· 7 years ago · Oct 05, 2018, 02:46 PM
1{
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "# The list of cities\n",
8 "\n",
9 "The statistics office of the European Union, Eurostat, runs an [\"Urban Audit\"](http://ec.europa.eu/eurostat/web/regions-and-cities) at regular intervals, carring out perceptions surveys in a series of cities across the Union. It is hard to understand precisely which cities are taken into account, as the list varies in length between 70 and 1000, depending on the data series.\n",
10 "\n",
11 "We took the longest possible list of cities, so that we'd have the EU code for as many cities as possible (e.g. Narva, in Estonia, is EE003C1). The rationale behind this decision was to enable us, later on in the project, to easily reconcile our cities with any data that Eurostat possesses on cities, such as questions of the Urban Audit survey related to perceptions of climate change.\n",
12 "\n",
13 "There is no clear logic in the way the list was constructed ; populations range from 20,000 to 8 million. We assumed that each member state gave Eurostat a list representative of its geography and of its urban diversity, which is what we were after.\n",
14 "\n",
15 "Later in the project, 53 cities were added, following requests by local media outlets. These do not have a Eurostat code and are refered by name only. \n",
16 "\n",
17 "You can see the complete data and cities list [in a Gitlab repository](https://gitlab.com/edjn/onedegwarmer-shared-data).\n",
18 "\n",
19 "## Cleaning the list\n",
20 "\n",
21 "We started from the population list, which contains many encoding errors, duplicates (e.g. \"Amsterdam\" and \"Greater Amsterdam\") and peculiarities (the French listed several city groupings as individual cities, for instance).\n",
22 "\n",
23 "The list was cleaned and geolocalized with the following program. Out of the 1049 cities listed by Eurostat, we kept 945."
24 ]
25 },
26 {
27 "cell_type": "code",
28 "execution_count": null,
29 "metadata": {},
30 "outputs": [],
31 "source": [
32 "import pandas as pd\n",
33 "import re, requests, json, time\n",
34 "\n",
35 "df = pd.read_csv(\"../Data/urban_audit_pop.csv\")\n",
36 "\n",
37 "# Creates a field with both cities and country\n",
38 "df[\"Country\"] = df[\"CITIES\"].str[0:2]\n",
39 "\n",
40 "# Removes countries\n",
41 "df = df.loc[df[\"CITIES\"].str.len() > 2]\n",
42 "\n",
43 "# Removes \"greater cities\", clean names and other non-clean names\n",
44 "df = df.loc[df[\"CITIES_LABEL\"].str.contains(\"CA\") == False] # e.g. CA Nord Essone\n",
45 "df = df.loc[df[\"CITIES_LABEL\"].str.contains(\"CC\") == False] # e.g. CC Val de Loire\n",
46 "df = df.loc[df[\"CITIES\"].str.contains(\"K1$\", regex=True) == False] # e.g. Glasgow (K1 in the code seems to be for greater cities)\n",
47 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].map(lambda x: x[0:x.find(\"/\")].strip() if x.find(\"/\")>0 else x) #e.g. Bruxelles/Brussels\n",
48 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].map(lambda x: x[0:x.find(\" - \")].strip() if x.find(\" - \")>0 else x) #e.g. Lens - Liévin\n",
49 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('Greater ', '') # e.g. Greater Amsterdam\n",
50 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].map(lambda x: re.sub(r'(.+), (.+)',r'\\2 \\1', x) if x.find(\", \")>0 else x) # e.g. LÃnea de la Concepción, La\n",
51 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('Ä', 'ø') # e.g. TromsÄ\n",
52 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('', 'ã') # e.g. Guimares\n",
53 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('ċ', 'å') # e.g. Västerċs\n",
54 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('ġ', 'ő') # e.g. Gyġr\n",
55 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('ÄŠ', 'Aa') # e.g. ÄŠrhus\n",
56 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('Athina', 'Athens')\n",
57 "df[\"CITIES_LABEL\"] = df[\"CITIES_LABEL\"].str.replace('Halle an der Saale', 'Halle (Saale)')\n",
58 "\n",
59 "# Some British cities still are unclean ; would need manual cleaning after verification of city status \n",
60 "# (e.g is it a conurbation or a real city?)\n",
61 "\n",
62 "# The following steps let us keep only the rows where a population figure is available,\n",
63 "# and keeps only the most recent\n",
64 "\n",
65 "# Removes all years with no population\n",
66 "df = df.loc[df[\"Value\"].str.contains(\":\") == False]\n",
67 "# Removes duplicates\n",
68 "df = df.drop_duplicates(subset=\"CITIES_LABEL\", keep='last')\n",
69 "\n",
70 "# Takes first lines only for test purposes\n",
71 "latitudes = []\n",
72 "longitudes = []\n",
73 "\n",
74 "for index, item in df.iterrows():\n",
75 " if item[\"Country\"] == \"EL\":\n",
76 " country = \"GR\"\n",
77 " elif item[\"Country\"] == \"UK\":\n",
78 " country = \"GB\"\n",
79 " else:\n",
80 " country = item[\"Country\"]\n",
81 " \n",
82 " time.sleep(2)\n",
83 " \n",
84 " url = \"http://nominatim.openstreetmap.org/search/?format=json&q=%s&countrycodes=%s&limit=1\" % (item[\"CITIES_LABEL\"], country)\n",
85 " r = requests.get(url)\n",
86 " data = json.loads(r.text)\n",
87 " if data != []:\n",
88 " latitudes.append(data[0][\"lat\"])\n",
89 " longitudes.append(data[0][\"lon\"])\n",
90 " print(data[0][\"display_name\"])\n",
91 " else:\n",
92 " latitudes.append(\"\")\n",
93 " longitudes.append(\"\")\n",
94 " print(\"Fail for \" + item[\"CITIES_LABEL\"])\n",
95 "\n",
96 "df[\"Latitude\"] = [x for x in latitudes]\n",
97 "df[\"Longitudes\"] = [x for x in longitudes]\n",
98 "\n",
99 "df.to_csv(\"../Data/cities.csv\", sep='\\t', encoding='utf-8')"
100 ]
101 },
102 {
103 "cell_type": "markdown",
104 "metadata": {},
105 "source": [
106 "# Getting the data from ECMWF\n",
107 "\n",
108 "After a telephone chat with climate scientists at ECMWF, we proceeded with gathering historical temperature and snow cover data on each city in the list.\n",
109 "\n",
110 "The code is based on [a file provided by ECMWF](https://software.ecmwf.int/wiki/display/CKB/How+to+download+ERA5+data+via+the+ECMWF+Web+API) on their website. For each month between January 1900 and December 2017, it queries the ECMWF database and receives the temperature (in degrees Kelvin) and the snow cover (in meters) for the coordinates of each city at midnight, 6 am, 12 am and 6 pm.\n",
111 "\n",
112 "The resulting data is then averaged per day, converted to degrees Celsius and centimeters and zipped. The program (written for Python2.7) ran on an Ubuntu server rented from Amazon Web Services during two weeks."
113 ]
114 },
115 {
116 "cell_type": "code",
117 "execution_count": null,
118 "metadata": {},
119 "outputs": [],
120 "source": [
121 "import os, sys, csv, math, subprocess\n",
122 "import pandas as pd\n",
123 "import numpy as np\n",
124 "sys.path.append('/home/ubuntu/eccodes/lib/python2.7/site-packages/eccodes')\n",
125 "sys.path.append('/home/ubuntu/eccodes/lib/python2.7/site-packages/gribapi')\n",
126 "\n",
127 "from ecmwfapi import ECMWFDataServer\n",
128 "from eccodes import *\n",
129 "\n",
130 "def get_met_data(date, param, area):\n",
131 " fname = date + '_' + param + '_' + area + '.grib'\n",
132 " fname = fname.replace('/', '-')\n",
133 " fname = fname.replace('\\\\', '-')\n",
134 " target = os.path.join(met_data_dir, fname)\n",
135 " server = ECMWFDataServer()\n",
136 " if os.path.exists(target):\n",
137 " print \"\\nUsing existing meteorological data file %s \\n\" % target\n",
138 " else:\n",
139 " server.retrieve({\n",
140 " \"class\" : class_ec,\n",
141 " \"dataset\": dataset,\n",
142 " \"date\" : date,\n",
143 " \"stream\" : \"oper\",\n",
144 " \"expver\" : \"1\",\n",
145 " \"levtype\" : \"sfc\",\n",
146 " \"param\" : param,\n",
147 " \"grid\" : \"0.75/0.75\", #setting grid to ll or F128 causes interpolation but is required for area.\n",
148 " \"area\" : area,\n",
149 " \"time\" : \"00:00:00/06:00:00/12:00:00/18:00:00\",\n",
150 " \"type\" : \"an\",\n",
151 " \"target\" : target,\n",
152 " })\n",
153 " return target\n",
154 "\n",
155 "param = \"141.128/167.128\"\n",
156 "met_data_dir = 'data/tmp'\n",
157 "in_loc = 'cities_list.csv'\n",
158 "lid = 'city_name'\n",
159 "llat = 'lat'\n",
160 "llon = 'lon'\n",
161 "\n",
162 "for year in range(1979, 2018):\n",
163 " for month in range(1,13):\n",
164 " if not (year == 1979 and month == 1):\n",
165 " print \"Now doing month %s of year %s\" % (month, year)\n",
166 " year = str(year)\n",
167 " if month < 10:\n",
168 " month = \"0\"+ str(month)\n",
169 " else:\n",
170 " month = str(month)\n",
171 " dataset = \"interim\"\n",
172 "\n",
173 " if dataset == \"era20c\":\n",
174 " class_ec = \"e2\"\n",
175 " else:\n",
176 " class_ec = \"ei\"\n",
177 "\n",
178 " if month == \"02\":\n",
179 " max_days = \"28\"\n",
180 " elif month in [\"01\", \"03\", \"05\", \"07\", \"08\", \"10\", \"12\"]:\n",
181 " max_days = \"31\"\n",
182 " else:\n",
183 " max_days = \"30\"\n",
184 " \n",
185 " # Output file:\n",
186 " # If this file already exists it will be overwritten.\n",
187 " out_loc = 'data/%s-%s-%s.csv' % (year, month, dataset)\n",
188 " \n",
189 " date_from = \"%s-%s-01\" % (year, month)\n",
190 " date_to = \"%s-%s-%s\" % (year, month, max_days)\n",
191 " date = \"%s/to/%s\" % (date_from, date_to)\n",
192 " \n",
193 " \n",
194 " # load locations list into pandas dataframe\n",
195 " ldf = pd.read_csv(in_loc, sep=',', usecols=[lid,llon,llat])\n",
196 " # get extent of locations list, plus 5 degrees either side as long as we are within 90/-180/-90/180\n",
197 " area = [min(ldf[llat].max()+5,90), max(ldf[llon].min()-5,-180), max(ldf[llat].min()-5,-90), min(ldf[llon].max()+5,180)]\n",
198 " # make the area list into a string to use it in the met_data_file function\n",
199 " area = \"/\".join(map(str, area))\n",
200 " \n",
201 " # make directory for meteorological data\n",
202 " if not os.path.exists(met_data_dir):\n",
203 " os.makedirs(met_data_dir)\n",
204 " # get meteorological data\n",
205 " met_data_file = get_met_data(date, param, area)\n",
206 " m = open(met_data_file)\n",
207 "\n",
208 " # get list of grib messages from met data file\n",
209 " mcount = codes_count_in_file(m)\n",
210 " gid_list = [codes_grib_new_from_file(m) for i in range(mcount)]\n",
211 "\n",
212 " m.close()\n",
213 "\n",
214 " # create output list \n",
215 " o = csv.writer(open(out_loc, 'wb'), delimiter=',')\n",
216 " \n",
217 " # write a header line to output file\n",
218 " o.writerow([lid,llat,llon,'Date','Time','Parameter Name', 'Short Name', 'Distance to MetPoint', 'Latitude of MetPoint', 'Longitude of MetPoint', 'Value at MetPoint'])\n",
219 "\n",
220 " # iterate over grib messages\n",
221 " for gid in gid_list:\n",
222 "\n",
223 " #iterate over location list\n",
224 " for index, row in ldf.iterrows():\n",
225 "\n",
226 " # for current location, get nearest point in current grib message\n",
227 " nearest = codes_grib_find_nearest(gid, row[llat], row[llon], npoints=1)[0]\n",
228 " # format nicely\n",
229 " result = [row[lid], round(row[llat],4), round(row[llon],4), codes_get(gid, 'date'), codes_get(gid, 'time'), codes_get(gid, 'name'), codes_get(gid, 'shortName'), round(nearest.distance,4), round(nearest.lat,4), round(nearest.lon,4), nearest.value]\n",
230 "\n",
231 " # write result for this grib message / this location to output file\n",
232 " o.writerow(result)\n",
233 "\n",
234 " #dispose of grib message\n",
235 " codes_release(gid)\n",
236 " \n",
237 "\n",
238 " # Splits the files by city and zips them\n",
239 " df = pd.read_csv(out_loc)\n",
240 " cities = pd.read_csv(\"cities_list.csv\")\n",
241 " file_name = '%s-%s-%s.csv' % (year, month, dataset)\n",
242 "\n",
243 " for index, row in cities.iterrows():\n",
244 " city_name = row[\"city_name\"]\n",
245 " city_code = row[\"city_code\"]\n",
246 " df_city = df.loc[(df[\"city_name\"] == city_name)]\n",
247 " directory = \"data/%s\" % city_code\n",
248 " file_name_city = directory + \"/\" + file_name\n",
249 " \n",
250 " # Removes any null or na values in the column \"Value at metpoint\"\n",
251 " df_city = df_city[pd.notnull(df_city['Value at MetPoint'])]\n",
252 "\n",
253 " # Converts date col to datetime\n",
254 " df_city[\"Date\"] = df_city[\"Date\"].astype(int)\n",
255 " df_city[\"Date\"] = df_city[\"Date\"].astype(basestring)\n",
256 " df_city[\"Date\"] = pd.to_datetime(df_city[\"Date\"], format='%Y%m%d', errors='ignore')\n",
257 "\n",
258 " # Aggregates per day and per city\n",
259 " table = pd.pivot_table(df_city, values='Value at MetPoint', index=['Date'], columns=['Parameter Name'], aggfunc=np.mean)\n",
260 "\n",
261 " # Convert to Celsius\n",
262 " table[\"2 metre temperature\"] -= 273.15\n",
263 "\n",
264 " # Convert to cm\n",
265 " table[\"Snow depth\"] *= 100\n",
266 "\n",
267 " table.to_csv(directory + \"/\" + file_name, sep=',', encoding='utf-8')\n",
268 "\n",
269 " # Creates a zip for the month and the city\n",
270 " bashCommand = \"zip -j %s.zip %s\" % (file_name_city, file_name_city)\n",
271 " process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)\n",
272 " output, error = process.communicate()\n",
273 " # Removes the csv file of the city\n",
274 " bashCommand = \"rm %s\" % (file_name_city)\n",
275 " process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)\n",
276 " output, error = process.communicate()\n",
277 " # Removes the csv file with all cities\n",
278 " bashCommand = \"rm %s\" % (out_loc)\n",
279 " process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)\n",
280 " output, error = process.communicate()\n",
281 " # Removes the grib file\n",
282 " bashCommand = \"rm %s\" % (met_data_file)\n",
283 " process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)\n",
284 " output, error = process.communicate()"
285 ]
286 },
287 {
288 "cell_type": "markdown",
289 "metadata": {},
290 "source": [
291 "# Deduplicating cities\n",
292 "\n",
293 "ECMWF provides weather data in squares of 80 by 80 kilometers. It analyzes millions of observations (data from weather stations, satellites etc.) for a zone, then averages the values for the square. Such weather data does not take into account micro-climates within the 80x80-kilometer square. This is bad for weather forecasting over a few days, but very good for long-term analyses, because micro-climates, especially in cities, change over time (because cities all grew over the last 117 years).\n",
294 "\n",
295 "For each square, we only kept the largest city (which is why Düsseldorf and Bonn did not make it to the final list of cities, only Cologne did) with the following program. 440 cities were discarded ; the final list comprises 505 cities."
296 ]
297 },
298 {
299 "cell_type": "code",
300 "execution_count": 1,
301 "metadata": {},
302 "outputs": [],
303 "source": [
304 "import pandas as pd\n",
305 "import numpy as np\n",
306 "import matplotlib.pyplot as plt\n",
307 "import datetime as dt\n",
308 "import random\n",
309 "\n",
310 "cities_df = pd.read_csv(\"../Data/cities_list.csv\")\n",
311 "\n",
312 "cities_df[\"population\"] = cities_df[\"population\"].str.replace(\" \", \"\")\n",
313 "cities_df[\"population\"] = pd.to_numeric(cities_df[\"population\"])\n",
314 "\n",
315 "cities_df.sort_values(by=\"population\", ascending=False, inplace=True)\n",
316 "\n",
317 "cities_df.drop_duplicates(subset=['metpoint_lat_era20c', 'metpoint_lon_era20c'], inplace=True)\n",
318 "\n",
319 "cities_df.to_csv(\"../Data/cities_list_deduplicated.csv\", sep=',', encoding='utf-8')"
320 ]
321 },
322 {
323 "cell_type": "markdown",
324 "metadata": {},
325 "source": [
326 "# Merging the ECMWF data sets\n",
327 "\n",
328 "The ECMWF data is actually two different data sets. One is ERA-20c, which goes from 1900 to 2010, the other is ERA-interim, from 1979 to today. ERA-interim is of much higher quality (there's not much satellite data to process in the early 20th century) and higher resolution (the squares for ERA-20c are 125x125 kilometers large).\n",
329 "\n",
330 "We had to slightly modify the data from the 20c data set (1900 to 1979) so that it can be compared with the data from the interim data set (1979 to 2017).\n",
331 "\n",
332 "To do so, we compared the data for the period 1979-1997 and looked at the difference in temperature between the two with the following program."
333 ]
334 },
335 {
336 "cell_type": "code",
337 "execution_count": 7,
338 "metadata": {},
339 "outputs": [
340 {
341 "name": "stdout",
342 "output_type": "stream",
343 "text": [
344 "Islington\n"
345 ]
346 },
347 {
348 "data": {
349 "image/png": 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\n",
350 "text/plain": [
351 "<matplotlib.figure.Figure at 0x7f4340931470>"
352 ]
353 },
354 "metadata": {},
355 "output_type": "display_data"
356 }
357 ],
358 "source": [
359 "import pandas as pd\n",
360 "import numpy as np\n",
361 "import matplotlib.pyplot as plt\n",
362 "import datetime as dt\n",
363 "import random\n",
364 "from statsmodels.tsa.seasonal import seasonal_decompose\n",
365 "\n",
366 "from_year = 1979\n",
367 "to_year = 1997\n",
368 "\n",
369 "cities_df = pd.read_csv(\"../Data/cities_list.csv\")\n",
370 "\n",
371 "test_city = random.choice(cities_df[\"city_code\"])\n",
372 "\n",
373 "for city, row in cities_df.iterrows():\n",
374 " city_code = row[\"city_code\"]\n",
375 " if city_code == test_city:\n",
376 " city_name = row[\"city_name\"]\n",
377 " print(city_name)\n",
378 " city_era20c = \"../Data/data/%s/1900-1997-era20c.csv\" % city_code\n",
379 " city_interim = \"../Data/data/%s/1979-2018-interim.csv\" % city_code\n",
380 " \n",
381 " city_era20c_df = pd.read_csv(city_era20c, index_col=\"Date\", parse_dates=True).drop([\"Unnamed: 0\", \"Snow depth\"], axis=1)\n",
382 " city_interim_df = pd.read_csv(city_interim, index_col=\"Date\", parse_dates=True).drop([\"Unnamed: 0\", \"Snow depth\"], axis=1)\n",
383 " \n",
384 " city_era20c_df = city_era20c_df.loc[city_era20c_df.index.year >= from_year]\n",
385 " city_interim_df = city_interim_df.loc[city_interim_df.index.year < to_year]\n",
386 " \n",
387 " city_diff = city_era20c_df - city_interim_df\n",
388 " \n",
389 " # The following two lines are needed because I forgot to take into account Feb 29s before\n",
390 " city_diff = city_diff.resample('D').mean()\n",
391 " city_diff = city_diff.interpolate()\n",
392 " \n",
393 " fig, ax = plt.subplots(1, 1, figsize=(15, 5))\n",
394 " ax.plot(city_diff.index, city_diff[\"2 metre temperature\"])"
395 ]
396 },
397 {
398 "cell_type": "markdown",
399 "metadata": {},
400 "source": [
401 "The difference in temperature before the two data sets is not random, it follows a seasonal pattern. This makes sense, because the squares being averaged to produce this data are not equal. The square under observation in ERA-20c might be a bit more to the South or West than the square of ERA-interim, which explains the seasonal patterns.\n",
402 "\n",
403 "To account for these, we decomposed the series above to extract its seasonal component with the following code:"
404 ]
405 },
406 {
407 "cell_type": "code",
408 "execution_count": 8,
409 "metadata": {},
410 "outputs": [
411 {
412 "data": {
413 "image/png": 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\n",
414 "text/plain": [
415 "<matplotlib.figure.Figure at 0x7f433fbaa2b0>"
416 ]
417 },
418 "metadata": {},
419 "output_type": "display_data"
420 }
421 ],
422 "source": [
423 "result = seasonal_decompose(city_diff, model='additive', freq=365)\n",
424 "result.plot()\n",
425 "plt.show()"
426 ]
427 },
428 {
429 "cell_type": "markdown",
430 "metadata": {},
431 "source": [
432 "We then corrected the original data by removing the seasonal component from it as well as removing the average of the trend with the following code:"
433 ]
434 },
435 {
436 "cell_type": "code",
437 "execution_count": 9,
438 "metadata": {},
439 "outputs": [
440 {
441 "data": {
442 "text/plain": [
443 "<matplotlib.legend.Legend at 0x7f43406b2358>"
444 ]
445 },
446 "execution_count": 9,
447 "metadata": {},
448 "output_type": "execute_result"
449 },
450 {
451 "data": {
452 "image/png": 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z/wMSQgghhBBCDJoROcP2p/1/Irchd1CvOSFoAo/Pevy0bSwWC6mpqZ2Pn3zySRYtWtSlzYYNG7jhhhs6H+/evZv4+HgSExOZM2cO69evZ+HChae9T15eHmlpafzrX//illtuYe3atXz/+9/nlVde4fnnn2fGjBnY7XYeeOAB1q1bR2hoKKtWreIXv/gF//73vwGw2Wykp6cD8Mwzz3S5vsFgID09nZdeeonrr7+egwcPEhQURGJiIo888gjBwcFn/HkJIYQQQgghht+IDNiGy+mWRC5ZsgSbzUZzc3OXNmlpaSxevBiAxYsX8/bbb58xYIuPj+8MDKdPn05RUVG3NseOHePo0aNcccUVADidTiIjIzvP/28gearrrrsOgClTpjBp0qTOfgkJCZSWlkrAJoQQQgghxDliRAZsZ5oJGw4rV65k+vTpLF26lAceeIAPPvgAp9PJ2rVrWbduHcuWLUNVVerr6zGbzfj5+fV6LQ8Pj87vtVotFoulWxtVVZk0aRJ79uzp8Ro+Pj5nvL5Go+lyL41Gg8PhOONzFUIIIYQQQowMsoetHxRF4dlnn2Xv3r3k5uayZcsWUlJSKC0tpaioiOLiYhYuXMiHH344oOv7+flhNpsBSEpKora2tjNgs9vtZGVlDdpzEUIIIYQQQox8I3KGbbj87x62q666qjO1fwcvLy8effRRnnvuOVwuFwsWLOhyfuHChbz66qvccccd/b7/D37wA+677z68vLzYs2cPa9as4cEHH8RkMuFwOHj44YeZNGnSwJ6cEEKIb5Wndz3N7ordhPuEMz1sOj+b8bPhHpIQQogBUFRVHfKbzpgxQ+1ImNEhJyeH5OTkIR+LGBnk9RdCiMGjqioXvXcRIV4hKCgUmArYv2Q/Xjqv4R6aEEKIdoqiHFRVdcaZ2vV5SaSiKNGKomxVFCVbUZQsRVEeaj8epCjKZkVR8tr/HfV1Bi6EEEKIr6fB2oDZZmZR0iLumXIPANUt1cM8KiGEEAPRnz1sDuBRVVUnAhcAP1UUZSLwBLBFVdVxwJb2x0IIIYQYJoWmQgDi/OMI9w4HoLpVAjYhhDgX9TlgU1W1UlXVQ+3fm4EcYAxwPbCivdkK4Iaer9Cnewy0qziHyesuhBCDq6ipCID4gHgifCIAqGqpGsYRCSGEGKgBZYlUFCUOOA/YB4SrqlrZfqoKCO+lz72KoqQripJeW1vb7bynpyf19fXy5v1bpqMUgqen53APRQghvjEKTYV4aj2J8IkgzDsMkBk2IYQ4V/U7S6SiKL7AWuBhVVWbFEXpPKeqqqooSo8Rl6qq/wT+Ce6kI/97PioqirKyMnoK5sQ3m6enJ1FRUcM9DCGE+MYoNBUS6x+LRtHgqfMk0CNQ9rAJIcQ5ql8Bm6IoetzB2kpVVT9oP1ytKEqkqqqViqJEAjUDGYheryc+Pn4gXYUQQghxiqKmIiYGT+x8HOETQVWrLIkUQohzUX+yRCrAm0COqqovnHLqv8Cd7d/fCawbvOEJIYQQoj9sThvlzeXE+sdhd7oACPcOlxk2IYQ4R/Vnhu0i4HbgiKIoGe3HngL+CLyvKMrdQDFwy+AOUQghhBB9VdJUgkt1sfGwi39+tJlLx4eiBgdQ3Zo53EMTQggxAH0O2FRV3QkovZz+zuAMRwghhPj2yKjJwEvnRVJQ0qBds7DJndK/qaKNiT5N7CvUYq5swxBqxOKwSPFsIYQ4xwwoS6QQQgghvr5Htz7Fz754alCvWWQqAuCv9rdZaf0pn194FNXuB0BN64C2mQshhBhGErAJIYQQw8DpclJjqaCk5figBlKFpkICDSFMoRx0ngRu/zW/9dwCQGlTxaDdRwghxNCQgE0IIYQYBnn15aA4AViTs3nQrltoKmQUwRgUJ5Z5z8JlTzC7JQuALXnHB+0+QgghhoYEbEIIMUCVzZWcMJ4Y7mGc06paRmaqeVVV+aTgE5psTWftHntL8zq///TElkG5pqqqFDUVEdimB8B3TDLMvJuw9myRX57IH5T7DIRLdbH0y6XsLt89bGMQQohzkQRsQggxQE/tfIrHvnxsuIdxzspvzOeKNVewvWz7cA+lm/TqdJ7c8STvH3v/rN3jSJU7OYiuLZmS1gysDuvXvmaBqYBmezPBLQ4AlJBx4BuG15gZ+LugvLmS/Brz177PQOQ15rGhaAPP7HlmUJ6rEEJ8W0jAJoQQA1BnqeNg9cERO0N0LjhafxSAneU7h3kk3X2Y9yEAmTVnLxX+icZiFBV+5BmMqtjZcGLH177mi4dexEfvw7SGNpq1geA1yn1iwtVE2m146erYmVf3te8zEAerDwJQ2VLJO9nvDMsYxMizKauKt3YVDvcwhBjRJGATQogB+KLkC1RUmu3NtNpbh3s456SO5aQHqg4M80i6MtvMbC7ejIJCRm0GqqqelftUtZYT4XDyg8rNqE4Da3I2fq3r7avcx7bSbdw+4Yck2asw+8afPJl0DRFOJ/4eNRwqMX7NkQ/MoZpDRPpE8p2Y7/CvI/+itrV2WMYhRpZ39hbz963Dt1RXiHOBBGxCCDEAm4o3dX5fZxmeGYtzXZ7RvYcr35hPvaV+mEdz0meFn2F1Wrlp/E0Y24wUNxUP+j0cThdtzjJiHHa8LbWE2qPJMu4b8PWcLifPHXiO0T6jmRZ4LQlKJWrw2JMNQscTrvPBoWvmUEnjIDyD/lFVlYPVB5kWPo1Hpz+K3WXn7xl/H/JxiJEnvuZzFlg+wGp3DvdQhBixJGATQoh+MlqNpFelMzF4IgC1FpkpGIgTxhPE+scC7j1jI8WHeR8ybtQ4bptwGwCZtYO/LLKwrgVF38AYh3uv2SVaFYdipNEysP1lW0q2cKzxGI9Mf4TKqjpClCa8Iid0aRMelIRZo2IyVlFjHto9ZKXmUuosdUwPn060fzTXxF/DpuJNOF3yJv3bzOlSudLyKT/WfUJZo2W4hyPEiCUBmxBC9NPW0q04VWfnG3oJ2PrPbDNT1VLF9xK+h7fOe8QsizzeeJyj9Ue5ceyNJAQm4GfwI6M2Y9Dvk1lRi13XxhiHC6IvINlSBMDRqrIBXS+nIQedouPy2MsxleYA4B81sUubiKgLAJhmOMjhIV4W2bF/LVBJoslq56IxF2G2mcmqzxrScYiRparJSjTVhChNVNbI31EheiMBmxBC9NPm4s2M8R3DZVGXAVDXem4tifzDZzn87P3BD0L6o2P/WnJQMtPCp42YgG1ryVYArk64Go2iISU0hYyawf9ZHSx3P/8x3uEwaQFRre7kNVnVlQO6Xqm5lEjfSHQaHa4ad601bej4Lm3Cx8wCINYze8iXRR6sPkigRyA/XV7G9a/sYoxnCgoKeyr2DOk4xMhSVmditOJeDm2qkhIpQvRGAjYhhOgHm9PG3sq9zI2eS4BHAHqNnhpLzXAPq89sDhfv7ith+/HhDTLzje4kA4mBicyMmEmBqWBE7AU8XHOYsYFjCfIMAiA1NJUTxhMDqsdWZ6nD4XL0eC6npgCAmOAkmHA1wU730sD8+oEHbNF+0QB4mApwoINRsV3apERMJxwtmSFlHCpuGNB9TlXdUs3zB57nmd3PnLHtweqDjPVPweZUKa5v4a43sonxHceeynMjYDtabsJi69vyzYyaDF7NfPUsj+ibob78BDrFXSPQWls0vIMRYgSTgE0IIfohrzEPu8vOeWHnoSgKIV4h59QM296CesxWB3XNbcO6yT/fmI+XzovRvqOZFeGe+RnuWTany0lmbSbnhZ3XeSw1LBUVlSO1R/jyeC03vbqbn6w8yC8/+QKz1d7rteosdVz9wdW8ePDFHs83mt2zYFGRMyEwhqAg92xYial6QGMvM5cR7ReN1e4kpK0Ek1cUaPVd2njpvHgs9EIKDHDC/DH29mLaA/Fa5mtc9cFVrMhewdq8tTTbmnttW91STVlzGSE69566174/HW8PLceLIjlUnUFO9cj+wCOv2sx1r+zkd+uz+9T+L3tW8I+Mf9BiaznLIzv3WapPZodUGwc/uY8Q3xQSsAkhRD8crXPXDpscMhmAUK/Qc2oP28ask3XjKozDt8k/vzGfsYFj0SgaJgRNwFfvO+z12PKN+TTbm7sEbFNCpqBRNGTUZvDOnmJyKps4VP8F6+of4p8HNvR6rXey38HisJCWm0ZNa9eApLHFhl4twNvlIrB9mWLw2PkA1DX3f4bN1GaiydZEtF80X5WZSFQqcIxK7LHtlcm3MdNiRQ3ezIGSge2XKzAV8I+Mf3DpmEt5eNrDgLu2Wm86/s9o2uLRahTmJIXxyQOX8L1xl6Hi5JblK08b/A63V7bm811lL1sOHKG04cwlPI43uoOQgxWyxO9MXA1Fnd97mEuGbyBCjHASsAkhRD9k1WcxymMUkT6RAIR6h46IpXx94XKpbM6u5mGfjSzVvUeFcWgzBZ4q35hPYqA7qNBpdFwVfxWbijZhajMN25g69qqlhqV2HvPR+zAxaCJrjq9lT3EeV071QRe6DoCsupwer2O2mXn/2PtMD5+OS3Xx5pE3u5w/Um7CoK8lyu5AiZwCgH7MdPydTlxtJf2u+1ZmdgdeUb5R7DhWSaxSRWD0xB7bKlEzeNzYglNj4y+Hfo9L7f8s2xtfvYGnzpNfX/hrjhWFAlDRXNFr+45grt7oR2yQNwadhgAvPX+45jp0igGbIZcTtSNzNqqgtpm6rzbzd8PfeFq3gpe25J22vdPlxKq6X4/dxceGYojnNENTMdUaDzK8Y/CzDmw5sBDfBhKwCSFEPxytP8rEkIkoigJAiFdItxmUkepwqRGT2cz9rOVG7U7KjcNT8LvB2kC9tZ6xgSfrhC1KWoTVaeW/J/47LGMCOFx7mBCvEKJ8o7oc/81Fv6HVbkGN+BelmrewOCzg8qS0uajH66w6topmezOPTV/K9WOvZ83xNVS1nJzZPFJuwqZvZozGEww+7oPhkwl2utArddQ12/o17lJzKQBRflEU5x7CoDjxiJzUc2O9J+PDU/m+UcPx5l38Ye/z/bpXSVMJ6wvXc8v4W9ib18bqfe5AK6+htNc+Na01GDQGSurgZu+DUOcOejy0HqT4xDLa5wCFtcMXqJ/O37/I53H9e5g0Cldp9pF5eB8Ftb0v/0wvK8Clce9bzK08OlTDPGf5WcpYFhrOkyE6wpxVUotNiF5IwCaEEH1kcVg4YTzB5ODJncdCvUJpsjXR5mwbxpH1zaasKq7WpePhbCacRirr+59IYzB0ZIgcFziu89iEoAlMDZ3K+8fe7/cM02DJqMno3Jt4qvGjxnOJ/8/R6BvJbTrAT8/7Kb7E0WjrvqTQ6rDyTvY7zAqfzY/eqELbdDku1cXz6c/TancHyF+VGjHqHUR7h5/sGBhLkAo6nfm0AUFPyprd4/DRhjO6rn1ZacJlvbZX4i7mMVMJSuMs3jv+Dm9nvd3ne71x5A30Gj1zIm9m6epM/A2jUFUtx+t7D9iqWqsI8w7DVFfF/TW/hX/NgxNbIXc9l5UepM7DTlnJ4Ne6G6gtxVv4e8bfKW1oxfLVh4Toi7giLp4HIsK5T/8Rr2zN77Xvl4UngzRjU/c9b29nvc3j2x8/K+POLDWy6ZQlzyOd1e4kwllFvoeeco2DEE0tlabhm/UXYiSTgE0IIfroWMMxXKqLScEnZy9Cvd1Lws6FZZGbsqu522cXKqBRVJrren+TfTblNbpnWDqWRHZYlLSIoqYi9lXtG/IxVbdUU95c3mX/2qmOFYWSYPsR34+ezx1JtxHmFY1VqcLl6rqk8LPCz2iwNuBvm0+lycry7U1cF3c7G4s2ct1H1/GPjH9w0Pob2jQK0YEJJztqNATp/LBrrf1eHlhqLiXIM4hDRa3M1WTQGpQM/qN77xB7ERrVxXOxl2A3T+Qv6S90BtGnU9Naw8cnPmbhuIUs+28ZHnotb3/XC1+7ntKm3pdE1rTWEGgIJcpV7j6gKLDyJlh1O+MVTwAq6oe3zMSp0o6l8Vrma7yy+2Me1a7iH+Ex2IEdXgbWR+ZRWni4175Zle7nEWezY3FWdEvssiZvDZ8WfsrxxuODPu7fbNzI45+tGvTrni1lDa1EKjVUah2oCjQY7FRWnzsBpxBDSQI2IYToo47kCZNCTgZsIV4TcHlnAAAgAElEQVQhANS2juzEI3anC1tdEZPaMvhF7ATmRI/hSOvqYQk0d1fsJsIngjDvsC7H58fNZ5THKNJy0oZ8TIdr3W/CewrY6pvbOFph4tfabB7f/ga6FyYwrzkbRWvheF3XQOWj/I+I9o1lwwEfLk8OI9jHwP7DM3lz/lsEeQa1p3tv5CeNRr6XeF2XvqE+YbRonRTU9G/msyOl//6cQqZrjuOZ/N3Td4iaCRo9860buND3h7icBpbtXXbGmc1NRZtwqA7OD7mWwyVGfnZxGCk77mei00hNa3mv/WpaazAogSRo2vco3bEOxs2HxHnEfe9lABotBf16zmdTxwcKu6pfBo9aPjGoLJ6wmGUznuCQpwcan3/2unSv3nSEEIeTSTY7Fn0zx6rMneeqW6opNBUC8GHeh4M6ZofTSZ76OrbA1X0uPzDcqqrLadLbceD+vTuh19NU0fvspRDfZhKwCSFEH2XVZxHqFdol0Oj4fqTPsJmtDhZqd1Co1/KJxoKX6iLb4wAL1i3AbDOf+QKDpMnWxK6KXcyPnU9RfStzn9/G59nuVPYGjQFfxwy+KNnRa/2ysyWjJgMvnRdJQUndzu3Mr0NVYVLTTohIgYS5zKxzB3j7ynI72xU3FXOo5hCjXBfS5nDx1Pw4nr1+EjmVTezPCeC9773H76at5rvF07nf2IRvZNfgMMg/mmathsbKnpOZ9KYjpb8z/wt0uNCMn3/6DgZvmPcLlGOfscz8F6iZx4HqA6wvXH/abp8VfcaEoAnsO65Fq1G4qeZFlKZyIh0Omm09lyNQVZXqlmpwBJCgVKJq9BA+BW5Ng++vITLqQgwulSZnxbAthT1VvaWeBmsDKcHTMOlM/CgiHIPWwD1T7uHaSUu4whVIibeZwrruy1atdietVJBotzPaLx6jzkl60cmfy/6q/QCMDRzLxwUfY3P2b6/i6azO3gSGWnQ6E0UDXOr8ScEn/HTLTwdtTGfSVH6cQv3J0hMFBj3W2sIhu78Q5xIJ2IQQoo+y6rO6zK7ByRm2kZ54pMli50btDv4enoiH1sDKimouqUrA2GY8bYa/wbatdBsOl4Mr465k+a5CCuta+Nn7GZQ2tLI6vYzjJaNAsXO05vTZ+Abbnoo9TA2dil7jfgPZ2GLj/N9/zuUvfMlLn+eR4lmDZ1MhTLsDFrxOvMM9i3HklHGuy1+HBg3pRxO4LSWAhLdncWXdCq6aFMFrXxZgtjoprtGSpClF9QgAv4guYwgOdu/pU419Xx5oc9qoaqnCSwnjPOsBbDo/9wzamVz8CNzwKuENB3mtZRsxPkk8f+B50qvSe2xeZi7jq9qvuDL2KtYdruDno4/gmfMBJF9LpMOJBTN2V/fU/KY2EzaXDavVlwm6apSgeNDqOs9rPXyJdrqw6Ezdkq18eqSSHy4/gNM1dIFcntH9eibor2eiBWp1WpZM/D7BXsEATPSLp1mrkF6c1a1vVoWJJkML0fiQMPo8VEUhp/BkYfC9lXsZ5TGKx2Y8hqnNxBclXwzauFfmvgOAS4GsyoEtdU6vSmd72fbOvZZnW1ttAYUG9+9CqGcwJ/R6MEotNiF6IgGbEEL0QbOtmSJTUZf9awBBnkFoFe3In2FracZgqGOL3spN42/GSx9ErMNdh62xrXHIxrGxaCORPpEk+CWz9lA5F40NRlXhR2+n8+v/ZuGvjQVge/HQ7WkqbiqmwFTAnOg5nce2HquhuqmNYB8DDa02fjK6fanW+CtBZyA8IAYPl9K5xM3pcrLuxDpivM+jrc2PRwK3g6UBdv6Vn53vS3Obg//sLearMhMphkqUsGT3Xq5TBIe6k9n4tOXR5ujbsrby5nJUVCprvZijzcSZMK9LQHRaqbdhn/0QszW5XOh9BwB3bbyLuzbc1Zl5ssPGoo0AhCrnU9vUwp3Nb8CY6XDdy4x2OFAVtccPLapb3TNMpiYfxuqqIHhctzbRqgcmg5XCuq57995PL+WL3Bp25A3dcuP89hpqJwrgD3VVLPRJ5AeTftB5fsYYdzCcW7atW9/tBXm0aWCsfxwxY84HoKluH3nVZhb8Yydbi3czM2Ims0fPZrTPaNbmrR2UMWfVZVHceoRZFnfCjrLKgSVw6SipcbqaeoNJYyyiUK8n2DOIqWGpnDAYMJgHVhtQiG86CdiEEKIPsuuzUVG7BGw78mr5OLOSYM/gEV8829LUyHJ/fxQU7px0J3bf0cS63EshjVbjkIyhydbE7ordzI+dz0cZFTS3OXh0fhJ/vimF3CozXgYtr91yFapLR2ZN9wx7Z8u20m0AzI2e23lsS24NYX4epP3oAjKens9V+gwInwyBMQBoQiYQY3dRZXEX+91buZea1hp0LbMYF6Qj+OhbMPo8UF2Mz/kbl40P5a1dhWSUNJKolkHYhG7jCPZ11/YL1pVTXN+3WY6OGmwlORWEKUa8Jp5h/9r/8Ii7AI2i4lPZwIaFG3hi1hNk12fzyuFXurTbULSBlNAUvsx2cLXnETytNXDxz8BrFEF4A3C8vnvh446ArbZBR6SzEkLGdmsTbwikXu/kRO3J30OnSyW9yP1Bwnv7u88YvZvzLj/f/nOcrsHdr5VnzCPAEIi2NJcEh41nznuIAI+AzvMT4i7Bw+Wi3PRVt77HStwZOieMmU5MrDtLp+LI5/q/7yKjKh+zo46UkBloFA03jLuBvZV7KW36+ol/VmStQO/Sc7/RHXA1NAysnICxzf3zH6oZd6/mUvIMXsQHJJAQmEipTodXW+97IYX4NpOATQgh+uBg9UEUFKaGTe089odPc3n2kxxCvENGfMDW2lTLR34+zPGdQIRPBEpgDONc7jfEDdaGIRnD1pKtOFwO5sfN5509xUwe48950YF8d0okLy5KZcVds5gRGwq2SAqbBpZFz9Rm4lD1oX71+aLkC5JGJTHa151Z0eZwsf1YLfMmhKHRKGBphJI97tm1DqHjGW+30Nye+fC93PcIMARwvCCGnwSlQ0sNXPFbmHUvZLzLz1Ls1DXbUFpq8HE1QVj3wtYdy+789LVsP96336eOmbC5bcdQFQ2M/U6/njuj3UXCPWsy8dR5siR5CdcmXsuWki2dexsLTYXkNuQyL2o+nx2t4n6/XeAb3vnzCPJy/9yyq7svZ+uYdfNqsaJT7RDcPWBL9I3EpSh8VX1y/1J2RRPNbQ5ig735PKeaWvPJshmmNhMvHnqRzwo/4+3svpck6Iu8xjwCdTHM1BxDVbQQNaPLeX3oBJJsdmpcJwMti83J7z/NobrWvUctMW4eAV4h+KkKOkMtEyP9uWuOe/awtMz9s7pp3E3oNfqvPX6X6mJLyRaSm/yJtbszUra2FA3oWkMZsNmdLgLbyijS64gPiCcxIBGXAg5NNTZH/4u5C/FNJwGbEEL0QXp1OhOCJuBv8Aeg1tyGs+oocS2ZBOiDqWsd+iWR/00/wW1/WonDeeY3OA1NZVg0Gib6u1Ppe4TEkeRyj7njjdrZtrl4M6N9RtPaNJpj1WbuuCCus+bZDeeNYUpUABqNgr8mjgZ70YCSULyW+Rp3briT/ZX7+9S+0dpIRm1Gl+WQ6UUNmNsczJvQnlwmfwuoThh/yuxVyHgSbTZcWiN7yvexrWwb80bfTKtNZb7xfYhMhbhL4JJHwcOPlLxXSI0OZJymfclXaA8zbJ7ugE3VWli9M6tPr2t+QzG49PxYuxsl6WrwDTtjny58wzAbwohsPdaZ+fCGsTfQ5mzrXAb5TvY7aBUtEdpZ+NtrSW7eA6lLQOve7xcxyh2E5TV2ny2qaa1BQSHR2Z4Io4clkQnt5Q2K60/ODO0rrAfgDzdOweFSWXvo5FK5d3PexeKwMDV0Kq8cfqVPJQn6wqW6yDfm47CGc5E+HyKmgIdf10Z6L8Y69FTrzDicDsxWO1f/bQf/3F5AWEAtwU4XgVHnoygKMXp/rIZWVk3YjvHYiwQ6Dby/z4rZaifUO5RrE6/lo/yPvtYHJjWtNdhcNqbZjWgCp6BXVdrsPSeAOZNKs3scQxGw7cirxU9TQ7NGJT4gvvN3oNXQREXj0OyhE+JcIgGbEEKcgc1pI7M2k+nh0zuP7cqv4zn96/xV/yoaV8CwzLB5ZS7nzdaHqWk0nbFtQ4v7TVhke30uQ1AMvoodAz5DNsN2rPEY08Ons2JPMQFeeq6d2nOtsGifsTiVVsqb+7c8SlVVtrQncnh699O02M9cz2x72XZcqou5MSeXQ36eU4OHDubUrYRPl8KOF8A7BMZMO9kxJIl4u739Xr8mwCMAb8ulfEebgU9zEVz0oHuPmncQzPghyvGN/OKyEOaHtP+sw5K7jcVT54mP1oN6rYYgcw7rj5x5L9HOkiP42j3wczXBzHvO2L4nLcFTmKwUkF/jznwYqE0gISCBdfnryKnPYc3xNdw64VaKavTcrP0SRXXBtNs7+4+KnECQ00mFqeeAzUcXSKLSvr8tpHvAFhfi/lmYWk7Oqu4taOCugENcuPOHXBAbwKoDpXyRW82fNmayIusd5kTP4aW5L+Gj9+HnXz45KFlFy83lWBwW2pqDmUweSszsHtvF68KwaVQOVOTy2ZFKpjZsZMeE1dQpVYzVeHXuIYzxjaJUp1C+6zl2eXpwmcUEFiMvf5FPpcnCHcl3YnVaeS/3vQGPuWNJ7ExHNR4JFxHk0mJVjP3+sENVVSxO94xqgfHs12fcti+dVoP7/2d8QDxx/nEoQIUBysqHpz6kECOZBGxCCHEGR+qO0OZsY0bEyeVRWVlfkaIpJFpTi9PqQYO1occseWeTR0sFXoqN+oqiM7Y1Wd0FaaMCo90HAtz/erg8hmSGzea0Ud1Sjb8ugo1ZVdx2fgxeWhUOLofmrskqJoW638AfrOzfXpwTxhNUtlRgN06normCv6T/5Yx9tpVuI8w7jIlB7iWKqqqyJbeae0YXY9j6W8hcBS21cP6PQaM92TFkHAntAVtVazk/mPQD9hW0co/PTvAJg+RTaqxNuQVUJzNbvuQHYy3gFQQ+oT2OJ8grhDqdngU+R/nXjoLTvvHOa8yj0naEa9qs7pmrhDlnfL490cdMI1FTSX5pJaUNrcz/yxYCnReSUZvBEzueYJTnKO5PvZ+M4nqWGL6E+Esh6GTRb31oIpEOByZL9z1sVa1VeCpBJCgVqJ4B4B3crY1f0DiCHU4srlJcLhWXS+VAUQMLDfug8Esejs6jsK6FHy5P543Md2lxmFkQfzvBXsGM09/CcWMOXxR8/SQ1x43ugNG/0YqH2gYxF/TYLilgPAA7iw5x/MBmXjT8g53mPRzXa5mfeG1nu+jRM6jQ63lg/DT0Hn78uLGRX41O55/bC5j9hy+46eV8Loy4lLTcNCztCYD6q+NDjViHDZ+xFzNK8caqs1Lf0r+SAVanFRfu3+di09ndR2ay2PHI39CZITLOPw5PnSejPdyZIhtLh27/qhDnCgnYhBDiDNKr0lFQmBHuDthUVSWg4JPO84rJvYSn3lI/pOPS2dyBlqmm+xvl/2WyuZc/hgfEuQ8ERAFgcGhptJ79LJFlzWWoqORXeKBRFO6YHQtZH8LHD8E/50JFBjQUwNp7uLPsP6iqhr3lR/p1j23tmfvaaufja/0Oq4+vJquue/r1Di32FnZV7GJu9NzOpZkFdS0U17dys2YbeI2CpXnur8t+3rWzpz9jPEJQVPDU+HNN7M2UlxYxy3YAUm/tXC4IQPhECJsER1ZDba57du1/MkR2CPYKpcE3mGu0+zlabmJPQe+/U38/9C8Ul47/ayxyz671cs0zCUyYBYC5IJ1/b0pnh/Z+4nNL0CgaCkwFPHjeg/jp/TCXfEWkWgMpi7teICiR0Q4nLc7uy4JrWmvQqoEk6apRgsf1PEb/McTZ7Tj0DVQ2WcmtMmOy2EmwuwOoWXUf8OebUlhxdyoR0ftQW8fyymd23thRwNYjBgDSy75+4e2OgtmpHdkue5lhS46ehbfLRWb5HqIqN5Gn9+aFUX5cNPoibr746c52MaPG4QJKrHX8dd7fiI6azU2OT3nnrmksvTKJxlY7E7yuw9hm5JNT/p70R1lzGagQ4XCixJxPsCGIBh2UV/Qv6OrIEKlVVaotA8sSmVlq5LHVmWdcyvvpkUrmkU6ebzgeWg8ifdzJdsYFJnLCoMdR3b86hEJ8G0jAJoQQZ5Benc64UeM6s8Udr27mMsdOLB7u2QKDyb3MreNNz1Ax2Nz7gqz1Z15C1ORwB3chgfHuA+0zbd4219AEbO1Lt/Ydg6unRBIZ4AWH3wa/9mWR/74SXpkFR1YTU7we2oLJqe/fG7ftZdvR2KKI9AmnstSdfv1oXe+zdB+f+BiLw8J1iSdnw7YdqyUQM7G1WyFlEeg8eu3vEZrEd5u0tFVfy+8+PsECzQ40OOG827s3TrkZyva7A9Me9q91CPYKpt7gjY+1kst8ynhu4zFcPdQhKzOXsbVsIzNNPvhpPN1B4gBpx7gLeLcUpxOe9S+CFTM3te1kUsDFpIamcsPYGyhtsBDd1r5kMXpW1wsEJRDhcNCsmLvNCNa01uCy+xGvqexxOSQAfhHEO5xYDM1klBjZV1hPGI14W6shMBZN0XZuiW3lRNtGjLZ6fjz1Pg6VGPnd+hwuiXdf81jd119Gl9eYR6jnaGYoRVh8osAvvMd2AdGTmdhmo6ApmyivdJ4cE4WHzpPfXvTbzsAfYELQBBQUnrrgKWZGzIQL7kdpKuMSxz7uvyyRQG895dURRPlGsb1sO01WOyZL/2bpS5vKGOVQaPJOBK9RRPhHU6PVYizp/YOKnnTMso+z2Wl1NtLmbDtDj+42HCln08FcjpT3/nfQpbp473Aa0YY8SgNDifWPRds+cx0fMpEivR5947F+31uIb7o+B2yKovxbUZQaRVGOnnLsGUVRyhVFyWj/uvrsDFMIIYaH3Wknszazc3YNICPzEFM0Rdhm3IdT0RHQ4p5ZaGoPoIaKl8P9xshpPPOn6c2qmVFOJ/qOJAqegdi03gQ4ndQPwR62jmyGzS0B3H1xPDQWQeF2mHEX3LsNEufBeUvgmr+goBJoD6S8Nb/P12+0NpJZm4nFlMT3Z8cyb1wiqktPTl3PMy+qqpKWm8ak4ElMCZnSeXxfQT13+R1AcdrgvO+f/qahSfzeXMvM4HmsP1LBIt2XuKLO7zkwmbzQ/a+zrcf9ax2CPYOpV+2g0TEldhPZ1nd56vM3Ouu9dVietRwVhUfMFWjGXwmeAb1csQ98QzHqw5jcup/bNRtxeQQwWVNEZOVc3rrqLbQaLYdLG5msFOLU+0BQYtf+nv6EqB44NS4qzCdn2awOK6Y2Ew6LFyGuegj+n34dNFpiNV5YtU7+b9UOnt94jLl+7QHYVX8ErQHzvld588ibXDzmYh648EoeuXw8lyeH8/rUejxcKnWmr594JN+YT5A+hlilGrWHbJYdlNAkJrfZMHs08MgYbwo0dn5z4W8I8+6a8CUpKIndt+7m5vE3uw+MvwpGxcGnS9F8cA9PjdrKvhO1XDj6QvZX7ufu5Xu5/z8H+zXmQmMJcQ4bphB30B0TNh6rRkNTdfeyA6fT8WFTss29lLKqpapf/QE8i99iSeST7MvpPVj8NG8nBZ7/4eroCPa01RAfEN95LiEwEYei4LL1/f+9EN8W/ZlhWw5c1cPxv6qqmtr+9engDEsIIUaGrPosLA5Ll/1rSvY6AAJmLqbFN46xLveytaEO2Lzb66hpm08uYdqYVUV2RfdxmJUWQpycXJKmKLT5jCHMacPU1v8kBf1VZi5DUT2YMjqKqdGBkPEuoMDUW8E3FG5Ng2tfgrGXAzBR8cCqNva5IPnO8p24VBeO5mQmhPvy+FUTcdmC+aq65zfy+6v2U2Aq4NYJt3bOirhcKvsL67lZs9Wd5TFiSo99O4WMR2tv5s2F0Xx+kwcJSgWa6Xf03DYwBmIudH9/uoDNKxijzcSW+OmsUPMwBO1kfeXfuGvDDztfI4vDwkf5HxHQMpHJznqUhEtPP84+MI2azMXaLLwUO5pbVgAQUbqRikb3G/jDJUZStMVoIlNA0/2tQ5DePdt8uOJkYNmR0t+zuX22pocMkR0SPN17+u681JvRgV7cGFYFGh0kzoVJN7K88BOabE082GyDwh08dPk43rhlLJ6bnyTC6cDR9vWWRNqcNoqbijG4RhOnVOMR1nvAhlcgV7RqWWhu5ve1jXx5w3q+E9NzOQVfg+/JBxot3PCqO3lNyV5uqf8HUU2HGO8/nVZHK0erD5BdXIm9D9lBO1Q0lxHraEMT4g6Gx7T/bplNuX2+BkBNi/tDm+Q29+tdbu7/PrYix2H+E+jDf0t/jdVh7bFNZqV7+fZNFhdjA8d2qX0Y6x8LgKKtwWwd2v3AQox0fQ7YVFXdDgxNKjEhhBgh0qvTATozRJY1tjLR+AVl3pMgMAYlbAITXO402k1tQxewuVwqTqWFHV6eeLYnFFFVlUffz+SlLd1rmJmVNkY5u/7JV/3HMNplwak6MdvNPd7H4XTx+pcnvvYbqFJzKaotiNSoQHA54fBK96xaRxKUDgExYPBjrup+07oyZ2Wfrr+tdBs+2lEstB/nsvVzSNTVgSOEaktZj+3TctMI9AjkqviTn0MerzETZT3O6LYTXbIg9irEnXyCkt2MPfR7MPjCxBt6bz/jLneb8Em9NulI7f+00sg4m4010/6GreYa6q11nbOUWXVZtDnbGGdsT4ISd8mZx3oGAQnuJaS2iTdC4lxskTP5nnYv7+53v8HOLK5joqYYZfR5PfYfF+gOGD478WXnsY6i2aFt7Qk1TjNrleDr3lM5PqaJzT+7jPM9it216vRe1E+7jXd8PbiyxUJy1qfwnxshbzNsXQatdUQ4XDg1DdQ3938ZX4cCUwFO1Ym21R9/pRVtb7OB7YK84nmmroG5wefj5z+m7zeKvRBuWwU/3YdLY2CuJgNbczwKGq71X8lLygscq+r5/+L/sjqsGO0NjHE48A5xBzvh7fvB2qxn3td6qgqz+0Onjhm23Lr+9QewY0SnqpTpanhgy4M9LqusMrl/J+4Ln8Pq69ZwTcI1neei/dx/C8x6KyVlUkBbiFMNxh62/1MU5av2JZOjemukKMq9iqKkK4qSXls7sgvMCiFEh2MNx4jyjSLIMwhVVXl99SdMVgrxne5e5uQzZhITVPfftI5Cw0PBbLWz0R9+EhFGtsEdsJU1WrC1WSio7r6HpEnjIFDVdzmmD44lpn2WzmjtOVNkZpmJP3yWy7v7+v8G7lTFTaXYbaOICfKGgm3QVNbzkkONBsInMsdRg904jeVHl1NgPP3siUt1sb9qP0GaKczTZ6NtrkT7/u0E60IxO6pxupxd2lc2V7K1dCsLxy3EQ3tyj9q+ggau1u5H1ehOLmE8ndAk978f3As12bDgdfDw7b19yi3w8wJ3MpNedBTPbnba+G2dkaTKTVwR7w7I9le6C4Jn1LozIl7eVo3DO/y0gVBfBU69BkbF4fmdpwAwTF1IsqaELTt28O6+EqxVue7MiZFTe+w/acxUzrdY2VPz385sqR0BW5yjva7WqLhe7z8mMIF4u4MvSraAqkLFoc4yCm/U7MWm1fPT2zbBI1nuPYDv3QYH3oCZ9xCs9aNN705WMlAdCUe8G9s/mDglC2ZPQuNTAPA9rw+/Jz3x8EWJu4jLdRkcLm7D0zaaMm8zszS5HCnpW/KiivZSHVF2B4GR7vF2LMt0qDW99utJTZO7/XibHY2qklNX1K/+Josdh8ZCjN3Bk3WN7K3aw9bSrd3aWU3H0KoqwRO/1+1csGcwnoqBUr2OhqLMft1fiG+6rxuwvQokAqlAJdBrDmVVVf+pquoMVVVnhIb2nM5YCCFGmqKmIuLaMyuuP1JJQskanIqewAvcS980YRMIUF2gDu2SSJOxnjqd+0/4myEOcuoKOFZl5kPD09xkeqvLsiqHy0GTViUQzy7X8AyJI8rlfjPdWy22mib30qZPj/Z/T0sHVVUpby5DtQURM8oLvvyzO619Ui/bnsMnEdqaT1vNd9EqHizbt+y0SzYLTYUY24zYm+OYoi11BwY1WSywH0VVHJ2BQ4cd5TtwqS6uH3t9l+P7Cuu5XP8VRJ9/2qCqk2+4O029hz/c+TEkd38T2s1pkpjAyTfct0+8ncnjvgcH3uT7Y6NRnR58fsJdDDyzJhNvJYL5yjG0iZcOODtkF5FT4aHMk/vMJrp/NveGfMVTHx4hWW0PmiNTe+4fnMDtpibsGiOrs927IzqWRI53mrB7hZw2mFUCo7miuYUDVek0VB4GqwnGTKeyuZJVx1Zx/djriQ9McNe1u2OdO2jzjYB5vyTSN5ImrYuj5QPP0prXmIdeo8e/of3/cFD8adt7JF7ifu2TvnvadqejjJtPAuVkfpVJglnhqIcBm9ZBdX7fShR0JPKJcjjwDI4BINQrFEUFm6YFe1vfSwU0mCvxcrnQeIYT7nBS3Ni/D2hK60y0ah34aXy4qtVdX62nrLmWtioCnS60PSw3VhSFKN/R/D977xkgyVXdbz+3qnPunp4cN+e8q7ySUEJCBElYSCSBTTDJBgx/sAkGAwYDBgP2S7LBAoGwMUE5rkCrVVhJu6vNYWZ2ZnZy6u7pHKvq/VA9szs70xM2h3q+zEzVvdW3errr3nPPOb/TbTKR7Z2daIqBwYXOSRlsmqYNaJqmaJqmAv8JXDJdHwMDA4PzBVVTORI7QpOniWg6zzce2slfmF9ALHkjOIv1pMoXIwFm1XxGQyITI0MMyzLlBQ1Z0/iHLZ+hpbuTZdIRVolWjoSOFo0eSg2jCfBJznHXEL4GAkXDrlQttoGiwbara4TuSOqExjqUHiKv5lDzZSwf+RN0bYXrvghm2+QdKpdhysW4sdqOFnoDr/S/wlNHnip5/e0DulBDaDBIjdKjqzte/49cldA1sg6Gxnvo9gzvwW/10+RpGjumaRptba0s1MhpqH8AACAASURBVDoQC26a2Y0JAfc8BB9+YaJy4gmyIriCf7v23/jbtX8L138R1AIbOn4K2Qb2hHajaRo7h3ZiTpQRZATRdNUped0JeGqg4QreKm3m5iVlrDYdQTM5Sis9li9mYzqDI+fg53t/iaIq7A/txyLZWcgwmq9p6tfz1nFTKoWKyjMHf6sfq1nLj3f/GIAPr/rw0baOAHzgT/DRrWDzUh9ciCoEnV2zE+w4luaRZhrcc6gp9KMhwNc4dYfld8CnW2dm2Jei+Dm7XHuNt2V60IRgq80KvTO7j1GDraqg6ZsHgFk245bsDJlkhrpaZjyUWHoIj6pSmHMtNYUCkWTHrG4l1N1K2CThdTdSwI/QJn+mZJQR/Kqqf74mocE3lyNmC6bwxLBuA4OLmZMy2IQQ1cf8eTswuyqnBgYGBucwg6lB0oU0c7xz+KeH97Eh8wJuLYG0/r1HG5XNQxUyNlUwlDr9BahHSUaHCcky9dj56nCIw7FD7Oj5DQCNYoDWwcRY2yNR3Tvmkz3jL+JrxFcMFywl7T8YzyJbBwCNJ07Qyza6sJRzHqpe+TpUrphagbFyOQCfXJEj1L8Gu+TlhZ4XSjbfMbgDvzVAbTKGhKr3X/9XNOQLAOzsG686t2doD8uDy8dJsB8eSrIio+crsuDGmd9c1fKSi88TQQjBDY03YJEtuqfwkg8i7/o1S6zVxJRO9of2M5IdoSlR9KCegvy1klz1CaSRI/xo6X7e0RBBVK8YXzz8WCqWInnruS1lYiDbzG0P3saTHU/SYL2MBmkQU3DqEEM8tSzM5WmU7DzdswXMDtotNh5ofYC7Ft1FlbNqfHvZPKaMWVWph2mmhl894VttibRQYW2kURog66guvZlwLJOIr8yKsnnkPI28W36aN+c6cElmHvL48CX3kMoVpu3ek+jBrApkERj3fykz+RgwySSjMxPsAUhkw7rna8H11BYKJCapqTcVyb5mQrJMpbeG7srrcavqWJjlsWRJ4lZNJT9H9e56eswmPInx39ldQ7v4wvNfQNVmLshiYHAhMRtZ/98ALwGLhBDdQoj3Ad8SQuwRQuwGXgd88jSN08DAwOCM0xHrAGAw7OYPO3r4VOAlfRHddIwqn8lK2lmPR1EZTp3+emajZGPDDJtk/NYyrkulqTPVclB5CQ2oFmE6+o4uuLqiuopkwBIYfxF/I/6ihy2SnXzsrSPNOOb+G41Nu3h0z4kV1B0Vy3iPvA8p1g23/EvphT+MqSgukTq5Yl45mVQlB8OlazPtGNhBo3M5S6WiFHzVcrB5CViDmFTBweGjyoWJXIK2aBsryvWQLFXV0DSNl9tDvE7aScFZrYtdnCtc/WmwuHlXvhmExg9fuxeA1ythNHfNtLlWJ8WCm6DuEsRz38Y0uLdk/hqgexsX3cJHY4fRCg5i2STfvvrbzCu8k2oRQpomxJDyRYiFt3BjeIBXlCj7qpfwN5s/gd1k5wMrPzBl16pa3bspci3TFm2ejGg2ymBqEKeop0n0o/mnGeupQgjMi1/PIqkbGcFb576Z5+xmftTUzre2/mja7t3xbsoLkLKNN2bLrAEGZZlMfObPo2QhhldVsc29kgpFkJTS5JTcjPtnQi0kJImqwBxsK96CT1Xo7R+vVJkrqGSkHC7JXvI69e56cgJsate4+oM/2/MzHjz84Njmj4HBxcZsVCLfrmlataZpZk3T6jRN+5mmae/WNG2FpmkrNU17s6ZpJzabGxgYGJyDdEQ7APjvZxPcVBGlPrZdL4p83M56vmwRZWqeaPbMiY7kYiGGZZkqXz0CWKcsYMScYJtNz5GK9hwNKeqN6oucMttx+cPOckzCiqwKhlKT5/90p3RDKet6mte6hukdmXlezNg1Et2gCd6ubIO518J0YXw2ry6DP7CPT920iGyygkPhFhLZiapzvYle+pJ9OLWFLBadaBYnFMPvTBWLqMprdMaP5uPsDe1FQ2NlcCWZvMLGb/2ZeZ97jK88uIur5b3IC288NTlhpwpHAC79a64tqpVu6X0aTbFxu3oYMWfj6R2rEHpYZrwXconS+WujLL4Vj5JmXcd1vKXs+9w852aUkU4kNJjOCDJZ4R3/w423/xJFCN5lChHJRPjxDT8mYAtM2bXKo+dvOeQB2oeTU7adjNYR3Zuj5apoFINTS/qfYsTC1+s/G6/g01d+mZ8FrmN5LsPv2386bUmLzng3tfkCOed4D2+5o4IBk0xuFh7/tJbGo6oIdxV+UxmagP2DMy9Gnk3oYcdl3kbKm5bjVVRix20CDcQyJGQVt8lX8jqjSpEpS5qBAV1UJZqNsqVnCwAtIzMP8zQwuJA4FSqRBgYGBhckHbEOZKzEEg6+6/+9Lsm+dmKdLbliMeVqhnhuojrj6SKR6icvBFXFnKKqPhNOBe736ouhwvDRkKKBWDeSphFwVo6/iBBknLW4FUFvbHKDLZTvAARJJYTZu43HTsDL1hXvQig+6pU+KCELP4HK5TCwj3WNfm6avwaVPHf850N0hcfn0Y3mr2Xjjaw2dyEqlo0Z1FL5IuYVsoSyvWPt9wztAWB5cDl/OjhIz0iauzbU87kVcVykELMJhzxTrLwLr6riLzjRUPBnA7jyYSgu9k8rc66GOdfov9dMY7A1Xgk2L+9xNvPMAd1YMEWP6OemUIg8liX1VzPXO5ego4L7brmP1RXTvCbgNDtxISPMIxzom30e6ahCZGHEQZmIIZWdRq/l8TRepX/WL/kgAJcsvIW7YvrGz2SiHaNomkZPvJu5hTTCWzfuXI27lqgsk0rMPKwxRRZHMVQx6NaN65297SXba5rGD59tpaNoIKu54qaQPYjLF8SrqiSV8d/V3oFeErKE11FaeG7UYOsymRg6rCtFbjqyiYKqh4i2Royi2gYXJ4bBZmBgYFCCjmgHai7Ixxs6cB3ZBNd8BlwVE9rZqhfh0xTS+TNnsMUzuvJhRXAROcyY4wO8KZ7mzw4rA7KMNdaBUgwPC6X6CCoKNndwwnUUTwN+VWWwhIctRScBeSFrKtbgqHiW5w9PzGPLKyr3vtBOZ2hyUZLOWBdy1oWMMnMJ+splMNwC+QwfvUoPQe1Lt/GJ/x2voLdjcAdOs4s97XYWiSN6OOQo5YuYm8+SZWAs92X38G6aPE14rV7+sKOHCreVr922gveUHdQLNc+9dmbjO5ME50P1ai7N6wIwN4ukrrK5+E1n5vVv/Q5c+XFdmXEqZDMsuInLlW0c6B2hK5zClSqGsE0XEllECMG9N9/Lg295kLm+mRtO1VYfIyaV9rbZi1W0RFpwm91IodCsxnpKMNt00Zplxfp9tWspG/3eTmGwRbNR0kqKxkIeS1EhcpRan/53OD0wWdcJaJpGSihYNT1vLxDU/8/DkdLlNFoHEzz95CP86Ok9KKqGhF7aJGALYHO4cCsaCW188ey+Ht0AC3rqJlxvlCpXFZKQ6DKbSHbp7R9rf4wmTxO1rlrDw2Zw0WIYbAYGBgYlOBRuo5AO8N7Yj6FsAVz64Unbmb01eBSVjHZiKoonQqKgL+aCzmpi5nJWSW3cEw+jAnfU1XD/nBe446E7UVSFSHaYckXB6p6oaGcqm0O5kiOcnpjvks7nwdLDNbFDfKjmelR5hF2Rp8dJ7Mcyef7q3lf55y0/51+f3TzhGqAbbL58sQbcTA22qpWgKdD8OHO9czEJE8uakuzpjo7LU9oxsIMyeSHWRB8ONTkmWAJA+SLqC3k0odAx0oOmaewZ2sOK4ArCyRzPHhrkLatrkHf+Cl76D1h4M9g8kwzmHGDFnVyR0EUcrou26qG5JsuZee3gArjxK1PnHY6y+Fbs+RHWiWbuf6WTGm2AgmQbUzGcCX6bH4fZMashVrtq6TeZSLRvm1U/0MPsfKYGtFDRo3Q68wKnw1WBVdZDQLvjpQ2u7oRuCNcWCrgrm8adq/DoenDx3Mxy2BL5BKoAm9BVZMur9FzFRLS0N2vbvj3k5/4YqfOfaRuMIcm6V3C0jqBdNZEU+XF9wiE9p62mvHRRcrNkpsZZQ4vJibVrC4OpQV7tf5Vb5tzCAv8Cw8NmcNFiGGwGBgYGk5BVsgxn+lmpxHAlO3WhjFILZHcVHlVFQSFTyEze5hSTVHRvXtAeJG2rZJ1opr6g8HdzbucqxcbqpExbrJktPVsYyY9QXlCwu8smXMdROZdyNU8iN7EO2+7+wyDlWZ0KcfmuP+IzV5E2HaKnmMc2kspx549e4qWODmzVD/BC7LtklfF5Zql8imguQlW+aOTN1GBb+Ho9fPKhj2OJ9tDkbSIv95BTVNqKYVjD6WHaom109VXxtvpivs6x9Z2Ci2gsKkW+3NVMX7KPUCbEivIVPLqnj4Kq8T75UXjoYzD3dXDHT2c2trPB8jt4QzLFV2N5Lk1nYN17z/aIJmf+DSBbuNu9m9+80kmjGCDjqj/teYFV/vn0m2R8kT3EMvnpOxRRFJW9Q4c43OtmY1lRWfVMiY6UwObXNx3aI6UNtrao7v1qyufxVjSNO+e16WHRmfzMwkNH5fdtJl11s7piPkLTiGaGSvZpOfwgRyxmzPaD/P5PLxIr2vJltqLBplnICJW8cvR/kYjpBnFlxaIpx1PvrueI3cOi5HYeaX0YDU032HwLOBI7MisxFAODCwXDYDMwMDCYhM5YJ6DxOikCFcv0hWgpXJV4VN3rE8+dGeGRpKYvLsvsZRRc1ZiFLs//3vWf4CvetfzT0AguuYz7D9xPTI1ToSg4PBPFG+RAE35FIa1OXNztHDgAwKJcDtH2Z5qsASRzmN3durH44M5eDg3E+dtiKlVO6udHO8er2/Ul9Zy3pkIG1erViw3PBJMV7rwXBPB/72Ghdx7hvJ4PNZqn9FibXqA5EV7E2xuL4y8qTALgqqBW6Ip0uwcOs3t4NwArgyv5445urilPUrX1a7DkzfD2/wHL+Dp15xSeGqwNV3JbqA9pwY3gn6ZO2NnC6oZ513EjLxFNZakXg6jT1TQ7BVR76hmRZZZJzWzrmLwIPMC3njjIL1/qGPv7v7buIK+l2FC9lL+Yk9M9gVMU+D4T2KvXYVVVeiOlRT9aI63ImkRDvoDkrx93zmPRvcRpZWbPosGE/n45iiqyDk85PlUlWSLEW9M0Qkk9d7TfolFz4OeEZQmHZMVm0sMqbRTFj47J681l9FzSMk8DU1HvrmfIpGIRGf5n769YWraUOd45zPfNp6AVxtR7DQwuJgyDzcDAwGASnm3bD8Bl2R6oWz91Y7sfh6Z7EGK5M1M8OyXSmDR9cSZ7dZW4pCUIjgDWigXUizD10tW81PcSSTKUKwom5yRqez5d2j9PdsLO9YHQAYQGcxUJrF4WpAeRLBF2dek78n8+NMiCMguFth9i1jSWxVz897572R/aP3aNgaTuJViixhDB+bPztPib4PafQN8uFjb/ieHMADZzkoP9+kL0wdaH0TJ1vHHJGiqSLbpnxOo+2l8IKsvmY1bhqb6f85UXv4JFsmDVatnROcLH/S+CkODmKbyn5xIr79R/bnj/2R3HdCy7A09ugDWilQYxiGWKELhTRaVDD7mstBzhlcMT638BvNYZ4YfPHubLD+1jZ9cI0XSeH23dBMBnrrseKdJ+1r1rAK7q+ZQpKuEpJOxbR1qpKFgpCBvYxqsueqy6wTbTEO2eId1b57IXw1btfvyKSkKZ/FnWEUoRM+vf63azjbdLzxCSZcqOUfO0Cz2kNZo9arAVFD2M22+buth4vbuehJbh+34/fflhPrb6YwDM8+mfIyMs0uBixDDYDAwMpqQz1sm9e+/lg099kO9s+87ZHs4ZY3P7PgDmp0egdt3UjYXAIvRd+WMXKKeTpJTDp5oQQuCtagKgECx6l4oqdxWhOsySnjtWUVDAOkl+lr8RX9E7OBoaNUp7vIXKnAmzpx42vI/aoVaEnGJHdz+ZvMLLh4f4N8uP2JnuZUle5eOhFC6Tb9znZCClL+xWaSHETMMhj2XRLfC2+1ik6tPV33t+xMG+GC2RFppHDpIdWcOH5w7Boceh8YoJ3U3li3n/SAZTbgk3Nt3IFy77Ak/uHcZEgVXDj+q1xry1sx/X2WDNu+Geh/Qxn8ssugVNtvIRxyacIou1/PTnhI0W1o6YVAZbtk/a5nubWvAG91MW7OLT/7eLf33yEKp1G0HMLH/wk9D96pkVHCmBu2IOZYpCLFs6JLF1pJWaHIyYKydsgngtemhjhpmFZw+F9bIXHnexPIDZjk/RSJYw+LY3H6HDpnv0B0wCRVIZlk2UOY8KMtlk/Vlz7DNFIY7QwGv1TjmeUaXIX/jcXJaCjXUb+dnz7fz1z7uQhTxWhsHA4GLCMNgMDAxKomkab3/07Xxn+3fYMbiDx9ofO9tDOmMcCrXhVO04NW16gw2wmfVd7oHk6S+enSuoxGUFr9C9Qt4KPeTM21gsbFwUTTCFBri56Wb9nCJN7kWyeXFpevjScGp8KFl/uo35OQU50ASXfYRaVV8Y7h/s4IXWYTaqr7Io8gz77E5WehewVuuhzrJmTCYdoD+lq0ouzQ/OPH/teJa+mYXvekS/J7mVzr5BHm57GIFMQ6aBxZs/DL56uOlrE/sGF/GR6CCZ1pt5/+LPcvuC23libz/vq2hGTg3C2vec2JjOBpIMc685t+rETYbNg1hwI9cpLwIgzoCIR7VTF9oYMMl4h3eQyBbGnd9+JMLm5gGsFf+Hs+p+Wgej3PdyCyZXCzfGIkgWFyy9bUxe/2wi+eopUxQShck3fxK5BH3JPuZlsqTtVRPOm2UzVk2QZWa5XuFYDwBlgaaxY25VIikm1j0EaDvwOMMmmfWeeWhAh8nCoMlCmf2oCq2zmA83ktENtlQmS17O4MKMJKZeeta5dRVJMzL/FOph09ZtfP2xA3QMZ3FJ1YZSpMFFiWGwGRgYlCSUCRHLxfj0+k/zjsXvYCQzMk4h8EImQz+1qgnMjunlzAGHVc/N6omVzp85VYwks0Rl8ElFJb3ROkyVy/SfxQVyZb6H66vvoj5voyFfOuTPZtHH3jFyVOQgnAmTUsOsyiUR/gZwlVNbuRKADMP87Pl2NpoOsN/pIacprK1ai1XkMcU1ItkIqby+Oz+QHEAuODADlJ14aFy5sxKfyUmLxUR9YgcPtz6CObuIe80/QSh5ePv/6kWmJ3TU/3fzRQ9P7uunK5xiT0+Ud5ieBXf1ue+tOl9ZdjsSRTXPGdZgOxkqHZXIQqbd7mONaGb7kfEbJ9/b1Mzlnp2ktTShQoQ3rDzEvLLt5IXKjbUb4Z4H4a3/CbVrT/tYp8VZjl+BFJMXqT8cPQzAynwcxT25d9ihyaSlmYmvRFO6J6+qfMHYMbdmJiEKk7YfHnkegLcsvguA9gXXELVYxgRHAFzF3wfi+rUHe9oZkQUeefo80QZPAx6Lh3c33ElNQaH/0W/wK9u/stX9GeIjPg6FZ26wtY20sXNw5/QNDQzOcQyDzcDAoCS9CT1JvMnThM/mI6fmSBcmX0RcSGiahiZHaMpnoHo1yKZp+7hGd/jjp9/DNhIdIWSS8JmKIY41a+C2H8GyO/S/bV4UexmNop+BkJ8vDAaxC3fJ67mdeihUe/ho7s+h8CEAVufiUBSNqKnfCIDH3MuLh0Nca21mV4VuHK5q0kVZXFE9v6wnoe/ad8Z6sRWKxmLgxA02IQSLg8v4g9vF/nl/YDgzRMOwh/pcG7zpe1C+cPKOxeMb/WGe2NvPE3v7qSZEQ+RFWPOuGf1vDU6AhTeDyQYI8E0tMnEqMMtm5njn0Orxs05q4eW2ozXM9vZE2dfSxpsdvwbAq6pEs79hbeVmAorK2hu+cdrHNyskCSc2klIeRVUmnB7N4VqbH0H2T17TzImZtKTOaIMtkQ3jUlX8lUf/T06sJGV1rGD1KN2RFBnTEWwa3DBPrwPYsfA6RrT8mKQ/gNuuh0cORnXRoVhvMxFZwmuZOn8NwG6y8+xdz/KJa/6BPsp5l7yJDebDVOW7WZhN0ZfsGdsQmo5vb/s2n3nuMzNqa2BwLmMYbAYGBiUZXXTXumrxW/WJ9vg8pwuRTF4FOUVVbmTGO+4+n75wGkqWLnZ7qohG+olIEmXWokdJCFj9Dr0IbxGpbB4LTYNsOxLGWoiTM5VWvqv060ZX2zGqdKMG26JcfkyRMDDvBuyqSq2tHS8J6vLt7HS4qXXVUlF7KQVhoS6u3/+osd8T78dfKIbwnYSHDeAfLvs8H1F9XJIqYMtu4J5MBwV7GSyZooC0twFMdq72hdjeGeE3r3byzsABhKbCyrtOajwGU2B1weJbdW/vMZ/L08niwGIOSQq1Yphd+/aPGSv3b+3g+9YfccBcwGt28eHGW9kt5dmkRHidZz6y59zLYXTJHjQBg8mJHvvWkVYswkJtQcERbJq0v1PYSEkq6dz0XrZkIYZH0XC7j4qXuCXdE3b883572yADthSLZR8ui4tKRyWvDb4GMN7D5qrApGmEirXkMoNthCWZshnW4zNLZoQk0Xvtd9iz/huYPn0IqlfzNqkDgFd7D8zoOofCh+hL9pHMJ2fU/kzwuwf/yHOvvHq2h2FwnmEYbAYGBiUZNdhqXDX4rPpkHsmefg/S2SaUTCCkAn4lP6P8NQBPoA6HqhJPlK6ddKoYCnegCUHQUV6yjSibxwK5j+0dYexqgry5dEHo6uol1OfzHBreMXZs19Au7HknflUd87CJmtXUKBoe2xCXSAcBjV2FKKvKV4FsIuFdyDpFz1kbLewbygxSVSigOCvHKzieAHO9c/nQ/Nv5j1AXrvYreIP0GvKKt4JsLt1JkqBmNUsKB9A0aBtKcqOrXZdvP9GcOoOZ8cbvwXsfOWMvtziwmMFCkrAk4Q29xtP7B0hkC1Ts+iEbxS52BmpZXbmO2zd+GbcwUxCCG9Z99IyNbzZ4rfp3u3m4d8K51pFWgqIMCfBXTy6S4pTtxCWJWHT6DbaUmsKpCsQxuZFuWf+u9ieGx7Xta3mJZquJteV6rbgmbxM7h/SQw4D9GJVITxCfojKS1vtrkQ4iskTVcSUIpmPdtW9hxRs/ohv9G97PhqT+XHmyefowx3AmzFBaD8lsj7bP6nVPF5qmccWOT2PZ8q2zPRSD8wzDYDMwMOAPO7p5dZLaRT2JHvxWPzbZjq8oHT2aRH4h05fQjVKPqs7YYLMHavCoKsnM6fewDcd0VbdKd3XpRg2X4VXC2COHcGpJFEtpg80cnMuqbI6+XIseDqpp7BjcQUW6mG8ymoMkydRYvGTlGO+s7KLP6mAwN8LqitX66ZqVXKp1YRJWehO9pPIpsmqSuWoaKXhqjCMx91oAvmL6b6zkESvvnr5T01XYhnazrExfkM5N74H6S8998Y7zHZsHPDVn7OUWBfSCzIfsLq53HOb7z7Tw0qYH+Bvxv7TPuZX2XJjVFatxmB28e+UHqHRUcmnD687Y+GZDoJibdnh4orR/60grwbxeX9ASmDzc1GVyEpUlkrHpN9hSZLGp4zc9PEXvfWd0fImEoaFnKQjB6qbrAT1cfjRM/lgPm90dwKcqRIsbfCLeTVSWCTqCnDDL30qtyYmsMaNabKNRAgCHRw6f+OueQkYSGSoJYc9f+BufJ4KqauQK6tkexjmJYbAZGFzkhJM5Pvv73fz0ubYJ53riPcTibn60+fBF5WEbKBpsDskx4/wb4a7Co6ik86ffoI2k9LyQ2hKLNQAW3oKG4CZpGx5SKFNJafsbWZXJkpNS7B86Qle8i3AmzIK0RN7kBPvRvJNaTwN9ksq16lZerdYXyGsq1gDgalxDmUjgEn56Ej1jkv6L1diJSfpPRvUq0rKbjfJeEs7GmYWsNl2F0BQ+tTjMO5eYMMe7oeHyUzMeg3OGRf6iwVY5n7cWHuXLw59kwba/o0eupfPqewB0bzDwoVUf4vG3Po55Ku/sWaS6TPec9Q6NF9gYyYwwnB6mIlPcbChRksJj8RKTJFLTiCBpqkpILuDCPu643657+PpGjnr4NE0jkdPHs6yYzzrHe9TDd2wOm9NXgUdVSeT1nNZ8Vn9mTVeDbUosDsyr30VjPs9wcnpp/+ZIMwCSkGiLTpzfzga9vZ086nbQIV348+iJ8LUnX+S6/+/nF4242WwwDDYDg4ucB3f2kFc0BmMTa/Z0xnrIZHw8urtvLIftTNUZO5sMp3Sjy+qeM3MvjKsSj6qSVeOncWQ6o/WZaqaqGeWuRKvbwM2mbXhIImxTGWxNLC/usD944MWxnJS12TQZZ92496C2YiVxWSIW72aL00m5vXxsoSxV6yqSzqyJ3kQv/Uk9PLIpHz/p/LUxJJl4tW5sSavvntn/p+4SkC1cZz3EP69L6McaLjs14zE4Z/Db/FQ4KjhUvwblui+x1Zfl9kYPv1/zEXZFmpGFzPKgHsonhBirUXgu0lCl11QMH+dJGq1BVpvKkpC9YLYf3xUAr81HWpJIxEvXcgN4dfeDRGRBuXnZuOP+Yq7Z0MhRD193JI1iCmHXBOUOXVSkydM0dn5cDptPD4lMqLo4iKbOrGj2tKx5J/PyeVKFjmmbNkeaKbeXM8cz55wx2Ab72vhqmZ/HXTOrkXexsanv14y4f0LzwOmfR883DIPNwOAi57fb9Am5/ziDTdVUBlJ9qDk/+/tiZHIWJCERyVz4O4PDKf0eXa4pQg6Px1WBW1XJaadfRTNWDKcJ+qaubyUteSNLRQey0JAcvtINZTML578Bu6rxSs+rvDb4GnbZxfp8BO04D2NNtR4i2mUy8WJ2iKtqrzqa+1IsK+BN5elJ9NAS1j9bVQUFypecyK1OSsW6t4BkxrFuBuGQABYH1K6HjuehcyuYnVC18pSNx+DcYXFgMQdj7UgbP8kDtQ0UhOC38V+xqXMTiwOLsZsmN3DONQIVi7CqKvGiN32UUYNtXjJBwjaxBtsofoduPI3E+6d8nQde/gUAf3HNh8YdL3PXIWka4dTR/nu7w8QtHWIIxQAAIABJREFUaWol19h3vtGj57daZStO81HJfosrgE9VSWoZFEUFEdPvyzpJ6Y3ZULaApnyemJQgr0wtqHIofIgmz3wqbPUnnMP2+J4+PvfHPTNqm1fyvOfx9/Bqf2lBkUMDO8hIEnH54g37e2JvP7d8fwutg4kJ50Zygwg5wzPN50bO4bmEYbAZGFzE7O2JcnCoG3/dJkLKXgrK0UlkKDVEQcuj5fUd0ZcOh/FavBeFSmSkmKfnm81usGzGoZnIipkVqz0ZEmoMp6pit09hhAEsunXsV9kx9b1Ylt/B8myWUPI1dg7upNq6iAYxhOmYYroAtR5dNOAxt5u4kmZj3cajJ61ukq5G5uSTxHIxdvbpOSQVSgEqTp3Bxqp3wCf2jNWbmxFzNkLfTmjdBHXrDTn/C5RF/kW0R9vZNrCNwUwnf73yr7HKVtqj7WOhu+cDwltHmaKSLIwPaWyONOMyu1msRCi4SucHBlx6SGMsNViyzbaOMF1qK3WKzJr568edc/qC+FSVaPqo6Ehf2z66zRJzj6n9Vu2sxiJZCNgC40RLkE04FIkUOQb7u0mY9BC3k/awmSxUqw40AW0jHSWb5ZU8h6OHOdztZst+ic5YFzll9s/m7U/dx4LtXx03N5ZiKD3EjsEdvNT7csk2h5O6umVU0tDU89xoS0fo+d37ibz6W/oi8RmHMW5/8XHeGvo27/7JcxzqP+pJCydzaEL/vD/XfqhU94sWw2AzMLhI0TSNL235Fs5536Lg3oTV/zyh5NEJbVQh0iaCBJwWtjQP47V6LwoPWyyth++UuUqrME6GXdjISIUZTe4nSjJbIKYmCUwszzSR4HxSXj13zOObJtl/7jUszUPMHOJw9DDRIT9OkcVWMd4oqnXqi7U/+oOYhInLqseHFlrqVrNK0d+/veGd2AsyZrPraHHvU4EkgWcW3k+ApqtAUyHcZoRDXsAsDixG0RS+u+272GQb7132Xv79un/Hb/WP31w417E48KmQOS7Eeu/wXuqdC6kVwwhfacXFCq/+/UhkJs9hU1WNnzzwKAdtGlcGJm6muPyVBBRlzJsPkOreQY/JRFNw8dgxWZJp8DSMC4ccxa6ZKQiNnu5DhCV9uXnSBhtQbdafyzv79Ry153ue55uvfHOcwdAea6egFugZDGBWq9FQ+a+tpQ2pyTgyHOcd0Z9xj/wUA9Hp6751j+jhpzvaSpccOKLoUQdhWSaZjM1qPOcaT71wL7cktjL41EdQvreaBx57dNo+qqoxlLyX/5pzmA9xL3f/9CW6wvp72zwQx2bW5479A22ndR49HzEMNgODi5T9w4dozj5EpbyCxXmVOlMH/dGjYZGjBluDp46r5gd5rmUYn9V/UXjYktkQQtMIuGdWM2gUh+wiJ8FAfGZFXWfLUDzL3T/dSlxkKGNmHiLHijcDUF05zb3IZhYFVqMWN8kdEb12ljSqEFnEa/XiNDuJKxnWVK7BbRkv1W+uX8/KolegP9NKUNEQFUvPviJj3SUgW/XfDYPtgmVxQDcm9ob2cmPjjbgsLlaUr2DzXZu5ouaKszy62eHBSkrKjBkiWSVLS6SFoFSPR6Sxl5UWHfIXFWRTucmf161DCVyJR8lIElcufuuE81Z3gICiklCOGozZ2G4UIWiqHO+p/Ojqj/L+le+feA1Nf4b0Dx4kIssIxJh41clQ524CYO+QHh567757+dWBX7G5e/NYm1GFyL9SdvLLipcA+LfNz9MZmvmzeddzDzJX6kcWGkP9PdO27+jVXzMTn9xg0zSNTln/f8Rlicjw9Nc8l2kb6UITgt/XvYkyESN4+A/T9tnfF+WgK0xSkgjatnBJ5gUe29NXPDdEphgq6hGt7Om58PPlZ4NhsBkYXKTsHdB3+j5s8bEgkyZryjIQm2iw3Wob5sY6heFEFhOui0IlMp0L41ZVTFPlfU2Cy6ILe3REhqdpOXtSuQJ3/vhFWgZH6LcVWGRyzKzj6nfq+VsVS6dtetnqdwAgNMHnVxUXg8flsAkhqCmGYl1de/XEi9Suo7agu/80FBqULOJUhkOeKGYb1G0AIek/DS5I6tx1OIrfjdvm3zZ2XJztDYMTwCs5ScoFwsXIh0PhQxS0AoGMvknirmwq2ddTlM9PFSZf9A7EMthd+5E12DD35gnnhT1AQFHGREMG4xnMogOABv94AaEbGm/g+obrJ1zDLvR8wXDkCHusFmocVZikkw9F9gXnU1ko0DbSSjKfZPvAdgB+8NoPUDV9wb93+CBCk/iM8hSL2jcjAGEZ5LmWqUVYjqX8wH1jv8eGOqdtPxjS867i6sTcLIC2kXaSssLKTBaA3nDHjMdyLpLM6Z+tbGMdw3Il1vT0NUif2L+VHov++zNlNXzH8p80t+iGbnPPwbF2fksXL7Se+nn0fMYw2AwMLlIGEnrowarOhwkqCjFZoz96VDCjfaQLLe/irzs/xw29PwYgmbYSzVz4u16ZQkyvwWYtXbtsMtwOPaG+6wQMtkS2MOX5R3b10RFK8ZXro6QFrC/Kk09LcD584BmYQXhn+eI3Up9XWJbLcuX2z+kH/Y0T2o2GRU4aYla9Co8GZkWfXmoLmRkZi2eEKz8O1/z9SRfwNjh3kYTEkrIl1LpqWV+1fvoO5zA+i5+YDF1h3cu1d3gvAP6Ybnya/aU9bJ5iGY+MMrnxEBvqps2RYamlHJfFNbGB2Y5XgST6Jt6+3hjCqs8ZxypDToW9WHx7MNXBVruNm+fcMqN+0+GsmsfcfJ6BxGG29m2loBa4c+GdtERaeKL9CQC2duxgfi5HLrAU+9r3UJMv0ORs5sXDM3s2v+/h93Kf/zD7fHrZkGRoem9YJKa3iYnspOc3d74CwLUpXSxlINo1o7Gcq6SKJRu60r0kLUGc2emN4dc6f4fQNG4IrOB5i0AWKWq7HkFRNVJD+vsjNA2bLcwLrae/pun5hGGwGRhcpAwVk8krMhHK/PPIS4LuyNEE9faRLhx5O7JWwH7kzyyucDIcNRHJRi74GilZJY5XVWEqKfxJCBSlsJ/evZuD/TPPT/jlSx2s/PKTU/b59SudzK9wke77DQBrL/u7WY1tRshmvrnqY3y56nq46pNwx39NatxsqNrA6vLVzPVOIvphdSHKl1Ch6IvKqoJyagVHToaFN8G1nz3bozA4zXz1yq/y4xt+jCTO7yVO0FmJKgSH+/Siz/tC+yizleGKFsMcp8gLHQ1VzpRQrY0O7mO/xcLl5asnv4AQODUTaUnRBTw6jhAx5/BKVrxT1XQ8BqdJb/eC3IkiBG+Yd+s0PWaGuWwOc3IFwoV+tnRvwWV28feX/D0L/Av4wWs/4IvPf4nu1F4W53LY7/453PIt5kp2nKY29rd2oKpTz185Jce20A62OO18qkrQZjaRP6YeXSnixbqTMUlFUScmGb/cuw2vorJA0ksiDCf6JrQ5n8gUkgB0xDvIOSrxKaEp1waqqnFE3cO6TJa7lr2HtJLl6bJ5XKHt4EBfDCmte9gWCRtxU4rtnWEy+Zkka18cnN9PMwMDgxMmkhxA1jTkxpsI1l4CQLRYaBSgL9lLZaH48E0Nc0twgJGEhbyaJ1U4PTla5wpZLaV72Gyz87BVBXQRgLaeVm7+3ha++cTBaXrAnw4O8OWH9qFq8HLb5AIBe3ui7Ooa4YMrZLaPHKRedlBRtnBWY5spKzZ8hEVv/iFc/4+w8s5J29yz7B7ue8N9JcPMRN065hb0nflKRTl3PGwGFwX17nqavE1nexgnTV2wCYAj3bsB2De8j+XB5ViT/SjI4C4t62+WzNhVyDJ5va/BWCuaECwrXzbpeQBnMQctko0Q79jJEbOZRufMxX6cRQn/VkuahrzEQv8pemb5GpiTz5MXeZ7oeILLay7HIlv4xNpP0JPo4fHWh9mQSXNF1V/o4dgmK3PnXE+nSWZN9mX29029mdYRbUcVGjckHGSEykcry2Ga8ggAqaJAiyoglJ74LD8Q2cW6TAafbwEAkfTMwzPPRTKKvg4YSg+ScJVRToSRZGklzqdadxK1pLg2b2Fd03V4rV62BCtYJ5rZ9FoLDqF7KNf4FzMgCyqUXp49VFrl9GLDMNgMDC5SkqkeyhQFdcXbCHr1sLdUQt/JLagFovkh5itpNFclIFib20YypQefX+hKkVnSeJXZh0T6vLrB9rGNHi6fW8bDuybflX348MPsGNjB/iN9PHb/f/B9z6/4V9c3aTkyeZ7E/a90YjVJvCn5O3ZYrayrvWp2N3SmqV1HQ15fKJbLzhmFYxoYGIynOqDnig0Ot5DMJ2mLtrE4sBRPrp+ktRwkecr+Tk0iU6LMSCyje3cqfHNK9xd6LmA4HUYM7OGI2URTYOZGl8deMfb7FcpJ1l8bd+EaGvN6rloyn2Rj7UYUVaPGsoZb1c+wub2Lf1QW8MY7vjHWZV7t5eQkwQrbq9OGRW47sAmASwI3cteiu+g2m5FmkJ+VOUag5XBofLjjcHqYSL6ftZksdfW6VzOam3yD7nwhox0N/ex32rEIhcHB0l7D3x18DKFpXBdYgVkyc23dtbxQCIFQ6NnxOBZzBAcSC2supSAEV3n28+tXOrj7kbv5+stfn9RreTFhGGwGBmeIV9rDDMYm3+08G8TzEQKKis1dRrA4Cecz+iQzkBpAQ2WNMoyY+zqoWcPC+FYKBX0Cv9CVIrMid0IeNq+vCYDm2EGuXlhOdyTNSGr8gilTyPDlF7/M/9v8GZL33cG/Sj8gY32Rf6qPEOv744RrJrIFHnythzuXuek5+FuissS6+nNcnrx2HbUFPSev2j+LWmkGBgZjVFfr+VPd+V3sD+1HQ6PKuoBqwmQd03u6nJqJtJg8Nzad042WoH9+6f6y/vxrHu6jstDCgMlEY2DRjMdvdwVxFGuNXWFbMON+0yLJBMVRAzAamsfyLz3Jrd/dxNvbv4tstlNzz8/18h9F1lcXhYZcHWxtmdpbtrP1z8iaxhVXvnusDIFWmN7TkyWNKIYEtveOj67oK4Y/NuXz+GtX4FJUovnzOx88Sw5bUXm/36FHW4z0d0zaNlfIsTP0JOsyWernXAnoYjVxJc2LDh9vyT9OzKRQbvZSW70OgLWBDl7oeo19oX385uBv+NTmT5FVJs8PvBgwDDaDi4JsQeFvfvMaR0LJszaG9z3+Ef7hqV+ctdc/nkQhRkBRsDi8BMv1kDVF1SelnrgemrAgH4P6DbDgRiqie3EWhSQuZINN0zSyUh63qsFkyfhTUFaxnDsyKveHthMq6MbX/t7x4Tc7BneQU3MMpgfY5exmx/rP871qPRdlROwmnsmPa//Qzl6SOYX3l+9nu1mfFNdVrjvR2zszlC/h1qzKZ0MRGstXnu3RGBicl9QEF3NbCna5e/n3HT8EwCXmUCOGEd7aaXqDU5hJS+qk9axSqv4MD7hKh1W6zLpK7rbuLiosugJig6e00MnxmN260uSqTJY6/yk02ACnoxa7AvM8i/iPTYPc6TvI9sAXWSW1YbvjPybUaax319NkC/KKXUbteIlcoXSNr8FUG9UFQWPDInw2/T0QRKbso6oaGSlPbbFNX/jwuPOxnD4PeFQV2VuDX9GIq2dvPXIqyJKntmDCYXIwYNINqUSoe9K2n3jsx2SlCO+PxqBWn7/WV+qiQK8E5nKVvI9BWabKXU2dW49UkdU2LK5mBIKPrf4Yz3Q+wwee+gDD6YtTPdIw2AwuCjpDKR7e1ctzzWcnZlxVNTTHATpTe87K609GUksSKApreFxVmDQNRJR0TuHwSAcADYWCLoE+/wYEKutV3QN3oYREPtv1LM91PzfuWDKfRBP67vSsa4eZrHzx1l9wQyrL//b8AmfFA/yh+eGxEgkAW3u3YkLmslSGn/gD/MrVTyqfolYRDDtC42rPaJrGr18+wuIqN42Dm9juDlDhqKDOdQqLUJ8OZBOBylW8KxZHVBr5awYGJ8oHHetYkinw2tA2apw1jMRMVIkwlrKJ6q3H45JspGSNeGaily0jEnhUsMiWkv09xWLYB7tbUCy6gTdThUgAu6eMrw2F+cpwCFfF9OOdFb4m3h9J4UrfypvSD/CV+Jdw2a3wnodh6Zsn7XJVw3Vss9m4jFfY2TX5puOR3kGGTGmqTXpZhEAxD0+WE+PK3hxPOJklLanUanoUynBsfEhkPKeHS7pVDdzVeFRBvER+4flCRijYNBPzfPPoVvTwzlxkYhrAH3e2sXnoN8wt+Lg8k4NqXeHYZXHR5GmizauL0wyYTFT65lLlqkJC0JsNEyw7BJkm/nLZB/j2Nd/mQOgAdz9yN/tC+87cjZ4jGAabwUVBKqfHPg/Fz447PZbJIIRKshCfvvEZIkmagKKA1YMQgoAqoZqS9Mcy7B08jKwKgpoVKpZB7ToUq4/XoYuSXCgetu/v+D7fePkb45StIsWyBQ5hPaFrmurW881rv8N1yTRyYCtPDn2X2x+8nYGkngPxUt9LLM3keXfESl4SPH3kad619F28yVZHt7XAy8fkse3ujrKvN8Z71/rh8J/ZbrexrnLd+VFTqlYP5zIERwwMTpzA/Cv5weAADuFibeVaBvu6sYoCzvLpPV1OyUFCglhqvGGQySukpSx+deocOLe9EpOm0ZN5lieduiHS4J65h83hLWddNsvcfAF/VelcuRPBGmzig/Fhdu928jeWR9CaNsKHX4Q5k9SGLHJlw+vISoKgczd/Ojh5iOOOFx6g2yyztBiOOhYSKafoDU9eIgFgcGCAmCzw2SrwKAoj2fHXD6X1OdOpCbAHcKsmYsX8wlA6xGef+yzR7PkVIpkWKlZhYZ5vHodHNyVj43PYktkCn//TD5FMCf4pm0QqXwIW59j5ZcFlNKtRVGBIlqlwVmGWzFTZg+y12YnRhTNezmN7+ri56WZ+ccsvEELwl0/85Xn3fp0sMzbYhBA/F0IMCiH2HnMsIIR4WgjRUvzpPz3DNDA4OVLZAu+SnyYWPTt1PUJp3VBLl6iJc6ZJ5VPkhVI02HT554BkJVcsnr1vqI3KgoZatRpkE0gyauNVXKG1IpAuCA+bpmn0JHroTnTTEesYOz6Q0O/NWSz6eiJYlryJ71dcw9NdCXyRj5NVsvzPof8hlA5xMHyQa1IxbI13cs/Se2jyNPGhVR/i6qoNaEKwo+vpsevc/3IndrPMm517OCxpDKqZsTCSc55ld0DjlVC14myPxMDgvMU57zKqFIX3pt/A5y/5ArsP6J4F2Te94eQ2u4hJEonY+Of1cCJLypTHN82mlNldxt9ERlBMMZ5yOamwV+AwO2Y8dpc3OPa7rax+xv1mgrtaz717v+lRAloEceXHwTT1/ayvXI9VyBx2pNm9a9ukEvSDHY+jCsGK+dcBRw22qCwID5UW1AgPtBGVJLzuWoKKSiw/flOzJ6Z7oCzmMpAkXJqFmKRvJG/u3sxj7Y/xYu+LM7z7c4O0BDZhZb5vPqFMiE7Ziyk1XpylY2gQl+9JrkumWJ2IwjWfGXd+WdkyBjPDHFz5VhQBFQ5dqKbW28RLdiuaEPwg9yQPP/JHhhNZlpYt5UuXf4l0IU1LpOWM3eu5wGw8bPcCNx937O+BZzRNWwA8U/zbwODcI9zK18z/TdPAprPz8indUJO0c8PQiWT1cXgVAWZdurnM5CIpFxiIpumOH2FBPo19zqVjfUyBRmpEBItwXRAetlAmRLqg1yg6NiyyP6FPrA555guTSWm8nEolDAMy19S9jt8e+i2buzcDcHk6Q82yK/m79X/HA295AKfZydI51+NVFAaTLwEQy+R5aFcvb15Vg6PlUZ4MVCAJiesarju5cZ0p6tbBXz4GlpN8Hw0MLmbKl5AVNiqHD7C7O40pXgw5m0EOm8fqJStJjIyMX0QPxrPEZY2AaeocXau7jL+Kxnm4c5ivp9z8y9X/Mquhm5y6sVNABmfFNK1nh6VM99h90PQYmn8OzLt+2j42k4315at53m5nefx59h2XX9wzksaiHQBgftliAHxWPYctIsvEh0sXug4PtaEKQbm3Fp8iEdfG56cNJCKYNDA79VqdLs1OQtJrvu0P7QcY+3k+oBVyJCWBTXIwz6erme6zB3Ac51ls795OWoYN3kvgk/tg2W3jzi8t0yMwnpunlxYaNdjqXHVoaPgtXlZaPHy0cC+f/d1uNE1jjlf/3x+70XoxMGODTdO054DjNUjfAoyqKPwCuA0Dg1PIHw4+xV8+8smTvk4hHSclBKbU2clhG8noD2+7FCGZnVy160wSLtaIcWlH8xfKHWWEZYnWzk5yDNJUyEPdJWPnhbsKu8hh1hwzMtiaI82TyvD2RzO8979fIZQ4u2pP3XE9OVog2NKzZez4UEo3Zl3miQWjZ0W9buyuFc1cEbydWC7Gd7d/F7tqYnEuT+1i/b2Vi9LcctVKLktniFg7GYpn+O2rXaTzCu9eV4bWuokn3R7WV64naA+WfEkDA4MLDNnEkHsp87IH+OWLR5hjKW76eaf3WI16h0LR8XlFw5EoYVkQLBojpbB59XIc5SLB9XVXs6Fqw+zGbvWgIBEzl49TbDwl+PWcOBs5xCUfmPH1r2q8gQ6LmbXWV3lk93iPWefhA0StKUxIY+IqJsmE1+QkLEtkJ8nPGiUabQOgMlCPR7MSk8bPb6H0CC5FQ/LUAOCU9PklnAmzb0ivs3eg+PN8IBWPkJQkbCYn8326t3O/04mnML54dm9Ef1+CVZeMbQ4fy5LAEgSCP3f9GYBKh27Q1rr0DYnLaq/AuvETrBUtRA49z31bj1DtrMYqW+mIdpzOWzznONlvUKWmaaOf+H6g8iSvZ2Awjp+/9gDbQptIZE+uUHMkNcDGhjq6RMepGdgsiab0nbyspDB8lg0V0CcJACdHQ0gq3dVEZInn976AJinUFTRoOqbel1tX3TIrlmkNtvsP3M9bH3orj3c8PuHcI7v1Ypivdpxdb2N3QjfYrqm7hu0D20nmdaN6NNfAPc1iZloqlqKanayTWsjEG1gRXEE0G2VhSiJsbUQcXzLA7mOD5iBtyvPeXz3E1x49wPpGP8ujm2kRCu1Kktc3vf7kxmRgYHDeka9ZxxJxhGf3HeFO5y6wecE+fQaK36mLhkST4w2T4eF2MpJExTRer2NDGh3zrpj9wCUJrF7cp1pwBMBZDiY7mB2w+p0z7nZVsYZlj3uArbv2jjMukp27aLVYqLfreVSj+K0+IpKEGisdEplO6sac39uIW3IRldVx1x7JxPCoCha/brC5iiUTBpL9HArrueF7B/dMGqZ5LjIc0XPW7GY3lY5KFvgXcJ89xmZvYtwaZ6A4zzYFJy/v4jA7mOudO+ZdHAuJdOsG21W1V8Gad6LZfHzO9zRfe/QALQNJGjwNhoftRNH0T1nJT5oQ4oNCiG1CiG1DQ+d3dXeDM0cop3/Zm0Old7ZmQiQzRE4SROTwWXkgJooerYys0R87+1K+owabSxwNVwu661GFAEmvH9NYvnx8HTK3vh9jyU+dw/Zc93N889VvAvBS70sTzj/d+hquRV9kV3/zSd/HyTDqYbtr8V0U1AJb+7YCEEnricxe20mm5Eoyon4Dl5ha2N8X456l9wDwunSUQuWqSbtc6dfDcNqyf+LD18zll/esgD9/nScrm5CExPUN04f9GBgYXFh451+BRSj8u/nfmZvcCTf984wUbCu8umR/+2DPuOOhSCsANUVvTylsnmO8+fWXlm44BXKgEXPVkhPqOyVCwPzr4bIPg33mm2tN3iauDK7mZ14vC1PPjFPlZeggrWYzi4Ljx+t3BInIMnKydPHsXFZf13odZXhNfgpC0Bc7Kj+fyI7gUxUsPt0Q8Vh0Y3pr1xbyKFySzpAkT3esc+LFz0FCUb2WndPiRQjBvTffyyVyFT8ps/H1l792tF2mH1nTaKwoXe9vWXAZALKQKSsqk15VcxV3L7pbn/MsTsSG97Mu/SLLrEP87W9eo8HVaBhss2RACFENUPxZsrKgpmk/1TRtvaZp68vLy0/yZQ0uBjRNI1V04B6aInZ8JmSzuuhHTk4TS5/5kMRE6mg0cdfI8ZHFZ56hlD6RjO7yAQT9ehy6x6HH8DcteMP4TkUPmy2vlfSwtUXb+Mxzn2GhfyEbazfyct/L4wzkXEFlb/hVhFRgX3jXKbufE6E73k2Fo4JLqy/FaXaypVsPi4xkRjBpGk5HYJorTI+ov5SFdHK4u5+bmm7iRudf8a7kEIGFl03avq7uEu6IJzD5X8BU/hj27T9Fi3bxlMfLhqoNlNnLTnpMBgYG5xf+hZcDcKO8A23F22DNu2bUr6y4ydbc38O2jqPzTjShz6fl0wiXiOIzMOdpHNuwmzXv/qNuYJ4O7v41XP+Ps+72/678MilJIhV8mUePCYsU0RZ6zCbmFfPXRvHbygjLZqyZ0s6GQlHW3mv1ErAX3/e+o6IY2cIIHlUFt25Ee226J+m54rxzR3Ej94nn7531/ZwNogl9ue8qeno9Fg9frHwzNyZTvNz/57F2kXyYCkXB7in9+RnNYyuzl42lCPhsPj5/2edxmouKkpd8ECGb+WHDnzk0EKNnyE13vJu8ki91WfqTUxdIP984WYPtIeA9xd/fAzx4ktczMBhjOD2MKnQ54vZI6VCEmZDN6yGJGTnL0FkISUxlju7ijapF/f/svXeYZFd5r/uuvSvnqs45TE5KSCiDQMkgmSAkwGBsHzDYxtcHjG1sONwL5mBjn2uDL9xjm2DCBRsTBQgMAkkojITiSKMJmp7UOXdXzjus+8eu6p6e7p6O0xO03+fhUVO196q1q6f3Xr/1fd/vO5eM5aZxmxK3a7ZOqzZipSwkfXHcpqRh91vnnhSwbrh+TSdZTC4YqfxF3y/IaTk+/9rPc3ntdYznxxnIzO4Y7h9K4ndZEbxk5uC88zeSoewQrYFWnIqT65qv47Hhx5BSki3FCZkmLv86mN62XY2CiW/yBf73QycI9+q4JXjEF/nPAAAgAElEQVQ7FnF6bLyEj0/FeXvLa/jaoa/xe0e+xF9276avMGGnQ9rYvEwRoSby/naKoS7EnZ9Zdn/IcMDaZAt783zshwdnGmhni9ZCtjbceeYBnD5Q3bg6VhddA8AXA/eZzU02mk2RTbw1sJnHQmWeOv74zOs5w2oOvuW0Jt9RT5S4qhLRpylq8+uyTVMihbUpHHaFqavUFw6OvTRzjGZmCZomVKKa0crv5mDyCF4TotpWHFJyrPfn63il68NEfmLe8z6VtcRr0De7iRiq72B3qUTGSJMtW0ZrSZmmUdfBs3gUdFeNFWGr1q8tSLABrnovTb0/4Lut32P/CRVDGgxmFt7MH0gPcNv3bpvJnLkYWImt/7eAXwPbhBBDQoj3AH8H3CqEOAbcUvn/NjbrQm+qd+bnoczadkpKlfqkrMM4J73YCuVZN6qJ1Lnf9ZnMTxE1JNI9G2Gr8VnpLyddTlqFEyVwWn2DO4imeqkxdHSpk9XmtyiIF+MEnUHy+QCf/oEVyXxq9KmZ9/cem0L1WqmIRb133vkbyVBmiNag1YD6xpYbmchPcDRxlHwpQcgwcQfWQbC1XolEcE/9MP/4y6NEUwcxUaFh98LHN+5BAT565Ak+kDPJC3jO7aAl0MIt7besfT42NjYXJL73/AjP+34x04ZlOYQqxhmXuQ4wNDbO157oA6CgWxkWtbGtZx5ACHjzv8Kr/3JVcz6fef+VHyJgmuTU/6SkG2i6zqTTSvXfUzu3FUnMEyOlQI2IM5aa3+x6KldCKladfcgdorXOEnxjFcMNAI2CFWGriDl/oAG/aWIi2V4uUQhtp5UQCXWK/YfO7WbmqQymB7n1e7fyzNgzc16vlnlEArNps8HaNjo067nfn+kHICXy1OrCag+0CNti21CEMlO/tii3fQqu/yBXTd3Lx7Da3/SmF15HjORGkEiOxs9t6cV6shKXyN+SUjZJKZ1SylYp5b9JKaellDdLKbdIKW+RUp770IHNRcNLUycAEFIymVtbDZtmWDfThAOmzkENWfEUwZZdZEdoI5kuxIkZBsITnnmtmjsO0BFcwIFMCMreehpMS/AuVMeWKCaIeqI83RvHLNdiaiG+fXA2PeLRE71kndb5WTWOaZ6bAuuyUWYiP0FrwBJs1UL0x4Yfo6CnCJsmTt8aTUcAPGFE/U7eEBvi2++7hlvDIxh12xe3ug81w47fRHgi/H7N5Xz3mk/x4Nse4edv+fmM45uNjc3LkFg3nL6JtgQhT4RWTx3POjT+39h3+OKjJzFMSdFMokpJOLCMNMfdd0Ht4vVHFyqRjhu4pSRIeid4aSTJ+NAJejwKMeGfJxyiniimAL+aYiRZmDfW+HSSsmrgw4FTcdLdbEWM4jmrdlA3DDRFswRbJcLmCsaoMaxo3a5SCRnbwmXt1/KS28ngLz53Ni99RfRn+jGlyXB2bh1krlIWEQs1zrymhJtorwi2gfQApjRJKWVippMz4XV4ecf2d3Bbx21nnoyiwK1/Dbf+T+7SLFH7/OjCgmx8rAeA4/0vwwibjc1Gc2T6BC4T2nWdbGFoTWNphnWTnVRV0tMbH+GqRvgA8vlzH2FLFOPUGTqKdzbC5nP68GPlj7c3Xr7geaa/ntbKd1k1LpkzbskSbC8MJgl7XdQ7d3Mk+TzffqafbEnnyLRlW3xVoUjGUWIolZo3xkYwkrV236oRtjpfHTtiO3hs6DGKRpaQac53cVwtba+EgSe5Ov4jdpjHcbZesfixQsDbvgm//4D13z13r88cbGxsXnYIIbht82/ylNfLZcUHuDT3OHuPT1ESOWImKOJlvAQUgktjl1BUJQ8df5p47wFedLvZ4t8079DqZpmqZhlOzN/wjY8NklQVgpXenbGGLfhNk3TJimQOpZJIAV7c4PQC4AvVzgi2naUynpYd7Gm5mpSqsi17Hz0n+87GVa+YqYJ1DZlyZs7rucomdG30lH6A/jqadeua+tJ9xItxdAEx5tv5n85fvvIveX3365c8DoCr/wCfK0hAV3no+KEFDxkYs4x1Tk6fWN6YFwAv479Wm/Od3lQfzRo06AZFY23Oopq0REZBUUgn+tZhdiujpM/e5LXyot48G0a6HKfGNHD4wnNer/b4aq9bOGVPCTXRYVipkNUb+alUI2zPDyS5rC3C+6+5HcWR4yM/eYC7/+UJ6t0HEVJyZzYHAp4ZPjfpClVLf6dZy76BBKYpubH1RvZP7ictrQjbmXLuV8TVfwjRTvjJn0IhDs2Xrc+4NjY2NktwW+dtGEgeaOjmz13f56uP91JSS0Tl4ilqLxdu2HUXAMcH72NyZB/DTgeXt86v14u6LcGWVgXJyfn19CODJ0gpCtHqJp/TS40hSRvWhmRv3HpW+pyzz1t/pJaaSk3hznKZmo7dM+YbvS4Y+Mn5UWFUfc6fXgJR0K3/Xx88xQhLUdHUGsK6g4H0ACMZ67uKKutcw+hwo+x8I1v1AoOZXsbT89NUUxVjtUl96Z6xFwq2YLM5bxnJ9dOtlag3DAqklz7hDOjmbN1aKtu/1qmtmLKZx1kp2tWMc3sDMaVJVk8TMwxc/rmipKbS+6QjuHDfHFekma2mNf/JwnwRnSgmCDojHJ1Ic7fvea5vsMTJ66/K0j+dx+frpVvT2BK2bJMPTpwjwVax9P/XB5Lc9c9PcN3fPcT4aBeGNCiKIiHDnNvSYC3Ub4c/ehx+/0G46SOw5571GdfGxsZmCXbGdtISaOGBaB3b6Oelnh5yDp2oWDrqcbFTv/UOOso6E8X99KUPAHBNx/XzjqtG2BKqQn56frbP+OAx0opC1DPrLBwxVTLS2qgdTE0D4D+l7CAUCtOgmQQNE185QEdLM1uiW3AIB49Ft3P99A/oH+hbt2tdLZN56zl/eoStWCkzCbrmirGip46GMvSl+jmZtL6rWsfaHZfnseduusslHM4xvr9v/u8kXUnZnBKFC6a33VLYgs3mvKRslElq42zVitTrOgW1hFHZjVoNupwVbLk1pleuBs0oEjRNvKaJaabPWe0WWDdeE4OYYeI5TbDV+ayWG+2hhe2enZFmWswCAjEvwialJFFKoJV8XMIJfvPIh2l++qt0hbvo1x7gpx+4mpwvzS7hYVPb1ShScrzSD2g5TGVL/Pzg2txCqwxlhnCrbganHVzRHqGz1sc3H5UEnJZIC5kmuNdJsIGV6th6Jdz0V1bTWxsbG5sNQAjBbZ238VRxjJSicJO6n7QqiTrOL+fGc4LLx3YzxKArRZ8xjCphZ+3OeYfFKkIsrioYqbnrh0xRozbxAgnVQaTi/AgQEwFSaplMUWOoItiiodn3vS4Hb0safHN0jGGlFZ/LgVt10xnuZLKlExcaAz86S+0QVsBiEbaSWcBjyhkb/iqmv5EOTaM/3U9vVbB5VlZ7uSw6b6RT8aI7yjx8rG/e2zmt0spJyAXLNy5EbMFmc14ykB5AIunUNOoNA1NITiQWb1q5FDqzvToK2sanJGqyhNeUhEyJVPOkCov3DjnbTBeth0fMMOY5ITb7mwk6g4u7NQUaUQE3fqYL03PeymgZdFMnlXNxhVLpP/PUF/jIzvfQl+7j8wf+moQw2BPegq+mm1ZdZzK3tGD7+qGv87l9n+MLjxzlD7+5j4d7JhjPja9p12woO0RzoIXpbJmbttXzmbdeBig0u6yIoJUSaQsrGxubC5/bO25HlwYPxZq40/M8SVVQ5zkLUY8LkC3hy8grgse8RZoNL16Hd94x1QhbUlFxpucKtmf64lwrDpFyOAmdkkZf720g4YDDgxNMp6yNxtpI68z7QgiE9NOt6SR8nTOvb4psYqA0zqHANXTG967npa6KxWrYSrKM15zfXkKNtbFdz5HR0vRMv4TXNIn4V9nD70woKl2tVn/CY2MHKJTntlvIGbMCs5pRc6FjCzab85KqVWuXplFrWjs4RyZX/0eno+GsrO+Lcr674dlGkyW80iSIilTOTS+4KvGKHW/MMFC9c0XJe/a8h2+8/huLF6NXmn56DM+8CFuyaKUgjCcd3ODtB28UhODagz/lt3f8Ng8MPAjA7rYbIdJBd1kjrZ/5dzqcHeazz32WLx34Ej8c+2sUzwB/9sifcMv3buHXo79e8bVXGcoMUeu2djubI16aI152NIXIJiw7Zr8pwGmnDNnY2Fz47Kyx0iJ/GI2xjf0YQtC4QsfJi5Wrd78dgGmHSrvavOAxbtWNz+FjyuHEXxyZs1l44PBLdCqjZIRJxD0r2NprN2MIweGjvyZfEQzhyNxSg3yltqsYnnXh3BzZzFBmiLS3jqC5tlKQ9aBa+lDtq1alSBmPnL9O8NV1slWzPAMOxZ+jQTdwh+vOyty6dlrlBVvdT/FU79wN5LxZJFjJyuqZuDiMR2zBZnNe0pfqA6BT0wmHrJvZifjqrf11oRMxFTympEQGY4NTEstoeE0ICieaqp2TXnBVqukBMcOc19Mn7A6zKTLfJWuGimDz6o55gq067sCkwqXiBHRcD9f8Ebz4bT5QdtONC5cp2brtjRBpp0vTySpxdFNf9OO+cuArKELhjy/9IAXlOP6uf6bktKJ3A+mBRc87E1JKhrJDBNR63qjs5VUH/wf87C/5QPQJjvd1sCsTZI+mLj2QjY2NzQWAEIL/tuu/sU9P8Y2QH4DG8MLi5OXGJTteTUvZWth3hRbpj4kVZZt0BWiUk8Rz5ZnXS8cfJasoSKznZ5WuJmus0bHnKFUalYeic9sj5FUr7d7RsG3mtS2RLUgko14XIXKUymXOFVLK2QibdlqETeh4mG9c46/rpF23MoiSepwmQ8cbrp133HrQsuk2GgyJCBxn77G565E8ZXZUvrujows7SV5o2ILN5rykL92HX3fhk5KGlqsAGF6Du6OOgVsqxKRC2VEgkd/Ym6COhhuFkOqlpJyb5t1VqsKqxjRWbqxREWz+8nyXyGpfNpnVqNWGrZqtG/4UfDV4Hvg4/zwywue8W3FFOyHSTremIYXkZHJh4TWRn+De4/fyxs1vZIfvDeT7/4DbW97J5cqnARhKr845NFlKktNyOM1a/shxH7UDP4d93+A3Tv4tjUaKd4yHiAm7vsPGxubi4Z5t93BZzR6+FrHu+bUL9dp8GaKoCm26JSj2tL9m0eOi7igJp5tWMclwpRfbSCpFY/ZJxl2WUDs1wtbYZLVvyWaOoWtTCCkJ1MzdDC07rQ3TcNuumdc2V0TdiEeiCEkyPt+NeaPI63kKunWt80xHhImb+f3VlEgbbZqOkFa6ZKNu4I+cnQiboqjc4mvjuLfIE6fVseWFTr1uEjMMhpN2hM3G5qzRm+qltuygoIZobr0CISWJ9MlVj6cpBi5UanBRcGx8hEsTBi6pEHEFyavmORVsA5kBHFIhaqzCWMMdQlPchDWD6eI0ppw1gkmULMG2w6wIqZYrrTqw9z0M73+Slg8Pcv3bf2C95/TSitWz5rmRIwt+1NcOfQ1Tmrx797t56uQ012kZ/n5sHx999W6k4eGl8dX1sxvJWpFaqcdoFlOYV/wO/P4vAbjFe5QgeXRn8ExD2NjY2FxQKELh49d/EkcluaQ20nVuJ3Qe0VTzPjZNd3HFlusWPSbqiZJyOGgVkzPNs//Ho5/kXzsHeKTBSqU/NcLWGOkEwDBG0Y0UAVOiBOdGNXPueuIyQHPbrJBrDbTiVt2MOq1N5XR89bX7a6XqEOlz+OanRCoSj3DPPynShhMIGlYtYJOu4wqcnQgbwM3dd1BSBKLwEyZOsffPKhIFP826znhhfczKzjW2YLM5L5kqxKnVDQreBtw1m6gxTLKl1aVElnUTTUjcwkGN6iN9DgSTJgzcqIQ9YbKKIJ4+d7npPfEe6jQfAgGuFUaShKDoqafBKKObOunS7HVUI3dXMYIUCjRXmm9H2qF+B6hz0ydaPVYN2YEFrP2z5SzfO/o97ui+g7ZgG0/3xnlfYC/q8fvZ/NynkIZ/RiCulJGc9e/IyDoJiQKOSCvU7QBfDXeGjhMSeQxbsNnY2FxkbI5u5n11V+M3JXULuCG+XPm917yRWy//DA2hxeuWo54oKcUkJrKMT1nPusHEcTKq4HOKtXl4qmALOoP4pABHCpQcQSnmPQN/3fJ7vFX7JK0x38xrqqLSHe5mBEsg5ZLnrm9rtX6tK9xFRsvM1u4ZOnkFPAu1hvDXowsH0bJ1rY26Ab6zZ3BzxSW/Q9Qw8YT2s/e4FY3UTI2CAopaQ4umEzc23rfgbGALNpvzkmw5S1SW0fyNEGmnzjDI69NLn7gAhbJBuSLY6t0REg6YXKDR4tmkLEzcOIh4YhQVhVzi3LgWSSnpSfTQVHZRUnygrPwWYPgbaDWs7+/UtMhEMYGCixtdA4i6HeA+sxgMx7qo0yTHkvMF22PDj1HQC9y99W4KZYP9QwmuMA+AK4DzwH8QMSXp8ur62VUjbM5EoTKRVut76LyR3eX9BMkj19PS38bGxuY84Q9f90Ueesv9eM/iIvpCo7suwB+/ZjNCzHc9rBLzxEiaJSRQmLRM0YrlCa4oFtkW7Jw5pooQggaHn4KziFAK+Beo97r7ut38wV234VDnPoe3RLcwqFvP1mL63KVEVp2gu8Jd6KZOybA2uou5JFmh4FF9809SFPKeRhorro2Nug7es/dvTfWEeK0aYdCf4pFj1rM9U9lIdjki1BoOkqKAYRpnGuaCwBZsNuclBT1PrSwgQi3gr6POkOTJLH3iAuQ1nbIicSsuGny15BWFqfjGCSbTlJQUiVs4Cfms1IBidnWGGWtlPD9OqpSipaRQUv2rGkMNNdFhWA1BT22enSwlwfCzwzwOLVcsOY6nrpvd5SKD2fmC7cGBB6nx1HBp3aU8P5CgyxwkoMXhtv8JzZezxxijqK2ut8pwdpigM4iz+iAMV2o5um7EWxijWx0nWnP2UjhsbGxszhVCUfCFWs71NC44op4oZWlQEAJ9up/RVAFNydNhqHz1jn/n86/9PG2n1QU2+eqYcCigFgmo89sF7GwOcc+V82sJN0c2M60lSSmC8jkUbKdG2GC2ji2bmianCHzOhTdl9UAzmzVL3NXrAlyrW2ssl1tbX01BEYxO/wKAeMaKePpdYaIEMIRVE3+hYws2m/OOslFGlxp1ZhFntBWEICLdZNXSqnpv5YtlSkLgVtw0VXLIp5PLb9i8VgqaTkmAW7gIByzTjmJu9Y6Xa6En3gNAR9FAW2XjVE+smU0Vu+FTI2xThWmUspOAmbYMR5ZARNvZXSqRk+NM52ejZSWjxGNDj/Ga9tegCIUne+PcoB603tx8C9zxGerMMrq5OsE2kh2hOdCMO1fJa68uXjpfBYBD6ngD9u6zjY2NjY1F1G31YourCo7MIHuPTVBQDEL+FoLuEDe13TTvnKZQO2Oqg5wCQefyRcvmSMUZ2+nCzK8us2g9mCxM4lScNAesdVPVKTKRHMUUAp9z4UwUNdrOW7Ip3jRWTx1+OEPkcj145aW/R9Aw0eWjAIxM9QPgd0epcVqGJyeTAwymB3l27NmzOpeziS3YbM47cpoVvfFLE19tOwARNURelYymlxdl++v7DvGxHx4AoJTPUlAEHtVDXdBanKeyfes/8UVIFgpIAW7FTahy4ysVxyhqGx+iPxK3DD42FbVVG2s4w80zEbZTm2ePZ6cJGRVB3bK0YCPSzs6K7e639s/2VHtq9Cnyep6b228G4OneaW739kC0y6qHq9lM1DApitUJ+OHsMLWeRmrNSUyhzjhfUrsFKoJ6xe6ZNjY2NjYXLdV0x0nVgy8/zAtHnkMKQWP9tkXPaYxuZdqhMq2qRLzRZX/WlqhlYtLjciHzq9uYPBNH4kd4avSpJY+byk9R660l5LKeh1XjkUTaMkLxn+KKeSre2g66ZZybsgYl58LHrCfO2q3s0BSKjkl0w2QqOQxAyFdPg9+KYH6/5z7e9tO38cGHPzjn3KJmYG5wm6fVYgs2m/OOGcFmStyxVgCi3gYAXppYXirjkyfjPNNrFZqWClmKwhJs9RVnrFxx41yD4gXrJudWPYTDlgB1KUkODqc2bA5VehI9NPtbqaOI6VqlsUawCb+UKNI5J8I2XUxQZ2qYqhvqti89TqSDnSVLsP3s6Oyu10MDD+F3+nll4ysp6QYvDkxzmXEQul9tHeAOEjLBECZ5Pb+iqUspGc2NEnTU0SymKXkbQKn0XBMCum60fvaEFx/ExsbGxuZlRa3XSpMf9NYQ08dJDz0GQE3DJYue01h53idVldgKGpU3+BoIOAMccflQiqur1T4Tn332c3zo4T9bsq5rqmAJtoOD1nO6KthSOStVMuhbWIS6ajpQhWSbMoi+iKhbb2rUECmnxlS2zHQlJTLib6S1ditCSn459CNyWo5UKUW6bGUIGabkb//uE9z70OMbMse1Ygs2m/OOWcFmWoYQQH0lN7x3fHmpjNPZEtOV5pbFXIqCEHicPmorPU4K+up6eK2GZEWweRxeQj7rpu1R0zw/sP434qXoiffQHthcMdZYpWCLtCEAt+Fmqjgr2LJakiajCDVb5rlhLUi4jZhpEpUeTqZ7mM6WMEyDXw3+ile1vAqX6mL/YIrtxjHcZh66KoJNCPzCKnaeXuHuY7qcJqflcFNLi5jCCJ5Wy9FZEWy26YiNjY2NTYU6n5VaN+6N0ComqTes8oJo/Z5Fz2nyN838HPQtX7AJIdgU2cQxlwvXKs21zsSBsUHS5RQvxV8643GThUncIsI//NxKMUxrltDJ5q3nfsi/SK13Zd3WKqYwVxBZXAv17lrSqqBveppUzppfTaSN+ubNdGsaLY42PvrKjwIwmrU27MemJnm26bscPf4PGzLHtWILNpvzjlMjbISsFMK2uq0AjE0v3bHeNCXTuQKJfBEpJcVCCikEfpefYLgDj2lSkhsnlhIFK43T5/QTqggBj7vA84MbazWb03IMZgZp9m0iKAqI1ab9RaxdQ6+mMlW5cRf0Aros0UUGpX4Z0TUApwca9nCJYaK4h/nML4/yzm/+J/FinCvrLeH01MlprlMqv/OuV82c6lctsdmfXJnwHs5aqRLoMZqYxhE9reB7y23W9TXuXtG4NjY2NjYXLzFPDEUoTHsDtIopmh2D1uv+xZtCN/obZ34OrdDoZVNkE4NOBbe2/pk4BdNa/+wd3nvG46YKUzhkGGlY9v3VCFu2aK1dFo0ahmefq8JXs9bpLovmoLUuOTHyIulKy5+GWDv+uk7+Y2ScV028ho6Alb5aXQf09D7LoNPJAOeu191KsAWbzXlHVbA5cEMlCrS55VIAprO9S56fLGg4G7+Ns+k/SBd1cgUrCuNz+REOJzUmlNUsuZJ+lq5gLpm8dfPwOv0EnAGEBJezuOERtmOJY0gkta5OguRRvasUbMEmDOEgbMB4pbFmspK20aGnl5cOWWXnG9mTmUBxT/HvzxzlUO5HSMPLxLiVuvp0X5xbvUegYQ+cspsXdFlpFgOplTk/VS39y4UAjSKOu6Zj7gGhJvjgAWi6dEXj2tjY2NhcvDgUB7WeWhIeF7Uijc9RES2exQ2qGnwNMz+HAk2LHrcQHaEOkqoEc33XCaY00bEiZQ8PPLbocZqhkSwlUcwQ0rQcLquCLV+xza+JNC98cnhWnDoDGyPYOiu1hKOTB8iWU/hNk2htI4Tb8EmJHh/kT//dcueurgN6x14AIClWVlpxrrAFm815R1WwSXU29zkY6yRmGCRKS++ETGVL+HzHCLoHiOfK5IvWDpW/Et2qlypFR4mJDWqena2k7fndQVRFJSBUDKXIaKrIaKqwIXOAWYfImNKMR2g4fKvMLVdUSv5m6nWdqYrpyFRFFEdNE5YbYQPY9aaZOra7Xj0Avpeo0W/nJy/E0QyTl/pH2K0fhs2vnXNapJJeMpJZmYNWdWdNJg1cwkCEbXtrGxsbG5ulqfPVkaxk+ycqvdNObZZ9Oh6HZ8ZdMuha2QZpR8jaTEw78hiLmGIMpAf4xuFvrGjcRDEBwsTU/RyOHyRVWjiCN120nq1qQXLQ+YcgxUztV0G3/lsfWkSMufyUK5uq/sjyU0HXwpaWywCYypwgZ2QJGpJgIGht9Do8fLjmcT5Z+lccpjqzDhhOWSU2cXVjNu/Xii3YbM47qoIN5ympBv56mnSdlLl0GuFIKoXmzONVU8RzZUpl64YU8FrRujrhJePQmNig5tn5SvSpKhjDDh9FM0cNKV7YwCjbL0/swyn8HOqxdslcqxVsgIx00GIWyWopNEPj2JSVEx4zjJVF2Gq3sLPSdPRXE/8fMU+Md+16B0fGMnz32SEu119ExbDs/E89rZIqO55dWY+akewIfqcfkTytB5uNjY2Njc0ZqPPVMWlaG71xVSXsCuFUnGc8p5oWGVphXXRnqBOAhLNMMl9e8JhvHP4G/+uZ/zW7ZloGE5UyhpuzOhKTJ0efXPC4yUr2TCiTIiBKuE2FrGatHXTNWreEzlAH74xZKYq+yOIpo+tJbf0e3KZJqjRC3izgN61aQISAy9+Fx+nkBvUQ7XqR/pRlXjdVsiJtkw5BKptd1/mY0uQtP34L9524b93GtAWbzXlHphJ2dzhPSTVwuKjTFVIsfWPqiVtpk3lFksiVKVaaPQa91k5YoytM3CEZTW9MGLxQ2cGqfn7IW0NaUXiV8yWeH1y+YLvvxH08M/bMqufx/NhhCtkG9h7uA8ATWL1gc9V00mlY3+t0cZqjU5YrU1iqlv3+CqjZeRcNuo5u6rx797u567JNOFXB3//8CDcp+zGdfmi7Zs45tdFWHFKSyK3M7XMkZ/Vgc2Qq51WKo21sbGxsbM5Eg6+ByWovMn+U6BnSIatUBVtwha7MbUHL3GvUJUikFo6C7Z/cD8ymKi6HgaT1rL4nP4pL+Hl8eGGHxGrT7EBFyPhMSBStyBplqxTBf4aG2KK6GerdmJ6mwhumUZekzTh5WcYn1dk37/gH+OMn6b/sw7TqOgMJayG5FsUAACAASURBVI0YrwQANCE41r9/XecTL8Y5mjjK02NPr9uYtmCzOe+omnS4T9uRqpFukmp5yd5bJyt/jCVFMJHOUK7cYP0eS6A0++sxhaAvvjHW/oVKGkHYb924Qv4GUg4nd/iP8PzA8oxHdFPnU09+iq8c/Mqq5mCYBmVlmC2RrfzHu3YA4PKvXrA5azrpqvRimypM0ZewbuCRcOfyHCJPZdebuKxYok718dbgFmLDD3PT1jpShTI3Ow+gdN8EDtecUzyRJiKGSbaw8hq2Jl8z/mpbBzsl0sbGxsZmGdR560iW05TcIRLe0Bnr16rMRNhWmBLpUl3UK0H6nA7S8fnPuZyWoydhlTlUI1/LYbDSQ63TKNIgtvD48OMLrqmqLXtiBUsshk2dyVwKyjmETOOUypmji5GKYPNtjGADqDWdpESevNDwmvPnFu3YSbOuM1m0voMppYDXNAHoHT2wrnMZy1nCeCA9sG5j2oLN5rwjWcrgMU0cpwm2WiWApsB47sxpcOPZWev/qfQw5Uq6gLeSa95WsXIfTxxdz2kvSqkiGEMVwRb1xhh2ebjEfJEXh5JohrnkGD3xHvJ6ftblcIX0TPeCotEZ2kKrt5KvvZbm0JEOag2rh8tUYYqRzBSqlERWkg5ZpW4b/yd1fKv3GN6vvA7+4x7+pOYZNokRGuUEbLll3ikiUEfUNMhry7f1l1Iykh3BI2ppYhrd4QPPxvSIsbGxsbG5sKmv1E5PvvNbxD3LE2y7anYR88TOWOu2GK3uOvqcTnILuCEfmDqAKa21Q6aSRbQchjOW+KsxDOpzESYKEwxl5/e3na7Upzfnrf/WmCUS+QTJvheIqwo+xXfmD6pmr2xQhA0gJgLEHRo5xcSDa977dZ17aNF08rJIopBiwmGyq2wJu5H4sXWdy2gl+2cwM7huY9qCzea8I11M4zcl4rT86FqnVeB6eLL/jOfHC7NOkpnUAJphpT56K/1AGivNs1OZE+s25zNRqjR3DlQ+/02b38Q0Ovd5czQYY5ycXDrNc9/EPgCGM8MzN+mV8NSwtXt0PTmoODyx2j5sANFZwfbc+D7GM2NEDRPRsHNVw4Vf9/c0XPN/wD1fg/Zr2XPg03ykoZJKsHm+YMNfT9QwKRjpZX9Gupwmq2XJ5oI0i2mrfk2IVc3XxsbGxublxYxg8/hJlFNEPUv3GHvDpjfw4D0PLlnrthAdgRb6nQ4KC7ghPzXy3MzPk7nlW/9PZMfwmSY+KYlmLdOzajToVBKlBAFnkBamMFEImia54jQTx56hz+mk2d8x75w5bL8DrvgdiK2sRGIt1DhryaiClCLxKt557yuBWiKG9Xv41cm9aIpgh8u6jsn8+gkrmP1OJwuT5LX1Kb+xBZvNeUe2mCIgzXm28w2V1IJjU2cWbCljBGclxJ/PD6OZ1k3JU8khb4xtrXzO+v6BLoam5xBS4q7c3K9rvo6bGq7ii5EwVzqfY2QZTpH7xi3BVjbLM8XAK+HA5EuoEt787Cfg0f/benEtzaEj7dQbBg35GF879FXynueImgbUbVvdeN03wa2fhF1vhjf+b4ShcUvyu1C7babv2xz8dUQNgxLLvxFWrXyHJrx0uxI4Inb9mo2NjY3N8qg2zx7LjZEsJZcl2IQQOJQVlglU2BzrJq8oxBeI0vzopSeQlbS/qfzyBVsyPzqz2dpSaTk0ukAteLKUJOAM0yymSdRcQdCUlPQM5aHnOel0sn2pzdlYN7zh86CuXKiulnq/lT1lCoFXCSx4jFexWi083vcgAJtiewgZJonyygzMluLU73S9omwXnGD7+6f/nr967K/O9TRsziK5chqfKXF450aAWiuRsYH48YVOA6y0twzxGav4YnEc3bTcID0Oa8clGOnEb5oUzJULn9VQNgt4pUS4Zwt0/+Laj6MJwXTd04ylzuxWKaVk38Q+6rzWw2Kh9IWlOJE6RkdZR/XGYOR560XPylM0ZvDXIxUXbxluIZB5O06p0lXWVuYQuRg1m+CWT1g/LxRdA/DFiJiSoli+02dVsB0fcVYibLZgs7GxsbFZHvVeK8J2LHEMU5rLSolcC5tqrA3Q8dOiPw/3jDNR7qG9bKX0x1cQYUuXJmcE207dKikYzS4g2IpJPCJAI3HKrdfikQolWURPHiarKmyr2bSqazqbtFQ24wF8zoXXNyFvJwD7E9Y6aFPjHuoMhQTLz9ZZDmO5MVRhGZ+8bAXbU2NP8cLEC+d6GjZnkXw5i980cfvm/sE11G0iaJiMpxdvnj1VmEJTdC4pWda75fI0urR+9jotwSZCTTTqOgU2xlJfM4t4TQmnOCq1hzt4p7uVfaHikhHDvnQf8WKcO7vvBGAos3LBNlY4zq5yCa7773DHZ2DL7eBdendwURSFor+FdjHJ6NBlfFm5kU/F09au2nrwyvfB7Z+Ga/5okc9XCeCiqBiUdG1ZQx5LHkMgoBggoCdsS38bGxsbm2UTdodxKa4Zs4+zLdg6ancBMK3PTVn8x4cfRaglfqvQB0Aqt3zzrYyepFY3KPka2coYQWeUsfz8lMhkKYnbdKEKibeuC6GEKQqDnLTq6LtCG5fquFy6Wy6f+TngXnh9E67bgdc0GTemcJsm2zqvIGa6SYj17cs7mhslpFSCDJn1MR65oASblJKhzBBThaklnQJtLlyKRp6AaeIKzBVsgZoWmnSdeHFxd8felCXmLilai3jDTKFLK9rmVSs5zU4v9YagqObIl89+w0RNlvBJE5xzLXBvbreaQU9PL9wHpcrzE9ZO0B3dd6AIZcURtngxTt5Msq1cRo12wFXvgXd+BxR16ZPPgIy00yqmqA24uVQdwRvbvH7pD4oC175/1mlqAQLCBwIGU8trnn1o+hBBtZkrlYpxyyrr7WxsbGxsXn4IIajz1dETtwTbclIi10JjuAOXKZk25z7jhgsvAXBj1oqsZfLLN9/Kyiy1honeei3tYpyAElswJTJRSuCu7IUGGrpwuBuQAo65LNnQFT4PBVvHZXgqro9Bb+2Cx4RaLadIgDZdx1vTQVSEmHLIddUVY9kxxqcjOAmtm1PkBSXYpgpTFPQCJaO0okaBNhcWRaOAT0o8/rmCTQk10qzrpPTFb04905aRSDdBhJToMoMuyqgSnKeIiTpcZB1lJtKr31XJa/mZPihnQpNlPCbz7O476i8DIFVYPMUT4Lnx54i6o2yNbqXR17jiCFv14bKtXIbIEoXCK8Bd20W7mOTPX+lGPfkQbHrNuo29HIJOK2W2NzG+rOMPTx1GL7TwlvAxECp03nA2p2djY2Njc5FR76tnPG89c6KLRHHWC1VRadYl02LWtr+sm6jKAWKGQbOvGb9pki0trz1QUS9SEhp1hoFn03W4hIHX9DGem/8MTRaT+EqWsHFE2/EErc3TFz1uXMJFg79hHa5wfXH5wjToluiKBBoXPKauaw/NupUS2qgr4HARc9dRVATD8fVJXdQMjeniNH7NhVGM0n8+CTYhRJ8Q4oAQ4gUhxLPrMeZCnJoHWu0RYXPxUZRF/KaJ9/Q+YcFGmnWDFItb2PbET+I0IeaqISjBFHl0NNynbZzUOYLkHJKh5Orzlr/T8x3e9V/vmqmNWgxN6LjlfDfCSM0WQoZBRj/z+fvG93F5/eUcn8hS721eg2DTILp+gs1Z00lUZHhb+quWALruv6/b2MshUklHGUoufS+YyE8wUZggHm/geuVFaL1ybTV8NjY2NjYvO6pOkQA13pqz/nnNpoMpdbZWezxdJOw5zq6yQeHGjxEwTXLl5a1jpotWpC5oqqiNewDwF8W8CFtBL1A0ivgrpSWEW4jUbgZgv9tNe6gLRZyf8Z6aigtkTWjhHqvuuk00VQRbrWm1JqjzW8ZmR/qfW/CclTKeH0ci+XPup6uUpTd15rKX5bKe3/hrpJSXSSmvXMcx52ALtpcHJakRMCWK5zTbeZefOkOhpBiky2ke7H9wXiPp3lQfTRrgbyAgVQy1hIaOW879p97osW60x+Irrwercjx5HIlk7/BeDHPxULqGhnuBPzURaqZT08mK+IKh+J54D59+6tMMZYfYEbuUt/zLE5wYda84JfJIvIeA7sCPC3zr+ICpROvEwe9b9r2hpvUbexnU+K0H50h26XvBoalDAHiKMeozL0H3xkYDbWxsbGwufKrmX8CqequtlCbpYcppoBlWfuJQIkfCqdHobsLduI2AaVLQl9c4u7pu9uGHGkuAhfNWxtqpvdySRau+P1LMkVYj4PTS3GoJvAmHypb1qlU/C0SkVXpSX7NIOYXqJGRax9Sp1qZva41lVtI//tK6zKEqgLv0PLuNNNPFCYr68g3SFuP8lMiLMEewFS8+wfb8xPP847P/eK6ncU7RTA1dGFbN1wJ9wqJYOyLPjD7DR/Z+hM8+91mOxI/MvD+cG6BL01CCDQSFE03VMIQxTzC1BpsB6E+uXrD1pfsA+GXvI+z5xP08fnzhf5OaMOYJRgAcbloMhawzT7o4t5bu20e+zd333c13j36X3+j8DRJjl5Eu6kwlAjOpwcvl0PRLtJYU8r6W9e07Vk2vVJxwwwfXb9xlUl/ZQZvKzC+YPp1D04cQKFynJRDIDU/ftLGxsbG58KlG2EKu0Kp6q62UJiWEIeBo3EqrOzo5RFkRtPsacQciBExJ0ZgtEfrWr55n7+GFU/Cm8tYaxa+EwF9LQQlQX7DE3qlRtkQlxbJOy5B2WxuxjdHZDdnzsX6tylalhVcWijTUdi56TMRhie5Gr+UUvan5UgBGUifXZQ7VHmyNhsFVumUIsxqzuNNZL8EmgV8IIZ4TQrxvncacx0BmAFVaxhHVLuwXE/f33c/XDn2NbHl5uyUXI9UGg35Tgmt+H42YaqVJfvzXn0AgCDgDfPHFLwLWH0m8NMYWLY8r3ERA9VJSDHRh4mKuwUZbtBOAieTqm2dXBdtzE0+TL5d4YXBh10lNmLhZ2OCjRfhIO3X647O2vOlyms+/8HmuaryKh+55iI9c+Td8+4kRHgp/krcKawdoODO8rDmWjTKDmT62l8vooXV2RYx1AQIuf+c5schviFlpDOncmVNKwRJsXpp5vec4uILQ8oqzPT0bGxsbm4uMai+2s+0QWaXdZZlnvDBmbUz3Tb4IwKZoF8ITJmCaFM3Z6M0rHvldyvd/fMGxxis9XAPOWhCCtL+DrrK1bjm1eXY1wtaupyhVepsFT9lAPx8dIqts3/PnXJJ+K6Hg4n1mLw/s4X9MxdlTa/kIdLTvwm+aTBWW3vxdDjOCTTfYpFlppX3rUMe2XoLtBinlFcDrgD8WQrzq9AOEEO8TQjwrhHh2cnJ1/a8G04MYxRaQykUp2OIFy0xjOLu8xfha+dRPDvPA4eUZNmwUVTMZt6nMM+kAqPVYhaSpUpJ373o/r2u/mwf6H+BI/AgfevhDOISLN2dzeGNNhB1+cqpEV0zcYu5YjVGrh0i6sLo/omQxSaqU4qrGq9BkEdXXx1BifhNnKSVlYeIRC+/Etbqtm/6BiVnjka8e/CqpUoo7mt+HoXv5wqMn+G3zR3SXjvAWw7pZ/+rE8kL3J5InMKTB5eUMjtj61a8B4IvB794Ht/3N+o67TNyhFvymSaYw+2+4qBl8/Yk+SpUcdbB+B4emDmEW27iWFy2zkQ1s5mljY2Njc3HQ4LPMNjZKsG2rbLRWBdtk0nr2b67fBe6QJdgqrYtMU9Iix4jkF66ZGslMoEiJ32utoxx1m7nMsKJuhydm10LVCNtmM4FZyWSpmnzB+R1hu/m6q/nAh/8GRVk8m6hry+W8PZOls3sHAB6vnwZdEjeWZ96yFGO5MbzSjVdK2ipth54ZOrrmcddFsElpNWaQUk4A9wKvXOCYL0opr5RSXllXV3f628uiPz2IXowidT+T+YsvJTJetATbUiYW64FpSr7+6z5+fmh9dhTWi6xmRReduBZ8PxKwFuldgR384OEO7n2kG4/Dw7vvfzcHpg5wk/pG2nUdNdhIxBMmoygYioH7NMHkDrcRMwyy5cVbBJyJanTt1Y1vQEoVR+Aog/H5aYpFzaSsgGsRwdYZsCJTR6etdgQT+Qm+efibbPa9ij/792le8akH+MWjj/MB571Qv5POkvX9/ODAi8uaZzVd9IpyBm/9Wcg777oR3PMjoRuCv5Yaw2AqPzEj0L7z7CAf//EhfrJ/9vc6mhslUUqgpnzU6yN2OqSNjY2Nzaqo1rCdbUv/Ki11bTTqOr2VZ3my1I/bNGmo2wGKisdUKAmrpCKTzfH5Gh9PehZeH49mxogaJkrF4bGmfRe7zSkUKXjksf/kxMBg5TOsCFujWcJRMSoLVDKeBIL2UPvZu+ANILjn9bD7brxd18y8VqM7mWR93OdHc6OEDDcAAV8DPkOwf3z12VxV1izYhBB+IUSw+jNwG3BwreOeTrqcJqOluNPood3IM5pdfqPAC4Wqg89GRNhSBQ3NkCTzy2s6vFFUUyJdiwg2d6yFL41OwOBb6RnLkci4uLrmTjLlDL+z83fozFZ6bwQaiPlipBWFvFBwn55rHmykSdcpmMvvX3Iq1X5vQ2MxjHwngehxBheIsGVLGiUhcSsLX8+W2i0ADKSsP+Yv7P8CutTZOVrPk94P8qu2L/PNyBdQnV747R8QCbXiNQW9yQHKurnkPI8mjuLASZuu467pXNW1nrcE6tlU1sg74+w9Zj2gvr/P+tv5rwOzgu3QtGU4ckmx8vvpvmkjZ2ljY2Njc5FQrWHbKMHmDjeyuawxXuwDIGWM06brKJUacjcOihXBloqP8+OAn3vDBoYxf30wmRmh1jBwhCqW/B3X4XC4adQ1Oh2HGfnVlwFLsAkEIdPEV299jkf14FAcNAea8Tq8Z/mqzzLBBrj738AzmzbZLIOMOvSZNehaGMuPEdEVNJyoW2+jUyszsA5OkesRYWsA9goh9gNPAz+VUv58HcadQ9Vw5LV6P41maSYXdy2cmrN7PlCNsG2EYJvKlnChkc4t37xiI6imRDrFwjcEf6yFPeUymfEkd13RQm3ATWHitXz6xk/zwVd8EFHtJxKoJ+KrwxSCKVXFrbjnDhRopFE3yCuZVTVL7E/341AcPPqSQbe6lbIyQiz3+Dy3yGQhjxQCj7rw9YSindTpOvHcCfJanvtO3sdvdNzB1ZNPEhNZurQTtBSOovzG30KoCXHpb9GulfA4xxiIL70bdCR+hJgRsSro1tHS/7zAV8vWskbelePH+/s5MZll/2CSGr+LR49NkipYmxEHpw6iCgc3aOPo7jDUbj3HE7exsbGxuRDxOX1c3Xg1r2jYoDromk1sLmuk5QS6qZNW0rRp5ozjs1u60IREMzXi8SEyqsKYU+HxwfkW9YniJLWGgStc6VHWdSN8bJzG5qsYUp2I1GDluATeSuV9uNEqHxFCEHQG6Qx3bsRVbzht7k5MITgwtnRv3aUYy44RK5tknVFov47NWom82Y95Bjfx5bBmwSalPCmlvLTyv11SyrNS0FIVbN16kWa9RKK4ushIlQf7H+S27902Eyk51ximMROG3gjBNpkt8VPXR7k9+a2z/llLkdfyPDn6JDCbEulSFhY4vhorn3pHIM8n3rCLN13WzCM9aa5vuA2n4sRVbfcQqCdUCftPO1Q86mmCzemh3lTJOYqMp1cuWvvSfTR4W+gZy/Nen5U/Xut9jrH0XOvWeNGyyvWonoUHCjXToemk9WEeGHiAgl6gw/kqrlEOk26/GT74InxkCK54l3X8pW+nVdcJuEY5OXlmwSalpCfRQ22pcu3r2DT7vMDpYat0IAU8dOIA33lmEEXA3961B82QPHB4nMn8JPceu5d65zauEicQLa9YX6dMGxsbG5uXFV++/cvc2X3nxnxY7VY2aRqmMDkydZKUs0Sj9Mw8x9zCer7nyjnG4n0zp/3s6L2AFQj4myf/lq8c/ApTZUuw+WJzW/A0BpoYdbrw5K21Z7KUxGdadf+e2tl1wz3b7uHNm9981i71XLKt/goAHu95aE3jZMtZMlqGBq1M0VUDHdeyvaxhOPI8O7S2KNsFY+t/PG5daJuuU2fqZLQEplw6JWwx7j1+LxLJ8eTxpQ/eAFLl1Mz1bIRgm07n2KIM06StTwf2tfCNw9/gvb94L5P5SXLliumI6l/wWBG0doY+ckOE0PNf5H3ye2iG5CcvWnV/vvIUJcUHLj/h4GzjRK/DN2+sbuFHUyR7+4/Me28p+lJ9uGUjIHldYi9e00TzTDIYnxtOT+Szi34+AKEWOjSdDHF+fOLHtARaKPaVqRdJwjtfax1zanuDWDdtnnpyrjzHJxd2pawymhslU87Qkjes78S7MSkcG8k2n/XvoaAM8m97e7lhSx237WygJeLlJweG+djjH6OgF9hk3s1WZRi17apzPGMbGxsbG5tl4g7QYlUd8b3DD2AIaHLM9n9zK9baIqNlmK5YxwdMkycm92JKk798+K/5z55v8dnnPktWFmjRdYI1zXM+otHfyJQKgZJVSpAsJgnokryYu274k8v/hNs7bz+rl3uu2Lrleup1ncMTz69pnGrmXrteRPfWQqSDLYpV//fL4/vWNPYFI9gOjh8nqpu4VB+1hoGJQaqUWvrEBUgUEzw+/DiwfHv0s03VIdLUAwxnRlaVprcSsvExfr+xnuf8E2f9s5Zi34T1j/hk6iTJiqmG2zm/Bxtg5R4DbU9/Eu7/KPXP/SN31o7zzScH+MB/Pk/EjFNwW3VscwSbc37EblclF/2p4eUZePy87+dMFaYwTIOBzABGqYbXBodwx4+ztayRdGfnCbbpjOU65HctLEAJNdGha5TUEk+PPs0bNr0Bs/cxAJybXr3gKVc0XYUuBAfH5qc8nEpPvAeA7cUCWW/zRRlZat3yejymSdQ/iG5K3rHdifjBe7l7u4tfT93LEyNP8BdX/QWh0XFUTGi58lxP2cbGxsbGZtm0BizDsEeHHwagpeLyCOBRK4KtlCWet8TCmzNZ4nqCf9r3Tzw5/hClidu4I/QlPqRdz++lMngijXPGb/I3YQhwiml0wyRRShDWy0y7Wy/KdcNCNHbtYWdJo6+0tn5pY5XfQZeeB389CMHOJmuj+PnxQ2sa+4IRbEPTR+jUy5g776KmUkxZ7dq+Un7Z/0t0qaMIhaHs2pvZrQfV+jWz0EpOz5Iup8/q5xUTQzztcTPsKc5r2ryRGKbB/kkrZ/hk6iSJgnXdHtciPTQ8EXB4oZCAmz8Ongh/7vkhPeMZHjg8zp5wiWCtJdRCnsjMaT7nfMG0PdyB05T0xJe2yB/LjfEXj/wFn3ryU4zkRtBMjXQmyjs8T4DqZrunnhG3Pq+ubDxh1dRFfYtcjztIi2GlHkgkV9bcytbCC+Tc9RBb2NXxigZLdIyknzzjnI8kjiAQXFlOUg5sfJ+0jUDd8Qa2ljUag30EPQ5uTn2PZ479mH3Z/wtn3U/ZGryGuzbfTV3qgHWC3X/NxsbGxuYCItq0iybNYFKzsoG6I5tm3vM4rM3tqXyKVMlaE9+dyeJA4asHv0pYbac8/WruP5CmM6chpRtcczN+Gv2WgMs5y4xPTpIoJqjTixQDF7Yb5EpQ3D46NCcTIremXsjVlmPdRhq1Yu4SaruaVk1jNPMyEWxJbYQWzcB11e9SY1gW3lVXxZXy05M/pTvczbbotg3rebYUVcFmFKyeG2d7XulMH1IISopOMl9e9/F1U19Wyurx5PEZo5HeVC/JUha3KXG6wwufIAS85cvw7vvhxg/BtX9M59QjfPMOD7/+6M10urOolShc+JQxfAtYzzsjHWzVNMaLS/fHODhlGZ8+OPAgPzn5EwCmpn1cV3gYtt/BjnA3BUVwfPrknPN6EpYYbQvVLzp2k8NKOdgeuZTjwy6uVg5jtt+w6M5WuG4Hm8tlEmbPGed8NH6Uem8Lm5nCDF+kN97GPWwRbhLKFP/1h1fwzEvf4t1NDQyVJwjn3sjYsbdwcirHbo6T8bWBv+Zcz9jGxsbGxmbZeJq2s1Urg5B4TZP2xm0z7/lc1jpnMpcko6cQUtKm6WzX61CEQlfpbfyp44fo+RT5+ChJZX5pRJPfqmkbVVUmB4+RKCZpNfPIRTaNL1ZaRANSwEvTy+tzuxCJopVVVW9quMMVN86WV7CjrFESfWSKq3dmvyAEm27opJQyPhGD+u3UVgTbaiJsI9kR9k3s447uO2gJtJw3gq0qPv9As9IDz/a88nkrsphXzXW39pdS8qYfvYl/2f8vSx77wsQLANR4auhN9ZIupPFLE8WzSEokwI47obWS2nb1H4AnzA2DXyJUGIHsOAQquxqnROmCC43X8gp2l0oURT+6Ycx//xQOTB3AoTiIuqN8cf8XAXhlaRSfnoJLf4vtNVYDxsnUbP5zppzhueL9XF0osjO0eKPJJl8z3UVBh+N1PPz4Y9SJNIHtNy0+mWgnryiWSLsniOfnGqbsHd7L1w99HVOaHIkfodHZRkjkcUTbznh9FyxCsLV2DylMXEe/wje8CnWKh58NDvFv27cxnDD48Hf3c7lyjHLjFed6tjY2NjY2NiujdhubytbGepum44rNbsAGKg2849kpsmaGsAkl4efN6Tq+dOuX2DzRzwcc3+efPF8iYibJOuYLtuaAVdPW63IyOdaDLjVqDB1v/aZ5x17MdIStddwTg6uvNYuX4qio+KXEXzV3abqUbWWNkivHk72rX9tfEIItXbTS5KSvA1wBQobVU6saelwJP+v9GQCv73q9Jdgyw2syL1kv4sU4QsJ7dCsis5LauoPDKZ7rX1mH9oJmpeplVUhk19fafzQ3Sn+6f+a7PhPPTz5PrbeWa5uv5WTqJLlSCr9ponoXSSE8HU8Yrnk/HP0Z/D+XQCkNYSsl0uPw4MaKUoV9kfnntl7FzlIZU9V4dvjYGT/m4NRBtka38t5L3osudTyKnxvlCUyHFza9hi2Nr8Ah5f/P3n2HV3KWB///+DxO5AAAIABJREFUPjOnd+mol12V7b25d2MbbOMCxtiGEBwILUBCTNr7kpfwJjEBAuQHTiAhBl/YDvCGYgK2wQaMccNlba+9zbveIm1R11E5Ov3MzO+POWqrdrQrWdr1/bmuvbwazZl55pFWPree+7lvErnRAibf2fUdMiS5I9aHck8dgHojdfxH2yA/ejJC45D9g0I1Xjz1YPxlbM5a5DWD3x0eLUFrmAZ///u/58vbv8xfPfFXHBs6Rjn2ipInWjvV1U57K5ddDcBjL36Dp71e3rXu/XjC9axs/QHv2FxL57FDVKp+fGOaZAohhBCnhfKVLMvZv1hfms9DePQXsAGfHbANDHUzRIqIoYg7SqlKDrC5YhvhhF0J/S08xznaXlKuiVkmQVeQ5mADL7vd9MXsQnwR06SkbtV8P9miUlO7iep8nh3Hnj/pa9gtEbwowB0prLC5fKz02sHbbw/vOOlrnxYB20DcThd0OfygFMoZxWFC90n0Ynst9hr1wXrqgnXUBmvJmtmT3gs3l2KpGH5DI2Ka6IaTY/Hi99bd+dBePvPAzlndL1NoGN2vaSQGT61FwomG0wdbB1tpGWiZ8rx7nj7M4y3Ps6l8E03hJrqSXcTSXfhNC2exARvABZ+CG/8dbvg3eOfdsPWPRj4VKjSs9kwWMPmjNDvtVMUnWqf+R2RaJrt7d7O+bD3vXvluqvxVBPU6NmotULUedCeu0maasjnS+jHSOYOORAf37bmPqmQTa7K58ZUeT+CN1lOhBvj0FU38ZfMxCNVBScPUz6sUG132uJ85PvqD5cnjT9KeaOei2ot4pOURAKIZu9hKoPTMDdiWr7gOgH8N+9GU4qYVN8O2D0LrU/yfbQbnue3/YXkbz1nIYQohhBCz5y9niWG/l6nP5SA0+v/zsN9+LxBPxYhrOUKWk5QrSiAX42gsSZNqI+Gtpb/+ChzKJOspm/QW26rPYYfHzVDCrhxeYhgEat5cPUsrmzayNpPl8ND0202m05fuw2faXytVyPYCWFO1GYBXTqHwyGkRsMUTdkDhLjQfznnLCBuKtqGuWV8rlo5R5rW/YWsD9jf9YkiL7Er2Ejbsao2BnJsDfUeLfu2xxAGOJF4rutqjZVlkld0fLK5pJPs7Zz/gaezu3Y2m7G+t3x373ZTnfW/7ThJmN5mhJTSF7VzpI6nD+CwTl2+KPWyTcXpg022w+Q9gw83juteHC/3PvK7Jr7e85iycpsWr3bumvHzLQAuJXIJ1Zetw627uvupuqjPvY53WglZbSLML17MymyPh7ud4f4qvv/R1TMtkY1dhSbx06pRILVyLhsknK3fjOPAIbLxlxspM1dFmqrIWe/pGA80f7PsBFd4Kvnb51/jMOZ9hRckKqpP2Dw49VDXVpU57IU+Eas3LoK5zef2lVPorYfP7wOmj9P4r+YLnuxiaE6rWLfRQhRBCiNlRilp/A+emUpybUTBmT34wWI7Tsoin+xjQDEK4yXvLiZh97OuI06zayJcuJ3zbf3LMu4rwysmzd7ZWbSWpaRw07YDNb+oQOHPfN0zGW7OatZksvdYA7UPtJ3WNvnQfXqMQWvlHaxeU151HqWEQS+486crsp0fAlrT7TXkKpdlNfyVRw6RjaPYrY72pXkoLOb91Qbty3mxWs+ZLd7JnpJhKeR6OFjkmy7KIeX6EKv8+vYniiocMpvPkdLv8vKUUg3P8/Lt7d7OqdBXLIst44tgTk55jWRZtaTt98NGXvHT32UFWxsoQMC3c/lmssE0jVKh+5PWXT/p5X+P5rMxlaY9PvkJpWRa/eN1exVpfth6ApaGlqPY+PGSgepN9osPFcrykHHkePPAoPz/0c9617D2szvWT1z0QrJ70+vYgCz1RHrzDTnW46NMzPpde2shZmTRt6T2YlsnRwaM8ffxp3rXiXTg1J7euupUfX/9j/IlC64vA1EVPzgQrC2Vzb1n9XvuAPwp//Bu46A5c0SXoG24Bh3uaKwghhBCLk79yNf/Z0c1Kbfz/yz3BEgKmSSI7QL8OIeWHQCXlaoDnD/XQpNpxV61C+Uqp++vnWHHlBya9/pZC4+hXXfZ7BksrA+20CBHmjifM+Sk3TlNx53N3nlRgFUvH8OcsDLRxPexU3TZWZ7LgOMLx/pPbhnRafDXiqeGAzS7NrocqqTSy9KZnH7DF0jGiHjuHdzGtsMXSMSpMO+Bams/Qm+4s6pslnsmjHH1o7h72dBb3G4GeoQwpfbTQyFDi5H6TMBnTMtnTs4e10bVcUncJL3W+NGmLglgii9u5BycOPuDr4pGf/hZd6QD4TRNPYBYrbNMIFQIlr2tilUgA6s9mTSZLn3l00vl+8NV2vv7UY+h4qPLaeeMDqRyVQ4UqQjWbR85d6bWXv+898EUqvBVcXvUeGlU7qWDj9CtmwwFbZgCu/iJM1bNtrMhSzkknyZPgzmfv5K6X70JXOm1HN/C+bz83cpoz1U0exxnZNHustzVdw5VLr+TsqrNHD1augcv/Fj78ONz4bws1NCGEEOKUeGvsghjhqvHZOr5QlIBp0pvpJaspIs4wjnAVQZXi2IFX8aosnuqZ96JV+iupMF20uO33QS7vmdkKaCYBXyM3xxS/O/Y77vzd98nkpy9Id6K+TB+hXJ6Eo2R8wFu+ipV5i6Q7zoHu/pMa22kRsCXSw7257Dfdrkg1VUaW/szsio7kzTz9mX4CTrsAhVt3U+4tXxQB20Cmj0rDDqLWGHEMMiOl/seyLIuj8dF0yc6BFE6Hfd5zx4vbzNgTz5BwGOiF+CSZmf1ewKkcjR8lnouzrmwdl9RfQt7K80zbMxPOOxJL0uDbwdp0gv+d+Arftj5PhdsOeHymhWuyIiEnIVxIhfQUUiMnKF/FihzktByHBlonfPqlI33o3qNkEjX8wbdfoDueYX9nnHXaYfIOH5QtHzl3Xan996yZ5FNbP0VsSNGgOjBLZ6i0NJyPvuJtsPKa4h6spIErEklCQ0389MBP+UXLL7h8yeU8dyDPCy2xkeDTk4kRd5Sc8c0vr226lq9e+lXUGf6cQggh3oTK7FL+emR8IOUORAiYFj2GvYARdpXgKbV/CVwZe6Hw2uL2oq3W7dU73bLwhN9cFSKHhZes55NDnVjper5/8Ot899ni95xljSyJXILSfJaM+4TiLprOOm8dpoKnTrKoyekRsGXsgM3nsgs3+EtriRomSSNO3iy+6XNfug8Li3uf6uVYn50SOF1p/7yZ54vPf5Ejg0dO8QmmlzEypM0kpYZBLtLMipy9v2yycT1+9HGu/cm1I8U8DsW6yRe+irt6Xi3qfr2DCfp0qNXslZxMbu6Kruzusb+510bXsqFsA2F3mCeOTkyLPBJLknBkKfdU0H/lV9GURalpp0EGLHNcjvapCLnta3ocUwRsms5S7zIAHj04MbDc2daL7mnniqZtvNYe56ZvPsMjuzrYoB3CqFgPmj56r9JlNGezBK0m3t70dtpjgyxRXbgrl0+47ji+UrjtB3DjN4sPrEoa8FsWW4+v44dXP8rXL/s6d2z+Gw50DZHOmXQPZQAI5ntJuSbfZCyEEEKI08DwL4fD4wuIKU+EgGnSp9lpdlF/JcFCVehztUImUJEB27qgfY+waeKpXDYHgz79hBu3ELBS/LB5M5qe5KHWHxX92uFFlkojS9478X3XBXXn4zZNdnRPvlVoJqdFwJYudB33F4pJOMPVVOfzgMXOnuKrIw5PZjzh4UP3vkgym6c2WDtlCf3WwVbu33s/39zxrVN7gBkMN9orNQ1ovIi6vB2EvtZzeMK5z3c8j4XFwf6DAByIje4/OzZU3FwM9bbR7dBp9tn7uzLGwCmNf6xdvbtw626aIk3oms75NefzXPtzE87r6OykR9eoCtUTXn05AOGkHaz4TROmSmGcpeEVNq/DO+U5zbXnU5nP87vWx8YdN02Lfb37QBlcv/pcvvehc4inc9zz1AHWaK246k/o61XSwN0dXVQctcvMp7sP41Am7soifliuvNoO3IoVsfuw1KsujsZMLltyGV0DTszCqunRWJJkNk+p1U92kh8cQgghhDhNlDbB274IG28bf9zpwWdamIXf9ZYHawlE7RW2c7Q9pB0h8Bf3HuDcOrv1TYlhEK5dOcPZZ6gNt8C6d7H6qX9leS5Ea+bxSVt/TXZs+L18rZmESeom+Gq3cl4qzdHUcye1P+60CNhS2QQAAW9hX1OwkrcmkngtL//y4r8U/eDDfdtCzhL2dQzyFz98hVp/LR3JDnLmxObRuzrtlbVftvySoULQOB+Gm2aXGibOpouoz+fAgh0dByac+0q33Xfr2JAdqB3tt8e4OpMlZh4qqqdcf/8hckqxMtIAQNaMz8VjAPYK26rSVTg1u1feqtJVdKW6GMiMDwoHOnaQ1RQ1kQZUZAk5nJTH04CdEjldGfzZOL/mfK5aehUR99QplqWrLuaiZIrX4y+TM0a/D472JTH0/QCsf/rf2fyTS3j0gv2cH+rFSxY1Zv8aACVLKTNMSlI9HOxOoHrtr5+KzrDCdjJcPix/JQ1aF88esr9/dh0foEm1sUXt50gsSU88S7nqx/Kf2QVHhBBCiDOaUnDuR0f3vI/hsUYzfWpLlqKC9i/jy9UgyVBT0Zk7a5eeRXk+T8Qw37wBm+6Ed/4nnPXHfLj/IIbex5NHfz/ulH2xfZz7vXPZ3rF93PHhgG2pEUcPVTJB+UouT6ZIqkH29c2+dcBpEbClc4WAbXhfU6ASv2VxztAyXu56mceOPDbNq0cNB0bf4Jv8su67VO25h2wqjGmZdCQ6JpzfctxescpbGX7Z8ss5eBLoHExz95OHMM3RIDOWslf+Sg0D6rbh1NwE806OxcenYmaMDHtj9hL3cLpk38AhAK5KJMmrLIcHJq7KnSget/dqNYab0S2LHMlTfzDsxs17Y3tZG107cmxZxF5WH14RHJYetJ+jIroSNJ1+bx1LE/ZGTLel2f9o5sD68vV85dKvoI9JXTyRvvQczk9lyZJlR/foPsDdbYPU+V+mOp+nqucweCOUP/kZvtv4a/uECQFbA2Cvem1vieEeLHwtovOTC65KG1jt7eOZA3ZK667jA/y9+37uct3F0ViK7niKKINoQQnYhBBCiDORx3IA4LAsqsobwBfFLLy9V2XF/8LYWbqEv4r18/6BIdSY5txvOpoG13yZC7MOPIbGf+0Znxb5nV3fIZVP8dMDPx13PJYppERaWVzhSVoiRJdxUTINFvz2yG9nP6xZv2IBZAw7NzfoK1S685djorGpz0tTuImvvvhV9vft56XOl+hPT119ZTglcnmmnWXpnXzWeR9mawsw+X6xzn47GCrP6vzk9Z/MybM89Go7//jQXvZ2jFZOHB5X1DAhUEW+dDnVOYvO1Phy+3t694zs2Rsebzx9DN2yuCxpB13bO16ecQxDKfu15ZFGgpZGVsuQzc+8MjeTgwMHSeVTrC2bGLAd6B+/WphP28FMRbndG8uINHFBpoez+0vYln6Dvy3dQZr0JnQLft3y+Mjhncf6GfL2sCmv4JMvwgcehfpz0fY9aKdsRk/I8faXYzl9rHD18kJLHyWpIyT08OxSHWejpIF61cXO4wMMpHLsPDbAJu0gtaqXzu4eBmKdOJSJMzxNSwEhhBBCnLZc2P1Wo4ZBqKQKNJ1B3X6/7C9UlyyKw82FWR9biILumI+hnj6Uwlm9iYuGTF7oemKk2nn7UDuPtDyCU3Py2JHHxmVljWxvMky8JZMEbE4vbnc1S9NOft16pgZseTtgC/sLb3w1naQzQiDXyx1b7+BI/Ag3/ewm3v/L9/O5339uyuv0pnpxWJDyLkP75IvkcBBtK6xYTbKPrTfZTsQweHd8kJ09O3m97/VTfpZ42g64trf0jRwbDtg8phucHlT5CpblU/Rm28a99pUuOx1yY/nGkfEmc12UGwYNOROvoXiubeaALZmzG46XlS4jaDnI6ln6U8X1cJvOY0ceQ6HGlVav9lfjc/jGrbDZwaG9olkVsiseeapX0qw6ubHHS5ip95vNF9fSi9mWSvPb1sdHjr3cfoi4w2BLsMFOKXB64Lbv2xt4l5w7sUeJUqiyFVzgep1nD/VSlT/GgG/J/A06spRQthPdyvPk691kuw4QKKS35nteJxWzv0d8pRNTKIQQQghx+nMru8domWGivHYmmuGz91C5qmYu6T9WYOlmws1nz3zim4BryTZuH+oib2V5+NDDANy/934UiuvqPkY8F+f37aPpkn3pPhSKoGninmyFDciWLOOCZJb9/XsnzeybzukRsBlpHJaF3z/amyvjKSdq9VOqNnLX5Xfx5Uu+zDnV57C3d++U1+lI9BA2wPCUgstHe3gL5wzuxKW5Jk0l7M/1UpE3uGWoDw2dBw48MO04LcvCMKfv2ZDI2gHbCy2jJftj6RhOEyyH/RsRvWI1a3JDZMyhcSuGr3S/Qm2glg3lG2hLtGFZFkn6qcwbpKPrWJGx2NXzKjkzN231zIxpB4vlwTpCmouMbtCfnLiHbzYsy+Lhww+zpXILVf7Rb1SlFM2R5nErbG39KXTHAMqCqNcufRqqXYVb5VmhjpHTi+hDNsfKNlzFxakU7ekjtA3ZgXJH39MAbK69YPREXyl8+Hfw7vsmv9DG22jM7CMysIdG1UE21Dj5eXOhpAFlmTQ6Y9zzdAvrGP2Fgqf/INk++4fB8AZkIYQQQpxZPMr+JXfEYKRydbSykNJYZIXIEbfcBzd8Yw5Hd/pSNZtZn00TzFXwxee/yD8990/8aP+PaPSdz3cfqcCj+fnl4dHtUrF0DK/lsQOrSYqOADgqV/GOpL2N5cnjT85qPKdFwJYzM3gsC6d79I18IFpLrWOQj/3XS6wvOY+3NryVs6vOpi3RRjI3+Z6srkQPpYaB5bVX6vTll7FWO0qVq549sT0Tzh8041QaBiWmSX0uyvPt0/dOuOvlu3jvw++d9pyO5HFc5b/k+ZbukWIpvaleQoYi47KDF61iJUtzdsDVWthvZlkWr3S/wsbyjdQGaknlU/Qke0lpCSoME1V/Fmelh2hPtbDlvi1svm8z533vPK574Dr+8dl/HCm4YlkWGTWEx7TwO/2ENC8JzaIvcWorbPv69nF44DDrwpeO25/31V/tp607PC5gO9I7RNaRpES5RoqTaIU862XqOHnn3FSInA1v47mck7Ln/IljT9A1mMbjeIWAabJs+bXjT3b57D+T2XgLpu7hg45fUK1iUDaPpXELc3ZdRTcvtvaxSTuI6fBgohFNt5LptxuiO0KT/6ZHCCGEEKc3d+GX3EFzTBpjoBI0x8je+qI5vXY2kYDaLSjgkqMrua75Br7/2vdJ5pPs2rMJcODLb+K3R39LxrDbKMXSMXxmof7CFJU5/XVrWZlP4VUBdnQV1zt52GkRsGWtLB7TGpeC5o5U0+xN0BXP8LH/eols3qQ5Yhd3OLHAxbDeVC8VRg7NZwdGlZvsJsXlCZ09vXsmVFgcUGkCpoeEHqFhKMuB/gOkCumZk9kb28ve2F6yxtTBz9HU73GXPU5vfh/H+uxrdad6KTFMcsPl1yNLWZK3V7yGe8B1JDroTnWzsXwjdQE7jfC13lYSjiyllgtv9UreF+9nm/t6PrHpE3x808e5YdkNNIWb+PH+H3PtA9fy/de+z1AmT1ZPU2rpKKUIOwPEdRhInFrhkYcPPYymdP71QR/3P2cHmV3xNP/+u4Mc7woTS8dG8nt72g7T61BUjK3cWNgPpisLw/nGr7Dh9KC719CQge/u/i47jvWQ9XawIWugV60v/jreElh3E+/UnwIorqT/yardCv4KrlHPArDFcQhVs5mkr5Ym1U68t5BSG5CiI0IIIcSZyOu0q2oHLffowbM+AFd/cc4KuL0phWrJuKNsMY7yziWf4v6rv0/p0AcJaY3curWG9uMrGcoN8cxxu4dvX7oPn1GIU3zRSS/pqFiFAiqNMl7tLq538rDTJmBzn1i5P1CJK93DP9+0jucPx7jn6cNTFrgY1p/ppczI4wjYK2yOmg0MahGa+mIkcolxDbJzRo64bhLCT7xiKxeluzEsg9dir005zq5kF6Zlcix+bMpzwim78mQo8NJIWmT7UAflRg6zkHOMJ0R9Lo+yFK2DdvAzXM5/Y8VGagJ2ittLHbvJaRZRRxAVXUapabJ2sImPbPwIH934Uf7m7L/ha5d/jZ/c8BPWla3j8899npfb95F05IgUcp5LPGH6NZ1Ef9eUY56JaZn8ouUXBM21YPj5/379OoPpHN9+6jB5w0TP2ys8w1+XTMc+Oh061WNSJ/GXk3PYK2vmHPVgm63skov5TKyTY0PHuGfPf9DtzrLJWzWuOXYxtLM+OPL3SP0sNvzOlqbD2htp7HuKMEOspgVVt4186TKaVRsMdZFW7jnraSeEEEKIxcVX6DcbVGMyf2q3wll/vEAjOkMohVm9mQ3aIR58pY1/+mmc1qPL+eYlBnfuu5p1CQOPHuDXR+zK4X2ZPgI5i5TmB4d78msWMqPqEg5aBlumLZR4otMkYMtNDNhCNWDmucH9Mhcsi/Ltpw5T7qnGrbsnXWGzLIt4rp+oYeAKFgIjTSNWeR5XFSoW7u7dPXJ+R8IOYMKOCP7lF/KWXCcAP9r5DC8f6WMyXUn7NcNB1mQM0+5H5grt4fnDMfbF9tEaP8SF6QT6cPl1dxgnEDI9I0Hky10v49E9rChZQW3A7mK/o/slAMrd0ZHS8Y7+iXvxGsONfOniL+HSXNyz6z4GdZNoYQk96ouS1RTxwfYpxzyTlzpfoiPRQWf7aq5YXUkskeULv3iN+3/fyts31PCW5o0A7Omxe5qp3gN06TqVhT5w9kGFUWI/g+6Zmx5ss1W+/krOTWeoSFSzK2GXa91addbsL1S7hb6QHah5K+ehB9tYa9+JZmT4G//PcZGD2q04K1fSqNqpUH3EHdGie7AIIYQQ4vRS7q1AsyyqtJKFHsoZx7N0K8u043zvqb3sbhvgCzeu5qxd/4CeT3GT71W8xipe6HgBy7KIpWOE8gYp59R9f/GWEHeUsmLIzmp7taf4VbbTImDLkcdlnfCmc/3NULMFfng7n61/ha54hp+/0kFjuHHSFbbB7CAGhl1uMzKaIhZc+1a25fpw4BgXsB3oOQpAiaeC4PILKTNMXDkvP979e275j2fJ5McXF8kYGfozdqR85IT+aWOlsdMgk84Uzx7bxY/2/wiHcnL9UALHcFUZT8i+d95Na7yVvJnn0dZHOa/mPJyaE5/TR6mnlP0D9he6OlAF4Xryykko0TppI/FSTynXNF7L9t5f0eVQ1AXs5dpoYWNkfJpVwanE0jG++uJX+fhvPo5L+cjH1/B3163hHZtr+d5zR0hk83zO+V3uHPgKluHh8UP26qJj6CCDuk5luGHc9TxVdvpgXeXCpPCVrziHQT3CXf37cVgKh2WxbvnbZ38hpSi58QtwwafANc/pnfXnQLCGW63CxtfarXirVuFROTaqg6Rd89RSQAghhBALripQx6+PHmeNq26hh3LGUbVb0bH41NoUj/3FpdzKI9C5E7wlXO7aS1dXLe2JdloHW4ln40TzObLu6d93xQNNnJvqRlf6rPaxnSYBm4HLOmGo3gi8/2fQeBErf/9XvL9sH//xxCGaws2TBmyjvc4MfOHRzYDRDW/FAdSYQfb0jhYeOdRpdyEvDy6B6o1YuodzHU7KStvJGiYtPeP3fA2vrsH0K2xplWVpLoeyLDryT/Pzgz9ng2c9EdPEGykEbLqTjPIQzTo4MniEZ9qeoSfVww3LbiCbN/n1nk5q/DUM5roBqC9pAE1nyFdPrdnGzuMDk947kLkcVI60plFRSL8s8dvBUSI5+5TIT/zmE3x393e5pO5SHJ1/yoXNtdSX+viLt67E5dD4Qs3TlO66h0jbk0SNILt69vP4vi60nD0/Ff4TOsEX9rE5vOETb/XG0B2EPvJL1pQ18uexGDcNpfDVn3ty12q6FK78v3M5uslpGqx9B8rM2VWJwvVo5Xbg26B1kvVOXqlICCGEEKc/pz9CuWGO1GcQc6hmMwAfbuiirH8nPHYnLLsSzv0TqlKv447b72N/1forACrNDIZn+q+DEV3OWtqp9zfPah/baRGwZTFwnhiwAbiD8J7/hlAtHwv8jkPdCchW0ZnsJJ6Njzt1uEpiqWGixn5Th2oYws/SjIO9vXtHyvIf77WDvtrocnC4URtvYUtfKwNGFx92/j8Odg+Nu/5wwKZQ4/bCnSitZWnK5tiQyeKMPkUin2Abdtrc2PLrGd1PRRaGckPcs+seStwlXFx7MT/fcZwv3fcA5O0IXlkW1aV2KqG/ZiXL9A4++z+7MU0Ly7K479lWvvCL1/jVnk6+/3SWyrxd6rXMbzdTDgfte6ay3VOOeTKxdIydPTv5+KaPc3Xlp+nsjXDb2XbPsdqIl9/cYHJL37/DqrdDSQPnGf0krePcfs/zuHX7XpW+yQM23Au456piNXzwV/zhef+bv932l1PnIS8m626y/1u7zU5/LBtNw7R8UnBECCGEOFNVVdjvpaqqpIXPnAtUQKgOfvN/4e63gGXANV+CxktQWFyp9+IixKOtjwJQa6amrBA5zFW1mpBKEshVn3kpkVll4WSKwg8ON6y/mcrOJ1lfkmFXi92P4sR9bGNX2PCNX64ccERpSudJ5pMjq2M9Q0dxmybVVYU3v9d/nfVXfAGAiwOPcPT4+KbWwwHbqtJV06ZEpjQDLx4uS2VAmehGJXUJu4qPJzJahCPnCFKVtYPH7Z3buabpGpy6k+TO/+FR918TO5YoPI+JM2zvaXOWL6dB6+LVozF++OJR7nxoL//np7v41hMH+dC92xlMZfhfeTtts77hUgAiQfu1mdzk+/Km8nKn3aD77KqzeeL1brxOnStWlcMLd8N33kb9Q+9Fla+Cd/wHXPjnrE92ojmS/PdbWrEF5k3rAAAgAElEQVScdrBbcWIwUdiHh3th9rCN0B1w3p/AuR9d2HEUq3YLrL4eNrzb/thfTkq351ALVU7zQiGEEEKczvxROxWyvLZ5gUdyhnrbP8GFd8BN34ZPvAClTfb7LleAqzz7cBvLRwoS1htD6IHpA7ayxg0A9B/OT1t5/kSnScBm4sIx9Qkbb0VZBp+u2sneI5MHbL1pe4UtahrgHR+wpTzlrEnYAdDwPra+TDcVhkFJZcPIeWtW3ohCscvtIt22a9w1hgO2bVXb6Eh0jPRlGCuTN0hqJj7Ny1vCK9EsSHSfxeHDLfYJY8qv51xBlmRHm1nf0HwDAO72FwFoLtyvwshD0F4to3ojupnlA9UtfOaBXdz91GFuP7+BnZ97K/d+4GwevvAwb2nfzUOr/4QtdRcBEAna/9CzZvGVasAOIt26m7XRtbT1p6gt8eJ67afw0KchPQCX/g384f/Yq2Ubb2OrZu/LO/jq5+kss5tJT1hhq9oAl/8trLxmVmN501PKbna57p0jH8f9DQC4ItULNy4hhBBCzK9oM3zkSTtVT8y9NdfDFX8H698F4cI+Qd0JSy9gk7GTbLxx5NQKM4crNH1mk165BoA7al2zGsZpErBZ0wdsFauheiPnDf0KM1uCQ7kn7GOLpWMoC9ymBxzjJynnq2Jtth+P7hnZxzaYH6Aib6IFR4OKgCtAY7CeXW43zp69467RmezE6/CyNroWC4ujg0cnDLM3MUROg6Dy0LD8av7nWBtXl15IyOwjhWdcgQrDFaQhn0JTOitKVrCqdBWD6Rw1aXtv3ZXqOACVeQOChZW51ddBoIpP+R/B5dD40EWN/N11a/C7HVxcbbBq51dg6YUsOeujqELlwHChF1qWxNTzO4kXO19kY/lGnLqT9oE01WEPvHw/hJfAR5+2A7bhANThZtV5f87qTJYfV9TTtfpqgs4gPucJzac1HS7+yxmXk8XMPNWrACivkk3IQgghxBmtesO4XsXiDdB0CRXZo+ix0MihEsPEG5khsylYCcuu4IquX+JWoenPHeO0+OpmFDjVDM3/Nt6Gu3sn11YOouUrJ66wpXrxmRo5xyQFLYJV1NBHY2gF2zu3Y1kWAyQpMXU7PW6MdRWb2Ol2UzL0OqY5Wo2xK9lFha+CpaGlALTGJxYe6Riy0zKDuheWX0VDPs/nvd9nqdZD3HFCOVZ3iBLS3Nx8O3+25c9QSvHKkT7WqRYM3cPWjF3VsdywzwXs9NBzPkLg2JPs+EgNn7lmNWr7t+HuK+CrayA7BNd+ZVyZd6fuxGdCjtSk1SUnE8/G2de3j62VWwFo60+zxjsAhx6Hze+d/IfG2R/ips1/wj4zwRPHn56YDinmVKjObivgjkhOuxBCCCHEnGq8GIALcu2EXfZ7+LBp4gkX8f723I+hhjpZphdfZG9OAjal1NuUUvuUUgeUUn8zF9ccZlomGU3hUjMsHa57FyidD0eeJxEvY1/s9XGfjqVjhAxFxjVxctwltTiVwdbA2bwWe40XO19kQM8RsjwTzt1csZmYruF3ttA2MJp7OhywLQnZhTcmKzwyHLAFHAGoXAtv+TsCBx7kSu0F/KUnpK65Q4RUkmvr38/FdfY3xYH9e4ioBMaW26nO54kaBqt1//g+W9v+CJx+XM/9G/zq/9gpikYWLvwU/PFvoGLVhHEFLY2cliGRNSZ8bjI7unZgWiZbK7eSyRv0DGW4LG1XyGHTeyZ/kVJcs/EDeHQPx4aOScA235rfApXrxhUgEUIIIYQQc6BiLXl3CWdr+2gObiJg+dAB5S+iWmfzW6BsBWtSnUXf7pQDNqWUDvwbcDWwBrhNKbXmVK87LJ1PA+DUZgjYAuXQfDlr+36Dma0glulhMDs48umuZA8leZO8e2JDO3+ZXTlxvbmMEncJX3vpa+QVhLSJS5XnVttl3nt8MQ52jVaKHA7YQq4QJe6SSUv7dyfswh5hV+G6F91hV7l0h/HXjp8yzRchSJJ4Oj9yLN5i719zbXo3ztJmfn3kODd5a8ffxFsCW94Hr/4AnrkLzvoQfOhxeMtnoWbTpFMXwkFWz9OXyE76+RO92PkiDuVgQ/kGOgbSKEzWdz9ol7KPLJnydUFXkKsargKg8sSS/mJu1WyCjz0NngVqkSCEEEIIcabSNKyyFTRp7ZwdvJ0bk+fbx31FbOtRCs75KA2Db2DABpwNHLAs65BlWVngB8ANc3BdAOIZexXLrU1c7ZpgzQ04Bo9yQcAuPNIy0DLyqZ5kL2VGHtMzsaFdpMoOMoz+Lm5bdRs7uu1GdhHnxCi5LlhHjSPEDq9G+1F7n5xlWSMBG8CS0JJJK0UOtxaIuMe8iV5xFdyxG67+0rhzHd4wHpUjkbD7vZmmhadnJwY6VKyFFW/DAajgJEUlzv0T+xvmok/DNf88Y15zUPOQ0g36k7lpzxv2YueLrC1bi9fhpa0/zfnabvypNtj8BzO+9qbldgl6WWETQgghhBCnK0f5MppUB72DHqoTpn2w2DoMG29liSq+ddRcBGy1wNgKG8cKx8ZRSn1YKbVdKbW9u7v4nl/9SbsJtEv3znzyqmtB6dzsbgdgb48dUOXMHN2pTuqMzPgebAXuiD3cXH87t666FZdmT2DUP/n+n/PKN/CC10PyuN0/oS/TR87MkcsE+e/tR1kSXDJpSmR/wq7sWHpi9O0Ojis4AnYjRIB0IY3yYPcQK4xDxEPLwOmBFW+1T5wsYCtZCn/xur2qNjZdcgphh48h3aIvMbGy5YmSuSS7eneN7F9rH0hxubYD0+Gxe67NYHPFZj699dNc33z9jOcKIYQQQgixGKloM2VqgK6eLtyZGFnlnvB+fkouP0vqzi/6Xm9Y0RHLsr5lWdY2y7K2lZeXF/26eCGN0O0oImDzlULjxZzf+yyWpbO72w7YWgdayVs51mWTOAKT5JYG7PQ8Ld5OiaeEiyIXAFBTOnlPi3MbrmBI0+jpfx4YLem/47DFX//4VYKOajqTnRP6KwymeoBJArZJuAP2BsZswi63/1JrjHXaYbThtMYl58HSC6DhoskvMItqQVF3hF5dY2Bw5tL+d++8m7yZ5/IllwPQPpBmieqEkiY7kJyBUorb190+UpxFCCGEEEKI004hTjB7D+HN9ZN0Ttx2NZ3axsuKPncuArbjQP2Yj+sKx+ZEPDmLgA1gzQ1EEkdwZkMc6D8EwL4+uxT+imwOZ3CSYMnhYlCL4Cps/rtKO5uvd3aztHLyrXjnFIKVTms/MBqwZdJBLAv2HrH32x2LHxv3ukQmhtc0CQZn3pDo9tsBW74QsB06dIAyNUiwcevImPmjh2Hl22a81kyqfBWkNI3BgYmtCMY6PHCYe3bfw3VN17GxfCMAbf0pGvRutNLGaV8rhBBCCCHEGSNqB2yugcOEzAEyronbrqbjbrq06HPnImB7AViulGpUSrmAW4GfzcF1ARhK2QGL98SeXVNZ9XYspVGbsziesAt/7O/bj45OUy6HNzz56l7CXY4/a6dqDrS1cVkyRVlNw6TnlnhKaDacHPMM0J/M0pm0A71UKgBYPHfALt7Rl+4bf49cP2HTxO2fOQLXvPY+NyNlp4Q6u+z0S1U9eeGQU1ETstMqY4UAd6z799zPZ5/+LM+1P8edz92JV/dyx7Y7Rj7f1pekni4oaZjzcQkhhBBCCLEolTYBUGe2U6oGMSapkzGtkuIXO6bpRl0cy7LySqlPAI8AOvAdy7J2n+p1hyVSdqVHrztY3AsC5bD0Arb1H+Yn2Tg5M8f+vv1EtShODqNNEbBlvZWUJo4xlMmTbd9FHgeOkqkrHm501fIz7TB7jrfRlexCUxrX9z/EXYHtvC33YQD6M+NTDJPGIBHDRPMU8Sweu5KkmbYDttLBvZhoaFXripmFWakqtCIYSI5fEdzRtYMvvfAllFI8cOABAD5zzmco846uUqb6O/CQkYBNCCGEEEK8ebj8ZLyVNBodlKo4VjEVIscqos7EsDnZw2ZZ1sOWZa2wLKvZsqw7T/Y6f/2jV/nxiyekERZK83udRQZsgGq+jC35PiwMjsWPsT+2nwrDXtXSp+iPYAWrqFJ9PL6vi1X51xiIrAHn1GmYZ5VvJa8Uj+67l65kF1FPlObcQZblX+fjVR0AdCXGr7ClzARh07SLjMxkuBx7epC8YVKVaWHAU1P8ZsZZqIzYEX480zFyLGNk+Owzn6XKX8VjNz/Gly7+Ep/Y9AluXnHzuNc6hourSMAmhBBCCCHeRMzSJhpUB1EG0QKzDNhm4Q0rOjKTRCbPA4fv494dj447nsrEAfB7iw/YiCylMWuXqH+562W6Ul2U5woFMXyTL1c6IzVEGeSBZ/ezUR3E03TetLc4v+mtXJhM8UDvj3m27VnKvOWELHus7838AoBDsa5xr0lbScKGAa7AzM/gtlfYtOwAHYNpKlWMjH9C8c05UV66DICE0Tty7Bs7vsHhgcN87rzPEfVGubrxaj6y8SPomj5yzlAmTzTbZn8gAZsQQgghhHgTcVWsYIU6hldlcYbmr2XVognYXjnejbv8ETqzD2BZ1sjxVDYBQGA2DYAjS2jM2QHbIy2PAFCVLmR/eicP2LzROjRl4Wz5LR6Vw7/sgulvEa3hi909RAnQlmijxFVOhCEMzUVF3x6UqdObOmGFjQwR0xwJxqZVWIVzZIc41peiWvWiwvMTsHn9lQQNk6Rpp3Amc0nu3X0v1zdfz/m1U5ccbe9PsUQVgtJpGmYLIYQQQghxptHLmgkquyq8N/wmCNh+d/hFUBbKdYS2/tFy+Jmc3Tg64JtFqcxwPQHLwpN381z7cwBUJ01yOKdMKQyV24Uur9WftQ/UnT3tLbRAGSHT4v3pTYRcIco99ZSoOB11byPvLSdsmOP2sJmWSVplCymRRaywaTppzYczH+dYzyCV9OEunaegSNOIGhYJZQfHR+NHyVt5Lqm7ZOQUy7KIp8c31m4bSLNEdZH1VRVV0l8IIYQQQogzxpgWYN5I5bzdZtEEbK+1PwXAkMPkmdbDI8fT+ULA5p1FwBaoxNCcRLNODMsg6okSzSRIOkJTbvBzhO0m2ZdrL5P210FokobUYzl9ZHBRmczy8Dsf5sqq9xFhCBWsIr31Q1SaGRKF6pEA8WwcS0HQUOAorrN5Wg/gzseJdR1FVxaB8vlbxYpaOgnNbpzdMtgCwNLQUjJ5g/9+4ShX/csTbPvHX7O9JTbymvb+FEu0LqyI9FQTQgghhBBvMtHRgE0LFN9nerYWLGAbyg6xL7Zv5OPO5C4chVTI51t+P3I8a6RQlkWw0JesKJpG1l9DXdYEoCG0DE+uH8M9zTVCdsDmVxmcjdPvXwNAKYb0EI5MH2F3mMRQArfK4wyW4VqyjYhpksqNpkQOZuziKb5ZFObMOYJ4zATpbruwh6OkfoZXnLwSy82gI0/eMGkdtNsh1Pjr+IO7n+OvfvwqDl2jIuTmo/e/yLE+O4huG0hTr7pwljXN27iEEEIIIYRYlEoasSgsBvlm7rN8shYsYPuvvf/FrQ/eSleyi7xhEtPbuSSZQrcsjvc+O3JexkjjsSw8viLSCMewwvUsz9krRj7qiKghXKFpqrf4yjCVXVBDX3JOUfdIOSJ4cnbaY2rA7uHmCZXjCpQSMk2SxtDIucPpkX6KW10DyDsDBKwk6VihEuM87WEDKNH8DOgmfckMrYOtVPoq+c6TbbzQ0sfn37Geh//0Qu65/WwyeZMP3fsivUMZumP9VKk+aZothBBCCCHefJweVLjO/rv/DKwS2Z5oJ2/leejQQ2w/fpiUI8vWjEFTLkdf7uDIeVkzg9eycLpnV87eFV3K+ry9qpUcqiSqhvBNtxlQ09CCVfbf64sL2LKuEvx5u09aZtCusOgNl4MnQsQwSFrJkXP7MvZqm18VH7AZ7jBBlcTsP24fCM1fwFbqDGEqRUt/Fy2DLURdtXz9sde5cVMN7zmrDvXSvSx7/OPcfX05+zvjnPeFx9i9dzcallSIFEIIIYQQb06lTaA5iysqeJIWLGDrTdsBzs8O/ozHDj8PwMpAM6tyij5nH30Je3Usa2XxmhY4ZlfUwlHawJXpXtY4b6btWDOVehH9EYJVdsn9yrVF3cPwlhK2Bklm8+SHeuz7BqLgjdgpkWRHKl72JAsrbFrxgaflDhIiSYXVQ1bzjfZmmwdlbntuWvuO0zrQyqF2H1UhD/94gQPuuRp+/qew9+ec8+g7ePLGLO/aWkeFUejbJgGbEEIIIYR4M6o/BypWz6oR9mwtWMAWS8dQKA70H+Cx4/+Dy7RYX72JZmc5CYfB0y32KlvOyuKyLNBmOdRwPU4g3LWSTNdRAmYcqjZM/5rV18HW22FMr7Fp+aKUqjg98SxmotDDzFsK7hBhw8RSFvGc3Zutu9BEO6gXn9qpeewVtmrVS9pXNa/fCBV+e3XxQPcrDGQHSAz6+VbtQwTuuRR69sMN34BPbofIEmoefj+fb3iVb15TyNWVgE0IIYQQQrwZXfq/4EO/nddbLNwKW6qXi+ouwqW56MztZk02i7dqLRsq1gPwdOuLAGStHG7rJAKViF2gI9l1mM3qgH2s7qzpX3Phn8Nb7yz6Fo5AGRGVoHswgRruueYrBU3HZzoBGEjbKZM9qT6UZeFzF79KpnnDBLF7sJnB+UuHBKguzNdrffa8f8b8JWsP3g0bboFPbIfN77WXfD/4K2i8BB78c5z7HwSHFwLz13dCCCGEEEKIRUvTQC++qOBJ3WJerz6NWDpGQ6iBy5ZcBsCGTAbKV7G+4SJ0y6Kly64UmSWP62QCtrAdgNSqHs5yHMByBezlyjnkCtnlOwf7utELe9Tw2pUoPYW9agNZO2CLpfoImKC7gkVf3+GL4FQGTaoDZ0ndHI58opqyRjTL4kDqNQAuzLeTu/JOuPEb4B9T9cbpgXfdA4FKOPw7e3VtHlf+hBBCCCGEeDNbkIDNtExS+RQl7lKWee2AbXPaDtg8NVtpzuaIZfYDkMM4uYAtVIOJRq3q5jzXIVTtluJTHYvki9gBW6KvC1e2n5TmB91eWfMoe6/acHXIvnQ/YdNA8xS/IdHpt3vPhVQSb9n89WADCEWqiBoGA+YAmqWoyedxNl8y+cn+KNxyn72vsFRK+gshhBBCCDFf5nf9bgp5Kw/AXb9qZ6C7kj8N1XCxnrTTCT1hVucMHvX2kM0bZDFwWScRV+pOMr4qlsXbaMgfhrob5/gpIFDoaJ4Z6MKTGyDtCeMtfM6nBYAYfSk7YOvPxIgaBrqn+BU2d2C0WbgWmd8VNuUro9ww6HY4COddaOhQtnLqF9Rsgj/6BcymobkQQgghhBBiVhZkhS1v2gGbZQT5t/ds5c/K8rgqC+mKms5SrZSUbvBKeys5ZeLk5FbGtEg9l+mvoGHMvH/tJDiDdmXFRH8XIStOzjW6P83vtAOZrmTM/m/qKEtyeRy+4lfY3GObhc9jSX8AfFEq8gYANVmTfn8jOFzTv6Z2i6ywCSGEEEIIMY8WJGAzTDswWFlezbXrq9B790P5qpHPLwstA+CZIzvJKgvXSS4Eussa8GK3B5iPgG24o/lQXycRFcfwjAZYfk8JyrLoSsRI5VMM5HtZms/h9M2m6MiY1avw/K6w4fIRtb8srMolyJcX19pACCGEEEIIMX8WJGDLFVbY1lbWQv8RyAxC+Wj63brarQC81vnSKQVsw4VHKG0aXzhjrvhKAcjGuylhCMs7GrA5vCUETYveVD9HBo8AsDSXx+2fRS+1sfvd5nuFDQgXKluuyQ8RWLpp3u8nhBBCCCGEmN6CBGzpfA6AsytK4Ie3g+6ChotGPl9eu5EluRxt8V1klIVTOU/uRpFCoY66s09xxFNwekkrD/78ACVqCM03GhRqvhLCpkFfspfWwVYAGnI5XLNYYRvpmO4Jg7v4/m0nK1TYgdeQy+FfIgGbEEIIIYQQC21BArZsPodleLj8xT+Djlfh3fdCxWhKJGXLWJnN0WUcxVQK10kHbIUVtrptpz7oKaScEcpVPyGVxBkcDdic/lIihslgejRgW5LLo9zFFx3BUwju3oDVNYANZik3xodYn8lC5fo35J5CCCGEEEKIqS1YSmSJAe72F+Cmu2Hl1eNPCNezImswpKcAcGruk7tR/Tmw9XZYM/cVIodlXSU0qg4AXMHykeOuYClh02Qo10/LYAsBy4fPsmA2AZvLD0p/wwK2sLucf+iJYThKIFA+8wuEEEIIIYQQ82rBqkRWWnlU1TpY+46JJ2g6S7TR/WAuzXNyN3L54bqvzWvwYXhKaFTtAHjDZSPHvaEoEdMkkY/TOthKiVFIaZxNaqNSdoPqaPNcDnlKViGlM1kytw3GhRBCCCGEECdnQfqwmRjUGUmom6IxM7DU3wTYzbPd+kkGbG8EX5SQslcCHYHRlEhvoJSIYZA0k7QOtlKfK6ysuYsv6w/A+38G/rKZz5sDgZIKaAFv/cY35H5CCCGEEEKI6S3ICpvCoCyfgUI1yMnUVK6ltNAXbDEHbHpgTDDlLR35q110xCRDlv5MP8FUoZeca5bFQ8qWw5jqk/Opttbe8xdcuvkNuZ8QQgghhBBiegsSsFnKJGoY0wZs4fq1rMpmAXA7fW/U0GZt7L614TL/AHgjhA1z5MOStCKvuWZuRr2QostBc8xrkRYhhBBCCCFE8RYkYAOI4hzXe+1EWtlyVmbt8v9eh/+NGtaseSMVYz4YE7B5wkTM0YCtOmthLuLnAKDxIvjLg3bfOiGEEEIIIcSCW7iALdIAmj71CWXLWZfJABB0z6J32RvMHbJX2Az08RUgdSde094iqKFTm8+jPLPcv7YQvJGFHoEQQgghhBCiYOECtvI105/gDnJOLsC32jtp9Ne/MYM6CapQWdHwlNhVHcfwFBpRB/RKIiqN7p1FSX8hhBBCCCHEm96CBWyl1VPvXxuWCTVyXjqDw72IUwkLAZsrOLGSo0ez9945zAqa9B60N6ifmhBCCCGEEOLMsHArbEsvmvGckiVrAVhaWTrDmQuoELBNVsnRo9upnPlUhCXWcaha90aOTAghhBBCCHGaW5CATQN8pTM3g3ZW2kVJ9MW8wjYcqHknBpUuZ5j39lqEOqpwYEClBGxCCCGEEEKI4p1SwKaU+pxS6rhSakfhzzXFvM6BQp2w32tSDRdCoAoiS09lmPPL4QJPeHxJ/wLTE+EPBxKszgzaByRgE0IIIYQQQsyCYw6u8S+WZX15VjfV3cWdWLUe/mLfyYzpjXXjN+0eZifyRAiTYLV2BEN3o0u5fCGEEEIIIcQszEXANmu67lyI286fVddOelj3RfCrDOu1w5hlq9H1BZluIYQQQgghxGlqLvawfUIp9apS6jtKqYmVNwqUUh9WSm1XSm13ZVxzcNvFz+G3p2OzOoCjWtIhhRBCCCGEELMzY8CmlPq1UmrXJH9uAL4JNAObgHbgK1Ndx7Ksb1mWtc2yrG1VZVVz9gCLmStg72tzqxyqav0Cj0YIIYQQQghxupkxR8+yrCuKuZBS6j+BB095RGcQTzA6+kHl2oUbiBBCCCGEEOK0dKpVIqvHfPgOYNepDefM4g9LwCaEEEIIIYQ4eadaBeNLSqlNgAW0AB855RGdQRx+OyXSCtagJin7L4QQQgghhBDTOaWAzbKs983VQM5InggAqkoKjgghhBBCCCFmby6qRIqpeCOgOaBqw0KPRAghhBBCCHEaksZg88nhhj/8mexfE0IIIYQQQpwUCdjmW8MFCz0CIYQQQgghxGlKUiKFEEIIIYQQYpGSgE0IIYQQQgghFikJ2IQQQgghhBBikZKATQghhBBCCCEWKQnYhBBCCCGEEGKRkoBNCCGEEEIIIRYpCdiEEEIIIYQQYpGSgE0IIYQQQgghFikJ2IQQQgghhBBikZKATQghhBBCCCEWKQnYhBBCCCGEEGKRkoBNCCGEEEIIIRYpCdiEEEIIIYQQYpGSgE0IIYQQQgghFikJ2IQQQgghhBBikZKATQghhBBCCCEWKQnYhBBCCCGEEGKRkoBNCCGEEEIIIRYpCdiEEEIIIYQQYpGSgE0IIYQQQgghFikJ2IQQQgghhBBikZKATQghhBBCCCEWKWVZ1ht/U6XiwL43/ManhzKgZ6EHscBkDqYmczORzMnUZG4mkjmZmszN9GR+JpI5mZrMzUQyJxMttSyrfKaTHG/ESCaxz7KsbQt070VNKbX9zT43MgdTk7mZSOZkajI3E8mcTE3mZnoyPxPJnExN5mYimZOTJymRQgghhBBCCLFIScAmhBBCCCGEEIvUQgVs31qg+54OZG5kDqYjczORzMnUZG4mkjmZmszN9GR+JpI5mZrMzUQyJydpQYqOCCGEEEIIIYSYmaRECiGEEEIIIcQiJQGbEEIIIYQQQixSRQVsSqnvKKW6lFK7xhzbqJT6vVJqp1Lq50qpUOH4e5VSO8b8MZVSmwqfu0Up9apSardS6ovT3G9r4boHlFJfV0qpwvGbC681lVKLoizoIpqbf1ZKvVa4xgNKqch8P/uYMS2WOfiHwut3KKUeVUrVzPezz2SxzM2Yz39aKWUppcrm65lnsljmRCn1OaXU8THXvma+n30mi2VuCp/7ZOFnym6l1Jfm87mns1jmRCn1/8Zct0UptWO+n70Yi2h+Nimlni1cd7tS6uz5fvaZLKK5mfSeC2EB5uROpdRRpdTQCcfdhX9TB5RSzymlGubniYu3iObmYqXUS0qpvFLqXfP1vMVYRHNyh1JqT+Eav1FKLZ2vZ160LMua8Q9wMbAF2DXm2AvAJYW/fwD4h0let/7/b+dsQ6Wowjj+e+pSYZRYJogKpnSlL1Go5YcEywizwN5TCgqFULMXoYISTPqkWfQlKOgFs0Qz64NGoQmpfbkJipovlQpW18ykoijBUp8+nLO2rnev9+7s7jnX/f9g2DMzZ/bM89uzM+fMmRlgf0xfDvwAXBHn3wUmVje2ZlIAAAUtSURBVClvMzAOMOAz4La4/GpgFLABGNOTfW/0lJGbW4G2mF4ELGpBB5eW5XkCeEP1I7iJ64YBa4HvgYGt7gRYADyduo5k6uYmYD1wYZwf1OpOKvK8AsxPXV9y8gOsK0tPBjbIzSkfZy3zHHYyDhgM/FWxfDbxHA1MBT5owfpSzc1w4BpgKXCvnJw6J/WL6Vk51JdmTz0aYXP3TcBvFYvbgU0x/TlwTxebTgNWxPQIYK+7H4nz67vaxswGExreHR5+maXAnXE/9rj7tz3Z52aRkZt17n48Zu0AhtYWUe/JyMGfZVkvBpK/UScXN5FXgWdJ7CUzJ1mRkZtZwEJ3Pxb365faIipORk5KeQy4H1je+2jqT0Z+HCiNHPUHfup9NPUlIzc9KbMpNNNJLK/D3Q91sWoKoeEOsAqYWBqRTEUubtz9gLvvAE72LoL6k5GTL9z9aJxtahs3F4o8w7aL8IcDuI9w9b6SB/j/pLYPGGVmw82sjXAg62qbIUBn2XxnXNaXSO1mOuHqXkqSOCgNpwMPAvMLRdA4mu7GzKYAB919e/Hdbwip/jNz4i0W75jZgCIBNJAUbtqB8fFWpY1mNrZgDPUm5TF2PHDY3ffWuO/NIIWfp4DF8fj7MvBcoQgaRwo3PSkzJY1y0h1DgB8B4sXmPwgjMbmRwk3upHYyg/Rt3KZTpMM2HZhtZluAS4B/ylea2Q3AUXffCeDuvxOHMYEvgQPAiQLl50wyN2Y2DzgOLKt15+tEEgfuPs/dhxHin1MkgAbSVDdm1g94nnw7sJCmvrwOjASuBQ4RbnPLkRRu2oDLCLenPAOsTH31u4KU559pZDK61g0p/MwC5sbj71zg7SIBNJAUbrotMwPUnquO3JxJyjbuQ8AYYHGtO99Xaat1Q3f/hvDcFGbWDtxekWUqFSc1d18DrInbPAqcMLPzgS0xy2pCI6p8qHMocLDW/UxBKjdm9ghwB+He4NS3vaWuH8uAT4EXCgXSABK4GQlcCWyPbe6hwFYzu97df65fZLWTor64++HSQjN7E/ikTuHUlUT/pU7g43gc2WxmJ4GBwBEyIOExtg24Gxhdr1gaQSI/DwNPxvSHwFv1iKXeJDrWnK3MpDTKibt3d5HwIGGUpTP+r/oDvxYMpe4kcpM1qZyY2S3APMLzc8eKxtHn8J4/eDic0x86HBQ/zyPcqz29bN15hD/jiIrvKG0zANgGtFcpq/LB3ckV6zeQyUtHcnEDTAJ2Ex/qbFEHV5XleRxYlbpu5OKmIs8BEr50JBcnwOCyPHOBFanrSkZuZgIvxnQ74dYla2Uncd0kYGPqOpKjH2APMCGmJwJbUnvJyE3VMs91J2X5K18i8Rinv3RkZeq6koubsuVLSPzSkVycANcB+ylr57Xa1NMfaznhlqF/CVdeZxCupH0Xp4WUncyBCUBHle/ZHaep3ZQ3BtgZf5zXSt8N3BXLPwYcBtYmF5iPm32ERtW2ODXtDYkZOfgoLt9BuJIzRPXjzEY2iTtsuTgB3gO+jvVlNWUdOLnhAuD9uG4rcHOrO4nrlgAzU9eTHP0ANxKulm8HvgJGy80pN1XLbAEnL8VyTsbPBXH5RYSR2H2Eju6IesbZx92MjfN/E0Ydd8kJ6wnt/lIbd3Xq+tLsqXQwEUIIIYQQQgiRGUVeOiKEEEIIIYQQooGowyaEEEIIIYQQmaIOmxBCCCGEEEJkijpsQgghhBBCCJEp6rAJIYQQQgghRKaowyaEEEIIIYQQmaIOmxBCCCGEEEJkyn/d31kUIsDXpwAAAABJRU5ErkJggg==\n",
453 "text/plain": [
454 "<matplotlib.figure.Figure at 0x7f4349634a90>"
455 ]
456 },
457 "metadata": {},
458 "output_type": "display_data"
459 }
460 ],
461 "source": [
462 "era20c_corrected = city_era20c_df - result.seasonal - result.trend.mean()\n",
463 "fig, ax = plt.subplots(1, 1, figsize=(15, 5))\n",
464 "ax.set_xlim([dt.datetime(1979, 1, 1, 0, 0), dt.datetime(1979, 12, 31, 0, 0)])\n",
465 "ax.plot(era20c_corrected.index, era20c_corrected[\"2 metre temperature\"], label=\"ERA 20c corrected\")\n",
466 "ax.plot(city_era20c_df.index, city_era20c_df[\"2 metre temperature\"], label=\"ERA 20c\")\n",
467 "ax.plot(city_interim_df.index, city_interim_df[\"2 metre temperature\"], label=\"ERA interim\")\n",
468 "ax.legend(loc='upper left')"
469 ]
470 },
471 {
472 "cell_type": "markdown",
473 "metadata": {},
474 "source": [
475 "The correction was then applied to the data from the 20c data set from 1900 to 1979.\n",
476 "\n",
477 "We did run the same program to reconcile the values for snow cover for the two data sets. However, the differences between the two were too large, even after a correction. The data for a city would show a very snowy winter in the 20c data set, for instance, while the interim data set would have recorded only a few days of snow, and vice versa in another year. We subsequently decided to drop the \"snow cover\" column from our analysis.\n",
478 "\n",
479 "At this stage, we had a single data set of temperature averages by day from 1900 to 2017 for each 505 locations. (A further 53 locations were later added to the data set)."
480 ]
481 },
482 {
483 "cell_type": "code",
484 "execution_count": null,
485 "metadata": {},
486 "outputs": [],
487 "source": []
488 }
489 ],
490 "metadata": {
491 "kernelspec": {
492 "display_name": "Python 3",
493 "language": "python",
494 "name": "python3"
495 },
496 "language_info": {
497 "codemirror_mode": {
498 "name": "ipython",
499 "version": 3
500 },
501 "file_extension": ".py",
502 "mimetype": "text/x-python",
503 "name": "python",
504 "nbconvert_exporter": "python",
505 "pygments_lexer": "ipython3",
506 "version": "3.5.2"
507 }
508 },
509 "nbformat": 4,
510 "nbformat_minor": 2
511}