Student_MLE_MiniProject_EDA.ipynb

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        "# Mini Project: Exploratory Data Analysis\n",
        "**Project submitted by Jeffrey Dick on 2024-09-02**"
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        "# Exploratory Data Analysis: Unveiling Insights from the NYC Taxi Dataset\n",
        "\n",
        "Data has become the lifeblood of the modern world, permeating every aspect of our lives and transforming the way we make decisions. In this era of vast information, the ability to extract meaningful insights from raw data has emerged as a crucial skill. Enter exploratory data analysis (EDA), a powerful approach that allows us to unravel hidden patterns, detect anomalies, and generate valuable knowledge from the vast volumes of data at our disposal.\n",
        "\n",
        "Exploratory data analysis serves as the initial step in any data-driven investigation, offering a comprehensive understanding of the dataset's structure, distributions, and relationships between variables. By applying statistical and visual techniques, analysts gain a deeper insight into the data, paving the way for more accurate predictions, informed decision-making, and the discovery of actionable insights.\n",
        "\n",
        "To illustrate the significance of exploratory data analysis, we delve into one of the most popular and widely studied datasets in the field—the [NYC Taxi Dataset](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page). As the name suggests, this dataset captures detailed information about taxi trips within the bustling city of New York. The NYC Taxi Dataset is an ideal choice for learning and practicing EDA techniques due to its richness, complexity, and real-world applicability.\n",
        "\n",
        "The dataset encompasses a vast range of attributes, including pickup and drop-off locations, timestamps, trip durations, passenger counts, payment information, and much more. By exploring this data, we can gain valuable insights into the dynamics of taxi usage, understand travel patterns across different neighborhoods, identify peak hours of demand, analyze fare structures, and even uncover interesting anecdotes about the city's vibrant life.\n",
        "\n",
        "The NYC Taxi Dataset is an excellent resource for aspiring ML practitioners to develop their EDA skills. Its scale, complexity, and real-world relevance make it an engaging playground for uncovering hidden patterns, generating hypotheses, and forming data-driven narratives.\n",
        "\n",
        "In this mini project, we will dive deep into the NYC Taxi Dataset. We will leverage various EDA techniques to unveil meaningful insights, visualize data distributions, identify outliers, and pose insightful questions that will fuel further analysis and exploration. By the end of this colab, students will have a solid foundation in exploratory data analysis and be equipped to tackle real-world data challenges with confidence."
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
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        "Load the NYC taxi dataset into a Pandas DataFrame and do a few basic checks to ensure the data is loaded properly. Note, there are several months of data that can be used. For simplicity, use the Yellow Taxi 2022-01 parquet file [here](https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet). Here are your tasks:\n",
        "\n",
        "  1. Load the `yellow_tripdata_2022-01.parquet` file into Pandas.\n",
        "  2. Print the first 5 rows of data. Study the schema and make sure you understand what each of the fields mean by referencing the [documentation](https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf).\n",
        "  3. How many rows are in the dataset? How many unique columns are in the dataset? **2463931 rows and 19 columns**\n",
        "  4. Which columns have NULL values and how many NULL values are present in each of these columns? **passenger_count, RatecodeID, store_and_fwd_flag, congestion_surcharge, and airport_fee each have 71503 NULL values**\n",
        "  5. Generate summary statistics using Pandas' [describe method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html). Do you notice anything unusual in the dataset? Find at least one anomoly and try to come up with a hypothesis to explain it.  **(1) Values for some fares are negative. A negative value might indicate a refund. (2) Some passenger counts are zero. This could happen if the driver did not enter the information, and so it should be treated as a missing value rather than zero. (3) The highest tip is $888.88. I would guess that this tip is much higher than the base fare for this trip.**\n",
        "  6. Drop all rows with NULL values and store the result. We'll ignore NULL valued rows in this mini-project.\n"
      ],
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        "# Load parquet file into a Pandas DataFrame\n",
        "tripdata = pd.read_parquet(\"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2022-01.parquet\")"
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        "# Display the first few rows of the dataset\n",
        "tripdata.head()"
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              "   VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
              "0         1  2022-01-01 00:35:40   2022-01-01 00:53:29              2.0   \n",
              "1         1  2022-01-01 00:33:43   2022-01-01 00:42:07              1.0   \n",
              "2         2  2022-01-01 00:53:21   2022-01-01 01:02:19              1.0   \n",
              "3         2  2022-01-01 00:25:21   2022-01-01 00:35:23              1.0   \n",
              "4         2  2022-01-01 00:36:48   2022-01-01 01:14:20              1.0   \n",
              "\n",
              "   trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
              "0           3.80         1.0                  N           142           236   \n",
              "1           2.10         1.0                  N           236            42   \n",
              "2           0.97         1.0                  N           166           166   \n",
              "3           1.09         1.0                  N           114            68   \n",
              "4           4.30         1.0                  N            68           163   \n",
              "\n",
              "   payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
              "0             1         14.5    3.0      0.5        3.65           0.0   \n",
              "1             1          8.0    0.5      0.5        4.00           0.0   \n",
              "2             1          7.5    0.5      0.5        1.76           0.0   \n",
              "3             2          8.0    0.5      0.5        0.00           0.0   \n",
              "4             1         23.5    0.5      0.5        3.00           0.0   \n",
              "\n",
              "   improvement_surcharge  total_amount  congestion_surcharge  airport_fee  \n",
              "0                    0.3         21.95                   2.5          0.0  \n",
              "1                    0.3         13.30                   0.0          0.0  \n",
              "2                    0.3         10.56                   0.0          0.0  \n",
              "3                    0.3         11.80                   2.5          0.0  \n",
              "4                    0.3         30.30                   2.5          0.0  "
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              "        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-39b13606-6988-450f-b423-3040eb08b7ce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tripdata"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the shape of the dataset\n",
        "tripdata.shape"
      ],
      "metadata": {
        "id": "jkQN2lBymKH-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3842de38-d39d-4fe5-9c0b-c91ee48b7f7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2463931, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "tripdata.isnull().sum()"
      ],
      "metadata": {
        "id": "N8JeOtV5mQJ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "603b2977-cbe6-4815-c7d6-bc8b2c61d41f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VendorID                     0\n",
              "tpep_pickup_datetime         0\n",
              "tpep_dropoff_datetime        0\n",
              "passenger_count          71503\n",
              "trip_distance                0\n",
              "RatecodeID               71503\n",
              "store_and_fwd_flag       71503\n",
              "PULocationID                 0\n",
              "DOLocationID                 0\n",
              "payment_type                 0\n",
              "fare_amount                  0\n",
              "extra                        0\n",
              "mta_tax                      0\n",
              "tip_amount                   0\n",
              "tolls_amount                 0\n",
              "improvement_surcharge        0\n",
              "total_amount                 0\n",
              "congestion_surcharge     71503\n",
              "airport_fee              71503\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VendorID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>passenger_count</th>\n",
              "      <td>71503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trip_distance</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RatecodeID</th>\n",
              "      <td>71503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <td>71503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PULocationID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DOLocationID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>payment_type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fare_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>extra</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mta_tax</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tip_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tolls_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>congestion_surcharge</th>\n",
              "      <td>71503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>airport_fee</th>\n",
              "      <td>71503</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary statistics of the dataset\n",
        "tripdata.describe()"
      ],
      "metadata": {
        "id": "h9q5SOrql5mS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "1fe6351f-0102-4cd0-96bf-85b09ebcc08d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           VendorID        tpep_pickup_datetime       tpep_dropoff_datetime  \\\n",
              "count  2.463931e+06                     2463931                     2463931   \n",
              "mean   1.707819e+00  2022-01-17 01:19:51.689724  2022-01-17 01:34:04.421902   \n",
              "min    1.000000e+00         2008-12-31 22:23:09         2008-12-31 23:06:56   \n",
              "25%    1.000000e+00         2022-01-09 15:37:41  2022-01-09 15:50:50.500000   \n",
              "50%    2.000000e+00         2022-01-17 12:11:45         2022-01-17 12:23:49   \n",
              "75%    2.000000e+00  2022-01-24 13:49:37.500000         2022-01-24 14:02:51   \n",
              "max    6.000000e+00         2022-05-18 20:41:57         2022-05-18 20:47:45   \n",
              "std    5.021375e-01                         NaN                         NaN   \n",
              "\n",
              "       passenger_count  trip_distance    RatecodeID  PULocationID  \\\n",
              "count     2.392428e+06   2.463931e+06  2.392428e+06  2.463931e+06   \n",
              "mean      1.389453e+00   5.372751e+00  1.415507e+00  1.660768e+02   \n",
              "min       0.000000e+00   0.000000e+00  1.000000e+00  1.000000e+00   \n",
              "25%       1.000000e+00   1.040000e+00  1.000000e+00  1.320000e+02   \n",
              "50%       1.000000e+00   1.740000e+00  1.000000e+00  1.620000e+02   \n",
              "75%       1.000000e+00   3.130000e+00  1.000000e+00  2.340000e+02   \n",
              "max       9.000000e+00   3.061593e+05  9.900000e+01  2.650000e+02   \n",
              "std       9.829686e-01   5.478714e+02  5.917573e+00  6.546806e+01   \n",
              "\n",
              "       DOLocationID  payment_type   fare_amount         extra       mta_tax  \\\n",
              "count  2.463931e+06  2.463931e+06  2.463931e+06  2.463931e+06  2.463931e+06   \n",
              "mean   1.635807e+02  1.194449e+00  1.294648e+01  1.006710e+00  4.914539e-01   \n",
              "min    1.000000e+00  0.000000e+00 -4.800000e+02 -4.500000e+00 -5.000000e-01   \n",
              "25%    1.130000e+02  1.000000e+00  6.500000e+00  0.000000e+00  5.000000e-01   \n",
              "50%    1.620000e+02  1.000000e+00  9.000000e+00  5.000000e-01  5.000000e-01   \n",
              "75%    2.360000e+02  1.000000e+00  1.400000e+01  2.500000e+00  5.000000e-01   \n",
              "max    2.650000e+02  5.000000e+00  4.010923e+05  3.350000e+01  1.659000e+01   \n",
              "std    7.079016e+01  5.001778e-01  2.558149e+02  1.236226e+00  8.291560e-02   \n",
              "\n",
              "         tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
              "count  2.463931e+06  2.463931e+06           2.463931e+06  2.463931e+06   \n",
              "mean   2.385696e+00  3.749773e-01           2.967234e-01  1.916937e+01   \n",
              "min   -1.252200e+02 -3.140000e+01          -3.000000e-01 -4.803000e+02   \n",
              "25%    7.200000e-01  0.000000e+00           3.000000e-01  1.130000e+01   \n",
              "50%    2.000000e+00  0.000000e+00           3.000000e-01  1.469000e+01   \n",
              "75%    3.000000e+00  0.000000e+00           3.000000e-01  2.002000e+01   \n",
              "max    8.888800e+02  1.933000e+02           3.000000e-01  4.010956e+05   \n",
              "std    2.830698e+00  1.680538e+00           4.374741e-02  2.559641e+02   \n",
              "\n",
              "       congestion_surcharge   airport_fee  \n",
              "count          2.392428e+06  2.392428e+06  \n",
              "mean           2.282322e+00  8.249935e-02  \n",
              "min           -2.500000e+00 -1.250000e+00  \n",
              "25%            2.500000e+00  0.000000e+00  \n",
              "50%            2.500000e+00  0.000000e+00  \n",
              "75%            2.500000e+00  0.000000e+00  \n",
              "max            2.500000e+00  1.250000e+00  \n",
              "std            7.432040e-01  3.125554e-01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cf1e4e27-5b0b-4487-9583-8eb6543e4e86\" 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 th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>congestion_surcharge</th>\n",
              "      <th>airport_fee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2463931</td>\n",
              "      <td>2463931</td>\n",
              "      <td>2.392428e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.392428e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.463931e+06</td>\n",
              "      <td>2.392428e+06</td>\n",
              "      <td>2.392428e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.707819e+00</td>\n",
              "      <td>2022-01-17 01:19:51.689724</td>\n",
              "      <td>2022-01-17 01:34:04.421902</td>\n",
              "      <td>1.389453e+00</td>\n",
              "      <td>5.372751e+00</td>\n",
              "      <td>1.415507e+00</td>\n",
              "      <td>1.660768e+02</td>\n",
              "      <td>1.635807e+02</td>\n",
              "      <td>1.194449e+00</td>\n",
              "      <td>1.294648e+01</td>\n",
              "      <td>1.006710e+00</td>\n",
              "      <td>4.914539e-01</td>\n",
              "      <td>2.385696e+00</td>\n",
              "      <td>3.749773e-01</td>\n",
              "      <td>2.967234e-01</td>\n",
              "      <td>1.916937e+01</td>\n",
              "      <td>2.282322e+00</td>\n",
              "      <td>8.249935e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2008-12-31 22:23:09</td>\n",
              "      <td>2008-12-31 23:06:56</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-4.800000e+02</td>\n",
              "      <td>-4.500000e+00</td>\n",
              "      <td>-5.000000e-01</td>\n",
              "      <td>-1.252200e+02</td>\n",
              "      <td>-3.140000e+01</td>\n",
              "      <td>-3.000000e-01</td>\n",
              "      <td>-4.803000e+02</td>\n",
              "      <td>-2.500000e+00</td>\n",
              "      <td>-1.250000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2022-01-09 15:37:41</td>\n",
              "      <td>2022-01-09 15:50:50.500000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.040000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.320000e+02</td>\n",
              "      <td>1.130000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.500000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>7.200000e-01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.130000e+01</td>\n",
              "      <td>2.500000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2022-01-17 12:11:45</td>\n",
              "      <td>2022-01-17 12:23:49</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.740000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.620000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>1.469000e+01</td>\n",
              "      <td>2.500000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2022-01-24 13:49:37.500000</td>\n",
              "      <td>2022-01-24 14:02:51</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.130000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.340000e+02</td>\n",
              "      <td>2.360000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.400000e+01</td>\n",
              "      <td>2.500000e+00</td>\n",
              "      <td>5.000000e-01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>2.002000e+01</td>\n",
              "      <td>2.500000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>2022-05-18 20:41:57</td>\n",
              "      <td>2022-05-18 20:47:45</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>3.061593e+05</td>\n",
              "      <td>9.900000e+01</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>2.650000e+02</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>4.010923e+05</td>\n",
              "      <td>3.350000e+01</td>\n",
              "      <td>1.659000e+01</td>\n",
              "      <td>8.888800e+02</td>\n",
              "      <td>1.933000e+02</td>\n",
              "      <td>3.000000e-01</td>\n",
              "      <td>4.010956e+05</td>\n",
              "      <td>2.500000e+00</td>\n",
              "      <td>1.250000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.021375e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.829686e-01</td>\n",
              "      <td>5.478714e+02</td>\n",
              "      <td>5.917573e+00</td>\n",
              "      <td>6.546806e+01</td>\n",
              "      <td>7.079016e+01</td>\n",
              "      <td>5.001778e-01</td>\n",
              "      <td>2.558149e+02</td>\n",
              "      <td>1.236226e+00</td>\n",
              "      <td>8.291560e-02</td>\n",
              "      <td>2.830698e+00</td>\n",
              "      <td>1.680538e+00</td>\n",
              "      <td>4.374741e-02</td>\n",
              "      <td>2.559641e+02</td>\n",
              "      <td>7.432040e-01</td>\n",
              "      <td>3.125554e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "        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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\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",
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              "\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",
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              "\n",
              "  @keyframes spin {\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",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"tripdata\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"VendorID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 871130.4415283408,\n        \"min\": 0.502137472729769,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2463931.0,\n          1.707819334226486,\n          0.502137472729769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tpep_pickup_datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.002463931\",\n        \"max\": \"2022-05-18 20:41:57\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2463931\",\n          \"2022-01-17 01:19:51.689724\",\n          \"2022-01-24 13:49:37.500000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tpep_dropoff_datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.002463931\",\n        \"max\": \"2022-05-18 20:47:45\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2463931\",\n          \"2022-01-17 01:34:04.421902\",\n          \"2022-01-24 14:02:51\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passenger_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 845850.3052382031,\n        \"min\": 0.0,\n        \"max\": 2392428.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2392428.0,\n          1.3894533085217193,\n          0.9829685911276167\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trip_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 862316.4394171573,\n        \"min\": 0.0,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.372751193113768,\n          3.13,\n          2463931.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RatecodeID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 845845.4591724741,\n        \"min\": 1.0,\n        \"max\": 2392428.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.4155067571521485,\n          5.9175732652200015,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PULocationID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 871079.3655220721,\n        \"min\": 1.0,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          166.07680937493785,\n          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\"fare_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 862381.8061838396,\n        \"min\": -480.0,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.946484016800797,\n          14.0,\n          2463931.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"extra\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 871129.4297891044,\n        \"min\": -4.5,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.0067101432629406,\n          2.5,\n          2463931.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mta_tax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 871130.2418174772,\n        \"min\": -0.5,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2463931.0,\n          0.4914538921747404,\n          0.08291560479694038\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 871092.0952594293,\n        \"min\": -125.22,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.3856955409871454,\n          3.0,\n          2463931.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tolls_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 871122.8810638053,\n        \"min\": -31.4,\n        \"max\": 2463931.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2463931.0,\n          0.3749772660029846,\n          1.6805380158244387\n        ],\n        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      \"dtype\": \"number\",\n        \"std\": 845850.499532706,\n        \"min\": -2.5,\n        \"max\": 2392428.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.282322498315519,\n          0.7432039804760401,\n          -2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airport_fee\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 845851.0111972755,\n        \"min\": -1.25,\n        \"max\": 2392428.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2392428.0,\n          0.08249934794275941,\n          0.3125554417761801\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with missing values.\n",
        "tripdata.dropna(how = 'any', inplace = True)\n",
        "tripdata.isnull().sum()"
      ],
      "metadata": {
        "id": "VP3EWMIEo4sp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "27adfe52-c59f-4a7a-d71b-58de2a9be430"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VendorID                 0\n",
              "tpep_pickup_datetime     0\n",
              "tpep_dropoff_datetime    0\n",
              "passenger_count          0\n",
              "trip_distance            0\n",
              "RatecodeID               0\n",
              "store_and_fwd_flag       0\n",
              "PULocationID             0\n",
              "DOLocationID             0\n",
              "payment_type             0\n",
              "fare_amount              0\n",
              "extra                    0\n",
              "mta_tax                  0\n",
              "tip_amount               0\n",
              "tolls_amount             0\n",
              "improvement_surcharge    0\n",
              "total_amount             0\n",
              "congestion_surcharge     0\n",
              "airport_fee              0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>VendorID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>passenger_count</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trip_distance</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RatecodeID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PULocationID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DOLocationID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>payment_type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fare_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>extra</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mta_tax</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tip_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tolls_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>congestion_surcharge</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>airport_fee</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature engineering is a critical process in machine learning that involves selecting, transforming, and creating features from raw data to improve the performance and accuracy of predictive models. While machine learning algorithms have the capability to automatically learn patterns from data, the quality and relevance of the features used as inputs greatly influence the model's ability to generalize and make accurate predictions. Feature engineering, therefore, plays a crucial role in extracting meaningful information and representing it in a format that best captures the underlying relationships within the data.\n",
        "\n",
        "Here are your tasks:\n",
        "\n",
        "  1. Create a new feature that calculates the trip duration in minutes.\n",
        "  2. Create additional features for the pick-up day of week and pick-up hour.\n",
        "  3. Use the Seaborn library to create a [line plot](https://seaborn.pydata.org/generated/seaborn.lineplot.html) depicting the number of trips as a function of the hour of day. What's the busiest time of day? **Hour 18 (6-7 p.m.)**\n",
        "  4. Create another lineplot depicting the number of trips as a function of the day of week. What day of the week is the least busy? **Day 1 (Tuesday)**"
      ],
      "metadata": {
        "id": "u2i5l3QNFAf3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new column for trip duration in minutes\n",
        "# https://stackoverflow.com/questions/22923775/calculate-time-difference-between-two-pandas-columns-in-hours-and-minutes\n",
        "duration = tripdata['tpep_dropoff_datetime'] - tripdata['tpep_pickup_datetime']\n",
        "duration_minutes = duration / pd.Timedelta(minutes = 1)\n",
        "tripdata['trip_duration'] = duration_minutes"
      ],
      "metadata": {
        "id": "Wef2rR1Npl8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create new columns for pickup hour and day of week\n",
        "tripdata['pickup_hour'] = tripdata['tpep_pickup_datetime'].dt.hour\n",
        "tripdata['pickup_day'] = tripdata['tpep_pickup_datetime'].dt.weekday"
      ],
      "metadata": {
        "id": "d13PYaN2FPFt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a lineplot displaying the number of trips by pickup hour\n",
        "\n",
        "# Use default seaborn theme\n",
        "sns.set_theme()\n",
        "sns.lineplot(tripdata['pickup_hour'].value_counts())"
      ],
      "metadata": {
        "id": "-Bf7bnS9uU-h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "4461d020-cc06-42b5-d51e-1cb195494e8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='pickup_hour', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a lineplot displaying the number of trips by pickup day\n",
        "sns.lineplot(tripdata['pickup_day'].value_counts())"
      ],
      "metadata": {
        "id": "iGjHC9lHuO8r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "39719db8-dc4e-4876-d3bb-3a365827567b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='pickup_day', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": [
        "In the realm of machine learning, understanding the relationships between variables is crucial for building accurate and effective predictive models. One powerful tool for exploring these relationships is the correlation matrix. A correlation matrix provides a comprehensive overview of the pairwise correlations between variables in a dataset, allowing practitioners to quantify and visualize the strength and direction of these associations. This matrix is an essential component of exploratory data analysis and offers several key benefits:\n",
        "\n",
        "1. Relationship Assessment: The correlation matrix provides a quantitative measure of the relationship between variables. By calculating correlation coefficients, typically using methods like Pearson's correlation coefficient, analysts can determine if variables are positively correlated (increase together), negatively correlated (one increases as the other decreases), or uncorrelated (no systematic relationship). These measures offer insights into the direction and strength of the relationships, helping to identify important variables that may influence the target variable.\n",
        "\n",
        "2. Feature Selection: Correlation matrices are invaluable in feature selection, which involves identifying the most relevant variables for building predictive models. By examining the correlations between the target variable and other features, analysts can identify highly correlated variables that may be strong predictors. This knowledge enables informed decisions regarding which variables to include in the model, potentially reducing dimensionality, enhancing model efficiency, and preventing issues such as multicollinearity.\n",
        "\n",
        "3. Multicollinearity Detection: Multicollinearity occurs when two or more independent variables in a model are highly correlated. This can lead to problems such as instability in coefficient estimates, difficulty in interpreting feature importance, and reduced model robustness. By examining the correlation matrix, analysts can identify highly correlated variables and make informed decisions about which ones to include or exclude to mitigate multicollinearity. Removing redundant variables improves model interpretability and generalization.\n",
        "\n",
        "Here is your task:\n",
        "\n",
        "  1. Compute a correlation matrix between the variables 'trip_distance', 'fare_amount', 'tip_amount', 'total_amount', 'trip_duration' and use Seaborn to create a heatmap of the results. Which variables are strongly correlated? **total_amount and fare_amount are highly correlated; tip_amount and trip_distance are moderately correlated**"
      ],
      "metadata": {
        "id": "yVQLUFXPGe4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute correlation matrix of numerical variables\n",
        "correlation_matrix = tripdata[['trip_distance', 'fare_amount', 'tip_amount', 'total_amount', 'trip_duration']].corr()\n",
        "print(correlation_matrix)\n",
        "\n",
        "# Create a heatmap of the correlation matrix\n",
        "sns.heatmap(correlation_matrix, cmap = \"Greens\", annot = True)"
      ],
      "metadata": {
        "id": "XCY8MrLAppQz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "outputId": "1c8fd751-cc9f-4007-901e-314ec61ad99a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               trip_distance  fare_amount  tip_amount  total_amount  \\\n",
            "trip_distance       1.000000     0.039426    0.491978      0.048724   \n",
            "fare_amount         0.039426     1.000000    0.024196      0.999878   \n",
            "tip_amount          0.491978     0.024196    1.000000      0.038133   \n",
            "total_amount        0.048724     0.999878    0.038133      1.000000   \n",
            "trip_duration       0.165854     0.008134    0.086206      0.009720   \n",
            "\n",
            "               trip_duration  \n",
            "trip_distance       0.165854  \n",
            "fare_amount         0.008134  \n",
            "tip_amount          0.086206  \n",
            "total_amount        0.009720  \n",
            "trip_duration       1.000000  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pairplots, also known as scatterplot matrices, allow for the visualization of pairwise relationships between multiple variables simultaneously. Each subplot in the pairplot represents the scatterplot of one variable against another. Pairplots offer several advantages in EDA:\n",
        "\n",
        "  - Variable Relationships: Pairplots enable analysts to explore the relationships between variables, revealing patterns such as linear or nonlinear correlations, clusters, or other associations. These visual cues guide further analysis, feature selection, or modeling decisions.\n",
        "\n",
        "  - Multivariate Analysis: Pairplots help identify multivariate dependencies and interactions, highlighting how different variables jointly influence one another. This is particularly valuable in identifying potential confounding factors or discovering hidden interactions that may not be apparent when considering variables in isolation.\n",
        "\n",
        "  - Outlier Detection: Pairplots can reveal potential outliers by showing data points that deviate significantly from the general pattern observed between variables. Outliers can indicate data anomalies or influential observations that may impact model performance.\n",
        "\n",
        "  - Feature Importance: Pairplots provide an intuitive representation of the relative importance of different features. Variables exhibiting strong correlations or clear patterns may be more relevant for predictive modeling or feature selection.\n",
        "\n",
        "  - Data Quality: Pairplots can help identify data quality issues, such as data entry errors or measurement inconsistencies. Patterns that do not align with expectations or exhibit unusual trends may signal data problems that require further investigation or preprocessing.\n",
        "\n",
        "Here is your task:\n",
        "\n",
        "  1. Create a [pairplot matrix](https://seaborn.pydata.org/generated/seaborn.pairplot.html) using Seaborn to observation the relationship between the following variables: trip_distance, fare_amount, tip_amount, total_amount, trip_duration. Note, pairplots can be memory intensive. Try sampling the dataset using the [sample method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sample.html) in Pandas. Which variables appear to have a strong relationship? Which variables seem to have no relationship? **fare_amount shows some correlation with trip_distance but not with trip_duration or tip_amount. The \\$888.88 tip was for a relatively low fare (around \\$10).**"
      ],
      "metadata": {
        "id": "Eh6TJ8iRJHDm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot matrix of numerical variables. If memory issues try the df.sample method.\n",
        "\n",
        "# Set the random_state to sample the trip with a tip of $888.88\n",
        "tripdata_sampled = tripdata.sample(100000, random_state = 4)\n",
        "sns.pairplot(tripdata_sampled[['trip_distance', 'fare_amount', 'tip_amount', 'total_amount', 'trip_duration']], height = 2)"
      ],
      "metadata": {
        "id": "l_v24Ym3p8A-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "097e1203-a7b1-44b4-ed23-1a43b8a00678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7eebc1372980>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 30 Axes>"
            ],
            "image/png": 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l5eEPf/gDHnzwQfzud7/zWzZhwgT89Kc/RX5+fqyLQUQpKM3oee412LQvpUVWmPUSoGk9UDJKBd7nqtm+qDdifKV4YrylZBeXyz4jRozA+vXrUV9fj6+//hqAJ0EZ56wmou6QBODWRaV4ekOF3w9xaZEVyxYUQ+APMHWDoGlYtqAY6zZVsX1Rr8P4SvHEeEvJLq5jKXJzc9mRJqKosmabcdtlJWh1yp5nXE06mPUSf4ApKiRNw22XlcAhq2hudbF9Ua/C+ErxxHhLySwunWpFUfDxxx/jq6++QmNjI7R2B4cgCFi+fHk8ikJEKUgSAItOPJM0ij/AFEWSAJzTLwMNDa2QZZXti3oVxleKJ8ZbSlYx71Tv2bMHt912G7777ruAzrQXO9VERERERESUjGLeqf75z38Oh8OBtWvXYvz48cjMzIz1JomIiIiIiIjiIuad6urqatxxxx248MILY70pIiIiIiIiorgSY72BAQMGhBz2TURERERERJTMYt6pXrp0KTZs2ICWlpZYb4qIiIiIiIgormI+/Lu1tRVpaWn4wQ9+gFmzZmHAgAGQJMlvHUEQcN1118W6KERERERERERRFfNO9X//93/7/r1+/fqg67BTTURERERERMko5p3q999/P9abICIiIiIiIuoRMe9Un3322bHeBBEREREREVGPiHmiMiIioq7SBAE2WcWJFhdssgpNEOK+/SaHguraejQ7lbhvn4h6Xk/HIYo+7lOKtpjfqQaA/fv3Y/369fjiiy/Q3NwMVVX9lguCgG3btsWjKERElCQUQcC6jVWoqKnzvVZaZMWyBcWQ4jBVY09vn4h6HuNA6uE+pViI+Z3qTz/9FAsXLsTf/vY39OvXD1999RUGDRqEfv364ejRo7BYLJgwYUKsi0FERG0k+lV6LchJDwBUVNdh3aaqmJe3p7dPRN0TjRjHONBzFA0x+Y3iPqVYifmd6qeeegqDBg3Chg0b4HK5cO655+Kmm27C5MmTUVlZiaVLl+LOO++MdTGIiJKCJgiwuxXYHDIsJh3MeglClK+cJ8NVertbCTjp8aqoroPdrcCii9114Ujbb3UpSDNEf98Qkb+uxMRoxbiejkO9Vd0pO55+vRIVNXUwGSSUl+WjeHhf6HUi0rr5u8h9SrES8071F198gVtvvRXp6elobGwEAN/w75KSElx++eX4zW9+g/POOy/WRSEiSmjx6OxGukq/YkFxQnQUbQ454nJLuqHHtn+0rhXvfnokoS5EEKWarsTEaMa4no5DvZGiAU+/XuHrUK+6ejy2fHQIG7bV+Nbpzu8i9ynFSswvxUiShLS0NABAZmYmdDodTp486Vs+aNAgHDx4MNbFICJKaPEaktaRq/SJwGIKf8030vJYb9+gFzlckCiGuhoToxnjejoO9UatTgUV1Z79V16Wjy0fHULlgej9LnKfUqzEvFM9ePBgHDlyBIAnIVleXp5fUrK//e1v6Nu3b6yLQUSU0OLV2e3IVfpEYNZLKC2yBl1WWmSFWS/12PZLCqzYX9sAILEuRBClkq7GxGjGuJ6OQ72RzeH2/XvEkJyADrVXV2Mv9ynFSsw71eeddx7efvttyLIniP34xz/Ge++9h4svvhgXX3wxPvjgA1x++eWxLgYRUUKLV2c3Wa7SC5qGZQuKA05+vMP+Yj1EXdA03DS/GCUF/tsvKbCifFoetmw/M8IqUS5EEKWSrsbEaMa4no5DvZHFpPf92+VWw6zZtdjLfUqxEvOzp2XLlmHx4sWQJM+Vn/nz50MURbz33nuQJAk333wzFixYEOtiEBEltHh1dr1X6b3D69ryXaVPkJMKSdOwYkFxzBO3hWIQgGnjBuK62aNw7KQNBr2I/bUNeGz9LjhcZ+6QJMqFCKJU0tWYGO0Y19NxqLdJM57ZfwZ9+Ht/XY293KcUCzE/E9Dr9cjJyfF7be7cuZg7d26sN01ElDTi1dn1XqVft6nKb1uJepVe0DRYdOKZxDHxLJ+moWR4X1R+eQIf7T4adBhiol2IIEoVXY2JsYhxPRqHehlJAG5dVIqnN1Rgf20DSgqsMYm93KcUbTHvVF900UW49957cdFFFwVd/uGHH+LBBx/E+++/H+uiEBElrHh2dnmVvuMkTcP/K7RibH5fPPtmclyIIEoF3YmJjHHJzZptxm2XlcDmknHB987B85v3MPZSwot5p/qbb76BzWYLudxms+Ho0aOxLgYRUUxEc17peJ4I8ip9x0kCMNCajtsuK0GrU4bNIcNk1EEUBLhkBSYdT9aJOqsjsbM7MZExLnk121xodXr2eZpZh+WXlsDhknmBhBJaXB4EE8KkvN+zZw8yMzPjUQwioqiKxbzSPBFMXJIAGPUSXtryeUznEidKdZ2JnYyJvYtT0fDE+l1B70xbdKefsWYboAQUk071K6+8gldffRWAp0P90EMP4YknnghYr6WlBU1NTZg9e3YsikFEFDOR5lBdwaFpKUfRwH1O1E2MnRQK2wYls5h0qvv06YOCggIAnuHf/fv3R//+/QPWs1gsGD16NH70ox/FohhERDHTkTlUfVfVKSW0OrnPibqLsZNCYdugZBaTTvXs2bN9d5+vueYaLFu2DJMnT47FpoiIekRH5lD1DVeklGBzuCMs5z4nioSxk0Jh26BkFvNnqn//+9/HehNERHEXr3mlKXFYTPoIy7nPiSJh7KRQ2DYomcV8DMW+ffvwl7/8xe+1jz76CFdddRUWLlyIV155JdZFICKKOu8cqsH45s+klJJm5D4n6i7GTgqFbYOSWcw71Y899hi2bt3q+/urr77CihUr8PXXXwMAHnnkEbz22muxLgYRUVR551BtfwLA+TNTlySA+5yomxg7KRS2DUpmMR9HsX//fixZssT39+bNmyGKIt58803k5uZi5cqV+NOf/oTLL7881kUhIoqqeM4rTYmB+5yo+3gcUShGScCqq8ejvtEBm8PNtkFJI+ad6ubmZmRnZ/v+/vvf/44pU6YgNzcXADBlyhRs37491sUgIooJzqHa+3CfE3UfjyMKJcNigOx0w6ITPC+wbVASiPnwb6vVioMHDwIAjh8/js8//xxTpkzxLW9tbYUoMj0+ERERERERJZ+Y36m+6KKLsH79erhcLlRWVsJgMOAHP/iBb3l1dTUGDRoU62IQESU9TRA4XDLONEFAk0PB8dp6mI06mHQi65woThjzei9FA2yyyn1PSSPmneqVK1eivr4emzdvRkZGBh5++GH07dsXANDS0oJ33nkHV111VayLQUSU1BRBwLqNVaioqfO95k3eIvFEIyZY50Q9h8df71V3yo6nX6/kvqekEvNx12lpaXj88cfx2Wef4YMPPsCMGTN8yywWC7Zv347bb7891sUgohjQBAE2WcWJFhdssgpNEOJehmabC00OJW5l6InvrAU5uQSAiuo6rNtU1SP1nupY55RMYhWXohVfO1s+Hn+9l1sDnn6tgvuekk6PzqIuiiIyMjJ6sghE1EWJcBfBqWh4Yv0uVFTHpww99Z3tbiXgBMOroroOdrcCvdSj4TzldKTOLTrmA6GeF6u4FK342pXy8fjrnRRBwLGTNu57SkpRPwtbs2YNBEHALbfcAlEUsWbNmojvEQQBy5cvj3ZRiChGIt1FWBGH+STjXYae/M42hxxxeaaJnepo6kid+7IWE/WQWMWlaH1uVz+Hx1/v420rl0waGnY97ntKVDHrVC9duhQGg6HHOtWbNm3Cf/7nfwa8vnTpUtx5552+v19//XW8+OKLOHr0KIYNG4Y77rgDF1xwQVTLQpRqEuEuQrzLEGl7rS4FDmdsEqpYInSYIy2nzjMZw9epyajDiRYXE+hQj4pVHIzW53Y1bjLm9T7etjJnWl7Y9bjvKVFFvWXu378/7N/x9uKLL/oNMe/fv7/v32+//Tbuv/9+3HzzzZg0aRK2bt2KFStW4A9/+APGjRvXA6UlSg6JcBch3mWItL2jda145NXPAER/SLhZL6G0yOo3DNOrtMgKs16KynboDAFASYEVlQcC67ykwAoAuGvNxwCYQId6TqziYLQ+t6txs0Mxj8dbSvG2lf21DSFjL/c9JbKUfyhh9OjRGDdunO+/s846y7fsqaeewqxZs7By5UpMmjQJDzzwAMaOHYu1a9f2YImJEl8i3EWIdxkifZ5Bfyaceoc2ujREJXGQoGlYtqAYpUVWv9e9J6G8SxoDAlA+Lc/XgfYqKbCifFoe1DZ1zgQ61FNiFQej9bldiZuaIDDm9ULetrJl+8Ggsbe00Iqlc8eCUZYSVa8dQ/HVV1/hyJEjWLVqld/rM2fOxKOPPgqXywWDgc9sEAWTCHcR4l2GcNsrKbBif22D32sV1XU4eqIVD7z0qa9M3bmbKWkaViwo5pytcWI26PDOziMoGpKDuWV5cLlVGPQi9tc24J2dR3DJ5KF+6zOBDvWEWMXBaH1uV+Km9zhizOtd2raVx9bvQnlZvi/2plv0EEXg48pvcO7YsxhnKSFFvVN94YUXQujk1XpBELBt27ZoFwUAMHv2bDQ0NGDgwIFYtGgRbrjhBkiShEOHDgEAhg0b5rd+fn4+3G43vvrqK+Tn53dr27ogB70kiX7/781YFx6JXg/B2jEALFtQjHWbqgIzw15aDL0oAHG4nnzLgmKsfaPKb5hYSYEVtywohl6KfhmCfWfvncvH1u8KWN/lVn3/9t6Fue2yEkhhiqVoQKtTgc3hRppZD4tB8ltfL+naJSXzLEz0dpQIQrXlYBQNWDpvDNa+UYUN22p8r5cUeNr4L3/7KRZNL8SIITm+DjcgdGobvQ3baHS0b2OxisXRiq+djZs2h4zMbJPvb72kQ5pRh1angpPNzqBxsaexbXdN+7Z8y4JiPLOpCvsO1/te0+tEZFgM0DQNW3ccxrgCq1/76I3Y3sLrqfqJeqf6+9//fkCneu/evThw4ACGDx/u68QePnwYX375JQoKCjBmzJhoFwNWqxW33norSkpKIAgCPvjgAzz55JM4duwYVq9ejcbGRgBAZmam3/u8f3uXd5UoCsjJSQu5PDPT3K3PTyWsC49ErIdI7XjV1ePR2OJEq93TAcxKNyLDEp8RHs02F17607+C3kl8acte3H7F92JSlrbf2WiQ8NHuo3hs/S44XErAum2HNgKejrVDVnFOv+BTCdadsuPp1ysCTo5vXVQKa/aZ9tFsc4Ws90RsR4kgUltu79sTLfjdX77A3LI8XD9nFGwOGWkmPXQ6AQf+3YCVV3wPr2zd59fhDravKBDbaNeFasfRjsXRjq9ty2fQS/i4MnTcNJt0ft8xVFy8eX4xMtIMcfvN6Qi27Y5r35a9bW5Mfh/cOG8snn9zj398LbTitstLYTHrOhXLUxnbW3jxrp+od6ofeeQRv7+3bduGbdu24be//S0mT57st2zHjh1YuXIlbr/99mgXA9OmTcO0adN8f0+dOhVGoxGvvPIKbr755qhvrz1V1dDUZAt4XZJEZGaa0dRkh6KoQd7Ze7AuPHqyHiL9MIVqx22l6UWk6Y0AANnpRoPTHbXyhdPkUPDp58fw6efHgi6vb3RAjlFZvN9Z0YADXzUEPTEMNrQRAJpbXWhoaA14XdGAp1+vDDr1zNMbKnx3uJ2KFnTO1+WXlqB/n7ReeTx15ASrI225LbtTxr+qj+OiCYPx8p+/8Ltbt/yyEvz+r/sCEum031fkjzE/su7E5GjG4ljEV2/5mhwKqmtDx00Bgi9GhouL6zZWYdq4gSgZ3hfGHj7g2Lb9dSUme9tc3jnZeO7NPdjdfp/X1AECcPP84qC/ob0J21t40ayfzlzAifkz1b/5zW9w9dVXB3SoAWDKlCm46qqr8Jvf/AbTp0+PdVEwY8YMvPzyy9i3bx+ysrIAAM3NzbBazyRDaGpqAgDf8u6Q5dA7UlHUsMt7E9aFR6LWQyKWCQBsjvAndDaHGxZd7E+0Oju00WLSBa1Tm6yGnXqm2SHDYtThxc17g55grt1YiVVXj0/YdpQIOlMvDqeC8rJ8bPnoECoP1MFkkFBelo8RQ3KQnWHC2jcqg76voroOrU6Zz/yFwTbaPfGou1jGV0HQUH562qT2Q8vLp+VBEDTfd7TJWsi4WHmgDnPL8ro1H3e0sW13Ttu68ra50kIr/vhuddD1K6rr4JIV6FM/z3KHsL2FF+/6iXmnura2FtnZ2SGXZ2dn49///nesixEgL88T0A8dOuT7t/dvvV6PQYMGxb1MRNRxscp6qwlCpxLjBCbT0WN/bX3QoY2lRVZY9BI0IGAbHZl65t1Pj2D2lDxUfnki4LMrquvQ2OJEmp4nG9GQZtZj1NBcAMDcsjyYDDpo0FB54ATyzw5/0TUeU8oRxVJ342u4OGrSSXjvU08SwPnn5SPdYoAkCWhqdUEviTDqdYCqQhEEHItwR9LlVpkkMEVYTDqYDBK0CH0gm11GWgbjKyWemHeqBw8ejE2bNuGyyy5DWpr/LfSWlhZs3Lgxbh3YrVu3QpIkjBo1ClarFUOHDsU777zjd5d869atmDx5MjN/EyW4WGS9VQQh6NDqSFm7BU2DRSf6OlIlw/ti5LDcgGcAly8ohgIE3cb1c8LnljDoRVRU10FVgfKyfL9nzbxa7W7f8E/qHoteRN8cM6r/3hCQqGzymLPCvDM+U8oRxVJ34mukOCpoGm4oH4MXt+xF0eCcgEcpSos8ydBe2rw3IMt+e968FbyQlfzMegk3zR8LWQ3fqzYZpTiViKhzYv7Lv3LlStx2222YMWMG5s+fjyFDhgDw3MF+8803cfLkSfzmN7+J+naXLFmCiRMnoqioCADw/vvvY8OGDVi8eLFvuPett96KO++8E4MHD8bEiROxdetWVFVVYf369VEvDxFFl3ce06BZb7swFFALciIInMna3XZ4YaS72aGmggGAtSG2UV1c36GpZ7xDHoNJM+s79Z0pNA3AS5v3Bjw3XXmgDl9+fQrjCq0Bz/wB8ZtSjiiWuhpfOxpHJU3D0rljsfaNyqC5CZ7ZVIWCQTnYX9uAkgJrwDqAf1zkhazkJ2gahg/KwbF6W9h9bjbqGF8pIcU8Ck2fPh3PP/88fvWrX+G5557zWzZy5Ej88pe/9EsoFi3Dhg3Dxo0b8d1330FVVQwdOhT33nsvrrnmGt86s2fPht1uxwsvvIDnn38ew4YNw5o1a1BaWhr18hBR9BklAauuHo/6RofnGb9uzGNqdythn2n2Di8MdRfm5vnFkBUVrfYz5Wh79xqaFva56Rc378WT/3E+nnsz8vPZbafqaluGrHRjzJKz9Tbh2sNLW/bi8dvL8PxbewPusHXlgg5RIupKfI0UR1ucsmeILwScqLeFXXfO1Dw8+vtdWHX1eAD+z1+XFloxe6onLvJCVuqw2d3QiULYZ+5dsgodM0FSAorLpb2pU6di6tSpqKurw9GjRwEAAwcO9EsQFm333Xdfh9ZbuHAhFi5cGLNyEFFsZVgMkJ1tkuZ08cQq0jPNNocMc4Yx9F2YjVUoGpLjGyocbNh4uG04XAqaW51YsaAYrS4FR+tafVPYtH8+O93if0faOydthsUQt+zrqS7SvvqmrtU33ZDFpEe6uesXdIgSVWfja6Q42mqXUfPvU/i48ihmRBja7XKrcLgUPLZ+F8rL8v2m9srOMOLedTswclguL2SlEJNRQkOzE0/+6V8B+9z7W3jXNeMxqF962EeyiHpCXMfLWK3WiB3pEydOYNq0aXj55ZeDZgwnIoqFjiTmCXcXpv2w7GDDxiNtw2zUQdA0pBkkvPvpkZDPMw7ItWDNnef7DS3Xi7xyH00WU/ih9DpJwIZtNSgpsOKWBWNhEMA7ZdTrRYpxqqahT5Yp7GMsXt6Lhw6XEjAf/JI5Y/Cr26bxQlaKMeolpFv0Afu8vUTK+E7klZCpEjUeJEQUZ97EPMF4hxdGugvTfli2d9h4Z7YBnHmesf263rvfoqrCohPRN90Ai07kiUUM6CQBpYXB95X3Wc6SAisun14IAy9oEAGIEOMKraj68oQvTnqPoaDrFlnRP9cSMgZadAJjXwpSVA2ZaYaw7WJ/bUPAbytRImBmByIidCwxT6S7MIYg01m1zUrbmeQ/oZKd8SQyPpptLlxfPgYvb/GfF7y00Irry8fg5Ck7rNlm9M0y8Q410WmhYlxJgee4WfXUdtx1jecZ6S3bDwZ/Xtr76IyqMgb2MoqqoqHJhevnjMbv/vJ5QOy9oXwMfvKb7QCY8Z0SDzvVRNTrhMreHakjG26ambaZaNtKM+v8tpdm1mH5pSVwuOSIJ4rtp+pi5y1+zEYdTpyyY9bUPFx6YQFa7W7kZBih04n49kQrdJIAQQDcigqXpvFkn3qlYLG0bRxttctodbixv7YBJ0/Z4XApfhm92z8vnW7RY0CuBWKEaZUoNRl0Ev7y8SEUDM7GNTNH4pqZo+BwytBJAr453oJPPv/Wl1+EGd8p0bBFElFSa7a50ORQOpydtiNzqAbryGqCAIes4MZ5Y/H8W3sC7jRfP8dz9/JHlxRBA1BwTvbptwuo+PIEXty813cycGYIo+i3DUocZr2EplYnFEVDn7Oz4HQraLF7OgfvfXIEV88YifxzsvH18RbodSLqmxwYN7wvRO5LSiHh4mukWGrRiTBnGvGnbdWoqK7DlZcUYVyhFe99cgT3L5mE9X8FKmrqsGFbDUwGCTfMHYOz+6WjvskBi0kHo0GHF9/ag0+/OBb08yn1GEQBi2eNRHOrC6oKSJLnwiUgYlReH9hdbvx5+yEMOzuLGd8p4Qhagj3AfOLECUydOhW//e1vkzpRmaKoqK9vDXhdpxORk5OGhoZWyHLvvhLLuvDoyXqwWjPCLg/VjhOFIgghh1IHO+nSBAFrgmTv9r4vVOIT78njviP1uHvxBJw4ZUefLBNMBh1UTUPVlyewZftBAMDqJZOw4f0avzmM206L1bZj3dE71h3Rm4+nSO0Y6EJbFkW4VQ3PbvJvLxNH9cf15WPwzKaqgH18+fRCDMjmcPBQenMb7ahEislh4yuApzsYSxVBwItb9uKHk4fBbJCgasCbf/sSw87OwoghOZAVDWf3S8eLb+3x+7xgcTPY5ycCtm1/XY3JiiDgxc17cfGkodjy0aGAKbUun16IdIseFoPUqy9gsr2FF8366Uhb9m23W1siol4p1PDpeJch5PRWITKDOmQFBYNzMGea/zQdW7Yf9JuLOtR2fjxnJIwGEcMHZUMSBfz2z/7PfC2aXojXttX4nQgAZ54XLC/L92U0raiuw3f1Ntz37D986/EuTOKQNQ3rNlYF7MshA7OCvu7925cJnCiOoh2TI8XXm+aNxb4j9Vg0vRAjhuQExNO2sVQCcPUPR+LlP3+OEcNyse9wPXbX1OGzfZ470IumF+LtHYc7FDe9ZQgWqym5edtcweCcgA41cKY9TBs3EKXD+/ZEEYnCYqeaiDol0pC/eAk3vVWoky5REFFd2+B3glZSYMWqq8fjsfW7AhKfaIIAm1vBwosKsXjWKJgNOnz5VQPSzAr6ZJtxyaShmDMtzzckePyIfgHTgJgMEsrL8jFiSA5MBh1GDMnxnXi22PznlA53QYDiy+FS/E7qstMNWLGoFP1yzMgbmIX55+VDVjWIAvw6FC5ZgeF0FneieIhFTI4UX51uBauuHo8tHx0KiKd3L54AURDg0gCnW4HDqcBi1uHGeWOhqCrG5vXFtTNHQYOGXfuOYfg52X6f0TZmutwqBvSxAPAkNvPesWaSqtTjbXNzy/IhSQKuuqQIsqLBbNT52sqmD7/E3LI8XlShhJRwnWqTyYT58+ejX79+PV0UImqnK3eHYyXS9FbBOsjPvxX6DmN5Wf6ZxCeCALcGPNvuu5YUWHHT/LF45S+f+z3nV1JgxYO3TMHxepvfZ5sMUsgTz1VXj4cUZCom3oVJDHbnmfaVnW7Ag7dMwQtv7UXlgTrffv1zkOGJF3zvnJ4oLvVSsYrJkeKrw6UEvZtYXVsPs6EQp1pcAaN22g/n9g7nbStSzPS+l0mqUo/NIcNkkNA324y3th/EH9+t9i3ztpWCQTlQVJUXVSghxS0q1dTU4O9//zu++eYbAMDZZ5+NsrIyFBUV+a2Xnp6Ohx9+OF7FIqJO6Mrd4ViJdFLVfnm4slceqMPCiwpg0UtQBAE2hxwwnYd3vefe3IOiITl+nerKA3V44a29uOoS/3hWXpYfdhjbnGl5QcvDE4aeZzHpfXfMzv/e2Xjuzb1+F2BC7dfnN+/hSAOKm1jF5Ejx1WzUBbR/wHNsHD3Riu27v4k4nLvyQB0EAbhmxki/94eLmeVl+TjwVQOTVKUgi0mH8rJ8vLRlr1++CuDM/p9aMhCj8/pAVvgcMSWemJ/9ulwu3HPPPZg7dy4ef/xxbNiwARs2bMDjjz+OefPm4a677oLL5Yp1MYgoCjpydzhevNNbBVNaZPWcdLURqWxGvQgZwJrXK3Gy0R62A+4Zyi1h0fRCrF4yEfcsnoC5ZXlIt+hRUnCmTCOG5AQ98fR+TpAb1QA4VUgiMOgE/OyGSbBmmyErmt9+DLdfvR0ZoniIVUyOFF9FIXjwGjU0F8MGZgV0irwqD9Rh8tizsGh6IUwGCbtr6mA26lBa6NlWpJhZPLwvlvGiVUoy6yWUFPQN+9vbJ8sERVUhhGh/RD0p5p3qxx57DG+99RauvPJKbN26FVVVVdizZw+2bt2KK664Alu2bMFjjz0W62IQURR09u5wLAmahmULigNO/LzPErY/6YpUtgyLAc+cTkDlcoe/Cu6WVay6ejyqaxvwwEuf4pFXP8MDL32KV/7yBZbOG+PrWEf6nGDLg10QoPiTFRWqBnxceRRH6/wz1Ebar/G8uES9W6xicsT4KgTv1KZbDKhvcoT97GMnbaiubcCqq8fDZJBwrN6Gm+aPRWmRNeKxZdCJTOSYogRNg04K3y1xuVW02mVoGu9UU+KJ+Rnwli1bMHfuXKxevdrv9by8PPzXf/0XWlpasGXLFvz0pz+NdVGIqJu8dy/aTrHi5esMxvGERwAwpXgg5kw9k827vsmBYNewg5XdO7y3eHhfOE8PozQZJORmGsNuNyfDiD+8W+17vrZtUp2mFheWXVoMm1NGpIvp6Ra939+hLghQ/EmiiDf/9iWKhuSgfx8L7lk8wZeMzGQIf9GDIw0oXmIZk0PFVxGAQa9DSYEV1bX1fvHPaBCR0S6uAYEJG+eW5aG+yYFf3jLFk1tCAMbk9cFAa1rYMvHYSm1moy7gN7VtVnmDXkSaWQe9JAHg7yQllphHJ1mWUVJSEnJ5aWkpPvzww1gXg4iiwHv3ItTcpfHsDGqCgLWdmHNaALDwwkKoKvySTXkT4tyzeILvtdrvmlFSYA06DLGkwAqdTgz6Gb7tF1oxe2oeDn5zKuTnlBZZMSDXgqd+cj5a7W6km/UwG3UQVV6BTwQuWcGMc4cGTZg0cfQATBjZ3zclUFs9cXGJeq9YxeRw8XXFwhJ89vl3mHdePox6zzSCG7bVwGSQ8NhtZQHxM1zysZFDc/HTZ3bg6hkj8b2i/hBOlz1RLtxS/GiCAJ0oYPWSSb425VVSYMXqJZPw3clWDOybBoMkAGrit4NEmH6U4ifmneqpU6fi448/xo9+9KOgyz/66CNMmTIl1sUgoiiRNA0rFhT3+A9FZxP02NwKHnjpE5SX5WNuWR4y04xY/84+34mfQS/6kuRU19Zj1dXjAcCvQzyu0Iob5o5BXYMdQOikOhU1dVA1YFReLspPJyNru05pkRW3LCjGC2/t8Ut4xnmqE4coiNjy0aGgCXNe3QpcO2skXLIasF850oDiLRYxOVx8zc004dMvjiFvUDb2Ha73SyL20pa92H/EP36GSz724ua9WH3DJLzy9j6seb3S1wH3Xvz04rGV+uxuBSaDDq+/XxO0rYgisHTuGDS1upCVbkTgeIjEkijTj1L8xLxTffvtt2PlypVYsWIFrrrqKgwePBgAUFtbiz/84Q84evQonnjiCZw6dcrvfdnZ2bEuGhF1kaBpsOjEMxmqe+AHorNTatkcsmcamO0HgbJ8TB57ll+HaX9tA8YV9AUAzC3Lg1tWcdUPR+DaWSNx4pQDOklAnywz7ntmB+697vsAPEl12s9L7VV5oA5zy/Lw6O93obwsHzeUj4bdKcNk1MGgk/DS5r1+HWqA81QnEg1a2GRLojAKc6blYW5ZHtJMeuh0IrLSDBxpQD0i2jE5XHx1uVWYDBJKC6y+aY9MBgmTxgzAiCE5+OGkoRBFAXOm5WH+efnITDeGjJMVNXW49MICXyfK4VLw2HpPzFx4UQEMOpF3+HoJm0OGqiHsxXK3rMJk1OHZNxP7dzKRph+l+Il5p3rmzJkAPFNqvf/++37LtNMNatasWQHv27dvX6yLRkRJrLMJeiwmnd8wxLyBWQDOPOs3amgusjNMqK5tCBh2Vj7N0zlevWQShpyVhV37j6OkIHJSHZdb9XXky0rPxh/fq0ZFTR1WL5kY0KH24jzViaHVHv6izbF6Gx5+5TMAwMPLpmD3geOYMHIA0vXcb5T8wsVXbxxttrl9f991zXj8fus+v06EN3bWNdjCbqvl9Od4OVwKNpwe/rvmzvM9sZAdkJSXZtahqdUddh27Q0ZmhjHhfycTafpRip+Yd6qXL1/O1PdEFHWdTdBj1ku4dWEJzCYJ180eBUHwf9YPAN7afjDs/KiapmF0Xi4mjOqPC//fIJxotIcto+F0B6u8LB8vbj4z92ZHskdznuqeFemiTU7GmWR2iqqhtLAfbHYZ6XruN0p+4eKr0SBi04cHsfCiAgDA/AuGh3xUAgCuuqQo7LYMYS5EMRb2Hka9DumW8OukWfS+qSgTuW10diQdpYaYd6pvvfXWWG+CiHqhziboETQNBYNzsPYNz7RZi6YXYkn5GN8z1FddUhR2KPePZ4+CzSFj8tizsL+2AX/46z6sWFSK0kJr0CvSJQVW7K9tAAAUD+/r99nhTiIBZrhNBAadGDFZHeBpb58fPomJowbAYuZ+o9QQLr5mpBnxw8lD8fXxFpQWWv2GgbdXeaDON11WqAug3jgZjMnIY6q3cLhk6CUpbNzVSyIcLgVAYv9OJtL0oxQ/3KtElLSMkoBVV49HfaMDNoc75LN3miDA5lZx8pQNS+eOgapqaLG7kWHRo8nmxI9nj4IoCHjolikwG3XQoGHXvmPY9OGXvh/w707a8MirnuG+JQVW3LqoFE9vqMCti0qhav5JdbzDHh9bvwslBVbPlDFt7K9tCJsVnBlue15TqwvzzsvHeaVnY+jATAACXC4ZGWkGKKqGFpsbv7z5XPTLsWDX/m/hcCnom2UC+Ew1pYhQ8dUhq2ixuTBiaC7yz8mCwynj1yvLoCgqBEGEJAloanVBVTXsO1KPoydaMXtKXkDysZICK26cV4yXt+wNuv2SAitEjnTsNWwOGRBkXHZBAZaUjwYAtNrdSDN7UpI1NrvQbHMBSPzfyUSbfpTiI+qd6jVr1kAQBNxyyy0QRRFr1qyJ+B5BELB8+fJoF4WIUpyiAa0tzrAdam8Gzn2nM9K+sHmv31Qv/3XDJLzy9hcBzwJePr0QBYNy8N+vfgaHS/G7u+x9/8WThvqS6swty4PFpIdeJ0IQgBOnHLjrmvHYX9sAu9N/KNiW7QeDZhdnhtvEYTHrYXPIGNAnDa+8vc+XEf6P/+ufmXZcgRW3XFoMl6wwSRmllFDxVSeJsOZY8Nybe3zHxTsffIkfTh4akOW7pMCK80rPxk+f2YGLJw3FwosKoKoaTAYJOp0ItyLjh5OHBmTS916YFAQNngkRKdVZTDoIIpCVJmHdxqqA9rDs0mI0NtuRnWnBigUlsLvlhJ2qKpGmH6X4ETQtunt2xIgREAQBlZWVMBgMGDFiRORCCELKJSZTFBX19a0Br+t0InJy0tDQ0ApZ7t0nYKwLj56sB6s1I+zyUO04EXRkugpNELDm9DqLpheiurbB74d60fRC1Py7IWiW55ICK6aWDEROpgkmgwRF1aAoKvbXNmDL9oNwuBSsXjIRD7z0qe89/718Ku5e+3HAZwXbtjdBWvHwvlBVDbKieubf7OL5Y28+niK1Y6DzbVkWPHORf1x51Pe4QPt96OWZIq0EOq131Xtn9eY22lGJEpNDxtdLS1B5oA4f7fY/LoqG5IQ+PgqtGJPfB063ihFDcmAy6OBwydhf24CJowfgT+9VY9jZWRgxJAcutwqDXsT+2gbUftuIG8vHJEUHhG3bX5disiBAEQSseb0yaDsaV2DF0vlj0NDkhCQC/7nuH75liTpVVazmqWZ7Cy+a9dORtuzbbre2FMT+/fvD/k1E1F0dna6ibQbOYNNfdWRKLINewn3PnvnxLimwYtXV4/HY+l0BCcdMRinoZ3nvTIsifFetHS7FczI6OAePrd8Fh0vBoyumoi+TlyQEu9ONPlkm38lduLZSUV2HFpsLWRZ9UnQAiMIJF1+P1duQmxl4XMwtyws7bdb15aPx6tv7AmZWOK/0bMycMgxv/f1gwLKbF4zl8dSLaADsTjlohxoAdh+og6J4Hr3p38c/o1miTlWVCNOPUvzE9Jlql8uFjz76CGeffXaH7lgTEXVER6eraJuBM1jGbUmKkDDMqINeL2H1kol+d1De2XkE5WX5fkPCSwqsUNTgP5jeuVcfWjYFl15QAFEQfHdqvB1qgMlLEondqUBWNCyaXogRQ3JgNuqweslEv5EKbTncCgxukdOkUNILF1/bT3/ljave/3tH4LS/63zilAPDzs7CZ/vOTCVYeaAOL2zei5HDclE0JAdzy/LgcqsY0McCVdOgaSog8HjqLexuFTaHHLINbdl+EHaHDINehD1Idm1OVUU9LaZncHq9Hrfffjt++tOfslNNRFHT0ekqzG06qe0zbpsMErLD3BU2GSRkpBvx4lt7gs69ajZK2H3ghN9ru/YdC5mAbOTQXBh0Eg6eaMRLW/YGdMqYvCSxpFt0yLDo8faOwwF30LwjFdruw/TTz2BzmhRKduHia/s46v3boBf9pigMdkdaQG7A5+2uqUP5tDzfYzQlBVbfUPJbF5Yw8V8v4klKpgvZhlZdPR4Wsw6VX9ZhwqgBQT+DMZh6Ukw71YIgYOjQoWhoCD1dAhH1Tt151qij01XopDPTIokC8P2R/TH09LN7JoMOkiRi+WUl6JNlCrgivqR8DF54a0/IuVeXzh2D4edk++5ePrZ+FwAETUBWUmDF7Kl5uO/ZHbh1USmKhuQyQVmCM+l1eP6tPX530NqPVPCe9JUUWCGAIw0oOUSKveHa8f7aBvTLMaOkwIrabxuRnWnCUz85H3anjF/eMgWHvmlEdW2933sqD9Thxc17cdUPRwb9TO9d7tJCT5z0XrByuGTedexFTEYJRr0O7+w8EjLuLp03FtZsMxqaHEE/gzGYelLMW99NN92ERx55BD/84Q+Rl5cX680RUYJpfwJn0UtQgIhJxsLp6HQVza0uLJ45Eq9uBfpkm3DD3DE40WSH2aiHwylDJ4n4R9XRgDvRq5dMgsWkw9o3KoNuv/JAHVRNg04S4JZVjB/ZH5PGDMCJUw5IooA50/Jw3axROFZvQ/8+Fmiahgde/ASnWlx+2cJdbhVn9bUg3ahjhzrBON2KL5tx2zsm4wo9oxK8U/14OwIQwJEGlLA0QYBDViCKIp7fFD72houvtd824vzvnY2hZ2UgzWzA82/6j+QZV2jFXdeMx6O/9x/JUVFTh8WzRgUtW79cC1YvmYjMNCPue3aH732869i7GPU6ON0yZpwbOu463TKGD8pBfaMDJoPk18Y42ot6Wsw71ZWVlcjOzsacOXPw/e9/H2effTZMJlPAevfdd1+si0JEcRYsg+ztl4/D9opvIiYZC0fQNNyyoBhr3wicduOWNu+3mPSob3RgzrQ8GHU6KKqG1/73QNhszpUH6iAIwNUh7qp4tdo9zxZqGtBic+FkowN9s8145NXPUDQkFzfOG4Mn//Qv3HXNeGzefggXTxqKDdtq4HApficLa+48nx3qRKQBWz46FDBSYXdNHQQAS8pH48k7zoNeJ+KP7+7HtbNGQ2D2b0pA3jg8Ylgu9h2uD2jT7WNvqOmAJozsjyXlY2FzuJGTbsKaNyoD4rj3+Jh/wXD88d1qv2XNrS5kpxuwYlEp+mSZYHPISDfrIKsannqtArddXurXSeJdx95FFDQIiBR3x6DV4cZfPj7kN1ooUUd7xSr7NyWmmEes9evX+/69c+fOoOsIgsBONVGKCZVB9mxrOipq6kImI7HLKixS+HmlNEHAS5v3Bh0i9tKWvb5pWMwGCdYcM557aw+WX1aM5zbt6VA25901dbg2xF0VL0XV/KbT8s5tveCC4fifd6vxwua9WFI+BvtPd9znlgWO1OGV9cSlAkGnWgM8d900Dbhn7ccYOSwXS+eOhQjuQ0o8bePwlRcX4Y/vVoeMvQ5Zhfl07JUATCkeiDlTPfHVZJDQN8eMdRsrceRoI35x85TQySJr6nDlJUUBnWqdTsSDt0zBC2/tDbgY+tCyqfhk71Hfa4yNvY+satAQIe5CQ7pFhx9OHop+uWaMH9EvYTurqiBg95cnkJvpebysxe5GfZMD44b3hZhgZaXoiHmn+v3330dubi7MZnPQ5Xa7HfX19UGXEVHyCpVBVla0sAltzv/eOVAFIeyPjt2t4NMvjuHTL44FXb545kgYdBJONtrx2v/WYHdNHZwuxe/HOlg2cL9tOGRMGNnfL1tt23JWfXnC7zXvSeK1s0bif96t9nTMZ47CS1v2AkBAlytRr6yTh3ckQigtdrdvmOtzb+7BtHEDUTq8L/cnJZS2cThS7L3ge+f4/ra5Fax5/czjL4umF6JmewN219ThkWXnRjw+NBX42Q2T8MWRemzZfhAjh+bCmmXGmjcC5yCuPFCH597c47vwyNjYO5lEAcdbw7crm11GnywT3tl5BNfOGnVmCspEayuCgOOnHL753L1KCqwY2DcdA7JNiVdm6raYd6qnT5+Oxx57DLNnzw66/MMPP8Sdd96JL774ItZFIaI4CpVB1mTUobwsH1s+OhT05OqFzXswfcJgjBiUHfKkKtK0GwIE1J1ywGiQsO9IPRZNL0Rru/K0z2Lbnqpp+PGc0RAF+HXe2ybTac/zfc7c4T7eYPMNZ+yfY8GaO8/nMLAkYQ4x57hvuUmHt3ccwsKLCvD7v+7H3LI8TudCCadtHLaYdXho2RT8fuu+oLH3+c17fEPA7U7ZN52cy62ifx8LNmyrQXa6AbmZZtic4WdgMBok7D18AtW1DVi9ZBLMRgmtTnfIOYgrD9RhyZzReOon5yPNwNjYK4lixLhrMEiQFRXDzs6CrKhQ9LoO5WGJN5eq4bVtNUGPMwC4ZcFYGMIPyKMkFPNOtaZp0MI0eLfbDUFgyyJKNaGfh9NQPLxvyKHXFdV1uH7OGLg1BQYE76CkmXW4Z/EE1DXa/V635phx77XfhyiKeG1bDa78QaHvrsy5Y8/y64hLkohf3nwuKr88ETDvsPdO9IGvGnDj3LG49MICNDQ7odeJAcl02nO0OdnUn+5geYYyihA07UzinQQ8EaAzjHqdL3N8+ws4GRY9THoJP7pkBFRNw+vvH/AM77PJgIUXTChxeONwdroBZqMOdQ320MO2q+tgd6uw6ARkphlR8+8GX5y+Z/EEAMAdV3wPJ5scSDPrUVJgRXVtfcDFzZONDoiigHPHDsTr2w7g9Q9qMCavD0YM7RO2rC12N/pnm3js9FaaJ+5OGNkfw07P0tH2gvnhbxohAHDJKkYNzUVTqwuvbP0iIA9LLJ5j7uxnOt1K2AtITrcCgyH8BQRKPjHpVLe0tKCpqcn396lTp3D06NGA9ZqamrB161ZYrdZYFIOIelCoDLK79h3D2Py+Yd97vN6GwQMyQs5RatbrYNBL+DjI0KorflAIlyyj8kAdbpo/Fn/8X8/V4nSTHv91wyS8tq3Gr0Nf2i5brXfOae+0Lt+ebMXm7YdQPi0Pj/5+F+66ZnzIDjUA6KQzWaEPfH2KQxmTlE4CLp9eiDSTDpdeWIDf/3Wff7spsmLRRYXok2nCgguGw6AXYXO6cc+6jzuVyZ4olrxxeM7UPKx7owozJg8Nu/6xBhsG9UvHy5v3+j0u4x3Z07+PBc+9uQfzz8/HlT8ohAYExtQiK4YPysIf/rLPl7Ds+jljwt5gAYB0i55xshdTBUDVFPx4zmg8/9aegN/ppfPGQlYU1J2yo1+OBSdP2U9fCDozQihYctTuxuNQn7l8QTEEIGhn2+EMfY4AAA6nggx2qlNOTDrVv/vd77B27VoAniRkDz30EB566KGg62qahpUrV8aiGETUg4JlkDUZJFizzUgz68O+N8Oih8MlIy3EUFqXEnpolSAAN5SPAQDIsupbR4OGDdtqAjPf1tQBAvCr28pwvMHmm3Pa23E2GXSorq3HFgDlZfnYX9vgu4PZXmmhFRUH6lBSYMXCiwrRJ8uI6d87hyeKScglq7BmmXDNzJF4tk2CO6+Kak+yspvnj8XE0Weh5t8NqDtl9y3raCZ7oljyxmGbU0F1bT2uuqQo/PoAntlUhYJBOX6PvQjwdCRcsoqKmjrcMNfTSX5py+dBs4kDwIihuSgtsOKP71ajrsGGQf0yMK7Ait1BYmdJgRUWoy7khVRKfTanDLNBwrqNVUF/p1/YvMdzgRrAy3/eizlTPc/ge6deC5UctTvxONRn7jtcj+OnHHj9/ZqgHfg0S/julcXMzPapKCZ7dcqUKbBYPHOzPvbYY5g1axZGjx7tt44gCDCbzRg9ejTGjh0bi2IQUQ+TNA0rFhTD7lZgd8rISDPiuU1V+PakLeQ8qCUFVtR+14zReaGHCjrdcsihVbtPZwj99coyv+f+HCESpwGnO0hz/LN5e6ma5pu6Y25ZHt7ZeQQ3zhuDF7fs9St/aZEVS+aMwYlTdiiKhgde+gS/um2abwg4JRdFBQRBw8lGR9i25nR7Lr70zTb7ktIBCLiDQtRTJE2Dw+nGqqvHo/a75tAXBYs8o2v2Ha7HNTNG+g2/HWhNw43zxqKxxQUAcCsqXBFi6pUXF0GWPZ2YnAwTfvuXz3HLpcVYtzFwKsSl88bAJcswiTxeeiuHU4YgIGybcrgVqJrn34tnjoLJIPkecQiVHNX73q7E41CfWV6WH/TCvrcDf/O8Ys9F9iDvLS20QgQfe01FMelUl5aWorS0FIAnu/fFF1+MwsLCWGyKiBKcoGmw6ESY9UasOX3Fd9+Rejz5H+fj2U3+V6TbDr0eOSwXyy8tgRjkzkW44dcmgwRBEPDK2/sw/7x8X7KdVnv4xDrBlpcWep6tHjEkBwCQZtJjxuSh+OkzO3Df9ZNw7axR+O6EzffM151Pbfcrm/cKerw021xociiwOdxMhtYB4Z6T0zTt9HPS4bPROlwKoAH//epnAe0y3vufyKtt285MMyAr3Yj/ea8G1bX1WHX1eADw6xCUFlmx8MJC7Ks9iVVXj8erW/f5YrPJIOHXd5wHaBosJgmLphdCQOSbyqriSUxZUmDFl1+fwjn9M2B3yZgzLQ9Lykej1e6GyaiDQSfip8/swL3XfR+mCMcL5/1NXZlpBpw6fdEmlFa7DJ3o6ZDWNznw6K3TYDJ4RjiESo7q1ZV4HOozw03JWVFdB5esYM60vIApwsYVWjFnWh4gaAA71ikn5uMPVqxYEetNEFESaHvF1+FSIAnAj2ePxslGu18yEu/Q64rqOticMtLbZelWRRFuOfTZXHlZPl59+wuMzsvFgD5pePPvB7FhWw1+vfK8sOVrn3W0tNCK68vHYNVT25F3xfc865h0aHW4carF5RmeLurxyKufhfzM0Mnaos+paHhi/a6Au+d8tje4SM/eOVwyNC1ylnizUQdB8LS79gnv4rn/ibzat+3VSybCmm32daIfW78L5WX5mFuW54u9A/qk4T+e/DseuHEy/vButV+Hu7wsH58fOomsND2GnpXlS2AWKaaaTBIECL4LpXddMx42u4wHX/4U9yyegH65FtzxxN+xeslEnGpxRTxeYvG8LCUOl6zCFGnWBaOEU81OAJ4u6YlTdvz540NYtqA4YvvpSjwO9Z6IU3I6Zbz7yREUDs5B+bQ8v3Ocdz85ghvLxzBZaQriLz4RxUzbuwrtfz+U0x2XYEOuvWx2GSa9EW5FgUnnuSNhc8qo+vJE6CGMBX1RNDgH9U0OPPdm22GGWsj3lBRYYdRLWL1kIlxuFQP6WnDilAMnT9nhcCkw6EWUFFhhOJ39e8LI/rCYPJ2pcEMpzXopLj+csXiWLJV1pL4y0ww4ccoR/hn6Ik+STb1OQnVtA1ZdPd53USie+5/IK1jbdrlVNLQ4fX87XErAXbZ7r5sAh0uBrGgBbX3EkBwYJAnWXDOe8XveNXJMlQTBd0y43CpyMj2nnekWve/9+2sbIh4vjHGpzzuqIlKbMuhFlBZ52k3ewCxfG1h+aUnIx8q6Go9DJVyNdLHVYtLhhvIxWLepKiCJX0cTl3JURvLhwytEFBOKIGDNxiqs+NXfcNeaj9Hq8B9G++neb2E2hr+uZzbpoAGob3Th+S17oQgCbHY3tmw/iPJpeSgp8J85YOLo/shIM2LLR4fQJ8uEihrPdEiLphdCUVQsnTcGpYX+7ykpsOLy6YUQBQEPvPQpHnn1M4gQ8O7OI/jiSD1KCqw42eRA+bQ8fHW8BX94dx+WzB2DJ//4LxxvsKN8Wp6vc+UV74zfHXmWjM7oSH25ZQ1VX57A4W8aMbcsD+PatZvSQiuumTESogC4Zc/0KVs+OoTysnyUFFhx47yxPAGiuAvWtg16EXKY0T0AoJM8p4PmIHfmFFWDNdfsS1LWmZj63Umbb/RGukUPo17EhJH9kZlmwJGjTSiflofabxsjxkvGuNSXZtZB0zRc8YPCgHg7rtAzs4emaahvcuCaGSOxZftBX+e2oroOTreMZQuKo/p77E301/4z65scAa+13Z5ZL/lyyqy583w8umIq1tx5PlZ0cFRF+/OnFb/6G9ZsqoLCKYgTGu9UE1HUBbur0P6O3x/fq8H3R50V9qp0q8OFVruAfrkm5J+TjXWbqnD9nDFwuJSAIYy5mUZkphvgdHkyfl920XCYDBLuusYzT/WGbTW++YYvvbAAoiDA4ZJR3+SAJHo6Rme268YPJw/Fe58ewS2XFmNH1Td4afNe3HXNeFRU1+HZjVVYeeX30NTiwl93HsbyS0vgcMk9dkU5Fs+SpbKO1JeqAVu2H8TdiyfAYpQwpWSg3zC++iYHZFlBi80Fg97zU1p5oA7XzR4FAFBVFZB43ZriK1jb3l/bAGu2OWys3V/bgNJCz2ic9s7qkwZN0zzPQBskrLo6eEzV60QIAvD1sRaIAtBkc2Lv4ZO+bfTLseB3f/kc15ePhiQIGHJWJgx6ETeWj4kYLxnjUp9JL6Gh2YGsdEPQeJuVbkCL3Y2RQ3Px02d2oGhILvbXNvje32qXYU4XfclRo/V73DbhqvczLXoJxcP7Yu0bgUn3bmnTgffmlPG1zQ7eoY40KoMSEzvVRBR1we4qbNl+0C9BjsOl4Gcv7sTPbzwXz4TIBnvfMzsw5Kws3LxgLMaP7I//ebca+nmC7+Rwy/aDQFk+RgzJgap6En8021zon2tGbqYJDy+b4pdwxzvsccO2GpQWWXHtzFHIzjCi1e5G3SkHSgqsWH5ZMWRFhV4nYvGMUbhnzUc41eLynXgCwO4DdbhGG4nsTCMWXFAAh0uGWS+dySwa5zuUsXiWLJV1pL4U1bMPJRH40//WBM/iWmTFgvPz4XSdOeE/dtKGA1814JIJg6Ah+BymRLFiCjL6x3tx6PLpnoSx7WNt+TTPrAazp+ZhR9VRX3z1dpg1aFBUDVnpBjyyfCpeefsL32e0jaklBVZMGzcQBYNyYDKIsDllbNl+EKVFVtw8vxgPvvwJ/n2sBVdeXIT/fO4fKBqSg6Vzx8LegQuSjHGpTwSQmWbEc2/uCZk1+6b5Y+FWVIzJ74MLxw/Go7/f5VvubQNd6chG0v4zNQAvbd6LoiE5frkJ9tc24KUtezt0oSiUjozK0Ets74mIe4WIoi7YXYW2d5evnzMKTpcCi0kHvShg+WXFaHXIsJ8+sVI0DSca7Lh5QYlveJckeYY9NbU4Me+8fPxgwiAUDclFq8ONVocMs0mP+iY7FEXFz26YCEkS0eBwhp/y5Qeeu9N/+egQrpszGssvK0ZjiwN7Dp7Eq1v3457FE3wdam+yHa9TzU784uUzz4P3ZMKcUM99ecvFZ3v9daS+ZE3Dz26YBItZH7YNXT97ND6qPOp7LcOixy0LiqEBWMukShRnoiAE3JF2uBT896uf4cZ5Y3HLgrFwuhXYnQqMBgmqqkEUgYUXFaCh2YmCQTkYP7I/3vmHCXPPG46GZieaWlyw5pjx0ua9uOLiESGPh8oDdZhbloffvf05ll9aAllW8eit03C8wY67T1+cBIBmmxt3L56AvlkmrH2jskPHCGNc6lM1DW5FCx1va+rgVjToJBGLZ47Cx5Xf+B4tCNcGYvFsst2t4NMvjvnN5d7W4pkjYdJJXdpuR0ZlZPIiUkLiXukhj6z/P9x5RWlPF4MoJkLdNfDe1SgbNxB90w3QBAFr3qjE5T8owj1rP/YbWth+updbFhQjO90As1kPh0vF8ME5WPNGpd96E0b2x03zxkITgDWvV2LG5KFhy6moGr6pa8HFk4bC6VKgahqaWmVs2HYAANAv14JfrzwPn+z91pdsx6v9o009mTDH+9zXuk1VQbN/8+6ov47UlySK+LquBTmZprCfZXN47sYBnrt+2RlG7D9Sjw//72smVaK4EwQN5dPyAPjfkS4akousdCMUVYUkijjVbMOTf/pX0Hg7YWR/LJ03xje09Rc3TcIzG6tQcDoBZDgut+qbuaHJ5kb1vuMBSdHMJh1e3foFphQP7PAxwhjXC4giWu3h25fN4T6dx0LFuMJ+eHXr/rBtIFYZ48N1fE0GCZIoouLLE8jNNHmmZrS7Ud/kwLjhfSFG2C5HZSQv7pmewvhPKayjdxW8w5wWz/Q8h1pelh9wggd4TrSe2VSFX9x8LswGHZr1roAh4yaDhB9OHgq3IsOtnLlrEo7FpMOwgVmo/bYJ1mwzHC4FoiigvCwfh79pxCd7vw06H2XboeDty2l3K2eGgceRURKw6urxqG90cJ7qDgj2nFzb+nK4FORG6FADgMkoweFSfKMZfveXL3DlJUURh+/1RBuh1GfSSXjv0yNBh6W+96lnKh+3qiHdog8Zb4edneXrUJsMErIzPEkf50wLH0+BM1mRW+1uWLPNkGUVq5dMxP7aBmzZfhBFQ3JxstGBiuo6zJka/PNCHSORjllKbg6XDEuk5KVGHZpaXchKN0LTVKy58/yQbSCWGePDdWwXXDAc9U1OfLT7aMCjFgP7pmNAtinsqIoOnT9RQmKnmoiiriN3FTRBgKJquGfxBMiqihULS9A3yxzQgfWqqK6D64cqAAVmoz7gRLC8LB+79n2HBecPR1OrZ/qYcNMhlRRYoagaJEnAR7u/wVMbdvstWzpvDP703n5Ys81+7ysttGL2VP+h4G31ZMKcDIsBstMNi+70bXSebIYV7tm7FrsbblnFwW8aQ0+pVeiZ4uWJO87Dzj1nRjNcecmIsNtlUiWKFUHTIk7loygastKNOHfsWUHjbdsLiQsuGO67K9eR48F7sdFs1OG2x//mW1ZSYMXqJZOQlWHAT9ftABB+rt9Qx0gsnpelxOB5/Esf9jdbFAR8cfgk9h2ux5LyMZ7fuhBtoCPPJnf14maojq/JIGHauLNxstGBmecOxVU/HAGdJODEKQd0koBv6lrQJ8sIfZjP7tioDGYBT0TsVBNRTIS7qxBqSNYV04vCfqZbViErKmQl8Ed0xJAcDO2fgWOnbFBOn6u1T47m21ahFQsvKkTtt02Q1cBnuCoP1OHFt/bilks9WTYfvPlctNjcMOhFZGcYce+6HX5Dwdsym8L9XFKyMBsliIIxZBsqKbDipvnFkFUVmqb5dU4czvDPxHH4HsVSpNj74hZPIqh7Fk8I+v62nd3xI/tDfzqLfU5G+OPhxvljcccTf0dpkRUnGv2H8VYeqIMoAGPy+/ierQ431y+Pkd4nM82A+tPTVwLBE+pB0KDB83y1GqFzaXfKWDS9ECOG5PiN2Niy/SAcLqVbFzeDdXxNBgmrl0zC8+0SrXnL/ujvd2HE0FyMHd4XkYarclRGcmLUIqKYCXZXIdyQrEsvKAAAX9bZ9j+GaafnrfZmZvYyGST062uEW9WgaZ6hu96OsCgKmDMtD/PPy4fDpSDdoockArmZJvTJMuH2X/8taNl3H6iDS1bxr+rvkGE24px+6ahvcsJs0GHE0FxfRvG2Sgqs0Eu8gpwKLCY9AAFj8/viy29O4apLinD59EKYjTpo0HDkaBMEQQM0LWAOYKOBSZWoZ3Uk9hr0YtBYm51hhMngeaxBkgSYDDqUFlmh14tYUj4GguC5g/3j2aMgKxpabC7UnbLD4ZIxclgubppXjHvWfhRQprZDyMcVWnGyMfjzszxGeie3omL3gRP4+lgz5kzLw3WzR8HmkJFm0uFEowMf/t+/ccUPRqDgnGwAnouXoqAL2enMSDOiurbB74JnSYEVq64ej8fW7+r2hZv2Hd/MNIMvc3n748pkkHDn1ePxq/W78NybHRt6zlEZyYedaiKKq3BDsqq+PIGJo/rj4klDffOgepUUWHHR+EEQBUBVNb9pX+5ePAFmyYD17+zDFRePwOubqoJeKX7/s3/jx3NGQxI903ccOdYc8o4zAHx3ohX/2n8Cyy8rRlOrE+f0S0OzzYU5U/OgacGvpDe1uNA3g0N7k52qqnDLbtwwdwye3VSFP75b7Vs2rsCKG+aNQWOLE+kWAxqanb5lJQVWZKUZmFSJEk772Pvl16eweskkvHZ6SiyvcYWeodqP/v4zmA06QFNx8/yx0CDgH1VHA2Lr5dML0TfbDLdbxbJLS3Dfszt8d6Pbc7lVX6wUgmQq5zHSe9nsMj7ZcxQ//fFEPNvuN3xcgRW3XFoMt6L4RlK4FRUrfvU33zptE5BpgoDn2uVdAc78Zt8wd0xULty07fjaZNXXoW47l7uvfIVnOvR2t3rmMS1KGexU96DH/vgvrLryez1dDKKYCDWNRbismVu2H8STd5yPZzYF/zFct7EKU0sGYlxBX9xyaTGe2ViF4oI+yLDo4FIV/OiHI3Gs3oZLJg3FnGl5vqFe3qGHs6bmQVVVGHQ6OGUZ1hxz8IKcJkkCKg94kqR5ryyrRh1WP78T5WX5AYmAHlu/C4/eOi0a1Uc9zCUryE4zo+KA5+7aJZOG+g0ffOGtvbhlQTH+secbDD0rC8CZkzpR9Zz0cfge9bS2cdho0GHR9ELf8FcNwIb3awJi7e6aOggCcO9146FqKkRBxMlGO1QVQWOrIAAjh+Vi8pizoKkqjtXbQ5anfx8Liobk+OYX9sbRNJMeaWYeI71ZmkWHn/54Iqq+PBE05j77ZhVuWVCCzDQDfnnLubA7Fd+ICsA/AVm4i/eVB+pww9yuzyMdivfcJmTC1Zo6qJpneavdDQsvvqccdqp7En83KEWFm8YizRw67DhcChwuOWiSEsDzY3jd7FHYfeAE/l+hFbcsGAtAgMPlgCSIWNduztO2Q70qaupw6YUF0OkkHPzmFGx2GRnpxtBJd4rOJN3xJjUx6yWYDBKKhuQGTfBTUmD1ZC9VQyfgoeRg0OngVFR8XBmYwdXbptyKguraBowr7IdHb52KDIvBb5oWDt+jnhQsDrdtvwXnZPuNwGiroroOKxYUQxMEHG+wY8P7B/weeWn7Obtr6nDZhQUw6iWIAkLG1HEFVuzc861f7NywrQalRdYzw2F5jPRapg7EXIdLxr3P7Ajy+pmOtfciUjh2hxtpnXyeOtJ8197h5MFmDPHyzkpiMjKDdypip5qIoirSNBbLLy0J+bxpSYEVja3Bhw16HTtpw46qoxg7vC90kgCbU0a62Yy17TrUwJmhXuVl+diwrQaSKEBRVAw9KwutDjceePET3Lqo1G9dbzlunl/s97y1zSHjpS2f4/DRRjx4yxS88NbegPcsv+zMXUpKbpqm4YW39oQcPlhelg+bQ8aS8jH4uPIblBT0Yz5WShih4nDb9hsu+zYAKABURcVr2wLvZrePrXqdCLei4GSzC+XT8iAK8B++W2jFDXPH4JW/fOH3ORzuTV6yqkaMua12d9DX23ZivZ3ecDr7PHVH5rv2ZgSPdFyZjTqYDOxUpyJ2qokoqiJNY+FwyUGfNx13ehqrUy3OgPe1TfphMuhQPi0P+w6fxPBBOUg3iGhxymGHennnqzYZPYnOZEXBAy9+glMtLjy2fhfKy/Jx3axRsLtkGPUSLCYdGlucuOua8b6hZ67Tz0sBwH3P7MCKRaW4bvYo2B0y0i16WIw6dqhTiMOtBE1GB5xpU2ajDo2tTowr7Ae3rMIghp7ehSieIg1/9cZEr/aJlYwGCU63AlXVwo4c8n6O2aiDSa/DLzZ8iosmDMbSeWPhcClwumSkWwzQSQIaGh248pIiLLyoAA3NTuh1IuqbHGD3goCOx9xgr7flvYscrWSRHZ3v2psR/FiD5/GHUAlXNU2DThQ5oi0FsVNNRFEVadiVzSHDohMDnjc1GnR45e3PceH4wX4/ht6kH+9/9m8UDs6B+XQG8LyzTUg7PSWL3RnYEW/L5VZRWmTFkaONGDE0B9+esPsS6ThcCjZsq8G5Y89CbqYJz26qChjmuHrJJOw9dML32qkWFx58+VPf32vuPJ8d6hTjcIZOYAcAggCIggCTQQ+3rHrufLBDTQkiUhz2PMOsR2mRFfsO1/vF2P59LLA7ZMiKDJc77Md4Ymuh57EXp+zCjfOKseWjQ/ifNsPKS4usuH7OGLhkFfc+uT3gM9bceX6X5wum1BEp5gI4PY2Wv7Z3hr0d5o7N9dwxnZnvWtI09M+1YOLo/rh4YvCEqxNHD4DdLcMisc2nGnaqiSiqOjrsKuB5U1XFj2eOgkNRcfP8Yjz7pufHsLwsH+9/9m/86Icj/IZcmwwS1t15AVQARn34ex3pFj1uWVAMAYAIAU+9VuG3vLTIilPNDmx++1DAlfLKA3UQRWDE0NyQn9+d+S4pMaWZw883bs22QBA1KG7A6ZJhMRqhCUJMhrFGepaPqL1IcTjNrIPZIOHHs0aj5quGgBhbWtgXt142Dg1q+Mdx0i163LSgGCKAXfvq8K/9JwITNFXX4WVtL6764cignxEsfrLN9z4RY26OBY4gV3m885237zBHa67nDt0oaNN+HS4ZP549JmTC1Ve3AkvnjulUGSg5sFMN4ODBg3jwwQdRUVGBtLQ0zJ07FytXroTBwJNkos7qzrArQdNgkkQ8v3kvbpo/Bi63BlnRUD84J7BD/R8ToWoa6hrtsJj0eOiWKbCcns9yzYYK353o0iIr+udaIKga9h46iWHnZMHhUrBoeiFGDMmBpgF9s81QNc13NTrU3K1vfvhl0Cm4ujvfJSUevSRiwsj+KBicjfEj+wMQ4HDK0OlEfH2sGUa9BAlAs+xGmkmPplYXLCZd1O+4deRZPkp88e4kdiQOC5p2OvFiDrIzTL4YazJIuPWycZBVDXqdiCfvOA+tbeYL9sbX0iIr+uWYoRME2GUZxcP7odWuBMyKsGX7QVTU1OHHs0f7ZWv2ah8/2eYTW6zaskEnYmrxWTh//GD0yTL5zVH9t13/hkEnotXuv53SQiv6ZJnx1E/OhygIcMkqJFFEU6sTRoMORr0IQRCQZtbBpAssZ/vvYjLo4HTLaLWf+W6dfT7b7pQBI8I+NuG5484sHKmm158JNjY24tprr8XQoUPx9NNP49ixY3jkkUfgcDiwevXqni4eUdLp7rAru1vBp18cw0XfH4LB/TNQ19CCPlkm38negguGY0rxQECU4JJV/Ol/DwQkDHvwlim475kdGHJWFpbOHYv/eXcfrrpkJJ59aw9WL5mEx28vwwub9/oNy7p/yUQACD3HZJEVj91WhpOn7PjiSL1vWprOPp8VC5ogoMmh4HhtvefZRp3Iuzrd1Gx34oa5o3Gy0YlX3t4XMJfuQGsa+maZYXcq0EkCMiwGNNtcMGcYI564dfQktKPP8lFi64lOYkfisE1W8YuXP8V//Oj/+WJsdroBP1s6GQAgqxpe2vJ50Pj6x3f34+oZI6FpGlQN2LXvGL5XNADVtQ0Bw129GZpbHW48tGyKL5+Ftzxt42c02jzvcsdOrNqyJghQVRXXzByFde3mly4psGLZpcVQNRVZ6Ubf6+MKrVhyOvndZ/uO+a1fPi0Pq5/fiaIhubh8eiGcbgXv7DyMG8rH+MoZKjt++bQ8X0bx0iIrloVJrhqs/ZoMOhxvCD2tHAA4nDLS9bxxl2oETevdkea5557Ds88+iw8//BDZ2dkAgNdeew0///nP8eGHH6J///5d+lxFUVFf3xrwuk4nIicnDXc/tR2apmHVj3rvPNXeumhoaIUs997nUXuyHqzWjLDLQ7XjUDRBgENWAAiQRAFOtwKHU4HFrIMIARo8V2cFAZBEAQbBBVU0weaU0Wp3I92sh9Ggw6lmOwx6HewON8xmPVrtMn7x0ie4e/EEmAwSPtlzFLOm5mPtG5VBrwaXFFix/NJibNv1FUYNzcXPXvwET/7HeWhsduGsvmlwuGTfVXBBOFOu+kYH3IqKd3ce8fuR9iotsuKG8jFodbhhMeo9c02adNDrRDTZXKeT9UgQAMgq4HTLsDsVpJv10EmC726myaCDS5ahqgLU03N3p5l0num4ALS2qQ9zhARo0T7JiccJabS3EakdA51vyy4Aew+exEe7jwZtY+MKrbh5/ljc/N8fAPDU+U3zi9HU4kB2ugk6wTNros2torHFiXSzHrKiocXmwolGO8YN7wuxzXcOVicOWcHyx/4WsozJ9ixqb4z5miBgTZBOIgD/qaROi2pMFgTI8OSNsNllWMw6mAwidIKIVpeCVrsbZqPnbl59kxOP/v4z/PKWKXj17S+wZO4YrH0jcPgqcCa+OmUFrXYZqqqhT5YJLlnGsXr/kULe9YuG5GBK8Vkw6iW4FQ02h9sX70QRMOp1sLs8dwjNRh0MehGi4Cn7Q7/7J47V22EySHjyjvMgK2rI2BEqHt6yoBi6GJ7u6nQidEY96hsdnu+Wgp35zrTlzsZkm6zBoBfDt7nLiuE8HSNb7W70y7Hglbe/CPpb7W1zG7bVoKTAiqklA1F3yo4DXzVgxYJiAAj5Xdq+FwAmju6PJeWe4dzBLlC1/Z21ySrcsoaGZgceeOnTgM/2euon5yNd3/XY3RtjaWd0pH46eh7Skbbs226XS5witm/fjsmTJ/s61AAwY8YM/Nd//Rd27NiBBQsW9FzhiJKIIgh4cfNeXDzJk5yj/ZVm79XfoiG5KJ+WBwEyzumfjbWvVwasu3TeGNz3zA4UDs7B0nljAc0zbUZDkwPbd3+D5ZeVwO4MP5+1w63gwFcNvteO19shiUJAR9xbtnd2HsEPJw/F/37i+f+egycChilWVNfB5Vbxh3eqg37G6ud2oqSgL66dNRrPvbkn+DrPe9ZZPHMUXti8N+AO6MILC/HAS5/4tu09mQh2QhjtO5nxuKOWLEM73bKG3ExTyDa2u6YOTveZ9lFRXYdnNlZhaslApOcZ4dJUvLT586B3QT7d+x0G9k3HgGwToGkh6+TGuWODDpf14rP8ia8zSY6iSRUEHD/l8JsOq3+uGb+46Vw83W76wXGFViyZMxq3X16KFzfvxZj8PnC4lIjxVRAE3LP2Y89nFFhxzcyReHfnEd9IIW/HuvJAHRZeVABF1fDMJv+4OGFkf1w3e1TAlIglBVZcPr0QogD84sZz8eBvP/XF1VCxI1w8XPtGFVYsLIEUo4SSTkXDE+t3Rex0JbNYtmWLzo0Wly58m3N5OkDZ6QY8/Lt/4p5rvx+0Q+1d35sV3PvvPlkmbNhWA/vpuN3R7Piffn4Mi2eO7NDz2TaHDFnxjN4INV97SYEVBp0Ez2VX6gmxOg9JnkvcMXLo0CHk5fmn48/MzITVasWhQ4d6qFREycV7MjNkYFZAhxrw/Eht+egQysvyff8eNCAn6FXpygN1eOGtvVixqBT//OIYfvvnz5Fm1qN4eF+c0y8du2vqYD99Jzccm13GTfOLsWX7QQBA32xT2LINO9tTdu93KC/LD/q59U2OsN9vyFlZAR3qYOs8326ea8BzYvLathq/bVce8JwQqmJguLa71QgnOR0/gYzUQdeE7j//FY9tREur3R1xvtH22WorD9ShT5YJz725B5IoBp0j2NvWXttWA5caviPw/OY9IdshwGf5k0FHkhxFmyYI2P3liYD5pe9aPAHPBGlru2vqUP3vBvTJNqOipg7fK+rfofgqCGdOPncfqMP6v+7DsLOzfPG7Lb1OxB/frQ6IecPOzsILm/cGPVZe21aDr463oOrgCdx97QRs+ehQ2NgRaRqxY/W2mMQY3zFcnfhxrTti2ZYV0dShNmcx6bD30EmsWFTqG+kQStv47XKrvr9tDjliWdvH/la77Euu2jfdAEuIR6xMRh0sJh10ooDyaXkoKbD6LfdeWHUrkTOdU2zE8jyk1/8iNzU1ITMzM+D1rKwsNDY2duuzdUGu2EneFPoCIEAIuk5v4a0LqZdPK5Do9dCRNtrk8JzMzJmW5/c8XVvtrxxHutN83exRAIB/7PkW184aAb1OhKx4fsRa7e6ImUItZh0kQfA9FyXLasT5Vjdsq/H7fzCh4m3b79eROujIOm1fszllZJv9Q3ZrsyN4YbzL7W5k5pjCruPl3YfBeDvomabuzSgbj22E05l4azJKMDjCr28O0ql1uT3tzB2iQ962rTndCuQgnW+viuo6XHpBQdC2UlpkRZpRBymJztcTPdbFgsUUIU6Z9J0+D4i0fpNDCTrKQi+Fbmsvbt7re5ba6VY6FF+Nev/23/Y3wBu/vcxGXdC7iiPaDLNtr20sVJTQ82V7Y0ekjlKLzQ17ujHqMaan41q8xLItt9jlDrU5k0FCbqYJuZmmiHm+DG2GV7f9d6Tv0X5973s68t1EWYNeL0LRNDy2fhfKy/IDEvc9tn4Xfn7jZGRld+y3OZjeGEs7I1z9xPJ47fWd6lgRRQE5OWkhl+skzw4Lt05vkZlp7ukiJIRErIdI7djreG09gMCru+21XR7pqrS9zQnSv4+1YkCuBe7Tneo0sx56SQw7vMpkkOCWZZQWWrFkzhh8U9fSobK1/39bpYVW7K9tCHi9/Wd0ZDudXafVLmPYwCy/1xps4U8iTUZdh2OMdx+G4nDJGHJW4AXIzojHNkLpaFv2srmbcbLREbKNlRZZg07l5j0Za3WEbt/e/etwKZDE8O3BoAvM4lxaZMVti0rRNzvxYkZHJGKsixWdzRU2yVFulgkZlo4P4e9IOz5eWx8ihoRukw6XArPR055NRgkmgxQxvlZUfxewzLvdtvG7/d26YOtHWh7p98LhkpGRFr4eDXoxJjGmJ+NaPMWyLX9TfwJ9skwR29ypZgdcbhV2p4yMND3GFVoDpsH0ru/9rS4psOJkowN1p+y+cnrLHOy7tH1vZ7+bra4ZiuLJMeBwKSEvGFk68dscTm+KpV0RrH5iebz2+k51ZmYmmpubA15vbGxEVlZWkHd0jKpqaGqyBbwuSSIyM82QFQXQgLuf3o57rv5/Xd5OMvPWRVOTHYrSexMt9GQ9RArqodpxeyaDJ5S0v7rbXtvlka5Kt70LKImejJqqUz7946rD/iMncdP8sUGfXb5p/ljoBAGyCIwYlosTp+zQR7jK7C2b9//pFv/ylRRYccPcMfjJb7Z36PtFe500sw4NDf4Jioz68Ce+Rr0U8J5QvPsw3PKOfla8t9GRk5OOtmUvvSRiUL90XD69EID/9CilRVbcOG8s9KL/rZK2F13CDc327t+0DtwxsZh0uO2yErQ6ldMJkPRIM0qQNLXb+yPeemvMD5mF+9JiyE43GpxnOozRiMkmgy5EDAnf3rzxxKDzTBcXKb6+/Od9AZ/h3a43fpcWWTF7St7pKYQCdfQ3I1LZTQbPrAeROkpl486O+nETj9iZKDraljsbk9PMeugEIWKbMxn1MOg9yewkUcScqXnQNASs783h4n023+lWsGvfd75yhvoubd8b6ruFY5REtMgyNM3zexAqqZvJ2L020VtjaUeFq5/OHq+dufjR6zvVeXl5Ac9ONzc3o66uLuBZ684Km5FPAzRNA7QI6/UCiqL2+joAErceOlIms95zMrO/tiFsJ6/tlWOzURd23ZONDr/3CQIwqF8GLp9eiGabA4WDc9DU4sTlPyjAdbNHwe6QYTbp4HC6YZAEQHXgP57wTKmhnL7DHalsbf8visDjt5dBllW0OtzYX9uAHVVHUTQkt0PfLxrrtH3NYtQF7AuDKATt9HlPJAxix+OLdx+GnjZE7Hb7jMc2wunMZwuiiD5ZJuw5eBLXzhoJYBQcThlGg4QMix46AHb1zLQpJQVWXD1jJH76zA6UFFhDDkX17l/v9wVC3zHxrqMpKiw6wZeUTFNURP9J3PhJ1FgXKxIQPMmRqkFWO58UJ1LdmfUi6psCR1m4FTVsp1MviZ742uqAXRKQlWYKiK/20/H1ubeqAhLotY2fJr2EB28+F2aTDq+9V42LJw0NGvP21zaE7Hx4fwcEARjcP6NDx8ktC4oDcnV4O0rvfXoE5gmDot72ejquxVOs2rLFqEOLywmL3hCyzXnVNzkwsG8ajp60+YZYL7yoAAad6BmmLXnmqf7vFVNh1EtQVA2AhhvLx/iVM9h38c5T/cCNk7v83SwGHX779ue4esZIAP4J0cYVeDLRS5oGWe5+orLeFks7K1j9xPJ47fWD8cvKyvCPf/wDTU1NvtfeeecdiKKIKVOm9GDJiJKHd07U2m8bwybn2LL9oO/fX33XgOWXFQddd+m8MVizoQKlRZ7M14e/acR/v7oLoirDmm2CzaZAJwoQJAG5GWaIgmcomU4SYM22QNTsWPbrnRg51DNH5ZbtB7Fl+8GwZTv8TaPv/5dPL4QkCMi06CFJGjZv98xZvenDLyN+v9pvG3HT/LER17lx3piAdUqLrL7ytn3f8suKg0+rpWnol23CtHEDsXrJRNyzeAJWL5mIaeMGot/pzNKd3YelRYFl6sj84omyjahRVUgAxuT3gU4S4XQpSLfoPR1q74P1qgH3Xz8RD958Lq6dNRK/eOkTFA3JxY3zxkASEPA9vW2g9ttG3/dNqjqhLutIkqNobmvc8L64fHqhX4x59NXPcEuQtua9COdQHbBmm9DikJGRZsKXRxthzbZAEs9Mg2jNtkAE4GzXIWgbR5ddWow//W81sjOMePPDA5h//nCs2VARNHYe/qYRN8wdE7JMg/qlo2R4X4iq2qHjRKdpWLGwBA/efK4vHhYNycF7nx7BDeVjYlLvve0YjkVbFlUVaXoDBE0L2eagaXA63Sge3heaoOGpP/0LDpdnlo/+OebT5RFgEDT0TTcgwyDBIABmSYBZCl7O9t9FVFWYpe59N0FVce3MUdiwrRoFg3N8v80P3nwull1W7LmbmWJtIpnE8njt9fNUNzY2YtasWRg2bBhuuukmHDt2DI888gjmzJmD1atXd/lzOzpPNQBfsoVVV/auOas5z55HKs5TrWme+ZcdTvn0lWMBzTYXjAYdRCHUPNUy0syeK8VNrQ6YDDqYDTpoqgpNFGF3ynA4ZPTNNEARREiqDEXUnc4E7nmv2aiDpMo40eK5wq2XRCiqBkU9XZbTyXXsTtl3FVw8PU+1AAGqpsKgkyAIgE4UIagqVEGErKp+c26bDBKcLs8dbLNRB0OYeao979FDf3qearPJ873azlPtLUua3zzVnu9kiTBPtbfe7W4VDpfsqTd91090OE+1P1UU/dqn2aiD90nqRrsDeun0HOQtLphMOhh1EhRNgUmSoAG+72kyetu+BpMu8PvGo957GmN+ZNGep9qlan6xSy8J0EsSbC4FNrvbk5BPL0EUBM/jDKoMVdQBqgwtWHzVAIcqQ4QEl6zA4fS0bQECIGgw6nVwKzIkwXMcGCTPZ7c43HC6FGSmGeBWNNgdbt/7BFGDSa+DPaBMgF4UgTbxr6PHSbyPp94wT3VndDkmiyJkzfNcq99vIABF1eDJOyXAIctosSV2rPT+LreebtNGvQSDKESlQ81YGl5PzVPd6zvVAHDw4EH84he/QEVFBdLS0jB37lzccccdMBi6Pv9npzrVp636ETvVvVEqdarjjW3ojN5cF7HsVAO9u26jifUYWSLFZO6vjmNd+etOTGZdRsY6Ci+a9dOZTnWvf6YaAPLz8/G73/2up4uBx/74LwC97441ERERERFRsuKd6hjRNA1qiMQG//X8zpCZMIHwU+89cNO5Ebe9+rl/dGi9RCBJIjMXoufqIdIch+HacaJgGzqjt9ZFR+bq7G5b7q11G22sx/ASLSZzf3Uc6+qM7sZk1mVkrKPwolU/nZkLnJ1qIiIiIiIioi7q9dm/iYiIiIiIiLqKnWoiIiIiIiKiLmKnmoiIiIiIiKiL2KkmIiIiIiIi6iJ2qomIiIiIiIi6iJ1qIiIiIiIioi5ip5qIiIiIiIioi9ipJiIiIiIiIuoidqqJiIiIiIiIukjX0wVIVYqior6+NeB1URSQm5uG+vpWqKrWAyVLHKwLj56sB6s1I+zyUO04UbANndGb6yJSOwa615Z7c91GE+sxskSKydxfHce68tedmMy6jIx1FF4066cjbdm33W5tiTpNFAUIggBRFHq6KD2OdeHBeug61t0ZrIvYYd1GB+sxuXB/dRzrKnpYl5GxjsLrqfphp5qIiIiIiIioi9ipJiIiIiIiIuoidqqpV9AEATZZxYkWF2yyCk3gkBkiikwTBDQ5FFTX1qPZqTB2EPVCPIdIPdynFG1MVEYpTxEErNtYhYqaOt9rpUVWLFtQDEljggciCo6xg4gYB1IP9ynFAu9UU0rTggROAKiorsO6TVW8MklEQTF2EBHjQOrhPqVYYaeaUprdrQQETq+K6jrY3UqcS0REyYCxg4gYB1IP9ynFCjvVlNJsDrlby4mod2LsICLGgdTDfUqxwk41pTSLKXzagEjLiah3YuwgIsaB1MN9SrHCTjWlNLNeQmmRNeiy0iIrzHopziUiomTA2EFEjAOph/uUYoWdakppgqZh2YLigADqzfIoMMsjEQXB2EFEjAOph/uUYoVjHCjlSZqGFQuKYXcrsDlkWEw6mPUSAycRhXUmdqhwuGSYDDqY9SJjB1EvwnOI1MN9SrHATjX1CoKmwaITYUk3eF5g4CSiDhA0DZkmCUPOykRDQytkWe3pIhFRnPEcIvVwn1K0cfg3ERERERERURexUx1HmiCgyaGgurYezU6FE8wTEfUCmiDAJqs40eKCTVYZ+4lSULPNhSaHwuO8mxQN+Pp4M7475WA9UlJJ+uHfra2tmDFjBo4dO4Y33ngDY8eO9S17/fXX8eKLL+Lo0aMYNmwY7rjjDlxwwQV+729ubsbDDz+Mbdu2we12Y9q0abjvvvvQr1+/qJZTEQSs21jlN+G8NymCxCEnREQpibGfKPU5FQ1PrN+Fimoe592hCALWvV7JeElJKenvVK9btw6KogS8/vbbb+P+++/HjBkz8MILL2DcuHFYsWIFdu/e7bfeypUrsWPHDvzsZz/Dr371Kxw+fBhLly6FLEdv8nctyEkVAFRU12HdpipehSMiSkGM/USpz3ecV/M47w7GS0p2Sd2pPnjwIP7nf/4Ht956a8Cyp556CrNmzcLKlSsxadIkPPDAAxg7dizWrl3rW6eiogIff/wxfvnLX2LmzJm46KKL8Jvf/AbV1dV47733olZOu1sJCBK+MlTXwe4OvChARETJjbGfKPXxOI8O1iMlu6TuVD/44IO44oorMGzYML/Xv/rqKxw5cgQzZszwe33mzJnYuXMnXC4XAGD79u3IzMzElClTfOvk5eVh5MiR2L59e9TKaXOEv+sdaTkRESUfxn6i1MfjPDpYj5TskrZT/c4776CmpgbLly8PWHbo0CEACOhs5+fnw+1246uvvvKtN2zYMAjthpTk5eX5PiMaLKbwj65HWk5ERMmHsZ8o9fE4jw7WIyW7pGyhdrsdjzzyCO644w6kp6cHLG9sbAQAZGZm+r3u/du7vKmpCRkZGQHvz8rKwt69e7tdTp3Oc80iTRJRWmQNeN4G8CRgSDPqIPXCR0UkSfT7f28Vi3pQNKDVqcDmcCPNrIfFIHW5jXnbcSJiGzqDdRFZV9qyogHNTgXHa+thNuo6dSwx9vtjG42OeMXk3ra/uvq7yeO869q25Uj1CAiwK1q3zmdSRW87Njurp+onKTvVzzzzDPr06YNLL720p4sSkigKyMlJ8/1966JSPL2hIiAz5G2LStE329wTRUwYmZm9+/t7Rase6k7Z8fTrgW3t1kWlsHayrbVvx4mKbegM1kVwXWnL0TiWGPsDsY12XU/E5N6wv7p7rPM477xgbTlYPZYUWDF7Sh7ufGo7HC6ly+czqag3HJvdEe/6SbpO9TfffIOXX34Za9euRXNzMwDAZrP5/t/a2oqsrCwAnumyrFar771NTU0A4FuemZmJ7777LmAbjY2NvnW6SlU1NDXZfH/rANx2WQlsLgV2p3zmjoemoqGhtVvbSlaSJCIz04ymJjsURe3p4vSYaNaDogFPt5uOAvAk+Xh6QwVuu6zE7wpvpJOz9u040bANndGb66IjnYzOtuXOHkuheGO/9w6YxaRHmrF3xv7e3EY7KpFicm/ZX9E41o2SiFVXj0dDkwOt9t59nANdj8k6ALcvGge7S0FzqxsuWUHVlyfw2PpdcLg8ico6G4NTUW85NrsqmvXTmYuYSdep/vrrr+F2u3HjjTcGLFu8eDFKSkrw+OOPA/A8M52Xl+dbfujQIej1egwaNAiA59npnTt3QtM0v+eqDx8+jMLCwm6XVZYDd2SGUcLgAZloaGiFLKtg2gVAUdSgddXbRKMebLIaNntmq1OGpZNDB5Nh37ANncG6CK0z9RLtY8miE2BJNwAANKV3x3620e6Jd92l+v6K1rGek2mG7HTDfLqn19uP844I1q50OhHn9MtA7bdNuOtXHwV9X1fPZ1JNqh+b3RXv+km6TvXIkSPx6quv+r22b98+PPzww/j5z3+OsWPHYtCgQRg6dCjeeecdTJ8+3bfe1q1bMXnyZBgMnhObsrIyrFu3Djt37sS5554LwNOh/uKLL3DDDTfE70sRRUlHsmd6T+yJKDQeS0S9A4/1xGRzuCMs536hxJJ0nerMzExMnDgx6LLRo0dj9OjRAIBbb70Vd955JwYPHoyJEydi69atqKqqwvr1633rl5aWYurUqbj33ntx9913w2g04oknnkBRUREuvvjiuHwfomhi9kyi6OCxRNQ78FhPTBaTPsJy7hdKLCk7bmL27Nn4xS9+gb/85S9YsmQJ/vWvf2HNmjUoLS31W+/JJ5/Eueeei9WrV+MnP/kJhg4diueffx46HQ9WSj5mvXQ6S2ag0iIrzHopziUiSk48loh6Bx7riSnNyP1CyUXQNE3r6UKkIkVRUV8fmJxCpxORk5Pme6a6N2NdeES7HhRBwLpNVQFZSJctKIbU7nC3WgOnlPP7rBDtOFGwDZ3Rm+siUjsGutaWO3MsUWS9uY12VCLF5N60v7p7rPemuuqI7sTktnXpVDTG4CDY3sKLZv10pC37ttutLRFRwpE0DSsWFMPuVjzPHJl0MOslCL34B4ioK84cSyocLhkmgw5mvchjiSjF8HczMXG/UDJhp5ooBQmaBotOPJPEgz9ARF0iaBoyTRKGnJXJuwJEKYy/m4mJ+4WSRco+U01EREREREQUa+xUExEREREREXURO9VEREREREREXcRONREREREREVEXsVNNRERERERE1EXsVBMRERERERF1UUw71YsXL8bOnTtDLv/kk0+wePHiWBaBiIiIiIiIKGZi2qn+5z//iRMnToRcXl9fj88++yyWRSAiIiIiIiKKmZgP/xYEIeSy2tpapKWlxboIRERERERERDGhi/YHvvnmm3jzzTd9fz/zzDPYsGFDwHrNzc2orq5GWVlZtIuQsDRBQJNDwfHaepiNOph0IgRN6+liERFRCIzbRMlHEwTY3QpsDhkWkw5mvcTjNgkw3lIyi3qn2m63o6Ghwfd3a2srRDHwhrjFYsEVV1yB5cuXR7sICUkRBKzbWIWKmjrfa6VFVixbUAyJAYOIKOEwbhMlHx63yYn7jZJd1DvVP/rRj/CjH/0IAHDhhRfipz/9KS666KJobyapaEECBQBUVNdh3aYqrFhQzCtxREQJhHGbKPnwuE1O3G+UCqLeqW7rgw8+iOXHJw27WwkIFF4V1XWwuxVYdJzdjIgoUTBuEyUfHrfJifuNUkFMO9VeLS0tOHr0KJqamqAFudI0YcKEeBSjx9gccsTllnRDnEpDRESRMG4TJR8et8mJ+41SQUw71fX19XjwwQfx3nvvQVGUgOWapkEQBOzbty+WxehxFlP4ao60nIiI4otxmyj58LhNTtxvlApi2kpXr16NDz/8ENdccw3Gjx+PzMzMWG4uYZn1EkqLrKioDhzaUlpkhVkvAXxWJOqY/ZOIusobt/cdrkd5WT5GDMmBy63CoBdR3+SAhXGbKOHwuE1O7c+TTQbJt/88e0uAJgg8h6OEFtNO9Y4dO3DttdfirrvuiuVmEp6gaVi2oBjrNlX5day9WQ0ZJKKPWSSJqDsETcPyBcU4fsqB17bVYMO2Gt+y0iIrSob3hdSD5SOiQDxuk1Pb8+R9h+ux6urx2PLRoYD9x3M4SmQx7VSbTCacffbZsdxE0pA0DSsWFMPuVuFwyTAZdDDrOf9eLDCLJBFFgwDg9fdrUHmAsYQoWfC4TU7e82S3Cjz3ZhX3HyWdmKbSKy8vx7Zt22K5iaQiaBoyTRKKhuQi08ShyLHSkSySRESRMJYQJR8et8lL0DTIisr9R0kppneqL7nkEnz22WdYsmQJLr/8cgwYMACSFDjwZvTo0bEsBvUyzCJJRNHAWEKUfHjcJjebwx1hOfcfJaaYdqp/9KMf+f79j3/8I2B5b8n+TfHFLJJEFA2MJUTJh8dtcrOY9BGWc/9RYoppy3z44Ydj+fFEQTHbOhFFA2MJUfLhcZvc0ozcf5ScYtqpnj9/fiw/nigoZlsnomhgLCFKPjxuk5skALcuKsXTGyq4/yipcAwFpaQz2dY5TzURdZ2kabjtshI4ZBXNrS7GEqIkwHOA5GbNNuO2y0rQ6pS5/yhpxLRT/Z//+Z8R1xEEAQ899FAsi0G9lKBpsOjEMwktGIyJqAskATinXwYaGlohyypjCVES4DlAcpMEcP9RUolpp/rTTz8NeE1VVdTV1UFRFOTm5sJsNseyCEREREREREQxE9NO9QcffBD0dbfbjddeew2vvPIKXn755VgWgYiIiIiIiChmxJ7YqF6vx9VXX40pU6bgF7/4RU8UIWVoggCbrOJEiws2WYUmCD1dJCKilKEJApocCqpr69HsVBhjiXoQz3mIbYASVY8mKhsxYgQ2b97ck0WIO7cGHPm2ES02N9LNepiNOoiq2qXPUgQB6zZWoaImMDuixGdPiIi6RRUE7P7yBHIzTXC5VRj0IuqbHBg3vC/E0zFWEwQmQyKKg/bHY4vdHXA8Ajwmk12482Se91Ii69FO9T/+8Y9e9Uy1LAhYu6ESlQfOBIOSAiuWX1YMXSeDgRYksABARXUd1m2qwgpOO0BE1HWCgOOnHPho99GAmD2wbzoGZJugADzBI4qHDhyP0DR2upJc2PNkBMZbgOe9lDhi2qles2ZN0Nebm5vx2Wef4YsvvsCNN94YyyIkDFUUsfZ1/0ABAJUH6rD2jSrcurCkU3es7W4lILB4VVTXwe5WYNH1yOh+IqKk51I1vLatJmjMBoDllxXjGZ7gEcVFpOPxlgVjoRd5syGZRTpPXn5ZMc97KaH1SKc6KysLgwYNws9//nMsWrQolkVIGDanHBAovCoP1MHmlJGu73gwsDnkiMt90xAQEVGnON1K2JjtcPHCJlG8RDoenW4FsijwmExikc6THS4l/Pt53ks9LKad6v3798fy45NKq90ddrnNLiNd3/FgYDGF33WRlsdbsGeciIgSlcPpOYEzGSSUl+VjxJAc33PV+2sbYHdEiOk8wSOKGodTCXksbtl+EA6ngkj5qnhMJra258nB9rUAASaDFLJznWjnvdT7sAXGSZpZH3a5xdy5XWHWSygtsqKiOvCqXmmR1dNpTZBhTuGecSIiSkRpZj1MBgmrrh6PLR8dwoZtNb5lJQVWXDh+EE/wiOIk3aIPeSyuuno80i16aBHOeXhMJjbveXKouFtaZMXqJZPwwEufBMTdRDvvpd4pLuNg/vnPf+LRRx/FypUrsXLlSjz66KP45z//GY9NJwyLUYeSAmvQZSUFVliMnQv2gqZh2YJilBb5f6a3s5oozw1FSqjWbHP1UMmIiEIz60XcMHcMtnx0KOgzfs+9WYUb5o4J+l7fCR4RRYVRL4U8Frd8dAhGveS72RAMj8nE5z1PLi/LD7qvK6rr8PoHNQFxN9HOe6n3iullO5fLhZ/85CfYtm0bNE1DZmYmAKCpqQm//e1v8YMf/ACPP/449Prwd3FTgaiqWH5ZMda+URU0q2FXptWSNA0rFhQn9NQRkRKqNbY4kdbmWXJOhUFEiUDQNIwYmovjDXbMLcsLGG5aUV2H6+eMCRgxxBM8oujxnhO02l2YW5aHoiE5nuHebe5Uep63lWHRiVi2oBjrNlXxmExC3vPk4w12bNl+EIumFwYd6r9kzhisufN8nidSwolpp3rt2rX43//9X1x//fW4/vrr0bdvXwDAyZMn8fLLL+Oll17C2rVrsXLlylgWI2HoNA23LSqBzSmj1SbDYtbB0o15qgHPiZ9FJ555TijBAkukhGqtdjfS9EYAnH+QiBKLJAiorm0IOtz0sfW7YHe4E/7CJlGyCnZO0Pb4a9ux9j4vnQw3Gyg0nabBqBfDDvW3O93ok2ZI2PNe6r1iOvz7z3/+M+bPn4+77rrL16EGgD59+mDVqlWYN28etmzZEssiJBy9AAw9KwsDc01I14vd6lAng0jPMHmfoYk0TFyLlIGEiCiKNEHA82/tCTnctLwsHxaTzndhs2+6ARadyJN3oigIdU7Q9vhrq+25Bo/J5JZhMYYd6p9hYbI5Skwx7VTX1dWhuDh0Mqri4mLU1QUfGkypIdIzTlnpnrvUHZl3m4goXsLFpMoDdSge3pfPaBLFSKTjb8SQHN/ffF46tciKGnZqLVnhRRJKTDEd/j1gwAD885//xJVXXhl0+WeffYYBAwbEsgiUAK6fMwbHp9ogAL5nYkYOy8WyS4uRYTGgwenmvNtElFAixSS9xPluiWIl0vHncntG+fF56dRjizhdoZvng5SQYtqpnjdvHp5++mlkZGTguuuuw5AhQyAIAo4cOYJXXnkF77zzDm699dZOf+5f//pXbNmyBZ9//jmampowZMgQXHPNNbj00kshtBkm/Prrr+PFF1/E0aNHMWzYMNxxxx244IIL/D6rubkZDz/8MLZt2wa3241p06bhvvvuQ79+/br9/Xu7UM9IP/kf58MgADrxzL5Ktnm3iSi1RYo5rQ43/rStmjkfiGIg0vF3Vl8L1tx5Pp+XTkFmU4QpaHk+SAkqppfab775ZsybNw8bNmzArFmzUFxcjLFjx2LWrFl47bXXMG/ePNx8882d/tzf/e53MJvNuOeee/DMM8+grKwM999/P9auXetb5+2338b999+PGTNm4IUXXsC4ceOwYsUK7N692++zVq5ciR07duBnP/sZfvWrX+Hw4cNYunQpZDn8VVIKL9wz0s+9WYX2P4GcCoOIEkm4mFRSYMX+2gbmfCCKEZMh/DSkaSY9n5dOQZogoLq2PuS+5/kgJTJB02Ifkfbv34/t27fjm2++AQCcffbZKCsrw4gRI7r0efX19cjNzfV77f7778fWrVvx2WefQRRFXHLJJRgzZgwef/xx3zpXXHEFMjIy8MILLwAAKioqcMUVV+Cll17C1KlTAQCHDh3CzJkz8etf/xozZ87sUvkAQFFU1Ne3+r2miqIn87fdjXSzHuZuZv5OZDZZxYpf/c33t8kgobws3zc9wtnWNKSbdOibm4aGhlbIsuq5sx1iKozO3glKpqm5dDoROTln6iGerNaMsMuDteNE0pN1l2h6c11EasdA19pyqJi0ZM4Y1Dc64FJUiAKQZtIjzZzYcSYR9OY22lGJFJN7Yn95z5McThlpZj1e2LwX+4/U+84fNADWbDOMehGJNBkr27a/rsZku6JiR9W3mFoyEF8crkdupsk3pVZ9kwNj8vpAUVWYdL071rK9hRfN+ulIW/Ztt1tb6qARI0Z0uQMdTPsONQCMHDkSGzZsgM1mQ0NDA44cOYJVq1b5rTNz5kw8+uijcLlcMBgM2L59OzIzMzFlyhTfOnl5eRg5ciS2b9/erU51e7IgYO3rlUHnqNalYGBo+zyUySAFnR6htMiKWxeV+hphtKbC4NRcRBQt544diDlTz8xTfbLRgZONDiiqij+3y1DLOEPUde3Pk0wGCTfNH4ubFxTj2U1VAecPPNZSjyiIOPDvUxh+TjY+rjyK3W3O48YVWpGbacKjv9/lycvD/U8JJm6ZVlRVRWNjI06dOhXwXzT83//9H/r374/09HQcOnQIADBs2DC/dfLz8+F2u/HVV18B8NyVHjZsmN9z2ICnY+39jGhQRRFr36gKOj3A2jeqoIqpl/Cm7TMv5WX5QadHqKiuw9MbKtA2kWN3p8Lg1FxEFA2qKGLNG1VY+0YlHnjpUzzy6md44KVPsfaNShxvsIWMaYwzRJ0X7DzJ4VLw7UkbntlY5de5AnispSJVFPH8W3sw7OwsbN5+KGCf766pw+btnunUuP8pEcX0TrXb7cYLL7yAjRs34rvvvoMaYqjzvn37urWdXbt2YevWrbj77rsBAI2NjQCAzMxMv/W8f3uXNzU1ISMj8LZ+VlYW9u7d260yAZ7hBwBwyi6HnR7A5pSRbU6txAtpkojSIisqqj1TX7S9wtxWRXUdbC4FGcboPCPT5Ig0NZeKTFNiPY8jnc4iLCVoNmFvO05EiV538cS6iKwzbTlc3O6TZQo44fNK1DiTCNhGoyNeMTme+yvU8Rbp/CFRjjW27a5p25ZP2WVU1NRhzrS8kPu88kAd5pblAUis/R9vbG/h9VT9xLQnt3r1arz11lsoKSnB9OnTg3Zgu+u7777DHXfcgYkTJ2Lx4sVR//yuEkUBOTlpAIBv6k+EXbfVLmPYwKx4FCuubl1Uiqc3VPimvgjF7pQxeEBm2HU66nhtfdjlDpeMIWdFZ1vRlplp7ukiBGjbjhNZItZdT2FdBNfZthwubkeKaYkcZxIB22jX9URMjsf+CnW8Jduxxrbdce3bsrcNRNrnbZcn2v6PN7a38OJdPzHtVL/zzjuYO3cuHnnkkZh8flNTE5YuXYrs7Gw8/fTTEE8Po87K8nRQm5ubYbVa/dZvuzwzMxPfffddwOc2Njb61ukqVdXQ1GQDAKSZw6fTSDPr0NCQuMmgukoH4LbLStAcYb5JszF6399kCN+kTYbEq2tJEpGZaUZTkx2KEt+EE5FOztq240TUk3WXaHpzXXSkk9HZthwubhv04a9+J2KcSQS9uY12VCLF5Hjur1DHW7Ica2zb/roSk71tINI+b7s8UfZ/vLG9hRfN+unMRcyYdqrNZjNKSkpi8tkOhwM33XQTmpub8dprr/ndBc/L8wwNOXTokO/f3r/1ej0GDRrkW2/nzp3QNM3vuerDhw+jsLCw22X0ZpyzGHWYMLI/hp2d5ct+bdCL2F/bgMPfNMJi1CVc9r5w2bM7m1k7zSD5hoK3V1pkhcUgRe37m/Vi2G2Z9WLC1bWXoqgJWbZELFN7iVp3PYF1EVpn6sVi1OHcsWfhwgmD0SfLBJtDRppJhxONDjS3ujCu0Bp0CHiix5lEwDbaPfGuu5jvL0GA2ajDk3ech9Y2x9maDRXYX9uAkgJr0KHhiXissW13Ttu68p4rA8CDN5+LFpvbd668ZftBOFyKbzpDIDH3f7yxvYUX7/qJaad61qxZ+Nvf/oYrr7wyqp8ryzJWrlyJQ4cO4Q9/+AP69+/vt3zQoEEYOnQo3nnnHUyfPt33+tatWzF58mQYDAYAQFlZGdatW4edO3fi3HPPBeDpUH/xxRe44YYbolZeUVWxdN4YrH3DP3ulN/t3T06r1b6DbNFLUICQ2bMFAGs7mFnb+9l2p4yb5hfjuTcDp6a5bVEpJE1FtGYFFzQNyxYUh5yaqzdPwUBEHSeqKq6bPQrr2iVJKi204uYFxcjJMEHTEDT7N+MMUQcJAtwa8OzGSr/zinEFVjx4yxRs/KAGN80fi+fe3MNjLcWJmoal88bgmXbnmCUFVqy6ejze+/QIZkwehkde/Yz7nxJSTOepdrlcuPfee9Hc3IxLL70UAwYMgCQFJhQYPXp0pz73/vvvx4YNG3DPPfegtLTUb9moUaNgMBjwl7/8BXfeeSeWLVuGiRMnYuvWrXjjjTewfv16v/csWbIEBw8exN133w2j0YgnnngCoihi48aN0Om6fs2h7fx7nqyWlUETaJUWWbH80pIe6VgHm3pqxcIS7Kg8GrKsU4oHYs3rlUGXrWgT4Np/tskg4Ya5Y1A0JBd2hxsWkw5pRv95qqOJ81R3TCLNidoVnKvxjN5cF7GYp1oTRawJE7dvnDcGDc1OpJsNcDgTP84kgt7cRjsqkWJyzPeXIECGgLVvVAa9Ez2uwIrlC0tw7GQrMtKMADQ4nArSzHqY9Z2fHSSW2Lb9dTYma4KAuiYHXvvfmuAxt9CKhdML0CfTBFnRYOrC7DCphO0tvJScp9rlckHTNGzfvh3bt28PWO4ddt3Z7N87duwAgKDPar///vs455xzMHv2bNjtdrzwwgt4/vnnMWzYMKxZsyagE/7kk0/i4YcfxurVqyHLMqZOnYr77ruvWx3q9uwuOXxGapeMtDhnWG4/9ZTJIKG8LB/552QH7TQDnrLOmZoXcpndrcCiE4NOa+VwKVjzeqVf51uK4UwI3qm5LOmeUQnoxcGXiDrPFiFuu90a7l33D5QWWnHT/LEwCGCcIeoEGcDxBlvILPu7D9Sh1e6GxazH7b/+G9bceT6sGfxNT0V2twKLUR865p7OCv7sm3tw07yxvbpDTYkrpp3qe++9F9u2bcPMmTNRUlIStezfH3zwQYfWW7hwIRYuXBh2nYyMDDz00EP4/+zdeXxU1d0/8M9dZs0eGFAQAwlJWEJCWiggEDfcUILg3gIuoCiitc9T6/JTaqlbta1aFdywLjy1oiCm1q1YLYqIYgMhsoRFcEEhkD2z3uX3x2SGmcyWhEwyST7v16uvyqw3Z84993zvOed77r///s44tLCaHdEnNzc7FCT5LhRdxOFRgwLqW+eMQ9nH+5AdIwt5tKyMdqcCa7Ix6LNbCwy+iYgSVax22+7yPl9eVQ2XR4XR2Pe2dSE6Hk63iia7J+pr7E4FZpPs/2//jXLqVexOBVqMONnt0VC+i+0tJa64BtWffPIJ5syZgzvvvDOeX5PwLDH2YI71fGfTBSGow1hakoOyj/cF7f/Xmm8ke2A/K26fNx4mowRV0yGLApxuFUaDiNQk78XOHiPbNy+MRJTo2tJu//mWEgACHC4Vkigm3JRUokSliSLsTnfMTM9JFhmCIOCPN5fAbPTOhOM51vtYzTKUGFG1r644XSpSGFRTAoprUJ2cnIysrKx4fkWPYDLKKM6zRVyb590GqmsuEr61zjOmHgueR2Rl+BOohcu0GTiS3TrRWunUbDz69//C6Vb9iSOs5ujVKtbzRETdzWSQImYdLsq1wWSQsXxNRdjkSa2TNhLRMYogYPnrWzFjanb07N55Nmyo+AGr1lX5+xvvb9qPBaUFPMd6GYtBgkvVIu7eEpj1O9Y2tUTdJa5zcC+99FK89dZbUFU1nl+T8AyigEvOzENRri3o8aJcGy45Iw+ADghxXGDcInCts+9CZjZKQXs7l63fi9Kp2UHHGjiSHWjr7mqUfbwPpSU5ALxTu5etqYDZKKM4P/hv9fFugcA7jESU2ERBwGXTwrfbl03Lw479R0PaRF8bqHdBe07UE+mi6M/u7NtWtHWfA/CeZ3POG4my9XsBHOtvZJ2YxnOsFxJ0HSZZwlXnj0ZxXmhdKJ2ajbL1e1sGotiHpMQU1yHDnJwcfPDBB5g1axZmzZoVMfv32WefHc/D6HYOt4KlKz7DrNOH4xfn5HszF5q8o9ObdxxCQXY/ZKaavYlu4nkcAWudy9bvxa1zxqGmaBC0gDu+TreKh1duRmlJDmaWZMPt0TDIlhQ0Qh2o9ZRx73oXJfq2VgDsigZ7kxvNHg1mrq8mogTjUlS4PCqmFA3yt4VGg4ij9U70SzPD7VFx+7zxIfuoMm8EUWR2d2g/5N2N+5GfleE/z5KtBtjSLdhQ8X3Qe339jVXrqniO9UJ2t4L6RhdKp2bjmhmjcbjOAQHeGZQPr9yM/KxMLJxVCJdHgUXib0+JJ65B9a9+9Sv/f//hD38I+5qOZP/uaXxrjIcPTsf/vbcraHSjKNeGSWMGwa2oMMZ5BDdwrbMveP79wklotHuCptw43ao/iC7Ot+GKs/Kjfq7ZKOPSaXn+TmWzQ4ElWcTi2YUh21rpAB5v4z7XRETdxe5U8IeXvkBpSQ76pZn9j+eclIYVZZX4fPsh/2O+fVQfXrkZTrfKvBFEEdidxxKT+fohs04fjp+NPAE1DU4YDSIq9hxB2fq9yM/KDDqvgGPJUnmO9T4Ol4KMNDOee7MSuw7UoLQkByOyMpB/cgYeWDQFn+/4EUdqHTAaRFj421MCimtQ/dJLL8Xz43sMq9kQdQr18/+oxPwZBV1wHKE/t9lkwGsf7MYFk7OhaQgJ+C85Iw9Wc/T1K063gl0Hav0XP9/3tN7WSgfwZKuAGjg2ZTJwn2siou5kNRuCbjACwKXT8rDrQG3YdhzwLpVZta6KeSOIwtAFAYoavIOI061CVXW88M/tMc8r4FiyKp5jvYsuCDAbZawoq/T/7q1z+ORnZSDZaoCBMxQoQcW1VfrZz34Wz4/vMYwGEYXD+0ecQl2+qxraBTqA+M3/9q4/EnD3/An+6TSSJGBFWSW2VFWjYs8R/5Rvj6Khf7oFJoOEg0eakZlqipk8wtcILphZ4F0zHSY45lZbRNRTmAwixubasCWgox+Y1LE139RUf94I3iAkCuLwqHC41A6dV8Cx/gbPsd7H4fHeXNkSoY+4dXc1LjkzF3u+q0Ph8P787SkhMYLpAk63AjFGUg1HjG2ojocqCHhidQUW//FDPPzyZuw8UIvC4f0xYfQJ/gbMNyLz0MubIUsiXnp7Bxb/8UPc/8LnuOWR/+CSM/JCko8FJo8AvI1eflYmnIqKI01u2BUtKJlIW7baIiJKBE6PggUXFgQlUPJNPY1E14HrZ3HGDVE4dqcCoyS2+7xyezR/f+PAD/Xe3Cw8x3oVu1OBwxW9DygKAp57sxIud99OfkyJK+7zZ6qrq/H6669j+/btaGxshKYFN56CIODFF1+M92F0K7tThdMdvbGwxGkqkyaKOFRjxzkTh6K0JAdpyUa88t4urFpXhdvnjQ95fbhp6k63iqUrPsP80gJcOX0Ufjxq9yfnCVzrBADVtXYsXbHJ/+/A9dKB07V8+16PyMrwJwDy7XNNRNTdBIh48a3tQQmUBvazRn1PitWAhmYX+rEtIwqiCwLMJhlmkxxyXg3qnxT1vYNsSbh2ZgEEQcd1pQUMqHshs0mGokb/XZ1uBU63isN1Dpw8IJl5eCjhxDWo3rlzJ+bNmwen04lhw4ahqqoKw4cPR0NDAw4dOoSTTz4ZJ5xwQjwPISFYzTL+u+tw5L0Y822wmmRAi363ti10QWiZYqUgJcmEp1/fGjTl2ne3d9veI/61SYF807DCBb07D9Qi9+R0PPjSF20+nsD10haDhOJ8G3Z8XRN23+v2JCzz/Z2BSdB4oSWizqLrOrbtPYJhg9OCHou2FGbzzsMoGTuoKw+TKOGpLSOMWYPSMKVoEM4cf3JQ1vz7F02Ouie8URZbdkcROO23lxIFAZLo3Zs83DLBsXk27P6uDoB3oSTz8FAiimtQ/ac//QlWqxVr166F2WzGKaecgjvvvBOTJk3CO++8g3vuuQd//OMf43kICcFkEHHwcCPmTR+Jle8gqMEYm2vDDbMLIXZCQK0G7EMdLaGOIAD3L5oMTQttwNweDWajFD7ozbPhzHFDMH7kQHyx4xBaKw5o9AIFrpdeNLsQW/ccCZu0ra0JywL/Tv93M4M4EXUip1vB7fPGo7re4X+socmNq84fDeCroMDad7Py/U37YRk/hB1/Ih9BQE2jE3Omj8ThWgeO1Dmw72B9UHbvLVWHcdm0PAChyVIvm5YHo8hgurcTBB0CdCyYWYDnyiqD2texed564HSrmDB6IDJSzdjxdQ3z8FDCiWtQ/d///hcLFizAoEGDUFdXB8B7px8AzjvvPHz55Zd46KGHsHLlyngeRrcziCLmTh+F596sRO7JGZgx9dhejAMzrZCAiBeMoJFnqxGKqsPu9ISMzuqtAs0RWRkoW78XV5yTj+JcGxRVh8UkQ2/ZG7uhyY0HX/oCt84ZB00/diEzGsSImcrLq6qxfHUFrr+oEG5FC80UfmYeKvcdCft3+La/kHQdI7Iy8cRrW8O+LlbCstZ/Z+D7eOeSiDpLWpIRqqpjw9aD/twTZqOEhReOwdUXjMZl0xSYDJK/TX1/037M59RUoiAKgL+/X+W/ZpuNEhbMLMAfFk/FoZpmmI0Shp6YBo+ihewJX9PgxIB0MwPqPsAiSzjqdCLNaMbUosGYe95IAAKcLgUmowSLScK7/6rCgpljsH3fUZSW5HBbNUo4cQ2qNU1D//79AQCpqamQJMkfXANAfn4+Vq9eHc9DSAgqdDy1xhsIth7hLc63YdFFhQi3Q7VvRHbHfu906ZXvBu9xHTg62zqztqLq+M1c72jzK+/t8j/uu/Ob2tIQPbxyM0pLcnDJmbkwyCLMRhmFw6WImTi37K7G0ToHZkzNDlpnuHHbD1i64jP8Zu64sO8LXE8duE9lONEaSmYQJ6KuoEPAq+uqggJq3wyex1Zt8b+uOM+GBTMLUFI8GB5FgyzFbxcHop5EFUUsD1iCFngOBd5YL86zYcbUbMiSAFu6BYdrHUi2GDCofxJEBtR9hslowIqySpw9cShe/OeOkIGbG2aPQU29HUNOSEFKkpHbqlHCiWuNPOmkk/Ddd98BAERRxEknnYSNGzdi+vTpALwj2SkpKfE8hITgdGvYsb8Gl07LC1mjXLZ+L5xuDUlycEdMbzWVO9Z06daZs/unm/HS2ztCtifwfcbUsYP8ez/6sndPKRqEH4/akRYjyU6j3YMBmVbUNTrRaHfjm0MN/iBcUfWQv/NovRMW47E1460bwtbrty0mGboghB3xaUsGcd65JKLj5faoQQH1r+eMwz8+CT+D55m1lVgws8A7E4tBNRE0UURDszvoJvis04dHPIc0HVh0USHqG534/fObcO/1p8DAad99hlvT0dDsRvaQ9LB1ZOvuajy9dhsWlBbA5dGgA9xWjRJOXIPqKVOm4N1338WvfvUrAMAVV1yBBx98EN9++y10Xcfnn3+Oq6++Op6HkBAcTk/YNcpFuTbcOmccHE4PkpKNQcm3LGY5aCp3tD2uHR41JFBVFC3qfn8zS7KRmWoOu356yfwJUf8eo0HE4Ro7HnzpC/8dZrPRO9Y+eEAy/rnh65C12IXD+8PQ8m9fwrLyXdWR129HWCMd684k71wSUWfw3cDztVEmoxS1TdV1HQ6XgmQDb+oR2V1K0NZHZqOEn408IWjmXKCtu6vhcCtITfb2S4yyyKUUfYirpe8brY6U76qGqulwuhUMyLCyflDCies82euvvx5/+tOf4PF4p/teeeWVuPnmm1FXV4fGxkYsWrQIt9xySzwPISGkJBnDjjRv3V2Nso/3ISXJCM2/l/RH+M0Tn6Cx+dgU6Vh7ONqdCsxGOWgf6dpGV9T3uD0a3B4t7PrpnQdqUZxnC/u+olwbdh6o9WcOL6+qxpvr96G0JAfzSwvw3NptYe9CP/VGhX/PakHXsWh2IYrzbZHXb7eMwuut9vf2BeThFOfbvHcuiYiOk8XkbUt8bVSTPfqyFYdLQZLZEPU1RH1Fs8MDs+nYTe7SkhzUNDijvqehyY2n11agtCSHN8j7GKdLRf90c8w60uzwIMksY8+3tSH9w0SkCwLsioYjTW7YFa1HHDN1XFyD6rS0NBQUFMBg8HY0BEHAokWLsHbtWqxZswY33XQTjMZjd/U9Hg+++OILNDY2xvOwupyiamG3igC8gbWiatiy50jQNKnAi1G4ra8CWcwGPLt2Gy6YnI2iXG/AaYixrthoEGE0iBiRlRFybGXr92LOeSP9n+Xjy3D79ff12HmgNuhvKBzeH8NPSo+53tlH0nXcfHERpo4dFLFsWr8HCA7IA/lGtnnnkog6g8kgoSjX5m8jY7XDZqMEI2/qEQEAkiwGALr/Wj0iKwOx4glF1VC+y9uf4A3yviXJYoCiaDHrSJLFAJNBxtNvbAvpHyYatdVg2eI/foQn1lRAZWDdayXUrcD6+nrMmzcPzz//PCZNmtTdh9NpHDHWATucCvqnWVo9qvv3bdx5oDbqHtfQdZx3yjC4FQ1XXzAKug5IkhB1P9Wj9U7UNDrx0/yBWDJ/Qsg679+v+AxLFkyE3ZmLJrvH/9y7G/fj3ElD8fDKzUGfaZBEHK61R/07W693lgQETQ9ry3sAb0C+eHYh96kmorhRNR2XTcuDw+Vtv2O1w0lmA+wuD5IZDFAfp4kiBEGDURaxcFYhlq+ugNujYd/B+qjnkO9mvYFTv/sci0HE4VpPzHbWZJBQ3+SdiZnIOXS4U03flFBBNXBsy63exBJjGpOm6+iXboHZKMHZEmRu3nHIv29j2fq9uHWON6v21t3V/sRehcP7QxQENNo9SE024tFX/otbrvgJ7E4FTreK62cVYtnqCn/jZDZKmF9agBFDM2F3eJB3svfO8Z7v6rDmwz1wulX/Ou+HV27Gncs24M+3nAq3R4XZKGNEVgYAb8ZwZ6tg2GKW4VGjT1MPN53Leze7fe8BvCPWVlk81qD2wnpDRN1H1TR4FA0n9LNiyfwJ8CgaThlzIvZ8V4fn3qz0t4FFuTZcckYeNE2Hy60yqKY+TRUEfLXvKIYNSsORegf6pZqxcFYBPKqGp9Zsxb03TMazayvDZHYuxG8e/xgAYDElXNeU4kzQddgyrHjo5c24fd54TB07CJmp5qCEt0MGJAMArBZvnoskS+LWE+5U0zclbo3sRUwGOeKdt6JcGwwGCc+XVfqzcQPAmg/3IHdIhn/fRlXTseiiQhytdyAlyYQVZZVBib3G5tpw59UTsOLNSnyx4xAunZaH9zbuR35WBmaWZENRdQy2JeHZNyvx5Otbg77/sml5yB2SgT+89IX/GEtLcrDrQC2cbgVLV2zCz8/JR0aKGSOyMpB9+U+CRrVHDsuEIHgD4Eij4/71zq2C37RkU7vfQ0QUb1aTAc0GBc++WRmUoKw4z4Y//bIE31c3Q5YE7DxQi6UrPsPIYZm4YXYh2yvqs3RRxJE6B/7z3+/w6N/L/Y+PzbXhhosKMW/6KLz41nZ/vyRwhpxvK6VdB2q9iU95HvU5ZqOEouH9kZpkxNr1e4Pa3bF53uWHb5RVYsGFY/D+Zztx3YVj/LvKJBruVNM3MajuAi6PgtKp2QDCjzQbJBG5J2dg1NBM/3ucbhV/eOkLLJhZgAEZVgBAdZ0DFXuOYNeB2pAAfcvuajz9RgV+cc5InHfKUCRbDVi1rsq/L/al0/Lwzw1fh02WBni30/IF9Vt3V+OSM3MxNteGzTsOYfzIgRh+UjrKPt4X1MgV5dqwZP5EiAJQ3+jCH//vSzy4eCqefqMiKEiOtt45xWrEotmFWLam7e8hIoo3RdOC9qn28W2hld9qV4byXdVwulUkx1h7TdRbeTQNq9ZVhYzQbdldjeWrK3DdrDH4y6ot/n5JazOm5uC0n5zEjmkf5XQruPKCUXh2bWVIu7ulqhq6DuRnZaDZ6UHWoDTY3WrIdrSJgjvV9E38VbtAk92Dh1duRmlJDmadmoN+6ZaQkeaiXBtKigcHTQEfOSwTRcP7Q9J12BUNTXZPyPZagXs8G2QJyRYZBlmE3enBkvkT/KPJ0bbl8m2x1S/N7H9MFARYzDKGn5SOKUWD8czabWH3vBYE73HmnpSOYYPTYBTQ7vXOJkngGmkiSigutxpzW8LW7A5uqUV9lCjC44485XXL7uqYOVQMsgiTxL2p+yqH0wOL2RixDvnaXadLxYisDByutWPogOSE7CsGbh3bGmdh9l4MqrtAksUAp1vFqnVVuHRaXtiR5q27q7GirBKP/upUqKoOWRbgUXTUNrpgNcsQICAzzYSa+mNbZQXu8exbd718zbaQtUq3zhkHjxJ9iozbo8Fqlv1Jy5IsMjZU/ICy9Xvxm7njInYut1RVo3RqNnQAi1qmPgoArLIIS4oJDo+Koy1/Q7RAmWukiSiRtM4b0ZocZj2cNYHX+BHFjSBAA2ImK3W4o0+JtZrkhAyQqGtkpJjxzeGmqK8xG2UkWWS4PSoUVUvYtcm+nWo4C7NvYQ+gC5iNkn9NdbQR4/Jd1Th4pBn/+GQfLjkjD0tXfObv2BXn23D1BaORbD0WHPv2T911oAa/mTceR+sdmFmSjfMmDQ1a81wG4Bfn5Ec9RqNBhCQKWPLMJv9jvoBcUaOf/G6PhpMGJEMKaCTUMJkPfY2JxMaEiBJcrGRJGSkmDMy04FCNA4C3fbOa5IRd40cULxq8AXOsibgpVgPG5tnC3qQvzrN5zzmeP32W06PFrEOarkMWRaQmGfHpth9gNRkAa2LObuRONX0Pg+ouoGoqbrq4EEcbHIi1Nbjb492nUdMQlLisfFc1Uq27MefcEbjx4iL0SzMjPcWMsvV7cdu88RiYacWb/9kbsmZ7bK4NLo+KFKsRE0YNxKbtoWuZivNtyEj1Tv2+dFoeytbvhdOt+ke8rzx/ZNRjTrYaYDGI/tFlbiVARD2dIAgoyrVh14Ea/xKbwEy0e76tw++uOwW/feZTnNgvGTfMLoTIgID6GlGEqmmob3Jjz3d1/v5J620687MyYTJIuHxaHgQg5IY7z5++TRcE2J1u7P6uDhNGDUTWoLSgNnfngVoc+KEeuw7UYNzIgRBFwb+08fZlnyTsoA1nYfYtCRVUp6Sk4IEHHkBubm53H0qnMggiBACpyeaYa4qMLUlutu6uxqxTc3DptDyMyMqAouoY1D8Jmg5srTqMQQNSMLHgRNxx1c+QmWLCi299hfysjIhrtovzbbh25hhoOoKShBTn2TC/tACKqmHTVz/i6+/r/Vtq+QJrRcmPmr18YKYVQsDFkFsJEFFPp+saLjw1ByZDHl5dVxXSni6cVYj6Rgfuv34yBABHG51ITzYFdep0QeAoBfVqmq5DB5CSZMDwk9Lxj4/3BV3/fQlN01KMeOX9nThpQApOKRyEK87OR6PDgwEZVpgMIuqbXLCYeI70VU5Fg8Ukw2wUcXVpAZavrgjJO7Rw1hgcrnXA5VbhdmvIz8r0723OQRtKBHEPqpuamvC3v/0NmzZtwtGjR7F06VIUFhairq4Ob7zxBs444wxkZWUBAEwmE2bNmhXvQ+pyoihC0XRU7juKrIEpuPuaCRAE+O/gBu536msgzEbJO/q8fm/I1lkLLizAi29t9z/+x5un4uyJQ/Huxv3IOzkDK8qCMyeajRJyh2SgpsGJWacNxy/OHQFRFNBkd2PL7iP49WPr4XSrKM6zYcbUbLzz6f6gUfLaRldQ9nIf391lqdXdZW4lQEQ9nUGWYHd6Ii6rWb66AlOKBiE9xQJJACxmOahTxyUw1OuJInQAbrcKh1OFySgh9+QM7Nhf4+/X+BKalowdjIvOyIOqarjtiU/wwI1TMDjJiJXv7MAnFT/4P5LnSN+k64AkCpg4ehCee7My7LZrL771Fa6aMRq1DU70zzDjwlNz8IeXvvB/BgdtqLvFNaj+8ccfMWfOHPz444/IysrCvn370NzcDABIT0/H3//+d3z//fe466674nkY3U7TdRypd+KTLQfxZJgkYg+v3Iz8rEyUTs3Gwys3A/BO/X5m7bawW1M827Kdy7a9R3DFOXmwmmV4FA1zzxsJDXpIQO1LZtb6rl/p1OygoL68qho6gLyTMzAiK8P/WoMs4qGXN2N+aQF+cU4+ahpcMBpE9EuzhK1A3EqAiHo6owDknJSO5asrwiZ/fHjlZswsycby1RW49Kxc9E+z+jt1FoPEJTDU6ym6jidfj3x++PoWvoSmz66txMJZBSgtyUFNvRMZqaaggBrgOdJXCaIOSZRwtN6BcycNjdhnVVUNLreKow1O7Pu+LiShJAdtqDvFNbp56KGH0NzcjLVr1yIzMxOnnHJK0PPTpk3DRx99FM9DSAiKBrzx0Z6wd97e/2w/7l80GZ9V/hh0ESoc3j/mFlgj543H4P5W6BDw939595e+fd74oNf6kplF2p86cEQaOHbxc3u8o8++0fP8rExkppqx5JmNcLpVFOXasPiSorBJRbiVABH1dLoo4unXt0ZtO90eDVt2V+OqC0bB2ZLZ2DdTh0tgqDfTRBFPvhb9/AjsW7g9mnc5mapjRFYGMlLMqG9yIRyeI32PUZTgVFQYDTJeXbc7Yr26dmYB0lNNOFzjwJic/iGfk2iDNlwC1LfEtfZt2LABV155JYYPH47a2tqQ54cMGYIffvghzDt7F7eiRL3z5nIpIev1DFL0i4nVbPBmyhQELAu4U+xbk+3Tlv2pQ463JegvzrPh2gvHoK7RCQD+oD9wepZd0UIaC24lQEQ9ncOtxNwv1cfpVgHdOzPIapa5BIZ6PbtLCZtnBQjft/D1TZodHugA9v9Qjx+ORt6Ci+dI36IL3t/caJCi1itN1yEIAjJTzSFbxSbaoA2XAPU9cQ2qnU4nMjMzIz7vmwre24mCiHc37g87Uv3uxv2YO30k7r3+FMiSCJNRgskgweWJntDMapKhQYeq6Zh1ag6umzkaBlmCput47H9OhcOlIsni3d/abJQi7rnqG5EOlGw1ID3ZhNyTM3C4xo7t+2swIisD2Zf/BCf2t8JkkFDb5IJH0bB19xH/FPLAxoJbCRBRT9bsUGA2Sph1+nD8JM8Gk1EGdG8wIYmCd/ssWUN6stE/OrJgZgHMxtiX1UQbTSFqF1GEKOhYMn8CVE3Hif2SoGk6mhweWM0yjtQ7kWyRkZ5sRF2TOzhfjElGssWAkwck4+k3tkX8Cp4jfYvTrcJqltHQ7Mbc6SMwYdSJUDQNTpf38aN1Djz2ajmcLgUZKWZoRg3NzmP92ngP2kQacY72+PEuAeIod3zFo3zj2mrl5OTgiy++wOWXXx72+XXr1mHUqFHxPISEoEOPOlItCMBdT32KR245FS+8tR1bd1fj0ml5ETNuF+fbsOe7OqQlm/DeZ/sxY2o2mhweqJoHr66rCkkm9pu54/DQy5vDBtatR7aL82wYmGHFXU9vwAn9kgEg6Jjvvf4U/PKp/wT9Db71U60bC24lQEQ9VZJFwm/mjsO7G/dj+OB0lL27K2yixj8vngK7qsEgScg72ZvrYkxOfy6Bod5JFKHqOlaUVWLH/hrcOmccnn2zMmRd9aKLC/Hg4sl46Z87ceb4k/Hwys0ozrPB6fLAlm6FqOsYOSyT5wgBABxOD2xpFphkCUJWv5A6VZxvw303TIYgAC++/RXmnjcKKWYJd8+fgIEZVlgMoj8g6uxgKdyI84TRAzG/JUt5uJFot3J8u+BwlDu+4lW+cV2wcuWVV+Ltt9/GM888g6amJgCArus4cOAAbr31VmzZsgVXXXVVPA8hIQgQIq5rLvt4H4SW7e5rGpz+15St34vSqdkoyrX5X282Slh8SRGuPH8Usk5MRU2jExefkYskiwHfHm4KCagB78n7j4/3obQkJ+S4ivOO3T0GvBVq7nkj8de3vsLFZ+b5E5n5FOXaULHnSNi/wff5vsaCiKgnMxsNKPt4H4YOSgvbfpfvqsbyNRVQRRFGWca3h5tw858+wkf//R7VdQ4sml2I4nxb0Hu4BIZ6Oh3wd0aj5WxZ9noF3B4dvzhvhD8Z68LZhbClWaBpCtCyTIznCAFAWrIJGoCKvUci9mWfe7MSBlnCaT89GU+9UQENAn6/YhMA3V9fVEHAE6srsPiPH+E3T3yCxX/8CE+sqYAqCB06rkgjzlknpuHJ1yOPROt69O+LtkQo1ii33sG/hbziWb5xHameOXMmDh48iMceewyPPvooAGDBggXQdR2iKOJXv/oVpk2bFs9DSAiarsdcIwIAgb+j063i8VXlWHxpMa6eMQqHaxzIOiEF278+Ck3TIUsCTrIlA4IAsWV9SaTvKK+qxkVn5Ibu+Te7EC6Pgp/kDYAoAuW7q3Hn8g1wulVcfnY+fvP4x/7R7eI8Gy6Yciw7eeu/IXD9FNdCEVFP53Ap/sSNkfJSlO+qhsPl7RyJLe23rx1edNEYLoGh3kUUYXcfG4GLlbMFAFRVx2/mjkNNgxNGUUBNox0ZyRaAy8QogEfV4VaUmH1Zh0tBvzQzyndV+5dJ+vqcnTHlujWHJ/yIc7S6X76rGtoF0b/HbIocfkX6Tt9nOzwqDBKXR3RUW8q3o0kS4/6r3HDDDZg5cybef/99HDhwAJqm4eSTT8bZZ5+NIUOGxPvrE0KspDUOp4Li/OBR4/RkI+6ePxGv/msXfnHuCNgyzACAgZlJePGfO0KmxVxyRl7U79A07/qnwPXcdQ1OaDqwdMVnIVPDmx0e/GbuOJiNMixmGaqq466nNrRpbTbXQhFRT9fs8AAIn3cikN3hgQ4hqP3eursaTreKFKPEJTDUa+gAqmuPJReLdW74zqF/fLIPN8wuRL3DDU0XgwIbLhMjALA7PdA0vQ3treIfiPLVL1+fMx7BUqT+e6zjdDiViEs4i3Jt/hmq7fnOwOdT2c/usHgmEo3br+JwOPCLX/wCl1xyCa644oo+Mc07klhBpsUsY+GsQtzy54+QnmzEzZcVY0CGBR5Vw7WlowFBQF2TCzUuB1Z9ELrVQPmualx0em7U71BUDUtXbAp6bNKYE1Hb4AwbKCeZDbhj2Qbce/0p2LjtB4zIyogYUAPH1mZzLRQR9QZJFgOA0LwTIa+zGqHretBSGQBwurxBNVGvIIpQNB22dIv/oZjnRss5tPDCMdhYeRAnZCZjsC0prodJPZOlZeTWN/MnkiSrAYrqDWiTLIagPmesYKnJrgDW9s2GiNR/j1X3LWYZM0u8OZO2BAT6Y/O8uZQg6ECEwDpWzMCBq+MTz/KN25pqi8WC7777DgLn/sNikoPWRgcqyrXBbJRR0+DE/Ysm4483lUDTdOgQ4HJrcKk6PJqKlCQTVC345AxUsecIivPCf8fYVmunfd/rcHlCHvc9J4oCivNtyEgxoWz9Xuw8UBv1b9h5oJZroYio17AYZf8Momhtn0ESYDbKKC3JgTkgiPYFFES9garreL6sEpIk+s+HaOeGb620xSRDlkS88l4VkiwGbwBE1IooCEgyefvCkerU2Dxve3u03onifBtEAUF9zljBkN3lafcaa4tBCln3D0Sv+0W5NlhNMpLMMiYXDcKS+RNw+7zxWDJ/AiYXDUKSWYY1yi4Rkb4TCBi4og6LZ/nGNVHZ1KlT8cknn8TzK3oEl0cJSToGHMv+fbC6CXcu24A/vPQFFF1DXZML1bV2uBXv3qeyKOOvZZVosnsifkfZ+r2YP7Mg5DuK8224bFpe0CjK2DwbFs4ag4wUE77+vj7sMR1pSbTz+r+r4HSrYROn+T7/+tljcGrxICxmVkIi6iU0XcNV54/GwcONmDd9ZMhNS19b2dDkxvfVTdh1oBa3zhkHs1FquVnKjg/1Dh7dm5wsZ0g6XB4VV50/CndfMwGjhmZi3vSRYfs21104BklGCZKuY9W6nRg5NBMDM6286U5h6fDWi9HZ/XD5WXkYmxfa17ywJAcNzW589OU3WHhhIaxGOajPGS1YCtzWrT0JqYQICfUO/FCPay8M7XOPHzkQCy8sgNOtwGCQMPykdEgt2+XuPFCLFW9W4u/rqqLO5oz0nRy46hzxLF9B1+P36+zduxe//OUvMXLkSFx22WUYMmQITCZTyOvS09PjdQjdRlU11NR49+GubnLjt89sRGlJDkZkZQStay5bvxd3XvUzvPqvXfjfK34Cp6Lh2bXbgtO8tyQJk0QB9zz3WcTvfPimKZAkEYAAt8e7T7XZIEGAgCanBy6Pd78/s0GCR9XgUTXU1nvXVbc+pj/eXAKrLARtTZBkkWEyyHC6lU5JKiLLIjIyklBb2wxFib4+pTfrznKw2VKiPh9YjxMR69AxfbksYtVjoP11udGtorHJA1umBc+XVSJrUJq//U62GmA1y1j63GdYuvAUHKlzYOmKTSjKtWFK0SBkpppxQj8LLFJc71v3OH25jrZVIrXJvt/rmx8bsKHiIErGnoSn32i1tdCogbimtAD1zS7UNrgwINOKjdt+wKnFg/H5VwfRP92KdV98h0UXFcKA3hsMsG4Ha2+brAgC3IrakrBXwI6va5CZavb3TY/WO9E/3YL+6WZA9yYAlkQRgB7UH9UBPLmmAuW7qmE2SigtyUHh8P4QBQEuj4od+2tQtn4vnG4VT/z6tDavsW69TZcsibj9yU9w9sSh/uuC2SghJcmIVf/ahbMnDg3JjO+7Efvwys3+PnZ7vjOwv836Fl1byqetW6+1pS77v7fDR9wG559/PgBgz549eOuttyK+bseOHfE8jG5naZXlTxQFpKeYMH7kQBTn2ZBiNeJ/Lv8JVHi31Tpn4lDMmJrtD3DLq6qh6cCMlpHicIkPxubZ8MWOw/5shEvmT8DtTx6bJVCUa0N+QLbCP/2yBCvf2RE2qYN3+oMIhNtrWtOYVISIej2XW8WATAu27jmCcyYN9Sem2XewHmXr9yI/KxNzzhsJc8sIBOBNUPaLc/Kx5JmNWHrdJFi4CwL1cNVHmyFJAsbk9MORekdI/2TT9kNwejTkZ2Vg9ze1yD05A7u/rcVpPx2MF9/ehUd+dSryszLQ0OxCvySeDxSeDMADAUZJxJbdR9AvzRwy2DMiKxPXzy5EbaMTKUlGVNc68LuAgSbfSONNswvhUFQIgohn1m4L2fnm1jnj8PDKze1KSCXoun9asDcIM2DOeSPx3JuVWNWSb+jSaXnYdaAW+VkZEbeaA4DSkhw0OzywpkT/bibxi694lG9cg+obb7yRa6oBSKKAJfMn4tV1VShbvxe3zxuPl9/2BrRmo4TZpw/H1LGD8cwbwSPUgSe/b9uq0qnerataZ/+eMSUbD7282f++wLXSgXfHfK9vaHJhxtRsaHroZ3F6Sc/Q1rtsRNR+6ckmKJqODRUHQxLN/GbuODz08mYsmFmA+iZX0PKamgYXnG6VyWSox1NbLicut4ZV63ZH7Z9ccmYuzhg3BH8tq8TCWYW4/6/exKiHjtqxal0VSsYO6o4/gXoIXRAgSwI8avQ216OocLgUrFpXhbnTRwZ9RuDWWWZZwhOrK0LyEAUGtu1po9Uw23UV59uwZP5E/w46vm22ZpZE3oZx6+5qXHT6cJhNXB7UG8X1qn/TTTfF8+N7DEkUsOoD72b2Cy/0TpO68oJRuMKjIjXJhMq9R/DUmm1R72qtWlcFt0fDo3//L0pLcjCzJNs/DTHJLOOOZd7trorzbbh+ViF+ONKMP9w4BR5VQ8WeI3h45Wbv83k2zDl3JP7f8g0AgAUzC7BgZgEcTg8DszaI53T49ojUwN8wuxAeRUWzg4E20fHQADy7dltIp2xLVTUEAbjkzFzYW9rNwJ0RjAaRuyBQr6DrgArg+X98FbN/Anh3Gfn5uSNxtM6Obw41AeD5QG3jacnoHbHNBTDr9OFwuBTsPFCL8qpqXHF2fsjn+LbOAhBxey3fTaBwdTLcYAWAiPtfA8CjvzoVDc1u/0f5ZjX5pp+3XvZpMUkticp4PvQ2vJXeBdyKhi1V1Th5YDLGjxyImkYXREGAqupwe1SMyemPle+EnwLvG6EGgGSrAU636r+AFeXacOPFhXArKpZeNwmCKODbHxvxn/Lv8NW+Glx9wSh8ue0wRmRlIH/OOPRLNyPZbIBbUbD0uklBQVdShOkPkUZDOzpKGhyUGiDb3VB1wK5oCT/iGhjImo0Sbp0zLmSKj2+kP54J2/QwATXgbeCffL0iaJp/VxwPUW/kaVl/9/Nz8jFu5EAAApwuBbIsorzqMH42+gSIAmAyHLuMFufbUNPg7PTZPpyV0vP1uN9Q9K411TQdV10wynvNNss4Uu/EE6vKUdfkDuqfJJkNUFUNVrMBv33mUwDePkpHz4ceV159SDx+G5dHhSgI+PpgPe66ZgL6pZlD6tzPzxkBk1Hyzwyqa3KF/axmpwJNi348RlkM05c1YOeBo3juzUr/jdLifBuumVEQdf9rRdXQP9kIe8vaXaNBDOojtp5+fsa4IZBEAYhxjNTzdElQ/eWXX2L79u1obGyEpgUvGBcEATfeeGNXHEZEe/fuxb333ovy8nIkJSVh5syZuOWWW2A0ds76H4dLgdko4Z75E+HRdPzfe7tCkhfce8Nk3LV8A+qa3CHvd3s0FOfb4HSrWDJ/AnQAA9ItMJtkCJqGL3ccwivvVyE/KxOlU7OxcdsPuGxaHkRRwE9HDIDJIMFslKFpGlweBWZZgjlZjNkwhhsNnTB6IOaXFmB5mFHSWMFb688zGyUsmT8Rr31Q1e7P6mqtA9nSkpywa2YCpx/FqwPg8KhR78D6OjlddTxEvZHDpeC2eeNhMkh48Z87gm+e5dlQMnYwTJKIo01OLJk/wbsVzPD+kIBOHZGLNCsl0dpIiqyn/oaKrmPZ6oqo/RW3R8PYPBv2/1CPgpz+qNh9CHVNbv+sOYOAdp8PPbW8+oJ4/TYOlwpJBO69YTKeXVsZts45nB4IEPwBb3pyaOJjAPAoGpLM0bc0tJrlsH9L4LIGp1tF+a5qHJ5ij/pZvrXZvuzjmg7MLy2IuK766TcqcMPsQo5q9kJx/U3r6uqwcOFCVFRUQNd1CIIAX7Jx3393d1BdX1+PK6+8EkOHDsXjjz+OQ4cO4cEHH4TT6cSSJUs65TssJhnL/mcCVCDkAgV4T7Jn11Zi8aXFuPf5TSHvT7YasPDCQtzyyEcYOTQTN1xUCLllrfp3NQ4MG5SOh26aCsC7fdec80bC5fHeqfvhSDNOyLSirtGFB1/6Ak632qbAONJoaNaJaXjy9dC/IVbwFu7zSkty8Oq6qm4JTNurdSA7ImA0uDXf9KO2ZpVsL7tTifq8b+pRVx0PUW+UkmTEgR8b8cnWg6FtVFU1nlm7DYsuKsKtf/EmhCzOt6Egpz88mgZZEiBDgA74b1xaTDIEQYAg6DDLoSM7YW9yIvK0w0RrIym8aDOLEvY3FEVoQNhrfev+SrLVgEvPzIMt3QwJwJAT0vD4r0+HKAAeRYUH4et8tFlwPa68+oh4/jZJFgNMBjFqnbvx4kK4WqZ2F+XaIIfp0xTl2lCx5whs6RaMzbOFTCUHvG212Sjjyde3hvwt4ZY1xMoM5VubLeg6bphdiCN1DvQbko4nX98a9vXlu6rhdKtINrBP1tvENah+6KGHsGvXLvzpT39CYWEhpk2bhhUrVuCkk07CCy+8gC1btuDZZ5+N5yHE9Pe//x3Nzc144okn/Ft7qaqK3/3ud1i4cCEGDhx43N9hNghQRSuaHZ6wmbsB74l81QWjQh4vzrehf5oZTo8HD99cAqtRQm29HYouICXZiD//7UsMG5yGhReOgQ7g+X/sCknwUDo1G+98ut/fSLQlMI40GtrRYDLc53VnYNperQPZ1oFruNe3Natke8VKrmEM01DH83iIeiNF1dEvzRyxzS7fVQ2HSwn69/LVFbjy/JF49V+7cNX5o/HCW19h0/ZD/tf4kka+v2k/FpQW+Ed2Io3+LJxViB37ayJ/fwK1kRRetJlFifobagDsLiVmf6U439ay9zSg6hoADelJJtz9zKc4VOMAEL7ORxvtdCs9r7z6injWZaPsHYGOVuecbhVmo+zfZra+1czO1kl5//TLEjwTZtT7+lmFcHmUNs/423mgFsX5Nv8a6kCt8wV4FA2SKKKxOXTWaSCHU0GygX2y3iauLdP69etx2WWXYfr06UhKSvJ+oSgiKysLv/3tbzF48GDcf//98TyENh3jpEmTgvbKPu+886BpGjZs2NAp3yGKEhwuBc0xRhgdrZ4vzrfhqvNHQxIEpJqNSDKI+L66GZ/vOoJ7nvsMz7xRid/MG4drZ47BwaN2PLUmfIKHN9fvw7DB3j1WAW8wG7Wj2HL3OJy2BJNtfbyjn9UdWgey4QLXaK/vTL4pRuG0zvzeFcdD1Bs1Ozwx26hmR3AbtXV3NexOBVknpuGZN7cha1BayPNlH+9D1olpWLamArogRB39efqNCpSW5ET8/kRqIym8WL9Rov2GqiDA7lLQ7PBEfZ3DqWDhrEJIAL470ozbnvgEtU0eLH+jAqf+ZIj/da3rvCaKUUc7dT36uGCilVdfEs+67FH0mHWu2aFAFoBfnDsSD6/cjMxUs3f72HnjsWT+BORnZfinbTvdKr6vbkZ+VgaWzJ+A+2+YjD/fcmx7t9Ztd2uBbX/Z+r1YOKswpN8VbrecJrsHJpN3v+poLOyT9Upx/VUbGhowfPhwAPAH1c3Nzf7nJ0+ejEceeSSehxDTvn37cNFFFwU9lpqaCpvNhn379h3XZ/umptQ5vBeoWIFNkkXGI786FTUNTgxIt8BgkGAUBeiahu+q7bj/xc+Dssxu3V2Na2aMwuEaO6DrUe/w+bKFA20cZY2wHiV2MGkIOyUn3Od19LO6Q5IkBt2p3HmgNuKe4cX5NiSZZEht3E1OksSg/2+LRbMLsWxNRdCd09Z3aTt6PK0lym8QTkfKrrdiWcTWnrpsNkkwOqO/PskS2qY32T3+WTgzpmSHPO9rj1etq4KjpS2ONvoT7jN8EqmNbIu+WEcjXUsDn2/vbxiv39yjAdW1DgiCdzpuNMlWA1RNw/YDR/HHVyoAAIqih62zgXXe7oo8Qli+qxraBdGnDydqne8LdTuedVnzKDHrXJJFxuaqH9E/LQn5WZlQNQ1LV4QumfR/tiT4Z0MumT8BTrfSsr3b4JjHFdg/HTksEyZJwM0XF6HZpbbs+mBAkklq6Vcd61yZTRIMsgSnW4nYRyzKtUEUhOOqx32hvh2P7iqfuAbVAwYMwJEjRwAARqMR/fr1w86dOzFt2jQAwKFDh7p9H+uGhgakpqaGPJ6Wlob6+voOf64oCsjI8N5I+L7mCJIsBhyudUQ9yexODzJSzMgamOKdQqBp8Gg6Pqk4iNfW7Q4KqH2aHQq2769BdqsRkdZ86fyB2MFsSpIRacmmsNNdYgWTmWlmpFhD79DJdnfI53X0s7rLTZcW4/FV5SjfVY2y9Xtx65xxAEL3+b750mL0T7e0+/NTU9v3nlvnjEN9kwvNDg+SLAYYDRKeeaMiqJ4cz/EAwfU4kbW37HozlkV47a3Ldk8jjjY4I7dRed51ea0ZDWLMG5i+x53u2CM7kUKMRGwj26ov1dFw1z6fjvyG8WqTq2vs8KgaXl1XhfkzRsNikqP2VywmGSLgD6gBwGzyng/h6r3vsVijma6WrUE7q7y6Wm+u2/Gsywdrj6Jfmjlmnfviq2pMKJCwcNYYuDxKxHXTgbP2inJtOFrvRHWdw3+cvmMO97cEvrd1H6p/jL/JqTbB6VYhSQJKp3pvLrWefl46NRsQ0CnncW+ub52hq8snrkH1uHHj8Omnn+KGG24A4J1WvWLFCkiSBE3T8OKLL2Lq1KnxPIRuo2k6Ghq8GQOTLAZYjDI+2vwNrruwIOwaj4WzxsAoCcfm42sKPJDgVlS8/PbOiN+TbDWgbP1e/GbuuKjHk2w1oGKP9wZHrGDWLItQXJ6wo6EHfqjHjRcXYnmrx4vzbVh0USEUlwe1rvBTeFp/Xtn6vVgyfyJEEe3+rO4gA0F3KpMsBtx8aREcre9c6hpqa5tjfp6PJIlITbWgocEBVY0+i6C1JIOIJIMvA6aO6y8cg7nnjWrz8cRq1APrcSI6nrLrbfpyWbSlc9LeumyQRJw8IBmXTcsDEHrz7PrZhZBb3RP2dcZ8S20i3cD0PR4uKG9tQIY1pPOXqG1kLH21joa7lkb6DburTdZ0b6LTrburIcsCJF3HoosKw2b/XnSRd9r3kYDrSlGuDb5bQOHqve+xWDP2LCa5XeWVKPpK3W7rb9PeNtlqliEBMevc/JkFWF/+HQ7X2PHgS1/g1jnjoOvhA9eHV25GUa4Nl03Lg8ujYvOOH/3HGe1vuX5WIRqaXSgZO7jdfTqDKEAwSHB6VLz/2X7kZ2X4Z4r69ql+f9N+XHfhmHb1E1vrK/WtozqzfNpz86NTg+qdO3di8ODBSElJAQBcffXV+PTTT+F2u2E0GnHTTTdhz549eOyxxwAA48ePx1133dWZh9BuqampaGxsDHm8vr4eaWnRR39jUVr2rLOYZLz18V7MO38UXnp7O2aWZOOaGb59Hw0wmyRIggABQH2zHZJkhCACRtnb6Yp2504A4HSrUQPlolwbMlJM/r39ogbGswuhqxoUABLgT1rWOkNn2Mc1HUqUffdCP8+AzDRzS6CqtOuzupNVFo4l/VK1oH/7yq4jVFXz15nOOr7jOR6fzjimeOussusNWBaRtadcBOjITDVj+9c1uHZmARRNg9OletsoowwJOr4/EhxUlE7Nxrsb9wPwtqfh8hv4Am9vghvR/9pIoz9Wg9ih9jaR9bU6GvFa2sHfsLPLThdFNDsVOFzeWU6fVf6AksJBkAUB80tHA4B/RhQA780kTcFvlm8EAH/gsnnHIRTnhdb7wDpvNckxkj6JHe5jJILeXrfjVZctJhkbt3+PiSMHRaxzTs2JT8qr8dW+GiiqDqdbxcMrN6O0JAeXnJkLoyx6p6BLIhqaXfjD4ikwGSSomg5Ax3WlBUHHGa2P2y+p430oSRBgMUr4+bkj8cJbXwUl5PWvw+6EvhnQ++vb8erq8unUoHrWrFl46KGHMGPGDADAfffdhxtuuMG/33NaWhpeeOEFNDQ0QBRFJCcnd+bXd0h2dnbI2unGxkZUV1cjOzvyWrb2kACcNzkb//h4H646fzQcbtW/xtpoECEKAlRNwbeHHXh45Wbcv2gKHHYPBEGAQdax6OJCLHs9/J27wzXeKeXRpiPfMLsQqqZi6XWTYgfGrbZDEHQdVlk8FkT6tkSL8Hgsge+TZREpViMUl6dDn0VEFC92VYfb5cDo7H6wuxQ4nCqSLDLMRhmKqsADARlpZvy/q34GSRKw80At3t24H+dOGuodiZg5Bi/886ugzwzMhByY4Cbi6E/Aa9hG9mwdvWbGmyoIaLS7AQiwtkzfXrVuN3IGZyAtRUKS2dyS5d47LcNqkiHoOuyqgKULJ0FRNBytd8LlUbH3uzpcdlYe7nn2M//nt67zoqa1qb4nanlRfH4bCcBPRwzCxq8OYkLB4KCdFSwmGWUf78FZE4bimx8bcNm0PCxd4a1jTreK3d/W4qzxQwL2ydbRP3C3E0kAIIQ9zrjUM12HKAhIMsst09RVOFwqkiwGWI0SBI1BcG/VqUG12WyG0+n0//vzzz/HJZdcEvK6cGuYu0tJSQmeeuqpoLXV7777LkRRxOTJkzvte4yigKwTU/HNoUb/NJD/7jqMsvV7MXJYJuZOH3Usa6FLQaPdg3c27sc1M0ajvsGJGy8uhNOtotmhIMkiw2qS4VEVmE0ibphdCI+qwuFQcMPsMfCouj9ot5pkiJoGWRBh6qTAmIiot5MlCS9/sBdZJ3p3TnB7NDhcHuw8UIsDP9TjmhkFqKtz4IR+VthdCqYUDfImwBGAa2aMhkEQcF1pAeaeNxJ2pwKzSYbYsk/1daUFQTcwpTbe5CTqTLog4MV/bseMkhyoqg6XW/GPIj/w4ucoLcnx1/1kq8G7jrplL2lBAEwGCenJJgzIsAICcN3MMRB0HX+8eWrUOs/6TiE0DQIEmE1GfBvQTy6vqvb3kw2yhKvPHx1UxxK27ug6RF2HUQCMRgkpRsn7OAPqXq1Tg+r8/Hz89a9/hSiK/ing27Ztg8lkivq+s88+uzMPo10uv/xyvPzyy7jxxhuxcOFCHDp0CA899BAuv/zyTtmjGgCaXAoUtxuFw/tjeeu9GfNsWFBagP+3fIM/wZQsCRjYz4rzJg2FIAAHDjXBYjHAZJAgCN5EH74LmiAoUFQVVoOEpJZMgkZZQFJKS6DME5iIqN1UTcW86aPw3JuVwdP38mxYMLMAiqbitQ93Y/HsQiSFWzut6xDQaoQZQJeOmBBF4fBomDZhKKwmGQ6XgodeLcd9N0zGc29Woryq2l/vi/NsuOGiQkiaHbpggaBpsIgCLMmtklK19DfaUudZ36k1RVMxcmimv/75+PrJiqZC10UYBc7cocQk6Hrn1cZt27bhl7/8JQ4ePOj9cEFArI8XBAE7duzorEPokL179+L3v/89ysvLkZSUhJkzZ+JXv/qVf9p6R6iqhpoa73q7Qw0u3LFsA26fOxbDh/SH3aWgye6BxSzjaL0TT6wqR13LJvbFeTZMHTsYP9bY/Rc031Tv58sqsWn7If93+Dp3/2/5BgwbnIZFswsDpr8kPlkWkZGRhNra5j69JqQ7y8FmS4n6fGA9TkSsQ8f05bKIVY+B9tdlDwS8/M52nPbTk9EvzQyHU/G32R99+Q3mnjcKggDIPajN7W59uY62VZe1yYKAo80upFpN2Lb3CERBwMdbvsfXB+ux+NLioDrvdHkwMM0MDQJE1vewWLeDdaRNjtXmXjl9NBxuD5INUjwPvUdgfYuuM8unLXXZp1ODagBQFAXffPMNjh49irlz5+L666/HKaecEvU9P/vZzzrzEBJCYGPR5NHwm8c/RmlJDopy+mNAhgW6gJBEYWPzvMk+nG4Vf3jpi6CtkYpybchv2fs0kC9T4ZJnPsUgWzIWt9qIPpGxUfBiUN1xvrK77fH1+PXlxd19ON2qL59P8QiqFQhQNB3Prt0WMmpy7YVjIIve5JJSxE2vqLW+XEfbqqvaZFUQoAN48vUK7DpQg9vmjYfZKOHVdVVBWxT5+hgGUeDMtyhYt4N1pE12aDokQcDTb4S2uQtmFuCV93fi8rNGwNp624U+iPUtuu4Kqjt9Sy1ZlpGdnY3s7GzMmjULp59+OoqKijr7a3oUq0nGkvkT8eq6Kn9QbDZKWHjhGFwzowBNdjdMRhlmo4RPtn6PNR/uCdmTeuvuaswsCU2cVr6rGtV1Dvx+4Sk4UuOAR/Ou4SDqUxjXUAfoghBxXadLUXH3U59i8aXFmHf+qKBRkzuXfYLfX38KnC7FnyWWKJFEq9sQBFTXOWE1y/7Epn946QvMPn045k0fiXnTR8HpUpBsMWDv93UMqKlLSIKAukYnzp+SHdLm/r/lG1DX5MZlZ+XDlzSPKNHEdZ/qBx54IJ4f32MIAF77oCooK7fTreKxVVswYfRAzC8twPI1FThnwlD87b1dET/H7Ql/UWuye/DaB7u9U8T/UYkFpQU9aho4EVFXUwUBy1rnuGjJQCzpOprtHtQ1uXHv85vCvr/ZoSDZEtdLKFGHxKrbbk3Hq+uqcGnLHuyAt0/yt/d2BfVB7p4/AaOz+0EQeN+S4q/R7oamCxHbXACwOxRYU009ZkYm9S1hsqtQZ3O4laCLW6BNXx2Cy6Pi+lljMMgWfYNxY7hkOPCOes+Ymg2XR8XMU4fD7lKgifxpiYjC0cMEHYB35s+yNRXQBQHWlv1RI/GN/hElkmh1+7mySmiiCLei4bxJQ5FijV7HB2RYIYuid0ciojgzG2U43dF3bzabJDg8atTXEHUX3mbvAnangvRkoz/5h92pIMks40hLkrIfj9hx3wuf439/8RP/dhatFeXasPNAbcjj40cOxAn9rFi+ZlvI/tQLZxV61/wJOsQIGWeJiPoah0eNeKOzfFc1HB4VJoOECaMHBm2pZTSI/i21TIm4jQv1eZHqttkoYfrkYXh27TZkD0lHca4NdqeC+244BVt3H0HZ+r0heVwsRgm2TCtqaxM3rwb1HoIgoNnhweJLipCZag5qc33bapmNEuqb3K2yyxMlBgbVXSDZasC9N0zGs2srgwLfolwb7r1hMkQBWPab02CWJOQNycDyNRVBiUKKcm249sICvPjW9qDPLcq14aoLRoUE1GajhNwhGThS5wAApFiNkCUBZllkJ5CI+jy7M/poiN2pIDNZwjUzCrBsdUVQgkjfbgwSdKiCwKU2lFAi1e0rzs7D2598jXMmDUXZx/vwSsA076JcG26dMw4Pr9wMp1tFUa4NN15cCCMnvFEXkiQgPysjZOvZolwblsyfCFkCZFGExczQhRITa2YXMMgSnnhta1DgC3iTjz27thKLLynypl3QNBgEAacWD8ZV549Ck8ODJIsBqqqjps6J2afnYM55I/DjUTsMsvfunUfRQgLqW+eMQ9nH+0L2Vl04uxCKosFslCEy6QgR9VHWGJ0yq1mGJghY9nr4dnv56gosurgIy9ds7VE7LlDvF6luj80biGanirKP9wXdtAe8dVoUgPsXTYYsiTAbJUhMBkVdSBNFyEDEvrIoANdeWACHR4VBksBV/pSIGFR3AYdLCWkkfLburobD5b2znGwQAV3HmOx+qK534vV/7w7Z2uKG2YVY9/k3+GKHd7/qolxb0OeVluSg7ON9Id9XXlWNp9dUIPfkDOw6UIsbLy7k/qpE1CdZDFLEpTbF+TZYDBKa3ZHb7S27q+F0K/6p4laZQ3qUGCLVbYdLwYgw23L6lFdVY8bUbNgyrNj/QyMG9U9qCV6I4s/e0g+O1OaWV1XD7dEgSyI8ihoxxxBRd2Kt7ALNDk/M5+2OY1O2BACrWu0VCXjX+i1fXYH5paNx7/Wn4PZ540MSjYzIyojaKPmef/L1CiYzI6I+SdB1LJpdiOL84JuSvgzJgq7HbLd9bXasqeREXSlc3TYbJZhNUsQdRHzcHg3NDg9kSYDdGb3+E3WmZocndpvrVKDpun8giijRcKS6CyTFyCLb+vmoSXSqqtFgd8NqlpFkMcAoSyjKtfkD6bZcNAHv3UC7S/GOjhMR9TGSrmPx7MKIe/nGaretLdtpxZpKTtTVWtft1Ja91JNjZPs2GkRYTBL+u+swSsYO6opDJQIQu70FAItZhsPlTfRLlIgYUXUBq0kOmabtU5Rrg8Ukw2o61kjEGvnQVMAoi0g1y9B0DdfPHoPiPO/nx5oSE/h84Og4UU/38Cv/7e5DoB5G0HVYZRH9k42wtkrkaDZKUdtts1HyTxUnSjSBdVtRNdjd3uC69ewMn6JcG47WO2EyyDjwQz3rNXUpi0n2DxKF46ufFhO3MqTExaC6C4iaEzdeXBjSWPgybEq6HpQ4LNbIR7JV9ncAm+we3PPsRiycNQaP//p0pKeY/AF2a6235fKNtBD1CkwRQJ1I0HUsnDUmbLu9cNYYaLrmnypOlMianQr+8OIXMBskXDtzTEhgXZRrw2XT8jBkQDKO1tuxoLSA9Zq6VKPd6R8kCtfmXnthAT768htYjTLrJiUsRlVdwK0b8c3BGtx0SRHsLgXNDgVJFhkWkwxdU6BADPoh2pJEx7fntNUs41CNA7c88h9cef4IjBtxAhbOLsTTb1QEvb8o14bSqdl4eOVm/7+tJhlgFnAiolCCiCa7A5edlYurLhgFh1NpmX7oQZPdhcxUC3dRoISnCwI0TcOpPxkCDRr2fFuH62cVosnhgcutwmySAejYf7ABhTn9vCMtDFqoixllby941bpdWDirAG6PBntLm3u03om/v7cTvzhvJEQBvIFOCYtBdRdQNR2SbMTjrbYK8AW6J/SzQpaObV/hSzSybE1wYByYREcXBDg8KgDBH4A//cZXeBpfwWyUUFqSg4vPyIVBEuFWNFTsORKyByU7hERE4Qm6jvRkM7buOYJ+aWa4PRqcbgVH650oGt4f0Nizo8Tl6yPo0JGRYsauA7V4/7P9uPeGyXjuzUoMG5yGEVkZaGh2I9lqwJjh/dknoG5jMUiwe1RccmY+XvznV8g6Mbh+XnpWPgyiwIEgSmgMqruAriPsNle+f187s8D7upaLoC9pzo0XFcHl8Y5sBybRUQUBy1ZXoLyq2r8vtaYd+zynW8Xub2tx1vgh+Nt7OzF57EmYNOZEjM21+e/6vfT2dlw9fRSn0RARhaEC+Pv7O3FBSQ4MkohmhwdJFgPSUkx45V87cflZI7h+ihKSr4+wY38NHvuf01Bd58B5k4bCaBCxqfIHnDd5GDJSTHA4FfRPN8BqkhlQU7cSdB0CBLz41lfIHpKO4lwbFFVHeopvJkU9TuifBKvJALvTE5JYkigRMKjuApquY9eBGlw6LQ8jsjLg9mgwGkTsPFCLsvV7oUOHKoj+QNnHNzJtSW7purWMUAe+zulW8fDKzSgtycElZ+bCKIv+xsapqPjwv9/jw/9+H/a4Lj8rn/urEhGF4VZUTC0+CS+/vSNoe8Oxed4ZRm5FhYHtJyUYPSCg/s3ccXhqTXC/YmyeDcMGpeGOJz+B063iL/97GgNqSgiarmPrniPIGpSGJofHPztoz3d1GH5SOv723q6gttjXR5YYWFOCYFDdBVxuBbfOGYeyj/dh1boq/+NFuTbcOmccRAghATXg3Zd62ZoKLA5IhhNuuy2nW8WqdVVYta4KT/z6NG+grOtojpHd2+5UYE02dtJfSUTUewjwzjDa0qq93VJVDQHA/NKCbjkuomh8fYQrzsmPWn9nnT4cr7y3C3aHgmQD+wHU/ZwR+so3XlwUti6H6yMTdSfeZu8CKVZjxOnfZR/vgwY98r7Uu6pb1k57xdpuK/D5WFnEub8qEVF4OhDSifMpr6qGzmw5lIB8fYDiPFvU+lvckmGZu4BQoojUV+6XZo5cl1v1kYm6E4PqLqCoekgj4bN1d3XM/aI7Gij7soiHw/1ViYgiiznTJ8bzRN3BapZhNkox8zkpqn5sFxCiBKCoWti+stsTvTLHGmwi6ioMqruA3emJ+rzZFD247Wig7Msi3vr1gVnEiYgolCVGuxyr3SbqDhaDhAUzC2CKcdPcYpK5CwglFEeE4NhoiB6qcNYlJQrWxC5gjnEn2NSOfanbst1WIEnXsXh2YVBWcWZMJCKKzmSQUJRrCztyUpRrixm0EHUHQdeRn5UJj6JGrb8WkwyZ/QBKIJH6yjsP1Easy637yETdiUF1FxAFIerFTdXiGygLug6rLB5LSpaAjU/r7cQY+BNRd1I1HZdNywOAoLa7KNeGy6blQdV0QBK66/CIggReQ3Ud2LqnOmr9NYoA0wJQIonUVy5bvxdL5k+EKKJNfWSi7sKgugsIgo7SqdkAQi9u3sf1Xhkot5VL1SNuJ8atEoioe+hweVRMKRqEmSXZ/q0Qj9Y74fKo8EYkDKqp+6mtttpcMn8C1ny4B7lDMsLW3/5p5h7dZ6DeKVJfOT8rEy6PioWzCqEoHHyhxMWguguYZQnvb9qP/KyMoIvbzgO1eH/TflxXWgDoeo8IlDt7RLnR7m7zdmJEsTz8yn9x6xU/6e7DoF7AZJDx9oavMWxwGvqlmf2PV9c58PlXP2LhrDGImQ2KKM50IXRLzp0HapGflYk/vPQFSktyguqvIACyAI5SU8KJ1ld+e8PXmFBwAsbm2rzbxgIJ2Uemvo1BdRcQdB0LSguwbE1F0N57PW3qSuu74cDxjyjXN7libifmb0CJYukZpxL1AC6PgnMnDQ3ZM9U3w8jlUWCR2DZR9/LtSx2obP1e736/QNg+B4MRSkSCruPqGQVYvroibJv78MrNuOuaCRiYYeEsRkpIDKq7yLHp3RqcbgVmowyLQewxAXW4u+HA8Y8oNzuiZ0a3O5VjI/dERF2k2aHg4ZWbUVqSEzJq8vDKzVh63SRY2DZRNwu3nZDTrfrr7jUzRsHlVjldlnqExmZX2JHqh1duhtOtosnuweoPd3MWIyUkBtVdSNB1pJolZJ2YitraZihKz5k6GO5uuM/xjCgnWQxRn+dWCUTUHcwmGU63GjRi0vp5ou4WqR766u6UokHon8BLyogCWUxyxDYX8G6vxVmMlKhYI6lNwt0Nb8/zkaQlm9q87zYRUVfxZaINpyjXBlFgkjLqfqyn1JtYWraYDaco14adB2oBdLzPSRRPDKqpTWKNGHd0RDnFasSi2YUhjWhPW29ORL2LLxNt64DFt75PENg2UfdjPaXeRND1sH1CX30uW78XAGcxUmJiraQ28d09DNwj0Mc/otzBANgkCe3aToyIKN7aumsDUXdiPaXexiQJ+OXlxfjuUBOa7J6QddXH2+ckihcG1dQmvruHy9ZUBAXWnTWi3BO2EyOivqO37NpAvRvrKfVG/VIt8LhVrP5wd1z6nETxwKCa2uxYBnOOKBNR79fTd22gvoHXZuqNOIuRehoG1dQuHFEmor6kJ+/aQH0Hr83UG7FeU0/CRGVEREREREREHcSR6jgRRQGZmUkRn09NtXTh0SQ2loVXIpZDrHqcKKSA/SpbH++Spz/F0oWndPUhdZtErEeJoDPqMsu2c7AcO6472mT+Xm3Hsmo79pOPH8souq4uH0HXOZeCiIiIiIiIqCM4/ZuIiIiIiIiogxhUExEREREREXUQg2oiIiIiIiKiDmJQTURERERERNRBDKqJiIiIiIiIOohBNREREREREVEHMagmIiIiIiIi6iAG1UREREREREQdxKCaiIiIiIiIqIPk7j6A3kpVNdTUNIc8LooCMjOTUFPTDE3Tu+HIEgfLwqs7y8FmS4n6fKR6nChYh47py2URqx4Dx1eX+3LZdiaWY2yJ1Cbz92o7llWw42mTWZaxsYyi68zyaUtd9n/vcX0TtZsoChAEAaIodPehdDuWhRfLoeNYdsewLOKHZds5WI49C3+vtmNZdR6WZWwso+i6q3wYVBMRERERERF1EINqIiIiIiIiog5iUN0NGu1uNDhVHGlyw65o0AVO3yDqKJ5PREREXUMXBNgVLW7XXFUHvjvciB/rnLymU4/CRGVdzKXqeGTlZpTvqvY/Vpxvw6LZhZB0Jhsgag+eT0RERF1DFQQsW12B8qr4XHNVQcCy17bG7fOJ4okj1V1I9zVGAQEAAJTvqsayNRW8G0fUDjyfiIiIuoYeJqAGOu+aG+/PJ4o3BtVdyOFRQxoLn/Jd1XB41C4+IqKei+cTERFR14j3NZfXdOrpGFR3IbtTOa7niegYnk9ERERdI97XXF7TqadjUN2FrOboS9hjPU9Ex/B8IiIi6hrxvubymk49HYPqLmQxSCjOt4V9rjjfBotB6uIjIuq5eD4RERF1jXhfc3lNp56OQXUXEnQdi2YXhjQavsyGAjMbErUZzyciIqKuEe9rLq/p1NNxLkUXM0kCbp0zDjX1TtidHljNMiwGiY0FUQfwfCIiIuoakq5j8exCODwq7E6l06+5kq7j5ouL4FQ0NDa7eU2nHoVBdTdIsRqhuDywyi3bA7CxIOownk9ERERdQ9B1WGUR1mSj94FOvuZKAnDSgBTU1jZDUTRe06nH4PRvIiIiIiIiog5iUE1ERERERETUQQyqiYiIiIiIiDqIQTURERERERFRBzGoJiIiIiIiIuogBtVEREREREREHcSgmoiIiIiIiKiDGFQTERERERERdRCDaiIiIiIiIqIOYlBNRERERERE1EEMqomIiIiIiIg6iEE1ERERERERUQfJ3X0AHTF37lx8/vnnYZ/785//jPPPPz/ia95++23k5OT4/93Y2IgHHngA69atg8fjwdSpU3HXXXdhwIABcTt+IiIiIiIi6h16ZFD929/+Fk1NTUGPvfjii3j//fcxadIk/2M/+clPcNtttwW97qSTTgr69y233II9e/bgnnvugclkwqOPPoprr70Wq1evhiz3yOIhIiIiIiKiLtIjo8bhw4eHPPa///u/mDx5MjIzM/2PpaamYuzYsRE/p7y8HJ988glWrFiBKVOmAACGDRuG6dOn4/3338f06dM7/diJiIiIiIio9+gVa6r/+9//4rvvvsOMGTPa9b7169cjNTUVkydP9j+WnZ2NkSNHYv369Z19mERERERERNTL9MiR6tbeeustWK1WnHnmmUGPf/755xg7dixUVUVRURF++ctfYvz48f7n9+3bh2HDhkEQhKD3ZWdnY9++fcd9XLIces9CksSg/+/LWBZeiV4O4epxokj0sutKLIvYOlqXWbadg+XYObqqTebv1XYsq45hP7ljWEbRdVf59PigWlEUvPPOOzjjjDNgtVr9j48fPx4zZ87E0KFDcfjwYaxYsQJXX301Xn75ZRQXFwMAGhoakJKSEvKZaWlpqKysPK7jEkUBGRlJEZ9PTbUc1+f3JiwLr0Qsh1j1OFEkYtl1F5ZFeJ1Rl1m2nYPl2HHd0Sbz92o7llXbsZ98/FhG0XV1+fT4oHrDhg2oqanBBRdcEPT4zTffHPTv0047DRdccAGWLVuGZ599Nu7HpWk6GhrsIY9LkojUVAsaGhxQVS3ux5HIWBZe3VkOsTpnkepxomAdOqYvl0Vbgozjqct9uWw7E8sxtkRqk/l7tR3LKtjxtMksy9hYRtF1Zvm05yZmjw+q33rrLaSnp/sTjUVitVpx6qmn4r333vM/lpqaih9//DHktfX19UhLSzvuY1OUyD+kqmpRn+9LWBZeiVoOiXhMrSVq2XUHlkVkx1suLNvOwXI8Pl1ddvy92o5l1T7sJx8fllF0XV0+PXoyvtPpxLp163DuuefCYDC0+/3Z2dn4+uuvoet60ONff/01srOzO+swiYiIiIiIqJfq0UH1v//9b9jt9jZl/bbb7fjoo48wZswY/2MlJSWor6/Hxo0b/Y99/fXX2L59O0pKSuJyzERERERERNR79Ojp3//4xz8waNAg/PSnPw16fPPmzXjuuedw1llnYfDgwTh8+DD++te/orq6Go899pj/dcXFxZgyZQruvPNO3HbbbTCZTHjkkUeQn5+Ps88+u6v/HCIiIiIiIuphemxQXV9fj48//hhXXnllyJZYNpsNHo8HjzzyCOrq6mCxWFBcXIzf/e53KCwsDHrto48+igceeABLliyBoiiYMmUK7rrrLshyjy0aIiIiIiIi6iI9NnKMtu1VVlYWVqxY0abPSUlJwf3334/777+/Mw+PiIiIiIiI+oAevaaaiIiIiIiIqDsxqCYiIiIiIiLqIAbVRERERERERB3EoJqIiIiIiIiog+IeVN9xxx3YunVrxOcrKipwxx13xPswiIiIiIiIiDpd3IPqN954A998803E57/77jusXbs23odBRERERERE1Om6ffr34cOHYTabu/swiIiIiIiIiNotLvtUr1u3Dh988IH/36tWrcKnn34a8rrGxkZ8+umnKCgoiMdhEBEREREREcVVXILqvXv34t133wUACIKArVu3orKyMug1giDAarVi/PjxuP322+NxGERERERERERxFZegeuHChVi4cCEAYMSIEbjvvvswY8aMeHwVERERERERUbeJS1AdaOfOnfH+CiIiIiIiIqJuEfegOlBzczMaGhqg63rIc4MGDerKQyEiIiIiIiI6bnEPql0uF5544gm8/vrrqKuri/i6HTt2xPtQiIiIiIiIiDpV3IPqe+65B2vXrsW0adPw05/+FGlpafH+SiIiIiIiIqIuEfeg+l//+hcuueQSLF26NN5fRURERERERNSlxHh/gSAIGDVqVLy/hoiIiIiIiKjLxT2oPvPMM/Hpp5/G+2uIiIiIiIiIulzcg+pFixbhu+++w913343KykrU1NSgrq4u5H9EREREREREPU3c11SfffbZAIDt27fj9ddfj/g6Zv8mIiIiIiKinibuQfWNN94IQRDi/TVEREREREREXS7uQfVNN90U768gIiIiIiIi6hZxX1NNRERERERE1FvFfaT6iSeeiPkaQRBw4403xvtQiIiIiIiIiDpVtwbVgiBA13UG1URERERERNQjxT2o3rlzZ8hjmqbh+++/x9/+9jd88cUXePbZZ+N9GERERERERESdrlvWVIuiiCFDhuC2225DVlYW7r333u44DCIiIiIiIqLj0u2JysaPH4///Oc/3X0YRERERERERO3W7UF1ZWUlRLHbD4OIiIiIiIio3eK+pnrt2rVhH29oaMDmzZvx/vvv45JLLon3YRARERERERF1urgH1bfffnvE5zIyMnDddde1O/P3mjVrcMcdd4Q8fu211+LXv/61/9+vvfYannvuORw8eBDDhg3Dr371K5x++ulB72lsbMQDDzyAdevWwePxYOrUqbjrrrswYMCAdh0TERERERER9T1xD6o/+OCDkMcEQUBqaiqSk5OP67Ofe+45pKSk+P89cOBA/3//85//xN13343rr78eEydOxNtvv43Fixfj//7v/zB27Fj/62655Rbs2bMH99xzD0wmEx599FFce+21WL16NWQ57sVDREREREREPVjco8bBgwfH7bNHjx6NzMzMsM/95S9/wfnnn49bbrkFADBx4kRUVVXhySef9G/hVV5ejk8++QQrVqzAlClTAADDhg3D9OnT8f7772P69OlxO3YiIiIiIiLq+bpsKPbbb7/F+vXrcfDgQQDAoEGDUFJSgiFDhsTlu/bv349bb7016PHp06fjoYcegtvthtFoxPr165GamorJkyf7X5OdnY2RI0di/fr1DKqJiIiIiIgoqi4Jqh988EG89NJL0DQt6HFRFHHllVfitttu69DnXnDBBaitrcWgQYNw6aWXYsGCBZAkCfv27QPgHXUOlJOTA4/Hg2+//RY5OTnYt28fhg0bBkEQgl6XnZ3t/wwiIiIiIiKiSOIeVD///PN44YUXcM455+Caa65BTk4OAGDv3r144YUX8MILL2DgwIG46qqr2vyZNpsNN910E4qKiiAIAv7973/j0UcfxaFDh7BkyRLU19cDAFJTU4Pe5/u37/mGhoagNdk+aWlpqKys7MifG0SWQ7cKkyQx6P/7MpaFV6KXQ7h6nCgSvey6Essito7WZZZt52A5do6uapP5e7Udy6pj2E/uGJZRdN1VPnEPqletWoUzzjgDjz32WNDjRUVFeOSRR+ByufD3v/+9XUH11KlTMXXqVP+/p0yZApPJhBdffBHXX399Zx36cRFFARkZSRGfT021dOHRJDaWhVcilkOsepwoErHsugvLIrzOqMss287Bcuy47miT+Xu1Hcuq7dhPPn4so+i6unziHlR///33mDdvXsTnp0yZgo8//vi4v+e8887D888/jx07diAtLQ2Ad7ssm83mf01DQwMA+J9PTU3Fjz/+GPJZ9fX1/td0lKbpaGiwhzwuSSJSUy1oaHBAVbUw7+w7WBZe3VkOsTpnkepxomAdOqYvl0Vbgozjqct9uWw7E8sxtkRqk/l7tR3LKtjxtMksy9hYRtF1Zvm05yZm3IPqfv36YefOnRGf37lzZ8QM3h2VnZ0NANi3b5//v33/NhgM/uRo2dnZ2LhxI3RdD1pX/fXXXyMvL++4j0NRIv+QqqpFfb4vYVl4JWo5JOIxtZaoZdcdWBaRHW+5sGw7B8vx+HR12fH3ajuWVfuwn3x8WEbRdXX5xH2y+bnnnovXX38dzzzzDOz2Y3ek7HY7nnnmGbz++uudkmX77bffhiRJGDVqFIYMGYKhQ4fi3XffDXnNpEmTYDQaAQAlJSWor6/Hxo0b/a/5+uuvsX37dpSUlBz3MREREREREVHvFveR6l/+8pfYsWMH/vznP+Mvf/kLBgwYAAA4fPgwFEXBhAkTcPPNN7frM+fPn48JEyYgPz8fAPDBBx9g1apVmDdvnn+690033YRf//rXOPnkkzFhwgS8/fbbqKiowMqVK/2fU1xcjClTpuDOO+/EbbfdBpPJhEceeQT5+fk4++yzO6kEiIiIiIiIqLeKe1BtsVjw4osvYt26dUH7VE+ZMgWnnnoqzjjjjJAtrWIZNmwYVq9ejR9//BGapmHo0KG48847MXfuXP9rLrjgAjgcDjz77LN45plnMGzYMDzxxBMoLi4O+qxHH30UDzzwAJYsWQJFUTBlyhTcddddkOUu28KbiIiIiIiIeihB13W9uw+iN1JVDTU1zSGPy7KIjIwk1NY29/l1ECwLr+4sB5stdEu5QJHqcaJgHTqmL5dFrHoMHF9d7stl25lYjrElUpvM36vtWFbBjqdNZlnGxjKKrjPLpy112YcbnBERERERERF1UJfMcX7zzTexevVqfPfdd6ivr0frwXFBEPDll192xaEQERERERERdZq4B9UPP/wwnn/+eQwcOBAFBQVISWn7MDoRERERERFRIot7UP3aa6/htNNOw5NPPglR5GxzIiIiIiIi6j26JMo99dRTGVATERERERFRrxP3SPe0007jemkiIiIiIiLqleIeVN999904ePAgli5diq+++go1NTWoq6sL+R8RERERERFRTxP3NdUWiwXFxcVYsWIFXnnllYiv27FjR7wPhYiIiIiIiKhTxT2oXrp0KV577TUUFRWhqKiI2b+JiIiIiIio14h7UP3OO+9g5syZePDBB+P9VURERERERERdKu5rqmVZRlFRUby/hoiIiIiIiKjLxT2oPv/88/Hhhx/G+2uIiIiIiIiIulzcp3+fd955uPfee3HdddfhoosuwoknnghJkkJeN3r06HgfChEREREREVGnintQ/Ytf/AKAN7v3xx9/HPK8rusQBIHZv4mIiIiIiKjHiXtQ/cADD8T7K4iIiIiIiIi6RdyD6lmzZsX7K4iIiIiIiIi6RdwTlRERERERERH1VnEfqQYAl8uF9957D9u3b0djYyM0TQt6XhAE3H///V1xKERERERERESdJu5B9ffff4958+bh+++/R2pqKhobG5GWlobGxkaoqoqMjAxYrdZ4HwYRERERERFRp4v79O+HHnoITU1NWLVqFd59913ouo5HHnkE5eXl+PWvfw2z2YwVK1bE+zCIiIiIiIiIOl3cg+rPPvsMV1xxBQoLCyGKx77OaDRiwYIFmDhxIqd+ExERERERUY8U96Da6XRi8ODBAIDk5GQIgoDGxkb/88XFxfjyyy/jfRhEREREREREnS7uQfWJJ56IQ4cOAQBkWcbAgQOxZcsW//N79uyByWSK92EQERERERERdbq4JyqbOHEiPvjgAyxevBiAd9/qZ555Bg0NDdA0DWVlZZg5c2a8D4OIiIiIiIio08U9qL7uuuuwbds2uN1uGI1GXH/99Th8+DDee+89iKKICy64AHfccUe8D4OIiIiIiIio08U9qB40aBAGDRrk/7fJZMJ9992H++67L+zrNU3Djz/+iP79+8NoNMb78IiIiIiIiIg6LO5rqturpqYGZ555JpOXERERERERUcJLuKAaAHRd7+5DICIiIiIiIoopIYNqIiIiIiIiop4g7muq4+Gdd95BWVkZvvrqKzQ0NCArKwtz587FRRddBEEQAABz587F559/HvLet99+Gzk5Of5/NzY24oEHHsC6devg8XgwdepU3HXXXRgwYECX/T1ERERERETUM/XIoPqFF17A4MGDcfvttyMjIwOffvop7r77bvz444/+rbsA4Cc/+Qluu+22oPeedNJJQf++5ZZbsGfPHtxzzz0wmUx49NFHce2112L16tWQ5R5ZPERERERERNRFemTUuHz5cmRmZvr/PWnSJNTV1eGvf/0rFi1aBFH0zmpPTU3F2LFjI35OeXk5PvnkE6xYsQJTpkwBAAwbNgzTp0/H+++/j+nTp8f17yAiIiIiIqKerUeuqQ4MqH1GjhyJpqYm2O32Nn/O+vXrkZqaismTJ/sfy87OxsiRI7F+/fpOOVYiIiIiIiLqvXpkUB3Ol19+iYEDByI5Odn/2Oeff46xY8dizJgxmDNnDr744oug9+zbtw/Dhg3zr8P2yc7Oxr59+7rkuImIiIiIiKjnSrjp31arFYsXL8aQIUPa/J7Nmzfj7bffDlo/PX78eMycORNDhw7F4cOHsWLFClx99dV4+eWXUVxcDABoaGhASkpKyOelpaWhsrLyuP8WWQ69ZyFJYtD/92UsC69EL4dw9ThRJHrZdSWWRWwdrcss287BcuwcXdUm8/dqO5ZVx7Cf3DEso+i6q3wEvYs2hf7www/xn//8B99//z0AYPDgwTj11FNx+umnH9fn/vjjj7jkkkuQk5OD559/3r+eujW73Y4LLrgAOTk5ePbZZwEAV199NURRxIoVK4Jeu3TpUmzYsAHvvfdeh49L1/WQEXCinob1mHoL1mXqDViPqbdgXabeJu4j1Q0NDbjxxhuxefNmSJIEm80GANi4cSNeffVVjBs3Dk8++SRSU1M79NnXXnst0tPT8fjjj0cMqAHvCPipp54aFCinpqbixx9/DHltfX090tLS2n08gTRNR0ND6PpuSRKRmmpBQ4MDqqod13f0dCwLr+4sh4yMpKjPR6rHiYJ16Ji+XBax6jFwfHW5L5dtZ2I5xpZIbTJ/r7ZjWQU7njaZZRkbyyi6ziyfttRln7gH1ffddx++/PJL/PrXv8YVV1wBq9UKwDty/Le//Q1//vOfcd999+EPf/hDuz7X6XRi4cKFaGxsxKuvvhp2Gncs2dnZ2LhxY8jdsq+//hp5eXnt/rzWFCXyD6mqWtTn+xKWhVeilkMiHlNriVp23YFlEdnxlgvLtnOwHI9PV5cdf6+2Y1m1D/vJx4dlFF1Xl0/cJ5uvW7cOP//5zzF//nx/QA14R44XLFiAK664AuvWrWvXZyqKgltuuQX79u3Dc889h4EDB8Z8j91ux0cffYQxY8b4HyspKUF9fT02btzof+zrr7/G9u3bUVJS0q5jIiIiIiIior4n7iPVsixj2LBhEZ/Pzs6GLLfvMH73u9/hww8/xO23346mpiZs2bLF/9yoUaNQUVGB5557DmeddRYGDx6Mw4cP469//Suqq6vx2GOP+V9bXFyMKVOm4M4778Rtt90Gk8mERx55BPn5+Tj77LPb/be2h6oDdkWD3anAapZhMUgQumZ5O1Gv0mh3o8Gpwu708FwiIuoFdEGAw6Oyj9QHqTrw3eFGNDa7+dtTjxL3oPqcc87Bu+++i8svvxySJAU9pygK3nnnHZx77rnt+swNGzYAAB588MGQ5z744APYbDZ4PB488sgjqKurg8ViQXFxMX73u9+hsLAw6PWPPvooHnjgASxZsgSKomDKlCm466672h3ot0d1nQOPv7YV5VXV/seK821YNLsQEhsOojZzqToeWbkZ5bt4LhER9QaqIGDZ6gr2kfogVRCwjP1j6qHinv178+bNWLp0KUwmEy699FJkZWUBAA4cOIBXX30VHo8Hd999NywWS9D7Ro8eHc/DijtV1VBT0xzyuCCJ+MvrW4OCAJ/ifBsWzy7sM3fkZFlERkYSamub+/SakO4sB5stei6CSPU4EeiCgCdadbx8+tq5BPTt8ylWPQaOry735bLtTCzH2BKpTe6O36untuus28E60ib31N++O7C+RdeZ5dOWuuz/3uP6pjaYM2eO/7+3bdvmTwgWGMvPnTvX/9++pGE7duyI96F1i2aXGjagBoDyXdVweFRYE3hfYKJE4fCoYS++AM8lIqKeiO1638Xfnnq6uAfVDzzwQLy/okexOz0xnldgTTZ20dEQ9Vx2pxLzeZ5LREQ9B9v1vou/PfV0cQ+qZ82aFe+v6FGsZkOM5+P+kxD1CrHOFZ5LREQ9C9v1vou/PfV0nEfRxZJMEorzbWGfK863wWKQwj5HRMEsBp5LRES9Cdv1vou/PfV0nX7b54477oAgCPj9738PSZJwxx13xHyPIAi4//77O/tQEo4uCLC7VFxx1ghcfEYutu4+grL1e+F0q/7shkzCQNQ2gq5j0UVFOFRjR5PdA6NBxM4DtTjwQz0WlBbwXCIiSlCRtswSdB2LZhdi2ZqKsLs6sF3vvQRdx42zC7FlzxFkpprh9mgwGkTUNDgxdnh//vaU8Do9qN60aRMEQYCmaZAkCZs2bYr5Hl/yst4s0hYRj9xyKjRdg1nmPnxE7aEKApa9Hrr1xg3ceoOIKGHF2jJL0nUsnl3Ifar7IB3Ahq0HQ+pG0fD+3XdQRG3U6UH1v//976B/v/zyy8jMzAzZMsvH4XCgtra2sw8joehhLiCAN5vhM29u4zYBRO0U7ZxavqaC5xQRUQKK1nYvC2i7BV2HVRaPJaZie97rtbVuECWquK+pnjZtGj744IOIz3/44Yc488wz430Y3aot2wQQUdvxnCIiyLDtyQABAABJREFU6nnYdlMkrBvU08U9qNZ1PWhP6tY8Hg9EsXfnS2vLNgFE1HY8p4iIeh623RQJ6wb1dHHJT9/U1ISGhgb/v+vq6nDw4MGQ1zU0NODtt9+GzRY+219vwW0CiDoXzykiop6HbTdFwrpBPV1caugLL7yAJ598EsCxzN6Rsnvruo5bbrklHoeRMHzbBARmsvTxbxPAdSJEbcZzioio52HbTZGwblBPF5egevLkybBardB1HQ8//DDOP/98jB49Oug1giDAYrFg9OjRGDNmTDwOI2FwiwiizsVzioio52HbTZGwblBPF5eguri4GMXFxQC82b3PPvts5OXlxeOreoxjW0RocLoVmI0yLAaRjQRRB0m6jpsvLoJT0dDY7Oa2K0REPQC3zKJIeF2nnizuCxQWL14c76/okfrA1txEcScJwEkDUlBb2wxF0Tg1rIfQBYEdaqI+rDdumdVod6PBqcLu9LBdI+qDuOq/i6hh9t8rzrdh4axCGAX0igsKUVdTdeC7w428o92DRGoLF80uhMTfjqhX6Gs3zlyqjkdWbg47bZntWtupgoBlr23l9YF6JAbVXSDahvbLV1dg6thBKBrenw0GUTvw4tvzRGsLl62pwGKumyPq8frajTO2a52D5Ug9HYPqLuDwqNixvwaXTsvDiKwMuD0ajAYROw/Uomz9XswsyW5Tg9HX7vwSRaILAp57sxK5J2dgxtTsoHPqubJKXFda0K5zg+dW13B41JAOk0/5rmo4PCqsstjFR0VEnUUTRRyqseOciUMxY2q2v58TKTDqDW0v27XO4fBo+PpgPe66ZgL6pZlhdypIMss4Uu/EE6vK4fBosMpcO0mJi0F1F3C4FNw6ZxzKPt6HVeuq/I8X5dpw65xxUFQ9ZsPb1+78EkXjVFScPXFo2HOqdGo2nIoKi9S2TgzPra5jdyoxn/evsSSiHkUVBCx7PXj2kK+f83DL1OjAfk5vaXvZrnUOh8uDe2+YjGfXVmLr7uA6dO8Nk+FwemCVWY6UuHjrrAukWI0o+3hfUCMBAFt3V6Ps433on24GELlhjjUlRmfWM+pjdF2Iek7petvOCZ5bXctqjn4fN9bzRJSYIrWlvja5tCQHwLF+Tm9qe9mudY60ZFNIQA1469CzayuRlmzqpiMjahsG1V1AUfWQRsJn6+5qb9ZiRG542zK1iKgv0fTo55TWxlEOnltdy2KQUJxvC/tccb4NFoPUxUdERJ0hWlu6dXc1RmRlADjWz+lNbS/btc7hdKtRr+tOd8+pE9Q3MajuAnanJ+rztY2uqA1vW6YWEfUlTlf0Oh/reR+eW11L0HUsml0Y0gH1TfnsaWspicgrVlvp9mhB/Zze1PayXesczY7ofWW7o+fUCeqbOCelC8Sa+pNsNURteDm1iChYZ50TPLe6nqTrWDy7sMcnJyLq6wKTjJlN7evn9La21yQJuHXOONTUO7lPdQclWQxRn7daeladoL6HNbQL+KYG7fi6BqUlOUEZwI/WOzEwwwpR02K+P3D/Q58JowfCbJRhdytwuBSkWI1QVJ2NOvVqFoOECaMHIuvEtJCM+gd+qPeOhoSp995OoIZmhwdmkwyLyfs5m746FPJa/6hKDzh/dEFAg1PF4QM1sJhkmGUxoc97QddhlcVjyXsS+FiJKJQmCNiy5wgyU81wezRkpgm48eIi/N+7O3D2xKFB7XJNgxMnZlohBPRzovVrjrft7a6M4ilWIxSX51iGarZr7WI1yfjZyIHIOTkdxbk2KKoOi0mGDh37DzYgySQDUfrKRN2NQXUXEFpGZjyaDpdHg93pQf90A2RJQJPdjaP1DthSzREbYEHXseiiIhyqsaPJ7vEHDwerGzFv+ig8+fpW7Nhfg1vnjMPKd3cFrUnpzEyavWHrC+odBABXXzAalfuOBj0+IMOCM356Enwpbnx11uFSkJJkwtOtEuMU5dqwcNYYAAgKrDt72l48z53ekkGXiHoGXRThcKvISPUmWd13sB6P/v2/GDksEw8unornyyqDdmUozrNhsC0ZtjQzxJY2yTdletmaiqDAujjfhhtnFwIA7IrW7jaT7WHPJeo6rr2wAJV7g6/rsiQi7+R03qSghCfoOmtpPKiqhpqaZu8/BAEeAM+trcSwwcdG1lKsBvRPt6ChyYnUZBMiTXwJe5HIs+HaC8fgxbe+wqbth3DptDzsOlAbNslDcb4t5h7YMf+eCBeq62aOgUfVYJRFGEUhaqPXOrBIMsnon5mE2tpmf7K2vkiWRWRkdE852GwpUZ8PqscJRBWAo/UufHu4Cf3SzEEzP4YMSEa/NBOAY3U22vlRlGvD5WflIsVq6rQZHiHB/Jq2d/LaE4DrgoAnwmTQ9X3H8Z73PUWsegwcX13uznO0N2E5xpZIbXK430sVBGwNGKH2tbv90y34w0tfYERWJvKyMoKCasDbzk4dOwjFw/uH3afaN9NO1XSIgoBn1m7zt2tmo4QFMwuQn5Xp3VYpQrvYne0h63awjrTJmiDiaIMD3xxuQr+A+lXT4MTo7H6QBAGyAAbXYH2LpTPLpy112f+9x/VN1CYKvAH1eaeE7qvrC45bNxG+Cw0gYEXZtpCLxI79Ndj+9VFcdEYuTh93Mk7oZwUA7DpQE5IhMdYe2K217tSbjXLI3pO+z33qjW3Iz8rArgO1uGxaHgakH7sTHShSUH7TpcWshNRumi5A04FPth4M2c/ysml50HUBRxscmHf+KFx8poIUq/eWVbjzY+vuaswsyUZGigCrWfYnyAnqtAkC3JoOl0eF06UiyWKAxRB+irUqCHjuzUpkDUrDuBED8P3hJsyYmo3ckzNQtn4vnG7Vv21M605ee0dZ2pJBt63nPRFRNLooorrWgY+3hLa718wYjd8vnISj9S5/f8TX3gHedvaq80f5+xYWkwxBECAIGqwGCWaDhC17juDkgSlotHv8beb7n+3HTZcWo+zjfXjita3+7wzXLrI97NlUXYeqAZ+0ql/FeTbkDsnA/oP1GDksk31GSlism13A6VYx/OR0lH28D1taB6ZV1Xj2zW2YP6MACnTIkgSPoqK+2Q2TQYLJIIVcJMxGCbfOGReyT29Rrg23zhmHh1duhtOtIj3ZiMWXFqNfmhmNdg90iwEWkwxR12EPubDpMMsSBACH6x0wmwzwqLp3fbZLibpVxsySbP+Ngkh3oiPtR/n4qnLMn1EAQG/X6CCnovdtmq7j1XVVYfezBIAbLy7CqnW7Q6Z6B54fgaxmAyr3HcVzb1b6nyvOt+GGWYUQBMDhPlbXrBYDBFGHR9PhUTU4nAosZhmyJELVdLz8z69w9sTQG2itv9/bydP86+9i7dsabpSlLRl0/euWiYiOg0fVIra7z//jK+QHjFCHa29rG504yWaFKdUMh0tBk92NZIsBmiCgpj58sL5kwUS8+M8dId8Zrl1ke9izRbqul1dVA299hRsuKsRLb2/HVeePjpqHiKi7MKjuAs0OD4rzbHjlvV1hny/fVQ39Ah2SJOLpN4I71XfPnwDAG0j7kpylJpmw8t0d2HWgBpdOywtJCPLY/5wKSRCh6DqeWlMRFMgX5dpww0WF0HQdyVYj3IoKp1NBssUAj65DEkSoGnDoqB1Ggxi0v2Qkbo+3cfMF2K3vBse6e3x4ih1LV2xq87qntozmMeju3dye6PtZHq61B00d9J07OoA/3lwCWRJQ3+SC1WyA1SRD1XVYzTJ+u2Aiki0GGGQRmq4BAkLq2oTRA3H1BQV46o3Qc+u6CwuQMyQ95IaX77gAoLQkx9/xbHJ4oJu9N7Z0XWv3KEtvy6BLRAlKFOF2KVHb3asuGIW8kzMgiwKcbhVmk4S//O9pEADUNbm82ZsFEU+8tjUkeF44awwO/FAf8pl2Z27E72zdLrI97NmiXdfLq6rhcCm46IxcONwKLEaZgTUlHLYwXSDFakBzjP317C4FBlnE1TNG42oADqcCq0WGySDhngUTYTZKsJoNcLoVuD0qZpbkYN70kXjlvV1Yta4KZqOE2acPx6QxJ0LTAafbA4dLxTUzRuNonQOPvVqOuiY3tu6uxtNvVODqC0bj6Te2hSQ1WzirEGnJBljMMhqb3BiRlYH+6Zaox240HOvouz3asbvBLVNmm2L87b6gPNqInE+s0bybZhdCRWggxEQlvYvTpUZ9XhQE3LNgInRdR3qqGTX1TgDAD9VNOLG/FW5Fg9ZSFTQAiub9PEEQoGo6GhudsKVasPLdnSF1LevENCxfUxE2aH7uzUrMnT4Sf4twA81348nHaJBw058+8k9bj6bZqcCSYgo6N+KZQZeICAA8qu6/Se0TeLPSIIvISDFDlgSkWGRYzQaYTTKcHg9qGlxIssjISDGj2aHgmTfCt51Pv7ENiy8txr3Pbwp6rskeY+/igNFntoc9ly4IcLi8Myz/5+c/QUaqBc0ODywmGSaDCIdbQUOTBxmpRmg68OTrW3HDRYWQ+XtSAmFQDWDv3r249957UV5ejqSkJMycORO33HILjMbOmSZkNcrQIQRdhAK3ACpbvxcWkwyrSYbDrUCEAKvFgGaHB9CBE/pZ0ehww2gQIYrez0rWddidCq48fxSunD4SBoMAwLvO1OVR4XCqkCQBGyt/wL7v6vDg4qnYUPE9Xlu3G+W7qnHR6e6w06meXlOBhbML8dybxy58V5yTH/lClWfD7u/q/P82GyWkJRvhgYCjdU68uq4qKIgIJzAoj7XuKdaot1vT8fQboWvQ2xKwU/do76wCXfCeH+HOp68P1iN/SCb6p5vhbJmyLYsCBtuS4VFVJFsMECFA8O14AsDhUpBklPDgyk345lATAGBsy4yOeeeOxM4DNThU4/B//4gwSXh8yquqMXf6qKh/r+8mUlGureUIvJ3KedNHRn2fAOCJNRVBN4eiZdDtzOzlRNQ7RWp/dUGAXVHhOdoMu0eD3aEg2SLjoRsnw2oxotnpDXhEUYAsCXB7NNQ1eZBkMUBVdax8Zwd+fs4I3PvK5zhU40Bxvg3XzCiIOdLdWmD/IJzA0We2hz2XoKtITTLgocVT0OR0wmyUoOs6mh0eiIIByRYj0swGuHQVsi5ix/4aLHu9AtfPLoSiqkH9hnjMVIx2nkT8LkGAKghwuBQ0OzxI9i3B5Ah7r9Xng+r6+npceeWVGDp0KB5//HEcOnQIDz74IJxOJ5YsWdJp32OUdSz7n1MBUYQK+E+yKUWDMG38yRB0HS63Cl0Haptd8CgaduyvQdn6vRg5LBMLZxVi5dvbcfq4k0Omlhbn27DwwjGobXTh7/+qCnlu5tQc/PUflZg4+kT/GqdId3/Lq6pxtM4R9BkCgEvOyIOmIWTK1iVn5qFy3xEA3kBkYD8rtu09Cj0giVR+VgaKcm0RMy9rOrBk/gR/YAT/hkihYq2ZcsU5UUlgA5pkkWEyyHC6lW6ZZh62MQf86+V7wrT3WFP5fX+j060gI8kMDTq27jmCwpx+WPY/UwFRDjqffjbqBJiMMuqbnBAEAVv3VOOND/dgRFYmZp6aA13X8X/v7Qqpx4suKsRvr5mAA4eb/PWwYs8RnDwwCb+77hT89plP/YG1LyiOxOWOXkeNBtE/Mr15x7FtvAQBUc8TWRb9N4duvKgoqN7deFGR92aaS4HZKEdMohaISySI+rZw7e/pPx2Mn58zEpLmgVE0QAOg6zo0XYeuA6nJZmi6it3f1OL1f+/Gb+aOx6oPqoKWwhTn23D9rEL87b2dWLJgIv730fX+pV7ROMJc33ceqEVxni1iRm+zMXjvYqllC1OnokLXBWi6DqdLgcujso1LYJoowywC0DSkWK1BgajZJOPDL77BaeOGwCTKePrNbbjjqvF44IUv4FZU/HCkGclWAwZmWAFdx5Nh+hQLZxXCGCZzeFuug4HJR0dkZaCmwYXUJCNsGRY8t3YbNm0/dh2fMHogFswcA4+iQhIF6JoGTddbchcJ3t6tEH2nHOq5+vyWWk8//TSeeuopfPjhh0hPTwcAvPrqq/jd736HDz/8EAMHDuzQ5wZvFSAALdtNeQAse70ibKf+pbe3Y0PFD0GPl07NxsMrN2NEViYumJqNf4RZqwkAiy8pwidbD4YkQgO8Dcq1MwvgVrxJlZKtBkAHjtY7sb0lcA9M3PSHG6fA5VbhVjWIAmA2ynB5VKia7l8rFTjK/pu54/CPT/Zh7rkj8eq/duGcSUMhCgK276/BiKwMKKqOwbYkPPtmZciFd+GFhVhRVokvdhzyjzwWDu8PgywiKUwDZ1c0LP7jRxHL/Q83TsFtT34S9FjgiKbFJCPFaujQxTWwAxIpWVxHppl3JPV/pGD0kjPysHTFZ0HJtiJdTICOb9+iC0JIp8V3k8HlUQAIkETBny3bapH9t0qMBhmKqsAgyVB1FaIgwulWj11AjTKg61D1Y9m2k60GmGQROrwdPFkUveeT7p3q3/p8uvbCAty1fAOGDUrDjKnZeOjlzZhfWoANFeHPkbG5Nlw/uxAHjzT563V+ViZuuKgQO74+iuFD0lHb6IKq6khLNuLOZRtCkp35PP7r0/F8WWXETuD1s8YAEOBWVP9P4rugS6KAF/+5HZ8HXKTH5nnbAU0Hqr6p9ddjh0vxH6vvxluT3ZvgMCj5YDuy8S+aXQgJnXdjpjMC97Z8RldsqfXwK1tw6xVjuX3JceA2MLF1tE3WRBF2XyBiNcBqlKFpmret9rWvVgMsRhmqpsGtaGh2eJOVWkwSPKoGAYBZbrmxretQADwZpr9y7YUF+Pv7O3HRGXn427s78UXAzUGf4jwb5kwfCallSc3vV3yGmy8rxtIVm0Je6/OX/z0NTXaP9wZi1WG88eEeFOX2xzUzCvBUq20Ji3JtWDCzACvf2Y4FpQUh11xFELC8ncvAorU1sdoh1u1g7W2TXdBgEmQo0MPWuRsvLsRHm7/F6eNPRn2TGy6PAodLhabpuP+FzwG0bPN64Rg43Qqqa50wyiIUTfd2vwHY0i0wyhKMIrx9DFHEoRo7muwef5/2wA/1uHpGARqbXbCYvLvfPLt2mz/5aOvj8vXRnW7V3y/89xff4OfnjUBjkztk28+aBifGDu8PURCCbgaFE63Osb5F15byaWv/hFtqtcP69esxadIkf0ANAOeddx5++9vfYsOGDZg9e/bxf4koQIN37WbrgBrwjv4uW12BGVOzg4Lq1omNrrxgVMSpU5mp5rDBAuAdoa1pcOGupz71P1acZ8OMqdn4+vv6kAydHlVDSrIRL7y1PWwD8ujf/xsUUJiNMuaeNxJmo4Ste47gzJ9l4cT+Vuw6UOufJms2SphfWoArp4/C4Vo7bBlWON0ePP+PYwG1L0gN2nKs1UUw1pqpJEvwbt9t/dxYWq/lLi3JCZuMqiummUdbV65pwYmwyndVY/nqCkwdOwhFw/t3yppy313bwIuMr5zf3bgf0ycPg8kghWTx9NW5//z3O/z8nBGobXQg2WrC8lZr7HxTr5//x1f4fLu3btxx5c8wINOCxiYXkpNMEERvVv3nyr4Kez49u7bSvz5Ph7dM+qVFPke27K6G061g6YpNQVlrn36jAqeMGYSbAm7kjM2zYcn8iUE3LwL/xj3f1OLaC8fg2Te3BdVT7/mTg++rm/HOp/tx7qTQi/TYXBuuv6gQ008Z5r95dbTeCUkUMTDdjH9EySi+fHWFP/uu71x9f9P+kA5nrLwEkwsHxdy6pi3auz1YvD6DqLdTBAFPBiT/MhslLL12IpKTTHi6Vfsa7ubr2FwbrptVAFkU4NGA2gYH+qdbgj7Tx9e+zpiajZfe3o7cIRlhg+ryqmpcPWM0muwevLquCksWTMTmHYciXr+Lcm34ZOtBf/tWnGfDbxdMhADgtic+xtkTh2LG1Gzoure/8/mOH3G0zoFNXx2CW9GCrrmqKIY99mjX50htzY2zC6GDeVrizSjK0AA8+Vr4PvKTr1dg8SVFcLgUNDS7vMlt87w3VsxGyb+jxlNrtmFK0SD0T7dA0/WQgaiiXBsuPysP/dPMYRPmlU7Nxoo3KzFscBpWravCvdefgqxBaW1KPurrF86Ymo1d+2txQr+ksNt+DuqfjIEZlihzMnnti7d4lW+f37Bv3759yM4OXvObmpoKm82Gffv2dcp3qAAU1ZvAK9p6on5p5rCP+7Jvh5sa5RNrSmrr6d7lVdUo+3gfhg32NhalJTkAvAFDxZ4j0DQ9bAMS+Fofp1tBXaMLz66tRGlJDvqnm7GirDLo/U63iidf34oX/rkde7+vR12jE06X6h+RixWk6i2LYH1rporzbUGv850MFoMY9FxbPzeW1mu5R2RlxMxIGi/R1pWHy9a+dXc1MlPN7fp7I/EFZK0vMr5yHjY4DUfqHBG3xSj7eB9KT81BxZ4jUFrWv7d+3ZaWm0xDB6X5P9tgEFC59ygU3fseh0uBDrTpfNpSVe1fdx1N4H6qvnpeviv0vNxSVY3X/l2F+aUFQY8X5dpwTWkBnl67DX/9x1e45MxcPHLLqbj7mglYMn8C8rMyUPVtLd5cf+y8C/e3L19dAUXT8eBLX2Dpik148vWtOFxrx7NvVkY9JwN/e9/jWSemhfzusfISZKaaQx5rb92JFbi35bM64zOIejtNFENG9kpLcvxtZbhr36stAYDPlt3V2P51DVweDctWV8BilmGPkem7X5oZ5bui7w7S7PDAYpZbsnh72+yFs0Kv375gpmz93mPHWVWNVR9UYeveI6hrcmPVuiosXbEJv39+E17453b0SzVj+/4a/99kb2nfdUHAoRp7u67P0dqaLXuOsB3qAh5Vi1nnHC4FdocCWfKGLt4taSuD6rKvbh6pc0QMhF9dV4Wte45EvJ4OG5zmr9dNdk/U/l7gddf3un5pZgwdlBpx+7lX11XBo0buj/DaF1/xLN8+P1Ld0NCA1NTUkMfT0tJQX18f5h1tJ7es3W1yKNB0Hc0x1gNHCpp9wYAlynYQsZJ5hHt+S1U1Sqd695ieWZLtH0l86OXNGJtrC/MpodmLi3JtOFrvRHWdA+VV1ZgxNRuKokUcEdy6uxqXnJmLij1HkN0SNAExkj+17OebapYAeCvtzRcXodmlwu70wGo2IMkkQWo5D66bOQZPtXQm2vO50dib3EH/jhWg2Z0KUtNDb5KEI7VcIHz/395jaS3csbk9Wrv+3kBywBr0Bqfq/50Dy9VXzr66EekCtKWqGtfMGI1+aWZYTIaoFyrfZ3mnOxuC3tPsiJ4RFgg+n46t148sMOFN4PeHK8/yXdW44qz8oFwAOw/U4midA063ii92HMJ5pwyFxWTA7wOy2S6ZPwGvvLcraG/3cH9764Q90UbZIx1r4B7ygb97R+pPe+uOr56E09bP6ozPaE3uYD6F9p6jFB7LsXME1uM6R2gg4msz29K++mSmmv03KmPlLgGOta/RroVJFgMO13pzUTTZPRg9rB8AHdfPKoTdpbQsGzLgk60Hg2bL+ZTvqsaMKaGJTrfu9l5Hnnuz0v+Y3eFBWoYZDU61TRnDA6/P0dqazFRzm9oh1u2O8dXlBrsn6sARADQ7WpaZacfafV8/NpDbo0WfmRbmPT6+c8NXr40GMWZ/z/e87//tTu+yimjnn8ujwmo1hH2+Ldc+s9H7Xta38KKdj/HoW/j0+aA6XkRRQEZGEgDg+5ojEAQh5h6JkYJmY8vo69F6Z8RERjUNTozNs4VfL5pnw84DtWE/29cIWM0GjBiWiYdeblkbYop8rIHZiy+blgeXR8WKMu/FTdeB2kZX1L9TFAT/WuzWnxmJ060g68Tgmx/9I7zWXt2I/KwMzCzJhskYvczDfW44za2OL1aAlpJk9P/+bZWaGn3rskjH0lq4Y/M91ta/1yewHgPA4QPekYHWv1fri0o0zQ6Pf+u1aAI/0+5Ugt7Tepp/OIHnk38tU4RzpCjXBl0LnvITeFENp6bBhQdf+iLoM1q/v3Unoa3lFOl9kUQ6Vt/jgb97R+pP68+IxVdPImnLZ3XGZwRqXZc7oq3nKEXHcuy41vX4YO3RkNe0p30N/LfvZmWS2RAtXyiAY+1rcoTAoDjPBotJxhOrygF42xWDLELXddz+5Ceoa3Lj9nnjUd8yCt3W4/Q5VNMcvAzNJCEjIwmHD9S0+/ocra1pb9+EdbvtAuvywdqjMa/rSRYZRllCU6ub6q1/o/YEwpGe89WhnQdqUTg8Um/z2PcF/r/VLMe8QeBwqcgenB72ubZc+1Jb6hzrW3Thyqez+xaB+nxQnZqaisbGxpDH6+vrkZaWFuYdbaNpOhoavJkukywG6DpQXeeImt33aMteuuEev35WIV7853b/3bXWa6RGDcvEYFsydD30uRlTvKPP4fgaAbvTg1da9tYN3OonnAGZVvzlf06DomkQBAStzcpMNaPZGf0usdOtwOlWcbTe6V9fFesiaDbKqK1tW5IhkyRi97fe9dxL5k/olM81y2LQWrCdB2oj/pbF+TaYZbHNxytJIlJTLWhocECNMiUo0rEEKsoNvYES+FjrvzdWkBFYj33vB0IDr9YXlWiSLAY4XErMm0yBn2k1y3C5j73HYpKh63qbzqexed7/Hpvb3zsa0+oc8U09bH0zyJelO9INqcC/NTBhSeDzrW+UtbWcIr0vkkjH6ntf4O/e3vrj055z0BzjZlZbPqs9n9GWYLl1XW4P393utp6jFF5727q+qL1tcrh21NdmRhOuDfcFNbIswCBLMdvX4nwbUpOMIdm5i/NtuGF2IZ5bW4G6Jre/XZk6djBefnsn6lpmy7TlehHpNbIU3P6aDBJqa5thNsr4fPuhdl2fo7U1be2bsG4Ha2+bnNSyTWa0OmcxyXArCprswbOtWl+Ldx6ojbosofV7Wku2GlCxx7urTdn6vZhYcELU4/JdM339wqP1TmSkmKJ+f5LFEPEa2JZrX0ODg/UtimjnY3v7J+25Gd/ng+rs7OyQtdONjY2orq4OWWvdXr6Mc1aTjB37a7Dpqx9w3YUFeGZtZUinftFFhXj57e1B7y/Os+HaC8dAEgXc99dNmDhmECwmCdfOLICm6bC7FBhkEQ1NLty5bAOmTx6GK88fCUEYBZdLhckowelWsObDPWEzFftGsAMbBd/o8/6DDWH/prF5Nuz9rg7VdQ7sOlCL/KyMoEzT+w/WIyXZFHELDN93FeXa0D/dgjnnjoSmxQ5SLQaxXRkOfXtVdubnBu5/WbZ+L26d4x1pD5f9W1c1xJ5AF0xVtQ4dS+B3+xLQ+AQGex0pRwBBr/etWW9droH1yJZuiVjmY/Ns8KgajtY7YTZFv4AGXqjG5vUPek/FnkMoHj4Qiy4qjJr925v5fgxkUYCoA+99tt8/iyFw2va7G/dj2OC0oM+oaXDismnB5RlY1gMzrbj7mgkQBO8xBk5f9F1YNR0Ry6k9N9hqGpxRA+Gj9c6QoN73PeF+97bWn8Dn2lN3fPUkUkLBtnxWZ3xGa8ebJbU95yhFxnI8PoFlZzXJIW2Jr81sS/vqU9Pg9Lfdn1X+gNPGnhS1ff37+ztx/exCfLH9B4wYlulNIgZgQLoFTQ4PPIqKTdsPByVNLMjOxEkDU7DkZydD14FB/ZNQue9o5Gt0hFl24fosBsnbHlgMIg78UB92AKIoN/z1OVpbE63tDdcOsW63j7+fbPRuCXrjxYURs39LAIyy7F9L73susC74roO20oKoszfDDWL5PsNqlv3r+51uFWs+3I0bLy7E8lbXzLG5NsydPhKNzd5ZF2ajhNN/ehJWvrMDV54/Kmq9sRqliPWkLdc+X6DI+hZduPKJR9/Ch1tqtWyp9Z///Me/tvq1117Db3/7207cUsubaa620YWyj/fijHEno1+auWWvY+/dOUVVoWsCVF1v2WtWgskgQYeOo7UuSLJ3W4qKPUf8W2BF20Kpocn1/9m78/Cmqvx/4O97b/a2KS0EEITSQlvK0lIHBpBS18FhK4riDi6IKKDCOI5+HUSdmR/iMoOjAiOLjtvMuOBSR0TEZRBkVMZCQZYCBUQQKLR0S9IkN/f3R5rQNEvTNGnT9v16Hh9p7u3N6cm5J/dz7zmfA61Wgl6twulKa8BMzJ9sPYwZEwfhWFktVJKAM5WuL1YFwPv/OejzO9dengGrTca6LYfw69H9PIGEO2Pz3sPlSIzXomsXvc+yQrmZJswsGILTFRbPUl4XZHbHbZMGo+ysGQlxWr+/E242PveyT6IgYmWjTMzhHjca61SHuzRCoHWqa+1OnKowQwC8llzy9/eGs3yLLAhYXbgL40a2LPt3ZY0V8QatTzIdT/bvwl34JkD271f+/QOmXdofSQl6mG0yFLiGlcfp1dCqJVSZ66DTqKGWBFTWWGGM12Lrzp+R1a8r3vqsxE9W7nPLYrjPoYM/nYVBp/Y5D4alm1CQn4bDP1di5ODzsLpwl8/x3NMi/vqv7/G76SPw9ueu92xYT4Gyf9951VA8vHyz52lObobrphsAv4Hw7Ktcy369+N5Or6A+UPbvYO1HAbDMz3uEnf27hccK9RhcUqt94DIwTQunT3YIglcg4p39e6fPjd9g2b8VCHipcBeuvqg/unZp2L+6vvPUKhFORYFOEvH4S9/gx5M1ruPW9+8ff30Ys64cgp/KagFF8SxTNGPCIPx+xRacrbG5ltsan4V/f3UQ0y7PwNlqm+/3hedp9y582yC7uDvjc8NrlmHp3itbuL+jUs5L9CSpjDeo0SPZACnAMkaB+hpP9u8m+iG2bW9h9cn1CaJsTqV+yTfXd7pGJUIjuradqbTgkdXfeH1X/3y6Fkp9W3Mvh3nlRf2hKAo+2BQ4+7e/z/TWiYPx6Mqvz33/NvicG39najUqrP5gJ7754Vz7dK/zXlljgTFO5xOI52a4rm9UTXwHNvXdx/YWXFP105zrk+YsqdXpg+rKykpMnDgRqampmD17Nk6ePIklS5Zg8uTJWLRoUdjH9ffFp4gi7LITdocry6Feq/KsJ6tVqVyJtpwKLE4nJOHcGr96nQpatQhBEGCpc8BaJ3uCcdnphNUmw1L/mloloKrWBp1GBVEQIIpwrdUnA3V2R/2awWpX2ncB0KhdT7NrzQ7EGdRQSyKqautgqO/I3O+n16mgUYmQnQqcigK1JMEuy56EDBqVBAVOqCXX8Sx1DiQYNHDISn0ysXNBn7l+7pZOK0GvVUEtCpAVwGJzAIoABa51j1u6Rq5X3UdgvdxoiUbnGKn19yK7TrXrs9XWr1MtiRJEKJ51VM0WBwx617qQoqLA4V6n2lbf3husUw1BgN0hQxAEqCURVrsTZosdWq0EnVqCShJxttYKrVoFtSShqrYOWo0EnUaCKIiw2Bye99NrVbDa7DBbXOXUa1QQnE7YFMBef1PG/TdqNRL0WjVsDlcynHiDClqVBItNhsXqgE4redaJrqxxvadWI0ESBFcZ688FteS6kQa46s9idUCvU8GgVUEEUFN3rnwGrQqi81xmW3+fq+t1Jyz1ZRSbWKc6Em2ntY7Fdao7Dl4INq2l61Sb64Nfg9Z7nWqzxYE4g6rBOtUKzA2+hxuuUy1DgM3ugEoSIIoSLHUOT1Ct17r6R6VRn+bu3/UaFaA4Yba5ckoY9Cro1CrYZFefqde6rk0gACpRQHWtDcZ4DRRF8Kw9HFf/O3anAxrJ+8a1TqOCpf6apXH/2FA4fQ/XqY6csPtkUYQM+LQ5d9oos12GuT6rPBQBgqhA5/VwQw1V/XWsTitBr1GhrsE1p1YtuQJ0P0HyuWvU0NtNwO9kUYTVIUMSRNgcst/v86ZwnerwtdU61Z0+qAaAgwcP4o9//COKiooQFxeHKVOmYMGCBdBoNGEfM9AXX6RPhFgOFpvCTsGlLesh3Au4aAinLbMNndOZ64JBdfvQmdtoqCLVJ0fi2oCfV+hYV95a0ierVCJUWjXKK63eD2XaybVta2B7Cy6S9dOcoLrTz6kGgP79++Pvf/97Wxej2bg4PHUUbMtERJHB/pTaszpZwdLXt0VkGhJRa+ICZ+0UF4enjoJtmYgoMtifUnvmab/72H6p/WFQ3U5Z7E0tXu6b7ZsoFrEtExFFBvtTas/Yfqk9Y1DdTpmbWFi+qe1EsYJtmYgoMtifUnvG9kvtGYPqdsqgCz4dvqntRLGCbZmIKDLYn1J7xvZL7RmD6nZKr5aQm2nyu821eLnkdxtRrGFbJiKKDPan1J6x/VJ7xqC6nRIUBXOmZvt0Pu4MiVx6gNoLtmUioshgf0rtGdsvtWccR9GOSYqCeVOz2+061URubMtERJHB/pTaM60k4IGbh3Odamp3GFS3c4KiwKASYYjXuF5gp0PtFNsyEVFksD+l9izBoIGjzg6Dqn4JLbZfagcYVEeJKApITo4LuN1o1LdiaWIb68IlFuuhqXYcK2Kx7toK68K/SLRl1m1ksB7D1xZ9Mj+v0LGuQsfr5JZjHQXX2vUjKApv/xARERERERGFg4nKiIiIiIiIiMLEoJqIiIiIiIgoTAyqiYiIiIiIiMLEoJqIiIiIiIgoTAyqiYiIiIiIiMLEoJqIiIiIiIgoTAyqiYiIiIiIiMLEoJqIiIiIiIgoTAyqiYiIiIiIiMKkausCdFSy7ER5ea3P66IoIDk5DuXltXA6lTYoWexgXbi0ZT2YTAlBtwdqx7GCbeiczlwXTbVjoGVtuTPXbSSxHpsWS30yP6/Qsa68taRPZl02jXUUXCTrJ5S27HnfFr0TNZsoChAEAaIotHVR2hzrwoX1ED7W3Tmsi+hh3UYG67F94ecVOtZV5LAum8Y6Cq6t6odBNREREREREVGYGFQTERERERERhYlBdRuoNttQZZVxusYGs8MJReDwDaJw8XwiolinCALMDif7KaImyArw06lqnDhr5blC7QoTlbWyOlnB0te3oWhfmee13EwT5kzNhqQw2QBRc/B8IqJYJwsClq8tRlEJ+ymiYGRBwPK3d/BcoXaJT6pbkeL+Ym0QAABA0b4yLH+3mHfjiJqB5xMRxTrFT0ANsJ8iaoznCrV3DKpbkcUu+3QWbkX7ymCxy61cIqL2i+cTEcU69lNEoeG5Qu0dg+pWZLY6WrSdiM7h+UREsY79FFFoeK5Qe8eguhUZdMGnsDe1nYjO4flERLGO/RRRaHiuUHvHoLoV6dUScjNNfrflZpqgV0utXCKi9ovnExHFOvZTRKHhuULtHYPqViQoCuZMzfbpNNyZDQVmNiQKGc8nIop17KeIQsNzhdo7jqVoZVpJwAM3D0d5pRVmqx0GnQp6tcTOgigMPJ+IKNZJioJ5U7NhscswWx3sp4gCkBQF916TA6vDiepaG88ValcYVLeBBIMGjjo7DKr65QHYWRCFjecTEcU6QVFgUIkwxGtcL7CfIvJLEoDzuyegoqIWDoeT5wq1Gxz+TURERERERBQmBtVEREREREREYWJQTURERERERBQmBtVEREREREREYWJQTURERERERBQmBtVEREREREREYWJQTURERERERBQmBtVEREREREREYWJQTURERERERBQmBtVEREREREREYWJQTURERERERBQmBtVEREREREREYVK1dQHCMX36dHz77bd+t/3lL3/BxIkTA+6zbt069O/f3/NzdXU1nnjiCWzcuBF2ux1jx47FwoUL0b1796iVn4iIiIiIiDqGdhlUP/roo6ipqfF67ZVXXsGGDRswevRoz2sXXHABHnzwQa/9zj//fK+f58+fjwMHDuCxxx6DVqvFs88+i1mzZmHt2rVQqdpl9RAREREREVEraZdR44ABA3xeu//++zFmzBgkJyd7XjMajRg2bFjA4xQVFWHz5s1Ys2YN8vLyAACpqamYMGECNmzYgAkTJkS87ERERERERNRxdIg51d9//z1++uknTJ48uVm/t2nTJhiNRowZM8bzWlpaGrKysrBp06ZIF5OIiIiIiIg6mHb5pLqxf//73zAYDLjsssu8Xv/2228xbNgwyLKMnJwc3HfffRgxYoRne2lpKVJTUyEIgtfvpaWlobS0tMXlUql871lIkuj1/86MdeES6/Xgrx3Hilivu9bEumhauG2ZdRsZrMfIaK0+mZ9X6FhX4eF1cnhYR8G1Vf20+6Da4XDg448/xqWXXgqDweB5fcSIEZgyZQr69euHU6dOYc2aNbjtttvw2muvITc3FwBQVVWFhIQEn2MmJiZi165dLSqXKApISooLuN1o1Lfo+B0J68IlFuuhqXYcK2Kx7toK68K/SLRl1m1ksB7D1xZ9Mj+v0LGuQsfr5JZjHQXX2vXT7oPqLVu2oLy8HJMmTfJ6/d577/X6+eKLL8akSZOwfPlyrFq1KurlcjoVVFWZfV6XJBFGox5VVRbIsjPq5YhlrAuXtqyHpi7OArXjWME2dE5nrotQgoyWtOXOXLeRxHpsWiz1yfy8Qse68taSPpl12TTWUXCRrJ/m3MRs90H1v//9b3Tp0sWTaCwQg8GAiy66CJ988onnNaPRiBMnTvjsW1lZicTExBaXzeEI/EHKsjPo9s6EdeESq/UQi2VqLFbrri2wLgJrab2wbiOD9dgyrV13/LxCx7pqHl4ntwzrKLjWrp92PRjfarVi48aN+PWvfw21Wt3s309LS8OhQ4egKIrX64cOHUJaWlqkiklEREREREQdVNSD6ssuuwyfffZZwO1ffPGFT4KxUH3++ecwm80hZf02m8348ssvMXToUM9r+fn5qKysxNatWz2vHTp0CLt370Z+fn5YZSIiIiIiIqLOI+rDv48dOwazOfD8H7PZjOPHj4d17A8//BC9evXCL37xC6/Xt23bhtWrV+NXv/oVevfujVOnTuHll19GWVkZ/vrXv3r2y83NRV5eHh5++GE8+OCD0Gq1WLp0KTIzMzFu3LiwykRERERERESdR6vMqW68ZFVDO3fuhNFobPYxKysr8dVXX+GWW27xOb7JZILdbsfSpUtx9uxZ6PV65Obm4vHHH0d2drbXvs8++yyeeOIJLFq0CA6HA3l5eVi4cCFUqnY/3ZyIiIiIiIiiLCqR4yuvvIJXX30VgCugXrx4MZYuXeqzX01NDaqqqnwyd4ci2LJXKSkpWLNmTUjHSUhIwOLFi7F48eJml4GIiIiIiIg6t6gE1V27dkV6ejoA1/DvHj16oEePHj77GQwGDB48GDfeeGM0ikFEREREREQUVVEJqidNmuR5+jx9+nTMmTMHo0ePjsZbEREREREREbWZqE8cfu2116L9FkRERERERERtotWycR04cABHjx5FZWWl3+1XXnllaxWFiIiIiIiIKCKiHlT/+OOPeOCBB1BcXAxFUfzuIwgCg2oiIiIiIiJqd6IeVC9atAglJSV4+OGHMXz48LCWzyIiIiIiIiKKRVEPqr///nvMnj0b06dPj/ZbEREREREREbUqMdpvkJSUhISEhGi/DREREREREVGri3pQff3116OwsBCyLEf7rYiIiIiIiIhaVdSHf/fr1w9OpxNTpkzB1VdfjZ49e0KSJJ/9xo0bF+2iEBEREREREUVU1IPqBQsWeP795JNP+t1HEATs2bMn2kUhIiIiIiIiiqioB9WvvvpqtN+CiIiIiIiIqE1EPaj+5S9/Ge23ICIiIiIiImoTUU9URkRERERERNRRRf1J9YwZM5rcRxAEvPLKK9EuChEREREREVFERT2oVhTF5zWn04njx4/j559/RkpKCrp37x7tYhARERERERFFXNSD6tdeey3gti+++AKPPPII/u///i/axSAiIiIiIiKKuDadU33JJZegoKAAixcvbstiEBEREREREYWlzROV9e3bFzt37mzrYhARERERERE1W5sG1Q6HAx9//DGSkpLashhEREREREREYYn6nOpA86Wrq6uxfft2nD59Gg899FC0i0FEREREREQUcVEPqr/55huf1wRBQGJiIn7xi19g2rRpyMvLi3YxiIiIiIiIiCIu6kH1559/Hu23ICIiIiIiImoTbZ6ojIiIiIiIiKi9ivqTagCQZRmFhYX48ssvcfz4cQBAr169cMkll2Dy5MmQJKk1itHmFEFAlVXGqSPl0GtV0KlECIrS1sUiImpViiDAYpdhtjpg0KmgV0sdui/sbH8vEVG4ZAX46VQ1qmtt7C+pXYl6UF1dXY2ZM2di586diIuLQ58+fQAAX3/9NTZs2IB//vOfWLNmDeLj40M+5rvvvus3AdqsWbPw29/+1vPz22+/jdWrV+P48eNITU3FggULcMkll/iU74knnsDGjRtht9sxduxYLFy4EN27dw/zL/ZPFgQsX1uMopIyz2u5mSbMmZoNiZ0FEXUSna0v7Gx/LxFRuGRBwPK3d7C/pHYp6sO/ly5dih9++AELFy7E1q1b8d577+G9997D119/jUceeQS7du3C0qVLwzr26tWr8eabb3r+u+mmmzzbPvroIzzyyCMYP348Vq1ahWHDhmHevHnYvn271zHmz5+PLVu24LHHHsMzzzyDQ4cOYdasWXA4HC35s70ofi6qAKBoXxmWv1sMRRAi9l5ERLGqs/WFne3vJSIKF/tLau+i/qT6008/xQ033OAV8AKAWq3GjTfeiNLSUqxfvx6PPPJIs489ePBgJCcn+9323HPPYeLEiZg/fz4AYNSoUSgpKcGyZcuwatUqAEBRURE2b96MNWvWeDKQp6amYsKECdiwYQMmTJjQ7DL5Y7HLPp2EW9G+MljsMgwqTm8noo6ts/WFne3vJSIKF/tLau+i3jrPnj2L1NTUgNtTU1NRWVkZ0fc8evQoDh8+jPHjx3u9PmHCBGzduhU2mw0AsGnTJhiNRowZM8azT1paGrKysrBp06aIlcdsDf7Uu6ntREQdQWfrCzvb30tEFC72l9TeRT2oTklJCbqs1ueff46+ffuGdexJkyYhKysLl112GV588UXIsgwAKC0tBQCfYL5///6w2+04evSoZ7/U1FQIjYaUpKWleY4RCQZd8AEBTW0nIuoIOltf2Nn+XiKicLG/pPYu6i30hhtuwB//+EfMmjULt9xyC/r16wcAOHToEF577TXP3OrmMJlMuOeee5CTkwNBEPD555/j2WefxcmTJ7Fo0SLPk2+j0ej1e+6f3durqqqQkJDgc/zExETs2rWruX+qD1X9MJU4SURupglF+3yHteRmmhCnVUHqhFNFJEn0+n9nFev1oIrh4VaxXnetqT3URVv3heG25XDrtq3/3ljTHtpoe9BafTI/r9CxrsLTsC2zvwwd21twbVU/UQ+qb7rpJpSXl2PlypXYvHmz95urVJg7dy5uvPHGZh1z7NixGDt2rOfnvLw8aLVavPLKK7jrrrsiUu6WEkUBSUlxnp/vuTYXz79V5NVZ5GaacO+1uejWRd8WRYwZRmPn/vvdYrEeGrfjWBWLdddWYr0u2qovjERbDqdu2ff7ivU2Gsvaok/m5xU61lXo/LVl9pfNw/YWXGvXT6uMpbjnnntw0003YevWrTh27BgAoHfv3hg9enTARGPNNX78eLz00kvYs2cPEhMTAbiWyzKZTJ59qqqqAMCz3Wg04sSJEz7Hqqys9OwTLqdTQVWV2fOzCsC91+TAbJNhqXNAr1XBoJEgKU5UVNS26L3aK0kSYTTqUVVlgSw727o4baYt66Gpi7PG7TjWsA2d017qwt0X1tbJMFvtMOjUiNO2rC8MJchoSVtuSd1G4+9tr9pLG21LsdQn8/MKHevKW7h9sgrAfdcOg8Umo8Zsh16r6rT9ZTBsb8FFsn6acxOz1SYoJCcnY+LEia3yXmlpaQBcc6bd/3b/rFarPWtlp6WlYevWrVAUxWte9aFDh5CRkdHicjgcvh9kglZC355GVFTUwuFwgmkXAFl2+q2rziZW6yEWy9RYrNZdW2gvdWFQCTDEawAAitw6fWFL66UlddsWf2+sai9tNFa1dt3x8wod66p5/NWVSiXi/O4Jnuvkzt5fBsP2Flxr10+rDTa32+346aefsHv3bvzwww8+/7XUunXrIEkSBg0ahD59+qBfv35Yv369zz6jR4+GRuO6sMnPz0dlZSW2bt3q2efQoUPYvXs38vPzW1wmih2KIMDscOJ0jQ1mh5PrHRK1E2197iqCgCqrjH1HylFdJ7PvICLqANr6u4U6nqg/qa6qqsKTTz6JDz/8EHa73We7+ynxnj17Qj7mzJkzMXLkSGRmZgIAPvvsM7z11luYMWOGZ7j3Pffcg9/+9rfo27cvRo4ciXXr1qG4uBivv/665zi5ubnIy8vDww8/jAcffBBarRZLly5FZmYmxo0b18K/nGKFLAhYvrbYa/3D3EwT5kzNhqQobVgyIgqmrc/dtn5/IiKKPPbtFA1RD6ofeughfPHFF5gwYQJycnL8ZtturtTUVKxduxYnTpyA0+lEv3798PDDD2P69OmefSZNmgSLxYJVq1Zh5cqVSE1NxQsvvIDc3FyvYz377LN44oknsGjRIjgcDuTl5WHhwoVQqZi6vyNQ/HScAFC0rwzL3y3GvKnZENiBEsWctj532/r9iYgo8ti3U7REPXLcsmULpk+fjocffjhix1y4cGFI+02bNg3Tpk0Luk9CQgIWL16MxYsXR6JoFGMsdtmn43Qr2lcGi12GIYaXjCLqrNr63G3r9ycioshj307REvVW06VLF6SkpET7bYj8MluDp7doajsRtY22Pnfb+v2JiCjy2LdTtEQ9qL722mvx0UcfwelkdjpqfQZd8MEYTW0norbR1uduW78/ERFFHvt2ipaot5y5c+fCZrPh6quvxpQpU9CjRw9IkuSzHxODUTTo1RJyM00o2uc71Cc30wS9WgI4d4Yo5rT1udvW709ERJHHvp2iJepB9cmTJ/HNN99gz549ATN8Nzf7N1GoBEXBnKnZWP5usVcH6s7y2FGTUSiCAItdhtnqgEGngl4tddi/lWJXS9phW5+7bf3+REQU+esZ9u0ULVEPqh9++GH88MMPmD17NrKzsyOS/ZuoOSRFwbyp2Z0myORSERQLItEO2/rcFQCMye6FyXlpsNmd0KhFlFdZwdVMiYiiL1rXM2393UIdU9SD6v/973+YNWsW7r333mi/FVFAgqLAoBJhiNe4XuigHSeXiqBYEMl22FbnriIIWObnbwBcF3U8l4iIokdWENXrmc5yXUitJ+qJyrp164bExMRovw0RIbSlIoiirSO0w47wNxARtVe1deyDqX2JelB922234Z133kFtbW2034qo0+NSERQLOkI77Ah/AxFRe2W22pvYzj6YYkvUh3/bbDaoVCqMGzcO48ePR8+ePX2yfwuCgFtvvTXaRSHq8LhUBMWCjtAOO8LfQETUXhl06ia2sw+m2BL1Fvnkk096/v3666/73YdBNVFkcKkIigUdoR12hL+BiKi9itOyD6b2JepB9WeffRbttyCielwqgmJBR2iHHeFvICJqryQB7IOpXYl6UN27d+9ovwURNcClIigWdIR2eO5vcMJqc0CnUUGvFtvV30BE1F51hO8R6jw4IYGoA+JSERQLOkI7FBQFRp2ElPOMqKiohcPhbOsiERF1Gh3he4Q6h1YJqvfu3YvXX38du3fvRnV1NZxO74sSQRCwcePG1igKERERERERUcREfUmtb775BtOmTcOXX36J7t274+jRo+jTpw+6d++O48ePw2AwYMSIEdEuBhEREREREVHERT2ofu6559CnTx+sX78eixcvBgDMnj0b//znP/Gvf/0LJ0+exK9//etoF4M6IUUQYHY4cbrGBrPDCUUQ2rpIRDGD50foZAX46VQ1Tpy1sq6IiFqBrIDfUdSuRH349+7du3HPPfcgPj4elZWVAOAZ/p2Tk4PrrrsOf/3rX3HRRRdFuyhtThEEVFllnDpSDr1WBZ2KCW+iRRYELF9bjKIS34yREuucOrmg5wcAM5PCeDgEASve3sG+hIgoyhRBQHWdjNpT1Vj53k72u9SuRP1JtSRJiIuLAwAYjUaoVCqcOXPGs71Pnz44ePBgtIvR5mRBwMoPdmHT9mOoqrXh6MkanDhrgSxG/SPodBQ/AQMAFO0rw/J3i3m3kzoc9w27fUfKUV0nB23jTZ0f3x84jXnPfInfvbAZ8575Ei+8Wwy5k54zsihi2TvB+xI+8Sciajn3dfLPZ8x4sVFADfAajmJf1J9U9+3bF4cPHwbgSkiWlpaGjRs3oqCgAADw5Zdfolu3btEuRptSBAGrP9iFcaP6ofCrUry1scSzLSfdhLnXZEPFO28RY7HLPp2xW9G+MljsMgwq3sygjqG5ozKaOj8m56X5vLb83WLM62TrgiqCgJPlZuzYH7iuzHYnXircxacpREQt0PA6WauRsJ3XcNQORb1VXnTRRfjoo4/gcDgAALfddhs2bNiAcePGYdy4cfj8889x3XXXRbsYbcpil5HSKxGFX5X6XKDt2F+GFbzzFlFmq6NF24nai3BGZTTV/m123yWj3BcynYnFLqPGbA+6z6kKM5+mEBG1UMPr5Kb6XV7DUayK+pPqOXPmYMaMGZAkCQBw1VVXQRRFbNiwAZIk4a677sLUqVOjXYw2ZbY6MDAlyesJdUO88xZZBl3wZt3UdqL2IpxRGU21f43afz9ktjrOrRPaCZitjoB14RYobGafTkQUuobXyVPy04Luy2s4ilVR/8ZXq9VISkqC0OCu/ZQpU7Bs2TI899xzPgF1XV0d3n//fZw+fTraRWs1Bp3K79OfhnjnLXL0agm5mSa/23IzTdCrpVYuEVF0hDMqI9j5kZNuwt4jFX63dbYLGYNOhb1HKpCTHrgvCVRXAPt0IqJQNbxObqrf5TUcxaqYu41eXV2N//u//8P+/fvbuigRo1dLiDeog+7T2S5Yo0lQFMyZmu0TOLjnOnameaHUsYUzKiPY+XHd5Rko3OSbOLIzXsjo1RKO/FyJgrFpPhd4Oekm3HVVtt+6cmOfTkQUmobXyYWbDvrtd3kNR7EuJr/1lQ52wgiKgh7JBuSkm/wmvfFcsHawv7stSYqCeVOzYeHSQNSBuZ86F+1rXr8S6PxQAGSlJnsdr7NeyAiKgjsKhmB14S5kpiRhSn4abHYn4g1q9Eg2QFIUn7pyY59ORBS6xtfJT7++DQX5/b363Z7JBojO4KM+idpSTAbVHZHkdGLuNdlY8W4xL1hbiaAoMKjEc/NAWcfUwbifOi8Po1/xd34IAG9GNSApCu4sGAKL3QmrzQGdRgW9WoRQf2EXbt0TEZG3xtfJ7jxE7j6VATXFunYZVH/88ccoLCzEDz/8gKqqKqSkpGD69Om4+uqrPXO3p0+fjm+//dbnd9etW4f+/ft7fq6ursYTTzyBjRs3wm63Y+zYsVi4cCG6d+8e8XKrFAX3XpMDq8OJ6lpbp79gJaKWO/fUuVHgF2a/wptR3gRFgVEnIeU8IyoqauFwnLuw44gYIqLI4XUytWftMqj++9//jt69e+Ohhx5CUlISvv76azzyyCM4ceIE5s2b59nvggsuwIMPPuj1u+eff77Xz/Pnz8eBAwfw2GOPQavV4tlnn8WsWbOwdu1aqFSxXz2KILSLCzp/5SSi8DQ+n3QaFQTBFf8qigKL3QlBUKBTxWZ/0J4oogiz3YmKY2dhqZMRr1d73bTgTQgiosgSBMCgU8OpKDhTXed1fdternup84n9qNGPFStWIDk52fPz6NGjcfbsWbz88suYM2cORNGVf81oNGLYsGEBj1NUVITNmzdjzZo1yMvLAwCkpqZiwoQJ2LBhAyZMmBDRcsuCgOVv7/BaAsc9rEUKo0OQ/axR25LjRUuwchJR8/g7n3LSTSgYm4anX98Gq032/Lzhm8O4o2BITPUH7YksCCirsODNjSVe+TBisZ8lImrvZEHA6vd2Ytyofij8qtSn3717ajbWfLAL3+w+6fU6+2OKBTGX/TsUDQNqt6ysLNTU1MBsNod8nE2bNsFoNGLMmDGe19LS0pCVlYVNmzZFpKxuip8LYcC1nunyd4uhCIFWPG2d40WLu5x7Dpfj2sszsGjmSDw0YwQm56Vhx4HTqDbb2rqIRDFFEQSYHU6crrHB7HB6ncuBzvsd+8tQ+FUpCvL7e/2ccl5iTPUH7YlTFLF9/2mfgBpw9bOrC3fBKYoBPysiIgqd+/stpVci1m89jMyUJM8146KZI5HeJwmr3t+FlF6JXr8Xa9e91Hm1yyfV/vzvf/9Djx49EB8f73nt22+/xbBhwyDLMnJycnDfffdhxIgRnu2lpaVITU31WkMbcAXWpaWlES2fxS77XAi7Fe0rg8Uuw6AK/R5HpI8XLRa7jD2Hy/HAzcNR+FWpJ/EE4Hq6NrR/N+hVke8IOTyI2qOmRp8EO+937C/DlPw0n5/f2lgSM/1Be2Kuc6Bros4TUOs0Egry+2NgShIcsoLepjgseydyI4+IiDoz9/fblPz+yOyb5HPNmJthwu0FQ+CsT1hWuOkgrDYZQGxd91LnFXNBdWJiIl599VVkZWWF/Dvbtm3DunXrvOZPjxgxAlOmTEG/fv1w6tQprFmzBrfddhtee+015ObmAgCqqqqQkJDgtwy7du1q8d+ianBym2uCP5E1Wx0wdtGFfOxIHy9qHAoWzxmDV9ft8Xnas2N/Gf72XjHuu3aYp65kBaitk2G22hGnV8OgkSA1M+auk5WAgYm2uQdrBZIkev0/1qhi+Esq1uuuOWQFPtNDgHN34e+emg2VJGHRzJGw2Z3QqEXsPVLhdWFhs3tnR3X/HDP9QRtrTluuraqDQ1Zw7eUZGJiSBJ1GBQUKduw/DUkS8NGWQ36fYC9/txj3XpPT7H6rM+hI52tbaq0+mZ9X6FhX4fF3nZwQp8FrH/teMxaVlGH1B64lDvcdqcADNw/3THkCOtf3HNtbcG1VPxEPqt9///2wfu/KK68EAKjVavzyl78M+fdOnDiBBQsWYOTIkZgxY4bn9Xvvvddrv4svvhiTJk3C8uXLsWrVqrDK2ByiKCApKc7zc609+FIACXEar/2bEunjhaPabENlTR1qLa4AODFeiwSDxrO97KwFawp3YPLYNGwP9lTdJuP87gkoO2vB828X+SxPc8+1uTB10YdcpqWvbwsYmDxw83CvMsYSozG0v7E1NW7HsSoW666hps4VAPjxRFXw0Sd1Ml768AevC42cdJPXhYVG7f0F4v65NfqDWNfctlxV54AxToOPthzyGWFz68RB+Ocn+/z+XtG+MlgdTpzf3feGLbnE+vkay9qiT+bnFTrWVej8XSfrNBI0KjHgNWPDEVgAUJDf3/Pvzvg9x/YWXGvXT8SD6oceeqjZvyMIgieobo6qqirMmjULXbp0wfPPP+9JUOaPwWDARRddhE8++cTzmtFoxIkTJ3z2raysRGJios/rzeF0KqiqOje/W6cSkZtp8goY3XIzTdCpRFRU1IZ8/Egfr7maehosK8Dz9U/drhjVL+ixasx2nC6v9ezfUNG+Mjz/VlHIT36qrLLfOnEfq7zSCkedvekDtSJJEmE06lFVZYEst+46jE19ATVux7GmLesuVKGOnKgxB2+Xpystfkd7AK4Li31HKrD3SIVnW066CXuPVLRKf9DWQrmQam5b1mlUeOHtHX7rvLzKGvR3q2ttHbq+w9Uezte2Fkt9Mj+v0LGuvIXTJ+tUIu6YMgSVtcFHYqrqnz42nPLUGb7nGmJ7Cy6S9dOcGzURD6o/++yzSB/SL6vVitmzZ6O6uhpvvvmm32HcTUlLS8PWrVuhKIrXvOpDhw4hIyOjxWVsuJ4pAMyZmo3l9Yvau+VmmnDXVdlQnAoczZyHF+h4c6ZmQ5GdcLSs+D4azlO2y06k903CnsPlXnNalr9b7Fm31R1ENH561pheq0JtnSPoU7raOkdIc2XM1uCBidlqhyEKc7gjQZadPm0mFsRimRqqNttQUWt3fbYxNn9eEQSs/mAX0vsmYfLYNK9h26sLd2HWlKGw2hwwWx3QaYMvMxeo1e7YX4Zpl6Ujs28Snn59GwB4Zf+OVn/QHjWnLVsdzoBPS5rKh2PQqSJ63nS0HBGx2te1F61dd/y8Qse6ap7GdZWZkgxHE0FQQty5UV42uzOq172xju0tuNaun4gH1b179470IX04HA7Mnz8fpaWleOONN9CjR48mf8dsNuPLL7/E0KFDPa/l5+dj+fLl2Lp1Ky688EIAroB69+7duOOOOyJebkEQcPvkwai93AG9VgW1SsR3u3/GfX/5Elmpyc1OcCMpiieAjfbFVqBlfBrPaXEnizBbz3Vte49UICfd5PPEB3DdBIjTSiirDP7kx2x1nFsHNgiDLniTbmo7tS91suIa7u/nxlIsJIuyOmTP0iCNhxAXjE3DqQozHl7xNQDgL/PzA58nGSavp9CNaVQikhK0+OPsC6HVSBAFAYKg4M6CIe06+GpLDfuwxtwjAAKNFNKrpYitV91elk4kImqpOpsdWo064HdhTroJsnyu3+tlisO8qdn8nqOY0C5nuD/++OP44osvcNddd6Gmpgbbt2/3/Gez2bBt2zbcddddWLt2Lf773/+isLAQN910E8rKyjB37lzPcXJzc5GXl4eHH34YH3/8MT7//HPce++9yMzMxLhx4yJaZocg4IV3duCeZ77EQ8s2476/fIlV7+/EqCG9kBivCXtJAEFRYFCJ6BavgUElRqVjCXUZHzd3gO9WuOkgCsamISfd5LVfbqYJ916bC0mIXDCsV0vIzTT53ea52KUOwdMu9/lOGYiV5TUURfBZaxM4d+7EGzRYNHMkrr08A7sPncbsq4b6nCc56SbMnpqNwk0HA76PQadCglbCwH7J6KJXwaASoJei0x90FsH6nMJNB3HHFP+f1bRLMwKOKmiu9rJ0IhFRiwkCjHE6VNfW+b1mdN+MrqlfijU304Q4TfsetUMdS6s8tisrK8M777yD3bt3o7q62pMO300QBLzyyishH2/Lli0AgCVLlvhs++yzz2AymWC327F06VKcPXsWer0eubm5ePzxx5Gdne21/7PPPosnnngCixYtgsPhQF5eHhYuXAiVKnJV4xRFrHhnB/bWr9U8MCXJMwx058HTeOjWX2LBX/7T5ksCuIcYWuocSDBo4JAVmK126LSqoMv43DppEIBzyxu4n5i7n+RYbTKefn0bCvL7Y0p+GhQAPZIMiNNK6NZFj4qKWq/9G2vOkx9BUYIOi2fn23HE9LJyggCbU4FTUTB+dD9MyU/zydS9Y3+ZJ/jKTElCjyQD9h0px6C0ZEzJ9x4qvufQGQzsl+x3ODJvFkWHTqPCsHQTtje6IaLTSLhjyhCYLTZMH5+FWycNQnWtDQ7Zib1HKvCHNf/FM/eOjUjbi+k2TkQUQbIg4HSFGeY6GQat5Ld/3fDfw0jplchrOopJUQ+q9+7dixkzZsBqtSI1NRUlJSUYMGAAqqqqcPLkSfTt2xc9e/Zs1jE///zzJvdZs2ZNSMdKSEjA4sWLsXjx4maVoTksNgcOHa/E4jljYLY6UGO2ey6WDx2rxJD+3aDTSLDa5JCHObdE4/l5BrUEGcDytcWeNaVfX7/P83TtoRkjgh7v5BmzZ3mDDd8c9gxBbxjcWm0y3tpY4jVssWHisUgGw605LJ7aTrDhue7t0T6XGlIEAVaHDEkUUV5pxZsbS4Jm6tZpJNjr547Z7U6cqjDDISvI6JOEJ1/9zhN8A65A7s/35WPl+7u8jul9fvCpZSTVORy4++psFB84ja6JOtjsTmg1Erom6vDGx3vwwts7PPu6n6C4b5pEqu3FWhsnIoqWOrsDifFaHCsrh5ioQ1Wt61r5TKUV3brocehYJW4vGAzZ6cQVI/rwmo5iTtSD6j//+c8wGAx4//33odPpcOGFF+Lhhx/G6NGj8fHHH+Oxxx7DM888E+1itCmL1YFHZo7CKx/t8bnILhibhpc/3IWrLhmAf36yL+pzfv3Nz5s3LQdbdhxHUUkZrr08w2e4alOJxjRq0bP/vGk5EOpHIogAZk4egtrLXU+7tWoRGlEI+MQ5ksGwe1i854KTnW+HEyvz5xVBgNnuxKmKWiQlaHHwWAU2bz8eMFP31EsGAADGZPfC6g92+eQpuO7yDEy9ZAD+0WDJJqtNRq3Fjnum5XgSm/FmUXSpRBEV1VZsKT7uNUIgN8OEyWPTsOPAaa9RB8C55V0i1fZipY0TEUWfgMoaGzbv8P7+zM0wYdaVQ3HDrzNRWW1FklHH7z2KSVEfN/b999/juuuuQ69evTxLXin1J8P48eMxefJkPPXUU9EuRpsKtJC9e15lynmJyM0wNTmMUxEEmB1OnK6xwexwNns+XaD5eclGnee1gSlJPuV0Jxrzx71sj/vvqbO5nqzIgoAX1hZj3jNf4MH6OeQvvr8TMgAIAiyyE1VWGYd/rsSJs1bP39Mac8SpY4jk/Plwzy13O7/nmS/wxzXf4Gx1HboadX4TrADAviPlGJPdC0kJOqx8f5ffPAVvbizB8Czf5Is6rQqi08nzo5UoCvCvT0t8htwXlZThw69KcVX9zRG3HfvLMDAlyavttbTPZo4IIuosVKLgM8ILcPW5L763E6U/VUKtVsEh83uPYlPUb3M7nU5069YNgGtdaEmScPbsWc/2zMxMrF27NtrFaFMOOfDSLO519pxOBB3m3NIMsIogoNbmOz9Pp5EgSefurdjsvqnnCzcdxAM3D/eU1839pN29jA/gGo6oM+qw/B3/a04vf7cY+Tm9YYzX+jwRb+7fw+HdnVukpgw099xSRBF22Qmbw4mysxZMHpuG9L5JKNx0EHaHM+igiIL8/lj1wS4UjE0LGHi7Xh/k9VpOugkGrQpwcumM1mILsqRWUUkZbhiXiX82GE0AuALxO68cCkFRIpK1mzkiiKhTEATU2RxBvxen5Kfh1XV7MGvKkFYuHFFooh5Un3/++fjpp58AAKIo4vzzz8fWrVsxYcIEAK4n2eGsMd2eWJqYF2ezO9E1UQp4odVUBtiGywn4CzYVAMvWFuOKUf28fl+nkfDAzcMhieeenvgb6t0w0djMgsE4XlbrmRPecDktANDr1DhRbg6aXOeGX2XijU/2+d6N9PP3+MMlZshNJQq4o2AIasyhTTFoLNRzSxEE1MkyVKKE0xUWr7vp7sRVz9w7FooClJ21BHy/gSlJeGtjCX7d6FxszFp3rs/ISTdh7jXZEBlQt6qm5jP7e1qSYFDD6XRCUUkh99lugW4UMkcEEXV0NqcCS50cfB+7Ezv2l8HJHCIUo6IeVOfl5WH9+vVYsGABAOCGG27AkiVLcPToUSiKgm+//Ra33XZbtIvRpvTa4NUcb1BDG2QYX6gZYBsHm13iNXhwxnDE6TW45rIMJBjUWDRzJA7/XIl+5yXCGKfF6+v3IKNvkmdNwEBrSlttMvYdqcCoIT3xyTeHA2bp3nu4HAmG4IlzHLIS8G5kUxltm3ODgTo2WRCw/O0d/m+uhHiMps4tq0OGRq3CqQozJFGEQQccO12DIz9XejL56zQqKIqCE+UWVNXWQZaVgGtsuptmU3kKEuLUeGJOHuL0Khjqh31T69JrfVuRTiOhIL8/BqYkQRQFLJ1/ERQo2LbnJPb/eBY/nqxG7oBuzc7a3dSNQuaIIKKOrM4uI07vulZu2M82XAWja6IWOo0Ei9UBg0YLkf0gxZioB9V33XUXJk6cCLvdDrVajVtuuQVmsxkbNmyAKIqYM2cOZs+eHe1itKlAS7MArqdQpi56iIIAKL4XzoogoNbSdAZYfYLWJ6D+091jsKpRtuCcdBPuvjobp86YYbPLKBibhv0/ncWVF7nWmm5qqLdGLWL2Vdl48T3v4Yg56SbMmjIUv3n2P/jd9OHB66OJmwzBMtpyiRkCIndzJdjTSJ1GgiiKWPbODq9hwCMH9cBT94zFirXFeGtjied1dwIrSRRx3eUZAHzPoWSjDgAC3rwCXMGURiWih7H+HGBA3Sa0asnrM3KP7Cn8qtTrc3cnlxs7rDek+rwQzcnazRuFRNTZ1dlkJBt1GDm4B8aN7Oe3nx2T3QsPzhgBtVrE9gOnkTugG/tGiilRD6oTExORmJjo+VkQBMyZMwdz5syJ9lvHjDqHA3dcOQSr39/lFVjnZpow+8psOGQZTgUwqCXPEL84vQpqlYQVa4sxeWxa0OMbdCpY7DL2NFgHu0eywWf5HcB1ke8+5qJVWwG4Oqv087tgcFoybpk4CKfPWnDrpCyopSGwO5yw1DmgUokoKjkFURTw5KvfoSC/PybnudacTjbq8O3uE/jpVA2sNjlowOBKeObdCTa+K6nXqjxJyxrjEjMERO7mSrDsyVddMgAr39vpM682pVcilr1T7DeZCgDMmDgIFdV1uG3SIIiCgOOnz02X+HbPCeSkmwLevMrNdAVoaiH0IewUJQK8bo4U5Pf3yQPh3gYAY4f1Qs4AE6AozcrazRuFRNTZGeO0MNfZcfvkIVi+1vf7dcf+Mrz04Q/Iy+mFXt3ikGzUsW+kmBP1oHrGjBm4++67MXr0aL/b//vf/2L58uV49dVXo12UNqM4gVfX7UZGShIK8tO8hrOsKdyFGRMGodZqx0uFP3gurq69PAP7jlRgx/4ypDcYnt1YbqYJOo0KFdVWr3WwZWfgIdY79pfh1kmDvH4WBGDGhCwAwF/+8T/PE5nGyxpcNKw3dBrJ6w7isAwTMvomQVW/8HSwgGHapRnYtuek5+8J9PQn0BxpLjFDQORurrizKzccdeG+yTN6yHk+iaiAc/Oi/SkqKcPksVZ8sKkUBWPTIDsVLHn1O69jP3DzcBQCnjwFU/LToCiAKUmPQ8cr0c2oY0AdA2rMNnRL1GHssF6Ykp+GLgm6gJ+7O4mO1S4jTiX4bVdunqzd9Z8xbxQSUWfnkJ0ABJyutDSZrMzmkGGzO9k3UsyJegTy7bffYtq0aQG3l5eX47vvvgu4vSNQAHy35yS+23PS7/bpE7Kg00peTysaXrgXbjqIB2eMQF5OL3RN1HmC8vIqK3IGdMPf//0Dbh6fhb81eKr2/+4eE7RMjZOnbS8pQ8HYNOw9UoGZBUP8PpEpKinDind3YtEdo/Dw8i2eBGUNfzc3w4SikjKvgMFmdyLeoEbPZAOgKDj401kU1D99z0xJ8v9eAYY+NudilTquSN1caZhdec+hcky9ZADGZPfCqg92YViAZeT8ZchvSCWJ2HekHIUAbpmY5bWtYdK/KflpMOjUMOhU0KgkOBUZQ/olczhbjEiM06Kiyoqsfsk4WW6Gpa7phJNmix1xCZpmZe3mjUIi6uzMVley0epae9D9XP2sA8lGrWcONlGsaJUWKQRZm/PIkSOIi4trjWK0maYuxqx1DqgaJS6yO7wv3BVFwZbi49heUuZ5kpY9oBtOV1ox7fIM7Co9g4Kxafj1qH7QqEXEN9HZ6P1cqNnsThRuOogn5uRh2Ts7/P7ejv1lEDAIf5h9IbbtOYnCTQdhtbnuGh46Vombx2fBqbj2c98UaJy9+M6CIbA6ZMyaMgRORQn81M/P0EcuMUNAZG+uSIqCe6ZmQwZwutKCiuo6/HpUP8Tr1X73byrJmCgKeODm4Xj69W1wOJw+o0ysNhlvbSxBbqYJc6/OqT8vFEAQeUMohjgVBQ4ncOKMGX986Rssmjky6P4atQhdg+RmoWbt5o1CIurs4vQq1FgcTX6/atQi9DoV9h2pQE56t5jvG7n8a+cSlaD6vffew3vvvef5ecWKFXjrrbd89quursa+ffuQn58fjWLEDIPO/8W5m16nhii4hoa6n/4mJWg92wvy++ODTaWe4dIPzhiB02ctsNll2B2up8CiIGD/T2eRfn4X2OxOqCTR89S4sZx0E85UWn1e79HVgIL8/ig7aw5a3pPlZix59TvkpJs8wUO8QY3bJg/GG+v3IDMlyesJtUGngkOWoRJdnaWgKNBLIlQqEaeq6oK+l7/hPVxihiJ2c6V+7rJTEGCpc0BRBMTrVbDaZGzbe8LvtIugScYyTCg+cBr7jlSgIL8/HE4FMyZk4bV18MmnMGcql8mKZU4FeHNjCabkp0GnkZBs1AUMfnPSTSivsqJXN+8bxKFk7eaNQiLq7HRqFWQnsOvg6ZD62TfW78HXOxNjOpEjl3/tfKISVFssFlRUVHh+rq2thSj63n0yGAy4/vrrMXfu3GgUI2aoJSFo4i61JODF93fiqksGeOZwqlQiRmT1QGrvRIweeh7SeiViSn4aBAB6jYTNO457ZaV99I5R+Gr7Mc/v6zQSFs0cBQjwzdJ95RAsXLHFpxxbd/6MfUcqMHrIeUH/HvedRPf73zFlCCQRKKuwoO95RqSf3wUOWUH3ZD0cDifKKizo2TUOWq1v8rG4AE8D3QINfeQSMyQpCuZdkwNznQO1ZgcMIS4/5blzXOdAlzgNBEHA9pIyz9QKq82Bs9VWjMnujQsG9sAbH+/1mrpx6FglZl05xG9m/ZvHZ+H3K1xTI1zDu1U4W1WHSWPTMO3ydKgk0TXkWyNBYEAd02x2GTv2lyEzJQnzrsmBQavCrRMHA8oPPhdJ112egeQEnWsVByg+/VFTTyt4o5CIOjO77Po+HDmkJ8bk9PZZYWZYugl3XjUUGkmATXbinmtz8fTr22I2WRlXdeicohJU33jjjbjxxhsBAJdeeil+//vf47LLLovGW7ULVbU2zxzixhfhV17UHw5ZweQ81/zKX2b1hAIFdXUO3DJpEFa9v8sngde0SzOw70i557UbxmXgWFkNJo9NwxX1w7/3HqnAU699h5vHZ+G2SYNRY7a71rzVqVF21oIFN/4CiXEaOGQFdrsDSYl6PLrya5wst+DAT2eD3ince+TcDZMd+8twx5QhUKsEzP/Lf2C1yZ5kTK98tMcnUVnjO3SJ8VoOfaSwBF2nOkCbkUURJ8vNqLXY0csUB0kUYXU4PVMr3HLSTejVLR6nz5ox68qhmDExC9W1dhh0Kug0Et78dJ/fERl/WP1fz2gTRQG0KgkGnco3SGJAHfMsdTK6xGswJC0ZSUY9zlRZoSjAxLw0TMnvD6tNRrxBjcQ4DQxaFcw2O6prHRAEBaZEvWcN1VCfVvBGIRF1VnV2GU5FASDi1fW7MXFMGmZMGASz1YF4vQo6jQprvyjB1ZekQ3YC67ceRkF+/5hNVsZVHTqnqM+p/vzzz6P9FjFPq5GwaOVWTL1kAG6ZmAVBECCJAhTFNd+6srYOe49UeOYn52aYcPfVOVi+doffBF5Op2tI+FsbS9AlXoORQ3phRYMlCHQaCTMLhuDRO0bjVIUZAoA9h8/ggszuPmvu5maacPvkIaiotGDRzFH4/YotWFO4C3+efxFWvrfT71rVT7++zatMZRVmmLrokZWajKJ9gZee8XeHLsGg4dBHajZFELD6g11I75uEyWO9M+qvLtyFOwuGQFAUzxNCS50DCXFa/O2dHdh7uBw3XJGB1F5GFO0vw1fbj2PfkXPL0bmPdeJMLQanJWPZOzt8bg7dNnEwnFBw4rQZGrWI4gOnPeevW9cuOmhUIroxSGqXEuLU+H93j8GrH+1GSq9ET9tIMKih16nw7Or/4myNDbkZJsyemg21JOHhFf9BbqYJ116WgR6JOjgFASfLzbhiVD9Mrk/mWLjpIJ9WEBE1YK2TkRivwcsf/oBfX5iK02ctEAVXrp86mwNnKq24+pJ0OJwKzBYbBvZLQmqvRGg1EswOZ8yN7OGqDp1Tq6XO+/bbb/Hll1/i+PHjAIBevXrh4osvxi9/+cvWKkKbidOpMTyrO34xsAf+tWEfrhjVzyfobDg/uaikDKcqzD7r47q5lxUAgHnX5uLFd70DavcSVQ2TjeVmmpDVryv2Hi73OlbRvjKsdu5CZkoS9h2pwJK5efjxZA3sdhmTx6bVzydUwWpzYO+RCjz9+javwMFNEARPcBx0ySE/d+i0ksChj9QsVoeMcfXnUcO25r7xUyfLUEuS5wmhe4m6HklazPvtJXDITs967fuOlPtd1m1Yhgk9u8Z5jQoB3NMpfsC1l6Xj462HAy51F69Tc4h3O6aRJKz+YGfAdvZI/U3IopIy2OyyZ0nBon1lgALMuToHy9/xHknh1c/zaQUREQAg3qCG3eFE/z5doFV7T3EEXH1nb1M8uibqkBCnxQWZPVBeZcWW4p89iT9jaa4yV3XonKL+qdpsNtx///3YuHEjFEWB0WgEAFRVVeHll1/Gr371K/z5z3+GWh18bm175nQ6cduEQThVacGtkwZj1fs7fS7E3T+7n0DXmIMvK2B3OHHt5RnonqTHnsPluPGKTIwY1ANxWjUsNhm3TBzkyiquElFUcgrvfXEAb24s8Ry/8XtPyU/DWxtL8OL7OzGwXzIW//1bT4Cyq/Q0figtDzgnfO+RCsTr1TCoNJg3NRunw0g+xqGP1ByKIvgdDeH+ec7V2dhx4LRnSkTPrgb06R6HwSnJkAHY6s+feL0af56fjzc+3utzrO0lZVAU+D1nivaV4YZxmbj76myfuV+5mSbczSRk7Z7V7kBKr0QUflWKIz9XYuHtI9E1UQez1YE4nQqWOjseuuWXWPLKtzBbXUu8uJNNFpWUocps8xn+17if59MKIiJAq5ZwpsqK4Vk98M5n+zF5bBpunTTI09+errTi35tLMWNCFhyyE3V2GcmJOhRuOggg9uYqc1WHzinqQfWyZcvw6aef4vbbb8ftt9+Obt26AQDOnDmDl156CWvWrMGyZcswf/78aBelzUgAHAD+9el+TMlPCzjPouET6KaWFTivWxxUKhF1NgeWzBsDvcb1UcqK4pqTbZM9AXXpT2fx5/suwpbiY8jok+T3eO61d4v2lWHG+EF474sDngvAQWnJfueE52aYMCnPNRw8f1gvAK7guKm1A3mHjlrKqSh+b/IAwL4j5VAAfLXd+073LRMyoaQkey1xpwCQZQUzJg7E7kNncLbG5nWshudkY9VmO/RaFW66IgszJgxCrcWOxHgtk5B1ELUWBwamJOE/3x/FU/PGwi47YZedkEQBoigAENC7qx4P3/JL6LUSTlVYPE+hrTYZdX5G9ADebYp9IRER4HDK0GtVEAQFt08aBLtTgd3uhCi4RkLG6VS4ZWIW7LIMAQIMOgnllVavkZPRHP0TKNlkoNeF+qU6a20yFAC1Fjvi9GoIAOI0DKg7qqh/o3/44Ye46qqr8Lvf/c7r9a5du+KBBx7AmTNnUFhY2KGDaqco4h8f78EtE7OgkkT8v7vHeO68vfBWkdeFvDu43XukwucuV5d4DeZdm4tuiTrUWuwwddHDoFXB6VRQXmX1LAHTeP7njAlZkJ1O/HJQT1htMp66Z6zn6bW7Q2oYxJdXWT1PUtwXgE+9tg0F+f1x66RBOHnGjHiDGlabjKdf34as1GSoJBGK4MruzTt0FG3WIGu/X3XJAK8pEX17xOOhW0dAcQI2WcHLH/7gMyR3zjXZePjWEaixOLzyGwDnzsnGuiXqAQAOhxMatQo9kw2up9MMqDsEg861tNqfZl+I05VWvLmxxCcfxZ1ThsJgkKDXqvDzaTM+rk+e89bGEui0gb9ebXYn+0IionqSKEGtkqEWJdhlBS++v9O7v81wZf/Wq9VY+cFOzCwYgniD7whXS50DerU2otMJ/SWbHDm4B2YWDMGKIEkoHQBWF/7gM4x97jXZrTf3llpV1D/XsrIyZGdnB9yenZ2Njz76KNrFaFM2hwM3XDHQK5kY4Dq5/nT3GCxcscUTWLuD20PHKnHzr7PgdLqebHSJ1+BPd4/xWcYnN9OEWycOxvHTtfhP0TG/ycEA4MKhvbznWGeY8Lvpw/HUa9uQmZLsldFbEICBKeeeaLuWGZKxr36ffUcqUDA2Dc+87vrdmQVD8NCyzUjtnejpTJh8jKLJnYW7IL+/V3Kx/T+dxchBPfHPT/ZBp5Fwy6SB+EVmT6xYW4z0vq68Af6GjC9/xzW/+k8vfeM179Vqk/2OGsnNNOHwz5UYktoVce674gymOxSDRoTeFIedB05j0/ZjPjkuivaVYeUHOzFnajas9e1vx/4y3DppEExd9BCEwP1cvEHNvpCIqJ6lzgGDVoWKaiv+8ck+3/62pAwr33f1t2nnd8GKd4tx+6TBXvvoNBIS4rR4IYJrQwdaGivlvEQse6fY7zX38neLMffqHCx72zfZ8I79ZVj2TjHumZYT+vKfzDUUFdGo36gH1T179sS3336LG264we/27777Dj179ox2MdqUWpKw6v1dXkvwuDMVv/Lv3Zh3ba7nYn7vkQrkpJswYUwqdpWWYc7VQwEIEARgxdpiv1mK9x+tQN+eCQGHwxbtK8PkPO8hrEUlZVAAzCwYgmSjzpPR212G/r0TPftq1CJyM0yYOWUIHA4nRg3pidNnrfjd9OGev2HcqH54a2OJZ04L112laDKoXWuzv7mxxGu+88jBPTBsgAk9kvVYfOdoOEURq9/fifS+SRg99LyACfTcwZD734Br3uv+oxU4U2n12ted3dmUqOMw745MEFFWYUZqr0Q899Z2v7sU7SuD1S7DXD/CAQBOnjHj653Hkdk3yTPHuqHcTNO5UQ1ERIRaiyuPkE6rCroUldXuRG66Cf/8ZB8wyXv7HVOG4MV3I7s2dKClsZpKyGuucwS8Jt+x37U9Psg0z1CXYqTwRKt+ox5UX3nllXj++eeRkJCAW2+9FSkpKRAEAYcPH8Yrr7yC9evX45577ol2MdpUnV3Gr0f7ZpDNzTDh5vFZUKkE5GaYMOvKoThVbsaQ1K7o2dUAU5IEURRhqXPAWufAjIlZ0KlVePH9nT6ZaG8clxm0DP6GsG4vKcON4zKxaOVWWG2y15JZf7hztKeMXRK0SO+bhN/+dRN+N304/rDmG59jjb+wHwDvOS1MPkbRoggCPtpcikl5abhl4rlkJjqNBJWg4Im7xsDuVACnE7dNHoIX3yv2ulHkj6XBEhg79pdh2mXpGJiShAM/ncWimSOhAOieZIBGJUItCnwy3cFZbK5l2E5VmIPuZ7Y4oNNKkCQBOo0EjVqszwD+A2YWDPFZhWEOk9gREXmJN6hhrbPDqQhB9zNb7JCdrmvJhstW5WaakJmSjBfe3uH398Kdbx1oaaxA08Lc3DcJAm93IF7tP0lloKfjDW8OUPhCqd9wHwBGPai+6667cPToUbz11lt4++23IYquBu10OqEoCq666ircdddd0S5G21Lgf93m+uzCd1+djSEDusIhy+jdzQDUB9IWq4BKpxm7Ss8go08STp+w+CwzAJwLAIIJlPhMUYAFN1wAtUr0LJmVmZIMlcr1dHry2DQ8vHxLk/NLG77OjLYUbVaHA9dfMdBnOsTEMSmYelE67IoCi82BGrMdAoD0vklIMmqDHjNe7z0/Sy2JSIhTQ6PuisR4LfQaCSLgCqadvEHU0QkA1hTuwuSx/hPVuel1KjhkJ3aXlmPRzFHQ6yToNBKKSlwZ4p+7/2JY6zhah4goEJ1GBb1GhRPlwW9iuvpbVx+q00p4aMYI9DLFIU4j4Ux18JVnaswOwNC8fjhQMsmmkgnH6YOvaBQsoW+gp+PAuZsDaomzssMVSv2Gm+wu6p/KyZMn8eijj+LWW2/Fpk2bcOzYMQBA7969kZ+fj5SUFJw4cQK9evWKdlHajAIEHAayfX8ZTldaMGpwT+jVKjgUxWcexrAME4amdYOpiz7gcYoPnEZuhslvQxmWYfKaM92QSiXiiVe+8/zsflrtcDgxMDUZT73mvS51oI6k4evMaEvRJgkSVrznPZ+pR7IeV+UPQJ3DiZWNkpzkpJtw2fA+yEk3BVwazpXR+Ry9TgWtWoKuiwRVfRI+jrboPBQAew6XY/qErKDtRqMS8d9dP2PH/jKIAjB2WG9PsrLyqjokG4FuHK1DRBSQ1eYAIOCHQ2cCXsvmZpqgUUtwyHZXoketChq1iFqLHaIAGOM0fqfcuJnr7Hho+eZmDfMNlHjXPVWz8feCTiPhjilDIIoC/vqbiyEIrgdNOo3kSU6ccl4iDFpVwNFugZ6ON9xu5HV22EKp33AfDEb9U7nsssvw9NNPY9KkSRg4cKDP9nXr1uH+++/Hnj17ol2UNmMJkqkYcD0R06okOAGcqrBg/Oh+mJKf5slC7F4vd/r4rIDHKNx0EM/cl+83kdm1l2Xg8dX/9fmd3EwTqmrqsGjmSK953k+/vg1L5uW55qw04J5v3VjD15nRllqDzSF7tfO+PeLx2MxRkBVXJvxfj+qHgrHnzqEd+8vwUuEuzJiQhVfXwSdhYMHYNPx8ptbrNb1WBUFRIAoih3p3QmaLAzeMy8CR41W4deIglFdZIQjwtKnMlGTMmJCFqlob3v3iAADX6KPJY9PQJcE1KkKjFnmTkYioCWaLHQoExOkk3DZ5MK6utaHGbPdclx46VomrLh4AWXbiyM9VuPPKofih9Az++uZ2zzFyM01YNHMU/rDmv7DaZK9kpooCxBs0uPbyDBRuOhjyMF+hPvHu6sJdSDkv0ZPPyBinwWXD+2D1B7vw3Z6TAFwB9aKZo/DBfw4gKUHnM0I1N9OEJfPGoqzcgjq7DL3kf6h7U98Z/E5pmWjWb9Q/GUVRoARptHa73TMkvKPSN1papUu8Br+58QJ0TzLA5pAhiiIcCvDi2h0+S/24sxA3TKTkj9Umw2K145aJWQAGwWaXIYqAQatCjdmOzJRkn5P7xnED8ciLX/vc1ctJN0EtiT6vzbpyCF75926f193zsDtjdu+2zM7o970BmDtBcriG85VW/O5CaFUG2J0K/tYoSUnDc+ib3Scx/sJUvwkDn359G343fTgAYFj9ElsS4Lo51AHrj5qWEK/CyCG98Ld3i/HXBonKcjNNWLrgIny98ziqa21ITtR59aHuqTC5mSaUV1mR0j2ebYiIKIjEeC0cThkjss7DineLfZYvnD4+C39Y/V/8YfaFyOqXDIdTQVKCzusY7qfJd0wZgtUf7MIDNw/3yWXU8Jog1GG+kqJ4ls9qfKwZE7IwcUwqrDYZ3ZP1eHXdHmT0TULhV6V+EwvvPHAaQ/ono7rWDn2Ap6EhLUtLYYvmsr9RCaprampQVVXl+fns2bM4fvy4z35VVVVYt24dTCZTNIoRsoMHD+JPf/oTioqKEBcXhylTpmD+/PnQaCIzL1gUXAm/9hwux5yrs5HVryusdgfO1tig16qglkSsKdzlM9ylYRbitzaWoLrWFnQY4nd7TnlO+L/+5iIAgCAIsDlk3DIxC6I4CHV1MuL0aujVEs7UWH2C7Zx0E667PAOSJOD/3X0hREGApc6V2Xbhii24eXwWbpk0CFAAp6LAUueATqPCswsugkYUmmyIDQPBOL0aKrMNsgKYHc52EQi6y2+pcyUxerGNsjMGylw47dIMz13a1ixPa4s3qDFrShZGD+4NhwLU2hw4VWHB5LFpSO+b5FlnuvE5JDsVvxk7czNN6J5kwHP3XwyDVuVKJNXB6oyaRyOpsOydHdh72PfC6IfSMxiZ1RMV1XXQa7y/RjVqERq1hNlXZkMlImb7MiKiWOFwKq41qN/fiYy+SSgY633j+81P92Hy2LT6kZ8q7PuxAqnn+SYfLdpXhtsnD8Ez9+ZjTeEuvzmIANc1gb81rXUaFersDtRazl2PAvBZj9p9rFfXAZn1mcAXzRyJ7SVlKBibhsJNBwMG9YPTuiIxyPBi99Px4MvSBk/oRoGFVr/hiUpQ/fe//x3Lli0D4ArqFi9ejMWLF/vdV1EUzJ8/PxrFCEllZSVuueUW9OvXD88//zxOnjyJJUuWwGq1YtGiRRF5D7VKxMyCwbDLMvRaDZY3eiKdm2nC5Lw0FB847fPUeMf+MkzJdyXKcchOFNQnzWn81HnSmDTPsljDMkzYUvyzz4k8b1oODh4tx6oPduOPsy+EWiVi7LBeXk/tyqus6Jaog8Mpo0eSATsPnkZivBZpvRJx73W5qKypg0YlBl7wPkg9NA4E3UNl3v6spF0sG9Cw/NdenuF3zeNIZA9sSrDMhU7nuQCytcrTFuLUEkYN6oU6hxMvNpo/PazBGuzuwNp9DiUbdT43pjztTaj/kuJQb4Ir+/eh45X4f3ePwWsf7/HpT4f074beyQZ8vee41+vlVVb0NsVBIzFDPBFRSBTAapdxhZ+VcoZluEZEduuigyCIqDXbsOaDXVgyN8/voU6Vm5Fs1AVMRuW+JvC3pnXD0ZdWm4zcTBNunzykyWMB50YpSZKIgvz+fhMU79hfhlUf7MRdV2W7/ugAuCxtdEWrfqMSVI8ZMwYGgwGKouDpp5/GxIkTMXiw9yLtgiBAr9dj8ODBGDp0aDSKEZJ//etfqK2txQsvvIAuXboAAGRZxuOPP47Zs2ejR48eLX4PWVFg0Khgk8WAa+hBAa66ZIDPPGbAdaLmZppwptKKNYW7UJDfH1Py06AoriBBEICHlm32dACN51DnZppwR8EQLPzbFpwstyA304TEOA3WFO5CSq9EJBtdQ2g0agk56SZITickScIL7+zwKeu1l2fgy++PNTuY9BcIFuT3x5sbS9okMG2uxuVvao3ClmQPbEqwzIUNO/jWKk9bUAQBpyst+NenJV4BNeBaKk6A9/nkPocgALOvGgpZduJ0pQXduhhg0LiGzTMAooYsdQ4sumMUXvloj98LoxffLcaca3Lw+jpXG3OP8hEFoFuiju2JiChETkWBCAGFX5UG/E6/o2AIREmAuc4Bq00OmHBKEIDyKmvQ99OoJb/X441HtxXtK8OpvOAZyd3BtDthrzFO0+Q1os0uQ91EBnEuSxtd0ajfqATVubm5yM3NBQBYLBaMGzcOGRkZ0XirFtu0aRNGjx7tCagBYPz48Xj00UexZcsWTJ06tcXvoREl2GQFtVZH4DTu9cuv+Auq4w1qzL4qG5Y6O/5w52hU1C8bsO/HChz5uRK3ThqMx2eNhkGvhkYtosZsw9P35sNstcPucKL4wGnc/9dNrqA7w4SZBUOwqj65wje7T3q9V26myXP3JpwF7wMFb/6O15aBaXM1Ln9TaxRGc1mxpjIX+itbh1rmTBRhtsnQ69Qhn0/uc2hL8TGUHKnA7ZOHoG/3BAbTFJDRoMHpSmvAFReKSspgqXPg8VmjodepoFaJEAUBagG8+CEiagarzQG9VuUTULsVlZTBCQW1tXZPYly9n4RS7sS5A1OSgr6fMU4T8sOJpgZau4PpvUcqkJtpgiwr0DQx79lc50BcgHWqqf2KeqKyefPmRfstWqS0tBRXX32112tGoxEmkwmlpaUReQ8nFNTZHagLkObfzb32XkO5GSaIIiAJwKvr9vgd/2+TZdcavApwusKC42dqMTitK5LjtfiprAYDU5KQdv0F6NHVgK07f8bpCosnW2Fj7mA23AXvAwVv/o7XloFpczUuf1NrFEYzO2NTx/ZXtg6RLVIQAEGAQ1G8EpUF4j6fcjNd55CiOLHr4JlzQ72ZhIyCsMtO1JiDt7NaiwOCAOg1KghQICpKsBF9RETkh9GgwdkaW9B9zBZXLp7CTQeRk27ySYLccOg28vsHXZpLFIKHyg2vT93BcqDEVr26xeGpeXmI06twyQXno6LaCqMh+LVr4wTG1DF0+k+1qqoKRqPR5/XExERUVla26Niq+qesNRYHLHUydE2cRI1PsmEZJky7LAOmRB0EBZhzdQ6sdQ6Y61zj/9WSiLJyC5ISdbA7nDh+uhaSKOBUhQWrl/4Hv5s+HH9Y843neA/NGIG3NpbgoRkjgpbDNb/A/8L1TQeTas/f3fj1SB2rLTQuf6A1CgFXJxunVSHAagk+pPpM65IU2t8aJ4kBO3h/y541tzyNtfVnINfHvk5FgaIAy94pxm2TBzV591inVSEn3YRbJw6GRi1CEgTMuzoHooD6uhDQkZJ9NLcddUbNacs1VXVN9lFxehUEQYAkAiqhY7WnaGAbjYzW6pP5eYWOdRWehtfJem3wp7s6rQStWkBWv2RMykuDWhL9Lglrtcko3HQQf74vHysbLTObk27CXVdlwyEHf6jTsO8v3HQQz/7mYrz4np/EVldnQysKMHRxTaM8a7Gja6IetVZ70MTCoiC06Dxmewuureqn0wfV0SKKApKS4gAAx8pPI96ght3hDHqSadQinpqXh2qLHd276KFWiVBJIkQAChQ4FSecioId+8vw7hcHYLXJeGpeHk6Wm/GYn3WoGz8JdncSTV0oJsRpkBivbdaC94Crg0lO1CHBzx06ldnmc7xwj9UWGpffndkR8E0ad++1uejWRd/s9zAaQ/+de67NxfNvFfl08O7s35EoD+DdjttCWXktnE4FCgRYbDKcTlf719UPrQp2Phm0EuZNy0FFlRnxWj0EUYQp2dDaf0Kra0476kya25bPWlzTdYL1UQatCgIAU3LbnSPtEdto+NqiT+bnFTrWVegatuXjFWfQJV4b9Dtdq1bB5nBgYl4a1m89jCtG98OHfpKBAUBmSjK2FB9HZkoSpl2WDgDQqET8eLIaCXGu68pQH05kpSbDGKfBAzcPR2VNHWotdsTp1UiM1/pco1rlGlhtMtQq0W9iYffTdAiIyHnM9hZca9dPpw+qjUYjqqurfV6vrKxEYqJvuv5QOZ0KqqpcyQ3i9Gpo1BIOHD2L6y53zS33t4yVRiUCejW6GnWAU4Egup56VJgt+OPq73yGxuSkm3DkZDXKKix+y9AweB6Wca6TKK+yBh3KolOJcNTZ/aacP/JzJeZek40V/lLRX50NR50dFXX+h0w2Pl7hpoNYNHMURBHNPlZbaFh+q03G069vwx1ThuCOKUNgsdph0KkRp5UgKU5UVNSGfFxJEmE06lFVZYHcxN1TNxWAe6/JQW2dDHOD9waAZ+rn04dSnqY69YbtuLU5nIATgNkmQ1GAarMNQv2QLZUkAooS9HxSSyIUp4yuRgNcp4LSrM+lvQmnHXUUoVycNLctG7QqHD5WGfDC6O6p2RAB1NisqKgIcBDy0pnbaKhiqU/m5xU61pW35vbJcXo1NJIQ/BpZEvDjmTp8+t/DuG3yYJyusGDGhCy8us5/4Pr069uQmZKMYekm1NllfPjVQdwxZQgc9deV/q5xvYaQw/t6FADi1CLi1FoA8HuNqhYFKGoRdocTG745jMyUJK8VdvYeqcCGbw7jziuHtuh6hO0tuEjWT3NufghK40kJncxNN92ELl26eJYAA4Dq6mqMGDECixcvDjtRmSw7UV7uOmGcooj3/rMfV+YPwJlKK46eqkHXRJ3XMlY5A7qdW9JHUWBTHDh2ygpTkh4CgNfX78E3P5ybB52bYcKtkwajzmbHopX/9VmKKyfd5Fk7LzfTNdylqrYOeq0KBrUEGQi4RlvDpawarivdMOV8oNeb4v17aiQn6lBntaO2ztEulg0I9+8ORqUSkZQUh4qKWjgcrds5mkwJQbc3bMetRhShALDLCuwOGQ5ZgQLXMhk9uhqwYOl/MOeaIRie3gMygB0HTiPZGOB86kTzptuyHbW1ptoxEF5bdggCVr+/C/16J3rWqY43qNEj2QDJqQCwwOzQxExCxVjXmdtoqGKpT+bnFTrWlbfm9skOAfjP9z/h4gv6BPxOFxUZFgew53AFKqqt+Mcn+6DTSCjI74/sAd2gUYmuaYOSiKraOmg1ErRqCbJTAaBAp/K9Xmt8TedvnepmX+MJAhwKYLXJePmjH5q8zg4H21twkayfUNqy531b9E4dQH5+Pv72t795za1ev349RFHEmDFjIvIeIoAp+QPw7pcHMPWiAeiSoEWdXYa1ToZep0KvbnGQBAGPrfkvfjxZg8V3j0FVrQ1LXv0OgCtAnntNNqbk90dVrd1zt+sfn+zB7ZOHIHtAN3zbIIt3bqYJs6YMhUN24tJf9IFGBKAo6Bp3Lm28BIS0RluglPPhpqJv+HsqlYgEgwaOOnu7WTaASxxEnutLzQlznR1d4rRQnArOVFo9y609NGMEjHEaaNQizlRakZNuwkuFe5Ay0whTkgE56SZY6lxfgnF6Ffr2SIAIMKs3tYhTEGCps2LWVUPr25cd3bqoodeqYLZaEK/V4YV392Pe1Gz2A0RELSAJIi66oA8+33YUl47o6+lz4/Rq9OmRgM3bjyIv53wokNHbFI81hbsAuALX/Ucr8KsRfRoEqgq6NUxyK9Xnu/DTT/tc0zmd0Esi9C25xlMUqAQBBq0Ks68cijq7DEudjDi92rWEJ69NOqxOH1Rff/31eO211zB37lzMnj0bJ0+exFNPPYXrr78+ImtUAwCcMlSihKkXp+NkhRk15nOB8eFjlbh18mB8uf0n/HiyBgCgkgSvods79pdh2TvFnifPDVltTtwwLgO3Tx6MuvoAWZJEfLXjGN774gCyUpNdd8X8FIsBIrU1pyBg9+FypPftAmOcFsUHT0NRgM07jnuGc2nUIlSSgL1HKvDTyWrMunIIVr2/C4+u+QYF+f0xMCUJCoDuSQboNRJEfmFRBDgVQKvWYsf+Ms9TE0udA+VVVgxJ6wpZAe6emh2zo2qIiNoLwemEAAGX/KIPTpafu04uKinDkeOupWMVRYEAAT276PDMvWNje3SjokCEAo0AaDQSEjT1V+G8PunQOn1QnZiYiFdeeQV//OMfMXfuXMTFxeGaa67BggULIvYeTlHCT6fO4nxTIiQR6NHVAIvVgdFDz8OwjG4oq7DgrY37AbiGdf90qgYnyr3nTDVeN6/h67dOHIS/vbfTs76fTiNhZsEQPDEnD6cqzDh11oIeSQYGGxRbBAFOxYmB/brCanOg1mJFZt8kCILguQsNuBLa9Uw24MjxSowb1Q//WL8Xk8em4dZJg2Cp/1KN06tdd6nZxilCRAE4W12HXqY46LVqWKwO6HUq6LQSqmrqkGzU4e/rduO2CYNi74KOiKidEQSgosoK0c91cnVtHWwOJxITtHAKAh8IUUzq9EE1APTv3x9///vfo3Z8m0NGt6QEvPj+LqTWz83TaVSotdhRfOA0CjcdhNUmY1iGCddengGrTcbK93f6HifAus5na+qQ0TcJBWPT4JAV9DbFYdUHu7DsnR2efdxDwp1OJ3QaFQNsaluCACcAh1PAi+/t8M6gnmHCAzcP91oa48EZI3DlxQPw3pcH0K9XIkQBOHnGjHiD2hVQC3A9WiSKkLO1deiSoMOOA6c9OTCsNodrCsKAbjhrrsPmHT/j+l9lck41EVFLCAIcihNJRh227/ff5z6y8mv85sZfQFGA+CZWsSFqCwyqW4EoiHjxvWLs2F+G7/a45j43TK7wi4HdoVG7Eips3nHMs1xWY/6WwtJpJHRP0uP9/1TgrY0luPbyDHy05ZDPEgNF+8rw4ns7kZmShH1HKjD3mmyoeHeP2oIgQIaAskoz3vx0v29bLSmDUwEK8vvjrY0lsNpkPPnqd5h6yQBcPy4ToijAYnXAlKSBQSuhe3J9Moo2+nOoY4rXa7HqA9eN0K6JOs/rZWctWPXBLswsGAIAriGI8bGx9B8RUXvkgABRELH6g11I6eW/z73ogj4w6FSwWByIV7PPpdjDoLoV2ByyT+Bgtcl4a2MJ3tpYgufuv9i1nBYE7Dlc7jegbrxuntsdU4Zg1QfnFrcf6GfetZt7CPlbG0uw7J1i3DMth0+sKeoaZtfU12fXPFluRpxe7Xd9ScB3uoPVJuMfn+zDnsPlmH3lUCTo1BCcTqiE1vorqLOxyzK+23PScyO0sRkTswAABh2/RomIWsJqc90W/2b3SXyz23+fO+Wi/lBLItS8iUkxiuMnWkGtJfhay2aLA2erbdCIrnXzcjNNXttz0k2YdeUQHDpW6fN6Zt8kz1xqIPAQ8cbbd+wvg7mOz/YoumRBwAtrizHvmS/xuxc2455nvkStxY4asx1ma/D213gcRW6ma21gjSQyeyZFXY05eL9da3YgN9MEvdpfGkgiIgpVrcXe5LWyWhJRVWuDXeYoS4pNvMXeCuL06qDbDfr6j6F+qauZk4fgVJ7Za7H4hSu2YNyofhh/YT/otCpY6xzYe6QCFdV1XsfyN0Q80HYzh9BQFCmCgOVri1FU4v00utbqgEYtNvmEz5Sox7O/uQjW+qUoNCrBtfY0A2pqBaH023OY/ZuIqMWa6m8BQKuRIAiAxWpHHJ9WUwxiUN0KDFoVctJNfoe65qSboNeq4FpKWoHFLqPW4nqCV3q80pPEDIBnWPfzv70ECQY1DDq1T2Cy90hFwPfKzfAeQu4J5okiTRBgdyqYPmEQpl3mgEoloqjkFN774gAMOhV27C9Dbka3oOfFqbMW9Eg2INkoAQqgYkBNrUivVWFEVg9PcsmGNzkPHauEXquCxPZIRNRieq0KioKA1wQjsnpAFAUI9ctNmx3O2FxKizo1RlWtwOm0YO412Vj2TrFXZ5GTbsLca7IhKU7I8H2ql5PunQUZcA2BNdQ/bRYFQFEEDMsweYaAF246iAduHg4APhmV75gyBPf/dZPn2DqNxKUIKLIEAXYFOHPWijc3lvi0wd9NH46K6jocOlaJS37RG7OvGooX39vpc17ceeUQ/HPDXu/lithWKcIazvdvvN6p4HRi1pVDsOydYq88Fe5+m1MQiIgiQ1IUQBAw55psLG90rTwiqwfumDIEf3vX+xo5N9OEOVOzXb9LFAMYVLcCp6LFV98fxbxpObDUOVBrcSBOr4Jeq8Kxshr07hbnd5isu1NxZ0F2dyAKgFUf7MK4Uf1g0EooGJsGQXBl+LbaZDz9+jbMLBiCWycOwqkKV0IoUQS2FB+H1SZ75mhX1dYh2cAhNBQZsiBgx4HTUBRg847jfrN6KwCG9u+KWyYNwt7DFVBLIuZcnY06m4xaqx0GnQpnKq14Y/1e3DJxEBQAzEVG0SD7mZ7gdZEmiljxzg6fdrxjfxlWvFuMuVfncOQEEVEkKApkRYFaFHH31KGoszthttoRp1NDq5Gw4t1ir/xBgOuad/m7xZjHaTgUIxhUtwK7rOCNDSV4Y4P/rNzP3X+xT0DttmN/GW6fPAj5w3q5nqIAeH5tMdL7JqHwq1JkpiTh0LFKDEvvhtsnD8apCgsEuIaBrynchcyUZFx3eQa6GnXI7t8NIxb0xJlKKxau2II/3XVh9P5o6lTc86cnj3Vl7A6U1Xt7SRkKxqbhH+v34rpfZUCrUeGlQtcSGgNTklBZY0O8QY3bJg3GH1/6L7p20fMLkyIu0Hz/hhdpFpsjYL9ctK8MFpsDcVyfmogoIqwOBU+/9l88cvtI7D9agWSjDpU1NvToavAJqN2K9pXBYpdhYF9MMYBBdSswN5HRsKmMh3U2Gd3iNYCiwOxwoqikDJPHupbG2nekHA/cPByFX5XivS8P4r7rctE1UY8LMtUYk30eVJKI//7wM97euN9rqS5P1loGKxQBFruMopIyXDGqX5P7Kgpw2Yi+ePCFzUiM12DhbSOhwHUedOuihl12YuGLW3Cy3IIfT9bwC5Mizt1e/XFfpIWS/TvOyJE+RESRUGux48eTNVj44tf43YwRUEsiai121PlZZrYhs9UBAxOXUQxgUN0KdNrgS640mWW2QTIy9zJE7qWx3MO9C/L7Y0p+GursTpyutKB7kgEGlQBZELDr4BmfgDrcrLXB5iBS59G4HbjbZVPZ5wGgaxcdVJKIP9w5ur5tC3jguU1+12cH+IVJkdfUcm5mqyP0VRuIiKjF3NfKJ8stuP/ZTZ7XF80cGfT3mlpJhKi1sCW2Aq1aCprlWKeRkJtpQtE+Pxm7Gz1RdnceDYMXq032SqQDAC/89mIAAiRFcQ1ljEAg3OQcxBZgsN5+uNvBnsPlKMjvj4EpSUgy6gC4ph2YuugDZ6DPNOH0WSvO66p3jb6AK4tnoIAa4BcmRV5TbcqgU0ETQr/NkT5ERJGhC9DnBl3VhqMuKYZwTGUr0IgCrrs8AznpJq/Xc9JNuO7yDKgAzJmajdxM7+3+nijr1a4A3N3J+OPpZOoJigKDSkS3eA0MKjHsJ9TB5iAqQvjppOpkBS+sLca8Z77E717YjHnPfIkX3i2G3IJjUnQoDQLqB24ejn1HKvCHNd9g686fkZNuQuGmg+jWRe+3vedmmDA5Lw2ffnMYOtW59ulu0/40bstEkRBKm1MJAmZfNdRvvz37qqGuJd6IiCgi1JLo99rh0LFKzL5qaEjXyERtSVAUtsZokGUnystrPT87BQHbD5xGslHnWe+0vMqKYQO6Qaz/CEJ9WisLAlYX7sK4kf1Q+FWp97JFUVpiwOxwYt4zXwbc/sJvL272vFeVSoRKq8bTr23zO78xN9PUKZJUqVQikpLiUFFRC4ejdbMJm0wJQbc3bsfudnDt5RnYd6TC0/Z0Gskzt3/fkXJMvWQAhmf1gAABdXYZoujK/l3601ncUTDEp33KgoDl7xZ7jdYIpS23Zd3Fms5cF021Y8C3LTfV5hwAzlbXwaEo0GvVsFgd0OtUsNTZoRIEdEnQcqhXM3XmNhqq5vbJ0cTPK3SsK2/h9MmAq1/evv80uiaeu1Y+U2lF9yQDyist6N0jHuWVdehlikOcpvOOaGR7Cy6S9RNKW/a8b4veiUImKgpyB3SDxe6E1eaATqNCSvd4rw7B/UTZM380QGchKQruLBgCq0PGrClD4FQUWOuiO2w6lDmI4cx7raypazJhEJNUxQ53OxiYkuQ15aDx3P44nRoalQidRgWNXUCtxYExQ8/D5Rec77d9RnKaAlEommpzFpuMRav+65niYKvvu/ceqUDhpoN4cl4eEjQcRUFEFCk2h4yysxZ0TdR5Xis7a8Gawl2w2mQsmjkSS179Dk/Ny0N8CDlciFoTg+pWJCgKjDoJKecZW3z3RFAU6CV3hyIgXh08EG+pUOYghqOpzOdMUhVb3J+zO1FeQw3n9j81L891M8TphF4SoW/iRhEQ+k0lokgJ1uasdbLffBUNtzOoJiKKnFqLI2CfC5y79mCuFYpFvM1DIYnWvNfmZD6ntuduB01l+ebnRu1dU31TU9uJOpKn//l9WxeBOoGmrh00apG5VihmMaimkAiKEnIyteZIjNcySVU74m4H5VXWkBPlEbVH+vqLN39cbZxfn9SJcOAQtYJgD3By0k0or7IyORnFLD5OopBFY95rgkGDOVOzAyYMYscZeyRFwQUDumFI/2548T1+btQxuW8gsW8iImodwfrdu67KhloAp4ZRzGJQTc0SjXmvWklgkqr2RlGgAfi5UYd27kbiuQSTenV4yxIStXdP//N7PHDDBW1dDOrgtJKAB24ejvJKK8xWu/e1BbteimEMqikmMElV+8TPjTq6SCaYJGqPlrz+P9c/2L1TK0kwaOCos8OgElwv8NqC2gFOCiMiIiIi/xrEM0xYRkTkn6AovP0TDYqiwOkMsM60JEKW+bQDYF24tVU9SFLw+2rB2nGsYBs6p7PWRVPtGGh5W+6sdRtprMfgYq1PliQRDy/f7PWaEGDfP8y+MPoFimFs2+e0tE9mXTaNdRRcpOonlLbsxqCaiIiIiIiIKEwc/k1EREREREQUJgbVRERERERERGFiUE1EREREREQUJgbVRERERERERGFiUE1EREREREQUJgbVRERERERERGFiUE1EREREREQUJgbVRERERERERGFiUE1EREREREQUJlVbF6CjkmUnystrfV4XRQHJyXEoL6+F06m0QcliB+vCpS3rwWRKCLo9UDuOFWxD53TmumiqHQMta8uduW4jifXYtFjqk/l5hY515a0lfTLrsmmso+AiWT+htGXP+7bonajZRFGAIAgQRaGti9LmWBcurIfwse7OYV1ED+s2MliP7Qs/r9CxriKHddk01lFwbVU/DKqJiIiIiIiIwsSgmoiIiIiIiChMDKpbmawAP52qxomzVpgdTigCh24QtSZFEGB2OHG6xsZzkJqkCAKqrDL2HSlHdZ3M9kJUj30pEdE5TFTWimRBwPK3d6CopMzzWm6mCXOmZkNSmGiAKNpkQcDytcU8BykkbC9E/vHcICLyxifVrUTx8wUEAEX7yrD83WLe4SWKMp6D1BxsL0T+8dwgIvLFoLqVWOyyzxeQW9G+MljsciuXiKhz4TlIzcH2QuQfzw0iIl8MqluJ2epo0XYiahmeg9QcbC9E/vHcICLyxaC6lRh0waevN7WdiFqG5yA1B9sLkX88N4iIfDGobiV6tYTcTJPfbbmZJujVUiuXiKhz4TlIzcH2QuQfzw0iIl8MqluJoCiYMzXb54vInS1TYLZMoqjiOUjNwfZC5B/PDSIiXzE9Rqe2thbjx4/HyZMn8c4772Do0KGebW+//TZWr16N48ePIzU1FQsWLMAll1zi9fvV1dV44oknsHHjRtjtdowdOxYLFy5E9+7dvfb7/vvv8eSTT2LPnj3o2rUrbrjhBsyaNQtChDNYSoqCe6/JgdXhRHWtDQadCnq1xC8golYiKQrmTc2GxS7DbHXwHKSgzrUXJ6w2B3QaFfRqke2FOj32pURE3mL6SfXy5cshy75ZJD/66CM88sgjGD9+PFatWoVhw4Zh3rx52L59u9d+8+fPx5YtW/DYY4/hmWeewaFDhzBr1iw4HOeSaBw5cgQzZ86EyWTCiy++iFtuuQXPPfccXnrppaj8TZIAnN89AT276GBQ8eKMqLUJigKDSkS3eA3PQWqSoCgw6iRkpiTDqGPQQOTGvpSI6JyYfVJ98OBB/OMf/8CDDz6IRx991Gvbc889h4kTJ2L+/PkAgFGjRqGkpATLli3DqlWrAABFRUXYvHkz1qxZg7y8PABAamoqJkyYgA0bNmDChAkAgDVr1iApKQl/+ctfoNFoMHr0aJSXl+Nvf/sbpk+fDo1G03p/NBEREREREbUrMfuk+k9/+hOuv/56pKamer1+9OhRHD58GOPHj/d6fcKECdi6dStsNhsAYNOmTTAajRgzZoxnn7S0NGRlZWHTpk2e1zZt2oTLLrvMK3ieMGECqqqqUFRUFI0/jYiIiIiIiDqImAyq169fj5KSEsydO9dnW2lpKQD4BNv9+/eH3W7H0aNHPfulpqb6zItOS0vzHMNsNuPnn39GWlqazz6CIHj2IyIiIiIiIvIn5oZ/WywWLFmyBAsWLEB8fLzP9srKSgCA0Wj0et39s3t7VVUVEhISfH4/MTERu3btAuBKZObvWBqNBnq93nOscKlUvvcsJEn0+n9nxrpwifV68NeOY0Ws111rYl00Ldy2zLqNDNZjZLRWn8zPK3Ssq/DwOjk8rKPg2qp+Yi6oXrFiBbp27Yqrr766rYvSIqIoICkpLuB2o1HfiqWJbawLl1ish6bacayIxbprK6wL/yLRllm3kcF6DF9b9Mn8vELHugodr5NbjnUUXGvXT0wF1ceOHcNLL72EZcuWeZ4im81mz/9ra2uRmJgIwPWU2WQ6t0ZiVVUVAHi2G41GnDhxwuc9KisrPfu4n2S738vNZrPBYrF49guH06mgqsrs87okiTAa9aiqskCWnWEfvyNgXbi0ZT00dXEWqB3HCrahczpzXYQSZLSkLXfmuo0k1mPTYqlP5ucVOtaVt5b0yazLprGOgotk/TTnJmZMBdU//fQT7HY77rzzTp9tM2bMQE5ODv785z8DcM2ZbjgXurS0FGq1Gn369AHgmhe9detWKIriNa/60KFDyMjIAAAYDAacd955PnOnDx06BEVRfOZaN5fDEfiDlGVn0O2dCevCJVbrIRbL1Fis1l1bYF0E1tJ6Yd1GBuuxZVq77vh5hY511Ty8Tm4Z1lFwrV0/MTUYPysrC6+++qrXf//3f/8HAHj88cfx6KOPok+fPujXrx/Wr1/v9bvr1q3D6NGjPVm88/PzUVlZia1bt3r2OXToEHbv3o38/HzPa/n5+fjss89gt9u9jmU0GpGbmxvNP5eIiIiIiIjauZh6Um00GjFy5Ei/2wYPHozBgwcDAO655x789re/Rd++fTFy5EisW7cOxcXFeP311z375+bmIi8vDw8//DAefPBBaLVaLF26FJmZmRg3bpxnv5kzZ+LDDz/E/fffjxtuuAElJSVYs2YNFixYwDWqiYiIiIiIKKiYCqpDNWnSJFgsFqxatQorV65EamoqXnjhBZ8ny88++yyeeOIJLFq0CA6HA3l5eVi4cCFUqnN/dkpKCtasWYMlS5bgzjvvRHJyMu69917cfvvtrf1nERERERERUTsjKIqitHUhOiJZdqK8vNbndZVKRFJSHCoqajv9PAjWhUtb1oPJ5LvsXEOB2nGsYBs6pzPXRVPtGGhZW+7MdRtJrMemxVKfzM8rdKwrby3pk1mXTWMdBRfJ+gmlLbvF1JxqIiIiIiIiovaEQTURERERERFRmBhUExEREREREYWJQTURERERERFRmBhUExEREREREYWJQTURERERERFRmBhUExEREREREYWJQTURERERERFRmBhUExEREREREYWJQTURERERERFRmBhUExEREREREYVJ1dID1NTU4Pjx46iqqoKiKD7bR4wY0dK3ICIiIiIiIopJYQfVFRUV+OMf/4gNGzZAlmWf7YqiQBAE7Nmzp0UFJCIiIiIiIopVYQfVjzzyCL744gtMnz4dw4cPh9FojGS5iIiIiIiIiGJe2EH1li1bcMstt+B3v/tdJMtDRERERERE1G6EnahMp9Ohd+/ekSwLERERERERUbsSdlBdUFCAjRs3RrIsRERERERERO1K2MO/r7jiCnz33XeYOXMmrrvuOvTs2ROSJPnsN3jw4BYVkIiIiIiIiChWhR1U33jjjZ5/f/311z7bmf2biIiIiIiIOrqwg+onnngikuUgIiIiIiIianfCDqqvuuqqSJaDiIiIiIiIqN0JO6huqLa2FidOnAAA9OzZE3FxcZE4LBEREREREVFMa1FQXVxcjKeffhrff/89nE4nAEAURfziF7/AAw88gKFDh0akkERERERERESxKOygeseOHZg+fTrUajWuueYa9O/fHwBw8OBBfPTRR7j55pvx2muvITs7O2KFJSIiIiIiIoolYQfVS5cuRY8ePfCPf/wDJpPJa9s999yDG264AUuXLsXLL78c8jH/85//YNWqVThw4ABqamrQo0cPXH755Zg3bx4SEhI8+33++ed49tlncejQIfTq1Qt33nknrr76aq9j2Ww2LF26FIWFhaitrUVubi4eeeQRpKWlee138OBB/OlPf0JRURHi4uIwZcoUzJ8/HxqNJoxaISIiIiIios5EDPcXd+zYgeuuu84noAaAbt264dprr8X27dubdcyzZ88iOzsbjz/+ONasWYPbbrsN77//Pu677z7PPtu2bcO8efMwbNgwrFq1CuPHj8fvf/97rF+/3utYf/rTn/D2229jwYIFeP7552Gz2XDrrbeiurras09lZSVuueUW2O12PP/881iwYAHeeustLFmypHmVQURERERERJ1S2E+qRVGELMsBtzudTohi82L2KVOmeP08cuRIaDQaPPLIIzh58iR69OiBFStWIDs7G3/4wx8AAKNGjcLRo0fx3HPP4de//jUA4MSJE3jnnXfw6KOP4pprrgEADB06FJdccgn+9a9/YdasWQCAf/3rX6itrcULL7yALl26AABkWcbjjz+O2bNno0ePHs0qPxEREREREXUuYT+pzs3NxRtvvIFjx475bDt+/Dj+8Y9/4IILLmhR4QB4gl273Q6bzYZvvvnGEzy7TZgwAQcPHsRPP/0EANi8eTOcTqfXfl26dMGYMWOwadMmz2ubNm3C6NGjPe8BAOPHj4fT6cSWLVtaXHYiIiIiIiLq2MJ+Uv2b3/wGN910E8aPH49f/epX6NevHwDg0KFD+OyzzyBJEu6///6wji3LMhwOBw4cOIBly5bh0ksvxfnnn48DBw7Abrf7zIt2J0krLS3F+eefj9LSUnTt2hWJiYk++73zzjuen0tLS33mYhuNRphMJpSWloZVdiIiIiIiIuo8wg6qBw0ahLfffhtLly7F559/DovFAgDQ6/UYO3Ys5s+fjwEDBoR17EsuuQQnT54EAIwdOxZ//vOfAbjmQAOuwLch98/u7VVVVV6JzRru597HvV/jYwFAYmKi137hUql8BwJIkuj1/86MdeES6/Xgrx3Hilivu9bEumhauG2ZdRsZrMfIaK0+mZ9X6FhX4eF1cnhYR8G1Vf20aJ3qAQMGYNmyZXA6nSgvLwcAJCcnN3sudWMrV66ExWLBgQMHsGLFCtx1113NyiIeC0RRQFJSXMDtRqO+FUsT21gXLrFYD02141gRi3XXVlgX/kWiLbNuI4P1GL626JP5eYWOdRU6Xie3HOsouNaunxYF1W6iKKJbt26ROBQAYODAgQBc87aHDh2KKVOm4NNPP/U8+W6YwRtwPXEG4BnubTQaUVNT43PcqqoqryHhRqPR51iA64l346HjzeV0KqiqMvu8LkkijEY9qqoskGVni96jvWNduLRlPTR1cRaoHccKtqFzOnNdhBJktKQtd+a6jSTWY9NiqU/m5xU61pW3lvTJrMumsY6Ci2T9NOcmZshB9QsvvABBEHD33XdDFEW88MILTf6OIAiYO3duyIXxJzMzE2q1Gj/++CMuvfRSqNVqlJaWYuzYsZ593POf3XOt09LScPr0aZ/guLS01Gs+dlpams/c6erqapSVlfnM2w6HwxH4g5RlZ9DtnQnrwiVW6yEWy9RYrNZdW2BdBNbSemHdRgbrsWVau+74eYWOddU8vE5uGdZRcK1dP80OqmfNmgWNRtNqQfWOHTtgt9tx/vnnQ6PRYOTIkfjkk09wyy23ePZZt24d+vfvj/PPPx8AkJeXB1EUsWHDBkybNg2A6+nz5s2bMWfOHM/v5efn429/+5vX3Or169dDFEWMGTOmReUmIiIiIiKiji/koHrv3r1Bf46EefPmYciQIcjMzIROp8PevXuxZs0aZGZm4vLLLwcA3H333ZgxYwYee+wxjB8/Ht988w3+/e9/Y+nSpZ7j9OzZE9dccw2eeuopiKKIHj164MUXX0RCQgKuv/56z37XX389XnvtNcydOxezZ8/GyZMn8dRTT+H666/nGtVERERERETUpIjMqY6U7OxsrFu3DitXroSiKOjduzemTZuGmTNnQqPRAACGDx+O559/Hs8++yzeeecd9OrVC3/6058wfvx4r2MtXLgQcXFx+POf/4za2lpccMEFePnll72ygicmJuKVV17BH//4R8ydOxdxcXG45pprsGDBglb9u4mIiIiIiKh9EhRFUcL5xaysLDz11FOYPHmy3+3r1q3D/fffjz179rSogO2VLDtRXl7r87pKJSIpKQ4VFbWdfh4E68KlLevBZPJdeq6hQO04VrANndOZ66Kpdgy0rC135rqNJNZj02KpT+bnFTrWlbeW9Mmsy6axjoKLZP2E0pbdwl77qqlYXJZlCIIQ7uGJiIiIiIiIYl6LFpQOFDTX1NRg8+bNSEpKasnhiYiIiIiIiGJas+ZUv/DCC1i2bBkAV0D9wAMP4IEHHvC7r6IomD59estLSERERERERBSjmhVUDx06FDfeeCMURcE//vEPjBkzBv369fPaRxAE6PV6DB48GOPGjYtkWYmIiIiIiIhiSrOC6osuuggXXXQRAMBiseD6669HTk5OVApGREREREREFOvCXlLriSeeiGQ5iIiIiIiIiNqdFq9TfeLECezevRvV1dV+M4JfeeWVLX0LIiIiIiIiopgUdlBdV1eHBx98EBs2bIDT6YQgCJ6gumFWcAbVRERERERE1FGFvaTWX/7yF3z66aeYP38+XnvtNSiKgiVLluCll15Cfn4+Bg4ciA8++CCSZSUiIiIiIiKKKWEH1Z988gmmTp2KO++8EwMGDAAA9OjRAxdeeCFefPFFJCQk4I033ohYQYmIiIiIiIhiTdhB9ZkzZ5CdnQ0A0Ol0AFwZwd2uuOIKfPrppy0sHhEREREREVHsCjuo7tatGyoqKgAAer0eiYmJOHTokGd7TU0N6urqWl5CIiIiIiIiohgVdqKy7OxsfP/9956fL7nkEqxZswYmkwlOpxN///vfMWzYsEiUkYiIiIiIiCgmhR1UT58+HevXr4fNZoNGo8F9992HoqIi/O53vwMA9O3bF7///e8jVlAiIiIiIiKiWBN2UD18+HAMHz7c8/N5552Hjz/+GCUlJRBFEWlpaVCpWrwMNhEREREREVHMCmtOtcViwbx581BYWOh9MFHEwIEDkZGRwYCaiIiIiIiIOrywgmq9Xo+vv/4aVqs10uUhIiIiIiIiajfCzv79i1/8AkVFRZEsCxEREREREVG7EnZQvWjRIvzvf//D0qVLceLEiUiWiYiIiIiIiKhdCHvic0FBAWRZxsqVK7Fy5UpIkgSNRuO1jyAI+N///tfiQhIRERERERHForCD6iuuuAKCIESyLERERERERETtSthB9ZIlSyJZDiIiIiIiIqJ2J+w51URERERERESdXdhPqt9///2Q9rvyyivDfQsiIiIiIiKimBZ2UP3QQw8F3NZwrnVzguqPP/4YhYWF+OGHH1BVVYWUlBRMnz4dV199tdcx3377baxevRrHjx9HamoqFixYgEsuucTrWNXV1XjiiSewceNG2O12jB07FgsXLkT37t299vv+++/x5JNPYs+ePejatStuuOEGzJo1i/PFiYiIiIiIqElhB9WfffaZz2tOpxM//fQT/vnPf+L48eN48sknm3XMv//97+jduzceeughJCUl4euvv8YjjzyCEydOYN68eQCAjz76CI888gjuuusujBo1CuvWrcO8efPwxhtvYNiwYZ5jzZ8/HwcOHMBjjz0GrVaLZ599FrNmzcLatWuhUrn+7CNHjmDmzJkYM2YM5s+fj3379uGZZ56BJEmYOXNmuFVDREREREREnUTYQXXv3r39vt6nTx+MHj0ad955J15//XU8+uijIR9zxYoVSE5O9vw8evRonD17Fi+//DLmzJkDURTx3HPPYeLEiZg/fz4AYNSoUSgpKcGyZcuwatUqAEBRURE2b96MNWvWIC8vDwCQmpqKCRMmYMOGDZgwYQIAYM2aNUhKSsJf/vIXaDQajB49GuXl5fjb3/6G6dOn+ywRRkRERERERNRQ1BKVXXzxxVi3bl2zfqdhQO2WlZWFmpoamM1mHD16FIcPH8b48eO99pkwYQK2bt0Km80GANi0aROMRiPGjBnj2SctLQ1ZWVnYtGmT57VNmzbhsssu8wqeJ0yYgKqqKhQVFTWr7ERERERERNT5RC2oPnr0qCfIbYn//e9/6NGjB+Lj41FaWgrA9dS5of79+8Nut+Po0aMAgNLSUqSmpvrMi05LS/Mcw2w24+eff0ZaWprPPoIgePYjIiIiIiIiCiTs4d/fffed39erqqqwbds2vPbaa7jsssvCLhgAbNu2DevWrcODDz4IAKisrAQAGI1Gr/3cP7u3V1VVISEhwed4iYmJ2LVrFwBXIjN/x9JoNNDr9Z5jtYRK5XvPQpJEr/93ZqwLl1ivB3/tOFbEet21JtZF08Jty6zbyGA9RkZr9cn8vELHugoPr5PDwzoKrq3qJ+ygevr06X4zZCuKAkmS8Otf/xoLFy4Mu2AnTpzAggULMHLkSMyYMSPs47QVURSQlBQXcLvRqG/F0sQ21oVLLNZDU+04VsRi3bUV1oV/kWjLrNvIYD2Gry36ZH5eoWNdhY7XyS3HOgqutesn7KD61Vdf9XlNEAQYjUb07t0b8fHxYReqqqoKs2bNQpcuXfD8889DFF13GhITEwG4njKbTCav/RtuNxqNOHHihM9xKysrPfu4n2S7n1i72Ww2WCwWz37hcjoVVFWZfV6XJBFGox5VVRbIsrNF79HesS5c2rIemro4C9SOYwXb0DmduS5CCTJa0pY7c91GEuuxabHUJ/PzCh3ryltL+mTWZdNYR8FFsn6acxMz7KD6l7/8Zbi/GpTVasXs2bNRXV2NN99802sYt3v+c2lpqddc6NLSUqjVavTp08ez39atW6EoitfT9EOHDiEjIwMAYDAYcN555/nMnT506BAURfGZax0OhyPwBynLzqDbm6IIAix2GWarAwadCnq1BEFRwj5eW2ppXXQUsVoPsVimxppTdx3p3PEnVttRLAinXhRBQJXVjp8rLNBrVdCpxA7VXtoC22jLtHbdBfq8OnpfGg627eaJ5nVyZ8A6Cq616yfsoDoaHA4H5s+fj9LSUrzxxhvo0aOH1/Y+ffqgX79+WL9+PS6//HLP6+vWrcPo0aM9Wbzz8/OxfPlybN26FRdeeCEAV7C8e/du3HHHHZ7fy8/Px2effYYHHngAarXacyyj0Yjc3Nxo/7lhkwUBy9cWo6ikzPNabqYJc6ZmQ+rkX2hEwfDcoeZgeyHyj+cGEZG3kIPqSy+91O8c6mAEQcDGjRtD3v/xxx/HF198gYceegg1NTXYvn27Z9ugQYOg0Whwzz334Le//S369u2LkSNHYt26dSguLsbrr7/u2Tc3Nxd5eXl4+OGH8eCDD0Kr1WLp0qXIzMzEuHHjPPvNnDkTH374Ie6//37ccMMNKCkpwZo1a7BgwYKYXaNa8fNFBgBF+8qw/N1izJua3envFBP5w3OHmoPthcg/nhtERL5CDqp/+ctf+gTVu3btwv79+zFgwADPMleHDh3CgQMHkJ6ejiFDhjSrMFu2bAEALFmyxGfbZ599hvPPPx+TJk2CxWLBqlWrsHLlSqSmpuKFF17webL87LPP4oknnsCiRYvgcDiQl5eHhQsXQqU69yenpKRgzZo1WLJkCe68804kJyfj3nvvxe23396scrcmi132+SJzK9pXBotdhiGGszUTtRWeO9QcbC9E/vHcICLyFXJQ3TjQ3bhxIzZu3IiXX34Zo0eP9tq2ZcsWzJ8/H/fdd1+zCvP555+HtN+0adMwbdq0oPskJCRg8eLFWLx4cdD9LrjgArz11lshl7Gtma2OJrcb4mPzKTtRW+K5Q83B9kLkH88NIiJfYd9K/Otf/4qbb77ZJ6AGgDFjxuCmm27CX//61xYVjnwZdMHvgzS1naiz4rlDzcH2QuQfzw0iIl9hB9VHjhxBly5dAm7v0qULfvzxx3APTwHo1RJyM01+t+VmmqBXS61cIqL2gecONQfbC5F/PDeIiHyFHVT37dsX7777Lmpra3221dTUYO3atZ4lrihyBEXBnKnZPl9o7qybTA5C5B/PHWoOthci/3huEBH5CnuMzvz583Hvvfdi/PjxuOqqq5CSkgLA9QT7vffew5kzZzj8O0okRcG8qdlcH5KomXjuUHOcay9OWG0O6DQq6NVcp5qIfSkRkbewg+rLL78cK1euxDPPPIMXX3zRa1tWVhb+3//7fxg7dmyLC0j+CYoCg0o8lwyEX2REIeG5Q80hKAqMOgkp5xlRUVELh8PZ1kUiignsS4mIzmlRNom8vDzk5eWhrKwMx48fBwD06tULJpP/uTZEREREREREHUlEFhI0mUzIyclBTk5OwID69OnTyMrKwtatWyPxlkRERERERERtLiJBdagUDg0iIiIiIiKiDqRVg2oiIiIiIiKijoRBNREREREREVGYWpSojJrPpgCHjlfCYnUgzqCCRiXBUueApU5GnF4FAQIgKNCqVXDIMmQZcCoKrHXNW7JCEQRY7E7UWuzQaVXQqkVoRIHZOalDsSnA4eOVsNgcMBo0sDmcMFsdiNOpoFFLUIkCLHYHqmvtiNerodeqICoKLA4ZiiKEdW5R5+IURZy1OHCs/DTi9WrotCpIno3MBE4dl0MBTp2phV12wmqXYa2TEadXQ6MSIQlARU0dDDo1tGrXdUyNpUE/y3ODiDoZBtWtyCEIWP72DmwvKYNOI+GBm4ej8KtS7Nhf5tknN8OEKfn9UV5lRXrfJJRVmAEAe49UoHDTQWSlJuOuq7KhFhAwQJYFAcvXFqOo5Nxxc9JNuO7yDHTvooPYRODgCsi59iTFNvf5tPdwOR64eTjeWL/P+1zKNOGOgiEAgN2HzuDtz/YjMyUZd00dirIKK977z0Gf/edMzYYUpK3z3OhcHIKAZW/v8GonOekmzL0mGyoFgMAbldRBCQJkp4Kys1a8ubHEp6+cNWUousRrcarcgrc+3x/gHFHYZxJRp8GgupU4RREr3nEF1ABQkN/fJ6AG4AqEBeDCob1wzzNfeF7PSTfhgZuH4+nXt2H52mLMLBgMg0byCZBlUcSyBu/j5n6fscN6IXdAt4Bfav4C8lCCDaLWpDQ4n669PMP/ubSvDCvf34XMlCTs/7HCc/7sOngGW4qP+91/+bvFmDc12+/5wXOjc3GKok9Ajf/f3pnH2VT/f/x17j13nX24lN0wd4wZs4RGlkkoWSKEZKksUShaKelb+Ubkl0IlfNtIlihKFPVFkm9qzFCYYZgsYZh97n7P+f1x3eOeu829d/a57+fj4WHuOZ9zzud8zvv9/nzen+0Nmy1duSULM0Ymw2yxQM7SqBzRwJBIYAVwNDsfP2e6t5UffnUMPZKaIal9Y+T9Uyw6b9eRmSOTsXJLJtlMgiCCAlpTXUPoTRZRxdKhdZRLRWUn41Q+GkUoRccyc/Kx4+dcDLurPTJz8lFSbsLR09fAM4yQhmcYXCnQuTjUjveIDldCb7a6Pc+7cRrs+Xlva5boWQRRm+hMVkFOvelSZk4+OrSOQkZ2PrYfyMWQ9HZoFKH0qCMZp/Ld6gfpRvChM1q8ylW53gxWKoXOaKnhnBFE9WLleegMFjSKUHrUgaPZtnbKB9uyMGNUqsv5zJx8lBssZDMJgggaasypViqVGDZsGJo0aVJTj6xTlOvFDS+T2fvIhrvzR7PzcXv8LVDKpSjTmV0cZL3ZijKducL76gzuG4F6s9WlArTjydkgiNqgXH9Tzn3VJbuDXVF6d/pBuhF8OMqYO3QGCwwmK3R6cqqJhoXBzEFnMPtkWzNO5aOx0yCAHZ3BvQ6RzSQIoiFS6enf2dnZ2LdvHy5evAgAaN68OdLT0xEXFydKFxoaioULF1b2cfUWlUIq+i2Xee/P8HS+oMSAIentIJdJBAdZHSoHYGvk+XJftdL9Z/fkbDuetz+LIGoTR33yR5dYacX9iO70g3Qj+AhRybyeVylZ6PQWqFW0iopoWJTrzQhRyqCvYBaG3bYaTO4d5BClZx0im0kQREMj4JFqk8mEOXPmYOjQoVi6dCk2bdqETZs2YenSpbj//vvx/PPPw2QyVWVe6zUKmRTJsRrh98m8QtFvR5JjNTiZV+j2HMMASe0b42ReoYuDrFayOJlXiBSt5/sWlBigkkndnvfkbPt6niBqCkd98keXwkLkuF5sQKoHHUmN07jVD9KN4EOlYL3K1fViA9QqFkq5e3tKEPUVpUIKlmVQUGLwqAMp2pu21Z39S47VgGU9T/Emm0kQREMjYKd6yZIl+OqrrzBmzBjs3LkTWVlZOHbsGHbu3IkHH3wQ27dvx5IlS6oyr/UahgFG99MKjfnt+89gSK8YlworNc62S/f2/Wdc7mF3ECQMg7MXi10cZJVMirx/ijGkV4yL02Df/TvFyyZlKpkUqXH+ORsEURswDCPokyddSo7VYEivGEGXkmM14Hkeie0aYWh6O7e694SHTcpIN4IPCc9j+gNJbuVqyv2J+PG3vyFnpWBpbSjRwFDKpDh9vgid2jXG6H5aV1upvWlbbXZVfH1yrAZTh3XC6fNFbu9PNpMgiIYIw/OBbcGYlpaG3r17480333R7/rnnnsP+/ftx+PDhSmWwvmK1cigoKBd+8xIJrhXrcblAh7bNIpBfqIdUwsDC8ZAwAA+gSZQaDM9DJpPivS+zXEJUDOkVgyXrjuDliWlgGMYlPBbPMNCZOeQX6hCqlkOtYFGmM0GhYKGSs5BLUGH4FyvD4L2tWcg4Vf27dbKsBFFRISgsLIfFEry759ZmOWg0YV7PO8txnUEiQbnRgjMXi9GueQSuFRsQESqHxcrBYuXBcTyyTl/D9v1nYDBZBf1p2kiNcr0JChkLg8kKtZIFzwNSKQMVK/Ea6qUmdSMQglmfKpJjIABZlkhg4iyQSliU683QGSxQKVlcLzbgx9/+xkP3doCEAZQScqp9JZhl1Ffqgk3mb9hXo9GMsFAFTGYOHM9Db7BAKmGQkZOPbT+dRlzr6Bt2VQWOuzFtXCUDAyBELgUHYGUdtplVCcm2mMrYZCrLiqEy8k5Vlo8vsiw8N9CHWCwWJCcnezyfmpqKn376yeP5YIPhOESHK3HpWjl4nsetjdWwWHmU681QK1nIZVJbY18ug8VqRc+UZhiaHgOTmYNcJsHJvEIsWXcE8W2joYlSg+c5FJYZoVKwUMuksAJuw/1MHZYEuT2mtQ91mJTnMWN4EsWVJOo2HAe1XIqY5hEo15vQJEoFK8fDbOYAnofBZEWH1lGIefA2QX++P3wOk4d0wtrvs92GeKlIxkk3ggyOAyth8fG3f6J351ZoFKGE/saOyH26tsKGXSfxyH0JAIXTIhoYzA37yjC2zcgUMimuF+shl7GQShnEt45G7Pgugl3t2vEWrNicKVzv6DiTzSQIIlgI2Knu2bMnfv75Zzz00ENuzx84cAA9evQIOGMNESnPI6l9Y+hNVqEdJpEwkEgkkEslUKhvbNrBSNCpXWOs2ubaw/v48CSs/foYDv91RTg+Y2QyDmZechu6YtW2G3F3/cgnw/NQs5Kbm4hQBUjUQRieR6iShZyVwmi2QG+0IlRt+/2+0+hISqwG4wfG48OvjnkM8eIpPrXzM0k3ggcJz2PCwI5YucX9zKE1Xx/DY0MSyUkgGhwSnkeIgoXJwsNotiAyTInVXx8T21WtBqP6avHa2l9F1zrbVLKZBEEEAwE71U899RRmzZqFGTNmYOzYsWjVqhUAIC8vD+vXr8elS5fw9ttvo6ioSHRdZGRkZfJb75ExQJNbwoUpCaGyGxWN42gHz0MOuPTwKuUsVn8ldqgBIDpcWWG4HzVLIcmJhgfLAJomoSgsLEeYfcMop9ERk4VD1ulrKC034bcTV9zeh/SEcIZnGKz4MgtD09shrnWU25lDBpMV4wfEk9wQDRJH+2qxcJg+IhmXC3Qo05khl0kQGabAi+8ddLv7N9lUgiCCjYCd6oEDBwKwhdTau3ev6Jx9mfagQYNcrjtx4kSgjww6nHt4dSaLi0MNVBynl0JXEMGGXXegZPH8W/8FAMyZ0NXrNaQnhCP22OT9u7XBpj3ZHtOR3BDBgsFkwbwPfhF+z5nQ1WM4LYB0gyCI4CJgp3r69OlgaNfTGsVTrNyK4vRS6AoiWHHUGdITwh/sskNyQxA2nNsgpBsEQRA3CdjizZw5syrzIZCXl4e1a9ciMzMTOTk5iImJwTfffOOSbvPmzVizZg0uXbqEtm3bYvbs2bjrrrtEaUpLS7Fw4ULs2bMHZrMZvXr1wrx589CkSRNRuj/++ANvvvkmTpw4gUaNGmHMmDGYMmVKjXca8AzjdUMPTxWUPU6v45o/O0LoClrHRAQBzjoUHiKHUi6FwWQlPSH8wm5vT18owvQHktEoQima/r19/xnEt40muSGCAp5hoFSwmDOhq6ADpy8UkU0lCIK4QZ3rRszJycG+ffuQnJwMjuPgLuLXt99+i5dffhnTpk1Dt27dsHPnTsyYMQPr169HSkqKkG7WrFk4ffo0/vWvf0GhUGDZsmWYMmUKvvzyS7Cs7dXz8vIwadIk9OjRA7NmzcKpU6fw1ltvQSqVYtKkSTX12rZwPW5273YMPWGPleu4UQhgi3n9yuRukEggOpccq8HIPlrwgF8blRFEfcSTDs2f1A2vrf0V3/96Dgse74HVXx132XTqcR82KSOCC5VMirSEptC2jMLX+8+I5Co51iZXmkglyQ3R4DFaeRfbmhyrwbA726FHcjN8uI1sKkEQhM9xqlesWAGGYfD4449DIpFgxYoVFd+cYTB9+nS/MsRxHCQS25SiOXPm4Pjx4y4j1f3790diYiKWLl0qHHvwwQcRFhaG1atXAwAyMjLw4IMPYu3atejZsycAIDc3FwMHDsT//d//CWvC58+fj59//hm7du2CXG5b+/N///d/2LBhAw4ePCgc8xd/4u/ZN8Rxt9lYapxGtCuxp1i500ck42hOPqLD3Y+m+LKzcU1DcfZsUJzqwLGX3bWCcry7OdOjDvVIaoarhXqcvViMts0j0KF1lEhP8v4prve7OAezPlVLnGoAVokEK7dk4qgbuUrRanBnanMkxTSq13JTkwSzjPpKXbLJLCsBq5BhyWdH3NrWFK0G6SnNcblA1yBtqj+QbIuhONXVC5WRd+p8nGq7Uz1lyhTI5fJqc6rtDrUnzp8/j3PnzuG5554THR84cCAWL14Mk8kEuVyO/fv3Izw8XBTWKyYmBvHx8di/f7/gVO/fvx933323yHkeOHAgVq1ahYyMDKSlpfmV/0Cwb4jjDucdND3FfdSbLKI4kd7uQRANjXKjdx2adF8itK14bNqT7XEHcNrFmXDGaLK4dagB4Gh2Pob0iiHbSjRoisuMHm2rXQfe3XTU7XmyqQRBBBM+O9UnT570+rumyM3NBQC0bdtWdLxdu3Ywm804f/482rVrh9zcXLRt29ZlXXRMTIxwD51Oh3/++QcxMTEuaRiGQW5ubo041Z42IHM877iDpru4j/7egyAaEjqDuVLnbWlIRwgxFdlVk5kjuSEaNOV677bTW/QR0g2CIIKJgNZUm0wmHDhwAM2bN0eHDh2qOk9eKS4uBgCEh4eLjtt/28+XlJQgLMx1yD4iIgLHjx8HYNvIzN295HI5VCqVcK9AYd300EqlEtH/AKBWyrzeR62Uub2Xc5rK3qOmcVcWwUhdL4e6JjeO2MssRFWx/FdEXdQRf6jrclQX8Pf7ViQ3cpmk3stNTUIyWjXUlLxJpZIKbau3HcCDSTdItgPD13YyIYbKyDu1VT4BOdUymQxPPfUUXnrppRp3qusLEgmDqKgQj+fDw1XC36zO5HYDMsC2HjQ6Qokwtffe3qq4R23hWBbBTF0sh4rkuK4QFa6sUP7tf9dHHfGHuihHdYFAZNmbXU2O1aCgxIAObaIbhNzUJCSjgVPTNplhvbctCkoMbq9rSDbVH0i2fcefdjLhHioj79R0+QTkVDMMgzZt2qCwsLCq81MhERERAGyjzBqNRjheUlIiOh8eHo7Lly+7XF9cXCyksY9k20es7ZhMJuj1eiFdIHAcj5ISnctxqVQChpWisMSAcr0ZISoZ1HIpnhie5HYDsidGJMFiNKPQWPH01aq4R00ilUoQHq5CSYkeVmvwbrRQm+VQUePMkxzXFexlZzJZMHVYElZt8yz/QP3TEX8IZn3yxckIVJbdyUxyrAaj+2nRJEpZ7+WmJglmGfWVumST7e2VacOS8IEH28rwrp2VDcWm+gPJtpjK2GQqy4qhMvJOVZaPP52YAYfUmjp1KhYtWoR7773XZU1ydWJ/Vm5urui5ubm5kMlkaNmypZDu0KFD4HletK767Nmz0Gq1AAC1Wo1bb71VWGPtmIbn+Uq/l7sd54xWHu99keG6e/fwJLcbkDEcDwvn2+6ZUqDS96gNrFaOdi9E3S2HupgnR/KL9Fi+8ShOnCvAkPR2uK9nDHgATaLUUMskIvmvrzriD3VVjuoC/paLlWGw5uvj6NAmGmPujoPFykOlZKGSs5BLADQgualJSEYrR02Vnb29cuKsZ9sKNHyb6g8k2/7hrayoLCuGysg7NV0+ATvVmZmZiIyMxH333Yfbb78dzZs3h1KpdEk3b968SmXQmZYtW6JNmzbYtWsX+vXrJxzfuXMn7rjjDmEX7/T0dLz33ns4dOgQunfvDsDmLP/111+YPHmycF16ejr27t2L5557DjKZTLhXeHg4UlNTqzTvvJs4uoBtd+KVW7MwY3iSywZk/uJuEzOCaKhYeWD55gxBpzbtyRbO2cPROUM6QviCo70+/NcVbNh9SjgnhDqsxfwRRHXi3F7xZlvJphIEQVTCqV63bp3w96FDh9ymYRjGb6dar9dj3759AICLFy+irKwMu3btAgDcfvvtiI6OxsyZM/Hss8+iVatWSEtLw86dO5GVlSXKU2pqKnr27IkXX3wRL7zwAhQKBd5++23ExcXhnnvuEdJNmjQJO3bswDPPPIMxY8YgOzsba9euxezZswOOUe3x3fwInUUQRMWUG61u1/oBpFNE5SB7TQQzJP8EQRD+EbBTvXfvXkRHR0Olcr8IXK/Xo6CgwO/7Xr9+HU899ZTomP33p59+irS0NAwePBh6vR6rV6/Ghx9+iLZt22LFihUuI8vLli3DwoULMX/+fFgsFvTs2RPz5s0Dy9587datW2Pt2rVYtGgRHnvsMURHR+PJJ5/ExIkT/c57RdjDsyjlUgxJb4cOraNgMnOQyyQ4mVcIvdECNRtcm3oQRGXQG80Y1U/rokvb95+BwWSlkC5EwOgMFo+2evv+MyRbRIOG5J8gCMI/GJ4PbJ5OfHw8lixZgsGDB7s9v3PnTjz77LP466+/KpXB+orVyqGgoFx0TGfh8Oy7B/DcuC7YfiAXmTnijW8eH5EEOYJn2hTLShAVFYLCwvJqX/PAM4zrmq86MkWtJsvBGY3GNeycI+7kuC5hAoP3v8xy0aUhvWKwZN0RvPVkr6AZTalNOapOfNHdiuQY8F+W9VYOVwsNuFakR6MIpeBUXC82oHGkCk2ilFBROBO/aKgyWpXUFZust3K4fF3vtq0ypFcMbmmkCir5r8gOkWyLqYxNprKsGCoj71Rl+fgiy8JzA30Iz/Pw5o+bzWbRBmEEoJJJMXlookslBQCZOflYtc22rrquOHsNBaubteypcRo8MTwJUirregvPMFjl5FADEH5PHpoIlUxK6/vqMbWpu0oZC4VMip8zL7k4FaP7aaGUsQBHjRmiYaKQsR7bKgAwc2Ry0Mg/tSEIgvAFv7oZy8rKcOnSJVy6dAkAUFRUJPx2/Hfy5Ens3LlTFPKKsG3mEdc62qWSsmNfp0RUHd42h3tvaxZ46vipt3hb85eZk4+41tHUQVWPqW3dNVs5bNyT7dap2LgnG2YKY0I0YAwmi8e2SmZOPgwmSw3nqHaobTtEEET9wa+R6o8//hgrV64EYNuE7I033sAbb7zhNi3P85g1a1alM9jQ0Bu8x2ykdUpVC2220nCx71HgCb3BjBDSpXpLbeuu0Wz16lQYzVbI5dJqez5B1CYV2ddgaavUth0iCKL+4JdT3aNHD6jVavA8jyVLlmDQoEFISEgQpWEYBiqVCgkJCejUqVOVZrYhoFbKKjgf8Ix8wg3UMGi4VKQrpEv1m9rWXYPR+6whg9GKMHKqiQYK2VcbtW2HCIKoP/hlFVNTU4UdtvV6Pe655x5otdpqyVhDJUQhRWqcxm0YoNQ4Da0BrWKoYdBwUclIlxoyta27ISrvHaAVnSeI+gzZVxu1bYcIgqg/BDxnZcaMGeRQB4CUAWaOSkVqnHi9uX3TC1oDWrXYGwbuEBoGRL2E4Xk8MTyJdKmBUtu6q5JJKng+TfkkGi5kX23Uth0iCKL+EHBILcI7FYUKuFZQjnKjxWN4BufwDUo5C6PZgnJ93QsJFSg1FRLAyjB4b2uWqMe9Lu3cSSG1AodlJWAVMhQUG6AzmKFWslDLpOABQX9UChYMw4BheCjZ+q83nmiIITZ81d3qCKkFABzD4Ojpa4gOvxlSq6DEgJT2jSFpoHJUnTREGa1q6pJNttvXwhIDeB7geB4GowUqJQsGDd+m2vHFDpFsi6GQWtULlZF36l1ILaJySBlAzUpursVxqJTchW9wjL1rMFnrlFNY15HyPGYMT6qzcaqJyhGmlsNiNEPN2nZhtQIe9ef7w+cweUgi6U09obZ1lwPwc+YlHHUMpaPVIKl948CneRFEPSJMLUe5zhTUNrW27RBBEPUDahfUAqU6E0oMVlwrM0Fn4UQhGTyFb8jMycf2A7kYkt4OAIVz8BeG56FmJWgcKoealVBl2IBw1CcT7+pQAzf1p/WtEaQ39Yza0l1OIsHKLVkihxoAMrLzsXJLFjgJVZ9Ew+d6iZ5sKqgNQRBExVCroIYxWXn8knUJVwt1KCgx4mqhHhmnr4G7USFVFHu3Q+so4be7uNY8w0Bn4dw67ATR0HDWJ73Ju/506dAE9/WMwbUSI+kH4RWd0XucXn2QxOklgheTlcf1IgPZVIIgCB+g6d81CcPgWrHBZcNMngeuFRvQJEJZYfgGk1m8NsAxnIO7aeM0TZxosLjRJ30F+lOqM+P1/xwWfpN+EJ4o15u9nr9aqEerJqEkO0TD5IZ9tXDe5ZtsKkEQhA0aqa5BzDwPjret0Xtt7WEs+vQ3vLb2MH7OvASOt52vKDyD3GnHWXt6T9PGaZo40VBxp0+GCkYPndWA9IPwREUhsxiAZIdosNjta0WQTSUIgrBBTnUNwvHAxj3ZLlMKM3PysXFPNjjee/iG5FgNTuYVCr8dwzl4mzbubpo4QdR33OnTybxCJMf6pj92SD8Id6gUrFdZ4niSHaLhYrevWaevkU0lCILwAZr+XYOYzFacyivAqH5adGgdJYRoOZlXiO37z8BktkIhl+KJ4Uku4Rscd/8GXGNFVjRt3HGaOEE0BNzpk1IuRVrCLfh0J0TOdqpWg8E9b+qPM6QfhDN6oxlD02PAMBBtVpaitdliyY2ROJIdoiFit68JMdF47P5ErPn6uMvSssE9yKYSBEHYIae6BjGarHhuXBdsP5CLTXuyhePJsRo8N64LjCYrwuRSt+Eb7HGqX3vsDrfhHCqaNl7ReYKob3jSp67xTfHwoHhIJR1hNFmhVLCQsRLMfnsfDCb3oyekH4QzBqMVDMOgR1IzDOkVI3SCXi82gGEYmG7EviTZIRoijvZ160+nMSS9He7rFQOeB6LDlZCxEjz77n6yqQRBEDeg6d81SHiIAtsP5Lqd/r39QC7CQxQer2V4Hiqp53AO3qaNO04TJ4iGgjt9UsqlaNs8AjqDBQaTFWolixC5FAoJg/i20W7vQ/pBuCM8RIGv9p3B2u3HcTKvEHKZBCYzh0YRSlwr0kMTpSTZIRosjvbVYLJi055sLP7sCE79XYhygxnlBjNenpSGUf20UMrFOkB6QRBEMEJdiTWIxcp5DdFisXKQs0xAu3gzPO922rjzNHGCaCg465NSLnU7cm3XgenDk7CS9IPwEYuVw6m8Ao+zixLbNcJ0kh2igeKrfbXPtFuy7ggMJivZVIIgghZyqmsQncF7iBadwQxVmMLrLt4znCornmGEaeIhKhbTRyTDYLII08adp4k74nhtRWkJoq7hrE9D0tu5nQniqDvOyyqcZb6+60SpzoQSgxU6g7le5r8uoTOYPcpUZk4+PvzqGGYMT6ql3BFE9eKrfc3MyYdEArz1ZDoAXliqVq6vHhta3200QRANF3Kqawirwxo8T6iVrE+7eKtZiXBPTyPa9jQuQbEd8kMxrYn6ijt96tA6SjSC4oij7qhZyc0NdBxkvb7rhNHK4+11R9yOxNeH/Nc11Eq2QpnSmTmEsBQ6iGhYBGJfcR8PhUyKlVsyq82G1ncbTRBEw4bWVNcA9hjS3kJTpMZpoJSzPu3i7XjPQOJSU0xroj7jSZ9MZu+dVt50q77rhJD/U/Uz/3URpZz11CcpcLVQR2VLNCgqY1+r04bWdxtNEETDh0aqawD76POJc7b1eYA43I8tXFY7HDtzDe1aRGLOhK6iUFuOu2vad9SseESbA8C7nSLlz2g4QQRKdU3T86RPcpl3mXXcjdY5bwBTr3WCdLrq0ZssaBSp9BgC0WCyggGobIk6jb92OFD7qlSw1WqDyMYRBFHXIae6BtAZLFDKpRiS3g4yqQTTRyTByvEwWqwwGK1QKqTgOB5f7TuDdzYeFa5LjtXg+fFdcPpCEdq3iIStGmRslaTx5qib/d6ODT+TxYoX3zsIg8kKpVyKyUMTEdc6GnqDGQo5i1H9tC4Ou2N+63t8yWBfd1Xb71+d0/TsI84GkxVL1h3B8xO6YuJ9CQCAVyZ3Q0SoHBYLh8JSI2SszQnK+6fYthstz8MqkeBKgQ5lOjMUcilKdGY0jlSKOrO+//Uc7unWBh1aR8Fi5SGVSqCz8DWyVjmQb1dX49TXthxWBp3ejFC1HDl/F7psfPfWk+ko1RkRGapEucEENVu/7SXRMAnEDjva1+WbMvDy5G5gJR0hkUiw7Ok7UVxmAsfxOHGuQGhDpGo1YBgGy2bfCYuVR5nOhOzzhZBKGHRq1xgWKw8rB5h4QC5hPC5L8wTPMLByvNcBh6qwcfXZXhEEUfuQU10DhKhYvDChKwpLDGgcpcS1YgM27skWRqtH9dPiVF6h+w1AGKB7UjO8tvawcDw1ToMpQzth+bO9cb1YD/AMvj+cB22rKDRtpIbOYIFUwmD5s3dBbzSD44DrxQbkF+qEyqhTu8ZYOL0nzBYrLBYeKgULHjyOnLgCtUoGnYULqGKpC5VSsK+7qu33r2ianvNme/7iHP+0aZQKH351XNip+ZNvT4h0KTVOg6nDknC91IgQlQylJXqYzLbGGM/zKCo1oGWTULS+NRQX83VIbt8Yfbu0xIlzBZAwDG5ppILRZEVxua0xeeTkVeT9U4xHBiWA4zkoWZuMV4XsB/rt6mKc+tqWw8oSGabAB1uPuZXjtTiOCQPisX73CYwf0BFg4LejQBDVSaB22G4rlHIpZo5KxcbvT6F/tzYum5SlajVYNvtO6E0WSCUSWKwcSspNkEklaK4JRQtNCMqNFuiNVqiULI6cuIyT5woxrHd7aCKVMFmsPm1m5s6OOO847pjvQKnv9oogiNqHnOoaQClj0ShCihZNGkNvtIBhgMlDEqBUsLheqINEKvG8AUh2Pu7rFSM+diofq786hqF3xoCVSiGXSTBhUEes2upaITw+PAlGswUhKhlCVCxaNgnF7R2bIFSlwKptrhXVxPsSwPM8LuWXQ8ZKBAfi0fsSIWcAjmGgM9p2Fw9RsmAYBmB4qGQseJ7HyhuVkn30PKl9Y8hYCUIcKk7B+SgzodzMQVmFU7aq26Gr69TU+3tzIKt7mp5KJkVaQlO0vjUCd3S6BSfOFWBoegzCQ+KxbtcJt7t/f7T9OKbc3wkWjsfGPdkuetKpXWOU6c1oGqUCw9ic7XYtIqDTW1CqMyEz55owMpIcq8GEgfEoLDVAKpWAVwJyGQud0Qyd3gKWleDgsX+Qe6EIj96XiNJyI1QKcRm5LT8g4G9nj1PvvKba/n72UXpHqrMDrCHoodnCe5XjEXfFYsLAjvj251y0aRaBxJhG1Pgmao2qWtJit693p7XB7hszdnb8nOsSXiu2VRQKSgyIClPiw6+OudjUKUM74UqBHqyUwR+nbO2ISUMSoTOaUVBqhFrBQqWUgeM5mDkeZisPvdNMIE92xJ6XIentsGlPdqVtXEOwVwRB1D7kVAM4c+YMFixYgIyMDISEhGDo0KGYNWsW5PKqmdLHcDxC5EroHKZs8wAMRgsaR6hwrdQoHHc3lVsTpcaqOX3AAADDwGCyolxvRqhaBqWchcFkQWGJAZOGJEAhZ8FxHPQm29Ryk8UKOctCFiqF0WxFQYkRahULi5XDxCEJYGxDLOB4myOhM5jQJEKF5ppQcDyPzioZeiU3g5XjYJFKYDBaUK43Q62UQSplkF9oQESoHFl/29aDX7pWhof6x6FHUjOs/vq40FngOAW9TGeEVCpBud4sTDW7pVEILJwVPHezbCqqBN1VlhU5dA19t96aWHdm79E/ca5AkFUeQNMoNVQyid9TkTmJxNZRozcjLEQOGSuBwWQBw0jA8zz0RgvUClsHDsPwULJSTBmSiKwz16GSy3Dg6CVk5uRj/qQ0HL3x7k2jVZj7cFdIpRIwAFipBOUGMwxGKyYNSQQrZVBcZhu5VshZFJUaYOUAyACFnMXlazqYrVZhZkd822j836w7wXEc5KwUOpMZISoZAAZleguUVh5KuW0Zh05nQffEW3FH4q0oLDEgVG17RrnRAr3BjIgwJVa5GRGZOiwJndo3Qoe20YhtEemyhldntkJ1Q991BgtUTmXiTxzu6h6VaQjrH8v1ZsS2iMCz47rAZLECvM026W/YJqWcRZlOj8G92sFktsBotkIhYyEFT6PWRI3CMQyOnr6G6HAlTGYOZXoz5DKp12vK9RZAJQMPHgwYcDdsrVLOYuJ9iTBaLOjfrQ2UcimOZucjMlSOZ8behqhwFcADHM/DaLKAYRhMHJKAMp0ZoSoZFHIpLFYryvQm3NJIhZ8z/xEcap7nIWMZqBUK20i1zoRQlQyFpQa8+ekRFJWZANhs0bRhSbBYrRh7bzweHtxR0L+S8pvTz7sn3YomUSqktG9cKRvXEOwVQRC1T9A71cXFxXj44YfRpk0bLF++HFeuXMGiRYtgMBgwf/78qnmIxLYe6OTfhYhpGoImkWrobjjGPIDocAVWPNcb4G9WbPYGs4QBWCmDknIjwtRK6Ay26dxKBYuCEiNkUjOUCimWffEH+nZthbSEWwHwMBitUCtZSBgGhSUGbPgh22VK7GP3d4LZwkGnN4PjAaVCim8PnsXYe+NhsVohl0khZ6Uou7G2MDP3GtZuPy5Mt0qO1WDK/YmY9/5BtGkWgVsaheC1x+5A1unr+PCr48LzlHIpnhvXBdsP5GLF5kxRHob2agcewNrtx3HvHW6mmLmpBL1VlhU5dFcLdWjTJLRKR+Rqa7q722czDOZPSnO7qRJQ+XVnvINDbf+mzutNJ96X6PUeKqUMBo4DK5GCA2AyWmyOikoGjodN9lgpVm27OfohdMq0isK1Epsz3KldY3y1LwdPjUwGD8DCcXj/hT4wmW0dSjaHmgHD4MY/BmYLB7OVg9HMA2DA8UB+kQ6LHRt0Wg2mDk+C2WxFabkJC6f3RGGJAf9cK0dYiBzZfxchMaYR3tuaJTjx9nefen8SQpQy/HX2OlZtOwYAQjll5uR7XOqRcSofa78+jnED4vHRN39iw+5Twjn73goMw2D55kw3mxzG4PvD5zB5SCKeG9cFBcUGr2u/a2JUpq6u8XbGm/5Ghskwd3wXcAwDnrc52aEqGaLClLByVpTqTQhXKfHT73+jV2pLGEwWfP7Nnxg3IB4KKVNlSwIIwisMg6tFBqFz0c6Cad29XqZS2pZ8sRIJPvzqmEsnaZMoNdreEgqAwao5fcHxPExmK8p0ZoSoWEgZBiolCwvHgZVKwEoluHStHKFqGZpEqXEgIw+De8WgX5cWYJiWKCo3IFKtgEwqxftbs1zs2ILHe2De+wdRVGZCxql8vP9lFiYMisfGH065bRskx2rQK6U5fvvrMpLbNxa9m782rr7YK4Ig6jZB71R/8cUXKC8vx4oVKxAZGQkAsFqtePXVVzF16lQ0bdq00s/gAehMBnS+EZ7CwvPgb/wDbIMaZguHj7854bHBPGFgR3y68y/07drKpXJJidXgtcfuQEGJEf/Z8aeLUzqqrxan8gpEeco4lY9V246hR1IzRIcrsWTdEcS1jsaEgfH4eMefeLB/B6zadtwlP47rmDJz8rH6q+OYMSoVC/5jW/PdM7kZGkUoRdcNSW/nkmd7HgBgZN9YjOkfh093up+661gJVlRZTqrAoavsbr32RrLeaEFYiMLtiGNNrMHy1LEwso8Wiz87Iur4cPxmlV13Zu/RH9VP6/JNlXIpYltGged5r1ORdXoTwkLkuF5iwDc/5+KuLq3QKEKJUp0ZoSoWcpbFmq+Pixzq58Z1wd7f/kZ0hArNG6ttI7S8BSN6x8IK2+ihbQYFi/wiA1ZsykBRmQmpWg3u6xWD3b+ew+CeMbBaeXzy7V9iZ1irwb8f74GX7A26bFuDrldKM3Rs2wiffPMnDv91RUifotXglkYhOHnOVafe35qFnsnN0LJJqLBnQWm5GUPTYxDXOkoU69V5Vkp0hBLZ5wtd7puZkw+GAXokN3O77wIAxLWOwntbs250aPCIDldCwnFuR0wrEznAV7zJmVIuRXiIPOB9G6qKikayZBIprDyP42euofWt4VDIWZTqzDBZOISHyHHw6N+4+/bW6Nu5FfKLdeDBYOyADlj33QmM6R8HlZx1G7N36rAkyCQMGE4cosibA07Oef2nur6h6caSFmfb8OfZ60jVatzqemqcBlIJg1KdGVaOQ4e20RiS3g4/HM5DF21jREWqwXMW24g0AxhvONNqJYsyvRmLPvkf2jaLwND0dlDKpS5LapJjNZj+QBLW7zqJ/ne0BngGjSKUYBgGq7ZmurVja78+jgXTeuDC1TKhQxgA2reKdNt+yMzJx3+2H0dsqygXR1lv5vwaea6qPSlKdSaUGKw1sqFlbUL2iCDcE/RO9f79+3HHHXcIDjUADBgwAK+88goOHjyI4cOHV/oZDMchXKG09fRaeazadszFWZ06rBPy/ikWXefYYF6z/TgG9YhxW7kczcnHsTPX8XPmJbdOKc/fXHskui47H0N6xeDr/bnC+XXfAbGtosBxvMcGvOO9MnPy8cjgjqL7SaVih9XRkXDGvjaxqNQocnSc09grwYocAvZ+xqNDlxyrwcm8QoSqZAH1Ojs2wr2NOFb3GixvHQsc5/p9ANuxnPOFbted+YO9R9/5m9od312HziG+TTRG9tGC48Sh41K1GtzXMwY8ePyZW4Cj2Vcxpn8HrP7quEtH0OAeMTh25hoMJiuGpLfD3t/+xtgBHVBeboKVA/b9/jf6394KFp7Hyi2eRz0ysvPBA9C2isKVAh0OZl5ykbOM7Hys2X4cT41Oxas3NgTMzMnH0PQYrNp2DHGto0RO9dFszzplv47j4eK8J8dqkHKjY81x9objPdxtwGN/5hCnvRWcn7lpTzbye+rw2trDQqOWdfOtKxqVuVKow+tOGyP621HkaY23Ui7F/EndRLMQAn1GZfBpJAu2DR6bRoe43fzu8eFJ2PJjNh7oE4dSvRXfHszFyD5ajOwXC47jYbJwOOGm42XV1iz0SmmOxHaNhO/jzcFnAGGvCudztIa7flCdyy2MZqtLPaSUS9GhVTR6JTd3aW/YO1+feWe/YGNStBqktG+MhwfFA2Dw+a4TGH9vvM2+upkdY7evX+8/g+5JzdyueX5/axbGDYiHhGHw8Td/oWdyM3Rq18hz/Z2dj6uFOiz69DfhOempzZHW8RbRzB3na+7rZbN99jaClWFwpbDca5k5jzwHsieFM0Yrj7fXHXG7/KYh6Slt6EYQngn6RSK5ubmIiRE3VsPDw6HRaJCbm1slz7BKJDBxPK4W610qOMBWAa3adgwzRqW6XJuZk48OraOQcSrfZQTYkehwpUen9Gi27R7uMJk54RmArZLq0DoKRjehthzz44jeoZHOSiUIDxE7rCazeETGmTKducI0dkegIoegVGfC1GFJSL7hvNixj/pv338moNFa50Z4h9ZRHr+FvROguvDWseDu+2Tm5COpfWO3a2v9xV52zt/LPhuhbfMIfLXvDF5b+yviWkdh/qQ0zJnQFfMnpaF7UjOcvlAEhVyGRhFK3NWllYtDDdjKb/sBW0cPYCvrPl1b4a/cAlh4W6dUv7TWMHGuDrX9fe0zKICb8h8drvTaIdMoQiU65qwbzs/wpFOsVIKNe7Jd9DEzJx/cjfL3NHsjM0f87s758YT9nP3/zJx8rNySBU7iauIrkn/nHQfsjibP+L4XAcPzeGJ4ElLjxHo4eWgiNu/N9ujM+vOMyuDLGkqTlcf5q2VuRwHtsxKG3tkeBpMFjSKUyDiVj417siFhJGAYBqu/Pub2O2Zk56NFk1Dh+1Tk4B89fa3Wy4sInIq+b2W/ocHoWtcMSW+Hq4U6fLTjT8EOvzm9JxbP6IkeSc3w2tpfXTrt/r5aBpOZw3tfZmFwejtYgQrta0a2rV3ijoxT+ZBJJdAbLMjMsaXTe2hX2HG0cfbRa66COst+jc5gEcq6ohJ1toGe7JWnPSmcEb7xqYatp9UtywRR3wn6keqSkhKEh4e7HI+IiEBxcbGbK3yHvTG9qExvcwRVCplHR8xxxNcZe6Wh9+JQVuSUejovl0lczpvMHCLDPIuG871UDhVUWIgcVisvmnZmf4YnKjoPAGqlDCwrgVop85pOpZBBIWXQK6UZhqbHiNYWL1l3BPFtoxGiYCH10/aXGMSNcF86AcIj3Tc2nLGP7DuP8Hu89421v55wlzc5K4FCysDVZaoY1mGaXIhUgtQ4jcs3s49c20dMAbidnTB/Uhp0BgtMZg5NG6m96sPQ9BjhfZo2UkPC3NQhe5x2X/Wpou8FQLSRIOBeNxzxdDwsRO4xX1mnryFVq/E6e8Px3d3lxx32c45pMnPyoTNaEKkS67L9G3qbzeGMfVp4uNL75keOsACefCAZ5Ub7dEib7jruq1DZZ/iLXZYr0iGdwQKFnPfakZlxKh9WK49yvUVYypOZkw8rx4OVMsg4lY/7erqfXWCx8sL3kTDed2n2dI+aKK+qxl9b1xBwrjscCfQbimyyyrVOtHf4/XbiCn47YZtls/CJHtAbLB71r1G4EjxsMiyTJkBvtPhkX73Z1nK9WWgfmMyc1zYM4GrjMrLzMWGQ+3aR8zVKBStM+45tFYXkWI3b/KfGady2AdzZqxCF9EY67/VmdXzjukh1y7KdYLQT/kJl5J3aKp+gd6qrC4mEQVRUCADgYsE1AABXwSChpwrHXmmovIwwBeK4pmhvNqAdz4eqZbCtBK/4XsmxGlwvNgh/8zyPMp0J4wbEg+Ntle/JvEKPFZxjI95bJRgdoUSYWg5WZ/I6Tcue7rYOEizflOEyFevJUaloHKlyubYirjqtSa+ovMNC5ML395XwcN/yVV6Bg+gub4HkBxDLsZ2Zo1Lxx8krou/lPFLqCZOZQ1Q4C+ONHawrSgvY3sfuiNuvKdebK8y7oz750nGjVNxsDDjKpadr3R1P1WpgtXrWne37z2DJk+m4WqDzmhfnckzRalBQYnCb1p5Xdw6xzmBB22YRLtfMHJXqVj8G94jBknVH3D7HYLKg9a2uHZAV4biFkPPeDlX1DF9wlOWKdCgsRI7iMlOF8lyut+0Cb3Uw7gaHzhlP1ysVNluuM1jAVtDD5y0P1Vle1Ymvtq4h4Fx3OOPvN3S2ye7qRHcyE6KSodhLZ5LJzAl21R/76s22hqhkuFqoF9Kx0oqXZzlTWm7yuDbc0fZJJYzQ2bp9/xk8N64LANclSI/d3wmNoz3XhY09nvFMVX/jukp1y7IzwWQnAoXKyDs1XT5B71SHh4ejtLTU5XhxcTEiIlwbo77CcTxKSmwNZ3tPsrUCr9qd02yvNFLjbM6rJ8fzerEBKVqN2yng9mtFx25s4LT4syOiyiwlVoPwEDlOny9ym0dnJ9i++7d9erWclSC/SI/vD+fh4UHx0BlioTNYcGdqc6z++rjLbsmOjXi3lWCcBk+MSILFaEah0VbRPzE8Ce+5Cx3kkM5jrzPPobCC9VbuUMrF38ZbR0FqnAZKVuLzc6RSCcLDVSgp0cNqrXhEVcn6N9LoLT8VOdqOcmyHBdBZq0Fiu8a2WOen8t2OlLpDLpPgerEBhSUGxLaM9JrW1rljK+vunW6F0WQRpu25G51xxq5P9s6jJlEqjzqSHKsBe2OqtF2Wl6w74rGh506nkmM1mDw0Efk3GpHuMJisuF6kR2SYwmvenTuuhvSKwS2N1C4yZz+369A5Ic+OqJWs2+/uTj9YqQSz3v6vaFqoI0q5+3v5g7MeVdUzfOkwcpRlbzpk1xeLioXO4N25CFHJoFawOHf5Zh3CShmh88edPtiWptjqAnuEBm9406mq+CY1ib+2riHgr8wHYpOd60R3MiOVMoJNdYdcJhHsqq/21Z0dtJMSq4FKwWLFpgyh8z3nQhGmDkvCqq3iKcSONtcZi5XD5KGJoogijtfYbR9ws6wNJiuWrDuCIentRDPWIsMU4Di+ynWmuuxaXcOf9/TXJjsSjHbCX6iMvFOV5ePPgFTQO9UxMTEua6dLS0uRn5/vstbaXywW24dUK2xxoQtLPTvFybEa6I1ml2M3w+V0wvpdJ4TKw9nxTGrfGC2ahILnXc89PjwJPA+88/Sd0BkskEoYZOTkY/Fnth2/7ZVZqlaDRwYnoKjEiKhwpUte7TvXlpQa0TO5GVgpg0vXdHhydCpO5hXi+8Pn0D2pGQpLDRh7bwd8+u1faN0sAh1aR+FifjkeHZwAiYRBmc4EqYRBmd6M7345JzTiHStBx7jHDMfD4tAhIQUwY3iS6+6TTukAQM0ywoYkvJWD97FRz6hk4ka4x57wG2uwAnmW1coJMlMRnjoWRvbR4rW1v1ZJfux4ypMcN78DYBuB8GVWwve/nsPC6T3BwPPshJRYDTSRKrw6pRtUShYhShlyzhdBqZAi+UZjzWrlvD7rerEBqXG2zdF2/3oO3ZNuQXONq44kx2owup8WHMdh/qQ0YalAh9bRmDYiCR9tPy66tz0UXPb5QlH4suvFBhzMuoSoMKVnh02rQXioHAUlRqTEanDU3btrNYgMU2DOhK7C0oXdh85h4pBEzByZLMSpVipsDllJuRFtm0e4bG6WHKux2R4vMiXSDwaIbxvtZbMeic/y6QlnPaqOZ3jD8d4eO+du6ItKzqKgxLPNTtXa5FDCWbFiU4Zw7MLVMlwu0HnseBndT4sjJ64I34epYLd8TzMUaqK8qgt/bF19pzpk3jm9c50YHqLA8dxrItl985Pf8PKkbh5HfQtKDNBEqpAcq4HZyiFcLfdqXw1GM6YOS0JRicHlnimxGjzxQBKWbfgDrW+NwOh+WhjNVhw5cRk9k27FlPsTYbwxHTxUJUP2+UIX+2V/TkGJAc0ah2DK0EQh5KhCJoXFapsV17Z5BL4/fA6PDUkUyjTjVD4MJqvLJpC9UpohtX3jKpe92rZrNUVNyLIjwWQnAoXKyDs1XT4Mzwf3dn2rVq3CBx98gH379glrqzdv3oxXXnkFP/30U8AhtaxWDgUFDj2TDGOLyetl92+5lIHeZAvXZG8wAzysVsBoNkMhl8FktoBhJJBKGZTrzYgIVdh2m5MACqkUFo6D0WyF3mhFiIqFUiaFhGGEFUEWnofRzAmjUzIpg5JyExRyKRQyKRgGKC4zQa2SQc5KoDdabDGvVba0BrMFSrlMGKG0I+z+CEBnDzmllsNi5YXwEgo5izVfH8PhP21rvOw7AW/+Mbte7JhpZRhRI1yIndw6GvpKhNBgWQmiokJQWFjul/K7DWsBW/n7GupCownz+gwXOfaWlmGwZvtx3JPmPqaovfMmvk00HhuWCIWEgQWM2927nxiRhPwCPUxWK5o2UkPF2sIbFZYYEBqiwL4/zqN/11awwHUzneQbDTp77yTHAzIpA5PFCqlUir9yryM6XCk4wwUlBsS1isSh45fRvkUkTGYOoWoZNJEq/PrnJRhMHGJbRILngSZRKlg5Hp/vOon/nbi5I/jNuO9WKGQseMDraExybGNMvC8R77uJdW3vGHEMi+ZpJ28AsDDuy9DbNd6+oSdHs6r0sTqeUZEcA+5luaLQMFaGQX6RwWWzMntnJctb8fSKX1BUZkKK1uYwG0xW7Dp0Fo8MTsDJcwWICFWIOl4aR6qw8+BZTLk/Ubz7t4cyYQCsrOZvUlMEauvqO/7IfFXZZHeym6ptjMdHJON9p7wkx2owYWAHRKrlsPDAF9+fxPh7OsDCuLev0x9IAsswuFakw9Ez18EDSGjbCDJWApWChVrBorjcAAkjBQ8eR05cwZkLRZg0JBEnzxXgvS+zBPvWNb4pHhncEWu2H3fJ0+h+WkgY20asW37KxgN9tC55dy5Hd2Vtv1eTSCUk1aQzNWE76wK+vmegNhmw2YklG47iuTEpQWUn/CFYbamvVGX5+CLLdoLeqS4uLsagQYPQtm1bTJ06FVeuXMGiRYtw3333Yf78+QHf162xuDG91MLb1p7o9BaoVSyUcinYG1MALZwVFg6QsdIbsXctCFGxUMtZWHgOZiG2q83plTI2Z8HC8dAbbPeTSaUo1RkResOp1RvMUClYW2xfhoeSlQoxnyuKNegpTaBxCsXXyRAdoYTRYEa50VIvYh5WR3zG2jSOVelUA7byMVis4HkGHM/DYLTcmFrMoFRngkJu6yxiGEAqYSBnTOAkSugcZF0pZ1FSboBSzkIlZ8EAsPKAlbdAJmFh5TiAYSDhrZBIWIc41bbrVQrW1rljNYCFHGa7DihlCJFJwDGM6HlqBQue52AwcyjX2XRIJbd1RpUbxXpaXGaESslCKWNhMFlRrjdDqZBCfqPziuM5SBkGDMPAygFGs7hTqqTcBJXS9l4miwUcZysnvcFiG5G/sda23Cl/Es67XHASCXQOOuTLNd6+YXXHIK3qZ1SmAVcRvEQCs9Wxs1IGtdyWX52ZQ7nBDOWNTkkrx4MHIGcZsGDAA8J7KhUsGDAAw0Mld/0+wRCnOpgbgr5+wyq1yQwDE8fDaLbesEMsZFIGMqkUOpMVOif7JZPY2iFWABarBQopCw4Q2Uu7fQXHodzCCx30KrkUpQYDlDK5zUY71QEMAA4cFDL2xv4YZkEnGAkPpYyF3iVPgJSRwMxZIJf4227hBPuskEkhlzCVCidZESwrAauQoaDYQHGqQU51dRPMttQXyKmuRc6cOYPXX38dGRkZCAkJwdChQzF79mzI5f7HMrbjzViQItigsrDRkJzqmoZk6CbBXBbV6VQDwV22VQmVY8XUJZtM38t3qKzEkFNdvZC8eae2nOqgX1MNAO3atcPHH39c29kgCIIgCIIgiKBn4We/47kxqbWdDYLwGXKqqwmJhEG0l7ANtA3+TagsbNTFcqhIjusKdbHsagsqC/dUhSxT2VYNVI6BUxs2mb6X71BZ+U5FsiyVSupF+6M2IXnzTk2XD03/JgiCIAiCIAiCIIgA8R5UliAIgiAIgiAIgiAIj5BTTRAEQRAEQRAEQRABQk41QRAEQRAEQRAEQQQIOdUEQRAEQRAEQRAEESDkVBMEQRAEQRAEQRBEgJBTTRAEQRAEQRAEQRABQk41QRAEQRAEQRAEQQQIOdUEQRAEQRAEQRAEESDkVBMEQRAEQRAEQRBEgJBTTRAEQRAEQRAEQRABQk41QRAEQRAEQRAEQQQIOdUEQRAEQRAEQRAEESDkVBMEQRAEQRAEQRBEgJBTXYOcOXMGjz76KFJSUtCjRw8sXrwYJpOptrNVbWzduhVxcXEu/9566y1Rus2bN6N///7o1KkThgwZgp9++qmWclw15OXlYf78+Rg6dCg6duyIwYMHu03ny3uXlpbixRdfxO23347U1FQ8+eSTuHr1anW/Qp0nGHTpu+++w+OPP4709HSkpKRg6NCh2LJlC3ieF9KMHz/erY6dOXNGdC+So8AIBjmrDFVp40lG6w4k9yTbtUUwyJ4vdTtQtbL1xx9/YPTo0UhKSsJdd92FDz/80OV5dZHy8nKkp6cjLi4Ox44dE52ri+XD+n0FERDFxcV4+OGH0aZNGyxfvhxXrlzBokWLYDAYMH/+/NrOXrWyZs0ahIWFCb+bNm0q/P3tt9/i5ZdfxrRp09CtWzfs3LkTM2bMwPr165GSklILua08OTk52LdvH5KTk8FxnFvF9PW9Z82ahdOnT+Nf//oXFAoFli1bhilTpuDLL78Eywan+gaLLn388cdo3rw55syZg6ioKPzyyy94+eWXcfnyZcyYMUNId9ttt+GFF14QXduiRQvRb5Ij/wkWOasKqsLGk4zWDUjuxZBs1xzBInu+1O1VKVt5eXmYNGkSevTogVmzZuHUqVN46623IJVKMWnSpNooAp957733YLVaXY7X2fLhiRrhgw8+4FNSUvjCwkLh2BdffMHHx8fzly9frr2MVSNffvklr9Vq+evXr3tMc8899/BPP/206Njo0aP5yZMnV3f2qg2r1Sr8/cILL/CDBg1ySePLe//xxx+8VqvlDxw4IBw7c+YMHxcXx3/77bfVkPP6QbDokju9mTdvHn/bbbcJMjZu3Dj+scce83ofkqPACBY5qwxVZeNJRusOJPc2SLZrnmCRPV/q9qqUrZdffpm/6667eKPRKBxbunQp36VLF9Gxusbp06f5lJQUfsOGDbxWq+WzsrKEc3W1fGj6dw2xf/9+3HHHHYiMjBSODRgwABzH4eDBg7WXsVrk/PnzOHfuHAYMGCA6PnDgQBw6dKjeTvmRSLyrla/vvX//foSHh6NHjx5CmpiYGMTHx2P//v1Vn/F6QrDoUnR0tMux+Ph4lJWVQafT+XwfkqPACBY5q07I1tU/SO59g2S76gkW2auobq9q2dq/fz/69u0LuVwuuldJSQkyMjKq+vWqjAULFuDBBx9E27ZtRcfrcvmQU11D5ObmIiYmRnQsPDwcGo0Gubm5tZSrmmHw4MGIj49H3759sWrVKmEqh/29nRWmXbt2MJvNOH/+fI3ntSbw9b1zc3PRtm1bMAwjShcTE9PgZcYbwaxLv//+O5o2bYrQ0FDh2P/+9z+kpKSgU6dOGDduHH777TfRNSRHgRHMcuYvlbXxJKN1B5J7MSTbNUcwy55j3V6VsqXT6fDPP/+4lGtMTAwYhqmz5bpr1y5kZ2dj+vTpLufqcvnQYo4aoqSkBOHh4S7HIyIiUFxcXAs5qn40Gg1mzpyJ5ORkMAyDH3/8EcuWLcOVK1cwf/584b2dy8X+u6GWi6/vXVJSIlrLZSciIgLHjx+v5lzWXYJRlwDgyJEj2Llzp2j9dNeuXTF06FC0adMGV69exdq1a/Hoo4/is88+Q2pqKgCSo0AJVjnzh6qy8SSjdQeSexsk2zVPsMqec91elbJVWlrq9l5yuRwqlapOlqter8eiRYswe/Zs0QCCnbpcPuRUE9VGr1690KtXL+F3z549oVAo8Mknn2DatGm1mDOCqF9cvnwZs2fPRlpaGiZMmCAcf/LJJ0XpevfujcGDB+O9997D6tWrazqbRJBBNp5oqJBsEzWBp7o9mHn//ffRqFEjjBgxoraz4jc0/buGCA8PF3pEHCkuLkZEREQt5Kh2GDBgAKxWK06cOCG8t3O5lJSUAECDLRdf3zs8PBxlZWUu1webzDgTbLpUUlKCKVOmIDIyEsuXL/e6Zl+tVuPOO+/En3/+KRwjOQqMYJOzqiIQG08yWncgufcMyXb1Emyy56lur0rZso/UOt/LZDJBr9fXuXK9ePEi/vOf/+DJJ59EaWkpSkpKhD1kdDodysvL63T5kFNdQ7hbP1NaWor8/HyXufzBgv29ncslNzcXMpkMLVu2rI1sVTu+vndMTAzOnj3rEpLr7NmzQSszQHDpksFgwNSpU1FaWuoS2sVXSI4CI5jkrLogW1f/ILn3DZLtqieYZM9b3V6VsqVWq3Hrrbe63Mt+XV0r1wsXLsBsNuOxxx5D165d0bVrV2FmyIQJE/Doo4/W6fIhp7qGSE9Pxy+//CL0pAC2hfgSiUS0M11DZ+fOnZBKpejYsSNatmyJNm3aYNeuXS5p7rjjDtFOfA0JX987PT0dxcXFOHTokJDm7Nmz+Ouvv5Cenl6jea5LBIsuWSwWzJo1C7m5uVizZo0oPqondDod/vvf/6JTp07CMZKjwAgWOatqArHxJKN1B5J7z5BsVy/BInsV1e1VLVvp6enYu3cvzGaz6F7h4eHC3it1hfj4eHz66aeif3PnzgUAvPrqq3jllVfqdPnQmuoa4sEHH8Rnn32G6dOnY+rUqbhy5QoWL16MBx980KfGcn1k0qRJSEtLQ1xcHABg79692LRpEyZMmACNRgMAmDlzJp599lm0atUKaWlp2LlzJ7KysrBu3brazHql0Ov12LdvHwDbVJaysjJB+W+//XZER0f79N6pqano2bMnXnzxRbzwwgtQKBR4++23ERcXh3vuuadW3q0uECy69Oqrr+Knn37CnDlzUFZWhqNHjwrnOnbsiKysLKxZswZ33303mjdvjqtXr+Kjjz5Cfn4+3nnnHSEtyVFgBIucVYaqsvEko3UHknsbJNs1T7DIXkV1u1wur1LZmjRpEnbs2IFnnnkGY8aMQXZ2NtauXYvZs2fXucGr8PBwpKWluT2XkJCAhIQEAFWre1VZPgzvPC5OVBtnzpzB66+/joyMDISEhGDo0KF1UqirigULFuDAgQO4fPkyOI5DmzZtMHLkSIwfP160xf3mzZuxevVqXLp0CW3btsXTTz+Nu+66qxZzXjkuXLiAvn37uj336aefCgbDl/cuLS3FwoUL8cMPP8BisaBnz56YN29eg6pgAiEYdKlPnz64ePGi23N79+6F1WrFa6+9hlOnTqGoqAgqlQqpqamYMWMGkpKSROlJjgIjGOSsMlSljScZrTuQ3JNs1xbBIHsV1e0tWrQAULWy9ccff2DRokU4ceIEoqOjMXbsWEyZMsUl3FRd5PDhw5gwYQK2bNkimoVXF8uHnGqCIAiCIAiCIAiCCBBaU00QBEEQBEEQBEEQAUJONUEQBEEQBEEQBEEECDnVBEEQBEEQBEEQBBEg5FQTBEEQBEEQBEEQRICQU00QBEEQBEEQBEEQAUJONUEQBEEQBEEQBEEECDnVBEEQBEEQBEEQBBEg5FQTIuLi4rB8+fIafeacOXPQp0+fWs8HUbfJysrCgw8+iJSUFMTFxeHEiRO1nSWCqFb69OmDOXPm1HY2CKLOcOHCBcTFxWHr1q21nRUiSKkr7eS6ArXXb0JOdQPmjz/+wPLly1FSUlLbWakRduzYgY8//ri2s0FUA2azGbNmzUJRURHmzp2LxYsXo1mzZrWdraBj/fr11JitBoLNVtckH3zwAfbs2VPb2WgwVFZWyYZUH9QG8h+yvb6xb98+cpx9gK3tDBDVR0ZGBlasWIFhw4YhPDzcp2uysrIglUqrOWfVk49vvvkGOTk5eOSRR6onU0St8ffff+PixYtYsGABRo4cWdvZCVo2bNiAqKgoDB8+vLaz0qDwZKt37doFhmFqMWf1n1WrVqF///7o169fbWelQRBIu8IRsiHVB7WB/Kc+t5Nrkn379mH9+vWYOXOmy7lgLA9P0Eg1AY7jYDQaAQAKhQIsW/t9LXUlH0TdoKCgAAAQFhZWJffT6XRVch+CqE7kcjlkMlltZ4MgCCKoqYvt5MpQlW2ghlAeVQU51Q2U5cuXY/HixQCAvn37Ii4uDnFxccJ6pNdeew3bt2/HoEGD0KlTJxw4cACA69qI5cuXIy4uDmfOnMFTTz2F2267DWlpaViwYIFgYPxhz549GDx4MDp16oTBgwfjhx9+cJvOOR9lZWX497//jT59+iAxMRF33HEHHn30Ufz5558AgPHjx+O///0vLl68KLyrff2JyWTCO++8g+HDh6Nz585ISUnBQw89hF9//VX0THvZrF27Fhs3bkS/fv2QmJiIESNGICsryyWP9jLp1q0bkpKS0L9/f7z99tuiNFeuXMHcuXPRvXt3JCYmYtCgQdiyZYvf5RbMzJkzB+PGjQMAPPXUU4iLi8P48eNx8uRJzJkzB3379kWnTp3Qo0cPzJ07F4WFhaLr7TJ8+vRpPPPMM+jatSseeugh4fzXX3+N4cOHIykpCbfffjtmz56Nf/75x688FhUV4c0338R9992H1NRU3HbbbZg8eTJOnjwpSnf48GHExcVh586dWLFiBXr16oXU1FQ8+eSTKC0thclkwr///W/ccccdSE1Nxdy5c2EymUT3sFgsWLlypSCfffr0wf/93/+5pPO0zsl5ne7WrVsRFxeH33//HQsXLkS3bt2QkpKC6dOnC50Z9utycnLwv//9T9Cx8ePH+1VOhCvebLWnb/Xbb79h/vz5SEtLw2233Ybnn38excXFfj334sWL+Ne//oX+/fsjKSkJaWlpePLJJ3HhwgVROvszjxw5ggULFqBbt27o0qUL5s+fD5PJhJKSEjz//PPo2rUrunbtisWLF4PnedE9dDodFi1ahDvvvBOJiYno378/1q5dK0rnba2sp3opLy8Pc+bMQZcuXdC5c2fMnTsXer1edJ1Op8O2bduEcqU16oHjTVZ9sUvebIivNjQQAmkDrF+/Hn379kVycjImTpyIf/75BzzPY+XKlUhPT0dSUhIef/xxFBUVuTxv/fr1GDRoEBITE9GzZ0+8+uqrLtOLPe2XMH78eJFddawz3n//faSnp6NTp054+OGHkZeXJ7rOUxuIcE99bifb5eLw4cOi4+7s6Jw5c5Camoq///4bU6ZMQWpqKp599lkAwJEjR/Dkk0+id+/eSExMxJ133ok33ngDBoNBdP369euFd7f/s+OurfHXX39h8uTJuO2225CamoqHH34YR48eFaXxte1Rn6CuhQbK3XffjXPnzuGbb77B3LlzERUVBQCIjo4GAPz666/47rvvMHbsWERFRaF58+Ze7zdr1iw0b94czzzzDI4ePYrPPvsMJSUlgkHyhZ9//hkzZ85E+/bt8cwzz6CwsBBz587FLbfcUuG1r7zyCnbv3o1x48ahXbt2KCoqwu+//44zZ84gISEB06ZNQ2lpKS5fvoy5c+cCAEJCQgDYHPLNmzdj8ODBGDlyJMrLy7FlyxZMnjwZmzdvRnx8vOhZ33zzDcrLyzF69GgwDIM1a9Zg5syZ2LNnjzBqdPLkSYwdOxYsy2L06NFo3rw5/v77b/z444+YPXs2AODatWsYNWoUGIbB2LFjER0djf379+Oll15CWVkZTdHykdGjR6Np06b44IMPMH78eHTq1AmNGzfGL7/8gvPnz2P48OHQaDTIycnBpk2bcPr0aWzatMll2uxTTz2F1q1bY/bs2UJj/v3338c777yDAQMG4IEHHkBBQQHWrVuHsWPH4quvvvJ5Otj58+exZ88e3HvvvWjRogWuXbuGjRs3Yty4cfj222/RtGlTUfoPP/wQSqUSjz32GPLy8rBu3TqwLAuGYVBSUoIZM2YgMzMTW7duRfPmzTFjxgzh2nnz5mHbtm3o378/Hn30UWRlZWHVqlU4c+YMVq5cGXA5L1iwAOHh4ZgxYwYuXryITz75BK+99hqWLVsGAHjxxRfx+uuvQ61WY9q0aQCAxo0bB/w8wkZFttodr732mvCtzp49iw0bNuDSpUv47LPPfJ4ufuzYMWRkZGDQoEG45ZZbcPHiRWzYsAETJkzAt99+C5VKJUq/YMECNG7cGDNnzkRmZiY2btyIsLAwZGRk4NZbb8Xs2bOxf/9+rF27FlqtFvfffz8AgOd5PP744zh8+DAeeOABxMfH48CBA1i8eDGuXLmCF198MbCCg61eatGiBZ5++mn89ddf2Lx5M6Kjo/Hcc88BABYvXox58+YhKSkJo0aNAgC0atUq4OcFO95k1Re75M2G+GtD/cHfNsCOHTtgNpsxfvx4FBUVYc2aNZg1axa6deuGw4cPY8qUKYLdfvPNN7Fw4ULh2uXLl2PFihXo3r07xowZI+jnsWPHsGHDhoBnnqxevRoMw2DixIkoKyvDmjVr8Oyzz2Lz5s0A4LUNRLinobWTvWGxWDBp0iR07twZL7zwApRKJQDbEiODwYAxY8YgMjISWVlZWLduHS5fvox3330XgK0NdvXqVRw8eNCnd8nJycHYsWMREhKCyZMng2VZbNy4EePHj8e6deuQnJwsSl9R26NewRMNljVr1vBarZY/f/686LhWq+U7dOjA5+TkuFyj1Wr5d999V/j97rvv8lqtlp82bZoo3b/+9S9eq9XyJ06c8Dk/Q95dbPkAABGeSURBVIcO5Xv06MGXlJQIx37++Wdeq9Xyd911l9d8dO7cmX/11Ve93v+xxx5zuQ/P87zFYuGNRqPoWHFxMd+9e3d+7ty5wrHz58/zWq2Wv/322/mioiLh+J49e3itVsv/+OOPwrGxY8fyqamp/MWLF0X35ThO+PvFF1/ke/TowRcUFIjSzJ49m+/cuTOv1+u9vg9xk19//ZXXarX8d999JxxzV37ffPMNr9Vq+d9++004Zpfhp59+WpT2woULfHx8PP/++++Ljp86dYrv2LGjy3FvGI1G3mq1io6dP3+eT0xM5FesWOHyHoMHD+ZNJpNw/Omnn+bj4uL4yZMni+4xevRokUyfOHGC12q1/EsvvSRKt2jRIl6r1fKHDh0SjjnrkJ277rqLf+GFF4TfX375Ja/VavlHHnlEJL9vvPEGHx8fL9LXQYMG8ePGjauwPAj/8GSrPX2rYcOGieRn9erVvFar5ffs2ePzM93pT0ZGBq/Vavlt27a5PHPixIki+Rg9ejQfFxfHz58/XzhmsVj49PR0kYz88MMPvFar5d977z3Rs2bOnMnHxcXxeXl5PM/ftL9ffvmlS7481UuO9pvneX769On87bffLjqWkpIiKkOicriTVX/skicb4qsN9SYnnvC3DdCtWzeR3Vu6dCmv1Wr5IUOG8GazWTj+9NNP8wkJCcK9r1+/zickJPATJ04Uvcu6det4rVbLb9myRTjmrNt2xo0bJyofe50xYMAA0Tt88sknvFar5U+dOiUc89QGIjxTX9vJdrn49ddfRde7048XXniB12q1/FtvveXyPHf1wKpVq/i4uDhR+/bVV1/ltVqt2zw7l8cTTzzBJyQk8H///bdw7MqVK3xqaio/duxY4Zg/bY/6Ak3/DlK6du2K9u3b+5x+7Nixot/26bj79+/36fqrV6/ixIkTGDZsmGhdbI8ePXzKR3h4ODIzM3HlyhWf82xHKpVCLpcDsK2LKSoqgsViQWJiIv766y+X9AMHDkRERITwu0uXLgBsPemAbX3vb7/9hhEjRrjsQG0fJeJ5Ht9//z369OkDnudRUFAg/OvZsydKS0uFqetEYNh7WgHAaDSioKBA6AF1V7YPPvig6PcPP/wAjuMwYMAA0fdp3LgxWrdu7TKtyhtyuRwSic2cWq1WFBYWQq1Wo23btm5lbOjQoaIRi6SkJPA8jxEjRojSJSUl4Z9//oHFYgFg2ywEAB599FFRuokTJ4rOB4J9VoWdLl26wGq14uLFiwHfk6geRo8eLZKfMWPGgGVZv76/o/6YzWYUFhaiVatWCA8PdyuzDzzwgEg+7DL7wAMPCMekUikSExMFWwnY6gipVOqyVGDixInged7nOsQdzjrdpUsXFBUVoaysLOB7Ev5TFXbJXxvqD/62Ae69915ROyUpKQkAMGTIENHa0aSkJJjNZqFd8ssvv8BsNmPChAnCuwDAyJEjERoaWin7PHz4cOEdANd2CVH11Ld2ckWMGTPG5ZhjPaDT6VBQUIDU1FTwPB+Q3lmtVhw8eBD9+vVDy5YtheNNmjTB4MGD8fvvv7vY54bU9qDp30FKixYt/ErfunVr0e9WrVpBIpG4rL/zxKVLl9zeB4BPleazzz6LOXPmoHfv3khISMCdd96J+++/X6S03ti2bRv+85//4OzZszCbzcJxd+Vw6623in7bHWz7mih7JabVaj0+r6CgACUlJdi4cSM2btzoMQ0ROEVFRVixYgV27tyJ69evi86Vlpa6pHf+1ufOnQPP87jnnnvc3t+fjTc4jsOnn36Kzz//HBcuXIDVahXORUZGuqR37oyxV6DOshcWFgaO41BaWoqoqChcvHgREonEZQqrRqNBeHh4pSoh5zzZp75TqJG6h7MdDQkJgUaj8ev7GwwGrFq1Clu3bsWVK1dE65vd6Y8/Muu4vvvixYto0qQJQkNDRenatWsnnA8UTzJbXFzs8jyi+qgKu+SvDfWXyrQBvMk6YJO3li1bCu2cmJgYUTq5XI6WLVuSfa5n1Ld2sjdYlnU7hfzSpUt499138eOPP7rsyxFI52RBQQH0ej3atm3rcq5du3bgOA7//PMPYmNjheMNSbbJqQ5SHHunAqGmw7wMHDgQXbp0wQ8//ICDBw9i7dq1WL16NZYvX44777zT67Vff/015syZg379+mHSpElo1KgRpFIpVq1a5baX11NoAN5p8x1vcBwHwNazPWzYMLdpHDd6IPxn1qxZyMjIwKRJkxAfHw+1Wg2O4zB58mS330qhUIh+cxwHhmGwevVqt99crVb7nJcPPvgA77zzDkaMGIGnnnoKERERkEgkeOONN9zmxXEUw5fjzveojP45NlYDeTbRMHj99dexdetWPPzww0hJSUFYWBgYhhHtOeCIvzLrL55k2pO8ens2yWztUBm75K8N9YeqagPUhLxZrVa3zydZr3nqcjvZ073tbU9nHGeC2LFarXj00UdRXFyMyZMnIyYmBmq1GleuXMGcOXM83quqaUiyTU51A6YqFTovL080KpyXlweO43zuybP3RDnuVmnn7NmzPt2jSZMmGDt2LMaOHYvr169j2LBh+OCDDwSn2tP77t69Gy1btsSKFStEaeybMPiLvRyys7M9pomOjkZISAg4jkP37t0Deg7hmeLiYhw6dAgzZ84UbeJ17tw5n+/RqlUr8DyPFi1auO1V9Yfdu3cjLS0Nb7zxhuh4SUmJsPlJVdC8eXNwHIe8vDxhpA+wbYpXUlIi2kglIiLCpafXZDIhPz8/4OdTzOTqwd9yzcvLQ7du3YTf5eXlyM/PR3p6us/32L17N+6//37RDsRGo9HtKHVlaN68OQ4dOoSysjLR6HFubq5wHnCdEWTHPnpD1A3cyao/dslbPV1dNrSq2wCesLdzcnNzRe0lk8mECxcuiNoC7uwzYJN3X2fgOUP22X/qazvZPprrbK/9mQ2RnZ2Nc+fO4c033xQ2lgSAgwcPuqT1tZyio6OhUqnctutzc3MhkUhcZnw0JGhNdQPGvntrVTSS7Nvp21m3bh0A+NyIa9KkCeLj47Ft2zZRfg4ePIjTp097vdZqtbq8Q6NGjdCkSRNRuA6VSuX2Xe29vo69XpmZmS7b+/tKdHQ0unbtii+//NKlwWd/hlQqRf/+/bF79263zjdN/a4cnkYSPvnkE5/vcc8990AqlWLFihUuPaI8z7uE5qooP873+O677wLaA8Ab9g4k5/f86KOPROcBW+fPkSNHROk2bdrkdeSvIlQqVb2cklXX8ddWb9y4UTSFdcOGDbBYLH451e506LPPPquUfLgjPT0dVqvVpQ75+OOPwTCMkOfQ0FBERUW5yOznn39eqeer1WqS2SrEnaz6Y5c82ZDqtKFV3QbwRPfu3SGTyfDZZ5+JnrVlyxaUlpa62OfMzExRG+ann37yO5yjI57aQIRn6ms7uXnz5pBKpfjtt99Exzds2OBzfu0jxI6yyvM8Pv30U5e09nKqyJZKpVL06NEDe/fuFU17v3btGr755ht07ty5QS/NoZHqBkxCQgIA4O2338bAgQMhk8lw1113BXSvCxcuYNq0aejVqxeOHj2K7du3Y/DgwejQoYPP93j66acxdepUPPTQQxgxYgSKioqwbt06xMbGeg1EX15ejjvvvBP9+/dHhw4doFar8csvv+DYsWOiUZaEhATs3LkTCxcuRKdOnaBWq9GnTx/07t0b33//PaZPn47evXvjwoUL+OKLL9C+fXuvz/XGvHnzMGbMGAwbNgyjR49GixYtcPHiRfz3v//F119/DQB45plncPjwYYwaNQojR45E+/btUVxcjD///BOHDh3C//73v4CeTdga4F27dsWaNWtgNpvRtGlTHDx40Oe1S4BtpHrWrFlYunQpLl68iH79+iEkJAQXLlzAnj17MGrUKEyaNMmne/Xu3RsrV67E3LlzkZqaiuzsbOzYsSPgEQdPdOjQAcOGDcPGjRtRUlKCrl274tixY9i2bRv69esnGr0cOXIkXnnlFcycORPdu3fHyZMn8fPPP1dq1CchIQEbNmzAe++9h9atWyM6Ohp33HFHVbxaUOOvrTabzXjkkUcwYMAAnD17Fp9//jk6d+6Mvn37+vzM3r174+uvv0ZoaCjat2+Po0eP4pdffqmS9auO9OnTB2lpaXj77beFGLoHDx7E3r178fDDD4vW4Y4cORIffvghXnrpJSQmJuLIkSM+z2TyREJCAg4dOoSPPvoITZo0QYsWLVxCuhC+40lWfbVLnmxIddrQ6mgDuCM6OhpTp07FihUrMHnyZPTp00fQz06dOmHIkCFC2pEjR2L37t2YPHkyBgwYgL///hs7duyoVMg3T20gwjP1tZ0cFhaGe++9F+vWrQPDMGjZsiX++9//uuwv442YmBi0atUKb775Jq5cuYLQ0FDs3r3breNsL6cFCxagZ8+ekEqlGDRokNv7zpo1C7/88gseeughPPTQQ5BKpdi4cSNMJpMQ7rChQk51AyYpKQlPPfUUvvjiCxw4cAAcx2Hv3r0B3WvZsmV45513sHTpUrAsi3HjxuH555/36x7p6el45513sGzZMixduhStWrXCwoULsXfvXq8OplKpxJgxY3Dw4EF8//334HkerVq1wiuvvIKHHnpISPfQQw/hxIkT2Lp1Kz7++GM0b94cffr0wfDhw4WYlz///DPat2+PJUuWYNeuXQE7th06dMCmTZvwzjvvYMOGDTAajWjWrBkGDBggpGncuDE2b96MlStX4ocffsCGDRsQGRmJ9u3b49lnnw3oucRNli5ditdffx2ff/45eJ5Hjx49sHr1avTq1cvnezz22GNo06YNPv74YyGW6i233IIePXr41RiZNm0a9Ho9duzYgZ07d6Jjx45YtWoVli5d6vd7VcSCBQvQokULbNu2DXv27EHjxo0xdepU0TR4wLaj5oULF7BlyxYcOHAAnTt3xkcffVSp+OjTp0/HpUuXsGbNGpSXl+P2228np7oK8NdWz58/Hzt27MC7774Ls9mMQYMGYd68eX5NZXzppZcgkUiwY8cOGI1G3Hbbbfjoo48wefLkqnglAYlEgvfffx/vvvsudu7cKcRef/7554Xdoe1Mnz4dBQUF2L17N7777jukp6djzZo1lZKxOXPmYP78+Vi2bBkMBgOGDRtGTnUl8CSrvtolTzakOm1odbQBPDFz5kxER0dj3bp1WLhwISIiIjBq1Cg8/fTToh37e/XqhTlz5uCjjz7CG2+8gcTERHzwwQd48803A362pzYQ4Zn63E6eN28eLBYLvvjiC8jlctx77714/vnnMXjwYJ+eJZPJ8MEHH2DBggVYtWoVFAoF7r77bowdOxZDhw4Vpb3nnnswfvx4fPvtt9i+fTt4nvfoVMfGxmL9+vVYunQpVq1aBZ7nkZSUhCVLljR428vw9XElOFFjLF++HCtWrMChQ4cQHR1d29khCIIIWrZu3Yq5c+diy5Yt6NSpU21nhyAIIuihdjJhh9ZUEwRBEARBEARBEESA0PRvotKUlpbCYDB4TaPRaGooNwRRdRgMhgo3MImIiIBcLq+hHBGEd8rLyytcJxodHe1xsz+CqC+YTCaX2LrOhIWFVTo0EkFUFmonBwfkVBOV5t///je2bdvmNc2pU6dqKDcEUXXs3LkTc+fO9Zrm008/RVpaWg3liCC885///AcrVqzwmmbv3r0+h3khiLpKRkYGJkyY4DXNwoULMXz48BrKEUG4h9rJwQGtqSYqzenTp3H16lWvaShWM1EfuXr1aoUh3xISEoQYuwRR25w/fx7nz5/3mqZz585QKBQ1lCOCqB7s0TS80b59ezRp0qSGckQQ7qF2cnBATjVBEARBEARBEARBBAhtVEYQBEEQBEEQBEEQAUJONUEQBEEQBEEQBEEECDnVBEEQBEEQBEEQBBEg5FQTBEEQBEEQBEEQRICQU00QBEEQBEEQBEEQAUJONUEQBEEQBEEQBEEECDnVBEEQBEEQBEEQBBEg5FQTBEEQBEEQBEEQRID8P2U6hpR1l3/jAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A count plot is a type of categorical plot that displays the number of occurrences of each category in a dataset. It is particularly useful for visualizing the distribution and frequency of categorical variables. Here are some key uses and benefits of count plots:\n",
        "\n",
        "  - Categorical Variable Exploration: Count plots provide a quick and concise summary of the distribution of categorical variables. They allow analysts to understand the frequency or count of each category, revealing the proportions and imbalances within the dataset. This information is crucial for gaining insights into the composition and characteristics of categorical variables.\n",
        "\n",
        "  - Class Imbalance Assessment: In classification tasks, count plots help assess the balance or imbalance of different target classes. It provides a visual representation of the distribution of classes, highlighting any significant discrepancies in the sample sizes across categories. Identifying imbalanced classes is important in machine learning as it can affect model performance and bias the predictions towards the majority class.\n",
        "\n",
        "  - Data Quality Inspection: Count plots can be utilized to detect data quality issues in categorical variables. It allows analysts to identify unexpected or erroneous categories that may indicate data entry errors, missing data, or inconsistencies in the dataset. By observing the counts for each category, anomalies or discrepancies can be easily spotted, enabling data cleaning or further investigation if necessary.\n",
        "\n",
        "  - Feature Importance Evaluation: Count plots can provide insights into the importance or relevance of different categorical features in relation to the target variable. By visualizing the distribution of categories within each class or target level, analysts can determine which categories are more prevalent or have higher frequencies for specific outcomes. This understanding helps in assessing the discriminatory power of categorical features and their potential impact on predictive models.\n",
        "\n",
        "Here is your task:\n",
        "\n",
        "  1. Use Seaborn to create a [countplot](https://seaborn.pydata.org/generated/seaborn.countplot.html) for the variables PULocationID, and DOLocationID. Keep only the top 15 pick-up and drop-off locations. What's the most popular pick-up location? **Location 237 (Manhattan - Upper East Side South)**"
      ],
      "metadata": {
        "id": "OA1p83hesFkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Seaborn countplot for PULocationID and DOLocationID. Only plot the top 15 categories by value counts.\n",
        "\n",
        "# Get columns for LocationID\n",
        "locdata = tripdata[['PULocationID', 'DOLocationID']]\n",
        "# Rename columns\n",
        "# https://stackoverflow.com/questions/11346283/renaming-column-names-in-pandas\n",
        "locdata = locdata.rename(columns={'PULocationID': 'PU', 'DOLocationID': 'DO'})\n",
        "# Convert to long format (melt LocationID columns)\n",
        "# https://www.geeksforgeeks.org/python-pandas-melt/\n",
        "locdata['id'] = locdata.index\n",
        "locdata = pd.melt(locdata, id_vars = ['id'], value_vars = ['PU', 'DO'])\n",
        "# Rename columns\n",
        "locdata = locdata.rename(columns={'variable': 'Action', 'value': 'LocationID'})\n",
        "# Get list of top 15 PU and DO locations\n",
        "top_locations = locdata['LocationID'].value_counts().head(15).index.tolist()\n",
        "# Extract rows that have top PU and DO locations\n",
        "top_locdata = locdata[locdata['LocationID'].isin(top_locations)]\n",
        "# Make plot\n",
        "sns.countplot(top_locdata, x = 'LocationID', hue = 'Action')"
      ],
      "metadata": {
        "id": "eigDjtkTruQD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "543dc498-0e82-419e-c0bf-2828ab4ddc5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='LocationID', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "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": [
        "A box plot, also known as a box-and-whisker plot, is a powerful visualization tool for displaying the distribution, variability, and outliers within a numerical dataset. It provides a concise summary of key statistical measures and offers several important uses:\n",
        "\n",
        "  - Data Distribution and Skewness: Box plots offer a visual representation of the distribution of numerical data, providing insights into its central tendency, spread, and skewness. The box represents the interquartile range (IQR), which contains the middle 50% of the data, with the median indicated by a horizontal line within the box. By observing the length and symmetry of the box, analysts can assess whether the data is skewed or symmetrically distributed.\n",
        "\n",
        "  - Outlier Detection: Box plots are highly effective in identifying outliers, which are data points that deviate significantly from the rest of the distribution. The whiskers of the plot extend to the minimum and maximum non-outlier values, with any data points beyond the whiskers considered as potential outliers. Outliers can indicate data errors, anomalies, or important observations requiring further investigation.\n",
        "\n",
        "  - Comparing Groups or Categories: Box plots are useful for comparing the distributions of numerical data across different groups or categories. By creating side-by-side or grouped box plots, analysts can easily compare the central tendencies, spreads, and shapes of distributions between different groups. This allows for the identification of differences, similarities, or patterns within the data.\n",
        "\n",
        "  - Variability and Spread: Box plots provide insights into the variability and spread of the data. The length of the box indicates the spread of the middle 50% of the data, while the whiskers show the range of non-outlier values. By comparing the lengths of the boxes and whiskers, analysts can assess the relative variability between different groups or categories, aiding in the understanding of the data's dispersion.\n",
        "\n",
        "  - Skewedness and Symmetry: Box plots offer a visual assessment of the skewness or symmetry of the data distribution. A symmetrical distribution is represented by a box plot with an equal length on both sides of the median, while a skewed distribution is indicated by a longer box on one side. This visual cue helps in understanding the shape and characteristics of the data, assisting in further analysis and modeling decisions.\n",
        "\n",
        "  - Data Range and Quartiles: Box plots display the quartiles of the data distribution. The lower quartile (Q1) represents the 25th percentile, the upper quartile (Q3) represents the 75th percentile, and the interquartile range (IQR) is the range between Q1 and Q3. These quartiles provide a summary of the range and spread of the central portion of the data, aiding in the understanding of the data's variability and dispersion.\n",
        "\n",
        "Your task is:\n",
        "\n",
        "  1. Use Seaborn's [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) to discern the relationship between payment_type and total_amount. Does anything look weird? Can you explain what's going on? **(1) The boxes are very small, and there are a lot of potential outliers. However, most of these are probably real values. (2) The most negative values are associated with payment types 3 (No charge) and 4 (Dispute), but some also are associated with type 2 (Cash). Maybe some of these are fares that were unpaid for some reason.**"
      ],
      "metadata": {
        "id": "Bbl-WZtctVbH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fM_2IGKrk8Vy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "e72a57b4-ec6a-40c7-daa9-d227b2147be6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='payment_type', ylabel='total_amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a box plot of total amount by payment type. Do you see anything odd?\n",
        "sns.boxplot(tripdata_sampled, x = 'payment_type', y = 'total_amount')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A histogram is a graphical representation that displays the distribution of a continuous or discrete numerical variable. It provides insights into the underlying data distribution and helps uncover patterns, frequencies, and ranges within the dataset. Here are some key uses and benefits of histogram plots:\n",
        "\n",
        "  - Data Distribution: Histograms allow analysts to visualize the shape, central tendency, and spread of the data. They provide an overview of the data distribution, helping to identify if it follows a particular pattern, such as a normal distribution, skewed distribution, bimodal distribution, or multimodal distribution. Understanding the data distribution aids in selecting appropriate analysis techniques and understanding the characteristics of the data.\n",
        "\n",
        "  - Frequency Analysis: Histograms display the frequency or count of data points within predefined bins or intervals along the x-axis. By observing the height or count of each bin, analysts can identify the frequency of occurrence for different values or value ranges. This information helps assess the concentration of data points and identify peaks or modes in the distribution.\n",
        "\n",
        "  - Outlier Detection: Histograms can assist in identifying outliers, which are data points that significantly deviate from the rest of the distribution. Outliers may indicate data errors, unusual observations, or important anomalies requiring further investigation. By examining the tails or extreme values in the histogram, analysts can identify potential outliers that may require additional scrutiny.\n",
        "\n",
        "  - Data Range and Spread: Histograms provide insights into the range and spread of the data. The x-axis represents the variable's values, while the y-axis represents the frequency or count of occurrences. By observing the width and span of the histogram, analysts can assess the data's range and variability. This information helps understand the data's spread and aids in subsequent analysis or decision-making processes.\n",
        "\n",
        "  - Feature Engineering: Histograms can guide feature engineering processes by informing appropriate transformations, binning strategies, or encoding techniques for numerical variables. They assist in identifying nonlinear relationships or determining optimal cut-off points for converting continuous variables into categorical ones. Histograms also help identify data skewness and guide transformation methods to address the skew if necessary.\n",
        "\n",
        "  - Data Quality Inspection: Histograms can be useful in detecting data quality issues or anomalies. Unusual spikes, gaps, or unexpected patterns in the histogram may indicate data entry errors, measurement inconsistencies, or missing data. By observing the histogram, analysts can identify potential data quality issues that require further investigation or preprocessing.\n",
        "\n",
        "Your task is:\n",
        "\n",
        "  1. Use Seaborn's [histplot](https://seaborn.pydata.org/generated/seaborn.histplot.html) to explore the data distributions for fare_amount, trip_distance, and extra. Use kernel density estimators to better visualize the distribution. Use sampling if you run into any memory issues. **I tried making the plots with kde = True, but I can \"see more\" by playing with the range and number of bins. The peak in fares above $50 is probably for airport trips.**"
      ],
      "metadata": {
        "id": "CnV0dIHquPtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore data distributions for 'fare_amount', 'trip_distance' and 'extra' using Seaborn's histplot. Sample the data if you run into memory issues.\n",
        "\n",
        "sns.histplot(tripdata_sampled, x = 'fare_amount', bins = 24, binrange = [0, 60])\n",
        "#sns.histplot(tripdata_sampled, x = 'fare_amount', kde = True)\n",
        "plt.show()\n",
        "\n",
        "sns.histplot(tripdata_sampled, x = 'trip_distance', bins = 20, binrange = [0, 25])\n",
        "#sns.histplot(tripdata_sampled, x = 'trip_distance', kde = True)\n",
        "plt.show()\n",
        "\n",
        "sns.histplot(tripdata_sampled, x = 'extra', binrange = [0, 5])\n",
        "#sns.histplot(tripdata_sampled, x = 'extra', kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "8HI3uXHCyI_B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "372898fa-0811-4cca-f2b9-72ac1a38f50a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}