\t129\n",
+ "\t119\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests, re\n",
+ "# re is a module for regular expressions: to detect various combinations of characters\n",
+ "import operator\n",
+ "\n",
+ "\n",
+ "# Start from a simple document\n",
+ "r = requests .get('https://americanfootballdatabase.fandom.com/wiki/American_football_rules')\n",
+ "\n",
+ "# What comes back includes headers and other HTTP stuff, get just the body of the response\n",
+ "t = r.text\n",
+ "\n",
+ "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n",
+ "\n",
+ "wds = re.split('\\s+', t)\n",
+ "\n",
+ "\n",
+ "# now populate a dictionary (wf)\n",
+ "wf = {}\n",
+ "for w in wds:\n",
+ " if w in wf: wf [w] = wf [w] + 1\n",
+ " else: wf[w] = 1\n",
+ "\n",
+ "# dictionaries can not be sorted, so lets get a sorted *list* \n",
+ "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n",
+ "\n",
+ "# lets just have no more than 15 words \n",
+ "ml = min(len(wfs),15)\n",
+ "for i in range(1,ml,1):\n",
+ " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Example 2\n",
+ "\n",
+ "Lots of markup in the output, lets remove it --- \n",
+ "\n",
+ "use BeautifulSoup and nltk modules and practice some regular expressions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[('player', 28), ('possession', 29), ('teams', 31), ('rules', 35), ('goal', 37), ('first', 40), ('touchdown', 40), ('zone', 41), ('nfl', 43), ('kick', 44), ('field', 44), ('overtime', 54), ('end', 56), ('football', 56), ('line', 61), ('game', 61), ('may', 62), ('play', 64), ('team', 110), ('ball', 123)]\n",
+ "[('strike', 8), ('called', 8), ('drills', 9), ('get', 10), ('may', 10), ('foul', 10), ('run', 11), ('hit', 11), ('team', 11), ('second', 12), ('home', 12), ('third', 13), ('first', 16), ('basic', 17), ('batter', 20), ('runner', 21), ('rules', 24), ('ball', 31), ('baseball', 51), ('base', 60)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests, re, nltk\n",
+ "from bs4 import BeautifulSoup\n",
+ "from nltk import clean_html\n",
+ "from collections import Counter\n",
+ "import operator\n",
+ "\n",
+ "# we may not care about the usage of stop words\n",
+ "stop_words = nltk.corpus.stopwords.words('english') + [\n",
+ " 'ut', '\\'re','.', '', ',', '--', '–', '\\'s', '?', ')', '(', ':', '\\'',\n",
+ " '\\\"', '-', '}', '{', '&', '|', u'\\u2014' ]\n",
+ "\n",
+ "# We most likely would like to remove html markup\n",
+ "def cleanHtml (html):\n",
+ " from bs4 import BeautifulSoup\n",
+ " soup = BeautifulSoup(html, 'html.parser')\n",
+ " return soup .get_text()\n",
+ "\n",
+ "# We also want to remove special characters, quotes, etc. from each word\n",
+ "def cleanWord (w):\n",
+ " # r in r'[.,\"\\']' tells to treat \\ as a regular character \n",
+ " # but we need to escape ' with \\'\n",
+ " # any character between the brackets [] is to be removed \n",
+ " wn = re.sub('[,\"\\.\\'&\\|:@>*;/=]', \"\", w)\n",
+ " # get rid of numbers\n",
+ " return re.sub('^[0-9\\.]*$', \"\", wn)\n",
+ " \n",
+ "# define a function to get text/clean/calculate frequency\n",
+ "def get_wf (URL):\n",
+ " # first get the web page\n",
+ " r = requests .get(URL)\n",
+ " \n",
+ " # Now clean\n",
+ " # remove html markup\n",
+ " t = cleanHtml (r .text) .lower()\n",
+ " \n",
+ " # split string into an array of words using any sequence of spaces \"\\s+\" \n",
+ " wds = re .split('\\s+',t)\n",
+ " \n",
+ " # remove periods, commas, etc stuck to the edges of words\n",
+ " for i in range(len(wds)):\n",
+ " wds [i] = cleanWord (wds [i])\n",
+ " \n",
+ " # If satisfied with results, lets go to the next step: calculate frequencies\n",
+ " # We can write a loop to create a dictionary, but \n",
+ " # there is a special function for everything in python\n",
+ " # in particular for counting frequencies (like function table() in R)\n",
+ " wf = Counter (wds)\n",
+ " \n",
+ " # Remove stop words from the dictionary wf\n",
+ " for k in stop_words:\n",
+ " wf. pop(k, None)\n",
+ " \n",
+ " #how many regular words in the document?\n",
+ " tw = 0\n",
+ " for w in wf:\n",
+ " tw += wf[w] \n",
+ " \n",
+ " \n",
+ " # Get ordered list\n",
+ " wfs = sorted (wf .items(), key = operator.itemgetter(1), reverse=True)\n",
+ " ml = min(len(wfs),20)\n",
+ "\n",
+ " #Reverse the list because barh plots items from the bottom\n",
+ " return (wfs [ 0:ml ] [::-1], tw)\n",
+ " \n",
+ "# Now populate two lists \n",
+ "(wf_ee, tw_ee) = get_wf('https://americanfootballdatabase.fandom.com/wiki/American_football_rules')\n",
+ "(wf_bu, tw_bu) = get_wf('http://myyouthbaseball.com/basic-baseball-rules.html')\n",
+ "\n",
+ "print(wf_ee)\n",
+ "print(wf_bu)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Plot the results: are there striking differences in language?\n",
+ "import numpy as np\n",
+ "import pylab\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%matplotlib inline\n",
+ "def plotTwoLists (wf_ee, wf_bu, title):\n",
+ " f = plt.figure (figsize=(10, 6))\n",
+ " # this is painfully tedious....\n",
+ " f .suptitle (title, fontsize=20)\n",
+ " ax = f.add_subplot(111)\n",
+ " ax .spines ['top'] .set_color ('none')\n",
+ " ax .spines ['bottom'] .set_color ('none')\n",
+ " ax .spines ['left'] .set_color ('none')\n",
+ " ax .spines ['right'] .set_color ('none')\n",
+ " ax .tick_params (labelcolor='w', top='off', bottom='off', left='off', right='off', labelsize=20)\n",
+ "\n",
+ " # Create two subplots, this is the first one\n",
+ " ax1 = f .add_subplot (121)\n",
+ " plt .subplots_adjust (wspace=.5)\n",
+ "\n",
+ " pos = np .arange (len(wf_ee)) \n",
+ " ax1 .tick_params (axis='both', which='major', labelsize=14)\n",
+ " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n",
+ " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n",
+ "\n",
+ " ax2 = f .add_subplot (122)\n",
+ " ax2 .tick_params (axis='both', which='major', labelsize=14)\n",
+ " pos = np .arange (len(wf_bu)) \n",
+ " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n",
+ " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n",
+ "\n",
+ "plotTwoLists (wf_ee, wf_bu, 'Difference between football and baseball rules')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "and\t2836\n",
+ "of\t2676\n",
+ "to\t2646\n",
+ "a\t2217\n",
+ "in\t1422\n",
+ "his\t1205\n",
+ "he\t928\n",
+ "that\t920\n",
+ "was\t823\n",
+ "for\t798\n",
+ "with\t797\n",
+ "as\t672\n",
+ "I\t505\n",
+ "you\t497\n"
+ ]
+ }
+ ],
+ "source": [
+ "#In case Project gutenberg is blocked you can download text to your laptop and copy to the docker container via scp\n",
+ "#Assuming the file name you copy is pg4680.txt here is how you change the script\n",
+ "# Please note the option errors='replace'\n",
+ "# without it python invariably runs into unicode errors\n",
+ "f = open ('pg4680.txt', 'r', encoding=\"ascii\", errors='replace')\n",
+ " \n",
+ "# What comes back includes headers and other HTTP stuff, get just the body of the response\n",
+ "t = f.read()\n",
+ "\n",
+ "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n",
+ "wds = re.split('\\s+',t)\n",
+ "\n",
+ "# now populate a dictionary (wf)\n",
+ "wf = {}\n",
+ "for w in wds:\n",
+ " if w in wf: wf [w] = wf [w] + 1\n",
+ " else: wf [w] = 1\n",
+ "\n",
+ "# dictionaries can not be sorted, so lets get a sorted *list* \n",
+ "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n",
+ "\n",
+ "# lets just have no more than 15 words \n",
+ "ml = min(len(wfs),15)\n",
+ "for i in range(1,ml,1):\n",
+ " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Assignment 1\n",
+ "\n",
+ "1. Compare word frequencies between two works of a single author.\n",
+ "1. Compare word frequencies between works of two authors.\n",
+ "1. Are there some words preferred by one author but used less frequently by another author?\n",
+ "\n",
+ "Extra credit\n",
+ "\n",
+ "1. The frequency of a specific word, e.g., \"would\" should follow a binomial distribution (each regular word in a document is a trial and with probability p that word is \"would\". The estimate for p is N(\"would\")/N(regular word)). Do these binomial distributions for your chosen word differ significantly between books of the same author or between authors? \n",
+ "\n",
+ "Project Gutenberg is a good source of for fiction and non-fiction.\n",
+ "\n",
+ "E.g below are two most popular books from Project Gutenberg:\n",
+ "- Pride and Prejudice at http://www.gutenberg.org/ebooks/1342.txt.utf-8\n",
+ "- Adventures of Huckleberry Finn at http://www.gutenberg.org/ebooks/76.txt.utf-8"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Comparing Word Usage Between a Football Rulebook and a Basketball Rulebook\n",
+ "\n",
+ "My goal was to compare two different sports rulebooks to see the similarities and differences between two rulebooks of two different sports.\n",
+ "\n",
+ "I chose to compare a PDF file of a football rulebook and a PDF file of a basketball rulebook.\n",
+ "\n",
+ "My results are presented at the end of my code."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# In this section of my code, I use the PyPDF2 PdfFileReader\n",
+ "# to get and store the two PDF rulebooks as objects.\n",
+ "\n",
+ "# Used pip install PyPDF2 in the terminal to use the PDF functions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import io\n",
+ "import requests\n",
+ "\n",
+ "from PyPDF2 import PdfFileReader, PdfFileWriter\n",
+ "\n",
+ "football_url = 'https://www.dfoa.com/wp-content/uploads/2021/07/2021-NCAA-FB-Rulebook.pdf'\n",
+ "basketball_url = 'https://www.quickscores.com/downloads/ftmeadesports_20212022_NCAA_Mens_Basketball_Rulebook.pdf'\n",
+ "\n",
+ "r_football = requests.get(football_url)\n",
+ "f_football = io.BytesIO(r_football.content)\n",
+ "\n",
+ "reader_football = PdfFileReader(f_football, strict = False)\n",
+ "\n",
+ "r_basketball = requests.get(basketball_url)\n",
+ "f_basketball = io.BytesIO(r_basketball.content)\n",
+ "\n",
+ "reader_basketball = PdfFileReader(f_basketball, strict = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# In this section of my code, I loop through each page\n",
+ "# of each PDF, storing the text contents. Then, I store\n",
+ "# each specific word into a dictionary and use some of the\n",
+ "# cleaning functions used above to remove stop words\n",
+ "# and other unnecessary symbols or characters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wf_football = {}\n",
+ "wf_basketball = {}\n",
+ "\n",
+ "for pages in range(reader_football.getNumPages()):\n",
+ " contents_football = reader_football.getPage(pages).extractText()\n",
+ " wds = re.split('\\s+', contents_football)\n",
+ " for i in range(len(wds)):\n",
+ " wds [i] = cleanWord (wds [i])\n",
+ " for w in wds:\n",
+ " if w in wf_football: wf_football [w] = wf_football [w] + 1\n",
+ " else: wf_football[w] = 1\n",
+ " \n",
+ "for k in stop_words:\n",
+ " wf_football. pop(k, None)\n",
+ " \n",
+ "for pages in range(reader_basketball.getNumPages()):\n",
+ " contents_basketball = reader_basketball.getPage(pages).extractText()\n",
+ " wds = re.split('\\s+', contents_basketball)\n",
+ " for i in range(len(wds)):\n",
+ " wds [i] = cleanWord (wds [i])\n",
+ " for w in wds:\n",
+ " if w in wf_basketball: wf_basketball [w] = wf_basketball [w] + 1\n",
+ " else: wf_basketball[w] = 1\n",
+ " \n",
+ "for k in stop_words:\n",
+ " wf_basketball. pop(k, None)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# In this final section, I sort my dictionary of words\n",
+ "# and then put each one into a list, sorting by the most\n",
+ "# frequent words used. Finally, I use the plotTwoLists\n",
+ "# function to plot the top 30 words used in each rulebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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mYm8NX3b2+yi+z6srcgXn86kksnR9pzw/R9f7m9x7kVapPdfSfIzGmA0i0hx4BrgXp++tiPwAjDLGfOBjvy2wF4aW+2h8QvrqptRK9XsBjMP2nY7F9uE9gr07A/+OCcksWfUbkBv7T0hyXLfFXU4YY3z9I+TrPczK+qWF87wwLY1PyKQGKPZqWW7gmDEmJpP24Wq41DLGpPU/Fl9fMFd+jxpj3kx/sZJ1ynlOzVUCV3nGG2MeT03mzpdzAjBBREpgP4cu2IEqNzsdyC84yf+DreCW+8oribw/FpEa2A7urUhdAzQ7PIsd1NLSGBPtuUJEhmMrc18yu+KBzP9cU1IyieWuUfB/eT1n2PlgjDnq/EC2cEZhRmAHLIG9ynQR2z+tgLPM+79xV5kKmsQd9H3JinM6vVxlK4Xtw+WttFc6z/S+eKdPi5J4XdUQkdxAMWwd4fIgtvE5ynhNwycijbEN0MyQ3vM5LVzvW0nsP3ue+3C9F4fTmGdqzzVIxzEaY9YBHZxzqR52+ryHsYOFfjfOwCIPfbD/vI4QkVzGmOe91qe5bkqDVL0XTv32CLbfaxPnboib+JglJIMl9xvgulPoq91UKA37+Avb7a1IiikTKiYifj4aob6+T1lZv9yNvTD1jojkMb4HdfqU4dNmiJ2K4xnn5fsZnb8HVzSk5jk0P182YL/ELUTE+9ZVUmnTVR5jzHFjzCfGmPuwP+Q3YgdnIXYuz9uARSbt8xO6KgSf0z7kEKHASe+K3HFLBu8rnrRdEcu0zzWVEh2/M+K8Nvb2015ncXrOB1fFmNz7sRw78rY/ttJeDmDsPK/fY6+Ket6a95TWMmXFOZ1eW53nCO8VIhKKnc3jF2PMqVSkz82/x+g560dqPg/wfU40c7bb6rEs1Hn+XyrzyChZcT673rfk3ou0Ki++o1BFOM/e7226jtEYc8EYs9ZpUD7iLPbVKD+FvROwCnhORF71Wp+euind3zHnrloz56XrvaiEbZd846PxWc5Zn11cV41v8LEuPA35fA8UdrpcpEVu/p0m0VOE8+z5fcqK+sXlV+xV0B+A6SIy0EcanzK0Aer89/Ih9iAOYYf7Z5ZZ2BNqhIg08FGWXOJjLrKkGGM2YU/Me5ypXhIRkRrOMaaLsVPcfIj9b+J18Zo3T+x8mQWdtMexff/CReQ58RHeVERuFJGKzt8BItLUR5o8/Nsd4pzz3Bb733ai2+8i0lVEbvUum7OuFP92efguNcecTWKAIs5UJW4i0gc7nUhG+gPfFZJPmfy5psYDIlLHa9lI7C2YD1xXUtN5PrhuF5VPZv+uq5rDneflXuuqY7uH/IENhuBpErbv1HixEby8y+Pv3JbkCo4hq0Q5z8+KiKsvqutH+XVs3fyOR/rPsLe7uzq3az0Nxl6ZXGYSBvtIzecBtjHi7g/pdKF62Xk5yyNdjPMc4bmx830aTuaJIfPP59nO8zMi4r4y5fVepJUfMNazLnXO60ew3QDe9UgbQxqOUUSaiNf8uA7XlUafdYLTqGuLPe+eFJGJHuvSVDc5UvsdayV2mh9Pg7D/QK8wxhx0lsU4z8089y8iQdiBNZk5biUlrv7o3nMZ1yBtV//HO88zxcfcxWLnrvU+x11eFo85PJ3vqmtaLM9zNSvqFzdjTCz2n4ydwCQReSKJ8ieQ7g9T/u2knIt/Q3E2w3ag3QB0MynPlZduxpg/RORenDCQTh+y3dhL6DdgO/oWJXFH7uTcj/0RfEdEHsHOs3YK+99CTeyPY2PsVDLpNcjJ5yHs3ItfY289VsRWNHfxb6fiQdg5Dl/ANhxWY/vSlMF2BK+PnVPzF2yDcrWI7McOwDiIPfZbnbRfGGNcV7fuwV7t+spH+RpiT6bfnP25OtFXBNo7+/kcO09YTjUB+16uFjto5y/sf6jNsOW+NwP3tRzoIiJfYv87vIQdZJZcAz2zPtfU+ApY47wvsdj3pBm24n/KK21az4d12B++wWIHq7j6Jr3l0dd3BfYqSwlgn0kY4Ww5tjFcHDvSPMHtMGPMPqchGQXsFpEl2CmE8mB/AJtj+5xXuYJjyBLGmLXOFaihwC6xA0nOYufpq47tkvCaR/ozzrHPB1aKyHzsP/n1sHczfgP6ee1mOba/7EwR+R/27sUpY8wkr3R7se+n5zygN2LnSZ7nkW6uk98EEWkJ/IT9HnfAzv2X5qAIqTSBTD6fjTFrROQt7C3sXV7vxZ+kL8zwDmx9ullEvuHfeUALYac58rw1OoG0HeNQbKNuFbaeOIP9Db7DKe+MZI71nPw75+MjTiP7Ied8S0vdBPaq1xFsHXgJWz8ZYJ5HoxLs1D+fih3cuh97x+UObKNngEfZfhORD7FdjLY571tBbH13HjtAsnZSx5bJPsd+57s6V2PXY+udjs66+1KTiTFmuYg8hf3H5iexffl/wfb5rIBtyK3G/qPgKRbbD3iXiHyBrffuxV7QmuL5m5NF9Yv3cf3u1AtfYy+w5TXGvJTSm5GmBwmn5jHYCaFPYH8cZzpvWq4kto3G95RKMaRxGiaP9SHYKyM/Yb+gf2PDH84D7vZKO5tkpiVx0gRjIzhtxp7U/2C/HIuASDymTyLlaWcMPqbowY4cfQZbQZ3D/jDswVZCJbzS+mMrhbXYSukC9ouxHPufSVEnXR7sl+0rZ/157I/x99jGrr9Huj+xDRdfZb4BOz/op9jK5W9sAzkWO6fe/3l/vkm9D+n9XH19TqRxqg3sj+L3znt7CjuYqkV6yuqsH4nvaZhKYLuaHMPejnKXkWSmm8nozzUV5627/M57sA373f4d+59z6Ss9H5z0bbEN0TP8W0eEeKXZ7Cyf7LU8j8d2/ZM5lhrOd+Sg876dxPYZm47vab0y/ZxOpqwpfa+6YH8MTjuf7W5s3ZA3ifT1sefm79jz8hB2TuEySaR/HNvAvIDXVEL8Ow1TAPCi855cwPaBHIHvuRirYQfIHMf+oG3G9g0Nwcd3nQyahokMPJ9J+lwW7Dnper+OApOxDaBkP8ekvifYhtu7zvt1HvtP6v1XeozYRsEs7O/GX85n8QN2+rIKqTxef+w/Dsb5nHKlpW7y+k4ud9LGe+7Ls+zO8a1zynoK2wAO8/E+BGKDb+x33rNfnc+hKL6nNYog46ZhSvYzxv4+foStc/7BDsq7Jz1lwP5z8bHzPbuIPae3YQdhhfsqG/a7OBnb6L+A/a4+gsfcyllVv5BEewrbh3+Ns260r/24HuJsoK4TYqMPfQ30NsbMyu7yKKWUUur6k+GDkFSO9x/slbovs7sgSimllLo+6RVQpZRSSimVpfQKqFJKKaWUylLaAFVKKaWUUllKG6BKKaWUUipLaQNUKaWUUkplKW2AKqWUUkqpLKUNUKWUUkoplaW0AaqUUkoppbKUNkCVUkoppVSW0gaoUkoppZTKUtoAVUoppZRSWUoboEoppZRSKktpA1QppZRSSmUpbYAqpZRSSqkspQ1QpZRSSimVpbQBqpRSSimlspQ2QJVSSimlVJbSBqhSSimllMpS2gBVSimllFJZShugSimllFIqS2V3A/Re4C1gFfA3YIB305lXOSAKOApcAGKACUDhKy2kUkoppZTKOLmzef/PArWAM8BhoEo687kRWAuUAD4H9gENgEeBtkBT4I8rLaxSSimllLpy2X0F9DEgDCgA9L+CfKZgG5+PAHcDTwGtgPHATcBLV1RKpZRSSimVYbK7AboC+Al76z29bgRuw95yn+y1bgRwFngAyJ+azEQk8grKorKZfn5XL/3srm76+V299LO7ul2tn192N0AzQkvn+Rsg3mvdaWANEAg0SmV+V+UHqdz087t66Wd3ddPP7+qln93V7ar8/K6FBuhNzvOPSaz/yXkOy4KyKKWUUkqpFGT3IKSMUNB5/iuJ9a7lhZLKwLl8HQlQtGjReuHh4VfSJUBlo3r16qGf39Upf/78cGXdcVQ2Klq0qJ57VymtN696F0Rkk8frGcaYGdlWmlS6FhqgV8z5oGYAhIeHm02bNqWwhVIqo4WHh2d3EdQVCAkJQetOpbKeiOwyxlx1Fei1cAvedYWzYBLrXctPZX5RlFJKKaVUSq6FBugPznNSfTwrO89J9RFVSimllFJZ6FpogK5wnm8j8fEEYyehPwd8n5WFUkoppZRSvl1NDdA82EhJN3otP4CdgikEGOi1bhR2/s952PlAlVJKKaVUNsvuQUh3Ow+AUs5zY2C28/cJYIjzd1lgL3AQ29j0NAAbivNNoLWTriF2jtAfgWcyuNxKKaWUUiqdsrsBWhvo4bWskvMA29gcQsoOAOHAC9jY7+2AWGAi9ironxlQVqWUUkoplQGyuwE60nmkRgwgyaz/Feh1ZcVRSimllFKZ7WrqA6qUUkoppa4B2gBVSimllFJZShugSimllFIqS2kDVCmllFJKZSltgCqllFJKqSyV3aPgfRKRaGCXMWZQOrcPAX4B6htjNnm/Tm7bnUf+IuSpRenZrVLKh5hX2md3Ea4Ls2fPZtCgQZw5c+aK8omIiKB69epMmjTJ5+ukaN2pVMa5HupNvQKqlFJKKaWylDZAlVJKKaVUlsrJDdDcIjJRRP50Hq+JSC4AEfk/EdkoIqdF5LiIzBeRstldYKWUymzfffcdjRo1IigoiIIFC9KgQQN27drlXr98+XKqV69O/vz5admyJb/88ot73YEDB+jYsSOlSpUif/781K1bl4ULF2bHYSilrnM5uQHaDVu+xkA/IBIY7KzzB0YAtYAOQDHgg/TuSEQiRWSTiGyKO/fXlZRZKaUyzeXLl+nYsSPNmjVj+/btrF+/nsGDB+Pn5wfAhQsXePnll4mKimLdunWcOnWKhx56yL39mTNnuOOOO1i6dCnbt2/nv//9L/fccw/79u1LV3lmzJhBeHg44eHhaN2plEqLHDkIyRELPGKMMcA+EQkDHgfGGWOiPNL9LCL9gb0iUs4YczitOzLGzABmAASUrmwyoOxKKZXh/v77b06dOsWdd97JjTfeCECVKlUAWL9+PZcvX2by5MncdNNNAAwZMoTevXtjjEFEqFWrFrVq1XLn98wzz/Dll1+yYMECnn322TSXJzIyksjISAACSle+0sNTSl1HcvIV0O+dxqfLOqCsiBQQkboi8rmIHBSR04BrZHv5rC+mUkpljSJFitCzZ09uv/122rdvz7hx4zh06JB7fUBAgLvxCVCmTBkuXrzIn3/+CcDZs2cZOnQo1apVo3DhwgQFBbFp06YEeSilVFbIyQ3QpAjwNXAOeACoD7R11vlnV6GUUiorzJo1i/Xr19OiRQu++OILbrrpJr7++msAcudOeFNLRACIj48H7BXR+fPnM3r0aFauXMm2bdto0KABFy9ezNqDUEpd93LyLfiGIiIeV0EbAUeBUGyfz6eNMb8AiMg9GbXTGmULsuk6mH9LKXX1ct1KHzZsGHfccQdz5szhtttuS3G71atX0717d/773/8CcP78eQ4cOEBYWNgVl0nrTqVUWuTkK6BlgAkicpOI3As8CYwHDgEXgEEiUklE2gOjs7GcSimVJX755Reeeuop1q5dy8GDB1mxYgU7duygWrVqqdo+LCyMTz/9lC1btrBz507+7//+j/Pnz2dyqZVSKrGcfAX0PcAPWA8Y4B1gvDEmTkR6AGOAgcAO7OCkJRmxU43moa5F10NUjetBYGAgP/74I506deLEiROULFmSbt26MWzYMGrUqMGFCxeS3X7cuHH06dOH5s2bU7hwYQYPHuxugMbExFCxYkXq1q3r83VKtO5U1yKtOzNPpjdARSSlUeVzjDE9PRcYYyI8XiYKx2mM+Qj4yHtXHutjknutlFJXo5IlS/LJJ5/4XFeqVCnatGmTYFlERASeYzkrVKjAsmXLEqQZMmQIYBucANOnTyc8PDzRa6WUykhZcQW0tMffHYCZXsv+yYIyKKWUUkqpHCLT+4AaY35zPYBTPpa1EJHNInJeRH4RkZdExD2aPaWoRyISISJGRO5w8vlHRFaJSDkRuUVEtovIGRFZKCJFM/t4lVIqu1y+fJlHH32UwoULU7hwYZ588kn3CPh3332X+vXrExwcTIkSJejUqRNHjhzJ5hIrpa5X2ToISURux/b1nATcDPQG7sX273RJbdSjUdhISQ2Bwthb9M9jIyhFOPmPTKIcGglJKXXVe++994iPj2fdunVMnz6dGTNmMGHCBAAuXrzIqFGj2L59OwsXLuTEiRN07dr1ivankZCUUumV3YOQngFeM8bMcl4fEJFhwLsi8qSxUhv16DljzCoAEZkGvAXUM8ZscZbNwTZuE9FISEqpa0Hp0qV58803ERGqVKnCjz/+yLhx43j88cfp3bu3O12lSpWYOnUqVatW5fDhw5QrVy5d+9NISEqp9MruaZjqAc84t8jPiMgZ4H0gP1AKIA1Rj3Z4/H3Med7ptaxEhh+BUkrlEI0aNXJPPg/QuHFjjhw5wt9//82WLVvo2LEjFSpUIDg42D2wSKMgKaWyQ3Y3QHNhb53X9njUBCoDv4tIflIf9eiSx98GwBjjvSy7j1cppbKcMYbbb7+dwMBA5s2bx8aNG1myxM5cp1GQlFLZIbtvwW8Bqhhj9vtaKSK1yMSoR75oNA+l1NVq/fr1GGPcV0G///57ypQpw/79+zlx4gRjxoyhYsWKAElO55ReWncqpdIiu68IvgDcLyIviEh1EakiIveKyKvOeo16pJRSqXT06FEGDx7MDz/8wIIFC3jttdd47LHHKF++PAEBAUyaNImff/6ZRYsW8dxzz2V3cZVS17FsvQJqjPnaaVQ+BwwBLgM/ArOd9b9nZtQjXzSax7VPI1uoa1W3bt2Ii4ujYcOGiAh9+vThsccew8/Pjzlz5vD0008zefJkatasybhx42jbtm3KmaaS1p3qaqK/A9kvSxugxpgFeEUkMsZ8A3yTzDYpRT2K9pHnAhGpJyJxwPfGmKbGmGnAtCs7AqWUypmio6Pdf0+aNCnBui1btnD//ffTqFEjDhw44F7uGSUpJCTE/Xr27NkMGjQowXqllMpI2X0LPjM9CEwBqotI1ewujFJKZZe3336bAQMGsGvXLvbu3Zts2kuXLiW7XimlMsI12QAVkXzA/di5PRcAfbK3REoplT3++ecf3n//fSIjI7n33nt555133OtiYmIQET744ANatWpFvnz5mD59Or169eLs2bOICCLCyJEjs+8AlFLXpGuyAYqdcP6gMWYnMA/oLiJ5kkqskZCUUteqBQsWUKFCBWrUqMEDDzzA3LlzE13lHD58OAMGDGDPnj3cddddTJgwgcDAQGJjY4mNjWXIkCE+89ZISEqp9LpWG6B9sA1PgJXYeUQ7JpXYGDPDGBNujAn3CyyYFeVTSqks8c477/DAAw8AcMsttxAYGMjnn3+eIM3DDz/MvffeS8WKFSlfvjwFCxZERChVqhSlSpUiKCjIZ96RkZFs2rSJTZs2oXWnUiotrrkGqIiEAs2wEZUwthf9e+hteKXUdWb//v2sXr2a+++/HwARoVu3bgluwwPuqEhKKZVVsnsi+szwIOAHHPIISScAInKDMebX7CqYUkplpbfffpu4uDjKl/83crFrZPuvv/5bFebPnz/Ly6aUur5dUw1QEckN9ACGAwu9Vs8DemEnv0+SRvNQSl0LLl++zJw5c3j55Zfp0KFDgnUPPPAAs2bNonv37j639ff3Jy4uLk3707pTKZUW11QDFGiPDd050xjzh+cKEfkQeEhERhud3E4pdY1btGgRJ06coG/fvhQtWjTBui5dujBt2jR331BvISEhnD9/nqVLl1KnTh0CAwMJDAzMimIrpa4TV10DVERCgF+A+saYTV6r+wArvBufjvnAK8CtJDPxvUbzyLk0coVSSYuPj6d///4sWLCAkydP0qRJE1q2bJmo8QnQqVMnnnrqKZYuXeozryZNmvDQQw/RuXNn/vzzT/r27cuMGTOS3b/Wndc3rZ9VWuWoBqiIpHRlcg4wMqmVxpi7kln3M14Rk5RS6lqxePFiZs2aRXR0NJUqVaJIkSL4+/v7TFupUiV3X9DIyEifaaZOncqwYcOoWLFikmmUUiq9clQDFCjt8XcHYKbXsn+AwllaIqWUugrs37+f0qVL06RJk+wuilJKpShHTcNkjPnN9QBOeS8zxnjOdFxBRJaKyDkR2SMit3rmJSLVRGSRiJwWkeMi8oGIlMrCw1FKqSzRs2dPHnvsMQ4dOoSIEBISwoULFxg8eDAlS5Ykb968NGrUiNWrV7u3iY6ORkQ4ceKEe5krMtKmTd69m5RSKmPlqAZoGr0EvAnUAjYCH4pIEICIlAa+A3YBDYA2QBDwuYgkOmaNhKSUuppNnDiR559/nnLlyhEbG8vGjRsZOnQoH330EVFRUWzdupUaNWrQtm1bYmNjM2y/GglJKZVeV3MDdLwx5ktjzE/A00ARoLazrj+w3RgzzBiz1xizA+iObYwmmnFZIyEppa5mBQsWJDg4GD8/P0qVKkVgYCBTp05l7NixtG/fnqpVqzJt2jRKlizJ5MmTM2y/GglJKZVeOa0PaFrs8Pj7qPNcwnmuB7QQkTM+trsR2JCZBVNKqex04MABLl26RNOmTd3L/Pz8aNy4MXv27MnGkimllHU1N0Avuf4wxhgn6pHrim4uYBEwxMd2xzK/aEoplTO5IsTlymWrS89pkS9duuRzG6WUymhXcwM0OVuA+4CDxpg01agazUMpdbW78cYb8ff3Z82aNdx4440AxMXFsW7dOndc+OLFiwMQGxvr/nvbtm3p3qfWnUqptLia+4AmZzJQEPhIRBqKSCURaSMiM0QkOLsLp5RSmSl//vz079+fYcOGsXjxYvbu3Uv//v05duwYAwYMACA0NJQbbriBkSNH8uOPP/LNN9/w4osvZnPJlVLXiyy7Aioi0cAuY8ygzN6XMeaoiDQFXgaWAHmBQ9gISBeS21ajeWQcjYyh1JWLiIigevXqTJo0KU3bjR07FoBevXpx6tQp6tSpw5IlSyhd2k6tnCdPHj788EMGDBhArVq1qF27NmPGjEkUNz61tO68fmjdrjJCjr0Fb4xZgI/IRcaYmCSWi9frn4B7M6t8SimVkwwZMoQhQ/7t9h4QEMCECROYMGFCkts0adIk0W13zz6hISEhCV4rpVRGuVZvwfskIrlExC+7y6GUUleT+Ph44uLisrsYSqlrSIY1QEUkWkSmichEEfnTebzma+J3J/3/ichGj0hF80WkrLNORGS/iAzx2qayiBgRqeu8Luj06zzu5LNSRMI90vcUkTMi0k5EdgEXgaoZdcxKKXWlIiIieOihh3j00UcpXLgwhQsX5sknnyQ+Pt5n+nfffZf69esTHBxMiRIl6NSpE0eOHAHs1cvQ0FBef/31BNv89NNPiAhbtmwB4K+//iIyMpISJUoQHBzMLbfckiD60ezZswkKCmLx4sVUr14df39/9u7dm0nvgFLqepTRV0C7OXk2BvoBkcDgJNL6AyOwkYw6AMWAD8BOqwS8A/Ty2qY3sM0Ys0XsXCKLgLLO9nWw0Y++dSIhueQFnnPKUw046F0QjYSklMpO7733HvHx8axbt47p06czY8aMJG+dX7x4kVGjRrF9+3YWLlzIiRMn6Nq1K2CnWOrTpw+zZs1KsE1UVBS1a9embt26GGNo3749R44cYeHChWzdupUWLVrQqlWrBFGSzp8/z+jRo5k+fTp79uyhQoUKicqikZCUUumV0Q3QWOARY8w+Y8zHwGvA474SGmOijDGLjTEXjTEP7dy582ageUxMzK/AhD59+nwChIlIIwDn1nl3bMMUoCU28tG9xpgNxphSxpia1apVy/vyyy8fAg69+uqrjwF+wCBjzBpjzI/GmNM+yqKRkJRS2aZ06dK8+eabVKlShfvuu48nn3yScePG+Uzbu3dv2rVrR6VKlWjQoAFTp05l1apVHD58GLCDjn788Ue+//57wE6/NHfuXPr06QPAihUr2LZtGwsWLKBBgwaEhoYyevRoKlWqxLx589z7iYuLY9KkSTRt2pSwsDCCgxNPIKKRkJRS6ZXRDdDvTcIe6+uAsiJSwDuhiNQtWLDg0htuuOFgcHBwr/r16xcB2Lt37zHg0bfffvvzvHnzfoO96gnQFhtu8z3ndT0gEPjd39//fFBQ0KqgoKC79u3bl3vp0qW7gJVFihSpkjt3bs6fP39HBh+nUkplmEaNGrkniAdo3LgxR44c4e+//06UdsuWLXTs2JEKFSoQHBxMeLjtdXTo0CEASpUqRYcOHYiKigJgyZIlnDx5km7dugGwefNmzp07R/HixQkKCnI/du3axYEDB9z7yZ07N7Vr186sQ1ZKXeeyZRCSiOQHvm7UqFH19957L9d777334vnz5yMA2rVrNwQYD9w0btw4ATqLSCC2IfqpMeZPj7IfK1u2bPiuXbsubt269cKjjz56W3x8fNi33357O/DA9OnTRwYEBBAQEPA0EJDlB6qUUhno7Nmz3H777QQGBjJv3jw2btzIkiVLAHtr3uXBBx/ko48+4ty5c0RFRfGf//yHwoULA3ZAUcmSJdm2bVuCx759+xg9erQ7j4CAAPz8dMymUipzZPQ0TA1FRDyugjYCjhpj/vb87x6oAhSbNm0aFStWjMH2Bb3bY/0IILJv3763DBw48E9jzEPAnUA7jzRbgJKtW7cODgsLCwZ2vPTSS0tfeukld4KNGzfG5suXLx7IBwSRwhygoNE8lFJZb/369Rhj3FdBv//+e8qUKUOBAglvHu3bt48TJ04wZswYKlasCMAnn3ySKL+2bdtSoEABpk2bxpdffsnixYvd6+rWrcuxY8fIlSsXlSpVyrBj0LpTKZUWGX0FtAwwQURuEpF7gSexVzO9HfLz87s8adIkNm7cuE5E7gDc/3qLyFvNmjU7lzt37sDg4ODz2L6kR4DlHnksA9bMnTs36osvvvjr559/vum+++7rJCKjRKQ5QN26dUvmsgGPtwF/ZPCxKqVUhjh69CiDBw/mhx9+YMGCBbz22ms89thjidKVL1+egIAAJk2axM8//8yiRYt47rnnEqXz8/Ojd+/eDB8+nLJly9K6dWv3ujZt2tC0aVM6duzIV199xS+//MK6desYMWIEq1atytTjVEopl4y+AvoedtDPesA1kj1RA9QY8/vo0aO/mj179p0TJ07sBIRiBystcaX5559/zgLFZ8yY8X6XLl2eA2Z59i81xhgRaQe82KVLl/+7fPlyQIkSJT665ZZbDr700kslgL4DBgzo9PDDD8cDnVJ7ABrNI300MoZS6detWzfi4uJo2LCheyS7qwG6bds2Tp48Cdj47XPmzOHpp59m8uTJ1KxZk3HjxtG2bdtEefbu3ZsXXniBXr16JehfKiIsXryYZ599lr59+3L8+HFKlixJ06ZN6d69e7qPQevOq5vW4SqrSUZFuUhHqM0ZQF/n8bZXXrNr1KjRdMeOHaFz586d2qNHj0igkjHmUDL5NcVO43SDx7Jj2CurUwHfk+rZ/UVip4zCv1RovdI9JqTyEJSLVl7qSoWHhyeYi/J6kVKozfSG4ly/fj1Nmzbl559/pnz58hlR1GQFlK6M1p1XL63Dr14ichA44bFohjFmRnaVJ7VybCSk+Ph4OXz4MAMHDvw/4Lir8elMeD9FRMaIyAkROd60adNFcXFxy4BVQNVp06YVrFChwttFixYtEBgYOCksLOyEiNye1L48p2HKosNTSqkU9ezZk5UrVzJ58mREBBEhJiaGPXv20L59e/dk9F27duW3334D4MKFCyxcuJB27drh5+dH9erVadasGevWrUuQt4gwdepUOnbsSGBgIGFhYaxYsYLDhw9z++23kz9/fmrXru2evF4plWOdcLVhnEeOb3xC9jZAXbMW+5w87u+//y5YoUIFLl26lAvY47W6G3AZaNK4ceOX1q9f3+7tt9+OBR4A9vXv33/qoUOHapw5c+aOTZs27XzkkUcKi8hCEamVaUejlFIZbOLEiTRu3JhevXoRGxtLbGwsefLkoUWLFlSvXp0NGzawbNkyzpw5Q8eOHYmPj+eDDz6gY8eOBAcH89VXX7FhwwZq165Nu3bt+OOPhF3hX3zxRbp06cL27dsJDw+nS5cu9OnThwEDBrB161bKlClDz549s+fglVLXtAxrgBpjItJw+x3gB+c5zNfK8uXLm7i4OMqWLbsGOO+1eo8x5nljzI9r166Na9myJR9//PFZIF5EbgS6AvdduHBhZbVq1ZYNGjSIatWq7cNGQ0pEIyEppbJLdHR0krfXCxYsiL+/P4GBgZQqVYpSpUoxffp0atWqxdixY6latSo1a9Zk7ty5bNiwgU2bNtGzZ0/i4uKIiYmhVatWVKlShbfeeou8efPy1VdfJci/e/fudO3alcqVK/P0009z/Phxbr/9djp27EhYWBhDhw5l586dnDhxwmf5NBKSUiq9svMK6Arn+TbvcgQEBOQuUKBAEeDc4cOHf/ex7Q7P5GXKlOHs2bOFnNd1AQH2iMiZvHnzPhwUFMTevXurATf6KohGQlJKXS02b97Md999l2AS+RtusF3fXRPJHz9+nH79+hEWFkbBggUJDg7m+PHj7snqXWrWrOn+u2TJkgDUqFEj0bLjx4/7LItGQlJKpVdGj4JPiwPAN9gG6EDgLdeKRo0a1cmVK5cfMO/ixYt5PbapEhQUFHjmzJlLHstWiQhFihQpA9TENmYNUP+VV14Jveeee+YDfh988EH7ESNG7Mzsg1JKqcwUHx9P+/btef311xOtczUYe/TowbFjxxg/fjwhISEEBATQunXrBJPVA+TJk8f9t2ukvK9l8fFJjuFUSql0ye5BSAOA48CbwGfAy8C3ISEh1c6fP38WeMYr/d569erV91q24ciRI/ud+T43rl+/vjsgH3744ZRhw4YtqFy5ckDlypUnPv/880uMMUcy+XiUUipD+fv7ExcX535dt25ddu/eTYUKFQgNDU3wcMVrX716NQ8//DDt27fn5ptvJjg4mNjY2Ow6BKWUSiQ7r4CCvQoaDryAjfXeDog9ePDg3rVr1/5KKiePX7Zs2ZpKlSqdBU41aNCgyf3332+GDBnS4q+//tpdsmTJt+++++61wBDgZ2NM4rAhHjSah1IqJwkJCWHDhg3ExMQQFBTEwIEDmTlzJp07d2bYsGEUL16cn3/+mY8//pg33niD4OBgwsLCePfdd2nYsCFnz55l6NCh+Pv7Z2o5te5USqVFdl8BBfgV6AWUBvyBCtHR0RsuXrx4yUdaWbly5Upfmfz888+HgQig8Pvvvx9w+PDh0f369Qu6++67XwcWAi2Ag5lyBEoplUmGDBmCv78/1apVo3jx4ly8eJE1a9aQK1cu2rZty80338zAgQMJCAggICAAgKioKM6cOUO9evXo0qULvXv3pmzZsowaNeq6nGtVKZXzpGoiehGZDfRwXsYBR4FFwNPGmD+dNAboZIxZ4GPbYsaYDs7raJKZsN47HxGJASoALYwxqzzSjQTuNcZU91iWB3gEuB+4CTv5fAywGJhsjPk1pWMNKF3Z6GTKqaeTF6uMcr1ORJ8RPCMd+dKjRw9GjhxJxYoV2bhxI+HhGT/lsU5Ef/XQevvaIiKbr8Z5zNNyC34Zdp7N3EA1IAoohJ3yKLOdB8YCTZJKICL+wNdAbWAUsBrbv7Q8cB/wBDA4k8uplFJZzrN/58KFC+nbt2+CZfny5ePPP//MjqIppZRPabkFf8EY85sx5rAx5hvgI+wI9qwwA6gjIvckk+Yx7G32VsaYCcaYTcaYQ8aY1caYR5z1Sil1zXHNEVqqVCkKFSqUaFnBgv9OkXTw4EFuvfVWAgMDqVatGkuXLk2QV3JRlpRSKqOkqw+oiFTCDhry1U8zM/yKnabpZRFJ6qrt/cBSY8xWXytNRgW9V0qpq9gzzzzDI488wvbt26lfvz5dunThzJkzgL2SmlyUJaWUyihpaYC2FZEzIvIPdvR6Next8azyMlAceDCJ9WH8G10JABH5wCnzGRHZnVTGGglJKXW9eOyxx7jzzjupXLkyY8aM4eTJk2zbtg2AqVOnJhtlyZtGQlJKpVdaGqDfYftXNsBejVyMnb8zSziDnV4GRohI/lRu9hi2zG8BSW6jkZCUUtcLz+hHZcqUAf6NdJSaKEueNBKSUiq90jII6ZwxZr/z9yMisgJ4DhjpLDsN+KqBCgEZ9a/xW8DDwOM+1v0IVPFcYIz5DUBEUjWfqFJKXeuSi3SUmihLSimVEa5kHtBRwDARKeO8/gGo55lARPyAWnjdGk8vY8x5bKP3SezteE8fALeKyFU3FYFSSuUEqYmypJRSGSHdkZCMMdEisgd4FhtScxwwy+lruRQIxF6tLIIdxe6pmIjU9lp23BhzNBW7noedUqk3ti+qy3hsJKVlIjIKWIWNpBQKdMTOX5oijeahlLpepSbKUlK07lRKpcWVRkJ6A+gjIhWMMR9gIxr1AjYBS4BSQHPXrXAPnYGtXg9ft9Vx8rjb9cIYEw8MA/J6JjLGXADaAC8B3bF9VvcBk5z8b03vQSql1PWgTJkyKUZZUkqpjJCqSEjZKaXISRlNIyGlnkbTUBlJIyHlDFu2bKF+/fo0atSINWvWpHo7jYSU/bROvj5drZGQckIseKWUUjnE22+/zYABA9i1axd79+7N7uIopa5R2d4AFZFoEZkmIhNF5E/n8ZqI+CybiPyfiGwUkdMiclxE5otIWWediMh+ERnitU1lETEiUjcrjkkppa5G//zzD++//z6RkZHce++9vPPOO9ldJKXUNSrbG6CObtiyNAb6AZEkHbfdHxiBHV3fASiGHQHvinb0DrYfqqfewDZjzJaMLrhSSl0rFixYQIUKFahRowYPPPAAc+fO5dKlrAp4p5S6nuSUBmgs8IgxZp8x5mPgNZIYlGSMiTLGLDbG/GyM2QD0B5qLSDknySwgTEQagXsqqO7YhqlPGglJKaXgnXfe4YEHHgDglltuITAwkM8//zzJ9BoJSSmVXjmlAfq9V6z2dUBZESngnVBE6orI5yJyUEROY0fcA5QH9+TzC7FXPcHGrC8CvJfUzjUSklLqerd//35Wr17N/fffD9hJ6rt165bsbXiNhKSUSq90zwOaHZwQnF8Dy4AHgOPYW/CrsLfmXd4G3heRwdiG6KdOKE+llFI+vP3228TFxVG+fHn3Mtd1gV9//dUdklMppTJCTrkC2lBcMeGsRsBRY8zfXumqYBucTxtjvjPG7ANK+MhvCfA38BBwJxCVCWVWSqlrwuXLl5kzZw4vv/wy27Ztcz+2b99OzZo1mTVrVnYXUSl1jckpV0DLABNEZApQAxtq80Uf6Q4BF4BBIjIZqAqM9k5kjIkTkSjgZeAIsDy1BdFoHkqp682iRYs4ceIEffv2pWjRognWdenShWnTpvHcc8+R8DpBQlp3KqXSIqdcAX0PuBfYAczEDhga77G+gYjsMsb8DvTARkbagx0Nn2iwkogY7MAmf2CWyemz7SulVDZ65513aNmyZaLGJ0CnTp2IiYlh6dKl2VAypdS1KqdcAb0M/ITtq5kg4pExJkJEgoAA5/VHwEde2/v6t7wQNv777LQUZOeRvwh5alFaNrnuaLQNpa4tX3zxRZLrKlWqRGr+h9e6M+20LlXXs5zSAE2WMeYMcCY1aUXEFbC4C7ZBeyjTCqaUUkoppdIsp9yCT0REWovIKRF5SERGisgur/U9RGSniFwQkWMiMsdZ1dV5LoBze15EhonICdfcoEoplZOdPXuW7t27ExQURMmSJXn55Zfp0KEDPXv2BODdd9+lfv36BAcHU6JECTp16sSRI0fc20dHRyMifPXVV9SrV498+fLRvHlzDh8+zMqVK6lVqxZBQUF06NCBP/74I8G+Z82aRbVq1cibNy9hYWGMHz+e+Pj4rDx8pdR1INsboMaYCO/b7iJyL/ApEGmMmea9jYj0A6ZjJ52vCbQDdjn5zXaSDQUOi8jrwMPALcaY7zPrOJRSKqM88cQTrFy5kk8//ZRvv/2W7du3s2rVKvf6ixcvMmrUKLZv387ChQs5ceIEXbt2TZTPiBEjmDBhAuvXr+fPP/+kc+fOvPDCC8yYMYPo6Gh2797NyJEj3elnzpzJ008/zQsvvMDevXt54403GDt2LFOmTMmKw1ZKXUdy3C14EYnERkK61xjzTRLJngMmGGPGeSzb7JXGDzv9UlOgqTHmYAr7jATwK1A8vUVXSqkrdubMGaKiopg7dy633norYAcJlStXzp2md+/e7r8rVarE1KlTqVq1KocPH06QbvTo0TRv3hyAhx56iIcffpjNmzdTt25dAHr06MGCBQsSpH/11Ve59957AahYsSJPPfUUU6ZMYdCgBNcJABsJacaMGQAaCUkplSY5rQF6NzYWfAtjzDpfCUSkBFCWlKdWeh07uKmhMeZ4cgmNMTOAGQABpSvriHmlVLY5cOAAly5dokGDBu5l+fPnp3r16u7XW7ZsYdSoUWzbto2TJ0+6BwkdOnQoQQO0Zs2a7r9LliwJQI0aNRIsO37cVo+///47v/76K/369aN///7uNJcvX05yEFJkZCSRkZEABJSunO5jVkpdf3JaA3Q7dh7QPiLiHZ4zrZZi+4O2I40j4ZVSKqc6e/Yst99+O23atGHevHmUKFGCEydO0Lx5cy5evJggbZ48edx/u+bw9F7m6t/pep42bRpNmjTJ7MNQSl3nsr0PqJdfgAjgNmCG+Jj12LmaeQRonUJei4FOwFQR6ZHB5VRKqUxx4403kidPHjZu3Ohedu7cOXbtsuMw9+3bx4kTJxgzZgwtWrSgSpUq7quYV6JkyZKUKVOGAwcOEBoamuihlFIZKaddAcUY87OItASigenOgCNvLwHjReQYsAgIBFobY97wymuhiHQC5ouIMcbMTWn/Gs1DKZWdgoKC6N27N8OGDaNYsWKULl2aF198kfj4eESE8uXLExAQwKRJkxg4cCB79+7lueeey5B9jxo1iocffphChQrRrl07Ll26xJYtWzhy5AjDhw9PdlutO5VSaZHTroAiIrmwI9iDgL7AF3hNNG+MmQoMdNbvwsZ+v9lXfsaYhcAgYI6IPJ95JVdKqfQRkQSDgV5//XWaN2/OXXfdRcuWLalZsybh4eHkzZuX4sWLM2fOHD777DOqVavGqFGjGDfOjsds2bIlmzZtSnc5HnzwQaKiopg3bx61atWiVq1ajBgxgooVK17xMSqllCfJaVEqRaQD8An2VvzPwEljzMVkN0o5zxDs7f36xphka+eA0pVN6R4TrmR31zSN3KEyS3h4+BU1nrJKREQE1atXZ9KkSRmWp4gwf/589+hzbxcuXKBChQo8+eSTPPHEEz7TxMXF8fvvv1OsWDFy586Ym1shISEMGjSIIUOGpJg2oHRltO5MSOtLlRVEZLMxJjy7y5FWOe4WPBAKxBpj1mZ3QZRSKjts3bqVvXv30qBBA06fPs3YsWM5ffo0nTt3TnIbPz8/SpUqlYWlVEqp9MtRt+BFZDYwHigvIkZEYkQkQEQmONGOzovI9yLSzGObCCdtMY9lIc6yq+4/AqVUztWzZ09WrlzJ5MmTERFEhJiYGPbs2UP79u3dkYm6du3Kb7/9lmDbOXPmUKNGDQICAihZsiQ9eiQcG3ny5Ek6depE/vz56dChA88++yx16tShVatWHDx4kHPnzrF+/XpuvfVWAgMDqVatGkuXLnVvHxMTg4gkuIq8b98+7rrrLgoWLEhQUBCNGzdm586dAGzcuJHbbruNYsWKUaBAAZo1a8a6dT5nv1NKqQyXoxqgwKPAC8BhoDRQH3gV6Az0BuoAO4ElIlI6uwqplLo+TZw4kcaNG9OrVy9iY2OJjY0lT548tGjRgurVq7NhwwaWLVvGmTNn6Nixo3tqo+nTp9OvXz969erFjh07WLx4cYJ5PQFeeOEFOnbsyPbt2+nevTuHDx9m9+7d/Pnnn3zwwQcAPPPMMzzyyCNs376d+vXr06VLF86cOeOzrEePHqVZs2aICEuXLmXLli0MHDiQuLg4AE6fPs0DDzzAqlWr2LBhA7Vr16Zdu3aJQnMqpVRmyFG34I0xf4nIaSDOGPObiOQH+gMPGmMWAYjIQ0Ar7CCkZzNivxoJSSmVGgULFsTf35/AwED37e7nn3+eWrVqMXbsWHe6uXPnUqRIETZt2kSDBg0YPXo0gwcP5vHHH3enqVevXoK8H3jgAf7v//4PsBGJJk6cyHfffedeBvDYY49x5513AjBmzBjmzp3Ltm3baNasGd4mT55M/vz5mT9/Pv7+/gCEhYW517dq1SpB+rfeeov//e9/fPXVVwn2mRyNhKSUSq+cdgXU241AHmCNa4ExJg5YB1TLqJ0YY2YYY8KNMeF+gQUzKlul1HVg8+bNfPfddwQFBbkfN9xwA2CjGh0/fpwjR47QunXyUxd7Ri3KnTs3xYsXTzS/p2eaMmXKACQ5B+jWrVtp1qyZu/Hp7fjx4/Tr14+wsDAKFixIcHAwx48f59ChQykftCMyMpJNmzaxadMmtO5USqVFjroCmkau4fvxzrPnVE15UEqpLBAfH0/79u15/fXXE60rWbIk//zzT6ry8YxQBAmjFPlK44rT4Z0mtXr06MGxY8cYP348ISEhBAQE0Lp160TRlJRSKjPk9AboAeAi0NT5GxHxAxoD7ztpfneeS3v8XTvriqiUup74+/u7+1EC1K1bl48//pgKFSokakQCBAcHU7ZsWZYvX86tt96aZeWsU6cO7777LhcvXvR5FXT16tW8+eabtG9vpwo6duwYsbGxWVY+pdT1LUc3QI0xZ0VkKjBWRE5g5/J8DCgJTHGS7Qd+BUaKyFNACFfQN1SjeSilkhMSEsKGDRuIiYkhKCiIgQMHMnPmTDp37sywYcMoXrw4P//8Mx9//DFvvPEGwcHBPPPMMzz22GOULFmS9u3bc+7cOZYvX57knJ4ZYcCAAUybNo377ruPZ555hsKFC7Nx40aqVq1K7dq1CQsL491336Vhw4acPXuWoUOHJnm7PjW07lRKpUVO7wMKMAz4CJgFbANqAm2NMbEAxphLQBegErAdGAU8nS0lVUpd84YMGYK/vz/VqlWjePHiXLx4kTVr1pArVy7atm3LzTffzMCBAwkICCAgIACA/v37M3nyZGbOnEn16tVp27Ytu3fvztRyli1blu+++46LFy/SsmVL6tSpw1tvveWepL5+/fqsW7eOevXq0aVLF3r37k1ISEimlkkppVxyXCSk7KaRkBLTaB4qK1wtkZBygp49ezJnzhzATkBfpkwZ2rdvz5gxYyhcuHCq8hg5ciQLFixg165dGVImjYSUkNabKqtcrZGQroYroEoppby0adOG2NhYYmJiePvtt/nyyy8ZMGBAdhdLKaVSJcsboB6Ri7wf0c76e0Rkp4hcEJFfReQZcQ33tOtjRORZEZkuIn+LyGERedJrHwVFZIaIHBeR0yKyUqMiKaWuJQEBAZQqVYpy5cpx22230blzZ7755hvAXiHt0KFDgvQjR45MNPm9t1mzZlGtWjXy5s1LWFgY48ePT/coe6WUSk52DEJaix2x7lIWWAZEi0g9YD7wIvAeNhLSdOBv4C2PbR4DRgCvAXcAb4rIamPMOqexugj4C+gAnAR6AN+KyE2uvqNKKXWt+Pnnn1myZInPUfipNXPmTJ5//nneeust6tWrx65du+jbty958uRh0KBBGVhapZTKhgaoMeYi8BuAiOQDFgIrsIOH3gVWGmNGOMl/FJHK2IFIng3Qb4wxk5y/3xKRR4DW2AnqW2KnYSpujHFNwPeciNwJPIAN7ZmARkJSSl1tlixZQlBQEHFxcZw/fx6AcePGpTu/0aNH8+qrr3LvvfcCULFiRZ566immTJmSZANUIyEppdIr26Zhcq5Uzgb8gAeMMUZEqmKvXnpaDYwQkQLGmL+dZTu80hwFSjh/1wMCgd897twD5MVGVkrEGDMDmAF2EFK6DkgppbJQixYtmDFjBv/88w8zZ87kwIEDPPLII+nK6/fff+fXX3+lX79+9O/f37388uXLJDdQNTIyksjISMAOQlJKqdTKznlAnwdaAPWNMWdTkd6zFrzkY52rP2su4BjQ3Ecef/tYppRSV53AwEBCQ0MBePPNN2nZsiWjR49m5MiR5MqVK1HD8dIl72rzX65+ntOmTaNJkyaZV2illHJkSwNURO4FhgItjTGHPVbtxUY98tQMOGyMOZ3K7LdgJ6qPN8b8fMWFVUqpq8CIESO44447iIyMpHjx4mzbti3Beu/XnkqWLEmZMmU4cOAA3bt3z9yCKqUU2dAAFZHqwBzsZPGHRKSUs+oi8AawUURGYkNt1geeIG0Tyy8D1gCfi8hQYB9QCmgLLDPGrEpuY43moZS6GkVERFCtWjVefPFFOnbsyKuvvkpUVBQtWrTgk08+Yc2aNZQrVy7J7UeNGsXDDz9MoUKFaNeuHZcuXWLLli0cOXKE4cOHp7h/rTuVUmmRHfOAhmP7aE4AYj0enxhjtgCdgP8Cu4BXnMcknzn5YOx9p3bAt8BM4AfgY+AmbF9RpZS6poSEhPD666/zxBNP8M4771ClShVGjBjBM888Q7169YiJiUlxjtAHH3yQqKgo5s2bR61atWjevDkzZsygYsWKWXQUSqnrSU6IhFQOeAF7hbIotjH6GXZU/J9pzKsuMKRr164dP/zww0DXwoCAgDMXLlxYCQwxxuxLLgONhPQvjeShspJGQoLp06fz+OOP8+eff7rjsl+8eJFChQpRqVKlBFGL9u/fT+XKlVm2bBl9+vRh0KBBDBkyJLuKfl1HQtK6UmUnjYSUPjcCm4FewAZgPPAz8Ch2SqWiachrELARuO2PP/6IrVq1aswPP/wwNyYmZsOXX365FsgHfJqhpVdKqQzUsmVLzp07x4YNG9zL1q9fT8GCBfnpp5/4/fff3ctXrFhBQEAATZt6d5tXSqmcL7sboFOw0yc9AtwNPAW0wjZEbwJeSmU+twFvYvt/Vly6dOnqvXv37g4LC+tRoUKFhrfeemsHJ88qztyjSimV44SFhVGmTBlWrFjhXrZixQpat25NeHg40dHRCZY3btyYvHnzAnD+/Hn69etHgQIFKFeuHK+99lqCvP/66y8iIyMpUaIEwcHB3HLLLQmuOM+ePZugoCCWL19O9erVyZ8/Py1btuSXX37J3INWSl2XsrMBeiO24RgDTPZaNwI4i504Pn8q8noN+Ae4H0g0Wl5E8gKdgZ0ek9MrpVSO07Jly0QN0IiICCIiIhIsj46OpmXLlu7X48ePp0aNGmzZsoVhw4YxdOhQ1q1bB4Axhvbt23PkyBEWLlzI1q1badGiBa1atSI29t/gcBcuXODll18mKiqKdevWcerUKR566KEsOGql1PUmOxugrprzG8A72PBp7Ej2QKBRCvlUB2o6+ZwEWjZo0OBmEbkjd+7c/4jIGez8n7dgG6iJiEikiGwSkU0azUMplZ1atmzJunXruHDhAufPn2fdunVERERwyy23uBug+/btIzY2llatWrm3u+222xg0aBChoaE8/PDDhIaGsnz5csA2Yrdt28aCBQto0KABoaGhjB49mkqVKjFv3jx3HpcvX2by5Mk0aNCAmjVrMmTIEKKjo5OcjH7GjBmEh4cTHh6ukZCUUmmSnQ3Qm5znH5NY/5PzHJZCPvWd5+NANPBt1apVwyMiInLt27cv765duw537dr1HmA58I2I3OCdgTFmhjEm3BgT7hdYMG1HoZRSGahVq1buhue6desoXrw4oaGhNG3alAMHDvDbb7+xYsUKAgMDadiwoXu7mjVrJsinTJkyHD9+HIDNmzdz7tw5ihcvTlBQkPuxa9cuDhw44N4mICCAm266KUEeFy9e5M8/fY8HjYyMZNOmTWzatAmtO5VSaZGdkZBctVVS/za7lhdKIR9XCM4+wBGg/UcffdTN39+/dGho6BHg/95///1X7r777lqdO3c+gY35/pxnBp6x4P1LhabtKJRSKgNVrFiRChUquK883nLLLQDkz5+fevXqER0dTXR0NM2aNSNPnjzu7Tz/BhARd4Sj+Ph4SpYsyapViadBLlCggPvv3LkT/iS4whm78lFK5UjFRMRzCpEZTojxHC07G6AZxXUV1w/oAqw7f/78fefPnz8HdAeqAOF33333Pdhb/YHeGWgseKVUTuLqB2qMSRCZKCIigm+//Zbo6Ggef/zxVOdXt25djh07Rq5cuahUqVJmFFkplX1OXI3TMGVnA9R1hTOp+zau5adSyMe1/jfs1E0uASJScseOHctz584d/sQTTwwFgoAvk8tMo3kopbJby5Ytef/99wGIiopyL7/lllu47777OH36dIIBSClp06YNTZs2dUdIqlKlCr/99htLliyhTZs2NG/e/IrLrHWnUiotsrMB+oPznFQfz8rOc1J9RL3zOeW1vA0QW7NmTYKDgylevHgZoJMxJjqtBVVKqazUsmVLLl68SLly5QgN/bdbULNmzfjnn38oUKAA9erVS3V+IsLixYt59tln6du3L8ePH6dkyZI0bdpUY78rpbJFlkVCEpFoYJcxZpCz6EZgP3YaphtJOBI+GBsRSbB9PM8mk3UgdgCSH1DMR9qpwEPYOUbHplROjYRkaWQPldU0EtLV7VqOhKT1ocrJNBJS2h3ATp0UAgz0WjcKO//nPBI2KKs4D0/ngHeAvMCL2EarSw2gJ3AZWJAxxVZKKaWUUlciSxqgIjIbOw/nQBExziOkf//+42677baLwcHBbxYqVOhCo0aNdsfExKwGHgN+bNy48QIR+UZETojI382aNdu7bt26vV55m6JFi8a0adPmr8DAwMEVKlQ4FxUV9fGePXs+ufXWW7flz58/b6lSpY6JiM4RopS6JkRERNC/f3+eeOIJihQpQvHixZk4cSIXLlxg4MCBFCpUiPLlyyeY4/Opp57ipptuIl++fISEhDB06FDOnz8PQExMDLly5Up0BXrmzJkUK1aMixcvZunxKaWufVl1BdQV230WUNp5XJo2bdp727dvf+err7769Ntvv/27WLFiVe+55576ly9fngg0+v777+OxV0GbAw1q165Nu3btEJEEMeJPnjw5ZPv27YO/+uqrqXXr1r00fPjwTo888sidXbt23TFx4sTex44d2wHMzqJjVUqpTPfee+8RHBzM+vXreeqppxg8eDB33303YWFhbNq0iR49evDggw+6Ix3lz5+fqKgo9u7dy5QpU/jwww956SUb7TgkJIRbb701wYAnsAOgHnjgAfz9/bP8+JRS17Zs6wMqIi8ATY0xrT3SFMZGM2pojNngIw8BjgJPGmPedZYZ4BVjzHDndXVgJ/CEMWacsywCWAEUN8ac8JGvex5QvwLF65XrPyuDjvrqpX2eVFbTPqCpFxERwYULFxKE2ixRogSNGzfmiy++AODSpUvkz5+f999/n3vvvTdRHtOmTeP1119n//79ACxYsIC+ffsSGxtL3rx52bt3L9WqVWPnzp1Ur17dZzlmzJjBjBl2usFtPx3iWq07tT5UOZn2AU27ekALETnjegC/OutuBBCREiIyXUR+FJG/sCE6SwDlvfLa4fH3Med5p49lJfBBIyEppa42npGPRIQSJUpQo0YN97I8efJQuHBhdzSkBQsW0KxZM0qVKkVQUBCPPfYYhw4dcqfv2LEj/v7+fPLJJ4C9+tmgQYMkG5+gkZCUUumXnQ3QXMAioLbXozKw0EkzBxtq8zGgibP+MOB9P+iSx98mmWXZebxKKZVhfEU+Sioa0vfff0+XLl24/fbb+fLLL9m6dSsvvvgily5dSpBf9+7diYqK4vLly8ybN48+ffpkybEopa4/WTkP6EXsVEkuW4D7gIPGmEu+N6EZ8IgxZhGAiJTE9h9VSimVSmvWrKFs2bI899y/UYgPHjyYKN2DDz5ItWrVmDJlCqdPn6ZLly5ZWUyl1HUkKxugMUADEQkBzgCTgb7ARyIyFvgdqIRtlD5hjDmNnYT+/0RkPXZaplexDdlMo9E8lFLXmrCwMI4cOcJ7771H48aN+frrr/nggw8Spbvpppto1qwZTz75JF26dEkQJz4lWncqpdIiK29Jv45tPO7BNjb9gabYCeiXALuxjdILzgOgNzZ85mbgQyAK25BVSikFxMfH069fP/bt28eoUaOIjo5OlObOO+/kySefZPDgwdSsWZOlS5fywgsv+MyvT58+XLx4UW+/K6UyVZaNgr9aaCQkHfGpsoeOgk+fhQsXcs899xAdHU2lSpUoUqTIFU2bNHbsWN555x1+/DGlKMgJXauRkLQ+VDnd1ToKPjtjwSullLpC+/fvp3Tp0jRp0sTn+osXL6aqQXrmzBkOHjzIxIkTeeaZZzK6mEoplUCm3oIXkfwiMteZZumYiAwXkYVOZCRE5P9EZKOInBaR4yIyX0TKemwf4URNukNENovIPyKySkTKicgtIrLdyXuh9+T0ItJLRPaIyHlnGqfHRERHwSulrhk9e/Z0T6ckIoSEhLijJA0ZMoTixYvTtGlTAPbs2UP79u0JDg6mRIkSdO3ald9++82d16BBg6hduzZnzpzh8ccfJywsjPHjxxMfH59dh6eUuoZldoPsDWwIzv8ArYBa2KhGLv7ACGd5B6AYkLhnvI0NPxhoCBQGPgKex04eHwHcDIx0JRaRvsAYJ01V4AlgGDAgYw5LKaWy38SJE3n++ecpV64csbGxbNy4EYB3330XYwyrVq1i7ty5xMbG0qJFC6pXr86GDRtYtmwZZ86coWPHju4GZtOmTSlWrBhRUVHs27ePN954g7FjxzJlypTsPESl1DUq027Bi0gQdhBRd2PMUmdZH+w8ngAYYzzjvv0sIv2BvSJSzhhz2GPdc8aYVU4e04C3gHrGmC3OsjmAZ6iP54ChxpgFzutfROQVbAN0ko+yekZCuoKjVkqprFOwYEGCg4Px8/OjVKlS7uUVK1bkjTfecL9+/vnnqVWrFmPHjnUvmzt3LkWKFGHTpk00aNCA0aNH8+qrr7qjJlWsWJGnnnqKKVOmMGjQIJ/794yEFHfur8w4RKXUNSoz+4DeCOQB3CE1jTFnRWSX67WI1MVeAa0NFAHEWVUej4YqqYt0VMLJszhwAzBdRKZ6pMntkX8CxpgZwAywg5BSdXRKKZVD1atXL8HrzZs389133xEUFJQo7YEDB6hYsSK//vor/fr1o3///u51ly9fJrmBqpGRkURGRgJ2EJJSSqVWtg1CEpH8wNfAMuAB4Dj2FvwqUhHpyGvyesO/3Qlczw8BazO21EoplfPlz58/wev4+Hjat2/P66+/nihtyZIlOXfuHGDjwyc1mEkppTJSZjZAD2AbjvWBnwFEJBCo7qyrgm1wPm2M+cVZf8+V7tQYc0xEjgI3GmPmXml+Sil1tatbty4ff/wxFSpUSBSuEyA4OJgyZcpw4MABunfvng0lVEpdbzKtAWqMOSMiUcBYETkBxALPYq9QGuAQdsL5QSIyGTtYaHQG7X4E8JaInAIWY7sC1AXKGmNeTm5DjeahlLrWDBw4kJkzZ9K5c2eGDRtG8eLF+fnnn/n444954403CA4OZtSoUTz88MMUKlSIdu3acenSJbZs2cKRI0cYPnx4ivvQulMplRaZPQp+CPaW+hfACmxfzk3AeWPM70AP4G5sdKQRwOMZsVNjzNvYAVAPANudMkQCv2RE/kopdTUpU6YMa9asIVeuXLRt25abb76ZgQMHEhAQQEBAAGDjwEdFRTFv3jxq1apF8+bNmTFjBhUrVszm0iulrkVZGglJRAKAg8Brxpg3UkqfHa7nSEga8UNlJ42ElD4RERFUr16dSZMSTfBxRUSE+fPnu0fFp+RaiYSk9aC62mgkJB9EpA721voGIBg7F2cwdh5PpZRSSil1HcqKyECPA1uBb4GSQAtjzGERiRaRaSIyUUT+dB6vuaIVpSJKUh4ReVNEjorIBRH51Znr07X+HhHZ4URPOikiK0WkZBYcr1JKuRljeOONN6hcuTIBAQGUK1fO3ady586dtGnThnz58lGkSBF69uzJX3/9O59mz5496dChAxMnTqRs2bIULlyYXr16uUet9+zZk5UrVzJ58mREBBEhJiaGuLg4+vTpQ8WKFcmXLx+VK1fm1VdfTRTVaM6cOdSoUYOAgABKlixJjx49AAgJCQGgU6dO7ghLSimVkTL1CqgxZiuQ3GXhbsBsoDFQE5iJHaw0jn+jJO3DjpYfi42S1MLZ9hFshKUuQAxQDrgJQERKAR8Cw4H/AUFAo4w6LqWUSq2nn36aqVOnMm7cOFq0aMHvv//O1q1bOXv2LLfffjsNGjRgw4YNnDx5kr59+9K7d2/+97//ubdftWoVpUuXZtmyZfz666/cd999hIWFMXz4cCZOnMiPP/5IlSpVGDNmDADFixcnPj6esmXL8vHHH1O8eHE2bNhAZGQkRYsWpU+fPgBMnz6dRx99lDFjxtC+fXvOnDnDt99+C8DGjRspUaIEM2fOpEOHDvj5+WX9G6eUuqZl2zygjljgEWM7ou4TkTDsFdNxqYiSVAH4EVjlbH+If+f9LIMd+b7AGHPQWbaLJGgkJKVUZjhz5gzjx49nwoQJ9O7dG4DQ0FAaN27MzJkzOXv2LPPmzSM4OBiwkYVatmzJ/v37CQ0NBaBAgQJMmzYNPz8/qlatSqdOnVi+fDnDhw+nYMGC+Pv7ExgYmCASkp+fHy+88IL7dUhICFu2bOGDDz5wN0BHjx7N4MGDefzxf8d+uiawL17c1oOFChVKkK83jYSklEqvrLgFn5zvTcJRUOuAsiJSQETqisjnInJQRE5jR8+DjZIE9sppbeBHEZksIu1dt++xI9+XAbtE5H8i0t+JkOSTMWaGMSbcGBPuF1gwI49PKXUd27NnDxcuXKB169aJ1u3du5eaNWu6G58ATZo0IVeuXOzZs8e9rFq1agmuQJYpU4bjx4+nuO9p06YRHh5O8eLFCQoKYvz48Rw6dAiA48ePc+TIEZ/lSovIyEg2bdrEpk2b0LpTKZUW2d0ATYpgoySdw06lVB9o66zzB3DiwIdgb7PnAuYAS0UklzEmDrjNeewA+gA/iUitLDwGpZRKF5F/owZ7TxwvIon6cnr76KOPGDx4MD179uTrr79m27ZtDBgwgIsXL2ZKeZVSKq2yuwHaUDxrWttP8ygQyr9Rkr4zxuzDifXuyRhz2hizwBjTH2gPtHK2xVjrjDGjsA3Yo0DnzD0cpZT6V9WqVQkICGD58uU+1+3cuZPTp0+7l61du5b4+HiqVq2a6n34+/sTFxeXYNnq1atp2LAhgwYNom7duoSGhnLgwAH3+hIlSlC2bFmf5XLJkydPonyVUiqjZHcf0DLABBGZAtQAngReJBVRkkTkcWwf0m3YkJ/3A38Dh0WkEdAGexX1GFAHuAE74X2yNJqHUiqjBAcH8+ijjzJ8+HACAgJo0aIFf/zxB5s3b6ZHjx6MGDGC7t2788ILL/Dnn3/Sr18/7rnnHnf/z9QICQlhw4YNxMTEEBQURJEiRQgLC2P27Nl89dVXhIaG8uGHH7Jy5UoKFy7s3u6ZZ57hscceo2TJkrRv355z586xfPlynnjiCXe+y5cv55ZbbiEgICDBtr5o3amUSovsvgL6HuAHrMeOgH8HGJ/KKEmnsQ3WDcAWbH/QO4wx54C/gKbAQuAn4A1gtDHm3cw9HKWUSujll19m2LBhjB49mqpVq/Lf//6Xw4cPExgYyNdff83ff/9NgwYN6NixI40bNyYqKirlTD0MGTIEf39/qlWrRvHixTl06BD9+vXjvvvuo127dtSuXZuYmBh3w9Klf//+TJ48mZkzZ1K9enXatm3L7t273evfeOMNVqxYwQ033ECdOnUy5L1QSimXLI2ElGDHItHALmPMoCzYlwE6GWMWpJT2eo2EpNE/VHa7liMh/f7774wYMYLFixcTGxtLoUKFqF69Ok899RS33nrrFeffs2dPTpw4wcKFCxMs/+233yhcuLA73GZmutoiIWmdp64VGglJKaWUT//97385d+4c77zzDqGhoRw/fpyVK1fyxx9/ZOp+k5tCSSmlslN234IHQERaiMj3InJGRP4SkQ0iUl1EejrL7hSRH0XkvIisEJFKXtv3E5H9InLRee7rsS7G+XO+iBiP10oplelOnTrFqlWreOWVV2jdujUVKlSgfv36DBkyhC5dugBw8eJFhg0bRrly5QgMDKR+/fp8/fXXCfLZt28fd911FwULFiQoKIjGjRuzc+dORo4cyZw5c1i0aJE7GlJ0dDRgR8wvWPDvjZ8rjbyklFIZJdsaoMaYCGPMIBHJDXwOrAZqAQ2BCYBr+GUAtg9oL2zEJD/gE9foeRH5DzDJ2aY6MBGYIiJ3OtvXd577AqU9XiulVKYLCgoiKCiIL774gvPnz/tM06tXL1auXMn777/Prl276NGjB3feeSfbt28H4OjRozRr1gwRYenSpWzZsoWBAwcSFxfHkCFDuO+++2jTpg2xsbHExsbSpEmTRPtwRV4KCgpiw4YNfPrpp6xdu9Y9Qb7LqlWr2LVrF8uWLeOjjz7i008/ZeLEiRn/xiilrms54RZ8AaAQ8KUxxjVPyD4AEWmILeOjxpg1zrIHgJ+B1tjJ5ocA84wxk5xtfxSResAwJ8/fnbbqKWPMb74KoJGQlFKZJXfu3MyePZu+ffsyY8YM6tSpQ9OmTenUqRMNGzbkwIEDfPDBB8TExFC+vI2zMWjQIJYtW8b06dOZMmUKkydPJn/+/MyfPx9/f38AwsLC3PvIly8fAQEByd5yf//996848pI3jYSklEqvbL8Fb4w5iY1q9LWILBKRx0WkvEeSeOxId1f6g9g5Pas5i6oCa7yyXe2xPjVl0EhISqlM89///pejR4/y5Zdfcscdd7B27VoaNWrEmDFj2LJlC8YYqlWr5r5aGhQUxKJFi9xzd27dupVmzZq5G5/pkRmRlzQSklIqvXLCFVCMMb1EZAI22tFdwEsicrdnkvRkmwFFU0qpDJE3b15uvfVWbr31Vp5//nkefPBBRo4cybx58xARNm7cmCjqUb58+bKkbFcaeUkppdIq26+AuhhjthtjxhpjIoBo7DygYMvYwJXOuTpaBtjrLNqLnfPTUzMSTjp/Cdt3VCmlcoRq1apx+fJlqlatijGG3377jdDQ0ASPsmXLAlCnTh1Wr16dZChNX9GQvGVU5CWllMoI2X4FVEQqAv2AL4AjQCWgJjDVSXIZGy3pUeAfYDywG9v/E+A17Aj3zcA32Kuo3YB7PHYTA7QWkZXABWPMn0mVR6N5KKUy0h9//EGnTp3o3bu3+xb4pk2bePXVV2ndujU1a9akW7du9OzZkzfeeIO6dety8uRJoqOjqVSpEvfccw8DBgxg2rRp3HfffTzzzDMULlyYjRs3UrVqVWrXrk1ISAhfffUVP/zwA0WLFqVgwYKJrmR269YtQyIvJUXrTqVUWmR7AxQ4B4QB87Hx349hIySNxTYkLwAvAXOB8sD3wD3GmUHfGPOZiDyMHYw0ATgIDDDGfOmxjyeAccCv2EZuSGYflFJKgR0F36hRIyZOnMj+/fu5cOECZcuW5f777+fZZ58FYNasWbz00ksMHTqUw4cPU6RIEUJDQzl27BhdunShSZMmfPfddzz44IM0atSI+Ph4ChQowMcff4yI8M033xAdHU14eDhnzpxhxYoVREREJChHYGAgHTt2ZN68eSxZsoS8efPSsWPHFEe4T5gwgbx582bW26OUuk5lWySk1BCRnsAkY0xQVu3zWo6EpJE/VE52LUdCSo/w8HCqVq3KmDFjyJ8/P0WKFEm0rGDBgvz+++8UK1aM3LlTvp5w5swZLly4QNGiRVNdjpCQEAYNGsSQIUOSTaeRkJTKHldrJKQc0wdUKaXUv/bv30+rVq244YYbKFKkiM9lfn5+lCpVKlWNT7BXY9PS+FRKqcyS7Q1QEYkWkaki8oaInBSR30XkUREJAB4A8ovIIWf+T9c2r4jIDyLyj4jEiMirIpLXWRciIvEiEu61n74ickJE0j+PiVJKZZALFy4wePBgSpYsSd68eWnUqBGrV68mJiYGEeGvv/6id+/eiAizZ8/2ucyV1vPKcVIRkwBGjhxJ9erV3Wk3btzIbbfdRrFixShQoADNmjVj3bp1Wf5eKKWuP9neAHV0A05joyC9gu3L+Rl2YFJlYA7wtoiUdtKfBXpj5wAdAHQBngEwxsQAS531nnpjJ6z3PYxUKaWy0NChQ/noo4+Iiopi69at1KhRg7Zt25InTx5iY2MJDAxkwoQJxMbG0qlTp0TLOnfunCjP5CIm+XL69GkeeOABVq1axYYNG6hduzbt2rXL9Bj1SimVEwYhAew2xowEEJFxwFPAJWPMRGfZC9jIRk2BBcaY0R7bxojIGOwgpOecZTOBmSLyuDHmvIhUBRphw3EmopGQlFJZ6ezZs0ydOpW3336b9u1tX8Rp06bx7bffMnXqVF588UVEhIIFC7qjG+XPnz/RMm8pRUzy1qpVqwSv33rrLf73v//x1Vdf8X//938pHodGQlJKpVdOuQK6w/WHM7r9OLDTY9kl4E+gBICI3Csiq0XkNxE5g52ayTN60ufARf6diqk3sMEYs8vXzjUSklIqKx04cIBLly7RtOm/Uxj7+fnRuHHjBFGJ0iqtEZOOHz9Ov379CAsLo2DBggQHB3P8+HEOHTqUqu01EpJSKr1ySgP0ktdrk8SyXCLSCPgQ+Bq4E6gDPAu4J71zGqxzgd4ikhvbl/SdzCm6UkplHM+oRJmtR48ebNy4kfHjx7N27Vq2bdtGuXLlkpzwXimlMkpOaYCmRVPgiDFmtDFmozHmJ6CCj3RvAy2xfUSDsY1WpZTKdjfeeCP+/v6sWbPGvSwuLo5169ZRrVq1dOebUsQkb6tXr+bhhx+mffv23HzzzQQHBxMbG5vu/SulVGrllD6gafEjUFZEugHrgNuBrt6JjDE/iMhqbKSkD40xf6cmc43moZTKbPnz56d///4MGzaMYsWKUbFiRcaPH8+xY8cYMGBAuvNNKWKSt7CwMN59910aNmzI2bNnGTp0aKpv33vTulMplRZX3RVQJ8LRa9iR8juAW4Hnk0j+DuAPrBYRIyIhWVFGpZTy5Gu6pLFjx9K5c2d69epF7dq12bFjB0uWLKF06dLJ5JS8smXL8t1333Hx4kVatmxJnTp1eOutt5KcJzQqKoozZ85Qr149unTpQu/evQkJCUn3/pVSKrVydCSkKyUiw4A+wP3ARqCiM01Tkq72SEga3UNdra6lSEgRERFUr16dSZMmAfb2eloiFmW3kSNHsmDBAnbt8jlu0yeNhKRU9rhaIyHl/JowHUQkCNsv9FFsHHmllMo2rohFSimlrBx3C16soSJywIl0tFNE/s9ZF+LcSv+viCwVkXMiskdEbvXK5lPsNE4lsZPUJz0RnlJKZaCePXuycuVKJk+ejIggIoluwUdHRyMifPXVV9SrV498+fLRvHlzDh8+zMqVK6lVqxZBQUF06NAh0aTws2bNolq1auTNm5ewsDDGjx9PfHy8e/2hQ4f4z3/+Q3BwMMHBwdxzzz0cPnzYvd47GhLA7NmzCQoKcv89atQodu/e7S7/7NmzM+ndUkpdr3LiFdAXgXuBgcAPQGPspPJ/ArudNC8BT2JHuD8LfCgiFYwxZ0TkBqA5MNl51ATGZe0hKKWuVxMnTuTHH3+kSpUqjBkzBrATz/syYsQIJkyYQMGCBbn//vvp3LkzefPmZcaMGfj5+dGpUydGjhzJW2+9BcDMmTN5/vnneeutt6hXrx67du2ib9++5MmTh0GDBhEfH0/Hjh3Jly8fK1asAGDQoEHcfffdbNy4MVVTPHXu3Jldu3axcOFCoqOjAShYUOf4VEplrBzVABWR/MDjwG3GmFXO4l9EpAG2QeoaHjreGYyEiDwNdAdqA6uB/sAh4BFnUvt9IhIGeEZP8t6vRkJSSmWIggUL4u/vT2BgoPu2e0xMjM+0o0ePpnnz5gA89NBDPPzww2zevJm6desCdp7OBQsWJEj/6quvcu+99wJQsWJFnnrqKaZMmcKgQYNYvnw5O3bs4MCBA+7BRO+//z6hoaEsX76cNm3apFj+fPnyERQURO7cuVPsNqCRkJRS6ZWjGqBANSAvsEREPEdH5QFiPF7v8Pj7qPNcwnmuCnxvEo6uWpfcTo0xM4AZYAchpb3YSimVdjVr1nT/XbJkSQBq1KiRYNnx48cB+P333/n111/p168f/fv3d6e5fPkyrupu7969lClTJsFI9kqVKlGmTBn27NmTqgZoWkRGRhIZGQnYQUhKKZVaOaUPaDkgatWqVYsBFi9efHrNmjXz77jjjhbYK5s3A7d5pHdHSfJoaHofSwsgDjBTp07tlVkFV0qp9MqTxx3AzX173HuZq3+n63natGls27bN/di1axe7d+8mJa78c+XKhffsJ5cueQeeU0qpzJUTGqA3ApuBXlWrVl2fO3fuuN27d//ZpEmTnosXL37fGPOnMWa/MeZgKvPbmytXrkbx8fFzgHMAq1evLptZhVdKKW/+/v7ExcVlaJ4lS5akTJkyHDhwgNDQ0EQPgKpVq3L06NEEt/x//vlnjh496o6wVLx4cY4dO5agEbpt27ZML79SSnnKCbfgp2Bvnz9StGjRty5fvvzik08+2f+PP/74pk+fPrft379/2h133LEciAe+SUV+03LlyjXs4YcfvtSxY8cpf//992MLFy6sm9rCaDQPpdSVCgkJYcOGDcTExBAUFJRglPqVGDVqFA8//DCFChWiXbt2XLp0iS1btnDkyBGGDx9OmzZtqFmzJt26dWPixIkAPPzww9StW5dWrVoBdo7SkydPMmbMGLp06UJ0dHSCfqau8h88eJAtW7ZQvnx5goODCQgISLZsWncqpdIiu6+A3oi9tR6DHbEO8Bww8tVXX61w880388ADD9zr5+fXCfglhbwaiYgxxtT57LPPcr333nt/tmvX7uHx48czaNCgFZl3CEopldCQIUPw9/enWrVqFC9enFy5MqaqffDBB4mKimLevHnUqlWL5s2b06NHD3c/URHh888/p3jx4rRs2ZKWLVtSqlQpPvvsM/ct+KpVqzJ16lRmzJhBzZo1Wbp0KU8//bR7H66pl9q1a0fr1q0pXrw4H3zwQYaUXymlXLI7EtKDwEzsAKB+PtZ/jW2gtgGWJ5eRiNwLzDfG/A6sAf4D9ARmYadtejY1BbqaIyFpZA91NbuWIiFlJRFh/vz57pHx2ZVfTo+EpPWjulZdrZGQsvsK6E3O849JrP/JeXZPJC8i/inkmQt4KC2FEJFIEdkkIvrrp5RSSqmrSTFXG8Z5RGZ3gVIjUxugItJdRP4QkQCv5e+JyBcbNmwo17FjRwIDA58XkbMiskVEOngk/SskJIR77rnnvyISJSKngPc88j7oRENa2KVLl7udbQYAx0TkhsqVKz9SpEgR/P39h4nIPhHp4qucxpgZxpjwq/E/CKXUtc8YwxtvvEHlypUJCAigXLlyDB8+3GfanTt30qZNG/Lly0eRIkXo2bMnf/2VcI7OOXPmUKNGDQICAihZsiQ9evRIct9jx46lWLFifP/99xl6TEqpDHPC1YZxHjOyu0CpkdlXQOc7++joWiAiBbG3x985depUnjvuuIOZM2eOA2oB/wM+EZEqnpksWrSoGbAPCAeeFpGGwGzsrfvaN91009qvv/76fif5x87zlIsXL/qvWLGClStXTgMGA6cy5zCVUirzPP3004wePZrhw4eze/du5s+fzw033JAo3dmzZ7n99tsJCgpiw4YNfPrpp6xdu5bevXu700yfPp1+/frRq1cvduzYweLFixOF5gTb6B0yZAhvvfUWK1eupFGjRpl6jEqp60umjoI3xvwjIu8Bvfm3YXg/8Dew6LbbbmvmLDvTrVu3/cBLInInNhTni0BBgJCQkB9/+OGHV135isj7wHJjzEvOojbdu3e/MG/evLweu69QpUqVn2rVqnUz8JcxZklS5dRISEqpnOrMmTOMHz+eCRMmuBuSoaGhNG7cOFHa999/n7NnzzJv3jyCg4MBG62oZcuW7N+/n9DQUEaPHs3gwYN5/PHH3dvVq1cvQT5xcXH07t2bNWvWsGbNGipUqOCzbBoJSSmVXlnRB3QmcKuIlHNe9wbmGGMuHzly5JehQ4dStmzZZ0XkTxE5g73KWd5JWxmgYMGCW7zyrErC6EZ1b7nlFlfj0wDm7bffrvHtt9/e3bhxY5599tlnNm/ebIDPfBXQ8xa8X6DGPFZK5Rx79uzhwoULtG7dOsW0e/fupWbNmu7GJ0CTJk3IlSsXe/bs4fjx4xw5ciTFvIYMGUJ0dDSrV69OsvEJNhLSpk2b2LRpE1p3KqXSItMboMaY7cAWoKeIVMc2MKMA6tat23T+/Pm8+uqrF/PmzRuBjXq0AfAHgoGmxhizd+/epAYpucw9ePCgK3b8O8A7ffr0eWfv3r3f9+rVi61bt55q1KhRfIcOHfIkl4lSSl2rXNMwpcatt97Kb7/9xuLFizOxREqp61lWjYKfiZ0S6UFgjTHmB4Djx4/XbNeu3YFu3bqV+Oeff1oAh7FzgwKMAvKfPXv27JkzZy565FWlaNGiRwDPDkmPjB49+gfn7wddj9DQ0OmRkZEsWrRo8uXLl59etGhRncw7RKWUynhVq1YlICCA5cuTnYnOnXbnzp2cPn3avWzt2rXEx8dTtWpVSpQoQdmyZVPMq127dsyfP5/+/fszZ86cKz4GpZRKxBiT6Q/s1cwzwAWgl8fy/wUEBOxbv379Hzt27DARERFH8ubNe/6uu+6KNdYPuXLlOgQM8cjPrFu3zmAjIw3H3qbvC5zACQ3v5D3x2WeffePAgQPms88+mwmsAJalVNZ69eoZpVTW03MvaUOHDjWFChUyUVFRZv/+/Wb9+vVmypQpxhhjADN//nxjjDFnz541pUuXNnfffbfZsWOHWblypQkLCzP33HOPO68pU6aYgIAAM27cOPPDDz+YrVu3mtdff9293jO/L7/80uTNm9fMmTMnxTLq56dU9gA2mSxoy2X0I+t2ZG+7/w3k91hWAVgmIueKFCly9sUXX/y7Zs2a8X5+fsYYM8EYUxgbJSlBA9R5w3sBh4B/gK+AQV4N0MN58uQ5FxAQYPLnz38WOA7MSqmcWokqlT303EtaXFycefnll03FihVNnjx5TLly5czTTz9tjEnYYDTGmB07dphWrVqZXLlymYCAANOjRw9z6tSpBPm9/fbbpmrVqiZPnjymZMmSplevXu513vl98cUXqWqE6uenVPa4WhugWRYJSUS+Ag4bY/qmkK4nMMkYE3SF+4sGdhljBvl6nZSrLRKSRvdQ1wqNhJSxIiIiqF69OpMmTcqS/WkkJKWyx9UaCSlTp2ECEJHCQHNsSM1amb0/pZRSSimVs2XFIKStwLvA08aYXa6FItJCRL4XkTMi8peIbHBGybvWtxaRXU6EpBUiUtFj3Y0i8rmI/JZEBCWllLqmRURE8NBDD/Hoo49SuHBhChcuzJNPPkl8fLzP9O+++y7169cnODiYEiVK0KlTJ44cOQLYrlihoaG8/vrrCbb56aefEBG2bPGeCU8ppa5MVkzDFGKMKWCMGetaJiK5gc+B1dirog2BCUCckyQAO8CoN9AYKARM88g2CNvv81aSiaCUWp6x4HUyZaXU1eK9994jPj6edevWMX36dGbMmMGECRN8pr148SKjRo1i+/btLFy4kBMnTtC1a1fATtHUp08fZs2alWCbqKgoateuTd26dX3mOWPGDMLDwwkPD9eJ6JVSaZLpt+CTUADbqPzSGHPAWbYPwAmzmRsYaJzpmkTkdSBKRMTpc7sd2O6Rn3cEpTQxNm7qDLB9QNN3SEoplbVKly7Nm2++iYhQpUoVfvzxR8aNG5cgypGLZzjOSpUqMXXqVKpWrcrhw4cpV64cvXr14vnnn+f777+nUaNGxMXFMXfu3CRjzoOdiD4yMhKwfUCVUiq1smoe0ASMMSexsdy/FpFFIvK4iJT3SHLB1fh0HMVOTl8YQETyi8irIrIniQhKSil1zWvUqFGCCeYbN27MkSNH+PvvvxOl3bJlCx07dqRChQoEBwcTHm7HLBw6dAiAUqVK0aFDB6KiogBYsmQJJ0+epFu3bllwJEqp6022NEABjDG9sLfevwPuAn4Qkdud1Ze9kzvPrvK+DnQCngNuIWEEJaWUUh7Onj3L7bffTmBgIPPmzWPjxo0sWbIEsLfmXR588EE++ugjzp07R1RUFP/5z38oXLhwdhVbKXUNy65b8IA7TOd2YKwzTVMP4JtUbNoMmGuM+R+AiOTFRlBKKWRnimqULcgmna5DKXUVWL9+vZ1Pz7kK+v3331OmTBkKFCiQIN2+ffs4ceIEY8aMoWJFO57zk08+SZRf27ZtKVCgANOmTePLL79MUyhOrTuVUmmRLVdARaSiiLwiIk1EpIKItARqAntSmcWPwH9EpK6I1MCOss+bWeVVSqmc6OjRowwePJgffviBBQsW8Nprr/HYY48lSle+fHkCAgKYNGkSP//8M4sWLeK5555LlM7Pz4/evXszfPhwypYtS+vWrbPiMJRS16EsvQLqzAm6D7gTCAPmA8WAY8B7wFggNR2OHgfeAVYBfwJlgc0ZUcadR/4i5KlFGZFVltDJlZW6fnXr1o24uDgaNmzoHsnuqwFavHhx5syZw9NPP83kyZOpWbMm48aNo23btonS9u7dmxdeeIFevXol6F+akpxcd2o9qVTOk9W34J8GFhtjNojI48AvzvIbgH5AC+Al7yhIxphoQDxeHwTauF6LiAFeMcYs8EgT4ZVHgtdKKXW1y507N5MmTfIZ7Sg6OjrB686dO9O5c+cEy3xFwvvtt9/w8/OjZ8+eGVlUpZRKIMtuwYtIIPAg9sqlp7ZAaeyApA3A/zwnpFdKKZX5Lly4wOHDh3nuuef4z3/+Q/nyOqmIUirzZGUf0HbY0exrvJb/YYz5zRizD3gGyAO0BBCREBExIpIgxqmz7N6kdiQiZUXkQ2eKpj+dqZ50kjql1DXrwoULDB48mJIlS5I3b14aNWrE6tWrAXs1VERYvnw5DRs2JDAwkPDw8AQRjj744AMqVKjAunXr+PLLLylbtiz9+/f3OaWTUkpdqaxsgDYHNhtf93wAEckD9HVeXkrvTpwrrSuA89gpmhoDscAyZ52vbTQSklLqqhIdHZ3g1vvQoUP56KOPiIqKYuvWrdSoUYO2bdsSGxvrTjN8+HBeeeUVtmzZQtGiRenWrZv7Nny9evXIly8fI0eOZOfOnXzyySds27YtwQT23jQSklIqvSSJ9mDG70jkM+AvY0wP53UItg/oP0A8kA/bIP4FCDfGnPRIU98Ys8kjLwN0cvX59HwtIr2xYTzDXI1dEfEDjgP9jTEfJ1fOgNKVTekeEzLqsDOddq5X14rw8HA2bdqUckKVyNmzZylcuDBvv/023bt3ByAuLo6wsDC6du1KmzZtaNmyJUuWLOH22+10y2vWrKFZs2b8+uuvlCtXju7du5MnTx7eeeffXlLbtm2jTp06HDt2jBIlSiRbhoDSlcmpdafWk+paJiKbjTHhKafMWbLyCmg+7FVJb/cDdbCT0f8E9HYiJaVXPaAicFpEzjhRkv7CRlG68QryVUqpHOnAgQNcunSJpk2bupf5+fnRuHFj9uz5d3a7mjVruv8uU6YMAMePHwdg8+bNvPvuuwQFBbkfrvwOHDiAUkplpKwcBX8CJ5Sml8PGmJ+An5zG4nwRqWaMOYG9MgoeI+CdW/XJyQVsA7r4WHclDVullLrqeE6llCdPnkTL4+Pj3c8PPvigz2mcypYtm8mlVEpdb7KyAboV6JlcAmPMShHZAzwPPAL87qwq7ZGsdgr72QJ0BU4YY06ltZAazUMpdbW58cYb8ff3Z82aNdx4o73RExcXx7p167j//vtTlUfdunXZvXs3oaGh6SqD1p1KqbTIylvwXwNVRaRoCuneACJF5AZjzD/A98AwEblZRJpg48An5z3sxPafi8gtTtSlFiLyho6EV0pdi/Lnz0///v0ZNmwYixcvZu/evfTv359jx44xYMCAVOUxbNgwNmzYwEMPPcTWrVvZv38/CxcupF+/fplceqXU9SjLroAaY3aKyAbsrfHJySRdCMQAzwGRQG/gbWAjcAHYm8J+zolIC+AVbKSlgsBR7Mj4P1MqZ06O5uFJO9UrpTyNHTsWgF69enHq1Cnq1KnDkiVLKF26ND/88EOK29esWZPvvvuOZ599lltuuYW4uDgqVarEf/7zn1TtPyfXnVpfKpXzZNkoeAARaQtMBKoZY+LSsf1soJgxpoPzOhrYZYwZlFFlvFpGwWuFqq41Ogo+ZwkJCWHQoEEMGTIkVel1FLxS2UNHwaeCMWYJ9upnuazcr1JKqdS5ePFidhdBKXUdSFUDVESiRWSaiEz0iC70mojkctb7i8hYETksIudEZKOI3O6xfYQTvag10A3Y60z8XtcjTVER+cDJ4x8R2S0ivZIp02zsRPMDnbyN099zv4gM8Upb2Vlf12dmSimVQxljeOONN6hcuTIBAQGUK1eO4cOHA7Bz507atGlDvnz5KFKkCD179uSvv/6dEL5nz5506NAhQX4jR46kevXqidKMHTuWcuXKUa5cOSIiIjh48CBPPvkkIpJgJL1SSmWEtFwB7eakbwz0w/bPHOysm4VtDN4PVAfmAF+KSC2vPF4GngLqAn8A78m/NVte7Aj2DsDN2Fv1051Gqy+PAuucfZd2Hoewsea9G669gW3GmC34oJGQlFI51dNPP83o0aMZPnw4u3fvZv78+dxwww2cPXuW22+/naCgIDZs2MCnn37K2rVrk41clJSVK1eyY8cOlixZwvLly/nkk08oV64czz//PLGxsQmiKXnSSEhKqfRKyyCkWOARJ7rQPhEJAx4Xkc+x0x6FGGMOOWkniUgbbEPVcwjmc8aYFQAi8gKwGiiLnQv0CPCaR9oZItLKyXu5d2GMMX+JyEXgnDHmN9dyEZkFvCAijYwx3ztRkLpjG78+GWNmADPA9gFNw3uilFKZ5syZM4wfP54JEya4G5ahoaE0btyYmTNncvbsWebNm0dwcDBgG4QtW7Zk//79aZpOKW/evERFRREQEOBe5ufnR3BwMKVKlUpyu8jISCIjIwHbB1QppVIrLVdAv/eK474O23hshp0ofo8r8pAzoXx7Ekce2uHx91HnuQTYcJki8oyI7BCRP5w87gHKp6GMOI3RhdirngBtgSLY6ZmUUuqqsWfPHi5cuEDr1olvBO3du5eaNWu6G58ATZo0IVeuXAmiH6VG9erVEzQ+lVIqs2XUNEwGqA9c8lr+j9drz/WuxqyrETwEeAJ7a30ncAYYg9NATaO3gfdFZDC2IfqpMSbFKZiUUupa4OrZlCtXLrxnOrl0ybuatvOIKqVUVkpLA7ShiIjHVdBG2KuY67BXQEu5bq+nUzPgS2PMPACnb2gYcCqZbS4Cfj6WLwH+Bh4C7gTapbYQGs1DKZVTVK1alYCAAJYvX07lypUTrYuKiuL06dPuq6Br164lPj6eqlWrAlC8eHG2bduWYDvv10nx9/cnLi71s+Vp3amUSou03IIvA0wQkZtE5F7gSWC8MeZH7O3t2SJyr4hUEpFwERkiIvekIf8fgdYi0kxEqgCTgIopbBMDNBCREBEp5hqV78wxGoXt93kEH31IlVIqpwsODubRRx9l+PDhzJo1iwMHDrBhwwamTp1Kt27dCAwMpHv37uzcuZPvvvuOfv36cc8997j7f7Zq1YqtW7cSFRXF/v37efXVV1mzZk2q9h0SEsKqVas4cuQIJ06cyMzDVEpdh9JyBfQ97NXG9djb5+/8f3t3H99T3T9w/PXeGJttzE3uNcMwFHJfarqhG3IRCWGIUKIrN4lKkq5KXUg35rpIKK78rlRorm5MhcxNREjUcn9fYe63z++Pz9l897V73+27zfv5eJzHvuecz/mczzmf7ezzPed8Pm/gn866vsBY4FXsGJ8ngDhs9KGsmohtcH6OfXT/nrPPiAy2mYztcb8N8He2j3fWzcLGlJ8N3CYiK4ByxpgMr6T5OZpHMh1UWan8LSkpicGDB7No0SJOnDjB9ddfT/369VmyZEm283r55ZcJCQnhxRdfZN++fZQvX57evXsTEBDA8uXLGT58OM2aNaN48eJ07NiRqVOnpmzbrl07nn/+ecaOHcuZM2fo2bMnQ4YM4dNPP810vxMmTODRRx+lRo0anD9//opH+e7y87VTr5lK5T9ZioSUGxGHMtlfJLbxmmmDMYM8mgOrgDBnylJ+BSESkl5MVWFUmCIhLVmyhM6dOxMbG0tYWBh///vfOXnyZI4aoN4iInz00Ud06dIlS+k1EpJS3lFQIyHlWSz43CAifsaYC27LigHlgBexnY/2iEiYVwqolLom7dq1i4oVK9KqVSvAvk+ZFy5evEjRokXzZF9KKXU1sh2K04mK9LaITBKRYyJyREQm5yAqUlmXZaHOsiYiEsrlR/dHneXvuez7HWd/R7F3OBGRvzvDNyUAh7AD0pcH/p6js6KUUjkUFRXFk08+yZ49exARQkNDr0gTExND69atCQkJoXTp0rRr147t27enSrN27VoaN25M8eLFadSoEcuWLUNEiI2NBSA2NhYRYdmyZTRr1gw/Pz+WL1/O7t276dixIxUqVKBEiRI0btz4ijuvoaGhTJw4kUcffZTg4GCqVKnCa6+9lmo9QNeuXdM9BqWUuhpZaoAaYyLdHr/3BC4BrYDHsRGRujnrshoVKT17gQecz/WwEY6Guax/GNvrvjV2gHmAJKcM9YC/AVuBH40xe7OyQ42EpJTylKlTp/Lcc89RpUoVDh48yLp1665Ik5CQwPDhw4mLiyM2NpaSJUvSoUOHlDjsp0+fpn379tSpU4cNGzbw6quvMnLkyDT3N3r0aCZOnMiOHTto3rw5p0+f5p577uGLL75g8+bNPPDAA3Tu3JkdO3ak2u6f//wnDRo0YOPGjYwePZpRo0axZs0agJQyz5w5M91jAI2EpJTKuZw+gt9mjHnO+bxTRAZge7DHkfWoSGkyxiSKyAln9kga72z+Zox5ym2bKS6z8SIyCvhERPoYY5KysE+NhKSU8oiSJUsSFBSEr69vulGEHnjggVTzs2fPJjg4mLi4OG655Rbmz59PYmIi//73v/H396devXqMHTuWnj17XpHX+PHjadu2bcp8uXLluPHGy9/3x44dy2effcaiRYsYN25cyvK2bdvy+OP2vsLQoUOZNm0aX331FS1btqRcuXIAlCpVSiMhKaVyRbYfwTt+dJs/gB0wvjFZj4qUUxvcF4jI7SLyhfPY/xTwX8APSP/KqZRSXrJ792569OhBjRo1CA4Opnz58iQlJbFnj/3evmPHDurXr4+/v3/KNs2bN08zryZNUvc9SEhIYNSoUURERBASEkJgYCDr169PyTvZDTfckGq+UqVKHDlyxBOHp5RSmcrpHVD3UBoG25j1IfOoSMl3JMVlXXbemk9wnRGR64GlwEzssEvHsQ3hD7GNUKWUylfat29PlSpVmDFjBpUrV6ZIkSJERESkPILPDvcoRiNGjCAmJobJkydTq1atlLFC3fN276wkIiQlZfrASCmlPMLTveB/IPOoSEednxVdPjd0S5N8pUwrypG7JtiG5pPOAPSISPusFtidRvNQSuWm48ePs2PHDt5++23atGkDwMaNG7l06VJKmjp16jBnzhzOnj2bchc0Li4uS/l/99139O7dO+Ux/7lz59i9ezfh4eEZbpeUlMTSpUtp3bo13bp1o2jRohoJSSmVa3L6CD5NWYyKtAvb0Wi8iISLSFtgnFtWv2PvpN4nIuVEJDCD3f7iHMdwEakuIt2xHZKUUirfCQkJoWzZssycOZNdu3axcuVKBg0aRJEil+8H9OjRA19fXwYMGMC2bdv48ssvmTRpEnA5znt6wsPD+fjjj9m4cSNbtmzh4Ycf5ty5cxluc/78eX766Sfi4+Pp2bMnCxYsIDQ0lK+++opDhw7xxx9/XP2BK6WUiyzdAXWGWHoH6AKUBjK6GmUYFckYc1FEHgLeBjYDm4BngJRxQowx+0XkeeAl4F/A+0BUWjszxvwoIsOA0dhoSquBEcDCNJKPcqZ05edoHsl0UGWlCi4fHx8WLlzIE088Qf369alZsyavv/56qo5JQUFBfPbZZwwePJhGjRoRERHB+PHj6dKlC8WLF88w/zfeeIP+/funDPM0fPjwDBug58+f56GHHqJIkSK0bt2anj17snjxYjp27MjixYupWrUqlStXJj4+PsP95tdrp14vlcqfshoJqT22Y08k8Ctwwn0A+PxORLYCi4wx4zNKp5GQlPKOwhQJKTd88skndOrUiSNHjlC2bNnMN8iC8+fP88ADDxAZGck999xDTEwMwcHB9O3blwkTJlC3bl26d++epbzyayQkvV6qwq6wR0KqCRw0xqzO6Y5EpKgxxr1jkkc5d2ol+V1QpZQqqObMmUNYWBhVq1Zl69atDB8+nA4dOni88bl06VJ+/vlnRo8endIJKSkpiW7duqUM55TVRqhSSmVVpu+AOlGI/glUc6ISxYtIMRGZIiKHReSciHwvIre4bJMc7eheEYkTkQtAOxG5KCItXNLtFZEdLvN3ikiCiPg58ykRjkRkv4j8S0RKuaSPcoZ6ute5w3kBqCsi14nIJyJyVkR+F5F+HjhXSimVZw4fPkyvXr2oXbs2jz32GPfccw/z5s3L8vYZRVtybXyCDR2a3PgUEcLDw9m7dy+JiYn06tWLDz/80PMHqJS6pmXlDugwbKegftjhlRKx73c+6Cz7FRvyMkZEahljDrps+wrwFLbj0SnsGJ6RwPciUhMoBZQVkQrGmEPOujUuj/eTIxz9ClwPvOlMvVz2URx4FjvQ/VHgIPb9z+uBO4Ez2AZ0aHoHKCIDgYEAvsHlsnBKlFIqd40aNYpRozJ8ZT1DydGWbrjhBs6ePcvEiRPp0KED27ZtS9X4dDdt2jQiIiK4+eabAVIaoXDlndDo6Giio6NtOo2EpJTKhkwboMaYv5zB3RONMYdEpAQwGHjEGLMUQEQGAbcDj5G6R/t4Y8z/kmdEJBZoA/wD29j8DvB3ln3oLItx2fcUl7zSi3DkCzxujNng7CMcuAe4xRiTHCu+D7YRm94xaiQkpVShkl60pbVr11KpUqU0txkxYgTdu3endevW7N69O2V5sWLF0oyIpJGQlFI5lZNhmGpgB45flbzAeedyDRDhlta9R0EscLOIFMU2Nlc4yyJFJAB7hzU2OXEWIxxdwvakT1YXe+c0ZdA8Y8zv2GhNSil1TUgv2tLevXuZMWMGAwYMuGKbXr168fbbb6c8qgcICAhgyZIlKWOWKqWUJ3h0HFDs2J2uEtzmvwOKYRuat3G5AdoGaIVtTMZBqghH24GuwE3YR/6QOsLR+XQ6HemdTKXUNat9+/YcPXqUGTNmsHbtWn744QeKFCnChQsXEJE0G6EBAQGcPHky1fzSpUu18amU8ricRELaje3sc7PzGRHxBVoCH2S0oTHmtIhsAAYAwcBG7N3UqkBPUr//mdMIRzuwDetm2DFBEZFqQNrPnNxoNA+lVEGXlWhLyY1QgJkzZwJQq9blx+jJjc/IyMgs7VOvnUqp7Mj2HVBjTAJ2UPpXnN7ndZ358tjB5TMTCzwMfGuMSTTGnAPWOstiXdLlKMKRMeZn7HukM0SkpYg0BN7jcix6pZQq1LISbQm44k7o3r17ef755wkICGDZsmVZbnwqpVR25TQW/Gjn52xsT/YfgLvdesCnJ9bZPtZt2W2uibIZ4chdFHDIyfcI8AJwXRa202geSql8KSkpicGDB7No0SJOnDjBihUr0m0gZiXaEkB8fDzVq1dPiTP/3XffceTIEZYtW8Ztt92WVtbp0munUio7shQJKS+IiAG6GmMWeTO//BoJSS+iqrDTSEgZW7JkCZ07dyY2NpawsDBKly6Nn59f5htmILkBum7dOm666SaGDx9O586ds934BI2EpJS3FPZISLmpCjABYOHChR8Ck4HF2LuWGcWcT1YC+BtwH9AY+z5pEsAzzzzTAfgU+86qUkoVWLt27aJixYq0atUqV/IXEaZOnZoreSullDtP94LPkFhPicgvInK+aNGiB5966qmfgb4Av/zyyxLseJ3D1q9fv7FYsWIrnWhGJ0TkPREp6ZZfn5IlS24tVqzYvOuuu+6he+65pzh2oPoPABo1atQb29O+uIiMFpFjrpGYlFKqIIiKiuLJJ59kz549iAihoaGcP3+e4cOHU758eYoXL06LFi347rvvUraJjY1FRDh27FjKsvj4eEQk3TvNFy9e5IknnqBSpUoUK1aMqlWr8vTTT+f68Smlrj152gAFJmGjFr0M1Pv0009/r1GjRgDwBMC4cePmA7cfO3bszfvvvz+0YcOG1bC92Tthh2malZyRiDwKzAgLC/u/uXPnjgRaxsTEvAU8jY2KxKlTp341xrRq27ZtDDAUuM0Y8717oURkoIisF5H1Gs1DKZXfTJ06leeee44qVapw8OBB1q1bx6hRo1i4cCGzZs3ihx9+oEGDBtx9990cPJiVV/HTNm3aND7++GMWLFjAL7/8wsKFC6ldu3a66aOjo2nSpAlNmjTRSEhKqWzJs0fwIhIIPAkMN8bMwg5o3xyIB94CpiWnrV279s5Lly7xxRdfXBccHPwrsMUJl7lCRGoaY3ZhG7JTfvjhh6cBHnzwQbC96VOsXbt26TfffDN027ZtTYB6zoD0V9BISEqp/KxkyZIEBQXh6+tLhQoVSEhI4J133uFf//oX991n33F89913+frrr3nrrbeYOHFijvbz+++/Ex4eTuvWrRERqlWrluEjf42EpJTKqby8AxqBHYT+K2c+eWTj/+G8s5nsxIkTYdWrV/8zODg4AEh+ZL7aSRchItcBlV3yStPcuXN7xMbGsmrVqpXpNT6VUqqg2b17NxcvXkyJ1w7g6+tLy5Yt2bZtW47zjYqKYtOmTYSHh/PYY4+xdOlSkpKSMt9QKaWyKa8fwbtKfq6zM62V586dSx63M9xtVZbvUN55553nDh06xKRJk47moHxKKVXgiAhgh2ICcB3p5OLFixlu27hxY+Lj43n55ZdJSkqiT58+3HXXXdoIVUp5XF72gt8OnAfuwA4yn9yhKK0Xh7b//vvvpU+dOkVQUFApZ1krbIN5uzHmiIjsd/L6Ir0d9urVq3L37t1/7d69e7fo6OgVxpg5aaVzHu8PBLjppps0modSKl+rUaMGfn5+rFq1iho1agCQmJjImjVr6NGjBwDlypUD4ODBgymfN23alGneQUFBdOnShS5duhAVFUWLFi3YtWsX4eHu9wJS00hISnlNWRFx7VkY7bxamK/l2R1QY8wpYCrwsoj0jYuLC4qLi6Nnz56RIjLDSfaRiMQDnfz8/C727t2bxYsXlxeRW4EZwH+d9z8BXsJGSXpSRIyIjBCRp5x1nQEuXLjw50MPPXQX0B94T0QmpVO2aGNMk4I4jpZSqmCLjIzk8ccfz9Y2JUqUYPDgwYwePZply5axfft2Bg8ezOHDhxkyZAihoaF8+umnVK1alfHjx7Nz507+97//Zfpu6BtvvMGHH37I9u3b2bVrFx988AHBwcFUqVLlag5RKZW7jiW3YZwp3zc+Ie/HAR2DHdvz2VatWl1fsWJFypQpUw+o56wfAHwEyKJFi97+xz/+0b1Lly5DgD7AJ8Cw5IyMMe+IyAUgudE5FvgYOyboAoB33nnn+R49evwKnHTSPCUiO4wx76dXQI3moZQqCF555RUA+vbty59//kmjRo2IiYmhYsWKrFu3jhIlStC6dWuGDBnCjTfeSMOGDZk0aRLt27dPN8+goCBee+01fvnlF0SERo0a8fnnnxMQEJBpefTaqZTKDm9GQnoEmDls2LA106ZNq2yMud5t/XKgLXAnmXQ2col6JNgxQA8Bt2Mf9SMiZYGjQBtjTGxGeWkkJKW841qNhBQZGUn9+vWZPn26t4tyVTQSklLeUVAjIXmzE9KKqKgopk2b1hKo5jxGjxeR9/z8/D4HbgbOHDly5HsRGSUiu51B6beIyMPumT322GOtgQ+BAw8++ODjIrJARM6JyA/Y4Z6UUirPRUZGMmjQIIYNG0ZISAghISGMHDky3Y498+bNo2nTpgQFBXHdddfRtWtX9u/fD9gORTVr1mTy5Mmptkm+Y7lx40YAQkNDU6UREaKjo+natSslSpQgLCyMefPmpcpj7dq1NG7cmOLFi9OoUSOWLVuGiBAbG+vBs6GUUpY3G6C7X3vtta+fe+45goKC/gQqAk0B6tWrVxsbYnNu+fLln8G+w/nYhAkT2rds2fI9YIaIpPpae9tttz0O7Fm2bNndH3300UxsRKUm2IHpU1+tlVIqD82fP5+kpCTWrFnDjBkziI6OZsqUKWmmvXDhAi+88AKbN29myZIlHDt2jO7duwO2Idm/f39mz56daptZs2bRsGFDGjdunG4ZJkyYQMeOHdm8eTPdunWjX79+7NmzB4DTp0/Tvn176tSpw4YNG3j11VcZOXKkZw5eKaXS4M0GKOXKlRtYrFix06VLly5ljHnXGPP3+++/v13lypWrAztnz579EvB34BFjTMyzzz775erVqycDM4HHnGzaAIiID7AiJibm1eLFiwcfOnToF2NMF2NMy2HDhmX4XE8jISmlclPFihWZNm0aderU4cEHH2TkyJG88cYbaabt168f9957L2FhYTRr1ox33nmHb7/9ln379gH2nc+dO3fy/fc2qFtiYiLvv/8+/fv3z7AMvXr14uGHH6ZmzZq8+OKLFClShG+++QawDeTExET+/e9/U69ePe666y7Gjh2b6XFpJCSlVE55tQEK7J4xY8aUhISE09jH5E8FBAQE7d+//zegRb9+/SoAxYEYETkdGBhIYGAgwGBsJCUA13dH+/n6+rZv2rRpsfLly48BngeeHzRoUBsy4NoL3jegZEZJlVIq21q0aJEyPidAy5Yt2b9/PydPnrwi7caNG+nYsSPXX389QUFBNGliX+1KvltZoUIF2rdvz6xZNjJxTEwMJ06coGfPnhmW4YYbbkj5XKRIEcqVK8eRI0cA2LFjB/Xr18ff3z8lTfPmmb+5NHDgQNavX8/69evRa6dSKju83QBlz549fx07duw49hG834IFCxZt2rRpG7a3fHL5OgANExISaiUkJNTC9ppv66x7D6Br165dAZkyZcqUb7/99ltAkqe6develndHpJRSOZOQkEC7du0ICAhg7ty5rFu3jpiYGMA+mk/2yCOPsHDhQs6cOcOsWbPo1KkTISEhGeZdtGjRVPMiogPMK6W8xusN0Exsww5ef70xZpfblF5oze1AAxEp4bKsRTpplVIq161duzZVRKLvv/+eSpUqERwcnCrdjh07OHbsGJMmTeLWW2+lTp06KXcpXd19990EBwfz7rvv8tlnn9GvX7+rKl+dOnXYunUrZ8+eTVkWFxd3VXkqpVRG8noc0GwxxpwSkcnAZLHPr74BArENyqR0Blv9ADtI/SwRmQBUwo4RmiUazUMp5WkHDhxg+PDhDBkyhC1btvDaa68xbty4K9JVq1aNYsWKMX36dB577DG2b9/Os88+e0U6X19f+vXrx5gxY6hcuTJ33HHHVZWvR48ejBs3jgEDBvDMM89w4MABJk2ycTtcXx3IiF47lVLZkd/vgAI8C4wHRgA/YUNvPgD8llZiY8xpoD1QC9iI7QE/2ln9ci6XVSmlrtCzZ08SExNp3rw5AwYMoH///jz55JNXpCtXrhxz5sxh8eLFRERE8MILL2TYWenChQt06tQJHx+fqxpDNSgoiM8++4yffvqJRo0aMXLkSMaPHw9A8eLFc5yvUkqlx5sD0ecpEXkPKGuMST8MCPlvIHodRFldKwrrQPSeHmg+Ob9evXpx880388svv+Dv70/ZsmUpUsRzD7U++eQTOnXqxJEjRyhbtmym6fPjQPR6/VTXgoI6EH2+fgSvlFIqtaSkJE6fPs2zzz5Lp06dqF69ukfynTNnDmFhYVStWpWtW7cyfPhwOnTokKXGp1JKZVe+eAQvIiVE5H0ROS0ih0VkjIgsce5aIiIhIjJHRP5woiF9KSL1XLYvIyIfisg+Z/1PItLXaweklFK5ICoqim+//ZY5c+bwxRdfsGjRIuLj4xGRlLvHsbGxiAiff/45N910E/7+/rRu3Zp9+/axcuVKbrzxRgIDA2nfvj3Hjx9Pyfvw4cN06tSJsLAwOnTowB9//EGLFi20p7xSKlfklzugrwO3AZ2AA9j3PlsDHzvr3wNqAx2xwzO9hB0bNNwYcxY7VuhG4BXgJDZ+/AwR2WOMyTCOvFJK5SZPhrKcOnUqO3fupE6dOimdhBISEtJM+/zzzzNlyhRKlixJjx496NatG8WLFyc6OhpfX1+6du3K+PHjefPNNwEICQmhaNGi/Oc//+Gmm25i69atDBgwgKCgIB5//HGPHYNSSkE+aICKSCDQD+htjPnCWdYf2Od8rgXcD9xmjPnGWdYL2AP0BP5ljNkPvOaSbbSI3A50BzJtgIrIQGAggG9wOQ8dmVJKeVbJkiXx8/MjICCAChUqABAfH59m2hdffJHWrVsDMGjQIIYOHcqGDRtSwnX26dOHRYsWpUr/6quv0qVLFwCqV6/O008/zdtvv51uAzQ6OproaDsYiUZCUkplh9cboNiIRkWBlEHnjDEJIrLVma0LJAFrXNb/JSJbgAgAEfHFxnzvBlQGigF+QGxWCuAM5xQNthPS1R2OUkp5n2vko/LlywPQoEGDVMuSxxg9evQoe/fu5dFHH2Xw4MEpaS5dukRGHVUHDhzIwIEDAdsJSSmlsio/NECvRvKVcQTwFDAM2AKcBiYB13mpXEop5VWukY+Sx/J0X5b8fmfyz3fffZdWrVrlYSmVUteq/NAA3Q1cBJoCvwKISABQ31m3HdtZqiV2IHpEJBhoAMx28rgF+MwYM9dZL0A48GdeHYRSSuUFPz8/EhMTPZpn+fLlqVSpErt376Z3794ezVsppdLi9QaoMea0iMwCXhGRY8BBYBy20WmMMb+IyCfYTkUDsY3Kl7CdjT5wstkJdBORW4BjwFCgOvBDdsuj0TyUUvlZaGgocXFxxMfHExgY6LFe6i+88AJDhw6lVKlS3HvvvVy8eJGNGzeyf/9+xowZk+n2eu1USmWH1xugjhFACeBTQLCP1gOAB0SkHNALmOKsLw6sAu52esADTMQ2OD8HzmJ7zc/HeUdUKaUKixEjRtCnTx8iIiI4e/Ysv/12OShcVFQUP//8MwCPP/44p0+fZsmSJVnK95FHHqFEiRK89tprjBkzBn9/f+rVq6c94JVSuSJfRUISkfbAf4G7gI+AqcBbxpg/ryLP98hCBKRkGglJKe8orJGQ8lJUVBTHjh1jyZIl/PXXXxhjKFWqVJ7sWyMhKeUdGgnpKohII2xv9wjsI/TB2DuiczJqfIqInzHmQp4UUimlCpCSJUt6uwhKKZWufBEJyTENGAtUxA6n9BcwUURSnh+JSKyIvCMik0XkKPZRPCLyqIjsFJFzInJMRJaLSBERGQ/0Ae4TEeNMkXl8XEopleeioqJo3/7yg5/IyEiGDBnCM888Q9myZbnuuusYMWJEqndIL1y4wOjRo6lSpQoBAQE0bdqU5cuXe6P4SqlCLl80QI0xP2DHA52AHYC+InBjOskfxr4n2hroLSJNgLeAF7DRku4AYpy0k4H/AF86eVYEVufOUSilVP42f/58ihQpwurVq5k+fTpTpkxh4cKFKev79u3LypUr+eCDD9i6dSt9+vShQ4cObN682YulVkoVRvniETykDC5/Ckg0xhyCy2PXufnNGPNU8oyIdAYSgE+NMaeA34Hkq+VpETkLnE/OMy0aCUkpdS2IiIhgwoQJAISHhzNz5ky++uorunfvzu7du/nwww+Jj4+nWrVqgO3I9OWXXzJjxgzefvvtK/LTSEhKqZzKNw3QbNjgNv8FttH5m4gsB/4H/NdpjGaJRkJSSl0LXKMjAVSqVCklGtLGjRsxxhARkXrwkPPnz3P77benmZ9GQlJK5VRBbIAmuM4YY06JSGPgVmzv+THAJBFpaow54I0CKqVUfuQaCQmujIYkIqxbt+6KdP7+/nlWRqXUtaEgNkCvYIy5BHwNfC0izwNHgPbYu5oXAF8vFk8ppfK9Ro0aYYzh0KFDtGnTxtvFUUoVcgW+AeqMHVoDG6bzBNAGCMKG8ASIB+4RkdrAceAvY8zF9PLTaB5KqWtReHg4PXv2JCoqitdff53GjRtz4sQJYmNjCQsLo3Pnzhlur9dOpVR25Ite8FfpT+Bv2J7uO7BRlR4xxnzrrJ+JvQu6HTgKvJj3RVRKqfxv9uzZ9O3bl1GjRlGnTh2aNm3KggULuP76671dNKVUIZOvIiF5imv0IxGpD2wBOgNrsHdAz6a3bX6JhKQRPNS1RiMh5T8iwkcffUSXLl0yTauRkJTyDo2ElH/VdH4uNoWxta2UKlQuXrx4RScgpZQqbArDI/h0OZGQPnZmk0REG6BKKY+JjIxk0KBBDBs2jJCQEEJCQhg5cmRKz/LMIgvFxsYiIixbtoxmzZrh5+fH8uXL2bt3Lx07dqR06dIEBARQp04dFixYkLLdli1buPPOO/H396d06dJERUXx11+Xx+FMjoI0depUKleuTEhICH379uXMmTMpaWJiYmjdujUhISGULl2adu3asX37dpRSKi8U6gYoNhLSAOdzciQkpZTymPnz55OUlMSaNWuYMWMG0dHRTJkyBch6ZKHRo0czceJEduzYQfPmzRkyZAhnzpxhxYoV/PTTT0yZMoVSpUoBkJCQQLt27QgMDCQuLo6PP/6Y1atX069fv1R5fvvtt2zdupUvv/yShQsX8vHHHzN16tSU9QkJCQwfPpy4uDhiY2MpWbIkHTp04MKFC7l6vpRSCgr5I3hjzGkR+dP5rJGQlFIeV7FiRaZNm4aIUKdOHXbu3Mkbb7xBx44dsxxZaPz48bRt2zZl/vfff+eBBx7gxhttROLq1aunrPvggw9ISEhg7ty5BAUFATYiUZs2bdi1axc1a9q3joKDg3n33Xfx9fWlbt26dO3ala+++ooxY8YA8MADD6Q6jtmzZxMcHExcXBy33HJLlo5dIyEppXKqsN8BzRJjTLQxpokxpolvQElvF0cpVYC0aNEiVdjgli1bsn//fr777ruUyEKBgYEp09KlS9m9e3eqPJo0Sd1/YNiwYUycOJGWLVsybtw4Nmy4HABu+/bt3HDDDSmNT4BWrVrh4+PDtm3bUpZFRETg63t5CGTXqEcAu3fvpkePHtSoUYPg4GDKly9PUlISe/bsyfKxDxw4kPXr17N+/Xr02qmUyo5CfQdUKaW8KauRhUqUKJFqvn///rRr145ly5bx5Zdf0qpVK8aMGcP48eMz3V+yjKIeAbRv354qVaowY8YMKleuTJEiRYiIiNBH8EqpPKF3QJVS6iqsXbsW1wE2vv/+eypVqkTLli1TIgvVrFkz1VS5cuVM861SpQoDBw7kP//5DxMmTEh51F23bl22bNnCqVOnUtKuXr2apKQk6tatm6UyHz9+nB07dvDMM89w5513UrduXU6dOsWlS5eyefRKKZUzegfUjUbzUEplx4EDBxg+fDhDhgxhy5YtvPbaa4wbN+6qIgsNGzaMe+65h/DwcE6ePElMTAwREREA9OzZk+eff57evXszYcIE/vjjDx599FE6d+6c8v5nZkJCQihbtiwzZ86katWq7N+/n5EjR1KkSM7/Jei1UymVHdoAVUqpq9CzZ08SExNp3rw5IkL//v158skniY2NZf78+YwcOZJRo0axb98+SpcuTbNmzTKNtZ6UlMTQoUPZu3cvQUFB3HHHHbz++usABAQEsHz5coYPH06zZs0oXrw4HTt2TNXDPTM+Pj74+Pjw9ddf89///peaNWvy+uuvX9ExSSmlckuhjIR0NTQSklLeURAjIUVGRlK/fn2mT5+e6jPYMT7btGnD0aNHKVu2rJdLeqWjR49SokQJAgICPJJffouEpNdQda3QSEhKKaWuSlJSEsaYVL3Xc0u5cjrknFLKe7zSCUlEbhWR70XktIj8JSJxIlJfRKKcZXeIyFYRSRCRFSJS3WXbGiLyiYgcctZvFJH2bvnHi8h4EZnn5HdIREbk/ZEqpa4FUVFRrFy5krfeegsRQUSIj48HYPPmzTRv3pyAgACaNGnCxo0bU7Z77733CAwMZNmyZdSvXx8/Pz+2b9/OH3/8QZ8+fQgJCcHf358777yTn376KWW7ihUrpoqMdMsttxAUFJTSiWjXrl2ICPv27Uu3zKGhoUyePDllXkSIjo6ma9eulChRgrCwMObNm+epU6SUUqnkeQNURIoAnwDfATcCzYEpQKKTpBgwBugHtARKAe+6ZBEIfA7c5Wz/f8B/RaSO267+DmwHGgPPA5NEJP23/pVSKptiY2OZPn06U6dOpWXLlvTt25eDBw9y8OBBqlatCsCYMWP4xz/+wcaNGylTpgw9e/ZM1Wv+3LlzvPjii8yYMYNt27Zx/fXXExUVxdq1a/nkk0+Ii4sjICCAu+++m7NnzwJw2223ERsbC8CZM2dYt24dxYoVS3mFITY2lho1alClSpVsHc+ECRPo2LEjmzdvplu3bvTr1y9b44IqpVRWeeMOaDC2UfmZMWa3MWaHMeYDY0xyEOIiwGPGmDhjzI/YcJqR4gxwZ4zZbIx51xizxRizyxjzErAR6OK2n7XGmJeMMTuNMTOA97GN0iuIyEARWS8i6zWah1Iqu0qWLImfnx8BAQFUqFCBChUqpDxGf/HFF2nTpg116tThueeeY8eOHezfvz9l28TERKZPn87NN99MeHg4hw4d4tNPPyU6Oppbb72VBg0aMHfuXE6ePMn8+fMB++7pihUrADsEU1hYGO3bt09ZFhsbS2RkZLaPo1evXjz88MPUrFmTF198kSJFivDNN9+kmz46OpomTZrQpEkTjYSklMqWPG+AGmNOAO8By0VkqYj8XUSquSQ5b4z52WX+AOAHhACISAkReVVEtonIHyJyGmgCuOYBsCaN+Yh0yqSRkJRSueKGG25I+VypUiWAVBGJihQpQsOGDVPmt2/fjo+PDy1btkxZVrJkSRo0aJAS6SgyMpKdO3dy8ODBlM5OkZGRKXdFV65cmdIAnTRpUqpITBnd0XQta5EiRShXrlyqsrrTSEhKqZzyyjugxpi+2Efv3wD3Az+LSDtntftIyMnPqpLLOhnoCjwL3AY0BOKwjVSllMpXXCMSJUcqco1IVKxYsSx3Okrevk6dOlSoUIEVK1akaoCuWrWK7du3s2/fvpQG6KBBg9i0aVPKlNwIzqysyftzLatSSnmK1yIhOY/SXzHGRAKxQJ8sbnoL8L4x5v+cR/T7gBpppGuRxvz2NNIppdRV8/PzIzExMfOEmahbty5JSUmsWXP5Ic7JkyfZsmVLymD0YN8DXbp0KevXrycyMpLQ0FDKli3Lq6++mur9z9KlS6eKwnQ1g80rpZSn5PmVyOnR/ijwKbAfCANuAN7JYhY7gU4i8glwEdvBqHga6VqIyBhgERAJ9AZ6Zpa5RvNQSuVEaGgocXFxxMfHExgYmOM7h7Vq1aJjx448+uijREdHU6pUKcaOHUtwcDA9evRISRcZGcnQoUOpXbt2ypBKkZGRzJs3jz59svp93nP02qmUyg5v3AE9A4QDH2Ebk3OA+cArWdz+78AR4Ftsb/jvnc/u3sA2bH8AJgLPGWMWXVXJlVIqHSNGjMDPz4+IiAjKlSt3Vb3HZ8+eTbNmzbj//vtp1qwZZ86cISYmBn9//5Q0kZGRXLp0KVVno7SWKaVUflQoIyGJSDww3RgzObO07po0aWIKWjQWpQqDghgJSV2m9aeUdxTUSEheewdUKaWUUkpdm7QBqpRSSiml8lSh7A5pjAn1dhmUUkoppVTa9A6oUkoppZTKU9oAVUoppZRSeUoboEoppZRSKk/lhwZoFWAWNub7eSAemIIT+z0bSjvbxTv5HHDyreKRUiqllFJKKY/wdiekGsBq4DrgE2AH0AwYBtwN3Awcz0I+ZZx8woGvgQVAHaAvcB/QEvjVw2VXSimllFI54O07oG9jG59PAH8DngZuB/4J1AZeymI+k7CNzzeAO5x8/oZtyF7n7EcppZRSSuUD3myA1gDaYh+Zv+W27nkgAegFlMgkn0AnXQIw3m3ddOB3oB025rxSSimllPIybzZA2zg//wckua07BawCAoAWmeTTAvB30p9yW5cELHfbn1JKKaWU8iJvNkBrOz93prP+F+dneB7lo5RSSiml8oA3OyGVdH7+lc765OWlcjsfERkIDHRmL4nI5kz2eS0rCxzzdiEy4M3y5fdz422ZnZ/aQFAelUV52IYNG86JyFZvlyMPFMa/8/SOqTAea3oK8rHWF5H1LvPRxphor5Umi7zdCz5fcCoqGkBEEowxTbxcpHxLRNbn5/PjzfLl93PjbZmdH7cLqCp4Eq+F3//C+Hee3jEVxmNNT0E+1oJadm8+gk++M1kynfXJy//Mo3yUUkoppVQe8GYD9GfnZ3rvZtZyfqb3bqen81FKKaWUUnnAmw3QFc7PtmmUIwg7CP0Z4PtM8vkeOOukd39/zMfJ33V/mflvFtNdq/L7eyXeLF9+Pzfeltn50fNXsF0r187C+Hua3jEVxmNNT0E+1gJZdjHGeHP/y7ENxCeAN12WvwE8CcwABrksr+P83OGWzwxsJ6I3gKdclj8BTHX2c7fHSq2UUkoppXLM2w1Q91Cc24Hm2DE7dwKtSB2KM7mw4paPeyjOOKAu0BE44uSzO1eOQCmllFJKZYu3G6AAVYEJ2DuUZYCDwMfAC8AfbmnTa4AClMZGUPobUBHbcP0ceA7Y5+lCK6WUUkqpnMkPDVCllFJKKXUN8WYnpLxQBZgFHADOY+POTwFC3BOKyBAR+U1EzonIBhFp7bK6tLNdvJPPASffKrlY9lwnIuNFxLhNh1zWi5PmgIicFZFYEannlkeIiMwVkb+caa6IlPJQEbNcf+koAfQEPsC+N5yADde6HvuusJ9rYhG5VUQ+FZH9zrmIclvvkfMhIg1EZKWTx34ReU5E0rqrn69k4fy85/77FB4efgSX+jt16tSbgYGB0SJyTEQSnPyquOVTTUQ+c9YfE5FpIpJcV7cCidinIRNz/6ivWZ66dgI0xv4N7nPyOgysBHrnWumzQUTGiMg6ETkpIked3736bmm8fS3MrkzrzzluIyLTXZalOs4yZcr8sHr16q+d7c8Bew4dOvS/mjVrfpVPjjNdIlJRROY4dXpORLaJyG0u6/NjnXbB9of5FjiJvc7Ny2FeV/v/M/cZYwrrVMMYc9hYi40x/zDGfO3M7zDGlElOC3QDLgIDsO+OvgmcBqo56X52tvvKyWexM3/YGBOWD441RxMwHtswq+AylXNZPxrbYHsAqA/8B/vLHOSS5nPgJ6ClM/0EfJaX9ZfBdLeT/rgxZpGTxwxjzEFn+SpjTHGXY7kXmIS9CJwBotzO11WfDyAYOORsW9/Z1yngKW//PmTh9yWz8/Me8AVQYerUqc337dt39Pjx46nqb9CgQea66667GBER0RnbMIkFNgG+Th6+wBZneWPgLuccv2mMCTLG/GaMOeXU30Rvn5NCOnnq2okx5nFjTKIx5pgxZo4xZpIx5l1jzHfGmAX54FjBdlLt6/w9NsC+AnYIKO2SxpvXQo/XH9AC+A3YDExP6zgXLlw4oWvXrqZChQpJR48e/dAY87IxZm7btm0vRUREmNmzZ7/r5ePMqE5LAb8C7wPNgOrAHUDdfF6nm5x6OmWM2e58npcbvwPeriNjTKFugC53TvZQt+VvOMvfTTkJsBaYmerE2BjyLxvbYDHGmNfd8nnCWR6TD441Z5VvG6Bb01kn2Pdxx7os83f+YB915utiv6Hd7JLmFmdZ7byqvwymhsaYnsYYP7flQcaYDU4+aTb8nH+iUZ4+H8Bg7Ddbf5c044D9OK/EFITJ/fw4y94DlqRXf0BJX1/fxHnz5qXUH/Yd8CSgnTN/jzNf1WW7h4Fzhw8fnmuMOWGMecbJWxuguTN56trZ1hiT5OQXlMZ+iuaDY71iAgKxd9k7OPPevhZ6tP4OHDgwC9sptw32i970NI6zqDHmz4SEhLM+Pj6n3Y/zm2++OW+MOWOMKebF48yoDicBqzJYn1/rtI0xppYxRowxkU495qQB6on/n7lfT94uQC5NNZyT/JsxxsdtXZAx5rQxJsEYUwL7GPYS0DXViYG3fH19vzX2j+y0ufIC6mOMiXf2UyDvgmIboGew3/p+AxYAYc66MOcPranbNkuBOc7nfs4frLisF6dx0jcv6u8q9tHD2Uea32bdG1ieOh/Yb+RL3fJo6uRd3du/E9n43UmvAfqnr6/v8Vq1apmHH374ZJkyZcq7rL8dMIcPH05Vf9i7Ci84nycAP7nlWw4wX3/9tTHGPGyMiXLqThugnp88cu0EVhpjNjtp88XdlqxO2E6sBrjFmffmtdDj9delS5dL/v7+bzhljOVyA9T1OMs7+WxO6zgTExN/dNaX8dJxZlaH27DDMi7EjoSzCXg8uX4KSJ1GOuc4uw3QvPj/6ZGpsL4D2sb5+T/s3RRXp4BVQAD2MURZ7GO/w27pDhcvXjwU+61olbOdqyTsoxvX/RU0a4Eo7AgEA7CP4FeLSBnnM6RxXlzWVQCOGucvE8D5fMQlTU5kp/5y6qLz81IW03vqfFRIJw/XfRRUMUDvOXPmTHv99df55ptvLh4/fvwrESnmrK8AJJYtW9a9/tzPYarzc+rUKfH19WXz5s3ryfn7UCprPHLtLFasWDXgBiefE06+I7DvXt9B/u5/MBXbYFnjzHvzWphdGdbfyy+//Puvv/7qu3fv3pg0tnU9ziPAUSC8bNmyZ13WVShatOifPj4+tbDn6LiXjjMzYcAQ7GP4dtg6/QfwmLO+INVpduXF/0+PKOLtAuSS2s7P9MJv/oIdAD8cO/Zomvz8/JI7PmSUD6QfBjRfM8Z87jovIt9j/2D7kHkEqtyUnfr7Kof76Of8TOtCrHLAGLPA+dgaoGLFitObNm36DHAfqaPkZKv+AgICZgK89dZb/x4+fLgni6yu5JFrp7+/f3Hn4xHsXbZb3ZJsAToDu3Ja0NwgIm9gH7PeYoxJ9HZ5ciDd+hOR2oGBgWHr16+nTJkyNTLJx2Aba/Puvffe+7ds2XIUeHnQoEHtYmJiKgMbgYc8WnLP8gHWG2PGOPM/iEgt7DFNT3+zQiEv/n96RH7+Fno1Sjo//0pnffLyUsAx7Ps+5d3SlA8JCTmTjXwKPGPMaezj0FrYl/AhjfPisu4QUM61B7fz+TqXNDmRnfrLicexd303YXsJZoWnzsehdPJw3UdBVxKgSZMme7E9n2s5yw8Bvrt27Uq++1zK+el+Dl3PT7/jx4/fn5iYyK5du37O1VIr8Ny186zzuT8Qiv0SUhL7T28etrPPUtxGovAmEfkn0B243Rjzq8sqb14Lsyuj+mt5+vTp4vXq1cPHx2e6iFwCbgOGOJ+Tg74kH+dHwO0HDhxIbNCgQSXg6aZNmzY6evQoiYmJs7E3K7x1nJk5iH0M72o7UM35XJDqNLty+/+nxxTWBmiWGWMuABuwvW1d3RUREfG7F4rkNSJSHBvu9CD2ndBDuJwXZ31rbNQpsI+oArG9A5O1xA5/tJr8qTN2KIpD2N6PFzNMfZmnzscaoLWzbbLknt7xWT+M/O/HH38MBCpjf5/A/p1dnDt3bvXkNM4QTHVJfX7qOstDgSnvv//+WuwwIhvyqOgqCzK5du5xPvti75Qtw3a++wU7/NJ6bGP0gbwpbcZEZCqXG5/uoZ4Ly7Vw8bJly2Zu2rSJxYsXvwk0xNbDAufzTlIf58Nnz579ctWqVUV+/PHH54GAn3766b6EhARZu3btdGc7yH/HCfYxc223ZeFA8v/0wlKnBZu3X0LNpek15yXc9Ia2me6sH+y83tENuAA8gv1nOBU4vWLFimeddG+mk88IZ/0r+eCYsz0Bk7HfgKtjQ6Auwf6TuN5ZPxr7bakzdpiKBaQ9TMUWLg9TsYWrH6YiW/WXjelvxpgLxpgDxpgrejFiLzYNnekMNopWQ5whZTxxPrDfTg8529Z38jpJwRiGKd3z46ybDLRcu3btjBUrVpiqVavGY++Aup6fd0qVKnX6iy++MHPmzHkJWEHawzB9vXLlyrhly5b94ePjcxA7DFNyWaKc+tdOSJ6fPHLt/Prrr8c66Q6mk884Z/0/vX3M2E5TJ7Gd5FyHpAt0SeOta2Fu118sVw7D9Ffr1q0f+/HHHy/ed999J9I6zlq1ap1ZvXq1GTVq1BAvHWdmddoUe3NhLFAT6OrU32MFqE4jnbrKbiek3Pr/6fl68nYBcml6xDnBM9JZnzxEwR0pJ8K+sBzP5Tsttxpj7nTSLU8nn+Qhmvrng2POfuVf/oO7gB0G6P+ACJf1gu0pfxA7CPFKoL5bHiHYR2onnWkeUCqv6y8LU1djzEVjzF5jh7lI63xEYt99cp/e8+T5wD5+/MbJ4yA2hGy+H4Ipo/OD7ay3HDji4+NzqVq1auaOO+74GZfhlJw8ij300EO/ly5d2vj4+JwDPksjTTVgib+/vyldurQZOnSoOXfunMnAYm+fm0I0eeraebuTbns6+TzmrPf6cDDp/E4bYLxLGm9dC3O1/riyASrA+BIlSvxVrFgxEx4evjet47zlllu2BwUFGT8/v3NeOs6s1Ot92HFOz2Hv7j7BlT3a83OdRjp1ld0GaG78/8yVqbCG4qyBfbk93vns2hMsCPsLl/wuR0IG+QRiX6JPwg7N4doT3gc7llqos49f3TdWOeap+kvWE5iDbWS3Qesqt3mq/qZhe2u6q4Xt1LIJ2+D5AXsXS109T9VdAPba6YvtLe+e9h1gEPA08IoHyq0sT9XfU9gnGnNJO2LVXOz4vE9ggw8oz4vEPiGajz3XWeXp/5+5prC+A7obOwRBKJeHXUj2AvYdjrmkPvl1nMnVaSddCew3JVePO/kvRxs0nuap+gPbo/99YA+20aJ1lfs8VX9PYB/tuk+znfVLnXltfHqOp+ruDPBvoDg2ZKprqNkG2OHfLgGLPFNs5fBU/X3r/OyCHU7LVUNnuQG+vtoCqxwriq039xENcvI74BWF9Q4o2EpZjW3lf4LtAdccewdsJ9CKy73+wP4xQeoLJUAZJ59w7B9bHPZdp47Yb/itsBWuPMsT9dcG+BL7RWsWsDeN/fyJ7ZSkPMtTf39picI2Ql/CRpFSnuWpugvGPtZsiB1zeBW2l3Fn7Csbw7HvjCrP8lT9zcKGKL2ADU/6O7ZR8zfs6AVTgCc9X/xr2t+cCex7yO2wN02SvxAcw46nC7YufuNyvbjK7u+Ad3j7HYBcnqoaY2Yb+yL8BWPM78aYKcaYkDTSJksrn9LGmKnO9hec/GYZY6rkg2MszNPV1l+UyVx8PjjOwjp56u/PfUquV+2ElP/rLtAY85IxZqcx5rwx5k9jzP+MDdPp7WMszJMn6k+M/VuLNcb8YYy5ZGwo3K+MMQ/lg2MsjNN4k7F4l7ShaSzL6e+AV6bCfAdUKaWUUkrlQ4X1HVCllFJKKZVPaQNUKaWUUkrlKW2AKqWUUkqpPKUNUKWUUkoplae0AaqUUkoppfKUNkCVUkoppVSe0gaoUkoppZTKU9oAVUoppZRSeUoboEoppZRSKk/9PymQMSDhsYcOAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ " \n",
+ "wfs_football = sorted (wf_football .items(), key = operator .itemgetter (1), reverse=True) \n",
+ "wfs_basketball = sorted (wf_basketball .items(), key = operator .itemgetter (1), reverse=True)\n",
+ "\n",
+ "football_list = (wfs_football[0:min(len(wfs_football),30)] [::-1])\n",
+ "basketball_list = (wfs_basketball[0:min(len(wfs_basketball),30)] [::-1])\n",
+ "\n",
+ "plotTwoLists(football_list, basketball_list, 'Differences/Similarities between football and basketball rulebook')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# My Results"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "By running the code above, you can see the two different graphs that show the most frequently used words in the football rulebook and the basketball rulebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Similarities\n",
+ "\n",
+ "* 'Ball' is the most used word in football and second most used word in basketball\n",
+ "* Both use words like 'team' and 'player' quite often, showing both sports have players part of a team.\n",
+ "* Both use the word 'foul' frequently, showing a player can get a foul in both sports.\n",
+ "* 'Game' is used often by both\n",
+ "* Both rulebooks use 'shall' and 'may' very often. These must be common words used in rulebooks across all sports.\n",
+ "* Both use 'clock' often, showing both sports use a clock."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Differences\n",
+ "\n",
+ "* Football rulebook uses 'field' and basketball rulebook uses 'court'\n",
+ "* 'Shall' and 'may' seem to be used more often in the basketball rulebook\n",
+ "* Basketball rulebook uses 'official' frequently, while football rulebook does not reference any kind of official or referee as often\n",
+ "* Football rulebook uses 'pass' often, while basketball rulebook does not even though both sports involve passing.\n",
+ "* There are multiple frequent words in each rulebook the only pertain to that specific sport."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
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