From b3d2e6a0c3fd77adfc6fccaf53da99e5c7b29f55 Mon Sep 17 00:00:00 2001 From: Asmit Dash <115637270+asmitdash@users.noreply.github.com> Date: Sun, 30 Jun 2024 15:10:11 +0530 Subject: [PATCH] Add files via upload --- Book Recomendation System/29June_Bookrecsystem.ipynb | 1 + Book Recomendation System/29June_CNN.ipynb | 1 + 2 files changed, 2 insertions(+) create mode 100644 Book Recomendation System/29June_Bookrecsystem.ipynb create mode 100644 Book Recomendation System/29June_CNN.ipynb diff --git a/Book Recomendation System/29June_Bookrecsystem.ipynb b/Book Recomendation System/29June_Bookrecsystem.ipynb new file mode 100644 index 0000000..72dbf03 --- /dev/null +++ b/Book Recomendation System/29June_Bookrecsystem.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1ibYR9SiNhBsiwQ1UEpaFCu5t7B5E9c2u","timestamp":1719635079469}],"authorship_tag":"ABX9TyNH6Oztif2nDu1RBNxBtPL1"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"source":["import pandas as pd\n","\n","# Load the dataset\n","books_df = pd.read_csv('/content/books.csv')\n","\n","def search_books(keyword):\n"," \"\"\"\n"," Search for books that match the keyword in the title, authors, or publisher.\n"," \"\"\"\n"," keyword = keyword.lower()\n"," results = books_df[\n"," books_df['title'].str.lower().str.contains(keyword) |\n"," books_df['authors'].str.lower().str.contains(keyword) |\n"," books_df['publisher'].str.lower().str.contains(keyword)\n"," ]\n"," return results\n","\n","\n","def display_books(books):\n"," \"\"\"\n"," Display the list of books.\n"," \"\"\"\n"," if books.empty:\n"," print(\"No books found.\")\n"," else:\n"," for index, book in books.iterrows():\n"," print(f\"Title: {book['title']}\")\n"," print(f\"Author: {book['authors']}\")\n"," print(f\"Average Rating: {book['average_rating']}\")\n"," print(f\"ISBN: {book['isbn']}\")\n"," print(f\"ISBN13: {book['isbn13']}\")\n"," print(f\"Language: {book['language_code']}\")\n"," # Check if 'num_pages' column exists before printing\n"," if 'num_pages' in book:\n"," print(f\"Number of Pages: {book['num_pages']}\")\n"," print(f\"Ratings Count: {book['ratings_count']}\")\n"," print(f\"Text Reviews Count: {book['text_reviews_count']}\")\n"," print(f\"Publication Date: {book['publication_date']}\")\n"," print(f\"Publisher: {book['publisher']}\")\n"," print('-' * 40)\n","\n","def main():\n"," print(\"Welcome to the Book Recommendation Program!\")\n"," keyword = input(\"Enter a keyword to search for books: \")\n"," results = search_books(keyword)\n"," display_books(results)\n","\n","if __name__ == \"__main__\":\n"," main()\n"],"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bn90ajE4I-5i","executionInfo":{"status":"ok","timestamp":1719599950418,"user_tz":-330,"elapsed":18807,"user":{"displayName":"Ruchi Jha","userId":"17183609542544808004"}},"outputId":"2f0c3c47-631f-4986-c929-85587ade2d2e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Welcome to the Book Recommendation Program!\n","Enter a keyword to search for books: Mark Twain\n","Title: The Innocents Abroad\n","Author: Mark Twain/Grover Gardner\n","Average Rating: 3.86\n","ISBN: 812967054\n","ISBN13: 9.78081E+12\n","Language: eng\n","Ratings Count: 8879\n","Text Reviews Count: 693\n","Publication Date: 02-11-2003\n","Publisher: Modern Library\n","----------------------------------------\n","Title: The Tragedy of Pudd'nhead Wilson/Those Extraordinary Twins\n","Author: Mark Twain/David Lionel Smith/Sherley Anne Williams\n","Average Rating: 3.79\n","ISBN: 195114159\n","ISBN13: 9.7802E+12\n","Language: eng\n","Ratings Count: 3664\n","Text Reviews Count: 58\n","Publication Date: 03-06-1997\n","Publisher: Oxford University Press USA\n","----------------------------------------\n","Title: The Tragedy of Pudd'nhead Wilson\n","Author: Mark Twain/Michael Prichard\n","Average Rating: 3.79\n","ISBN: 140015068X\n","ISBN13: 9.7814E+12\n","Language: eng\n","Ratings Count: 3\n","Text Reviews Count: 0\n","Publication Date: 01-01-2003\n","Publisher: Tantor Media\n","----------------------------------------\n","Title: Huck Finn & Tom Sawyer among the Indians & Other Unfinished Stories (Mark Twain Library)\n","Author: Mark Twain/Paul Baender/Dahlia Armon/Walter Blair\n","Average Rating: 3.85\n","ISBN: 520238958\n","ISBN13: 9.78052E+12\n","Language: eng\n","Ratings Count: 5\n","Text Reviews Count: 0\n","Publication Date: 3/15/2003\n","Publisher: University of California Press\n","----------------------------------------\n","Title: Huck Finn and Tom Sawyer Among the Indians\n","Author: Mark Twain/Lee Nelson\n","Average Rating: 3.57\n","ISBN: 1555176801\n","ISBN13: 9.78156E+12\n","Language: eng\n","Ratings Count: 242\n","Text Reviews Count: 29\n","Publication Date: 4/22/2003\n","Publisher: Council Press\n","----------------------------------------\n","Title: Huck Finn/Pudd'nhead Wilson/No 44 Mysterious Stranger other Writings\n","Author: Mark Twain/Guy Cardwell/Louis J. Budd\n","Average Rating: 4.06\n","ISBN: 1883011884\n","ISBN13: 9.78188E+12\n","Language: en-US\n","Ratings Count: 33\n","Text Reviews Count: 3\n","Publication Date: 08-01-2000\n","Publisher: Library of America\n","----------------------------------------\n","Title: The Adventures of Huckleberry Finn (Adventures of Tom and Huck #2)\n","Author: Mark Twain/Guy Cardwell/John Seelye/Walter Trier\n","Average Rating: 3.82\n","ISBN: 142437174\n","ISBN13: 9.78014E+12\n","Language: eng\n","Ratings Count: 1049912\n","Text Reviews Count: 11391\n","Publication Date: 12/31/2002\n","Publisher: Penguin Classics\n","----------------------------------------\n","Title: Adventures of Huckleberry Finn\n","Author: Mark Twain/E.W. Kemble\n","Average Rating: 3.82\n","ISBN: 486443221\n","ISBN13: 9.78049E+12\n","Language: eng\n","Ratings Count: 144\n","Text Reviews Count: 17\n","Publication Date: 05-06-2005\n","Publisher: Dover Publications\n","----------------------------------------\n","Title: Adventures of Huckleberry Finn\n","Author: Mark Twain/George Saunders\n","Average Rating: 3.82\n","ISBN: 375757376\n","ISBN13: 9.78038E+12\n","Language: eng\n","Ratings Count: 687\n","Text Reviews Count: 37\n","Publication Date: 8/14/2001\n","Publisher: The Modern Library\n","----------------------------------------\n","Title: The Annotated Huckleberry Finn\n","Author: Mark Twain/Michael Patrick Hearn/E.W. Kemble\n","Average Rating: 3.82\n","ISBN: 393020398\n","ISBN13: 9.78039E+12\n","Language: eng\n","Ratings Count: 185\n","Text Reviews Count: 18\n","Publication Date: 10/17/2001\n","Publisher: W. W. Norton Company\n","----------------------------------------\n","Title: The Wit and Wisdom of Mark Twain\n","Author: Mark Twain\n","Average Rating: 4.2\n","ISBN: 486406644\n","ISBN13: 9.78049E+12\n","Language: eng\n","Ratings Count: 970\n","Text Reviews Count: 53\n","Publication Date: 12/23/1998\n","Publisher: Dover Publications\n","----------------------------------------\n","Title: Mark Twain's Helpful Hints for Good Living: A Handbook for the Damned Human Race\n","Author: Mark Twain/Lin Salamo/Victor Fischer/Michael B. Frank\n","Average Rating: 3.86\n","ISBN: 520242459\n","ISBN13: 9.78052E+12\n","Language: eng\n","Ratings Count: 513\n","Text Reviews Count: 71\n","Publication Date: 10/18/2004\n","Publisher: University of California Press\n","----------------------------------------\n","Title: The Complete Short Stories of Mark Twain\n","Author: Mark Twain/Charles Neider\n","Average Rating: 4.28\n","ISBN: 553211951\n","ISBN13: 9.78055E+12\n","Language: eng\n","Ratings Count: 5710\n","Text Reviews Count: 142\n","Publication Date: 03-01-1984\n","Publisher: Bantam Classics\n","----------------------------------------\n","Title: The Autobiography of Mark Twain\n","Author: Mark Twain/Charles Neider\n","Average Rating: 4.05\n","ISBN: 60955422\n","ISBN13: 9.78006E+12\n","Language: eng\n","Ratings Count: 2871\n","Text Reviews Count: 209\n","Publication Date: 11/28/2000\n","Publisher: Harper Perennial\n","----------------------------------------\n","Title: Collected Tales Sketches Speeches & Essays 1891–1910\n","Author: Mark Twain/Louis J. Budd\n","Average Rating: 4.39\n","ISBN: 940450739\n","ISBN13: 9.78094E+12\n","Language: eng\n","Ratings Count: 207\n","Text Reviews Count: 10\n","Publication Date: 10/15/1992\n","Publisher: Library of America\n","----------------------------------------\n","Title: The Adventures of Huckleberry Finn\n","Author: Mark Twain/Scott McKowen/Arthur Pober\n","Average Rating: 3.82\n","ISBN: 1402726007\n","ISBN13: 9.7814E+12\n","Language: eng\n","Ratings Count: 364\n","Text Reviews Count: 27\n","Publication Date: 10/28/2006\n","Publisher: Sterling\n","----------------------------------------\n","Title: The Adventures of Huckleberry Finn\n","Author: Mark Twain/Peter Coveney\n","Average Rating: 3.82\n","ISBN: 141439645\n","ISBN13: 9.78014E+12\n","Language: eng\n","Ratings Count: 1980\n","Text Reviews Count: 66\n","Publication Date: 1/30/2003\n","Publisher: Penguin Books\n","----------------------------------------\n","Title: Mark Twain's Adventures of Huckleberry Finn\n","Author: Mark Twain/Richard P. Wasowski\n","Average Rating: 3.82\n","ISBN: 764587277\n","ISBN13: 9.78076E+12\n","Language: eng\n","Ratings Count: 12\n","Text Reviews Count: 0\n","Publication Date: 05-01-2001\n","Publisher: Hungry Minds\n","----------------------------------------\n","Title: Collected Tales Sketches Speeches & Essays 1852–1890\n","Author: Mark Twain/Louis J. Budd\n","Average Rating: 4.29\n","ISBN: 940450364\n","ISBN13: 9.78094E+12\n","Language: eng\n","Ratings Count: 67\n","Text Reviews Count: 6\n","Publication Date: 10/15/1992\n","Publisher: Library of America\n","----------------------------------------\n","Title: Mark Twain's Own Autobiography: The Chapters from the North American Review\n","Author: Mark Twain/Michael J. Kiskis\n","Average Rating: 3.8\n","ISBN: 299125408\n","ISBN13: 9.7803E+12\n","Language: eng\n","Ratings Count: 9\n","Text Reviews Count: 1\n","Publication Date: 10-01-1990\n","Publisher: University of Wisconsin Press\n","----------------------------------------\n","Title: The Adventures of Tom Sawyer and Adventures of Huckleberry Finn\n","Author: Mark Twain/Shelly Fisher Fishkin\n","Average Rating: 4.08\n","ISBN: 451528646\n","ISBN13: 9.78045E+12\n","Language: eng\n","Ratings Count: 33489\n","Text Reviews Count: 453\n","Publication Date: 12-03-2002\n","Publisher: Signet Classics\n","----------------------------------------\n","Title: The Adventures of Tom Sawyer (Adventures of Tom and Huck #1)\n","Author: Mark Twain/Scott McKowen\n","Average Rating: 3.91\n","ISBN: 1402714602\n","ISBN13: 9.7814E+12\n","Language: eng\n","Ratings Count: 1507\n","Text Reviews Count: 105\n","Publication Date: 10-01-2004\n","Publisher: Sterling\n","----------------------------------------\n","Title: The Adventures of Tom Sawyer (Adventures of Tom and Huck #1)\n","Author: Mark Twain/Guy Cardwell/John Seelye\n","Average Rating: 3.91\n","ISBN: 143039563\n","ISBN13: 9.78014E+12\n","Language: eng\n","Ratings Count: 667590\n","Text Reviews Count: 6783\n","Publication Date: 2/28/2006\n","Publisher: Penguin Classics\n","----------------------------------------\n","Title: Historical Romances: The Prince and the Pauper / A Connecticut Yankee in King Arthur’s Court / Personal Recollections of Joan of Arc\n","Author: Mark Twain/Susan K. Harris\n","Average Rating: 4.38\n","ISBN: 940450828\n","ISBN13: 9.78094E+12\n","Language: eng\n","Ratings Count: 296\n","Text Reviews Count: 13\n","Publication Date: 08-01-1994\n","Publisher: Library of America\n","----------------------------------------\n","Title: Letters from the Earth: Uncensored Writings\n","Author: Mark Twain/Bernard DeVoto/Henry Nash Smith\n","Average Rating: 4.21\n","ISBN: 60518650\n","ISBN13: 9.78006E+12\n","Language: eng\n","Ratings Count: 6568\n","Text Reviews Count: 376\n","Publication Date: 2/17/2004\n","Publisher: Harper Perennial Modern Classics\n","----------------------------------------\n","Title: Mark Twain: Selected Works\n","Author: Mark Twain\n","Average Rating: 4.22\n","ISBN: 517053578\n","ISBN13: 9.78052E+12\n","Language: eng\n","Ratings Count: 41\n","Text Reviews Count: 1\n","Publication Date: 10-02-1990\n","Publisher: Gramercy\n","----------------------------------------\n","Title: Adventures of Huckleberry Finn\n","Author: Mark Twain\n","Average Rating: 3.82\n","ISBN: 440300282\n","ISBN13: 9.78044E+12\n","Language: eng\n","Ratings Count: 90\n","Text Reviews Count: 2\n","Publication Date: 11/15/1977\n","Publisher: Laurel\n","----------------------------------------\n","Title: Who Was Mark Twain?\n","Author: April Jones Prince/Nancy Harrison/John O'Brien\n","Average Rating: 4.16\n","ISBN: 448433192\n","ISBN13: 9.78045E+12\n","Language: eng\n","Ratings Count: 529\n","Text Reviews Count: 56\n","Publication Date: 5/24/2004\n","Publisher: Grosset & Dunlap\n","----------------------------------------\n","Title: Roughing It\n","Author: Mark Twain/Henry B. Wonham\n","Average Rating: 3.89\n","ISBN: 743436504\n","ISBN13: 9.78074E+12\n","Language: eng\n","Ratings Count: 5846\n","Text Reviews Count: 494\n","Publication Date: 4/29/2003\n","Publisher: Pocket Books\n","----------------------------------------\n","Title: The Complete Essays of Mark Twain\n","Author: Mark Twain/Charles Neider\n","Average Rating: 4.33\n","ISBN: 306809575\n","ISBN13: 9.78031E+12\n","Language: eng\n","Ratings Count: 195\n","Text Reviews Count: 15\n","Publication Date: 11-02-2000\n","Publisher: Da Capo Press\n","----------------------------------------\n","Title: A Tramp Abroad\n","Author: Mark Twain/Dave Eggers\n","Average Rating: 3.86\n","ISBN: 812970039\n","ISBN13: 9.78081E+12\n","Language: eng\n","Ratings Count: 26\n","Text Reviews Count: 9\n","Publication Date: 10/14/2003\n","Publisher: Modern Library\n","----------------------------------------\n","Title: Las aventuras de Tom Sawyer\n","Author: Mark Twain\n","Average Rating: 3.91\n","ISBN: 8497646983\n","ISBN13: 9.7885E+12\n","Language: spa\n","Ratings Count: 113\n","Text Reviews Count: 12\n","Publication Date: 5/28/2006\n","Publisher: Edimat Libros\n","----------------------------------------\n"]}]}]} \ No newline at end of file diff --git a/Book Recomendation System/29June_CNN.ipynb b/Book Recomendation System/29June_CNN.ipynb new file mode 100644 index 0000000..9fe0989 --- /dev/null +++ b/Book Recomendation System/29June_CNN.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"Y0zm6tRgU2zF","executionInfo":{"status":"ok","timestamp":1719639805142,"user_tz":-330,"elapsed":13394,"user":{"displayName":"Ruchi Jha","userId":"17183609542544808004"}}},"outputs":[],"source":["import os\n","import random\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","import tensorflow as tf\n","from tensorflow.keras import Sequential\n","from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten\n","\n","from tensorflow.keras.datasets import cifar10\n","from tensorflow.keras.utils import to_categorical\n","from matplotlib.ticker import (MultipleLocator, FormatStrFormatter)\n","from dataclasses import dataclass"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-IF4x4gYU8wP"},"outputs":[],"source":["SEED_VALUE = 42\n","\n","# Fix seed to make training deterministic.\n","random.seed(SEED_VALUE)\n","np.random.seed(SEED_VALUE)\n","tf.random.set_seed(SEED_VALUE)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12497,"status":"ok","timestamp":1719634796608,"user":{"displayName":"Ruchi Jha","userId":"17183609542544808004"},"user_tz":-330},"id":"aFJtAg94VBKh","outputId":"35272783-ac67-4912-af2a-c0ae30f9830a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n","170498071/170498071 [==============================] - 4s 0us/step\n","(50000, 32, 32, 3)\n","(10000, 32, 32, 3)\n"]}],"source":["(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n","\n","print(X_train.shape)\n","print(X_test.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":573},"executionInfo":{"elapsed":3065,"status":"ok","timestamp":1719634799658,"user":{"displayName":"Ruchi Jha","userId":"17183609542544808004"},"user_tz":-330},"id":"Ulz66qwSVGcm","outputId":"4045c0ac-19bd-4ccf-b3d6-c2096ac2fd1e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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/4uGcVJomItNlZlHs4W5dzzF5+wfyf3BdIpGHguL2MfVutzbhB5eF8b4fpOhfIZ0imvncfDiD16C2ECZ1PkOx9V4itlhksmxHZVwrAfG/lh51LhJgeMxMwza6iU536oe7jsrxiYvU3uN4RD74uU3XhXlnb0HUOfC+fMQW1yUxjlVwwCnMExlM+UInRc41j2j7X5aTrOFYZxnmWTr/ZVpuHcMjI1vhwNftqmX4V5+wfiAaijz4MkQ94/dPt6rp76hCh9zg+5n55wL1HWh9b9Phqn4MJbP0DDGwo9ef0OUL1/Cvf7jF89ALCzJcXvuHBrLDXPMC9sP5d6+18fc6ypo5P6Zrz4jyq/9+DtQZ2wYtfYT+Zz7Q/yWnx/jN8mpQO6TJoNHZ19z7Rru4c5dkOv/+SvYZzev34DYcCRzeb2J+ag/xvH91vtvinJj/TGos9DEb7ZUrdH3b+Ia4Ap8hrmS/N4snGEuVpJtMN9egTqDI1x733tX3muujnuiZgvnXLIg147hJl63td2B2PnT8rpaA++d5tgG8UT2VVjC6w4PcGyMhnJ8e8a5xnFQ72BuDdUa1szwu7taxpinhlotxDqTCRoNjgbyO7uoYZvuPMB7vZrJ77FJbKwdxv5+7bQcI2vraKJc7cjfs77Mrc/1Skl2bGGcaSXWeVVV3mxqjKtiius4nOOVMUdWl3Gfnlblc0115znnCmM/B2a+xh5oFvifw4QQQgghhBBCCCGEEHIC4eEwIYQQQgghhBBCCCGEnEB4OEwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAlkZkO6uTJWrSoDmHYdjX6WWii2n+XSTACtBZwLQkMN3Jdn2dPcMGMzjBFCJcicGcYqRWAIbO905XUJPml/JAW3RxmKRjeqaMjllHFLYJgr+YbYdKBExsdDNAmoRfh7oTJZmEzwOccJitfnyk6pO8Df646wHwbKPHCSPBp/h5iO0FSwoQyQWvNoWvT8s5+C2MYFaS7QN4we3r95D2I9NWYG3S7U2e+iAcHDLWn+02rjczofhdR/4x//M1GO/jL2xde++GVZJ8I+XV1Fcz1XSKH6rmHk9ZNX34BYGMncUW/imE0Nofh40BVlY9q6paV5iGVqXu4foJGd79DcQOeTTgeF48kngzbxsfLj7oGcN7dv34U60wOcW82KNBsYDdCU4b3XX4XYqjKY6ayegjqW2ZEOnTQzwf8fi4sLENOGCvEE15yVVTSxqFXk/qMc4B5ibQlzZpLIfLy/twN1moZxXhjJ5JPHuIZHIfar78vOH49wrGkPW7+C7zI1TGynyvCjrPZozjk36GGOrjdk7rMMn/YPDiFWjqQpkjWMY8OEpD/QZm14YdzDZ4hjuS5pY9/jojsw9pSFfCfPsKYMS7g/rilDuMDHPa1ldDhRO+nU+N+PvrH/GQ9lrGw48TQKHEeBeqyojPv/idovfnAPTT3vPNyCWKcl19mN06ehzpKROzpz0kgrNAxCA8OgxTLg1RjbEZercWvdpzB+LwdDup+OId7/v5S9A4it1eS+tmMYK87PYd/fKmSeqVexHcrGGq7Hf1LHsZcYJs6TqcwzmTH+LWPDUlm+z+rGGtRZP70hynvGd9BWD3PA5z//OVE+2Max/hf+4pcg9lu/8Tui/NKLP4A6Z556HmLfeObTovzB5k2oc+v7P4bYUSzXt0GKfXX1s/h740SuCYuLaEp1XNy7i+tH4WQf9Rbw+yz20VguC+V468zhN8ZjV9BYc3tH3muY4Lh5423cj6bKhLGziEZ2rsB1PCrL+8/N43M2atLcq9/DtXdvG9fsPJbzsmLsiXoxGhm+ObkgytN5zNv+MhpH1yqyXQ67mJsePsA2SKcyXyVTbPPBEPdcqfp+r5Qw7xwH555Ew8DyRM7PtI95dHOzC7H335Bt6he4t5j20KzdS+W88Q3jtVsvG4b1JXn/1FgLF1dwL3+oDOnq+TNQZ7l1VZRX19DosFbGdtFrTmyYfQ4Mc+m4J88QBrd3oU5vB/fIcV+Ov7HDtWvx8gbEfLWmVpYbUMfr4P5Gm51Hxh5oFh6NEztCCCGEEEIIIYQQQgghnyg8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5AQys+bwUge1hJqR1LKoGLp4foCaH9Wq1NJIUtQG0lpezjlXFFLzI07x3lmMeh55IWOFoQtchKjh1o+lFluW4fuNMqX9kqGmSn+Iz7R5IO8d+Xhda4BtkGxJrdTxEerDnFm8BLHlZanZ5jVRH2Z6iNpHg4F8zqM+6vfsHaFmy+178v6ZFqg7Jspl1EtLAqmdNK6itsstQ0/ste/9SJQP9gdQZ/PBNsSiQGvCYN9PUxyjWid6bQnbdGcLtZtaZTm2+13UW7p265a899oi1Iki/L21Danzs76Buj93t1DX6/03ZWx5DbVAb99FXWCXyLaydD6zEPOJ1o8qhzgOxhO8rtWSWshh+GjoUJ1MZL4vCuyvzfv3RfnW3ftQ594N1OFbbMo5f3oRtUwf3sW59ebLUr/vM1/vQJ1ay9CpPpkSw4BvaO3HSiMuMzRrUytnTuRaGBqC5D1Dt85TWq2Fobe7+fAhxNoNuW7UjD1Eb4rrrNYlLVUwr2pNzcRoA8/H98vVXioP8F3Khs6tlsMdjfH3SmVDr1Npx9cqOLDLZWyXI6Wzf9TFdmpUcN54SkfanFvHwNjSm4b10tjTZrg3LJQmnWf0oSHV6mKlZ5kYW65mDfc2faUx2LO0rHN8v1JJ9muzZHhkBLLOMMVxFeQ4jqd7cjx0u7i3qjdQ53ZtTfoiXDx/Aeo0DC3JsnqXJMF+SbAJXOHkeMxn1DPWIUvP+Dgo1XHQXGhKTcjzhk5lu2RozR7JtbfWwXYflvD7JY/keP/Mp1DrdmUZdSpv3rghyvfuor61H2DuK1I5byo+zrcvfl4+wy4+tvvRd74NsfffPyPK2di4sI46rd2hnCcDw7PlxkP8XhvmcjwOU8NLp4tzcFqReeGxszhvOivoObK7L5/hG994EuocF+kU+7q7I7+hkpGho1/HyTi3KrV7izJqki5fwtzay2XeGozxu67qUBd4f1+OyWYJ17n10x2IJU76JRzl+L0+VH4rlQDvbUjou2ZLzt+0hG23M8R5+Vu/Kt85Lx5AnYslvC4o5Fjee4DfrfHEWHOUz8PEyOWFYY7QaMp28IpHY5P+i3/uKxAb3pb9/NJvoyZ5MEV/gZHyccgyQ5fd8EZo1+RcqkeYIxcC3B92ampsWV5iiXFuuCn7+rXf+D7UufPaO6L89Z9/Aeo89fg5iNXVuWXpCOeyt2d4btyV3w6T9/CbYLiFOsQT5XH2oNeFOneu4xlJuCDbrnYG14knfu5piEU1OU8T40xyFvifw4QQQgghhBBCCCGEEHIC4eEwIYQQQgghhBBCCCGEnEB4OEwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAlkZpew9SU06GmVpJBzo4bGI16BYuDa/cQzTBymhnC/rww9FpoopF6vozFC70gKsLeVyZRzzvUn+Jx3NuV1gykKZ5fUo5+qYZOGkWHYtt8V5WmB944M15F2S5rgvPDEZ6BO7yGKaRcjea/2Ior1T0f47IOB/PtBOcLrNlabEFteXhHl7R4K4x8HtdoKxHa6chzfuIfi4O+8/RbEfGU4k01xDI37KAofKDOl8RSF9rt9jPWH0tzg9v13oU69in1x5eIVGTDM7r7/x98W5bPnz0Ody1cuQ2xBiaaXDXOldguNSPxUGs4Mp/h3qvEIjTPG3b4oZxmOq0oVx+igJ69rNTEHlA1DzVgZXI5GhqHII4POo7MaKnxE44VCFw1XHSO3O2UG4c38N0p5XZ6jkYA28uqPcHzc30ZTsm0VyzI0yDi9jM/53o+lKeXy6hrUufzZz0FML72+YX5hmU7pprI8M6z1dCa8T/5vxZ4xZkol2TaWoVNqGHlNJ3KdnaviniXyscFCX+aLSWys82XcV8RTZZDbw1xfMkyztJGXF+HvZcq4q1rB+ySG+W6z1RHlSgWf2/Nwf9AfyLUliQ0TtAjzONzfMH+ZGnk8i+VYK4Vo5tOaR6OeJJFzvjd8NPLxeIp5ZqpMpDzDBMfqHz3arTmeG8lBx4ZDNHGrVA3DQDX+sgTraFMV55xLPZlnCuOZSr4a22aKwetCZVxj3bs/wvc7ui73RHv7aGrbNIwOT5+Shs1zc2j+UirjHIQ1KcU1KTXScaoaIjPMVY+DQYx7p3Yg82iyh0ZU97po/vblZx8X5XGM+fGU0TaVmuzrL3Qwjz+xhIbJo1xet1fGfDU6wmfX/uRh3Ic6Z+9Kw+ZqF/t5fqkDseStV0XZMsR76R3cx7//QJp2TQwjx03DbHdnXxoife65L0Cds50NiP3f/ttfE+V4vAV1XvkxzqXt7Q9E+flvPg51jouyZ5iPj2WenltFA+3NbTQR703k+C78a1Dn2afw++iLvyDvXy/h91kywti1azLf9g7R6KpaxfGdlWQeud+7C3UWmnKNXp/Dc5vmvLFvUTlrmGJO/uA+mjbf/J781ov7H0AdbwMNaUc78ht47SwanlU7+OzOl33sB1inVsOxESuzwMjH3zsOnvrUKYjdUIbBR4e4D1qo4bhK1f5sr4/fQmtGm17qyHuFDteryMNv/7mW3N+UjD15ZmwKKmq/W6/jnuRoRz77+7/xR1Cns/UMxJbn5Ld/OsHzkDzG34vGcryXcxz/oy7mSP1Znh1hX3X3cM2p7cr1MulinelzaBwanFPnUtYR7AzwP4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATyMyGdPNNFCgP464olyO8Xa2Mot7TsVRITgxjoU4HDSG0MU2c4dl2kqAxSK0hzU4e7KK4/wd3UBB9ty+fa4SP6c5WpXHGn/vKp6DO6TU0W/mnr9wU5ZduoAFAmqNQdujLNuh3Uah+NMD3azaVAHtmmaOgSHtJmXTVDJH/NMOGObOxLn//AMW0j4POPBpZ3LgnzQUe3r4FdWoRtunRUJpbDHo7UMfL0XGj25dGKt0xjtmwjO28uCJNsqqGIeOpc89CbEP14a3XX4I6gSfHWpKh4Pzu3j7Enn76qihfegwF0jfWliDW+MJzovzGe2icMJ2gWc80ku2ZOzSWywscj1tb0uCjZJiVtOfQhMw5KQo/HqMxz6OD5WA2y1UzGNJZt1b52DIOKxz2BRjQGUZNnvFMs0TOnDsnyjXDeLA3NPpQmbG9dQ/ncjXEMRMqM4O3X/wO1Fk4hSaYc6flPPEMcw/PcKLSfZX7eJ0RmgmjGz52fB/X8EIZPVTruPeYeJhXS3VpdpENMWc7wzRjdUX2T7pvNKBh4lkvyfEw7aNBVnsVTdVmMbVcXJE5czrA3w+MtThSpnEVw0RrMsbnLJdkPb+Ee5Yjoz2TRK4TgbEXmBhmvy6Xa1LVMGYLS2iMMklkO+zu4f7nOIgNE0gvU2uVsRfIDYNEoGzkgQDnTe7Ltg+N3X0SY+4rhbLtG1Vs91GMe5RU5fapMW2mKq+VfXyowKEhY6HWCOsbITVMcXQ+2TrAPP5givuYG3fk/mPJMDxbX0cjr0ZDGvVUDOPKQpvyOeeSQhnSGfut42ApwOc/pfqn1UKjo9cO0RztcCq/qc4aZq3/zg4aH0fK2HPhOt67/MFDiGW5zDPnjKkVGd89vhr/mZFXpz/6iSi3DYO4fNEwW9JuhD3s51aAuXY6lG0wj0PI1Qqcy70taQh26iqapDUN0/bPXZSmVztHuN5sDXDdGo2kKdTN69fxQY+J/iGuc61FmY/2eziOKg0cI4OhzD9Jiv343jv43fhwU+aVZhPbfmUF88ryOZmDR3fQzPHeLhq7VZtyvC0s4f53rqUM23ycX2EJn7Pky+/NNMYcmRtmpi6X38lXn8azlsfPY6xZk3NsbgnXz9EI51wcy7br76PBYBbjvaoldVaVfcSN9E+Zdhvz0Z76Fo98bIeGkcsPc5UzClzXS8Z3x5mmvH+1bJi3G/9qOlX7jb5hxlaq4npSRPIZah6+y/KiHH+l0DCIu4fnag935J4x1Y6kzjnfN8xnC/nOobEvs4wcpz05jmvGHuFgYBgyKpP0tnEG2/AMU0q1D4w/4jDmfw4TQgghhBBCCCGEEELICYSHw4QQQgghhBBCCCGEEHIC4eEwIYQQQgghhBBCCCGEnEBm1hxenl+A2PhAadcYen6DEerNjWOljeahfskoMfTE9H0S1ArpzKHGTqy0Y27efwB1DgwtqCKU2jWBofPWqsjrlkPU1q0coD7VY61VUX44j/fe7qJe2nQk3/nVa9egjq91rpxzSV21Sxt1MJ2hB9duSx2eZo4CJpMY+7iIe6J8bgk1cY6DDz74EcTe++CGKD94iFpOWR81n5pt+U5XHjsHdZ66+hTEHu5KHZ47u3jvpVXsn7MXpT5bcwE1crcP8V7FntTCunsH9X13u1LD6OoTUMX93OWrEBsO5LvkhnReEeM8ffsHUvf4sSufgjorpzoQ+8GPvivKW9s9qJMkhu7lWD7D4SHO02oDfy9XWpLDEbbvo8NH+1ufN4MmkaUn7FQuyAvs/MTQai0pLVHPfABLb1dXwXVjbk7qUH35q1+HOm++9h7Ebt+SWn2ZoSl3I0D9qso5qa2evY+ae29+5/sQ+/yvSE3Zag11Bw15RNAFthRL0xm0py1N55k3Az9FNndRa0uPtfoU17NGG9eTSSz7zNJcO7WGXgblmmyL4BCquLka6rB2avL+zVXU4JsaAtDXlP55p4N7lqnSs58YhgeR8X5JT9abTA1tTGPeBJGMDQaYH1NDqlvvrZY66DEx38I2v96XngsLc1jHeEzXUvrTeYJ6dcdBamgOazJDN3ditHOoxIKtPBD6mFe1VGAUWXPcmOVaC9nIx42S4TWhlpvcWH4SdW9T388zdMfVHjYz9IWzwMhzqpq1bHmWb0Yif6/3AJPAnYe3IVZW2py1Go7/iqGnXVZrYBThMzn3jBH7eHm8ic9f398T5cDHsX759GmI9beVHrihZXnKGGu1ksrHStfWOec84ztEj6ypoWfvSqjRGKlBEhrfT5GvfHKahk72CHNtqoS4M2PdXTHm8jeqcn2LPVx/snX8Rqjcvi3KI7zMOUMz+snHL4ny2gifac3YV1++KPc/lxZxH3NceLmlLy1jg3EX6qys4HdV4KTe7oMH+M3bK3Ce9w5lO4YV1MjfH2Ks3ZTrYaWBeqOtBZxz1bLM7ytzqPONerH4LtpL4E9i8huxiHB+9Q7RX6altjdf/zk8Syo7POtYW5VjqWTo3F57E+fqwaHUtZ30cONSGPv7thq71jfAcVA1cpannq1/2IU6vrE/DD3Z14VexJ1zaYpzOEnk+lSvGTnSOB/rq3OTUgXHcbOBzxmVZF8Ph6gf7jI51uc7xjeBsf/V8v7JFHWQJ0Ncc/p9Wa9Wx+Q618C22+nJHFCp4Bpb5LgP1Odq9+7i9+f5e5g7ls/JvJDlhvfKDPA/hwkhhBBCCCGEEEIIIeQEwsNhQgghhBBCCCGEEEIIOYHwcJgQQgghhBBCCCGEEEJOIDwcJoQQQgghhBBCCCGEkBPIzB40c4soND6nRNJ9H00Vuj00dkiUuLSvFaKdc7lDwesiko/bMISsE4exd29K07bhFE2lKhUU/a6U5O9V6ygkPRdIkf5XbmxDnTTGZp62pSHd0hw+t+fQqCZJpQngKEax9eHIMGtI5XN6hpmf5W4U+TJY+CgKH4X4fqkSAi+yGVyvPgF+8N3fg1i4ckWUL159GupUYxyPV594TJSvXEaDgGxiGFf4ss+Gbg/qhBGOhyDoiHKS4pgd9lFIva0MIFOjL+7uyHlaaWzifQxjoQsXz4lyYfy9adxFwff3fviavG6M7fvUL/wixJ5+5oK898toSPfBjdsQqynDr3YHTRHAzcY511P5a2qI1z8yaMMXy63MvE6Oh8IwNDONzwo5rq7fQDO28Rhz7eNXpbFh2TCa8LXzmkFe4HW5WtJe+NJXoM7dWzi2/97f+XuinI4xP97d7UKsXJNz8DHDWPT9P34ZYkun5Th+/EufgzojZ5iQKdenktFOByM0eZvGMh9bZhvnV85D7ONmapj/HBzIHFYbTaDOvLF+RarvKw3DoGKE+WKgzd6MoRek2BfTvmzTpSaaUbx//RbEGsqQolFFk47pVK4Rc2vzUMfLDGMtZYpUMXZ4/Qn2fbks15utbTTtdTk+Z6PdEeXJGPNjmqDhTbUi527TMPc46KMJyWQqx0LTMAA5DqbGePTU3MwNEy3L6DNVfT821pzIMIgLlLFbOcQ6hYfzzVN5NNcGdc65wnCa1a8zynCOxGof7xv7x9jIYVGh952GybJvmEirZ/IDw9XQw3yivcus3WpuOO7FYzlGe0PDxMgw4XNTeZ0eK3/CXzNiHy8HD25CbJrKZxsH2BejNs7DqjIjn7xrGD0H2F5pXSYtP8D2Kxvrhqe+/VLDAC+zxrYyA7T6XsfC5QtQp9nF8TFR2/j4LO6h51LMc/WJfOe0i3NrsIPr/OiBNL99+PLrUKf15GWI7W9JY6O4huuNZUg62pf7415kjPVjYtBHk6dgKPuoGeECmYww3/pOxqplNHnyPfxma851RDkLsB/HMZpKjbZlO54/9STUaVfxTMYlcqQmRzgv5/Q5htFno4lhvB3KZ88DbLubN3DNmVuRe+TnP43fXlX3GMSSTM6LyRDnc5rgeUs8lv1eDvA7uVrHmF4qPGPNORYMI8hIpc3I+O7utNF4spbLMXqvh/08zbBf9Z4xinCOhGVs01Tti05v4BlJewFzzd6+ND9MjP1Vqh4zMYzvyxHuKydj9S1k7FlHPZzfvQP57VCkxn54CfN7ovpvMMT98GhqmbnLuTzZw3x269o9iC1+UZqEhpGxB5oB/ucwIYQQQgghhBBCCCGEnEB4OEwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAmEh8OEEEIIIYQQQgghhBByApnZkM4ZZnNehDFNuYJ1ak4axYTGGbWvHSKcc4kytyhX21BnbwtFm0d7UjT/wjwKx0/Ro8JVlHD7lYun8DnVhWmA76tNrZxzLgykmUCzhOY5C3MXIXbxsTOifOvuj6HOe9fQcKkUKoO4Ak0QUq3w7ZzzQynobRmhWAYmuXL18bxH4+8QO/fQ/O25Z/+0KJfLKPQ/b2h6r61Lw8CDLo69ezfQIC7OpXC776EYeRBim2aFEkk3+iubomtEkcl7NdqLUGd/IIXpfWM85oZ5Dlh1GBr+jQoaK55b3xDlSoD39h2O0aefkqZZnU4H6nxr/LsQ23oo5+Cp5XWokxlGNZEyrOj10NDqUUH3j2d0l2WAVCgjIXOqGoY59zbvivKv/9ZvQJ1eD01TXtjbEeWf+do3oE7ZMDfQ72fZRaR6rDfRlOGX/+wvQ+zG+9K09Pd/G40re4YxxHubW6I856FJQWWCDfqDfynHaLiAxiH+Sgdiw65sz8gwinrYuw+xo768bjLBsX7+T/1NiH3cLM9j/6QTOe+bDRwLRYrmE0Eo27laRTMKK4WNlPlgnGJ/lQ1nt6tXLony1haao0yn+IOLS3J9STM0qMidXGdrhrlePMIZEFTlPA0M067hAc7JI2Vi2G5hzh4YRrdZLp+9bOwJE8PM79QZmf/1fsE55w57mP/1XqMzb5jyHAMjYz6Feg+bG9ttY+80HspxVCphu8+voLFLVXW1bxjEBdac8GUfHh3uQ53xANe9s+elkW8/wTF6eCjHVbmMps6W2YynzGHNvQe+HtSzfJBLDuebr4yi0sQyMzMWRrVYFobZdd5F05j9TWX8Vjwa++P9QRdi94bqGyfHhi95qxCrzcl95v4Y98erllmUWi+zHvbXNMaYW5S/V798CapMDPO3wZ4c2+Uc+z5QBtvTXXwXV0YzIq8j1/XQ2JTlPcwd1SeV4V0J9we1HcOMfFN++3Xfu4G/dxfXqaZahw86mJf2t7DtHu7Ivcb50hrUOS6CsmGOPZHjZnAH+3G6h+26vC77rV7FcXs07kKsqb6751fwQ3J31zBHy2R/Z1O8bjJAI62yJ3Owr0zMnXPuYE9eF9Zxj7DfR0Ou8UD1f4j3vreJa9zaabkGVBq4loQTXAPGY7lWFFP8vdOn8Lq2OrfZuoM5ud7Adajw5b28Dz/e+kTo7eP50VDF5mq4j66UcFzFUzn+8xD7fuTh+D+cKiPHFjZOZHwjturye6jTxnZvNnBPctSVz7VvfEcGTs6RJeNbwmIyUWM7xpwcx5j/BgOZpwdDzIflMr5L5st22TOMMg/1MznnJkmuyljnwSaeZ2EfW+c2H86jsSMhhBBCCCGEEEIIIYQQ8onCw2FCCCGEEEIIIYQQQgg5gfBwmBBCCCGEEEIIIYQQQk4gM2sOa60e55zzEq1NglpUwyHqy8SJPJNOfdQAHoxQl6OnYqc28PGLFK87uyg1Py6uo17KaIJ6KacuPyvKpQK1oQ6PZLtUOwtQx+2jXtDGqtRm6g5RF+fC449BrDVXU+Wr+EyGHtbhkdKqNDRl/QI1ahKlaWlI5LnM0OJUMium1ulxUGvMQyxSj9bt7kCd8nwHYqNUacIYutXVOdTBAU2zCer+FMbMnCRSK6pSNTSiPdRgyn1Zr7GAerulQmojB1XUTytKOI5zTz6TlxnjKsDnjOpSm6dq6A6lUxzH+5tSL22hjpqTf/ZP/QLEXn79tigPxthOk+kuxKZjmeM6zQ7UeXRQ48gQDz409CSPDmXfewHmwq1dnBMvvfwjUX7l7dehTu+gC7Gp0ph88umnoM7yEupiB2oc9fqoudbtyt87dxq1OddPL0Psr/+NvyrK9zY/gDo/fP0NiE2Hck5cv78FdWqrOG/233pLlEf/HKq4i196HmKHAzknRiNcX6deF2Kx0qvK80cjHzfK2DZXL0pd/WoNdcqsnLJ176EopylqdNUb2PddpSUWeJiLPEMTt38k+2J3B/W/EkMa0yk94YHW8nPO5YW8cDTC/cHA0KpsKe252NBXLTxDi1bp47YMre5qDds8DGX/NZu4lwt8Y91QG4lbd1GX1QuxH0qBvFd/ZCy6x0Bm6CprOf65MuqRt+o4tse6nY01PRqgLmBFaWUvL+NYn1Sxf+JU7WEr+ExBDZ+9pnSpO3XUG11d1HkHN5ATY284UvW2dlEnNRl2IRapeROmOD6CHNszSeRcDgNsg9xh2+m9lTN0dXsPbkNseijfZzDAXHUcHBqb2K2RzE9JD3PR4gruw4oNOf7K1l64h/MmfCD3YbGhrTowHAeyhhyj0dkzUCc0/D3qHXn/5NpdqJMojeOJj3m1+dUnIDbqqjXh/fegjjM07t1Ded0070KVaBX38atf+4Iol6uYew+u4d6mM5L12mfxW/CuoalfVV4hUYQ5+7jwChxbhfrWWmoZ+8wxXpf25Zqdl3EtjCc49/f25FwpItxH1CP8ZlpSnijLC/icSx3M7y6R/RgF2B9JIOdzb4jfPfe3b0Fs677s/wMcDi6dPgOxZkfef2vvHajT9jDf1kpyPi2vX4Y666cwp3ipzNP9q7h2xYb2eKa+ZUeGf89xkBv66on69plvYDscdfHbYHcsz4EWz+J3/lwdz8e21HdNa4JrfTnE6xbUuUmjhmtoGGAub7VkvQd3cV0aDuVcsvYWA2PtmIxkzNgOuENjb93ty4p5gReGW/gNUGrK+T0wNPuPjP3jtJDvNzW08Cc55vdUfdtlhqfDLPA/hwkhhBBCCCGEEEIIIeQEwsNhQgghhBBCCCGEEEIIOYHwcJgQQgghhBBCCCGEEEJOIDwcJoQQQgghhBBCCCGEkBPIzIZ0mSHkX2RSRNkyHatWUAy80ZTi4w92Ufj71n0USQ+Vc1hp+wHUmWzjdY8tS6Hsb34djd4+2DyAWPOUNFlYXFiFOjvKKKPTMQy5chTqLimTlp3dTagTVroQ2+1K053NhyisHkUo7t5pSbHu8Rj7qgjxbwWer0W/cRz4Hgple8rgJns0/I/c2pnzENPPOpmgkPt2D6dKqSNNApLUMDKKsO/HyoAoKbDdwxANIdJAxrQhjHPOLS90IVYcyPkVGwaCXi6foVrFeWv4CrlcmT5kmTE+IrywCOTvDYZo5uAZAvNl1Vc9w6imWkPTwa9+URolvP/BHajz1jtoJjZQ5iulCAX1jwc0sIG5idPSHfVQMP+PX/yeKN95cB/q7PW6EDtUfebXcfxXppgPd/blM/zxi38Mdc6d24BYuSzH/6axRiSxFN8fj7pQZ9DHWKSm99XPXoA6r914E2JxXya2+4YJRK2Ec/l0W46jWy//BOoEZcwL/roc20cpGi4Y09S5QvbNdPpoGCA1DJPLek2OmaiEObTdwTleVeP9cB/NF99+9xrEUpX7yqUG1Jmvo3HHg025Zu/v4dyapJgvesrIzjKOLFTq63YPoY7lMxFPZbBWw/adX2hDzFPPME2N/Z5hYjieyLWlMPJSapltqPGXGfuKag1zhyZ8VAyQUuyMtjIH7BiGfpsP0fxqrPLFNDPW6y1cv84vSIOi5Y1TUOe9B7hnLpTRSW2I+/F2Hcfxm/ekAWljFY3KGmU5d29dQzOizJhbncfket1YvwR1hnfehVgwkPm3VeD+eDToYqwvDVdLEeaA3gTnUrUjvxEWdBJyzg0MU0i9Nus96HGxsYEGrv4tmeeqhk9TFmNuKHuy7w8Nc/IX7+FeY10Zez3u8AenhknTWOXj+Cc41sbaJdI5552S82RyGb/zRqn8pnrmIprPDX0cM2NlRlg6QqOjtIU5LL4r80KyjXMrWkaT4NGKzAHRPOb6uW+i0W1Xmbl2FnGsP984C7Hf+55cl8odNCY8NhJs65IyzWoYe7MowzydxnJ98sp471oF77W/I8dpZvinXr2Ae91TC/I7NTTMWSdD3BdFTn63WebSAzVX37+Fa9DDLsb8RJnTd/H35wucq5fnZG5LDRPZODSMbBO5n7JyZKmK91pZlOc7iy00puwNcT81VabN9XAB6hwHofE/nJEnx2g8xn1Xr49r31gZtn75516AOk8+gWZz3/tHvyXKe5vYz2ttPI9oN2VOjGPsr6mxP8wz+ZzTqbHZzeQmef8Az/Bcju1SqL3mcIDnDF0jT2eenN++YcC3tY9r3FpHtYth7tvP8fxjqr5LUg9zclDDNSfTewvvox2+PRo7EkIIIYQQQgghhBBCCCGfKDwcJoQQQgghhBBCCCGEkBMID4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmBzGxI1+mg8HEaSiHpwQBFnIsEjUaO+keifOcumkoNBiimXa3Is+yHt1D8eaWCwu2nTkkh/c46mpJFfRSldhUpOH362c9hlS1l1pCiSVLmsF2GQxlbq6GQf5zhM3l12Q+n6+tQp9lBQ4X+vjTb2tlGs57EQ4HtSawEvX0Ut66XUUw+Hsv+s0yFjoPCEPVOlEHbqI/i4GXDoK3fkwLo8QTFz0c9vFekBMObdTQyWJpDw6XWvDToWergM2UhGlCMy/L9Ds7imJlm0pDCJWh0lRmmO7kys8l8Y8wahnSdeWlCk2fG7xnGee22fOeSIbbeNQzHikSOx09dxTnSaWI//MZv/K4o726j6dRx8Pa7r0MsVAL52pzNOecOu12IdQcyH999iOaY7WU0Z5hXfbGwiDls94OHEHv3LWns9nu//3v4ey0c20Eox9HUMMGJpzKv/svfwdwbGX8SXT8tjVxqi5ivnv3U4xB79Xvvi/LI4fi/tm+YJmZyLs+lTahz4wevQKy7JHPtgTHfohjzcapz3Ajnm/uPMPRxc3p1GWLanGyug4ZVgZHHo0VZb3UJx+wf/NF3IJbn8l6dJpq4bD00jE/mZDt32rhH6u6gccfejlyLO3No5FFX5o5to06zjmtEsy3zf72B4zgd4zPdvCENzgLDAGdkmILEKsfEU9zvBQFOOE/Nk6ph5pMZ+5EkkUYlydRw+DkG/AwNslYbcjxsH6KBVGKMtbApc4FvjPU0QUOds88/KcqHRi6K59CsOFDmNn4L80fX2Mf0lRlhbph/Ticy77SNe98z9vrDXbk/PdvpQJ31K89ArPuOHA/DTTTuO9zGWG8ofy9LccwejbGvqnNyzWtu4BqYjvA7ZaIMhHzL7fcYWF1fgVh/U+55anOG061nGHspM+uHe/jN8fdefxtiVxbkvPlPKmhMWTPW8GIox9HBm2hId7CE++ObU2n2FhumdeuX5Z75zBzeJ36I63xDGb15uWGs1Mf2LPty/9MbG/vjmzchVjyQa8uhsaetX0HTwfXzF0V5soXvsmQYhD73lDSK3DiP9z4uWm3MdZW6bNcixLavW2cdmZyvaYoGgYMjwxx4IMdSOcR9rRsb38ZjaXbuhZhXshSfs6yMNBNjXTpSS0fRuwp1qolh+FvI5ywHaHi61X0ZYudCucc7XXkK6iS+YTA5kvP5KMZvifzgCGJeLvNtp475N/dxXvR7cu9SMoxSj4NygeN4dUnO11cynK+HDsfj+pOyL174OhprPn4VzwcWlJnuv/zv/gDq9LqG+etQ5oyDPeyLODFM40KZ4PtTy1hRjpk5w5Sv7HA/mikDvG4f2ylOcQ2ISnLvMklwzB5OrO8xea9xYBiXOswnsdq/jVJs38DI7zVlHpwVNKQjhBBCCCGEEEIIIYQQMiM8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5AQys+Zwv4t6UWEsdcgizzhrNqS0wkAGRwPUjZlror5RR2lpjA9Rv2R5HXUGTz3zNVF+6z7qPl27gbEX1qTuTreLdVYuPivKvqHzEk9Rh7hTSD2R3g62bzVGTZO1efVMmaH19Qxq5Yy7Uq/n+7/1Lahz/x4+ZwBawaj9MjYkTRL1dwff0Gc5Fgzd3FDpgLVRFs9ttPG9H7/QEeVGxdBJNebEsNcV5ckIx3+1ju115THZ9xtnUd/Lj85CbKB0ZjfW1vDet6QeYmseG2He0L0MlTZlboyFwsgBlbrUUUonqC9syFu7yJftOXGoM7SwiJo+A6WxOuxuQZ1TS6jr9ed+5edF+dd+8/fxoY6BF3/0IsTGPalbVDe0+n75l/8sxNJC5pBX3nwP6rSbRk7Jpb7j+jLqFSbbqG96NJR9Mbr+PtSZK+O8qbfl+zTmsL8qdZlX2x0cfO0WjuNWS46ZagN1vr7+jc9D7GhPzt233kIdwCzB3HG3K9suilB3LtzCOdE/lLG0iTnHry5CbFNpH/Z6qHF1HBQFanSV1ZpjadYmQ3z+ciDbudDi7s65LMd7+b78PfMv5jnm47NnpXfBopE/Tj9EnbByWf5eq43zNFDvsrODOuAvfB49EFbXpV5cWqAmb28f1/nDPSlEuN/F9g0DTMhLi1J7MzcWgDxD3be20uM9PEJN28LH/ovH8n0sXfrjYL6FmuGLDRnrHqAu4HwF531ZjVutF+6cc8sXr0DswtqGKL99F3NRp4xa0mki9z/Lqx2o4xtr6lDpAvpNvPfhrlxnzy7jnmVUMrTxMzn+Dg5xzPprZyB2+okviPLmfVzLJoZ+a6RzR4bjODBywLQr9027Dsdxaui7+yqnGVPkWDjKUMs6LOQaF4X42RgbuaGbyrX/wPhQSAu8Vy+Sa9pmhGtxp8A5EfsyVhS4NzzKsS/u78ix1vJx73uoltlvbeL305VTqMF6Ue2jF8rodTG8jbk9G8tnKjJ830NjTuhxGxta7skR+mbEb1wX5Zqhuzw1ctXZJ6TOefIA9byPi2CK75B5sh2TAuf0yPjuGA1kf0QlrNTycJyWlZZ4KTX8BQL8ZgumUlM2H+Peuhp18EEzmVc8I7GsNeXvrXa+AHXGGeax4YGcz7d2sK/nQtQQbyvN3DPLF6HOu1sfQMz35DdH5GFfWR4Hk7GMjRs/hDpZCffNvYmcq/0uahy7p/80xj5mRj18b78sx9HUkLJeP7sBsV/8d2VfX7qC3wqlKo7tJ78stYlT4+Twe3/31yH22gdyD+JN8cIsNfy+SnLeHBh6wvPK8yOs4v5jbHklHMk9+dCQgQ8CfM6pOjs6muDeemR4B7y7KfP03T38wb7hL5YrreCpcfbWWkTt+4Y6WzkYfLRvPf7nMCGEEEIIIYQQQgghhJxAeDhMCCGEEEIIIYQQQgghJxAeDhNCCCGEEEIIIYQQQsgJhIfDhBBCCCGEEEIIIYQQcgKZ2ZAuQC1kl42lsHNhCCb7DoX0M0+KNh8aXmW9HopiF1Mp5LxmGLl89md+BmKnr0gR7n/+9/8rqLNaR8ONIJYC7Js3UTR99YIU6q4sXII69QJFsUcH0siimqPhU2wYZ+z1ZayzdB7qLKyeg9h4IAXMfdTFd1kJBbY9ZQqTJCim7aUoCu8VMpZaCubHwNe++GmIXXhCmgo+2ESDiFPr8xC7/JgU1l9dWoY6QYFzot/vivI0wX7W7e6cc426MuRqoHFGYAjtR8pwbzxEI4vnn5ImBecun4M6iWHIUqi/L6U5zvfCSB5BJMdDMjGMjAwjHl+Z4HgVIzGF+DevqTJEDAM018jiLsSWlBHPl7/yWfy9Y+DmbTQbOtqRZjKPnX8M6lSrmDMfPJC56M6tu1CnUcdxpcet10PzuXHXMIxSY/vSxQtQ5eISCu03lSHizo5hZDov+35tA9+338P5VlJ+AJUcjQVaxjP93C/K9ebAMEndvr8Dsb2p/MHakWGuahjnhZ6cJ6eamJfqK2h6s3n7tijHI1yTjoO79+5DTOe5fh8NFSxjrdjJOZ6FOMdrTTQOi8dyjC4v4Vpc9nFsX7wgDYjKxjP5Ec6bkjKkq1bxOX01R4ox9te0h2Z3SVs+58Iajlk/xXc5uyGNwsoVHI+9YRdipZLM46GH63xqmNEGoZxf2RT3FYFhqFmk0pikUcfxfxycXcXn+Au/9A1RvnPzHNTpT7APpxPZFukUc+i5dTRjK5QZYLGIeeDI2L8NR/IZTi/iPiY1jCMHQ7lfLAzzq0Yh51KQ415xpY1zZLgj9yiDTczZyRSfqb4ix/H6k1+BOnmC68bOA7m3Hw2M/Gg8e6sux3HocG4ZnmsuGcl7Wd9Ox0HJ6OdQ7fsWfcxXcYBjNFRjbTTBe1smwKfPSyOlzQG2qStwv1hShmme8c0R52hstLYgTZlCY8vSU8aKxQGOxwf7uE4d1eSacGZqmEvt4feGU2uSn+Kedpzi740y2eaFYa5XG+NYe7gp1+Gah3WGKTZMR+WmxWcuQ53jIt8xvimqcgzGPn7zlgxjq1Ikje79GMdyYZid52oMLq9/CupEGZqL7j6QOdEygUyrxtlKLMf3eIzPVKnKMeEb+andQdPyUkuZhC1hG5TqaMrXm8jvku3xW1CnsYrju5LJtWM6Mc5osnWI6Vy6dfAq1ClHuA+cn39GlP0Ef+84uL+P5ukvvikNyZcu4j7vL//NvwCxC0/IXOeFmFunU8wrcSzXq6c+fRXq3PkJno/9/j/+Q1EuxbinSwxTwVwZjraN7/yNNWUA6uF8H8SY7w/VOtSd4r7F+q/ZKJL370d476iD4//e/X1R3urjdYtncM/14L7cA6UJfpP6Huaq3qHcu0xS/L1Z4H8OE0IIIYQQQgghhBBCyAmEh8OEEEIIIYQQQgghhBByAuHhMCGEEEIIIYQQQgghhJxAZhaCNeQ8XKa05Dwfz5oN+U9XjNV1KF3j5hdQu2O1JnVInv8M6htdfeELEDvckZpq5RQ1xy6cPg2xXD3Y6jLqY6UT+UyjLmr8xIZWUzKWTZ851Lf5YBP1GN9862VRfuEL+HsLqwsQ6/Wl7mWEzesWz6EeTK76NItRHyY19AKPdruiPO0bP3gMfPqZxyH25HNSc3j81EWoU2+j/qcetoWh0+Ub2rbzdakFWBhzxPqrTZ7LX0wNTV5n6DtOp1JX6OIl1CuslmTfj4c4RwpLnEppTBZGosgNfbhMtVWeY514jHpIWS6f0w8tnXNsvf6+1Ii7c+se1PnSl5+D2CiR+j01S+P4GBgeYf+MJrK9yjXUmzvq43V37t0W5Y4x1rOhoUc+kVpGD7duQJ2HD/bwOl9e95f/Impj5YMDiP3h974tynfeQK2+hbbUYNq6jv11ytDrPEq2ZSBCneD5hRWIPX3lKVGO/xzOkf/q//0PIDbuy/Z80EXtUReintRUad0N9vahzrrRfyWla7u43MHfOwZGY9TDypVmXGxo2s8vocZrrvTOJxPMhRsbGxB75633RTkycsraKq79S0qbODA2MhGmf1cqyzFSM+ZpoHXax6gfO+6hLvDBrhy3haGpWDVymH6GVhPzcW+Ec7LIZBtXK6gf6xnjWHsXtKq4P8iMfmgpDc8IZdiOhVaA7fzF52We+dyTp6BOf4TjP1EbgiTFvkhHhr67ysfnY/y9kaHvNxjKe0UR5rBDY6xVzsu+GE/xXYqO1Djc3HoIda4bGvdPzEkNvru7OPacoQufVaSWZOPs81DnKxfPQezgntRLfP8nr0Cdna33IVb3pJ6mM/QaJxk+p6f2cuEjMpCrY5yHD1KpZ7ls5JS5cRdi4Y7s67R/CHWuPoGeKWeuSK+Eg9ex3dc8o72UJmRkbKyrA+yf0MnrajXMYdc+uC3Ki0O894VzuCbdL8n8uH0Dx3+1j2PbU3PeM8bQxNB5jtX3WjzEOgcZ6mnXanLP0De0OodTzEMHm3LfFJ7Bdeq4eOI0+stkNakvmhkL9JrKWc45V1F7Ki/HtWl3F/PYgWr/oIKeRJNJB2LjRM6xShX37XGM83A8lN85wyGO9yzLVBnHSMvwZqg25LzYNHLyJMD88VB53DT2cRwFczjnkt5tUa75uI+Yq56DWFiSfZNO8bp6Gc9ITq/KvBM5XD+Pg9WLeDaVNuT+6VOfeRbqXHoW52JWyO+MJMMxFGeGCZjaj5YauEc48zR63Ax+9Y9EOUyMfeUQc01JHRx+6nH0pTl3XsaOhvgNNdzBfdLWSOXkEe7bgwD3SUEo82ZjFXPyl/7UCxDb/vUfifKD5AHU+bP/45+F2Hf/8CVR/sF37kCdzfvoH5VM5b7Ts9bKGeB/DhNCCCGEEEIIIYQQQsgJhIfDhBBCCCGEEEIIIYQQcgLh4TAhhBBCCCGEEEIIIYScQHg4TAghhBBCCCGEEEIIISeQmQ3pcsMUZjyVQs6lOpqqhSEKvge+FNO+tDoHdSpVPLc+d1aayTz75Z+BOmtXnoHYay/9fVE+s4G/t/rk0xArLUljsrDWhjqjiRTBHvdQ7H/7AZpfHW5Ls7ksGUGdahONahYXZXvee/Aq1FlZQyH1dCSfszCMgLwhGkZkhRT0tgzHqmXs49KqjPXKj4aRV7WOpnuNijQpqNeMaRGiqLf2UPMsQzojlhdy3uQJCqJbJm7a8DEFSzznfKOZC09e1+igcUaayXtlhtmLM0wYCifzgm89QIaxTOWFwhmOlykaHXq5/L2y8ZxRhrmjPpH1im0Uqt+9uQ2x01ekGcCebxiHHQPxFJ9/pMxwbtxCg7hf/bV/BrHvfec7ouwV2F/bPXzv3Tsyr0WGsWiS47pRWpV59Pvf/WOoM+2hkd0716+J8nAbjTS6u/L3OguYQ3e38LrekWy7uQ4aZMTZNYh9+9s/EeVqC40u5haXIbaXSCO50RSfabOPZhGFyqO1IzQcCXbRTK+zINs8CGZe+j9WLMPO6UTO+7JpzIfrV7ki571v5NUsxnnTP+yK8miA5lvnz6BJaVX1RaOGJi5ty2gllYYYWYZ5Lgjkuywu4r13DLONh8ok5pW33oA6lwxD0p1d+c4PHqLRReqwzTst+VyRsSaVyzgHU7WeTic41o3lxtXmO6LcGzwa+XhwgHun+7feEuXTp9B869QamlyGahzlHs7V3h7mx25XPsPCPOai4RjNZkZjOf6GhmlXf4B73ysXpSGMZX40UaayS9Uy1Imm+Eyf/rw0djkYYZ3bW4ZJky/HWjbGceXm0Fxy/RnZN0vP/BzUSQ9xf3Dw7g9F+dZbP4Y6ex/guuGXZFv5obF4HgNHQ2znbx/JtSnFYeW+lGMOq+5siXLF+MZ57tPfgNj6hjTt+vUfvYnPOcV+zUL57IlhxFM19jaT+/I5g3ncH1+YkyZlkwzHXljHdeqZL39OlA8whbqDV3C9nqqPizzEeTM23qVeV51Txe+dccn4llmQ38QTh3W2DAOyo67MQ4fvXYc6vwyRT4Znnv06xPy2zK1+A9unU0FTtaAs2z9wuG95+/2XIbZ/V+aMW1s4B6LQMI1tyPYvJXiuUCQ43oZHMt+mhWH2VZLPPhrgvW/e/gBijYr8vSzHdWmQYB7Y7cu97sXkHNQ52MS8c/f2u6IcxTgmOw3Myevn5Fp1lOK4zTvYx/ORMs4r457rOOisYT76D//Tvy7KJeO8LPGxX339vW4cAVar+N5FIa9LcxxX62fRAO/yVWlSd/9N3FcWGd4riOS+OQ5xD/naB9KgbaeLOXlr1/huPZJjtGesE36Ae+tGRY7Rz//MV6DO537p8xB76fVbojy6geeB9Q7O5V/5C18V5Wtv/yrUee3ltyD29V+Rbb56Ds87Z4H/OUwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAmEh8OEEEIIIYQQQgghhBByAuHhMCGEEEIIIYQQQgghhJxAZnaliQwDm8O+FFfPJiiQX62hIUvgS7H95QUUB7/3sAuxi8//oiiffvoXoY5zKL6c9KX5Q7uJ5hpLlz8FsWEohcDffhXNJqZjee9erwt19jbvQixQJjSVCrbvqfNoLPfMZWnWkAYoqB8FHYyVpJh2aBjAjO5sQkwbEabGnxMGAQp61xbkc62sGy4Wx0CzjeLuhTJFGk1RVL+Yomj6dPrhRi6xIdA/VQYsaYpmJEmCAv2JutdohOYGoyGK0Ke5vH9zHsd/s90R5U5zEepUSiianmkjEg+NtXyHsaYyW9zfwXaajFFMPs/l/PYcPlNuCNy3mtJQ4uwZNAIaj7D/ilw+e7uJ8+04aBt9mKi52TOMtd557TWIbd+SgvmWSUHNMBYt+bLtixj70He4JpxWhpnzTczZhyM0BLhw7ooo38nQBKp7IM0vsnIH6mwPjdw3knmue4BGF56R5yaefIbuCI08/BKugXmg2s4wiRkZ5l6ZyhV1496NNranNjjLCzQKPA5WF9HEohzJZ62VcY5XaziuUrWmRtox1DnXqmAuunhK5oKOsWdZX+5ArFGWfdaqo2nGxMd7lXL5Pr0jfKZKXV4X1XD+WWYb9w7kmvD+DRzHWzs4/ntH8l5Jgvd+4uoaxBoV+VzZyHBcMkxDC2W4Winh+2WGCbKn9qFphm13HHQM46f+vjS6epjjfF5cxXHcVu9Yb3bwB9toGhN4cs/QxKHn2g3DbEbl8dTYs7z7znsQW1qSxm61GhodjtSe6NlzuKf92meeh9g4leNjZHTzYxs4Prb35brxYAvNiLZuoSHM3Uz+3sQwl6x2TkOs85T8BvnUlS9CnVO30BTyjRd/S5R3t25BneMg7j2A2I19mUPGhhFW5zTuF5+N1HgMsRPPb2xArNWQe/SpYdg5HWGsFMnxMCmMOj4+eymWzzU+wDHjh3JO5gGuLdtqvjvn3OG774hyrYK5sF9BI/d+VX4TT415axlA1hZl2x3EmOv7Rl71EzlvHm5h/vcrmON6KlfUe2gKdVxceuazECsiZVgZ4ndWGBgmv5m8zqsa+7W3sF0378n96MFkH+o0G9j/6ZZ8rloZ6yzPo9HxQkt+FwyMb5pYjYlkgvNk0MVvh4n6FvINE8rBBHPrQF3Xy/Eb1fNxPkWe3Je9cwP31u1FvNdhqExy69jHA8Pgb/9QjvnzK5+BOp9e+asQ+7gZTvFZ6/NyPOYO31GbyDnnnKe+A9Ip7kmKwvqfUdk/cYJ5pbOCOepX/uIvifJ/v/UtqDPqWmascn7t+zjWFpfVWE8x90wTnKdhXebWaoDr0vISng98/otPiPIXfvbTUMfrYNutn5c5Oc9xr3vjBq7/v/KnpZnplSu4/37lJ+9D7P7th6J89tI61JkF/ucwIYQQQgghhBBCCCGEnEB4OEwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAlkZs3h6Rg1Rmplebln6ClFPup5FEonrtrA6/7Mv/tnIPbCL31TlFuLqAuyffNdiAXqGbp91CbZvY3aHQ/6UrPl27/2a1CnUZX6IZMpajWtrqA+aEtpl966j1o9sdF28+vnRPny06h74rIyhA6690V5ZOhDH47x97xC9vFkjPowgwL1goqBHC9XO/iYx8Gvfeu3IZZFfyzKh4eo0zg42oOYlknSGsTOObe9jffKlBbm/BJqR80tokZzWWkRDg+6UOfadRz/vYEckxvnz0KdIJLjuNXE3z9/HjUFT29IzdDzF1BTcL6MY62ptCrzdgvqOEPjNVG5Iwjx71uB8Xsr56QmXqWFcyQxNJqUNKybnzee8xhoGJrDocop8T7qje1dwzyz0ZD38gxdvr6R/ycqP3lV1Fwte9iHu9tS0++VH74OdVaaqF+1f9gV5aMx6hIPVHoa76F2mjN0kEPV0dUIc9rE0FTe7cpnynxDfz1E8U/Pl+PWN9ZOZ2gOu0Jqiw2H2Aa9HsbmFjrq1tgGx0Hh4/ytKL3FyJjjURljk77Uu00SnM/tJs7fT31K5gar76MI50QYylhmaMo6H+dNuSTzeKNh6HmrHFbkuFWLjLZ75z25jxmOUIvOZZgXtA5+KcBn8n3MmYUnnzP3sc17xjztj2S76PnnnHNxjPuRdCqviw0vgONgzcjHXizb9GB7B+q8/sYNiL36luzDlVOoy/qVr30VYqeW5DNMDtGTIDBykVP5PgxxrJ1ZRx3zqlrDyyUcj62S8hNpYj8nGd67P5ZtN84wX717/TbEDqe7ovz8hSWoM1jG97v1UOrFvnsHNZZfv4l91Vea9ost9E95YgX3RJ/56s+J8qsv/R7UOQ5+/izqyu4eSL3TH9/CcfV7t/GbqnpB3qvWwPzRDLC9kr6c45mHOWU4xbxaUfvjLDD+98nDWK7y6MEQv+GKicxFJcO3IOkaXiUfSL+ZmvH/WHEN16Q3U5nXbu9h7qgYy00pl7k2MrxsvATn0qQr92TDAvdfobFOZZG819m5Dj7UMVFrY05Oc9n+RlpxLsJ1Jy/kmK8YZxbJcBdi29el5nTRwPm1tPokxG68L7W/x56xhxzi2heeknsXz+Fe5uHd26I8HOEeeTTCORBkch56Be4jXKULoUJ9W97bwm+QuTa2y8YZqfE+nWIbjGN8zlidwTTncdxODK3dWOlllx1qHLunMPRxk6aYV3KdRoxv19DQ203VWU1hHAEWBcaSVOa7wsf2SyMcjxvPnBPl6irmuqN30evKUx43G58/D3X+zF/+eVF+uP0Q6uzsdCHWHyrPJ8Mn6dQaauifOSPPaWJDr/xwjJrip89KzeHQx7F+8xq2Qf0vyTb+zPOXoM6rP7kOsfFQjpcssTSdPxz+5zAhhBBCCCGEEEIIIYScQHg4TAghhBBCCCGEEEIIIScQHg4TQgghhBBCCCGEEELICYSHw4QQQgghhBBCCCGEEHICmdmQLi9QFNvlSqA8NUSqCxRt9jwpil0pGyYxn0ajtbISNn/ntVehzuEDFBGfKvOC/uEB1Ll34x2IDQopgB5laELQCKXod6tiCM7PoTD+w21pgJEm2E6jPoqt37t1V0XehjqDQR9ilVC2eVpGE7T9FPuhqkymak0Uha+GaDTRVyL3aY6i38fB7/3RixDrnL4iykWG7f7qi38EsbOnpWD+4gKauG3e34JYquZNbb4DdWJD8H1bmRZ+83NfhDqfegbNDUZq/PuRYchy944oX7uO8+jNt3C+ddrSrOQv/jt/Hup86cnLECsV8u9Sp9fQdCc2DOk8XxkgGWaIiUNxfj+UsXIHzdOqhsFTHsi8h9YGx0NumP8Uyl2jZBiyRIZJ15mWFMxPDVO1vmEqFbRk3/slbNPxNhrVTLvS3KO/j/lqDxwXnOtO5XXnnn8G6mztSkOA7iH+fsMwBZmMpLlGEuG7TKaYw8ZK7N/30eGkYrRL4cl8nxnmc4FhDOWncrznhgnazm4XYqnq9rD0aBjSxQm2aX8o+9lvomnRuItjJkllm9aqaKoTGGaL3X05RqaGId3RAMe/NtIqjPERhdjOkZpfo8wwVVP9FY+xjjYEds65rS1pyjEtcOxNA9xrlJS5XmAYJI5GmDtSZdJYLmH7Hk2w7bb2D0W5cIYhY4Ft5ylzqqrRBsfBG6/+GGLFvlxT2wtojvbK22h89p4yWvvSz3wT6vzDf/QPIPYr3/yyKM9VcBxXjDkRRnJ+jSdoOLa0gPvFvCzz6OEM5oCesSYlxv+oeCr/3rhzH+r87f/L34bY3o7c23/+C1+GOr/8l/4axJZXZd/UUxyz6ymOx7e7Mv/mhon0jtpbOefcY2ekmfaFK09AnePg8jrOp/+gJo2IN8ponvOH7+Oe+Q9uyzzzqbPrUGfwwS2IddV4CIw1rhsbY7Qmx3ZWGKZhOea+3ULef6/WgDqTUPZr08N2qrdxbuXaVHMfzb/KZdyP3Fc5cz/Dubwa4W60VpfP3qzjvQvDXHgvlr8XBoaZ5QHGnipkvm/0DQPUY8LYxrpCGVonCZ5rpMZ3fl6SuS033tMboBlVOpCG5HNLaKw13UXT8uGO/NZLDQPhZIBjaV/dKyhjI4zHfVXG+/RH+C6Br8Z8gO10+jzOi+U1ea5Qw+MCVxjfccNEfjufP4eG6GGGZp+jWJ6J+CGuHXGG5xj1hvyeN1LFseAZBtr6vCgMsZ8tf+TRSI5jy3zOMsLO1N46qmDuiY1/Na125HM11jtQZ2uIe/m2Mqhfvoimte1zMtdV1s9CnUsexpKxnPODCe5b8gzXcd/XhozYTuUAB/fikjwXarZwT16KME/XmvLc8NnPPQZ15n71OxDT4/aj7pH5n8OEEEIIIYQQQgghhBByAuHhMCGEEEIIIYQQQgghhJxAeDhMCCGEEEIIIYQQQgghJxAeDhNCCCGEEEIIIYQQQsgJ5N9AqRjFl/NUCjtrYwvnnMu0E45zLnZS7HmljWLTv/Ot34DY/IoUGl+2TKxGaEAURVIkulFH47XQUK+vK8H/1WU0HBv3pbFK1RCk3t/dg1gSy3ZpVlAgPR6gycP1V18W5YfvXYM6U8NMw0Xy/TLrfU+jKLaryz72yyhCXzHM5uacfJ+rT6IQ/3Hwl/5H/z7EystS6HvURxO562++DrG1VTn+fMPQrFrBsRbnsn8uP2UIja+hAcxoUc6TX/6ln4U6lmHgUBnSGd4GLlXi6pMU+3lnB40c79x6IH+/hu+7dR/NDW6/fV2U/Qn+3s2tHYh97uc/I8pnz6HJSWKJyVeUUVKEecmzTBOVAVLJM1T+j4GuYcg1Hcm5Wo9xji+tYnvt35HtfOM2GujsJtg/8/PSyM43ctgwP4RYlsgBmI7QEGAyNcyvlJHp7hbm1eFAmqYUCRpd1Mq4TsXKpMUrYx5PDeOCkjJ8KTIcH5Mpmp7kvnyuOMU65QjNvUoVtZYZ5jlVI5aodrBy1XGwd9iF2LpaZ7VBnXPOpbkxHhfkeOz3jOtSjE2VqVqOQ8a9dwONk3yVCywDyDNGfvIbsg8nQxzrmXqmNMY1vWz8njZgvLaJc/n80hrE5pX5RTiPeXw4RIeWw1T+XljCLWXfMEA6VLG8MEzJjO1p5MkcPTRyx3Gw28Vx9V60K8rBDq6Ddx8+hNhXv/l1Uf5f/2//N1DnP/8v/kuI/eavf0uUHz+F+9WoZOz7mrKvswzH43x7HmJL89JULTQMNEvKoNA3jLwGxnodh3I8/D/+zt+HOu+89ybEdM781W/9E6hz+srTEHv6MWmaWy2jaUyrwOdcV6k2DXEcDzPccBWxHLdnT6HZ0nEwNYze5ivy+b94eRHq7A1x3XtlU+aGd7dxL/CYYVYZqxxSGOa0fWMtLqay76MKjrXCSu4qZvV9v5D5qqcMBZ1zbuHJxyEWqGZ583fQQGjDeJfTc8q80thDVEJs86NEtudwH/tz1dgfrC/KXFHS5mPOuegAv63PKtP0jU4H6hwX4xjXnXgsc9vEWFezAmNpKr99Uof9MTrCPblflnMnrGO7dvfQEG7voTRRiwt8lzTDvm105NqeTgyjMjXHR+NdqDPJ8NvLK8nzkNAw7l08jXuLS5flt//WPhrwlXC74Txf1ouH+P25Ooe53Plyz1U0sH3ffw9z0dqSnNN14zvhOBjH2M6B2vuVjLU3dXjdSOWR8cQYs+a3gbxXPcAcknl4ne/LcdtZw7O+NEBzO1+d2c3P43X6Oz92uD/1U8ytnq7nG2eUhlGlpwySC6N9SwF+szVaMrfOLeL7rp3C74TMl9+WC2fw985cxD2eNqYPvY9mPv5ofCESQgghhBBCCCGEEEII+UTh4TAhhBBCCCGEEEIIIYScQHg4TAghhBBCCCGEEEIIISeQmTWHc0OotBRKPRtLA8n5ht5WILU08hi1Qvb2UPd1sCtj1QS1ZHKHGjvzc1KXo7O+BHXSDLVJNh/I37M0RnylzRSnqEsWeKgxUq9IPZvUaLrACirdzSxGHSjf6KveSGrsxGXUVWquYxsMq11R7ueoxTIZ4t8YFloXRHnR0Gs+DsolfNZr770lyr0jHHtFgX2fKF3IwWAIdTxD76VSluMhGaHuz9Eu/t723Xui/Nu/89tQ57Bv3Gsgx0izhQJP7TmpKVhvoebq/fsPILa8eEqUKy3USv7j38TnPLj+hihnRg64sYXaVPeH8v0eu4p6ze0WakW156SmZrWGunLtOs7TqCLzSa2G7XIsjPFZnZq+qYf6R0NMj+6hJ4MPjbwziI1ctC/HVRChBtoox+sKlZ/GRs4sCtSBKik9yU1Dyz1Vmr+ew/m3e4h6Y07N08LQ3YyqqKncUpqalsa+lTsCpU1ZdYbulqEpG6k28ErYx4XR5p66l6X9eRzce4A5JVL6+Jbe7sbGKsS0/mxvYGkOG32h9PdHhv7zuzduQkz7FDy4h/qxi4ZWWrvdEeXr129AHb3X+DN/+otQp1xgHp/rNEW52sO8ut/tQixX81v3gXPO9QaYV4dTueaNjL7yS5gzJ4mapwGOx9wYx4dqLVs0NPaPg1PnLkEsc3KtSgzd9lIdtfvWNuSaWng4ZjfWT0Ps9//FPxPl/haOvVoV+6IMeQ1zZjnE/KT1zmtVHB86Z1dK2F9FBZ9pdyzb7u1334E6P/uz34TYs596VpT/7t9DreKXvov7kQurHVEu1XD8723hvvD169LzI6rj+620OhDLlPZp1diXHgfWPPSUbuNaB/dOL5xvQ6ynNF9vG7rcowDH2vKG9PIISjiuJkYen6i9b5hYewjsH/3k6TZqsLaUvuXU0LM/SDBfdebkHOwYupzRBO91SnkZlIz/4/LqOG+8SF7nD3AtWwmxPZWstPMNz4eR8W3RDuSzXzyDY+O4yIzvYC05XSk1oU4yxe+4uCvX9oOkC3VqCx2Ife3nvyLKD0a497x3sAmxpYuyb3Nj3GQJjpvYSQ3oegu1THfUPmUS43h/7FOoMe+qsvH2j1BDv7NsrMfq/GM8wH6ZX0K/o7SQbbW4gjlmacnSuZWa6N0xjvelDl5XDmS9nQeGd9MxMMEtnPPV3igxNLCTxNDbVXuJUhm/H6xvmFxNHMtHZWJ8IyZqOWm2cb8TGD4IkfKvKUeocz8dyd9LfXzffIpzJMzl7+X4uq4w9kBpIteA0djwL/GxPQ8OZD4ZG7r+NWPfsKc03lNjPas3cU4MlYfJaGQMoBl4NHYkhBBCCCGEEEIIIYQQQj5ReDhMCCGEEEIIIYQQQgghJxAeDhNCCCGEEEIIIYQQQsgJhIfDhBBCCCGEEEIIIYQQcgKZ2ZXG91D8vlKWIsqFQ2OhumFSUW9KcemRYdSx0ERh51DdPz5Cw6rcEIQeRVK4emXlPF4Xo8D2lWek6ceLf/QHUCcupLh0ZBiQjQ1jnFZTmsmUQuyKwDOMoSayrW49RIH7bhf7YepJUeyly/h3gVMdFMWOC9meh3v4LqWJYbh3ShrQjUeG6vcx0N9HU5E//Be/Kcr3tu5DHT9Bcfo33lCGiEbfp4bZllP9+nu/8YdQpRThfPvUc8+LcmyYKfQMAfabd3dEeX//XagTT+QzPdi6DXVu3cbrPvPcp0X5P/mP/zOo86MfvASxVJkZ9KYoJj82DCBvvixN+f74FTSBqocowB4p0fugjO3bNAzpTp89J8p/9i/+e1Dn0xD5+AkNk8tEGZ8NxtimBz008TyIZb00wlxUpGgaMBnLXOQZJgVJgTnMV0Ze9TYaawUB/l6gcmRh/GlTm7+Z9zFivjJO9Y1750bQV/cKDFPWzHA8KPTvmc9kmNDoHGMYleTG7+k0ZOalYyA1zPr2j6QRQ8swj7TM5vT4sMxph4aJhG7mIjfMWqt4r50Dea/X3rwDdepVNHuZgsMIjpmSMsJ89zree6WGJh06h62uYp39O7gGeqEcVzu7+NynT6OprDb9mRpGUaMhGhml6rrMavMWmpfEyhhlaBllHgOpwzmX5dr8xdgLY+qDsb29g32xd4D7vvtbck0tUlwH9Z7dOecSZXSCPehc2VgT6spYNwhxjlQrcu5WKtgGuWFKdndX7e0LrPPn/vyfh9gLL7wgyvfu4V7uV7/16xB79fWzopxNcC073Ebz53hfmkmFGe7JRukAYjcP5T6mZhgDHQeF0c6FWk9KOe4rnpjH8bG7Jufv0NjjpWP89ltckIbhlQaa7nS1s5hzLlGmxqlhcjwN8Pd8ZcjbMtZ+vQLFPRwLboL3Lrbk3vu0YXQUBbgWN8fy/ssBzttDw+Cv3JQGeHmCL5OOuhDT3w2GH53LDaO2tSekAfX5M2j2flzExtrgqSMPLzc6O8OxHCnTzEoH53ljiLH+TTnPP/Mkts/FJw2XaH9FFOMxPuePv3sPYnt7MidXm0Y+Gst81J7Hb4lnPnsWYrd23peBJo7l9TNoFDw3tybKjTqa5I1TPMvpK4PhvMDnvL/3FsTmO3LPMx1h/mhX0aw1USah0wnmq+NgGGN+SBO5PoURjo9+vwuxZl1msqUF3NMVEeZW/V01NtbH8Qj3cFkg52CW47v4JRxH3YH8Tr1zC/c7c2tybAdVXGeLDNeAPJHzrT/B557E2Pe6DZIE750abXdXGUAe9fEb3Df6rzeQ7+MXuEcYT/D3rt+Qe5Ijw5R6Fvifw4QQQgghhBBCCCGEEHIC4eEwIYQQQgghhBBCCCGEnEB4OEwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAlkZkO6UojnyCNlMBBU6lAnD9D4aaTMvQJDxLlcQgH+KJL3L9UMofEWPsOWMrcYnToNdZY3LkFsc2dPlJ/87JegzmD3gSjfvPY21BkOuhALA9kGbcOUyTOMah5uyt+7eweNEfwytkFrRRqBLM0bv2cYKngH8l5zhzhkTi3PQ+x0R7bxjXfQBOdn0E/kY2dtZQ1ij52TBoWF0e6hj7HA06ZSOEcKwzijpOdJhIZL6+unIPb1X/gFUW7W0NylXUGh/Xfeel2Ur934AOqsnjonyhPD7SswzCXfuvae/K1r16BO7dxViD14IJ9zroPPvVxCAfZaQ+aFgy00atrfvAGx3T2ZAyaZYWiSozD+w64c7y98E+scB4M+iu/3etIwZDhAof3h0Jjj6pVaHcwN5SrmcbiPYaBWDbEPo5K8l2UQFxkGSNpwLMtxTmrTAMteyfBAc4F+dg8rZZll9CYNFvD3nUsM87dMPZdl5hQaJqX6/pUK5g7LPEqbCpUNQ8bjYG4BDdNaag2vGO9z0EOTs6rKT0mM/RWnGNNmHiXDHCo2jC12DuQzTFIc//PNDsROX5DvnCQ4PnrKTOT2fTQlKy2hQYtfyHs1avgu3jLm2lZVzvlBF00zbt+5DbGLl8+IcmwYWsUZ5hy9xFqmdWeMPUq1It9nOkZjlONgr7sPsSSV7x0a+bEwxuOrb0iTnaefRcvTV994E39P/a9HHBoGwwnmmYcP5T53MsX+sgyTI3Ura2WMSnKMWnk9M0xLB8okZn5xBeosGmY6fWW4urqGBkkHhziXfvd3f0uUJwM039rfxzV3qAxBQ2OdDIw5MbcijamWV/A5j4PcMDjNtLGnYXTYDvEdn9uQeW6/fwB14m00FE6Gsu1LdRzHE+M5E7Vn9XN8zizB+eZl8tlT495xpN8Pc7ZnzOUsUPnXx3bKjP1Bob7FKhnm+iLB3LdV6YpyYqxlubH0R8rIdDTCe5eMebqkDMgqxn7vuMiM9T9T7RqGuF/zQsscVY7BbNyFOpt30bD7+lvyW6RZeRzqTObx23is+nahegbq+Dnm6aW5y6JcruJZwDSR/dhe7ECdJMX+7/flOnHqNJrrecZa/50//KEoRzUcR8tnsK9K6uxo6wHm7TjDdfdgIA3v5iv4Ld1u4N4iVWdcqfF9cRz0B7julCI5z8oh5odSCSe67ylDRg/X4zjGPhyNpGGlNrF1zplOtjqUFJjrggrm225XGtD95m/9PtRpLfwpUT53AQ2MM2eYxmXyGUaGcbvV5vpbT+9tnHPOzzH2cFuOUfMbpGz0g6qXGSZ51hh9cFeeEVr7llngfw4TQgghhBBCCCGEEELICYSHw4QQQgghhBBCCCGEEHIC4eEwIYQQQgghhBBCCCGEnEBm1hxeWTI0nvallsY4Q/2LIUp3ucKXWhqWtmKrhXpipUjqeYyHqItXNTTNXCxjL7/4IlS5cGUbYvfvSy0g39CLqpXlMwWGxnLV0P3ReqDjMeocpYbuT0Npmr3w3GWoU2kaejqB1EvJkhHUGd9DrRm/LzUtl2tNqPPc5SchttyRGnGvPLwFdY6Dg13UPfvC518Q5Re+9jWoUy4bmqBKY9g3NAVzQ6crUBpuljbmOMb+2b8v2/Bggno6B3v4fjeVxvCDHdS4aixLnSZXRi1Tr4Saw3EqdXB+7zvfgzpnLz4NsY15qQNV8XHe1iKcS9OJ1Ka82UON74Yx/jOldbR1iDo8i4vnIDZS+lx/+J0fQZ3/8G/8+xD7uNnbNzQu1TiaTDB/xDHGoorShaygbpyVn7TGtu/jHHFGrFAajFoDyjnnfEPjvlqT48HSONaCwpYusYWnhJc9U0ET0Vpcli5xaGkAq7XEehf9TM5ZmsbGcxraX5WK1Mx7VDSH+yPMc7nSilxfWYY6JUP/fDSVY7teM3T1Q0NzMpANFpVwzHiGnvBoLO9VqmLObCygDlriy/Gehjj+Kx35frmhKdcfYNs9duGsvPeWoZ02xLl8NJDrxmOXHoM69+9dh1iidNE8Y0s56Bl9rP4voWHo51t6ycOhvFdg7EeOg8wzxozSGx0YY31saNtt7crc/n/9z/8LqHPnBmrtD1T+v7GJGo2WB4LOWYmxj/cy1LsLVB9aOdNTc6TwDK1WiDjI49U6/v6+sQaWlU9B7wi/EaZTfIbbt+/LZzJ0ARNjKSkqctwaqRf0IZ1zrl6WeWE0NDQcj4GS8a0SqHeMuzhmLS3fdZXDnj7C74t3u/jdtfXgrij3xtiHA2Ndn6g1NDLGelrgc/qFzFlDY90dqT1LaPxfVT7FZ8qVfrdnfEM64zknap3KDV3ioXVdWc0TH9+3Yuyr80ytnTnOt0srmGvnSvIZRvtdqHNcGTqK8PsoUWtmWML96STbg9iD7TdE+b2XUfO9GeBaX0/knuDdb78GdcrncEzsK23k2sUO1Dl3GtfM+9uy37IYx02ocuSKofebFzjH85G8rubjOLr1Pu4RXvyhzK2nn8A9Qt7E+RSl8gwo7WEenV/Ce92+Jb933zvCb+Kf/5mvQGz1tNwjD1NcX46DqqEbXlHfaKUI268yh55cZaUJPh5jTj7qoo/VeCznTcPQbNa+Js7h95H176j1No7j5z77vCjfNvaef/f//g9E+Wtf/RzUefyZDYi1V+S4LQrrbMc4/3Dy/VJjbu0edSF244PbMmC0gT6fcM65THkgjY1v92rDmDd9tZ59RF8O/ucwIYQQQgghhBBCCCGEnEB4OEwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAmEh8OEEEIIIYQQQgghhBByApnZkO7MBopitz0p2nzjHhpubO+iaH6cSUHoRgMfYzhCUewslyLp2hDDOecOdlFEvD+QYs+TBO8dFBhrNuZEeXsLhc3vD6Wgd16guPzKEprrecp057B7CHXKdRR877SlvH8pwDaYGgZnThnaDKd4XTxA05t6Lutd2liFOuur+H737kujif1dHBvHQb2Gbbrfk3346huvQJ3l5TmIrSwvinKSoAHC4WEXH0KZDYQ5Xnfq/DrENuZk329eewh1hgM0klhekX1WW+hAnaAiBeZHhlD92toZiG09kGYDe/s4j9bW0ZXSU4Yzgym2gQuxrxIlel82DFTKhqFIvK/MeXwc6yunzuF1yuQK/MCOiSQxROYLOVdDw8TK8iErV6URg+UQ5BkrRRBIIX/DH8VlRj7UBkiBYVoXGEYhvjJdKBnvpw3bLIM4NHVDDG8F03Cy0+mIspUDpoaRQObJZ5jNfM65VBnTpKkxbzIj5j68XY6DWh3NKDJlxDo12jSMcHxEyvhJj88/AftQp4Iwms3EcKrythfi79XauG/q96WpZlXPP+fcrjJODUPDDKiK71LryDzeqKD53MoSGpXsFXL/Uavh3FpexnW+35NmUdbWw/JgarU7otxsYRv0DHOPvT1pFlT4aAJ0HMwvzBtROR7GA1wHp3V8ft+T/do19hALS2jS2J5fEuXUSMh5gbkoTZSJkWF+lRiGY3ny4TllqtbP3Mq9hmmvr+Zpt4emZN9/8fsQ+5mf+RlRfvudd6GOlfpi1VbaNNg553IP55s278usfUyM73zvzj35e+VHw1jRMpD1PJkLQpyqbuLje0fKrOzMGub6W/cN09ypnCdZjnW6hln3ntqkNI38r/edzuHae2Sk/y2V2PQcdc65wNjraKz/xoqMsbat1pYjh4N2YDznKZVsO8a8DQ76EFsJ5bf8p43vvIsb2PG1sfwmn2bYL8c1sg+TexCLp3I9HBqfpdtdNJt7cPgdUd7b6kKd1QiN2Rc82be9MV4XbaG5V2ksc/D97BrUufKNsxDbz+X9Dx/gxn1pTY6JZz5rmJnV0ZBrb09+/+k9inPO1RvY21evnhbl1mls9CLDfUqWyGff2sT1c3iA7xcrE8juAL9JN68uQqzelGvqw703oM5xEBlz31fzrBLg3CwMe9RCGXnmGdYpG2b0JWViWDW+u/t9w6g0k31dqeG9U4f7jYtX5Ni+/PQK1PnNfyzn5K/+t7gf+Pnh8xD7zDflvXMfx1Bq5E1P5XxtrO6cczs71vmjHI8bZ/EcpT/AnLy1I88sQuM52wsY8yM5jgdDnDezwP8cJoQQQgghhBBCCCGEkBMID4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiAzG9K15tCgZKxMxuaWDQMYw3Bmb1saYEwMw56whCLtulpuiEYnGRpyHY2l2Uq9iq5MkxEacI0n0vwkNn4vU7GiwDYY9FCAvaUMWFotNIkZj/G6vX35Lo0GCoN7hnGSl0rh8ZLhKmHokLuSMoY6d+kcPucIRc2/+913RPmNazt482OgbJgNTSddUX7xxT+AOkWC46NVk22YJCisPhmj0H6o/iZz9twG1HnqC09A7OIZaVLXvXcf6mwd7kGspMb7xQU0m9jdlWLyT195Cuo8+fQViP33//C/EeXQoQFTMsS2i2MZK1LDJaaC7RkoR7Vz5y9AnZ177+O9lNFK1TB7vHr1MsQmI9kuG2toBHQcLCygOZTvZI7ODLOBJMXxr83RJhMcs16A4vtaoD/P8d5xhrEgt4zCVB3DTCYv5Bix3sWz3PR0HaNKrgyJUmM8WuYNgTIh04ZxzjmXWLFcxnzLPGcGkzqrnXzDiEKbRVl9dRxUqpgvfE/GxjGu6WVjDFXL8jrPMLooGUZ2To3tVhvNxSY9NDWJQ7khCcvYpuMYc18QyOdM8PVcPJZ9+HCCeX3+1CmIJQ/lOlv1cCxUmtgGS22Z1/b27+LvtXFPpt38Bim+zJU1NFfN1T5pNEJDq9EQY/PKyM5Yco+FzGHf6zkWlnGsl8u4Pw5DuS2fm0PzHGflJ5XDrJySxrinzJW5TWbkbCtfaG+v1OiMwVAZVk1xfFjmqpl6P+u63/jN34TYW+/IfefLr/wE6niGGW2m1o3UMC7LDOO8ItUGP9gG1hD11X6kUlgmosdAbhhcq+8Qy3jNM1wni1i+U6OO3yqLLez7g12Zw/pb+O1wZBhxv6hM3OYM78OWZ5huq3U28fHCnvp+mhgmUdbOI1DfYiVjTtbsK0Up9HDs1YznzNUcjDO8d9V49nZDjdIEDSAHh/gMvZZsT88wyDWy1yfC4cAw7O5tiXI2RrOm7uADiOVqT9yuYduPjm5ArD4v+9Fv4BoaVdCUtJXI8wB/BdeJuSX8YG+1ZX/ffb8LdTw1tg62jTmf4n5jZVUay93bxLVkfw/bs4jkHF82zhnKZev7QsamUxx/D6/hOK1H8gcuf+o81BkYJnV7h7JPo/KjYdqcGnvIVJmcGl7IrlbDMx4wbTZMzkoR7lP0d8d0gutxbrgR+5nMD+nUOLMzNsAHh9LY7YtfvQp1Pv/lz4jyD77zNtS5dQfPSFbvyW//cgPnX9v4BojVPqXXw7HeH+CceOyJi6Lc6eD5S2sOO7B7JMe2Zdx+5jH8BpiM5HwexTSkI4QQQgghhBBCCCGEEDIjPBwmhBBCCCGEEEIIIYSQEwgPhwkhhBBCCCGEEEIIIeQEMrPmcFjBqpWW1CaZb+BZczhGPZGoKrVjeofGY2R4r2pF6uJlhn5sNu1CrFST949C1FQJAtT0mSqNMa054pxzhdLfMiT+XGFoxmQqFIWoheVKqIvaPZSaw+MYNZ7aHdQ1CpX2lW+0wchQR9ve64vy4QDr9Ieo3/P7335P3gelWI6FkaHj7FTb/MIv/TJUyQ3dlkDpe+WGVl9haIwFqu0rhi73Vhd1X/vda6J8MMa+8Coo6PT+azdFef+lXahz4bzUE/7spcegTjzGcVxVY7RIDO1I4zo/kHMyN2TXxobOYag0/c6eRs3hyWAfYk+0pN7dj155Feo8uINaxeOh7PdidAh1joNWC+d4rvXlCkNLzMgXPaWrHBq6rIER0zq2hpSdiwz98zTXOo2GfmZh3ExpHHuG9qHLjQQMVQytPjV3C+PvprmhORmP5ZqQGOM/NzSAndJotJ7a1PlUNWvGfC8ZAmS+0m/TuqbHRcnQjqzVZD6EceacC4zBFijt4CzDvkhTYw1Xz9DvY/uNe6htp5+hYuyRYkOHNVF5e3SEeyTtCdCc70Ada3+QjOS6EZRwZJUM7dsiks/ebBmeBMa46swvyfv0DqCO52NfTfoyr45HWKdSw3URdLiNuXwceB62TRSpfGXotjtDEzSK1F7Q2lMaeuRlvdcw6pSMae85mUMs7eDM0igvPlzjeGFRavdZvgyFkVe17nGe4/gYDnEvt7W9LcrnzqHeZN/Qsh6BNwQ2+iw6xIXRTla7+Ho/bmj2HgeZsX4WKuYZObtkfE8UY9XOxjheruN1P3nzLVHef4D71dTDgbyrtHt7Rq6vGXv0mmr6svF+RUk+p+4/52yPgFB911m61T1zndJeNnhdyfrXLv1NYryLHxr7HyefoTvoQp2gwLlb9pui7OWPxr7COefG/S2IeYEcS1ETv03aekA456Y35VrUXMI+SxaNtS+S+W99Hr1c7m/icx5dl9/UT5xCD5pGA/tx47Qc8/sP8JluviOvG/eM/X4Nc2upKnPkyjrqsm7dR63iaa6+nY086hma/a2O3N+cvzgHdXZv3INYmsj1rHeA+6uth7ifm2ZdUV5Y7ECd42Bo+DEkSts7SXGexzGO41pVtr21t3bGd1WgvtczQ1840fneOTdS50Xbm/htvrKEquRzyltiZOgSn31a7j0PJ0tQpxRiuwxU1ye+4U1SNfZASnc+NPwiVk6dhti5C3Icx7FxbmPk8jiR8/LI8D2pN3CfXq2o56wZZ4szwP8cJoQQQgghhBBCCCGEkBMID4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiAzq8cPBoaocdAQxUYdxd2jKoqP18tSMLzdRjHyQQ8NuQY9aTYxMExMkgnGmqUFUa5oww/nXDpFwetQiVlbBgBRWYpGe4aydK2BzeyrUJpZoth4XasjRbAPDvpQp2+YF7TmZRuMDLOG67dRLPy9N6Xg+8o8GmGtnEZhbufLZ1hsN7HOMVBvoAFGWw3R5tJlqDM1xkdF/W2l5BmmHFXD2Kcm6+WTAdTp9w0DpJps++WLHahzsYaGANdvfSADlnlOTYqmbz68C3UWFtEQQMfiMRr3TacopD4cylwxHWEbJFM0RQgrcqytrKMI/Z2H2xDbvivbYDLAZ/rg7dcgtrCgDJfm0IThOPCMv+t5yg0zNkT8J1PMq4ky2rQMdLShpXPOFcrcJU4xh01TzMeeMt/xjHtrAzXn0AQmTw3zHP1bUMMZ1hdo8GQZMBWeYTYUyuuiYDbxf+35UFhmR5lhHKNDRq73LXcDVS9NDCOKY6BumKqFqtesv2BXDCO+wUDmkMAYx6Uy/l5VGYKadYyHGB91RXll+QzUmRjGeZ26fPZoyVg3VLcmDueytWeoNqTxZlTDe1uTIlHjf3GpAXVKhtlQoAyXymXsl6LAZ6/V5P2r1nMa/TdWxmG6fFwUBT5roVxWPaPhjTQHRpRgUOecc4Y5oDbEsnKodV2g8mpkmJJZRptgZmP8nDYzCzxj722MY931lrFptdmB2Kkzam9lvMvYMtPRRl5G/reM2HTetq6z8pBuO2t/eRz4xliLVBNaptteYJmKy3fMhrjHW2vit8NCJK+LJjjHW4aD8USte9Y6mIbYP0PVZ2PL41KZxgXG3sOa3776zrLWeWtfoe8UWXt2o82r6p0Nj3hX93D8RxDCOlNjb6+7tOYb34LHxPjgPYgFZTnPpkbbl5q4hq09uS7KibF/SsuGifGR/Gbr7eA3zaCLsfFDOebf/PE1qLPQMs4VIrmufuHr2B/nzq+I8vwS5p7WsrEHWpDt4vurUGdvEw1Adw5uiHJexm9LlxhrXC5zecnYI3j4mK7Z0GameEYyGOCakypjskoFv92Pg+7Rh+9xsgzPc0Zj49srl+84NXKrNp9zzrmy2m+XjH37YITnf4nKk815PAf64tc+DbEz59ZE2Y+wv5rzcq/7qc+iaWOthONfm7lPndEG+oDOOeep88CyjznZMl2dxLJdrL1UxTgnajZlW1nfJYHhMByrvYR13SzwP4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATyMyGdPfvYGzalSLVzSUUja5UUXy5rbxO5ufxMQZDFGnvKuH2w30UKD9ETzUX5FI4OjfNfwyDnlzGrJN0ba4UhPgu48wwslBNFeXYTunoAJ9zLNsgC1HIvTvAttMeHAeG4d/tG9h43X1pQhAPsZ1W2yhMf/XsKVE2fu5YGPVR2N/lypDFQzOe7W00MLv+zm1RroQoKl5qdyC2uCxN3NYX21DHMgBbaEtTwcxw1pqMDyG2vCwF2E+to6naw60tUb527V2ocy5GswFtpNLvYzuNRmgQ1zuShnuWIV0WG0LxZSlC//Zbi1AnnqI4//KyNGE49cxTWGdpBWKLS3JsV9TvHxeW8c1Uvbc2mnPOuThG0wDdXnGCeTw3jM+0AYtlvFMxxPB9ZYqUGUZ2lnGLfmfPMATQz6RN7JxzrmQ8p2YywXZKjefUZk5WG1jvoufNaIRjXRtMOYdGbPr3nXMujdFgRJvzVCofzaTgp01ktI2vDKpKhkGGaf6jDQuNOVKyzGhTbUZl7GOMdm435TrhG4ZcFcMQI1eLca2BdRI1JydjXNMts8eaMqiIDOOQ4QjvVWnKNWIcYxuMjbwaFbI9A2NO+gEa/Ogt0WiMfdXt4lqm+6pUMozsjoHYMELW89fwMzON1vS4tfaUnmEsVyg3lNxwR7EMk31lEhdVcY4UAe5Py9YL4S/K+xjz3cqrSSzHmrX+WNeNYlnP2tdPUnwXyLUBTubCuFeh+soaj6HRf5pa7dEw8vKNZw0K1c9GXzjTkE6Oo9BIkA0Pc8pXlfnX0QjrvHoXjZf3pnI8TAwzwqmxbuTq2XPjSy9T9/INVz7L/9H3LXc7SWDMyVBdVjUMkmo+ztOmMsht+thXC0ZX1dTDR84wSDfepVBr9cQwuDouVg1D91FZvmfoDAPV0Ngzzsn3ig/RWGu0g89w+K78pi4N8NuyNV2AWBrJZ5gWOAfyDHPG4bbct/aNb4AL5+U309TY7x/cw7MAfyBfsGK4HZ4//yzEVk7J7+LDCe5JdnfRNC6P1V63hBPs2c+fg1iQyX1D7gzDv9TYb6ux4M0wdz8JcodrSqTPfYxcMBjiO2bq22A4QJPJwBj/c51A1cF544xvvUpNPteqYaBWX8Rv/2pTPkOWGzkyl78XzmEb1I3v9UitccnY+F7KcKxpE++ecdYxNb69tJFdaLSBtaSW1TdaaHy7DEfGs/vyukEfv2Vngf85TAghhBBCCCGEEEIIIScQHg4TQgghhBBCCCGEEELICYSHw4QQQgghhBBCCCGEEHICmVlzOItQ2zMpfUaUp7mhf5GiNlSlLfU8OkuoXzLnow7O/EgKc3QPUOO1u4dabOOhfM0sNXTqtK6Wcy5P5e9NxqjdoTXGAkMLrj9BQZHxQN4rMjSFmj7qGuW+1GpNEuzCch21ciqR1CHplAwtIteB2NPPSs2WK8+gptC5S5cg9rkvSJ2f+w9QV+Y4yA3NVV/9jSRMsA9bEfbhKz/4jihvbeNY9yLU4fnc5z4tyl/+4megztER6tm88ZMfivLQ0EW9dvcexG7evi3KY0NzsijknKy0lqBOr4e6UP1D+c7DHupEGlJsLlSafu0m6metn0eN47mFNVFeXke96/XnnobYfEuOY0t31tKLdZ6KGXniOEgS1E3UGsOWJqMzNB9BE9HU8kV0e1n6voWhM5io57I0GS2tSE9paAYBajD56tkt3V5L93IW7Ujr/bQ2sdXmkaEVNUvbWW0AmquGdnCtjHNJt4LVLsdBtYRto9+7yLEdrL5vtaRurqU5bL231rYtDM3hdhX3Gg2lHVbkxt5jaoxjpV+ZJ7huNOtSm9AYss5wSXBDpXkWJdhOY0NjLfWlPt3eEeb6wX4PYp2O3BfuDzH/V6pGXihk2x0e4JrUN9apquoHXT4u9Pr5J8hYZmhEOw9jZaXdZ+X6LMNYpOaSNf5DZ8w3pTmZGmPN1IBX+dg3cr2eb56R56KysRZHMv9a89bKj/qdE0Nf2Dfmd67ulRr3Dow+zlU+NtcWa/LqZzLa5VgoGVqSSn/Ws97HWMNT1fa58bmpNWudc25NLV+//OwpqLNi7MdvbMv8tD3Evj9MsQ8nKm9PjddLPdXPlnb3DHtKK0tEhjZyqF6vbmg6l41nKHvywlaA43guxKeoK/3wSoS/Z3zaQm4aGfnsuFhM5yA2XZN7hJ37Xaizcx89UtKaXDPDGH1i/E1898qBGt++4fWQtiBUvyQnwcJFHCOB8QxupyuKWzfxXbJDua4unzfexdjLVKfy2+vgCPVqo+wuxBZWpJfL6vwT+EyTTYjd25TPXjW8GeaWsD3TicxhYWTMuj1Dj/xI9l8yMb6fjoE4MXT61bwbjzHXDQ3frnKkz6tQk9eSjy/Ud7DldzE1TJCSWI6Rwti1llvG2uHJ7yrL0yGbyt+bDnFfGweGT4bSa947QLHw+bkOxLRX2d7DXagzifH3FtfkGUVm7GUOjHMTp/dXRsc8fIDX5Wo9yYzvp1l4RHYkhBBCCCGEEEIIIYQQQj5JeDhMCCGEEEIIIYQQQgghJxAeDhNCCCGEEEIIIYQQQsgJhIfDhBBCCCGEEEIIIYQQcgLxilncEgghhBBCCCGEEEIIIYT8WwX/c5gQQgghhBBCCCGEEEJOIDwcJoQQQgghhBBCCCGEkBMID4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5ATCw2FCCCGEEEIIIYQQQgg5gfBwmBBCCCGEEEIIIYQQQk4gPBwmhBBCCCGEEEIIIYSQEwgPhwkhhBBCCCGEEEIIIeQEwsNhQgghhBBCCCGEEEIIOYHwcJgQQgghhBBCCCGEEEJOIDwcJoQQQgghhBBCCCGEkBMID4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5ATCw2FCCCGEEEIIIYQQQgg5gfBwmBBCCCGEEEIIIYQQQk4gPBwmhBBCCCGEEEIIIYSQEwgPhwkhhBBCCCGEEEIIIeQEwsNhQgghhBBCCCGEEEIIOYHwcJgQQgghhBBCCCGEEEJOIDwcJoQQQgghhBBCCCGEkBMID4cJIYQQQgghhBBCCCHkBMLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5AQSzlrx//x7tyGW5Zkq51AnMu5V8uWZtBeUoE6cexDrx2NRDqyj7ckIQq1aWZYbFaiTpnirfhKIsu/hMyVOtkFeYB3PiH2cFEWBMZfrSlAnN2LOzfDs1mX6Lkbb/e9+6dyHX/hT5m/9rf8FxI62HoryZDiBOmG5jjdT4/jipYtQ5cJFjOm237x/D6q88+MfQ+z2zZuinBnj349wSperNVHuNFtQp9Vu/2vLzjk3Nz8HsXZ7XpRrDazTbOK9qg35TJVaDepUqtjmQakqyrkxPjELOVfM8mewzJgTKqf5RtL57LNXZ7j5T5fPfeNzEPNy+fx+hi2RG3O1Wpft3Db6XreDc871+335ex7evFLCFWAylDm6WsJ8XCphO5frcmyXI7xuMklVOcY60zHEPF+Oo0a9gb9fsdaNRJTjGH+vXK5CbH+vK8rb27tQJwjLEPMC2Z5BEECdJEkgpp/r8PAQ6jy8dx9iHzd/+7/8f0LszIocf2HahzrVAN/x7Kl1Wae+BHU2e5gvfv97r4ny4OAI6jRbmNd+e29BlIMnvgZ1ej/+7yD2zfBVUf7rf/WvQZ1xTf5eng+gTmhs3w52ZL/+v/7O34c6R4ddiP0v/1f/mSifP38O6rz88ssQu3T5MVGuVnCsNxo4lw4ODkR5MMD3W15e/tDryhWcI89/9vMQ+7j5R+8NIZZnxqZSYe2LdBb1jDXOuAzrzLAv+7cNa+/78f6gLOYFrpOJsSNJ9Xqa4HP/zc9g/vq4mQ9xPQGsNjYGpO9/+KbLGv+zXDdLP1v3tmKzoK+b9d76M3bW0anfb9bf+6jvNwvWHlA/Z5ZlUGdvhN9TnwR/4z//DsRydWZhtpaH75B5ckwWxslGZIxJfyi/LevxJtR57soZiB3ty/3g977/fagTT3AfOz8vv8cqxp61VCqpOriGXr36BF4XybU9CIzTHXPRkePGXPPM+ay+Z/zZxrYep9aYtGLwrWfkob/yV/7KTM/w0+Rv/OMtI6ra1Dx3wqsKX89hnNNBjudxueqz1Jgj1jEX/NyMGXC2Wt6/tvgnN/qo+4GP9n+zhfHkcPZmXWc+pl6LjRc0z/pUjjPmzX/9761DTMP/HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5ATCw2FCCCGEEEIIIYQQQgg5gcysOVwY+jKg92kcNY+nqLs2yeR1JUMI09LJCH35uF5uabrhQ2gd4OEENZACD3VWPF++s6VB4+s2MORFLM24nxaWVIl14h+o9vQdasYkiRH7cLkUU2sG+Bi1sP5NmFtCrZWlhRVRPnP6LF43vwix2JPjwwtxDFlaShOlFXVl9RzUufj4MxC7ee2aKB8dHkCd7gHG7t65Jcr37t6COqHqnqqhFZvFqOcdKY26SgW1OcMy6l5VmlLnttpEXcrOAmrudeZl/7U7+HuNNmoqN1Ws2mhCnaCMusdBKHNOaGi8HgfTGDU6y4F8VktD3NIJ0xpFwxFqvEYRju2q0nKfWlq+emA55xpt2dcl31iGctTuLSkBK0s7fjyQWm1+gTmtWkWNNZ3m4hR/3xmhWk3qsHm+JfSFSbTRlGNtb8/QszeE8AOV3a38YmkOaz21MJx56f9Yee7q4xCLVD/vPMD1urNyCmI5bGdwJVzoYG745V/4pihv338Ade4/QN23SyqvDSLMvStn8feyh7J/vvcj1BOsLp4W5csXN6BOY64Dse+/+yNR/s53UHfRM7Qjf+93f1eU/8Jf/AtQ5+mnnoTYZCznvJfjfCsZOu1NNW8axpys11C/uBTIdThJjEl5DISBpX0/w/9ezLAvsnUxDY1BVUa9v3+THzhmZpAKtPT9ZtmMmtfNgLWP1/cqDHMD31qH1XPmj8i/6cykWfsR9/JW15ixGX5ulh60nnK23ZvRz/p7dAatcKveT3OqmX2lhEa9mce6vFdhaZjO0O+WB8Jx4RW4f/KNvRjWMc4jVDk3vp8tD6RSRe6boxDXtN/+3d+G2Advvy7KwyFq2nse7uE878MTifbJyI098voPXoLYN7/5c6L81JPPQp3YXI9lm6ep4YVi7EkC1aDWdamxR55Fj9z6LtCdHBu/dxwE1jeFOjPzLA1g6yzM6TMtvHXm4718ta+rW0uvkWtS1YeZkYFTc82U/WrnMXRnAEwN7Bn2CB9Zq9h0PPrQq+zUOsP4m0Vn+SOu14/IloQQQgghhBBCCCGEEELIJwkPhwkhhBBCCCGEEEIIIeQEwsNhQgghhBBCCCGEEEIIOYHMLDyYGPorRab1jRDfR40Rfa88R41E39Lp0KI+GT5TqYTadWkgY6MEdWqqkaF7Esr7F9YbKq0cW6vEahkVm1kaSmmVGVo9ljaU7324VqWlGTOL9Mos+iwfXcPlp8vlK1chdv3966K8d4Saq7VmG2LlqtTvmUxQB7ZUQq3WPJY6jcMpavkuLa9B7Iunzony5t3bUGd01MXrvvRlUX64vYnPGck50jE0ed9648cQ+84f/JYoZzs3oY5v6IcXaowGZWwnq+2CXF4XGXXCMuaAWl1qfbWVzrRzzjXnT0Nsbm5elBcWFqDOp59C3dSPG60T7Bymw3Q6hTqViqGrnMtxXK2i/nOrhdqpA6WDFqeoDVuuoS5wVY21wEgN07Ghe6X0o466qPGaZ3ItiSLUWE6MdKz1zSztvDDE2DSW76x//09ihh6p6quyMf7TMa5TVr6H6wwdNn3dTNqSnwAby6jlnmXy+dMxjivPx3GcgdQW9n3daGdPjav2BcwDZ9aXIXYpknrn7+3jfJs7gzq9jV1Z7+HDXagzOtgT5eL0KtQpl1G/cOPseVE+e+YM1JkOcZ16+mmpcT+Z4JpULeN2sanmd5piG9y7dQNidbW+WLk+maDOYqD2TanpO/HJExprnPa6+KTn3Azyk//fih/rY3wkZnukGXVfoZIp1DfTL+JVH76P94wh6qn9cG6Mn0eVn+Y4tlv9wzUTZ9FWNSV5jf+HmuVtcnWz2ZV8dQ6Y8cKPCN7/I353zWQkY/3+ozOOrZz84WrCtkxpoPQ/fQ+1dXs7dyD2wc03RXn//jWok/Z2INZUWsWNOn6PxYZHkP7OTlNDG1nvbQ2fgJvGmj34F3LfMJngWq/3Ec6hv4V13mONmyzTZysf/r4WnqEzPTH2QHvKw2fH+L44DqzvDnglo/1yD6+L1Lwup4aPkI8L1kJTxuajI6izvYX72Otb8rrKIvo5lZu4t3bK76uY4bvnp4mpuT7DSpEbOsGFp769PuKGyx7qVlD2u2cJS88A/3OYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5ATCw2FCCCGEEEIIIYQQQgg5gcxsSGcamH1EkzHPm8HEzTAE0vUsEfNkOoZYyUnx+FKIJkloXYMklti0Ks+sx/+RL/xwLNOiRLeddV1h/a3gw4XAZzEheDTs6Jyba6Kw/4VLj4ny/XtoLHBwsA2xljKpK1fQIKhkuG3VS7KdxxM0NygMEyvtM9Vuz0Gd2Bj/aSbvv3HxItSpVjqi3Kh1oM7ixnmIjdS4+t1f/cdQJ0ixDUqBnHFRjm2QjzHmK8OviWUEZIzHXTWOixvXoY4LDLM2ZahZNszu/if/8/8I7/UxUzWM5RJlEOH7mN7tuSrbJggxD+SGGYQ28qrWMa/GKfZhKdIGFTg+mh00gAwDOQEebG5BnXJZtosfYGb3jHdxgTJINAxKE+NdhgNpbFEyDFijCMeMbuNWG00A4xRNM6ax7CvLcE8bgDjn3FSZEzaNPHgcpDHmq9FIxqoVHFeh0c6wP/CxbeIxmpwdHRyK8soyGmRUavh7CxXZzqeq+HsVy8O2eVmUTy9u4DMpE758imMvjXFtfvIpaQjzla98BeoszqG55C/84i+I8s2baEiz/eAhxJo1ObbHQzRzPTg8hFi7I9cubULonHNhiO2pzRZHhrHMY89+BmIfN9or2TnL2OiT3QXxPz/+FXyMnlm58S2TGwahfibXIM8wDzoOcqtxZmgv03Jb7TWsvYd5a2Uk6puTC0N5puthm3o+9k+ozH1N43FtBG58F/metUfS37qzDT40cbZM+WYwVpzRlRKG7Yc3gflzhbGXOy5Ca4zodjX6w/JvCj353fHBWz+EOjff+B7ERuq7sTC+9ZbmcO+3ogzJQ+PbMopwfPf7PVFOM8OQTr1gnOAeLDeuO+zKe//6b34L6tx/gGbnzz79rCi32x2oUzL2sdBVhrGcZYB72JVmabvbaJS2s/UAr1NtNzHMpY8D39jPO3U+EBbYDr7x/RAk0mRvzkPTvcoUzeaurJ6SdUJsm9HN2xAr7cq936SP5yj+3CmIVZYvyfvUO1An96Rpo5V6PPP8aoYcZeSFAjZ0Ru6Y5Qxt1rM+lZStp7bPYOX9fRrSEUIIIYQQQgghhBBCCJkVHg4TQgghhBBCCCGEEELICYSHw4QQQgghhBBCCCGEEHIC4eEwIYQQQgghhBBCCCGEnEBmNqRLDDlkbwaTMyvmK0HmJEHDhsAwpPOUsHLmLLF1/L1aJH+vjtruLh2NIDb1pbnR1DA4gGc0YpaQumWW8HGihatnF7f+afExuoD8G/Dum69DrLUgDYiqhiHX4f4OxMbKMG15FYXVnY9jNFGi5bFh2OYZ6uq+ikURTt85w2zo+9//I1FuVtEg64knPyfKU8OcLTZ8vFpLq6KchDi5Dg1Doloo50TNMA4rG0L8Xiif3RqxljC91pc352SMZkp6TvRHj4bhRmS0c66Gbb2FdcaGIdd4Is2vtKmFc855Rkvnqg3THPN43Ui2hcrb1RoajgXG2M7U3zKbi2gcppe0fg9NGQof+z5SC0dS4LtkhpHd4sqiKJeMvJ5n+Hu56qwkNn7PMOnKczmQtUGXc7YhXRzLXFWr4fw+Dl578ycQGw+VQUqK7VctlyDWanZEeb7zLNQZ99CA494H10TZSydQp260VzUaqDo41oMQnz3sLIhy1MD1ZnL3lig/eHgf6tQMI4/DgeznK1euQJ1f/LmvQ6ylTGIWFhahzvb9uxDr7kpjl5Yx331j3ox6XVGuGm0Xj9FkxVP7CM/IOceBbxg/Fcqgysqhs++aPxx9leHVapttzWTQMnNQPZP8veIjm/L99PaPM/nBzNhV+n2KHMd6FuM4TqZy3Hoh5rNjwfqAUtgmch9umOZbpnueYRgbyf1AYBiIB03MT6vPviDK9eXLUGfzAHN7f0/mVn/7XagTHsp87MVo2pSYJnmyny3TOttsTpuvG5eZfMR54unvw9kmgJ7fzvtwA/NPCm0m7RzuWa39WpChQdv7r31XlN/6we9DnckAjc/09ix0xnj3cXwvLK2IcnMeDYRLxnfqUa8uyqMhnmvUG7KOtc+sGibK+nigO8RvidEYv6FeffVH8t6GmXZomGYtLszL64zvhB3DKP7Bwz1RPjxCE8CJ8X+Rgfq2rFTRFPs4qIXGfjSXbZ/2b0GdSoLf3ZVcftudWp2HOtMhjodOVX5T6LM455wrVXEPt7Yu17XCx3XuaHgHYv1b0vx40liHOtVVmd9LzSWoUxj9nGt/OCuvGXvW3FdrtmE+Z8UK2BtaBxRG6CNvebQR7Ec7a+R/DhNCCCGEEEIIIYQQQsgJhIfDhBBCCCGEEEIIIYQQcgLh4TAhhBBCCCGEEEIIIYScQGbWHLb0L7SmWWBod9jX+R9ax9I8CpUOpW/8XhDgdYnS1JkMUBdn8OAhxBYvPyXvY5ylaznE3BA8td7PU9qRltzvR1Wjm0VPeGZ94Y8ssaou/Fj1jGfnoIu6UG+99kNRjgyNy9XzZyEWq3o1peXknHO12hrEtA6O8XOmdpOW0EoMLbv3Xn8FYj/59u+Kcr2Oz7m2JJ9zZQP1g0qGDuzTT0hdz/Cv/c+gzuY91BQ66kpdqL6hBTpQupTOOTdUOlfjMeqDJUkCMZ1PPEMjsmTo+JYiqRH2qGi1Wlp9jYbSzAqxThRhLMmlLllkaJnFCY41LYSXGzpNlSq2VzKR9xoafTicYKarNRry93wcj8OBvFe1hbphoyGONS1U3Wyhxts0Ru0yreVbGMm+VEL9tqnSea4YOuC5oV8ZKG1uSy/O+r1yWcasOXIc3L2PWmmhyo81Q/9uOsS28fWa41l7CENrTuVVS5fbGf4GRST7vl2x1n6cb0VZ5pmghON446xcb2ot1JJ3FWOM7sp9zPPPfxrqNFsdiGVK83p9bQXqTHrnIBYqDceyIW9m7eXiVLZnFOKFWWboc+p9hOnncAwY+paFyoehM/SRTa25j/Y/G3q4B0Yuygzx0vwjbvI8l6uyVUdrDiPW72u9Wl12zpzeH12ZWN3L0sk21a0DratreExMUfc+VjKS5cojojls7NOttoc6s9zbmCOW1nKg1qqaoSV55Zt/GWLt535GlPe39qBOJcJ9zLh5SZSni6hVP1U6xJV734c64Qh14TNP/p5fYK7XXiLOOVd4cn3OZ9by1eYaRg1bNPpfd5d/JbCvNrRIjwvfw7auqzfLJqhZ+/Yr34XYmy+/JMrj/j7UKbSYqXPOU3uXsIzfGGXje+z8hfOiPLfYgTqBoSWqzx+s76N4KsfkvU0ct6MBXndqWe4JyiHuy+I6zi9fJeojw9Pk3ZsfQOzSpaui3J5DnfGH26iru3so822jhfljrjEHsVZbfiu0rT3XMXCl9ABitUzmtkEJNdB97B5XJHI/avr6VHA8NpqyLZIUx0epjJrQni/HQ7mCdcoVfIb2VC6QXSO3Dm9JH6isfRrq1BYvQCxSevWphw0VGnvPQs03z9hfWXkzUx439lHYbOeGM6Eu/Kg5+dHJ5IQQQgghhBBCCCGEEEI+MXg4TAghhBBCCCGEEEIIIScQHg4TQgghhBBCCCGEEELICYSHw4QQQgghhBBCCCGEEHICmdmQbvPWXYgFSiTfMhXxSmjI4gXyTLocoSmBb5jxRFN5XW6IaVcCQ8VZmZ+kBf5eefUcxA5HyjjJMAoJA3mvwnDJyA3TFE+dy/uWaLRhVIBq1oYJoBn715f/VXjaddCS3C7w2bVRQe49GgZIWnTeOedujaSA/d4WmhSMc3z+5uKyKFvGHVVDgH1haV2UQ8M4bDoeQaxalWPt+rV3oc5L3/tjiPlKXL27h0YdD+7fE+VycwHqlGoNiHXaUtj/K1//Bv4+jCHnxhNp+jQaoQHfsI8i+9v3pbnd7VtoaHX9xg2IaRO+06c3oM7CApowVavSQGJ+fh7qHAdJhjlF+7NN0gnWKXAc54msNzXyVWQYaQQlOR4bDRwfnrOMptSDGuY/obGWHHXlGPEyNL+YDORcbjbxmeYbmAO8XJqLBTmO2dTwxxqNZNsNU7Qt6rTxXXxljJbkaHZXbaBRwmgg+w/zs3Op8Qx6KTFe71j49DNPQgyMtQzjFc9wdSiX5Rz3fBzH7Xns+0tXLotyaJg2Rtq1zjlXURPOMl8sDBNPT10XGeun15Bmc14d885+H6978nJHlJcWcPyPYxwf07Fsq0YL3/fipUsQy0ZyUgTGOl8YJl2Z6j/PyDm5MY61AZ1XmDZhnzjWeNR7PM/oZ4uPat2rt76xYbzsGfvjklrjdN84Z5vkFTMYlVlmhBr/4/wfFevnLYPoGS6z9tV671sY3xbjIe5jJiNp6GN9Ax0HuWGqZX6bzACYChpz1c9xDQ892RatKrb74gRNmkrvSiOx8RHe+3IZTab6gVw37hl7/YepzL+Dpa9DnUqM+/HSwXuiHE1xTubGN2OqFmzPtEO05t+Hm7LOkl+sPrfGxkyPdEyUjWEbH0oz4u/+0T+FOv1d/KaoleWLVSurUKfeQIPYqjIp9x22YdPYI6wo87dqHfeC1RIah1WUKVjb+N5NUjm+F27ehDp3P8DY3JycO6Up7pOOevjdGqvvi8Me5sODLs6LS5efEOWNM+egzks/fBlincUzonx64wzUme+gSV1D9V9o7PmOg7UADbTTiuzDwMO9p/6mcc65sTJI9o113TP2a4HKB4XxfRYa49j38Bk01rpaqcg9yZJhrteIZRv0h2h83x3sQKy0IL/960tnoU5UxXmTqn279dzWPjbSprxmAjbO7GbIpYV5MxrSEUIIIYQQQgghhBBCCPmI8HCYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5AQysyHdT+4+xKAyErJE7CPLxE0JJluGXJEhpB8pDeyJIdi83EbDgXPzMrZawddu1FDcfTyRQupejiLcWlx9HKMJVGYYqwTKgKJUQsVty8wjUCZ80wmaLniGuLWvzCGmMQqFW8+pzXmqFTSm8j1sT/3k6aPyZ4gQ27kzJ80mtm/ehjoVwyCud1+aNG5vo5HdKz/5CcSeeOJZUa7VcczGU8NMTHXrGz/5EdQ56nUhlionrdwwMwNDFkPoPInRqGNQSGO5Guriu3KEY6aq3rk9twx1KoaZZcmXsd4R9ss3vnERYisr0uCh0cQ2Dyv48NqEo2IYDB4HVv9MY5kLamVsv3oN+yKL5L38AK+z2mZrVxobjqZDqFOvYTtXItmGaTI26hhLUy7zkyX+X43kSM4SnEcNwzgsHst8GBt5NTBMGCrazMkyg4OIc7W6fIaJMd9bLTQTGw5km1cruG4VOSbbTBncWCY4x8HV82hyptvZGuumYQ4YfmF/1Yy+9xfldZYhXckwv/VVHrXa1PIg89XqGFimaqHcHyQ+Pne6j8Yu9bo0VSkbY9ZFmMP2D+R4n/Rw/HfqmDtyT85dr8BxbP0LQp7JeWoZo/g+PnueqXE8i2vHJ0DkcD+V53LMeIaxonNo/uKrvGbNVGv8H+3K/ccf/CqaLTUN09DLj18R5eocmrHUl9DAp9aQ+6bM6ItCmVZb20DbqK/41xb/VcCdZhwe2lwvM01cjDykclNRYJ443Mdvp9sfvCXKL3zxl2Z4yo8fq7nAbNEwIrT7VfchroRFhvMmGfdEeX8b8+P+e3ivr3/qaVE+3UKjoX5imDHvvS3Ko1vYX0Eqc+b46legTnf5mxCLb74hyrXrvwl1Sv0PIOYnuu0sk3FjUmgzKWudty7ztGmSYbA5iwGl7bZ0LIwO0FjupT/8ligfHeB4CEJc51Y3pIl4qTYHdRpl6xteju/JdAB1Vlq4R04zOeZjXI7dwTaabT333HOi3GkbH2SqHyvVK1Dl1Coa4I4SaRp3cxsNwCZeD2K9I2moltfQfG5tA838Lj8mjcOee/azUKfIjPMlZZQdRjhuAx/7Sq+pWWY4UB8DNePMpZ/KdyoZZpGJ4aCdqz1xnmBuLYx76VkdGXtka0+COWo2h1i9l8+NvFJS/dw28lPTeJcjlRe6B5tQp75yDu+1Ls8VvArOW2OJAzfwWfKoc7Ntecw1QC/Xhmn5LDwqR3aEEEIIIYQQQgghhBBCPkF4OEwIIYQQQgghhBBCCCEnEB4OE0IIIYQQQgghhBBCyAlkZs1hr97BoNbbMq6bGkGtMpWZuhmohVJT2h1Jhhqo9RFq3hUNqS/TmcfXXmsaujQdqc+2d4Samh/sSM3TG/tYxwssrTl5nWdoQ5UN7c9I6fBZ2rSWpIkOWZrDSYLtqXVkKqbmML5focRXSlYTuCet4MfKJEVRmJLSU9W6zs45lybYXoXSc9x6gBpQH9y6B7GXXvqBKJsarwE+w9J8RwYMPdXQ+HNPvyc1nhaaqDtYKkv9Hs/QD8pyQ8MolrFIaWk751y7g/pcWvd4MsF3ufb+uxD7/rf/UJRv374JddbXT0Fs73BflAtD5yg09Fu1/mhqzJFv/sLPQuzjplo1tIOV3nlg5B0rVlU6lKGhf57k2F5ad6owNLr6h12IhYW8ruTjdfUWPkOgtM3HU+yL5UWplznJMa+mxrqh+9nSAK6WUas1VIrCWtvdOefSFH/v6Ehp0Rnj35pLgZ7gxroRRjh3A6XhlRhz+Ti48fb7ECtV5Xs32qhHt7i0CDFf6chVyqi3F1pbHthqGNrBRkzrS3uBsfAa469QXg3W0hiosRYa+rvtOo7HUqD0ag0N7Pu7uEd5/77UDt44hfml1cBYEKp10dSzN8ajeh+vMNYWa1uotbPTR0Pj0s9RazH0ZC4y9VwNRXI91iwdz8DDPUN3b0uU3/jBt/HeE9zH3HpDaju2Tq1AnXNPPwOxL37lF0TZ83A8ZkpzGHRoHer22hgaqEaLzhYx5qTWXDU0sLMY5832gweivLK8AXWyGLXBb994VZRbhueJ+9lPY+xjxuoLWNMsPVrrXuq6WWXufbWPyMeo03r77m2I9dfkdeEU/T6GB7gW52rtfdzQnG2trYny7iL26YtG7tuMLoiy13ke6niTQ4gFqRxXhfmNhe8CDgcztvlPSyv4UdIc/uGLv4tBX65zjz35NFRJDN+gXJ1HWN8BA0MYOI3l2M0zrNM6hd8rdaVDvLezBXWuvfsOxG4/kHqqjRp+62ld9O0t9MqZxOjlkpfl/Lq+fQPqrJ/DtePsRkeUqzXDt2WMe93hVHr6FN5zUGdpsQOxsTrb0L4xzjlXZPh+mdobPiojeWEJ/XjyfTkeen3cf2TGWYfO3VEJ273wcc+o83tonll8+DmQqVdvNTRo+eO76H49uIEa42GKv1dXHlONBmoH93bwXOGgK8936suoaV9fw5hXVWu7pWVt5M1ct5V1rme2napjfDvMAv9zmBBCCCGEEEIIIYQQQk4gPBwmhBBCCCGEEEIIIYSQEwgPhwkhhBBCCCGEEEIIIeQEwsNhQgghhBBCCCGEEEIIOYHMbEhXTFFIXZuDeIbYdG7KemuhZcvOAAWoU2VuUTFMTPwcxbS3jqQIfW7Uud1FgfJpLoWcu0PDWGgk7zXK8H17Cf6er87ldVs651xoKnXLZ9D3cc45zxDvBr3rAoXI8xyHQ6HfJ8U21wY71g+aXXwMdBZR3H37ujQ+s4TVJ2McH64k2ysK8SWrZWzTwUjOJcvcIA+xf3rdPVHOJmiQ0u50IBarsTUx5vJgII0TLEO8wcQwU2hKMfc8wbG3ZxgeDIfSJO/9a2g+9/KPfwixmzelgdVwgGYlt+58ALEoku+TF4YwfmAYgKmxkBoGT/+H/+P/HmIfN7Uamm111XhIDbMmbUbhHL6j5SkyGo0hpq+rGEZ2zhgPmTK78CKss9Jeh9gtZf6z2EEjgbk5aX7YG2NuGo1xviXKNC4soeGCYe/gMmWKYJlfjMfYduWybCtt7uecc3mGuT1UhnS5YSwX+IahZqpMOkwLoU+ef/w//FOIXXn8MVF+7tPPQp26YWpSr6m5apj8aRNR55wrq1hmGEb4xpowSwsWhhlEOZJ9f7iNRqb9ra4oN9fPQ53eAV7323/0e6J8NMbJvF+sQqzakUZa66tPQZ3ASAypMuHJM8O41bguS5SxXIZ5tTBi2rjOMio5Dh7cQ3OgtY3PinJuPKtljmYZ+GmstslS2RftsrHGGfvT4c59Ud7vPYQ6u91diFVDmX+fef5L+HtlbVqNc8ub/TNE3nsG42XLSNJa4ApluArGn865+3fQPPMH3/kdUf7c574Mde5+8DbEdh/cEeUfj3Bv5dx/asQ+ZmbYqFvfeVYM6swck9HIqFTkuIbPLUgjuastXFO/++oDiNWqchz7xphJRtIAqvz6P4c6T1Vfw3s7mbfvOdyzjJq4vlUTuT8OEjTAM7+aYV9rfQsaOUebBxr9aV33KBnQaQ728LvjzGlpLHh4hGaADWPAjZQBWGJ8B+hvIeecW+lIU9LIM4ykDWPxew82ZcBYO0Jj/7vvyb31jQ+uQZ1bt+W7dLf3oE7F2I9Gan/lInzutQ00Cl48kmbg43Ef6oyH+B13953viHKQ4l6qf4T9oA3Q4wl+11Vb2A9hRbVn8Wj876TnWXNYjtGpcS6TGecK2jO5XMXvyMw469AtMbO56AwVrVbW54bmKaIKlo1zjXKMbXc0le9XWUVDyPYqGiumE2n6N9zEM4tBH/fk8+vnRLk2vwZ1XNkwpAWDUyNvz9Iw/kcbx4/G6CeEEEIIIYQQQgghhBDyicLDYUIIIYQQQgghhBBCCDmB8HCYEEIIIYQQQgghhBBCTiA8HCaEEEIIIYQQQgghhJATyMxOEJkhwK4Fkj3DIcIy6AGzMkMwWZsSOOdcqoSWmz6KcFeM4+69gRSgniQotu538cJRLH+votW8nXO5Eu6vG88UJ4ZYeKYMiYxzesu8I1fPYJmcFJaAOWhbW4YDEHL5LIYDpiq2EhR/NHxj3MbGOYhd+/GLorx/hOYP40MUOz997owo+4aJg2+NbVXNMtXJC5xvaSzHQ72Kpky9Por994fy2avGM73yk5+I8u0dbINmew5i9ZoUUi95OLeuXXsPYofK4Ob27etGnX2IZdqQyDBytJw6tMmUNR61KY1zaLhh9edxYBnjaRORJEZzqF4PY0FLmhJ4Rg6zxPCrVWnqkIzQyGBxHsdMEMpnjzK8Lu4ZphV9aexWd2iAt/tAjqvuCE0Z/DLOm6giTSusvJqleK/xVJpylAwDskajAbF6Xc6bnvG+pQhNM0ZD+XtHR2jkkRrPGZXk+6WxtZ5/8vzkXTRrqs83RflTxTNQZ9BDIxmXyj4LPDRMqdWwTQNlvmn1c1pgzFOGEYmRiraPuhDb2ZPPPuqjYWFDGYUs+/jc/+gf/DcQe/H7ci3LGmehTucimmY9V5NGMuODTaiTtOchNtqX8y1O0CQ1z3HtzNT4y4xcZZlOaSM2yxBp+VNojPZx88G1NyG2fkoaTfnG2mgZgIGlVIBrTmqYw157/RX5ewnm1WUjF93eUQZ0Hpqj5Ec9iP3ht35NlOsRXvfEc0+LcmqZyBnfDXp7mhnrvN4LOOdcqNZnz8O2841YoNa3dIrv+/5rL0HsnVf/WJQHRzhvHty9C7GuMsJKDGPR48Dcw+rYjIZ0OlZY/Wztj9V4t0xEx8Ym79qWnBM/ZxiZPu91IHZ/T+bfu9u4990fy/EQp12oM+ehYeEXqtKgaKmxBHVuhmji5fty3hR7r0KdPD2AGDYLtq/5Ta6Y1XxO3+tRMqgrGfnh4LY0q7Z8FGttzJHr8zLWarWhztIS9m21Kveokymu9Tv7+J3z5pvSTK/awNx6r2cYfSuzt+EWGok+PJAGdGGA++HBAe5H/T3Zt6UQ2/d73bcgVi/LMdJp4+9Vy8a+eUsa57396j+BOqMh7htOnZJmYvsHuNedRtjHn//SC6K8toam2MdBbozjWJnNWXO6FKERnzZo1d8FzjnnT3HfjMywTjg03LUMeE2jtRmu07/mG99svmEs22jJvp8a39KWKXVZmTSGxvtOhmhI131fzsH+wgbUmT9zGWItvd821srMytOq/FGPLB6Nkw5CCCGEEEIIIYQQQgghnyg8HCaEEEIIIYQQQgghhJATCA+HCSGEEEIIIYQQQggh5AQys+awb+hFgc7UjFpUWpfIqmPphnrqLDsr8Gy77KPuyCCUen29BOvUq/iDYUk+ZznC5joaS+2XemRo55TwutuHUitnZJzTR4bGsW4DQz7NFg/Wt7KkX4w217c3tageEb20WagZ+kprSoc4qaKWaTpF/cOp0qTu9lCrJzG0nSOlFay1K51zLjM0BVNfat4UAT5nWEZdw3Aq+2dqzJu3rkvN3/1XXoM6tSrqNJVCObYL433HY9Q+1DpKlu5yEBgajU7NL382bTRf6Yo6Y25Z8wbvZVz3iKB1bKcjS48Wx2icyFxkyOY6U6ZOaQO2DR22xBjHFfUDxQTHx9bdexDrdNZEeTLoQp0jpY05MIRgWyuYj1O1bsSGDlVYRn2ukopNeqi52mq1IDZS+syRsbYEhtZoWc3vPMfnNJZqVyrJ66y18zgYG3r1iXq09nwH6sy3UIOvFOj+MbRFPVyrBj2pMTmZ4ByxZn2Qy/snHvbhb/3BtyH2B9+V2qVRCfPqc48/Jsql8g+gzhtvoM7t8mmpMVw5+0WoU7Qfg9je5g1R/sEfvAJ1wmcuQqy/2xXlegfHequJ8yZQesJ5ZmhgZ7jm6nqz6Gd+EhztodZcNpG5KKwuQx3r8T1P5uPCx3XwwPi9D974/7D3Z0G2Zfl5H7b2dObMPDnfvPOtW3NVd3X1iEY3JsECIEIiCQUkkyA0UBGSQ7bDfrBfbUco/Gi9OGQrAiHLlimZFEUBECmCAAGw2fNQ1XPNt+485L05nswzn7PP3n5A6OH//z6wjktdlSnn93tbK9beZ+81/Nd/7cz4vtdMeYHknUtVzBn296wmXk50slcG+KDLa3ZVvPv6N6DNrbd/bMqtNmrQv/KZT0Odz5EKJpxHFqXX7h0PUZNy2MV9sef8De7fRS30t17/OtQVTuNz5+EdaNMlv1drWk3xOD0d8zhNcc748xnVFybjAxqUbAzJZpUkNj9IiOZwnKIG+w/v2b3krexFaPP5v/23oe7SIzv21R+gbmq4e8cU8wnmLPkQdVqLrl2nn6o+gDZXmpgj/TDYXKo3wj0i6aE28nRm+6oocf7/LAFd6VOkObxcxfm23LD9eH7rHLRpEl32tTWr/0m9eEhdWnG5LskRJkTj9d137X4c/PklhPDw8C7UPXvBxpVXz6Oe9cV12+bGI5y3u4+InrV7zJSc2XZ2yVnPf7MIOG/jQLxBnPdJSs6DWYZ19Xduu+uIhj45p96//54pLy1hLvPv/lu/C3UfOXRNuZjMzq7se46Lwckcnkj4a9zzZjolscbNkYjoJwe69bn3oSZBthiRZGo0wdhaqdscZEzObMdPUM97c93Gioick5nOc+LW/PT4MbQ5eHsP6rrrF0154xL6hzTabajzc5vNjXk4HSdEIYQQQgghhBBCCCGEEB8r+jgshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlkbkM65v7ATKSwzQeLIdM2BdbNnFL2iBh55T0Udi4jK+6fVVFwfnMRhaTrzhDoyhqKu1/bsOLuzRoxwSHd9PX3rSj1P7+Bz30wIWYN4YPN/PKcGWvZMjUBpIZcH2zARYYKYD93Eoy6KJh/4fwlU261V6DN8MkQ6g4OrbB+f0BM5IhwuzfhKOYw3gkhhIkb+8PjY2jjjadCCCFyvzcco3B8z5kijKfsXVBMPnF/X2LTZR6zEmYsxOZVTAysPDMSF5D5RNrRPHOuyz5yZsSILHXdnBBzzJiYGE6daVudXFcj5kaJM1EridFnt49mPIUz11iqNqDNYIgmHYf3H5lyWuC6qTkzyUYN37e9tg51T/atAUHJ5scU57+fDynpu8EADQ9S13f1Ghpl9rpo3JG6PalawX1rMsFxGLs1X62goc9JUF3A917bWjVlZsyaxjgfS+fOyowYioBj2B/Yfh73cY8Y9bDu4c6hKU9TjL2vfQ+N5O7dtOafewPcW9561xp5ZcT9YvMCGlRsbdq6JyPsg6VVrHvn3ddN+ShGk5pry2io9oPXf2DKB6MOPufKAtS99PRTpvypT6J5VDnDPaic2Xk8yzEGnAQH+2g0dfvWT0z5uZd+AdpEMa7DzM3jhMzj+3fuQF2n0zHly1uYr4Y+9pe/fUlyj2EfY9HyijN2OUKTvDde+54pVyqYCxy+/2Ooqzlz1XqLxCuSIHR2rbnYsIux98G9e1DX67r5XiHnjxxjQBzZWJvH2HetKs7/4czevygwBpwEz3zqS1DnzbemOTFQZvmby8NYjkcNrqEG438M5qMh9Arb7u/8YzTVDMvXoOrTL3/ClH9+BdtcO7QGXYMuGnZ19x5BXW9v25TLo11oU2mgSePC6IIp/+k3oUkY3ce9LBvZc2ReEtekOc7yH9ZYzhtenSSf/9SzUHfpvDWVKojJWfcY975Gw8ajGTFh9/M9hBBil5RHU4y/gwGex+7cfmjKS6ub0CZJcQ185hP2W8fLGzi3/uz7Nm9ZIIbJC2vEXL1n50RMjlnVksVIV5GTQ1RBjLKDjaXjAs8JjcU21D3zsj3Pv/DsJ6DNk/s3oa7vDP6ardNhrsjWlDfiY4bWbD767z7zrtfYmYR2jg+hzfYjjH/FzK0vauRI9oV52vizOdk5+HcFF+tIvtM53Ie6sTtX1Vu4r9cbeN6sOBPgjJ1dyHPmT2xOuX2Me0773HmoW7lgjeyqC21oMw+nJ5ILIYQQQgghhBBCCCGE+NjQx2EhhBBCCCGEEEIIIYQ4g+jjsBBCCCGEEEIIIYQQQpxB9HFYCCGEEEIIIYQQQgghziBzG9JNiWmW/7IcR/iteS5h+3nN0dztZ+Tps4AGSJ9tW0HoVz7zWWizsUgEvd0PVmIU97+0bgW3YyJUn+d4XfqcFZg/HuJ1f3KzA3VladtFRHQ8JeY1ZeyNw1ifE4V5J9bNhPjZXxjA0GkOY7uPg/EIjT9SZ5C1vIgi/jm5zr/iYIhtKimOxXBkxfcLYlKQEhMmP2RxjGtkNGKGAG6EyNhPJmiK4GFruXDzkc4rIraOs2jO33OdHoPbwV9itvghf8/HodNhURBCPkEDh9LPGbIwC2JQ4o28mGHh+lIT6loLtu7hwyfQZpbhWMyciVpeR6OJSn0J6g7etkZeMTGj2mxY46LWCpqPsn2j0rDPMCV9EGZs9O3cbhLjpK43OwohpJntg2mO5lszYgoZzez4JWTPnU6wX3Jn5JWlp8OQbnUZx3l93cbfckKMyZjnZGoHNibGO4H483hjxQqJvZU69tfX7lkjre+/8y60uXvnNtRlbiyKHE2znhzZ9b1cb0Ob/cMO1JX3rClI9QKaL1Zi3KfecQZ46cUL0GYYoXHN8sXrpvxP/+DvQJswxVj1zjvWEObS1evQZnMDf286tmspTk7H/zdMhmjQ8ujhW6b8zHOfgjb9Ho5F7ozdvKlRCCH09jDWjt06GZNc7XAPTeOOBjZn9uZLIYSQpmSfLZ05IDGtW2/a/DgpcC0f3vwp1I2HNo/JSSxk23W9aeP9ygLuLcX+LajLnSnUM8+/BG1qFTRk7LnnvLuLpjGdKZ5JoqbNq2sLp2Me/99+7z+BusIZ/03JPjiZEgPliTePxDazGTH2cvliSYwHSUoZCnevgwMci4KcQ/Y61nyoJGfdRsVet9PFMb3/GGPAQsPOx2IFNyC//kII4VzL3uuVFzA+fn+Iuf541+5ByRjXexxhn8+gX05LpvvhWSNGqItLNh4Mx9gXE2LqXXXGxmN2XiIObVM3J/0cDSHAGTuEEKJg88OyiknrkOR5r37CGu79yktPQ5v/5z/6A1M+jvC822ihKftgZveqiBhCzwL2+cwbcFFDOvIdw50t0wKvW2hhfv+0yyWuXELTrsMOGsjmpTVCXmqg4d9JMJniXPM+chmZH/QM484rZWCJNBK5dnGKv9dsorn0wO0LJTn5lxF5Bv/5iJmZuqpphZitD7EPor7NJRKS7yfEdPXYGYT3esfQpkIMyTfP2/lXrWN+RT7jhbozamc50NFdNFZM3FhdegXX9zycjoxECCGEEEIIIYQQQgghxMeKPg4LIYQQQgghhBBCCCHEGUQfh4UQQgghhBBCCCGEEOIMMrfmcElEMby+aEk0UOm9vI4n0ROJiF6g10dJUtQ4SRau4r0a9hv4mGijHaSoA7LQsPe/sYsaI6+90zHl/v4jaNM4dw3q4pl9v+kA9XtaRMNo5HR3ygiHkOq5lvb+MybaxTRena6n1wML4S/Rx4Vbzz3VPlIGA9QFu3vHapnWa6g11F5ELaWx0wqOO/h766uo3eS12IYD1A6bEB3iidOYSomecUI0GKdO9yfPieaYH1em+U10yGA6RCQGEA1gHwOYTjCNC0Rj+GcF0xyGmnk01D8GZkwDO7HzIcvm08zya7ogmnv9HpujVgMpZzElwTmauznSJ5paa8uolVqr2hhdEu1U2CMyHK/xGDX+phN7r5LowKVEc97Ph8kQ9VVrFRyH1GkFs7WVM82wwj4D09X1Gup/0dBeNyL66CdBo4p7+NT1IUsryNaI70RixYyIFXd6Vsc2Imvr3ArqjW6c2zLln/z+H0KbaoT6hefPXTLlgzuogerjYauO/VRO8Dk32lanrLmKec1rX/8zqOt29kz5URPn+t//438Adb/8BevfcH1rC9rcuY26aPcePTTlN995G9qcO/dFqItdvyRE++4kmAxRV/ze7TdN+dYNfMdqgnHu/e/9c1NeqOMcionGa57bGP3dn/wQ2qy3UH9u6LQdZz2Mj2sb+JwzF7f7vQ60WW3b35tNyP49IYt5aN+vQYJASvK0ratWczMhet4Pa7i/HY9dnks0RRdauJYurlmdypWFNrT5e3/8p1C38YyNJ+0LqL1+Ehw8+DHUlS6OViqov766ugZ1yYJdmxGJhVmGfZpAzoBzpiB5mM9rixnm3kx0fuex1eU96uD5sOfyn9kYY+9SC+dj7LSKf/hjjPU//hFqbicu/6iQGFAvcE8oGjb+jqu43svhNv7e0Okusz6HGmQuj6GPiSrRIPWePQnRX2a+QXAOJvkhyy28Xnco8d4JOTNVXc44nmFMLmPUIK1lLrYVTBfVjS3RV00T7LsksjExjkgfFHj+BL1a5oVCtJhn/jsR0b0fFrgOuxO7VscF7gFRiu+8f2yve+WZ06E5PCRniqnbeytEJ3syxvnBvt94IvJ9IHbfI9hZqEbOm1Gwc8R7FIXANeWxDVtbdj5kaxjvywbGyLHPIas41y/VcD8buek+GOC8Gg5wrHL33SaJsc2Y6PGnqd8/oUnIyHei2szev5mwL4LkLOvQfw4LIYQQQgghhBBCCCHEGUQfh4UQQgghhBBCCCGEEOIMoo/DQgghhBBCCCGEEEIIcQbRx2EhhBBCCCGEEEIIIYQ4g8zt5pEwgzgnGM7ErqnJkxcaJ22YQZU3/4kKFOG+P8C6d46saPNb+/ehzdIKGo4VM/t7nSMUP58+eMuU08M70Oav/y00pNt9aI3rri+hMUNcw2f61l1rqJYQLe+lCg7rQtUKUHvB+xBCiIh51NiJaQ8H2AdHIxS83h2fDqMYz/de+yrUPbx325SzFDuVma2kNWvM0Wq1oM1FYtBzdGDvdUjE+Ot1FEk/7NjrmLB/Towjh0MrnJ4EIrT/IY0kYJmydTuHIR3jw1pbUHO7OWLOPJwWw40oRyH63JnqsOheqWJlVrfrPkkxhrIxjJyofbuNhgC7ewdQ11iwplkVcu/mAprerLj79zs70CafWjH+3vE+tGlvorlSx5nUVYmpW0YMzgpn5tHvo9nAhfMXoM6zt7sLdZUU12k1s303GqF5TlTi3Ji554yzDzYk+DjoddHIa3fH9oU3dPjLrvv2j35kykkV5/E4R6OpQc/24avPvwBtcmI2s7LizH4iDMhdsl+ut2wMqRDzl1rDzv/lBcwFRgM0qZm4NdEZ/gDaHNy/A3VRYfv4oPME2mzvkvc7vmLKVWZaSsw2es5M7+ETNEmakX0x9n1MxuUkiMm+0Dl4bMqPnQlfCCH8wmdehLoXfvlLpnzzLTQJ6z3cg7o0tv3VCdjvS1Vc91vX7RjefxsNBMfejSWEkK3YeZsRc0lvIjTJ8fejSgPqxsEaKSXEJLWWYHxsOROqJKApz3obzd92u3af2OugcXE0w3uVY/ucW6u4tyzVcH2PB/ZeddLmJPjR974FdQ0wY8Z9cI28d6Nhx9UbI4cQQrOJOXO9bmMfsyAuybr3RnYpyWOqVcwrllv2unqC8/jB0OYxGxfb0KaSYR94A7SsxD549x1cb08e2dhRHhADWWJklqX2/eLKKrQJDdxL8tzugTkxCaacjnSYEpMD0nTqjeXwOm+gFgKabzLTZvb9I3L/f8faMDOqmctToiF5UJLfjxM7tt0JroGRiz1JHQexuYjxqHC5dTwjOXJBzl5uX6pleF2zSswcM/sMnT6aYlfoWd2dAYjBahbj7y3V7b7w5S/jej4J2JzJXW5UJca8aOyJa4KtkZiZ1jkDzjjCe8/Itwc02ibnSPqp74MDi7/ueERM08k6XVq2MZEZ4kXEkLbu9pO4gXlLo4H7mTeWmxEzS3K0DLOp7fOCGAwmxJCu4vacRvrhznr6z2EhhBBCCCGEEEIIIYQ4g+jjsBBCCCGEEEIIIYQQQpxB9HFYCCGEEEIIIYQQQgghziDzaw4TDZAieO08vB3T6vMaO1xfhNSV9lt2FFCHZ0w0b/ZH9hkqCbZZGPWhzkuDtEao8zYqrebYlLxvfoh6eo/vv2vbEC2qL/7Kb0DdWt3qYW20UFPo0ipqStUz2581ovHjtVFCCGHm9Gdyonty+3EH6v6zb9wx5W2iS3wS3Hz3Dag72LPj+tRTV6BNtY46ZKOJHbPJBPVGsxT//uLnbUJEd5hWZRk73ega6qflfdTiLJ0+0aTAuYayO0zpDQFFIaZNO2fdx8mH1Q5mGk0nQSXD9VvEH6yrXJCxzypEY9iR57h+a1WnuUQ0V9fW16AudlqYlRrRrypQ8yl177e63IY2h32nuXqIOlStpUV8JrdGWi2MoTOifetl/5oZrsl+B9dkteo03XIcq2qC49I96pjyZIT9NB1j3cztnQnZq0+CEdE2POrZ/uoOcQwfPMA99cdv/NSUswbG7AHRJYucRtczV69CmynRCWvV7ficP4dz/Yc/ehPqHpR2vudkna40rQfB+vIytDnMcY843rlnytu929Bm3MV9KnVrskHmXmWKfXfrTauHe7D7GNrkRMOxN7bPMCB5xYzkUqnbN5he3EkwmxC/BqfLlxCtxbzAMazUbGxfbOB1Wy1872vrNh7X6hiLsoXLUPfKp6wvQjHCOD4ZkTnj9sKSrOU9p4G9zTToiU5f1eumj7GfalPcA48OrF55ROZslcToiYvtgwmeB0KKviCHhzZ37B3g/K9EuG/EdXuvxVV8ppOgUcN3BNlGsuZGPZwf9dTG33oF4/GY6IjWndZ+o4najswjw+eUMTnnlTk+Zz517cheHJW2TZ1oF1+4cB7qukdWy7pF/GCIDUSIE7snRSXTz8V38XXRsIM3JyET8sKI6I4yfXeXAJ1sVm/pEl+CwdA+74jlTyNcr1nSsRUJjseY+IAUbt6w7yEj7xUSQsjdWXKd5OiPj3Eg/94/uGHK322jv1JUsWt88wrqUq9tPAV1t999x5QPd9HPI+/iDPAeJlEL4/bWs89C3fPPPG/K3/rK16HN7pNHUHfnzgNT7nXRl2MSYb6RujV2eHw6vlnERJA29nnQnL5d85xfZ1Psm8hpYM8inI/9Icajwp2rfP72lwEeQSRolW5tsTPx7hP8Znd8ZPf2OvFKWGT+Mi6PnZA8YhQwD0z8dzVy75Rpbrs+yLuYk1TI96Xeoc25CuIfEtY3sc7//ge2EEIIIYQQQgghhBBCCPH/d+jjsBBCCCGEEEIIIYQQQpxB9HFYCCGEEEIIIYQQQgghziD6OCyEEEIIIYQQQgghhBBnkLldaSrETCOKrdj0Ur0KbQZE3H94bIXi2RfqefyiKkQUviSS+KkTgb+8iM/54mYb6g4OO6Z81EXzhGlh+2DnuAdt/vlXvwp1L3/2i6ZcJa4Eyy00Yri0uW7K68SQrt3A94udwUCjRgSwSX96o45OD/vg3fsoCj/zxggFCnWfBHsPHkJdMXNzpsCxqDfaULeza4XvW3U0Vun2DqEuq9jfGxGzlyH6JIR6wxppHR3hvUtiitBw5ifHQxTaL9w69YL3fwEx+AgfbEjxYc3n5jGNi2OcV+y6D2tA55/9pI30/geyGs417zE2IiabU2JiNXTzIY4xphTEm2E4sJO0tohGb1sXzkHdeGgNIgYjjJmtGsawmvMN6O4fQxvvfRXNcC0f7aNRyWRgDVmOc2xTJ4YHqeurQQ/7/GjUgbplZzBWjfF9O4do3rR/YNd8o4lGZVXynKOpH8DTYeQ19Y5+IYSeM2PZI3Hu7XfegbpHu9Z4YXVzA9owQ7p9d93Ne3egTTPD8TnnjA1/+6+hgeyDbdxvZmM715IM11vkTCtmxLAtH5A5Gtl710tcuLM+9mfsDIlWIsw9Gke43o4m9rmGM/y9ATGXHDrzsqyCc5ZROoPcDxvXf9aUAZ9/0LfPNhzheO3s3YW61MW+WguNT159AQ2Dth9a88Pdn9yDNpee3oK6K1vWSDH5JN779W99F+q6R9bsJW0QM7OhnTOHxEBojxxDlpzZYy3FcW42sM87fft7Q2JK1UdvydCf2PmYD3Cu52Ed6mrOFLi/T9Y72XOXFq0hTL11OvKKmzdvQt0ssuNT92aqIYRBD/fwJ4+tOV+rhfMjI+fKiYt17fYSPhNJSCoV+1zs3nlOjC/drZok189dXv3uu7j/zIjZ7/HA5gM/eg/X+94+GgblIzv/4IwSQigKNDcDIydigMauw8RpTpN4X3c6pnEI4S/pn6kzESf7apiR84M/IpJ9NSb948/UxNc25DPynK7h5hLuxyV5zu9+9459zk+h0dvmszb2dBfwDPXZX3oZ6s5fs+/86C4xJTvA/KrnDP56ZJI8jNBg+Mkdu78MWmguVsR43j1yZqJJFfOPJMF9YbFm3+f76CUc/iZWfeSkKfZz6kzOcmJCXZDcyJvIMoO6jJijlckHn4NXVleg7rBnzdFoeGCG9fAAeJmPbT7+hxBCxR8aA5of18h3r4KY63pzvdEIF/PhmCzw1K6vlOxLKcl/0xWbb0ynONfTCuaG92+4iTvC/eWvPfO/wOd06D+HhRBCCCGEEEIIIYQQ4gyij8NCCCGEEEIIIYQQQghxBtHHYSGEEEIIIYQQQgghhDiD6OOwEEIIIYQQQgghhBBCnEHmNqRrNlEQPUmsSvQBMY4ZTFBJeuaF1IkoNjV+cgLUMTElYKYAn77YNuVffAaFswsiJH3kemeWo0j1oGvNlVqLaJ7wymc+C3Wf/bkv2+uIidyEiIzHvlu8Un4IVPW74kwkmLj1gzsPoO5rr//YlF/fRiH3tzs4DkcTaz4Rp6fDqeB4iOZvjcyKlh93OtAmraOwecPVEZ3xMB6h4UHLGbeMRmhYUo5xfKalnQ8lmY/Mn2fmKpkBgp80UUTMHj+k+c/P0jTI3yshsYMJ8c+IUdKHgZt5fPwktQWo6w12TTmuYEyr1ckkzZ2wP5nIMzIfhiM7/w4OMf5HGa77Rs3e6+gYjde2Nlah7plnz5vyG9/H6wZd+y6jKc6FaY7mOdXEmgZ0ibFcTvozcvG3P0BDDmb6EBW2LiOmZNMJxoDIrdOEGDJW8FZhAsawpyMed/o4FvceW9Oq249wX9rroWHUgyfWACltYMy+/szTeK89a5qRJNinxLMi1DI7Hz776jPQ5su/8Bl8znt2nWwf4J565Mxwq8RIbzbBvSxPbJyLiD/GyiL2y8QZclXJHlErcC0dHNu+65L4ckT2spnL5ZrErIrlgD6Olz+juP4/lmdeQuOfw44ds+ERGoO88RM02fneju3TbIj5wf/+f/O/hLrfWrR92F5FI+T+Hhr/NHdumPKzLZxXN3HKhAf3rLlWcukqtJm6uDMucSH1jnH+D/t27Ft1DGpxgg/VdcaiBx3s3z6Jq52+fWcS6sPNuxiHLq3afD/LMHaMyVpKXdwuc/KDJ8Df//v/GOpStzelKY5hAgcTjKMJM1Yi5qn1ujXZIakH/bcmb9KUsKBN8rfCxbV6Hc+6M2cQdkDMOeMU3yWqWNPScUEMkjC0h5Ez+2WmTRz/fsRcjV7nakkOzc2YbV3JzqMnxPISGiSHwq7zKsl1RyOMD7WKHTdmiMjqStdnZYznwYhs0rPCPkNCjNc+cRHX091tN08P7kCbwwO39157HtqsLOMaeGbVmpmOnsZnSogh+qMD+0x/8Ce4B12+hgZ4cd2u3/LiNWjTSPC7yTtv/9CUr1+HJuFzT+PcmLnvLXfvnA6z28Q7jYcQ8twbSJJYl5C1mHhDOmyTNXDsR5mNyezbQ62Kz+ljMO6OIRQsIn2IMFKr41y4eOk81E2dOXdENhNqSOf2jtoE22yQcchdTIzIeTAm57jcfcucEHPRQHLr0plEv/PGG3jdHOg/h4UQQgghhBBCCCGEEOIMoo/DQgghhBBCCCGEEEIIcQbRx2EhhBBCCCGEEEIIIYQ4g8ytOXx8jBpLs6nVwJgQoZCS6GtU5vjVkuiQ+DslEbZ5ehO16/7WL71kykd91FQ7POpA3bLTUHnYO4I2n3z5RVP+wpf/JbzPyjLU1Z3+VrVE7ZBlog1Yc51XiVGvaH9vF+refOddU/76t78Dbb759W9C3WHaNuWVn/9Xoc0gR+2jInL6S0QL+iQYEr25JDidpr1H0GZ98xzUXTi/Ycq1KmqOHeyj5t3e7r4pFzN8pgbRmKo4XZqN8/hMj/dwjh4eW13P+TSH5xP98e3YdR+l5vCMaMhRjVf3XEyDmF33Qfc5KWZEo6jasLpQtSY+az3Ddzx85HRfp2StEmnP1D2C1y0NIYRxF/eNemJjdE601ftEi3apZeNMrU40/o6tPmdOtOTjFOuaS1Zna5doqy+1UKds2Le/N53gvTMSF7p9e/8G0fTPyX5aOE2rkuyllYholPX8GJ+OvwuX3n8ghFDJrHYY00DrEs2zkdPkOjwgupAF9s1me82UayQ21BPUCnzQuWnKsxbGovV17Ofvv26fa5hjm6rLPUZjXA8lWZRFbufMQRdznbSJeuUbW+v2OtJ3u0Pck4YT5wMR4zMNvT5eCKHuNBwXG0RzmMyN8dTH/9OhC7h6Dn0sNjZtn4YC1/Mx8enYPbaajN2H2Obe9g7UnV+z+nq/9ku/Cm3u//j7UHfwyPpKxOttaLO1hjns+zffNmUyzBDDekQ/OSIathOX/x8Ncf0Nn2Bulbj42B1jPpQSf4/IaRofEh3kfg+ffTy02vTn11vQZjDFNVF1e1dC+uAkKKM61GE6gPO4T3wzfG4GOpkhhAnpm7L0Hghknye68BloDmMb5kfhz5pe8ziEEJKanTMTpq2bkDi3ZO/VaGDsTWJc34WLfTHZ53la7Sqjj9YjA/J/qip6Mmzv7EPdUtOu84Lk8yRMh8L146zEeZvPSN7shiMh3zXqNaJx7XK4e49RmLqf4n684LxIohSvq9TsCx5NMY6+/uZbUFfu2HPxzt372KaBcWy6aOdEb4xzcvYIx6ps2OfMyYSvNcjCiGy8eG/7HjQZ9vC6sm/HrzPEuHMS5ESP3vtR1YnebjzFtTjPeX1CztR9twmUoG0eQo+Ip8PZnwUtZpvln2uOc3dOfLSon5Nb4DOyL+XMy8I9Q1rB+VH1h+IQQu5uNSRnRO/NEEIIM/d9cxYRPwPy7MXQ3t+fb+bldGQkQgghhBBCCCGEEEIIIT5W9HFYCCGEEEIIIYQQQgghziD6OCyEEEIIIYQQQgghhBBnEH0cFkIIIYQQQgghhBBCiDPI3IZ0EyLQ7I0D0hRFo6OEGFS5W+XkG3WFGVs5ZefNFgpC/9bnn4K6i23bbnCM5i6bbTQKWK5aAei15hehzQvPvWDKi0toTDKZoJlGNbHvEhNDuoOdbai7e8ea4Hzv9R9Am9d+8GOoe//mLVPu9ojBIDETWP7CXzfl4QxN8iJiDpQlbkzL0/F3iHyIBiWFn38zYmhWooh4mtr5f24LDeI21jah7p/c/CNTPr91HtrU0eMvDEa2n/vEzCMnBj3+/eKYmHLM4evDxOvnMWgriMC9F6rn92HmIR9873mM5VgbVuef82dprvc/hjTD/hr27BxNiGFVNcWJ1axZM6h4QkxMCmLgl9l4sdBAw7asgr9XdbFhrY0xs0FMOgYja67VH6DZQOrej3h2hEYD943V9SVT7hwcQJsyYAzw+9uEmD2WxLwkcYadUcAHLTLsu2lsf68gBmAlcXhI3N5ceJeEEyJiTodTG+cyYiLaIEYM7Zrdm/oj3Of3DtGstebMPAZDYkY1wn3jvX27F8cjYg6RoCHcNLcmVsf7+JxRYXOGBWJqyDysus5slxlWLDTRcOnKpS173foGtPnJT9/BZ1iwa37r/Dq06bx7A+qazoBuZRFjR2CmIB5i0nEiRBgbymDncelNekMI9Rau1c0Ltu/rMcbCKYnHPWduF5UYPz73L/821N140+Yo4ynmq5XX7kJdvWXnUUn28I4zes6ZMTExloaEhOy7KTGgiVx8rK/hXH/1C5+EuvUVa9ryz//pd6HN4/sYOx4e2OfqjbDvpuQM1Fy1Y1qckmmckD08dUZvlSoxyiYGRRMXf6dj7Js6Mb8tgzPrXmhDm8ifL0IIceLmOzGyY4Z0hTu/VEkfxIm7F1kjaYZzrb7szwQkZyepL/rdzZcfQzt67prnXh/y904R//iP/xnULS/YvX6RmMW3mmju1V60RpM1YlqYkXytmrp5Q9zuZsSMKgr2us4Qzb52qjiX0oFdc6sB12o2tfnHKy/g2h0eYbx/+67NiZ/cwZyoXMLfq7ozwPIF7N9+jjnQyJlLj0mb3gSfIV+yv3d3hP303i08OzTHdozHR/iN5CQYkz3FG6qzsysz5IR2ZM8ekDhdc2bj7Ih91MW8uXDfCLmx3AfHFXaVr6Nmo+TW3iiV7gnkOt93ZUS+F5BvmaXLeQpiMEiOjfBtg53ZRmRudJ88MeW9Ozgu83A6vtgJIYQQQgghhBBCCCGE+FjRx2EhhBBCCCGEEEIIIYQ4g+jjsBBCCCGEEEIIIYQQQpxB9HFYCCGEEEIIIYQQQgghziBzG9JFVDTaCitHJd6uEmPdkjMEGhO56ZyINifOgOtiC79tP7eFAvNDZ+QVzVDE2ZsyhRDClWtXTDl+6gK0qVasuPpsgkLn3b3HUPf999835TfffBPa/PDHaCx385YzlusSYznSd4UTME/IcNZW0TxtYd2+c8nuTUxGSjC3I4rbJ8DlNTR3WV2xde1l7IeMmFSMZnZe7e7tQJsrF65D3aULl015fa0NbfIZmq08evNtU97roNA48xKLnJB6xAxg6Pr+YOYxaONmc05wnvpaEAM8mEcfziRvXgF/FodOA0mORlc1J5CfHxMB+ymaR+ZT26f1BGN2SeaHv3ulggYwi4to9BmcMdNyG9dWhTzDoGvNJ8DsIKB5Tpox8X8c++Mju97iGE0z1jfQbCt1hiOPDn4IbbIKmnQkznFyQgytms4EJYQQmk0bqyZTNCoZdLGuWrPvMxqcjnic1nGt1tr2WY+J6UhIiEHooh37ATH/eTLrQF0U2TXxaIZ76lqBfXrj2Bq7bN9CA9l4jPP/qRfsnjr96U1os/3YPmdO4txKC+dV7nKy9vIStLlMjFMbbk/4hS9+Dtq0iJnlN75jjbsa1Yt47xo+5+baqilvkbWVsPV9Sv2PYpIDTVyemVUx7gz6aLKTu/dOahhX//Af/j7UvfqUzVt2dnDdbLzwC1BXd/nO699CI6d7e3tQ11iwOfN4jOPVbNix9/MzhBBWN1ehLnZ7cULmXoXs1xcu2Ll98SWc62tbuN9UIxs7OiS3+pOdr0Pd1DnJdcc4QTeuEKPiy9aENargvnwSZA00h82csVu9ijl0TExXxz1nKDXBHCImhmnVuo2ZrTbGlCLC/MD7ekUVNA0jaUXIRzaOpyTX985GZSDG1hHmDElqn6EocJwLamxk59H8Rsh+/s0bMOdpd0qD71/Ck23cj2dDG7MePHyEF9Kzga1bWsI50mziN4T2Utu2WcQ23kQzhBCqqR3vp7ba0ObLv4J75uP71ozqcI/k+7ldBJ9exLi9W+tD3cHT9p2b5zGWD8dY1w3OmJKYRHcLrMzdnE+Z2RcxhpxG9n2iKu4djQ1iLn1k66bHp+Ps53P+EEI47tvxYedZdg727Zihe5hhrEmc8W8lw0C6SUyMe338JuKh5/U5jOC9aVyVmDb7PCKEEMZjO9dmxLQ5THA/m7kz8HjC1hbeKnffLWcTPLtPRuQc17N7zNFhB9rs76JJbu/YXre0hPNnHvSfw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQebWHK4mqNviZWWfPY+aI9e3UBfnyorVIev0UN/miNRVnM7mwvQQ2kxGRPvK6eAsLKAGR4PoaDnpmtBsonbe4aHVVPnKV1CX7Fvf+i7Uvf2O1Rnc2yfvkqOezsxrxBB9GKYfmzixraSC75utXoa6yLWLiWZWxDRKnW5eWZ4O/Z7rl9agrrFgtT2zZhva3H2Emnv7Tu950Me+2b18AHXnLmzZNruoSX3rzn2oe/jY6ctEqKdTsjonzjOPJu+HhWkDxURTCzRsifYRlyq2lQXRuiuJjh2uCXLz/wnJrpVD1KqMnZhXOcM+7Q8xpiROK7heQ62+GdH/PB5bTcGU6FAxTavCaXUfEN30NtEhjt3Yr6ygvvzE6RpO8NahN8JYdJzYfqk3MD52jjtQN3PzPakT3as67p1jor3pSZmWuxO1YvrhrRaO3+G+17k6HRN59TzG44PMjsV3d9+HNjm+Yphds/MhnmH/3c9RS7SSOf3zaQfa7N9ET4AbD+2ecOv9J9BmOUV9sV/63C+a8vkN1NT8+//gn5hyHmNcZ6rRn/v0J0352uUr0GaT6PuGoc23nt4k++TnXoW673zrW6Z8633UT/b6cSGEsLVun2FtuQ1tEpLHZH4vKU7HPO4NcF4NnI4c2ZpDr48eFcF5d8yIbvof/+lXoG777fOmvNPDexdv4vh4HeDxGPVUKyu44CaPbc466OFePHR53/pl1LT91/7Gr0FdVLPjGifk97vYL+fWbAwYJrgBDKe4dzbqNt4/8wJ6RXzzq69B3bhrY3tM9s5nX3oO6jZWbD8Mpzh/ToJ6E9d9WnETlzzr/bvvQd3xsc19ZyQesxwvczrcRYp5xdrW01AXJ27PJlrntQz353Fk64oUx7Bw8zgOeD5leWfstJFnZL+OyTE89R3DZDmJpn7kG9Jc+MPCzpof/JwnxblVzA9fev4pU+4cY6wb5fgS775/15Rv334X2jDPjYrLBxttzCsXWugLcOm8rWsFzNsn9zG+/3u//WlT/n/9V9+ANo8e2/y7XUW9350Yf++gtO8yIGPN/IfyiV2HzQmOS4vM04mLF/EM9ZrrbO3kNubPxthPzENlOrNzoV/iPnESTKbYp+OJHZ8pEXL232BCCKHq5mj3mByQSL6WuLyrkmEywzwAvFcZ+z7gv0+EEEKR29jG5lVR2mcajbEPBgOWX9kiu25CvHn8/uU17kMIoUHOenFi7z+ekf2TxJOjA5tfzYZ4luiRs3PszuFFjN4186D/HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEGmduQ7pc++QzUtRtWkfn6OpoINWcomr+UWmHnaYpC1sMmCjvnfWsCMB6Qb9sxqXMmAI0KtsliVJfu7T2y5Uco/vzn3/2hKf+X/+AfQ5u9nV2o8z5NBflOXxAHk7h0plPElibKqlBXcYZ7lQr2b7pxAepC6kwdChzPgojlg+kZMQ47CZpLKGofV9umPJiRsUiwLnVGFvUqjle3j4YH/ak1qrl15za0OTjAuZaDcDs6AoAhRWAi8Pguvg0Tjp/LyI4YbpTkstQJ3BfEIKBkZmbu/aII32VKjE+8oRrxyKPGIPhczJTjBCBmld6sqdlA44cZee9xacX3B0MU8c9IvGg27VqKiSEBM36oV2x8Wl/EfaNWxxh24AT6kwRfptGwa/Li4gK0eefOXairNWycm47RjGI4wX4BP1AysQqyJnw4Kbz7aeBmixCG2Dwm41Ct2fHrE7Oqk+C5Z56Fuvc690y5m2DfVJZwXDfa1uQpHmP/DYZoNJH4mOLicwgh3Ln5AOrGR3YvXpqsQpt6gesmccYSF5fRBOrcqjX3fbiDZnfri2ju9fJVa263ukgMcBKcj2nTmTB10SB3vYbz6ld/7rOm/E++g6Zd3TH2+ULd/t5kgH0+jln8d0YlLN87AZgZZzmwz09SJ7p/ZTVbV6+jQdYzL+O6eWrF5m/x8Q606cQ4Fpurdv41Vq9Bm+kAzVAOH1ljle5BB9r4nOXoCONqd4TmXonzdpoQZ9FohmvryZEzjangczNjwENnDDhLcY00iJH10Y59duIBGw73OlBXTu1YJTPyUCfAl77wCagrXa7xvW99FdrkY1y/ldSOD0mrqSGdrxsd4TyetHAs2uefN+WyhkY8Pu8MIYQkt7nGmJhj5cG+S+TN70IILWJGu7li96lJjuuvPMT8p+zZuqJA06RZgXMb0i1q2ElybV8m+f88Js6nJDsOIYTQXsJ+XVq2Rm9RSgzsJ1j38vP2+8fXD9FUapwTc+yRXTvJMfnW0d+Huhef2jTlNZLvPNjGuZSP7Dz59V9G48Y//MfvmLL3OQ8hhCc9YoB3bPehskvet4Vrx6X7IY2JeVqE71ctbE5ekm80kwrJt123pCPcJ1gwuphYk9xhQjrmBBgSI7Ka69TJhPQpOT+P3NmuINflM1KX27peD5+pe9TB3xvY/T4iZo+zKT6nN4SbUeN5u5ZGJLcfDDC3mEztvY57mJP0Sf573LEGq0+/8DK0+blP4/754I41a313B02BJ70DqGu6s2yHvMuUBNzmkp3H9U2MAfNwOjJrIYQQQgghhBBCCCGEEB8r+jgshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlkbkO6f/NzaFJRqVo15LvbKOD9ra9+Hepe2rDC5lGGQv4TYuJz8903TPlpYmYTBzSj6jy0AtD9QzQJe7yNpgc3btrr7u+hcHzeOGfKKxewn8oEzZVmE2ecQT7Tj6dEcH5ghfDrGQp8x0S8e+TErGc1NMGpL29AXenEyXPiqlIGrPPmZTNiEnYSLK2dg7p727ZP2TyeEeeMydC+ExNE7/SJIYozrxlPickfERpPU3tdQdzFCmbi5quI+ZWHGVJwkzr3jMS4ryjx90oXepiJYkncXRL3gwUxvMzBJSyE0rniRcR0JIowHEa+r6LTYaw4neJ6ai7WXRs0lShiNMQYz+y8rRPHnhnpZ28kMCZGBosNNIBcciZxVfJMJXm/3Bl+VKs4Z2o1a2LRJX0wLdCMLarY31tsoOHMZID3GhxbM4NFYlqU1XB/S6p2Pk5mGDt6PdynLmzY+NUbdPA5RxhzKhV8htNAcYBjca1pDRWaZD7W8hrUVd32XM1xPVfraNKYuvmXj4kZaANNTQo3Z+I1vHeNmN9GYzuuxC4lPL+1Zcq94w60+flPofnFi5fsdfEY+7dOsr4osU9Rz8hzkxj9y1/6vCn/mJg9du+gmV97wZpFDYkpSERyuTi166ZM505hP1LyMcaGVt3GAr9/hxDCqCDmqS4fiGO8bpnMte7Qztvrr1zGey+iuZ2Pv4cDHIussQR1S+dtvvjoDsarSy5ebR89hjbbjzCvXq/a+VGQubC0hLE2cflH2kBTMmZuU63Ye2U1nOsXr1+Euoc3rdlMKHDdPLi3DXXDsTVPy5r4eyfB7/z2b0LdyBnx9PdwPR/30SxnNHR1BTOuxtiepHaONomx3BdfwrPfF3/ly/aZpnhdTH5vOrTz/Yjk8T7/7/VxjVw8hwahLz33nClPiKntV/4ZzplvfsPefzrBvpuRnKHwblxkrsfExLN0OXqe43pjubY3bD5NhnRXrlyCusKdYdbW1qHNkBgyLy7Y/bG9hHNr57ADdS1n2vyJF56HNmkdc5liaM+gFy7g3Hrt+2hkfvMd++wvv4RGb2s1u75uvYe5YbGOZn6/dMkacP3wzg+hzd3dO1D37CsvmfJSDft85zYa7h5P7H6SrGGMbKY4DtHYvs+F2hVoExMvx1/9xJdMebv1JjY6AWYTXOfBmfxm5Nx9TOZxmdt1vrCK34FGxABvbdnOv1t37kCbhw9xn9vf2TPlagvjSkSMLsfuO9OUxKPpwPbB8T6auu3u4bza2bPtDjpoLjk6xnuNp7Zfagtk7pWfgrrza7bvOiskl/rcq1DnTXJ/UmBuHa3j3D739Cum3Fo9D23mQf85LIQQQgghhBBCCCGEEGcQfRwWQgghhBBCCCGEEEKIM4g+DgshhBBCCCGEEEIIIcQZZG7BtmGJTQ+cnuo726jd8c033oK6Bw2re7LaQh20pQw1RhYXrH5OfQG1Ox5s70HdjbtW0+z7P/oBtnnwCOq6I6c3mqLmzb/06oum/FdeeAra1Mgn+FrF3uvhDmoeP9jBdznuWR2S9958A9q8+/1vQZ3Xi6psPYNtmDbywGmvEM2umGhGo+bw6dBqHRPp4wePbN8/eIyawxMmAuz05fIJ3rzRRM3V1On+zKZEI5f8Xux0IImUL9Uc9neKyN+EmA4Z3vuDNYcjpjpGtIr9fEiI7mxEnqninr1MUK+IaSP7fimInnFBdN1i18lxckpU1VKigRrbZ8sL1AAuicJpmti+r6REA57on08m9vcmRFc8i3AM0+W2Kc+IvnCS4nyoVq02WxTj+zVbtk1nH/ekS1dR88yPa7NBNHrJghvtDEy5tYh7UpW8S5zafqlVsU1exT6vVO1z1QrUqxuP8J39emP6pyfByoDo1bt42CR7R4N4ElSC7cOMxLnWAu5xFaelOx1gTKlVUOO10rLtkohoXOKwgk5uFGH+83rm1iTRlNtcaUPdRttqBSZTvHeSYAyY+XVaYh+kGV739FXbL09dQQ202w9Qi+7aZasHudjCfTKaoWae17gcMz2+E4DtCg2nWx6RWNjrdaAuCjbOpBWcs41F7K+Vto0FDaIn2AnYX1OnHZ9kGFO6RFN59aLVHM4WUAPzlVec5upPiAb8BOPq2uqqKZcJxvpGBftg6vwGCjJnmS+CzxlqNVw3T5Pc/s3v3jflFtHY9+MZQggz53nQbuO+cRKwsV8/Z3Wjf/PXfw3a9Ia4h9/ZtmeqMdH/j8nKWWzaGPaJZ1Ff+Hf/6m9A3eUXbLtJQN3UBhnX2dRqI+90BtBmUth4OByi5nCS4rtcvmznzIDoee88eQHqjo7suWtIdEATsocXM9fHXoM4cP8Br/3MPC1yUufzan/uO0m2zm9C3cOHVi97PMZY0Kyjlrk3gVldxnXeOUbN9cLFo5ycMZ6+jtrwe49sDvdkB3W+owr29ZN9myt9guSeq0t2/I/yq9BmEuHe0RrYe6V9jMnjA4x1/aaNwXEd18DxAeqpHh3a7zbPNjG3qBC/o0c37tmKGfbBlUXcUzvvfseUt9qnI0e+8RZ+Q5u4/bhIieYw8SyJnb7v0QHq/R8foeeGt9yISE6SJNjPnT37baVOvnWMRrgmnuza6/aP8EzTd1rBx7v4Da3bw3cJmd0DVtYxTgyJf07m+rhDNMZ3dzDXffaKvf8rn/8MtLn1EMfh0Vt2TaxcfRHaVNsXoK5St7lEzA4hc6D/HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEGmVtx+zuPDqFu7ISkt5+gaHSDaLsfDGy7249RSPr8Apq7/Ot//RdM+cVPvAJtKvUFqFvdsuYnG88/B21+hZiJbaxYYed2HbtryYnXV2to6NAkdZkz2+oRw4+DAZoJbHesMcHX1lE4fkiMwx7tW8HrkhhrDQ7QlG/mNO/rDRyXkpmJOWMCZhJ2Egz7aDYxnVrx8ZgYx8yIIVfwxjHE6CQh7+19KyoBjQWKKgq+T3Ivvs/MH5ghnLuKXBbHtpL42lH8dcx8JSGmAd6IJJ4Rw40YH7TuTDhSYrjgzTVCCCF3Y5wTQ7oQUITej3FCDPBOAuLhE+LECu1Xq2jGMxnjPK65uVavE0OifTSRiJxpVo2MVzFCU6k8t7EuyXDdTCc4H9o1G2sPJ3jvfmHrFjYwXmVjNGQpXPgfTzBOlDHO49WNFVOekv71ZiYhhDAd2j7Iath3UYS/lzmDpfEhmQjEPNbDzHNOgivE2ALNKrFvshjfMfPGisQgI+Q4rklif6/axHuzmFm4PTxiZpXk2ZN0wTXB5yxiO64jYrw2IwaQC0vO2GpGDCGraMrk/01gRowrK+RfCRJX2V7C/KvZwN/bWLbPycJqjxhqFm4/LfM5N6qPmJz0zcyNPdurKiRGj/vWIKvWwHi8srEKdTWXQiYZrq1yijGl7ky6Eh8MAzeounjVGpXduYq56NKmffaXXkFzsUYT58fCojUTGxCTzQnZI2bu2aMYTclmM1ynw74172HGZfUW7hvnr9l3vnwFDWIePXgMdbt77vfOocHVSZA1cM5kbn++dh3H8D/8XTzoPdm3pmrbxOiIGQZd2bLz6qVraNi1ub4BdbPM9mFEYljM1puLkSWJ2d4gcVbgvNrbe4L3Hts5mpPEepzj2uo647puF/upyDE+TsdD3wjaZNSQbg4DcXaGc9dViAHkSXHpEq5Fv2e+99570KZTdKAucWe7hSb2YYWZgnVtvvHWe7egTZ2YH6+17Xqakri9uYYxw58RW62L0OaFF20s7U0xj759gKbsh4e27uc/hc/0C0vY51/5sx+Y8vYxzuXf+KsvQV27Zu/VjIl55TLmG7fatt39uwfQ5m/8FprbhZHN+47GZA2cADNiRtkf2hwhbWH89ee6EEIYD+x83NvFmNU5xP764dB+P1rZwnnV7xMjbPfd5N4dNK3d30Mztru3bbt0gRi2unNVr4sGfDMSb5tLtq/qTZxDfZKzjlwsH40xth4d4ljdKu37vXMfv3feP8Tvf5PY7jmNDeyDKMFx9wZ0cfzh/gdY/zkshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDDK3Id3hARrSeR39aIYC+ZUIxdYnsRXKPreCQuMXn/4U1D31yudMeaGNQtJMfHmxZUXzN1fRkK7CTLpKK2YdEbOvyJmCzZho/4yITTsjFWZK06iguP/mkh2yL3z2s9Cm2mpD3X//z/7clO89uouPWRDzqMwaisQJPlMacIy9KLY3PDgpRj0UTc+H9r0jYlaWkLH35gbMCK2c4tin3vCCdE1ZRROavLT3mhAji5Ka1FmYSDsY/czpV1WWzsiO/D77C1Qjtc/QyPC6RWKO0mj4+Yh9nqYY1nxcKEvsAzZFvclgxlyZToAxMRqJU9s3aSDGQsQwLXKDPSXzqlLDsfBmW8xYsU5imDf3KIkhXe8I12nm1mVR4nPee7xnysvn0SRpMkKjibEzqoxSbMOMjLzJVFTgu+SkPye5HQdmgjMeo3nacGiNalIy/3Mwrgwhq9i5UZR9aHMSeBO5EEKI3Tyiewcx/yzdui+IOU6FzMdawxq7MMOeJMOYUvh25JmyFH+v6tZSQvrg2hNrrnFpF/OvtIKmGUsrdr5PRzj3kgruLYUzDR3l2OclMYiD+xQ491rEXLjmTHrZdTExcPNzI4nmTmE/UpI6zpnBzK7xaop92lpCYyufa0xJXh2RmDno2pjZLPCZWBgPU2ui4vPeENCcOYQQ8oYdn5c+g0ZliXuEp5YvQZt7u2jYdnRo53tWxXeZEhPn3BnbNqrEkI7Ex4W6MzMjfdBsYudduL5uypefQaO04z4a5Rwf27EaDDH3PgmqJPb5bZ2Zfl+8hgZPT3/iU/Y64lV2+yYadC0s2rm23CKTlsTVSt22mxAzzow8xHhsr2s1cZ9fWrTzIyeGbZ1DjNE+z8yIIV53iPP43iNrWtQ7wntPBmgSXDrD1bL84Jj9F+3sOxdk/pdzuFQnJPc+KRp13B+fefppU15eWoY2d+/cgbqRMwC71sL5Xsa4dt56xxprHRzhmL3+ozeh7uUXr5ny5ho+ZyPG93u0befN3/mvfgJtrl21Y/S3f+cT0ObtO7hXvf++jWOvPI/r5MVP4TP9rV++asqTHPOPhWVc41/75j1T3u1gjLx+Aa/77V971ZT7Hcyj4xqeg959wxqxHQ1Oh2lz+zzumZ27dl61l9Cg9sL5c1DXcUaDJckt7pI87+Y7do6uEYO4eokxsfS5jDfMDCEsNNFY0Z9XLl/E9eZ9vt/v4dqaDfEcGTvj6OEITeSiCON0VNh25HXDj95Hg78ks7E7j3DOJnXMG+ru21uIicF2SXLkyBvSfTiT0NPxpUMIIYQQQgghhBBCCCHEx4o+DgshhBBCCCGEEEIIIcQZRB+HhRBCCCGEEEIIIYQQ4gwyt0DQ1hLqgkyd7tI0akObahPr7jmJpcoS6kL+wi9+BupWnHbdNGfaqSgE0nPNKil+E18gUluetCR6qk5nMCHakSEi3+Cdxl5ZEI0/JvzqqtqLqP/13PVrUPfWu1um/PAhag7nRPcvcTonXmOWPVMIqE91OtR7Qihy1JdZWbSaLGmK82qM0o2hdJp+GdFjrjBtNNenswLbHBGd0prTvcxrOBaTCT57PnV6YkQ6zOsQs7nn9bVDCCFx+j0VotW61ESNqU2nYbhUx1BUqxA9Hbd2mR5pkuC9UjcO7Looxnf2eqBeL/ekqDVQ2+t4YHXRaiQWVch1UWTfOycTpFprQN14arWpvG51CCFUiZ6Uv/tkgJp7M6LpV0T296Y56oYtLrRNucxxLoxnuLbGTnN1mejVtUnf9Y5snx8RjfHJhGnO22eoNvHeK8srUDdy+lhsnbLfm05trzOt4pNgYQXf0e+XFaL3W6thf6VVq+UVE/1Mpkfu9W9Z30RM29mvL7LemJ6w1z9nsai1aLVSN9bWoc1oSvYpl0cwD4SC6MzN3PzPS6KxTNZk5OIhU6VskhjQaNh4wuYx070snNBcVJyOecxk3cYjG5/yAel3okGa1GzfRzHxQMiI1n6jbcqjnHiAZKh3Fzkt5GSG8zEjLxhldiye/QTmncHPGaJlPSD655HLY5YWcf/ZH6B+4XRinykmczYh8T+DnAHnY4PE6OaSjTFrm6jFeOESxrix2yeqp8OSI1RIzMxcXBtFuL8MibTt1Gn7N8gZJ0uxT4PTZKxWUbM8q+M8LvwYesHrEEJEdBt97PG5YgghjMfurEt0q9lxuubypjwQbXXik4O+AfPtLUXhvTVIG7ImfPhluT47x/q+Y3reJ0U1IzlCYp/vymXcmy5duQx144mN5ZMJ5p6fehW1ba9e+rEpv/bDt6DNwyd7UHfjttVhz8g3i1qKeqq9Y7sQb9zDZ9ru2rXz+dv4Lt0DqArl1D7Dwx2Mv6NvY2ztde0c6Q7xDH41+xTU/cq//AX7+2S+v//e21D3f/g//4Ep1+s4xk+98AzUHR26uJOhju9JkJDnr7Wclj6JIXVyhpk27fei/+4Pfx/bjHBc+0d2Ht25eQ/aTInn0mHH6lRPJiQHIj4tjQX7flOy3mYuRlWrmCNMyFwLkDOS3IbsHXnwnib4e0c53muxYd+lWsP9LCL5VQHfGsi3BxKT4RsFyR/n4XR86RBCCCGEEEIIIYQQQgjxsaKPw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQeY2pHtqbRHqZoUVie4QI6/BUhvqnlleNuXrn3kF2ly4gKLwE2eAlCTExA1qsNKbmoTAhftTbzZHvqVHYELD7v3BxnKMghhD+Wevpvjciw00AHv6su3Pm7duQZsHB8dQV6b2XnFExLuJoU7s+qUkfX4SRAEF0ddXrPj4+ioRBydmfXGwAvZJPN9y8uPKxnlxgALsWdUK0/s+DiGE8Qif0/tTefO5EHCOsjkbEzOPSmbXRL2C/dtqoHlIo27F3JnRW0JE9mO35lmfx8wdyK3dki1A+qcyd90pmccZMdbyT0Z8hcKAGKY1nAlNcwFNLocTNJqInJHArMCxH4yxLqva35tNyb1JTKk27TzKiOFS4eJ4NMN+GoxwbVXcM7FxrtVwXvWduYs3aPyLOnyX2diuQW8YF0IITWK6M+hZs4iSmDkUBfbndOqeM8Z7nwQXrj4FdX5tZsQgiOHfekbiI9uKR64yJmMfE6u1cuzM0YiJT1TB8ZnBk+J1vY41mxkO0FjmyS66xjzaPTTlZpXExzEajhTOlLJM0GyjGpE+cHvCwhLmiRkxuSrBbI6M1RyxlsWJE6FkMcw+/5SYzI4nxDDNxQtmojgjYzF15iQ+Xw4hhCF5htnM3qvZxLGfknt548bqAjEJ87kGMZG++NQ5qKvV7ZxhW3qdGN1mNfsMw0EP2uSkD9LY5Vakf2NiOHzuvDUtapBc56nrl6BuZ3fXlKvZ6fg/nZIcCb3Bb1LBd2yR7WTgzAejGfbpyhqaPqXOwCerY/xgJm5jZ5iVk9hQRjj2uT8fkuumbn17E8e/rI7tJJ4sI+/nz1klOXtG7KzpytTonBiuuv2tJMZ583Bq4nEIISJxM3XmUzF53oKMWpp5k0RiGE5Mm3/hy79gyi++9Cq0uXEXzeG/951vmfLeDprP1Ws4jq0FGxMvXUPjtQf3npjy/+p/98fQZkBMjX1uHRfEJIyYHY7dAotTNFj7pV9Fk65zW9ZMrNfD7xPv30BDute+c8eUP/OZT0CbxXMbUOfXWJbiOegkKMmSWltfM+ValRhPkpx15qb2T994B9pk5JtSyxkI/7PvfB/anLtwEeqi1K6bhTb26WhM9uNju28f93EfT9x+nFXItylyBpi4nCQj3zWypTWou3TRnlVWrzwHbdorW3gvZ9QXs/MM+Y7hj3YsG46JIV1weZk37puX05GRCCGEEEIIIYQQQgghhPhY0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4gcxvSrS3UoW46sZf3Bigs3Xj5M1B3yZnbPffUOrSpkO/WcWZ/LyM6yxlqSwevr82MY1Ii7u+1rJnWvjdGYCZhzFjFC/4TPxMwQQghhNLdPwn4wszI6JOfeMGUx0Te+p9+43Wo2zmyovBMwD9hotiuj0+NUQFxJErT+F9YDiGELCPmJ4nvZ2b+gL/nRfsnxOyLGa0tLFpR+KJEQ4CIzIfg6qIYTQPQ3IIYbjDjQVdHZwIZen+viMwh9ntehD4hYvLMkC6KUteGvB9bu/6NmDvACZCS5/AGmt5kJARuxBe5+e5NC0IIoYxwPtYadk8oA87H0RiNtEK3b8s5XrdIjH26A2uSUZA+GI1sm4xscSUz+vQvTQyCmJFR7szf1tZXoE1zjHN0/MCaghC/S/p7k4k1E8tS3JcbxKjJG9B1DtFw5CQoyYY9dQaFM2KwNxnjnOm7uhmZH8MRmrENh3aPyzJiykTiTD60Jl2ldzYKaHQYAu4J3hAshBB2tu382N/dxzbEqOn2/YemvNQgvz/DsfdxIcrQNKZVITGgbuv6Q+zf0Rh/r9ezMWBG1jszVPF7Qj4jidMJMCPmmKWPKWSRD4k5ZoidgQ/ZG5khV+7yip6b1yFwYzm/JSyM0Byo1cD50HQmNSkxWhk5A9QqMY2ZkjH05qYxGeY6OZM0IzvfR0Ocs6wPfD5QqWAMjchecvnaBVNmhkz1BTSq2qo5E74E5/pJEEe4DnMX19Iqzo96Dev88aWeYXxke9zMmWDnzKw4wnjs0+gpcx+NsZ99fKpUsQ9in1uR3JQZR/q4ytbtEjFtT9y9mIn0h81E+VHsg/NxdpaZx8j6pJiS/di/O9vr4znOPnGGcSxLifF2zV7XbC1Bm9UNzBk3VpZN+YffRQOwaYF7bb1pn+vO3fvQ5p03bW4xIWM9jjBGjqbWFC8pSB9Q2yxn5kiOqP/Nf/vfYaXL35ixc4OY8l26dN6UWy3cuybk2avOzHSQn44ceUxyi6W2nUd1kmfmJGbE7mPYX/nN34Q2x4eHUHfv7j1T3txC87krTz0NdW/fuGnK/SHmO8WEfCNx5oc5yZG9sdulq9egTW+IRnZl1e4vjVU0J2yvorHc6tqmKadV3NeTBMfB71URMfwLZF/w55eS5MOBnfHdxpuR7xrzoP8cFkIIIYQQQgghhBBCiDOIPg4LIYQQQgghhBBCCCHEGUQfh4UQQgghhBBCCCGEEOIMMrfmcEn0V7xWU53oNL709GWoO79sNT/qRAM1JvoyiddFZZJSRPPIX8Z0cyOmp+Qeq4g/WHMpnxHtEKJDNnV6dP0Jaof0Rtjnw7FtNytxCIc5/t7MabVuXbwCbVaX70Dd/rHVLIIxCCFEJdHDAr3H06HVGhFtlySxGjAVootXq2Fd6vqUaVkXRPfHzwemgdrIUE8vS7zeItEOJppqXrKN6+16vVomFIxVfg0yyTGu1W37nK3JwLSs/XVMc5iIWsHv0XlAnqH0/XI6/p7WIBrAfiyohnKGmkiFW9MTpgNYEG0216cl6/cK6rB5nbckwXsXRGOq03H650Svtl6z64ZNoQqboy4es/k/JpqhkdNhrRO99/3DI6hr1K0OWpVors5mqNecgoD+fDpUvu50ROMQBkQ72Me1EdEJ9pq1IaB+66zAwWeaw6ORnVcsHpe0x2zdjOy7jKxi1yDTaUyrdm956upVaHP9KdzD1zat3l41YWYG2AczN7fLBPef2RQ1bN9732nK9VHj7dKlC1D38OEDU57sP4E244ho/rl9Igu4L38er/rIiYgudurjIdnj9g4P8GZuH1pYXMQmZB/aP+yYcreP48V0UTO3JxyTtcXWxNRpxS8uoZ7maGLHMC9QPzEvMAaUbg+qkPyrSnKdasXtSQXZk4i+r9f9Zs/JNPV9XJjkRBsxIbmj2wPzcDr0LYdEH7xZs/PDexSEEELaWIC6lvNVick5b3qMv1erWS3HrEZyNeLJkTph6lEf41xM4kXqdSLJvuG9PLwufgghjIh+eN6071yr4+/PqOa2rSvJuYvXufkXMX8RqCLMlyGcZs3hpEJyXRfH2I5dSdiZ4oM1mekHicLr5pK5RbS4X3n5ZVO+tH4J2jzYuwl1vb7VBZ4VD6HNMy/aXLPWQH31KckrB+57xGyE+0uWoA7rdGrfb+CeMQQ+29rLVnf56aefgTab62tQt+L0eFvk/WpNEj/c9yuv3XpSJOSs2h/Ys0H3CPuUeVkc7D4yZeYJk5Jz47ktq8F7+dp1aPOt770Gdds71iuj0cRcZkZMbqZT++xpheSjbm8/6OJ4rV96Eeuu2HnUWEb95EoNdap97sS0fDOSXxVudnufmhBCKMk3NK9VzLywFhfwOa9stk352tYqtJmH0/GlQwghhBBCCCGEEEIIIcTHij4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEGicrTpCAvhBBCCCGEEEIIIYQQ4mNB/zkshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4g+jgshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4g+jgshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghG28qvgAA/Q1JREFUhBDiDKKPw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4g+jgshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4g6bwN9/YPP7BNHOO35nnqoiiCNhG5rkzKD3wGei9XTsoPbjMv5FZzURSFLc/IvUt8X1/n7/OX4a8jtw4hsN/zZfw99gyT3L5QXuC9r17YZA/xkfK//Q//PXyOK+umXG/i3Ds4wgH6wQ/fM+UHj7ahzXg0hbokzVwNmY8RPkNR5K4NXleSMUySBOrg94JrQyZIxFYJPAO5jq5vW87zHNrkM+xzfyc29/J8AnWFuxe7jq03/+zsunfffRfqPmq+9Wd/DnX+Wdm407FwdfGc0dDfn8V6hu/Defqd1c0T67MY+yBNcdub596Mwj37jLxLEUiMds3m/b3I3T8icfWDd0nOs5//zIe88sNz7iLuAec2Vk05m/WhzbMXV6Duky9cMOXPf+YT0OaN9zFG/xd/+E9NeWNjHdpcO7cGdfVKxZRnMxznjY0NqMvc/CuGGK+ee8E++8EE4+P7Dx9AXVKxe8uV81vQZnOZvN+1F0z51v1daPPnX/821Pn5/8z1a9DmcH8f6n784x+b8trqKrS5dGEJ6j7z6S+a8rWrPwdtnn8J3/mj5vf+8/8M6tY3bV/sH3WgzXH3GOqS2M6PaoYxbNg9groscXtVSXJvyD0w9vT7uN7Y/5EcHXftM/VxzkzHQ1O+fOEqtFlo1D7w59Y3cUyTrAp19x8+NOXjDq73LMf3G/Zsf+4eYP/OQgXqNs7Z55rkGH1j8pztFRu/3n7rDWjzn//efw51HzWz+ktY5x4/GpO8bDwmd7N1RYQxbFB2oa4e6qacJMt462YdqorU7eENnLPkEZAZ2UETd2bFIQ3FAHP9MHJ7AlmT0Ygc/txeEpXk3jHmDLPVlr1uiTwoOVtED3q2PMDzfhlhrAqly6UijNlpcRev+xj4n//mL0Fdo25j6ep5zCPu3MR9tZY0TDlOsA/HI1wDvZ7t10YN522/h7GmyO14v/TCc9DmuSuXoO7OTXsWOegNoM3tHTu2OyTWNRLcJ9qLdmzHBS4mFsvXFm0fpwnG0Z097PNG3T7D8mIb2jz9Asarr377O6ZckuNvkmCu1mw1TbnT6UCbP/zTn+DNPmL+T//+b0Dd48c7pry03IQ29QaOYeLiT72K8SGrYocl7mw3nWE8Gk1xPsQuPtQq+EwLTaybTm3eUKnhnFleXbAV7KMWGfxu3z773tEQ2hyTWH50bPOGyRDziI027lX1ul0TO50DaNM5xtjarNpYUZBvJD2Sqy232/Y5J3i++E/+m9egzqP/HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDDK35jDTBPVaZfNo5DKoViXTBHVqikylsZxDu7EkV86j00jbzCEVOU+/lES8mF03j17nvM/ws2rDdFgLp4VZzD6sqvPPFqaZXDrBZ6b/+Xj7EdTduHnL3idC7Zw0RU0fr5VXFmw+krr4g8dnOkWtnCixy9zrB4WA0sElG1My9vW608Uh181I7AhuHNiyTYg2mu+WGdE+mhJ9Tt+O6jWT7k1Tq1k0r8b3Rw2Lx/Nq/nrm0Rxm+sWof47aeUyH1ffhvFr18JykjX/OZA7tYlbH58cc+xubxzHZZiH+z7d3wnMS3UGvrx0C7p0l0So+CeopjmEa7POnCfZf32s5hhC6A1s3JFqOccC6S+ttU95aa0ObZZIqFUO7Bh91UHM1a+C6OXfFaiMfV/GZ3htbXbKDDuqiDYmW9fm21ZxcWmpBm2qG71Kt+D0CmoSEzLV8YuPqjMw9ph0P+Q/+XFhZaUPdxnmrUT3JTsf/N1QrDagbDEamPJ2Q+Uhiw+LCoikvNFHHsSTj2mrYvThOUaevJP8P4mPm4SHqjeY5zrW1sdWy6/cWoE3vyM7jxcVFaEMklUO3ZzX4jo5QF5O93+KC1cVEhc8QDh7dgLqq20taNRyXvUPUBdzbtv1SraP247kLF6Fuw83tmzWcPycB82PxmxpJc0PktWdDCNHUzXeyyKNAbhZsrItmeO9kgnWl1zvNyLuwZwBfFbIXu5wlIhLAEbt53T1Dn2hS50Rz2N+LBcgUF07s9DujlPTBhLzfzPmZkH2SY5+hnMPf5OPiuadfhLpHD++Z8hs/wliw/WQH6hp1p+VM8kOvLxwC5p/n1tG7YNRD3dDza7bd9S2iL/wQz6T/7I2fmnJ7qQ1tNttWA3g6QK3kehPjWOb0Ygd9zEm2d7Du1q0nprxI9q7NLdwXJpFdczfuoy7xTh81yx89sf0SE53btS3cq1J3Dl9cJFr4J0C9jnt20+WVKRFTr2XEX8Dt/2kF1yv1/3FBskr6dHEB97DhwOYIE3I2z2o49o0Fu3PXqhjvYzc/6EcEsp9VXMJRJ/ceEe+AzOXIxx3U8i0Dbgxp6r1J8LrJGOvaTZvLLC6hPno1JdrwpY3dtcqH+/Z2OjJrIYQQQgghhBBCCCGEEB8r+jgshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogzyNyawwyvu8N0Kee6LsbrmJZiCF6nlGlH0l/8F5T+gnn0drnG8Qffhz0n3I00mUcb88NqDs+rcenr2BjPpcN6OiSHQ840eUGPE98nIzqNzabVLRoQLa9KBXWLkthq0DCZt/EYn3PmhCCrVbx3nKBWmNeYZLrEsVtvTItoPEFtKi8rF0VkLRMBy8j1cVEwDd0PnleRF4wLIaQJ6iGhjtJ8urppavtuzuX2kTOfjvl8scjXsHA1Tyzi/ffB2r3zag77+/uxYdfFbMBI1Txa7qwP/Lqhz02eE/WaiVY302r1Gs7z7je+2SmJx3Wih1WrWb2xiOiy7o6wn1+/ZfXubu98H9qUsxHUHXVtny5WMRYlGV43cfFwqbUEbVoJ6qLu3b5vn4m8XxZbfbF4F38/GWBdq2nfZYHEwmYTteFGQ9t3iy3UHGzWcb8ZjfEZgHm0s8mc7ZP3Gznd3rJ5Oiby3t4B1FWd3mif9NX+AepUj5btWBSry9CmzHEvjlw/k/QgJBnOR6+VedTpYBsyFjOvHU/09ryW3nCImpQ5+ReVoyOr7+vzjBBCaC22oS5J7XyvE73mBpnbnX2racw8HzKSy02mNlY83kNdzOEUY3tvbK8bEM3Bk4D5ShQ+xyJrLmrhvApOFzjqkxxvSnTMS6vBOi1x7qXjDt5rYtdJ1ES/jygj7+embcmsLlwflAnR+idDGI1crjMmeRteFvxZF8shFFWSV9d8DkZ+j2jxB8iP59BBDiH4/y8ryDnppFSIu0SnvHdsY123g3q/9RoqlY/HA1NmuVmrhVq666tWO5jpwC418PcWGzZGvX/rJrR51NmHuqhq9/Y+03hdsmN25cJ5aDMmY90d2T4oSJuYeEhkTgt8QPbB/hj7peU0bCcJ/t4o4H5y8alz9vdGuC81F7HPS7cG5sptPgaGY9SybrrYxs5Z5zZR37rbt+80It8ZYnJej903OtgTQggZeYaRi1vTKeYtKdEvrtWddnqJ85h9M0DY+dM+U4X4VtRruJ/1hzbAJ2QtV6p4XdPpJ9e7JI/eRp3z4dCO1cbqOrTJyLPv79t7lXPrx1v0n8NCCCGEEEIIIYQQQghxBtHHYSGEEEIIIYQQQgghhDiD6OOwEEIIIYQQQgghhBBCnEH0cVgIIYQQQgghhBBCCCHOIHMb0jGjHW609sHXecFrZj7njX5CCCFy37LjOUyS/ocr/0XFELgh0Fxmb/7W5PepuZFvQ953HlOkD2ssx5jX3M7DjKgS359ETP4kiImxQz61QuP5BN8nJuL7Uydgn+c4hpMIRdOrzsxiNsU2kxGK4RfObKVCjK5qFTTh8AZtvS6K+NcbVki9ykwZJihePxrZvsuISDv7C1TqBO7LEkXT85wYHrh4EhG7i5SY7kQT259srrN5fFoM6DzcHM32DRsLahrqBihKibEiMdZKCnvhoD+ANoeHh1B3fGzrOodorDEc4r38mDWbaCy0uLhoyq3mIrRZWEDjsI2NTVOu19G0a0rmf1HYZ2IGiczIBYeP7T+szhlyFR82Zp+OvwvPcnzHWsMaaSxvXoc2ZChCXLXz4Y2bP4Y2B4/vQd10YI1rnjw6hjZrCzjX2svWWOLyMhrSME/NeGIrc7Lexl07QUbEVGVSwTHcndjY3th/Am0WltDYYjiw682btIYQQkyMLWLnpurLIYRQFqQObw6MhsQA1Zl0rG7hfncSLC+vQl1aaZtyi+Sd9QY+f8MZFEVkb2SuoT4+esO4ENB0J4QQ+n1rypQzI0yyEfr9JokwH5k489vBAON6m6wtf29vmhdCCAXZ+2dujkYR5jqVmMRxnzPM8N5NYrbljfoSYkrWn2Lf1UYudzwleUY0wxeI+26ujXBeRTUyH5fdHrqARjzRAOd/MrbzweduIYQQpjiuoevmCDM1X8ScyD97NCOD4U3TyXkmmpCz7tCOc0TWH4u1JeQDZP3V8V1gSbCJNcD1HRVu3ZBYzzMbl49XyTOdEHu721A3dnllNSOmhSmO0caq3duZaV17uQ11WWLvv/N4D5+JGH3vOMNRNkdyMk/bNfsMFWIsfti3+c6VzYvQ5gkxJe307HU1YlDbXsK67rHt83xKTO1znG/e8KuxgvtEpYljtbJijeLrXbw3O1/PnNnz9EPm1j9rkiqeRcrS7mEJNf4mpqpVu4eNc9wLGw38vcztj2w+jkiczl0Osrq2Am0qGTl3z+xzsbPQzMUs9s2Qnd9jd0arVYhhOPmG0HdmyGmFmLBGuAd4U172nMwAb+piQG+IOVDGnt2d8Y+P8TwzD6fjhCiEEEIIIYQQQgghhBDiY0Ufh4UQQgghhBBCCCGEEOIMoo/DQgghhBBCCCGEEEIIcQbRx2EhhBBCCCGEEEIIIYQ4g8xtSBciIiLuyjFxeiP6zCAIHSJicEAuTLz5Cbs5wRtzRMSMp0LEpafOTGNGjDq8LjcziqImdVAxj2lRCKW7MiJ9Xs5hFMgM+OY10/Mw00EvMj4jguInQZ8Ym4TSmslMp2jKwXo0cSYVzP8iFMSAwplZrHnjjhBCv49zrXPcNeXxUR/aFBW818wZh5XEyGXQs+9cEKH68RiNE/w8KskamRETDr8Ec2JIkBEDGPYM+HvErap0P8jmo28T8P28acFp4t49a7b18OFDaDMao2lA1ZkY1ogYfznFfh8OrND90TGabRyPu1A3GdrxmQ5xvFg/+xhdkhg2cvOjT0ytmi00qds6d8GUX3rpJWjz6qufgbqldtuUizmN5XIwsyRxgsTjNHVrl5hmsNj+gfc5IZIEDWFafny8GVAIYTxCk4WhMzosSpxXCTHMyac2zvRIn9Zq+AyLbWtSk6cYe3vJMv5e2xrA1ImxYmhZ07hhgUZezGxoOrVrZEJMko76uL9VnbHFcIKGT8MR1vkcbE4/xhDALAXneqWGcyOO7c06e2g6FC6j4eRHzauf/izUTbwRGckPvKlKCCEUYA6F65nlGt6gmaVu3kAzhBCW29Ykhj0T2/YiZ5BbFhhrx8+96Nrg72ckf/QPPyGmveyMULgJWJbEIY6YriXufBPFxNQ2wXxkOrV9xSJvTOJX6syW8hm+34lADKxC6tZ4Qt5ySnIut/fGq2jYWTbJEbTj8uoU42PEHEn33J5wgLlHWRLzw7Yz0SJ9UIzd+IzIeW3IFhw8AT4TNX9z7dh2XcO+A8NyNq1G7Bzr8njyTBx3tiDmUidFx+UDIYSwtGBzi2oL1zkzcas33BwhAXh/D/PfncfWbJmkeeHCpfNQN3PnxsEAz3pXLl6GulHPrrlxjnv2wBldHZF7V2vEPNLNyYUGmvKlJJRPG7Y/SUoeMvJ7o4mN06MZMan26zKEEHUP7O8RQ7o4JzmlO/fPaOLy8ZMHYgZetf1VFhgPH+90oC6t2XXOTL3ZLlaUH3wOTlLMrdfPufyXfB+YDjFOJ4kzCSV7L5p24uRj+YZfusybuyD5hjfqi0jesrNLDCcHdqz6Y1yT7eUFqKtX7HU5Oc+wo97UJZqt9ho2mgP957AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4g+jgshBBCCCGEEEIIIYQQZ5C5NYdzqmPodEGI4FzErnPtIqprS+7lhJfYvZMUXylO7HXHPdQ4efToEdStrVmtjoUF1AVJ3e8xjd6C6Kxgu/n0bUDpiH3eBz0/rgs8D15nmb0frXNaLznR8T0JhkPULZpOrAhStY4aP7Uq6h9mbl6lRDs7Jjoxl89vmPLv/s5vQ5uD3SdQ9//5O/+lKfeJVutwglqcZWmffUaWvdc5LHOmL4zj7LVSqU4q0Q4u3EyeTlELl8nY+XnM9FVB0zzgOmX64Uz31dehztHpwa9Dps989849qBsNrEZXpUTdpEqC/eV1IHsj0n9EU7bftQO78wh/j43FxQtWm219fR3a+PEZk3k16+P86N2yums/fesNaPOd174Hdb/92/+GKT/99NPQxmvXM5IENdDYWvI651GM78Lmtr8Xu/dJwDT49l3sGz7ZhTYsrlbdKy0Qvfdz51Hfr9ezumhDIop37hzO461rNh9YXsL5uJuhLt84b5tyhWjw1Sr2nYsF3D+zIcbVYdO26xa47+8d4Q/WnObfYIz92yf6fmnsdGehBdHPDCFAJhPhuxwPUcNur2uv22qeDl1AJigcBdtfzCMjJtqVpdO/jWPsh3n8IVgcIGEmpE4rkEWGkuSPZeL3YtSNXFiya4vOhZzk8e4p/P4dAtf881rFTBtxRvR954mHLM+FZ5jDo+M0E13CM46ftiXZc+IJCeTjyb+4HEKIWkTzsu71dkmfLhNdce8b8xg1Z0OH+HR47faUzIWBi4clWZNsm4f8dI5YGAJ6YlRJfkA0h+HuXvc8hBCmLIf1sYJcR86VXve7rMxvY/RRU1/AvXdt08Yj740TQgijPg7knYePTXm/j+esKolRvaGd8+0V9CCo1nEN7D2xOVA+xVh+PDiCulrd5injDua/kcshnxx1oE1CYmvTvV/MzvSkP1c2rab940P8vSlZAtORPavnBf5eJcM9x0kVh4LEqynRoh+4+FSr471PgiTF3NNr5Iccx6vXR3+ljUUbN1sLG9BmZx+/j/X6VieXfetLM3yG3PXzbID3rqXEb8jF4Eod/TzSzHss4DNNpqjv6z97jsnk6xzjugnOz8mvtRBCiEi8nbo9ICNxgnnVzFxMHpF3yYmHkOfSBdQmnwf957AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4g+jgshBBCCCGEEEIIIYQQZxB9HBZCCCGEEEIIIYQQQogzyPzq8SkKjXujHW9CFkIIxBMmJE57mflKMB+72ImdV7wBQQih20Wh+LffeceUv/71r0Ob999/H+q2trZMmZkNPfvss6Z87do1aLO8jCL03lhr4lXUAzcUAdFtZoBHjAP8dfMay83ThpmC+fd57fXXoM3Fv3bhA3/vZ81oiALeva4VSY8SFKKnfnpuvpcFGy80N7h4wRoXXb64Cm0aCRrn/dovvmrKjx4fQJsbd9C86dGeNeGYxbhukqTiyjiHmEmY13L35o8hcEO6xInXe339EEKYEEO1SsUagbBnYtYys9yOzbz+jElin71gAe0EYAY9Pj752BRCCNMpPv9oaI1bBr270GZ/B+vu3rplyrdubUObcY6i/aFq59riIs513+8hhLDUbtmKCNfbaGzvNS2IsVYPY0CW2mdKU1wj79++AXX/9d//e6b8W3/9X4c2zz/3PNQVznBmfsNQZ+ZKjJSyDM1y4C6nxDhpYRGNJmYzOz69Ls6PcoxmGysL1tglIunNED1cwhiMFPG6yiLO42pun3PzGJ9p8By+3zfdWorJmvxCa9GUL27jvZN9qArlORszuxEaNzUmONcmsX3nAfG5yEu8LoN5y8yVSK4BNbgB9IoW1N18Yk05GkvEze8EYDnQXDnXHPdmexzDxxAWU+bJ+9gzFTkmQHt71kh0bRUNGUdubcUxjnNGYv3UmcbN2ween6nxJus7kqPPg3+u0xKPQw3njDfdi4iJYlTD/TJMnVnliJlj4WXRio1ZZYeYLBMTw2jdxswyw2cq93EDiNyZICLGeZCgsgenQ+jWFjGtpvh7kXxvrlhLDJKiks1ZZyxNXqYk/0tWun6I0tPz/2bXrqL5bP/Y7h8Hu3iGWltZgzpvEDgZEXMoMh7+7MMMSLvEEG5xwe59zND98AANFxtNa8LnjWZDCGFv3yYOPhcNIYSEPOfaWtuUh8zw7OIm1B2M7fkzq5OcFWpC6B3ad05ifJdqhmZ+w6E9z5fknJCRb0eZi1e1CjHLPAkmmOM0Fp1xaJWY58VoyJhVbYzsjTCvPRoy80NbV5T4TBGpu3nrkW1DDMKfu4bfgSrObK5GTGRrbs+p1HAWFSRE9py56O4BzuNJTsx8K/a6IifjQkwM6y4GFOTe/QEapU5KlzuRM+n5VTQUzJ05bJV8u52H0xPJhRBCCCGEEEIIIYQQQnxs6OOwEEIIIYQQQgghhBBCnEH0cVgIIYQQQgghhBBCCCHOIPo4LIQQQgghhBBCCCGEEGeQuQ3p3n73Hai7cMEKSVeJyHdMtO8TZ0pBPKxCIKL5248emvK777wNbd577z2o63SsCcHS4iK0eeWVV6DOG+7dcgZMIYTw1ltvmXKjgQY0ly9fhrqnnnrKlH1fhhBCu92GOm82xAw3ihkKzHtzu3kN6bzRyjzGK6yuXkdx9JPgsIeGFF/7phVNT4kYPxN3LyMrWF9tLECbETH/mTlx9aKHzkL33kIDv6x335Q3Euz3bBMX0+pi25S3j/BdOhNndEXWZJSjuVeU2vmYEGH8KTFtKWInME/E1mNiUlS6B/O/H0IIETN3Kax4PPHOpGYRsXfP/HD+Mz9z2LqPnCFGQgyJKiQ+LTjzq3LtKrQ5dw5NYdbWrIlnmuGc3dlBg8SRMxpcWkLjhykx6ASzQ2KkkSX2nRcbOB97QzQTGY2tUQKbH0mG5g13Hj4w5X/0R/8I2tSbuN4uXbxkytRYjszt1PXB3DZ2bk2wOH4SPPvSy1B35Iyudh4/hjYXz6NpTKtl5/b9HYz1/R4aP/SPbV1rAcd5NMExfNKz82iY7UCbB32MYQ8Te12yir+3G9v5v3jnIbRZPMB5PNxsm3K5gmanyQLOq3rNGuCMiHlUwWYbxExmkDtPzkCMeogv1L0dG4dWlzAunQTz5EDz5k4fFn//ec3Y/HXskR48REPSd9/7qSl/7rNfgDY7Ox1T3ljfgjZrazhH/UMULCDPYefH+neefqFt6J774Tg1BnQe6hDnTMdIG5+XhRBCqNrjJcvLmD9bVLNxfJxiLOqP8RkWKu5cuYqGloEYoMaHzsDqEPeI0LOmqNEMTVJDhMfpyMdMEgsDMxB3B+fZCBPPqIOGSMm63afKKfm9grlr+9ye5Qcs/ttiSYysT4rjIzSbe3T/iSmnxOTs/OY5qHvmmj3Dp+Ts9XgH9/+llbarIaauxMSvcGetvjNNDyGEGTFa67mzf3MD14A3rGJLt7GA1w1djlymZM+eYU6yc2zPAGmBP1hJMAfyJtGdQ7x3nGDf+VtVavh7NWbanNv3GQ/xvHsSRMQMNkxtX2QV7Idz65gjPzmyYz+c4rzqkVgzmdi+qGTYpxPy3Wn30M6Z8QDjZq2F4xq55ZWT5xy7+dhsYmzfPIcmub2BvddxH88JKyvLUDd1JpS1Gsb7CTEzXXC5dUQ+IswyNNxbXrR5UaWKY9xexHU6dSakk8mHyzX0n8NCCCGEEEIIIYQQQghxBtHHYSGEEEIIIYQQQgghhDiD6OOwEEIIIYQQQgghhBBCnEHm1hz+v/zH/zHU/c2/8TdM+Utf+hK08Vq3IYQwi60GRka0vG4Q7eB/8t//d6YcEc2xK1euQN0LL75oys0a6gey5/R3Z5px/b7Vp9rdRY1NplX8zjtWw7nVQu2Q1VXUYnv66adN+dq1p6DNyipqzSROq5JpeuasD5xO2LxaxZHTInzppZegzUkwGqPmzULVihTVKqh/lFaITm/Pzoch0dzJC5zbd963Wq133kGNq917t6EuHjp9RZSpCdcubEDdX/nSr5jy7//5W9Dmx+85XSjSB6MRagNV3Su3ltrQptPpQF3kdNZSolU8y8lcm9m6KdFBjokmXs/pPJcz7DymzANrfk4dx48ar8kUQkCxSNKmZLqXTm+OyQ7Wm6gLfP2ZT5pyq9WGNj/60Xeh7vG21U/NpzgWTPPUx1pGs2HXaUx0l6tEb+zQ6c4WMW6NU7K+pzPbd2++i2vrv/4Hfw/q/q2/9e+Y8qULl6BNTMTgQK+f6GTPox1/WjjO8bmOu3YsNlbb0IZp9CduXPf6OK+KgDqNF9etnmDhg1oIYbuLe2N3Y8WU31/CmNkZoZb6yuJ5W1FFrbRbw54p956/CG02Mox93dzOj40+rqOki3N0a9neqyhw3Uwn2C+R00ErSdApiw8Wai/InF1rr0Ddhcs2/2kvLn3gvT8O6PryudPP8Pfm1ROeB6+1zzRl79zBHPbmjTdtBckfG3U7hhc3cR5PJ2QPT/wzQZMwj+LvvPkq7BNs72Rz292Ljcs8+sI/y/H8H0VMBEh937A2zDgG9l58x5z2jW03rWN8/OcXcN1/5tDGzKdmeMaZZPh7sxUXtxcxZoeH9v0GB6gvGwfMT2rONyMiusRlIL4ZwT5TPiH6wijDGRLvKUH0Q0NJtFS9fi0ZFjZDy8jPf9LohDg8xrPe6qbVPO92sBNv3b4JdVcu2D372cuYr106vwl1u0dWz3R3B/1lnnQOoa7uzl/eTySEEFZWcH9caFnfmzTF+XbpvH327qAHbWZkz+72bV/FRHO48+AJ1MVODzcnc7JsYvxInYdVXOIamJGcxHdVlmEfFKQ/B117vi3y0/G/k2WKZ+MysX3TG+EYHg8x/z0Y2b6YkD7NiU75zJ2zZyQ+TMl5fWnZfo8o2njd9iHJUZ2WNNPynYztM8VEl/tJF/tgChrO+L47++hzUnHT6Pw5/D5Xkhxo4vWhyYebrXNtqLv81DV7HYmtj7e3oS6r2PmSUVO3D+Z0zH4hhBBCCCGEEEIIIYQQHyv6OCyEEEIIIYQQQgghhBBnEH0cFkIIIYQQQgghhBBCiDOIPg4LIYQQQgghhBBCCCHEGWRuQ7rtBw+h7r/9/d835ay5AG1eeP5FqKs6vX8vah9CCK02GrS99OJzpnz5+nVos0hMTHInlM3sUarkGVIn+n28hyLVsTPXa7YuQ5uKV7IOIUzHVgi8f4xi2r3+EdT9+Z/+kSkvr6H53NVrz0DduXNWUH+VXNdqYp97d6q8ZMZGqJQdOyM2Jqh/EvxLv4imifXUvmOrhQYYkxxnzVe/87YpH3ZQaJxo9ofxsR3X1776NWizUMW/29SzRXufAsXkty5tQV1t0T7Euau4Tn/6vhU2TyI04EuJUU1eOHOLEZpyZMQYxJvGlcSYKqYGibZd7H8/hFAy0w+3vJlRWaWCxiBTd/sk+VnaCn14mIGNN3WiJpvUAMb2RcIc6ahvjG23sY7GipubOB9HQztHjjoY5/Ic11LmDMfmMfqJExzTcojzsSjsvYoSf39GYkBwxoohwb574403oO4P//APTPnf+p1/G9qskL1sMrHPkKVoNjCPudFpMUAagTlECNVFG+eKMRq93Xq4h/dyizUja3WjibFhxRnJHVWxT/emB1C3fdsaiy4t4P651OxAXXLOzvfG1eegTWXZ5hG9Nq6jnwY0sjk3tvGxneFeducNNNw56tu9ZNOb5oUQEmJSl0KsZWaIcxh5QYsQhodo+nR/ZOfLuaVXyZUfP3Q9Rb4NNmEhbJ61OU/sm6cN/z0yhsQ0bqNtTcHKHA1rGw0bfx88fgRtzl9Cc6eFBWeAOqexHIJ9SbuX7Xnwg1hVOJNSOsbkQm8CGJ+S/9Mp/X4WsOu92XQIge57pTNUjYjJa0pylOnAzqO3h5jnfrskhlkur2gQY6VmDZ9zwe/rJBctW9bkpz5BM6KjAZqR7xf2merEfG6RzL3jyK63xzEaTl0d47xK9py50xjzmIiegP29yHiyBeDHj+VIJ8TtB2j+Vs06przSXoQ2Sy00AEsiOyeqzLA4xv45PrJ7tDf2CiGEq1cw/l1w5/UGMWXc2cOcpOJM3B48fABtYmcoef4c/v7uIfZdp2fnss9FQwj040pzZvOrhTrmSTuHx1BXyex1i35PCCEEkuP56BSXaDCZRljXrNu1OR5j3DkJpuQsXlZtHfGsC/fudaCuSGwfxhiOQknOPoUz3mbmc8RXHPaTGTH1LonBaeI+ErJcPqrY64ZDzD96YzS782afxJs+DPv4Pc5/xhuN8NzaXsK4ee2aPRcvtzB2tBdxbsexfbDV1Q1okyZ4r5u37Jo/7KAx5zycjoxECCGEEEIIIYQQQgghxMeKPg4LIYQQQgghhBBCCCHEGUQfh4UQQgghhBBCCCGEEOIMoo/DQgghhBBCCCGEEEIIcQaZ25Duk1fQEOXg2Aqi/93/4v8Nbb70hV+Cur/6r/4rplwhosrLFTShubhqDXqadVTh7vZRgHowtCLY0wqKYi9laLJQr1vR7/09NMGp1Z2YNhHXbi2goHh3Yp+zJIYfsb93CKHmhPGLKYpNP3x4H+refe89U85I/26sb0LdNWf6t37xIrSJyN8YUic8nsxpjvJRU0vxWWtu7IsZCpvnxDjDT9uEmZwRE4fVhp0Pg30U/2+20TRu6MTjmfVDr4/PftCxYv8jMtf6Tsw9GuK7jCdoHjV1gu/HRyjSniY4j2OnhD8h4v/MqLJ08585GVG/FGeYwoxXioKI5YPJG1GvPwGYaZF/VvY+80HMSOjytZUpMeloL6Gp2o4z1yiJYRUzDGw2mx/Y5ujIzvUHTzBm94a4R8SpffYsxXdptYi5nZuAOZmQzMjuB9//gSnXK2g48jf/zb8BdUtL1kCFGfclZL35+cL67iR4so1Gt1UXWJeaaNZAPONC7Mz5UhLrqyQ2DEbW7Oegh+Yo187jGF66bo1V1i/j/rm6tA51/X07Jx/tfwPadGbWxGKhTswea7hulnI7t997iAah565fweesLJvy7AhjPZlWwYdoZj43V8wk49LZw7mxu/+WKT/7LJrpnAzU5cyWiPEUN6n74FyJxf95jOzYnuCvY/Hj4oULUPfwxo9MeTzG3OPBY2sqeO3ZT0Obp55HQ0ZYbcyAFWoCbFRsPjJDtdLld7wvyXWuzHKPku3V/sridBh5RVViflTafJHNTpZPebPWSR/zzpsj3IvfdL/w36f4i9vHuD+/eWBNlX9EjGc/XUMzrOdiZ37EnJXcMxV1/P3dZAnq7hd2LZEQGpYKvNeTqc2Hp8TI7DeIIfUzLkfnuzwbweIDW9BaZxBdTk5HfhxCCDmJdfWKnd+HPTT66x4z50k7chk5mywsYg73pS9+zpTX19FU6oXnPwV133/t+6Z86/0b0Kazh4a0TWcqv/sITRJXV62ZYr+DOcLRHuZAraq9994h5tb1Kn6TiZwx5KXLuJfEGRrE7e7Zc3FMAv6YnNXT0s76bISxqVrH8/Ws6s4z5JvQSZCTc7f3cb72NM6rB3fQsDB2pomVFN+x20Ezttx9H2iQca56x7YQwsSvL7Imt1bR3DNyuftwipGz17PfvkZknL35YgghVFL77Suf8yjkTcp7E3yX4T6upfuP7Tp9+kufgjZphPv/2N3/4Aj3ypu37mHd+3dNeTTEvWMeTscJUQghhBBCCCGEEEIIIcTHij4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDDK35vCzK6iv0a1bnZibj1Dr9s/+6L+Bupeev2zKX/ziF6DNcIDfrSdOS+b4CPWCaq1lqMsS++zNGDU4UGkrhLt3bpvy0WEH2mwkVgMyRVnKsNRuQ13f6brEKdHceYKaMcuR1V9MC9QOXl5Zg7oo6phyrYq/9+DOTaibOh251jLqalWrqOMFenunROMylKjt4rUAY6KdWq1hXep1kgqcV1GJGlwNp/NTmeLaGvdxbh9OO6Y8Iwpm3Z+iPtHPXX7BlN976zG0KWZ24nrNnxBCKBKcM9Ng3zkhumTVDOdomti60RTHJSdalWnVXjcj45kQnaGa05AdlkyxEOtmTouwIGN8WphHq5JqMHodczJnQ8E0BW2RrfDxGPtrMLDaSb6PQ+CauL6u30d9p+Ou1UqrVHHuXVpDHVj/dvmUjDPRyxqMrO7fUR814DOyKUzdD37lK1+BNkyr+G/9zt805ZUV3O9mM3xOryM6z1z5ONh/grEoccKd+fIitNnaQI21RtX282CE/Xc8+uAYXVnA66qXcI87umCzhqM2rpHtKmriPfOyzX8+l+H79TpOh37wANq0MtTz/qPXbA729hPUpbz6uZ+Duqev2T1i/6fvQZsn97DvCrfqmaZtnmOdD0NM3S+JMA7lzmMhZSKeJ4DXtA8hhMLFTLbmPqx28Dyw+7C4ilr1+JxbW6gTmTp92h/+5Id43aVrpvzic89AmyTC3Mpv6+xd+NDbZ2c62Sw/iLJ5xorlKDbfKkjsZZ4cEZ3xpwCi/QkC0Cx3YvuJ1/bvYCz62qADdX+vbfUsZ+R8URCt2J1j63fx3hj34h9EGMMuNKxWbI0I0/u9eEw0gEMD53Fesee1KCITknTdZGzv1SIeIIMB7i1/Nbf5zwsR6uAWMZnbbvySef9vzGtNk/31pFhbx9xo6LRTO8eorTvq4Rlqd8fOrUtbeMb+9d/8V6Du8194xZR3dnegzZtkr/3+920sPdhH7eCVJp7F/blxnZzX24tWb3eX3HsywPW12rTv3Kqg7uxqG9fq0HneTLqoabtcQV+Ju/t3TLkMmA8sbuKXG6/9HM9wzR35/CqEMJzY+FRr4to5CZoxxs3MiQ53tnG80hzjQxFsfJ8OsU/HffQNyl0u3SDa9G3irTUd27FmZ5oFnEYhyVwuT856cWFjVC3F32dS/v6bVrWOZ8RKDR+q07X94n1qQgih38e947Uf3DLlhQauyaevYn61u2u1ih8/fhPaPN7Gc4E/q7ebqK89D6fki50QQgghhBBCCCGEEEKIjxN9HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDDK3IV1SoEj1ct0KQL9wvg1t3t1Dwfftd35iyocXt6DNjYcPoe57N6xwe87MH4j5z3hsxbuXChTvXl9Ckej6+nVTbjRRNH0ytfdmNj8xc8Vwxi0NYhBXGeK71DMrkl7EeF0cEVMJZzKyRMTWj8ifCh7cvmHK93fQQKi1hAZP66ubptxsoFj49Svn8QdPgNL9jaQkphFpBftrYcEZHjxCs4FKggLlCw07Plu1VWiTVfAZHu3tmfLuLjFTIDPwO1/9vikfPESjjoYTVy8SFKoviVFfntu6WoJtMjKvvNlQnZiAjLxrVwghzWy/xCUxLvMmGSGEWW5/jxnzeGPCEELInDA+8Vs6Nfh3omZHzAjHVcUJvmQMrjQhxM4SaPvJI2jzkzd+BHXHHTuPC2JIx4yt/Psw06CFlhXfXyImigWZH6X7vRExvwhkfkTu2RPyLtUKPkNZteskJc5a337t21DXHdm962//O/8utDm/hfvpbGKNYkryLifB6vI5qPNjUaujocK0RMOIZs3G6JUFEosiYirYs/NhuLyN975MYtGKNbLolmia0S3Q3ObWxD7DLjHIXT9n95aNJprP9bp7ULc6sv3y7Abuu50ZPucDZziyRK5rbRHnEG8cTBIgFjJn/jJiclWtYP7z1FVr5rf76B65++nAx2NmvMlimDePnNegbh6TSX/vEEKYevPNCO8zyTEeDp2JbLWGc6Zes2ukSlLhjLzfzE2klDw3N9ez86jfR/OjnUM0eu46k6TxaARt4hTH78IFm+cuL6NRZjFjxoA2NpXMOedEIHMt9euerGhiPFsc2Tyzeoxxp5ZjP3edgVRCzIiGB4dQNxtb46YK2VPzDOueOPPlhMyrkRufwRDNnpoxxqumM9rK5pzHUWbnR5fEwq+RdTo4tvnB75J5tRVwrPxTLRCTSGZECLOFGPKeFOMxGnkdHdnvGMx4uEXOxlV3rjkmpmo33nsf6o6PrPHZvQf3oc2bb9yAusTlrVevXsE2BcbknR1rLtdoYu4Zxfa6yRjfJSNngMnYrsPrT12FNu1lNKSrObPWwQGu51mOa+C5K/b+d3buQJtmE818B3077k2Sf5MlFyJnVjmdnA5zxfMXMffrj+w66w4wz0wbaLQ2mNqYUSkxHi1kuPbH7rxweIDfHvYe4/lvwZl/5yRG3r6N35QuXbD7aDPDWJe7d5mQQWVny/HYzv8p+YaQpCzW2b5KibFtQe41nNr590d/+jq0WV95C+r8d6KlFs7jWgXHKnV9vLyI3zbn4XScEIUQQgghhBBCCCGEEEJ8rOjjsBBCCCGEEEIIIYQQQpxB9HFYCCGEEEIIIYQQQgghziD6OCyEEEIIIYQQQgghhBBnkLkN6Z5MUKC5Vrdiz2WE4s9PLaJJxeT9m6b8zb3fhzbfun0b6t44tkYSZYEi1d7MJoQQCmdMUBmhmcDGEgqpf+6XrZnAxiIRd3fmRgkx5GLmSmlsr2Ni3iFFc4ikac15hsQoKp9hv3gjqv4RGgzeu4nC+IOJ7av7HTTzKxM06qvW7HPGxAji3/jXfg3qPnKI2VzpTP3iDOdCEYjJn6tLYhyvehXnw+KiNTxYXkQDhDglJhXO8CVN9qFNnuDYH+1Zc8dWhiL+i4mdR5U6vm9nivc+9MaKRCC9WqKwfxpZ04CiRgzpiElB4Zr1hijEHyXYnyP3nJG/UQghIeaBM+ecFEcoqP8/JUriGBV5U4IZMWQkJgWPHt4x5ddf+wa0OTp8AnV5bufDLMdnYiYtPo4yo6bYmQQMiXHM0REacKTOkLFCTCy8mUkI2J/TKZqgZGRNTCbWiCcnHj+1Ou6dr732XVN+vI3mab/7O78LdT/3+c/bCrJ3ngT/9t/6Dz6wTZKQv2GTx4+8QQQxv6qkeOHQeSK90/0qtFk8vwt1s6aLmRka52VVjBctZz5bCcSgpbTzaJLinrQ/w/h//jn7DEcJGgQ9fBMNMaaPbG61snIR2tRWcZ9fcH1QEpOckpjyRc7UqtvDvIIZflRr1hzr5o03oc1pwccnH2NYmxAwzjGjObp6XSUzwDs4QNPcVsvOrYUFzA8ODjtQt71j51+tgfNj0Lfj+r1vfxPa/NpvoOHOYGjn/0NiUL27i2ty2+XR9+7fhDa7O3idN6SbzZghKTOku2DKv/xLmNP+3Bd+Eeqqfk8gueNJUIxxrQZnKhgyNDpidf5sFBKMhdf3cOM7d2Tj4aMG9rs3WA0hhMj1oY8xIYQwI2ejidsAqv59AxqvzciaHJIzcpLZuhkxg2NxLnF1xNMw5MTE/Nuur2oDNPz7RRKPr7r7L5Tk00DEDLqcCTLJf04KZkjnDTm94XQIIaTErLfixqNCzpFf+QrmDVevWAPVyQzH4+q1y1h35bopr6+tQZtxrwN1Pm3ukX3VT91r1y5Bmz1i+Fi4sT48xnvfvYs58tNXbC7x4gtomDwi7/J80z5X9jb2+Z1tNDMb+oSOnC0rxPC9Xrf7YMF32Y+dy5dw7HcPOqbcI+vc76EhhDA8sms/JjnrxkWcj91j+3v1Ksa6PRLLx6WNUVkT89g9YlAYHtk9+vLFVWgyyn2+j+u238c5Wo3sM03JtwBmzOr9OPvHmFuPhljn87lWFfOkew/RXHqxYd9v7WU0pVzbWIa62JniXb+M4zkP+s9hIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4gc2sOf+UH70Bde8lqIraI7lRWRY2R93atDuU4R+2k5pWnoe7c+jlT3ruHOmTFFHWRcqenOiIaI70x6lr9p//p/92Uf/mLn4Y2/7Nf/SX7Wzn+fkxkvLLI6tm0M9S3GTD9yobtq1kfNU5m5BmKqX2/PEdNtc1F1Jq7/8TqwUREaysn2jxdp71SovTniVASragQ23nbOcJ+v3H3FtQ9fHRsygnRqqoSbaPU6QknVbyuS8Z1OLLaNRcvXYA2SQ11f45LO/ZrE1yTNTeuUzI/7h3iRF52OoNLLdT7XWkQ7cjYaQ6X2AfTAsNTHqxG2OMd1Mba7uL63j+287gscb1nRBe7KGy7UyINSGHa5nPhli/TTX+yfR/qvvedr5nyg/u4RnISw3wImeY4FtUq7iVTF9uZntrSUtuUxxO89+Ehzhmv/dlo4DxmmsMgGUrCS79/DHUTF48HI6LVR/50W3Oaq9tEc+33fu/3oO6u0/D/zX/lr0CbTfy5j5zjI9RA9fMxJlpiTKs1cvGX7UtJivOh07d6bYPqAbRppySvcLdaWdmANvU6arpVXaxbruO+26jZDTMPD6BNEqNWW++obcrFDNfycICaq+WSffashdcd9YnO3MBqpV2q4rjkJdMhtvdi+02F6PVHXnec6IqeBPNoB7M2GEACvFFB9G8zoo+furobt9BD4uEjjBef/8LPm/KExOwf/fhHUOd1iK9fRy3JhtNv/elPfghtHhHd9L0Dq2d8584daNMnOZLPvyOiE8w0zH1cZW3KAu911/XxPzzEWL+1gXnayy9/xpSHE3yXk6AkGqyzno2PSU40ImvEa8V5TURrqKH/6Sr2zf/xiY2/r5Nl89UKrokf5lY3ekxiStRHv4HMaYFPyBnSx6tqHfODKckp+2OnZ1wyjVtMGire44HIn7LIN3NeCV+bYc5+f4RX/qarOhfhD9ZIjl7E9jmj8nRrDnsvCxaTR2M8rDYXl0z5C5/9PLQ52kUPgNjdf7G6BG0uX8Y1sLdv4/SD+5h/FyRmrK1Y/fbBANtUnFZ1PiH7S4zztOJ8hPZ2MP9+/8ZdqHvmKauVeunaeWiTxpg7hYr9vW/95D1octTB91tesfnU+mob2hw6zd4QQpi6s0K1gfHqJFhbwu9j9YqdR0ddXJtpgXF6NLBnn/4M10irjX4TVzbaprx7Fz2yugPsLx//AvFyiVOMfxOXXA+HGFsfP7G5Z1rD319cxG8d/b77FkDOiKUXGA4BRIenU+JLQKJyEpeuBcbWNKtBXW9g49BxD8fqc599Dupqrj8vb10lz/nB6D+HhRBCCCGEEEIIIYQQ4gyij8NCCCGEEEIIIYQQQghxBtHHYSGEEEIIIYQQQgghhDiD6OOwEEIIIYQQQgghhBBCnEHmNqSLCxQoHx9bIefNyyi2vnn9M1C3//hdU+7voXD21iUUxU5qVly6QgykyiEK8Fec4cAsQdHomHwmf/uGfc79IzSbGA+tSPRySswuiOi9Nz1IchTBX1pahroyc4LeE2KSxEwB3CvHCfZdMsC+a2TWeCEm7nqzQMy9KtaIpMrMWE6AMkajq+09K6x/694TaHN4jH2apFYAvUrMqLIqipbHqe2LhFwYTbC/ktQb3KBxxsULuG66hR2z7GgEbaZO7Jz4sYQXn0EjgUtXnzLlIsd750NcN8XY9vmsZO+LIu0hsfP/4kYTmvz0NhpDHDnjq2mCwvgVYh7lnysusM9PK9QAieBjX6+7B21+8P1vQt3t2zY+dvbRyCvM8BnOnbfGRU+e3MHryLMvLdn9hRmODJ0BR858BZghgIuHFWKIt7CAxmFHx9bgYThEg4yUmPxk1ZprgzHgqEOM85xRSCVFE4ZuF9fb3/27f9eU93bRlOw/+vSrUPdRU8yY+Y8dn5IY4YCrYQghirzxAzGtI/PxqNMx5UEL8xHitxTi2O57sz5xXY0wplRSF3tGuH96g840QWONy6vENDFZNeXBAE1jnpyDqjBw62SjhePSP8A5uj2z62TWwzkbE9MTb8oRk7FKY2JI54Z9NsW+Owk+rBnojMzjmYt9NWLMNxtg3vfWOz815bv30Bzo1c99GeqqFRuLuiM07ao2MR5+6cu/YMqbGzgfdx5b45r9J7hHvPkGmtQd9ewzzGbEXJLMj1rDrreE7CNxgvfyZrSVCt47IcZhRWHzu2od2/T6OFbehGc6YOY2Hz8x2fdmLl6Vu7i/hAcdqCrcPBpPMa5WWhjXPr9s99lPJpgH/rUY877/dmjH9f9RoPni4QRjdDaxsbaS4hmn7szWl86tQ5veCPf+UcfOd5YLjCcYH6fOWJHNvZwkN5lL3PMG5rlvFsSAbGyf/RMB8/jrgeTjqXuf8vTkx+MxjmOzaWNUkxkPk7zSmyF3DnFN12t4r4EzzTw4wrNJIOe4xUUbx5pN7PsZMSVdXLTfDHZ2Md7u3LL7gjexCyGE5WWc3/VG25Q/9QoakF557hmo+4VffMWUr15A87nHT7A/v/aN75vy7Vtoyru4gN9I2ks2pjSauAaOjnA99Z0BWOeYxLkTYLGJOX7V7VcsP10gRvdjZ6L27jbJv9nZq23n41rtCrTpHmOueewMsyu4REI8xX72oW00IXm0O0MddvH3SYoQajUbs5oL+FCHB+SbhYvJFWKkF0icnuQ2DvX6+JwlieWp+0Zxj+yxjx/junn52Wv29zGUz4X+c1gIIYQQQgghhBBCCCHOIPo4LIQQQgghhBBCCCGEEGcQfRwWQgghhBBCCCGEEEKIM8jcmsMrRPOmvWB1n86trEGbxTrqeZROF2c2Rk2VnYeoe9mdPjTlnGgK5T3UlKo6zeGVjTa0abdRu+YLX/i8KZ8/jxo75cj+3mINNbSYzu3B2Oq67B4TLaz2JtTVnMbTmEjd9buo7VWUVptvSjQuHxCNy5XzF0z56gJqvzzc7UDd2orVFVqonA7N4Yc7+I63ncbwaIp9U2+iLlPp/raSxqhlV63hHA2x1a6JiHbU8lobL3NTFLQrA9eSrsZ27bZxiobD3D7D+voFaHP+0iWoW3AxYDTo4L33UIttktq4gD0XQp3EjkluNZPyKWomXdvC9bZ3YO/1/mPUFRtNcTHlTuswLT+ctuTPmpJqrjotazIXIiKunrref//2e9Dm3oMbUHfo9HbjiGwnBY79Qs0+Z3sBJ+TdB/egbnnFag5HJeqNDrpWg6nZbuPvL+F+EwXbV90e6m4OiKbsyqrVZovIn1sPDlH3zU8jEjpCQsZ4MrYCUnmO+oGVDOd/5B7sH//JH0Ob/wgf4SOn00ENPj+3/bP/RR25mevECtOBTbBvHh/Y+D8Yo0hXo0ANvvMrtl2PjNdhfwfqpu7h0wXc51OnHVmN0c+h3UTx4EbFxuO7vfvQpjfpQN2jRzbfuriB+vL1Gq6bqeurJ2Q+LjUwDlXqdhzIUqaDXKv58WM7x8dPmmLsw3lMJu0cdfsHqA/+k9e/BXW9IxtnXvrUp6HN1uWnoS4v7PjUsza0+Y2/8lehrhrZ9TUh/hd/+k/+yJQjIgK4vIy/lzmd5dEI86iywLhQrdg5WuY4sRKiOVxxv9ckOo9M99h7h1y9jv17+eo1qCvcBsD0808CNh3TmsszLxHR8otYF7lzSLSNXh7hCPfG8ZGNWWmKD3V+AePhf+DOHItHGOv/r5NHUHfoNNF9jhlCCM2GjauVJmoehyHuGyPnSZNVMGdn21TXnW19nA0hhAWil1ur23mbkHw1jvC6d0r7zt+a4Hq7EpG8yce9GdmroebjoVrF5/XanrMhrruIxJWB82Tpdom3xCKObUjseT0h+cdoiGN05bI9Zz91DdfXcgNzkoVFm0u88PxL0ObRgzumXMkwJh/3sF9qC/b7TnOxjb/fJnMksjH4uz/As8Sf/fNvQN1bb9lzSL2O36DI0RnW3COi8Tom89vrfLOz5UkwJj4ZrUX7PaK10IY2/T7q5m71bBx5RHxilms4/48PbQ5SJWnX09cwZ5yMbU7w9DPoifSgjvNv131TSskcTV0sjROMkZ0j/BbWcJrD7Tbmp+0l9JcZj+z92XkQzL1CCLnzxRiNcX+hfgYutz0gXlH372NuuLXSNuXOh/RJ0n8OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPMbUi30EAF6mbDCmVnGTEQCSj83XKmIqvPXYc2lTaav/VzK8icxSjizAwovIlPYxFF07NKBnWbm1bcfWkZRaofb1vjpIUcu/S1d38MdV1nePGJp1+GNrf30TytV9q6jQ0UpS/HKJR95bIVtK8uoylBt/M81D113Y7NPjGtG7/+BtRNnej2t376NrQ5Cd6/jYYUXh+83sJxLiJiNle187heRUOCRgXHYjDYNuXREOdsrYWGF7WWnTPFFK8bj/D3hk58f4Hce/maXW/r55+CNlkNDQEODq2RUSXBvzeVJdY1mtaELCZGacxsa9C3AvPjERpDNFK81/NOLH/nCI2aBjMUbvcGLbPZ6TCkm81w7KnhkSMlbfKZ7eeDfTQJK2bE/HNijR+WFzagTVTFsfBztErMMck0AmOa81to5HXn/ZumXJAuqRNTzcj9nbRRxxjQPepA3eGB7avLV69CmzTFveXu3bumzAyJtrZwDzw8sgYPA3JdQQwIStcRETFAOAmaJBaFuYy8iPGNiyG1QHKBCMei+8jO/8c7aGDy4C4xrVi0e30Lw2NorqPRbeyePSaWPaXzrZlV8PeHxGzIm4u1Etznq1Psu70DG1ff30Ezk+vn0ThyGttnyBur0GZtE3OUxbo10ujcug1tQkw6NLJ7LDPmPDW4YWUGofT53bgeddEcc3EVY+2nPmMNlJvL2O/dCe6p9Zqdx8UE101GTC5jlxM1Mlxbr37mS6Z8fMxMVdCgKHLmVymJVxPyLovOODshaysl+UGjYd/PG+KFEMLhMcaFRs3uE6986vPQZvM8Gvn2hjZvaTKDs5OA5BWli78xc4Eihoxh1ZrGZStoIheGmFfM9uyeWj55DG1Ge2huFy/avfBvXkQjwIM9HPvf6z2wv0+MqBIXs/tHaL54RPKDUW7fr05MjxeJIW+vZ+PxdILzv0bOG5nLY0oy/5Mq1g1rdp/42hTH5ZehJoQL7gxe4pI8MdIGjvWk6wx9ial9IOcVnx92+3juiEmM2nR7X5Tg+I+HeK/bbj/0uUYIISw1cD3duvWuKfeIcd6zz9qzXUbMvt6/iefkRts7ouP73rnzAOq2nRHl6z98DdrsH6MxWrVhnyshxpSsbuLGdDDAPqjUcK+qOIOzRuN0xOQbdzHWra9aQ7omGPWGkGXEQNgZMmY57sfVMeZ+k2Bj4r3HOF5LC5j7XfTnIXLOby2vQN0ot+2mOa7TsfuuV6vheLHsMJ/Y6w73OvhMixiT221bV8zw7NU5xu8v07Ht8zjGuTcjgbN0+VWN5Fcbm9jndWcAffPdW9BmHvSfw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4g8xvStbFpXLWCycMZijFHO2g0UiTW9GB7B4Wd947fhbrICYYzE4eYmNRVKvbZF8YoNt1ooHj3xBl5PXqEIu0/ev11U/5aSky7RiiaHjXbplydPQdt3nj3faiLI9t3ywv4vi9cQ6Oml5+1ddUW/l3g5774aairVq0IdquFgvrvvIfPcPuxFTUfh1Ni5BXh+NSck1BrqQ1tekTUvupE4Bt1NP/pHaG5V+nMHUcDFIWPiMFHpWb7OSa+IENyr9HQri923fLGFVMeEKO3lJjnTJwBZKOO6ygQo4ZK1RnHEKH6EPBdUudUxkwAx8QsYnPJmpBdXENTsjs7eF3k5n9BDCVOgqL4cM9BjezcuFaqKHzfbuG45s5sa38PDXtqDVxvg7E1N5hNcJwXW7iWaqmdR0vEzOycM+g87OO9FxrMAMY+e0Yc8a4/dRHq9pxp6ONt3CNWVtfwOc9Zg9Dt7W1ow8b44gVrrHh0jOYRvW4f6nJnRJGmp8OQLktx/c7jMRYxQ7pg3ymK0eRhSPr0yPXNOMb5cfM+xoatLTv/2sv44GWJRhpJxc7tguQMg9jHbBzToz4aldVD25SvtDDO1bwDawhh7IyLHh7j7yXpHtRlsb1/J8bco3zhs1DXuHDHlI+30cgmynGsTqsB3WiEcWbqjK1qxNB1TMyv8tLG6HNb56HN1vkLH/hMwzEzLcU+Hc7sWMfExXMWMF6M3FjEJe4bq2vWjO3a9WegzdEhxsyRM2MpifluSmJA5pKbJTL//XkghBAqVft+zOhoMMFzSqVh98DzF/H9CnLMKmMbFz7YSvZjghjuxt4sh+QQgZgtRokzSUow1kd1XBPxRTu3y02MKdk2mjTlj+08qlcwVvw6MZr9k759rlskxyvdAFV7mOs8M8J+2XbrZkL6NyN95z3/GiSPYibq/u4RuTc7is3cufltsrbeLzFWXXTmdiWJASdFmRGzQze2KVmbowGu89j9H90uMW1uk3Nj5mJNs4lroHOwA3Xe6HKW4/hHVTQhOz62++jmJpoaX3vGmszvddBcMa6j8fz5KzaW/+AnP4A2f/THfwp1By5HHk0wb2ku4rt4M8XRkOUkUBX8/zxOJmSPLXCttpxR9ZI34Dsh3nrrJtRFwdYxU8ELFzBu7h10TDkmsW5KDOmCm8c1EreZ/duiM3Y7InHz3jbmlaORfa4qMZEtCvt7SYZt4hj7Bd6YmMGNp1jnu3iTmMHlY7J3FPZeMxKAR2SO1ip2TVxYw3PJpS18hl7XruedQ9wr50H/OSyEEEIIIYQQQgghhBBnEH0cFkIIIYQQQgghhBBCiDOIPg4LIYQQQgghhBBCCCHEGWRuzeFhgXovVacT2ptim3iGGiNZ0+pX7h+gxsk3vv0TfAinYbW0hJowRz28VxRZ7Zpf/tVfhDZf+MLnoe7mLavrMuoRDdSx7cIHB6hF1O2jvs3FDdsH3/7uj6HNeIKaSaO+1e95mGD/PnUBdY32n9wy5QvNp6BNNSb6jxOrIx2X+HvVBdRQefST9+zvrS5Bm5MgL3HKJ24ZjCeoN9Mg2nWp08AuiHIc03itO23iWgXbjImG4XLF6lDlRF/4+PAA6kJp1+mQ6TaWbgyJCN7xMWpFLTqdrTTBezea2Hf1ul3LUYTXReQZWk3bdxm5rlKQdeP0iTaWUEP3EdHMJVKHp4KcaFUmTqeazb1pjnO74vR1vdZRCCEUOeqwba2vmPKgh3Nv/xi1WoexffYFDDuBSD6GWW7HdUJ0scdOl2zUx+eeDLGu77R7E6LNef48anh5TfY793ehTafTgbqLF61+8eoqakfdv38f6vb27P0vXkTt0RbR4t95Yq+bjDF2nARMOrssPlhXlsWL2NXNYrx5L8c5E5p2vtcrOM73th9i3T27bqZkXtVXUTu7WLDXzTJck/2R1Q3LC1xbGy3cUytTu3BikntERDu10bZruTfDBXj/EWojxm5LiOsr0OboHfSPWKvbe7WXsZ9CF/dqn8v58kmRZh+stZmT2BsRnb6a05IsyTt6DfEQQoidAGPm9/QQQihwj3t01+Zqx/uoQXn96ZehLluyMSsJRD+2tMH9mWefhzb3bv0U6nbHdp0267gnDYe4z0+cfv00x+s2iFbgymrblIuC6BCSceg5bcThAH1X8pxpP7vyaVEdZiKe/l+IiIcEe/qy8DkK0WQn2rYhsWspaqNOcLyEsS9tt005JzqVy0SHe909w04F5/Gqi4cbRLP/t5evQN37R1ZP87+a4trq9nDOTMZ2bmckvtB90uWAJdEcnpCk9rpbpy9U16HNErmuiN0zNHG9nRRVlsdW7FjnA3wnFqe9l1FvSHLPHHPy7cdW77NGct0LF1EXOIvteA+HONbf/e73oe6v/sZvmHKzgWvnwWObCz4k+sn7A9wnfviP/tCU33oH4/bDJ5j/5hPnaVLBdcn8o2o1O5ePu3j+DCWZ3xM7prUa5hb9Ca65mYtiHXLePQlmY9znfE5w3MXxOiJ1jZZdE5UmnoOPx/gdLwv2umYdJ/KEzJmBWxOLJGc9d24D6m7csutm0sO1NRq5fiG6va1F/Paw6PyHxqR/jwf4HfGwY+fD1ip+f1xsoS7wOLf3GpL4skj8cypun/30C9egTZPoLL99267B+2RNzoP+c1gIIYQQQgghhBBCCCHOIPo4LIQQQgghhBBCCCGEEGcQfRwWQgghhBBCCCGEEEKIM4g+DgshhBBCCCGEEEIIIcQZZG5DukMibr1et+LLFWIAwAwHpjNbt7qOJiZXrqFhWn9kjVQqFRTpJ55qIXbGeZvra9CGGYN84qWXTPnmjfehzdGaFXzfJ0LWvW4H6g6q1pAlSfHB8xyFso+OrCHdq59Ekeqr51Eo+/GB/b31S89CmyjC3xs4Q4W8IKY7K2TcGx1bUTkdhhvLqzj2sRN37/WJIDsxhKg4w4M+ua5KzDtqVTvWtRqaImTEECu4qjExQGJ1SeQM97yQewhh7AyqFlbOQZsoI+Y5dSton/iHDCGsraPgvJ/b0wkaZCXE9KO9ZNfblDg8jFJ8hsNjZ1KQ4rtMRrh2BxM7t0tm5ncClGQ5Tb0hBjHQKYkDWOFMHaqLaGKRRjj/2xU7r65vYdwZ3kHx/d19G8PKJTQOaZJnOOpYU67lC+ehzbUr1tyjvkuMBQ5RoH/noTVAWKjh/lNP8Tn3h9bMo4pTNvQHaGwx6FljmvPn8V36fTRvePDwkSm/d+MmtFklMW5xuW3Ko+HpMKQriWnWXGuMGNIVbm4nAQejN0QjkmFh+6LSwrnX20ZTk/fftestIvt1c4xGcs43KVSbuLay1D5TSowxix7G+mFu49XdGw+gzc42zseoYedanZjrrBAjmTtP7plyq4bPlCXYBzuDx6acEuPIRmhD3Xhqn4HtyyfBgO39VW+6ivE4S4jRlMtFma+cz2lZw4jsg6MRMb/KbV2zjSYuNWKYknqzqxJjfeFMUZdXMBd4+ZOvQt039rZNuZqw98X+PB7aefvMJz4JbT73uc9Cne/PCTNtvIP5//e/+11T/rN/+F9Cm1//134b6p56zhr89ZjZ0kmQkiMhzFuMqyXJ+4CYXJfi/I9cXRGTZIfcK9qyJmrpFPeIXbd/hhDCvot1n15Gw8LfiO2aeDDE9b6xhfv1Z11cvXtwB9p8pcR9I3em2BkxCiyIId3MVVWJaePnZ5gz/9a6zZterBGDsD6asJV9m19FCe4bJ8Wgg+8eOXO0GTH+ZvlH7M4iswI35KNjNBtcvXLVlMcsFxxgv265M9PuHu6h3SH+3mNnGru5gevkv/4H/9CU7+4/hjbefCuEEIZ921f1Js7JtM5yLvvtKCpwzXcO8PvSxqZdO5cuo3HfwT7m92NnLD6bYWyqVtGIbTq1a25CjOlPguVFNMiM3N47muBY1JqYs46dkXlO+mZI5uPEXddexjPGiBhA+28kO4eH0ObBY5xrfT/XYuyDhYaNySX5/dQbZoYQ6g37TDVihjgg3yNCsPN2dxefu1rDuV1zvzfskHlFTGufum6NsV987hK0efQEjbLff2i/2Q1Gc+zNBP3nsBBCCCGEEEIIIYQQQpxB9HFYCCGEEEIIIYQQQgghziD6OCyEEEIIIYQQQgghhBBnEH0cFkIIIYQQQgghhBBCiDPI/IZ0uyjsnEVWRDlbQ6H7PjHOODqyYs8rGxehzRe//AzUjadWaHxCRKOZQckIROfxXTpEKLvfs7/3B3/w+9Bm1ZkXTCYogs+MtSaFFfiORyjkX6kQ0wVnxFMlpnzFDAWvp054PKbOfcT1xvXVkJjgLNfRiGrRGZWlCbv3xw/ztkicuHuFmWQQTe/EmWIwcfc6NY6xxZSYTRBvx5A7s5ViQszFcpzb+czNSWLmMXWC6JMpMXOI0ZDL+5tlpO+8MU8IIfT61kwhIsZ91JjKmXAwI8mEmPzEiW2XMEOrKcaT8ci3Ox1/TytI33hDrv8fbmaKK2sXoMn162js092+Zcrry2jysHGExi37HWcASeZxrYVjmGbWlOCAmGasr9lYdOEqxqalPpobrG1YEzI29dIKxrC1NWvKWm+gCcTEu8SEECK3BsfEKOrxYzQKmbmxarTQPGo0xb0kc0ZDtQaO1UlQSfH5Yd2TmM3MvaLI7ntJhnF1MsPr0rqdD8wkaUbW/a0bHVPOZhjDnnkBjT1zt5lkXTQAqTozMWYIWUxxL+70bc6ydweNbKICDfcWWtb8ZWuVrGVigHR/aA0Re7M9aPPuY3y/+siO1bUKPlOL7Bszn7IWxAHyBJjNMDb4vDMlZl/MWM7nkOw6Vpe7dR8RE7dGvQ11n3zl50y5mOF1BZnbsTObZu8yHttxjiJ87hde/BTU/fg1a/TWyDBfLSOM/7Ulu5Z/7a/8FrSp1zH+T13fsZwlInnauz/9qSlPiNHn4d4TqCuftYZ097axzYlAcojS7VX+DPIXlWQd+jlaIX1KYq2/e0nWVkz2uOAMpQKZxzWysf98ZmPPlxcx//n5JWvAdP8BGtsNpvicRdPub+cHxNiRnBlnbi3lpA+GI4z/5930+40Gmuv9+jl8v4ttG/+TlJgZ52i4Vx66faKD8f+kON4nxuIuZiVkKjMwJ8H+ebKHRsfNuh3/1SWyz7UwriyvrZjywuIKtHnnxntQ9+Ofvm3Kv/SLm9DGv8pRB03d+gOMY8dHtt1R74MNyEIIodmyc2Sp1YI2BTHkerRtzfVS4va8QIyre87cNsFHCjmJYVN3DmFnrJMgiTDWFc78NSXfbmZTzLsyZ6o9HmMMmRIzVv8Nq9PpQJsnu2gqWK3bsT7u4bzqHuHvRS7XrFVxjVTqNieo1HB+PP0sGovfuHXbVpDvGuyUH7vawQDPbIEY3SeZjRUZiR11Em+ff9qalE8m2L/b+x2ou/XI5vwX1zF2zMPp+NIhhBBCCCGEEEIIIYQQ4mNFH4eFEEIIIYQQQgghhBDiDKKPw0IIIYQQQgghhBBCCHEGmVtz+AnRITncfWjLAbVR2guoCTMrnebkzh1oc+0yXre5ZnWf9ojGz7PPPgd1jx7Z53z8GPWi1ldRm+nh/QemXK+iVtnhgdMqJtqAjInrz4hofcVELCdJXN8NUFepSnQoG073MiN/Fhh5bdoQQppZHZdain3QJu/87EWrUToZEOHIE+DwADWxmg2ri1Mh75gU2GG50xhjCkVTr4MWQhgN7XWHM9ThYXqBIbZzZLGO+qYVMme6bo4UMT7p8ZHVAF65ch3vXUOtqBCczg/RaRoQHb6R00vLifbRqI9zezKwujvjAerwjMaoBdR1VTHp34xogydOx/RDqvr+zGGaq147m7XhszRxLbBvrj37aai74+L47ZtvQZtmFe918dy6Ke8d4zgPxyQeuuecEI3v0Y7VRas18PcjP2dDCFnN7jczohP8ZA/nWmvBrsGNTdRqnZJ7HTmd2SdPcC/LSBxKK5EroxannwchoKYm0xo7Cbz2fgionc1mcUTE493WGCYR6rD1ibbdwsqGKWdLRAeZaKwtxVY/8t5b29Dm9js/gbpnn7O/d2UDc531RXvvVoPovY9xbk8Hdq5ViL5wpYLvV2vZdkyX9eDmfagbH9g1UdRwPo76ONcaCzaXO3cR95sVst+g9OPpyCuWllCPM8/t3sF0idk69Nq9/j6sTQghRK6uLLFNHDA+jfo272NSi1Wi+ecfgWneZ04rmA3XOCcxLLVjv7qxAW32Oneh7lOf+pwpt9w8CyGEaY4xJ63Y+T4geq7tNup3Vht23BtNfMGMrKXu0Mb/aXk6tLNDgmPh5xXzrIDJEEIoq27sU+JzQvbwMrfzMSY5NE1j/Pyv4Jy9fAH9bf79BRsPl5qYV1cX2qZcIb4uf9hBj4C3dq2WdCfHM9ZqE9fksdMUncxwzn46w/j4OyvXTPmVDZyzLb9RhhBK79VTEI8YpvPfdnqW5N4nRsnWou3rksQsr/cfQggz78kywfFgcfrBtv32kJHcrNFCTdDe2P7ez7/6CrS59/AB1L1903oAfPIzr0KbX//VL5tyWsPz+4/ffAfqmhWbkzQXcP7du4c5QuriRYOsrxmZ34OhzS3u3EZd9tYSangvLdvnymoYB57sYr7tU9GY6KGfBEzHeezmWpW844TkG15PuJLhfLx8ETXJvWfV4x3i0dXFMTw4tOexpILjtdjE+R837LM3yAer1MX3WpN4CYzxbJk4z4/ZDPf6hSrxnXLrJCNGVFGKfe77uNLGeH/lEsbp4+OOKb/24Ca0eejOuyGEUJa2X7a20PdkHvSfw0IIIYQQQgghhBBCCHEG0cdhIYQQQgghhBBCCCGEOIPo47AQQgghhBBCCCGEEEKcQfRxWAghhBBCCCGEEEIIIc4gcxvSLT6DJiZJYgWZ+yUaQRVEuL/ifrZPTMLe/umbUFdOrOB0RL5t//mf/hk+gxPXZ6YYP3jtdahbXl425fYimo5s950JATFr8OZbIYSQZvZd0ggdFooS+64MThif9W8dRb83m1akfUaMUKYlMY5xpmdJge9334nuhxDC4cAKvudjNGI4CXJibhE544KcGJbERBDdG8xUiTnUtIfjOoutsHk+60KbckiE1J25S3MFzVZmi/h7nYHt+zwiZmyNtinXiUi8n7MhhFA4ofqUiLQfDfD9/NIticFUQdZSEdvYMSExgHichMi982CEsSqfYt+lif29nLn1nADMiKl0z8ZN6zDkwzsyA6QI4//l561JXV7g720Tk7q7D3ZMee8ARfUzYgZRcevLv28IIUSRNT9caOGczac4QTqH9pmYaV2VmG0tLti+ay/hdWDoE0LY37dmG0dHOB+HE2LW47o4qeDvsfGrVq2pxcIiGpWdBOMJmlWCIR0zwiF13qRrQvqhO8G6rGENEqczvHcrW4a6L7z8lL33GuYxf/LVP4G6b37jtim/tURMXp3R4UKDmBOSUBS54NclpiQ5Mb9Yq9v3q4wwZj+4h/t8d7djygkxzlvYxN979ulnTfni1mVokwzZXm1fmsWzk2CeOcqMIlmdn8feTDKEECaTOfIpkudmxMQqTaxBCvMbi2KcR1FkG6Yp2VvSD95b2DqNnDnsmPTB0grmKK9+9rOmPCBG2mzfyJwhF8tHmov4e0tta5RXIR5GiTf7CiGkLm5vOZPWEyPCMSzdhGD7WcjYOnRGtyOcs958LoQQgp8PZLzYGPqJW5I8t7qIZljrmZvbbG1NbTxsEZPBdID3Xji2111P0YzoW1PcA2uZjaO/tXkF2vwb556CumvOAL5gfT44grq4Yg3ZSxJXI3IuKn1uT0zbT4pFkuP4eMRi6yxgPBq7Mzy7d63GDLHsde+8h981dvbaUPfL679synv7OGb1BsbyoTvf3nuAe/YLzz9nyp98+RPQ5vK1p6HuW9/+nilv76CpW88ZLYcQQhL7fQLj4ZSsgdHQjg3bX/b30RhtYdl+/zg+RiPpXg/3hSS2a26SY05+EkTk3D10azgiptf1BsajRRe36sTUPoqxn71B65jk0T0c+nB4bPPRjHwjKQsci2Jqx6xSxbxya8t+/2AGgju7mJMvO8Pp4RB//8r6FtQN+vaZhhOcH5Umfnsrpvb+1Rj7bpXkMu/esOeE/SeYk+8Rk/T2atuUW2QezIP+c1gIIYQQQgghhBBCCCHOIPo4LIQQQgghhBBCCCGEEGcQfRwWQgghhBBCCCGEEEKIM4g+DgshhBBCCCGEEEIIIcQZZG43jyoxIitz+225mKHY9KjA78/egCLJUUg6ImLPT7afmPLWhQvQpknEl0tncNMfoIhzv48GYP2ebbd57hy08YL2rQUiAk7E673Jw5QY8yQ5mmK0l6wI/WSKYtrv3boJdavPv2TKMyLE3x+joninb8chTlD4/Kd3foTP8MgK7zczFBQ/CSoZGkKE2BldBTSCmsywnxecaUpKltMBMUcbRPb3ltZRjLw8xPk4dQYfETEBmRFThJHr+1c++3PQ5vorti6uoQEZ8bcJjYZ9pkEfDQImJTMbsCZkKTHmqbXQcCGu2HepLaJRVDrBvrv/0D7Xzt4TaDOaMFMh28cJMas4Caghy4e/myklxIwnZ/4vzhDjuZdfhTbVAhdA1xlNpfUDaPNkH+t8LArEOKE/sDHs4AiNPBaIaUCl6tYWiQFJjDGgWbdrOQnYUf0ezkff5wtLZL0NMEYf9ey6OTrE9wukz0tnVDbs4R54EkQJ5gyp65uMGVMWxCDLzYcjsqceEfOJ0Y41rZiW2H8bM3yGo2M713aJsW5axf0my+29ZmRPuu/j/y6aNkZknENp+2Vax9zq6rM41xoVu28UY/y97hTNjZJ1Z9KbYOyoLuL7LazZZyhinOvePCWEEOquP5mh20nA4vHMjQ9rkxBzL2/axszuqLGLG/vJBPO5oyPs54WWzT96vQ60GYxwPqwuW6NBZjTkGRODuMEIY9G5C9bobXUJzaCzBsbVSt3GgCLgnC1IjC7c3E6JiVxBDFeDMxdebOPaYmZ6aWrHvXY6pnEIJK4Glw+UxLSOdCkau5F+YL9XjmzcLpmDeA3PE1Fk1005JkaHxKQucpktO3vOevumvEyMHf/XdTznzc61TfnWEeY1ecA9/LnzF035t9bPQ5tKneyLrotjZshYkBw2sfM4IrE+Ivtw6Yz6otPhOx5CwHN/CBhbmbFnlZxV63W777CYzIhTN7dImxE5w//oxz815YeP8LyysYmG5EnFPud7t+5Cm6kzL6u3MLZS007nttnt4p7gjY9DCCG4uHl4iNeNx9gH/hiyuIB7XpZi3cFBx96brIFQYnwfubgTotNx1mu0MK7sHdk9s3eMe+FSexXqltttU15YxP3q8WM0GvRmbP7cE0IIly+2oS7btTnINMc1OSLf48rUvk+rScZraPPtWh3nca2C8f6oa78F9Lr4+5Up5k5xZPeqKkl3ZiTnyhJvPtuGNodHaDZ31LfBvDcg30iIwenGkj3f8vPnB6P/HBZCCCGEEEIIIYQQQogziD4OCyGEEEIIIYQQQgghxBlEH4eFEEIIIYQQQgghhBDiDDK35vC5+DrUJam9PE1RIy6toAZNxV2XlPgYh4uoXVqv3bDPtIUawJUMtVAGQ6sDUq2i8s94jNpDvZ7TASGCQRfWrWbL51+6Bm3eH6H2y92+1bMZ7O1Dm0qCGl3X163uSVxBjZOf3PoO1PW7HVNuRTie0wJ1T/6/7f1ZkGVZlp6H7TPc+V7369fH8AiPOTIicq4hqzJr6m5UVzcbBMButAwESU2EQANeCNFEM0lmkpEmiRJolJmMEh8IkYLMSIAQ0GiA3egRha6uqs4asiorM6tyiox59PDxut95OpMeii9r/as7riUqw524//e2t+1z7j57r732Psfd/r/bk+OSX8A+3dr/AOoKFTlYEcp4HQmhofGna6LE0HI0tGb7A6nlEht6OkaV22pKzaWrayvQJj+Hsb13KOe67OF6Cwyd3le+/LwoX7ryLLTpe1JjKvNQC27OuPdoIGMmNa6r1VDjNUykvlNijLmlYbig9Jcsjat+FzV2bm3KANw5wFhPPNT08VS8eJbu4DFhGh00rZHunHPxRGnAG9tCHBg6tkrjsmyIMK2euQh1nZbMdTUMY3eygTpbdx7vivKeobO1sXFZlK31frCPmlrzFblvrGktVedctYjjUirIutEQ83ExxOu8rmx3aMSsM3SPKxU5WEFo6A4muAZDpRdYNrT5j4JeH/W+okTG49Dh8/Ri3K8jtTY3W6htt2foSWZKmzhv6ObuxKhb9/D6h/I+HcwpXob7RhbI50knOM8lpVM2Ms5ISQ77GSotuIW1E9AmNjQVt/alfttKDdvUT57FPhTlWrbW29IiaiNev3VHlJfPPwdtlmt1qPPV+c7SwTwKJoZG40RpXo6GGHu+MV6R0sU09fgNbwF9Ph0bmpuW3Oih0mj86PY70GZlvQ51el4zQ8ex1ZJ6qlGMeS5LURt8dU36iTz/7MvQ5sMPr0Hdez95W5QvP/cCtAksDXM1f1ZU7e7ivrG4KPWaa3N4RmoZ2pw5ldtDQ1/7aMCckmlNXsOTAARCnXPZROa5rId7oxdhbverSkfROFekpt666qfhyeEZa8lTZ8+sgGckX+nCZv1daBOExjl3ri7KlxYwF/7vwzN4nTpr+6mhHTzCMfDVO3jmG2LQlTrWqbOjZ+SzzLjO029PPdRUPioGRr7VeXPe0DLXZyXnnOt11fuRMT5WLp+bk/khNc4RY0P3eLcp42tzZwva5G+g3qg+264soZ/NvQePRHkywXi3dL4XFuV76soKvrfuGTmyoL4Bzc/XoU3b0FydjNSaM/TJE2d4J6mzdWRcZ2nt1pQ3wsQ4Kx4Fn37lFaibX74nyjc/+BDa1I09paI8G9ot3I8HQ+OQoM7WecPvZZLimqgE8l6FCq6RgfGen/Pl+1+tjL+nv1GEPu4vYRHnPlbeWnEOY6g3wHuNRnKd5ozviL0xnmXmanINjiO87vFjXDd3722L8mIZ89KzV1GLPlD92t7DvWoa+J/DhBBCCCGEEEIIIYQQMoPw4zAhhBBCCCGEEEIIIYTMIPw4TAghhBBCCCGEEEIIITMIPw4TQgghhBBCCCGEEELIDDK1Id2nn/sC1GmzpnzeEJYO0djEUwYH7U4b2lTLaEi0uCKFnScTQ6Q/Q5H+RlYXZUs4Po5RhDtRxjijMYpblzwpWh752KdhHp9F+3bFPgqDFxdRcN4r3xflfBV/rxOgAcbdg8eiXOjXoU2/sw11YVEKt4eGocg4QfFu31OxYIz5UZDPGQYwKo4sOzrrzyixMvfKMmyUZCgiro207h+gIPrFU2hsePmyNGlZXF6FNodtXEtnzsl7dQ3zxbAmDaryRTSsur+Jpgi9TkvV4L1rRRyDaCTF6wd9FP8PQ1xvaVXGVesQBeAfNzH+3/lImjA0u9hP52E69LVBi3c84tjKYdqQzjKoSyxTB21gFhimjcZze8rALzFMYqoNNGC5dFUaIn7w5j602X38GOourS2I8uWLuEZ6ytyrPzDiqo5mFMWCfJbQGLtiAQ0eqlVZ16hjro8Ng7hVZbZx2EFjtp1dHJe9A5lrjS3JjUeYT0aerOtFRvwfAc9dQcOoZiTz47XdR9Bmt4UGDpNY5pRDw+gqCHFea0W5zyYj3FOL8wtQt7Z+WpSXDdO4LMFzRdeTY58ZpnX7hzKHtYwNaGSYcTZOyD3h2WcuQZutfYy1e4/kGM95GMcnGyehbrwv11clMNaIhyZd+205f1kB26xubEBdXuUhy7T0KMgcmrGk6izqG+aRVo7O+U9+Jiun6N/LBbiHhwWMtQ/f+5Eo1yo4h6dOoGnWYCjXV2DEqD4Tlcto2jUaovl0pSKNopIUx+T0GTRVfuudN0X5+9/5HrR57dXXoC6n3l0Swyjt4YP7ULe2Lg0fLaPPR8ZeNlb7km+sm6Phyaa2qZEf3dDYT/oyPjLD6NMt4Lr3DFNxjblElNmcdZ/MMKSDd8YS5lVXlfHoDdFAKzPMflPV0cAwaA/z2M9UGQAn5tnUOJOp/SYt4FnHK2GdG8o9wXiVcZ5hQuXm1fmuiO+sR0VoGAFPlPmb1SY2jKm1AZ2Vt61vCB19rrOM/gxTtSyTdUEOA363ieZ/RWU41h3gGcjfVyayxrPsH+C9N7eksZVx9DRr10+ekn0yzJdjw6hPm05GhnFesYprp1avy9/r4/tgGOI85NU7QLW6CG2OgvIynrs+f/a8KC8tYV/vXf8I6to9ue8McUhdd2CYlkcytvM+znNgzH1jXsbtwgLu/5MJxvbignye1gGeEXo9FUfGOTpnOMueWJZn+WIB49H3cT8eDuXz9Y215QWYT0ZjOZ5379yENt2uYWSeV/FYw4cplDCOByM5x/EE+zkNx+NLByGEEEIIIYQQQgghhJCnCj8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg/DjMCGEEEIIIYQQQgghhMwgUxvSLTTQkCVQ4v6WSVJgiNh7ylRkbBjL5QwB9rInxaxLJRS/zxvi/rqf47FhGjdE46IolkLSSYRGLmEof++xQyHrQ6Mu7khR+EkXRdOzNRyDSVUKVw9D7FMSoDB3UZlBhA7HrrOL96ooA7e5EhpueNh155Rhio+62UdCt22Yn9Sk+Lnhq+V8w8gijnRDfMgJtHHO92XM/OT2LrQZGAYsZ0rrovz23Q+hjWWa8rVflHN96RKaFEWZ/L0/+N1vQZt33nob6nLKZKdkmM/N1zDW+m1peGCZDQQBjkFBGS5MJhjrj3ZxjvfbyjhSGyY656IE5yrwVd0xiWPL/EKbZkyLvmo0RgH7ZIhmAwVliOUbxhaRUVdekUZylz+FfZrEr0Pd1sPbotwoYKwV8zI/tXaa0KZg7FPVoszRiWEu0zRMOnoDuZZrNTR7qVbRhKE2L/tZquB1tSqa9TSW5NwctHHfahmmlM1D2fdOFw11joIvfOFVqBsrs7zXjHjsTPC5h2rOJjGaJLW7LagbqbVUKOF8VZUhkXPOVVUO8frYz9EQ67KivK4ToRnFnW1pDts2/o6/bczhfF32c6mM/b7XvAd1G3My/l5YW4c2L5x9Bupyr3xFlEvGes+XMf+n6jyyVEEDvBM1rCsq47CicQY8CrotzA06P3qpZXpsGISqA4g2vv3T0Om/FGIc37z1JtR1WndE+cWzPw9tcg7zUxDKfmqDUuecK+q8muKabB5iHC8vSWPFzMd7l6oYH59/TRpn37//ANpYpqyVvByrwQDX5PYummCePntWlJeXl6HN4mM08t1TZlLrytzyyLCMZzO1pg+MvaNnmNQty/nx5zEeMyNfQB8Mg1zPqIMUad0aq5xTsZUFhmlYQd48yxlG68Z+46fyXqlxREtjjEft9xQYZpbWaU8bQFrvLW6aMTfeW5yPayLT82DFD97pqeAZ5zwwoDPeMSLr2VP5FPk83jv18Dod36GRx6zvEdrgtBAaZp9GBJQq8lxZLOD3kLY6N9SqGMvWs8TqO41nfD5aPoEG1MOhMlE2ntc3zConsbwuZ3zbMV7ZXBDK56kZBtRZzjgb9tV74/DJxphPg9/+3W9C3Ze//BlRXllH07q33sS9fjSRY7px/jK0qSQ4rzc++kCU54q4bkpGqilXZOXSIu7ZUYTxUCrKsT/Yx1iPYnVOygyTvBDncHVRfu/JIsNIUseCcy6vzI97xrmsYpy3W1s7otw2zorWdqbfGwvGOm0b3y31J4vGXB1vPgX8z2FCCCGEEEIIIYQQQgiZQfhxmBBCCCGEEEIIIYQQQmYQfhwmhBBCCCGEEEIIIYSQGWRqzWHQ6nGoy2W1qVYMbSalw2PpC5cMbdswkvolWpvNOVtzWGtxZhlqheRzqAvs+7JfKUpmuTCQ2jV7Dn8/DXBcQq8vyoFDDZzMw37GgdQwjDzUS0ljHJc0kHXjGPWjJg7rSoHU9IsMrdPA+BuDp5SmfENr6SjY330MdZmTfcsbujHm31FUHKeGAFKa4XWJJ9fE4RCve/PaJtR9T9VZGt+hod30Ukdq8Sz2Ub/n9//wD0T5vXc/gjbRxNDk9ZSmWoIx5PktqEuciiOta+ec8zzUEBqNpJ6qZ8xLbOk4OjnmmRZ1c85lnqGbp3LHUemnabaVJqlzqJseGZpIi0pvyTnnRiP53FYc53KY1/Iqb/cHqH9UqmDSXF/fEOW501ehzYuGCFNDaYfdvnkN2hQqMq7OnlyFNq0uaqtrjSvPN3TYHMbMUGllbzdRgD1o45rQioGJoTs4MbT4R2pOo8jQ2TL2hMWq3N+WysdDTy1nrHE9F6sLqFNmaU5mKofkjFyYprgmIjX2kZGLfGPle76swydBf4Wf3kvWWRqoA62fbOS5aIxnBjeRdXrfd865nz97EfuZyXZn67gHrlTQd6KkNA1zhrmAlYdcQeaO0NAOzIxx2TuUGuKv/9H3oc3/7K/9u3izT5j9XdSVrSnN5N1d9BawdNrrC3Kcm03UTc+Ms2+5IjXKq6t4hu4PMfcVCtrLA9dblj5ZwNXzMB7jRNYNBoZ+eA/z4/qG7HtixH+W4Rj4SmPwwgWMdUvnWe+doxH2c+P0WagrlOWY9waYszfOXoA6fa7YM/SMnUM9yE8c40zpYtlX3zgLuBOoj+/UnmNp3eoc+tMf0O8KllCvUadFtw0PhszQUvV0M+Mdx9PnQGNvcQVD/1y9+3mGlnXQb+HvKa3gLMV7ez6+N+v3rszQd9V9cs45p3X2jXNFFhn7TazH4bickJ3zjb2oVle5zcOxaDfRs8FP5B42HuP4pCneS8sXe0bcJka8ab36Xg/z0WSEc9vpyPPnpIjfNVK1dxy28cxaMb6/xCM5BqGxng/3Uas1035Hxnei0QjfHSoqf1jvIO0OjsvWtsylxnbmqsvG2inIfo3702n9f9K8+8Y7ULesvH3SCerAb97Dd8Rzz39WlJfWNqDNfqsPdbt7Uie3fgY18nM5nNdE6bDnc7hP7O/henvY2Zf3DjGO+0OlgW3ktXiE+dZ38t6WcHWlhO9/g4k67xseEoMW/t7unlwTrUN83kIR+75xVr67zi/VoU0ywpxcVu/cB008800D/3OYEEIIIYQQQgghhBBCZhB+HCaEEEIIIYQQQgghhJAZhB+HCSGEEEIIIYQQQgghZAbhx2FCCCGEEEIIIYQQQgiZQaY2pDs8RKFxbSxXMcyHfMNwIIqkkLNlIrewgOYn+ro4tgTDLRMaKR5vmWvEhii8NgQah8bvhfJehcgQch+iWHihJEWiAx/vHfo4PX7qqzIKdVtGXloIPx/i85ZKeF2uqEW4pzB0cM75ykQiMMTrj4ITa8tQt7MnDV+WCzVo4yX4kJOJHEMt9O+cc4Ehkg5GRsYasXxBtDGHH05n8veNb78pyt99411os69MGLwQx8A3zBS0AYbL0CTBMn9LlTFIatw7M9Zy7Mt4t0z5PId98JShhG8Yini+YUTitCHd8TBWHBtGI3ostKmFc87tGaZI1Zqc64vPoIlPYIxzrMzYdNk523CpGMp40PuIc87NnTgLdevKgCWcx7X84P51UW63Mffm82hGMR5LQ4y5eYx/yyS1qoxb2oZxSGqZmSnDmYmxRkZjjMdhH80iNEsNNB1cVPupZbhwFFz/6H2oK5el+Y5lWOsZ8ZhTZlShcZ0Vx2Go4sowTAkCY90rs7mcEVf63s455ynrOt8wTqqqTTWfw98PykaOVqFmmW/FCcZV+1AajuQjzC+hYdgZqPx47UM0ifzWt74FdefOnxPlSxcvQZtxhP3c2ZNmM4eq30dFyZj7YU8a/dTKhjmQYYYSKROVqrFWS0U0qMrUXIRlvPeJM5jbe8qwpFDBvBobJpculfHQH+BcPN6UBsCnTuLvP//CC1CXK8hcm3kY/4YfnYuVSYxvmBqaRr5qzddquLauPPcc9kHldusMWA3x/SYM9DneOEQfAZlhZg11RYx1qw6eyHp3MM2sZTvLeNCcfJ1HDTNQ69+hMjX2Rjp2TpstGYaoLjCM+lTcWs+bGeaHqTIb9RYNo3XjfOcy9a5rGYFb3pIFmZuyirHeDTNLTxluZ8fkPc8555YX0fxqMJLnp8HYeO82vopUlIGvZUbVGxlG2IkyZs8MA2HjbAHG70ZQVkq4n2gPvmEP50ybw0cTNLXyEozTWkX+Xprg2KXGuhypcbEixMqbdWWK2263oI21VrUx8XiIY16JcOyCTM5xPH7yWftp8MzJFahrbz0S5b09fK+LIxzpdleeLfYP0Bxt8zEa2YXqO0ZqnCv7xjj7gay7fuMhtOkZhrT6e9xiw9j/VS61toRCEd/ZWh05r0Xj+2Nt4QTUHfSV2aORy30jJ1997mVRfuHFV/D35vA8t3ZCvrMVKobhaWJ87xzLgShtYmxMA/9zmBBCCCGEEEIIIYQQQmYQfhwmhBBCCCGEEEIIIYSQGYQfhwkhhBBCCCGEEEIIIWQG4cdhQgghhBBCCCGEEEIImUGmNqTzDUOIufk5Uc7nUeRbm8g5h2ZKlmmKRagMuCwhd0vYPI6VaVzBMA4w0P2yjPM8ZXoT9Qzjpv4W1NXmpQh8roCGDnXD5CQXybrAM57XMuVThkejQQfaRD4aI3jq/kGGY54Yvxcq04/JGO99JBhGArWaNC6wxNadYUiXKuMMy9SwmDOMJFSMppajn2Gmoc1/Ag+Xr+WlcdiRseYb5jKBMk3xDCMLbXbgHBpSaDMR55zzDWNF7ecxGaMpgmVIl9cXGn0KQrxupPNCZpjPWeZ26v6+ZXJyBJhGfMoMJIrQMKJSRZMOnUfffx9NwqqG2eiJlVVRLhUMc0yocW6iDTCMRvkAc19j9aQo9wdotjGnTKC8Aj7v4QGaq6bKaHBvvwVtBsbvVZRJQF4b1zjn5pWxhnPOObUmRoFhSlZpQN3qUl2UvQwHr1Kbg7pOX5o+3HyAe9JRYJkN9QwTFY1lYqiNVqw25nUq/kPjXDHVsjfcUcycovqgjU2dQ+Mia91aBsD63plhyBUbBjQHTWn0ZpndDPto7qgNarf39qFNfQkNzjy13+w0cU1apk8F9czPv/QyXncEaHMU53BPtc6m0cQwo1JnZmueI8M4TJux9ftoNuOHhtGQMrAcjtEgJnUYRwV1tul2htDGqbxqmUsWyvh8mTpXJMbZahpTMuv3MmOdakM6a64SI9fC+jYMiLUhtnN4jsjnj4fRbXyIa9xX5yk/b702Ggcx/Z5luSwbc6jnx7fm2Zgf3YfMcBYDA+Wf3kwWjTO7KyqjIeP3M8Nc0mmjwRwaJHnLZ/A6/Q5imFa7PJofwWv6ENdyNkFjUVeS+4tnGKla/0vmaQO8wfEw8XLOuWcvn4e63lA++2CIY7H9eAfqslSepfsDzHWGH7Mr5mVus96FLHNpnY9ylkku/hzs/3FsGFer3FMxzE1D4/0vUka2lvH9eGyY2ufk/RPDENfa4/R5bmCNeWwMuvpOtFZfgybdJq4Lp0x/MzwmHQnVCuaVUknGQ3WhDm16GY7XI2UQO4yMvdDI08vqjBAneG4pVg3DXVU+6LSgTZZhrpkoM+7hCOM4r9439XnLOecmEcZjXpnUzTdWoU1l/iTUnarIcTl1GfPv3Bwa2Z47Lc2XU+MccXiA5+ZxJHOpteMZn7Pc+ors+6WrH8/s9nh86SCEEEIIIYQQQgghhBDyVOHHYUIIIYQQQgghhBBCCJlB+HGYEEIIIYQQQgghhBBCZpCpNYfLhi6Y1sUzBU+nwNIBNDW/ptAmtrQ4c0oH0vg5U79Y65Xlc4YOa1G2ud9DEZC6j9pvFaVf0jZ0f6qGPlE+lXqSmUMtlsw9Wfep10PN4STECcyUppRvTEFm6FFPlG7eoP9kHcmnwdjQWyyWpCZobMSxb+jURUpkKjA0Eq141JrGeUPjLNVaZc65wNNljI/Y6GdOafFYyzRVAkuZ8ftGlQtUjGaGMHFq/KKvbhYYmmqWnqruvda8/OnNDe1PtehDQxvQzC/q9tZ8HgWg2+tQq89qUyphnkGdMsxh1r2GQ6lpVTX2CCuvTiJ1L0vfLDV0zAOpMXXy3GVoU1SaT/du3oQ2zkOtKF8vLkNjc3cPteh0OFTKOL6ppckeymfJGX0KDN0/nXP29vagzcMtrMuUzlx/dDwE1ax4tPZ+jZUZQG/X0qq0NIehako9SXWddY6xdIh1Vc7cN+SaSCLc57ttnEM9dtY4uQxju6g8Dyzt+F4HzzF6T5ivo9714iJqZ+u5Sa1xMvYN3S7wnxwrT4OJcWiIlR6iuXcYZ7ycOmdaWrfDEWr+5ZTW4v3bD6DNgaFtt3FKap7eumloshv5eG5OzuupU6idurKk72PoLg9xv9FjZcVxYIyn1n629vRp6goFzMdaY9k551KVF6y9MzTWt04C0+S8p0Gnh5rDtYrcq9I+all6NUNLVz+2cebKjPMBJEhjbDxLq1jHg9XG8A7x9Puh4RuQaQ+EzNA6NeZen6OzxNDtna/jvbRvhiFo61k6xFrP1co5Q8P/ReveGhq3WRHnWD+zZ/gyTP2R4WfMygLuO9WyHMfd/Sa0efG556Fut7ktypkqO+ecF+C6GA+ktm1mePZYOQPeV6x5NLxqdLzpfd05fPep19ATw/IrGYzl82VwZrbPrNorSn+Pcc65hYUFqOv15HeMCxcuQptJgmM+GMu+5/MYt829A6jTHgf2++fT59HuJtSFyt8rinFMD9st424yProHOA7LqyegrlqVY5gzvK6SFGOmq+bQ0o22/I0qJbn/Wp4D8UTu2cMhfvdq9fDcvHFGvjeev/xZaFObX4c6Py912ZstPCc1D/DdazCU4zLsY/699/Ax1FVq8n16r7kLbbwUx25hQWoO94z9ehqOx5cOQgghhBBCCCGEEEIIIU8VfhwmhBBCCCGEEEIIIYSQGYQfhwkhhBBCCCGEEEIIIWQG4cdhQgghhBBCCCGEEEIImUG8TLsYEUIIIYQQQgghhBBCCPlXHv7nMCGEEEIIIYQQQgghhMwg/DhMCCGEEEIIIYQQQgghMwg/DhNCCCGEEEIIIYQQQsgMwo/DhBBCCCGEEEIIIYQQMoPw4zAhhBBCCCGEEEIIIYTMIPw4TAghhBBCCCGEEEIIITMIPw4TQgghhBBCCCGEEELIDMKPw4QQQgghhBBCCCGEEDKD8OMwIYQQQgghhBBCCCGEzCD8OEwIIYQQQgghhBBCCCEzCD8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg/DjMCGEEEIIIYQQQgghhMwg/DhMCCGEEEIIIYQQQgghMwg/DhNCCCGEEEIIIYQQQsgMwo/DhBBCCCGEEEIIIYQQMoPw4zAhhBBCCCGEEEIIIYTMIPw4TAghhBBCCCGEEEIIITMIPw4TQgghhBBCCCGEEELIDMKPw4QQQgghhBBCCCGEEDKD8OMwIYQQQgghhBBCCCGEzCD8OEwIIYQQQgghhBBCCCEzCD8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg/DjMCGEEEIIIYQQQgghhMwg/DhMCCGEEEIIIYQQQgghMwg/DhNCCCGEEEIIIYQQQsgMwo/DhBBCCCGEEEIIIYQQMoPw4zAhhBBCCCGEEEIIIYTMIPw4TAghhBBCCCGEEEIIITMIPw4TQgghhBBCCCGEEELIDMKPw4QQQgghhBBCCCGEEDKDhNM2/PN/6YtQlw5Hopz3JtDmuZcuQ10uLIjy6VMnoc0o7ENdp9UR5b3bh9CmdziGOjfJi+L9hzvQ5Cd3b0BdrVIR5YVaHdo05udE+aVnL0CbXFiGumZLPl+734Y2++09qAvlo7hysQht/AzrXJQTxXF/BE3qi3WoWzu5IMqvfe1laFO6HEBdEspYyFU8aPNLp//X2M9PmL/33/1foM4PUlHOhfg85UIB6vI5ORkFo03RmJ+wIJddzsPf8xKocpORHNP+ZAhthkOc1ziOZD+L2M9KpST7GBqpIcugKknl2EVRBG0mE1yTSSIfMItTaONSrNP3H4/x3mmC1+Xzcq4CY45zaj6dcy5fluNSrc5Dm1de+59D3SfNf/KXz0HdWI3NyBj3YrEEdQ217hcWGtCmWqlBXRDIGMmbsZ6DurwnxznMYW44zDBGb+/I+CskGOvrdXldOcSF5HkYH14g48HPYXz4AT6L78s6z8c2nrW+nVo3xrN02jHURRN5XR6n0+WLGMe6D1mGffrlv/G38WafMC8uL0Fd4sl5HseYU4xs4fyCjL/5+gK28fHv4TqHzM3NQZvIWEuhupeV6xcWsA+1ObmWer0OtNlryr2/Wq1Cm/X1dajT7Zq7eNaJBni20uk3LGBgndw4A3WDgdyDHt2/D21Sh/tGbV7m0eoc5peRkds/vH5dlHv9AbT56OZdqPuk+T/9P/6PUKfjyoqF7cdbUFcIZQ6pVCvQZnt7G+q6aizWT21Am9RYS/22PEcvLi9CG+tVYdTuyhaLuG7KKh8mxvlk4DBH1xflHnS4hXFcqeCaKNdlH1qH+I4QBJj7nIrRcgXHfBwZe0mgcq2P45QZ56aHDx+K8vIS5sH/6//mPzL6+cny5ltvPLGNlUOnqfM83OetOt8PVJvpfs+6l8aai2mu0/Ex1SXWvY3fT41zrr7O6qM+1//09qkq4+9ZdboP9nXYzzTN/syyc8599jOvQd3T4D/+3+H7ZazeDULjPUCfzZxzLgie/H9008Skfu/5067TddZ1Fjq3WfOo20wX/3gv613P+j3r+aYhSeUzp8a9LaaJZYs4ludta8z/s//8/zPVvX6WNLu4h+m+WXOYGv1//VvfFuUXXngB2pw5g+c8PabTzqke+48ba71eD9rUanKv73TwG9phqwV1JzdOywqjT1bMHKqzxPe+8x28LsF3Nvu88eQ246E8px829/FCI7QzVTke4ZnrP/zf4nlVw/8cJoQQQgghhBBCCCGEkBmEH4cJIYQQQgghhBBCCCFkBuHHYUIIIYQQQgghhBBCCJlBptYcdgnqCYdO6S2iBI07fNyEup0tqbflD1D3pGToXhYLUrNtASXHXBCjLkc8kdpoL75UhzYnzn8W6lL1PJ0D1D2pFKWecN5H7ZDJCLXz5qtSL6XRQC3TT33mOag7c/6EKN+5fRPa3LrxAOouPXNVlNMC9unERRzzXENpsZ1FHZukivcaDaX+3WRsqUQ+fYZd1FvMac3TAi6LgaHfk+RlndYscs65yNAFK6ZSpy7LoVZlLsM+xKoPiaHTa2kB5ZUWZ04LVzvnnNIgDbSWnnPOc4ZeUCKfz9JP0xpyzjkXx0pPyrjOGfpl02icTQwtrPFE1uXzqA1bQmlwl9NzZegjHgWx8YygZ2dpbVm6cUqHLTM18PBWgZpX3/hbY87QavMyebPYYXwMEyNm8nKC+j3s56J65LMNnNQ0Rg2midoKJ0Y4jsa4B8Yq/vNF1Kq0dO18X+knG9rMjXnMC4OB7EOcYe4dD/D5AqUhrjW4jwrPtzTI5Njkcjg2YQH7nylNaFunEXP08vKyKK+trUGb/X1Du3cix3llBffPDcNPoaj6vruNffJTqcFbm69Dm8U6arzm8lJzu3La0Anu4jlmqGI7MuL/zv2HUHf/gTxrdPt4b9/QDJ2rtkT55IkT0GZtZRXq5lUOSEa4Jo8CK0a1LqC1x1na/sOh1HHOG7Fu+RvE6ZP1/RJDE0//Xq/bhTbzC6iJq68LBvgsZXXOtcagN8CYKVbUPBs+AqYuqrq/td6tM1K5/GTPhYnRB00uxDiwzoA6XnLHJB+bXhOKaXSCf1r3s9IcttpMp0M8HVrfdxqd3o+nXTyt/qnOHVZ+sfbwaXKOrQubPbGNrY2c/pnlo0S/LzlneJ0Y5+HAOJP4/pM1oM13mCnaTBM302q1aiwtU11nrRtLb3caLd+f5bNATjHmaprcYI35NNrP0+o8f9JYe32m93rjPSs19j7tizGNHq5zONf2ez72YZr7W3PY78vvNJa+dU7tVbu7u9Dm+g30EhtOnuxbZP2e7pPWIHbOudDIHdrDZNr9rFCQ75L1BRzLafJ0Wvt4OZn/OUwIIYQQQgghhBBCCCEzCD8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg0ytORymKPC7MCd1cnMZ6r/VCqivcfLqZVFerNagTX+I+hpa8nHxJOrUnTi9DHXRpCPKsaFx9koJNX/zntQhyztDvzKRWlAHI3zeZge1a+4/2pYVAWqHtLqoodKNpZbvyavY79deQa3iC5cviLIf4u+V6qhrNVLaNo8eo6ZzOcYwWlyQeoET1e+jIjR0cwNdZ2jLpYYuaqa0e8tFnIu5+QWoK5Xr8j7xENr02jj3kdJKzZyhYWho/GhtstDS8AylrlEuMDQUDX3fLENdI7w3xofWykkMKTZLM2kaXS9Lhwf1o/D5DBk55/lax/F4aKrFhnZkquI4McYqMsa035drs1BArdswQN2rMJT3LxtryzN2GE/FUZKgdt7AkvsqyPUVGcKo93fvifJpQyuzYmiKByr+54z4Hwxxf9veb4mypVUc5wxttlCPFT6wtTnnc+rvuamhNWZopRXUGizkj8ffhRNj0aVq/Wr9UeecyxUxRlPvyZpxlgZapSK1vSwtx4UG5vEollplpRqekaoLdagrKg25gaFlna/Ie1WNM1J9HvebyURrwOP4HnRRP3lnr/lnlp1z7v6jx1AXKx3AuUYd2mQR5pz9+49EudfB80EhwDmu5GXdKId56SgolrCvWsvO0g2tVjFmHrdaolyalKDNZIIxo+Pd0tKzdOjLZbm+6kbMan1h55zrD6QGX66Eayspy+ebVqvw46LHoFTCsZvmPKJ1+K02P/09GdupwzG3co7WfpxWi/aTxhobrZHoGRriluawbqd1W617T3udBfZzWo3jaeLvY2oOq3m1ZtnyeLC0sjX2mMu6ac7LzqH2rhX/qXH2TX113ZS/d1RMM9eWD8I069O698dd1/q6aTW2p1kDmml1kHVumEaf/E+7/zS/p8+BmXVWnEJPeNp50XXTPt8njakJnalnNMbvsN2GOq3La/lrnD59+ol9mmb8nMP5mVZventbfh9rNvE8ure7J8oPHqLXlnVd84c/FGXrLGXt2fpZ9vfwG81yA31HPm48eurl2fMMn5XMelFWOdls82SOxxsiIYQQQgghhBBCCCGEkKcKPw4TQgghhBBCCCGEEELIDMKPw4QQQgghhBBCCCGEEDKD8OMwIYQQQgghhBBCCCGEzCBTK27XaihcnVOizUXjbqNxF+pSZVBSMMx/Snk0YGn1pGjznb0OtKkvoZD0/II0xWgZhnSn5utQNziUQtU7XRS3ztQz9yI0JimWDVOkmjRS2dvbgzYrZ09BXa06J8qTviEMHqJI+933t0R52OxDm4N9fL7WUI7x4+YI2vyNv/nvQN28MtzY76CR3VGQGCZuvpNzUS7OQZu11XNQt75+UpQXFtAgqFxGw5nAl2PTM+Jqy/AxSJT5W+obpnFxhHWpum4aoyZDNz5NcO7TWK6RzDCycIYpkvZ8yIy/U2UZruWcNkUIDOOEHCYibc5mmQ3kCij4HoZyjLVxx1GRGIYlXiCfyTeMY6YxcIgMA6mxYZqVZfL+oxG2SQ2jtUIoDZDCojHuPhpNebEyW8nw+e4+PhTlXIx57rMvXoK65Tm55gNjjVTK2M9aTcb2/iHuLa0mGhfML8pcsbiA+102xHno9ZQxlLHnzlXRwK1UkuMZHA+vDTcxjBXDvFxzQR7znBcYf9dWoW0Z0mnzLatdv48xkyQY206Za+wd4Fmn1MB87IcyjvcHhtmMMr/tG/v8whruN9WKbHf92jVo85P3P4K6/QO5bjIfA6RQxhgtK/O+crUCbVaX0SS4uS3XRKeF5ikf3boDdSvzcj/V+8GRMYXvkGUyFRhxrM3KLCM73cY55/pDuT+PJ2iOtjCPZ5uaMtOz9sbQMDBuKPOVIZhsOtftyjURxHifirEmtUnMaIRnj5xhHBmovcQaJ+v5Oh0Zf7kc5o5WB9f3aCzb1Rt41rd+T/fLMsA5CqYxubKM0HwjX6Cx3HSmWrpqWmO5aQykpjHfsn/vzy7/adchhvGQcdjW/bT2siyzDPD+7PtMW2eaz5nXyX4l3pON9J4W08SNGX/ek822zHtbxolqyKzfC0Jj7asuWL9nxcQ0ay4Mn9zGNNKbok/Wdb76PcuA0XoWjWkUaLTTcWqbfX1cY8rjwTS5plzGs1i9XhflWg3PdB93HKz8MI0h4zQG8pubm9Bma0t+0zo4OIA2Y8NsLgJDdIw9yyi4viBNqa0ZsPowp98tjb3eXG7KSC7zjfxrfI/Q7azvANPwP57VQAghhBBCCCGEEEIIIeRnBj8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg/DjMCGEEEIIIYQQQgghhMwgU7t5pAmaP0ROmq1Ypi1ra2jQcPfGTVGOI8PEZK6EncikcUUQo2j0wS4an/UHUqA5n8ffG6Ro1HHQk0Zhm1soNl1oSLOQ7gRN8g4e3oU6bfDUaaGR0WGCBhjtd+T9U4cGAOUymjltbt4X5SDFNqUiSmyvXZJj/u/8jb8BbVbW61DXGcqx++ZvfwfafO1v/XtQ90lTm1uEutMbz8jyqQvQZnnpBNQV8nr5WKYRhpHXRM51pYBzce7c81DnKQO6xBRyx9/r9aXZSquFBnijsYyrSTzENhM0DYq1UVli/L3JMnhQRhaZYUyVGULxuUAbSqBZT2gIsOs7BaZRg2Hwp/TeRyM0mDoKLBF9MIqx/Eoswwh1XWgYhGqji5/24cnC/nGMnSjm5L3SCGN2fx9jrakMlgZ9zJkPd2XOvH4DzeB6ET7Lay9dEeWhce+x4bXS6st42G5izvZD3DvLDTkuxSLmgG4H++Aph5NiCffJvGHSGCijmCw+HsaKvmHIVVDPFBiGXKkR2xEYfhnjYJhBaMOGXs+Y+yGa1JWq0sxj6HAO37uD5xGvJI0t4hBNQVJtRnGA62HsbUNdTp0/3v7hj6DN3fuPoK7ekKZxi0sr0CYwDMB6PTkukwGu5dEQc6aXk2NVXcRzYrPdwusyea/FRh3aHBcKarwsI9j5OTQVrOTl2KRGHFsmI101F/NzeKYtGQZtw66MGctUpVytQ11VxX+Yw71lviDXsp9gm+YQ11s8lrl+PEZDurpvGQDL82oUYewNh3i20WZmoZFzpjG4sozzSkaO1kZ94zGaBx4FVn6cxsTLOh/oc8W0JlCeOi/6hhFVYhgf7+3JXDtnmC2VysZ75cfqk/V/VU82srPNgaZwszQw/J9cpsyWrPPeNKbE2sDZOdsEWZts+unx+X8zy7RWm/9ZhqCW15deFaHxf3WWsaB+HbJMrAzvP5eqmLBOa4FhSJj5ch69wIgtdTMdM87Z5yttUqj7+NN74XU+PLVhwGj9no5Bw8zUim/9bmcaKVrXqdx3fExCLeO/J5toViv4nUsb0FmGrdMYFNqGnJbR2pPz0TTmopYp71Dttbdu34Y2PeM9Lp3if2KXDRPlQkGOlWXMOh4O8PdU/NlGjkaMqj0nM8w+TZM6a2P4GByfTE4IIYQQQgghhBBCCCHkqcGPw4QQQgghhBBCCCGEEDKD8OMwIYQQQgghhBBCCCGEzCBTaw632qhTmgukTtZivQxtzlxch7q7Dz4S5c4YdXorEeqlxUo+rDVAfa/Ux7r9A1nX7aJ+4Gf+yhegrp+T/Rp3Dd0/panpr6AO2v5gB+q2Hkt9rNEA9eiSTahysdLnrJZQ5/DUCdQLTNT9x4bOUHEBNQW/+LXPivLlF/De+33UPnz961Jj+PXf/C60cX8Lqz5pfuHVX4K6RkPqCYeGjq2LUUsxHsk4Sg19Yc/QIfYSGchBDnV/wjzWZeq63AS18yYTjO2geyjKjSJq5XRzcu1ubqNWZm+AejqJ6kNgxJWlYRv4MvUkDrUB0wA1dnxPxmhi/H0rNvTocgV5naUnZekhac3Cn5Wez780hpRYHKnxsjSHLdGzKW7uG3EcBlqrGO+Uzxm6r0pP6t7mHrS5fg+1gqtrShcywz4FocyHrTF26nf++G2o+8GbH8jfquL6C4w1Obco8+HqBuqVhwFus7rnQ0NzEjV0ncvn5VoKDU250NAcTpWmt6UPfRTM1etQl2mNNVOrEuu0tmdq6Jt2e6gJXVB6z3lDW9c6M/SUlu7uAHPRpIx1Gy88K8pxAb0TOt2WKIfG+nvz/ZtQFzcfiHLf0I/1fIzHckVq0VWqxvnLSCiLS7Lvhuys22viXtIdyb0kNeY4VzL2xZLSMQ2Px/83xMb5IFJrulFfgDZlo//rq9IXoTfGNu8dYDwm6jzip8aZJcXYjpXeXTTBvbjZw3O1Du0zqw1oc+GkzI8TQzfyjXc/hDo/kP2sVfHdopjDOC4oHcDYOlcYMeqpvSxKMfcuLaEu9uGhencx4tjSE4YuWCKOR0BgnNV0b6fXHNblJ2vyWr+XA28P595/G2Pm7/yXf1eU/8Jf/AvQ5td+7V/HX1PnCEs7MwCvC6ONkVf1AyaGtqRnnLe0t4Bv6V0b+TiDe1n6lpZ2qyxbms6x4QuiNYfj+HicK5xzGIDOWHeWdrSlgZrJdiVQIbZ1c8e++kXr/cg4p8Tq9onxvlIOUT974OQ3itQ33j+1LvSUOrB69NLM0EA18oCXqT17yt+DSDbGzuzlFBLetu7rx9P+/qQx0ib4+Ng5xHoeua6T1PCDsLSk9fkzs85dT97DptHtd865TOWfZy5egjbNQ/ldoziP5+iRIegdT2TdaGB9RzHGRQ2LlZOniSGrjfU+5qncZOV7w84JNL4/blQfj5M1IYQQQgghhBBCCCGEkKcKPw4TQgghhBBCCCGEEELIDMKPw4QQQgghhBBCCCGEEDKD8OMwIYQQQgghhBBCCCGEzCBTG9JNksdQp/XpWy00rNo52IK6OCfFnrd20bBtPDBMaJw0KDlooUleGqCQ9GgiO3r27FVos7i4AXUfvfmmKHuGIH9nVxrclGoo/3z6NBpZDJTBTbuJBkxRiiL01bI05vBSFLKuGgZnRWXw0TIMBxqn0Gzu7OXTovzhB+9Dm9dfvwF1b3/jLVGO94+H4cbqAhokZmNl1hSh+YoXY2x7Tl7npWg8YhnVaHOoXAHNVlxg9EENYZCgkLoXYV1ROTk2D9AALFbGOFXDmKQ9NsTdI2WcYRh1RAnOfbPdE+VeHw12KoYp2Py8rAsNFzTLwKRQQONG6GeEuUOLx1tGdkeBZX6SqLjyAss4xhC1V44HgWFikQ8Nc7SCMojLGW2MPvRGcp18cBcNq3ZauJaCusy16Rj7Oa+Mi0aGidDOLv7e7ab8vaIyO3DOueocxuMLp86IcmMFc2hqmBsETsb/wDBX9UPLyFGOZ2oYSkxiHHNteGAbjjx9ChU0VZkoY0Udn845F+ZwbPJq38sMA4dOH81vYxXvkwRz/SMjHjwVfsMEzb4qyy9AXaFxXpS1Z41zzoVDOQa9w9vQZryHcVxVrji5EGO2m+HzBSoefMNwJzbMONc3ToryYhFNQXLGXO2q884owvV+8uQq1K3NyzyeGNcdBWGIc19Woa1NVpxzrprHtRqOZaz1DwyzEiOvFpRZZb/XgzYlH+NBe0hZa8tZppqxPGssGmv50plTonzQwTxXMGJ0pOIvX6tBm8To00ifR3I4LzljD0/VOTo0907DyLEk95sgwN+LjHWjfckKRj+PgmnON9beYZnUaQO60NjPrOsyNThWn7pdjO33lLFhrYoGkL/41Z+HunnDSF2jjdaaTTxD7+5gPs4X5Jq4dAWNlQo5fD4wPzJ90558vpt6n1f3snyVLDNmfT62jOyOCtt4T9bhidU2moqVrdPYMP1NjHOKNqSzzMVSw2htrM7IfhFjNC5hvm2ofag7RAPevsptno+jUDDOTvlIXlecYNwmxmEm9bV5mvEOYpjyhco9O7IWgQHMuxGSqWEMmarrjov5uG9ZisEQYl9N41DwIjTWtHEvaGfllY/pfGat03giv5tYMxGq9/znX3oR2sw3cA94cFuaNu/vYi6/eO481HXUOwCaf05n9D1tXGljOdME0Jg/3S1vOhd64/cJIYQQQgghhBBCCCGEzBz8OEwIIYQQQgghhBBCCCEzCD8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg0xtSHfp2WWoe3B7W5Q7XTR72d5Do7UkkIrJu4ax3EGCRiOZ8vayzHiKRTS3mKhbvXT1M9Bm3Mfv5KOOfJ50gqZZw1gabCxXUTi+1EAzjX5X9t0yF3j8CM38EmU4Flli0zGKVC8VpZHLp15GU77V505D3YfvSCPCH33nbWjz4CaaoIVjaT5R8AyTkyMg62CMJqkypEswrrwJPqNT8ZekaD43HBpGdtr8x5ivHHoNuEi1GwzRlGNi9DNzsp9BijHTeijnOTFMMtwQ+7m/15L3NgxZum1cy7duPRTlwQCF3ItljJm19TlRfu2LL0MbP0CheEucf5o22jzBPyaGdJZhziSWz51ZwvdGutDPbRnAWM+tzQdTw8SqN8YffO/mPVG+8cDYI4oVqDtot0Q5TDHX+76MP98Yp/nFOagLQrkV5gyh//k5I7crU5BhHw1AKobB01xBjlWYYc6xzBa1eVQco5lIkBmGS8mTTX6OgsgwcMgpYy3LsEIb8zmHcx3m0ISyXsDn7o/lHn7/4QOjjREPBRl/qTEXhToaYriKNG0LjXxcHtdFuWP8HV/vB845Fyrjzcoints29/Bc0W7LfTGfw/V35lk8MxTUGHS6GP/1eh3qInVuSjOMg4Uamtvl1fYyTnDPPQqsvSNVZ7rEmK85Y48rjOTYZIaB4EQnAufcOJJjMeoYJjXFOtTl1ToJjbcCw2fWjZVpTMU4e5e0kdI+GtKVQ8yrI/V8WWiYGOXx98bK3MnKoZlnrBtlCFc2nmU8wvUdhnLs9HpwzrmCkb9Kap1aRr5HgWlYq8+rpvkc1uk95oMPPoA27XYb6j7/+ddEuVrFw7Ded51zLlSB+95716DNvXubUPepz1wRZW2k55xzP/qRNNj+O3/nv4I2zf2W0U8Z2//Bf/gfQJuvfOVLUJfFT3Z3+rimsp51zvWeHH/2mUH34XgY3To3nfHTNCZ7zjkXKfPL1MghnmGQmVdnvzjCHJIZpqTayDkw8tjmfYzvmvqusHpyDdqkDZmjYstg0jC/0j7HfgGfNzPenWNPPnNqmSsa5l4+eKBNF1vW/EEbo+5jmzn+K4ZvnDV1fs8ME8XMND5TZoRT7C/OOZcvyv14MMAz0P6ufG/sjrGNNml3zrlYnZOuXn4G2jxz4SLUff873xXl1IiiaXLONPFp3csyD5yOj+cUyP8cJoQQQgghhBBCCCGEkBmEH4cJIYQQQgghhBBCCCFkBuHHYUIIIYQQQgghhBBCCJlBptYcfu/9W1AXpPLyfIg6UKfPnYe6E+ektu3Du38IbdpN1FPtHkpNkVyAmoLFAeqXnD15QZQb84vQ5odvvQ51QWNelF946eegzU5H6iX3DA28YhX1+xZPSC3ASYZabPUqaoXsPJJjkCaGnmUZtfq+eFXqBZbr89DmvYeoRbjzrtQEm+yjpmwtNPShQ6kZNxmhXvNRkIwNnd5U9jWK8XkSQ88mU9dp7WLnnBuP8F45HaIR3vuwtQ91t7Zaonz/MbZpHqJ+q1OaT+vrJ6DJfvNQlDsDnK/BCGP00Y78vTFKTrnBAK/LlM5m2YjZrYfbUDeJpabyF76AOSBnZTWl12PJSZlau06uL++YaAM6z9JVlg+VGNpGiaFVqXVrI6PNJDK02dSfFiNj7h/to8b3Ozek7t8hhr+LI9QuTUL5PNUiageHak8w9eMizNGVmty7luZRK9YzdFH1NOw+fgRtaiH+Dfb556UWXMkIK8/Q8Iq0Zp0Rx8UC5mitnX1csLTEFhfl/mxp1oYh7nvNA5nDMlMTDPvweFN5J7Qx91WrDajT+d+UiSxiPydKYzCLcW6Cqtyfn3nli9Bmu4gPM3goz2mZoblmSZe1lJ53o4FnpMAIoY8+krqHdUMrvJjHhFypyHb9Pu7Ld27dhrrlBanhWasY4vxHgqFtq/a4wIhZS4OyPi81IZcNTeoHA8yrn37xeVHe2zuENvuG50JQkP06sYRnw5eeu4x9uHdXlBtz6K2RVx4EoyaeWVYrhlax2mdb/Ra0KRveArWqvFdvjOeviTHm5Zrsez6HfXJG/s+UvnvO0Dkfj9EHolyUcZtOqUN4HLA0IqfRHH746CG0+Y1/9I+h7pt/LN/Ffv1/8mvQJrT0XdW+t7e7B23e+P4bUPfpzzz7xHvfuSNj/b1334c2pRKeYVstud7+4T/8DWhz+RlcWydWl0RZ7zX/MlheFBnMqaUNat5NlU1B2SNhGl8F02fEWItap9k3hNnzxrOv1eS5YWIckpt9zMlhXmmSO5z/lTrmmoNtmV/HA8zlRaU5HBnnD+sVKvC1pwmeowNDr7uvXhS6Rt42JI5dTg1VaKRI63yv66bVeD2umsPT9GPavur3gMTw/BhHuGfG6hxp7Y/TrLfIiv99PBNo/7KDA/QlS5W+dc7w7ers43U5tXYnI3wHuXXzI6jT9jWJobv8cTWHrbqfVfh93JMF/3OYEEIIIYQQQgghhBBCZhB+HCaEEEIIIYQQQgghhJAZhB+HCSGEEEIIIYQQQgghZAbhx2FCCCGEEEIIIYQQQgiZQaY2pHvnTTQTaMxLU5GzG+tGGzQ2OX3+jCj/83/2TWgzKqGw86gpBdDHhlHH0soG1C2unxTl7/3oe9AmLKEId+nCy6J89StfhjZfPS+Ni37rn/wmtGnuX4e69dUVUbaEuoeGMYI3lMYtyQi/75dzaEgU5KWA+IMtNPu6c+cm1LlcThVR3vrEeTSFUT4kbjg5Hn+HiCcoPj6O5diPJ2igNuqhsLk2LghCFGRPDXOv4VjGcStGYf/vvouC6N95/478/TwaYPT6baiLY/k8/rvXoE2pIOdwYQENuU6dOgl1S5mMq7sPHkOb7gCfT5ugDSaGSVgZ42rthFzfW48PoM1rr30K6rTB32CIhmeWcnuuKAPZ946HIZ2RGpyfyHw4GRtmVIbK/Wgs42MwNAwEDXO0kifnJ/KxUw/28V6tkeqn8TfKJMb8X5+XBnRZiiZW5ZLsQ5Jgn4YZGkMVPGnuEUXGGBjmXi0Vft02rr+aMVeDs3Ltlgwjj3zOiDWVcyzzudBwDsvSj2fS8UlTKKCpSrksTcdWVlagjdX/wVAaPzUPW9BmxzCoaO7LdoViGdqEAcZMvyPnOsmhGZvpjqkMwGLDbKhQVeZextqqnUYjo+b2jij3rH0rxOfTRhqJYTZz/y6aEo+Gsl2+bhjZGfH41V/4iijfuI570vvvopFXQRn1eN7UR9hPlGoFzTEjX8W2kdMGxrkoC+Se2pjHZzy3hvF4Tp1z9+fRIO7DW/ehLqfm58wyPksDl6kL1+W6XF1agjajoTxv7d77ENrUcrjP19R4npjHPFf0cTx7fWlC5jnseHUOzSU9teYtM8vMMKQrFuR1hQI+SymPfS+ovJ3L4XweCYYJDhg8TWlxo6/7ype/Am1yIY7NP/3N3xHl//Rv/9+hzSnjXTNRZ0rLIPq738N3v69+TfZrcRHj+NFDea61jAfzeazT5ybLyO6b3/wW1P3bf/WviLJtTmSZH33cff3J1013ZDge5wrnbLO5adpYdZk6nwXGhOSGGG/7H0hT1eUTa9CmbBzmx2ocY8PtNpxbhTrvgnxvG9QxlhfUvhD30MS8aBjEpjfk/h88xG9CQR2fL3zmguxjHdf8yDDcM2YBaqbCmCtrOVnmbMcB66yrz2vTxLpz+IxvvIEGnVadNol+/rkXoI1lKj9R7/XNJp5H33rrLah7+FAagBaMPbRak7/XMN4lSmU862ojOcsUeNDD+A+U+3hqODvr96x/GdBY8WPf6WNddTy+2BFCCCGEEEIIIYQQQgh5qvDjMCGEEEIIIYQQQgghhMwg/DhMCCGEEEIIIYQQQgghMwg/DhNCCCGEEEIIIYQQQsgMMr2bhzKecs65XleKTe/stKDNu2+jAcX5c1KgfH6+Dm3GEYo9v/SZZ0X51MopaDPqobh0u7UpyuW8YZy38gzU3dyVQs6n22gQdC6R5haFBRTqzvf3oS6NWrKc4nf6uZUTUNdrShH4snZ+c861DDH5f/bHfyR/L8Pfy1dwjn0lur3d2oM2CwmKflcbUvS+UEOTk6Ng0D+EurESaR+O0LTOTdAcJ1NGQuOhIarv4Tj3BtL88K2bD6DN+49bUJdbWBbldhvbJIa5Qbsn+5XFaH64siTF3PMlNFYZjNGkaDiWz9Jq4fhq8Xzn0IiqWMQ4PnkSzQ0ePpRreWgIx3/tF74Gda4m125q5JfeAPs+8eS8e/7xMKTzPRSZ174LqaFgb4naT5QZZmQYMwSGV0OQyNg+6OIa2TrA+emM5c06gz60WahjvlhZlPG/YuRHl0hjn1s3tqBJdRUNiRZUvto7RMPO0RDXTapM/w4PO9AmyqP9RW8kr6uWcd36huGSzie+ZVYRG0aEqt20BhafNHNzOM/VqjSaGI/RLHbfMLaIVV7r9zGu9vfQwNJTZm+5HO5n0QQXwEj1K/bQEMMzDCoCXWWlFGWA04lxvqobV6DutC/z6PDhDWjT2cfYjsYtUW618MwyMow7agvSFCc1jBUvXUHjvK98RZpAPX6ARmmhsXdqD5AoOh4mMvPzaOA6DtQ5IsH17OXwGbtqLqw8cH4d98a0L01WKz6OzWefvQh1iTKNmSsZZ5YmxkymzABDY+53NuV1lQBjqN/GHJ0NWqK8vIB5omQY+Y725HX3HqFB6JnP/BzUramzxigxDKqLWJelcvFWKmhK6RtGdrlQjl2haDj+HQGe9f9Calp9o41nmD7prcnK9b/yK78CdWfPXBLlv//3/x60+eY3vwF13a5cb6UyzsXNW5gP//b/7T8T5arxrrKzLWM0DNFAUJssO+ecUzlsMsEzxO/8zu9A3Ze/+AVRPnsGjdZTI64+SaYzsT0+hnTOeO/QwWwdgzzDaMpTxoKZMdWpsQbufyhN3vfe+QDanP4SfjOIG/IM1Df6GYZYeaDm6KM7+L5e3padv3L5DLTJT/CcNG7KZ1kd4/eCzgdoap8p497GK/i8h/P4vjlSj+clGFvTnGMzy2jOdngUWO+tR0G3i+bpJfV+buVfa2yKRTlnlkGcZY6qc9tohHtv4ON1Y2V2vr+P50ptzuqcc0X1faDZxDj21Dtwcw+NFXM5vLd2c68Y+0RoGVWqMQ4CPLgnhrn0xzX/1pelptndJ5dvj8cbIiGEEEIIIYQQQgghhJCnCj8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg0ytOby6VIO6aklqdXgJCvHsPtyBuoHShlpZWYI2+y3UvFlclL93+TxqB9+/9RDqzi6dFGVLG/nr//wPoc6vSx25xV99Fdo8aElNzQ8fooZLZugcliZSHyWXoe7JMEFdF39O6rps3UItuGLJ0D6cl/dvbWGfTsyhhtDcnNS2aXVQZ2XSwuse7Mr7H4xwXI6CfgefO1K6LeMJassVcjimUayuM3R4xpMR1L17R+rmfuPta9Bm4eRpqPtzX5Hxd+cO6qfdvHkb+6A0lLcPUVt3cXFRlLWeoHPO3b3/COq09pqluZcaOjzDoRyXXIhxdec2PktOabjVK6j1eOfmY6g7dVLq1VZL89AmGqNGaacnNWT93PHQHM6coYumZKc8LRZoNXLOBUqDydI20rHunHOZ0g7e2Ud94f4YNfYKFZlTaqGht2v83bKr9ItLPq7Jg32pDVg0NLjrDaw7eUr2qVDA+Lh7FzWtOi2pBzY2tGlLecyPzY6M/6UGxvEkMjTWVPzHhu6mpZSm53gKybWngtUPrfUWRfiMA0NPOFA5JDO0A7W+tnPOhUqXLPBRp2xs9EFremcxzr1nxEMwknVJZOjZaw32IubVrIp1p06eE+WhoYV75wffhLrxQJ7der0WtEkMX4R6Q+p+R8YZcHUN9XG1rt3DB6i732ujfrfn5DMvLS1Cm6NgeRF1zCdqL8yMvJrPYS6KJiqvRag5WDD0mHNleUavVFE7u9dEfd8kk7kvLKD+Y+JhnV+Ui/fxFp69W7fleesQJYDdd36AviQnVlXMnMcz2VrV0BNMVVzdRa3CSYjnrYuXpOfI8jK+k0SG5uVIeReEPs6nOVfGnncc8AwvEq1n6Vki6Rkmck/pNlrSi9ZZ48rV86L87/+tvwltVgzfgP/6v/67otxqo75lNcH32Hffe1+UazVso/U7y0bujY38PxzK3J4r4Fngzn3Mfb//h38gyv/e/+rfhTZhaIw5VE270et5+LhalsdHcziwNOtV9wzrDuf71rlLrQHj4JIY791Xvijf2Sabm9DGAxMC5zz13phlqOd64cpLULd2Wq7NR7u4d9x+KM+x2208E+XDOtTNXf20KC8v4PNecvh7b771PVlhaOGHgZF31DnQS6eL5an8NAx9fD2nkXFWPAre/RD3qxMrcn+slDE+asb+X1YxOlfFXDcY4nl0MJT7r++jL1NgfDNIEzmGA8MPqz6P58Oy0lRuWv/Hqvx/AkOD26V4bghUrFkay5Zvi+/JuA2scDR04EP1XhJYZz7DO0OfF633+WnkjD+u5jH/c5gQQgghhBBCCCGEEEJmEH4cJoQQQgghhBBCCCGEkBmEH4cJIYQQQgghhBBCCCFkBuHHYUIIIYQQQgghhBBCCJlBpnZFuHICDRriiTKHWqhCm5ph5DVR4tYra2gucOM2iignEylu/cO33oQ2jcoq1LUPpUh61EPB7VqExmF37kqjjO98/yfQJr8iyzceoIlW+BiNw15QBmC1Ghp+DIpoqJMpM6XJBo7v6ZOnoK6ndOKbB2j2Uq4ZBh+eMtzIYz+9AAW9g1DO36tfuIL3PgIGLTSpGCvB7kSbATnnhhkulXZXxnHzEMd0Zx8N8N6+fkeUt1oo0h7l0fzqj/+FNE08s3EC2sQDvJeuyxli/FEkBdh399BAMAjQiGRemTZOIhRy395CExztW2GJpg9HuCaDkhTQ/+jmXWjzL77xLaj7X/xPf12UK0UU6x8W0YSsO5K5YjJGgfujIE0MNfzsyaYZvjGHBTUWXoDxnxnmHv2JzA07HcyribHFLMzLfWIyQeMHz4iHnDI/bD7GtXz7xkeifPr0SWjTsQw4irKfS0vGdR0cz909uZY9w5DIMwzObj+QRkk1w8ykXsJ75ZUJjZF6neFj55xThgfHxJCuXMYzQ6TMILpdzGk9w5BuWRnb6pzmnHNxYtj1aYMUI9YrVexnMpJ74TjBteWPMF/klEnjuI/7xnxdnmM6RTQyGoe4F48Lc7KPRk4Li3jdsCf77vv4LIHhwLG1JQ12qhM0KskMk6JCQcb72gqe2z56F89bYSCfb2kJz6VHwVxlDuoSdX6z9rhUr0vnXJbKfOFNMPb8CMfZZXLOvAzbeCkaK7abMm9/+wd4rt7fx5xZX5d7/6VThjngvsxzP3j7JjS5v2MYLwey7ycWcI0s15ahbnNHnrc8w7R02MF7NR/LOF4wTLK1kY1zmH8t75eCYRymDa6OjUOouTHoumnaWC0sszurpVw3Gxu4F6+urkCdNpCKjfzfMsyYdS6yznh67a6uYr6yjLBG2pDOGIOFBczRv/d70pBudQlj/d/41T8Pddr86ONinR2nMps7Pn50LjRMH/U8WnPmGwZx0MbYH60z682BnP/a5RehzbMXN6Cu+fCeKPfuo7HmziGaIr746edFOV9GM7OT6/IdankF11fFOCbt3ZLJLjAMQUun8FuOK8tx6RlmX5a5V1nllMgwHNPmxVZdahgTuynM7aYytnsKfGgY0n3wE2miuVjH86Hn4b56eChzm+Gh6YYjvC7My/GyDOszw5Q0UGNYKhhmbEM0MUyUIWMc4xwG6ltUbQ7XQ95hrGXqnTQwTEIHA3y/8NV7QmoNnhFr+bx6ZuOlzTK7zdS7CpwZftoKamBNGPMyDccj+gkhhBBCCCGEEEIIIYQ8VfhxmBBCCCGEEEIIIYQQQmYQfhwmhBBCCCGEEEIIIYSQGYQfhwkhhBBCCCGEEEIIIWQGmdqQbhKhSH9JmUNlHooj98ZoKtVWBllzdTSy8FLDNKUvxaXbbTQkOtxFs4nFyrooXz7zPLQ5uY7i0meVrvP2dTQX2/3Jj0W5XkCR9nELBb5zJ2W7kWEsNDZMp6oVaQLSKaCY9+7eNtQtN6ShwdVn0HCjZph0be+1RTlOcZxqy2hW8pU/9wuiXNnA+TwKogM0pBiov5EMG2gQcdBFg7gf/UQKxe92MNbXz1yAurAm779qiJHXajgXB00Zf5t3H0KbsWGA1FiUJgGGH4AbqetKZWxUyKPge5jL/5ll55wbDNCorN9RcZuhkUFjGWP03t1HotztoOlO50++CXWrq3I8v/aVz0ObhTk0/dA6+AdtjIOjYDzCdRgr4XnL6M0FKJg/UYaI5RLeO8zjdbtDGe9bBzgXkxTF/qNIiv3P12vQZnER4yHz5TopGMZaqytyDvd2dqBNa4j5OAtlXp1bwOdtNjF3tFUc1+cXoI3hmeE2d+S9ioaRzHoD11tB7ROWGWiUGKZ4x9QAKQwwPpotmed6A4yrNMPxGoxkfHQHRvwbZnNxKtuNIjTAW6piPI4K0iis2zUM8NpoJDPZuSXKh5ub0KYYyX03fxb3Ea9smMMqg4rMMtGaQ4OzfCQNnvIFjI/6Il43itW+YZjozhtmu6EyKtlYX4c2a6t4Ltw4JY2gFo3ccRQ09/EcFquN1jK48fLG/2d48jrPOB8EqWF+qOrysWFy4uH+vN+U8Tcx9uudG4+h7ic/lOZywVdfhjbpSMb6gWEGV8xjXKXKFKxiOBbFY+Ncocwda1XMLzltQOmcu31TmkZ3jPF1IY5dWJCxnRl51TP2XB0LUYy56qUrL2Mfjim2hZfKRUYry6QuCOT+1W5jzPzJn3wX6kbK6DxvmKFbJnUjFe/DPu43gdqfTWNiYxB01WoF81W/h+ZHmw/k2f7/+9/8PWjz4ov4Hnv58kVRToz3tWm2fss80zap09c9+d5PC9OQWa07zzRStMZMGXIZDxoae+3Ovoyt33r9h9Dm1c9jfnj1c58W5XMnMR/dun8P6tpvyH3o/Dqalp9ekXWNBdyfLaPj2pLsU2aM040PP4C6yVgZgOWMM5gzTCB9OS5eZhlaTjHHxneUZBojO+vgfgTc+QDHNEvkftFawFzXN8zp/bw8x546fRbaLFbRXDdTeTpyODaxj3W5TMZINME92zoDZamKB2PP1tk1Z7xLhEaM+srYMDXOZamxviGuPHxeKx7TVN7L860kaRnM6+LTTa78z2FCCCGEEEIIIYQQQgiZQfhxmBBCCCGEEEIIIYQQQmYQfhwmhBBCCCGEEEIIIYSQGWRqzeH5pTrU9ftSPyTMUPdkewc1Oh/sSk3cQgW7UUT5EDccST2dfBk1wLrNNtRdWJTaTK9+8avQ5vs//AHUjZtS96TbMjQMM6U7UkWt2EEHtUkGI6Vzpe/jnIt8Q+dZaa81FlCXdXsTNYfDnNQs+tf+4s9Dmw9+fAfr7kutuaCB+kRf/dW/CHVzS1J7894eatYdBf0+6gJ3nBRY2uyiTukHdx5A3aMdqXldmkeNxK3H+NwHzaYoh4be6NYWrpslpcM6GaF+T81YOGfOnxfl7D5qFR+05dqajFGXtVzC2B4rTfHUiGNLn6tclvcqlTCuLM2nQkE+X1JBrZ5WGzXcvvu9d0T51AJqw144vQZ1jZrUfR0NcQyOgszQkhtNZN04MrQODa3WkZpDS07Pz+M4N3tyXicTQ/8rb2hjhnK91Ru4bhYaOD+TiezY+upFaHP+medE+cb196HNaUPeyVNa2aMJxn+ni7qi1arUy7Ti39J+jgpyXAYRrpGxMVdRKjvvp/gwhsQlKFr5/vHQHB4OMR97Tj53akhtxUblg0cy17ZaeBYIQzxrJErLazDEeZ4Y+3qQl+edchXPP719zLV3R1LTcn8XvRO2H30kyvUd1Jd85gs/D3WVU1LTMjTyXKGBec5XOWAOH8XVFlB3eb4k13Jk6Nw9eoR75zvvvCvKXUPn9twF1Fn2M5kDdo399Sj4R//wt6BuoLSyC3k8r+ZrxkFX6eulMeaikhYfd87lPFn3xZfOQ5vTc3WoOzyUsRb6qMv61S/jXHzjm7dF+c1vvwNtXnhJasCvLGK/B7hdu8V5OS5LC6jVWsjjWs4X5RgvhKhn7BlvPQ/v3xXl736AMas1/Z1zLq/ORJmPzzeJDa1MX+a4wNAr/V/+6l/Bjh4B+vxmnct8Q2sxgV1nOg1PfR4+OGhBm+1tPKPrrdc6w06DdV7VWsXdCeogW3qTYU7GQ/sQfWus/U3Hx9Y2ate/+96HUPfMM8/IigzPiab2M3Qdn8UaF+R4nCuccy5J8NkD2P8tHWxE640axzx39+5dqDuzJvNmrf4stHnrw1tQ93BfxtfLr+B1z17E/B4r3e3rtx5Bm82CfP9s1HFfqhg6xHNLKpca+rHNB+ifUFPBNTHGfGT4VYF+uyE7a+UiHaeW1v808W1f9/TZe4jfZRoNuR/ubWGuK5Zxz/QLSjs4MbSDjfeVYvnJ7zmpocHrq7lOjTU5Nr5jTFRs5Qyd6iBQ7wnGmoyN5wvVu09/iAeQoXEeDVWitnSJLZJEeUj4+F5n7R06TxcLeFbMG2fKXk+ewaPI+G45Bccj+gkhhBBCCCGEEEIIIYQ8VfhxmBBCCCGEEEIIIYQQQmYQfhwmhBBCCCGEEEIIIYSQGYQfhwkhhBBCCCGEEEIIIWQGmdqQrrGExiZ9ZRB3fxPFz7uGcdJeS4ryr1Uq0GbjAhpJbD+Uxi1xhuYPX37pi1B3akGaufzW7/8utPn+j38EdbmyFHxvrL0CbRZXToryaIjGAVGMAtTDdkuU+wEKSwcLhsmDJ+/lOcMgK61DVb8vzWS+8/1DaNM6QAHzxVNfEuVTVzfw3vEc1H30xpYo7++iUY37m1j1SdMzNMQfdGXffvJwC9rsdQdQV6zWRTk1xM8PD9A4I03l32T6PTRl2t5GU8G2MliqVDD+L1xE45hYGYFsnDmNfVJGAlrU3DnnnCHAXlLGctUKrlttPuecc74yd1laRmPFVgdjVIu7nzp5Etp022gWsrkjTRiu30MjoxPLdair12Rs1yuYB48CQ9MeTUV8TO+jyDKOkTfLPHSjShyK4Q/HUrQ/SfDe5RLm9lxexq1vPExjEeOhr0wC0jzmq0QZ+3z6C38O2vTaaAB2/540fShV0JCj20XjgtKKjO1eD/PEZIRmEV5OjnF/giZQY+NvtzmV/wPDWM4P0IAgVftw4h2PvwsnMfY1VgZc+3toztkbGOM8lvfKhZgflw2DtgO1F48inK/h2DDOU+urWjEMclt4JkqaMvekgWHS1ZJnq+gn+LyFBPNx/qsyrqrG8zY2noO67cf3RTkeYawvLH8G6s6/+KIov/ltPFv99u/+IdS9+QNpFFkrY85p1HA8vbFcJyPLzewIeOP7b0OdZYik8UOcw1xerk0rP1p0OnLP9idorLj8JZz7w0PZLihgLjp3DnPt11JpCHrzFp5Z5nIyj55bw/1gYuxJ+VCOQbmG+Xg8MfJxScbREH1lXOZwfedVrtjZa2E/Y8MgtyLvlXm4lp2He+dEmcQUCoYD5DElM8yIrFD31BnPMjSzTJ/0vUZDnK9oYv2gupflUGSgjY2snTFWnbKexTIVipSx7djYt3JFjO2yMoDyjPfDN3+IOecX/9wviPJ8HddbZhoDHh8juZ8V1hPFyqQwl8NxDQPjs4i6WRZilNQbeGY9e+Ws/L15fF+5dAXfqZ06Hw4N0663vvMB1D3zzDlRvnj1Et5b5b+R8f6508T3v919+T62XMV3vdwifgvoqfNVFuH+EhqrTr3qoUGds9eh3nc9I8dMDMNpnYus9XwUvPY53LMrc3Jd+8YZMjXe2R635HMnhslqZLyvhHmZMxLTQBDHOVY5uFTF+Dg8wG9mk1hOfqWMsZbEcp4HMZ4HwhRjLZfJ9R0b762R9Q7Ql+ek1Dh/W+Zv2jQxML5BWXGsr5ufn4c2a2toLn39+nVRnlgO81NwPN4QCSGEEEIIIYQQQgghhDxV+HGYEEIIIYQQQgghhBBCZhB+HCaEEEIIIYQQQgghhJAZZGrN4SyPWkmXXpR6F8kD1Fc9v4I6PKkvtVMPWqgV4pVRF+n0S4uqBvWUfu7nfgnqfqz07XYz1GpaOoeaH4Ou1PioVJ6BNmurL4ny1p3vQZv5EurBVFOpdZQ51HQ7aOGYt5UUSpKgHp0Xnoe63Y4cK0sfZqGxjHUFeV3chibunU3Ub03H8v7pCLVmjoKDDLXKru1IzZtbOwfQJpdDTbiFZRkfrUO8Lp83dBOVpmu/j3pSmaEFtLy8KsqVGsZVu4M6g6dOybHvtFFPSuvbNBqNJ7ZxzrlSWeqltQ2934ahe1nM6zHAWJ+rYczsF1qi3Gw2oc3Fs+tQd6jG5fY+ztX6Nt7rU0tqzEuofXRcyBVkOvcMWb7JGOMqdTL3lQz9qpGhy6TkP11gXFc29OQLRamFlRnqcGNDlyyvtPnyJYwPfe+hoUvZHeK9FxZX5H0MDcjBELXZWi0Z70mM904NPSn9fIMxrq0oQq2ogspDvmfp46HWWKry3nHRUwsNzeSJ0tcr5PEZwxD3y9FAjtfq6iq0CY183OnKTa1YwDZxglpiUSTjoVxA7ciCM65Tmsqpb2h8x3It1UYYQ4fX3oG6W426KF/+4lehzdqll6Bu593viHKvhftIVsLxPPOs9CTYefwQ2vzBf/87UNduy3g8t7ECbVyGcbxek3WVKs7VUdDrYm6wPAg0Rqi5ZCTXRGjoYhYMndKxipG9A9yLt7ZR309r553duAht8iVcb6dOyTk8u4F+Bw8fSi+DWtnQ5PPRk+D+pnyX6E0wFop53G+KSnN4dIhjMGdoWc+Fsg9RhAddvd8555yv9JLzRcxVmcPz3Xgk718q1vHmR0DmDEFERWB4GQThk3WxR4Zu+507d6Hu9q3bonx42II23S7Oq9Zt9I1zhbXtZepc6xvvRoWcfD5LW9IaOr31jw39xyCP+T8I5RjnjTZ//O3Xoe7Tn3lZlP/Nv/qXsU9T5KV/FSjlMCYj9eiGfLbzUswPk0zu2bUVfFd+6VX0O3p/V54/dzfxG8lXzp+Fusqi8nIJMP/dXMc987bKt++9i/4ajbUTonz2FH6j2Qhxfxm05WD9k6/js+Rq2M9Lq3LvmPcwt6bG+SpJVH7PcF36geEFoRd5hnGQD3GO9Rk5TZ+cC58Gl6/i++xgLPvqB6hHm2T4rjoMZDyOY3xJLFRwDp3S4c4ivC7Ri8sZmvIh7vWb+xgP88rzZcXwJNL+W4HDDdqfQnc+zOE5omK8t45iuY9rDyjnnAtD3BtjNcaZb70jWnuVrLO+dXS7eE4fKF37jxvH/M9hQgghhBBCCCGEEEIImUH4cZgQQgghhBBCCCGEEEJmEH4cJoQQQgghhBBCCCGEkBmEH4cJIYQQQgghhBBCCCFkBpnakG4cTKDu1DkpNP7ll56HNvNLaGzV3JZmUP0miornPRSJ3jhxRpRzZRRk39lvQZ1fkYLMlWX8Jr55gH1YP3tFlC9ffRXaZMr8J/BQIDosovj59QMpNp0aTglJHsW7a3PSFKa+gELdwxGKcBsS2FAzidEswVdC2cMRmmvEGYrJxxN5Xc4wsTgKPtxDI4vNQ2nQNh7i8/R6+NypEhpfWEBR+EZDmyg6NxzItbS1hb+3sFCHuvMXpFHM3gGayzx+/AjqqnPyXr2+YQCpDEWuPv8ctLl3D81DtAtHyTBk6fgoiD5fl2L5m49R3H0uh4ZjRWUU1jpEUwTno1nE/LxcSz3DAO/H129D3dyc7OdzVy7j7x0BnrF+AyWsnxlmX75hrJjp9RtY5jKYj11OznVgbSeGuUulps2NMD4eKGMN55yrqetq8yegzfKKrOt1D6HNoIextjgn95KD5i60GY1wj9AGM4Mh5okwwPwfJTIjJxGOQTTEXOWHcv4scyCX4lxl2hQBrzoSQuPP0ydW5fpdWsYzRJDD507VJjeZYF59tIXmqWEg18mSkbO10aFzzu1syxhZqKKJy4IRo9uPZGzv99AgNKdMyOZyaJDRTzCPtzelIdzBDhrSVOt4blp77rOifPdtPO+9fw/v1fp9aYo0buKz5OfQyG6uLnN73jAdHPRbUBfNy/OOlzNcwo6ASYyxpvNFZhiDhMa8DgcyhyQJ7lXlChrC9LU5iWXqaZjb9SYyh/3+6x9Cm1/5hc9D3YI6G5aNPSn15V7y0QOMoSSPaytS5kOxYXi2YBjyBspAuVjFPfDKFVzfN3fkGISZYYJp7KfRWM67tXMOY1wT45FcX5Yx1lGQpIaLrdotrl27AS22t7efeN3Nm7egxfvvvwd1up217x4coKFwpvpuDek0NqyZMRm+MmTyjTNSbJnRKkMk05Auh2eGYV+uZW2255xzowjH5e/+vf9WlPMlXO+/8su/CHXa9NXzpj0h6BE9Hka3zjmXGhEQF2X/IuP4FAc4j+sL50R5dQ2/dXzjB2jG+nBPnjd+/gy+01RS44xakHnFK+NB6cJ5fPdfPyVz224HY+vDm/L88QffxrPu1YuYk8+urIny9ffwLNU8wAHN/aL8brMyfx/aLFcwlgNP1iWe8d5qGHlpg+vMYU4Ljf0kTVVspIbp5BEQRTiH7a6sGxkGmQdtfDf+3g9/JMpd4+x5/iK+477w8qdFOfAwr4wMk7qJJ9dgYHwrWl3B831R7bWhYSynve6iCY5TFGOdp4zTS2X8bmOZ3X50T67vGx9dgzZnL+HY6U97fWPMLZPQUPUzNua428PvjfD7T2xhw/8cJoQQQgghhBBCCCGEkBmEH4cJIYQQQgghhBBCCCFkBuHHYUIIIYQQQgghhBBCCJlB+HGYEEIIIYQQQgghhBBCZpCpXcLu3kcx8OWLUqB898YDaPOFNTSHGo6k0U6vgyL93hBllPORNCRqPkajjlZwD+oWlqQZ1V4LDYm2DrAPK2tyeErzKNKuvMXccIAC0Yf30KyhnZOC6IEhON+oVaFumMofjB2OQRgaZj1K0DtvGOxYwu2dXlOUtwzDs3wVxcn9QBmAGWYNR8G9nSbUNfekIYBlrHXi1Emo29raEuWNjVPQJjCMK6JYzmGYw1h/7tlnoa6nTCru3sX1tnYC11tjUZoUtDotbNOQouzlMhqXHR6iCUiojCtqFYyrdgvH3FMWib0u3ntpEc0bnn/ugii/9urL0KZYwjG/eV2a6Q37aICws4PGDDdv3xHljZNr0OZoQMMQvcYHQ1xzSYqxnXmy3WSCZlQjY03E6vdSD/OH7pNzzuXzBdUG+zkcYj7Wpiy3bl6HNi1lyjRXwxiq1hawTwW5BpsHaBJimTd4ym1AP5tzaGbjnHNppsxzEjQbGPdxnxoq04wypl7nGUYanpoH3e8jw5h7X5kYWuaL2kDHOeeq83Ku2x3ci+draAC2syeNO1aW0bDq0rnzULc5Jw0qTp3agDanNs5A3fvvSsOv777xfWiTRWqfz3CfH0SG2eNEjqenDyjOuU6Aa2vxRWm22w9wnPbvY37cf0saSvkO18jJM7iXJR15JgoDnOOXrlyCOpeTOWC/cwfbHAGh4axYKslcEBmGImEBjU8SdQ7zEsz1sbHGKxV55nq8ieZvH1xDU9nttjRteetDnOck+wHU/du/KM1XIiMetw/ks7QSzMfDCHPRojKF9Et1aDOO0bDzkTIcTvNoNrN2GtfkfirHKotvQpvRBOehUJDnnTjDtZUaqdZ3MjYmhon0UWCZsXW7ckx/4zd+A9p8/3sYH8WSNOjsacNEZxutxep8PBrjWS2Xw3Wjzd5Sw1zP1y5Gf0ofoE+JvhdOqnXW0fuste+ODBNbz5d7fxDi89YN0+rNx9Ik7D//f/6/oM36CTRJffVz0nDKOpPZZwb9fMfn/82iDOc/p84SFcNI1H8fjROL5+Q7xR/cwpjsJrhn/ooy2xp8/Z9Cm81LeLZ49t/6dVEeR/hOUyngWXNZfes4iUcSd/kZaUj7nXfQIO63/+gnUHfuTF2UP/ca5tHv/9EW1N17uC7KH97Gs8znzmNuWFfGdXGI+T6J0AQ48OVaSTM8A3meYdqc6fLxsG3OElx3gTKmHo0w1v/gX/xzqHvrhz+U9zHy4Y1r70PdZCTn7POvfhn7aewdA3WOzfr4np8fYF2uKF9sHt/Bb2h99X6bM85Sfoh90u8JQ8OcPo5x7t9/T75vbm3i95eVNTRfjpUxcGwYrKYj7IMrytguz9fxOuPYoI1RLSPkaTg+mZwQQgghhBBCCCGEEELIU4MfhwkhhBBCCCGEEEIIIWQG4cdhQgghhBBCCCGEEEIImUGm1hyuV1BP59SJ06Lc2mtBm/3bqCfS25baUO091Fw6bOJ11brUyml1LR0+1EZ+tCu1SKIMdYZeeA7FeSoFqdUx7mM//VDqV55bewXa/KU6avr87oe/I8pvt1CrzIXrUJX3pbZMboRaRNU8ahVHsRQn6Vg6K2OjbiTHs9NG/disi39jqM5J7dtBjLo/R4Jv6Pco3aylNdSVnV9ArTyt7VIqof6RpVvUV7rUi4t1aHPh4jmoe/NHPxLl0Rhj3bkG1NQXpFb3Ugd/b3tTapW9+2O8dzzB+Bj2Zfx5EerprC2hhuf8vNQCfO4K6m5VDP3iispDlr52b4h9TxIZ/80DjGNDGtbtrsrxvHsLtQhfRPmlT5zI0D90Srs6Z2hAlfJY5wcyP44MHbZ2H/W+Ml/qm2Ue5qLEiIdRV+rpFYoonHtieQnqikX5ezsHqFPW3JPaVGmEOTs0pPPuHErNyWiC47SyipriY6XPPOhj7PW6qMOaqnycGWNw6hxqrl46IddytWBoNmaGvjtoI2OuOgoyQzRrOJB5Jja0AzNDc7t9IONqYQHzzoULF6BurylzQc5IBBfPor5eRcW7F+AcRkNcS3NzMqecaKxAm909qe0/MOJ46OPvNXKyrmBpehZRq7Ab10X5zBd+CdpceB7XW6R06IslI4l2UcP23g/lfvPpF1GX+Nw6njmvP5Yaw0OHfToKqmXcq0ZKW84z4jga4bkvUtqp1hmiULT0b+WavnEHx/3xA9QcHozlGpwkqNM76GE/81UZ/5MOxnq+JM/H/QHG8fXHD6GutC1z5sZKDdqcuIzaqff35L0Oh3hm+eU/jxv2+StyDS4uoeZmdxv3wILS/awUMf4LHq7TvtLnL+SmfhX7RAkNbdv9fbk33rqFmqwdQ99d+wZYOsHO0JP3lA5mzhgby8tDn7+H/Sd7BFh1CegLox6p5flg1emfC4x/x0qNc9pQnSPqC+iTkBmaytpjodvF88hv/tPfhrrnn70iyhUjnxndhOez9uWjIjC6srAvzwi5DzEflq69B3WHr78ryuWzL0KbL/+bfwXqzi3JcdzLPg9tqmfxnDefk9ql+Srm5OEIv5Hc+kjmLd9IKyeUL82vfw6/fZxZRe+av/Obb4lyvYzvxL/+165C3Te/Ic9Xm/dRl/VRCe+1NCfjO0gxfwQBflfIlJ+Ny3DR6fdB546PxrDG0jKP1HvHj36I+9WPf/Q21FXVO1TR+K7XH+Be/8d/9MeifMGI2YUFnNdhV/azkGH+tb6rNXtyrx1NcA57PXW+Mr6FBXn8vbbKiaMRxtB4hGeZclnuL4uLeG5vqvzinHPf/MY3RVl7UTjnXCWPfS8qzeHVdfS9KhvfnLpK2388+Xh+X/zPYUIIIYQQQgghhBBCCJlB+HGYEEIIIYQQQgghhBBCZhB+HCaEEEIIIYQQQgghhJAZhB+HCSGEEEIIIYQQQgghZAaZ2gUhaqHQ8u4taSrijVFY+offfwPqRl0p/J0O0bCh18V7DU5LAepnnkEh9RuPbkOdl5Mi3J9+6bPQZt8wN9IeMC3DbMiVpFD8iZMvQJPnu5tQ9/Vr3xLluXk0ALh8GkW/s6o0m4sMMe9O6xDq0kSKbj/eQZO81DBiyCuDCssEwQsNIXD1Z4fjYriRL+I4z1Wk2UmhhG0sM42GMonY3t6GNiXzXlIkvVpFUfjBEM1P6soU7+d/Ho1VNk6jaHltTsZ/uYjPcn5Dmm1ZBiOLC3WoC5QBRSWPa9ky+PB9GTM5w8xs2EMzmZ3Hcoz7PVyTE8NwcqCMcMplHPPTZ9AAsqhiY9g/HgZI4xjNE8KcHEPLIDFfRLNKp4y0Wi0c0/4IRe0TZW7nh9inNEJzg25LGlRExhqpFDBG20M5h2mKZgP723uiHBumjbVqGerGau8q5PDvpoGPffK1AUuKz6LNZZxzLlZrYmz4dT7aQkOpUiZNdgoZrpGxYQKo8X18vi888aqfPamP5gxJLGPGt0xEjf73E3ldnOGgFkOc+8WGND98/BDz+IPNHagLC3Ld9Azzn3YH83hOxdG5s2h0OBrJeR75uLbmCjh2jWX5fMMhGm+GPYzRgjLbmCS4bqvz+HuL5boojw/wrHPt/W9BXSWUZjqFAuaXnSbG/35bGo55Pp5/joKKca7Y29oS5VwB98Ygh3UukXOtzwvOOTdfxd/b2pHnvsTIKUENfy9TZ7Oukf/X1y9C3dySNC26s/cA2qShjJmHe1vQ5u4Wnlfnq7JP24cYH1keTZp2OjIebj9CU7Jrd/ag7pUvfkqUn3sWDSgf7aDpz+qyNL18+cVnoM2b796Aul63JcqRsU8eBaFh9FZV7xxLhsHw3u4+1A2VIWPPMLVNYpzXIHzy/yxZxnLapC7w8VmsdxxfXWedV42fmxL5LJ7l6pYaRnbqBapn7CNWn3xl9Jwv4n73/ocfQd3DhzJvP3v1MrTRBtzOoVGf5x0fUy8/wnnc++BDUW68dQ3aFI1nWPXlvC1dexPatP4bNNYc/Ft/VZQv/vqvQZukgeZvo6Z8z3jjR1+HNv/8t34L6t75kTSNs95bz2zI3PbcM1egzeXP4XeMX3pFvgv9/X/0Q2hzYg6NZX/5F6XB+++10QSwsY592OvId7TiCPPC4qlHUBencv9KUzS7S1PMt5lam5YR3FHgOVzo+j349W/+CV5oxP+5S9Lo/sKV56HNg8ePoe6ja++L8gcf4F74yqtfgTpPvR9ZpuWugGeZQU8eXnb2W9DGD+R1mWVuasxhSRm9lY2z1Fwd102tXhfla+/jAeveXTz//uTdD0Q5NQzDczkcl7oyodQGdX8aev8KQvz29n/4j/7PT7wP/3OYEEIIIYQQQgghhBBCZhB+HCaEEEIIIYQQQgghhJAZhB+HCSGEEEIIIYQQQgghZAbhx2FCCCGEEEIIIYQQQgiZQaY3pEvRwOnHb0vzt2IdzXGW1hpQt3hBGrDcegeF3Md9FJLe3ZKmB1dfRNH8V155Feq6XSkcfecGmtYNhyhQ3mlJYe4oQcONJCeva3loWHVqGY2uRgvSTMwboZD19kEL6tyhFFsf9tEocNhF84J4Io06YtMAwzATUCYqgXaac84VAhTvjibSjCI+JuLumdGPojK/8o1njAzTiP5QPuP2NhroaJF755y7ePHME9ssLi5A3fPPS7F/z8flOzHmtXkgDViyFAXRtb9TPEFTq8AwaojG8ve6Y7yuYJj15PNyzHOGYWHOoUj73liZmcU4drFh1BEpkfbeAHNVf4DmRmXV972D4xHHg4khvu/kvG4fYCykDk0KxmobaI0MQ78U5yfI5P1rhvlFFmMf+h0ZI+MBXhePcH7ySpB/aMTHUK3Jgwx/P/BwT8orE5wsRrOBSYxj11O51jJtKYQYkJFabxPDke7OnXtQN96T81720DwqifBeOm+HhuHeUeCFOPfjgXymIMC8UyqjOcOiMkqyzFgsk5Gc6kOvi3v49Zs3oW6hURflpTrm7MThXOTUXJzaQEO6pjIA3m6iId5cowZ1z1+Q5jbdFNfy9mN8Fm9O3itXNkyCjbNGc1eeW3ZufQBttm+9A3WrVTkP9+4sQZv5BTTP7HTlGXAevXyOhNEA1+HLL78oyq1WC9r0OpjnqsosdWkZDXVqc4aRYyRzz2iMsX75mdNQ12isiPL3vvMDaPPZz6Bh0N2HMh7eeh/Px5cunRflKMX1kGSYiwbKAHXTMK0LC2i2VarIsYoSHN9vfwfNpD716kui/LnPvmxcZxjLteX993fR7K51gKaQqTq3DPu4To+C1DiLnjhxQpT/+l//69DmwUOc+/v3pfHUtWto/vXgPl63uyvHa2icy0xzNPX+EhpnyskY9/AokmOfaZe1/+Huf3YZzdmcQzNVy7BNmzZZddEEzzGWSV2pLNdEZQ73pOYB5vF3fiwNpp65dAHaWM+njQHNoTsivMx4EViSZ7/OmVVoErcwR80PpbFlI8Uzgn8H4/TBb/y+KA/m16DN3Qhz1Pf+4PdE+d2PcA+tFHFfWF2UJla9Du5L1z+Q5mLvvPsWtPH+CebkpUX5HSMsoSHoe9/F9fy1X5BWx7/yS7gGt/uYNx/elPPXSIx8v4RnPP0u6Rv7i5Xn0kz2y84DTx/rzBpNZM4a9TD2SnkcG200ubSxgfcOcN009+U3uo9uvAdt1s9hzqhV5HrrGd8HRrHxvjmR+b3dx+eL1Lvl2HhnCwwz3/qcjFvT2NMwwe72ZL7NGUbqzz7/HNS1D+R3oT3jLD8xTFCrdfk+0+/jHtBuG9//lHF7NDGciafgeLwhEkIIIYQQQgghhBBCCHmq8OMwIYQQQgghhBBCCCGEzCD8OEwIIYQQQgghhBBCCCEzyNSaw+df/gzU9TpSh+TESfzWvHEWNX0Kfl2U9x6idlI/RX2NiS+1SQaGTvDjA9Su6R5KTZ/2Dv7e4weo+aulBwtnUDvYD2Tdtc23oc3Kz70CdZ99+S+Icuv7/xja7LZQGznryWdOM9TemkSoz5JEUrcm9AwN0dDQ5lGahbkcat0Vc6i9Mu7LMR8b83kUJD3UdxoNpS7T1t0DaDPJcLwipYtTMPQzc4buT7cjx/TiJdQB1Ho6zjn3xhvfFmXf0FytVlCnUfchX8Q5zNStcgV83jRB7Ro/k9qUoaHzWa1in8JQ3T/AOA591FqqKR3K/BzqUB0coh5YkknNsN4Q42Cvifo9fiq1hx7dw3v/+1DzyZMaf9ebRFIjq9lGLadhhOM8UtrVqbHGI0N/qxTK+UkMXbzE4RwmSi8w9g19X0NzuKi0OIMaap4VCjKQh4MetGn7+Cw5FY85Q5MsNjStOm25l4yGmHvzBcyPWhctMXSevRDXdz4v64qhoT2ax+v0etbjdFRY+aJcVfNsdLU2h3q7nmo4meCYjkaoeVavyzg6exbz8eJCHerSRMZIzci9UYgxU63InJXq5OucC4syr2pNeOecW2+gD8S5urxXx9DCdTHub529LVHOlzFm3Qhz32BT6oomzcfQJm/qzMlxOdhDTdl8GWOjUpN1oTG+R4PhG9CQep+Joa3XNvaqsRqvbgfzlaUnvLYqf29gaNTFMebV3R15jj9zCvPq5auoTfgb/7/fFeV+F+O4sSrvlXk4X3kjB9Qqcn1bz1IqYfzXanoN4tjduI6a2++8JTU9l5dQG7SYx98bdGW/7ty6C23yhvbt/GpdlLVO4FGRGB4OTskvvvDCC9DkxZeeh7rRSD5Ts4nayw8eot/MrZt3RPnmzVvQ5s6dO1C3syO1HAc9XFv9Lp4HBsp/wtIj1ccf38dYt3K0ryotGdPA2OB8tfcnCZ7bYm1c4Jw7PJB5NDPeEYIc6sl/45t/Ispf/tKXoM3J9RNQl6VaK9MQ8DwiIsMLZzMv995rHo7hpzZw7V9R77MHLdyvDo218+59+Q5/4z/5j6HNbor7Y60u9YRf+fRnoc0zF85DXVF5pFheFv2+PBO02rgnHB7gN5Lmnly//eE+tCmMcDwf3ZFn1MYqfkep1zC+T33loiifaOB3lEJwBuruXv+eKE8izDu+j7+XKt1XS4v2KMgX8IyfU7GdGh40FeP8VFReI/0h5siDNo5XkspcnvMMT4od/PZWW1Zn3QHG+qN7uAcEygcq8/FZRkor3hnr3Rnawf2B3LMNiynz+0t/IMdqbq4ObUrGt5VyXmoHz8/hOaKygN9JT12UPghDY44Hxvm+2ZTn+52dbWgzDfzPYUIIIYQQQgghhBBCCJlB+HGYEEIIIYQQQgghhBBCZhB+HCaEEEIIIYQQQgghhJAZhB+HCSGEEEIIIYQQQgghZAaZ2pBup4ni48VUit1HTfzW3K2g0cJYeZ0srTWgzSiHQuqFUkmUb9++B21KNTRSee/NH4tyvbAEbVYqK1C3+0CaHsSL70ObRk2KRHtNNLu4/gYKfA8bV0Q576EofNBrQV1PGyrkUHB7khqmHwXZLjXMUZIYwyFf0KYq2oDAuSzCutCTsTFKUIj8KFhUQv/OOXewK2P7cBuF1RdPoElRoSRjLTPMT9ZPoYlDqETSu33DlOY+1uVyckwXGrhu6vN1qNNokwznnIsiGTNJEefLNwwXYmUSad57bBkkKqOOHI5dEmBdQ5n87LfQOCGJDdcPZShYyKEo/N4OGjXNKYMH33LHOgICwxDFU+YagZEb8oYZSarMBQaYGlxkrHtPGSeFHsZHYLg6JMrIq2yYX4FhoXMuUIaP1QrOYZLKe0cDzKuxYco30UZlRn4cGiaG2khIm+0559zEGLvanDRq8n18XssPo7FYF+XVurWF4xzHymzDMjI9CspgIOXcqbOnRDkyDFarVTSiTBM5YuMR7oPDEd6rWJBzHRpGgNqw0DnnPDApwty3sornirk5uQdtbu9Cm/1DeWaYGGaIJ5ZwLyuN5XWDVgvaLBimZMVE9b1vxNUEr6vOy+tGjzGu+hGae6yovcsy7ZpEaLZRqMv1bRmcHQWNBhoktjvStGhvF+d5/STGx62bH4lyzjC6PXsKDUxKBXk+/nAXDY2tdf94S8bMnLEmIyOn+L7M2yfWMR7XT0lToTT5DrSp+riWta3LQRvPQ+0Ur/NCmeeKOVzvJzfOQt2NG3KsyiU8W2lzWuecK4RyDPLoR+NCy6lM5Y4Ta3VscwRYJp6RNv5xOKaeYfyjT2GWgeDpDTSUqs/Lsd84jWfvs2fxumvXrony9mM0x9Tmc1bdoI95Z6jOB9Y+nyS4z8fKSC4yxtcYOpcpU7wsxbNVuWysU7XnbT9Cs6c54x3h/kNpWvST929Am5PrJ6HOU4Z02TH6d7OJcYa7cf++KL9rGBvem1+Auivz0lSqaJyR73fQ7PAgkOOzWMV7v/LyZ6Du6pWrotyo4v4SG+/5Ogats7U+O62tWUb0xvuYiuXRCPf13X18d35wX8ZSd4jjdPLsBahrNOTeeO7Zs9Bmfek5qKvU5F7x1ht/Am0MvzGXZuobiTEGR4FlBL+gTM2+9NqnoM2Dh1tQNxzL3NbfN9bIzQ+g7sSiPIO8uHEJ2mxf+wjq2jfleouMM91eGw3T1i7J+xeKJWizvy/f/YtV3F9Cw6TOU29WvmGIWyriuskXVTsjJw8Ngz+n3pN9H89S+Rz+3qnTco+rLSxDG9ybnet1ZR+2tnegzTQco1ROCCGEEEIIIYQQQggh5GnBj8OEEEIIIYQQQgghhBAyg/DjMCGEEEIIIYQQQgghhMwg/DhMCCGEEEIIIYQQQgghM8jUhnTdfRT3r5SkqU6zi0YF/XEb6s5dkSYVO4YRVKeHws5rJSnAv2kYDjz/4mW8bl0Km8d9dADo9LCf0VCKq2eHd6FNX2mFD/qPoE1zC4XB9zsP5HUD/P14gMLtqZOC87EhSF0oo8D2L/35V0W5No+GY2//CA339rdkH0KHBmtjbWbjnCsofe2iYdZ2FJTyGPINZVLXbragTb+JpoIT9UzV+jy0WVpBAwJtCBCGOH6nz2xAna8ckAZGzCZTjHPPWFvjnjTlGA9wLWcJCrD7SpQ9MYyTLNF0TxmVxQUcg4VFHLv5eTnGh10UuM8SNAvR3mylHJpOHe6imcK9u9K5YHkJjWqOAssWL1BDmMvhmHoe5oZUGYv2x+jWoEX8f1onsQxZhhOcC20uUKyg2UYub4j2qzrfeJY4kX1PsUuuYIj/hyU5Bq1Dw0R0hHE8GMp4DwzDwsSIf78v82oxMIwCjb6XSjJ3rK+hCVQhNOZKmRVGxrwcBWEe57CiDLHiGNvk8jjO7abMa56xSixTnTt3pRnVJMK90TKVWqxLk5rlJTS6TTLMmbvK3Pedd9+BNps78mxTn8c1UjfMVZuP5Bml0z6ENsUSXlfOSzOPLMN9JDYMOFpqnxj1W9Amb4zd6ZPSdHCuhoYjqYfnn1xBrreRYXByFBSLeK4oqrNGmmAeCENc5CdPybi6fBHNtxp1NGNrLMjrHj7E83HfyGG5nHRR6w1xD3/nvVtQd+HSeVE+2EPDvd5IGYQaeefkEhqtFIoyHxZLGI/tCdZVKvJZPv0yGucsLaGZXwp7F47BM5fQPDBTbmKNFdxbJkNcN6OhvC7MGQ5XR0Cngwa/TXX2PTjA9zWrrteT69faG606nXvGxnnEMo3LK2OhctkwOiygY2C9Xhfl1Mhz+gxrnWn1+cQ557pqDEZDPFdb9+oqUyFtfOucc6OxZfIt48q6tza7c865Rw+lcd3Xv/51aPPZl9D8a0Wdh4+LiZdzzmUOc+vVq/L7QLGAc/bWHTTy/O6WHJ+6h/l+/vQa1L14+ZwoP3se3+uW6vhOESZyHCdGvGf5J/9vnxXLui5JMUaCAM+Q2oy7UsWxO1Ndh7ragjy73Df2pRvv/wjqel15dokneB7wnnse6i5e+bQoT4zz41tvfBPqoliuMd+In6Og3cbcWlbvK3/pL34F2vzeH3wb6m6rde6XDVPvMY7zpQvPiHK9ZJjTL+KeOY5bolwI8bozz5+Hul4sc/fWY/zWMenIcVlewHNtY3ER6rSR9DjDWI8znHvfyXWSDHFNalNg55xLAn0OxDHY3sRvi9/7+j8V5aUVPLdYe45+moFhHDkN/M9hQgghhBBCCCGEEEIImUH4cZgQQgghhBBCCCGEEEJmEH4cJoQQQgghhBBCCCGEkBlkas3heh61mrr7Ukt3cRk1V52HmlKdjtRKmqBUiTvROAV1CxWpzXPooXbqlqFns7wqdUd+8vaH0KbbQ/2mstKmXA9RuybzpEbXcIi6a+MYx6Ck9HMsbaCRoR+b+rLOui6OUNctVBqNP/crn4c2L30Rx/zHb94R5T/5xgNo08dpcFkqtVeCFLVYjoLFBYzRlSWpJXd5AzVw3vrgOtQ1lW7ovKF5s9fEeMhXpG5zpWJoTmnRZudcrSr1gfwA46PVQo3JgdKFHHQMzeG2rEsMbcDMiKtI6XPGiaWDhlQqUle0bOhghiGmp9FI/V6MfdJ6TM45VynIee92BtCmEKKmz8FBS5R9D/WJjgJLDytQ2kmBFldyzgWGTlmcqLyWoUZRYOiGBkpLyTNyvSFdCpqCA0NPLzTmNdVahEaM5pTuYGhpABu6eKHS5LX2Ld/I/1obORdaY4A5WmsIpj4+S6mCelKP96T21uK8oaFYNrQdVTk11vJREBtj82BT6vbX5lA7spji2DzclHt/rYw6vYuLmGt1nREyLhrgISUey7rDA8y9maHd9+F1uZd8+NEH0CZJ5b1X1lHrf3Ud9+ussyPKfoB5oriIeS70ZT4OPIyrgxZq3+0rX4SgjPt83tC5LZbk/dMUc4BzuE/lCvJe/cnH01P7WbO2VIe6Tltq0p05ieO+cgLj8cKXXhLlOUPb8XAffSwaC7LdC8+hVnGc4L36Yxkjev0559yd+/eg7hde/awoRxHq1Y4jGR+nT6Eut9P7j8P4uHwVnyVwGFeVorzX2qVz0GY8MrTxRzKvFkLcI569gmsw8mQfzl1A/WR/gmvpow/lOTqXPx75WJ+vnHOu3ZZjc//+fWjz0bVrULe9vS3K1ruKb5wrtB+FLv9paH1d61ksLXXdB+v3dN3yMs7z3Jyh/680ji2N5VoN9ymtJWlpB/d6mDM7XbkG221ck3t76K1RLMl3C+3R4Zxzh4e4v60sy3frzJjjoyIxNN7nlXb/K5//DLRZPYVzu3lP5sTlGmqZnrtwGurKi2puLS1fY26HygNmYpyHPcOvQcebPg87Z605XBPWuT21TDDgTrie6zW5LuauYrw/ePAQ6m7+WHox7D9CH5DRIe7/L33mNVF+/qUv4XVj1It96wffEWXP0J09CmJDE1dt2W40NDTrQ9x33v/gx6K8YOQxz9DE7bdkPHoNPEecfwXntT+W8ZAr4nUHB6gn/OgDeb7p7eFcrC7IM+uki/lpe4RnyEZDnrly5Sq0SQzfkUlP5tLY0HwvGn42ReWxY+XRrcebUHegvEmuf/ge9tPw+dGMTc3h//cTr+N/DhNCCCGEEEIIIYQQQsgMwo/DhBBCCCGEEEIIIYQQMoPw4zAhhBBCCCGEEEIIIYTMIPw4TAghhBBCCCGEEEIIITPI1IZ0kwEKXufL8vKwiKLp585vQN2jXSksXqygcPbcEgq+z9WkkHSjhKZSj+7cgbrzX7okytUcGipkAYppl2vyedYMg5tQC76PUOi8N0KB7zSWItFwH2ebTkWRNKpJEkOo3lCTf/1bUsz6wrPPQJsrL1+Eus//ojTymltHQ7fX/wiFsjevS5HxYGyYFR4Bn/4UGvHNKZOC5tYOtAkMI4G3rt0Q5UmM8zw2jM/ufChj9PQ5vG5RmeQ551xOmYmNDVH9kWHuNVGGQHGE8dHvS3H1QR/73W6hcHy+IPtUm0NDoiVD9L6mTAoyw8iu0zWM88ayLhhgm8AQpq8Wpeh8tYCp79NfQHOK+vK6KFuma0eB9Ve9UOWLEqZVFxsZP4nk3Qw/Apdaj+3LyiTFXmFWcy5TOWxoGIfkctjRZCJj2zLJK5elsUrBMJGLDROriaqzjA7zhkGiUwZ0OR/zRJjDiRiqZukEc32c4r0e7sp1GRZ70Ga9gWuw4Mn757zjYRxjmRRFkZyL/V3MRWHOMDJSxh3lIhrZ7e6iQWhZnT/KFRy/vjE/vprrzUdoqrKwggZcqVqnI8PYIlBxtX4KzefKNTRAyim/j2IdzyyxYcARDeV49o3z3qkLeD5IlMHIgy3MvXnjmFlW5qphgMZJYQHHvFSSY254Mh0JZzfQWM4/sybKFeP82BvgeL2ojOTu3bkBbSolXDdLC3KcX/v8VWjz4YcYo4WhzMdnz70IbbwA57BUlPPz2VcuQ5u5hjzHv/IZPHe2DzCHafPR566sQ5vA4fknGSvDzpN4jur6GDQ7m/KdJJehcc6CYRx279FtUa7kTkKbXIBzVcnLHLdx2jDqOwLGY9wbtbHbYID52DovxspoK45xPVuGOtrANTUMZFPDyDRT7TLDaMsywJvGkE4byVnjpI37nHNucVHGvzaac865YhFNjE6elHF04gSaIS408B25XMY9T9M1ztU5dU5aXcF1s2Kc4xN9WDwmhs3OOZcZ78+xel/2YoyRs4b565kT0mwuH+I4F3zDHD5RceLjQTq0xqwk48Q6W/vGvmqZeGu0KWNmnDMtQ2bnZDvL3DE2xtM5OS5hgM9y7iR+J1qsyG8G9+6jadfr/+K3oO72nY9E+XNf+gq0uXQZ96rDA2nUePfau9DmKNhu4T736IE0K9veQrPgnSau81DFzM4mfusoFjEerv3knihncR3arJ7G95xIvUcVi3j2vPERmuvevdES5YqPe29OfUMbTPDM2jH2qv0teb4vlvC8b72XDNS3hrqRf+fm8ByI75I4vt0uzvFwKJ/PMpfs9nCOU7WnZtYL/RTwP4cJIYQQQgghhBBCCCFkBuHHYUIIIYQQQgghhBBCCJlB+HGYEEIIIYQQQgghhBBCZhB+HCaEEEIIIYQQQgghhJAZZGpDusoaiiGHRflt2c+jGHllDn/C35cGGDllIuScc+0xCkk3lLlLYwVFqq/dmEBduyOFqk+eOgttHilDCuecG3uyD5UifktfUeYucwUcp1a3BXWhMgRKDacoyzwh8JSYvKH/nhqGYwePpXD1f/tf/jNo81f/2r8GdZ/7OWlO8uqraEpz9swFqPtn//ibovzO67ehzVGwtIrGJk4Jm1cqaBBxZrUOdbdvS2Hxe4coDj7poNlQuy1NaO7ffwBt9pponHThgjSqqRmOY1mE4uOeMu8IDdOsojJvevDgEbQZGmZ3p+al6U7NMN3JjHjceiRF6NMJrvfxCNdyOpLi7mfrmDvOnccYXVxcFeW5Wh3bnEZThPqKNKdILfOGIyDNsB+JMmRJjDb9IY7pYCzNJyJDwD42TGG8QM5FaPxe2TAOC5w2xEDjEM8wBPBUHzyHphmDnjS2yhfRbCA2nm8ykeOSNwwoC3nM7ZnKx9EI10jBMC9Znpf7Rq+H8Z857OdwIvvVMYzDGnOYF0ol2ffAwzE/CoaGEUOgDApzRgw5Iz7KyowlbyzVbhuNz3Z3ldFhHuOqZMTRYCTnrGDs14M2niuCUI793AIay1XLsi6Z4D5y/y7uqXNFOc+eYWJhmQYd7kuDkwd38N6/0PgS1F09I/fT7qXT0Gbcx1g7vSHzcZBDY7aJZZ4ZyH3D89E48ii4fAkNxcYj+dyhYY7pdrH/aSSNrVYW8TziZxhr8yqM2h00ehsP9qGuUpL5aXkB4z+fRyOZSk7G/2oD43g4kfG/MGfsEUZ+1Gff1SXMaZMIDcC0cd3iIp4PNhPMAeursu9eioZj9Sr2YV69u3QO8N7VEu6dFy9Ig8nlVRzfo0AbrznnwD/HMmtNjDpf/e9RYJigZkYu0tuzZaiTGmcGbZCVGoZ0ltGQp2LNMrrVxnnapM8522xO/97CwgK0KRQwrjodGUeW0ZxlZFepqLVsmMhdvYJmXEW1T1jjpM+XP22n6o7H8fh/wDIflJtKLsQ5syzVEmXQNvEsk0SsC9Xv+cYYxsZYp57sez6Pcx0apnE6TqMIz4cYNzhORpfAgNf69jCe4PtFAPseXqhNKJ1zrlyVZ66rz56DNnstzLeb29dF+R//dx9Am8uXn4O6i+fl+7WVB46Ct3/8EdT1+3Jex0PjPO9h/zfWpdHk3gGeu0ZDPJP3D+UZ+YO38H1l6xHu/yV1Hk0ivG77MZ5TorHcT0YhznNzKO+V+Xhg9Azzw0GvJcq91h60sZJAouoWFjC32iaN8llyOVy3S0u4L2xvS7PAfh/vHUW43vTvWQar03A8op8QQgghhBBCCCGEEELIU4UfhwkhhBBCCCGEEEIIIWQG4cdhQgghhBBCCCGEEEIImUGm1hwuraEmzCiR2hbVxiK0iUNDZ0rpgNTm8LqgjBpcj/akNuuZk6jztnHpJNTduH9XlJ9/7hloc691H+rKSns2LKFmXCmQQ3jhFGrabl1D3cEkkppmqaEdEoSoTRKo3ysaWkTWtIZKQ7O3h1oz/+C/+l2oqxaknvCVF16ANktzqJfyq78m9WcOt/6B0c8jwNDb1eJJpRxqgC3Nz0PdudWGKN98gNpG3QDjeHFZXrezswlt7t28DnUPb98S5flqA9osLqLmrx8oPSJLw20kx6BvaHP2+qhF1FJ6UhNDcy+KUfcqVX0w9VW7qCl4ui7j6pkrl6DNidOoezm3IjUu5xuYO0pVnGOtUeaFx+PvaVr/yDnnRqqyP8JGrRHOxTiTumupoVE0mhhz6GQOK+cM/TZDSyyv9O2iyNArNETPCoG8zjf0pMYTea9+B2PI0lTWWm3lOdSYzRmCeplKtaMealWOBvh781WZt8uGfvihoaemNSEjQ2Pc0krLKT3EnDsemsO9Huq0h7Hsm9ZLds45l8dnLBdkri2WDJ0+Y5wnTak5FhuCYwdGHCVK82yphrk3jXCctd7zpQuopTdflWeikuFlMOri2PUP5DkiMM4HQQn1Mgcq13pGzh608MyQjmS/1lfq0KbTwrON1j30AtSGHcf4zHEk12DO0lQ+AuZqhkhjTeUrQwOwkMc9vNuWfgOnT61AG0sT13myLqvh753eQJ28pRV5/9Ic5vEwxXVTDlT8jY0zbCIT5OoiaqdOcNmAvqXLUKtw2EetwI2NuuzSGM8sp06iNuJ8TfYzjh9DG0vjdXVF7RPGvGSGNmi+FP6Z5aOiXsez/ETt/X1DJ77VPIC6kdLfj4eYUyyNWl+dPyxdYktPMzXOGhpLT1ZrGpsasLrOuJGl7NhXZ+aSEUPjMcaMrrM0jodWnfIF0b/vnHOhoSsdl2Qc5yx9dONcAXqWlhDtEeEbnhQhnFHxmSzd0FBpDHsZxrIVpzpsDKliF5tDJu/lmx4jeLbQcTqN3qh1Rje9jIzf01jfLPQ7d2J1yVjPcE43nqWxhN+OFhoyhx22WtBm5/4tqBu35X5SNHwmjoLxECdD+/GMxuh1MplgXgnVuX+xjs/YCzAeoo7cf0dDnK+PbrSgLhfIvje0MYJzzmXYh3JV5p+S4TviqbPgxNLzNr4rOPUtzDdephPrHTGVsd0b4PhWDF8mHbWWZr+lO6/13LUvjnPOFYt4nda+j42z/DQcjy8dhBBCCCGEEEIIIYQQQp4q/DhMCCGEEEIIIYQQQgghMwg/DhNCCCGEEEIIIYQQQsgMwo/DhBBCCCGEEEIIIYQQMoNM7YIwMsSQ/Ux+W7bM0Q4NE4JeIgWo1xbPQ5vVE2jC8fU/vCbKjRKamLz0MprNvf7t10U5LGKfLl49BXU716RAebWCv1cZyzF48QQa0j0+2IG6+01pUudpZyPnnLNE6JV2dhSjKHbO8KiLMmVUY4nQG8YI/8V/8Z+K8soJNPs6cQpNRn71L39RlL/wlRexU0eAMaQuUwZIgWFkUJ5DI5cLpzdE+fy9h9Dm/Z0e1CVqrq9eRVO11UU0Brl9U4ro37+Novr3b6O4eqEoBcrLBctwRpmAJGg+UCmgcPxoIE0xIg/zhGWAocXVW300nPEnaKZx8uIZUa5XMPaqeexnqSAXRd4QcresjbxI5grPMEE7ChLD/mQ0kWt6GGGb7gjrYmVAYJndRYbYf6aE9QuWIZ1vzL0yoIsjzMehcZ2ncpbpDagqI8OUQa/3n95bPl8a4XWdPhqAlcoy/vJ5jCJtLOCcc82Dlvz9HMZs2zA80M88HGIby+BvrMxrAsPg4SjoG/NTVflikuF8+VaMKreXrUM0grXWb6DyY2YYEnX3cQ/Pq5gZG6ayltlLpGJroYamZPWaNMeslDFfaRM555zz9e8ZRpLjCV6Xqb6vGoadwx6abWw+lEa+ownm8X4P56+knicwjIs3H7egrrgh2/kFzP9HQbWC57coln3td9C0q15Dw1q3IOc+yOPYeIbR4UiZoawsr0KbagXPFaEy9fONo2gxj65xw66MrSQxzqLK/KU+j3luaJiklsuyXbFg7Afz2CdtJjaeoGnXYh1NcfK+HM/QMIizjHUDX55RkgkmpmoZTbJHsVxv/SGum6OgahiBh2tyLMol48xVw7rKvHxfunXzJrRpHuCaSPR+GeCZxTLo0oZVlqmWlduhmXGdNpM0PfKMM1mi9mLLVMgypNPGcrrsHK5353Cfj4zfswyRtP+SNb7mmKtBtpocGaZB3JNNC33DeA9jBIPEuk5fOc3v//T2ygBvCmO5P70PEmv+p2E6czus0wbJYYi51TIBDNRZbRpDPOecS9W9Fhv47j5Xw31QmzdaJpBHwdj4hhapvmnjN+ecy1fwXSRRY5o3PgGG2gzWOfd4T54ZA2vvDTCuckV5r6iIc9jv4N63WJB79Nw8msUnI/l7oWEiFxpxFatvZpZpXRwZZp9j+XuPHqFpbfMA3zm0cXpknN2iCY5dX31b0evBOefKZdx3dY5JDGPnaTgeb4iEEEIIIYQQQgghhBBCnir8OEwIIYQQQgghhBBCCCEzCD8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg0xvSDfApjlfiii3D9FAqlAyzKGUcdG92x9Bm/ff/QHUpQMpwN+8j4LQS1cuQ935U9I4zDMMiS6fPAN1rbe3RDlqobnY3JK894Ih1L22iOYubSWmvd9BAWzfEAbP5aQxh2UAEHkosH367FlR/jd+9ZehzeHoOtQ1+9JEot25B2129qDK/YN/dEOUtXnbURGGKM49VIZYmWGQleXQjLChTHs+/+JVaNP+4btQV16TRjHPvoBmfZ3mFtStr0ph/VIeDcB6hmnQ481d2SbBOK5VpEHQvCEAXzbWsvKAcolhuNAboOB8tydzRdsw1jo9h2LrK4t1UQ5zlqC+8Tcv1a/YMGoajTAvhJ5cb4Hpgvb0sWwtJqr7fcMcZ5xg/HuhfMY4NeLfMKOYqDw6tozQDHc7bcAS+vg0RQ/ndaBMeyqG2UCo9pbMMBGyXDNSZdIxNmIhMYw8YnUrw3sOjKmcc643lkYxA8PMaWgY1VRVvGeGCY61b2jDj2nNPY6CSI2zZ8RHGOIzDpXR2+4ung/yRs48e+6sKOc8XOPrq2tQl9NdMMZ01Mdc66nckzdiRjsE9dtohmjFdqDM/Ab9DrTxQxwDTXkOzzGWl05BmZSOYzwDTgyTut1dafAXGqZT21t4r2pePnNj7cnP8jRYXECDm0Stw2oOc682unLOuYlyW6xUcd/NRkbQWLlOsbCAxoaJMp/1DDPQQgn76Tl5RvAzzI+TsTyPeEYQzddxn9emRWa+yrBP2oyoXMVzmzYJc865hfm6vPWUZ6vFhhyDRG8Izn7J8pV5T3JM8rFlOlUoyJhZWUGzcMuQblm129jAd4D33/8A6h7cfyDK3TbmsMQw+gRzNMNZzkq1+jprDHxVZxl/Wddpc6CJ8W5hmdRpA7qBcYa26rSJlmWqZRng5ZWZvG/kgP+xYZmc6Tmy2lh107SxYkKva88w17XiZpp+Wuh4m2YMLD5un6y9Yxqse2kj59g4R9vPIudhYrzrWaZ8047x08Y3zKOLmTwTxIaB9tjIK/odMQzwTJI3zF/LC9IgrmeMaWIZ56Vy9+uMsU3X+PZVLcl9NWeYzIdOXucbhnRehHXabDwzzhE541tHTR0JxiOMoXRkmISqHDAxYs96R8yptVQs4EmiZNS1OvKdw8r303A8vnQQQgghhBBCCCGEEEIIearw4zAhhBBCCCGEEEIIIYTMIPw4TAghhBBCCCGEEEIIITPI1JrD62tnoa7fl9pQowlqxLkUvz/3D6RWUsvQ06vVqnirVGqoxCPURjk8OIS6sxfOi/J7Nz+ENnOnV6Hu2S9/VpQf37kPbUqrUo9rZ4i/n0c5LnfhpNQwHPYfQptRYmi4KJ0TL8Qp9Awdylwg9XTyJbx3rWBoCFXl/C2s4cPE51Cf5dFNOQ5vvI660kdBGhnaRp7WKDL0HX3U6vOqUodn+cQpaPPCxg7UbY3k2N971II2foJrqaR0ILuHGGsbJxpQt74q9RCv38Q4bnWVVk4RtXNyhj6RF0k9ncEY9YO6A0P3DLRoMWZXS5gDqqGMNUNe29aZnUjdnbSPem2Z0l1zzrlE6T9mRj47Cix5rIka05ER65mPc6h1el2MWlWW5rDWBNMaxM451zJ0Gj2laVyv4rhPLK2oWMZRzoiZQOm+eYY6s9YXds65WGkBToxxKhSwn5ESfc8MDdHuENfyQM1Na4hrJDU0PAtzck2kiaXFiVVaD8/o5pEQGH1NtB6zoe1V9lBLNFT6txVDbzQwHrzXkpqWYYBtqoYGWUHp/1tye4ExGVFX7i85H/fwocpXjx9izl5q1KGuVpH7c2poHMYTXJOJWsvjCDWHEyOP5/J6X8RnCYwzynCk8q9hnlDMo+59+0D23c9jbBwFpnalSj2lMsZQHOH8FPMykHKGVnES4ZjmCzKPW/raQYhBqvseGr8XGzq9+v6JFjR02PfUeF6tL+wc5qvI0Gq1+uky9XzGvjWeoA44/J6pZ2+MixqWXGDoNRfx7JipdRIEU7+KfaJozVLnMD4snc9yGXPt6Y3TolyfR13u9RMnoe76DelzcvPGDWiztbkJdf2unNfM2Oe9j6kn+7PC8i2wNIe1TqSlHax1ia3rLL1Jq66Ql3WBsZlNpVX7xBZPDyuWNVqj37mPr9Nr/R7E25T31n3QuvB/2nWgHz+FdvC06Bxs6fYGOUPTXg3LNPPinDFUxhkhMXRmwV/D1CfHKmufPQ5Y716+9k6a4Fz4Pu7/hbLad4wVOzTejUOlO1+29LUTw3NmJHOb3p6dc25tcRHqiupdazjCnOVFMida2tIj4z0yimSffCMHWHnBV/GeGe+7Voym6h00Z/gWhTn8vZp65ygUcD7HhsZxor4bTrveNMfkFZEQQgghhBBCCCGEEELI04QfhwkhhBBCCCGEEEIIIWQG4cdhQgghhBBCCCGEEEIImUH4cZgQQgghhBBCCCGEEEJmEC/7JNX3CSGEEEIIIYQQQgghhBxL+J/DhBBCCCGEEEIIIYQQMoPw4zAhhBBCCCGEEEIIIYTMIPw4TAghhBBCCCGEEEIIITMIPw4TQgghhBBCCCGEEELIDMKPw4QQQgghhBBCCCGEEDKD8OMwIYQQQgghhBBCCCGEzCD8OEwIIYQQQgghhBBCCCEzCD8OE0IIIYQQQgghhBBCyAzCj8OEEEIIIYQQQgghhBAyg/z/AT99II39Q8sRAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["plt.figure(figsize=(18, 9))\n","\n","num_rows = 4\n","num_cols = 8\n","\n","# plot each of the images in the batch and the associated ground truth labels.\n","for i in range(num_rows*num_cols):\n"," ax = plt.subplot(num_rows, num_cols, i + 1)\n"," plt.imshow(X_train[i,:,:])\n"," plt.axis(\"off\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":483,"status":"ok","timestamp":1719634800136,"user":{"displayName":"Ruchi Jha","userId":"17183609542544808004"},"user_tz":-330},"id":"dDvu78xxVL08","outputId":"82e792c5-4388-4ad8-c82a-f22f4f71099a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Original (integer) label for the first training sample: [6]\n","After conversion to categorical one-hot encoded labels: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n"]}],"source":["# Normalize images to the range [0, 1].\n","X_train = X_train.astype(\"float32\") / 255\n","X_test = X_test.astype(\"float32\") / 255\n","\n","# Change the labels from integer to categorical data.\n","print('Original (integer) label for the first training sample: ', y_train[0])\n","\n","# Convert labels to one-hot encoding.\n","y_train = to_categorical(y_train)\n","y_test = to_categorical(y_test)\n","\n","print('After conversion to categorical one-hot encoded labels: ', y_train[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pZ-5Wp8AVQkT"},"outputs":[],"source":["@dataclass(frozen=True)\n","class DatasetConfig:\n"," NUM_CLASSES: int = 10\n"," IMG_HEIGHT: int = 32\n"," IMG_WIDTH: int = 32\n"," NUM_CHANNELS: int = 3\n","\n","@dataclass(frozen=True)\n","class TrainingConfig:\n"," EPOCHS: int = 31\n"," BATCH_SIZE: int = 256\n"," LEARNING_RATE: float = 0.001"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mZnl9_YRVV-H"},"outputs":[],"source":["def cnn_model(input_shape=(32, 32, 3)):\n","\n"," model = Sequential()\n","\n"," #------------------------------------\n"," # Conv Block 1: 32 Filters, MaxPool.\n"," #------------------------------------\n"," model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=input_shape))\n"," model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))\n"," model.add(MaxPooling2D(pool_size=(2, 2)))\n","\n"," #------------------------------------\n"," # Conv Block 2: 64 Filters, MaxPool.\n"," #------------------------------------\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(MaxPooling2D(pool_size=(2, 2)))\n","\n"," #------------------------------------\n"," # Conv Block 3: 64 Filters, MaxPool.\n"," #------------------------------------\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(MaxPooling2D(pool_size=(2, 2)))\n","\n"," #------------------------------------\n"," # Flatten the convolutional features.\n"," #------------------------------------\n"," model.add(Flatten())\n"," model.add(Dense(512, activation='relu'))\n"," model.add(Dense(10, activation='softmax'))\n","\n"," return model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1719634800137,"user":{"displayName":"Ruchi Jha","userId":"17183609542544808004"},"user_tz":-330},"id":"nx9277zQVaZn","outputId":"e39083ba-ad8f-48c8-8b2a-fba51c93f6b1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type) Output Shape Param # \n","=================================================================\n"," conv2d (Conv2D) (None, 32, 32, 32) 896 \n"," \n"," conv2d_1 (Conv2D) (None, 32, 32, 32) 9248 \n"," \n"," max_pooling2d (MaxPooling2 (None, 16, 16, 32) 0 \n"," D) \n"," \n"," conv2d_2 (Conv2D) (None, 16, 16, 64) 18496 \n"," \n"," conv2d_3 (Conv2D) (None, 16, 16, 64) 36928 \n"," \n"," max_pooling2d_1 (MaxPoolin (None, 8, 8, 64) 0 \n"," g2D) \n"," \n"," conv2d_4 (Conv2D) (None, 8, 8, 64) 36928 \n"," \n"," conv2d_5 (Conv2D) (None, 8, 8, 64) 36928 \n"," \n"," max_pooling2d_2 (MaxPoolin (None, 4, 4, 64) 0 \n"," g2D) \n"," \n"," flatten (Flatten) (None, 1024) 0 \n"," \n"," dense (Dense) (None, 512) 524800 \n"," \n"," dense_1 (Dense) (None, 10) 5130 \n"," \n","=================================================================\n","Total params: 669354 (2.55 MB)\n","Trainable params: 669354 (2.55 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","_________________________________________________________________\n"]}],"source":["# Create the model.\n","model = cnn_model()\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1-PYBeeJVfZI"},"outputs":[],"source":["model.compile(optimizer='rmsprop',\n"," loss='categorical_crossentropy',\n"," metrics=['accuracy'],\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kkFlsRYLVh4A","outputId":"e8024562-f135-4559-c74e-2dfceca072c9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/31\n","137/137 [==============================] - 204s 1s/step - loss: 2.1228 - accuracy: 0.2298 - val_loss: 1.8997 - val_accuracy: 0.2854\n","Epoch 2/31\n"," 45/137 [========>.....................] - ETA: 1:54 - loss: 1.8398 - accuracy: 0.3361"]}],"source":["history = model.fit(X_train,\n"," y_train,\n"," batch_size=TrainingConfig.BATCH_SIZE,\n"," epochs=TrainingConfig.EPOCHS,\n"," verbose=1,\n"," validation_split=.3,\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WOAJvRroVnIv"},"outputs":[],"source":["def plot_results(metrics, title=None, ylabel=None, ylim=None, metric_name=None, color=None):\n","\n"," fig, ax = plt.subplots(figsize=(15, 4))\n","\n"," if not (isinstance(metric_name, list) or isinstance(metric_name, tuple)):\n"," metrics = [metrics,]\n"," metric_name = [metric_name,]\n","\n"," for idx, metric in enumerate(metrics):\n"," ax.plot(metric, color=color[idx])\n","\n"," plt.xlabel(\"Epoch\")\n"," plt.ylabel(ylabel)\n"," plt.title(title)\n"," plt.xlim([0, TrainingConfig.EPOCHS-1])\n"," plt.ylim(ylim)\n"," # Tailor x-axis tick marks\n"," ax.xaxis.set_major_locator(MultipleLocator(5))\n"," ax.xaxis.set_major_formatter(FormatStrFormatter('%d'))\n"," ax.xaxis.set_minor_locator(MultipleLocator(1))\n"," plt.grid(True)\n"," plt.legend(metric_name)\n"," plt.show()\n"," plt.close()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dRjtFP4QVqjR"},"outputs":[],"source":["# Retrieve training results.\n","train_loss = history.history[\"loss\"]\n","train_acc = history.history[\"accuracy\"]\n","valid_loss = history.history[\"val_loss\"]\n","valid_acc = history.history[\"val_accuracy\"]\n","\n","plot_results([ train_loss, valid_loss ],\n"," ylabel=\"Loss\",\n"," ylim = [0.0, 5.0],\n"," metric_name=[\"Training Loss\", \"Validation Loss\"],\n"," color=[\"g\", \"b\"]);\n","\n","plot_results([ train_acc, valid_acc ],\n"," ylabel=\"Accuracy\",\n"," ylim = [0.0, 1.0],\n"," metric_name=[\"Training Accuracy\", \"Validation Accuracy\"],\n"," color=[\"g\", \"b\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N9USbcwmVu_s"},"outputs":[],"source":["def cnn_model_dropout(input_shape=(32, 32, 3)):\n","\n"," model = Sequential()\n","\n"," #------------------------------------\n"," # Conv Block 1: 32 Filters, MaxPool.\n"," #------------------------------------\n"," model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=input_shape))\n"," model.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))\n"," model.add(MaxPooling2D(pool_size=(2, 2)))\n"," model.add(Dropout(0.25))\n","\n"," #------------------------------------\n"," # Conv Block 2: 64 Filters, MaxPool.\n"," #------------------------------------\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(MaxPooling2D(pool_size=(2, 2)))\n"," model.add(Dropout(0.25))\n","\n"," #------------------------------------\n"," # Conv Block 3: 64 Filters, MaxPool.\n"," #------------------------------------\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n"," model.add(MaxPooling2D(pool_size=(2, 2)))\n"," model.add(Dropout(0.25))\n","\n"," #------------------------------------\n"," # Flatten the convolutional features.\n"," #------------------------------------\n"," model.add(Flatten())\n"," model.add(Dense(512, activation='relu'))\n"," model.add(Dropout(0.5))\n"," model.add(Dense(10, activation='softmax'))\n","\n"," return model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nsw_-uX2V0RZ"},"outputs":[],"source":["# Create the model.\n","model_dropout = cnn_model_dropout()\n","model_dropout.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sgx9e9N8V3Lo"},"outputs":[],"source":["model_dropout.compile(optimizer='rmsprop',\n"," loss='categorical_crossentropy',\n"," metrics=['accuracy'],\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J0DCLCozV6Rh"},"outputs":[],"source":["history = model_dropout.fit(X_train,\n"," y_train,\n"," batch_size=TrainingConfig.BATCH_SIZE,\n"," epochs=TrainingConfig.EPOCHS,\n"," verbose=1,\n"," validation_split=.3,\n"," )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yNh9bGvBV9lw"},"outputs":[],"source":["# Retrieve training results.\n","train_loss = history.history[\"loss\"]\n","train_acc = history.history[\"accuracy\"]\n","valid_loss = history.history[\"val_loss\"]\n","valid_acc = history.history[\"val_accuracy\"]\n","\n","plot_results([ train_loss, valid_loss ],\n"," ylabel=\"Loss\",\n"," ylim = [0.0, 5.0],\n"," metric_name=[\"Training Loss\", \"Validation Loss\"],\n"," color=[\"g\", \"b\"]);\n","\n","plot_results([ train_acc, valid_acc ],\n"," ylabel=\"Accuracy\",\n"," ylim = [0.0, 1.0],\n"," metric_name=[\"Training Accuracy\", \"Validation Accuracy\"],\n"," color=[\"g\", \"b\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"massHVOzWA-U"},"outputs":[],"source":["# Using the save() method, the model will be saved to the file system in the 'SavedModel' format.\n","model_dropout.save('model_dropout')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p17OmbueWF6p"},"outputs":[],"source":["from tensorflow.keras import models\n","reloaded_model_dropout = models.load_model('model_dropout')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N50tLYAuWIWb"},"outputs":[],"source":["test_loss, test_acc = reloaded_model_dropout.evaluate(X_test, y_test)\n","print(f\"Test accuracy: {test_acc*100:.3f}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"207LhzdzWMW1"},"outputs":[],"source":["def evaluate_model(dataset, model):\n","\n"," class_names = ['airplane',\n"," 'automobile',\n"," 'bird',\n"," 'cat',\n"," 'deer',\n"," 'dog',\n"," 'frog',\n"," 'horse',\n"," 'ship',\n"," 'truck' ]\n"," num_rows = 3\n"," num_cols = 6\n","\n"," # Retrieve a number of images from the dataset.\n"," data_batch = dataset[0:num_rows*num_cols]\n","\n"," # Get predictions from model.\n"," predictions = model.predict(data_batch)\n","\n"," plt.figure(figsize=(20, 8))\n"," num_matches = 0\n","\n"," for idx in range(num_rows*num_cols):\n"," ax = plt.subplot(num_rows, num_cols, idx + 1)\n"," plt.axis(\"off\")\n"," plt.imshow(data_batch[idx])\n","\n"," pred_idx = tf.argmax(predictions[idx]).numpy()\n"," truth_idx = np.nonzero(y_test[idx])\n","\n"," title = str(class_names[truth_idx[0][0]]) + \" : \" + str(class_names[pred_idx])\n"," title_obj = plt.title(title, fontdict={'fontsize':13})\n","\n"," if pred_idx == truth_idx:\n"," num_matches += 1\n"," plt.setp(title_obj, color='g')\n"," else:\n"," plt.setp(title_obj, color='r')\n","\n"," acc = num_matches/(idx+1)\n"," print(\"Prediction accuracy: \", int(100*acc)/100)\n","\n"," return"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zSLsX4yXWPGn"},"outputs":[],"source":["evaluate_model(X_test, reloaded_model_dropout)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MO8YeQA5WTEH"},"outputs":[],"source":["# Generate predictions for the test dataset.\n","predictions = reloaded_model_dropout.predict(X_test)\n","\n","# For each sample image in the test dataset, select the class label with the highest probability.\n","predicted_labels = [np.argmax(i) for i in predictions]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s8LsysyDWVYH"},"outputs":[],"source":["# Convert one-hot encoded labels to integers.\n","y_test_integer_labels = tf.argmax(y_test, axis=1)\n","\n","# Generate a confusion matrix for the test dataset.\n","cm = tf.math.confusion_matrix(labels=y_test_integer_labels, predictions=predicted_labels)\n","\n","# Plot the confusion matrix as a heatmap.\n","plt.figure(figsize=[14, 7])\n","import seaborn as sn\n","sn.heatmap(cm, annot=True, fmt='d', annot_kws={\"size\": 12})\n","plt.title('Confusion Matrix')\n","plt.xlabel('Predicted')\n","plt.ylabel('Truth')\n","plt.show()"]}],"metadata":{"colab":{"provenance":[{"file_id":"19ii9YsluuKf7STUu3HYRIxYQD_CFZ6rv","timestamp":1719635065362}],"gpuType":"T4","authorship_tag":"ABX9TyOZzzBtg6buSkjgdtUR02/F"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file