{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Test notebook for Pastas with PEST++ GLM and PEST HP Solver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n", "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Fetched release '5.2.16' info from 'usgs/pestpp'.\n", "\n", "Downloading 'https://github.com/usgs/pestpp/releases/download/5.2.16/pestpp-5.2.16-linux.tar.gz' to '/tmp/tmp9hm8j3fj/pestpp-5.2.16-linux.tar.gz'.\n", "['pestpp-glm']\n", "\n", "A *.tar.gz file ('/tmp/tmp9hm8j3fj/pestpp-5.2.16-linux.tar.gz') has been downloaded and will be converted to a zip file ('/tmp/tmp9hm8j3fj/pestpp-5.2.16-linux.zip').\n", "\n", "Extracting 1 file to '/home/vonkm/repos/pastas-plugins/docs/examples/bin'\n", "pestpp-glm\n", "\n", "Updated pyemu metadata file: '/home/vonkm/.local/share/pyemu/get_pestpp.json'\n" ] } ], "source": [ "from pathlib import Path\n", "\n", "import pandas as pd\n", "import pastas as ps\n", "from pyemu.utils import get_pestpp\n", "\n", "import pastas_plugins.pest as psp\n", "\n", "get_pestpp(bindir=\"bin\", subset=[\"pestpp-glm\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "head = (\n", " pd.read_csv(\n", " \"https://raw.githubusercontent.com/pastas/pastas/master/doc/examples/data/head_nb1.csv\",\n", " index_col=\"date\",\n", " parse_dates=True,\n", " ).squeeze()\n", ").iloc[-300:]\n", "prec = pd.read_csv(\n", " \"https://raw.githubusercontent.com/pastas/pastas/master/doc/examples/data/rain_nb1.csv\",\n", " index_col=\"date\",\n", " parse_dates=True,\n", ").squeeze()\n", "evap = pd.read_csv(\n", " \"https://raw.githubusercontent.com/pastas/pastas/master/doc/examples/data/evap_nb1.csv\",\n", " index_col=\"date\",\n", " parse_dates=True,\n", ").squeeze()\n", "pex = (prec - evap).dropna().rename(\"PrecipitationExcess\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Model" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ml = ps.Model(head, name=\"PestGLM\")\n", "sm = ps.StressModel(\n", " pex, ps.Exponential(), name=\"pex\", settings=ps.rcParams[\"timeseries\"][\"evap\"]\n", ")\n", "ml.add_stressmodel(sm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solve with Pest GLM" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2026-03-10 16:17:15.804463 starting: opening PstFrom.log for logging\n", "2026-03-10 16:17:15.804758 starting PstFrom process\n", "2026-03-10 16:17:15.804797 starting: setting up dirs\n", "2026-03-10 16:17:15.805236 starting: removing existing new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp'\n", "2026-03-10 16:17:15.807524 finished: removing existing new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp' took: 0:00:00.002288\n", "2026-03-10 16:17:15.807612 starting: copying original_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/model' to new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp'\n", "2026-03-10 16:17:15.809125 finished: copying original_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/model' to new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp' took: 0:00:00.001513\n", "2026-03-10 16:17:15.809449 finished: setting up dirs took: 0:00:00.004652\n", "2026-03-10 16:17:15.826062 starting: adding grid type d style parameters for file(s) ['parameters_sel.csv']\n", "2026-03-10 16:17:15.826314 starting: loading list-style /home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/parameters_sel.csv\n", "2026-03-10 16:17:15.826398 starting: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/parameters_sel.csv\n", "2026-03-10 16:17:15.827231 finished: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/parameters_sel.csv took: 0:00:00.000833\n", "2026-03-10 16:17:15.827310 loaded list-style '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/parameters_sel.csv' of shape (3, 2)\n", "2026-03-10 16:17:15.828187 finished: loading list-style /home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/parameters_sel.csv took: 0:00:00.001873\n", "2026-03-10 16:17:15.828337 starting: writing list-style template file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/parameters_sel.csv.tpl'\n", "2026-03-10 16:17:15.834590 finished: writing list-style template file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/parameters_sel.csv.tpl' took: 0:00:00.006253\n", "2026-03-10 16:17:15.836909 finished: adding grid type d style parameters for file(s) ['parameters_sel.csv'] took: 0:00:00.010847\n", "2026-03-10 16:17:15.836998 starting: adding observations from output file simulation.csv\n", "2026-03-10 16:17:15.837034 starting: adding observations from tabular output file '['simulation.csv']'\n", "2026-03-10 16:17:15.837091 starting: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/simulation.csv\n", "2026-03-10 16:17:15.837977 finished: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/simulation.csv took: 0:00:00.000886\n", "2026-03-10 16:17:15.839709 starting: building insfile for tabular output file simulation.csv\n", "2026-03-10 16:17:15.843697 finished: building insfile for tabular output file simulation.csv took: 0:00:00.003988\n", "2026-03-10 16:17:15.843780 starting: adding observation from instruction file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/simulation.csv.ins'\n", "2026-03-10 16:17:15.847541 finished: adding observation from instruction file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp/simulation.csv.ins' took: 0:00:00.003761\n", "2026-03-10 16:17:15.847993 finished: adding observations from tabular output file '['simulation.csv']' took: 0:00:00.010959\n", "2026-03-10 16:17:15.848051 finished: adding observations from output file simulation.csv took: 0:00:00.011053\n", "2026-03-10 16:17:15.848450 WARNING: add_py_function() command: run() is not being called directly\n", "noptmax:0, npar_adj:3, nnz_obs:300\n", "noptmax:100, npar_adj:3, nnz_obs:300\n", "\n", "\n", " pestpp-glm: a tool for GLM parameter estimation and FOSM uncertainty analysis\n", "\n", " by The PEST++ Development Team\n", "\n", "\n", "version: 5.2.16\n", "binary compiled on Dec 1 2024 at 10:51:08\n", "\n", "started at 03/10/26 16:17:15\n", "...processing command line: ' ./pestpp-glm pest.pst /h :4004'\n", "...using panther run manager in master mode using port 4004\n", "\n", "using control file: \"pest.pst\"\n", "\n", "in directory: \"/home/vonkm/repos/pastas-plugins/docs/examples/pestf_glm/temp\"\n", "on host: \"asuszbs14\"\n", "\n", "processing control file pest.pst\n", "\n", "\n", ":~-._ _.-~:\n", ": :.~^o._ ________---------________ _.o^~.:.:\n", " : ::.`?88booo~~~.::::::::...::::::::::::..~~oood88P'.::.:\n", " : ::: `?88P .:::.... ........:::::. ?88P' :::. :\n", " : :::. `? .::. . ...........:::. P' .:::. :\n", " : ::: ... .. ... .. .::::......::. :::. :\n", " ` :' .... .. .:::::. . ..:::::::....:::. `: .'\n", " :.. ____:::::::::. . . ....:::::::::____ ... :\n", " :... `:~ ^~-:::::.. .........:::::-~^ ~::.::::\n", " `.::. `\\ (8) \\b:::..::.:.:::::::d/ (8) /'.::::'\n", " ::::. ~-._v |b.::::::::::::::d| v_.-~..:::::\n", " `.:::::... ~~^?888b..:::::::::::d888P^~...::::::::'\n", " `.::::::::::....~~~ .:::::::::~~~:::::::::::::::'\n", " `..::::::::::: . ....:::: ::::::::::::,'\n", " `. .::::::: . .::::. ::::::::'.'\n", " `._ .::: . :::::. :::::_.'\n", " `-. : . ::::: :,-'\n", " :. :___ .:::___ .::\n", " ..--~~~~--:+::. ~~^?b..:::dP^~~.::++:--~~~~--..\n", " ___....--`+:::. `~8~' .:::+'--....___\n", " ~~ __..---`_=:: ___gd8bg___ :==_'---..__ ~~\n", " -~~~ _.--~~`-.~~~~~~~~~~~~~~~,-' ~~--._ ~~~-\n", "\n", "\n", " starting PANTHER master...\n", "\n", "IP addresses:\n", " 0.0.0.0:4004 (IPv4)\n", "\n", "PANTHER master listening on socket: 0.0.0.0:4004 (IPv4)\n", "\n", "\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 1\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 0\n", "\n", " calculating jacobian... \n", " running model 4 times\n", " starting at 03/10/26 16:17:15\n", "\n", " waiting for agents to appear...\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/logger.py:100: PyemuWarning: 2026-03-10 16:17:15.848450 WARNING: add_py_function() command: run() is not being called directly\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "03/10 16:17:16 remaining file transfers: 0 \n", "\n", " 4 runs complete : 0 runs failed\n", " 0.000958 avg run time (min) : 0.0123 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:16\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:16 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000915 avg run time (min) : 0.00188 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 1\n", " starting phi = 66.2227; ending phi = 41.7098 (62.9841% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 2\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 14\n", "\n", " calculating jacobian... \n", " running model 3 times\n", " starting at 03/10/26 16:17:16\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:16 remaining file transfers: 0 \n", "\n", " 3 runs complete : 0 runs failed\n", " 0.000922 avg run time (min) : 0.000967 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:16\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:16 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000881 avg run time (min) : 0.0018 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 2\n", " starting phi = 41.7098; ending phi = 33.4205 (80.1262% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 3\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 27\n", "\n", " calculating jacobian... \n", " running model 3 times\n", " starting at 03/10/26 16:17:16\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 3 runs complete : 0 runs failed\n", " 0.00085 avg run time (min) : 0.000917 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000948 avg run time (min) : 0.002 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 3\n", " starting phi = 33.4205; ending phi = 29.3008 (87.6733% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 4\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 40\n", "\n", " calculating jacobian... \n", " running model 3 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 3 runs complete : 0 runs failed\n", " 0.000894 avg run time (min) : 0.00095 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000956 avg run time (min) : 0.00192 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 4\n", " starting phi = 29.3008; ending phi = 26.7954 (91.4491% of starting phi)\n", "\n", " Switching to central derivatives:\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 5\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 53\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000978 avg run time (min) : 0.00102 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000858 avg run time (min) : 0.00183 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 5\n", " starting phi = 26.7954; ending phi = 25.9024 (96.6674% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 6\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 69\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000894 avg run time (min) : 0.00105 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000896 avg run time (min) : 0.00183 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 6\n", " starting phi = 25.9024; ending phi = 24.33 (93.9297% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 7\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 85\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:17 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000822 avg run time (min) : 0.000933 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:17\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000912 avg run time (min) : 0.0018 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 7\n", " starting phi = 24.33; ending phi = 23.2434 (95.534% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 8\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 101\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000864 avg run time (min) : 0.0009 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.00096 avg run time (min) : 0.00187 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 8\n", " starting phi = 23.2434; ending phi = 22.4447 (96.5637% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 9\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 117\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000906 avg run time (min) : 0.00095 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000871 avg run time (min) : 0.0018 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 9\n", " starting phi = 22.4447; ending phi = 21.8342 (97.2796% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 10\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 133\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000878 avg run time (min) : 0.00095 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000862 avg run time (min) : 0.00175 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 10\n", " starting phi = 21.8342; ending phi = 21.3529 (97.7957% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 11\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 149\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000853 avg run time (min) : 0.000917 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000852 avg run time (min) : 0.00183 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 11\n", " starting phi = 21.3529; ending phi = 20.9654 (98.1853% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 12\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 165\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:18 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000889 avg run time (min) : 0.000967 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:18\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000873 avg run time (min) : 0.0018 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 12\n", " starting phi = 20.9654; ending phi = 20.6459 (98.4763% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 13\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 181\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000914 avg run time (min) : 0.000983 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.00085 avg run time (min) : 0.00172 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 13\n", " starting phi = 20.6459; ending phi = 20.379 (98.707% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 14\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 197\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000839 avg run time (min) : 0.0009 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000858 avg run time (min) : 0.00172 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 14\n", " starting phi = 20.379; ending phi = 20.152 (98.8863% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 15\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 213\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000839 avg run time (min) : 0.00095 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000894 avg run time (min) : 0.00185 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 15\n", " starting phi = 20.152; ending phi = 19.9576 (99.035% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 16\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 229\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000828 avg run time (min) : 0.000967 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000877 avg run time (min) : 0.00177 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 16\n", " starting phi = 19.9576; ending phi = 19.788 (99.1503% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 17\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 245\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:19 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000872 avg run time (min) : 0.000933 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:19\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000854 avg run time (min) : 0.00182 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 17\n", " starting phi = 19.788; ending phi = 19.6395 (99.2498% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 18\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 261\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000925 avg run time (min) : 0.001 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000875 avg run time (min) : 0.0018 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 18\n", " starting phi = 19.6395; ending phi = 19.5085 (99.3327% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 19\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 277\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000878 avg run time (min) : 0.000933 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000871 avg run time (min) : 0.00182 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 19\n", " starting phi = 19.5085; ending phi = 19.3915 (99.4006% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 20\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 293\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000889 avg run time (min) : 0.000983 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.00091 avg run time (min) : 0.00187 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 20\n", " starting phi = 19.3915; ending phi = 19.2865 (99.4583% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 21\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 309\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.00121 avg run time (min) : 0.00175 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000831 avg run time (min) : 0.00182 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 21\n", " starting phi = 19.2865; ending phi = 19.1912 (99.506% of starting phi)\n", "\n", "\n", "OPTIMISATION ITERATION NUMBER: 22\n", "\n", " Iteration type: base parameter solution\n", " SVD Package: RedSVD\n", " Model calls so far : 325\n", "\n", " calculating jacobian... \n", " running model 6 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:20 remaining file transfers: 0 \n", "\n", " 6 runs complete : 0 runs failed\n", " 0.000856 avg run time (min) : 0.000917 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " computing upgrade vectors... \n", " computing upgrade vector (lambda = 0.1) 1 / 5 \n", " computing upgrade vector (lambda = 1) 2 / 5 \n", " computing upgrade vector (lambda = 10) 3 / 5 \n", " computing upgrade vector (lambda = 100) 4 / 5 \n", " computing upgrade vector (lambda = 1000) 5 / 5 \n", "-->starting iteration FOSM process...\n", "2026-03-10 16:17:21.103524 : recv'd terminate signal2026-03-10 16:17:21.104887 : recv'd terminate signal2026-03-10 16:17:21.103496 : recv'd terminate signal2026-03-10 16:17:21.108140 : recv'd terminate signal\n", "\n", "\n", "2026-03-10 16:17:21.108192 : recv'd terminate signal2026-03-10 16:17:21.103293 : recv'd terminate signal\n", "\n", "\n", "2026-03-10 16:17:21.103496 : recv'd terminate signal2026-03-10 16:17:21.103914 : recv'd terminate signal\n", "\n", "-->finished iteration FOSM process...\n", "\n", " performing upgrade vector model runs... running model 10 times\n", " starting at 03/10/26 16:17:20\n", " 8 agents ready\n", "\n", "\n", "PANTHER progress\n", " avg = average model run time in minutes\n", " runs(C = completed | F = failed | T = timed out)\n", " agents(R = running | W = waiting | U = unavailable)\n", "--------------------------------------------------------------------------------\n", "03/10 16:17:21 remaining file transfers: 0 \n", "\n", " 10 runs complete : 0 runs failed\n", " 0.000867 avg run time (min) : 0.00177 run mgr time (min)\n", " 8 agents connected\n", "\n", "\n", " testing upgrade vectors... \n", "\n", " ...Lambda testing complete for iteration 22\n", " starting phi = 19.1912; ending phi = 19.1042 (99.5466% of starting phi)\n", "\n", "-----------------------------------------\n", " --- OPTIMIZATION COMPLETE --- \n", " Reason for terminating PEST++ simulation: PHIREDSTP / NPHISTP criterion met\n", " Summary of termination criteria:\n", " NOPTMAX = 100 ; NOPT at termination = 22\n", " NPHINORED = 3 ; NPHINORED at termination = 0\n", " NRELPAR = 3; RELPARSTP = 0.01 ; NRELPAR at termination = 0\n", " PHIREDSTP = 0.01; NPHISTP = 3\n", " NPHISTP lowest PHI's:\n", " 19.1042\n", " 19.1912\n", " 19.2865\n", "\n", "FINAL OPTIMISATION RESULTS\n", "\n", " Final phi Total : 19.1042\n", " Contribution to phi from observation group \"oname:simulation.csv_otype:lst_usecol:observations\" : 19.1042\n", "\n", "\n", "...starting posterior FOSM calculations...\n", "\n", "\n", "pestpp-glm analysis complete...\n", "started at 03/10/26 16:17:15\n", "finished at 03/10/26 16:17:21\n", "took 0.0833333 minutes\n" ] } ], "source": [ "solver = psp.PestGlmSolver(\n", " exe_name=\"bin/pestpp-glm\",\n", " model_ws=Path(\"pestf_glm/model\"),\n", " temp_ws=Path(\"pestf_glm/temp\"),\n", " noptmax=100,\n", ")\n", "ml.solve(solver=solver, report=False)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = ml.plots.results(stderr=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solve with Pest HP" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2026-03-10 16:17:22.344834 starting: opening PstFrom.log for logging\n", "2026-03-10 16:17:22.345291 starting PstFrom process\n", "2026-03-10 16:17:22.345375 starting: setting up dirs\n", "2026-03-10 16:17:22.345454 starting: removing existing new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp'\n", "2026-03-10 16:17:22.347364 finished: removing existing new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp' took: 0:00:00.001910\n", "2026-03-10 16:17:22.347436 starting: copying original_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/model' to new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp'\n", "2026-03-10 16:17:22.348156 finished: copying original_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/model' to new_d '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp' took: 0:00:00.000720\n", "2026-03-10 16:17:22.348508 finished: setting up dirs took: 0:00:00.003133\n", "2026-03-10 16:17:22.366650 starting: adding grid type d style parameters for file(s) ['parameters_sel.csv']\n", "2026-03-10 16:17:22.366819 starting: loading list-style /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv\n", "2026-03-10 16:17:22.366909 starting: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv\n", "2026-03-10 16:17:22.367891 finished: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv took: 0:00:00.000982\n", "2026-03-10 16:17:22.367989 loaded list-style '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv' of shape (3, 2)\n", "2026-03-10 16:17:22.368840 finished: loading list-style /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv took: 0:00:00.002021\n", "2026-03-10 16:17:22.368949 starting: writing list-style template file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv.tpl'\n", "2026-03-10 16:17:22.376908 finished: writing list-style template file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv.tpl' took: 0:00:00.007959\n", "2026-03-10 16:17:22.380008 finished: adding grid type d style parameters for file(s) ['parameters_sel.csv'] took: 0:00:00.013358\n", "2026-03-10 16:17:22.380118 starting: adding observations from output file simulation.csv\n", "2026-03-10 16:17:22.380161 starting: adding observations from tabular output file '['simulation.csv']'\n", "2026-03-10 16:17:22.380224 starting: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/simulation.csv\n", "2026-03-10 16:17:22.381543 finished: reading list-style file: /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/simulation.csv took: 0:00:00.001319\n", "2026-03-10 16:17:22.383675 starting: building insfile for tabular output file simulation.csv\n", "2026-03-10 16:17:22.390587 finished: building insfile for tabular output file simulation.csv took: 0:00:00.006912\n", "2026-03-10 16:17:22.390717 starting: adding observation from instruction file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/simulation.csv.ins'\n", "2026-03-10 16:17:22.395949 finished: adding observation from instruction file '/home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/simulation.csv.ins' took: 0:00:00.005232\n", "2026-03-10 16:17:22.396603 finished: adding observations from tabular output file '['simulation.csv']' took: 0:00:00.016442\n", "2026-03-10 16:17:22.396679 finished: adding observations from output file simulation.csv took: 0:00:00.016561\n", "2026-03-10 16:17:22.397013 WARNING: add_py_function() command: run() is not being called directly\n", "2026-03-10 16:17:22.404732 starting: Converting parameters to shortnames\n", "2026-03-10 16:17:22.406571 starting: Renaming parameters for shortnames\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/logger.py:100: PyemuWarning: 2026-03-10 16:17:22.397013 WARNING: add_py_function() command: run() is not being called directly\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " find/replace long->short in /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv.tpl\n", " find/replace long->short in /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/parameters_sel.csv.tpl... took 0.00 s\n", "2026-03-10 16:17:23.307873 finished: Renaming parameters for shortnames took: 0:00:00.901302\n", "2026-03-10 16:17:23.312390 finished: Converting parameters to shortnames took: 0:00:00.907658\n", "2026-03-10 16:17:23.313072 starting: Converting observations to shortnames\n", "2026-03-10 16:17:23.315729 starting: Renaming observations for shortnames\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n", "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " find/replace long->short in /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/simulation.csv.ins\n", " find/replace long->short in /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp/simulation.csv.ins... took 0.01 s\n", "2026-03-10 16:17:24.484335 finished: Renaming observations for shortnames took: 0:00:01.168606\n", "2026-03-10 16:17:24.493175 finished: Converting observations to shortnames took: 0:00:01.180103\n", "noptmax:0, npar_adj:3, nnz_obs:300\n", "noptmax:30, npar_adj:3, nnz_obs:300\n", "./pest_hp pest.pst /h :4005\n", "\n", " PEST_HP Version 18.46. Watermark Numerical Computing.\n", "./agent_hp pest.pst /h asuszbs14:4005\n", "\n", " AGENT_HP Version 18.46. Watermark Numerical Computing.\n", "\n", " Performing numerical speed test...\n", "\n", " PEST is running in parameter estimation mode.\n", "\n", " PEST run record: case pest\n", " (See file pest.rec for full details.)\n", "\n", " Model command line: \n", " python forward_run.py\n", "\n", " RUNNING MODEL FOR FIRST TIME .....\n", "\n", " Running model 1 time....\n", "\n", " Waiting for at least one agent to appear....\n", " Speed index for this machine = 1009.0 \n", "\n", " New agent has appeared:-\n", " Local working directory \"asuszbs14 x86_64 Linux /home/vonkm/repos/pastas-plugins/docs/examples/pestf_hp/temp\"\n", " Agent speed index: 1009.0 \n", "\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:26.401559\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:26.413691\n", "starting arr mlt 2026-03-10 16:17:26.413713\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:26.413814\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Sum of squared weighted residuals (ie phi) = 66.223 \n", "\n", "\n", " OPTIMISATION ITERATION NO. : 1\n", " Model calls so far : 1\n", " Starting phi for this iteration: 66.223 \n", "\n", " Calculating Jacobian matrix: running model 3 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:28.170198\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:28.178418\n", "starting arr mlt 2026-03-10 16:17:28.178439\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:28.178658\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:29.790057\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:29.798909\n", "starting arr mlt 2026-03-10 16:17:29.798928\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:29.799021\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:31.456294\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:31.466373\n", "starting arr mlt 2026-03-10 16:17:31.466441\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:31.466581\n", " Model run complete.\n", " 3\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:33.138147\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:33.146866\n", "starting arr mlt 2026-03-10 16:17:33.146884\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:33.147045\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:34.665711\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:34.674236\n", "starting arr mlt 2026-03-10 16:17:34.674263\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:34.674384\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:36.175363\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:36.183849\n", "starting arr mlt 2026-03-10 16:17:36.183879\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:36.183992\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 2.2074 upgrade fraction = 1.0000 ----->\n", " Phi = 34.416 ( 0.520 of starting phi)\n", "\n", " Lambda = 1.0246 upgrade fraction = 1.0000 ----->\n", " Phi = 34.082 ( 0.515 of starting phi)\n", "\n", " Lambda = 4.7557 upgrade fraction = 1.0000 ----->\n", " Phi = 34.752 ( 0.525 of starting phi)\n", "\n", " Maximum relative change: 1.156 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 2\n", " Model calls so far : 7\n", " Starting phi for this iteration: 34.082 \n", "\n", " Calculating Jacobian matrix: running model 3 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:37.633968\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:37.642451\n", "starting arr mlt 2026-03-10 16:17:37.642504\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:37.642620\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:39.070069\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:39.078673\n", "starting arr mlt 2026-03-10 16:17:39.078694\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:39.078780\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:40.778661\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:40.787436\n", "starting arr mlt 2026-03-10 16:17:40.787456\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:40.787592\n", " Model run complete.\n", " 3\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:42.549884\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:42.560372\n", "starting arr mlt 2026-03-10 16:17:42.560401\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:42.560522\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:44.585895\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:44.596050\n", "starting arr mlt 2026-03-10 16:17:44.596359\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:44.596553\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:47.706186\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:47.717736\n", "starting arr mlt 2026-03-10 16:17:47.717761\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:47.717894\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 0.47557 upgrade fraction = 1.0000 ----->\n", " Phi = 18.820 ( 0.552 of starting phi)\n", "\n", " Lambda = 0.23244 upgrade fraction = 1.0000 ----->\n", " Phi = 18.156 ( 0.533 of starting phi)\n", "\n", " Lambda = 0.97303 upgrade fraction = 1.0000 ----->\n", " Phi = 19.618 ( 0.576 of starting phi)\n", "\n", " Maximum relative change: 1.717 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 3\n", " Model calls so far : 13\n", " Starting phi for this iteration: 18.156 \n", "\n", " Calculating Jacobian matrix: running model 3 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:49.714533\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:49.725737\n", "starting arr mlt 2026-03-10 16:17:49.725764\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:49.725883\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:51.538417\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:51.548459\n", "starting arr mlt 2026-03-10 16:17:51.548484\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:51.548597\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:53.897806\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:53.910750\n", "starting arr mlt 2026-03-10 16:17:53.910777\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:53.910902\n", " Model run complete.\n", " 3\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:56.064577\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:56.077840\n", "starting arr mlt 2026-03-10 16:17:56.077864\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:56.077989\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:57.898720\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:57.910288\n", "starting arr mlt 2026-03-10 16:17:57.910310\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:57.910420\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:17:59.633567\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:17:59.642016\n", "starting arr mlt 2026-03-10 16:17:59.642037\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:17:59.642227\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 0.11361 upgrade fraction = 1.0000 ----->\n", " Phi = 8.9037 ( 0.490 of starting phi)\n", "\n", " Lambda = 5.68029E-02 upgrade fraction = 1.0000 ----->\n", " Phi = 8.6576 ( 0.477 of starting phi)\n", "\n", " Lambda = 0.22721 upgrade fraction = 1.0000 ----->\n", " Phi = 9.3930 ( 0.517 of starting phi)\n", "\n", " Maximum relative change: 1.833 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 4\n", " Model calls so far : 19\n", " Starting phi for this iteration: 8.6576 \n", "\n", " Calculating Jacobian matrix: running model 3 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:01.370504\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:01.381519\n", "starting arr mlt 2026-03-10 16:18:01.381543\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:01.381655\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:03.167283\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:03.177904\n", "starting arr mlt 2026-03-10 16:18:03.177975\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:03.178217\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:05.015639\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:05.028414\n", "starting arr mlt 2026-03-10 16:18:05.028439\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:05.028566\n", " Model run complete.\n", " 3\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:06.843510\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:06.853621\n", "starting arr mlt 2026-03-10 16:18:06.853642\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:06.853781\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:08.712191\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:08.724466\n", "starting arr mlt 2026-03-10 16:18:08.724489\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:08.724598\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:10.636503\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:10.646395\n", "starting arr mlt 2026-03-10 16:18:10.646421\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:10.646575\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 2.84014E-02 upgrade fraction = 1.0000 ----->\n", " Phi = 4.9242 ( 0.569 of starting phi)\n", "\n", " Lambda = 1.42007E-02 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8848 ( 0.564 of starting phi)\n", "\n", " Lambda = 5.68029E-02 upgrade fraction = 1.0000 ----->\n", " Phi = 5.0909 ( 0.588 of starting phi)\n", "\n", " Maximum relative change: 0.4878 [\"p0\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 5\n", " Model calls so far : 25\n", " Starting phi for this iteration: 4.8848 \n", "\n", " Calculating Jacobian matrix: running model 3 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:12.459150\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:12.469801\n", "starting arr mlt 2026-03-10 16:18:12.469824\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:12.469935\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:14.360386\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:14.371277\n", "starting arr mlt 2026-03-10 16:18:14.371309\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:14.371487\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:16.290336\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:16.302000\n", "starting arr mlt 2026-03-10 16:18:16.302030\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:16.302175\n", " Model run complete.\n", " 3\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:18.149376\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:18.159749\n", "starting arr mlt 2026-03-10 16:18:18.159882\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:18.160486\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:21.018615\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:21.029816\n", "starting arr mlt 2026-03-10 16:18:21.029847\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:21.029997\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:23.094785\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:23.106837\n", "starting arr mlt 2026-03-10 16:18:23.106862\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:23.106980\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 7.10036E-03 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 0.997 of starting phi)\n", "\n", " Lambda = 3.55018E-03 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 0.997 of starting phi)\n", "\n", " Lambda = 1.42007E-02 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 0.997 of starting phi)\n", "\n", " Relative phi reduction between optimisation iterations less than 0.1000\n", " Switch to higher order derivatives calculation\n", " Split slope derivatives analysis begins during next iteration\n", "\n", " Maximum relative change: 4.3184E-02 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 6\n", " Model calls so far : 31\n", " Starting phi for this iteration: 4.8722 \n", "\n", " Calculating Jacobian matrix: running model 6 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:25.139260\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:25.149738\n", "starting arr mlt 2026-03-10 16:18:25.149762\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:25.150561\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:27.316597\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:27.328734\n", "starting arr mlt 2026-03-10 16:18:27.328761\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:27.328901\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:29.336944\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:29.348090\n", "starting arr mlt 2026-03-10 16:18:29.348114\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:29.348224\n", " Model run complete.\n", " 3\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:31.408572\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:31.425096\n", "starting arr mlt 2026-03-10 16:18:31.425133\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:31.425564\n", " Model run complete.\n", " 4\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:33.567926\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:33.579054\n", "starting arr mlt 2026-03-10 16:18:33.579080\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:33.579240\n", " Model run complete.\n", " 5\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:35.411108\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:35.421682\n", "starting arr mlt 2026-03-10 16:18:35.421706\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:35.421814\n", " Model run complete.\n", " 6\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:37.233512\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:37.243534\n", "starting arr mlt 2026-03-10 16:18:37.243561\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:37.243682\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:39.049792\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:39.062373\n", "starting arr mlt 2026-03-10 16:18:39.062398\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:39.062515\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:40.965917\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:40.975927\n", "starting arr mlt 2026-03-10 16:18:40.975953\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:40.976076\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 7.10036E-03 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8719 ( 1.000 of starting phi)\n", "\n", " Lambda = 3.55018E-03 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8719 ( 1.000 of starting phi)\n", "\n", " Lambda = 1.42007E-02 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8719 ( 1.000 of starting phi)\n", "\n", " Maximum relative change: 7.9581E-03 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 7\n", " Model calls so far : 40\n", " Starting phi for this iteration: 4.8719 \n", "\n", " Calculating Jacobian matrix: running model 6 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:42.722969\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:42.733602\n", "starting arr mlt 2026-03-10 16:18:42.733640\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:42.733796\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:44.379122\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:44.390122\n", "starting arr mlt 2026-03-10 16:18:44.390144\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:44.390367\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:45.949337\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:45.958137\n", "starting arr mlt 2026-03-10 16:18:45.958156\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:45.958236\n", " Model run complete.\n", " 3\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:47.587825\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:47.598466\n", "starting arr mlt 2026-03-10 16:18:47.598488\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:47.598612\n", " Model run complete.\n", " 4\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:49.190915\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:49.199183\n", "starting arr mlt 2026-03-10 16:18:49.199444\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:49.199582\n", " Model run complete.\n", " 5\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:50.764520\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:50.774748\n", "starting arr mlt 2026-03-10 16:18:50.774769\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:50.774904\n", " Model run complete.\n", " 6\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:52.388378\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:52.396933\n", "starting arr mlt 2026-03-10 16:18:52.396954\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:52.397050\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:55.044713\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:55.053256\n", "starting arr mlt 2026-03-10 16:18:55.053281\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:55.053398\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:56.626743\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:56.635707\n", "starting arr mlt 2026-03-10 16:18:56.635727\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:56.635827\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 1.77509E-03 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8721 ( 1.000 times starting phi)\n", "\n", " Lambda = 5.86869E-04 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8721 ( 1.000 times starting phi)\n", "\n", " Lambda = 5.36908E-03 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8721 ( 1.000 times starting phi)\n", "\n", " Maximum relative change: 1.6414E-03 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 8\n", " Model calls so far : 49\n", " Starting phi for this iteration: 4.8721 \n", "\n", " Calculating Jacobian matrix: running model 6 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:58.258839\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:58.267825\n", "starting arr mlt 2026-03-10 16:18:58.267844\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:58.267934\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:18:59.893013\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:18:59.903051\n", "starting arr mlt 2026-03-10 16:18:59.903077\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:18:59.903209\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:01.535953\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:01.546257\n", "starting arr mlt 2026-03-10 16:19:01.546279\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:01.546402\n", " Model run complete.\n", " 3\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:03.049160\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:03.057930\n", "starting arr mlt 2026-03-10 16:19:03.057949\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:03.058049\n", " Model run complete.\n", " 4\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:04.550590\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:04.558568\n", "starting arr mlt 2026-03-10 16:19:04.558589\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:04.558720\n", " Model run complete.\n", " 5\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:06.056184\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:06.065975\n", "starting arr mlt 2026-03-10 16:19:06.066002\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:06.066129\n", " Model run complete.\n", " 6\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:07.744531\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:07.753408\n", "starting arr mlt 2026-03-10 16:19:07.753429\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:07.753529\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:09.404267\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:09.412952\n", "starting arr mlt 2026-03-10 16:19:09.412973\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:09.413071\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:10.969464\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:10.977967\n", "starting arr mlt 2026-03-10 16:19:10.978050\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:10.978208\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 1.94027E-04 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 times starting phi)\n", "\n", " Lambda = 4.43556E-05 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 times starting phi)\n", "\n", " Lambda = 8.48739E-04 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 times starting phi)\n", "\n", " Maximum relative change: 5.5003E-04 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 9\n", " Model calls so far : 58\n", " Starting phi for this iteration: 4.8722 \n", "\n", " Calculating Jacobian matrix: running model 6 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:12.512771\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:12.521462\n", "starting arr mlt 2026-03-10 16:19:12.521484\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:12.521589\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:14.042305\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:14.050671\n", "starting arr mlt 2026-03-10 16:19:14.050690\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:14.050804\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:15.689280\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:15.699871\n", "starting arr mlt 2026-03-10 16:19:15.699895\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:15.700004\n", " Model run complete.\n", " 3\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:17.376594\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:17.385189\n", "starting arr mlt 2026-03-10 16:19:17.385208\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:17.385357\n", " Model run complete.\n", " 4\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:18.971934\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:18.981321\n", "starting arr mlt 2026-03-10 16:19:18.981341\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:18.981429\n", " Model run complete.\n", " 5\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:20.563768\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:20.572048\n", "starting arr mlt 2026-03-10 16:19:20.572069\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:20.572163\n", " Model run complete.\n", " 6\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:22.122624\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:22.131159\n", "starting arr mlt 2026-03-10 16:19:22.131185\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:22.131307\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:23.717579\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:23.725924\n", "starting arr mlt 2026-03-10 16:19:23.725944\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:23.726053\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:25.344257\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:25.353966\n", "starting arr mlt 2026-03-10 16:19:25.353986\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:25.354076\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 4.43556E-05 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 of starting phi)\n", "\n", " Lambda = 6.20006E-06 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 of starting phi)\n", "\n", " Lambda = 3.17323E-04 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 of starting phi)\n", "\n", " Maximum relative change: 1.9987E-04 [\"p1\"]\n", "\n", "\n", " OPTIMISATION ITERATION NO. : 10\n", " Model calls so far : 67\n", " Starting phi for this iteration: 4.8722 \n", "\n", " Calculating Jacobian matrix: running model 6 times .....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:27.940904\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:27.949793\n", "starting arr mlt 2026-03-10 16:19:27.949812\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:27.949902\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:29.524065\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:29.532911\n", "starting arr mlt 2026-03-10 16:19:29.532933\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:29.533039\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:31.115358\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:31.123968\n", "starting arr mlt 2026-03-10 16:19:31.123989\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:31.124121\n", " Model run complete.\n", " 3\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:32.725342\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:32.734570\n", "starting arr mlt 2026-03-10 16:19:32.734596\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:32.734727\n", " Model run complete.\n", " 4\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:34.344274\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:34.352745\n", "starting arr mlt 2026-03-10 16:19:34.352766\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:34.352863\n", " Model run complete.\n", " 5\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:35.877907\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:35.888171\n", "starting arr mlt 2026-03-10 16:19:35.888192\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:35.888477\n", " Model run complete.\n", " 6\n", "\n", " Parallelisation of lambda search: running model up to 3 times.....\n", "\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:37.434014\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:37.443681\n", "starting arr mlt 2026-03-10 16:19:37.443699\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:37.443807\n", " Model run complete.\n", " - number of runs completed...\n", " 1\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:38.997645\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:39.006616\n", "starting arr mlt 2026-03-10 16:19:39.006636\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:39.006728\n", " Model run complete.\n", " 2\n", " Running model ....\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vonkm/repos/pastas-plugins/.venv/lib/python3.13/site-packages/pyemu/__init__.py:37: UserWarning: Failed to import legacy module. May impact ability to access older methods.ModuleNotFoundError No module named 'flopy'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "error removing tmp file:simulation.csv\n", "starting list mlt 2026-03-10 16:19:40.663760\n", "number of chunks to process: 1\n", "null mlt file for org_file 'org/parameters_sel.csv', continuing...\n", "process 0 processed 1 process_list_file calls\n", "finished list mlt 2026-03-10 16:19:40.673075\n", "starting arr mlt 2026-03-10 16:19:40.673094\n", "number of chunks to process: 1\n", "process 0 processed 0 process_array_file calls\n", "finished arr mlt 2026-03-10 16:19:40.673190\n", " Model run complete.\n", " 3\n", "\n", " Lambda = 8.66649E-07 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 of starting phi)\n", "\n", " Lambda = 3.26280E-08 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 of starting phi)\n", "\n", " Lambda = 2.30195E-05 upgrade fraction = 1.0000 ----->\n", " Phi = 4.8722 ( 1.000 of starting phi)\n", "\n", " Maximum relative change: 1.1589E-04 [\"p1\"]\n", "\n", " Optimisation complete: relative parameter change less than 1.0000E-03\n", " over 3 successive iterations.\n", " Total model calls: 76\n", "\n", " ******************************************************************************\n", "\n", " Note carefully:-\n", "\n", " PEST-HP does not run the model one last time with optimised parameters.\n", "\n", " Optimised parameters are recorded in file pest.par.\n", "\n", " Use PARREP with this file, together with PEST control file, to build a\n", " new PEST control file with these optimised parameters.\n", "\n", " Then set NOPTMAX in this file to zero and run PEST.\n", "\n", " ******************************************************************************\n", "\n", " See file pest.rec for full run details.\n", " See file pest.ofr for objective function record.\n", " See file pest.sen for parameter sensitivities.\n", " See file pest.seo for observation sensitivities.\n", " See file pest.res for residuals.\n", " See file pest.svd for history of SVD process.\n", " See file pest.rmf for run management history.\n", " See file pest.rme for run management efficiency.\n", " See file pest.par for best parameters.\n", "\n", "\n", " Execution of agent terminated by HP manager.\n", "\n" ] } ], "source": [ "ml_hp = ml.copy()\n", "ml_hp.name = \"PestHp\"\n", "control_data = dict(\n", " phiredstp=1e-3,\n", " nphistp=10,\n", " nphinored=10,\n", " relparstp=1e-3,\n", ")\n", "\n", "solver = psp.PestHpSolver(\n", " exe_name=\"bin/pest_hp\",\n", " exe_agent=\"bin/agent_hp\",\n", " model_ws=Path(\"pestf_hp/model\"),\n", " temp_ws=Path(\"pestf_hp/temp\"),\n", " noptmax=30,\n", " control_data=control_data,\n", " port_number=4005,\n", ")\n", "ml_hp.add_solver(solver)\n", "ml_hp.solve(solver=solver, report=False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = ml_hp.plots.results(stderr=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solve with SciPy Least Squares" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fit report LeastSquares Fit Statistics\n", "==================================================\n", "nfev 22 EVP 88.64\n", "nobs 300 R2 0.89\n", "noise False RMSE 0.13\n", "tmin 2001-05-28 00:00:00 AICc -1229.98\n", "tmax 2015-06-28 00:00:00 BIC -1218.95\n", "freq D Obj 2.44\n", "freq_obs None ___ \n", "warmup 3650 days 00:00:00 Interp. No\n", "solver LeastSquares weights Yes\n", "\n", "Parameters (3 optimized)\n", "==================================================\n", " optimal initial vary\n", "pex_A 850.706567 215.674528 True\n", "pex_a 177.047388 10.000000 True\n", "constant_d 27.508892 27.902000 True\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ml_ls = ml.copy()\n", "ml_ls.name = \"LeastSquares\"\n", "ml_ls.solve(solver=ps.LeastSquares())\n", "_ = ml_ls.plots.results(stderr=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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f9wj0rUxycjKefvppjB07Ft988w26d++OkpIS/Pvvv+jSpQseeOABvPzyy7jvvvuQnJyMiooKpKamip22+Ph4UBSFv//+G/fffz9UKhUCAgLw888/Y8CAAejVqxdmz56NLl26gKZpHDlyBBcvXrQL/QCA/Px8h1F/25hmC5MmTcKoUaN8IlTkZsdd174xyGQyvPDCC/jxxx8hlUoxY8YM9O3bl+QTqIeqqiqHZ2Xy5MlYuHAhnnzySbHqS0ZGBlavXo1FixahWbNmCAsLw4IFCxATE4OcnBy89dZbdvv49ttvERMTg+7du4Omafz222+Ijo4Wn7WEhAT8+++/GDBgABQKBZo1a4aRI0diwIAB6N+/P6Kjo3HlyhW8/fbbSE5ORrt27SCVSjFx4kTMnDkTYWFhiIyMxLvvvguati8YNHjwYMyZMwf9+vUDwzB488037bzNkpKSkJOTg9WrV6N3797YtGkT1q9ff81zda3vTceOHTFz5kyMHDkSrVq1Ql5eHo4cOeKQf4FAIBAIBMJNAkfwGe68807upZdespv38MMPc+PGjeM4juOqq6u5F154gYuNjeVkMhnXokUL7umnn+ZycnI4juO4K1eucHfddRenUqm4Fi1acHPmzHHYZ2FhIffAAw9wCoWCa9myJbd8+XIuPj6e++677zxzkDcZ48aN4x5++GGny4xGI/f+++9zCQkJnEwm42JiYrhHH32UO336NMdxHDdjxgyuTZs2nEKh4CIiIrgxY8ZwpaWl4vYfffQRFx0dzVEUJV5jjuO4goICbsaMGVyrVq04mUzGBQQEcLfddhv31VdfcVqtVlwvPj6eA+Dwb8WKFdyVK1c4ANyJEyec2n7ixAkOAHflypUbPUVNFk9f+7p+LzU1lQPAVVRUcBzHcUuWLOGCg4O5devWca1bt+YUCgU3dOhQLjs725WH36QYN26c02dl4sSJ3KVLl7hHH32UCwkJ4VQqFdeuXTvu5Zdf5liW5TiO47Zv3861b9+eUygUXJcuXbhdu3ZxALj169dzHMdxCxYs4Lp168b5+/tzQUFB3JAhQ7jjx4+Lv71x40YuMTGRk0qlXHx8vLjNXXfdxUVERHByuZxr2bIlN378eC4rK0vcTq1Wc8888wzn5+fHRUVFcV9++aXD+zw/P5+75557OH9/fy4pKYnbvHkzFxwczC1ZskRcZ+bMmVxYWBgXEBDAjR49mvvuu++44OBgcfkHH3zAde3a1eGc1fe9MRgM3BNPPMG1aNGCk8vlXGxsLDdjxgxOp9Pd8LUiEAgEAoHgeSiOc2MBaAKBQCC4lKVLl+Lll18mYT+3IIMGDUK3bt3w/fffe9sUAoFAIBAITQj62qsQCAQCgUAgEAgEAoFAaIoQUYBAIBAIBAKBQCAQCIRbFBI+QCAQCAQCgUAgEAgEwi0K8RQgEAgEAoFAIBAIBALhFoWIAgQCgUAgEAgEAoFAINyiSL1tgKthWRYFBQUIDAwERVHeNodAIBAIBAKhycBxHNRqNWJjY0HTTWtsiWVZHDlyBHq9/pZpQxqNRsjlcm+b4RGa2rE2teNpCnAcB6VSid69e99078cmJwoUFBSgRYsW3jaDQCAQCAQCocmSm5uLuLg4b5vhUo4cOYK+fft62wwCgXCTc/DgQfTp08fbZjSKJicKBAYGAuA/VkFBQW77HZPJhG3btuGee+6BTCYT5/f9xfoxOfjUQbf9flOkrnNKuDHIeXU95Jy6B3JeXQ85p67nVj+n1dXVaNGihdjeakro9XoAwA8//IBu3bp51xgPsG3bNnzyySfoPOplhMcne9sct1J07jAubFmKH0cnomurcG+bc8NsO1eCT/7KRM8pPRHRJsLb5hAEyi+X4/C8w+K75GaiyYkCFnevoKAgt4sCfn5+CAoKsmsUSFQScdqdv98UqeucEm4Mcl5dDzmn7oGcV9dDzqnrIeeUpym611uOqVu3brjjjju8bI37yc3NBQAEN09CZFI37xrjZmrKrwIAurUMxO1tw7xszY2TW853OkNahyCmS4yXrSHU5mZ8P95cwQ4EAoFAIBAIBAKBQCAQXAYRBVxMEms9pSZTjRctIRAIBAKBQCAQCAQCoX6IKOBiVDantKoy24uWEAgEAoFAIBAIBAKBUD9EFHAxDDhxeu+h77E/9X0vWkMgEAgEAoFAIBAIBELdNLlEg97GbCMKzCrbD5QBqbn3IbxFPy9aRSAQCAQCgUAgEAgEgiPEU8DFmDnOYV5Z2SUvWEIgEAgEAoFAIBAIBEL9EFHAxZjgKAqojWovWEIgEAgEAoFAIBAIBEL9EFHAxZidzNOYiChAIBAIBAKBQCAQCATfg4gCLsbs1FNA4wVLCAQCgUAgEAgEAoFAqB8iCrgYM+U4T2Oo9LgdBAKBQCAQCAQCgUAgXAsiCrgYk5N5GgMJHyAQCAQCgUAgEAgEgu9BShK6GGc5BdQkpwCBQCAQCARCkyEhIQE1NTXIz8+HTCYDAKSmpmLw4MF46aWX8P3339e57a5du6DX63HvvffWuc68efOgVqsxc+ZMV5veYP565zFIpHJIZAqwjAmJd45A0l0jGr2fs38tQvt7x0AiU4jzis4fxvlNS1BTWQK5XxAomkLrAcPR5o5HxN8eOPVzNGuRbLevQ0s/RtaBzbjn3SVo1rItAMCk12LjGw8hICIOw2Ytu+7jTXh9JxQyGiqZBEaGxfTB8Zg+JKHR+5m94RLeeqANlDKJ+HdljQnfP9VRXGfp3lxsOH4VG17sdd32Xi+/jf0Ngz8YjLA2YW7Zf+GpQjAmBnG94sR5FzddxMW/LoKiKTBGBmFJYbjzzTvd8vuE64OIAi7GafiAqcbzhhAIBAKBQCAQ3EbLli2xceNGjBjBd5RTUlLQq9e1O3m7du1CZWVlnaKA2WzG888/71Jbr5d+kz5CsxbJ0JYV4p+PxiIiqStC4hIbtY9zfy9G8pDRoihQdP4QDi39BAOmfILwNp0BADUVxbi8Z2OD9tesZTtc3rcJPQVRIOfovwiMjgfHMI2yyxlrpnZHt5bByC6tQZf39+D25FB0aRHUqH18+Gc6Xr6nlSgK3GoUnS6CUWsURYHSS6U489sZPPTTQ1AEKsBxHMoyyjxmD8uwoCXEOf5aEFHAhXAsCxPlqApoGJ0XrCEQCAQCgUAguIsJEyZg8eLFGDFiBKqqqnDw4EE8+eSTUKt5D9Gvv/4aa9euhdlsRmRkJObPn4+KigrMmzcPDMNg165deOyxxzB27Fh069YNU6ZMwfbt2zF27FhUVlaisrJS9Dj44osvsHLlStA0DZVKhZ07dyI/Px/jx4+HRqMBy7J4+OGH8fHHH7vlWP3DYhAY3RKVeelIT/0NlXkZYExGhLXqiB5PvgaJVIZzm5Yg+/A2SKRyAMDAaZ/jwtYVAICdX00FRUtw50vf4dzfS9DxgQmiIAAAfs0i0emh5xpkS1z3O3F570YwI2dAIpPjyv5NaD3gQWT+96fLjjc+3A9to/1xKrcaP+3IwqncauhNLPq2CcGcZzpBLqXx8cZ0rDqYD4WU7/z/+WJPfLYpEwBw+6cHIKEpbHv9tmv+1q6LZZix8ix6xAfjeHYVFFIaKc92QbeWwS47nmtRklaCo4uPwlRjAsdy6DK6C1rd0Qosw2L7rO0wqA1gDAxCW4ei/8v9IVPKUJVfhb3f7IVJZwLHcWjZtyUSbk9A2uY0sCyLotNFiB8Qj2atmkGmkkGm4j1qKIpCeFK4+Ns5B3JwdPFR0BIazXs1R/q2dAz/cTgCowMdvBo2vrARvZ/rjZiuMTi77iyu7Loidvr7TO2DyA6RAHhviIQ7E1B0qghBzYNw+2u34/jy4yg8WQjWzCKoeRD6v9gfikAFLm25hLPrz0IilYBlWQx8eSAi2kV47Nz7CkQUcCEMY3Q6X80YPGwJgUAgEAgEAsGdDBgwAD///DMKCgqwceNGjBo1ChIJ30H85ZdfkJaWhgMHDkAikWDFihWYNm0aNm3ahOeff96uw5+VlYWqqip07NgRX3zxBQBg9uzZ4u8sW7YM69atw969exEcHIyKigooFArMmTMHDz74IN5++20AQHl5uduOtTI/E9VF2biybxMS+j+A3mPeBsdxOLLic6T/uxatBw5H2vZf8dCXf0EqV8Bs1IOiKPR6+g1k/rcBg2fOhdwvEABQkZOGHk+8ct22SORKRLXvjfyT/yGkRSLAcQiKTnDRkfKcya3GxUItFu/JxYSBLbBwQhdwHIdJS87gh+1X8NwdLfH11sso/H4oVHIJagwMaBqYN64z5u/KwZ53+iHETybub9WBAuy6aL0+5VojesRbO/3n8jX44amOWD6pG9YeLsATc0/gwqd3gnIy2OhqDBoD9v+4H3d/dDf8wvygr9Jj44yNiOwQCb8wP9z51p1QBinBcRwOzDmAC39eQJfRXXBh4wW0uK0FujzRhd+P2gBFoAJt728Lo9aIPs/3AQCY9Wac/f0s1o5Zi6jOUYjqFIU2d7WBIlABXaUOe7/di/u/vh8h8SFI25wGQ3XD+k2JQxLRaUQnAEDxhWLs/WYvHlv0mPW4qg148IcHQVEUTq0+BalCiuE/DgcAnFx1EseXHUe/Gf1weOFhPLbwMfiF+YE1s2BMN+5xcjNCRAEXYjZbPQLGBXfCsqqzAAAN6yz9IIFAIBAIBALhZmbMmDFYunQpNmzYgFWrVmHVqlUAgA0bNuDIkSPo2bMnAIC5hmu7TCbDM88843TZ33//jeeffx7BwXwnslmzZgCAO+64AzNnzoRGo8Gdd96JoUOHuuqwRA4sfB8SmQISuQK3jX0Hx379GmnbVuHSjtUAAMZkAEXTkKr8ERAZh0OLP0RUh9sQ27k//JpFNuw3UmajuuAK9NVluP+jNZCp/K+5TasBD+LsxoUIiUtCq/4P3NAx2jJ67gmoZBL4yWksfrYLpq04i6+2XMa3/1wBAOhMDCQ0hSCVFElR/nhmwUnc0zEcD3SNRFyoqs79Pt0v1mlOAQsJ4SoM6cCPnj9+WywmLz2D3HI9WobVvU9XUXy+GOpCNbbP2m43vyqvCn6hfjj3xznkHckDy7AwaU3iaHx052gcWXQEJr0J0Z2jEds91un+pUop7v/mfpRnluPq2avI3peNM2vP4JG5j6DkQgmatWqGkPgQAEDSsCQcnHuwQXaXZZbh1OpTMFQbQEtoVOVVwWwwQ6rgu7dJdyeJokrO/hwYtUZk78sGADBmBgFRAQCAmG4x+O+r/9CibwvE9YpDcJznPDR8CSIKuBCzWS9OP3/Pj9D/Mw1rqi9CwzlLP0ggEAgEAoFAuJkZO3YsevTogeTkZCQlJYnzOY7D22+/jcmTJzdoP35+fqDpxsU9jxgxAv3798f27dsxZ84cfP/999i8eXOj9nEtLDkFLBz75WsMeP5TBEa1dFh36FsLUZZ5BsWXTmDH55PQ77kPEZHUzWG9kJbJKMu6ICYK7DdxNgBgzZT+4Di2QXaFt+4EXVUpqouycd/sVajITmv8wTnBklPAwtTlZ7FuRg8kRwc4rHtw1gDsz6jArotl6Pvxfvz6fHfcnhzqEjsoCnC/j4AAB4TEh+DB7x50WJT5byYKTxXivi/vg9xfjvMbzqPwVCEAIGFgAiLbR6LgRAEubLyAcxvO4Z7/3eP0JyiKQlhiGMISw9D+4fZYP3k9Ck8VOsT61/aMoCQUOJYT/2aMvLjGmBjs/N9O3PvFvYhoGwGj1ohVI1aBMTGiKCBV2XRzOaDvtL5o3rO5g22DZw1GWXoZCk8XYvus7egxrgdaD2rdgBPXtCBZF1yIrSigkgfjkXZPAAC0aNgLjkAgEAgEAoFw8xAbG4vPPvtMdPu38Mgjj2DevHmiS7/JZMKJEycAAEFBQaiqqmrwbzz00EOYN2+euE1lZSUYhkF6ejqioqIwduxYfPnllzh4sGEjrDdC826348I/K8Ey/ICXUVsNdXEeTHotDNXliEjqxucLSOyCitxLAACp0g8mnUbcR8f7J+D8psUovXxWnGc2ND7/Vo/HX0G3ETMgU17bs+B6eaRHFL7YnAkzw7flK7QmZFzVQq0z42qVAbcnh2LWQ0kYmNQMJ7L56xOolKKqpnFewlmlOqReKAUA/H6kEFFBCsSFKl17MHUQ2SESmiINCo4XiPPKMsvAmBgYNAYog5SQ+8thqjEhfXu6uE5VfhVUzVRIHJqIXs/1QsmFEgCAzE8Go9YaUl2ZW4nyy9bQCW2JFvoqPQJjAhHZPhIVVypQmVsJAEj/Jx2sydpvCooNQslFfr8laSWoyuPPMWNkwJpZBETyYs2FjRfqPcaW/Vvi3PpzMOv5+9asN6MiqwIsw0JdqEZ4cjg6j+yMhNsTUJpW2uhz2BQgngIuxCIKUBwHiVQOPxWfFENLcfVtRiAQCAQCgUC4SZkwYYLDvKeffhplZWW46667APAVBZ599ll0794djz76KFasWIFu3bqJiQbrY8yYMSgoKED//v0hlUrh7++PHTt24Pfff8fKlSshl8vBsizmzZvnluOzpduol3B6/Vz88/E4UBQNipag64jpkMjk2Df/XTAGHUBRCIxsgYR+9wMA2g59Eru+fxlSuRJ3vvQdYjr1Re8x7+DUuv+DrrIEisBmkEik6PHEq5Aq/cTf2v3DK6Al1q7K0LcW2tkS1d795fy+e7ID3vrtIrp9sAc0RUFKU/jy8fZQymiM/L/j0BoYUBSQFOWPcQP4bPuvDWuFu78+DD+5pEGJBgGgY/MALN2bhxdXnYdcSuHX57u7NZ/Atne3gZZax4YHvTMIJ1aewOGFh8EyLAIiAjD4g8FIHJqInAM5WDdxHZTBSkR1ioK2WAsAyN6TjcydmaBlNDiWQ/8X+wMA4gfEI3NnJv6c9ifiB8Qj7rY4HF5wGLpyHaQKKTiOQ68JvcTkgQNeHYCdH+0ELeUTDSqCrKUre4zrgT1f70Ha5jREto9Es3g+dEbuL0ePcT3w10t/QRmkRKs7W9V7vJ0f7wxmFYO/Xv4LlOCD0fnxzgiKDcLeb/fCoOZDEJTBSgx8daDrTvRNBMVxXJPqsVZXVyM4OBhVVVUICmpcCZHGYDKZsHnzZtx///1ifdqiwuO4e9s4yDkOx8afRXHxOQzZ8gQkHIcTY0+DaqRb2K2Gs3NKuHHIeXU95Jy6B3JeXQ85p67nVj+nnmpneYP//vsPd955J3bv3o077rjD2+a4nVWrVuGZZ57BwJf/D83bd/e2OW4l69A/OLT4Q/w3sztu7+A89t1b7LpYhpd/OY+TH93e4G1WHcjHMwtOYsgXQ9Cyq2Mox83ML4//IlYfuNkoPF2IrW9svSnfIaSX6kJMgqeAVJBZ/P35chYMRcGgr/CWWQQCgUAgEAgEAoFAIDiFiAIuRBQFhL9VqjBQgiOGRlvsFZs4lsX+A9+gKE+IM+M4aHMPAQypiEAgEAgEAoFAIHiTQe3CGuUl0NR5au1TN6WXwM0OEQVciJnh62paRAGalsBP8BqoqfFO0oqjJxdhyqWluPvfSQCA9X89h747n8P6bddfH5ZAIBAIBAKBQCAQCE0DIgq4ELNZEAVssjRY8qFqako8bxCAc4VHxGmOZfF+xWEAwPeFO71iD4FAIBAIBAKBQCAQfAciCrgQi6eAzKayqL9wirVeyikQGWBNplJRkSFOl0sk3jCHQCAQCAQCgUAgEAg+BBEFXIhWz9fgtBcFJHbLPA1lY0tWzh6v2EAgEAgEAoFAIBAIBN+EiAIupLQ6HwAQJVGK8/xpvlyR1lDlFZuMjF6c/iftd7tleq138hwQCAQCgUAgEAgEAsE3IKKAC2FYPqO/DDTy8vKQmpoKJSd4ChiqvWKTSchzAAAXdVftlhUWnfC0OQQCgUAgEAgEAoFA8CGIKOBCGNYMADDq9YiPj8fgwYNRklsIANAa1V6xycgaxenjtH0ZwvySs542h0AgEJo0FkE4Ly/P26YQCAQCgUAgNAgiCrgQhmMAAOrKarAsy8/U8/PKNN5JNGhijHUuK6zK8pwhBAKB0MRJSUkRBeH4+HikpKR42yQCgUAgEAiEa0JEARdi8RQAa61JyOl5caBSV+kFi+oXBcp03imTSCAQCE2NI0eOYPLkyaIgzLIspkyZQjwGCAQCgUAg+DxEFHAhrOApQHFWUYARPAUYKed0G3djrE8U8FKZRAKBQGhKpKSkoE+fPlYPMQGGYZCRkVHHVgSCZ8nLO4jOyzrj6SXdwdW6VwkEAoFwayP1tgFNCTPLCwDhoeGQSPLAMAxYHS8GGCjGKzaZWJPDPAnHgaEolJm8k+eAQCAQmgp5eXmYPHkyOM5R+JVIJEhMTPSCVQSCIzO3TwVo4DRtRmHRccTG9vK2ST7Ltm3bkJub620z3M6+ffsAAMUXD8NUXexla9xLacYZAMC2c+XIqfLOQJ0r2ZfOlzovOlEEY1ndA4AEz1KV451qc66AiAIuxBI+EBIUjKysLGRkZKBAswGflf0LLeudB9boRBTw4wA1BZSZa7xgEYFAIDQN8vLysHbtWgcPAQCgaRrz589HXFycFywjEByp5MzidHFZGn7b/R7Oawvw5aO/Q6kMweur70H/sM548pEVXrTSuxiNfFvtk08+8bIlnoOmaVzauszbZngEmqbx8eZsANneNsUl0DSNc6vPedsMghMs75KbCSIKuBBL+ICUohEXF4e4uDj8u/8IUAZonXTOPcE5TQ5AAxP8E7FEy7uxxtEKXIAR5Zx3bCIQCISbnZSUFLscArbQNI2DBw+id+/eXrCMQHBOnsQ6venccqw2FgAyYPbfYzAg6jbskjLYVXUST3IcQFHeM9SLyOVyAMCCBQvQs2dPL1vjfjZv3oxZs2Zh5cqVaN++vbfNcStN7Vib2vE0FY4dO4bJkyeL75KbCSIKuBCzIArQlDVVg78yGACg5bwTPnCM5kcGlmgz8FaLB5BauA/Te7yMsYdno4wiMYUEAoHQWCwhA84EAYlEgvnz5xNBgODTnNJdFbNK7eK0uM1GBCgtOY/wyI5essw3aNu2LXr06OFtM9zOhQsXAADt27dv8sfb1I61qR1PU0Gj0XjbhOuGiAIuhBFyCkgoqxzvrwoFAGjh3Q54XzoQTw/+HE8DqKrOBwCoaRoGXTkUgo0EAoFAuDbp6elOBYHvvvsOI0eOJCEDBJ9jQ+q7dn/rbAYqzBQFf5m/+He1puiWFwUIBALhVoNUH3ARrNmIedVnAQASW08BVTgAQEN5J6lJF04GABiZcL84LyggBlIhKVZ5WQbgJEEWgeBLMIwZo1f0xb3LezlN6EYgeJKkpCTQtP3nUyKREEGA4JMUFBzFrJyNdvOyJPbrmBlrOKFWV+oJswgEAoHgQxBRwEUcOrlQnLbzFPCLAADUUJS1BJBJ5zG7JOBdAiW01SaKphHK8fPPXFiLqQva4+CuDz1mE4HQWC5np+I8q0U+Z8DfB77wtjmEW5y4uDgsWLAAEgn/XrWEDBBBgOCLFJel1bu8BQOYbfIeqYkoQCAQCLccRBRwEUbGmmXSZKO4B/jzogBDUdDryrH17+fx9OKuyE/b5BG7WGFUlabshwXChMiR1wr+wV6lDJOyf/eIPQTC9VBaZc0UvCTtVy9aQiDwTJw4EVlZWUhNTUVWVhYmTpzobZMIBKfU6CvrXa4DB7NNZQKtvsLNFhEIBALB1yCigIvgOGt8aYVZLU6rVGHitFZbjJll+3BaqcD/9r3nEbtY8KKARqNFamoq8vLyAABhEqXDupZlBIKvUVR+SZy+TRnjRUsIBCtxcXFITExEeno6eX8SfBa9SWv3t5K1D8GqpAGTzcCG5hoiAoFAIBCaHkQUcBFam49oudkaHlBQUAh/IWwg99xacf5VeKYagUUU+P77HzF48GDEx8cjJSUFYbIAh3UtywgEX6NSXy5O1+hMpANG8AlSUlIQHx9v924lEHwNg6nG7u8ozr7coJmiUKmzvmM1hmqP2EUgEAgE34GIAi5CY6gUp2uESgOWBqPSzP89NmeduI4BnkmWxgjhAyzD28CyLKZMmYJAys9hXcsy0uEi+BpVhipxOi0vg3TACF6ndllC8v4k+Cp6s1UUmOCfiEha4bBOkb5MnNaY1A7LCQQCgdC0IaKAi6i0icF7t+/7dg1GBeNYuspjooDQYLWJbgDDMJAZ5c7XZxhkZGR4wjQCocGkqC+K04xSQjpgBK/jrCwheX8SfJHPcjYDAAJZDq+OXI9IWaDDOkU2YY8ao9ZhOYFAIBCaNtctCnz22Wfo3bs3AgMDERkZiUceeQRpafYZbouKijBmzBhER0fD398fPXr0wLp16+rYI8/s2bNBUZTdv3bt2l2vmR5Boy5AYc1VAMBTfq3Rud0jdg1GucmJKOChEoUczbsJsjYxhBKJBM3DExzWlcv4ZYmJiR6xjUC4HtKi/EHRpANG8C51lSUk70+Cr6ET2gFq4f9IZai4LFxoG5SyNjkFzJ6rkEQgEAgE3+C6RYHdu3dj+vTpOHjwILZv3w6TyYR77rkHWq1VYR47dizS0tKwceNGnDlzBo899hgef/xxnDhxot59d+zYEYWFheK/vXv3Xq+Zbsdk0qHfH8OwzlgEAAhTBAOwbzBKnYkCoBzmuRPKUppQKJ0VH+MotESEK0hZLcJNwQOTo0gHjOBV6ipLGBYeeM1s7wSCN1AJAxVRftHivHChElEpZW2naBgiChAIBMKthvR6N9y6davd30uXLkVkZCSOHTuGO+64AwCwf/9+zJ07F7fddhsA4L333sN3332HY8eOoXv37nUbJZUiOjq6zuW2GAwGGAwG8e/qaj5BjslkgslkqmuzG8ay74JCe4HDXxoAk8mEqKgozJ07F9OmTYPU6MxTAG61z4IlneG777wHydvd0aZNG8TFxSEj61+Hddf9sQQ9eo70iF3OsPyut36/qdIUzmtzBsi3qapZ070Zfv75I0RFRXnluBp7TvPy8pCRkYHExEQiutXDzXavjh07FoMHD0ZmZibatGkDvxAaD/56O8opFv+N2AWlMtjbJt505/Rm4GY7p7EMhwIJhU8Sn4HJZEJYQHOgmF8WRisBaKGx8XpRs8Z6j+1mOW4CgUAgNJzrFgVqU1XFJwILDbW6pfXv3x9r1qzBAw88gJCQEKxduxZ6vR6DBg2qd1/p6emIjY2FUqlEv3798Nlnn6Fly5ZO1/3ss8/w4YcfOszftm0b/Pwck+m5moOH/rP7+0j+cWSt3IKkYA5RUVGYP38+jur/D4D9R5SjKGzevNnt9jEcC4BCQeFVhAZqcfr0aZw+fRp6U4nDumkZJ1B01f3n7Fps377d2yY0SW7m82riWNg6NpXKJYhqFuWRZ6g+GnJOt2/fjp9//hkcx4GiKEybNg133323B6y7ebkZ79U/dr+PpdRJ4TalsH7jdwgOuM3LVlm5Gc+pr3OznFMJxwGgkJddhs3lm1GhtuYPUBopoFaKoSqjrt53a01NTZ3LCJ4jISEBCoUCKpUKOp0OEyZMwFtvvYU5c+bg/PnzuHDhAr744gtxYM4XSU9Px7hx41BaWorg4GAsXboUHTt2tFtn165duO+++9C2bVtx3oEDB6BSqepd5mkaciwsy+L111/H1q1bIZVKERYWhoULF4pej1999RWWLVsGlmXRtm1bLFmyBCEhIR4/lpsZch2uH5eIAizL4uWXX8aAAQPQqVMncf7atWsxevRohIWFQSqVws/PD+vXr6/X5bdPnz5YunQp2rZti8LCQnz44Ye4/fbbcfbsWQQGOibHefvtt/Hqq6+Kf1dXV6NFixa45557EBQU5IrDc4rJZML27dvRuXM74Lh1fmFFP2wo54c00/93DwCg6I8/AH2Owz7uv+8+gHJvGMGCFe8BADp36oKw4C7iaGVsbAw+Xv2D3brxbaLQv9f9brWnPizn9O6774ZMJvOaHU2NpnBev1n5rt3fLEXh/vt9/17Ny8vDY489Bk6oAsJxHObNm4fXXnuNeAw44Wa+V3v88p7d3/FtItG/p/fuUQs38zn1VW62czpXaAd07dIdndvfj/zCSHyT+jcAID40DtBctFtfL0G971eLRybB+6xZswbdunVDfn4+OnTogMGDB2PGjBkAgJdeeglFRUVetrB+pkyZgsmTJ2P8+PH4/fffMX78eBw5csRhvbZt2+LkyZNO91HfMk/SkGPZuHEj9u3bh1OnTkEmk+Hjjz/GO++8g7Vr12L79u1YsmQJDh06hMDAQHz88cd499138X//939eOqKbE3Idrh+XiALTp0/H2bNnHWL/Z82ahcrKSuzYsQPh4eHYsGEDHn/8cezZswedO3d2uq/77rtPnO7SpQv69OmD+Ph4rF27FhMnTnRYX6FQQKFwLK8jk8k88rHes2c34G/9+1D5UDsbAMBf5gfoHbc16YvhF+TejoElfODgwUN4afoEsCwLmqaxYMECh6uv0Vf4RAPHU9fuVuNmPq/OnFV94ViudU6zsrKcZqjPzs5Gq1at3G3eTcvNcq+aGBOktBSUE3FXrS/1qWO4Wc7pzcTNck5Z8J4CcqkcMpkMzWP58E2aAzrF9AbS7UUBDbh6j+tmOOZbjebNm6Ndu3bIzs5G9+7d8f7776NLly546KGHvG1anRQXF+Po0aPYtm0bAGDEiBGYMWOGOHh1M9HQY6EoCgaDAXq9HlKpFNXV1eIAwalTpzBw4EBxAPT+++/HoEGDbonOqKsg1+HGuOGShDNmzMDff/+N1NRUu5GvzMxMzJkzB4sXL8aQIUPQtWtXfPDBB+jVq1ejTmxISAiSk5N9Mst4aWkpVqxaJv6tYB1zBwCAv9S5S35FlaP3gKux1BxYumS5Qz3tbnQIACAR/Ae+Wl/udnsIhOvBKPS5RksjxHkGg++PVgUEBJAM9S4gLy8PqampPlWCUqsrR4+VPdBleRd8u2Wyw/KKGscQLQLBG1gGByor1UhNTcXVolLsf/w/rB64CmVFBof1NR6qjkRwHRcvXkRZWRkGDRqEMWPGIDU1FQcOHMAff/zhbdPqJDc3FzExMZBK+REqiqLQsmVL5OQ4to0zMzPRo0cP9O7dGz///HODl3mKhh7L8OHDMWjQIERHRyMmJgb//vsvPvroIwBAz549sWPHDhQVFYHjOKxatQpqtRrl5aRt3lDIdbgxrlsU4DgOM2bMwPr167Fz506HUS9LzJmzBnHtkbP60Gg0yMzMRExMzPWa6jYKCwtB2yQ/K/04A6zJ8QMbHBjrdHu19qq7TBNhBVmAYRxHK8fGvY8dI7ejqyISAFBlqHS7PQRCY+E4DkZhJHby0B9ACa74anWhN826JikpKejbt6/d+46mabzyyitetOrmIyUlBfHx8Rg8eDDi4+ORkpLiEyLBsTMrxOklxQccludX5XvdRgIBACxvoBkzXhSfoxemvoJObbvj9dc+dli/hqbBmJy4NxJ8jtGjR6N9+/bo0KEDXnjhBURERGD16tU4ePAgFi1ahMcee8zbJt4wPXr0QF5eHo4fP47169dj3rx5WLt27TWX+SJHjx7F2bNnkZ+fj4KCAgwZMgTPP/88AOCuu+7C66+/jgcffBB9+/ZFRAQ/CGLp4BJcB7kOzrluUWD69OlYuXIlfvnlFwQGBqKoqAhFRUXQ6fhSNu3atUNiYiKmTJmCw4cPIzMzE9988w22b9+ORx55RNzPkCFDMGfOHPHv119/Hbt370ZWVhb279+PRx99FBKJBE8++eT1H6Wb4NUo/hTG1RiRfdkIzoko8EC/txBDyR3mq7XuH0kSuyOcvXurRCJB+6T2iPKPRpAsAABQbfT9kVfCrUdhyXlxWqEIhL8wiKXVFnvJomuTl5eHyZMn2wkCFhfzr7/+WuzcEuqn9nlkWRaTJk1yEAm8AX2Nz+fJzDNet5FAAABGGBwwmazPkSWJVkmx2ek2Wg8MWhBunDVr1uDChQvYtm0b3nrrLZw5c8bbJjWYFi1aoLCwEGYzfw9yHIecnByHxOJBQUEIDuYrucTFxeHJJ5/Enj17rrnMkzT0WJYvX47BgwcjJCQENE1j3LhxSE1NFZdPmzYNR48exaFDhzBo0CDExcW5NT9aU4NchxvjukWBuXPnoqqqCoMGDUJMTIz4b82aNQD4mLPNmzcjIiICw4cPR5cuXbB8+XIsW7bMLoFNZmYmSktLxb/z8vLw5JNPom3btnj88ccRFhaGgwcPikqNLxEeHo4RIx8FANAcB4lEgmZB1gQDLMt/iP2Vwfjt4T8dtq+u8YSnAM+kSVPs6ml/9tlnSE9PR15eHoIVIQCAKpPW7fYQCI2htOIKZmwZJ/7tpwxDgCAKaGp8VxRIT0938IjiOM4hhIeMItePL59HM1t/WTaTH/++Jdea4G0s4QMM4xgWYKxhIWMd52s0vp2gjmDP0KFDMXXqVLz33nvXXtlHiIyMRI8ePbBy5UoAwLp16xAXF+cQXldYWCi+89VqNf7++2+xrHl9yzxJQ4+ldevW2LlzJ4xGIwDg77//tkvQXljIe0DW1NTg/fffxxtvvOGhI2gakOtwY1y3L4Qlm3Z9JCUlYd26dfWuk5WVZff36tWrr9ckr9Ctexfg0jGo5ApkZWVh7JoMVJfwnWu1wYxgFR+vHxAQ7bBtUXWu2+2zNKfvHno3srJeQUZGBo4ePYo333xTTDr48ZzhgB9Qbda53R4CoTF8+c9kpMPqfSNTBiIANAAO6prSujf0MklJSaBpWmysSCigdnucYRhkZGSQKgT1UPs8OsNb59Fkrt+9ukZl/bySa03wJozgKMgyzpcHmRiUKeybgxqSE+OmY9asWUhMTMSxY8fQs2dPb5vTIObPn4/x48fj008/RVBQEJYsWQIAeO655/DQQw/hoYcewrp16zB37lxIpVKYzWaMGjUKEyZMAIB6l/nisUyfPh0XLlxA165dIZPJEB0djXnz5on7uOeee8CyLIxGI8aMGSNWkiA0HHIdrp+mHyDhZhhhtEgukQoNPmtCxGqdSRQFJBLHU63xwMg8KzQGaFoqNkiHDBliN9K2c9se4JFYVLNGt9tDIDSGHF2J6M/USmjZZkj43vWOjD/Rt+cUb5lWL3FxcViwYAGmTJmCJ1+MQEGHEGR/chmZWdZnjCQcvDa255FhGNA0DY7j7ERpb51H8zXel1Vya8IZcq2vn7y8PKSnpyMpKUn8hjmbR3BOXl6e6ClgNlvFtTvvvBN79+4FwzgKAgCg0fmu6ErgqT2o1qxZM5SVlXnHmOukbdu2OHDAMSfLokWLxOkZM2bU2Smrb5mnacixKBQKLFy4sM593EzhH74KuQ7Xzw1XH7jVMTN83IqU4k+lwWT96FbpnLuXRnJ8Y1FrrnGzdVZPAZOgDjhzx9VU8HZWcc5jCwkN41z6Cfz61w/ETdiFhNFKcVpRq+zbGr37PW1uhIkTJyL94nGc7BqOYpkUD73RxS6EZ/78+aRD0wAmTpyIrKwspKamIjs7GwsXLvSJ82hieFEgslYS17ZCD0wtlUCpoMi1vgFsk0y2bNkSM2fOFHNykHwNDSM9PR0s+HcnY+MpsHv3brAsi9dff93pdhpd08+0TSAQCAQrRBS4QSyeAhLho2u0aSBW1tiLAosGfonXEh/HoyEdAAA1HnDXt1Qf+HY778Fgcce1Ravmba5GHb6FhGuSkpKCaTufxKfli/D0q91JQ9VFhAlJMAGAgmMteF/nr+PW+E5poJ/Yuc3KysLEiRO9aNnNRVxcHAYNGgSAjwU8cOCA18+jmeHf7+2l9smH5ty3HHLBk+G33+eSa32d5OXl4aflr2HI1CiA4kMWv/76a8ycOdMnckrcLCQlJYkeg7VzCnAch++++w5306HivFbCoIVGX+ExGwkEAoHgfYgocIMwLD+6LhFGMQ0ma8e6tqdAnzb3YfyAWfCX+QEAdIxjpQJXY5EozhbyoQoWd1zbkbZpU/gEGlU0Ba4R5SIJPJYM6eVKPlSk/IFo0lB1FZT1FVUliFaTle0AAK19zLHFWZ6VzZor4rSJM4udWzJq3HhsR4379u2LzMxMr55Hs+ApIKNoPKNKEOdLJHJECL2wuIRAcq2vk/T0dDDPtkBh7zAMGR1W53qWfA0E58TFxdkkGnRczjAMFDrrd7+VhBdiNYYqD1hHIBAIBF+BiAI3iFkQBaTCqbT1FKgrfMBPGP2sYdwfw18jegVY41tt3XGzsrIw5plpAAAjRUHvw8nbfBVnIRmBQSANVRdQblKL0wU03zFcO38bAMBgMvqMR8b/rX8Sdy7tjAsXN4jzCnIPIN/62MHEEU+c68VZaUJvC28mwUtMSkkQrrJ2WmVSFcJpXiAsrcz2im1NgdZtWonToW3961yP5GuoH5YxgxMGLb75+jsHT0GKovDHgpMAgFCNEayWH6zQkBLFBAKBcEtBRIEbhBEbhnwCLIO5blEgLy8PqampYI38B7qGq7+k1Y2iqbKNueagNViHVm1HLP1UYZAIo5wZmcfdalNTxFlIRptkP9JQdQH6WnkuJk+eDHUVP08npb3eMbQwr/osKmgKC458Jc6buf15u3U0rI+5NtxEOBPevD1CbClJKKMkaKYMF+crlMGIkPDeYBevHvOKbU2BAH/r8yKpo6VC0zTJ13ANGJuEmA88MNzOU9Dy3bp0TIPC/2Vg38wMFOfypQg1Ro3njSUQCASC1yCiwA1SoePL9gRIlDAxHGw9iCt11o+xretrysJlAIAaN3cSKqqso1RFpniUqJ2HKyxevBgBgpgxdsJInxl9vVmIi4vD/Pnz7OY9Pm4Yaai6AJ1NDa036V5gWRbVlfxzUyOhvd4xrE2+QS2KFBdpe88AFSVxtgmhATgT3rw9Qry57BQAgKIkCPaPEOfL5YHIM/MeLvM1adi44RufEK5uNkw2nVJtVIDDcpqmcfDgQZKv4RqwjHXwQULL7DwFf/31VzHsqSxTD0bHghVCCTQeSIRMIBAIBN+BiALXQWbmdoxY0RM55b+h3MAn44lUhkJnsu8EVAueArVdX/Uafj2tm7P91wiJggIZDiwnR4nGURSw2OZn5m0KCKJ8ZvT1ZuKppx6z+zsgRu4lS5oWOiEadkH7SRh01/9A0zQqBVFAT9NQKr3vOmwyWEuLVjJGMSP6g8pYu/VIyc/rx1kulPnz5wMAUlNTvfK+Os/xiWL/NhahVVR3cb5EKkevwATx719O/+gzWfKvagrB1FWs3scwmq3P1RUljS+//EgUhiQSCRYsWIDevXt7y7ybBos3IwDQEj6sxeIp2L9/fwexjdXx94fGA4mQCQQCgeA7EFHgOvhm3yxckXBYQJ+CVvhwBsj87ZIMAsCvh3MxYclhpF26ZOf6qtPy69W4OcZ492neI0Et4cMVansK5OXlYe3atWBZFkojb59/sNSnRl8tIRe+LlLoapVvyqrOvSns9nX0HH9fqpShYsdQo7ZWIfjxh4+97pHx+/Z3xGmdlBbj3Zla74PTtBlnTi3ztHlNhtq5UAD4RGm6GIZF6zb34IPIO/B9q1EAgL7R94nLWTntEzkQDp37BUPX3YMP/hztNRsaguWdn19on4/hgQfaIzs72+tVJ242zCZr514i5Lqw4Exs69nlNgCAxgOJkAkEAoHgOxBR4DrYw1lHMNabrgIAlFIl9CbHzP2paSXwi4y3U+P1gnuezs0V1n7Sptn9XVytF6ct4QyvvfYaAEBm4Dswbe9uBpnU+6OvgH3Iha+MtNWF3lBp9/cWrgJfn5uC7j1a+7Tdvki1oQpbziyDwayHXiipqVQEAuA7hpcvZyFAENnuGTbAa3ZaKC4vEqfVQvAzwzDQG/nn7UGp1bX87aNfwVfxZQGuQl2A3/f+D83C/MTShL6SePCrHvw7dOR9/4chd7wPAKiutC5nZdZ7wpti65LTPwMA/qxOQ06ObyZAtH3nf5Lyqt2yvJIzpHrHdbB0xyvitEzmmLDRIratXbsWv/zyCxLj+eouGuLZRCAQCLcURBRwEdnVuQ7hAxb8gsPs1HiDINzrPVx23RI+UDucAQBoPT+9M9AfP38/yuuNLl/MNl4fOl2lw7ycQAUGT4z0abt9kdl/PYM3jn+NXqt6o0Z4RpSKEHF5XFwcAjl+gVpb5GQPnqVlpFVAYygKMhk/4kbJ+Oc9TBEsLlf76BvX1wW4mX+OwoeZa/Hhn08C8G7iwVW738OwZT3Ev6NiHF3YO7QfKE7rVNZRWG+KrSFWLRuDH+jgc9e49js/sJnUbnlORaY3zLrpWaixDg7QEqnTdf755x888cQTGD16NH7+cSEAQOPmRMgEAoFA8C18tInq23RiHT+sD3edhKs2I/G2VOlMdq6v69dvBgDoKQqcs8LBLuIhWSQAoH0VXy6ruJoXBZw1qCmd1Q5zc+/fFr6Ybbw+DCY+KVZALZu5IJlP2w343gjxdm2WOK2l+c6/Uhlkt06gkLSvuqbEY3bVBVsrN0iLFko+3l14jMJsytX5Yv2Bm0GAO8Tw5dG2GAoAeDfx4OdZf6IA1g6TUhnssE6bNp3EaVWNWcyB4C2xtbS0FDtUVlWgx33NfO4a137ns7U+Qz9UnUJpxWUPW3XzE8lw9S6v/fzXaPi3lIaUUCUQCIRbCu/3/m5CNE4SBLZrfTeyy51n67WUJrS4Pia05N3zWIqC0Y21gBnhoy438I3WIkG0cNag5mqsjTGjD8QSOrORpmn4+9ddr9qbMAzvahnE2bt/cFLK6yOE9eGLI8R9Gccs/SpFM7u/Ayk+NlatK/OITXWxeHEKVu/9xW7ewiVfYuLEiTBb8iFIVeKyGDgfqfMmvi7A1e64Xi05V2fiQW90ulXKUKfz34oeBAAIDWnm9Rj4wsJC2D5VTLjcp64x4OSdL4TixNv0Tb/c8pyHrbq5YRkzKoVTGmcGluy74rBO7edfJyRCrkH9YgKBQCAQmha+10K9CVBTHAD7zp9M5g+FtBIA0DrCH5dLrKMy1Xp7NzylytrB0esroFDZd3hchVEoechy/GU+X8ALEJYG9ZQpU8AwDCQSCbq07YqLyOVt8gFRoLaNAD+C2bdvXyxYsMDnkkwxQtknWa37gpVQPltHu64R4mHDvFtOsZk8GGDsEzcGh8Tjz5P5CFLJcFfbSATScoAzQC1U2PAGpaWl2JHxMS52sn9+yzQ5AIBiYzUgBQx6E16KGogfru6Fygd1WEtnzLZj4CtCVkpKCiZPnowOizuI84qKzyIqoiMmTpyIYcOGISMjA4mJiW67Zw9dLkOrCH9EBiodlkk4DlK5c6Ey0j8GAKCXcl5//mNiYtC6VIe0CD8AgM5fColEAn9/f6SmpiIpKcnrNgLAq6++iu+++w4Mw4CS8u/S1rIgZLP8t2uPodib5t10/HvgSxgp/jyGV7TEh3+dx5i+8ZBKrO+h2s+/Rs0gAHzOo5SUFJ/71nqKbdu2ITc319tmuJ19+/YBADZv3owLFy542Rr30tSOtakdT1PhZr4WRBS4DrRO5lE0DbPgptcmIgAqmQTnhE54lc6EvLw8pKenOzS+Th2egzuGfesWO03CSCUnXOYyrRFVNSYE+8kcGtQnMuZjbTb/ATQwvpFgaOLEiejSpQv69u3rcx3X2pgFIUUCCr/2+BZPHueTOwWEBGPihGe9aVqd1DdC7M1zyzgZodqXWYaXVp8EAFz+9H4ESZWASQ21ocpjdlVpr+K/M8sxuNskyCX+KCwsdBAEACCnJAOLFn6L03L+OH5fsw5D73gEUAHVbi5Dej04Ewl9QchylvsEAHIL09G1Iz8dFxfnVjv/OlWAF349gTuTI7DsWT4re0uGQ45Q0YWhKFC0c6EnyI9PMFnNev+ah4eHQ8H4A8KzVeInw9PPPCW+WymKwhdffIGZM2d6xT6L+GOx5fXXXwfX6iDOcZVI9m+OVDX/LZUCKClLR0RYklfsvNnIqUgXp3X++UAp0OuTHVg0thd6JfAeLpbn33L+tRpWEAUoTJ3qe99ad2M08u2fTz75xMuWeA6apjFr1ixvm+ERmtqxNrXjaUpY3iU3E0QUaCQmkw562n40uI0Q/GgWGq9SmoLRbG3I7jl0DC8MeQosy4KmaSxYsEA880d0hbjDXbYK4QOcTQ6Ev88UQK03Y2y/eLsGdXqejfcC6zsJhjQajU92XGtjFs4ZxbDo2m0YRnwRjwsR/jguMWHIks6Yf8c3SEwc5mUr7fHVEWID49iJ+mabNVlWfqUOgVJ/wFQCtRvDb2qzZNuLSKk+j8GXN+PrUdsQExODdiU1uCiMvlooM5Vj5YL3gRmtAAASGbB04WrgxQSo4VihxNvk5eWhdevWOHDgALRarVtH3RvDrmO/YOjsljg6Lx/RBhOKFHzISM7V9Gts6Tr2pPM5K3ZfsuauUFESoAHXMcg/CgBQ7SvXXCYFhFwIeprG9h1rxWef4zi88cYbYofck9QWfziOw3fffYepi3oDFBDuF4V3ZQH4pPwIKmkKg/9+DGMCkvDGiD88aufNSJBNglZU8942lTUmjJx3AFmfPyAumjhxIgIDAzF69Gio1QyiAHAUBZWC9blvrbuRy+UAgAULFqBnz55etsb9bN68GbNmzcLKlSvRvn17b5vjVprasTa142kqHDt2DJMnTxbfJTcTRBRoJEYnI5NREj5m2OIpIJXQGNI+CunFfPK57bv22Y10T548Gfcv6IIsqRnhEpXD/lyFiWMACmA5a23id9efBQDkVdTg40c6i/NV8kBx2uBDWYd9teNaG7MQPmA2GsGyLHI2lACTeLfiYgmF9/57A6t9TBTw1RHishoDYN/PxvGcSnH6u+2X0EYeAOiAapMzvx338EflOYCmsNNUiry8PJw4cQIXe/s5rJdZmgPGRhSEhEJFhRGBAKopABwHUB4uPVIHtiO0FsHSUvLP28wrX4GqlgHo+m4rSM0coODnzzUfR8t9n+DBAe+63YboIGvIgNHMQi6lwXD2niwcx4Fycj2DAvnwgSoayMvNRXpGhlfd9I2wTxzXc0IUDi8pQnG+dTTjzTffxBNPPOFRG+vyWFKb9YAMCFAE47Zuk/DJP0+Ly1do0jHu6hlERXWuvTuCDZb8QM2NwNGKp+2WmRgWMpswgv79+4Omaeh1LCiOA0dRCA6R+9y31lO0bdsWPXr0uPaKNzkWV+f27ds3+eNtasfa1I6nqaDRaLxtwnXjewGuPo7RaO2EPGyOhYTjMKXnSwCsngIymsJLQ5KQHBUAAKAU9h0HlmWhulwKAEjPc1+iJ5MwQsVyMiSE2dvw36VSu78tdeABwOAD7q4A8Mmm81h9TuszCcXqgxFqOtMs32GoKLM/h2Umg08k8auNbVUMbydDs8AIYS8hdVTmiAv1Q6Bwv6rNzpN7uoMAmyzeSW3b4Oeff7Zb3uEK77WgC5CimU05tTP7qlFdxW9roCkY9JXuN7YB+HrVgSrh61TiL0eJyl6/fjtjNVjW/dnRQ/ysSr+luozRJrxFybKYtPyY022Dg1rw61MU2rdv5fVknnpBzFAJ1zsrMQiJH7SxW4dlWY8nH6yrkoSO5u31VwQh1Em4QGmF7yRJ9FUsHmxhekfxsrDSvlqSbfJOS1LKmW/28blvLYFAIBDcAxEFGolRGJmUchx6h0/D7pG70b3DE2BYDkv2ZfHLJBRUcgmmDuIbXBJloMN+2Bq+QXu5MMtljXBOb+9KbRQ6VwwnQ2KkvQ1crbjt5nH9xOkaHwgfqNAasXDPFcxJzUD7QQ/7XMfVFoNRgxfSlgIAhHYsSovtz6FeSvtUh8sWS1UMX2n8WQo4tClti9tNFB5U249USWkKgYJbbLXZeRlQV5OXlwel2pqAM6mtEpzNiHGb/67i3HrexfxKoBLl4+LFZVkZJnz5xf+BEtZXqws8YvO18PWqA5GM1TaDk7j9ySsHuN0G27dkQaUOHMchR+gxDTcq0TzrMey4cBUs65gHw98vEhLhmgeH8PZ7U3jRCSJKc6P1XFbL7St9eMMLy9IZlQVHIHrc9/Br1QPz58+HjuKvf4CyGaQyR4+6glLfS+a06fh8nMv5z9tmiFg8BSjOsaJLsdrx3WkRic2C58uCwHKHdQgEAoHQNCGiQCMxGtUAACGHGGTSADzyf/vQ5p3NKKziP7ISoQEbrOLd9lu17SiOdFswa4V4fyXtkkb4X1tfQJc1A/Deou54PaU7fvtzHM7R/G8wnAxJgteCBVmtRnZEcDyejuSFgUqzweudV4uLblTAIUxfsQTNmzf3qY6rLX/t+Uic9leqIJFIUFHBiB0CANDRlE91uJyRl5eH1NRUr197UPx5q2FCUKj7Eb/m2Zchq6gxIlAoUaj1UKhLeno6OJtHplVHFR57OUb8O/98DTIvGe2uuYWDBw/iuecmIUBYdPzUEneb2yDqGqH1FXfheMox278thzgt/trl3gRLtsJPQZUOV4vPin/3Sp6Fk4a+AIAtZ4sctqVoGgGCWBBi4zni6fcAx3H497IWJkFsCzLYC8QPv9cS/YYGedULa+LEiUiYvhSK6EREPP4RJkx4FlpB1DbBUVQHgIKqLA9aeG3OX9mBt87MwROp033jPQrAJIS1yWnHSNGr1c6rDPniN5ZAIBAI7oeIAo3EaOTdlS1OpSdyK3Eqzz7PgCVML0jJiwLygGBkZWVh7dq1YiPcqBY67ArXNMLfuboLAPCnzIx/pGZ8VHlcXGZmFWgbZd+wKqrW2zV4ASAghw/arWZNXq9Zb2JYBEoLUdNiPcytlqOyxvtlEp1xPmMrPszbIv4tlcuRlZWFNWvWItBsP9JpKQG2du1arF271icajRZSUlIQHx+PwYMHo2XLlpg5c6bX7GNhSTgmweHTaQ7Lq2pM8BfKeGo8lM0/KSkJrE3ceECCCpe6hYl/mwzAnDnzEcE4igK9e/cGAKiFBKWvFfwDs9FzuRDqwtZdGPC90BwpbS+k2noOWHgne4NbbWBt3pF7j59HxuWj4t8mVVdxek6q805+kPCJDQyxHounhZeZc1bjgnk7yqWCt4I+1m55ZmIQ1M+09LoXlpHhkBS9EIHt38Ibvz+KGuFxe3tDNjJLHGM0y3SlDvO8SdnV0+L0G18+5vVvKAAYLQlwnXgKWMJhrsXWvya51CYCgUAg+CZEFGgkRiGG2eIpoDM6xrVq9HxHxeIpUKUzIS4uDqNGjRIb4XoNvx0VqHJJI7xNPZUvKFBoEWrvflljZKAxWDtUeXl5+PEHvgGjldJedXNdsj8b/T7biSip1c36fJbzuF1vM3qffQmvYtYgXusQ2Ccfe+aZZ9CnTx+MHj0ao0ePRsuWLb3eaAScZ//++uuvvdKozcvLg1momsFyEsjCHJ+NSp0JgSq+Q67h3B9XDvAdaKlKIf5tiLV/nlYs24CJEydCUivh3DBZhNP9FduMOHsTX8wpYUHL2HuBhFGyOtZ0H7ZRAUvX/ol3ZvOlRhMMHGZtPC8u85c7droAIITiR2iDQnnbPS285OXlYc+pv1AUaRUzSmvaOV23eWys0/meItYvD0XNMgEA/+gyoRFENB0bhBUHsh3WLzNW+ox3U15eHs6ct4Yz6J+I84kcHRZRAIIosHpyX4zvnwAAKFY3TGifWX7QHaYRCAQCwccgokAjKSi9CIDPKA8ATgYGUVHDf4gtokC1ziTGnFoa4RPHTQcA1EhvPAt5Xl4eFEXqOperuQDQFIWoIIXdfNuRgvT0dJSX8sqChqYhlXgvvvjTLfzosERizZGQnrPH43ZcD0W09YbIktk/XsuXL7fzzuA4zuuNRsB5bDngnfjnS5cu4bKfMKIJ5x2tyhoj/IUa8BoPJvHnpFZ7Lgfbu7Z36jgYACCj7G0e0fZxp/vS+NAop6/llACAJUuWoMiss5t3mTJjpDTSo3ZUVFSK05KgCIT0CwYAZCnsy84eza6A3uQoUOkET5aCx5p7RXhJT09Hy4hCu3ml5jhMMd+FJ6T2glWZlxP3tQ12DMEAgBomCLEhSnzVYjgULIt2DP/Q52nKRO8mb47KW7ys/ty0TpwXYeKvu7dDxkyCKMCx/HspzF+OaOHdVdxATwEASFm0yPXGEQgEAsGnIKJAI/k9c704XVpaitOnzzisU6njP8RBgijAcoDWaB2Vj4uLQ69uAwHAJXXL09PT672SObrukNAU5jzVAy8NSULLUD4TsW1MYVJSEqqFPjhHUWgWIhHdXE2ll1B0ZAHgpOPoTkxR/4rTueWOI6u+MErUia2VK4KydhaoWuEZcqmjguTtRiPgPLbcgsfjn/3yxWmWc14xtVJnQoAf3znUUgDnofvS5CRfQDdGgjPjzoASzt9n/T+0Wy6TWMWDh2TWDq1WV+YmKxuHLzxDFs4XVOOt9WeRWViOqVOnwiCxvycNFIVXH1yCN0N6INqmQ+7O619Sar1OsmaxuNoyoM51D2Q6XtMMm9dDh7YhHhdekpKSoFDYnx89q0J5s7EY2fsVu/lFNu7v3sAkce49o+UCUaox4t7Bn+Lgk/vxUifenb3UrPNa5Yz5/13BtjwKubm5opeVNMD6vjIJXmLeztFhEioJccK7VEJbBwfyKnVOt8nLy0ObTPukxS+9PNUn3hEEAoFAcB9EFGgk/cK6iNPTP/gaH3z4ocM6CiF2UymTQC5MV+nsXWGDAnlXzWqaAldH6bWGkpSUBEicD5n2rg4FQIGmKPROCMUrdyeLokBRlXWkIC4uDnP/bz4UQiMrNFQuurk+//sDuPv8Tzi5/8sbsrMxKGg1ShRWIWWt+ZTdctsYeG+OEjm7clvO8iNzy3vZ11FPWtgJj0+Nspvn7UYjYI0tdyYMeNq+PYf3idMyyvlI1uUSLcqEZGksRUFX45lRd72TzmeSKtru747JD2NiiPUdERGWLE7behFodZWuN7CR+MozZGHs4sNYd7wAP2ZFgmVZGAT38VY63oMpmGEQGNwSzzy8DBse3yFup9E4H2F2BaFh1rwRAVES1AhCRd+KYId1SzQGGMx1v8uzCo+43sBrEBcXh9CIELt5Rs4PakaGVi3vsJtfUO6Yv8OTnAq86HQ+B4mYxFeqDEZ4MF/ZQy23Fw09JWDqjAy+3p6OTbkS/HeK97KSKijkDLa+2ytlErRIUuKVV17xqgeOibOIAvy7R0rTSBIqEZ3MqcTgr3fh/T/txZj09HRsn2/vXRLgD6+L1wQCgUBwL0QUaCQSIYtvZ7US0c98DYpyPIWfPNJJnLbNK2BLYCCfudxEUdDfoCtxXFwczsfUPYLF220VDaKC+NHLolrugxMnTkSgUA9u0ZIfRDfXwyp+/bXZ/9yQnY1BRTkmYrNkUval+uqsULTsblaJbloayTl34YyQeLJbpyfxbswQu/XP97G67NI07TOJ3SZOnIjs7Gy8/vrrXks8l5eXh917rOW8MjS317nuu39li5n+M684euu4A7NQFaGZzSi1vJY3A0VRePnhVfil/2f4vuMUxLfoLy6T2ogCGkOFm62tn9//+hhbCv6HkGb88+4L8c+lGqvnEi2hoRdEqtz/y0GfnCp83nqKuNw/MAZ+QkhWeQUfh+4OrwelnxTtmv+AFoG7IU/8Xpyvd/LeX7z3Cjq+/w/+OG79/Y/iHxany9X5Dtt4ArnS/h41cEoUVukhV9oLG4XVOZ40y4HmQl6cwdUMlLUEuKs2AnZ4aBIAoEpKw1bH9JSAabKxTaeKBE3TaN1a4bBevyci8NJLL7ndnvowCp4CDMvfA1IJhRbN+EEBI8PicqkWyw9k47JNIsekpCToS+3FrZAQmdfFawKBQCC4FyIKNJBt+z5F52Wd8c/VQwAAynLqamXIPj37HiTZZPq35hWwz5LurwoXOzXV1a5tiH+WMEGcNkgsZRKtokB0MN+AcRZTGCx0XOQKM6AtswsZMHOecdOODVZC4iSsosfKHgCA/fv3+0x9dUvpxG5+d2FPzqc4ph2GRXuviMtHDf7KYZspU6Zg7dq1yM7O9qnEbnFxcfjqq6+8lnguPT0dqih+5L2NHqjkQutc93RuBfyEbPRPPDncI6PclmZygto6enzy0hmnv9056UEM6TXDbp5KYX0vaA1VtTfxGCkpKfis9FektQxEt0+sDf0RU8OxbPuzHg8pKNMYUK41ItRfjlC/k1BKi/HjnG/E5dmFNPrFfoCBg+w7WGGCgFlWleU2r4f0i58hP6gQlXFb7Oab1R0c1r1YpIaZ5fDqWqtX06ODPkZn8KJqua7EJTY1FjNnzUI7pCwYAI2CKt51/GmZdXT7qtZ9HhcNQc7yNX3Umm5Izh8AgPcOAYDDWeViPpaQkFagOA4sRSE6ht/GkwKm7Wfwi10FmL9gPpQxjuUzk2Jae13wNVqStrIWTwEKQSqp6M1oYcoKayJfi+eYLS+9Mtnrx0IgEAgE90JEgQbyWsavAIATFN/Aolj+1Fk8BfRZJ7F3RlexDKGFIGGUpranAEVRCBRClKtd6P76fdtn8eCdr6KvlC/ZxunaAABo6tqeAgAQKGTLvpy9C2NX9sVfv40Ul+n0Oo90FIJUMtCU81JzCxb9H5544gmH+d5ywzexfKNrwcKldvMtiSULi64iOd++nNaiRYvQr18/n21keSvxXFJSEhT+vGAl5RxfTVIbYcvE0VAJooAqgPbIKLdZ+PmsigetduTrG/zb420EIo2hup413YfFy8YoDLEWK2WIjJRCqaBwvlcEVtOFuP32RI+FFBjNLIZ9/x/6fvov2jRbBFP8asiSvoUhwVp55OlvtjgVp0KFagRZBRfd4jmUl5cHbcklh/mDNAYc0twv/v38nW0c1lmyzyoMJslDAABaQ93JYN0Jw/Hfnh7lEdhQ/DYAoKBSB47j8Obof/C8Px/iUmH0jn0WLKVIWU6KfZqH0CevBwKznhaXF1hCCGRKNBO+nb/9vsDjAiZTK7fIRW4/2LHWd2V3A38c0maOQoGn0TC89w0jCC4SmgJFUWKyQQvpxRpwHIcNJ/JxobDa4Vz27tPZMwYTCAQCwWsQUeA6sXgK0H68C2aHDu2ddqJsKxDUxl9IRqTV37grcXNhGDNCyFXwzaN/4Ifur+FM9VMAnIcP2CYatBAk4Ttln2jO44RSiXf06eKyq2UlHukoXCxSQwL+fClY+wbY19/MtMvgD3ivvnpeXh6MwkgWC3sXXYsrdHp6OrL+LLZbNmJcqM/EZ3Ich8MX16FCbR9D6o0EdHFxcYgXwmAkRgbmSqtND3SJwV8vDMSARH6U3lRRAKXgxu8fKPGIp4hJENbMnBzUjwVISi3ChsXFDf7tsOB4jFI0BwDUCKVNPY2l0kSITez7wDdb4r4vWot/+/l7LqSgSmdCqcYII8PiYqC1xN+Sq/zIvILl8NcF553VUCGJY2buebd4DqWnp8NodEx2efrqFLvKGP3bhDms8+Ff55Fbzl9jfynvrq02aRzW8wQas/Ce56yCtd7EorLGBEoiQVwz3h2/3MyHbHkr+SQrhOf0SIgEQGGH+nFcMHYTl+dXWBPjhVnOv0TtIGC623621vfnH5k1V0SCmcX9LYYCAMpM3hVZzCYdDlP8tWctOQWEnBiWNoAFmgL+Sy/Fy2tO4r4f+Eo/sZRcXF6jr/SAxbcGarUaAQEBTkWsJUuWgKIo7NljX21p/Pjx+P7778W/jx07hnvvvRetW7dGr169MGDAAGzYsMHNll8bi/0WW4qLi3HvvfciKSkJnTp1wn//WcMDjxw5ggEDBqBr167o1q0bdu7cWed+V6xYga5du6JTp04YMmQIcnKsoU733HMPunTpgm7duuH222/HiRMn3HZ8N0p6ejr69++P5ORk9O7dG+fOnXO6XkpKCpKSktCmTRtMmjQJJpNj/4FQNzd6ng8cOIBu3bqhW7du6NixI6ZMmQKDoWElXG92iCjQQJS1OqcQRjJHjOPjXFu2cN4hrSunAACohNOvM964K7FJiG2XCp36IL9wDO4yHmqDkAXZxlMgWhQFHD0FgiQqh3kiFOX2joIlXJumeU8BCTh0sQm9iIh1rFX+66+/esUNPz09HYWCZwhbq4a6JbNzUlISKkrtOyzn74iChPJMHPy12Hl0DiYemo2R6+7Fv4e/x6NLe+CH+S94LQFdYAjfgfKXK9Gzg9Xz4/+e6oH2MUH4YHhHAACtCIDMxJ9Xpb/EI54iFk8BEytHWmE41i8rBcs2zktFJeWfPZ3ZeeZvd5OUlASVSoJKm/KKl8L8kB5ife4Dgq0dYXeLLbaJ+drrHKs7KIUOWH6lDj/sSEe51uoKHyR0tg+fdKyj7or7ISkpCU5SB0DN2Mfit4sJdFwJ1vdroIwXurReEoI4ij/HMlqO/z3cEWH+fGfPEkLQLIDPb1POGryWfJLjODE/S0SAfX6cAAV/PxbYZMsPp/nvXKm6wE4E8IT9bK22QJTJ+n7PktL49yL/vSo1aV0mTizYNAmdl3XG+cvbGrxN0dWT4rSeCQFg9baKriUK9Ixvhr3p9uEtSx+ylljUGCobZzChTtasWYPOnTvj999/h0ZjLxSmpKRgyJAh9d63586dw7BhwzB9+nRcvnwZR48exW+//YaqKu+FpAFAVlYWFi5ciL59+4rz3nrrLfTt2xfp6elYsmQJnnrqKZhMJnAch0cffRQffvghTp06hbVr12L8+PHQ6Ry/ixcvXsTMmTOxdetWnD17FhMmTMDUqVPF5WvXrsXp06dx8uRJvPrqqxg/frwnDve6mDJlCiZPnoxLly7hzTffdGrrlStXMGvWLOzZswcZGRm4evWqQzgPoX5u9Dx37doVR44cwcmTJ3HmzBkUFxfj559/9vBReAciCjQQplZyf0pQ3g2Ca6htp9sWS1nCar0TUUCI39e5wK3U0m3OLLE2mstsEneZbUbSLKMExWqDQwNHWUcZOAAQQnjd2lGoEEymWf44aI5DZqb1AxDcIwi25eslEgn69evnFluuRWIbG7dhzj63hKUBGxcXh7ff+sJh22lZvvGS35fxFwCgmGLx07mlyKBM2IjtXkvieKWU95pRSpVoFemY4d3SoZH4BUFm4u9dRaDU7Z4iHMvCLDzjDCeHPJT3yLF4qWzKNOC+H/agsKr+zr6fILrVmBteI9yVxMXF4a1vhtS7TkCI9V52t9jy62HriI+EcRQkDcI5n7j0CL7bcQmvrDkpLlMKI9+0v/1nzFWeQ3FxcYiKiXaYX8MGimIvAEQGKpEU6Zjo1SIE+8t50UDtBSHIzLCiKNA7PhJj+iUgJoR//xdW8vdghJDNvwgmryVw1RoZcIKnQHRIkN2ydtH8+cu3EQXCpPz5Ppl2RBQBWrZsiUmTJrnd/trjAyFm+3d/fjVv71WzwWXixE+lvPA1es9rDd6mWnNVnD6tuRuA1WPQUpbQQonaAIlN1kadkUFMSAIG0/w7WOPFHChNjZSUFLz55pvo168f1qxZI85PS0vDlStXsHz5cmzYsAHV1c5DzD7//HM8++yzGD58uDgvNjYW48aNAwDk5+dj5MiR6Ny5M7p06YJZs2a594DAP2vPPfccfvrpJygU1ntr7dq1eP755wEAvXv3RmxsLHbv3o2ysjKUlJRg6FDeqyY5ORkhISHYsmWLw77Pnj2LLl26ICaGFy/vv/9+bNmyBWVlfAnYkJAQcd2qqipQdbTFvU1xcTGOHj2KZ555BgAwYsQI5ObmOrSlf//9dzz00EOIjo4GRVF4/vnn8euvv3rD5JsSV5xnPz8/yGT8N95oNEKn0/nsfeVqiCjQQBwKTQmdwMNZfCcmxE9eew0A1/AUsIgCLnArtXgKfL3jsjhv23lroyDWZiQwPEAOigIYlkN5jVVEAADKVPeNz0ncX3tZa1E3dPx5pTmgSNIG3av4hlZupxA88HoL0Q5vZu+PjrFWEsgptG9k2bq6Tpr0osdsaizhSmsyv0yav8uLlTK7N4OnkjiaGBaUkLODMdEY0y8BANC5uVUcCPHj710AiAvnGwkPPnKv2z1FzCbr9TRxcrzx4Rf43//+h/T0dEycOBGfbbmIC4XV+HJr/WXdVDJ+dFvHeM8VLba5Y0fXFr9g/p3liedr4R5r7D1HOSYX1QudmItFvHC6+5J1NFNi5JdRKvtOmSs9h4KDHL0A9r7zMD56mPdYCRVEqoRwf4f1LF4NgQq+k6v1wjU3MixYQRQIUPD3Xkww/y2weArExfQCAFTSNORy+x6vp579vIoaMMI3LNjPz26ZxRPDVhQIV/DvhPPZF0QRgOM4h9Ayd9hfO6eAymQvZlUxfPLGKhn/EvVWZQ+d0JGPNXKwvNAtCQZrhw+UqA3wk1ufI0v4W4Dgeaj1cr6JpsL58+eRmZmJBx54ABMmTLATi1JSUjBmzBjExsZi8ODBWL16tdN9HDt2rN6BkGeeeQY9e/bEmTNncPr0abz4ovvbH99++y0GDBiAnj17ivPKyspgMpkQHW393iQkJCAnJwfh4eGIiYnB2rVrAfChBGlpacjKynLYd9euXXH8+HFcusTnd1m5ciU4jkN2dra4ztixY9GiRQvMmjULK1ascNNR3hi5ubmIiYmBVMoPvFEUhZYtW9qFQgBATk4O4uPjxb8t54zQMFx1nrOystC1a1eEh4cjODgY06ZN88wBeBkiCjQAljGDra0SCQ0tszBs0LyZc7f7ekUBml+md4GngMWTwcxZxYm3/7C6qCtl1g++VEKLI661QwhCA+rO+M5y7u8oaAUfbZrhhRKJ0P6SMNZOd3b7YK9kx68NYzPa2+whezU+u/zarsKlxc7jnDxJM1W40/l3/5QsTnsqiaPRzAI0/5wEK1ToGd8M21+5A79OtrojSmgKgYI7sb/QwWYkzpNSuhK9zUiZiVOgipGjc+fOiIuLw4kca04Q27J6zhBFAdZY73ruxE9ef/nS4SPu89jz9UDnGHGacSIKqNi6K55EBEcCAFilvWeDKz2H2FoVV1qZTAgPVGJ4l1j8+GR3bJzBZ8lvHuL4/i8R7gV/RQgAQMN5Pi7UaGbB0EKYjYy3MU74VuUJwmVAYKxYCScs3F7c9tSzr9GbxZwCMpkST/RuIS5rG82LKrZCa7jw3uIC6/ZsA9xjv8W7jrZk9q+1vMLEd4KMNI2AIL6J5Y3qODohmamcs7ZdLKNdtRMNao0MtAbre9Ry7/pJ+XtF46V8GE2NlJQUPPXUU5DJZHjkkUdw8eJFXLhwAWazGcuXL8eECXzlqGefffa6vEs0Gg327t2L116zepRERETUs8WNc/bsWaxbtw7vvfdeo7b7888/sXjxYnTv3h0//PADBg4cKHbkbElKSsK8efMwduxY9OrVC2VlZQgJCbFbd/ny5cjNzcXHH3+MN99884aPiUBISEjAqVOnUFRUBIPBgD/++MPbJnmEJisKmNRXr71SA2HMjg19o8y+I3++wLmrl6UagbNEg5yw31U5W2/URJiFpIUMK+M7V9cgMtAaQmBLeFDdH5Dw4BC3dxTOV/DHERfFj7zJWRYla95FYYW9y3PHri285iFQWnEFn/75JC7mWJPmmGslGswq1dr9/U3yOIf95Befdo+BDSQvLw/FJc7LpBX6yxEWJvGoNwYvCvAN09hgviOQFBUoxhRbsITkKCn+Hlab7M+1Oygp50cp/FkWes4fuRW86KMxmDHrz7PierYNa2f4CfHlOtZ7iYMYoXZ5H86xtjoAGGmDx6pPmBjru8oSU97/agIey2+BkeVA1JWRDttYQnOimvFJGxkFLwq441612AQASpbF7I6TAQA0TeGhrrGIE2q+xzkRhUuEd2uAkq8Eo2HdL17VprLGJIotfoIoEB/K27zgv8uorDGComkECYf5znsvQCJx3/msCxPDgRX6rhKJEs38reJEqzD+W7D7UgkyivnvbnQQP8JzPi4QYWFWUYiiKLfbb0k0qJBWYmDrX3A6yCoA98nvjBouCH6CmBUVbfW6uRFxQmbjnWA0NKyDrhNG96WsYzOvdk4BwL4akXjvCnk7tCbv5MNoSphMJqxYsUKMb1YoFHjyySeRkpKCv//+G5WVlRg2bBgSEhIwffp0HD9+HGfPnnXYT8+ePXHgwAEPW183e/bsQVZWFpKSkpCQkICDBw9i8uTJWLt2LaRSKYqKrNW1srKy0LJlSwC8B8DWrVtx4sQJrFy5EgUFBejYsaPT3xg5ciQOHjyIo0ePYurUqdDpdE6fp3HjxiE1NVUMLfAlWrRogcLCQpjN/HeA4zjk5OSI58NCy5Yt7bwgbM8Z4dq4+jwHBATgiSeewKpVq9xruI/QZEWBM5f/cdm+zDZun4l6vuVytfgxu3VsRzZsCarHU2CPkBk4XS4Ha7qxGGNLEjQzZNh8hs/a3q1FCABgYKLjaHCkEFN46HI5Hv15H3YIoQb+8iCHdcXfkFJubyAK7XsYLC6vtBRpe/5GuaIvemutDaNn/nzM2eYeYdHO1/Fr5VmMOWhVxs2cfaLByyX2HdV7+r2ONrXyNahrvFO3HICYkGtP+qE611m0+BuPemMYGRYnQ8oBAAaH8TcrFqFNRvMNVk9k8tcLibaULABQyC3nO6afbUnD2XyrIFhjtA80UutNeGrhQTw+/wA0BjNUQnx5Def5DqIFRiijGSUPQifWMXFnlYdchc0Mi79PWytMWEa0y8zRWFY9HUuufo4zxtsctrN0WAJVvFeTLMTffZ4NQmfsTmMQDgxbhR59X3G6Wqi/Y/iYaKcf//7VOAahuZ0SjQGMMAIvF7xUWoZZ3fO7fbQdABAsjCb36dsJWVlZHvfEMrOs+MRLpQrx2wXYe+HN+IXPLN46trc4b+gbfENOIpFg4cKFbrf/i6V/AgASYxbhlMIq7HYsScSOar6EYohwqcMiZS4RJ5rZvA6r1fkN2kZn5MUDmZAUeeawtuKyiECrIOgvhA0UVTmKAv5yS5JM7+RAaUps3LgRsbGx6Natmzhv/PjxWLFiBebPn4/vv/8eWVlZyMrKQnZ2Nl599VWn3gJvvPEGFi9ejE2bNonzioqKsGzZMgQEBOCOO+7AN998Iy4rqUP4dxVTp05FYWGhaHvfvn2xYMECTJ06FaNGjcK8efMA8CEC+fn5uPPOOwEAhYXWd//ChQvh7++PwYMHO/0Ny7oMw+DNN9/E9OnT4efnh8rKShQUWMvXbtiwAWFhYQgNrdvj1VtERkaiR48eWLlyJQBg3bp1iIuLcxA3RowYgY0bN6KoqAgcx2HevHlOy3ATnOOK85yRkSFWIjAajVi/fj26dOni2QPxEk1WFNAZXVcH3Gijkp+98gHUFz7HFUM3u3UGt4t0um194QOBNqGJ6urc67aPZUxieIOZk+HlNSeRflWNQCXfCR3Rs7nDNpFCo2De7kycyKnEc8uPAgD8lSF1/o7OAx0ZSxKnbs358+YvkSEuLg7RwUpkX31KXK/AiauxpyjRFjnMYwRPgRahfAO2qFrvMGr85R1f2/2t1pW7ycL6sdSqZ1kWXIRfnevFJ4R5ZJRw69lCLNl3BWqdVXw7X325zvXPF/LPdrmab8yqGfc3WHUG/h0gE+7Pwmo9zCyw9ph9A11rtF7zi0XV6Dx7G/ZnluHwlXLM25UJlYIXBTzxLNWFWfBSkFA0Up74F7Pj7rdbXuUBzwsA+P2YfYx1ppI/uZ0C6j83ljwogYILuRqs2zwbOKGrylE0pLHdgDqSDdkmHryvE+8+Lnas/HjvKw3lWF3B3WgNZlwWzqtCxo+4twx1fOaDKP79VaW9iri4OI95ilgwMxzMlKWCjhL3dIjChw91xLqp/e3stXhmRIRaw5vORdmLQu60Py8vDxvTawBwyAq0LyXsL6kUpwMZ/nxOmvYMDhw4gNatW7ssp0C1puDaKwHQCm0gCSsBTQH3trIKV7Y5BVoKnhi2XoNiTgFBxNR64B3b1ElJSUFmZiYSEhLEf6NGjUJxcTF2796NUaNG2a3/9NNPY+XKlTAa7UPNOnfujC1btuCHH35A69at0blzZzz22GNo1oz3SFqxYgWOHj2Kjh07olu3bpgzZ47HjrE2X3zxBfbv34+kpCSMHz8eK1euFBO4LViwAMnJyUhKSsJff/2F9evXi+Et8+bNw/vvvy/u59lnn0WHDh2QnJyM4OBgfPrppwD4xIKPPPIIOnfujK5du2LOnDn4+++/fTYp3Pz58zF//nwkJyfj888/x5IlSwAAzz33HDZu3AgAaN26NT788EMMGDAAiYmJiIiIwJQpU7xp9k3HjZ7nnTt3onv37ujatSu6d++OqKgojyTs9AXqD8i7idG5cLRLb7R+EC1u4l3jgnEqzxpnTNP1Nxar9Y4NXRUHWKSLyupcBIclXZd9tknQGCGnwPA5e8XRFtuswhYs4QO18RdcXZ1Rw7l/pIsR2s0c+E6YQkjGGBmoxOECR3ED4Btq6enpSEpKcktDMP2qGs8uO4JKrQlLJvTGNs5ecJJy1kRORjOLZn4yVNSYcNsnO7D/7SHiPZDceghgU364Wu8dFzdLrXoAUBokda5X6UT8cDUGM4PnVx4HALxzrzXpy7s9Xr3mtlllFBADaBn3x+erdfyom5SjQFO8eHWw2PGZv1rFV/SgaQovrz5ptyy/UgeVEB+t47wnapmF57i6hgEtDULXhCFA3mZxebWHOgB/nbZ2bh6PPQpL3un//v0TaO8YNmDB0tkO8ucTuqnd2Nm2JK6jUX8jMznKmpBwXP8EbDlbZLUziH8naSgKOk0xVAHOBWRXw3Ecxi85gsD2lhn8fy1D7ZMiMiyHIFoBwIwqXalHbKuNiWHF8AGpVAmKojCuf4K4/H8Pd8SsP8+JVXSCguw98wYNGiSWJnTXdwDg3526y8fRZTCH2mcqu2KYOC03ywAYcLUyB3379gXLsqBpGgsWLLDzXsgvPY+oZkmQShw9dmyxvcPVmoaFRm4vOghQgJRRoPLIRiR/+ZD4+0qZBFtfvh0My+G77ZdwobAaxTbhA2Ua/p3qLxeqD3gxB0pTYfPmzddeyYYuXbqIo/xLly61W9a7d29s2+a8PGVsbCzWrVvndJkn2LVrlzgdFRVVp50ffPABPvjgA6fLLBULLDirSgAA8fHxOHz48PUZ6gXatm3rNPRj0aJFdn9PmjQJkyZN8pRZTY4bPc+TJ0/G5MmT3WafL9NkPQX0LoyBMxr5TjfFceAEUaB1uB96hvMNlJRxvercNkgljMA48RSQ2jQ0KxvoEugMs9k2M7qQvNDEisq/zIlgUbskkYUAZd1uVzq4f6SL4YBo/yP4jU0FABwRQiyigxWoZu1tq6z2TF3q0QsOIrdcB7XBjGdSHN3tzTaq9NVqAypq+GutNTJY8F9mnfst1rou70VjSEpKAi0IRbUHrCmOwwDwo3GVHghv0BqsQtN3/1hdceMS761zm9YRlk4N79qq9kCDtUJrFQVaR/C/+9sVR0HFyLAoE7LO1w4hMTEs/JR8I7vGA89SXVjCB8o0Znyy6QIiwtvZLa9iPZMlf1+GVRQ78Z81Y7S5qP7fF93yA/gReTXFl4x0B5xwna418tQi1A/Lnr0N66f1F12zLXaGNWuDAJYDS1EoKnGMEXYX5wqqYZsGr3UsH4ohl9I4/5G1A5tRrEGwkFCuWm8/+u0pzCwn5sWRyBwF6zbCM5dTxn/XaYn9eIYnvgMA/+40V+SgNGavwzI1F43pd/FlaqVm/hhOpZ+ss0Tif8fn495No/HK6ruv+bu2d3d1A97LlRVXxG8nxSjBmQ0Ov98uOggdY4MR5m+pMGB9F1vaKwGC56DWi55NBAKBQPAMTVYU0LlQFNALCQFtmyEmgw5PJ7L495WBGNI+qs5tLaPERjMLvYmB3sTgbH4VOI5DiMyaBbyyou7OIwAxttUZJpt4P9vqA5ZOicSJKBBRh6eAn19Ynb9T4wH3V4YDtC2tKrclYROfGMn+dt19cIHb62ozLCeWFgN4seVaWDut1o6BhRkR1kz6hTrv5BSIi4vDggULIJFIQEnsO7YcRSFE6CBUecCT4UqpNWmWlLIKZzKp82oeAPDWvXwnNsSfdx/XesCDRW/k3ydSUOidULc3DWCNzb2/s33pP52RgUoQ3XRecCW3YBYS3lEchd+O5SIo0H5UtcoD5xMAYm0yoJtqrKE0ew/XoHzHfABA+xjHHCeWEc2gQN5zyExR0NW4Z4TbEj5AXcNTAADuTI5A95bNRFFAbTBDZ2RAUZQYs1+lbpjrtyvILquBBNbOnEJlfbf7yaVicsQtZwsRLOQbqLKpsmEZefdEKT0Tw4p5caROnv0WQgjB5VIt5u5y/Fa6+ztgIS4uDk8/Ya0P38powsxKP3wfMQj/fTAGM4fx7ybKzH/bqSD796ttFYJfzvDCxS6z/Xs2K/8I9p9eZjfPNuFldQO8OdQaq5eXyRgFzmxy+H0LoQGO+TAqRVFASJJJRAECgUBo8jRZUaCo6JTL9mUQwgckNu34X1euwM4d253GZ9oSoJDC0iev0pkwaflRPPjTXqw7no9P75krrvftJec1aQEgdcebmLKwA7Zt/NlpQ8dsKwrA0Q1RJnESPuDEU8DEsAjwq9u1VUdR9YoTN4KZYfHVtkti9QELr0T0BwAMEJIlxtvEbJeUXRUbghZcXfqpsErnME92jXPw1UhrQpLalSCm3L8QnyY8CgDIN7ku70VjuX/kU5iwIBWlwY7H4k/zokalvtLtdiyyqVNvKwo46xhYsCR1qzbw66jrSUroKgxCIlApR6Fz85B617XUf6/93CnlEqiEkTd3Pkt1UVqahu/XP44LVZaOqQR6EwtzLTOqhHwd7u4U6oVn48WBsfDvxN9zUTVGcABqzmzD24NbYPWkvg7v2GUHsvH1P2lQqcKE0B2gWnP9nlb1IpybhogCFgIVUrHme45QmjTEJmbfU5RqDJBRVlFSJrN/pixlaefszICU489xtRB256mRdwsGkx6M4I2hVAQ7LLf9Xn2x9SJMDIuv2vBJocIY1u3fAVu6d+8mTr+XOB1jXzqEIff/BIWUv+ZP9G4Bs5kXs5gA+++xbRWCIInzd9zwHc9iyomvcebiBnGe7dE1JBcNYxNSdaRipFjpyFkVhJhgxwECi6eAvzBIoAHrEXGIQCAQCN6jyYoCqyQapCya3+jtOCcNdb3QIZDYqPWs2Yi5c+de80NJUZRYgaBaZ8KedF7lX7TnMlpHdRPXuyyXgTM7d4N+MX8z9iukWHHpa6cNNIsoIOE4/PKcY41upczRzTky0FEUqKgxwt+/blHASFEwuak00S+Hc7BgTxaqTRSam6wjlSGCi3D3lvyIxdUrr6OFiRcGpCpOdIO34Oq61GbG8X6wyBI9K62jmE/exse4dm4ejA4x1kYt66TflxDRGQCQ5yE3bWd8u2kTDkqmoUDh2KEuLOcb55Um92ehbxdtPYcWUUDCcQ4eDLaE+PHPU7me70hq3ZhTyNIxrtRU8raBxkPdYh3W2/7KHaJngKWeum25PQAo1xjhJ2TMN1MUTA0sLeYqPt06CSnVF7BPIohRwuj1xUL766ymKCxaNN/tnUKLB86lChY5HUMAAFf95KBpGj//9COm3NMFwX4y/Px0D4QHyPFA5xhx2zmpGQBFiclaqzXuyX/Bcg33FLBAURQ6CB4OaVf5cxtM8x3wyppiF1tYN78fy4PMVmiT2ecSGNsvAQDvun8qh1+vyqy1S0QKuHfk3YLJxkNB4UQUsHS4LYxfchjdkh7kbaYph7w5rv4O2MKx1u9T5z6OpWbjmqmgN/OdaSZYVWeJRKWTPAJmmzbAkQw+IZbOUI0qG4Gx2ljlsF1tTEJIYQjDwgwFOLOxzioI8WH+DttXCck8d+/i47W1gEfEIQKBQCB4jyYrCgDA7A9ealRD5sO/zmHgF6mo0Np3zmt0fJyl1qbhoYhOAsuyyMy8hts/nFcg0JkcXXTz8w86zLO1v8xf5rSBtv8k/6FmKArhgQp8Paqr3T44J/HLEU5EgXKtURzJrAt3uemW2rjZB5j4BnQIw+CuntPs1ithoxFWEwIAMNJ60Q0ecFOdckEkClJKER6gAAUzOGFEK710FDrVULitJA7v3N8eX4/qipTxvaCSS/BYd9612dl1DvLnO481XkyQW6NeWecydQ1/b3giC71UYj0JMlrIgXGNAfQQP/7+KNEL5bJoCkwdgtqNYDtaunsvn+NCChoBCilah/MN6ecGJuDA24ORFBUoZkbPreCFM6MgCgwVwosuFlVDZePCXaP3bPWJC8ba8eL8c3OhqBofxN2HFkKmN46i8M47MzzWKRze1SqySDgOCxYswIQJE8R5nZoH48i7Q/G/RzrZbXc0uwKBgrCh1rqns81RDcspUJsE4f7IKuWfIUvMfqXOc8lFz+RXQWrjKSCV2I8It4m0hq+ZGX66VK/G/v37PTryDgAGoeQnxXFORYHa7Msogw78c2WmKMyd94VbvwO2MIzVW42inAjuQUqoTby4XiFFnSUS5ZSj2/6eYz9bf0cQH37e/JzdOtUNqKxkCSmUCu/SV196oc4Sjc7yC5VrjcjLy8PsD74EANTQFCi4XxwiEAgEgvdo0qKAUsU1qiGzZF8W8it1WHYgy27+jtMbHNZltOWgaRpt2rS55n4tosDIedZsmJZ65pGU9YOsrnFsMKanp4vTOil/uWo30N6/mipOS2gKSTaNPcC+/JCF2iMvAD+Sea3Gr85NZfQUNt4Mlob4u4lPQBFoHR187wE+jbaE5RvYP1WcwAPD+7q1LrVlpD9aeQwJUR+ic8z/icuqmVAcyP4M/5bOgL9cipE948SqDvd05BusR7PKMX93JnQ2SZz8hI5hjRsTpF0Ljnaez6BXRQiMZj6TeqVNFnp3uZIbbEQTP7oSABB0DVHA8jxpGauXgc7FWdNrj5ZKZPyzRwsd5zlPdMWjCQxeuKs1YoL5+7GFEKOdV6EDx3HYfIYfvY4O5p/xihoTtp6vEF3e3fUs1UW8pFaok42nwMghX2LzhNPwE274sDD7RG7u6BQqhXOaHK1AO47/vVnh/REeHu6wLkVRYtiIhVHzDoil9NTuSoopXKvGeAoAQCtBFPh2+yUUq/Vi/pgqofPrCQIUUigFrxA/lgVVazS9jU3uE4PwLJUatHjiiSccvgM0TcPf33FE2VVk5p8DAIQy9XsJ2VJtVEIl3K/DhrnnO3A2vwpTVx7DlVKrQMrZ5NygaccCTtFBSpSbeKGrjKaQU7zZaYnEaieaa6VNzgmLKLC0+oL9dg0Qa01m+5DHxKTkOkWSaCftg2q9GRfSLqGyghdAOIpCUCDtdnGIQCAQCN6jSYsCAUGy63IhrF0pIKOY/zj7sSyKV81EzaUDqPx3AaZOndqg0YggpaOboF7oJK5+9C9xXmm+Y2mVpCRrmcIaQRSo7RoZZOOj3jxEZVceq5mfTMzcXJvwAPsRgjLttUdba9zUkdl+3hpra+kmS2s1uO5M5ut9s2ZrLOYjm0e7tS61JZykJvJXpMkrcSWkUFxmYK2drNolKUP9rR3Bz7ZcxFf/pInLLMkcWYrCP1tXeGXkJUjivMoEy/hDz/AjdZUc/xy4M77YYJNzQSWt5G3j6n8tySQ0AhVSGDgVaMv10bpWFLAt2wgAtIy/viaDCSkpKUiKCsCgGA5+cus9KnoKlNcgs8QaGmD7nL27/gxUwuPqrmepLmIUIXZ/cxzf+Vq87wqeW3YUb/5+GiHCIUvfSkTLOOt7yx3u2JbT+/bW+3GR4jsfgcHx9WwB0UPDgkoQE6psPJhcKWBZEg1eqyRhbeLDrO+G73ekI1gudLprKjyWvC8mWIkgCS80BzvRHgNtvkt6QRTQyWjxnSex6ZyzLIu+ffu6zX38eBafE0LeCI20oFKHEEHYqqjOdct34KmFB7HlbBHu+nqXGA7E2oQP1BZaAF6EL2esSUannPkJJSUXHNYzmKznt6yCz61i+wmu0EpRVpnlsF1lrRC+i5f+Rp7Qdth18FucvbDOxlOAPz+292NtglUyyKWOxxER1wpmEwWJxVsuWOLWsAwCgUAgeJcmLQo8N3nsdTUQ1Hr7TLtRwfyXupM+GJcObMPaF4fi0tmTuPvua5cSAqwjm7bUCCOkETYj4dPy/3ZYz9b+Gpp26ho5RNlcnFZIaajk1sZGz/i6M6VvfnEg1k7ph/s68Q2Y8gaIAjo3jXSdzLXuVy50wGqPwlhG4RnG2jHQNtKtt7FY9JYqJ4NXRljFidqN5dgQ+9GXXZes7s1Km7KPy3e/49ZYTbXehJ92ZqKoViqIar19WEMzhkGMicH58kdRY+bvmSqOcXt8sSW84u4OUVBI+FjZICejb7UJ9pMBoOEnXB+ti92ybcs2AgAlFe5JBnUef4tQ/n7Ir9DBaJO9zzZNCUVRos3uepbq4mq5fSyyTGL1BNlx4SrWHM2Fn01HZcAk/t3kLndsRjgxaZx15FNeR/I1C7Mf6mj3t0GIwaky8AKLqwUsS/hQo8MHbOK0K7RGBMh5ofZSTqbHkveVlFeKITkSoxlLliypc90aJgQAoBU6hxzHYc6cOXbPgDvDSNpF851WaT3nuXbp39yKGgRSvH0aJ152rqDapi1wOo9/fjghEz9dR6LQqCAFWNi/w4pLzzusp6Ct9/rTGx4BABRVWV/UOnMNhq8fXnszVDDW5Lclxecw6sDbuG/HRHz128N4IW0Jnjw8G0bBm8DiKTAw0dH7xgJFUYgIcAwh8AsJx4IFC+AniCFBwTK3hmUQCAQCwbs0aVGgR6+O115JQG/jxrz1rH3SKjP4Ub8IVdB1jUYEqRw7OYyzDHQNwKlrpNA46V7WXGy8WhLfPdytOeoiMkiJ21qFim65tT0FgneXICzdPn7x8olfr8vuhtIhhAUljLpLaPueuOU8Gm3cxgHnySFdBVvHvruX66G98J/4d+3GckywClIb74FqnbVxWVhk9YowGji3NrYX7rmCH1Mz8dkp+3vQUpYOAO4oD8LVSx8hPeMLlDBxUAtJsipo4FJamlviizmOw+yN57D8QDYAQC6h0S2e78A0lwXWtykAoJmQV8DSwXZ1fL6lbKOlU0QLogAYCgzDOM0lEtfMDxTFl6K7/8c94vw+ra0iUEK4P1TCa1env3bCsOuloFKHGptKHXl5eaiqld/CYHb8fQVjFTCDWkS5LSwH4J8tGvZeWQpZ/dVc7kiOwKrn+oh/04yQ/0Jf6RYBy/JuoanGfSoTbDwaiqr1oM3888coaJfZVh95eXkoq1JDSgkjxiyHadOmOfze7pmDAAAa4ZnX0jRomheCQkNDPZZbQKsX7KxHFBjSPgqH3x0i/l1QqUcgxd+vag+UT92XwXujMIzFe8Q5wSoZFLVG3msMjnkAJDaJBvNpYZ82m3EwQu3kRyo46zNTXG71QFtec1mcPpbDhxRKOApd44KvKWrlVzpW2anWmTBx4kQECnkTfvzxU7e8BwgEAoHgGzRpUSA3d3+D15298Zw43czffmRfD169D5A51sxuCCqZ85HP6+nMOhMjtEa+kcCx1k70p492xs7X7sSDXWIc1q+NpTRVuZbvlK28exHuV/bG/pXF+O/zXFx665K47tumNOir3Vdr+74WrFiTmabsz5ulYWNxb7egVrupHBn4jkuI/LLD/PJTLExl1gZ27cayhKbQzCYGWq23NuTS09MRq+HPNVfDON3eVdi6sdvebyz43+1T6YdNV9+BASpwwuugkuHDNMwUhbgWoW6p8JBRrMHS/Vni32qDGRA8BWLrKYtpwVKBQCW4x7rDFX/ixIk4ePAgaJqGeCsy/PE7yyWilEnsRogt9Gsdho+FJHkSClAJHcwag3tEgdzyGvT/fCc6vP8PLhZVg2E5pKeng4O9B9S/27s6bCszWz1cqmFya1gOxwEq2r4Cw9EjJ6657QDbUU+Gt7fKqHYI+QBu/LliLeEDjfQUCFBY311SmoJByz9vZrn1WXJnfHZ6ejooqRwSmhd6JQzn9Pfiw/zx/J1tUM3wwhVHUQgL40eE+/fv7/bqLhauCgH21DXCByIDlfjoYV7sL6zSIVCo6qBxU/nUjrHWb/632y/hbH4VDEJS07ruCIqiEBWkRK8Kq5deldaxOoYTj33YFh80cc6r01TZrGNbCcGWBRpeLCiWs3ZhInWhclKhqFr4ZsVKeaGu0nDFYR0CgUAgNB2uWxT47LPP0Lt3bwQGBiIyMhKPPPII0tLS7NYpKirCmDFjEB0dDX9/f/To0QPr1q275r7/7//+DwkJCVAqlejTpw8OH3aMtW8Iv1acbvC6q4/kitNlGv6jX6Uz4bGf90EjuOuxNbiukR1LQq3alGoaljFdaiseOBESijW8aMHB+mGnKAqtIwIa5PYaKooCvD1dY/vghQEfgWIocAwHY5G9nQVOXCFvBFsvjQAZYIYlptVRTGkXHQit2T4kovBqw69zY+E4IDJukcN8Q1kFqo9sgPHqZVTtX+O0sdzMz9oYs42dT0pKQrNiIRGUynmeCFcRb1PjPbfCOhpkAn9NW0fE4csRXey20XOBUAqeLCpVjVsqPFTXCtF5eWgSdEJiwwD5tcU3SwUCJSwd7Gtn5L4eYpM6Yfznq0BbnmEG9R6/s1KfFEWhbTTv/VCuNcJPUBh0bir5eOCyddT03u/34J0/ziApKQkXg4Xa4yyL5lkPw2ikcHdyiL2tjPV+0XLOOxyuwOIpVVsUWLX0F5SWXjs/xJrJffkJlhcFtIzeIeQDuPHnir3ORIMA8Hgv/h6JCFSgeVQrAIDRpifozvjspKQkXhSgrKJAXb8XGaiACUrxmd+y9TdMnDhR9JbxRFZ/WsgpIXMSo18bS2K8oio9AoSKCmo3CWy1y4o++NNerD/OtxXoejT96CAljhVbR9UrnVTtkTg5VI6z/t425NotU6VPBcCXYLRUW2G5+lUULU0jUHntcKwnBM9CAOjfhvcasYRRtvPjkyZeqiQJBgkEAqEpc92iwO7duzF9+nQcPHgQ27dvh8lkwj333AOt1hofOnbsWKSlpWHjxo04c+YMHnvsMTz++OM4caLu0aA1a9bg1VdfxQcffIDjx4+ja9euGDZsGIqLG19yKkp+7dJGgH2nFOArA+hNDD786xyO51TCKOE/jof2HruuWFClExUeaFgMPwCxFjcA6LSOWbYtI+uWxGGNJVSIJyyzESlqNwhtcbXbs+15CJEDpcIodqi/o5fDH9P6Q8OE2c2rqOaFGndkyOc4oFDh2PAyGRhwxhoULn0R6v2/OG0sX7rqvA59XFwc4qNbAgBopcR9Mdssh9+PWc/FkO/24s+TvFeFkRbqWEuDMaqX/e/2ax2GYIa/p8qqsjFx4kSXZ/bWGOxFgVA/OXQsfx8opI7ZsGtjEVwUwj2vvY5OQUM8dQZ8vhP/lgWCEjKvN49qXu/x244QA0AvIaeHJdyhTGuESsiZUFpRjNTUVBw5csSl963FdTlWfgn9WszG2QvLERsTA5MgECZVReCirh8euG8YFkzoj9nDO4jbWkrTAYAWjcj61kgs+QRUUquAMfRcKY4c1aCwsLCuzUQsuVIYoRKJljG4pRN7veEDADAwife4KdUY0TyaFwX0Ms+UzYuJbQ5KIgNN8UIQzXD4+eefnf5epFCSLlAQBZQqq1eTO5792pRqDKCEe60hooCl2kdhlR6Bwii22uT8XXujmBjHdwQtuPrXJxNFBilQzYbjLkHArnQS3kBT9vuuqLxi18k31RL0y8187h+OolBVzQsGZeX1V91IrpHY5Riqi0eEMMMuccFiYuRqIeFyuJADp8rs/hK1BAKBQPAe15aQ62Dr1q12fy9duhSRkZE4duwY7rjjDgDA/v37MXfuXNx2220AgPfeew/fffcdjh07hu7duzvd77fffotJkyaJdarnzZuHTZs2YfHixXjrrbcaZWNAA73zN55ydIcvqNSJceBGoRGgqzKKsaCDBw9usB11eQo0VBSQ2xxHReVlqALs3avlcqHxcJ2iQHgtTwELEydOxLBhw5CRkYETJfOxuOYsANcnSLP8boBCCo7TokKIxY+O6OSwrp9cii5tOuGQzbxqXQm+mzcZm//9Azt+LwNN01iwYIFLGrAsxyGQYaGW0Oip0+OYiu+wTp40FV99MgJarRaJiYlOG9t3tY1Aapq10VZQqUNkoAJSCY2E2Hig8jTik1shK+ugWzoHvx3NRbHa3gX1pdUnkX1oG0zCyNzmP7dCoV4MgG9wvjw0CZklWuiqJbgq41BWlQOAFzJcaaNtOAUABCil0At5DpTS+uPKASBMqO4g46QATNA2oHa3LbnlNRg5bz/G9kvA9Lucj9baiQYUL1Qp5fUnwnvvwQ7496JVwHztnrYArN44ar0ZKkoGcMCOHb9j6U/WEpeuuG85jsN7G/jntE3wPzgZoAcC/sEv263vIM7EN/J79+wBiqLQwsabxGS2CqlaN+XwTCtS448TvADSIuAIKoX5339VBIlEgpiYa4c8SSU0IgMVYBhBFBAEJdt3Vl3PZWNghE6ahG78ybB9rwYIITE6KY3U1FSX2FYfBjN/v56L4b2otCF+mDBugtN1LSVr/Vj+HilX238PXf3s12bCkiOQC++j+nIKWIgO5u0t0RgQIPMH9EC10T1eN0bBw+v5O9tg3m5LHhH+3NKou4FhOacyTgWgAlXOREsKsN3F3O0vQcpF1blPMxTit+iDTWMxqedLKCqqX0BTaVqhe7eQetcBgK4tQvDva3ciJlgphlJavLksSTK1TMPaKzc7lvf+yZMnvWuIh7hwga+McezYMWg07hHXfIWmdqxN7XiaCpZ3hzvznbmL6xYFalNVxX/0QkOtSbX69++PNWvW4IEHHkBISAjWrl0LvV6PQYMGOd2H0WjEsWPH8Pbbb4vzaJrG0KFDceDAAafbGAwGGAzWjk91tbVzoGGNMJlMzjazw2QyO8y7VFQFgzBfT7MAaOiqefdmhmHEUImG7D8+1HlHIq9cA1NLe1dpo14Hqpbb/FWJtaFUWpGNiKiedsstZbPaRYc0yJ7aBAnJr8q0Bofto6KiEBUVhbztayGkVoBGV3ldv1MXj8/nr63GYIbZXCHOVwU0d/o7If6BaHthGNIS/gEA5JfkYLHqAPBgDDpcqsH50zpRuLnRxqxOr4Va8PP87ME1eOWfcbhKsbh30HMIDLGWT3Nm53ejOqPbxzvFv/t/vhODksOxcEwPqCwdXwWNqKgol55PC2/9ccbp/Hd/WoEhL/D3jEFjwJQpU7Bk2zGcKePw3IB4fLXtEkrKZQCMKFHnu8W2Kq29WKGgOV4UoPgM9Nf6zRAVL4BJWf5Z0RrUjbJzzs5LuFptwFf/pGHyQOdl8CwhH1KqBqwgCshoOUwmk/hbtX8zLliOn57oihdWnwIAqKT8OiqJ9eMgETpfR3tGICyoBGXV9onxbuS+XX0kD2q9Ef1afIiTAdZz/MXVLeL04fKnAABpRdUwmUyI8LctTRciTmuphr3fGsuw760JOsHyv93cZMYFiQQ//fQTwsPDG/S7UUEKGKt5Dw4tZxa3sbyzgBu33+I+LqPpRu8rWMlf51K1AUoFL7ppKQr9+/UFRUvccm4tqGuMkFPWkV0NzdT5e3HBvHgRaFQAcgMyi0+jrxttq82Z/Cr0DhLEF1DXPC9BcgpyKQ2jmYWC5juslSaNW86n5frf1zECyZF+ePW3M6KHAIW676+IAOGZYvwAGVBhtH8/XS27iHxTHmwi/lCur0A47bxKQIKBwxkAgQwFtQTYxVZj15H/4eu4seJ3WVz3nyJkDYsW7Ffh0a7RDTo3LUMUADgECJ4FlUJ7QCnjvYe0rKHO915TQqnkBZ2XXnrJy5Z4lsmTJ3vbBI/R1I61qR1PU8HyLrmZcIkowLIsXn75ZQwYMACdOllHd9euXYvRo0cjLCwMUqkUfn5+WL9+fZ1xlKWlpWAYRmzQWYiKisLFixedbvPZZ5/hww8/dLqsyqjD5s2br2n/masUAAn8qWqESouQa0rG86tOAgA6+e1CtjDSrxFismmaRn5+PsLDw7F9+/Zr7p/jgLtiaKQW2nsM7D5yCruXfYWgYDWqk/nGzZtvzsCgwQ+J67DmSrttDh/fhSuX7W80PacGIINGXdOg461NtREApKjQGvH3ps1wNihWXF4qBptcuHQaVSWN/526qDFab0OG5RuyKpbF1n92OF2/sojGUd1duEO9FycCtbiUfQEQ0gy0SFTi/GkdGIbBqlWr0Llz5xuy7XKVNQ5//6HTeDrkbYA1YM/+cwDO1b2hwEsdgR/OWY9v16VSbN68GRXlVQANlOvU13XNGobzxzuo/xMw0D8CoKBXm8AwDLL3/4UBnTvj323ZKMunIGeUAIy4lJfmFvuOFvLPHABIZGVY8PcP0JgNgAzIyym85m9ml/HbsyYaUAK5V3MbZWdBHg3LDV3Xdjoz0C9sOc5FpCGdA/D/7F1nmBRV2j1V1TlMTgwzDAMMOWdQEIkKZte8hl1cMK2rfq6uWXdNq65rRnEx54Q5CwiSg0iGAWaAHphhcudUVd+PW7G7emLPDGCd5+Ghu6q6+07VrVv3nve85wWFgCeoOj7R/X9lCYXqIFD+6y84sJlsszAMgiyFow0skEa2DZ3gxNLv5Chie/vto+sZDLCtxDaHtkkZAIR5QlKmByrw9dcu+CKA2Fe8UXlRwlNU0q99dUD+LQCghJJ5vUIWvPTSS8jKIr/fonHVTyMslCf1cNF2tZXngdggNccDrGBcGPB6W/39XuG8NgQi+GUlUW9wFIUvv/wAjKFlqW1tRX0IyDAchkgL3MDMTHhOeR5gKAbGcCqAo/hP7VqE37kBuWmzO7SNMgygBNKNi7TsOqYYGNREKVRXBQELUB3smHHUF2AAUFiz8heQqokGUIJ5o4+mE57TwzVkfPL6DIAFOOqrl9rH8zzuabxHRQgAgDcYABNuABxxXwf/4QsBADaWhlJecKhCTjkq/OYIvv+oHqdccy1OCgexJbALu/zn4acfvmvV31zpIm3fvmc/vmb34kgDUbs1RkLS3+D3+5v4huMbY8aMwZo1axAMBltdivR4RTgchslkav7AEwAn2t96ov09JwJ4nofFYsGYMWO6uimtRlJIgeuvvx7btm3DL7/8otp+zz33oKGhAT/++COysrLw6aef4sILL8SKFSvavVgTcccdd+CWW26R3rvdbhQWEtOcEENh9uzmJzaN6w8B+3eid/GDKDPT6LP/IuwNjUC+cR8OFMlpEt7GCBiGwQsvvIBLLrkEP/zwA2bMmAGjsXl335ksh4H3qxe5zowcPLZgAXiGx6CXiaPyRx+/jRtvuluKFDY2HgC+ekL6THb3FMw+Rf6bqg9vxN3LyO+np5hb9PfGIspyuHfTj+B5ChNOmYZMjZrFX61YAxwiUqWcbhmYPTV5E8a/rf5e0RYigbLxSPi3VK06gB8P7waiJgA+pOakAhGiMDA75Zzdyy67rN1KgTV79uOVDeT1jOkzYbVlt+rzPM/j6XvVE8dpM0+DYd2vQPlORI0t66OtQYTlUOkOAqvJ/fjq5cPx8GebUOomi2BjRneEKR4AhYCPiztXvo0V+HaVHYAbsNOYPXs2PlpyG3rnjsCIQZckpY1ly/YD5XvRI8OK+tzH8VoAgHAbDRowDONGNH1Ocg/U45U960HxFgCNsKRYWnwev9lWiRWrZXPKRJ+r9oTwkPduABQCwtwwMyMHs2fPRiQSafL+1/pGsZ9znHx/WZ3xxnjt6bdvHl4Hti5xKsVgrxG9xxViar9sTCohC3Ce53HnBtJH3RF1/05239x+2A1sXiO9pwVSoFtGLq74wxXNnlclfglvx6bthEytp3nMPv30+JV9C+ALRXHugjUY3TMd543Ix8BuTthMBhz1hPDJgf8AALIzM1p9LliOxz0bfwDHAxOnnAHmy4fBUhTGjRuEnNyhzX9BO1BW48MnpT+iDEBvFjBnntrkOX1s53JwUblyxrNYhU2zn+vQNgLk2YPVP0qkgIkxtug8v1e1ATX765CV2w9o/A1+hk96XwWAuzYtAaJRTD31FPTMtGPgOB9ue+85iDReonOaVV6HN0o3gKWI/03AwGPo0KHYu3cvioryAQ3hY649AyaLE1pWHhzPINUklgyVZfw/RJcDDFAUAlInvImbzkzDkJ656J/nxIsvr0NhuhWzZ09q1d9cvfoAvj60G2k53TB79jBs2HIU2LYKIUYeD5SKzBMNNE1j3LhxzR+oQ4cOHScY2k0K3HDDDfjyyy+xfPly1UR23759eO6557Bt2zYMGkQWvMOGDcOKFSvw/PPP48UXX4z7rqysLDAMg6qqKtX2qqoq5OXlaf6+2WyG2Ry/iAWIUVZLFuyUYCJVJsjo89OWYm/VCPTo9poqFrzghdcwePAkFBQUSPI5o9HYot9QHtInx4G9R72orG0kJbQ4wMJxCNI0hnanceDAARQXE2MqQ4zxkjtUp/q9Dfu/kF7zJnuL2qLVtjSrEfX+CNxhHnkx37FqXw02H5Ijj4Gov02/0xz65jgkpYANdMLfyEkR0jE4kq8ZouVJktEuG/eJ57BdUFhMm0zWNv3dj543RCXlH/zAj3hwiuCazkeTei63uBpw1nMrVdvGFGfi2oEceo2YiHMWkAVZUOhWoQAfd67y0qxgoykAjqAu6sG+su/wcOWPQOWPWNfvTFit6uoPbUEwSs7r9AF5+DCmmmAkyDd7TvLSyAImGiXHBdhgi8/jje+rq1VEeVrTjItFvEQ21epQ/U5L738VeHnYNdrk301GvzUbGUTo+JrjIqJ1g1BsWY+pA7V9CxrYbCgdHZJ9n0eFEpLdUi2YNiAHlNeIzSxgN1pbfV6znBZUhnuDBuChabz+1VUY1H08Joy9sVVtWr6jGmW1fpTV+vHhxgpM7puNN/48FvUBP8SoLEMzrT4XRgAZdjNqvCE0hnk4eKCRAoKh2g4ZP5WI8jTMQmWHdIpEkZo6pzkpFkQ8avVCOFiDD5fdiZMHXIw+fWZ1SDu9YbG8H1kJN9bV44033mjWV0M0GwzxhNiqA9sh51RMH7CaTTAajSjJS8PA9AaIPvyJzmn3dBLurw8I/7Mh9OnTBxzHISXFgB7P9I/7jD/AwKQ9lQEHBnvevg9D/maGkhTYIZggHzHy2LafA47U4aOtdXj1KhKhYsNBVFVVtYpkTLeLFR3IOU2xE6LQB3lc7uj+q0OHDh06Oh9trj7A8zxuuOEGLF68GEuWLImbyIryMq0SUbG1pEWYTCaMGjUKP/30k7SN4zj89NNPmDBhQqvb6KVaZvIglscSwTPEO8AYUdccn3rqme2KPL/2pzH4+6x+uGg0UTIwFod0fsxCGw5e1xt5ZrkUERdTFqw+qF5BOcyyJ0FR0XVtbpuoDpj53+VSfWIRl768Fr+55IVGIJo86SCnOPcvXDYcEQUpkLitZJLLCSZj7qjctsGjByfVJZtj5XPB0G2TaI3rlRm37Z115DoGmikp1Vp8sqlC9T7dZoTZyIChSM3tnplkyRcQckTeevPjuHOV5TAjFE0DANSyAXj9slniyk3xZF5b4A+TyazNGH+P3nrLHc1W+BD7ABsl//vZEA7s/xH/evtUVBxc2dRHAQAUohia8iUyDYdQ69OW2vtD8YtrcbLfFozvJdSC5+QJ9eSpJ2PdunVJc3cvr/HDoCAFiqLq/lW9dTvmz5+fsNIBC3Ufj4ST4zj+vxX78a8vdyAYIe1JsRjx4DlDYDAI/cDQtIGjFjLtJvi4NKmU3tPurZi382WEW1kdJTZdavkeQS4dkO99ug0lCQEgS+inO4944BAIEa9GebpkIxRlYWDIOO1swbiV4zQjFFWTAuM/mYn/1G3AuStv7ZA2AsDuMuFZxwsLXZ5vsn+KyBXMBhtD5Pnn6SCVt0gKmBT1A4fmjQYAFDaRVy8aDTaE08j/FC/NexijdmN90aCqJKESnu0/g/XWg+a0r2U4phNf9wax4i3bV9rqikkpVjI+iSUJUwTvnHoaYEN6BQIdOnToOFHRZlLg+uuvx1tvvYV33nkHTqcTlZWVqKysRCBAJqT9+/dHnz59MH/+fKxbtw779u3Df/7zH/zwww8455xzpO+ZNm0anntOlinecsstePnll/H6669j586duPbaa+Hz+aRqBK2Bn6LAs83X2o6tOrTL2QAAoHj16TGZ274gAIAp/XJw/al9kGIlkUKWNkoltMyKesifHnhHes1xMaRAjMt6eQ0pddTPz6Ah2Pa8oky7/Nl31x6M278nOFJ67U8iKRBREEQZNiMiPOk/dipxJQXReV4sn7Y9LJsTwkIn1Smb5RQLA9rQprKHYl1tJSI82RZswsG6LRAnciJiy2EWZthgQFAqd1VcNCDuO7IcZvijZAFby0UQVbhOl9ftTko7vUI7d2+NX8CH/GyzCwOH2QATQyMqnMf6oBdnrLgZH0RrcN0P85q9RqPTPkZZ91+A3s+pSnEq4Q/ET4C7pbW9rnzPTEIy8rx8r1lTrRgzZgymTJmSlH7rD0fRkE70TblRDl//ZScYwQF3ZIUDyz/dCZZlsXevuub41zdOwvkjye/3PypXhvF6K9vdJp7n8eBXO7HolzKsLydkmFnwafGzhJCxGe0JP58IYkUHRwype9C1RuvwhAhFtBdi+6q9EJUCbc0t3nuUROtv/fA3OIQxzRuIL0/XHIIRFofqWj7uBiMcaJoc7zAkCD8rkJNihi8aT152NNbtKCMvOELEUxyv2T9j0U0gBT7cQsbnAE0ljcASwXI8xK5lUJACnHA/9eQTzwdE5ZFbGEc9DC3VMDRZtJ/TUUSk8sKxCB7YBs7f0OIKQwHBzNSc10cyMG3pM8tpIfMTMTiQnz0ENM8jTFGor2/6uujQoUOHjuMXbSYFFixYgMbGRkyZMgXdunWT/r3//vsAiLzs66+/RnZ2Ns4880wMHToUb7zxBl5//XVV7t++fftQUyNHTi666CI88cQTuPfeezF8+HBs3rwZ3377bZz5YEsQpSgEWzABiy0bERSi97yQ55jNAU+WXN7q308Ep1AH2BuKSnWgnYwcPTxcv196gPO8epFXH1FPfPxhMpmieQrjijPQVoiRVwCo98dHQHyRfPSKkqiMPxps8+/EIqyIZJoYGhGOkAJiLXctiNG3iGAyVqGYJzVEkmuAFFZEg159/Q0UFRVh6tSprYq+aEnTwxyJ2AdbqGZpKdgYFc7QAhL9q6mpwbJly9AzhYaVlkvXWG3xbteZDhM8USIZraV4hBVKDF8kORNvMQq75Ot34vbt2hVsdmFAURRSbUawLDmPNV45OrzfQDd7jcy2UgAkwlbl1u7P/qBMwI1kGfzFXoLhQ//YzF+WGOJiguPle92f5P4aiLDwiZUOWBY8z6PqH7thfm4/3rhrDfhIEAzDxJm9DsxPwUPnEpPY9bUXwS6shjzepkuetQTekDyGHagl/cdiIPeEXygl2B5SQCylJ/1eoOna7bHYdLA+btvhhgDu/Ww7QImkQNselVEFYWEVFnSeGLVXS3Dpy2sw6bGl2FbRMhVEMMqCFhRvdqZ5FUaO04KGaOufse2FKU3wsBAW9BQHzf4Zi4HdyLPIx6VJ25LRV5XwheV+a1OM4eJ9TLVAPdIYJaUoOYpCZgb5DpNZ+9nGMZGESgGa5sH6G8GzbXOzbgnRIiJFmJ+IJZkZxoA0oRvXNpa36fd16NChQ8exjzZ7CrSk/mJJSQk+/vjjJo8pLy+P23bDDTfghhtuaGvTAAC00D6frxJWR06Tx8amD4igaPKAvjRzJGZMvK1d7VFCYuKFh25BQQHsjBEQcpiX2kz4bnwv3PbAApwxZ7Tqsz/xHtQd3oSMfBK5jwqRbBo0RhW1gxSwy9Ekb0hbFlkczcN+gxt+NnmkQEQh0zAyNCI8iRxa6cQ5i2Kqw5HgIACrVPvqI4nzqduC0iqyYKB5HvPnXyNJQMXoy6xZs1oU4T2lbzZ+3iMvVsIcmagH2yhLToSI0Jevm9IbgQiLs4fm4N//uxKP3f0hhkxKAWgGg8f8HdsAmHgeRlP8YsxsYEAZCFngpykcqpMXTZ5wchaxIimQldUgVdQqenEfdpaFwfItWxjYjUAkQs5jxBC/aGvqGtGcGQAhR0RSgOV4HKzzo2emDVGOxzdby6TjX7tyIyimZVG6RLh0XBE+2OACp5AA+6LJ668Ll+8DH3WjzkD6FPPtUQBAZVUUlVVkrKFpGi+99JJmn7UYGWTaTaj1hWHhePhoCh5fVdxxrYU/LKudPt18GACwTlAMSKSAUAu9NRDHLAtHQ+nO5vG3LhL/toYyauKjYilRcj+1NX2gf54Tuyo9pJ0wAAjDG2xo9fdsOkg+M//NjfjvRcMxthkCeNGKMpSnEJWHowWES47TjOpoERJdhUjED6PRFrfd5XKhtLQUJSUlbVK6cML4w9oahfeJ+6cSRYLqhoMRNo6Dn6bh8RxGRkbblTyx8AlklpGhYBbGF5fLhaPV5J5ojhQ4e3g+Ptt8GNksBzdDI6+bGbW1flisaqWAg+XgZWgE+GhCUoBnAXAs2Ej8GNQzSGNP+Z2an3Ov/xRAy8ZTEaKS0aNII0wHgzpwqHdXJPqYDh06dOg4ztFmpcCxDpuw1vT4mo8akbxB9cN4Wt4G/JpCJnNGJrnlPtKESYEyZ9USIws0PdQPL/3nBlRWxUc//v3D9dLriJDzbmHa5xmZlypHILwxEnQJglRblPwmA6JSwEBToGkKrKCMMFGJ/x6GprD+rulw8z3i9nmRoO0x4DgejRqKCCUCYRZvrNoPgNwosV4YrYm+LLxiFNJtinrwHJnUBmkKHJu4za56P15fVY5AuPk0GEBw8waQn2bFfWcOwo4dT+B921YUPdkf7nPz4T47F6W5rwEAwk1Ioi0KBYFHobapCXi1Dlfh1ZVlWPxr01JVse/3KpFJhq/WBLC/ipUM95pbGBi5iKS4iBi0/5aE14iVSbCfdpFJ/uPf7capTyzDRxtd+GrLEfyw9QD5HZ5vNyEAAMML0zB9QC5YRfWBzd5yLPlyPviodgpDS1FW48PDX+/CqMy3pG21LvV9StM01qxZ06RvQfd0QrLUCougxRufbVe7ACJ9TwQ/R/q+3dz6En0ZgmLIyKqvjSfQ+kh8IoiUZVvTB96YO1Z6bRLSRnwxKWCtQUVDABe+tFrKdU+EX/bWoFuEtL4u1Ly6IFcjxUmJ+rr9cdsWLVrUJuWUEgdrBd+DdIHET7e3yFdDme5mF05FMggsJcTnoN1sAEVR0t+74pcVAIBQoGlC71/nEOVNKkv6zj2PXY+lS5fiuRfV95TY/gCiCdMHnnt5KZYuXQq7JZ4M4qtmIsQ7MC3Tg2tO6Y2vb5SrDURqD7V4PBUhegqEopyQQgOkC74U9Q1lCT+nQ4cOHTqOb5ywpIBDYPG9nuaZ7Sd/2IPeZrUj+br0j6TXhiai1m1BqvDQbQjICwGDxrx50BW5cB06ELd9PysvpJRKgfYgx6lUCsiNUaooeJEU4JpeTLcGIilgFHI2Rcd3UxPpAwCQ7TRjQt/4SY4XLVs83/HJVgz75/e44Z1NmqqXvUc9mPPsCjDC99G8tmlmS6MvZgODn/5vivQ+yMlRt1ATk/aLXlqD+z7fjn9/u6tFvxMVlBdGhvT/bYdXxR0TYprvK6mODEltc7hW7oPeZgghV70fD3yxAze//1uTCxeRFOid3xsAMMQTAE3TuPXWW1tsuJeb7kBY8JUIJvibtK5RpuEQQhZZ/bBmTxn84She/HkfAODvH23Bzko3jEK5PGMLVFEtRb88B1hevtdKTSb8rXYVvvz+pjZ/pzcUxalPLAMA1KfKk/aKQ2qi4ZZbbmm2bm73NLXU/H229fnvsQhF4/vByX2ycKRuL7bRgtFgG0gBcWHIsOrxefvRX1v8Hc0p3kTZeFvTB3KcFvz5JGLCaxSuuyfcPLHWHKo9IRxuCOCv7/6KXzXSHwCZ9JvYrXmT3mxh/B9QdYbm/sNHNwOA5Kmyfv16zJs3L0451RqvFQB4bz0xGjQKNS8mW5pW9YmgFcZ6zii5Rq6aHa367ebgEZQCDjPxkpH+XuGnPW5Pk39visWI+af0QkCoYPPg0W8wZcoUOBxqLwKrkP7ip1hAoz+WBGhMHNILU6ZMAWc/N27/wSCp7vT6HX/EH4c4MDA/BfefORCjChz44InbW21g6jDJz95p//kZj3y9E2ahWXdUfNPi79GhQ4cOHccXTlhSoEGI/C9c+UCzx3I8QBXG5zaLMDLNGzW1BiIpEIxwCEXJotNpis/7rMiwIEujFGODYuEbFeS3TDsvpdJTwBuKIBhh8eT3u/HJJnnSw/Ekuh3gWxaNbwnCoruzEJmMCtUWTC0gYjLtJgzzqK9NSytOvL+BTEa/3HIEnwmSZiXu+3w79lf7QAspJDQgmUICaHX0BZBzoAEgyMuS3mATcuKKBhKN+mpry/JlxfQBsZRlfkr3FrdPiWynBTbhu0oP7ZG2B/imI9oNCvVFZWPiNJOjHrLgzu9O+neeNRsHDhzA448/3uJzmpeRIp3HEBMfyU10jcIlz2OHQ47ypTI1kiGciB4ZNhgokRRoUXNaBKfFiCgfrzz68OjaNn/nla+sk15nBOV+VVkp36c0TeNvf/tbs9+VL5ACffykjcVc+x8RWkZ+QwtS8dGqh6T3bSEFLEYGNhMjpILIqA63vPqAFmEhtcnEICeFnIeW5I8ngqgQogRSwBttvy9HpTuI+z7fji9+O4xzX4gn/uwmRoo5m1pwbnNSSNs21J+M5ed8E3fdK2r3qJQB48aNa7NyyheKYltFI9bulwknszCuZjtbPp7eOJWQfY4IIatrku0poCAFSktLpb9XVI3wPI99+/Y1+R15KRa4FQRGOORVebQAgJUjf7uP5sFppQ/QPMyCB0dqWhHYXXdiRL1cFtbHkesrnn+Xy4WicDmePqc3Tp9+aqvTOuiYSgYvLd+PHRwJRLAUBb4JZZsOHTp06Dh+ccKSAqJZ4DJ7fB6kEosWLYJ/90pUGROfClOS0wfsZlnuuu8omSDajfHEg8fIwJlKHvgmRcS+UrEAYoXqBEwbI1kiuqfJ58kdiGLBsn14Zsle/P0jWUHBMGTB4Y2GWx0RSoRYpUBUkP+3hBTIsJuAYL5qm4emwCcoeZkIK0rVJcI+21yBlXvJhJUSCBgGPHr16oXVq1e3q3zch9dMQLrNCB4G6ZoGg9qRPiV8oZZNxMT0AYPQR+gm+vUzRecn3Jdh5GARu5xV7m/BZtIz6v0yaXC4QVteK5rNAQAooeyVzdnqyWua1YgAK5hfxqg4WqM4cBpqcdZz6ioIHMfDSBGCw5xE3weH2YCIhllYnqH1lU3eXnsAK0qrsfGA3H84mvTXS/09VQTWwoULW3R+RVd31I0DAFh4tLraRiyC0Xj1joGmkKogWwpyhrTpuzPsJlAxZdoCrVAy+ZtIy/GHWWlhTbdjfE0XyEDRJM6TBB+JysYgjjTK36Ms7RphOfjCLKICQWoyNm80mGk3g6YIQR6msvD5n37De+P+Je2vqq9QKQO0FBYtVU799d1fccazv+CihXKVCDH7zdiKsq9/mdwLAEALqUANwXq89cWfseW3N1r8HU1BTB9wmA0oKSmRlGJiJgkFoHfv3k1+R16KBVmKIbOhsRyhiFptZRHTSmgKvNDjSgKKZ7yC6HaYDfDzKfit+io4WQ4jGtKkMqIMw2DDhg3tTunQBC3PWUJt8MTQoUOHDh3HPk5YUqAlECWBoBnVojsWjW0oIdUUlOWN7v+clA/TikR5aRp1O0kagylmEla/fykAICJMgBm0L+e5X54Tk0pIHnmtLySVDlPiaB35LQ8bScqEo7zGhzfXEGm6WVIKtI4U4Fj1hJenKPj9R1vVjo83ubDziBsPfbUDNd4Q7lq8Tdonpg9QPDB16lSMHz8e+/bta3P5uDE9M/Dan0iesVm4poFQ8znGTS1elBDdzkWSJdSE3H/fnsQGZKlsAywc6ZPKNWyAappwUfpk7Dii/Xet2iffT5xwvY1NeEgkQprNCB9HrNHYmJzv1igO7Ib4vt4YiMhKgSSSAk6LAYfC/eO2f8PWgY+2fDH7xW+Hcdfibbh80TrVdoPA5AwdMBzl5eWtJrBEb5EgS85rQyTU7gXGbsFoT4myWj/cwmnNirJIzY4vjdkSZNpNYI3qfhbgW3YeOY7HG6vLpffvzRuPi0YXSu8vGl0oLX7bajQIyAohVriRfEmo3nL7x1uQlyKPfa8r/g7xHmTFEnhM8471DE0hSzBwPeom/b5/37MxkyfVAaobq+KUAQBg6zUKjmGzWqWcWrIrfnz+Ea33gXBajLCbGEBQiixu2IZ/163HZZsfb/V3aaHWRwhOUdl3yy23EKJNeHynpqQ2+/fmplqk9AAAaHC7sH6DWhVkEDwxIhSFqGC0242WvQOUI3+6TfAjYnNxeM9jWH7kHwCAqtdvwiOPPILbb7+93SkdWrhr+F3Sa3+S50M6dOjQoePYwO+aFBAlgZTBhAGexO7XxmBe0iLjsRBzORPNOa+t/1lz9+QVN6KxeqdkzMdQ7TdC+/f5QwEAtd6wpjmYq5pMWEIM1e4JhzsYwZQnluHddcT5W8yBjwpToJaoM3pk2BDl4pUgXl/rSAGATLJfXlGG0Q/+qCqhRlGip4BQTi4JEy1x4SVe08OH1yU+uJUQlQKMIAENsYnl/n/92x0J/47hfYtgEiazYZPc+wLNpGcopeKxSoEIy2HnETfqhMn24O4pkieGsQ1Gmak2E4KcdoSdTWDcp/X3Wg3xk9wPN7pAU+Q7kk0K+NgsDPFOjdv38Zc3tjgqX+2JJ3u+/OvJqBXa7K4mfXjKlCmtIrC6pZKFZoAlCqWAQNa1p9/f/em2uG1RlsMvRzcBALJbUDIvETLsJvC8+jEW4Fqmqvl62xE89WOp9D3je2Xi4rEyKXDJuB6y8Vs7lAJpNjFdjPydXq51ppKlVfGkiicYlST/AKS/A5BTeKJCLrvJ0LLzK37fUQ8hLR7/bjeONgjjnonX9FTJvuABZJ72V3yzdkezxBPH8ZoKAzENAECctL75NlvAC2VJj7bAK6U1EFOcGo6Uo6ioCE888QQ4jkNxMfGIcNiar+qQl2JBWiBNer91xxosX7dEdUzYEwYlnJegINOnKQrD/GRMHGMYJR2bZosny28fY0Hp+qUYPXp0u8xwm0Jxt9lghDbqpIAOHTp0nJg4YUkBh1eOFiWKcImSQIuJx2+pJKc4O0amnflRBS674KHkSvEA3DqzLwBIngLWZibGFICio4NV2zbv+RxfMeUAkkMKiL4CUY6XoiRKhEDyGMXUjPZMOFbtVUv2xdz51pACJ/XJgtlYE7fdH2hejq+EyUBji0s7D7k4RYwUymjvREuMPLmFSez1pW81dbiElpQBlZUCZCH7dqAcADDDbcKuP2/DiN3yuQkGE/8d/XoWwCiQAgHFPNTfzIihlIofjVm4PvDFdpz+9Ar89wfiUTC1fy7CIinQjLGkFkiuNgOrhsonmGDi+snqeMNGo4FcezPtwQD7ElCI4kCtHwZaML1so/O8FpxCDXBfYFjcvo+3fdPiqLxJowRjlsMMv5CT/MhDT7dpzBLTB/xCWoZXsdBqa78/e7ic4vP6n8dibHEG/m9mP+QaCaFT0IyTflPIsJtxqOY81TY/WvZ9SgWD1UjGz5JcmRw20JSU4023ow+ISgFfmIzxa6nWKQVm/He55nZllRixzC0ANAgpPOIT0NBCUiDXSa793Nc34LWVZcR4U1A3BBDS9FQRUc81/Ru7Kt3odefXKL7j67h9p/bPQabQB07qG2+k1xQy7CawbDwx2NoUMi2IKVtLv/9GlTZRfjDe/DcRsp1mbDw6X3q/8NVn4ExX37s8y8EujGF+jjwHKVB45A/f4f8y/4rbLl0oHZufFn+eLz19MgoKClQpDiJaY4arxMyBuar39b6wpMb6deNLWh/RoUOHDh3HOU5YUqDhTVJ1wMGyCSNcBQUFWLhwIcb3knMb/TE52D9/SRZRyZTiAUCfHDL5FKOmRrrpRT0N4HDjFNW2/9sj506W8+2vH2w2MEgRJpcGOn4SHGQyAQABmgJNtX3CAQD1MeUAI4JrflSY0JtakFtqNxtwwD0lbrs/2LwUtShTVhg4zAZM6JWpedykEkKE0IoFeXv+boAYpCnBUVSLpOP3CakmibB8T7VEbhhoGgcrZRf2w6YQOND4+L8VGP1bDTJeLmvy70i1GWEUDbAUC9CmyhgCaqWAKEMGSDWHt9YQVYhIXDjMDCJCVNfYhgofYmlPiwZZEtDwaQhGWDy5MT7Cvi2LOPYP7/4kXD2+x4Tshcgz7sP+QhLRMyVxmHSYyf11NJgety9sJ+c7dqzRIoOiGgvp6qojCAlKjlCQb9OYJSqXfBxpX5SiYBM8Jdra74d0J6qDs4fn45S+2fhg/gT0yXHALhi45huK2jyuZtiNOBQahEyFsCmQoNZ7LES5PCAraxxmA/559iBcOq4HBnZLQZQXS6a2vQJNjrDYbgjJYxrPtiwdqCmUKswxXfUBiQwQx9aocKsaTU376kjtVCgP7v+COPnTQrrQ4kgV5s6di7KyMrzx+U9YsmIlegoRcwB44vvdTX73gmXahnznjyzA8MI0eITzn+LM1zwuEdJtJoTYeJXfb5sXSZUS2tq3qtyEvOFC/pg95B6jWjAuGBkaFnsuBnuFsSrVAMQ+W3keNqHL+kGuYTQSBRUK46oz5sFqlvvN9AG5OGNoNyndDpAJIXE+0x4zXBGialCE8nl9Z+OmVn+fDh06dOg49nHCkgKBRrLYiFJUkxGuuXPnIjVdzt+zNXFKkiXFA+TokfiwzUhverJN8UBDVF2uKaSICjiQFfuRNiFLWBTsq453yA4YugEgi9iMdEObJxyAvGDMEtQJc08mE8yIoBQwtzC6NbT/qehRdjZ6lJ2F/LBQl9vTPCnAKCZmDf6wZPCkxI3TSjChH3HHF5S47ZpoKZHlMCFTwQP8vPoxzeOUJeLeWN10hOqBL2TSwEBTeOZT2W2+7H9luOyyyxCKMHjtv5VYuSbY5N+RYjGA4chCqFERLY5SFCKhxO7pSjd3UYa8xdWA6U/GRzttJgP2hcninWkLKSBIaUXvAyV27Po0bpuW5B4gf1MqUyVVJNiadRAlGZ9L+9uTTx4LcQJfF4zP8w465HMgjjVvrC7H4Pu+w5Jd6hrsUQ11xIHyMkQE0iYY5FTf01KIXhQ+zilJmlPTDO3q9yKnERttr/MQAmvVL6vbrMTKsJPxapT5MViEc9JciosIsdwgAFXK0BUTeuLhc4eApimwENNxWq9kkdtoQpbDjH3+sdK2bVvfbNN3XTWxJ4YVEJJlZ4xnx7oyMu6J5IBI4JkMLSMFsp3xfdJnVfe7A0Er7lkZwMX/24CZZ8oKjQZ/06RmikV9fzvMBpQ/Ogf/uXAY3J7DUlvTUgq1Pp4QmXYTQtGUuO2Xb30Gkyb2brMfRoTl8OUWUs2AMqivvejO39JRwWZiYBAUF6ZUBoxR/cn0tHTYhBQYn0AKNDY2araboSk8d+lI/HbfTJw3ojsWXj5KVS1g1qxZeOedd/DBBx+02QwXIOaYyjLFDf7Wpbzo0KFDh47jDycsKeD3kclckKZhMjYd4eIVj/cFk59IeFx7I8RKZNjJJElUClw06X5MSSnBfYPnax5PAeCQOHr+r7OT47icZU9cfjHM22AQZvg//vRZmyccAFDnIwu0i8f0wKp/TMXtpxHztbAwCTcbmjfHAkgKwfbgBGwPToSJJ9exwdd8+oAy+Mrx2pPam6eXgBeqOxgYpl1VB2Jx7ojuMFecJb2v8MaXRQTUC5fmEFRE6f3uOhx2kQi4geexboMf77zzTourJ1AUBWOC/uZrIqdU6UUhpg8s3VWteazdzGA9RyTce72tV7qIaRhmDVLg+v3vxm0Labjgi8gz7VdviMr5wt1auKhqCZxmo9CW+Gj2QZMRaU51VP7ez7bDF2bx/FISaQ1GWERYTirlqcSQfr0lojAY4FTf0xqsuO1UAIwkaX5xwWPt6ve8FFmV4XK5sNIiREWptiuxMgVydV2FGcayPwMAWpqVriRW3AHtRS0rKQXaTgoAQGGGFSFelrkfDcSnPbUE9505UPJ9iMW8Nzdi++FGaSyLiEaDxuZz3wFF5QkFjJ4S6XUk4sf7q4l3gSm7Jxi7TKZP7hW/MFci1qMmRZHuUFn1GwAgk+Vgb6VSIMtpQpDT/u0x55D2taVvKauoQJGaR9M0+vYjqX8tJQsP1flBRckYQqcYQRvUn7PabLDygipLNHLl5XavX78+TvFgMTJ48qLhmDlILlksloy86KKLcPHFF+O7775r8d+rhY+vnSi9rtNJAR06dOg44XHCkgJBxczw+eebdiLneDJhOZl3oF/vWXhq4DxYeeDywLCkSPG0ILkIByKIshysRhuePfcT/GHUDZrHN3WhTov2QM+coqS0K8vZlGyfgkOYs1it7ZO/vi5EvR0WA/LTrFKOdEgiBVq2EFNOr0RjPHeg+TrlXIwku8YbH0WmKAqsIG9nQLXatK0ppNlMCPHy32g2aJMxWhFhLVy+aK3ky3BynyzQDS5sLUkDAJRsIyQJy7Lw+Xwt/jsMlHab6j2JSYFyRblBTzCKYIRVpWoooayoMC6lWPOYpiAqBYyc9t0RKx8WU1S0cLjnZ6r3LC//7bdO+2+r25YIDsViKEujYkhRH4s01phSZPXPxgP12HigDrOeWo7Jjy1VVXnIT7Vg2a1TUFe/VNoWDHJtHrMKM8j1Ev0EWEtZu/q9dKspbtbSUtkYzy6cEpZlm637HgtRcVXVGEJIyC0PUi2T54cVxEyi+ywqNL496QOAvOAujgp9lW/Z+Fmn8HYZVZQOiqIko1IR0/rLCrJ/frEDnmAEFKLghOi70dQyUqAwPf4+XVd3qfTa465A1C+rEyiDfE5+LG2QlApaiK2ecrhR9lXw+Alp6AQt1/trIbqn2RBIQApQisV3axUzAUV73Rs/V+2rtJN7r45q2UI5P80KTkxxcJhAxSgFQlwIFpBz6ReHBF5u9/jx45tVPIiVlJJZeaAww4arBQVfc0oQHTp06NBx/OOEJQXWrd0svT7n7FkJj+N5HoEIWfiJKrxpY/6KtVduwW3XvNWmsl4tQarVKM1/GmKiVHf2uzzu+KamSkwbyrklgjLPVgsiF+BpZdk/JZRRo56Z6gnrbyayz2FpOvIkIs0mkxgGIdriDWqXwjtY68ehOpIfynKxpACZ4F03hdSdzhcm3pxACiT7RkmzGXEwOFB6zyVwTI/ERITDGhHmN1eXY0WpHHm8eUYJ+vbtK71nhL+1tVFjE6Wt1jhSl/jaH6hV598edYc0UzMA4Iyh+SgWovzF3Se0uF0iHGYDGJqCgYvv/yVhPq5ed+y5bAoURa7H6KANefljWt22RGBoSlJ/PDV1Mf6YPU61/867bsPq1avRq1cvnPmsOuXi/AWrcaDWjyONQSwVyrrdc8ZArLpjGhz8IVzx23+kYz/55MukjVn/OvJTuz4vcwLyKFZSIkeg968hahGGYZqt+x6LdIEUCLMcAkIknqcoBEMNzX62Jf0hGekDgOwrkMoLi78W1np/U5Ey9OA5xGg2NqJ/2fge0uvdVR54QlGYFGaGJmPLCNZuafH3OwsTHMJ5anS70DPThxHF/8DJGa/BPmCy6tgLX1qt+b3VnhC+2nok4e/+b8dr5PvbkKWTYTfByyYgBRj5C1s79okkBuurB+drkLZzHIc1PHm+lNKJ06hi2xiOkpQPY5YTOXnqVL8QF4IV5Lkreisoh0zlQn/evHn44IMP4hb7YiUlJZKR7ijeX67qekzlCfk0g4n3Q9GhQ4cOHcc/TlhSoLCwh+RKrmU6JmL7YTcoceKnOB2UsGIvKChIaoRYhIGhJflzfYzTv397vOkdxUNVQ1uJZOY8N0cKWISorKeN8ldAHXWYNUh2OS47slF6XZwzvEXfNXNgLv44vgfumj0AFkqYcEe8cccFIywmP74Ukx5binCUQyIj/3G9MvHxtROw+PqTAACskD7Q/toOaqRZTeBgwsgAiSB5w+qyYyzH4911B+GqV4uh62L6ylZXI+75TG1AWJBuQ0FBAazCJHH/SjdomsYLL7zQqn5sYrQjjPcsXqu5HYjvP9XeYFyE/su/nozyR+cg1WpEQLgQNktai9slgqIopFmNYDj56mQIpImHj8ZNpg8fIfnRxhZUcchKJRN+qzF5hJsI0VeAoTNwy8znVfuqqiukyGCVJ3F0bk8V6eNi3m91ndrobeqps9o1Zs0/pRdShOs2AE2PCc1BPN3KILCyf9Z62DarGnoqVCgBTu6vgUBds0ZzWikYsRDTB9pLCoiKBhNPvscb1iYuY7G8VE69EU0qY5UCk0qy8c5fCLnU4I/g++1VKlLAaEpcbleJXlnq+332kDzccGof2IXT1Og9gjrvi9hrAX7L3YWUMedgdMpinJy1oMnv/d8KOTVnztBusBhp/HL7qdK2lWHyLKlvw2wk02GSKmXEghLUZzRN4+abb27V9wYE4pqPqBVkDMNgXIR877RoRtzntHDO8O4IRMlCugFRMDFmxkE+BCtN7jGekkmB2EoCABnLLrroojjVQDIrDyghqrE+/vxb7F+yBQDg8cWXyNShQ4cOHcc/TlhSAADEzMumSIHSox6ItLxB4yHckcgQotzKhZ4oA4wFDeCBswchPxS/PN3JJsf8EJAjA4lgEhZgnibOaVMor/HhpeVEIuw0GyTyBQDe3ChHOguL4+u4a4GmKTx4zhD8ZXIvmAQJpiccnz6wr1omCur9YUkpkB5T99lAUxhVlIHcFEEpwItKgeQRL4BC+s6SyaA3hsj4ZJMLd3yyFUYqiF6WDYBAXMWmOZD+K+PbmyYhN8WCUNiLoHBu777zCSxcuBB/+tOfWtVGsyG+1BcA2PM/1FQsAPFpGUfdobiIbI7TDJ7ncd0Hp6FS6M7WNpACAKmSIBoiAkBalHxho5GB8nbmOA7vffAhAIBRNNGZQDa+xEw8Huym5JMC4uJu44F6GA1m/LP4fGnfp599GBfxa+67eI7Df9Y8LG3ry9Kg2jmWzRnSDUU1AwAA2UzL/D0SQctTgGwnW5767zNtVjVkKkgoHgaYhev54cdvxilFYhGJNk8OiR4nxhaUSG26neTzYl/1heOJSy0oxyfRHDUvRb4eNEXGrIm9s3DVxJ4ASHlXE0XGCYrnYWjh9aMoCk9eOAxOiwFf/vVkPH/pSBxuCMDGCmlZviqEY9Iedndfi9+yD2CAbUXC71X6Z5w3ojt2/et0FGikKszmWt/P0m0mRGGRvG6UKO7XF7feeisA4IknnmiV4eDHGwmRZEjLi0shtArpGE5DXsLPK3HlxJ7wRrMBAPUUhyivJvvCiMIWM9ZmZWVhzZo1msQAEJ8ekMzKA6rfCZDnC2V1ICqYl9b7PaioaH+1Ix06dOjQcWzhBCcFyCQqEEycY17lDoESzH2YJNYjbwnEheG32yulbVoyQIDQFhYjg6Ouq5Eb41yeb27Z5KQlENULIi4fr/YqMAikgL+Fk9pY3PDuJry6shwAYFGY6P1W+hU+dZMa8mMiaHVuKQCUGRsAAB+y8eWxznthlfR631GvtHjNiCFBmJhyUSxLJnDtqVOuBXFhyEukgFqK+ulmMukaWfAIqos/wsmZxK28NkYpkK5In+idbUf/PBI1Kz/4C3iKgo3jMHDQVGRltb46hdWoHWEsMwONfm07tzhSwBNCNEYpkJNigcdXhRUBeWJps7RNkppmNQIKUsAZTAMABGgaaWnq4e3DjxcDUClzkc03PQQm+7oDgFNwYr/v8+040hhAmilN2me0tG5ItpkYbN72DtZCTtt4/vRX2t3GTIcZESFHv5ENNnN000hUfUD0hxw1cnS7Fi/98+R+ahZ+7LVXnmw2v7ohIN9LH8zXTl/xCKSg09KyqHAiiIaIFCuQAhpqJi0o06PEsUlpNGgxMhKxevFYWUlmFJQCJh6tIojOG1mArffPwuDuqaAoCoEIC7OQnlPlPggjr60aSTEmTg+wm+VxPhCJ91LIFTZdMe72FrdThHheoxr3aWM4jCefbL4fxCIYYfH22oPSe2UK4Z/+/Ccs44V0F6plRBFDU5gzajQAwM3QCMeQAiEqCnPMebWYLRgzZoxqoR+L2PSAuXPnJj3d0VtL5iaMNQURgRSIMhT279/f1Md06NChQ8dxiBOWFHDV+WEV/jx/uCHhcb5QFHLdYXli0d4axy1BuZB//erKcskZXUsGCAAhYVN9uBh7y+6HZ+ejKPCTyeHdc15MWpvSYkiBB84ahA13T8e/zh4EAKBbGemKxbYKWTardNbfVfa99JpuY1QuIyJHdWOvm3LxvHT3UYgB4syYagtGRj25jLBk4WBMslJANJxjRVIgql5kr9xLzPx2OEjE70gGSRGojVEKKKPwvbPlaNMPPxJzrNQwi779BuCHH35odRvtTUTvd5bu1NwuKjCcAulx1BOU2njG0G4of3QOAMDrUVdbsFrjU2ZagjSbCRQnX8NoKF9KD3CmkDbYh0xH7iWPgLISwoRVXMr9TNPRYqYDhkinwmzw7OdW4vGfZFLQbFcvACY4F2N8z9sxwLxO87vsZgMeVihsACCv26h2tzHTbkJIkGU3cu0zGeOF6xG7bhN7LkW3LzknW1E6zS2YI3qvVRtXauVX1wo+Ijec2gdji7UX/W6hlSmOXM39LYVYOpFjyTjkjbSsRoJyPBbVOTkp8t+r9Gfpn5eC0wQ3eiNNxon22SMCd80ZAEuYjCs7qspR2SCPNwzkfmGylcW1R4Q3KPulnNpPXVbX5XLBK5DyTnvrz3Gq1Qg6wdAc4sJtyrN/9Jtd0uu/TSuRUgi7d++OF1bcK+2j0HKz3YLcYqnEp5tXE8BBPozf1m9VbRMJNHGh/8EHH7QoPSDZ6Y4De5OgAG1NQUioaBI10OjVq1dSvl+HDh06dBw7OGFJgUP1AdiEUkKBUOIcOF+IhTg9pSlyOsTSPm2tcdxSKCdLD31FFlkFBQV46aWFSNupnjgEhEnCqCI5ovrKZT9i5SUr0S0zOWUSAVm9IIKmKWQ5zLALizyRFPBH/XGfbQm6p8lRLmUEmePlydtZOWrztZbC0DASAFASoNGjRw/8/e9/l8iB6QPlyWiE5aWItijrFcHETLyirDi5Ti4pIC6awxGi+vCy6sV+rGN/QPj52PQBZb7+/FOISdvO0s34ruYbAICJ5cBxHBYsWNBqgsseE713KAiIP155seZ9IZ5XMe+ZpA+QbRajvPhb/Lm6ZOCHH33VqraJSLMawSlIgUjUKXmJiKRA1uybYOkxBFln3oKJhfepoorjeAscTVR4oDpAKaBMSTjqCWGHR45Sjz1pFBiGQco4klKwN381tlsppOYtjvuedKYSBq4Bu2h5HDnf3C0pbbQYGfjZNABAI1qezqAFLU8BQCYFkmXiF4tRo+R7SGsBJRKxuRql+ES4hTan2Nt3XsVxJhwlfbUx7G0R6WxW5J+LY6fyPortuk9fMhwjeqShZyY5pn1JD8SfxAByfvbV1qrutck5z0mvN6eRdDKt0o5h4f6/ZUZf6TkCkOdsz+Ii+IQx97vvVra6fTRNSdUyRAw9Skwsw1S0TXn2S3bJRqpDuqdKrz9c9RBeKpOrlBTYp7S4nYWZadI4s9WqVnuFaApBj3obRcntLigowAUXXNAh6QHNYUAvoj6hLQ6EBB6LNRnQvXv3Dv1dHTp06NDR+ThhSYHKxgCsgit/oAmppi8UBUWRhzUNqkNK+ySCRTHhe2P1AXwnpBFU50/EITwN795bpf0hIRzy7CUjcNqgPHwwfwK6paQgxdQyl/6WIs2qPY20iQsZjuz3tZEUSFFEvnply8ZWoSiRu54UCOP0aU+0+ntdLheObiFGhSE6Cp7nVXmkShWIOxCRFq+xJIghJuzk8pNrYqCSe6uIk+MIK5R/49STwtiIWlBolxjdFCFG4U/ukyURRh+s/AfKC0l0zxiV+3Fry72l2dXRe6ciMJY5aKTmfSEG5iRSwBPC5kNkwSCeWpfLhTfffkn6DMPzuObam9p0j6XajOAUucgsZ4ZVaMM1116lkt6m0LXY6pBJlXONuXj49Fdhb0Is4HUn31TLZo6NjBswrJHcx9n5WSgvL0f6lD/hJOfHCAqLml1W9cI8gzmCaN+ncOlPc1Tbbz7t5aS1088K5mgUwEXbXqdcPr2x6QPkPdXO6in+sHbljpxi0i8SLaBEUtLMaN/bwaAbYaGNKSntWwSJMvdwhCyqyytdLSKdxUX/n08qBp0oJK6A2cBg8XUn4R8zSYTX2LKKpk1/J03ICJ6OSKl2ALAhs0p63U+opVfjje8nUWGMMjBqJd68efPgdMrbbrrp7jaNARN7q8epYIS8D9DRNi2keyueS8o2P1L6vuo4qhUEXGG6DY4EwoJUfwT+BjXZK85FlMRRR6QHNIdspxk2EwOKZjDvhjsAyHMRHTp06NBxYiH5LlrHCO75bDvOGmEAeCDQhNTdG1akD1AUVq1alVBymGxW3mJk4FaoBea/uRHlj87BS8v3A6DBR+LzwPPTrHjx8vbLgxMh1tlaxKB8gXwQIkX+aNvyjEOCvPTkPll44KxB0vag8H0OJhNgWi96LS0thbfGC8CGYIzB3Pz583HtK7IRljsYkWTuseaSSk+BSCSIx1zfAQDK+bYvirRgMzGgKCAsOKZ7Y/JMY70NIsLiJHbS7RMWRFYhFcPtrcJHlJyrL5IcNE23utxbmjNbek3xPMyK2yJ9wFCw7Idx9wUrKgUEM7Sf98ju6R9scOGxPwxDaWkpLHYaoham4JVy/NbGeyzNagLLyuqTCGeFSaiQMXLMYJSVlWHS88Q122mog9Lz/abTXkRGRh/Je0QLVZVVcLlcSb33lWkzIihBgdMQ9Aq/9Ru2FKyX9rMxYfZB6YuxGXK/AID3xt6H1DS1B0h7UBnuiQyOR4imUFGxFoVFk9r0PVpKAV4xxtLtTB9IhLl//gtuu/409OnTR/P6hTUWq0p4hBQXmudha4O0XYkUixEMTUl9lRUIYXF8mjVLu1qESF7GrsNsJkYqm6eFsJCeYEqCwslmJASjy+5GTar2s9QsKCCq3EEMzFcT1VFhrDUqxlrROyc1nZAlFo6DP9C2MaB7mhVQcHdhjrTXT3GYO3cuZs2ahb179ybsB7Eg6kGS5jOphIyBm0u/QrQdp7JbmgVWjoGSIhuyKwTOEcba/x1BZr4dgKzM8ni8KCoqAsdxoGkaCxcuxNy5c1FQUICCggKJMCgpKelQtQBFUVLais9WDISAAJUEpukYBsdxWL9+PYLBYIcoxU5khMNhmEzt1Scdmzhe/7bjtd3HM3ieh8VCfGESmcUeqzhhSQEAMFNGQgrEmLgp4QtFQVNkEuBxu3HxdRfHHZOM0j5acFoMOOpRRwhu/fC3pP9Oa2AyyB34D6PkyUZhhg3njuiOqoMCKRAjd28Krno/Xvp5P/50Uk8p5/S20/qhlyIHPiSYmRnbWPyvpKQEnlofaNjgi/kKlmVR3ygvB92BqLRQ+eP4Iry5Rq4Fblb8/XX1cu6puRWO8C0BRVHgeSDECiUJefX3xxr2iaj1qc878cSQ0xE+W/kv1X5+ZR0YhsE111zT6sljVnq+/D0UBYNiDUI17NW8L/iY9AEtlJSUwOok7e1b58cnK3xtvsfSbEZEOJkUCPNW2HkKAA9/sEFakACAgVITKqJs/Z+jb8cVv/5b+wc4PumEoGZFA06UlSceq6alvY6fGq4UmhVv+JaRWhy3rT145PyReGUzcNgEbNv7pSYpwHE8nvqpFMMKUjFtgPbCWav6AMfJnYluZ/rAySVZ+GZbZdx2i9WIKVOmJPycqBQwJlAKuL2EFHDyPOg2EJVK0DRF6tULJGBEoRLTIp0b/RH89b1fsadSMLWLYQUuHtMDr6wsw5ie2gad4WjySIEUaxoAoMaQeHLDCiksC5fvx6n91SqniAb5InrnpGcZEQVgZ7k2jwGx6SNhwSAzJCxexYV0S+EOEoL2hctGgqEpfPnb//DGVrV557IpL+OXzVVaH9eEkaFh5QyAwodhQP4sPHDzArAsC4vNoJqMHa06GqdWFImjRYsWSWpGJWHQURDH0G93+oE8ksroOnSow36vq7F+/XqMHz++q5uhQ4eO4xxr1qzBuHFtS4fuKpzQpIBRcAcORBObOvlCURzIOAiAxlJrUFrUiOjI3L2nLhqBM5/7RbXto40dZ2zYWsTm208bkIO3ysgEzMe1PHL+p1fXo/SoFz/vqcbhRrL4V+bFAkBQMvRrW5csKCjA9X/5GxbgHfgpCrTZBi5EUhwYhoHN7gDQAABoDMhKAZuJQbrNiHo/mazlKzwPfAG57OItPU5vU7uaQ1AgBTwxedsJOIG49AHRl0JMR6jzyRPVN/reisaiIjz+cBG2bNnS6rblZ6kXmSaagZgJnpt1RPO+EM9rz0x1zXMAuHsOKXFXUFCAyadOwDsoBxPh23WPEVJA/q0wZ0MaxwCIwhdyY+cemdgxUGqZOSV4jowY+kdAQQoUflGBQ2cKcnEOSScErRpKATFXWyTbnOb4+2Bdt51iF4bVEM/8M4y2M3xbUZLrxGETWcjd5voapyOeOFlXXodnfioFAJQ9MlszsqalFOAUypj2Gg1eNLoQRprG2OIMnKmwpmgIuBN/CPJiNdZcVIRHuJeczVSoaCky7SaE3aSvhgzyb2othj/fchjLFSqb2PN6x+z+6JVtxyl9s6GFsKC8MiUh0plhzwASc1UAgO2OMLINB7Baw5ReJF8MjDpPfuHChfjo29vhApDhi7R5DFAaLwJyJZNgGyPaoi9CisUIt7cKd2x+WrV/+bRFcOSOADZ/3arvtcAMJSkwasRolJeXY+/evcjOonDxxhukfXxMxRalQaJWemMipUky8NepffDskr3gQ+Q3WYrCmDFDO+S3jgUEg+TeefrppzF8+PCubcxxhO+//x4PPfQQhlxwE7KK+nZ1c5KKyu3rsPOb1/DMRX0wrLj1lZy6Ct9vr8ZDX+zDqPmjkN1b+1mhI/mo21+HdS+uk8aS4wknNCnARhiABvyRxPnv/jCLgDXxpO/dd9/FBRdc0BHNw5CCVJQ9MhvFdySeXBh4XrPcUkfiuUtH4OONLlwzWS03T7UaEeHJotnPa+fxaqH0KJGcHqyTr0Nuijq685afzCbrqbbL9OdedR0WvP8OeIpCVrd0HC33SwvOTRYrlKSAWBqLooC3rx6P2c+swCVjCyWyYtWW1zH/V+JtkMnyOG1G630OWgK/4PDupymyemriWucb92FrRW+8vqocVwo1yb2C1FWsZlAXagAAzAlbkF04DSMKChCJRNpECqQ71JNtM20EQBatWwsP450r46NT4lw2xWrESX0ypSoKADCmp+zwXlBUAFSXIy8jG+Xly9s8oU2zmRDkZMVJhLcKZTOjaAy6MbKkF07NvAEw1aGifrrqs2KE2uVyofj7SpTNzMPAjTX4aHE9BgqkANMjLemTbS2lACeoHXwcOb/eUBiOuKNknNo/E2uPqrfV1NRh+47kSYpznOrrz/M8vtteiT45TvTJEaKxihr07mA0rqQpIAumlb4evEIp0F6jQQND48IxxBCt+Mg4lHVbCwBw1dc1+TmZFNAe//0hUsrWliTrnUyHCZUN5LwFhah7IkJMVACJiE0fMDI0/jg+caqIqBQwJqHtOSlZzZICAFBY8BIyAs/GbY8KC9hYv5a5c+eCyVqL/zSsRn5qFuZe1bZod5bDjP51+diVcRj963Nx0GOSItotxVF3ENXeEAblp0opfSlWI1btflt13N/MRUgvGItIpPUVOUKUWuXFwSipGIKBBmCjcmd8cKJPnz6aJYs7Kr1RhJg6uHVvNTKELme1UkhQlfa4h0jADR8+HJMnT+7i1hw/OCSoR1K7lyCnZHjXNibJ8NcRgnh4Dycm9WtbpaSuwKE6sihN65WGbkOTY0Kso+U4HtOPjq9kh1biSAOZeAaakLoHIiyGeMgCte/aGtU+hmEwYYJ2/epkoblOY23CGb2jcMbQfLz6p7FIt6ujkWlWEyIcMcbzcy0nBZQVB0RoLR4AwN6O8mdmS6pUju7Kex/GkiVLJEOmr7bIdbQr3UGMynwdzgH/wCWfjUdBSgD7H56NR86Tox8v//q89Lqhg+6Swgwr/Bxxtw5TFMKKKhli+gCjkAywvYg5332fb8env1agyh2UFg8OIbJc29gAACjdeKjdlTOsMWoOIyW/LwnQcaoaQE4foClgQi/1w9OhKMUXYsmMMtXmaNdkNs1qRFThiJ6XkQ+DkJ/vDniQnZuHDTkHsCHNg+6pK1SfFSXhpaWl+OKdGpRdux0fPFsJjgOyBa+GMZklbW5bImgpBcRc8wAfwcYDdTgjW5uEmpb9NKZlPg+Wj79PTpkyLakVU2Lv2zX763DNW5sw/cmfUe8j5J1ynXe4IcEqQaMkIacYP2gqeZ4CWxrOxWC3QFhwTa9aIjHpA7HGbmLVGmuSTEazHGb4ObLACjJ0k4ZxsURFbPpAc/jiAPFCiSRhTpLubFlkrNTKIaKRZiWe51hSAACiNOnHqUZb3L6WIsthxvqq6+A4cBHWV96AG2YSz50wTSHcRDqOEle9uh5znvkFq/bVwCuMqSkWA/6+TS73+1DIiqvP/6jN7bTF1IIozpYrG5jNah+GHgU9NA0StUoWd1R6owixnC9ly4ZBuJft9uNvsqtDhw4dOprGCU0KOISJRpOkQJgFL8gMRw0Z1eklf5qD5Rjy9MlwmCS3fH8rypRl2Js3ObELk8nBlpPa1jjpe8gJ23DgIKqdxFjqp53xuZ97ckgJyAaKx9tL/q5y9uZ4Dqxi4hVr8pYsfHfTZPg4p/Te56/Gz3uqcaDWJ0muCxTuVn5Fm256fzNmPbUcrgaivrCbGLhcLlTXEskxz/HtrpwRS1gpUzvMrBHuQDwxJEqFGZpCUUwKgVNBCgSj5J400+0zwEmzGRFSeAoYLNkwUoQkcIe9kpkcAGzIUJN+4mLU4XCApmn4AvLNtu/BcvzVMRxzp6ulw8lALNkCkLQHAAjwUazeV4tlWdpR7nVZR7Au5xBq/TVx+3w+QoImq2IKRVHIKCcqKZrnsadKJq1eXE4qWYQVSoEjjdqLcFkpIIPjZaUA1U6lQCwMLLn+ok9JIihz3bXK0AaFqjWWdlZHEJFhNyEgpAv5aRqTTpqQ8Pkieq+ISOQxkgibgmTMO8SH290Ptm7a1eJjqz3xz1pRKaClyPAK59jZDlIg3W4EYMAR/wgARpzUT14gV9VV4ZFvdmLl3vj7RYkdR0iqyRPf7Za2OS1GaREMAGfNWwcYE3ulNAcLrVbe0JDHz4rDh1X7cnPyNCsNiGkXnTlP6ZZKxldDWh5swj3TPad9Hhs6dOjQoePYwwlNCtA0iRh5myifF4iw4IUyS/1L+nd6yR8AuP/MgQn3Mfyxw8hn2mWptq8VpECEVR97sSD1VUI0xKIMGXH7WgM7R77HSrtx20dbcP3bmzD39Q1NfmZDwx7V+9d+/D/8yrUswtQe2EwG/N/MQbAIRMaOg/tx5SvrcMrjy2RztiYuf4M/IsnzHRYjSktLpQ+IOaksy7a6FKESo4LkevcO0TAqFkdBJhxnegjIju4mA42pMYZjKRZ5Iiku2CxMO0kBqwlHIiUoachBSV0POCx2iRTwRf0IJShXB5D0gUWLFmH8+PEqSS7DMLj33ucw7/w34UxN/mR7SEGq6n2vbDvCLCFQfODwxPd7tD6mwqGAOneg2BNEQEFqKHOQ24O8DDI2cRQFu1E+l4erGwCo0wf+8sZGaEH2FJA7c6X7oPSaoZO7wKCEsqnBZkgBMa2pvrZGM0/7s4NLAQDWJJEW6TaTpAwCAL//aMJjQzGkQKyXSHOoF0jO3mur0adPH/zwww+t+rwIl8uFp/7zXIuP9wSjCEXVbReVAo0N9ZISQ1RlVPoJ+eU0NpUs0zTMBjXJ1iMrU1KMLfp5E176eT8u+99aHKjVHtOVz6dNBxsAkHLBJgON4SAkwOMFc7Q+2rp2xpACwRAhIkRCSokshxUFBQWYMmVK3IK/s0sTFmbIpGs6S+6Fhqt7duhv6tChQ4eOzscJTQr4w2SBeZRNLCMNhFmwglIgFIyitLS0xaWLkoWrTipGlkN7cUQfQ0oBi5GBwZAGAPC3gqtQOsBbjQxundUv7hhxyktT7VsgWARTMDNDoppfbT3S1OEAgHW8erL4gks9gX6+6Px2takpZDnMUorIgWq5rZEo2dZS6sVhZlBSUgKeUZMCDMO0uhShEvef/S7Oxkj8c9rrMCoWRyGGQ61PvVDhOB57hYWW2UDDbjaoaogrKzuIxpLmdprjOS0GUBSFTUduwaaq62A3G2CiBN+LaBChSGKV0JEjR1WLQYCUbly9enWHTrT75jrxwmUjpfe3zeqHOcPIPdHScl+lEZLvPpI34c+WvlhyR5lqf7Ikxc9dIfswrF7xjfT67dcXYdGiRSqlAJsg1YlH/Pazv7tSem0yO+P2twe8SApwia+9sq3rdx3SzNNezRGj0WV84pK2rYGRoRHmrVL02aeh9hARiCEFGvwRcDyHcDSEaDOpWy6XC+kRcsz2dV5wHIcFCxa0STFQWlqKeoVCBAAG7AnD1ERaW13MuBAVFt3XXzsfU6dORY8ePdCjRw9MnToVX7GEGAm1Ih2tORgYWhpT91fKY+qrK8s1j69sjCePRAIzJChaLLb25xH3Tu8uvS5igUH9zoHL5ZLGIOtCuX18M54FiQiDjoCSzDOzJ/SUUYcOHTp+1zihR/iGIJEkehKY4rEcj1CUAycoBd547c2k5uS2BgXp2vJJU6TtEZSOQKpQu95P0+COtkxWKk4Kn7xwGL7468nIcsQvBMXcV5pu3yLRwpGokZFu3USe53kcqt8PnufRQ+E2fk3KYEyecn+72tQUMh1mmAU1CAO5zWTBzYPVWFBpwWEmplUpaSQSyXPJkZb2zOuJB698HUN7D0e2Xe6jAZpHWbWaTNla0Si9FieSF40phNNiwN1zBqgml2HBO8LCtF2OC5BSb0p/CoeZgdlAou4/0LW48+N1CT+7d9/+uMUgx3Hw+Xxx+eXJxuwh3bD3odOx+o6pOG1wN6Q6yH3lZijMTn1Vdexf0uKdvsuF4OjU7JG4+aKP8fRTL3WIpDg7NQ1mYYH1/RLZ2p/necyfPx+V1eqFrZIkkI8l/ydUvSQxPeeeMwaCFTwmghq+CyKU0eEzx/QmedoU0G24A7SFxsCxMlHxR2NeUtqWn2YBQMHGiaRAdcJjY1NzuMDdOOX1YTj5rdE4982x4IV+W+U+FOftUVpaiqAg1fd5ZfVDWxRDJSUlCAZJ+oiIaB1gT5jOwKlUDTuPuKXoOxcl14Pn+bg226PJnYqIaXc0L1egCIRZzWNjCRiAmAwCQBRi5YT2V/aYe/p/cBJvw+1Zk/DZ5RtgMjtVxoFHD8vnzetNrG7sShi4E9qbWocOHTp+1zihSYFGQfocW+5NhJi3KSoFIiG1fLSjFgRaUEZRTQyNYYVpAIC9h+chz5sB28HzOq0tTcFqk02nzvnsHEQUZfsSQZSPFmfZJddyJdhoBGFhYcDQ8aaErYGZJ5M5ozOesLjh1D7on6cdlXzxm3mY/fnZGPrGUBxRyDOYdpZLaw6ZDhPMQspDMCxPYMOeXejX53ZUGGIXTNp92W4m7TRZyeT1tJmnJV1aajPIE2M/Tao4KKE0QxNfnT28O7bcNxNXT+qlOtYvRHGthvZdb4CYDYrIdlpgUUiRrZ4vEn6uX78BmqZdGzZsiMsv7wgYGFrK181IywUARCgKK/J3q47jj5gwEdqkYZadOAp3pKTYJizgHKmymomxOMGyLA5XqiXwsX0CiK8+cKBKroRxRtuLjWhi7snFGNqdLOLDLSQFBvUpwsKFC3HyJTnIvKknpj5QDOO1PaT9t533cVLadsbQfACAVfhpbxNKgbIaQrjZTQx6Zdux2uxCAwUEKKAcEbi9h/H92qcwffFs/Pvzy1Sf7dW7JwJCv/a4yTOOpuk2KYbEHHal4e3ggUPhSDB1MFMBlYLohWUyEcFH1Rd72DjZcyTN0L/VbWsKZoHYpRQqD1+CVCJvKH57iuB/IpICDNP+FBeLLQMvXrUWf5zzAhgjGUuVxoFer9wn01LS2v17ycRl48j9wHC6l4AOHTp0nKg4oUkBH0uipl4KmoXf/WE1KRANK6SwScrJbSlMClJg4z3T8epVY/DuX8bDYe2B0kO3oco3ttPa0hRS7XLOf5nJiMrq7c1+pimjKQAIhmRigaHbp4zItZHP/+YMYqRDXepxQu9MpNniJzXpHI8XqtdI772KdXimKa1d7WkOWXYzjBw5L0pSwIyXcdgYf77ybNqpMKKJHyv084LuPZIuLTUxSlKARqNfHc1S5hIPF0gtQLvChk9IH7Cb26+ESbXJi9WCNCusBvn9tsKVmp95quRyTdOuRx55BLfffntcfnlHE4S56fkJ933yyWKkUNqRSqtJPn8dJSm2CqSVPVWOEhqziDt6WobamX7WU8slZZCIWKXAmd/Ii9h/XrokqW0FAKeVEH9BaEeGAZmoBMi4NHfuXHSfSoiZylyrylyUsqTEfb4tYGgKpw3Kg1m4333BxCUT6/zk/nj+spFY8n9T4vbX1O7B09tfAQC83bBVtS8jXVbfuN0sGIbBtdde2+Z+MXfuXFgVj8++vfvCkaBihJ1uRK1XTtuwKsYw1tegOrbnYJnoKi6ITylrDywapECNVzudRIvIEtUNolLLkGTfCxHKMcjrkftresqxVfbswXMGAwBonRTQoUOHjhMWJzgpkA4ACNA0IkF33P44pUBEESHu4DI/sVAumJ0WIzLsJkzonQmbRvmyrkRsJYGmJLAiojGlv2IRENQGFM+DotvuQg0AKSZ58WRLVRufle79HwoMhCjIi8gLlxCAIg3ZMwCcNfm+drWnOWQ6TBIp4Au5wQgLmUMW7SjiR3/RnjzbhZKErKAkaG/tdy3E5v/7vOqqDkHhnPbNdTRbatMnpA/YTO1fcCmVAilWA8yGpv/20bwZ0ybeBiA+wj569OiEdcA7EkXZOaryk0pwLAdLVPtvsppTOjzVwSossAqLZONIc34/vPjiS7A41MqbOl9YlUYCyJ4CYo9Qeqca7dlJb6/TkgYACFOJSQGRuKApslj/8bdX4Wbi+2xhJHm57gCQbjdJpICnCVJAfDYlKt1aXb8P2YqF6pY9siJGHJMZnseXX/6I0tJSzJgxo13ttipSqixGK+wJKjLYGLcqfUBpAsjVVwAgJCFN0+AGyPf+2LGXtqt9955BDDHvnjOA/C7I7wYish+C0uhUiUZ/PCkwqYSQXVE+eekDiSCOQd9+KxNk5iQoqJIJcTzXlQI6dOjQceLixCYFOHnS4fHJhkPBCIurX1+Pl1fsByCTAryQ2N4V5Qi1apcDwE3TSZ30c4YnjiR2JtJtalLAG0g8sRWhLP2lhYAwObbyPKh2yvX3BOWFKsXL32WnG/CfqrfxHfMpzJQPBsXKxE9TyDOoy+cBwHuj7oK5AxYtSthMDAy84OjsP4Ihvf6B03MfQYWGSgAAwsEaDBXc6y2KYxxmtVKgI0iBGSOvUb0PBNSEkKgUsGiU3ItFQPD5sJlTmzmyeSiJKqvJAHvqiCaPN8TUnVdG2LuiDjgAZNjNKom2EjRFo5yr1dy3cf2WDk91sAgLLINZTZaMnnU+Hvt2d9zxcZFXhVIgHJFN3d7pd3VyGyrAaSN9KtiETeebaw4AADge2Fn2I27e/CQOUfHH/3fMHUltW6bdBKOQl+0NJk69Eqs6xDrri1hf8QtyFGPWU2selF77fOS+tPM8Tp06NSnPMYuCFDAbrXAkKCVqpd2qiHy9oHi454yBKC8vw9KlS/H+kkcx8JWBKMsiC99BnKHdvhJ/Oqkn1twxDXNPLibtEAxrKUpWM0UT3F9if1USME9dNBwAJK1JsitkxEIcg0QMKJ7Wob/XFqRajeANyTHd1KFDhw4dxx5OcNcYA6wcjwBNweutREYWyVv84rfD+HGnnAsrmtw9+ugT4CIlnV59AAAK0rUjA+cM746hBWkoymhfBD1ZSI+R3/uDDc1+RpyMGWntha74HdYklF80UwYAZJJHsbKMtpt5N0S6wMnUxtn3ublwHEXWrdtIdDQoioIRRgBBrOd2w2VmsM/ciOwIj2pj/Pnw+Krx/rwL8MWWw+if58R5L6xClsMMu0lUCnSc3LWw+1j8fNbnmPPpmfDSFIIhNSkgKgUsCRYySvgEV2+7Jb3d7VKRAkYGaVlTgOrHEx5vaIILFeW88+fPB8uynUYQ0jQFGwd4NU6d0UKhJhwEjPELsf+9/EZcqsOsWbOS2l4rjACi8IcaVNvPeV47NcMTVEfXJU8BisKhCpKmY+Z4DBo1P2ltVCLNTqTXIRrYfKhBlcoi4tklsvLjyGHtkqV9whH0G3ZFUtuWYTfBwBoBhOEJxavXRIhKAUsCcnBh3SZcZu0JCCqr9by8+PUGiMrImcRythaegehnYjXZ4GDMQNQTfxzjkTwF7v1sG77cQsj4DDsxQi0oKMCQ1/+q+sxVRbPa3T6KopCXKo/3ZpgA+GGg5fPiCWp7TIjVEk4fnIfGQAS5KRZkCma4xFOAgsHQcUoBJb6ZvgiNbhe6dz820gWVePS8IXj752OoHJIOHTp06EgqTlilwOzBxGzKLixIPQqpcyhGKh4W5k65ud07rcxPLK4+uRdynGZcLUQ6RFAUhd7ZDhgSSO87G+kx6QPV3sRmWSLE9IFESoF1FasAAFa0fxI7LEU2tEu3yyusqp6Lpdd2pjGOFGjg46XGqSlFcds6AiaKnNOo4s/PD6iVCyliP/ZXw2picOHoQgwtSMMXfz0Zn99wEmjB5I/lOy59AAAy0ouRKtw+fp/aZE5cyJgTLGSU8AnqHLsg9W4PMhXlPG0mRlUGUQtcJNKk1L6z64CLOJrgvFmcDFyvVWju83vV/bZDUh2EtCqKDmGC43P0NO1s8nB3zOJLdJqnAISFhXAqD/z8y+oOSXkoyCTeAAEaeOWXsmaOBgwJpNrFlizN7e1BpsMEWiiZ6A4nJgUkpYCRSaj+qK6Tx14rJzv6i6SAA8lLPTMr0gWcVjvsCc6Z1dCAjza6cMv7m/HG6gPS9jSbtrIAAFJTeiTc11akm0haC8X4YQJRLsRWdBAhkgI5TjMW/HEU7j9rkLRPHJMZJnH7k4mC7mMxaMCxYSoci1SrEd5Iblc3Q4cOHTp0dBCOjZVmB+CfgjGOTairW+c5JO1T5umn0tVoFBbcJkPXReOznWasvXMa7hZyI49VxKYP1PsaExwpI8I1nT7wWPmnAABXEuawc09bAKMwOV5pPAwG8fbmudYdiA2iNWjUhxcdojsaZppEuGhlE2Laky402BNQy8gHdEtBToocIZPcsjtI7rpo0SKYhUVfTfVO1YJFVAooTTMTQYzf2aztX3hl2eXrZDEyKMq044lBib0gqr2NzUrtO7MOeHMIhDhsW+/T3BcMqvtJslMdXC4Xwh5yD0Uc5dhWuAq1vV9v8jOxiy/JKoECIlGSPkBFox2W8pCX3RcAMcMsNDZNYACAP6w9hnmbMCpsKzLsJkBQMLnD2lJsjuOl0o511VWYN2+e5nHlHpmUC9AU9pT9CADwCuoBexKJQRJ5J7Bb7DAm8BTwZZISoJ/8qiaxlM8NR4yMf1jJmclqpoQeqYQY9FtrkN33HszI+U+zSoFYwhuQ0wcMCdIlfk9IsRqxtfryrm6GDh06dOjoIJywpIDFyMBuYmBjyeSlPiBHVaIK5+ke1m3S66zM5JZFai2aM2c7FhBLCjQGmyYFWI6XFgWJ0geSCas1HTflTAQAhGgKZ/d8DwAwIiS322KqBBejFQgIkfapPJmwp7PJXxAkglkowxhQEAFRWr2wShNyZD1N5CFzPIcqYRrbEUoBl8uFefPmwRwQopjWkMqZn3gKRHE0cg2ue+fUhN/DslHpfNtt7ScFlEoB0Ztj1ug/oCen3d9K061dUna0rVj6ObnmOaH4Rc09dz+kqp6Q7FSH0tJSsG6yeN1tle8JI9RO7oUZcuS4zqfepyxJeLTmMGmrsDDsiOvgdHaTXh+qe6XZ430JZPyrueTnT2fYTeBYcq7cUe1a9N9ur5ReHyrfH2d8KcLjVN/jz677N3ieh1dI83AmkRi0Koz2TEYbfvTu1zzuoFn7GSamnfEcB59wiIPj8Er/q2FLLUxaO6XfE9KSdll5uBkaazKr4Q5qKwVEhZOWqa9ECnRS+sCxjFSrESxnh7n02q5uig4dOnTo6ACcsKQAQCSLDCe4EIflSFt5rfzaaSIKgiwOcDrzOreBxyHS7UYYFOtpT4JolwhlPfBESgER96QMb0/TJFgVBlx15n24+uRi9HHKskeeDkkLlVjH9/P6X4Ifxj2Eb85OXN8+2bAaiULFqzg/UVpNSjiECb4nQVQTAF744irUCnd0Mupqx6K0tBQcx4EWSncyBlYlVw9GOBSad6PcGMaKSA18Ceqw7z30i/TalgRSwKlwFRdLMwJAkaHpcoedXXa0LRi0rR48Sxb8GVT8Nb3yivkdmupQUlIC1kfKYCrvlAkZ76iOG14oe0Mo69QDsgGl2UDjSJVICsj7O/I6GPnuzR5T26jdT+dZkp8+lGk3I8qS8cnNBlX7Vu2rwch//YDr3t4kbRs6oG+c8aWII1Y1QftzqArv/Xw3vALJ4aAtWh9rE7Jt8r1kMtiklDsA+OLUF3B3HiEBB/jjycgLRhWgR4YNBys3Y8+hX8AL5PdPFyzBmHF/S1obldB60qRyu8BqmA1GONGHJf48izRCR4ynxxtSBWLHy6V1bUN06NChQ0eH4IQmBVKtRjCC03NQiMqEoixeWLZPOsbAEIJgrCn5+aMnIjLsJrDl8sLDHw00ebySFEhUktAkLMzHj74hCS0ELEaZFNhIh/C3yWmgFFH4DCctuV2nxJACDG1AXv+zYM/snZS2tARWI5lwRxRKkbDCCb0/x6DISqogNEXCvFT/awe1kEBy5g+TRR5lYMEwBkmuHoiwMFFylLiqervm95SWy6W3qmu0ZfGtQX6avPgRDRcB4PZpTzX5uc4uO9oWDB89RlrwZ5njK2SYzI4OTXUoKCjAkH4kx5pT9E/eXKk67putR/DY+UMByHJsEZIBpZFBejrJ9VaScR1xHaZwpAJBOEE0XsTYNB8Wu7QNEycNvDipbQIIqRrhyDlwc+rzdOnLa+POXUFBAR54/hzVtpQmVEzv7f8CvggZI2xJjG5nWOVKPiaTHf0ZmSTo2WMSsp2k74ViyMz+eU48fsEwHKrajDnfXY4/LLseACk/a7E27f3RHnjC8SaIvTM+RbUnFLc9Ek2c3iZ6ChgMySNYjlc4TAbQFBDkHMiqHdzVzdGhQ4cOHUnGCU0KpNmMgFDuTVy8xtUkpskkzKk/9FsEm8kAd7AEw2qIOZSfbZoUUKZqGGhtpYAYjUlWLWirSb14+s/XV4FVGAmazZxUASuFV98CdAcZ9DUFu8kZty0sGAycz/TG+1dsQKqRHOOJJCYFhnNy2/sWTk5yK2VnfghKAZqO4OkXZLl6nS8ECy1LsX8r+0Hze7btJCk7/YMh9CwubndOeUG6DY+eNwTPXDICjKKPFeaPgSVBGbKuKDvaHP7V5zLp9ammHPSBGfNOe1pa8KcwskT/L2lD8UTJH+FyubB06dIOTYOwZGl4rXDqKPXzl42UqkCIC1tOML+TDCgNNKyC/wMjjAsddR3sDBnPw5w2KSCORYsfvh6uNO2x39gBteLNBgagiarCzcty9srGYNyxpw0iyrX3rXukbVfx+TiZz1Add7VZlt8fpTgEhGedJUnjKQBYFASD3Z6Le6c/BwC4xN4LLpcLlRUNAIAgrU518AsE4nNL/k+13coDNNNxY+3MMTfGb6RY/OOTLfi/D34DpxgXokJ6hhZpnexn0/EMmqaEFEcG5TUXdHVzdOjQoUNHknFCkwLpNhMoSSlAJkreUExeIU1IgmROoH4P4DghD56Pj7woEVYoBRgNUoDjWCkCaWCSQ8xYYxbZ6/xHEOXldvi5sCSFTokxzOqIUn7NwW5OidsWFEiBqKcBNGOAQzjGk0CZsbvsJ2wWfAie6nk+0rM7xh9j7ty5GD10BACAoyPoOf50aV+NJwwTI0foqt0H4j7vcrmwfjcpS2esDCYtp/zisT1w1rD8uO1BjT53Pl/QqVUFWoppQ6+UXv913D+w+MoNyEiVndlTFWkxN579Nlw7s1BUVNRhhn0izFrO66xMFDx54TDMGpSHDMHbodYbBs/zuPjlNZj9zC/SmGsxMoiwZLxIczg7tLqDQ0jJifDa9wvHi54GUWRFtXPNLR1ACgBAVgrxPPBAHpPeW38w7rip/XPitl17wVsoTFenRJw74Q480302AMBLU1jvJfedhU7eM81mlJUBdnsOuuePxpYrtiC38SwUFRXhkQefAgD4GDUJV9FAzn9azPPV1sGV7QrzRsRvpFks212Njze58PMeuZxqRKyOEzNWcGxUSnVg9PkBAKBPdtMpWTp06NCh4/jFCU0KpNqMoISIVkCYjMaRAhQhBaxMx0wAT1REBbOsEK89oRbhqieRuvxUi6aRYjQiT9oNxuSQApYYUiAAXqUU8HMRaTqeEuMqTXcBKeCwpsZtCwh3ZumeUrhcLjjN5BgPGx9RBIA/LL9Jep2SknzjLiXSbGkAAJ6K4vp35PznGm8IRlpuX6OGhLe0tBQGB/H54BpI3+nInPIbMtX1vrvzDO674qtjSiEgwm7PgV2oMlFYMFG1z+VygQ+wqvfz5s2TTOg60jhx+rCr47ZRNBlPTQYa540k57IwnSzEKxoCaPBHsK6sDjuPuLF6H6mYYTHS2NtISgQeoKMdWt3BaSKLlyhCqqiwCHETTVFId5NnwLQ6LwZ/5sJDzgm41dILvXuf1iFtS08nZVMbaAohPzk39b74KilapT0NBgsyYmT3Kc7uOHX6v3E6nQYA2C4808xJTB9gKNmETzTdq6iokPpgo3Av+2gKUJAdYg4/G3MJbEkoP9tasLSsEtx7VFZcSUqBmKopIUWqltnYdZWJjiWIvgI6dOjQoePEwwlNCqRZjeAFUiAokgIxDsScEF21GnVSoDWIcGSSFKSaJgXqfWQilu3UnqBGFZHvZEk0nXZ1hM1PAVElKcBHwYnpAzHqBEMHSloTwWnLiNvmF0yveJ7H3r17kWIhx7i5+Brw//5Unftss8STDMmEaIzIxVRIqPOFQVNy+xpiSIEwGwafGQBM5G9jRcPCDszt//Os5/HPvpdj7cWr8c3p7+CLy9aA6oQqGG0BTTP46dJVWHnxL7AoUmAWLVpElACPLAXN8xgcoiXTRyU6ilwZ1HuWRmPJeGpRLKSyHCZYhIXs0z+VSttDUdlT4KXa9QCAGo0SoMlEmoUQgywdgSdmzOcVfgZP/ucJlAqVE3b+7Mb0OU/grPMW4sqLPgM6qJ9kZ5TAzPHgKAo1dSQ1oKw2Ps3BYox3wzcabMh0qA1xRbPOU7qfrP58kpRXAJDmjDdsVPbBxgYyvkYpCmYqXp0RjPFPOJiE8rOtRUQxXpUpzIbFFLfY6jjBkFzpxWxJhw7A2IxZsA4dOnToOH5xbM6Ok4R0mwk8T0iBjf4K1PnCcWWJJFLAoEcCWop/nj0IUYkU0C6XJaIhQBaJqTbtOs9i3XIAMCRJrtuneAayFNFBP03hW1ae4Pl4uSBhSsx17wqlQJojnhQQQQHo06cPUmzEaNAdo8zYuPVtvNUoG/oxPI/iQvXiINmwCkaObIypmD/MglJMvBtiTN7+9dnF+Nuvt2NHEUmF4MJ8h+f2G40WnDvhNtjMDhTkDIExSWqUjoLd5ECKWSZ1lIqAsj0hlP3fLnz0tx1wOBxxrvQdSa70iaoXAzwdTwZSFAWbYPT42qpy1b5M6xYcqvkcs0Ei+AUR7ZrxyYJTSLeJ0lHVAhCQVQIAcOGFZ0uvL7jq3E5JKclLtcIhNKLRQ6oxLFfI2UVokQIUwyDDoV6gG4X78eThc0ErCA9zEtMfpoy5ERfYe+GfPc+TtknGowA8Hg6U8NtOWh5rJ/cl41aQ69jr3RJYovL5aAzI7RFT3GKNBoNCuV0Tz4M5xseNzkIis2AdOnTo0HH844Qe4fvkOmCiyYTwoNGImQ+9iQXL1JE0liILG4tJz5VrKa6Y0BPzTiFO46FmIn7i5CvVqr3YjnYAKUAzBjwz9r6E+/3gJIFro09do5zpAqPBjJTshPv69etHTObspKSiO4aEcVVvUb3/9bKNsDk6trSmWC2BFdoiRl4DERa0QjnSwKn9Jj71lKreTz1l+jGZ238sIVYR4KljEQ1G4fP5sHDhQjAMWTh2NLlij7kvWIZEfoNRdX+Mdc8XEe75Dp47+Bz8BjIOzEgb0AGtlGEXiJUozWLt/lrVPk6xcI5EZDXL5D4zO7RNInJTLLCx5NHb6CNVHFIs8eNOMMKCZeX7ab69LwAgM7WntM3I86g4cgRLly6Fx2/BLFr2J9H0gmgjGMaAe//wGc495QFpm2g8yjAMeB6wCUTHfbO7Yf1d0/HY+UPx9EXDwfM8NkYbAAA9I+Tv+XzsP5PWtkQwxFSW4RRjk1tBCkhKgRhSIMlFYgsAAQAASURBVBAipICF71hVy/GEm6aTPujd9lMXt0SHDh06dCQbJzQpUJhuBauIaJ2Z9SwOucoBAHOGdsObc8dCTCm3Gu0a36AjEbqlE8lqiAKibGK1gOg8btOIegEyKcDwfFJl3WkpieuTByh5YbD1U3XZPKYLlAJZqYkX8fn55O9wCgv9OprCx9/fLO3PVEQNB3EGUMaON8QSCbSI4DQeEK5xIMKCgpIUaDo6WNS9+JjM7T+WoIzGihAVAXPnzkV5eXmHGvaJsMfcF2GGXNtwtGmlEADQkPvBsiiJIitz1DsCdiGFJkxzcWXolLXqo2GZFCwZ+IcObZOI4iwbLBy5pofrK8FyPDyC143NJJ+XkhwHvlv5sPT+rLHkvs8UPAlEKM0mBwTOREEkAornkWsp6eg/RdUHnYJPQHF6CNlOMy4cU4h0uwkrNr+CWoE8vrbPhdh6xRYUDzi3w9tmjVnLK+cCSsWg+PwyxNxnhw8T/wsLr0vmRWRRXlQ8exnqf3ypq5uiQ4cOHTqSjBOaFMhJUUv+PspkMTXvPwAAp9mASSXZ4AW3ZKuuFGgVMlMIKRCk1VLMWEjlyDRMswAgKhjnGZIcjCnoPh5T6HhXf4B4DIjC93271cZ9TBKjay1FmjMr4T5KmGinpMiL5/uP/IjNW98BALCKdIIqdI5E12oi51UkBer9EbAcj3CUA6VUClCiRPoIrnp7Utz3eEINHd/Y4xzKaCwQrwgoKCjoUMM+EbYYUsDPsJrHPXb+0Lht/zoz3viSoTuWFHAIZnxBmkN9TBnaF5bKarFomCjJLFxyScmm0CfHCRNLzmdlYxVuen8zxGD0wG7ymNUr24GPy76Q3lMCkeJUGIlGKEplNjn32ruw+/79yFxYhklTru+wihRKiH3QIUwnvAG1MuPbHW9Jry0mO6BhONsRiK1wEFWkO3mEZxbP8zgslINUVsdZtGgR/u/vpKwhE4l2ynk8HlBaWoqov7Grm6FDhw4dOjoAJzQp4DQbUMuqS7N9l04WLQ4zkWuGhFJ1ZoOuFGgNnHZh0k1RqPNol/0C1CZjWohGBFIgye2jaBpPXvyD5j6WohAWJoAcxyM/IEuezV2gGDGZbDAmkKiK82e7LQeM4pi9lRsAAFFWbvtJxsTkQjJhFfK1w8Kiv94XhicoLLwoxcSbAqLRED5b9bAkH1ZiZOEpHd7WEwGdqQhIBFuMCWilEWAQnypQkqsmV3+7byZmlsSnBTFUx6bp2ARjziAN1PvV7XxmiUwKhCPEYd6MzpWIO4VqNw3BRnzx22Fpu80snxeXy4WIwnuBYQiR8Oprryf8Xp7nUVYRwbLV/g6tSKEFh5Bi4g3Uq7anm2Siw9CJpOtIi9pwViQxAUjKDFHlBADpdtI20cfDZCGDr4nt3PN4LENLuaRDhw4dOk4MnNCjO0VRyM26QnNfmokHeB5BYTJoMetKgdbAYkkDAPAUhTpPvElWWY0P68rqEBKVAoZESgEi7TV0wKTc2EQZqSglkgJApkeeeOfmDE56O1oCq0bZNACghVuUomk4FYe4gw0AgCgrt/36KY92WPuUsFgEUkC4pA3+CL7ccoS8UUh0eYrC9z98jAO79qg+f6OnN34Z/QD6DbqgU9p7IqCzFAGJYI0hBSIUBRtN8vG37foEVUe3AQAGKCLd15zSG6lWIyLReGd9d9QXty2ZcAiO/H6KSuhzYGNqcdHqWwAA5k6WiFtoQgp4FBU6zh6ej34KUqWoqAjBWjkqS9MGacHaUrAsi3379iWhxc3DTpFFtSekJgUoVj63FdVlWLp0aacssO86402cYcxH7/VEuRBW+LFUe0LYXelRpZJkCqSA6ONhspABzsDyHVo29XiCqFzSiQE1DAYDhg8fjsGDB+OCCy6A3x8/5nU2Hnvssa5uggofXDsJ3/3rSnzzwGVY+dJdiIa1yysnQv2hPajcsVa1rWLzcnz34JX45v5L8e2/rsCGtx+XvveLO89DJKi+DmWrvsL78yeitmyHtG3nt2/i/fkT4as50sa/DDDM/RrD712BwXf/jAue3wh/SFtJlwibDzbi+23yPPq1Xw7h1vd2qI7J+uv3bW5fW/HOhe90yPceWHUAjS752Va1vQpf3PgFPr32U3zyl0+w66tdHfK7OprHCT+yZ9jjXYPTqVqsL/sD/vfebISExajZqJMCrYHVIjvmN7hrVPs4jsc5z6/ERQtXY0uFYNaU0FNAIAU6YVI+ijOqou0AIQVWPn0IA0Jh3Js2AmZr15SeMidKzVZIbTMhn0O3kAsdFfL2x8OKbgXjO6x9StgEE7eQ0LR6f1hqZsDcoDr25lvmYuXy9aptr3+0FJ7UsR3dTB1JhE3DBNRGe9DDvA2XrL0P07+5BAC5z7McZHHVK5uobsKR+AmyEx3sKSBU6wjTFDw+bbnz0Iz3pddmdC4pYBeUaV4FYfL3Wf1w47QSzOybhuqP/wmO40CHFAvZ6lppwZpIWRQLhmHQu3fv5DY+AZyCCsCn8GlYtGgR1m/ZKL3/+9ULJP+Djpbkp6Z0x8xu/4BrPWlPmFafs2vf3ghlVU9aGMTEaLhRUAowUb5DK3scb5g7dy62bdvW1c04ppCWlobNmzdj27ZtMJlMePHFF1v0OZZt3eKxNWgtKRBb4jbZMFodmHXP6zj9vrdBG4zY9/PiVn2+4VApKnesk97XH9yNzR8/h4nzHsLp97+DmXe9itTuvRD2eZr4FiA1vxcObZSNMiu2/AJnbo/W/TExSLMZsPmfk7DtwVNgMtB4cdmBVn1+80G3ihQ40XFw1UG4K+TnxMr/rsTkv0/GOQvOwdkvnI1uw7p12G9zTXig6WiHavuRRx7BJ598gl27dsFqtWLixIn497//jX79+gEAysvLUVxcrPnZDz74ABdcoB0lvOqqq/D662p55KxZs/Dtt9+2qZ1OiwHpbh71BnnSNzz9E2ywGLAp7IJT2Gw2a+ef69AGwxhg4nmEKQrVjerBLMxyks/ArwcbADSlFOiY9AEt/Hng5bhnxyLUKef/PIUb/vYw/j7v75qfcblcKC0tRUlJSYdGaRNFKinFYmW4tRv2hSoAAB5B9hyKktQNSwcbtylhMacBIKkjAIcGf1gyczNE1aZuqZkG0LGlvgIk6qabDB4/sGqQAhbGg14pe7FBeM+xUdCMAY//YRi2VjTivBHEBDMi9NEMjsdDuafibddPOGP8VR3aXptdlo6HgtqTrVSj3C87mxRwmlOAKFAf9iu2GeG0GHFJcRgv7xUmv0F50XDg4D4MHzYbNE3DwnGIME3f80r/iS1btjR5bDLgMFgBth6eEJmUi6qGC+4k/WD4jjq8VUWeC2Jqw6xZszp0HCgpKUEwwIECSSVRYn+1D5/9ViG9Fz0FxGj4F6vvBACYQmyHVvY4HtG9e2Ij3987Jk2ahC1btmDNmjW4+eabEQqF4HQ68cYbb6CoqAj3338/ysrKUFpaihEjRuDyyy9PeNzBgwexZ88eVFRUYMGCBfjss8/w888/Y+TIkXjrLeLV8d133+H+++9HMBjEoEGD8Morr+CBBx5AQ0MDhg8fjvHjx+PFF1/Em2++iWeeeQbhcBjTpk3Dk08+ifLycpx55pkYNGgQNm/ejPXr1+OCCy5ARQW5L5544gnMmjUr6ecou88wNFTsRTQUwMZ3/4PGw/sBnsfQc69F3sCxqNq9Eb++91+ApkHTBky/42Vs+/x/YKMhVO3cgCHnzMeBdd9j4Oyr4Mwh9yVNMyiZcn6zv53TfzSO7t4EAPBWu2BxpoNLYoncSX0zsOWQG75QFNe/uR3bKzzgeB6PXtAfMwZlY+nOGtz49g7QNCnxufaek3Dv4j0IRjj8uKMWD5/fr8nvL6/x46ynN6B/Nwe2utwYW5yGRX8eCkMnlQt1H3Zj9XOrEfKEYLQZcfLNJ8OZ58Sur3ah9NtSsFEW6UXpmHTrJNAGGtsXb8euL3eBMTHIGZiDkhklOLjmICq3VWLT65sw8+GZCHqCsKSRAC5jZJBakCr91s+P/Aw2wiJ/RD4qt1XirGfPwq9v/gpzqhkDzxoIgCgaLv3gUoR9Yfz0z58Q9oYBHhhz9Rjkj8zHkd+OYPM7m8GYGIQ9Ycx6dBbWPLcGDQcawPM8Rv15FLqP7I4jvx3BmgVrQFEUaAONs549q1PO6bGENq/Ffv75Z1x//fUYM2YMotEo7rzzTsycORM7duyA3W5HYWEhjhxRy3EWLlyIxx9/HKeffnqT333aaafh1Vdfld6bzW13VGcoCraoEfUGWdYcUZTRE6OdFrOzzb/xe4WVA8IMcLi2SrU9qiGFTxTYigg58YZOmJQ7rVmw8RTqFNu+/PIrDBkyTfP4RYsWSTXiaZrGwoULOyyf2woGQDyDqSQFJhRMwsf73gMAfBCqQP1bkzEig5R2s9KmTiMwxNQRjqJgpoKo90ew8SCRDPMcB6UAyZlmAG1QX9tIkNejbscZbBpeG38Y7kTvzEHYUEYknR6PC6lpPXFq/xyc2l9elIukgJEHTp79LE7uhPYajVZYOB5BmgKitYiyHAwMLRmfAsCw7kVY5SURnVAnewqkWdMADxCl5cmoQyhLKEaqOY4DH5LbNaDvOGnBumT1ndhycg5GuTw47dZbkZubi3/84x9gWRY0TeOWW27B3/72NxQUFKh8CToSDoMNCAG+CEkNkcppCsNB0K8e30RJfkeOVQUFBfjr9bfhOXwskJhq3PuZXH1G4TOIuXPnotHxNfYF96Ckey/MvVwvm6qjeUSjUXzzzTc47bTTMHDgQPzyyy9gGAaff/45HnzwQbz88ssAgL1792LZsmUwmUxwu90Jjztw4AB+/vlnrFmzBrNmzcKSJUvwwgsv4KSTTsKvv/6KwsJCPP7441iyZAmsVivuvfdevPzyy3jooYfw0ksvYfPmzQCAnTt34rPPPsPq1athMBhwxRVX4KuvvsKgQYOwc+dOvP322xg6dCg+/vhjZGZm4ttvvwXP8/B4mo66twUcG8WR7avRbdB47Pj6deQPmYhxV92NkLcBPz12DU5/4F3s+eE9DL/gRuQNHItwwAuaZjD4rKvReHg/hv/hrwCArZ8tRP+Zl7X692mGQXqPvqjZvw1Hd29C4aip2P3De0n526Ish2+2VOO0Idl46Iu9OGNYDl67ehhqPGGc/PAq7Hz4FDz5XRmevGQAZgzKRqM/Aoam8M9z+2Kby4MnLiaL3Nd+OYQ3VlXgxx2yaWuDX17DbKvw4JU/D8Xo4jRc+uKveGt1Ba46Od7QtyOw+rnVmHjjRDjznDi8+TA2/G8DTr37VBRPLkb/OcTDbd1L61C2vAy9p/bG5rc348K3LoTRYkTYF4bJbkKP8T3Qc1JPFI4jbR5wxgB89KePkD88H4XjC9Hr1F6gGRprX1yLIRcNQc+Te2L9ovVNNQsAYDAbMP2+6TDajPDX+fH9Xd/jnAXnAABqS2tx3svnwZZpw8ZXN6JwXCEm3ToJwcYgvv6/r3Huy+di+yfbMXbeWHQf2R3hBGmHJzraTArERu5fe+015OTkYOPGjZg8eTIYhkFenrrU2uLFi3HhhRfC4Whaqm82m+M+21YwNIWqyouBnrIDcoZRJivCwkRBjH7qaDlsPNAI4Oede7HvzQ146fLRAACWjZ9gF6THRxoB2VOgMwoBDh98Gewbn4By8d0tL1/zWDHKpXT27sjIlhE0NEkBxUS2e0Y/QJEe/ANbD1sD2RD2BlBUVNQpBIZFkWJhpnwor/Fh5V7y8OJ5FkpSwJrKgKLVk/G/Xn+3HnVrIzqL+ImFFikwrsiEiOJer284gNS0nnHHhSMCKdDJ0fg0HqgE4GRq0RCIIMthxsB75edWqs0OEMENPFTnkgIZjizAA4SFKg4mA40jhyuka7tw4ULMnz9fOn5ikEbvkmEAyIJ11syZWLv6OQw971yUDCBpQxdffDH27t2LPn36dMn95RBS8MSUCMmUTrj/+RiyuLMk+RddeCWe+/xjhGgKDCII1lbBmKk+PzSlHmsBSLOjFIueWqijaYhReQCYPHky5s6diyNHjuDyyy/Hvn37wHEc0tPl5+bZZ58Nk4mk29TX1yc8bvbs2WAYBkOGDIHT6cTYsSTtbvDgwSgvL0dFRQW2bNmCCRMmAABCoRDmzJkT176ffvoJa9aswejRZI7m9/sxatQoDBo0CH379sXQoaRqzJAhQ3DTTTfhtttuw7nnnit9bzIQCXjx3b+uBABklwxD8Uln4qd/z8fhrSux42uiDo6Ggwi665DVZwi2LF4Ad2U5CkdNBaxN34ONh8uwZtH9iAS8GH35P5A3YEyTxxeOmoZDG35Cbdl2nPK3/7abFGjwRzH83hUAgMn9MjB3ciEmPrQKX/52FA9+QbxIfCEWVe4QTipJxz8+3IWdh724YEw3pNq0Z79XTOwukQSA2lOgT44do4vTAAAXj8vH579WdQopEAlEULW9Ckv+uQQAMbY1CGR23f46bHp9E8L+MCK+CBgzUbJl98vG8seWo+ekniiaUKT5vSMuH4FeU3qhYmMFti/ejsObDmPybZNRs6cG0x+YDgDofWpvHNnctO8Dz/NYv2g9qrZXgaZpuF1usEIgIGdQDmyZxGesYlMFDq09hN/e/Y38XcEIAvUB5AzMwcZXNqLxYCN6TuoJk73zq5F1NZKm2m5sJHmbGRkZmvs3btyIzZs34/nnn2/2u5YtW4acnBykp6dj6tSpePDBB5GZmal5bCgUQigkS5bdbpKnEolEEIlEwPMc6gODkV8zAp6sXwEAS9Pj3fIZxtqqiIp4bGdFYY5FWEED4GFm/PhuexXqPAE4LQYEwvEM24jClLhz5Qu58fLOtwEADKikn9NXX30VUIhMXv7f/2A1qiPyLMtp/t7OnTvjcuxYlsWuXbuQm5ublPYpkUgpwfO81L6S3mfAtO5+icgCgJXBSoChUHXwSByBMXXqVFWkMHl91QgDzyNKUbDQPnzyqyzBtVotkAs+AuYUA7igejFw5Z9uPe7vm664/1999VVce+21EvGzYMEC/OlPf+qU37YY4k07PYE6QNFvaxsPorvG+QgKZf+MaP58JfO8ZlAGVIKFzVCL6kY/UkwUlOtSkZAEADfVudfyYOkBwCGb39m9LhQVnSFd2+ef+xf+/PIIHODIArtXeg9V+3Lz8nDWuQ8CkNudm5srjU3KYzurr9oFUsATDSASiSA3NxcLFizAkqMPkwNYSAoIhmHwwgsvIDc3t8PbZTSlSa9ttBcBNv73GJqKa0cgSlLbTLQpbt/v/fn/e/27E0H0FFDi3nvvxZw5czBv3jxs27YNV111lbTPZrO16DhRJUvTtEoxS9M0WJYFwzCYM2eOSlmrBY7j8Je//AX33Xefant5ebmqLX379sXmzZvx5Zdf4pZbbsFll12GG264oaWnoUmIngJK8DyHSdc/BnumOod8wGlXIG/QBBzZuhI/PvoXTLttYdz3pXTriQbXXqQXliA1vxiz7nkda197EFyk+QhvTr+R2Pjuf5Ca3wtGS/srTomeAkpwHI8v/jYaRVnqZ+c/5vTB6UNy8OVvVRj/4Cqsumtiq39PyV9SVKdVeQXP8bCmWXH2C2fH7fvlv79gxj9nIK1HGnZ8vgPeKsK4T//ndFRuqcTB1Qex45MdOPPZMzW/O7UwFamFqeh1ai98eOWHADSIWgEUQ6liaOLCf//S/YgGozj7+bNBMzTeufAdcEIFNIOisg/P8Zj+wHQ4YqolDb1oKArGFODQ2kP48uYvMefJObBn/b4q0yWFFOA4DjfddBNOOukkDB6s7d6+aNEiDBgwABMnNn0DnHbaaTjvvPNQXFyMffv24c4778Tpp5+O1atXS7W6lXjkkUfwwAMPxG3//vvvYbPZEKqhADCorZkNk0AKaGHJktWg6dafjh9+0C5793uAmaMA8DDSZOL60VffI9cKNIaB2K61etmPiAkYY3HNI9hoIAuGRrDSuUzGOa2pqcE111yDCx4oxI4eKei/sQbXPn8trnxhCKDwnlyx4hcYTXvjPrt+/XpQFAVekfdA0zQOHDiAr7/+ut3ti0Ui47CjR6tVv3cdMw1PcUvktgr5+lwonsB4++23MWTIEGlbMvuqhefhpShY6BgTOeEip7Ac3AyNogE9kWHMwF4cwsQgj4n0fPz73/9Gt27dkJXVOSUUOxKddf+L/VnsjxzH4dprrwXDMJ1yHo80HI3btnPPVtC0zKRv2PwLXOXx3iyHGkh+/FGKb/G9k4zzao9SgAkwGxrwzZLl6G4HlONSzdEKiH6HHEV1yH2thZqaGnzwxvvAdT0RFMzvKg4dVJF6S/Y8j53DMwDB5d3j9rS7fR3dV+vqGgAaqAt6pbbm5uYig88EEMbwYSNw+sI/4MiRI9L93xnnnOd5BYnpRqT+MCI1B2AfOEU6JsLG981adx1gArz13oTt/L0+/48Fd/1jHW63W/JeeO2119p9nBYmTJiAG2+8EQcOHEBRURHcbjdqa2tRXFwMhmEk4mDatGm44IILcMMNNyAzMxNHjx7VNDk8fPgwMjIycOWVV8JisXR4/84bOBZ7lnyIERfcCIBUGEgv7AtvtQvphSVILyxB9d4t8NUehsFiU1UT6DfjEqz5333I7jMEjmyi/GHDIc3fiQXNGDDs3Gthy0h+gEfEzMHZeObHcvxHiPZvPtiI4T1Sse+oD8N6pGBYjxT8UlqPsmo/nBYDPMGWm06WVvmwqbwRI3um4v21hzFjUHZH/RkqmOwmWNOtOLjmIHqM7wGO5dB4qBHpPdMRDUZhTbeCjbAoW1aG7AHZ4Dkevmof8kfkI3dQLj644gNwLAejzYiIXyYWXetd6D66OyiKQl1ZHew5ZCGeWZKJg6sPomhiEfYv2y8d78hxoHJLJQAS9Y8GSGpF2B+GNc0KmqFxaO0hhNza/aH7qO7Y8dkOjJ1HlDe1+2qR2TsT7sNuZPTKQEavDFRtr4K30quTAm3B9ddfj23btuGXX37R3B8IBPDOO+/gnnvuafa7Lr74Yun1kCFDMHToUPTu3RvLli3DtGnxud933HEHbrnlFum92+1GYWEhZs6ciZSUFJzO8yhYcxDDu6dg7ooHwWswTwaexxlntM5QIhKJ4IcffsCMGTNgNHaG+P3Yw+dvPgggCANDBup+w8dhYu9MuOoDwMYV0nHT+2fjjDkjVJ+NRAK4+8O7pfd9KDNmzJiRtHO6bNky8DyPjx44iEnTU/HNL25wHI8UxgJALoc28aSxyM6WiaxXX31VtfgSIUa2rrhCu8Rle/HDu48CvDdue3Z2JmafPlt6X3bAhqdWLok7jo15oDAMg8suu0xSCiS7rz795l3wAjDHkAJmsxFAFJmg4QZgz3KgILU7UHMIBt6MP19zU5dEupONzr7/xf6sBMdxKCoqwimnnNLhv799TxjPb1it2padnwmbKQU4SHL9UnPNmD1tdtxnz337foACfDSF2bPj9yuRzPO67oNngOhRGBg3+g8djWGFqcC6nwEA/71gCHxV2UD1bun45tqWLCxbtgxeTxQmyOZ3PBuNOUp9rVNT09rcvs7qq8vW7gD2bUOI4WGz2aQ0hp/fewLgwiju2QsXTu+Y8bM5PPbW3fBQAH1wBep/+A4UOBUpAMRf/0ffJvOV7nkFmD1Lve/3/vwXFZk6EuO2227DlVdeiXvuuQennXZau4/TQnZ2Nl5++WWcf/75CIfDoGkaTz31FIqLi3HllVdiyJAhmDx5Ml588UXcddddmDZtGjiOg9lsxmuvvQa7Xb3g2bp1K2699VYwDAOr1drhFUIGzfkTNr3/FL795+XgORbphX0xfu792P3j+zi6exMomkZGzwHI7DUYkYAPO799E989eCWGnD0f+UMmYug512Dli3eBYyMwWh1I79EPmb3lQMg3910MUc3Wd+qFMDvTpH0FI6d06N92z1l98Le3d2DoPcsR5XiM7JGCt+aPwH+/K8PSXbVgaApjilMxoU86Gv0RPPrVPoy4bwUeOq9po0EAGNzdiX9/vQ9bXG6MKU7DpeO102Dbi7A3jPf/KFfpGTN3DE65/RSsenYVNr2+CRzLod/sfkjvmY4RfxyBz//6OaxpVmT0JopxnuOx/LHliPgj4MFj+KXDQTM0ik8pxsqnV2Lrh1sx8+GZKP2hFGtfXAuD2QDGxGDS/xHVxbhrxuHnR37Gr2/+ivzh8t9YdHIR9v64F4uvWYz84fkwpxAVTe9Te+PH+37E4msWI3dQrkQuxGLYpcOwdsFafHrNp+A4Dpm9M3HK7adg++LtqPytEhRNIatvFrIHdA7Zciyh3aTADTfcgC+//BLLly9PmMf40Ucfwe/3t2lB1atXL2RlZWHv3r2apIDZbNY0IjQajdLD+i+TSd6ibTng01CjmHi0+cGu/J3fExYtWgT30Vog3w6GJukYNb4oqn1RnPqkTAjkOM2464xBcedoW+kn0usRwRDmDb1KOiYZ53TAgAFEqspy+Pk7ktrCMAwyLCkAK5MCebmDQTPkt1wulyYhQNM0Vq9ejTFjms5Taw8MFBO7DgAA7HaXqc5FYf4ozc+zQVkpILqOx1b/SGZfrRYUCgVpP6K0kuQpTh+QKym60mkTyhBGAxsAz5OttdU1qmjoddddh9mzZx/X/gKddf9L/ZlTX+f+/ft3yu8XdhsZty0Y9cNilL1CHqpaios12sJEQ4BQkrSlbU3Gec00pwLRo6AMPnjCLCIc6bMOswHnjuqB179WE2mdNY4PGDAAAT8PEwA/TdRW4NSkAB+TcsPQdLvb19F9NcVGFCv1oQBmzpwpeZuwJtJnjUzXPSsdPAUPgMfmTQH/5/kSYXH16xvw405ilKtsWzDkgVfwmTgUOJqw3b/X5//v8W9uCjU1NXHbJkyYgD179kjvH36YpNHcf//9rT7O4XCgvLxceq8seThjxgzMmDEj7vcfe+wxVVnCyy67DJddFm/Mt2HDBun1rFmzOqTaAACc++Q3cdsMZivGXnFH3PZRl/xf3DazPQUz73xFta1g5JSEi/szH/5Ec7sWZt71SvMHNYGaZ2fGbbObDfjfn4fGbX/u8ng1dYbDhPX3NW3Bq/wNk4HG+9fFP5OTjau+vkpz+6yH4/tI/zP6o/8Z/eO2z3ky3uMid1Auzlt4nvT+1DtP1fydlPwUKd3AU+lB5TaiDjBajDjtUZlAG3fNOACAJdWCM546I+57ug3rpipzaLSQqgmxmHB98jw0jle0uYYFz/O44YYbsHjxYixZsiRh+UGALCDPOussZGe3nnVxuVyora1Ft27tr1tpTeAl1XnF3E4MiCZ8VIhMqmma5F7uOVSF/60oUx279s5pKNaQ39TWy8e9MX8PxkyIfwi0B6JLt5hyIi6UM+yy58UZhmyJEACAVatWxRECAFnA+ny+uO3JhIHSvhVrourftdq0PTuiClLg3Xff7TCTwVhsSG+QXttMDHiB2chkyGJxGedGVFzwxBhQiu7jOppHov7cWYRKRlr8+O6P+KTqIU3hvDxihDc0zMHlciW9bYmQIpSZ5egwyg7XwB8m45XVRM4hGxed7xwUFBTg7rvIxD9KUTBTao8bmqYxZOgQ9ba2P6o7DWFB9hAQys+K3iZh4Twb2pCelyzYhfHVbOUxZcoU6b7JSdGubOT1HJZeT80Z3fEN1KFDhw4dOroYbZ5pXH/99XjrrbfwzjvvwOl0orKyEpWVlQgE1BOcvXv3Yvny5bj66qs1v6d///5YvHgxAMDr9eLvf/871qxZg/Lycvz00084++yz0adPn6Swl/YEf67OebcOUqkpgRTgabIw2Hu4BilW9cQvkVFIQ4DUDj+F6rh8nblz56K8vBxLly5FeXk55s6dixSTnPMcZIPS60WLFqlSV5ToDJdsA6VNTaVoGLxpIRIgpADDMEl1DG4NrEYGnECqZBhlA5dn6klN4KhR/Td2lvv4iQKt/txZoOj4sfOFxq144LA655SNxpMEBw8RI0q2youioqIOl6SKqHKRMYalo3js6efxzsefASDkFQBEeTmncTrbuU+BP/9JNu+y042gTOQ+p2kaa9asQV53da6r2+3uVEKlLaivJc/+WpMBk2eQcZZlWYSj5DwzXUgKOITx1ResV20/exiRpPbIkMdZl8uFVWuWSe8nnnR7xzdQhw4dOlqAnlk2bGhGVXAiwpnnxFnPti7NW0fr0WZSYMGCBWhsbMSUKVPQrVs36d/777+vOu6VV15BQUEBZs6Ml9cAwO7du6XKBQzDYMuWLTjrrLPQt29fzJ07F6NGjcKKFSs0UwRaC1uChZehc6tRHfeQ6mgLC1GOJfmFYYMNFsXC78u/Jh64GoXJWSrTskVvW1FQUKCKDDktadK+SweTfHZR+aClEqBpulMisolIgb9PfyZu2zilU6KAkJ/r1OjxSbQTgNog0WpiwAlKgYEZ8TKy3QWOLot0nygQ+zMALF26tFMXitl885oqv79a9d7lcuG330jZHx5y9Lij2+1yubBmBTE4ZOkoUsaeh7cqiMrGZiKL06gQwe4RieChc1suNU0GDAYTLEIpBCvjAW2xg2EYLFy4EGPGjEEwqjZI2rZ1W6cSKm1Bn16yVLbush4AyH0OhkwzGLrr6Hc7RQwxvcFG1fZxvTKx+LqJ+PT6kwAQcrioqAh/v+0mAEAqywGG319ZKh06dOjQ8ftDu9IHtP4py6kAJD/q4MGDpF5xgu8RP2O1WvHdd9/h6NGjCIfDKC8vx8KFC5NWAs5Oa09qmU6un328Q5Qyi473LOsBALjDFFhhonv+yAIM7p6a8DvcIUIkpBg7lhSIRYpFlt9bBYJAUj7E4L777sOBAwc6JSJrSNA3u+cNj9s2NnQB+q2uRlZYlj/Pu/r6To0eXzuCuAVnRNWkgPguxZaLkzWir10V6T6RIC5cpk6d2qkLxS8vWYGXRt+B27pp5/8BgNdbqXpfWloqW2UILzojbaS0tBRBP1EyRWi1d4CoFAhyRNUwKaUPbOk9O7Q9WhBHPivtRfc+g1X3RDgmLYMy0p1GqLQVffqozWQdDgNeeukl8MLjtSvTB5yMQAqE4w3yRvRIR4bdJJHDHMfBbCHzFQPXuSkvOnTo0KFDR1fh2E9UTCLEaEEsum6qcvxi7ty5OOu0cwAAKVlk8V/pDiLCksW12dh01/IKufIOhcy8MyCaYQEAI0wUReWDEgzD4Oqrr+60SLYxgVJAhMvlwtKlS/Hll1/i2mvvwMcvVSG7Vk5/KMzv1alRd6ed+IP4FafNpkgfoGgaPexqH5C/WIrilBs6WgflwgXovMg7ANjMTkwcdClKsrTLzgKAN0YpUFJSgli7jM5IGykpKZFSaiK0WgEkkgLVIaJWivrCXbLwswmrZTPtRXaaA2HvRvgaSTu8MV4isIs+CMeuD4fJ7FS9/2npO5g7dy4aeEJedqlSwEA8TnxhT8JjlOSw2Uw6rZHlj9nzrUOHDh06dCQTvytSwMYkIgV0pUBbkJ2eBwAI02QiVe0JIRQlrw100+e0IkxknJ1NCjhtOdJrkRToahM3ADh18OUAgBTQ+OjU5wEAc9NJ5E0ZGT7zzDOliStdL0cTa2viyxl2JBx2ot7x0RQg1BywKowGaYpBuE4tgWbqizq1jScitFQtnb1QtFvSE+7zBWpV7wsKCjBkCDHNo/jOu7cKCgrwh3MvAQCEKPX5EkmBOk8DAGDTis1dIs3PoklKXN/MBtw8YhfOXns3xn96OnZsew8fh9WKC15YpB5PPhz3b/knNm9/H6WCUqNLPQUEUsAbSTxOKslhUzppK83juDnfOnTo0KFDR3vwOyMF5FzsdMU80ZDADE9H07Cb0wAAAUTB0BQ4HqhqJNFrQ4J0ERErORKxSbEkTjHoCDgdedJrg4Ik6koTNwCYOOgSvD7lGXxxwY/o12Mytl65FTed9UZcZFiJ4FHZKK24eFBnNlciBTiKgo0i15J4ChB4PV5sXL5Z9ZlFL7+hS3HbiUSqls5cuNiaIAUO1+2J2yYSAN27d+/Ue2vGdFLKKBgzFNlMBrhcLtQ3NgAA2CjfJdL8LKFCR3HWYdxa/qy0/aKND8HKqdUNnIE+5n04Ys/dIQZ4YeN/pfeGBKR8Z8AukM+eqL/J42655RYwDIPU2UQJddhqPGbPtw4dOnTo0JFM/G5IAZfLBdYvL6L60XIuu64UaBvEiKGPZ5HjJFEvVwNxoDYwic9pINAgve7XbWzHNVADaSmF0utQWB016mpp+8iiU5FhI5NRMV1g1apVmoQAAHiqFaRAj3hjv46ERbEwtNJE6mwzMWAFpUB9XT0O71ZPwD3usC7FbSeOBVWLzapdFhMAbi//BD5vVYLP2Tq3nYJ/SCwpUNkYJIoLYTsnlMrsdMWFgYyZLwfL4/Zl8erxs09a/jHtwyGqmWKxmpfTILq0JKGRVLnxsyHN/WL7n3jiCXAch0PC86xnOKJ5vA4dOnTo0HGi4XdBCogP/F1bdkrb+jjkyalOCrQNdmFx4AOP3BSiwqioJ6QA00T6gFgDmuZ5DBhwQQe3Ug27sxv6RIEMlkPPgpM69bdbCmW6wMUXX5ywrGPpFnKu0zkeNnuO5jEdBZoxSNFMCyMoBYwMogIpkJ2dh19/CyFHYYbo9+tS3GSgq1UtNmtmk/tLy39SvefRNeVdxHYGaBo05H5oYCiUlJSAF8YoTvAh7GzFhUNQCsTCxPMICefs1qzxOMuQhX+c+/oxG7FuSs2khJGJr5rSWbAJSoG6iA+NtWo1S2z7R82Q1WvX9b2o8xqpQ4cOHTp0dCFOeI895QM/GpAnLQXmQsBLJgcRvunJjA5tOKzEtM9H8SgRSQFRKdAEKRASzJ7MvHb9847Gh5evRTjshtWe1fzBnYzYCSrP86AoCgzDgGVlF3WGYXDffc9jxil9QDNGMF1QNssKIADAQovRQApBYTGTlZGDhQsX4rPAvwGhBNwD9z9xzC5sjjcUFBR02bm0WZu+byqPHlJvEM0nO5l7tSlMRa2UDz6eLPZeunwUbCYDrA47AB5ctHPLeUptSiCnN/I8IsLJOqn/Bbiyt3Y532MFSp8LK8chkGBMHznkj53ZLBVsJkIKrKGCOPnL87H8jE+QnlkCIN6nI2dOFg4Kr+3mzk1v06FDhw4dOroKJzwpoHzgR/zyoorzy3+6B2zc53Q0D5vgQO+jaQxmP8G3mCjta0opEI6QRWRXZZgaTDYYTJ1bCrGl0DKS43kezz33HPr37w+73Q6fz4c+ffp0+QLbxlOoA2CiSJpAzwzgkOD0bjSYMXfuH7Ht1ZdRBnK95869vquaqiOJeOOt95p8chyJIQVEpQDVyYosizkVNM8T3wvaDR+bih9ungybQFIxJiOAMC44/0K8/NTdnX4/bWjcB2gUHfHRNCCocEydXLK1LRB9LjiOg4HXVoU8WDAHjLELlQIxi/sNOz/AjJPvAqBuP0CIDRFcbOmMDoTL5UJpaSlKSkq6fGzXoUOHDh2/P5zw6QNKY66wT178d+9WLL32UF0jbz3eYRfy3wHgJepz1b5ESoEtez7HA7/cCYAoBY4FiPn7x4IJnpaRHABcf/312LdvH8aMGXPMlPSzChPmM4Y48d+LhsFz9BtpH0WRhdeNJ9+LkgiLq4zdukQVoiO5EJUsTcEu5GOL6CpSgKJp2IQxxiqkuNjNMpsRFRawPYs6t5yniKaeO2HhVJmEXPhjGUqfi8wVNZrHpNq6VpVlM6eo3rsVVTJifToQka9Ln6zOMXBVpox1RSUMHTp06NCh44SfpSsf+EEFKVDYva/02q0vVtoES8xESwkDo31Ob1p5NzZxJHJsPga8HI61yZjYX2OJga5wR28ONopMovvn0jh3RIFq4t3oIyXViktm45Ord+D/Lv2+S9qoI7nQUrIAAKOIEHehyXwcRFLALKS4KA1QWaFWRle54s8ddJX02skDoymZABDTB4ymzi3Z2laIPhc3X/4unut7S9z+VKFaSVfBJlTKEdEYrFe9V/p02LLIsX+hs1HQe0aHty02ZexYHOt16NChQ8eJj9/Falh84F8372Zpm9OWgylWEh26NrNzHfBPFMQa4Fkp2c0/kVKgmpYXD6YuJgWO1cnY3Llz8e6778Zt72x39OZgE9QAfsEjIsKGpX0j+53bJW3S0bHQUrJkfVSBcyP50ntvxKPan0BR3imwCY84kRQwKtoummIaaGPnNwzAnNE3Sq978QY8MefNuGOOB6WAiIKCAkydOg2nTPhT3L6mKlZ0BmJ/vyHUEHeMWH0mKJBFI/qe1RlN0yTajrWxXocOHTp0nPj4XZACAHngDxk4WHrvcHbDMxd8jdemvoA/n/Z8F7bsxIGdbmj2mAGc3OVqqK71cjiWJ2MTJ05sth59V6c9WIXFVEAo7RiJEG+B7lEOjrT48mQ6jn+IShYl/njO45g35xHpvTcUQwqI6QOd7TQIOcXFyJC+aTXJSfyswFZ0lVKApuW2hMDB6egWd4y9k6uKdBRWLNvVpb8vlqcU4Y0GVO9f//5vuPbtyQgEG+HjyXPJrii72pHQIto6uxKGDh06dOjQ8bshBQDAliovVNau2YKKigqMKpwEi6HrDJCOd5yfPkR6/ffp8gS2R4a2QZYyr/g0c/wkuDNxLE/GmqtHfyykPdhokjvuF4wjQ1Gy8ErpwnrkOjoesSUQ586di275ozDX0Q8A4Il4Vft5vms8BQBZzXJSLwuevHAYLEZ5Ib6XIe1ijoF8hyDPwWR2wBQjq6CZ4+9eiiUp2UdKcc21t3epAstqU5fRDHFh1fsnjizBL9F6vPXjLfAJXg92a+eQAs2N9Tp06NChQ0dn4HdFChTnj8G0cG+YvqzC9Okzjokc8uMdd815HUZhIjskj8Mbfx6Le88YiBkDtXNIw0LU8P+cA3H7OR90Wju1cKxPxhLVoz9W0h5swmIqIJAB4QiJvhl/X8OKDgFOkxMA4BH6gwyRFOh82AU1S+9sGueNlO9rXqEQqnYfivtcZ0McFx0KTuCpkiu6qDXtQ2lpqer9zt2hLldg2axqUiDIRTSP21izDWLvtVkyNY/pCCQa63Xo0KFDh47OwvEXhmgHXC4Xnr3mi7jF1KxZs46ZheDxBiNjRAFPo4zi4QvUYfLIbEzum53w+AhPzv3Q3nNgtHVtnilAJmOzZs3C3r17j4kyf7HQqkffVNpDp9ZZN1iAEOAXpLjhaBAAYKI06qzpOKFg4HlEY9IBnELZNw8bVG0X17kUqE4vu2ajTQAL+GPUC9Go3EYuEktidD7E1jiFMp8AYDR0vYKhLSgpKYHhgLp/dLUCyxhTgjbERaXXL//veUCwldgZdSNgJNMiuz3xc6wjoDXW69ChQ4cOHZ2F31VIb9WqVcdsDvnxDIcg0fXGODqL2Lv/R1z/5kRs3/mJFBEzGa2d1r7mIBpMHS8TsmMl7cHGkGsYEBZYdY2kHJnhGCk1qaPjYFFcY9HXwik4vHvZkPpgQUlUX1/f6SkvoppFTHERoSQFJo++rsPb0RxCgmQ9hZJ5eq/Hl+jwYxoFBQW4LEBSSfqvqz7mFFgAEBJ8A1wuFx5//FZpe51RPv/Kkrs6dOjQoUPHiY7fDSmwaNEiXHzxxXHbuzqCcSLATpEwi1fD0RkArvn5ZiznPLhqzb1oFIJHRqO254CO5nGspD3YBGd0PxvEokWL8P5H7wEAGqrr9LScExxmxWtxkb97RxkAwMNHVceK/EHF4YpOT3mxGQhxFU8KyEZzRiHtoSsREqLqNkWq+6MPP3bc3ke3Xvsx3hv1LK47/51jUg4fFPpoaWkptGwbDDz//+ydd3hU1daH33NmJpPeeyGEFkIv0kSKiKAIit1rV+x47XrFz17Acu1eC4oVCzYUERBEem+hBkiAQHrvZer5/jgzk4QUkpBkUvb7PDxMzpyyzp5T9l57rd/CxRb5IhAIBAJBV6BLOAXsOdjKaSJO7XEGoyPiaZuNKzMU1/l9lu0qq5Qlym2lCvW6jlF/u73SHnJQ3WyOnRJTJXfddRf2ym6yRWkXpR0Frcc4F/WZ6WdQB1dWq5Ufvl0MQIlSs6qIvfoAp0WQtEWUlpvNKVBYUVKjUoe5WjSDVuN8oVmzpKZWlKblOZZptHTo+6j/gImcP2lSu3y/2isM9O7dG3eP2l4Bd0VBkrtE90ggEAgEAqCLOAXqysEG+P7779vdDEZHxMPWqS411u0U8LLWjiePihjdqjZ1BZyd9uBum2EtM1ditVqRdbbHidkq0nI6OecFzybsnyyOvHTcsayoQBVvK5Fq3u/K6d4AG20RpeWhVR1XCceP1EhbOJa+zbGOrNG1qg0NMdlVrcAy3T2axMREqss0uAdqxX3USpTbtG0iIyO55fbaEYQeijNkMQUCgUAgcB5dQmjQnoNd3TGg0WgYM2aME63qPHhq3cAIZaa6c2Aj0HKYmrOHGp2+znUFHQeTbbK1UrIiyzKyTu1ISxZFpOV0cvrHDWX1wrwaz9SyEgUfoFRS1f3tM632CK2oiEg0mgwsFkvbRWmZbTa4qP/b0xbiFsQ5VnHmjPArM3/hosM/Mq7vteTnFKJsqrLln8X54j5qJcqxsmbNGnr37k3/Qb0hcWuN7z26xnyJQCAQCAQOusSbr73kYHdWPGy55aX1qHiLOZfOx4IFC3j3rf8BUGSo4KabbkLjpt5fkkER91cnp65n6jPPvA6AVZIoL8+utU1AQGCbp7wYy9T0Bouu6lVnsVjqW73Ncdd7MXXwLNz1nkRGRuIXWFUGz2SUxX3USpRJkiNyZOferbW+d5G6RNdIIBAIBAIHXebN1x5ysDsrnjZ9gLLTSpHZsZwWPjzS0iUCVDotdo2OylJ1cGXUyCxcuJDw2GgAxg07T9xfXYDTn6l3zLofrS0qoKQ0s9qa6jKJtk95CQ4IB8Ciq1ker73yrxF3ANDbrBHvqRYmVqlKEzHKElqdGjmybfemWusektuP40ggEAgEgragS43ORB3g1sHDllteqxSZDctpAo//N+G1VrdJ0HrYNTrKy9WOs0EjY7FYKLBWgAYi/KOdbKGgrTj9meqlQIGkOgVCbctOF3htS0IDIyAbjFrV/22PEnuHd5xmU0OMG3YPP/j1JDpsOJ5uAWfeQNBoPrtiCd8ve5UPDesAiInWk5hkQOOhOol8rFaKhLigwMbKlStJSUlxthkdhk2bVOda9uHtmIprR4p1ZHKT9gOw8mA+p4o6Ts3lTYn5AGTuycSYZzzD2oKWouhUkbNNaDZdyikgaB08baWbyqx1P3TskQJT8STOuzsxMZPbzDZBy2PX6CgvteAOGDQSGo2GIlkN1Q7wCneugQKn4aVIFACl5bmOZY4ulNT2iUQebv6qDa4urFmzhl69ehEZGck7X73T5rY0BkmS6N9jirPN6JT4ekcy47wn+XC16hQYck8EiY8dR+OpdoNicWU76jtsQeztTrNT4FyMRvUaeOWVV5xsScdDlmWOrvjK2Wa0CrIs8/Kyk8BJZ5vSJGRZ5uAPB51tRpfE/izpSAingOCs8XT1BaD0tPrkduxLbxr+IIMH1FZ6FnQs7Pnkzz53H+5AhSTx8ccf8hPvA+DlHuxcAwVOw0PSABaKqmkKKI70gbZ3CrjZnk3lwMSJE9v8+IL2RfWoFsVgRaPR4BcVBBiZEDwU36x4gl28GDn6YecZKXAqLi5qieX58+czfPhwJ1vTcVi2bBnPPPMMCxcuJC4u7swbdCA66rl1VLs7Ort27eKuu+5yPEs6EsIpIDhrPGxhrmXV6pOnpqaSmJhI7969MaMAElonlv4StCyzZs1iwoRzuHzTjSiSxPXXX8E3i94DJFz1Xs42T+AkEmy52D8e/ZGJox8BqjsF2h53VzVSoFyonQps3Ondn0+LDxIdEs6WLT/y6d57AfBw8eHN23c62TpBeyE2NpZhw4Y524wOQ0JCAgBxcXGdrt066rl1VLs7OqWlpc42odmIBDrBWWPPfS1FLU+2YMECoqOjHerOZltOsUYWToHORI+Y/o7PFeV52BUlXF28nWOQoN1gqOYgtOcPOCNSwN32bKqQJawWU5sfX9D+iPBSowUKzGWMHj2a/EI17zYp6bgzzRIIBAKBwKkIp4DgrPFwDwLUMk+pKSncddddjvrlVqvVFikAGk3HC6UR1I+s0eJmVX/b8op8DLYxn15ECnRZHg8cA0CAxr3aUucJM7m7Bzo+V1TTOdDYHJWf9b2jzW0SOBd/D1UCM8dUhtVqxSqrD64N6zaQmprqTNMEAoFAIHAawikgOGs8PdQc8kpZ4uiR/Q6HgB2LbbAonAKdDzfb/9n5iZhtQnKuNuFJQdfDz6Ynsc6UR3rWPqDKJSA5QWjQVe+DrNgdV3mkpqayZs0a7E+onkLUr8vh7x0FQJmLWnVAsTkFzCYrSUlJTrNLIBAIBAJnIpwCgrPGw6NKWC483Af5tLJOFlvYsE6rb1O7BK2Pu6L+ts/t+q9jmXAKdF0CPMMAKJclpq64gW//ftRRktAZaf2SLONu80r8svhbR1qTYnNQSJLGCVYJnIm/j1oytVhncwrYLkxFkejVq5ezzBIIBAKBwKkIp4DgrNFpXXG1hZF7eUnMnz8fjUbtcGk0Giy2DrhGI5wCnQ13SX2EnNRUhYi76IWmQFfFPgtr59W0leSaitU/nBApADicAp9++n6tKKbs7BwnWCRwJgF+PQGolGV8vLWO9IGLLry4RnUCgUAgEAi6EsIpIGgRPGz/l5bnMmvWLJKTk1mzZg3JyckifaAT41bHTKusEUVNuiKpqakcS8qrtTzDWAQ4R2gQqhxXrh7q/9UDmU6mpDjDJIETcXMPdGih/LXqa/RuahLU8GEjnGmWQCAQCARORTgFBC2Cp+1SKqtQBwWRkZFMnDiRiPBwR665cAp0Ptylmg6Aq1zCnWSJwJnYK45cdtnttb5LVdS6FFonhep7oB436vIgPN2lGgEL3bvHOMUmgfOQJIkKW3TACwfmYtWon7WycGYKBAKBoOsinAKCFsHe8S6tLKix3Go1Oz5rRfpAp8PttDKT46IvcJIlAmeRmprqqDhisdT+Pts2AHNx0v3vabtGD/q5c9lDEYw+vyq9JSJMhIt3ZVI0YLFpXmiF01ogEAgEXRjhGhe0CJ6yFjBTVllYY7nFYnB8FpECnQ93WQ/V0rRDbPm6gq5DYmJirVz9utA76f73lF3AWgHA3j6+0MfX8V1GZhZe3hFOsUvQPrCIkrkCgUAgEHTtSAF7eSpRm/js8ZDVDlWpoajGcrO50vFZI6oPdDrcT+tI94+70kmWCJxF7969a1QciduaXed6LlrXtjKpBh4NRCiMGDGSBQsWtKE1gvbArR5VVQbSZbtTQFff6gKBQCAQdHq6rFPAngM7adIkoqOjRcfwLPHUqB3+MmNxjeXVnQJajXMGBYLWw63aQG+qxs+JlgicRWRkZI2KI79+mscDlZfVWs9Z6QPuDTx3zGYrd999t3AMdzFuHPeC47PRJjKhE+ltAoFAIOjCdEmnQPUcWACrVXQMzxYPrargXGoqA6AoYy/5aTuxWIyOdbROmikUtB7uGjfHZ70Q6uqynF5x5M67X2bjjMU11tE7ySno3sBzR1HAYrGQlJTUhhYJnI2fb22BSYOpwgmWCAQCgUDQPuiSvfi6cmDtHUNRp7h5eGrdASgoL2T16pW8lvww2RqJLwY/4lhHlKrrfLjrPByfXWQRftuViYyMrPH89PHvRR+LzFGN+qzVOckp6G57NtVFZaWCRqOhV69e9a4j6Hy46L1qLdMLp7VAIBAIujBdMlLg9BxYQHQMzxIPF08AEo4f4drLLyZDK2ORJH4+tNDJlglaEzdd1YBLLwuhLkFNNNXK/+l1bvWv2Iq4VXNcVSfwq5PIsoZPPvlEOIMF9IwRlVMEAoFA0HXpkk6B03NgNRrRMTxbZLPaloWhboS938+xPNVQCMA5iFmYzoi7S9WMm4gUEJzOjZGTHZ8H95ruFBvc63EKPPTQyyQnJzNr1qw2tkjQHnin9401/pZF+pNAIBAIujBd9i04a9Yspk6dSlJSEr169RIOgbPEWK4WKE/R1xwYZioGQMZXFiJOnRF3l6qa7yfLM51oiaA9cumFbzI+ZRtajyA8/Xs4xQZ3WxTT6Vw46VrcPUPa2BpBe6Fn+GhIrIpkk4VTUyAQCARdmC7rFIDaObCC5hMW2A0ydtRanmOLH3YRszCdkrDAvmDTaMuyCKEuQW18o0Y59fjuep8af/e1yHx4yTfCIdDFCQroU+NvESkgEAgEgq5Ml0wfELQ84cHRdS432co9CWX6zkn3yHMdn6+ImOBESwSCuvH2qDn495Z1BIUMcpI1gvaCh1cYsqI4/hZCuAKBQCDoygingKBF8HAPaPB7kW/eOXH3CKr6rPduYE2BwDmEBfar8bdBsdazpqCr0c0qnXklgUAgEAi6AMIpIGgRPN0CG/zeRSjTd1rmxVzJpS5hTB3zpLNNEQhq4e9fs6rMUQxOskTQ3vCVhLNaIBAIBAIQTgFBC+HhGdbg9yJSoPMyffzzvPKvlehc6q8HLxA4C73ei58mfuD4u0IWrz2BipdGOKsFAoFAIADhFBC0EP6+3Rv83kV0vgQCgZPoGz2B6fpwAB4MGuNkawTtBW+Nm7NNEAgEAoGgXSCUdQQtgiRJXKwLYrkpB4BgRUO2ZHF8L5wCAoHAmcy77i8ezk8kyK/XmVcWdAm8tG5gdrYVAoFAIBA4n2ZHCsybN48RI0bg5eVFcHAwM2fO5MiRI47vk5OTkSSpzn8//fRTvftVFIVnn32WsLAw3NzcmDx5MomJic01U9CGBOt9HZ8v8axZk1yxWhAIBAJnEuzfG0kS4nICFS+dh7NNEAgEAoGgXdBsp8C6deuYPXs2W7duZdWqVZhMJqZMmUJZWRkAUVFRZGRk1Pj3wgsv4OnpycUXX1zvfl9//XXee+89Pv74Y7Zt24aHhwdTp06lsrKyuaYK2oggt2DHZ41U89JKLE1ta3MEAoFAIKiXEPdQZ5sgELRbbrvtNkaMGNFmx0tJSWHixIn069ePQYMGOSYQX375Zbp160ZgYE1Ba6vVyuOPP06fPn2Ii4vjxx9/rLXP5ORkJk6cyMCBA7n44ospKioCID8/nxkzZjBo0CDGjRvHqVOnztr+t99+m/79+9OvXz8eeOABFEVh4sSJ9O3blyFDhjBkyBAqKioAeP7554mMjHQs37BhQ639vfHGG47vY2Nj8fX1dXy3e/duRo4cSf/+/TnvvPPO2vaORFPaOTc3l/PPP5/evXtzxRVX1DmWfPjhhx3bxcTEMGTIEEC9dsaOHYurqysffPBBre06I81OH1ixYkWNv7/88kuCg4PZtWsX48ePR6PREBpa84W7ePFirrnmGjw9Pevcp6IovPPOOzz99NNcdtllAHz99deEhITw22+/cd1119XaxmAwYDBUqUkXFxcDYDKZMJlMzT29M2Lfd2seo6Ph715VD1w6zSlw7eB7z9hWok1bB9GuLY9o0zOTmppKUlISvXr1IjIyslHbiHZteUSb1s/0sc+x//cDjI0c36T26ept2lXPuythNBpZs2YNXl5eHD9+nB49epx5o7NEq9XyzjvvMGTIEDIzMxk+fDjTpk1j6tSpzJo1i4EDB9ZY//PPP6e4uJijR4+iKAp5eXm19vnoo49y7733cu211/Ltt9/y2muvMXfuXF555RXOO+88/vjjDzZt2sSTTz7Jd99912zbc3Jy+OCDDzh48CA6nY7x48ezdetWAH7++WcGDBhQa5snn3yS+++/v959Pv744zz++OMAfPbZZ2zcuBEAs9nMbbfdxg8//EBcXBzZ2dnNtruj0dR2fvXVV7nyyiu5//77eeyxx/jss89qtfnbb7/t+Pz000+j1apDY29vb9566y2WLFnSymfVfmgxTQG7983f37/O73ft2kV8fDz/+9//6t3HiRMnyMzMZPLkyY5lPj4+jBo1ii1bttTpFJg3bx4vvPBCreUrV67E3b311dBXrVrV6sfoKOQUFzk+F+YXcYfLuXymbAbgZKJC6rFljdqPaNPWQbRryyPatG5WrVrFhx9+iKIoSJLEfffdx4UXXtik7QUti2jTuhnh9RDGIli2rHHvp+p01TYtLy93tgkC1JnMSy+9lL59+7J//35GjhzJggULiI+P59FHH6W0tJTw8HC++uorCgsLueSSS9i2bRs6nY4RI0bw/fff1xpo2/nrr78YP348ffv2ZdGiRcyZM6fVzycsLIywMLWSVWhoKIGBgeTn59cbrfDJJ5+wePFiQNW1Oj2SACAhIYFJkyYBMGnSJF566SXmzp1LQkKCY0wxduxYLrnkEsf7qrmYzWbHTLTJZCI4OPgMWzSeH3/8kYcffhhQf5sRI0YQFxcH0KLH6Qg0pZ2XLFnCjh07ALjxxhv5z3/+06Aj5qeffuK3334D1PHsqFGjWL58ecsZ385pEaeA1WrloYceYuzYsXV6wwAWLFhAXFwc5557br37yczMBCAkJKTG8pCQEMd3pzNnzhweeeQRx9/FxcVERUUxZcoUvL29m3oqjcZkMrFq1SouvPBCdDpRbg/g6Akd725ZDUBwYBCzLvmAmTmHUBQLEcF1v3iqI9q0dRDt2vKINq2f1NRUrrjiChRFAdQIsI8++ohHH330jBEDol1bHtGmLU9Xb1N7RKbA+Rw4cIDPP/+cc845h+uvv54vvviChQsXsnjxYvz9/fn888+ZN28eb7zxBrNnz+bRRx/F29uba6+9tl6HAMCiRYu49tpr6devH1deeWWbOAWqs2vXLiwWC1FRUfWuk5KSwmeffcYff/xBdHQ0H374Ya0I5UGDBvHrr79y99138+uvv5KWluZYvnjxYkaMGMGff/5JUVER+fn5BAQENMveoKAgHnvsMbp164ZWq+Wee+6hZ8+eAFx//fVoNBpuuummGuOVt956i/nz5zN27FjeeOONeqOoc3Nz2bt3r2PCNDExEbPZzOTJkykoKGD27NncfvvtzbK7o9HUdi4qKsLHxweAiIgIx+9fF/Hx8ej1eoezpSvSIk6B2bNnc+DAAUdoy+lUVFTw3Xff8cwzz7TE4Wqg1+vR6/W1lut0ujZ5WbfVcToCIYG9HZ9lSUKn0xEdPrjJ+xFt2jqIdm15RJvWZseOHVit1hrLrFYrH374IW+88Uaj9iHateURbdrydNU27Yrn3F7p1asX55xzDgDXXXcd7733Hnv37nXMjpvNZvr37w+offUJEyZQVlbGtm3b6t1nZWUl69at4/PPP8fFxQWtVsuRI0eIjY1t/RNCzfe/+eab+fTTTxtcr7S0lKCgIHbt2sUnn3zCo48+yrfffltjnTfffJP77ruPTz75hEsuuQQPD1VcdM6cOdx///0MHTqU0aNH06NHDzQaTbNtLigoYOnSpSQnJ+Pm5sbFF1/M+vXr+fbbb4mIiKCoqIhLL72U2NhYLrnkEu69917HmOiJJ57ghRdeqPf9+Ouvv3LppZc67juz2cyWLVvYunUrkiRx3nnnce6559K3b99m299RaGo7N4Uff/yRa6+9tpUs7xg0W2jQzv3338/SpUtZs2ZNvbNAP//8M+Xl5dx8880N7svu4cvKyqqxPCsrq5b3T9D+8PWOdnwuNYqZBIGgq7FgwYI607xAzdtLTRWCowKBQNBSVA93lyQJi8XC0KFDiY+PJz4+ngMHDrBo0SIASkpKyM3Npby8vEHx7mXLllFQUECfPn3o3r07x48fd+yjtTEYDMycOZMnn3yywchiUGd+r7jiCgCuuOIK4uPj61zn999/Z/fu3dx9992OyAMfHx+++eYb9uzZw9tvv43FYqkh5NdU/v77b3r16oW/vz9ubm5ccsklbN26lYiICMfxrrnmGkcoe0hICBqNBo1Gw+233+5YXhf2qI3q5zRx4kT8/Pzw9fVlwoQJ7N+/v9m2dySa2s4+Pj6O9Pa0tDTCw8Pr3bdwCpyFU0BRFO6//34WL17MP//8Q0xMTL3rLliwgEsvvZSgoKAG9xkTE0NoaCirV692LCsuLmbbtm2MGTOmuaYK2git1sXx+aCoNiAQdClSU1O56667HGkDp2OxWEhKSmpjqwQCgaDzkpiYyO7duwF18HjrrbeSkpLCrl27AHWQffjwYQAee+wxZs+ezR133METTzxR7z4XLVrEt99+S3JyMsnJyezcubNNnAKKonDrrbcyadIkbrrppjOuf+mll7J27VoA1q5dW2fYd25uruOd9Morr3DXXXcBUFhY6BDMfOedd7jhhhvOyvaoqCg2b95MZWUlFouFtWvXEhsbS25uLqAKNy5fvtwRtZGRkeHY9vfff3csP53s7GwSEhI4//zzHcumTp3Knj17qKysxGAwsHXr1i4RJQBNb+fp06fzzTffALBw4UJmzJhR53537dqFj48PvXr1apsTaac02ykwe/ZsFi5cyHfffYeXlxeZmZlkZmY6ykDYSUpKYv369dxxxx117qdv3741hEIeeughXn75ZZYsWcL+/fu5+eabCQ8PZ+bMmc01VeAEBgSdWUNAIBB0HhITE2ulDVRHo9F0+ReuQCAQtCQDBgzgtddeIy4uDlmWuf7661m0aBEPPvgggwcPZvjw4ezdu5e///6bI0eOcN999/Hwww+zd+9ex4C6OmVlZfzzzz9MmTLFsaxHjx5otVoOHDjQqueyadMmFi1axG+//eYoEbd//36eeeYZIiMjKSgoIDIykrfeegtQUwC+/PJLBg0axP/+9z/++9//AvDxxx/z8ccfA7B69WpiY2Pp06cP7u7uzJo1C4D9+/fTv39/YmNjSUhI4Omnnz4r20ePHs20adMYOnQogwYNomfPnkyePJmpU6cyaNAghg4dyoABA7jqqqsANWVg4MCBDBo0iN27d/PSSy8BqjDes88+69jvL7/8wmWXXVYjtcHf35/77ruPYcOGMXz4cP71r381qA/RmWhqO8+ZM4effvqJXr16kZSU5BiLVr9GQI0SuOaaa2ocq/r1Zr8GOz1KMwHq/PfFF1/UWG/OnDlKVFSUYrFY6t1P9W2sVqvyzDPPKCEhIYper1cuuOAC5ciRI422q6ioSAGUoqKi5pxWozEajcpvv/2mGI3GVj1OR+NUzkHls7VzlNLKpre/aNPWQbRryyPatDYpKSmKLMt1vhc0Go3y2WefnXEfol1bHtGmLU9Xb9O26mc5g3Xr1imAsm7dOmebckZOnDihDB8+3NlmKIqiKAsXLlQAZdeuXc42pcXpqOfWUe3u6HSkZ8jpNFtoUKknRPR05s6dy9y5cxu9H0mSePHFF3nxxReba5rAiUQF9mPWhPp/b4FA0DmJjIxk/vz53H333VgsFjQaDfPmzWPEiBH06tWra3jZBQKBQCAQCDogLVJ9QCAQCASCWbNmMXXqVJKSkoQjQCAQCFqR7t27s3PnzmZvv3///lq5+zExMY6UXoFA0LUQTgGBQCAQtBiRkZHCGSAQCATtnIEDB9ap2C8QCLomZ12SUCAQCAQCgUAgEAgEAkHHRDgFBAKBQCAQCAQCgUAg6KIIp4BAIBAIBAKBQCAQCARdFOEUEAgEAoFAIBAIBAKBoIsinAICgeCsSE1NZc2aNaSmpjrbFIFAIBAIBAKBQNBEhFNAIBA0mwULFhAdHc2kSZOIjo5mwYIFzjZJIBAIBAKBQCAQNIFOV5JQURQAiouLW/U4JpOJ8vJyiouL0el0rXqsroJo09ahtdo1LS2NO++803HPWa1W7rrrLs4991wiIiJa7DjtEXGttg6iXVse0aYtT1dvU3v/yv7sFwgEAkHHp9M5BUpKSgCIiopysiUCQdfDarXSr18/Z5shEAgEglampKQEHx8fZ5shEAgEghag0zkFwsPDSUlJwcvLC0mSWu04xcXFREVFkZKSgre3d6sdpysh2rR1EO3a8og2bR1Eu7Y8ok1bnq7epoqiUFJSQnh4uLNNEQgEAkEL0emcArIsExkZ2WbH8/b27pKdgtZEtGnrINq15RFt2jqIdm15RJu2PF25TUWEgEAgEHQuhNCgQCAQCAQCgUAgEAgEXRThFBAIBAKBQCAQCAQCgaCLIpwCzUSv1/Pcc8+h1+udbUqnQbRp6yDateURbdo6iHZteUSbtjyiTQUCgUDQ2eh0mgJthV6v5/nnn3e2GZ0K0aatg2jXlke0aesg2rXlEW3a8og2FQgEAkFnQ0QKCAQCgUAgEAgEAoFA0EURToEuQnJyMpIkER8fX+86a9euRZIkCgsLW/TYkiTx22+/teg+BQKBQCAQCAQCgUBw9ginQDvi1ltvRZIkJElCp9MRExPDE088QWVl5VnvOyoqioyMDAYMGNAClgoEAoFAIBAIBAKBoDMgNAXaGRdddBFffPEFJpOJXbt2ccsttyBJEq+99tpZ7Vej0RAaGtpCVgoEAoFAIBAIBAKBoDPQJSMFFEWhrKysxf8pinLWtun1ekJDQ4mKimLmzJlMnjyZVatWAWC1Wpk3bx4xMTG4ubkxePBgfv75Z8e2BQUF3HDDDQQFBeHm5kbv3r354osvgLrTB5YtW0afPn1wc3Pj/PPPJzk5uYYtzz//PEOGDKmx7J133qF79+6Ov3fs2MGFF15IYGAgPj4+TJgwgd27d9d7fkajkfvvv5+wsDBcXV2Jjo5m3rx5zWssgUAgEAgEAoFAIBCcFV0yUqC8vBxPT88W329paSkeHh4ttr8DBw6wefNmoqOjAZg3bx4LFy7k448/pnfv3qxfv54bb7yRoKAgJkyYwDPPPMOhQ4dYvnw5gYGBJCUlUVFRUee+U1JSuOKKK5g9ezZ33XUXO3fu5NFHH22yjSUlJdxyyy28//77KIrCm2++ybRp00hMTMTLy6vW+u+99x5Llizhxx9/pFu3bqSkpJCSktLk4woEAoFAIBAIBAKB4Ozpkk6B9szSpUvx9PTEbDZjMBiQZZkPPvgAg8HA3Llz+fvvvxkzZgwAPXr0YOPGjXzyySdMmDCBU6dOMXToUM455xyAGjP6p/PRRx/Rs2dP3nzzTQBiY2PZv39/k9MUJk2aVOPv+fPn4+vry7p165g+fXqt9U+dOkXv3r0577zzkCTJ4fAQCAQCgUAgEAgEAkHb0yWdAu7u7pSWlrbKfs+W888/n48++oiysjLefvtttFotV155JQcPHqS8vJwLL7ywxvpGo5GhQ4cCcO+993LllVeye/dupkyZwsyZMzn33HPrPE5CQgKjRo2qsczubGgKWVlZPP3006xdu5bs7GwsFgvl5eWcOnWqzvVvvfVWLrzwQmJjY7nooouYPn06U6ZMafJxBQKBQCAQCAQCgUBw9nRJp4AkSS0a5t+SeHh40KtXLwA+//xzBg8ezIIFCxxVA/78808iIiJqbKPX6wG4+OKLOXnyJMuWLWPVqlVccMEFzJ49m//+97/NskWW5Vo6CSaTqcbft9xyC3l5ebz77rtER0ej1+sZM2YMRqOxzn0OGzaMEydOsHz5cv7++2+uueYaJk+eXEMbQSAQCAQCgcBZrFy5UqQ2NoFNmzYBqlZVQkKCk61pWTrquXVUuzs6Hbmtu6RToKMgyzJPPfUUjzzyCEePHkWv13Pq1CkmTJhQ7zZBQUHccsst3HLLLYwbN47HH3+8TqdAXFwcS5YsqbFs69attfaVmZmJoihIkgRQQ6gQ1IfOhx9+yLRp0wBVqyA3N7fB8/L29ubaa6/l2muv5aqrruKiiy4iPz8ff3//BrcTCAQCgUAgaC3sExqvvPKKky3peMiyzDPPPONsM1qFjnpuHdXuzkB9k6PtGeEUaOdcffXVPP7443zyySc89thjPPzww1itVs477zyKiorYtGkT3t7e3HLLLTz77LMMHz6c/v37YzAYWLp0KXFxcXXu95577uHNN9/k8ccf54477mDXrl18+eWXNdaZOHEiOTk5vP7661x11VWsWLGC5cuX4+3t7Vind+/efPPNN5xzzjkUFxfz+OOP4+bmVu/5vPXWW4SFhTF06FBkWeann34iNDQUX1/flmgugUAgEAgEgmbh4uICqPpIw4cPd7I1HYdly5bxzDPPsHDhwnr7nR2VjnpuHdXujs6uXbu46667HM+SjoRwCrRztFot999/P6+//jonTpwgKCiIefPmcfz4cXx9fRk2bBhPPfUUoL7M5syZQ3JyMm5ubowbN44ffvihzv1269aNX375hYcffpj333+fkSNHMnfuXG6//XbHOnFxcXz44YfMnTuXl156iSuvvJLHHnuM+fPnO9ZZsGABd911F8OGDSMqKoq5c+fy2GOP1Xs+Xl5evP766yQmJqLRaBgxYgTLli1DlrtkdUyBQCAQCATtjNjYWIYNG+ZsMzoM9pDpuLi4TtduHfXcOqrdHZ3W0KxrKyTl9KRxgUAgEAgEAoGgi7F+/XomTJjAunXrGD9+vLPN6TB8++233HjjjezatavTDUA76rl1VLs7Oh35GSKmZwUCgUAgEAgEAoFAIOiiCKeAQCAQCAQCgUAgEAgEXRThFBAIBAKBQCAQCAQCgaCLIpwCAoFAIBAIBAKBQCAQdFGEU0AgEAgEAoFAIBAIBIIuinAKCAQCgUAgEAgEAoFA0EXROtuAlsZqtZKeno6XlxeSJDnbHIFAIBAIBIJOg6IolJSUEB4ejiyLuSWBQCDoDHQ6p0B6ejpRUVHONkMgEAgEAoGg05KSkkJkZKSzzRAIBAJBC9DpnAJeXl6A+rLy9vZuteOYTCZWrlzJlClT0Ol0rXacroRo09ZBtGvL09Q2VRSF6e9v5GReOQA/3TOauDCf1jazwyGu1ZZHtGnL09XbtLi4mKioKEd/SyAQCAQdn07nFLCnDHh7e7e6U8Dd3R1vb+8u2SloDUSbtg6iXVueprbp4cxiUkpB1rsDkFkhM6oVn08dlc5wra45ko3ZonBhvxBnmwJ0jjZtb4g2VREpmgKBQNB56HROAYFAIGhvLN+fWePvY9mlTrJE0JqsOZzNbV/uQJZg61MXEOzl6myTBAKBQCAQCM6IUIjp4iiKwpsrjzDn1318vvEEZQazs00SCDodqw5lAdAvTI0OSMoRToHORkp+OQ8tigfAqsD+1CLnGiQQCAQCgUDQSIRToIuzL7WI9/9J4vvtKby49BB3f7MLs8XqbLMEgk5DhdHC4cxiAG4eEw1AkogU6HS8tzqRogqT4+/9acIpIBAIBAKBoGMgnAJdnIyiCgBCvPW46TRsTMrljZVHnGyVQNB5OJRRhFWBIC89Y3sFAnAit0w43zoZdifA2F4BAByo5hQ4kFbEd9tOid9cIBAIBAJBu0Q4Bbo4WcUGAIZG+fH6VYMA+GTdcdYeyXamWQJBp2GfLYx8UIQPEb5uuOk0mCwKp/LLnWyZoKUwmq2O6I9rzlFL4u5PK6KgzMh/ft7HjA828tTi/Y40EoFAIBAIBIL2hHAKdHGyiisBNVJgxuBwrhuhdmhXHMhsaDOBQNBI7DPIAyN9kGWJHkEegEghOBtKKk1YrIqzzXBwLKcUs1XBy1XL5LgQJEl1uF7/2TYW7UxBsZkqUgrOHkVR+HT9cTYl5db53WcbjvPjjhQnWCYQCAQCQcdFOAW6ONklaqRAsLeqkj2udxAACRnFTrNJIOhM2AXnBkX6ANAr2BMQYoPNZVNSLgOfX8nwl1fx4A97OJFb5myTHJoRcaHeeOi19AxSf+OEjGJcdbLD2Xo4s8RpNnYWdiQX8MqyBB78YQ+KUtMxdDC9mJf/TGDO4v0YzBYnWSgQCAQCQcdDOAW6OFWRAqpToF+4qo5+OLNE5L8KBGdJmcHsGPwPiLA5BWwDRhEp0DzsUUyF5SZ+j0/npgXbKCw3OtWmwxnqYL9vmBegporYuX1sDFcMiwTgiHAKnDUH01UnW26pkeS8chRFoaBM/f2X7E0HwGJVyCt17jUhEAgEAkFHQjgF2pC9KYXM/m432baBeHsg26YpEOKtByDa3x13Fw0Gs5XkPOfPwAkEHZmD6cUoCoR6uzpq1tsjBY4Jp0Cz2JdaCMD95/ciOsCd1IIKHvwh3qnpBAm2wX7fUNWpOtAWFeLnruOeiT2JDVGdBWmFFRRXmureiaBRVHes7DpZwIKNJxj60ireW53I0r3pgHod5Nii4AQCgUAgEJwZ4RRoQ95bncif+zL4estJZ5viIKukZqSALEvEhqod2EMZYlZLIDgb7ANY+yARqjkFcspqhT8LGsZotpJgey5dfU4kH90wHL1WZt3RHJ79/YDT2vOwLd3KHilwxbBILhsSzjvXDcXbVYePu45wH/UZK6IFzo6E05wCf9iiA95adZSc4kL69nyKQd2fJqe4wlkmCgQCgUDQ4RBOgTZCURT22USm7PmnzqbSZKGwXJ21CrHNYgL0C1Nnuw6ltw87BYKOil1Yrno4eXSABxpZotRgJrMdRQ11BA5nFmO0WPF119HN351+4d68ec1gJAm+3XaK1/9q+3KqeaUGhzaLPSLAx03Hu9cNZUKfIMd6dmer0BVoPlarwtFq7bf+aE4N8cZebttJc1E44WYmPVuU1hUIBAKBoLEIp0AbkVVscIQzJrSTGXi7PXqtjLeb1rE8zuYUEGKDAsHZYRcZrB4p4KKViQ5wB4SuQFPZa2/PCB8kSQJg+qBw5l4+EICP1h5r8+eWfeY/OsAdD7223vVibakFR9qJU7gjciq/nAqTBa2s/vZphRVYFYgJ9GDG4HACPBIc6+bm7naWmQKBQCAQdDiEU6CVsFqVGqGs1Wcz0gorKCp3fl6pXWQw2Fvv6GBDldjgIeEUaDUKy40idLyTU1Jp4rhNGX9gtUgBEGKDzWVfSiEAgyN9ayz/18huTO0fAlSJzbU2VqvCgo0n+GzjCQD62iIB6sP+/eF24hTuiNijLPqGedHd5lgDGNsrgPeuG4J/UJ5jWUHp4Ta3TyAQCASCjopwCrQCiqJw+1c7GPHKasds/H5bbrGdhHYwW5RlFxmsljoAaudVktRIAiHW1PK8uvwwQ15cxffbRS3tzsyBNPUej/B1I8BTX+M7R1lC4RRoEvtOK+9YnUsHRwDwx970NnG4bUjK5aWlh/jncDZQJTJYH3a9gSOZJcIh2EzsqXexId4Mi/ZzLB/bMxAUhb2WKud7kTG1ze3rLFisCq8uP8xve9KcbYpAAED37t0ZNGgQQ4YM4fzzzwfg1ltvpUePHgwZMoQhQ4Zw7NixWtulpKQwYcIEhgwZwvDhw9mxY0eN75cuXYokSRw4cOCsbbz88svx8/PjqquuAqCkpMRh25AhQ/Dx8eGdd94BYO/evYwaNYohQ4YwduxYjh8/Xmt/b731luOcp0yZQlZWFgBvvPGGY5+xsbH4+vqete0dhaa08bXXXutYHhERwcyZM2vtT1EU7rnnHnr16sU555zjuIaSk5Nxd3d3bP/444+31Sk6FeEUaAW2HM9j7ZEccksN/B6vvlTtegL2CfnD7WAW/vRyhHbcXbR0D/AAhChWS/PV5mQ+Xqc+dBZubT+Ck4KWZ39aIVA7SgDat1Ngf2oRKfnlzjajFttP5JOYrT6PBkf51vp+Ut9g3F00pBZUEG+LKGhN7I7ebv7uXDIojH+N7Nbg+jGBHsgSlBjMDg2C9ojRbKXCaHG2GXVifx/FhXkx3OYUkCQY0zOA1Iwd5MtVEW9FSl6d+xCcmb8Tsvh43TH+88s+US1D0G7YvHkz8fHxrFmzxrHsvffeIz4+nvj4eHr27Flrm9dff53rr7+e+Ph4XnnlFZ5++mnHd5WVlbz99tuMHDmyRex78MEH+frrrx1/e3l5OWzbs2cPvr6+XHbZZQA8/fTTvPjii8THx3PTTTfx2muv1drfHXfcwb59+4iPj2fGjBnMnTsXgMcff9yx38cff7zOwW5npSltvGjRIsd3559/fp3t9Oeff5Kbm0tSUhLPP/88//nPfxzf9evXz7H9G2+80ern1h4QToFW4JN1VR6/3+PVWasDNqfAeb0CgfahK2DvmAZ762t9ZxfMai+iiJ2B3acKeOGPg46/D2UUcyyn/Q0K2ytF5Sau+XgLn22o7VFvj+yrQ0/ATlUFgvbz+yuKwkdrjzHjg41c+8kWrE4s8Xc6+1ILue2L7VgVmNIvpJYjE8DNRcOF/dQUgj/2ZrS6TfZn+A2juvG/64cR6lPbpurotRqibc7W9ugMsnPL59sZ9/oaCsuNzjalBkazlZ0nCwBVtHFCnyA8XDScHxuMr7sL8Yl/AqCxRWEUaNqfY6s9Y7ZYHb/5n/vU+8dgtrLiQKYzzRI0QHJyMoMGDeKaa64hLi6OW265BbPZzM6dO5kwYQLDhw9nxowZ5Ofnc/z4ceLi4iguLqaiooIBAwawf//+Ovd77Ngxxo0bx7Bhwxg5ciTx8fFte2ItiCRJlJSoz+qioiLCwsIc373++uvce++9uLm5tcixJk6ciJdX3WlkW7ZsITQ0lJiYmDPaZcfbuyr6rLy8vEaar50ff/yRa6+9tiXM7xA0pY3tGAwG/vrrrzqdAkuWLOGmm24C4JJLLmHz5s1dOpKv0zoFTBarU46bkFHMuqM5yBJoZIn9aUX8dTCL3FIjGlni8qFqiGt7SB/IridSAKpCXYVSdsuxJD4dqwIXDwh1qJIvbYPBS2dh9eEstifn8/pfR8grVR1azrrPG4Oj8kAdToGeNk2B3FJjuxl8vbQ0gddWqHnY6UWVHM9tPwPXd/9OpMxoYUyPAN7719B617t4gNqx2pCY0+o22QUN7cKsjcH+u7cnZ1B1KowWthzPI7fUwC7bALy98NueNHJKDAR76RnR3Z9IP3e2PHUBH904DIC9WbsAGK2oWgOZWgXF2n6fD+2JXSfzmfzWOkbOXc2y/Rn8nZDl+M4e7Shonxw4cIAnnniChIQETCYTX3zxBY8++iiLFy9m165dXH755cybN48ePXowe/ZsHn30UZ5++mmuvfZaBg4cWOc+w8LC+Pvvv9m9ezefffZZuwidliSJCRMmMGLECL799lvH8scee4zBgwczZ84cLJbaEU5PPfUUX331FZGRkTz88MO88MILgOpQ2bp1qyMMvbU5ffD++uuv88gjjxAZGckXX3zBI488Uud2r776KtHR0Xz99dc1ohwAcnNz2bt3L5MnT25V2zsK9TlIli9fzpgxY+pMs0hPTyciQh2XSZKEn58feXlqlNmRI0cYOnQoF1xwATt37mxV29sLndYpkJjlnMHs/PXqLOa0gWGM761GBdyzUO2sDI70YWg3NeTxSGYJZicPaLJK7E6B2pECDlGsduC86CxsPa4+aKYPCmfG4HAA/tjXNvnPnQG7g8potvLN1pPM+XUf/Z/9i23H21+YcFG5iZN56kxlXekDHnqto259e5g1Tsgo5vNNqmCev4cLADuS28+g0C7Y+MAFvXHVaepdzx6VcSK3rFUdRuVGMyfyVJua5BQIbt+RAsm2c4IqTYz2gMWqONKu7hgX47gGvF116LUaDMZSVpWr6ViXRF+ErChUyBJp2Qn17lOgsu5oDld/vIXkvHKMZiv3fbubcqPF8RzYfCyPzCJROrW9Ys+FBrjuuutYtGgRe/fuZdKkSQwZMoS33nqLU6dOATB79myOHDnC2rVrmTNnTr37NBgM3H777QwcOJCbb76ZQ4cOtcm5NMTGjRvZtWsXS5YsYe7cuezbt4958+aRkJDAtm3bOH78OB9//HGt7b777jvuvfdeUlNT+fTTT5k1axagOhNeffXVNrFdURR++eUXrrnmGseyDz/8kI8//pjU1FT+/e9/1+sUePLJJzl58iSzZs3i/fffr/Hdr7/+yqWXXopOp2tV+zsCdbWxneZEU4SFhZGcnMyePXt4+eWXufrqqzGbzS1lbrul0zoF9qcXnXmlFia1oNyhfH33+J7MtEUFAIzo7seb1wwh2t8dN50Gg9lKcp7zwhsVReFEjtoBrDNSwCaalZhVitliJafEgKUdhRMD/G9NEufOW93uZrTqIq/U4BjUju7hz5T+IbhoZJKySzmRW3aGrQVQM2rl3dWJfL89BaPFyp/721e0RUGZkd/3qrNr3fzd8XV3qXO9XrYUnQNpbf+sOh27WN4FfYO53pYbv7OdOAUsVoXUAvVZ2a2a4nxdhPu44uGiwWxVSG7F++pwZgmKAoGeeoK8ajtV66NXO48UqP4sOuCEd2h9rDqUyfHcMnzcdFw/KrrW98s3v0q+LBFiUZg69gmCzeq76vCJLW1taofjo7VJjrScsb0CHMuvHh7JiO5+KAos2SuiBdor1UPKJUnCYrEwdOhQRy70gQMHWLRoEaCKsuXm5lJeXk5lZf2OnnfeeYfu3buzb98+Nm7ciMHgfA0U+2xuWFgY06ZNY/fu3YSFhSFJEq6urtx88821RAQBFixYwNVXXw2o4eH2VIjdu3dz6aWX0r17d7Zu3crUqVNbzfmxceNGoqOjiYyMdCz74YcfmDZtGgDXXHMNmzdvbnAfN954I7/88kuNZYsWLepSqQMNUVcbA1RUVLBq1SouvfTSOrcLDw8nLU19vimKQkFBAQEBAej1egIC1OfhmDFjCAwMJDW184vXdlqnwMHUtp/lWLDxBBarwtheAQyM9GH6oHCevLgv7143hB/vHqMKTckScbbQ/JWHnJerdzizhPSiSvRamaFRfrW+71bNefHVlpOMeOVv3lud6ARL6+eHHadIL6rk7m92kVFU4WxzamG1Kjz9236e/GUfG5NyAVWrIcBTj7erjjhb6cf2oC/REbCLc2plierBFe1lRntHcj7XfrKF4S+v4tnfVe2IuvQE7Iztqb5wVtsG5M5k7RHVhvP7BjO8u/o82HUy35kmOcgoqsBkUdBpJELrcGBWR5Ik+gfnImMksRVn46tSBxouQ3g6PduxwCTUdAocbAfOKjt/HVTD2a8bEYWnXlvjO0VRWHhiKQD/ChiGi96TALPqiDuRc/aK4p2ZpOxSth7PR5bghcv689GNw4kL80ankbhyeCSXDlEHYqsTnP+MEtRNYmIiu3fvBtRB4q233kpKSgq7dqkRqgaDgcOH1bSwxx57jNmzZ3PHHXfwxBNP1LvP4uJiwsPDkSSJL7/8stXP4UyUlZU58u9LS0v5559/6N+/PxkZ6oSA1WplyZIl9O/fv9a2UVFRrF69GoCtW7cSFRUFwPHjx0lOTiY5OZnRo0fz119/0a9fv1axv66Zan9/f7Zu3QrA6tWriY2NrbVdYmJVn/v333+nb9++jr+zs7NJSEhwVGLo6tQXDbBs2TLGjx9frw7B9OnT+eabbwBVdHDMmDFIkkROTo4jHeXo0aNkZmYSHh7eeifQTtCeeZWOSWNnORSrlXu/G4+77MJb1//T7OMVlBn5wVZi7u7xqgKqRpa4Z0JtNdQbRkWz+1QhH689xg0jo/Fxb/vQn1WH1E7WuN6BuLnUDseVZYk+oV7sTSnk3b+PArDmSDYPX9inTe2sj6JyEyn5qiMgt9TAQz/Es+juMU62qiZ/7s9g4VY1bM8u3DS6h7/j+z7BnuxNKeRoVgmXUFtkxpnsTy1i6f50Hp7cp8Fw7bYiv8zoEMZ8+pI4Ptt4grsn9OSZ3w5wJLOYkkoTXq7OC6GrNFm446udFFWoSt3d/N2JC/PigUm9693mwn4hzFt+mK3H8yiuNOHtJPuLyk2OaJuJsUF4ueqQJEjOKyenxNCkmfDW4JStEkKUnzsaubbQUnW+//tRErxW0j9G5nDG10wb2Dr3ld0p0K8JqQNQJTCZVWxw6m9enT2nCnh9xRGenh7H8Zwqp0B6USX5ZUZHGLmzUBSFzcdUp6pdi6U6O/d/yxHZgqtV4apxzwHgbfUCCkgrPtGWpnY4vtumvp8m9Q0hzEcVW/tt9rkUlJkI9XGl2PY8yxDpA+2WAQMG8Nprr7Fv3z5GjBjB9ddfz4ABA3jwwQcpKSnBYrHwzDPPkJqaypEjR/jkk09QFIVx48axdu1aJk6cWGuf9913H1deeSWffvopl19+eduf1GlkZWU57LBYLNx5552MGDGCSZMmkZubi9VqZfTo0TzwwAMAjjSCe+65h//+97/ceeedzJs3DxcXF+bPn99qdk6ePJm9e/dSVlZGZGQkP/30E6NGjWLx4sW1ohg++eQT7r33XqxWKz4+Pnz++ee1bH/ttdfYunUrGo2GqKioGukRv/zyC5dddhkajfP7Z21JU9oYVGfB6SkFS5YsYefOnbz44otMnz6dpUuX0rNnT3x9ffnhhx8AWL9+Pc8++yw6nQ6tVsuXX36Ji4tz34VtQad1ChzLKaPCaKlzwFud/IJjbLIUgQUMhmL0+qZ18uws3HqSCpOFfmHejLNpCdTHzKERfLrhOIczS/hwXRJzLo5r1jGbwrL9GZgsVi6zef7tQkJ2te66iLM5BYor1Tyao1klWKzKGTvmbYHd6ePrrqOw3MS2E/lUmiztYgALat77f1cecfxdYlDbcEzPqvDMPrbw8aNO0r9oiKd/28/e1CK6+btzQx3hum2NXduim787t46N4daxqrrsJ+uOkVpQwZ5ThYyvY8DQVizbn0FRhYkIXzd+uGs0Uf4Nh7kD9AjypGeQB8dyylh7JIdLBzvHC70uMQerAn1CPIn0U+2ODfHicGYJu07mc9EA5zqs7OURG2pTxWrll7X/x9y0lQAku1pJSv4PsKJVbLJH9zRFTwDUHPhgLz3ZJQaO55QxpI7Sim2Joig8+/tB9qcV8dXm5BqaAgAH04sY19t59xWoehJZxQZctDLDomtHtf2V8B0Al+hD8fFXnfBecghQQIax9QUnOyoZRRX8vEudyLhhdFU5Tb1WQ6iP+h4N9lIjc7JLKlEUpU71c4FzcXFxcaQH2Bk+fDgbN26sta5dkE6SJDZt2lTvPnv37s2+ffscfz/33HMtZG3z6NGjB3v37q21/J9/6p7Iu+eeexyfBwwYwJYtDacRrV279qzss/P333/XubyusPMJEyawZ8+eWsur2/7ZZ5/Ve6x77723GRZ2fJrSxkCtewPg0ksvdaQTyLJcp6Poyiuv5MorrzwLSzsmnTZ9wGJVONiIaIFKQ2HV58rmhUtWmix8uTkZgLsn9Djji1MjSzxxkRoq9OWm5FYPfU/MKuGhRfE8+EM87/6dyPYT+exLLUKS1BmC+ogNrRluU2myOmbtnI09D3tMjwA8bI6ftML2k0KwaMcpTuaVE+ip56lpasiXRpYYGVPNKRDavpwC+WVGLFaF3FIDe23l9PaltI8QYnt98tOvyRHd1ciLnU7Wlfh+uzrjdt2IqAYHr5nFKZRWFDr+vrBfKFAVueMM1trSF86PDXYss9eAbw+pGfZnTrd62vXQ0aVc9/U5vJCihpCPMKuz7xv0aazf9WGL22O1Ko5Uln7hTXci2ysQtIcUgi3H8hxVMvanFTvSB3oEqYKI89cf59IPNrLigPN0OzYfU4VEh3fzq9Ppe7JSjSIYGjLcscxTrzoNs6zOb+P2SGG5kZsXbKe40kxsiBfj63H82MsVV5qsjskBgUAgEHROOq1TAHAMbBrCYKgakBkNzdMh+HlXKnllRiJ83bikkeGq58cGM7K7PwazlXdWtW6ufs8gT249tzsAb/99lGs+Ub2mQ6N8GwwNtosNVudIO6lGcCBdtWNAhA+RvnpAIbWg/TgF7OJ3907syV3je/LudUP48IZhNUJx+4Sog4PkvHIM5tqldNqKzcdyuW7+Foa9tIqHF8WzMTHX8V17ERuzOwX6nuYUsA9enZn/fjSrhB3JBWhkiWtGRNW7XnrWfqb/ejG3/zgZq1X9vaf0V51yaw9nYzQ7pxrJ7lPqwH9sr6oIp/bibAEcVRzqcgqYzQYe3jiHQ5IJd6uV+7zieP6yfxhRoD67Pt5f/0xLc0kpKKfMaMFFK9Mj0KPJ29tTCNqD2ODHtmo5oD7b88vU8pjTB9pLO+ayL7WIn3Y6T2Bps02PpboIXnVSLOpzPyqwKh/Yx2cAAKkaK2mZtWcYuzrPLTlIYnYpod6uLLj1nHqj/1x1GnzcVCebvYSxoP3QvXv3syqVtn//foYMGVLjX3tIFxAIBM6hUzsF9qUWnnGdymqOgEpj02dsLVaFTzeoHas7x8Wg1TSuSSVJ4j8XqzPIP+1KadUSirIs8dS0OOZePpAofzcCPV0I83HlrvG19Q6qU30A5u2qZppUV4B3JvZIgXDrOjTe/+bcqGdIaydOAUVROGRzWozpoXZkLxsSwdT+oTXWC/V2xUuvxWJVnFaBwGpVuOvrXWw9rg6ql+xN591qgpJHs0qc6rCwk+BwCtR0VJ1jE8Xbc6qwVUvQNYQ9SuCCvsF1VvKws+XgQgySRAIGNu1V8weHRKqOuRKDmU1JufVu21qUGcyctM3E96826213thxMK6LC6Nzf354+UFflgbXb3iJdA35WK8um/8S9V/xIpL8PKcVq2N8RjJjNLaucbdcT6BPi2ejnfXV6BrWPsoRHMktYfzQHWQJPvRZ7cZlQb1fO76tGjdiD3vJszoK2xmpV2GIrOTqmZ+20PJOpgkxZNTwyZKhjeVDwaHpWKhhkifuX30JZedvfW+0Vg9niiEz64PqhjpSh+gi2TRxkFTtfgV7QsgwcONBRpcD+b/Hixc42SyAQOIlO7hRoRPpANUdA9aiBxnI0q5STeeV46rUNzhLWxfBoPy7sF4JVgbnLErC2csm/60d1Y8MTk9j59IVsmXMBFw0IbXB9Pw8Xhkf7EeSl56Yxal75kXbgFCipNHEit4yRXkt4NvUdUnQS+z3NpOW2j9J0qQUVFFea0Wkkx6xgXUiSRG9btMDRLOcMEPLKjJQazEiSGvoONRXITRbF6b+51apwtJ70gT7BXgR6ulButDjCjNuS/DIjP+5Q83KvH9WtwXX3Zsc7Pi888AWgOuzs0UXzq83athVHs9TSekFeegI81c5/SuYe3KQMQrz1mK0K8SmFbW5XdRpKH/g2US3RdJVPPwKC1JliWZbw8BuKu9WKUZJIPrWhRe05ZNcTqCOSqjHYKxA4uxSpXVdmUt9gh3MNYKDfCTRlq1ly/1g+uVENyc8rc86AcF1iDoXlJjz1WgbVUckjMyseiyShtyoEBVUpjwf5epGXOosAs5Uk2cLHy+9uS7PbNdtP5FNutBDspXc4/xrC7ujMLhGRAgKBQNCZ6dROgRO5ZQ418PqoNFYNxgympg9+7CHrPYM9cXdpum7jE1Nj0coSa47k8OySAyhK6zoGmsr3d45m7WMTGW2b8Xb2ABFwzMIbg2rWdc3L3VfX6m3OwXT7TKIXLtqGbzH7IPeok9o106YqHeSp5+np/Qix5ZB66rWOSgkH0pybMnIqv5wKkwW9Vqb7abPFsiw5nFtL96a3uW3zN5ygzGihf7h3vXm5duIrqkqQbraWcOyUKvR05/ge6DQSW47nsTO5bdMgDp+WlpGWvosrlt/ETb9dzjnd1GXOTM0orjRRUK4+w+1aDZkZu3ngq9Hc/uVwdkoGNIrCNec+VWO7niH+RBjU/PMjqS3sFEi3lyNsnlPAPsjKLXXuzKs9MmVc7yAGRVQNuFNd3uGWbc/jYz3gEEPNK3VOpMBHa44BqsNSVy0qo6jwJJUVBaRkqakBkYqMVE2FO9BTT4qpD32KVafG7pK2rUKgKArz1x/j9RWH2ZyU267e6/9U0xBpjHCgXVdARAoIBAJB56bTOgXCfdWOl70DVx81nAKGps/WptrE7SL93Jq8LUDvEC/euHoQkgQLt57ilT8T2lUHwkUr46HXOgavyXllVJqcG068P60ILQZSdGo7udoiLEpKjjrTLDKKKjiWU8qhJpQr6x3sXLFBu8hlmI8rnnotz83ojyTBxQNCGdpNnUXa7+R65faBa+96wrWnD1JV+/86mNmmeflFRli4TY0SeGxKLHIDVTmKitM4Iau2nWNRBy/fbnsVgAhfN64arkZpvPdPUmuaXIvTS+v9vuMtKmWJVFmhj/s6wLm6AqdsegIBHi6O+vQ/bp7LGsrYIakD1SnaAEJDh9bYrk+IJ14G9ZyO5Bzg9V+vZtIXg8jOPXzWNtnbrLlOAbuuSGG5CbOTUl4qTRbH7zq2VwADbE4BN6mYVK2EWZLYeOgH3DSlnBv0Eb4cdKSRWCxVgnPl5Xko1tY5h53J+WxPzkenkbhjXA/H8szMPVy4+BL+/eMUUvPUCi+R2praDnatnJMlavRIomLEYm54gqAl2XmygLnLDvPh2mNc/9k23l7l3HdTddYeUSsy2FNEzoS9AkGW0BQQCASCTk2ndQrYQ03PpOxvMFeFcFaamh7OmWKLFIg6Q15eQ1w+NJJXrxgIwGcbT/DmyvbTgbAT5KnH38MFqwKJTgp1t3MwvZgo/WGMsoSbVWEIaoew0pjiNJsUReGaT7Zw8bsbHErd/RuhTG6fiUt0Un5xVnElYXIKA3T/UJJ9iGkDQtnwxPk8NsGNINOvgNWh3+As7OUI6xK+BFUUL9hLT3GlmQ2JaofXZDGycsd7vLb4av73x62tMnDZkiVhMFsZHu3HxNiGowT2Hv0NgGgL3D/gTgD+KDtBYYka3XDfxJ5IEqw/mtOmne/DtlD4vmFeWK0WluRXRdukl68CYNfJglZPbaoPuxhf9YoO24rV2eMb3GN4I+Zqnr3sh1rb9QjyxGJQnUXripP4tjiBHFlhx8Hvz8qeogqTo8pJY5x+djbu/ZL//DiN7Yd/xc/dxZGrb4+CaGt2nSzAaLYS7KWnZ5AngyJ9AQh2OelYZ1vOHr79Zzb7A08SHfwDeWUGXvlxBmO/HsLJUxs5fmI15y+awAuLLmoVGz9aq/7OVw6LJNSnSqtj5+FfqZAltlLJxqztAES51hQhDPNxQ6+VSSrvi6tVoUKWSElruCxZS7LyoBoVFG6z++N1xx3aGM7kRG4ZJ3LL0GkkzjtD6WQ79uixnBIRKSAQCASdmU7rFLB7tzOKGu5gV5qqXtSGZjgF7OJ2Uf7NixSwc+2Ibrx4mZoT+cGaJFYcyDzDFm2LJEnE2gawh51QgcBssXIovZjvt59i87Fcgl3VGaLekguRrmqYu0nKdZqCe3aJgZT8Coxmq0MfoF947RzY07FXIDjppAiM9IIyvHq8zxLXnzh3+bV89OvVRPq589pft/N2/veMC/qQI5klTmtXqL/ygB2NLDHNlpf/7O8HGf/6Gv79yQ08euhTFhYf5uP8Xew5VHvgeLYcK1FHdjOHRpwxDHdvqpoqMFgfyLDhdxNnhkpJ4ueNLwLqoNc+MFt/tG1qqyuKQkI1h8uufd+QJoNsi1TaYMrAy8VMSaWZo9nOiWRZeVDNex8Vo97jJUUpHJDUgfSt417iovHP4ulVu+JLoKcLeRW9ATguW7Hafp+M4pO11m0K9lKEEb5u+LjrGrWNoii8vPstllWkMGvbc1zw9WCCez/J0JCPKCh3Tlj+JoeifyCSJBHiradnkAeB+qoUnJ3mIn4tViMril0qyS8z8nfpCcpkiT93f8hvu/9HuSyxt6Ll31WHM4tZfTgbSYK7J9QUxD2ad9DxeY1V/T0ivWrq+bhoZQZH+mJBR4xVjcw5nLK+xe2sC0VRHEJ+T0/vx9heARgtVl7/60ibHL8h7KkDI2P8HZE3Z8Ke7iIiBQQCgaBz02mdAmE+dqdAw5ECNZ0CTffk2zUFzqTg2xhuHtPdIVZmn/FsT9hTCNpaVyC7uJIxr/7DtPc2MOfX/WQVG3BzVUtk9XELIdJTHRRYdCWOHPnc3MN8vnQW5WVt0451RU/EhdU9iK1OgKcOX3cdVsU5auSFufvI0FU9Bn4sSsBqMbHDrIYWHwhIIUq33alaEkfqERmszozB6qxwWmEFp/LLyZXUMHzJNsA90sKzhCaLlWSbU2CkrXxfQ+wtUu0ZHDAASaPlxshJAHyfuRmTxYjBVI7i9hBhfZ7k9/hbiU/6s0XtrYu0wgpKKs1oZYmeQZ78dmghADNdQgiyWCmRJcaH/Q2oM8ttTZnBzOrD6uDKniKy88B3WCWJaKtEaOjgerf193AhuXKgw8FhJ6M866xsqkodqH0tbtj8BrM+H8qBfd/WWH4o6U/SZAWtov7LkxTKNZDum0xOsXMqpmyyiXLay1BKksQ3s0Zx8YCq9iqRJYpsKTG5Oivp2cfJ1ah/byxI4O8itUpJOS0fRWKPEpg2IIyY08o+Hi1Nq7V+pF+vWsvs4olBFtU5ezS7bTRnkrJLSc4rx0UjM75PEE9Ni0OS4I+96ew55dwSn3/bnBXnxzYudQCqVR8QQoMCgUDQqem0ToFgH/VFlnmGSAGDpep7g7lpHTRFwRFKGtVMTYHTsQv62fPS2xP2mdojbZz//ndCNjklBtx0Gs7tGcC9E3ui97UNFP36EOHTHYAKbQWpBapjZ96Ku3g7bzu/rHumTWw8XRMgOsAdL9f6ZxILilO5feG5zPh6OAODVCG3RCfMxpaXxgMQblG1GXI1Mms2v06hrD4azJKES8TPrDt8djOszaXCaOFEnhrBU1/6AKiVPF65fACPT43l69uGcspVnU0eUK7+BkcLEuvdtjkkZJRgtEp4u2rp3UCFCQCz2ch+q3pdDom5EICLznuaAIuVbFnh140v8eeW1zihqaRUA3t12Ty1YU6L2lvfOQD0CvbEbMhjlUGd8b18wK1c7BEDwAa3NYwIf43U/LYfzPydkEWlyUp0gDsDItTffnuqOts7yj2ywW39PFyoULyIOC06P8NQeFY22dssLswbQ0kGn/14GfHb3gfg4yPfsl1j5p5dczmeuNyxzV/7vwbgAtmHlRM/YFG/+3CxKpTLEqkZu87KnuZQVGFiv61c79heVWH34b5ulJvrdpqUyzLHkn9z/L1fNpNi0/Urk87OKaCYjWz4ew6FmfGAqiPxh0009N6JtcvmJlpqR/RFBQ+qtczuFLBUqEKkh0vbJr1spW3gfW6vADz1WvqH+3DFUPV6fb+NNUOqk1lUydYTqjPoTJWHqlMVKWBQtSiS89uV7pFAIBAIWoZO6xQI9W5c+kCFubpToGmRAmVmKLOJL4X7toxTwJ6neiSzBIuT8njroymRApm5h0nM2InJevY5s9tsHZm7xvfguztH85+L+nJcUX+rPmEjCQuIBaBQZyG1sAKjoYSNJnWgnVnWNmUK7ZoAlw4Op3+4N7ee273edXPzE7ntl+nssJSQIlnQaNVBhTPKEhpM6mC5p+zFORr19/0k8UcAelgk/CxWUl3gwOGv29w2UB0liqIKzdnFw+rjhlHRzD6/FwHspEKW8LRYUYoGAHC0MrtF7dp1qhCAYd18GxQYBEhKXk25LOFhtdKzxxQAXDyCuMNPnel+88Ri5h/7DYCRJWpIb6ZkbTUBNzvfblMdPcOi/Vi57S0qZInuFhg84AbunfohF+OBIkkc9ikgL+3tVrWlLpbuU+/d6YPCOLj3S577+jxWliUDMCpyXIPbeum16DQSIeXqLPFFknptZ1jOLq/bnm4RFygz5+dLebfiOE8e/JiczHj2yeq7oEiWuW/D45gMpShWKyuL1BD8KdEXEtR9Iv1G3Eu0Wb1m0rJatjJCY9h2PA+rAj0CPQjzqfneyqhQ0wrCLeq7R29V8LaJIR4trNvW8jML2DfIkr8f4760pbz41z0AfLbxOFYFxvcJcggg2inIP0a2LVphhFL1PAgPHV5rv8NsQqkpRd0BOGJpG6erPUR/clyIY9n9k3ohSep3SU5KxVm6Lx1FgRHd/ZoU2Wh/7hrNVq79ZAtXfbyFDYm5rWWmQCAQCJxEp3UKhDRSU8BQwynQtPC4PJvuToi3HledpuGVG0lMoAeuOplyo4WTec6tY306PYLUGdHsEgOlBnOD6/6y8SWuWHkbL/906VkdU1EUth5XnQKjbCXyigqOk2nrGPbufgHhQapIY55GIi23gB37vqLcNlArNLXNQNve0bsgLpg/HxjHbWNj6l133vI7OSZbCLJY0SoKu1yKifNaSWIbR2AoikKFpM5qdXcP59wgVcE9QaMOAkZ7RnOuTg0zLTPuc0oJtepCeI1lz4m/AIizupFZWaU+brU0fM02Bbty+zmNqPO99/hKAAZJ7mh0VYJp11/yKSPNMhWSRJpsxd1qxc/tEQBMkkRZeeulvmw/kc/aIzloZYm7xvXgt1OqqOBlAUORZBlP3268fvMWZioRABRZklvNlrooqTSxzqaSPn1QOG/ueZ9flSKyNTKyojCi/78a3F6SJPzcXdiZeQ9vRd/Lfee9BEAGlmbPcpotVo5klqDFxI59t7FKVt8XaRqZ+f88BkAvq4yXVSFNI3E4aRmHkpaRJiu4WhXOG36vY1/hii8AmaWHHMsycxI4mbGzWbY1hc221IFzq0UJ2MkwqU6PW8Mnco7iwkPB5xJpUaNt9qL+HtrT2s8kSZiMzXe2/JGxEYCN1hJMFUWOAectY6JrrXs0WU1nibLA5dFTAQixKLi6107h8XV3oXewJ6cq1SiCbFni93XP8cf65+u+BsxGDHn1zOSbjWTu/ZaVf9zBr79ch7mi7siZMoOZ+JRCACb0qRIfjQn0YHJcCKN9fuDj369vdYdfXfwer0ZfXDokoknbueo0+LjZroFUVXTWWdVyBAKBQNB6dFqngF2tOL/M2KCAW6WlaqDTVKdAvkEdeLaEnoAdjSwRawuTtoeqVhgtPPD9HvbaOhvOwsdNR6CnWk7rRE7DDovE0lMA9PJsOMz3TJzMK0dXfpjJ3Z7k0M5ZKGYTR0+sBiDCAl5eoQQE9EGnKFglieyc/aw9vsyxfWEToz+ag6Iojln+XmcIJT98bAUrzXlIisKHo1/gJje142sIWc2xrLaJarBTXGmm1EW9xvoE9WVM36tqfD8wZCjDglWHS6VbjqOUVVtiL0cYG9J4pffduQcAOMe/DxmmvrjY1MfTMna0iE2KolRzCviecf34XDWXebB3TUeR7OLByxPfxNM2QLjCNZIh/cbhZvs7v+BYi9hbF/9dqYqeXTMiCk3lPnZJBmRFYfqoh6tWkiTCvfoCUEDbpjNtOpaP0WKlR5AHvQI0HEQV5LvMLYq5vW/Ez7f7Gffh7+FCieKPPvxqQkPUqIwyWaKkpHZOemNIzivDYLYwPeQtfpaKkBSFSKv6Cl1kVFMvLg4cymCN+gzYn7KOlQfUCJtxGh/cPatmjoN06n2faVYHaiZjBTcuvYZrVtxKUVHrhrk7RAZ71lafz7Sq78O4iDF8cesubrxkPoGy6pDLszliB5So51fdOVBe0byZ49yMeEdpyQpZZue+70i2OcMHR/nWWv9o5m4A+uh8uGjMk9yoj+SxHlfWu/9zuvtTYvUjwqza+nTyrzx14he27J5fa92Pfr+eMX/MZOf2D2osN+Yf56Wvx3Jh/Ks8mr+N50oP8tfmV+o83o7kfCxWhUg/txoVM1AUYl3e4GB4PH+5JLPrwOL6G6UVOJ5TqpbylSUuGVhbmPNM2CsQ2Mkrc45ApkAgEAhaj07rFPBx0+FqE1BrSDW30lr1cqvuIGgM+bbdtpSegB17CsGhjCJySw1c9+lWluxN575vdztVBR6gR6DaITye2/AM/FGTOqPQJ2TYWR1vW1ImPcPns80D3q08zv9+uoyjmepsWm+d2k6yRkuIRe2wlpYcZl3ZKcf2hZbWF0fKKTVQVGFClqBnUMNOgf9tfhmAizS+9O13FfdM+5Rwi5UcnUSI7gPKjS03m30mMgsryHJRHWa9w0fQM3oiwdVSVgbGTGVwzGQATrkaWb9/K9d+OYy3f72mzv19u+welqx+okVtPJJlU8dvZKSAoijEm9SZ0JExk4iNCCbCpF4bR0+1jPr4idwy8stMaCWlVnhzXew1qM6UIRHn1vourOdk3u55PZda3bjr/NcZ3t0fb5sPM7+4dQaHWcWVbD+RjyzBvyf1YsmO9wAYI7kTGjq0xrpRgWpFlFxt25bOO2ZzOg6N8uPEidVUyBIeVoUXrlzCJWOfbNQ+/NxVB2ZBuRE3j0D8bM6WjGYKzh1ML+ZS//+x2l91CP0nZiZ3x8wAQLFVN5jY/0YGevcAYH/eIf6xpw50u6DGvoJ81OfiKY3qtNyydwFZMpTLEkeTVzfLvsaQXVxJYnYpkgRjetaMFLCYjWTJ6v0fGjygylZdTedBXv5EJpRE83L0ZehsjoGK8rx6j1lekIxirvv6+XvPx47KEADrk1ZwvscvzAx/ETdjeq31jxaqjrI+Xt3QuXrxn+uWc9HEF+o9dj9bWdhwc03H/fwDnwGQcnIDlTbb1xYlYpIkPj/0lWO9wpSt3P7rdH7UVCIpCt62x+Phwro1SrYeV9PW7NpAANkZ8TyxcDxfGaqiQhIz2rbs8PYTql0jY/zx93Bp8vZ2XQE7+U6IGhMIBAJB69JpnQKSJDnyJRtKITBYTNU+N+1Fl2eLFKgxI9AC9LMNgNYdzeGKDzezN6UQX3cd71w3BBdt/T9ZudHc6oNKuxL08QYiBcorCkiV1A547+iJZ3W8I/Fz2O2hoLF1Pj8xpvB5phpuGuvVzbFeqKzOZJSYNpKhqepkFiitP8hOskUJdPN3bzCN5OCR31lrLUJWFO499zkA3L3C+b/e1wNw2C+d9XtbX3XeTnLaIYo06vUU020ckiwzxlWdRfK2KnSLHEPPmCm4WRXKZZm0ypc5JJlYVHSoVvhrQf4xXs3ZxHMpyzAZWi7txZE+0EDlgeqkpu8gR5bQKgoD+sykR6AHPgb1mj2a0zLq4/aZ1mhP0DdwPwLk5R8jxTbQGthnZp3rjB7/f7xy23b8QgcT4OGCh0XdZ35xaovYezqH0lVHS88gT8J83FhdqA5WpnebUmvdHlGjAMjRSlRUFFJQnEJ+K89kAyTnqtdQjyAPDpz8B4D+sjsaTePKqAH426Ka8m2zmqGoIdCZec0rDXdy9wusD1bPfVbQaG6Y8DITht7lqHAQYYXeMRcwMHwMAOuMOSTbqg6MHXpnjX2Fhk1EUhTyNBK5Bcf4K2mJ47vkVlTJt6cO9A/3xte95uAwJ/cQZkm9d4IC4hzLQzxqlvtLMfRnRfp9jBvzPO42J2J5ZX6dx4uP/5Kxv0/niq+G8sfyf2M5Lc3gr6xtAPRW1N/mn8ok9kVsY7VPOe8sm1Vj3dKiFHZUqhEZfWwpY2fCri3kbhhNjFnhUd+hasoWlTz//RSmrb2Pp3+didVi4oSkvis2UkHGyQ0UpG7jjr9msVenwUuB/416ngejLgLgZGXdUVNbbKluY3oEkLD/O17+bjIzVtzAcmuhoxIKQF5p25YcTrf1gbqfVsmhsVw1PJL+4d7ccZ4a7ZRf1rZOQoFAIBC0Pp3WKQCNK0tYPVLAYGlaSFy+zYcQ2dKRArbZjQNpxZzKLyfK341f7j2XEWcofdbv2b/o9+xfLWrL6fQIUjsVJ3LrH/gdO7kGRZIIsFjxD+zb7GOVF6Wy0kWtSX2d9zgeDlPLuGXbBrJ9gqoUp6Nc1dzuXb7qgK27bba18CyVsRuDXWSwV3DDA9fVh74H4EKNLzE9L3QsH3/eU4yocMEiSXy+/0Ws1vrTXVqKE4d/4+QJNYQ12Kzg7qZeWxf0uASA0S6BSLKMVufKQEm9j466qXaVyRJZOftr7K+gSBWtM0sSeQUtMwuWU2Igr8yIJEHvM7StnT2JfwDQX9Hh6hFIj0APqFTDto82s0Z9SvI65v5wMR/8ejVF5QbesymID/A/c9TO3qO/A9DLIuHt1/2M6/u6u+BqUQe+2cWtk05yMF2N4ukf7k1JcRpJkvq7jh5wY611YyLi8LQJzR08vpbrfpnGFb9eTGVlUavYZue4LYS8R6AH+2116Qd4dW/SPvzdazoFwjTqsyuj4HjdG1hMUM957dv2Ht9Im7BIEudJ3XjwYjX83M+nG0Ml9fk/0bMHkiQxsPd0QC3pBzBCcsPLp+bAOjAgigiT+mzan/gnayqrZsWTi+qxrwWoL3WgODeRjGw17SbEKqHRVjkMwgKqnuFBZislVn/MVoV/f7cHve0WKK+oxymQugGzJJGklXgqey1fL7/H8V12+k52Serg8pkxzwKQrpUx2CqfLDJns3/PAgAM5bn8+9fLSNNI+FsVRvS/oVHna3cKbC+5lCWzDnDrZV9zmYvq+PzFqN5fG0wFpKdtp8J2XEWS+GTdU8z663aO6GQCFIlvLvqacXFXEWN75yRba/cpSipNHEhTr5/ensn8a9dcFpmyKJdlBiku/HDuPEZVqs+xgvK2FerLsFVJCjttxr+xXDYkgj8fGMd4m05CXpmIFBAIBILORqd2CoT6nFlssLLaTLLB2ninQKXJ4qhTfqY88qbSN9Qbe0Tl4ChfFt839oxh6S1FUvY+/u+PG0gpqFtwyREp0ED6wNE0dfanj+wO1UJDm8rv65+jSCMTZIKbJ73B7VPe5e7AkY7vY7tNcHwOcw+pse11Yao6eYksYTK1rq6AvZRgn5CGf6P9xWpnf2TwaSkVkkRPvyfwsFpJ0Bj5ee3/tYqddpKOLuXSbc/wvzI1KiHMWtVRPH/UQ3w54hmenVFVa31QHYOx4ymba/xdWk0ULze/ZcpuHbYpvccEeODm0jghz2WnVCGyEV7qjFZMkAfFBvWzPaWlKSz9Zw4z1s7me0Mqn5Qc5r+/fENOiYGYAHfGh57Z4RSfprbTYNfG1QV30crorWrUS05Zy1ZMsGMvd9ov3Jv9R35DkSSiLBAYVNuB56bXEWxSXxOL935Mugx5skRm1t5WsQ3UUq/Jueo9GxPkwYEKVQxzYNiIJu3Hz6OmUyDc5jhML6sdlg7wf9+MY8bCMWSd2lTruzcPfk6lLNO9zINbz/sCqdpz7ZFzn2WKPpRbz38NAF+/GKKq+fUmBte229/DhUCD6kz4z6H5DgcCwInyussCni2KolQTGaxyCnz06zWM/fMKPtz9DgBhcs388ZiwqpSSQKOOKH/V7nVHc3BRVLvLDXXfW9kV6nPBnmZwtKQqteunzfNQJImhuDI0dibdbfoMYQYN5xGEIkm8uPsdzIYSPl1+NztlE55WhY/Pex0f3261D1YH9lz43FIDZptz6/axzzqiO0BN2Vix5yMAx2z+L0ohiVqZQEXi84u+oqctraZ75GgA0mVQzDXfgZuS8rBYFbr5u3Ps1GIskkS0VWL+4If55ubt9OszAw9ZjSosNRU2yv6WItOWQhl2llWSAmzRN3mlQlNAIBAIOhud2ilgjxTIbMgpYDVX+9z4kLh/DudQYZEI93FlaNSZFcibgodey/9Ni+O2sd354c7RBHo2XIrNzqEXp3LoxanNPq7ZaubxFbNYkr+Pt/+6r8517BUITuSU1avinZiv5tH2dm98LeTTsZoq+DZ7KwD+hQMI9VadEbOnfcZTUdN4KHQC0VFVOdrhXlWChnFlWn5MusrRwSsqOkVrYhcZ7N2AU8BqtXDAVqt+YLfza30fHT2a/rndAXjn5FJyW1FkbvdxNZrEnssboqk5azi83zX4eFcpVA8KH+34HGlU2/T4aaH4pdVmvnKKklvETnvpy9hGpg4cP7WBTUopkqJwxTkPAqoTK6VCzY9OkRXKG8h9rou/UtdhqTYAPJKqDhifmxHHGTIHANhTcgKAwUGDG31MF0UdOOTXo3B+thy0pQ/0D/dhT6qaijNEX1t0zo6vRR1IrDBWpTPkFSa3im2glnr1MicyPvpxvlsxhURZHcwN6DmtSfsJ8KjSFAAI9VBniDPrEMWrKMlkKaUk6zS8+Pe/a+TAW4wVHLLNaOdmXc2AiJoRW4N6z+DN61YRWi0qaqBLVU75xEG31jqev4cLlfnjiTCZqbBdX71sjoRkc+tUTDmWU0ZaYQUuWpmRtqizTVve5MOSBAC2Sursb7hLTVHPnt2GOAb17kYfbhpdVRXAxTaQr88pkGtb3seWHpBjO7fK0hwWFavviRv6qBolDwz5N91L/Uk99W/uGv8hPlaFw1r49I9b+M627nOxNxLXu/HXQYCnHo0sYVUg1zaQ7RY9jpe7zeAB38GMRb3XfsmLB1RBSHsAUC/0LJzxMz2q6WwE+vXCw6qK2lYYbLoCisLxnFKeWqxGT50fG8SOrF0ATPHtx5ghtyPLqlPTU6tqkJSY21a9P90eKeDTvEgBO0G2voj9nhIIBAJB56GTOwXUzmx6YUORAlVTOgZr4/PPF9vK+1w2JOyMdcqbwx3jevDcjP6NniEFcHfR4u7S+Jzb0/lx08skKWpb/VOZTmbhiVrrdPN3RyNLlBktZJfUHUJ4tFwNy+ztF9tsWzZueYOTWhl3q0I+NzjaWJIk/jXpNWZNrakQ3bPaLOLx9LvYm1aBty3ftbAVnQKKojhKCTYU4p58cj2lsoSrVaF3z9qOmz4hXmzIm0Uvg0KJLPHp6kdazeajhWp4f5BJwcOiMLrHFQ2uf+6wu5is8WWW11CCSlVHz/GimtdGSbWc4txmqrufToJDT6BxlQe+3/Y6ABMkD6K6q1EkMYEe5FvC8LFYUSSJU6mbG9pFLVJsg5hgkzpS8PVM55np/Rjbs3Y5t9NJTtlMvGREUhRGx13d6GPqJfV8C00tr/hfXGniZJ7qnOoX5k18sep8GhJYf462J+oA0ljNOZJX0nq6AlkV0NtnBXvcNSyWyrBIEkFWCAnq16T92CMF7LOaYd7qYDajjgHZ4aRlDifZeo2JpX894Pju1Kl1VMoyequCi8dgPPRnfsYO8ldz8ntbZcIjRtb63t/DhR3lF5CW9BwvB47nejmAV0c9A0CarGA0lJJwZAkGQ8tdAxuSbKVdY/xxc9GQm7mXOQmf11ov1C2oxt+ebm4E23wknppunNer6nudwylQt53Ztvunn81BnG2rbvDnxhco0MiEWyUuGGFz4EVcx/6UJ1CkCAZFx/JIzEwAPixLpESW6W6VuHDUY006Z40sEeylDmQzqwkOz5g0jzsvW8g5tt8p1aZDE+sZyWujnuXOkLF8c83fRAT0qbE/SZLoLqnXVUllEs9/NZKnFgxj9mcryC8zMjDCh8em9GG7UXU8jeg+ucb23nr1XipT6k9pbA4ZRRVYrHU76RVFcURLnq1TwH5P1XMogUAgEHRgOrlTwBYpUNyApoBSlRfc2EiBnBKDo4M1c3D4WVjYfigozeKDY78A4Gm1YpEkftpUu+ySi1Z2VFuoS2xQURQSbTPifSJGNduexalrAYgujCDCL6jhlYEBcVfyeo+rebvfK4yMG8nwaD88bBUJCkpaR7ANILWggoJyEzqN1GAayb4Taq36frigdaktTNknxBMjetzyhwBwpLRlBtZ1cbRCDUv3zxmBS957XDXhzgbX1+u9efvGDdwz4wuMlepsa1JFTaGt0spCx+fc8pYJe7dXHjhTpEBO4Qk+WHobv5Wqjoob+93k+M7dRUuYjytBRtW5lpwVr+478U8qyuvOg7ZjtZgdgpnhpeo1qPcpZtZ5MQ1tRqWxDKti5aftbwIwTnInLHJ0g9tUR6/xBaDQ0rIDB6gSbgz3ccVbD/ts9+rgHvVHGHloatc1zyttvfKZOZUSZnfV6RpiC0+/0LtPjZD9xuDvXjNSIMxfHeCdUoxYzDVnOg/a0jxcbTPi87LWk3NSjQo5kqJGU4QbNfQLb1xU2OXjnuMalzCeHvZwnd+76jR4uGgowYtBo15nzk1r6RN3Fe62Wej3/7iZa7b+H58tu7spp9wgGxLVgeoEW174p2ueoEAjE2vVcr5c5XgLr6OMbEx5BKEmC+HBl9Iv3JtXrxjIa1cORGtV76syQ90z37m2lLz+AWq0To5kBUVhUbpaCeT68PFobfoFCRlVlUY0ssTM8S8wjKpB7K3RFzdJaNKOXTm/ripEQ6NrVoWI8YtldL+reeCij/F0861zf91d1GvgH8tulujM/KEz083zZXr5a/nithEUZG4iS6MKNg45zRno56GmEZVJLTfT/tueNMbM+4fPN9Z24oNaerbcqE5+2CdKmotOI+PnrjurfQgEAoGgfdKpnQKhjUkfoLpToHECb0v2pmOxKkR7Kg7hvY7OF2ueoESCWJOVZ4POA+DnnO2YzLU7Lw3pCuTmJ1EoS8iKQo/o2mHyjWXeNcuY5noZCXlXNlrI8eJxzzJ5xKV8eMNwJseF4G5TcS9qRaXn+JRCAOLCvGtVHlCsVp75/kIeWziBTRlbABjoGXX6LgBVYC7IS0+xUXUyZVlbR8hJsZg5aqv5nl0Zy1XDoho92HLVaTBK6sDqhFLTvtJq4cO5lfkcOfQzMz/rx/JVjzfLTrPFSqItLSOugXKEB44uZebiGXySt5NKWWKUVcfIYffWWKdHkAeeJtURczL/KOu3vcNVm5/ktd+va9CG7Jz9GGyVDPoGqzN+iQ2EdlutFhb+9QDjvx3FpV8OZXGxqnJ/be+rznzC1dDr1EFbES1/DdhFBkcGneL7v/5Nua3UX6+YC+vdxsu9T61luc2sS98YssutnHJVz/2Nkc+w8bqNPH75oibvx9+hKaA6e3vFTMbbqpAvS6zd9laNdQ/aSszd6j+UfpIrJRqZF1f/G8Vs5HCuKsDnafAhLqxxUSvuXqE886+VDBt8a/32nVYdQZJluqMOer8tVaN5DhTXPdBrLClHl/PatxMwFO1ie7KajjIxNois1O38bFAdj48Ne5Cr+lSVGQ3z7VFrP9kuz5GU9BoDY8cCcN3IblwyKByNoj7zSo11OAUURXUCAP26jVfXk2UK8xI5YksJuWj4bMfqDq0LWxvLsoZnJ7yBq1Uhwiox/bxnmtUGdl2BupwCA2JnOlIjAHqEDT/j/rp7qc/wg9Wi+DZ7W5gR+jGBnnp2HP0NgEGSK26uNUuWBvmoDrYSueXEZL/bpkbCbT1ed2qUvf/j665rUuRhXWSVpBPmtw5ofTFcgUAgELQtbeIU+N///kf37t1xdXVl1KhRbN++vcH1f/rpJ/r27YurqysDBw5k2bJlzTpuuM0rnltqxGCu+yVmoKpDYFQa96L7ZZc68zwi6Mzq4y1FZnHrhcBbrBaW5u4B4N7u05l8/ssEWizkSwrbD32P0Wzg++1vkVWizt5V1xU4naPJavmwblYJV4/685TPhKvWFYt8NSXmCCKaIY7k7aZFb1FnNArK6y4f1RLstTkFhkT51vpu574v+c2YyV+WfFaY1Q7bwJBhtdazExviRb5J7TRmycrZVyGoVm5TshigopC09O2UyTI6RSHV0JcrhtWeBW4I2WM4kqJQqJHIz6/SPSgxVQ0KcozF/H7gS47pNLyYuozcjPgmm56cV47BbMXdRUOUX90lPw8e+Z07Nz1JsSwRa4b/Rk7jo3+tQdLU7PjGBHogGdSw3ZNlafxzXH2erK1Ir1VasTopGWpecJAJ+vVQS5GlaaDUdh+Ulx/kke/G8cGvV/Pt8tlc9dUwXstcQ4UscVK2UiJLhFsUxo54oN5j1IWbqxpqXdQKHe9D6cV4ygVs0/yX13JtIoiyew21+dPx9qvKqe5ri77JM7SO3gGAZDhIgVbGRVHo33s6PnoftHLTZ4j9q2kKWK0Kbu7+XOvbH4Avj/6oKhraOGhUo0YGhY/h5UnvoVUU1mpMLFv5IIfL1MGzuSK80foWjbNPHazanQIA3XW+AJhsjrrsOlTum8LXu95hkVTCQsPPuFlyiPB1o2eQJ5+t/z+MksQw3Bg16BbOHXY3ERZVELBnt3G19vPUtDj+PakPFw2o0olx02mQreozttRY+11QVppJuU3RPzpiNB62mPPtB7/HKkl4WRWCq1U2sCv3V3e89Ow+kT9m/MwPVy5D79I8sV17BYK6Jgf0ei8GVItGiIk674z7617NZjerQlSumoKwyHKUoqwDbMtWnxsjfGunz4X5qyksJbJSryZPU8guqWTHSfXaPZVft6BuepFdT+DsqyS98ectpHgvoa9361Y5EggEAkHb0+pOgUWLFvHII4/w3HPPsXv3bgYPHszUqVPJzq47xHjz5s3861//YtasWezZs4eZM2cyc+ZMDhw40ORj+7rrHHXEs4rqnnWrrDZJWj1qoD4SMoo5lFGMTiMxLKBtEuuOpGzkkl+nMXfJjZiaWDaxMexI+JEcWcHbYmXc6EfQeQQxwTZbuTZpCT+se5q5CV9wz+LLMJmN1SIFancEd6erIbd9dI2bUWuI1AK1kxNZz6CwIbxddWgtaqe7sLLmDMr2Hf/jygUD2bjlzbO20R4pMDjSt9Z3vxz8GqBGfepBPS+ud1+9QzzJNUUiKQomSSK/vtJpjWDzljcZ99VgVv7zFCgKy3Nf4aKfJ7LlwEIAwo0SAyMDm9y2QX4hBJttYoMpGxzLS01V10KepYLDZWp4eaks8/bfTRsUQ5XIYO8QL4eehKE8lw9+vIwd298H4O2tr1AqS5xj1fH11cuZesFr6E6bmQPoEehJhVEdzJw0FLKrQo0cydNInEqprTRvJyVXFTfzNemIDO1BsEU976QTquNrdelPrKWMT0oO82r2ehJlK+5WK3MCx3CbazcCLRYeiL4Eja5pebyeHqqyepFMiwwcqnMwvZj+Xn9TKsv4WBWmaQN4aHTD1S4CAnoTVwHdjVZmBKv58Xmm1hHDA9DI8QDEKq646Jo/kPG1hTlbrAollapezPXjXkSnKMTLJuL3q/dCaeEpkmW1nfv1vJjekWO4J2wiAPMy1zsEQnMr+zTLQVkf/jb78quVd+vuWdNJl32WjqGTBnXAeMJFw+jQ9xnfO4CslC2Ocnyzhz2AJEloda58Oe1rfhj7GiEhg2rtZ3i0H49MiUWnqeoyaGQJjaI6XsqqVXh54YepzPl2Elm5qoChh1XB3SOIIEXddqvtHdFT0juilAxmCzuS8x3Hqk5oYF98vWunNDSWEEcaYd0Rg8O81XSgUCu4u59ZKyQmtCqaYLCpB4dybqK7WUuJLPPCsltZZSkEYGzvmbW27RbcC4ASjUxB6dmLDf51MMvh20otqKjzeZFR2DJ6AlCVeubjdnYRLAKBQCBof7S6U+Ctt97izjvv5LbbbqNfv358/PHHuLu78/nntQWOAN59910uuugiHn/8ceLi4njppZcYNmwYH3zwQZ3rN4QkSY4XYUZR3TMuhmpOAUMjnAKL96izRufHBuHRBql1RrOBp/55EKMkkZGbgFZpeVHDpQe/AWCqSxAunmppv4lREwFYX5zEunQ1pzZJqeTTfx51pEycOM0psHzn//gsbzcAY/wHnLVdqQXqb9bY9IHqeLvpkC3qb19QLbQ9J3Mvj+//kKNa+PP40rOyz2SxcsAWjj34tEiBosJkVpnUEOuP+93NJbIP1+nCCA07p9799Qnxwogr/rbBZ1buwWbb9vWRHyjUaPgo+Q/27v2CjXor2RqZjzPWAeBt8KR3SNNnPSN83QgwqgOB41l7HMtLzFWDgmzFxGGqOuBLrAWsX/9yk46z+5Q6E923mo2fLruLTyqO89SBTyjIP8ZORT3mixe8j3sDg4aYIA/yjWrH/whGkjVVHefdiX/Uu90pW+i23uRJsJeePlp1pvJoxnaOJi1jm4sZWVE4T/JksKLjP0FjWTlzKddfMp9Hrv2TNbcd5JILXmvSeQP4+nYHwCBLVDSxWkJDlBrMJGaXoPVSr6srvfvy2g1riYu9rMHtgrzdiE9+CXfDO0T49wYg39zyegcABrMVg/4kAEM8a4exNwV73j5Avk1XIDAwlhmu6sD7q3i1DF3CsWUokkSYFQL81Ovk9slvESe5UqSRKdLISIpCSuVAR0paS2CPFMitVt4txr9mqkahLGGobHopTTsp1SINNvlW0sfyJZ9teAaTJHGO5M7IgTc6vg8NHUqf3pc0af8y6jmUmdX7vaQ4nZ8N6Sw157Dl8M8ADmdAkEZdd2uFGmnT07VKK2ZXcgGVJitBXnr6tmA0BlRFCmQX1z0xcH7cdciKwmi3xkVNRUeMwccK7laFrSlXAjI3954FwCrZgFmSmKgNYGi/2uKiIYExDifxqYyzL9264kCVtkeFyVLjWrKT6YgUOLtr12IxkyqpTiqzrvnXpEAgEAjaJ82Xqm8ERqORXbt2MWfOHMcyWZaZPHkyW7ZsqXObLVu28MgjNZXXp06dym+//Vbn+gaDAYOh6mVfXKzmJZpMJkwmEyHeepLzyknNL2NYVM3Za4vFVENR24CCyVS/2KDZYmXxbjV1YMbAYKyn0htcvzks3fMR7x/+irmjX2R4zBT+99e9HMWIn8XK/016D7MCtOAxK41l/F12CmSJi3td7jifYf1vRn/iZ9JlSLdWzQp+mr6G96LUHOlT+eWUVRhw0cqsOfANcw58hFWSuErx5NLz32hy29jXN5lMmC1Wh2JyiJeuyfty14LVos6CFxqLMZlMWMwGnlx+O/m22a5Cc/lZ/X4H04upNFnxctUS5ePCkhUPkl+ezfWXfMnvG+dilCT6WjWcM+RuRgy9BwCzxQKWumf/egSozg8/i4Y8rUJ6zmH69Gi6fSWFJ9hGBSCRpIFX9r0PWls4sn2mrzKE7v5uTT7/EG8Xjhp9wSOXxIIkx/Yl5ionQKZGAtRc/JkePfi5/AT/OfY9XwX0JaZPPQNQReHUvm/w8Y3BGjSKH3ao6TIT+wRgMplIT9nMlyVHQZbI1Ej876/ZWCSJvhaZ0LCRDZ5HlK+edEMvtKgD7erszt7N9Hq2TS5TIwoUYwB+rhp6uUewsewoR/KPsit7LwCTNX68et0/NbY722eCl0cQeqsVgyyTnZdIhEvt6Ifm8OuuFGRrKUke5YDM+X2uapStfq4ajOhIK5Xx9VA1L3IVU4s/+1AUtqx9j5PupYCGEdHjz/oYfh4ulBkryC4qJ9JHdWZdP+Jxft34MKutxRxLXMmuE38D0E/nV+14Es+N+y83rpuNWZIINUmckD3w0kktdt7d/NRB2uGMIsc+owIHwYmfa6yXnrmPyIjGC1XaMRvLyLDd7r1KfEjyKuLz0pWUyhJIEncPefCsz0VjcwqUmyswmUxkZFc5MldkbgUZAmUXTCYTQVoPMFeSYlP67+7V3XH8dUfUGejzevpjNje+ClBjCHBXuzkZRRV1nm+/XpeyxC8Wf6+IRrWHRtaz4OIf+fmv9XxqCiTI04XLRt/F7ye+Zi8VuCoKj016r559SXhbFYo0EimZifSPqb/qx5korjCx9bgaXeHuoqHcaOFEdjG+rr411rNH3IV4uZzV752ec8CR1lKkax3NG4FAIBA4j1Z1CuTm5mKxWAgJCamxPCQkhMOHD9e5TWZmZp3rZ2bWLRY3b948XnjhhVrLV65cibu7O9YSGZBZuz0ebdqeGutYLDXD94woDeoXJBRK5JRq8NAqGJPj0cqwatWqetdvKoqiMD/vM3K0Cq9veJqpu3bxpWE7SBK3WePYtjcX9jZPX6E+8kq3USZLBJotpKWHk55Vtf9hJg1b9OqsRqTJQg+LzHpXiVe2PICX632UVEby7e8rMCjb+cLwOxZJYkqFxNCgB1m+ak2zbVq1ahX5BrBYtWgkhZ0b/qGpVR+zKsBiUWecssoLWbZsGQdzPmS7rmomJddU3my9CoBNWRKgIUxvZMnid3i5cjVWSUL384usKN8KLjBSiWH58uWN2l+FGUCLm8kF9Abij26jPK/p9qXkfYdZU9VgSTaHgKQoKLZOXbEhhvyTh1m2LKFJ+87IkzBWhgO5JBSnOtqvwFAGp6Wlx5glBmhv4rBpHgd0Jh7e8ix3HjYhad1xrUxHYzVhcAnErPWgvGAFr7MBd6vClOKLKDNMIsJdwXB8J8uOK/yd/SIGfdU5/WhIBUlikDX0jL+hVQEDXoSZreTY0om6mRVOaSV2VWTUu/1JYzHowGAOY83ff+FS6g4SbCpPUR0fksRg+fyzuobq4liBhI8FsmVYt+EPfD3PvpqDosD8/RoGeP7DUVkm2GzlRJILJ4+f2fbcSgAtWUUVrNqeBS6QJyn8+eefTa4IUJ9x2uIdbC37kz/czFi0GvzMkJvqx7KMs2tb2agBJFat30Kmf1WEyEijC9tdjLy25nHidWaQZSLM3Wr9ltPpy28cwbsiEC+tlRUrGncvN4byAvX5sT0xg2XLVGez1WJiiBE8FA3JGhNpWpm1G3/D36vhShl1UVl+GIsk4aIoxKc9Qt+YF0hVx/AMNunIOuHBshNn175Wm25LUWUZy5YtI6+46rkfL6sDUE+TzLJly9CVSzWeEcZ8naO9/9yn/k6epaksW9ay5S6zKgC0pOWXnuFePdbAd7UpMaiVWPw0lSxfvpxJ+svIK/+RSdpziN9+gnjqDrH3skgUaSD+0FasBc2fvU8pVd+RUfoM4gK+50DuJfyxxkJGUM0UggPH1T5QVvIRlpXV3e9qDNnFax2fc3SdvybhypUrSUlpvdKrnY1Nm9S0oGXLlpGQ0LR+RXuno55bR7W7o9OR27pVnQJtwZw5c2pEFhQXFxMVFcWUKVPw9vYmYVUiO9afwDesO9OmxdXYtqDgBFTr4xkkmDZtWr3HWvPzfiCDK4Z34+KpvVi1ahUXXnghOl3L5BEcOLma1E3qyzZRaybL8DNWSWKG1Y0bb1oIcsPKwWO+GwPAluvrjsKoi78274VkiJZduWTGzBrfVS5fwpYCVRTyPLcwZo34DzdueIg0LbhFf4Bb+qUQHMdXp37HJElMMWt5+bplaJspMGgymRxtuietBHbvJMLPnemX1Ba+OhM5JQb++kj9ccu1ENEtn+cK0gCJK/SR/GpIpURu+Pc+E398uwfI4YIhPUku/sRR57zILYkEkxWQuHLSI0Q1YZbvnSPr0JrcAQOyt1SnfdlZ+/lsw5Pccu5zRITXroH+6HfzABiMK3ttYfwjTC4oOg07UUNJT1UM5I2p4xoso1gX0enFbPhiF7CPk1oLF190EZIss3Dhs7XW7e8RxqXTZzKu5Bxm/j6DUzoN3XtZ8NLB9ds+wCBJRJaZ+XfQWL6zbMOslSjWSKzxWs6wAjfuuOxeLuwXzJ7dH7O2yISsKNzo3Y+vSxIczo2rxz9Ez5jJtY59Ov89vJ4Ao44crRqlcWvUFF5KX0mKVmbUyB4EBPa1te0+7l55K+e6R5GhsQIyskss06ZNI/G4jvlb40mzORaGG7VcdfWTLXb/24lMLWLzShl00K1HIOed0/xr1M6+1CJSt26jR4Qa4XCBZwzTp89o1LblRjMv7fkHkyLx9fEINH3ViIsJE8/B0yPkzDtoAGt5Pl//cQMfWTMxuasRJt1L/Di3+9PMnNr86iV2fs3dzanEXHrEDWTa8KoUk5CDhWzf+1+26NXfeITsyQPXfIZ82jP2YuVigjcv5c2VVs6J8mPatNr3W3MZWWrgk8PryK6UmDh5Cu4u6ut4OpcDcOe3o0nDSFCEB1PPa/w1UFmciixp2XP0JByBUItEnqInjAfIUd7GIEn8e/STDIs9++tq5SdLALBo1WfVig074LRxVDffMKZNm0bByj8ht6pqxcUX3E5o0ADyyoykblkLwH1XTiLQU3/WdlWn1GBmbvw/VFokJlwwBQ/92Xd7TCYTiz9dDcCYft2ZNq0vMI2beOKM23775XOABQ9f+azeP38nZMP+eHqFfsYW9zKG8DMB0V8wbWLNtJt3EzcC5Vw0bhSje/g36RipRSf4fvsbTOo1k/KTEqiZH1hawhnYTjEa1YmDV16pXZJZ0DCyLPPMM82rEtLe6ajn1lHt7gzYnyUdiVZ1CgQGBqLRaMjKyqqxPCsri9DQ0Dq3CQ0NbdL6er0evb52J0Kn06HT6YiwCalllRhrdd4tlpo58QZJqreDX2G0sCpBnbG7fHiUYz2dTkd65g483IMIDOhd57aNZcV+VWfBxapglCWKJYVQs4UnL/kUnf7MMwoGWwmzpgxSskvVHlyYzqvWdhMG3cTL61SnwLhu5xPaZwo/WF/j8Q3/YaeLBiKX8Mmp3zFKEhNMEq9e/Qc6n7BGH7s+dDodmcW2cFp/92YNuvy9ZMotvgBkKiae2vMmFo3EJbogbh0zh1/XzqZQUpo9oEsrrOCfI2pVg+l9ddy59gTYlLa/KzqAWZYJtUBM9HlNmk3tE+qNku8DFJBlyK/Tvm82P8fPxgyMm57lletX1/iuvCSDzdZSkCXmjH2BRzY8SbqsMMZ1Ep7BFnZmrMLXbCVdCaVniA86bdNkRboFenHK0B9X5RcKZYmiomMEBfejVKmtx9HPPw6dTkewfwz9ZHe2UcHJnL3klVWpkp/U6XiscDtoJTytEIQLJ7RGfCJ/5aL+z4Fi5r8H54MGrvCI4a7Jb/LtrxdjkSS6WyRie01Fks98DuG+brgZPMFdzYU9UnwhPa1/k6RRiE/8lYvCngNg0daXOamBU5WnUGQ1l1zjOQCdTkdc74u4et8nnKrIpodrID1cz3c8Z1qSIG933CxawExhRfZZ799qVfhw3QmGe/7JUc9iQObC2CsbvV8fnY6p/UPYmJhLucmbIKuVClmmuDgZP9/mC8CVZOzl8aU3sMlFjboYpvMjLe0S9ucN4D9TBrdIu0YFuEMinMyvrLG/UYNvpl/8exySjHhbFeZO/wJ9Pc9YN80QsCYQ5tu8Z1F9hPnpCPbSk11iICm3guHRNQdswTovMOeRV5be6OOWF6dx9e/T0SsS14WpSvr+Nm2VAd3H8khsD/JLMxnV/5qGdtNodBpVX6YSMzqdjvyKrFrrBLuHoNPpCPWOglzVYe1hVYgMHYwky+w4qT5H48K8CfNrXoWBhvDT6fDUayk1mMmvsODr2TJikZk2GZW+YT5Nui480QPlFBvyzup6yi41EemSwG63UkCi3KWM9KKa17miKGTatBQiAzybdLyv1j/L+8cXY5AgPmc3I71imm1rR8LFRQ1nmT9/PsOHn7lEpUBl2bJlPPPMMyxcuJC4uLgzb9CB6Kjn1lHt7ujs2rWLu+66y/Es6Ui0qlPAxcWF4cOHs3r1ambOnAmA1Wpl9erV3H///XVuM2bMGFavXs1DDz3kWLZq1SrGjBnTLBvsZXgy6ihHVGkorvG3RZIwmyrR1qEW/ndCFmVGC1H+bgyN8nXkPaZn7OTK1XcRpEgsuWkbOl3TlfIBTBYjKwoTQIanvQcyt3gflbLMSzFX4h06uFH72HZ100P2M2x502GutVWXQ6MncIPZlRRLGaMHq0JKgX1n8GnwQF5ZdDk/u5sxShKjjQpvXrEYnU/zBwin4xAZ9G1ee7rqNBgUNWIh1xZKH2WBZ678HrNNJK1MljAZStHpm94R/X7bKawKjOkRwL6ENymRZbysCiWyRJFtkDrSNaTJ4dV9gj05kBUAJJNVj8L7gbI0kGFfZe2w8s17PsUgS0RaJfr1vJj5HiGk5CSQd8KLCaNHs+H3eNJyI9H4ueHSRIcAgJ+7DlnrTZgJ0lwg8dRa1SkgqTOugVaFXFuuR1xk1T3bwy2EbRXJHCtI5FS5OnC4M+AczFo9X2SpIW73dZ9OctlFpOXM5riLhsOHFnEkdTMJGvC0Ktw/5QN8vKMYIbmzlQou8I1tlEMA1OdAfmoAUER3M3y208qUqCCSPLPZlLqBi4DK8nx+LTkKsuyIRAgyKwR6q/eGJMs8e/XvgDpT2NJpA3Z8PXToLC6Amdyys0sdOHLkd+ZveJccazYnoiRApr9Vy9AB1zdpP5/cpApkjp67Gm8LVMiQV5hMdLczl3CrE0Xh1RV3sclFQq/AU/1nMbLXXZz32hpkFAZFtIyOwoBwdT92QVA7kiTx+MgnmbfjNR4aNpvQamXmTsdeyq4l1Ntr2Rfhwz+HszmYXlzLKRCi9wNzHlk2xXcUhUV/3Ma2vIOMChjI1NGP4hvcv8Y2K7e9SZpNN+TXzE2gAZ1Rfb4NifJhQHT959kctBo1RavCViUhu7z29RrkqTqKg3y6O5b1lFwc9+7BdPU9PDzat0Vtq06wt57SHDOZxZWOkrpnS0a5+ozo00TBVk/ZHSin1Hh2ZT0ziirpHvgre23PqgKduVZZwuIKM+VG9bdpyvWbeGoD/z2xGGyvr0TFgF+ZGiYgtXBFlPZKbGwsw4bVX0JYUBN7yHRcXFyna7eOem4d1e6OTmlp61Vnam1avfrAI488wqeffspXX31FQkIC9957L2VlZdx2220A3HzzzTWECB988EFWrFjBm2++yeHDh3n++efZuXNnvU6E+rj/l5lcvvBc9Fo1XLFOp4BR7Yx4WapmOQ2Gwjr393u8+kK8bHBEjYHeP/sWYJAlUjWwemvzS9xtPfgD+TL4W6xMv+h9vujxLxaEXMDoibX1EurD3T0Qd/emhe5n2kpWhZ5WCgsASeLJm9fzv5u34uJdFQGg9e/OsJG/MDMtkmuKdLw743v0Ab2adNwzoaDg7+FCRDMqD9ixaqvZrCi8MeYFPDxD8PKKRLZ1bAqLkpu8X6PZ6hDC+1d/A19kqtUZZkdcgK+1Wt5yWNNDjXsEeVJiUkOys6y1xZzMpkqOoIYkJWugML9m2cKNqesBmOjVA0mSiA4bzqi4awHwcPPn3N5fsLfg2mZ3jCVJItzXFX+j2sFMzIpHsVoptd0TMbL6e0mKQp+YKVXn5dsTgOPlGey3qPfd2O5TeOSij/l64nu80P9Orp/wMgfSXOhRptr28975vJupVku4J+ICAnzUGt9zJrzOrZ6xzLrwvUbbHebrSlLRhfQ1SgxBdVZkFqgiXxsrM1EsFpZvnkuRLDuuDQA/kzqb25Z46bVobSKZ6aW1Z17PiKJwPGkFr/xwEdds+T9W6nI4oZeQFIUb3Xvw5XVr0Gqbd07eblo8zGqIfV7xqWbtA2D3ljdZIpcjKQrzx7/BFSMeYs+pQgAiPMDNpeFUqcbS3+4USCuuVa7tnH5X88stOxk3+LYG95FhK2UX4t0KToFwb5t9tdXcg93V50C2QR08/rzsHl4u2MUquZKXC3Zw79Lajp3fUqscw4dtTVhRoZb4G9xCjpbq6GylZytslXtybO9Pz2rPwSDbfRvsXxVJ10Nf5YQ+nKk+D2JDz76MbX3YKxBk1VOWsKkUV5goMqnPvN4hTXuW+mjV8yy3FJ9hzYbJyUvmoHfVPnI1Eul5NbUn0m2VB/w9XHDVNf6eOpysRqANscj4WayYJYntNnsHWVq92ygQCAQCJ9DqT/drr72W//73vzz77LMMGTKE+Ph4VqxY4RATPHXqFBkZVWV1zj33XL777jvmz5/P4MGD+fnnn/ntt98YMKBpJe52GjJJspSw6dCrAOSWGjCaa4Y4G4yq0KBHtcWVhtq1gwvLjWw+egoJM5cNCa/x3drcKvHC74793iQbq7M04TsALnIJQecRyIAJTzPyonccIemtRYZZTaEI860nNFDnBq61O2vRIf58U3w/vxe/jnto8xWU6+OhyX3Y/cyF3H9+850Nsj4EF1vn9KHgc+kfd4W6XKPFx9ZnLShq+sBmxcFMckuN9PIu5rsjD5ChkQi3wuXnPcsIXdVs38i4pofo+nu4kG9UHTRZsoLVWrNSwfGTa6isprq472jVNadYrWypVCM/zu1+YZ37P56j/t49Aj2abJudcF83tAa1U3+06DgVlfmYbU6B7q6qUypa0eDuXtUWPUOGArDbWkq2RkajKPTrpebTDo0+nyvOeQBFkdiXWkRZsTor/ZO1gDyNTLSi4frzX3fsq0f3iTx65c94eTU+VSXcx41McwwBrl+wv/IWAA6XnYebVSFXI5GQuITvUlTR0Dt9BuJmu27cTB4Ee7etU0CSJDQW9RpIqmyaU6CiPI+7vxrJZZse5wdDGlZJon+pG1dIU1h1yY/85+rfcXXzbbZtPm463GzicrllGWdYu26spkpeSfgCgCu8YxnW4yIAdp1UB78xXi03E9kn1BOtLFFUYSKtsHllFFszUqCfzWlhny2vTrCXGnmVZS5j3YaXeSlHjaiZpFPvsUOyhYqyqhz9U8dXs0uurdxfbgwj2FXB263la+jqbZUxKiT1N8u1vU8m6au0JoL81Gd4YGBV+GpP76r3zZFM9Z0b18KlCKtjdwqkF7aMUyAxW50JCvNxxdu1ae3qo/cFoEI5u7KeZUV7MUsSgRYFV6tNRLbiMLtPFXD/d7sZ++o/zHhfdViHNtGhdTz/CAB93ILpJ6mOXvszfrxv7FnZLRAIBIL2SZu4fO+//35OnjyJwWBg27ZtjBo1yvHd2rVr+fLLL2usf/XVV3PkyBEMBgMHDhxolhjPaIPaOVpSGI/eVj7n9FmCSoP6YnexSo7Bo8FQe8bmr+1/4NnrGXr3+j8O7H/x/9k77+g2yqyNPzPq1XJvcXolzemVECCNhFAXSAi9LSX0zkdf+gKbhaUsnSyE3lsgBBJqKum9OcW9yZKsYkkz3x9TJNmyLdtqtu/vnJxIU6Q7Y2k07/Pe+1w0iMKBx1uOzWIdv4LnsYnxYNfe5vueN0e9x4afXIL79KmDmvY2jiWlEM5TTlrb/BD6iIPKKocHNneUW5MFwba17UAQZp0O/ctH4UL1CFw4+8WQdRaxd3advbjNr/vdX/sw3fQR2OxHsEPBIZXj8fKM/0KvT8f4LGHwW8ABuTmFbX7tNIMalT4hk8HHMKipCe1lvePwqpDnm0sCppKHi35CiYKBiucxZmjoLGK5C/htfzUOVAqf+Y6k0OZbdHC5CwAA+zxVcNgFIYLleQxME278R+hCDej69hTMIu2iyDUAKui0oTOXu8vscHn9KPbOgiFopvG2kYuhUnVsYC4N6ErqXNhbLnx/G3gdBnmEG96n1zyK3SwHDcfjwhMfxzyxZ7nP3QNZpugPBlujgRGEtv1wg+ea+jU0x9e//QN/MG4oeR7j/Qb0PDITB62P4e6FTyE787gOx5WiU0HpE85HtbOqla3Dc6x4DfYqWah5HjfOCnwvNx0RRIHeURQFNEqFnN69vbh9M7OSKJATk/IBQXDdW26HxxcqAGanCoZxFZwX/9z3ATiGwZm6nliy8CekcTw4hsHBw0JmAO/3YemfjwEAJjJGpAV9f6obekb1nAaj0QrCn0scMFZyQhbTjD5zoeOEwWpWplDioNOmwCTG1TdT+HzXOb1yFt/AGIoCQ3KF8/zngeqovN6+CkH8GJDVdnE1zZgFAHAyHRMoGjyCoJ0FJfLF37NU9REseGUNvt5aimKrCz7xfE/p37Q8sCUOOoTfxb6m3jjOGCgLVPI8JvWf16G4CYIgiOSky+aBPbvgR/Ty+WFngFEZ3wBoWkLgFmu2lTwDjZha6mloWguy6eDbcLEsSlUM7i9ejmnLJuH/PpyJ9db/gWMYDOFYzFIKKZrLNj7f5lh/+usluBkGPX1+DCtsOZU1mjhdtbCJg+7crMh8CyRMWhUyxbTqQ5X1rWydGMw6FdZaF6DfwCfBKkLtMyysMLtTWx/5TKynvhI3vjEG2/lrsLHHRpQqWeRwDF6a+iT6FEwGAMyfeh/ma3Jx+9Ar2hVzmkEFHzRI8wufx7LK7SHrd1ZtAwCkijd7W+2Btld/7Bb6m49m9NDrUuXlPM/jlV0KXPr2RvxxQBjI9c1sf6ZApkmDcrcwW3QQPtSJN5AGHjht2oO4r+/ZuHH2S6HHZekTUloxQt+0XOUvcVA4siAHJ+t7AgAmKSw4ofDydscqkWcRBv87S2xwNvihFD/3fpsw8FrPCOLeAtMApFh6464zP8LYmtlYW7Mw7uUDAODVjYGK5+FgGRSX/RXRPjzH4b1iYZB4Q8Zk7Cp/FDvqT8Y10/u1KXW4Jcw6FeAXPjvVnvbVRFfUCkJXDs8i1SiIR26vX54t722M7gB2qJiiv7OkqeDbGhzHy2JyLDIF8i06WPQqeP089pWH/vZkpQvfsTKWx2GFUJJz+ymvg2EYDGCF8pK9JWvhdVnx0Lsn4gOfUM+/cMj5mGHoLb9OeUM/ZGpjIwroRFHAzTLw+xpQxQgCVq+cUXh90sN4dfy90OsDA9L5liHoDxVGD1sAIFA6kG/RtXnGvS3MPE74nK05WI06Z8dF7L1ipsCANnZvAYBss5BtWM/6m5S0RArH8fBywm9XlkKPfKXwnTSoS9Hg49AzTY9lV0zA73edhDV3n4x75rbNZOygV/iu9MkchqFZhfLyHhzQt9dJ7YqZIAiCSG66rCjA6ixYlCbM2lYbNwDgUFoXmq7n9gqDWSXPQmq76/E0nU3a5S8CAEzyGZDl51HPMvjOV43lKisA4ETLcTh/uDAI/NZTglpr+P7E365/Hie/PRo7Dq8OWf71ga8BAKea+oMJY3IYK8oqtgIATBwHY0pBm/fvL8427ylvWnKRDJi1ghAQLpPBohDOszWMMVZz/LTuX/hJ0QCrQgElz+OClGH4YuGvGDogMHNi0GfgsQU/4MTxN7Yr5lS94Faa4hO+mhW1oZ4BO53CrPzZZsEwbBvngt8nzM79WbkZADApfUTIPsesLlR5hEGwVxQb+nUgUyDdoMaxhoHQcDw8LIPdRwQfAzPPQK8x49zjH0RWar+QfRiGQV828NkeHnSjKSHVlI8qsODmU17B1bnT8djp77fZrDEc0oBOmjkbmG3CqJ4W7LdNk7cxcDwuO+lpfLG5GIdqgQ11M8FBFffyAQAwGSzIF7vZ7GmUHdIcG7cuxT6Wg5bjobFcjpI6N7LNGiwc3zNqcZm1Kvh9wiC7sqF9M++VdYcBAJls4LzuLLXBx/FIN6iRFuXTPSxfMhtse7xV9R74OB4sA2RGuVUeIHwvJNHilV8OosoR8BHJTBcGclKr0wG8EiaT0IVngF4ondlbswdLvjgfn/BWMDyPuwpOwUnjrsec44RMoRwf4OV1MMZovK0LMqittR6EQxTbMtMHYfjgs1A4dEHI9nef+RE+u/gvGPWZAAK/HYNjmCUAAL0zDBiUbYKP47Fydzt8OkTqXF78uLNcFrDaIwrkpQvfR4eCg83dtNwjEmqcDWAUgiiXo01FvkYQZ5QqQfS9+oR+mNw/A/kWHXJStBFdQ1f+9Qqe/PICOFxWHGWErJW++RNxXO+T5W16KvQwpBTgRmVecy9DEARBdFK6rCgAAKcf/wBMHIcyJYdext/kNFAJt1dw6lVyLNS8tCx01rusfAsOqnkwPI9zRj+HFRdvxv9G3YmLtL2R5+OQ6ucwf+z1GDn8QhznZ9HAMPjkt0fCxvPZrndRAS++Wr9EXlZuLcIan/DjPm94x2dEm8Pn92J/8ZqQZaVVgjNpDq8A2jHwklIyd5cmpyhgEmeebK6mN16pSmGmrdZd02Rdc6w8Kog5x1mzMM+yFHee8R70jVLgO0qKTgWGATR+QdCwB8Xn9bpkk8HTRl8HA8fDyTL4+ffH8ewH8/AbL8xeTR50Zshrbiiyhjw3aZXIMLa/VUq6UQMOSvQQhYu/yjcAAIxMy5eTftos+fGIfqc0WS9lCozulYoMcw9cN+t5ZJjCGGC2gzSDGpqgbguDc0yY2j8DFb7e6CtqRhenjsS6Ej1ufH8zLn9rA+ziDXtmAsoHUvUqWDyS6Lapla0F3t32GgBgnjYPSzcIg8vLpvSJWpYAIHw+bS5B8PnLb4PX1/YU6AqHYNqapQwMqLYctQIARvQwt+dS1CJDWzDzaw3pNyPTpIFSEZufy9NHCp/xL7eUYM6SX+AS3eJVaj3Sg7JrRhkC3wWpTGefsxTfOwWR5eF+52LRSYL3xrhh5+PJ465EpvtKAIAhRn2G9DqLbMx5pFhoX6vleBiNkfl97BJ/OwbnxlYUAIBZQ4VsgR92tF8UuOuTrbhi6Qb8JQqY7REFctIEP4U6BYMae9u+Pw6PD7/tq8KxWhc4pXCvkq3PRL5oFOxT2ZFh1OCs0W27bu45vBq3b30O79RuwbPfXgo/w0DPccjOHYOcHpOQJpox99RkAAyDhed+2qbXJwiCIJKfLi0K6DMG4my1MLNiTv+5SfmAxyeIAiyvgIoX7kQbGokCP296AwDQz8NiZL8RYBVKFI64ADed9SmuSfsHVi7aiB4Fk8GwLM7vKZi7fVC5Dj5x9jaYvX7htXfVH5OXffzHY+AYBqO9QM9B86Nx2E0oL9+G6UtHY8GKK+ByWeXlpVZhFjpX0T6H/yHijdyu0o65KMcKs66FTAG1MFCwhvGQCEeDsxq/+q0AgArr8Zg9tO2ZFZGgVLBI0amg4ITYHUGZK0eP/QkPy8DA8ejdcxqGiSnENxd9jDfdR+BjGMxUZWJQvzkhr7lBNHA7YUAGUnQqzBzS9laJwaQbBEHB4hXef6fYm9zItDwd2TdFSNU3cTx6508MWVft8OBwtfB9LCywtDu25mAYJiT9e1COCeP7CLNr2toFuDF1FC6f8xLe/L0IAGRTOo2SlTNO4kmOWQvGLYgou+1FrW6/aes7+JGrA8PzGFtwBXaX2aFTKbBgXPSyBABBFNjpmoRUPwc7y2DT9vci25HngQbh+lfpFPrSZ4qGa0CQKBADh/whuYLQUGH3oKKNg7CAn0B0etuH49xxBXj3ignQqxWocjTgcE3gNygrqGvw6JxAN5OB4vfnL7hRrmCh5HnMHheanTR33A0odgq1+0ZVbMoHjDoVdKIocLhKKHXK4JmIW4XuiUPnAYnZQ4V7gdV7K+H2+lvZuinVDg9W7AwIChoFj/7t8BSwiKa+PoZBaVXkRreHqupx2vO/4YLX12LJj3vhVopdMYz56GERrq31Kg/umDOoTUJgg9eNe36+GV7xN+Fj+z4AQB9eCUYh/Bsh/tb0M/eO+HUJgiCIzkWXFgUA4Pxxt0LB8yjSu1BSszFkndsr3PizvAIKURRwNwRuyDweG74vFRyfU50Fcg29BMMqADbw4ztnyj1I9XMoY4Gf14e2S6uyFqFGPNu7OBf8fh+8vgZ8XCHM3i/ocVLMOg1kZQ6FgefhYRis2fqWvLzULogTOer23YhLmQK7ypq2+0oGpBpVezhRQCvU3Fu9kfUTXbPpNThZFuk+HpXceEzo0zbjpraQqldDwQmxO4JStCvFWuxsngWjUOCyYZdhMKdEOgcMgxovDF+MZxauBMuG3hBKosDC8T2w7v9OxrPnFXYovnQxjVrhFUSh/Ywwo25iW84+mDh0IZQ8j5NNfZvEuL5IyIgYmG1ESgxc0gEgN2hgNyjHhDG9UqFkGaytLsSc41/GQSuw9lBo5kiWWROV8oW2MjDHhDq3MCO/19ey6Mb5fXhi49MAgDM1ufjioDBAOHtMPlL00T2XKToV/FBhcINwzVi1/wvs3PUJvvrp7uZ34ji8+t5cnP72GJQe+hkVDVYAQJY+kDmy9Zggzo3oEX1RwKBRysaoktN9pPx5UDCm69GB1qiRMKV/BgpShYFXhS1QQpCt0MuPRw08XX7ct/eJYHheHsiNZHRNjDsBoMYhiNOxKh/Qq5XQij6Yh0WRObOV64AEx/HYK/ooxLLzgMTQPDPSDWq4vH7sr2h7H+lvtpXCx/EYlm/GGxePxvXH+aFXt10w1OlSoREzQCpqDke0z19HanHGC7/jYJVwf7JqTyUcSuG6m23pgx6imWOtmsO5Y9smWL/30+3Yy3iR6udg8XNCFwMAfdUWeZs7xtyGm9ksnD713ja9NkEQBNF56PKiQO6AUzDNK9ykWN1vARCM15xeJ9w+URTgFFBywg+hx1uP6tqDePXryzB72WRsZIUbNKV6VquDA40uDX8zC+ZQy/Z+GLJuX1Ggf7SLZXC4ZB1Wbvovqhge6X4/Zky9p+MH2wwMy+IEvXCjsOrQcnl5mUuop88NujlvC/2zjFCwDKxOL8qi1P85mkgtuMKVD1jEWthaMVukNX4q+h4AkOvIwMiCNKiVsfvqpOpVYDjhM1sfJFpUiZ0S0sWb7sljrsZHl27Cqku34b2LN2La6L83+YxWOTw4WCUc45ieqdAoO55KniZmCrjdgrDSIL6nUdFymn3/gin4ecFvuP/MT5qsW3NQGIzHUmzJtQTiG5xjhl6tlOvN1x6swdI/hRv04L9tIjoPCPGZcMQlzPIWs4DNXtLstl/8fBd2sn4YOB4LJz2DH3cJs5mXTG6mzWgHkL5Taq9gTPqD7QAuX/MA7jn6Nbbt+CB8fMuvw3PeYzioVuHXPZ+iUmxblymmPNc5vfJgZ3h+bGaM80RBKLhmvzXK6tx4d60wk7tgXGwyg4KRvCsq7IEYs0TBNqdRNxOdNgW9+MDndHyYNnENPg52sQuPMUbJLga1Ehrxt/OI6HeSESRktESx1QWHxwe1gkXvDrRIjRSGYeT3kbKS2sJnm4Tr75mjeuD4/hkoaL8tC8yikFJTd6zlDSFkKFzzzkbUubwhBrG1SkFYyEodgPzcMcIylkG9o6zV19xX9DPe+n4x7PVVWFayCgBwQ+4JWGAMdCHqawx85guGn4fLLlwJlZm8BAiCILoqXV4UAMPg7L4LwfA8dukq8fYfj+Ky907ElHcnYK3tgLAJp4RSvMH67vD3mPHFaXiuej2qWQbZPg7DS0cgKz8yx91zJ90FBc9jA1zYc2ilvHxf2YaQ7XYWrcR7u5cBAM7R94XKGNrCLdpM7yPUcK92HQPHCamTZeIsdI6pfTe8WpUC/cSblGQsIWjJaDDVIJzvOq71QYLf68bPHmGgVWsbi+NyY5vqmmZQA35hMGoPEi2q68WbbmXkN9AbikQzKh0PS5RmjVP1gu9BvTe0btikbH021aK1QKVoGoc0Qz+hb1pUYgyHNDBM0amQLQ7ApPf7dlspPvtLuOm/c85geZ9EdB4AgN7pBtSzucjyCqOHvYd+DLvdHxtexKNHBaHv71kTsceaDp4XZtz7t6PeuTWkLI4D7hlQ8TzKFZDN5XYc/a3J9pv+egUPVfwqPy91FMtt6zJTegMAthZbAQC90vWy0Wa0SRWFrGpH07Ku5njh5/1o8HEY1zsVU/tnxCSuYKRMtMogUSDfKAzCxmgym2w/QGWRH4/v29Sjo9YpHCvLALoYiQI6tQJqMcvusE/IwsjURJbtIXmIDMwxQhUjv4bG9EwTBIvgEo1IKKqqx6YjVrAMMH9kZH4JLWEU7zdqHaXNblPn9OKPA1W46YPNKLd50D/LiK8WT0WGUQMjWwunmFmYlTUURlMeUsTsg2e+uQx3vHsijhWvl1/L47bihU/Pxbbt76GsfDsu+/l6PFO2Gud/eDJKWMDi53Dq8Q9hwdT75PbMfTIGNw2KIAiC6LJ0fVEAwKRpN2OWVexHvu99bPBWw8cAG/zCQJbhlVCIP9IrfDXwMQwG+Rk8mj8HvdmX8If1fAzKjiy9MSd/PE5ihUHje+uflZfvswqp3woxzf6LIz/gL84BBc/jbxNujc6BtsC4ERfDwHGoZhns2PsVeJ7HIU7IlMhPHdDK3s0jlxAkodmgnCkQrnxANMKq5VuvLS0t3YgahdBXfU/9VPmYY0WqXg2eE0QBR5CRW7VLcJZOV0f+/lJafj9z9Mo7lAoWqXo1qhtCxSSjqn2zfXVOr9yaTKrzjwVSCviQXJOcUTFBfL+Vuyvg8voxskcKLp3cW/ZNSJQooFSw6J9pRFaD8P7L93wsi3kSO/Z8juu3vwgPy2A6Y8IFs57DRrFUZGyv2JxHSRQod5sxngmdEd5n3RfyfN265/H3Lf+GlxF8MACgxF2FCrFtXVZafwABP4GRPSwxiRkA0kRBTBoot0ax1YX31wtZArfMHBSXEhIpKyXY9+Csqffj2rQxWDz9n022H2DuBUAw9hvRyFwUAGrqhWO16FVgYxS+QaOAmhN+O4tEx/pMbWTZPj/vFjLVjh/QVPCIFZIocLSmbZkCX24RMnWmDsiMSvaQkRc9Y9yVYdcfrXbgphcW4tF3nsGv+6qgVrL4z/mjYNAoMbV/OtJVQoaBkeOg1wuCVb7oP/GR+yi+81XhvB8uxS/rhDLGL1bfj5ftu3D5+kex+NtFsIofiCJW+C7+zTQQWmMm0vPG4G5LIU7xKjB1ZOyMjwmCIIjko1uIAmqVEvu8d+M4t3DTouOEH0Kpdo7nVWC50LTqe8fegfknP4UdZcKAcmCEogAAnH/cxQCAb+oPwyam8u0Vf/yP54UbijU+KwDgJF6H7D4ntuew2oRKl4IpCgsAYMWu93CsbDMqWUDJ8xjSf17LO7eANEDemZSZAi2UD4iu9lam9cFyaeUOAECGF+ChwnF5sc8U8HHCANYRlMlQ5bECANK1rQ/4Nh2pxfR//ow3fxfaY/Y1RdfzIc2gRrm3N5ggLwmTun11weuKasDzQN9MQ0zT9eeOyMWFE3vh9tmBVOuxvdNkt3uWAR49czhYlsGJg4WSmj5xSGtujsE5Jqjsgq/AB85DuPG9k0KEgY83vYwGhsFk6PDseSugUullUWBMr9SYxCSJAnUuL87sPRcAMEK8du4PGuCUHP0T1+74L1wsiwmMAXf2Eerh9zfUwiXOcGZmCA760rVjeAxMBiXSDIK4Ig2UW+M/P+2D189jcr90TOoXu5KWYKRMgeDyAYulF66Z/xZ65I1psv34fsJ1+3hNJtTqpin70rHGKvsCEMoHlOLfv4FhoOV4TB+6qNX9/ByP1XuFz8v0gfEXBdpaPrBqjyBgzB2WE5U4jGI7TmdD+O433/z6X2zI2oPynl/hb303442Lx2Fwjhk8x2FygQ8pKiHDIJMP3LdkBhkGGzgeNpbBLTteQU3Nfqyp+AuAULq4h+Vg4DhcoRPKixQ8j/MmBzxB/nbmO3jqis3QmDqeEUEQBEF0HrqFKAAAGZkFqDi8GPcojsfNaaE3WDynAhv046rmeRw38DT8eaAapXVu6NUKDG3DDeuYUVegp5+Hm2WwZdfH8Pt9OCC2kjuj16yQbRcOOq8DR9U25vScAQD4xroTG/YKLYWGckpoTe0vXQhkCiSjKNBC+UCKMMtWzzLwulvOcigVszyMPhXUChb9MqOflh0Sm0ENn18YjDq4wCCmWvQXSNe3/vd6f91RFFU7wfFAvkWLIZboigLpBjUaeB0yucAyYxsyGIJZK5q5xdJPABBEon+cMQxjgmbRzVoVhuUJ3+1Lp/SRPQbum3ccHj9rOBaMj657f1sYlGPCr7UX4jT3cKh5Hqt8Ndi6S/Bj4DkOv7iE2cILh1wAlcaAeo9P/h6O7mWJSUySKODneEyZeC/Wnv4N7j/+MQDAPt4DXhRcV23/Hzwsg+M4JV5Y8CP6ZY8CAOxnhc+hkeOh1wnChWQ2NzCGZnNpBiHuSESBI9VOfLRBOLe3zhoYs5gaI2WlVNoi8z0YM3QBPjrpJTx85sdh10vHKnmAxAK9WgGFKAroOB4vjroF/fvNarKdz89hzcFq2fV/6zErap1emLRKjI6RgBWOXultFwXqXF5sFrNZjo+SgGES3fxd/vDdbw5UCKn/HpbBOuUy9FLtBAC8+PkC/GP/ZTBa/gAAZLMBETUryDD4m/mfoK+fgYdlsGbr/7BObHvcnxM6VTzY71zc8LfP8Uj2iVjS91zk5Ac6WxAEQRDdk24jCvTJMOAQ1wv7UhYjP2t4yDqeV4eIAkN5NdRaM97+swgAcNbofBg1kRdlMgoFhquFgceusg04UrIeHoaBluMwbfzN0Io3zv19HMaOu76DRxY5J4y/EWaOQwULvH7oKwDAaGPHDLSktoRFVfVyf+1kIWA06G3SHcFkypf7a1ttRS2+TonYpUHr1WFAduzrX9P0ang4QXhwBJU3VItZA+mm1ntQbzoq3AT+e0Ehfrr5eBii7D6eIXYgyOQC3wuT1tKu11pzSBAFJsbQT6AlHj9rOO6YMygkgyBFr8LC8T3b1Nor2gzKMYGHAmvr/46JjPB52HF0NQBg574vUcEy0HE8xo0QMpM2H7WKIpAupNNCNNGqWKgUQmpFncsLvaUn+hacACXPw84yKC/fAgDYULUVADAjYyQ0aiNyM0cAADgxLSMT4uyyj0ORaDLYnp7vkSJlCtTWNxUIG/Pvlfvg43icMDAzRECKNVlypkDkpq2DC6bCqAsvpsmiQJQ7UARj0CjhsE7GQKcCzxXeiXGFlzXZhud53PzhFix4ZQ1e/03IXPp5j5AlMG1AZtz8BACgpygKlNa50ODjWtla4I/9VeB4oF+mAfmW6HyvUlTC76abC+9tUNVwFADA8jxqFSxu+mkxDh74Aa/ZBHFgm0lslRkkxF486f8wnTFh6ag7kZ4xCFNMQibA/w59gTqWhYHj8eEFa7D6zO8wZ9oDYFgWp895DtOn3R+VYyIIgiA6N91GFOgrzu4erHIgu1ENPcepgaDBTaGpF47VOuWexBdP6t3m9xuSItTL7qw7gA37vwQADOKVUBkzMVSsJ1yYOR6MKn41y2p9Gk7RCMZVh8X6z9G5E1vapVWyTFpkGNXgeGBveXL5CkjlAxwPfLG5BBVBHRJYhRIpok5QWxfoFV1XvQ93vDUBq1Y/JC8rE/uqw5sSc5NBQKgB9viFm8YQUYAXyiAyUlqevba5vdgnttya0j8DbAwKiqXZx1QE0uuNEZQ1NKbS7sH2YmF2O15p2o0Zlp+Ca6f3T6gAEI7BYu/2Q1X1OM7UGwCwrWYXAGD1LqG7yRRlCjRiK7pYlw4AgoN7SpDYBgAqjQG9xLryvUd+Aef3Yb04Mzmuz2wAQHpaP6iChLksMX26qLoePo6HSaNEbkrsSkdSRVWsur7lWfgDlQ58tkkQAW+eGb8sAQDIMkueApF3SGgJuXwghpkCGiWL7fWzsPHwo+jb/5yw27z26yF8JdbkSy0hpdKBEwbFr3QAADKNGuhUCnC84BsRCb/sE7xcoul9ILXEdaNpDMdqnahVCdfEW7JPgIXjsVfB47LVN8PXyNsiK0gQ6tXreDx/0R8YNeICAMCEghMAANtZ4TdkjMIElUoHc0rsO2kQBEEQnY9uJAoIg5eDlfXIzRoZss7PaQA+IAqMypuEd9YcAccDU/qnY0Ab/AQkjssT0vF2ea1YeewXAMB0i1BD+8CY2/GQbgD+NuOZdh1LRzhj6AUhz0cNObvDrykNXpKthECrYmXH/Zs+2Izxj63E9H/+jNs/2oKPNx5DpmjMtLMo0CXik9/+ge8YJ5458DEgDmJKxL7qXm96zE0GAWHA7RJFgXrxHpDz+1AjflvTU/u1uP/Wo3XgeaF+VprRjzbpRmGgoect8jKTvu0O7b/uEwYHQ/PMCWv/l6xkmzUwa5XwczxyzIUAgB0eYYCyqlaYMTwhb4q8vWQqGUtRAAhk4NS5ArPuA9TCe+6r2Iz9B5bDyrLQcTyGDhK8BFhWgZygFnqZ4kypJCT2zzbG1MxPErFqnS1nCvz7x33geGDGkCwUFlhiFk84pEwBZ4MfDk9TH5S2IpkqpsXQU4BhGOjVwnXU6REGn08t342HvtoBnuexvbgOj3+3S96+3OYGx/HYLf5WjO8d3+wghmFkX4EjEZgN8jyPXyQBI4reB2nitdLDNC1nWXewEiVqIYvh+MF/w029TgUAVCtYsDwPExcQ17INzXscjBm6UDY2BoAJGSOb3ZYgCIIguo8oIBqGHat1Qm3sAT0XSB3082qAC6RYFhTMxbtrhZ7l7ckSAIBBfecAAEpYYK1YNzhj2IUAgD4jL8BZ534KVh//dOmhwy5AX59wo9DfD6REoe2QVEKQbKIAwzB469LxuHhSLwzJNYNhgKJqJz7aeAy3fbQFAyAc+0dHV8oCwOqa7QCAIoVglgYApZyQYVDvzY65ySAgzOw5/cLsr4MBOM4Pa91h+MVBU5ro2t4cm8RWX6N6WmIWo+TOz3CBG2WTvu03zbLZWJxnDDsDDMPIgptfIwz+i1ge+w79iF2sHwzP4/gRQrp2hd2NPw4IZRhT+sc24yIlnCggOuHvtxVh/f6vAQCjWQNUqoABXm5Q/XOmppGfQFbs/ASAYFGgAX6OD7vN3nI7vtoqzGjHO0sAEFLxDWohWyU4q6m9VMuZArErHwAEXwEAqG/wwepswIurDuDN34tQWufGHweE1PsMUUQst7lRYffA4+OgYBnkp8amzKUlpBKCI9WttyU8UuNEsdUFlYKJarvULLPgC+NifXA1+HGg0iH7LWzZ+ztcLAsVz6Ogx0ScOf1RjOCF83emrgCzdHlBr9Or2fcwmnIxlA/87ScMPC1q8RMEQRBdj24jCmSaNDBqlOB44GitO2TWys9pwTDCj2cvP/Dcb17Y3T4Myzfj5CHtM+Ezp/ZGgV+4+fQxDPr7ePQeMLfjB9JBGIUC52aMBgBM00cnjTCZ2xIWFljw0OnD8N2Nx2Pz/bPwxiVjMfM44W9qw/lQ8jy2Kjjs3Pkh6moOYEvQzM0fu94H7/ejTOxQUNNQgCE5ccgU0Kvh4CwAhA4ZTmclqmuEdm8WjgsZaIVjk2iKNSqGM53pYgaC2xeYqTIa2vZd8XPBs3BZ0QuuCyGJUGtKTOghXk+W/CbUAI/kVUjPFIStT/8qhp/jMbqnBf1jPMAOdPUIEgVEz4B9DTVYX7UNADA27biQ/XKDjNCyxM/K/grhmjEgO8bmneJsOc+HihnB/HvlPvA8cMqwHAzNi10nhJaQSggqo1BCUFsf+0wBQBAzAMiDW4miqnqUWAVxQ2o1Wm7z4LA4GM+36OLqJyDRlg4EO0oEofu4XLOcERENMi3CwN6p4PDxxqM4+ZnV+Of3ewAAZeWCGN3Lr4BKpQPLKrBk/vu4I/dk3HHaMkwLMivObiVrbLxZWJ/G8RjQd3bU4icIgiC6Ht1GFGAYRi4hOFBZj5ygWSs/dIBf+JEeijx8uaUELAM8fuYIKDpQjz1EZZEfn5wyEIhDr+tIOH/uq3in3wW4dt7rUXk9WRQoszUx9EsmUnQqnDQ4G2ePFoz6Sl3pmKkWBqMfbn4Ff2x5Q56NB4A/yjegtnYf3OJnoJ7pjZQYmnZJmHUqNEAPpXguHY4yVNUVAQDS+Zbr3nmeD8oUiF0auZQpUOHuIS8ztrGF1bbiOsGBXKOMaVZDZ+aMUcJn9autJRiiFP6ev3DCQHpezmQAwt/8w/WCMdl542JfLxw2U6BgKgDgIOPHer8VADC+3ykh++XqAsJPpmiWKWUKtKdEqy2oFKzcjSRcBwKe5/HTLqHt3DXTWx5oxRKpLeHRWhde/+0QyjuQMVAT90wBPw5UBGbfD1XXy3X7UimGy+uXB9rS4DzeSB0IIikfkDwQBkW5M4bFJFw37Qrgzd+LAADbi4WMQi9/AADQVx24fmemD8CFs5ZAr0vFxJGXwsTx0HI88nNGt/g+cwuvhJbjcVbKELBscnmmEARBEMlF9KTvTsCQHDO2HqvDmoPVwqyVV7jhYhg9DnCzkHOwB7aljAXAYcH4nhjeo2OzRUPMffCDdTMAYMbg+LUebA1GpcHIqXdG7fX6ZRqhUjCwu304VutCQYJu9iIlU6xdr7B5cN8JV+G7DY/im4ZyFBctB1hgLGPABr4ea/x1OFa6EQCQ5uPg08c+SwAAFCwDi14LPcejTsHAUV+JansxACCDbdkjoKjaiVqnF2olG1P/A8lTYK+9NwrTFDAwKujaaDS4WnQgn9I/IyEzhp2BwgILxvRKxcbDtTD68wHWCgBQ8jzmjL8ZALC+qBYHq+phUCtw6oi8Fl4tOjQ2GgSAvNyx0HE8XCwDL8NAz/EYMmB+yH555gKgTuhKkJXSJ6TzwMAYZwoAQgmBze0LKwqU2zxwef1QsExcfEOaQxIFnvhuN6ocHhyodOCxM4e3sld4aoIyBWKZw2WQPQV8YTIFBFGgX6YRZq0SNrcPGw4L3hdSGn+8aYungCQKDIyyaGUxC6KAk2Whsv2Fib3ehsU1DW7veDSoBN+QAebeYffV69Px5vH/hKfB0app4IB+s7G+H2UIEARBEK3Tre7EZw0VUlaXby9Dti7IFI01wKBWY59nNHZVCl4DU/q13TStMSPzhJm8nj4Og6Jg6JesqJWsnLK8uyz5SggaI/cDt3tQOORcHMer4GYZrGGFm+hrCq+DieNhZ1ks3/4/AIDFp4j5jFswqXoVdKLthcNViep6oRNGmrLlG+ktYunAsDwz1MrYfb3TxRZvVjeD1xZtwEsXrQXDtu39ftufGAfyzsblU4XWYrvLA/XD01gzLOmCt8Qy0f/k1BF5cip3LAmXKcAqlFiUMgRpHI98Drg6ayJUqtB68ZygVOfMtAEhnQdyzLE3mZRc+MOJAodEcaJHamJS2iWka1OVQygfOBxB3Xs4eJ4PGA3GsPsAAOiCMwWCRIFDVU5ZFMiz6JAt/o3XHRIymRKVKRAsCrSW2SYZYQ6OctmY2dQDjPje/dI/ww49h1rtb6ipb0CtWhArhuSManb/Qf1PwYjjwnd7IAiCIIj20K1EgSn9M2BQK1Bmc0PFBA1EWAO04o2NdI8QjZmrcWP+jicyj8e/J9wPRtG1kzKS1WwwHNJsXIOfg93tx79PXYZBoiGTieMx+rjzcIJo5vSuR2hPpvdq5brkeJCqV0MrtnlzuKpR7RJmj9I1KahyePDEd7vlG+5g9ok12rGe7UzRqeTSGqvbB4ZlcbTGidK6yNp8ORt82HTECiA6AlxXZtZx2cgwqrGjbiRY8QJ1Wp95AICtx6z4Qmz3dv6ElltVRgs5U8Ad6pB/41kfYfWl27H80m24dN5rTfbLywzMeGemD4pb5wGJ9BZEgSJx8N073dBkXTxp3IGjrK595QN2jw9e0YMiNcYlTwaN8Ntpc3lxoDIgYuwqtcndHvJTA6KAJHgkShTokaoHywhdHspaKM9we/3y52JgTnQzWRRKtdxFoNggZE44FF5U2lyoUAnLe+eOiep7EgRBEERLdCtRQKtS4CTROLDSEUh1VihN0KkCp0KlYNA7Iwo3hyyLeXNfRP9hyVM6ECuGJGlbwnBoVQq5vrjC7kZOxmC8veAnXJo2Cv8YcR2USjVunv0SUjmAEwcrCp8RljiKAik6FdSyKFCLarEtYoY2HY9/uxsvrz6Al1YdaLLfQfGmvG9mbNOxWZaRRZIqhweuBj9O+fevOP0/vzfr7h7M+qJa+Dge+RYdCtLi70DemVAqhFKQej4F5zIDcIYiFdMm3ASO43HfFzvA88AZhXkYGacWepIoIM1ER0pB/njM1BVggWU4NBoT9kl+AlmxLx0AAmaD4eKWyhj6ROO63wGkTAGJxqIAx/GyS304/ByPm97fhDNe+B2AUO+vVcW2lnyAmCX2zbbSkJR8yU/ApFHCrFUhyxx6bIkSBdRKVjaSXCWWMIVjf4UDHC9kWmTGoLWriRd+W8rFe486BY+DxXvgFjOucrJHRP09CYIgCKI5upUoAAjO0gDwV2nAL4BRmqALunHqm2GkGuc2EuhAkPyiABBw+a4QXb4NWgtumb8UJ4+5Rlif2g+PjLpJ3p7zpsZ8xi0Ys04FFScIFw6PFVVeYdBi1mbi222lAAL1rsFI6bv9MmM/uJHajNXUN6Dc5obD40OF3YMSqwslVheuXLoBGw/Xht33jwNC5sPkfulxmSXu7EgDL3/ao/jHBb9ApTbgu+1l2HLUCqNGiXvmDolbLFIbuSMRuLcHwzIsnj33W/zf6csAAAfFgXj/OIkCacbWywd6J6jOXaLxwLm+wQ+7W5ht53kel7+9HmMf+VH+/jTmm22l+HxziSwOxuPcnjeuACwDbDxcCz/Hw6BWQB30+5lnET4v2Y1KRBLlKQAAc8T7gOXby5rdZrfsJxCbTBYTH5o9WKdgceiY0Hkgzc9Bo0mctwVBEATR/eh2I9/pgzKhVbHYXpMPi59Dus8PVpUq10UCsW+P1RWRygcO1zhR7/G1snXikWZ+Wmr9Na3wctyQcwKy/Sz22E6Ke6aA0i+KAg02VHPCjGFVvQkucaZwb4U9pCbWz/EoEgdq/WKcKQAEapWrHQ2wBtWXF1XXY+mfh7FiZzmeWr477L5/HqgGAEzunx7zOLsCUjmTVB4CQG7nuHB8gSxyxQPps3W4xokGH9fu1zlQIQhYfTPiJAroIygfSHimQNO/o9SBYOWuCvy8pxIOjw9Xvr1B9g+R4Dgez68UWpdeOLEXXrlwDF67eGzMY86z6OQ2r4AgRARn/+RZhGPKDsqCSNWr5NaWiUASBf44UNVsi8pY+QlImJimvyeltWsBABn+rl1uSBBt4ejRo5g+fTqOO+44jBgxAh999BEA4Pjjj0dhYSEKCwuRmZmJm266Key+J5xwAgoLCzFmzBisX78eALBu3TqMHTsWKpUKX3/9ddLG/9ZbbyErK0ve7oMPPgAAFBUVYcqUKdBqtfjPf/4T0/g7A3SOo0O3++XRq5WY2Dcdq/ZwGFN/DfaUO6HprQ1JsYy203B3IN2oQZZJgwq7B7vL7BjTK3bt8KKBNCNXYW+5ZvfK2f/B5nf/wjfe0vhmCmiVYDg1ACccDQ5U834ADDaXBG62rU4vqusbkCEKHMW1LjT4OGiUrDw7F0ss+oDhnDUoJbuoqh7biq0AgA2Ha1Fb3yCbvEnbS+23JvUlP4FIkFr2SQMVALKL+4Q+8RVWss0aGNQK1Df4caSmXjYZbQscx8uz833jkNUCNG80yHG83LM+0eUDBWk6ZBjVMGqUYFkGByvrUVbnQZ8MI54UBTaTVgm724fbP96CH24+Qd73iy3F2FfhgEmrxO1zBsmDbq83/KA3mlw0qTe+3yGYofbLNMLmDvgLhMsUSFTpgES/TCMGZBmxr8KBn3dXyK0/g4lV5wGJLK0B8IV6sFT6iwAAqUjuDj5E4li1ahX0ej3Gjx/frv03b96MiooKzJo1K6Lti4qK8Le//Q0bNmxo1/tFA6VSiSVLlqCwsBBlZWUYM2YM5s6di19//VXeZurUqTjjjDOa7PvUU0/h/PPPx9///ncsX74c9957L77//nvk5eXh9ddfxzPPPJPU8QPARRddhKeffjpkmdlsxrPPPosvv/wylqF3GugcR4dulykAALkpoguyYwh2+YZCq2LlXstAfNpjdUU6UwlBJJkCElINcjyNBs06lSgKALYGO2rEb+pfJUYwTMA8TKrLBgKlA30yDLIJYExj1AZa0wXPth2sqsfWY8Kg38/x+HlPRch+G4pqwPFA3wwDclLiN8PdmZGyl8ptHtS5vKh2eORBV7wFOIZh0E9MS99f0T53/FKbGy6vHyoFE7cWps0ZDZba3PD4OChZBvlxENNaQq9W4vubpuHz66bIsZTZ3Phk4zHsq3DAolfh6+ungmGAveUOVDk8cHh8uP+L7bjlwy0AgEun9In7LPzkfumyuNMvyxhi2CiJAsHZLMnQtlYqJfx+R6CEIDjzShIFBuXERhRIVQdeVym+7zGVcA1PU1pi8p5E52fVqlVYt25du/ffvHkzfvjhhyhGFHtyc3NRWFgIAMjJyUFGRgZqamrk9cXFxTh06BCmTZvWZF+GYWC3C9/luro65ObmAgB69OiBkSNHgm1j16R4x98caWlpmDBhAlSqxGVcJRN0jqNDtxQFpLRnyXlYo1KEeApQpkD7CBYF1h2qwUVvrGu29jXRBDIFIhEFhAGvJc6eArxfuIk+5qkGxzBgeB61viz0zTDIA8HgdHJJFIjXzKtZdqH3wuoMiAKr91bCHuRM/+Ou8pD9pFrdET1SQESGWauSxcx95XbZq6F/ljEkCyNeSCUEB6scrWwZnoPiZ7Vnmj5u/i3NZQpIJoM90/RQJoGXTLpRA4teLc+sl9vcePOPIgDAddP7o1e6QTZn3HTEioe/2oGlfx4GzwNnjc7HtdP7NffSMYNhGDxx1gjMGZqDc8b2QJ+ga1C+nCkQKB/olWDvBgCYNlDoQPTXEeG79MovBzD8wR+w8XAt6pxe+f4gVpMEFrVw/WN5HkMahHMjmQ6ma7Kb3Y/oPLzxxhsYMWIERo4cidtuuw1//fUXxo8fj+HDh+Oiiy6C2y18xnr37o0HH3wQhYWFGDduHEpLBd+g999/H0OGDMHIkSNx+umn4+jRo3j55ZfxxBNPoLCwEJs3b8bnn3+O8ePHY9SoUZg3bx6sVisA4JJLLsGNN96IiRMnYsCAAVi9ejX8fj/uv/9+LF26FIWFhfj222/Dxr1//36MGzcOI0aMwHPPPReXcxUpGzduhN/vR0FBgbzso48+wtlnnx12gH/PPffg7bffRo8ePXDzzTfjoYceime4TWhr/ADw3nvvYcSIETj//PNRXl4edhsiAJ3j9pP4O6AEkCb2WJdM0rXKgCigVrLoleC2VJ0VyVdgd5kdr/xyEL/srcTXW0sTHFV4pNrdClvrooA1AZkCKToVOE64md7vEwYtqRwPHzTomaaXU7ZDMwWE7eLhJyDFCAA2ly/E0V0yOZM6PKzeU4l31hzGhiJBtQ3MwJGRVlsIlBA4ZFFgXO/ElOn0FdPsD7QzU+BgnD+rAJATNMj2+AIO/rLJYIJLBxojxVtsdcn+C1It/Oiewt99w+Ea/LBTuIF5fuEoPHtuYcy7DTTH+D5pePnCMcgyadEnTKZAZpCnQKLLBwBBxGYYIfum0u7BF5tL4PD48NzKffhpj3BO+2QYYIpR1oVFJ3RAGsQrkc5YQtZlmXrH5D2J+LFt2zb861//wi+//IItW7bgnnvuwcUXX4znn38e27Ztg8FgwIsvvihv36NHD2zevBmnnHIKXntNaOn66KOP4ssvv8SWLVuwdOlSFBQU4Oqrr8Zdd92FzZs3o7CwECeccALWrl2LTZs2Yfbs2XjhhRfk16ypqcGaNWvw3//+Fw8//DAUCgUefvhhXHTRRdi8eTPmzp0bNvabbroJd999N7Zu3ZpUs6Q1NTW46KKL8Morr4Qs//DDD3HeeeG7fC1btgzXXHMNjh07hldffRWXX355PEINS3vinz9/Pg4ePIitW7di/PjxWLx4cTxC7bTQOe4Y3VIUSG80s6ZVsdCK5QP9Mo1xSb3uihwnZgpsO1aHH3eVg2GAy6f2SXBU4ZFuUCsdkYgCwix4vEUBn1+4mS4V7/FzOeHHuWeaXp4pDM4UOBjvTAFx0N84U0DitMI8ZJk0qG/w497Pt+O8V9agrM4dJApQmU5bGCj+zfeW27FeFFjG9kpraZeYIZUPSNkpbSWQ1RK/z0BuihZmrRI+jsf+ikDc0uNE+wk0JlvMDNlYVIsGv+AVIs26S6LAJxuLYXV6YVArZMEgGQgWWCSjQY1SIf/29kxL/Lk2aJSyuLX1mBX7xM/B6r2V+PePgmHj2aObeg1Ei2kjLsNoRofLBp4Hk8ISsi43Y1DM3peIDz///DPOO+88WCwWAADLsvB4PJgwYQIA4MILLwypdz7zzDMBAGPGjEFRUREAYMqUKbjqqqvw2muvhZS2BHPkyBHMnDkTw4cPx3PPPYedO3fK66T66eDXjIT169fL8SxatCji/WKJx+PBGWecgbvuuguTJ0+Wlx85cgTHjh0LWRbM66+/jnPOOQcAMG/ePGzevDke4TahvfGnp6dDoxHuV6+88krZKJFoCp3jjtMtRYG0JqKAQk7NLSyglOb20ifDALWSRYNfcCSfMSQ7rjOBbUHqB15ha9lo0O31y27/FkM8jQZV8HKh5y7FL8wUF6Tp5RpzaUDT4OPkx/Fycw8uHwjn4D2yhwX/PGckTh2RizSDGn6Ox4bDNfKAkMp02oZ0vrYX12GbaNQ4NkGZAtL3+kClo9mb1ZaQMgXiJWABQoq7VOK0syTgeyKlj48ssMQtlkiQMgX2iOaSfTIMYEXBenQvCwCgShQ1x/dJS6o2ujlmLUb1tGB4fgpyUwI+Ddee2B9zh+ckjRHt0Dzh9/6braUhnTSKqp1QsgzOHVvQ3K4dJjdnJN6+aB3mTLkbZk1WyLre+aNi9r5EciINShQKBfx+4Z7jpZdewmOPPYaDBw9i7NixcLlcTfa74YYbcMcdd2Dbtm1YsmQJPB5Pi68ZCcnWJpjneVxyySU46aSTcOGFF4as++ijj3DOOec0G3NBQQFWrlwJAFizZk1ISnm86Ej8ZWUBz5PPP/8cQ4cOjWmsnRU6x9Ehee4i4khjUUCnUuDUEXl45cIxuHPO4ARF1flRKtiQ+surpvVNYDQtI2UK2Nw+uL3N/1hKM+BKloFJE79mHSl6FRr8oQMmhVeYFS5I08uDsipHA3aX2bDw1TWorm+AXq2QZ3FjTcBo0BfSfUBiRA8LThiYif+cPxozhgg3vV9vKYWP42HUKBNu6tbZkISgDYdr4fXzyDRpEpaG3StdD5YB7G4fqhxN//atIQlD8RYNA74nwkC73uPDDlEgGJskA1WJnEZtJoPPVd8Mo5ypAwCT+yVXFw+WZfDpNZPxxXVTQjLvLp/aBy8uGgO1MjluPYblC5+H77YLN4XGoGv8rKHZcWv1aTH0kB/rOA69suPvC0FEl5NOOgkffPAB6uoEAZfjOGg0GnkW8t13323V9OzgwYOYNGkSHn30UajValRXV8NkMsnGeQBgs9mQn58PnuexdOnSVuNqvH84xo4diy+++AKAkH6faH7//Xd88MEH+Pzzz+W2cdu2bQMgpIWfe+65Idu//PLLePnllwEATz/9NP79739j5MiRuP766+W08q1bt6JHjx746KOPcMkll2DSpElJGf+SJUswbNgwjBw5Em+//bbcGq+2thY9evTAs88+i/vuuw89evRAd4bOcXTodi0JASDd2LR8QKVgMWto8qRfdlaG5JixvdiGUT0tSXeTHUyKTiVkNfg4VNo9zbphS7XyFr0qruq5WauEiwutube7BfOpglQ9DBoleqTqcKzWhdOe/x0Nfg4mjRJLFhSG3NjGNEZdoHxAqRDOjXROdSoF+gXNAgszcsewcrdQqzsw25h0sxHJzoBGmRUXT+qVsHOoVSlQkKbH4WonftxVjoJUPaYOiGxg6mzwobROyNDpF8dMASBQ4iR1SNl81Ao/xyPfootLG8+2kJ2iCXkenFXBsgxG9UzF6r2VAIDJ/ePbljISGIZBsn/FpUwBKRts3vBc7Cy1YXtJHS6e1DtucWRY+gDC2BHpPgY6TfLUcRPtY9iwYbjxxhsxZcoUKJVKzJo1C2+99RauueYauN1uFBYW4pprrmnxNW677Tbs378fPM/jzDPPRI8ePTB//nz87W9/wwcffIC33noLDzzwAObPn4+0tDSccMIJOHz4cIuveeKJJ+KJJ57AqFGj8Oijj4b1FViyZAkWLlyIBx54ACeffHKHzkM0mDp1KjiOC7tu7dq1TZZdffXV8uNhw4bhzz//bLLNiBEjcOzYsegF2QIdif+JJ57AE0880WSb1NTUuMXfGaBzHB26pSgQrnyAiA4XT+6NY7Uu3HXK4KQe9DEMg0yjBsVWF0rr3K2KApKpXrwwapRwNxIFShyCSlmQJgxeBmQZcazWhQY/h6F5Zvzn/NFxrYsObkmoEP/Ww/LM+OuIFcPyzSFO7kPzhGPx+oVU81i1+erKGDVKTB+UiR0lNjx82lCcMjw3ofH0zTDgcLUTd38qqPE/3DwtopKQctHc06BWwBJHnw4AOE78HO4qs4HneWwoEkoHElWG0RIZBg2ULAOf6IjbuNRitCgKWPQqDCHTznYhXZckBueacOcpg1FidWFYfvxKCbMzBwPiWM7ij383ESI2XHHFFbjiiitCloVrJxhc73/qqafi1FNPBQB89tlnTbYdOHAgtm7dKj8vLCwM23v9rbfekh8bjUb5PdLS0lqtme7fv3+3rqsmiO5KtxQFNEoFjBolHB6hbZpSdXEPAAEAAElEQVSGRIGoMSw/Be9dNTHRYUTEkFwziq0ubD5ai/F9hNR8juPx+eZijO+Thh6p+oSYDALiLJsqdPavxDMAqXqV7IZ96ZQ+8Pg4/G1MD5xemB93g8yAp0Cg/eCC8T2xr9yBs0eHpllJTt9S+fkg8hNoF29eMg48D7m2PJH0yzTi5z2V8vN95Y6IRAGpJWAiWin2zxKMZK1iy7kNhyXDxuQTBViWQZZJgxIxq6KxV8gpw3Pw4qr9OGdMj6T4PHRGLHo18i06FFuFWu3BOWakGdRNJg5iTV5mfyh4Hn6GgZFLTh8egiAIomuTHIV9CSD4R1+bJPWNRHwZ30cYCKw7VCsv+2JLMW75cAuuW7YJQHD5QPwHMCpNpvw408/DxZtDasinDczEsisn4qzRPRLSMUPKFPBzPGpF8WT6wExse2g2FozvGbKtQaMMaVM2kDIF2gXDMEkzADxnbAGODyoZKLY6I9pP8p+I98ALELLCpJKF7cU2/HVYyhRITBeH1pA6EABNMwUGZpuw8+E5uGfukHiH1aWQfAUAYHCCrkvpRi3SxCwqA5ucn0Wi6/Hmm2/K9dfSv0cffTTRYREEkSC6ZaYAINyQHqkRbmKpfKB7Mr6PMBO/vqgGHMeDZRl8u00wnNpy1IqdJbagTIH413ia9EY4OB5ulkEup8ZBoNkyh0QgeHEwckkAEMgeCMdxeWYcFHvCU6ZA52dQjgn/u3wCnly+Gy+tOoASa8udPCQkASkRQhsgZK3sLXfgs03HUN/gh0mrTNpOGJLZYKZJI2cIBUPtczvO0LwUfL+jHNlmTUKyVwBBYDX7FKhU8jCq8xISA9H9uPTSS3HppZcmOgyCIJKEbjtFnh6cKUCiQLdkaJ4ZOpUCdS4v9lU44Grw49d9gXToDzccRW0CU53NWhX0Yj1xKi/MZiWTKMAwjJwtAAhdPFr6LkmmXhlGDdKNmma3IzoXkkGflILdGlKmQCKENiDQgeD7HYLp5eieqUk7uM4WRYG+cfQK6W6cMDATLANMG5DZ+sYxgmUZ6OqmY4TNgJSs5OgLTxAEQXQvuq0oEFI+oOq2p6Fbo1Kwcr/sdYeq8eu+Sri9HNSiQd5nm4qxX2ydZknAACZFp4KOEwYrSp+Q1ZCoFnTNEZwZ0No5mjYwAwqWwbSBydU+jegYPURRoCRCUUD2FEhgpgAglL0AwOmFyTszKxlyFhZYEhtIF2ZkgQW/3nkS/nHGsITGUc6ehd+L70NGavJ+HgmCIIiuS/ctHzBSpgABjOudht/2V2FdUS00orfEwvEFWLGzHCV1bqwSjdQSMYAx65Qw2BUAONjcQo1+QWqSiQJBvdJb69AwNC8Ff9x1Utw7ORCxpa2ZAoHygcR8Dsb0SkWfDANMWiVumzUoxBch2ThnTA/0StOjsKcl0aF0afKToB1lhlGDg1X1yDRRFhVBEAQRf7qtKBBcPqAjUaDbMk40G1x7sFpu/TV7WA4m9E3Hf37aD5fXD7NOhRMHZcU9NrNOherS83B67z34uPokAJ07UwAIpEMTXYc8i/A3tTq9qPf4YNC0/LOSSKNBQGjt+NOtJyR1y1QJpYLF5P7JK1oQ0eOGkwfgm22lOGlw/H9rCIIgCKLbigJphoAaT+UD3ZdRBalQKRhU2IXe6Sk6Fcb3ToNSwWJugvvAm7UqFHlGoic/A25fJVgGyLUk16A62FPAoqP+2t0Rk1YFk1YJu9uH0joX+me1bNqXyI4eEp1BECC6F1MHZGBqEmetEARBEF2bbjsaDs4U0CgpU6C7olMrcHphPtQKFuN7p+GffxsBpSI5vhZSmv2Wo1YAgsmgKklikzDrArpiotLBicSTL5cQtN6BoLY+cR09CIIgCIIgiKZ040wB8hQgBJ4+ZySeOntE0vR/l5BS8+tcwiCqf6YxkeGEJSRTIIEzv0RiybfosLvMHpHZYK0zsUaDBEEQBEEQRCjJNe0YR6j7ABFMsgkCQFPjvv5ZSSgKtNFTgOiayGaDtS2LAjzPwyoaDSaqJzxBEARBEAQRSsxGwzU1NVi0aBHMZjMsFgsuv/xyOByOFveZPn06GIYJ+Xf11VfHJL4sswapehUyTRro1d02YYJIYhqLAv2SURQI6j5goa4C3Za8CNsSOhv8aPBzAKh8gCAIgiAIIlmI2Wh40aJFKC0txYoVK+D1enHppZfiqquuwrJly1rc78orr8TDDz8sP9frY+O2rlEqsPymaWAZBooknCUmiOABN0CZAkTyInUgaK0tYU29UDqgVrLU9YUgCIIgCCJJiIkosGvXLixfvhzr16/H2LFjAQDPP/885s6di6effhp5eXnN7qvX65GTkxPxe3k8Hng8Hvm5zWYDAHi9Xni93hb3TdMp5G3birRPe/YlwkPnNBS9KlSs6p2qSbrPanCMRjXbbf529FkNJdsoCELFVlfYc/LRxmNYtu4YrpveFwCQqlPB5/M12Y7Oa/Shcxp9uvs57a7HTRAE0ZWJiSjw559/wmKxyIIAAMyYMQMsy2Lt2rU488wzm9333XffxTvvvIOcnBzMnz8f9913X4vZAo8//jgeeuihJst/+OGHmGUZBLNixYqYv0d3g86pgJBlLXxFU1Q8fv2pY+clFuf1kB2QYty6YQ2qd0X9LZIa+qwKWD0AoESJ1Ymvv/kWjZOvXtiiQLGTwYvf/QWABet349tvv2329ei8Rh86p9Gnu55Tp9OZ6BAIgiCIKBMTUaCsrAxZWVmhb6RUIi0tDWVlZc3ud/7556NXr17Iy8vD1q1bceedd2LPnj349NNPm93n7rvvxi233CI/t9lsKCgowKxZs2A2mzt+MM3g9XqxYsUKzJw5EyoVpU1HAzqnTbl300o4G/wYWpCOuXPHtr5DGGJ5XvdVOLBk+x8AgFNnnYTcFG1UXz9Zoc9qKH6Ox8Obf4SfA8YefxJyzIHPgdfP4bZ1KwHwKPNqATSgV3Ya5s4d1+R16LxGHzqn0ae7n1MpI5MgCILoOrRJFLjrrrvw5JNPtrjNrl3tnyq86qqr5MfDhw9Hbm4uTj75ZBw4cAD9+vULu49Go4FGo2myXKVSxeXHOl7v052gcxrArFXB2eDHgGxTh89JLM5rukknP84066HqZnXi9FkVUAHITdHiWK0LxXUNKEg3yesO19rh9fMAgEqH4CmQZtS0eN7ovEYfOqfRp7ue0+54zARBEF2dNokCt956Ky655JIWt+nbty9ycnJQUVERstzn86GmpqZNfgETJkwAAOzfv79ZUYAgujIpOhXKbO6kNBkEgCyTBqcMy4FRo4RO3b0EASKUYXkpOFbrwtZjVkzsmy4v311mb7KtRU/tCAmCIAiCIJKFNokCmZmZyMzMbHW7SZMmwWq1YuPGjRgzZgwA4KeffgLHcfJAPxI2b94MAMjNzW1LmATRZSgssOBApQOT+qW3vnECYBgGL10wJtFhEEnAyAILlu8ow+aj1pDle8OIAtSOkCAIgiAIInlgY/GiQ4YMwZw5c3DllVdi3bp1+P3337F48WIsWLBA7jxQXFyMwYMHY926dQCAAwcO4B//+Ac2btyIoqIifPnll7joooswbdo0jBgxIhZhEkTS8/hZw7Hxvpnon2VqfWOCSCCFBRYAwOYj1pDl4TIFUilTgCAIgiAIImmIiSgACF0EBg8ejJNPPhlz587F1KlT8corr8jrvV4v9uzZI7vYqtVq/Pjjj5g1axYGDx6MW2+9FWeffTa++uqrWIVIEEkPyzJI0dGsKpH8DO+RAoYBSurcqLC55eV7ygVRgAnqSECiAEEQBEEQRPIQk+4DAJCWloZly5Y1u753797geV5+XlBQgNWrV8cqHIIgCCKGGDVKDMwyYU+5HT/vqcCWY3UYkZ+CIzWC8Du6Zyo2Hq4FAKQaSOgiCIIgCIJIFmImChAEQRDdi8ICC/aU23H3p9vA8YAkC2cY1RjTKyAKkNEgQRAEQRBE8hCz8gGCIAiiezFS9BXg+NDlg3JM6J1ukJ9T+QBBEARBEETyQKIAQRAEERWOH5ABtYJFnwwDPr12MkwaIRltYLYJvdP18nZpJAoQBEEQBEEkDV2ufEDyKbDZbDF9H6/XC6fTCZvNBpWK6mOjAZ3T2EDnNfrQOQ1PihJYfu1YmHRKaJQKPDinD15cdQCzB5iRqeXANzihVrLgG5yw+Zgm+9N5jT50TqNPdz+n0v1VsC9UV0E6JqklNhEZu3btAgBs3LgRDocjwdFEl856bJ017s6OdO3ojNdHhu+MUbfAsWPHUFBQkOgwCIIgCIIguixHjx5Fjx49Eh1GVFm7di0mTpyY6DAIgujkrFmzBhMmTEh0GG2iy4kCHMehpKQEJpMJDNN0Jipa2Gw2FBQU4OjRozCbzTF7n+4EndPYQOc1+tA5jQ10XqMPndPo093PKc/zsNvtyMvLA8t2rSpUjuOwfv16uN3umN5DdkUaGhqgVnfN0rDOemydNe7ODM/z0Gq1GDduXKe7Pna58gGWZeOqXJvN5m55UxBL6JzGBjqv0YfOaWyg8xp96JxGn+58TlNSUhIdQkxgWbbTze4RBEFEg84lYRAEQRAEQRAEQRAEETVIFCAIgiAIgiAIgiCIbgqJAu1Eo9HggQcegEajSXQoXQY6p7GBzmv0oXMaG+i8Rh86p9GHzilBEATR1ehyRoMEQRAEQRAEQRAEQUQGZQoQBEEQBEEQBEEQRDeFRAGCIAiCIAiCIAiC6KaQKEAQBEEQBEEQBEEQ3RQSBQiCIAiCIAiCIAiim9ItRQGe51FfXx/1fx31bJw+fToWL16MxYsXIyUlBRkZGbjvvvvk1/V4PLjtttuQn58Pg8GACRMmYNWqVQAAt9uNoUOH4qqrrpJf78CBAzCZTHjjjTc6FBdBEARBEARBEATRNVEmOoBE4HQ6YTQao/66DocDBoOhQ6/x9ttv4/LLL8e6deuwYcMGXHXVVejZsyeuvPJKLF68GDt37sT777+PvLw8fPbZZ5gzZw62bduGAQMG4N1338WECRMwb948nHrqqbjgggswc+ZMXHbZZVE6QoIgCIIgCIIgCKIr0S1bEtbX1yelKDB9+nRUVFRgx44dYBgGAHDXXXfhyy+/xPLly9G3b18cOXIEeXl58j4zZszA+PHj8dhjjwEA/vnPf+Kpp57CggUL8Mknn2Dbtm1IT0/v2IERBEEQBEEQBEEQXZJumSmg1+vhcDhi8rodZeLEibIgAACTJk3CM888g23btsHv92PgwIEh23s8npBB/6233orPP/8c//nPf/Ddd9+RIEAQBEEQBEEQBEE0S7cUBRiG6XCaf7xxOBxQKBTYuHEjFApFyLrgrIeKigrs3bsXCoUC+/btw5w5c+IdKkEQBEEQBEEQBNFJ6JaiQDKzdu3akOdr1qzBgAEDMGrUKPj9flRUVOD4449vdv/LLrsMw4cPx+WXX44rr7wSM2bMwJAhQ2IdNkEQBEEQBEEQBNEJIVEgyThy5AhuueUW/P3vf8dff/2F559/Hs888wwGDhyIRYsW4aKLLsIzzzyDUaNGobKyEitXrsSIESMwb948vPDCC/jzzz+xdetWFBQU4JtvvsGiRYuwZs0aqNXqRB8aQRAEQRAEQRAEkWR0S6PBZGX69OkYOnQoOI7DsmXLoFAocM011+CRRx4BwzDwer145JFHsHTpUhQXFyMjIwMTJ07EQw89BJVKhdGjR+P111/HwoULAQBWqxUjRozAwoUL8eSTTyb46AiCIAiCIAiCIIhkg0SBJGL69OkoLCzEkiVLEh0KQRAEQRAEQRAE0Q1gEx0AQRAEQRAEQRAEQRCJgUQBgiAIgiAIgiAIguimUPkAQRAEQRAEQRAEQXRTKFOAIAiCIAiCIAiCILopJAoQBEEQBEEQBEEQRDdFmegAog3HcSgpKYHJZALDMIkOhyAIgiAIosvA8zzsdjvy8vLAsl1rbonuIQmC6Aid+frY5USBkpISFBQUJDoMgiAIgiCILsvRo0fRo0ePRIcRVegekiCIaNAZr49dThQwmUwAhD+G2WyO2ft4vV788MMPmDVrFlQqlbx84rKJ8uM156+J2ft3RZo7p0THoPMafeicxgY6r9GHzmn06e7n1GazoaCgQL7f6kq09R6yK30WusqxdJXjALrOsXSn4+jM18cuJwpI6V5msznmooBer4fZbA75YCh0CvlxLN+/K9LcOSU6Bp3X6EPnNDbQeY0+dE6jD51Tga6YXt/We8iu9FnoKsfSVY4D6DrH0h2PozNeHztXsUNng7o9EgRBEARBEARBEEkMiQJRZhAXyBTwOKsSGAlBEARBEARBEARBtAyJAlFGjUC6SE3RaqB0awKjIQiCIAiCIAiCIIjmIVEgyvjAyY8vXHMfFn35N3itRxMYEUEQBEEQBEEQBEGEh0SBKOMN8hEoVyqxVavB0SO/JDAigiAIgiAIgiAIgghPl+s+kGh8aGouaGuwJyASgiAIgiAIgiCITgDPA5wf4P0A5xMecz6A54IeS+u4oOdB2/Gc8Dry46B/4JtZH257PrC9xgwMnpvosxNzSBSIMt4wy2yeurjHQRAEQRAEQRAEIcPzgN8LeJ2Azw14XcI/nwvwuhs9DtrG5wH8DeI/byuPQ5cp/R7MsNdBuf/OwGBfHswHDfZ5rtmwOQB+AH6GkR9zDCP+D/ghLmcADgz8DCAO68GJ63hGeg7wYITHjbdjAu/HAZjo9oDJHEKiANF2vOABhPamtHmsCYmFIAiCIAiCIIgugM8DuGpD/7ltgMcONNiF/z0O8bn4f8hjB+CtB3gOXgAehhH+sQzcDIMGRvjfE/TPLa6XnnsZBj4w8DKAj2HgBQMfg6bLGQY+AF6WgU8BeNNZ+BgeXkYBH5TwMow4mA8MxqXHwsA+SABgmJbOSkzZzPSHIrVnwt4/npAoEGV84UQBd21igiEIgiAIgiAIIvloqAccFeK/cvFfBVBfAThroHDWYHrFESgP3A24auHzOmFjWdhYFnUKVn5cz7JwMgycLIt6lkE9w8LJis8ZBk41A6eGRT1rgpNNgZthEjrQjgUKRgGWYUP+ZxgGLMOCZVgwYITnYMEwjR4jsB2AkMf8/I8AtnsMl7vHUcYRb5jvGJUPEARBEARBEEQ3wW0D6o4BdUfFf8cA61HAVgI4ysE7KuDwOlCtUIj/WFQrFKgRH1sVCtSxLGypogCQmoZ6NiMmoWoUmpB/WqUWaoUaWoU2sFwp/K9iVVCySqhYVehjhQpKRhn6v7iO4Rhs2bQFE8ZNgE6tg5JVQskqww7k5f/ZwPOw27Chz4mOQ6JAlPGiqSpARoMEQRAEQRAE0UXgeWFWv+YAUL0fqBb/ry0CV3cU1V47yhRKlCkVKFUK/5cplShTKFBlUKDaZEEDm9qutzaqjEjRpMCsNsOsNsOgMsCgMkCv0gv/lPrAMmXoMr1KD51SJw/21Qp1zAfVXq8Xnu0eTMmbApVKFdP3ItoPiQJRJlymgN3njH8gBEEQBEEQBEG0H54H7KVA+Y7Av6q9aKg+gGO8C0eUKhxRKeV/R1VKlOWY4WNSInp5vVKPdF060rXpIf9bNBYYlUbs2bIHJ085GemGdJjVZpjUJii7STo7EV/oUxVFOL83bI2Oze9KQDQEQRAEQRAEQUQExwmz/cUbgZJNQPkOuCt24KDfgX1qNfapVdivUuGQSoXSPAt4pvmZfpZhkaHLQK4hFzmGHOToc5BrzEWOPgeZ+kykadOQrkuHTqlr9jW8Xi/4nTyGZwynGXYi5pAoEEV83kBGwERtDta4ywAANr8nUSERBEEQBEEQBNGY+mrg6BpBBCjeiLqSTdgBD3Zo1NipUWO/SoUj2UZwjCns7nqlHr3MvVBgKkBPc0/0NPVEgakA+cZ8ZOgzoGJpIE90HkgUiCLeIFHgoVkv45Jvzkep3wkb70tgVARBEARBEATRzamvAg7/DhT9Bm/Rr9hVdwBbNBps16ixXaPGkdzwKf8WTQoGpA5Ef0t/DEgdgH4p/dDT3BPp2nQwXczFn+i+kCgQRbzeQJlAtqknnhl7J85f+wAc4BIYFUEQBEEQBEF0M/xe4MgaYN8P8O5fge11B7BBq8UGrQabdBq4DDlNdulh7IHhGcMxNGMoBqQOwMDUgTT4J2KG3+/Hgw8+iHfeeQdlZWXIy8vDJZdcgnvvvVf+zPE8jwceeACvvvoqrFYrpkyZgpdeegkDBgyQX6empgbXX389vvrqK7Asi7PPPhv//ve/YTQaI46FRIEo4vMJogDL81AoVTDqMwEAdoZPZFgEQRAEQRAE0fVxVqNn9S9QfPIxjh75Bb8qfPhVr8MGnQbuRiKAWWXCqOzRGJYxTBAC0ofCorUkJm6iW/Lkk0/ipZdewttvv42hQ4diw4YNuPTSS5GSkoIbbrgBAPDUU0/hueeew9tvv40+ffrgvvvuw+zZs7Fz505otVoAwKJFi1BaWooVK1bA6/Xi0ksvxVVXXYVly5ZFHAuJAlFEKh9QiRqAyShcfBwMA87nAavUJCo0giAIgiAIguh6OGuAXV/Bt/1TbCxbi9U6De7jdDicbQ7ZLFWdgrG54zEmewzGZo/FgNQB1OOeSCh//PEHTj/9dMybNw8A0Lt3b7z33ntYt24dACFLYMmSJbj33ntx+umnAwCWLl2K7OxsfP7551iwYAF27dqF5cuXY/369Rg7diwA4Pnnn8fcuXPx9NNPIy8vL6JYSBSIIl6fGwAg2YoYDbkAAJ5h4HSUw2jpmZjA7OWALhVQqoXnHAewdBEkCIIgCIJojNfrhdfrjWi74P87M53uWLwuMLu/ArftI2wsW4sf9Br8ZNCjJidT3kTJKFCYWYjJeVMwOXcyBlgGhJQB+H1++OFPRPQR0en+Js3QnY5DWmez2UKWazQaaDRNJ4cnT56MV155BXv37sXAgQOxZcsW/Pbbb3j22WcBAIcOHUJZWRlmzJgh75OSkoIJEybgzz//xIIFC/Dnn3/CYrHIggAAzJgxAyzLYu3atTjzzDMjOj4SBaKIJApIJ1WjNkDJ8/AxDBz1pQkRBepKt+DvX52DmcoMXH7ZHzi68VW8seZJXDrhTvQce2Xc4yEIgiAIgkhmfvjhB+j1+oi3X7FiRQyjiS9JfSw8D4vzEApqVqOufiO+1ivwg0EPa06GvIkBWgxSH4dBykHop+oHbYMWKAL2F+3HfuxPXOwdIKn/Jm2gOxyH0ylkjRcUFIQsf+CBB/Dggw822f6uu+6CzWbD4MGDoVAo4Pf78eijj2LRokUAgLIyoZNddnZ2yH7Z2dnyurKyMmRlZYWsVyqVSEtLk7eJBBIFoojPL3gKSOUDDMPAxAO1DGCvr0BTO5PY8/Vf/8EOjQY7YMflPI/r/noGh4w62Dc9h6dJFCAIgiAIgghh1qxZMJvNrW7n9XqxYsUKzJw5s9P3kU/qY/E6wWz7CLV/vYpvXMX4l8mAAyaLvNqiMuGkXjMxo2AGRqaNxM8rf07O42gjSf03aQPd6TikDIGjR4+GXEPCZQkAwIcffoh3330Xy5Ytw9ChQ7F582bcdNNNyMvLw8UXXxz9g2gBEgWiSKB8IJCaZASLWvBwOKsSEpNeH1COnHVHcUgtfIi/VwNPJyQigiAIgiCI5EWlUrVp8NLW7ZOZpDqWumLw617B5m3v4F0Njx8Nevj1qQAADavCjF4zcVr/0zE+ZzyUrDCkkdK3k+o4OkhXOZbucBzScrPZHJGwePvtt+Ouu+7CggULAADDhw/H4cOH8fjjj+Piiy9GTo4wpVxeXo7c3Fx5v/LychQWFgIAcnJyUFFREfK6Pp8PNTU18v6RQKJAFHE5qwEAqqB6JRMUAHywu6oTEpOODXxoDx5YHrqS5wFqsUIQBEEQBEEkC5V74fnlKSw/tBzvmg3YlR4o5RiRPhRnDDwbc3rPgUltSmCQBNFxnE4n2EY+bwqFAhwntLPv06cPcnJysHLlSlkEsNlsWLt2La655hoAwKRJk2C1WrFx40aMGTMGAPDTTz+B4zhMmDAh4lhIFIgiZfZjAIBsNpAiYmJVAHywu2sTElOD3y0//mbPRyHrPPZSaMyROVISBEEQBEEQRMwo3wnn6ifw0bGf8VaKCVWZYlYAo8S8fvNx/pBFGJQ2KMFBEkT0mD9/Ph599FH07NkTQ4cOxaZNm/Dss8/isssuAyCUot9000145JFHMGDAALklYV5eHs444wwAwJAhQzBnzhxceeWVePnll+H1erF48WIsWLAg4s4DAIkCUcXP+QAAakYhLzMqNIDfBYfbmpCYPL6AKLDdcRTQBDIHjpWsQz/zGQmIiiAIgiAIgiAAVB+A/ccH8H7JaixNMcGabgEAZGvSsGDohTh7wNlI1aYmNkaCiAHPP/887rvvPlx77bWoqKhAXl4e/v73v+P++++Xt7njjjtQX1+Pq666ClarFVOnTsXy5cuh1Wrlbd59910sXrwYJ598MliWxdlnn43nnnuuTbGQKBBF/JxQx6REIA3EqNACfsDeYGtut5jS4PfIjzdrQutfDldsQb/BZ8Q5IoIgCIIgCKLbU18Nz6rH8N6+j/Cq2QhbmgUAUKDLxhWjrsX8vvOhUnT+GnSCaA6TyYQlS5ZgyZIlzW7DMAwefvhhPPzww81uk5aWhmXLlnUoFhIFooiUKaBgAqKASakHGgCH15GQmLxBokBjSm1H4xgJQRAEQRAE0e3xNYD78wV8s/F5PG/SoDQ1BQDQ15CPK0cvxpzec2TjQIIg4gN946KITxQFlEGigFFtBJyAw+tMSEweX0Oz6ypdlXGMhCAIgiCIROGvr8SHS0/GwIKpGHPqi4kOh+iuFP2Gzd/dhMcUduxKNQAAstQWLB57C07rdxoUrKKVFyAIIhaQKBBFwmUKGNVCOwq735WQmBq45jMFKj3W+AVCEARBEETCeOPzRXhOzwPVv+KPmgMwpfVLdEhEd6K+CrXL78K/SlfiM5MRgBpGVoPLR/4dFxx3IbRKbasvQRBE7CBRIIr4eT8AQBlkNGjSCClRjhbS+GNJg9/bZJmB41HPMqhIUEkDQRAEQRDx5c2GEoAV2hAfK92Iqq+uxiHrAZx34Y9QaS347/9OwsiccZh8+qsJjpToavA7vsBnK2/Hs0Yl6kxGAMCZfebipvF3Ik2bluDoCIIASBSIKr5wngLixc7ONZ/GH0v21e4FNAqcp+mBDzxCy8RUVo16eFHJJyYmgiAIgiDii10UBABg9da38ALKAIsB5V8sQmGPKXhR6wesa7DN7wMUdHtIRAFXLcq+vhEPVv2B31N0AICBxgLcd/xjKMwqTGxsBEGEwLa+CREp4UQBo04UBcQsgnjzu0bIWvjUfRSX9ZiBfIUe94y/CwBQCQ7g+YTERRAEQRBEYvjJXSw/fo+xo4IPZBXWVO1OREhEF4M/8DO+eH0KzrJvxO96HTRgcdvom/DBmV+SIEAQSQhJwVHE5ygHACiCywf0GQAAB7iExCQxUZuDm0/+F24GYHNWAev+AZuChdtRBq0pN6GxEUQk1Fdsh1Jlgia1V6JDIQiC6DTwvgYsWToNCPJvc/o9ACu0evMyDIwKnbzOaj+GtOxh8Q6T6CpwftT9/Age2LMUK016AMAIc188ctIS9Enpk+DgCIJoDhIFooSzai/+6xdEgWBPAaM+CwDgYBIzIz+SV2ML04AzepwoLzPp0qHlebgZBpXF62A+tgnmkeeDyRyYkBgJojXc9nJM/G4hAGD9WSugNeUkOCKCIIjOQVHRT3hDUR+y7LAqtPe7L6jE0VZfEZe4iC6IowLbPrkQt3uPoNighxIMrht5DS4ZcSW1GCSIJIfKB6LEmr2fyY9ZBOr2jMZsAEA9w4DzulC/51tsfO8McPayuMTFiOUBCiZwMWYYBpm88Kf/dNW9mFr8CZ5+b1Zc4iGI9rD/6Gr58fNfXZTASAiCIDoXVbZjLa7v6efh4wLlA3ZXVaxDIrog/NH1eOftE3ARX4xilRI91Ba8c+p7uKLwGhIECKITQKJAlFAqNPJje5Crv8kopObzDIN6RxmuWXUjLmk4gI+/uiwucXEQRAG2Ud/XTFaI9zUxY3Bpiiku8RBEeygJqnHd5atLYCQEQRCdC1eDvcX1tQwPX1CnIpuTRAGibbi3vIe7vlqIJ41K+BgGM7Mn4MOzv8XQ9KGJDo0giAgh6S5KuD02+XGVLyAKaFR6qHgeXoaBY+932KQV+rB+6C7GuXGIq1lRQGUCvO44REAQHaeyvlR+XGjomcBICIIgOhceb2jpQAYHVAVNCdlZFg63NfDcYwVBRATHofKnB3DjgQ+wzaCDEsBto2/G+cMuBcMwre5OEETyQKJAlLC7a+TH1bwvZJ2JB2oYYN6uFwHxIumJk/Gg1POAZUL/1JkaC+CtjEsMBNFRal2B75e1gTIFCIIgIiV40iKP0SCD4VGF0JbEpa6Aj4DdQ9dYIgJ8Huz85CJc79iKCq0GKYwKz854EePzJiY6MoIg2kFMywd++eUXzJ8/H3l5eWAYBp9//nmr+6xatQqjR4+GRqNB//798dZbb8UyxKhRG1SDd8HwK0LWmcTT7A1STV2Ij/EgD8lTIPRPnSUaIBJEZ+C/9p3y45pGs14EQRBE87xc9KX8+H9nfY0cVdNyweIgsdXmbbncgCDgceC3d+fjEud2VCiV6KNJw7IzPidBgCA6MTEVBerr6zFy5Ei88MILEW1/6NAhzJs3DyeeeCI2b96Mm266CVdccQW+//77WIbZYco3/w97StYCAEaq0zB/9DUh641M04QMd5y6EUiZAkzj8gFjfpNtfW5bk2UEkWys5G3w1lcnOgyCIIhOwZGgTgNZxhzk6TLl52li0mIZ75GX2b3OuMVGdEKcNfj2ndm4ni+Bi2UxyTII7571NXqaqbSPIDozMS0fOOWUU3DKKadEvP3LL7+MPn364JlnngEADBkyBL/99hv+9a9/Yfbs2WH38Xg88HgCP2Y2mzCw9Xq98Hq9YfeJBtJr19eVY8aWpwC94BVwvKE3fL5G5QOMCkBoLG4wMY1PghO7D4APfb80Y0GTbWtqDiE187iYx9QcUnzxOC/dia54Xl/6YhGuOeerhL1/VzynyUBXOK9FW96Gt6EeA8Zdm+hQAHSNc5psdNZzagELr9eLbEMuYBfMW7MYJWrgQyXDA2LnJJvP2eKxdbbjJqKIvQzL3puLJ9QN4BkGp2RPwKMzX4JKoWp9X4Igkpqk8hT4888/MWPGjJBls2fPxk033dTsPo8//jgeeuihJst/+OEH6PX6aIfYhOUr3gl5bquowLfffhuyTNnAAerQ/Tws02S7WODjOEDBYNfuPagoDryfzd3UXfinVZ9DZyiKeUytsWLFikSH0CXpzOc118ehVBlIbPrcdRS94vD9aY3OfE6TmU55Xj3HUFr7KV7QCrXZDx9TgdU0zchKFJ3ynCY5neWc5vl4lCgZnM9Ow7fffgt7XWDiIsWrANQ+1LOB62u1p77F+xOnkzIJuiX2crz63il4TuMDwGBhrzm464QnwTLUyIwgugJJJQqUlZUhOzs7ZFl2djZsNhtcLhd0Ol2Tfe6++27ccsst8nObzYaCggLMmjULZrM5ZrF6vV6sWLECxw0fCGwMLHemqDF37tyQbTd8/ALQUIrGNN4uFrz6v3sBAMOHjkDhsMD7Ody1eOrTt0O27TcgB4WFsY+pOaRzOnPmTKhUpDpHi65wXv/9v/8LeV6pZOPy/WmOrnBOk5HOfF7ve2cKvtG65OcF/bUYOTxxn1GJznxOk5XOdk5fEe8Dxg0fi5FD52LPQRVeXPMrAKDAnIW17qMh27sVLd+fSBmZRDfCUYnXZEEAuHbgQlw98W7qMEAQXYikEgXag0ajgUajabJcpVLF5ceakav2Ba6c+a8m72tUGdDI6BcAwPqcUOhSYhme3ONApdKExGVRZjbZ1u6uTIobnHj97bobnfm8eoJSWyWS4Vg68zlNZjrjef2GdYU8r3OWJdUxdMZzmux0lnMq3aWoVTqoVCrk5xXK63qnDgRKQ0UBO/wtHldnOGYiitRX4833TsG/NULZyA2DFuHKiXclOCiCIKJNUokCOTk5KC8vD1lWXl4Os9kcNksgGfBxoaP9PEvfJtuYVIaw+1bX7kOWbmxM4pKQRAGWCTUaDKfu1jmblhQQRDLgRdPPq9/rhkKlTUA0BAHwPi/ueHMMflP68cn4piVs1Y6yBERFEE3xQxBVFWLdd6opD9f2OR1ezotBaUOB0pUh29viZIRMdAI8drz93il4Vi14dy0edD4JAgTRRUmqQqBJkyZh5crQH6cVK1Zg0qRJCYqodXy+gMnh/YMvDruNwRC+/Z/NXhKTmILxSy0JG3UfAICXpv8blw04F6eqcwAAtW5ydCeSExcriAKfnPiSvMzhiP33hyCa469tb2O5moeDZTF7Q1NRoNJJogCRHEiZAko2MMN/zbRHcMP0J5GR2q/J9naGAe8Lk95IdC98Dfjs/dPxtErIgrp2wHn4+8S7ExwUQRCxIqaigMPhwObNm7F582YAQsvBzZs348iRIwAEP4CLLrpI3v7qq6/GwYMHcccdd2D37t148cUX8eGHH+Lmm2+OZZgdwi9mCvTlGJwz4baw25w87oawy+vqy8MujyZypgDbNN1vaq+TcPPk+5CuEUoYrJ66JtsQRKKxVu6RH2eZ8qHjBKHLZitOVEgEgXpvfYvry12UeUUkB34x0YoN4xCfmT64yTKOYeB0xP7+hEhiOA6/fHI+HuIF49RLe83FNZPvTXBQBEHEkpiKAhs2bMCoUaMwatQoAMAtt9yCUaNG4f777wcAlJaWygIBAPTp0wfffPMNVqxYgZEjR+KZZ57Ba6+91mw7wmTA5xdqrJRh0psleqT0xtenfthkudUe+0FNc+UDwVg0FgBArdce83gIoi1UFq/HBV+cIT/XG7JgktpmxUFUI4jm4Dhfi+vL6HpKJAnSJ1WhUDdZZ9alQck3LRewOUh07c5s+fZ63OrcBT/D4LTMsbj5hCcSHRJBEDEmpp4C06dPBx/mx0birbfeCrvPpk2bYhhVdPH6hUyBlkQBAOhhGdBkWYn9aJgtowsnzxA0/6e26NIBAFYftRkikot/rrwRh1WBz65abYCZZ1ABHjZb7L8/0cBXvhO1B1cic8K1QJgyHqJz4g0qHQtHOeeOUyQE0TLS5IAyTMYgwzDI4FmUNfIRsDnKkBuH2Ijk4+i6/2Bxxc9wKxSYauqHB+e8Ql0GCKIbkFSeAp0RHydlCrR8KhVhBuUOryMmMQXDyZ4CzYsCqXqhDWStv+WbXIKIN8VBA6u+osK1XyF8pl/f8nJCYmorN316Gk7a+wq2//7PRIdCRJFWRQGGA1oQxQkiXkieAuHKCAGgjG36ObU7K2IYEZGs6OzbcdOuV2BVKDBMnYZn5i+DqpnPDUEQXQsSBTqI5CmgjEBF7akyAwDGqYSZ+Xpv7Gfm/WIGA8O0kClgFOYD6tByOizRMj/8cCseeWM8vE4ybIwWmQq9/FjT6HK1Vts0FTbZKNryDlbrhc4p7xxenuBoiGjiFUXU/lxo9seZlmEAgHqWhaPuSJP9iMjhG5xwBXmKEO3DJ96eKBVN2zc3h426EXU7/NbD+NTxPxxSKZEFFZ6b/yH0Kn3rOxIE0SUgUaCDyJ4CTOun8u0zv8D7p76P8cZeAACHz9XKHh1H0v/DZSpIWMw9AAC1oFmtjnBr6Q/4QOHCmA9PSHQoXYbUoHaeXl5IgtXwnSeN8ZH1T8qPG3gS3boSkihQoDCgPxNojXndxHtgFs0wyyq2JSS2rsLd756IE74+C6WHViU6lE6NVD6gCGM0CAALdL3lx+MZ4Zprd9fEOCoiqWhw4oUvzsPvOhU0PPDcrFeQacxOdFQEQcQREgU6iCQKKCI4lRm6DAxNHwqj2ggAcPpjX3MqtSRkWjIaTBFEChvLwOe2xTymrg7PMPC6qJNDNKgLcniXygaeGXoFAGCIP3kuX+t/fgBPvD4GdSUBPxRvbRHWBt2De3l/mD2JzopXLB1TsSxmmAfKyxVKDbJFu57ymn0Jia2r8A3rhItl8fY6Kr1pLzzHwS9mMrJhjAYBoCB3lPw4TSFkNtnc1pjHRiQP3355Cd5SCZmvD46+DUNzxyY4IoIg4k3y3FV3Uvy8lCkQ+eylQWwB6OBi2wfY66yGixX+xEwLRogpYvkAzzCwUbprVDhy7PdEh9AlcHBN67ZTjXkAgDokzyD7/w5+iHeVDXh91Z3yshc/Pz9kG5c4iCS6BpLJrIpRIl2fIS/XaVKQrRRSbkuLViUitC6BzXpYfkwmuO3H7w/cZyibEQXOnXQPTsueiCUTH4JZTBe3N9AEQXfh0NoX8aB9BwBgDjMUs4ec38oeBEF0RUgUaC+uWoDnYRfr7rQKbSs7BDCoBW+B+hgPEiqqA7WYloymvYgllAqVnO5qrTvc7HZE8/D+0AHqgZJ1CYqka+HkAuf1yowJAIAUSRRgkqfcpVQpzAxv9ATMuV5jQ1vSKVvI1iE6H7+XrwcglI6lGgJpthqtBW6xNOwhz0FU7iMvifbgCpqprnRVJi6QTg4XJAoolOE9BbRKLR6d8ypOHnQWTCohk9EeByNkIvG4yrbh1q3Pw8WyGKfJwmTzeYkOiSCIBEGiQDs4uO09nPvB8agu/y8qxLq7fG16xPsbtKkAgPoYpxPX20vkx0ZtSovbpkIYsFgdJS1uR4TH7a4NeX6wZneCIulauMTvyMP9zsP1c18FAKSYCwAIRm5eT+J7wTfYy+THdUHf6VPUofWYdnct4IttdhARP/5QCB4RP3jK0CtvgrxcqdQg21QgP39p/dNxjy0cvN+HfVvfhcdRnuhQIsLTEPhur1MxcNtLExhN58UX1FWoue4DwZjESQtbHIyQiQTT4MSTX1+EfWol0sHi0Tlvg43AH4sgiK4JffvbwcN/PoT9ajX+pT0Gu1O4wTKrTRHvb9AJooBDtv+JDV//8UTE26aINwu1NftjFU6XxtnIqfmQrQjOol8TFE3XwSV+R/pY+sp9kk3GQPdsm+1oQuIK5vs/A2aCdUHfaQNCMwO2ajVYs+KOuMVFxIccRo3BfU7GjT3n4R9DLgUAnD/0Ynm9NQ6GspGw+o9HcdamJ3DVe9MTHUpEuD2hviwHDnyfoEg6N46KHfJjRTPlA8GYtRYAgC0OnkdEYvn6qyvwicoHhufxxNQnkGEgY0GC6M6QKNAONmoCavvnrHDDp/BG/gNq0AlZBc4Yu/2/qWq5j3YwqaxQ/nDv0a/hrzkQq5C6LE5XaBvCb1GP43++Boe3vZ+giDove/d8ifuWTkVZ8Vr5O6LTWOT1CoUSJrHcpc5WnIgQQ/AoAzfaVgUr96b3id0GTlPnyOvvLV0R3+C6CLu3vos73hiDo/sTPzB028uwa+s78vMnpz4GALjixCdwxvhbAACZ6QHjwXC+GIngo6PCZ+8vtRJeT/Knhi/f8lrI80MVWxMUSefmnz/dLD9WB3VzaQ6TNg0AYI+x5xGRWEp3fIxHbVsAAFf3PAUT+52S4IgIgkg0JAq0g4wwLdEc+pbT84PRi+UDLgbyACIW9EHkPYkt4s2Cg2Xx7Z/k9NxWnI3KBwCggWXw8paXExBN5+b63+/B53wdZv54BaoVwndNL85eSaSIxpk2R+JTii3q0O++Uyzb8Yp+CFm6gAkdWQ22j6s2PIrvFA24btVNiQ4F9356Bs7dFMgOybT0abJNRmpAFKhNkq4TuqCf++9/fTiBkUSGhw/NpFt5aDnAxTa7riuyXBlohcqwrd/ymcVJCxu1UO2ycM5q3PfHA3CwLEYqLbhq+uOJDokgiCSARIF2kM41FQUWTb4v4v11OkGJd7Ms/N7YpZYOVwolDTekj2t121SxjhAAjniaDnCJlvGKKcJZXKjI4+ZpGNhWShRNv1/Sd0YiRWz5Vlef+PpoX1DbRAAoLRPaEnrF7ggZ2kDsPNVrtotahVCKcUilTHjb1O8R+vfWiSJvMCqVBibx5zVf7ESQaH5CIDtgQ8WmFrZMDhpEI169KJz/qFXg55/uSWRInZJxvsg7IwGAURQF6mNc3kgkjve+vARr1Sx0PPDonFehZJWJDokgujXFxcW44IILkJ6eDp1Oh+HDh2PDhg3yep7ncf/99yM3Nxc6nQ4zZszAvn2hLY9ramqwaNEimM1mWCwWXH755XA42pYVSHeo7cAephVaij4r4v31QTOHbldNVGIKR4Oo9Osi6IyQEpSe7aZawjYjmTmpG2WRuDmabWkrcxRpTZaZzQUhz1MUQhZMnauqybbx5K/fHsHtR74MWVZctQsA4BONulQKNYyiv0BfpvWaXqJl9h76MdEhhKDVNv28AsDdvc8AADj8yVE+MIgL3PgfaYjd70608IqiwGAmIKr888jXiQqnU3J495dYrxRElYXmQRHtYxTbazqSqLsLET0ObnwN/3IdBADcOuhC9EpvvjMVQRCxp7a2FlOmTIFKpcJ3332HnTt34plnnkFqamDC4amnnsJzzz2Hl19+GWvXroXBYMDs2bPhdgfGa4sWLcKOHTuwYsUKfP311/jll19w1VVXtSkWEgXagS3Mj6VSGfnNvkYdqOs7tnlpVGIKR4OYvqxupg1RMKnBQkWS3MR2JqSe5UoweHrkDfJyN+8H2uA3QQB8GK8NtTq0FjZFoQMA1LnjN7jxO2uxb8N/wQfVY19x5NMm25VYD8LvrMVPrNjHXqHBP4f+HQDgiHEb0u6ALcHZIWmNsoEUyvCO7lKbwrokqc22MAFR4ADnjmnpWjRoEAXVgfqAJwfD+cFbjyUqpE7Hir0fy4/rIuw6YTAIExxOhgHvJ1G7K+Gvr8K9m/4FD8tiiiYb5068PdEhEUS358knn0RBQQHefPNNjB8/Hn369MGsWbPQr18/AEKWwJIlS3Dvvffi9NNPx4gRI7B06VKUlJTg888/BwDs2rULy5cvx2uvvYYJEyZg6tSpeP755/H++++jpCTyrnKUM9RG/F43HBHU5bWE5KIOAF/VbEFk+n3baRBrWVWK1kUBS4gokBw3sZ0JnyQKMAxmjbwC2PIcAGAD48Elrw/H4yctQe7AeYkMsdPgi6AG26wyAL4q1DVyKI8lb31/HZbYtuGqvZ/g6nO+AQAMgQa7ECqilTiKsf/Y7/Jz3ueGxSR0TKgLk2VEhGff7s/x4cbncM3M55DK8ahlhetmVYI7TmRBgZoIUqstpjwAQG2SpGG7eR9EKw7UKFjUVu9Fakasfn06jlsU0PqYemKQpwp7/HYcUSkx4otTcIeqABcu/AZg2pYa392wBLVK7pE5PKJ9DPpMAICPYeBx1UBrjDwLsivh9Xrh9bYu4krbRLJtonn/myuxTcXCxAP3zXwFPl+o6NOZjqUluspxAF3nWLrTcUjrbLbQUkeNRgONpul47Msvv8Ts2bNxzjnnYPXq1cjPz8e1116LK6+8EgBw6NAhlJWVYcaMGfI+KSkpmDBhAv78808sWLAAf/75JywWC8aOHStvM2PGDLAsi7Vr1+LMM8+M6PhIFGgjHre1ybIpiqY1pa2hBAMfeGTGsN60gfcDDKBRtl4+oNcGPAXcNJvZZiRRQAUGDMPgzVF34lLRjGyjTosHfvs/vEKiQET4+dYHUSlqE+AC6oJ6mceaL2u2AkoGr3hLcbW4rLEgAAAlnmog6Bg8DIsUUw8AQJ1kLkqDmVa57fd7cVDJYPfXi5ABFrViBsndR78C/2UF5p/2WiuvEBt8Ec6wW0xCyUsdwyfF39zTSJx4/IsFWDz1YfQcND9BEbWMXezaYNKm4j+zXsHM7xbK657yHsUpZVuQkVuYoOg6Bw1BWX/Xzf5PRPvogzwyHLaj3VYU+OGHH6DXR35/tmJFkneWsf+F/3j3AiyLUxUTsOG3bQC2hd006Y8lQrrKcQBd51i6w3E4nULpaEFBaNnrAw88gAcffLDJ9gcPHsRLL72EW265Bffccw/Wr1+PG264AWq1GhdffDHKysoAANnZoS1Ds7Oz5XVlZWXIygq9ViuVSqSlpcnbRAKJAm3E0xBIHZ6JfviRP4ArJt7Z5tc5R98b7zkPxXSms4HnAAZQR+ApoNMFZhRcSZLu2pnwOYXadqU4FZee2jtk/VFQGmakeMUBdRoH1DSTlCN5YNT56sNvEAM4VgGIA6uGMF4gp5n640v7fhR7HTh2IPCDMX74BbIo4GJZeJxV0Bgy4xJzZ+agUvgubVZwUPM85GluAPfUrsXUQ6uQ2md63ONqACfHktKCPmCx9AQgGMq66iugMya2B7jk5q/jeLhYBt8pffj99zvxe9KKAl6AFUSBjLSBTdaXV+8hUaAVvKIocKoqE2yEJqcsq5Aff/7xubjihu7ZonjWrFkwm82tbuf1erFixQrMnDkTKlX4UqJEw/s8uPXd++FUsShUmHHruS+E/Tx0hmOJhK5yHEDXOZbudBxShsDRo0dDriHhsgQAgOM4jB07Fo89JrQ3HjVqFLZv346XX34ZF198cZSPoGVIFGgjHnFmUsnzmGa5BPfOnIY0Q3ijqZawaFIAZ/RmOhvKd2LryrsxcvYzUDEsoDbB4asH1GqolbpW9+/d60Rg7QMAqO65rdSUb8MNhz8DACjFH9rglmQA4AhTJ0+ExycOvG/sORdrq7fh+B4nNNkmRTR3s/lj172jMdmsGkUQ/CH2HfgOTFBt+119/4ZJfWfjyx+vxHYFh5sqV8nr+uWMBs/zYHkeHMOgrq4IWSQKtEq+HygWxycNYWbZp/1yPdaZP4MuvX/cYvI21OOI2B3j/JTjcP6E5gVhgy4DSp6HT/ybJ1wUgCCsHMfqsFH8HNsUCvC2UjD6dKANvjjxoF4sIzLq0sJ69hRVbMJQnBfvsJrF73XjkQ9mY3j6UJx1youJDgcA4BEz2DTtdJf/d4oeV0QzoE6ESqVq0+ClrdvHkx9/vBmrVByUPI8HZjwPjbrlktJkPpa20FWOA+g6x9IdjkNabjabIxIWc3Nzcdxxx4UsGzJkCD755BMAQE6O4KtTXl6O3NxceZvy8nIUFhbK21RUVIS8hs/nQ01Njbx/JJDRYBvxNAgKkJYXvAFMalO7Xkca1FijNNP52JcLcan3IEZ/fSaGf3U6TvzgBOxXCzdSalXrKXAWXSpu63cOAKAuSdro8X4f7lw6Gc99eHqiQ2kWzuvCbV+cKz+XMgWMhkyogtKM7Syli0eKlJ5tUBvx5Lnf4tTJTQdeUi9texwFLG9QScD2Y79hiest+fnI9KHolTMWOi68+MMwDMziumc+Oy/pTd6SgR6K1sXM//vsrDhEEqC09C/58TUnPo1euaOb3ZZhGFjEP7PVVhzr0FqE8bvhEoXJYcaeIetGfDYLTy09PhFhNcFpK8by7xbj6MEfYRfjNenCC2il9uQyHFy/5U187K/BAxW/wm2PPF0zljRIZW1M+2/CXfuTq9sH0Tac1fvweMlPAIBLc45H/5zmr1kEQcSfKVOmYM+ePSHL9u7di169egEA+vTpg5ycHKxcuVJeb7PZsHbtWkyaNAkAMGnSJFitVmzcuFHe5qeffgLHcZgwYULEsZAo0Eak8oGOzqmkiMZ+dVFy+v9EHVovWqUMpABqIxAFAGBczjgAgC1JjLEOHFqJb3k7XnUdhLO+ovUdEsB9b0/Gek1gFqZeHKQyDIOMoPaEfqohjxjJU0DJNv8tM4lmWLYITAmjRbAosK12NyqVgb+7wZQDhUKJnBYuqVaFsO5bowH2mu6ZktsmGgknvbmm36EVqviKK9V1h+THlpSCFrYUtxFbUdY6SmMWU2vYrUfwSN3DqBU/fyPymt4g/E/hjHdYTfC4ajHhszm4vWI15v56M+rEr5Khmaya8gS3I22MP+hadM8nySFkN4i+DGpF+0WBc39eHK1wiATw2veLUaFUIJ9X4KqTn010OARBNOLmm2/GmjVr8Nhjj2H//v1YtmwZXnnlFVx33XUAhPHETTfdhEceeQRffvkltm3bhosuugh5eXk444wzAAiZBXPmzMGVV16JdevW4ffff8fixYuxYMEC5OXlRRwLiQJtxGkTWjvUdnCMZxHbVVmjNCs/S5ne7DpvhKnrFrHu2ZokrYgavIEsin0HktOc5EtV6HkqCfIO8HGJP4edkb2ieZ+yhRtZk14wVLHHUcBqCHqvb/lQL5Bs0dn7MBMaz//1OTvsa1VZi6IbXBekigsVTL0RGFDGmm1FPwMA8v2RxaIXhcGP1z0Ts5haY8Pu9+EK6pgztNfJmKpuah7nSvDs9v6ilSHPfaKQahJ/K4crQrPyKhtCnZ0TjcsTiGcFk3iRBQi0ddS0ILC2RpFaRZlNnZRju7/C2x4hS+n2wsXQqlrPviIIIr6MGzcOn332Gd577z0MGzYM//jHP7BkyRIsWrRI3uaOO+7A9ddfj6uuugrjxo2Dw+HA8uXLodUGPOPeffddDB48GCeffDLmzp2LqVOn4pVXXmlTLCQKtJFPdr0DAOA7OPNrMQp1IdYotShracJs6KDIWlGkiDNfDSwDd3BP44rd8K97FeDi207t7Q3/kh/vKVkT1/eOlJxGYwNpNhhAyEwyAMBRSTdXrVBeshFOcQCjVDR/I2s2CjVSdpaJ2zn1NvM+z058AHptCgDgvsEXhazrb+4lPx6jCNSWOZzJmfmSTNQ1EnyKFQzemfJk0w3j+J16q2q9GEtkP51bFcI18wejAe7aoliF1SKVjtAexQZdGl5auBJvjL4rZHlx6YZ4htWEgyXrwy43iN/15077CLf2ORMP9BZ+zyr8yTHwlqgPMg3O9yfHdb7BL0w6qFq4loZjcCMBxt8QP0NXIkpwHJ794wE0sAwmKFNx0sjLEx0RQRDNcOqpp2Lbtm1wu93YtWuX3I5QgmEYPPzwwygrK4Pb7caPP/6IgQNDvcvS0tKwbNky2O121NXV4Y033oDRaGxTHCQKtJEsc8/WN4qAFHNQizKu4zNgPl94w7XzdD2hVxsieg29JgVK8Qa7LqgX+L/fm4Vp2/+FY2ue63CckeJ21uBbzio/f75kZfMbJ5A0KJpd9/Cg0AHi8E9OwjefLGxmawIADpSslR97WyitMZkEUa2BYeB2Vsc8LgBoCJNxc5Y6DzMH/U1+fvrYm9BfmSI/11t6y497KgLfQ3uSpT4nI05Rd9UEnfaR/edi28Xb8ONpX8jLXHEqLbJWbEelaDI4vR1taHcUJaY22+MLHTxrxZZzg/vOCll+tDJ8e7J4cc+xb8MuVyoFU7QMcz4umfYwBmWPAgBUJIn3DQB4PQ7cGxR/sYJB+dE/ExiRgEcsZ9MoWjaWa8wNk+8Pee5wJIdHAhE56357DCsUXrA8jzunPw2GShgJgmgFEgUiheeBqn1IVwkK+kzW0qGXs5gD7arcQS7m7eX7ZtIVFW2Y3WcYBiliuqs1yMTpNUsKbAoFXj70VceCbAMujzXkuVWhQH3V3ri9f6T4xYGiFgzSocATgy+V15058XackjosZPu76nfENb7OBhPUJmnUkOadxfW6dLCigGVvNBMaK6QynJQgDc/cyK9DpVTj0/N/xSMjb8CVveZhSK/pgXVBx2YP09Iwnmz49XHc/epI1BYHzc76fYAvOdqRcpwPLvEe9rVJ/8AI1oglw66R12dZ+simjlVVu2MWh9dtxyPvzcLqP57CtG8XyMt7aiPrOHN1wWz5cVXdkajHFwkeX6i4ptEIGSumRrX6xbWJ9bmQPFhGcy075WdlDAYAVLEMOK875nFFwrEwWQ5v/PFIAiIJxcsL5QPqNooCx/edE/LcnkBPDKLt+N12PLnvPQDAuZahGJA/PsEREQTRGSBRIEKWvjMDw785C+8WCy6uqna2+JEw6tICs/J1R1vZum08OeZ2+bG1jTXXkjFWne0IfKufBMoCs0e+ONb0+sMMTiZ+E74+O5H4xb/hCyNuwKqLN2PehFtC1j8+73+JCKvTItXljuCUSEnp0ex2LKuASZxBtsdpFssrDlLnpQ6Vl9V66ppsxzAMTi+8EjdMfyJkdsacEiglcLhrYxdoBCze/w6+VnOYvuJSQfDkeTzx1gTc9+ZY8AkSBni/Hx9+eSHq7b/D466VS7QGFhyPdy/8EyePuVbelmEYZIg/X5UxHMx+tvr/8EFDKRbv+19IydjkfvMi2v/aE/8pP65KUMmI2x8YOF+SMgxMkL/A2SmBNkglzo6L0x2hByvUO1/Uczb+O/oOAEA63/QWJT21P1ieh59hUFO9p8n6eONxVmPlpv82WW6I0OA3lni49okCjXE4K6MRDhEnvv35LuxVsjBxwHUz45fhSRBE54ZEgQj5Jyfc0B0R+0+qmfBp4zzPY3txHbytGFExDAOz1K7KHr2ZzueHL8bcYRfh9LRCAMCkfnPbtH8KKxzfX5vfwuSDS/HC+4GbX1+U/A8iwecPPwPkiLKA0lEkK0G2GZFIoVDiNMvQkGX+ysTfyCYrLrG7hy4C0c0ktn+0RSHTJhKkZOWFowKD0wn9T4t4/0umPy4/tifYJK1eHBhyDIMjm99G7c7P8K6yAZ+reRSXrEtITLe9MwVPOHbgcf93uOOTUwEADM9Dq7WE3T6TEeqkK+oOxywmV5iyrPmGPphcGFn3doZhcLYmHwBgDyMgxQO32JbuMsMg3HrGeyHrHjj9fVyVPhYAUNWQmPgkJNFZqdRi8vAL8cr4+/Hu/A+abKdUqJAuZhVU1CQ+e+y5ry7Cv+2BDLCprJCJUeVObDYQAKz3CX9TrSqyEsLmsJMHSqfB66jACyWrAACX558om1oTBEG0BokC7aS5TIH5//kNpz7/G6Y88VOrr2EST78zCjcPeaKxUbqY1nrf3Nfw/qnv4/SxN7bpdVIUgpPlizoeLpbFy6mB+mh/PDMFxBtZbaO+70ebMaNKFG4xPbMlU7wLh10S8vyyT08VTAeTAEfNQdz75jj8vvohuOuOYdtvT8HvTNzNrMsrigIR9NU2QfgO2p3xqc+X5s/VKj2+P+1rXKE9F3NGXxfx/im6NJynE7IF6r2JNe4yBvmYzNv6DKZteEB+Xp+g0oYfEDgnvysECUbHC1kh4UhXCjOx1TGc4Q43w3rDlIfbVJ9rVglGP3bxsx1vPKI3h07Z9FgYhkFfc28AQHWCP5M+MatNKZ7zSUPOQX764LDbZorXhwrrwfgE1wLvuQKiVG8/MCv/eABAhdcetfeoOvwbfvz22jZl8TjrjsIeoSFmOGZnjJIf28gDpdPw0Y+3oFjJIpNjcP70xxIdDkEQnQgSBSKk8eBU2YwosL1YmAGssDdvkiahF1P166MwgyTNWKuUQgqmRqHB0PShbTaXsSibn1GIa/mAKAooAdzX81R5eXHVzrjF0Co8jzKl8DdkWjCbzkgLdQj9S6vF8++cFMvIIuadn+/EF6wbVxd9jP9+dxXOP/A/vPLVRa3vGCNc4sBEx7YuCpjFbeyuOBgN8rxcPqBS6pFpzENv7Yg2v4xB/H7W+xLnnH5wy7twsM1f+uvsxXGMJsBJbEqTZS010LKIs5+2Rv4j0YQLc83TGzLa9BomteBDY/clZtDtlszmlNqw6zNFf5sKPrF+EoFMgdZT3bMUgiBUYUt85lhPPiBaFSmALLFMqKKZbLf2MPunq3Fz5a+4/fXCiPcpqdwuPx41rO0Gt4/OeRV68RbR4ba2eX8i/jhrDuK/NZsAAFf3OxM6dducxwmC6N6QKBAhvkZja3UEg5bW0IkzYE5Px1OJveKoVNnMjV+kmFtIM4ynKOATRQEFgHNPfBwnK4UMiJsPfoC1f/yzhT3jR7DvQUbm0Ga3Sw3TseKVJGkXbOd98uPXPMIN9ovu2KVjt4ZLHCzrImihZRJnFO1xuGH1+dzgRIFNHWE3j3DoxTpjp6910TBWfL7/sxbX18apHKMpTZW1arZ5UTNFNH2ti2EpBh/Gk8Wgb1s6rlljAQDYfIkxxZNEAa0i/EWnR65QPnCU5eFvCN/FJh7IooCi9d+wLI0gIFUm7LMaoEAZOvDKSu0PAKiMYrldg/g9+F7bfLebxkgZPxoeKGjh96k5NAoNpom/u/G4xhId538/3owaBYuevAJnTvm/RIdDEEQng0SBCOD9fvgazbgrm2kjaFBH/qOtZ4WBTzTKB6R6Z1UHRQGj2tTsunAt2WKFT0x5lc5mrnhjDQBX7FsatzhawucP3ECnGJsfKCgU4bNKXLWJG3xLZDUzwHn2rSlxjkTA5RXOqS6CgYFJHOTY41AL3dAQSAVWdcBAzCCmkjujOIvYVrJbGdTWJshUrIHztb5RECmi14A1hmnvPN/0mqdQtk0QNoktAO1cYoQgjyj8aZr53GamDwAA+BkGtgTOvEvdPSIRtjO1QrZGhSfxdfvmRiUmWWLJg1XBwpPALiNOMYOmZxizxkgxiplNiSp9ISKnrmIn3qrfDwBYfNwlUEUgrBMEQQTTZUUBf21R1F4rnOndMW/4gYhOHXlXApXYTun7g9+0L7AgfKLpmkrVsSloUwuigD6OHxePR7gJ0YqGUscPOCtkva0ycWUETlsxln5ybkgfamUr7s4X5ExtsuxAceL7WDc3I/8mY0N93bGw62KJS6p/jsAt2ySlj8fBtK9adDnX8Dy0usja0YXDIM5u13OJS9Xm+JYH37UJMkhr4IWZ1VO86egH4e//wvDrm90+RZsOAKjzx252m2skCjzc6/Q2v4ZJL8Rpb6PoES0k3xOtMrwooFJqYRLL42pjaNrYGj5JFIjgNyw3pTcA4FNvBVwJalXr99nx1cpbsNod6H5yddoYmE35UIufm8oYtMv0NUQmgkkZiHqm/Z2SpGusvYFEgWRn2ep74GBZDOBVmD3uhkSHQxBEJ6TLigJbj6yO2mv5wqR9zhv597Db6oMyBdzeltMH1zDCAOg3vQ5chD/0YQmqd44k9bIljM04fQOAk/M2uy7aOD1Cyza92Nt98siL5XaJAHDN54lrT/jODzfin45dmPfHHfIypbLlG9lbZjRtCxQv1/yW2F25tdl1lQlw9n7XVQQA8EQwky4JWDZv7OvzHU7Bt8DMNW98Fwk6jSQKxO+71Bi/ODidr8pEJpoOGKwxrNFviQYxVb+vqh8+Ov9PbLt4G6aNvqrZ7VP0mQAAWwwFFl5MaZ+hsODt8Q/g9GkPtfk1zPosAIAtjt1bgnGLx6BpoewlVbwVsNrjLwRKSJKJspkyh2AG9pgsP37qy/NjFFHLrK9+CQ+Ur0KdQrge3Jg+DtfNfwsMyyJTFLMra/ZF5b0y/QFxqq7uSET7OMXspg6JAuI11pFADxSidRw1B/A/h/BZu2rIhWCZLntrTxBEDOmyVw5XQ/Scf33ewEzUmnN/wR8L/8DUQX8Lu61KESgzsDpbvvFXIrCtzVrU7vg4vxd+JjqZAkZN87Og9a3MMEYTeZYjSAh4/YRn5cdb25CREW3W2Q81WaZoJVVPpVDhnen/CVlWlwS9n8taMBerifOsIecPfL4Ouls3DzSLdcX2GM4USzSIwoMabTPubIxBLINx8okZIAKATxQFlIwCy87+Flf1mBmyvjYBqcI8x2EzI1wvWUSW9iq12rLG8FxKRoMGVoPRQ/7WLkHIZMgBANgYHghTjhBrtrNi+UALJrIWceBY6yiNS0zhkDIFVKrWhe3czGHy448V8S/L4P0+fK0JzVDyBmWCZDFCpktFXVFU3i/4qlNni0y4kcwBDR1IIzfJ19jElTsRrfP+z3fBzrLowyswc2zz2VUEQRAt0WVFgXp39FKKpfp2ANBpzC2m2Df4A14Dtc6WZ7D0QT/1NR24efAF9dJWhTEKrLR74Ociuxk165t31q4PY7oVK45VC+UB3qAb/vysRm7vCbjBBoC1bOjfVcHzYFpwc5cY2euEkOc1SdD7ObWFgUKtoySOkYR+ju844YlWtzdphFptmz/2g4IGsW5d08ZuHo0x6ISYnXH8LjVGGrwoGBY5xlzMG3BmyHprAgYA23d9KD/2IbK/Z4opHwBQx8TuXHLi36kjf/a0dMF4zs6ysCUo1R1o2dU/VcwwsyZQqJQzBSLw7Egx5cU2mFZ48KPZTZbNG3W1/DhL/B2uaCHzosFRgU8+WYiSfctbfb/gT3hdJNdljsNT5b8AANLVTbt6RIrshxGHayzRPpzWI1haJ9wvXTlwQbMeRgRBEK3RZUUBdxTrjKXBCsvzrc4Uub2RiwKqEFGg/QZP3qD06cYmTX8dqcW4R3/E7R9viei1jC2IAvEayLz5zSV4qno9AGCXIvCehkaxHdzXcS+GaOBv54ihNM6D7nCoGs18szyP6awZAFBTH1/RwhtkGJfXWAAKg1mu1Y59Kr5HvAZoOnjJ1Is32fEU2Brj56VMAeHmMSdzeMj62gT4HQS3PEvTjY5onxRzDwCAjWHAxaibg1Q+wHYgQ8RizIVFFGXLa+MrCnD+wHejT1DKfWOkVrS18Wjv2QyyKNBKKRaANrfajSZejwNfcbVNlmcFOfxnihlBFS0Iv+98vxgPOrbjtN9uDV3B8wAXmv3CBR2uNYKys7LSjbI5cmqQSW9bMWnF7gNxzBIk2sZHP9+FWgWLAp7FKRNubX0HgiCIZuiyooDTHr0Bl+QpEIn+6gnyEaitb3mwMrXXyfLjTRtfanY7+55vsP79s8E5ws/iBIsCqkZmUu+tFeoPP/0rst7jRkPzzuT1cboPe74uUOc+mWm+z25RUB/mzsKLk/4hPz7mrkpgJAJ+vvHNJ4M00SG/xhXf+IK9O8JlvDTGJNaU2+NQqy2JAuoO1moadIKQEa/vUgg+D2q2fSiXQylFgVOvs4RsVov4DwDqxOt1P46FTlMQ0T4pJkEU4BkG9ghTqtsKL6a0Mx0sG0kVy6BqYxRncwSXvrX0nUoVs98S5SfBcxwaxFMcyXcfAHIYIS1eGeeMMU9QaeJCXR/8OP8zLJ/7PrTawIx8tk74HS33NBUPJDY5hd9kT6Mss5fen4srXh8BjyNgYBh8hJGUnXmDZvYnDDm31e2bwyQK8fG4xhJtx1NfibdqhQmfK/ueBaWi462yCYLovnRZUeBfzr1AM20D24osCkRw7+HxRZ4pcPPxj8qPn9NyTWYHJP6+6mZc5tmLT766rMX4WJ5v0i7LoAlIGeHaazXGZMxpdp2TYcD74juLOCsvtDXefX0DBoP2BLmkSzehE1oQLJrj+IFn4KXhgjPwIX/sWqm1hsdRjiffmIDPvU1nslLFdNPaOA8QvN5ARo5C2XodrMkgGbjFftZdik3DtN9kEAD04k22i2XBeeObpr98+Q044a9/4HWX4ImhbOZYrAwD3h/fQcCDhz4BABxgI/9bqlRa6MUZ+LoYtdKTug901LgrVWw/WxPn7KDg7Bt1C2n5KWIpTq03el48bcHjtoIXZ7Z1+si6e7xyomDequXj69XABXkHXH/KG8hO64/8oCwBAMgx9wQAlLfgz2EKZwrM83ix4RjWqll89es/pDeU2zUCQF0EfiveoFKssQNPa3X75jCKJpmOOLYjJiLnm18eQpWCRTYHnDr5rkSHQxBEJ6fLigIA4IzQpbc1vGI7Hifb8mwRz/OhokB9K54CjW7S6srCO8Fv0wq1oB+6whu/HdzxAQBhlrcxqfrA4KrO1XqadeMU/WB4hoErDnXwY/lA7ev4RrMc5x7/IOaphBniRPRT5zlOTsu8bLRg6DOCbZs4kJsq1BhXx7AWujW+++MJvKMI7yidKrbcq4nzAEGq24+0ItJszAUA2FkGvD+2s9tSpoCqg6KAQcxuAACXM76ZGE9V/h7yXMkGzvQQdWAg5mEZuJzx7YxRH4EnRzgs4gx+XYwM8qJRPgAAaaKjfryvWd6gTAFlMy0JASBVzGCxJshl3uUOzKjrtJGJAhlpAwEADpaFJ47nlQ/KrlI1U+qQI17jy1swcg3XdnXP9vfkx5JAs/zHW+EI+n5Y3eHbIQcjGaNmROgl1Bym4GusL3EdU4im8D4vlpasAgBckDcNqhY8QwiCICKhS4sC1v9n77rDpKiy76mqzml68gw5JwEBQZKiIEHBtGKO66q7Kq5xdXWNq65h17C65qxrDhhBAZEgkjOSMwOTQ+fcVb8/6lXqruow0zPsD/t8Hx891dXdr6urXr177rnn5mihuHlveiMgQOknAAAtaboPJKLWk9rtXSsj+sDezzRfI+cJaj3pM5P6NE7F/g4MZB7udha6dj4xabsgdX27YRXcNRs7ZCx7d36Dx96fhE+/vlLcNrjryfju7K/w2kULsnovITD0UxS4HKlZssWaBnWPiZG0HUVmPjvUEmt/V385vCSLas9wHWu38gvWOEUh2M6lDkKLxLYqBYwGG2iS1fR3cIDYjVYGhTpZq7K3Zs7Be9PeFvurNx9Y2mHjikb86BPnJ6orrX2zem0BoZByNdcnQlAKtLWGvVDPz1kdXbPvJySugePApJAWi50c2KNjKNfQtAMA4GAzUwkBvP+Nnvw+TU172m1siWBlij6aVqcwy0sHAQCOMBR2LH9adR+LCimwpW69+DhGCKmHD89X7OOKpvdLihAyyMC17bwVlIMxikLof6BbTh4Slq18Cnt1FKwsh5knZd8qNY888sgjEcc0KeDPUaAQpjKLUoJRpeT2rV/249tNqeWiX8yQXLfrtn+Vcl+3xjgadNq51YhMuVDrbrtcuSNIgTiRKtr06hn4IiJ1bWYYXPHdxe0+HgD467J78VG8AY96pGDaZCpE98LesBqyUwpYiew9RlHwVK/L6TgzRbm5VHX7ic5+KLLzwXZzBwcIzV6+xrYQmQXeZpMTlBBgt7MpYoQY2Rnb0PMb4INLK7mM/cGOLX/pnmA4xsgCGpvBhuEVI1HK8beEM9b/A417lMFIe+HaD07GHoY/KEPKMjMZFFBAZPnt5Zovdh9o462ykLR2a84goMslmkhb0aJ46ntYIckIt2d7x1QIEKPJgiyCWIqiUEz2b05DqOcSclJAq+tMafEA8fEFu9+Bq35r0j4WmXKj8RCv4jHKiAKHwY76PfPhZZSf4Yoqy842fHM9dnw7C+A4LPzmGmxe+hgiggdKGxUuFpNTJDG9/qPXrjKPBHAc3t3NJ4POKxgIu1Vb4ZlHHnnkkSmOcVJA2+QnGwjO2GfrtU34ACAQSZYw//mjDWjwagdX/UoGio9neVIHiFoS2zNNnTVfI2+RmBNSoAMCmTjJkDAaWZhCs3QD3G/oGGOdnSpDMRiz9xQAAItZkse+88tDrRxR28AkZD5NoFAIGjNPfgBFDt7orbmDzaWaidlcIZXZb0rRNGwk1vH52pcUEJQCBrrt55vQijTQwaRAYt11KJJc71xGS1naF1Y+3u5DAsdhPS0pqowZtKOTo5AEVu1FCojlA21UCjiEfu8drL4JEVM8U5pg20naO7YcpZKmGOmSwGQZxBYQAtHdgZ1ShPIBOoWPgS5B7XCEKCHkMMvO9YsXXAsAYGWkTDAawJk/35b0uqa4dA7VHV6JK1t+wQXNS3H9K31xa8tqXLb/I4TJmkXXxvOWoihRueXzdWxJUR7a2LntU6yio2A4Dpef9ODRHk4eeeRxjOCYJgUC/tr0O2WAFlLvWGh0pNwvFFUPouoykO23BQxZnMxyDkt6buU+Sa6aSfmAHHcPuR6Xd52q2DZ34Z05M3DUgqAU0HLSLbQqzRDbu55cDacbK1v9WnkfYVMKY8f2RFTWxm9Wt+lYfsU6LL5yA8qcvVDk7AUAaKEpsB1oLHnItRcA0JkoQTKBlQQRuVIFaSES54+DMSekAD/tBjrYKDOS0Loxbk7uX16qk4iu2pi2SVquEI0os57mLFU3QilRSzsdSy5H5QMO0orSE+9Yc8kQOb7pMsZOYoznpWnEwu3/uyciTlRJ2QaxDnI9ejqwU0qcXEfZLJ7UFHZyT486ovZjOeneGogFEFRJBDTJ2gM2yowrf7FI/gb/2vIKgLYrBQDALnTOaO64Eo08UuO99S8CAKYYy9GpbPBRHk0eeeRxrKBDSIEXX3wRPXr0gMlkwujRo7F69WrNfd955x1QFKX4ZzKpuPRmgMW7v27tkBVoIZJPZ5pgpdatrghwZektkC2iZJGgVwmiNx+WTIkyVQp8e+63eGDsA7hw2B9x18SnsPQiqb74vQIHXLUb2zbgNBBIAVpDql1kVyojmhqSpZm5xLfzbk3adlrnk9v0nqMZJwDAeJQ8BaLEQfsqax9cP/FJ6Gm96LAukAJxioLbtb/DxlRHFvadLKkVOXLYyBTma+ese4S0+DLkoOWTlfgS+Du4u4NQAiHgthlvJe1TapCIguYUJmm5QiAhmGPozOrJBTgNPFHrSuHy3hawwlzUxuDKQczzPGzHdm8JE6l5utpyh6OLWIrj9uTGoDcbCEqBbItzHMTB35MjVWAm4Ej5QLrF00P9Lhcfq3Wd4LjkuV++LUiy/QJ6M3yrxiaKFVU/nEbHor0G/jraRrdd7dWLrHt21Giv2/LoODTWbsTcOH+/u2rk7Ud5NHnkkcexhHYnBT755BPcfvvtePDBB7F+/Xocf/zxmDZtGurrteV+DocDNTU14r+DB1tXL7iey42TcjNxZC6yqNdhC9jfpN5iLl1rwnQwyxyE2Ujyd4qShYEuTRazqiWz49GjoAcu6HcB9LQeFEWh0KQkQ+q97dtrO0gWRkaDer/qQoeyj3l1w6/tOp6/1S5M2pZtRjMRPUnw5Y0cnRZgTSQgLTQVJz2n15tQQM65hqZdHTamIMnGWzPsUw4ANkIc+UPtGxSEBKWAijlYtrCSMQfC7fvbs8EWbF38d0SaeAXGfPCB8wRjBZZeuES1zV6JrDTHpxK05BoBWQZVx3HoWplsLJoKNqLe8sXbR5YvlQ+07VbpIPcObwfX7IcEUiDNrV7H6EWZuNvdPu0dU0HIvjNZLkkcpKuDJ+LK9ZA0wYrlA6n3O2f0HeLjZpXyBjah/ICLhhVKgfejSqXju+d+CYAvIwz5+esmzmamkovFWdz+yUY8M39nRvvLMcDWBQCwN5gvH/hfwOwVTyJGURgKIwb3nXG0h5NHHnkcQ2iba1YGeOaZZ3Ddddfh6quvBgC88sormDNnDt566y3cfbd6X1WKolBRkZmsOhwOIxyWMmAej2Tk1JuxIxpte5bexUYAGrAbi8T3S/wfAHTECLBHsQUHmqQAvMkbzHgcavvZOUBY8jY27ERhglxMUArooEt6fe9SK/Y28AvDzYfdiEQirZLC9tMVYFeMVx14/U05Oa6JiEajYNkYamgOAIXSwgGqn+Nw9FD83eQ5AvehNQgH6lDYe1rOx6UGHW1q0zGwklpob9TfLsdSDrVz1U1kzIWmYtXPLwMDN1jUNe9Gz3Yen4AgKwTeyceW4zhc9tZanNDNiTumSA71VloPcBG4g83tehzDhBTQU3pEo1HVY5opLLQeYIPwemvadczfLr4fD9YvwcQdH+LWsQ+J28sZC2w69bnRKScFwLb7ubl2x5fi4y8nv0Ou7d0Zf66VuPp74uH2mZMI4eoPx9v0/hZi7OnpgGMqx5d7vwH0AMCl/dxC0PCAQ6PrILp24BgBIBzl7+EMqKyOj11vAyJAS8jVYcc1QsZKZXBMLzB2xWfhKjQFG5P2ZRMC+nk/3Y24RochADAbiqHnOEQpCssW3IlRo25BNJraDPYa5zBEo1HM21qH2Rt4I9dzh1Wga2Hm3h0OHX+NeWPBNs17ebQd8UgQn7VsBhgaF/c882gPJ4888jjG0K6kQCQSwbp163DPPfeI22iaxuTJk7FixQrN1/l8PnTv3h0sy2LEiBF47LHHcNxxx6nu+/jjj+Pvf1dvx9IS9GLu3Llt+xIAPPEoQNM4tK8WcxuV77dggdSObkMdBYCBnfWhs4XCkQAffK/etBWFTZlls+fO+Q5IyEo1yf5cuHg2LDalvNMbCgAGoK62Pun7xoMMQKSv3lAMH331PQoMylaFmeDEcAl2MTwpsHHLWhyuyqyXdLaIx12IkMGtX1+FTRvVDcQGx8z4VcdTJb/uWo+Hdr6DZobBI5v2gjL1yNl4WDYGI8shTFO41fA7vBiejShFYd8+PxqrW39uBVqCgA6o9zbl5BzNBPJz1R0JAgag9kiD6ucXxBjAwGLD9pVoru/dIePzhPnzuL66CV99OxcGWROC5XUU1hxgsOZAC/qFd0Mw5KbDLGAA9h3a3W7HUR/zo8XnAgyAu9mj+Bz5Mc0UXCgOGIBHW1bD+vWniGt02Wg1OBaBI//C66YWQKfDIrMBizY+Jj7dE1M1j1WjR1I7+Wiq3c/N3U3bIDSb2LTuCAA+cMn0uNa6eBmtKxJsl7EebnQDVmB/g79N7x8I82o3L0WpzvHtATYexhoiHtumi6c9ps44g4N0DKs2zUPNkcxLeHKBIy07+dtULJ7VcY64o4AOqHYn3/vaC4Egr56ikf485fxxQAdUNR9RjE8faUJN8w5AVhn5zaFl6MN0g1bzle+//x5FcaBOB9zmWYcBcy/GdHNypnhk3Im1jAsAEG7mr4uFR/i1CQBMemYZZvaIY0JlZl2VmpvdAA00B33idwgEcqPCzCM7LF31DGoZGk6Ww9Qxfznaw8kjjzyOMbQrKdDY2Ih4PI7ycuUCo7y8HDt2JLvxAkD//v3x1ltvYejQoXC73Xjqqacwbtw4bN26FV26dEna/5577sHtt0t1VR6PB1278vLyiB6YPn16xuONN+9BqGE7rP3PErftW/syDrn4BdyJI0/BgD78+0WjUSxYsABTpkyBXs+vvJpWHgL27UCXTpWINQVwJMDLg8u69MT06QOghRc/+ydqiG/B5IljYbBIku5ooAn3fSVF8N16FeHEkcrvNOfNOwCY0aNbL0w/RfncG4dWAj5JPfFlQzGqmoN4+dJhGN7NmemhQXDRYqCGlyF37VGGieMyP66ZIhqN4ptveIMkI8vhrDPP09x3Oqbjvk+nYG6sCfYSC5qb+QWPqcKN03I4tkjYiwe+eAgA8Ltp12Bw/Qi4Aw2YOOT3bXpf/ngeAGugszpHWwO1c/Xd/94PABg6cCRGD0/+/DWfv4C1kVoYC3XtPj53yz64W/bhs2b+PI/buuLO1Tr85+Ljcfpx/Nxxy/1Sezx7v1E4tR+feV33+UtAJAhbsbVdxvnTj3/Bcw3zwFEUAB26VnbF9CnTVY9pptj0xatAmA++K7q24PgRF+Z0zPt3fY2Zbi+0pveLz/2j5mu376Xw4qolAIAoRbX7b//BD98AzcAZTBGmT8/+uK7d3AD8uhIhJru5PlP89M4LAIAYR+GMM85oteGgL9iEx758HRGawqRTR8NkTV2KlgvU1KwFFvGPL8DgtMd021dvYVNgH95gduK9wm0YNLbjgo55y9YBh9bCqNdn9TvGlq4ADh9AyMC1+7kq4MABM7Ccl/GnO6a+hQuAumpEjbLzk+Nw3VvHYZ1Z6ZVkMBpQUVoB1CeXbD193I2YePx0fPT+Q6gDrzDYYTTg9oH9gK08MTHaXImpfc/H5L4z8eKi2zG/eRMuP/tJlDm6KeZPAPjiAIMnrpma9Dlq+GnVDmDvFkR00neQKzLz6Dh8svdLgAJ+VzAIxjaWMOaRRx55JKLdyweyxdixYzF27Fjx73HjxmHgwIF49dVX8cgjjyTtbzQaYTSq1/n6uHhWC/bbvzgTi60WfE+z6DLofNTtmYfzd70uPu90VCS9n16vF7exJCNv0DN49HeDMfNlXg3hCaUex7tnfYaps3nZeyTSAmuBVDrhZ5W1sq5AneK9mvcvwgozX1fJRtxJnyPYEdiNOnjDMWys4rP9j36/E9/cdFKaIyLBIqvzDkY8WQdCmSLO8kSKFcDmah90NIXjuzpV93XqHUCsCZ6IZKboDTfldGyxqJRNMRnMGD3g3Jy8bwFpS+hjo+12LBMhP1f9YAFQcNhKVD+/zFQCRGrRGG6GnqHx6Wfno0/5MIw4NfftjyZ+fz7/gCRPF+3ks1B//ngTDjyRnAn7amMtphzXCQBgN1iBCO/U3R7H8S/1PwGy9zUbLIrPkR/TTGHVmwGi+m0M1ud83DVebQ+WAbQl5eeVl/RT/N3e52aA+LXYdeZWHdcCOz9XNoCFnqEBWiPN2kroGX5Op0DBHwUKrdKYOI7Dm8v2o0uhBacPTl3uVqArA81xYCkKoVA97M5OOR2nGqpq+Ra33eJA/+JL4ItyeH7+Dpx/QlcMU5lTK6zlQGAfWIrC5fs/xJYJ9yTt017giGxeR9FZnXOlDj5R0MxGOmweXfjr2+LjdOdpsa0SqAOa4yFxv2jIm0QIAIDdYIOWn2XPwl7Q6/Uo1pkBVvIieWHdM4DJiL4sjTculAL/+858B/chddcMX4RDoTW9sWeBjSewfJDWMR11rPOQULV/IX6hwqA4DheMuetoDyePPPI4BtGuGsaSkhIwDIO6OqVBTV1dXcaeAXq9HsOHD8eePdm3w/GmqM9Tw2IrX2f3+bYPAABP/qIMgGzW1JLKjVUuAICeoXFC9yI89rshANIbDVbaO8FETIfq5t8NyNvAJZgRNQWUx3LF4Z/Fx7qyQUnvHSeswMBKZTvFBm/qWsREGA1SDaI30n5ZgijLm6FZOQozX16Oc178BU0+9bE6icmYS2bW586xCzUbl34LmsnOGT0VbMSN3MdlZhSVa/iJ/4XNrJ6xLCPnekPEg507vsIjoT246uDn8DVmb1SVLSKs1FrLHYziuR93K54f3FlyyLcRsson9H/35tAMS6UPuVFnVtkxO5gZKSDwhd0p9mwdgikMDK/pdkbK1xY5eyo3pOjFngv4CCkgeGxkiy5deALZw9B47PVhmPP5RTkbGw/y/Tkqyaj1lz1NeHTOdlz//rq070JTtGjk5/EmO9G3B4Q2fWU0T5rf//U2vL/yEM598RfV/UsSWr02712IJ//TCxt/fkx1/1xCMBqMx4BDTZlL04uJ6WwTOm4e3R/IfI4ptvGdclo4qQY/GlP/foU6s2pHAgBgSPvCYp0yO7zZxP+2u2nl64TOTalwxJWZOafdwvuMZLueyiO3+HTNswCA8YwDXTuNPMqjySOPPI5FtCspYDAYcMIJJ2DhQsm9nWVZLFy4UKEGSIV4PI4tW7agsjL7vvBeqnULWlegCUBytwGbSk95luXwl8824ZwXluG7zTUApOxSsY0PIlsyaEloIUO9ILYPe39+UtweT+gt3pTQes0ka9s3Y8QNSe8rkQJ2xXarMTuRiMlaJj72R9qvj3U0zsuqhbZtAHDCoz+q7ltAuiK4olIdtDuS2yArJjv+TA5JAavJCQAIHIWFFsey8JMFo1VDxlxKFtsN8YBCibF445s5HUs0mrxANuilc/WJ73fg2R+Vclo5ySYYzfliQaxd+Ddc/NHJ2LxY3WNEjqCrCv96ZyzWL3tSc59YLLmFJ6XRJjMbmPUSseBvB4KtvkUiUYbqld4f3XucmvK1ep1SdRVp51ZvAULmZNNxQg6HpRgWMsd9ZALu9m+Dvzl3bTQ5SPeQQ83Kc9UVlM5DgRBOBTu53XpVnOjbA54Qfx+z00bsdFGYty315xbblV1dTll2K953WHHFvo/abYwChPucPxTHhH8tAkfIKJbl4AtrB/wFdl4p4KHQ7gSWgBElvNFvZSz95xUWdAMANFPSPB+NqQfj/mhQ0X1ADmHesbWSlLSr3O+rMyQFikr4ZEMTTSHq65hzNw8lwsEWfOXj57WLBlxylEeTRx55HKtod7ej22+/Ha+//jreffddbN++HTfccAP8fr/YjeDKK69UGBE+/PDDmD9/Pvbt24f169fj8ssvx8GDB3Httddm/dk+ilJt4ZcOX3D8QliXsMjQ65Ilf5uOuPH5usPYdFgKnBiaD7gKLXwQ6Q6mJwWssp/i/SMSicIltLBqSgh6QyTrMBpm6FVaEsbJdziuU4Fie507OeBJheP7zxQf+zUyHblAhOMDfEuC25I3pO2UvoCTAitXjgkLNt4+pIDZxP8eQXTMQlaOcKgFMZEUKFPdp8zZCwBQz0UVi9gjviM5HYu8JZ0Ak7Wb+Hj1/qak55t8UjBmN/LH0c9GcPXhb7HVaMRlBz9P+7nvL7wN71E+XLX3fc19Iirn0vEDfpf2vdNBrjbwRnNPsH3SvBkAUMRy+ODSJTjFzGcrnxxxJwZ2PzXt618cJ2WGPZ72bU/nI10wbG2ojy1I0FwfbtjcpjHJIZICHI2qZv46WL2/Gfd9tQXBiDQ33/fVlrTv5SBzmieobp6aa3jD/Lyo5wx4abtyPo3Fk4PP4oJuSds6CjHixE+R37LGHQLHcej1t7kY/OA8zax2ARlzSNamr70hBO592PQBuqC8cdM0omH+Wo9G1e+9vngoqU2hAIaUxVhaSZ7ZTMmkQE2Ga4CKwt7QcRxiFIXG5o5rUZuHhEWrnoWLoVHOAicPv/5oDyePPPI4RtHupMBFF12Ep556Cg888ACGDRuGjRs34ocffhDNBw8dOoSamhpx/5aWFlx33XUYOHAgpk+fDo/Hg+XLl2PQoGRpfDpwFAW/vzb9jhqIkIDwVHNnfH3WbNV9PCoBvy/EL3AKLXyQnq58AACssiD483gTQCTALKskBRZH6sEFJLVAlMjbDZR6La2gFOhVqlxMeMMxeFQCbS04zYW42MGbJfo1Mh25QIR4KISCysWR2qKwOFHqDMAVzy1hIVdq0EzuLDjMRicAINBKNUtWCDQBHIdQyz5EWg7C75eCEou5WPUlxc4eAEh2SLaI9clUGbmAx3NY8ffxtBUGq1TXLrTTlEOhFCCkgI/N/FwGgJpY+u8RlZEC9x//Z/x32tvoWn58Vp+jBrNOuhb9KkqJNoHjECXqk+EWngx49vxvsfbytZg+5MqM3mJC37NQQOYNt699pe7+OF8aZDE40uypDWeCNY7H1/o5PxFCAMhBKh+48NUVeH/lITzw9VZxP4s+/dzgYASSuDnNnrlBlYf3lohHkslitYCwS+fRmu/FxeOaz+UCAvFIExJmU5ULTX7pOn/zZ3X1h81SAoYE0m7PIdV9cg3hnMjEctJZ0E1MLjQ18ebKsQRSXXjeHw9rlg8EQnyiwm6wqz4/3tYj5Tiicf4znpw5BDNH8OqKandm93GKolDM8d+2sWVfRq/JI7f46sD3AICznYPB6PJ+DnnkkUf7oP37IgG46aabcPDgQYTDYaxatQqjR0uLj8WLF+Odd94R/3722WfFfWtrazFnzhwMHz486880khutx5PhojaBoY/WbUWNm1+IDLN0Qq+ivmqvQoxNDuqExUwBIQXcwagYnGvBQisXlfe/PhTxxl1gE2rO3QyD776T3MOjZFGt12hxJXwuQ1MYlOArkKl8UEAXE5+Z98ezUxlkgzjHHzuGVZIcR1qSxzqo1+lJ29zx7LwS0oGN88efybE01WzmSx+CFAWObb8Sgj3bv8DkLyZhT+2TGPf9+Tjpq+l4b94s8Xlaw5jNRuqLoxQFb0jK1ntzTAqs2CpJk7dctQXvX7ESoWjqAEQeLNhICYk/S28Gh159cS1HmJACDMfhwmF/xLCK3NRxmuT+HM27wW36NGfS5zXLn0IN6dd4UT++q4Ge1sPIqJuxasFN1E47t6uTobmC8LvZCLnDteI4OGilgsejoj5pLaTyAQpVCeUDQdl5uvpAM5r9qclfO80rzbyh9i3JEDA/yh8HWkWNtK0muWxFKGlSg8/bvoqR7xt4X4YYzR/TGz5Yj4Myb4FSu/r5S1EUCsjXc3eQV4NgikhlQAvQNIMSElA31PEKlmhCWZIwfi8b0Swf6N5jIgDAZihIeu7p/lfhlZnfphxHnNxjRnQrRP8KXpVTm4VasITir7EmV54U6GjUVq/FcvDrn3NH3XKUR5NHHnkcy+gQUuBowE5uxF5XZvWl7sOrFX+f981MLDPzCxEDnbwg8RI1gFo8J2QynWb+Rspx6ooCOWwJ0v+v7DZ8Ov8WMSiV40OfVDMcJWoGfRqlgI6m8dzFw/D4eUPQv5wPiGpc2QX3VpKl8MfTKx9aixj470Nzyu9zWIUUsKnUw7u47DLG6cCSDHQmF8pD32zFKf9ahJY0wQEAmInRYJyiEA23n3Hjl1veQTPD4B0zH+AGaRpvxdPXhVplfhotAUlZ4IvnViXSGFJmTT2hKNYcSB00bapyicGjlSgdfFl4M8RaDoIOSESHIOtNRIRI+w1k0b50VwNO//dSrD/UtqDOpJek8l9bjBi68RHsX/5Mm95TwIu7PhQfdykf2ub3+2vzyja/hxaiYR82UPy1YjU58fXGIzjx8cXY7c6u7Z8zoaxrf8OmnI2RkxkNbq/xIqoiuxew/mDq88JBzBQ97WAuqYbe4O8pFJWsBtI6h88uUlfC1NRsyN3AVNDHSK5jRiIwnlkgmZrWebTvVQVEZVfXuK2dRqcES2763giFSCz9vGMkX+nqjU8DSCYFnGT8bi4GVmUeG8jpYSblNQN6T0t6fmjv5A4tiYgRpYCOodHJyZc97Kn3YfX+ZhxuSa9WKiJ3wI+3aZdb5dE++Hb1v8FRFEbAhG5dxhzt4eSRRx7HMI5ZUiBKavG/XnJfRvvf/4PSs+CAQQrSDTplJuqR77ZhxD9+wsd7abFmX46R3YvI62jYiMFPuhICqwrx8EmwKkkpAAAu2TahfEBHq8tXBVKApoG+5XZccmI3dC3iF6eZug8LsIn127kNvOWIku9GJZACamOlKAo2ThlAuHNs3BdniXIhgwTmO8sP4GBTAF9vTF93bzZLBnDBYHLdfK7gNBWl30kFDKMTDdyaZePzsrklhCykvv5MHU9CrNyb2bH4eA2fubRZiXolC2+G6z6ZjNdlxIhLI/sVifCqCOHqv/Kt1dhR68WN76/P+LPUYDQk1wU/vjs3Zm5FMgVERVnrSQHhxmBux+qW2Yv/Jj62mopwy8cb4QpG8cK27NoKOvVKP4I9WbjDp4NcudDoC6Pvvd9r7rvpsAvP/bhbMwPrIPXg7eEjoYY4uSYYJHsFqJGsAPDwjHfx6oSnk7bvq88d0aIG4TgXebqI2+QdcrTGCwA99LwC7rDnQPsMLgFRUtIXY4ENGRhMHqH57xamacQi/iQD0wKKX2t4EFdVDOll247reRqeGH4betKS2qgwAy+IKCEydDLF4NZqDy58dQVOenKRqseEHPtIWd4vFjM4lURFHu0DLh7HV838tXdut6lHeTR55JHHsY5jlhRwM/zC8v2CzGpVF6XwkDMkSG/fXMarD1bU02ATygJO6lOCu88YIP7tJCUErjRKAbNKUF9Pc+DIAkS+MDisk362KAnStJQCgnGRjpZe09nJZ9ayLR+wEHlptlLtbBAjraUoVnk8tLIZfyxV1sG66dzK8QWjwXQXinxR9dj3O9K+r15nFGtJgwnZ8lwiliKIv6tr6vZ0QqjVLDO39OWYFIiw/MLfQMoYgmlKBwS88BPfotRq4b1JfHTm2eW1CT3CmzXURBFSKmFIWKfXpshaZgKzigTY0sqWfIkQ5osbSsckdRLIBs8O+AMAoDebXYCeDbwyhUznimGK54QgMROTVjuj/D0DOTxHWVn5QDr856c9ePbHXfjDO2tUn3eQFqqeHJfgaCFMxh6LJ9cgH25Wn08ZmsG4nlMx59zvMKvPBeJ2TzsSlwAQIUaDnIwM3lUnkSc/btcmeooJKdTir8eH/52MLUsfbadR8hCk+BQo/GNu+hatFTLi2tWyP6n7QCE5fz0UJZYP9OKk+180gfCcMfQPeOn0twEA5YxZlWRMhKAU0DM0OhcmGyS+vHhvytd7ZHXskXa8X+WhxIbN7+IQA5hZDtNG3360h5NHHnl0AJ544glQFIVbb71V3BYKhTBr1iwUFxfDZrNh5syZqKtT3hcPHTqEGTNmwGKxoKysDHfeeSdisezitWOWFMgWphQZMV9Eu++3XCnwwqXD8f61o2HSSwsbkRRIoxRQW3J6aRqbv7xadXw7v7uJNxUjiyktUiAmegpI2wT5YLakgNVI6rfbsY2eoBRAglJga7W6xN5BDPsExCkKQV+N6r6tQZwEWelCI7/MiTwTSSkgZWEDQVcrRpYZgjFtj4WzR/455Wtt5Fs3y4IYL5dbwzHBzFPonPH5usOpdhchKEdsNr57QiyxJ3cWtelNbnWDsjAxATSQq9Okz8102VWlLeDCeAvi3rYb5LmJ30d30h+9tXASTwlPO/Z/d4UlCfvdc3w4sYekamnyR/Dqkr04/u/z8f2W1NdzdVDpIRDIoZJJKB/gUtwqB1Qo/Sm21XhU5flCpwxvO3qyyBEhAWYknsx4V6XIvANAt4LuuH78AzjP2AkA4GrnQHBOXCAdtI+zW6O1byEh2b50b8PjbB0u3f9JroengFj3z1HYXuvFjtrU5V9FlBRQN7sPIJZQfuckCpIwTSFI5pwTTOXi82rfukvpIKy7fB0WXLYq7Xg5jpOtAygYdUySR8PTC/iuAvsb/aqdfp4Y94j42Cczqs2jffH19g8AAFNNnWCxqJsC55FHHscO1qxZg1dffRVDhyqVnrfddhu+/fZbfPbZZ1iyZAmqq6tx3nnnic/H43HMmDEDkUgEy5cvx7vvvot33nkHDzzwQFafnzs79f/nOD3G4Cu9esAzbMB5ir8NDI0IyQwLXgEn9y3BmUM7Jb1WaEvY4k+zUNXof343yeonkgbnNy3Bj3sXIEqMFNXaEQKy8gFZ0CSRAtktTm3kptSepICgFOA4/vt0KjCh2h1CVXMA0TgLPaNcNJpUDNQ8/jpYHG0LigTEyQIuLSmQ0EvbHYiKRpNaCJKfZMO6l9Gr95TWDjElan1HNJOcVot6O0IBNooBwKKZDYlrdW+OWyhGSFbXQM7fE7oX4ufdjTDpaVgNOoWp4D/PH4q7PufNusrIotZiKgTFceASSIFYNABdBhk0AGjQcC2PCKQAee9OBWbsa+QJEsFTpDUosVXggSE34OEtLyu2fzH/Vlw48+NWvy8AVHNhABQqi/ql3TcVHDaBFGi/+oEtfv7c7BkCftyvZLz31PvxOFHc3Pjheux/XLtu2mEtAzxS0BrIoZKJS3qQjFsn98X1CSUl5720HOvvn4IiqxSQO0z8/NlRSoEIOAAUdjYk39ea/RH4wzFYVfrXy1GgtwNhwBVpP98TAOI1zFLAmUMr8d3mZCLoQJMfx1ucSdudpiLAA9TqOmY5I5kB8vPCrA/W4/Yp/XFq/1JQFGDUMWJbYgA4qXIMNtf+DABo8VSh2askPm06M5gQhzhFwRXljzMNCucVDsHsli24ftiNquMwZNgiV25yrGfIXOY0K8ozAGBXnRdTn12KARV2/HDrBMVzE/qeBSzny30CjTtQ0KVrRp/9/xnRaBTRaHqCUdgnk32zQTDQgB/CtQBN46yBl+f8/dXQXt+lo3GsfA/g2Pkuv6XvITzn8Sjvm0ajEUajtoLT5/Phsssuw+uvv45HH5UUb263G2+++SY+/PBDTJo0CQDw9ttvY+DAgVi5ciXGjBmD+fPnY9u2bfjxxx9RXl6OYcOG4ZFHHsFf//pXPPTQQzAYMrtfHLOkwGTnICwKp5f2AUD9gaUiIVAe51DHSDf096e9iyEVI8S/Y3FWJAQAYD9xSGY05MtOgRRI5ymgMwEpjPNpAI8Nuw1/2/isuG3V6ufwOkcyLBEvOI4DlRAcCcGLvHxAIAWy9RSwWEj9NgU+E5uYnc0BBC8IsDr8flwP3DdjIAY9MA+ROItad0j0QxCgJmcP5NB9PE4CjHQ5Yl8CKXCw2Y+hKgtYOYTs9kPB3ZjZ2gGmAhvH9xR/fk4IM/j31Wvw7Zqn8OBu3oxOl0ZebqMNABdFMxeDsAD25/gnF4wyBVIgFOWvrUtP7I4NVS0iKbDvsemgST3smf9ZJiO7aFg5wJcwroC/AY4MSYGaBr613NY1L+GTTa/j5mkvoaTzidi27TMAgJH8+jrZvCB3Rm8NTu0+JYkUWO0/hAvb9K6AcEXbNFpNZgqHjSc4PTQFNhYBrcvshpINihkTwIbhCCd3grjva8k0juN4VdOXG47g0hO7odCqHMs1E/+Jj78+W/w7oOHg3hoIxm9dCu2AhoL+uE7J5SAAn3WVkwJ2Un61Rtd+pKocwu0kxkrX+SUndsXcLbVwB6M43BJE/4rkYy+Hg5jLrnS3b396J8ehhaJQ7RuFKxNIAT1DIRrncKg5gOO7OpNeW0juS3Kw/kbQ1uTtuUBiS8K9DX7M+nA9RvcswrZqD0Z0L8S7fzhR3P/a0/6Nlz44AQDQ7KtGKEEpQFM07BzgogA3ISIpisKDZ72PG/x1qLBVtmm89bLgX0dI9c5OEzbJGkoM6+oUVVo7arWVkQDw4eK/4YbL24fE/l/C/PnzYbFkXta1YMGCnH7+4ebPEaBpdIpxqNlvx9yDc3P6/qmQ6+9ytHCsfA/g2Pkuv4XvEQjw83jXrkry9MEHH8RDDz2k+bpZs2ZhxowZmDx5soIUWLduHaLRKCZPnixuGzBgALp164YVK1ZgzJgxWLFiBYYMGYLyckllNm3aNNxwww3YunVrxl38jllS4JrB12LRujvhyKC+fNGOT8XHjQnR3/EyQgBQysQB6QbKaATIhbK2hKnAGKxAigQSDeDk3mcAMlLg3qiU4ZwfbcH7D87DC5eNwMT+fAZY3iKLlcmpOxH1Qa0nhDjLaRIaibCR7GGQphELe6AzqS+G24Iw+OPLcXo8eNYgUBQlmiT98GstrpvQS7H/mePvxaOf8OZfBSwHN03Bn0Opq+ApkM5oMLGN3qHmAIZ2cWb8Oe4DS1DQ45Rsh5cSPyy6V3xs1fFB3nnj7kHn0sHoVT4s7etttBGI+9FM8RlHAAhR/DGhmdz0So4QqbeBMYDjOLyyhK9t1TMUuhVZsOGQCwBAk3O0ooA/d5sDEVE5YgWFROs2X8M2OAp7ZDSGFyNVuC7swx+3vACPnsGReX/EjYOuwlMx3jBSKB8QCAsA2NfoV0yezyzYhS83HMYH14xBt+L0C0lnQfekbQ2xtmeQQ+RSNhlsqXdMA4edV9qwFAW/rwZ2Z/J42wo9rQNYAOHk4O1gQs37H/+7Fr8e8WD9wRa8+ftRiucqnD1xeedJeP/ITwByq2SKkwDQwOhx4IkZuPOzTfhs3WEYdTTCpEyoa5EF3YstSURRYslYcdkQgHAdgfrtsJQNzNk41RAh50KU40mB7/88DgM7F2LLETfcR6Koag6kJQWqXPz1uDvDLENrwMYicJP7py9WnBT4D+lcgPWHXDik4YPgtJYnbZv08QQsunwNKGNmxGBW4xXvpcr75qr9/H1nya4GhapNrzPgdF0xfog1ocFXA6NeWdNPg4YDNFzg0MKGAAqgQIOm6DYTAv5wDOOe+En8W0fm0c5O5RhcgYiC4w9F44oySDnetxpwQ5tG9f8DU6dOhcOR3pMqGo1iwYIFmDJlCvT63NwXAeD2D58AAJzhHIgZZ56Zs/dNhfb6Lh2NY+V7AMfOd/ktfQ9BIVBVVaWYQ1KpBD7++GOsX78ea9YkexLV1tbCYDDA6XQqtpeXl6O2tlbcR04ICM8Lz2WKY5YUsBB39wBFgYvHQTHaAnCDzOTrovKx+LA+uQ3XTzvqcP376/HRdUpju1X7+drREpv6j52pUmDCiBvw9sLrwYASXaPloDmggNT5qiFeNxX+SBxXv70GB57gpbbv/CIZqFU6JTOuMrsJDE0hznJo9IVR7jAlvZ8a7DK5udddhcL2IAWIUsCkt4iqB2EN9o+525NIAaupAFuu2gIA+N3bx8MNFv5g7vqAp1MKLNvdiFs/2Yj7z1Qu8DPJJF9ePg7v1y0HAMzZ/gkuzTEpsLFpq/j4xMLfi49H9z0ro9fbdCYgDgRkKhOOohAKNsNiS16ItwafhvnA2x/1IxqXzvu1B1vwyDmD8fXGapw2QDrviiwG0BTAcjzpVe4wwQYadQmB4G2Lb8Un/aZnPI6amnXwkDlitR4YWCvV6kYogRSQiJ8DTX70IY9ZlsPzC/k2oU/O24EXL1USiWrQq2Te1zMsEA0B+syuxyRwHMJkYW80tu3aNBltMHIcwhQFj/dIu5AChyNugJZKhVLh1yP8TXbhDvV2mnee9izi82fho9plCFC5K3kQlAIU8Wx5/Lwh+POkvjjQ5MeVb63G0C78cR5Y4Ui65ht9SulXvy7jxcfLdn6BqWWZdcZpLYRzIcLy55NABndxWvDrEY9mkC1HwGgFyHzKxWOgmNwvGaqOrABLUTCyHEJ0Z1Qk3I+GdnHypIDGnFqgQgo06Rj8uPRBTJnyVM7HK/cU0MLBJj/6lEmES4nRCcSa0BhsQAmlLNuiABRQOgBRuNgowCjL/dqCz9ZWKf42EpPiTgmkQK0nBIte+m3rPCF0L849ofL/CXq9PqvgJdv9U8HtOohlnBegKMwY/scOD6Jy+V2OJo6V7wEcO9/lt/A9hO0OhyMjYrGqqgq33HILFixYAJOpleu/HOGYNRoUpO4xikI0lDpIZGXmaX+Z9pL4+NzCweLjP7yzFpEYi5kvr1B9D4dZfbHkNPMnR4uGSZKAkV3G470z3sOPF/6k+jwNJJUGCDjH0h1hNrl/8fPEoR3g6xwFMDQl1mTXaLTPUoOO0cNOZNsub2aGcNkiQgmZOfVsazimbXRnJb4M/rArZ+Nh44LRoPqxv/zNVWj0hXHLxxsV2/c2pG87dk7vc8THTIb1odlgtYdvtXeOqTP0Ki0v08Gm4YgfDObedCzERhVlOVeN64FBnRxYec9pePnyE8TtNE2hwKxU39io5Il5WwpGVg2Pz/uj4m+5Ud+NpKZXTgrsb5QClPnbpHr4Zl/bnO8PHlza6te6d81BnMwRJkNmXVdSQSiIqdo7v83vpYbNNCHcaCl4/uPJPdK+LrHjC8BLsK8ZfhMAQgTnqANJnJACNDnHdAyNbsUWTOhXii9vHIf3iEy8e0nytZI4tzKygNqka98bPxePI0rOhQjHf5ZwD+hRwgd7B5rSK1Num/If8bHfW53rYQIAWjw8MeiMcyh12EFRFC4bLbXZO64Tfy5/srZK9be3q5ACALDL1z73qKYQb7RHU9r3ord+OaD4u9TMr0caw25E4kqyKBKPwEHz87+bKOW07vXZItH7RHjfRFIgFGUViQuttpp5dAwWrnkOMYpCH5ZG317HfqlGHnn8lrFu3TrU19djxIgR0Ol00Ol0WLJkCZ5//nnodDqUl5cjEonA5XIpXldXV4eKCj5ZXFFRkdSNQPhb2CcTHLOkgFmWxfanqTEXSIGJtAN6Wo/Vl63GU+Mfw19Pfz3jz9MyHiu0ZtZ9AACGlw1HibkEYwqSTcJSLRHKdTZUFkg3+Uxu6IIMu6o5gMU76+FRcRxWg4OcMu4cOvzLsdHI/xY0LWUpVt5zmvi4zq1tvCCQAoGwR3OfbBFnM2tJmIjddelJgYrOUt1pezR+203c8gt1qaXku+u8CEaSF7g2vXqmKJBDUqBPnD+zp/Q5F2FZ0H3mEF42W1FggkGnPPqC+sZFiDabislmUYosnhqWmpSkTJx09ehBGTFx2LUAgJCsq8QBkrVcd7AF17+/TtzuDSdfR95QFNtrks/JVyY8jSJaGRx6wq07tr7mvTh1xd3i3yazs1XvI4dAMDy559M0e7YNrlBf8fGYnkUp9uSxu1792lIQwWG36j7ZQlBt0SpGsMO7FYrnYpfCZFKgTqV15Wjw87Q3DVHdVkSiUl14hLPAopPKxHqV8tf1eysO4m2ZmkwNnQr7wEwC8Rb3AfgbduL+1wZj2bzctUeb9+s7APh7nGCEVyxT3gnjBYCP1iSbgto1JPbReNsIOi18F+MXWlVWD/553mDVfZoSVCKlVn6MDTEfwgnj8seCcJC2hC3kN6IzaIGZCWwm9WRFpwJz0jZ5N6I6b/J9tjvD/w5j6NQlJ3m0HXOP8MaU00syqwPOI488/v/itNNOw5YtW7Bx40bx38iRI3HZZZeJj/V6PRYuXCi+ZufOnTh06BDGjh0LABg7diy2bNmC+npJTblgwQI4HA4MGjQo47Ecs6SAjtHDRBYzgTQ9lgU5oJANNuvMmNbnLNhIXS6XQXuzmyb1Ud2eGMBkgtfO+Txpm1amGgB0bBwbq1zi3+Of/AmhaBwn9+UXyTerjE1YFPxjznb8/u01uPLN1RmNrYAsjj1+7b7RrcWRw8vFx43hnuLjigITepHs1mGXtuTVSrLt/hQtJLMFS4JDraC93KHMSDvIImxPvU81qyWH01KCHjT/O/hyqG5IxPje2vWIy/c0YsqzS3HJ68klMzaNbHMuWz4K9d9h1ioG3XqGEj0E1BAlioKdpBWYTdaBoojE7bYUmWJdBtdzrZsPlvqR34dlOUWryf1NfnAc8Pt31yleJ98nEInhvq+2YMhD83HGcz9j+R4lOTm+51T8cPESxbZQuHXn7uGGLYq2jAZ92zwF5IilMaRsLYRgszkimfEY9TS6WVP/PloEq9ksEQq5MhsVygcYOrVsvsiSrPRRI2ft5Fz1hVxtH1wKhGXEaIQ1wSHjzYS5FAD+/u02pIOT3HtcnsN4b9Fd+MpI4YZa3mTpp7mz8N4H0wC29a1K34/w80mtnhbr8C89sRusBgbTh1SgV4l0Lr+1LJnE0CIFImz7tNOsiBF/E78TI7o7Vfepalaa+JYU8MqHRjaMcIJSwBcPwUEIWOEapqncLM2CMqL1s+vHio/LC6RrWlANVrtlpIDKuXtFOf/6NJdnHm1EY/2vWEMsY08fod55Io888jh2YLfbMXjwYMU/q9WK4uJiDB48GAUFBbjmmmtw++23Y9GiRVi3bh2uvvpqjB07FmPGjAHAe6AMGjQIV1xxBTZt2oR58+bhvvvuw6xZs1J6GSTimCUFAEDI3fgzJAUojcA7nia4A9QzRYDUkjAbUkBNOihsGah3Jj23wr1b8Xec5fDA17+K4+5dlhwgCEqBWpLNkpMKqSDKHHMsIeficTy+WMo+DR+oLIcQ5I41KdooWghhUdeyN2fjEpQCHAf89fPNqHWH4A5G8fLivTjcEhCl7AIKLHoYGBrBaBx3z96MWDy1jHmcibR+0yAyXFUr8ePXf0A0y97QIV8DKBL8dik7XnO/rzby0l2139+mUZd+2/K210JzLIsb3zkRNSQz+Oj3R7D1CJ/dLdXw5xBwmPRYv/9r3jPBxkjZ9i4k895AcYhH1Akkvexytmhc2u8FDwIAGDJFhhLKVvzhOGqCSvNBQBkI/vOHnXh/pZTZFNy95TDrLbixiyQPDUXSK0wSwYa9uGfpX8W/e8MAik49tQciMaw50JyS8Lynklfo9KGTs4q5gHDkODC4aGRX3DypD0Z1L8Stg1MHmGpZTEBJBPsDuemlHhU9BVKfk8J8CgBvEyPEWk/yOG0MUQq0c4u/CDmPKI5DDHoYZaxm71Ll/SAd6V1IrIdafNVo4pT3sVsaluJfsWps2vBWm8fc128W3fErCkxYc99kvHDJCEW3iX7lyVlqo8EqznVyCCamuUZJnD8XuGBXdHGqXxtVzQHFcS119gYANFBs0rh8bBgOvfJ7UTkiBUJEAXbZ6G4Y1UMizcrsJlw+phsuGtlVPKbyuUtN5WIhnSgC7XRc8+Axb+1/wFIUhnJ6dJWpCfPII4/fLp599lmceeaZmDlzJiZMmICKigrMnj1bfJ5hGHz33XdgGAZjx47F5ZdfjiuvvBIPP/xwVp9zjJMC/NdLJ3cWyge0OghEVAK7c46vhFXH3/RvOLW35ntLngJtkzIKMtanzngHx1uVbS56lw1N2v/TtYfFmmudSoBQWdC6mlY7IQV8OV7Urlz3MpbT/DHqFaKh1ylz84JJVnWKNoorwnwg8EYkd7WkMWEBFOdrWm/4YB1O/MePePKHHTjrP8sU5ngAv7ASzpdP1x7Gx2uqEt9SATtZDHqi6sHg9d9fhdtca/D615dnNe4DR1aAoyhYWQ5lxdrSIblBZmJwYNMwkjyk14ONte189ngO4WdK+i3dESvumc0bRmZrcGXTS4TcUHsPAHyHDK/3iOr+cdllbtLIcAr12AJBJ2T+7EYdupPuAk9uSs4ee0Ix8bp7Z/kB5XtqkItDCqT5I5Aoe/c3IbjpI0CD4ACAtaufxx6ZO/x/zv5Mc18Bd36+GRe8sgIfrEqWYwsoIrXaLfH2qS9mye/AsjTOG9EZt0/tD4qiwKS5K9W6tecAisyTuze+nZMx+knduE6fusXjiG5OXHBCF9x8Wl+JcFUZp3CueltB/mSDMHl/AwcANGpkp0+h1YBnL5KIwnoNkkVAMfEWqW7eBYuGz0iVa4/q9kxQRuZQT/10GGRtPy0GnagYevOqkQCksp1EcCr37mg7BK/RiB8bTfzx4jgdGJrCqr+dJq4B+pMA2xuOYV+j5NlQUsyXBLppGr6o0svBy0ZRYHQqtuWqfEBQCliNyXPVo+cOwZPnDxWVAo0yPxQ1DyQLIYkDXPsoMPLgMbeOV22eUTE2zZ555JHHsYrFixfj3//+t/i3yWTCiy++iObmZvj9fsyePTvJK6B79+6YO3cuAoEAGhoa8NRTT0Gny84c+JgmBawCKZCmvlRUCmiw89FY8mLeqKPxl6FxPHz2QNxyWl+VV/EQlAKBSBzhWBzBSBybqlwZlSTIIThqdyvsjffPn4stV23B+RXjUcSYcduU/6BrUXLGYms1H7jL+6sLqFSpKcwEVmKQ5c/xolZofQUA5phBsTgEJKXAgu3aZQsjbbl3SI9E+YBIaEm44ZBLbEXWEohif6NygReNc4qSgi2HU597DiMv0ffG1AOdrUb+/Pk8pB7gasFH5NOlHJ0ya+yStcpMXAjaTNqBUCCg7gKfKTwJAXuQdaCJtNAc1aMw5WufnDkEAETnd6tMKj+geIAoS/f5k8dYte4tsZsAADSn6EoCAAyZEwIRfiFcYNFjQJo2bkeIkmHyQKUBmppvAwAUyZQcvoRa89e+uhgnbnwMPy74i+bnXbPvY8XfXQt7aezJg2U5zCF94J/4fofmfkVElt3CtU9mUJgB42AwqJOyVOV3w/jPTlTiAEBtCl+RIDnX/9y8ErEs1TVq8NL8KA36spT7URSFf11wPG6f0k90z28JRJNaldrJuerLQfvJVIhE+Pc3kPtMhFXOp78b3gU9SRnBNxtTGwh2I/NAXbARNoNE2AWOSKUz3+z7tnUD5Th4yND88UKxfCARvYi6YXuNB2+qlBCowZ9jMqulcSdGfDRG/LuQtDssd5hw59T+ePvqUfjoj9Lz8nIhh60SDPktaoPK89LLxeAwKee8xO4Dn687jBs/WJex948AQcmk1V4QAErtySoYtSSGQAr4udaXiuSRGkeOrMZmOgaa4zBt5M1Hezh55JHHbwzHNClgoQU3+jSkAJvaYT4cT74JGvUMiozAJaO6przh2k06COXRrkAUf/zvWpzz4i/4TEVKLMeTYx5EV4O0UAioZEIenPYKlly+Gg5jAeJxbZLBoLLQqlBRCkTTSN0ByZE+MdvRVoRj0gKuvnG6KCMVIARYmw+78fJi9fKAad0mAQCGsLmz7XMF+MxUNpmbJ2ZKyo1U3RIAwE4Wg540C1h/Fm3WVqz6N67e/CwAwEapH4tDTQH8/u3V+FCWKZ69/jCueHMV9pHOCTZZjTYAlMgy3YFA6pKcdPD4lH1T/aysfZfKIlUOIUDwEELDbpBe6zAWQghbEiXk0ZAH0399VrHtxi5TUZiiPIgmU6QQ0Jv1DCb0K005vqoW/pwptCgDWqNOfbod2FVqVSdX4ISqVuM/Mf44PVW7WPEaLhrGy//pg28/PV+x/f4+F6UcGwB8KmtT5gvHFD4IcjjtvCLJBVbqC5pDCFfGFWN6wm5SHqv7ZwzATRP74JubxqNHsTI7/dYv+/HA17+mff8Vm9omaWfjUXgE4zdj5u69ToseZnJPSHT4t6chAXOFCFEeCaUyEyqSf2MrqSn4x9ztWL5X24PBRogMb8wPs6xU54LvLxMfrzC0bs4Nh1wIkWPsiRcnzfsC5Mq2R77bpnnOAsDD3c4GALjjqRUQ2eKFBX9W/F0XO0N8TNMUJvYvQ5HVgMkDeQJJrsCgaQZO8lus0ivvJX5wcJiVBGxiguIvn23C3C21uPfL9Oe9HIJSwJxijZLYiQDg270mwmpy8u+J9OuEPFqHhZveAACMgAmlJf2P8mjyyCOP3xqObVKAtJEKpJG6i32HQam2kkuUiANIymRrgaYphdngz7v5xZeaYZIc0/ufj7mXSO3JYmlaFAnS5Ld+PzLpObPKgk2tfEBtIZAIKzFE8sXS97jOBnsb+RKP4z027AueCEvCmAd3lqTsT/6gnt0U+lV7cpXJ4DjcV/URAEmWrIXrT+Hlo6f2L8XYXtICL50dhcPEB97eNFLXYIYtqrhYFPf8+pr4N82pL+CeWbATi3cqg+ZH52zHz7sbceMH6wEANqsy+HXK7BbTdfRIB49MaTDAawULKSh0mJKzw3II6htBWWCVeR+YDXbYyLTmTfASaW5Wem+8NO4x/GnSv2BJQfgIJUXC4tpiYHDOsM5J+33355Nw5lA+uy30U08sO0rsWy+AoihcZOZVLnJZ+Z/mSkHXkYRymu8X3YOXHGb8LbhT3Daz6HhcMO5eze8iYH9CoLrliDppWlo6EADviO5p3Km6T2vBsawo+T7QnDzv2E16/GVaf3QvtuLly09ApwITZo7oIj7/3oqDaY08A/G2KRy83mqwZIyMMfk31wJFURhCVCyJXSdsRCbuZXMbsCYiTJQCeg4osRlwTvfkeeDakyRFyaWvr9J8LzsxHD3sr8PrtT+L2w/JejRP16UmyrTgIa1tGY6Dny3QvK8mEu8nPvaj5u9fbOMJHDebu+4DzU278GlEabBKaxCuw7vxRO+RhFK3Qg272iv7zhTvXeJ7ayzNvt2UXVtIiRTQXuolekwA6msBCyEuAmnuhXm0HgvrefXN5PLRR3kkeeSRx28RxzQpILnRp5a6C2Zyte4QTnt6CbZWKxfJ4WhykPnz7swzpYKvwMYqSRoc0JAStxZC5qRroQUvXKpsYxNTITXKVLKxDWlqSwEpa5RraWaYvB/F8uqOVOoLLTisxLQvR5kMeRB5yMCplmgIuHVyX7x99Sg8f8lwmPQMrjmJ757gTSP3dFj4xbQ3TZ2mWs2sGn5e+RSaZAHkkAL1rhhqPhkChJZvNqvk6q3jOFhki+BAqG1Gkx5CKgyO6rDm8P2K5+wabbQEFBPjMW+Iz3LbZdJbs8kBGzFG8yd4iSSWEQ0uOQ40RWNqitpNWiwf4K9Xk56BzajDyX34BfJDZw3EpgemYnDnAlGOLWSHBXPR6UP483LV/mbRbyARdpFskwL29SYlcbdj5fPiY4+KpP+6E27JqL95opHjrxqkQKG9E8pY/tw7sOOrtO+bDTgZWTVtcKeU+w6sdGD5Pafh7+ccp9j+/E+7NV5BPqON7vNuD6+oMLMcdHr1Wnot9CHmrp+uUSrChHPV185mbWFC2uo4Cr1LrVATqQyoVJbBaJmi2gnp9osujhaNcptoKwNwl4tXKtlZDgCtWT6Q9LpANIncEmA38113fDm6D+zaMw+vL1S2YOxXOwxaDVI6a5jiFtHK6+7JvrPQxfdXFNiugyOBFAiqrDmyBcdJZUJqiQEBlc7MEgQWohzz01S7KId+62hs2I4N4Ndgpw277iiPJo888vgt4pgmBSyk/VMgjdT9u3re2KXawAftwo1UgFoW5dLRXZO2acFJZMR//WKLuE2oUc4FwrG4GGxYjDoMqlTW5wpZKznUZJoNGplMOYSsrC/H0swYyZxRLH+s1LLFr15xgvj4kIrhlMPOB7EemgKnUvKRLQKymuR+vk4Y2tmpua9RR2Ni/zJx3ONJ0LhoZwPGP/ETdtepdxewW3mpaa6IjA11a8XH5xQOxk3T31TdT6tbBsB3r+A4DoXOHuK2GEXBKCMFDhxaqvLKzCHUzpuQTAAkSskTUWDWiz3XWwIRWOWkgNEJK82/XtHmkeMQiynPWZrsN2vK84rtU2xSBlVHsntixo0srp+5YChuPi6GS0d1QQG5vgWDxANNfoSicSzZxZ8/nWXy3Ce+3676nWykBCKVrPyiHTIFiIr/iY7JzDw0lpBhXbCtDpuqXNhT78W2amVm20vOy2v2/Dej984UcVkQ2b1EvfVlImwJZmn//nF3ynk0nUIsHdxePitrj3NJNd7p0JeQAiv2NWHRDkkVYyfZ1nQkYFsRjUqkwFVj1L1W5K3+AKBGpQ0dANjNqT0+AGAB64anbkva/RJxoG4DAKAoxl9X3/9am2p3BYQuJImwkmPsz8Gc+vpP63HxsjvwPulGAgAXGqZhXcvFmvoiQY5fnWA0WcgoSeWlO73YXlWIWz/ZBIdNSYxtqvJhZ636PSNTPyL58VFTO4rjVfEX8oVjSdeWlZAtEYpCtJWtU/PQxk8bXgVHURjC6lBROexoDyePPPL4DeKYJgWs5CacjhTYS5y7q/X8ojPRzEfO2u/5xxn48fZTcNmJmZMChSo9rP3hzIJWJoMFQED2XhUOE3rI3NtHdi9UNesCgD9P6qMIWDJSCghmQznOdP2g443nOB2/mJ12XHnSPvISgrNfXJb0vMPOy4vjFIWAP83ikuOAkAdcUD1LunXTezhj0fXi3ztdd4jGdmpIzNCWO6QA7YgriDs/36z6OjtRN3ip9NmX2h3fpHweAFpIa8Pu0OPRsz+CzezEweYAEpOA6fwjrn13LXRMQk28jBR4oGZR2rGkQogEv3ou+dzUqr0XQNOUWK/f5IuIgRYAmE2FsBGFkI8oAz796Ew88+ZIRBPaPgq95406E26sPBUAcIq1Ox4/+yNxn0NB3tgyJCsfAHiir7dD+bv3LOGJlgONAeypl9RJcpLjB42gR5Bo+wQFjjd5P5aigHgMYFnEVLLgDJM8z8hxuCWAu7/YLAb+Alm5bE8jznnxF0x+ZimmP/+zQt0iGPeF0rQ4zBacrOuDnsncHfeqscoA9+aPNir+vrrzaeJjv0abz0zhJ2oYE0tnTQr0kbWB/XKDZKppI4GVt53rssMyUsBpUZ//DQnXWaL/gQBbQr27Fh7+7sosRsjDFeQVQ5YYf+4yWul3ACf2VHqcHJCZvE4tGAAAmGDrKZECGcyp6TBv5ctiJxIAmG6sRFEFf1/QGqpQmlfjCilKHAIJ6rq4bO5zFHRRPAeOxpJdMjJJRoj5M1QZysuVph2n7YlhNjCq50ijV6kWsJBzFwCCgdy2JM4DWFizHABwWsmwozuQPPLI4zeLY5oUENon+dOYOp3K8MFe/2Y+0F+0Q1lrfXJf/mZ4zxkDoGNoxYIvEzhVSIFM5YFafdTliLJC9wR+UUXLVisTB2i7Zt8xtT9+uXsSzj+BX5BkQgoIWVlfO2W64qwRS/8yQVUGLS95cKm0TDIZHdCTRaDHk9qt/43Pf4chn4zHiE/Gw1W7Ken5Fze+oPibphiF4uK1K05I6ULfI6GtntaxtRN1Q5imEAm2JD0vJ4VmLr9b8/MEREiweH7JCADAkl0NmPzsMnywV3mpz9+q3cUBABbuqE/KFJkoaWHam25dS0sBQSJvDgT573fZ6G7ic2q9yBNRREoIWgIRmGRGgzZbBWwkY+4jQeEjkYN4Wx/B1yv/pXgPmpa+z5+mPIcvz/4Sz573JYx6C4qJH8lxlXyfaHn5gBYEpcARVxCPfLdN3F4hI4jUWoMBgI2YePni/EL8pY+mqe739Fsn4j9vnYhYPFneq9dJn6OWTXzg6634eE0VviF1yf3K1I9znad9690B3sRPgD4NmSHHnacPUPz9Y0I3kpsnPYXjGEKwtNEMNRDmyRM9RyUF0Okgv0fIndztRCbuo9CuEuwPd/DEVoyOJ/mzyPHAmVK70oMa7f4c5sz8Aubpsr8nuIliSLhmb5us3cnn5ctG4NT+0lgOt0jjfXD6m3h45N14/Kz3YSPHOExTiEbadg7s7LxSfDyWK8aj53+DOAn0teiLigITKIov0WqSyfBtOmVG3qSXrlezUUk4c6AVRK5R5gnQ7MusVMMT4n+PQZUO1Q4DcqjdT11B5efo9UboyDkbTOigkEfb4HYfwmqOP58nD/3DUR5NHnnk8VvFsU0KkPZJiQx9IhhykzdE+f0TF4CCND/djVUL6WqkU8Gagev9vgZ+4SNfY/50xyn40ym9cNGo9IoGoVe9lhGaHHahrhC59UQwkt9ggGmcqgkigKR608TAh6IoOMgmj19ZApKI5wJ8B4MYReG9ZX9XPOdu2IFlnHKBTFPAEJlSwW7SY0wv7Qya1ahTZF+0asmtsuyLV0Xd0JOTFvSeNO3zACBGTBYZEsC/u/wAAGBdo/LYpaoxFZAoJzbS0veppFJL/NPBE+KPL8Xx49xe48H+x6djzz/OyGhscrNBa4F0jtus5eLiO9FL5EOd8vvQsu9DUzT6FPaBnmybPfMH3NXvUlw7kScShO4DqQKsYqtBvJZW7ZcyaacPqcBEEsyoOX0DgE0wnCRk28tG9UzyO4YoXtOHUe9LNhwzE3+KFn8EIx/9EX94Zw1cgYh4nRxIaJ/Zp1yd3JQHsR+c8pz0RA6D2LhMaURnoRSwGXU48MQMzed1tA4jTTwRGoi2zQxVJAVYRrWDSyrIiaAmWRBns/EBa4CmEctxW1c5DoI/vgGKSklk/eGknrh6fA8AQFWz+vGyWVO3Y2wLPKTEIxTkzwGtEgYAKLYZ8c7VJ4okvdzIz2Fw4HfHXQaHwQGLzCA14E9NfqaDUZbpn7/jTtR5WbH8Rks8omdolNv5379aNkZrQnmPWSddA4kkOMcxsJHuEOFYHI2yc6jJnxlpJ3RncZhbt/5QIwocHD/OqoX3Jz2XR+uxdN0riFEU+rI0unc/+WgPJ4888viN4pgmBRwk4GqJpl58RUggxZIARQiOvaEornhzldgxQK0MIBNoLcpaMnD712ewDr9LRZreq9SGe84YKAYpqSCQHRkpBUjWyJdDB+JwLA4jCTjKTJmrMLbXJMuDHeSU9vgyXwx+7FE6q//z+2sVxn4FMb5vtN2kx82n9cVJfUowvJsT15zUEwaGVi11AKA49r6wehaNYXSwkkWmT2XMmdaPCogTWbKOZMHlGctaj7Tg9pEs0iPnDsaMoZVQQ50nhBu68Bnr3xUdryAFGuJta6nmi/CvFzwkZk3sA4qiNFuSJaLYxl+Lzb4wKov74899L8Ldx10Lvc6o6JCRyluCobWJjSJrGa4Yew88IR3e+HmfOCekau1FUbypWyIcJj3+OIHvTqFFvNktgjlaZmTb/mC94u/fd5sGg4FXRi3d3YAmfwQ/7ajHsIcX4CXSwlMwQpSPS42Aa5TNAz1KpExyJOTKaGyZYMlqycfBaEg+ZunwT1nbz0QXeuH397exQ8o9h77mH1Ax6HXZlQ9QFIWBxNulp+ycsMvMO/0+9VKSXKCeXEb2hjEpnecBmTGeRkBus2XejjFbrHTzZpE0uff2T6G+EnAF8UjQ8hTQ64zi/SSxLWm26BHhr/cBR3jF0ElPLsLzC/kxr27QPicE8z45KSC08xVAGaW5Oc5yqKSk9QXH0fCF43AFIrjx/fWK12VynwaAt3/hOxyl6+YCABeOlMoXRnbn1YBqRHYnQgbfpHNlNIY8MsOPh5cAACY7B6XZM4888sij/XBMkwIVDj6DWJ+m/VOYZOdYVnI1D0XjeGzudpEQAACHRm1+OmgFEvUZ3NwZOj3Lf0gjw5MpsiEFhDZ1fgp8fXMO0OiLIErWV3ZDahLj57smio8TWz4BgIMsWjxZLAa9CYHoz3GX+LiIpRA8eJ1YP3r7lH54/9rRMOkZdC2yYN39k/HSZSdADfK68lSwETWIT6XNXyxL8iVGXN0ZWo9wLI7Xlu4Tnzv5X0vxxs/830Ld+Ml9SvDCJcpuFUJLxQZvGH+c+ATemfoW7p3+Jowymbe7jW0fg8SokiN1tWptsVKh2MqfJ4I894/j7sNlI28BANj1fGBR79qHWIrSIW80Peky5vGFeHTOdnywindJtxhSX4+JapZ+JBtfauePndY1ZrPIyLaDy1N+BgBsZPms/znWXnjxpCdx26n/FJ9jE77Tv+bt5I0jrUpS08BQWHjHKWKmWICcuLBZSkGR9/P6UqtvssFfqyR/DINBu6uHFn43QmoR2JiQObWIHVJaXwYRDUkmhXV6LmNXfDmuP4U3rJSTLHq9ESZCYnjS+Z7IsGPDW5j91RX45ttr8OP829Pubye/mTvUK20nlwpCDNVqkAKJJnj3Vk6CJV2v1QyxnZAtLMXPJ6enqH0XIJikapECAGAlw1ObU7NBjOLfKMYl35eMKU4JyWxQOqYj+p0tPp5ZNBRFxVJG+K1l+/HKKc/I3oGBLxzFGc/9jIU7lARgYwblA/XeENYfcgHIbN1ywUh+rdSr1CqS2S4VUsBBty4xkoc2goFmLI/z/jenHXf5UR5NHnnk8VvGMU0KZNqiLkCC2zgn3fAOtwTQ4lfeFAs1DJvSwWxQP8xNGcj1Lxx8FQBghL2H5j7nDEvd0isdSkjWNaPuA0RKGqZpfPrSICDW+oW3gA0HmhEhmXl9Ggf1rkUWTB7IZ+brvcmLWGHR4mnYlvRcKmxc+wrOeGMAvvjkdyjlpAzQmZaBaIr01mz1ZjfpNc2xLjhBaR716Zoq1LiTF7JWchnK2/zF/A14591TcTBxPZ8miI1zklLg7V8OJD3/6JztOOIKimZVdpNO8d3uOr0/yh38orDOE4KO1uGEylEwMkaYZAvCthqliaQAUQpkW2Ij+EvUq9S/W408KbDQbEBL1S+a7zHi0Z9w6ycbNZ9XIwxSlQ8AwOPnDVH8/dBZfBs9gcQQ2igmQqg1b2FonL7gGsVzj/W9LGl/oZRkWGF/TOg9XdY6MYbbPkn2yNjb4E9qPUhRFCwGHSb0U9aMy4MOmmakACtHme3vN+5Q/J3KXE4LeoYWVQ7VCa3frAaeFAhkSQrs2fUtzn9zMH5c/CD+8+WF0vvVTNH0gkiFMiIhT5xXBfrL569Hprhg87N40L0R9zavxm01CxAJuXFk/yLc/uZwbFyt9D/hWBY+ck374wUp1S2AZIxX1aJOLuv1JjHzDgBGnRl2jbK2eDTzVrUttZLCzR3gM6RljtTzPwB0LuQD7mZ/BH4NBZZQdhcIts0QL05IgRN7JpdQnNFVew7sVJCsFDh1yFV4bNgt+Pz0/+Khsz6ATlYO9o+522GzSJ/BcTRcgaiqeiOTMj+5+bCQ+U+FUT2K8M1N4zH7hnGiMbE7kEw+2DPscJJH5liz+V2EaAqd4kC/Pmcc7eHkkUcev2Ec06RAAclweCguZTAVJdlYVkYKfLWhGj9sVS6Ci6ytY8lH91SvPT+gYewkx6Uj/owbB/wLnPsvmllGwa39zmn9WzU+cfGaUfmA9F0ecRjR2Io2VIm445NfECeLWIZJLx8tc2gHhA5ST/6AdzNqd83N6PPNLIfbNj2Pw3o9HgrtwS75GppIzFtjvv7o7wYr/r7ri82Y/PSSpP1sxNXfJ5Nnf7HgdjyNpqR95RlMNcREUkCPJ77fobrP91ukjK/gjP/JH8fg1sl98ceTe4mdExIN5ww6KVvW1raPfqLeYVmLYhyZQjgH6lSIIZPM0Ovsn1NlVWl8vTG5Nl9AWCV4T0cKdC2y4OM/jhH/FrKa8jaKaw8kBypyifYRvTIALTY4MIUpSnwJ/74m5fbnF+5R3W/yM0uwI6HF2T7iMdC1UJmpTww6hADQm0UQq4VIjMX7v1wq/l158CwwWTr7i68VXd6VRJvVyMv2/Vx2HVKeWv4wduoo3HZwNt6OSEal232TVdu2pYNwjjZ4Eo8nfw615Xg2NGzFE0vuwgJdDFdsf1XxXDjUgphACrDOtN08+hDDyRp3SDvIlt0+jToT7BpLh2xKIhZufkt8HIhm1uEA4K8lByERq1XUYgBghTCntpEUIGuDQZ2K8cG1oxXPpTqsncSSDGl8FEXhrOOvRf/yYQCSSUfBIBHgOxMI6qREJF6f1a6gomMIoJy7zk8gp7UwtIsTTotB9MJR8xRILIHIo+1YcmAeAOBka1dQOe7ykkceeeSRDY7pGcjh4EkBL00jHtaWckdJ1jMmIwVeWJS8uM6kNk8Nx3d14pXLRyRtzyQIZ2gGT34Zx9KdPtz/1a+q+wgLgHSLPy0I5QNC2UQqJLapa/aldvnPBBZaCnRpOr2MvJTIG59buBuLdioX1jZZNvuNtc8qntu+8jnsXP0SAKBAJn/VgUOjTj3YGznwVn5crQhcjDoGK+85TbFNrZ2UhZQ8+MNuwFMDRIP4rnF90n4A4ElzvGPkXGYY7XP10TnbxceCqeboXsW4dXI/6BhaPB/qPMqA2yTLEsUpCsE2mHgFScAWi5sxsNKRtbu7QGSpEUOMrHWiX2WRdQJnxKmG99J+htq1kK58AADG9CrGnJtPwttXj0K3Yn4RTdOU6Fp+6RurEI4p39tiLhJl+omgKQZFBvXrwmxUkmhfydrfzRhaqWnaCQA3nsr7HAjEhYDEoMNGAqxsSnK0sK/Rh802KdjYFRgPU5qady1UatTCW8W2qZmTVp/PeRp1KXwyKlIcRy0IahZvQs93OzEB9QZbL22va9qJRlkJT9We+eJjQdFBcRxYpkDRjUYNBWY9bEQJUetRz/TLSQCjziyWaSXCq2KAqYUVtavEx/tCwzG8mzPj1wrnrJa6wUrK7vxh9ZazmSJODp1RZ8D4PiUKteDwYu1Eg0AKHHFpKycSy3zMpAMJIJUyqkF+fTb5whj3xE8Y8tB8NPsjaPZH4AlFxfmlU4EpY58WAQXkO6p5CjjySoGcgmNZLAkcBgCc0vP0ozyaPPLI47eOY5sUsEmmTl7vYc39JKWAEb8f10N1n1kTe6ddXKWCvE+wjryPliO9FnbUqmeJI20kBRwmnRiUqREVvnAMW6vVF1dtrdkEADPDZzBNLKdoE6cFIQMHAFe/vUbx3H5Zq6SY7PcKeGtw4c43cP72lxEJtqCYk46Vj6IwBsmLnTeH3wmbja8Lbg0pAPDBRGLQc++XW+CWZWGspFa/uWUvrv1wAt56cww2agRKHm/qum6hfOCd5VXitsV3nIzrB8bTGo4JEJQCiZ4Xo4+/WjmWNtSYB4mhXr/ySnx547isXy8QF2q+HGWdRqb+7HAc326Sgpe4Rn20WtvQTBM5x3UqwMT+2q7tiZJ3mqJh04gxKAA1GsfaYnQq/u4nM2p77qJh+PdFw5Jes/a+ydj/+HTRCC+x5rwpoWbZTq5JXyi5ZWa28AelIK5/9UhMHliuWZqTDqUaXVMsRD0RyNC0ccmab/D3xnewR8VMsGvVJFgMjJiZzgY2o06U7svJqwKGH7enDdL21bu/QaVeIoqeXSa5wXsJWWfhOJj1manbKtP4CgiZdwAw6C2wabSR9GZAFK7+5Z8Y8u4QzOd4or5vkALLGfCyhjeLGoQSAq2g20rIYX8bzTEFpYBBz3/e+vun4O4zBuD9P4yEMYVoSFCWaCkZdtV5k1ofrz0kjXV/eDAScc1JPQEo79HyeWTEIwsw4pEFmPH8zwhF+ftAOj8JNQjlA2qeAp6wK2lbHq3Hrj1zUcdQMLMcThx61dEeTh555PEbxzFNCugZg2iI5PFqZzCEJfDATqXopeIe/vh5Q3DntAFJ27MBRVGYMbQShRY9rhjLuyd7QtmRAomLdQFriBw522yrfGxCVuuOTzclBUnnv7wcM55fhqW7kjOFvmCyxD0bcBwHM83LmC0ZOu2XJnRUkMswaVpaBDn0UoC0bf3r4mO3p0pREc9RFNxs8rHt3WW86GzeBj4In1+vDHo/WHUIF78u9b+2kiDhS9dWrDKb8KyZRQGn/oGeNPLcIMkQcbJWhp2dZgx0clh/7yQxI5gKIimQkDUc0m0C3pvyhmwsrScF/MRYzGoubtXCVRhjkz+MWFwp8x/RR7tlHQBwceWxbdboAiIsrAUSz6ijMam/eqeJbNGiUq9r0zj/g2EXqjWULBZZdhEAaolc+ZXLR0DH0Di+q/L5C0d2QYnNmDIQTyofoAUD1tYHsesONuOnHXWorV4BANBxHJ669lk8d/GwVr+nlkGq1czXUPsz9L3YdmSt6nYHC2zzTUWZPfXx0gJFUVKZi+xacjB8wOgOt55keTGwB6UGh/j3AkoiW4Q5whpP3S1DjgqVGng57LJOHQadWbO2PJOuL3/arVTpUO4h0DOU6GWSCYSOCUc0zAbFY9zG8gFWUAro+bFRFIXrT+mN0T3Vy3kEdHJKJXmfrq1SPMeyHKY+uxQr9invnW//sh+XlN0H85HpaAr3UzxXbDXgjMF8YkFe1hVXmTOqmoNYsotX0LVmTeA089e7W6V8IMDlxlw4Dx5LdnwCABjNOGA0FaTZO4888sijfXFMkwIAUEDqYd1aSgGOQ52QIaLN6FaUXDOntoBvDf5z8XCsuXey2CYuW6WAV6Xec3+jXzQGE4KY1kBYYK8+0IyvNyol6kIt8pcbkqXr3jZmD6NxDj2cPwAAdBrmVYlINKPyEPM2fziG8d0luX5UZjR29YHPxMduTxW4BFf/ZhU3fWdBdwj8SGuzmQDQJaFmGwC213hE8sVKFrAh2WdM0CtrbJ1kX3cg9aI7ygmqFz74FxbPAKBjaAzuLAUS15/SW/U9ysTygXBS3evwTqPRnayUs3FPT0SAGHhZTJnXEstRbDWApnirkKaEoJ6maJg0SBUAoBLOM60yniAp9Si0GnDgiRnY+egZorS2NZAbT6q1I63RkPm6Aw14bNJ/VJ8zm5QmYgLB0b2YJzdNegZXEhISAG6a2Ff1fR45V8pMJpKPNhIAegONeO2tsVg9/y+q76EFjuMw8+UV+MM7a3G4hVcWOeMcepQ6W2XgJ0BLKSC0TW1gaHCx9HMsA/XA+XiOv1YKWtmKFpBUXBe9tlIkGAtIy0R3OLU/SDpE49LvpOM4xML8PO0l6i0zS8OUxgNDQFqlgKwsy6i3wq5RW364Zl3az4olzKXBSCUKzIas5lhhXntlyV5FaYaAQkKYtAQbsfPH+xA4vCZpn3RgWQ7COwukQKaQ+w/d9flmHJaVOYRi6goWlgXKbKNQ75mQ9NyH140RidBaT0icl+Os+j3/xUV8G9J9Df6sxg1A9BRQW5/07jEp6/fLQxtLmrYCACZUjE6zZx555JFH++OYJwWKyGKmwa1u2lN38GfxMcfYUKliKDW+d0lOxkLTfB92wZvAkyUpoIbNh13i41T1w+kgz77v1milF4knL0C8bZQTRuIsVhfypQn1TIakgF25QGv0hXHui7/guAfn4cyR96MzzR+H94MH4a7ekPT6lTs+T8ohtqhcCQyjQ5R859a0JBNQoNESSigHser5BTYlIypirHKhWyTWdacu12CFUhiyv9AST0AvWeu/uzSMKYXsZjAahzccA8dxeG3pXlEpUkwT6X7LftXXb1v3OvZt/Uz1OQF+kn132LQl9qlA0xQKSbCmlun/7JzZmq8N6pUL5VqPerZRKB9obc17IuQlKFrqBDWUdh2PgV3GQKfWDSHBaNBPXMetMu+Dh88ZjG0PT8Pa+yaLHgeJuGJMd/z692kAePJxnsxk1UaMG79uXI//MD5cUzMv47EDyjKMg4185lafgrTJFCVCm8cEUqC0VOr1ve7nR9O+T41L3SvhUMwFAIiqGE5mCrnfwYYq/v2cxPPAFfGqvSRjHArLupVQFLZs4685D/EqMMaZjJUCwn2vRsNTwCZTBhj0FoSD6mTwUw3p22maE5RoOwLj0askWaGXCgMqJRXYg19vTXq+iFwXX/v34/wjX+Oqudm3egtE46KngNWYncFeIsEhPw+iMXVFUI8Sa5LPgACGllQnkRiLZ3/cjd11XqjckhVQu2eng1Q+kDxHXTnmr+hibt2cnYcSzc17sIXij/GEhNK8PPLII4+jgWOeFBACGLdGRnvnYallWdwwKCmI+v6Wk5MkuG1FKiY+HRIzt/LFh9CqrzWQ1+knOhkLSJRpA4CvjdkueXu23mxmp2NpIingDWNbDT+OlftcuKJUYt2f/fEmAMC5RqltY2PYnUQKCC0Rry85EQBwqoFf+MzfxgdHrS3NAPjfSE2BItTDW0ltsJuSflsfqwx0BHIrlRx215YPscfAv8fQLsWYPLAcV4/vqdjnr9MGoGuRGTef1lfTI8Ni0IktAus9YWw54sZjc3fgyrdWwx+OoViUP7uSXutu2o2Lfn0e56x9GGxc/TwKB1vE4+10VKrukwkKSTZOLeveo7APBnLqWej9BiVJI9QlByIx/LKnEXGWQyzO4o2f9wHIXIKdDucO7yw+zlR95KR0GNvvXABAVy55HBYLr7TgOA5P/rBDDMAtCQXPFoNO7D+uBZtRJ2Y4//TfdfjhV/7ct+n4gG2/7Bqo2/19RuMHeANTAVuqeFkzk4M296U2PlBt9CqPpdx8cZl3X9r32V2vrngJketjd33rg3dBFQYAh5p5MkroGOGOZZ/FlWM/qySzrtz8LGoPLYeXlCXoWZ2q54YatDo5CLDppfnLqLfhJ1bymDnV3kd8HEtjuhGLBhEkx/VUYwWuKX0CLGdAl6Lsujv0l3lnfLYuWQUokALNpO3fDqMBXAqzYTV4Q1HEyBRpMWXvuj95oBQ8y0mBsEbXllA0rhnkMzQFo44Rr8/nF+7GlGeXIqahFBDQmnbFIimgUj5g1pnx38mvZf2eeSRj2ca3wFEUBrI0ysuHHu3h5JFHHnkc+6SAmQRTgaj6guCQm8929g0YEI3ToCgKs28ch0GVDnxw7WjRjCuXEDKcmQYGxTIpYmIJQZgEAaf2L22TEWInmczcE1SvG4zG+ZX8WRVSjbw32raFbTgWh4lkju4f9beMXqNnaCy84xTxb3mmsMEbhkUvZZ1+irWAi4ZglJ3q/lhQzKgnZq2GlA7Flqu24D+XLIQ3FMX7K3mFyT4N9USmmH/bBIVztTBWQCIFPLIFtY9VLsicDNknhTLjn6ufFB9XFljwxlUjk3rQF1j0+PmuSbh9Sr/Elysg9xXwyc65eVtrYWG0r6m6lr3i46bG7UnPA8CydS+Lj0sKW08KFAlKAY3raFqvMzN6n5dJp5H7vvwVl72xCq8s2YuvNlbjexIUGzXq+bPF2N7FGNOLD1a0rrFEnG3uJhJ/xXSyjN1EygeW7WnEy4ulY2/NoEuCGuTk1eck2LKR6ykuIyD/u/6FjN9T3sWBIZkxXQ6VAo2+sCjNF9CX4s/fSkP6Ol2jQb0kx9HAk4sPn5Ns+pYpPvvTWPGxUAvuNPPKM1c8s4C9SoOAqVdRLz32063wEsd9Jq5XkK6pIHgKJHZyEGCTzakGg0VsqwgA/zzrA/yz14UAgOOgXmrBRvw4/Y2BuOmt4eK2p8/7Chtq+fkpHWGViBJr6v09Kp0d5iy8K6vP8IZiiJNSI70uexXe0xcOEx/LvQ+0fhNPMKqpFBC6CSSq5C59XergcOe0/rjh1N6Yf5tUfnBKwvyfCYSkRTjGYv7WZMLMbs+eaMgjGUuqlwEAJjjb5leVRx555JErHPOkgIWYuAVj6q2L/CSoNUdNmEP6t4/oVoi5t5yM8X1yUzaQCCnDGU3K/KtBXjOdWPMpyAMNbZC3A8rSA0EpwHGcWFcNQJTSPzj5eYww8aoEbxuzXZEYK2ZjKpzq9c5q6F1qw/QhvPFSo1dJCsgzhS0Mgx/m3Sy68gOAPx5CjBx3W1x5/HXEUGvlviYMeUhq82Vvhfu4HCY9g6V3TVRsmydkYo3JxJOXeByURHSwV09B1Eey8ykkx7to6TsaLD0198sEoq+AN6ToeX2g0Q8LkRIHVK6pgw2Soef2PQtU3zsiC4ZK7OlbUGqhKIVSAACuOunBjN6n2h2CKxDBbOKZ8a95OxWL+Gyk/ulwYg+SJVZRCY2wdE7aNvr4P4iPCxmJuLODxjhzJzCMDnsbfLjizdWK17W25KG7rLxAKKvwqRBR4Sz6aQcVpAD/vQ1U2289xSQwjLFc0vEcpHcCALxplEwsy2F1oUv1OX+kEjdP6oNLTuzW6jEWWg344wS+g0kTIS+dpB+9i8tMKTZ9uRTMWuJAf78yMCyVKay2sgEEovx1SbMG3DtjYEafIZQPaLUktOikc89sKcEfB0tyZ7PeggIrnxV3serf6a3vrsERvQ6/kNI5A8eBpUxYQkqSEk1N0yEdAd5dpfZ9q69KZU9tyJUCula04isw63HTRF5FITdwTCQFhGvVFdReD/Qt4+9pqUoEZ03sg7+ePgD9yu144dLhOP+ELpg+JHvSVW5G+8f/rkP/+5SklFFvxrfnfpv1++YhIRYLY0WUV/2d3PfcozuYPPLIIw+C3wApQAKYqDopIAQ2FKtXmLC1J5xEnheJs6ptz+RIlPInkgJhYi7YFnk7oMzUeEIxHGj0o+c9czH078n1w0bGiMkFfD26L6bd2zsThCMx0XhKp89u4SWMWa4UaPSFYTEof8eX6lcgJjMS9MXDYvmANaFkgaF1qPOEcPFrKxXb75iqXn+fDewmPfrK5MRRVjDbcybtKzind/GWoNp9GnxB0sZMg4Sp2/uj6IvQ0+tEUWn2bf7kEJQCh5qCohoFAFoCUViJyVhA5bdv9khZ13XV6q7uh128OmeI16Iw5MoWArnW7FcPRHQa7S0rgslS4Ld/OaD4u9gmjetfF+RO2ukg175a55GnpkgKildG3Yc3pryOk/ueLW5zylrQ/XjpCrw4cw4A4LSnlyje59xhnVptjCk3xWwhx5VVCeArTJkTpnIDVJooBXJBChh0tJjVTPQVsBO5uy+aWvofSDH/2s0W3J6D615QegkGjgU2PtPqzrBlohzevXfBFFaqH2IHL8R4A58RrmcobPceAABQnC5jYruSuOW7AlEFESzAIjv37LZOOGfkzfj3+H9g4fk/AgCcab7T3pDSt8HCcnj2x13i35NaUfr20mUjAAB6hkpSipw6ONlDIKpBWGjB7Y+I6phs700CpNaJMlIgoUagzE46qfjCquUDI7o5wRASpCJD36Azh3bCUxcc36rOLolzRzjGJpEVpZbsFQh5SNi68yt4aQp2lsPg/ucd7eHkkUceeQD4LZACJMMRiKtnIgIxsp014qJRrc8IZTUmmSP0h6vUDRAFxBIy2VpKgbZKnAd3khaaLf4IHpvLS7+jss+Xtyq0kUDWm6EEVgvBsBTk6jUcrbUgkAIfr5YyQLxSQEkK+MEpSAE/GxEt/UxxZeBIU3pV46qZMuf4tuDTP40VXf+F7JE1odc8APiIv4DglB+OEqWAGgnDspi87DbpT9co6LLI5KphSGf+fNh02KUI6loCEVj0AimQfE25ZAZkLUH1LO1eF6+QYKIOMahrDYqs/Gub/drn4EVFxydtO1R9W9K25xbuVvwtqGJmDKnEuBwZjQIyUkBFKVBol86xIb2mYnSnMYoFulPWacAbpNHn3nkY9MAPivcYUGHHsxcNa/X4uhRK16CgkBg5+NKk/UIaJKsa5Fl8q5FXYzRQuVFfCAapiR0k7Do+iPVpKMQE1HtCGBiQrpU+LV0woLkL+vos+NU7JCdjLBa6JJDjWVTEK6KaaQpRn7rJoRZirAlsTDm/uaMVuHXiVziJ5rcvo/hjwbEGscQmHexGHazkvlTjluYY4TowyAg2o8EKmqJxWp+z4TCUoNkfQYGdV7m4hJYgCbAmlL6YWODVJZLfw/BW+PZMGVQOiuLvUYkdSNRIsXTnQiJcfsm/xaDPzghRgFCWJ1cKJBoNCqqsRl9ELB+Qk8dypZZWS9lsyy+yhV+FKMqj9Vi+h1dajNE5wWTZ2SKPPPLIo71w7JMCQgCjRQqQ7RxrgKmN2fZMIV+wfLOpOsWeyX2IE2s+BSliW5UChVYD3rxqJADeAE/N70Ce3bQb+QDFm2X2RQDHcehx9xxc8qqU5dTpszObEhZT8gVhgy8MR4GS3PFTHGKy/sp+LiaSArq4crH6zI/78INKHWWuUGg14OJRXQEAe+p94DgONnNyz2tfwqI2FOPloy5W+btw0TD+9pqy5jkUt0GfYScHLQzqxAcY+xp8ippwVyAqejYE2ORgPByVye4jSlLA3bgTP/50n1R2EDe0icwqIvLxZhVDLAF/nvICxpcOx+dnfY7nT/4nFsycD45NX2cudFpoi0+HGoTOI2rlAzpGj9lnz8YnZ34CR0KrQQA4bSRvmjlU5xTbgwZki3WDjsabvx/VpvaZ8haWgoppQv+ZSfv5svASeV5GuGwt4n0K6nI015ZotCUUSnI8KZRM+xv9mPT0Imy38HPoU70uws1nvIs1dTdhfdUDeOK8ETkZo6A6EcqcKor7wcBxiFEUGpp3pXppEiKcEeGYcr4IsE488NVWTOs+OWFvA8wZtiSkKErMQgvE8+bDLvS993s8PX8nnCXqZQhTn12KEY8sQJTmibMoRSEUaEraL5hAHtcmlLd0VTFiTQc9Q4v3gGoNg0Q5PFxmPh4Cqpuk+4DZ3LrWqcL1JC9HSmyhKBjnNvjCIikwrrf0eXLiVC34//OkPlhz72lJ23OJJl/byP88lFjRwiddxpWPOsojySOPPPKQcMyTAoLR4DeRekDFDT1Iej2zrKlVUrvWYsogXi7ZSaUFohyJxkPP/7Rb0QVAMCAy5mCRPboXvxDxhWOqzsNyczSbmQ9a/K2QwALA8r38wpGhpMWGTp/dwlBNGtvgDaNPt1NQSknBfoCm8UPcJf7t5+JgSdykjysz1c2BZP3me384MatxpYO8e8LPuxthUVlwckJgRwzZQjEngOSF7Yo1z+Nbs3Temlhgf2hom1ooAtLis8kfUZACzf4ILHqeoAioXE/hmPR7elhlZu4vX16C26q+xiIdb4TWoziZDMkGglJAy1MAAApMTrwy/T30L+qPib3OgMdvy8h8bdFOnhTQ5ZoUMPOZPk9IPUDpW9gXg4oHqT53XOexmHveXLx0/newqmQMl9x5qiKobw3k5QNy9GOVc6M/nnnWtSUQwcSiNzCl7GmMiPPzRuccJR7FgCpRKUDa/vlSKJk+XnMIRkoK1pr8tRhYIWXhe2TZJk8LgimeQFxQFIVCcl23eLKrcwcYBKLKeS/KGXCw2Y+JI25QtK000NlJ3oWs9o0frseinfU4+wW+M89/ftqDU4//A2aWnYi/D75e8ZpDzfx5sOEIQJPP9vqSiW5/wu/AyMb532taP78K0vvE318N5brs/Et8RClg4DgwutaVOXUiZRnecEwk1fc2KAk1gdiIxFiRLKQoCm9eNRIjuxfiH+dKipViFVLg0tHd2kQEqiFxHklUYuTReng91dgM/nwde9zFR3k0eeSRRx4SjnlSQGjhFqcoNG35OOn5ADF7irOmnLUeywTnDuPllk0ppM8AkNhxKM5yeHUpL7ts8Ibx4iLecTwxU9Ya2GQSUrX+xnKlgIVI3gNIb5SohsMt/GLSQEnvqVNxV08FtQCmwRsGTdH4z+gHNF/nAyd6CkShJAXYhDZ2/zp/aJKDf1shLx/5/tda2KzafZ85Uj7gJ8GUC3HAVy8+3xKXghqGAxp2Po44Z4aujaSAUAftDcUU3QdcgQgsxMgxoGKUJjcR9Mp+270NPqw0KPc3G9oWdAldPDI1AozEWEx9dqlim2ACpwU6x4vtghTlA1qQ10t3tXdFgbEgqYYaQJuJIEDZhQSQSoZslPK6cEfTZ2YFXHxCF6wt34OVxQ0IMfx1P7Ugc1PRVFDzFQEAO2lJ52O1z41yuwlGSiI3Jo+6GRUFJlw0siuGd3NiYKVd87XZQGj3Wu8Nix4xheCPZ4v3SMrXttRsEB/3ruODQ1esQtxmYDkANJp8ETD6ElzslAJIvS678Vc4JF+Bq99eo3iOpmg8dMabOO+EWdhZ64U/HFOcg++uqIKN/OnxKkmBoKcaCzleNdQjaIQtRiOy5w7x+RN7tp4cLLWrK0UAPpiXw6+hFtRCi5fvYGBSudYyhcUgtfkU1AKJRL9Fdt8VyA2GpnDawHJ8fsM4BTl1xuCKpA4Epe1QOvD6lSMVfwt+GHm0Hat/fR9xikKPONC58+j0L8gjjzzy6CAc86RAROY6f+G6x1C95HHF8wGWD3hiHawUECWlaW62ieUDAO+OvmhnPf70X8nILVeZgjKyMDzYlJwJ9MjckS1E8t5aUqDWzS9+9LTQoowDlWUdPEVRGNHNqdjGcryioaJSW/rrpzhx1DUuZUcAFlLwM3VQOc4bkRsvATnkv1WXQjOsKUybBHm9J86rCVwMgws/PBnhloMAALvMVLFzlAOEFlptzHAXmPWiuVW1rGTFHYzCQqTZfhU5blROCtB8OnhLVTPuf+WvMCUwXCam9X4CgNR9IFNSoN6rDApeumwE7pzWH44UnSVyrhRIUT6ghreW7ceAB37Agm3KtnlRFdJO30YfCQBJc6BwbC208rdqZLMwGGWl1pXraf5766i2dfMQILQlrPeEceMH6/DkDzsAADaivvFyKYwETTqYaClrW1bMt+l88vyh+PLG8TlrRVnuMIntSPc38p/nJF1xWgL1mq8DgC9+eUR83OAbickDy1FUKLUTFdzxYyyHbzdV446z3sNolKEgBoRMp2c1zlTO9kJXguV7GjHt30tx4asrFCVmG6tcsJO5x+tXfqePf7pTfGxsGYqa3Y+Boiux5x9n4MATM9p0nEs1ykcAwJpwawrEMw9so3EWGw/ypS7mNrbPtBr573fGcz8DULboBACaAkrsEnEkbFN/Lx1W3HMaNj0wFd2KLLh5Up82E8BqGNTJoWihmy8fyB1WVC0CAIy1doyHVR555JFHpjjmSQHKIkmz63U6/GnXu4rng2TRGGPNrW7j1Rpo1cImQi0jCABXv70G6w+5xL8fPvu4nIwrMQuhGAsHsazAShbdAUrdWCodhADtghF8QKxvZTLmIlKfL4crGEGxoyueHXm36muCNC0upqNxpaQ0JlMKvHrFCWJgnGsIGeoWfwRmle4DAgTDQE9MkgxvNxqwcMNrAICYbKHLcdLiurANrv4AX0svLArl9br+SBxGPS/NDnLJgWlUlpl10RQ4lsWi5S9hS7elCCUErRzTtjGKpEAgAo7jEIuz2FjlUpTXyJG4GB/fuwR6hhbN/9SQc08Bs9QDPHE8anj4u22IxFg8s4CvPd9V58Weep+qkqetbTMF/Hi71Of8UDMfxNoY5bxQzUXBxTOrAWCjzUnbdFRuAm4hKFy1rwlzt9Ti5cV7cbDJD7uFv1680C4VicY5GGme/LQmSrJyjF6l/DxT1cxfS0J7yZZgY8rXLfIdEB9zYPD6lSegW6mkFGBlBOM9s7dg1X43RnZ/FYd3Pw6nvXdWY5SbTCZi82G+5OfbzbwKYGu1R9G+sFeJFQ5CqHoSvlNVWPr94yxPPNhMupwEs4IKo9qdrAJwJBBZvgxbQAL8vGwg54YJbZsD5ObAoWg86bqPs9J6oI4c01QKJYamUGDRY+ldE3PSHUMLX944XnycLx/IHZb7ebJpXLeJafbMI4888uhYHPOkwEUJvcoPGMhCIRIAOA4hsmiMsuYOVQqU2CR5dqrgIFFqqIYbTu3d5iBQgNCKTgvDH1mASIwV6+AjNIVoml7gaviAdF2wGvjv3tqccVilPlzIwp42KNk1XYAQoHKgUSJbKzbHOomPc12nKYc8oKVpBlYN8kf4/VkYYJbt0xzka97lpADbeBIAviXdwMq2t9cUxpho4kUzpHxARSXiCkr7hmkKdU2H4YvvUOzjjFGwxyj073drTsYXibEIROL457ydOPfFX/DUfHXztkiC6zdDzBjvnKa9sM41J2Q36iCcVl4NXwEBP++WnOm313jw4apDmPrsUkx+ZokoRXZa9DixRxFW/e20nBEYfcok2flFr/KtOa0JfdobGRqhIO8LEq5aDc5To/l+8XiyKWE8RQY/GwgZVnlQWO0KwUbUN0IXDzVEQi70Lv8vAMDQvpyAWOoklE05iFmnJ83caYU0r08aUAmKopLKpqYPkUiCR+dshzccA0CJpSqZoluxNilQQ+aAQlk3A/ncu6/RDwNp75pICjhlaiYjzatG6jy5yTx3JUSGcD3IW+fdM5YvIevNCMaomRsN+iNx6Gn+PW1029YFcrKlxh1KakPc7A+L6wFRKdBOZHQ26FFixZ9O4cnrfPlAblBVtRxVDK+MHDX4sqM9nDzyyCMPBTqEFHjxxRfRo0cPmEwmjB49GqtXr065/2effYYBAwbAZDJhyJAhmDt3bqs/u8LeSWFqBABxTzWefG0IFnx4FoIksOloUqDArBelyankz0JNb6osoCGH8sFUSgEB+xp9sJilzHUgkDrblQi5dLpXMb9wbW2Oc1QPqR5VMEcS1AyZBPUsSyN25EIAgJGlcO85E+G06PG74Z1bOaLMUJRQD2/VyEaxMumqXbaWdIX51n/BKB8M9QrS2OGZDgD4/fieuRmjSAoos3BL9vAL16BKwBWilc7j+6t3wG6oVGwbT83EAOZNTB/SttIMs54RDTab/RG8Rrw2XlmyV3X/RMk9Q86Ps4/vhEkDeF8Hm1GHLQ9N1XxNW0HTFOxGwWwwdebyijeV8+TfvtwiPv7+V94Z/a5pA/Dp9WPTknmtRYzMPxZd8vv7/fVwVa/HpAW/x83vn6T5Hlw8udQglKLWPxuo1VNvPuyC3cafc36aRjyiXupQd+hZrCOF8IbWl41nBOH3EWrGHQaeePFEvalfGJFmxvNH9gCQbAL3F1m2eHedVwyQ1cwoU6F3qbYRnxCsykmBS15bqdgnFuKvRW/IpdjeEpDKRw6G+U4pQ7uk7wCSCYTykUZfGC3+CCb8a5HYTnd8v99h6UVL8cCwmwEA/hSqkUT4wzHoaH7eM7ex1EVuLFvtCipavPJjj4hKAcEE9X+AEwAgecukavuaR+ZYsf0TAMDxMMJqr0yzdx555JFHx6LdSYFPPvkEt99+Ox588EGsX78exx9/PKZNm4b6evVayuXLl+OSSy7BNddcgw0bNuDcc8/Fueeei19//bXVY7Al1ATOW/4E3rdbcHvsIEKEFAhxlpQ1lbkGRVEyXwHtG66QKWZoCq9ecYLqPi6V9oGthSDHTAVXIAq93gg9GVswkHmvbY7jxGwZRQGdnfwp2NrygYGVDnx700lYfe9pYgZNq1677+GTUJAYGDI6VAVHwLvjYTTufBydnGasuXcynrkwub99LpFYD2/VuBTDsgVkYUjKuLn8dUDYi4ONvDTXSNzh+5fbMaAiNwZpxcQ1XW40CAAfruOlxAGKLw+QI0Qr/z7SsA/xhLaVkwb2wFu/H9XmbBhFUbJFa/prIFFyL1QzUBSFf188DM9dPAy/3D0JdpMeb/9+FI7vUoBrT05tRNgaCCUEmfoKqEH4vsIckmu8eCnvydGTmJzZdMmmkP5AA1bu/Q4ehsFiqwVcXD0Ty5FOBY44i6mmTjBzHCb1vzAn4+yvcq5vrfaIpAAA+P11SfsAgCcuZen1bawbT4fSBEPEAmLU6hbac2qAYqXfl+L437xzoUVR7tCj2Ir9j0/HkM4FiLEcvtrIS/yzJQVKEwjhFy4djlkT+RKElxbzRJucZ01UaTGkvas3oRVpg5//jl2DRvxpygX4J/FsyAXkZXizNxxBVXMQry3dh2veWYNQNI5CUyGsJtJVJwtSYG+DD3qG/x4OJv09MRX+ca7UMraqOZCkFGjyhZO6CjDtqFLLBsI9IF8+kBusqFsHABhbqN7iM4888sjjaKLdSYFnnnkG1113Ha6++moMGjQIr7zyCiwWC9566y3V/Z977jmcfvrpuPPOOzFw4EA88sgjGDFiBF544YVWj6HSoJRSN8hciEPk5tu9pCzrRVRbISxohDpTNQhxDENRmEraGCYil0ZDQu/3VBAy8RYSyPuyUAr8a95OzHh+GQA+6xQjLex0bajbHNKlAGV2kyiX1Qq2muJlMLPKz3FaSNaN4xe0OoaCnqHbtXSA/1zlWK3QUqlI44j6BoiPP2Fb8Od3xyDG8plGHatDnzIb5t02IWeKlyKNkpQgy2cU41Ry6QiTEFzVuasQTWhd2KWkArlCoawMIx2iCUGMTuZx4DDpcc6wzuI5NHFAGb6+6ST0K88NwSKH8BnnvbRcbCnaWtjbac4aQJz3G0mG2GpIziL7Ao2wmSSljsd9SPW9WJafb40chacvmodVV23BCUO0S3uygZ6h0T1B9t4SiECvN4mu8R6Ntn86SH4zXDvfCRNbJzrIcfOkccQ3URJRbXPyAXpnpxlFjcMAAH19BaBpChRF4ZqTlAohqyH7eWDTA1Px8mUjsPPR03Hm0E6il4CAQET7fKVY/jsmlkRQNP87FPtLUWQz4sKRXXPm1SIc1yZfRNGWd+GOenxIStRsxF8iG/+bWz7eCEbHz63F+rbNAX3L7bh8DG8qt7/Jj2DCMWwORFCaSO79j5ACRWRc+fKBtoONx7Am5gIAjO6VnQloHnnkkUdHoF2j4EgkgnXr1uGee+4Rt9E0jcmTJ2PFihWqr1mxYgVuv/12xbZp06bhq6++Ut0/HA4jHJb1RvfwC5LaFh/MZj7gu+eUZ3DVwmvFfdYc+gkgwWCYLE66l1UgGs08cyfsm81rEuEnGdhZH67Haf2nqC6UIlFBCg/EYuqZuOvGd2vTOORwGJWr4+9mjcX8bfUIRuN4fdkBAECTN4hoNAobKLgBuH11GX++kHECAIueRijCZ5H0aPsxFcwLP1x1EBed0Cnp+YOBURjMzlZsc1gMQIv0N8WxOTuWqWDR8b+1JxhFNBqFkVKPSoSWhOUOI+pD/QFIrcIWG2lQ/p2ADqBZPYqtetWxt/a4Os3qQUWQtUIIEd3eWjhlWWQuwWeg2VebRFqVFffP2TF2kgC7wa0k1uTvv/pAMzYddqNXsTLbHY9FFUZt2aAt56rdKB3X6/+7Fk2+CJ6+YAh6FCdn49PByLRtDtJCoYlIwcMxePwhmFV6vHv8DYjL1AH1jbtgsScbf8biyde4FlpzXMvtRkW3lCZfGNFoFEWgUA2gvnk3yjuNSXpdMMwCJOb20Vy7XveF5Fqq84T4uZMoBTxsRP1zOQ6xw6thovhgrEfADIa2IhqNotymw7amS1AW6osdEelamjKgBF2cJhwm5T5mPYVoNJrVMbXogckDSgCORTTK4orRXfHzbp70Xbu/UTS8VEWcv6d6Yn7FZ4n+ERyDwZXWnB5nh4E/T2Msh0hUeX880sKPw2Dg27mGaArBgAs6FYJLjk/X8kZwIJ0yzLQxaczZnqc9ivhjs6/el0QEnj20Ek6zcilGo33Px0zhJHOVcE0J+F8Y2/837N4/H26agpnlcFz/3x3t4SjAshxCsTg8/jCaQsDOWi8iHIVgJI5gJI5InEU0ziIW5xBjWUTjvLFvjOXEx1GWA8tyYDkOLMerQoXHLMeBI//Lny8wG3D3GQPSDzCPPPLoELQrKdDY2Ih4PI7ycmWGu7y8HDt27FB9TW1trer+tbW1qvs//vjj+Pvf/560/dMflmJIhVR7eZZhCr6NLAAALLGYk/avq3W1yrtgwYIFWb9GwMEmBkIm+NbX5+GMrnwmk+OAQAzwRIGWMAWAQTQcxty5c1FqYtAQ4l/zh35x9C3gsGrpwlaPIREHvID8tNi7/mf0BrC6nh8HAKzYsAW2+s0wxwHQwMYtq3G4qkTl3ZJhYRgE4vz4K6NLcO2WLwEADMuJx7K1x3R3DX88t9V4k35LZwzwgoKBZQBIizI26APgFP9evXIF6re26uOzgisMADq4gxHMmTMXXDQGqCbm+WNl4ULYHzoOvWN61OukBdlK2g+ABsUaMEDXmPIczva41tRKvzkAGGgOEZYCBz0MHIcIRWHRT3NhNEsu54kGcg2eIzBRVoAk28rjNH75eROATVmNRQshNw2AxvJ1mxRjnTNnrphsu2VF8jTXr4DF999/3+bPb8256nfxYwaARTv5gGvKv3/BP0bGYCPecMvrlMdeC+tW/YLDm7MeQlrwCVX+uL315Tww8aakfTZvWwtORmb9smoetm1PLoWqqz0EOACGRcZzbDbHNe6TjicAVDd5MHfuXDjjNKppFms2LkXVoeKk1/mDXpEUcNN0m7xr0qEmAAA61LT4MHfuXDR6+PuZJx5V/dyo+xf8Mz4HQULSOsMWrFu2CJvEr6lDvX8UAOUxvaoH8PhGBiwoBA5swtwa6Tpr/b2KPw8ue30lkELRFY/zio0Gv/JeGo5GAANQYKCxZcVibNF6g1bCoWfgiVKYu2ob5OfB9j37MXfuXsRk/hVz534OnaEs5fv9ewO5L1P8PBvwBDTPjUyPaUMLfz0v2C6VTV7QM45CI9AruAu7twCK++7unZgbUF8jdSRayH2q0RfCd3Pmil4HgUDqspc8krFm97cAgBG0FXpD9gRwtuA4Dq5AFEdcQRxxBVFN/jX5I3AFomj2R+AKRNASiMITispENDpgg3rSLtfo7DTnSYE8fvN4/PHHMXv2bOzYsQNmsxnjxo3Dk08+if79Jb+gUCiEO+64Ax9//DHC4TCmTZuGl156SREvHzp0CDfccAMWLVoEm82Gq666Co8//jh0usxD/Y7Vy7cD7rnnHoWywOPxoGvXrijsPgDTT5Pa9E2OTsS3n2nfwAcPGIrpk/pm/LnRaBQLFizAlClToNe3zjv//g0/wUMcyH84TOPcU0bgtAFleOz7nXh77UHFvhaLGdOnT0DfkT7c8dkW3DSxl2Y5QVtwsCmAZ39dJv49fTpvXsdsrcMHe/kFZnnXXph+en98+eGjAEKo7FqCKSdPz+j9X96/AjtqvQA47Oj2pbg9TFOYMmVKm45puLIad83+Fd2LLJg+nTc+ozccxP3bXsdI/QxUAdDHdZCTAj07V2KVrGPaKSefhOM6td25Px184RgeXP8TWI7CpCnTMO/zxwEku7QLifdBPSqxf0stTrVcgU8jUulNkEjg7QYL7rvyDNXPavW5uqUWn++XIs4KpwWHSKmLieUQYSgcP3wAevWaIu7zyVs8QVcc49CkoxDXhRHheFOx0WEbXr56aeafnwHWzdmBdY2HUNG9D3Bwv7h9xEmTRI+QW1bMV7xmVI9CfHjNqDZ9bluu/0WBLdjSkuzWf+9aHXY/wpsc3nK/NOZXLh2G15cdwDpZC1IBM6aelpE5aGtw60p+DO8fsODp00cDW9cpni/vXAKjwQIcWAUAqOzmxKRxyfPAhv/ybWCrDJQ4n2ihNcf1+483AY2Sb0CIYzB9+jQs/PgpbGNdcJSZMH1q8ucufvMTxd/pxtYWtAQieGLTYgRiFCZPPR17D3DA6vkI0Oqf++8v30QwKAW4RRYDzj5T2k9+Tie+/qrz+EycoDxr673qud2/YF+jH2E2taomFucDnYhe+Tt/+/YTAIDSgoJ2OcZLQr9i9oZq7A0YAUiEaXF5J0yfPhQA8NgHf0eEojDyxIHo1Gl0yvf7onEdGnY3gaOJh0NZZ0w/QznubI/poCY/Xt3xi2LbxDEnYMognqA40OTH81ul5wcPGojp43ukfd/2Rpzl8OSWhQhGWQw68RT0KiVdMzzZdxz6rWN1I38vHVWUez+BWJzF9hovNla1YGedF7vqfNhV5xVLPbOBgeZgMxtgMehgMTAwG3QwMBR0NC2WV+poCnodDT1NQcfQ0JPnGZoCTVGgKZCyJr69JgWI2ylK2idVO+A88vitYMmSJZg1axZGjRqFWCyGv/3tb5g6dSq2bdsGq5Wfc2+77TbMmTMHn332GQoKCnDTTTfhvPPOwy+/8PeNeDyOGTNmoKKiAsuXL0dNTQ2uvPJK6PV6PPbYYxmPpV1JgZKSEjAMg7o6pdFTXV0dKirU64orKiqy2t9oNMJoTF4UPzlvN66dPARGHZ9t0+v10HMcoipyYSPLodBmbtWCSa/Xt5oUMBsYkRQAgOs/2IgDT8zA28sPJu3L0BT0ej0GdS7E97dOSHo+V+hWoqyfFL7b8B5Sps0dikOv18NOGwE2hEDUm/ExEMyppnatg5yLHmvrLr5Ha4/pwE5OAEAwGhdff9aJN2PqCX/CQ1/vBFAFXdwIQMpm2mwFACR3bJOx9b9nNnDqdKApgOWAKncYepqBmg+WRcczFl2K+Cyci1XvPW5krGnHne1xLS1QKmpK7SaRFDByLAAadQd/Qv/+0oJZSDY4OQZNYOGO+0CFbICVl2vn+tiW2PnAvyWglA5PeGopDjwxA6xKq0ejjsnZOFpzrtrN2uaAjYEYKhOOu8moh1+jlrvQZoJe377cLssB3SpHAAkKmmDMB0YnffdPd3yEaac8iER8wewWH2d6rLI5rom/cCjKIsbRKDMWAEEXmkKNqu/FJqha2vO6L7HroGcoROMcPBEWTuI87qPUP7fQXAwEJZJLRyvPWYuBEev72+OYytG7zIZ9jSqEZQIixGvEy0UVn8OSiU1Ht8/c2pu00GxJCICCUWm+sXFAMwWEQs1pxxCMsqAQx0YHP9exXFzzNZke0x6lDnG+F2C3GMTXVjiVmWOjXtch96F00IM/HgDw4ZrD+Ps5vGni/8LY/j8hHotibcwN0BRG9Wy7nwDHcdhT78OC7XVYvqcJ6w+1aPp9lNiM6Ow0oXOhGZUFZpTajSi06OG0GFBkNaDQokeB2QCrkQHDsfjhh+8xffrE/G+cRx4dhB9++EHx9zvvvIOysjKsW7cOEyZMgNvtxptvvokPP/wQkyZNAgC8/fbbGDhwIFauXIkxY8Zg/vz52LZtG3788UeUl5dj2LBheOSRR/DXv/4VDz30EAyGzEyp23U1aTAYcMIJJ2DhwoU499xzAQAsy2LhwoW46aabVF8zduxYLFy4ELfeequ4bcGCBRg7dmzWn1/nDit6L9s4oEUl2aEDB5up4yfAfuX2pH7NPe6eo7ov3UHGQwaZWdP5J0gt4zo7zbhybHe8t+IgWogTsU1nAiJu+NL02l53sAV3fb4JD58zWDQum9qvHit4k2y8d9K/0K9zcs1vthA6JzT6woiznJgpMzJGsUc9FTcB4MdbFAMuGXcC3lixRHwPSzsHWAIoihIXiDOeX4ZzB6l7CviMPtw+pZ/Yhqw2WMqv1BJgYHIvRyyWmU6a9LSiLWYDkSPNqpmHLXhK3C54CjhhBBCEx+iDM8QvKql28DUVzBC316qfg4kdBwBAzxxdE69UhqaHW4JJpABNURhY6SAKGyXM7dhG9bmLh+GWjzeiW5EFncuSu3H4I14wtGSEt1pjDp3KdsN8Wt2EMBe4aFRXzNuqJJJnbziMYlMhEDyIpohb9XUs13F10TRNocRmRI07hAZvGF2sfIY4QNOIRwJgDEqzxMSS7UTj07+ePgAPfrMVF41M9nDINfqW2bBgm/L46mhKbFcpIBjjFUGeBLIlTkgBhm6fe6xW1yB5RxIHaDSDgy+DTjmuQBROpgECzWjQW1Lunwn0DI1yhwk1bslY0mKQ5gFbwpzA5NA8uK0QCKiV+5rT73wMQe7HkW4/+f9q2L77W3hpClaWRd9e01vtyXCoOYAv1lfjuy01IkEvwG7SYViXAgystKNvmQ19y2zoXWrNwniYQ5T4Vv1/94zIhefX/wqOle/yW/oewnOJiiqtJHYi3G5+zVJUxBsSr1u3DtFoFJMnTxb3GTBgALp164YVK1ZgzJgxWLFiBYYMGaIoJ5g2bRpuuOEGbN26FcOHD8/o+7V7BHT77bfjqquuwsiRI3HiiSfi3//+N/x+P66++moAwJVXXonOnTvj8ccfBwDccsstOOWUU/D0009jxowZ+Pjjj7F27Vq89tprWX92o19JClhBoyUprwTouOSbckfgn+cPxUWvrsSh5vT1eblya84GI7sXKv4e17sE7604KDq923RmIAJ40/TanvnycgDAZW+sErcFI/zibBwsGN6bZ87bOlkUWw2gSDam2R9RtNgS+s1zcel8cEZ1MDA0ZgytxJzNvJy7k7Pj2lLKEY5yqldjxGjEzaf1xc+7+eNV74kAyeXRMLXRIVsN8u4DDpMeVkP6a0QgBYp0NgBB7Dfo0LNgHwAdSm25DwqEMe6u8yU9x7KcBilwdBfcNqP2Is1DulEUWQ1iUMPQFB46+zh8ueGIuN+MoZUY1sXZrl0y+pTxmd+NVS40+JPnze/rViNIU4Aso8TFY6AY5XlipfhgcVykfcpyJvYvw1ezxqN3qRVDHuJl9fd++SvuGs2TK34Nh3+Wk9QlpWmk8blAqZ0nBeo8YQwol5Rvfn89HIYein3rvcrzOZFQu3Jsdwzq5FBtyZhr9C1XGvNdcEIX7G/0Y+3BFsX2AMv/zkcYCvvWvIJeo64HIFMKMO1DClQkkAJmPYNgNK5oo2endACi8AbTd8pxBaMwUX5RP3bRpH/mZJzFNoOCFNDJ7umJ17H+KNzvtSAQUJm0Kz6WMH/+fFgsmRNCqfwltje+A+iA42IGzF+wKKtxcByw001hYTWFXW5pHtBRHPoWcDiukENvO4cKSww0FQJidUA1cLAaSNactv27/H/CsfI9gGPnu/wWvofgudK1q5K0f/DBB/HQQw+lfF+WZXHrrbdi/PjxGDyYV2bV1tbCYDDA6XQq9pX77Wn58QnPZYp2j4QvuugiNDQ04IEHHkBtbS2GDRuGH374QRzsoUOHQMtag40bNw4ffvgh7rvvPvztb39D37598dVXX4kHJxs8+t02zJb1Q7aBAZDs4M8cJVKgssCMpXdN1FQHHC0s+supWL2/CTNHdFFsFwIwQSlg1/OLRV80tbQ0UTYJADGWMGH63GW4dQyNYqsBjb4I6jwhBSmwg2SS43FpgRtrHg+apvDYuUNgM+hw4aguOW3vmA10GqZyg4tmAQDKiEx+d70PPR0MGvXKbJxJX5DzMRVapEV8vTcMa4pgVoAQgney9gZIVm4/IRN+pFs0XtWWMfLnpND7u1uRRSTZmvwR1c5ee+qTCYSORCqlgDvImz7Js5wU+DaGI7sXioHYi5eOaO9hootTWhCPf+In2BNKYXcZk+VoAX8drI7OCVuJSifXAySgKArDujqTtgej/PXvi6u3U5OXD7w59fV2GZscgvdDvTcEg75cLGfz+2vhKOyh2DfKhRUHLFEpRlEURvUoQkegW5EyMDq+qxOekETgltmNqPeG4YtLJPI5217ElpF/AigKcfL76+j2uccKc6OAQZ0cWHewRbxPAYCdNgBcFN6Q+hz0xs/7sHxvE567eBjcgSgqdXwW1s5yMJtyM7cm/oaWhJaRb/9+FK5+h+8u09HtkVNB+P1/a20Jp06dCocjPZGZib/E/I+eADhgfMUwTD89c1+N5Xub8PSPu7H5ML9+oShgfO9inD+iM07tV5Lz8yQXXln/CzhWvgdw7HyX39L3EBQCVVVVijkkE5XArFmz8Ouvv2LZsmVp920PdMid56abbtIsF1i8eHHStgsuuAAXXHBBmz93fYIxl5XWQYsUsGQQ8BxNuDLow54r9CyxomdJcrAukALC4sBGstO+WDBpXzn6lduTpM+eiAsA4NSnbg+VLUrtJjT6IjjzP8uw+C+nokeJFfXeEHaRTHIkLmXWotFCMBSFAoseT54/NKfjyAT/nDkUd33Bmw9pSesdNr6sQk5w1B+cha4lX6KhoAoRss60kBZnuUQiQSKXu1YePBs13b9B57iS7eEo/u9CYwW6eBkcZrT7mucCxQn9vYttBvjDMTT5I2jyh+FQkbRnUh/dnki1kGvyRfD1xmrFNhdRD3RymoGDuSdWtFBgUR67G3q+jbdWvoMypgZVJeqt6by+6iRSgOM4EuB2bPZTz/DXup9VVyCxFJ+1PZfpgp6dU5vP5QKlJHitJyVjdlLn7vU3oDJh3ygbUTSf4Ljkrg4dBadFeY2Z9AzssuvqpL4luHpcT/zuxQWQF76Eg00wWkrAElKAoTOracwWiRnsB84chHNe/AWeUAzROIu1B1pgpQwA54cn5FJ9j0fnbAcA/GPOdkTiLAx60tYxWSDTanQrsmDzYZ4Mv/uMAehbrlR5nNxX6uBztNVMcgj3nl11XsTi7FEjzjsa2XpwaO0fi4WxPu4FaAqj+8zI6D2rXUE8Omcb5m7hM3wmPY1LTuyGP4zvia5FbS9nSYe2eGX9L+FY+R7AsfNdfgvfQ9jucDgyIhYF3HTTTfjuu++wdOlSdOkiJWUrKioQiUTgcrkUagG5315FRQVWr16teD/Bn0/Lk08Nx/TsflIfZZs8u8aihAGVkTS6vXBq/9K0+ySaKB0NCIsvbziGYCQOG8mgeOOpF6yJGZIKhwkeoi6w55gUkDuxT3/+Z7y4aA/OfUFydQ7FpQuU5XSgj+IVcOGorjh3WCcAQBRm1X2EI1do0Yu18P5oJ+yomYU+PplfhqV9soYVDj6Q0dGUQk0TiDsBAB4qgRQg/+sYBmPNSunUHwqS69LbisKEgMVu0ivIq0hMxb3xKEPwhxAgvzzWHGjG/V/9qnheIBHUiLqORImlHI3NZ4AK9NDcx+OrU9navkoBLcQ5fm7xc8lEML+dJwrN7eDHoYZSQmA1+fn50kpuv15fNbiQF9+9OR7735kGsCyao0oPBh979IgsZ4JDuElPY/keSYZ/wym9Ue4wIsYpz+ttG98FG/Fjq5E//jqmfe6x9gSSTV7u8PGaKlzy+krUtfDzgDeS7D3CSb3Y8PGaKgCAiSgFjDk8a+WKhutPSTaMlQfbQunO/wJ6l/JjibEcTvnX4qM7mP+H2LH7O/hoCnaWxYA+Z6Xd/+uNRzDt2aWYu6UWDE3h9+N64Oe7JuHBs47rEEIgjzzy6FhwHIebbroJX375JX766Sf07NlT8fwJJ5wAvV6PhQul9vM7d+7EoUOHRL+9sWPHYsuWLaivl9reLliwAA6HA4MGDcp4LMc0KVDvVdaSWhn1enEdlyzl60g8d3FmBhBHG3ajTjQ2O+IKwE6y0z42tYohxioDs0/+NAaL/fyi15HjDHdcVqcQiMTxr3k7US2r4/THpeA5xunBdJCBoxZGEglwgLkCAFAYKIJvz50AAGfdOJFQoSgqKWPHRJziY5tFSYDlCm9cNRIjujnx3jUnKtQ03hgvFfbSNOIRfgHNcRxiZIGtYxj0cCrLTyb1Tb8gyhbOhGy23aQT1QNN/oiqp8Dfph/dvshjehUrDOL+MrU/bp7UBwAwP8HQrbPTjJMJuXnZmG7oXmzBJSe2v7mcgK9nSeVXwrkYZrUDFm+gPmkbJ1JFHXuthVg+2PeptPVgWQ5xirj3w9kh4xHInfdXHkIoGkcFwwfR1S27MfvLS3GPzoOzqWp89t9JWGlS3o8C3NGTbhckkgI6RtFKrFepDcU2IxLL4K/Z9TbmLPiL+LeeaZ+a9MR6fItBBwcxRf2UBPlUnJSSRJJLh+R1/gI62XhiLqjiQdRaTOiXfo7+9qaT8OZVI/+nSAGzbG10xBVEKNq+6q9jDWv3fQ8AGME4wOi1r4FwLI6/fLYJt3y8Ed5wDCO6OfHdn0/CQ2cfp1AK5pFHHscWZs2ahffffx8ffvgh7HY7amtrUVtbi2CQX1sXFBTgmmuuwe23345FixZh3bp1uPrqqzF27FiMGcOriadOnYpBgwbhiiuuwKZNmzBv3jzcd999mDVrVkZlCwKOaVIg0dnfppMyGV056UZHc9RRJQUKzHoc3yX3NeG5BkVR6EGylUdcIdjMfEDr08jECYgSifl1J/fEa1ecgG5OE+pJq0hbjkmBQCT1WHwxyaWPApXDJV/rICgbXP5ueHT4Zzh08A5w0WKMod5GVfPZioW2Sa+8XKMRSWHitLcPKTC4cwFm3zge43qX4PwRXcTx+FmnuE8gyHsHbKvxiAEgTelQWaBkO61mFYfENkLP0IqgxW7UiV0Tnvx+By55bWXSa647uVfOx5ENGJrCk+cPxbr7JuONK0fi+lN6a2aALh/THTQ56GV2E5bcORGPn9dxpS7Hd3WKXSeafPx8GmS1ze1eW/kE74wlg/BnR1AC982QjA/8MVI+oHKVR+IsOIonC8odHaQUkC3sv91UjSJCCvx337d4KHJAfO5hNMGaQKRGcPQCMR1DK7LxJj2DK8Z2F/9maAoMTaHIqlx4RCkK7xz5Sfxb305Gg2oQ1EKiwSzLJwQ8sWTFhS+cfM/Yb+PbQTbmsFPJqf3L8MrlJ+CnO07R3GdIlwKcNrBc8/n/Bfx3RWvt636bWN/Ml6acUDhQc58WfwRXvLEan687DJoCbp3cF5/+aSwGVraPOWseeeTxv4OXX34Zbrcbp556KiorK8V/n3zyibjPs88+izPPPBMzZ87EhAkTUFFRgdmzZ4vPMwyD7777DgzDYOzYsbj88stx5ZVX4uGHH85qLMc0KeAORrH+kFSDa5W1FuptkEyRGO7oG/vI28YYGBq9S4+uVFgLJUIW1heGnQR5XpVMnByChHvG0E6YelwF3O4D4nOnjbkjp+NLzGrJccWY7igt7iP+7efMKLa2T51rpigj8vxGbxgWXSGEQuJ60rpRngQb0llJHDVFu4mPCws6te9AwY9128On43fDOyPGmaAj0Z7fz5MCLAtwZLx6RodRQy6HURYg2kgbtlxD3iWhosAkKgWOuIIKB3KA76jRno792aDYZsTkQeVgaEozE+QOHv2yoVIbPzYhoxqIaxOYy81GHNwzT7GtI5UC157cC7Mm8tJsT4SfQ/00lURUROKsOC6bsWM6jpx1vHSNGnQ07ISk3q5i2GgnUypDhj194FXtPr5UkPtLmPQ0LhnVDc9dPAzL/jpR3F6mcg7LzSgZpuOId0FVJZECpBNFLARElR443pDyGuuq34MaQsBeauuDXOL0wRXoVfq/owLIFNedLBG8/5i7/SiO5P8XOJbFhhjvIzG8x2TVfeo9Icx8ZTlWH2iG3ajDe38YjVsn9/vNeDfkkcdvHRzHqf77/e9/L+5jMpnw4osvorm5GX6/H7Nnz07yCujevTvmzp2LQCCAhoYGPPXUU9Dpsottj/lZ57yXlouPbbL69TGlkmSfAmDUHd1DYZB9/qYHp+LLWePx5Y3jUP4/1gaoWFavLQR5vjT5dmFhJtTEB4kDtJ7jYLfkNnt87wxtNv6MIRUoddhQXH0qKhsH4/JTZhz1AFFYSDd4wwpZZrNfIAWk8T1/8XCcMViaBPb5x6KLuxJdW7qj2JH7LLwaTHqGEC8ULCRwCQSbAAChWBxx4jHQpagATnsnhSLHZm2fDJi8S0LXQouCJJDj61nj8ebvR7XLGNoKLVJgQt/2UYBkgxIytjoPTwr446mzV4daDiRs6VhPgX7EwK05xP8fpShEgkqDxmiMBUvOVYOuY+ZYPUNjxhDeUrDZH4Fdo5zNGWchUFn39LwTH53+Hi476eYOGaMW5GU6Rh0DmqZwzrDO6FIoEe1lDiP0nPa94Lj+Z7bb+BJLF4Q5IEwI6VicJwXmcx7c8cZQxAPN4r4tfiUp0KvsQ/HxiZVj22O4/+9w74zMa1LzkLD/0FK4aApGlsOgfsnlcw3eMC59YxX2NfjRqcCEL24ch5P+B+b8PPLI47eJY54UkMNulDJcBkbyew7T7FEPDg0yVthsYOAw6TG8WyG6Fv5vGcsUk6xhoz8Mm4UP8lwMjfpVL2m+Jkbq/AVH5VCY70Rgagftfp8yO1674gT1ccQ5VDhMOOA+HbsaLle0fTtaELLakTiLBq9U7tJMOjzIT0sdQ+Ply+Xfjcb26luwrfYGUeLdERACBMGZ209IgWDQiyoDP2ADyYIWM1LJjqWdfA/ksmWHWa/acQDgpfCplCRHE92Lk5VBH143GmN7dwzZkwqCUkAgBSJcahVTVWOD4m9OJAU6Zo4VzCcbgtK550vwOmj0RURSQM90nFqoVEYCHvaoy7BdDC12FSmxlWJw+fCjfn9ymqVjlFjGJKDUZoQpsfcswQB3DwzveVy7jA3A/7V35+FRVOn+wL9VvabT3dn3hLCGfQ0SEAGVAAIDyuDICMoysikoMzgOMi4Iv5kBl4srjnMdwWeuiMtc0dHL4CDggiIIJuwEwr4kBALZt+6u8/ujuqu7k3QIoauql/fzPD52V1foc05Xd9V565z34H8eykG7WBP+56FBAIDUaDHgcv6aOCrAJrjPo/8xGfHjvrXS82uNVva5pnePJNAreGyQ0JNfKC433YszQG/wnnZV02DHzHW7UVhShZQoIzbMHSwFNAkhRA3hFRQwupPM/X2H+05BVQCsRth4aTWXF3/VF33So/DXafKvS94arnKWVNTDHOkOrEw69IbPv7HZXSMFeBzb/x5e2boIgDxBAQAw++ggp0YbkWR13527q1frl+mQi0GrkTrZrgtYAKhuEEcNNF65AQAWj8oC4L1qha+OsBxc2ciNzrkC1c6RH+fPfiHto+HEL9XMfg+DZ0C2xgqel+eLlmBxf3ci9RqvqTjBIlKv8Rot9LvcLNzaKV71ziDg/s67l3htuUyV9Y0zvCubucMVFCi4VIMIwXuKi8ucf+yRggJaBTt+rulXV6rqcYz3Pe2qwfm5m42B0Unwnj7Q/Pcr0WpAdHnTrPoAcFuq/1ce8TS0czy+/cMdGNZF/E1sHPxrcHgP2S+pKpIeu4LD2ZnilEKTw33+iIvNkqW8wWiic/pLMOQ/ChQ/l+QBAAZYvPPrCALD7z7Mx6GLFYiL1OP9OYObDQwTQoiSwiIo4FpyyOKR6KxGcJ/YygNg7lauj+RCHeIj8a+Ft2Fs78YrWasj3nlXdmPeBdz1xl5pe4VG02TerkuDx/SBJ3f/Bdt04n7+XO7JU5dEi7RKgqfOiRbEeAwtVzO5pCfXndgLZTVNXmuuhR69szP2LRuNtx7IxqgeSVhwRydFO8KuDoLBOTWg2rn2t52527aiThw9cFufGfh5ej7+PvUb2crjaj9AHGXjukvo6e/TB8r2/v7AcZzX8om+goRqEFoYEt6cqsZBAVeiQYUCHJ5TrkzSaBbvoMDZqzUQoO5IgWk9vPMEpPHukQ0NzvHwkRGB0QEzeyzZ62tUUoLZgMOXZ8JY3HTudIxF2fOXqdESw/WCd4errNa9pOIVZwLN7MwYrJ+dA73z52OENhY9uoyTt6BBZNKANADAvvPl+P1H+eoWJkjk1YmryfRPv81r++vbCvHloUvQa3j89/Rs1ZebJYQQIISDAh/OHSw9rnHedbV6jBSosscgvkJc2qtDufpLAo7qkYTX7u+PrS1kJg4Enp2V81e9l3Kqr25ujXLPnAI8jnt0XuUa8J5gMWD3UyNxeMUYaZvrbny3ZPedN10ABIMA8Q4b4D1SwKW5kQIcxyEqQgejToO3pw/EE2OUXWLPNZRYJ4jtV+Nc+7vB5j4e+veaKj3W8BpoefmmNyR4jP6INGgxvEsC5g3viIdv74RjfxqL06vGI7dHYGf0bizeHDi5RK437N9QNAZZV7pIz2sayr1eZwqPFEj0OB4inKNZqmqvNtnP4ayWsiMFnNOvqhpwX/aj0va+ghbv/+LDJvubIgMjKOC5LJ3Fx6ikBIsREAy4fK1pUCDWIk+SUV/uH9TO63ldoxUzrta7c0xcrqxHJGqQyV3E0M7xm5ipxwAALgpJREFUqHUuVfnrrF/JX9AgEuOxJO7mQ82f64nblSsFOKsBOMbQt9svpe17z1zFq1uPAQD+8sveyM6M9fVPEEKIogKjVySDHqlWKXmga3igVus+qVU6YnDqwiOoPrUQ5fUPqlJGTxzHYWLfVHQK8MzEcS10Vioqi5pscwgMrmmmOg2PHoK7c2iT8c6hxaiDSa9F34xoAEBX51y9Lh7rPzfX4VaD6053c0GBACmiF9ca5RrnZ1nlzBFR1yAu95ViB8wyLD/oi2fW8widmARt6bjuWHJXN68h+cEkkNalHpGV0OLrV8ruwJmaKdLzKltls/splVMAEKdfAEAEEz//6kaJBgFAcC5JqGRQwHOkgEbj/i2sYwIslqYriMRZ1Z/iBLinPQDiEoTN8XXMWuwcOrZXNtjdONlobaPkmFV2dwCzqt6Owel/xpqix1By+ltUMfG4UPI3LBjEmgJn9FIwyCsQlwvrImhgjRKDVJV1Niz6IB8CA37ZPw33ZqerWURCCPESnFfMrcBxnJQp3xUUSM5wD+GyMxMADkJdOgxaOtm1VuMl/CbE/Vp6XFlV3GR/1ygBQJw+wDyWL3ywvfxDM5+8qxtu75qAP93TC4B3UMOzbGpyXUw3t/xcRV3TNbTV5pqvyznn3lY7O4ENzjXAIwVlf1aiPeYPK5lbQU6e2d7V1ppRFgMyO6FvqdiprW60FrzD2clSMijgGtGkF8Q73FX15Y32YDinF49TXsFyub7rpdX1EDyS8tVBwA8nK6QcCACQVGMOmOP5jm7inX6tj4AA0PyShACQZn8VWUnqZlSvErxHXNQ63EldGxwMuy0OVGp4/G3n/0OVM1hkNrUcDAs30ZGBcSwGi7yLPwIA+pvcwb4XvyzA+Wu1yIiNwPK75Uu8SQghbRGyQQEA0vxxV1AgPjIR/527FtUnH4PnbO1AGUYeDBrPdU43T0Gas29dWXO5yf7eQQEe9c75yWv7/g7T7lglX0GdhnSKw7uzBqGLc6SAhufw+9FZ+OWANPRMbXlpNaW0dFd429HAG6YpdVgd4v9rbGIuhDrn/3UKdrIAICPWnVncV5LJYPDQbe5kVO1iA2vVkbt6tnzH+seTpeiZLM4br2XeI15cnV++hQ6lv7nubOuco1mqG+U50MAdbDt+KU+xcsU5c7LYHMwrCFgLhhlrd8PsXpUUlqvqrzrg0jM1Cv+cPwRfP3G7z308f8c0HnkoPp53hyprrg9oFy2WhefggPec7VrB3fY2j6VgD9aWoMqV5DFS2SkPgc5i0LYYFCLe8qrOAgD6J4v5bA6cL8f//CiuOPL85D4+p+EQQohagvcKuhViGwUFAKCDuTeEeu/lqSgo0HqNEziVVNTDAi0AOyo9kje52B0M6ZqzuC3qQ1QXJ6DeOb9Yr1Mvsc7CO7tcfycFeS731dhT4wJvfWgps7cglrvaLnYCa+prAC1g4JRN4JgaHYE1UwcgQs/7HNocDJ4a1x2/viUDGbGmgPtNim0UDDQKDHUebf33Gbdg774vgXKgFuLv7bFLlbA5BDicHUQla+QaEcQJ3qNZXAwad6cwp9cDipVLr+URb9bjSlUDTpW6R1TUOVdCMAruNu2RGqNYuVpjYPuW5z5HGtznhvTqaJwxlyPept4lxlsPZOOPGw/gwSHtsfxfh+AZsq5l7qBQhP2Y9PiwRoDrhoElMrjykMiN4zhpeWHSspqaUhzhbAA4DMi6G4LA8PRnB8EYcHe/VNzaSd2RM4QQ0pyQDgo0nj4AiHO6GtMH2AV4MLlcWQ8Lr4MYFGiazKu81obeKW/g/yw8yjbPQq0GAHgYdIF1J1RN1gjfdwwGdwy8JESuDitziB0vV1Cg1iYGBfSc8j8r4/sExuocN4PnOWlES6DxnE9s1PEwMoY6Z+dp82+HoVuyFadPJALlQA1vh0NgGP3ytwCAsRnO5TUVDNhI05zs4nerylbl9boG7uHjqXFdFSsXAPROi8L2gss4dNE9esHVlia7HjCI56vMuMBIMtgWxy7OR2bc5zh/bZRqZUi0GvH3GbcAAJKjjF5BgRq7+zqACU1zYHCMIcJEHbfG+mVEI/9cmdrFCHgHCzbCwXFIcjCkpGTj0/yL2HeuDBaDFk+N66528QghpFkh3RuWpg/UuIMCFc0EBXTa4L27qLYrVfUwa8TOYWV902ReF8pqscMiHmbf63lcdXYo9frA7PyoofGa2p4CZfhwc5ggZnmvcc7PrXWuPmCUcaUBog7PxG3ZmTEw17rvYndLFqfhZCaIS5bV8gIKS9yd8Op68a4szyl3unEN0xccYrldU1wAcYlaz6CAVuEApWtqyMUy9zQL1xKEBrt7WUI9a3quChYNjhgcL5mOWltgBOsaT9Gq9hgpwDmaBgXMDOD5wFiyNpAYdSF9yeg3+8/tAAD008fAJjCs3iKORpl/eyev1VEIISSQhPQvvOtu0aUKd6bhitqmiduMWjr534iuHnczr1Y3wKIRT3KVjdcnB1Ba3QBTM0MOM9KGyFfAIBNISeVuhF0QOzDVTAy62QRnUEATnPUhvnnmEkmyGnH84sOIL+uAyIvzpO3xVjGTdhUPjHnlW4+/dk4fUDDAZY3QiolNnaNZquzuDjhjwADTd9JzjYKrDwDidBdADArkRGYCAOIrxZwNGo9ErJVcddM/DnCu1V4CTUKjVXNqOPc5iWONk1ACkSxwg7FqCrRpTYFqf/lxAEDvmG74eM95nL1ag3izHrOGtle3YIQQ0oKQ/oV3JSA7fqkKPxReQe7qb/CvfReb7GfUU1DgRvzjoUEY31u8A3S1ugFWZ36AyoaqJvteq25Agq3pBZZOFzhLrqmtpZECgSpCp4FdEL9f1YIYaBOcwQEDH3z1IS3rEO/OAfLjiVLYHdE4VTQPZTWdpO1W55J6VTwHDTzucju//kadcr+zHMehV1oUBFfgyiPbvIMx/JS+12tfJbmCAluPlOC/JqzHM7csQW3VowAAgXMnvevSaY6i5fKHl+/ri1E9kvCne3qhX0Y03p+do3aRADQzUsAzKICmQQGzgqNagslI5yoUJj21jy9MEHDAJh5TPdNuw5rthQCABXd0bpKTiRBCAklI/7J3SRTvaF8oq8WC939GYUkVNuZdAADkdk/E4lFZ6J5ixe9HKzunNNglWY1YNkFMgHetpgFmnRkAUGlvemerqt6ucC764NM4KDCut3jX8IO5g9UoTqt89fgI2FxBASZ2ZATnnU0LTQ0JOb3T3PPbZw/rKD2us7nvbFudIwUEjoOF95xKJHbADBplg6/9M2LgcAYFqgT3FDKHysnSXNMHqurtcNgjcG+3aSitFDsLtXB3XlOjAivRYGt0TDDj7ekD8cDgTHy6YChu7RwY8/LjG40UqOY5MMG1VGbTEW4CC4zlagPNA4Mz8fzk3vjnw7eqXZSAVXQpD1c0HLSM4VzDQFwoq0W8WY/7B7VTu2iEENKikA5bpjnvyHgmGnSJizTgsZFd8NjIwMpEHyxc+RoEBhg0Yiew0l7TZL96mwMCGDyXgJwW3VuRMgYLU6ORKqvv64cVd9ubXMgGktQoI2xMDAZVQ4BDYGjQiJ9/oomW8go1HMdh37LRuFxZh86JFqz44nCTfQz6SBgEhnqeg0V7FWUN4nHgWr9e6eBgvEWPBkEc4VDtMT9/7fenFC6JN88Ay9HiCmzYfQ42hzNQwc+FwfECYsu6eu1Hbk6cWY+kq11xKbYAAGDnONRXX4bBnIh6R9ORAidp8GCztBoeU25ph4qKpoEUItpfuAkAkMW0eGeXmHx5+pD2io6UIoSQtgjpkQLWCC1ifMzXtkaEdDxEdjoND6tzTXgt7xwp4DFE16XW5oDHKluIZMC8O15QpIzBovHwZYOWD+iAACCWWacX72TWgKG0qh51WjH4lmzNULNoRCZRETp0Tmx5FEiUs29r5ssAALv+OBLpMc7keQoP04+PNKDB4QoKuIflv7C5QNFyNMbzHIZ1Ee+gX7hWi889prRFRmThyrE/4VjJzIBOMhps4s0GFF6aiapjT0nbtm3/I2ptDvAacYRTusPd3sP1CYqXkYSGA8V7AACdtIk4eKECRh2PBwZnqlwqQgi5vpAOCnAch+zM5pd0sxpp3vPNco0W0PDiHa1Kj7tx/7v3PF76sgA1DQ44nPM3X+oxB/+c9AVinMOMSfOCpTNgMMYBEIfibj5YhCqN2PFKjKLPN9RpfCwvaOXEu2ER2jLMG94RSVYjmHP6AKfwWIE4sx71ghiwrEJgra/uGsV2wWMFgtm3dcBtneMA0B1FfxMTZXJgDndQa21pHhwCg0MrjnAabu0svfb8Pf9UuogkRByoPg8AcNSKgYBfDkj3Wr2FEEICVcjfLk+wNP9j3NLa8KR1oiN0OAOAca6ggNgptDsEPP7xPgBigidrkrh/amQK0qMoYh4qzCbxbpqd48ALlah3hhjNEc0H4kjoiIrQNTstq5AX52InWX7End2ec251BQWUFWc2oNbZCfRMLBcIXEGBH06USttmD+uIOLMeAgNGZNGdan/y7JQl1xlQbKxHZKURggDYeTFRagdrJj4Z+kcY9Wb6DSNtYrPV4DCrBzgOey5mAQCm5VAuAUJIcAjpkQIAfGZ7tRhDPh4iO9dIgat1zukDzuW0GhzuJE2XK+vhGpWp1QT2kHhyYyyR7o7L4TOnUe/KMm+gudCh7i+TxLwgj97ZudnXy60MOR3FkSRMpf54XKQeNYIVAFDNcWB223X+QjlpzikVu09dlbZFReig0/CYP6ITuqdY1SpaSDJ4LDscWy6umGHX1KHviv9A4MTzlZ7XoUvKQGTEdVOljCT4HTvxJep5DlaHgJN1WeidFoWeqXQ+JIQEh5APCvgY5UrTB/ygS6IYDFi/R1yKsIoDwBjsjbJ7S0EBLQUFfBnfJ0XtItywBIuYWA4Avis4jjrnly3CGK1iqYgS7uqVjL1P52LxqCyv7Q/H3QIAyNCZPLaqN32gxiFekDOOQ21NCVZvOSaWxRmpWNHlfkXL5OIaKeDJqAv503FAqLc7c6FoxSCR4BxFotPSEG9ycw6c2QYAaNdgAIMWU26h/DqEkOAR8lchvI+ogJlGCty09s61y6sc0QCAWp6HrbYMdkejoIDz/xoNXXT5Yg7C9YvjzHpEOIMCHfSHYXfmQogwRKtYKqKUOLOhSf6LdhZxqOwmxzXs/XoFwJg0m1/pXBkmvRYarQW8MwBQVX0Zr2097rXPsKxfKlomF9dIAZcRWQlBk0sk2FlMYketQiOOEGCuoABP5ydycw6Uiquy6GriYdDymNgvVeUSEUJI64V8UCDd447MqB5J0mOzIfg6YYEmNUps21rBPdS1suoi7A7vNZ5dIwV0NH3Ap9E9xWMzUh88ScbiIvUwMvHDzcvYLW03mmg+briKj3LfGZt55mP89p1+cAjinG01urxxZgNMzsDV1Ypi51YG5uyAc5w637dkq9HruU4T8qfigMEbxLw217QceLiWzAW0Gho9SG7O/rrLAIDK2s4Y2T2RRqQSQoJKyF+JTLmlHaYMzMCb0wbg1k5x0nbKKXDzkqNcF7Za6Y5xZVUxymq95+7and0Brdb7Qpi4jeyehH/8ZhC2Pn672kVptdhIPQxC066eXhepQmlIIEiI7uT1fKtOwMU6ZzI9Fe6Ex5kNiHAeo6WVJWIx4A5a8rw6QQGthvdaLlevpVECcls7cyD6t4vG0nvGAABsHIcEvkhaHYdy3pCbUVFxHqc14rF0uiYbv+hDowQIIcEl5HvGei2P5+/tAwD4Yr/HetA0UuCmpUS5O/mRAlDLA8eLzmD253XS9rhIPezO612aPtCy4UGWcTzObIBe4AF4jwyhYdDhKyG+aZK28/YqQAPwKsSg4yP1sDWIx2PZ8S+gxTSYDBppSgPPqRcXv1bjDp5q+ZCPz6vuzm5JuLObc0SWIKCa5zEmZQPyXDlveLqrS9ru6MnNAIBkm4ALmjTc0TVR5RIRQsiNCasrEc8pAzR94OZFeSzraHTOEdh98pTXPruXjoDAuUYKNE2uRYJXXKQeOsH7e/Rc5kSVSkMCgSUyqcm2U87s7gYV7sTGmfUwCOJp7smGI7jb/B56Gw9Kr6s1fQAAsjNjpMcOtZZoCFPVziDMRmupNH1AQ9MHyE04UiROoUuoMyG3exIigmgqICGEAGEWFPBcGsagDauqy4LjOAzuKM4fNwjiCVCvqZRe75ZsgeBwr2VOqw+EljizHppGQYGkRsPHSXhpbpRIgzPZq0GF73+c2eAVuPpPxlHsT10vPed59YLDfdOjpccVtYGzXGK4oSVziT8cKS8EAGjrEjGud7LKpSGEkBsXVj3jBIsBmx4bhu2/v52GOPvJ29MHYvGoLOnC2+ZwBwU2zBkMm71Wek4XXaHFbNBCI3jfXRvcZ7pKpSGB4lfW7s1uN2iUzykSF6mH1uH7DjCv4kiBRSO7SI/LKSigqD46dzLUCo1rSUI6P5G2O2wrAwBU1XfGbV2CayogIYQAYRYUAIAeqVZ0iKdEaP5iMepwS/tY6cK7wS4GBe4flIGYSD3snkEBmj4QUjiOg5ZzX0gPsVmgVfHOKwkMS+9ej3eGPt9ku16FRKNxZj14wXcuE07F4zXKI9FgQXFlC3sSf3th5OvS40rnyg9aWpKQtJHNVorzzvhibMIdND2VEBKUwi4oQPwvLTpCuvCuE6oBAFlX1uDA/y2Ew14v7aehkQIhR8+7O3p6ji6ECKDjdRjUeRy+/eV/vLYbVQgKJlqMQEtBARUTDQLA3OEdAQDPTuihajnCTUJslybbauuuqlASEgrKa/cBEJMM3tqjr8qlIYSQtqGreHLTkqOMgCB2Dmsc1dCiAW9rd2D1FR7/tcuZxIkxaLSUyCnURGjco270lL2beIixpKCrg0OBc3i2XmdSvAyp0RFggu9gpMEY5fM1JSwd2w0zbm2P1CharlVJel3TAFVcEnXmSNsU1xcCOiChPgJ3dqNVBwghwYlGCpCbptfy0HFi57DeUYalMcukIZlfXskDADgoh0NIMhus0mMDDb8ljSQYoqXHVnOK4u+fEmWEIDTf4d54+xuq55bhOA5p0RGqlyMcJcI7iJka21WlkpBgd9pRAgCwOFLRnqanEkKCFAUFiF9EaMXO4fcmB15Jdi+vdc5eBQC4nbeoUi4ir2iTx4oecKhYEhKIFg37MwAgk4/AwF4PKP7+Rp0Gkfrmf3sMPraT8PD6kBVezzUaCmqStjmrEXMnpUb3V7kkhBDSdjR9gPhFpDGm2e0X4QDAw8DR0PJQlBDTASgSH+c5LqlbGBJwurUbhgMzDqhahhhT09+m++L6Iz2ZLuDDWXxcltdzNZenJMGrsuICLurEkT79ssapXBpCCGk7GilA/CLKFN/s9nLnNAIDzTcPSYO6j5Ie3xHbT72CEOKDKcJ7ebAcZsQzv/gHDdkPc/Gxnb2eU1CAtMXeo18AAJJsAob1pkAjIaRt1qxZg/bt28NoNCInJwe7d+9WvAwUFCB+EWtpObmOUUNBgVDULd19t61dTKaKJSGkeTHR3pn96xlNcyEAz/FIFdzPOZ4uh8iN23P6BwBAqi0SsZE0BYUQcuM+/PBDLF68GMuWLcPPP/+Mvn37YsyYMSgpKVG0HHQWJH6RHJPW4ut6SkIXkjiOwyMZd6GnLgajcx5XuziENJEQ383r+VHU+9iThJsEjs5L5OacrD4FAEjVZahcEkJIsFq9ejXmzJmDWbNmoUePHnjrrbdgMpmwdu1aRctB4+WIX3TO6AKc8P26gZI4hayH73wRD6tdCEJ8SLTGwHZmJnSZ7wIA6uiOMHGK1hgAoUHtYpAAZLPZYLPZrrvfWb4SAIcuiQNatX8gc5Wf6hE4QqUu4VQP12sVFRVe2w0GAwyGpkskNzQ0YO/evVi6dKm0jed55ObmYufOnf4odqvJFhS4evUqHn30UXz++efgeR6TJ0/Gq6++CrPZ7PNvbr/9dnzzzTde2+bNm4e33npLrmISP+md0anF1w0a32uFE0KIXEZ2S0RdTTd0qcrGafNeLO8+S+0ikQARpTEBQqXaxSAB6D//+Q9MJlOL+9Q21IJzLrakr0nEpk2bFCiZ/LZs2aJ2EfwiVOoBhE5dwqEeNTU1AICMDO/RQ8uWLcNzzz3XZP8rV67A4XAgKSnJa3tSUhKOHj1684W9AbIFBaZNm4aioiJs2bIFNpsNs2bNwty5c/H++++3+Hdz5szBihXupYKu96NMAgPP87jHmIZP6y4AAAbp47G74Yr0Oo0UIISogec5nF41HsB42Bw26Ci/CXGK0psBG62aQpoaPXo0rFbrdfebaJuI//3Xx5g88VfQ6YL7t8Vms2HLli0YNWpUUNclVOoBhE5dwqkerhEC586d8/oNaW6UQKCRJShw5MgRbN68GT/99BMGDhwIAHj99dcxbtw4vPTSS0hNTfX5tyaTCcnJyXIUi8gsMSIBcAYFeuljvYIC5yvOqlUsQggBAAoIEC9GTYTaRSABSqfTtbrzYo2IvqH9A12o1CVU6gGETl3CoR6u7VartVWBxfj4eGg0Gly65B2gvnTpkuL9YVmCAjt37kR0dLQUEACA3Nxc8DyPXbt2YdKkST7/dv369XjvvfeQnJyMCRMm4JlnnmlxtEB9fT3q692Jo1wRmtbOB2urUJkf40+JphTgWj4AgIfG67V2yQOv21bUpvKgdvU/alN5ULv6H7Wpb7kDHsHbXz+CHlrrDbVPuLdpuNabEEL8Ta/XIzs7G1u3bsU999wDABAEAVu3bsXChQsVLYssQYHi4mIkJnovUafVahEbG4vi4mKffzd16lRkZmYiNTUV+/fvx5IlS1BQUIBPPvnE59+sXLkSy5cvb7K9NfPB/CFU5sf4w9WKGulx+bUy9NDE4DB/DQBgKuvV6vl21KbyoHb1P2pTeVC7+h+1afMWWxbDzJvbNB88XNvUNWeWEELIzVu8eDFmzJiBgQMHYtCgQXjllVdQXV2NWbOUzYF0Q0GBJ598Es8//3yL+xw5cqTNhZk7d670uHfv3khJScHIkSNx4sQJdOrUfCK7pUuXYvHixdLziooKZGRktHo+WFuFyvwYfzp8ksebP4qJIhPjE/HkuLXYkPcG9LwGk/pNue7fU5vKg9rV/6hN5UHt6n/Upv4X7m3aOKs2IYSQtpsyZQouX76MZ599FsXFxejXrx82b97cJPmg3G4oKPD4449j5syZLe7TsWNHJCcno6SkxGu73W7H1atXb2h+RE5ODgCgsLDQZ1DA1xIPSs1bCZX5Mf6Q7LEeOM+JQ2Jm5Cxu4S+aR20qD2pX/6M2lQe1q/9Rm/pfuLZpONaZEELktHDhQsWnCzR2Q0GBhIQEJCQkXHe/IUOGoKysDHv37kV2djYAYNu2bRAEQerot0Z+fj4AICUl5UaKSVQSF91eelxe5XuaCCGEEEIIIYSQwCBLToHu3bvjrrvuwpw5c/DWW2/BZrNh4cKF+PWvfy2tPHDhwgWMHDkS//jHPzBo0CCcOHEC77//PsaNG4e4uDjs378fv/vd7zB8+HD06dOn1e/NmLhorNzD22w2G2pqalBRUUFRcw+OWgcAYA9XdsOfAbWpPKhd/Y/aVB7Urv5Hbep/4d6mrnO763orlNzoNWQoHQuhUpdQqQcQOnUJp3oE8++jLEEBQFxFYOHChRg5ciR4nsfkyZPx2muvSa/bbDYUFBRICWv0ej2++uorKblCRkYGJk+ejKeffvqG3reyshIAkJGR4b/KkBt2BMBH86PULgYhhBBCZFBZWYmoqNA6z9M1JCHEH4Lx95FjwRjKaIEgCLh48SIsFgs4jpPtfVwJDc+dOydrQsNwQm0qD2pX/6M2lQe1q/9Rm/pfuLcpYwyVlZVITU0Fz/NqF8evbvQaMpSOhVCpS6jUAwiduoRTPYL591G2kQJq4Xke6enpir2f1WoN6gM8EFGbyoPa1f+oTeVB7ep/1Kb+F85tGmx3wFqrrdeQoXQshEpdQqUeQOjUJVzqEay/j8EVwiCEEEIIIYQQQojfUFCAEEIIIYQQQggJUxQUaCODwYBly5bBYDCoXZSQQW0qD2pX/6M2lQe1q/9Rm/oftSlxCaVjIVTqEir1AEKnLlSP4BByiQYJIYQQQgghhBDSOjRSgBBCCCGEEEIICVMUFCCEEEIIIYQQQsIUBQUIIYQQQgghhJAwRUEBQgghhBBCCCEkTFFQgBBCCCGEEEIICVNhGxRYuXIlbrnlFlgsFiQmJuKee+5BQUGB1z51dXVYsGAB4uLiYDabMXnyZFy6dMlrn7Nnz2L8+PEwmUxITEzEE088AbvdLr3+ySefYNSoUUhISIDVasWQIUPw5ZdfKlJHNSjVrp6+//57aLVa9OvXT65qqUrJNq2vr8dTTz2FzMxMGAwGtG/fHmvXrpW9jkpTsk3Xr1+Pvn37wmQyISUlBb/5zW9QWloqex3V4K92feyxx5CdnQ2DweDze71//34MGzYMRqMRGRkZeOGFF+SqlqqUatOvv/4ad999N1JSUhAZGYl+/fph/fr1clZNVUoeqy6FhYWwWCyIjo72c22IWtasWYP27dvDaDQiJycHu3fvVrtIktYc47fffjs4jvP6b/78+V773Mj1mFyee+65JuXs1q2b9Lq/ryvl1L59+yZ14TgOCxYsABC4n8m3336LCRMmIDU1FRzH4dNPP/V6nTGGZ599FikpKYiIiEBubi6OHz/utc/Vq1cxbdo0WK1WREdH46GHHkJVVZXXPnKf21uqh81mw5IlS9C7d29ERkYiNTUV06dPx8WLF73+jeY+w1WrVilaD1mwMDVmzBi2bt06dvDgQZafn8/GjRvH2rVrx6qqqqR95s+fzzIyMtjWrVvZnj172ODBg9mtt94qvW6321mvXr1Ybm4uy8vLY5s2bWLx8fFs6dKl0j6LFi1izz//PNu9ezc7duwYW7p0KdPpdOznn39WtL5KUapdXa5du8Y6duzIRo8ezfr27atEFRWnZJtOnDiR5eTksC1btrBTp06xH374ge3YsUOxuipFqTbdsWMH43mevfrqq+zkyZPsu+++Yz179mSTJk1StL5K8Ue7MsbYo48+yt544w324IMPNvu9Li8vZ0lJSWzatGns4MGDbMOGDSwiIoL97W9/k7uKilOqTf/85z+zp59+mn3//fessLCQvfLKK4znefb555/LXUVVKNWuLg0NDWzgwIFs7NixLCoqSqZaESV98MEHTK/Xs7Vr17JDhw6xOXPmsOjoaHbp0iW1i8YYa90xPmLECDZnzhxWVFQk/VdeXi69fiPXY3JatmwZ69mzp1c5L1++LL3uz+tKuZWUlHjVY8uWLQwA2759O2MscD+TTZs2saeeeop98sknDADbuHGj1+urVq1iUVFR7NNPP2X79u1jEydOZB06dGC1tbXSPnfddRfr27cv+/HHH9l3333HOnfuzO6//37pdSXO7S3Vo6ysjOXm5rIPP/yQHT16lO3cuZMNGjSIZWdne/0bmZmZbMWKFV6fkef3KlivUcI2KNBYSUkJA8C++eYbxph4YOh0Ovbxxx9L+xw5coQBYDt37mSMiQcWz/OsuLhY2uevf/0rs1qtrL6+3ud79ejRgy1fvlymmgQWudt1ypQp7Omnn2bLli0L2aBAY3K16b///W8WFRXFSktLFaxNYJCrTV988UXWsWNHr/d67bXXWFpamtxVCghtaVdPvr7Xb775JouJifH6PViyZAnr2rWr/ysRYORq0+aMGzeOzZo1yy/lDnRyt+sf/vAH9sADD7B169ZRUCBEDBo0iC1YsEB67nA4WGpqKlu5cqWKpfKt8THOmNgBXbRokc+/aet1rr+19P2S83pdCYsWLWKdOnVigiAwxoLjM2ncmRYEgSUnJ7MXX3xR2lZWVsYMBgPbsGEDY4yxw4cPMwDsp59+kvb597//zTiOYxcuXGCMKX9uby640dju3bsZAHbmzBlpW2ZmJnv55Zd9/k2wXqOE7fSBxsrLywEAsbGxAIC9e/fCZrMhNzdX2qdbt25o164ddu7cCQDYuXMnevfujaSkJGmfMWPGoKKiAocOHWr2fQRBQGVlpfQ+oU7Odl23bh1OnjyJZcuWKVGVgCFXm/7rX//CwIED8cILLyAtLQ1ZWVn4/e9/j9raWqWqphq52nTIkCE4d+4cNm3aBMYYLl26hH/+858YN26cUlVTVVvatTV27tyJ4cOHQ6/XS9vGjBmDgoICXLt2zU+lD0xytamv96Jz1c2367Zt2/Dxxx9jzZo1/iswUVVDQwP27t3rdXzwPI/c3Nyb/t7JpfEx7rJ+/XrEx8ejV69eWLp0KWpqaqTX2nKdK5fjx48jNTUVHTt2xLRp03D27FkA8l2vK6GhoQHvvfcefvOb34DjOGl7sHwmLqdOnUJxcbHXZxAVFYWcnByvzyA6OhoDBw6U9snNzQXP89i1a5e0T6Cd28vLy8FxXJNpX6tWrUJcXBz69++PF1980Wv6RiDWozW0ahcgEAiCgN/+9rcYOnQoevXqBQAoLi6GXq9vchAkJSWhuLhY2sfzS+l63fVac1566SVUVVXhvvvu83MtAo+c7Xr8+HE8+eST+O6776DVhs9hLGebnjx5Ejt27IDRaMTGjRtx5co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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ps.plots.compare([ml, ml_hp, ml_ls], figsize=(12.0, 8.0));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fit metrics" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PestGLMPestHpLeastSquares
ValueValueValue
Statistic
rmse0.2523510.1274350.127442
sse19.1043354.8719244.872432
mae0.2171380.1033600.103327
nse0.5547770.8864610.886449
evp55.51134688.64607588.644891
rsq0.5547770.8864610.886449
kge0.4882140.9172820.917220
bic-809.048810-1218.976723-1218.945455
aic-820.160158-1230.088070-1230.056802
aicc-820.079077-1230.006989-1229.975721
\n", "
" ], "text/plain": [ " PestGLM PestHp LeastSquares\n", " Value Value Value\n", "Statistic \n", "rmse 0.252351 0.127435 0.127442\n", "sse 19.104335 4.871924 4.872432\n", "mae 0.217138 0.103360 0.103327\n", "nse 0.554777 0.886461 0.886449\n", "evp 55.511346 88.646075 88.644891\n", "rsq 0.554777 0.886461 0.886449\n", "kge 0.488214 0.917282 0.917220\n", "bic -809.048810 -1218.976723 -1218.945455\n", "aic -820.160158 -1230.088070 -1230.056802\n", "aicc -820.079077 -1230.006989 -1229.975721" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat(\n", " [ml.stats.summary(), ml_hp.stats.summary(), ml_ls.stats.summary()],\n", " axis=1,\n", " keys=[ml.name, ml_hp.name, ml_ls.name],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameters (and stderr)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PestGLMPestHpLeastSquares
optimalstderroptimalstderroptimalstderr
pex_A216.986076.070757855.763330NaN850.70656739.041745
pex_a55.842824.105269178.369950NaN177.0473889.157405
constant_d27.81030.37241027.506585NaN27.5088920.019103
\n", "
" ], "text/plain": [ " PestGLM PestHp LeastSquares \n", " optimal stderr optimal stderr optimal stderr\n", "pex_A 216.9860 76.070757 855.763330 NaN 850.706567 39.041745\n", "pex_a 55.8428 24.105269 178.369950 NaN 177.047388 9.157405\n", "constant_d 27.8103 0.372410 27.506585 NaN 27.508892 0.019103" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat(\n", " [\n", " ml.parameters.loc[:, [\"optimal\", \"stderr\"]],\n", " ml_hp.parameters.loc[:, [\"optimal\", \"stderr\"]],\n", " ml_ls.parameters.loc[:, [\"optimal\", \"stderr\"]],\n", " ],\n", " axis=1,\n", " keys=[ml.name, ml_hp.name, ml_ls.name],\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Standard error for the PestGlm optimization seems a bit too small. The PestHp does not have a standarderror, nor a covariance matrix (`pcov`). PestHp does have parameter sensitivy values but I'm not sure how to interpet those." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Covariance matrices" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PestGLMLeastSquares
pex_Apex_aconstant_dpex_Apex_aconstant_d
pex_A5786.76001118.4200023.7832001524.257821318.811811-0.687605
pex_a1118.4200581.064008.302430318.81181183.858060-0.142915
constant_d23.78328.302430.138689-0.687605-0.1429150.000365
\n", "
" ], "text/plain": [ " PestGLM LeastSquares \n", " pex_A pex_a constant_d pex_A pex_a constant_d\n", "pex_A 5786.7600 1118.42000 23.783200 1524.257821 318.811811 -0.687605\n", "pex_a 1118.4200 581.06400 8.302430 318.811811 83.858060 -0.142915\n", "constant_d 23.7832 8.30243 0.138689 -0.687605 -0.142915 0.000365" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.concat([ml.solver.pcov, ml_ls.solver.pcov], axis=1, keys=[ml.name, ml_ls.name])" ] } ], "metadata": { "kernelspec": { "display_name": "pastas-plugins (3.13.5)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 2 }