notebook.ipynb

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      "source": [
        "# **Proyek Analisis Data: Bike Sharing Dataset**\n",
        "- Nama: Afif Ramadhan\n",
        "- Email: ramadhanafif172@gmail.com\n",
        "- Id Dicoding: afif_ramadhan_nC7s"
      ],
      "metadata": {
        "id": "cP5zh7Ww85Fm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Menentukan Pertanyaan Bisnis**"
      ],
      "metadata": {
        "id": "ggwOFmpI88dG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Apakah ada peran dari cuaca terhadap jumlah pengguna sepeda?\n",
        "- Bagaimana kondisi yang tampak ketika sepeda digunakan pada workingday, holiday, dan weekday?\n",
        "- Bagaimana tren penggunaan sepeda dalam setahun, apakah tahun 2011 atau 2012 yang lebih unggul?\n",
        "- Bagaimana variabel seperti temp dan atemp serta humidity dapat mempengaruhi jumlah total pengguna sepeda (baik Casual ataupun Registered)?\n",
        "- Apakah ada pengaruh musim terhadap penyewa sepeda berdasarkan pelanggan Casual dan Registered?"
      ],
      "metadata": {
        "id": "6irqmTtg8-IC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Menyiapkan semua library yang dibutuhkan**"
      ],
      "metadata": {
        "id": "EueHsNk89AsY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sebelum memulai proyek, pastikan kita telah mengimport semua library yang kita butuhkan untuk mengerjakan proyeknya."
      ],
      "metadata": {
        "id": "s3ZACUZHhx_2"
      }
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    {
      "cell_type": "code",
      "execution_count": 1,
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        "id": "4cmWv4qc8xWu"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Wrangling**"
      ],
      "metadata": {
        "id": "7DwSpkkZ9BqV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Gathering Data**"
      ],
      "metadata": {
        "id": "ZuJuLGxe9D9O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada tahap ini kita akan mengumpulkan data yang telah kita kumpulkan. Karena saya memilih **Bike Sharing Dataset**, maka saya mengimpor file **day.csv** dan **hr.csv**"
      ],
      "metadata": {
        "id": "BsgkgIxEiMkt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Load tabel day**"
      ],
      "metadata": {
        "id": "muVsNbDsjdyz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df = pd.read_csv(\"/content/day.csv\")\n",
        "day_df.head()"
      ],
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      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1        0        6           0   \n",
              "1        2  2011-01-02       1   0     1        0        0           0   \n",
              "2        3  2011-01-03       1   0     1        0        1           1   \n",
              "3        4  2011-01-04       1   0     1        0        2           1   \n",
              "4        5  2011-01-05       1   0     1        0        3           1   \n",
              "\n",
              "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
              "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
              "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
              "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
              "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
              "\n",
              "    cnt  \n",
              "0   985  \n",
              "1   801  \n",
              "2  1349  \n",
              "3  1562  \n",
              "4  1600  "
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              "      <td>2011-01-01</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.248309</td>\n",
              "      <td>120</td>\n",
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              "      <td>1349</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>0.160296</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.186900</td>\n",
              "      <td>82</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### **Load tabel hr**"
      ],
      "metadata": {
        "id": "iKit5fiCjiPQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hr_df = pd.read_csv(\"/content/hour.csv\")\n",
        "hr_df.head()"
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
              "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
              "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
              "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
              "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
              "\n",
              "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
              "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
              "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
              "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
              "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
              "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>dteday</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-35ed04f5-ebde-4c21-93a2-64497f020423');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "  }\n",
              "\n",
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              "  @keyframes spin {\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Assessing Data"
      ],
      "metadata": {
        "id": "BvZAfrSH9F2U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Menilai day_df**"
      ],
      "metadata": {
        "id": "ctCvHaW9jn7t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Mengecek tipe data day_df"
      ],
      "metadata": {
        "id": "vPss7RTjpHCn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.info()"
      ],
      "metadata": {
        "id": "fx7PaLsV9G8M",
        "outputId": "9b44562e-d81c-4e24-9a21-63db71dcbbd3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     731 non-null    int64  \n",
            " 1   dteday      731 non-null    object \n",
            " 2   season      731 non-null    int64  \n",
            " 3   yr          731 non-null    int64  \n",
            " 4   mnth        731 non-null    int64  \n",
            " 5   holiday     731 non-null    int64  \n",
            " 6   weekday     731 non-null    int64  \n",
            " 7   workingday  731 non-null    int64  \n",
            " 8   weathersit  731 non-null    int64  \n",
            " 9   temp        731 non-null    float64\n",
            " 10  atemp       731 non-null    float64\n",
            " 11  hum         731 non-null    float64\n",
            " 12  windspeed   731 non-null    float64\n",
            " 13  casual      731 non-null    int64  \n",
            " 14  registered  731 non-null    int64  \n",
            " 15  cnt         731 non-null    int64  \n",
            "dtypes: float64(4), int64(11), object(1)\n",
            "memory usage: 91.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat kita lihat diatas, terdapat 1 tipe data berdasarkan kolomnya yang tidak sesuai. Data yang perlu diperbaiki adalah: dteday -> datetime"
      ],
      "metadata": {
        "id": "aehGEBWCkA2U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Mengecek apakah ada missing values"
      ],
      "metadata": {
        "id": "bAqLYE6apJuU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.isna().sum()"
      ],
      "metadata": {
        "id": "yAuILIlYpNob",
        "outputId": "2d90ad5c-3d6c-4637-dfd2-51e69eba8dee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "instant       0\n",
              "dteday        0\n",
              "season        0\n",
              "yr            0\n",
              "mnth          0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weathersit    0\n",
              "temp          0\n",
              "atemp         0\n",
              "hum           0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "cnt           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat kita simpulkan bahwa tabel day_df tidak ada missing values, berarti aman."
      ],
      "metadata": {
        "id": "7DBZ61QHphyj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Mengecek apakah ada duplicate data"
      ],
      "metadata": {
        "id": "3DNowUifptLY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Jumlah duplikasi data: \", day_df.duplicated().sum())"
      ],
      "metadata": {
        "id": "fXnuxiD5pA8L",
        "outputId": "4b21a63e-2a6a-4a15-df8a-15c2c4294439",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah duplikasi data:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pada tabel day_df terdapat 0 data yang double, artinya semua data bersifat unik."
      ],
      "metadata": {
        "id": "ltdz1GLdp_1c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Mengecek apakah terdapat keanehan pada nilai statistiknya"
      ],
      "metadata": {
        "id": "qfYOgQSqqZIv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.describe()"
      ],
      "metadata": {
        "id": "-O50DO_aqgKP",
        "outputId": "5314537d-ea47-4c28-a372-2bd46f88bc7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          instant      season          yr        mnth     holiday     weekday  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
              "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
              "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
              "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
              "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
              "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
              "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
              "\n",
              "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
              "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
              "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
              "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
              "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
              "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
              "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
              "\n",
              "            casual   registered          cnt  \n",
              "count   731.000000   731.000000   731.000000  \n",
              "mean    848.176471  3656.172367  4504.348837  \n",
              "std     686.622488  1560.256377  1937.211452  \n",
              "min       2.000000    20.000000    22.000000  \n",
              "25%     315.500000  2497.000000  3152.000000  \n",
              "50%     713.000000  3662.000000  4548.000000  \n",
              "75%    1096.000000  4776.500000  5956.000000  \n",
              "max    3410.000000  6946.000000  8714.000000  "
            ],
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              "\n",
              "  <div id=\"df-cefcf639-9364-42ed-9956-985000228bce\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>holiday</th>\n",
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              "      <th>workingday</th>\n",
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              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>cnt</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
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              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
              "      <td>731.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>2.496580</td>\n",
              "      <td>0.500684</td>\n",
              "      <td>6.519836</td>\n",
              "      <td>0.028728</td>\n",
              "      <td>2.997264</td>\n",
              "      <td>0.683995</td>\n",
              "      <td>1.395349</td>\n",
              "      <td>0.495385</td>\n",
              "      <td>0.474354</td>\n",
              "      <td>0.627894</td>\n",
              "      <td>0.190486</td>\n",
              "      <td>848.176471</td>\n",
              "      <td>3656.172367</td>\n",
              "      <td>4504.348837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>211.165812</td>\n",
              "      <td>1.110807</td>\n",
              "      <td>0.500342</td>\n",
              "      <td>3.451913</td>\n",
              "      <td>0.167155</td>\n",
              "      <td>2.004787</td>\n",
              "      <td>0.465233</td>\n",
              "      <td>0.544894</td>\n",
              "      <td>0.183051</td>\n",
              "      <td>0.162961</td>\n",
              "      <td>0.142429</td>\n",
              "      <td>0.077498</td>\n",
              "      <td>686.622488</td>\n",
              "      <td>1560.256377</td>\n",
              "      <td>1937.211452</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.059130</td>\n",
              "      <td>0.079070</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.022392</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>183.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.337083</td>\n",
              "      <td>0.337842</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.134950</td>\n",
              "      <td>315.500000</td>\n",
              "      <td>2497.000000</td>\n",
              "      <td>3152.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>366.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.498333</td>\n",
              "      <td>0.486733</td>\n",
              "      <td>0.626667</td>\n",
              "      <td>0.180975</td>\n",
              "      <td>713.000000</td>\n",
              "      <td>3662.000000</td>\n",
              "      <td>4548.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>548.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.655417</td>\n",
              "      <td>0.608602</td>\n",
              "      <td>0.730209</td>\n",
              "      <td>0.233214</td>\n",
              "      <td>1096.000000</td>\n",
              "      <td>4776.500000</td>\n",
              "      <td>5956.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>731.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.861667</td>\n",
              "      <td>0.840896</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.507463</td>\n",
              "      <td>3410.000000</td>\n",
              "      <td>6946.000000</td>\n",
              "      <td>8714.000000</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    0% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat kita lihat diatas, tidak ada keanehan pada nilai statistiknya."
      ],
      "metadata": {
        "id": "GaYS5rfFrEvx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Menilai hr_df**"
      ],
      "metadata": {
        "id": "I331x435rKGz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Mengecek info hr_df"
      ],
      "metadata": {
        "id": "Hb4726QmrSWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hr_df.info()"
      ],
      "metadata": {
        "id": "VnzWhMFrrVU2",
        "outputId": "98d81675-1a68-4921-d21a-927db271a538",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17379 entries, 0 to 17378\n",
            "Data columns (total 17 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     17379 non-null  int64  \n",
            " 1   dteday      17379 non-null  object \n",
            " 2   season      17379 non-null  int64  \n",
            " 3   yr          17379 non-null  int64  \n",
            " 4   mnth        17379 non-null  int64  \n",
            " 5   hr          17379 non-null  int64  \n",
            " 6   holiday     17379 non-null  int64  \n",
            " 7   weekday     17379 non-null  int64  \n",
            " 8   workingday  17379 non-null  int64  \n",
            " 9   weathersit  17379 non-null  int64  \n",
            " 10  temp        17379 non-null  float64\n",
            " 11  atemp       17379 non-null  float64\n",
            " 12  hum         17379 non-null  float64\n",
            " 13  windspeed   17379 non-null  float64\n",
            " 14  casual      17379 non-null  int64  \n",
            " 15  registered  17379 non-null  int64  \n",
            " 16  cnt         17379 non-null  int64  \n",
            "dtypes: float64(4), int64(12), object(1)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat kita lihat diatas, terdapat 1 tipe data berdasarkan kolomnya yang tidak sesuai. Data yang perlu diperbaiki adalah: dteday -> datetime"
      ],
      "metadata": {
        "id": "o-cusj4priX7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Mengecek apakah ada missing values"
      ],
      "metadata": {
        "id": "nAUwIgsnr6kU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hr_df.isna().sum()"
      ],
      "metadata": {
        "id": "tmkCxpy5r-dI",
        "outputId": "ad9c4493-2772-40c1-9735-d5c3e2641211",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "instant       0\n",
              "dteday        0\n",
              "season        0\n",
              "yr            0\n",
              "mnth          0\n",
              "hr            0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weathersit    0\n",
              "temp          0\n",
              "atemp         0\n",
              "hum           0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "cnt           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat kita simpulkan bahwa tabel hr_df tidak ada missing values, berarti aman."
      ],
      "metadata": {
        "id": "XrdbcgojsJw1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Mengecek apakah ada duplicate data"
      ],
      "metadata": {
        "id": "otnf-COvsOyL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Jumlah duplikasi data: \", hr_df.duplicated().sum())"
      ],
      "metadata": {
        "id": "XrBsRXemsVrO",
        "outputId": "508a33f8-44a9-43b2-d4fd-1763660adab6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah duplikasi data:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Mengecek apakah terdapat keanehan pada nilai statistiknya"
      ],
      "metadata": {
        "id": "PspYBv-bFlQU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hr_df.describe()"
      ],
      "metadata": {
        "id": "88TeXHajFrnj",
        "outputId": "73610e8a-762e-4507-e73c-03a86d219100",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          instant        season            yr          mnth            hr  \\\n",
              "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
              "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
              "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
              "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
              "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
              "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
              "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
              "\n",
              "            holiday       weekday    workingday    weathersit          temp  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
              "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
              "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
              "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
              "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
              "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
              "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
              "\n",
              "              atemp           hum     windspeed        casual    registered  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
              "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
              "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
              "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
              "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
              "\n",
              "                cnt  \n",
              "count  17379.000000  \n",
              "mean     189.463088  \n",
              "std      181.387599  \n",
              "min        1.000000  \n",
              "25%       40.000000  \n",
              "50%      142.000000  \n",
              "75%      281.000000  \n",
              "max      977.000000  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weathersit</th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>hum</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17379.0000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
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              "      <td>17379.000000</td>\n",
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              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
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              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "      <td>17379.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>2.501640</td>\n",
              "      <td>0.502561</td>\n",
              "      <td>6.537775</td>\n",
              "      <td>11.546752</td>\n",
              "      <td>0.028770</td>\n",
              "      <td>3.003683</td>\n",
              "      <td>0.682721</td>\n",
              "      <td>1.425283</td>\n",
              "      <td>0.496987</td>\n",
              "      <td>0.475775</td>\n",
              "      <td>0.627229</td>\n",
              "      <td>0.190098</td>\n",
              "      <td>35.676218</td>\n",
              "      <td>153.786869</td>\n",
              "      <td>189.463088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5017.0295</td>\n",
              "      <td>1.106918</td>\n",
              "      <td>0.500008</td>\n",
              "      <td>3.438776</td>\n",
              "      <td>6.914405</td>\n",
              "      <td>0.167165</td>\n",
              "      <td>2.005771</td>\n",
              "      <td>0.465431</td>\n",
              "      <td>0.639357</td>\n",
              "      <td>0.192556</td>\n",
              "      <td>0.171850</td>\n",
              "      <td>0.192930</td>\n",
              "      <td>0.122340</td>\n",
              "      <td>49.305030</td>\n",
              "      <td>151.357286</td>\n",
              "      <td>181.387599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4345.5000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.333300</td>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.104500</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.484800</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>0.194000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>142.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13034.5000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.660000</td>\n",
              "      <td>0.621200</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.253700</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>281.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>0.850700</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>977.000000</td>\n",
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              "\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cb6fbc2b-9f0a-44ab-8725-8de2b57a82d2');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    20% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat kita lihat diatas, tidak ada keanehan pada nilai statistiknya."
      ],
      "metadata": {
        "id": "whVHKiPIGHbR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Cleaning Data**"
      ],
      "metadata": {
        "id": "Uz5dgcJm9HW5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Menghapus tabel yang tidak diperlukan\n",
        "\n",
        "Sebelum kita masuk ke tahapan-tahapan selanjutnya, kita sekarang akan menghapus terlebih dahulu tabel **hr**. Alasannya adalah karena kita tidak relevan dengan pertanyaan bisnis yang sudah kita tetapkan, dan juga sebenarnya tabel hr dan day itu bedanya hanya di kolom hrnya saja, selebihnya sama semua."
      ],
      "metadata": {
        "id": "tlFcZd1PIbIB"
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    {
      "cell_type": "code",
      "source": [
        "del hr_df"
      ],
      "metadata": {
        "id": "ddrzMvOUGsu_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Menghapus beberapa kolom yang tidak akan digunakan\n",
        "\n",
        "Berdasarkan pertanyaan bisnis yang sudah ada, maka untuk kolom yang akan didrop, yakni:\n",
        "- instant: Karena indeks catatan ini tidak memberikan informasi tambahan yang berguna.\n",
        "- windspeed: Tidak ada pertanyaan bisnis yang terkait dengan efek kecepatan angin terhadap jumlah penyewa sepeda."
      ],
      "metadata": {
        "id": "szj41GktHpb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drop_col = ['instant', 'windspeed']\n",
        "\n",
        "for i in day_df.columns:\n",
        "  if i in drop_col:\n",
        "    day_df.drop(labels=i, axis=1, inplace=True)\n",
        "\n",
        "day_df.head()"
      ],
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      "execution_count": 13,
      "outputs": [
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          "data": {
            "text/plain": [
              "       dteday  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
              "0  2011-01-01       1   0     1        0        6           0           2   \n",
              "1  2011-01-02       1   0     1        0        0           0           2   \n",
              "2  2011-01-03       1   0     1        0        1           1           1   \n",
              "3  2011-01-04       1   0     1        0        2           1           1   \n",
              "4  2011-01-05       1   0     1        0        3           1           1   \n",
              "\n",
              "       temp     atemp       hum  casual  registered   cnt  \n",
              "0  0.344167  0.363625  0.805833     331         654   985  \n",
              "1  0.363478  0.353739  0.696087     131         670   801  \n",
              "2  0.196364  0.189405  0.437273     120        1229  1349  \n",
              "3  0.200000  0.212122  0.590435     108        1454  1562  \n",
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          },
          "metadata": {},
          "execution_count": 13
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    {
      "cell_type": "markdown",
      "source": [
        "3. Mengubah beberapa detail tentang kolom\n",
        "\n",
        "Karena nama-nama dari kolom diatas agak kurang sesuai dengan style saya, maka akan saya rubah. Dan juga saya lebih suka dengan tipe data yang bentuknya kategorikal, jadi beberapa yang sebelumnya adalah angka akan saya rubah. Serta sebelumnya ada tipe data yang juga tidak sesuai, dan itu akan saya rubah juga."
      ],
      "metadata": {
        "id": "-s138dPIK9rp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengubah nama judul kolom\n",
        "day_df.rename(columns={\n",
        "    'dteday': 'dateday',\n",
        "    'yr': 'year',\n",
        "    'mnth': 'month',\n",
        "    'weathersit': 'weather_cond',\n",
        "    'cnt': 'count'\n",
        "}, inplace=True)\n",
        "\n",
        "day_df.head()"
      ],
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        "id": "7RYNcLaQOwFV",
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        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      dateday  season  year  month  holiday  weekday  workingday  \\\n",
              "0  2011-01-01       1     0      1        0        6           0   \n",
              "1  2011-01-02       1     0      1        0        0           0   \n",
              "2  2011-01-03       1     0      1        0        1           1   \n",
              "3  2011-01-04       1     0      1        0        2           1   \n",
              "4  2011-01-05       1     0      1        0        3           1   \n",
              "\n",
              "   weather_cond      temp     atemp       hum  casual  registered  count  \n",
              "0             2  0.344167  0.363625  0.805833     331         654    985  \n",
              "1             2  0.363478  0.353739  0.696087     131         670    801  \n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.226957</td>\n",
              "      <td>0.229270</td>\n",
              "      <td>0.436957</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
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              "\n",
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              "        document.querySelector('#df-cbb18dc2-66c3-45eb-a5ca-ab76b029240f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cbb18dc2-66c3-45eb-a5ca-ab76b029240f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengubah angka menjadi keterangan\n",
        "day_df['month'] = day_df['month'].map({\n",
        "    1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n",
        "    7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n",
        "})\n",
        "day_df['season'] = day_df['season'].map({\n",
        "    1: 'Spring', 2: 'Summer', 3: 'Fall', 4: 'Winter'\n",
        "})\n",
        "day_df['weekday'] = day_df['weekday'].map({\n",
        "    0: 'Sun', 1: 'Mon', 2: 'Tue', 3: 'Wed', 4: 'Thu', 5: 'Fri', 6: 'Sat'\n",
        "})\n",
        "day_df['weather_cond'] = day_df['weather_cond'].map({\n",
        "    1: 'Clear/Partly Cloudy',\n",
        "    2: 'Misty/Cloudy',\n",
        "    3: 'Light Snow/Rain',\n",
        "    4: 'Severe Weather'\n",
        "})"
      ],
      "metadata": {
        "id": "ey0OrZDeQRwP"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengubah tipe data ke datetime\n",
        "day_df['dateday'] = pd.to_datetime(day_df.dateday)\n",
        "\n",
        "# Mengubaha tipe data ke categorical\n",
        "day_df['season'] = day_df.season.astype('category')\n",
        "day_df['year'] = day_df.year.astype('category')\n",
        "day_df['month'] = day_df.month.astype('category')\n",
        "day_df['holiday'] = day_df.holiday.astype('category')\n",
        "day_df['weekday'] = day_df.weekday.astype('category')\n",
        "day_df['workingday'] = day_df.workingday.astype('category')\n",
        "day_df['weather_cond'] = day_df.weather_cond.astype('category')\n",
        "\n",
        "day_df.head()"
      ],
      "metadata": {
        "id": "viYvYTZwTrf-",
        "outputId": "8a8a08fa-d35f-41cf-afc6-72701667e279",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     dateday  season year month holiday weekday workingday  \\\n",
              "0 2011-01-01  Spring    0   Jan       0     Sat          0   \n",
              "1 2011-01-02  Spring    0   Jan       0     Sun          0   \n",
              "2 2011-01-03  Spring    0   Jan       0     Mon          1   \n",
              "3 2011-01-04  Spring    0   Jan       0     Tue          1   \n",
              "4 2011-01-05  Spring    0   Jan       0     Wed          1   \n",
              "\n",
              "          weather_cond      temp     atemp       hum  casual  registered  \\\n",
              "0         Misty/Cloudy  0.344167  0.363625  0.805833     331         654   \n",
              "1         Misty/Cloudy  0.363478  0.353739  0.696087     131         670   \n",
              "2  Clear/Partly Cloudy  0.196364  0.189405  0.437273     120        1229   \n",
              "3  Clear/Partly Cloudy  0.200000  0.212122  0.590435     108        1454   \n",
              "4  Clear/Partly Cloudy  0.226957  0.229270  0.436957      82        1518   \n",
              "\n",
              "   count  \n",
              "0    985  \n",
              "1    801  \n",
              "2   1349  \n",
              "3   1562  \n",
              "4   1600  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dateday</th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>holiday</th>\n",
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              "      <th>atemp</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan</td>\n",
              "      <td>0</td>\n",
              "      <td>Sat</td>\n",
              "      <td>0</td>\n",
              "      <td>Misty/Cloudy</td>\n",
              "      <td>0.344167</td>\n",
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              "      <td>2011-01-02</td>\n",
              "      <td>Spring</td>\n",
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              "      <td>Jan</td>\n",
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              "      <td>Sun</td>\n",
              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>Clear/Partly Cloudy</td>\n",
              "      <td>0.196364</td>\n",
              "      <td>0.189405</td>\n",
              "      <td>0.437273</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan</td>\n",
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              "      <td>1</td>\n",
              "      <td>Clear/Partly Cloudy</td>\n",
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              "      <td>0.212122</td>\n",
              "      <td>0.590435</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>Spring</td>\n",
              "      <td>0</td>\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a8cd5a94-c91b-4671-85ae-3c468b2b5eb5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a8cd5a94-c91b-4671-85ae-3c468b2b5eb5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d04d76f3-d0b3-4c21-b1f6-6ec5437b2b1f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d04d76f3-d0b3-4c21-b1f6-6ec5437b2b1f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d04d76f3-d0b3-4c21-b1f6-6ec5437b2b1f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5euS_y6fMLTe",
        "outputId": "ba0aecac-9481-4de2-9d1d-f74c98c3f736"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 14 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   dateday       731 non-null    datetime64[ns]\n",
            " 1   season        731 non-null    category      \n",
            " 2   year          731 non-null    category      \n",
            " 3   month         731 non-null    category      \n",
            " 4   holiday       731 non-null    category      \n",
            " 5   weekday       731 non-null    category      \n",
            " 6   workingday    731 non-null    category      \n",
            " 7   weather_cond  731 non-null    category      \n",
            " 8   temp          731 non-null    float64       \n",
            " 9   atemp         731 non-null    float64       \n",
            " 10  hum           731 non-null    float64       \n",
            " 11  casual        731 non-null    int64         \n",
            " 12  registered    731 non-null    int64         \n",
            " 13  count         731 non-null    int64         \n",
            "dtypes: category(7), datetime64[ns](1), float64(3), int64(3)\n",
            "memory usage: 46.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Exploratory Data Analysis (EDA)**"
      ],
      "metadata": {
        "id": "EyPJaCDm9KFb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Eksplorasi Data day_df**"
      ],
      "metadata": {
        "id": "hxgJ6qd69Mce"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan bulan"
      ],
      "metadata": {
        "id": "jZEeEP2kXQtj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.groupby(by='month').agg({\n",
        "    'count': ['max', 'min', 'mean', 'sum']\n",
        "})"
      ],
      "metadata": {
        "id": "Xo68yguo9LTl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "2f14cebc-6320-4639-a648-b3baac95c0c3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      count                           \n",
              "        max   min         mean     sum\n",
              "month                                 \n",
              "Apr    7460   795  4484.900000  269094\n",
              "Aug    7865  1115  5664.419355  351194\n",
              "Dec    6606   441  3403.806452  211036\n",
              "Feb    5062  1005  2655.298246  151352\n",
              "Jan    4521   431  2176.338710  134933\n",
              "Jul    8173  3285  5563.677419  344948\n",
              "Jun    8120  3767  5772.366667  346342\n",
              "Mar    8362   605  3692.258065  228920\n",
              "May    8294  2633  5349.774194  331686\n",
              "Nov    6852  1495  4247.183333  254831\n",
              "Oct    8156    22  5199.225806  322352\n",
              "Sep    8714  1842  5766.516667  345991"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8cbbb235-acfe-4806-9615-4127157bcb9c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th colspan=\"4\" halign=\"left\">count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>7460</td>\n",
              "      <td>795</td>\n",
              "      <td>4484.900000</td>\n",
              "      <td>269094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>7865</td>\n",
              "      <td>1115</td>\n",
              "      <td>5664.419355</td>\n",
              "      <td>351194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>6606</td>\n",
              "      <td>441</td>\n",
              "      <td>3403.806452</td>\n",
              "      <td>211036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>5062</td>\n",
              "      <td>1005</td>\n",
              "      <td>2655.298246</td>\n",
              "      <td>151352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>4521</td>\n",
              "      <td>431</td>\n",
              "      <td>2176.338710</td>\n",
              "      <td>134933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>8173</td>\n",
              "      <td>3285</td>\n",
              "      <td>5563.677419</td>\n",
              "      <td>344948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>8120</td>\n",
              "      <td>3767</td>\n",
              "      <td>5772.366667</td>\n",
              "      <td>346342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>8362</td>\n",
              "      <td>605</td>\n",
              "      <td>3692.258065</td>\n",
              "      <td>228920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>8294</td>\n",
              "      <td>2633</td>\n",
              "      <td>5349.774194</td>\n",
              "      <td>331686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>6852</td>\n",
              "      <td>1495</td>\n",
              "      <td>4247.183333</td>\n",
              "      <td>254831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>8156</td>\n",
              "      <td>22</td>\n",
              "      <td>5199.225806</td>\n",
              "      <td>322352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>8714</td>\n",
              "      <td>1842</td>\n",
              "      <td>5766.516667</td>\n",
              "      <td>345991</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8cbbb235-acfe-4806-9615-4127157bcb9c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8cbbb235-acfe-4806-9615-4127157bcb9c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8cbbb235-acfe-4806-9615-4127157bcb9c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ffe488fa-a74d-47eb-bff3-83c1f172589b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil diatas, dapat disimpulkan bahwa bulan yang memiliki rata-rata terbesar dan jumlah terbanyak adalah bulan Juni. Sedangkan rata-rata terkecil dan jumlah terkecil adalah bulan Januari."
      ],
      "metadata": {
        "id": "i4gmhtVdVvBC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan keadaan cuaca"
      ],
      "metadata": {
        "id": "YFpD3BIIYvoo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.groupby(by='weather_cond').agg({\n",
        "    'count': ['max', 'min', 'mean', 'sum']\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VXGbaEgpYvSO",
        "outputId": "22eac42c-631c-4387-ebd9-370a9b506bc6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    count                           \n",
              "                      max  min         mean      sum\n",
              "weather_cond                                        \n",
              "Clear/Partly Cloudy  8714  431  4876.786177  2257952\n",
              "Light Snow/Rain      4639   22  1803.285714    37869\n",
              "Misty/Cloudy         8362  605  4035.862348   996858"
            ],
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil diatas, dapat disimpulkan bahwa para penyewa sepeda cenderung menyewa pada saat cuaca ketika sedang cerah atau mendung. Sangat sedikit ketika salju turun dan tidak ada ketika cuaca sedang sangat buruk."
      ],
      "metadata": {
        "id": "-A-NrypSZBHn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan holiday"
      ],
      "metadata": {
        "id": "4JckYoe8Zdhr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.groupby(by='holiday').agg({\n",
        "    'count': ['max', 'min', 'mean', 'sum']\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "QaCN9uJQZ4gD",
        "outputId": "df7132be-626b-4f76-92b3-f51dff1c710a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        count                            \n",
              "          max   min         mean      sum\n",
              "holiday                                  \n",
              "0        8714    22  4527.104225  3214244\n",
              "1        7403  1000  3735.000000    78435"
            ],
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              "        document.querySelector('#df-9afc259e-4488-478c-9b48-4246017ce928 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9afc259e-4488-478c-9b48-4246017ce928');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-11d6f7a9-4da2-4ca2-8c0a-c5b64e718fd6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11d6f7a9-4da2-4ca2-8c0a-c5b64e718fd6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11d6f7a9-4da2-4ca2-8c0a-c5b64e718fd6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil diatas, dapat disimpulkan bahwa para penyewa sepeda lebih suka menyewa sepeda saat bukan hari-hari libur, melainkan pada saat hari-hari biasa."
      ],
      "metadata": {
        "id": "j-Z3OD0dZ_wT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Bandingkan penyewa sepeda pada saat hari kerja (weekday) vs akhir pekan (weekend)"
      ],
      "metadata": {
        "id": "uCcVWS9baec-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.groupby(by='weekday').agg({\n",
        "    'count':['max','min','mean']\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "uxBGRCVaaeAT",
        "outputId": "87f0fb60-abdb-4369-9c53-8596809e3d88"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        count                   \n",
              "          max   min         mean\n",
              "weekday                         \n",
              "Fri      8362  1167  4690.288462\n",
              "Mon      7525    22  4338.123810\n",
              "Sat      8714   627  4550.542857\n",
              "Sun      8227   605  4228.828571\n",
              "Thu      7804   431  4667.259615\n",
              "Tue      7767   683  4510.663462\n",
              "Wed      8173   441  4548.538462"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17252506-e27c-429b-9a0c-55071665a732\" class=\"colab-df-container\">\n",
              "    <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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>max</th>\n",
              "      <th>min</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weekday</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fri</th>\n",
              "      <td>8362</td>\n",
              "      <td>1167</td>\n",
              "      <td>4690.288462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mon</th>\n",
              "      <td>7525</td>\n",
              "      <td>22</td>\n",
              "      <td>4338.123810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>8714</td>\n",
              "      <td>627</td>\n",
              "      <td>4550.542857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>8227</td>\n",
              "      <td>605</td>\n",
              "      <td>4228.828571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thu</th>\n",
              "      <td>7804</td>\n",
              "      <td>431</td>\n",
              "      <td>4667.259615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tue</th>\n",
              "      <td>7767</td>\n",
              "      <td>683</td>\n",
              "      <td>4510.663462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wed</th>\n",
              "      <td>8173</td>\n",
              "      <td>441</td>\n",
              "      <td>4548.538462</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17252506-e27c-429b-9a0c-55071665a732')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-17252506-e27c-429b-9a0c-55071665a732 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-17252506-e27c-429b-9a0c-55071665a732');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ddb94b32-14d6-46c8-abf1-5b33f7935f7d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ddb94b32-14d6-46c8-abf1-5b33f7935f7d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ddb94b32-14d6-46c8-abf1-5b33f7935f7d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil diatas, Urutan rata-rata jumlah penyewa dari terbesar ke yang terkecil yakni: Jumat (Fri), Kamis (Thu), Sabtu (Sat), Rabu (Wed), Selasa (Tue), Senin (Mon), dan Minggu (Sun)."
      ],
      "metadata": {
        "id": "PIyi-iS4dIgk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan workingday"
      ],
      "metadata": {
        "id": "YtjH57CmZZpn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.groupby(by='workingday').agg({\n",
        "    'count': ['max', 'min', 'mean']\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "EbCeIRJLZuP4",
        "outputId": "7c215d0c-5eea-4b9a-e888-c30d009b7c2c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           count                  \n",
              "             max  min         mean\n",
              "workingday                        \n",
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil diatas, jumlah penyewaan sepeda terbesar jatuh kepada workingday atau hari kerja dibandingkan dengan hari non-workingday. Namun dapat kita lihat juga, hasilnya lumayan berbeda walaupun tidak banyal."
      ],
      "metadata": {
        "id": "gMmO5SembrBU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. Kelompokkan penyewa sepeda (baik casual dan registered) berdasarkan season"
      ],
      "metadata": {
        "id": "dEUpRUyFSgu6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.groupby(by='season').agg({\n",
        "    'casual': 'mean',\n",
        "    'registered': 'mean',\n",
        "    'count': ['max', 'min', 'mean']\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "cI5EWqdkSp1F",
        "outputId": "8f2af272-654a-4f55-a191-29c337e2998e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             casual   registered count                   \n",
              "               mean         mean   max   min         mean\n",
              "season                                                   \n",
              "Fall    1202.611702  4441.691489  8714  1115  5644.303191\n",
              "Spring   334.928177  2269.204420  7836   431  2604.132597\n",
              "Summer  1106.097826  3886.233696  8362   795  4992.331522\n",
              "Winter   729.112360  3999.050562  8555    22  4728.162921"
            ],
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil analisis diatas, para penyewa sepeda baik Casual Dan Registered, suka sekali pada musim gugur (Fall), dapat dilihat dari rata-ratanya. Sedangkan yang paling sedikit adalah pada musim semi (Spring) dengan rata-rata yang paling kecil."
      ],
      "metadata": {
        "id": "VRAx9hE1S_F7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "7. Kelompokkan temp, hum, dan humidity berdasarkan season"
      ],
      "metadata": {
        "id": "jAUKAskXTds2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df.groupby(by='season').agg({\n",
        "    'temp': ['max', 'min', 'mean'],\n",
        "    'atemp': ['max', 'min', 'mean'],\n",
        "    'hum': ['max', 'min', 'mean']\n",
        "})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "pSn3r2vxW1bg",
        "outputId": "810612e4-2824-4e09-ec6b-a462084d8ce5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            temp                         atemp                           hum  \\\n",
              "             max       min      mean       max       min      mean       max   \n",
              "season                                                                         \n",
              "Fall    0.861667  0.469167  0.706309  0.840896  0.242400  0.655898  0.939565   \n",
              "Spring  0.572500  0.059130  0.297748  0.548617  0.079070  0.296914  0.948261   \n",
              "Summer  0.808333  0.253043  0.544405  0.757579  0.250339  0.520307  0.922500   \n",
              "Winter  0.657500  0.220833  0.422906  0.607975  0.227913  0.415539  0.972500   \n",
              "\n",
              "                            \n",
              "             min      mean  \n",
              "season                      \n",
              "Fall    0.360000  0.633482  \n",
              "Spring  0.000000  0.582903  \n",
              "Summer  0.254167  0.626948  \n",
              "Winter  0.333478  0.668719  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fa258911-5869-430d-b2b8-57950e7913cd\" class=\"colab-df-container\">\n",
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              "      <th colspan=\"3\" halign=\"left\">hum</th>\n",
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              "      <th>mean</th>\n",
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              "      <th>mean</th>\n",
              "    </tr>\n",
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              "      <th>season</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fall</th>\n",
              "      <td>0.861667</td>\n",
              "      <td>0.469167</td>\n",
              "      <td>0.706309</td>\n",
              "      <td>0.840896</td>\n",
              "      <td>0.242400</td>\n",
              "      <td>0.655898</td>\n",
              "      <td>0.939565</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>0.633482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spring</th>\n",
              "      <td>0.572500</td>\n",
              "      <td>0.059130</td>\n",
              "      <td>0.297748</td>\n",
              "      <td>0.548617</td>\n",
              "      <td>0.079070</td>\n",
              "      <td>0.296914</td>\n",
              "      <td>0.948261</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.582903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Summer</th>\n",
              "      <td>0.808333</td>\n",
              "      <td>0.253043</td>\n",
              "      <td>0.544405</td>\n",
              "      <td>0.757579</td>\n",
              "      <td>0.250339</td>\n",
              "      <td>0.520307</td>\n",
              "      <td>0.922500</td>\n",
              "      <td>0.254167</td>\n",
              "      <td>0.626948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Winter</th>\n",
              "      <td>0.657500</td>\n",
              "      <td>0.220833</td>\n",
              "      <td>0.422906</td>\n",
              "      <td>0.607975</td>\n",
              "      <td>0.227913</td>\n",
              "      <td>0.415539</td>\n",
              "      <td>0.972500</td>\n",
              "      <td>0.333478</td>\n",
              "      <td>0.668719</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    90% {\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil analisis diatas, dapat disimpulkan beberapa pernyataan sebagai berikut:\n",
        "- Rata-rata temperatur dan atemp tertinggi terjadi pada musim gugur (fall season) yang selanjutnya diikuti oleh summer, winter, dan spring.\n",
        "- Rata-rata humidity tertinggi terjadi pada musim terjadi pada musim dingin (winter season) yang selanjutnya diikuti oleh fall, summer, dan spring."
      ],
      "metadata": {
        "id": "u_YyyX4CXTsY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "8. Hubungan antara penyewa casual, registered, dan count"
      ],
      "metadata": {
        "id": "ffyY2e11jW6Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "correlation_matrix = day_df.corr(numeric_only=True)\n",
        "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
        "\n",
        "sns.heatmap(\n",
        "    correlation_matrix,\n",
        "    annot=True,\n",
        "    mask=mask,\n",
        "    cmap=\"coolwarm\",\n",
        "    center=0,\n",
        "    fmt=\".2f\")\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "56MaGBIdjxmV",
        "outputId": "a31ab3cc-e1dd-47ac-efa3-f89e308384cc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil analisis diatas, didapat pernyataan sebagai berikut:\n",
        "- atemp dan temp sangat berkorelasi (0.99).\n",
        "- hum memiliki korelasi lemah dengan temp dan atemp (0.13 dan 0.14).\n",
        "- casual cukup berkorelasi dengan temp dan atemp (0.54), dan sedikit negatif dengan hum (-0.08).\n",
        "- registered memiliki pola yang sama seperti casual, dan berkorelasi moderat dengan casual (0.40).\n",
        "- count berkorelasi kuat dengan temp, atemp, casual, dan registered (0.63, 0.63, 0.67, dan 0.95), dan sedikit negatif dengan hum (-0.10)."
      ],
      "metadata": {
        "id": "lh99J-cElh2G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Visualization & Explanatory Analysis**"
      ],
      "metadata": {
        "id": "SCNmtOLp9OKB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Pertanyaan 1: Apakah ada peran cuaca terhadap jumlah pengguna sepeda?**"
      ],
      "metadata": {
        "id": "SG59_Fbj9Qr6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.barplot(\n",
        "    x='weather_cond',\n",
        "    y='count',\n",
        "    data=day_df)\n",
        "\n",
        "plt.title('Jumlah Pengguna Sepeda berdasarkan Kondisi Cuaca')\n",
        "plt.xlabel('Kondisi Cuaca')\n",
        "plt.ylabel('Jumlah Pengguna Sepeda')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dtRoaxqd9PdU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "5a150bc7-d70e-4d26-b8c8-40674e8b99a4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan visualisasi data menggunakan boxplot, dapat disimpulkan bahwa jelas terdapat hubungan antara kondisi cuaca dan penyewa sepeda, Kondisi cuaca saat sedang cerah/setengah mendung menjadi kondisi yang paling diminati, kemudian diikuti dengan cuaca berkabut/mendung, dan terakhir ketika cuaca sedang sedikit bersalju/hujan menjadi yang paling sedikit diminati."
      ],
      "metadata": {
        "id": "9e72oZginRSN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Pertanyaan 2: Bagaimana kondisi yang tampak ketika sepeda digunakan pada workingday, holiday, dan weekday?**"
      ],
      "metadata": {
        "id": "F2Bo_ypQ9RfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(15,10))\n",
        "\n",
        "# Berdasarkan workingday\n",
        "sns.barplot(\n",
        "    x='workingday',\n",
        "    y='count',\n",
        "    data=day_df,\n",
        "    ax=axes[0])\n",
        "axes[0].set_title('Jumlah Pengguna Sepeda berdasarkan Hari Kerja')\n",
        "axes[0].set_xlabel('Hari Kerja')\n",
        "axes[0].set_ylabel('Jumlah Pengguna Sepeda')\n",
        "\n",
        "# Berdasarkan holiday\n",
        "sns.barplot(\n",
        "  x='holiday',\n",
        "  y='count',\n",
        "  data=day_df,\n",
        "  ax=axes[1])\n",
        "axes[1].set_title('Jumlah Pengguna Sepeda berdasarkan Hari Libur')\n",
        "axes[1].set_xlabel('Hari Libur')\n",
        "axes[1].set_ylabel('Jumlah Pengguna Sepeda')\n",
        "\n",
        "# Berdasarkan weekday\n",
        "sns.barplot(\n",
        "  x='weekday',\n",
        "  y='count',\n",
        "  data=day_df,\n",
        "  ax=axes[2])\n",
        "axes[2].set_title('Jumlah Pengguna Sepeda berdasarkan Hari dalam Seminggu')\n",
        "axes[2].set_xlabel('Hari dalam Seminggu')\n",
        "axes[2].set_ylabel('Jumlah Pengguna Sepeda')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "jkO5kTJq9S1Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        },
        "outputId": "82b235b8-9428-4863-e439-8532c0b00efe"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan visualisasi data diatas menggunakan barplot, didapat pernyataan sebagai berikut:\n",
        "- Jumlah penyewa sepeda lebih banyak pada hari kerja (yakni senin - jumat) daripada akhir pekan (yakni sabtu dan minggu). 0 menandakan akhir pekan dan 1 menandakan hari kerja.\n",
        "- Jumlah penyewa sepeda jauh lebih banyak pada hari biasa (bukan hari libur) dibandingkan hari libur (nasional).\n",
        "- Jumlah penyewa sepeda pada hari Jumat ialah yang menempati posisi pertama sebagai hari dengan penyewa sepeda terbanyak, dan hari Minggu menempati posisi terakhir dengan jumlah penyewa sepeda yang paling sedikit."
      ],
      "metadata": {
        "id": "zD8bnWwdqdSG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Pertanyaan 3: Bagaimana tren penggunaan sepeda dalam setahun, apakah tahun 2011 atau 2012 yang lebih unggul?**"
      ],
      "metadata": {
        "id": "HwLq3Uazr2Qx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "day_df['month'] = pd.Categorical(day_df['month'], categories=\n",
        "    ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'],\n",
        "    ordered=True)\n",
        "\n",
        "monthly_counts = day_df.groupby(by=[\"month\",\"year\"]).agg({\n",
        "    \"count\": \"sum\"\n",
        "}).reset_index()\n",
        "\n",
        "sns.lineplot(\n",
        "    data=monthly_counts,\n",
        "    x=\"month\",\n",
        "    y=\"count\",\n",
        "    hue=\"year\",\n",
        "    palette=\"rocket\",\n",
        "    marker=\"o\")\n",
        "\n",
        "plt.title(\"Jumlah total sepeda yang disewakan berdasarkan Bulan dan tahun\")\n",
        "plt.xlabel(None)\n",
        "plt.ylabel(None)\n",
        "plt.legend(title=\"Tahun\", loc=\"upper right\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "MxXENgFIsQAK",
        "outputId": "03f49ecd-0f14-4647-84ff-17e522023027"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan visualisasi diatas menggunakan lineplot, dapat disimpulkan bahwa tahun 2012 (yang diwakili oleh angka 1) jelas menang unggul baik dari januari sampai desember. Puncak penyewaan sepeda pada tahun 2012 terjadi pada bulan September, sementara bulan yang paling sepi terjadi pada bulan januari. Sedangkan pada tahun 2011 puncak penyewaan sepeda terjadi pada bulan Juni dan memiliki penyewaan tersepi pada bulan Januari."
      ],
      "metadata": {
        "id": "RqPCsCmQBg4G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Pertanyaan 4: Bagaimana variabel seperti temp dan atemp serta humidity dapat mempengaruhi jumlah total pengguna sepeda (baik Casual ataupun Registered)?**"
      ],
      "metadata": {
        "id": "tI-rFa8qCOZx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 6))\n",
        "\n",
        "# Scatter plot untuk 'temp' vs 'count'\n",
        "plt.subplot(1, 3, 1)\n",
        "sns.scatterplot(\n",
        "    x='temp',\n",
        "    y='count',\n",
        "    data=day_df,\n",
        "    alpha=0.5\n",
        ")\n",
        "plt.title('Temperature vs Count')\n",
        "\n",
        "# Scatter plot untuk 'atemp' vs 'count'\n",
        "plt.subplot(1, 3, 2)\n",
        "sns.scatterplot(\n",
        "    x='atemp',\n",
        "    y='count',\n",
        "    data=day_df,\n",
        "    alpha=0.5\n",
        ")\n",
        "plt.title('Feels Like Temperature vs Count')\n",
        "\n",
        "# Scatter plot untuk 'hum' vs 'count'\n",
        "plt.subplot(1, 3, 3)\n",
        "sns.scatterplot(\n",
        "    x='hum',\n",
        "    y='count',\n",
        "    data=day_df,\n",
        "    alpha=0.5\n",
        ")\n",
        "plt.title('Humidity vs Count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "O359gNh3DB4P",
        "outputId": "1fbf4441-51a8-49b9-e641-cfef61069630"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Humidity vs Count')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 3 Axes>"
            ],
            "image/png": 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pIoWUGseDDz6Ic889F8FgMO9eLrroImSz2XF3Ynw+Hy655BI88MADebvk999/P975znfaz9sqOPjQQw9NmF6XSywWA4CKdoGz2Swee+wxXHHFFXkhvosWLcJ1112Hv/71r/b5JsuaNWtw7rnn2v+uq6vDypUrS/52CGK+8IMf/ACPP/543n+AGdoeiUTw/ve/P+9vnuM4nHnmmbZ+hcNhPPXUU7j66qsRj8ft9w0PD2Pr1q3Yv38/uru7S17b7/cDAP70pz8hlUrNzg2Pctddd+H/+//+P9x55534f//v/9nHX3/9dezfvx/XXXcdhoeH7ftJJpPYvHkznn322UnpUy6VPtNccouBBwIBrFy5Em63G1dffbV9fOXKlQgEAiW15uMf/3he2tXf/u3fgud5PPLII1Me09/+7d8WHcu1U8lkEkNDQzj77LNhGEZROnm1KBzHZG1uIddccw1effVVHDx40D52//33w+Fw4D3veQ+Aqf9mJ2OnALPIeWNjox2JAACCIODTn/40EokEnnnmmYqvXcj//b//N+/f5557Ltkp4rillI17/PHHq9JEIVefOY7Dhg0bYBgGPvKRj9jHLd0e72+st7cXr7/+Oq6//npbYwCzbt+aNWumPD6/34/3vOc9+K//+i97/p3NZnH//ffjiiuuGLcuHc3dSRMXAlS8m5gR9u/fj2g0ivr6+pKvDwwM5P0713kEjE0WW1tbSx4vrM/AsmxR7vGKFSsAwE4dmOyYCjszWTz33HO47bbb8MILLxRNZKPRaJ4RqgalxrF//368+eabdu2FQgrvpZBrrrkGv/3tb/HCCy/g7LPPxsGDB/Hqq6/iu9/9bt57fvrTn+KjH/0obr75ZmzevBnvfe978b73vQ8sW94n7fP5AADxeHzCexscHEQqlcLKlSuLXlu9ejV0XcfRo0exdu3aCc9VSOFvCgCCweCEtT0IYi4544wzShbv3r9/PwDT2V4K6+/uwIEDMAwDt956K2699daS7x0YGEBzc3PR8Y6ODnzuc5/Dt7/9bdx3330499xz8e53v9uuWTFTPPPMM/jDH/6AL37xi3l1lYCx+77++uvLfj4ajSIYDE76upU+UwtJkoo01+/3o6WlpaiGhN/vL6k1y5cvz/u3x+PBokWL8uzUZMbE83zJDj1dXV34x3/8Rzz88MNF45iJDZBS45iszS3kqquuwuc+9zncf//9uOWWW2AYBh588EG7pgcw9d/sZOwUYKabLF++vMj2WekXpWpBVUKp3xTZKeJ4ppyNszZKp0OptYQkSaitrS06Pjw8XPY81t9zoV4D5sbBa6+9NuUxfuhDH8L999+Pv/zlLzjvvPPwxBNPoL+/v6IUNJq7E/MdciwRM4Ku66ivr8d9991X8vXCiVS5TkHljud662dqTKU6rx08eBCbN2/GqlWr8O1vfxutra0QRRGPPPIIvvOd71S0O1CqiB0Au1hnIaXGoes6tmzZgn/4h38o+RnLqVaOyy+/HC6XCw888ADOPvtsPPDAA2BZ1i6IZ1332WefxdNPP40//OEPePTRR3H//ffjwgsvxGOPPVb2u1m1ahUAs5itVZeqGkz2uVXzt0MQc42lLffeey8aGxuLXrdaHVvv+/znP1+2EOiyZcvKXufuu+/GDTfcgIceegiPPfYYPv3pT+OOO+7Aiy++OGMthteuXYtIJIJ7770XN910U54z3bqfb37zm2X1xOPxTOm6lT5Ti9myU5MZk8PhKFosZLNZbNmyBeFwGF/84hexatUquN1udHd344YbbpgRO1VqHJO1uYU0NTXh3HPPxQMPPIBbbrkFL774Irq6unDnnXfmvW8qv9lcO1VNqmWnCOJEZ7J/S0Dpv6f5NhfcunUrGhoa8Mtf/hLnnXcefvnLX6KxsREXXXTRhJ+luTsx3yHHEjEjLF26FE888QTOOeecko6RaqPrOg4dOpTnUNm3bx8As8tAtcb0u9/9DplMBg8//HCeV71USH85MbV21iORiB2yCkxux3Pp0qVIJBIVGaJSuN1ubNu2DQ8++CC+/e1v4/7778e5556LpqamvPexLIvNmzdj8+bN+Pa3v41vfOMb+PKXv4ynn3667LUvueQScByHX/7ylxPuwNTV1cHlcmHv3r1Fr+3Zswcsy9pRa9V4boWU+44IYr6xdOlSAEB9ff24f/dW5KYgCFPWh3Xr1mHdunX4f//v/+H555/HOeecg3/913/F17/+9SmdbyJqa2vx61//Ghs3bsTmzZvx17/+1dYi6759Pt+U76cclT7TarJ//35ccMEF9r8TiQR6e3tx6aWXVm1MO3fuxL59+/CLX/wir1i3lVaZSyV2KpfJ2qnp2txrrrkGn/jEJ7B3717cf//9cLlcuPzyy4veN9nf7IoVK7By5Uo89NBDuOeeeyZ0Tra3t+PNN9+Erut5DrQ9e/bYrwPVeW6FkJ0iTkRm4m+pUqy/ZyuCNJdS89VCxvub5TgO1113HX7+85/jzjvvxG9/+1t87GMfq8jJTHN3E9LE+QvVWCJmhKuvvhrZbBZf+9rXil7TNG1GOgz9y7/8i/3/DcPAv/zLv0AQBGzevLlqY7KEP9dzHo1G8bOf/azovW63u+Q5rYVDbh2kZDJZ1LZ4PK6++mq88MIL+NOf/lT0WiQSgaZpE57jmmuuQU9PD37605/ijTfewDXXXJP3ejgcLvqMtYuRyWTKnre1tRUf+9jH8Nhjj+H73/9+0eu6ruPuu+/GsWPHwHEcLr74Yjz00EN53Y76+/vxq1/9Chs3brTDc6vx3Aqx8tlnq+MVQUyVrVu3wufz4Rvf+EbJ7jmDg4MATIfE+eefjx//+Mfo7e0t+75SxGKxIu1Yt24dWJYd92++GrS0tOCJJ55AOp3Gli1b7DSF9evXY+nSpfjWt76FRCJR9Lnx7mciKn2m1eQnP/lJ3rV+9KMfQdM0XHLJJVUbUyk7ZRhGye5l5TSwvb0dHMcV1ev74Q9/OOH1Laphc6+88kpwHIf/+q//woMPPoht27bl1SGZzm/29ttvx/DwMD760Y+WtJmPPfYYfv/73wMALr30UvT19eH+++/Pu4fvf//78Hg82LRpE4DqPLdCXC4XALJTxImFz+dDbW1tVf+WKmXRokU49dRT8Ytf/CIvdfjxxx/Hrl27Jvz8RHPLD37wgxgZGcFNN92ERCIxbje4QmjuTnP3+QxFLBEzwqZNm3DTTTfhjjvuwOuvv46LL74YgiBg//79ePDBB3HPPffgfe97X9WuJ0kSHn30UVx//fU488wz8cc//hF/+MMfcMstt9jh9tUY08UXXwxRFHH55ZfbBuHf/u3fUF9fX7SIW79+PX70ox/h61//OpYtW4b6+npceOGFuPjii9HW1oaPfOQj+MIXvgCO4/Af//EfqKurQ1dXV0X3+4UvfAEPP/wwtm3bhhtuuAHr169HMpnEzp078etf/xqHDx8uyikv5NJLL4XX68XnP/95cBxnt4e2+OpXv4pnn30Wl112Gdrb2zEwMIAf/vCHaGlpwcaNG8c99913342DBw/i05/+NH7zm99g27ZtCAaD6OrqwoMPPog9e/bYrVG//vWv4/HHH8fGjRvxiU98AjzP48c//jEymQzuuuuuvGc/3edWyKmnngqO43DnnXciGo3C4XDgwgsvLFsThCDmCp/Phx/96Ef44Ac/iNNPPx3XXnut/dv/wx/+gHPOOcd2rv/gBz/Axo0bsW7dOnzsYx/DkiVL0N/fjxdeeAHHjh3DG2+8UfIaTz31FD75yU/iqquuwooVK6BpGu69996S+lAKVVVLRoiEQiF84hOfmPDzy5Ytw2OPPYbzzz8fW7duxVNPPQWfz4ef/vSnuOSSS7B27VrceOONaG5uRnd3N55++mn4fD787ne/m/DcpZjMM60WiqJg8+bNuPrqq7F371788Ic/xMaNG/Hud7+7amNatWoVli5dis9//vPo7u6Gz+fD//zP/5SsUbF+/XoAwKc//Wls3boVHMfh2muvhd/vx1VXXYXvf//7YBgGS5cuxe9///sJ6yLlUg2bW19fjwsuuADf/va3EY/HixZR0/nNXnPNNdi5cyf+6Z/+CTt27MD73/9+tLe3Y3h4GI8++iiefPJJ/OpXvwJgFl3/8Y9/jBtuuAGvvvoqFi9ejF//+td47rnn8N3vftcueFuN51aI0+nEmjVrcP/992PFihUIhUI46aSTcNJJJ035nASxEPjoRz+Kf/7nf8ZHP/pRbNiwAc8++6ydjTDT3HHHHbjsssuwceNGfPjDH0Y4HMb3v/99rF27tuQmRy6Wrn75y1/GtddeC0EQcPnll9sOkdNOOw0nnXQSHnzwQaxevRqnn356xeOiuTvN3ec1s9+IjjgeKdey+Cc/+Ymxfv16w+l0Gl6v11i3bp3xD//wD0ZPT4/9ntx207kAMP7u7/4u75jVEvSb3/ymfcxqc33w4EHj4osvNlwul9HQ0GDcdtttRjabreqYDMMwHn74YePkk082JEkyFi9ebNx5553Gf/zHfxS1zezr6zMuu+wyw+v1GgCMTZs22a+9+uqrxplnnmmIomi0tbUZ3/72tydsxV1IPB43vvSlLxnLli0zRFE0amtrjbPPPtv41re+ldfOejw+8IEPGACMiy66qOi1J5980njPe95jNDU1GaIoGk1NTcb73/9+Y9++fRWdW9M046c//alx7rnnGn6/3xAEwWhvbzduvPHGonamr732mrF161bD4/EYLpfLuOCCC4znn3++6JzTfW6bNm3K+x4MwzD+7d/+zViyZInBcRy1LyXmDOt3vH379nHf9/TTTxtbt241/H6/IUmSsXTpUuOGG24wXnnllbz3HTx40PjQhz5kNDY2GoIgGM3Nzca2bduMX//613nnyv3NHzp0yPjwhz9sLF261JAkyQiFQsYFF1xgPPHEExOO32qdXuq/pUuX5t3jRH+rL730kt2yOJVKGYZhtpV/73vfa9TU1BgOh8Nob283rr76auPJJ5+ccGwW5VoUV/JMLTtTyKZNm/JaZJe7L+ven3nmGePjH/+4EQwGDY/HY3zgAx8whoeHiz4/nTEZhmHs2rXLuOiiiwyPx2PU1tYaH/vYx4w33nijqKW2pmnGpz71KaOurs5gGCbPjg8ODhpXXnml4XK5jGAwaNx0003GW2+9VXSO8cZhGJXZ3PH4t3/7NwOA4fV6jXQ6nffadH6zFpatq6+vN3ieN+rq6ozLL7/ceOihh/Le19/fb9x4441GbW2tIYqisW7dupKt0Kf73Eq1537++eeN9evXG6IoFrU2J4iFwEQ2rpSWplIp4yMf+Yjh9/sNr9drXH311cbAwEDR34D1NzM4OJj3+Up121pbFP49/8///I+xevVqw+FwGGvWrDF+85vfGNdff73R3t6e975Sf5Nf+9rXjObmZoNl2SK7ZxiGcddddxkAjG984xsln8d40Nyd5u7zFcYwqBoWsbC54YYb8Otf/3rCHQSCIAiCmAt+/vOf48Ybb8T27dtLdkQiCIIgThzuuecefPazn8Xhw4dLdkIjiIUI1VgiCIIgCIIgCIIgiBnGMAz8+7//OzZt2kROJeK4gmosEQRBEARBEARBEMQMkUwm8fDDD+Ppp5/Gzp078dBDD831kAiiqpBjiSAIgiAIgiAIgiBmiMHBQVx33XUIBAK45ZZb7KYNBHG8QDWWCIIgCIIgCIIgCIIgiClBNZYIgiAIgiAIgiAIgiCIKUGOJYIgCIIgCIIgCIIgCGJKUI2lCtB1HT09PfB6vWAYZq6HQxAEMWsYhoF4PI6mpiaw7Im1F0HaTxDEiciJrPsAaT9BECcm09V+cixVQE9PD1pbW+d6GARBEHPG0aNH0dLSMtfDmFVI+wmCOJE5EXUfIO0nCOLEZqraT46lCvB6vQDMh+zz+eZ4NARBELNHLBZDa2urrYMnEqT9BEGciJzIug+Q9hMEcWIyXe0nx1IFWGGwPp+PDAxBECckJ2I6AGk/QRAnMiei7gOk/QRBnNhMVftPvMRpgiAIgiAIgiAIgiAIoiqQY4kgCIIgCIIgCIIgCIKYEuRYIgiCIAiCIAiCIAiCIKYEOZYIgiAIgiAIgiAIgiCIKUGOJYIgCIIgCIIgCIIgCGJKkGOJIAiCIAiCIAiCIAiCmBLkWCIIgiAIgiAIgiAIgiCmBDmWCIIgCIIgCIIgCIIgiClBjiWCIAiCIAiCIAiCIAhiSpBjiSAIgiAIgiAIgiAIgpgS5FgiCIIgCIIgCIIgCIIgpgQ5lgiCIAiCIAiCIAiCIIgpQY4lgiAIgiAIgiAIgiAIYkqQY4kgCIIgCIIgCIIgCIKYEvxcD4AgiBOLhKyhO5JGUtHgEXk0BZzwSCRFBEEQxwuk8wRBENOHtJRYSNAvkyCIWePYSAqP7+pHJKXaxwIuAVvWNKAl6JrDkREEQRDVgHSeIAhi+pCWEgsNSoUjCGJWSMhakYEEgEhKxeO7+pGQtTkaGUEQBFENSOcJgiCmD2kpsRAhxxJBELNCdyRdZCAtIikV3ZH0LI+IIAiCqCak8wRBENOHtJRYiJBjiSCIWSGpjL+7kprgdYIgCGJ+QzpPEAQxfUhLiYUIOZYIgpgV3OL4Jd1cE7xOEARBzG9I5wmCIKYPaSmxECHHEkEQs0JzwImASyj5WsAloDngnOUREQRBENWEdJ4gCGL6kJYSCxFyLBEEUURC1rC3L47Xukawry9elSKBHonHljUNRYbS6nBB7VMJgiBmn2rqPek8QRDE9JmOls7EHJ4gKoEsPEEQecxke9OWoAtXrW9FdySNlKLBJfJoDjhpsUEQBDEHzITek84TBEFMn6lo6UzO4QliIsjKEwRhM1F706vWt057ceCReKxs9E7rHARBEMT0mEm9J50nCIKYPpPR0tmYwxPEeFAqHEEQNtTelCAI4sSA9J4gCOL4gTSdmGvIsUQQhA21NyUIgjgxIL0nCII4fiBNJ+YaciwRBGFD7U0JgiBODEjvCYIgjh9I04m5hhxLBEHYUHtTgiCIEwPSe4IgiOMH0nRiriHHEkEQNtQqmiAI4sSA9J4gCOL4gTSdmGvoF0YQRB7zvVV0QtbQHUkjqWjwiDya5tHYCIIgFhJzqfek5QRBENVlrufwpOsnNvRNEwRRxHxtFX1sJFXUStXaiWkJuuZwZARBEAuTudB70nKCIIiZYa7m8KTrBKXCEQQxKyRkDXv74nitawT7+uJIyJPrTpGQtSKDBZgtVB/f1T/p8xEEQRDTYyq6TlpOEARxfDFdXZ/uGoGYH1DEEkEQM041djG6I+kig2URSanojqTnZZQVQRDE8chUdZ20nCAI4vhiOrpOkU7HDxSxRBDEjFKt3emkMv77UhO8ThAEQVSH6eg6aTlBEMTxxVR1nSJYjy/IsUQQxIzSHUlDYBnUekR4HTxqvQ7UeUSwzNguRiW4xfEDLF0TvE4QBEFUh+noOmk5QRAnIsdzutdUdb2SSCdi4UDWmyCIGSWaVvByZxj98Yx9rMHrwDnLatEXkyvenW4OOBFwCSUNUMAloDngrNqYCYIgiPJMR9dJywmCONE43tO9pqrrFMF6fEERSwRBzBgJWcNf9w/lLT4AoD+ewXMHhlDjFivenfZIPLasaUDAJeQdtwwztTMlCIKYeaar66TlBEGcSJwI6V5T1XWKYD2+oG+LIIgZozuShpo1IAksZFUHAHAsg6W1brglHgLHQtV0JGStosVES9CFq9a3ojuSRkrR4BJ5NAectBAhCIKYJaqh66TlBEGcKMxFw4KErKE7kkZS0eAReTTNgr5ORdcpgvX4giw4QRAzRlLRIPIsltR5cGgwATVr4LTWAF7uDGMgLuO0tiB6ovKkwoE9Ek8dgwiCIOaIauk6aTlBECcCs53uNZdpd5PVdSvSqdx4abNhYUHfFkEQM4YV4uqTBKxZ5IdX4vHqkRHIWhZ1Xgkeh/m6FQ581fpWMiIEQRDzGNJ1giCIypnNdK+J0u7mox5TBOvxA31jBEHMGM0BJ0JuARzDwADAMsCKBi+a/BK6o2l4pbFc7JkKB54qcxFGTBAEMd/JTV2QBBYtQSdisorFtW64RBY1LgeisgrdmHldJ50mCGK+E3KLaAlKiKY0OEQOjGFgOKlAN6qf7jUXaXeVMJFWUwTr8QFZX4IgZgyPxGPD4hB+/txhdIVTEDkWh4YSaA25cNm6RrAMA90AMqoOh8hBVrNzPWQAx3/3DoIgiKlipS48ubsfEs/hmX2DePHQMCSexYaOEESOQ71Pgq4bMAwD6Rnq6kM6TRDEfMfSqa5wCocGE5BV3e6gKWtZbF5d3XSv2Uq7m4xTn7T6xIEcSwRBVI1CQxN0i3i5M4xajwMOnkUioyGSFgEDSMhZHB5O4vBwyv78YExGjUecU0OzEMOICYIgZpOAU8T6thB+s+MYFE1HvdeBs5bWYHvnCP66fwhukUdz0Amvg8dJzf6qX590miCI+U6uTlmpw3FZhZrV0R1J44NnLUad11HVa+am3SmajrisQsnqEDkWXkmoStrdZBxFpNUnFvRNEgRRFUoZmpaghK5wCj5JQI3HAa8kYCSloC3owlN7BvKMiSSwULPGnBua+RpGTBAEMR+wtJ5nGbzdE4NL5LCkzoMXD4XRH5MBjO2aR2UVL3eGsazeW1VNJ50mCGK+U6hTIs+ixjPmSAonlao7lqxU5dwIKYu2kAsOgZnW+SfrKCKtPrFg53oABEFMj4SsYW9fHK91jWBfXxwJeWbSDiYaQylDE01pODSYgKKZhs3qJOR3C+iJpqHpBgDTqbSkzgORZ21DM1fMdvcOgiCISphvWp8ZXbCklSxaAk50hVMYlXSbJXUeJJVs1TWddJogiPnOXOiUR+KxaXkdErKW51Rq8DqwoT2IZ/YOIiFrU7YnlTiKciGtPrGgiCWCWMDMl7zlcobGIbCQVTMU19ql8UkCXCKHkFtEjVuEx8HDKwkQ+TE/91wamtns3kEQBFEJ81HrHYKp2QaAhJKFx8HDKXBgGYBlGTT6JPhGGzRUW9NJpwmCmO/MlU6puoFTWvw4ucWPjKbDwbNgAPSNRpQeGIhjx9HIlOzJZB1FpNUnFhSxRBALlInCUWdzN7ucoWFg7pKoWT3vuOVYWuR3osbjyHMqma/PnaGxwohLUe3uHQRBEBMxX7Xe0ncAEDgGalYHywAeSUDILSLgEu33VlvTSacJgpjvzJVOJRUNgwkFQwkFcVnDUELBYMLsQlfjFvHYNOzJZB1FpNUnFuRYIogFymTDUWeScoZmOKngnGW1aPBJecc9Dh4bFgeLHErA3Bsaq+NRoSG0dnOoyCBBELPJfNV6W9+9DiRlDU1+J1iWyUttBmZG00mnCYKY78yVTo3n/DEAxNJTtyeTdRSRVp9YzKljKZvN4tZbb0VHRwecTieWLl2Kr33tazCMsSR9wzDwj//4j1i0aBGcTicuuugi7N+/P+884XAYH/jAB+Dz+RAIBPCRj3wEiUQi7z1vvvkmzj33XEiShNbWVtx1112zco8EMVPMp7zlcoZGNwBZy+KDZy3GpesW4fyVdbh03SJsWdOI/3NaS1lDA2BOa4m0BF24an1r3pivWt9KbVGrAOk+QUyO+ar1umGmVpzREcKqRV586Ox2nLe8DmsW+e0UuIBLwKYVdeiOpKuu56TTCwvSfuJEZC50ajznDwB4pfKvTWRPpuIosp7BRasbcHKLH6e3B3D2khoEnGLRe4mFzZy6Ce+880786Ec/wi9+8QusXbsWr7zyCm688Ub4/X58+tOfBgDcdddd+N73vodf/OIX6OjowK233oqtW7di165dkCQzCuIDH/gAent78fjjj0NVVdx44434+Mc/jl/96lcAgFgshosvvhgXXXQR/vVf/xU7d+7Ehz/8YQQCAXz84x+fs/sniOkwn/KWLUNTqgbI5tUNqPM6ijpfeCQeV61vRXckjZSiwSXyaA44EUkrePDVo3NeS8Qj8dSpYgYg3SeIyTGftV43gMGEgoBLwGltQVywsiFP0x0Cg2f2DiKcnBk9J51eOJD2Eycqs61T483JVy/yYiihlP1sJfbEchQVzt/Hiz6KpBW8ciQ853N7YmZhjNytgllm27ZtaGhowL//+7/bx6688ko4nU788pe/hGEYaGpqwt///d/j85//PAAgGo2ioaEBP//5z3Httddi9+7dWLNmDbZv344NGzYAAB599FFceumlOHbsGJqamvCjH/0IX/7yl9HX1wdRNL2jN998M377299iz549E44zFovB7/cjGo3C5/PNwJMgiMmTkLUiB4xFwCUUtfyc7rW6I2kkFQ0ekUdTGQNiva9SQ1Pq87N1T0RlVFv/Foruz8S9E8RUWKhaT3q+cJkJ7SPtJ4jZpZROA5h1XR6MZ/Dnvf2IpjQ4RA6MYWA4adZ8Ilswv5iu9s1pKtzZZ5+NJ598Evv27QMAvPHGG/jrX/+KSy65BADQ2dmJvr4+XHTRRfZn/H4/zjzzTLzwwgsAgBdeeAGBQMA2MABw0UUXgWVZvPTSS/Z7zjvvPNvAAMDWrVuxd+9ejIyMFI0rk8kgFovl/UcQ843Zyls+NpLCg68exSM7e/HM3kH8YWcvHnz1KI6NpEqOaWWjF6e1BbGy0TvpMcynWiLEzDBfdR8g7SfmJwtV60nPiVxI+wlidiml07Nd8+jYSAr3vnAYv361G4/v7sfv3+jBy51hNPoksAzZguONOXUP3nzzzYjFYli1ahU4jkM2m8U//dM/4QMf+AAAoK+vDwDQ0NCQ97mGhgb7tb6+PtTX1+e9zvM8QqFQ3ns6OjqKzmG9FgwG81674447cPvtt1fpLgli5phKOOpkmKgbUbV3GeZTLRFiZpivug+Q9hPzl4Wo9aTnRC6k/QQxP5hpe2Jh2ZW+mJx3vD+ewXMHhnBGRwiDCYVswXHEnDqWHnjgAdx333341a9+hbVr1+L111/HZz7zGTQ1NeH666+fs3F96Utfwuc+9zn737FYDK2trXM2HoIYj+nmbo+X+lDJjnM188bnUy0RYmaYr7oPkPYT85tq1Okop/czofWk50QupP0EMUalacczxWzUfbLsisgVJ0j1xzOwavGQLTh+mNNv8gtf+AJuvvlmXHvttQCAdevW4ciRI7jjjjtw/fXXo7GxEQDQ39+PRYsW2Z/r7+/HqaeeCgBobGzEwMBA3nk1TUM4HLY/39jYiP7+/rz3WP+23pOLw+GAw+EoOk4QxxvHRlIli/tZxfRme8fZ6mRRLve72i2ridlnvuo+QNpPHN+Mp/czofWk50QupP0EYTLR3Pt4wbIrXkmAJLCQVT3v9Yymky04zpjTGkupVAosmz8EjuOg6+YPr6OjA42NjXjyySft12OxGF566SWcddZZAICzzjoLkUgEr776qv2ep556Crqu48wzz7Tf8+yzz0JVx/6AH3/8caxcubJkSCxBnAgkZA1P7u6HwDKo9YjwOnjUeh0QWAZP7u5HQtaqtuOckDXs7YtP2G66mrnflV6TmF1I9wli9knIGrZ3hsGzjK31dR4RsbSZ6iaw408Hy2n9eDo7GT0nvT7+Ie0niInTjmdT+ybS3enqsrWGEHkWS+o8kIT8v3+/k694bk82YmEwpxFLl19+Of7pn/4JbW1tWLt2LXbs2IFvf/vb+PCHPwwAYBgGn/nMZ/D1r38dy5cvt1uPNjU14YorrgAArF69Gu9617vwsY99DP/6r/8KVVXxyU9+Etdeey2ampoAANdddx1uv/12fOQjH8EXv/hFvPXWW7jnnnvwne98Z65unSBmncKwW0XTIfEcnjswhP54xn5fg9eBc5bVojuSrsqO82R3ZqqR+32i7AYtREj3CWJ2yO0IlFY1PLGrH0PJsTbTltb3xWQIHDNpra9EZyvRc9LrEwPSfoKY/RIT5ZhId6uhy7lrCJ8kYM0iP+KyCjWro8En4fyVDajzThwpOJmxzHWK4YkOYxiGMfHbZoZ4PI5bb70V//u//4uBgQE0NTXh/e9/P/7xH//R7uZgGAZuu+02/OQnP0EkEsHGjRvxwx/+ECtWrLDPEw6H8clPfhK/+93vwLIsrrzySnzve9+Dx+Ox3/Pmm2/i7/7u77B9+3bU1tbiU5/6FL74xS9WNE5qO0osdEqJ8tI6Nx7Z2YuREgauwevAR85dglNaA9MyLnPRbppaXFeXauvfQtH9mbh3gpgtcnW7ziNix9EIXjkcRtAtwsFz9vsavA6c0RHCSc1+1Hodk5q8V0NnSa/nJzOhfaT9BAG81jWCZ/YOln39/JV1OK1tZiPrJtLdbSc34fdv9lRFl6froJqMjaBNiukzXe2bU8fSQoEMDLGQKSfKbSEnvvfkftR5JXAsU/S5z1y0HGctrbXPMZUIor19cTyys7foOMsANW4RS+s9cAhcVXcVyl3T4tJ1i2ZlN+h44UTWvxP53omFS6Hm13pEPLVnAPv64+BZpkjzt528CGd01GBlo7dirS/UWUvTDQAZVcfaZh/WNQcm1HTS6/nJia59J/r9EzPHfNC8icbwziUhvHgoXPb1wjFOFCU01TVEJWO1xkKbFNVhutpHT5ggjnPKhd3KahYOnkNGyxbVz5AEFhwztvCYqHtEOaNSqiAsywCNPgnPHRjCzu4oGv1mikW1dhWoxTVBECcyhZqfGS2Y6hZNTS6l+VaqWzmtL9T4SGospS5X06206v0Dcezpi0+o6aTXBEGcSFSrqcF0Ur4m0t1YunJdriRKaDod6Cq1EfMlxfBEhxxLBHGcU06UDQBtIRci6XwhlgSzyJ7fJVZ0/vGMSqni3zVu0V6ALK8fC123ChdOd1dhvILjLAM4BQ57++KUf00QxHFJoeY7BBZpJYu2kAtd4RR0fSxQXRJYrF7kHVcDS2l8S1BCTDbrZuRquoXAsRVpeiUNIqhmBkEQxwtWU4Ny8+ZKtG26KV8T6a7PWVnjnoSs4ZE3e9EVTkHJ6hA5Fl5JqNp8vpKxWmOhTYr5AVlmgjjOKSvKBuB2cGjwS2AAqFkdwqhRqPc5Kto1mai7xbaTm4p2ZgwA/fEMJMG8VuHnrF2FUosJABMuMMrtBrEMsLjWjWf3DSKpZO3jlH9NEMTxRKHmMwDqvQ4MxDNoDjoRcovgWQYCx6It5MKqRn/Zc5XT+IyqIyFrkHjO1nSLXG23ND3kFnFgII5oWkXAKWBpvRd1Xse4u/chtwCBZYrSG0izCYJYyLQEXdh2chMODMQRS2vwOXksG9XEiZho3l2JM2eiqKll9V7s6YtPGFW1fyCGWEaFQ+DglQQkZBV7+2Nor3EDQFWihCqN8KpWF2tietBTJojjnHKiPJxUcOHqBvRH5ZKOFgATRvZMFHoaTipFOzMZVbejokS+uMV1WtFK7sbwHNAWcuPwUBLWhnupBUa53SBztz4JbexWwTKAwDJ44eAQ2kJuBF0i7YYTBLGgKdT84aSCc5bV4rkDQ4jKKuq9EkSerUjry2n8cFLBhvYguiNpO9UOQJG2swwwEJfx078cQlc4Zb+vLeTCDecsxtomf9nd+00r6/D0nsGSC6gnd/fj0nVNCCcVimQiCGJBUWqOW0nqMFCdlK9y82S3yOG00aY965p9OBpOm1GuhqnlbSEXWkNO7B+IQ9cN/Pb1bjy9dxBWueYmvxNndISwuy+GlQ2+qkQJVRrhVa0UQ2J6kAUmiOMcS5S3d4aRyGjIqDocIgePyGF9exABp1hUVC+SViraJS5XQ8kq4np4KIGOWg+2nWwuAFKKBlnNYjiplHQqAYAkcEWhtU6Rw/7+OPb3JXBGRwiDCbO+R7kdmtwW17KaRUrRkJA1BFwOMIaB4dGW27l1QZbXe1DjcdBuOEEQCxqPxOOCVXXY3hlGNKXBIXLI6jo2r65Hc9AJjmUr1vpcjS8s0K3pBrasbkBS0bB/IG5HvOZqu18S8NjbfRhKZCByLHjOrN13NJzCz587jH9416o8vc61Q92RNGJps6uddU2HyIGDAY5jce8Lh8Hk1AIk7SYIYr4zlYij3Aj+kaQCRdPLzqFznTnjpREX6m5W13E0nMYz+wbtzVu3yGHTijowMLMNXu4M4/BwCnUeEX89MASPg8e7T14Ep4OHg2MRTaswDOAd7SH0ReWqRQmVsxG5z6kaKYbE9KGnTBAnCIPxDLrCKTvlrS1UuqjeZIxeYehpYRHX5fUe7OqN28JupbiNF2Ibk1U8d3AI8ugueFY3oBsG6jwOJBQNTpEDy8A2fOV2aDwSD7eDw/MHhxBJqeiNpnFkOIUGrwPnLKuFput5dUHUrF72PgmCIBYKx0ZSeHrPIAZiGcRlFWpWR4NPwhWn1mJJTl27SrTe0vhSBboBoD8m44pTm7GmyYdwsljTGRbY0xtHTzQNgTMXQk6Bw+JaN4YTGRwYiKPO6yhZ3DWlaCWv+Y72IIaTCnTDsJs/FI6btJsgiPnIZCOOCqObaj0idvVGsaTOA19BOQlgLOVrMkW1rY5qls2wNnUVScCOoxFsO7kJv3+zx85uYBigJejE9s4RsAyDN/YMoHMoCZ9TwCK/hLaQC+csra1qlFAlBcArcUARM0tpdydBEMcN1uIhqWRR43Gg0e9EjceBpJLF47v6kZDzo44qMXoWVuipRW4R18I6G9a1rF2F3M8Bo6kPK+qwrz9uO5UyWhaDcRld4RR2dkcxFM/g8HASjT4JOd2yS4bbFi6axNFFTX88g+cODEESuKJis+XukyAIYiGQq3siz9qazzAMntk/mKf3lWi9pfGlCnRLAgs1a+CZ/YPYtLKuSNPdIgeBY9EXkyFyLGJpFcOJDI6NpPDG0QjispbXXa4Qp8AVXRMANMPAq0dGwHPFU1jSboIg5jOTKTJdyvnPwIwEPTSYgKLpeZ+1Ur4m2jQoNe/vCqewqzeK/QMJHBlOYf9AArt6o+gKp3BgIH8zWBI4vHIkgoCLx2tdI+gZ1dxYWkVvVEZS0XBsjnTYckCd1hbEysbxG1MQ1YccSwRxnDMZRxEwsdGLphTs7YvbxmTT8rEFRW5h7sIaSgOxDHZ2R/Ba1whSmSy2ndyES9ctwvkr63DpukW4an0r1KyB0VRtZHUDI0kFmm6AZYC4rELgzYXMcweGUOMe61pXKty28L69kgBJGHMuyepYsaVShcSpgwRBEAuNyeh9JQscj8Rj0/I6GAAODCZGUyaMPI2PpFTE01mctaQGp7cHcEqLHxetbsB5K+qQymjgWAbRtGpHhQKmnuswMJRQihY5FmrWQFQuvhc1ayCjZfO62xWOmyAIYj5SaZHphKxhZ3cEu3tjGE5kbCeSVTPPLwmIpBQMJzLojaYBw8CmFXXwSPyk5/2RlIJDgwl7U9dCVnUcGkwgkizeAOiPyWjwSegKp8CyDJwiB0ngoGZ1OAUehgFy8p+AkBuPII5zJtuCczyjF5NVHAkncezgsH0s5BZwwao6ZFQDh4cSWF7vKaqzEZNVHBpMoMYjIj66iMhNj8sdKwOgwetA53AS2ujCgWNZsIyOeq8DSVlDfzwDa0lRrihf4X2LvLkQsoyntdtdygmmaDpkNYvXukaoKCxBEAuGyeh9JQucYyMp/OXAIHiWQYNPgqYbkHgWTUGnnYYRk1W8fnQEQ4mxxUfAlcLSOjd4lkVzwInhRKbo/A1eB7JZo2yxWVXX8zTbwimyCLpF6IYBRdPzUje8kkDdfwiCmLdUUmTaSmPLZg0cGTabHlhzVZ8kYCAu49yVdRA5BtGUBrfEQ2QZPHdgECLPTnrerxtGkVPJQlZ1OASu4FgWXgcPA2YEFQDb0S/yHESehciz5OQ/ASHrSxDHOZNtwVlo9KyJu6xmIfIsJD6/xlE4qeLpPYO4an0rAGBXbzzvfIqm2wsDB8/CerVczSZrNyapZBEe3SVhGWBpvQfnrajD797oBQBkNH3conyl7tsnCVizyI+4rGJxyI1TWv0IJxSomm4XQ4zJKhKyhoMDCbtI+HhFYccrjkgQBDGbTEbvyy1wFE2HwDFIyCrePBYBxzAwAKRG62somg5lKIk1i8xzHRpMYEmtO+8ckZSK/piMwUQG5y6vw0hKwdGcrnCtIRcuXtuII8OpsosPt8jnabZVHzDoFFDncSClZBFOxqHpBtJKFgbMrkUOgSl5vkogPScIYiaZqMg0APu1Ws9YZL4VPXRSkx/1Xif+59VjcAqcrctNfglb1jTixYND8DtF1HrHmtUUBncWzvtdIo8Gr6Mo7RgwNwDqPQ64Rc5uquN1cGgJOuFx8HAIHBw8C8MwS0rUeh3wOnjaoD1BoW+YIOaI2ZrATrYFZ67R6wqnbKeQT+KxosGLp/YM4J1LatAXk4sKaJe6lumU0tHgdaBwul9YqLA54ITPKaAvJmN9exDLGzxQswYEjgHPMFBVHUtq3dB1A6e0+iHxPAbiGaQy2aLnV+6+RZ5Fg9+B3lgaS2s96IsM4ehIGpLAoj3kRlLRcFKTD2/3xMCxDLySULYobCXFEQmCIIDZ0fzJ6H2pBY7lWN/QHsTDr/dgT38cDV4H3nXSIjT6HOiLmQsPWTU3HACz3kcpV05G1cGxDHojaaxvC+K9pzfDMABNN8z6S6OF8spFGOXeS43HYY/vuYPDWN8WxM7uCHb1xuAWebSFXHA7OGxoD+KJt/uxcbmOlJqd1HMmPScIYjYIOEVsaA9hIC5D4BjUeyW0BF3wSDz25jS3sSL4LYePrOpwO3j8ee8AwgkFzUFTzzmWQVPAiftf6QLLmP//2EgKfknAOctq8+brpeb9ksDhnGW1RTXtGrwObFxeC90w0OCX8FZ3FP3xDBwcC1nLgucYvKM9iL6YDJZl4OA5uB0cOI7BUCJTdoN2Pjnw59NYjgfoyRHEHDCbE9iptOBsCbqw7eQm/HlvP5oDTjh4FiMpBS8eCiOrmzWOzugI2QYDGKvHUXgtJavbndj6YnLRtXJ3q3M/PxDLYHdvzHZKWZ+v8zqwuNaNHUcidoeK3PvJ7XZR6r7dIocGv4TDQ0kAwBkdIbOVtaaDZxmo2Swe3z2A7KgVtsKPAeQ5wabSMpYgiBOT2dL8yep9bhedaErBkXASGVVHX0xGWjP1tT+eweO7+rBlTQMee7s/r5NmjceBdc3+kto+nFSwcXkt3jwaQTyj4U9v9aMnms5zBF24uqFs56DCe7GiX/2SAI+DQ4PPicW15uZDwClgcY0Lrx+L4sBAHA6Bs1PzKnnOpOcEQcwG5W0BD4/E56WxWRH8uQ4flgFGUiraQi4kMuZ7l9a68XJnGD3RNFY0eKFlx9KIc+fr5exAc8CJFw8N5c2HHTwLBuZm7IudYaTVrP26mtVxxpIQXukM48LVDXjp0LBdX7W9xo10JosNow4ni0hKxZO7+3Husjo8s39wXjjwaTOh+pCVJIhZZqoT2Ol41afSgjOcVJAZTV/LqDokgcPSWjcODiXzahxZuEQeCVlDMpPF6W1BpBQNHMMgaxg4OJDI2zEp/Fy5sZ61NIT+mGwvdHQDWFzjQlc4CS2bf55Sz6/UfRuGgT+93WePxXKOKZqOPX0xrGz02k4lYCz8eM0if54TrDuSRiytos4jmoZY1eEQOTv0uFzdEIIgTiymovmzqfdWF529fXEcOzgMljE7fDoFzqyXx7FIyCrisoozOkIAA7Bg0OiXIPIsjoRTqHGLRSkXugH4nSLec1oL/uOvh+CReKyQRh3zGQ2qrqMrnKz4Xo6NpNAUkMAAeLsnhkND+Z/1So04MGB2Fc3kdEuqxDlEek4QxExTiS3ITWfWDWAgLmPLmgYoWR3JTBY1HhEXrqrHcweG7Hm4W+LREzULZWu6AYFj7TTiVEbDsnoPltYDLMMglcliMJ5BOKnk2ZfNq/M3JOIwnSwtQSc6h8w05tzN5LisYXmDF0vr3Dip2V/RnF9gGTx3cAg8y8Dr4PM0drYd+LSZMDPQEyOIWaaSbg2FE9hqeNWtxQNQ2aIlmlbwcmfY3iXJ6gZ4jsH6tiB2HI3kTdxDbgECy+DBV48WjXHT8jrs64+XdCqVK7xtjXVlo9ceq7VAUjQdh4dTxSdD6eeXe98A8FrXCHQD9uLJqh0i8QzaQy4oqo6sboBjx5I7kpksBuIyYrKKfX1xNAWcSCkaGn1SydDhc5bVIk1FCwmCwOQ1v9p6D5iav7cvPq7mJxUNLANb13qiMgbjMjTdQJPfidPbgzg2kkK9V8IrR0ZQO2Kmp+3qjY6bctEdSUPkOaxq9OXVSvJKArQsJnTaWPeSVDS8eSxqH0+NNntY1eizi3mvWeRHNK0AhoHeaNou6F3Otuaei/ScIIhqUG6OXYktCLlFtAQlRFManA4OfonH47v60RczI4Ka/E7E0io2LA5i++ERZHUDanZsgi3xY12OJYHF4hofnt0/BGG0YU1uurOl15Z9KbUhsW8gXnK8umE6mtY2czilNWAff61rJM8BZcEygNcp4Lc7euxIKwB5GQmVOPCtZxtJKdANAy6Rh1PgJp3CNpW1GDEx5FgiiFlmst0aqu1Vr2TRkpA1/HV//gSbYxnIahavdo1geZ3HLsQdcAnYtLIOT+8ZLDnGZ/YP4oJVddhxJIJERrN3gj0ih3d0hCYceynH0HhM1IXCLfJ5i6eukRRGkgoMA6jxOPC+9c14Zt8AvE4BDp5DRstiJKmgvdaFo8MpvHYkgoBLwKmt/qJFCGCmjTx3YCjP0BIEceIyGc2fiV3USh1VbpFHjVu0dY1jGQTdIkaSCnqiabx4aBgbl9Xi2f1D8Eg8RJ6FopndOocSCl7rimDjshocGkrC5xxLubDuX+RZu1ZSufsfD2snPyarCCcVZLMGTm8P4tWuEfSMpLG2yYeDQ0m0h1xYXu/BsZE0srphpzOPdx2nwJGeEwQxbcbT24lsQX8sjecPDtn1TduCLnSNpCCwLJwCi446DySeQ080jb19caxq9ODtnjgEztwI9TsFNAWddpdjS899TgE1HgcUTcf+/jiiaVND17cHEZe1PPtS6EyZbAOgcu+vcYv4895BDMTlvM9YGntGR2hCW2A929z6r5Zj6oVDQ9i8uvLNl8muxYjKYCd+C0EQ1aQSkbZ2l1/rGsHO7ggElgFbojqq5VW3yP3cvr44EvLknFTW+4+NpBByizi11Y/T24JYUe8BN1qYT8saWNHoxcktAVy6bhGuWt+KjGqM6/kfimcwGM9gb18c+wfi2Nsbw2CJ7hOVMFkjV0hzwIm2kMvekR9JKtB0AyzLoD8m442jUbxzSQ1GkgrUrI6RpII6rwMbl9bi7Z4YhhMZDMQyOBpOI5wq3pUBgKis5u0gEQRx4jIZzX+pcxg8x6LOIxZpfqHeW4yn+5VovvX5SEqBV+Lhdwp2xKaD51DnldAccIJhGLgdPGo9DvgkATFZxa7eKLrCacRlDbt6owinFGxaUYer1rfaE/zparZFc8AJt8jh0GACMVnFplV1eKs7iu6RNLwSDzVr1skzDOCPb/Vh6WinOiudmWfLT3nVrIGoXNqGkZ4TBFEJE+mtMI4GKZqO/piMSEq109haQy4MxTOIplU0B51IZTTEZRXtITfSahZtITeW13vgFjmc3hbASU1+yEoWw4kMFE2HAVO/rAimoYSMo+EUwkkFe/pitobHZLWsfbGaKJQiN+sg1460BJ1FNswAEEurcPBc3jk4loHfKcApcggnlZJrl9xnOxDL2E4lYMwxxTFM3jpmIqpll4h86KkRxCwzXteeUillvdE0ZCVblGZgYXnVJ9qVTsgadnZHsLs3ZqcHWLsawNiixe3g8Ps3exBNqZBEDtG0goBTwNY1DXhizwBYBmivceXt4I7n+Vc0Hbt740gq2bzd6qSSnVJ9kcl2uSvEI/FoDTkRlVVktCy00QfqFjm0hpx4tWsE7z+jFSGPAyxjpsnJahZvdEXsmh6SwMLnNMOE+2KybeCs15bUeaDpesnrEwRxYjEZze+NpnFkOJWXHpCr+YW7qKV0P+Q2o0gzqoG+WBoHBxJ5em+lAYMBnjswiJisIaNmMZxU0DmUNLtyjqY8Z3UDbgeHJXUe+CQBatawI5VyJ/ccy8Al8lCzBnYcjWBZvTdnPCIMw0BfTC6yPZVotoVH4nFGRwhv90SRzGSRUXQMJ81FTEvQCZ5jkTWA3pgMwzCjmSz8kmDv6pdC1ceK3ZKeE8SJRzW6g02UXiVwTFlbIHAMMqPawzJAc0CCwLF4R0cIWd3U3d1DcWR1A06BxeJaN1pD5lxcy+pY2+THI2/1YjhubngaRgp1XhFL6jy2Zg8lFHvOC5gO9XJ1RC0qaQhRaIdKpdsBQFPQCSWr59mN01oDeLkzjGORNFY1+vDmsWjJiFrr2VqdpnOx6r5OJoWtlF22bKNDYBFJKXbpC6q1VDn0pAhilhlPpEullIkciyPxVMlObMDYbne5XZLcLgzZrIEjo/WJrAmzTxrbiZDVLF48NISQW8RLh8wOE7oBZHUdy+q9eO9pzdg/EIfAsXita8Q2vuN5/s2W1FLJ12JpFcdGUmAYxjbmosDgmb2DCCfNLkBxWYXPKeDiNQ1YVu+1n98jb/aiK5yCktUhcizaQq6yXe4KYVkWaxb50RtNI+BSwLMMXCKHaEpFWslif38Cfz0whI5aNxgAp7QE8NrRSM5z0pFUNOi6WdOjsG6IyLO020EQBIDJab44WgcjNz0gV/NzdaWU7rMMIPEcfvDUQdR6HKhxi9g/kLD1PuAU7DTgeEZDXNbAsYztyAonFBwbMdMsTm8L4NhIGh4HD2l0l9nnNK9fanIPAA6eRTip2LreH0tjIJ7B0joP9vbFcHQkDa+Dx8pGH1qCTpzWGsD+gTjcFS7kDAAXrW5AVzgFj8Sj3utANK3ipUNhtIZc4FkGIscgo43VHbHuTVazZc/rFnk7SoD0nCBOLKrVHWyi9CpZzZa1BasavXh0Zx+UrI7lDR68cHAYCUXD610Rs35ckx8b2k2Hf1rVcXgoifed3gq3yOFHzxxEdySNpbVurGz0QuAYLK31gOMYDI46muIlIjJFzpz7AkB3JAVZzSIha+N2Dy1sCFHKDvkkARLPoTuSxubVDZAEznZsLaljbAf+0lo3th8OQ9V1+Jw8BuMyDAD9MRaRpIK/OWsx6ryOvGerZEs7+a26r5WmsBXaZatExitHRkadZbL93VCXuMohS0kQc0A5kS612+GVBEgCW7ITW25x1HK7JBzD4Levd4NhGNR6RPt47i6FtXtsdXV4+XAYA3FTVFkGYDkWR4aTeKFzGO1BFw4OJOzFTsAl4IJVdWV3YXxOAaX2iS0Rf/iNHruooGl4MtjQHkQik7Y7/ADAsXAKF6yqxzs6QgCAOq8DTpFDRtMhCSwavA70R81FzES7TW7RrA/icfDojcrgRQ77+xNIqVnUekSsXuSDRxKwrM6NlzrD2NMfz+sUBwDRpIqgRyxZN6SSXfhq7I4RBLEwqFTzLb2XVb1I8wt1pZTu59ZIcvAsmgKmU9/S+wtW1Nuvu0SzhpxL5G1H1ultQewbMFOWlzV40Bs17YAksNiwOIhl9V7s6YujN1qcMtHgdYDL0XXDMAt7R9Mq3A4eF69uQH88g4ymo8HnQHNAwjP7Bu3d7NxIq3K66BJ5RNIq+qIy3DKPff1jhWUZBjgwmEBHjRutIRErGjxYXOMCA6AvJuPUtiDKkbt7XYmek34TxPFBNevaTZRe5RT5krbAITB45bA511xR78Efd/ZhIC6bG7cOHnFZRX9MxsudYaxq9GLfQAJ+SQDPAb99vRtdYXPDeP9AAs5RR1FPRMal6xZBGM08sxwybtGse9fsdyKrG+geMXVsVaMPBwcS2NMXL+lIKax3alFu/WGtKySBsxvxWCl1lgO/JeTCkXAKMVnD4aEUIikzosot8mgLufDM3n68c2ktWoIu+9lamy+FWHVfrU2ASjQ697uQ1Sye3N2PWo+jKJuDusRVDj0hgpgjSol0qd0OkWftEP3cTmy5Yajj7ZKY3n8ZjX4nGJiTf6tAqayaEUE1HgcCLgEsw8AAMJRU7KKtVtisbgDd4TTOXVqLI+GxrmyRlIpn9g5i03IzKirXwLhFDutagnj49V5wLJOXAlFYVBAwd1S6wimkMhoa/I68HfH+eAYpRcPuniiGk2aYrUPk4BRYOHgWbxyNAizgdQhwOXi7OHipXQZrEaGMOqVyn72D53BwIIGukRR8Dh7dI+mSxiSR0fDuU5vwVk+sbHhwOaq1O0YQxMKhEs3P1XtZ1W3NL6UrpXTfAGx9V7N6nubLqg4lq+cVqM6td9EfzwCMWQB2OKHk1RXySwLaQm44BQ5b1jRgJKnY0a9Z3UDQJeDkVj/istm5Leg2NzGSGbP5wWA8g9+/2YtVjV70xmQMJ8wOR5LAweXgwcKAg2fx8I4e+JwCtKyR1+TB0kXLweOWeBwZSqIl5MKxUXskq1l4JR59MRmNfgnq6A659fzGc/ZXku5hQfpNEMcP1ewOVmmphsIuzQ++ehTCaOSoW+LRM+q47wqn0BpyoW/03z3RNNYvDqLR58CWNY3oGUnj4GACKUWDxHPwOwV0hVO2bWjwOdAScGE4lYHIsUgrWbSFXFB1Hae3BfGnt/qQVDQ0+Z3YuKwGw0kFuoFJOVIqLYJtaeyTu/vBMQwMSBB5FuGUAoFjEJdVGAYgcCyUrI6jIykommGPpXDenrs+aPA6wOQ848lotPVd7O2Lg2EYiHzxVjh1iascciwRxDyi3G6HFaK/tslnTsRzwlDH+xwAZFTdjggaTio4Z1ltXvcbNauPdazIZO0cb4nnsLjGjaxhQMsa4Fgg6BIRTilFdZ7CSRWqbuTtwmR1HUfDaXQOJpHMaOiPZ/LS76yigs05Im/tqHSNpNBaky/+PMvA7xTx532DODSYhDS6DdPgc+Ad7SG8emQEXaNFx10ijwavAyzLIOAUi4xj7iJC1jw4PFo7qcnvxBkdIezui2FJnQfhpIIzOkLoi8tIhcd26Bu8DrxzSQ18TrFseHA5ZqLrE0EQC5NS2p2bkrWiwYMGn7OkrpT6bCZnsi1wbJHmp0fTwSTBLBBuoWUN8JxZ+Doua2gOOrFmkReL/BIcPAsGwOGhpD25/uBZi9EacmIwriClaogmVTy1ZxAnt/jROZxErc+BtJLNq2PXE03jHR0h1Hgd+NPbfTgynIKm63CJPBbXuHDG4pCd/gGYDitrIWbpuEfisXqRF8dGUtjVG8O5y+vMc4+kkchoaAm4IPIszlwSwnByzKlUSZr0eOkeFqTfBHF8Uc3uYJNxUFtYji2WAc5ZVotjI2Mbt2pWRzKjYW2TH2nV1NK2oAsnNwfw+K6+0W6XWYRHm8x0DifzUn4zmoG9fXGc2hbAxmVmswOWARb5JSQUDZef2gSnwEHNmudgR4uLT8aRMpki2C1BF85dVoffvt6N/piM09uCSMgaYmkVumHYmQECx8LvFOAQWfQPZ+yx5M7bC7vCyZqZZghgShodSSlm0fPR8hqFdWipS1xlkPUjiHnEeLsd9T4H1jUHSgrieJ/zu3h4k2b4qW6YKQFndIRgwMxJXtvks8+bkDX4XTwYAB4Hn7fzoRtAyKXiotUNiMtannOJZczdYivsVGRZHB5K2uG5uQsbK/0OgF1U0MIKcc3qRt5uOQNgbZMPT+zqx2tdI0irWYg8C7fIw+Pg8N/bu1DrcaBrBNDtRYyM37/Rg3qvAzVuR14YbELWkMxkcXpbEKsaPBhKKoilNYgcg+GkgtWNPvCcWexwd18MV5zajGhaRUbT7UWWrGXtRUeh8R0vBLeau2MEQSxsymm3yLNYWu/B6W2hso6KUp91CFb6gTkxLtT8ep+EwXgGXkkwHf6xGKLpsc+va/bj1NYAeiJppJUs4rKGeM4104rZRS6cVFDrkTCUUBBNqjg4lERWNyCwDJr8ThwZSmLRaKpFLi6Rw1/2D+FYOIUVjV7omvl6NK3iv7d3odHnxOGuFFjGtDlDiQxGUgqa/BKW1HsRTirI6sDqRh929cahZQ1ceXozAMa8Pseg1iWi3i/BUWITBhhfn8ule1iQfhPE8YGlAyNJBbVeBxjDsCN2cplsfbVKHNS55M6x+2IyFte6saLBCwZmcWueYyBrprNI1XQ4BBaPvtWLqKzilJzNZY5lMBTPmBGgo0E3TpFFlmNxeDiFNU1+XLCqHq91jeC1IxEzOyGRgciz8Ek8TmoOYDiZgazocIhc2Zp0hfoZcosVN9RJyBqe2T8IhmHQ6HfC7eDgkwT0x2QYhmm/dN2AmtXNtHDO3EC2nDq5zzaaUpA1DLhEHpLA2c/Y7Ew3OY0+NpJCVziJ/QMJ+1hhHVqqs1cZ9JQIYh4xld2OiT73jo4QEpmsfVw3kFcfKddZ5ZF4vKMjhDeORvBy54ht8HiWgcfBwy3yODCQQKNfss9h1Up6cnc/GMa0ZsOJDGJp1e5qVMqZ5XEIdpqChVVfJKUweR18nCIHUWBxaCgBljVfO7nZj5BHhMfBwzm6472rNwaWZZDRzPSL3mgaXeEUXjwUtp8hYO5mxNKqXcQ2o+mIySp0A3ndmESexWltAWR1wx5rfILvY6IQ3GrujhEEsbCZquaX+ywDoC3kgkfibae9pfkBl4DT24LoiaQxEMvgSDgJkWfBsww03UCT34m+qIz+uIxrNrRh/0A873rWYsXqYDecMHeS670OXLW+xUzHEDhsPzwCAwA7ajdyHVceB4/hRAbnLKtFS9AJNWsuDLK6ju2Hw1hS50FW16EzDGQlC90wsLffTE3+3Zs9EHhzIeKVOGxoD+LlzjD+cmAQgGmnVi/y4ZJ1i3BSmU2Y6aaxkX4TxMInVwcUTceu3ij8klDUiXMyXStzmchBnUtuxI9umJu0jT4Jrx4x5+B+p7kJ4OA5rG8PwsGziMoqltR5wLMMfBIPLuQancMayOo6WI5Fa8gFnyQgmTEdRClFQ3PAiWzWwN7+OLrCKYykFAgci9NaA+A5Fm/3xBCXTQ0bjMmo8Yh5uliuC+kZHSG83Bme0IYVOuaVrI6TW/zIaFl0jmYOAEBLyIVTWv3IZM2xW02Kch1aa5r8JTV+shptRaFaqYi5pUKsjfB6n2NKv4MTEXIsEcQ8Y7K7HZV8bssatuKFy+IaD644rQX9sQx6ImmwLAMHz6HB68CyRg+2d47gEn+j/f4at4hXjoyMFrwzVx5WHY/crka5nY0kgUNHrbtol8OqL1LjVqFoY7sl9V4HfA4B4ZQKt8Dh3OW1eOXICP56YAgBl4iBuIyNy+pw/oo67O6Loz8m2+kX2mjkUySlYntnGIPxDJJKFnWesSK3Vm52YjRlzxq3qhvYsqYBAadY0fdRSZrEZMKGCYI4/pmq5pf77NlLa4vq3Vl6X+d1YMuaBjyw/ShkVYeD51DnlRB0CThzSQiHB1OQeA7hVKZo574t5MLLnWEkFVOblayOlJLF4eEU0koWZ3SEkFayqB+dnGtZHasafUhkNETTKpr8TuiGjvNX1uHNY1G8cGgYLtHcXW8PubB5dQN0wwDLMEiPOpUAwCmwCCcUdEdkszvSIj+cgoj9/UkMxjMIuESwjFkvKq1m0RVOohTVSGMj/SaIhU2hDuTWtcuds1aaQjtdCiNPrc2BAwMJu7mCS+QRdAlY1+JH0CXaTXeGkwrOXlqL5w8OgWWBGo8DAsdicY0L56+sx1BirJ6eS+RxbCSFp/cMICZr8EoCZDULv1PAa10RHBxK4uylNXirOwZJYKFmjTxdLKef4aSKVw6Hse3kJoSTyrg2rNDpYxjAwYEEFte6cUqLHyzLgmOBcELBgf4ETm0LIOQWILCMvaFhUW5DYLIaXZiKmFsqRFZ1CBwzK7+D4wV6SgQxh5QLyZ/Mbkcu5T5XbuECAHv74kXX51gGm1bU2RFGVvpX53AKAsfkFRF3CGze7jiQ3zK7sJMdYAp7uZ36tpALm95Zh5FkBh21ntH3czg4mATHAKe2B/B2Tww9I2M1j1iGwWBcxkhKwbI6D7oj5mtukbeLcwNmwe2ucAo1HkdekVsD5uJiab0HDMy89qX1nrxoLqurRXckjX0D8aIUioSsYWd3BLt7YyXzs60Q3EqLOxIEcfxRbc0HSut+vU8qq/cpRcMZHSGE3CI03bD1fTipoMbrgCCwMAqEO+AS0Bpy4vDwWP2P3O48ltaHc2o6CRyLoFvEOzpCUDUdZ3SE4BI5PLFrAMNJBV4Hb9uHvpiMoaSC95zSNNopbmwAPkmAPFqrSdHMhhO6IeHIcBLNQSdCbhE8y0AY1V0ti5LpDqXS2JTRaNUjw0k0BZxo9juxuNY9qfTD3GdE+k0Q85tSOpBb166txoV3dNRU7NyfLoVz4awB/OXAENa1+NAUMDdxLY0ejGfQUevOi0bti8l4x+IQnCKH1YuSaAm6wTLmnDPkMVP8soZZr+7VrrA97+VYBkG3CIFjEZdVxGUV0qp6OwVM5Nm81DHruVkanFuLKJxUEU4qE9qwQqePrGZx+mjk6Y6uCFwih2RGQ0etG2ctqwED4OQWP57Y3W9vaFiU2xCYrEYXpiLmZlc4eBYntwSoKcMkIMcSQcwRs91ZpnDxMd71XSKfF2FkJUR4HHxREfFISsGxETnvWrkts3OdUNY1LGEfd6e+zoOVjX50R9Loj6XRH8vgpCZzt6Y/JsPlMNMnjFGDyTAM+mMyTm0NADAN2Pr2YH4hQ1WHOlogPLfILWA6l7SsjpDbgbisIpbW0BNJ2wu/8Z6Xour47evd4FgGu3tjcPAc3A4uLz8bMENwp5P6QhDEwmU2NX8iva/1iHh670CRRgHmIuvU1iBEns3T5X0FqXG5Og+YE3Frcn7Bqnosq/faxblDbhHhpIKB0WjSoEsc/UwWum6AZc3UNwB5kVJeScDp7UHs60+AZxkoMOvosYyZIq1lzYKv9V5pwkKrhbvlMVlFbySNQ4NJJBUNNR4H9vbFcHJLAOevrEPAKRa1qCb9JoiFTblUKZFnUeNxwCsJs14nLXcufHgogSW1bjh43k5Ly1VejmHyHCdWqjPPAmubAnj0rT57cxUwN2tvOGcxPBJvR/BbOHgOAsfAKXJmRzaWtaOhLCwtTSoaYrJqF822EDgGzQEnDg8lwQBFmpm7meIWOLhFznYSGQbQE0ljSb0b61r8ZtkLjkFXOI0dRyJwCRySmSyeOzhU0laVqpk0WY0uTEUsXPtYjYKIyiALSBBzwFx3lpno+ttObqq4iPjevnjRe3JDix08axvFUsI+3k699VpzwInOoSTOW1Fnd8xgGYDlWPicAk5pCeDYSAq6YcDt4LCiwYt6rwOntQXQGx1zejkE1u6Q58iJZALM8GMA2NUbhazqWNnoxa7eGAIuAResqsPTewZLPq+/7B/A290x7B9I4rS2AMJJZbRmiWjnZ1tG2grBnU7qC0EQC4+51PxS12YA+CWhSKMAU6c7SkTtFO425+q8mVZnar3Padb2K3SW1XkdSCoa1jb77c9YmigJLJqazA2HVY1eJDNZM7XDJeLUVj9++UIXGvwSOJbBsgYPwkkV+/pNyxJJixhJKRMWWs0dv6Lp6BlJo3MoaS80WQY4Gk5hIJbBcCKDdc1+Ox06915Ivwli4TJf01lz58K7eovn1RZ+l1jScdISdGFvfwwNPgkukYOa1e0ozpc7w2gPuVHvlfI2AwCzA5vAmbX2Ai4BIwU2ynoeAssWOZUyWhZ9UQUDsQzO6AjhDzt78zZLCjc0WAZYXOtGVzgJLWtGyW5YHMJTuwcwzCjQDQPHRtLwO826TS91hnFmR01evaNcWwWU3kSYjEZTFGp1IStIEHPAXHeWmej64aRSsce/nCj7JAHnLK3FOxaHkFaz05p8eyQem1c34Mnd/XbHDE03IPEsmoJOBJwiAi4RcVnF6W1BtIVcyKg6eqNy3u63x8GjLeRCUsmCAexCfQyAOq+IoYQCXQcW17gRcAoIJxW7NtNALFNk0BRNR3dERnfEdF4lZQ1Nfid6omanEZ41Q4xrPI4iAzWd1BeCIBYWc6n5pa49nJOyZmkUMH7kTSmtt1JIBI7ByS2BvO48pXCLfF7aSe7iR+RZvKMjhBqPA51DCTAMg6SsoS8i45TWADqHkljT5MWrh0cQcoto8jsxEJfh4DlkVNNRJLuzCLpFwDCQkLWytspK5bCcSov8EnoiaWi6AU3RMDCa2lfO8Uf6TRALk/nuSKhkfB6JL3KcKJqOw8MpO/IqF8vGdNS6sWFxEK8cHslzEPmdAtpCLvAsU/J6gBmZ5JcEyKqZSpfVDYwkFWi6gXqnYG/a5m5QF64hdAM4PJREW8hlR7Rm1Cx8UhOisgoGDI6NpKBkdeztj0PVzGLkWd2ArOp5tsqinCOwUo2mKNTqQk+LIOaAue4sU8n1VzZ6K/L4TyTK1UrxaAm6cOXprTg2ksLBwSRiaTWvjpHVnvsdi2sQSSt4fFd/nlPJ6pAHmF3hrIXV8wfNrnC9URm9URlNfidqPSKe2jOAdy6pQV9MRjSllTRocVmFQ+DsQuEHh5J2d4yeaBoZLQs1q5OBIogTnLnU/FLXzq0n0VbjglcSJnT+l9P6ep+jYq3PXTQV6mnAZTqc1izyY09fFLtHd+05BmgOuczOdQEX3u6JYySt4uxlNXZraY+DR+dQErph4NTWAB59uw8+Z74Nyh1/bzRtp4U0+Z04rT2AP73VZ49F0w07jXs2NnsIgpgd5rsjodLxFTpOXusaGfe8VimG/3NaCxwch65wynbsn9rix+I6Dw7ndGYrvF5azeYVt86M1r1r8jtxRkcor1B4JKXiwEC8pHNMN4DDwymsafJjXaMfr3WN4MCgeV2fxGN/fwJd4ZRttxp8DvAcA1nN2qUscsdYDUcgRaFWD3piBDEHzHUobqXXr9TjP1ui7JF4rFrkg0fixzW6pXZzcsdjvZZWNJzc6scbR6Poj8tIK1kkZQ07jkaQ1Q27Q4gBFBk0wOyK5JF4u/ZHVjfw+tEITm71451LawDDwGltQaxrCaDO6yj6PEEQJwZzqfnlrm3Vk3hHR03FTpPpan2li6YNi2uwarTGnnWdC1c14M1jESyucUHgWGhZAxuX1cIpcjg8nMSyBg/cIme3Cy8VbWSNf2d3BIcGkzg6kkJS1vDiwWF7gwAAeJaxU/tYxiwyW6rRBUEQC4/57kiYyvgqtTEtQReuO7O9ZHOH8a7nEvm84tbhlGLPmV8/GsF7T2+GwLHIqDocIgdF08EyKOouamFtpuSOO+AS8pxKAHBwMIGzl9bi1a4RsAyT995qOgIpCrU6zI+/III4wZjrUNyZuP5sinIlRreS2k2AWSOqNypjOJHB/oEEALNbxop6D9wSD4YBXAKHk5r9CCeVPCMpcix4hkG914HDwykwAJwCh7/sG0JS0bBmkQ8NPglHwqkZK8pOEPOFch3PiLnV/Gpfe7paX+miqdR1WoIuNPrHxjuYUFDrEfFyp7lbv7zekxcJVa6467rmAPqiMnpG0kXdS90ij3qvAwxMp1KjT8KTu/vBlFjUkKYTxMJkvjsSJju+qei8gbH6ohNdrznghM8p2MWtrTkzA6Cj1o3OwSS2HxmLmjql1Y+ltR7b0V+I5ejKHbfAsfA7BduxxLMMOJbFjqMRbGgL4uxlNXCK/LxzBBJj0DdCEHPAXIfizuX1Sy0+AUx6QVqtSYFlwKwOR2rWwGmtATudbUmdBxwDnNYasEOFLSPZFnKhMSChVXEhrWQRz2j2bktryIXzV9ZjKJGBbmBWirITxFwx210uFxpzqbkzfe1CTQ+OdoAbT8+nqt+lFk9Wh09JMGs1FVIqzdAj8XhHRwgsy+DJXf3oiRqjUUoc1rcH7cYPNW4RrxwZQa3HAZEfcyzNVqMNgiAIYPyNG+u1tU0+PLd/CErWsMtEFOr8VG11oR2x5syNPgltIRf+cmDIfq8ksDAM4JUjIzilxZ/Xac26nuXoyj3vUDxjl5MYiMsIukVwLINat4jWkAsukcepbcHqPFBiRiBrSBDTYDo79HMdijsX15+oQ4TFbC5IrTBcq8MRA8N2KgGwC4SHkyr8LgWntPqRzmQRcItYVu9FRsviyd39OKMjBKfIYVdvHE6RhU8SbKcSQHU6iOOXue5yOZssVM2fqWvnaroV3fPKkRF4JN7u0FZNPS/lJHMILCTB1O/CBgtAfpph4fd39tJarGzwYiAuI6VoULMGGACRtAq3yMMr8Vje4EFayRbtupOmEwQxU+Rqla7rOBpOoyucsnXI0lUAeRpc53XAIbBo8Elo8DnzdH66trrQjmxeXYehhIL/fOEIsqMDs7TYJfLQdMPuwKxoZvFtn1PAaa2Bkufd2R3B717vwQWr6uESzfqlDp4FA7MmYCmnEkVKzy/oyRPEFKnGDv1ch+LO5vVLGbQat4gnd/UjKqt5bURnc0GauwPukwQEXQK2Hx5BwCnA7eDR6JdwNJzC6kYf/vBmLzwSD90wsKTOgz19cWxZ04ArTzcN7eGhBDJqFiLHIpnJFl1rpouyE8RcMNddLmeLha751b52oabXuEW7sKsksLamV1vPCxc3ToEza30oxZqbuzM+3ve3alE9AODQQAK/fb0b/TEZAsdiT18MiqbjnGW1JVM6SNMJgqg2uVqlaDp29Ubhl4Q8HbI6Fg/GM7b2WXXzACCRyeL0tlCe5lbDVhfakZcODWNJrbuowydgdg1tD7nRUesebcYggQHwzL5B7DgaKWqusK45gD2jDRms5gnx0euUSuejSOn5R/HWDkEQEzKR1z8h02SzkFIGzQDQH8/YbURzsYzcTGPtgAdc5u66ljXgEnnUeh1YtciHo+EUmv1OO4pJG217emgwgYFYBo/v6gcArGz0YnGtWd+j1K45MPNF2QliLpjrLpezAWl+MYWabuk5gCJNr7aeW4ub09qCWLXIh0tPXmRruEVuCkgl319C1vDM/kEwDINGvxM1HgckgUN/PIPnDgyhxi0WjYM0nSCIalKoVXFZhazqJXUoMVp+oRSlNHcmbHXAJaLG47A1s3D+6xR57OyOYSihYCihYDCh5DVXyLWdhfPxsWsUp22TTZ6fkEUkiCkw33boZzoUtBrnL2XQrNoYQOmua7O1IM3dAe+PpXF4OAmvJCAuq0irOtwSb6fGWR3grIWTyLP29z3XRdkJYi6Y6y6Xs8F803xg5nS/0vMWanqungPFmj6Tej5Rql8l35/1/3Ox6ogUFvgGSNMJgqg+hVql5OhooQ5lVL3k3NmiUHPdIo86jwhj9LMOkQNjGBgebUxTzlaPZxMmmvcKHDMp21lp2vZ8tMkEOZYIYkrMpx36aoSCjmc0qhVqWmrxaeVeA4DAjV8bY6axdsCbA04cHEwiklKR1Q2sqPcg6BJQ75UgCSwknkVaycLA2MLJ+r7nuig7QcwFJ4JDdT5pPjA9Xa6W3hdqeq6eA8WaPtN67pHMBYh1bz2RtH1vlXx/pbpiW7X3Dg0m7NQMgDSdIBY687U2T6FWiQU6mqtDDoEtOXe2KNRcgWXwxrFoXpRTg9eBc5bVQtayJW11YR29Grdo13Bq9DnRFHCOO+8dHI1iLUe55goTOYXmm00mTOb+L4ggFiDzZYe+GkVzxzMaDV4Jz+wbLKpdMZWaGaUWnwxMoxaV1aJuPqUWpLMxEbCcQ0/u7offyeOPO/ugw8CR4ST8TgHhpILWoAuJjGYbdJ5lsbcvbo9r28lNCCeVOSnKThCzzYngUJ0vmg9MT/ctvY+lVdS4RduZsnqRF+01nkmdt1DTLT23aizlarql5zOp4eM5xSbz/VlFZpWsDnG0ZsiaRX6sbfJBEjjSdIJY4Mzn2jyFWmVFTcqjEaEOnrXrDnkcPNpCrgnrywGwU309Ep93vv54Bq8cGcHfXbi0SNNybY2W1VHjEfH03kEMxGX4nQLWNQdQ73Ngy5qGoiij0Gh30HBSQa3XAcYwMJJSEHTlR0w5Ba4qz6mQ4yFSeiFCT50gpsB82aGfbihortGwOvrkFl+9YGU9njs4hCV1HrvDz2TOn3ud7kgaS+vc6I9lkFGzGE4qGE4q2LymoWxXuFwjN5sTgZagC5eua8LPn+scNcIcVjZ60RVOQcnq6Aqn0FHrhlcSwHPAgYE4Dg+P7QBZ46IwXOJEYa67XM4080XzganrvqX3sbSap/UA8MTuflxy0iJ0hVNFWl/uvLkOxVhaBcMAF66qx5FwCh6Jh4NjMZxU4HOaehhJKzOm4RM527ad3FTR98dzwOtHo/aiCzC7HG1YHMS65sBx83smiBOV+d7FNOQWYRgG+mKy7di2oib9kgBm9H0Bl4B3dIQAoKJNHctu+EYd5XFZzSu4nVGLYzatz8RkFQwM/HHnWMfkWFrFIr8EkWft52bZh2MjKfz+zZ684uMhl4gtaxrwp7f70Bcz7Y4ksEhnNHgkftI2YD7ZZGIMspAEMQXmyw79dENBcxcouR19AKuGkGYXqs7t2lbp+ROyhj19UbMbBMOAMQz0RmVwLIONy2vhd4q2+I+3IJ2picB4u+fhpAKeY7G41o3OoSRObwtCyxroiaaRVrNwihwYBqj3SDgwkACfE448XyYoBDGbzHWXy5lkvmg+MHXdt/S+zpOv9YCp94PxTFmtL3felqAL205uwu7eKPb0xpFUsvA4OHQNpeCWeFy8pgHL6s3fxIOvHp2xxZx1b6WijSIpFeGkkvf9lWt93RZyY39fArI69mz8koC2kHvKYyMIYv4wn2vzHBtJ4cnd/WgOOHF4KIkj8RQkwUzHPWdpLc4YdSQ5C+bJlWzq5NoNkWdR43HkvZ6r79bcuHMogZBbQFxWIAljtUYBQNMNDCUU1HqkvOdWOF+30okZGLj/laNgGdM1Zt1XUsnazv++qIyBuAyBY9DgldAcdJW1C/PJJhNj0FMniCkyH3bopxsKmmtocjv6WAicaQCsQtWFhmi88x8bSeGRN3vx3MEhe/fXyuXui8l4uyeWt5gYz5DPxERgoggo69n4JAErG3xIyBrOW1ELnmUQz2SxuMYFSeDw39uPQuCYoqiuuZ6gEARRXeaD5gNT131L00ppPWDqfTmtL3feiXR+x9EIltV7Z3wxl1Q0xGQVhwYTRdFGS+o8SCkaVjZ6cdX61rHNjoLW1+uafTg8lMQZHSEzVUPT4eBZMAAODyVJzwniOGC+1ubJzyBQ83TI7+Rx/soG1HmLdRmobFOnUruROzceTmTwxrEIJJ7DljUNYBgGhpEf2WTZC+u5ldJ6nyQg6BKw/fAIltZ50BxwwisJ9gZGVziFx97uxWO7+vPsyOY1DVjfHiwbzTRfbDIxBj15gpgGc71DP91Q0FxDU9jRBwB0Y6xmRmHnifHObxnIrnAqb5JvtUs9oyOEwYRS8US92hOBSiKgcp+NyLMQeREpxQzplVUdi/wSYmkNWd1AVjdK7vRT8UCCOL6Ya80Hpq77lqaV0npgTO9LdRkqV/OuUp2f6cWcwLJFTiUAdsQtz47p8s7uWEnt390bNyO3Eop9PJ7zHtJzglj4zNfaPLkOGd1Avg7JGsJJpaxjqRIqsRuFc2OvJMDBc+iJphFOKgi5BAwnzXFZz9GyF9ZzK6f1WtaAS+ThKIiWUjQd+/vjEEc3Niz64xk8uasfum4g4BTHjVyaa5tMjFG+lDxBEPMeKxQ04CoufF1JKKhlaIDijj6SwCKZ0XDOslo0eB15nScmOr+dllBigZLbLrXSiXq1JwLdkTRiaTMlpNYjwuvgUet1oM4jIpY2d89zn41FXFYhqzoavA4wyH9m1k7/eONKyBr29sXxWtcI9vXFkZBpoUIQxOSYqu5bmlao9UCB3vukis47GZ2fqcWcpanRtIJGnxMukbNrkFj4JcGOvh0vcgpAkYaXGyNpOUEsTErN7SzmsjbPTDvfK7Eb3ZE0BJax58VNQSc2raiFXxJwZDhpRw65RbNoeFrJQuDYvOdWTustu1PYxS4uq4imVTBMoXKbdiSRMdPyciH9nb9QxBJBLHCmGwq6rtmH3b1xuEUO72gP4rWjETu1i+dY9MVkXLCqHsvqvdB0vaLzWwaysE2qhdUu1ZqoT9QpqNpF+lKKVlS8FhhN4Vhei4yaRXckjbVNPjy3fwhK1oDIs1CyplPpzCU12NcXR53PAbfIoTcqg+cYyOpYBfLCcc3nLiQEQSwspqL7HonHBavq8ObRKM5YHISmAwlZRXc0jfYaN3iOhaxl8cGzFlfU1bKczmd1Axkti3BKwXAiA55lZ6TQaq6mDsZkLApIGEpk4HcKUHUd2awBh8BibbMf+wbi0LLGuIs3BoDPOfGCk7ScIBYuM1GbpxrdLl0Ch1qPaHdLYwwDw0kF+qiHfjLO93LjKWc3AGBvXxyHBuOIpFT0x2QcHEoiqxuodYu4ZN0ivNUTxWUnNcIr8RgYdfg4BBZtIVfecyun9QyAtpCrqAO0ktVR75WQLOMcymh6nlOtEv2djQ7SRGnoKRPEHFIt8ZtKKGiuOFuFTD0Sj3ef0oSj4ZRdjNrnFHBKawBq1oCq6EW7waXuJaNmUecRoWX1vLamFg6eBTc6Ua/ESFR7IuAUuCKnEgAMxDN4vSsCRdPNFqkeEW01LmhZAzUeERuX1+CNoxE8tacfDBhsPxzG6e1B9MVkDMQzaPRJ8DrVIkM7GM/gge1H87p8iDxLRb4J4gRgpia5k9X9YyMpPL1nEAOxDMLJDA4OJhF0CbhwZT329yegZnWc0hKAc7QLZu749/bFi8Zv7UzntsPOaFmMJBVouoG0kkUsreLAQBxBt1BVDS9M2cgaBh57qw/tNS6017jhdHBQVB0Bp4D/3t6FLWsb8dsd3Xjvac3wSjxgoGjxNpxUcPGaBuw4Gik7xsLrWrazN5pGJKngb85aPK10FYI4kZgrB0A1a/NUw9F8bCSFZ/cNlq1V53NW7nyfaDyFdsN6/0Asg1ePhNEdSaMt5MKZS2rwelcEKTWLzuEkWkNOxGUV65r9djTq6kVerGr05z23cvP1rGHghnMW4+XOcN7xRp+E1Y1ePL57oOT9OHg2bxPa6kJa5xHNGlSqDp5jsb0zjIBTxFBSxvbOMKIpzdb4rGFg8+rqdB8lh9X40NMgiFmglBjNZOvlSsZT2LXBynmOpVVcfkoT0mrWzIcWGDyzdxDhZPlxFhoyRdMxlMhgQ3sQsqbjwEA8z1h6HHzZNqlA6U5BhRMBnmUhcAwG4xmkMtlJCbyaNRAtkfLgFDm8cSyC1Yu8RRFNVstpiefAMAy6hlNIKhr+sn8Qaxb5sGllHZr9TjgENq/I4rGRFF44OISXOsP2dayCsr7RjkVUFJYgjj9KdcUcTirwOWc/uiVX80WeRaPfiZDbgaGEjJ3dUZyzrBaxtGoXsrbGN94iJXdnekmdB/v74+iLmk6lJr8TPMPgnGW16AqnEEmruGp9a9U0PDelTdF0276+2R3F68eiuHh1A373Zg8a/BLeuaQGciaL1Y0+/P7NHhweTsPj4MGxTNHibVm91y42XmrBmXvdwmLhR4ZTaAk5cdbSWopcIogJmOvIv2rU5qlGx2LrHEkliyV1HltTrFp1F6yqxzs6QhVp42THk/v+uKxCNwBJ4LC3L45IWsXikAtvdkfhFnl88sJlWNHgtdcG4znirPn6sZEUBuIy1KyBBq+Eeq+Es5bUoD8uI5s1UOeV0OiX8NCOYxA4Blk9vzC4tV7I7R4dS6slMw4afQ4sCjjx29e60TmczDvHOctq8eTuflx5+tQ3cef697pQIMcSQcwwpcTILXJo8EuIpScW/5nwkI9XZyKpZMEwDE5rCyIhaxO2iAaKnUMiz8Ij8XjlyAhOafFD5Fi7tbPVftoj8abxmkSnIGsiMFmBL3yGKUXLM+AWPMtA5Fm4JR7P7Bksasd9eCgFkWNxSosfu3tjAMZari6p8+DQQBJL6t04MBDH0ZEU3AKHZ/YN2u1Vc891ZDiJ85bVgeMYHB5KgAFo94MgjhMm6pY23qJjNjV/IJ6BrOo4qVnF0GixWEvft53cNOEixdqZBoD2GjcEjkXII+L8FfWIpxX0xWToRr6eT0XDC7FS2hRNx0BcxkhSgcfBIyZrULNZKFkdalZHNKViRaMXhwaTeLkzjN6YjCW1bjttudzirXDBmdt+eziRgVPkShYLj8satneGMZJUoeo67WoTRAmq4ZCZD1Sj22XuOXySgDWL/IjLKtSsDoFjsazeW3GK12THk+egH62Vl1GzyOoGjoVTOL0tAADI6jo0XYeaNWAAE2YupBQNDICXO8NIKqbWxuQhJGQNG0aj/HUDCLhS2LKmAReuakA4peLlzjAyWhYOnkOTX7K7wln3mFQ01LjFkhkHMVnDz5/rRF1BR9PcZhJT3cQ9Xn6vswE9BYKYQcqJUVc4hbe6o3bXnFxyxX+mPOSVFgmsxEhZ/78QnyRA4jksrfdgbbO/5A7HVIoVTlbgSz3DlqBZnLbQgMtqFjFZA4zS7bgzqo7ukTTeuTSEy09pgpo1IHAMkrK5U75pRS1e7gxjZ3cUjX4naj0injs4hHOX1+Wdh2MZrG704bmDQ4jJGpbXe7CrN067HwRxHDCdrpizqflWMwJgrO6dRSRlprBVskixopAODyUxEJfBADg0mEDB5rOt59WYpLtF3o4Y0nUgMrpJI/EcnAKHWq8DKxq9UDUdx0bScIpmZyMACCcVnNTsB88yJRdvheR+J7UeEfsHEvBJPKJpFQ6es9/HsQzqvA48/nY/Xj0yYkcBk64TRD7VcMjMB6pRcLvwHGJB1zRNN7W5Etsw2fHkvl/kWGS0LAwALgePrK6DYxjUex04a2kNXjoUxmtHImV1LXd8dR4RL3eGEZXNaFaJH3PEZ9SsbQNjaRWvHhnBYExGc0DC+89ohazqo1H9biyv9+XZArfIw0Dp+TkAdA4lsbjWXXTcaiYx1QLox8vvdTagrnAEMYOUEyMlq+d1zSkkpWgTTr6n0wXBKhKY2w2NzdmCmKhtaO44x3uPyLOQBA6ntQWxstFbtFiYSqegSp1dQPkFTEbV7edX43Gg0e9EjcfsfOeTeETSKmQlC4ln4RI5iJz5v06RHT1vFju6InirO4odXRHsG0hgcciFlw6F0R/P2F0vMqoOWdXRE0mh1i3a119a68bLnWH0RNKQBNYuZljqu6XuFwSxsJhqV8zZ1vzctAMHXzwdLIyoLcQavxVJurjWjaGEgsGEUuRUAsb0fDIaXo6QW0RS1sAyDJwiC45hIHAssoaBOp8DyYyKoXgGzQEnmgNO+J0CFvmdqHGL4BgGPMvkab+1eCuk8DthYEaeyaqOkaSS9wxPbw3gr/vNnXQ1q0PRdAwnMtjdG8OD249isMxiiCBONGa6A9psUY1ul5Wcw6zT2YU3jkbQOZTAQEyGoulFtmGy48l9v1cSbEc5y5jd21wOHic1+fF2TwwDo7pmkXvtwtpHTpHDSEoFyzDoGUkjlrOJkWsD6zwi9vfFoRtAWtGRUnUYhoFjI2m8fjRaNP6J6kyJPAs1W3plldH0KXcfPV5+r7MBRSwRxAxSToysLjqFu8QWLpEvqiERl82Fisixdk2JqXjIJ1MksBpGc7z3TKVTUFLRip6HVQwbyBf4cguY4aSCDe3BvAVMTFbBscDpbUGEkwpEnsWBwQQ03QDPmguRjOZCwCXAKbJo8Drydk38bgG7++JwOzjbUWS1V93Tl8BFq+vxelcE/fEM3BKPnmgazQEnltR57LEDsxOxRhDEzDHZrpgWs635p7YFcCRsOr1LpTaU65BmUTj+SvV8MhpejnBSwfkr63D/K0fRF81A0w3EUwo6aj04e0kNdnZHsXlVA97sjuA3rx3DuhY/9vbF4HMKOKnJX9SZqJydKrQhw0kF5yyrxWtdIzg0lEBGM+uNNHgdWFbvwStHRgAAumFgV2+U6i8RRAmqMbecTcqloFWj2+V45wi5BQgsgxcPDePRt/pt28KzDJoCTqxa5AMA2zZMdjy57xd5Fkvr3IilVSQVDU1+J5KyhnqfhN6ojERGszdNLXI3AnJrH9X5JBwaSgAwv2uXgwMD2A6ljKaDZQCvU8BLO3qQUjR4RjU5dz1i3Vduil17jQsCx+Q5kCSBRa1HRFzW7A3gQvxOfkrdR617GI/59nudS+hJEMQMUk6MrC46Dp5FvOA1S/z3DZivFBYIBUwRPWtpaNKLjMkWCazUSE3VsE6l25uu63kTdiC/GHZu94hjIyn0RtNFCxfdAPpiMjavboAkcIimFBwJJ6Fo5iLHrXAwYCCWNndcfE4BQbe5wy8JZlTTOctq7TxvSWDhEni4HVyeo8ja3e6PZ3CgP4EzOkIwABgGsGaRD4tr3SUNUiURa5TTTRDzk1Ld0nLJ7YqZi7VomC3Nf/XwCN7ZEUKDT0JfTM77XMBlFrLeU6YOXiltr1TPK9HwiWqJyGoWj+/qR1vQhbVNfgRdAoYTCo4MJ/HIzl6866RG7OyOwsFz6ItlEE4oaA25EEuriBU0bhjPThVuDlm2Y+My00Gkw0DIZTrmhpMKDAACxyCW1oq+92haI+0mCExtU3GumGiDb7rdLsfTzU0r67DjSASHBhN5WqTpBnoiaXAsg3XNgbzo0cmMp/D9IbcDHbVuBFwCzlpag2RmNH05o8GRE12fS0rRYAB5tY+agmPfX1LR0DmURNAlIjVab8nBs5B4EX/eO4ieaBqhnIj+3JTxlKIVPf96r4jFNa5R55QZqeqVBGi6jkV+HSwY+CTe7kxqAGgLuSougF6KhfR7nWvIshHEDFJOjETe7DDmcfB2wVQgX/zdIg9F00sWCJVVHf0xGQlZm5RQTqZIIFC5kZqOYZ1M29eErOFoOA2/JEBW8wtrHxpM4JyltWgOOG1DxLMMjgynAOQvXADY3S9WNnqxty+OYweHAZghwK0hJzYurwPHsuiPyfBKPBw8hwavA2ctqUWjz4XWEItVi3xIKRo4hkHWMBCXtbzoI2t3+7kDQ2BZxq6n1RKU0BJyld3lKIxYK4Ryugli/lLYLS1Xw3O7YpZKDZ5tzX/n0hrs7o3lpa5Z2l3ndUxa2yfS80o03CEwRU0jCiM1U4qGnuiYM4xjGSytdaO91o2moAsrG32IyRpSShZ1XgcYBrjy9Ba8dmQEQ0kFcVlFjccxoZ0qtTmkG8ChoSQafRK6I2nbhtd6REgCi0afZNudXBw8i6EytbUI4kRiKpuKc0ElG3yTmcOWo9w5uiNpJDIa1BK5xZpuIJo2u7nlziUnO57C92f1OhwNp3FkOAXdMHXNMTp/FkukTFvXzq19lJTNiCerrp2WNSP/AdMGWtFL4aQCnmXyatUBY+lyPMsWPf+hhIJ1zQG8cmQEIbcDIs8iJqtIyhouOakRj+/qH03DA9pqXGjyO3HFac1YXOOp9OsoYqH8XucD9CQIYgaZSIwCTrGs+DcHnBA4pmiBAZjCnFEnnxpRaZHAXCoxUpN1DpXaia7kProjaXSFU3nRQhZ+ScAZHSEAY13q6jyiHTFkLVzWLPJD5Nmi1AwL3QDCCbM7xYp6D05rC8ApcvaudF9MxopGL05q9BfdV+HuvrW7fcGqeiyr90LTzRzvkFtEIpMdd/fDilgrB+V0E8T8JFf3gbEmAYVdMQuZC80XOBZXnl5eu6eyaCqn5wlZw87uCF7uDOPUtgBePWw6eSwsDX9m7+CEkZosw+RFg2V1A/sGzNQLSWCxoS0Al8jDJfKoHb3fREbDWUtrYACo90pYXOue8F7KbQ7pBiBrWXzwrMUIJxWkFA1OgUNayeLgYKKofqK1mAJIuwkCmJq2zDbHRlLgWQZeBw+HyIExDAwnlaJOl5XOYcej1DmSioaMqhc5aix03cgrXzHeuSZz7VWNY6lnlq5Z3d1yKRetc3AoiTM6Qni5M4yBuAxJ4OB28Ai4RLsrnFvkIXIsmgLOkjX5ADP6s5T29sVknNLix9J6DziGwZFwEhlVx8HBBNa3B3F6exAZTYffyeP8leYmyXRZCL/X+QA9DYKYYSYSo3Li75F4bFxei8NDyTwHSm7+8WQnqFPNE67ESFXynmq0mLaMipVWltF0OHjWnrTn7tDnRgxZzqW4rGJpvSdvl6HwuTgENm+hsrzeg1yfW7nntK7Zh929cYBh7AmIzyngHR2hovubaPeDcroJYuEyVYfMXGj+RNpdjUWTpf3ZrIFDQ0kcCaewqtGD09qDAGBreFxWEU5OHKkZcIlF0WDAWGRqICe1wkI3YEeNntFRY9fu2NsXL5tyN97m0ObV5oIld9HikXg8sP0oDudELOV+fwBpN0FYVENbZopjIyk8/EYPXh2tmwbk/y3rxsw7id0iD4fA5jlqcp1LTQEnLp6BiJnC78Uj8XjkzV50hVN2Xby2kCtvHr16kRdP7O6HrOrI6gZ2HI1gXYsPTYFGAMDaJh+W1XttR7ysZjGcVCBr2ZI6vnqRF2m12JkFjGn52mYOLpG3Mw4A5HXajssawkmlKo6lUs+FKIasG0HMAlMVI79TLOlAsYzaZCeoc5knXK0W00D+AgGAXafKKfJF0UeFTqgVDR6c3pafhlL4XLJZAz7JTEfzOng4xbEw3VLPKddhZhWlLRWdUBitte3kJtvIlopYo5xugli4TEX3jyfNt8jV/lqP6fDJ6gbe7onj4GDSjiIFgEUTjMdayDUHnGgLuSDxHOKyClnNjtY3YuFz8GgLuSasD1Wpbk/GSdgSdOGDZy1Ga8iJaFor+v5Iuwli/pPb5SyX3Po/gwllRp3ECdlscuB3CmgNurC3P45VjV6sXxyEmjXgFFm0h9xYVl8dR8dEde3qvA44Rc62Sx5H/r2vavTjnKW16Aqn7FRrB88jLmsIuASsaw7AI/G2kyc3yr8wRbst5MKqRv+EHUJdBXP+UlCE6OxCjiWCmMc0B5x4XjfsyXFuctRUJqjW7uv2zjASGTPE1iFy8IjctArbVUI1agZVskgqNESFTqgGX/GCIHdXuiucws7uCFY3+qBkdQgsi/39cXTUeYp2aIBih1luqsmOoxHb6I8XrVXqvimnmyBOPKqt+UD5SMrZ0pFc7c9taADAjiK16h3VeyUAxW2mLayFXK4+yloWR0dSkFUdDV4HmloDeGpPv73DX0o/gbGU6cJi6cfCKbuRhRVpOhknYZ3XgbOW1uLxXf1laygSBDF/sTSrVAMGq/7PTDqJc+eLiYyGRr8DfbG02Z1twCykvWFxEGctramKnow3Pw04xaLX4jBrHUXSat6m8OntZumISmxN4RzXmjfn6uRU5vyFUITo7EJPmyDmMTPlXBiMZ/J2FdpCU2t/PNEORy7V2FWo5HlMdYe+JejCtpOb8Oe9/WgOOOEUWKxq9CIqq5DV8rnalTjMmgPOKUVrUU43QZxYVFPzK43IKWQyul4JudpfmJ4MAGpWz1vEVKrfhZpdGB30yuFw2ajQvaM75aWKpffHM0hkptfBjbSbIBYulmaJPFsy5RbAjDmJCzcrPQ4eWlbHmR01cDs4tARcqPdJ6Kh1V+X6E2UTbGgPTTjHdTu4KdmagFPEWUtq0B+Xkc0aqPPm39dMzvmJmYEsHEHMc0pNUENuEeGkgte6RiY18c9tPZ1bwDWpZCc9iZ5svaRq1QyaaMJeyhCxjNlutDVkFsUu98zCSQU9ERk1bhG6AfREZTCM2dr66HAKqxr9RY6l8Rxmiqbj2EjKLADJsajziHbhR4uJorUop5sgTixyNS6taJAEDmrWwEA8g1QmW5HeVxpJWch06+CVIlf7S6Unr23y2WkSQOn6cyG32Xq70OEVTio4NmLWLhrWdKQyGmq8ItwCB1nVcWAgnnduC0u346MbB4Uoo+keO7sjcAjclBxspN0EsTDJ1axS3TRPbQ1OWQ8notRmJc+xyGg6MpqOdy5xldWVqWwKlLoeywA1bhEGgEODcQwnMvBKAkSezXsto+oYSWbwzN6oXdx7qrbGnKeryGgaWJa1x19uzg/Aro+3rtmHo2GzuY81v6YI0bmBnjZBLAByJ6jHRlL4/Zs9FU38C41MRtOr0sJ+KvWSqrmrMNGEvXBhBgAvd4bzCqrmPjPrOR0eTmBJnQd/3T+Iff0JhJMZaLqBJr8TZ3SE8OaxEQTdQt5zLucws9IrmgISdnZHcWQ4VVT40YJywAmCyMXSuEodPdXQ+mrUwStFofbnpifn1t6wKLWQEFgGT+8ZLHI2rWzwotYjIi5rAHTUe13441t96I6kwbMMdvcGsacvXvS8LN1WssVOJQbmwumpPQPY2R1Fo99pj3U6DjaCIBYGhZqV6ywJuAR01Lpn7NpTje4vZSvcImc78d1lHE2F12MZoNEn2VGlF6ysw/6BBCSBxbJ6L1bUe/IiTnXDwHMHh7CkzgOfJOSdazxb8+Tufggsg1qPCEXTUeMRcaA/gd+92YOVDT67e7OlubnnKHevm1bUATDrrVKE6NxAT5wgFhCVTvwTsoYDA3E8Nlp80NppqPWIiMlqkfhbVOrgmEq9pNmuGWQtzBKyhgdfPVrUKjWSUvHk7n6cu6wOz+w3FywOnsXTewYQSSkIeURkR50/PdE0Xu4M44NntRctsEo5zKz0Cr8kgAEgcmZh2sLCjxaUA04QRCGV6v2xkVRRx57l9Z5Ja3016uCVYiran7t5YGl47mdZBpB4Dg+/0YM9feaO+ppFPgwmMmDAgGEYaLqBZEbDQCxTVreHE5mia7fXuHBgIIH+eAbL6z15z2A6DjaCIBYGc1njcirR/aVsRUxW8frREbzVHbXnnKWc44XXq3GLBY4jsy5eT1RGRtHw2K5+DMRlOHgObgcHA2atvEODibxGDBblbI3Ec/Z1XCKH7pE0/E4BZ3SEkJA1iLxYUnPL2cWkksWOoxHS5zmGnjxBTIFq16ColEom/m4Hh+2dYTy9Z8A2DFYL5lqPWFb8gcodHFPdUZlM3YlqPePcZ2blfmd1A60hF1wihyf3DEASOdR5ROiGgYG4jLSaRVLJonY0dQ0AomkzDLpwgVVqAhKXVfglwY5Oyi0AaRV+tKAccIJYWMyW/ldav+1/dxzDK4dH8lK6Am4Be3pjOLU1WLHWz2R3nYBTxIb2EAbiMgSOQb1XQkvQVdFzs56DltXhdvBgRp1KA3EZTpEDz5pOpJBHxF8PDMErCaj1iJBVHZpuIC6rEHm2pG6rmm4X/gbMBdTG5XX4r5e7IAksvBXuwBMEcXwxV3XSphLdf2wkBZ5l4HXwcIgcspqOnd0RyKoOWR2bc5Zy1BRezwDy1g7JjIb1i4MYeKsPBgMcHExAzZrd6lY0eOAa7ZqsZg14JR5eibcbAzGGUdLWyGo2z3kFmPYnqWh4uTOM81bUQtPHxpyrud2RNAZiGcRkFcmMBt0w4BZ5BFwi6fM8gBxLBDFJZqIGRaVMNPGPphQ8fzAGnmXyBNvaTWgPuuCXBLsLTy6TcXBMp15SJXUnqvmMrWdmpaapWQOntQbw9J4BgAFiaRUBl4gGrwMXrKpHndeB7oiMuKxiUUACYN5vW8hlj6dwgVU4AYnJKo4Op+yUt8ICkJlRi0k54ASxsJhN/a/E0dM5lCxyKgFANKnCABBJKaj3SXmvldP6atXBK6T8M+Mr0r6koiGR0eBxcHh6zwB6omk0+iUc6E9gab0HF69pwMOv90AbDTGNyyraa1yo9/JIZDSoo+lupXT7ujPbcXp7wOyaBzMNbjBmOr+W1HlKOuUodZkgTgzmok7aZKOljo2k8PAbPXj1yIh9zCfxWN3ow46jEWR1w55zAsWOmsLrZUZtibUhrRvAM/sGsbzOg5aQE901Zu1RAOiJpLGi3osmv4SmgBOvHhlBIjOmj20hF85eWlt0jylFy1ujWNoNmBkCDp6DlpNlkKu5/bE0OocSODSYtG0kzzJoCjixapGP9HmOodUMQUyCmapBUSkTTfyzhtmm2usotUOgo3MoiXOW1eJIOJX32mQdHDPZhaHaz9gt8nmdf1bUe/ByZxg90TTqvRLUrI6sbqA/nsGLh4axutGHzGjhVq9DgK/B3LFOZDR7kVFqgZU7AdnbF8drRyJ5r+cWgFzR4EGDz0k54ASxgJht/a/E0XMsJ9oml4NDSaxvCyKt5qcAj6f1M6Hr1XhmAsvCMHQ8fyCCnv+fvT+PkeS+77vxd91VfXdPz/QcO7Mze++SXPFaUiQlURR1WeZjKz/FjmM5cBz/IsCwkScx8EvgHAZsBA5iBInjILDhAImTwH4Q63EsOLJsi6YkSiIpLrm8ltx7dnbn7mP6ruq66/dHddVUd1dfMz3H7tYLMEzN9nRXdU+/v9/v53h/KvZoadO0vT0Wc3W8yTN45sQYQiyNZJgFSRAQGAp1RYcFgKF66/aT82M4Mxl3EwOyZiBfV32DSt2eJyAgIGBUDFot5ehrtdGqr06V55nJKG7k6uBoEjXPv/dKjmarDdzZEl0Lja26AlExcCNXR5i3K0YlT9BnqSDic+cm8b/eXkZRVF195BkSEZ7GqzfzmIjxLddOEoRbxQ8ANEW4/0aTREtVP7CtuXVZR1nSWoJKAKCbFtbLDVAkAZr01+2A/SFYHQMChmCvPCgGpd/G3xFfjvEXVlk3sFmV8RMfmwZBEDsu793L/vNRv8czCQEMRbgLWJin3cMJz5AoSYCiGwixNHI1BY/PJcHRFKqW1rKAZqIcCAx2wOr2ObE0ieMTETw+lwoCSgEB9xj7rf+DBHo2qw3f3zVMC++ulPGPPnMCyTA3kNbvha6P4j1jKALJEOfqNgA4ZweKJFAQFTzExlAWVWSiPNYrDViwWzqcdrZ+ut3u6XRtsxaMrw4ICDgwBqmWcvTVa7cA2Lq4Xmngifmku3f10is5OpMQsJgXty0kPAMOaILARJRrGYQj6waKkgKepvDwdBymZYGhSDcw5afziRDbUsUP2IkURTeQDLMIsZS79/Zq7lrZXgPiAtNR0aubFgSGAkO1323AfhKcbAIChmCQ0fKD+m7sxKej38ZfVGwhJmAHQrylpoCduY0JzMDeFr3Yq/7zUft8RHganziZxp2CiGxNgdYsuQ2zNJIhFkVRhdkc0dZQDYxHOUzHeTAUAZYiIcFwp7nJujHQAesgjR8DAgL2BlHVXZ82xyTb2TwD/bVpWM0fREcyUd5X6wEgHWaRCLFDBbtGreuj0POGZiAd5RBmaff5NN1EqlmdZJoWQgyNK5UqnlpI4cP1CoDtVo6JGDeU7gb6HRDw4FGXdayWJGRrMnTDQibKYz4dPtTfd0cP2+0WONr2nmMowvX6dBgkyO7VP2f4TCbK4ehYCLNjITRUw11zGIqEZQHTSWHgYREzCQFzqRB4mkJNtv1LJ6Ic1soNpEKsGwhr11xR1VGoKXhqIeV2HjhMxwU8fSwFua1KN2B/ObzfloCAQ8ggo+U/WLU3tb18N3bj09Fr41+XdXvSjajiuRPpFnM8niExlwr13RgPc/jZi/7zvfD5iAusO3KVpUncztvXXJJUzCZDqMrbpoW6aeKFMxOYTQkwTbu9MMTS4BlqqAPWQRk/BgQE7A2maeLKRqWl9cwJXsR4pqc27VTz++nIkWQIL57L4JUr2ZbgUibK4cUhfJ/adX9UWjUKPQ+xNAzTxEzSPgzppgWGJDCTFHA7L4IkCUR5GqczMTAUgf/7xZNQdBMUQSAeYnd0L4F+BwQ8OKyWJFy6W2rRUZ4h8eR8En/rsSN75p+620EQXn312i1ohomFdAgnJ6JYKoho5k4HDo579a8iqbhbFKFoJtYrdoDK2U8DwKOzSQBAwTPpuJ12nfcGr7wtx8fHI3hqIQUAEHw0N8zSYGgS766UcWYyiifmk9AMCwxFQJR1rGw18MmTgUYfJMG7HxAwBIOMlnfo5iExCs+JbgEdr1hvVuUW8T87FcWZyXjP5+53+NmPaUh74fMxkxDwumn7T41HWEQ52t086KaJj80m0FANxAQGTx5NjaSiCzgY48eAgIDRU5d1rBQbiPMMZK1zMMJzx9NdtWm3mt9LRyI8jSeOJmGaFuqK7vrDRTj754Po2GpJwitXs6AI29tC0UzEQwwuLCQxPxbp+/u9GIWezyQEXGHpFt1WYbcpz4+FcWIigvNHEnhqYbjgfz8C/Q4IuP+py3rHJGXA1va375TAURR+9umjI9/rjmIQRLu+sjTpDuZJhBg8czyN05OxHQXHvfo3nRTw8pWsG6DK19WOs8GwOr+T4P1MQkCEo5EOs7iRq7f8G8+QvuvwQU3xflAJ3tmAgCEYZLS8F7/eYq/nBEkAY2HWHp/MUABB4M3bBcymwjsWv51mWnsdfl65msUnT4zj1Zv5PZ+GtBdtCN7n9FZzVWQNC+MRhJqLzX5M9gsICLj3WCs3sFyUOipBASDOM3hqobtv2lq5gWrDDmpbsNu40jEemm6gLGm4vFbGIzOJHW92jyRDSAjsjqpr6rKOV65mwdNUx31dXCril54/jmMTOw8ujULPIzyNCwspkCTRUlHAMSQePhLb04qCgICA+5u1cgN1RfdtJ5Y1E8tFqWMfv9tgxagGQfTT1/Eoh/Eo1+MZBqPfuWKnOj9s8L7bWuBUl33p/JT7WrZXXsWe+EkQICwLW6KKmLA/U7wfVILAUkDAkPQbLd9Oe2+x0xNNEsBkjMePbm9hOiG4/cJTcQGPHIm7bWs7Eb+dZFp7maxSBIFvvLcGgmg1xduraUjDBscGWeS9z9lQdXxsNgHNsKCbZtDmEBAQ0BNR1WFaaKkEdaqD+lmFSqqOyRiP124VUBBVPDabwF9+uIlKQ8NcKoQb2RqubdZ2tdndaXXNWrkBiiA6gkoAsFyU8I331vC1Tx3flTb20/NB9TshsDidiSJXk6EZFiaiPBYOuQdKQEDA4UZUdSg+kzUdNMNs2cePotJolIMghpkgt5tgWL81Zr/ahwdZC1ZLEr71wQZeWyy4reuOV+pmVd6XKd4PKsE7GhCwA/qNlvfS3lvs9ESPhVm8dquAuMC0mNApuoHb+Tp4mtpX8etlsmoByFZlTCcEjIVZt12CYykQlrUn0/AGPSgNs8gHrQ0BAQE7wdFt07LbABycEc5CD68ggdmuBjo1EWnR++WihEyc37MgfT9EVbf1vS2oRJEEjqfDCLEU3lzawmwytKsWgm7aO6x+n5mK4cxUbEfXEBAQENBOmKW7TlIGbHNq77j7UVQa7cWQml5721EEw0ZxHaN8HWctcAJmN3I1RFgayTCLl69ksVyUWvwQszUFr90q4KmFFPJ1dc+neD+oBIGlgIBdMqyHhPN4ZzM/nRTcQwZNEuBoCrJmTx5iaXLfxK+XyaqimeBpys26t5vEnjogcR7VIh8QEBDQi914BWmGhUpzQECYp1sm2Si64U6lHDZTPQrCLN2RradIAo/NJnBxqYj3Vss4VxAxGRdGfhAJ9DsgIOCgcXx7/KZrOkNvvOPuR1FptBdDarpxP+usX8DsSJLHclGCZnRWoWVrius7O2zwLmAwuodoAwICBsLpLU6EWsdsdustdh7voBm2zNEkgWSYBUUSIGBPvtmqK7hTEHFjs4a6vLci6Byc/IiHaCykwx1BJcO0sLQl4tsfbeL6RnXPr7GdQRZ5L3VZtyvMlkv78p4GBATcHwyr814008Sx8Qh4hnT1HtjWfNOyoOrmvuq9w0xCQDzUeu3H02FcXCoiV5PBUCR008JGpYHFXB3f+mBjZNc2jH4H2h0QELAXOL49L57LIOPxI/Lz7RlVpVGv/Xa3RMVONXBQnb2XNLYu67i8WsF3r+VAUyTGIyzIZk96RdJxO18HTfmHOBTdDjiNMngXsE3wrgYEjIBhe4uPJEN4dDaJ65s1jEdYpMIsOJpyg0oRjsbtfB0EQeD0ZBRXNqp7UrbqpZf53oWFFN5aKrYElRTdQElUwdEUFvMiLt4pQr+9tatrHLYHfJhFfphS4GCKREBAQDs79ZAIs7Q7CjoZYpAQGJDN6lS6uRu+slGBrJn7pvcOzqHq4lIJy0XJvl6eRq4mI8ozkDUDRVGFpBoAgJWShMePJvDk/NhAz99LSwfV70C7AwIC9pJBPdxGVWk0rNn1blrZ+ulsQ9X3pVVuVNrsXOuNzRo+XK/AMC3MJAR8+vQECnUFHENC1kyYpgW++d9eOJoEtcMJ0wH9CVbbgIARMWxv8UI6jOMTETAkgYWxsBu0EVgK65WGPaksxrnGsN3KVke5kU4ILJ48mmpmqglMRHkcSYYQ4Wksb0muSBum5QaV5lIhd8x1rVlyu5PS2p0sbIMu8sOUAu9XL3pAQMC9x048JLxtdCGWwvHxiKv3cYHBWrkBzbCQie6v3nuf628/cQQ3sjVcXa8izNIYi3CQNQPTcQF1ZftgImsmrm7UcGYy3vd1+2npIPodaHdAQMB+MIiH225aotsZxnR7N61s/XSWZ6g9b5W7navjG++tYbMqg6VIRHkGEzFuaG123ovloj0wqdrQcG4qhlSExe2CiFOZCEIMhek4D9OycGw8gtv5eouBd4SzkylBwmFvCN7VgIADwslYvHI12zLCmiYJsDSJ6TjvTjBwaO/hHuVGuvtz0YjwNDIxAeem4qjJGqqyhqgz0UexzV85mkTN5xoHYacL56CL/KB98fdCL3qQkQ8IuLfwZqe3RNXV+4qsISbQKDe0lok1Dvut92GWwk9fmEVN1nBnS0RRVF19b6efxg+ipYPo9/2g3YFmBwTcHwxbadSOnxb02yvv1tepn85qhjWyCXV+3Nmq4/deXXQrYgG7zVDWI0Nr81q5gVxVwe18HQxF4pMnx3FpuYQf3ioAAM5MxZCJcvjSI1O4ka3BAuGeW2ICg8+fy+DERDTQ3z0keGcDAvYJvwXlSDKErzxuZyzOTMXQUHXImoEtUYWkGKjJGggCKNdVhDgaLE267QGj3Ejnawr+5K2VlmwCS5Mdh4CJGAeWJqEaJrLVVgNv78jtYU3xdrpwDrrID9pyMcoRsH7s9oARZOQDAu4d2r/vL52fRlFU0VB1fGw2Ac2wsF5uIFeTQcD+fgssDYIAdMNCTKAha4b7XLvRe++1hBkKr97IQ2y2tzmIqoF3V8p46fw0MreLWC01Op7H0fp+Gj+olvbT7/3S7r0K/gSaHRBwf+DViAtHU6ApArJmQOjTEu38XqWh4rWbBaiGBZa2/X8G0YLd+jr12yfn2gzLh33+XtRlHW8tFVuCSoBd+epMvx5mXy2qOmqyBlkzcWIigou3i7i7JcKyAIIANMPE3aKE713P4//+7EnUFQOyZkBSdZAEsSNfpSAxMBzBOxMQsA/021x6M9J/8tYKvn8zj5KoQjctTMcFPLWQwtXNKo6Ohd32gMtrZVzdqLYEghyGCYKsliS8sVjAm0tF92c8Q+LYeAQxnvE9BGzVW4NK7Zn2YcV7NwvnIOXETikwSQBjYRYW7El3HEuBsCz3ekc9AtbLaknCtz7YwHJRgmqYYCl72siXzk8NdMCoyzpeuZoFQxJIR9iW63/lahZfefzgq6kCAgJsemm+V5dZmsSVjSrqio4IR+G713ItU0K/9IiMsQiLsqhhMVd3tcOr+f30vv1a0hEWry0WXI33UpY0FEUVnz+XwWpR6pgA6mh9P40fVEv76XevNg6SAASGwvXNGpYKdaSjHAjLwpaowmwrs+ql3f3W52EPFs7jy5KK5aIEhiRAEnCv6TBUUQUEBAzOIAFiP50oN1S8cjWLVIjFnS0JddVAiCUR5ig0VGMgLRiFr1MvnRUVO8HQb3+8E9bKDVQkf+11pl8Ps68OszTU5rQ3iiBQlFSQBOH+zDQt0CSBME+jrhgIcxReXywMHdh3PststYFsVYHSTPibVpAY6EewogUE7DGDZpqdxzU0A6puQm/uQtcrDVxcKuJMs9yfpoCvX1qBYVi4u2VnAbyBIIdBxNp5TcNo3YU72YRzU3G3Sqou6xAVA4/PJXFyIoKPVRr2dAUL2KzK7qZ52D5zYPcLZz/fk5mEgFSYAU9THZPt5lIhPHs8PZLr6EZd1vFn767i7TulFiPBlZIExTDwC88e63vAWCs3fK/fOezt95jygIAAf4apLppJCAizFGqyitdvld2gEgBwNIXbeRF/+s4KFsYiuJmru//Wrvnd9N7vWhTN7NB4Vbc3+aphYq0k4ZEjCbx4dgJV2fbP42gSBGytjwn9NX4YLe2l393aOEgCmE+H8f1m5dVWXcHNXL0l+OUNLnXT7n6f1QtnxvHda/mBDybeA2ivaxpFBWxAQMDeM4ielxtqx2Pmx0JYr0hIh3n88FbBbdcCgNlUCP/X+WkU6kpfLRiVr1M3nU2FWRCwEOJovHojj5KkgWoOlfDuj3eCqOrgmO4D6DXDHGpfPZMQMBnjcTsvoirrqMo6QiyFKE8jzNJIhlhEeBo8TUHWjI6gEtA/sO9oeK6quEM1vBoeJAZ60/3TDggIGAmDjvp0HtdQbcNU78Z8vdLARIzHo7MJvLtcQlnSWsTaOSSo+nbQYhCxdl7TT/hFxUCuJmOjYpfwvnxlE9/+aAOXV8tYKoioyToSAgvC0wM3aJ95OzsZvToMEZ7G86fH8fbdUktQhmdIRHgar97Moy7re3YdSwWxI6gE2J/b23dKWCqIfZ9D1oyOoBIAZGsKXrtVcFtmAgICDpZBNR+wtemphRSSIa4lqBRmacylQiiKKj5crUIzOrXDq/nd9N7vWhy9dzLGVVnD7Xwd680qm5qi47vXshiP8ojxNERFR6GuIl9XERMG0/hRaanTxtH+XHOpEJaLotvOF+UZ8Azp6uFYmB3o9Xp9VrmqgreWil0PJu3juNsPoE4W3e+agN1VwAYEBOwP/fR8qSD6Bp7qio6tuorv38hjvSy3/NtKUcL3rudcTeilBd00cKf7bS+rJQnfuryOU5kYfnizgCsbVeRrMhTd6Ngf74QwS4OAnQD1IxPjh9pXR3gaP35+GhNR+30zTBOSaoAAgWSIRbYqQ1INtxJq0HW4Luu4vlnD+ytlfP2tFeSqittyB3RqePvvB2wThNoCAvaYQVsCnMephom6omMmaYut3iztHAuzuL5Zw+yYnSV1xNoJNMiaibKkgiIJxAQGsCzUZb3nouO8ZvtzKbqBkqhCYCnEOBrfv5FHSdTwuXMZ/PVHm9hs+isxFIHH5xL4xMlxxAV2oNHbfuzWEHEQFM1COsKBo0lohgmmzUtqUM8PP/q1SuRqckdQyUHWTORqMoB4z+uXVL0jqOSQrSnBISUg4JAwbEutBSAVZnAqE3X1HrAPJrJuQNYpKM2sqaMBhmmhKKrYqDRwNBVCqi1o0etavHovawYKdQV3t0REeQYWLPzV5Q0s5kUcGw/j/JEEnj81DgB9vUS8jFLT/do4VN3Ena1t3w6WJt0JQNma4hqO93s9UdW7toDczNZRafh/ln5VBu0HUJbaTth4r8lhNy0mAQEB+0M/Pc/VZN8AhqQaIAkCt/J1jEc5mJZdaemwXm64mtBPCwadIDcM3kA4RRIgCcJdg3iaxHzatt7YjUfdTELAjyyrZUCRw1wqhC8/NjO0z+jFpQL+1mMzWC018MyxNPI1GYZloSZrduuyoiMmMCC9WW8fnHXYW2WajrD40VIRPENivOkn6HxG7Roe7Ln9CVa1gIA9ZtCWAOdxLEXCgr0oOagADMtCQzfc6Wve6ULZmgJFN7BZlRHlaDwyE8dffbTpZpe79QI7r+l9rvWK7PF34nFmMoqXr+ZwPB3G/3p7pUWsNcNCUdTw0Xp16LJQv4Vo1AunF1HVwdIkxiL+mZNBPT/aGaT3nqZ6L3BMn38HAJIg7EkaPgEqniFB9VlEAwIC9odhW2pDLA3T2tZ81fNvRjPQZGJ7c75cklyNPjVJYDoh4FuX1/Hi2U6t97sWr96bloXbeRE8Q8KChbNTMXz/Rh66af98LMLh3ZXyjvRdVAycm4rBbPp08Ay1q+SD92DzznKp4zExnnEnAE1EeTy1MNb39cIsjckY79ti/MhsHAJDoib7/277waL9AOpUUTmarXgqikdRiRsQELD39NNzv/1dVdZgWCZqso6GZqDa0EASaEkckCQBRTcH1oJ+lg/DslZuuJ6dpmdbqekmVN1EQzXctWo3HnUvnrWnXz+1kLKD97qJuEDjwkIK82ORga/XGwgjCQJ3CxI+c2YcP7xVQKGuIsRQqCs6JqIcPn8uA2GAdbi9ylRparWsmVjekhDm6JazmDJkV8iDSPCuBATsMYP2RzuPU3WzI4DgTOKZjPHu9DWz6W301EIKumlho9LAqUwUDdVwvRz69QJ7r815Lkk1kK0pSAgM5sdCeH1xC4ZpISowKK2qmIzzYCnSXUw1wxzaL2JQY9tRMirPDy+DeqlkonxLtYGXTJTDRJTv+1qJEOtm5L1/G47XSjzkX7EQEBCwvwzriTGTEBDhaF+NiHA0JqKc62X3+FwSc+kQGqoJhiJwbiqGpYII04Kv1vtdi7N2vHBmAnGBQTLMYTYp4OJSER+tVZCOcjBNgCLtqUej1PdRJQq66bmTPJhPhwe63lSY7WiRBprZ6ZUKPn8ug1xN9f3d9oNF+zV5q6hkzXSTQqN+LwICAvaOfnqeadu/qbrdpjyXDCEZYkESBIqShum4gPVKA6YFsBQBjqYQF2g8czx9IFpQaai4uFREtqbgsbkEbmRrbgt2XdFb2q936lH3U0/Mtky/3k3S2FsRallAUVLxF5c3cTwdxtGxMCIcjbjAIMLRODFha3+/dbi9ypRjSBimBUU3YFpAmKNhmJbrOeXV8CAx4E/gsRQQsMcM2h/tPG4ixuHYeAR80wfDMY0zLAtffmwGhrVdjGlaQL6u4laujo2yjIZqIF9vnYjTqxfYe23uc+XryFdlTEQ53C6IoEgCFEkgITAo1FVc26zhdqGOG9ka1jzjqActC3UWompDw3iERTrCIsrRoCkSby0Vd9zL3Y+98E8a1EvlSDKEF89lOvrMM1EOLw44XWImIWAuFcK5qThOTkQwPxbCyYkIzk3FMZcKBYtcQMAhYVhPjAhvZ2/bNYJnSDx3YgzPnUi7E2lu5et4a6mED9cqWC81UG1oHYbQg1xLTGBwYSEFAPhwrYLVcgP5moLNqoIbmzXcytVwfbOKxXwd1SEm9+RrCt5YLMAwLKSjHMYjLEiiuy/RThmVnhdF1TZ7bfMZ5BkSyTALRff3rusWIOx4n5tVVE8vpHD+SAJfemTKPWwFBAQcfvrp+ZFkqOXfHG+exYLdTnx8PALLsrBRlTEWZnFmMorH5pJ49vgYPn36YKaL1WUdP7y5XaUpyjqm4wJEVcdyUYLAUmCarbw79ajzrkdOsvaxuSROT0Z3FEjzVoQ6VbfpMIsbuTreXS5jpShBNy1cWEghwtO2t+rJcfu9rzSwVVegNivEnHW4vcq0oRmgKQJFUUW5OXFOUnUouuEm+IPEQG+CdyUgYB8YtL3K+7iKpMLwaSFgabIjQxATGDwyE8dm1b9mv9ehoP3aZM3AYq7uVj1FeQZnJiMoiipqsoaxCAej2Wms6AbWyg2kwtzAZaFrZfsw1K39YCEdxqNzyYGeC+jvb+SwFz5Og3qpRHgaTxxNwjQt1JXtKUsRzv75sJ4lzpjx3V5/QEDA3jBsS+2RZAgJgcXpTBS5mgzNsDAR5bGQDqPcULElqihLmmsI7Z1S48VP63tdS0nUwDMkJiIc1soN1ORtbSQJArph4Xa+Dprsn4dcLUn4k7dW8OZS0f1Z+zSdbpVPg+q4w6j0XFT1lha6dv+9iRiPmmIM9BrdrmkixgXjqQMC7mH66bn3e+9odDpsB9X/1mPT+MFNFusVGTRJIBPjXT/P8S6m1nvNWrkBzbDc7ojFgoinFlK4uFTEeqUBmiQQ5ZmBPOp6sVsfIu+6oGgGxiOsm2Rxuiyc9rqHpmN4ZCbhXutqScIPbuUxEeUwHuVaWvAcLfZWmaq6ictrZTwxl4RhWM3qMguZGI+pOI+f+Ng0kmFupBYd9yPBOxMQsM9YAHq54fRrw/Jb4CzLwl9/tNlSqeSlX9DH+5p1Wce1zZr7XHZrQQjvLpewkI6g2jx40CSBZJiFZlhgKGLgDLGo6hgLs10nnH37ShYnJgbLaAzib+TF771LhVkURRVv3t5yvUAEhsJ08366HXbqsr3QbVQaYD0HES/e9905OO6mHHgvDBwDAgJGz7CBEsDW4TNTMZyZinX83Pner5YkrJcbIICW8fUO3bS+27qykA7jyfkkeJZEPMS4gSWSIBDl7aRGnGf6+sA5lajtgS5nms5TCynk66rvQWNYHXdo10OaJEEAuLFZw+18HZkoj5nm73f7LFxvQ9rW8JpsHwxrsoYozyATE/D4XGqoAGGg0QEB9x+99ube771Xo9ea0+AemYnj4Zm4bwDkIHA8R72tuu+ulHFmMoqnj6dwOhPFqUxsII86wA7KONrp3Q93W4/yNQW3cjVUGhoSAoPjE9GOIJt3XVB1eziRaVn4xMlx1Br2lNJ83W5TToSYlve0W4teTdZRV/L4qSd4RHh7/38kyaMi6TA5CzNxAR+sVXBmMopPnRrHWJiFYVkgACTD3J5ZddxPBCtdQMA+sNONczfaF7i6bE9BGNTTo99zd2RdLXtC0U88OoU3FovI1WRwNAWKJJCJcvjEycF7xMMsDQvoOuGs2hjMz2NQfyO/+3Oee7Uk4ZsfrGO5KLmLq5Nhf/nqBo4kw7jT9C8Btj8zwPYzYUgCsmrgbk1yvY5iPOM+tv19H4X54qgNHAMCAkbLqPUe2P7ezyQEfP3Syki03nnev/XYEXz3ahafPJF2M9YcTWEswmI6zuO5E2nImn9LmIPTEuGdhObgnabTftDYqY57r//0ZBSrJQl/9u4q3r5Tcj3oJmMcfvz8NG7mqvDGs7yfhdO+5l0DHOZSIXAMMbTmBhodEPDg0UujuwVADgonINRerWkBMAzgVCY2kIbNJATQFPDeSqXD+/PJ+aTvevTRegV/+NodLBe3p3rOpUL4+8/N46Fpezqyd12oypqrzYpu4G5RwpNzScymQtisyu6QIu97OkiLXpij8PKVrKv9+ZqCMEvjqYUU1ssNPBzlWpI3wRS4wThwj6W1tTX83M/9HMbGxiAIAh555BG8/fbb7r9bloVf//Vfx9TUFARBwGc/+1ncvHmz5TmKxSK++tWvIhaLIZFI4Bd/8RdRr9dbHvPBBx/gk5/8JHiex+zsLH77t397X+4v4N6iLuu4vlnDO8sl3NisjcQPot/GeRSvMaynRz+c7MuXHpnCp0+P46GZGM5NxSEqBl44PY6ffnIWX3x4Ei+dn8JTCynEhcGNo3sdfHjGznQMIuCD9nZ3w/lcclWl5UCRrSn4/o08tuoqXrmSxZhnjHdZ0vDWUhHf+mADZUlz+7wzUQ6yZhs2tvdwB3QS6H7AQbMXWu88717q/ai1HrD1/tG5FBTNxItnJ/D//cQx/NzHj+Irjx/BUwspbFblvhN2nJYIZxJaO92mH+1WxwH7Pf/WBxstQSUAqMo6/ujNu9iqq1A903y8n4Xjw1GX9Y6BGU8eTeLV6/k98/17EAm0P+B+Zy80ehgGWdu8fnDOwIPJuICxCIeJGDdUgmIuFUacb73XOM9gLhXueGy+pnQElQBguSjhD1+7g3wz4eysC44RuqPNHE1BNyzEBAZ3ixK+8NCkr2ddvxY9WTPcddoJrp2dioFnKRTqCj5zZqKjIjiYAjcYB/oulUolPPfcc3jhhRfwl3/5lxgfH8fNmzeRTG77q/z2b/82fvd3fxf//b//dywsLOBf/at/hS984Qu4cuUKeN524v/qV7+KjY0NvPzyy9A0Db/wC7+Ar33ta/jjP/5jAEC1WsXnP/95fPazn8Xv//7v4/Lly/gH/+AfIJFI4Gtf+9qB3HvA4aO97LIma4gJDD5/LjNwa5Yfg2ycR5HdHHUJvl97XFnS3MwLgB1NR4jwNM5ORfE3V7O+0816lc96kVQd4xHW7q/WTHAsBcKy3P7rfsEp53NxjBa9LJckzI6FWjLtDnXFNjcci3AD9XkHtBLofsBB06uiyGlXHaaFzct+6P1etFstpMOYHw+jLGnQm+OVnTHLg2i8t6XMb3ql3/Sjuqxjs9pAlKM79Nth0CTDclHq0HEAuJWr4+xUzPUHdPB+Fppp4WNH4jh/JO7633lbDUe1Rj/oBNof8KBwUC2xg1bLOsGvV65mQRGEu4+OhxhcWBjM9xOwtfFO05/J2QM7+nmnIHZo561crSOo5LBclHArV8N4lHMDQ377c4okQJIEGIoEQRC+19pvCrSk6u57RBLATIJHJsZho9IASZAd+/5gCtzgHOjJ59/+23+L2dlZ/Lf/9t/cny0sLLj/bVkWfud3fgf/8l/+S/zkT/4kAOB//I//gUwmg2984xv4mZ/5GVy9ehV/9Vd/hbfeegtPPvkkAOA//af/hC996Uv4d//u32F6ehp/9Ed/BFVV8V//638Fy7J46KGH8N577+Hf//t/HywyAQC6l10CwGpRwgtnJloM34Zhr83tvOxVCf6oja/PTMbx3PE0lotSh1nqoAJOAO6oVAevUWy/4JTzuahG52HEMC1ohmNQ3vrvima2jGF1puk58AwVBJV6EOh+wEHSq6Loz95dxZFECHe2tje+w7aw7Zfej1rrd6vx3pHc7e0VmRiPT59uNap1DkCLuTpu5uxqE69+O8GlQZIMoqr76rje1HDNsFo028H5LERV70iY+D0uYHcE2h/wILHfLbHDthUfSYbwyRPj+MZ7a8hWZXsfLjKoKzo+d44caM0TVb1jD+zVz3btrDT8ky4O1Yb9eNe7yUe3AYCjSdR8nt/Bux61kwgxIAnbM5Ak0DJISNENlEQVuXoKTx1NdW21C+jOgbbC/fmf/zmefPJJ/NRP/RQmJibw2GOP4b/8l//i/vvS0hI2Nzfx2c9+1v1ZPB7H008/jTfeeAMA8MYbbyCRSLgLDAB89rOfBUmSePPNN93HfOpTnwLLbre0fOELX8D169dRKpU6rktRFFSr1Zb/C7i/6VZ2CditUXVF33EbQ3vknCSA8QiLdIRFlKMha8Y9UWrf3h63m7HJEZ7Gl85P4fhExC2/dYJKgwh4XdZxcamIity6aDhGsXOp0OAZdh9PEIokXLNars2Qm2NIdwyrH0G5bG8Oq+4DgfY/CHSrKFJ1E2/fKaGutGrxsC1sfplSr+YXRXWkrXejZDca397+4bRXnJmK4acvzLYElbwHIG/rnKPfTvvxoEmGMEv76jjd1HCGInw129HqftntQNNHQ6D9AQE7Y5D2tmHbiuuyjldv5kEQRMs+fJg1b1jtjAtMl0faxAT78U5gyE/XM1HOHYDUa1hFr3bERMjWhvZBQhxNYTzKAxZ6ttoFdOdAV8vbt2/j937v9/Crv/qr+Of//J/jrbfewj/6R/8ILMvi53/+57G5uQkAyGQyLb+XyWTcf9vc3MTExETLv9M0jVQq1fIYb1bE+5ybm5stZbgA8G/+zb/Bb/zGb4zuRgMOPb3KLgG7aqXWnPIzbAbCGzlvj47zDIktUcWNbA3Pnx6Holk7bsHYD0aZgdlNqbDdqmL4tlxUZA2zKaGj5aK9vcX5XFTddEeuOswlQ1B1o2UBc4hwNOZSIYhqp5ntKMpldzJN6l7isOo+EGj/g0C3iiJH+9srFIHhWtjaM6Veza/IGh6ejmOj3ADHkMjEeEzGhEP1Hd+Nxg+q6d4DUHvrnNN+3J5k6KWLMwkBc6kQVkqd7XAnJiJQdQNRvvOA4Wh1v+z2sJp+v2v4Tgm0P+BBYZQaMGh726DVss61bVYbWMzVXW30TnVTdXOgNW9Y7TwxEcVcKuTbDjeXCuHEhP16TmBI080WXfdWtfbT5l7rUV3WkQgxvoOEwhyFiSjfs9UuoDsH+m6Zpoknn3wSv/VbvwUAeOyxx/Dhhx/i93//9/HzP//zB3Zdv/Zrv4Zf/dVfdf93tVrF7OzsgV1PwN6z27LLXnhbDBiSaAkqHRuPgGdI8DSF//ydRaSbGQPAf+G43zasgxxi/O7ZWUDbWy6cljqK3M5y9FqUnc9F1iMdU+EKooyPzSZxpyC2/N6FhRQAjKwt0MteTJM6bBxW3QcC7X8Q6JZhdbTf0fp2BtX+9pYyJyNakTV3U+1dA85NxTER43y/4/ei3g+i6aKqd4ynPpWJoqEa0AwT0wkBTy+MuffaTxedCljFMFoMvGM8jZfOT+NGl6lwzvOPstX7QdDwnRJof8CDwCg9/IZpbxukesh7bVGOxs1cHSRhVxKVJc31FuIZEs8cT/XV8mG1czzK4e8/N991Kpy3svVIMoSfffooHj+awNUNe1V2vO8GbU/rth451/3da7mWn/MMiaNjYXdtWitJ++KNdT9xoO/U1NQUzp071/Kzs2fP4k//9E8BAJOTkwCAbDaLqakp9zHZbBaPPvqo+5hcrvUPQ9d1FItF9/cnJyeRzWZbHuP8b+cxXjiOA8dxHT8PuH9xou5bdaXj3wYpu+yHEzm/vFbG5bUKTk5EXE8hbykm12wfADoXjsOyYd3Pw063e35kJub+b5a2g0nOQlCTNdDNwNIgi7KT0ahIKgzLQoilwTOUmwnpln0ftTHjbsdu3yscVt0HAu1/EOiWYWUp0rdC0WEY7fdmSrPVBq5t1jCTDGEmwbeU3cuarVcsTXZ8xw9K7/dD303TxJWNChTNhMBS7s/TERbpCO8Gi5zrGUQXjyRD+IVnj+HTp0TkajIYisBE1H6uZ46n+2r1KMx2HxQN3ymB9gfc74zaw2+YYRD9qodSYRbf/GDd/XeOIWGYFjZrMrbqKmaSgjuwQdZMZKuyOzmzF8Nq50PTcfzTL57BrVwN1YaOmEDjxES0JagEbK9FJEnisdkkaIqArBkQRmSEfiQZwqOzSVzfrLmJaYoicLcgotFMTqyVG/j6pZWun9G9mPzZaw707p977jlcv3695Wc3btzA0aNHAdimfpOTk3jllVfcRaVareLNN9/EL/3SLwEAnnnmGZTLZVy6dAlPPPEEAOA73/kOTNPE008/7T7mX/yLfwFN08Awdsnfyy+/jNOnT/uWxAY8mDw2m8BmRUaMp6GbFhqqgYkhyi77EeFpcAyFyXjrc3hLMdsNRp2FYyYhHIoNq3PYyVUVN5AzFePxk4/O4NhEZKSv1WuBXik2EGYpiKrRYbaeiXK4lashGWYgKsZAi3KvrEy3fxu1MeN+TQ88aALdDzhInEzltz7YwHJRcitmphMCnlpIYclToeiwE+139EFUdTdZ4Fd272i+9zt+UAGK/dD3uqxjpdhAQmAgkgaWi5JbgXo7T+CJoylwzHZ4bxhdjPA0HjkSBxDveOwg2rlbTX9QNHynBNofcL/Ty8PvvZUKEmfZlp/30/Re7W0kAciageubNTew8fzJcbx6M++bkCiKasvPCQDJEIONSgN62+tkohwUrbMdrlsgZVjtHI9yHYEkL/uVWFlIh3F8IuJ67F7ZqLScJQh0/4wOS7L/sHGggaV/8k/+CZ599ln81m/9Fn76p38aFy9exB/8wR/gD/7gDwAABEHgH//jf4x//a//NU6ePOmOHp2ensaXv/xlAHa244tf/CL+4T/8h/j93/99aJqGX/mVX8HP/MzPYHp6GgDwsz/7s/iN3/gN/OIv/iL+2T/7Z/jwww/xH//jf8R/+A//4aBuPeAQ4YhDtaFhNinYoy4VHUdTIYiqPtKpAH6lqorHE8LPYFRS9UOxYXUOO8tFqSWQc3dLwnpFxi9/5jjmx0YXXOp1z8tFCc+fGsfFpSLeWyl19F8vFyWUGxoen+u9idzPaT/9Mhv7OT3wIAl0P+AwMB7lILCUOx45wtE4MRFBSVJRFEfX4urVfMXHv8+r+c53/CD0fr/0fa3cwHJRwufOZfD1S6stujcR5fHITAyvXs8j/TiPCE8fGl0cJDN9WK71sBJof8D9zqg9/Lq1tznefa9czYIgtgPxqTCDF87Yfq3t1UPvLJdafp8kgC8+PAmaIpCvqWAoAgTQklD3atZ+BVL2M7HibeVbzNV9vZyc1/Z+RkF1ancO9K4vXLiAP/uzP8Ov/dqv4Td/8zexsLCA3/md38FXv/pV9zH/9J/+U4iiiK997Wsol8v4xCc+gb/6q78Cz/PuY/7oj/4Iv/Irv4IXX3wRJEniK1/5Cn73d3/X/fd4PI5vf/vb+OVf/mU88cQTSKfT+PVf//Vg7GhAhzg4IzNV3cTtgogXz2bctqhRiIRfqSrXnIjDM2SHwShgt2Achg3rWrmBXFXpMMsG7EDPW0tFpMN81/dp2JLRXvfsjKL+1KnxlsOh039tWrbAt78vJGFPgbBgH/KciXx7vQAMsiA/KJOJAt0POEj8NoQ1AIW6inJDw0vnp1EU1ZG1uHo139F6h3bNd77jB6H3u9H3YbTdGU9daWiYS4ZwdioGzbDAUAREWcfNbB3jMd7dxHebsufouDNl7yBastsPVA+Khu+UQPsD7ndG7eHXrb1tLMzi7bulpi/rdmCpKGr47rW8b2DDuTbvQImVkoTJGI/TkxFMxHgcTW4n1E1rW7P81k1VN7GYqyNfU/CTH5vGjKeFeTfsd2LFaeW7tFzEfDoMjiZBEoBhmkiFWSiaCY6lIGvbA3sOQ7L/sHLgq9xLL72El156qeu/EwSB3/zN38Rv/uZvdn1MKpXCH//xH/d8nfPnz+MHP/jBjq8z4P6kmzg4Bto8Q41UHPyM7gjYxnURnnZf18FpwWgfE9rOfmxYRVXvOjUPACqN7lPzdpLp6LdJF5oBt0IzGOi3WFME0Xci37XNmnsde9EvPWhmY9STiQ4zge4HHBT9NoRFUd0zzSdgZ0K9wxu8wxqc7/hBBCh2qu/Dartzbw3VxI1cvePfTzZb7pyDVr8pe+em4vhgtdLymqPU8WEy0w+Shu+UQPsD7mdG7eHXzRybY0jfMwPQPbDhXJt3iBBFkriyUQVHU5hJCljM1vHUQgqm1apZ7etmuwXFVJyHfntrJNVL7YmV9kEPFUnd1fP7EeFpTMYEvHO3DLHtrOCQr8oYi7A40gy+9eJBrk498MBSQMBBchDi4Gd09+zxdNe+6MMSdAizdNepeYCdifF7v3ZaMjrIPfcLuMVDbM+JfCxNutfxwplxfPea/2ewm4Vy0MzGKCcTBQQE+HPQmn9qMoof3ixAM6yOCaAHGaDYib7vRNude6PJziOWt4LLOWj1mrLnDcztlY4P6/EUaHhAwINLNw2YS4WQifMtU4Yd+mm635mhLKlYLcldf8dvHXOu7Y3F7YAJRRKYTgjuVDhJNWChU7O866aqmx2VrYpuotZcD3bbBuZNrLQHsADgblHEdFIYuY+RX+DNgWdIaIbl3l9QndqdB/fOAwJwcKXrfkZ3E83yf78WjMOwYZ1JCJiM8bjrmWjh4GRi/N4vv425t5XhzaUtzCZDHVnlQe55kANYhKe7TuRzyFUVvLVU3JMy32EOsqOYTBQQENCdw6D556biPb/jB6H3O9F3R9vbW4w5lgJhWb5Zc+fe3loqutVbAFqC/e0HrW5T9tqz9e067mS6NyoNlEUVP/fMfE/DWD+GDUQGGh4Q8GDTTQPKDbXDQHtQTW8/M1zf9KvR36bbOnYkGcJcKoyTExF3EpoTzK/JGjTDxHRCwNMLYy3X5F03/SpbnRY/v2qpXhWkfv/m7Ov9WrMdU/G98DHyC7wB6EhEOwOVDjrZf1gJVrqAB5qDFod2Ue21AT3oDWuEp/HlR2ewUZGxXNw+fDgmd6phIBVmWyZUTCcEX58jb5np/FgIk3HBN6vc754HPYB1m8jnUJM1VBrb1znKMt9hD7KjnjYXEBCwzUFp/jBaD+y/3u9E32XNAE3aptvtGd5MlMOpLjp2JBlCQmCxkA7j283BGU6wv9tBy2/KXjteHW/X8LtbEo6kBDxzPD2Uhu8kEBloeEDAg42fBjhJzl6aPmgb727WsUSI9dVQ52dHfJKo3tdrr2xtb/Eb1PAbQMd01rlUCF86P4XPncvgT95a6QgqOYbapoU98THqFnjzJjEkVT8Uyf7DyoN75wEPNF7xfmQmhpWiPanGMYXeD3HYie+Q32K1F75A3Tg2EcEvf+Y43loqotLQXcNs1TDw+NEUvvnBesf9PDabAElsG247rQzOIcSZitStfaLfJn3QA1ivA4JqmG7GZdRlvt02ACRhl0eruol3lkt7/tkFBDzoOFp5fDyMbFWBohnYElXXT2KvNH+n03S82rcfOj+svluWhVOZCF65mmsJKgFAtqbghzcLODcV973OCE/j0bkkTkxEhwqeDaLjWz4aDtg+UcNq+DAHuP1ciwMCAu49eu1nb+fq+MZ7a9isymCbAY2JGOe7TngrP+uK7laKRlgKFxZSPXXH0bRqQ+uoNI2wlG9QyhtI2aq3JhC809OA3obfgL3Xv3SniDsFEe+slFt0eqUkQTEM/MKzx/Di2QzGo5zvcB5g73yMugXeHJz7O+hk/2Hlwb77gEPNXm3S/Db5YZbC86fGAdim0HstDqMaVblf4z+9zI9FkA63tu2lwmzHoQOw7+fiUhFzqRDuNFssLKCl/cE7FWmn0xQGyRD3OiBMxng34zJsma8f7X+7L5wZx6vX8+4Yc5IA5tNhLBdF930B9v6zCwg4rOz1obxdK1XdBEMR+MTJNOICu2eaPwqt30+dH0bfNcPC64tbiHCd1+94UvTTy2GrewbR8W4m5BxNoiiqWC1JIAhioL+1QTPTB7EWBwQE3B/c2arj915dbKkW5RkSsh7puU7kawqWi5JbXTOXGkxrHpmJYakgIVuRka3KWCyIYCgCT84nUW6oiPC075r8U0/MYqkg4r2VEgB0BHt6GX570U0Lry1ugSBavfZkzcTbd0r49CkRPEP1HM6zV23rwyQTgurUToLAUsChpNcmLSGwOz6AdNvki6qBd1fKI+/Z7cYoRlWOKji1E/z6vbvdj6gaeDwloNzQUJY0KM0Nf/tUJIe9ykL0OiA8f2ocP7iZB4Chynz96Pa3+/zJcWimBUnVITAUvn8jD91o/d39+OwCAg4boziU9/NxaH9+R3c+Wq/u6fdtt1p/EDo/qL6zNAlJNXAk1Zrh9mr7qPV8EB33MyHPRDm3DfvP3193K2Wd3+31t9YvM32Qa3FAQMC9TV3W8dZSsSWoBNhBltv5Onia8vUtevlKFqJqtFTXiKrRU3OctTZXVXB5rYxKQ8NElMdnz05gq65CN9B3CMIjR+JIhpmewfa6rGOz2kCUo13PPac62L43A6KiI+JJLHvvO1eT8fTC2IG0rQdtbrsjeHcCDh3dNmnVhoZLd0vIVmSI6vaJfJgDyCgCOqNgFJOJDsu9AP3vhyLJFvPVO1tiR9+yw15OU+h1QGBpcqgyXz96HTBevZl3F3vbp8TwfY79/uwCAg6S/ajoOUit3K3WHwad73UPEY5GlGO6elLshZ730/GtutpiQu7ouGGaeO1WATGBaTmMDfK31iszfRg+o4CAgHuTtXIDFclfY2XNHkDQvk7sRHO8a61t1G0hxNKoKzreWy7jqYUU8nXVd5iN87yOTvbSYGc9XszVcTNXB9Dpj0STJEifyaAODEUcaIAnaHPbOcE7FHDo6CaYY2EWr1zJDrwp9Mtg99vE+/37Tls0ev3eKCYTHcTY7G4Mcj/OxnwmIWAxLx6YYXq3A4KzkAxa5uvHoIv9YfrsAgIOklFW9DhTwFTDxFZdgaab+Nmnj+7q+zaI/u+l1h8Greh1D1GeQSrMQFI7PSn2OqvcTcf/3jPzmE0JLT5Rm1UZqTCLiqxhxicJtZsA0GH4jAICAg433dYJUdXBMZ1JVgfNMDvWiV6aQxJ2RVD7IB3vWtte1ZmtKWhuczuG2Xjx6mQ3z1dnPY7yjN3Op5nI1hS8dqvgBq/iIQYLY2HEBQZhnoZmWGApEnVZQ13RMRHlARxsgCdoc9sZQWAp4NAhqrrv+OIYT6MgqhBYquN32jeF3TLY7UbS7bSL905bNPr93igmEx3U2Gw/hu1JPqxlphGeHqjMtxuDHjAO02cXEHCQdPvOOGtAtto7qONsltungAG2EejjRxOI8mzPa+j2fRtE//da6w+DVvS6h4kYhwsLKdQV/7aJg9Dz8SiHZ46n8fKVrOvR4eDXfu2w0wDQYfiMAgICDi+91okwS4OAXdXTPgQBADIxvmOd6KY5TrvvK1ezLf5FiRCDh6Zj7vmHpTo1UNHttdM7zMaPXjq5WpJAk4TdAhelkAwz+NHtLYiK4QavEiEGZ6aiIAkCf/TmXdxqVjUBwImJCL769NGW81UQ4Lm3CFa7gENHmKVbxtE7HEkKeGw2gbrsL2qO2PVqrWg3kvbiN+HF+zzeYNd3r+Xw2GwS8+mwb/Z6kNaO3QZXdjLZYa8Y9n4Oe5npTq9v0APGQY08Dwg4bPh9Z5zN8Wu3Cri2WXMrVP2C+qKq+05yBOw2gqsbNXzx4amhv2+D6H8mbm/g91LrvVrRnnCJhxikwr2DZqOg3z3YeskfKj3303BVNzsCTV52GgAK9Dwg4OA5rFMZ+50JXjo/DcOy8NyJdMe5Zy4Vwpcfm+m4j26aMxZm8fbdEtIRDiy9HVgqSxp+eLOAiSiHfF1tqSZycIJJ3mE2fvRKxPz5++u4dLfk/iwT5fDimQxubNYg6wamEwKeXhgDAPyf7Dp4msKpTBS6aYEmCbAUievZKp49nu5xBfcvh/VveBjurasNeCBINYWxPXJfFFWslRv44rlJlBqdGzhH7Hq1VrQbSTs4xp/eL7Simy2HivZg1/XNGo5PRDoOOmvlBhiSQDrCuoEex7jOW1nVvvGlSRIMRSBfUyAphiso3YTG2exfulvCK1eyLZPWvJMd9othgzGHPQuxk+sb9IBxmKu2AgL2E7/vzFiYxWu3Ch1tS35tz2GW7joFzKEoqj0Nn/301buOdNP/VNieJkcSmnvdTtCHpkisliScmYr5amMqzKIoqnhnueRrNt4+VfIHN/JgKcq9Bscgu67o+zJ5rJ++H0Y9b7+muqzvSQAo0POAgIPlME9l7NfuXRRVfPLEOP78/TWcyETw8EwcFoBUmMGFhRTmxyIdv9dNcziGdL3m2tEMq6XlbjLGYzEvQtENzCVDINA5zKadfomYatvZLFtT8ObtLXz8WAqqboEA8MNbebA0iSjL4MREBJWG1uLPpxt4IH3pDvPf8DAEq13AoaMo2gGR9mg6S5MgCSAq0NBNqyVgExO2xW4YI2lng8wxRMs4eABIR1hUZQ0xnnEPOt5gl2aYvgedSkPFxaViy2O9xnXeMlJn4+snKKkwgyfnU7jYZqLnFZqEwCJbkRETGAgs1SLMBzGN5iAOF4cpwj/MAeOwV20FBOwHft8ZC0BV1jCfDrueSWxT29rbnmcSAmJC52QZYHuio6TqOD0ZHUj3ne+qdx3ppv+bVRl3CiI+fiwFiiQ7HrNRbiDC0ziSDLVo42pJwjc/WPfVCAC++vHs8TT+8vI6BJbCyYmIa5C9n5PH9kvf90rT9zIAFOh5QMDBcNinMvY7k2SrDSwVRKQjHAgC4FkKFEEgEWKhahbqsu57/X6aU5ZUrJZkn1exz1CZGI/Nqoz3VipQNBNRnsbxaBifODmO6TiHubEIiqKKs1MxvHazANWw3CBVL510gmd+lVC2hQmNV65uwLxpQVINsBQJzTTxYw9PgqXJDnuSB82X7rD/DQ/DvXGVAQ8UoqojxjM4NxVvTi6wI9kkQeBmroYb2RreW6kAaJZZnsvgiaPJocxSvRvkuqzj65dWfDMKt/N1nJuyswftFVTOuGLvQacu6/jhzULHY73Gde1lpN0EhSII/OFrd5olrdtZBq/Q2Jvv1nGj3sftJOp/mAI1/dhJhH+v72+YA0a/g9q99FkEBOyU9u9MUVQxlwrjTkFs2aA6VTrtwfnPn8tgtSh1DeY7mjuI7jv6+uTRlPuzXvp/tyYhwjP4G0/VqEO10bkp7LWBfGupiHxN6ZgYWZY0/MUH65iIcrB8mhR2ovWHVVv8ND3MUnhqIQUL9vq+m2vdywCQ8/flvLc3crVD9d4GBNyPHLapjO3aypDdjblV3US2KqMoam5l7KvX8m5V6rmpOCZinO+e1k/D+5EMcTiSCCFxloWim+5wg9v5Ou5s1bFcbODOlgSSsL3qOMYORmViQk+ddIJnLG2v0d7W9PmxEL53PQeCABqKvbbRFIHbBQl/+eGmbxdKv+nLh3Ht2g2H7W94N9zbn0TAfYkTGGJp0g2YSKqO91fKEBUdY2EOx9JhmJYFhiKRrchICNs+E8P6HbSYzXmqoAgAcZ5BTdagaCwM04KiGzBMC1GObjERdw46a+UGNMPqiNgD9sGEY8iO1+8mKBaA5aIEzvM+ODhCM+ppNPdSKeZOIvz7dX+jyOzfS59FQMCosACEGBK3C3VoRmsaU9ZsLyW6baN+YiKKF85MoK7oLZvlXE3GXCoEVTc7Ws56beSqDQ00BViWhc2qDJokYJgWqOZ4ZJ6xq6ec/25oRkdQyXlM+6aw1+vWFR3LRck3UbBZlTEe7fy5wzBaf1i1xU/Tq7KG91ZK+HCt4k4U2u217mXl1WF9bwMC7lcO01RGv+///FgINAXoRufjGYqA0jwrtFfGypo95ZSlyY49bTedeeHMeMv5xzspdSrGg6bsc4VTIeQYdKu6iSsbFSTOsi1efhVJB6Dg7FS859Rtb/CsvTDgSFLAeytl1BUd3hU9zNJYKUodE+p6tSXfr/p6mP6Gd0v3MGpAwAHhBIYcqrKGOwURa+UGQk0hWq80EOJojEU4iKqBtXLDfbxT7u59DsC/jNMxm/vmBxt4+WoW33x/HReXipiM8ShJKp47kbbbLAggX5NRqKuQNQMsQ+Lyahkl0TYCdaLroqq7EXu+bXwo34z8twc7ugmKs9hohr93iKTqI51G0y9Q0800/aAYJMLv5V66v3vpWgMCdstqScLXL63gW5c38Or1PPJ1FXozkN9OnGfAUK1VOxGexoWFFHTTQk3WUair2BJVzI2FsVqW8DdXs3j1eh5/cXkDX7+0gtWS1HWj5mSN/+KDDcwkBMiqAVHVka3KqMsaDNPCZMwehexoPdrK+J3KKqfS1PtavTaQimZ21XuWIt2pPX4MqvWHWVvaNd1ryu4dh30YrtWPw/zeBgTcrxyWqYzdvv/LRQlzqTDCbROtEyEGnziZxlbzHOFXGeusB949bS+defV6Hs+ftINLVVnDlY0KbubqkFXbt/UvPtjAZIwH2Vb46vgUaoaJyRiPi0tF91z0/15axf984w5WS/bQo/b1+i8ub+BWrgbac3tOYcBkXABNktAMq2WZbKgG5lIhhFkaDc867z2n1WUd1zdreGe5hBubNeRrSs9hGfeyvh6Wv+FRcO9cacADg9cHIVdVcDtfh2kC03EBTy2k8O5KGYZpuW1qLE12HBIGKXfvZTb32q0CHp9L4sp6FS89OoWNsozH5pJYKzegaAaWChIsy0JV1vGFhzNudN0RB79WvijPIBPrjMJ3ExTHZI/xGQsKwL2nUZmR3mulmMNG+A/i/nZasjvItc4khPuuHDjgwcNvk1yoKXhiLolLyyXoxnalkNPeJmudAad2zRcYCt+/ke/IEjub0GeOjflej5M1DnM0ag0dJzMRTMZ4PDITQ0nSkBAY3N2SsFmVcWw8grlUCKcmozg5EWnRem/7sndT2GsDyTFkV72P8gziAo2az+Z5GK0/zDrfruntpuzewNpBXms3Xd/Ne3s/tncEBOwHh2UqY7fvv2kBdwoivvDQJAiCaDmTrJUbbvWQk0wmALcjoqEZ2KoriPJMS2dEN50piho008JL56fxvetZzCQEt4J3syqjLGlY3iq41Z+AHcCvKzpYisRMUsB3ruaQa7fzqMruBLtuwbP5dBjZigxRNdxKqZjA4MREDC9fyYL0RLMs2BW6M0kBj0zHkQyzLec0v8oky7LcYRntnkwHvXbtlsPyNzwKglUr4FDiHBIur5UxFmGRDDF4b7nsBpWA7TLRsQjn61vUb5PWy2xuuSRhLh1CSVKxvGX3Ac+PhbBclNzsAmAfEk5PbAuZVxzYtha2buLQTVAI2KNGnZYLL85zjdKM9F4rxRw2wr/f97ebkt1BzB5fXyzcd+XAAQ8efptkhibx7koZJ8cjmE2FQJJEy+b40blkx/O0a76imx1eRQ5lSYNmWL66awHI1xV7KmhDQ6hEIVuRcWE+ic2qAlk3YcFef+qyjudPjWMiyuP4RGSgTWGvDWSEozGXCvle90SMw4WFFOpKfldaf5h1vl3T21sknHHYDgdxrb10vd/1dPv3+7W9IyBgPzgsUxl7aatp2UGix9rWLu96wDH2GhfhaCwXJSi6gfEojyxp+y29eHa87+sA2zrjmHh7NTPKM1gpSW71UFXWcDtfR1FUwdMU7hYkvLtcxlwq1NK6xlD2oIhbuRpyVaVjqAZLk27wrK5ouLpRA8CDgF2dFOMZbEkKOE9ZkwUgHeHwyJFES5t3t4osZ1iGNyjmd9/3Ioflb3gU3DtXGvDAEeFpcAyFmqyDp0lUGpobVHLQDLNj4z7oJq2b2ZxhWig1PZaeO5FGWbJf9+UrWTw8E8eFebvlgqEIiLKOlVIDl9fK4BgKEZbG8yfH8erNwTf/3QTFsCz8/efmu06Fc55rJ2akfoG3QQM1u82sjiozO2yEfz9LTXc74aHXtTpmj/fD9IiAAL9NMgEgHWZxI1eHapiYjAvu5ribT17798071dMP3TR9ddc0LcSFVl2pyBpeW9zC2akYnl5IodzQEOFo0CQBzbCG2hT2euyFBdswvNf69VNP8DsynnZ0tyZrbga8fSQ1SQACQ+H6Zq2vPu9FhU27prOe6i1nwp+X/W4P6Kfr3argHPyu936aBhQQcFAchqmMw+4xHQ09Ph5GtqqAp0kcHQvh3eUyFN1AMsy61bpxnsFKsYEzk4NZYHQLPrnt22htNZ5LhnBiMoJcXYGo2l5/M0kBkmq0eAoW6gqubFR8h2rEeAbVhoaPNqotelaSVHzliRn86aU1VGTNvae5VAh//7n5Du/ApYKIxeba7w1csRSJuzWpvfO86/u7W3a6xu309w7D3/AouLeuNuCBwxHQLdH2O2of55yJ8S0b92E2aV5x9rauVWUNUZ7G/FgYq6UGUmEWumFBNy28t1LGclFyBUzRDcTDDD5ar7otCqmwbaCnaNbA4tBLUI6mwn2FZhgz0kFN/7w4h7ndZlZHmZkdNsK/n6Wmu2036XWtXrPHnTx3QMBhwm+T7NV7b2uY33e7m+YDaGmXbifE0r66u1WX8ZcfbrqbV8O0wFIkiqKK713PIRPjcHmt6m6mH5qJAxhuU9jvsb3+bSfG017dHY+wqDY0rJQk9zAA2EGl+XQY37+Rb6mY8tPnvaqwadd0p5o4zjPuhD/v6+13e0A/Xe9WBQd0v97D3JoYEHAvsZem/IMwzB6zXUNV3QRHE/jxR6ZQFFWUJK2jBXy5KLk2CP1ep91j1EuMZ/DobBInJjSMRVi3GnhpS4LA2mulE5jy+gWqup14bx9M5AzVODcVh2FZHdelm8Bivo4vPzaNMEdDMyzEBBonJqIdQaXVkoT3Vkq4mau7P3OuwVkP/LwGR70e7HSN2+3aeNB/w6MgCCwFHAiDRnS9ArpZld2Rw4puIi7Q+PTpTIswDbNJ68iONlvXVMMELLt807Ts7Pl0gsftgi10ZrNqyjAt8AyFiqghE+XdjHpR1PDda/mhM43dBGWUQuM9hHknPyiaibfvlPDJE2m8tlhAUewURaAzkw4Mnlndi8zsMIe5/Sw13W27Sa9rfWg6hjcWt3b83AEB+00vvffbJJuWXfb+wpkJnJiIQjfNrt/tbprvnerZPmXNuwlt19f3VwxMRDk3gcEzdrWsZpg4kgq5r+U3oW4Yre712L3SfGA7aPej21sgYCEZYqAbFo6kBFzPVmGarXVB7fq81xU27Zr+4tlxrBQbLZOMDqo9oJ+ud6uC63W9h7k1MSAgYHAG3WP6aShLk7AArJQkPHc8DYoiWiacOkF1WTNaqpwUzcCWqMK0Wl+nV/ApzFJgKAKVhj0YyakIinA0kgKDc1MxrJcbiPI0ZhIhNzHDUAQogkDGsz46yJoJhiK6Vg3pJnArL+LTp8c72gEdnPeFbnMW9waujo1HOrwGR70e7HSNC6pPbe7/Oww4dAwT0W0XaqevNhFi8MzxdEe0e5hNWrdFYDLGYyYhuEK+Jar45Ek7W3orV3cN6JIhBqcyUayXGx0l+oc10+gcwpzJR+0VYMtbEr786Aw0s7Pa6vpmbVeZ1b3KzA5zCBsmELWbVo9RtN11u1av2eNOnzsgYL/op/fddDgm2K1hO/UjcwIod4tSy8/7bUJ5hmqpjiUJAjxDIRPn8eTRJD5cq4Bt+kT4Tag7bLTrrmkBuZqMF89O4Ac3C3hnuYQYz4CmCLxzt9xSxeTg1ef9qLBp1/Qzk7qvZu+34fUguj5sO8P9NA0oIOB+ZVCtGeT730tDTQu4la+7yRAnYe3s2V+5mgVB2GuOqtvBnE+cTCMusB2VrX7rKk0BmTiPv/5oE6kwi5u5eksrW0XW8NmzGbx2q4CYp13aSWq+eXvLv3skyuETJ9PgmdbJd+300jPnfRmPsB3BK8dT9/hEBJ8+nUFRVPesXWyna1xQfWoTrFgB+8pOIrrDbNSG3aT5PXcqzOJbl9fdw7tp2T2/f+fJOdzdEkGSBEIshZKkYikv4uPHxlpK9B0OY6bROYQ5k4/asw7ZqoxXb/pXW+02szrs7w+ykO/kYDFIIKr9MEwSdj/4bEoASZJ9X2tUbXd+13o/TY8IuL8ZVO934y3QTfOdqqef+Nh0xySeXs87kxDwo9sFtzoWFvDM8THc2Kzh9VsFJMP2hr/XhLrDhJ/uJkMsvv1RFtmagvmxEMYiHHTDaskMt7cPOvq8k3XAT6cBDKzdfjroF7BMhRk8f9puQ9+LYNOg2jtMsiPQ84CAw82w7U29vv91WcdqScJGpdHiH+RAwE6qtDMWZvH23RLSEQ4sbQeWnN/7aL060NmJJkncytVwpyC63RhOAMer+37Vwqkwi8VcDSulBgo1FY/PJTuqqpzg1k71zFlbulmfxAT7PR+Pch1FBe3v8W4SDjs96wTVpzZBYClgXxkmotsuDoNkKXcjahaaYz4ZCi+ebY306yaQr8v4sfOTrneSrBlIhVhsVmXfCpK9yDTuVjCdQ5gFdASVgO3JD36R9d1mVvsZUsuagXeWS4iwtiluNwN0ZyHvt9jvxnivPag0GePx3Ws5VGTNXXz7bSz2qu3ufpoeEXB/M4zed9uM52sKbuVqqDQ0JAQGx9t8GXppfkxg3O+nowXr5UbfIIZX/9MRFn/9YRZnJiP4u08fBYC+E+pGxSgqcvx016v/jocVx9j/3ztt1Yuj78MOeag0VLx2swDVsMDSpOvltFwUoXticv18KLzvRZih8GqbFxRJADxN4T9/Z7F5+CIHet5h2AvtDfQ8IGBv6Kafw+jqKNubnD0rTRK4u2VX0nqrhQA7qPL5cxm8u1JuqzSytbMoKmAoEoZpwbBs/z9VN9211O/enJ9fXitjq64iFeFAWBZKUmsAx9H99mrh1ZKEb36wDoYkIKsG7m5JuFMU3euuYTSTqp21xUkKea1POJrEhfn+Fcyj8P/b6VknqD61eTDuMuDQMPiYTP9s5JPzqa5T0nq1VXQTtV4i1C+DXpd1XNus+QaVRp1ptF+rYo/wJAgQloUtUXUj+IMKpnMI8zN/9k5+8Ius7zaz2u33q7KGuqxjMVdHvq5iPMLi/dUKIjzd0pLhXciB3n5PL5wZx3ev9Q5MdWOt3ABDEkhHWCiaiVSYwe28iIKowjAtd/Htt7HYywkP98v0iID7m91m8D5ar+APX7uDZU87mzNJ5qFp2zS7n+aXG+rQG03v90vWDORrCjTDcn0dek2oGxV3tup4a6mIiqSDYykQln2QePHscEESP9119N+r+d7stWa0rg/e+xxkHXDWVYYkcHGpiGxNcQ9Qx9NhvHIl2xKkB3of1NrX6XSExWuLhZYDmbcKN8RQmElEXP/ANxa38OnTVM8s96DshfYGeh4QMFq67e2fWkjh7TtFXx9RP10dVXuTN0DlbfVqrxKNCQxOTEQxkwzhVq6GakMHxxAoixpWmoODlouSOzWOoynwDIlnj6cQ5qie9/zRetUNaDkVt7nadgBnIspjPh1u0Z58TcEbiwUYzcTA5x+y19rN6naV00SM2/WkaqB1bTEtuNYnAECFmL7r3qiCgDs96wTVpzbBqhWwrwwS0e0mDhRB4A9fu9OSjQQGa6tIhVkURdWtiHFK8fuJUK8FY78yjaslCd/6YAOvLRbcaQzOorBZlYcSTOea31gstPzcO/kB8I+s7/Z+/X5f1U3UZR1PHk267YQWgOWiBJ4hO1oynIXc+W8/clUFb7UFH53HD/JeVRqqexiy3wsKdVnHUwspvLtSbjl09dtY7OWEh/thekTA/c1uMnj5mtIRVAJsbfjD1+7gn37xjBso6LaRBYA/fWelJVDsBGleuZrFVx7vrgXe79dYhN3XipLbuTp+79XFlnt3NL/fdbfjp7scQ3Zo/qCT+PqtA8D2upqOsK6OOgeoo6mQ+7P2yig/PfXbDyia2XEgc6qwKJLA7JjQouEAsFKU8NMXZkdWuTRq7Q30PCBgNHQ7Q+Sq9prysSPxlp/32huOqr3JG6Bqb/Xy+ge1J0NU3cSVjQqePJpCPMRgKS+611QSVYxHeciaCVE18K0PNlqqONvvmfXoeram4LVbdsu3E8B5amGsRYNWSxL+5K0VvLlUdH82HefxxYenoJsmJNVAhLMnVycEtuV1d6Jnuz1jjCoIuNPrCKpPbR6Muww4NAw6JtPv352AA00SoEgCqmG6Pcpe0WgvBaVJAt/8YL3ji/7YbALVxu5EaK8zjc4CuVyUWkZ8ti8Kw5jCHUmG8OnTGawUG8hWZTBtfd69Iuu7vd/235c1A0v5OnTTRCrcPPjRFE5NRLBYEH1bMhqqDoLoPCg6kzFqsoZKw3+x7/e51mUdP7zZ2tetGxbWKw1cXCrizGQU7QVqD0rfdEDAsOw0g1eXdVxeLeOj9QookgBHU+7oZcO0cG2zivdWSjiaCrttDH4b2eubNfA05Ws0+tyJ9MC6uZ8VJXVZxzfeW+sIqHk1f1gT0PbrFxgKDdVoOYQMM4mv1/txfbPmBvIsC3h8LomGosMiAIGlYFoWHp9Loi5rMHzKfdv11G8/4Ne251RhHU+H8ebtIqpy6/Nkh0zCBAQE3Jt0O0PUZA3LRQnn2wJLQPe94ajam7wBKr9Wr1OZCB6fSwEAvn5pBdWGXdkkqQZOTEQR42nEBQbjUQ4CS9tnIN2AppuYHgtBVHQsF6WO/bL3nqM8A54h3bNEtqa4+9n29dg5e7T7x2ZrCu4URNzZEnFnS8L8WAiT8frIWo53s9b2CgKShD1R7/pmbaAWyEGvw6/18EGvPn1w7jTgUDBIRLebOCiaCUU3sFmVIXk2xE7mVVL1jvLXXm1V376SxXScbym39DJowGAvM43OAqkana1r3kVh2ODGeJTDT1+Y3VFkfbf36/39d5dLoEiy5eDnrRAqia2fjTOF9JWr2ZYsireCSzXsfuwa/On1Xq2VG9AMq2XxJUlAM0wsbYl44mgShtV6GHpQ+qYDAoZlJxk8R8Mpwh6HDAA0SSAZtjOiJVGFblrYKMv4cK3ac0Mra4b/kIJmkObMVGyoe9mPipK1csN3GASwrfk7CWa3X3+Ep3c8ic/v+Ry8FZ8hlsLtfB0/fn4Ky1sS3lut4GgqhDtbIqbjAn7skUnUZL2lnbxdT/32A07bXq6mQDctbNXtVjtJ1RETGFzdrLmBSIde/oEBAQH3D93OEM4+WtE799PA3lhAOLQHqNpbvTKx7cB8taG5U5sXCyIUzcAnT6SRCrNYLTWwmK/DsixEeQbPnBjD0/MplCWto4W5/Z5Z2j4r3c7X3f2topu+67Fz9vBWOQF24P4713LuY53q1p14TnVjp2tttyCg30Q9oH9LfL/r6GWl8iCvMcGJKGDf6RcJ7iYONEWgJKpIhlpLLp2SeKCzta1XW1W1oWEqzne9zsMQMHAWyHZxd3AWyF7X2s2ocKeZgVGOeBaYzmoCAKg0NFxcKuKzZycQ5mjXK+NISsDri1sdwR9vNt8wLfQaAE6TZNeshajqLYtvpaFB0Q3QJAlZM0AQwM3NGhaa3h4PUt90QMBOGEZnvC0Mc6nt75UdPFDBUiT0ZhQizFHYEntvaCVV9x1SANiacRirDUVV76r3gK35/damQTS63+eyE533q/h8Yi6JNxaLqMkaFtIh8AyFhMAgV5Nx6U4Jzxwfcw9Yfnravh9QdRM3s3V8bDaBq5tVLObqIEkCBIBUM/iomyYocnvsdT//wICAgPuHbmcIR1e7JR73wgLCYdAAVaGuwLKAv7i8gWxVhqIbmB8L4/s388jWZHzmTAY//+xR5GoqaNK+V92wg0ZM27qh6qbrC2qaFlTdRIxncG4qjppsB6Iemo7hkZlE1xbA9iqnME9jvdLAMTbSoqvAdtWX03myF5M5e9HtPfabqOdc706DYaM0db/feDDvOuDA6RUJ7iYOkmrgWDoCvhkc0g0LNGWLRIynoZtmx+845fF+k268gtjOYQkYOAtku7g7cDQJqse19puQMGxmYBQTF7xohoWKbD8XAbtVAgBmUyEouoHpZAh/c2UTm1U7I/3Jk+N4c2kLx8YjHZmXbE0Bx5D48mMz+MHNvO/r0RSwmK+hLutusCoeYnBhIYn5sYj7fsd4BqcyUdwpiFB0E1NxHstN08NGM5D53PH0A9U3HRCwUwbVGW8LA0uRODERwa2cnTRQdMOtQjkxYa8D4xHW/R5fXit3bJBJgvDVTcAONlBErxD0wRBm6a56DwBxge65Ng2j0d0+l53qvLfiU9FMkASwMB7B927koRomYoLtEbKQDiPE0SiIaksrhp+eevcDVVnD7XwdmmFhOsHj7GQUZzMxhDgKUZ7Gx4kUlvKS6z1CkcRA/oEBAQH3D93OEFGewVwq5Jt43EsLCMA/QEUS9jCK2ZSAW7kaVMPEeysl5Osy3l8tQ9FMMBSB506k8friFggCePN2EWulBjTDRKWhYTzKYzYVQoSjMZcKue3NjlaSBIHxKIeVooTlkuQOPBiLcEiEGN+gErB99mivctIMW7F5msSRVKglWQ8A2WoDry8Weq4do0xO93uPAbt1OsLTHdcK9J9G3u3aRuXndD8SrLABh45u4iBrOn7umTn8zzfu4mbzsAHYh4wvPDTp+yV3vBgAdJSJsjSJs1NRXF6rHojR2iAC5l0g2wMpmSiHCEfjwkLKt+93qSDivZUSaMo+gDkeRDuNqO9FhF4zTRwbj2ApXwdNklguShBVHTRJYDYVwo1sFQxN4uRExA4EWmgxbfVmXhiKxNFU2D1EtF9rmKUwmeDRUI0OY9eLS0X80vPHW97vhmq4Ph2qbuLhmTjOTsUwFuEGHn0aEBAwON4WhjtbIv7Ok7P4X2+v4FauDtMCTMvCiYkI/u6FWdQUveV7fDNXw7XNWssGNhFiO3QT2G6fjrdVv+4XvbR/JiFgIsZB1juvey4V8tV75zlHofm70XlvxedGuYGtuoqVkuR+rqZpIS4wkDXbUy8VZjER5fHUwljXg5qzH/jWBxt4b6UEzbDw2GwCVzdqeH+1DNUwQZMEHp6O41OnxzGfDuHjx8bQ0AzEeGZg/8CAgIDDw26CD93OEBMxDi99bApv3ym2PH4/LCCA1gBVo6mJF5eKuLMl4cR4GP/vO6uIcDQmYnYC3LQsKLoFUTHsaisCIAi7vY1nKCSbfrMAcGHB9md6+UoWuebENlkzMRnj8IWHJvHylWzL3rl9kls73r2wt8ppPMJiJiFgPh3uCNKruolsVe65duxkSutO32MnCFiWVKyW/NvLgd7TyLtd26hM3fuxV0G4veRwX13AA4ufOJiWif/y/duYTYZwZioGzbDAUATE5kb4Zz8+1/E83hHK7WWiiRCDM5NxHB2LNMd6au6oz1GMJe7FoALmXSABuOIeExh8/lwGJyaiXUczL+bqbgDO60HkHDSGjajvRYQ+zNreVyczUby/UgZDEUg1R6gyFAnNsJCvKW4bo59pq7cKzTko+v39WJaFS3eLePN2saM9Zrko4RvvreFrnzruvt8blYb77xNRDo/PJbFUEG2DcAANrXX6RkBAwO7wtjDoJrCYr+NvP34EqmGi2tCQirAgQaAkqXhjsfV77HjoeIMfMwkBc6kQeJpqCUBHeQYTMa7DrHQ/NnCDVJE6GuS97kyMx5cfm8H8WKTrc45C83ej887nx9MU6ooOjiFBEvbhhyQIUBSBkqRDYGmUGxrGoxzm0+GBhmR86tQ4BJZCiKNw8XYRVXnbe1A3LSyXJLx2s4Cnj6Xw+FwSd4tSz+l2AQEBh5NRVMb3qjI6mgofmLmyE6Cqyzq+fmnFrTBSDRO3cnXQJIG/N3sUEY5Go/lvBAFwDIUIR0FSDcQFBskQixBr75MfnU2678tPPTGLy2tljEVYcDQJAvY6+sTRJB6H3Urdrf2t/Tq9wTmeITGTiCId5XD+SBwN1baI8FYBMRThdom0U5Y0LBVEvH135xObB6U9CHh9s5vjqk2vaeTdrm1Upu69GHWHyH4RrLABh5Z2cbi8WsGWqGKt3Bl55hkSNEl2lL86Yz3fvltqaX0Ls1Qz61nBD28WoBqWK5DtWe9RM6yA7dSjxGv47TdadNiI+l5E6J2syFZdAUEQiHg+o1SYhSjrLUEkb6CwvQLNyUa3HxBPNoNv7yyXYFrAesXuWzdMq2XiVLYqu4cmZ3H+aL3qLs65moyx8HbrjawZqMt6cFAJCBgR7S0MugncyosAbM02TeD2ltgyxh6w9V9gKWzVFWxUGi1tcc7m2LsBbg8y7NcGblDtP0jN343OO5/fYs5uV2MpEhVJxdGxMCqSBk03oZsWFN1AiKURE3pXEHm1vC5rIACIso4rG1XEeAaaYYIiSZCEXQ3lmJsbpokXzoxDUg0YhoXxKI+FdDjQ6oCAQ84oK+O7VRnt1yAGoHvCoj2ALyp2EEk3LXywWsHTCyl7OIFhV3mem45iKS8iGWLBUCTiwnY720I63HJvHEOhJustPlJeo3CeoQZ6D48kQ3jp/DQWczU0VANvLG1hpSTh5EQUr90qYMXTWpcIMXhoOoY3Fre6Pl+uJiNXVVBrJgVYz0TqvWwf2800csA/oTIqU/du3MseTofzqgICfHDaprq1NQBwDxG5qoKqrEFUdNRkHV99eg4USUI3TRimiZViA1fWK/hRs3rFeY4Yz+z5F3cnGeGdepR48U6RA4aPqHsj9CQBpCMseIaCrBlgKAq6aQ4VaHEW3IemY1grSQix9ghsC3bw6NOnJ/D/XFwGsN3GuCWqeO5kGu/cLdmToSoNsBSJuVQInzuX6VlmG2Zp1GQd+ZrsGgAD2xOnGIp0D00RnsYjMwlc26yhUFfdqRKO0TjPkNgS1T0PQgYEPEj0M0oFgHJDc7Oihmk1tYjD5dUyDNN+jtt5EbdydZydiuLMZLyvSfV+beCG0f6D0vwwSyMTZRHhGTRUA7ppQWBINDQDhbra9Tn89JwAcGWj2jIVDrCDQJkoh883g3t+h692Ld+qKyjUFXziZBrVhh1kokkSoqKDZynXwJsmSVy6W8JKSfa0wElIhplDuxEPCAiw6aaRqm5iMVfHpeUipmLCPdES1Cth0R6gD3O2vyhNEkiGWIR5CpfXKri7JeHyWhk/89QcOIoCCEA3TESbwRxvgsTR0ZKoIh3lQFiW2w7tZdC9v3P9DEm4bec8Q0I3gacWUu7a8uhsEpNxHou5GtbKjZaAkRfLsnBlo+J7fovxzNDJ6UGrjJ19xStX7Wmz7f6qzr065wm/a2+/tlGZunfjXvZwOtzfyoAAD07bVLuvjiMC9ibTwEI6jIkohy1RwWZFRq2h43e/cxOPzSXw4w9P4we38iiKWkvW29t7vNfR873szfU+t58BrDNFbicRdSdCX21omIzzeG+5jEt3S64n0sPTcXz+4Uk8cTTZN9DiXXBJAkiFOcQFFuNRDrppAhZQa6hIh9mONkayeS0xgYFuWuBoEhGOhqqZ+MGtfIsYO5uRfE3BFx6aRFygYbQtsnpzWobAUi0LrnfhYEiiJajk+DjdC9mDgIB7iX7VOk414YfrFYiKrT0frVVQVw3MJHgsFyWslCS8u1zGd6/n8GMPT2EhHQJJkr6bz/3cwO2F9o9a8xmSgKga+LN317HebAcOszSeOJrEM8fHfJ+jm55PJ3hsVmTkqwrGIhxeOj8FzbCDSuNRDicmor6HrzBLIRPnUW3YP1N1E5phD+d4f6WCpxfG8KOlLTfTrDb3AgJLoSSqHUatgU4HBNwb+GmkY0Qtaybm02G8c7d86FuC+iUsnjk25v6MJIBkiMGFoynwLImPNirYqqt4eCaOx+fswEeYpfCpk2kkIyziAoNMTGhZF1dLkhs4cZKuUY7ByYkoFvM1NJeBgdcB7/W3n5Vu5Wpgqe02OEXX8c0P1sGQBGTVwN2a1BIwcq5fN62OgRTes9cwye5hq4yPJEP45IlxfOO9NWSrsn12FBlsVhuYS4VRkzXcbQ7oab92wD8YNwpT927sl4fTXhCssAH3DN7SQ6+vDmBP+7qVq+FGto7b+TqWCiLiAoOnFlJYLIgwTAtv3ylBVu0JXwA6eoHbJ8ft1Rd3kN7cnfp9eJ+7fZoDsD1FbicRdSfQ8tZSET+6vYV3l8tuUCkZZlEQVbxyJQvTtJAQ2K7P377gmhZQk3Vc2aggzjNu6wZJoKONcSzM4uKdEiI8jUbz83JKapcKdWRivPs63s0IACQEu3XiwtEk3rpbgmVZ7ns2HRcgMFTHgussHJfXyri8VnFNxNsPLYc5exAQcK/Rq1onwtM4MREFQ5GoyjpCLIWaomMszGKl2HCD3xRJ4OxkDN/+aBOqYbpJg/bN535u4Py0X9W3dWwn7bWj1Py6rOP9VTthkKttt5yLqv3zyQQHINPxO930PJFj8IWHpnB9s+YeTHiGRCbK4cJCCg3NwJ+8tYLNqtySKV4uSvhwreKu37fzdRRFFXVZR0lS8enT48hEeWRrMmYSgu3Lx5CYjPMQGKrlQOAQ6HRAwOGnXSNV3ezQsxoOf7C4X8JCM6ztRG2Mxzc/2MCx8TCqso7LqxWEORofrlcQ4WiczkSRrSooihr+ztE5PHIk3vJ8dVnHK1dtTz4nAaroBkqiioV0GD/39FFka7bX3tmpwfRvtSSBJglEORokQbR0FHjPSqpu4upGzU0qPHci7V6D1yj8sdkErqxXXCsLL3Jz+t2gye6dVBnXZR2v3syDIAhMxu3XUXUTb98p4eZmHZ87l3Gvrb3QoFcwbq/aKvfDw2mvOLxXFhDQRoSn8fxJO+Ls3YgmQwwycR53CqK7QVd0A+lIGIZp4dljYxBVA3VZw3qlgclmYMk7Mc7B69uzV1/cfr25HEPg65dWduT30f7cToVXVdYgMCQW0mEkBBYJYWfTkI4kQyiJGn54q9BitO1MpsjWFNQVvecG3m/B9R6InIIi0wJk3cAvf+Y4FM2CpOqQNQMbFRnxEIOqrIFjKER5BnVZw8WlEn7mwixIQnUXBm92pKEbuHi7iBfOjCPC07izZS+cgF2G/ImT6a5ltBxDuYuRH4c5exAQcFjZaQC9KKp48mgSimag1NQShiYRDzF44mgSH65V8PB0HO/cLWEmKSAd5RBiKSTCLAjLwitXs/jK4/bmcz83cO367A1+Z6IcFnP1odtrR6n5a+UG6oqOkmSPsVZ0A6ZpgWx60eWraoe299PzoqS0tE047YnlhopLd4tQm20dLEVC0XWoBgnNMJGtKdBNy31/DNOyPaQIYCLKI8rTGIvEoOj22O3HJ5L49Onxnh4fgU4HBBxu2vWsJmstk5AJz2MPc7C4X8JCN003UfvdazlkawqyVQXPn0rjk6fSaEod0hEWYxHeTWbqZqc59lq5AYrYrqoH7AEK82NhaIaFl69m8eRCCmJDx2u3tnB5rdpzjVktSfjz99dx6W4JAPDYXAJrpQbmUiHUFR0Wts9KNVkDYJ+pTAvYrMr4+LGU3UqtGYhwtlm60WzL8waeHDJRruv+G+jcJyi6OXSVsd865fxtyZptneK9Nid4dnwiciBDH/baw2kvCQJLAfcMqyUJP7iVx0SzjF7RTcQFGicmIvj+jQJMy56sYFnAJ0+O49JyCT+8VcCpySg2KzKm4wJePJtxBdFrBO3gtFzt5Re3V2/u86fG8er1/FCReL/nfmupiLqiQ9FMEAIgMCSm4jwu3bUNrHdTRqyZJiiy1Wjbi6KbPTfw3RZc50A0nRDw8EwcNEmCoQiURA3hpgn3zVwN0wkB/+eDdawUJfd3p+MCHptLYGmrjvEIj2ubtY6SW4G2S3G/cy2Pn7kwi1OZKBTddM254z0OXvdy9iAg4DCyG8NsUdWxWZXx1EIKAkvhykYNMwkeby0V8f0beUQ4GlGBxvGJCD5cr+KN21tICAwiPONOS3M2n/u5gfNqv3ckdPsEt0Ez8c6G+/h4GNmqAkUzsCWqkHUDqm7i/Ex8KM0XVd2t5KVIokPXNKNT2/vp+VTcrihy2gQA+7N/524JF5eKePNOEc34PqbjAp49MQat2bdRV3RXx53khaqb2BJVzCQEPDqXQEnSwNEkLsynQBBEh5+Il0CnAwION+37Y2cggVcjvRzWYPEge0YnUXvpbgkCS4GnKaSjPJa37CCKadmV9mFOwvHxMFJhzlfDRNUO9jhnGQJAhKNxd0tErqZgpSQhGWaxXmq472Gvyp6Xr2TdNmTAHpgQFxgsFyXMJAVIquGelWIC0xLsAwCKJPE3V7LI1hTMj4UwGa/DsizMJATkarKbaBhk/+23T0hHWFRlzbcyFQAaqt4RjCpLasfjvMMuGpoJUVFbru1UJoLH51IHUhG31x5Oe8nhvbKAAA/dSh9rzalhsmaCpUmwFIlTmQiublRBkwTmUiGEWRpjYRYbVRmX7hbxlSeOoNKodUTPeYb0NcTbC5wWq6WCiFxNBk0RmIzyUHcQifcjX1OwXJQgawYqDQ2pMItMfLtNbDdlxGGW7jCJ9cLRZM8NfK8Fl6VJHEmGEOaoluAYx1KIsBQW0iF873quJagEAOuVBqxlCz/x6DQstC4YgL0pYSnS9R/J1RTUZHtDUkP/A+S9nD0ICDhs7NYwO8zSMC170k0mykKg7bH2MYHBJ0+moeoWxsIsvnc9j1KzRJ/0VFW+dquAM1MxAPu/gXMm7VxeLWMqziPM02BJAne2RDcoMojWt2+4Vd1uJ3j2+BhWyxJk1XQDVc5z9ntvwyztW8nrwFCd2h5m6ZZ2Pm9Lm6Pnzn041zwWZvFn767BhAWpab5NkwTWKw28fmsLnz49jrtF0Q0mAQBH24+ZiPKoNTTcyNVxNB1GTdZBhRg3YBbodEDAvY3Xu2a1JGG93AABtOiZw2ENFg+6Z9RM07XfGI+wuJ2rIy4wqMgaZNUAQxGoyhqqDQ1PLSTBMe1hHFuDvdYeAkthuSihImswLQskQUAzrI5Job0qe7x+fYsFEU8tpHBxqQgALWelx2YTePVG3v39sTDbUpHkBKA0w8Lbd0v42JF4y4S6XvvvbvsEAC2tal6cJePrl1ZQbWjuJGeWIhETaHAU6Rqae88xHE2iJrdOz8vE+ldQD1N1PWyF9l56OO0lh/vqAgKa9OpXBuD2+8YFBtMJAd94b92NUBclFQQIzKYEyJqBVJh1Bd/JenMMiUyM7zDE20vKDRVv3y223JdmmDiaCvkuoIB/dsYrVmGGwqs38hBVA2MRDlt1BSulBkqShqKo4pEjcWzVFESb0+92UkY8kxAwlwphpSR1VAVlohwiHL2rIE0qzOL1xYJbHtzy3B+bbsmkmJY9Wtq07PYYhiKhN8dce3/PydI47RlOn77zmn4HyPZF4PmT43j1Zv6eyx4EBBw2dmuY7R0kQDU3im8uFe3vq6LjZCaCZ46PoSSpIAkCdLOVyyFbU1q0dD83cE5w5epG1TULba9YArpn4uuy7rYqVBtaSxAHAF5f3MJElEO+rnYEfFTd7PneziQERDja1weDZ+zpm+3azjEECnU7keF97LHxSMvjvYeEEENhvdLAWJhFmKNRV3TwDAnTAu4WRRDEOJ6cTyLCbb//FEng7FQMp6eieGvJbtHgaBJMmMHzp8fdz+6x2QQuLhUhqob7u4FOBwTcWzjeNTMJocMawuEwB4sHTVh4E60WgHdWyriwkISo6lhRJJAEAQMW4gKDp4+l8er1PNKP8y1aNpMQEA+1aptT8eQEdhhqO7HiHC0qkorrm7WWQIdTgdru1/fBWgWfOTOB6SQPkiCQjtjDFwSGwrsrZfcevZVTTgDKeT7bVsIziIcA5lIhzKYE3MjVwJIkaIpAQzPsYFmXRDsBIM4zLZ64DnOpEC4uFdHQjJZJziGWQrYqY2EsjE+dGsdmVXaDZ3G+s+pqkL+tYaqud1qhvVceTntJsMoG3BP06lcmYJdjAsBUnMd6uQHNMO0DBUXAMC0omoGypOHjx8fAUOSBR4G7ReKrDa0lo9BOe3amXazSERavLRbciQaO31RJZ1oW6wABAABJREFUVLFRaeD4RAQ3c3V347+TMuIIT+NL56egGIZtiO7pf3/xXAZPHE0O1K7XTWS36gpeaZbResnWFPz1lU08e2IMSwUJAktB0QxQJImarKEiaVjekvDCmQk8OpvEeyv24cOb6YrxDJ47nsaFeds41m/0+Fq5gUpDxWs3C5A0Aw3VgGqYmInz+PHz0zAs3FPZg4CAw8ZuDbO9Lb/fvZZDrqYgytN4bC6B9XIDim5iuSjhSCKEXE1GMsy2VL/wDAmKIDqec683cF7d9wa/2zPJgH8m3tF7miRc/4v2CTabVRnjUa5jeIHz2GeOp3oao19YSIEkiRYN5hkST84n8aXzUy16V5d1vHo97/pdeScH1WUdz58ab5la5JjBMjSB8zNxXN2sYSrO425RgqKbMJtRNcO08JmzGUxEefAMiUpDd1smVkoSpuI8ohyNs1NRRHkW3722HfB3DiuPpwRQpF1hlQqzKIoq3lkuDeXlFRAQcLD0s47YiUfffjFIwsKbaFWaXnJvLG7haCqEpxdSYBkKBOx2tHxNRqXR6WHq6PbFpRKWixL05uhjliLBcSTGIxxEeXtNVXQTVVnD3aKIVY8nXSLE4JGZmPu/nXZmSdExOybgzdtFLBc5TMR43MqJrh+g9/NxKqd4hsTRsXBHJevRVBjnjyTRaK7xF5eKuLMluetVnGfcJEsq7N/y5nSb3G3rXEiEGMymBNzZkjAeaa2caqgGpuMC8vXWtfbJ+STmUmHcKYgtz9MtEeGcEcqSiuWiBIYkQBLoWRm82wrte437504C7mt6tU9tiSo+fy6Dd1fKsAAYlgWeoezpMALtljzyDAXDsBBi6QOPAnfL2Ed5BislCX42Ee0RdD+xUpptgU6ZKEUQKIkq9Kbqac0Fx3kMTXZve+jFkWQIv/DsMXz6lN3Kx1B2i8KRZGgggey14L56I9cRVHIoSxqeOZ7GxaUSlgoiTMsCQQCTMQEPz8QQ4WislRr43LlJJMPMwBmCuqzj2mYFVzdqCLMUlgoibmTrWClJYGkSHE3h7paE1bKMX/7McZyeTO7ofQsICBiNZ1m7PwXT3Lg6o4M5msRMUgDHkK7uAduBmHhoZwMMdoNX972tBkBrJtkvW+rV+6inkqd9gg1LkZBUoyOo5Dw2W5V7Tp47kgwhIbA4nYkiV5OhGRYmojwW0uGO31krN1AUNZCE5uub4bzv7WawcYHGZkXBcyfS+PaHG0iEWCTDLHTDAs+QWEhH8PadIr7y+CyeOZ7Gy1eyKNTVlsPH2fkYbmzW8O5KBRGeRoxnQBJ2K0Zd0XF1o4bHZu3WkW9dXkdRHN7LKyAg4ODx2y9yDIFXr+cP/fe631nDGzhzhskouonFvAjNsFyzbAA41Xwev8TL/FgEv/T8cXzjvTUsFyWUGywYigRHk3h8Lol3V8ruY2mSQF3WOyZjlyUNK8UGwizlVnyyNImZRAQXl+xBCwnPuukNjDifT7bawJ0tERRF4G5BRKMtsfHi2XGcnoyiLuv4+qUViKrRMvlP1rYDPxb8W94ck/Cf+Ng0CIJARVJhWPbZrlBXoOpmS+UUYFdS1RUdx8Yjtk3KWAgXFsbcdXaQQgNvIn+rruBmru5bbdxedb3bCu17jSCwFHBP0Kt9KiYwODERxYmJKN5c2kJZUvHwdAwlSWvJUjuP7VXeuNMpRcP+freMvVN+2o5fBN1PrJwyU2eigTPNR2++nlMOC9ilpN7/Pcz1A/aCaI89jfv+ez+6Lbi64RdWszk6FsLrtwooSypkzXCN2G/JNXAMiRfPZPD+atkV6kEq01ZLEr71wQZeWyxA1kyEWAqyZuDsZAzXNqsQFWA8yoMiCSwXJby1VEQ6zN9XGYaAgP1kVJ5lXn8KL2MRuyV3OiFgKi6gJmvQDNMNPk3EuJYWrX6at9t1wcGr++2tBoB9oOiWLfXqfbsPknf8c5RnmhrWOT0oE+WgaK3tcN3u7cxUzPWh6nc/jt+Vg9NmLDVNVNvNYHXDgmlZeH+ljHPTMbyzXIZu2i3MZyaTqDRUFEWtRceXCiLeWynhWDrsVqGmwiyWixJ4hsTD03HMpUItWeor61XopoUnjyZBEtpQflMBAQGHB+9+0QlK3C8VII7v3s1sFS+emYBhmiiIKq5t1NygkjMRjyQAgaE6WtgiPI1jExF87VPHXa0EgIZm4Ee3t2A0xS8T5UCRBJ48muwwQgeA5aKE50+Nd7S3VWQNx8YjHZ5G3sCI07p4baPm7qe9xHkGK8UGzkzqLeuZd/IfsJ1k8ba8RZv/36l+mkuFcCQZQrmh4vXFakvXxpWNCqI+n78FO6jmrJPe80e/wE57It/xcfWrNgZag3+7rdC+17h3vnkBDzSD9isfSYbw4VoFnz2b8R1p+fkePgu7mVI07O/3ytjHeAaPzibB0mTPgIifWHkn3TlBl7lUCMtFCXGBccthnSi7rG37UOz2/gdhkAOa0/7gdzDiaQqGqYKmSCTDLCzLgm5aiHI0kiEWa2UJprUt1P2yRc5icbdo97KzFAmKJCBrBn60tIVzUzF8sFaBohtuFYVfKXJAQMDgjMowe9BKVu9m2Psag2jeKHWx/XqdVgMn8PXQdAyPzCT6JiP8Jpo6ej8R4/DQdAxvLRU71j8ns+ro427vbZDKs6WCiMVcHbJmIMbT0E3L9r+I81gpSjg9FUNDswOE6QiLJ44mIaoGxiOs2y4R4WmwNIlCW3u4pps4NRFBmKdxJCngneUyaooOAvYhQlR1d1pe+8b/fswUBwQ8CBymCpBRJB28OlyVNdzM1qCbFp5oVhqlwyw+cTIN07Qnlf3Zu2sQFd311/NqtpPwdSr2RcXA6UwMNVlDTGDw+XMZkASB717P+fq4Oj/zJmWLouprlO3gDYxEeBpPLaTw/koZRVGCYVqgSAJzyRCeO5HGclFy3y+H9mE7gJ1kERUdz51I42auhisblRbbjUycR0GUW9qgge1glKTp7ms7eD2fhjV9b/+ba29lb38rvc//oE2Vvr/uJuC+ZtB+5ZjAuKbc3tL8CEfjxIT/YrPbHthhf79fxt6v7aAdP7HyTrpzTPvqio7H5hL4xMlx5GsyTk1G3Yzvo3PJkdz/IAx6iFlIh/HkfLLFvwloZmpYCjVZQ5ijQVMEKIJAMsRCM0yURNUtux1UqNfKDSwXJVzfrGGj0gBgmx1qholUmEU6aldDmJ4VmGsG/IDRVTIEBDxojMIwe9BKVr/XGETzAIxUF/2ul6VJjEW4pr+Ff1AJaNX79ommgK1bjp6KiuHbmuaU64fYwe6/3731W8c4hsDFpRJu5uoAAEW32x6m4wJERcdYhMNUjMeZC3PYqDSwWZHxJ2+vwjAtZKIcHp7ZroZtT6SQBDAe4/CXH25ivdLAFx+axA9v5RFmacylQqgrOkii07DW+d2xMItsNdDugIB7jcNSATKKpEO7Dsd4ex0o1GUU6gp+5sIsarIGAkCupuJ7NwooNR/rtHUD6NDsXuvr9c0aTGtbBy3Anb5MWBaENruQ65s1fLBa6XoP7fvtmqwhE+cwOxaCZlhgKAKqbuBGro4IR0NS9Zb1zG/KtDOlbbko4cn5FI6OhVvWsjsFEapuIFdVWgJeztq4UpSQDDGoNpPpznvlBOKGNX1v/5trb2VX9O2zSvvzP2hTpYNVNOCeYph+ZW92kgoxuLCQ6trisFqSQFMkxiOsO4rSYZAMyLAZlFFk7P3Eyuk9fuHMBE5MRNFQddwtilCaPhze+/IK2lq5gVxV8R0ZPYoM0DCHmAhP4289dgQcZY9MdYzYKRI4khTww5sF6KaFQk2BblooSxpmkgIs2IsRNYRQlyUVt/N1KLoBhiJhmhZIws56bFRkfOqUnd1wRpU75cghdrBqh4CAgO7s1uuun44C6Br4HUSznf/u9Zhhrn83uu/Ve0fnneARADw6m3QTEnVZx+tNbQS2W9Oc15pJCG4lUbveD3Nv/Yx1X72ed4M7AMDRFGiSRLXZVkHAHrjx1x9lsV5pbcuoyBouLhVxYiKKCE93JFLGwix+eLNgtwCGWZCknWhQDRMblQZmUyGEPV5UzsafJOBOC7q2WXNbKQPtDgi4NzgMFSCjSsZ6hxo4gZ0tUcV0IgRVNzEe5XB0LIxXrmaRCrNuUAlo9dfrds7w0/CZhIBUmAFPUx2dHXOpEJ49nu54fLfASJilYFmWOxghGWaRrcn4aL3W8VieIXFuKu4GuZznbA/SOPtswJ4GWqgpbrWq91krkt4yGc6ZgrpZkXF0LIS/9/Gj+NFSsWV66k4nhLb/zbW3sjuTpv2ef1QV2vcK99fdBARg8Ey4NzCwUWng7pbka8QG9M+A7CSDstuMfTexigl2EM3ZIE8nBbx8JdsRVPIKWrbaaCk1BVqnDe02A+R3iPNmS95c2sJsMuQe/I4kQ/jZp4+6742s2Wa0DEng6FgI62UZR8fCqMqau+BkoravijeA2K+iyLQslEQNW3W15TPkaNv0MC4w7qhyt31QN5AKs/jmB+s72lTsd5VTUFUVcD/TTUfLDbXDh8MbPBhEs7u7vW0/ZlTX2+872a73jq+RX0Ck30a23FDx3sp2JRHQOV1u0Htz7me1JLlm35koD7U5KnoszCIT5ZCrKRBYCoDtsaQZJk5nopiM8yhKrS1uzrWIquEellJhFkeSPCqSDo6lIDAkVooSjk1EcG2jipqsI19XQBCAwNgHqpjnwOJs/MfC9rSgiqxhxvOelSUNr1zN4kuPTKMoqodKLwMNDwjY5jBUgIyiHa99qAGw3bK8WpIQ5WmUJQ1FUcX8WAgg7ECL4dnMe/31BtXsCE/j+dPj+M/fWWwJKvEMaQ/QuZlHVGBadPD5k+N49WZr2xlNAZk4j7/+aNM9X1iWhVOZCKbjfEeyQNZMMBThrnfeNerYeARL+ToyMd7urqjKOJIUcHw8jNduFTrugSSAZJjBh+s6VMOEZVmoNnRUGppt+l0QkY6y+OkLR6BoVovBt6QYPQdY+OH3N+e0sjMUgfNHEuAZqutaPooK7XuF+++OAgIwuLeOIxJOKWY3I7Z+GZCdZlB2m7EfRKz6PaYu68hW7QOB41ehGbaRqqLr0A1z1xkgvzYGJ2ucrSmYHwthMi509Io77827yyXQJIm/uZrDdELA7byI9UoDIYbCsXQY82Mh/OSjM252Gxi8TJkk7NJd0wJ0wwRBECAAzCRDOJII4e9cmEOUp0EAkHUDL57NoCiqO9pU7HeVU1BVFfAg0K6jg2STR5H13qku7lT3h9mcdnssAHz90gpYiuiq9zRFDnVv5YaKN25ved7vCjTDxNFUCLmajOdOpvHechmX7pbctYClSWTiPDTDwsPTcVQarQbrTvVUQ9VdHVsuSm6G+Kn5JBJhFqslCZZlf+YL6TDWyg2YpoVCXUUmJuDYeAR1WXcz4N2MaEnC9vD7n2/cAeGpsjpovQw0PCCglcNQAbLbdjy/oQaAfQb5/s08nl5I4f+8v4G3eBrZqgzdsPDUQhKPzybwzkq5Jbjk+OsNo9mKZiEd4cDRZIfuLhclfO96Fqul7cBQKszghTPjUDQLkqqDJkncytVwpyC2JK3zNQWWZeFz5zK4ulGFbgJ1WcNiQXS9orq166m6iRvZGt64VQBJEvaU7KKEmYTQkux3zg9LeRGiomO1pCFfk8HRlNsGPRHlIKsmXr2exydPjOOjjequNLTb39xEjBv4eQ56Gvl+EQSWAh5I2rMN3lLMdj+GQTIgB5lBGUSsej1mrdxAQzHwzLEUXrmaw3qlAYokQRLAdFzAV544suvr92tjaPcHAeyD37c+2MCnTo1D0gw3Oysw2yW72ZqCM5NRPDGfhGZYSAgMPntuArPJsJvVDTMUXr2Rd0emOrT7p+RqMh6eiUMxTKwWJbA0CY6hcHQshGeOjYGjCbxwZhyFmgK1mYlPCCxytc4yXy9+m4r98LE6yNcLCDgsDJJNHlSzDzoz3s4wm1O/x17frKHa0DA/Fsbtgojr2RpIAqBIEkcSAn7skUlQJNFxb92qZrrpTLWhuUkawzAxFmbxmbMTrudGjGdwp9mONx7h3ExzOzxDuc/vNTuP8AxM04LAUKAIEoW6gk+cSOMHtwpYLUpoaAbKkopTk1E8f2ocmmF1GNE6rROqYWJ+LIzv38gjwtOYjG/f+0HqZaDhAQH+HHQFSPue1qslLEWCJv2Nrh2cNaq9DcwJGP3odhFbogKKImBZQE3RcX2zjpjA4Hg6jBuealPHX2+Y9UhUddffr/0+bufrHc9VFDV891re1ZzrmzXc2ZJaHkMSwMlMBH95eRMfrlfdn6ciLP7uU7OoNTTEBbbld5w1yjvpbzzGu++nrBko1BU8c2wMt5tBLOf8UFd1vHgmg+9dz2Gj0oCu6lguSnhsLoHH5+ypd2NhFt94b60lWQAM55/rXfdeOm9XtI7yb+5+q0i9d688IGAXtGcb2vtlHT+GQTIgjig8NB3DazcLUA3LzYTeCz202WoDy2UJm2UZuZqChmqAJEzwLIVKQ8OdLXHXr9F+iLMAN6jkndRQlTW8t1KCwFJui5ttahtHvfmZGabVsqjyDImnj6Xw9UsrqDbs1guBpbAlKpiI8eAossU3y+ufohsWFnN1zKdCeHwuCY4moegGVosSfnS7CJIg8FcfZd3RrB9Yleb19B7D7ZjjehcL07I6slMOezHJ5DBNTgkI2E8GySYPmvVuf4yq2+X8D03HsF5u3HObQFHVIbAU/tfby9B0u1KpJmsgCRPLJQmX7pbw//vi6ZZ76lU1IyqGr85EeQYrJQkWAMMC3vK0ezheGyxNoiJrEBjKbcujSAJnJiMYi4TANSfBMSQBkrA9BFmaxHiUw2wyhDcWt3Bts+YOV1gvN/DJk2k8f3IcEZ7GyUwUzx1Pt9zLtY0q1hMN1GQdpmWCALBaaiAVZvHhegVPLaQ67uWg9DLQ8ICA7hxkBYh3T+tMcqs0NBimhZmEgGubVSTDTNdKFm/lZuvZw0BMYHBto4axMIfVUgM0SUAzTKyWJTwaTWAixrt7YJ4hMZcKDXXOqMs6FM3o8HUyLTSDOdutw168muO3xo6FWbyxuIVcTQbPhqE2z1FSsYGXP8rihTMTXYNfjtaputk0Lm+d4DmbDGEyxmOzKrdUnRbqCh45EsfxiYibtDg3FcNSMwhlAchW5ZZkgd/9+NFr3RvV3939WJF67+yGAgJGiF8bhDcbeioTQSYm9I1Ge0WBJIDxKAeOIZGJ8QP9/jAMEtUeNvJdl3XkajJm4gIaqonPncuAZ0hUGhrubklgKdJ9zlGa7CrN7Ix3UoOTKfEG9gBb/L9zLYePHxvDj25vYSYuIMzT7oSIEEPho7UKCnUVc6kQXrtlT824XaiDJgk8PB3Hp06NI1eTkQzZnk53CnUILAWOJvHkfBI/ul3EWrmBQl1FTdZwJBXCJ06kcSNbQ0nSWsZVlyUNK8UGwizVUREF2CXDDEl0+Ls4rSHt/l0Oo55kclgmpwQE7DeDtrkN20qcrTaQrcpQNBNvLG7BtPZuE9hPy3ea5WRIEnVFQ5xnkIpwODcda9H8GM9A0bYFql/VzLkp/yC7c2AC4Oo9AIQ5Ch8/NgaBoSCpBgyTxkxSQFxgkK/JeOb4GN5aKuFHt4t4cj6Fy2sVKJqBz53LoNLQ0FBNpMIMrm9W8bHZOHI1GStFO3NeklRcz9bw449M4eWrOZw/Eu8IkH3/Rh7fv5nHSlGCblqYjgt4aiEF1TDB0RSqDR3piNkxWvsg9DLQ8ICAw4mzp/3WBxu4uLSF9XLD1ZNTmSj+8sMNbFYb+IVnj/nqsrNGqboJTTcxFRegGSZkzUCIoRDmKNzZkmBZFjTDQpSjUVN01GUdY2EWL52fAgCcnYrizGR84HOGc2bJVRXXV9XxdcrVZEzEOGRiPEgCSEe5lqATYLcmdwtMWQAKoopkmAXf3NM7VGQNs6nua5So6qjKGm7n7clx0wkeumEhwtNgaRJRnoJqmHjx9ARyNdmdLKrpJiwAi7m6W/GajnDu9Sqa6XZE+NFNQ/ejWvR+rUi99644IGAEdGuDYGkSxycieHyuc4JcO+2i4JipAkBdMQZ6jkEZJKq9k8j3WrkBnqbw6t08vncj7/78SCqEJ+aSWMzXER2BeTfQeUC7syW2eGk4mRIAHdmSSkPDiYkwXjo/jR/eKuB6toYtUYVhWpiO8/jx81NQDAPfvZ5DSdIQckxiTQvLJQmvLxbwhYem8FcfbiBbU3AsHYZumuAZEuePJDAWYZEQWFxcKoIggWJdxfsrZXeqUHt75HJRwvOnxvHuSrlzGtLpcXz3Wr7jb8vbGuL173IY9SSTwzA5JSDgIBimNXnQVuKZhIDXFwv7sgnsp+W7yXIyFIGpmICXr+TwQ48p6pFUCM8sjEE3rRa971c1Y1rdLc5jPINHZ5OoKxpu5mrgaQqnJ6N4f7XsVsdKqo5sVcGzx8cAWLi4VATPUojwNEzTrqgq1lV8/dIq5pIh3MzVERcYkCTw7PE0vvzoNAwLWNmSXO3O1xWkwywmorx7Lc56LaoGZhICclUFuqpjvdLAxaUi/j+Pz2ApL6LS0FomDTkchF4GGh4QcHg5krQnqFUVDedU28RZlHW82/RAevtOCZ8+JeKRI/GO351JCKAp4L2V1qE5mmHizGQUNVmH5dFW1TAR4eypa0dSwo4S194zixP4Xy81UFN0vHW3iC89PIW//mgTRVG1Xx9oGWjk8PVLK76BKVGx1424wGA+HUZDNVr8m0zTbsX2S4YwJIm7WyJOZ6JuOzNFEpA1EwvpML72qWP4+qVVrJcbuDCfQjLMQGlW3DY0HWMRFlt11Z0O7Zwf4iEaUZHp+p5009D9qBa9XytSg1Up4IFkFOZ/+yUKg0S1AQwV+Xay3ZuVBv7P++uwQIChSNcEcLUogSFJvHh2AqbVewM7TObcOcTNJAQs5sWW61WNzlGjgN0el6vKaKgGfnirgFxVgdb07FivyChJGl69nseT8ym8/FEW483DRJRzMh0MGpqB5aKEmqKDJoGarOP6ZhW5uoKrGzVkYjxgAckwix/czCPM0Tg7FcNaqeFeh7eKysmG+FU7dPu78LaGtLMXfi2HYXJKQMBBsBfmrodF7186P72jLKej09majA/WKsi2TezJVmRcz9bw8WOpFr2XVB3jEbvSU9HMjtaJEEv31JmFdBgAcG2zBp4mcG2zhuWmB5JpAaJq4N3lEhqqjkfnEnh/tQKaJDAe5aEZJmICg2xNRk3W8chMHPGQ7c20VBBxK1fHqUwUSwURF+ZT+N71HHTTwolMBC+2BdnaP7+ZpK1/umnZ4755BmGOQk3R3XXQex8HoZeBhgcEHG6yNRlvLZV8/03WTORqMoDOwBIAzKXCuLlZh6xtT2ZLhlgkwywmYxwKdaXl8SxF4kgy1Ddp3W1P7reGiao9SU3WDLy/WsZKqYGY57m9A43CHI2LS0WIqtHSwuc85rPNjodj4xGEWBohlna9kjYqDaxXGrjRTAq3V/sSAE6MR/DGbbv6i6ZIyJoB07KQryt4fbGAhbEQ7myJuJGtI8rTeOtO0X4fkyH82COTKEkadMNEmKVARFgYloULCynUu7Rr99LQ/agWvV8rUoPAUsADy27N//ZLFJzFgCTsHub2Db7jFzTooceb7Z5LCfhwvYqJGIeJGO8KOUkQqDa9Lxia7Cq+O82c+x38WIrsyI6ouomNcgN3tyQouonXb21BM+0pdfNjIUzHeYxFOFxeq+CJ+SR004KiG2A0AsfGI/hovYLNqozZVAhLhTo2KjLOTEZxdaMKSTPAN709Hp1N4HZeRClfxxcemoRu2hMzxqOcOzFpIspBVHQ3qCSwtG+1Q7e/C29riJe98uE6DJNTAgIOilGbux4WvV/M1YYOcLVr/pV12/9DM2wxI5vGpne2RHzx4ckWvScAXFwqtoyl9uo0z1ADe1V9uFrB967fRlG0Rz/DAuIhBuMRDu+tlHFh3vY3cnWcIlGWVJSaGfSqrOPS3RIYkgDPUMjXFDw5n8KWqOLqRhWfO5fBnS0Jj84mEBcY3MjV3IOV9/NhKBKSp41Zhe3D8ezxNF67VWhpnThIvQw0PCDgcENTRM9/Z7r8+1q5gTsFEU8tpGyNb1o71GQdr9/awmfPZVAUNaxX7P19mKXxxNEkPt/ne9/TD8+jgapuYr3UsI2/BduXVNZNmKaFSkNDXGBQbnqCZmsKOIbEbErAnS3JXZvSERaTMR4EASiagfEIi+eOp12LCKe1TVQM0BSBS3dKqDQ0d+0oSxpeuZrFJ0+M4/3VEmaSglvJytEkTMtCTGAwHeexmBfx8WNjeG+ljHJDw99+/AiurFeh6AaOT0Tw5++vI8LRyMR45GoKMjEeX350BvNjEXzuHDm0hu5Htej9WpF6b151QMAuaI/m7/SwsV+iIKq6O17TO0kNsDf4pyajPXuIge1DT3s2XNYMhFka+ZqKmQQPwzAhaSYM2FlcjiHx4ll/8d1tf3D7wc8ZX7pclNzgjb0wiYgLDEzLQoijoeoGVN3EZkXGo7MJSKrdk+68B6ZpgWcoLObroEgCqTALkiAQZmnQJGF7pDSrj2iSsLMqhmVnqnUTE1EOAkvj9VsFfLhegW5aCLM0iqLiTppIhBikwqxvWW+vvwunNYSlyaEPuzvxVDnoySkBAYcBC0Dv7X9/DovezyQE18jaj/YAl5/mK5qBVIjDeqUB0wJIwn6yuMBgNhly9aEu67i4VERF7hyJ/dqtgmvGGuHpvjqTEFi3UolnKJiWBdOy1wtnuifHbK9jHE1BYCmslRswLIBjSEQ4CopmgOJokIT9uRqmBcuysJiv4/lT4ziWjuD6Zr2lwjQRYvDYbMJ935xJTIpmQmi2TUc4Grpl4f/62BQm4yHozQTGfuuln84HGh4QcDjJRHlkolyLTm//G9fSjutFVPUW+wwAqMEO+EiqDsOw8PeeOYqGZkA3LPAMCZokcGKie1Vsvz35k0e3BxNUJBUCSyHNsdANC3GBwViIQZSnkasqmIhyOMILYCj7dY+mwiCb06K7rU1PHxvDl85PuR5OjqH5WJjDqckIPlipwALw2q0CHp9L4la+DooE/uTSMs5kYihKKr7w0CQ4ikRV1nBtowpJM3F3S8KJTASGabpBq7pqYCYp4Nh4GB+uVVGoq7bBt8ek+9WbeUzE+B3tg/ejWvR+rUgNVqaABwonmu9MD3P25sMa4AH7JwphlnbHa7YvXtmagh/eLOBLj0z1fA7n0NNeCmsBmEuFsFyUsFaWMZeyJ/EAQDrC4vG5VE9/pmEz536bZu9jkmE7S+I8r6joiAsMnlpIoSiqzbHYBFTdtMt3dROSamAuFYLAUJiOC5B1w/1djqFQk3UcS0ewUWmgruigSAI0SeDsZBypCAtVNzGbFFCoKQABCByFb32wgUJdQZijQZEEMjEed7ckNFQDL56dwIlMFH/2ziqWi5I7XnYuFcKXzk/5/l045cAxgQFLEUMfDHbjqXKQk1MCAg6KQb8zgwZsD4vev7G0hblkyNenDegMcPlpvuPTNBZmERMYEAQBniYxnRQw5bkP+30xWqYWOXQzY+0WxLMDRJabANAME2ozM12XdUzFebA0iem4gEpDw/Fx26NDNy1Iio7ppICSpELRTWiGimSIhWZagGVBM0ycn4ljKsFjvSyDZ0gAJFaaPh2qbuLiUhFzqRDubElgaRJHx0Io1FVs1VVMRDnUFA1vLG7h0bkEzk7HD2Qiz35MIQoICNg57evFWITDi+cyeOVKtiPQ0t6O66VbooKlSSyMR0BRBBbz2xOZB6lS7LcnZygCiRCDQk1BIszg3etlrDYHH2RiPCbjPObHwtiqKyg2J7Q5rW3xEAsAvmuTYVpY2hLx7Y828VNPzOKl89O4sl62p24SBGTNwF9/uAnAtptYLkmYH7fb3xICA8MEvn1lE1uiiivrVZiWfS45MxXF92/Y1hSGaSHC0W6bMkMRkFS7qnW1ZN+DblrYqivunlxSdVxeK4NjqKGLCJxq0VeuZkERhFs5HA8xuLCQHElg/36tSN3RVX/mM5/B//7f/xuJRKLl59VqFV/+8pfxne98ZxTXFhAwUpxofrWhdUTc/+ZqFs8dT+NL56cG3lDulyjMJAR8wJC+GRGeIaEZ9tSIQQ49He0clj2lx/GbCDf9MqI8g4kY5/pj+NGrNYQk7My4t6KHZQi8ej2Potj9oNeeWchWovjOtRx+cDOPh6bieP5UGiAIyKoBiiQRF2hc2aghzFGI8jT+wSfm8f7/n70/D7Lsvs/74M9Z77n70nv3TPesmAUY7ACxiCRIkCJFMrIqLNpyKZIsWfIrRpYsy2WyXBUncWLHcar02ik5kRU5iV4lkmXJihibpLiBJEiCALETy+wzPdM9vXfffTn3rO8f554z5269TWPQA9ynClWYXs793XP7PL/v77s8z3yJhWKDiCyhmzaT6SiPHMryjbdXAJAEePzoEC9fL/D9y+toisRrc0UUWeQzZyaIKhKCAKPJCKrsOcelowrDcRVZEpkeivPt8yu8fK3QdtCaL9Rp2ja/9MSRtr8Lvx04rSmcmUrztbeXSUW37yS1n5wjBtw/wJ2A7T4zO0nY7he+d1vdO73QK8HVj/P9BFIn509losHhaXa9ynAywkhCJaZIlBpmmxirJHrr6HUfc3HPyKBputQMi6pukourTGdjzBXqiIKAJAo4rve+ZnIxYhGZX3hihh9d3SCpqaxVdCq6yfGxBKcmUlxuWWw7LuTrBg9Me92ff+PhaV66nudbZ1c5v1ymrFscGorxgcNDvDZfRJG80egHZzKsVZpcWKlwYblCsW4wnIiQ1GSeObvCJ+6ZoNR4dxx59hPP98KA+wd4v6Mfzz1yKIfjuFSbVjDSloh4o2v9ntnNChXTuRhPnRgjXzN21KXYKyYPj1TP5Ws8cDDDmwtF/p9XF4OkUlJTmMxEOb9cZqnY4NEjOZZLTQw8naiqbpGLq0QVqWtvalo2hZpBRJa4slbjxWt5GobNREbjzYUytuMSUyWsVovtRtUgpkgMJzSev5xnNBXhW2dXuFGoc+/BNJmYSrFusFhsYNoOjx0ZoqpbJDUZUfCKwqNJjZruvVd/nFuVRCzH5fpGHUUWiMgi1+fqZGNq0L26UwfXA9kYHzw2wpdeX2ClrHv7Xk2h2rT4+GkxuM5mxamtClfvxamCXa38u9/9LobRXS3TdZ3vf//7t7yoAQZ4J+Bn80cS3Rl33XSYy9d3HMDdDlJIaN7csKaIbYkMRRIYT2nka03WKjofPj7Cs5fWNj30dFZJNmpGoCuxUmkylYkylIhs67DUr+Lit8o+c24FoaXdYVgO69UmD89kEQUzGOPoFTT7HTY3CnXm83Veny/StGxUWWC53OTHN0pUdAvHdXloJssHjw0znFDZqBk8fWqMDx4f5cXZDUZTWuDS8e0Lazw4k+XVuQLjKY03bpS4ka8Tj8iMJSNc3/D+/4dXNvjZRw5yda1GRTeJqt6IXTqq8PBMjmxcZb3S7Eoqgfc3FHYB+dxDB5ldr/HGjQL3TKZQJJH1SpNcwrNwfebcCp99cOu/tf3kHDHg/gHuBGznmZnKRHd8kH83+V5TRGaGvC6eREQG18Ww3cBVsx9n75Tziw0juC8b1SaXVqttmkrhEbyYKvdMiIgCaLLE//LtKwwnIqiyyEa1SbFu8DMPTPEXr97gwnIFy3GxbIcD2ShPHh/GdaBYN/iVDx2habpcXq0wmvJGTb721jK6ZQcHj6lMlIemM1zbqPPKXJF602IkEWmN1nn7uYDAyfEkF1erzK5Vqeo200MxCg0DVRIxHe/QNLtWY76o87W3l/nkaU8EtpNXdzOKvBPsJ57vhQH3D/B+Rr/Eb75m8tK1PJ86M7mjRNBWhYqRpKfxuRN0cn3n2Nrx0QQjyQiTGY2RpAokcXExLIfLq56JzWJRJ6UpzK7XqRsW09kYD89kydcMTown2/Ym23GDpNJ0LhYk1ubyda6sVTk6HOdiqxgQV2VqhkXTsrnvQIYXrmywWGoQVSVuFOs4rstbN0qcOZBhvSpTbVq4LhwZSTC3UeOJo8PM5es8MJ1hZjgeCKarkoAmS7i4LBTqbNQMhuIqpYaJKAg0TAvL9jrBdpqkr+oWz15aQxCEthG78HXC+2XnZwjdpkq9klvvtamCHe2Kb7zxRvD/Z8+eZXl5Ofi3bdt87WtfY2pqau9WN8AAewg/m+9Cz2qwaTs7DuDe6WDTx1gqyumJNBXdC5od16XcsLi+4bmMLRQbzBfqfOSkVyHu3Nz8deqm3XYgcVxYLus8ejhHRBGZycVJx9RtHZb6VVyG4iovXy+0DhReYqmim8zl6zRNm0cP59rGOHrdc38TVyWBh2ayNEybF68XWCw0GE1FOJiNefPhCZVK0+IjJ0c5ENIGGUpEWCvrbZ/za/NFjo0kuHsqTbVpEVMlinWT6xveuMRQQkUWhZbVqveefLfXUsPk/HKZh2ZyOBAc+CRR4OhwnFhERjc9gcL1qk5Vj5PQZAQ8UcZvn19jtaITkSVvtK51UNvO39p+cY546623gAH3D7D/sZ1nZrcH+c4gsKpbfS2Ud4tOvlckEUkSuL5eo2E6nJxIMpKMEFFExlJam/V0556Ui6ttPL0Z54NnJe3/rK9FFHYG8rnb747qdR/D4xIRWWQoEQlcMb9zfpUnjg5zfCyBZbtIkoAqikQUkQvLZX7usUPBgcofoXh9vsix0QTJqMJQXGWppDOXr/HC1TwnJ5LUmxZ3T6axHCcYlXBdWCw1eOhQFgBBELhR8Lj+xdk8rgu27aC0EnMHs1EMy0GWBFbLOguFenBPb2UUebvYLzzfibfeeotEwjOdGHD/AO9XbLZf5GtmkHjZCp38/Jl7d5aQ2gxTmSi5uBKMbsVUiRdnC1SaFlHF6zJ1XFgs6pxdLDOa0ri+Xse0HVygYdgMJSIcGU7guJ4GqevCa3MFMjEVAcjGIsHeVNZNkn5nTtPCBSKy6I2taTJHhhOoikREEjk+muAHl9ZZrTY5kIvyw8vrxFUZy/Y0TCu6ieW4XFguc3TUS2AJwD2TaT59ZgLdtLl/OktEEfjW2yscGY5j2g5JTeFALkq9abPUMv1RZJF8zeDwcIK6YdM0HYYS3p6ykzPeVjHC7HqNl6/nexanXprNs1ZpBppQ4e9t5tT9Tp8lbwd2tOr7778fQRAQBIGPfvSjXd+PRqP87u/+7p4tboAB9hJ+Nr/Z0Wniw9d+2G4At1mwmYmqe0oSU5kod40lqDYt6oZNtWmiySaVpsVw3CP8fM3kO+fXuggrvE5RgMlMlJevF0hoMplWoK6pIgcyMWzXpVg3EGDTNfskePdkiucurbdVziOKSEKTg38DGK1gf6XSxO1xvc57HnZGun86Q6Fm8Pp8kXTMczISBDg6miClKYB3aAivVVMknjw23NaZZrfcLgBGUxqjeAmjibRGRJEQBSg3TBzX5f6DGd64UUIIiYXUmhaKJARWrJIo8MDBDM9f3eDKahWnlYWqGzaVpsXRkQRX16v81ZvLgbOHLApk42pwUDs5kdrkU/ewX5wjPvjBDw64f4A7Att5Zm71IF/VLS6vVvhGa7w6qSmosrgnCYepTJTRVARNERmKq1iOy1KpwamJNJIAIrDSSvBUm3ZgP91rTxpNehp1l1YqlBsWcU1GFQWKDYPHjw63rdMbD2sPksdTniPP7EaNB2e8JE24O6rXfQwXb/xEj++K+fZCCdt1WSzqWI7LZFrj4SM5ZteruHiJfT+xFOZxv/ItiwKPHcnxmXsnqTUtRFEgXzUo1A2SmsxIMkLDtJEEAVkSA82n0WSEaETi0kqVi8sVZElkIq2xnG/g4iIJ3mvfP50lF1dZKjX481fm+cjJEb5zfq3n4WEvR9T2C8934id+4icGcf8A73v4PNfPrbOxjXPDrWqobZV8SGgyDx/K8YfPXQu6e35weY10VOH+gxlUWcSwHCKyp08kCp4rZyaqIIkCluMSU7zun7OLZaKK5BWELZuTEynOLpU5NBQjFhFR5QiG7bBSbteVEgU4Ppbga28uc23dmzqoGRZT6Sg/ec84C4UGx0cTXFyu4AJl3WoVKLwCtIsnLD6cjHBkJMGh4XjXvfnZR7Wga7hp2gwnVf7DyzdwWzG448Kx0SSPHc1xeaVKPCIH+zNsb2/3R8E3qs3gd32tVF/DabnUP/FUbVrM5etBQiuMzZy6fex14eJ2Ykc71ezsLK7rcuTIEV588UVGRkaC76mqyujoKJIk7fkiBxhgL+B32Mhit7So1srmw/YCuM30EP7ytRscyHgioT5ulSSKDYMbxTovXyuQrxnkawaT6SgfPTnKcEJlsaQHrx8mrM51+tXq+w6kg2TSC1c3GElq/MmP5lipNAOxvulcjA8fH8F03LaNbLWs86XXF1gu60RlicPDceKaFFTOi3WDGwW9bf1qyLUu7Nbjo/Oe1w2LkYS3eVd1i9FUhKdPjXJlrYaAJzToJ5X8nw9jKhPl3FKJj50eCwRgvfZdG1n03NlUWWy1/Fa5slrFsB1005sNPzGeJKZKvLVYDq45mYnyE8eHSWkKY8kI6ajCj2bbk0pJTaFQM7i4VOHFqxvcdzATOC/ZjkPTAstuMpbWWKk0t5XE3C/OEa+//jr33XffgPsH2PfYzjOzUGxseo3N9oEbhTovzeb5zvnVIIHi8yZwywkHf0zilesFnjm7wuxGjXzNCCynDw7FAoezzUb7ZNGrMP9/v36RQsPwul0dlyMjCX71g0c4kI21HVY2Kk3Wq02sVjJoodjAsl2SmkwSGUUS+eDxoTaji14JkWbbyPZN7k9pCsdHk5wYS3IgG8WyHaKKRKlhBCN2pboRdID5nUwfOJLDcW9acgvAallnKBGh2rTI100ECFxAr23UqZgWAmCYNoeH4zx1coS/fHWBAy09weFEhOWSTrFhel1VrcOiYdlcXq1wcjzJ5bUaL83mWS032wolPnrtt7stKO0Xnu/EG2+8QSKRGMT9A7yvEVflTR3R7plKb/r7t6qhtp3kg+/iOZyIEJFFoopELq4SkT13zXRUpaKbVIFTEyksx2E4HvEKuQ0DELj/QJq3bpR49HCOb59bwbAdxtNRii0x77l8nUPDcVZKeltc73fh247D63NFRlJeYujYWCJweVst6Tx9ahTTvunuVmmYiKLA4eE4iuxZfaaiCmMpjclMtK/7ss+5r84VePtGmRNjSR6cyWI7LifGEnzr3Cp/+uIcSU0hHVUo1A2OjHjF6K32dv8+DydULq1W0RSRqUyUxWKDRmhve2A6Q1k3284iPuqGTTmUhAontrzv93bq3unfxX7EjlY7MzMDgOP07vgYYID9DD9Yf2k232YP6h8I/GrzdgI4v6OmM4MdVSUurlTInFLbfv5WSMInHsuG0xNplkoNMjEDWRS4tlEjFZXbdC/CyYperZy+xelIQuWHVzYYTUb40dWN4H7opsPsWhXb9qoW9x1IB+MP42mVs4sVLq1Ug86ja/kaR0YSQeW8F/yRCt30DgaV0Pd63XMBeHE2H6wppkpUdYtHD+d4bb7YlRzs3CiKDYP5Qr1NC8l36bjvYJpK0w7uS6lh0rRsBEFgOhdjpaTzzLlVfvLuMX7x8UOsVJpEVZHhWIR01BsZefr0GHMbdb5+drktqXR4OI5hOViuy0JR5+iIZ629XmkGP9c07WCERRK6k5yd2C/OETMzMwPuH+COwHaemd0e5NcqTf7spXmiqsTsRi0Yb9VNh6trVU5PpPdEEycTVVkp6aSiCjO5OMOtyufseo1a02obS+s32ndoKM6/f3mey6tVcnE14MnVSpP/+4Xr/B1V4vuX1wOTgXhE4qXZPMdGE1xZq6KbNtmQe6rjury5UObk+M1DVK/76IuLhws24FX7j47G+f6ltbbCi38oubha5Xq+xo0rG8H3ZAmmc3GurddwXM+SOxNT+GTLCdXfzytNi7mNOmvVJmOpCJoiMZqMMJ2LsVE1kARYrxrk4ioHczEkUaBmeMkn3fTci2aGYjQMG0UUKbXGoUt1i4pu9qw8+/cebr3qvF94vhPT09OkUqkB9w/wvsZUJsp0LtZWTPBR0k1enM1zbDTZ9zm9FQ217SQfAN5cKGLbLpPZKIKrIQg3Y2PdvHlWuVFo8ODBDNWmxUvX8siSwFA8wmgqwgPTGb78xhIHczGGEhEMx+WusQTfOb9CJq5yZCTBtfUan7h7HNN2eX3e0zoS8IrWwwmPX1+6luf8UiVwaZ5MR3ni2BAjCZWhFi/P5etcy9f5a/dN8qXXF7i8VkeTRUaSGpos8VP3jPPVNxc3NfyJqzKxiMzZpRK66XDXaILX5opcz9exbBdZFFpGPt7+/OTR4b57e+d9FvD2psWSzuvzRcZSWvCzY8kIiiQGe344aVTWTWzXoaJbQUeXf85MdTQw7Hdtvd1g1zvVpUuX+M53vsPq6mrXhvNf/9f/9S0vbIAB3gkcyMbIRFUOD8f7jjBsJ4CrGRbFhsFioYFueroM4LXpe4mK7iBstyQRJh615TaxVNIxgGsbde6ZSre15+ZrBheXK0xmopt2xLjASllnJJRk8yEIAueXywiCwL0HvEOEYTksFHV+dDXPVDZKvVVx8Alba1VFeh00/DGIqm61WVH3uud+1cUP7H2UGt7mfeZAiogst10jvFF0JuLCOiUrJZ3hk2NBAO91G8FIUiMbU/jAkRz/zysLqLLE9y6uk75P5fJqxdsQIg6u63JptcKhXAzdsDk+mvRaikUQEYipEhXdwrRdLMezwR5OqDQMO9BtAogoEumoQnSb4w37yTliwP0D3AnY6pnZ7kE+3IXiOA6LRZ0XZ/PcPZUmXzOC8VY/ePWTELeqieO9ph0kNNaqHkcL4GlmqBLJiExElYgqUs/XM2znppNauPrQut7zVzeCAsnVtSrT2VhQ3W1aDlZLnHUkqTGZ9jQvOvexXvdRwHM26hyJHoqrvDpXpNZsX+tKpcn3Lq5xYjzZNapu2bBS0vnE3eM0TLvrc/T38y+/sch6tcl4KkqpYRBTPI2Pb55bRZEE7juYIR6RuLZR54PHR7i8WuX8cgVJFFBFgYO5KE8eHebHN4pUm1ZQkIgoYjDO1wv9BMxh5wWl/cTzvTDg/gHer0hoMgdz0ba4VADSUYVUVObyWpU3F4qcmcr0fF5vZfR6q+TD+eUSby6UObdU5norYT+WjPCTd48xmdaCiQazVQC3HZeFYoNP3jOOpkqevAQuC4UGV9drnBhP0jBsHjsyxGtzBb53cY1UVGkrnjRMmwems2TjShv3aYrEK3MFDNshG1dpWjaO46JbNpdXqnz05BiHhhJ8/sNH+dLrCyiSwPcurXFsNMGjR3LEFJlkS6bj3704jyqLWK343W6NhBdrBv/F454Wnz82rlsJrq5ViWsyf/XmEh88PkKkNQottQrRac0bC99u8m+jZvDksWG+dW6FpVIjSCz5hZCqbpLWlLbCg7+XnplMM5qMBAWU8L3z3Vdv9e9iv2JXu9Uf/MEf8PnPf57h4WHGx8cD5yfwDqSDDWaA/YyEJnP/dJZjo8ldB3Cm5fDSbD7Q7AEvc34wF6PQOmz0wm5IopN4wt0/4BGZ355b0k1OT6R540aJTEzhgYOZYGSiE03TQZMlYqrEA9MZTNtFlUSquslSWafWtEhoSpAkq+gmEUXqSYT+gapuWH0PbNO5GB++awTT7hYXD8M/UB0Z8TYK3XRoGDbTuRiLpQaTmRiVltVoXJV44GCGy6sVooqEYbusVnRkSWSk5RSnyjcrzTXDDg5Fn3voIK/M5Tk0HA/GKzZqBg8fynF+uey9/4jM6Yk0sYjIWFrj628v47he5f3wcJyRZIR8zQg2roruCRgqkmeLWjdsXBeGEyoTGQ3HAVGEkVarsiJt3bHkYz84Rwy4f4A7CVs9M1sd5MNdKIblcHapxL1TaRIRGbX17IaTL5IoBEmIW9XECfOsz/lN0yERkZnL1zm7VOGthRKaItJodTB1cn2teVM4VOzYkyRRQBQ9birWvX2j0bR47OgQl1crLLUOI5bjko0pgSscdO9jve7jE0eHu1xKI4pIOqaQiMgU6gbVpoXUqiivVZt8PDfG9fzNTqZwwWSh2OBgNtZTV+T+6Wyr4yhO03IC7r2Rr3NkOE5SUxBFIdhT3lwo8dGTIwzFFRwgrsgIIvzVm0uB+4/fWeuPXvfCZgLmPnZaUNoPPN8LA+4f4P0OURTbipXQSka0zgFvL5Y5v1zp2aV4KxpqmyUfDMvh3JKnjRceTVupNPnm2RU+cfcEX3triZVKE6U1kjWd85zebhQavDZXBDyem8pGmcxEPU1U16Vp2Zxd8uQgIrI37urH+v56O7m/UDOwbDf4+fD7Wm+JlAMcGU3wdz50lDcXitwoNBAFAduGpuDQrHqFjRdnNzgykkASBZaKDRqmjSKJnJNFJlrSFAeynmTHl15faMXVEglN4cpalU+eGSeuym0j1Juh8z770iFnDqQ5OppgMh1FEG52ZwE8eWy4bc+qtJJNM0MxDrY6YMPTIIokbOrU3Yl3S1vvVrCrFf/Tf/pP+Wf/7J/xxS9+ca/XM8AAtw27DeCqusXFlQpxVW5LLNUMi7phtTqF7J6/uxuS6CQeTRF58tgwZd2kYTiMpzXKDZOprMZ9yQwRSWSjZlCsex0+07l2vScfmbhMVE3y4myeH1xeD74+mY7y+NEhNloVcj/ANmwnIEOrR6bKtJ2em02pbmC7LrGWA8RWCTyf3FOa0tVxdHgkzuHhOLm4iu04zOcbfP/SGqPJm4m1bMxzDeq0yPYPKSvlkGNSLEJFL7aN5mXjKg/N5KjoJvceSJONRbi8WglGMXwIeCKFtaaXPKrqJlfWa9iOiywITGU0lkoNHjmU48XZfCDgHVdljo0keHA667n0hbDfnSEG3D/Aew399oHOLpSKbqKbDqbjMpevMzMcYzIdZbHUwHK8IDymejpEe6GJ4/O+KMBURmMooba4FIaTKjO5KK7rIAgiNcPuyfXxiBfc++MAPiRR4PREileuFVkoNqjoJqWGyWQ6ymNHcnzgcI4jIwkMy2EkESEbU6joFrlEBKHF5du5j6MprS3ZVKwbnF2qMJevEY/IxCOyp38ni6SiChu1m1pLnXomh4ZijKejxFWJRw/ncFv3yOdITZFYD7mNAoyEEkKjSY3pXAxNlijWDZZKOvOFBkslHUUSSERkxNZ43MxQjHTLBcnF5WfunwqSZP74eyrqFW5g64LRnVh17sSA+wd4vyOueh2YQ4lIUGgw7ZtBYUQWWa8aPbsUOzv5w0lz8IrVVd3qGe9tlnzwOuE9nussOi+Xm+TrzS4H0Igi8OyFtaCbVBIFHjmU5e3FMi9f8xwzdcvm4ZkcT901wttL5aB4Cp4GUnh/C3P/j65ukIhIQeLZst1gosN13Tb5h4QmE1EkxtPRtvtRN2xkUeDuyTSSKFCoG4iiQLzlvpyvGcyuV1mr6vxn903y/ctrjKUizAzFEFqxdzamUqpbNIxWYbz1mptNCfS6z44LG5Uml1arTN0b7dpjlss6P33fJIIgUDcsyrrJQr4eGBZ9+MQICAJNw8bB5cxUpi3puF+19W4FuzqxFAoFPve5z+31WgYY4I7AQrHB2cUyjx5uTxgAJCMyjx7JcXW11hbUwu5JIkw84WDbb2+9uFKmots8cTTHM+dWGY6rQUKlZtg8mItSbJhd4x4PTGf5N9+9ynq1STzkkrRYavDK9QKnJlJto2uqJFLTLSbTUWRRwOhY51hK69ps4hGJH14p99Wd6JVICZO7v4mHcSAbYyoTDWyxRxJqm5hirWlhO26bRfZGzQju2/nlSnDNQ0MxZMkbtwhDlUWOjiZ4cDrHQrHRdljzP4NvnVuh1rQpNgzWKk1GkxqPHs6xWGxw10SSoyMJvv7WMi9fL3ByPMlDh7IoksDR4QQ1w2K5rHP/dDa47p3gDDHg/gHeL+jsQvEDxZpukY4qXN+ot+0BTssgYDoX2xNNHN86WpMlnru8zlyhzmq5SbFhcHI8xdGRBOsVY1OuVyWRk2MpNurNtoPB0eE4Z5fKmK1uVP97i6UGL1zN89GToxSqBtm4yjfPrqC3kmbgdZ4+cXS477o7OT1cSHjzRinoQg3D6FgHeIeMMK8rLQHY1+cLvLVQCjSmfI7cKkA/PBwPxjYqusm5pQoPTmd5a7GEKomYjsullQqm4/JYJsqfvjjHVCbK33ryEEdGE4ymNM4vlzi3VAG8scBnL67x2nxx085guDOrzp0YcP8A73eEOcYvNPgYS0aCWLlXl2K4k7/cMNumDI6MJFgPcVlnvLcZt6Wiys0YvSU5EeZY3XSQRIGnOhxAhx/UuFGos1TSSURk3l4oUdEtRpIatVYnqeU4zG7UOTWeClw5x5IRfnKT/S0TU5nMRHl9vthWePed6dKxdv3ZTlH0xZI3LaEpIqIo8NB0lu9eWA0mJ1JRr+NKEkV+dDXPTC5Ose7dz2cveA54hZrJheUKB3Mxjo8lA12jrc5g/e6z3+XVq+MpFVU4kI0F9+PCcoXlot5T4P3JY8NoSrvRwX7V1rsV7GrFn/vc5/jGN77Br/3ar+31egYYYN+jZnik+9p8MUgYmLaLIgk0DJvhRIRyo71CuVuS8IP0uydTPHdpnXT0ZrCtKZ7e0o1Cg0Ld4KVZL4FxcbUaJFTWqgaSKPYc91goNkhoMmsVPRDSqxkWsihQbVoczMbIxdWg5TOpKVxYKfPRk6Nc26i1JVumczF+5oGpLq2kzXQnetk4Z2IKHzk5si1HJ//7YWtr8Lqp/PPJSqWJy81DSkk3mQptrmGHi1qoy2wzO+3wgUfAszU9PpqkZlg0LZu/8+EjHMzGSWgyo0nNm8tvWEEr7vV8Hcdt3+TuFGeIAfcP8H5B53PvjxlcWa/x6OEcyxW9bQ8Yjke4fzrd5pi2FTbrUExoMh8+McL/8u0rLJZ0CjUDw3bIRFUkQeCbZ1c4MZbckuv/3sfi/F8vXGcu1K4/mYliuS5LLWe8iCwht+ymPRdLl4+dHuX/9/x1Vis6I0mvSKIpIglN5tlLa4ymtK73uVVyXJEE0pqCbrZr+gHk4mpbYinM65py0xhDNx10sxlU+sMcuVWAntDkYAT60kqVqCJycjyJ6TjMrtc4MZakUDO4ulYLRuhenPUOLwBvLpR7cvSmncF3aNW5EwPuH+D9jnASYClUUA53x/sIu36FOf4z906yUW3yH3+86HX+ZGNB51C/eG+z5MMDBzM8e3Et+Fpnp//dk6meuk8JTebkRIqEJvP8lXXWa16pWBIFhhIqiYg3kaEpEvdPZ5hpSUYkIjLHRvtPeuTiKqbtdbVKooDjuIitjlnTdsnF2xNLYVH0uUKdQs3AdSEe8dzqmqbDPZNpXpnzRMLLrWRVsrW+im61xeTrNSMo+JQaZpuu0VZnsH73eTQV4TP3TfDytXzbz/c61+XiKi9fL3Tp1q5Umrx8vcBHT411ve5+19bbKXa16mPHjvGP//E/5oUXXuDMmTMoSrvV3m/+5m/uyeIGGGA/Iq7KJDUFRRKCLH4YZw6k+PnHD5FvzRPvliTCQboowEgyQkLz2vWPjyZwgSurVfzO0sVSg4cOeR0wfkIFvGpprzGFmmGR0hROtTah4WQE03YQ8AT4Tk2muLxSYa3SDFzv7ptKM5WLkorK3DOVpmk5pKMyjxzOcWgo0Xb9zXQnVstNXprN9wzSn72wxhNHh/nKG4ssl/XAqjO8MYQPfZ1irw3DZnooxlqliW46wXy1XxkKi8k6LoHDhd/K2vl5dbbHhg88Lt6YSbirShTE4HdHkhEePzrMV99Y4sJyJbiPnV0Nd4ozxID7B3i/oPO5D48ZvDZf5GcfORgYNaSjMk+dGGMk2ds5rBe206HYNF2GExEc1yWqSriuS7nhaeC5rsuDM1kurla35PovZKJcXq1QbliBi6huOWiKFFS301GV9WqTSGss7UahgSZLLe0mIdDnUGWxJydtlhz/6htLfOiuEa7na9w/neGVa4XgIAPeweyhmSyHh+O8teglb3xe9910Gobd1iEQNsgIr2erAD2hyYynorx6vUhFh9WKwXBC5cXZQvAzx0cTAaf71/b/v9MJ1ne969cZfKdWnTsx4P4BBriZBHhzocjbi+WgYOhLLviIqXJfjj8zlUKRurvxoX+81y/5APDafLHLLGcoEWm9Vm8x8fB1p3Nxjo8mAsmJpKagWzYN06Zu2BTqXgJHiik8cthzfr6wXOlZEMnXDB6eydI07a6OnYdnsuRrRts+6Yui5+uGp8/kuCitIs5UJspSSefYiHfeEYCD2ahXAKgbNAwb23VoGA6LJR1JFLAdNyj4TGWjyJLYN7nW737020NmcvEtkz/5mtEazRbb9iu/KNP5/sP3YT/E+HuBXe12/9v/9r+RSCR49tlnefbZZ9u+JwjCYIMZ4I7DTrRtOl0IwuQxnYvx8KEcI8lIG3lUdauLiIG+r9kZpDsurFUNdNOhUDeYSEfZqHnJo3C1OTzv3bScTaul/sGp17gZQCIiMZSIEFGkIDmT0mTuPZCmaW4uwA2bCw56mh69v39to048UmC0dQ/DyauwxagP39rahwtoshRUbe4aS2DabpclqA/HJXC46IXO9thmx2YRttOG3noao6kI42mNhmlj2Q6pqIIVctW5U5whBtw/wHsN/bi/87kPjxmkNYVywwzGFx4/Otw3qdTr+g3T5vkr69i2y3DS0y3ydfHCFeuaYaHKIpIoYFgOtuNSapi4rsfzPt9vxfWd+9GFZU9xwq9ur1d1NqoG4+kIoiAQUURcB8YzGpmo2vOanZzULznuj69FVW8E4FvnVjk5nuCBGY9v/YPZUknnvoPZIKhfKTe4tlELklnhDgH/98LaeP56thOg74bT3dZ7Ce/5YVeojWqKR2ZyyJKAbtpE7/CqcycG3D/AAB4SmsyZqQznlytdmjvgJY9ycZUvv7HYM9F+bqmCYTk941Fo59bNRot93OooVSamkmy5mxm2lzhPhjqf7hpLMJaKMpWJUmwYgQxF52sdyMYCmQdfBy8snL1c1nvGsqIoMjMUR5FELMclqog0DJs3FkocHopzZDTOp86Mo0gia2WdZ86v8MFjI6RjCvWmheXAifEEiiQSVeRA79QFhhIRNEXaEQ/320M6v97rXOcX7Dv1Yf19bL/E8u8kdrXjzc7O7vU6BhjgXcONQp1nzq0gCYJHhKZDOqbwyOFsVxcOtLdLarIUkMdYSuNnHpjq+p3OqoUowKHhOHP5Wpu2T5ic/SA9LGjXNB2yCRVREKjoNx0gpJbddaFmtLmMpaMyjx8d3jRB5gfXnVXYyUyUhUK7tlAFWK8a5Ovmtkaz/OSPYTmUdZNa08JxXeKq7CV/FJGK3v47vlXnVCYaOL+B57ZWba7xuYe0rkOfgFcNCY9N+CQe1kl640ap71o308DobI/1E1l+Jb0zOAhfq6pbPHNuJdBJCVdwpnMxPv/hoxwZTdwxzhAD7h/gvQA/WF8pN1gpN2madiAcHebhzoA9pSk8eXSYR1tV262SB/24/8JyhVevF4JOo/AoRblhcqNQR2jx/EbVc/MRaOd6r7LbslHegus74fPnarlJsW4EhyPTdklpEobp4ALX12uoY16QH+7QUWURWRTbgmrdtLt0hnw+9ztHNVlkOK7y9mKFK2u1tmS/nxjzg/epTJQra7U2vSgfYT0THzvhyN1wevi9gJdUSkRkZtdrNC2buyfTnF0q7ztdvL3CgPsHeD+jV4Fgs4ROvlUo6IewRX0nfC7bru7mbkapwu/Hsh2iishrc1VqhqdRmozInBhPBTF0QpO3JdkQV+WgEB681x7vLYy4KmPaTmB6ZFoO6aiCJAgstTSXXriSp2F6ot4N0yYdU/joyIgXW1/Z4HsX14goEvGIzIFMlCeODWG1Ci+7iZ+3ajbYrBsN+hfsO9ey3w17doM7e/UDDLALhB/kuCLx/UtrJDWl6+D/4mw+OPh3YrtE3ouIh+Iqz5xdoaSbbYF1mJxrhtXligNw12gCx3XRLZuRhBa0W0ZkiXsm06Q0hUNDMcZS2pajGX5w/Rcvz/PK9Xxg/TydjXHPVBrDcnoKkm53NGsqE0WW4MJylatrtaArRxYF7plKk4tH2Kgabdf3BRE7q9Gdrxs+GBTqBj959xjfv7TOaqXJcMKrsIcrNv1E+UTBS/AYlsOrc4W+xB7+vHXTZq3SpGHaNAybpVKjbVwvrJv05kKRRETmxdkClabVchnyMJev86XXF/g7Hzr6nnSGGGCA/Yirq1W+9PoCq5Ums2tVbBcm01qQ2Anz8K1oH2zG/YslnaQmB4G0bzTw2JEckijyH3+8iCKJjCRUyg2TfN0I+CEiS4wkNbIxhbgq8YHDuR2N4fn736nxJK9dLzC7USPfGks7NprgE3ePc2WtSi6uMpGO8uP5Ypu1fFQRuWcq7TllhgoPrus5foZHQsICtxHZcyt98thwsKdVdJORZITpXIyDuSgXVyttHBw+uPmjiJmowsdPj1NqGCQjMhFVIqFKO+LIqm5Ra9qcnkjhuC4JVWat0sS03a6kks+/Nwr1Nn2oqCoFuoSTac8J1rAcrqxWWas0+Wv3TTIVEnUdYIAB7kxsluDptz+8Olfoez0BT/S5F3y+2anu5nY6NX3uLzUMnru0jtFKvJxbKjGa1Hj4UJb/9ONFrFZnbCKi8LlHDvSVbBAFSGsKhu1Qbpi8eG2DE2PJHceyU5ko4ymN6639xAUsx+FALkompiKLAsdG47h4xY9MVGE4rpKKKvzo6gbLJZ1kVKFuWNiOw2pF58JyhZ84NozpuDuOn7dK6K1VmvzZS/NtUh3+iPh8vkFcldo0W/u9/zvBsGc32NWO98u//Mubfv//+D/+j10tZoAB3mn4D3K5YTIUV4mqEpIIL13PBwd/8ILG9WqT//tH1/kbDx/sGSBuh8h7jQeENXo6qxZ+8iSuyl2uOOAJxz5wMBMIsYZHM/yD0cmJFB8/vb2DhuU4CILAgzM3BcgNy+a1uQK6afPIoVxb5cHHdts5T4ymePlaoW3UazSpcXQkwY+urHN8LNl2fcN2elaje72uf+i7vFrhW+dWmBmKc2oiheW4XaNzvUT5wp1j4QNSP2IPf96yJPCHz11rE8SdzsX4zH0TJDSZaxtVXprNs1jUSccUyg2DlZLOZCZKtWkFyaWVsh4ky+4EZ4gB9w9wJ+PaRpXfe/YK8/k62bjiObQIUNYt3lgo8viRoaBLJpzE3o32wWbc37Rskh3h10qlSUJT+NbZFVJRhaFEpC0Rs1Ztko4qFBtmkAjTLZunT20/qeTvf4oo8Mr1AumowhNHhwOzA/8w89BMFgGP01YrTarNm7w7ltJIRxUur1aRQ11Epu3y8vUC9x1IB5zuO+n5nO64tI1JjKU0RpMRXpzN9+Xg8MHt6VMjFBsmX/nxUuCKqikiDx/KUmwY2+LJXsF8Lq7wyXvGubBcodq0aJpOkLB65LBXrW+YdltSDAiSSh84nOP6Ro3lsh4k0ibSGtbVjTv+kOBjwP0DvB+xnQRPr/1hsy70jZrBT54e69JGCsd755fKyKIQJM/9cWnH7V/c3az7Jcz9L87mAwOg8ZRGrWnz4kaeE2MJ/u5Hj5OveV2yIgKLxQaHhxJd2qai4K33r95aZr4VB798vcBUOsrnHjnAGzdK245lE5rMz9w/xVJJZy5fRwBEQeDMZJp0TOHPXppnLB0lIosczEV5+tQ4r10vUDVsXrtRYiimcu9UOtDCS2oKhVYn6lM76OT17+FWpkMvzeb50exNIW+/0zWlKczl63z4rpFNP9vtvM5+MezZDXa16kKhPRNrmiZvvfUWxWKRj370o3uysAEG2Gv4D3LY6rNQN5keivLNt1cYTUY4NOxlxec2vErkalnn8HB81wGiT8T+qJluelnsatPrSPL/HUbdsDg+muQNRexyFrAdl3PLZT50bIQTE0k0RUIWRZSWrsP909kdVdRfms3z3JWNru+5rks8IuP2+D3YXmvpQrHBWlVnOhvj1EQqSFzVdMvbgDJalz7SeEoLqt7bfd3X5otIokjTcgJB187ROejuMosqEt+7uNY2jgiesPifvTTP06fGiCpSVwdTVbd4+Vqe+w6kPb2p0Az5q9fzyKLIv/3+VebydeqGhSyK6JbNQzNZXr1eYCytBZ0KinRz5tpf341CndWKjmm7jCX7a5y8Gxhw/wB3Kny+m8/XSWoyFd3i/HKFcsMbp41HZKZzccbTGsul3loQO0GY+2+OAjvUmp5uUq/kecO025wrOxMx4y0ntpgqoynSjjR8woHscEINEjN1w0ukTGWjAS89CNgufP/yOj9xbJikJgc8l44q/OlL8xwZjrcVRVTZEycNc7oqiV2OSeExiUcO5Xj+6kZXdbczuPYPUFXd4k9+dJ14RObQUCzQrrBs+gbj4cOWKopcWq0EzkLh17u8WmWppLNYbAS6GNO5m/t9TJVZLus8diRHQlMwLIfHjnjC5muVJnMtt08fTcuh0rrnd/IhwceA+wd4P2K3xiqbdaGnogrHRpMcG022dTvl4ir5msGLsxt87a1l3rhRCpwywzzquN3F3c26XzJRtY37fUdjURBYKevEVInhhKdnOrtW5f/98WJwjceODjGW1Lh/OtuWLMtGFS6tVjk6Emcm5zl+RhSJc0sV/vylG/zG08eoNu1td/oeGU3w6x89ykuzeUzLxXIcLq9UuVFo8DMPHsBxQcBtmUEsMpyIIFgCruuyXmuyUTeYzsWYSGsokohpOyRUmXrTpqpb2+bf7ZgOlert9143ncB9zu943arT+U4x7NkNdrXT/eVf/mXX1xzH4fOf/zxHjx695UUNMEA/3Mo8qv8gjyRudgLFVAnLdnFcl5LuObrUDSs4FIiicEsBYlyVA8HPUsOkUDOIKCJLxQbpqEKpYZKMmqRCYqG+s89YSuvpLDAzFKfQsgHtJzi9nXu2UGx0EaQPx/VG1sLOOz62O5pVMywahtPTOQ+8TW0mF+feA9m2jfVrby226Ur51Rrb7W5p3Sk5hw8pnj5I+4EmLM46kowE4rzhpOJCsUG+1v6a/tjeSELlL16dDzqZIrKEIMCNVe/fh4Zi6NZNt6OkprQly4oNg+evboTeU2lftcYOuH+A24m91B/w+S6qShTrJkulRpBgcAHdspnP17m65nBmKn3LumY+9y8VG8EocDwyEiS2Dg3FcFyXRtjV0qWnc6WfiLlnKt2X87e6V2GuDItVR2SpK9HStBxwQZEEKrp1M2Hf+l3bcTHt7r0hpSltnK6IIkulOiuVJnG1vfKeiiqYtrsj/vben71tN6XOw9ZGtUm5YbYd0MAbUQx3ivmoGXaw7/sHRUkU+dbZFSpNi4srHvMfGorzwHSG1+dL2K2L+uPcd/ohwceA+wd4P2K3xir9rOs7O1d8XrhRqPPlNxaDjqJK02KtopONq0RkKRiXfvSwN0XQqeW5WffL40eG2rjf14eby9cpNgwiskTTspFEgYcP5QIzIIB81eAbZ1c4NpoMOLDcMElGFV6+XuBGK9ZVJJHTkyk+cXqMlUqT1+eLHB9Ncnw0ue09eziucXI8zVy+5hXiLYfVcpOVsldgtx2XQs1gNBVhZjje9ruS4P0XV2Wu52vopsN6zeD8m0s7iqFrhtXT+VOVxcB0qLMgDl5yyZ9AifZxZ+18nc1wJ4t871kJRRRFfvu3f5unnnqKL3zhC3t12QEGCHCr86j+gxweRQOItdxqXBcc3CDZIIsCEVm6pQAxF1cDPQdfcDVfNbyulaaFLAptme5w0mYsFe3rLOCte3dt//49qxu9CRI8kdh4RCYdldtEtHcymhVX5b7XB28jSsfUrvv54Eyu55jZ33ryUNfr3go5d/5upzirf5jq5dTUDy6wWPDacQUgqcmIgsCx0SSW43ByIslr86WgdXY0FSEXV7mwXEE3bb59bgWjQ+djv7fGDrh/gHcCe60/UAvxnYNLpWkhCgJOy2ENF1xcVitNIop4y7pmubiKadlt+nL5qsFkNkq54dk3Hx9L0jDswPzhrvFk0EnUC/04fzv3KsxbYV72BcHDiMhiXyFr/3cVqTe3hzn9RqHOdy5WePlaIeDVsWSEp0+P8dBMltWOjtxOdPL3Tvi+12HLsJ2uAxp4vL1aaRKLyGxUm20HivC+/+ETI/wv374SFKUSERnLdlmt6PzwssVdY0murteYTGttHWl38iFhMwy4f4D3Om7FWGU3Wqx+R1FMlYjIEoWawUhSQxIFViqeE3RncXerLpvr+TpLpQaSIBAfjiMIcHW9SsN0kMSbxhAbVYN8zeD0RIo3FkrBey83bnLgx0+P8dJsnu9eWG1LKg3FVQ4NxflPbyxiWA5zG3Uujla3vWeH97ClUoOloo5u2jx6OMdr80Vsx0USBZJRmURERpEEijWTyXSU1YonM3FkNMG1dS+pFJbU2EkM7TgOZ5dKXQX9IyMJDPvmdELYMMiHaW/uzhrGnWLYsxvs6cqvXLmCZb03N9AB3l3sxTyq/yCHq7UNw0aRRA4PJ1guN3AccFwXuRVs32qAmK8ZPDyTpVg3Arvks0tlPn3vBHMbdeqGHWS6j44m2pI2U5koo6lIT0vS7ZDXdqoY/QgSvGTOUyc8d4udCtf6609E5J7X1xRvzKDzPVR1ixdn8wwnIkRksS2h9uJsnplcvO31b4WcO383LDQL7XbW4cPFZq/ZNB0iitRWEaoZFookUqwbFGomTxwZotQwGU5GePRwjq++uUi+5gUUL8zm2+a16fH6+xED7h9gL/FO6A/EVRkBL+FTrJs4jht0hDquy8FcjHwr0TCW0m45iZuvGXzgyBAXlqtBQiTM/fP5BmMpjaFEpG1k4fUObQYf/Th/u/cqzFudvB+RJcZT3uElFVV45FCOoUSEatPuuq6vvZTUusVnw2v012XZdBVIVko6mahKrdk9Ch5GJ3/vhO97HbZ8Zzn/gObDsBwSEZmra9U2oXKfi/19v2m6wd5k2Q5DCZW3Fsrops2VtSp3T6WQJYGHD+fYqN3UDryTDwlbYcD9A7yXcavGKjvVYvXPJw3DZjoXYy5fp2nZbRzSWdztl3D3O/APZKNcXKlQqBk0TJuRRIS3FkqIgoCmSsQjCmXd5GA2ypXVCgdyMeKqzHQuhuU4JDWlTbKhUDP5zoVVsnHPpRo844dX5rwOprvGk0EBYjt7ducepkoisiSwuN7gxdk8J8eTweRDRPbG9h4/MoTtuHzs9BgvzuZx3JsxfOf4tb+OrWLoqm4xn2+0mTTAzVG3eybTCNBlQuFjLKVtu/D+Xjbs2dVu99u//dtt/3Zdl6WlJb7yla/wi7/4i3uysAEGCGMv5lH9B1kWbwaOLvD2YpmPnx7jx/NFGqaNbsrEI3IgkLpa0RlJeKNZ+ZrBxeXKtkcyaobFclnnzIE0R0cTgc5QXffa+T84maKiW9w1dtPS08d2W2l3e89M2xsv60WQ07kYP/PAFCPJyLaFYTuR0DwBbVEUeObsSnB9X2z1U/dOdL0Hf829rDp7fc63Qs6dv2uERjt6CYj7G+tmr5mOyUGX2uz6zU4Fr5Khko4p5GsGP/PAFEOJSJBUgpsBRa957fDrv5sYcP8AtwPvhP7AVCbKC67LUydGaBg2l1cr2I5LRBE5mI3x2JEcr1wvcHI8xVgqestjeDXDYq3c5OR4kocOZbu4/56pNIeHExwajrcl7HfK+du9V2He6gyMNUUkE1MZTUXaqsu91mK7Ln/ryUO8OJvfdI3hdXXyec2wWSg2dszfO/n5Xoct31lON522Me9MTGGx1CCmykgh4ve5WBbF4Jr+ezEsh3NLpZamh0Cx5W4nIvDdC6ucGEt1dSHfyRhw/wDvR9xqHL4ddHaTSqLA0WGviHp0LEEqIuO4LusVg/sPZru6f3ol3MMd+KosoCkSluPy2lyBX3ziEFfWaszla9iOS1SVmEhrTA/F+N7FNU5OppnKRrEch8OtrtVwYst0vA7bRESm1BqjziVUfnB5HQBFFMmFumC32rM797CkpqBK3vj0YqnBQ4dujn9risjhYW/c2r/3D83kWCg2uLZe48R4EgHaRp19bBVDLxQbzOXrPc9Eac37vH98o9ilfdi0HNJReUfurLfj7+rdwq5W/tprr7X9WxRFRkZG+J3f+Z0tnSMGGGA32It5VP9Bfmk231atlSWBYsPkqRMjpKIKhbqBKAgIwGpFZzTpCX2XdJPTE2neuLG57k34QNI0bYbiKpdWqpxfqXT97LHRBBXdYizV+9CSiao8diTHjUKDuuFd68R4iqgitTSC+h96trpnluPw9KkxXrmW58MnRtBNG0WSSEVlTk4kOTSU2PKebvbe/XU9fXKME2PJQJB6NKlxeDje8/3u9HO+FXLu/F2/mt2r2gE3q86bveYjh3OtKr9Bs0MVfDytEY/IgI0gCORrRptWU3g8JTyv3fn67yYG3D/A7cA7oT+Q0GSePjXGy9fyPHY4x3QuRqFuoMoihu1wdqlCKqpwdCSOIgr8+SvzuxrD8zmwUDNIx7yunjdu3NTe8WE7Lh85OdYVbHdyfjameHpvlSb1pt3F9ZvdK98gwt8rzkylmM97wbMfGEcUkbGUxlgq2tWR6o9yzK7XWK3oyJLAeFJjKhtjJhffdMRjO5/hTvl7Jz/f67ClymLgpBruSPWE22MUehULNAWllW0KX7Oim4E+loCnL2VYblBZ79WFfCdjwP0DvF/ROdLmG+as9eHknSLMK5IAHzw2zLfPr7LYmnLIxVUOD8V5+vQYhzu0haB3wj3cvZOvGjw0ncW2XRZLXgLm8HCcRw9n0RSJbExFlUS+c3GVeEQmG/WS4b70RmdyPK56BhL3H8zw+nyRsu7dk0RL+mE0FQmS8T4227N102Y4obZpqhq2443srdUwbW8M7uR4gqMjCQ7mYiwWG8F9D3eFnV0qd11fFDwdPd20eXWusOmZyU8aPTidxbAdGpZNVJZQJW8E7ulTN/cff5Q6E1N4/Ojwjovw2x2VvNOwq9V/5zvf2et1DDDAptiredQD2RiZqMrh4TjfOLvCSllHAJqmTd2weerkKEBAHL7Qd75uMJWJBjachuX0bO/s1LowLIf1apP7D2aYL9bbWv/9rph+Fc0bhTqvXC+0dftEFZF7ptIcH0tybb0WZOR7HXq2e89Wyp6jTdgJ567R5JaJq17r7Rfwn5xIcXIitenv72TNYdwKOYd/t1Q3uJ6v0TSdrmpH52e02Wt+/LTIWqXJSFKjadk4jstkJspTJ0ZZr3qfY92wuhz3OsdTwuK4+6XqPeD+AW4H3in9AZ//L69WEMQi55bLbFQNNEUiHpF4+FCWj909xnfOr/UcLfvqG0t86K4R6qbdkxfDHBhUjC2bBw5mAp0I6D8KHOb81UqTRMSr2E7nYnzkxCjLZZ1UtJ3r+90rUfBc5J45t9I23hVXJT581wgA0ZATUc2w2oL14H03DF6+3rs7abOuse1+hjvl7+3+fL/uppSm8OTRYR45lKNheiMmpbrBR06Mdo82tIoMvntr+JrhDlcXiEckDg3FyMQmaFpOzy7kOxkD7h/g/Qw/ebHX2n/QzisuMJevB51AvtZrSTeZy9f6ru3Dx0f40usLLJd1VEnEclzGUxGeODrM6/NFrufrQffscELFdGpc26jTMGxmhmKMp6Oe7mfSIhGRg6Jmr6S9v16AJ48Ns1FtokgCTdPxTInqJqVGqU3SYTN9wGfOrfCj2XzwNZ93tVZ36JNHcnz0xAjfvbjGa3NFzi1VSGpKV4dtL87398GXrxdYKOrBFMBmZ6Ziw+TVuUKXztI9B1J7ngzazqjknYZb2vHW1ta4cOECACdOnGBkZGRPFjXAAJ3YTgv8dkcXEppMJq5w74E0pXoMq6W14Y/IhYljpdzgxzeKntPARj1IBmiKiG4l2to7e2ld+BbMr88XeezIEM9dXm+bAdYtu2dF07fG/s751bZAVxAEvn1+ldm1Go8fHQoy5r3mmLe6Z7m4ypffWOxy2Vkq6/zes1e470C6LSO/2ca5V5ooux1t24yc1ypNLq9WKDVMMlGFo6PJtspC+Hcns1G+eXalK6nUr3re6zUPZGP89H2TTKS1wKJbANarzeC6vTbZzvEUXxx3P7bGDrh/gHcSm/FALq4Egve7GVMrNgyubdTIxFT+xsPT6K3k7/RQjNMT6b6jZWXd5PX5AlFVYr0HL3bpRMgik9koS8UGby2WODoc5+Jqte8ocCfnx1Qp0Gg7t1RGEoSA88Oc2u9eDcVVXr5eYDgRQZVvJpZqhs1r80U+99BBig2DL7+x2PeQdCu8vhMu34y/e+3rPlzoGlkOX3Oz7qbwXnZhucJyeaNttMHn7eWyzv0tJ77wNTeq7QmoJ44OczVU7OnXhXynY8D9A7xf8U5o/0E7rzguzK7XmMpGkVsmOomITFJTsGx6jpRdXa3yH3+8gCwJHBtJ4OKZEg3FI1xZqyCJArZzs5tSEgX+5qMH+ebbK9RbOrPgSWB8+K4RTNvdNGkSXm9FNxEEgaQm89cemCQiSdwoNFguNwJJh9FUZFN9QNN22xywwwYLo6kI00Nxfv97V5ldr9G0bCzbG987OhKnWDP4Lx4/xEgy0pPz/X0woclbmuJMZaLEVYnX59uTSuB1rs7nG5wct96TyaC9xK52vVqtxm/8xm/wR3/0RziOd/MlSeIXfuEX+N3f/V1isXffFnuA9xa2ChKLDaPv9zJRtS0wFQX4t9+fZXbjZvbfT/Q8c26Fzz54MCCOYt1gtdLsIhlfe6FUvynQ2e9AktIUNFnizFSa+w9ksF2XmCqjKVLfTPdCsUG1afUU1S41TOYKdR5jqO3rnXPMW92zfM3oWm94LvveA+m2a2+2ce6VJsp2Rx22m0R8e7HU12Hu7sl018/vVTXiQDbG81c3qOgWnQOQ4UNV+OAVntuOKCIzuTjpmLqvWmMH3D/A7UA/HsjFFR4+lNs0GeKjX0LilesFvn1utasz5enTY5yeSPcc4QrzYlibJ8yLvTgwpSlowxJl3eTDJ0Z46uRo31HgXpzvr8Vy3DbOD3Nqv3sVUcSuYDq87tn1Wlcn0nbeU/jnNuP1rbgc2DI52Ks7QJZgOhffsmMXdtbdlIoqQSEFCHi7X7fq7HqN1+cLAF2aHvulw3QvMeD+Ad7veCe0/3z4vPKj2Q1mhmJdLtA+OkfKrm1U+b1nr7TFuForfnzTLHHvgTR1w2lL3AzHVSoN85ZizQPZGJ+5d5LX5gr85Ws3+P6ldeKqzKXVKumYwqMzWS6sVFEkIYjdO/dkx3UpN8y2EeVwcimiiDxyOMdLs3kurXri47rloBs2juuyUW1y92SaZy+s8NjRYQ5kY8G6Lq9WKDdMFFnEsl1KutmludTrzPTo4RxvLZTaxLv98+Fcvr6vTXT2C3Yt3v3ss8/yn/7Tf+LJJ58E4Ac/+AG/+Zu/yT/4B/+A3/u939vTRQ7w/kaYjB6ZySFLArppE20FiUCXHgZ4FpmvXC+wUtKpGV4ru2E53CjUPbeFQj0YTQhnyMPE4bhuV1LJh2462C2r6qpusVxukIzIwYzwRs0IiEyVRTRF4oHpbM9rdcLTZ+p+Xcv2Lug4btsBx0fnptM56uUnteotHaBOhJ3ROq/vk/BUJtp1YNtK82QnmijhNeumTd3wbMHrTZuqbm2aRAwfLNYqza6kEnhtxn/43DW+8MmTPWei96Iasd0EWefPOC6YjstTrU1yv2HA/QO8E+iVBOqVFPC7LLeqGPcbV7j/YIbvdnSBgsf/z5xd4cRYb9fHMC+GtXn81/bX6Js8hLUiNmoGqhxhKBHZlP87Od/neh+dnB/m1F5aSJbloMlS39dbrehd99HXonCBH81uoEgCIwm1bS8LI7yG7X6GU5koxYaxpYZVr+4Aw3J4fb7EpeUqjx7Obdqx62M7fL4braczB9Jk48p7Uny1FwbcP8B7AbdiyvBOaP+FkdBkDmS9sbR+CHe7+12unTGubjpcz9c4NBwnoohtiZu0pgQaoqmockux5ka1ydffXuaNGyXAm6gYTqgosshqtclnHzrAXWNJDmRjPfdk03aYycW8tWhKl3voTC5O03TJ10wKNQPDdoOkEnhmSoW6gWG5Af/754PVcpOKblKoGyQiCh88PsTseo3OY1PnZ+ZC385Vx73585v9He32e+8V7Ord/MVf/AX/4T/8B5566qnga5/61KeIRqP89b/+1wcbzAB7hu3MM19YrvSsIgzFVZ45u0IqqpDUFCq6SbVpcWm1QsO0+fipUW4UvXnkqm5yZb2GSzvRxFQ50L0RgKjqBeqW7enmJNWbh5grq1UutVpNwwLQm40/9UNcldvEnH3ILRFRURS6Djj9XiOhycQjEj+8Um67TweyGmXdbLO1D+tGdF5fFKDUMPjhlfWuz+OBgxlEgZ6Hj37r2gw319z+WoeGYtwo1unQxe55sLi8WunacH3M5etcXq3sSGxvuxuC/3N1w+KJI0MYtovlOD2r5XeaeN+A+wfYa2zF8eGkQD+uFwVQRIE3F4rEVJlvn1vBsF1PmNvyhPCXSg2ub9QCDu/ESqXJakXnwelcu1uk5VBtWqiSyGQmSibqOTuGua5hWAjAi7P5nho9y2V9Sw7s5HxZah/y6uT8zut1aiENJ1TOLrXrXITRdf2WFoU/inuoVTUvN8yuvcyHv4adfIbbHSfp1R3gJ/h0s9mlUddZfd5pAL8TLt4Jx79XMOD+Ae503Ko+0jul/RfGTkaIvYJxdzLLdlzyNYNsTCUTVTkzlUE3baSW6Lhu2tw/nb1lrlqp6ORrRjBK5zguZd1bz3JJ574DGbQDGaq6xVffWGIuX8ewHdRWN1ZFN/nexTXunkpzbaMWfN3v0krHVGqtvdVyXGzHCZJKPnTTIaKIrFSa3CjUef7qBnP5etD9VDcs8jWD5XKDv/HwNJdWK237WGeiTjdt75qqhCaLXUWVmNq/cOV34u7me/uxkLxb7Oovql6vMzY21vX10dFR6vXeB7kBBtgpthuA9qsiuHiHBVEUmC/U0U0HTRbJxlR+PF9kOKHy7fOrSKLIVEbjweksxYaBbnqdMQnNG1d78tgwP7yyTq1pB5oXk+kowwmVb51f5vBIkvWKF+jajkvTslmtwHy+zuGROIWaSbqlabRdTGWiJCJym5izj3RUYTob69KW6Nd+3+8+Nk2Hqm6hyVJA5GFntM7rD8VVfnBpvU0IFrzP48XZPNO5GNc2up//znX1C/jDX1dFkUutVta299K0ePlagdMT6a4W4c6DRanRvTGHUW5sv7q03YBkN4HLnTSvPeD+AfYSO9Ws6MX14YTImwsl7plK88JsHk0RmcpEWSw2AveuuCoxlY3StGxkUUQ3bc99Bq+rtGk6bd0rfoCarxloshTsGY8dGQoEVg3bwXQcXpotUDUs7hpNENdkTNtFlUTm83WOjSW2HI3qxflx1dvfZFFo4/xenNp5HwU8XQhf5yLMl5mYwlhSa3v9objaJl7tj2LMF+pBN6/fISQK3kixYTn8qBXICy4UawYNy0bdxOBiu+MkPUcSQ4WPzTp2d3uA3A4XvxPivXcCBtw/wJ2MvdBH2q0G6E6wk+7JmmF1FaCblk2hZmA5LpmYwXJZZ3a9xqOHc1iOQ0SWOTaavKWEkh+rF2sGD0xniKleEdjvslUkkXRUQRa9Pfj8connrqx3iWFPpqPcKNQ5Mppgqeh12qpSk8lsNDC3WCg20C2byXS0Tb7Ex0RaC4T2Vis6q+Vm20idJkuMJCNUmxbrVZ3RZIS1apONisFUNhp8Zj6vr5abnF0qtWnh+kWVsDZtP3OPkWQk+F64A/iHl9dxXa8AFi7C36o+137Ert7F448/zn/z3/w3/NEf/RGa5gUnjUaDf/JP/gmPP/74ni5wgPcvthuA9qsiNE0H23GpNS100zs8pKIyL18vUGyY1A3PCU7A5sqaTc2w+YXHZriyWuX8coWPnx5jKhPlhavrfOzUGNc26swMx1EkgZpucW65zFhS4/zyElMZjaVig5Gkyo18g5MTSZ67usHbS2Uc1+XISIJq09p28JnQZB45nEMUhTZXONd1+ejJ0cAVzsdm7ff97uNGzeDhmSwLxUbwtaSmMJ2L8fBMluWy3vbzEUX0DktyZ8rJE4R9MBel2DA33Qw7g3JRoOVIofG9i+uUG2ZQyehVKW+aDrrpdSCEBcd9hLvN0tHuKn0Yqej26G+7Ack7Jey4nzDg/gH2EjvVrOjF9eGEyPHRRDBOVmvavD5fZCx1M4EiSwKuC5VWZbKqW0EFNBdXcXC5UagHOg3fvbDCVMYTUZ3L1wJnt+9cWGUyo3F1vcaDBzMsFhu8NlfgscNDPHN+hRuFm5x6MBfjJ+4a3vLZ7+T81UqT6VwscIX70F0jLJf1nlzf6z6GzQDCfBnWHgwfkvxCDHhBv1859kco/KKtKMCh4Thzec9VaKPa5K3FElFF4gNHhlhYKNE0PU2Puml1fYbbHSfp9Vn7hQ/o7qgFr5r8TvLw+4Hj+2HA/QPcydgLfaSdjszuFtvtnoyrcpubsO24QVIJYDKt0TBtnruyzlsLpaA4cCuJ8BuFetB9VNZNXpsrciAb5TNnJvnym4tYths4Gt895TlBn1uqdEmK1Jo2by+VGE1p1JoWV9erwXsaSao8cDDDpdUKMUXCtBwePZzDdFyurN5k/RPjSR45lEVvSZ2Ytts2ti7gnQNmN2qslnVez3nueCICHz05ynhGo9jwiiX+Zxre88ISKabj9tWm9TGXrwcd0Z0dwDFVYr3a5PBQvOtcc6v6XPsNu3oK/tW/+ld88pOf5MCBA9x3330A/PjHPyYSifCNb3xjTxc4wPsX2w1A2yyAW2MPhu0QH0kgCgQ6SlFV8sSUdYuI7M0dO46L7biYrdnddFTh0moVxyUIFJ8+NcbzV9b5weX14LU1xSOfUt3k4kqFu8aSNEybjarBqckUby6UWC3rTKY0pofiqLK44+DzQDZGRJaYzGisVzxHsclMlOOjHvlsd3yq3330xaKfPjWGpkjBtSKKwLMX1rqc0cZSGjcKes9rAUiiuOlm2BmU+8R7eaXKn740j2W7SKKApnhdZWFS9yvlfnXGtHvrXoXbWo+NJj0trR7jcNO5GMdG28cz+o1NbDcgeSeFHfcLBtw/wF5ip5oVvSrGfkJEU0SiqoTjesGl60LDbJ+ZtWyXmeEY37u0Rq15M6mU1BTOTKU5u1Cm3rTJRFXyNaON71wEFEnAsBzOLZU5OprgZ+6fpFA3WSzqjCQjfO3tZYp1g3RUQcAbX7Nsl+9dXOeeycymvF/VLWpNm4mUxs8/MYNuOBi2Qy6uElWkTccXet3HsBnA9FCMpKZ0cXL4kOQn5Py9ze9w8rUvJjNR7plKE1UkvndxLRhH1k2vQl4SBL59bpWpjMa51iFhodjgI3eNbpkcDMPn8F6fdVJT0BSRtKb07dhdKDYoN8y+ele3wsPvB47vhwH3D3AnY6/0kW6XfMF2uienMlFecN2ggDC7UQuSSifHUzx9aozX5vKcnkhT1U38YYPdJsKrusVfvnaDl695rmmSKGDaDm8tlnBd+Kl7xjm3VEWWBA7mYmRjalvhOoymZbNQaKBKEo8e0vjP7pvEtF1EwdtT5gt1Lq/WEAU4M5Xh62eXOD2R5MxUCsNyyMZUJtIaby6UuP9ghkxMYTSpBV2tvk7TxdUKtaaNKovEIzLJiIIgwLWNGqmozDfPrvDwTK6N1zv1no6OJjgz5e3fr84V+t4fw75p7tHZAWzZLo7jslJp9hz/u1V9rv2EXT0JZ86c4dKlS/zxH/8x58+fB+Bv/s2/yc/93M8Rjb63nDAGePew3QDUryL85Ws3eH2+FGSrRxMRDo8kyFebQRBa0i1SmkI6qlBqmOTiKpbjkojInJpI9c0iT+fiHB9NBGSV1BR0y6ZQN7xgtdDgrYUSSU3h8dgQf7VSBdel3nGw2Unw2avlfrXSZCQZ6dKt2O19dFzQFKnrWsMPal0bp7dBFPteK6bKm26GnUG5T7yT2Sjz+Tq5uEpMldFNh1rTwrQdZjdqnJpMsVFtkmwdJsaSkWCmO4zOVuSRZIS/9eShvq5wvr7SVqMN2w1I3mlhx/2AAfcPsJfYqWZFr4qx3x0zlYlyaaWCbtjEVZnZjRqO4yIKAgJeAiqpyRwdjnN8JEm5aWLZLqIIQzGVwyMJXrpeYDgZCZLMYfjB5mpFJ6ZKHB9J8J/eWOL8cpmnToxg2A4XVyrk4iq1psVIUkMSvZ2n3Nic998p7Q/HhbWqwSOHh3q+dviQtFJucG2j1tOJSJXFYM/xnNxu7msunmhrqWGyXm1y5kAq+F6pYXKjUGet0iRfM6gZFnFFIq5KbdcIv2efw3t91qos8vChbOAK13mvEppM3bDaKsU+/LGGxi3w8PuB4/thwP0D3MnYS32k/SJfkNBknj41xjPnVnj0cI5TkynWqwajyQjZmMIffO8KiyWvODKZjvLgTBZR8DSDdpMIn12vBUkl8Pa1g1lP9/T8cpkHZzJYjsNkOsYHDuXQTRsH2rqqfBiWg2k7jCRV5vN1Lq5WsR2XtYqO5bjcM+W5NjsuXFgp81NnJlivGORrBnXTolQzeX2+yGNHhtAtmw/fNYJhOaQ0mbJuslpuMpnRWC7pOK7LzFCccsNEFLwJi2sbde6ZSlOsm6xWugvmqiwGXb6aIgUJuM3+jlRJDDppwx3A4HVLi6JA07J5a7HEwaEY11vSIZoi8vSpkW1/Dvsd3aezbeCf//N/zp/+6Z/yq7/6q/zO7/wOv/M7v8Ov/Mqv8O/+3b/jX/yLf7GrhfyP/+P/iCAI/NZv/VbwNV3X+fVf/3WGhoZIJBJ89rOfZWVlpe335ubm+PSnP00sFmN0dJR/+A//IZbVvrl/97vf5cEHHyQSiXDs2DH+8A//cFdrHOD2wq9Y9kJnEiETVTmQifGxU2N8/PQYn7l3gsmMxqnxJIbtEFUl7xAhQDqm8MG7hik3TCazUaayUWKqRKlhdiUs/EAxE1MZSkQYT0cDsrm6VqVh2iQjMkLr1yq6Sb5uEG2NEuimTUU3e15zM2zVcl/Vtx/A7uQ++vA3zgems4Gl9W6uE0ZnUO4TrxlyugNvE1Ikkaruie6tVw0urVY5u1RidqPO06fHmM61H7b6tSLfPZnmC588yW997Di//ORhfutjx/nCJ09y96S3aW3nPm83INns5wzLQTdtXp0rcHG5sqPPbz9hwP0D7CV2wyl+MuRTZyZ46sQId0+luGssGWgpXWnpSRzIxjBtT7wzqkqMJSM8cXSYuY06mbjCY0eGeHAmyyOHcoylNF65Xmhp5Hm/03MUSxaRRAFJFFgu65xfLgOQrxpEFQnDcig3TATBCyDh5lhZP97fC66/FW72uf7B6RxHRxNdSaXOa3TyuCKJxFUp6CINu9lNZaJEIxL/1/PX+OqbSzx7YY2vvb3MWFqj07CuF4d3ftafOjPBLz1xhKdPjvHJe25+7XMPHQwScFFF6koqwU3nV03p75S3FRRRZKPaZKnUYKPaxOjQedoL8d79igH3D3An41bj1/2KA9kYn33wII8cHuKusSSnJ5JUGiZfen2xLXm/WGrwwtUNhkJarztNhK9W9LaRNkGApbLOSCLC0RFPR/BTZya4eyrdGguTiatyMJY9FjLLcYEDuRg/cWyYK60iQdOyg46r8D5iOfD2Qpn7D2Z4+tQYHzo2wqfuneBvPXmY+6ezfPjECN+/tMbX317mg8eHSUZkqroZTKsczMa490Ca1+eLbfuL312kSN3yHmGEeX2zv6PpXIxExPvZTldvvzPJH1U0Q+8vrSnM5xt37LmgE7vaBX//93+fP/mTP+n6+t13383P/uzP8sUvfnFH13vppZf4/d//fe699962r//9v//3+cpXvsKf//mfk06n+bt/9+/yn//n/znPPfccALZt8+lPf5rx8XF++MMfsrS0xC/8wi+gKAr/w//wPwAwOzvLpz/9aX7t136NP/7jP+aZZ57hV37lV5iYmOATn/jEbt7+ALcJ4YpluWEGImgApybas+wLxUabcLQ/hWvZDkdH4tw9mQ46Ya5v1Pj6W8sApFoOP941U13t9f3a8gN3GsPmgeksbUYFrhvoV5QaJnpH19J2gs+9bLnfq7nwW71O50HNJ14l5HQH3sji1bUqY+kIK6Wb39dN79B2ZirN0yfHtt2KPJKM9HV/28593q5gY7+RTNd1icgiV9eqrFaMtnt2p4m9Drh/gL2EzynPnFtBEoRgdCkdU3jkcLbvMx2uGFd1i+evbAQC3bbj8tp8kdMTSR4/miOuykykNRqGzXJZJxf3KqT5mkG9R9dMRBYDTun13KuSyGgy0qZBd2m1ykOHsqSiCrWmhet6be/hsbJ+vL9ftD+2e41OHpdFgQPZGIW6SUU3A7e5yXSUn7pnnG++vUI6qgQW2o4L19ZrwTjyVm5q/boD+t0T03Yp6b3vZ0k32wL67aKqW5xfLrFY1DFtl7VKk4ZhE2l9vilNuaMPp9vBgPsHuJNxu/SR3g34HDmVifJXby3y0nVvXMvvmAWPt1cr7W6aO02EdzqJRmQJSTBZqTTRDZum5fDdC2uAl2SJKALDcY1UVAnGsl28hI5lOyyXdK6u1Yi0ihm1pmdU8cB0loQm0TDktjHmhumdt8Ko6hZ//sp88Jnm6wZPnRjlnqk06ajC6ck6K6UGz1/ZYDQZabnjCRwdjpONKeCCLIocGvJkMzqdTzt5fau/I4Biw0QO3XtNEZnMRqk3LdYrTSzDCs41fiftXL7+nhml3tWTtLy8zMTERNfXR0ZGWFpa2tG1qtUqP/dzP8cf/MEf8E//6T8Nvl4qlfjf//f/nT/5kz/hox/9KAD/5//5f3Lq1CleeOEFHnvsMb7xjW9w9uxZvvWtbzE2Nsb999/Pf//f//d88Ytf5L/9b/9bVFXl3/ybf8Phw4f5nd/5HQBOnTrFD37wA/7lv/yXgw3mXcZ27ID9iuXl1QrfaCWYkprCetXgzYVy27hS+DAftq10TaEl0g3nlspMZqJMZKLopucMJIsCh4bjfPD4CFfWqsFrb9aW78/xHshGOTGe5PxSmZ++fxLXgYmMhmE5PHtxDU2VKTVMklFzR8HnXrfc79VceL/rAK0Rie7P0v+cddMG1w1swH29pJpucTAXa6tQVJoWkbrEA9MZTk+kGE5EiMgiAt7BYa9akbdzn7cbkPQaybQdF1kS+PDxEVRJDNwg7lSx1wH3D7BTbMXzB7IxPnhshC+9vsBKWfdGjWtKy+xA3DL5mtBkfuL4MNfWa0GXimE5rFWaHBqO893zq3z4xGiQREpEZJ48NsRrc8Wua40lIyQiN7mx13M/nYsxkYny/76+AHiJpnzN4LnL6xwfTVBpWmQ0haGkymQ6hiqLm/L+ftL+6HWNXNzTm3p1rkCi9e9MTGG13KSim63qq6cFdf/BDIeH4yQ1hZpuUWh4HafDHUYLjgvXNuqcnkxzZjy97fVtB6bjBOKrnS5ER0YSWE5vfb5+8MVqn7uyjmm7PHAww3K5QSIiU21aXF2r8uTR4Tv+cLoVBtw/wJ2O26WPtBm2c+7ZLRKazFhKQ1NEdNMhIkvIokBElpjOxag2raBLZzeJ8KFYhJQms1TSSUU9vTtVitIwbRIRGT+XMpaM8PBMlmcvrPHZBw8G+6ivlVrWTUzLZjwd5S9fvYFuOcRVmalslA8cznF9o87/+9rNjis/+dIrEdZZmKk0bP79S3Otc4NKpWFSNxySmsz1jTojSY0HDmZ4e7HEerWJ5bicXy4zmtI4PppoSy71Szpu9Xf0uYcOcqNQZ6mkB2dWVRapNy2mslFGkxGOt5xiBQgkWN4ro9S7+ms+ePAgzz33HIcPH277+nPPPcfk5OSOrvXrv/7rfPrTn+ZjH/tY2wbzyiuvYJomH/vYx4KvnTx5kunpaZ5//nkee+wxnn/+ec6cOdNmgfqJT3yCz3/+87z99ts88MADPP/8823X8H8m3HrbiWazSbN5s426XC7v6D0NsDV2qinx2nwRRRLbnMDCh3PHcTi7VMK0XY4Ox4lrMo7rkIwoRGSRw0NxZEng2GiCF65u8MnT40iSQNNyiCoiY2mNc4vlTQklTCY3CnUWiw0E4Op6javrNeqGTblh4uLym08fZ67gtTZajrvj4HMv58F97FUypvM6m32WcNNtQRQ88fGXrxdIaDIjCZWxZISFUoOfumecV64VWKk0sWy3VQWP8uB0ltn1Go57swttL8l3u/d5uwGJP5KZOaXStBwcx2U+X+f7l9cZjqttQuR3otjrgPsH2Am2w/NV3eLZS2sIghB0tcDOkq/pqMqD01kM26Fu2hiWTUKVKdUNzhzMMJXRGE5EyMYjTGWiFBsGoii06UWMJSM8fXqMh2Zudkr1e+4vLJeQBIFMVGGppGPaDm/cKPGJe8Z5ba6I7broxs0AfjPe32/aH+Fr3CjUu6yVc3GFew+k+fcvznM9X0fAc9RLtRxFv31+DbvVrTWRGSIekUhqvUcH3olAOq7KXeKrSqjQtJP76Y8pzuXrwd/Ja/NFjg7HGU1pDMVVbNflkUO5O677dKcYcP8A7wW8m/pIt6qlB+2JKVUUkSWBhunpCk5mooylom3cd3g4RrlhUWqYuHgdudIuurRuFOpcXqvw8EyONxeKvDpXpNa0UGSRsaTGwydyTGSi/PR9E7juzWSJH+P6+2ipbnA9X8OwvAaAk+Mp5gp1HMdlMq2xUKyDAPXmzW7ilUqTl68X+Oipsa51dRZm4hEJy3F5Y6HE2aVyy8nNpNwaMzs2GuftxRIx1Us0KbJAWbe4UWhQbhh89qEDgLBl0nGzv6OEJnNyIkVCk9s+b0USSUZkHpzOciPf6OqOeq+MUu/qXfzqr/4qv/Vbv4VpmkFV4ZlnnuELX/gC/+Af/INtX+dP//RPefXVV3nppZe6vre8vIyqqmQymbavj42Nsby8HPxMeHPxv+9/b7OfKZfLNBqNnqKD//yf/3P+yT/5J9t+HwPsDDu17N1qVGB2vcZ8vkEupjKZifLibJ7FUgOr5fh234EM07koV9ZqZGIKP/PgFE3T7eq4OZiNb6sLx2859dsvk5rCwzM53losUWtaTGXjXF2t8cSRIY6NJtioGaiyuKPgc7vjV+82Nvssv/rGEiPJSPA936XovgNpIorIoVyce6bSvDibp1g3gzZZVRKpGRYjiQhl3SSutrfD7iX57uQ+bycg6RzJXCo1AoG+lY42ZOh/sHonq1q3ggH3D7BdbJfn92IULKIIvL1UZr3aZKWk8+BMlu9eWGOjZpDWFC4uVxhORPj8h4+S0DyTgV964ghP3VVjtaKjSAKjSY0D2Vjbc9bvOTwxnuahmSxxTaJu2NQMC0kUee7SOk+fGuXhmRxLJZ3Tk6nATaYf9ivX9/v8lktNzi3e4IPHhznTMGlaDjFFZGYoxtmlCo8eyhGLSKiiSCIqtTnMdeKdCKTD93Ooo1Nqp/fT/9s0Qi6ktuNycbXKxdUqn7l3gvWq0eU+6GO/8vhuMOD+AQbYPXZ67umFcGKqrJtcXauS1pTAvj4VVfjIyRFGU5E2zh1OeNMcqajCI4dyJDWZuXydtxZKZKIKR0eTfeUiwmsvN0zuGkuyUmnwkZMjWK3pg0rD5OximatrVR45dLN4CjdjXD9+vrBc4caVDcBzhn786BCPMUTTcsjGFF6czbNS1ttiZU0RSWgy+ZrRtc7OwowqiRwbTXB5tYrluLxyvcA9k2keOpQlIkvceyBFuWFxdb1KVJWCcUEXuLRa4/JqlZ+6Z3LHbnm9eL6zMCWLIpdXK9saubuTsasd7h/+w3/IxsYG/+V/+V9iGN4fkKZpfPGLX+Qf/aN/tK1rzM/P8/f+3t/jm9/8Jpqm7WYZ7xj+0T/6R/z2b/928O9yuczBgwffxRW9t7DTg8RWowKrFZ25fJ2Pnx7n3788x2LJywTrhk08IqPKIt88uxIQ3nfOr/Uk8e124fiHj/CYRDauctdYkuOjCR4/OtTS2YDrIQLpF3z2wk7mwd/N4HWzz9IT72sXSvVdigDuPeAJgx8bTXaNXvzwyjrfPLvS5erz9OmxPSXfvZ677/xbVTvE4JvbEHvdi6rWO4UB9w+wXWyX5291FKyqWzx7YY2HZ7K8OldAk0VemStwI18nFVU4kI1SN2zm8nW+9PoCf+dDN5NLZw6kgd6jWFs9h5+6d4Lij0wODXvft1oV1wenc1xZq2I57W4y/bBfub7f51fRTWY3atw95TkQiQIkIxp/9vINyrpFLq4iiwJjKY3Hj01Sa9rka7cvabaXnO7/bXbyuA+fz+80Ht8NBtw/wAC7x60WUMKJKcNyglFf3fRMCfxu+GcvrPHh4yM8e2mtzU3z6GiCj58eo9Qw+VffutTTKdk3tdls7Ru1JrPr3u9ajktUEak2bRzHpdigq3jayY3h/T58HgCCXz7Vp9u0VDe6iv2dhZlrGzX+xsMH+fcvz3NtvYYkilxcrQbvsWk5rWSSQC9aLzWsHU0SbOesGL5WNq5QbJjvOZ2vMHb1LgRB4F/8i3/BP/7H/5hz584RjUY5fvw4kUj/jGcnXnnlFVZXV3nwwQeDr9m2zfe+9z3+9b/+13z961/HMAyKxWJb9WJlZYXx8XEAxsfHefHFF9uu67tHhH+m01FiZWWFVCrV1yI1Eons6L0MsDPs9CCx1aiALAk4LuTrTURB4K6xJLppY9gOpuUJductJyC8rUh8u5WFzmy0btpcWa1yfaM7Gw07r85uZ/xqK1J7pw8im32Whu10JVLCCFcypjLRmxbfrsu1tWqXAGtJN5nL13pd6pZwIBvjM/dOcnm1QrnhVXWObVHB6YfOv9WkpgTz7kBgRQq9D1Z7UdV6JzHg/gG2i+3y/K2Ogi0UG+RrJqLgdT1WdIvlcpO7xpOYlsNG1UBrJbhXyvq2gsbtPIcHsjF++r5JJtIaTcsJNOCurFUD/t8u598q12ei6p7zfL/Pz+/e8bl9qFUI8EcNmqZNNKZi2i4/vLLOR06O8J3za7c1kA7fT920qRsWoiBQb9pUdWvbr+v/bXbyuA9/rORO4/HdYMD9Awywe9xqASWc3PHNg3yEu+HzNRPTcXvuJw3T5g+fa08qgVcE/sPnrvGFT57sGfeG164bTpvphSwKGEGypr142ivG3Wy/jygiiiSiymJXt2lZN7merwXdTv71P356jI+cHOGl2TylukVElSg1DH7+sWlkSaRpuiQ1iZSmYlgOTdNznhPoToKBx+nbKWb5e8u3z60EurE+NuP5/aDz9U7jlt5JIpHgkUce2dXvPv3007z55pttX/ulX/olTp48yRe/+EUOHjyIoig888wzfPaznwXgwoULzM3N8fjjjwPw+OOP88/+2T9jdXWV0dFRAL75zW+SSqU4ffp08DNf/epX217nm9/8ZnCNAW4/dnqQ2GpUYCzpVb7ChFfRTUoN7+d9ggoT3mbEsZPKQqc70fnlSs+k0m6rs5uNX20WvD5zboUPHmuvWvjr2O08d68Dy2afpSqJbYmUTvifc+eBaTih8tp8kUPDcWzbbataWDY7qiZsB70ObOeXK5vep373pfNvVZXFQEg2rSmB62C/g9VuqlrvRsfagPsH2Arb5flbHQXzg15P/NJmvtBgtXLTsS0TVQAvsaRIWweNsP3n8EA2xvNXN6joVhfP7ZTzd8P15YbJK9cLrJT0Nmvp7fL8ZtzR7/Pzu3cCbheg1rRZKHjXycVV1qqGl4ixEjTN3oecd5yjNJl4ROKHV9Z3vQdOZaLk4gqSIDCUUFmr6IiCSFU3qTYtEhGZRw7n7lge3w0G3D/AADvHrRZQwsmd8Giuj37nGxeCuNMfw+qFuXydy6uVnoml8Np90x0fDcPm6Eic5bLeEgz39oV+Me5m+30iIjOdi7XtZeCZcVR1K3CS9hHe/3xXUtN2GEtpPDgzBECpYfDcpfUg+TOSUKm1uLvatNqSS2PJCAKbfxbh88JwQuWF2XxgDJEKaQn6Mi2qLHZx+rup83U78K7tWslkknvuuafta/F4nKGhoeDrf/tv/21++7d/m1wuRyqV4jd+4zd4/PHHeeyxxwD4yZ/8SU6fPs3P//zP8z/9T/8Ty8vL/Ff/1X/Fr//6rweVh1/7tV/jX//rf80XvvAFfvmXf5lvf/vb/Nmf/Rlf+cpXbu8bHiBAJ7GE3dwmUhq5uArcDLTqhsUDBzO8OJvvGTxnop5LTdje0c+etxFiKMmxGXHstrJwu+1MNwteJUHgS68vIAjt9qC7nef20RmUb7ZJTOdiJCIy6+FW19B1pjLRngempunQMB2urdc4PZHu0ufYzsFwu0H6birLW92Xzr+BlKbw5NFhHj2cAyC6ycFqp397d+K4xYD73x/oxQ0+16eiCrhu0D3Sjzc/fNfIls9xmOMF/ETSTYitvUBTvOT0drqItvsc3i7O78f1Q3GVZ86ukIoqfY0t+q1hK+7ox+3Jlki3v7MIeIeSmmEFDkQAuumNa5TqBifGkzsKpPciybIXXUMJTebhQzn+8LlrzOXr2I5L07KZzsX4xScOcXoi/b7l8d1gwP0DvF9xqwWU8D7XazTXP994253LX721GHTwCK6Li8tIUiOmSp5BTsvuvmHYQXKl3OjNW+G1C3gJGF+mIqKI5OIRcvEIiiRw74EMmiL1jXE32zMfacXInd9TJIGHZ7Isl/W2a3Xuf/4eWNZN/u33r/Kh48OBpIaf/BEFeOzIEN8+t0pUlYJmBN95Trfsts+iUyz9UmuyAQgSXf5eFz6vlHWT1+cLbeef9yKn98L+K4eE8C//5b9EFEU++9nP0mw2+cQnPsH/+r/+r8H3JUniy1/+Mp///Od5/PHHicfj/OIv/iL/3X/33wU/c/jwYb7yla/w9//+3+d//p//Zw4cOMC//bf/dmA5+i4iTCxz+XowKzyWjDCZifK1txZ5cCYXiDqDR5bTuRgP5qJIothV9fz46TFems0HhBeRJdJRhcl0lGrTCjLRsDWJbzl6J4o9Rb3h9rY5bha8unhjH2GXJR87nefu/N1wUL7VwQrYdJ74wnKl6zX8iohueofQzpbYrQ6GOwnSwwe2cIJTlUQMy+m6T9sdkdnt38BOqlrvxXELHwPuv/PRyQ1hsdEzU2m+9vYyqejN57LzmYkoAs9eWGvT5+n1HIeD3o2awfHRZCDe6Sc6/KByNBXZVhfRTveAz9w7Sb5mvGOc34/rXbwxiE4tO9ic57fLHb24fTQV4TP3TfDytTzg6Uj5SaVsXA2KOuBxuO32Gjjoj1tJsoQPAU3TZrXc7Ckcvl1R+Kpu8eJsnuFEhIgstnXPvr1Y5vREb02SAY/vHgPuH+C9iFstQoT3uc7RXP9845+T/uKVG1xavSkbMZ6K8Im7x7myWmV2vYbZ6niKq16HkN+5k4r2d0Dz175RM3jy2DDPXV6npJuBOUMvjt6uqHXnntn5Pd20+e6F1a5pkF77X1h/yrCdIAEWTv6sV5t84GiO0WSE9aoRjLHrls3Tp25+FuG9SBS8jmfbcRlNaTiOS1KTkUQB23Hbziv+Go4Mx9vW+37hdMF1d7jrvw9RLpdJp9OUSiVSqdS7vZz3DNYqTb57YYVSwwoe7I2awVBc5cc3Sgy3Egr1psVQUkWTJSKyyEMzOQ4Nx7sezKpucXm1wjda7gWSJHB9veZ1jIRcE7YKUIN1hbL9GzUDxwVZggOZWJvz17uVhb6wXOGrby71/F4yInNto9aVlPHx1IkRHpjO7uraAJ86M9GVcOm3SWz2vVfnCjx7Ya3t2iMJ1XOGqDQ5NBRrS45lYsqmpFzVrcCtrxO9ftd/ff/QG55b1xSRv/0Th/nQXaNt9+Vrby0xFFdx8SoW4b+RT94zcUstruH1iwJtr5OOKTx1YjRoVd7pZ7RbvJ/57/383vcKVd1idr3G6/MFgIDn/SCx13NZ1S3+4tV5JEHAclwquoXS0tJTJYGffXSmr/6QLMLh4Tg/vLJB3bBJRGSSmsJoKrLt5ESwXkFo4394d/aAfs96MiLzzXMrHB9N9OT6fjy/E+7ox9/+15dLDf7vF65TqJttSSXwDjx/+4NHuO9gZlvvc6f8HUZnQioZkfn+5bWuEQUfW+2BAOeXyrx0Ld/F8/7fQj+O3cn72AsefyfG6N7v3Pd+f/8DvDPYLB7eClu5wk3nYlxarfLq9ULbiFdMlXBclw8cyvHclQ2ubdxMOsVVmalslOFEJNBY6scn/tebpk21aVJr2kQUsaej6k4LBJtx2E72v/Vqk8ViA8t2+al7xvnmuZW2rqzwzz55dIh0TO17bgnH4uMpjW+cXeH8cpm0pvD4sSFGEhEissRatUmlYWI7LiMpjY1qk3LDDATVO7FXsfk7hVvlvvduymyAfY98zeBGwWttDOtTuNCaA/aCuFPjKb5/cZ3FUgNZFHhrscTpyXQXQSU0mfuns20uY7IookgCumlz/3R2SxL3ybCzk+rJY8NUdJORlMa19XYB6XLD5KXZPIWaiek4t00fYbPW2nRMJlnrDqZ97GSeuxc6W/k3mxne7HudlV1R8P77+N1jfPfCGpbjYlhOW0Vks/u6U22LuCq3VTjC0E2HlbLeJvZaNyzGUxrPXV7vcqx78tgwjW2M6W0GvzL0zLkVNFkKXsfvuKg2reDv/lbFIAcY4HYgoXnOnL1GYqH3c7lQbKDJEt+5sMq5pTJW6yQ/mY7yxLEhLiyXeOjQUPDzvSqgHzgyvOMuoq0C96giMZb29oDOxO/zVzZ46oS0K9H/rdCP6yOKGIz49UI/nt8Jd/Tj7/DXP3ZqrC8naoq07cTHbp2TenX9RFoV/c4RBR/b6Xz9jz9e5JXrha73tFzWW5petz4Wf6s8/n4ZoxtggPcCbkVfp5d9ffh8Y1gOL1zd6ClKfXm1yt2TKT54fATbdZlvaS3VDIuJtMbPPTbDSDKyJZ/0162T25Iym+m/fuqM1+Hr7weiAF9+Y5HFko7a6ggNF4K2u/+VdZMLyxWWSg3A06JaKDTaurLMkD5VOqb2/SzCe9FQXOW5y+usVnRkUeDBmSxnF8vM5etkYypRxeuY+qkz41R0i1TU68ruHN3z8V6PzQeJpQHeNfQLqJqmg+24rFcNDmSivDibZ7FFFFbr66vlZs+Wwl4BLBB8bbHY2JbeTkpTOB2yvFwoNvipM+N86+xK22EiGpFIazLfPLvCK9cLQSZ8LwK7rYLxrWaVq017T+a5e2GnDnf9EN4w/KrAc5fXWa8ZnBxPMJ2LI4kCpyaSnBzvrWcRxk6D9KlMtLUxd4shjiUjNM32cbioInUdoMBrx33u8vq2K/Ob4UA2xqfOTPJ/PX+NqCpxfDQR2K2GW2lv12c0wAC3ip0+l7pp872La21JJYDFUoMfXt7g6EiCE62EbydPHh9NBjwRrr5eXK3sSG8tvAdcz9f56fsmAfj628uIgtcV9f1L6ywW9UCvYj5f568/cnBHvL+dpEs/rk9EZB4+lMWyO6+6Oc/vJXdMZaK8cNWzu3ahzSHPsG0UUejq3um3P+42ydIrIRXWAukcqd5qD/T/FnwtDR8+z/uV6M3u03ZHom/lsxiM0Q0wwHsH290L+iVDXp0roPTQX7Jsbw9tWi5LxQafPD2OYTs0LJuoLPHwTJa7J9OsVZr82UvzrFaamLaDgDfqbFgO3zy7wmfundwW33TysV+EEQSPz/7g+1eQBM/9rdgwuLHR4OREkhuFBrbjBsYPW41lh/c/v0DcbG2Gk+koDcMmHVWYy9eZykapG3Zwf7baA8J7kT9yF5El7pmK8cpcgVrT+36hbjA5mcJ1YbXc5GcemAK8OKGXkRO892Pz9/a7G2Bfo19AFVFEmpZNEpm4JgdJpTAquokqi8Ghv3MMzj+IyxJM5+JcW6+1jV5spbcDdFlerlWajCbbu1XuGk0wV6ijiCKacnPOdyeBXa/NpNgwtlWF3Cx4/fhpcU/muTuxW4e7XghvGIootHXouAgUWq//5kKZk+O99SzC2GmQntBkfuL4MNfWaz2r7ctlve0wY9ouJb13Rb2km5j23kwW52sGgiBsqpF1uz6jAQa4Vez0uawbFnOFeltSCTznt+v5GjXD4s2FIhMZbVMdpt3qrfkI7wGCIATB5tGRJP/+5Tkur1bb3uNQQt2U9zu5Xt2mjhT05/rN9op+PL+X3JHQZJ4+1R70+65AHzk5wnfOr2078bHbJEuvhFRYCyRcpd5J52unlgnctPbezn3aTnfCrXwWu+3wGmCAAd493ErMv9m1mqZNVJWIKiKNEGf5hY+oKmJLIoVQwrxm2MQ1hRuFOi9cWQ86ccK6eemogm4luLzarYnqI8w3YT4OF4zTUYXzyxXqhsVwMsJMLs5SucH5lTJl3eTkeJKLq9Wg01STpTYH1s32vyut34vIEgdzMR6azvLDqxs8etjT64WbBh7b2QPCe5Ev0i2JApOZKD+4tE42ruIP2Jm2y6HhOBFFQhAEpjJRUtH3b2w+SCwN8K6hX0Al4AnQ1Q2766DuP+x+oFg3LG4U6rw0m+c751eD5ICmiMzk4szla1xarrbNuvYLareqlpqW29WtEtdkLq9WiasyY2mt7efDRNuvEtHr4BNXvXGLzmppv3X3C15vRUT6djrc+et8c6HImwultg4dH9sNkncTpKejas9quz/uED7MmI7DkZFETz2mIyMJLKe782k32E7l/na7EA4wwG6x0+dSFG66i/mwHBfdsIlHZDaqBuWGxf/z6gIPz2QRBTMoHPg8ud3qqo/tPHNxVWYornJlrdqWVPJ/f27Da41/c6FIRJE25XrDclivNvuuv1dyqhfXJzR5xzy/19zRb6/ZaeJjt0mWXgkpx/U4/NHDOY6OJtAUadt7oP+3oMpiT74H9oxjb+WzGIxDDzDAnYW9iPn7XWskoVKsm0xmoiwWG23JpZNjKVKaQq3Z3t6aiSnk4ipffmMRVRKCpBJ4e26hZiCLIlfXqhRrvTXpfOOba+s1BCCmSIiCx8H+GNlKpclkNspiqUEurqKbDueXy2Ri3hjbYqnBQ4duXt8Xw95qLNvf/16Zy3vJHVmkYdq8cHUDw3J4bb7IyfEk909nGElGGEtFt7UHhPci31AIvPeUiiokNRnHcRFFgfGUFuj4DWLzQWJpgHcR/R4+23X5xScO8ccvXEeRboqBhh0M/HZGWfS6cmRRaEv4+KQVj8hBhbFTE+PNhSJnpjLBQ75VtTSiiF3dKmbQYmrj9Oh79BNfPa20j4/w/cvd1dy5fJ23Fko9hd92WoXcy3nud9LhLqHJRBSpZ4eOj+0Eybsh9KlMlB86blu1Pfx74cNMXJW7xiR9lyBVFvesxXW7lfvb+RkNMMBusdPnMhNTOToSp9wwqRkWjkuQVJrKeC3tmajIXL5O07S7uLJYN7esrs6u11BlMUj2K2L3CEEY/rP1hiJSbHRfVxa9kdpr6zXeXixT0a3gPfbi+opubrr+d5rn95o7eq1hN1p9uwnI+yWkHBdMx23b57eDMP/24vv7D2b3VL9ot5/FYBx6gAHuHPQbXd1NzN/rWhs1g4dnsrx8vcDxsSQNw8a0HcZSGh8/Pcarc4W2xJLPq/maQbFuMp2LdnG25bg0LRtJFIgo3e6jYeObE+NJzi6ViasSh4a9SRF/jAy885Lv1ApQbVpBYsn/PnidQUeH40xlo+RrBheXK5vq1iY0mfFUlFevF4P4/cRYKuBsF7hrLMWZA1tPPYSv6e9F4bHqqCoylFCD96ApIpmYGvzeIDYfJJYGeJfR7+EDeOrEKJIosFZtkm+RbbVpEQm1MyqS4LWsR3qMHTQt4hGZmCohCQKjKY2NSpOVss61fJ2G6SV9xlIa46koubi6abU0qSld1UtFEoJ2UaeHwaKf+OpVNf/S6wuM9hB69S0y+w1V3c4q5HYPLHvhSrNXQfJOCX0nh5nwAabThWkvW1x3Urm/leThAAPcLuzkuZzKRDk6kqBYNzFsB920MWwH03IQBBDxrIGhFbQKXrU27NRYbvTnybJu8vp8gfWqERQchpMRak0T3XS6Oib9Zy6hyYylNK7n623Xk0WBTEylWDdIxxQishgEuP243mh13fbj+tvB8zvhjt1w/G44fTcB+V5XiDv5NzwSmYkpHO6wkd4L7IbHB+PQAwxw56BfB+dWMX/DsLr4t2k5PRPpy2Wd+w6ke3ZpHh5O9OTVV+duGhRMpqNd8iOO4zKWjDCSiLTxTdj4ZiwZwW8DqBk2c/ka07kYG6FEWVQVycbVwD1UEgUs2yWuytQMz/lVEgUeOJjhpWt5bhQbXN+oY7suEymNv3b/FEdGE8H1wvdEFUUODcWYy9c999g94OzwXnTXeJIfXFonF1OZzsbaTHX8WGEQm3sYJJYGeNfR7+F75HCOZ86t8OhMrssdy3cMWGtlwsOtij4kUUAUBObzdZbKOl9/a5maYTGVifIz90/xvUtr/ODyBpoicnoizXg6Eszj9gpOa027q3qZ0hTumUyzXjO6RPPCia9eWC7rPR2E1NZ1mlbvsarbXYXs3NCycbXN0WEnOiGbYa91P3ZC6Ns9zNyuFtf3eyvtAO9NbPe5TGgyn7p3gqZt8/K1AqWGSalhMpmO8pETowwnVN5aKAPe6LQsivywY0z5M/dOUNbNLqt5Pxg+MhzvMgx45FCWpWKV+UI9sKnvfObGUlGGYxFOT6RYLDYQWxXYpmVjOS6TaQ2BdvTiejW0X/Ti+tvF81XdYna9xmpFR5EExpIaU7doHe1jt5z+bndg3Sn8e6esc4ABBujfwblZzN/KwXQZIAwn1J77m+PCWtXg7imJB6bbR9f68apfANBb3bNhwySAyUyUJ48NoypSG99UdLPNOTvsgmbZcGw0yXja5NJqBUUSGY5FgqQMQESWkESB6VwM03Go6RZHh+O8dC3vjbQZ3t4UVSXWKk2Wyzo/++hBjo+mempSyRJBp1Snpu5edOKenkizUGxweirFDy6tY9puW1Jpq9fZiwL8nYD33jsa4I5Fr4fusw96geLJCa+tsWk6KLLAUMxz+ynUDIaTETKazHgqwnL55sEiIoscGYlx74EUw4kIn753gosrFZaKDb70+gLHRhMU6mYwy6vKIi9fy/OZeyd72lRXdaurW8W0HT501wgvXy+0WT77JLPW4R4WhiqJPTcSXzA0XPUOX/d2ViE7DxRl3aSqWzw8k2W5rKObO9cJ6Yd3O0je7mHmdrW4vp9baQd4b2IngdWBbIxfeuIIT91VY75QAwQcx6Wie8L29xxIk4hKTKaiJCKeRsVUNkZVN1koNbBtl6puoclSW/dRRTdJawoC7foPAC9dK/ATx4ZJttZ0/8Esh4fjXV2LgggfOzXW9ru243JsNMEHj49wZa1df6kX14eFoTu5/nbx/I1Cnb987QYvXysEXbhjyQhPnx7joRlv3OtWnMduN6fvZYX4TuHfO2WdAwxwp+NWEwP9Ojg3i/mnczFenM1TM7qtP6+uVTk9kW7b33x0FiY2W7tfAHBdWCw2ODme5KFDWUzbJap6CSHdsklEJObzdY4Mx7Edl4Zpc2I82aZJGoblOJyZynB+2RtLL+lmYKiwUmkSj0hMZqM0mjYfPjGC47rYjks6puC4LpWGxWqlyey6Z9hxcaXCSCrC5dUay+VGlxuqZcNKSecTd4/TMO0958JeSabtcu5uizN3IgY7zwD7Aps9dCfGk9wo1Hl1rkCxbgYzvWlN4fGjQ1zfqFKOqnzorhGWSzrLJZ1LqxXuncrw9mKFWssuWpZEHjiY4Yljw/zlaws8OHMzm++LgedrJvma0TM47RUkO643kvHrHz1K03S7SKZTKC+MpKaQjsqBFocPVRZ5+FCWRERmPdRGerurkJ0HinDbq68Lcn65smc6IXDnBMm3q8W13+u8XyofA7x3sJvAKqHJnDmQJq5K/N6zV5jP19EUkUurVdJRhc89fIAfL5SIyiLRiEy1aYIAHzkxQrFu8vBMloVie1t/KqpwZirNclknF1fbupxsx+X6Ri3QelNlsWfX4tOnxrxu2g7R/6F4hCtrla4AuxfX+8LQVd1q63C6XTxf1S2++sZSW1IJvNG8Z86u4Dgumah6y85jdwqn98K7NcqwU35/v45cDDDA7cJeJAb6dXBuFvMfzEW5tlHvvBQCkNYUKvrmsgz9HLPDa/fPNs+cW+GxI0M8d3mdi6vVYEJkKA73TqX53WcuM7tRC17nqbtGmC/UGUneNC4Ka9nmawaLxQYfOTkSTDX4hgoRRWQspTHWkiFZLNT5+tkVbhTqXF2rUagbTOdinBpPcX65HFy/YTgUagYvXyv0TKrVDBtBELq6tfYaOx0j321x5k7Ee+edDHDHYquHLuzuE05umLYnOJevmXzz7RWiitSqLiv8whOH+cGlNWpNi/WWq0G9afHGQglR9Egy7DgXHmPr1LYIB3kRUeSxIznWKk0s22UkqXVVtMPYbBRgNBXhkcM5qs21npuVH9S/W8F454HCb3uFm7og74ROyH4MkvdTIuf9VPkY4L2BWwmsqrrFs5fWSGgySU3m7FKZcsMkpkq8NFsgqkr8yYvzaKrEcEJlKK4ymY4yFFdZLus8fWoMTZECHnVdl6+/vewJO1sOd40miGuy19YuiSQ1GdN2cNze/FXVLWpNm/sPZqkbFpIgkI6p5OIqX31zkV4TzP24fjoX48N3jWDa3UWJdxoLxQZz+XqX2xl4fF5t3uS8zbBdU4V3m9P3E4dvhgG/DzDA/sJeJQa26uDsFfNfWq106QcKrkuhbvDkseEuvb9wYaKfY/aREU+nKLz2A9lY24RIeG9LRKSupBJ43U0rpWZLy1ZuGy0v6SanJ9K8fK2AIgl88PgwtuMitq4Z3uequsWr80UUSSSlKYFe7VJJp9gwOT2R4o2FEuDp2jZMO5g06Uyqwc09ab9w/q0WZ+407L9ddYD3HbZ66MLuPuHkxtHhON8+v0pCkxlJajQtm4gi4bgucxs15jbqrFc9MlVkEcX2kkuFmsnxsUTgOKe1xMB9hFtIw0FeuFPKnydORetk48quRwG8aq7WN4H0bpJN54HCTyL5aFrOvtEJeSexnwL991vlY4D3Bm4lsPJ/N6V5Fr+K5AmATmVjvHQtz4fvGkEQoGnaCHjB6LfPr/Lzj8+wUjHQFKnt2lXdIhVVKDdMRlIR/uqt5UBPQhYF7plMe92vZb2LvzbjgpFkhKdP7Z7rbzdqhtXF6WE0LYe6Yb0nnMf2E4dvhgG/DzDA/sNeJga26uDsvI4AvDibb+us9TWNVis6P33fJIIg9JTu6OeY7Y/Qda69XwHg+SvrXUklgCvrNR44mMF0vH3EHy0v6SZHRhLolh00Alxbr/Ho4Rym43Z15Ibvb1JTSERkSg0Tx3G5ka/z4HQG8ITFa7pFruXCZvbZv2KqvK84fy+KM3cSBjvUAO86tnrowu4+4UA4rskslhocURNIokBMlYnIIi6eeJ3UcmyzHBdRAE2V0A0b23VJRGRqurWpqn84yAt3Sulmk+curwejX1sFfFttJPuhmtsLnQcKtUOcPCKL74pOyE6rELdStdhvgf77rfIxwHsDtxJYhX+3aTlBMOk4LqWGieW4CIAL+NvDYqlBw7R7cpCf7H9pNs8PLq1TanjPk+/uuV4zeO7yOh85Odr2u9vhgjuJ6+Oq3MXpYURkMVj/dgW4b1eFeCevs984fDMM+H2AAfYf9joxsBO35Rdn85T0dk5YqTSDPepAh9GCD59Lejlmh7t9trN2f4/shO24vDZf5P/zoSOMp6OslBucX64w1UrcnF0qdU059OLd8P1VZZGT4ymqTYt6S1fKsl0m01EePZzj3HKZoyMJxpKRLsMk8PakXFzly28stnGpYTlcWa2yVmny1+6b7DKoeCexWXHGsDzX21fnCvu6k3YnuLNXP8B7Als9dIokkIzIRFSJiCxyo9DAdtxglE0WBfypZJ9oFEnAcSEbVynUDCzHRRYFYhGZlKbwgSNDvDFfJBGyle7UtggHeeFOKWgf/dos4OsMgI+PJndMGu9WO2fngSKcRPKtRW+3TshOqxC3WrXYb4H++63yMcB7A7fS9RJXZQzLC4RdFybSUQzLDtrlZUlAlkQs20ESuTmK5tKXgw5kYxRqJq9cL3DvwTRzG55FsW+DXNJNDuaifauqnQhzQfjQsFvuvh2cP5WJMp2LMV/oHocbS0ZIRG4mxbYjwH27KsQ7fZ39xuGbYcDvAwyw//BudW16e4DNkZFEUNj20WuPCsPnkl6O2XCz22c7It+ZqNLrEoCXXFJlkRPjSWqGFYymbVSbXfuKP9UQ5t2qbtE0bZZKjdYoukI2rvLYkSGvUcCFqWyUY6MJVio6h4cTlHSTp0+PsVLS20TN/X0gXzPaON+fNvHXM5HWsK5u3LbupX7FGd8M6cpqNdCn3Y+dtDvFILE0wLuOrR66uY0637+8hm46DMdVHjmU5aXW3G6Y8MMjbU3LYjKtcd1yODQUx3ZdLNslqko8OJPh0UNDPHpoaNOxhHCQ12tkIDz6FQ74fGIuNQyeu7SO0cOScruk8W62c3YeKMJJJN8VDm6fTshOK897Uaneb4H+e2EsZYD3H3ZrOw8QUQTWq03m8nVM22EuX0eVPOHPo6MJ1so6ogAJTSEekYkqLvGIzF3jyZ4c6fPzQtHTphhJaIwkNCq6iWk7KK3gVhI7RUE3f9YbhhVcu25YwQhDr8B3M+6+XZyf0GQ+de8ETdvu6wrn8+NWnVi3qytoN6+z3zh8Mwz4fYAB9h9uZf+6FfjcldIUTk+kt9yjwvC5RMDj9JUOh2pFErvW3m/veeLoMIeH4j3H4aZzMY6NJtteE3qfmcJTDQ3DCl5PEQV0w+Z6xTPnODaa5MRYku9dXGOl0uTccpl8zWA6G+NDd42gWzYPzWTJRFVuFOqsVnRM22UsqZGJqqxVbs5O+NMmpu0GeoquC7Ik8tJsnkxUvS2O053FGcNyus5SsD87aXeKO3PVA7ynsJ2Hzs/Yr9cM7OUKJ8cTyILAQzNZZtdrXSNtCU3lsSMpvnN+lVeuF6gZFrIocGoixXQuTrFhcCAb27RSGSbJXiMDYZL0A74wUfpz0f7aUpqyI9LYDy38vQ4UubhKvma8K2KzO6k870Wler8F+u9WgDPAALeC3drOV3WLZy+s8fBMlqZps1JpMp7SgkDssw9M8UfPXyeiSIynNGKqjKZ4DjvHR7uf7XDgPJxQuRR2vukQAe18tjfjglajE3/+yjzFuslIQg1GGHzuh625+3Zz/oFsjF964ghP3VVjtaKjSAKjSa3neMVm4xu3qytoN6+z3zh8Mwz4fYAB9h92u3/dKtrOILK45R4Vhs8lGzVP5Pu5y+ttAt7TuVjb2jfbe354ZZ2ff2KGP3zuGnMhsfDpXIy/9eQhRpKRttcs1s2uM5M/5eBDU6Tg9USBtjW6rsP3Lq5RNSzOHEijyVKQVFsoNvj5x73XvFGo8/zVjdCaS2RiCg8czCAKnmu393suDxzM8OJsnsVSg7vGktQNm7FkhMPDce5/hx3koPsspZs2V1arLJf1LhfZ/dZJu1Psnx11gPc1tnroOjP2Hz05xnAiQlk3+cGldc/Rp6MrKCJLrJR1ZoZiWI6nqySLAtfWa+RrxpZBepgkw2Ng0E6SfsAXJubhxE0ba18s766xJA3Da/l8c6HImanMpq+/X1r4ex0o/I3kdmKnlee9qFTvt0D/3QpwBhjgVrEb2/mFYoN8zUQUTB49nPOccSwHUYBq0+LoSIJP3TtBoea1kSuSFzB/6t6Jrut2Bs7hSq4vZtpLa8/HZlwwnYu1dSdZjsvsRg3bcdENm3sPZoJDwGbc/W5wfkKTOXMgDaR3fY3b1RW0m9fZbxy+GQb8PsAA+xO72b9uFbfCXWEuWS7rwf4JcGoiycnx9I5GvWVR5AufPMnl1QrlhkUqKnNsNNl2Fgi/pmE5bdIZvuGRv3bTdoPXc1za1qjKIqVGnumheLAnh5Nq+ZpBNJSY8kflDdtho9qkYdgcGopxdb2OYTscHY4HSaVwsm6l0uQbZ1c4tguJkt0gfJZ6da4QjL/1wn7qpN0pBrvUAHuCvdCE2OqhC2fs4xGZkxMpAE5PpHuS/fmlMgCyKBLXJEQ8kddcXKVpOlsmdzqDPL9rKuwKFw74LizfdK9rdswWlxom19ZrlHWPLN5eLHN+ubLpeMNeB+v7xXpzt9hp5XkvKtX7MdB/NwKcAQboxG74ZKfi1T050AVFkchJIrm4yt/9yPFNnwV/nTcKdWRJZCShslEzuiq5vphpv2d7My44mItybcOr5JZ1E920yLeSXaWGSSqqcCAXCzqX+nH3XnL+7eT729UVtJvX2Y8cvhkG/D7AAPsTCc17Fr0kjEG+1iSmykQVaVf8uhVH3wp3VXWLWtPmweksdcNCEgTSMbUvl2xn7zkxntyyqBzmr8eP5lgp6zRNJ2gS8Ne+2jGa57gEZ75kqwnATyr1WoufCOvUTwKYL9T5Ox86QrFhslFtBkZPcVVmOhej2rz5XsuNd6c76E7qpN0p7tyVD7Bv8E5oQsQUieGElwCKqBKC67JRM4KWwfBD1+uwcqNQ5z/+eJFXrhcArzp9eDjOdC7G9y+vYzsul1YrWyZ3OoM8WRRRJAHdtLl/OttG0mFiDgvm2Y5LoWYE+k/gjdGtb+Eot5fEE/6MRMGzBY0onk7JeCp6RySZdlq92atK9X4M9PeTu9QA7z+8E5zfK8iOqzKiAOMpra2VH7xuo3um0ps+C+F1LpUaXN+ot1VPw1XS0aTGoeH4ps92Py64uOoNRft6DqdaRQ8fuuW0dUX14+694vxrG1Vems1TqlvB/mm7Lk+feme0+W5XV9BuX2c/cvhmGPD7AP9/9v48xrHzzu9Gv2c/3Jcia6/qqt7U3VJrabVka9q2bMvLjMaTTOLMzXLfuTNZ/kiuM8DM4CaDAEGABAgGCC4QBMibCS5y3zg3wLw3Y9zMElszY41sty1ZVkutltVS793VtReLLO7L2c/94/CcOiQPWSSLrGJ1PR/AsN1FnoXk+f6e57cSRg/bnqxkq44zw7Yn7z7K9KSv3drQfrSr07HbvW+/tqfZfs9EfU5zbq9rr8h622MJHO058c19LRVFa5jW7caqEqng9YvT2JqX8PFaDmcn6gM1ZM3J3LL78h5GdlA3tuyoJgOM/hUSRpph9IR4tF3G+493sJaTIKk6fBwDlqZwYsyPjYKEsK/zQtW+pqJrRKaPZ3B9OYcH22Wcmwzh3nYZHEN3dZ3dLvLcwuwus5A13ZlKp6CxjK5TecOgFuvu76h5kyZyNC5MRTAeFkZ+EkGv0ZtBRqqPykL/qBoiwtFhEJpv/07zVQWGaUJgafzkXhqS1ljS/OqZJBbG/Hjr9nZL89GCpOLaUrZtGnvzddo9H+xRzS8vxpEuK06U9OXFsa6ecS8tsLXfnh5akTRMR3zYKNQAWJNLq4qOkqTi1HiwrXYPQvMfbZfxB1cfNvTCsDc/b91O4ZuXBt+b76CygvZznqOi4YOE2AMCYTDY9mS7KGMpXbYyZxkK2yUZ7z3O4gs96GuvNrQX7erHPpclDaZpQtUNFGtW6w93ttBetmcvJ1mv+5ugYGUWuQdfNF/Ler7WMq27mWxFwcXZCGRNw/dvpRpe6+7LexjZQXvZsnxNObTBTfuFWBjCvhh0T4jHO2W8dSeFNz9NIV2WUaip0A0Tp8aDCAgsToz5cXkhjqDItl002dfk4xmYpomyrIFjKJRkDRVFw4sLsYYJcoPqXeEWSneZxYO06mw8mmuNgfblDftdrHuVgNAUGiL/kmrVJvMsfSQmEfQavTlqker9cJgTBAnHh/1qvjvqu5Quw8cxEFgGlxZi2ClLCAgMaoqOfFXF1ftpPD0dRkHaPZ9umKApq8/bSraKpUyl3iOo83W6++SlSjLc/TL3m1Vja/9m3ZH0MFPBy4txq4G3K8AR9nXW7n40320HeZrGjdUc1vO1hte4nWnDSvs/KK09Tpq+H4g9IBAGh21PSnVb9CBddoLXn24UMBEWMBkWW/S1LGlYyuwOR5gIiQ39hZrZ736kV/ts60SxpuJE3I93HmSwmqs6Qye6GbDRT6Cpk617aTEOAB3t4EzUh7CPazkusBu8t/dW5yYjuHIq4UyWtafq8WzrZLxh07xv/caz0y3DkIDdQSBujsrEuNG9MsKRYNA9Ie5slvD9T1NOpNcWjmxFwfXlHH77zFnMxvwdF00VRUNRUrGULjuT2CqyjqqsQaw7m9wT5Hq9znY0C6VdZvHSYhwcQ0MzDKDepM49BaDZW96N8OwlKu1KQL769AQylcbeVWp9LOiwmsMOOmq6V/TG63xPeqR6FCYIEo4H+9H85qgvS9NYyVaxkq3i7nYJC3E/cjUVv/LsNDJlub6Q15zBDWVZQ0XWoBkm1rI1mAA+Ws0hFuBaNsvN18mztNMnT1INyJqle4PIqrG1P1dRsLxThW6YuLGax2cW4zg9HsRORQHP0nhpIe65qe9X85vt4E5ZxmZBwgtzUdxYzUN3GRrbmTaotP92uj4Ire2m98iTrun7gdgDAmGw2PZE0Qzc3y43VEQAQLas4MZKHhend4Mca7kq/vjGGj54nGsY/PPVpydQljUEBe9ncD8a3Yt9btaJ5ibfz8/FsJgIDG3I0F5BAq82JOmSjKqsYzrqw9cuTGAtW20pkbeD9yxN4+5WCVVFw8t1Z5U7Cyrq5/Dq2eSBZXV22re6PyN3v95mjsLEOGJZCPuim7rcbhwLZUnDzfU8ypKGdFkGx9BQdcNxegBAqiQhW5X3XDRdnAnj0/WClalE05iKiBA4GjsVDixNYSIsolBrFN9BpUJ6CWU8wOONmxvIVvYub+hWeDrRqQTkR3fTOJUI4N522Xm9u5Z50LXGBx01Pa5R2lGZIEh48mmn+XbvNknV8eFKrkHrbRuwVazh4XYZJgCaorCSrUJSrYXeWraKS/NRfLxewI/ubuPVs0mkywo4xmriGRI5rOaqnqnvb3y8iS+cTaKq6g39mZpxTxc9OxHERNg3sGyX2Zgfv/7KAubiPhRqGgSWBgVgOVt1mpZ6aVC/mu9lBxXdwHZJQrGmOiXfbmTNGIitG6bOHlcNHyTEHhAIg8W2J5KmtziVAIBlKKxmq8jX/1aWNLzx8WaDUwmw1uFv388gIDBQNMOzQXU7je5mL9VLr6T1fA3FmopkkLcmrqoGTIpyetryLN0xU2k9X8NSpoydstxSPmez156iU5DA/ls7m/Clc0l86dw4yrIGWTMcm7tVlEDTwIPtkjNUg6as6a2X4j4wtFX+JnAUrt5NN+zNhmVrenH2H9SU1WFBHEuEfdGpTjYesLKN/l8/foitogS+nn7Y3M/HFo3bm0WcSgawU3csRXwcFJdjyTBMqLrZcdG0kq1iNibCNOGUIORrCubifogsg4iPa9mYDDoV0ksoXzu/d3nDoKKMnUpAshUFSde1uUsCgcFOIjjoqOlxjtIedUNEODp4ab7du+2D5RzW81JDn6SXF+P44HEW2YqKkMDi/nYZhmkiGRSg6AYYmgJNW13nNN2KlW7ka07UdDwkIurn8HC7tUnnREhATdXxzsMMfDyDTL1fkr3o9LJNPEvj1HgQl+bjA9eDZEjAK6cSePNWyrkW+3q8sqL2o1ledpBnaAgsg+2ShBcXYi3vifjYfdu6YerscdbwQULsAYEwWGy7ZxhwAt82s3E/smUFAZ6FVs8SXc/X6oGT1kDIdknGpVgMqaLkTLq2abcf6dbh3kufvqqitR2MceV0ArU2OuG+lkSQx/3tstOzKCw2lqftVZGxV4ZQJ5tw9W4anz+dxNX7aZQkDaX63wI8g4mIiMeZivN6wwQe71iOv197cQ7AwZab9eLsP+oT40b76giHRrcPf6c62csLcfy/f/IIy64moiJHQ9KCzoML7NbR8gwNChRCIoeSpDpjmm0BDwgsxkNi20WTPSHgVCLg9LfYKNSssolcDZ9ZHMPCmB+r2SqSYdG5zoMYOdxNX4hBRRk7lYAAVsNBoLF5HTB4B9tBR02Pc5TWnp41FtiNPLmnKY66ISIcDv2UqXpp/liAxwfLOQRFtiFquV2U8e13HuO5eg8kgbMiij6OQaooIVdVILA0dMOEpltNvAGApinImuFk+Xz1Aot0U/PuiZCAz5wcw1t3Ug2lbcDuovPVM9aic5gNpZvppQfQfjTLyw6GRA4BgYFm8DBNs+Fv83E/XlrcvzNtmDp7nDW8E70+p0d9Y0IgjBq23dss1BDxcSjUVKi6gdm4H5dPxHD9cQ7zcT9CrqwTd2DcTU3RkawHRdx42aaypOFxpoL/eWMN2YqCoMA62UFeTpBe+vT5OKbFqQTs9uR7bi7acu3Njh73oCL35FP7nN1WZLTLEOpkE7IVFaphtthb0zTxl59uNbQcsbHtiP2/vRiGrenF2X9QU1aHBbEuhBZ6ffjblX9dvZtqcCoB9hjIMkSWcR5uOxUz5udgmiY+dzqB25vFhqk2ER+HK6fHsJgItDQmtbEnBPh4BjdW8zg3GcKLCzGougmOoRDzcQiJLJ5/fhoixxx408+9+kLsJTyFqoK7W6U9F5d7lYBcmo+BAgVVb5zANOjN1kFHTY9zlHYm6sNCIoC3bqVaIk+vXZgYeUNEOHj2U3LUrPmSqjdkKtmUJBUr2SqerTuWKAAnxvz4cDkHH88AsGzCWIBHIiRAUg3MxfwQWBpRH4vPnkogKLIIiiz+2nPTmIqIDSnv97ZKzthigaWdiCXQftF5EJrfbQ+g/Wi+l867gwhTER8qsg5VNzARFvGrL8xgYSzY3w31cM0H1R/kuNDPc3rUNyYEwigyG/PjN15ZQMzHIy9ZwfCSpGItV8NEREQ8wOP0+G7WCc+0loYBgAlAMwz8teemQVFUW9tk92iSNQN/+emWc9yTyQCmoj6ERQ7bRRk31/MQOMaxEVEfj1dOjiFVkqDrJpIh0bNXkqqbDYMx3BQkFare6plpdvS4BxWlSjJKkoqxoDCwioxubEKzvf1wJefpVHK/p8OfndcMkl6c/Qc1ZXVYjPbVEQ6c/XT4b20+5v1g2pPI7AfXnYqpGwaeno4g4uNweSEGnqFhAJiL+fDq2XEA7RdNim5gIiSAZ2gkAnxLf4mFMT/OTYVxcSba84N5EGN7OwlPUVKxnK1g7eGO82/tFpftPh+7BOSlhTG8tDDWdrM1qHs96KjpcY/SrmQrLYuEgqRiJVtp8w7CcWUQJUduzf9wJefZX8GO2NrZRDsVBZ87k8CjdAU1VcfCWAAsTSHq53Hl9BiuL+dQqCk4PR7C83OxBm2bjfnx7qOdhpT3mmY5lewpMM14LTp7Ydi6vx/Nb6fzYZHDlVMJfPbkGJ6bi3Z0pvVzf8PU2eOu4c0MY+LSUdiYEAi9cBDrc5uZmB9feCqJb7/zuCH4PR/34zeuLCAZskrbZqI+zMf9nn0BJ0ICAjyL2Zi/JTvJDiQEOAZv3dnGB49zjrMKsBwtj9LWmk4PmXiUKSMgMFjJVsHXpz8vJIN4nKk4zpWov4pYgGt1LBlGw0ALG7uaQTNaM66aHT2G2dj0ezwkYiERGFhFRj82YRB2ZNC2pldn/1Gefjr6V0g4UAZZjiVw3t56wJpEZqcsulMxeZaBYZj48rkkogEeqaIMgaXBMzSu3kvjxmoeX70w4blomgyLmIn6sJKt4BefmcLDdBn5mhVVKEsqxoJ8X4sq9zhOu9QIAM5PhXBuMjKwB72tw0wzUJY0yE3Gqd3isttFpdf3OMjGqQcdNT3OUdr1fA2aDicrzT1SVdNxbEtICN4MuuTIayGnaAZ0wwTP0IgFeIgsjZ2KgnRRxoXpME6NB1GoKkgVZSzvlPEf3rqHqYgfv/TMFKYjIt55mMFkpLH596lkAKmiDFnVreaiDN0wBaaZ/SwOm7XQbv45F/eBpumBbGD2q/mddH4vve5X64eps8dZw70Y5sQlAuFJ4DCa/T89HcE//8VzeLBdQrGmIexjcXo85DiVAGsd/vqzU5B1vWUq3GsXJvDiiVhLdpL7PhJBHjdWcijUVKd9hT1oxx54sbxTwVquhrKsYXmnCj/P4MOVHObjJXzudALpep+/dnuFAM82VDO41408S3fttDFMOOd6eXHMU5P6zUbtxybY73Hv2ez2EEGecd5zkLZmL3sNwDM7+Siu24mFITQwqFT0AM821N42M1F3Ai1ldrMsGJrCC3NRXFvKIlOWsZytgqYojAV5pymcWyC9yu/+4pMNJIIi/vLTTRQl61o1w8RczIdfujjd1ajn6absHdup1Nzk7q9up3DlVAKvPzs1EAPWTng4hsLlEzHPjVO7xaW9qFzKVLBdksAyFCZDIqI+vu35B9049aCjpsc5Sms/tzxLtzSDBI5nCQmhPYMuOWpe/BUlFY/SZVRkHSxD4aNla4F85XQCmmHAMEy8cXMTIksjW1HAsTROJkKgaWApU0EiyCNbsbQtIDANz7SiGeAYCp87k4CfY3F/u4SV+uQ1N90uDr30H0CLU2kyLOKHd7ZRkFSnj8R+NzD71fx+dN6+5361fpg6e5w13Iv9PqdHdWNCIHTDYTb7T4aEBkeSF7MxP/7+L5zEF89a+swxFMZDomemUvN9yKoBSTWQqygo1VQsjAVwe7Po9JxNl2SkihKen4uipuz2airUVNzeLOKzJ8carsVrr+C22902Ee/V0WPb152SjIDAQFJ17JQU+IXGfoztgkD92AT7PdeXcw3tIUSOxuWFGPI1pd678WBtTTtnf76mtDQSP8qTUI+XlSbsyaBS0WeiPvzMNBtqb23m43786gszCIpsQyrmfMyP95eySJdlXJgK4drjLMaCAiqy3tAUzi2QzYumz59N4n//wUNsFXfPJ3I0BI7BTx9mPGuYO0U77IhhMsi33IekGljJVgdqwLyER1J1/Ojudtua4XaLy3xNwQfL2a7FahiNUw86anpco7SkhITQC4P+vbgXf9tF2Umtn46IuLwYx4/ubqMi6/jpwwz++vPTKMs6rj3OIh7gkS4rqA+FA0tToEDhxRPWRDNJtd7j1iV7MfrpRhG/9uIcogEO+Zra1+Kwnf6/MBdtGCk9FmjUf7uPxCA2MPvV/F51Hti/1g9TZ4+rhntBdJ1AaM9RaPYfFFlcnI0AiLR9jdd9CBwNlqGgGSZWcjWcmwwhX1Wxlq/CNK1AfNTP48SYHxXJcizZU1U1w0RZbt0XNO8V9uO06eY9tn1dyVaxkathKVNBxGdNir29VcSJsQDCIrdnEKgfmxD18UgVJIR9HHw805DBb9vsw7A1zc7+J3ES6tG6WsLQ6dcb3ZztExRZvHZ+Am/dTjm1t7JmIOJj8dJi3Gki6k7FjPk5fLCSg6IZqGkGlPp/0iUJmsE7i3mgvTNFVk0kggIElm5J62w2NN080HbE0AQ8M69U3Ri4AfPqV9WpEZ3X4rIfsRpW49SDjpoexygtKSEh9EK/v5dO2Z32Iu3meh5jQd5psL1TUfDURNgarqDp2MhLCAksTNPqwVSVNYg8A5G1SuaY+kQ4wNKcbjYO/SwOO2nk92+lMB0RnfT+Zv13j5oehP73q/nDbIja6zUPkuOo4V4QXScQ2nOUmv13sp1e90EBGA8JWM/VYJomfnwvjbm4H+enQ/BxDGZjPrz/OIfvf5rCV+qlVCyz22mQpamW5ttee4V+nCvdvMe2TXagSVYNzMf9WMlWcW0pi3OTITxKl3HlVKKrIFCvNsH6rHXPDH63zT5sW3MUnKO9QhxLhAb68Ua3i5TOxvz45qXO4uNeOJkAqrIGirJKAex6Ys0wkasoTl0x0D5SV1G0tuVAQKOh6eaBtiOGzb0ubOxr7NeAddN0sJ/FZT9iRaKjRxdSQkLohX5+L930sgiKLASOaWiwDVhZRiGRw+pmFZJmQORozMR8EFgaVUWHrOqI+nlHTwWWBuPnQFNeLbl3sXW3n8VhJ40s1lRMRUTn/zfrP9c07acX/R+k5h9kQ1TCwUN0nUBoz1HRsb1sp9d97FQUXDoRA01RSBUl7FQUbBUlzMb9ePlEDD+5n8GjdAWSqsNtnQI8i4iP66k0vJP97JQ80Mnm2rbJntYNAGVZw0zMuoZnZiM4OxnCSwvxoZR7HRWn41G5zl4YjaeOMFL04o3eK1K6l/i4F06qZkCrq6GkWqM4VcOAppvgGAqJoAiKAsK+9gLZi6Hp5oE+Mx5C1M+BpVs3OCJnbZaaj9stbmNDU1a5hcDRmAiLmAz7GgTcXWZSklQouoGpsIhXzyYHFpEeRhYD4eAgJSSEXujl99JLZkw7DbYXmAJLAyYQ9XEICiyemghhp6LAx1n9F8qyhqBgZbZWZN3zWDb72Th00khb123cgyjcut/rdQxa84fREDUe4BAP8J6NRAkHD9F1AsGbYWX09bum7aZfn43bdnrdh2ECWwUJnz0ZR0BgsTAWwHRUhKqbyFQUPDMdweKYH1fvZcDXAx01RceLJ2I4mQwgX1XB1v+9X0f0fhqj27ZJcWX3mgBKkgZZ07FVlJAuynhqYjjZOEfF6XhUrrMXjt4VEw6Ebr3RXuyVvuclvnYJxWdPjaFQVXFizI97WyV8uJJHSVLBMTRemI/hrz0/3TJNwU0vhqabB9pe4L+/lG1oRG6P47QbuPZqwNwbNbsxrN3DQ+RoXJiKYDwsNGR/felcEu8vZVGoaU6ZyU/up8GzdIvI9yNWw8piIBwch53WSzhadPt76UXv3RpsO09M7DbAjvo4rGQr+OqFCfyPD1bxg7vb4BgaAkvj9HgQv/ELC7gwFXGmwQ2rFKiTRvIsjfNTIdxcLyJfVZ1BFAVJdXS/1+sYhub3uyhtp/XxAIfLC3F89+MNoukjBNF1AqGVYWT09bumbfe+izPhrmyn132EfRxeXIgjW1aQDAn4y09T2CjUnL9PhEX8Xy7P4tRECBEfB4GlERZZPDcfhayafTmi7f1ZvqpgJVsFR1OgKTgZUN32/rFtE+/K7pU1HbmKAs0wUVN03N8uYzlbwXTMN3DbclTKiI/KdfYCcSwR+qLfSGkn0Y4HBLx8Io7VbBU/uZfBZlFCImg1p/PzLKJ+DqmC1HHiTS+GptsHejbmR9THYzERwPfrE+Lsvk39GjD3Rq25MaykGihJKkSOxvtLWeQqKqqKhpVsFbJqoCJrKLmGBXmJfL9iNawsBgKBcHTpRe9tDX7rdgoiyzjaZpomwiKH+9slfPXCBP7qdgo0ReHsRAiaYWI8JGA8JOLTjSIuTEUajjWMUqC9NPLcZATnJiNYz9dQUzQ8MxPBtaUsKq4JPL1cxzA0fz+LUi+tjwf4FqcSQDSdQCCMJoPM6Ot3Tdvpfbc3S1A0oyEY4ca2nZ3uoyxZtqBQazx+WdKwmpPw2VNjmAyL+85mdO/Pdsoy7m+XMREScOV0AltFqcG5tFfvH9s2KfXS94q861SajvhQkTRMhATIqtHw2Q6qAuKolBEflevshaN3xYSRoJ9I6V6i/Y1npyFpOhaTAXy6WcQiH3BK0HTDwHhIREXR9z2xxhauqqLhhbnonpsF+/UmgL/+3DQU3YRmGH1HAiqKhrJkTZrbqSiejcENw3TGW19fzgFATyK/H7EaRhYDgUA4uvSq97MxP16/OI3//u5j+HgGZ8aD8PEM7qdK2Cpq+Mn9DAI8i/W85S0RORrjIdFzyEK3G4deF6TdauRTkyHn2J85OYaqooGhKET8fNf6X5Y0bBVrCAksBJ5BWGSRqSgNr+lX8/ezKPVqGk40nUAgHCUGldHX75q20/sANAweasZtO9vdh6qb2CzUnP5EmmE6e6NUsQaBtSfP9Y5t2yRVxw9up6DoJniWdkrYUiUZ7zzI4OXFuDPMAti794/bvkpaEJ+uFxyn0suLcaSKEn7xmSlkqzJ03cQn63mMh0RcvZ8eSLZsWdJQkXVcmo/1ZbMPkiet3PloXjXh0BlGQ+lsRcFr5yfwwzvbqNYdPQoay86A/U2sac6YoilgPu7HpbgPDE23PNCDKvVqPs5OWUaxpuLK6QQqHmNBZ+N+J6Lt4xnYffh6Eflhi9WT2HSOQCC00o/eZysKKIrCZGT3b4vJIB6ly9jIS3h6JgygVd8B77HInTYO/ep0NxrZ6djdaKn9/ofbZdzfLtfP68MLc1HcWM1Dr3uL+tX8Qeo80XQCgXBc6Vf/Or2PglXS5kW3pU6qYTi2026E7d4baYb3cKG9cNu2RJDHz5ayu/bYVcKWKsloHlLaTe8ft21aPllBSVIBClBUHeenQvjLTzexVbSC6jVVw0q2hqBoTQq36Sdbdr82+zB4ksqdvXPzCIQ9sL3RUX+jYHZ6eLsR7dmYH8/PxXBmPIiFMT/OjAdxYSrSIDT9NjPzypgyTODxThU314s4Mx5yxk+2ez2wK3RlqbtFttdxQiKHgqTinQcZJEKNkQyRo+HnGSeLiWPovkXeFqsX5mMN9zYInsSmcwQCoZVB6X1Y5JxeQnYmU7O+A71px351upNG7vfY7veHRA5ivQl4tqLg2lIWpxIBAPvX/EHpPNF0AoFwXOlX/zq9b6ei4Gs92k6v49u202tv1I8uN9s2e/KppBp4lC7DxzOOvQIAWdt1XvXS+8e2TSfGAljJ1rCyU4NuAN//NOU4lQBAYBmsZKt4lC5D0RodZXa2WD/35T5GL/s2Qv+QVQKhb3qNlHYr2ouJAE6NB3uKjrvTOauKBpqiEPPzDeUQvaa5DqrUy+s4PGtFBR6ly1B1w2kM7kQgdGsb4Z5AJHK0E63oV+QHyZPYdI5AIHgzKL3nWavsbToiwivQ2qt2rOWqYGkKIYGFT2AQFlgUJBWSYoBlaKzlqjg3Fe76eG72awPc73drPgAUaioCIjtSmk80nUAgHFf61b9O7wv7OJweD+H0eKjvrFL38ZtL6vrV5Wbb5p58KqkGaoru2Ct7mmsJ7R1ie5Wiu++huf2HyNFO4MTuN9h8n91my7rXAwLPgDJN7FQUGCYp5z4oiGOJsC96Sd/rVrR77Q+0lqu2NIq1F+vzcb9TDtFrmuugygLaHceOQET9PP63V07g7fsZqPX6ZoGjW0pEehH5g+BJbDpHIBDaMyi9Hw8LeGkxjrLs3U+hl5T3P/v5Bq4v58DQFF6Yi+L7GwVwNI2yrMEEsJm30uv7mTqzXxvQ/H5b862FM49EQMCFqcjIaD7RdAKBcFzpV/966dd3kNfViWbbZE8+tR0+qm5gLGjZJ46h8OxsFCLHeDrEuilFd9+DnR0F7Jbz+XnG+TdVb404dZOV5V4P2DT3KCTl3MOHrBIIB0YncXz1bLLF291tw9Y3b6XA0VTLlJ1H6TJElnHqc3tNcx1UWcBeY61nY348NRnChamIc78+jkFN0Ruaitubkr1E/iB50prOEQiEweCl94pmgGMoPD0dhqKa+Maz08hWlL7HIr9Zn9IJAKcSAVxbymKjUEOAZzET86Gq6CjW+p9otl8b4PV+nqWdaOxnTo6BZ+mR0nyi6QQC4bjSr/4NWzcHffxm27RTUXDldMLZR3H1UuzxsNCxV2Evk/Tse7i5nsf97RI4hkbEx2EqIsLHM3h5IQbNAEIiC1U3nGEV3WRlNa8HbJp7FJJy7uFDPmHCgeIljgJH4erdNLKVVm/3Xh5+O50zEeRbJqvZKZU8S2M9X+s5zXUm6sPCmB9lWYOsGuBYChVZx2q2ivGQgHiA7+qee8nUap7s1izYe4l8twxqpKfXdRMIBALQqPepYg2pogRZNfDuwx0YZvc674Wt/XbvooDIYqNg9WGwo7F2WdleKfDNehgL8MhWFEiqDpimMynHTTeL3b20fzERaNDdUdF8oukEAuG40q/+DVs33cff7xq+2TYZJrBVlPDyYhwCR+NEPNDVFLVey8WDIouLM1Hc2SqhWFMxGRbxzoMMtksygoJlwxfHAvjC2SS2ipKVePBUa+JB8zU1rwckV1aU3aOwl7LBQe6RjhvkUyIcOM3i+J3rq115u72wNxDu1Eo3dkplVdF6zpjK1xSs5av44HEOhZqKXEXBeEjEl8+NIxHk8cbNDbx2fu8Ff79prFEfj8sn4tguSeAYCuMhEbMx/77FbVCT7vaCCDOBQAiKVmT1pw8zfeu8F7b2272LTLO5vTUaysoKVQV3t0otetSsh0VJRVnScPlEDNslCdNRHz5YzjVMq+m2BKFX7R9VzSdaTiAQCKPBINbwXrbJMAHVMPHFU4muj9NPubh97veXsvjhnW0nKUAzDLy0GIeum1jOVvHXnpsGz9D44R3vknn3NTavB9wT9Gy67Q2VryltP9+ojx+YLXxS7erRvwPCkWa/zVHtdE534zk3djqnnf7YbcbUwpgfa/kqNB04OxHC40wFIZEDS1N4vFNB2MciW+l+U2SfdylTwXZJAstQmAyJiPq8s57aGw52X8LTS9rqfjgo5xWBQBh9BjUIwY07lT8scoj5OcQDPAzDBE1TmAyLjiOoKKlYzlaw9nDHeU/Uz+FL55INi1ZFM5wFqazqeHkxjq2ihOdmIz1Fcd3Mxvz4xrPTeLBdQrGmIexjcXo8hGTTNNBR1Xyi5QQCgTAaDHINP4jyun7LxWdjfuQqKq4v5+DjGXCMlV3szgxWdRPvPkp3da/N6wG7l6GqG+AYGs/PxVrslZdtc+/93BRr1rWmClJDuXqzLezWWfQk21Xv3TiBcEDstzmqnc5pN55zY5dBNKc/ukczz0R9+OGdRqcSAJRlDR88zkHRrOkIRUmDohmoKjoe71Sd2t9exmDmawo+WM7i47UCPlzO441PtvCd66tYy1Ubzz3EcZndbPD2Cxn3SSAQ3AxqEIIbW/ttWJrC4lgAQdFyMEX9ltNe0QyUJa0lqzVfVfH+UhbbrpHHJUl1opx2+rxhAumygrWchIifx1OToZ4W3mu5Kr778QZ+9iiLW5tF/OxRFt/9eKNB90dV84mWEwgEwugw6DW8ez/Uq20DWu2wm71Kz1TDahA+GfFhLCi0lJtvl6Su77X5OuxehpMRH06NB7GYCDS8v51tc+/93IwFeLx1K4WVbON+zW0L13JVfOf6Kt64uYmrd9P43s3NA9/jjQLEsUQ4VPbbHNVOqdRNE1dOJxznkj1pwO5P0U4s24m0rBpOjybFY0KBe/RzN5uiXoRkmM6fYWzwypKGu1slfLiSw72tEtZy1ZYGejaDcl4RCISjw6AGIbixtd9eTNrNR+fj/oYSOI6hcPlEDDsVpeUYhaqGkrSrVc1aLzctLnvVx251f1Q1fz1fA0dTSAR5hAQWiZCAZJAHTREtJxAIhINmGGv4/dBsh226KRffa13AMlTHv7vvtdfraGfbVG137+fGhBVs8ppYl6+qWMpURmKPNwqQUjjCoTIT9SHAM1jJVqHoBnhXOmS3jdZmY35885KVznluKoyqooGhqK5KFtqJtF1ap9avqeXv9dHPQHebol5KQYZpOAa9wfNK51R1Ayfifme8ZzNk3CeBcLxo18TanhInqTrubZV67jHglcr/5fMTDZPmJFXHj+5ue2qRwNENC8VmrXfrPNC7Pnar+6Oq+YWagmtL2YbBGO7xzUTLCQQC4eAYRpBmv/RbUrfXcIuJkNjx/c332s112KVq67kqbq4XsV2SUFN0mLBs21efngBDUy0OJDvjmfPYDwLdZVcdxB5vFCCOJcKhYD/cqWIN01EfijUVH67moRsmRI7G5YVYV81RbfqdxtBOpO3SOrvu1z1lYCIkgEJvm6JehGSYhqPXyXidaBeNL9bUhvGezZBxnwTC8cKOJr51OwWGomACKEkaxHrfoh/f24Zm9NdjwEv73f2L7m6VPJ1KQF3nw7uLV7fW2zpvE+AZmKaJD1dyXTfa7Fb3R1Hzy5KGt+9nWqatusc3Ey0nEAiE/um1gfMg1/CDpJ892F7DLaI+3rlXmrLK0UxYTp5IvadiL9dhB8I5msLVe2nc2iwiwLOYj1vTv1MlGW/fz+DSXBT5pqoLgaOd9ipe9JJdNYrOwUFytK+ecCSxH+7tooxbmwXIqoETY3783ZfnkC7J4BgaQYFt29h6kLQT6Z2KgtcuTCBVkJCrqhgPCciUralwnz+TwM/X8shXVVw+EXOi4Z02Rb0IyTANR78T6rxoF40PiRxWc1V47eUO0/ARCITDYzbmx+dPJ/EnH61jI19DoaZCM0zc2SziF05ZGTCDHiIAdNZT3TTxq8/P4Op9q0GoPVHGPRUuGeRBUQDPMri+nINpmtipKAj79naCdav7o6j56/kaVN1sGd0MWM4lgaOJlhMIBEKf9NPAeZBr+GHQq6NsrywjOyAlsgzeeWAFOuxWJ2VZ62myqf2ZJYI8clUVLE2homhYyVYxE/OhquhY3qnic2eS2MzXGpp0BwUWlxdiLU29gd6zq0bVOTgoiGOJcKC4H253o9THO1XUlJST5ZIpK31NCuqVdiId9nF48UQMmmHg/aUsxoI8nqYpVBUdtzYKuDQfRa6iNJR7ddoUdSMktiBXFQ0vzEVxbSnrOX1gv4ZjEJMggPbReHtz1syoGD4CgXDwlCUNV++nQVEURI7Bas7qI1BV9IYMx36nxLWj00L8tfPWonQ8LDboYTzAI1tRUKgpuHo3jQfbZRRqqpMub5eC7eUE60X3n54O4537GSi66fSHOkzNryha29HNIkdjIiwSLScQCIQ+2M90t0Gt4QdNv5POOmUZzcb8eP3iNP77u4/h4xmcGQ867VJ6CUS5A+GyaoChKcQCPHIVpWEvI3A0Ij4WX3rqRMvnm68pXWVXNeM1QKp5TUJTwHzcj7m4D/e2S11nRY8iR++KCSNHLx5q98MtqTqqigbdMMHQFDYKZkOWy0HVmbYTaQD4zvXV1lIvScOf39zCy4vxlhKL5k1Rt86iZsGyReZS3AeGpgduOPotHXTTKRofFjk8PxcDz9IjZfgIBMLBUJY0LGUq2C5J4BgKHE1huyiDZ+mWJtn2BDabQWv/XgtxLz30cQx++jCDQk1tSIt3l4KlmwIgXrawU3TZrfs0ZZXwCXWnzUTYd6iab+u71+jmkMhhIny0o6oEAoFwWHTqv1esqVjLVUFRVNt9VT9r+F6ziXp5z34cZXuRrSigKAqTkVab020gqtl5BAACyyAZEiFrOgL1NYFt27w+36DI7pld1W0mmXtNUqtf27WlLB7vVFve20trgFGA7PII++LxThnvL2VRqGoQeAaUaUI3TScS3Iz9cBclFYWaiqxrUg9LUyi5Ouc315n2I4rd4iUid7dKnsKv6EbLRsiNvSlq9t63cxYBrQ4sw7SyuPI1daBlIYNkr2j8YiIwktdNIBD6pxsdXstV8cc31nBjJY+ZiA8BkcVYgIfI0fDxDHws03Jc9wS2YfQY6HUhbi/8vaaCuvW/nd4DuwvDbgIXhgmnJ11Z1nFpPn6o+unWd3t0s82TkK5PIBAI3TLo/Ue7jH+aAibDIv7s5xsNjaL362Twsk/xAIdXn0pCVk3P++olA6mXAUW9Mohm1+5AuN1DN1WSwdAU/DyLqJ/DWFDY07btlV3VSyaZfayypOE711cbkg6AwTjlDoOjc6WEkePRdhl/cPUhVrK7Hla7TOCt2yl881LrwxDgWSiagUfpMmiKQoBnHdHQDBNVVYOuA+NhoeHh7jfFcj+0LfWqi33zKGobP896eu/bOYturhXwcLvcMhUP2L8g90O3BnTUa70JBMJg6UaHy5KGNz7exI2VPM5PhnFtKYuNQg2TERFL6QouTIfxi09PYjVfRUXeXUjZE9hGxWlh67/XVFBgV//b6T3QuDD0ClxsF2WUJLVF+4el+71sjoi+EwgEwnD2H+0y/scCPN55kEHYxzU48/fjZPCyTzQFiCyD//0HD5EICi3l11Ef39amvXU7hdcvTiNbLyML8izy1dZBPW4Oe5q1O1CyU1Fw5XSioWdTSOQGYtv2CmB52eBhOuUOA7IyIPRFWdLwJx+tNziVgMYyAa+HYSbqq09SM0DByuBZyVZRUTRMR3woVFTEAjy+emECgLX4llQdP7idaug9AQzfm9tOzOzJQc2jqIHdTVG3QrGWq+Kj1Rzub5edv9uN6cL16QMHOXqyVwM6qrXeBAJhsHSb6r6er1nNMCM+x6kEAKpmwM8zqMgaPlzJ4+tPT2ItV0W+oqIsa6AwWk4LW/+bp4LaCCwNpke9d5Mq1nBrs9DSu8jW/kHrfj+bI6LvBALhODOsEq92Gf8mgIKkYsZDk922pN8WJDa2AytVkiG4MlLt+3rl5JinTbMdUv/93cegqN1JaLMxEUVJdfYtzRz2NOvmQMlWUcLLi/GhlZ570c4Gn0oGOr7vIPeAg4CsDgh9sZ6vYasoef7NLhPwehiCIovPnUngcaaCVElGWdYwE/NhPCTglVNjqMgaLs5EAeyWCSSCPH62lG1xuADD9ea2EzOepXF5IYagwCJT3vXSuzdFXtlO7nGZjzNlqJqB+9sl0E1TKiXVyui6MBUBz9KOIA+zFNA+fj8GdBD9mggEwmjTrfOkomhQdAPxAO84lQAgX1NxdiKER5kK1u6nMR4W8MHjHKYiIv6vn5lHLCCMlNPCrf/NDawnQgKCAouXFuNt9d5NTdEa9JunaVQVHareWFAtqQaW0mWcmQhBUnV8uJIbiNbvZ3NE9J1AIBxXhpVN0i4jFABOJoMNQXQ3NUXrOUjgZZ9MWHs1AFCbyr3zVRWpkvf+znZI+XimoeeRrBooSxpElmm59lGZZt1LoKTf/Va793WywamiBEUz2n7nw2gNMEyO1tUSRoaKorUtEQCsMoF2D0PEx+PlxTjM+usElgYFYHmnCsO0HDfuB1CuL+abHS42w/Lm7iVmUR/fVqCas53sumk7QnBm3JqaVqyp+NrTE5iOiNgo7Aq5pBooSSpOjQcxE/UdSCngk5aOSSAQBke3fQ4CPAueoVucJlEfhztbJfh4BomgAB/P4NxkCCGRw+2tEn7txejIOJWARv0H4DSwDvs4fO3CBE6Ph9rqvRs7cODup7RTlqHqBj5/OoGfPMhAr0+BoACwNI2tgoSH22Wn59J+tZ5oO4FAIPTOIPr7tMPL0aFoRkPAuhmRY3oOEnjZJ9mVKct57OU03buLrO2QsvcwNjsVBZdPxLCerzX8+6hNs+4mUNLvfqvT+yqy3tYGy6oBjqE8/zYqrQF6YXRWcYQjRYBn25YIAEDEx7Z9GGaiPvzUMJ2HzF1OFvVzUHWz4QG0O/gDuw4Xd+3xML25e4lZO4GaifqwMOZHWdYgqwbGIyLeuZ/Gtqued6ciI1WS8eatFL7+9BT+4pNNJ4IAAGEf55QEDmvagpthGlACgXC06bbPwUzUh/m4H0W5Ua84loak6vBxDAICi7ifh1E3HaPq3Oh2MRsP8JiNiQ1DLHYqCgzTKvdungaq6AZWdqqgKQqX5qJ4fzkHAPDxDFTDwGfn4thxDbbYr9YTbScQCITeGUR/n040OzrKktax7Kt5f+SmnR31qr6w91X2fqSZ8ZCIqL/aci5ZNTzfY5jAVlHCa+cnIHLMUEqnvZxCg67k6De7d6/3XZqPtT3nTkXB584k8OlG8YnoZ3i0rpYwMsxEfRgPC5C0xhIBwFpI2yUCXuyVCbTtcq4AjR38gca0zYPw5vZTCpCvKVjLV/HB4xwk1cAL81HcWMnjZDKAqagPPEs7GV9bRRnZqtySxfXSQhyzMT/ubBbB0hRCAtuyaRnkhmzYBpRAIBxduu1zEBRZvP7sFH5yfxtzcT9W6334GIpCLMAjJLKYjohojs+NqnNjL/23o5Qr2apjC+0hFpKmYy7uaxghDFgNwU0AS5kKLi/EMBERIWsGYn4ON1cL0HUT8QAPWTUaNL9frSfaTiAQCL0ziP4+vdDr/qiZdi1Imo9p97gNiqxn6dpiIoBYgGu5joifbVuqZ5hWRtVBBYiGUcnRb3bvXu/rtL4xTKuSp9eMrGG3R+mXw78CwpHELVQiy6AkqVB1AxNhEb/6wgwWxoId398pEuyeFASgpYO/nbY5qt5c23Ot6bvlEz6OgcDRKMtWDTLQ2BTWysTaFR7Gz2E25sdaroo/+/kGrtcj2sDu5L2togTDHNyG7KANKIFAODr00udgNubHrzw7izPjYbxxcxPZigKashZQ0xHR0S83R9G54Y5ShkXO0XtVN7Cer+HXX1nAaq7a8j639ueqqqP9NIBIgMPHawVkXBlLtubX+tR6ou0EAoHQO4cxHbOX/VEz7eyo1zF/4VQCV++n295XUGRb3hMP8Ci3Kes6SFsyrKbq/Wb37vU+hqL2tMG9JDEcxqT0bjl6KznCyLDfmtd2D1HzIthOsbQ7+J+IBxDx8yPV6NWN23PN16ctxAM8/DwLVTedUj6etZqRb+RqiPk5wAQEnkGQZ/DSYhyAVQJXrDUKkXvyXrqsDGxDRsZLEwiETvSi+UGRxeWFOM5NhrGer6FQVbCcrUBWDccpbnNUnRvNUUreNV0HALIVpSFbSNGsUm5FNzAeElCsaRA5Gnaf1OmYD9duZJGremv+c3PRvq6TaDuBQCD0x2FMx+x2f+RmLzvqdczxsNjxvrzeMwq2pFOGULGmYi1XBUVRPWfz9Jvdu9f7In5+YJ/bsJxqg4KsJgj7YhgTY7wWwYYJqIaJL55KHLo3di+8PNfucr7mCQwBgUWqKGN5pwKOoTEft+7PFk6vXlb25L1Bb8jIeGkCgdCJXjXf/frpmA9v3kq1OJWOqnOjm+jmmfEQon6uoVTOZibqw7MzURimCR/PoqpoqKneEemCpLY0RO8Fou0EAoHQH6MyHXPQQYJ+7msUbEk722sPSvqzn280NCXvNpunX8ddN+/zygDr53Mb9WEcZEVBGElGQbj6xctz7S7ns8VO0azxnJ9ZjGOrKDmjOyuKjjdvpXBhKgwATmZT86YEwFA2ZKNiQAkEwpPFUdZ1L7qJbgZFFq+eSeIPrj5s0O+JkIDPLMZxYzWHb16yIowfruQ8tV7kLBugGa2DMnqBaDuBQCAcbUbBjh62LWlne8cCPN55kEHYxzVkD3ebzdOv467b9w3icxv1YRxHczVHGAqH2Qis3bmP4iLYy3Ntl/N96dw4To+HoBkGJFXHw+1yS1kIYImgYe7+Y3P/Do6h8fxcbOSztwgEwmhxGDrffM6j7Exy0210UzVMPDcbwbOzEWc4AwU42m9HGAM866n1IZEDz9JHsg8VgUAgHGUGaTMHdayjuj8aFO1srwkru3fGY2/UbTZPv467g3L4jfowDrJKIQA43EZgB3Hug9xMBUUWXzqXxPtL2Ybx07pp4qXFuHNP7z3awZ2tEhTdAO/aPNj4ebZBON39O+ypDQQCgdAth6HzB3nOg3aaNUcpacqKmAocjYmwiI18DdNRH6qKhvW85PRXatZ7O8LoXiy7o63A0e1DRSAQCEeVQdqvdsd69UwSqmF2tFujMAFsFK7Bpl2GEIC2U+uA7rN5+nXcHYTDb9SHcRDHEuFQG4EdxLndYt688J8M+wYujmu5Kn54J43totw4Le/5GccQreWqWMlWcH+77LzPLncIi1z9/zMj0SSPQCAcfQ5D5w/ynJ02AFEfP7QFsTtKWagpePt+BqpuYi0nAcgj6udwfiqEu6liw0Qft97bEUbSZJtAIBBGg0Har3bHWslW8QdXH+K52QjSZWsSaLPjahQmgI3CNTTjlSGkaAYyZaXtew4rm2eQTrlRXyeQVQphX43A9npY9vr7sJuQucXcbur2zoMMUiUZIkfjwlQE42FhYOJon892KtnRaVU3cfV+GuNhEYA17Y2jKaehNwBIqoFH6bJzTYNs9kYgEI43B9HwsSxpWMpUsF2SwDEUfDwDgaFAU/As9x1Uk8l2i/ZiTcX15RxSBQkVZdepM+gFcVC0dPmnDzPO5E9b+6uKhkfpEi5MhvH+cs55j633V04lGiKMo9A/g0AgEI47g7SZXsdSNMPpp/fsbKTh2LbjCsCBBWfa7ddGeQpZc4ZQWdIQ4BmsZKst2cGHlc3Ti1OuWwfUKK8TDv8KCIdOv43A9npY7L8XayoSQR4ix+DGSg5jQR5zMT9mYv490xI7/b2bB9At5skgj9VsFdMxH5JhsS44LIq17sWxG0eZ1/QfkaMhaUGs52sA4Di67IbebucSx1ADb/bWC82bw4mQiJmYfyQEi0Ag9MewGz6u5ar44xtr+OBxztG+oMDi5YUYnpoIYadiaZzIMQAASbP6zLWjlwhfuw3AWIDHW7dSTiNPO2PVBPDDO9t4YS6GhUSg7XF7vQYv7TdNExEfhxdPxLGSrTpaDwARkcPLi/GWYw5D8+17yVcVGKYJP8/CxzGHWs5AIBAIg2AYZVqDtJnNx6IpICSyOD8VhqqbEFgGySCPnYoCw9x1XAHY07k1E/X1de/2ZyapOnJVGdtFGapuwjRN7FQUhH3Wfq4i6yM9hcxNvqZgIiLik/WCY2tFjsblhdihZPN0Cnq9v5RFrqJCNQwINA3FMJw9s9sZ1i4INqp9tshqgtBXI7C9PNjfeHbaeUAmIyI+Wsnj+nIOFUUDS1N4ZjqCrz0ziYW43zOa3encQPceYFvMaQoI+Ti8d2MDG4UaDBPQDQPnJsP44lNJZErynuLYzTnzVcVzepsdnS5UFbD12l+7offLi3GYgNPU9dnZ6KGllnptDidCAl67MIEXTwyuWfgo1WoTCMeBYTZ8LEsa3vh4s0E3ACBbkVGQVLxxcxOqYeDhdgUVRcN0xIeXF+NIFyWMBfkWXek17b7dBsAEkCrJ8PGMk7H643tprOSq0A0TH63m8dRECK8/O7Xva2in/WVZQ76qoibrLVpPtf1EB4t9L27HVyLA48WFGLIVBV85P4HT4yGiwQQC4cgxrDKtQdpM97FsW/T9Wync2SoCAB6lywgJLK6cTjhDHaqKhjZbI+c4hZqCnz7M9Hzva7kq3rqdgsgy+PG9ND7ZKEBgGczH/QgIDH7hlHUd7gnV7eg3KDXofYC9Ly3W1BZbGxRYRH1838fuF6+gl/39//DONq4v58CxNCiYeJiugKNplGUNQr1UHkBfWWGHucciq4gR5yB+HP00AmuX1lmSVGwWajgR94OjKSSCPD5czuHGSt5Z/GuGiZVcFW/dSuHzZ5OYj/vxeKfa9bl7Scu0xXwswONHd9PYKNSgGSYkRYdhmljJVvHnN7dwcTaMVLFzyV835zRMs2VjYSOpBnTTRMRlYAwTTl01AJSwG9E/aNptDlMlGW/dSsEwTER9/EB7Xtkcdq02gTAqDEvzh9nw0c7WkVQDFAAfb2nY+akQ3l/KIuzjkCrKjg3YKNRwfSWHX7ww2aLZ/aTdt9sAyHUd4xgaYwEeP7y7jdubRWj1SMZ2SUamLEPWdfz9Xzi5r2top/0MTaFQU8FzNFayNeffS/X/9g2554O7PNt2Ksmajk82CtguyTg3GcL/8fYSvnRuvGG4BIFAIIw6wyzT6mQz4wEO8QCPu1ulrmx1w2CGAI93HmSwXZIA7NqvVEnGOw8yVtClrOzpuBoL8Hj7fgYU1Rii2Ove7c+Moym88yCDlVwVmmFCUzSsZKuYifkarsM9odqLfoJSw+iJ6N6XNu+rMmXlUDKrvIJe9vefKsnwCyxW02WcnwrjwXYZAZ7FTMyHqqI7rVF6zQo77D2Wd9t0wkiwlqviO9dX8cbNTVy9m8b3bm7iO9dXsZZrdcLsB7sRWNTPNfx7p0ZgzQ9LUVJxa7OA+9tlLO9UcXOjgGtLWQR4FumS3PJ6wzCRKsmQVB1zcV9P5+6m7tnGFnMTQLaeYmo7lTjG+vlvFGrgWQapooSy5O157/acfp7FREjwfN1ESHDqYJvv133fh9XR3705bCZVklGWtYbPth/2WgS0+/wJhOPAMDW/H53vloqiQdEtp1JQYLGeq+FeqgRVN3Brs4SSpMEvMM4CmKUpSKoOhqFaNLsXfbdpp6kCR0PkrB4LmmE2OJV2r8PAB49zWMpU9nUN7bRfYBnMxf3wSk86CL2376UkqVZwwzCRqyjQDBMbhRoCIuvoO9FgAoFwlOhHq7ulnc2MBzhcXojjux9vdG2r3ceyM2kFlkHEx2E+7ket3gMwVZJhYtc2dNovCJzVv7XXe7c/M/s6dJdNtPdq9nUAuxOqvejHhu3VE/EP31vuaw007HL/fvAKetmfOwCougFJNZzv0X0PkmolawDdX/so7LGIY2lEOegfh90I7PWLU/jiU0m8fnEKv/biXFvvpvthcTegs/GxDFIlGQ/TZbBM64qapq1/kzUDDE33dO5exMMWc+e6ONpxKkV8HFTdjmhTkFWjrRB3e06RY3DldKJlgzEREnDldAIixwx1g7cf7M1hO2TN2LcwD3MRQCAcZQ5C83vV+W4J8Cx4hoav3jTTyU7VTdAUYJrAdlHGXMyHeIBHMiRCYBnImqU3bl3pZ3HYTlODAovLCzHwLI2SpDU4ldw2TFINJ3rc7zW00/7piIjXL07BbIr6HpTe2/dia7us6Q2fg72glTWDaDCBQDhSDNuZ4GUzX784jWtL2Z5ttX2s6agPC2N+nJsM4aXFODTDaCl5s21Dp/3CRFgEz7bfxre7d/szszN6Gbpxj2bbB9s+2xOqB7Vn2asn4kq20YnU7RpomOX+/eLlGLQ/d5HbLYfnXPvkRvtsvbbbax+FPRYphRtRDmJamleqYbfHdKd12pFQm4mQAJ6xIsWqR/MklqYgsFaphMDS8PNsT+fuVTxmY348PxfD3a0SwiKLB9tlVBTdWWgHeBanEkEsZ6tthbjbc85EffjZo4xnLw1J0x3P/ih29Lc3h+2wv6v9MIoRBQJhFBiU5u9VSjeMho8zUR/m436kilLDM84yFEIiB900IWsGRI5p6KcnsDRKaNTsfheH7TQ1X1Pw5q1UQ6PwAM9iPu5HWd69VvfCrp9r6KT9umni1bMTyFaUA9d7+15sbdebbLJ93/Z3QTSYQCAcFQ7CmdBsM+9ulRxb7R4IIasGWIbGWq6Kc236EgVFFrMxPyYju1k+/ikWJUmFqhvgGBrPzzX2M21n2ywnQb7tdbe7d/szEzjLJggsA5amHIcGS1NQYNkEpp6RNMgJ1d30RGymmzXQMMv9+8V2DLqDhnYm9clkEGrdeVeRrN6TG4Wa8/kDVhl/L9c+Cnss4lgaUYb54+im/rKbzYn9sGwWdj2gdmbOVlHCyWQQiqYj5hcQ4FmncXcswIOhKUyEBAQFtueHfabu7S/LGmTVgMAzoFxTDLyOt5gI4NR4EA+3yxgPiwAsrzBLUxgPCagoGgyzUYjdnwFP01gY82MlW21pNO5+6IMii9fON4pICd6e/VHr6G9vDldzreVw/X5XzYxiRIFAGAUGofm91tYPqp9TUGTx+rNT2C5JuJcq7UbcTODyQgwr2Sp4hmqIxE2EBFD163P3quhWa9tdR7Om2gvi21sFPEiXka33XijLu41RJ0ICxkOi855+bMxe2p8MCUi2KZMeJvZiW9EMiByNqrLrQJuO+FCRNOe7AIgGEwiEo8NhOBPcQ4Emw2LDZGcA2MzXHAdSN9fMszTGgoJzzYuJQNtzm9itqu7HTrnPT8GyfamSjFiAR66iOEF/e83/kmtq6aD2LM37ANs5R1FWAEQzTCia0ZKNtdcayMuJAxx+NUizY9DHMagpupXgULfLDzMVvLwYxycbBed9IkdjPu7v6dpHYY9FVhAjyrB+HN00urMjvHttTuyH5eZ6Hp9uFJ3orD3RICxy0HQDX7kwhliAw4fLORgmHKeSPWmsXXO5dhuefE3BWr7adnKZ1/FswVE1o8FxEgsJuDQfw1ZRajBCXhs0lgEWEgE8zlScDY+XYI1iNlI32JtDWdd7+mx7YRQjCgTCKLBfze+1iemgGzzOxvz4Wy/OYT7uB8/SMAEoqoFYvVHlvVQZIktD0QwnACFp1qS0N25uIFvpXWu7JSiyOD8ZwRfOSHjrVqphE2Drm/ue+7Ex9mcwatrvXmxLWhC6UUKxpmI8JOLlxTg28jUnGEQ0mEAgHCUO2plQljTIqo6QwCIe4PAoXUGmojS8pljr3Dy712v2stV2n6d+7JR9/rdup3DldMJxjM3F/ZiJ+jAWFPC1C8ObFOreB7idc9MxH1ayFdRUHZmyjJPJIMLibhlZN/veUbTBQKtTLiiyznd6MhnEo3QZG/ka/vbleWSrMkzTGn5ybjLS07WPwh6LMpsL/wktFItFRCIRFAoFhMOdxy4OirKk4TvXV9v+OPqddHB3q4Q3bm62/fsvPTOJdx/t9HTebq4VAJYyFWyXJHAMhfGQiNmY3/Me9poWYJ/LnkJnp49OR3348rlxVFW9bfS9LGm4s1XA7U1rJg8FON59e0PV6X4CPIMvnE2ipuojI1iDpixpXX9X/XDYEwsIvXEY+jcqHOS971fz99L21y9OOQubYdmXsqThrTupBucNQ1O4NBfFU1NhJyrp51mIHIN4gG9xKtl0o7W9Zlyt5ap4fylrRXhdY4jd09Dcn02zjZmP+/H3PnPiSGq+/VkVqgqqqoZiTUNJUmGarTaQQDjOug+Q+z9q2Po2TGeCvXbdLsq4tVkATVEoSxpeXozjxmoeumFC5GhcmIqAZ+kGm9vvNbez1ckgj5+vFZCoZzr1Y6fs80uqjqqigaEoRPz8wD67TvbZ/iw5msK1pSxSJRlPT4ewkZeQq9+r+7Pcz7pkVHF//yxNg2OsoSa+ff5+97vH2q/2PTnf0BPGoLzwzQ92vqp0fP12Seq5z0e313pxNgIgsuf1doq6v3JyrGHBr+gGeIYGTVF4b2kHIZFFpl7q4PUgBUUWlxfGcG4y0lbQO/U6qSg6KIrCC/OxjvdxlAmKbFffVb+MakSBQDhMvHRU0QxwDIWnp8PYyNc6Ok56KaUbZg+/lWwFBWn32Lph4uZGAQJP40tPnWy4/rtbpQanUrOuq7rZVmv7WTzNxvzOKON22uP+bNwlCoCl/4cxsngQNEdMD2IjRiAQCAfBsFtLuPcmPGv1x3mcqWCjUMO1pSzOTYawkqviZDLolHAVqopT4t1vv8N2ttoEsJKtQqjbqH7s1DA/s73ss7vi5ZONImZZGhXZymC+/jiHTEVxpqKdGg8eainbsBjW53/Ye6wn61t6wtjvj8PrwZ6NiShKakN6oZt2oytt2tW47uda3c4vWdWxXZQ9Jx3kqypSJQlFSW2YQufnGaSKEniWdqYY2K9vl47a6YF2b9CaNzohkSPNTQfAqPWXIhBGAbeOpoo1pIoSZNXAuw93YJidHSe9lNINq4ffer4GTQcuTEWcCCpNUaBpCmvZGm6u53FxJurosfs6mnUdAD5azSEW4Frut9eyPzd7aY8zRc1D+3mWfmL0n2gwgUAgdEezgycscpgMi8iUZVQVDXNxP4J1GwFY9mw5W8Hawx3nPb1m5pclDWu5KjYLtQYbBOxOFlPbTHI+TDvVrX0OiixU3UCuokDRDWi61R/qdDKIF05YAaWzE0Fcmo8/cU6lYXOY9p18UyNOvz+Odg+2rBooSxpElmlx3kT9XL2BaQHt6FTj2s+1Nju/QgKLW5uFltpaG0U1WjYfAFCoqWBpCmzT2Mx+ou8BnoWiGchWZDxMVyBrOgSWAUNTEDkar51P9nSPBAKB0C1B0XLK//RhpifHSS+19cPq4Wc7ZexMn2Zn0acbRdzZKjmLa/s6FM1b1wF43u8wMq7sAEdZUrFVqGE9X2sItNhTXEhzawKBQDheeAVjon4e8QAPSTVA05Szp1I0o96LqdGedRP4sLH3RixNYXmnCmDXBoVFzpnoxrWZ5HyYdqpb+1yWNBRqVusN9+e7slPFyWQAi4kgJs4MLtNmUMNKCJ0hn+gTSrsHe6ei4PKJWH1M5S7uHkb9Nv7q9aH1cn6JvFXWtpatIuLjIHJMg5c+IDCIiBwkdbf5qlZf/I+HRFQVHc306rkXOAolScGNlbwjdvY0u4mQgNVsDecmta7rl4mIEQiEXujHcdJL+XS3TqheNcztsPJyFgksjUxZcRbX9nU8TlcwH/MjILJQdRM8Q4OhAIYCMh73O+iMK3eA43QygK2ihNVsFbEA70zJkepBmXiA7+qYRP8JBALhycArGGOXxD1KlyGwNEr1f+cYCpdPWEOBmukm8OHeGyWDvDO5TaoH1i9MRUABmI/7EfIIwB/2EIZu7fNaroq7m0VEfFzDeyqKhkfpCs5OBNveRz/9FfvpO9TNeYitb+T43vkTTrsH2zCtqW2vnZ+AyDGeZWv99Hbq56Ft3jzRFBD3CzBME7c2S4gH+HqTV0u85+N+BASuYYoBALAMhemIDy8vxrGWrSIZFhvO04vnvixpuHo3jc+cHMPdrbLzOWqGCZFj8JmTY1jJVvc0DKRBNYFA6Jd+HSfdliR344TqR8PcDquSpDY4ldwj7d2L669emMCP76bxxzfWsVGwAh4BnsWLJ2KYG/ODplrvd5AZV80BjqKk4pnpCKqyju2ShGRIdCaZXj4RQ7aiIBkSOh6T6D+BQCA8ObQLxoRFDldOJfDSQtwZNCGpOn50d9uZaNrMXoEP995op6I07HnsvkO6KeA3ryzg2lL2QKbh9UK39jlVkvDhah4vL8ZxbSnr2H8AiPg4vLw41vOAJy/72m/pfDfnIba+FeJYekLp9GAbJiByTFvHSK/9kvp9aJs3T2MBHm/e2nIW9bb42pHiXziVQK4i46PVPM5NhXHpBAPNMBHzc1hKV/Dhah4nE4GGY0b9HOIBHne3SshXFRimCT/PwscxiAV4ZCtKg5d5PV9DtqJC102cmwzhxYUYVN0Ex1CoSBrubZUQDfAdDcN++n8QCATCfhwn3ZYkd9L5fjXM7bDadC0SJ0KCM9LextbQqI/HVrGGoMjiJB90ypmXMhVUZGvijp9nG6KCAY5BgGdQqWeo2v2QNMNE2MeiLKm4t1Xy1Pjm624OcNQUAzdW847+BwUWER8HClZQZq9NAdF/AoFAeLLoFIx59WwSqm7ChDVpmqaoFqeSu2efpOooS+2rHtx7IzsZ4OXFOEwAsmY09B06EQ8MvEnzfjNwus2I1nQTumE22Fv3fkvRWkvj+7Gv/WSA73Webzw7jZ2yjD/7+QaKNbWhsua42/rjd8fHhF76bXjRS7+kXh9aW7RyFQWJkADKtBq2mQA2ChJSJRnnJkOYi/tB0xQElkZN1XFzPQdJMVCRNVy9l3YymSqy1TgvVZIREjlHwMM+Ds9Mh/GnN9awXpCcsozpiIivXpjAj+6mERBZp5dT1M/h6ekwaArgWBr3tsst97MwZnmgO23shjlxiUAgPPnsV7+7pZ3Od9KwYk3FWq4KiqI8F57uaaMdl8UAAHAjSURBVC+fbhQhsLTjlHEvtm0NtXoZAYZpQtEMuOeWpkoyBI6GwFENI5dpClhIBPA4XcZmQcLDTAU+zipZGwtE8J9//BCfO53AJ+tFBJs0vjmS2BzgEDgaumE6+r8w5oca2f2898qGIvpPIBAITx5ewRiBo3D1bhpbBdlxHD07EwENE0Y9R9fda3AiJODhdrmh12AzzYElwwTS5V3LOBHetbeDbtI8iAwc2wn3/lIWZdnqNSXwDII8g5cWdxtxj4dEiBwNSTVa9lsiRyMZEluO3Y997ScDvNN5VrJV/OhuCpJq4Ppyzrled2/g42zriWPpCaWXfhv7pZeH1i1aimbg1mYBEdEqb6vI1ut0w8RKrupMWNipv+7zZ5KoyFpDWqhdbyxpOr715VPIlGTc3iwBEBH1cfjD91ZQVXQUJdXZ1AQFFv/jg1XQFIVUScKFqQh4lka+quLt+xmM18sc7LpmNzRFwTRNSKqOe1slT0/+Xp/HXiNICQTC8eYg9duLdhpGU8BkWMSf/XyjoWlo88IzKLK4OBPFna0SMmWl5Thu51hF0Rp6VbjL50SORjIo4urddMPnYJjAx2sFjAU4XJgOYzbuR1nSsJqr4r2lHZyfDOPPP9mCppsICEyDxjdHEpsX8RR2tV83TGiG6UzlmY/79+wz2G6Kj82TMlWOQCAQjhtuR05Z0vCd66tYyVYbbNdaroYvPZVEVdUgKWaDU8nO3DVM78EUwMEFlpoZdLZtuiRjJVuFqhvg6vbTzWIigMsLMXzwONdi9y8vxLDYVIECtK5Nmqe3Fqqt6412GeD2e4v1DGf3XqzdGsjuHTkT9QGuQJm7/5Vt84+rrSe72SeYXkva+qXbso1m0XJvJt55kMFXLkwA2PX82g+n3atDYGmUpNa00Kenw7g4EwUA3FjOA7Cm3ym8gaDAoiRr2MjXnF4ZAZHFg+0yzk6EnHrlsaDlTFJ1EwJHYyMvtfRyoimAoS3Rt+unvTz5nT6PQYwgJRAITz4Hpd9etNOwsQCPdx5kEPZxjmYC3gvPbp1j9rnCIocLUxGUJNVZiIbq02+ylcaFrqIZeLBdwjJD48qpMQQF1mq27eecc69mq4gHeDA01aDxzZHE5kW83dPih3e3sZKtIltRUFV0TIQETERE5GtKx34M7ab42JCpcgQCgXD0Wc/XsF2UWwIiumHih3fT+L+9cgJBgcVYkPfM3B3EII5B388gsm3tvV6ufizb/5JrWicERRZ/44VZCAzT4oB6/dkpz/t0r02ap84CwHK2gumYr2E/5eWos98bETms7lTx4XK+YS/Wbg3k3o8207yfPK62/nje9TFi0GmSXnTrXfcSLfdmIubn8ZnFuDUVyPXQKrrR0Pi1OS1U5BgERRYfreTwwzvbSJVkUADCPhYsTeOLZ5OYiYioqQbWXCOktbq6q/quKPEsjYmwiLKsNziwDNNEPCCgWFNaDEPzhqrd5zGIEaQEAuH4cBD67UU7DTMBFCQVMx5OcK+FZzfOMfe5eJZucFhF/Zzn4sxyPpl4bjaMa4+z2CrIeLBtzeSZi/vx8mIcUxERLE3DxzOO1tu4I4nNi3jDtFLdL0yF8YtPT2KnooCvbwoeZyrIVpQWre5mig/P0oc+rYdAIBAIg6GiaC1DKmx0w8RarobFRAAlSXMmxjWz1yCOpUwF2yUJLENhMiQi6utuKmk/DGra6nq+1pLFBViBFkkLNqwTZmN+/L3PnOg6gGavF7wcehMhAbJq7BnksrOO7GoZu/ejey/Wdh/XtB9trmyx95PH2daTXSxh33TrXd9btHR8+dw4ri1lnaasgFV6MRP1eY7uBOA0dv3+rZTjVAoKLNZzlrgt71SRCHIo1KwyOnuLwdIUFKChpAOw6pcvzccbhM40Tfzlp1ueUx6aN1TtPo/9jiDtFTICk0Ag9EM7DQPQkE3ajNfCMyhaC0VbizbytQYt2st+VGS95ZiKbuBUIoD3H2eh6Abo+uXQFAWGppwgRlFSwTE0KrIGH884mUPNzqpmB5ik6ni4XcZyttqi+V5a3c0Un1PjwaFGm4neEwiE48Io6F2AZ6HorU4lG4GlwTFU278DnbNa8jUFHyx7T30bRoXDoKat5qtKi9MH2C0Xay5X6yWAZq8X/uj9VciqAT9v9VaMB3h88dw4SlUFxVrnINdarorpqOjZ+7F5Ym3zuqR5P9pc2cIx9EhM5jtMvFeHB8Tv//7v46WXXkIoFML4+Dh+9Vd/FXfv3m14jSRJ+Na3voWxsTEEg0F885vfRCqVanjNysoKfvmXfxl+vx/j4+P4Z//sn0HTGhe4P/rRj3Dp0iUIgoDTp0/j29/+9rBv71hhP7SvX5zCF59K4vWLU/i1F+f2LBErSipubRZwf7uM7ZKEq/fSSIYEfP7MmHOcX39lAZKmezp1bK/wer6GYs16+H28lVZZUXRwDI21fBWTER82CjVcW8oiJLA4PR4EYHnQQ64yBft4ttC9MB/DU5MhVFXv89s0b6i8Po/Xzk+0iFinY+yHtVwV37m+ijdubuLq3TS+d3MT37m+irVcdWDn6IWypOHuVgkfruRwb6uEsnQ8a48JFkT7Rx8vDXt+LtZQ1tWM18KzGy3qZD/syKEbnqEREFkUaypoUFA1AyGRQzIkYLMg4fZGASeTATA0DZa2JvQ8SpehaEbbSKJb8wWOQbqsdK3V7ab4fOPZKXz1wgS+9vREiz0cJMPQe6LZhGFAtJ+wX0ZlfTsT9WEy3NpgGoCT1TIeElvsl02nrJa9+h0NQ4+9bG0319qMYZqeWVyA5VzSzQ6bqS6Yjfnx2vkJ/J2X5vDMTBhPTYbg5xj8n++t4GePspgMi6i1CXI9NRlCLMAjU1ba2njbvnutS9z70WZb/41np/C3X5rft60/6rb3UB1LV69exbe+9S387Gc/w5tvvglVVfG1r30NlUrFec3v/M7v4H/9r/+F73znO7h69So2NjbwN//m33T+rus6fvmXfxmKouCnP/0p/tt/+2/49re/jX/1r/6V85qlpSX88i//Mr70pS/ho48+wm//9m/jH/2jf4S//Mu/PND7HQWG9YNtGAXdJpWxWbTsdES7qR0F60F9vFPFzfUizoyH8NRkCMmQgNfOT7QIntsrXFE0hEQOUR8HjqEgqVaUOx7gwdIUGIaCwDLYKNRgmCb+9uU5JINCQ/S9k5e5H09+s3NK5JiOzqlB1eMehkHqxKgsAgijA9H+4TIonW/WsMVEoEWHFc3ATlm2UsBNs+FcvWhR87maM5riAQ7JII9EkMdc3I+JsIiF+vUomoGZqA8RH4dSTcWnmyX8wukEToz5Eav3WZJUAxxDdRVJ7FXv203xyZQVlCStYYrPoBmG3hPNJgwLov2E/TBK69ugyOJXn59paUptN+nWTROzMT++eqHz/sWLbvodDRrb1na61m7WFn6eRSLAo1ovFawqGvT65mciJAxkryOwNH5wZxvXlnK4sZLHve0ydMNEqiTjnQcZiPUpsV647TVNwVlXhAQWiZDgTJgFWtclzftR29ZrhokvnRvHxdnIvmz9k2B7KdPcp+twgKTTaYyPj+Pq1av4whe+gEKhgGQyiT/8wz/E3/pbfwsAcOfOHZw/fx7vvvsuPvvZz+LP//zP8Y1vfAMbGxuYmLCaP//n//yf8Xu/93tIp9PgeR6/93u/h+9973v45JNPnHP9nb/zd5DP5/EXf/EXe15XsVhEJBJBoVBAOBwezs0fAIMYI7nf47pfu1OWcX+73DIpweb1i1NONlJV0eDnGCi6Cc0wWupw726V8P99fwV3NotQNAP3UlZFs92z4288P42H6QpMAF9/ehInxgKIB/h6Y9a963rt6Q/t+kh10x9pEMfohrtbJbxxc7Pt31+/OHVgfVsO6p4Jw+Mg9I9o/+AYls57Hd/dANPW8LBv91y9aNFepQ2Ptsv4k4/WkSpK4BgaCwk//ueH65iO+mDUDQdDU1jJVkFTFL55aQYsTYNhKMia1Wzz2dkonpuL7nmPverWYercoPWeaDYBODjtI9pP6IVRWt/aPN4p4/2lLAo1zWnSrZsmXju/a3Nt+9btII4PV3K4ejfd9u9ffCqJF+ZjLf8+iBLBdtfa7dri+uMd/Ph+Bj99sIONguUAY2kK56fC+NJT43i+7qTZDzfXCvh/fv+OZ2aUyNH4f3ztHC7ORtre33eur6JYUzEZFhtK2USOxpVTCbz+7FTH9VKv32c3jIrt3a/2HWrGUjOFQgEAEI/HAQDXr1+Hqqr4yle+4rzm3LlzmJ+fx7vvvgsAePfdd3Hx4kXHuADA17/+dRSLRXz66afOa9zHsF9jH6MZWZZRLBYb/nPUGZaXv9fjulMLP38miV96ZhLnpsL4aDWPbFlBzMc5nuPVbAVv3trCX3yyiR/dTeONT7bwwXIWyZDQENEGrMyksqTBMC0Ps49nIHIMGJpCzM/Bx7GYifkxG/PjxFjA8Tx7Rci96MaTvxeDOEY3DKoB3yA4jKgL4ehBtH8wHEQ019bwr5yfwMlEAF85P4GXF+NOYMB9LknVEfNzYGkKPo6GwNLQXD0pbC3aK0pXljRcvZ8GRVGYjPgwFhQgsgwWxwIo1lSMh0VMR31IBAVE/TzOjAchcgxyNdXJGsqUlY5RTDe9avVBabsXg9Z7otmEg4RoP6EXDmp920vW78JYEL/0zDQ+fyaJZ2YieGlxDN+81FgO1S4jtx39VEkMKtvF61rTJRnvPsxA100kQgKSQR401bq2KEsaVrI1rOxUcW4yhF95bhq/+MwUfuniFKajIkqSOpCm1qph4GQyCJFrdGPYk1g1o33vK9tez8f9LU6lk8kgKoq+53qp1++zG54U2zsyYSfDMPDbv/3buHLlCp555hkAwNbWFnieRzQabXjtxMQEtra2nNe4jYv9d/tvnV5TLBZRq9Xg8zX+yH//938f//pf/+uB3dsoMKgxkl7HLdasSTgmAFk1IPAMKNPETkVpO0rzqckQSpKC//6zxyjUVFAAPntyDH/+6SZkzQBL03WHENOQzdRuglq2ouDyiRhkVUdJ1hD18agoGqYjPlw+EcdSpoJogN9Xp/5+xn97RQ+GPUJ8UA34BsEoObkIownR/sExLJ1vJiiy4FkamXJjE06aAsYCPCgKuL6cxU/up/EXn6ag1HsSzMb8+MKZBMqyhqDAOoMXOjnDbL1s/vtORcEXzibxzoMMWJrCWFCAohmYj5ueQxJ61f699P4wtN2LQes90WzCQUG0n9Arw17fliUND7ZL+P6tFIo1FSGRcyZ6dpP1awLo3K67O7qdtu2+7jdvpbBdlFGSVCi6AZ6hoWitU9J6ZS1XxR+9v4r3lrLOv7krTdxrC3si3GdPjuGdBxnc2y43vOevPTczEJsY4NmGqeKqboBjaOf72ut3MBvzI1dRcX05Bx/PNLwXGM5Apb14UmzvyDiWvvWtb+GTTz7B22+/fdiXgn/xL/4Ffvd3f9f5/8ViEXNzc4d4RftnWD/YqqK1pBICu6Lj1UANsETw/nYJmmEiW1Hw7EwEP3mQwUauhpBoLdI1V73sy4tx7FQUjAUsB9Z7SzuYi/mdNM+KojlN1EABLE0jXZKxXZRwYzWPuZhvINHjXqYXdEobHaZY9WqQhskoObkIownR/sFxkAuT5nPRFBxbEPFxeH8pi7V8DapmQNEM6IaJh9slKJqOyydiCIQEp9TZ1irbMWUHKViGxlquiqraOhnO3ThzfsyPkMjBz7MQOApX76Ybyqr71f52en9Y2u7FoPWeaDbhoCDaT+iVYa5v13JVvL+UxQ/vbLdksQBo66AZRvl5t9O2bWyHTvM0NpGjIWnBvp0ktsOqOVDj3puly4qztqgoWoNtNgGnFH0QDjcb9+9gLCg0/K3d76A5GFRVtJb3ujloR86TYntH4ir/6T/9p/jud7+LH//4x5idnXX+fXJyEoqiIJ/PN0QvUqkUJicnnddcu3at4Xj29Aj3a5onSqRSKYTD4ZaoBQAIggBBaP9jO4oM6wfr45gWpxKwKzrt+lksZSr4wZ00vn5hAu8/zmE25sNHawUIHA1QwHTUh62C5BwLrk1LqiRjYcyPyciusyjAs04TNWB3gxL1cziRCODp6TAuzkQPrDdEN1H4YV1LrwZpmIySk4swehDtHywHuTBpboB5MhHA450qkmERczEf3nmQwVK6gumYD6miDFnTYRgmVrNVvPpUEi+eiDlBAfsYXkGKzXwNXz6X9LwGW/NfWhxrWDQnLolDyxw6TG33YtB6TzSbcBAQ7Sf0w7DWt7auszTVYH8k1RoydGEq4pnFMkx70EuVRL6qtDiVGJrCfMyPgMDg0XYJFNBzzyU78MMzrZ1zUiUZdvzGXlvY6wL3fgwASvX/9g1oDdLr78DL+TcbE1GU1LaTbg/akfOk2N5DdSyZponf+q3fwh//8R/jRz/6ERYXFxv+/uKLL4LjOLz11lv45je/CQC4e/cuVlZW8MorrwAAXnnlFfzbf/tvsb29jfHxcQDAm2++iXA4jAsXLjiveeONNxqO/eabbzrHOA4M6wer6iYKknfpRUFSoereveHTJQlnx0P4/qcpRP0cwj4OYwEeAkcjInJOfSwFwMcz4Bkaq9kqZmJ+RHwcaMryfdvi/Y1npxvuzy1qUT93oE4l4OBKUtrRT9neMBglJxdhdCDaPxwOcmFin8tugPn2gwzefpABAHz+TBIr2SqmYz6sZasYCwqIB/zQTRMMRSEscChJKm6uFZCrKEiEBERFFj9wRYptijUVq9kaAjyDitKaueR1X71klvbKYWu7F4PUe6LZhGFCtJ+wX4axvrV1PSS0HkNSDZQkKzOmOYtl2PagW1tmmqblRBJZqLoJkaWRCPL480+2sJ6vYfOkhDupcs+ZVHbgJyRyVvZTU7NsWTMabPBBrkG6/R20c/7JqoGypEFkGacETtGs7zrs45wptwdl854U23uoV/mtb30Lf/iHf4g//dM/RSgUcmqjI5EIfD4fIpEI/uE//If43d/9XcTjcYTDYfzWb/0WXnnlFXz2s58FAHzta1/DhQsX8Ou//uv4d//u32Frawv/8l/+S3zrW99yog//+B//Y/zH//gf8c//+T/HP/gH/wA/+MEP8Ed/9Ef43ve+d2j3ftAM6wdrN1DzSr/s1EDNxzO4tpTFRqGGrSKNsI9zJrmFfRwun4ghGuCg6SZWdqpYzdXwP2+sAwDm4n78yrPTyJRlp+9StqL0dH+DmJzQiVGolR3m5qoX+l0EDPs7IhweRPuHw0EuTOxz2aUDufr5WJpCSGRRVXSrfDkoWJFNE8hVFeiGia+cn8Cf/nwD2YqCE/EAVrIVTNQbcKdKsjOaWOSsvgcr2SpePZvEjdX8vu5rEJoyCtruxSD13kuz7SmqH67kiB4T+oZoP2EQDHp9a+u6wHnPtFLrQyeas1hGxR4wNIWVXBUP6j2Nnp2J4L0lGRQohAQWHLPbO6iXTCo7A4lnac+9XsTH4pVTCedYB+0c6eZ30M75t1PvzWs3xS7LGkzTwERYRCIk4IPlHG5tFvHSYnwgE3W7oZ/ejqNmhw/1av7gD/4AAPDFL36x4d//63/9r/jN3/xNAMC///f/HjRN45vf/CZkWcbXv/51/Kf/9J+c1zIMg+9+97v4J//kn+CVV15BIBDAb/zGb+Df/Jt/47xmcXER3/ve9/A7v/M7+A//4T9gdnYW/+W//Bd8/etfH/o9jhLD8PL320DNNE0Uaio4hkZRUrFdkjAX92M1W4Ws6o6Ib5dkhH0sClUFimYdu1hT8YM723jxRAwlSQPP0qgqGp6aDHV1f8Mex21/Lp3oJsXyKAhIt/S6CDiI74hweBDtHx4Hma3oboBJ0xTyNR4Cy0BSdExFRNzaLMIfERHgGRQlFbpp4rm5KJZ3ykgVLQfSo0wZszE/1nJV3E2VcWEqhHupMmgKSIYElCQVgJWqvp/7GpSmDLPccJQ0363Za7kqvvvxBtFjwr4h2k8YBvvVTlvXKVg9YpszZzmGdjJu3OeSVR2KZjgZL80cRDlVWdLwk/sZcDSNAG+Vl8eDPN5+kEE8wOPp6QhYerfDUS+ZVO4MpOa93kRYxBefmkAy1FhCup81yDBsYDvnn90L6rXzE2AoCks7Zbx9P4OlTA41RYcJ67dA0xSiPv5AM5eayy3vbpWQKtastgKqFbQzzNG0w5Rpmt61SgSHYrGISCSCQqGAcDh82JczUpQlDd+5vto27dH2ins1Tfvx/TQyJRmSZiBVqCEZFvHxWgGFqoLZmB9+gQVHU3hhPopPNop4kCqjpuqoKBpEjsHfeGEGtzeLOJkM4u+8NN+VSHZ7vYP4XN68tYWyrLVMygv79j7PcXasHNR3ROiO46x/x/neu6Esabi+ksX9rTI4lsLyTgV3tqyI6fNzEXzwOAdZM5Aty1hI+HEyEcTZyRA2CxJyFQV3UyWkSzJOJoPgGAr3UmX89eencXOtABNwFnciR+O3vnwalxfG+r7OQWlKWdLw//twFQxFtUxC1U0T37zUnz6NquYTPT6eHHftO+73f5QYhHbaOmeXdjePob9yKoHXn50CgIZzJYM8fr5WQFBkW3r19KOP3TpWmp1bdzdL+MmD3aEVp8eDePOW1WPszHgQp8dDDc6vLz6VxAvzsa6uqdvPd79OoWHZwLtbJbxxc7Pt31+/OAXTNPGffvigxaEIWM6l//uXTuPc1MHrgP2ZbBdl3NosQFKNhol8tnNpkHZ4v9pHVgOEfdFN2qNbLOwGrW8/yKAkqdgoSChUVUxFRJyfEnBxJgyBY3Ai7ofIMVjZqeDdR1lERBaaYdSj14CkWFECqV4jGw/wXV3vQfXHyNcUrOWr+OBxzkkbnQgJeO3ChNO0th2j1hz2oBnFHiYEAqERW9cfbpdxv55+H/NzeGkhhvcf5/Dhch6JoIAvPpVEQGDxV7e3cT9dxu2tEu5slXBmIohnpsL4USkNrb4aDvAMdsoKHqbLSIZEMPUoa0TksJqt4dxkf/0OBqkpQZHF5YU4vv3OY6xkq86/z8f9+M0rC31d3yhrPtFjAoEwqgxKO917GfdEMwA4PxXCuckIALQ42e1yqg+Wcw29evop/erWsdL8us1CDZKi4wtnk7h6L42KrIOhKai6AZamEBDYloyqXjKpuslA2q9TaJg2sJu+T9dXsp5OJcBqUr5dkg7cseT+TEqS6uwlmyfyjZodfnJ3p4R90YvnuZPoNIvFWIDHOw8y2CxIqCoaYn4OG/kaCpKKB9tlPDsXQVXWYRhAtqLgbqoM3TAhawbGQyJU3URJUmGYJmjKctZcPhFDtqK0pGN6cRD10PY9azpaSgRTBQlRX2cn2HFfyI9KzTqBQPC2BcBu1DYkcoj6OCi6gYqsYzMv4a8/P42fPdoBz9CI+Tn8xacpvH0/DYoC5mJ+mKaJ5UwVVUXHhakwJM1ATdExP+ZHUGChGSZkTYefZ53o3Eq22rf2DVJTypKGa0tZJIICBJZuKP++tpTFiXig5wXwKGs+0WMCgTCqDFI793Kg3N0qtZzLLqd6bjaCU+NBiBzjvM9+Tzf7qG4cK4DlwPmzn2+gWFOdliM8Q2O5VMX1xzl84XQSD9JlBHgGF6bCyFVVBJsakvfTRLtTO4tBOIWGaQO7SYDQ2gyasmk3iGqYuD8TRW/sV+yeyAeMlh0mjiVCC/14ntuJTrNYmLAeCIamwLM0fByD08kg1vI1/Ph+GhMRAfe2yqAo4JuXZrEWqmItL0HVTWwWJSSCPKaiIsZDAp6aCGFhLICtotT1Q3UQ47jd98yzNMaCuw6viqLvKZDHfSF/kCPTCQRCe9rZgoszYeffJE1HUVKxka9Bq/dMmov78dKJODjGGt2crSiI+XlUFA2qbiAkcpBUHWvZKj67GMfjnSpMAAGBRcTH4Veem4aPZxD386AAJ+W7X+0bpKY445ebtB3ofwE8yppP9JhAIIwqg9bOTg6UTr160mUFT88wTnlZr/uovRwrd7YKuLleBEtTuL6cA7A7JMme2JYqyWAYCmNBAYYJfOX8BD5YziHkKtEbRhPtQTiFhm0D93IajodEz6l3gPU5j4fEfZ2/H9yfCc+09vCStd1rHSU7PDpXQhgJuvU8d5vR1CwWsuuhFVgGYZHDTkUBz9AYC/JgaBqruSp8HIObG3lcOZPAj+6kUZI1mPUeRdMRH04mgvhkvYhovQSu24fqIEZh7lcgj/tC/iDHlRIIBG862YLbmyVUFQ1VRcfdrRJkTcdYUICqGzAMExQFbBVrmAiLkJR6NlLcj5VsFdmqipmoD+myjGJNhcgxqCk6JkICXj07jv/z2gp0w8SZ8SDcQ0Wt0me9r6lkg9SUYSyAR1nziR4TCIRR5SC1s9tz9ZPB08muKJqB25tWtlTIlX0kqQYepcu4MBVxJrbZzgbDtII+3/ryKciquWcT7f30RxqETez3e+zlujs5DRcTAVxeiDW0LwEsp9LlhRgWE4E972HQuD8T23koNeyhaZQwenb4yd6hEnqm2fNMU1b5mt2k9OZ6HlNREVfvppGt7O2JbxYLgaPrpW06dMPEeFhAVdEhsDQoCgj7WJweD4ICsJ6tYXEsgGdmIxgL8Hi8E4aqm6hIGm5vFfHURNg5dzcPlS1AT0+H8c79DBTd3Fc9dDv2a+iO+0L+oMeVEggEi+aGoBxNgabgNAS1kTUDjzMVaIaJzYI1qpelKcQCPPy8lXW0klUREFhwjIFogIdhGJiN+cAyFBiawqnxIPJVBYuJAKajPlAASjUFiQCPgqQ2RFmLkoqypOHhdhnpsgKg0ebstbgcpKYMYyMzyppP9JhAIIwqtnYWa2rDXkXgGQR5ZqDa2a1O95PB42VXFM3qK1uUVJyeCCIJWGPrXEiq9ZqxoIALUxE8PR1uKMfrRp/32x9pEDaxFxto2/tBTkkLiiz+xguzEBgGK9mqU+I+H/fj9WenDtzOlSUrmULVDafs0XYe2g28KYymHR6dKyGMBG7Ps91o2z0d4c5WERRF4fKJGGhKdTYc7TzxzWJRU3WwDIXNgoIAz0JWdciaDh/HgmUo3FzN4+ereXAMjemoD7GAgGuPsigEVczH/XjnQQYFScXJZBA8S3f9UDU3EE+GBAgcjYmwiImwb6DjuPe7SSAL+YMdmU4gENo3BHVPHwGsxe5Gvoqwn8N2YbfZpWaYyFUUPDMdQU3VcS9VQjzAIySwyJQkPEpXEPFxMEwT01EfJiM+zMTC4Bga63kJgGVzXrswgZVsBZq+e76ypOHyiRi2ipJzvnxVxVu3U/j86SSu3k/vuSgelKYMwwk06ppP9JhAIIwitnZeX87hrVuphklulxdiyNeUgelUtzrdTwZPs10pSqrjRAgKLD5azqFQU/GLz0xhMixgq7hre9V6/53xsICLM9Gep9Dttz/SIGxit5/tXlPS9tPsezbmx9/7zIlDt3P2PRZrKk7U972ruSpOJoO4MBUBx1D43JkEIj5+JO3waF0N4dBxe57tRtvuTvkmgNVsFbKqOx3pbbw88W6x2C7KuLmex4vzMQgsDY6moekmWJqGj6Px4okYrt5LO30rclUFG7karpweQyIkgKUonBwPQFIMqLqBZEjEYmLvRqnNwmnXQwNAWdZxaT4+0AdzEJsEspDvnLZKIBAGh9fi0m4I6p4+AgAlScWjTAV/9+U5vH0/g0eZsvOe8ZCIFxdiePtBBpJqYDwk4OrdNC7Nx6DpJrZLMiYjIgzTOuevf/YExkNii84BcP6tImv4eK2Aj1bzYGjKaVgKAAxF4U8+WgdFNYZx2y0uB6Epw3ICjbrmEz0mEAijSNTHI1WQEPZx8PGMM0xB0zHwqZrd6HQ/GTzNeyV3ZsoL81G8eXsbumHizVtb+OqFCXz/010nGsd0H2RvZhD9kQZlE/f6bNtNSdsoSPir2ylcnI1gpyRD0Yy+m30ftp1rXos1Tyh8fi7W1b73MBndKyMcCm7Ps91o20bkaHD1BmLNHeltvDzxtljcXM9jLMjDx9E4NxlCQVJBg0JRUvHBcg4/uZ9paEY2HfEhVZQQFFlwLIV0SQEoClS911LYV0UswB3qtAEbr1KM/W4SDlvgCATC8cBLI90NQd1ar+gGEgEepZqKz51OYDbmR76mgmMoVCQNn24UUZGtnkkcY70/U1FwcSaCgMgiWG/QTcGatNJO52aiPtzZKmB5p4L3H2cBADVFh1BvWBoWOctGFSVMRlojosOaplaWNFRkHZfmY6gqGhiKQsQ/mMgh0XwCgTDK7KcXz7CwrkdvGaYADMcO7KXT/WbwNO+VBJYGBWCzIIFjKOiGiY2ChEJN3Zezwf0dliUVySDvlJI1023PwEEFRjp9tl5T0mRNR66iYLNQw6nxIO5vlyFyNF45FT+StrR5LeZOhACsgVCH/bztxWhfHaFrBiX2bs+z3NTA7MRYABVZQ0lSwdAUqore8n6Wpj3HawZFFgLHoCRpKEnAdsl6UGgKmI/7kSkrDeMUpyM+vLwYx3tLOxB5BpmKjPeXrEkI7rTHbqIRw5420Kk+eSbqc76XjXxtJIwwgUA4+gxyge+lkTxLtzQEBazy6JmoDyvZKspSGXNjftxYyWGzIIFlKCyMBRyNztQDE7ph4t62ldm0MOaHWncEtdPetVwVb3y8iXceZnB+Kox7qRICPIv5uB9lWXMalsqq4QQ7vBj0NLVOWj/ohqgEAoEwSuy3F8+wGLWpmkGRxatnkviTj9axVZTA1zOoxsPCnhk87r3STr3HkqobmAxbmb2qbqKmGtgpK05JlMC2t4HNNH+HO/UhGs0l7za99AwcdmCkeUqaXi+/1+oXrerWf0uqgVRRQlnSjpy93c9veVTWG0frEyd4Mmixd3vN72+XwDE0GIbCcqYCiqKg6SYKNRUlWUVF1hGuN1llGeDBdgmPd6qe1+GVHmqPkL4wFUIylEBZ1p3I9/XlHNIlGX6eQdblsU2V5IbyDHc0wuvBGubUiHb1ycWaiuvLOaQKEiouB9woGGECgXC0GbTmt9PIsMi1NASNB3j80QfL+HSjAJam8f7jLE6M+XF5IYawj8P5yTA2CjVsFSXE61M73bgdQV7aa2vqSrYKSTVQkTRMR3zYKNSwkq1iJuZDVdFRklScTPoRqlj2x250quiGs5Af5ESgXntRjOomjEAgEHplEL14hsWoTdVcy1XxkwdpjIcEJEMCZM1AxMfipcU4oj7eM/juJsCzDT2WAKtnd8THIexjsTDmR8THQlYNvPtwp+um1V7fYUjksJprLXkHDn9wRDPNU9JoCo5TCQA4xiqJnwgJkNX+y+EOk35/y6O03ujezUkYSfYS+7LUn6c+KLK4OBPFuakwQiKHh9tl7FQUpOs9Mk4lgyhULOFTNAMBnsF8PICVbLXhOO7rsNNDmzFNy9Ocr6l4sF3CjZU87m2XUVE0LCQCmB/z42Gm0vAed3mG7cFdy1XxneureOPmJq7eTeN7NzfxneurEDjK87zA/oWzXZndWIDHW/XNkZv9fi8EAuF4MwzNb6fNABDzc4j5rWk7diej2VgAE2ERK9kqCpKKj9cLuLNZAstQ2CnLuJ+yFsQUrEWejcjRzrS3dtpra6qdwfowU8HLi3FMR3wN0bywj8NLi3GMhwUUJRW3Ngu4v13G8k4V97fLyJRlCBzVcvx+6aak2mZYdplAIBAOg17076DpZL8O2jlia3+2oiJdVpApKyhJGjbyEn6+WsAfvrfcskdZyzXuE+IBHmVJaxgtbwLI11ToBmCaJtZyEtJlpWWAUifb4vUd2pnJBclqf6JoBnbKMlTdwAtz0QF9KoPB/T3zLI35sd2EhemIDxVJc7KldyrKgWeqDYJ+fsujtt4gGUtHHC+hoCk4YzffW9rBXMy/Z0qcnelTVTT4OQaKbkIzLGH5/q0trOVqzuQBP8/gG89NQdNNnEgE8PR0GDE/j7/8dMuzRtdd4+zV4E03TfzmlQVcW8rCz7FO6mdIZPH8XAwfPs5B9ziwXZ7h59mOD9bVu2m8eqb95KD9RFnapS3a/al8PNPyt2H1/iAQCE8+bs13a72sGqAp4PaWlUkUaIqGdkqTbtd8k2WAiYjYoO2zMRF3tsq4cjqBWICHqptOlun7SzmcSQZx+UQM6/kadioKrpxO9DTN09ZUvp7ZpBsmbqzmcW4yhBcXYpiO+EBRwEsLcSyMBfHqGeAPrj5sWIRPhARcPhHD1btpJC6JA4mk95KifhB9/QgEAuGgGLVyMzejNFVzPV9DWVJxOhmAohuoyDoCIouYj8Wf/XwDAcHqL2hn1iqa0ZLxla0ouHwiBlnVG/rcToQEXJwJY9v1b272si3tvkM7MzkZsjOMRVAArt5L48ZqfmSybJu/Z5FlMBPzYTwk4JVTY6jIGkwTTknfQWeqDYJ+fsujtt44ep86oQFJ1ZEI8pBVAwLPgIYJnqHxk/vWNLeFMT8mI76OKXHu0YaTYbFhE+DnGPg4Bn//ygLWcjUwtLWB+ItPUuAYCs9MR+DnWaRKEgI8C4FnnObabl+QbXSaG7yxNA2OoSCpOn7h5Jjj0PLzLEzTxF9+ugWBZyBytLNxYGgKpxIBxPwcBJaGqhlYy1VRrHk/WNmKCtUwhzJxp13aot2fql3/j6PoSScQCINhP7Xw9uKQpuDodaokO00sLy/G8fKJOLaK1pScr16YAIA906SjPh6vnBxDqiRB103EAjxWdip4nKk0aHmhquHBdgnjIQE3VvIt1ydpOraKEl47PwGRY1CsKj1N87Q1NSRyCAgMZiI+BEQWqm6CgpV9mqsqznWrhonnZiN4djYCWTOchqf24nJQi6peUtRHeRNGIBAIvTJq5WbNjMpUzZqi4VQyiP/xwSoebO9OTP2bl2ZgmMD9VAksQ+NUIoCAyMIwDRimibVcFeemwgAs++GeBua2a59uFHFqPOh5bpqy9oTtSu32+g5LkoaMqxQO6K/UcZi9ftzfc6GqYDlbgawaWN6pNqxTvLJ7RqUH0V70+lsetfXG6H2ihK5Zy1Xx1u0U3lvKOv+2MObHwlgAmYolDrZjo5042Jk+xZqKk4kA3n6QQa6qgmUobORqSIQE7KRl0BSFSyei2CnvbkxU3cRYkMdbt1PIVhTcr4uou7m2/aC7jY7d4G2vmtCypCHs42CYqtNEVtVNvDAXxScbBWyXZEzHfMiUFai6gRNxv2fzOcB6sIbRWK7d9AeBoxvKPpo5bCNMIBAOh/3WwtuLw7EA7ziV3E0ss2WloV/C+0tZpEtyQ683ANguyvij91fxlfMTMEwT15ayDa8xTQNnJkIICSw4bjdgINSd/O2KzDiGhmECIscgIDD46cNi0712nuZpa2qxpuLVs0n8+SdbWK2XFAd4FiVJwd/9zAnn/RVFa+gLUWo63qAWVb1M+hn1TRiBQCD0Qr+Tzg6SUZiqKXB0i1MJADIlBdeXc3hmJoLTySCuLWWxUbDKB1maQroo438TWacfrXsamLt/oG6YiPk5VGStYa9jB5reup0CRe1aZ/faotN3yDFUw8Am+5i9Vr8cRK8f9/c8HfPhzVupFqdSc3bPKPUg6oZefsujtt4gPZaOKLZDSNVNiNzu17hdkvGDO9s4lQi0ODa86qDX8zUnU+nxThVvP8jgUaaMe6kSljIVVGQNa7kafvZoB0uZCv7s5+u4s1XCC3NRnBkP4vpyDqpuOqOpgd3m2mP1xq3tPMd71YTaKYFRP+ekan7udAKPdyqYDIvOyGnAapbtPmcz/T5YZUnD3a0SPlzJ4d5WqaVW1X2NDf8usLi8EAPvMa1hVIwwgUA4WAZRC28vDu1yW8AauasZprPAcPegK8taS683uyfRe0tZPN6p4P94ewnvPMygKKnO33/6MIv/z0+XsZSp4Ls/38C1pSwmwyIYqt4cU9Mb+icBuz2Uon4O8QDf173amjof9+P64xw03UQ8wGMq4sML81GERB7XlrLO+4exqPLS/XZa77WIHaWeHwQCgbBfetG/48xOWcHjpp6wAMAyFDIlGSfi/ganEmA1oE6XZc9+tM39AyVFx3quhsmwCNoV3RkL8Pigvh+zoSmAoym8+zCD9x7tYCNfw5fOJREPtH6Hnztj9SVyv3cyLOLaUhbf/XgT3/90q21PKJvD6PVjZ/e8fnEKX3wqidcvTuHXXpxrcBaNWg+iQTNq6w2iBEcUu6bSPRJaUg1ouomNQg2fORXHSTbY4thojt5WFM2JfCfDYsPfSrK1uOYYChEfB6oeo94o1HBtKYtfeW4KkqpD5GiMBXiMBXmkSxJoikZZUkFR7Y1OtzWhzSmBkqqjJGkt92VPNvBIVnIerG7TIO3XFWoK3rmfgaKbzvm8PNzt0hbzNWUkar4JBMJoMIhaeHuB/8M7286/6XWn0nzcj7Jsabzdg05WDac/HmBFP93TZmquPg6P0mWcnQjhUboMWdORrSh4cSEGYDdg8JmTcVw5nUCqWMMvPjOFh+ky8jUVft5yKgksjdfOTyBbUfq+19mYH7mKNVnTxzPg6lPebB12v38m6kM8wIGhKKfXlF2SrZtmz+nwe0U2u0lRH6WeHwQCgTAIRqXcbJQpyxpiAd7JILbJVVQsJAMQOKbBqQRYwRGOoVv60b7x8SY+Ws05ttquBrm3XcZqLofnZiNOVpPA0QiKLHiWBk0BySCPkI/Dj+6mka0omI76kAgKiPo5vHomCdUwG77D9XytIevHnREN7F39Agyn1083+7a9snsGfV2jVlI3ausNogZHlIbpOPVsnpKkoixryNd4hAQODNWaLdMcvQ3wLEwAmYqCF+aj+OJTSWj1ZqypkoxH22UERQE7FQWxAI+zEyFohgmWpuDjWSSCQkOfD90wIWs6xkMivnJhAi+eiHds0NoOtwPMLRofruQ8s4BsB1sz9oPVycnjdhLZmwqOpnBtKYtUSYbI0U52VDtRdV+juxF6c98oYoQJhOPLoGrhZ2N+PD8Xw92tElTdsErgKgrKsuY41wWWRgnWgtPd680ajmDi7HgQAZGFrhu4NB9DWVLxMFNBtqJAUg0ILAOWphoioKmSDMMEFF3H15+Zxvc+3sB2yZogU6gCVJTCV56fwWzMjw9Xcvu6V9UwMBYU2v7dfn9QZHF5IY5vv/O4ITNrPu7Hb15Z6Mlp1O1I7Wat91pgkk0YgUB40hiFcrNRJuLjILAMkiERsqbDMEzQNIW1XBUvnYiDZxqLyO2AEFtPP3L3o/3C2SR8PNPSOzAosOAZGqfGg3h6hoGfZ5GvKljLSU6m0Wq2ivdubDhOrJKkgZ+11gFX76cdW2bbsHxVwWzMB1nVsVNRGjKi21W/NP8OBt3rZ1Dla4O8rn6u6SAcUaO03iArnBGkmx9hc/o/z9IYCwoIiVZjUz/PoNrUU8MuT3A3dosHeNAU8MJcFO8+3MEnG0WU6uUQMzE/PncmiUeZCubifuQrinNMmqMR83OQI2KDV5uhKWtKm6zh+nIOL56Ie95jv+ULnd4XFjk8P2eVn7kfLAD4zvXVPTcL7k1FIsg79ySpVoT/wlQEPEt39HAftTpeAoFwcHSje90uQhYTAZwaDyJfVaFoBjLl3fK3iZDg9EAKCtbC1e6fpBtWn7r3lnaQrShYGAvgk40C5uN+fGZxDGs5ayHK0BRiAR4+vtWR//mzSfzwThoURWGiKdP16v00xsPivkvUun1/WdJwbSmLRFCwhjnohpPhdG0pixPxQIu+u3HbgV4im91oPdmEEQgEwvHh9HgI83E/VrLVBhunGya2izK+cDbREKAHAM0wHMeN266lSlJDBq57KBLP0hA5Bi/MWxnFd7es7oIRkcNf3U5hLu7HcrYChrYymGRNd/Yxti0LCEyDDStKKsqShssnYqjUM5/twPpe1S/AYMvSuw3ydMOgrqufazrIPeGorDeIY2nE6PZH2K4JG8/SuLwQQ1BgG7r7R/0cXl6M442bG9gqyE4juJmIiM+eTOB/fbyJtVytnhIJVGQNO2UZ1x5n8fozk4gHBNzbKmFhzO8s2mdjfuSqSsM4TBuRo6HqZlsHTL+NAPd6n9e0obtbpa42C+5NRXMTO0m1mufZEXQvUR2kEBIIhCePvfRL4KgWJ3i7RYid/vzGx5vYKctIhgSs7lSRCO4OT4j6Oby0aDn3bW2ai/vxV7dTSBUlTEd82KkoYGka91NlGCbwhTNJbBUlAMB8zI+EX8CZ8aDjsHl+LgZZNffU1P02e+32/e6y8OYMp3b63u6au41sEq0nEAgEQjPJkIDfvLLQkkG7mAjg7748h7VcFSGBRaokQ4HVNHsm6kO2ImMyLCIe4J194MPtctdDkWaiPrAMkKnKuLVZRDzAo6booCkDIZGFwDIN+5hCVWkZrBEWOYgsg/V8Db9wagyPMpWGEnQ3Xs6YQTZ4H2T52qCuq9drOq7rhCfvjo4wvfwI96qpjPr4hpS4eIDHGzc38Hin2tBfY3mnikRIhGlakwZqqo6FRAAcQ0E3TJgmsJgI4mG6jKirMXbUbzmWtksSxPqUIBu3h7tdimG/NaH9vK/bzYL7dQLXKqTuPiVeojqM+mICgfDk0Em/Xj2bxNW76Z4XIcmQAB/PQNEMvHJyDAGBRYBn8Px8rCEV2s7IWctVnUEHm3UHkt1jYSVbRUhkIXI0IiLnLGRth43tvL+33Tx7rRF7Cud+6v67fX8/+t7udd1GNonWEwgEAsGLp6cj+Oe/eA4Ptkso1jSEfSxOj4eQDAk4OxHGfDyA799KIV2SUKxpWN6pYjwkYDrqwzsP0ljNVaHpcIYiSarh9Di0p716OUTm4wE8SFm2ma2X3DE0BYFjnNfY+xjd9A4O2U6kgMA5GdHNtHPGDLLXzyDL1wZ1Xb1e03FdJxDH0gjR649wr5pK92vvbpWwVZAbnEo2SzsVJIICCjWr94ZumDAMExEfh4DAoFhTEQ8KTipm2Lf7ME6EfU5/J3cJgi1OnVIM+60J7fV93W4W3K+jYEUI3NlYdp+SdqI66PpiAoHw5NFOv9bzNWQr+4uGFevTTaJ+Di/MN/a3s9Ok13JV+DgG6ZIM07RCn5tFqd4Em0ciJOC3vnwaq1nL0WRHR92LMLdWukch83X9tzV1v3X/3by/H31v97puI5tE6wkEAoHQjmRIQDLU2iMwKLJW0Cfmx4/uplCoaQ39k+IBHh88zjmtN9zDmexpr14OkfV8DY8zFSwkAjg7EQLH0HhmJoLNggRJ1SFrOvz1JuFRP7dn+ZdmGH05YwbV62fQ014HcV0cTWOnLDesddzZXM3XdFzXCcSxNEL08yPstqayomgoSWqLUwkAWJpCSdJwYiwACrse7fV8DfmaimdmdHy6UURIYPG1ZyZwZjzsCOZM1IfxsOCZKtnOAdPcQ6Qf0emllrTbzYL7dTsVBVdOJ5z+Ue4x2u1EdRhjrwkEwpOHl34dVDSMZSiUZQ0zMR8M04SiGaDrE9WyFQUxP4fLC2M4N6m1XYTZWrmS3c2AZWgKpxIBhEUWm4UaKMDpD7WfqNxe7+9H39u9rtvIJtF6AoFAILRjr16J2YrVbBsA3Pm/smo0lKy5hzOpuoHpqA+fWRxr2YNUFA2GCdQUHSGBxb1UGZfmY7i2lMVGoQbDMCFyNObjfnz1wgQqcmMP3mb8PNu3M2YQvX76LV/r9Lnv57rWctV6Bpra0NTcHuzkdU3HdZ3wZN7VEWUYP0L7IStJKoqSCt0wwdCNUwkqkoapiAhNNzAZ8UHRDNzaLEDVrfK4tVzVaeiarSj40rlxvLQYx2zM33OK4UE1MmsWly+ds8pM3BkBzdfYfC9bRQkvL8YhcDQmwiImwr6OojrI+mICgXC86FX/bUeUV8ZQpzLkiZCI8ZCAlVy1ZSTyXNyPUBeLsKDI4tUzSfzB1YeOU+mFuSg+2ShgsyDhvaUdnJ+KYDwsDHVwga3zp5IBpIqyM9HGMPfWd5vm13WzmCZaTyAQCAQvutnntAsksQyFqqI577XteTIkYCzAw88zuLddanGa2OsHd1D8xmoe5yZDeHEhhkRAwPPzEZybjDjDLLqxYYfVELqf8rVh7S/t7PBiTW1IOLAHO105lfC8puO6TiCOpRFi0D9C90OWDPL18gcJsQAPgWWcCPNkRMRkWERV1UEBuJ+yItCJAI/Tk0G8v7Q7NjpVklGWtYaeH916tQ+qkVk7cXn1TBKqYXa8xv2kSw6yvphAIBwvetX/AM+iKKkt5c12FM3tiHI72nmaxq88N4Vvv7uMtKvUdzriw6tnkljeqeLMeHhPvVINE8/NRvDsbAR+gcG1R1nQlJUNZQIoSVZD7WE1qWzWeUUzwDEUPncmgYiP35e+77WYJlpPIBAIhGa63ed4BZKKkoqSpEBkGVQUDemyVS1xejyEs+NBfLCcw3pecipE3E4T9/rBDoqbAGTNQMTH4otPTTSU5h0FG9bLfmyY+0s7O5ymrBLBV59KQlJ1cAwDkaNxdiLk6bg6Cp/xMHgy7+qIMsgfYfNDtlNR8PkzViPWx5kKpiK+hgizbho4PR6Cj2PwxaeSODsZRKGm4e0HGeiuiDZgCVWpvlGxF9/deLUPopFZJ3G5ej/dlbjsx0M/qPpiAoFwvOhV/+MBHmVJaylvllQDZUlDvD5swcvRruoGvvHMFFbzVciaCY6hwFIUEkEeyzvVrrTYWvhak0cT4PF4p9rwd7ukehhNKr103l5sf7pR7Kjzg4rAEq0nEAgEgptu9znNgSRFs7JfVN3El8+N4/FOBY93qpBUA6Zp4L2lLCL+xp4+zU4T9/rBts1RP4dXTiU8+z0dBRvWrb0e5v6yomigKWAyLDrZSjYTIQGTEbHte4/CZzxontw7O6IM6kfY/JAZJrCUqeBvX57Hw3QZyRCP68t5qLqJqqLiZCIAlqZBURRurOYxHhLweKfS4lQCAIGlUULnxmNeda4H0chsFLrwH1bqKIFAONr0ov/ZioLLJ2KQVb1loXP5RAzZigIfx3g62nfKMnbKMj53JoFSTXOmYEqagbEAj1oXWuyOuMpNzi3dMKEZJjYLNfAMjUJV6elz2ItR0HmAaD2BQCAQdul2n9PsCLJ74E6EBCSCPMI+Fs/MRKBoBhIhHm/fz6Aqa1A1o6FptNve9bN/HAUbtlc/qm4Y5v4ywLMYC/AtTiXAquJ5+34GF6YiQw9mHRWIY2kEGcSP0Osh0wzg/nYJiSCPeEBApixDYGkILI3lnSq2ihJOJoMQWQYCR4NnWhtyT4QE2B2a2vV8aleKdnEm3PGaB9HI7Lh24ScQCE8GvQxkaE55d0+XqSpaWwcMx9B4sF3GKydN/OzRTotj6pmZyJ7nd0dcbccUAMiaDkUzkK0oqCpWg9DlbAXTMd/Aei0RnScQCATCqNFLr0S3I+hxpoKnJkOgAGwUJBgmnCyZB6kybm0UERQ5AI1No4FGe3fUnBiD6os0zEbZM1EfPuboFqcSYH0Xqm4eWDDrKNDqOSA8EbR7yAwTME1geaeKqqI7/zEBpxEZAEyERczH/RBdG4aJkIArpxPYqSgdJ761K0VbzdYQ4BnP6xpUI7Pj2oWfQCAcLwI8C8ME0mUFmbKCkqQhU1aQLlvNq/0829YBExI5RHwcHmbKLYulgqTi2lIWZamzc8aOuEb9HChY9kE3rClz0xEfanWn0kRIgKwaePNWas9j9nLvnSA6TyAQCISDxg64eOG1z7EdQQuJQIP9BuBkyaiGCdo1dMneqymalSl8VO3dXn2Relkv9Pq590JQZDERFhv2w8Cug6/TsJTjyNH8NRL2pFMjWIGjGyYBubHHXE6Efbg0H8elE1Hc3rSGYdqR8LCvfc+n9XwNHG316pBVAwLPgDJN7FQUrGSrePVsEjdW80NrZHZcu/ATCITjRTdat56veb6XZ2mEfSyarYC9UKooelcRuNmYH994dhoPt0uYifrw9oMMNvI15GsqTOwGI7aKVgR2UFE9ovMEAoFAGDX67ZU7E/UhHuDAUJSVgawa8PEMIj4OmmFgPuZHprJbUm7v1U6NB4+svRtkSfuwG2VPhH24MBVBSVKh6gY41wRe4Og694YB+SSeUDo9ZBNhESs7VUyEBM/UvrCPc+pyLy+M4dxkBOv5GiRVR1XRQFMUqrKOsqS1PKyFmoJrS9mW0gp7cwFgqI3MjmsXfgKBcLzoRus6OWCSIREJv4Az40HPhVK7CJy7H4JhGFjN1rCStRp3R/wcon4eyZAAzTAAE45TqdMxh3HvBAKBQCAcNP32Orq8EMe333ns2FOeoaEaBn754iTOjIfwzv3GHj+dgvxHgUGXtPfzuXfb32km6sN4WGhonm5DglmNHM1fI6Er2j1k6/kabqzkceVMAh8u57BdkqHpJliGwnhIwNeahCoosggIDH76MNOxDrYsaXj7vndzs3ceZPDyYhw+nh16DbB932u5KrZLElTdxERIRNTHD+2cBAKBcNB4aXw8wCNbUfDhSg5BnsWrZ5K4ej/dot0vzEVx9V4aY8HWaTGAdwTO3Q9B0Qzc2iwgInJO4GC7KOP+dhkTIQEvL8adyTSdjjnIe7cXd3e3SvtqBEogEAgEQr/0us8pSxquLWWRCAoQWBqqbkAzTCzvVPH+Ug6fPRlv6af40kJ8YH0L213Tfptqd2IYJe3uz32v6++lvxMJZnUP+SSecLzEzY5i0wCSQQGxAA9Vt0ZO+znGaQhns1cdrD3qcj1fg6qbEDm6ZQR2qiRD4OgD8+rmawrefbTjuuZCXw3hCAQCYZRxa/xarorvfrzRoNXxAIcvnUtCVs0WB0xzWbKNVwSu2Q7YU2wkdTdwIKkGxHqTy+Yyu2FE9Zrt26AagRIIBAKBcFDYZWE8SzvBHkUzkCnLSJVk6CaQcQVqGD83VJt2ELZ0mCXte11/t/taN4Oa2v6kQ5p3H0OCIotXn0ri2uMc3l/O4cZKHp+sF3B7s4hcTcXV++mGpmnd1MECVlojz1o9OryanE2ExYE+gGVJw92tEj5cyeHeVsm55kE2hCMcXdr9PgiEJ5F2upetqPjhnTRmoj68MB/DU5MhBEW2ofm2m3YRuGY7oOi7wQPbkeTWf1nb/Xu3Ub39PLNE959ciJYTCIQnGa+ysP3a036xbWmxpiIZ5JEI8ggJLFiGxvtdDPboll7XIN3SzVqg232t1zU/NRlqWEsR+9QIcbMdU2TVbEi5dPfXaG6aJqm6ZzNuwJpakCpaqYayqiMZ5EFT8GxyNhEeXLS6kze6LGlgaQohgW24XsPsvSEc4WhCMhcIx41+GmF2E4GzF2FLmTISIcHRU55pDB7YC9+wyOGZ6Qiem4ugpuhdlyL38sx6pbgPshEoYXQgWk4gEJ502pWFhUUOF6YieHo6DJFjDiRLZj1fQ7GmYjIs4p0HmZaeuYuJAJ6fj3U8RrdldL1kAXV7zG7WAr32d2p3bmKfWiGOpWOKnV3Urr+G/VCt5ap463YK7y1lnb9NhAR8/kwCBoB37mdwZ6uEsaDgpG1ePhHDVlECz+4ee5BlEO280cWaiuvLOdzaKOKj1XzD9bonE5GxkE82/aS4EghHnX4bYXbqBeFeNO2Ud/snXTmdgKIZDWXPAkujBICmgLOTIdxYzqOi6PUjdS5F7uWZbbeQe3o6DJoC2gw8Jbp/BCFaTiAQjgOdysLGwwIuzkQPTOsqioaxAN/iVAKs7OTv30rh9Hio7fX06mzpph9VL8fsZi3US3+ndud+9UwSP3mQJvapCVIKd0zp5qGyF3V23ySbVEnG8k4VHy7nUJBUhOo9mXiWRlBk8cFyDmOB3ej0oNM223mjxwI83rqVQqbs3TzcviYyFvLJpt8UVwLhKDPoRpjNm/qQyDn9k955kMFURHTS9CdCAqj6++bjfqxkKy6nkkWnkrRun9lOjoa372ca7E4zRPePHkTLCQTCcWBYZWH9EOBZmIDn1HDACuK3095hlKT3esxu1kK2I88LdyJEp3P/yUfrYCjK6xDH2j6RldYxpZumae5mcieTQTxKl53otGaayFYUnEwGG8YvhkUOIsvg1HgQT88MJ22znTfaFsLFRKClgbjdA2QYDWSHPTmB0BuDHmFKIBwFBt0Is3lT77YDtp6GRQ5XTiXw8mIcAODjWSiagcc7Vc9jtitJ6/aZ7eRoUHUTAucdKxuU7hOtP1iIlhMIhOPCqDSH7mQrRc5qbdJOe4dRkt7rMbtZC3U75c3r3IpmoCSpyFUVnEgEkAScditujqt9IiuiY4r9UL11OwWGoqwRlqqBiJ/DS4sxBEW2YVFn1/nafZMCPIuIj2uZIAdYwuPnWYgcg4qiYSNfG8gC3F7U5yoKxkMCwiKLoqSipli9n2iYoACwNNXiCLMZtOef1NeOHsMYYUogjDp7LZQA4O5WqWuniNem3m0HxkMiXl4ca1j4liUN11eynv3tbLwWW17PLE1ZWaiggGxZxrsPM1A0A/mKAr/ANgQ0AMvxNREWUZb1oYwDJlp/8BAtJxAIx4luysI6Ye+TJFVHVdFAUxRifr6nPVhQZHF+KoS/up1q2EOJHO0kE7TT3mEEA7yOaa8PTACPM2VQgHOP3TqNunHkNZ+7KKnO3rKqaLi1UUS6KDW0W7EZlH06agGt0b2yJ4B+fgwH+QOajfnx+dNJ/MlH60gVJavJdoVDWdbw1Qt0y6LO3ZMp6ueQKjItx6QpYDIs4q3bKVCuFMH9LsDdi3pFM7CUKaOm6nhmOoIbq3nohonPnU4g6uecJuTNDcSfn4s55x/E50z6P4wmwxxhSiB0Qyd9GabGt1so5WsKvnN9tSenSLtNvW0HFhKBhgWwrdEPt8u4v10G0NrfDvBebDU/s7Yd+enDDGTNQFnWoOomFsb8SAQF3N4q4sRYoCWwMRH24dJ8vOeI717fCdH6w4FoOYFAGBV6sd2H4Qywe+KKLOP0R7KdQfNxf097sHOTEVw5lcBKttoy4KmT9g4jGNB8THt9YN/jmfEgbm2WGtY03WZ/NTvy7Alv9vfG0bsBLEUzGhIWBJaBj99tD/DyYhzpsjXYalD26SgGtMhKaEj082M4qB+Q26P9g9tWD6XJyO4DYC+Wv/HsdNtFXVBgMR/3t/TRGAvw+GA5h0RQAM/uOpb2swD3WtQXaio28jVUZR3nJkO4t11Grio39IJqdoQtJgIABvc5kylEo0m30QoCYRh00hcAQ9f4oMg6pcwVRcN6roqr99Jtex610+ReNvVujbZ7MUmq0bLgarfYan5m7cahsmagKKmOU2q7JGOrKGE+5sejdBkXpiJO5pI7xb0X3e3GHhCtPxyIlhMIhFGgl33DYTgDbBtMmcBf3U5hPV8DQ1PQDQaP0mWILNPTHiwosnj92ametXcYwYDmY7obi///27v34LjK+/7jn9WuVqu7rKslkPEt8QXsxBjsmEsNxMENxYk7vwlMyHgMQ0jSmDTgaQgtBHMLuDTNQClJGrsE+huCkzQk09aOA7h2WoyJwZf8nNiY+BabRJLv1s3SrrTP7w+xa+1KK+052t2zu+f9mvGMtTc95+js5/uc55zznMiledLQPk0q+gITa0rk80p9/VJHTyjmDK6mysDA9C8fzD0ZOVkpVfUpVw9oZV+L8oCdjSFTG9DgL05tmV9vHT4dHdEefPT3bHdIp7uCCTt1V34wp0b8c0WFAxN4x1+mEPnMVFxf2/HBjkZdeUA9ff26aFyxjKTiQp8WTK4YMmHa4C95Ktcz8z9kr2y5Vh3uMlK+vH34tE509Foe4LEqvnNUW+bX1oMnh2R85HcnymQrO/WDMzp+Tr7B89uN1Nka/J1taz+vd1s71B82Onv+wu8+H+xXWZFP9RUBvXe8Ux09IdWUFdnuyCVbD8h655DlAJxkZb/BqcGAP549r6Onu1Va5NXelvbo474Cj/rCfnX0DMyXa2UfzE72puNgQPxnRubTHXxpXoTd/cxEf7ejp7s1sbZUbed61NJ/YVBp8NnYI00PMBa5ekCLypwGdjaGTGxA8V+c3g9GXntC4SFHf6WBzvK08eUjBkv8c+e6g+oNhaNzNsXPs5GK62uDH3y5vQUelfh98nkvnJnU2t6jj89oUKDQO2x7U7memf8hu431WnXAqpHypbO3T0dPd0ezarB0Zbw0kMOJMl4aOZOT7VjGZ/TguZjCYaNJtaWqLPbreEevunv7E14WEPnOdgX7VFNWpJZzsQcJjAbWY02pXzfPblR9eUATa0ttd+SSrQdkvbPIcgBOsbLf4NRgwNnuoA6d6NTU+tjP7gsbnekKqic0cEDL6j6YnexNx8GAwZ955GSnPlRfFr00L56VZYxcwfP+mW75vAWqK/PHzAsZNtKRk11afOl4zbq4Ur/7U7uKfAXySNFL/BNNDzBWuXpAi95QGtjZGDKxAcUH3uA76PSEwtGjvxGRzvJIwRL/3O6jZ7T98OmY21QOHtlNxfW1fm9skBT5CtTxwf/DRgoUehO2N5XrmfkfAAw2Ur70hsIK9YcTPp+OjJcu5PxwGS+NPiiSTMdyuIEXv69AdeVFGl8R0PbDpy3NuRf5vPislwYGl/qN0cnOoOZNqhlTRy7ZekDWA4A7WdlvcGowIGyMekJhFXo9Q57rC5voZVqZOgiSjoMBgz9zb0tHwtclu4yDz+5uOXdefzjVPey8kGEjnQ/1a9ZFVXq3tUMnP5hHabB09ANy9YDW8PfmxZjY2RgysQHFB55HA4M+EYN3eux8STp7+rT98Gmd64ntfEfm2ZhQXTKm62sjInN46IP2D47R0dqdyvUcOT1zcNsibWD+B8B9RsqXosICFQ4zUBKRjoyXYnM+fmArVZ2h+IyOiMy5F+qPvQ9v5LKAzp7hO9mRzxuc9RGRzE9F25OtB2Q9ALiTlf0GpwYDSvw+NZQXqaunT02VsXWx1O9ToXfkSbdzSaL+hpR8vyD+7O7IQazI/mpNqT/m9SV+X8b7AalYTicwsJQGdjaGTGxA8YF3qiuoq6fWRnc6Ijs9dr8kA5PF9mtyXdmQnYFzPSE1V9s7FTL+yxyZw2NCdYmunlqrU10XZuEfrd2pXs+R0zNvmtWo66bV6aZZjfrM3Oasna0fQPqMlC+RGx4MJ10ZL8Xm/OCBrVR2hhJ1uJKZc2+kz6uvKIqpJ5Gjif3GpKTtVuoBWQ8A7mOlTjg1GBAo9OrqqbXq7O3TvEnV0cGlUv9Av6O+vChvDoKkYoAn/uzuwQexBk/EHfncyN8tk/2AXD2g5THGmNFf5m7t7e2qrKzUuXPnVFFRkdR7svGucJ09fUNuOV3gGTiqXFRYoEuqS1VZ4rd9LezOo2f0q/0nJA3clrGjJxRzm8obL23QnAnjxtT+wdfsVpf6dboraPka3ly8fSPgFDv5ly9Snf1Seu8KN1zGSwM5P6G6RFPry9UXDqdtAuT4jD7bHdSbB08lfP110+pGrAmRzzvXHVS/MSrx+xQo9Ka07dQDYCg3577E8iNWLtwV7qc7j8nr8cjjkQJ+r2QGLsH3+zxaOK1BdeVD53fMZfH9DSv9gsH7qxHtPaHoTUc+MbNBHT19WdEXGMty2jHW7GNgKQl2V7KdjWGsG1Dk/V3BPpX5fUMmSU1n4O1v7dCGPS0Jn79pVmPWTMCZ6S8qkKvc3MFOR/Yn81yi/E5GNg2U5EpNoB4Asdyc+xLLj6GSrROdPX16/0y32jp61N9vVFce0KTa0rTXlGyq/dkuUd8kclLEjZc2qKGi2JV9gbFmn7vWVobZmbxsLBOeJRMq6bx1by5NcDrSek7Fzt1I0v35AJxl5YYHEanqFGbT7dmztSYMl8HpHOAi8wEgtyWzfzZ8He/WuNLCtGd+NtV+OzJZJxP1Tfy+Ak2pL9PlE6rT9rvzvT+QP0vicsPdZlq6MEnqZ+Y2RzfcdN26N3I9aKKdo1z44qR7xJ8jCgDiWcnvZGTL7dmzsSZkOoPJfADIf6mu43ZkS+23KtN10qm+iRv6A9m/p4+kDHeb6YjIJKmZCJtUjJg7NZqb7qKQDUUHQPbJlvxOh2RqQqYyP9MZTOYDgDvkcx1Ph0jdP9sd1NHT3Sos8KjAI4U/mKAn3XUy02d4uaU/kPtLAEnD32Z6sO5Rnk+ldF/Oly7pLgoUHQDDyab8ToeRakImMz/TGUzmA4A75HsdT6XBdf9UZ69+f7wzetfX1vaemMGldNbJTJ7h5Zb+wNB7ACMnDXeb6cFKRnk+G4w2mtvZk95QTndRoOgAGE4+5Lcdmc78TGcwmQ8A7uDWOm5VfN0P9oclSW0dvdp64KRqSv0xr8+XOumW/gBbeZ7I1klSrXB6NDeVRWG4SzsoOgCGkw/5bUemMz/VGTzaJXxkPgC4g1vruFXxdd/vvXCOS1tHr+JvVZ8vdTJT/QGnJwfPj78WHJuILJUbsNOjuakqCoku7Vj4oTpVlxbqdBdFB8AF2TjJdTLGmv+ZzvxUdvyTuYSPHQ0AcIdcreOZFl/3ywOFChQWqCc0cOZSb184+lwu1slE/aJM9AeyYXJwtvI8kumJyFK9ATt9dDcVRWGkSzt+9fsTun56nTa/e4KiAyBGrt0qOBX5n+nMT1XHP9lJONnRAAD3yLU67oT4uu/3FWhyXZkOnehUTyisIl+BOpSbdXK0flE6+wPZMjl47vy1kJRMTUQ2lg3YydHc0Yy1KIx2aUdvyFB0AAwrV24V3NnTp7cPn5avwKPyIp+K/F55jNGprqClDowTmZ+Kjr+VS/jY0QAA98iVOu6U4ep+RaBQMxsrVej1aPbFVQoUenOuTo60X7xpX5tumtWkrt5+zWysUNgYlfh9KV1Op6eTicidvxiyit0N2MnR3GQlUxQSDY4lc2kHRQdALjtwvEOb3z2uto7e6GOD7+iSbAfGqTN67GTw4Mw/0xVUsC8sv2/4+5/EX8JH5gPu4PT8JkC2S1T36yuKMnrJVqol2i8u8EgBn1f/d9sReTye6OOp7uc4PZ1MBGkHW+xswMmc5ZStR3cjnYXuYJ88krYfPq2uYH/0+UhAOH05HwCkU2dPn17d2xYzqCRduKPLvEnVljow2ZL5I+0Qxh8QqS3za2/LOU2uK1NFoHDIZ5HzgPtkw/wmQC7IlrqfSon2i2tK/dp64KSK/V6Nr7xwFvZYL1GL77MUFgx/oCsiU/2S3P0LwlF2BlCSPcsp247uDu4s1JX5tf3waZ3rCcXsVEQC4ubZTY5fzgcA6fLHs+fVfn74HI/c0cVqB8bpzB9ph7Cq2D/kOY+kykChDp3o1MzGypgzl8h5wH2yZX4TIFc4XfdTLdF+sdFA3+hD9WVDnrN7idpwfZaJNSXyeaW+/qGvz2S/ZOThLSCByDWyw0m0AWfLaXpWxHcWIgHREwrr0IlOBQfdveBsd0inu4IDOyNx6yYXJ6EDgHhdwb7oXVwSyaWBldF2CA+f7Bry3KmuoK6eWqvKQKE6epiUG3C7ZA6cAshfifaLe0NhBQoLVD7M2c2S9X3fRH2Wo6e7NaG6VKV+b8zjme6X0PuBLXbmxsjFy8TiOwu9oQsDST2hsDp6QqopK4o+1h3s07Tx5Xl3iicASAM5Hn8Xl4hAYYFmNJbnVNaNtkN4vKNnyONhI7W292jepGpNqClReaCQnAdcLBcPnAJInUT7xZUlPk2uK0s4J6PVfd9EfZawkY6c7NLiS8fL4/E4tv9JDwi2XTyuRDfPbtKB4x1qPx9SRXGhptaXq668aNjXZ8Nd36yK7ywUxR2lD/WHY36OBES+neIJwB1Gm3x28FG5mY2V6ugJKdQfVqG3QBOqSzR9fKVTTbdltB3CQq9n2MfDRjrRGdSVk2rIesDlcvHAKYDUGm6/eEJ1iTp7+1O27ztSnyVspPOhfs2ZMM5y21OFpINtw13j+W5rR8KJCp26A9BYxHcWPBq4+1Fk4tpCL3NrAMgPyUw+G5/jkTM2sznHRzLaDmF9eSDnDogAyKxcPHAKILWG60O919aheZOqtf3w6ZTs+2b7IHZu9QCRNexOVJhrdwKI7yxE5tbYeuCkzvWEotfM5upOFQBI1jI913J8JKPtEF48rkSfmOnLqQMiADIrFw+cAkidRH2o010hvXPktG6e3aTTXcEx95myfRCbpIM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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil analisis diatas menggunakan scatter plot, dapat kita tarik pernyataan sebagai berikut:\n",
        "- Hubungan antara temp dan count ternyata menunjukan korelasi yang positif diantara dua variabel.\n",
        "- Hubungan antara atemp dan count ternyata juga menunjukan korelasi yang positif diantara dua variabel.\n",
        "- Hubungan antara hum dan count ternyata menunjukan korelasi yang sedikit negatif, tetapi tidak terlalu signifikan karena condong ke kirinya tidak terlalu nampak.\n",
        "\n",
        "Kesimpulannya jika temperatur (temp dan atemp) meningkat, maka pengguna sepeda juga cenderung meningkat. Dan pengaruh dari humidity sepertinya sangat kecil disini, walaupun cenderung dapat menurunkan jumlah pengguna sepeda."
      ],
      "metadata": {
        "id": "e2Ra7X9tkvKu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Pertanyaan 5: Apakah ada pengaruh musim terhadap penyewa sepeda berdasarkan pelanggan Casual dan Registered?**"
      ],
      "metadata": {
        "id": "QvSvgj6SCfS-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengelompokkan data berdasarkan musim dan menghitung jumlah penggunaan terdaftar dan tidak terdaftar\n",
        "seasonal_usage = day_df.groupby('season')[['registered', 'casual']].sum().reset_index()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Membuat\n",
        "plt.bar(\n",
        "    seasonal_usage['season'],\n",
        "    seasonal_usage['registered'],\n",
        "    label='Registered',\n",
        "    color='tab:blue'\n",
        ")\n",
        "\n",
        "plt.bar(\n",
        "    seasonal_usage['season'],\n",
        "    seasonal_usage['casual'],\n",
        "    label='Casual',\n",
        "    color='tab:orange'\n",
        ")\n",
        "\n",
        "plt.xlabel(None)\n",
        "plt.ylabel(None)\n",
        "plt.title('Jumlah penyewaan sepeda berdasarkan musim')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "IHOYVJZqC052",
        "outputId": "1d84870a-9f82-41c5-de0e-46b68f0d3dad"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Berdasarkan hasil analisis diatas menggunakan barplot, dapat disimpulkan bahwa di semua musim yang paling dinikmati bagi para pengguna sepeda (Casual dan Registered) adalah musim gugur (Fall), lalu diikuti dengan musim panas (Summer), musim dingin (Winter), dan diakhiri dengan musim semi (Spring)."
      ],
      "metadata": {
        "id": "AFRrJzk6tfHN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## **Conclusion**"
      ],
      "metadata": {
        "id": "J2CnXYGT9Tc2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Conclusion pertanyaan 1:\n",
        "Dapat terlihat jelas terdapat hubungan diantara keduanya. Para penyewa sepeda sangat menyukai ketika cuaca sedang Cerah/Sedikit mendung dengan Q3 diatas 6000 dan Q1 kurang dari 4000 serta mediannya hampir 5000. Lalu posisi kedua ditempati ketika cuaca sedang Berkabut/Mendung dengan Q3 diatas 5000 dan Q1 dibawah 3000 serta mediannya diatas 4000. Terakhir pengguna kurang menyukai ketika suasana sedang Sedikit bersalju/Hujan dengan Q3 diatas 2000 dan Q1 dibawah 1000 serta mediannya diatas 1000. Dan untuk cuaca ketika Badai Besar tidak ada datanya, karena memang tidak memungkinan untuk bersepeda ketika kondisi cuaca semacam itu.\n",
        "\n",
        "- conclusion pertanyaan 2:\n",
        "Kondisi yang tampak pada visualisasi ini terbagi menjadi tiga bagian. Untuk jumlah pengguna sepeda berdasarkan workingday, workingday lah yang mengungguli non-workingday walaupun hasilnya memang tidak terlalu signifikan, tetapi tetap ada perbedaan yang jelas walaupun keduanya sama-sama diatas 4000 jumlah pengguna. Lanjut berdasarkan holiday (hari libur), disini non-holidaylah yang menang dengan perbedaan yang cukup signifikan daripada yang holiday. Hal ini mungkin pengguna lebih menyukai sewa sepeda saat hari-hari biasa saja. Terakhir berdasarkan weekday, berdasarkan data diatas, Hari Jumat (Fri) lah yang menempati urutan pertama dengan mean 4690.288462 dan di urutan terakhir ditempati oleh Minggu (Sun): 4228.828571.\n",
        "\n",
        "- Conclusion pertanyaan 3:\n",
        "Pada visualisasi kali ini, dapat kita amati pada kedua tahun yakni 2011 dan 2012, terjadi perbedaan puncak dan dasar bulan pada kedua tahun tersebut. Puncak dari tahun 2011 berada pada bulan Juni dan dasarnya berada pada bulan Januari. Sedangkan Puncak dari tahun 2012 terdapat pada bulan September dan dasarnya sama yakni bulan Januari. Dan dari kedua visualisasi tersebut dapat kita amati bahwa jumlah total sepeda yang disewakan pada tahun 2012, seluruhnya lebih tinggi daripada pada tahun 2011.\n",
        "\n",
        "- conclusion pertanyaan 4:\n",
        "Dari 3 visualisasi yang ditampilkan, dapat disimpulkan bahwa temp dan atemp memiliki korelasi positif dengan count, sehingga jika temperatur (temp dan atemp) meningkat, maka pengguna sepeda juga cenderung meningkat. Terbalik dengan Humidity yang memiliki korelasi negatif dengan count walaupun sangat minim (tidak terlalu signifikan), tetapi tetap dapat menurunkan jumlah pengguna sepeda ketika humiditynya meningkat.\n",
        "\n",
        "- Conclusion pertanyaan 5:\n",
        "Pada visualisasi terakhir, ternyata hasil yang didapatkan cukup mengejutkan dimana musim dingin (Winter) tidak menjadi yang terakhir, melainkan yang terakhir adalah musin semi (Spring). Hal ini bisa diakibatkan oleh banyak faktor seperti kondisi cuaca, temperatur udara, hari kerja, hari libur, dan lain-lain. Sedangkan urutan pertamanya ditempati oleh musim gugur (Fall) yang mampu meraih penyewaan sepeda sebanyak lebih dari 800000."
      ],
      "metadata": {
        "id": "f73FBkLw9V8C"
      }
    }
  ]
}