpZElvdZsXMF4b01u/OMnDOS7v26U7N2TTYf36yjx9aojUza/KZmS+Zt+0m7coM+EYNJPXeVc3tOc2TbPsJiJzEvYTn38wrYEL5UZ/z2qI8Zt3mOZlvRbftJv3KDXhGDuH7uD37dc5ombd0YuWYata3r8kygF70iBvJB8HSdeZzZd5p2/l4sPvARf8lb1pYSE7+YN0OmAbBp7tryLWsTdLesPb3vFD7+Pqw+uE6zRXZkuZ5Ldi4norfmtrThc0bQrZ8fNWvXZMPxTQZ6ePh7s+RAHEWFRayJXFHW9l58LHNCNLvubZy7pmyb1lI9SopL2Ba1kYmb5yBZWnBsWwLpV27wfMQgrp37Q7blfobFTuLthGXcyyvg4/BlBj5N3p9IW38vPvxlFUWFRayfvqqsLTp+EVEhkQB88tY6xsjb/55NSNLxyTdRHzNW9smJbfvJUPDJCD2fLNTzyel9p/D29yHu4FrZnuW6xu5cxtTeUwBYMzfOYMtxc8S3uriEn6I28ermmUiWFiRv+4XMKzfxmzqAtLN/cnlPIk5t3Ri8NoJa1nV4qocnfhEDWB00U0ePr6M2MkG2RalPQmSfnN9zmqPb9jM0dhJvJSzjfl4Bm/R8UlJcwpdRGwmXc+SIVo5cO3eVs3tOc1jOkXe0csS7T6ey8Z9HreeNzXOxsLTg8LZ93Lpyg34RL5Fy7irJe05xcNteRsdO5r2EFdzLK2CNni1K82x82Tw0edZb9un5Pac5tm0/Q2InMjdhKffzCvgkfLlhbO07jYe/F4vkPFunlWfz4xczV86zT+Q8e7JWDc5qxbc54ipp32k8/b1ZdmB12VbIpXwQv4SZIZpdzTbMXcOExZN5slZNziSUH2yVFJfwfdQmRm+ejYWlBSe3JZBx5QbBEQO5ce5PfttzmsZt3Ri2Zip1rOvSOtCLoIhBxGrpUVJcwmdR65kq++SQ7JP+sk9K43NM7GQWKPikpLiEH6M2MWzzTCwsLUiUYzMgYgA3z/3JpT2JOLd145U1EdS2rkOrQE8CIgawMlg3Nj+P2sAbm9/UbGW/bT+3rtwgNOIlUuW4OLRtH6Niw4mRdVirsAaY4tMyGSbWHFN1eJj4btrWjVFybLUJ9KJ3xEDe14utU/tO4uPvw7qD6zVbjmuth8t3rmCyvB6OmDMCP3k93HT8Ex09TK1bx/edoEOAL58e2sSDB0V8OHVRWduan+MY11Oz49nYN0cT0N+fmrVr8uXJLToyEvedwsvfm5UH1lBUWMRHWjnyYfxSpododsJdP3e1vEV2DZL0fJokr2VLD6yW17JyGQvilzBbzrOPy9Yywzz7MmoDU+S6d1iue33l+Dwrx+fI2HDeleNzffgSfPp0Lhv/bdQmxmyejaSVpz0jBnJdztMmbd0YLufp04FeBEcMYpEZ89Rc9jQ2LnxlWwBMf/t9TiadJS/vLoH9hzBh1FD++ecfAF564Xm6dWrPwaMn6T14JLVr1eLdOYa7O56W5/GRPI+VWvNYHL+UafI81s5dLW85XoNEvbh41BFbjhuHZA7DSZLkAvygVqvbyK83ya+/Lm0DugEbATfgPjBWrVaflSRpHlCgVqsXyWPPA33UanWKJEmfA22BncCPQKRare4j91sJnFKr1ZsqUU09wmVAJc2V83HKdgBCmoYYLSP+WjwTXAYbPR7go5RtRLi8bPT4JSlfAjCk2YtGy/gs9RsA+jXtY7SM76/9wKvNXjB6PMDnqd8yyeUlo8evTNkKwHAT4uKTlO1MNcEfALEpX/JC075Gj//2muZZNeaI73ebvVZFz4p5K3ULk03wB8DylK2MNyFH4lK2ATDaZWAVPStmfcrXAEwxwa/LUr5kqAk5BvBp6jcmxVasnOsvNetvtIytqZrns8xwecVoGQtTvmCkCf4A2JjyNVEuxsdmdIrmAHmMi+Ez4h6WdSlfAZjk109TvzGp3oCm5piqA5ge3wB9mj5vtIwfrv1oUs0CTd0KbBxs9Pi9N3YBMLBZqNEyvk7VPAfpFRPy7IvU7xhnQmyukWMz0oQ8XZTyBYBJubox5WuTbAkae5pjLfs7y/BZSg/Lk/ZuALxowly+0cRFlRuUPQrUr+v2SH9runvvj0fSjmY506RWq1OANlqvwypoM6gwarV6nt5rbTn6D0BJ0GpTflCSQCAQCAQCgUAgEJiR/+TleQKBQCAQCAQCgeC/SIm4PM8oHuWH2woEAoFAIBAIBALBfx3xpUkgEAgEAoFAIBAIKkFcnicQCAQCgUAgEPw/QS0ebmsUZtk97xHmsZ6cQCAQCAQCgeCR4ZHc9U2funVcHunj43v3Ux5JOz72Z5qCm/Qyeuyu65oH6bVVdTJaxtn0oyZt9Q2a7b7NsW1ujAlbS7+Zqtm+171he6NlXLx90izbEJtjy/GZJmzT+kHKFyaNL5Xh3zjI6PH7b+wGTN8uHOD9ZkOMljEr9TNaOvgYPR7gcuYpPFVdjB6flH4YwCzbEDeza2u0jNTss/Ro0tPo8QB7rv9slu3CTX00AEC3RoFGyzhwcy/POHYwejzArxnHTd7SGcDH6TmjZZxKOwiYHhem1E3Q1M4ujQKMHn/45j4AOjUy/gGcR2/uB0zftrxnk95Gjwf4+fpOs9ROc8gw1SfmOD4xx6MrTLWFKcdIoDlOeqJGI6PH//PXTcAs24WbZdtywePLY/+lSSAQCAQCgUAgEGgQu+cZh9gIQiAQCAQCgUAgEAgqQXxpEggEAoFAIBAIBIJKEJfnCQQCgUAgEAgE/094zDeB+7fx/+ZL04R3xtM+oD1FhUUsmrqY38//btAnbMZwggb0wMrain7uLxi0z5wfwXOBnXlQ+IC3przLhXOXddpr1a7JonUxNGnWmOKSYn7ZdchAxtB5o/Dw96KosIi1kStJOW9406FLGzfGLQ6nRq0anNmfWPb+M34evBw1AgtLCw5u3ctPcd/pjHuixhOMjA2nWRs3CvLyWTtpCdk3MnX6uPm1JfjtoUiWFpz5MoGjcTt02pv4uhP89hAaujfl2/CVXIw/oWBNeDNmGt16dOFB4QNmh7/Db+cuGdhi6fr3aerSmOLiEvbvOqjT3sbPg1ejRiDJc4lXmMtoeS738gqImxSrM5fWfu0YGBWGhaUFR7buY3fc9wbjh8ZOpGkbN+7l5bNx0jJy9GzR0q8doVHDkCwtOLl1Pwlx/9Jpd/V1p2/UMFTuTfkifDnndhrawhwyAMKjJ9AhwJcHhUV8EPEhVxTic9SMEQQP7EE963o67zf3a0tP2adJXyZwRM+nTWWfOro35ZvwlVxQ8KmrX1t6vD0UC0sLkr9M4JhBXLQi8O2hNHRvwvfhK7kUf1JxHnPfi8SvRxcK7z9g1uR5/HbWMC6Wb/hAjotig7gAmDH/DboEduJB4QPenhLDRYU8W7huPo2bNaKkpMRg/MToCXSQc31hxCJFW46cEUbQwCDqWVvRp1U/g/Z5C2bi3+M5CgsfEDnpLc6fvaCnQy3iNi6iqWsTSoqL2fPzL4Z6vDMe3wBfigofsLCCmjNiRhhBA3pQz9qKvu6Gmx6EzRuNp783RYVFxEUu50+FeuHapjkTFk+mRq0aBm1j3hmLt78PRYVFLJu2lD/OXzXoM2T6UPwHBFDX2oqXWxtuODM5eiIdAzpQVFjEgoiFXD5/xaDP6Jkj6TUwCCvrevRqabgJxeyYqXQL7Exh4QPenPwuFxTqRey6BTRxaURJcQkJuw3jYvi80Xj4e/OXbAul2unapjmvV2CLyHen0CWwIw8Ki5j3xntc0osrgAmzxhAysCf1G9SjWwvDTT3MERem1s43oifRKaADDwofEFOBP8bOHEmvgcHUs65HUMvnDdojosPpLMt4N+IDRRnjZo6itywjsGWITpu7XztejBqOhaUFx7buY49e3bOs8QRDYifSpI0r9/IK+ESh/gKMf+d1fAPa86CwiMVTF/O7QnyGzRhOjwGBWFlb0VgKD20AACAASURBVN9dd1Ol6tbNkFaGGwWYQ4Y5fFLdYxQDHd8Zi7dcK1ZMW6aY669NH0r3Af7Utbbi1daD/y22MMdx0pLYaHr3CuB+YSGjRkWQdOa8QZ8fd3yGysmRJ56wNNRx3hi85Lq3MnIpfyjUCrc2zQlfPIUatWqSuP9U2ftz34vlwOET2No04LvPVhuMU6vVLFi6moNHT1KrVk1i3pzG061aGPQTPJ78v7g8r71/exq5OjPiuZEsnbmMye9NUux3bPdxwvtOUWzrGtiJZm5N6NNpENGR7zP3gxmK/T6J+5x+z73M4B7D8Wyvu+NSO38vVK5OTPObyIbZqwmbP1ZRxoiYcayfFcc0v4moXJ0AkCwseDV6FMvCYogKisA3tAtOLRrr6jg4gPt3Cnizezh7NvzAgFm6u6JJFhK93g3jy+ELWdNjBs+EdsL+Kd0da+7eymLHtDWc//6Iom4A3QI708ytKT07vEjUtPd4e+EsxX4ff/QZIV0G8WLga3j5lttCsrBgSPRoloTFMDcogg6hXXHWm8tzgwO5d+ces7uHs2vDDwzSmotkITE4eiQfhS1gftBUvEO7oGqhO49OgwMovHOPd7pPYf+GePrNetXAFv2jR7Ax7ANigyJpF9qZhnoy8m5lsS1yNWe+P6w4P3PIAOgQ4Esj10YM6RrG4plLiVgwWbHfkT3HGN8n3ECHXu+G8fnwhcT1mEEbBZ/euZXFvyrxqWQhEfzucLYNX8i6HjN4OrQjdk856/S5eyubH6et4bdK4sKvRxdc3JoQ5PsCb02L4Z2FsxX7bVj1Kb06D6R/wGt4+bbTaesa2Immbo3p1+kl5kcuZM4HkYoyNsd9wYvPvcrLPcJ03vcNaE9j10YM6zqC2JlLmVKBLY/uOcZEPVuW4t+jK65uzfBr34fZU6OZv2iuYr+1qz4hsGM/QroPxsfXU1cP//Y0cm3E8OdGsGTmMqa8p/xZx3YfY1JfZR09/L1RuToxxW8862Z/xKj5ryv2Gx0zjrWzVjHFb7zO+97+Pji5OPN6t7GsmrWS8TETFMef2HOCyNCpim0dA3xp7NqYV7sO48OZsUxdoFwfj+w+yrjnJyq2PRfYmWauTejdcSDzIt8naqFy7dwUt4W+XV9iYI+heLbXjYtSW0RUYYuRMeNYN2sVEXq26BLQkSZujXmh8yvETF/I7PenKY4/sOsww0PGKbaZIy5MrZ2dAjrQ2LURL3UdysKZsUQueENx/OHdRxnzvLK/OwV0oIlrIwZ1HcL7MxczY0GEYr9Du48w6vnxBu9LFhKDokeyJux9FgRNwyu0C44G9defwjsFzO/+BgkbfqSvXv0F7XV5FMtmLie8knV5ssK6bErdNKcMc/jE1GMUL39vnF2cmdBtHHGzVjEuxtBvACf3nGBGqHLsm8MW5jhO6t0rgKdauOL+dFfGj5/JqpULFGW8/OrrePsE0c5DdwdDL39vnFydmeg3jtWzVzF2vrItxsWMJ27WKib6jcPJtXy96x8SxOrY+YpjAA4ePcm1G7eI37qBeTMm8+6ilRX2FTx+/E9/aZIkyfAnBgU6B3di9/a9AFxMukjd+lbYNrQ16Hcx6SI5t3MUZfj37MaObTsBOJv4K/XqW2Hf0E6nz4PCIk4e1pwZ+ufvfwx+TfUO8uXQ9gQAriZdpm79ujRoaKPTp0FDG2pb1eZqkubXmdL+rh4tyExNJ+v6bYr//oeTOw7jEay71bNHcHuObNf8snk6/hjundvotDt7NCcnJYO865mU/F3MbzuO0TLIW6fPnRtZ3L54HXVJxaduA3v78f22HwFIPn2e+tb1cFCwxfHDpwH4++9/dM44uHm04HZqOpnyXI7vOIxHsO6WvJ7B7Tkiz/1U/FFad362rM3FowVZqRlkX79N8d/FJO44Qlu98W2DfTgu2yIp/hit9GzRxKMF2anp5Mgykncc5Wk9e+beyCL94rUKT2ObQwZAl+BO7Pp6DwAXEi9UGJ8XEi8YxKezR3NytXz6645jtKqmT51kGXe04uIpBRmZVcVFLz++3RoPaOKinnU9HBwrj4tfz17Uaffr2ZUftmm20j2X+Cv16tdTzLNTWnmmTZfgzuz6WrMd8IXEi1jVr1uBLSvO9aDe/mzfqjnTlnTqLPWt69HQ0V5PhwccPXSybB76Zxw0NUf2aVIlelRSc9oH+XJAzoErldaLOlxJMjxr4hvcgf3bNVtNX066RN36dbHRG1/alns7V1GHrj278PPXmu3Yf0u8gJW1FXYK8/gt8QLZFcwjoFc3/vWVXDtPn6/Qpye064VC7Two2+L3pMvUqcQWvyvYwq9XV+K/0sTV+cTfqFffCjs9HUrbsm9nK87DHHFhau3s2rMzP8nx/WviBepV4I9fK/FHt55d2Cn79NfEC1hZ162WjGbyWqRdf5/Vq3ttgn04sf2AZp7xx2nZ+RkDOZ2CO7LHYF02jE/NumwYn6bUTXPKMIdPTD1G8Q3uaHKum8MW5jhO6tu3J59u+RqA4ycSsW5gjUrV0OCz8vMLAHjiCd0LpnyDOpCwfX/ZfJVsYSPXistJms8u7Q/g4/Es1vV1r+bQZv+hY4T2CkSSJNq1aU1+fgGZWcr2EDx+/Me+NEmSFC1J0htar2MkSZoiSdJ0SZJOSpJ0VpKkd7Tav5Mk6bQkSb9KkjRW6/0CSZIWS5KUDDzUwwHsVHZk3iq/NCArLRM7leGCWRkNnRxIv5VR9jojLZOGTg4V9q9X3wq/4K4679mobMm+lVX2Oic9GxtH3aJk42hLTnr5op2Tpvm7gaMtObfK389Ny6GB3gFpA0dbcmX5JcUlFObfx8qmPPnrqWzJTyuXcTcth3oqw8JaFY4qB9K0bJF+6zaOToZFrexz61vh37P8WSmauZTbITfN0A7affTnYu1oS66OLbKxdtSdh3af0vF1tWxh7WhDnpaMOwoyqsIcMgDsVfbcvnW77HVWWhb2KvtKRpRTX2XLXRN9Wk9lQ35aedHPNzYunBxIv5Ve9jrjVgaOCotd2efWtyIgWPcZOpo8K7dFRtrtSvPMqr7uZSr2ermemZaFfTVzXeXUkFs3y+eRfiuj0viuX78ePXr66elhb7Ie+vUiOz0bW708sdWrF9rYqezISisfn5WeXe26p4lN7XlkPnRsltLQyYH0m9q18zaOVdTO7nq101ahdlbHFg4q/bjKpKFT9eZhjrgwtXY66NWK22mZOFTTHw4qezK0ZGSmZVVLhrWjrU7dy0vLwVqhfmvX3wf5hTr1F0pzVSs+07Kwq4YeptRNc8owh09MPUaxU9mRnaZXK4zKddNsYY7jpEbOKm5cv1X2+uaNNBo5qxTHx/+whbSbyTrv2arsyNKypaZu6trC1tGO7HQte2nZrioyMrNRNSy3i2NDezIyH378o4L6Ef/vUeU/eaZpIzAMQJIkC+BlIB14CvAFPABvSZK6yf1HqtVqb8AHmCxJUmnU1wWOq9Xqdmq12vBi2EcAS0tLPlgdzefrv/pvq/Jfx9LSksVrYvh03db/tiqCRwhLS0uWrI1h83rj48LS0pL3V88zn1JG6rBi3Qd8vPbz/6oejwuWlpZ8uPpdtqzf9t9WxSTMEReidgoeZ8xxnBTS5zUaN/Uyo1YCQeX8x740qdXqFCBbkiRPIBhIAtpr/Z0IuKP5EgWaL0rJwDGgidb7xcD2ij5HkqSxkiSdmj17dmpWVhZxP60i53YODs7lv3bYOzmQXcEvkkps2/MJWRlZqJwdy95zdHLgdprhja0AUYtmkfrHdf6vvTMPz6K6/vjnBJQdlD2KharFrcWCICou7AKtC6LQuuICKCoYbGsFBKu1qC1S+rPIIrKLUhW1FQiERWRflEWLBWUTlLDLDgLn98edJG/eJGTevK9wQ87neebJZGbO9z1z58yduXPvnBkbXOxemNSfFyb1Z/fWXVQ6J+sJRcXqldiVnr1bd1f6zmxPiComu/nd6TupeE7W8rOTK7I7Pfs+7E7fydmBflKxJEqVK82+XXsz1+/dspNyyVka5ZMrsndL7l31eTFxxji2pu8gOaIsqp9TlfTvtua6/XP9e7Jh7UZGDx2fzc+KEeVwdnLOcojcJnpfvk/fydnZyqIS36dn34/IbTLs90eUxffpuzgrQqNCLhr5Ea/GsNTBDEsdzI6tO6l6TtbT5srJldm+JdyTqz1bdlI+zmO6d8suyiVnPSkuF6PGBzPH8cHMcWxL3071iCeC1c6pRvqW3OPi+Vd6sX7tN4wa4uLirbSRvJU2ku3pO6geURbVkqvmeZ71/tsf2Lh2U+b/Q1JfY0fUuV4luTLbYzjXJ82awNb07ZxzbtZ+VD+nWp7x/eKAPqxbu4E3howFYPCUQQyeMihHnROLHy9NGsBLkwbkqC8qVa/EzqjzZGdUfZHBgMn/YNfWXVSO6E2pXL1STPXe8KlD2JG+g6rZ9qNK6Nh8d/oY3p0+xtWd50bWnVVJz+OYPtv/aTas+4YxQ98CoN+kAfTLpSwqxlAW46a9wfat0XFVha0xPF2ONy4mzhhX4Lrz6FE3BHXk1KHsSM9eV1RNrsK2kMcDYNTUYWxP30G1CI0qyZVj0vg+fWe2eu+s5Ip8n0v9HVn/lixXKrP+HTTlVQZNeTU4RyLiM7lytqf/uXHTfS7JSLz1ZqI04j0mr035Z9z3KK9MHsiurTuplBxVV8RwrsdbFhPSRsV9n7Rk8VSWLJ7Kd1vSqXFe1jtG59ZIZnPECIZoDh8+nDnff9Lf2bV1J5UjytLVm9nLYmf6jmy9mpVi6HWuVqUSW7ZmlUv61u1UqxJbj5xReDnZ7zS9DnQE7sf1PAnQT1V/GUwXqupwEWkMNAeuVtXLcY2qkoHGIVU9ltcPqOpQVa3fr1+/mpUrV+aRVo8yL3U+Ldo1A+Diuhezf+/+PMfk5kb75vcxY8psbmrfGoA69S5j7979bM9l7PtjT3WmXLkyvPzM3zOX9WrzJL3aPMnSqYu4tl1jAC6oW5sDew+wO2p88e6tuzi47yAX1K0NkLn9+uVfUbVWMpVrVKXYGcVpcFMjlk9bks122bQlXNPODQe5os1V/G9e9owz3y5fS8WfVqfCeVVIOqMYl950FaunLQ1dDgBtm97F9MmzuKW9y/5z+RU/Z++efWzLpSy6//FhypUvy196v5Jt+brlX1EtYl8a3tSIZdOyZ2Nz++L2vX6bq/kyYl82LP+aKrWqU6lGFYqdUYx6N13DiqiyWDltCQ2Dsqjb5ipWz/si2/pNy7+mUq3qnB1oXH7T1ayKsSzi1eh048N0uvFh5k6ZS8vbmwNwSb1LYorPjGN6VnBMLyvAMf0ul7j4atqn+RsG3NLkLm5pchdpk2fRtoPLsnX5FT9n3559bEvPGRdPPP0I5cqX5YVe/TOX/aZ5R37TvCMzp8zm1+1bAfCLepexb+++XM+zrk91oly5svz1mYGZy7rc+Ahzp8yj5e0tALikXuznepvG7Zk6aQbtOtwEQN36ddi7Zy9b03PePPyu52OUK1+OP/V8OXPZw6268nCrrsxNnUeLdsExrXsx+/ceCO3HU21SeKpNCounLuT64Bz4Wd3aHNi7P4/64gA/C+qLDFJad2NB6nyatHMvSdeuexH79x7I832G3HiwZRc+SZ3Ljbe3BODSepewf8/+PN/LiKZds3to1+wepk+ezc13BHXnFT/P85h2+2MXypUry4u9B2Que7pNCk+3SWHJ1IVcF5TFhfmUxYVRZXFXiweYNfkT2tzh4urn9S5l3959eb67lBvxxkXbpncVuO588w33JL5jy87MTp1DqyC+L6t3CftiOB4A97XsxOzUubQOjullMR5TgI1B/Vsxov79PKrO+XzaUq5s5waNXN6mIWsi6t+urR6ja6vHmJc6n+YR1+UDe/fn+u5SJP8e9R+AuOvNRGnEe0weafVo3PcoPVp3Z2Hqgmzn+oEYz/V4y6J98/vivk+q36Al9Ru05MMPU7nnrtsBaHhlPfZ8v4ctUQ/fypQpnfmeU7FiWa+2P9nmCRZNXUjjdk2AvMtiV1BX1K57EUDm9mFofO1VfDhlOqrK8s9XUbZsGapUzvnul++oqteTr5zslOMTgeeAM4A7gaPA8yIyTlX3ici5wA9ABWCXqh4QkYuBq+L50UUzFnFl0waMnPOGS+f5ZNZN/GtT/skjrVzWp4d6PkiTWxtTolQJxi0ak03jk7R5XNfsGj5a8C8OHTzMM09kZVeZkDaK9s3vo1pyFTqn3M/a1et5e9rIHH4sm7GUy5vUo//sQRwJUo5n8MKk/vRq47LajOw9lM5ByvHls9zN6/Fjx3mzz3CeGN0LKZbE3Akz+XbNJm5O6cCGlV+zPG0JcybM4MFXHueFWf/H/t37GPr4gGy/r8eOk9pnJL8d/ZRLLT3hY7av2cz1Pdrx3Yp1rEn7lOQ653P70BRKVijNz5rX5fqUdgxt8VQ2nY/T5nJ980ZMXTSRQwcO0bP7c5nrJs4YR9umd1EtuSqP9HiQr1ev473pY7PZHz92nLF9XqfH6N4kFUtizoQZfLtmE7emdGD9yq9ZlraE2ROm0+mVbvQL9mVIxL4cP3acCX3e4NHRPZFiSSyYMIstazbxq5Q72LhyLSvTljJvwkzufeUx+s4ayP7d+xjx+MAcPnzQZyQPjn6apGJJLJ4wi/Q1m2iRcjubVq5jVdpSatQ5n3uH9KBUhTJc0qweLVLu4JWWv0+oBsCCGYto2LQhY+eM4vChw7zU42+Z64alDqbTjS5TWJdeD9Hs1qaUKFUi2zGd0mckd45+CgmO6bY1m7khOKarg2PaPuKY3pDSjsERx1SPHWdqn1F0GP0HpFgSK4K4uC7Q+CrtU6rXOZ/bhj5ByQqlubB5Xa5NacfwFtkzf82aNpcbmjcibdH7HDx4iKe7Zb6iyAczx3FLExcXXYO4eH9G9rgAmJM2n2ubXc2HCyZw6OAhnn3iL5nr3kobyW+ad6RqchU6pXRk7er1jJ82Ipv9whmLaNj0SsbMGcmhQ4f5a0RZDkl9jS43ukxKnXs9RNNbm1CiVAneWjwum8aMaZ/QpMV1zF7ykUst/fgzmesmzZpAm8btqX5ONR5/sjNfrV7LRzNzDp9aGNQ5o+eM4PDBw/z1yawG4uApg3i4lcui1anng5l+jF+UvTw+m7GUuk2uYODswZlptjN4adIAnmrjsp4N7z2Erv27cUbJEtnsl85YQv0m9Rn8yTCXhvh3WTcoAyb/g5TWLjvWfT3v5/pbbqBEqRIMXzgym8aC6Qu5umlDxs8dw+GDh+jX46+Z64ZPHcKDLV2muYd7daZ526aULFWCd5a8lU1jdtpcrm92DZMXvsuhg4fo3f35zHXvTh9Du2b3UC25Kl1SHuDr1et4J210jvL8bMZSftnkCv4+ezCHDx5mSERZ9Js0gKeDshjRe0iQcjx7WcydPp9Gza7i/flvcejgIf6UkpWRa9y0N7irxQMAdOv9CDe2bU7JUiX5aGn2AQ2JiIt46875wfGYMHcshw4e4i89shpmI6cOpWNL9/pv116dadG2GSVLlWDikux+zJu+gGuaNuRfc8dy+OBh/tzjpcx1o6YO476WnQB4tFcXWgYaHyzJGi55/Nhx3u0zgkdG93QpxyfMZMuaTbROuYNvVq7l87SlLJgwk7tfeZTes/7Ogd37GPX4P4hm0YzFNGjagBFz3uDwwUP0fzKrjh805VW6tnIZ5B7s+QBNgnNkbMR1uSD15oTF2YdLJkIjEcekIPcokSydsYQrmtTntU+GBud61vXulckD6dHaZdy7t2dHrgvO9WELs9ediSiLRNwnTZo8nVatmvK/VXM5cPAgDz2UldlzyeKp1G/QkjJlSjPxvRGUKHEmSUnZn/0vnbGEek2uYNDsIUHK8azY6z/p7zzZxr1aP7T34CDl+Jl8OivrIeHv+77I4s9WsHv3HprdejddH7wns7e3Q9tfcf3VDfhk/mJat3+AUiVL8nzP3LNPGqcncrJbdCIyGNitqn8M/u8OPBSs3gfcDWwC3gdqAf8DzgKeVdVZIrJPVXN+pCB3tOV5rQrs69RvXLalOtVD5ZvIlRVb5nN3zdvy3/AEjN3wHp1q5fx+SliGrXdPKl+oeVeBNXptcDeWF1dtkM+WefPl1sU8UOv2AtsDvLH+HR6r1aHA9q+udxerp2r9tsAaL60fH5d9hkaTGi0KbD9zk8vW9Hwcx/SZ4Ji+WPPufLbMmz9uGEvtKvXz3/AErN62hLrVGxXY/rMtLp17sxotC6wxfZPLJlazUp18tsybDTtW0Py8nN/2iYW0b1LpUDPn95rC8vYG972zW36S81tJYflgo3uaf/25zQqsMXvzdC6r1rDA9gBfpC/kt3GUxfigLOonX5fPlnmz5Dv3jaR44yKeehNc3dno3Kb5b5gHcze7zGpXnxv+iXo08ze7DGPda/2mwBoD17/Fjee1LrA9QOo3kxNSdyZCI95jkoj7k7Y/uanAGhM3umyQ8ZZFPPdI4O6Tip95bv4b5sHRI5sBuK1mzu9GheW9De5bYz9sz/lNp7CcUfl8cCOovOfMEjX87c4Bjhze5GU5ntSepiABxFVAZgtAVQcCA3PZPNeaNYYGk2EYhmEYhmEYEfg8BM5nTmbK8UuBr4Dpqprzc9mGYRiGYRiGYRgectJ6mlT1v8D5J+v3DMMwDMMwDMMwEsHJTgRhGIZhGIZhGMYpwgbnFYyTnXLcMAzDMAzDMAyjUHHSs+edZE7rnTMMwzAMwzC8wcusb9EUP/Ncr++Pjx7Z7GU5nu49TXKiSUS65LdNUdHwwQdfNHzwwRcNH3zwRcMHH3zR8MEH2w8rCysLKwsPy6JQcPTIZvF5OtXlkxene6MpPzqbhlc++KLhgw++aPjggy8aPvjgi4YPPiRCwwcffNHwwQdfNHzwwRcNH3zwRSMRPhiFmKLeaDIMwzAMwzAMwzgh1mgyDMMwDMMwDMM4AUW90TTUNLzywRcNH3zwRcMHH3zR8MEHXzR88CERGj744IuGDz74ouGDD75o+OCDLxqJ8MEoxJzu2fMMwzAMwzAMwzDioqj3NBmGYRiGYRiGYZwQazQZhmEYhmEYhmGcAGs0GYZhGIZhGIZhnABrNBmG8aMiIlVPtQ8AIlLpVPtg+IXFpmH4jYjUFJHmwXwpESl3En87SUSuOVm/Z/iPNZoAEbk/hm0vFpFmIlI2anmrGDSuFJEGwfylItJDRNqE9zhXzdFx2F4b+NAyBpuGIlI+mC8lIn8SkX+LyEsiUiGkRjcROa+gfgcaZ4rIvRGV6p0i8qqIPCoiZ4TUOF9EficiA0XkFRF5OGPfQtpXEJEXReRLEdkpIjtEZFWw7KyC7luE/uSQ25UXkX4iMkZE7oxaNyiEfXUReU1E/ikilUTkWRFZKSITRCQ5pA8Vo6ZKwCIROVtEKobUaBUxX0FEhovIChF5U0SqhdR4UUQqB/P1RWQtsFBENojIDSHsPxWR3iJyQZjfy0OjvojMFJGxInKeiEwTke9FZLGI1A2pUVZEnhORLwLbbSKyQEQ6xuDHjxafJys2g+3iis/TJTYDu7ji06PYLC4iXURkSlCOK0RkclAHh6q/89HPN9uZiBQLfHheRBpFresd8ndKi8gfROT3IlJSRDqKyIci8rJE3SvE6P/qGLevEzF/RhAjH4rIX0SkdAj7xyJi80IRmS0iu0VkoYj8IqQP74nI3fHsd4RWJ+AdYEiwqAbwfowa14i7L7g3Ywprq6rHgX/G8nvG6Y1lzwNEZKOq/iTEdt2AR4FVwC+B7qr6QbDuU1WtF0KjL9AaKA5MAxoCM4EWQKqqvhBC48PoRUATYAaAqt6cj/0iVb0ymO8U7NNEoCXwb1V9MYQPXwCXq+rR4MJ0AFe5NQuW3xZC43tgP/A1MB74l6puy88uSmMcrixLA7uBssB7gR+iqvflY98N+DUwG2gDfBbotAW6quqsED6k4sp+lKpuCZZVB+4Dmqlqvo1REckrdgT4j6qGuSl8F1gDLAAeAH4A7lTVw2HiU0SmAB8BZYA7gXHAm8CtQHNVvSWED8eBDVGLawCbAFXV80NoZPoqIq8DW4BhwG3ADap6awiNlar6i2B+JvAHVV0sIrWBN1W1fj7264B3gfbB748H3lbVb/P77QiNRUBf4CzgZSBFVd8RkWbAn1X16hAaH+DOzbTAlzLAW0BvYLOq9gyhEVd8+hCbgUZc8Xm6xGZgF1d8ehSb43H17SjccQB3TO4DKqpqhxAaeTV4BViuqjXysX8dd/1YBNwDfKyqPYJ1YWNzAvANUAq4CHeP8DZwM1BdVe8JobEXyLghk+Bvady1VVU13wd5UfHZH6gEjMCdI5VU9YQNBhH5QlUvC+Y/Al5X1Yki0hh4QVUbncg+sNsMzAea4mJjPPCRqh7JzzYXrWXAlcBCVa0bLMs8f0LYjwEuAJYBx4LFqqrdYvDhb7j9eU/thtlQ1SIxASvymFYCh0NqrATKBvO1gCW4hhPAZzFoFMNVhnuA8sHyUsCKkBqfAmOBxsANwd/vgvkbQth/FjG/GKgSzJcBVob0YVWkP1HrloXU+AzX29kSGA5sA6bgLpjlwh7X4G9xIB0oFvwvYcoz43gE86WBWcH8T2I4pv8ryLqo7Y7hbmxn5jIdDKmxLOr/XsBc3IXz0xD2kXGxsYDH9MngGP4iYtm6MLa5xVMu+xTWj1VA8WB+QfQxj9GH64BBuJvTmUDnkD6cqDzDxtbyqP8XB3+TgC9PRnz6EJuJiM/TJTYTEZ8exebqgqzLJT7XAusipoz/j4SwXxExXxz3LZ73gBIxlMWy4K8Ex0Ei/g97Xf8HMBqoFkd8Rh7XZcAZsfgRWR9kHM/cyimMD0B5XCN0Eu7aPgJoGeP+LIzSLB7Wj2D7VRnHoqATsBc4DhzB3bPtBfbEo2lT4Z2KU3SoBtwI7IpaLsC8DNbbgwAADEZJREFUkBpJqroPQFXXB09f3hGRmmQ9GcqPo6p6DDggIl+r6p5A72DwJDQM9YHuuBuP36vqMhE5qKofh90PETkbd3ETDXp3VHW/iBwNqfG5iNyvqiOA5SJSX1WXBE9Lfwipoeq6v6cCU4PhGK2B3wJ/A6qE3JczcQ2+0kAFYCfughd2eEdx3IW3BK6nClXdGMPwkA0i8gfck/x0AHFDdTrinj6GYRXQRVXXRK8QkbAaJUQkKShTVPWF4KnfbIL9yofI4brRwz1DDeVV1f4i8jYwIPC7L1lPT8NSVUR64M6p8iIiqpqhEXZI8SBgkoi8CEwRkYG4G6GmuJuJ0KjqJ8AnIvI4rke4A+E+cnhI3JDXCoCKyK2q+r64IVjH8rHNYL+IXKuqc0TkZlxso6rHRSRsnRNvfPoQmxBnfJ6OsQkFjk9fYnOniNwBvJsRGyKSBNxBzmt1XqzF9ZhujF4RMj7PzJhR1aNAZxHpg3tQENMQM1VVEZmUERPB/6FiTFW7icgVwHgReR94ldjjs4KItMXFYglV/SFGP94RkZHAc8BEEXkC15vYFMhRvnntSvCbe4AxwBhxQ2HvAP6Iu96H5WMR6QmUEpEWQFfg3zHYfw5Uxz1ULhCqetLeoTIKAae61XayJlxPxrV5rHszpMYM4JdRy4rjLuDHQmosBEoH80kRyysQ8olrhE0N4F+4ynVjDHbryXoStxZIDpaXJfzT0grASNzQuoW4htJa4GPc8LwwGnk+xcsooxAaKcHvbgC6AdNxw2VWAn1D2HfH9TgOA74E7g+WVwFmh/ThbOClwH5nMK0KllUMqXE7cFEe624NqfEybphS9PJWwJoQ9s8R9KRGLb8QeCeW2AzsbsYNx9oSo13fqCmjJ7Q6MDoGnca4ITKfBfEwCehM8PQ1H9u3Yt3fXDQuB1KBycDFwEDcUKQvgEYhNerghg3tAuYAtSPis9vJiE8fYjPR8VmYYzMR8Zng2NwdR2zWCsphG7AaN4RzW7DspyE1HiWPaw7weAj7sUCrXJY/BPwQ0ofX84jNC4A5MR6bJNy17BPg2xhtR0RN1SLic3pIjY64a/p2XK/Kf4G/ABVC2oe6bsZQFp1w9znvBPP59hzhGlYf4npedwWx/mHGVAA/zsYNE7w+Y0rUPtpUuCZ7pykGRKQGrhJNz2VdI1WdG0KjhKoezmV5ZVzjZWUB/PoV7kKX7xjyfHRK4yrZdTHYlAd+ims8bsqtbE5gW1tVY3rRNQ+dcwBU9VtxL7Y3xzUiF4W0vwy4BPhcVb+M1x8jCxEpBVygqp+fal8MIxKLTb8IeiNQ1R2n2pdEEtUbGYtdMlBXVSf9CG6d1kg+CVU0/KgcROQh3MPVGrie4KuA+araNC4njUJJkcmeJy6jzRPiMqt1EZGCDE3cDvw2N40wDaYsV3L6oarbwzaYovalMy6BROgGU7R9hA8HwjaYMjRwT6Cuwo1vD91gCtgY7zERkZK4l5B7ikgXYJ+qvhNDg6kkbkhLY+CGAsbFifRDZ2b0WaOg9qp6MOOm1If9SISGDz7EqiFZWT/LRC0PlfVTEpM19JRrRNpHxWah2o9EaPjgQ7DtlSLSIGgsVROXybV1WPtIjWA+5oy08dqfSCOWBlOkBq534+JE+XGy7BOlEdiuE5G10VN+dqr6cdAwapMxH7ksRje6Aw2ADaraBKiL6101iiBFpqdJ3Fj2H3Bd3q1xJ0D300Rjvao+cYp9KJQaifAhH/1QmRl91/DBB180fPAhFg2JM+tnvPa+aPjggy8aPvgQbNeX+LPJxqXhgw++aPjgQ5RW5PfLSuLei6qoqn1C2ueIQxFZoap18rLJRWOxqjYQl8mvobqMn5lZBo0ixqkcG3gyJyKyEuFO5pjeHzqdNHzwwReNBPmQiMyMp1zDBx980fDBhwRqxJX1M157XzR88MEXDR98iNCIN5tsXBo++OCLhg8+hNBfGmKbRwI/9pO93lwHjIvx9ybiUvM/i0tc8wEwKd79sKlwTkUpe15mRjd13xYqyho++OCLRiJ8SERmRh80fPDBFw0ffEiURrxZPxORNdQHDR988EXDBx8gMdlk49XwwQdfNHzwIRPJ/o24JFzm4DD3rW/ikpz0w2Xsy2Cvqu6MxQdVbRvMPivuu2oVcJ8uMIogRanRdLmI7AnmBZfCck8wrxriw3GnkYYPPviikQgf/oN74pojXbCIzAph74uGDz74ouGDD4nSSBeRX2ZoqOo+Efk18AYQ5iOR8dr7ouGDD75o+OADwBERKa2qB4ArMhaKSAXct3FOhoYPPvii4YMPkfSPmD+Ky/zbPj8jVf0e+B73Dnox3MOn4kBZce8zhk2fDoCIXAv8TFVHiEgV4Fxcr5VRxCgy7zQZhmEURSTOrJ/x2vui4YMPvmj44EOwXdzZZOPV8MEHXzR88CGRiMhjuGF16WQ12FRje6epL66H6yJVrS0uW++/VLVRov01/McaTYYRJ+Iy8D2M+17MSmC4uo8kFioNH3zwRcMHH3zR8MGHRGj44IMvGj744IuGDz74ouGDD1FaJYB2uHfmIrMVPxfS/itc8oYCp7EXlwCiLu5957rBspiSSRinD0Um5bhh/IiMwj2JWonLGtT/xJt7q+GDD75o+OCDLxo++JAIDR988EXDBx980fDBB180fPAhkg+AW3BD8/ZHTGH5BjdMLx6OqOtdUACJ+myDUcRQD7JR2GRTYZ7wIAtgIjR88MEXDR988EXDBx9sP6wsrCyKTllE2H9eUNvAfjgwB3ga6JExxajxO2AIsBboBMwHHo/HL5sK71SUEkEYxo+FD1kAE6Hhgw++aPjggy8aPviQCA0ffPBFwwcffNHwwQdfNHzwIZJ5IvILLfh7UBuD6cxgKghHgDRc+vSLgD6qOq2AWkYhx95pMow4EZFjZA0ZENz3KA4QQwY+HzR88MEXDR988EXDBx9sPxKr4YMPvmj44IMvGj74EKX1X9y7UeuAwxEaMb1PJCJlcYb7YrELbP8M/Ab4FJcZMlXtxrnIYo0mwzAMwzAMwyvEffMrB6q6IaT9z4ExQMVg0XbgXlX9IkY/BGgJ3I97X2sCLsHF17HoGIUfSwRhGIZhGIZheEXQODoPaBrMHyC2+9ahuHeYaqpqTeBJYFgB/FBgSzAdBc7Gfcj55Vi1jMKN9TQZhmEYhmEYXhHvN5JEZLmqXp7fsnw0ugP34nqpXgfeV9UfRCQJWKOqF4TVMgo/lgjCMAzDMAzD8I22BN9IAlDVb0WkXAz2a0XkGdwQPYC7cVnwYqEicFv0kEBVPS4iv45Ryyjk2PA8wzAMwzAMwzfi/UbSA0AV4N1gqox7Lyk0qto3r3eoVHVVjP4YhRxrNBmGYRiGYRi+MUFEhgBniUgnXOrvWN5JugD3TlQSLuV4M2B2wr00igz2TpNhGIZhGIbhHSLSApe5TnDpvkN/I0lE/of7OO3nwPGM5WGz7xlGNNZoMgzDMAzDMLxCRHoAb6vq5gLaz1HVaxPsllGEsUQQhmEYhmEYhm+UA6aKyE7gbVzmvPQY7PuKyOvAdNzHcQFQ1fcS66ZRVLCeJsMwDMMwDMNLRKQO0AFoB2xS1eYh7cYCFwNfkDU8T1X1gR/FUeO0x3qaDMMwDMMwDF/Zivuw7A6gagx2DVT1oh/HJaMoYtnzDMMwDMMwDK8Qka4iMgs3vK4S0ElV68QgMU9ELv1RnDOKJNbTZBiGYRiGYfjGecATqrqsgPZXActEZB3unSbBDc+LpeFlGJnYO02GYRiGYRiGd4jItcDPVHWEiFQByqrqupC2NXNbbinHjYJijSbDMAzDMAzDK0SkL1AfuEhVa4vIObgMeo1OsWtGEcXeaTIMwzAMwzB8oy1wM7AfQFW/xaUhN4xTgjWaDMMwDMMwDN84om44lAKISJlT7I9RxLFGk2EYhmEYhuENIiLAf0RkCHCWiHQC0oBhp9Yzoyhj7zQZhmEYhmEYXiEiK4EeQEtc5rtUVZ12ar0yijKWctwwDMMwDMPwjU+B3ar6+1PtiGGA9TQZhmEYhmEYniEiXwIXAhsIkkEA2HeWjFOFNZoMwzAMwzAMr7DvLBm+YY0mwzAMwzAMwzCME2DZ8wzDMAzDMAzDME6ANZoMwzAMwzAMwzBOgDWaDMMwDMMwDMMwToA1mgzDMAzDMAzDME6ANZoMwzAMwzAMwzBOwP8Dgke++2Jti8sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVYKuhLIfyeG"
      },
      "source": [
        "As,clear from the heat map,there is an existance of multi collinearity between many features ,mainly, among p_i(i goes from 1 to 37) features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTZkZMTegPH8"
      },
      "source": [
        "***We have to deal with multi colinearity***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWMHSSLi4DlR"
      },
      "source": [
        "**To know which features have multi-collinearity**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1BCQR2XgV2F"
      },
      "source": [
        "def corr(dataset,threshold):\n",
        "  col_corr=set()\n",
        "  corr_matrix=dataset.corr()\n",
        "  for i in range(len(corr_matrix.columns)):\n",
        "    for j in range(i):\n",
        "      if abs(corr_matrix.iloc[i,j])>threshold:\n",
        "        col_name=corr_matrix.columns[i]\n",
        "        col_corr.add(col_name)\n",
        "  return col_corr"
      ],
      "execution_count": 606,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDDKBTfmiOhJ"
      },
      "source": [
        "corr_features=corr(df,threshold=0.9)"
      ],
      "execution_count": 607,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "snfgtLXriZWv",
        "outputId": "8d230c0b-c741-4cd0-e71e-dcd01baaefb1"
      },
      "source": [
        "l=list(corr_features)\n",
        "l"
      ],
      "execution_count": 608,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['P18',\n",
              " 'P31',\n",
              " 'P25',\n",
              " 'P13',\n",
              " 'P26',\n",
              " 'P36',\n",
              " 'P35',\n",
              " 'P10',\n",
              " 'P32',\n",
              " 'P9',\n",
              " 'P12',\n",
              " 'P34',\n",
              " 'P16']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 608
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vq_hsl_wi9Z_"
      },
      "source": [
        "There are 13 features those are highly correlated,so we can drop it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOXHVBSrishU"
      },
      "source": [
        "Now let's try to **visualize the trends** in month and year to understand how they affect the revenue."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K0aOPDApGW9g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "db17ea97-d990-49cf-9058-2d3ec0d903b2"
      },
      "source": [
        "sns.countplot(df['month'])"
      ],
      "execution_count": 609,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5eea85450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 609
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "xhiXZMDgNQ3z",
        "outputId": "0e281835-58dd-43d6-d5d2-8914775a6772"
      },
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "sns.countplot(df['year'])"
      ],
      "execution_count": 610,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5edcaa8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 610
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKf13mRAi_GC"
      },
      "source": [
        "From the above plot we can look at the occurence of various months(viz. 1st month couurs 11 times in th data set...) in the dataset. We have the most data for the last 5 months. The highest of them is from **August** and **December**. Now let's see in which month did we have the most revenue. For this I will try to find the **mean of the revenue** for each month."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njQ5znG_XYSk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f9458a1-76d2-4ad2-95c4-965410a6b07a"
      },
      "source": [
        "df.groupby('month')['revenue'].mean()\n"
      ],
      "execution_count": 611,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "month\n",
              "1     5.900851e+06\n",
              "2     4.189109e+06\n",
              "3     3.969557e+06\n",
              "4     4.726097e+06\n",
              "5     3.657800e+06\n",
              "6     3.776214e+06\n",
              "7     3.458596e+06\n",
              "8     3.883020e+06\n",
              "9     5.390856e+06\n",
              "10    5.236838e+06\n",
              "11    4.721400e+06\n",
              "12    4.299763e+06\n",
              "Name: revenue, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 611
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7StNl_DklCS"
      },
      "source": [
        "From here we can see that the month **January gave the most revenue** to the restraunts. **September** and **October** followed January. Let's try to plot a bargraph with the same and visualize the same trends."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZKng8EsW2hU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "4b81325b-375f-4cd1-d50c-ab9fcb3412ba"
      },
      "source": [
        "sns.barplot('month','revenue',data=df)"
      ],
      "execution_count": 612,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5edbd2f90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 612
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7q9kP7-Pz19"
      },
      "source": [
        "95% occurance probability is being lied within this vertical line magnitude"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjU39D7hnNH_"
      },
      "source": [
        "So these bargraphs are giving out the same information. Now lets try to do same kind of stuffs for the newly generated feature **'year'**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXanR5irG5Gc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "6b11f3ab-6b2a-481a-bab4-11fc1a0abbc5"
      },
      "source": [
        "plt.figure\n",
        "sns.countplot(df['year'])"
      ],
      "execution_count": 613,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5edc58b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 613
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sTVnxP6v2dh+wYsRaLwbWDow/CfhQHb4IeBXlnd/igYX5pjp8LrB8YNqb6vXLgXMHxp8LrBul1gzP7fWUUBiptzr8IsqpR34J+ECXea3DD+7Q46iP22HAVTPM88i9DYxbAXyyY39HUVbU/wJ4Sn1OX9Ch3m8D/2lg/HmUNzGz1hq47Vk8fsV4FHDZwOXfrX9z9jZbvTnWJZ3q7VDnS11r1efhWkr4jdwb5Y3VWkqY7DQEdsdjAjew/VxDb2T7F8+uB34hIvaKiGdTVg4HR8Qz6vXvjohrKSvQPUett0MvbwE+26W/zPwB8CuUXTtfAX6E8gIapbdvAs+LiCURsRfw2voYLAEOp7zLOCAzN9c6dwEH1OEDKWE5bVMdt+P4B4HnjFjrMRFxJGW31rZRe4uIPYBzgN8aqNtlXgGeHBHrIuLL9dQlo9Z6LvCdiLgwIr4WEf81In68Y2/T3gJc0GVeM/Nq4ErK1thmyke2v9ehv+uB4yLiKRGxH2UF9hM7qTWbme7j+UP0NrQhH7th6yyjvP5GqhURe0bEdZQ9AldQdn916e2/Ab9D2WW4U7tjCJwCvDMi1lM2xb5fx6+iLCzrKA/Clyj7d/eiJOOXMvMIStIu7lBv0ExfdNulehGxNyUEDqe8m3+Y8q5nl2tl5rdrrU8B/4eyiQqwGjgjM+8fbDTL24ZkSBGxT53ni7rUiojFwCeAdwKf7tDbO4FLM3NTvbw33ef1WVm+jv/LwPuBPx6x1l7Av6UE1IspWzyf79jb9GP3b4Cr6DCvEfETlHf+B1FWuj9LCYKR6mXm5cCllGXxrym7hNZ0XeaqH6Hsquq8DMNjy3Hn10Sts4bymj191FqZ+WhmLqU8F0cB/3vU3iLi54Etmbl+mHmA3TAEMvP/Zeaxmfki4ALKvkMyc1tm/npmLs3ME4BnUPar3Us5uHRhLXEp5WDMqPUAiIgXUl7oGzr2t7Ref2st8V3K/tqResvMSzLzJZl5FGXL4F9Tdh1Mz//ddUUyvULZUsfPdjqPOylbE9Mr2DsoK4tRahERT6cs5P+Zso+zS29HAadFxO3Ae4FTge90qEdm3lnn9RzKu9utI9baBFyXmbdR9vv+OLCpS2/Vm4DPUIK+y2N3IvDlzHyQcnBxX+Dmjo/d2ZTA+wFlOfn0TmrN5rH7qM/F7wBfHaK3Ya1muMduVrWvCym70z44T709RHmDemuHei+j7CW4nbKb9JUR8ZdzTbDbhUBE7F//7wH8PvAX9fJTIuKpdfhVwLbMvLGm5yWUg2lQHqRHRq030Mpyyoq5U3+UBf6wiJg+098+wMZRexuYZhHloNEXMvN9Ay1eDJxch0+m7HucHv+2KF4KfLduhl5GOQj2CeA2ykJ62Si16mlC1gAfpxxI3Nilt8z8xcw8BHg2Zd/phsw8ZtR6EbEoIqZ3x32LciD8xlFqUfaHP6M+r+dRdkEOLi+7Wm/acsqnjjo9dpQPEPxM3W24ivJue+Wo9eoujR+t87qVsqvv8p3Ums1jp5ahfApuESUIdtbbMA5k+MduRhERlPk8CPhcx1pTEfGMWvNjlOMCwzwPM8rM383MgzJzCWWr/fOZ+da5ppn4gd+5/igvms2UdxabKO/0Tqe8670ZeA/bD5wuoawINgJ/S9msn67zLOCLwHcoAdCpXr3+NsrxgPno7x2ULYAfUPbj/WOHWhdQVlzfomw6fp3tH2k9nvIJorWUTzT8LbBvnS4ox0tupRyfWDZQ849rrUcoK4+RagFvrfN4S633vTofI/dWr//pWu+eLvNK2QK7daC3kee1XvcqytZYUg74X9+x3hLKCrbz80oJpXMpuwyz1u1S78lsX+YeGvJ5fSbldXM/5bW5ie0fgz6+Pv5J2Rc+TG9z1buAsnwkZbfqMM/tjPXYvrxNLyffq4/HKLV+kvJRzunlpPO8DiwvRzPEgWFPGyFJDdvtdgdJkuaPISBJDTMEJKlhhoAkNcwQkKSGGQKS1DBDQBqTiNhz57eSxssQkGYQEX8YEWcMXD47Ik6PiN+OiK9GxNcj4g8Grv9MRKyPiBuinHhuevyDEXFORFxPOc2FtKAYAtLMVgFvg8dO2/EWyrc5D6Wc6G8p8KKIeHm9/SlZzvG0DPi1egoFKCdSuyYzX5iZV41zBqRh7DXpBqSFKDNvj4h7I+Jwyul7v0Y5OdqxdRjKeZ4OpZyS5Nci4sQ6/uA6/l7KmWdXj7N3aVcYAtLs/gflx2qeSdkyOIZyaulzB28UEUdTTsV8VGb+U0T8HdvPVPtwZu54CnJpwXB3kDS7NZTfun0x5cyplwGnRDmPPBFxYD1r678Evl0D4PmUn12UdgtuCUizyMzvR8SVlN8oeBS4PCJeAFxdzvzLg5Qzo34OeEdEbKScPfPLk+pZ2lWeRVSaRT0gfC3wxsy8ZdL9SH1wd5A0g4g4jHKO97UGgJ7I3BKQpIa5JSBJDTMEJKlhhoAkNcwQkKSGGQKS1LD/DwFWP8nYCOeoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EnUFT9Ynu7d"
      },
      "source": [
        "Here we are having a bit of trouble in visualization because the labels in the x-axis are not clear. Let's try to zoom in a bit!!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HtNF37uHAGz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "e3ab1551-d877-43ec-c57a-f855dcd52677"
      },
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "sns.countplot(df['year'])"
      ],
      "execution_count": 614,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5edaa3c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 614
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vl3C3/uiM1626BABgkxmpAwAAGJhQBwAAMDChDgAAYGBCHQAAwMCEOgAAgIEJdQAAAAMT6gAAAAYm1AEAAAxMqAMAABiYUAcAADAwoQ4AAGBgO1ZdALA5znv1k1ZdwrrOPuutG3rcT7/+G2auZP/9xNPesuoSAIBNdOsvXbzqEtZ11A8+YZ8eb6QOAABgYEIdAADAwIQ6AACAgQl1AAAAAxPqAAAABibUAQAADEyoAwAAGJhQBwAAMDChDgAAYGBCHQAAwMCEOgAAgIHtWHUB++u2X/mtVZewrp3f94wNPe7m//HjM1ey/475/hetugQAAGADjNQBAAAMTKgDAAAYmFAHAAAwMKEOAABgYEIdAADAwIQ6AACAgQl1AAAAAxPqAAAABraSUFdVp1XV+6vq+qp6/ipqAAAA2A6WHuqq6uAkv5zkG5OcmOTMqjpx2XUAAABsB6sYqTslyfXd/aHu/kyS30lyxgrqAAAAGN4qQt2xSf56zf0bp3kAAADso+ru5W6w6qlJTuvufzfd/64kX9ndz7nT485Jcs5090uSvH/m0o5M8pGZtzE3PWwN26GHZHv0oYetQQ9bx3boQw9bw3boIdkefehha1hGDw/t7p17W7Bj5g3vzU1JHrLm/nHTvM/T3ecmOXdZRVXVZd29e1nbm4Metobt0EOyPfrQw9agh61jO/Shh61hO/SQbI8+9LA1rLqHVRx++c4kJ1TVF1XVoUmenuRNK6gDAABgeEsfqevu26vqOUnekuTgJOd199XLrgMAAGA7WMXhl+nuC5NcuIpt342lHeo5Iz1sDduhh2R79KGHrUEPW8d26EMPW8N26CHZHn3oYWtYaQ9Lv1AKAAAAm2cV59QBAACwSbZtqKuq86rq1qq6as28L6+qS6vqvVX15qp6wDT/0Kp65TT/PVV16prnHFpV51bVB6rqfVX1lJF6qKr7V9UVa24fqapfGKmHadmZ0/wrq+qPq+rIZfWwyX08berh6qp6yZJ7eEhVva2qrpm2/9xp/hFVdVFVXTf9PHyaX1X136vq+qnmR65Z11nT46+rqrNG66GqTppeu6un+U8brYc163tAVd1YVS8fsYequmPN+9NSL5q1yX0cX1Vvraprp/XtGqmHqnpcff6+4u+r6ltH6mFa9rPTOq6dHlMD9vCSqrpqui3tvWk/+/jSWryX/kNV/cid1nVaVb1/6vH5g/Zwl33/SD2st57BerhPVf1FLf4/dXVVvXC0Htas7+CqendV/eEsBXf3trwl+dokj0xy1Zp570zyddP02Ul+apr+gSSvnKaPSnJ5koOm+y9M8tPT9EFJjhythzut8/IkXztSD1mc+3nrnn/7JD+b5AWj/T4leVCSv0qyc1r26iRPWGIPxyR55DR9/yQfSHLi9O/5/Gn+85O8ZJo+PckfJakkj07yjmn+EUk+NP08fJo+fLAeHp7khGn6C5PcnOSBI/WwZn2/mOS3k7x8tN+ladmnl1X3zH38aZInTtOHJbnvaD2sWecRST42Wg9JvirJ/83iImwHJ7k0yamD9fBvklyUxX7vflnsZx6whf8mjkryFUlelORH1qzn4CQfTPLFSQ5N8p4kJ47Uw7TsLvv+kXpYbz2D9VBJDpumD0nyjiSPHqmHNev7oSz21384R73bdqSuuy/JYqe01sOTXDJNX5Rkz6jbiUn+ZHrerUk+kWTP90ycneRnpmWf7e6lfTHiJvaQJKmqh2fxC/d/Zir5Ljaph5pu95s+dX1Akr+Zt/LPt0l9fHGS67r7tulx/2vNc2bX3Td397um6U8luTbJsUnOyCJgZvq559P5M5K8phfenuSBVXVMkm9IclF3f6y7P55F76eN1EN3f6C7r5vW8zdZfGiw1y/z3Ko9JElVPSrJ0Uneuoza99jMHlZps/qoqhOT7Ojui6Z1fbq7/26kHu602qcm+aMBe+gk98kiRHxBFv8BvGWwHk5Mckl3397df5vkyizp/XV/+ujuW7v7nUn+8U6rOiXJ9d39oe7+TJLfmdYxu03sYb19/+w2q4e7Wc9IPXR3f3q6e8h0W8oFQTbzd6mqjsviQ5vfmKvebRvq1nF1Pvem8u353JegvyfJt1TVjqr6oiSPSvKQqnrgtPynqupdVfV7VXX0cku+i33q4U7PfXqS13f3qq+Os089dPc/Jvm+JO/NIsydmOQVyy15r/b1tbg+yZdU1a6q2pHFm8CdX6OlqMWhYSdn8YnX0d1987Tow1mEhGTxxvXXa5524zRvvflLdS97WLueU7L4T+AHZyx3r+5ND1V1UJKXJrnLIR7LtAmvw32q6rKqenst6XC/vbmXfTw8ySeq6g3ToTX/raoOXkrha2zW30QW+4rXzVbo3bg3PXT3pUnelsXI+81J3tLd1y6h7M9zL1+H9yQ5raruW4vTDB6Xrb2fWM9I+4ktbbN6uNN6lure9jAdtnhFFh/AXtTdw/WQ5BeS/Kckn52jvuTAC3VnJ/n+qro8i2HUz0zzz8viDeeyLP7R/zzJHVkc/nBckj/v7kdmcSjHzy276DvZ1x7WWtmO+k72qYeqOiSLUHdyFofKXZnkR5dd9F7sUx/TqNb3JXl9FqOlN+Sur9HsquqwJOcneV53f3Ltsinwrzr036PN6mH6ZPw3k3x3d8/2RrvOtu9tD9+f5MLuvnGmEu/RJr0OD+3u3Un+bZJfqKqHbX6ld28T+tiR5GuyCNhfkcWo/LM2v9L1bfLfxL/O4rtkl+re9lBV/yLJl2Wx3z42yeOr6mtmKne9Gu5VD9391iy+8unPs9hfXxr7if2ih42tZ26b0UN339HdJ2Xxt31KVf2rWYpdxya8N31Tklu7+/L5qjzAQl13v6+7n9Tdj8rizfKD0/zbu/s/dPdJ3X1GkgdmcdzsR5P8XZI3TKv4vSyOrV6Z/eghyeKiHlkcHjTrL9RG7EcPJ03LPzj98fxuFudOrNT+vBbd/ebu/srufkyS92fNa7QMU0A+P8lru3vP7/Utaw7nOyaLT8KS5KZ8/ifEx03z1pu/FJvUQ2pxYZv/meTHe3H409JsUg+PSfKcqrohiw+bnllVL15C+Zlq3JTXobv3/PxQFuelnTx78WtsUh83JrmiF4ea3Z7kD7LEfcVmvRaT70hywXSExNJsUg9PTvL2Xhz++ukszll7zDLqn2rcrL+JF037jydmcerBVt5PrGek/cSWtFk9rLOepdjs16G7P5HFaPzSDknepB4em8URXDdkcSjy46vqtza71gMq1FXVUdPPg5L8RJJfne7ft6ruN00/Mcnt3X3NFCDenOTUaRVPSHLNsutea197WPPUM7M1Run2p4ebkpxYVXvOeXpiFsc1r9T+vBZrnnN4FiMtsx1bvZd6K4vDVq/t7petWfSmJHuuYHlWkjeumf/MWnh0kv83HW7wliRPqqrDpz6elCV9qr9ZPVTVoUkuyOKclt9fRu17bFYP3f2d3X18d+/KYoToNd29lCvMbeLrcHhVfcG0ziOz2PEt7T12E/8m3pnFOVF73qMenyX1sYk97LH0fcUm9vBXSb6uFoe+H5Lk67KkfcUm/k0cXFUPmtb5iCSPyBLPmd2PPtbzziQnVNUXTe+3T5/WMbtN7GFlNquHu1nP7Daxh501nQ5VVf8si/8Dvm/zK97rtjelh+7+0e4+btpfPz3Jn3T3Mza94F7C1WNWcctip3RzFicr3pjk2Umem8UnXh9I8uLkn758fVcWoybXZnHxioeuWc9Ds7gYxpVJLk5y/Gg9TMs/lORLB34dvneaf2UWQftBg/bxuiz+s3dNkqcvuYevzuIQgSuTXDHdTs/iqpwXJ7luqveI6fGV5JezGIF8b5Lda9Z1dhbnCF6fxaGLQ/WQ5BnTa3nFmttJI/Vwp3U+K8u9+uVmvQ5fNd1/z/Tz2QP/TTxxWs97k7wqyaED9rAriw/R7nLl5BF6yOKKi7+WxXvvNUleNmAP98nn9hFvz5Lel+5FHw/OYp/4ySwuCnZjpqt1Ts/7wNTjjw/aw132/SP1sN56BuvhEUnePa3nqiT/ZcTfpTXrPDUzXf1yz39CAQAAGNABdfglAADAdiPUAQAADEyoAwAAGJhQBwAAMDChDgAAYGBCHQAAwMCEOgDYRFV18KprAODAItQBcMCqqp+squetuf+iqnpuVf3HqnpnVV1ZVS9cs/wPquryqrq6qs5ZM//TVfXSqnpPkscsuQ0ADnBCHQAHsvOSPDNJquqgJE9P8uEkJyQ5JclJSR5VVV87Pf7s7n5Ukt1J/n1VPWiaf78k7+juL+/uP1tmAwCwY9UFAMCqdPcNVfXRqjo5ydFJ3p3kK5I8aZpOksOyCHmXZBHknjzNf8g0/6NJ7khy/jJrB4A9hDoADnS/keRZSR6cxcjdE5L8THf/2toHVdWpSb4+yWO6+++q6k+T3Gda/PfdfceyCgaAtRx+CcCB7oIkp2UxQveW6XZ2VR2WJFV1bFUdleSfJ/n4FOi+NMmjV1UwAKxlpA6AA1p3f6aq3pbkE9No21ur6suSXFpVSfLpJM9I8sdJvreqrk3y/iRvX1XNALBWdfeqawCAlZkukPKuJN/e3detuh4A2FcOvwTggFVVJya5PsnFAh0AozJSBwAAMDAjdQAAAAMT6gAAAAYm1AEAAAxMqAMAABiYUAcAADAwoQ4AAGBg/x8L65Gi8SfomAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLCMxc1KoRk2"
      },
      "source": [
        "From here we can see that the most of the data is from the years **2008-2013**. Out of them the most of the data is from the year 2011. The other years are contruibuting really less on the basis of number of data. This is also going to affect the results as well.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FLYvpodYWVE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc20a008-1a9b-4122-9f4a-59d846a29547"
      },
      "source": [
        "df.groupby('year')['revenue'].mean()\n"
      ],
      "execution_count": 615,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year\n",
              "1996    3.903884e+06\n",
              "1997    4.286645e+06\n",
              "1998    4.251905e+06\n",
              "1999    7.329030e+06\n",
              "2000    1.359602e+07\n",
              "2002    4.991022e+06\n",
              "2004    3.482435e+06\n",
              "2005    6.611118e+06\n",
              "2006    4.203972e+06\n",
              "2007    5.079333e+06\n",
              "2008    4.876962e+06\n",
              "2009    4.652457e+06\n",
              "2010    4.383878e+06\n",
              "2011    4.147879e+06\n",
              "2012    3.837859e+06\n",
              "2013    2.532287e+06\n",
              "2014    2.464944e+06\n",
              "Name: revenue, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 615
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Wo0BWkRYgIt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "94adc7e6-ffce-4aa5-94c3-6cb34462e724"
      },
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "sns.barplot('year','revenue',data=df)"
      ],
      "execution_count": 616,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5ed9ed790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 616
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjzQbhB9pGgP"
      },
      "source": [
        "Out of all the years, the highest revenue was generated in the year **2000** and after that **1999** and **2005**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ibl5ULCEpbhZ"
      },
      "source": [
        "# **Data Preprocessing.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29pLv_qyHL5G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f38711c-e87e-4a86-c8dc-9787e2e2446b"
      },
      "source": [
        "df['Type'].value_counts()\n"
      ],
      "execution_count": 617,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FC    76\n",
              "IL    60\n",
              "DT     1\n",
              "Name: Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 617
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5KiyJs_pn1N"
      },
      "source": [
        "There are 3 distinct values in the feature **'Type'**. We can encode the values as this:\n",
        "\n",
        "**FC as 0;**\n",
        "\n",
        "\n",
        "**IL as 1;**\n",
        "\n",
        "**DT as 2;**\n",
        "\n",
        "The order or the numbers can be anything."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "axx56ry9HfVZ"
      },
      "source": [
        "ty={'FC':0,'IL':1,'DT':2}\n",
        "df['Type'] = df['Type'].map(ty)\n"
      ],
      "execution_count": 618,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIl5nRSFH8vR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "87d6f98a-ecd1-42a3-a895-8b2f780782f4"
      },
      "source": [
        "df"
      ],
      "execution_count": 619,
      "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>City</th>\n",
              "      <th>City Group</th>\n",
              "      <th>Type</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>P9</th>\n",
              "      <th>P10</th>\n",
              "      <th>P11</th>\n",
              "      <th>P12</th>\n",
              "      <th>P13</th>\n",
              "      <th>P14</th>\n",
              "      <th>P15</th>\n",
              "      <th>P16</th>\n",
              "      <th>P17</th>\n",
              "      <th>P18</th>\n",
              "      <th>P19</th>\n",
              "      <th>P20</th>\n",
              "      <th>P21</th>\n",
              "      <th>P22</th>\n",
              "      <th>P23</th>\n",
              "      <th>P24</th>\n",
              "      <th>P25</th>\n",
              "      <th>P26</th>\n",
              "      <th>P27</th>\n",
              "      <th>P28</th>\n",
              "      <th>P29</th>\n",
              "      <th>P30</th>\n",
              "      <th>P31</th>\n",
              "      <th>P32</th>\n",
              "      <th>P33</th>\n",
              "      <th>P34</th>\n",
              "      <th>P35</th>\n",
              "      <th>P36</th>\n",
              "      <th>P37</th>\n",
              "      <th>revenue</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5653753.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ankara</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6923131.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Diyarbakır</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2055379.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tokat</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7.5</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>2675511.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gaziantep</td>\n",
              "      <td>Other</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4316715.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Trabzon</td>\n",
              "      <td>Other</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5787594.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>İzmir</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9262754.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Kayseri</td>\n",
              "      <td>Other</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2544857.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7217634.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>Big Cities</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6363241.0</td>\n",
              "      <td>9</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 43 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           City  City Group  Type  P1   P2  ...  P36  P37    revenue  month  year\n",
              "0      İstanbul  Big Cities     1   4  5.0  ...    3    4  5653753.0      7  1999\n",
              "1        Ankara  Big Cities     0   4  5.0  ...    0    0  6923131.0      2  2008\n",
              "2    Diyarbakır       Other     1   2  4.0  ...    0    0  2055379.0      3  2013\n",
              "3         Tokat       Other     1   6  4.5  ...   12    6  2675511.0      2  2012\n",
              "4     Gaziantep       Other     1   3  4.0  ...    3    3  4316715.0      5  2009\n",
              "..          ...         ...   ...  ..  ...  ...  ...  ...        ...    ...   ...\n",
              "132     Trabzon       Other     0   2  3.0  ...    0    0  5787594.0      6  2008\n",
              "133       İzmir  Big Cities     0   4  5.0  ...    0    0  9262754.0     10  2006\n",
              "134     Kayseri       Other     0   3  4.0  ...    0    0  2544857.0      7  2006\n",
              "135    İstanbul  Big Cities     0   4  5.0  ...    0    0  7217634.0     10  2010\n",
              "136    İstanbul  Big Cities     0   4  5.0  ...    0    0  6363241.0      9  2009\n",
              "\n",
              "[137 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 619
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jleKY28_H_Ic",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "436da117-3915-498c-9425-37688d588ea5"
      },
      "source": [
        "df['City Group'].value_counts()\n"
      ],
      "execution_count": 620,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Big Cities    78\n",
              "Other         59\n",
              "Name: City Group, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 620
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OARNT6aIO1W"
      },
      "source": [
        "cg={'Big Cities':0,'Other':1}\n",
        "df['City Group'] = df['City Group'].map(cg)\n"
      ],
      "execution_count": 621,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsRy3EtRIjpP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "0854a775-2572-4030-de8c-1d5c75eba0d5"
      },
      "source": [
        "df"
      ],
      "execution_count": 622,
      "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>City</th>\n",
              "      <th>City Group</th>\n",
              "      <th>Type</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>P9</th>\n",
              "      <th>P10</th>\n",
              "      <th>P11</th>\n",
              "      <th>P12</th>\n",
              "      <th>P13</th>\n",
              "      <th>P14</th>\n",
              "      <th>P15</th>\n",
              "      <th>P16</th>\n",
              "      <th>P17</th>\n",
              "      <th>P18</th>\n",
              "      <th>P19</th>\n",
              "      <th>P20</th>\n",
              "      <th>P21</th>\n",
              "      <th>P22</th>\n",
              "      <th>P23</th>\n",
              "      <th>P24</th>\n",
              "      <th>P25</th>\n",
              "      <th>P26</th>\n",
              "      <th>P27</th>\n",
              "      <th>P28</th>\n",
              "      <th>P29</th>\n",
              "      <th>P30</th>\n",
              "      <th>P31</th>\n",
              "      <th>P32</th>\n",
              "      <th>P33</th>\n",
              "      <th>P34</th>\n",
              "      <th>P35</th>\n",
              "      <th>P36</th>\n",
              "      <th>P37</th>\n",
              "      <th>revenue</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5653753.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ankara</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6923131.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Diyarbakır</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2055379.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tokat</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7.5</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>2675511.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gaziantep</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4316715.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Trabzon</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5787594.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>İzmir</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9262754.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Kayseri</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2544857.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7217634.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>İstanbul</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6363241.0</td>\n",
              "      <td>9</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 43 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           City  City Group  Type  P1   P2  ...  P36  P37    revenue  month  year\n",
              "0      İstanbul           0     1   4  5.0  ...    3    4  5653753.0      7  1999\n",
              "1        Ankara           0     0   4  5.0  ...    0    0  6923131.0      2  2008\n",
              "2    Diyarbakır           1     1   2  4.0  ...    0    0  2055379.0      3  2013\n",
              "3         Tokat           1     1   6  4.5  ...   12    6  2675511.0      2  2012\n",
              "4     Gaziantep           1     1   3  4.0  ...    3    3  4316715.0      5  2009\n",
              "..          ...         ...   ...  ..  ...  ...  ...  ...        ...    ...   ...\n",
              "132     Trabzon           1     0   2  3.0  ...    0    0  5787594.0      6  2008\n",
              "133       İzmir           0     0   4  5.0  ...    0    0  9262754.0     10  2006\n",
              "134     Kayseri           1     0   3  4.0  ...    0    0  2544857.0      7  2006\n",
              "135    İstanbul           0     0   4  5.0  ...    0    0  7217634.0     10  2010\n",
              "136    İstanbul           0     0   4  5.0  ...    0    0  6363241.0      9  2009\n",
              "\n",
              "[137 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 622
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rehbcrl6IlLf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b45e850-d4c8-4975-be16-fea4b71500b2"
      },
      "source": [
        "a=df['City'].value_counts()\n",
        "a"
      ],
      "execution_count": 623,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "İstanbul          50\n",
              "Ankara            19\n",
              "İzmir              9\n",
              "Bursa              5\n",
              "Samsun             5\n",
              "Sakarya            4\n",
              "Antalya            4\n",
              "Kayseri            3\n",
              "Tekirdağ           3\n",
              "Adana              3\n",
              "Diyarbakır         3\n",
              "Eskişehir          3\n",
              "Aydın              2\n",
              "Muğla              2\n",
              "Trabzon            2\n",
              "Konya              2\n",
              "Gaziantep          1\n",
              "Osmaniye           1\n",
              "Isparta            1\n",
              "Kastamonu          1\n",
              "Elazığ             1\n",
              "Uşak               1\n",
              "Edirne             1\n",
              "Bolu               1\n",
              "Amasya             1\n",
              "Denizli            1\n",
              "Kocaeli            1\n",
              "Balıkesir          1\n",
              "Tokat              1\n",
              "Şanlıurfa          1\n",
              "Kırklareli         1\n",
              "Karabük            1\n",
              "Afyonkarahisar     1\n",
              "Kütahya            1\n",
              "Name: City, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 623
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdjGwMqEqXYX"
      },
      "source": [
        "Here manually creating the dictionary is inefficient. So we will store the city names in a list and then use the element as the **key** of the dictionary and the index of each element as its **key values**. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhnWUJ1rIsEb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1bc86f6-1a2d-4f10-d34f-3ec573bc9448"
      },
      "source": [
        "b=a.index # city names are the index of the a\n",
        "b"
      ],
      "execution_count": 624,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['İstanbul', 'Ankara', 'İzmir', 'Bursa', 'Samsun', 'Sakarya', 'Antalya',\n",
              "       'Kayseri', 'Tekirdağ', 'Adana', 'Diyarbakır', 'Eskişehir', 'Aydın',\n",
              "       'Muğla', 'Trabzon', 'Konya', 'Gaziantep', 'Osmaniye', 'Isparta',\n",
              "       'Kastamonu', 'Elazığ', 'Uşak', 'Edirne', 'Bolu', 'Amasya', 'Denizli',\n",
              "       'Kocaeli', 'Balıkesir', 'Tokat', 'Şanlıurfa', 'Kırklareli', 'Karabük',\n",
              "       'Afyonkarahisar', 'Kütahya'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 624
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAs53-oVI8iE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "498e59aa-bbf4-4157-a6b6-21199727f3d8"
      },
      "source": [
        "c={}\n",
        "for i,j in enumerate(b):\n",
        "  c.update({j:i})\n",
        "  print(c)\n"
      ],
      "execution_count": 625,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'İstanbul': 0}\n",
            "{'İstanbul': 0, 'Ankara': 1}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26, 'Balıkesir': 27}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26, 'Balıkesir': 27, 'Tokat': 28}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26, 'Balıkesir': 27, 'Tokat': 28, 'Şanlıurfa': 29}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26, 'Balıkesir': 27, 'Tokat': 28, 'Şanlıurfa': 29, 'Kırklareli': 30}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26, 'Balıkesir': 27, 'Tokat': 28, 'Şanlıurfa': 29, 'Kırklareli': 30, 'Karabük': 31}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26, 'Balıkesir': 27, 'Tokat': 28, 'Şanlıurfa': 29, 'Kırklareli': 30, 'Karabük': 31, 'Afyonkarahisar': 32}\n",
            "{'İstanbul': 0, 'Ankara': 1, 'İzmir': 2, 'Bursa': 3, 'Samsun': 4, 'Sakarya': 5, 'Antalya': 6, 'Kayseri': 7, 'Tekirdağ': 8, 'Adana': 9, 'Diyarbakır': 10, 'Eskişehir': 11, 'Aydın': 12, 'Muğla': 13, 'Trabzon': 14, 'Konya': 15, 'Gaziantep': 16, 'Osmaniye': 17, 'Isparta': 18, 'Kastamonu': 19, 'Elazığ': 20, 'Uşak': 21, 'Edirne': 22, 'Bolu': 23, 'Amasya': 24, 'Denizli': 25, 'Kocaeli': 26, 'Balıkesir': 27, 'Tokat': 28, 'Şanlıurfa': 29, 'Kırklareli': 30, 'Karabük': 31, 'Afyonkarahisar': 32, 'Kütahya': 33}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LsVIPTdJMvH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e35b2751-c779-4e0e-a727-fa32a46956dd"
      },
      "source": [
        "c"
      ],
      "execution_count": 626,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Adana': 9,\n",
              " 'Afyonkarahisar': 32,\n",
              " 'Amasya': 24,\n",
              " 'Ankara': 1,\n",
              " 'Antalya': 6,\n",
              " 'Aydın': 12,\n",
              " 'Balıkesir': 27,\n",
              " 'Bolu': 23,\n",
              " 'Bursa': 3,\n",
              " 'Denizli': 25,\n",
              " 'Diyarbakır': 10,\n",
              " 'Edirne': 22,\n",
              " 'Elazığ': 20,\n",
              " 'Eskişehir': 11,\n",
              " 'Gaziantep': 16,\n",
              " 'Isparta': 18,\n",
              " 'Karabük': 31,\n",
              " 'Kastamonu': 19,\n",
              " 'Kayseri': 7,\n",
              " 'Kocaeli': 26,\n",
              " 'Konya': 15,\n",
              " 'Kütahya': 33,\n",
              " 'Kırklareli': 30,\n",
              " 'Muğla': 13,\n",
              " 'Osmaniye': 17,\n",
              " 'Sakarya': 5,\n",
              " 'Samsun': 4,\n",
              " 'Tekirdağ': 8,\n",
              " 'Tokat': 28,\n",
              " 'Trabzon': 14,\n",
              " 'Uşak': 21,\n",
              " 'İstanbul': 0,\n",
              " 'İzmir': 2,\n",
              " 'Şanlıurfa': 29}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 626
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8l3Aq9gKJf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "c8e3d076-6939-4733-f1e1-9bafcb8a0bff"
      },
      "source": [
        "df['City'] = df['City'].map(c)\n",
        "df"
      ],
      "execution_count": 627,
      "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>City</th>\n",
              "      <th>City Group</th>\n",
              "      <th>Type</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>P9</th>\n",
              "      <th>P10</th>\n",
              "      <th>P11</th>\n",
              "      <th>P12</th>\n",
              "      <th>P13</th>\n",
              "      <th>P14</th>\n",
              "      <th>P15</th>\n",
              "      <th>P16</th>\n",
              "      <th>P17</th>\n",
              "      <th>P18</th>\n",
              "      <th>P19</th>\n",
              "      <th>P20</th>\n",
              "      <th>P21</th>\n",
              "      <th>P22</th>\n",
              "      <th>P23</th>\n",
              "      <th>P24</th>\n",
              "      <th>P25</th>\n",
              "      <th>P26</th>\n",
              "      <th>P27</th>\n",
              "      <th>P28</th>\n",
              "      <th>P29</th>\n",
              "      <th>P30</th>\n",
              "      <th>P31</th>\n",
              "      <th>P32</th>\n",
              "      <th>P33</th>\n",
              "      <th>P34</th>\n",
              "      <th>P35</th>\n",
              "      <th>P36</th>\n",
              "      <th>P37</th>\n",
              "      <th>revenue</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5653753.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6923131.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2055379.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>10</td>\n",
              "      <td>7.5</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>25</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>2675511.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4316715.0</td>\n",
              "      <td>5</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5787594.0</td>\n",
              "      <td>6</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9262754.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2544857.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7217634.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6363241.0</td>\n",
              "      <td>9</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 43 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     City  City Group  Type  P1   P2  ...  P36  P37    revenue  month  year\n",
              "0       0           0     1   4  5.0  ...    3    4  5653753.0      7  1999\n",
              "1       1           0     0   4  5.0  ...    0    0  6923131.0      2  2008\n",
              "2      10           1     1   2  4.0  ...    0    0  2055379.0      3  2013\n",
              "3      28           1     1   6  4.5  ...   12    6  2675511.0      2  2012\n",
              "4      16           1     1   3  4.0  ...    3    3  4316715.0      5  2009\n",
              "..    ...         ...   ...  ..  ...  ...  ...  ...        ...    ...   ...\n",
              "132    14           1     0   2  3.0  ...    0    0  5787594.0      6  2008\n",
              "133     2           0     0   4  5.0  ...    0    0  9262754.0     10  2006\n",
              "134     7           1     0   3  4.0  ...    0    0  2544857.0      7  2006\n",
              "135     0           0     0   4  5.0  ...    0    0  7217634.0     10  2010\n",
              "136     0           0     0   4  5.0  ...    0    0  6363241.0      9  2009\n",
              "\n",
              "[137 rows x 43 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 627
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErH2IUh2Wnhu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9909e66-63ce-4f96-dfa0-44c0541caaff"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 628,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 137 entries, 0 to 136\n",
            "Data columns (total 43 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   City        137 non-null    int64  \n",
            " 1   City Group  137 non-null    int64  \n",
            " 2   Type        137 non-null    int64  \n",
            " 3   P1          137 non-null    int64  \n",
            " 4   P2          137 non-null    float64\n",
            " 5   P3          137 non-null    float64\n",
            " 6   P4          137 non-null    float64\n",
            " 7   P5          137 non-null    int64  \n",
            " 8   P6          137 non-null    int64  \n",
            " 9   P7          137 non-null    int64  \n",
            " 10  P8          137 non-null    int64  \n",
            " 11  P9          137 non-null    int64  \n",
            " 12  P10         137 non-null    int64  \n",
            " 13  P11         137 non-null    int64  \n",
            " 14  P12         137 non-null    int64  \n",
            " 15  P13         137 non-null    float64\n",
            " 16  P14         137 non-null    int64  \n",
            " 17  P15         137 non-null    int64  \n",
            " 18  P16         137 non-null    int64  \n",
            " 19  P17         137 non-null    int64  \n",
            " 20  P18         137 non-null    int64  \n",
            " 21  P19         137 non-null    int64  \n",
            " 22  P20         137 non-null    int64  \n",
            " 23  P21         137 non-null    int64  \n",
            " 24  P22         137 non-null    int64  \n",
            " 25  P23         137 non-null    int64  \n",
            " 26  P24         137 non-null    int64  \n",
            " 27  P25         137 non-null    int64  \n",
            " 28  P26         137 non-null    float64\n",
            " 29  P27         137 non-null    float64\n",
            " 30  P28         137 non-null    float64\n",
            " 31  P29         137 non-null    float64\n",
            " 32  P30         137 non-null    int64  \n",
            " 33  P31         137 non-null    int64  \n",
            " 34  P32         137 non-null    int64  \n",
            " 35  P33         137 non-null    int64  \n",
            " 36  P34         137 non-null    int64  \n",
            " 37  P35         137 non-null    int64  \n",
            " 38  P36         137 non-null    int64  \n",
            " 39  P37         137 non-null    int64  \n",
            " 40  revenue     137 non-null    float64\n",
            " 41  month       137 non-null    int64  \n",
            " 42  year        137 non-null    int64  \n",
            "dtypes: float64(9), int64(34)\n",
            "memory usage: 46.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2-uTi0zq-ct"
      },
      "source": [
        "Now everything looks just fine. So we can go ahead with the data and start model building."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oimcF56RPteI"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfDX8Vpej7Oi"
      },
      "source": [
        "# ***WITH TAKING COLLINEARED FEATURES***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QO7zaFShrVk5"
      },
      "source": [
        "The first task will be to **split the dataset** into train set and test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrZptn58X64O"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x=df.drop('revenue',axis=1)\n",
        "y=df['revenue']\n",
        "X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=0.30)\n"
      ],
      "execution_count": 629,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KmYWsAoxrzEr"
      },
      "source": [
        "Let's check the dimension of train and test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYM6EVfGZAve",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3273269-29ba-48f9-fcbc-5b2eaac6b929"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 630,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(95, 42)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 630
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkVP10bGZFXz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2482edb9-b042-4797-d20c-dfb919eb88db"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 631,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(95,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 631
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lh2e0i8uZISM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a449791-0b26-4b7e-925d-32c21902d966"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 632,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 42)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 632
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVNqv_Z3ZRiB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d7e2c3d-3c6a-4962-f3b1-9a2379c61475"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 633,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 633
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtymfkxKsBQD"
      },
      "source": [
        "Before moving ahead, I am importing all the models from sklearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UQrNCGgZTfH"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from xgboost import XGBRegressor"
      ],
      "execution_count": 634,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "am1_4p4xr6Lm"
      },
      "source": [
        "## Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epTvCycHZgIn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2c7539d9-42c1-4984-8d42-fe83116e241a"
      },
      "source": [
        "'''\n",
        "lr = LinearRegression() #create the object of the model\n",
        "lr=lr.fit(X_train,y_train)\n",
        "'''\n",
        "\n",
        "\n"
      ],
      "execution_count": 635,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nlr = LinearRegression() #create the object of the model\\nlr=lr.fit(X_train,y_train)\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 635
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMHDUHfjSxLz"
      },
      "source": [
        " #create the object of the model\n",
        "lr=LinearRegression().fit(X_train,y_train)"
      ],
      "execution_count": 636,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoYCXT3oTDGs"
      },
      "source": [
        ""
      ],
      "execution_count": 636,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMj7wJU-aVMX"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score"
      ],
      "execution_count": 637,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2iAeTfJ7lqI",
        "outputId": "ac1d9275-183d-42c0-d929-a0a69dd886e0"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 638,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 42)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 638
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6_UswhlZ5eA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dc4a096-e022-4753-9bed-d5c71f91d78b"
      },
      "source": [
        "pred = lr.predict(X_test)\n",
        "s=mean_absolute_error(y_test,pred)\n",
        "s1=mean_squared_error(y_test,pred)\n",
        "s2=r2_score(y_test,pred)\n",
        "\n",
        "print(\"The MAE with the linear regressor is: \"+str(s))\n",
        "print(\"The MsE with the linear regressor is: \"+str(s1))\n",
        "print(\"The R2_Score with the linear regressor is: \"+str(s2))"
      ],
      "execution_count": 639,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The MAE with the linear regressor is: 2498286.3837149017\n",
            "The MsE with the linear regressor is: 11462276115125.28\n",
            "The R2_Score with the linear regressor is: -2.4113295417981107\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdP6Gaoo67KA",
        "outputId": "266c3067-67c4-4c90-b94f-8e5eee2dcb32"
      },
      "source": [
        "len(lr.coef_)"
      ],
      "execution_count": 640,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 640
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rhTS2xKqyUfz",
        "outputId": "f08036d2-67f9-49ad-c57c-bb939495dd44"
      },
      "source": [
        "weight_lr=pd.DataFrame(lr.coef_,index=X_test.columns,columns=['weight_coefficient'])\n",
        "weight_lr"
      ],
      "execution_count": 641,
      "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>weight_coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>-2.074147e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City Group</th>\n",
              "      <td>-6.158846e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <td>-1.290419e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>2.918061e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>-7.390225e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>-4.522112e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>5.179220e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P5</th>\n",
              "      <td>-2.959782e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P6</th>\n",
              "      <td>1.358481e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P7</th>\n",
              "      <td>1.709102e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P8</th>\n",
              "      <td>-1.035483e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P9</th>\n",
              "      <td>2.185381e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P10</th>\n",
              "      <td>-1.274010e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P11</th>\n",
              "      <td>-1.072631e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P12</th>\n",
              "      <td>-3.665156e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P13</th>\n",
              "      <td>-6.044888e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P14</th>\n",
              "      <td>-4.543317e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P15</th>\n",
              "      <td>-1.995096e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P16</th>\n",
              "      <td>-5.112422e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P17</th>\n",
              "      <td>1.511529e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P18</th>\n",
              "      <td>9.549564e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P19</th>\n",
              "      <td>-1.460639e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P20</th>\n",
              "      <td>-3.596981e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P21</th>\n",
              "      <td>6.599505e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P22</th>\n",
              "      <td>-9.562944e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P23</th>\n",
              "      <td>3.647243e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P24</th>\n",
              "      <td>-1.595840e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P25</th>\n",
              "      <td>4.912403e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P26</th>\n",
              "      <td>-7.483904e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P27</th>\n",
              "      <td>3.172328e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P28</th>\n",
              "      <td>8.966305e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P29</th>\n",
              "      <td>3.286893e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P30</th>\n",
              "      <td>-1.188371e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P31</th>\n",
              "      <td>5.861998e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P32</th>\n",
              "      <td>-3.226834e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P33</th>\n",
              "      <td>-5.377985e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P34</th>\n",
              "      <td>-2.014453e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P35</th>\n",
              "      <td>5.456235e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P36</th>\n",
              "      <td>9.561483e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P37</th>\n",
              "      <td>5.961838e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>3.377328e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>-1.734351e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            weight_coefficient\n",
              "City             -2.074147e+04\n",
              "City Group       -6.158846e+05\n",
              "Type             -1.290419e+06\n",
              "P1                2.918061e+05\n",
              "P2               -7.390225e+04\n",
              "P3               -4.522112e+05\n",
              "P4                5.179220e+04\n",
              "P5               -2.959782e+05\n",
              "P6                1.358481e+05\n",
              "P7                1.709102e+05\n",
              "P8               -1.035483e+06\n",
              "P9                2.185381e+06\n",
              "P10              -1.274010e+06\n",
              "P11              -1.072631e+05\n",
              "P12              -3.665156e+05\n",
              "P13              -6.044888e+05\n",
              "P14              -4.543317e+05\n",
              "P15              -1.995096e+04\n",
              "P16              -5.112422e+05\n",
              "P17               1.511529e+05\n",
              "P18               9.549564e+04\n",
              "P19              -1.460639e+05\n",
              "P20              -3.596981e+05\n",
              "P21               6.599505e+05\n",
              "P22              -9.562944e+05\n",
              "P23               3.647243e+05\n",
              "P24              -1.595840e+05\n",
              "P25               4.912403e+05\n",
              "P26              -7.483904e+05\n",
              "P27               3.172328e+05\n",
              "P28               8.966305e+05\n",
              "P29               3.286893e+04\n",
              "P30              -1.188371e+05\n",
              "P31               5.861998e+03\n",
              "P32              -3.226834e+05\n",
              "P33              -5.377985e+05\n",
              "P34              -2.014453e+05\n",
              "P35               5.456235e+05\n",
              "P36               9.561483e+05\n",
              "P37               5.961838e+05\n",
              "month             3.377328e+04\n",
              "year             -1.734351e+05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 641
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3C3o-okxJQN"
      },
      "source": [
        "## ***Ridge Regression***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zNTXqL9u7dzJ"
      },
      "source": [
        "Before I discuss Ridge Regression , let's discuss some of the aspects of the Ridge Regression:\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VkX0J2CeS8f"
      },
      "source": [
        " One of the algorithm to deal with this multi-collinearity is **Ridge Regression.**\n",
        "\n",
        "Let's discuss some of the points in Ridge Regression.\n",
        "\n",
        "\n",
        "\n",
        "1.   Ridge Regression is a Regularisation Technique.'Regularisation' means by using some hyper-parameter,the value of the parameters can be regularised.That has been accomplished in the following way:\n",
        "\n",
        "\n",
        "*   Instead of using mere a cost function like this:\n",
        "![1_6hJC8qMP3AFBVuJuBzE9Hw.png](data:image/png;base64,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)\n",
        "*   We will use this:\n",
        "![1_hAGhQehrqAmT1pvz3q4t8Q.png](data:image/png;base64,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)\n",
        "\n",
        "Where, $\\lambda >0$ is a hyper-parameter.\n",
        "\n",
        "\n",
        "2.   As,the cost function tends to decrease during training, $w_i$ s will be updated such a way ,some of the $w_i$ will be updated  $\\to$ 0.\n",
        "\n",
        "As, $w_i$'s  $\\to$ 0,the collineared features get less pripority and that overfitting ambiguity due to multi-collinearity can be avoided.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5FtTS57xPmc"
      },
      "source": [
        "from sklearn.linear_model import Ridge"
      ],
      "execution_count": 642,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW2CWy5-xiH6",
        "outputId": "0a393835-4561-49c1-b72f-442ee1ddec53"
      },
      "source": [
        "rr=Ridge(alpha=0.8)\n",
        "\n",
        "fit_r=rr.fit(X_train,y_train)\n",
        "\n",
        "pred_r = rr.predict(X_test)\n",
        "s_r=mean_absolute_error(y_test,pred_r)\n",
        "s1_r=mean_squared_error(y_test,pred_r)\n",
        "s2_r=r2_score(y_test,pred_r)\n",
        "\n",
        "print(\"The MAE with the Ridge linear regressor is: \"+str(s_r))\n",
        "print(\"The MsE with the Ridge linear regressor is: \"+str(s1_r))\n",
        "print(\"The R2_Score with Ridge linear regressor is: \"+str(s2_r))"
      ],
      "execution_count": 643,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The MAE with the Ridge linear regressor is: 2354458.1154690315\n",
            "The MsE with the Ridge linear regressor is: 9994439819900.09\n",
            "The R2_Score with Ridge linear regressor is: -1.97448146152741\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mQk5vITA11tA",
        "outputId": "631d5529-a1d7-4259-fdbf-4ad7d17a395d"
      },
      "source": [
        "weight_rr=pd.DataFrame(rr.coef_,index=X_test.columns,columns=['weight_coefficient'])\n",
        "weight_rr"
      ],
      "execution_count": 644,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>weight_coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>-2.088469e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City Group</th>\n",
              "      <td>-5.389436e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type</th>\n",
              "      <td>-1.156941e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P1</th>\n",
              "      <td>2.749351e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P2</th>\n",
              "      <td>-9.376050e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P3</th>\n",
              "      <td>-3.957577e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4</th>\n",
              "      <td>5.098787e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P5</th>\n",
              "      <td>-2.640218e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P6</th>\n",
              "      <td>1.619385e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P7</th>\n",
              "      <td>1.814993e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P8</th>\n",
              "      <td>-8.788122e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P9</th>\n",
              "      <td>1.625279e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P10</th>\n",
              "      <td>-9.229407e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P11</th>\n",
              "      <td>-5.594964e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P12</th>\n",
              "      <td>-1.213009e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P13</th>\n",
              "      <td>-7.430015e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P14</th>\n",
              "      <td>-4.416876e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P15</th>\n",
              "      <td>-3.087818e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P16</th>\n",
              "      <td>-4.725122e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P17</th>\n",
              "      <td>2.300748e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P18</th>\n",
              "      <td>1.839556e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P19</th>\n",
              "      <td>-1.624493e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P20</th>\n",
              "      <td>-3.464357e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P21</th>\n",
              "      <td>6.032722e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P22</th>\n",
              "      <td>-8.635682e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P23</th>\n",
              "      <td>3.378896e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P24</th>\n",
              "      <td>-1.000501e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P25</th>\n",
              "      <td>4.505622e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P26</th>\n",
              "      <td>-7.699923e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P27</th>\n",
              "      <td>2.981962e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P28</th>\n",
              "      <td>8.469967e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P29</th>\n",
              "      <td>-4.413902e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P30</th>\n",
              "      <td>-9.094835e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P31</th>\n",
              "      <td>3.178505e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P32</th>\n",
              "      <td>-3.112551e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P33</th>\n",
              "      <td>-4.556988e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P34</th>\n",
              "      <td>-1.585748e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P35</th>\n",
              "      <td>4.350924e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P36</th>\n",
              "      <td>8.042126e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P37</th>\n",
              "      <td>4.687888e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>3.129443e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>-1.801568e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            weight_coefficient\n",
              "City             -2.088469e+04\n",
              "City Group       -5.389436e+05\n",
              "Type             -1.156941e+06\n",
              "P1                2.749351e+05\n",
              "P2               -9.376050e+04\n",
              "P3               -3.957577e+05\n",
              "P4                5.098787e+04\n",
              "P5               -2.640218e+05\n",
              "P6                1.619385e+05\n",
              "P7                1.814993e+05\n",
              "P8               -8.788122e+05\n",
              "P9                1.625279e+06\n",
              "P10              -9.229407e+05\n",
              "P11              -5.594964e+04\n",
              "P12              -1.213009e+05\n",
              "P13              -7.430015e+05\n",
              "P14              -4.416876e+05\n",
              "P15              -3.087818e+04\n",
              "P16              -4.725122e+05\n",
              "P17               2.300748e+05\n",
              "P18               1.839556e+05\n",
              "P19              -1.624493e+05\n",
              "P20              -3.464357e+05\n",
              "P21               6.032722e+05\n",
              "P22              -8.635682e+05\n",
              "P23               3.378896e+05\n",
              "P24              -1.000501e+05\n",
              "P25               4.505622e+05\n",
              "P26              -7.699923e+05\n",
              "P27               2.981962e+05\n",
              "P28               8.469967e+05\n",
              "P29              -4.413902e+04\n",
              "P30              -9.094835e+04\n",
              "P31               3.178505e+04\n",
              "P32              -3.112551e+05\n",
              "P33              -4.556988e+05\n",
              "P34              -1.585748e+05\n",
              "P35               4.350924e+05\n",
              "P36               8.042126e+05\n",
              "P37               4.687888e+05\n",
              "month             3.129443e+04\n",
              "year             -1.801568e+05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 644
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuMEhPXE6u0a",
        "outputId": "4ed97bee-d662-4f76-d78e-0ace33164525"
      },
      "source": [
        "len(rr.coef_)"
      ],
      "execution_count": 645,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 645
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uqg0phAxR5fC"
      },
      "source": [
        "# ***Conclusion***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "U0ZEHzghjGYs",
        "outputId": "aa4609d8-1ec4-46e8-802b-6ec99ed1f4f8"
      },
      "source": [
        "plt.plot(rr.coef_,alpha=0.7,linestyle='none',marker='o',markersize=5,color='red',label=r'Ridge',zorder=7) \n",
        "plt.plot(lr.coef_,alpha=0.7,linestyle='none',marker='o',markersize=7,color='green',label='Linear Regression')"
      ],
      "execution_count": 654,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa5ed8ebf10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 654
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fhQP_trhZ_I",
        "outputId": "90e7a9f5-f4df-4b44-e367-c7e40f99db9a"
      },
      "source": [
        "#Comparison:\n",
        "\n",
        "print(\"The R2_Score with Ridge linear regressor is: \"+str(s2_r))\n",
        "print(\"The R2_Score with the linear regressor is: \"+str(s2))"
      ],
      "execution_count": 655,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The R2_Score with Ridge linear regressor is: -1.97448146152741\n",
            "The R2_Score with the linear regressor is: -2.4113295417981107\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5epkrq-WSIFC"
      },
      "source": [
        "The sole purpose of this project is to implement **Ridge**  regression.As,it is evident from the above  discussion, $Ridge$ is superior in case of multi-collinearity case and throw a merginally,good R2 score as compared to other one.\n",
        "\n",
        "Future Motivation:\n",
        "\n",
        "*   Although,all the weights in this example becomes non-zero in case of Ridge(i.e. the dimenion of the feature space is not reduced.).With the help of $Lesso$ , that can be implemented.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHYpdS2dR7LY"
      },
      "source": [
        ""
      ],
      "execution_count": 649,
      "outputs": []
    }
  ]
}