From a9df79bbacf6be3212c5e2fd799dcdf8dc155309 Mon Sep 17 00:00:00 2001 From: Sayantika Laskar <127471376+SayantikaLaskar@users.noreply.github.com> Date: Sat, 8 Jun 2024 18:31:40 +0530 Subject: [PATCH] Add house price prediction --- .../House_Price_Prediction.ipynb | 3900 +++++++++++++++++ House Price Prediction/README.md | 39 + 2 files changed, 3939 insertions(+) create mode 100644 House Price Prediction/House_Price_Prediction.ipynb create mode 100644 House Price Prediction/README.md diff --git a/House Price Prediction/House_Price_Prediction.ipynb b/House Price Prediction/House_Price_Prediction.ipynb new file mode 100644 index 000000000..29b0f1df0 --- /dev/null +++ b/House Price Prediction/House_Price_Prediction.ipynb @@ -0,0 +1,3900 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## **Installing Modules and Libraries**" + ], + "metadata": { + "id": "-BXUCl3p1hJe" + } + }, + { + "cell_type": "code", + "source": [ + "pip install pandas numpy scikit-learn tensorflow seaborn matplotlib" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Oin1JH9D1qsw", + "outputId": "c37ef970-1e14-4a0b-8d26-a63291cbca1b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n", + "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n", + "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) 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requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ozu1tq-E279q" + }, + "source": [ + "**LINEAR REGRESSION**\n", + "\n", + "Linear regression is a fundamental supervised learning algorithm in machine learning. It aims to establish a linear relationship between a dependent variable (target) and one or more independent variables (features). In the context of house price prediction, the dependent variable will be the house price, and the independent variables can be factors like the size of the house, number of bedrooms, location, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mDWl_oEU3CK-" + }, + "source": [ + "**MACHINE LEARNING PIPELINE FOR HOUSE PRICE PREDICTION**\n", + "\n", + "1. **Dataset Collection:** Gather historical house price data and corresponding features from platforms like Zillow or Kaggle.\n", + "2. **Data Preprocessing:** Clean the data, handle missing values, and perform feature engineering, such as converting categorical variables to numerical representations.\n", + "3. **Splitting the Dataset:** Divide the dataset into training and testing sets for model building and evaluation.\n", + "4. **Building the Model:** Create a linear regression model to learn the relationships between features and house prices.\n", + "5. **Model Evaluation:** Assess the model’s performance on the testing set using metrics like MSE or RMSE.\n", + "6. **Fine-tuning the Model:** Adjust hyperparameters or try different algorithms to improve the model’s accuracy.\n", + "7. **Deployment and Prediction:** Deploy the robust model into a real-world application for predicting house prices based on user inputs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6YLpNWDu1ni-" + }, + "source": [ + "Importing the Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PbF6vplH1fa9" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UQ_RaOJs1s7x" + }, + "outputs": [], + "source": [ + "# Load the dataset from CSV\n", + "df = pd.read_csv('data.csv')" + ] + }, + { + "cell_type": "code", + "source": [ + "df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 808 + }, + "id": "NxOCK1wuOmUW", + "outputId": "f04b1718-ee00-4e2d-d69c-df9c971a8c58" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " date price bedrooms bathrooms sqft_living \\\n", + "0 2014-05-02 00:00:00 3.130000e+05 3.0 1.50 1340 \n", + "1 2014-05-02 00:00:00 2.384000e+06 5.0 2.50 3650 \n", + "2 2014-05-02 00:00:00 3.420000e+05 3.0 2.00 1930 \n", + "3 2014-05-02 00:00:00 4.200000e+05 3.0 2.25 2000 \n", + "4 2014-05-02 00:00:00 5.500000e+05 4.0 2.50 1940 \n", + "... ... ... ... ... ... \n", + "4595 2014-07-09 00:00:00 3.081667e+05 3.0 1.75 1510 \n", + "4596 2014-07-09 00:00:00 5.343333e+05 3.0 2.50 1460 \n", + "4597 2014-07-09 00:00:00 4.169042e+05 3.0 2.50 3010 \n", + "4598 2014-07-10 00:00:00 2.034000e+05 4.0 2.00 2090 \n", + "4599 2014-07-10 00:00:00 2.206000e+05 3.0 2.50 1490 \n", + "\n", + " sqft_lot floors waterfront view condition sqft_above \\\n", + "0 7912 1.5 0 0 3 1340 \n", + "1 9050 2.0 0 4 5 3370 \n", + "2 11947 1.0 0 0 4 1930 \n", + "3 8030 1.0 0 0 4 1000 \n", + "4 10500 1.0 0 0 4 1140 \n", + "... ... ... ... ... ... ... \n", + "4595 6360 1.0 0 0 4 1510 \n", + "4596 7573 2.0 0 0 3 1460 \n", + "4597 7014 2.0 0 0 3 3010 \n", + "4598 6630 1.0 0 0 3 1070 \n", + "4599 8102 2.0 0 0 4 1490 \n", + "\n", + " sqft_basement yr_built yr_renovated street \\\n", + "0 0 1955 2005 18810 Densmore Ave N \n", + "1 280 1921 0 709 W Blaine St \n", + "2 0 1966 0 26206-26214 143rd Ave SE \n", + "3 1000 1963 0 857 170th Pl NE \n", + "4 800 1976 1992 9105 170th Ave NE \n", + "... ... ... ... ... \n", + "4595 0 1954 1979 501 N 143rd St \n", + "4596 0 1983 2009 14855 SE 10th Pl \n", + "4597 0 2009 0 759 Ilwaco Pl NE \n", + "4598 1020 1974 0 5148 S Creston St \n", + "4599 0 1990 0 18717 SE 258th St \n", + "\n", + " city statezip country \n", + "0 Shoreline WA 98133 USA \n", + "1 Seattle WA 98119 USA \n", + "2 Kent WA 98042 USA \n", + "3 Bellevue WA 98008 USA \n", + "4 Redmond WA 98052 USA \n", + "... ... ... ... \n", + "4595 Seattle WA 98133 USA \n", + "4596 Bellevue WA 98007 USA \n", + "4597 Renton WA 98059 USA \n", + "4598 Seattle WA 98178 USA \n", + "4599 Covington WA 98042 USA \n", + "\n", + "[4600 rows x 18 columns]" + ], + "text/html": [ + "\n", + "
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datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
02014-05-02 00:00:003.130000e+053.01.50134079121.5003134001955200518810 Densmore Ave NShorelineWA 98133USA
12014-05-02 00:00:002.384000e+065.02.50365090502.0045337028019210709 W Blaine StSeattleWA 98119USA
22014-05-02 00:00:003.420000e+053.02.001930119471.0004193001966026206-26214 143rd Ave SEKentWA 98042USA
32014-05-02 00:00:004.200000e+053.02.25200080301.00041000100019630857 170th Pl NEBellevueWA 98008USA
42014-05-02 00:00:005.500000e+054.02.501940105001.00041140800197619929105 170th Ave NERedmondWA 98052USA
.........................................................
45952014-07-09 00:00:003.081667e+053.01.75151063601.00041510019541979501 N 143rd StSeattleWA 98133USA
45962014-07-09 00:00:005.343333e+053.02.50146075732.0003146001983200914855 SE 10th PlBellevueWA 98007USA
45972014-07-09 00:00:004.169042e+053.02.50301070142.00033010020090759 Ilwaco Pl NERentonWA 98059USA
45982014-07-10 00:00:002.034000e+054.02.00209066301.000310701020197405148 S Creston StSeattleWA 98178USA
45992014-07-10 00:00:002.206000e+053.02.50149081022.0004149001990018717 SE 258th StCovingtonWA 98042USA
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datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
02014-05-02 00:00:00313000.03.01.50134079121.5003134001955200518810 Densmore Ave NShorelineWA 98133USA
12014-05-02 00:00:002384000.05.02.50365090502.0045337028019210709 W Blaine StSeattleWA 98119USA
22014-05-02 00:00:00342000.03.02.001930119471.0004193001966026206-26214 143rd Ave SEKentWA 98042USA
32014-05-02 00:00:00420000.03.02.25200080301.00041000100019630857 170th Pl NEBellevueWA 98008USA
42014-05-02 00:00:00550000.04.02.501940105001.00041140800197619929105 170th Ave NERedmondWA 98052USA
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datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
45952014-07-09 00:00:00308166.6666673.01.75151063601.00041510019541979501 N 143rd StSeattleWA 98133USA
45962014-07-09 00:00:00534333.3333333.02.50146075732.0003146001983200914855 SE 10th PlBellevueWA 98007USA
45972014-07-09 00:00:00416904.1666673.02.50301070142.00033010020090759 Ilwaco Pl NERentonWA 98059USA
45982014-07-10 00:00:00203400.0000004.02.00209066301.000310701020197405148 S Creston StSeattleWA 98178USA
45992014-07-10 00:00:00220600.0000003.02.50149081022.0004149001990018717 SE 258th StCovingtonWA 98042USA
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pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovated
count4.600000e+034600.0000004600.0000004600.0000004.600000e+034600.0000004600.0000004600.0000004600.0000004600.0000004600.0000004600.0000004600.000000
mean5.519630e+053.4008702.1608152139.3469571.485252e+041.5120650.0071740.2406523.4517391827.265435312.0815221970.786304808.608261
std5.638347e+050.9088480.783781963.2069163.588444e+040.5382880.0844040.7784050.677230862.168977464.13722829.731848979.414536
min0.000000e+000.0000000.000000370.0000006.380000e+021.0000000.0000000.0000001.000000370.0000000.0000001900.0000000.000000
25%3.228750e+053.0000001.7500001460.0000005.000750e+031.0000000.0000000.0000003.0000001190.0000000.0000001951.0000000.000000
50%4.609435e+053.0000002.2500001980.0000007.683000e+031.5000000.0000000.0000003.0000001590.0000000.0000001976.0000000.000000
75%6.549625e+054.0000002.5000002620.0000001.100125e+042.0000000.0000000.0000004.0000002300.000000610.0000001997.0000001999.000000
max2.659000e+079.0000008.00000013540.0000001.074218e+063.5000001.0000004.0000005.0000009410.0000004820.0000002014.0000002014.000000
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\n" + ] + }, + "metadata": {}, + "execution_count": 51 + } + ], + "source": [ + "# Summary statistics of the dataset\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xlR7G2Lvm4rF", + "outputId": "c47b1600-8854-4a11-c227-9a72a2ae8a47" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 4600 entries, 0 to 4599\n", + "Data columns (total 18 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 date 4600 non-null object \n", + " 1 price 4600 non-null float64\n", + " 2 bedrooms 4600 non-null float64\n", + " 3 bathrooms 4600 non-null float64\n", + " 4 sqft_living 4600 non-null int64 \n", + " 5 sqft_lot 4600 non-null int64 \n", + " 6 floors 4600 non-null float64\n", + " 7 waterfront 4600 non-null int64 \n", + " 8 view 4600 non-null int64 \n", + " 9 condition 4600 non-null int64 \n", + " 10 sqft_above 4600 non-null int64 \n", + " 11 sqft_basement 4600 non-null int64 \n", + " 12 yr_built 4600 non-null int64 \n", + " 13 yr_renovated 4600 non-null int64 \n", + " 14 street 4600 non-null object \n", + " 15 city 4600 non-null object \n", + " 16 statezip 4600 non-null object \n", + " 17 country 4600 non-null object \n", + "dtypes: float64(4), int64(9), object(5)\n", + "memory usage: 647.0+ KB\n" + ] + } + ], + "source": [ + "# Information about the dataset\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eM9-8Z6Q2Afl", + "outputId": "31166e0e-7933-4a68-fca4-f4ebbdd3976a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "date 0\n", + "price 0\n", + "bedrooms 0\n", + "bathrooms 0\n", + "sqft_living 0\n", + "sqft_lot 0\n", + "floors 0\n", + "waterfront 0\n", + "view 0\n", + "condition 0\n", + "sqft_above 0\n", + "sqft_basement 0\n", + "yr_built 0\n", + "yr_renovated 0\n", + "street 0\n", + "city 0\n", + "statezip 0\n", + "country 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 53 + } + ], + "source": [ + "# Check for missing values\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ipLlh5Zdm4rG" + }, + "source": [ + "Data Encoding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "booMa4XZm4rH" + }, + "outputs": [], + "source": [ + "df['yr_renovated'] = df['yr_renovated'].apply(lambda x: 0 if x == 0 else 1)\n", + "\n", + "df.rename(columns = {'yr_renovated':'renovation'}, inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4oTcfHpt2E4b" + }, + "source": [ + "Correlation Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yEe9JoDzm4rH" + }, + "outputs": [], + "source": [ + "df = df.drop(axis=1,columns=['date','street','city','statezip','country','yr_built'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 541 + }, + "id": "1XCTTTPs2CVz", + "outputId": "f4c7f243-655e-4618-ee5f-4fbfb31065f1" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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cuUNcXBz29vZ4e3szatQoHjx4wNKlSwEYNGgQs2bNYuTIkfTp04fY2FjWrFnD1q1bX5qOYramQCAQCASC/zecOnWKypUrU7lyZQCGDRtG5cqV+fLLLwFISkoiPj5eK+/n58fWrVvZvXs3lSpVYvLkycyfP5/mzZu/NB2F50wgEAgEAsErp7zCmjExMTxtiVd9q//HxMRw9uzZl6iVLsI4EwgEAoFA8Mp5UbM1/4uIsKZAIBAIBALBa4Qwzl4xd+/eRSKREBcXV96qCAQCgUBQbkiNJC/k+C8iwpqvGC8vL5KSknB0dCxvVZ4L+7rV8B/eF5sqFTF1d+ZUp8Ek/773pdV3aOevxG5eTHZmKh4+IXTqPQqfwAi9skf2ruXkwc0kJdwAwMsvnDZvDzUob4htWzawcd1qMjPS8fULoN+gDwkOCdMre/TwQdatWUFS0gOUJUrc3D1o17ELMY2aaWVWrVjMHwdjSU1JwcjIiIDAYLr36EtwaLhBHdZt282vG7eRnplFgK8XH/frQXhwwDN133PoKOOmzKFejSp8N+pjbfqCVevZ+8cxHqWmYWRkREiAHwO6v0mF4ECDZV08vIK4AwvIz0nFwS2Uuu1H4+IdaVD+1rkdnNg5nZyMB9g4+lCr1Qh8whpoz8eu+oxrpzfq5PEKrkub/vMNlnlszwr+2L6Q3KxUXL1CafPOF3gG6Nch+f4N9m6YSeLdS2SmJtKq22dEN++pI7N3wyz2bdTd3NjRzY+PvtedFv8kJ/au4PCOBVodWnYfjae/fh0ePbjBvo0zSLx7iay0RJp3HUXtZj31ygIc2jqPveumULNJD1p2+9yg3L7tq9i9aQlZmWl4+gbTte+n+AXp/14f2r2OYwe2kBivmX3m7R9O++5DdOQ3r/6Jk3/sJCPtIUZGco1MtyH4BZfKXDq6gvMHF1KQm4q9ayjRb3yBs5fh+3/7wg5O7Z5BbsYDrB18qNFiON6hpff/9J5Z3Dq/jbzMh0hlchw9wqne7COcvStpZX79oTG5mYnaz78AvXv15K0uXQD4ffMW1q5bR0ZGBv5+fgx+bxAhIfpXlf/j8GFWr15DYlISJSUleHi407FDR5o0bqSVadGqtd68ffv0ofObnfSeO7BjFXu1fVIwnfuMwtdAH3N4z1pOHNxMYkLpvWj79oc68nHH9/DH7t+Iv32Z/NwsPpu4Bk/f0DJlNakio1qIDDNjuJesZtOREtKyn76jRq0wKfUijLA0g4fpajYfLeF+qiaPmTE0qWJEoIcEW0sJeYVw+Z6S3aeVFClKywhwk9Ck6qsxDURY0zDCOHuFFBcXY2xsjKura3mr8tzILMzJPn+NhMXrqLZ29rMz/AvOHNnBhqU/0qXfGHyDItm/bRk/fTuQL6ZuxsrGoYz8zUsnqRLdEr+QUcjlxuzZtJCfvhnIZ5M3YGvvoqeGsvxxMJZFv/zEoCEfExwSxuaNa5kwZiSz5i3F1taujLyVlTVvvvUOHp7eGMmNOHXiKDOn/oCNjS2Vq9YAwN3Dk/6DhuLi6kZxcRGbN65l/JiRzJm/HBsb2zJl7v3jGLMWrWTEoN6EBwewZvMOhk2YyK+zJmJna2NQ96RHKcxe8iuVwss+rLzcXfm4fw/cXZwpKi7WlDl+IqvmTMLOxrpsW8Zt4/Dm72nQaRzO3pU4f2gJW+b34+2R2zG3LNv2D++eYffK4dRsOQzfsBhunN3CjiVDePOjdTi4BpfqEVKPRl2+1X6WGRkbvJ4Lx7ex/dcfeKPnOLwCIjmycymLJ/Xnox+2YWldVgdFcSH2Tl5UrN6cbSu/N1ius0cgvUcu1H6Wygx3exdPbGPn6u9p8+44PPwrcWz3EpZP6ceQb7cb1MHOyYvwai3YucqwDgAP7lzg9IHVuHg+fcuak4d3snbxZLoN/AK/oAj2blnBjK8GM37mJqxt7MvIX790iup1WxAQUgm53IQdGxcxfcJ7jJ22DjsHze/Axd2Ht/t9hqOLJ4riQvZsWcG0r97j61m/Y2Vjz8nDOzm29Qfqth+Hs1ckFw8vZfvC/nQZvg0zPfc/+d5ZYleNoHrzj/EOjeFW3BZ2L/+ADkPWYv/n/bdx9KXOG6OxsveiRFHIxT+WsG1hP94asRMzy9LrqNrkA0JrdAagdfg9zM3NAThw4CC//PILHwwZQkhoCBs3buSLMWOYP28etra2ZXSysrKia9e38PL0xEgu58TxE0yZOhVbWxuqVa0KwMrly3TynDp1mqnTp1O3TrTee3H6zz7prf5j8A2KYN/W5cz+ZhBfTvtdb5904/IpqtZpSeeQKIzkJuzetJDZXw/iiynrtX1ScVEBAaGVqVK7GSvnji9TBkD9SBm1w2WsPVhCRo6aJlVl9G4uZ9r6YkqUerMQ4SelVU0jNh4u4X6KmugKMnq3kDNlbTF5hWBtIcHKHLafUPIoU4WtpYT2dYywNpewMrYEAFd7CT2by9kfp8THRQTWyhPR+v+CmJgYhgwZwpAhQ7CxscHR0ZExY8ZoZ4H4+vry1Vdf0aNHD6ytrRkwYIDesOalS5do06YN1tbWWFlZUa9ePW7duqU9P3/+fMLCwjA1NSU0NJQ5c+a8smtM2XmQ62Onkbxpz0uva//WpUQ37kSthh1w9QygS78vMTY249i+DXrle3z4A/Wad8XTNxQXD3/eHjQelVrF9QvHn7vO3zf8RtMWrWnctCVe3r4MGjIME1NT9u7arle+YmQUtaLr4eXtg5ubB23bvYmvXwBXLl/UytSPaUKlylVxdXPH28eP3v0Hk5+fx707t/SWuer37bRtGkPrxvXx8/Lgk0G9MTUxYcvegwb1VipVTJj6E327dsTdxanM+Wb1o6leqSIers74e3vyQe/u5OUXcOtegt7yzh1cTHjNzoRW74S9SyANOo5HLjfl6ol1euXP/7EM75C6VI7pi51LADVaDMXRI5yLh1foyMmMjDG3dtIeJuaGjc3DO5ZQrUFnqtbviLNHIG/0Gofc2JTTB9frlff0j6BF10+IrNUaI7lho08qM8LK1kl7WFiVNbofc3TnYqrU70zlep1w9gikTY/xyI1NOXtIfzt4+EXQrMtIImq2RmYkN1huUWEe6+aNoG3PrzC1KGscP8mezcuo26QjdRq1x90rgO4DR2NsYsqRvRv1yvf96DtiWryFl18orp5+9HhvLGq1mqsXTmhlatRrRVilWji5euLuHUjnXsMpzM/l/r0b2jpDq3cmpFpH7FwCqdt+HEbGplw7pb/tLx5eimdQXSrV74udcwDVmg3F0T2MS0dXamUCo9rgERiNtb0X9i5B1Gr9GYqiXNIfXtMpS25igbmVE+ZWTtjb22NqagrA+g0baNGiBc2aNcXH25sPhgzBxMSUnbt26dWpUmQkdaKj8fb2xt3Njfbt2+Hn58elS5e1Mvb29jrH0WPHqBQZiZubm94yY7do+qTaDdvj5hlA1/5jMDY24+g+/fei14ffU//PPsnVw4/ug8ahVqu49kSfVKN+W1q+OYiQiFp6ywCIriBjX5ySK/EqHmao+e1ACVbmEO5j+JFdt6KMk9dUnLmh4lGmmk2HSygugarBMgCSM9SsjC3haoKK9By4naRm1yklod5SpH86sCL9pDxMVxMbZ8ACfMGIsKZhhHH2L1myZAlGRkacOHGC6dOnM2XKFObPLw3bTJo0iUqVKnH27FnGjBlTJv+DBw+oX78+JiYmxMbGcvr0afr06UNJieZNZsWKFXz55Zd88803XLlyhW+//ZYxY8awZMmSV3aNr4KSEgUJty8T/ESHJZVKCY6oxd0b556rjOKiQlQlJZhbGjYAnkShUHDr5nUqRVXVqTMyqgrXrl56Zn61Ws35uNM8uJ9AeEX94R+FQsGu7Vswt7DA169sSFGhKOH6rbtUq1RBR4dqkRW4dO1mGfnHLF6zAVsba9o0iXmmngpFCZt2xWJpbk6gr3eZ88qSYlIeXMIzqNR7IJFK8QiqTfK9OL1lJt+LwyNI19vgFVynjHzirRMsGhfNyoktOLBuHIV5GXrLKykpJvHuJQIqlK64LZVKCahQm4Sb+nV4XtIe3uOHofWZPKIpa37+hMy0RL1yJSXFJN67hH946XVJpVL8w2tz/9a/02Hb8gkER8YQUEG/h0arg0JB/K0rhEXW1NEhNLImt6+ff666iosLUSpLsDDwOyhRKDi0ex1m5pZ4+QZr6/QILG17iVSKR0BtHsXH6S0jOf6cjjyAZ1Bdg/LKkmKunliDsakVDm66IbxzB+azdEIt1s/oyG9r16FUKlEoFNy4eZPKUVFaOalUSuWoKK5cvfrMNlCr1ZyNi+P+/ftEVKyoVyYjI4MTJ0/SvFkzvec1fdIVHSNKKpUSElGTO9efv09S/o0+CTThR2tzCbcSVdq0IgXcT1Hj7azfEJFJwd1Rws0n8qiBW4kqg3kATI2hqBhUf0ZLZTIMeuZeBhKZ5IUc/0VEWPNf4uXlxdSpU5FIJISEhHDhwgWmTp1K//79AWjUqBHDhw/Xyt+9e1cn/+zZs7GxsWHVqlXI5Zo37+Dg0rDQ2LFjmTx5Mh07dgQ0i+FdvnyZuXPn0rOn/rEtRUVFFBUV6aQp1CrkktfXFs/LzkClUpYJFVjZOPAo8c5zlfH7iqlY2zs99Y30SXKys1CpVNj8JXxpa2vHg4R4A7kgLy+Xfj06o1AokEqlDBj8EVGVq+nInDxxlCk/TKCoqAg7ewfGfT0Ja5uyHXRWTg5KlQr7v5yzt7Xm3gP9RsS5y9fYsvcAi6Z889TrO3zyLOOmzKawqBgHO1umjvsUW2urMnKFeRmoVcoy4StzS0cyH+lv+/yc1DLhTnMrR/JzUrWfvULr4RfRDGt7D7LTEji+fSpbFwygw5BVSKWyv5SXiUqlxPIv99/SxoHUpOe7//rw8o+kU/9vcXT1IycrhdiNs/nlm3f48JvNmJhZ/EUHTTv8NXxpYe34r3S4cHwrSfcu0//Ltc+Uzc3583dgq6uDtY0DDx/cfa761i+bho2dk46BB3D+1EHmT/2U4qJCbOwc+Wjsz1ha25GZ/giVnvtvZuVAZor+6y7ITcXMUnfcrJmlAwW5qTpp967sI3bVCEoUBZhbOdGqzwJMLUp/bxWi38XRIxwTMxuS48+yes0k0tPTebNTR1QqFbZ2tjrl2drakpCg3/sLkJeXR/d3e2h/m0PeH0yVKpX1yu7ZsxczMzPqGAhp5mYbuBe2DiQ/Z5+0acVUbOydCH3OPgnA+E9PUG6B7viy3AI1lmb6DRFzU5BJJXrzONno7/fNTaBhZSNOXCu1xm48UFGngoxI/1fzrJD+Rw2rF4Ewzv4ltWrVQiIp/YLVrl2byZMno1RqvvDVqlUzlBWAuLg46tWrpzXMniQvL49bt27Rt29frbEHUFJSgo2eB/1jvvvuO8aP1x3L8LbEnu6y/41JCP+E3Rvnc/bIdoaMXYjc+OVtgAtgZmbOlJnzKSwo4Py5MyyaPwdXV3cqRkZpZSIio5gycz7Z2Vns3rGFSd+P54cpc/SOY/s75BcU8PX0nxn5Xl+9htaTVIkIY9GUb8jMzmHz7n18OWkm834Y99RxbC+SoKjSwdcObiE4uIWw4vumJN46gWfQy9uT7kmCK9XX/u9KCJ7+kUwa3pgLJ7ZTrcGbL73+rPQkdvz6Le8OX4hc/nK/lwA71i/k5OGdDB8/v8zvIKRidUZPWk1uTiZ/7F7PvMkj+ez75S9VH/eAmnT8YD2F+RlcPfkbe379mPaDV2sNwch6vbSyDm4h1PDNZMbMWbzxxhv/qD4zMzPmzJpJQUEBcefOMe+X+bi6ulIpsqxne+fu3TRqGIOxseGQ+L9h18YFnD68g6Hjnt4n+XvaUqdiqQ5xdxQGZV8UJnLo2UzOoww1e8+UGmc3H6jZflJJ+zrCNChvxB14yVhYWDz1/NN2tM/NzQXgl19+oWZN3bdgmUymLwsAo0aNYtiwYTppsfZVDUi/HlhY2yGVysjJStNJz8lKK/Pm+ldiNy9m76aFDB79Cx4+Tx9s/SRW1jZIpVKyMnVDbZmZGdjalR14/RipVIqbuwcAfgGB3E+4x7rfVugYZ6amZri5e+Dm7kFIaDiD+7/D3l3b6NSlu05ZNlZWyKRS0rOydNLTM7Nx0DPo+cHDRyQ9SuWzb6do01R/jnFs0KknK2dNxMNNM/DYzNQUTzdTPN1cqBgSSNfBI9iy9wDvdtJ98Jla2CGRyijI1W37/NxUzK30G/TmVo7k/1U+x7A8gLWDF6YWdmSl3itjnJlb2SKVysj9y/3PzUrD0ubFvVSYWVjj6OpLenJZz6i5laYdcrN1dcjLTv3HOiTevURedhpzx3fUpqlVSu5dP8WJ2BWMmXdex4toafXn7yBTV4fsrDRsbJ+uw65NS9ixYSEfjZ2Lp29wmfMmpmY4u3nj7OaNf3AkY95vy+G9G2jatgdSPfe/ICfN4P00s3Qs4yUryE0r402TG5tj4+iDDT64eEexelJzrp1aR1TMAL3lhoSEoFQqKSwsQCqVkpmRqXM+MzMTO3vDLzhSqRR3d3cAAgICiI9PYPWa38oYZxcvXuT+/ft8/tmnBsuytDZwLzLTsH7Gvdjz+2J2b1zIkDHz8PApey+eJP5hNrLrxaXX8OfLvqWZhJwnPGGWZhKS0lVl8gPkF4JS9dizppsn5y/eNGM59Goup0gBK/YqtCHNxxy+qOTwRSXf9n35LxMSqfCcGeL1jXP9j3D8uO7g82PHjhEUFPRU4+lJIiMjOXToEApF2bclFxcX3N3duX37NoGBgTqHn5+fwTJNTEywtrbWOV7nkCaAkZEcL/9wncH8KpWK6xeP4RtUyWC+vZsWsnPdXAaN+gnvgAoG5fQhl8sJCAzmfNwZnTovxJ0hJPT5y1KpVXrvn46MSq1XRi43IjjAl9PnLz8hq+L0hUtUCCk7Rs3bw42l075l0ZSvtUfd6pWpUjGMRVO+xtnRsCGrUqkpVpSUSZcZGePkUYH7N49q09QqFQ9uHsPFJ0pvWS4+UTy4cVQn7f6NIwblAXIzH1KYn4m5tXOZc0ZGxrj7VuD25WNP6Kvi9uVjeAUaLvPvUlSYR/qjBKxsy06iMDIyxt2nAneulF6XSqXi9pVjeAb8Mx38w2rx3oTfGTRug/Zw961IZK22DBq3oUx410guxzsgjCtPDOZXqVRcPX8C/2DDy1rs3LiIrWt/4cMxc/ANfL7vrkqtpkRRrK3zwa3StlerVCTeOoazd5TevC7elUh8Qh7g/s0jBuW15arVKEuKDZ6/ffs2UqkURwcHggIDiTsXV6qvSkVcXBxhoWWXnXhaffp+dzt27SIoMBB/f3+DeTV9UhjXLv61TzqOX7DhPmn3poXsWDePwZ/Pwec5+qSSEhUFxWiPvCI12flqAtxL+2wTOXg6SYh/pH8pDaUKElPVBLqV5pEAAe5SnTwmcujTQo5SBct2K17p+DJ9SGTSF3L8FxGes39JfHw8w4YNY+DAgZw5c4aZM2cyefLk584/ZMgQZs6cSdeuXRk1ahQ2NjYcO3aMGjVqEBISwvjx4/nwww+xsbGhRYsWFBUVcerUKTIyMsp4x14GMgtzLAJLB5Gb+3liXSmU4vQsChOSXmhdMa17sGLOF3gHVMA7IIID25ZRXFRAzZj2ACyf9Tk29s607fYRAHs2LWDbmtn0+PAH7J09yM7UvMmbmJpjYmr+XHW+0aEzM6Z8T0BQMEHBYWzZtJbCwkIaN20BwPTJ32Lv4MS7vTRh5XVrVhAQFIKrqzsKhYIzp45zIHY3A9/XrDFWWFjA2tXLqV6zDnb29uRkZbFt60bS01KIrttArw5d32jJNzPmERrgR1iQP2u27KSgsIjWjTUhua+m/4yTvR2D3n0LE2Nj/H28dPJbWmiu9XF6QWEhS9f+Tp3qVXC0syUzJ4f12/aQmp5Bw+gaenWoVL8Xsas/w8mzIi5ekZw/tARFcQGh1TUen72/foqFjTO1WmnGT0bWfZdNP/Ug7sBCfMJiuBm3lZT7l2jw5gQAFEV5nNw9G/+IZphbOZKdlsDRrT9i4+CNd0hdvTrUadGTdb+Mwt2vIp7+ERzZuZTiogKq1usAwNq5n2Jt50KzLprvfUlJMSkPNDNglSUKsjMekXTvCsam5ji4+ACw/deJhFaOwdbBg5zMR+zdMBOJVEpkLf3rXdVu3osN8z/D3bciHn6RHNu9BEVRAZXratph/S+fYm3nTJM3h5fqkFiqQ05mMknxVzA20ehgYmaJi6eu50RuYoaZhW2Z9Mc0afsui2eOwTcgHN+giuzdsoLiogKiG7UDYNGM0djaO9PhnQ8B2LFhEZtXzaHvR9/h4OROVkbp78DUzJyiwgK2rfuFStVjsLF1JDcnk/07VpOZ/oiqtZtq61w0YwxOHhVx8org4uGlKIoLCK6qaft9az7FwtqFGi00bV+xTg82z+vB+UOL8A5pwK3z20h9cIl6HTTDKRTF+cTtm4t3WEPMrZwozM/k8tGV5Gcn4xeh2Sw6+d5ZHiWcxz2gJnITCx7di+PMzl9o1LAhVlZWdOzQgUlTphAUFERIcDAbNm2isKiQZk01Ov84aTIODg706d0LgFWr1xAcFISbmysKhYKTp06xNzaWIe+/r9O+efn5HDr0BwP69dPb/k/SqE0Pls0ejbd/OL6BEezbtpyiogJq/dknLZ31OTb2LrTrNhSA3RsXsnXNbHp++D0OBvqkvNwsMlKTyEpP0bRD4l0ArG0dtR65I5eUNIySkZqtJiNHTdOqMnLy4fK9Us9Z35ZyLt1VcuyKJu2Pi0rerG/E/VQp91PU1Kkow9gIzlzXWGAmcujdQo7cCNbsV2BiDI99Y3mF8HiryXoRMq7f1++hE7w6hHH2L+nRowcFBQXUqFEDmUzG0KFDGTBAv8teHw4ODsTGxvLJJ5/QoEEDZDIZUVFR1KlTB4B+/fphbm7Ojz/+yCeffIKFhQURERF89NFHL+mKdLGpWpHae0vXBgqfpFk4M2Hpes73HfVC66oS3YLc7HS2rZlNdmYqnr6hDBr1s7bDykhL0nGDH969BmWJgkVTdI3UFm++R8vOg5+rzrr1G5GdlcWq5YvJyEjHzz+ALyf8oA1rpqQ8QvKE17GwsJB5c6aRlpqCsbEJHp7efDTic+rW1yx0KZXKuJ+QwL69Y8nOysLK2prAoBC+mTgDbx/93s7GdWuRmZ3D/FXrSM/IItDPm8lffoL9n2PDklPStKGO50EqlXLvfhLb980gKzsHaytLwgL9mf3NaPy9PfXmCYxqRUFeOid3ziQ/JwVH9zDa9PtFG9bKzUzUGVvp6luFJt0mcXznNI5vn4qNoy8tes7SrnEmkcpIT7rGtVMbKS7MwcLaCc/gOtRoPtTgWmcRNVuRl53B3vUzyM1Kxc07jJ4j5mlDipnpSUikpfciJyOF2V+Whgv/2L6QP7YvxDe0Ov1GLQUgO+Mha34aQX5uJhZW9vgEV2HgmFVYWOsPW1es0Yq8nHT2bZxJblYKrl5hvPPxL1odstITdb6DOZmPmDuug/bzkR0LObJjIT4h1en96bIy5T8P1es0Jzcrg99X/aT5HfiF8OHoOVj/Gd5PT03SuRcHd66hpETB3EkjdMpp02Ugbd96D6lUysMHdzm2fzi52ZlYWNniG1iBT75eiLt3oLbOyzdyOL1nxp+LEIfRsvc87f3Py0zS+R24+FSmUdcfObVrOid3TsXG0Yem78zUrnEmkcjITLnN9TMbKczLwNTcFifPCNoOWI69SxCg8djePr+NM3tnoywpxsrekw7t29Oxo6Y9GzSoT1Z2FsuWLdcsQuvvz9cTJmBnpwlrPkpJ0bkXhYWFzJozh9TUVIyNjfHy8mTkiBE0aFA67hDgwIEDAMTE6H9ZepKq0S3Izc5g65o55GSm4uEbwvuf//TEvXio0y6HdmvuxYIpw3XKafnmIFp30fRJF07tZ/mc0pn7i6aNLCNz8LwSYyPoUMcI0z8XoV20U9fTZW8lwcK09Pov3FFhYVpCk6pGWJlBUpomT26h5ry7gwRvZ42uI7rohiwnri4iUzOKhmBPKTGVni/y828REwIMI1E/bWt2wVOJiYkhKiqKadOmlbcqz2Sr/PnHYr0sZCcvlLcKeFumPlvoFeBUfL+8VWDFzerlrQIezuXf/ZQoy/8B4WZTWN4qcOr6yx9j9Cw6Vbpd3ipwM9fr2UIvmX2nylsDXsmYs+O1az5b6DmoefT517b8X+G/GawVCAQCgUAg+B9FhDUFAoFAIBC8ckRY0zDCOPsX7N+/v7xVEAgEAoHgf5L/6ur+LwIR1hQIBAKBQCB4jRCeM4FAIBAIBK+cJ2dfC3QRxplAIBAIBIJXjtghwDDCOBMIBAKBQPDKERMCDCN8igKBQCAQCASvEcJzJhAIBAKB4JUjwpqGEcaZQCAQCASCV46YEGAYYZz9P+F12DpJWT2ivFXAb/e48lYBAEluZnmrgFyufxP0V4mJUflvsHz5pvLZQi+ZIk+z8lYBpar874VD5q3yVoEiW9PyVoGCAtvyVoHSbdEF5YEwzgQCgUAgELxyRFjTMMI4EwgEAoFA8MoRszUNIwK+AoFAIBAIBK8RwnMmEAgEAoHglSPCmoYRxplAIBAIBIJXjpitaRjRMgKBQCAQCASvEcJzJhAIBAKB4JUjwpqGEcaZQCAQCASCV44wzgzzPxfWjImJ4aOPPnqhZS5evBhbW9sXWqZAIBAIBALDSKSSF3L8FxGeM4GWQzt/JXbzYrIzU/HwCaFT71H4BOpf1f/I3rWcPLiZpIQbAHj5hdPm7aEG5f8N9nWr4T+8LzZVKmLq7sypToNJ/n3vCyt/1b4TLNl9mLSsXII9Xfm0a0si/Dz1ym46cpaxSzbppBkbyTgxe4z285jFG9h89JyOTHR4AHOGvmtYhz/OsWT/KVJz8gl2d+SzDg2J8HbVr8OJS3y5encZHU7+8IH2c1pOHtO2/MHR6/HkFBRRxd+DzzrE4ONkp61v0f5G5Oek4OgeSoNOY3D1iTSo34247RzbNp3s9AfYOvlSp+0IfMMb6MikP7zF4c0/8uDWSVQqJfYuAbTuMxMrO3cAMlPj+WPTDyTePo2ypBifsHp0G/A5VjaO2jIO71rJga0LyclKxc07hPY9v8A7QL9eD+/fYOfaWTy4c4mM1ETeeOcz6rXsoSOjUinZtW42Zw5vJiczFWs7Z74c/y0N61ZHbgTpuXDvgYr0HIOXDkD1YCnR4VIszeBhhprtJ1Ukpqm152VSaF5VSgVfKUZSuJmkZtsJJXmFmvNmxtCxrgwXWwlmJpBXCNfuq9gbp6JYoZFxtYUWUZr/V6xYwYIFC0hJScHJM5QmXcbg7qe/HVISb3Do9xk8jL9EVtoDGnceRY0mvXRk4q+f5PiuBTyMv0huVgqd3ptNcFQTHZnLx1Zw8dBCCnJTsXMNpXabL3DyMvyduHNhB2f2zCA38wHWDj5Uaz4crxDNd0KlVHB693TuXz9ITvp95KaWuAfUpnrz4ZhbO2vLWPNjY3IzE7WfFwLvd21Lrzeaas7vOsjyLbGkZWUT5O3BJz3fpEKgj0GdHrPryGm+mLWEBlUjmDS8vzY9v7CIWb/+zoHT58nKycfd2Z63mjegU5O6BsvaunkjG9etISMjHV+/AAa89wHBIaF6ZY8ePsRvq1fyMOkBJSVK3D08aNehMw0bN9XK/Lp8CYcO7iM1JQUjuREBgcG806MPIaFhOmW1qGFM7QpGmJpIuJuk5Lf9RaRmqf9apQ51IuQ0qizHylxCYqqK9QeLiH+ku/ODj6uU1rWM8XaRoVbDgxQVc38vQPHnRhlNqsoJ9xWmQXkj7sBzUFxcjLGxcXmr8VI5c2QHG5b+SJd+Y/ANimT/tmX89O1Avpi6GSsbhzLyNy+dpEp0S/xCRiGXG7Nn00J++mYgn03egK29ywvVTWZhTvb5ayQsXke1tbNfaNk7T15k8tqdfNGtDRF+HqzYe4zBM5azafwQ7K0t9eaxNDVh44Qh2s8SSdk3tzoVAhnfs532s7GR4Z/ajrPXmPT7QUa/2YgIb1dWHDrLe/M2sOnTnjhYmRvQwZhNn/Z8QofSc2q1mo8WbcZIJmNa77ZYmhqz9MAZBs5dz/pPenDw8m0m/X6QBl2+wsWnEnEHlrDp5768+/kOzK3K3uukO2fYsXQ40W2G4RfekGtnNrNlwfu8PWI9Dm7BgMbwWjujG+G1OlGz5YcYm1qS/vAGMiPNFjCKonw2/tQHJ49QOr6/BIBj26azaNL7DBn/K1KplLij29m84gc69RmLd0Akh3YsY/73Axg5aSuWer6DiqJCHJw9qVSzOb8v/15vO+3bPJ+je1bRddB3uHgG4mWTT/WKHqzaGItvhRhC3KW808iI2ZtLUBrYvaiCj4RmVaVsPa7kfpqaWqEy3mkkY9bvJeQXaWRaVJMS5CHlt4NKihRqWlaX0aW+jEW7NE88NXAtQU1snIr8IjX2VhJaVZdhVkPC+sO620eNm76NNfO+o22P8Xj6V+KPHUtYPaMvA8bvwMJaTzsUF2Dr6Elo1RbsWfOd3mtQFOfj7BlCZJ1OrP95SJnzl09u48S2H4huNw4nr0guHV7KzsX96fTxNswsy9aZfO8s+9eMoFqzj/EKieHWuS3sXfEB7d5fi51LMCWKQtISL1Op4Xs4uIZSVJDFsa3fsXvZYNq9v1anrCqNPyC4emcAenofxMJU853ZdfQM05Zv4LM+b1Ex0Idftx/gg+/nsHbyaOxtrPTfLCAxJY3pKzdSOTSgzLmpyzZw6vJ1JgzugZuTPcfOX2Xiot9wtLOhQdWyL5aHDuxj4S8/896QjwgODWXzxvWMG/Mpc+YtxtbWroy8pZUVnbt2x9PTCyO5nFPHjzJj6kRsbG2pUrU6AO4engx47wNcXd0oLi5m04a1jBv9KT8vWIqNjS0AjarIqV9Jzso9haRlq2lZ05hBb5jx/cp8SgzsNhYVaET7usb8tr+Iew+VNIgyZuAbZny3Ip/cAo1R5+MqZWBbM/aeLmb9wWKUKjUejjJUT9h8RjIJ526W4OcmM9jGLwoxW9Mw/5MtU1JSwpAhQ7CxscHR0ZExY8agVmu+XUVFRYwYMQIPDw8sLCyoWbMm+/fv18m/ePFivL29MTc3p0OHDqSlpemcHzduHFFRUcyfPx8/Pz9MTTV7rcXHx9OuXTssLS2xtramS5cuJCcn6+T96aefCAgIwNjYmJCQEJYtW6ZzXiKRMHfuXNq0aYO5uTlhYWEcPXqUmzdvEhMTg4WFBdHR0dy6VbrH3Llz52jYsCFWVlZYW1tTtWpVTp069aKaE4D9W5cS3bgTtRp2wNUzgC79vsTY2Ixj+zbole/x4Q/Ua94VT99QXDz8eXvQeFRqFdcvHH+hegGk7DzI9bHTSN6054WXvWzPUTrWrUL7OpUJcHdmdPc2mBrL2XjkrOFMEnC0sdIeDnqMOLmRTEfG2sLw3onLDp6hY62KtK9RgQBXB0Z3aoyp3IiNJy49TQUcrS20h4OVhfbcvdRMzt97yBedGlHR2xVfZ3tGd2pMoaKEHWevaesLr9kJB9dAGnUej5GxKZePr9NbV9yBpfiE1qNqo37YuwZQu9VHOHmGc+7Qcq3M0a1T8QmvT903RuLsGY6tozf+FRtrjb3EO2fISX9Ak27f4+gegqN7CE27/8D9Oxe5efkYAAe3L6Zmw85Ub9ARF89AOvYZi9zElBMH1uvVyysggjbdPiGqdiuMjPS/PN27HkeFqo0Iq9wAeycP6lQLYcOWPezYsY3sAjh7V4WVOYR6GQ6N1AqTcuamirjbalKzYMtxJQolVA7UdJ8mcqgcIGXnaSV3k9UkpcOmo0q8naV4OGrKLSyGUzdUJKWrycqDOw/VnLyuwtu5bL0Hty+iUt0uhNXshJVTIC26a+7P+SP674+7bySN3vyU8OqtMZLrb4eAig1o0P5jQio31Xv+xJ5FhFTrTHDVjtg5B1Kn3TiM5KZcP62/7S8fXYpnUF0i6vXF1jmAqk2H4uAexuWjKwEwNrWiRZ+F+Ee0xMbJD2fvKGq3HU1a4iUdTxmA3MQCcysnzK2ccLS1xuxP42zltn20bxjNGzG18Pd0Y1TfLpiaGPP7gWN6dQLN/qBjZi9lQKdWuDuXNSrP37hD63o1qBoehLuTAx0b1yHI253Lt+7pLW/ThrU0a9GKJs1a4O3ty3tDPsLExIQ9u3bolY+IjKJ2dF28vH1wc3OnbftO+Pr5c+XSRa1Mg4aNiapcFVc3d7x9fOk74D3y8/O4e+d2qUwlObtOFXPxjpKkNBUr9xRibSEhwt/wS15MlJyjlxScuFJCcoaa3/YVUVyipmZYaZ72dU04dF7B3jMKHqarSMlUE3dT98Vkx4liDpxTGKznRSKVSV7I8V/kf9I4W7JkCUZGRpw4cYLp06czZcoU5s+fD8CQIUM4evQoq1at4vz583Tu3JkWLVpw44Ym/Hb8+HH69u3LkCFDiIuLo2HDhnz99ddl6rh58ybr1q1j/fr1xMXFoVKpaNeuHenp6Rw4cIDdu3dz+/Zt3nrrLW2eDRs2MHToUIYPH87FixcZOHAgvXv3Zt++fTplf/XVV/To0YO4uDhCQ0Pp1q0bAwcOZNSoUZw6dQq1Ws2QIaVvt927d8fT05OTJ09y+vRpPvvsM+Ry+Qtrz5ISBQm3LxMcUUubJpVKCY6oxd0b556Ss5TiokJUJSWYW9q8ML1eNoqSEq7EJ1IzzF+bJpVKqRnqz/nb9w3mKygqpuWoqTT/bAofzfmVm4mPysicun6XhiMm0u7LmXyzYguZufkGdFBy5f4jagV5PaGDhFrB3py/l2RQh/xiBS2+XkCzCfMZuvB3bj4sfcFQ/PlqbWJU+uYrlUowlsk4fet+mfokUilewdEk3dVvkCbdjcMruLZOmk9oXR7ejQNArVJx9/J+7Jx82fhTX34ZXZvVUzpz63ypMa0sKQaJBNkTRpRMboJEIuXutTOUlBTz4M5lgirqfgeDKtbm3o04g+3wLHyCo7h56RgpSXcxNwZTYwk7tm4ktFI9AEqUcD9VjZeT/g5eKgV3ewm3k3TDSbeT1Hj+aXi52UuQyXRl0rIhM1eNl6P+ci3NIMxbwr1HuuUWFxfzMP4S77SPplkkOFtr7o9vaDQPbj/lheFfoCzR1OkeWHqPJVIp7oG1SYmP05vnUfw53AN0vxMegXV5lKBfHqC4MAckEoxNrXXSzx+cz4qva7FxVkeWbd5LiVKJoqSEq3cSqFExRCsnlUqpUTGECzfuGKxj/vod2Ftb0a5hbb3nI4P8OHjmIo/SM1Gr1Zy6dJ34hynUjCgbplQoFNy6eZ1KUVV0dKgUVYVrVy8b1OExarWac3FneHD/PhUq6h/uoVAo2Ll9KxYWFvj5aTx9ciMZ1hZSrieUusgKi+FesgpfV/2PbJkUPJ1186iBG/eV+Lhq+gFLMwm+rjJyC9R82MmMCX3Meb+DGX5u/5NmwH+e/8mwppeXF1OnTkUikRASEsKFCxeYOnUqzZs3Z9GiRcTHx+PurhnnMmLECHbs2MGiRYv49ttvmT59Oi1atGDkyJEABAcHc+TIEXbs0H0TKi4uZunSpTg5OQGwe/duLly4wJ07d/Dy0jzYli5dSoUKFTh58iTVq1dn0qRJ9OrVi8GDBwMwbNgwjh07xqRJk2jYsKG27N69e9OlSxcAPv30U2rXrs2YMWNo3rw5AEOHDqV3795a+fj4eD755BNCQzUdSFBQ0FPbp6ioiKKior9cjwRjYxO98nnZGahUyjLhSysbBx4lGu4In+T3FVOxtnci5AkD73UnIzcfpUqNg5Wu58vB2oK7D1P15vF1cWRcj3YEebqQW1DE0l1H6PXDAtaNG4yLncYwrVMhkMaVw/BwtCMhJZ1ZG/fy/szlLP20H7K/uPEz8gr+1EE3fOlgac6dR+n6dXC2Y/xbTQlycyS3sJgl+0/Tc+Zq1n/yLi62Vvg62+FmZ8WMbYcZ82ZjzIzlLDt4huSsXJIys7X1PXlnza0cyEi+rbe+/JxUzK0cddLMrRzIy9a0UX5uGoqifE7t/YXarT6iTtsR3Lt6iK2LhtDx/aV4BtbA1TcKubEZR37/kdpthoFazeEtk1GplGRnppCXk4lKpcTSRrceS2sHHiXq1+t5aNi2P0UFefz4SWsqV6lKk5UrCIpsRJU6bbUyeYVgYarfiDI30Ri2j8eOleZR42ijyWNpBiVKNUWKsjKWf3GYdqwrI9RTgtxIwrX7Kn4/WvowzS+C3acyUCqV3M1yILhIMwZtyxmwsHYg7eE/b4enkZ+bgVqlLBO+NLN0IDNF/++/IDcVU0vHMvIFOfp/NyWKIk7tnIx/ZGuMTUt/b+G138XBPRwTcxse3TvLok0TSc3M4p02jVGqVGXCl/Y2VtxNTP5r8QDEXb3F7/uPsuLbTw1e6ye9OvHt/NW0HvIlMpkUqUTCF/3epkpYYBnZ7OwsVCoVtna64UtbWzvuJyQYrCMvL5c+776FQqFAKpUy6P2hRFWppiNz8vhRJv3wNUVFRdjZ2zP+m4lY22j6DyOZxpjKzdc13HPzVViZ6/+eWphJkEkl5BTo5snJV+Nsq+lzHKw1eZvXMOb3w0U8SFFRPdSIwe3N+GFl/jPHs70M/quD+V8E/5PGWa1atXTG+dSuXZvJkydz4cIFlEolwcHBOvJFRUU4OGg6nitXrtChQwed87Vr1y5jnPn4+GgNs8f5vLy8tIYZQHh4OLa2tly5coXq1atz5coVBgwYoFNOnTp1mD59uk5aZGTpIFsXF834rIiICJ20wsJCsrOzsba2ZtiwYfTr149ly5bRpEkTOnfuTEBA2fEUj/nuu+8YP368Tlr3gaN5Z9AYAzn+Hbs3zufske0MGbsQuQED8L9CpQAvKgV46XzuOHYWaw+e5v12jQBoUb30XgZ5uBDs4UKb0TM4de2ujpfuH+vg604lX/cnPrvR4Yel/Hb0AkNaRiOXyZjSsw3j1uym3pifkUkl1Azypm6oL4WKFx+uUKs1MRH/io2pHNMLACfPMJLunOHi4VV4BtbA3NKelr2ms++3ccQdWoZEIiW4Sms8fMORSF7Om7uHvYQITwnNKr6P0bcfs+PYAwCOxq4CuSXV6rd/KfU+jZ2nlBw4r3lQNo6S0byqlG0nNe2XXQA5mRq5jDw4fA2szKCCJ6w1XORrj0qpYN+qj0GtJvqNsTrnKtbtpf3f3jWExq7X+XbBaro0r/+36sgrKGTsT8v4vN/b2BoYKwqweudBLty8y+Th/XFzsufslVtMXKwZc1YzIsRgvr+DmZk502bNo6CggPPnzrDwl59wcXUjIjJKKxNRKYpps+aRnZ1FRupDGjeIxsTEBIlEQkKSfgP33/L4kXnkoib0CfDgj2KCPGXUDJez9WjxS6n3qTqJMWcG+Z80zgyRm5uLTCbj9OnTyGS6gxktLQ3/YPVhYWHxbKF/yJMhycdGpr40lUrTaY8bN45u3bqxdetWtm/fztixY1m1alUZI/Mxo0aNYtiwYTpp+68afkOxsLZDKpWRk6U79i4nKw0r27LjNp4kdvNi9m5ayODRv+Dh82I6t1eFnaU5MqmEtJxcnfS07DwcbZ7v+yKXyQjxciMhRb+XC8DTyR47S3MSUtLLGGd2FmZ/6qAb9kzLzcfR6vm+g3KZjFAPZxJSM7Vp4V4urBn+DjkFRSiUSuwtzek+/VdC3Z04eyexTH35OWmYWzuiD3MrR/L/4hHJz0nD4k95Mws7pFIj7F11XxjsXQJIvHNa+9kntC69xuyhIDcdqdQIE3NrFo6NJsq5JRZWtkilMnKzdOvJzU7Tmc35d3iYqeabT3tQq1EXqtZti5W9ZgZuk7a92Lz2F61xZmEKyRn6vQb5RaBSqbEw1U23MJWQW/CnjgWaQdQmcnS8ZxoZ3XLzCjVHWraagiIlfZobcfBi6YAfc0s7JFIZ+Tma32JqNjjbQF52Whmv4ovicZ0Fubq//4LcNMwt9ddpZulIYW5qGXmzv3hYVUoFsb9+TG5mIi37LtLxmumjQqAvSqWK/MJiZFIp6Vm602jTs3JwsC07GeB+ciqJKekMnzSvtO4/xyHXeucj1k7+Akc7G+as3sKPw/pRt3IFAIK8Pbh+7z7Lt+4tY5xZW9sglUrJzMjQSc/MzMDO3t7gNUilUtzcPQDwDwgkIT6etWt+1THOTE3NcHP3wM3dA6mkAv37DyC6bn1atGqr7f8tzSVkP+E9szSXkpiqfzZAXoEapUqNlZluP2/1RBnZeZq/yem6M1+SM1TYWQoP1uvG/6TZevy47qDzY8eOERQUROXKlVEqlTx69IjAwECdw9VVsyxBWFiY3vzPIiwsjISEBBKecGdfvnyZzMxMwsPDtTKHDx/WyXf48GHt+X9DcHAwH3/8Mbt27aJjx44sWrTIoKyJiQnW1tY6h6GQJoCRkRwv/3CdwfwqlYrrF4/hG1TJYL69mxayc91cBo36Ce+ACv/swsoRuZERYd7unLhSGrpRqVScuHqbSH/9S2n8FaVKxc0HyTg+5W09OSOLzLx8vQaf3EhGmKczx2+Ufq9UKjXHbyQQ6eP23DrcSErF0bqsMWdlZoK9pTn3UjK4nPCIxpFBZepTq1QkXD+Km29lveW7+UaRcEP3NxJ/7QiuvlEAyIyMcfaOIOORbggsI+UuVnYeZcozs7THxNyahOtHyctOJ7xKI4yMjPHwC+fmpdJ6VCoVNy8ewyco6rna4a8oVXDn9k3Ss/LIK4KcQigsVhMS4KX19hlJwdNRQkKKfuNMpYLEdDX+rroPL39XCfdTNXmS0tUolboyDtZgaykhIdVwqOixJ0P2RC8sMzLG1bsCd68cBcDeEvIKVdy7ehQPf/3359/yuM7EW6Vtr1apSLx1DCfvKL15nL0r6cgDJN46grNXqfxjwyw77R4t+izE1Lzs7Ma/cv3ufaQSCS72toT6eXHy0vXS8lQqTl66RkSQX5l8vu4u/PrDZyz/bqT2qF+lIlXDg1j+3UhcHOwoKVFSolSWmV0tlUq1E8qeRC6XExAYzPlzpWP9VCoV5+POEhL6/H26Wq2m5Ckea5Vazf3793n0KAVFiZJiRQnZeSqCPUsdDCZy8HGRcveh/inFShXcf6Qi2Ks0jwQI8pRx76HGoEvPUZOZq8LZTvex72QrJT3n1Yc0Qaxz9jT+Jz1n8fHxDBs2jIEDB3LmzBlmzpzJ5MmTCQ4Opnv37vTo0YPJkydTuXJlUlJS2Lt3L5GRkbRu3ZoPP/yQOnXqMGnSJNq1a8fOnTvLhDT10aRJEyIiIujevTvTpk2jpKSEwYMH06BBA6pV04wn+OSTT+jSpQuVK1emSZMmbN68mfXr17Nnzz+fZVhQUMAnn3zCm2++iZ+fH/fv3+fkyZN06tTpH5epj5jWPVgx5wu8AyrgHRDBgW3LKC4qoGZMewCWz/ocG3tn2nb7CIA9mxawbc1senz4A/bOHmRnat6iTUzNMTHVv/zDP0VmYY5FoLf2s7mfJ9aVQilOz6IwwfCg+efh3Sa1GbN4A+G+7lT01SylUVCsoF205kE4etF6nG2t+bCDZk2ouVv2E+HvibeTPTkFhSzZdYSk9Cw61NUMGs4vLOLnLQdoUiUMB2tL7qdkMG39bryc7IkOLzuuBeDd+lUYs2oXFbxcqOjtyvKDZygoVtC+huYB8MXKnTjbWDC0tWYtpp93HSPSxw1vR1tyCopYvO8USRnZdKxZUVvmrnPXsbMww83OmhtJqUzcuJ+GFQOIDvEhO7+QMat20cBnAy7ekcQdWEJJcQHhNTtq8i4fiYWNC3XaDgcgqkEP1s18lzP7FuIb3oDrZ7bxKOEijd+aoK2vaqO+bF/yMR4B1fEMrMm9q4e4c2kfnYYs1cpcPr4OO5cAzCzteXj3LAfXf0u9Fj1wdtc8bOu37MXquaPw9KuIV0AEh3YspbiogOoNNB7iX3/6DBs7Z1p11XiFS0qKSb6vmdWsLFGQlZHMg7tXMDE1x9FVsxZWWOWGxG6ci52DGy6egRw5XUijOhV50HMQVmYQ6i4lJx+uJpQ+nN5tLONqgmY2JcCxKyraR8tITFfzIFVNrTApciOIu6U5X6SAs7dUNKsqo6C4dCmNhBQVD/40zgLdJViawoM0NcUl4GwroWllGfGPVGTlga0dhHtCTgE0aNWbtb98SnSNiuR5RvLtlCUoiguIjNbcn82LRmJl60JMh+F/XnsxqUm3tP/nZiaTnHAFuYk59s6adiguzCMjJV57jZmp90lOuIKphQ029u7UaNKbLYs/xdGjIk6eEVw6spSS4gKCq2ra/sBvn2Jh7UK15pq2D6/dg23ze3Dhj0V4hTTg9vltpD64RJ32muEUKqWC2JUfkZZ0mSbv/oRapSQ/JwUAEzMbZEbGPIo/S0rCeVz9ayI3sSAlPo4NOzfQsm51rC3N6daqIeN/Xk6YvxcVAnz4dft+CgqLadugJgBj5yzDyd6GIV3fwMRYTqBXaagfwPLPGdKP0+VGRlQJC2TGyk2YGstxdbTnzJWbbDt0ko/eaY8+2nV4k+lTfiAwKJig4FA2b1pHYVEhTZpqxgdPnfQ9Dg6O9OjdD4C1q1cSGBSMq5s7CoWC06eOsz92N4PeHwpAYWEBv61aQY1a0djZOZCdncW2LZtIS0ulTr3SdQMPnFPQtJoxKZkq0nM0S2lk56m5cLtEK/NeO1Mu3FbyxwWN4bc/TkG3JiYkPFJxL1lJg0rGGBtJOH6lNM++swpa1DAmMVXJg1QV1UPlONtJWby9dFClraUEcwNjMF80/1XD6kXwP2mc9ejRg4KCAmrUqIFMJmPo0KHasV6LFi3i66+/Zvjw4Tx48ABHR0dq1apFmzZtAM14tV9++YWxY8fy5Zdf0qRJE0aPHs1XX3311DolEgmbNm3igw8+oH79+kilUlq0aMHMmTO1Mu3bt2f69OlMmjSJoUOH4ufnx6JFi4iJifnH1yqTyUhLS6NHjx4kJyfj6OhIx44dy4wp+7dUiW5BbnY629bMJjszFU/fUAaN+hlrW02YIiMtSeeHdHj3GpQlChZN0Q2ftnjzPVp2HvxCdbOpWpHae0uXJAmf9DkACUvXc77vqH9VdvPqFcnIzeOn3/eRmp1LiKcrcz58R7s8RlJ6ls6bdnZ+IV8t20xqdi7W5qaEebuzZGRfAtw1C2tKpVJuPEhm87E4cvILcbK1onZYAO+3a4SxXP/PrUXlEDLyCpiz8yip2fmEeDgyp3977fIYDzOzebIPyykoYsJve0jNzsfa3IRwT2eWfPAWAa6lIeiU7DwmbTpIWm4+TtYWtKkaxsCmNXXqm719BnnZKTh5hNFu4HztoP+cjCSdcWBuflVo3mMSR7dO48iWKdg6+dKm72ztGmcAAZFNadh5HKf2zOPA+q+xc/KjVe8ZuPuXDoTOeHSHI1umUJifhbW9B9WaDqJN59KFeaNqtyQvJ52da2eSk5WKu08o/T6dqw1rZqbp6pWdkcK0L0pfUg5sXcSBrYvwD6vOe6M1a6m17/kFO9fOYP2iCeRmp2Nt58yYsV/T950O2kVol8fqLiVgbyXB3LTUWLt0T425iYqYSJl2EdoVsUqdSQI7TqloXhW61Jchk8GtRDVbT5SGoEqUUCVISvNqEmRSyM6HK/Eq/rhUWrFUAtUDoUF4K3xt01m0cAZzpqTg7BlGlw/na8PI2em67ZCT+YiFX7fXfj6+eyHHdy/EO7gG3YdrfjdJ9y6yckrpAr17f9OshxZRuwNten1PePVWJD9M48zeGRTkpGLvFkazXvMw+zOsmZelW6eLT2ViuvzI6T3TOb1rKtYOPjTuPhM7F813Ii/7EfFXYwHYNEt3+EXLvktw86+BVGbM7QvbOBs7G2VJMVZ2nnRrGUP3VprJU81qVyEzO5e5a7eRlplNsI8nMz57DwcbzWzPh2kZf/vh/s0HvZi9ajNjZi8lOzcfV0c73uvS2uAitPUaNCQ7O4uVyxaTkZGBn38AYyd8j62dJqyZmvII6RM6FBYW8vOcGaSlpmBsbIKHlxcfjxhFvQaaa5JKZdy/n0DsN+PIzsrGytqaoOAQvvtxGt4+vtpyYs8oMDaS0KWhCWYmEu4kKZm7uUBnjTNHGykWZqXfn7ibJViaSWhRwxhrC4lmcdnNBTqh9YPnFMhl0K6uCeammoVqf95UQFp2qUzLmsbUCHtxqwEI/hkStT5/ruA/x464Vz/Y868oq+ufTv4qabR7XHmrAIAkN7O8VWCB7L3yVgEvJwMrar5Czl41sPrsK8THs/wfhsmp5d8O7/ntfrbQSybRtvyHaMzdblveKjB1yN8bp/1PiB/U8YWU4/2z/vX4/pf5n/ScCQQCgUAg+N9GhDUNI4wzgUAgEAgErxyxlIZhRMsIBAKBQCD4f8Xs2bPx9fXF1NSUmjVrcuLEiafKT5s2jZCQEMzMzPDy8uLjjz+msLDwqXn+DcI4EwgEAoFA8OqRSF7M8TdZvXo1w4YNY+zYsZw5c4ZKlSrRvHlzHj0quxUfwMqVK/nss88YO3YsV65cYcGCBaxevZrPP//837aAQYRxJhAIBAKB4JVTXuucTZkyhf79+9O7d2/Cw8P5+eefMTc3Z+HChXrljxw5Qp06dejWrRu+vr40a9aMt99++5netn+DMM4EAoFAIBD8z1JUVER2drbO8df9pR9TXFzM6dOnadKkiTZNKpXSpEkTjh49qjdPdHQ0p0+f1hpjt2/fZtu2bbRq1erFX8xjnV5ayQKBQCAQCAQGkEilL+T47rvvsLGx0Tm+++47vXWmpqaiVCq1+1o/xsXFhYcPH+rN061bNyZMmEDdunU1O0cEBBATEyPCmgKBQCAQCP5bvKiw5qhRo8jKytI5Ro36dwuUP8n+/fv59ttvmTNnDmfOnGH9+vVs3br1mYvX/xvEUhoCgUAgEAj+ZzExMcHExPD+0U/i6OiITCYjOTlZJz05OVm7B/dfGTNmDO+++y79+mm26YqIiCAvL48BAwbwxRdfIH0JS4IIz5lAIBAIBIJXzosKa/4djI2NqVq1Knv37tWmqVQq9u7dS+3atfXmyc/PL2OAyWSaTeZf1iZLwnP2/wRvy9TyVgG/12DrpNim5a8DQM1R+vfye5WYNyxvDSBGsqe8VeCBY/PyVgEPO/2Dl18lBw9klbcKSFP3l7cKFDauUt4qcO3MnfJWAXj52+2V1w4Bw4YNo2fPnlSrVo0aNWowbdo08vLy6N27N6DZv9vDw0M7bq1t27ZMmTKFypUrU7NmTW7evMmYMWNo27at1kh70QjjTCAQCAQCwf8b3nrrLVJSUvjyyy95+PAhUVFR7NixQztJID4+XsdTNnr0aCQSCaNHj+bBgwc4OTnRtm1bvvnmm5emozDOBAKBQCAQvHLKc2/NIUOGMGTIEL3n9u/fr/PZyMiIsWPHMnbs2Feg2Z91vrKaBAKBQCAQCB4j9tY0iDDOBAKBQCAQvHIk/2Drpf8vCLNVIBAIBAKB4DVCeM4EAoFAIBC8cv7uMhj/nxDGmUAgEAgEgldOeU4IeN15rc3WmJgYPvroo/+ZcgUCgUAgEAj+Lf9pz9n+/ftp2LAhGRkZ2Nralrc6rxXbtmxg47rVZGak4+sXQL9BHxIcEqZX9ujhg6xbs4KkpAcoS5S4uXvQrmMXYho108qsWrGYPw7GkpqSgpGREQGBwXTv0Zfg0PCn6rFq3wmW7D5MWlYuwZ6ufNq1JRF+nnplNx05y9glm3TSjI1knJg9Rvt5zOINbD56TkcmOjyAOUPffaoez8K+bjX8h/fFpkpFTN2dOdVpMMm/7312xufEtEZDzOq0QGppQ0lyAnlbV1LyQP9ClDa9P0HuF1omvfj6ebKXT9d+ljm6Yd7sTeS+wUikMkpSEslZNQdVVrreck/vX8HxXQvIy07B2TOUpm+Nwd0vUq9sSuINDm2ewcN7l8hOf0DjzqOo3riXjkz8jZMc37WA5PiL5Gal0HHQbIKjmjy1HdbsOsjyLbGkZWUT5O3BJz3fpEKgz1PzAOw6cpovZi2hQdUIJg3vr01Py8pm5q+/c/z8VXLyC6gcGsAnPd/E281ZW9+C338gPzsFR49QGnYag6uP/msGuH52O0e3TSc7/QG2Tr7UbTsCvwoNtOenDQ3Rm6/uG59QrXE/7ec7l/ZzbOdsUhOvYWRkQkiFqgwYWXrvDuxYxd7Ni8nOTMXDJ5jOfUbhG6h/UdDDe9Zy4uBmEhNuAuDtH07btz/UkY87voc/dv9G/O3L5Odm8dnENXj6lv0O/ZX2MRbUr2KKuamUmwkKlm7N4VG60qB8sLecFtHm+LobYWslY+aqTM5eK9ael0mhQyMLIgNNcLKTUVCk4vLtYtbuySMzV1WmvDVxt1h6+gZpeYUEOdkwsmElKrraG6w/p7CY2UcuE3vjAdlFCtyszBkeE0ldP822PHnFCn46cpl9NxPJyC8ixNmWETGRVHhKmX9l55Z1bF7/K5kZ6fj4BdB74McEhujv544fOcDGNUt5mPQAZUkJru6etOnQlfqNWjx3fY95p70zLerbY2Eu4/LNfGYvfUDio2KD8l1aORFd1RpPNxOKi9VcuZnHwrUPefCwNE+LBnbE1LQl0McMczMZnd+/RF5B2fvw0hBhTYP8p42zF4lCoUAul5e3Gi+EPw7GsuiXnxg05GOCQ8LYvHEtE8aMZNa8pdja2pWRt7Ky5s233sHD0xsjuRGnThxl5tQfsLGxpXLVGgC4e3jSf9BQXFzdKC4uYvPGtYwfM5I585djY2OrV4+dJy8yee1OvujWhgg/D1bsPcbgGcvZNH4I9taWevNYmpqwcULp2jT6ZvvUqRDI+J7ttJ+Njf7911xmYU72+WskLF5HtbWz/3V5T2JcsToWLd4id/MySu7fxqx2U6x7fEzGjC9Q5+WUkc9eNQeeWJVaamaJ7eBxFF08VZpm54RNv88oPHOI/NhNqIsKMHJ2R12i0KvDlVPbiF37Hc27jcfdtxInY5ewemZfBozbgYW1Qxn5kuICbB09Ca3Sgr2/fae3TEVRPi6eIURGd2LDXP3rCT3JrqNnmLZ8A5/1eYuKgT78uv0AH3w/h7WTR2NvY2UwX2JKGtNXbqRyaIBOulqt5pPJ8zEykjFpeH8szExZuW0f7383mzUTP+fQ2UtMW76BmM5f4epbibP7l7Dhp770/GIH5lZlrznxzhm2Lx1OnTbD8K/QkKunN7N5wft0G7EeR/dgAPp/9YdOnruXD7J71RcEVSrdheBG3E72rB5DndYf4xVcC5VKibzgsvb86SM72LD0R97qPwbfoAj2bV3O7G8G8eW037GyKavXjcunqFqnJZ1DojCSm7B700Jmfz2IL6asx9Zes6hmcVEBAaGVqVK7GSvnjn/mvQBoWcecJjXNmL8xm9QMJR0aWjL8HVu+mJ1GiQH7zMRYQkJyCX/EFTDkLdsy543lEnxc5Ww+mEdCcgnmphK6tbDiw7dtmPBLho7srmv3mXLwAp83jqKiqz0rz9xkyPrDrO/VFHtz0zJlK5QqBq//AztzEya2qYWzpSlJOflYmZT221/tPsOt1Gy+alEdJ0tTtl2J5711f7C2Z1OcLc2e2SZHDu5l6fxZ9Ht/BEEh4WzbtIZvvxzG1Lm/YqOn77S0tKJDlx64e/lgZCTnzInD/DTtO6xt7IiqWvOZ9T3mzZaOvNHEkSnzE3iYquDdDi58NdyPQV9cR1Gif/ugiiEWbIlN4/qdAmQyCT07uvDNMD8Gjr5OUbEmj4mxlNMXczl9MZfeb+rfV/JlIsKahnntzdaSkhKGDBmCjY0Njo6OjBkzRruX1bJly6hWrRpWVla4urrSrVs3Hj16BMDdu3dp2FCzP42dnR0SiYRevXppy1WpVIwcORJ7e3tcXV0ZN26cTr0SiYSffvqJN954AwsLC+1KwD/99BMBAQEYGxsTEhLCsmXLdPLFx8fTrl07LC0tsba2pkuXLjobrI4bN46oqCgWLlyIt7c3lpaWDB48GKVSycSJE3F1dcXZ2Vln5WG1Ws24cePw9vbGxMQEd3d3Pvzww3/cpr9v+I2mLVrTuGlLvLx9GTRkGCampuzdtV2vfMXIKGpF18PL2wc3Nw/atnsTX78Arly+qJWpH9OESpWr4urmjrePH737DyY/P497d24Z1GPZnqN0rFuF9nUqE+DuzOjubTA1lrPxyFnDykvA0cZKezjoMeLkRjIdGWuLZ3e6zyJl50Guj51G8qYXv92QWXQzCk8fpOjsYZQpSeRuXoZaUYxpFf1bPKkL8lDnZmsPeWA4akUxRZdOamUsmnSk+PoF8netRfkwHlVGCsXXzuk19gBO7FlEpTpdiIzuhKN7IC26jUcuN+X8kXV65d18I2nU6VPCq7dGZmSsVyagYgPqt/uYkMpNn6sdVm7bR/uG0bwRUwt/TzdG9e2CqYkxvx84ZjCPUqVizOylDOjUCndnXcMl/mEKF27e5dM+XagQ4IOvuwuf9elCUbGCnUdPa+urUKsTDq6BNO4yHiNjUy4d03/NZw8sxTe0HtUa98PeNYDo1h/h7BnOuUPLtTIW1k46x62Le/EKrImNoxcAKmUJB9Z/Q703PiGy7tvYOfvh4BpIlehS4y12y1KiG3eidsP2uHkG0LX/GIyNzTi6b6NevXp9+D31m3fF0zcUVw8/ug8ah1qt4tqF41qZGvXb0vLNQYRE1HrmfXhM05pmbD6YR9y1Yu4/UjJ/Yza2VlKqhBreYPrCzWI27MvjzFX9Hp2CIjWTl2dy8nIRD9OU3H5QwvLtOfi6y7G31n0cLT9zgw4VfXmjgi/+DtZ83qQypkYyNl28p7fsTRfvklWoYHLb2kR5OOBuY0FVTyeCnWwBKCxREnsjkQ/rVaSKpyNetpYMrB2Ol60la8/dfq422bpxFY2bt6Vh09Z4evvR7/1PMDYxZd/uLXrlK0RWoUZ0Azy9fHF186BVuy54+wVw7fL556rvMe2bOrJq8yOOxeVw934hk+cn4GBrRO0q1gbzfDn1LnsOZxKfWMSdhEKmLLyPs6MxQb6l/eGm3Wn8ti2Fq7fy/5Y+gpfPa2+cLVmyBCMjI06cOMH06dOZMmUK8+fPBzTerK+++opz586xceNG7t69qzXAvLy8WLdO08leu3aNpKQkpk+frlOuhYUFx48fZ+LEiUyYMIHdu3fr1D1u3Dg6dOjAhQsX6NOnDxs2bGDo0KEMHz6cixcvMnDgQHr37s2+ffsAjcHXrl070tPTOXDgALt37+b27du89dZbOuXeunWL7du3s2PHDn799VcWLFhA69atuX//PgcOHOCHH35g9OjRHD+u6VzXrVvH1KlTmTt3Ljdu3GDjxo1ERPyzfc8UCgW3bl6nUlRVbZpUKiUyqgrXrl56Zn61Ws35uNM8uJ9AeEX94R+FQsGu7Vswt7DA1y9Qv0xJCVfiE6kZ5q+jR81Qf87fvm+w/oKiYlqOmkrzz6bw0ZxfuZn4qIzMqet3aThiIu2+nMk3K7aQmfsadzwyGUZuPihuXSlNU6tR3LqMkWeA4XxPYFqlHsUXT4DizweiRII8OBJl2kOse3yM/cip2Az4AuPQynrzK0uKeRh/Cd+waG2aRCrFNyyaB7efYii/QBQlJVy9k0CNiqVhQalUSo2KIVy4YXifwfnrd2BvbUW7hmU3LFYoSgAwkZd6TqVSKXIjI85cuVmmPolUindwNEl39V/zwztxeIXo1uMTWpeku3F65fOyU7l76QAVar2pTXt0/zK5WclIJFJWTGzPvDF12fBzPxLjbwBQUqIg4fYVHSNKKpUSElGTO9fPlalDH8VFhShLSjC3tHkueX042UqxtZJx+Xapp7WgSM3t+woCvF5sBMHcRIJKrSa/sNQDpFCquJqcSQ1vZ22aVCKhhrczF5L0h+UP3k4i0s2eH2LjaDp3K12W7mHhiasoVZpylSoVSrUaEyPdvRBNjGTEJaY9U88ShYLbN68TEVWtVCeplIioatx4zr7zQtwpku7HE1Yx6pnyj3F1kmNvKyfucq42Lb9AxbXb+YQFmD93ORZmmuvOyTMcln7VSCTSF3L8F3ntw5peXl5MnToViURCSEgIFy5cYOrUqfTv358+ffpo5fz9/ZkxYwbVq1cnNzcXS0tL7O014wicnZ3LjDmLjIzUbsUQFBTErFmz2Lt3L02blr7ld+vWTbsRKsDbb79Nr169GDx4MKDZPPXYsWNMmjSJhg0bsnfvXi5cuMCdO3fw8tK8KS9dupQKFSpw8uRJqlevDmiMuIULF2JlZUV4eDgNGzbk2rVrbNu2TdMRh4Twww8/sG/fPmrWrEl8fDyurq40adIEuVyOt7c3NWrU+EftmZOdhUqlKuOCt7W140FCvMF8eXm59OvRGYVCgVQqZcDgj4iqXE1H5uSJo0z5YQJFRUXY2Tsw7utJWNvof0Bk5OajVKlxsNL1fDlYW3D3of5N2n1dHBnXox1Bni7kFhSxdNcRev2wgHXjBuNip6mnToVAGlcOw8PRjoSUdGZt3Mv7M5ez9NN+yF7D8Q1ScyskMhmqvGyddFVeNnInt2fmN/Lww8jFk9yNi7VpEgsrpCammNdrRd7eDeTtWotxUEWsug4ma/GPlNy9rlNGfm4GapWyTPjSwsqBtIfP51H4t2Tm5KFUqcqEL+1trLibmKw3T9zVW/y+/ygrvv1U73lfdxdcHe2YvWozo/p2xczUmJXb9vEoPZPk1AxtfQ+eyGNu5UD6I/3XnJeTirmVo06auZUD+dn6v69XTm5AbmpBYKXSsZlZaQkAHNsxi/rtP8PawYMzsYuYPr4vX07fjKK4CJVKiZWt7r2wtnUgOfH5NsPetGIqNvZOhP4NL9lfsbbU/Fay83THH2XnqbCxeHG/IyMZvNnEkuMXiigsLjXOMguKUKrVOJjreukczE24m6Hf+3s/K4+khBRahnoxo300CZl5fB8bR4lSzYDaYVgYy4l0s2f+8av42Vthb27KzmsJXEhKw8tW/zCKJ8nOzkKlUmJjqzs+zcbWnsT7+r15APl5uQzq2YESRTFSqYy+7w0jsnL1Z9b3GDtrjTGckV2ik56ZXYKdzfM9wiUSGPi2G5du5HHvQdFz1/3SEWFNg7x+T6u/UKtWLZ1xRbVr1+bGjRsolUpOnz5N27Zt8fb2xsrKigYNNANz4+MNGxmPiYzU9fq4ublpQ6KPqVZN1/i4cuUKderU0UmrU6cOV65c0Z738vLSGmYA4eHh2NraamUAfH19sbIqfQi5uLgQHh6us9Gqi4uLVp/OnTtTUFCAv78//fv3Z8OGDZSU6P5Qn6SoqIjs7Gydo7jo3/0gzczMmTJzPj9O/ZnuPfqxaP4cLp6P05GJiIxiysz5fDdpFpWrVGfS9+PJzMzQX+A/oFKAF21rRxHq5Ua1YF8mv/cWdlbmrD14WivTonoEMZVCCfJwoVFUGDPe78alu4mcunb3henxOmFSpS4lDxN0Jg88fpMsunqWwqO7UT5MoODQdoqvn8esWkw5afpiySsoZOxPy/i839vYGhifaGQkY+JHfbn3MIXGAz6jXq8RnLp8g+hK4a9krMulY+sIrdoWI3mpgaFWaYydGs0GERTVHBevijTt/h0SJJw9uutf17lr4wJOH95B/xHTkBsbDj/+FVdbKXNGOWoPmezlt49MCu91tkEigWVb9Rtcfwe1GuzMTfiiSRXCXOxoFuJJnxohrD1famxPaFENtRpa/LKd2jM2sursLZqHeL3UlepNzcyZOGMR306dz1vv9mfpgllcOn/GoLydlQnr5oRrjxdxLwa/446Phynf//zsZ6Pg9eC195wZorCwkObNm9O8eXNWrFiBk5MT8fHxNG/enOJiwzNYHvPXwf0SiQSVSvct0cLC4oXq/LS6n6aPl5cX165dY8+ePezevZvBgwfz448/cuDAAb2TFL777jvGj9cd9Dv4g2G8/+FwrKxtkEqlZP3FaMrMzMDWzvCMJalUipu7BwB+AYHcT7jHut9WUDEySitjamqGm7sHbu4ehISGM7j/O+zdtY1OXbqXKc/O0hyZVEJaTq5Oelp2Ho42z36LBZDLZIR4uZGQoj/MAeDpZI+dpTkJKek6IdTXBVV+DmqlEqmF7tgRqYU1qpysp2eWG2MSUYP8WN0ZrJoyS1CmJOmkK1OSkPuUDTObW9ohkcrIy9YN7eTlpGFh7VhG/mVga2WBTColPUv3IZ2elYODbdnJAPeTU0lMSWf4pHnaNNWfY1FrvfMRayd/gaeLE2H+3qz87lNy8wtQlJRgZ21FrzGTCfH1IO7qbU19TziR83PSsLDSf80WVo7k5+h6yfJz0jDX00YPbp0i49EdWvWapluGjRMA9i6lIWsjI2McXDxIT03C0toOqVRGTqbuvcjOTMPa9un3Ys/vi9m9cSFDxszDwyf4qbJ/JSVbxYpNpd7bx3NorC2kZD0xi9LaQkp8suEXw+dFJoX33rTB0UbKxKWZOl4zAFszE2QSCWn5ui+VaflFOOqZDADgaGGKkVSC7AnD28/eirT8IhRKFXKZFC9bS37pUp8CRQm5RQqcLM34bOtxPGyeHR60trZBKpWRlanb32RlpmNrV3aixmOkUimu7poZ6L7+QTy4f4+Nvy2nQmQVvfJZucVMmFb625Ubaa7HztqIjKzStre1NuJ2fOEz9X6vuzs1Klkx8vvbpGX8+3v3IhGL0BrmtW+Zx+OuHnPs2DGCgoK4evUqaWlpfP/999SrV4/Q0NAyni9jY81AZaXyxcTYw8LCOHz4sE7a4cOHCQ8P155PSEggISFBe/7y5ctkZmZqZf4pZmZmtG3blhkzZrB//36OHj3KhQsX9MqOGjWKrKwsnaP/QM1sOblcTkBgMOfjSt/cVCoVF+LOEBJa4bn1UalVKBT6Z/6Vlqs2KCM3MiLM250TV+48Ia/ixNXbRPrrX0rjryhVKm4+SMbRgOcEIDkji8y8/Oc2+F45SiUlSfeQ+z+xjIlEgtw/jJL7hidTAJhUqI5EJqfo3NGyZT64i8xBd/aVzMEFZWbZsTUyI2NcvStw92ppOWqVintXj+Lhr3+c2otGbmREqJ8XJy+VhlxVKhUnL10jIsivjLyvuwu//vAZy78bqT3qV6lI1fAgln83EhcH3bC9pbkZdtZWxCc94srteBpWr1SmPrVKRcL1o7j56r9mV78oEq7rTk6Iv3YEN9+oMrIXj63F2asCTh66y1U4e1VEZmRMxqPS771SqSA9JRF7J3eMjOR4+Ydx7WJpv6dSqbh+8Th+wZX06gWwe9NCdqybx+DP5+AT8Py/Y60OKniUodQeiSlKMnOUhPuXvvyZGkvw95RzK+Hpv/tn8dgwc3aQMWlZJnkFZWcbymVSQl1sOZlQ2qer1GpOJjwiwk3/S2Qld3sSsvK0RjrAvYxcHC1Mkct0H3VmciOcLM3ILizm6L1HxPi7P1NvI7kc/8BgLpwr9dSrVCounjtN0N/oO9UqFSUKww4ElVpN0qNi7RGfWER6poJK4aV9mJmplBB/c648YyD/e93dqV3FmlET75Cc+u/u28tAIpW8kOO/yGvvOYuPj2fYsGEMHDiQM2fOMHPmTCZPnoy3tzfGxsbMnDmTQYMGcfHiRb766iudvD4+PkgkErZs2UKrVq0wMzPD0vKfP6Q/+eQTunTpQuXKlWnSpAmbN29m/fr17NmjmcHXpEkTIiIi6N69O9OmTaOkpITBgwfToEGDMiHSv8PixYtRKpXUrFkTc3Nzli9fjpmZGT4++td/MjExwcREN6RhbFLqoXqjQ2dmTPmegKBggoLD2LJpLYWFhTRuqll7Z/rkb7F3cOLdXpr1otatWUFAUAiuru4oFArOnDrOgdjdDHz/YwAKCwtYu3o51WvWwc7enpysLLZt3Uh6WgrRdRtgiHeb1GbM4g2E+7pT0VezlEZBsYJ20ZqH4+hF63G2tebDDpq1seZu2U+EvyfeTvbkFBSyZNcRktKz6FBX8waaX1jEz1sO0KRKGA7WltxPyWDa+t14OdkTHa5/YsLzIrMwxyLQW/vZ3M8T60qhFKdnUZiQ9JScz6bgyC6sOvSlJPEuJffvYFq7CRJjEwrPaF4ELDv2RZWdQf6e9Tr5TKvWpfjqWdQFeWXLPLwDq86DUNy7juLOVYwDK2IcUomsRRP16lCjSW+2LP4UN5+KuPlGcip2CcXFBURGdwRg86KRWNm6ENNhOKCZRJCapDEeVcpicjKTSU64grGJOXbOmu9lcWEeGSmlYZTM1PskJ1zB1MIGG/uyD8NurRoy/uflhPl7USHAh1+376egsJi2DTRLDoydswwnexuGdH0DE2M5gV66ZVj+OSv3yfQ9x85iZ22Ji4MdtxISmbx0PQ2qRVIrMozsvALG/7ycGKcNuHpHcubAEhTFBYTX1FzzzuUjsbBxoW5bzTVXbtCDtTPe5XTsQvwqNODamW0kJ1yk8VsTdPQoKszlRtwO6rcrOxbOxNSSiDpdObZ9JlZ2bljZuXM6dgEAVWppxqY1atODZbNH4+0fjm9gBPu2LaeoqIBaMe0BWDrrc2zsXWjXbSgAuzcuZOua2fT88HscnD3Izkz9sy5zTEw1HqG83CwyUpPISk8BIDnxLgDWto4GPXK7jxfQpp4FyWlKUjI1S2lk5qg4c7XUmzXiXVvOXC0i9mSBpk65BGf70gH3jnYyvFyMyCtQkZ6tQiaFwZ1t8HEzYvqvWUgkEqwtNA/Xv66v9U6VIMbuPEWYsx0VXe1YefYmBQolb1TQfL++3HEKJ0tTPqhbEYA3K/mz5txtJu0/x1tRAcRn5LLo5DW6RpV6KY/cTQbU+NhZkZCZy/RDF/G1s6RthWevpQfQun1X5kz9hoCgUAKCw9i2aQ1FhQXENGkNwKzJX2Hv4ES3XoMA2LBmGQFBobi4afrOsyePcmjfTvoOHvFc9T1m4+5UurZxJjG5iOSUYt7t4EJaZglHz5R6O78d4ceRM9lsidW8gA1+x52YWrZMmHGPgkIVdtZGf7azkmKFxoC1szbCzsYId2eNI8PX05SCQhWP0l+RIfcfHcz/InjtjbMePXpQUFBAjRo1kMlkDB06lAEDBiCRSFi8eDGff/45M2bMoEqVKkyaNIk33nhDm9fDw4Px48fz2Wef0bt3b3r06MHixYv/sS7t27dn+vTpTJo0iaFDh+Ln58eiRYuIiYkBNKHITZs28cEHH1C/fn2kUiktWrRg5syZ/6oNbG1t+f777xk2bBhKpZKIiAg2b96Mg4NhV/rTqFu/EdlZWaxavpiMjHT8/AP4csIP2rBmSsojnRkwhYWFzJszjbTUFIyNTfDw9OajEZ9Tt34jAKRSGfcTEti3dyzZWVlYWVsTGBTCNxNn4O1T1uvxmObVK5KRm8dPv+8jNTuXEE9X5nz4jnZ5jKT0LJ2xINn5hXy1bDOp2blYm5sS5u3OkpF9CXB3/lMPKTceJLP5WBw5+YU42VpROyyA99s1wlj+777qNlUrUntv6bIp4ZM+ByBh6XrO9x31r8ouvniSPHMrzBu1R2ppTcnDBLKXTUX95yQBmY29ZkDNE8gcXJD7BJO1ZLL+Mq+cJXfzMszrt0La6m2UqQ/JWT2HkvibeuXDqrUiPyedQ5tn/LkIbRhvfTBfG9bMTk/S+U7kZD5i0TfttZ9P7F7Iid0L8QqqQffhmnZKuneRX6f20MrErtWsh1axVgfa9Pq+jA7NalchMzuXuWu3kZaZTbCPJzM+ew8HG03I92Faxt9+S07NzGbq8g2kZ+XgaGdNq7o16NexuU59P/8+Q7MIrWcY7Qc9cc0ZSToPD3e/KrToMYmj26ZxZMsUbJ18adt3tnaNs8dcP7MV1GpCqrbRq1O9diORSo3YuWwkJYpCXH0q8eGX8zG31Fxn1egW5GZnsHXNHHIyU/HwDeH9z3/C+s9JAumpD3XuxaHdaygpUbBgynCdelq+OYjWXTSTly6c2s/yOaWLNS+aNrKMzF/ZfjgfE7mEnm2tMDeVciNewZTlmTprnDnby7AyL9XF192IT3uVei3fbq4JSf8RV8DCTTnYWkmp/OdSHOMH6XrAflicAU9E6ZqFeJJRUMTPRy+Tll9EsJMNMzvUwcFCE9Z8mJPPk0PFXK3MmdWhDpMPnKfrsr04WZrxduUAelYrnZGbW6Rg1uFLPMotwNpETuMgDwbXqVDGs2aI6PqNyc7KZM3y+ZoFvP0DGTVhsrbvTEtJ1hk7XFRUwII5k0lLe/Rn3+nDkOFfEl2/8XPV95i121MxNZHyQU8PLM1lXLqRz5dT7uiscebmbIyNValh3KaR5vsy8TPd4RxTFiSw53AmAK0a2tO9nYv23I+jArQygvJFolary/qUBf85Lt9MLG8V8Lt/oLxVILbpuPJWAYCao/SvYfYq2dxwQXmrQCfrneWtAitSmj9b6CUT6Fb+M+h+3fiMMY6vgBkuU8tbBW42HlbeKvD5dw/LWwW2LfxnyzX9HbKnfPRCyrEeNu2FlPM68dp7zgQCgUAgEPwHERMCDCJaRiAQCAQCgeA1QnjOBAKBQCAQvHJe5vpy/+sI40wgEAgEAsGrR4Q1DSJaRiAQCAQCgeA1QnjOBAKBQCAQvHL+qwvIvgiEcSYQCAQCgeDVIxahNYhoGYFAIBAIBILXCOE5EwgEAoFA8OoRYU2DCONMIBAIBALBK0ciwpoGEcbZ/xOciu+XtwpIcjPLW4XXYtskgOPf/VHeKuDZqfy3DHpoGVbeKmCRW94agKtFZnmrgLW9RXmrQPzOE+WtAt51rpW3Cjh7B5W3Cq8G4TkziDBbBQKBQCAQCF4jhOdMIBAIBALBK0ciFqE1iDDOBAKBQCAQvHrE9k0GEWarQCAQCAQCwWuE8JwJBAKBQCB49YiwpkGEcSYQCAQCgeDVI8KaBhFmq0AgEAgEAsFrhPCcCQQCgUAgeOWI2ZqG+X/VMmq1mgEDBmBvb49EIiEuLu5vlzFu3DiioqK0n3v16kX79u3/cX6BQCAQCP5fIpG+mOM/yP8rz9mOHTtYvHgx+/fvx9/fH0dHRyQSCRs2bPhbBtaTTJ8+HbVa/dzyI0aM4IMPPvhHdb1I1m3bza8bt5GemUWArxcf9+tBeHDAM/PtOXSUcVPmUK9GFb4b9bE2fcGq9ez94xiPUtMwMjIiJMCPAd3fpEJwoMGyVv1xjiX7T5Gak0+wuyOfdWhIhLerXtlNJy7x5erdOmnGRjJO/lDalmk5eUzb8gdHr8eTU1BEFX8PPusQg4+T3VOvybRGQ8zqtEBqaUNJcgJ5W1dS8uCOXlmb3p8g9wstk158/TzZy6drP8sc3TBv9iZy32AkUhklKYnkrJqDKiv9qbo8Dfu61fAf3hebKhUxdXfmVKfBJP++9x+X91cO7FjF3s2Lyc5MxcMnmM59RuEbGKFX9vCetZw4uJnEhJsAePuH0/btD3Xk447v4Y/dvxF/+zL5uVl8NnENnr5l2+5Jtm7exPp1v5GRkY6fXwAD33uf4BD9eY4cPsRvq38lKSmRkhIl7h7utO/wJo0aN9UrP3vmNHZs30q/Ae/Rrn1Hgzqc2reCY7sWkJuVgotnKM3eHoOHX6Re2ZTEGxzYNIOH8ZfISntA0y6jqNGkl47M4e1zuXZmF2kPb2NkbIqnf2UadRqBg6u/QR22b1nP7+tWkZmRjo9fAH0HDSUoJFyv7LHDB1i/ZjkPkx6gLCnBzd2Tth3fokGj5joyu7Zv4vbN6+TmZPPjjAX4BZRdhb5lTWNqVTDCzETCnSQlv+0rIjXr6f1b3Qg5jarIsTKXkJiqYt3BIuKTVToyvq5SWtUyxsdVhloND1JU/LypAIVSc75fa1M8nKTYDNyOMjeHvPNneLT0F6xq1cWhfReMbO0punuLpPkzKbxheAV/+zYdsWvxBnJHZ5Q5WWQfOcij5fNRKxQaAakUp7d6YNOgCUa29pRkpJEZu5PU35YbLHPt9lhW/L6D9MwsAn28GNa3GxWCDN+7x+z+4zhfTptH/epR/PBpaT/11awFbNt/REe2ZlRFpo3++K9F6NChoQUNqphhbirlRkIxS7fkkJyuNCgf7COnVbQFPu5G2FnJmLEqkzNXS3cFkUmhYyNLIoOMcbYzIr9IxeXbxfy2J5fMHJXBcgWvhv9XxtmtW7dwc3MjOjr6hZVpY2Pzt+QtLS2xtLR8YfX/E/b+cYxZi1YyYlBvwoMDWLN5B8MmTOTXWROxszV8PUmPUpi95FcqhYeUOefl7srH/Xvg7uJMUXGxpszxE1k1ZxJ2NtZl5Hecvcak3w8y+s1GRHi7suLQWd6bt4FNn/bEwcpcb/2WpsZs+rSn9vOTY0nVajUfLdqMkUzGtN5tsTQ1ZumBMwycu571n/TA3ESut0zjitWxaPEWuZuXUXL/Nma1m2Ld42MyZnyBOi+njHz2qjkgk2k/S80ssR08jqKLp0rT7Jyw6fcZhWcOkR+7CXVRAUbO7qhLFHp1eF5kFuZkn79GwuJ1VFs7+1+V9VdOH9nBhqU/8lb/MfgGRbBv63JmfzOIL6f9jpWNQxn5G5dPUbVOSzqHRGEkN2H3poXM/noQX0xZj629CwDFRQUEhFamSu1mrJw7/pk6HDqwn/m/zOX9IR8SHBrG7xvX8+WYUfw8byG2tmUNbCsra7p07YanpxdGcjknjx9j+tRJ2NraUqVqdR3Zo0f+4Nq1K9g7lL2WJ7l8cht7fvuOlt3H4+5XiRN7l7Bqel8GTdiBhXXZvIriAuycPAmr2oLda77TW2b89RNUbdgdd98IVEol+zZMYeW0vgwcvxVjk7Lf9cMH97Lkl9kMGDKcoJBwtm78ja/HjGDGvBXY6GkHSytrOr31Lh6e3hjJ5Zw+cYTZU7/HxsaOqKo1ACgqKiQsPJLoeo34ecZEvXo2riKnfiU5K3YXkpatplUtYwa1M+P7FfmUGLABKgcZ0b6eMWv2FXHvoZIGUcYMesOMb5fnk1ugMep8XaUMfMOMPaeLWX+wGJVKjbujDNUTNt+NB0p2nyqmzZ6+GNk74tJrED5fTUbu7ELSz9MouH4Vh7Yd8fnyB24O6YUyK7OMLtb1GuH8bn8SZ/1IwdVLGLt74v7hSACSF/0EgGOHrti1eIPEGT9QFH8X08AQ3D/4BFV+HulbN5Qpc8/hE8xYspqRA96lQpA/q7fu5uOvp7JqxjfY6+nXHpP0KJWZS38jKkz/Nky1oioy+v0+2s9y+dMfxa3qmNO0pjm/bMgmJVNJx4YWDH/Xli9mp6Eo0Z/HRC4hPlnBwbMFfNjVtsx5Y7kEHzcjfj+YR8LDEizMpHRrYcXQt20ZP++fv0T+LcT2TQb5n/MHrl27loiICMzMzHBwcKBJkybk5eWhVCoZNmwYtra2ODg4MHLkSHr27Kn1iPXq1YsPPviA+Ph4JBIJvr6++Pr6AtChQwdt2t/lybDmvHnzcHd3R6XSfeto164dffpofoiGwqKTJk3Czc0NBwcH3n//fRSK0gd5UlISrVu3xszMDD8/P1auXImvry/Tpk372/oCrPp9O22bxtC6cX38vDz4ZFBvTE1M2LL3oME8SqWKCVN/om/Xjri7OJU536x+NNUrVcTD1Rl/b08+6N2dvPwCbt1L0FvesoNn6FirIu1rVCDA1YHRnRpjKjdi44lLBnWQAI7WFtrDwap0L8B7qZmcv/eQLzo1oqK3K77O9ozu1JhCRQk7zhp+0zaLbkbh6YMUnT2MMiWJ3M3LUCuKMa2ifw9OdUEe6txs7SEPDEetKKbo0kmtjEWTjhRfv0D+rrUoH8ajykih+No5vcbe3yFl50Guj51G8qY9/6ocfcRuWUp0407UbtgeN88AuvYfg7GxGUf3bdQr3+vD76nfvCuevqG4evjRfdA41GoV1y4c18rUqN+Wlm8OIiSi1nPpsHHDOpq3aEmTZi3w9vZh8JChmJiYsHvXTr3yEZGVqB1dFy9vH9zc3HmjfUd8/fy5fEn3O5SWmsrcn2Yz/JNRGMme/hA8vnsRUXW7UKlOJ5zcA2nVfTxGxqacO7xOr7y7bySN3/yUCjVaYyQ31ivz9tAFVIruiJN7EC5eobTt/T3Z6Yk8vKf/u755wxqatGhDo6at8PL2ZcCQ4ZiYmhK7a6te+YqRlakZXR9Pb19c3Txo3a4zPn7+XLl8XivToFFzOnfrRWRUVYPXXj9Kzq6TxVy8oyQpTcWK3YXYWEiI8DfcZjFRco5eUnDiSgnJGWp+21dEcYmamuGledrXM+HgOQV7Tyt4mK7iUaaauJslKJ/oJg/EKbiXrEKR8oiCa5dJXf8rxu6eZO7ZTlbsTorv3yPp52moioqwbdxCry7moRUouHqR7EOxKFKSyTt3muxD+zALKn2ZNAutQM6JI+SePo4iJZmcowfJizuFaZB+7+yvm3fxRpP6tGlUFz8vd0YOeBcTE2O2xBreG1epVDF2+i/0e6ud3r4SwFhuhIOdjfawtnz6vqbNapnz+8E8zl4r4n5yCb9syMbOSkaVUBODeS7cLGZ9bJ6Ot+xJCorUTFqWyclLRTxMU3LrvoLl27Lxc5djb/NqTAOJRPpCjv8i/1NXlZSUxNtvv02fPn24cuUK+/fvp2PHjqjVaiZPnszixYtZuHAhf/zxB+np6WzYUPomNH36dCZMmICnpydJSUmcPHmSkyc1D9RFixZp0/4NnTt3Ji0tjX379mnT0tPT2bFjB927dzeYb9++fdy6dYt9+/axZMkSFi9ezOLFi7Xne/ToQWJiIvv372fdunXMmzePR48e/SMdFYoSrt+6S7VKFbRpUqmUapEVuHTtpsF8i9dswNbGmjZNYp6rjk27YrE0NyfQ17vs+RIlV+4/olaQ1xM6SKgV7M35e0kGy80vVtDi6wU0mzCfoQt/5+bDNJ0yAUyMnvBqSSUYy2ScvfNAf4EyGUZuPihuXSlNU6tR3LqMkeezQ7wAplXqUXzxBCiKNQkSCfLgSJRpD7Hu8TH2I6diM+ALjEMrP1d55UFJiYKE21d0jCipVEpIRE3uXD/3XGUUFxWiLCnB3PLveZIfo1AouHnzOpWiqujoEBVVhWtXLz8zv1qt5lzcGR7cv0+FiqWhVZVKxZRJP9CxU2d8fHyfWoaypJik+Ev4hZV61iVSKX5h0dy/ffbvX5QBigo0RrqpRdm2UigU3L55ncioato0qfT/2DvPMCmKrQG/3bOzOeecM+ySQXJOCkgWRYnqVVFRQQQVRf0UAyBBzERBJCNIzmHJYckZls15Z3Oc6e9Hw+wOOwOoBK+33+fpZ3e6T1WdrurqPn3qVLVITP1GXLxg+sXlFpIkcSr+GKnJSUTXrXfPOqnNVDjYiFxKqnaRlVXAjQwdgZ7GHxMqEXzdDdNIwKUkLYGecj+0tRII9FRRVCoxur8Vn4y05tW+VgR5mX70iLZ2OLTtBEBx/LGaJ0fxqeNYmxjeLblwFsuQcCxvGmNqDy9sGzWl6Fj1x9RLL5zFJrYB5t6+AFgEBmMdFUPR8dofXK+srOLitRs0iY2q1k0UaRITzZmLV03qP3fFWpwc7OjVsbVJmeNnL/L4iDd46vV3+fLHX8gvLDIp6+akwtFOxblrFdXnUS5xNbmSEF/jLwR/FStLEZ0kUVJ276E6fwtRuD/bv5D/qmHNtLQ0qqqq6Nu3LwEBAQDExMg34unTpzNhwgT69pVjSb7//ns2b65+43ZwcMDOzg6VSoWnp2Fck6OjY619fwUnJye6d+/Or7/+SseOHQHZ0+fq6kr79u3vmO6bb75BpVIRGRnJE088wfbt23nhhRe4cOEC27Zt48iRIzRuLN+wf/75Z8LCjLvL70Z+YSFanQ7n24ZjnR3tuZGSajTNyXMX+WP7buZN+/SOeccdOcGkabMpK6/AxcmRrye9g6O9XS25vOJStDqp1vCli6011zONu9MD3Z346KnOhHm5UlRWwYJdxxg6aymr3n4OD0c7At2d8HKyY+aGOCb274iVuZpf9hwnI7+IrIJio3mK1nYIKhW64gKD/briAtRuXnc8VwAznyDMPHwpWjNfv0+wsUO0sMS69eMUb19N8ZYVmIfVxW7QK+TP/4qqhEt3zfdhU1SQh06nxc7RcNjO3tGFjFTjsXe38/vir3FwdiPyHr1kt1NQkI9Op8PJyXDYztHRieQk495XgOLiYoY9N4jKykpEUeTlUa/ToGG1d2jl8qWIKpGeT/a5qw4lRXlIOm2t4UsbOxdy0q79yTMyjqTTsXXpZ/iGNMTdJ7zW8cKCfHQ6ba3hS0dHZ1KSEk3mW1xcxH+G9KOysgJRVPH8K29Sr0ETk/K3ozaTjaXCEsOHcmGJDnsb4w8/GysBlSgYSSPh4STn52Ivp+3W1Jzf48pJydLRJNKMUX3k4dKa8Ww9W5gT+cIfiJZWlF65hCAIVOXnGeRdpcnDwscPYxTs3YGZvQNBn84AQUAwMyN301qyV/6ql8letQTR2pqQWfNApwNRJHPxXAr21I7d1OjvlYbDl/K90vhL5Mnzl1m3fR8Lp3xo9DjIQ5rtmjXCy92VlIxMvv91FW9+Op2fPn0Xlaq20epgK+/LLzIckSko1umP3Q/UZjCwky2HTpdRVv6QjDMFk/xXGWf16tWjY8eOxMTE0LVrV7p06UL//v0RRZG0tDSaNWumlzUzM6Nx48Z/Klj/fjB48GBeeOEFvv32WywsLFi8eDGDBg1CvMOU4Tp16qCqEcfk5eXF6dOnAbh48SJmZmY0bFjtUQgNDa31EKtJeXk55eWGruzyigoszP/8W1ZJaSn/N+N7xr080qihVZOGMVHMm/YpmoJC1m3dyQdTZvHjF5PuGMd2r9QL9KZeoHeN3170+WIhyw+c5tXuLVCrVEwb2oNJy7bSeuL3qESBZmH+tIoMROLBXAMWDVtRlZ5kMHnglou9/MIJyg7IExhK05Mw8wvFqnE7Cv+BxtnfZcuaORyL28ToSXNRm5seZnkQWFlZMeOb7ykrLeXkyRPM+el7PD29iImtx5XLl1i7djXTZ36L8A9Z7HLTko/ISr3MkHG/3l34T2BlZc1Xs+ZQVlrK6ZPHWPDzbDw8vakba9xj6+PlRnRw9ZDbjVTNfdXnFreqff/NoU+AlH0VhPupeCxazR8Hqr1BO45XEDB3FGo3D9wHD//TZVnXqYdrv2dI+3EmpZfOY+7ljefIUVQNeFYf8G/fsh0ObTqS8vVncsxZUAgeI0dRlZdD/s4tf+tci0tL+WjWz0x4aegd75WdW1U/p0IDfAkN8KP/qPEcP3uBJrHRmDu68f271W3z9WLN39LrXlCJ8MoARxBgwfq/F37xp/iXDkneD/6rjDOVSsXWrVvZv38/W7ZsYdasWbz33nts3br17okfEj179kSSJNavX0+TJk3Yu3cvX3/99R3TqNWGweqCINSKW/szTJ48mY8+MgzAHvvK84wb9QIOdnaoRJHc/HyD47maAlwcHWvllZKeSVpmNuM/m6bfp7tp8LbtN5Rfv/kSHy85ANzK0hJfL0t8vTyoGxHKoFfG8sf23TzXr5dBnk42VqhEgZzCEoP9OUUluNrdOfbiFmqVikgfd5KyNfp90X4eLBvzLIWl5VRqtTjbWjN4xhLq+HoYzUNXUoik1SLaGL4Zizb26ArzjaapVsAci5imlOz43UieVWizDN+stVlpqANMz1x9lNjaOyGKKgo1OQb7CzQ52Du63jHttrXz2bpmLq9O/BGfgNqeoHvF3t4BURTJyzP0lGg0eTg5m34REUURb28fAIJDQklKTGT5siXExNbj7Nkz5Gs0jBhaHVKg0+mY+/MPrF2zijnzDWfoWds6IYgqigsM66G4MAcbhzvXw72w6dePuXxqF0PeXoS9k3FPvZ29A6KoIl9zez3k4ujkbDJvURTxujlUFxQSRkrSDVYvX2TSOEvPzOVKUrWX+pYRZWctUFDDE2ZnLZKSZXw2QHGphFYnYWdtaPjWzOPW3/Rcw/tZRp4ORzvDdMVlUJGaTEVqMklpyYT/8CvW0bGUXqwe1jZzdKJKY9y77v7McDS7t6LZtgGA8sTriJZWeL38JtkrFoMk4TH0RbJX/UbBvp16GbWbB659n65lnDnq75WGnnX5Xln7hTMlPYu0zGze/nymft+te2WrgS/w28xP8fV0r5XOx8MNR3tbktMzaRIbTUVBLh+vrj7HW5EaDraigffM3kYkMd3EbIA/gWyYOeDiIPLFgryH6zX7h7w0/RP5rzLOQDZcWrZsScuWLfnggw8ICAhg+/bteHl5cejQIdq0aQNAVVUVx44dM/A4GUOtVqPVmp6O/GextLSkb9++LF68mCtXrhAREXFXHe5EREQEVVVVnDhxgkaN5OGaK1eu1HqI1WTChAm89dZbBvsKrsnBwWq1GeEhgRw7dY42zeRhUp1Ox7HTZ+nbvfYSBP4+Xiyc/pnBvp9+XUFJaRmjRz6Lu6vp2W86nUSFkalEajMVUb7uHLqcRIeYUL3soctJDGp5b3EyWp2Oy2nZtIoKrHXMzkr23tzIyuNcUiajupmYnavVUpV2A3VwFBUXbsYUCQLq4CjKDu+4Y/kWdZogqNSUnzxQO8+UBFQuhg9flYsH2tuMn38KZmZq/IKjuHjmEPWadgDka+LSmUO06fa0yXRbf5/L5lU/M+q97wgIqWNS7l5Qq9WEhoZz6uQJmrdoqdfhZPwJnuj55D3nI0mSfjJN+w6dqF/f0Dj5YOIE2nfoRKfOXWulVZmZ4+Vfh4QLB4hoIMc8STodCecP0Lj9s3/11JAkic1LPuFi/FaeG/MLjq7Gh+VArofg0HBOxx+jaXM5Zkmn03E6/jjde9x9aPYWuhr1YAytVktFpeF9L79YR5ifipRs2QCwUEOAh0jcaeP5aHWQnKkjzFfF6WtyXgIQ7qdi7yk5TW6BhKZIh7ujoYfEzVHk/I073HdvTuW0qhmoLwjYxDQgd+Mao0kECwsMpoAC0q17uyCAJCFYWMrDmTVlbg5v3o5abUZEcABHT5+nbVP5Hq7T6Th6+jz9u3eoJR/g48WiaYYvxT8uWU1xaRlvjngaDxfjxnVmTi75hcW4OjnePHctmbctkaEp1BIdZK43xiwtBEJ81ew8WsLf4ZZh5uFixhfzcykuVYYz/yn8Vxlnhw4dYvv27XTp0gV3d3cOHTpEVlYWUVFRjB49ms8//5ywsDAiIyOZNm0aGo3mrnkGBgayfft2WrZsiYWFxR2HC++VwYMH06NHD86ePcuzz/71mzpAZGQknTp14sUXX+S7775DrVYzZswYrKysTA7VWFhYYGFhOLxUXmNIc1Cv7nw680ciQ4KICgtm2R+bKS0r54mOsmH7yYzvcXN24qXnnsLC3JzgAMOHia2NHCt2a39pWRkLV6ylZZOGuDo5oiksZNWGbWTn5tG+RVOjOj7XpiETf9tCHT8P6vp7smjPcUorKundVA72fe/Xzbg72DD6CXnW5PdbDhIb4IW/qyOFpeXM33mUtLwC+jarq89zy8lLONlY4eVkz+W0bL5cs4v2dUNoERFgsn5L92/Brs9IqlITqEq+jmXzTgjmFpQdj5PPte9IdAV5lGxbZZDOslErKi6cQCqtHc9WGrcJuwEvUXnjEpXXL2AeWhfziHrkzzO+hMG9orKxxia0eoKFdZAv9vUiqcjNpyzJ9ESKe6FDjyH8Mvt9/IOjCQyNYeeGRZSXl/JYu94ALPzmXRycPXjymdEAbF0zl/XLZjP09c9xcfehQJMNgIWlNRaW8vVRXJRPXnYa+blZAGSkJgBg7+hq1CPXu08/vp72JaFh4YSHR/D776spKy/TG1LTpnyBi4srQ4ePBGD50iWEhoXj5eVNZWUFR48eZueObbw86nW5HHt77O0NvaJmKjOcnJzx9TVuIDXrPJy1897BK6Au3kGxHN62gMqKUmJbyrGsa+eOw87Rg/Z9xwDyJIKstKv6/ws1GaQnncfcwhpnd/m62/TrR5w9/AcDXvkWc0sbivLl+rCwskNtbllLh559BvLNtMmEhEUQGh7F+t+XU15WSvvOjwMwc+qnuLi4MnjYfwBYtWwRIWEReHr6UFlZwfGjB9mzYzMvjBqjz7OwsIDszAzycuV2Sk2R49ccnZxxcpZfsPbEV9KlsTlZGh25N5fSyC+WOH2t+gXrld6WnLqmZd9N42tXfCXPdLIgKVNHYoa8lIa5mcChc9Vpdh6vpFszc1KztaRk62gSqcbdSWTehjJANgD9PFRcT9WidnNH7emN+9PDqcrLxbZRMxzad6H08gVcevRDtLREs12OJfZ+/R2qcrPJXDQHgKIjB3Du1Z+y61duDmv64P7McAqPHNAbZEVHDuDafzCV2ZnysGZwKC69+qPZvsno9fB0zy588s0cIkMCqRMaxG/rt1FWXk6P9vILxEczf8bNxYlXBvfDwlxNiL+vQfpb98pb+0tKy5izfC3tH2uEi6MDyemZzF60Al9Pd5rVN/2Cs+VgCT3b2JCeqyU7T0vfDjbkFWoNZmKOG+LIsQvlbD9cCoCFuYCHc3W4jKujCn9PM4pKdeTm61CJMGqgAwFeaqb/qkEUBRxs5WdKUelDWudM+UKASf6rjDN7e3v27NnD9OnTKSgoICAggKlTp9K9e3c6d+5MWloaQ4cORRRFRowYQZ8+fcjPv/Pw1NSpU3nrrbf46aef8PHxISEh4W/r2aFDB5ydnbl48SLPPPPM385v4cKFjBw5kjZt2uDp6cnkyZM5e/Yslpa1b+z3QsdWj6EpKOTn31aSm5dPaJA/Uz94G+ebrvqMrBzEP+FuFkWRG8lpbNw5k/yCQuztbIkKDWb2p+8TfNvN6hbdGkSQV1zKt5sPkF1QQoSPK9++0Fu/PEa6psBgEk5haTkfL99GdkEJ9tYWRPu6s+C1pwjxrPbcZRUUM+X3PeQUleBmb0OPRlH8p3Oz24s2oOLMEYqt7bDu0BvR1p6q9CQKfvka6eYkAZWDM9wWt6hy8UAdEE7+gqnG8zx/gqJ1v2Dd5nHEx59Gm51O4dJvqUo0PRv2XnBoVJfm23/R/46e8i4ASQtXcWrkhL+Vd6MW3SgqyGP9sm8p1GTjExjBqHe/w/7mJIHc7HSDKet7ty6jqqqSOdPGGOTTvf9LPDHwFQBOH93Fom8n6o/Nmz6ulkxNWrdtR36BhsW/LCAvL4/g4BA++vgz/QtTVlYmQo2LoqysjO++nUlOdjbm5hb4+vkxZux4Wrdt95frIbrJ4xQX5rJ77UyKC7Lw8I1i0Os/Y2svG5P5uWkG9VCoyWTOJ731vw9umcvBLXPxD2/Kc2Pltjq+ewkAi6Y+Z1BWj2GTqdei9mK4Ldt0pCBfw2+L5qLJyyUwOJT3Pp6iH9bMzsow6J/lZaX89O00crOzMDe3wNvXn9fHvk/LNh31MkcPxjF7evU6bF9/IXt4BjwzjKcGy8v8bD9eibla4Kn2FlhZCFxL0/LD2lKDNc5cHURsLasf3CcuV2FjJdC9mTn2NgIpWTp+WFuqX+MMYPfJSszM5CU1rC3lhWq/W1NKToEsU1EFscEqujc1x6rfAqrycig6cYTkKZ9g16wlboOGYebkRPn1qyR+PB7tzUkCajd3g76ZtXwRkiTh/sxwzJxd0RZoKDx6UG+8AaT/NAu3Z4bj+eJozBwcqcrLIW/LH2Qtq+5XNenUsil5BYX8/NsacjQFhAX68fV7b1bfK7NzEf/EbEFRFLl6I5mNu/ZTWFKCq5MjzerV4cVBvTFXG1+LEWBDXAkW5gLDe9phbSlyKbGCqYs0BmucuTubYWdd7eUM8jZj/LBqb90z3eQ4uH3xpfy8pgAne5GGkfIz5JOXDUdAPp//kNY5U2LOTCJIDzti/iEybNgwNBoNa9asedSq3FeSk5Px8/Nj27Zt+lmhdyPrXO2p4g8bu2vH7i70gCk6fPTuQg+BQ5NNr5P0sDA/euruQg+YANuMR60CB5NrL/fysGng8+jrYc7Ge4v3fJC8uOPeh7AfFJ4f333B5AfNWyv/2mz8+8n8ScZjde8nZatm3F3oHrDsO/pPp5k9ezZfffUV6enp1KtXj1mzZtG0qfGRHgCNRsN7773HqlWryM3NJSAggOnTp/P444//HdVN8l/lOftfZceOHRQVFRETE0NaWhrjxo0jMDBQH1+noKCgoKDwX8cjWqNs6dKlvPXWW3z//fc0a9aM6dOn07VrVy5evIi7e+1JGxUVFXTu3Bl3d3dWrFiBj48PN27cwNHIJLr7hWKc3UadOnW4ceOG0WM//PDDHReTfVBUVlby7rvvcu3aNezs7GjRogWLFy+uNctTQUFBQUHhv4ZHNKw5bdo0XnjhBYYPl5ds+f7771m/fj1z585l/PjxteTnzp1Lbm4u+/fv1z93/8oXhf4M/2rjrOYq+/fKhg0bTM5y8vB48G5eY3Tt2pWuXWvPLlNQUFBQUPhfx9jansYmxoHsBTt27BgTJlTH6YqiSKdOnThw4EAteYC1a9fSvHlzRo0axe+//46bmxvPPPMM77zzjsEapfeTf7Vx9le49eUBBQUFBQUFhQfIfVrnzNjanh9++CGTJk2qJZudnY1Wq63lbPHw8ODChQtG87927Ro7duxg8ODBbNiwgStXrvDKK69QWVnJhx+a/hrE30ExzhQUFBQUFBQePvdpKQ1ja3sa85r9VXQ6He7u7vz444+oVCoaNWpESkoKX331lWKcKSgoKCgoKPyLuE+eM1NDmMZwdXVFpVKRkWE4QzojI8PkN7a9vLxQq9UGQ5hRUVGkp6dTUVGB+V/4NOLdUBYZUVBQUFBQUPifwNzcnEaNGrF9e/XH7nU6Hdu3b6d58+ZG07Rs2ZIrV64YfFbx0qVLeHl5PRDDDBTjTEFBQUFBQeFRIIj3Z/uT3Fp4fsGCBZw/f56XX36Z4uJi/ezNIUOGGEwYePnll8nNzWX06NFcunSJ9evX89lnnzFq1Kj7VhW3owxrKigoKCgoKDx8HtHnm5566imysrL44IMPSE9Pp379+mzatEk/SSAxMRGxhm5+fn5s3ryZN998k9jYWHx8fBg9ejTvvPPOA9NRMc4UFBQUFBQU/qd49dVXefXVV40e27VrV619zZs35+DBgw9Yq2oU4+x/hMVXmjxqFVCrTX8a42Fh3f5RayDj26/87kIPmIrGsY9aBX6bf/ZRq4CZmfbuQg+Y4tJHs4ZiTZydq+4u9IBJ/WjDo1aBDZcf/eLePv7G19r813GfJgT8G1GMMwUFBQUFBYWHj/Lhc5MoNaOgoKCgoKCg8A9C8ZwpKCgoKCgoPHyUYU2TKMaZgoKCgoKCwsPnEc3W/G9AqRkFBQUFBQUFhX8QiudMQUFBQUFB4aEjKcOaJlGMMwUFBQUFBYWHjzJb0ySKcaagoKCgoKDw8FGMM5MoNfMXkSSJF198EWdnZwRBID4+/k/nMWnSJOrXr3/fdVNQUFBQUFD470XxnP1FNm3axPz589m1axfBwcG4uroiCAKrV6+md+/eD6TMhIQEgoKCOHHixN826s7ELSZ+9xxKCrNx8YqkVe/38fA3vWL81ZObOLx5BoV5KTi4BvDY42MJiGqrP77jt/FcPLbGII1feCt6vPCz/vfJvYs5vmMOJYVZuHpH0rbfRDwDTJd5OX4jBzfMoCA3BUe3QFr2HEtgdFsDmdz0q8St+4qUq0fQ6bQ4e4TwxIhZ2Dl5A6DJTmTf71+Qeu0Y2qoKQuq0pvOgidjYuwJwbNdiDm2ZQ3FBFu6+kXR+aiLeQcZ1ykq9zN51M0m/cZaC3BQ6DphAk47DDGQSLx/h0JY5ZCSeoSg/i74vzSa8fieT53iL3Zt+Y/u6+RRosvEJCGfAiAkEhsYYlY3btoLDe9aRmnQFAP/gaHo+/bqBfPyhbezbupzEa+coKcpn/JfL8A2MvKsed8O5VWOCx4zEoWFdLL3dOdrvFTLWbr+ntAEvP0PwWyOx8HSj4NQFzr7xyR3lo/wF2seKONpCTiFsP6HjSqpkINMuVqRBqIClGpKyJDYc0ZFbWH38qbYink4CNpZQWgHX0yW2ndBRVCoff7qdSJhP7XfUiiqJKSt1tfZH+kLbGBEHG8gthJ2ndFxNqz4e4QMNQkU8ncDaQuDnzVoyNdXHHaxhVE9VrXwXL17MN9/OIV+ThatPJO3v0jcundjIgRp9o1XPsQTVqe4b00dHGMg/88wzjBw5Eg8PT3KLzdh3HjLz5WNVVRX8Nm0A2SkX6P7iCpw9o+5L3VuaQ/fGIuG+ApIE5xMlNh3TUXnzQwQONjC69+2PIDMOX6mgoEQua/em39i+dj4F+dlEREQy8f33CYuK4VKqlpxCw/aJ27aCQ7sN+0Wvp18nMEzuF9qqStb99g1nj+8lOzMZK2s7ImKa8eTgN3B0dtfnc/bAYk7tmUtpUTbOnpG06PUe7n6m2+La6U0c3TqTorwU7F0CaNptDP6R1W1xbNs3XD21gWJNOqJKjatPNE26vIG7fz29zJIvOlKkSTXI9/+mLqJbh8ZYmsONTIm1+6vIKTBsg9tpFiXSuq4ZtlaQnifxx4EqkrPlNFbm0LGhGaE+Ao42AsVlcO6Glm3HtZTf/CCBlQUMbKvG0/nhxIIpMWemUTxnf5GrV6/i5eVFixYt8PT0xMzsv8fOvRK/gbh1n9O48yj6v7EKF+8I/vj5eUqKcozKpyccZ+uvY4hs2p8Bb6wmqE4nNi14lZz0SwZyfhGtGTpxr37rPHiqQZl710ymWbdRDBq7GlefSH7/fiQlhcbLTLt+nE0LxxD9WH+eHruG4JiO/DFnFDlp1WVqshNZMfMZnDyC6fvqLzwzbi1Nu76CyswCgMryEtZ8NwJBEOg7agEDRi9Bq61kxeyXkHQ6zh/dwI4Vk2nVYxTD312Nu28kS2eNpLjAuE5VFaU4uvrSrs8YbOzdjMpUlpfg4RtB50Efmm6A2zi2fxOrF35F9/4v8c4XS/EJiGD2py9RmG9cj8vnjtKoZXdGfziHMf+3CEcXT2b/30tocjP0MhXlpYRENqD34DfuWY97QWVjTcGpi5x5/aM/lc5rQHeivprA5f+bzb6mfSg8dYFm6+dgbWFc3tcV+rUUOXFVx48btFxMkniqjYibQ7VMi2iBphEC6w/rmLNZS2UVDG6vQlXjrpaQIbFir5bZ67Qs36PFyVZgQGvZOIoOEAjyFNh6QscvO7ScTtBRViGRXSBxIan2Q9DHBXo3F4m/JjFns45LKRL9WxrqpDYTSM6S2HnK+EO0oBRm/K412KZ+/weTJ0+mSddRPPP2aty8I1n9nem+kXr9OBsXjqHOY/0Z/PYaQmI6sm7OKLJTq/vGC5/s029T5h/h3ffeZ/a33zJ/UxY5hdCjsfywBtj3+5fY2lcbJ/er7vu2FHFzEFi0XcuSXVr83QV6NKv9yPllm5apK6uYurKKPefKKbxpmB2L28SqBV/xyqhRzPtlBW7eYQwfMZKDZzIor6xdv5fOHqVxK7lfjP10EU4unnzzfy+hyZH7RUV5GUnXztOt/38Y/8VSXhg7jYzUBH744nV9HsfiNnFw/Rc07DiKPq+uxMUrgo1zX6DUxL0x48YJdvw2lojG/ejz2ioCozuyddFr5Na4Nzq4BtKy1/v0e+N3er60CDsnHzbMfZ7SolyDvBp1eo3B7+5h8Lt7mDr3MD06N+L3/VV8t66SykqJYV3VmNW26/XEBIk83tSMHfFVzF5bSXqunMbGUj5uZy1gZw2bDmuZubqClXsrCfcV6duq+tklG9FaFm19SJ+PEsT7s/0L+Xee1T2yYsUKYmJisLKywsXFhU6dOlFcXIxWq+Wtt97C0dERFxcXxo0bx9ChQ/UesWHDhvHaa6+RmJiIIAgEBgYSGBgIQJ8+ffT7/iw6nY6PP/4YX19fLCwsqF+/Pps2bdIfDwoKAqBBgwYIgkC7du3+0nmf3DOf6GYDiGzSD2ePUNr2/Qi12pILh1calT+17xf8I1rRoN1InDxCaNptNK4+0ZyJW2wgpzIzx9reTb9ZWFffzU/umU/d5gOJbtYPF89QOgz4CDNzS84dMl5m/O6FBES2plGH53H2DKH542/g5hvNyb2L9DIH1n9NQHQbWvUah7tvNI6u/gTX7Yi1nQsgP8QKc1Po9MznuHpH4OodwRPDviAt8Qw3Lh7k8LZ51Gs5kNgW/XD1DqXbM3I9nNpvXCevwFg69HuH6CZPoDIzNyoTUrctbZ58k4gGnU03wG3s+GMhLTr2o3n73nj5hjDohYmYm1txYOcao/LDXv+cNl0H4RsYiadPEINfmoQk6bh4+pBepmmbnnTv/xIRMY/dsx73QtbmPVz6cDoZv2/7U+mC3hhO0pxlJC9YRdH5q5x+5UO0JWU0CDH+5twsUuRKmsSB8xLZBbDrlI60PGgSIRrI7D2j41KyRKYG1hzQYWcNkX7VeR66IJGSA/nFkJwNcWd1+LqCKEDzSJHjVySOXJJIyoJ1hyS0OnC1Fzh5rfbDv0m4wNV0OHRRIqcQ9pyRSNdAo9Dq8s7ckNh3TiIh3bhxJklQXGa47d28gM6PDySqqdw3Og6U+8bZg8avwxO7FxIY2ZrGHeW+0eKJN3C/rW/Y2Lvpt6ZR9mzcupcjp1KoUnux+yxUaiHSB66f282Ni3G07v3Ofa17V3sI9RZZd0hLSg4kZcGmozrqBgjYWhmeT0mFpK+Liiq4VXPb/1hIlyf607dvPwrEQPqOmIja3IrNf6ymqKx2/Q4fLfcLv6Db+sUZuV9Y2djx2gc/0qhFVzx8gggKr8dTI98l8do5crPS9GVGNhlAROO+OHmE0qr3JMzMLbl4dJXRtjgTtxDfsFbUazMSJ/cQGncZjat3FGcP/KqXCa3fA5/QFtg7++HsEcZjT4ynsryI3PSLBnmpLWywtnPD2s6Ntg3s2XVSx/lEHRl5Esv3VGFnBVH+ph/ZLeuqOHpRx/HLOrI0Er/HVVFZBY3CZYsuUyOxZEcVF5JkD+e1NImtx7RE+ouINy/hsgo4fEFHSs6dPXQKD57/WeMsLS2Np59+mhEjRnD+/Hl27dpF3759kSSJqVOnMn/+fObOncu+ffvIzc1l9erV+rQzZszQG1FpaWkcOXKEI0eOADBv3jz9vj/LjBkzmDp1KlOmTOHUqVN07dqVXr16cfnyZQAOHz4MwLZt20hLS2PVKuM3jDuhraogK+UsvmEt9PsEUcQnrDkZN+KNpsm4EY9PDXkAv/CWteRTrx5m3qQW/PplN3avnERZcZ5BmX7hhmX6hbcgLeGE0TLTEuLxC29usC8gshXpCXKZkk5HwrldOLkFsua7kfz0fnOWThvA1VPVRoO2qgIEwcCQMjOzQBBEblw6THriWQKjDHUKjGpByjXjOj0IqqoqSbp23sCIEkWRiJhmXL908p7yqCgvQ1tVhbWtw92FHwGCWo1Dwzpkb99fvVOSyN6xH19X48aZr6vA9TTDB8TVVEkv72gLdlYC12oYQeWVkJKNyTwtzSEmSCApCxDAy1ke5qxJaQVUVEokZddO7+MikJBhKH8tTcLHRHn3gptdBZcunsU5wPA69L9D30i/Ho9fRO2+kXazb9REFMDNXmLL+pXUeay/fn9KDrjalrP9t4l0e/ZLzNSW+mP3o+59XQVKyyXSajiHrqVLSJJcjzUZ1FbFmH4qhnVW4WovP5KqKuV+0bpVc/JLdET4mNG2jiWtWjUn/cYpo/VyOxUVd+8XpSVFCIKAlY2dvkyf0Oq6FUQRn5DmZCbGG02fkXjSQB7AN6yVSXltVQUXDi/D3NIOFy/DMIOTu39m4cePcXD5y9hZC1xOrvZelVdCcpaEv7vxa00lgreLwJXU6qFeCbiSqsPfzfT1aWkO5RWge1S2mCDcn+1fyH/PWNx9Ji0tjaqqKvr27UtAQAAAMTFybML06dOZMGECffv2BeD7779n8+bN+rQODg7Y2dmhUqnw9PQ0yNfR0bHWvntlypQpvPPOOwwaNAiAL774gp07dzJ9+nRmz56Nm5s8lObi4vKXyygrzkPSabGydTHYb23riibzutE0JYXZWN8ub+dKSWH1E8wvsjVBMV2wd/ahICeJQxu/Zv2cF+nz6m/6Mm95tKrzcCEv45rpMu1ca8kXF8hllhTlUFlewtHtP9H88Tdo2XMsNy7sZf28V+k7aiG+oU3xDKyP2tyK/Wu/onmPt0CS2LdxKpJOS0FOCpJOi429oU42di7kpBvX6UFQVJCHTqfFztFQD3tHFzJSjbfH7fy++GscnN2IvM9esvuFuasTopkZ5ZmGQ0PlGTnYWhm/sdpaQlGZ4b7iMglbS0F/HKC41FCmqEyq5ZnpWF+kSYSA+c0hxyW7tFhbgCgKFJdJgJynSpRjwkrKjZ+HraXs3THQqbxal79CkEs+Wq2WCrF238jNNH4dFpvoGyUFtS1KS3P5PDWFJTTr2kW/v6RcwqoqhZiWg/DwjyE/J1l/7H7Uva2VXDc1kSTZ+L0lU1EFW45pScqSjbYof5EW0WacvFHFlQS5X3h6uOLuIJKu0RGfUImZpTMlBdcJcldxPVNrtH5usWbRnftFZUU5axZ9TaOW3bGytkWTm4nOyL3Rys4FTZbxvlhalI2VrWFbWNm6UFpk2BY3zu9kx29jqaosxdrOjcdHzMHSxkl/vE6L53D1icbCygFbIQmAI9t/wL/Ry3oZuX6N9xdrC1CJAkWlhlZWUamEm6NxH4y1BbSrb8aRS7XrcWC7h2QaKF8IMMn/rHFWr149OnbsSExMDF27dqVLly70798fURRJS0ujWbNmelkzMzMaN26MJD2414uCggJSU1Np2bKlwf6WLVty8uS9eVBuUV5eTnm54Z2xqtIcM7WJAJ/7QFj9J/T/u3hF4OIVweLPO5N69TBO7sH3vTxJkt8Qg+t2pEG7YQC4+UaRdv04Z+J+wze0Kda2znQfNoOdyycRv/cXBEEkuskTePjXQfiXvG1tWTOHY3GbGD1pLmrzB9e+/83sP6/jxFVwtBFoEyPSu4XIukO1g/0jfAVUKig1YZzdb8xUEO7zcMoKiGhp0P8zk87i72FJk87/eTgKGKG0HA5eqL6npubqCPYWCHBTcSXh5k4BKqvgfLI8i6C4XKK8UsLX5c7G2ZbVcr944yPj/UJbVcmcaWMBiUEvvH8fz8o43iHN6PvaKspK8rhwZDnblrxJ71eWYmXrQpQfvP7VcL3sqv3yZI6E06vxqTfSZAjF38FCDUO6qMnSSGw/XrseNxyqol7wHQLcFB44/7Nmq0qlYuvWrWzcuJHo6GhmzZpFREQECQkJj1q1v83kyZNxcHAw2LatmAyApY0TgqiqFeBaUlT7bfwW1nautSYLGPNs1cTexQ9LGyfys2/oy7w9wLmkMAdr+zuUWZhdS/7WLEsrGydE0QxnzxADGWePEAprzHoKiGzFsInbeOGT/bz4fwfpOfwrijQZuHiFIIiqWsH/xTXKeBjY2jshiioKNYZ6FGhysHe8sx7b1s5n65q5jHr/B3wCwh+kmn+Liuw8dFVVWLgbeiQsPFxqvenfoqistkfKxlLQe3Ru/bW5zUtmaynoZ2LeorRcnll5LV1i5T4tYT4izrag00nYWFYb6fWDBQpL5KB9UzrZ3K6TRW0v070S6Svg5mK6b9iY6F82JvqGsb507cIxqqqqiG3axWC/SioiNeUGs8bEMOPNaOb/n3x8009PkZOn+dt1X1Qq101NBEGehHB7+9Qkv0SHtbmArZ3cLzIzcigur75GCvNzsHN0xUItmBzN2rZ2PlvWzOXVicb7hWyYvU1udhqvTvwRK2tbWf+bZd5+bywtzDF5r7Oyda3lJSstyqnlTVObW+PgGoCHf33a9vsUUVRx8agcU3glDRZul/TbrZcDZydHSvJT9HnI9Wu8v5SUg1ZX27NmayVQVGKYxtwMhnZRU14Ji7dXGh3SvFMb3U8kQbgv27+R/1njDEAQBFq2bMlHH33EiRMnMDc3Z/v27Xh5eXHoUHVwdVVVFceOHbtrfmq1Gq32zq52U9jb2+Pt7U1cXJzB/ri4OKKjowEwN5ffoO5WxoQJE8jPzzfYOvWfAMhB+24+dUi+ckAvL+l0pFw5iEdAfaP5eQTUJ+XyAYN9yZf3m5QHKNKkU1aiwdreXV9m0mXDMpMuHcArsIHR9F6B9Um6fNBgX+LF/XgG1tefh7t/DHm3DcXmZSVg51TbHWFl64yFtT0JFw5QXJhDeP3OePrXIeGCoU43LhzAJ9i4Tg8CMzM1fsFR+qBlkCeGXDpziKDweibTbf19LptW/sgr735LQEidh6HqX0aqrCT/+FlcO9SIzREEXNo310/zv53kbIkgT8ObbrCXoJfXFEFhqUSQR7WMuRn4uGIyz5vF3vwrkJaLvgwHGwhwB7UZpJhIn5IjEXhbzE+Qp2BS/m7UCxa4kW2Oi1c0SZfuvW94BtUn6VLtvuF1s2/U5OT+5Vy6fI3oMF+D/c2aNgGHWAa/vYbBb6+h939+BKBV/ymkaSz+dt0nZ0tYWQh4OVfnEeQhG1R3Cja3sxQpr5IwU8v9Im7/Aawt5HJ0OnnSS5269SivlIdCb2fr73PZuOJHRr1nvF/cMswy02/w2sQfsbVz1B+7VWbK1eq6lXQ6Uq8exN2/vlF9PfzrkXrVsC2Sr+w3Ka/PV5LkmFhkz6CmuHrLKQRNQRnNW7TAwkZ+obFQg6+bQGKm8brT6iA1RyLEu/qRLgAh3iKJWdVpLNQwvJsarQ4Wba2k6q89ru4fymxNk/zPDmseOnSI7du306VLF9zd3Tl06BBZWVlERUUxevRoPv/8c8LCwoiMjGTatGloNJq75hkYGMj27dtp2bIlFhYWODk53TVNTd5++20+/PBDQkJCqF+/PvPmzSM+Pp7Fi+VZke7u7lhZWbFp0yZ8fX2xtLTEwaF2sKuFhQUWFoavrWbq6g5ar80wdiwdj5tvXTz8Yjm1dwGVFaVENpFj7LYveQcbB3cee3wMALGtnuP374YQv3suAVHtuBK/nqzks7Tt/zEAleXFHNk6m+CYLljbuVKQk8SB9V/h4OKPf0QrgzI9/Ori4R9L/O4FVFWUEt1MLnPLonHYOHjQsqdcZv22Q1g56zmO75xLYHRbLh3fQGbSGTo+9bH+PBp1GMnGBW/iE9IE39Bm3Liwl+tnd9Lv1YV6mXOHVuLkEYKVrTPpCSfYs/ozmnQchotnME07DeeP+e/gFVAXr8BYju5YQEVFKbEtZJ3WzRuHnaMH7frIOmmrKshOuwqATltBoSaDjKTzmFtY4+Quxy1WlBWTl5WoL1+TnUxG0nksbRxwcPY22u4degzhl9nv4x8cTWBoDDs3LKK8vJTH2vUGYOE37+Lg7MGTz4wGYOuauaxfNpuhr3+Oi7sPBRr5zd3C0hoLS2sAiovyyctOIz83C4CM1AQA7B1d7+qRuxMqG2tsQv31v62DfLGvF0lFbj5lSWkm012fPo96c79Ac+wM+UdOEfj6UMxsrIg/JF+XTzYXKSyFHfHycOOhCzqGdlbxWKTA5VSJugEi3s7wR43hyEMXdLSuK5JbqENTLNEuVqSwBP0yGD4ucpB0YpZEWQU42Qq0ryeSWyiRnC1x4IKO3s1FMjTg7Sw/JAUBTl2X0/dsJnvSdp2Wfx+5JPFsB3kJiaupEtH+Al5OsPFodd+yNAd7a7C76VVysROA6tmIt3CyBX83WLpHR3SLYcStnoCHf108/WM5vlvuj7f6xuabfaPVzb7RoO0QVsx8jmM75hJUpy0Xj28g47a+AVBeVsTl+E3sP9mTYQPCycqHjHyIDQRztYqkAjdcvd3oEAO5Gk9Wfwe2Tn4cu2rO0M7C36r77AI5GL1HMxXrD2tRidC9iciZG5LeKxMbJKDVyetxAUT5CXg7i5y7OYTZsccQFs5+n2aNY/AMqMvChQuorChj+LP9ScrRsmDWuzg6e/DkYLlfbFkzl/VLZzNs9Oc4u/mQn1fdLyytrNFWVfLT1DEkXT/Py+O/QafT6WVsbB0wU6vp2GMIC755Hzefurj5xXAmbiGVFaWEN+oDwM5l72Bj70HTbm8BULflENb9OIRTe+fhH9GWq6c2kJ1yltZ95KVmKitKiN/5A/5R7bG2c6OsRMO5A79SUpBBUExXQF6OIzPpFN4hzVBb2JB5I555ScmMGvUaaw6oySuS6NRQRWEpnE+sboMR3dScu6Hl4Hl5X9wZLf1am5GSLZKcJdGijgpzMzh2M6bMQg3DuqoxN4PluyuxMIdbT4niMjkmMNxXxNZKnnyg8Gj5nzXO7O3t2bNnD9OnT6egoICAgACmTp1K9+7d6dy5M2lpaQwdOhRRFBkxYgR9+vQhPz//jnlOnTqVt956i59++gkfH58/PUT6+uuvk5+fz5gxY8jMzCQ6Opq1a9cSFhYGyLFvM2fO5OOPP+aDDz6gdevW7Nq160+fe2j9xyktzuXI5lk3F4SNosfzP+ld90WaVIOYLM/AhnR6ZgqHNk/n0MavcXANpNvQb3DxlIcMBFFFbtpFLh5dQ0VZITb2bviGt6Rp19H6eInQ+o9TUZbHwY0zKS7Iws0niif/87O+zMK8NIQab0BeQQ3pOmQKB9ZPZ/8f03B0C6THyNm4eFUPU4TEdqb9gEkc3fYju1f9H05uQTw+fCbewY31MnmZ19n/xzTKSvKxd/ahRfeX9AvHRjV+nJLCXPaum3lzEdoonnrtZ/2wZkGuoU6Fmkzmfdpb//vw1rkc3joXv7CmDB7zCwBpN86w5OshepkdN4eT6z7Whx7DPjfaHo1adKOoII/1y76lUJONT2AEo979DvubkwRys9MN9Ni7dRlVVZXMmTbGIJ/u/V/iiYGvAHD66C4WfTtRf2ze9HG1ZP4KDo3q0nz7L/rf0VPeBSBp4SpOjZxgMl3a8o2YuzkT/uHr8iK0J89zuMfzFL+yRM7XRjCI6UzOhlVxOtrXE+lQXx6WXLpHR1aNLrj/nIS5mUSPZiKW5pCYKbF4pxbtzedXpVZe2qFtrIi5GRSWyrMO957RodXBuRsSNhY62tQVsbeWh+tW7tPpA9ntrQ11SsmB3w/oaBsj0i5GIK8IVsQZ6hTmLdCzxlpefVrI/+89o2Pv2eq8YoMECkrgWjoExXRH1Gk4sGEmJQVZuPpG0fulGtdhXpqBd8A7qCHdhkzhwIbqvtFz5GxcvQ2H8C4dXw+SRJFFLAcuQpMwOQg8uwD+OCoH54McoF9RVh1fdD/qHuQ8ujcRea6jSl4/K0li01HDWL82Nxf01ekgpwBOJ1aRmS/LNGrZjcKCPKZ9PZMCTTaRUVF89/1PlOBEQqaWvNv7xRa5X/w81bBfPD5AvuY1uZmcProLgMlvDzCQGT1pDuF1mtCoZTcuXC3g2LaZNxfojqL78B/196lijeE9wSOgAR0GfcXRLTM4svlrHFwD6PzsLJxv3RsFFZqsa1w6voay4jwsrR1x842h54uLcPaQ7+sqM3OundrA8e2z0VZVYOfsS1bkE8ReVNG7pZl+Edr5mw09Xc52AtY1huVPX9dhY1lFx4Zm2FlBWq7E/C2V+pcCbxcBf3dZ9zEDDF/ev1pWjqYIKqskGoeb8XjTh7UI7b/T63U/EKQHGeX+L2LYsGFoNBrWrFnzqFX5S0xf++ibWa1+9LEB1n9jZt39xNf5IUWd34GKxqZXPX9YHJt/9lGrgJnZo78unRweffB1Vk7Vo1aBFjGPepwN4q+oH7UK5Gke0iKwd+DTEQ9+glHRoXX3JR/bZj3vSz7/JP5nPWcKCgoKCgoKjw7Fc2YapWYeIHXq1MHW1tbodiuOTEFBQUFBQUGhJorn7B6ZP3/+n06zYcMGKiuNu6c9PDz+pkYKCgoKCgr/xfxLl8G4HyjG2QPk1pcHFBQUFBQUFG5DGdY0iVIzCgoKCgoKCgr/IBTPmYKCgoKCgsJD59+6uv/9QDHOFBQUFBQUFB4+yrCmSZSaUVBQUFBQUFD4B6F4zhQUFBQUFBQeOhLKsKYpFONMQUFBQUFB4aGjLEJrGsU4+x/Bx/3Rf77Jwkx3d6EHTDth26NWAYB026hHrQK//QM+ndRoWJ1HrQKnFp971CpQWPzo+8aW3+IetQqMDcx+1CrwySKvR60C5lYP/tNJd2VEo0etwf80inGmoKCgoKCg8PBRPGcmUYwzBQUFBQUFhYeOspSGaRTjTEFBQUFBQeGho8ScmUapGQUFBQUFBQWFfxCK50xBQUFBQUHh4aMMa5pEMc4UFBQUFBQUHjrKsKZplJpRUFBQUFBQUPgHoXjOFBQUFBQUFB46yhcCTKN4zv4EkiTx4osv4uzsjCAIODo68sYbbzxqtRQUFBQUFP7rkATxvmz/RhTP2Z9g06ZNzJ8/n127dhEcHEz//v0ftUp/mYPbFrNv41yK8rPx9Iukx7Pv4RsSa1Q2I/ky21fPIjXhLJrsVB5/Zjwtug41kNm++ht2rpltsM/VK4g3Pt9gUoe4Lb+ye/1cCvOz8fKPoPfQ9/A3oUN68mU2r/iGlOtnyctOpdez42ndfYiBjE6nZcvK2RyPW0ehJht7J3cat+lNp94vIdwh8HTZlj0s+mMHOfkFhPn78PbQ/tQJDTApf4st+4/x3jcLaNsohiljXtDvz8kvYNaStRw6dYHCklIaRIbw9tD++Hu5m8xr/brfWbVyOXl5uQQFhfCfl0cRHhFpVHZ/3F6WL11CWloqVVVavH286d2nPx06djYqP3vWdJydHHjjjTexs7OjvKKCrOy8O55blL9A+1gRR1vIKYTtJ3RcSTX8ykS7WJEGoQKWakjKkthwREduYfXxp9qKeDoJ2FhCaQVcT5fYdkJHUal8vG2MSNvKi7XKriouYbNjA5O6ObdqTPCYkTg0rIultztH+71CxtrtdzwfUwS8/AwtnxCxtYRMDWw5oSMt17R8pC+0qSviYAO5hbDrlI6r6fIxUYA2MQIhngKOtlBeCQkZErtOSRSVyTIO1tAyWiDAXa6XojKY9f0iViyfS2lRNs6ekTTv+R5ufsb7AcD105s4tnUmRZoU7F0CaNJtDH4RbfXHj2/7hmunNlCcn46oUuPqE02jLm/g7ldPLxO/83uSLu4mJ+0CKpWa5z44bLSskYMD6dnFEzsbM06fL2DKt5dJTis1qduz/f1o28KVAB9ryit0nL5QwHfzr5GUUp3G2VHNKyNCaFLfCWsrFYkpJSxclsju/bW/DrB0+wEWbNpLTn4R4X6evDO4J3WD/YyWvXbfMT6cu9Jgn7mZGYd+/BiAyiot367eyr5TF0nOysXWypJm0aG83r8r7k72Js8JYMQgP3p09sDWWsXpC4VM+/EaKWllJuUH9/WhzWMu+PtYUV6h48yFAn745QZJqYZp6oTb8vzgAKLCbNHpJK5cL2bsJ+eN5jm0nxfd27tha6Pi7KUiZs5NJCWj3KQOg3p50qqxI37elpRX6Dh3uZiff0smOa06zegR/jSsa4+Lk5rSMq0ssySZpDTT+So8HBTj7E9w9epVvLy8aNGiBQBmZg+++ioqKjA3N7+veZ4+tIGNS76g19BJ+IXEsn/zQuZPeYE3vtiArb1LLfnKijKc3fyo26QrG3793GS+7j6hDB83V/9bVJmun/gDG1m3+Av6jfgQ/5BY9m76hZ8/f5FxU9Zj62BEh/IyXNx9qdesK2sXGddh57qfObDtNwa9NBkP31CSr51h2Y/vYWVlS6tuzxlNs+XAcaYvWs34EU9RNzSAJRt389rn37Ji6vs4O9iZ1D81K4cZv66hQWSIwX5Jknh76s+YmamYMuYFbKws+XXDTkZNns2yL9/FyrL2Z1n27t7Fzz/9wKhXXyc8Moq1a1bxwcQJfP/jXBwdnWrJ29nZM3DQM/j6+mGmVnPk0EFmfD0FR0dHGjZqYiB7YP8+PDzcGDtmDNt37CIqui6Ojvb4eLljbaGjxMg92NcV+rUU2R6v43KKRN1AkafaiPy4UUtWvizTIlqgaYTAmgM6NEUS7WNFBrdX8e0fWrQ3v0SUkCGx74yOojKws4LODVUMaK1i3hYtAPvP66h8vLVB2c02z0dz7LTJegdQ2VhTcOoiSfNX0njF7DvK3gmvAd2J+moCG09KpOZKNAkTbp6n8XrxcYEnHxPZdVriSqpEnQCBfi1F5m7VkV0AajPwdBSIOyeRmS9hqYbODUT6txKYv02uFBd7eYLapmM68oog7eJGVs3/gmGvTCKHWM7uX8imeS/Q/60NWNnW7gcZN06wc+lYGnd5E//Idlw9+QfbFr3Gk6NW4OwZDoCDayDNe72PnbMf2soyzsQtYNPc5xkwZjNWts4A6LSVBNXtirt/fS4dXVmrHIDB/fzo38OHT6dfIC2jjOcHBzLt4xiefeUIFZXGPwfXoK4jq9ancuFyISpR4MUhQXz9cSzPvnKEsnK5Dt5/KxJbGzPGf3KG/IJKOrd15+Nx0Tz/1nGg2kDbfPgUU5du4L3nelM32Jdft+7nlWnzWPPZWzjb2xot39bKgtWfvaX/XfOVrKyikvM3UnmhZ3vC/bwoKCnlq1//4I2Zv/Drh6OM5gfwdB8f+j7hxeSZl0nLLGfk0/5MmRjN0NEnTNZDvTr2rN6YxoUrRahUAi8MDmDKh3UY+voJfT3UCbfly4nRLF6Vwoyfr6HVSoQG2iDpJASVYX5P9fCgd1d3vvwhgfTMCoYN8Gby+DBGjjtLpQkdYiNtWbsti4tXi1GpBEYM9OHz8WE8P+6cXofL10vYsT+XzOwK7GxVDOnrzefjw3nujTv3wfuGMlvTJP9Of+ADYNiwYbz22mskJiYiCAKBgYG1ZPLy8hgyZAhOTk5YW1vTvXt3Ll++bCCzcuVK6tSpg4WFBYGBgUydOtXgeGBgIJ988glDhgzB3t6eF198kYqKCl599VW8vLywtLQkICCAyZMn/+Vzidu0gMZtB9CoTV/cfULpNWwSanNLju1ZZVTeNziGboPeJvaxJzBTmzYURZUZdo5u+s3GrrZhcYs9G+fTrP0AmrTti4dvKH1HfIjawpLDu43r4BcSQ49n3qZ+88cxMzOuw41L8dRp1IGoBm1xdvMhtllXwmJaknjN9I3m1w076d2+Bb3aPUawrxcTRg7E0sKctbsPmkyj1emYOHshL/Z7HG93wwdoYnoWp68k8M6IgdQJCSDQ24PxIwZSXlHJ5gPHjOa3ZvVKunbrTqcu3fD3D+CVV0djYWHB1i2bjcrHxNajeYtW+PkH4OXlTa/efQkMCubcWcNvZeZkZ/PDd7MZ9coo1q37gzNnz1FRWUVmVi6SpKNBiPEbY7NIkStpEgfOS2QXyN6htDxoEiEayOw9o+NSskSmBtYc0GFnDZF+1XkeuiCRkgP5xZCcDXFndfi6yh4mgMoqKM/I1m/mHi7Y1Qkjae4Kk3UPkLV5D5c+nE7G73/vO6lBbwwnac4yTidI5BTApmMSVVUQG2S8XhqHCVxLh0MXJXIKYc8ZiXQNNAqT5csr4bc9Oi4kS+QWQmoubDmuw8tZwN5azuNaOqw/InE9AzTFsH39fFp0GMDzQ/vh5BFKyycnYWZuyaVjxvvB2f0L8Q1rRWybkTi6h9Co82hcvKM4f/BXvUxI/R74hLbA3tkPJ48wmj0+nsryIvLSq72UDTu9Rt1Ww3DyCDdZPwN6+bBw2Q32HcrhakIx//f1BVycLWj9mKvJNGMmnWbj9gyuJ5ZwJaGYz6ZfxNPdkojQ6hedupEOrPwjhfOXC0nNKGPBskSKiquICDU0uBZt3kffNk14snUjQnw8eG/Ik1iam7Nmr/F+JCPg6mCn31xqvGDZWVvy/dgRdGkaS6CXG7Eh/ox/thfnb6SQlqMxXQ89vPhlRTJxR/K4dqOEz2ZexsXZnFZNnU2mGffJeTbtzCIhqZSrCSVMnnUZTzcLwkOqz3HUiCBWbkjj19UpJCSVkpRaxs79OVRW1Ta2+nTzYPGadA4cy+d6UilffHcdF0c1LRs5mtTh3S+vsGVPDjdSyriWWMpXPyTg4WpBWJC1XmbDzmxOXygiI7uCKwmlzFueirurOR5u99chYAoJ8b5s/0b+nWf1AJgxYwYff/wxvr6+pKWlceTIkVoyw4YN4+jRo6xdu5YDBw4gSRKPP/44lZWVABw7doyBAwcyaNAgTp8+zaRJk5g4cSLz5883yGfKlCnUq1ePEydOMHHiRGbOnMnatWtZtmwZFy9eZPHixUaNw3uhqqqC1ISzhNRprt8niiIhdZqTdCX+L+V5i5z0G3wxug1Tx3Zm2fdvo8lJNalDyvVzhNV9zECHsLrNuXH5r+sQEF6fK2cPkpWWAEDqjQskXDxOZL3WRuUrq6q4cD2JpnUjDPRoWjeC05evmyzn51WbcLa348n2zWsdq6ysAsBCXe01FEURtZkZ8RevGZGv5MqVS9Sr39BAvn79hly8cPcPckuSxMn446QkJ1Onbox+v06nY9qUL+g/4Cns7e04duyoQbri0jJ8XY0bIb6uAtfTDB8QV1MlvbyjLdhZCVxLr5Ypr4SUbEzmaWkOMUECSVmgM/6ij/+IARRdvE5e3J0evvcHQa3GoWEdsrfvN9ifkCnh42L8HHxcBBIyDJW/nm5aHsBCLbdRWUXtY9qqCnJSzxHToLn+uCCKeIc0JzMx3mh+mYkn8Q41vO58w1qZlNdWVXDxyDLMLe1w9jI+TG4Mbw9LXJ0tOBJfPfxdXKLl3KUC6kbeeQiwJjY2sguooLBSv+/MhXw6tHbHztYMQYCOrd0wNxc5cVqjl6msquL8jVSaRYfq94miSLPoEE5dTTRZXml5Bd3f/pJuY77gjZm/cDUl4476FZaUIQgCdtaWRo97eVjg4mTOsZPVuhWXaDl/uZA6EaY967djay3fDwqL5PuDo4OaOuF2aPIrmf1ZXVbPbcyMT+oQE1k7T083c1yc1Jw4W6DfV1Kq48LVYqLDbO5ZBxtrlYEOt2NpIdK1rQtpmeVk5VQalVF4eCjDmveIg4MDdnZ2qFQqPD09ax2/fPkya9euJS4uTj/suXjxYvz8/FizZg0DBgxg2rRpdOzYkYkTJwIQHh7OuXPn+Oqrrxg2bJg+rw4dOjBmzBj978TERMLCwmjVqhWCIBAQcOd4qPLycsrLDcdlKivUqM0tKCnUoNNpaw0d2jq4kJ1m2iC5G37BsfR74TNcPYMozM9ix5rZ/PTps7z+6TosrAxvIMV6HQzfwG3tXchMrW3A3Cvte75AeWkxX739BIKoQtJp6TZgNA1b9jQqryksRqvT1Rq+dHawIyHV+E09/sJV1u46wOLP3jF6PNDbA09XJ2b/to4JIwdhZWnOrxt2kpmrISevoJZ8QUE+Op0OJydDL6OjoxPJSUkmz7W4uJhhzw2isrISURR5edTrNGjYSH985fKliCqRXr37IggCubl5OLlUx7xpq3TYWhk3KmxvxkIZlFcmYWsp6I8DFN8WelRUJmFrZbivY32RJhEC5mYCyVkSS3ZpjZYpWpjj/XRPrn75k8lzvp+YuzohmplRnpljsL+4DFxMPHNtLeXjt8vbGn+uoxLluLxziRIVRp6H5SUaJJ2WpnVcib9WbfRZ2bqQn2W8L5YWZWNla9hvLG1dKCk0jNdKvLCTnb+NpaqyFGs7N7qNmIOljWlP9u04O8mekzyN4UM6T1OhP3Y3BAFefyGUU+fyuZ5Yot//wRfn+GhcNBuXtKSqSkdZuY53PztrEMOVV1gi983bhi9d7G1JSMsyWl6ApxsfDu9LuJ8nhaVl/LJpH8M++54Vn7yBh7NDLfnyykpmrthEt2ax2FoZb0RnR/lcc/Nvr4fKP1UPr44I5NT5An09eHvI4Q3DnvLjuwU3uHK9mC7t3Jj2UR2GvRFPlqb6enB2VMtl3q5DfiVON4/diw4vP+fLmYtFJCQbXsQ9O7nxwtM+WFmqSEwt453Jl6jSmniDus8o39Y0jWKc3SfOnz+PmZkZzZo10+9zcXEhIiKC8+fP62WefPJJg3QtW7Zk+vTpaLVaVCr5zaZx48YGMsOGDaNz585ERETQrVs3evToQZcuXUzqMnnyZD766CODff1HfsDA5z/8W+d4J8LrtdH/70kEvsGxTBnTkdOHN9K47cOZOHHq0CaOx/3BM6O+wsMnlNQbF1i7aLJ+YsDfpbi0jA+/+4V3n38aRxMxL2ZmKr58YySf/LSEji+ORyWKNKkbTot60UjcvxuelZUVM775nrLSUk6ePMGcn77H09OLmNh6XLl8ibVrVzN95rd3nAjxMNh/XseJq+BoI9AmRqR3C5Elu3S15Dx7d8bMzobkX1Y/Ai3vP6IAfZqLN+PLjLf7LWfNjSyJBNOx5X8Jr+Bm9HltFWXFeVw8spwdS96k18tLjcaxAUT7wavLWul/j/v478ccvfVSGMH+NrzyzgmD/c8PDsLOxozR750kv6CS1o+58vG4aEaNj6dmzNmfpV6oP/VC/at/hwTQ7/2vWbHrMKP6Gk6WqazSMu67JUgSvPtc9T1Z5ebHxsXVLznjPzUenP9nePOFYIL8rXntvTP6fbf65botGWzckQnA5evFNIpx4JWhATSKddTLvv/Vlb+tw2vD/An0teLNj2tPwNkel8Px0wU4O6kZ8LgH778ezBsf1ZZ7EPxbZ1reDxTj7B+IjY2hp6lhw4Zcv36djRs3sm3bNgYOHEinTp1YscJ4bM6ECRN46623DPb9ES+/YVnbOSKKKoryDT0GRfk5tTxZfwcrG3tcPQPJzag9BGGj18HwRlxUkIPd39Dhj1+n0L7n89Rv/jgAXv7h5GWnsmPtT0aNM0c7G1SiSG5+ocH+3PxCXBxru0+SM7JJzcplzJQf9ft0kvzgfezZN1gx9T18PdyICvbn18nvUFRSSmVVFU72dgybOJUoI7PM7O0dEEWRvDzD2ZMaTR5OzqY9HaIo4u3tA0BwSChJiYksX7aEmNh6nD17hnyNhhFDB6NWqzl+/DiiKDD35x9Yu2YVc+YvQmUmUlRk3GgoMuINsrEU9N60W39trAw9bLaWAul5hnmWlstbbqFEVr6WN/ua4euqI/m2Z7DfiAFkrt9FxW2erAdFRXYeuqoqLG6LGbQx4jW8RVGZfPxu8qIAvZuL2NvAkl06o14zW0sY2MmZhZ+q2HEkm+DqEWlKi3KwsjPeD6xsXSktMqy8sqIcrG+TV5tbo3YJwN4lAHf/+iyf2pVLR1dSr92LRvO9kga//Fw99G2ulh+aTo5qcvKqx2SdHM25cq3IaB41efM/obRo4syrE06SlVOd3tvTkv49fXhu1BG9F+lKQjH16jjQ9wlv4IZcjp213DcLDMvKKSgyiCO7E2ozFRH+3iTddk1VVml557slpGVr+HHc8wZeM21uGs9PP1mdh1o2opwd1OTmVXuunBzVXLlefFcdRj8fRPPGTrz2/hmDerhVpwlJJQbyN27Oan3p3WqjUG0m6+DkoCZXU30xOTmouXrDML0xXh3qR7MGDoz55CLZubWHK0tKdZSUlpOSUc75y8Ws+rEerRo73jXf+8GjXOds9uzZfPXVV6Snp1OvXj1mzZpF06ZN75rut99+4+mnn+bJJ59kzZo1D0w/xWy9T0RFRVFVVcWhQ4f0+3Jycrh48SLR0dF6mbi4OIN0cXFxhIeH671mprC3t+epp57ip59+YunSpaxcuZLcXONz/i0sLLC3tzfY1OayG93MzBzvwDpcO1cd8K7T6bh27iB+ofX/yqkbpbysmNzMJOwc3WodMzMzxycomitnDXW4cuYgAWF/XYfKilIE0fCSFkURSartqQFQm5kRGeTHkbOXDPQ4cvYiMWFBteQDvT1Y8sV4Fk0ep9/aNKxLo+gwFk0eh4eLoTFla22Fk70diWmZnL+WSNtGMbXyVKvVhIaGc+pktXdBp9NxMv4EEZHR93zukiTpYxvbd+jErNk/MPOb75n69SyKi0to3749ffoN4KP/kyeSWFtZkpxt3DhLzpYI8jS8aQZ7CXp5TREUlkoEeVTLmJuBjysm84TqiVkq0TBvq0BfXNo1I2nenScC3E+kykryj5/FtYNh/FaAu0BKjvFzSMmRCPAw1D3Qw1D+lmHmbAdLdusoNRJrZmsFg9uL5BSpcfaOJu1adT+QdDpSrx7E3b++UR3c/euRetVwskrKlf0m5fX5ShLaKiPK3KSiClLSyvTb9cQSsnPLaVyv+pq2tlIRHW7PmQu1h+dr8uZ/QmnT3JXR750iLcPQcrW0kO9zutu6pFYnUfOyUJuZERXgzaHz1V4jnU7H4fNXiQ3x517Q6nRcSU7HtcaL1i3DLDEzm+/HjsDR1vq2RFWkpJfpt4SkUnLyKmhYw5NlbaUiKsyOsxcNX+puZ/TzQbRu5swbH54lPdMwzCQ9s5ysnHL8fAzjAPy8LElNLyM1o1y/3UgpIyevkgZ1qs/D2kokMsSGc5fvbCC+OtSPlo0dGffpJdKzTLf/LQRB9uqp1f9u02Dp0qW89dZbfPjhhxw/fpx69erRtWtXMjMz75guISGBsWPH0rq18Tjm+4niObtPhIWF8eSTT/LCCy/www8/YGdnx/jx4/Hx8dEPZY4ZM4YmTZrwySef8NRTT3HgwAG++eYbvv322zvmPW3aNLy8vGjQoAGiKLJ8+XI8PT1xdHT8S7q27DaUlT9NwDuoLr7BMezfvJCK8lIate4DwIof3sHeyYMuA2XvW1VVBVkpVwHQVlVSkJdJ2o3zmFta4+Ihx79tXPIlkQ3a4ejiQ6Emk+2rZyGIIrGPPWFUhzbdh7H0hwn4BtXFLySGvZtkHZq0lXVY8t14HJzceXxQtQ4ZydU65OdlkJJwHgtLa1w9ZR2iGrRnx5ofcHLxwsM3lJSE8+zZuIAmbfuarItnHm/PR98vIirYjzohASzZuIvSsgp6tpWHpz/89hfcnB14dVAvLMzVhPp5G6S3tZFvrjX3bzt4Aid7WzxcnLialMrUhato2ziWx2KjjOrQu08/vp72JaFh4YSHR/D776spKy+jU+euAEyb8gUuLq4MHT4SgOVLlxAaFo6XlzeVlRUcPXqYnTu28fKo1wH0Bvktiksr6NWrFza29gQHBeHoYIcoiMRfk5+QTzYXKSyFHfHy70MXdAztrOKxSIHLqRJ1A0S8neGPQ9VP1EMXdLSuK5JbqENTLNEuVqSwBC4kyYaKjwt4uwgkZsnB8E62Au3rieQWSrUMOL9h/ShPyyJz0x6T7VQTlY01NjWGr6yDfLGvF0lFbj5lSWn3lAfA9enzqDf3C2JOCvJSGuECajM4dV3Wr0dTgcJS2H1a/n30ssTg9gJNwwWupElE+wt4OcHGo/JxUYA+LUQ8nWD5Xh2iUO1pK62QDRJbKxjcTiS/BLaflGjYdhjbl07ALygGe88YzsQtpKqilPCGcj/YvfwdrO09aNJV7gd1Wgxh/U9DOL13Hn4Rbbl2agPZKWdp2VsOY6isKOHkzh/wj2qPlZ0b5SUazh38lZKCDIJiuurPvUiTSnlJPsWaVCSdlpzU81SVJaEyd0MQZaWXr01h6FP+JKWWyktpPBtITm45ew9We+6m/18sew5ks2q9PPlnzMuhdGrjwYRPz1BSWqWPlyoq0VJRoeNGcglJqSW8PSqM2XOvkV9YSZvHXGlS34lxH5+hU43Rx2e7tuKDn1cQHehL3SBfft0aR2l5BU+2kifPvP/Tctyd7Hm9v3xeP6zdTmywP37uLhSWlrJg417ScjT0aS2HiVRWaXn721+5cCOVGaOHoJMksm96zR1srFCbWBpp+R9pDOnvS3JaKekZ5Yx42o+c3Ar2Ha5+OZ42KZq9h3JZvVFe9O7NF4Pp2NqV9yZfoLRUW6seAH77PZXhT/lxNaGEK9eL6dreDX8fKz74qvaQ4upNGTzT24uU9HLSssoZ1t+HHE0lccc0epkvJ4QRd1TD71vlmLzXhvnRoYUzH067SkmZFicH+fyKS7RUVEp4upnTrrkzx04VoCmsxM3ZnEE9Pamo0HE4Pt9oXdxvHtWw5rRp03jhhRcYPnw4AN9//z3r169n7ty5jB8/3mgarVbL4MGD+eijj9i7dy8ajeaB6qgYZ/eRefPmMXr0aHr06EFFRQVt2rRhw4YNqNVyx2zYsCHLli3jgw8+4JNPPsHLy4uPP/7YYDKAMezs7Pjyyy+5fPkyKpWKJk2asGHDBkTxr13YMc0ep7ggj+2rZlKUn42XfxRDx/6oH9bU5KYZeKAK87KY/UG1gbNv41z2bZxLYGQTnp+wEICCvHSWfTeWkiINNnbOBIQ35D8Tf8PG3vh08/rNu1NcmMvmFbMozM/GOyCS59/5QT+sqclJQ6jRcQvyspj+Xj/9793r57F7/TyCo5rw8vsLAOg99D02r5jJqnkfU1SQi72TO491GEinvi+brIsuzRuiKSjihxUbyNEUEB7gy8zxL+PiIBs36Tl5COKfc71nawr4etFqcvMLcXWy5/FWTXm+b1eT8q3btiO/QMPiXxaQl5dHcHAIH338mX6SQFZWpoEOZWVlfPftTHKyszE3t8DXz48xY8fTum07o/kXFZewdMkiRowcIS9CW15BSlomxWVyXTvYCEhStcGUnA2r4nS0ryfSob682OrSPTr9GmcA+89JmJtJ9GgmYmkOiZkSi3dWr3FWqZWX1WgbK2JuBoWl8ozPvWd0ehkABAHfIX1IXriqtjvFBA6N6tJ8+y/639FT3gUgaeEqTo2ccE95AKQt34i5mzOtP5uIjaVApgaW7ale48ze2rBeUnJg7UEdbeqKtI0RyCuClXHyGmcgr+UW7iO308iuhp7wxTu1JGZBkIeAs52Asx281lMFPXuwKFjDnDmzSM/IwsUriq7Df9QPaxZpDPuBR0AD2j/1Fce2zuDolq+xdwmg07Oz9GucCYIKTdY1Lp9YQ1lxHpbWjrj6xvDEi4tw8gjT53N82ywuH1+j/73mG7l/O/iOwdxanr28eGUSlpYqxr0ajq2NGafP5TPmw9MGa3v5eFrhaF8dlN7ncXmo/ZvJ9Q3O/9PpF9i4PQOtVuLtSWd4aVgQX0ysi5WVipS0Uj6dfoGDx3KhhnHWtWkseYXFfLdmGzn5hUT4eTH7zeH6Yc30XA1ijX5RWFzGxwtWk5NfiL21FVGBPsx/9yVCfDwAyNIUsDteHi4cNGmWgX4/jXuexpHBGGPJ6hSsLETGvhQi18P5At7+5JxBPXh7WuJQox56d5Mnjc38v7oGeU2edZlNO2XjacUfaZirRV4dHoidrRlXE4oZ89E5UjPKMbcyXA9x6R8ZWFqIvDEyAFtrFWcuFTHhi8sGa5x5eVhgb1f9SO/VWZ4ANHVihEFeX/2QwJY9OVRWSsRE2NK3mzu2Niry8qs4faGQ0R9dQFNgfEbn/eZ+TQgwNgnOwsICC4va60pWVFRw7NgxJkyovleIokinTp04cOCAyTI+/vhj3N3dGTlyJHv37r0vet8JQap591H417L84L09+B4kFmaP/lJrJ/y9tbHuF+mOxr1oD5PfDnrfXegB02hYnUetAqcW333JkgeNSvXoZ62tnf/gHzh3Y8v4vz4h4H7RfarXo1ahlnH2KNhaY2LEgyLl0v1Z7PanX1fWmgT34YcfMmnSpFqyqamp+Pj4sH//fpo3rw5rGDduHLt37zYITbrFvn37GDRoEPHx8bi6ujJs2DA0Gs0DjTlTPGcKCgoKCgoKD537NSHA2CQ4Y16zv0JhYSHPPfccP/30E66u92/S3N1QjDMFBQUFBQWFh879ijkzNYRpDFdXV1QqFRkZhmtZZmRkGF3D9OrVqyQkJNCzZ/V6mbqbIRhmZmZcvHiRkJCQWun+Lv/uKRkKCgoKCgoKCjcxNzenUaNGbN++Xb9Pp9Oxfft2g2HOW0RGRnL69Gni4+P1W69evWjfvj3x8fH4+dVeIul+oHjOFBQUFBQUFB46j2qds7feeouhQ4fSuHFjmjZtyvTp0ykuLtbP3hwyZAg+Pj5MnjwZS0tL6tY1nNhxa6WE2/ffTxTjTEFBQUFBQeGh86iW0njqqafIysrigw8+ID09nfr167Np0yY8POSZvYmJiX95NYT7hWKcKSgoKCgoKPxP8eqrr/Lqq68aPbZr1647pp0/f/79V+g2FONMQUFBQUFB4aHzKD/f9E9HMc4UFBQUFBQUHjrKh89NoxhnCgoKCgoKCg8dxXNmGsVsVVBQUFBQUFD4B6F4zv5HqNI++jeUc1e0j1oFUlxNf+PyYWJT9Kg1ADOzR98e/4RPJ8UOjn7UKhB5ceOjVoGyspaPWgUWFj36R9Jzox61BpCW+XC+bfmouV/f1vw38uh7goKCgoKCgsL/HJKkGGemUIY1FRQUFBQUFBT+QSieMwUFBQUFBYWHjqT4h0yiGGcKCgoKCgoKDx1ltqZpFLNVQUFBQUFBQeEfhOI5U1BQUFBQUHjoKJ4z0yjGmYKCgoKCgsJDRzHOTKMMayooKCgoKCgo/INQjLO/SXp6Op07d8bGxgZHR8dHrY6CgoKCgsJ/BRLCfdn+jfzPDWu2a9eO+vXrM3369PuS39dff01aWhrx8fE4ODjclzxNsWvXLtq3b09eXt7fNgQPb19M3KY5FOVn4+kXSffB7+MbHGtUNjPlMjvXzCQ14Sz5Oal0HTSB5l2Gmsx77/of2b5yGs06DWHc+HcJ8QC1ChKDVaw/rCW38M66NQkXaREtYmsF6XkSG4/oSM2R9MdVInRtJFInUMRMhCtpEhsOaykuk49bmUPfVio8HAWsLKC4DC4m69ger+Pk3sUc3TGH0sIsoqMimThxIrGx1ec9fweUVlTrcunERg5smEFBbgqOboG06jmWoDpt9cenj44weg6ter1N447P639fP7uLg5tnk516ETMzCwLCmzBg1Lf640d3LubgljkU5Wfh4RtJl6cn4hNkvD2yUi+z+/eZpCeeJT8nhc4DJ9C00zADmbiNP3Dx+BZy0q9hZm6Jb3ADOvQbi4tnMAAR3tCrsYitJWRoYMtxHWm5ptsk0hfaxog42EBuIew8peNqWvXxCB9oECri6QTWFgI/b9aSqak+7mANo3qqjOat1Ulk5MGWE3fXoU3dah12ndJxNV0+JgrQJkYgxFPA0RbKKyEhQ2LXKYmismodWkYLBLgL2FhCURmcvSFRoFYjVVaaLtgIzq0aEzxmJA4N62Lp7c7Rfq+QsXb7n8rjTqxbt46VK1aQl5dHUHAwL7/8MhERxq+1uLg4li5dSlpqKlVVVfj4+NCnb186duyol7G3t8fW1haVKHLx4kUmT55MQUEB773/Pu7u7rXyjPIXaB8r4mgLOYWw/YSOK6mSgUy7WJEGoQKWakjKkthwRGfQty3NoXtjkXBfAUmC84kSm47pqLy5+L2LHTzRVMTVQcDSHApL4Ps5i1m1dA7FBVm4+UQy9KWJjOhr2A+qtPDzNvn/Syc2sn99df9s3cuwf379uvE6a/1kdf/MSDrLvrVTyEg8jSCocPYMojAvnZLCHNx9I+k4cCJegcb7IsDF4xvZt24G+TkpOLkH0rb3WILrVutQXJDN7jVTSDi/j/KSQnzDGtNp4ESc3APluvaFGF8tlmodFy9d4tNPJ5OQWkRMmxfxj+5Su238BNrFijje7Afb43VcSTNsm7YxIg1CbrZNtnwPza3xVRJnO+hUX8TPTUAlyveAXad03Mg0zOdBoyxCaxrFc/YXqaiQn+BXr16lUaNGhIWFGb3JAVT+yRv/g+bM4Q1sXvo57XqN4j8frsLDL4JF056nqCDHqHxlRRlObn506j8GWwe3O+adcv00x3YvxcM3gic6NyfcC45cha2noKIKnu1ghuoOV12dAIEujUR2n9Lyw4YqMvLg2Q4qrC2qZbo1Fgn3FVm+R8v8rVXYWcHANtUPfgm4mCSxZJeWb9ZW8fsBLcGeIk6lm9mzejKPdR3F6E9WExkZyXNDRzJ7TQ7zd9Q2zFKvH2fjwjHUeaw/g99eQ0hMR9bNGUV26iW9zAuf7DPYOj/9GQgCYfWqPxN1OX4zmxaNo07Tvjw77ncGvrGEOs166I+fO7KBbcsn07rHKEa+vxp3v0h+mzGSYpPtUYqTmy/t+4zBxt54eyReOkyj9oMZNmEZz7wxD622il+nj6SivIRAN2gcAvvOSszdoiNTIzGorWhQxzXxcYHezUXir0nM2azjUopE/5YibjXeRdRmAslZEjtPGb+5F5TCjN+1+m3DER06nURllcTC7ToyNBJPtbmzDk8+JnLyuqzz5VSJfi1FXO1vlQ+ejgJx5yTmbdWxKk6Hi51A/1bVF5uLPQgCbDqm4+fNOrbF62gQIhD5f28aL/QOqGysKTh1kTOvf/Sn096N3bt389OPP/LM4MHMmjWL4KAgJr7/PhqNxqi8nZ0dg556iqnTpvHtt9/SqXNnvp42jWPHjgFgY2ODi7MzX37xBd9++y1OTk4sXLiQYcOHY25uXis/X1fo11LkxFUdP27QcjFJbpua7d0iWqBphMD6wzrmbNZSWQWD26sM+nbfliJuDgKLtmtZskuLv7tAj2bVAloJTl6XWLxDy+x1WqZ+v57FP0/m6aGjGPz2alx9Ipn1fyNJTc9hwU702+I9cvrUa8fZsGAMdZv3Z/C4NYTGdmTtz4b988X/22ewdXlG7p+hN/tnUX4GK2cPx9HVn0FvLaNh+2GkXovH2s6FIRNW4+YTyfJZIykuNN4XU64eZ93cMcS06M/QCWsIq9eR1T+MIuumDpIksfqHUeRnJ9HnP98y9N3V2Dv7sGzmcCrKSwh2h8fC4Mc5C3j+lbepNPNk/oKF1G3ak73Lx5Cbdr5W2/RtIRJ/VcdPm7RcTJYY2Pq2tokSaBousOGIjrlb5bZ55ra2GdRGhSjALzu0/LxJS0aefA+wsTR6mg8MxXNmmn+8cfbHH3/g6OiIVit/BzA+Ph5BEBg/frxe5vnnn+fZZ58lJyeHp59+Gh8fH6ytrYmJiWHJkiV6uWHDhrF7925mzJiBIAgIgkBCQgIAZ86coXv37tja2uLh4cFzzz1Hdna2Pm27du149dVXeeONN3B1daVr164EBgaycuVKFi5ciCAIDBs2DABBEPjuu+/o1asXNjY2fPrppwB89913hISEYG5uTkREBL/88ovBuQqCwM8//0yfPn2wtrYmLCyMtWvXApCQkED79u0BcHJyMijvz3Jg83wathlAg9b9cPcJpceQj1CbW3Ji70qj8j5BMXQZOI6YZk+gMlObzLe8rJiVP46l59BPsLSxp2v7ppxNgpRc0JTAmv1a7Kwh0s90Z3osSuT4FR3x1ySy8+GPQ1oqtbJXBsBCDQ1CRDYf05KQIZGWC78f0OLvLuLjKudbVgFHL+tIy5XIL4br6RJHLunYsm4BdVsMpM5j/fDwCeWjjz7CTG3Jsb0rKa0wNMwATuxeSGBkaxp3fB5nzxBaPPEG7r7RnNy7SC9jY+9msF09sx2/0GY4uPoBoNNWsXvVp7Tu9TaxrZ7GyT0IF89Qohs/rs/j0NZ51G81kHot++HmHcrjgz/CzNySk3HG28M7MJaO/d+hTtMnMFPXfrgCPD16DvVa9MXNOwwPv0h6Dv+cgtxU0m+cJcoXLqfBqesS2QWw8ahEVRXUCzLeLk3CBa6mw6GLEjmFsOeMRLoGGoVWy5+5IbHvnERCunHjTJJkD+atrV6wQGkFnEuUSM+DTcdkHWJN6NA4TOCaMR3CZPnySvhtj44LyRK5hZCaK3sDvZwF7K3lPK6lw/ojEtczQFMMV1Ll/Dx71/ZO3I2szXu49OF0Mn7f9qfT3o3Vq1fTrXt3unTpgn9AAK++9hoWFhZs2bLFqHxsbCwtWrbE398fL29vevfuTVBQEGfPngXA0cGBXbt2kZaeTs9evSi/+WLZvl07ox74ZpEiV9IkDpyXr49dp3Sk5UGTCNFAZu8ZHZeSJTI1sOaAzqBvu9pDqLfIukNaUnIgKQs2HdVRN0DA1krOQ1MEJ69JZGggvxh2bJhPh24Defqpfrh4hdJp4EeoLSxZs7q6f9bspyd2LyQwSu6fLjX6Z/yd+ufp7fiFNcPxZv+8dmYXKpUZHQZ8iLNHMNfO7CCyUXcyEs+gUqnp8rR8bzyz33hfPLZzIUHRrWna+XlcvEJo1fMNPPyiObFL1iEvM4G06/F0HjQJr8BYnD2C6TJoElUVZVw4up66/nAhBeb/PAuX4HYcT3FBqxN5evAw1JZ25KSeNSivafjNtrlws21O32ybsOq2aRohsves/BKVqYHfD+qws4JIX7ltrMzBxV4g7ryOTA3kFsGOkzrMzQTcHf6dhs5/I/9446x169YUFhZy4sQJQH6rdHV1ZdeuXXqZ3bt3065dO8rKymjUqBHr16/nzJkzvPjiizz33HMcPnwYgBkzZtC8eXNeeOEF0tLSSEtLw8/PD41GQ4cOHWjQoAFHjx5l06ZNZGRkMHDgQANdFixYgLm5OXFxcXz//fccOXKEbt26MXDgQNLS0pgxY4ZedtKkSfTp04fTp08zYsQIVq9ezejRoxkzZgxnzpzhP//5D8OHD2fnzp0GZXz00UcMHDiQU6dO8fjjjzN48GByc3Px8/Nj5Ur5BnHx4sVa5d0rVVUVpN44S3B0C/0+URQJjm5O8tX4P51fTTYs+pjw2HaE1GmBp4cbjg62pOdXHy+vhORsCT834zcAUQRvZ4Frt7nor6VJ+N40vLycBVQqQ5mcAtAUSfi5Gs/X1gpCvSo5f+4sfuGG5922dQvMik7QozF4OhqmS78ej19Ec4N9AZGtSEuIN1pOcUE2CWd3U+ex/vp9mcnnKMrPQBBEFn/Zmx8ntmL198+TmSK/WWurKkhLPEtQVLVegigSFNWC5GsnjJbzVygvlcebrO0ccLGDtDzD49czJL1xezs+LgIJGbXbxJT83RBF8HICG0uBk9er803IlPBxuXcdrqeblgfZkJckibIKkyJYqKEiL9+0wEOmsrKSK5cvU79+ff0+URSpX78+F86fN53wJpIkEX/iBMnJydStWxcACwsL1q1bh4+PD++/9x5PDxrE7j17KCkpMZqHr6vA9dv64NXU6j7oaAt2VgLXahji5ZWQko1extdVoLRcMhimvpYuIUkYbTOttoLc9HO0b9uC1JtpBFEkKqYFp0+dYHAbeLYNdG0ATjby8bSEePzDb+ufUa1Iux5v9LyKC7K5fnY3dWv0T21VBaJKjSCKaKsqyEg6i29oEwBSrh5DEEUCIluQet14X0y9Hk9ApKEOgdGtSL2pg7ZKvvhU6mqXsCCKqMzMSU+Ix9VOfpHwDmrAhWMbKS3WkJIr4WyeibaqAs/AJgZ5+7oKXDfSF/VtYyO3zfXb2yYHfX8trYDsAonYQBG1SvYmNwwVKCqTSMt9yMOaiufMJP9448zBwYH69evrjbFdu3bx5ptvcuLECYqKikhJSeHKlSu0bdsWHx8fxo4dS/369QkODua1116jW7duLFu2TJ+Xubk51tbWeHp64unpiUql4ptvvqFBgwZ89tlnREZG0qBBA+bOncvOnTu5dKnaRR4WFsaXX35JREQEERERuLm5YWFhgZWVFZ6engYxZ8888wzDhw8nODgYf39/pkyZwrBhw3jllVcIDw/nrbfeom/fvkyZMsXgfIcNG8bTTz9NaGgon332GUVFRRw+fBiVSoWzszMA7u7utcqrSXl5OQUFBQZbZUU5ACWFeUg6Lbb2LgZpbOxdKcrPNpbdPXH60HrSbpyjY/+3AHB2cgSo9WAsLpMfysawtgBRFPSxY9VpJP3btq0VVGklyitNy9yibysV7w4yY0w/NVnZGnQ6LdZ28nmXlMPus5CvdeFqYjZFZdCrKfphMoDiwmys7VwNdbRzoaTAeD2dP7IataUNofWqPTH5OUkAHNz0DU27vMyTL36PpZUDi6Y8R2mxhpIiuT1sbm8POxeK/0Z71ETS6di69DN8QxriFxCOKEBprfrD5JCGrSW126Rc3v9XsDaX21lTJJFSY7SouMx0nkZ1uIO8SpRjos4lSlRUGZdxspW9f4k//fbnT+IBUVBQgE6nw8nJyWC/o5MTuXl5JlJBcXExffv0oVfPnnz44Ye8/PLLNGzYEJVKhSAIpKamsnzZMho1bsz/ffopNtbW5ObmcvrUqVp52d6MxzPIv0zS1/Wtv8WlhjJFt/XT4nLD45IkGwa399PhXVT8p3MBkk6LZO7CkSvVx8xtXLiWmM2mE7D9NAhA72ZgYyEbW9b2hv3Txs6FkkLj/ebc4dr90y/8MUoKsjm6/WeK8jORdFountgkn09+FiD3+WITfb64IBsbu9o63JJ39gzG3tmbvb9PpawkH21VBYe2/EihJh2VVIYoynXS6/np6LRVfPN2M/ZunIdKm0Pbp2Zg5xJgkLexflBUJmFTo97BWF+RDPrKoh1aPJ0E3hmg4t2BKh6LEFmyS0vZQ47AUYwz0/zjjTOAtm3bsmvXLiRJYu/evfTt25eoqCj27dvH7t278fb2JiwsDK1WyyeffEJMTAzOzs7Y2tqyefNmEhMT75j/yZMn2blzJ7a2tvotMjISkGPKbtGoUaN71rlx48YGv8+fP0/Lli0N9rVs2ZLzt70N1wxOt7Gxwd7enszMzHsuF2Dy5Mk4ODgYbL//MvlP5fFnyM9NQ8g7yvbNa3m6tQX9HwOVynjw98Nk81E5bm3JriocbQyPaYrhXBKUlkOlFnadkYNiYwOMZnVPnD24kshGPTGr8ZYs6XQANO3yEmH1u+LhV5fOgycjCALnj27664X9CTYt+Yis1Mv0efHrh1Le3TC7eWlcTn0wb+miAH2aizfjy4yXYWsFT7UWuZAskTRn+QPR42FiZWXFN7NnM33GDIYOHcpPP/3EqdsMr8eaN6dPnz6EhIQQERmJra0tGzZseEQaV7Nyn5ZF2+V+4ukI9QOrjxWXQ1mlPCkhLQ+2xMu/o/3+fDlnD64kqrFh/3T1CqPrs59zbMc85n7cGQBbezes7V0RxL//0Fep1Dz54ixyMxOYNbYpX79Rn8RLhwiq0wahRvb71s2gvLSAga/PJ7rZk1jZurJ3+RjyMi6Zzvxv0L2xSHG5xPxtWuZs0XIxReKpNqq//MKlcP/5r5it2a5dO+bOncvJkydRq9VERkbSrl07du3aRV5eHm3byjNjvvrqK2bMmMH06dOJiYnBxsaGN954Qx+8b4qioiJ69uzJF198UeuYl5eX/n8bG5tax03xZ2RrolYbxnQJgoDu5gP+XpkwYQJvvfWWwb41x+TYJGs7JwRRVSv4v7ggG1sHwzfAeyU14Swb16/lyOGD+n3qm0/gBZP7MeCtZYii/NvGEjLyjD8wS8pBp5NqeXBsLAWKbr6lF5WCmUrAQo2B90yWMcz3VnxTToFEUbE9KpUKbZnheZcU5ujffDPzwbOGw8LGzrXWW3hJYU6tt3WAlKtHycu8zuPDphvqfnMChbNHiH6fmZk5jq5+5OemYW0rt8ftwf/FhTnY/MX2qMmmXz/m8qldDHl7EfZOnpRXgk4Cq9tCB22MvJHfosiIV83GorZ35V7xcxOQJIn0264DGyMemzvqYEReFOTJC/Y2sGSXzqjXzNYSBrcTSc6R2HhUwvQ8vIePvb09oiiSd5uXTJOXh/Nt3rSaiKKIt7c3ACEhISQmJbFs6VJiY2ORJAl3d3c8PD318mYqFRUVFWRmZdXKq8iIR9LGUtDX9a2/NlaG9W9rKejbtKhUvkZqIghyvFPRbR63ghLQ6hwRBBUHTufw+nA4mSBP7CkpzDHwXuskyC4Ae2vZ23+7F7v4NvlbJN/sn08Mn17rWGTjnkQ27klBbhpzP+5ISGwHLhzboI9LKynMwcZInwdZh+LC2jrUlPf0r8uwd3+nvLQQbVUl1nbOLPpyALj7o9OBtiyLE7sXMfz9P3D1DsPFBXRaHS7edbh0eAnNen6oz8tYP7C1FPRezFt1e3vfsKnRNoEeAmHeAl+t1Or7x8ajOoI8VcQGCew///CGNpXZmqb5r/Cc3Yo7+/rrr/WG2C3jbNeuXbRr1w6Qp5Q/+eSTPPvss9SrV4/g4GCDYUkAc3Nz/eSCWzRs2JCzZ88SGBhIaGiowfZXjazbiYqKIi4uzmBfXFwc0dHR95zHrZlVt+t/OxYWFtjb2xtsanP5TmlmZo53QB2unz+gl9fpdFw7fxDfkPr3rEtNgqMeY8g7S3h8xAz9VlJlSZ6mgLc+/F5vmJmr5ZiJpCzjnV+ng9RciWBPww4b7CmQnC2nScuV0GoNZVzswdFWICnb9E3FTG1OnTp1SLxYfd6STkfSpQN4BTaQ87GDkho3NM+g+iRdOmiQT+LF/XgF1q+V/5mDK3D3q4ObT6TBfne/uqjMzMnLvK7fp9VWkp+TgoOLNyozc7z865BwwVCvhPMH8A1uYPJ87oYkSWz69WMuxm/l2bcW6B80Okn2Qnjd9pwP9BBIMVF/KTkSge6GbRLkaVr+bsQGCZRVyPGDNQlwF0jJMa1DgIehfKCHofwtw8zZDpbs1tWa4AGyx2xwe5H0PIn1Rx5ufM29oFarCQ0L42R8vH6fTqcjPj6eyKioe85H0un0s8TLy8vp1q0bycnJ+uNWVlacOXPG6Azz5GyJoNv7oFd1H9QUQWGpRFCN9jA3Ax9X9DLJ2RJWFgJeztV5BHkICAJG21ilMsfZK5pLZw4gCrIhJ+l0JF08gFdQdT8QAGdb+UXOK7A+ibf3zwv78QqqXyv/sweM98+a2Dt74eFXh1NxyzBTWxAQ2RJJp+PGxQN4Bxnvi95B9Um8YKjDjfP78Taig4WVHdZ2zuRlJpB+4wxBddqTXQh+brKPRBDkx7GPk1x/gigiSYYv5snZhvUOcl/Ut03xzbbxvK1tXND3V/VNz3WtVpAw8OY9DHQI92X7N/JfYZw5OTkRGxvL4sWL9YZYmzZtOH78OJcuXdIbbGFhYWzdupX9+/dz/vx5/vOf/5CRkWGQV2BgIIcOHSIhIYHs7Gx0Oh2jRo0iNzeXp59+miNHjnD16lU2b97M8OHD72oI3Stvv/028+fP57vvvuPy5ctMmzaNVatWMXbs2HvOIyAgAEEQ+OOPP8jKyqKoqOjuiYzQvOswju1eTnzcarJSr7L+l0lUlpfSoFVfAFb99A7bVkzVy1dVVZCWeJ60xPNoqyop1GSQlnienIwbAFhY2eLhG26wqS2s2LLrKI/VdcPHWV5jqk8LFYUlcCGp+rbwXEcVTcKrL8OD53U0DBOpFyzgag89momozSD+qnyTKq+EE1d1dGmkItBDvvk/2VxFUpZOf/MJ9RaoHyzg5gAONhDmI9CjqYqefYdyfO8yzh1ejat4lcN/TKKqspSWHfrSMhJmfjmOL7+qPu8GbYdw4/xeju2YS27GVQ5snEVG0hnqtX7WoD7Ly4q4HL+Juo8NqFXXFpa2xLQcxMGNs7hxYR+5GdfYsWwSAFGNugHQrPNwTuxdxqn9q8lOu8rGxZOorCgltqXcHmvnjmPnqmq9tFUVpCedJz3pPNqqCgo1GaQnnSc384ZeZtOvH3Hm0Fp6j5yKuaUNRflZFOVnUVlRxvlkCPOCmEABFzvo3lhAbSbP3gTo2UygXUz1De/IJYlgL2gaIcu3riPg5QTHrlS3o6U5uDuC680wSBc7AXfH2m/5Trbg7wbHr0rUDxaICZDz7NbIUIceTQXa1tDh6GWJYE9oGi7gbAetbulwWZYXBejTQsTLGdYe1CEKctk2lvIEBLhpmLUTyS+B7SclrC3k4xYef95DqbKxxr5eJPb15Ie9dZAv9vUisfTzukvKu9OnTx82bdrEtq1bSUxMZPY331BeXk7nzvKw25QpU5g3b55efunSpRw/fpy0tDQSExNZtXIlO3bsoH2HDgBo8vPp1q0bzk5OHDp4EGsrK6qqqpgxYwY9nngCdzc3A6/coQs6QrwFHosUcLGX18zydoYjF3UGMq3rioT7yO3cu4Vo0LezC+BKqo4ezVR4u4CfG3RvInLmhqT37tQNFIj2l/u5oy106jGM/duX8eP81WSlXWX7sknoqkrp3L0vdlaw87dxJB6cip0VXEg20j83yP2z/u39s7SIS/GbqNu8dv8EiN+ziIyks+RlXsfZM4zEiwcJrdeJ4vwstvwm3xvrNpf74vr549izprovNmo/hOvn9nJk21xy0q8S98cs0hPP0KBdtQ4Xj28k8dIhNNlJXD65jWUzRxBarxNB0a04kwj1wxx55rnnubDvexr45KASdSxf9htpVw8wcfyrdKhXfX88fElHiNfNtrGT1/3zdoYjl6vb5vBFHa3q3GwbB/mFpbAULiRXG85llfLSNB6O8ppnHevL66bdWssuzPvfafD8N/FfMawJctxZfHy83jhzdnYmOjqajIwM/eKM77//PteuXaNr165YW1vz4osv0rt3b/Lzq2djjR07lqFDhxIdHU1paSnXr18nMDCQuLg43nnnHbp06UJ5eTkBAQF069YNUbw/9mvv3r2ZMWMGU6ZMYfTo0QQFBTFv3jz9+dwLPj4+fPTRR4wfP57hw4czZMgQ5s+f/6d1qdv0cYoLc9m5ZhZF+Vl4+kXx7Js/6Yc183NTDeItCjWZ/DCpj/73/k1z2b9pLgERTRj+zi+18r/F+q0HqNOoPU1C5Le3GxmwaEcV2hovg852AtaW1Q/5szckrC10tItV6RehXbxDazDktumojq6N5LXNVCp5Jtn6w9VGdJUWGoaJdG0sL7BYUALnE3Wkil1p/aSGAxtmsmNpFhGRUcyb8zPRdV3JKYQzF9OwsBepdzMf76CGdBsyhQMbprP/j2k4ugXSc+RsXL3DDc7z0vH1IElENOqBMVo/OQ5RNGPzL+OoqizDM6Aeg8cswMpGtmSim8jtsXvtTIoLsvDwjWLQ6z9ja3+rPdL0b9W32mPOJ731vw9umcvBLXPxD2/Kc2Pl9ji+W15CZtHU5wx06TFsMmrzvliooU1dARtLgQwNLN2t0wdw21vLw463SMmB3w/oaBsj0i5GIK8IVsTpyKoxyTHMW6BnjTWs+rSQ/997Rsfes9V5xQYJFJTA7tPyQ7r1TR0yNbBsj46SO+iw9qCONnVF2t7UYWWcjuwC+bidFYT7yNfsyK6G8Y6Ld2pJzJI9N852snH3Ws0FcXvFsV5tfLFSUzg0qkvz7dXXfvSUdwFIWriKUyMn/Km8bqdt27YU5Ofzy6JF5OXmEhwSwseffKKfJJCVmYlYw8VRVlbGt7Nnk52djbm5OX5+fox9+239S2txcTEqlYoJEyZgYWHBhQsX+OKLL3jp5ZepU7cuZmZmBl6U5GxYFaejfT2RDvXlhU6X7jFs7/3nJMzNJHo0E7E0h8RMicU7tQZ9e1Wcju5NRJ7rqJIXoU2S2HS0WkAnQYtoUV5/DtAUP4G5oGHOjzMpKcjCzTeK9z/7mS4tXLG2gPU/pJGfJ7L6EOQVg3dwQ7oPncL+9dOJWzcNR/dAej1fu39evNk/I030z/QbpziwYRaV5cU4eQQT3fRJkq8cYcHkJ3H3jaL/qz/rhykL89IQajwTfEIa0mPEFPaunc7etdNwcgukz39m41ZDh6L8LHau+JziwhxsHdyo0+xJmnd/BYBrmWBpLvDGm29ibS5x4eIlhg8bzbXkfFr0+QxPT0/yi6tbJzkbVu/X0T5WpH2s3DbL9t7WNucl1GYSTzS52TZZEr/uqm6b0gr4dZeW9rEiz3aQ1z/Lyoele3VkaKrb5mHwbw3mvx8IUs07oMK/liVxj76ZL103MW3uIeLuanqdtoeJjdXdZR40yen3xyv8d3jYwyjGiB1876EFD4rIixsftQosPuD/qFXA3fXR+wvMHr0KpGU++nvlxKcffEUcv2R8cd8/S8Nwl7sL/ZfxXzGsqaCgoKCgoKDwv8I/4B1BQUFBQUFB4X8NZVjTNIpxpqCgoKCgoPDQUZbSMI1inCkoKCgoKCg8dBTPmWmUmDMFBQUFBQUFhX8QiudMQUFBQUFB4aGjDGuaRjHOFBQUFBQUFB46f+7DhP9bKMOaCgoKCgoKCgr/IBTPmYKCgoKCgsJDRxnWNI1inCkoKCgoKCg8dJTZmqZRjLP/Ebwcyu4u9IAp93303yzycSp/1CoA4GmjedQqUFzq8ahVoLD40Ued/BM+nXQhovujVgGnNRcetQp4uzz6T4qFOGY+ahVYm+P+qFVQeMQoxpmCgoKCgoLCQ0cZ1jSNYpwpKCgoKCgoPHSUYU3TKLM1FRQUFBQUFBT+QSieMwUFBQUFBYWHjk561Br8c1GMMwUFBQUFBYWHjjKsaRrFOFNQUFBQUFB46CgTAkyjxJwpKCgoKCgoKPyDUIyzh8CkSZOoX7/+o1ZDQUFBQUHhH4Mk3Z/t34gyrPkQGDt2LK+99tqjVsOAnRt/Y+vvC8jX5OAbGM6gke8QFBZjVHbv1pUc3P0HqYlXAPAPjqb34FcN5Nct/Y4j+zaTl5OOmZlalnnmVYLCjecJcGznYg5tnUNRfhbuvpF0GTQR76BYo7JZqZfZu3Ym6Ylnyc9JoeOACTTtNMxAJvHSEQ5tmUN64hmK8rPo9/Jswut3umtd7N70G9vXzadAk41PQDgDRkwgMNS43nHbVnB4zzpSk6rroufTrxvIxx/axr6ty0m8do6SonzGf7kM38DIO+qw8Y9VrF35G5q8XAKCQhj50mjCIqKNyh6MwCD/7QAA6k9JREFU282qZYtIT0tBW1WFl7cvPfs+RdsOXQ1ktmz8nWtXLlFUWMBXM+cQFBJmUN6ypcspKcjC1SeS9v0m4hlgvO4BLp3YyIENMyjITcHRLZBWPccSVKet/vj00RFG07Xq9TaNOz5vsK+qqoLfpg0gO+UCVnZuVJTm4+wZSfOe7+HmZ1qH66c3cWzrTIo0Kdi7BNCk2xj8Iqp1OL7tG66d2kBxfjqiSo2rTzSNuryBu189vUz8zu9JuribnLQLqFRqnvvgcK1y1q1bx8oVK8jLyyMoOJiXX36ZiAjj5xcXF8fSpUtJS02lqqoKHx8f+vTtS8eOHQ3kEhMTmTd3LqdPn0ar1eLv789777+Pu/tfX2zUuVVjgseMxKFhXSy93Tna7xUy1m7/y/md2reYEzvnUFKYjat3JG36vI/HHa6JK/GbOLhpBoW5KTi4BtCix1gCo9sayORmXGX/H1NIvXoEnU6Ls0cI3YfNxM7JG4Cdyz4g6fIBivMzUVtYExxRnyeeHoO7dzAAcVt+Zff6uRTmZ+PlH0Hvoe/hH2Jcp/Tky2xe8Q0p18+Sl51Kr2fH07r7EAOZz0Z3Ii87tVba5p2epu/wiUbz3fjHatbc7JuBQaE8/9LrhEVEGZU9GLeHlcsWkZaWgrZKi5e3D736PkW7Dl0AqKqq4teFczh+9CAZ6WlY29gQ+//snXV4VMf3uN+VbNzdXQhJCO7uVtxKCxRpoVhbqNBCCzVoixUrBYoVaHEoxS24BoITnATisvFs1n5/LN2wZBcr0t/ne9/nuQ9k7pmZc2fvzD33zJm5sdV5e8C7ODm76MupFiKidrgIGwvIkMOusxpSc4xWCUCEDzSKEmNvDTkFEHdew8003TmxCBpFiwj2EOFgAwol3EnXEndeS+FDe5N3ry/GzQGsLaC0zHRdLxKNEHNmEsFz9gqwsbHB2dn5dauh59SRnaxbOo32Pd/ji5/+wMc/jFnfvE9+nvHef+3SaWo2aMNHkxby6ffLcXRx5+evh5Gbna6Xcffyp8/gz/hy+jo+/nYJzm5ezPxmGAUmyrx8aht7102mQfvhDPxiI+4+EayeNYii/Gyj8sqyEhxcfGjSZQzWdq4mZIpx8wmnVZ+vnrot4o/uYOPyn2jbfSif/rAab/9w5n43lII843pcv3ya6vXbMvqr3xjz7QocnD2Y++1Q5DnlbVGmKCE4oiqd+37wVDocObiXZQvn0uPNAfw4axEBgSF8O2EsefJco/I2tnZ06/U230+dx7S5S2jasi1zZ0whIb7c0FAoSqkUGcNb7ww1WV+d1sN58+ONuHpFsPGXQRQXGL/mlNtn2L58DJXrdKfvx5sIjm7Olt+Gk5VyTS8z5JvDBkfLPt+DSERoldYVyju8+Uf9kBxRsyedhq/HyTOcHUuGUFJoXIf0u2fZv3osYTW60XnEBvwjm7NnxUhy0sp1sHcJoO4b4+kyejMd3luBjaM3OxYPpqSw/B7UqJUERrWmUu3eRus5cOAACxcs4M2+fZk9ezZBgYFMGD8euVxuVN7W1pbevXoxbfp05s2bR4uWLZkxfTrx8fF6mdSUFD4eOxYfX19++OEH5s2bR58330Qmkxkt82mRWFuRfz6Ri6Mm/atyAM5aw+HNU6jZeji9PtqAs1c4fy0YbPKeSL19hp0rxhBZqzu9xmwkKLoF25aMIDu1/PfIy0pi/ew3cXQLosv7y+kzdjM1W76PRGqul3H1rUzz3t/T97OtvPHeIgAWThmMRqMm4dh2tqz8gZZd3+eDb9fh5RfBoinvUmiibyoVpTi7+dCu90fYOrgYlRn1zRomzD2gP4aM09VZpXbF+xTg8MF9LFk4j55vDmDqrIUEBAbz9YSPkZvsm7Z06/U2U6bOY8bc32jWsi1zZkzh7IO+qVCUcuvmNXr06cfUWQv45IuvSbmXzOSvP9eXYWdjQfMqIg5f0rJ4t4Z0uZZejcRYmRutEm9n6FRHzLnbWhbv0nA9RUu3+mJc7HTnzaTg4SDiyGUtS3Zr2HBEg7OtiO4NDB//dzO1bDqm4dftGjYcff1f7vi/jmCcvQAWLFiAl5cXGo3hDd2pUycGDhxodFpz0aJFVKpUCQsLCyIiIpg3b57+XPfu3RkxYoT+7w8++ACRSMTVq7rPq5SVlWFtbc2ePXueS989W36nQYuu1G/WGS/fYPq+Nx6ZuQVH924yKj/og8k0adML38AIPHwC6TfsK7RaLVcvlBsDtRq2o1KVOrh6+ODlF0KPAWMoLS7k3t3rRss8uWcJVRr0JKZ+N1y8QmjTdxJSmQXnj643Ku8VEEOz7p8SWbM9UjPjD7XgqMY07vwh4VVbPnVb7Pt7OfWad6Nu0854+gTTe8gEZDJLju033hYDRk2hUeve+ARE4OEdSN+hE9FqNSReOFHeFo060rb7UMKj6zyVDls2rqFFmw40a9kOX78A3h0xBnMLC/bt2mpUPiqmKrXrNcLHLwAPT2/ad+qBf2AQVy6f18s0btaaHm8OICa2usn6KtfphrNHCM176tr+0nHjbX/2wHICIhpSo/lgnDyCqdf+A9x8Ijl3aIVextrO1eC4eXEvviG1sXfxNSjr9uUD3E08ou8r/pHNcXQPoX6niUhlFlyL32BUh0tHl+MT2oCYRoNwcAumesvROHtV4srxVXqZ4NgOeIfUw87JF0f3UGq3+wylopDctES9TLUWI4lqMABH9zCj9WzcuJE2bdvSqlUr/Pz9GTFyJObm5uzatcuofExMDPXq18fPzw9PLy86d+5MYGAgly5d0sssW7aMGjVrMmjQIIJDQvD08qJOnTo4ODgYLfNpydx5kGtfzSR98/ONAw9zwB4q1+lBZK1uOHmE0LT7JKRmFlw5afyeOHfod/wiGlCt2SCc3IOp03Y0rt6RnD+8Ui9zfNtMAio1pn7Hj3H1icTexY/AqGZY2Za/qEbV7YV3cE3snHxw86lM6x6jkGenkZN5n4Pbl1K7aQ9qNu6Ku08IXQd+hZm5BScPGL9HfIOj6fDmx8TWbYdUanyMsLFzws7BVX9cOXsAZ3dfgirVNCq/ZeNaWrZpT/OWbfH1C+C9ER896JvbjMpHxVSlTr2G+Pj54+HpTYdO3fEPDObK5QsAWFvbMPG7adRv2BRvHz/CIyozeNhobt64RmaG7gXP2cGac7e0XLijJTsfdsRrUakgJtC4l6lGqIhbaXAiUUt2ARy8qCVNDtVDdfIKJfx5UMPVe1pyCiAlB3ad0eDpJMLOqrycU9e0pORAfjHcN27/vnC0WtELOf4XEYyzF0CPHj3Izs5m//79+rScnBx27NhB3759K8ivXLmSL7/8ku+++44rV67w/fffM2HCBJYtWwZA48aNiYuL08sfOHAAFxcXfdqpU6dQKpXUq1fvmXVVKZUk3bxCpZja+jSxWExETG1uXTv/mJzllJWVolarsLaxN1nHod3rsbSywTeg4kNQrSojLekSgZXK9ReJxQRE1OP+rbPPeEXPj0qlJPnWFQMjSiwWEx5dm9vXzj1VGWWKUtQqFVYm2uJJKJVKbt24RkxsDQMdomOrk3j10mNy6tBqtZxPiCflXjKRUVWeKG+sPpFYjF9YPVLvGG/7tNsJ+IbXNUjzj2hA6p0Eo/JF+VncuXSAynW6V0jf++cEWvb5jpy0GwbnRGIxXsF1yUgyXmZG0jm8Qgx18AltYFJerSoj8dQaZBa2OHk+fkr5H5RKJTeuXzd4kRKLxcTGxnL1ypUn5tdqtSScPcu9e/eIiooCQKPRcOrUKby9vRn/xRf06d2bDz74gKNHjz6VTq8CFXDPHHzDDPujT1hd0kz8xml3EvANNRx//CLq6+W1Gg13rsTh4BrA5l8H8duX9Vg7sye3Lpg2JJWKYk4f2IiTqw829s7cv32Z0CjDvhkaVZe7143r9KyoVGWcObyFmo27IhJVfMArlUpu3kg0eMERi8XExFYn8erlJ5b/tH2zuKgQkUiEtY0NIsDS3Izb6YaBVHcytHg7GzdCvJ1F3HlE/naaaXkAczOdfqamLy3+nVP3qXmdMWdz584lICAACwsLateuzcmTFUMc/mHhwoU0bNgQR0dHHB0dadGixWPlXwRCzNkLwNHRkbZt27Jq1Sp9rMm6detwcXGhadOmHDp0yED+q6++Ytq0aXTt2hWAwMBALl++zK+//kr//v1p0qQJo0ePJjMzE6lUyuXLl5kwYQJxcXEMHTqUuLg4atasiZWVVQVdnkRhQS4ajRpbB8NpVjt7Z9Lu33mqMjb8PhN7R1cDAw/g/OmDLJrxKWWKUuwdXfjgq/nY2DlWyF9cmItWozZ4gwawtnMmO+3Ws13Qv6Aw30RbODiTnnL7qcrYvHIG9k6uRDyll+xRCvLz0GjU2DsYtpODgxP3k5NM5isqKuS9ft1QKssQiyUMfv9DqlQ1/vZvqr7Mh9KtbJ3JyTDe9kUFWVjZGk4TWdk6U5yfZVT+yqmNmFlYE1KllT5Nq9Wya9VnRNfvjZ2TN1ptxWkTSxtn8jKNt3tJYRaWNoY6WNg4U1xgqEPS1f3s/3MsKmUJVrautBn4GxbWFe9BY+Tn56PRaHB0fOS3cHQk+d49k/mKiop4+623UCqViMVihg8fTrVq1QCQy+WUlJSwds0a+vXvzzsDBxIfH893337LlClTiI4xHdP1qiiSgEYElo/0RytbF+QZxn+P4oKsCv3XytZF/3sUF2ajVBQTv28hddqOpl6HsSRdPcS2pSPpMmwZ3iG19PkuHFnF0S1TUZYV4+oZyJBxi1CUFKHRqLGxN/zNbeycyUh5MWPEpdN7KS0uoEajLkbP6/qKBgcHJ4N0BwfHJ/bNIf266++Hd9//kNiqNYzKlpUp+H3JAho0bo6VlTUSiRiRSESx4pEyS8HZ1nh9Nha684/K21gYl5eIoUmMmMtJWspUhueaxIioHiJCJv3f9Eb9w+rVq/noo4+YP38+tWvXZubMmbRu3ZrExESjcaBxcXH06dOHevXqYWFhwQ8//ECrVq24dOkS3t7eL0VHwTh7QfTt25chQ4Ywb948zM3NWblyJb1790YsNnROFhUVcfPmTQYNGsSQIUP06SqVCnt7nfclKioKJycnDhw4gEwmo2rVqnTo0IG5c+cCOk9akyZNTOqiUChQKAx7d1mZBpnMRNDCM7Bjw2JOHdnJmEmLMHukvPComoyfuprCAjmHd29gwbRP+GzKCuzsnUyU9v83uzb9RvyRHYyeuLhCW7xsLC2t+Gn2b5SWlHDhXDzLFs3F3cOLqJiqr1QPY1w6vp6I6h2RmpW3ScLB31GWFlGz5XsVDKoXiWdQbbqM3EBpUS6Jp9ay748PeWPYaixtXl7Mp6WlJXPmzqWkpIRzCQksXLgQD09PYmJi0D54ra9Tty5duuiMgODgYK5cvsy2bdv+E8bZy+Af4zuwcjNiGw8AwNW7Eql3znLx2J8GxllYtY74htWjKD+TxGOLWTHrI94eNeOl63gybgPhVRpi7/j8izKMYWlpxbTZiygtKeH8uTMsWTQXdw/PCn1TpVIxdfIktGh5b/iHL1QHU4hF0KWuGJFIN136KCeuajl/S4udNfRpLHnp+ryuTWinT5/OkCFDeOeddwCYP38+W7duZfHixXz22WcV5FeuXGnw96JFi1i/fj179+6lX79+FeRfBMK05guiY8eOaLVatm7dSnJyMocOHTI6pVlYWAjo3KQJCQn64+LFixw/fhwAkUhEo0aNiIuL0xtiMTExKBQKLl68yNGjR2ncuHGFsv9h8uTJ2NvbGxyrFv0EgI2tI2KxhAK5YVBBfl429iaCaP9h1+Zl7Ni4mNETfsHHyHSluYUlbp5+BIXF0G/4RCRiCUf2bqwgZ2XjiEgsqRBsXJSfXeFN+WViY2eiLeTZ2D2hLfb8tZTdmxYzfPyvePsbj196Gmzt7BGLJRWC/+XyHBwcTRu1YrEYTy8fAoNDeaNrb+rWb8zGtStMyj+pvuKCbKxtjV+z9UMekYflrewqyt+/eZrcjNtE1e1hkJ58/TipdxKYPSaaRV810advnteDA2t1g2FJYTaWJnSwtHGhpNBQh9LC7AoePTOZFXbO/rj5xdKw23eIxRKunTYeN/UodnZ2iMVicnMf+S1yc3FyNO19E4vFeHl5ERwcTNdu3ajfoAFrVq/WlymRSPDz8zPI4+vrS0ZmprHiXjnWahBroeSR/lhsxGP6DzovmWl5S2tHxGIpTh4hBjJObsEU5KYapJlb2uLgGoB3cE3eHj2DjNTb3L52BrFYQmGe4W9emJ+N7QsYI3Iz73P94jFqNelmUkbXV8TI5YaLmuTy3Kfum5269qJu/cZsWLvKQEalUjF1ykQyM9OZ+O1UrKysAVCrNWi12grB/9YWGKysfJjCUt35J8mLRdC5rhg7a/jzgKaC1wygpAxyCuFOesVzLwON9sUcCoWC/Px8g+NRB8U/lJWVER8fT4sW5Sv5xWIxLVq04NixY0+ld3FxMUqlEienl+d4EIyzF4SFhQVdu3Zl5cqV/PHHH4SHh+unNh7G3d0dLy8vbt26RUhIiMERGBiol/sn7iwuLo4mTZogFotp1KgRP/30EwqFgvr165vUZdy4ceTl5Rkcbw7+GACpmRl+wZW48lAwv0aj4er5kwSFmX6L37lpCVvXLWTUhHkEhFR+qjbRaLWolBWDGiRSGR5+lblzpbwjaDUa7l49hnfQq/P8SKVm+AZVIvFieTC/RqPh2sUTBIaZjhHZvXkxO9Yv4P3P5+Ef/HRtYQozMzOCQsK4kFC+uk+j0XAh4QzhEU9ftkarRalUPld9Wo2G5GvH8Aww3vYegbEkXztukJaUeBTPgNgKshePr8PNtzKu3oZxXk26jqfvJ5vp+/Em3vpkM45uunu9ae/p1Gj1AVqNhpSbx3Hzq1gmgJtfFVJuGupw/8ZRk/L6a9NqUauebl8AMzMzQkJDOZeQoE/TaDQkJCQQUcn41glG69Ro9L+FmZkZYWFh3HtkWvT+/fv/ahuNF4kU8FFA8nXD/njv+nE8jPzGAB4Bsdy7bvggS752VC8vkcpw84uqMC0qz7yj30bDKFpAq0Wr0eAdGMmNS+W/uUaj4cbF4/iHGtfpWTh1cCM29k5Uqmr6JdfMzIzgkHDOJ5wx0OF8QjzhEca3uTGGVqtF+dA4+I9hlppyj4nfTcPWrjxeVQuUKJQEuBt6lPzdRNzPNh5cdT9bi/8j8gHuhvL/GGZOtvDHAQ0lr2irjCfxohYEGHNITJ482WidWVlZqNVq3N3dDdLd3d1JS0t7Kr0//fRTvLy8DAy8F40wrfkC6du3Lx06dODSpUu89dZbJuUmTZrEqFGjsLe3p02bNigUCk6fPk1ubi4fffQRAE2aNOHDDz9EJpPRoEEDfdrYsWOpWbMm1tbWJss3NzfH3Nzw1UsmK9H/v0XHt1k6ewIBwZEEhEax9++VlClKqNesEwBLZo3HwcmNLm+NAmDHxiVs+XMegz6YjLOrF3m5urdZcwsrLCytUJSWsG39QqrUbIK9gwuFBXLidqxGnpNB9brGV07WavEOfy/9FI+AKLwCYji1dxnKshJi6uni8LYs+QRbB3eadBkD6AK8s1Jv6v9fKE8nPfkKZuZWOLn5A1BWWkRuZnksiDzrHunJV7CwtsfeyfgDoVmHfvw+dzx+QZEEhESzf9sKFIoS6jTpDMDyOZ9j7+ROpzdHA7B702K2rplL/1FTcHbzJl9e3hbmFroYwKLCPHKzUsnL0XlG0lPuAGDn4GLUI9exS0/mTJ9McGg4IWGV2Lp5LYrSEpq2bAfArGnf4ezsQt8B7wGwYc0KgkPD8fDwRqks48zp4xzct5Mhw8foyywoyCcrI53cHJ1+Kfd17eLg6KSvT21fCw+/GM4c0LV9ZG1d2+9c8QnW9u406Kgrr2rjfqyb9Tbx+xYTWLkxiWe2kZ58kea9vja4DkVpIdcTdtCo06cVrtHukfaPadiXA+u/RZ5+A0e3YI7sm4SqrISwarqpvwNrP8XKzp2arXX9oXK9fmxd2I8Lh5bgG96YW+e3kXX/EvU767aRUJYVc27/r/hVaoqlrSuKYjmXj6+iOD+dwOjybRIK5SkoivMokqeg1ajJTrnCTbsUvLy8sLS0pEuXLkyfNo3Q0FDCwsPZvGkTCoWCli119/HUqVNxdnbWT4esXr2a0NBQPD09USqVnD51in379jH8odXW3bp108WXRUURU6UK8adPc+LECX744YcK7fQsSKytsA4p98hZBfpgVyWCspw8SpNTH5OzIo3zYNXxtbj5RuHuF8O5A8tQlZVQqZbunti96lOs7dyo10F3T1Rp+DYb5/bjbNxiAio14drZrWQkX6Jpj/J7omqTQez8/SO8gmrgHVKbpKuHuH15P13eXw5AXnYy189uwy+8PpY2ThTK09i3YiFmMnMqxTbCTGbB6l/H4RMYhW9wNId2LKdMUULNxrp75I9fPsPe0Y12vXX3iEpVRvq9f8YIJXm56dy/cwVzCytcPPz1emk0Gk4d2EiNhp2RSB7/COzYpQezp08mJDSc0LBKbNm8DkVpKc1atgXg52nf4+zswlsD3gVg/ZqVD/qmFyqlkvjTxzmwbxfvPpi2VKlU/PT9V9y6eY3Pv5qMRq0mN0fngbSxtcPMzIxseRGxQQ6k5UBKjpaaYSLMpHD+ts7Y6lBLREEJHLig+/v0dS19m4qoFSbiRqqWSD8Rno6w/bTuvFgEXeqJ8XCEtYc0iEXlnraSMtBowMsJPJ1EJGfpFgk42jzT7fPaGTdunP7Z+Q+PPgNfFFOmTOHPP/8kLi4OCwsTgX0vAME4e4E0a9YMJycnEhMTefPNN03KDR48GCsrK3766Sc+/vhjrK2tiY6O5oMPPtDLREdH4+DgQFhYGDY2up7SpEkT1Gr1Y+PNnoaa9VtTmJfLX3/+Qr48C5/AcEaNn4fdg8D4nKxUg9VLB3euQaVS8uvUsQbldOj5Hh17DUMsFpN2/w7H48ZQmC/H2taBgJDKfPztYrz8DKc1/iGyZjuKC3M49NcsivIzcfOpRM9Ri7B+MFWWn5OKSFTu2C2QZ7D42876v0/sXsyJ3YvxC6tF3zG/A5B69yKrppfP/+9dq3tziq7bhQ4DphjVo3q9NhTm57J1zTwK5Fl4B4Qz/PNfHmqLNAM9Du3WtcVv08cYlNO2+1Da93wfgAun41gxr3xDyyUzP6kg8zD1GzUnP0/OnysW6za6DArhi6+n6qdOsjLTET/0eyhKS1g4bzo5WZnIZOZ4+fgxaux46jcq3/j09PEjzJ1Z/uY44wedEdPjzQH06juQ/Dw5a1bP0m1C61OJzkMfavvcVHjomr0Cq9Gm31SObZvJ0b+n4+AaQMdBc3HxMpzOvXZmK2i1hFfvYLStHyaoclMOrP+WKyf/ICFuPs6elWj9zgL9tGah3PD3d/evStNePxG/+2dO75qBnbM/Ld6ajZOHTgeRSII88xbXz26itCgXCysHXHyiaf/uChzdyzffPbNnNtfPbNL/vWlOVzYBU374gZiYGBo3bkx+Xh6/r1hBbk4OQcHBfP3NN/pFApkZGQa/RWlpKfPmziUrKwuZTIavry9jP/7YIOygXv36jBgxgjVr1jB//nx8fHz4Yvx4Kj9Y0fm82FePou7e3/V/R07V7ZWVvHwD5weNe6ayqhbB3Y6fcHLHbIryM3H1rkTHdxfqpykLclMMxgTPwGq0emsqx7fP5NjWGTi4BtDunTk4e5bfE8ExLWnSfSLxexdwcON3OLoF0nbALLyCdKsfJVIZqbfiOXdwOYqSfKxsnQmNrM7wr1ZhY+9MbN22FBXksHPdbArysvDyj2Dwp7/qpzXl2Yb3SH5uJjO/KJ+mPLB1CQe2LiGoUk2GjV+mT79+8Rjy7FRqNu76xHZp0KgZ+Xly/lixBHluDoFBIUz4+scn9M0ZZD/om94+fowe+wUNGjUDICc7k1MnjgAwZqTh5sxfT55BVExV8gtLuZKipWGUCGsLERlyWHNQo18kYGcl0scygm7bi7+Oa2gUJaZxtIjcQlh/RENWvu68rSWEeet0HNTaMI5s5X41SZmgVOtkGlbWGYKFJbwSXtTu/sYcEqZwcXFBIpGQnm44d5ueno6Hh8dj806dOpUpU6awZ88eYl5yvKhIq31RzSPwXybu4ivqbY/hTpbl61YBb0fjcQivGg9r+etWgcM33J8s9JIpKHr9m112q3rndavA1fC2r1sFbm26+rpVwN/99d8PwQ4Zr1sF/jrz+qe8x/V8+QsC/j5jJPDtOehQ7dn8TLVr16ZWrVrMnj0b0HlT/fz8GDFihNEFAQA//vgj3333HTt37qROnedbnf8sCJ4zAQEBAQEBgf8zfPTRR/Tv358aNWpQq1YtZs6cSVFRkT5coV+/fnh7e+vj1n744Qe+/PJLVq1aRUBAgD42zcbGRj+z9aIRjDMBAQEBAQGBV87rmrfr1asXmZmZfPnll6SlpREbG8uOHTv0iwSSkpIMtsH65ZdfKCsro3t3w421v/rqKyZOnPhSdBSMMwEBAQEBAYFXzuv89NKIESMMPpP4MA9/oQfgzp07L1+hRxC20hAQEBAQEBAQ+A8heM4EBAQEBAQEXjkaYTmiSQTjTEBAQEBAQOCVI+wVYRphWlNAQEBAQEBA4D+E4DkTEBAQEBAQeOW8rg+f//+AYJwJCAgICAgIvHKEmDPTCMaZgICAgICAwCtHiDkzjWCc/R/h9LWX8xHYZ0Gtef2fZjl4IO91qwCAnZPpD9e/KpycXsynU/4Nu/488rpVoLS0/utWAcf/wKeTgjpHvG4V+G3Y1tetAu8O9Huy0EvmRNzN160C9Ax7sozAS0MwzgQEBAQEBAReOYLnzDSCcSYgICAgICDwytG8xi8E/NcRttIQEBAQEBAQEPgPIXjOBAQEBAQEBF45wrSmaQTjTEBAQEBAQOCVIxhnphGmNQUEBAQEBAQE/kMInjMBAQEBAQGBV46wCa1pBONMQEBAQEBA4JWjFVZrmkSY1vwXBAQEMHPmTP3fIpGITZs2PTbPgAED6Ny580vVS0BAQEBAQOD/XwTP2QskNTUVR0dHAO7cuUNgYCBnz54lNjZWL/Pzzz+jfQ1RkJeOreT8wcWUFGbh5BFBvTe+wM03xqT8rQs7OL17FoW597Fz9qdWmzH4RTTWn4/fM4eb57dRJE9DLDHDxTuSmq0+wM2vil7mjx+aUyhPMShXZmGHWlWKo0cEdTt8getjdLh9YQdn9syiUK7ToUbrMfiG63TQqJXE7/6Ze9cOUpBzDzMLG7yC61Kz9Ris7Nz0Zaz5qaIO/lXew6fyWwZpnZtY06iaBVYWYm4kK1m+tYCMHLVJ3cL8zGhTz4oALykOthJm/ynnbGKZ/rxEDF2aWRMTYo6ro4QShYbLt8pYt6cIeaHuSwlta8uoU1mKpbmI26lq1u5XkJX3+HujQbQZzaqZYWslIiVLw/qDCpLSDb+8EOAhpk8Lc9wcdO9eCiUs+KuEW6k6ucHtLfB2FWNrJaKkDG6nabmboaVuhBgHG8gugL1nNdxIMdSlSYyYqiEiLMwgOVPLtlMacgrKz1vIoG0NMWE+IrRauJKkZUe8BuWDDxHYW8PozoZDzpd9G/Pe2DNcSiwvaNiAQLq198bcXIxWC+kZpUybf4Pj8TlG2+St7r40rueCv7cVijINF67m88vSWyTfL6FrOy/6dPXF2VFGcakaESCTiUm6X8zyNUkcOJplUFYlPxFNY15uOzjbQvtaYlzsRVjIYMnSlSyd8hu5OVm4eEXQqMt4GtePoWUVQ6+CSq3llx1wI2EHx3f8TEHOfexd/KnXYSwBkY0NZHPSb3L076mk3DyFRqPGyT2YtgNmYevoBcD+NV+SfP0YRXkZmJlb4RlQlZ5m4K402sSPxalBDYLGDMK+WhQWXm6c7vY+6X/tffaCTNC7nRMt69phZSnm6u1SFqzJJDXTtKKRwRZ0au5IsK85TvZSpixM5eSFIgMZC5mIt95wpnaMDTZWYjJyVGw9IGfXkfwn6nNo5x/s27KUfHkW3v7hdHtnHP4h0UZlj+5dx6mDW0hNvg6Ab2AkHfqMNin/OPp0cKZlfXusLcVcvVXC/D8yHt8OIZZ0aelIsK8FTg5SJv96nxPnDNvB3lZC/84uxFayxtpKzKXrJSxc8/hyXyTCggDTCJ6zF4iHhwfm5o//TJK9vT0ODg6vRqEHbNu2jeNbf6Ba8+F0GbEeZ89wti8eQklhtlH59Ltn2ffnWMJrdKPLyA0ERDZn94qR5KRd08vYuwRQ/43xdPtgMx2HrsDW0ZttiwdTUmj4AK3eYiR9Pz9I/c5fIZaYUb3Vh7wxfD1OHuHsXPp4HeLWjCWsRjc6Dd+AX6Xm7F05ktx0nQ4qZSnZKZep0nQYnYavp/mbs8jLusPu39+vUFa15iPp/dlBen92kJpdNuEZ3s3gfNv6VrSobcnyrQV8uygHRZmWMW85IJWYblNzmYjkdBUrthUYPS8zE+HvYcaWg0VMWpDDnNV5eDhLGdXHHoDm1cxoVMWMtfsVzFhTQpkShnayfGydVUOldG4oY8fJMqb+Wcz9LA1D37DExrL8IR7gIeb9zpa42IvZdryMhVtKuJumZnCHcrnr99Us3V7K3C1q1h5U4+4gokMtMWdvaliwTU1ispZejcS42pfXXS9SRK1wEVtPavhtpxqlCvo2lSB5aATpWl+Mq72IFXvV/BGnxs9NRIfaFYeY3/eombZexbT1Kt54+yhXbxTqz73dw5fenX25nVzEdzOuEn8uFzOZmLy8sgrl6NslyoENW1N47+OzfDjhPFKJiBlfx9CmqRsjBgez5I87XL2Zj1ajRSoVMXJcAgePZvH1J5GEBtnoy/FxgW71X347qLVw7raWlfvUfPDt38yYPoURI4bz/ewNOHuF89eCwRTkZaNQavltT/mxdB+k3j7DzhVjiKzVnV5jNhIU3YJtS0aQnVreN/Oyklg/+00c3YLo8v5y+ozdTM2W7yORlo9Nrr6Vad77e/p+tpU33luEFi2/esLzfGBNYm1F/vlELo6a9By5H0+XFg60b2TP/DWZfDb9HooyDROGeWEmNT0dZi4Tc+e+goVrM03KDOjiQtVKVsxcns6o75P4O07OkO6u1Iyyeqw+Z47uYOPyn2jdbSgfT1mDl38Yv3z/HgV5xsexG5dOUa1eW0Z8uZgPv1mBg7MHv3z3HvKc9KdrgAd0aelIhyYOzP8jnU9+SqJUoeWrkd6PbQcLmYjb9xT8ujrDpMy497xwdzHj+1/v8+H3d8nMUTJplA/mslcz3ajRvpjjf5H/GeNMo9Hw448/EhISgrm5OX5+fnz33XcAXLhwgWbNmmFpaYmzszPvvvsuhYXlD4R/phqnTp2Kp6cnzs7ODB8+HKWy/O0hIyODjh07YmlpSWBgICtXrqygw8PTmoGBgQBUrVoVkUhEkyZNDOr6B4VCwahRo3Bzc8PCwoIGDRpw6tQp/fm4uDhEIhF79+6lRo0aWFlZUa9ePRITE5+6bZYsWUJEzR6E1+iKo3sIDTpPRCqzIPH0BqPyF48sxye0AVUaDcLRLZgarUbj4lWJS8dW6WVCYjvgHVIPOydfnNxDqdP+M5SKQnLSDPUyM7fGytaVa6c3El6jB5Vq98bRLYT6nSYiNbPgWrxxHS4f0+kQ3XAQDm7BVG85GmevSlx+oIPMwpY2AxcTFN0We9dA3PxiqdtxPNkplyp4yv7RwcrWFZmlMxKppcH5lrUt2XKwiITEMu5lqFm0KR8HWzHVIkwb2hdulLFxfxFnrho3GkoUWqatkHPqsoK0bDW37qtYsb2AAC8znOzENIo1Y9epMi7eVpOarWHl7lLsrUVEB5l2ZjeJNePYJSUnr6hIz9Wydr+CMpWW2pHleTo3NKdYoeXoRSW7Tyu5fFfD/M2lBnIHEpTcTdeQVwT3skCh1I1uJ65qycqHuPMaUnOhZnj58FA7Qsyhixqu3dOSIYdNxzTYWkGEr24Qd7GDEC8xW06ouZ8NyZmw47SGKH8RNobNTXGZlqJSKCqFHLkStbp8dO3bzY/CIhVDP05gx/4Mxk+5jK2NGV6ejxTyEGMmXmD73nRuJxVz404R389MxMPNgre6+7FlZyrb9qYTEmDLnMU3KS5RUyPWkWVrkigsUhEeUm6c1Y4QcyNVy7ErL7cd5IVw7paWdDmciVtGcNXuRNTsRq3YEJp2n4TUzIJj+9fr2kpRfpSUwblDv+MX0YBqzQbh5B5MnbajcfWO5Pzh8vHo+LaZBFRqTP2OH+PqE4m9ix+BUc2wsnXWy0TV7YV3cE3snHxw86lMnbYfIJdCznPMpWTuPMi1r2aSvnnPs2d+Ah0aO7BuVy6nLhRxN6WMWb9n4GQvoVaM6W/Tnr1SzB9bczhxvsikTESgBXEnC7h0o4TMHBW7j+ZzJ0VBiL/FY/WJ27qces27UadpFzx8guk5+EtkMkuO799oVL7fqB9o2Lo3PgERuHsH0WfoJDRaDdcunHi6BnhAx2aOrNmRw8nzRdy9X8bPy9JwspdSu4qNyTxnLhezaks2J84VGj3v5WZGRJAl8//M4MZdBSkZSub/mYFMJqJhDdtn0u950WpfzPG/yP+McTZu3DimTJnChAkTuHz5MqtWrcLd3Z2ioiJat26No6Mjp06dYu3atezZs4cRI0YY5N+/fz83b95k//79LFu2jKVLl7J06VL9+QEDBpCcnMz+/ftZt24d8+bNIyPD9BvJyZMnAdizZw+pqals2GDcCPnkk09Yv349y5Yt48yZM4SEhNC6dWtycgw9UF988QXTpk3j9OnTSKVSBg4c+FTtUlZWxqVLl/AOqatPE4nFeAfXJSMpwWie9KRzBvIAPqENTMqrVWVcPbkGmYUtzp6GH08+d2ARyybVJvPeeZRlRWjUKr0OXiF1yTRRZkbSObyCDXXwDmlARrJxeYCy0gIQiZBZ2Bmknz+4iJXf1mHTnK7cu7wKrab8g9+uDmIcbCVcvlVuiJcotNy6pyTY18xkXc+DlbkIjVaLpYUIe2sx15LLp01Ly+BuuoYAD+NdUiIGHzfDPFrgWrKaAA+du83GUkSAhwQ7axFhPhK+GWTFiK6WBHiKDeQexkIGHk4i5IWGb6A3U7T4uOgMDgcbsLUUcSutXEChhPtZ6GV8XESUKLSkPnTb3krTotWCt7PhW3jvxhLGdJMwoKWE+rXKDQYvdwtsrKVcSsxnzNAQ/lpel19+rEp2joLoSoa/6eOwttZdp6+3FafP5QJw8WoezRq6ce5SHpUj7Gje0BWZTMzZC3J9Ph8XEbdTDUf6l9kOanUZOamXCalUF39XuJ+t6xc+YXW5fS0BMwn0bwoDmkH76uBkA2l3EvANrWdQjl9EfdLuJACg1Wi4cyUOB9cANv86iN++rMfamT25dcG04aRUFHPl5AaclOCgMin2ynF3luJoL+VcYrE+rbhUw/W7CsIDHm9EPYmrt0upGWWNk73uXokKtcTLVca5q8Um86hUSpJvXSYsuo4+TSwWExZdhzvXzz1VvWWKUjQqFVY29k8WfoC7sxlO9lLOXzVsh2t3SgkPev52+MfrplSW389aLahUWiKDTb8MCbwa/idizgoKCvj555+ZM2cO/fv3ByA4OJgGDRqwcOFCSktLWb58OdbWuretOXPm0LFjR3744Qfc3d0BcHR0ZM6cOUgkEiIiImjfvj179+5lyJAhXLt2je3bt3Py5Elq1qwJwG+//UalSpVM6uTq6gqAs7MzHh4eRmWKior45ZdfWLp0KW3btgVg4cKF7N69m99++42PP/5YL/vdd9/RuLEuruSzzz6jffv2lJaWYmHx+M6Zm5uLWq3G0sbZIN3S1hl55m2jeUoKs7C0cTGUt3GmpNAwPufulf3s+3MsKmUJVrautBv4GxbWjvrzleu9jYt3JBq1mu2LB3Ln0m7MrRyo3e4zfZmP08HCmA4FWUblVUoFp3dOIyimPTKL8rfJyLpv4+wVibmVPRl3z3Ji+zSUJdkEVh8JgJ2NzhjKLzKc0Mkv0mBv/eLeXaQS6N7ChhMXFFg8mDIoKDY0BAqKNdhZG59OsLYUIRGLjOTR4u6o09PZTpdXLBJx7qaKczfU1IyQMryLJSevKLGzKr+ejvVkNKwiQSYVodVqOXzJ8PqLSrXYWOjKs3lwixWVGOpUWKrVe4NsLKFIYXheq9V5e/6RKVPBrng1yZk6Y6WSn5jJX1Rm3HeXOHIyGydHGQDVqziyOy6djyddwNvTkvEfRVAtxpGnQSSCUUNCuHwtn8gwO3JydUb3lz9cZtInkbRo5IZWq6VqlAOff3+J+6mlVH6Q18YCCksNy3sZ7fAPnWvm86dWTb92bqTkwPEHM5NWti5k3rnN3vOQVQAyKVQLgu714NuCLAMP2D/yxQ/6RXFhNkpFMfH7FlKn7WjqdRhL0tVDbFs6ki7DluEdUkuf78KRVRzdMhVlWTEOboG8n/rfeiA42Om0ySswjP2UF6hwtHvM/P9TsGh9JsN6ubHom0BUat39+MsfGVy+WUqDxsbzFOXnotGosbU3bH9be2cyUoyPY4/y18oZ2Dm5Ev6QgfckHB4YkPJ8Q8s5L1+No93z/2L30srIyFbydicX5q1KR1GmoWMzR1wczXC0fzV3wv+q1+tF8F/qi8/NlStXUCgUNG/e3Oi5KlWq6A0zgPr166PRaEhMTNQbZ5UrV0YiKe/wnp6eXLhwQV+GVCqlevXq+vMRERH/Onbs5s2bKJVK6tevr08zMzOjVq1aXLlyxUA2JqY8cN7T0xPQTbX6+flVKFehUKBQ6J4Q/0zfqlUvPsDTK7g2XUduoLQ4l6un1rLnjw/p/P5qvSEY03AAAEX5Og9jZJ23uHB4MTVafYREKnthemjUSvb/+SFotdR74yuDc1ENBuj/7+QRTmSQN+/3q4lEagEimLkq74XpYYp6Mea808kOEeDhIuXAmZIn5nkeRA/ZdZfvqLmfpeH+4TLCfCV4uRg+zPadKePKfTEO1iL6NhNTI0zE2Zsvd6QsUcDxq+V1ONlp0Wjg+88royjT8MnXuv6WX1DGj3OvodFA4s1C3u7hh5/3073JfzQ0lCA/a76YfJHFM2vo0wf3DcTWWsregxkE+FkRd0QXczb8s4QXeo3PwraTOoM47pyGIb10BtiZW7pzSjVcvV8um5YLfRsb/sbG0Gp1ZQZWbkZs4wEAuHpXIvXOWS4e+9PAOAur1hHfsHoU5WdyNm4xy91vMzIFzF7TA9OrT0dW/hSk//u7X1MeI/3vaN/IgbAAC75fkEJmjorIYEuG9HAlJ+/luQ53b1rE2aPbGfHVYsxkpkMmPB3F/DE9RP/3t7/cNyn7b1Br4IcFKYx4y52V00JQq7Wcu1pM/MUieEU7XPyvxou9CP4njDNLy3/vgjUzM5zCEolEaDTPEx77cnhYP9GDEdqUfpMnT2bSpPLg3NDQUE7uWUjn4PKBuaQgGytbF2PZsbRxqeAlKynMruBNM5NZYe/ijz3+uPvFsnpqaxJPrye2ybsGchZWDojEEixtXdBqVBTm3sfeNZCSwmysbEzrUGpMh0d01qiV7PvjQwrlKbQdtMTAa2aMS3et6Ny5E5FNp2Np44X0QQ+wsxaTV1jennbWYpLS//1ALRFDjUgLMrJVLNiYT0kp+jptrUTkP+QJs7UScz/T+ArRohItao0WWyvDUfPhMv75V/OIXHquBl83CanZ5ddXVAo5BZBToKWgBDydxPi4aLj3oMmtLUR6L9I//1pbGnqWbCxEpOXq6iwsAetHnjciEVjKdOeMce2eluV3kunc3pNhnyQgM9N59jKyynj41haLwdxcglQqQqUyPZp/+F4I9Wo6MWLcOTKzFajUWpwczfDysKB7R2/eHn6KN7v6kpJWypI/71Klsj1d23txPqf8Om0ecUS/zHYoEzkgEkm4fDOLo4nQNBrO3oLigqwKfVOjhcx8cHRyobjAMPj8YXlLa0fEYilOHiEGMk5uwaTcjjdIM7e0xdzSFgfXADz8q/DbhRguWEE106FaL5X0LftY+EOy/u9/pt3sbSXk5pf3CwdbKbfvKSrkf1pkZiLe7ODMj4tSib+smyq8m1JGoI+MTs0dSDdRtLWdI2KxpELwf0FeNrYOzsYzPWDflqXs3byY98cvxNs//LGyGXkafllcsR0c7KQG7WBvJ/lX7QBwM1nBh5OTsLIQI5WKyC9U8+PHvtxI+nflCvx7/idizkJDQ7G0tGTv3orLtytVqsS5c+coKiofcY4cOYJYLCY8/PGd5B8iIiJQqVTEx5cPbomJicjlcpN5ZDKdZ0itNr0dQ3BwMDKZjCNHjujTlEolp06dIjIy8ql0M8a4cePIy8vTH1FRUbi4++jPazUaUm4ex80v1mh+d78qpNw8bpB278ZRk/L6crVa1KqKAfISqQwXr8okJ8YhEomxsHHS6+Bqokw3Izqk3DyKm2+5/D+GWX72XdoMXIyF1ZOnvrLSEklKvkdukRUZuWpSMtXIC9REBpUbvxYyEUE+ZtxM/nfeRokYhnW3x8VRwpSlcu6mqvV15hVpCPUt92aZm4G/u5g7acYNbrUG7mVoCPUpzyMCwnwl3EnT3WM5+VrkhRryi7UGcq4OYmwsRXq5R0nL0T7Qt9ygC/IUcS9Lly4vhIISLYHu5edlUvB2QS9zL0uLpbkIT6fycgPdRYhEcD/buEFVpgIXF3Mys8u4n1rK7aRiikvU+HpZ6j1EVpYS/H2syC9QPtEwa1TXhdFfnCc1vRSVSsu1GwVUj3HEwlzXFlqtbsr0UmL+gzbV8tAlcy9LS6CHofH7MttBIpHh5BlJ2u3jiECni1bDvevH8QiINZAVAS62EBIRy73rxwzOJV87qpeXSGW4+UUhzzCcZpNn3tFvo2EKLaB6jXuCqguLSMtS6o/ktDJy81TEhJWvoLS0EBHqb07indLHlPR4JBKdwfOo10ajKX/xNYZUaoZvUKRBML9Go+HaxeMEhFYxmW/v5sXsXP8rQ8f9gl9wZZNy/6DWQFqmUn8kp5aRk6ciJvzhdhATFmBB4q3nb4eHKS7VkF+oxtPVjGB/C06eN76I4EUjLAgwzf+EcWZhYcGnn37KJ598wvLly7l58ybHjx/nt99+o2/fvlhYWNC/f38uXrzI/v37GTlyJG+//bZ+SvNJhIeH06ZNG9577z1OnDhBfHw8gwcPfqzHzs3NDUtLS3bs2EF6ejp5eRWnz6ytrRk2bBgff/wxO3bs4PLlywwZMoTi4mIGDRr03O1hbm6OnZ2d/hg8eDDX4jdwLX4TuRk3Obx5EsqyEsKqdwFg/5pPObljuj5/VP1+JF87zPlDS5Bn3CJ+zxyy7l+ict03AVCWFXNq5wzSkxIoyL1P5v1LHFj3BcX56QRGtwZ0W2FcOLyM7NSr5Ock4+oTRcqNo7j6VqGkIIujf01C9ZAOB9Z+yumd5TpE1u3HveuHuXB4CfLMW5zZq9Mh8oEOGrWSfas+IDvlEo17/oRWo6a4IJPigky9gZiRdJZLR8p1uJmwhTtnZuMa0AqprHw10u4TJXRoaE1smAxvNwmDu9ghL9Bw5mr52+PYtx1oVrP89zY3E+HrLsXXXecGc3GU4OsuxclO16UkYni/hz0BXlIWbshHJBJhZy3GzlqMRAwHE5S0qiGjcqAET2cxb7WyIK9Iy4Vb5d669ztb0CCm3GiMS1BSt7IZNSOkuDuK6NHUHJlUxInL5Xn2n1FiZS6iXpQZzaqa0b2xDE9n3X5hJy6r8HcX82EPS3o3N8feGgLcRdhb6+LOvF3A2Q4aR4vxcoJTieWG4omrGhpGiQnzFuHmAJ3riSkohqvJupExKx9upGjoUFuClzP4ukLbmmIu3tXqPUYxgSIq+4twttPV06CyiPYtPFi/pXzaZv3f97G1kTJlfBT1ajgx/etoRCIRa/8ql5n5bQxd25cbGmOGhdCqiTuTpl6huESFk4MZTg5mrPv7Ph1bexIRaktqRgkzv43B2kpC/Llcenf2oVZVR6ytyycPTlzVEOwlok6E6KW2Q1SAiEg/ES52UL1Jf26dXUfWtY3sO36TfWsnoioroU/Prvi6QNzqT0nYPY1WsWBrCRF13ybp6mHOxi0mN/0WJ3bMJiP5EjEN+up1rNpkENcTtnPp2BrkmXc5f2gFty/vJ7q+ru/kZSdzes+vZCRfpCA3hdTbZ9i+bDRmWqhkOh7eJBJrK+yqRGBXRbcYyCrQB7sqEVj4ej57YY/w9wE53Vs7UjPKCj9PGaPecicnT83Jh1ZiThzuRduG5QH2FjIRAd4yArx1L8huzlICvGW4OOp+65JSLRevl9C/kzOVQyxxc5LStJYtjWvacuIJRkmT9v04tm89Jw9sJu3eLdYu+oYyRQm1m3QGYMWcz9myaqZefs/m39i6Zg59hn2Nk5s3+fIs8uVZKEqfraG37MulR1snakZb4+8l44P+HuTkqQxWYn49yod2jR3K28FcRKCPOYE+5g/awYxAH3N9OwDUq2pDVKgl7s5m1IqxZtIoH06eKyThynPcCM+BRvNijv9F/iemNQEmTJiAVCrlyy+/JCUlBU9PT4YOHYqVlRU7d+5k9OjR1KxZEysrK7p168b06dOfXOhDLFmyhMGDB9O4cWPc3d359ttvmTBhgkl5qVTKrFmz+Prrr/nyyy9p2LAhcXFxFeSmTJmCRqPh7bffpqCggBo1arBz5079ZrYvgnbt2rF2Xzbxe2ZRXJCFs2cl2r6zQD8VUiRPRSQqt9Pd/avSrPdPnN71M6d2zsDexZ+Wb83GySMMAJFIgjzzFtfObKK0KBcLKwdcfaLp+O4KnNxDAd0b/K3z2zizdy5qVRm2Tj74RjQlJ/Uqm+Z0wcmzEq0GLNBPlRblVdShSc+fiN/zM/G7ZmDn7E/zvrNxdNfpUJSfQdLVfQBsntPF4HrbDlqGZ1AtxBIZty5s4+y+Bzo4+uAV0ROviF4G8tuPFGNuJqJ/R1usLMRcT1IyfYUc1UOOJjcnCbYPBdQHeEn5dED5b9Sntc7YO5xQwuLNBTjYiqn6YCuOSUMfcqMAPyzNZe8ZJTIzEb2ammNpLuJWqppf/yoxqNPFXoyNRfnIc/a6CmtLEW1ry7CzFnE/U8Ovf5VQWFL+6njgnBKpVLePWsf6uodTeq6GP/YoKCzRTXe6OojxchFTIxwKSnQrEk9d01KnkpgmMbrpztUHNWQ+9D5x9LIWmVRLh9piLGSQlKFl5X416ocGxg1HNLStKebt5hLd5qvJWnacNhw5G0WLsbfWDajZ+fDVj5eJe2gj2F+X38bRwYzWTd2pV9MJRZmGtX/dY9mau3oZbw9LHOzKjdYu7bwBmDM51qCu72ZeZe7imwzs44+zo4ySUjUqlZZZ38dyP7WEu/eKUZSV63cvS3cNTauIaRb78tpBo4V6kWKc7UDUpgO/ecmZPWs28pxMXLwr0fHdhbi5uRDsAes0KTiaiZCZwdqjYONejVZvTeX49pkc2zoDB9cA2r0zB2fPMH35wTEtadJ9IvF7F3Bw43c4ugXSdsAsvIJ0MbMSqYzUW/GcO7gcRUk+VrbOeAXVYFQK2D7Hg86+ehR19/6u/zty6ucAJC/fwPlB4569wIfYuEeOuUzM0N5uWFuKuXKrlG9+SUH5kBfVw8UMO5tyT3GwnwXfjPLW/z2wq25x1r4T+cxZqYt/nb40jbc6OvNBP3dsrMRk5qpYtTWHnYfzeTfMwaQ+1eq1oTA/h21r5pIvz8InIIKh4+Zj56Abx3KzUxE95I49snsNapWSJdM/MiinTfdhtO1RcU9Gk+2wOxcLczHvv+mOtZWYKzdL+HrOfcN2cDVshxA/C7790Ff/96Duus259x3LY9bvun3WHO2lDOzuir2tlNw8FXEn8lmz3fiebQKvFpH2dWxXL/DKmbrh9b9eqF+/CiReMr7a81Vj5/T4zS5fBU5O/247ghfBrj+PPFnoJdOqd/0nC71kHB3+3erDF0FQ54gnC71kfhu29XWrwLsDKy6yetXMX3DndavApnlhTxb6l8zf+WLKGdr6xZTzX+J/xnMmICAgICAg8P8PgmvINP8TMWcCAgICAgICAv8rCJ4zAQEBAQEBgVeOsM+ZaQTjTEBAQEBAQOCV8+JC3l/jHjAvCcE4ExAQEBAQEHjlCDFnphFizgQEBAQEBAQE/kMInjMBAQEBAQGBV87/6gayLwLBOBMQEBAQEBB45QjTmqYRpjUFBAQEBAQEBP5DCJ4zAQEBAQEBgVeOsJWGaQTj7P8I3arcet0q4Cy/+bpVQJwV97pVACBp58nXrQIpk7a9bhUYG/D6P6e1vPD1D4NezuonC71k/gufThr0S/vXrQL7oxJetwqYW5m/bhVeCcK0pmmEaU0BAQEBAQEBgf8Qr/+VUUBAQEBAQOD/HNoXNq8pbEIrICAgICAgIPCvEWLOTCNMawoICAgICAgI/IcQPGcCAgICAgICrxxhQYBpBONMQEBAQEBA4JWjEeY1TSIYZwICAgICAgKvHMFzZhoh5kxAQEBAQEBA4D/E/6RxptVqeffdd3FyckIkEpGQkPBS6gkICGDmzJkvpWwBAQEBAYH/ZbTaF3M8D3PnziUgIAALCwtq167NyZOP3xh87dq1REREYGFhQXR0NNu2vdxNvP8npzV37NjB0qVLiYuLIygoCBcXF0QiERs3bqRz586vW73/BH9t+Zt169eTm5tLUGAg7w8bSnh4uFHZw0eOsHr1GlJSU1GpVHh7e9G1S1daNG+ml2nTzvjO3oMGDqRH925Gz63ZdZAVf+8jOy+fUD9vPu7fncoh/k/UfdfReL6Ys4zG1aOZOmaIPr24VMGcP/7iQPx58gqK8XJzolfrxnRr0cBkWWsSbrI8/jrZRaWEutrzSdMqRHk4mZQvKC1j7tHL7Lt+n3yFEk9bK8Y0iaFBoAcARWVKfjl6mf03UsgtVhDu5sDYJjFUfkyZAI5tO+HcuSdSBycUd26Sumg2pdcTTco7deiKY5s3MHNxQ12QR/7Rg2SsWIRWqdQJiMW49uqHfeMWSB2cUOVmI9+3k6y1K0yWeWDHn+z5ayn58iy8/cPoOXAcAaHRRmWP7FnHiQNbSEm+AYBfUCRv9Bmll1erlGz5cw6XzhwiK+Mella2hEfXplPfD3BwcjOpw+q9x1i24xDZeYWE+Xrwad+ORAX5GpX963A8Xy1eb5Amk0o5seBrAJQqNfM27ubw+UTuZeZgY2lB7cgQRnVvjZujnb6+37bPpSg/E1fvCJp2n4CHf4xJ/a6d3c7RrT+Tn3MfB9cAGr4xlsDKjfXnZ4wy3ocadvqYGs0HA5CefInDf00lPekCIpGEkNhW9B70MeYW1uXtu2sVB7YupiAvC0+/cDr3/wK/YON6pd27zs51c7h/+xK5WSm88dZnNGzbz0Dm+9EtyM1KqZC3bos+dH1ngsnr7d3OiZZ17bCyFHP1dikL1mSSmqk0KR8ZbEGn5o4E+5rjZC9lysJUTl4oMpCxkIl46w1nasfYYGMlJiNHxdYDcnYdyTdZ7pNwalCDoDGDsK8WhYWXG6e7vU/6X3ufu7yW1aXUqiTFUgZ30jRsPKwkO//xVkDdSAmNqkixtRSRmqNl85Ey7mWW5+na0IwQbzF2ViIUSribrmH7CSWZeeUywV5iWteQ4ubgi6JMy4H4Qv7cLkejgR6t7Wle2wZrSzGJtxUs2pBDWpbKpD6Vgszp2MSOQG8ZTvZSflqSwelLJRXkvN2kvNnekcggC8QSuJ+uZNqyzOdotWdH85rmNVevXs1HH33E/PnzqV27NjNnzqR169YkJibi5lZxfDp69Ch9+vRh8uTJdOjQgVWrVtG5c2fOnDlDVFTUS9Hxf9I4u3nzJp6entSrV+91q/Kf5MCBgyxcuJCRI0YQHhHOpk2b+GLCBBYtWICDg0MFeVtbW3r37oWvjw9SMzNOnjjJ9BkzcHCwp0b16gCsWvG7QZ7Tp+OZ8fPPNKhv/DfYdewMM1ds5LOBvYgK8eeP7QcYOWUe66aNx8ne1qTuKZnZ/LxqE1Ujgiucm/H7Rk5fvsbX7/fD09WJ4+ev8uOStbg42tO4ekUjY1fiPaYfvMDnzWOJ8nBi1ZkbjNhwhA0DWuJkZVFBXqnW8P6GwzhamfNjhzq42ViQWlCMrbmZXuab3We4mZXPN21q4mpjwbYrSQxbf5h1/VviZmNp9Jrs6jfB/Z2hpM6fScm1qzh37Ir/lz9wY8QA1HnyivINm+H29hBS5vxEydVLyLx88Br1CQDpS34BwKVLbxzbvEHKrB9QJN3BIiQcr5EfoykuImfrxgplxh/ZwYZlP9H73QkEhESzf+sK5nw3lK9+/gtbe+cK8tcunaZGg7YEhsViJjNn96bFzPl2KOOnb8DB2Z0yRSnJt67Qpvt7+PiHUVyUz9olP/DrD6P49Ic/jbbDzpPnmbZ6G1+83ZmoIB9W7T7K+9OXsOn7j3CyszGax8bSnI3ff6T/++GtKEvLlFy5m8KQjk0J8/Ukv7iEn1b9zQezfmfVV8P19TXt+Q0e/lU4c2AZG+YNYsD4HVjZVrzmlFtn2LZsDA06fkRg5aYkxm/hr0XD6fvxBly8wgB499vDBnnuXD7Irj++IKRKawAK89JZP/cdwqu2pWn3CZSVFhK34XtWz/+Cfh/MBCDh2Ha2rPyBbgO/wi84hkM7fmfRlHf5ZOpWbIz8FkpFKc5uPlSp3Zq/Vkwx2k6jvlmDRlP+iai0e9dZOHkwVWq3NioP0KWFA+0b2TNrZQYZ2Ur6tHdiwjAvRn+fhFJl/KFqLhNz576Cfcfz+XSwp1GZAV1ciA6zZObydDJylMRGWPFuD1dy80wbGk9CYm1F/vlEkpeup8a6uc9dDkDjKlLqR0lZE1dGToGWVjXMGNROxvS1ClQmvrIVEyShQ10zNh5SkpShoUG0lEHtzJm6upSiUp3MvUwNZ6+rkRdqsTSHljXMGNxexpQ/FGi14OkkYmBbGfvOqpi2JAMnewlDujkjFkF+kYa2DeyY92cWGTkqerZ24PMhboz5KQWliWYzl4m4m6Jk/8lCxg4w/kLk7ixl0nAP9p8sZO1OOSUKLT7uZiZ/3/8Vpk+fzpAhQ3jnnXcAmD9/Plu3bmXx4sV89tlnFeR//vln2rRpw8cffwzAN998w+7du5kzZw7z589/KTr+Z6c1161bR3R0NJaWljg7O9OiRQuKiopQq9V89NFHODg44OzszCeffEL//v31HrEBAwYwcuRIkpKSEIlEBAQEEBAQAECXLl30aU/i5s2bdOrUCXd3d2xsbKhZsyZ79uypIFdQUECfPn2wtrbG29ubuXMNB4akpCQ6deqEjY0NdnZ29OzZk/T0dACuXbuGSCTi6tWrBnlmzJhBcHC58XHx4kXatm2LjY0N7u7uvP3222RlPf83CTds3EibNm1o1aol/n5+jBwxAnNzC3bu2mVUvkpMDPXr1cPPzw8vT086d+5EYGAgly5d1ss4OTkZHMeOH6dKTAyensYH6FXb9tO5aT3eaFKHIB9Pxg3qiYW5jL8OHDept1qjYcLc5bzbrR1ebhUfUuev36Z9w1pUjwzFy9WZrs3rE+rnxeWbd42Wt+LMdbpEBfBG5QCCnO34vEVVLKQSNl80Lr/54h3ySpVM61iXWG9nvOytqe7jSpirAwClKjX7rqcwqmEU1Xxc8HWw4b26kfg62LDunOlvmzq/0R357m3k7dtJ2b27pM6fiUahwKF5G6PyVhGVKbl6kfxD+1BmplN0Lp78Q/uxDC332lhGVKbg5FEK40+gzEyn4NhBihJOYxEaYbTMvX8vp17zbtRt2hlP32B6vzsBmcySY/s2GZV/Z/QUGrXujW9gBB7egfQdOhGtVkPixRO6+q1tGfnlAqrXa427dyCBYVXoNehzkm5dJicz1WiZK3YepmujmnRqWJ1gb3e+6NcJC5mMTYfiTbYdiHCxt9Ufzg8Z9rZWFswfO5BWtWII8HQlJtiPz956gyt375OaLdfXV7lON5w9Q2jRcxJSmQUXj683WtPZA8sJqNSQGs0H4+wRTL32H+DmE0nCoXJvpLWdq8Fx88JefENr4+Ci8/7duhiHRCKlWY+vcHIPwsM/hha9JnHh1C6y0nT33cHtS6ndtAc1G3fF3SeErgO/wszcgpMHNhjVyzc4mg5vfkxs3XZIpTKjMjZ2Ttg5uOqPK2cP4OzuS1ClmiZbtkNjB9btyuXUhSLuppQx63edwVArxtpknrNXivljaw4nzheZlIkItCDuZAGXbpSQmaNi99F87qQoCPGv+EL0tGTuPMi1r2aSvrniGP2sNIiWsu+sist3NaTlaFmzvww7KxGVAyQm8zSMkXLyqprT19RkyLVsPKREqYKa4eX+j5NX1dxO05BbqCUlW8vOU0ocbMQ42uheKWKCJaRma9l7RkV6toortxSs2JpL6/q2tG9kx4Y9eZy+VEJSqpK5f2bhaCelZpSVSZ0SrpayeoecUxcresv+oXcbB85eLWHlVjl3UpSkZ6uIv1xCfqHmOVru2dFqXsyhUCjIz883OBQKhdE6y8rKiI+Pp0WLFvo0sVhMixYtOHbsmNE8x44dM5AHaN26tUn5F8F/0jhLTU2lT58+DBw4kCtXrhAXF0fXrl3RarVMmzaNpUuXsnjxYg4fPkxOTg4bN5Z7An7++We+/vprfHx8SE1N5dSpU5w6dQqAJUuW6NOeRGFhIe3atWPv3r2cPXuWNm3a0LFjR5KSkgzkfvrpJ6pUqcLZs2f57LPPGD16NLt37wZAo9HQqVMncnJyOHDgALt37+bWrVv06tULgLCwMGrUqMHKlSsNyly5ciVvvvkmAHK5nGbNmlG1alVOnz7Njh07SE9Pp2fPns/Vtkqlkus3blA1NlafJhaLqRoby5VHjERjaLVaziYkcO/ePaJNuHNzc3M5eeoUrVu1Mq6DSsXV28nUiio3JsRiMbWiwrlw/bbJuhdt2IGTnS2dmtY1ej4mNJCDZy6SkSNHq9Vy+tI1ktIyqR1d0SBRqjVcTZdTy6/8jVIsElHLz40LqTlGyz94K5UYTyd+2JdAy1+30nP5HhafvIr6wXJwtUaDWqvFXGo4iJtLJSSkZBu/KKkUi+Awis6dKU/Taik6fwar8EijWYqvXsIiOAyLB8aYmbsnNtVrURhfHjNRcvUS1jFVkXn56HQICMKqUjSFZyrGVaiUSpJvXSEipk55W4jFRMTU5ta1c8b1foSyslLUKhVWNvYmZUqKCxGJRFhaV/SMKlUqrtxNoXZkiIEOtSODOX8zqYK8vkxFGW0//pE2Y37gg1m/c/N++mP1LCguRSQSYSEzq1CfSCzGL7weqbfPGs2beicBvzDDe8+/UgNSbycYlS/Kz+L2pQNE1emuT1OryhBLzBCJy4deqZnOKLmdeAaVqoz7ty8TGmX4W4RG1eXudeP1PCsqVRlnDm+hZuOuiETGP3vj7izF0V7KucRifVpxqYbrdxWEBzy/EQVw9XYpNaOscbLX9ZOoUEu8XGWcu1r8hJwvH8tAH+ysRFy/X+4iK1VCcoYGPzfjj0uJGLxdRFy/V55HC9y4r8bP3XgeMynUCJeSna8hr0g3fkgloFIbeqyUSi0yMzEOthIuXC83skpKtdxIUhDq//wfSBeJoGolS1IzVXw+xI0FE334dpQHNSob9/C/DLRa7Qs5Jk+ejL29vcExefJko3VmZWWhVqtxd3c3SHd3dyctLc1onrS0tGeSfxH8J6c1Ux/ENnXt2hV/f10MUnS0blpq5syZjBs3jq5duwI6d+TOnTv1ee3t7bG1tUUikeDh4WFQroODQ4U0U1SpUoUqVaro//7mm2/YuHEjf/31FyNGjNCn169fX+8GDQsL48iRI8yYMYOWLVuyd+9eLly4wO3bt/H11b05L1++nMqVK3Pq1Clq1qxJ3759mTNnDt988w2g86bFx8ezYoXubXzOnDlUrVqV77//Xl/n4sWL8fX15dq1a4SFhT3V9fxDfn4+Go0GB0eHCm2TnJxsMl9RURF93+6HUqlELBYzYvj7VKtW1ajsnj17sbS0pL6JKU15QRFqjabC9KWTvS13Uow/XBOu3uSvuGOs/P5Tkzp+PKAb3y9aTfsRXyKRiBGLRHwxuA/VKoVUkJWXKFBrtThbGQ5uzlbm3MktMFr+vbwiUpMzaRvhy6zO9UiWFzFlXwIqtZZ361bCWmZGjKcTi05cJdDJFicrC3YmJnMhNRtfB+PTclJbe0QSCaq8XIN0lTwXc2/jsVb5h/YhtbMn8LufQSRCJJWSs+Mvstav0stkbfgDsZUVwbOXgEYDYjEZKxeTf7BiHE5hQS4ajbrC9KWtvTNp900byw+zacUM7J1ciYiuY/S8skzBphUzqF6/LZZWFdsit6BYd088Mn3pbGfDnVTj8S/+Hq589U5Xwnw9KCgp5fcdhxnw/XzWffMB7k4VjUSFUsmsdTtoUzsGpUqtr+/hu97K1pncdONezqL8LKzsXAzSrG2dKS4w7sW+fHIjZhbWhFQpf0nxDavDwY1TOL13EVUb90NZVsKhv6YBUCDPpKhAjkajxsbesB4bO2cyUkx7X5+FS6f3UlpcQI1GXUzKONjpHg15BYbzePICFY52pj1IT8Oi9ZkM6+XGom8CUam1aLXwyx8ZXL5ZivHXrleHhYcrAIXFhkZSYYkWWxNOKisLkIhFFD7ioCoo0eLqYGic1YmU0K62GeZmIjLkGhZtLUP9wEl1LVlDgygpVYIl3L0ODrYSurUsv4/zCgy9WXmFahxsn/+3sLMRY2khplMzO1Zvl7Nyay6x4ZaM6e/K1/Mf/5LzX2PcuHF89NFHBmnm5s9vuP4X+E8aZ1WqVKF58+ZER0fTunVrWrVqRffu3RGLxaSmplK7dm29rFQqpUaNGmhfcGBhYWEhEydOZOvWrXpjsaSkpILnrG7duhX+/mcF55UrV/D19dUbZgCRkZE4ODhw5coVatasSe/evRk7dizHjx+nTp06rFy5kmrVqhERofP2nDt3jv3792NjU/GBdvPmTaPGmUKhqODSVSgU/+pmtbS0ZN6c2ZSUlJBw7hwLFi7Cw8ODKjEVg5R37t5Ns6ZNkMmMT7E8K0UlpXz1y+98PrgPDiZijwBW7zzIhRt3mDZmCJ6uTpy9cpMfl+pizmpHGw/Ufha0WnC0MueLFtWQiEVUcncko7CE5aev8W7dSgB83aYGX+86Q5uF25GIRES4OdA63JcrGfJ/Xf8/WFWugku3N0ldMIuSa1eQeXrhMWg4qh5v6QP+7eo3wb5Rc+7P+F4XcxYYjPug4ahys8nbb3z6+nnZtfE34o/s4INJizGTVbzH1Colv00fC2jpPWT8C6u3SogfVUL8yv8O9qfb+BmsizvJ8K4tDWSVKjWf/PIHWi18/nYnikvLXpgeprh0fD2VanREalbeJi6eobR+awoHNk7h8JbpiEViYhu/jY29s4E37WVyMm4D4VUaYu9Y7jX2dhKx8qcg/d/f/Vpx8cCLon0jB8ICLPh+QQqZOSoigy0Z0sOVnH8Rc/a8ePXpSPS8Sfq/T73x3kutL+G6muv3NNhZiWhURUrfFjJ++UsXy3b9voZtJ1R0b2xGn2a6+9pUjNuLQPzAa3r6YgnbDuleSO+mKAkLMKdlXdNxvy8SzQuaPTU3N3/q55uLiwsSiUQfXvQP6enpJp03Hh4ezyT/IvhPGmcSiYTdu3dz9OhRdu3axezZs/niiy/004WvgrFjx7J7926mTp1KSEgIlpaWdO/enbKyFzuoe3h40KxZM1atWkWdOnVYtWoVw4YN058vLCykY8eO/PDDDxXymornmjx5MpMmTTJIGzVyJB+MHoWdnR1isRh5rtzgvFwux9HJ0aSeYrEYLy8vAIKDg0lKSmb1mrUVjLOLFy9y7949Pv/MtIfLwdYaiVhMTp6hhyonrwBnh4qDwr30LFIycxgzdYE+7Z9VPnXe+oB1077AxdGeeav/5qePBtOgamUAQv28uXb3Hiu27q1gnDlYmiMRicguNjRis4sVuBhZDADgYm2BVCxCIi6fCgp0siW7WIFSrcFMIsbXwYaFPRtRolRRqFDiamPJZ1tP4G1v/LVbVZCHVq1Gam/Y9lIHR1Ry49Orbm++g/zAbuR7dEu5FUm3EVtY4jnsQ7LWrQStFvf+75K14U/yD+/Xy5i5uuPStU8F48zG1hGxWEJBnuHUa0FeNnYOhh6cR9nz11J2bVrMyC8X4O1f8UVBZ5h9TE5WKqO+WmTUawbgaGuluyfyCw3Ss/MLDeLIHoeZVEK4nxfJGYbXoVSp+fSXP0jNkrPgk8HYWFpgbiYtr++h8bW4IBsrW+PXbG3nQnG+oZesyIT8vZunyc24Tft3ZlY4F1GjIxE1OlKUn4WZuSUiRJzZvxQnNx+sbR0QiyUU5hnWU5ifja3943+LpyE38z7XLx6j3wc/G6SnybX8tuKe/m8zqe4et7eVkJtfbiE42Eq5fc94LM/TIDMT8WYHZ35clEr8Zd005t2UMgJ9ZHRq7sDLMwmNk75lH/KT5VP3YnPdC6WNlYiCkvIXfhtLESnZxh0AxaWg1mh5dL2PraWIgkc8cKVKKFVqyc7XkpRRxsT+FlQOkHDupq6ND11QceKqCgrlFJdqcbYX8/m7uhvU3laM/CFPpr2NhDspz/88yi9So1JruZ9uuPr2foaSiIBX43V60U6Vp0Emk1G9enX27t2rj1XXaDTs3bvXYFbsYerWrcvevXv54IMP9Gm7d++u4Jx5kfwnY84ARCIR9evXZ9KkSZw9exaZTMbevXvx9PTkxIkTejmVSkV8/OMChnWYmZmhVj/9a8iRI0cYMGAAXbp0ITo6Gg8PD+7cuVNB7vjx4xX+rlRJ50WpVKkSycnJBtOFly9fRi6XExlZHk/Ut29fVq9ezbFjx7h16xa9e/fWn6tWrRqXLl0iICCAkJAQg8Pa2nhg7rhx48jLyzM4hg19T98OoSEhJJxL0MtrNBoSEhKoFGE8WNwYWq0WpbLikvodu3YRGhJCUFCQkVw6zKRSIgJ9OXXpmoEOpy4lEh0aWEE+wMudP374jBWTP9EfjapFUT0ylBWTP8Hd2RGVSo1Kra4QQyMWi40OAGYSMRHuDpxKzijXQavlVHIG0Z7Gt72o4uVEcl6RwfLvu7mFuFhbYCYx7EqWZlJcbSzJLy3j2N0MmgR5GW8MlYrSm9ewjnloilgkwjq6KsWJl41mEZmbwyOfPdH+c28/uH6RuUWF11Ltg+nNR5GameEbVInEC+X9SqPRkHjhBEFhVSrI/8PuzYvZvm4Bw7+Yh39w5Qrn/zHMMtLuMnLCAmxsHUyWZSaVUsnfixNXbhjocPLKTWKC/UzmM6hPo+HGvTRcHjLw/zHMkjKymD92IA42Vibr02o0JCcewzPQ+HS9Z0AsSdcM+3vS1aN4BsZWkL10bB1uvpVx9Tbdp6ztXJCZW5N4ZhtSmTlhUfWQSmV4B0Zy41J5PRqNhhsXj+MfWrGeZ+XUwY3Y2DtRqWpjg3S1BtKylPojOa2M3DwVMWHlLxWWFiJC/c1JvFP63PVLJDrD79Gv9mg0mIx/e5moC4sovpmkPwov3yC/WEuIV/l0obkZ+LqJScow7uZRa+B+lpYQ7/I8IiDES0JS+hNcQyJdrNnDlCnh2t0y7qUrCQu0ICtXRW6+iujQ8pdGS3MRIX7mXL/7/IayWg03kxV4uhn6aDxdzMjMfYkuu/8AH330EQsXLmTZsmVcuXKFYcOGUVRUpF+92a9fP8aNG6eXHz16NDt27GDatGlcvXqViRMncvr0aZPG3IvgP+k5O3HiBHv37qVVq1a4ublx4sQJMjMzqVSpEqNHj2bKlCmEhoYSERHB9OnTkcvlTywzICCAvXv3Ur9+fczNzXF0NO0lAggNDWXDhg107NgRkUjEhAkT0BjxwR45coQff/yRzp07s3v3btauXcvWrVsBaNGiBdHR0fTt25eZM2eiUql4//33ady4MTVq1NCX0bVrV4YNG8awYcNo2rSp3kMFMHz4cBYuXEifPn345JNPcHJy4saNG/z5558sWrQIiaRizIExF2/2Q3937dKFqdOnExoaSnhYGBs3b6ZUUUqrlrqpoJ+mTsPZ2ZmB7wwA4M/VawgLDcXT0wOlUsmp06fZu28fI4YPN6ijqLiYQ4cO8+7gwY9tW4A32zVl0vwVVArypXKwP39sj6OktIyOjXVT1l/N+x1XJ3tG9H4Dc5kZIb6Gxo2Nte419Z90M6mUapVCmLVqMxYyMzxcnDhz5QbbDp3ig7c6G9XhrWqhfLXzNJXcHInycGTV2RuUKNW8UVkX5/jljtO42lgwsoFu4UP3KkGsOXeLqXHn6BUbTFJuIUtOJdI7tnxl7dE76YAWf0dbkuWF/HzoIgGONnSsbHr/tuy/1uE16lNKbl6j5PpVnDt0Q2xhgXyvLpbSa9SnqHKyyFjxGwCFp47h9EZ3Sm/feDCt6Y3bm+9QcOqY3iArPHUMl+59UWZl6KY1g0J0q0L37jCqQ/MO/Vg+dzx+wZEEhESzb+sKFIoS6jTVtd2y2Z/j4OROp76jAdi1aTFbV89lwOgpOLl6k5er8/SYW1hhYWmFWqVk4bQxJN++wrDP5qDRaPQy1jb2SM3MKujwVusGfLloHZEBPkQF+rBq9xFKFGV0alANgPEL1+LmaMeo7rrtH379ay8xQX74ujlTUFLCsu2HSM2W06Whrm8pVWo+nreKq3dT+Hl0PzRaLVkPvLX21pb6+pp4bMTDP4azcctQlpVQubYunnXH759gY+9OgzfGAFC1cT/Wznqb+H2LCazcmMT4baQnX6RF768NrkNRUsi1hB006mzce5xwcAWegVWRmVtx9+pRDm3+kfa9P8TSWrf3WqO2A1j96zh8AqPwDY7m0I7llClKqNlYFyP2xy+fYe/oRrveuhgblaqM9Hs3AZ1BnJebzv07VzC3sMLFo/y+02g0nDqwkRoNOyORPHno//uAnO6tHUnNLCM9W0Wf9k7k5Kk5+dBKzInDvThxvojth/IA3R5mHq7lv62bs5QAbxmFxRqyclWUlGq5eL2E/p2cKVNqycxRUjnEksY1bVm6KQufJ2plHIm1FdYPTXFbBfpgVyWCspw8SpONrw42xeELKppVk5KVryE3X0urmmbkF2u5dKfcYBnSXsbFO2qOXXrg8TqvomcTM+5lariXqdtKw8wMTl/TTdU62YqICZZw/Z6aohKwtxHRJFaKUgVXk8rLbRQj5VqyGpW7GbWirejc1J4Zv2fi5WZGl+b2pGaqyMhR0auNA7n5Kk5dLF9EMf49N05dLGHnEd09bi4T4eFS/ju7OUnx9zKjsFhDtlxX55a4fD54y5UrtxRculFKbIQl1SMtmfRLOg2qmV6V+6J4XZ/W7NWrF5mZmXz55ZekpaURGxvLjh079EH/SUlJiB96ka1Xrx6rVq1i/PjxfP7554SGhrJp06aXtscZ/EeNMzs7Ow4ePMjMmTPJz8/H39+fadOm0bZtW1q2bElqair9+/dHLBYzcOBAunTpQl5e3mPLnDZtmt5a9vb2NuoFe5jp06czcOBA6tWrh4uLC59++in5+RU3SRwzZgynT59m0qRJ2NnZMX36dFq31j08RCIRmzdvZuTIkTRq1AixWEybNm2YPXu2QRm2trZ07NiRNWvWsHjxYoNzXl5eHDlyhE8//ZRWrVqhUCjw9/enTZs2BjfPs9C4cSPy8vP4/fcVuk1og4L49uuv9QZrRmYmooem7kpLS5kzbx5ZWVnIZDJ8fX34ZOxYGjduZFDugQMHAGjSxPCt3Bit6lZDnl/Ir+u2kS3PJ8zfh1mfDcPZXveASsvONdDhafhu5ADm/rmFCXOXk19YjIeLI8N6tje5CW2rcB9ySxTMP3aZ7GIFYa72zO5SH2dr3RtqWkExD7/Me9haMadLfaYdOE/v3/fiamNJn6rB9K9RPmVaqFAy58glMgpLsDM3o3moN+/Xr1zBs/Yw+UfikNjZ49p7AFJHRxS3b5L09WeoHywSMHN1M9gGO3PtCrRaLW5vvoPUyQV1vpyC08f1xhtA2sLZuL75Dh7vjkZq74AqN5vcXX+Tueb3CvUDVK/fhoL8XP5ePY8CeRbeAeEM/+IX7Bx0iwRys9IQicqv4dCuNahUShZNG2NQTrseQ2nf833kORlcOB0HwOSPexjIjJ74G2GVK27h0LpWDLkFRfyyaQ/ZeQWE+3oy98N39NOaaTlyxA/dEwVFpXy9bCPZeQXYWVlSKcCbpZ8PJdhbN8BmyvM5kHAFgN4TDfvcwk8G6+ubt20WxfmZuPpUosuwRVg/CPovyE01uGavoGq07T+Vo1tncmTLdBzcAnhj8Fz9Hmf/kHhmK2i1RFTvYLSt0+6e59i22SgVRTi6B9G81yQatOmoPx9bty1FBTnsXDebgrwsvPwjGPzpr/ppTXm2oV75uZnM/KJ8o+cDW5dwYOsSgirVZNj4Zfr06xePIc9OpWbjrkb1epSNe+SYy8QM7e2GtaWYK7dK+eaXFIM9sDxczLCzKX9BDPaz4JtR3vq/B3bVBdjvO5HPnJU6L/X0pWm81dGZD/q5Y2MlJjNXxaqtOew8nM+gp9KsIvbVo6i7t/zejpz6OQDJyzdwftA4U9mMcuCcCpkUujWUYfFgE9rF28sM4r+c7ERYW5Tfi+dvqbG2hFY1pNha6aZAF29T6BcJKNVaAj3ENIiSYmmuW2BwO1XDvM0K/T5oAOG+YppVlSIRe3A3RclPSzNIuFoKl0owl4l4t7szVpZiEm+XMnlhhsEeZ+7OZthal3vSgn1lfDWsfM6+fyfdjEDcqUJ+Wa2b+j91sYSF67Pp3Myedzo7kpKhYvryTBLvPL9H7lnQvsYPn48YMcKk5ysuLq5CWo8ePejRo0dF4ZeESPs6Jn1fMAMGDEAul7Np06bXrcp/lts3bzxZ6CXjLL/5ulVAfCrudasAQNLOx38q5FWQMunlfn7kaahX8PfrVoHlhca/YPEq8XJ+/dNIS5c/3crcl8mgX4x/aeRVcnBuwutWgTvXMp4s9JJZPfXJX2v5t3z+24sxAr8f9P/3ykxj/GdjzgQEBAQEBAQE/i/yf9Y4q1y5MjY2NkaPRzeFFRAQEBAQEHixaDTaF3L8L/KfjDl7VpYuXfrMebZt22Z0tSFQYSdgAQEBAQEBgRfL/0BU1Uvjf8I4ex7++fKAgICAgICAgMB/if+zxpmAgICAgIDA60P7ar6v/v8lgnEmICAgICAg8MrRCNOaJvk/uyBAQEBAQEBAQOC/iOA5ExAQEBAQEHjlCAsCTCMYZwICAgICAgKvnP/VbTBeBMK0poCAgICAgIDAfwjBc/Z/hBuFvq9bBRQOFq9bBUqbV3vdKgDgVz/xdavAtusVPz7+qvlmhefrVoG3h79uDSDY4fV/rufdgX5PFnrJ7I9KeN0q0Gh47OtWAbe1V1+3Cq8EYVbTNIJxJiAgICAgIPDKeZ0fPv+vIxhnAgICAgICAq8cYSsN0wgxZwICAgICAgIC/yEEz5mAgICAgIDAK0eY1jSNYJwJCAgICAgIvHIE48w0wrSmgICAgICAgMB/CMFzJiAgICAgIPDKERxnphGMMwEBAQEBAYFXjjCtaZpXOq2p1Wp59913cXJyQiQSkZCQ8MxlTJw4kdjY2Beum4CAgICAgIDAf4FX6jnbsWMHS5cuJS4ujqCgIFxcXBCJRGzcuJHOnTu/SlX+v2XAgAHI5XI2bdr0r8o5sONP9m5ZSr48C2//MHoMHEdASLRR2SN71nHy4BZSkm8A4BcUScc+owzkE07s4fDutSTdukxxYR6f/bgGn4CIJ+qxdcsmNq1fQ25uDgGBwbw7bCRh4cbzHTtyiLWrV5GWeh+VSo2XtzeduvSgafOWepk/Vizj0MH9ZGVmIjWTEhwSxlv9BhIeUemp22bn3+vZsuEP5Lk5+AcG8857HxISHmlU9sTRA2xas5y01PuoVSo8vHzo0KU3jZq1eer6ANZt38fKv3aQI88jxN+Xjwa9SeXQoCfm2334BF/OXECjmrH88OlIffo3c35jW9xRA9nasVHMHP+hvr4/1k+gpDALJ48I6r3xBW6+MSbruXVhB6d3z6Iw9z52zv7UajMGv4jG+vPxe+Zw8/w2iuRpiCVmuHhHUrPVB7j5VdHL/PFDcwrlKQbl2rh1x8a1vUHawN6+dGjpjo2VhAtXC5i+4Bb3U0tN6ta3qzeN6jjj522JokzDxav5/Pr7XZJTDPNUDrNhcF9/KoXaoNFouXG7iPdGzebX8eMpys/EzSeC5j0n4Blguh0Sz2zn8Jafycu+j6NbAI07jyUoqrwdivKzOLBpKneuHEZRXIBPaA1a9JyAo1uAXqYwL5MDG3/kztWjKEuLcHQPhLd7Ubd+eTnb/97IpvV/Is/NISAwhMFDRxEabvwePn7kIOvXrCA19T5qlRpPL2/e6NqLJs1aAaBSqVi1/DfOnD5OeloqVtbWxMRW5+0B7+Lk7GLyWh/l0M4/2KcfM8Lp9s44/E2MGUf3ruPUwS2kJl8HwDcwkg59RpuUf5iW1aXUqiTFUgZ30jRsPKwkO//xHpa6kRIaVZFiaykiNUfL5iNl3Mssz9O1oRkh3mLsrEQolHA3XcP2E0oy88plgr3EtK4hxbvPGVRFxdz/fROJE2agVaufpnlwalCDoDGDsK8WhYWXG6e7vU/6X3ufKu+jXDq6knMHf6OkIAsnzwjqdxr/+P55fgendv2s658u/tRuO9agfz7MoQ1fceXEaup2GEd0w/769B1Lh5GdcpXSomxklvakHanL2LFjcXd3f65reBqED5+b5pV6zm7evImnpyf16tXDw8MDqVSYVX0dxB/dwcblP9G2+1A+/WE13v7hzP1uKAV52Ublr18+TfX6bRn91W+M+XYFDs4ezP12KPKcdL1MmaKE4IiqdO77wVPrcejAfhYvnE+vN/sxffZ8AoOCmTjhU+TyXKPyNra29Ojdlx+mzebneQtp3qI1s2b8yJn4U3oZL28f3h02klnzFjLlp59xc3Nn4vhPycuTP5VORw/uZfmiOXTr8w5Tfv4N/8AQvv/yI/JM6WRjS5ee/fhm6nx+nLOMJi3a8cvMySTEn3jqdthz5CSzlq1mUI83WPrjV4QG+PLhtzPIyct/bL7UjCxmL19LbKVQo+frxEbx98Lp+uPrD941qK9a8+F0GbEeZ89wti8eQkmh8d8//e5Z9v05lvAa3egycgMBkc3ZvWIkOWnX9DL2LgHUf2M83T7YTMehK7B19Gbb4sGUFOYYlFW9xUj6fn5Qf1g5tzA436eLN13bezJt/k2GfnaBUoWGqRMikZmJTLZDlcp2bNyeyrDPzjNm0iWkUjFTv6qMhXn58FY5zIYfJ0RyKkHO0E/P894n55k5Zy259/+gXvvh9Bu3EVfvCNbOHkRRgfF2uH/zDFsWjyG6Xnf6j9tEaJXmbPx1OJkpunbQarVs/HU4eVnJdHlvHv0/34idkzdrZr1DmaJYX862ZZ+Sk36brkN/YcD4LYTFtmTalEncuqkzZA4f3MeShfPo+eYAps5aSEBgMF9P+Pix/aJbr7eZMnUeM+b+RrOWbZkzYwpn408CoFCUcuvmNXr06cfUWQv45IuvSbmXzOSvPzfZpo9y5sGY0brbUD6esgYv/zB++f49k2PGjUunqFavLSO+XMyH3+jGjF++e89gzDBG4ypS6kdJ2XiojDmbFJSpYFA7GVKJ6TwxQRI61DVjb7yKWRsUpGZrGNTOHOuHvhZ3L1PD2jgl09Yo+G2bApEIBreXIXpwW3k6iRjYVkbiPQ2Hanbm7Jsf4t6hGRHfj3nqNpJYW5F/PpGLoyY9dR5jnLWGY39PoXrz4XQdtQFnz3C2/TbYZP9Mu3OGvX+MIaJmd7qO2khAZAt2LR9h0D//4fbF3WQkncPKzq3COa/g2rToO4OeY7fT8q2fSU5OZvTo0f/qWp6ERqN9Icf/Is9snK1bt47o6GgsLS1xdnamRYsWFBUVoVar+eijj3BwcMDZ2ZlPPvmE/v376z1iAwYMYOTIkSQlJSESiQgICCAgIACALl266NOell9//RVfX1+srKzo2bMneXl5+nOnTp2iZcuWuLi4YG9vT+PGjTlz5oz+vFarZeLEifj5+WFubo6XlxejRo3Sn1coFIwdOxZvb2+sra2pXbs2cXFx+vNLly7FwcGBv//+m/DwcKysrOjevTvFxcUsW7aMgIAAHB0dGTVqFOqH3rqettydO3dSqVIlbGxsaNOmDampqYBuSnfZsmVs3rwZkUiESCQyyP+07Pt7OfWad6Nu0854+gTTe8gEZDJLju3fZFR+wKgpNGrdG5+ACDy8A+k7dCJarYbEC+UGSK1GHWnbfSjh0XWeWo/NG9fRqk07WrRqg59fAMNGfIC5uTl7du0wKh8dE0vdeg3w9fPH09OLjp27ERAYxJVLF/UyjZs2J7ZqdTw8vfDzD2DQu8MoLi7izu1bT6XT1k1/0rx1R5q2bI+PXyCDh3+MzNyC/bv/NipfOaYateo1xsc3AA9Pb9p16olfYDCJl88/dTv8sWUXb7RoRIdmDQj09eKTd9/G3FzG3/sOm8yjVmv46ueFDO7VCS93V6MyMjMpzo72+sPOxtqgvvAaXXF0D6FB54lIZRYknt5gtJyLR5bjE9qAKo0G4egWTI1Wo3HxqsSlY6v0MiGxHfAOqYedky9O7qHUaf8ZSkUhOWmG3xA1M7fGytZVf4jF5gbne3Tw5Pd19zhyKpdbd4v5ftZ1nJ1kNKjlZLItPvnmCjv2Z3InuYSbd4qZPPs6Hq7mhAXb6GWGDwxk/bZUVm28z53kEpJTSjl1bD1Wjo2IrtsNF88QWvWZhJnMgotH1xutJ37/cgIjG1Kr5WCcPYNp0PED3H0jORu3AoDcjDuk3k6gZe+JeAbE4OQeRKveE1GVlXL19FZ9OSm3z1KtyVt4BsTg4OJL3bbvY2Vtw80burbasnEtLdu0p3nLtvj6BfDeiI8wt7Bg365tRvWKiqlKnXoN8fHzx8PTmw6duuMfGMyVyxcAsLa2YeJ306jfsCnePn6ER1Rm8LDR3LxxjcyMxxtL/xC3VTdm1GnaBQ+fYHoO/hKZzJLj+zcale836gcaPhgz3L2D6DN0EhqthmsXHv/S0iBayr6zKi7f1ZCWo2XN/jLsrERUDjBtnTWMkXLyqprT19RkyLVsPKREqYKa4eUv/yevqrmdpiG3UEtKtpadp5Q42IhxtNFZZzHBElKztew9o6L4ZhI5h05xZdxP+A/ri+RBv3kSmTsPcu2rmaRv3vNU8qY4YA8RtXoQXrMbju4hNOwyCamZBYmnjN+XF4/8jm9YA6o0HoSjezA1W4/GxSuSS0dXGsgV5aVzdPO3NO39E2JJRcdITMMBuPvHYuvojUdANYYMGUJCQgJKpfJfXY/A8/FMxllqaip9+vRh4MCBXLlyhbi4OLp27YpWq2XatGksXbqUxYsXc/jwYXJycti4sbzj/vzzz3z99df4+PiQmprKqVOnOHVK5/FYsmSJPu1puHHjBmvWrGHLli3s2LGDs2fP8v777+vPFxQU0L9/fw4fPszx48cJDQ2lXbt2FBQUALB+/XpmzJjBr7/+yvXr19m0aRPR0eXu9hEjRnDs2DH+/PNPzp8/T48ePWjTpg3Xr1/XyxQXFzNr1iz+/PNPduzYQVxcHF26dGHbtm1s27aN33//nV9//ZV169Y9c7lTp07l999/5+DBgyQlJTF27FgAxo4dS8+ePfUGW2pqKvXq1XuWnxCVSknyrSsGRpRYLCY8uja3r517qjLKFKWoVSqsbOyfqe6HUSqV3LxxjSqx5R8iF4vFVImtRuLVy0/Mr9VqOZdwhvv37lE5yvhUiVKpZOf2rVhbWxMYGPzEMlVKJbduXCM6toaBTtGxNbh+9dJT6XQh4TSp95KoFBX7RHmdjioSb92lZkz5lJVYLKZmdCQXE2+azLd43V842tvyRvOGJmXOXEqk3cAP6DXqc35c8Dt5BYVG6xOJxXgH1yUjKcFoOelJ5/AOqWuQ5hPawKS8WlXG1ZNrkFnY4uxpOEV97sAiln9dhw2zunLu4G9oteUvL57u5jg7yog/J9enFRWruXK9gMrhtiav81FsrHQPnoJCFQAO9mZUDrNFnqdk7vdRbFxcg2lfhqEqvYPMurI+n0gsxj+iHim3zxotN+V2Av4Rhu0QENmAlNsJ+usGkJiVG5wisRiJVMa9m/H6NK/AqlyN305JkRytRsOV01tRlpURFR37oF8kEhNbXS8vFouJia3+1P3ifEI8KfeSiYyqYlKuuKgQkUiEtY2NSZl/0I0Zlwl7ZMwIi67DnetPP2ZonjBmWMrAzkrE9fvl90SpEpIzNPi5GX9UScTg7SLi+r3yPFrgxn01fu7G85hJoUa4lOx8DXlFOq+LVAIqtaEHRl1SisTSAvtqlY0V81JQAffMwSe0fFwXicV4h9Ql3VT/vJuAd4jhc8AnrL6BvFajYf/qT4hpPAgnD+Oe9ocpLZazZcsWqlatipmZ2fNcylOh1WpfyPG/yDPNK6ampqJSqejatSv+/v4AeqNm5syZjBs3jq5duwIwf/58du7cqc9rb2+Pra0tEokEDw8Pg3IdHBwqpD2O0tJSli9fjre3NwCzZ8+mffv2TJs2DQ8PD5o1a2Ygv2DBAhwcHDhw4AAdOnQgKSkJDw8PWrRogZmZGX5+ftSqVQuApKQklixZQlJSEl5eXoDOKNqxYwdLlizh+++/B3QP/l9++YXgYN1Dv3v37vz++++kp6djY2NDZGQkTZs2Zf/+/fTq1euZyp0/f76+3BEjRvD1118DYGNjg6WlJQqF4rHtpVAoUCgUBmllZSCTmVOYn4tGo8bWwdngvJ2DM+kpt5+q/TevnIG9kysRz+Ale5T8/Dw0Gg0Ojo4G6Q4OjtxLTjaZr6iokIFv90KpVCIWixk6fDSx1WoYyJw6cYypP3yLQqHA0cmJSd/9iJ39kw1JnU5q7B0MvTT2Dk6k3LtrMl9xUSFD+3dBpSxDLJYwaNhHxFSt+cT6AOQFBag1Gpzs7QzSnRzsuHs/1Wiec1eus2XvYZZP/cpkuXVio2hSuzqebi7cT89g/qoNfPjdTCaPeV9f38Omn6WtM/JM479/SWEWljaGsUmWNs6UFGYZpN29sp99f45FpSzBytaVdgN/w8K6/PetXO9tXLwjMbe0Jz3pLKd2zEBqXQc7jz4PrlkGQE6e4Zt6rlyJk6PM5LU+jEgEIwYGcP5KPreTdFOJXu46Y2lAL19+WXaXG7eLqFNVhFarwd3d8LqsbJ3JSTfuZS3Kz8La1lDe2taZonxdOzh5BGHn5MWhzdNo9ebXmMksOb1vKQXyNIryMvV53hg8ky2/fcicj2sjFkuRyiz4bPw3eHr5kJOdpesXj9yDDg6O3E9OMnndRUWFDOnXXd8v3n3/Q2Kr1jAqW1am4PclC2jQuDlWVk/2ChX9M2bYG44ZtvbOZDzlmPHXyhnYObk+1rNuLtV5sQqLDR+2hSVabK2M57GyAIlYRGGJYXpBiRZXB0PjrE6khHa1zTA3E5Eh17Boaxlqje7ctWQNDaKkVAmWgFiMuYcLoeOHA2Dhadwz/TIokoBGpOtfD2Np6/L4/mlbUb6koLx/JhxYiEgsIar+24+t/8S2qVw6uhKVsoTY2Fjmz5//nFfydAirNU3zTMZZlSpVaN68OdHR0bRu3ZpWrVrRvXt3xGIxqamp1K5du7xgqZQaNWq8FKvWz89Pb5gB1K1bF41GQ2JiIh4eHqSnpzN+/Hji4uLIyMhArVZTXFxMUpJucOvRowczZ84kKCiINm3a0K5dOzp27IhUKuXChQuo1WrCwsIM6lQoFDg7l3cAKysrvQEF4O7uTkBAADYPvYm6u7uTkZEB8Nzlenp66st4WiZPnsykSYZxD2+99wX9hk14pnKMsWvTb8Qf2cHoiYsxk5k/OcMLxtLSiplzFlBSUsL5c2dYvPAX3D08iY6J1ctEV4ll5pwF5OfnsWvHVn6c/A0/zZiDg4Oj6YL/BRaWVvw4awmlpSVcSDjN8t/m4ObhReWYak/O/IwUlZQwafYixg3tj4OdaW9SywblfTHE34cQf1+6D/+MC9duvHCd/sEruDZdR26gtDiXq6fWsuePD+n8/mr9gyam4QC9rLNnOPWqBdOnQzSiB1Obn3135V/r8OGQIAL9rBj5RflUt+hBYNGWXels36frS1ev6eK36tV4cfeERGJGp3dns2PFF8weWwuRWIJ/RF0CKzeCh8bBw1t+RlGST89RS7G0ceT6uT1MnTKR736cja2t3WNqMI2lpRXTZi+i9EG/WLJoLu4enkTFVDWQU6lUTJ08CS1a3hv+4b+63qdl96ZFnD26nRFfGY4Zno5iKvuUT1fG31K9VD0Srqu5fk+DnZWIRlWk9G0h45e/FKjUcP2+hm0nVHRtaIas6AIaRRk3vpuHc8OaaDWal6rXyybz3kUuHv6drqPX6/uCKao0HkR4zW4U5qaQdnYun376Kb/++usT8z0vgnFmmmcyziQSCbt37+bo0aPs2rWL2bNn88UXX7B79+6Xpd9z0b9/f7Kzs/n555/x9/fH3NycunXrUlamm3bw9fUlMTGRPXv2sHv3bt5//31++uknDhw4QGFhIRKJhPj4eCQSwziHhw2vR129IpHIaJrmQcf+N+U+q4E7btw4PvroI4O0Qw9Cf2zsHBGLJRTIDYNL8+XZ2Dk8fuXWnr+WsnvTYkZMWIC3f9hjZZ+EnZ09YrEYea5hkLNcnoujk+n4IrFYjKeXzjAPCg4hOSmJdWv+MDDOLCws8fTyxtPLm/CISIYO7seendvp3uvNp9BJQp7cMIg9T56Dg6OziVw6nTy8fAAICArl/r27bFq74qmMMwdbWyRicYXg/xx5Ps4OFb1999MySc3I4uMps/Rpmgf3R4OeQ/hz1nf4eFQM9vV2d8XBzgZ5fkF5fQ/ZJSUF2VjZGv/9LW1cKnjJSgqzK3jTzGRW2Lv4Y48/7n6xrJ7amsTT64lt8q7Rcu8VONK5cycc/ccglblh9iDo38nejJzccu+Zo4MZN24XGS3jYUYPDqRuDUdGjr9IZnaZPj07V/f/O8nlQfliiS1isQSpuNCgjOKCbKztjLeDtZ0LRQWG7VD0iLyHXxQDPt+MoqQAtUqJla0TK37sgbtfFAC5mUmcPbCCd8b/jYuXbnrJzSeCwntH2P73Rga9N0rXLx65B+XyXBwcn9QvdPdgYHAo95LvsmHtKgPjTKVSMXXKRDIz0/n6++lP5TXTXfeDMeOR4P+CvOwKHvhH2bdlKXs3L+b98Qvx9g83OJeRpyGvqNzwEYt1v7+NlYiCkvIxz8ZSREq28TGwuBTUGi02lobptpYiCh7xwJUqoVSpJTtfS1JGGRP7W1A5QMK5m7op0UMXVBy6oKLlt61Q5uZhFeBNxPdjKb5977HX+CKxVoNYS4Xg/5KCrMf3z4KK8pYP5NNux1NSlM2qyeUzSlqNmuNbf+DCkWW8+dk+fbqFtSMW1o44uAbyab8QGjduTEJCAlWrGhr5Ai+fZ14QIBKJqF+/PpMmTeLs2bPIZDL27t2Lp6cnJ06UB3uqVCri4+MfU5IOMzMzg6D5pyEpKYmUlPIl+cePH9fFTYXrOv+RI0cYNWoU7dq1o3Llypibm5OVZTioWlpa0rFjR2bNmkVcXBzHjh3jwoULVK1aFbVaTUZGBiEhIQbHs0y9PsqLKlcmkz2xvczNzbGzszM4ZA/eWKVSM3yDKpF4sfy30mg0XLt4gsAw0zEquzcvZsf6Bbz/+Tz8g/99DIaZmRnBIWGcP1ce36PRaDifcJbwCOPbVhhDq9WiekLAqlajeaqgVqmZGUEhYVw4V37fajQaLp6LJzTi6a9Zq9GgUpY9WRAwM5MSHuTP6QvlXiONRsPpC1eICq8YJ+fv7cmK6ZNYNvUr/dGwRhWqVQ5n2dSvcHc2/gDPyM4hr6AIdxfnCvVpNRpSbh7HzS/WaF53vyqk3DxukHbvxlGT8vpytVp9HJYx0u5eISkpmdRMCffTSrmTXEJ2bhnVYhz0MlaWEiqF2nIpseCxdY0eHEjD2k588NUl0jIMp/TTMhRkZivw9S5/govEUkJCI0g4c7JcX42Gu4nH8Ao0/iDyCowl6aphO9y9chSvwNgKsuaWtljZOpGbcYe0uxcJiWkOgKpMN/8mEhkOvWKJBK1G+6BfhHM+oXwBk65fxD9zv1A+dA/+Y5ilptxj4nfTsLV7+nhR3ZgRaRDMrxszjhMQanrM2Lt5MTvX/8rQcb/gZ2TMUGuguKz8KCzVkl+sJcSr/OXV3Iz/1959hzdVvQEc/ybde9EJnVAoUEbZQ7ayBSwyBGQjP0FAhooLBBEQ2aDgYMoSkanssodA2btQRhndpZTuJrm/P0LThrYMbW4Kns/z5IHe3OS8SU6SkzPeg7ebkqi4wnuv1Bq4lyBRrnTebRRAOS8TomKf0eOloNBVoFnRcWgys/Dq1p6MqPs8PPXs+abFxRQokwX3rh/VHZM0Gu5f/xv3ot6fvtW5F3lU79i9a0d05wfW6MDbH26i84gNuou1vRtVmwyg7YBfiowlt2Mht1PDEDSSVCyXV9EL9ZwdO3aMsLAwWrZsiZubG8eOHSM+Pp6KFSsyYsQIpk6dSmBgIEFBQcycOZPk5ORn3qefnx9hYWE0bNgQCwsLnJyePcxgaWlJnz59mD59OikpKQwfPpyuXbvqGjmBgYH8+uuv1KpVi5SUFD766COsrPI+mJcuXYparaZu3bpYW1uzYsUKrKys8PX1xcXFhZ49e9K7d29mzJhBSEgI8fHxhIWFUbVqVdq1a1dUWE9Vvnz5YrlfPz8/duzYwdWrV3FxccHBweGFJ2w2b9+bX7//Ap+ASviVq8LerSvIysqgXtNOACyf/xkOzu507KFdRr1r42L+Wvs9fYZPxcWtNCnJ2oauhaU1FpbaySBpqQ95kBDNwyTt3JrY+7cAsHcsVWSPXMe33mbOzG8pF1iewPJBbNn0B5lZmbz+RisAZk2fiotLKXr3GwjAut9WUS6wPB6eXuTk5HAy/Bj79uzif0O1cWZmZvD7mpXUqdcAJycXUlIesvXPTSQmJtCwUeE5f57UrlN3fpj1DWUDgyhbviJbN60lKzODpq9rX5/5M77G2cWVHn3/B8CGtb9SNjAI98cxnT5xlIN7dzBgyJjnfj3eebMlX89fRFBZPyqX82fNX7vJzMqifbOGAEyY+wuuLk4M6dkZC3MzyvqU0bu9rY32Ncg9np6RyaLfN9OsXk1cHB24GxPH9yvWUcbDjbrVK5OVnc3X8xdR32Yjrt5VuHB4OTnZGZSv+RYAe9d+go29O3Vaa3tfgxv2ZstPvTl3cAk+FZoQeW4rCfcu0ugt7dB5TnY6Z/b+iE/FZljbuZKZnsylo6tIT4nFv4r2tYy9fZq4O+fwKlsXMwsb4m6f4ehfU7F0qI/SJK8H5/c/o+n9dhnuRmcQE5tF/3e8SUzK5tDxvJ6kmV9V4uCxJDZsiwFg5HsBtGhUis+nXCEjQ42zo/b9kJquJjtb+wWzZtN9+nXzJvJWOtdvptGqmSuO5v35ZOxYSpWvh6dvVcL3LiMnK4Pg+tp5s38t/Rg7R3cad9KmU6jZrDdrZr3Lid2LCQhuwpXwrcREXaBlz4m62K6e2oaVrTP2zl7E37vKnt8nU67a6/hXeg3QzktzdPVl5+pxNA39BEsbR66f3c3Z0+F8Nn4KAG++1YV5M6dQLrACgeUrsmXTOrIyM2n+RhsA5syYjItLKXr11fZI/rF2JWUDK+Dh4YUqJ4eT4X+zf89O3ns8bKlSqfhu8nhuREbw2fgpaNRqHiRpe1ps7eyf6/OjabverPzhc3zKVsanbBX2b/2V7KwM6j7+zFgx/zMcnN14s8eHAOzetIita7+n9/BvcS7iM6Mwh86raF7DlIQUDQ9SJFrWNiMlXeLirbwfpIPamXPhlpqjFx/3eJ1T0bWpGXfjNdyN1/BaFVPMzCA8QjtM6mynoGpZE67dVZOWAQ62CppWNyVHBVei8u63cVVTIu6osa1UDo9OLSn78SBOvfMhPOewpomNNTblfHR/W/uXwb5aENlJD8m8U/j80cI0eQirjv+Oa5lgXMtU5fyhZeTkZFC+lrZe7v3tE2zs3ajTRlsvgxu+y5Yfe3PuwGJ8gppy/exfxN+7SKPO2nqZ2xuWn9LEFGvbUji6anMpxkWdJe7ueTz8amJhZU9K4h1GrZmDj4+PQXvNxLBm0V6ocWZvb8+BAweYPXs2KSkp+Pr6MmPGDNq0acMbb7xBdHQ0ffr0QalU0r9/f9566y29FBeFmTFjBqNGjeLnn3+mdOnS3Lp165lxlCtXjtDQUNq2bUtSUhLt27fnhx9+0F2/aNEi3nvvPWrUqIG3tzeTJ0/WrXgE7QKEqVOnMmrUKNRqNVWqVGHLli26uV9Llixh0qRJjB49mnv37lGqVCnq1atH+/btX+TpKqA47nfQoEHs27ePWrVqkZqayt69e2natOkLxVGzQWtSUx7w19ofeJScQGm/Cgz9bAH2j4cokhJi9H7ZH9y1FpUqh0Uz9XP+tHn7f7Trql0lez58Hyt+yJvTtmT2xwXOeVKjJs1ISXnIql+X8uDBA/wDyjJ+4lTd8E1CfJxuqAO0C0EW/jCXxIR4zM0tKO3tzcgxn9KoSTMAlEoT7t69w55vviLlYQp29vYElq/AlO9m4+Pr91zPTYPGLUh5mMzaFb9oE4AGlOPTiTN0MSXGx6JU5j03WVkZLPphBomJcdqYyvjywehxNGjc4rnKA3i9YR0epDzilzUbSUxOIdDPm1mfj8T58bBmbEKS3vPwLEqlksjbd9m27wiP0tMp5eRI3WqVea97J8zNzHTl/fTHXNIfJeDiWZE2/X7SDZukJUfrvf7uviE07/4d4TvncGLHLBxK+fJGr3k4e2iHthUKE5LjbxBxaiOZaQ+wtHbEtUwV3nxvBc7u2qE7E1Nzbpzbyqmw71GrsrFzLkOV1/oQFaWfWHX1hntYWSgZ87+y2NqYcv5yCh99fYnsnLwPcS8PSxzs8xoUnVprf5TNnRSsd19T5l1j+17tj4V1f0Zjbqbkg35+2NmaEnkrjYXLfbF168rhP+c+TkJbkbc/+EU3TPnoQTSKfK916bI1aN9/Ogc3z+bg5pk4ufrx1uDvcfXKG+JPfRjP3nVTSXuUiK2DK5XrdqR+m7z6b2JixttDf2L/xhmsX/A/crLScXT1YdioT6lZWztZ/rXGzUl5mMzqFUtIfpCEf0A5vpw4Ld/7IhZlvjlAWZkZ/PzDrLz3RRkfRoz5nNcaa4exkhLjOXHsMACjhw3Ue44mTplVYF5aYWo0aE1qShJb135PSnICZfyC+N+nC3U/vB4kRqPIV0cP71qLWpXDkpn60ytav/0+bboU/nkAsP+sCnNT6NzIHMvHSWgXb8tGlW+wwNlegY1lXlnnbqixsYKWtUyxs9YOgS7emqVbJJCjlvD3UPJasClWFtoFBjejNfywKYu0fHmKK3graR5iinn7P0g5d4Xw0KHE7zjwzOcml0PNYOqH/ar7u9J0bR65O8vXc27Ap899PyFpENnuY8J3ziP9UTwuXhVp2/9n3fszNfm+3hwwD78atHhnOid2zOb49lk4lPKjZe/5uvfn8zA1s+TWhV2c3DUPVbZ2QU+7Vo0YMmQI5ubPtxhHKF4KyYDrUIsrm73w7+06m/XskwzM2zb+2ScZWKZG/kUMhfHJvPrskwxs8bUXS8NiCFtW/P3skwzs3aHGfx7q+z5/z4qh3El9+vwxOew9/mJTXAyh8dDqxg6BK79fMXYIjO5kmEUA+fX+snjq/fKvPYvlfkoSkaJfEARBEATZvarZ/YuDrNs3PY/KlStja2tb6GXlypXPvgNBEARBEISXmEF7zpYuXfrCt9m6dWuRK+sMuQGrIAiCIAjyEQsCilbihjVzdx4QBEEQBOHV9apuvVQcStywpiAIgiAIwn9Zies5EwRBEATh1feyb41lSKJxJgiCIAiC7MRqzaKJxpkgCIIgCLITc86KJuacCYIgCIIglCCi50wQBEEQBNmJVBpFE42z/4i94caOADIyHI0dAldP3TR2CAC4+QQaOwRK+xSeT1BO5lbG304rOk5l7BDYnOhm7BA4ti/S2CFgYW38+uBWArZOCuoSZOwQIMfwW8yJxlnRxLCmIAiCIAhCCSJ6zgRBEARBkJ1GEqk0iiIaZ4IgCIIgyE4MaxZNDGsKgiAIgiCUIKJxJgiCIAiC7CSNVCwXQ0pKSqJnz57Y29vj6OjIgAEDSE1Nfer5w4YNo0KFClhZWeHj48Pw4cN5+PDhC5UrhjUFQRAEQZDdy5CEtmfPnkRHR7Nr1y5ycnLo168f7733HqtWrSr0/Pv373P//n2mT59OpUqVuH37Nv/73/+4f/8+69ate+5yReNMEARBEAThCZcvX2b79u2cOHGCWrVqATBv3jzatm3L9OnT8fLyKnCb4OBg/vjjD93fZcuW5ZtvvqFXr16oVCpMTZ+v2SWGNQVBEARBkJ1GoymWS1ZWFikpKXqXrKysfx3f0aNHcXR01DXMAF5//XWUSiXHjh177vt5+PAh9vb2z90wA9E4k9WtW7dQKBScOXPG2KEIgiAIglEV15yzKVOm4ODgoHeZMmXKv44vJiYGNzf9BNGmpqY4OzsTExPzXPeRkJDA119/zXvvvfdCZYthTQPp27cvycnJbNy4UXfM29ub6OhoSpUqZZSYXq9hQq0KJliZw+1YiU1HVCSmPH3Mv15FJY2qmGJrBTFJEluOqriboL2NlTm8XsOUcqUVONoqSMuES7fV7DqpJitf8vmyngper2mKu5M5CgAFKBVwM1rN7/uySHj49BgaVjGjeYgZdtYK7idoWH8gi6g4/fw4vh5K2tUzx8fdBEmCe/EaftycQY768WOvaUYlP1M8B1VGpZbo+sElvdv36uRG68bO2FibcOl6Ot8vv8f9uOwiY+ra1pUGNe0p42lBdrbE5etpLF4Xw72YvNu0buJE07qOlPO1wtrKhC5DL5KWUXRen7ea2dCkhhXWlkqu3clm+Z+PiE1SF3l+eV8z2jawwdfLFCc7E+auSebUlbxfiyZKCG1uS9VAc9ycTEnP0nDpRja/704l+ZE2jhYhJtSuYIKlOdyOk9j8HHWibkUljYIf14kHEn8+USda5NYJm7w6sftUXp2wsoCuTczwcFZg+W4IySkqjp5MZvHae6RnaOjT2ZM2zVyxtTHhYkQqcxdHcS+26F/B3Tt48FotR7y9LMnK1nDpWhq/rLnL3ei824zo70ONYHtcXcwwUSoAuHU3g5k/337qY63oraBpVSWONpD0CMLOaLgerf/8NKmiJKSsAkszuJMgse2EhqR884Wd7eD16kq8XRWYKCE2Gfad03A7Lu9+Pu1qUqDsjUc1XL5T+GsRVAYaBytxeBzXvnMaIh9/VygV0LiKgrIeChxtISsHbsVK7DsnkZqZdx9vN1Ti5gg2lpCZDfV9PVi2MZ4HD/Pq3DvtXXijoQM2Vkqu3Mhg4eo4ouOL3lmiUjkr3nrDibLeljg7mjLlx3scO5umd46DnQl9OpWiekUbbKyVXLyWwc9ri77fLq0caFHXFhsrJVdvZvHL+iRiEore1aFigAVvNrXHv7Q5zg6mfLckjvCLGQXOK+1mSo92TlQKsERpAvdic5ixLJ6LR1Zy9sAiMh4l4OwZRMOOX+DmXbXI8m6c286JnXNIfXAP+1K+1G0zBp+gJoWee3D9eC4f+4367T+lSqM+uuPbl75P4v0rZKYlYm7lQCVXaJ8EDkW//Qvl/FotAkYPwKFGMJZeboR3HkLs5rAXuxMZScWU5+zTTz9l1KhRescsLIrebWLs2LF8++23T73Py5cv/+u4UlJSaNeuHZUqVeKrr756odu+VD1n2dlFf1m+DExMTPDw8Hihrs3i0riqCfUrmbDpsIoFm3PIVkn0a2WGacHvBJ0q/kra1jUl7LSK7zflEJ0k0a+1GTaW2uvtbRTYWcO242rmrM9m3YEcypdR0rlR3uPzcFbQp5UZ1+5qOHJB++GbkSlx+pqKrBz4Xwerp8ZQvZwpnV4zZ8eJbGb8ls79RA2DO1hha6XQneProWTwm1ZcjVIz+/cMZq5N59D5HPIv4jE1UXD2uoqt+xILlPF2m1J0eL0U85ffY+SkSDKzNHw92h8zU0WBc3MFV7Dhzz2JjJoUyeczbmJiouCbUf5YmOfdxsJcyckLqfz2V3zRD/Cxtg2teaOuNcv+fMTEX5LIypYY/a4jZk+pKhZmCqJic/j1r0eFXm9upsDX05TNB9IY/2Mi8397iIeLKSPecQSgUZXHdeKIigVbcsjJkej7PHWijil7zqj4fnMOMUna2+TWCTtrbZ3YflzN3A3Z/HFQWydCX8t7IJIEl6PUrNiVQ78xF5n+4y1Cgu0Z0d+Xbu3d6dTKjTlLbjNs3BUyszRMGRuImVnRr0XVIFs2745n+PgrjJ16DVMTBVPHBmJpkffxdu1mOrsPaV/7FRvuc+7yIzzdLJgyNpCidgwqUwpCGyg5E6nh5+1qrt6V6NpIiatD3jkNKiqoU17B1hMaFu9Sk6OCHs1MMMn3ydq9sQlKBfy6R80v29XEPpDo3kSpe84q+Wgf25kbGlbsUXPhlobMbEnX4H1SaRfoWE/J2ZsSi3dquHZfonNDJaXstdebmYKHo4LDlySW7NKw/rAGFzsFb7+m/3F/O15i41ENP27TsP6IBg9XMz4ZlDeH5q03nGjf1JGFq2P5+LsoMrMkxg8r/dT3haW5gpt3s/jxt7giz/l0sBfupcyY/OM9Rk6+TXxSDhOGl9F77+Tq0MyeNq/Z88sfSXw+N4bMbInPBrk9/X1hruD2/RwWb0gq8hx3F1MmDPXgflwOExbE8PGMaP7Y9ZB7kXs4+udUarYYSujw9bh4VmDrooFkpBb83ACIuXWKsNWjCar9NqHDN+BX6XV2Lv+ApJiIAufevLCLuKizWNsX3KrLq2xdXu85i65jtvFGrzkkmsEy96IfY1FMbKxJOXeVC8MnvPiNX2IWFhbY29vrXZ7WOBs9ejSXL19+6iUgIAAPDw/i4vTrskqlIikpCQ8Pj6fG9OjRI1q3bo2dnR0bNmzAzMzshR5TiW6cNW3alA8++IAPP/yQUqVK0apVKy5cuECbNm2wtbXF3d2dd999l4SEBL3bDB8+nI8//hhnZ2c8PDwKtFijoqLo2LEjtra22Nvb07VrV2JjYwGIiIhAoVBw5Yr+/mqzZs2ibNmyAKjVagYMGIC/vz9WVlZUqFCBOXPm6M796quvWLZsGZs2bUKhUKBQKNi3b1+hw5r79++nTp06WFhY4OnpydixY1Gp8n4VPs/jeR4NKpuw94yay1EaYh5I/L5fhZ01VPItugq8FmzCiasaTl3TEJcssemwimwV1Cyv/faOfSCxao+KK3c0JD2CG9ESO8PVBPkoedw5QVV/JTFJEnvOqKlVwZTtJ7JZtz+L6oGmrNufib2NgioBRX/SNq1uxtGLORy/rCL2gcTve7PIVknUrZh3m06vWXDwXA5hp3KISdIQnyxx5roKdb4fZduPZ7P/bA637mYWKKPTG6VYsyWOv8884tbdTGb8cgcXR1Pq17AvMq5xs26x+3AyUfezuHknk5mL7+JWypxAPyvdOZt2JfL71niuRKYXeT+5WtazZvOBNE5fzeJurIqfN6TgZGdCjaCiP2DOX89m/Z40vd6y/DKyJKb/msyJi1nEJKqJvJvDiq0p+HuZ4eygpGFlE/ad1daJ2AcSvx9QYWcFFX2KrhMNg00If1wn4h/XiZx8dSIuWWL1E3Vi10n9OpGZDcevaLiXKBGXkM3pi4/YsjuO4Aq2vNXanZUbYzh68iE372Tw7YKbuDia0bCmY5ExfTbtOjsPJHL7XiY3ojL47sdbuJeyINDfWnfO1r0J1K7mwNY9Cfy6PoYffr2LlaUJOTkS1QMKb2zUKa/kerTE0SsSCSmw77yG6AdQOzDv+alTQcnBixoi7knEJcOmvzXYWUFQGe19WpmDi72Cw5c1xCVDUirsOavB3FSBm4P2nHoVtPd3/b7EnQTYclwiRwWVfQuPq1agghsxcOyqROIjOHBBIiYZagZqz8/KgTUHNFy5K5H0CO4nwc5TGjydFdjnPSWciJC4nwQp6XAvEf7YkUR5P0tdw/LN5k6s3Z7E8XNp3L6XzZxlMTg7mFK3mm2Rr8WpS+ms2pLIsbOFpxrwcjMjKMCKhWviuH47i/txOSxcE4e5uYJGtewKnN+2kR3rdz8k/GIGUdE5fL8mASd7U2oHWxdy71pnrmTy2/ZkTlwo2FuWq3trR05fyWDlX8ncup9DbKKKk5cyiLq0jqA6XahQuzNO7uVo9NYETM0suXrij0Lv58LhX/Eu/xrVmgzAyb0stVuNoJRXJS4eWal3XtrDWI5smkSz7t+hNCn4eVe1UV/cfatj51QaD78aNE+G2xbwgh1nxO84QMT42cRu2v2CtzQOY6XScHV1JSgo6KkXc3Nz6tevT3JyMidPntTdds+ePWg0GurWrVvk/aekpNCyZUvMzc3ZvHkzlpaWLxxjiW6cASxbtgxzc3MOHz7M1KlTad68OSEhIYSHh7N9+3ZiY2Pp2rVrgdvY2Nhw7Ngxpk2bxsSJE9m1axegnYDYsWNHkpKS2L9/P7t27eLGjRt069YNgPLly1OrVi1WrtR/c61cuZIePXro7qNMmTL8/vvvXLp0iXHjxvHZZ5+xdu1aAMaMGUPXrl1p3bo10dHRREdH06BBgwKP7d69e7Rt25batWtz9uxZFixYwKJFi5g0adJzP57nZW+tIPJ+XmslKwfuxkv4uBX+BWCiBK9SCq7nu40ERN7XFHkbAEtzyMpG12tlYgIqNTjZgb2Nkog72t4Fc1MFro4m3I7V4OdReDU0UUIZN+1t8sdw7a4aXw9tY8DWSoGfhwmpGRLDO1sxsb81Q9+ywt/z+aq2h6sZzo5mnLmU92WSnqHh6o10KpYt+gvgSTZW2ngepb3oxym4OpngaGfCpRt5PcMZWRKRd3MoW8b8he/vaawslWgkCWtLBXb/pE64FKwT1+9r8HF9/jrxJBdHM16r5cS1G2m4OJlx+mKK7rr0DA1XItOoFGjz3I/Rxvrxa5Ga9yPH1ERBeX9rTl1IwdJCSasmLkTHZXHqQgplShUee5lSCm7G6gd9I1rSne9oA3ZWCm7G5J2TlaNt6JR+fE5GNiSkSFT1U2JmAgoF1CinIDVTIjpJQqkET2ftbVvVUDKio5I+LZQ8TIfSLoXHVdpFwa0n4roZIxV5PoCFmTZtQWYRgw+W5tCkjj1XbmSi1oC7ixnODqacu5L3wyI9U0PErUwqBLz4F02u3F63nJy8+CUJVCqJSmWt9M51czbFyd6U89fyGlkZmRLXo7II9P3nG6QrFBBS0YroeBWfDXLjp6/KMGm4ByFBpqQ/vEaZwLzPaoVSSely9YmNOlPofcXePkPpcvqf7WXKN9Q7X9Jo2Pvbx1RtMgBnj8BnxpeZnswpW/DLgqd0Yr8SSnqes4oVK9K6dWsGDRrE8ePHOXz4MB988AHdu3fXrdS8d+8eQUFBHD9+HMhrmKWlpbFo0SJSUlKIiYkhJiYGtfr5vx9K/JyzwMBApk2bBsCkSZMICQlh8uTJuusXL16Mt7c3ERERlC9fHoCqVasyfvx43e3nz59PWFgYb7zxBmFhYZw/f56bN2/i7e0NwPLly6lcuTInTpygdu3a9OzZk/nz5/P1118D2t60kydPsmLFCgDMzMyYMCGv29jf35+jR4+ydu1aunbtiq2tLVZWVmRlZT216/OHH37A29ub+fPno1AoCAoK4v79+3zyySeMGzcOpVL5zMdTmKysLL2VKiYmJtjY2JCaoV+JUzMkveHB/KwtwUSpKPQ2rg6FN3ysLaBZiCnHr+ZVwGv3NDSsbEL1stqPGQXQsra2wWFvrSA1XYOddeEx2FgpMFEqePREDI/SJdwctTG42Gtv26qOOZsPZ3EvXkPtIFOGdLLi21Xpz5zP5mSv7Wp+kKI/hyU5RYWTw/O9PRQKGPyOJxevpXH73ouvEHKw1T6Wh6n68y9S0jS664qDmSl0fd2WY+czsTTX3m+B1zfzKXXC4il1wrHoOtG0uiknIgp+KHVtasqXPUOwtFBy9GQy63fE0bC2Ew8e6s89evAwByfH5xsSUCjg/XfLcOFqql4vqYOdKSYmCj4b6o+FhQlR9zP5ZEoE7Vq4UtGy8MdrawlpT3S0pmZK2Dx+fmwftyWePCctU8I2X/tlxR41XRuZ8EkX7XzItExYvU9NZo72PpRKBacjNZy/JaFSg7+HgibBCh4V0fFTWFxpmeiVmZ+JEppWVXIpSiL7ialaTasqqFlOgbmpgis3TPlmwT0AHB2079fkJ94XD1PUONn/86+NuzHZxCXm8G7HUvywKpasbA1vNneilJNZgfebo502hoeP9N8XD1PVuuv+CXtbJVaWSjo2t+e3bcms/OsB1StY0be9CUu+02Bl66J3vpVdKZLjbxZ6XxmpCVjZFTw/41HeaM6Z/T+jUJoQ3PDdp8Z1bOt0Lh5ZiSonA19TGPh8880FA1u5ciUffPABLVq0QKlU0rlzZ+bOnau7Picnh6tXr5Kerv0hc+rUKd1KznLlyund182bN/Hz83uuckt846xmzZq6/589e5a9e/dia1uwWz0yMlKvcZafp6enbtz48uXLeHt76xpmAJUqVcLR0ZHLly9Tu3ZtunfvzpgxY/j777+pV68eK1eupEaNGgQFBelu8/3337N48WKioqLIyMggOzub6tWrv9Bju3z5MvXr10ehyPtyaNiwIampqdy9excfH59nPp7C/Pnnn7Rq1Ur3d9u2bdm/f/8LxfaiLMygT0sz4h5IhJ1SU62skk4NtdVLI2nnvAEM62zFrhPZlC2t/aL6t3KfuiMXtEOfAPcOZRNYxoS6lcz466h+V0FQWWtsrJT88UMlAMbPfvqE8OcxpJcXvqUtGTMl8rnOb1rPkeF9XXV/z1qZ/K9jeJYGVS0Z0MkeBeBRyoR9J4se8ikuFmbQu6UZ8cnaOvGk2zEaKpRWkpWtoW6IA5piqBDD+vrgV8aKkROvFnr9tIW3eJCioktbd74YHsC5y0Vn+i4ubWopScuSWLpbg0oNIWWVdGtswqIdec/J+VsS9x5Pa4pNlgj0UuDp9O/LVirgrfpKFArYfrLg83vsisS5GxJV/BXUq2DJ0qllyc6RmPS4kVbc1Br49qf7fNDLnZUzyqFWS5y9ks7JC2k4O5qw7Ju8z+Wpi4r+jPs3lI8/NMIvZLD1oHa+5u37ObjaF38PTPzdC1w49CuhI/7Q+5wvTLUmA6hQuzOpD+5z+fsBrHLTNtCefquX28uw8bmzs3ORCWcB/Pz89JLpNm3atFiS65b4xpmNTd5wRmpqKm+++Wahqyw8PT11/39y4p1CoUCjef5K4OHhQfPmzVm1ahX16tVj1apVvP/++7rr16xZw5gxY5gxYwb169fHzs6O77777oXynryIF3087du311s88ddffwHaIcD8vVC2Vgqikwq/n/RMUGtye1H0b/NkT5a5GfRtZUZWDqwM007Evxyl4U6+1Y6mJgo+7GzODxsyyMyRaN/AgsQUDbbWZtxPKLyrNy1DQq2RsHuiJ8fOWkFKujaGlDTtv7FPPI7YBxqcbAt+rEVGZZCeqWH4hOtA3jCLk70pDx7m9RI42ptyI6rg/LQnvd/TizrV7Ph46g0SHxS9giy/Y2dSGLcwbwgndwK+g61Sr/fM3kZJVMzz3efTmCihdmULYhNV/LQ+hfQsSVdmgTph+ZQ6kVV0nUhNf6JOmGob6/nrxJNOX9fwxzptIyrQz4ovhmvndDo5mJGUnPe4nRzMiLz97Hl7H/Txpm6IA6O/vkpCkn7v28NHKtRqCZVa4vyVVC5fS2P9T9XIzNKQmln4B2lqJrpJ+7rHaqkg7XG7NvXxvzaW6K2CtLFUEPNAe59+7goCvRR894da12u1LVyDv4cJVf0V/H1VQqORCixKUKklTEyUmCjRmztZVFxPxgDahlmn+krsbWD1Pk2BXjPQDrtmZMPRyxJb/rrLlDE+zFscTUqq9j3paG/Kg5S896eDvQk37/67/FGRd7IYOSUKa0slpqYKUlLVTPvIm4hbmcz6NW/ife5708FOSfKjfDHYmnDr/j9fHJaSpkallrgXq19HUjJtUSpNCkz+z3iUgLVd4SvsrWxLkfGo4PlWj8+PuXmSjLREVk1prrte0qj5+69vOX94GT3G7tEdt7RxwtLGCUdXf2rFwkRf7bwzv3+frqvEEhufF63EzznLr0aNGly8eBE/Pz/KlSund8nfiHuaihUrcufOHe7cuaM7dunSJZKTk6lUqZLuWM+ePfntt984evQoN27coHv37rrrDh8+TIMGDRgyZAghISGUK1eOyEj9XhNzc/Nnji9XrFiRo0eP6rWyDx8+jJ2dHWXKlHmux1MYCwsL7OzsdBdbW1tS0iXKeuW93BZmUMZVQVRc4W8OtQbuJ0iUyzd3SwGU9VLq3cbCDPq3NkOtgV935aB6/JCzc7RL/HMvcckSKWkaArxMqBFoxoNHGuKTNfi6K7kVU3hjQK2Bu3EaynvnDWEogMAyJtyO0RaU9EgiOVWDm5N+VXZ1VJL0qOBjy8mRkCSIjssmOi6bqPtZJCXnUK1SXm+slaWSCgHWXH7GRP73e3pRv4Y9n067SWxC0ekFnpSRqSEuSa273I9Xk/xITSX/vPlllhYKypYxI/Luv1uhbKKEIV0ccHUyZcqSB9yKVunKfJQuEfCidSJRvx7p6kS8fp3o97hOrMhXJ56UrYL7sVncj80i6XHDOPlhDiGV8yaGW1spCSprw6VraYXfyWMf9PGmYS1HPv4mgpj4gs+ZSi0RcTOdkMraRR4KhfZHjn8ZqyJXRd5NkPB312/g+3sodOcnp8GjDAl/j7xzzE21qynvPT7H7HHVLVCCpI1Bo4HoJG0jLj9PZwU5KqlAwwzgXqKE7xPn+7kruJeYV0puw8zZDlbv15DxjGqUrYL4xz8uUlLV3InOJumhiqoV8uZdWlkqKe9nydUbz/7R8jzSMzWkpKrxdDWjrK8lR0+nEpuo0l3uxubwIEVFlcC8lqiVhYJyPhZcu/3PWyxqtbaB6Omm3zfh7WGNr39F7l0/qjsmaTTcv/437j7VC70vd9/q3Is8qnfs3rUjuvMDa3Tg7Q830XnEBt3F2t6Nqk0G0HbAL0XGmPtKql7lbjPhqV6qxtnQoUNJSkrinXfe4cSJE0RGRrJjxw769ev33BPtXn/9dapUqULPnj05deoUx48fp3fv3jRp0kQvC3BoaCiPHj3i/fffp1mzZnrbNAQGBhIeHs6OHTuIiIjgyy+/5MSJE3rl+Pn5ce7cOa5evUpCQgI5OQW/vIcMGcKdO3cYNmwYV65cYdOmTYwfP55Ro0bp5psVlyMX1TSrbkKQjxJ3JwVdmpjyKB0u3c779B/Qxox6FfPKPXRBTa0KSkLKKXF1UNCxoSnmpnDq8fyh3C9hM1NYfzAHC3PtHBpbq7zhRtCmbHB3UnAqQkXbeua8XtOMvaez6fG6JSlpEudv5P2kf7+jJa9Vyesp3Hcmh3qVzKgdZIqbk4K3m1pgbqrg2OW82+w9nUOjqmZUK2tCKQcFbeqa4+ak5NilvOfc0VaBVyklrs7mKJUQ4G1JgLcllhZKNu5KoHt7N+pWt8OvtAVjBpYhMVnF0VN5E9Mnj/GnffO8uSVDennRrL4j0368Q0amBid77eRl83wpH5zsTQnwtsTLTdvo8iujLdPWpuB8mZ1/p/NmYxuqV7CgjJsp771lz4NHar2VmB/3dqRFnbweNwtzBT4epvh4aL9kSjma4ONhivPjOYEmShja1QE/LzN+/OMhSqUCB1slDrZKTEzg8EU1zaqZEOStrRNvNzblUYa21zNX/9b6deLwBTW1yufViQ4NtHXiZL460beVGeamsOFQ4XWifBklNQKVuDkqcC9lTp3q9ozo78OFq6ms2xpLj06e1K/hgJ+3JR//z5/E5BwOn0zWxTDt00A6vpE3LDysrzctGjoz5fubpGeqcXIwxckh77XwcDWnewcPjoQ/oG3zUvR525PJH5dDqQCFEs7e1H4VdqynpHm1vMd6PEJDWU8F9YIUuNhp84p5OcOJa3nPz/GrGl6rrKR8aQVuDtoG0aMMuHJXe593EyQyc7T37e6ozXnWoro2b9r1+9pzbsVJhJRVUD9Igb879Gqm1L7Prmuvb19HQZMqefUq/JpEgAfUKa/A2Q5eq6wdAj15TXu+UgFvNVDi6Qyb/9agVGh71mwsIfdjxcsZapZT4OYI9tbg6waj+3sSHZfNlZvaxteWPQ/o0saZ2lVs8PUy58M+HiQ9VOmtxJw4vAxtmzjq/ra0UOBfxgL/MtquQDcXM/zLWFDKKa8h1CDEluBAK9xdzKhT1YYJw8tw/GwqZy4X/DG09eAj3mrhQM1KVnh7mDH0nVI8SFFx4kLeuV8MdqNVw7wGvYW5Al8vM3y9tJ8jbs6m+HqZ4eKY977bsi+FBtVsaF7XFncXU1o1tKNmJSs8ynXmyvHfiTi5gQexkRzc8BU5ORmUrxUKwN7fPuH4thm6+wlu+C53rh7i3IHFJMfdIHzXPOLvXaRyg57a58PGCWeP8noXpYkp1ralcHQNACAu6iwXjqwg4f5lHj24x73rf/OrO7jkgN8LtoNNbKyxrxaEfTXtFBxr/zLYVwvC0tvzGbc0DkmjKZbLq6jED2vm5+XlxeHDh/nkk09o2bIlWVlZ+Pr60rp16+duzCgUCjZt2sSwYcNo3LgxSqWS1q1bM2/ePL3z7OzsePPNN1m7di2LFy/Wu27w4MGcPn2abt26oVAoeOeddxgyZAjbtm3TnTNo0CD27dtHrVq1SE1NZe/evQUmApYuXZqtW7fy0UcfUa1aNZydnRkwYABffPHFP3uCnuLAOTXmpvBWQ1NtwtFYiSU79Hs1nO0U2OSbHH3+pgYbSxWv1zTFzgqiE7W3yR0+8XJR4OOmfd7HdNUfl5n2WxbJjz/Dy5dR0rSaCSYm2snS5qYK3mxgwc1oNT9uydCLoZSDEhurvDfbmesqbK0UtK5jjr2NQptcdkuG3qT0A2dzMDOBjq9ZYG2pTVS7cFOGXjLVNnXNqVPRDND2BMyfoF019cm3N1i3LQFLCyXD+pTG1tqEi9fSGTfzJjmqvNt7upnjkG8Scm5DbdrYAL3HPXPRHXYfTgagbTNnenbMS1b03adldeecu6N3M7YeTsfCXEG/N+2wtlQSEZXNjBXJ5OQbinJzNsXOOq/B6e9lyti+zrq/e7TWfkEdOpPBLxtTcLJXUiNI2+vw9fv6k5anLk3i4HltneiUWyfiJJYWUiesC6kTLWo8rhNJEkt35ugmqOevE6O76NeJ79Zq60SOSqJWeVPa1lHwv/aViU/M5tCJZNZsiSEtXY2lhZIPB/hia23ChYhUPv32mt7qPk93C+zt8j66OryhzRs148sK+uX9eIudBxLJyZGoUsGWQH9rTJTwTgdPFAq4eTeD2b/cxq2iti7YWyv0erHvJsCGIxqaVVXSrKq2B3jtQQ3xD/PKOHJZwsxUol1tJZbmEBUvsWqfWtfjlZENq/apaVZVSa/m2vxn8Q/ht4MaYpO159yOk6jil9sAU6BSw9ErEvvPS4XGdS9R2+hqHKykSRUFD1Lhj8MaEh7/lrCzgvKlta/ZgFb6PwRW7lUTFQ85au05jSorMDPVDtEePZXF79uSUD2u9xt2PcDSQsmQHu7YWCu5HJnBxPn39N4XHq5m2NvmlVHOx5JJI/PmjQ14W/va7Dn6kLm/atMVOTmY0v9tVxzstFMJ9h1LYe22wvOIbd6bgoW5gvfedsHaSsnVm5lM+TlO733h7mKGnU3ej5iy3uaMfz9vIVafjtr3yL4TqSz4TVvOiQsZ/PxHIp2aO9CvkxP341TMXB5PhnlD6rb7mPCd80h/FI+LV0Xa9v9ZN6yZmnxfb+6Yh18NWrwznRM7ZnN8+ywcSvnRsvd8nD3KF/p4CmNqZsmtC7s4uWsequwMrO1cqZINrz948S9oh5rB1A/7Vfd3pemfAXBn+XrODfj0Be/N8MSwZtEU0suwLbzwr322yPgTFzIynn/oz1Cunip81ZXc3HwKJqKUW2kfR2OHwPGwC8YOgcbtqxk7BExNjD9+dWzf8y1oMSSLojICy6jOaz7GDoGgLkHPPsnA2uUUvqimOL3+Tnix3M/u1bWefdJL5qXqORMEQRAE4dVQXNs3vYpE40wQBEEQBNlpxLBmkUTjTBAEQRAE2b2qk/mLw0u1WlMQBEEQBOFVJ3rOBEEQBEGQnVitWTTROBMEQRAEQXZiQUDRxLCmIAiCIAhCCSJ6zgRBEARBkJ0Y1iyaaJwJgiAIgiA7sVqzaGJYUxAEQRAEoSSRBOE5ZGZmSuPHj5cyMzNFDCIGEYOIQcRQguMoCTEI/47YW1N4LikpKTg4OPDw4UPs7e1FDCIGEYOIQcRQQuMoCTEI/44Y1hQEQRAEQShBRONMEARBEAShBBGNM0EQBEEQhBJENM6E52JhYcH48eOxsLAQMYgYRAwiBhFDCY6jJMQg/DtiQYAgCIIgCEIJInrOBEEQBEEQShDROBMEQRAEQShBRONMEARBEAShBBGNM0EQBEEQhBJENM4EQXipJScnGzsE4T/swIEDqFSqAsdVKhUHDhwwQkTCq0Cs1hSEl8DcuXMLPa5QKLC0tKRcuXI0btwYExMTg8XQvHlz1q9fj6Ojo97xlJQUOnXqxJ49ewxWdq5vv/0WPz8/unXrBkDXrl35448/8PDwYOvWrVSrVs3gMfTv3585c+ZgZ2endzwtLY1hw4axePFig8ewZ88eGjRogKWlpcHLepbs7Gzi4uLQaDR6x318fGSL4fr160RGRtK4cWOsrKyQJAmFQiFL2SYmJkRHR+Pm5qZ3PDExETc3N9RqtSxxCK8W0TgTnurXX39l4cKF3Lx5k6NHj+Lr68vs2bPx9/enY8eOBi//1KlTmJmZUaVKFQA2bdrEkiVLqFSpEl999RXm5uYGj+FJarWa8+fP4+vri5OTkyxl+vv7Ex8fT3p6uq7MBw8eYG1tja2tLXFxcQQEBLB37168vb0NEoNSqSQmJqbAl1BcXBylS5cmJyfHIOXm5+/vz8qVK2nQoAG7du2ia9eu/Pbbb6xdu5aoqCh27txp8BiK+jJOSEjAw8Oj0F6U4mZra4tKpaJ27do0bdqUJk2a0LBhQ6ysrAxedq5r167Rv39/jhw5onc8t2EkR6MkMTGRbt26sWfPHhQKBdeuXSMgIID+/fvj5OTEjBkzDB6DUqkkNjYWV1dXveMRERHUqlWLlJQUg8eQKywsjLCwsEIby3L8aBCKjxjWFIq0YMECRo0aRdu2bUlOTtZ92Do6OjJ79mxZYhg8eDAREREA3Lhxg+7du2Ntbc3vv//Oxx9/LEsMH374IYsWLQK0DbMmTZpQo0YNvL292bdvnywxTJ48mdq1a3Pt2jUSExNJTEwkIiKCunXrMmfOHKKiovDw8GDkyJHFXva5c+c4d+4cAJcuXdL9fe7cOU6fPs2iRYsoXbp0sZdbmJiYGF3j888//6Rr1660bNmSjz/+mBMnThi07JSUFB4+fIgkSTx69IiUlBTd5cGDB2zdurVAg81QHjx4QFhYGG3atOH48eO89dZbODo60rBhQ7744gtZYujbty9KpZI///yTkydPcurUKU6dOsXp06c5deqULDGMHDkSU1NToqKisLa21h3v1q0b27dvN2jZoaGhhIaGolAo6Nu3r+7v0NBQOnbsSKtWrWjQoIFBY8hvwoQJtGzZkrCwMBISEnjw4IHeRXjJSIJQhIoVK0obNmyQJEmSbG1tpcjISEmSJOn8+fOSi4uLLDHY29tL169flyRJkqZOnSq1bNlSkiRJOnTokFSmTBlZYihdurR04sQJSZIkacOGDZKXl5d09epV6YsvvpAaNGggSwwBAQHS6dOnCxw/deqU5O/vL0mSJB0+fFjy8PAo9rIVCoWkVColpVIpKRSKAhdra2tp0aJFxV5uYTw9PaXDhw9LkiRJ5cuXl9auXStJkiRduXJFsrOzM2jZ+Z+Hwi4mJibSpEmTDBpDUS5cuCD16dNHMjU1lZRKpSxlWltbS5cvX5alrKK4u7tLZ86ckSRJ/zMqMjJSsrGxMWjZffv2lfr27SspFAqpW7duur/79u0rvffee9LkyZOl+Ph4g8aQn4eHh7R8+XLZyhMMy9TYjUOh5Lp58yYhISEFjltYWJCWliZLDJIk6brnd+/eTfv27QHw9vYmISFBlhhyh6sAtm7dSpcuXShfvrxu7pEcoqOji5x0HBMTA4CXlxePHj0q9rJv3ryJJEkEBARw/PhxveEbc3Nz3NzcDDrXLb/Q0FB69OhBYGAgiYmJtGnTBoDTp09Trlw5g5a9d+9eJEmiefPm/PHHHzg7O+uuMzc3x9fXFy8vL4PGkCsiIoJ9+/axb98+9u/fT1ZWFo0aNWL69Ok0bdpUlhgqVaok23uwKGlpaXo9ZrmSkpIMvnXRkiVLAPDz82PMmDHY2NgYtLxnyc7OlrWnTjAs0TgTiuTv78+ZM2fw9fXVO759+3YqVqwoSwy1atVi0qRJvP766+zfv58FCxYA2gaDu7u7LDG4u7tz6dIlPD092b59uy6G9PR02RolzZo1Y/Dgwfzyyy+6BvPp06d5//33ad68OQDnz5/H39+/2MvOff2fnMNiDLNmzcLf35+oqCimTZuGra0toG28DhkyxKBlN2nSBNDWPW9vb5RK480KCQoKwtXVlREjRjB27FiqVKki2wT4XN9++y0ff/wxkydPpkqVKpiZmeldb29vb/AYGjVqxPLly/n6668B7QIZjUbDtGnTaNasmcHLBxg/frws5TzLwIEDWbVqFV9++aWxQxGKgWicCUUaNWoUQ4cOJTMzE0mSOH78OKtXr2bKlCn88ssvssQwe/ZsevbsycaNG/n88891vSPr1q2T7Vdiv3796Nq1K56enigUCl5//XUAjh07RlBQkCwxLFq0iHfffZeaNWvqvgRVKhUtWrTQzYeztbU1+AToyMhIZs+ezeXLlwFt78mIESMoW7asQcsFyMnJYfDgwXz55ZcFGqGGmGtXFF9fX5KTkzl+/HihE6979+5t8BiGDx/OgQMHmDhxIn/++SdNmzaladOmvPbaa4X2JBlC7vugRYsWesclGRcETJs2jRYtWhAeHk52djYff/wxFy9eJCkpicOHDxu8fIDY2FjGjBmjm4gvPbHGTq7VmpmZmfz000/s3r2bqlWrFmgsz5w5U5Y4hOIhVmsKT7Vy5Uq++uorIiMjAe3Q2YQJExgwYIBR48rMzMTExKTAB5ChrFu3jjt37tClSxfKlCkDwLJly3B0dJRl1WquK1eu6BZIVKhQgQoVKshW9o4dO+jQoQPVq1enYcOGABw+fJizZ8+yZcsW3njjDYPH4ODgwJkzZwzSQ/i8tmzZQs+ePUlNTcXe3l6vx0qhUJCUlCRbLMnJyRw8eJD9+/ezf/9+Ll68SEhIiCwNk/379z/1+tyeRkN7+PAh8+fP5+zZs6SmplKjRg2GDh2Kp6enLOW3adOGqKgoPvjgA90PuPzk+nx4Wk+hQqGQJdWNUHxE40x4Lunp6aSmpsq2Gq0wqampBXop5Bg6EbRCQkJo1aoVU6dO1Ts+duxYdu7cKcsKvT59+lC9enVZe8qeVL58edq2bcvkyZNl66UqSmJiIvv372fv3r3s27ePS5cu4eTkZPS5YP8ldnZ2HDx4kOrVqxs7FOEVIhpnQpFu3ryJSqUiMDBQ7/i1a9cwMzPDz89Plhg++OAD9u3bR2Zmpu64nEMnACdOnGDv3r2FDmPJMVygVqtZunRpkTmM5PhVbGlpyfnz5wvUh4iICKpWrar3+hjKpEmTmDFjBi1atKBmzZoFJmEPHz7c4DHY2Nhw/vx5AgICDF5WUYYPH67XGGvcuDFNmjShadOmss4/S05OZtGiRbph7sqVK9O/f38cHBxkKb9cuXL06tWLnj17FqiXcqlUqRIrV64sdPGUsdy9exdA18svvHzEnDOhSH379qV///4FPvSOHTvGL7/8IkuOr169eiFJEosXL8bd3V32Sc+gzTH2xRdfUKFChQIxyBXPiBEjWLp0Ke3atSM4ONgoz4OrqytnzpwpUB/OnDkjW4/qokWLcHR05OTJk5w8eVLvOoVCIUvjrFWrVoSHhxu1cRYdHc17771H06ZNCQ4ONkoM4eHhtGrVCisrK+rUqQNof6h888037Ny5kxo1ahg8hqFDh7Jq1SomTpxIzZo16dWrF926ddOtrpbD7NmzGTt2LD/++KMsP1iLotFodD9eUlNTAW2v3ujRo/n888+NuoBFeHGi50wokr29PadOnSqQouD69evUqlVLlj0NbW1tOXnypKxzq57k7u7Ot99+S9++fY0WQ6lSpVi+fDlt27Y1WgwTJ05k1qxZjB07VrcY4/Dhw3z77beMGjXqP7NKbNGiRUycOJF+/foVukqxQ4cORopMXo0aNaJcuXL8/PPPmJpqf+erVCoGDhzIjRs3ZN1XMiIigpUrV7J69Wpu3rxJs2bN6NWrlyyLM5ycnEhPT0elUmFtbV2gPsg1B/HTTz9l0aJFTJgwQTcn9NChQ3z11VcMGjSIb775RpY4hOIhGmdCkRwcHNi3b1+B7vqTJ0/StGlTg+TUelKzZs34/PPPdSvDjMHT05MDBw4YbdgEtAsx9u3bR/ny5Y0WgyRJzJ49mxkzZnD//n1dXB999BHDhw+XvTcv96NL7nKf1gMh51C7sbdWs7Ky4vTp0wVWLF+6dIlatWqRnp5u8BgK8/fff/P+++9z7tw5WV6LZcuWPfX6Pn36GDwG0L4XFy5cWODHwaZNmxgyZAj37t2TJQ6hmMib81Z4mbRv317q0qWLpFKpdMdUKpXUuXNnqXXr1rLEcP36den111+Xli5dKoWHh0tnz57Vu8jh22+/lUaMGCFLWUWZPn26NGTIEEmj0Rg1jlwpKSlSSkqKUcpetmyZFBwcLFlYWEgWFhZSlSpV/nOZ0X/44QepVKlS0qRJkyQrKytdZvwlS5ZITZs2lSUGNzc3aceOHQWOb9++XXJzc5MlhvyOHTsmjRgxQvLw8JCsra2lbt26yR6DMVlYWEhXr14tcPzKlSuSpaWlESIS/g3RcyYU6dKlSzRu3BhHR0caNWoEwMGDB0lJSWHPnj2yzHX5+++/6dGjB7du3dIdUygUsi4I0Gg0tGvXjoiICCpVqlRg2GL9+vUGj+Gtt95i7969ODs7U7lyZaPEUBLMnDmTL7/8kg8++EBv6Ob7779n0qRJRl3FKadKlSoxefJkOnXqhJ2dHWfPniUgIIALFy7QtGlTWVZrDh8+nA0bNjB9+nS9Ye6PPvqIzp07y7L/7pPDmc2bN6dnz56EhobqEhTLITIykiVLlhAZGcmcOXNwc3Nj27Zt+Pj4ULlyZVliqFu3LnXr1mXu3Ll6x4cNG8aJEyf4+++/ZYlDKB5iQYBQpEqVKnHu3DldDiErKyt69+7NBx98oLd1jSH179+fkJAQVq9ebbQFAcOHD2fv3r00a9YMFxcXo8Tg6OjIW2+9JXu5ISEhz/145UilMW/ePBYsWKA3l6hDhw5UrlyZr776SrbG2f79+5k+fbpeMt6PPvpI9yPG0ErC1mrTp09HoVDQu3dv3dZiZmZmvP/++wXSrRhKUFAQtWvXZujQoXTv3l22XUPy279/P23atKFhw4YcOHCAb775Bjc3N86ePcuiRYtYt26dLHFMmzaNdu3asXv3burXrw/A0aNHuXPnDlu3bpUlBqH4iJ4zoUSzsbHh7NmzBt838Wns7OxYs2YN7dq1M1oMxjJhwoTnPleObWwsLS25cOFCgfpw7do1qlSpIks6jxUrVtCvXz9CQ0P1kvFu2LCBpUuX0qNHD4PHUKlSJaZMmULHjh31es7mzZvHkiVLZGko50pPT9clqS5btqysud+uXbtm1LmgAPXr16dLly6MGjVK77U4fvw4oaGhurQWcrh//z7ff/89V65cAaBixYoMGTJEtj1fheIjes4EPefOnSM4OBilUsm5c+eeem7VqlUNHk/z5s2N3jhzdnaWZXuikuifNLhWr15Nhw4dDLIRdLly5Vi7di2fffaZ3vHffvtNti/pb775hmnTpun10g0fPpyZM2fy9ddfy9I4Kwlbq+WytramSpUqspaZK/c1P3nypF4vphxpPHKdP3+eVatWFTju5uYmezJgLy8vsSrzFSEaZ4Ke6tWrExMTg5ubG9WrV9fN73qSXPO93nzzTUaOHMn58+eNlrbgq6++Yvz48SxZskTWXoEaNWoQFhaGk5PTM4cX5ewpeZbBgwdTt25dg+QBmzBhAt26dePAgQN6vVZhYWGsXbu22MsrzI0bN3jzzTcLHO/QoUOBRqOhDBw4ECsrK7744gvS09Pp0aMHXl5ezJkzh+7duxus3NDQUJYuXYq9vT2hoaFPPVeOeZBxcXF069aN/fv34+joCGgT4zZr1ow1a9bg6upq8BgcHR2Jjo4usKXY6dOnKV26tEHLLmk/poXiIxpngp6bN2/qPtBu3rxp5Gjgf//7H6DNsfUkuRqIc+fOJTIyEnd3d/z8/Ao0EA3VMOrYsSMWFha6/xtjrts/YciZEp07d+bYsWPMmjWLjRs3Atqhm+PHj8uWod3b25uwsLACvbm7d+/G29tblhgAevbsSc+ePWXdWs3BwUFXD5/cV9QYhg0bRmpqKhcvXqRixYqAdiFTnz59GD58OKtXrzZ4DN27d+eTTz7h999/R6FQoNFoOHz4MGPGjDF4nrWS9mNaKD5izplQqJycHAYPHsyXX35p1E2mS4JnzbuSY67VyyT/vJtX0YIFC/jwww/p37+/3irFpUuXMmfOHAYPHmzkCP87HBwc2L17N7Vr19Y7fvz4cVq2bClLouzs7GyGDh3K0qVLUavVmJqaolar6dGjB0uXLsXExMRgZd++fRsfHx8UCgW3b99+6rm+vr4Gi0MofqJxJhTJwcGBM2fO/OcbZyXBwIED6dWrF02bNjV2KM9k6MaZWq1m48aNevs5dujQwaBfgk/asGEDM2bM0MVQsWJFPvroI4Mmfy1pw9zNmzdn/fr1uuHEXCkpKXTq1EmW/V6L2nT89OnTNGnShJSUFIPHkCsqKooLFy6QmppKSEiI7AsVDhw4QIMGDXS7NeRSqVQcOXKExo0byxqP8O+IYU2hSJ06dWLjxo1Gzx1l7LQFufJPOq5cubKsGx3Hx8fTunVrXF1d6d69O7169aJatWqylV9SXL9+nXbt2nH37l3dll5TpkzB29ubv/76y+ALN1QqFZMnT6Z///4cOnTIoGU9Kf8wd6dOnWQtuzD79u0jOzu7wPHMzEwOHjwoSwzNmzdnxIgRrF69Wrci8d69e4wcOZIWLVrIEkMuHx8ffHx8ZC0zv2bNmhEdHV1gePvhw4c0a9ZMDGu+ZETPmVCk3E10W7RoQc2aNQusvpNjk+mSkLYgLi6O7t27s2/fPqNNOgZ48OABv//+O6tWreLgwYMEBQXRs2dPevToYdQNl59kyJ6ztm3bIkkSK1eu1OXaS0xMpFevXiiVSv76669iL/NJtra2XLhwwajP+cCBA+nZsyfNmjWTvezciefVq1dnz549ejkP1Wo127dv58cff9RLHG0od+7coUOHDly8eFE33+/OnTsEBwezefNmypQpY/AYJEli3bp17N27l7i4ODQajd71ciWIViqVxMbGFvg8ioiIoFatWrL2Igr/nmicCUV62nCmQqHgxo0bBo+hYsWKvPfeewV672bOnMnPP/+s68kypG7dunHjxg2WL19eYNJxuXLlZJl0/KS7d++yevVqFi9ezLVr13RJQEuC4OBgtm3bZpDJ8TY2Nvz9998FUjecPXuWhg0bkpqaWuxlPqljx46EhobKtmdiUTHs2LEDV1dX3nnnHXr27ClbT6pSqdQNqRb29WFlZcW8efPo37+/LPFIksTu3bv1cnvJuRfviBEj+PHHH2nWrFmhibKXLFli0PJzV81u2rSJ1q1b63pXQdtYPnfuHBUqVGD79u0GjUMoXmJYUyhS/tWakpE2mS4JaQu2b9/O7t27dQ0z0A6tfv/997Rs2VKWGPLLyckhPDycY8eOcevWLdmyogcEBHDixAlcXFz0jicnJ1OjRg1dY/3ChQsGi8HCwoJHjx4VOJ6amoq5ubnBys2vTZs2jB07lvPnzxfaoyxHepdNmzbp9aTOmDFDtp7UmzdvIkmSLtFq/p4ac3Nz3NzcZJ3/p1AoeOONN3jjjTdkKzO/X3/9lfXr19O2bVujlO/g4ABoP6Pt7OywsrLSXWdubk69evUYNGiQUWIT/gX5tvEUXka//PKLVLlyZcnc3FwyNzeXKleuLP3888+ylV+2bFlp4cKFBY4vWLBAKleunCwx2NraSqdPny5w/NSpU5KdnZ0sMUiSJO3Zs0caOHCg5OTkJDk4OEj9+vWTdu/eLdtm6AqFQoqNjS1wPCYmRjI3N5clhnfffVeqXLmy9Pfff0sajUbSaDTS0aNHpeDgYKlPnz6yxKBQKIq8KJVKWWJ40p07d6Rp06ZJQUFBkomJiVFiMJbdu3dL7dq1kwICAqSAgACpXbt20q5du2Qr38/PT7p8+bJs5RXlq6++klJTU40dhlBMRM+ZUKRx48Yxc+ZMhg0bprdX28iRI4mKiio091hxGz16NMOHD+fMmTOFpi2QQ0mYdFy6dGmSkpJo3bo1P/30E2+++abe8IUhbd68Wff/HTt26H6pg3bYJCwsTLb5V3PnzqVPnz7Ur19fl29OpVLRoUMH2erDk3OKjE3OntTNmzfTpk0bzMzM9OpFYeToQfzhhx8YMWIEb7/9NiNGjADg77//pm3btsyaNYuhQ4caPIavvvqKCRMmsHjxYr1eK7mJlD6vGGO3DoWSq1SpUtKqVasKHF+1apXk4uIiWxzr16+XGjZsKDk7O0vOzs5Sw4YNpY0bN8pWflRUlFS9enXJzMxM9+vczMxMCgkJke7cuSNLDD/99JP04MEDWcp60tN6iszNzaXy5ctLW7ZskTWmiIgIafPmzdLmzZula9euGbw8JycnKT4+XpIkSerXr5+UkpJi8DKfxRg9qfl7T0tCD2Lp0qWlefPmFTg+f/58ycvLS5YY0tPTpVatWkm2trZScHCwFBISoneR0++//y516dJFqlu3rlHjEP49sSBAKJKjoyMnTpwokK8nIiKCOnXqyJLgsaSQjDzp2FjOnTtH5cqVMTExwd/fnxMnTlCqVCljhyU7W1tbzp07R0BAACYmJsTExMi2Srcw+XtSe/bsKWtPaklia2vLmTNnCuzWcO3aNUJCQmRZINK1a1f27t3L22+/XeiCALl6tObOncvnn39O3759+emnn+jXrx+RkZGcOHGCoUOHij03XzKicSYUadiwYZiZmTFz5ky942PGjCEjI4Pvv/9etliMmWPMWErCPob5GyJFLQgwtFGjRj33uU/W1eLyxhtvEBsbS82aNVm2bBndunUrcghr8eLFBokhv59//pkuXboUSAD7X9OjRw9CQkL46KOP9I5Pnz6d8PBw1qxZY/AYbGxs2LFjB6+99prBy3qaoKAgxo8fzzvvvKOXzmbcuHEkJSUxf/58o8YnvBgx50x4qkWLFrFz507q1asHwLFjx4iKiqJ37956X5qG+lIsKTnGjJEIN/8+hvnnecnJ0dGRGzdu4Orqyu3bt40y32rJkiUEBwdjampa5N6BYNiVxCtWrGDWrFlERkYC2sSemZmZBivvWYy1+m7u3LnPfa6h8iDmj6FSpUp888037Nu3Tzcv9u+//+bw4cOMHj3aIOU/ydvbG3t7e1nKepqoqCjdvFwrKyvdquZ3332XevXqicbZS0b0nAlFet4ElwqFwmBbtZSEHGOFJcI9dOgQGzdulC0RrrG89957LF++HE9PT6KioihTpkyRaRIMlfdOqVTqNnc2Vu9dfv7+/oSHhxs1BmN5MvdhfHw86enpej+crK2tcXNzM1h9eN7t5OTKxfjXX38xb948Fi5caNTExAEBAfzxxx+EhIRQq1YtBg0axODBg9m5cyfdu3cnKSnJaLEJL040zoQSrSRsbFwSEuEa0/bt27l+/TrDhw9n4sSJ2NnZFXpe7mq54ubi4sLWrVupW7dukVnQ5ZKTk0Pr1q1ZuHCh7HsnljSrVq3ihx9+YNGiRbqttK5evaprFPTs2dPIEcrDycmJ9PR0VCoV1tbWulXEueRqFA0cOBBvb2/Gjx/P999/z0cffUTDhg0JDw8nNDSURYsWyRKHUDxE40wo0UrCxsYWFhZcvHixwKTj69evExwcbLDhrWdtbp2fHBtd9+vXj7lz5xbZODOU9957j2XLluHl5WW03rv8XF1dOXLkyH++cVa2bFnWrVtXYP7nyZMnefvtt/WSWL/Kli1b9tTr5dpJQqPRoNFodBufr1mzRldPBw8eLFuSZqF4iDlnQolWEnKMeXt7ExYWVqBxtnv3boNsUZSrJGxunZ+ht6Epyk8//URoaKiu927QoEGyNxDz69WrF4sWLWLq1KlGi6EkiI6OLnTbMLVaTWxsrGxx3L17l82bNxMVFVVgI3ZDzYXNz5jbeOWnVCpRKpW6v7t370737t2NGJHwb4jGmVCizZ8/nw4dOuDn51dgY+MVK1bIEoOxEuH+kyX4q1evpkOHDgW2FCoOz1oxml9xrx5t3bo1oO2VGTFihFEbZyqVisWLF7N79+5Ct2+So0FQErRo0YLBgwfzyy+/UKNGDUD7+rz//vuypZkJCwujQ4cOBAQEcOXKFYKDg7l16xaSJOlikkNkZCRLliwhMjKSOXPm4ObmxrZt2/Dx8aFy5cqyxFCuXDl69epFjx49KF++vCxlCoYjhjWFEq8k5BjbsGEDM2bM0M0vq1ixIh999BEdO3aUNY5nsbe358yZMwQEBBT7ffft25cNGzbg4OBArVq1AO2X8cOHD+nUqZPeEKyxetnk8LSFMoZcHFPSxMfH06dPH7Zv3663W0OrVq1YunQpbm5uBo+hTp06tGnThgkTJujSR7i5udGzZ09at27N+++/b/AY9u/fT5s2bWjYsCEHDhzg8uXLBAQEMHXqVMLDw1m3bp3BYwCYNWsWq1at4tSpU9SoUYNevXrRrVs3PDw8ZClfKF6icSaUWDk5OVhZWXHmzBmCg4ONEoNKpWLy5Mn079+fMmXKGCWGF5E/v1Fx++STT0hKSmLhwoW6OV9qtZohQ4Zgb2/Pd999V+xlCiVfRESE7odTUFCQrL02dnZ2nDlzhrJly+Lk5MShQ4eoXLkyZ8+epWPHjty6dcvgMdSvX58uXbowatQovfff8ePHCQ0N5e7duwaPIb+IiAhWrlzJ6tWruXnzJs2aNaNXr1707t1b1jiEf0f57FMEwTjMzMzw8fFBrVYbLQZTU1OmTZtW6Nya/5rFixczZswYvcn4JiYmjBo1SpbEqyXN9evX2bFjBxkZGQBF5l971ZUvX54OHTrQoUMH2YfTbGxsdPPMPD09dXnoABISEmSJ4fz587z11lsFjru5uckWQ37ly5dnwoQJREREcPDgQeLj4+nXr5/scQj/jphzJpRon3/+OZ999hm//vorzs7ORomhRYsW7N+/36g5jEoClUrFlStXdGkTcl25cqXEbQZuSImJibotexQKBdeuXSMgIIABAwbg5OTEjBkzjB2iLNRqNUuXLiUsLIy4uLgCdUCO4d169epx6NAhKlasSNu2bRk9ejTnz59n/fr1usTZhubo6Eh0dHSB/GunT5+mdOnSssTwpOPHj7Nq1Sp+++03UlJS6NKli1HiEP450TgTSrT58+dz/fp1vLy88PX1LTD5Wo4UEm3atGHs2LGcP3++0AngHTp0MHgMJUG/fv0YMGAAkZGR1KlTB9DuGDFlypT/1C/zkSNHYmZmRlRUlC4xMmgTJo8aNeo/0zgbMWIES5cupV27dgQHBxt0h4aizJw5U7d/5oQJE0hNTeW3334jMDBQtoUZ3bt355NPPuH3339HoVCg0Wg4fPgwY8aMkXUo8cnhzObNm/Ptt98SGhqKra2tbHEIxUPMORNKtAkTJjz1ejk2Fc6/PP1JCoXCqMOuTzLknDONRsP06dOZM2cO0dHRAHh5eTF8+HBGjx5dZO6xV42Hhwc7duygWrVqes/3jRs3qFq1qiybbZcEpUqVYvny5bRt29bYoTyTIVcxZ2dnM3ToUJYuXYparcbU1BS1Wk2PHj1YunSpbO8LpVJJ7dq16dGjB927d8fd3V2WcgXDEI0zQXiFBAcHs23bNoPkX8vIyECSJKytrUlJSeHWrVuEhYVRqVIlWrVqVezllVR2dnacOnWKwMBAvcZZeHg4rVq1IjEx0dghysLLy4t9+/a9FGkbDLmKOdedO3c4f/48qamphISEyJ6k+Nq1a//5xMivErEgQBBeAgEBAYV+6ScnJ+t94Vy4cMFgiXE7duzI8uXLAW0vWsuWLZk5cyadOnViwYIFBimzJGrUqJHueQB0Q1nTpk177v1oXwWjR49mzpw5L8VCCDli9Pb2pm3btnTu3Jm0tDQePHhg8DLzy22YnTx5khUrVrBixQpZpn0IhiHmnAkljpOT03PPX5Fr37qwsLAiJz7LsVLx1q1bhQ6fZmVlce/ePYOXD9r5fbNmzQJg3bp1uLu7c/r0af744w/GjRsnS06pkmDatGm0aNGC8PBwsrOz+fjjj7l48SJJSUkcPnzY2OHJ5tChQ+zdu5dt27ZRuXLlAntKFnci4pLqww8/pEqVKgwYMAC1Wk2TJk04cuQI1tbW/PnnnzRt2lSWOOLi4ujWrRv79+/X24i+WbNmrFmzxmj70Qr/jGicCSXO7Nmzdf9PTExk0qRJtGrVivr16wNw9OhRduzYwZdffilLPBMmTGDixInUqlULT09PWSc+b968Wff/HTt24ODgoPtbrVYTFhYm2yrS9PR0XWb+nTt3EhoailKppF69ety+fVuWGEqC4OBgIiIimD9/PnZ2dqSmphIaGsrQoUPx9PQ0dniycXR0LDSFxH/NunXr6NWrFwBbtmzhxo0bXLlyhV9//ZXPP/9ctgb7sGHDSE1N5eLFi7qFKpcuXaJPnz4MHz6c1atXyxKHUEwkQSjBQkNDpXnz5hU4Pm/ePKljx46yxODh4SEtX75clrKepFAoiryYm5tL5cuXl7Zs2SJLLFWqVJHmzJkjRUVFSfb29tKRI0ckSZKk8PBwyd3dXZYYSoLbt29LGo2myOuEksfW1laKjIw0yH1bWFhId+7ckSRJkgYNGiSNGDFCkiRJunHjhmRnZ2eQMgtjb28vHT9+vMDxY8eOSQ4ODrLFIRQPMedMKNF27Nih21cxv9atW7N7925ZYsjOztbtqSmnc+fOkZOTg0ajwdfXVzekmnvJysri6tWrtG/fXpZ4xo0bx5gxY/Dz86Nu3bq6nsydO3cSEhIiSwwlgb+/P/Hx8QWOJyYmFsh19V8QHx/PoUOHOHToUKHPy6vO3d2dS5cuoVar2b59O2+88Qag7WmWcwWzRqMpMLQM2mTe/6U8hK8K0TgTSjQXFxc2bdpU4PimTZtwcXGRJYaBAweyatUqWcrKLyQkRDenTqFQGCWPVH5vv/02UVFRhIeHs337dt3xFi1a6Oai/RdIklToa5GamoqlpaURIjKOtLQ0+vfvj6enJ40bN6Zx48Z4eXkxYMAA0tPTjR2eHl9f30IbLsWhX79+dO3aVZfrLXff32PHjhEUFGSQMgvTvHlzRowYwf3793XH7t27x8iRI2nRooVscQjFQ8w5E0q0CRMmMHDgQPbt20fdunUB7Yfe9u3b+fnnnw1W7qhRo3T/12g0/PTTT+zevZuqVasW+JA3VLJLR0dHbty4gaurK7dv3y4Rv349PDwKbKScm5D2VZdbJxQKBV9++SXW1ta669RqNceOHaN69epGik5+o0aNYv/+/WzZsoWGDRsC2kUCuXnv5FjBGxAQwIkTJwr8UEtOTqZGjRrcuHED0K5iNpSvvvqK4OBg7ty5Q5cuXbCwsAC0W5uNHTvWYOU+af78+XTo0AE/Pz/diu07d+4QHBzMihUrZItDKB4iz5lQ4h07doy5c+dy+fJlACpWrMjw4cN1jTVDeJGUCHv37jVIDO+99x7Lly/H09OTqKgoypQpU+QwSe6XkGA4uXVi//791K9fH3Nzc9115ubm+Pn5MWbMmP9MrqlSpUqxbt26AqsR9+7dS9euXWUZ4lQqlcTExODm5qZ3PDY2Fh8fH7KysgweQ0kiSRK7d+/WbURfsWJFXU+e8HIRjTNBKMG2b9/O9evXGT58OBMnTtStlnzSiBEjZI7sv6tfv37MmTMHe3t7Y4diVNbW1pw8eVJvCyuAixcvUqdOHdLS0gxWdu4q5k6dOrFs2bJCVzHv2rWLq1evGiyG/NLS0ti/fz9RUVG6jdhzDR8+XJYYhFeLaJwJJV5kZCRLlizhxo0bzJ49Gzc3N7Zt24aPjw+VK1c2ePn9+/dnzpw5BRpGaWlpDBs2TJY8Z/369WPu3LlFNs4EQW4tWrTAxcWF5cuX6+baZWRk0KdPH5KSkgy6YOdpW6qZmZnh5+fHjBkzZFksc/r0adq2bUt6ejppaWk4OzuTkJCAtbU1bm5usvZqGzsfo1B8RONMKNH2799PmzZtaNiwIQcOHODy5csEBAQwdepUwsPDWbduncFjMDExITo6usDQSUJCAh4eHqhUKoPHIJQs4eHhrF27ttCekv9K8tXz58/TunVrsrKyqFatGgBnz57FwsKCnTt3GuyH07lz56hcuTImJib4+/tz4sQJSpUqZZCynkfTpk0pX748CxcuxMHBgbNnz2JmZkavXr0YMWIEoaGhssTxrHyMGzZskCUOoXiIBQFCiTZ27FgmTZrEqFGj9HqNmjdvzvz58w1adkpKCpIkIUkSjx490luJp1ar2bp1a4EGm6G8yAf8f6VxYCxr1qyhd+/etGrVip07d9KyZUsiIiKIjY39TyVlrVKlCteuXWPlypW6OU7vvPMOPXv2xMrKymDlhoSEEBMTg6ura4lYxXzmzBl+/PFHlEolJiYmZGVlERAQwLRp0+jTp49sjbOFCxeydOlS3n33XVnKEwxLNM6EEu38+fOFprFwc3MjISHBoGU7OjrqPvwL29xZoVAwYcIEg8aQy97eng0bNuDg4ECtWrUA7R56Dx8+pFOnTkb/gvovmTx5MrNmzWLo0KHY2dkxZ84c/P39GTx48H9qh4ApU6bg7u7OoEGD9I4vXryY+Ph4PvnkE4OUW9JWMZuZmemGWd3c3IiKiqJixYo4ODhw584d2eIwVj5GwTBE40wo0RwdHYmOji6Q3PP06dOULl3aoGXv3bsXSZJo3rw5f/zxB87OzrrrzM3N8fX1xcvLy6Ax5HJ3d6dr164sXLhQt2JTrVYzZMgQ7O3t+e6772SJQ9DOgWzXrh2grQdpaWkoFApGjhxJ8+bNZWuwG9uPP/5Y6A+nypUr0717d4M1zjp37kyTJk10DeFatWoZdRVzSEgIJ06cIDAwkCZNmjBu3DgSEhL49ddfCQ4ONnj5uXLzMcq1rZ1gWKJxJpRouR/yv//+OwqFAo1Gw+HDhxkzZgy9e/c2aNlNmjQB4ObNm3h7ez91ErKhLV68mEOHDul9CZmYmDBq1CgaNGggGmcycnJy4tGjRwCULl2aCxcuUKVKFZKTk0tc8lVDiomJKbSn0NXVlejoaIOV+9NPPxEaGqpbxTxo0CCjLpSZPHmyrj5888039O7dm/fff5/AwEBZJ+FnZmYaJR+jYBiicSaUaJMnT2bo0KF4e3ujVqupVKkSKpWKnj178sUXX8gSg6+vL6DdjqWwCeBVq1Y1eAwqlYorV65QoUIFveNXrlwx+rDOf03jxo3ZtWsXVapUoUuXLowYMYI9e/awa9eu/1Qmdm9vbw4fPlygV/vw4cMG71HO3dLt5MmTjBgxwqiNs9xpBqAd1sy/e4aczp07p0uC/GTSXTHt4eUjGmdCiWZubs7PP//MuHHjOH/+PGlpaYSEhFCuXDnZYoiPj6dfv35s27at0OvVarXBY+jXrx8DBgwgMjJSl5H/2LFjTJkyhX79+hm8fCHP/PnzyczMBODzzz/HzMyMI0eO0LlzZ9l+MJQEgwYN4sMPPyQnJ4fmzZsD2lQOH3/8MaNHj5YlhiVLlshSzvOIi4vT5VULCgrC1dVV1vINlQxbMA6RSkMo8RYtWsSsWbO4du0aAIGBgXz44YcMHDhQlvJ79uzJ7du3mT17Nk2bNmXDhg3ExsYyadIkZsyYoZt/ZEgajYbp06czZ84c3ZCRl5eXbqscOTdY/q/r3bs3zZo1o3HjxpQtW9bY4RiNJEmMHTuWuXPn6nqTLS0t+eSTTxg3bpwsMZSEVcyPHj1iyJAhrFmzRvdDzcTEhG7duvH999/rJciVw/Xr14mMjKRx48ZYWVkVuResULKJxplQoo0bN46ZM2cybNgw6tevD8DRo0eZP38+I0eOZOLEiQaPwdPTk02bNlGnTh3s7e0JDw+nfPnybN68mWnTpnHo0CGDx5CRkYEkSVhbW5OSksKtW7cICwujUqVKtGrVyuDlC3kGDhzIgQMHuH79OqVLl6ZJkyY0bdqUJk2a/Ge2bsovNTWVy5cvY2VlRWBgoG5vSTn07dv3uVcxG6qXrVu3bpw+fZp58+bpfUaNGDGC6tWrs2bNGoOU+6TExES6du3K3r17USgUXLt2jYCAAPr374+TkxMzZsyQJQ6hmEiCUIKVKlVKWrVqVYHjq1atklxcXGSJwc7OTrp586YkSZLk4+MjHTp0SJIkSbpx44ZkZWUlSwxvvPGGtGDBAkmSJOnBgweSu7u7VKZMGcnS0lL64YcfZIlB0Hf37l1p1apV0uDBg6WgoCBJqVRKpUuXNnZY/ykff/yxNHDgQEmlUumOqVQq6b333pPGjBkjSwzW1tbSwYMHCxw/cOCAZG1tLUsMkiRJ7777rtSqVSvpzp07kq2trRQZGSlJkiRt375dqlSpkmxxCMXDeMvPBOE55OTk6E24zVWzZk3ZMvNXqFBBN5ekWrVq/Pjjj9y7d4+FCxfKltfq1KlTNGrUCIB169bh7u7O7du3Wb58OXPnzpUlBkGfk5MTLi4uODk54ejoiKmpqez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+ }, + "metadata": {} + } + ], + "source": [ + "# Correlation matrix to understand feature relationships\n", + "correlation_matrix = df.corr()\n", + "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n", + "plt.title(\"Correlation Matrix\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u8hKTaTw2LkJ" + }, + "source": [ + "Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P1EtDR5v2HBV" + }, + "outputs": [], + "source": [ + "# Selecting features and target variable\n", + "x = df.drop(axis=1,columns=['price','sqft_above','sqft_basement','renovation','sqft_lot','condition'])\n", + "y = df['price']" + ] + }, + { + "cell_type": "code", + "source": [ + "# How data looks like before Transformation\n", + "x.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "-YbIJ9aYMztr", + "outputId": "a222e7a7-15a9-493f-fc53-907525d0b0fe" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " bedrooms bathrooms sqft_living floors waterfront view\n", + "0 3.0 1.50 1340 1.5 0 0\n", + "1 5.0 2.50 3650 2.0 0 4\n", + "2 3.0 2.00 1930 1.0 0 0\n", + "3 3.0 2.25 2000 1.0 0 0\n", + "4 4.0 2.50 1940 1.0 0 0" + ], + "text/html": [ + "\n", + "
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\n" + ] + }, + "metadata": {}, + "execution_count": 61 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xWu7BQMSm4rJ" + }, + "source": [ + "Splitting dataset into Training and Testing data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AWE_8DGK2OGA" + }, + "outputs": [], + "source": [ + "# Splitting the dataset into training and testing sets\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)" + ] + }, + { + "cell_type": "code", + "source": [ + "print(f'Size of the data : {x.shape}')\n", + "print(f'Size of the training data : {x_train.shape}')\n", + "print(f'Size of the testing data : {x_test.shape}')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2Mj_dxy9NADv", + "outputId": "0ad77cc4-2a2e-4fb5-edda-656d68c4bfc4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Size of the data : (4600, 6)\n", + "Size of the training data : (3450, 6)\n", + "Size of the testing data : (1150, 6)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aAWg0GjRm4rJ" + }, + "source": [ + "### **LINEAR REGRESSION MODEL**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1plypLYP2TFA" + }, + "source": [ + "Building the Linear Regression Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L9bhgg_32RC3" + }, + "outputs": [], + "source": [ + "# Building the Linear Regression Model\n", + "model = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 74 + }, + "id": "sabeTz6x2WKu", + "outputId": "b838a67f-453c-461d-cbbe-fb879ae15f8f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression()" + ], + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 65 + } + ], + "source": [ + "# Fitting the model on the training data\n", + "model.fit(x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AHxkvhlO2ZzE" + }, + "source": [ + "Model Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ibk-DaHm2YKX" + }, + "outputs": [], + "source": [ + "# Model Evaluation\n", + "y_pred = model.predict(x_test)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**MSE (Mean Squared Error)** : average of squares of errors\n", + "\n", + "**RMSE (Root Mean Squared Error)** : root of average of squares of errors\n", + "\n", + "MSE and RMSE help show spread the approximated values are from the actual mean.\n", + "\n", + "**MSE and RMSE** should be **closer to 0**.\n", + "\n", + "**R2 Score** : Helps understand how closely our model approximates values. Lies between 0 and 1.\n", + "\n", + "**R2 Score** should lie closer to 1." + ], + "metadata": { + "id": "o4u06sm3QZmO" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XzeL90ta2b0O", + "outputId": "10180fae-b08f-4540-92b5-7c005f3a02cf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Linear Regression RMSE: 898376.7867491037\n", + "Linear Regression MSE: 807080850969.6445\n", + "Linear Regression R-squared: 0.0418310828673496\n" + ] + } + ], + "source": [ + "# Mean Squared Error and R-squared for model evaluation\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "rmse = mean_squared_error(y_test, y_pred, squared=False)\n", + "\n", + "print(\"Linear Regression RMSE:\", rmse)\n", + "print(\"Linear Regression MSE:\", mse)\n", + "print(\"Linear Regression R-squared:\", r2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fnAFZj-B2gmC" + }, + "source": [ + "Predictions and Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 587 + }, + "id": "lYq6CfZc2eau", + "outputId": "8fd3aacc-f6a8-4719-d3cf-42b4e060f21c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " This is not a good prediction :( \n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Predictions and Visualization\n", + "# To visualize the predictions against actual prices, we'll use a scatter plot\n", + "print(\" This is not a good prediction :( \\n\" )\n", + "plt.figure(figsize=(6,6))\n", + "plt.scatter(y_test, y_pred, c='crimson')\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "\n", + "p1 = max(max(y_pred), max(y_test))\n", + "p2 = min(min(y_pred), min(y_test))\n", + "plt.plot([p1, p2], [p1, p2], 'b-')\n", + "plt.xlabel('True Values', fontsize=15)\n", + "plt.ylabel('Predictions', fontsize=15)\n", + "plt.axis('equal')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "source": [ + "# This is just for explaination\n", + "# There is no sense behind this\n", + "\n", + "print(\" This is how a good prediction would look like :) \\n\")\n", + "plt.figure(figsize=(6,6))\n", + "plt.scatter(y_test, y_test, c='crimson')\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "\n", + "p1 = max(max(y_test), max(y_test))\n", + "p2 = min(min(y_test), min(y_test))\n", + "plt.plot([p1, p2], [p1, p2], 'b-')\n", + "plt.xlabel('True Values', fontsize=15)\n", + "plt.ylabel('Predictions', fontsize=15)\n", + "plt.axis('equal')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 587 + }, + "id": "ED0cG7MCNW-n", + "outputId": "b5c16fe7-7d2b-47b5-99b7-2cf5832ebb37" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " This is how a good prediction would look like :) \n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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YAEAgK5zykopeXixVVkr6ZQ7Ifedq64kuiomR3npij86dnyfHoV9fE5qUoLhp9zEHBEHDUAj53e9+p9tuu01paWnq0KGDdu3apfj4eA0bNszV58iRI/r444/Vv39/w8UCgD8onPKSirIXutq/zgHpoma2Mi0a+Z4GPnidnFn/1NGtn6ki77DCElsosmcaIyAIKoZCyC233KJdu3bpb3/7mz755BO1adNGb7zxhqKjo119lixZovLycg0YMMBwsQDg6yqPHz85AvKLmiahdnr3C1UeH6aQ8HBF9WZnXAQvm9PpdBo9yLFjx1RcXKz4+Phqzx08eFA//fSTOnToUCWc+Lri4mLZ7XYVFRUpNjbW6nIA+In/vbJEh5+YKanu23Bb/OlenXX3jVaWCjQad79DvbJiakRERI0BRJKSk5OVnJzsjdMAgM878c33kupfB+RUPyCYGbpFFwBQVZOU1m6tA9IkpbVFFQK+w/BISH5+vl566SVt2LBBP/zwg44dO1ZjP5vNptzcXKOnAwCfFjb6GtddMLUFEIWGKHbcNZbUB/gSQyHkyy+/VN++fXX48GF5YWoJAPgVp8NR5e6WyrQ0jbguvO4AIsl+9yiFhIdbUDHgWwyFkAcffFCFhYW6/vrr9cgjj+i8887zq8mnANBQpSvWq/CxF6psRnfn0ee1pexixcRIi0a+p04rvpAqT3tRaIjsd49S3JR7rCka8DGG7o6x2+1KSkrSF198IZvN5s26LMfdMQBqU7pivfLGPS798tvzzDkgy57Zo4EPdq62YmrsuGsYAUFQMOXuGKfTqYyMjIALIABQG6fDocLHXqg1gMy1P6BzF+TLmbVEIeHh3IYL1MHQ3THdunXTgQMHvFULAPg0p8OhollLq1yCqXYXTNhuOb7P19Gtn1lcLeD7DIWQKVOmaPv27frXv/7lrXoAwCeVrlivA11GurUQmSRV5B22qlTAbxi+RfcPf/iDrrvuOt18880aNGiQ2rRpo5CQmrPNZZddZvR0AGC6+uaA1HQXTFhiCwsqBfyLoYmpISEhstlsrttz65sb4nA4Gnoq0zExFYB08hLMN51vUOUPhZLcCCC2k7vhttuxhM3oELRMmZh6++23MykVQMByOhzKv3+6RwFEkuKm3UcAAdxgKIS8/vrrXioDAHxL6Yr1KsiarsqfiyW5dwkmNClBcdPuU/SwvlaUDPgdr2xgBwCBpHTFeuWNfdzVdieAtPjTvbKPv54REMADXgshx48f165du/T99yd3hmzdurUyMjIUzsI8APyI48gR5d8zzdV2awSkdQIBBGgAwyHk6NGjmjRpkl599VWVlpZWeS46Olp33323pk6dqsjISKOnAoBGVTjlJRVlL3S13QkgEnNAgIYyFEKOHTumgQMHasuWLZKktLQ0paSkyGaz6ZtvvtGnn36qv/71r9q0aZNycnIUERHhlaIBwNsaGkDOfmgcc0CABjK0WNnzzz+vzZs3q3fv3tq1a5c++eQTvf3223rrrbe0c+dOffrpp+rTp4+2bNmiGTNmeKlkAPCuyuPHVfTyYlfb3QASmpSgs++/3cxSgYBiKIQsXLhQ8fHxevfdd3XxxRdXe/6iiy7SihUrFBcXpwULFhg5FQA0muI5y6TKk9vduhtAJCnuz1yGAYwwFEL27t2rfv36KSYmptY+0dHR6tevn3Jzc42cCgC8zulw6MimT1Ses1WS+wHEFtNUiXOncRkGMMjQnJCwsDCVl5fX26+8vFxhYdwNDMB3lK5Yr8LHXqh7M7oaA0gzpfx3uUK48w8wzNBIyMUXX6wPPvhA+/btq7XP/v379cEHHygtLc3IqQDAa0qXr1Xe2Mc9DiCySQkvPkIAAbzEUAi56667dOTIEfXr10+zZ8/WkSNHXM8dOXJEc+fOVb9+/XT06FHdfffdhos1Q3Z2tlJTU5WZmWl1KQAaQcnytcr77WRX2+0RkOimSpzDJRjAmwxtYCedDCKzZs1y7SETFxcnSSosPLnXgtPp1F133aWXX37ZYKnmYgM7IPA0ZCVU2WxqdnV/Jb4yiUmogJvc/Q41HEIkaenSpXrxxRf10Ucf6fjx45Kk8PBw9ezZU/fee6+uv/56o6cwHSEECCyOI0d04KJr5Swuk1R/AInql6mmA3oqdtw1XH4BPGRqCDmloqJChw8fliS1aNHCryejEkKAwFE45SUVvbxIqjz5686dEZCkZS8qqndnK8oF/J6736FeTQlhYWFKTEz05iEBwJDCydkqemmRq+1OAAk5O1aRPZlMDzQ2QxNTAcCXFb+12uMAIkn2O0cy/wMwgUcjIZdffrlsNpveeOMNtWnTRpdffrnbr7XZbMrJyfG4QABoiPxHX1DJrDddbbfvgjk7Vmfff5uZpQJBy6MQsm7dOtlsNtcCZevWrXP7tafungGAxvZNt1FyHDjkanuyFHvCcxMZBQFM4lEI2b9/vySpdevWVdoA4CsaGkBCmscq/tmJrAMCmMijENKuXbs62wBgpaI33/M8gNhsSlw4Xc36ZTICApjM0MTUefPmafPmzfX227p1q+bNm2fkVABQJ6fDocI/PONquz0J9Z7Rih7QkwACWMBQCLnjjjv097//vd5+s2fP1tixY42cCgBq5XQ4VDRrqXT8hCTPAkjclHvMLhfAL0xZTayyspKJqQAaRUN3w41/dZJirxtkdrkATmNKCNm3bx+rjgLwutLla5X3m0mutrsBJGrYZQQQwAd4HEKefPLJKu1du3ZVe+yUiooKffXVV9qwYYMGDeIvPADvKV6Wo4K7prrabt+GGxKiVn+v+XcWAHN5vHdMSEiIbDabnE6n65/1SUhI0L///W916dKlwYWajb1jAN9V8MRMFb+yxNX2ZB2QxLnTuA0XaGSNtnfM3LlzJUlOp1Pjxo3TpZdeqt/85jc19g0PD1dSUpJ69uypiIgIT08FANV8f8tDOvr+r3flub0S6lkxSnj+IQII4EM8DiFjxoxx/fsbb7yhoUOHVnkMABrLoVsf9jiANBs5WLGjhyiqd2duwwV8jKGJqWvXrvVWHQBQp5JlOTry3iZX250AEto6QYkzHyF8AD7K0Dohe/fu1Ysvvqjdu2u+9ipJu3fv1osvvqh9+/YZORWAIOZ0OFT4x7+62u5egombdh8BBPBhhkLIjBkz9Mc//rHOSScxMTHKysrSiy++aORUAILUqYXIKotLJbkZQEJsSpz9JPM/AB9n6HJMTk6OMjIy1LZt21r7tGvXThkZGVq9erWRUwEIQsXLclSQNV0qOblzt/sLkU1W9Ij+ZpcLwEOGRkIOHjyo9u3b19uvQ4cO+u6774ycCkCQ+f7miSoYP8XjABJ7942KvWaAydUCaAhDIyGhoaE6duxYvf2OHTsmh8Nh5FQAgsjBQeN1fNd/XW13A0jkFZco/k/3mlkqAAMMjYScd9552rhxo8rLy2vtU15ero0bN6pjx45GTgUgSBQ8MbNBASRqcG+1XvBMtccB+C5DIeSGG27QTz/9pN/+9rcqKyur9nx5ebnGjx+vn3/+WTfccIORUwEIApXHj3u8EmqIPVoJs6Yoaf5fzC4XgEEeL9t+uvLycnXv3l1ffvmlEhISdNNNN6lDhw6SpNzcXC1cuFD5+fnq1KmTtm/frmbNmnmt8MbGsu2AuSpKS3XgomulsqOS3F+IjHVAAN/TaMu2n65p06Zas2aNbr31Vn3wwQeaMWOGbDabJLn2lOnfv7/+8Y9/+FUAAWCuhs4BiR09hAAC+DFDIUSSWrZsqTVr1mj79u1as2aNDh48KElKTk7WwIEDlZmZabhIAIHrQOZoVXzzvavtbgAJOTtWUb07m1kqAC8zHEJOyczMJHAA8MjPi95tUACRpPjnJjIKAvg5r4UQAPDEoQee05E33na13Q4gTcKU+NoUVkMFAoBHIWTDhg2SpO7duysyMtLVdtdll13mUX8AgSm3/RCp5Nc76twNIKEpSWq39f8xAgIECI/ujgkJCZHNZtOXX36p8847z9V2lz8tWMbdMUDj2Hf+MDkPF7nanlyCSfn2fYVGRZlVKoAGapS7Y26//XbZbDbZ7fYqbQBwR+75w6UGBpCowb0JIECAMbROSCBjJATwrn0XDJez8H+uticBJDzjfCWvnmVSpQCMMmWdEABwx48PP9fgABL38uOy3zDYpEoBmIkQAqBRFTwxU2WzG3AXjKSEWVMUw464QMDyKIQ8+eSTDT6RzWbTE0880eDXA/A/BZOzPd4L5hT7hJsIIECAa9DdMWe+5PTJqaeeO/Mxm83G3TFAEClZlqP88VNcbY8WIps1RbEEEMBvNcqckLlz51Z7bMuWLXrttdfUpk0b3XDDDUpJSZEkHThwQEuXLtW3336rO++8U7169fLsHQDwW8XLclRw51RX25MAkjj7SUWP6G9WqQAsZOjumG3btqlv3766//779eSTTyosrGqmcTgcmjRpkp599lmtX79ePXr0MFywWRgJARom/4mZKmngJZjEudNYCRUIAO5+hxoKIUOGDNF3332n3btr/oVyykUXXaTWrVvrvffea+ipTEcIATz3w60Pq/y9Ta622wEkppnaf/0uK6ECAcLd79AQIyf56KOPlJ6eXm+/9PR0bdu2zcipAPi4gsnZDQogEUN6q8O+VQQQIAgZCiEOh0P79u2rt9++ffv8alIqAM9UlJaq+KVFrrbbIyBNI9X69T+bWCkAX2IohHTv3l3btm3TvHnzau0zb948ffTRR+revbuRUwHwUQWTs3XgnKGutkdzQLIfZwQECGKG5oRs2rRJ/fv3l8PhUN++fTVq1Ci1a9dO0sm7Y5YsWaJ169YpNDRUa9euVe/evb1WuDv279+vcePGKS8vT6Ghodq6dauaNWvm1muZEwLU7/ubJ+ro6i2utrsBxHZWrBKen8gkVCBAmTIxVZJWrFihcePGqbCwsNpmdk6nUy1atNDs2bM1YsQII6dpkL59+2ratGnq06ePfvrpJ8XGxla7g6c2hBCgbt8O/K1OfPqVq+1uALH/8Xa1eHAcIyBAADNt75hhw4Zp3759evPNN/Xhhx/q0KFDkqRWrVqpT58+GjlypKKjo42exmP/+c9/1KRJE/Xp00eS1Lx5c9NrAALVd6MfbFAAibn7RsU9PN7MUgH4MENzQk6Jjo7WHXfcodmzZ2vlypVauXKl5syZo7FjxzY4gGzYsEHDhw9XUlKSbDabli1bVq1Pdna2UlJSFBkZqR49elS5A+frr79WdHS0hg8fri5duuipp55q6NsDcJrvRj2gYzlbXW23L8HYY5Twp3vNLBWAj/NKCGkMZWVlSk9PV3Z2do3PL168WFlZWZo8ebJ27typ9PR0DR48WPn5+ZKkiooKffjhh3rppZe0ZcsWrV69WqtXr671fMeOHVNxcXGVHwBV5XYapmMffORqezIJNXnHQrPKBOAnvBJC3n//fV177bVq3bq1IiIiNG7cONdz7733nrKyslyXadw1dOhQTZs2Tddee22Nzz/33HMaP368xo4dq9TUVL3yyitq2rSp5syZI0lq3bq1unXrpuTkZEVEROjKK6/Url27aj3f008/Lbvd7vpJTk72qF4g0OUm9JF+KnK1PQkgYSmt1cRuN6tUAH7CcAj5wx/+oKFDh+qdd95RSUmJTpw4UeX5Vq1aacaMGVq8eLHRU7kcP35cO3bs0MCBA12PhYSEaODAgdqy5eRM/czMTOXn5+vnn39WZWWlNmzYoAsuuKDWYz7yyCMqKipy/Rw8eNBr9QL+LrfN5dJpU9g9DSDtti+q8TkAwc1QCJk3b55mzpyprl27aufOnTVewkhLS1NycrL+9a9/GTlVFYWFhXI4HEpMTKzyeGJion788UdJUlhYmJ566ilddtllSktLU8eOHTVs2LBajxkREaHY2NgqPwCk3I5DpWO//s+F2wEkNlpt964ggAColaG7Y15++WWdddZZevfddxUfH19rv7S0NH3++edGTtUgQ4cO1dChQ+vvCKBGuW0HSkeOudruBpDQ9m2U8hFzQADUzdBIyO7du3XJJZfUGUAkyW63Ky8vz8ipqoiLi1NoaGi1Y+bl5ally5ZeOw8QzHLbDGhQAJFNard5vomVAvBXhueEnLlAWU0OHTqkqKgoo6dyCQ8PV9euXZWTk+N6rLKyUjk5OerVq5fXzgMEq9z4PtKx4662J3NAEl6bwkJkANxi6HJMx44dtXPnTp04cUJNmjSpsU9JSYl27dqlCy+80KNjl5aWau/eva72/v37tWvXLjVv3lxt27ZVVlaWxowZo27duql79+6aMWOGysrKNHbsWCNvCQh6ufF9qrQ9CSBNh1yqmGsGmFEmgABgaCRk5MiR+uGHH/Twww/X2ufUXSejR4/26Ngff/yxOnfurM6dO0uSsrKy1LlzZ02aNEmSNGrUKP31r3/VpEmTlJGRoV27dmnVqlXVJqsCcJ+RABL7u1Fq9Y+nzSgTQIAwtHfMkSNH1LNnT+3evVvdu3fX1VdfrUcffVR9+vTRNddco7ffflsbN25Uly5dtHnzZoWHh3uz9kaRnZ2t7OxsORwO7dmzh71jEDSMBJBzvs9RiB/8/QZgDtM2sCsoKNAdd9yhlStXymaz6czDDRo0SPPnz6938qqvYQM7BBMCCABvMm0Du/j4eL377rv69NNP9f777+ubb75RZWWl2rRpo0GDBql79+5GTwGgEeW2HVil7UkAsU+4iQACoMEMhZDrrrtOrVq1UnZ2ttLT05Wenu6tugCYILfD4IbdhquTASRuyj1mlQogABkKIf/+9791zTXXeKkUAGbiEgwAqxm6O+acc85RWVmZt2oBYBICCABfYCiE3HTTTVq/fr1rvxYAvs/Qbbj3jCaAAPAaQyHkkUceUZ8+fdS3b1+9/fbb1XbQBeBbjC5EFj91ghllAggShuaEdOrUSZWVlTp48KBuuOEG2Ww2JSQkKDIyslpfm82m3NxcI6czxenrhACBJLft5VXangSQ+FcnKfa6QWaUCSCIGFonJCTEs4GUysrKhp7KdKwTgkCSmzJIKjvqanu0F8ysKSzFDsAjpqwT4k+hAghWuUn9pRMVrrZHc0DuvpEAAqDRGN5FF4Dvym07sMEBJGpwb8X/6V6zSgUQhBo0EvLvf/9by5Yt08GDBxUREaG0tDSNHTtW55xzjrfrA9BARu+CYRIqgMbm8ZyQW265RYsWLZIk1z4xNptNERERWrRokUaMGOH9Ki3AnBD4MyMBpO3eFWpit5tRJoAA1ShzQmbPnq2FCxcqLCxMt912mzp37qySkhKtWLFCW7Zs0e23364DBw7Izi8wwDJGAkjU4N4EEACm8SiEvPHGGwoJCdHKlSs1YMCvk9UeeeQRjR07VvPmzdNbb72lsWPHer1QAPUzEkDC0s5T0vy/mFEmAEjycGLq559/rp49e1YJIKc8+uijcjqd+vzzz71WHAD3GQkgTdI6ql3ObDPKBAAXj0JIcXGxOnToUONzpx4vLi42XhUAjxgJIGqdoLY5c0yoEgCq8iiEOJ1OhYaG1nygXxYu8/e1Q7Kzs5WamqrMzEyrSwHcYiiAhIWqw66lJlQJANWxTsgZJkyYoC+++ELbt2+3uhSgXoYDyA/rGr9IAKiFR7fohoSEyGazNexENpsqKirq7+gjuEUXvs5IAAm9qINS1r5uQpUAglGjLdve0K1mDGxRA+AMRgJI5MCear3w/8woEwDq5FEI8ff5HkAgMBJAIgYQQAD4DuaEAH7ESAAJzzhfbRYRQAD4DkII4CeMBJCYO29Q8upZZpQJAG5r0AZ2AMxlJIAkzJqimGuqLzAIAFYjhAA+zkgASZw7TdHD+ppRJgB4jBAC+DAjAaTNF28pIj7ejDIBoEGYEwL4KCMBJCT+bAIIAJ9HCDkDy7bDFxgJILbYZjrni+VmlAkAhni0YmowYcVUWMXQbbgDeih50V/NKBMAauXudygjIYAPMRJAmv3mOgIIAL9CCAF8hKERkLTz1PIv95tRJgB4DSEE8AGGJqEmt1RyzmwzygQAryKEABYzEkAUHqZzdv7ThCoBwPsIIYCFjAaQDt+vNaFKAGgchBDAIoZ2w73pSgIIAL9HCAEsYGgOSLtWavPiI2aUCQCNihACmMzQQmQxTXXOx0vMKBMAGh0hBDCRoTkgYTa13/eeCVUCgDkIIYBJDAWQEJs6/LDBhCoBwDyEEMAEhgJIdFN1yCOAAAg8hBCgkRkJIGFpHdVhP5dgAAQmQsgZ2EUX3mQkgMS/OkntcuaYUSYAWIJddGvBLrowykgASfn2fYVGRZlRJgB4HbvoAhYydAlm5FUEEABBgRACeJmhSaiS2r30cGOXCAA+gRACeJGhAGKTOhR8aEKVAOAbCCGAlxgJIM2m3qkO+QQQAMGFEAJ4gZEAEp5xvlrec5sZZQKATyGEAAYZCSCRV1yi5NWzzCgTAHxOmNUFAP7MSAA5+/kH1PzWq80oEwB8EiMhQAMZCSAh8c0JIACCHiEEaAAjASS0XZLO+eIdM8oEAJ9GCAE8ZCSAtJj5sFI+XmxGmQDg8wghgAeMBJDYe0brrNFXmVEmAPgFQgjgJkPrgPz2esVPnWBGmQDgNwghgBuMrQPSSS2f/v9MqBIA/AshBKiHkQDSJO08Ja/+uxllAoDfIYScITs7W6mpqcrMzLS6FPgAIwEkYlAvtc2ZbUaZAOCXbE6n02l1Eb6ouLhYdrtdRUVFio2NtbocWMDoJFTmgAAIVu5+h7JiKlADIwHknO9zFBIebkaZAODXCCHAGXITL6vS9iSAdChgJ1wAcBdzQoDT5LbqJ1X+eoXSkwDS5ou3TKoSAAIDIQT4RW6nq6QKh6vtSQBRdKQi4uNNqhQAAgMhBJCUm3at9FOxq+1RAJHUYf9qM8oEgIDCnBAEPSOTUCXmgQBAQzESgqBGAAEA6xBCELQIIABgLUIIgpKhANIklAACAF5ACEHQMRRAzm+nDofWNX6RABAECCEIKoYCSHgTdfhwvglVAkBwIIQgaBieA/L9B41dIgAEFUIIggKTUAHA9xBCEPAIIADgmwghCGiGAkjreAIIADQiQggClpEA0iL7UXXYxYZ0ANCYWLYdAclIAEmY/aRiRvQ3o0wACGqMhCDgEEAAwD8wEoKAktuyb5W2JwEkcfaTiiaAAIBpGAk5Q3Z2tlJTU5WZmWl1KfDQgbsmS45KV9uTABL/6iQCCACYzOZ0Op1WF+GLiouLZbfbVVRUpNjYWKvLQT2MXIIJ799dyUueNaNMAAgK7n6HMhICv2d0KXYCCABYgxACv2YkgNjiz2IpdgCwEBNT4beMBJDWn/9TkS1bmlEmAKAWjITALxkJIDF330gAAQAfQAiB3zESQMLSzlPCn+41o0wAQD0IIfAruQkNDyBN0s9Tu5zZZpQJAHADIQR+Ize+j3TaDeWeBJDIAT3Vdg0BBAB8CSEEfsHIJZgm6Z3UetH/mVEmAMADhBD4vNxW/aq0PQkgUYN7q+2av5tQJQDAU9yiC5+W2/tWqcLhansSQFK+fV+hUVFmlQoA8BAhBD4rt/1gqaTc1fYkgLT/cZ1soaFmlQoAaABCCHxSbvIA6ehxV9uj3XDnTiOAAIAfIITA5+S27i8dr3C1PQkgzV+YqOhhfc0qFQBgACEEPiU3oeG34Sqmmc6+ebg5hQIADOPuGPiM3NaXGwggTdVh3ypzCgUAeAUhBD4hN7GPdPyEq+1JAAnt3UUd9r1nVqkAAC/hcgwsZ2QhMkVFKmXZCyZUCQDwNkZCYKncjldVaXsSQELatlKHb1ebUSYAoBEwEgLL5La5XDrWsEswrXb8PzVtm2xWqQCARsBICCyRe96VDQ4gYSmtCSAAEAAYCYHpchMvkyp/vQ3G0wDSbvsis0oFADQiQghMZWQSatu9K9TEbjejTACACQghME3upbdUabMXDAAEN0IITLE/dbhU8D9Xm71gAABMTEWj+6bbjao0EEDYCwYAAhMjIWhUP0x8Xo4DP7jabgeQsFC1/y6HERAACGCMhKDRFEyaqfK5b7nabgeQs2PU4QfmgABAoCOEnCE7O1upqanKzMy0uhS/lv/YCyp+eYmr7cklmA57/m1WmQAAC9mcTqez/m7Bp7i4WHa7XUVFRYqNjbW6HL/yw60Pq/y9Ta62JwGk3f6VCouONqtUAEAjcPc7lJEQeFXBpJkNDiDhGecTQAAgiBBC4DUly9c2+BJMeMb5Sl49y6xSAQA+gLtj4BWOI0eUf+9TrrYnAaRF9qM668ahZpUKAPARjITAsLyHZ+ibtldI5UcleRZA7PeMJoAAQJBiJASG7OswVM7iUlfbkwASc9dIxU2dYFapAAAfQwhBg+3rOrLBAST2ntGKJ4AAQFAjhKBBvr3hj3J++6Or7XYACQ9T4suTFD2iv4nVAgB8ESEEHjuQOVoV33zvarsbQJqO6KeWr01hJVQAgCRCCDyU//gLngeQppFKmPmoYhj9AACchhACtzmOHFHJq2+62u6OgKT8d7lCo6LMLBUA4Ae4RRduKV2xXgcuutbVdjeARF51GQEEAFAjRkJQr9IV65U39nFX25O7YJJmP2lWmQAAP8NICOrkdDhUkDXd1fYkgCTMfpJJqACAWhFCUKcjmz5R5c/FJ//dk5VQJ9zERFQAQJ24HIMaVR4/ruI5y1Sy9H1JHgSQEJsSZ01lHRAAQL0IIagm/4mZKnl1ieQ82XY3gEQN76tWs6ZyCQYA4BZCCKo4OGi8ju/6r6vtbgBJXPRXRQ/oYWapAAA/x5wQuBRMmtmgABLSPFbN+nUzs1QAQAAghECSVFFaquKXl7jankxCjX92IpdgAAAeI4RABZOzdaD9UFfb7RGQpHglzp2m6GF9zSwXABAgmBMS5A7d+rCOvLfJ1XYngDQbOVj2W65SZM80RkAAAA1GCAliBU/M9DiAyCYlzJiokPBwk6sFAAQaLscEqdLla1X8iudzQGLuupEAAgDwCkJIEHI6HCqY+Jyr7W4ACc84Xwl/utfMUgEAAYwQEoSObv1MlYf/J8n9ABL7uxuVvHqWyZUCAAIZc0KCUEXeYUnuzwFpt2+lwqKjLagUABDIGAkJQmGJLTwYARlNAAEANApGQoJQZVqa7jz6vLaeuLjuvWAG91b81AkWVAgACAaMhASZ8nJp+NWh2lJWdwCJvftGJc3/iwUVAgCCBSMhQaS8XBo2TFq7VoqJkd56Yo/OnZ8nx6Ff+4TEnaX4Z7IUPaK/dYUCAIICISRInBlAVq2SLrmks5xZ/9TRrZ+pIu+wwhJbsAoqAMA0hJAA5XQ4XOHiuD1OI6ela+0622kB5GQ/W2ioonp3trZYAEBQIoQEoNIV61X42AtyHCo47S4Ym2KiKrRqVZgrgAAAYCUmpgaY0hXrlTfu8TMCyMnbcGdH3Ku0n9ZbXSIAAJIIIQHF6XCo8LEXJGftC5EVPv6inA6H1aUCAEAICSRHt35W4wiI6zZcp+T4Pl9Ht35mdakAABBCAklF3mG3VkI9tWw7AABWIoQEkOP2OLeWYg9LbGFBdQAAVMXdMQGivFwaOS1dW0/Y6tyMLjQpQZE906wpEgCA0xBCAoBrIbJ1J2/DnR3x6xwQF9vJf8RNu4/FyAAAPoHLMX6u2kqoa8I09B+jFdoqvkq/0KQEJc6ZpuhhfS2qFACAqhgJ8WM1L8UuSX3VbOilLMcOAPBphBA/VXsAOYnl2AEAvi6gQ0hKSopiY2MVEhKis88+W2vXrrW6JK+oL4AAAOAPAjqESNLmzZsVHR1tdRkNdvpGdGGJLVSZlqbhV4cSQAAAfi/gQ4g/O30jOunkUux3Hn1eW8ouJoAAAPyez94ds2HDBg0fPlxJSUmy2WxatmxZtT7Z2dlKSUlRZGSkevTooW3btlV53mazqW/fvsrMzNSCBQtMqtw7Tt+ITvp1L5gtZRerma1Mbz3xCQEEAODXfDaElJWVKT09XdnZ2TU+v3jxYmVlZWny5MnauXOn0tPTNXjwYOXn57v6bNy4UTt27NDy5cv11FNP6bPP/GPPlNM3opNq2IzO/oDOXTCNjegAAH7NZ0PI0KFDNW3aNF177bU1Pv/cc89p/PjxGjt2rFJTU/XKK6+oadOmmjNnjqtP69atJUmtWrXSlVdeqZ07d9Z6vmPHjqm4uLjKj1VObUQn1bIbbthuNqIDAPg9nw0hdTl+/Lh27NihgQMHuh4LCQnRwIEDtWXLFkknR1JKSkokSaWlpfrggw904YUX1nrMp59+Wna73fWTnJzcuG+iDqc2mKtvMzo2ogMA+DO/DCGFhYVyOBxKTEys8nhiYqJ+/PFHSVJeXp4uvfRSpaenq2fPnrr99tuVmZlZ6zEfeeQRFRUVuX4OHjzYqO+hLmGJLdzaDZeN6AAA/ixg745p3769Pv30U7f7R0REKCIiohErcl9lWpruPPq8tp64uOYAwkZ0AIAA4JchJC4uTqGhocrLy6vyeF5enlq2bGlRVd5RXi4NvzrUdRfMXPvJOSAubEQHAAgQfnk5Jjw8XF27dlVOTo7rscrKSuXk5KhXr14WVmbMmSuhLntmjzLbVg1abEQHAAgUPjsSUlpaqr1797ra+/fv165du9S8eXO1bdtWWVlZGjNmjLp166bu3btrxowZKisr09ixYy2s2jOnr4Z63B6nkdPStXad7bSFyDrLmfVPNqIDAAQknw0hH3/8sfr37+9qZ2VlSZLGjBmj119/XaNGjVJBQYEmTZqkH3/8URkZGVq1alW1yaq+6vTVUH+dhGpTTFSFVq0Kcy1ExkZ0AIBAZXM6nU6ri/Al2dnZys7OlsPh0J49e1RUVKTY2FivnuPUaqhy1rwQ2dB/jOZyCwDAbxUXF8tut9f7HUoIqYW7f4CecDocOrLpE/34m0ly/q+k5ttww3crNClB7XYs4bILAMAvufsd6pcTU/1R6Yr1OtBlpH64/v7aA0iT3ZJTrIYKAAgKPjsnJJCcfvlFqn8lVInVUAEAgY+RkEZW72Z0NQQQidVQAQCBj5GQRlbvZnRnBhBWQwUABAlGQhqZu5vRSWI1VABAUCGENDJ3N6OTWA0VABBcuBzTyOrdjE5SyNmxSvz7k4rqncEICAAgaDAScobs7GylpqYqMzPT8LFq3IzujN1wZZPin5uoppd1JYAAAIIKi5XVwuhiZWduRvfWE5/o3Pl/ck1SlaTQ1gmKm3Yfl18AAAHF3e9QLsc0gjMDCJvRAQBQHSHEy2oOICefYzM6AAB+xZwQL6orgAAAgKoIIV5CAAEAwDOEEC8ggAAA4DlCiEEEEAAAGoYQYgABBACAhiOENBABBAAAYwghZ3BnxVQCCAAAxrFiai1qW+2NAAIAQN3cXTGVkRAPEEAAAPAeQoibCCAAAHgXIcQNBBAAALyPEFIPAggAAI2DDezqceON0ocfEkAAAPA2QkgtTt009OGHxYqOlpYulS66SCoutrgwAAB8XPEvX5b13YBLCKlFSUnJL/+WrNJS6YorLC0HAAC/U1JSIrvdXuvzrBNSi8rKSh06dEgxMTGy2Wy19svMzNT27dvrPZ47/WrrU1xcrOTkZB08eLDO+639hbt/Zr5+Tm8csyHH8OQ13vp81vU8n0/fPKfRYzb09Xw+G48Vn82GntfpdKqkpERJSUkKCal9+ikjIbUICQlRmzZt6u0XGhrq1gfbnX719YmNjfX7v0SS+39mvn5ObxyzIcfw5DXe+ny6cxw+n751TqPHbOjr+Xw2His+m0bOW9cIyCncHWPQhAkTvNbP3WP5OyveZ2Oc0xvHbMgxPHmNtz6fwfLZlPh8Gn09n8/GY9X7bMzzcjnGD7i7/C1gBT6f8GV8Pn0bIyF+ICIiQpMnT1ZERITVpQDV8PmEL+Pz6dsYCQEAAJZgJAQAAFiCEAIAACxBCAEAAJYghAAAAEsQQgAAgCUIIQEgJSVFaWlpysjIUP/+/a0uB3DZv3+/+vfvr9TUVF188cUqKyuzuiRAkvTVV18pIyPD9RMVFaVly5ZZXVbQ4RbdAJCSkqLdu3crOjra6lKAKvr27atp06apT58++umnnxQbG6uwMHaLgG8pLS1VSkqKDhw4oGbNmlldTlDhtwGARvGf//xHTZo0UZ8+fSRJzZs3t7gioGbLly/XgAEDCCAW4HKMxTZs2KDhw4crKSlJNputxuHA7OxspaSkKDIyUj169NC2bduqPG+z2dS3b19lZmZqwYIFJlWOQGf0s/n1118rOjpaw4cPV5cuXfTUU0+ZWD0CnTd+d56yZMkSjRo1qpErRk0IIRYrKytTenq6srOza3x+8eLFysrK0uTJk7Vz506lp6dr8ODBys/Pd/XZuHGjduzYoeXLl+upp57SZ599Zlb5CGBGP5sVFRX68MMP9dJLL2nLli1avXq1Vq9ebeZbQADzxu9O6eTeMps3b9aVV15pRtk4kxM+Q5Lz7bffrvJY9+7dnRMmTHC1HQ6HMykpyfn000/XeIwHHnjAOXfu3EasEsGoIZ/NzZs3O6+44grX89OnT3dOnz7dlHoRXIz87pw3b57zlltuMaNM1ICREB92/Phx7dixQwMHDnQ9FhISooEDB2rLli2STv7fQElJiaSTk6s++OADXXjhhZbUi+DhzmczMzNT+fn5+vnnn1VZWakNGzboggsusKpkBBF3Pp+ncCnGWkxM9WGFhYVyOBxKTEys8nhiYqL++9//SpLy8vJ07bXXSpIcDofGjx+vzMxM02tFcHHnsxkWFqannnpKl112mZxOp6644goNGzbMinIRZNz5fEpSUVGRtm3bpqVLl5pdIn5BCPFz7du316effmp1GUCNhg4dqqFDh1pdBlAju92uvLw8q8sIalyO8WFxcXEKDQ2t9pckLy9PLVu2tKgqgM8mfBufT/9BCPFh4eHh6tq1q3JyclyPVVZWKicnR7169bKwMgQ7PpvwZXw+/QeXYyxWWlqqvXv3utr79+/Xrl271Lx5c7Vt21ZZWVkaM2aMunXrpu7du2vGjBkqKyvT2LFjLawawYDPJnwZn88AYfXtOcFu7dq1TknVfsaMGePqM3PmTGfbtm2d4eHhzu7duzu3bt1qXcEIGnw24cv4fAYG9o4BAACWYE4IAACwBCEEAABYghACAAAsQQgBAACWIIQAAABLEEIAAIAlCCEAAMAShBAAAGAJQggAALAEIQQIIDabzaOflJQUq0uu1c033yybzaY//elP9fbdtm2bbDabEhMTVVFR4fG57rjjDtlsNq1bt64BlQJoKDawAwLImDFjqj22ceNG5ebmKj09XRkZGVWei4uLM6kyz912221auHChFixYoCeeeKLOvvPnz5ck3XTTTQoL49ca4C/42woEkNdff73aY3fccYdyc3N1zTXXaMqUKabX1FBXXHGFEhMT9dVXX2n79u3KzMyssV9FRYUWL14s6WRwAeA/uBwDwCeFhobqpptukvTrSEdN3n//feXn5+uCCy5Q165dzSoPgBcQQoAg9frrr8tms2nKlCnas2ePRo8ercTERIWEhGjZsmWSpJSUFNlsthpfv27dOtlsNt1xxx3VnnM6nVq4cKEuv/xynX322YqMjNQFF1ygKVOmqLy83O0ab731VknS4sWL5XA4auyzYMGCKn3/97//aebMmRo8eLDatWuniIgItWjRQkOGDNHq1avdPrekOufNnP7nd6aKigq9/PLL6tWrl2JjYxUVFaWMjAzNmDGjxjkrBQUFevjhh5Wamqro6GjZ7Xadd955uv3227Vt2zaPagb8CZdjgCD31VdfKTMzUy1atFD//v31888/q0mTJg0+XmVlpW699VYtXLhQ0dHR6tatm84++2x9/PHHmjp1qlauXKl169YpKiqq3mN17dpVF1xwgb788kutXr1aQ4YMqfJ8WVmZ3nnnHdlsNt1yyy2SpK1bt+q+++5TSkqKOnXqpF69eunbb7/V+++/r/fff19///vfNW7cuAa/v/ocOXJEV111ldauXavmzZurZ8+eioyM1EcffaT7779fa9eu1dtvv62QkJP/D1hSUqIePXpo//79Sk5O1qBBgxQWFqZvv/1WixYtUvv27dW9e/dGqxewEiEECHKLFi3S73//e82YMUOhoaGGj/fss89q4cKF6tevnxYuXKiWLVtKko4fP6577rlHs2fP1tSpU/WXv/zFrePddtttevTRRzV//vxqIeStt95SWVmZ+vbtq3bt2kmSOnXqpC1btqhnz55V+n7yySe6/PLLdf/99+vGG29UdHS04fdakwceeEBr167VqFGj9Oqrr8put0s6GTZGjx6t5cuX67XXXtPdd98tSXrzzTe1f/9+jRgxoko4kU6OkOTl5TVKnYAv4HIMEOTi4+P1zDPPeCWAVFRUaPr06WrWrJkWLVrkCiCSFB4erpkzZ6ply5Z67bXXVFlZ6dYxb7nlFtlsNi1btkxlZWVVnjs1V+TUpRhJOuecc6oFEEnq3LmzJkyYoOLiYq1du7Yhb69e+fn5mjVrlpKTkzV37lxXAJGkmJgYzZ49W+Hh4Xr55ZddjxcUFEiSLr/88ioBRDr53+aiiy5qlFoBX8BICBDkBg4cqKZNm3rlWDt37lRhYaEGDRqkxMTEas9HRUWpa9euevfdd/X111+rU6dO9R6zbdu2uuyyy7R+/XotW7bMddklLy9POTk5ioyM1MiRI6u8xuFwKCcnR5s3b9YPP/ygY8eOSZK+/vrrKv/0tnXr1unEiRMaMmRIjZebWrZsqY4dO+rzzz/XkSNHXH8ekvR///d/SkxM1FVXXaWYmJhGqQ/wNYQQIMi1bdvWa8f65ptvJEmrV6+udULrKYWFhW6FEOnkJZn169dr/vz5rhCycOFCORwOXXfddVVGHL777jsNGzZMn376aa3HKykpceu8njr1/mfNmqVZs2bV2fenn35S69atNWDAAN1///2aMWOGa52TLl26aNCgQRo3bpzat2/fKLUCvoAQAgS5yMjIBr2upssppx4799xz1bt37zpf36JFC7fPdcMNN+j3v/+91qxZo/z8fCUkJLguxZy5Nshvf/tbffrpp7r++us1ceJEderUSTExMQoJCdFrr72mu+66S06n0+1z16au95+RkaH09PQ6Xx8REeH69+eee0533XWX3nnnHa1Zs0abNm3Stm3bNH36dC1cuFDXX3+94XoBX0QIAVCr8PBwSVJpaWm1iZwHDx6s1r9NmzaSpPPPP7/GhdMaym63a8SIEVqyZIkWLlyowYMHa8eOHYqLi6syWbWsrEyrV69WYmKiFi9eXG2ey759+zw6b5MmTVRaWlrjc3W9/0svvVQzZ8706FydOnXSxIkTNXHiRB09elR/+9vf9OCDD+p3v/sdIQQBi4mpAGrVqlUrSdKePXuqPVfTmhuZmZmy2+1av369fvrpJ6/Wcmry6YIFC1xrg4waNarK7cRFRUWqrKxUq1atqgWQEydO6O233/bonK1atdLhw4d1+PDhas+tWbOm2mP9+/dXaGioVqxYoRMnTnh0rtNFRkbqgQceUKtWrVRQUKD8/PwGHwvwZYQQALXq27evJOnpp5+usljYwoULtXDhwmr9IyIiNHHiRJWUlOi6666rceTh+++/1z/+8Q+PaxkyZIji4uK0fft2vfLKK5KqX4pJSEiQ3W7X7t27tWnTJtfjDodDDz30UI1hqi6n3v+0adOqPD59+nRt3LixWv/WrVtr3Lhx+uabb3TTTTfVeHvt3r17tXTpUld72bJl2rp1a7V+O3bsUF5enqKjo3XWWWd5VDfgLwghAGo1YcIExcfH680331RqaqpGjhypjIwM3XbbbfrDH/5Q42sefvhh10TSCy64QD179tRNN92k66+/XhdddJGSk5P17LPPelxLkyZNNHr0aEknJ7V27NhRPXr0qNInLCxMEydOVEVFhfr27asrrrhCo0eP1rnnnqtXXnlFEyZM8OicDz30kKKiojRjxgx17txZN9xwgzp16qQpU6bonnvuqfE1L7zwggYNGqSlS5eqQ4cOuvTSS3XzzTfr6quvVseOHdWxY8cqIWzdunXq1auX2rRpo+HDh+uWW25R//791aNHD1VWVmrq1Kmuy2JAoCGEAKhVYmKiNmzYoGHDhumHH37QypUrZbfbtXr1ao0YMaLG14SEhGjevHl65513NGjQIO3fv19Lly7Vxo0bFRkZqQcffFBz5sxpUD2nj3ycvjbI6R599FG98cYbSktL06ZNm7RmzRqlp6dr69at6tatm0fnu/DCC/XBBx+oX79+2rNnj1avXq0OHTpoy5YttW6oFxUVpZUrV+qNN95Qjx499OWXX+rNN9/Uxx9/rPj4eE2dOlXTp0939b/jjjv0xz/+UUlJSdq2bZuWLl2q/fv368orr9SaNWuUlZXlUc2AP7E5vTFNHAAAwEOMhAAAAEsQQgAAgCUIIQAAwBKEEAAAYAlCCAAAsAQhBAAAWIIQAgAALEEIAQAAliCEAAAASxBCAACAJQghAADAEoQQAABgif8fyFH5FpIT0DgAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "aLYcTjD-2m7m", + "outputId": "b6b7eded-677b-4e67-f3b1-edfdb97b8b64" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "# We can also create a residual plot to check the model's performance\n", + "residuals = y_test - y_pred\n", + "plt.scatter(y_test, residuals)\n", + "plt.axhline(y=0, color='red', linestyle='--')\n", + "plt.xlabel(\"Actual Prices\")\n", + "plt.ylabel(\"Residuals\")\n", + "plt.title(\"Residual Plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "source": [ + "# This is just for explaination\n", + "# There is no sense behind this\n", + "\n", + "print(\" This is how a good prediction would look like :) \\n\")\n", + "residuals = y_test - y_test\n", + "plt.scatter(y_test, residuals)\n", + "plt.axhline(y=0, color='red', linestyle='--')\n", + "plt.xlabel(\"Actual Prices\")\n", + "plt.ylabel(\"Residuals\")\n", + "plt.title(\"Residual Plot\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "3FhokAoPNdej", + "outputId": "42852288-8f18-4147-c419-39f07e2eb412" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " This is how a good prediction would look like :) \n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EpCVNa5R2pWo", + "outputId": "77559335-0f92-40ec-b922-db295c5c5838" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted Price: 386747.7629630785\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# we will use the trained model to make predictions on new data and visualize the results\n", + "\n", + "# features : bedrooms,bathrooms,sqft_living,floors,waterfront,view\n", + "\n", + "new_data = [[3, 2, 1600, 1, 0, 0]]\n", + "predicted_price = model.predict(scale.transform(new_data))\n", + "\n", + "print(\"Predicted Price:\", predicted_price[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nbTjQg79m4rM" + }, + "source": [ + "----------------------" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**NEURAL NETWORKS: ANN**" + ], + "metadata": { + "id": "2cRWRRuCnes3" + } + }, + { + "cell_type": "markdown", + "source": [ + "Neural Networks are a technique under Deep Learning which use these three operations:\n", + "\n", + "1. Forward propagation\n", + "\n", + "2. Backpropagation\n", + "\n", + "3. Activation functions\n", + "\n", + "Under the architecture:\n", + "\n", + "- Input Layer\n", + "\n", + "- N Hidden Layers\n", + "\n", + "- Output Layer\n", + "\n", + "Data is fed to a Neural Network in a downward direction such that it passes through Input layer , then Hidden layers and finally Output layer. During each iteration or \"Epoch\", Data moves in \"Forward\" direction for processing based on Weights and Biases, worked on by Activation functions to create an activation response or \"nerve stimulation\", and finally \"Back-propagated\" for weight-bias updation for better approximation using an \"Optimizing function\", while the error is monitored using the \"Loss function\" for model behoaviour." + ], + "metadata": { + "id": "RwaBBtdaqKAe" + } + }, + { + "cell_type": "markdown", + "source": [ + "Importing Dependencies" + ], + "metadata": { + "id": "B8JzCoY8sH40" + } + }, + { + "cell_type": "code", + "source": [ + "import keras\n", + "from keras.layers import Dense" + ], + "metadata": { + "id": "mXMBiv5Nnw8O" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Building the Artificial Neural Network model" + ], + "metadata": { + "id": "GzeHW9PPsLre" + } + }, + { + "cell_type": "code", + "source": [ + "# Splitting the dataset into training and testing sets\n", + "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=42)" + ], + "metadata": { + "id": "r6I4AbRPnnH0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Layer addition formula : model.add(type of layer(number of neurons, activation = __ , input_dim = __ )) wherein input_dim argument is for input layer only." + ], + "metadata": { + "id": "gGR4ltnl6_q1" + } + }, + { + "cell_type": "code", + "source": [ + "model = keras.models.Sequential() # creating a model object\n", + "\n", + "model.add(Dense(10, activation='relu',input_dim = 6)) # input layer\n", + "model.add(Dense(20, activation='relu')) # hidden layer\n", + "model.add(Dense(40, activation='relu'))\n", + "model.add(Dense(1,activation = 'linear')) # output layer\n", + "model.compile(loss = 'mean_squared_error',optimizer = 'adam')\n", + "\n", + "model.summary() # gives a summary of the architecture" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BwdXB5w5nk39", + "outputId": "af8623b7-9580-4a31-88c6-bf5403551206" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_4 (Dense) (None, 10) 70 \n", + " \n", + " dense_5 (Dense) (None, 20) 220 \n", + " \n", + " dense_6 (Dense) (None, 40) 840 \n", + " \n", + " dense_7 (Dense) (None, 1) 41 \n", + " \n", + "=================================================================\n", + "Total params: 1171 (4.57 KB)\n", + "Trainable params: 1171 (4.57 KB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "keras.utils.plot_model(\n", + " model,\n", + " to_file='model.png',\n", + " show_shapes=True,\n", + " show_dtype=False,\n", + " show_layer_names=True,\n", + " rankdir='TB',\n", + " expand_nested=False,\n", + " dpi=96,\n", + " layer_range=None,\n", + " show_layer_activations=True,\n", + " show_trainable=False\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 533 + }, + "id": "38qeTnEZG-ak", + "outputId": "06e56811-1b8b-49bc-d18d-d67768c2bb87" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 76 + } + ] + }, + { + "cell_type": "code", + "source": [ + "history = model.fit(x_train,y_train,epochs=100,validation_split=0.15) # fitting data to model for training" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hNdjOoK_nvcz", + "outputId": "48a7cad7-2c96-43c6-f920-26b46f5c589b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/100\n", + "104/104 [==============================] - 3s 9ms/step - loss: 653400539136.0000 - val_loss: 402941378560.0000\n", + "Epoch 2/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 653336182784.0000 - val_loss: 402769182720.0000\n", + "Epoch 3/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 652820873216.0000 - val_loss: 401796202496.0000\n", + "Epoch 4/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 650867703808.0000 - val_loss: 398778695680.0000\n", + "Epoch 5/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 646005587968.0000 - val_loss: 392130887680.0000\n", + "Epoch 6/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 636355149824.0000 - val_loss: 379982839808.0000\n", + "Epoch 7/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 620199018496.0000 - val_loss: 361028124672.0000\n", + "Epoch 8/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 596324122624.0000 - val_loss: 334115831808.0000\n", + "Epoch 9/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 564000391168.0000 - val_loss: 299562205184.0000\n", + "Epoch 10/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 524256116736.0000 - val_loss: 259020587008.0000\n", + "Epoch 11/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 479717621760.0000 - val_loss: 215870586880.0000\n", + "Epoch 12/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 434687082496.0000 - val_loss: 174542651392.0000\n", + "Epoch 13/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 393394716672.0000 - val_loss: 139567202304.0000\n", + "Epoch 14/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 360238514176.0000 - val_loss: 113647788032.0000\n", + "Epoch 15/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 337473273856.0000 - val_loss: 97468162048.0000\n", + "Epoch 16/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 324156653568.0000 - val_loss: 89319858176.0000\n", + "Epoch 17/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 317577265152.0000 - val_loss: 85734539264.0000\n", + "Epoch 18/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 314598326272.0000 - val_loss: 84387897344.0000\n", + "Epoch 19/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 313252478976.0000 - val_loss: 83948675072.0000\n", + "Epoch 20/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 312711053312.0000 - val_loss: 83713998848.0000\n", + "Epoch 21/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 312347459584.0000 - val_loss: 83558055936.0000\n", + "Epoch 22/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 312064344064.0000 - val_loss: 83412877312.0000\n", + "Epoch 23/100\n", + "104/104 [==============================] - 0s 5ms/step - loss: 311814553600.0000 - val_loss: 83261513728.0000\n", + "Epoch 24/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 311584129024.0000 - val_loss: 83092324352.0000\n", + "Epoch 25/100\n", + "104/104 [==============================] - 0s 5ms/step - loss: 311370383360.0000 - val_loss: 82925166592.0000\n", + "Epoch 26/100\n", + "104/104 [==============================] - 1s 5ms/step - loss: 311099162624.0000 - val_loss: 82786213888.0000\n", + "Epoch 27/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 310905405440.0000 - val_loss: 82623406080.0000\n", + "Epoch 28/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 310636052480.0000 - val_loss: 82436825088.0000\n", + "Epoch 29/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 310477094912.0000 - val_loss: 82297143296.0000\n", + "Epoch 30/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 310204366848.0000 - val_loss: 82133508096.0000\n", + "Epoch 31/100\n", + "104/104 [==============================] - 1s 7ms/step - loss: 310028271616.0000 - val_loss: 81994719232.0000\n", + "Epoch 32/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 309801615360.0000 - val_loss: 81859100672.0000\n", + "Epoch 33/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 309566701568.0000 - val_loss: 81675141120.0000\n", + "Epoch 34/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 309372878848.0000 - val_loss: 81483145216.0000\n", + "Epoch 35/100\n", + "104/104 [==============================] - 0s 3ms/step - loss: 309142618112.0000 - val_loss: 81349050368.0000\n", + "Epoch 36/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 308988477440.0000 - val_loss: 81219133440.0000\n", + "Epoch 37/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 308749500416.0000 - val_loss: 81059479552.0000\n", + "Epoch 38/100\n", + "104/104 [==============================] - 0s 3ms/step - loss: 308565639168.0000 - val_loss: 80893485056.0000\n", + "Epoch 39/100\n", + "104/104 [==============================] - 0s 3ms/step - loss: 308363001856.0000 - val_loss: 80758218752.0000\n", + "Epoch 40/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 308175994880.0000 - val_loss: 80584187904.0000\n", + "Epoch 41/100\n", + "104/104 [==============================] - 0s 3ms/step - loss: 307965067264.0000 - val_loss: 80446267392.0000\n", + "Epoch 42/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 307777175552.0000 - val_loss: 80297435136.0000\n", + "Epoch 43/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 307586269184.0000 - val_loss: 80173121536.0000\n", + "Epoch 44/100\n", + "104/104 [==============================] - 0s 3ms/step - loss: 307420233728.0000 - val_loss: 80050962432.0000\n", + "Epoch 45/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 307187449856.0000 - val_loss: 79899467776.0000\n", + "Epoch 46/100\n", + "104/104 [==============================] - 0s 3ms/step - loss: 307018596352.0000 - val_loss: 79767920640.0000\n", + "Epoch 47/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 306826543104.0000 - val_loss: 79608086528.0000\n", + "Epoch 48/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 306672992256.0000 - val_loss: 79467659264.0000\n", + "Epoch 49/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 306482610176.0000 - val_loss: 79354699776.0000\n", + "Epoch 50/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 306296389632.0000 - val_loss: 79213109248.0000\n", + "Epoch 51/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 306122031104.0000 - val_loss: 79070232576.0000\n", + "Epoch 52/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 305937842176.0000 - val_loss: 78984265728.0000\n", + "Epoch 53/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 305748180992.0000 - val_loss: 78829395968.0000\n", + "Epoch 54/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 305608425472.0000 - val_loss: 78676975616.0000\n", + "Epoch 55/100\n", + "104/104 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4ms/step - loss: 304331390976.0000 - val_loss: 77724303360.0000\n", + "Epoch 63/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 304213393408.0000 - val_loss: 77613416448.0000\n", + "Epoch 64/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 304089661440.0000 - val_loss: 77509255168.0000\n", + "Epoch 65/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 303914680320.0000 - val_loss: 77383204864.0000\n", + "Epoch 66/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 303800123392.0000 - val_loss: 77307199488.0000\n", + "Epoch 67/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 303617376256.0000 - val_loss: 77129367552.0000\n", + "Epoch 68/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 303493447680.0000 - val_loss: 77049528320.0000\n", + "Epoch 69/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 303384002560.0000 - val_loss: 76971147264.0000\n", + "Epoch 70/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 303218360320.0000 - val_loss: 76866887680.0000\n", + "Epoch 71/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 303056453632.0000 - val_loss: 76718555136.0000\n", + "Epoch 72/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302943830016.0000 - val_loss: 76617252864.0000\n", + "Epoch 73/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302785232896.0000 - val_loss: 76523913216.0000\n", + "Epoch 74/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302685749248.0000 - val_loss: 76408602624.0000\n", + "Epoch 75/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302593572864.0000 - val_loss: 76343148544.0000\n", + "Epoch 76/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302413447168.0000 - val_loss: 76241379328.0000\n", + "Epoch 77/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302322188288.0000 - val_loss: 76130181120.0000\n", + "Epoch 78/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302180958208.0000 - val_loss: 76036218880.0000\n", + "Epoch 79/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 302002176000.0000 - val_loss: 75906629632.0000\n", + "Epoch 80/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 301922353152.0000 - val_loss: 75816853504.0000\n", + "Epoch 81/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 301825458176.0000 - val_loss: 75717230592.0000\n", + "Epoch 82/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 301658013696.0000 - val_loss: 75615281152.0000\n", + "Epoch 83/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 301539098624.0000 - val_loss: 75525488640.0000\n", + "Epoch 84/100\n", + "104/104 [==============================] - 1s 7ms/step - loss: 301425000448.0000 - val_loss: 75417591808.0000\n", + "Epoch 85/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 301318078464.0000 - val_loss: 75321032704.0000\n", + "Epoch 86/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 301200244736.0000 - val_loss: 75238629376.0000\n", + "Epoch 87/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 301059178496.0000 - val_loss: 75134730240.0000\n", + "Epoch 88/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 300946128896.0000 - val_loss: 75051835392.0000\n", + "Epoch 89/100\n", + "104/104 [==============================] - 1s 6ms/step - loss: 300848709632.0000 - val_loss: 74949050368.0000\n", + "Epoch 90/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 300733431808.0000 - val_loss: 74849828864.0000\n", + "Epoch 91/100\n", + "104/104 [==============================] - 0s 5ms/step - loss: 300622675968.0000 - val_loss: 74745815040.0000\n", + "Epoch 92/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 300537544704.0000 - val_loss: 74658750464.0000\n", + "Epoch 93/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 300415516672.0000 - val_loss: 74565812224.0000\n", + "Epoch 94/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 300293849088.0000 - val_loss: 74477289472.0000\n", + "Epoch 95/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 300173230080.0000 - val_loss: 74385334272.0000\n", + "Epoch 96/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 300081709056.0000 - val_loss: 74304897024.0000\n", + "Epoch 97/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 300007751680.0000 - val_loss: 74212392960.0000\n", + "Epoch 98/100\n", + "104/104 [==============================] - 0s 4ms/step - loss: 299857707008.0000 - val_loss: 74114678784.0000\n", + "Epoch 99/100\n", + "104/104 [==============================] - 0s 5ms/step - loss: 299761958912.0000 - val_loss: 74028376064.0000\n", + "Epoch 100/100\n", + "104/104 [==============================] - 0s 5ms/step - loss: 299650908160.0000 - val_loss: 73940516864.0000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.plot(history.history['loss'], label='Training Loss') # plotting training and validation loss\n", + "plt.plot(history.history['val_loss'], label='Validation Loss')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 445 + }, + "id": "JkyCv5n9n_d-", + "outputId": "3fd17b1d-d24b-4925-ca61-0dc15eae1440" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "y_pred = model.predict(x_test) # predicting on testing data" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "L9rS_LQ5oCNS", + "outputId": "ee51aa89-8dca-4506-ca59-82265b3106fe" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "22/22 [==============================] - 0s 2ms/step\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Predictions and Visualization" + ], + "metadata": { + "id": "M0Q2VmdCs0w6" + } + }, + { + "cell_type": "code", + "source": [ + "# To visualize the predictions against actual prices, we'll use a scatter plot\n", + "plt.scatter(y_test, y_pred)\n", + "plt.xlabel(\"Actual Prices\")\n", + "plt.ylabel(\"Predicted Prices\")\n", + "plt.title(\"Actual Prices vs. Predicted Prices\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "hrJFrdsaoFNe", + "outputId": "3251dac3-4326-4004-8215-f649fd342af8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Model Evaluation" + ], + "metadata": { + "id": "8vs9nXXFs5p5" + } + }, + { + "cell_type": "code", + "source": [ + "# Mean Squared Error and R-squared for model evaluation\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "rmse = mean_squared_error(y_test, y_pred, squared=False)\n", + "\n", + "print(\"ANN RMSE:\", rmse)\n", + "print(\"ANN MSE:\", mse)\n", + "print(\"ANN R-squared:\", r2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y94jzlCAoGZ8", + "outputId": "dd483d78-c0ef-4f91-aded-db9157232f1b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "ANN RMSE: 550039.5822673787\n", + "ANN MSE: 302543542060.87244\n", + "ANN R-squared: 0.11848765484246582\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Price prediction" + ], + "metadata": { + "id": "QURozOidtCNZ" + } + }, + { + "cell_type": "code", + "source": [ + "# we will use the trained model to make predictions on new data and visualize the results\n", + "\n", + "# features : bedrooms,bathrooms,sqft_living,sqft_lot,floors,waterfront,view,condition\n", + "\n", + "new_data = [[3, 2, 1600, 1, 0, 0]]\n", + "predicted_price = model.predict(scale.transform(new_data))\n", + "\n", + "print(\"Predicted Price:\", predicted_price[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OH_dOSBNoLv-", + "outputId": "789baa23-4b50-4cf6-b5f4-0c015dd809e3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1/1 [==============================] - 0s 95ms/step\n", + "Predicted Price: [443651.84]\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/House Price Prediction/README.md b/House Price Prediction/README.md new file mode 100644 index 000000000..1bd3f8021 --- /dev/null +++ b/House Price Prediction/README.md @@ -0,0 +1,39 @@ +### House Price Prediction Using Machine Learning + +**Overview:** +The House Price Prediction project leverages the power of machine learning to forecast the future prices of residential properties. This project is part of the GirlScript Summer of Code, aimed at providing hands-on experience in applying machine learning techniques to real-world problems. Participants will gain valuable insights into data preprocessing, feature engineering, model selection, and evaluation. + +**Objective:** +The primary goal of this project is to create an accurate and robust model that can predict house prices based on various features such as location, size, number of rooms, and other relevant factors. By the end of the project, participants will have a comprehensive understanding of the end-to-end process involved in building a predictive model. + +**Key Features of the Project:** +1. **Data Collection and Preprocessing:** + - Gather and clean the dataset containing historical house prices and their attributes. + - Handle missing values, outliers, and ensure the data is in a suitable format for analysis. + +2. **Exploratory Data Analysis (EDA):** + - Perform EDA to uncover patterns, correlations, and insights within the data. + - Visualize the data using graphs and charts to better understand the relationships between features. + +3. **Feature Engineering:** + - Select and create meaningful features that improve the model's predictive power. + - Transform categorical variables into numerical ones using techniques like one-hot encoding. + +4. **Model Selection:** + - Experiment with various machine learning algorithms such as Linear Regression, Decision Trees, Random Forest, and Gradient Boosting. + - Compare the performance of different models using appropriate evaluation metrics. + +5. **Model Training and Evaluation:** + - Split the data into training and testing sets to evaluate the model's performance. + - Use metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared to assess accuracy. + +6. **Hyperparameter Tuning:** + - Optimize the model's parameters using techniques such as Grid Search and Random Search to improve performance. + + +**Learning Outcomes:** +- Develop a strong foundation in machine learning concepts and techniques. +- Gain practical experience in data handling, preprocessing, and visualization. +- Understand the intricacies of model selection, training, and evaluation. +- Learn how to deploy machine learning models for real-world applications. +- Enhance their problem-solving and analytical skills by working on a tangible project.