Repository: neurolib-dev/neurolib

Issue:
aln: add example notebook with external stimulation
c/p form Issue #10 

> * Add stimulation functionality (i.e. you specify tDCS or tACS stimulation parameters and under the hood this is automatically translated to model parameter changes for the specific model the user chose)

This is actually already possible! At least with the `aln` model, you can set the external input parameters for stimulation before each run and they will be integrated over time.
 
They are called 
- `ext_exc_current` / `ext_inh_current` for current input in `nA`, and 
- `ext_exc_rate` / `ext_inh_rate` from other spike rate sources in `kHz`.

I actually wanted to know how fast and easy I could implement this with `neurolib`! Let's see what happens if we ramp up a current from `0` to `0.1 nA`:
```
from neurolib.models.aln import ALNModel
import matplotlib.pyplot as plt
import numpy as np
aln = ALNModel() # comes with default parameters
aln.run()
plt.plot(aln.t, aln.rates_exc.T, c='k', label='no stim', lw=3)
aln.params['ext_exc_current'] = np.linspace(0, 0.1, aln.params['duration']/aln.params['dt'])
aln.run()
plt.plot(aln.t, aln.rates_exc.T, c='r', alpha=0.5, label='ramp stim', lw=2)
plt.legend()
```
![image](https://user-images.githubusercontent.com/7763212/73984315-b6acde00-4938-11ea-96b1-abb53ed714bb.png)

🤯

PS: I'm gonna add this to the examples!

Discussion:

Patch:
diff --git a/README.md b/README.md
index 25c0764..4556cdc 100644
--- a/README.md
+++ b/README.md
@@ -88,16 +88,14 @@ pip install .
 ```
 
 # Examples
-Example [IPython Notebooks](examples/) on how to use the library can be found in the `./examples/` directory, don't forget to check them out! 
-
-You can run the examples hosted on [Binder](https://mybinder.org/) by clicking the links below:
+Example [IPython Notebooks](examples/) on how to use the library can be found in the `./examples/` directory, don't forget to check them out! You can run the examples in your browser using Binder by clicking [here](https://mybinder.org/v2/gh/neurolib-dev/neurolib.git/master?filepath=examples) or one of the following links:
 
 - [Example 0.0](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-0-aln-minimal.ipynb) - Basic use of the `aln` model
 - [Example 0.3](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-0.3-fhn-minimal.ipynb) - Fitz-Hugh Nagumo model `fhn` on a brain network
 - [Example 1.2](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-1.2-brain-network-exploration.ipynb) - Parameter exploration of a brain network and fitting to BOLD data
 - [Example 2.0](https://mybinder.org/v2/gh/neurolib-dev/neurolib/master?filepath=examples%2Fexample-2-evolutionary-optimization-minimal.ipynb) - A simple example of the evolutionary optimization framework 
 
-A basic overview of the functionality that `neurolib` provides is also given here. 
+A basic overview of the functionality of `neurolib` is also given in the following. 
 
 ## Single node
 
diff --git a/examples/example-0.5-aln-external-stimulus.ipynb b/examples/example-0.5-aln-external-stimulus.ipynb
new file mode 100644
index 0000000..f0bb82c
--- /dev/null
+++ b/examples/example-0.5-aln-external-stimulus.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stimulation example\n",
+    "\n",
+    "This notebook will give you a simple example of how to construct a stimulus and apply it as an input current to the excitatory population of the `aln` model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# change to the root directory of the project\n",
+    "import os\n",
+    "if os.getcwd().split(\"/\")[-1] in [\"examples\", \"dev\"]:\n",
+    "    os.chdir('..')\n",
+    "    \n",
+    "# This will reload all imports as soon as the code changes\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "except ImportError:\n",
+    "    import sys\n",
+    "    !{sys.executable} -m pip install matplotlib\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    \n",
+    "import numpy as np\n",
+    "import scipy\n",
+    "\n",
+    "# Let's import the aln model\n",
+    "from neurolib.models.aln import ALNModel\n",
+    "\n",
+    "# Some useful functions are provided here\n",
+    "import neurolib.utils.functions as func"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we initialize a single node."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ALNModel()\n",
+    "model.params['duration'] = 5 * 1000\n",
+    "model.params['sigma_ou'] = 0.2 # we add some noise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can construct a simple stimulus using the function `func.construct_stimulus`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stimulus = func.construct_stimulus(\"rect\", duration=model.params.duration, dt=model.params.dt, stim_amp=1.0, stim_freq=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stimulus = func.construct_stimulus(\"ac\", duration=model.params.duration, dt=model.params.dt, stim_amp=1.0, stim_freq=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The stimulus is then set as an input current parameter to the model. The parameter that models a current that goes to the excitatory population is called `ext_exc_current`. For the inhibitory population, we can use `etc_inh_current`. We can also set a firing rate input, that will then be integrated over the synapses using the parameter `model.params['ext_exc_rate']`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.params['ext_exc_current'] = stimulus\n",
+    "model.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we plot the timeseries, we can see that the oscillatory activity locks to the stimulus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GuIDpgAF7m26++Wa++tWvArA/0Pztb7tFsIKMceVvJkwoccdFKsyePfCVr8Drr4fbu3d3E+Knnx7/PmvdAlg//Wl0kuOss9wYUSujS7158EH44Q+j7Zdc4iYEu3aNf9+yZa6W8dKl4fYDD3TjvIMPLn5fRWpAqgbrLmtthoMrT/nXYL0KmAQ8g7Vnxbz+B+Bq4CqsvaPwjlYfpdlJ/Wprgx/8IBpcHToU7r47c3AVXIbqFVfAL34RzWK45x63gIJIPXn22WhwtVMnl9H6xS9mDq4CjB4Nd97pMsWDVq1ygde4UgMitWrTJvjWt6LB1XPOccdYILgKEJwo3wIu8HP11XgbudqTy5eXpMsiFevnP48GV/v1c+ecTMFVcMHTCy90x5w/yff00y7DVaSezJrlSkD5/vVfXfA0U3AV3F1MkybB+PHh9o0bXVa4koxEqsXjQBtwKsb0C71iTBPwEaAVeLT8XasMCrBK/frf/4Xnngu3DRkCt93mBt/ZOOUU+O//jgaP/uM/3O3PIvVg1So3WRHU0OACPWeemd0+evd2GeGHHx5uf/VVd0yK1ANrXV07v+bq2WfDTTfF1ods8ycgjIEvfSkaZN2+3d1d4QduRWrV44+72qlBffq4c8r73pfdPkaPhl//Olo3/NZbXRkPkXqwcaMLpAZv8we44Qa3oGI2und3d1KMGxduX7jQTcaLSOUw5isYswBjfhxqt3YNcA/QCNyKMcEgyE+BvsDdWLuubH2tMAqwSn1avNgNjoN69oRf/QoOOii3fZ18MnznO+G2lhYXcNq9u7B+ilS6tja3wJsftPnGN+CMM3LbV6Zj8I47XBkBkVr3179GF1scPRr+3/+LX2SRmABr2nXXwYc/HG57+224+eYidFSkwq1dG822a2x0Ga1DhuS2r5Ej3b6CJQGsdSUGtm7N+DaRmmCtK02zYUO4/bOfhY99LLd9de0afwxOngzPP19QN0WkHcachzGv7n24ACmhNmPOC7yjD3AkEFe/4xvAUuASYAHG/Blj3gS+BiwGvlXKb6XSKcAq9Wf3bhcQCgY/jXGD50MOyW+fH/lIdAZ38WJXk1Kklt11F8yZE2477zxXizgf/fq5mnfBYFK6nIduIZNatmIF/PKX4bY+feC//qvdBXUyBliNcZN/w4eH2++9V5l3UtusdZMSfvDzu9+FESPy2+fJJ7vM8KB169xdTCK17NFHo3f8nXSSq22cjx49XCarnxX+ox+5TFkRKYW+wPjAIz1jGGzrm9WerF0PnAD8GheovQjoBfx/wAlYu6Gdd9c8BVil/vzpT9G6q5dfDiecUNh+v/ENGDYs3HbHHW6xEpFa9M47cPvt4bZBg+Db3y5s8Y+xY+Haa8NtK1bAH/6Q/z5FKpm1bpJv165w+w9+4IKs7cgYYAVoanIXrU1N4fb/+I/owj0iteKxx6KTCB/6UDSjO1dXXAHHHx9ue/hhTVhI7dq82QVDgw44wGVvNxQQRhg6FP7lX8JtGzZEJxlFpDisvQNrTQePOwLb35hquzLD/jZg7dewdgjWNqW+fh1rN5XnG6pcCrBKfVm3Dn7/+3DbsGFuEZ5CNTbCv/97uB5rW5vLxgssQiJSM372s3CQpqHB1Yn0sxLyccUVMGpUuO1//scFWkVqzXPPRYM0H/+4yxLqQLsBVnC3Yn7ta+G2d95xx5NIrdm2LRqk6dfP1Y8sVEODm/To3j3c/tOfqiSU1KZbb4Xm5nDbd76Tezm1OOefDxMmhNseeQRmzy583yIiCVGAVerLL38ZrhVpjCsX0M7tlzk54gi46qpw24wZ8NRTxdm/SKV46SWYMiXcdumlMGZMcfbfqZO7kA2WCtizx92OqQkLqSU7d0YzhPr1g69+Nau3dxhgBResHTs23DZpEqxZk2UnRarE7bdHa0X+27/B/vsXZ/8DBsDXvx5uW77cld4QqSULFsADD4TbzjwTTj+9OPtPl7HxJyx+8hOXoCIiUoUUYJX6MWcOPPlkuO2ii/Kvx5XJlVfCwV496F/9SrdjSu1obYVf/CLc1rt39Lb+Qg0dCpddFm575RV49dXifo5Iku6+2y3IE/Stb2WdCd7a2trxRg0NrnRH8JbOXbu04JXUlpUr4c9/Dredeqp7FNOFF8JRR4Xbbr/d3U4tUgusdYtRBSe0u3Z156ZiOuig6Nhx0SJXekNEpAopwCr1wVq45ZZwW8+e0QULiqGpKToAWbs2OgssUq0eecStRh70ta+5hQuK7XOfi9agvPVWZbFKbWhujt6qf/zxLksoS34Gq810bBxxBFxySbjtySejNclFqtXtt7sJwLTGRrj++uJ/TkNDtOTA9u1w553F/yyRJEyb5u7AC7rmGujfv/if9YlPwGGHhdtuv12JKSJSlRRglfrw2mvwxhvhts99zhVqL4WJE+HYY8NtkyaFyxOIVKPdu+F3vwu3HXVU4YuHZNK9e3QiZP786Iq2ItXorrtczcg0Y1xAKIdF4vwAa7slA774xfDtmNbCb36T9WeJVKylS+Hxx8Ntn/wkDB5cms8bPRrOPTfcdu+9sH59aT5PpFysdRPZQQMGwKc/XZrP69wZvvnNcNu778Lf/laazxMRKSEFWKX2xQ0U+vWDj32sdJ9pDHz5y+G2DRuit66JVJsHH4zWbbzuusJWk+3IeefBoYeG2267TTW6pLr985/Rc8I557iFF3OQdQYrQK9e8JnPhNumTIG5c3P6TJGKc9tt4Tsbund3iyWW0rXXRstuTJpU2s8UKbUXXoC33gq3ff7zxVuvIs6JJ8Ixx4Tb/vAHJaaISNVRgFVq39Sp0YvHUg8UwC32c8op4ba779ZgQarX7t3wxz+G244+OquVzgvSqVO0RteyZcpilep2991ugau0hoa86hj7NVjbDbCCq2vcq1e4TUEhqWaLF0fPB5/+dPT3vNgOOQQ++tFw29/+5iZPRKqRte72/KAhQ+D880v7ucZE71basAH+/vfSfq6ISJEpwCq1z69vN3gwfOQj5flsf7CweTNMnlyezxYptieegHXrwm1f+lJOtzPn7ayzXA3JoDvvVC1WqU5bt8L994fbzj/fXcjmKKcMVnCZfVdeGW6bMsVNWohUI3+c17Nn6W5n9n3+89Cly77nu3fDPfeU57NFim36dLfIVNC117qJ7lI75pjohP3dd8OePaX/bBGRIlGAVWrb/Pmu/mrQVVe5ej/lcOSR8Vms2az6LFJJrI1exB5zDIwbV57Pb2iIBoXeeiu6CININXjgAbcoTlpDA1x9dV67yqkGa9oll7ggVJB/fItUg7Vr3eRf0Kc+Fa41XEr9+7syNkF//Wu4trJItbjrrvDzIUPg7LPL9/n+efDdd+Gpp8r3+SIiBaraAKsx5lhjzA3GmAeMMauMMdYY02EqkzHmSmPMdGPMVmPMBmPMo8aYkzt4zymp7Tak3jfdGPPZ4n03UjL+BeNBB8GHPlTePvg1wFavhqefLm8fRAo1dapbRCTo8svL24czz4RBg8JtWrVZqk1LSzTD7fTT816MJ+cMVoBu3dzKzUGPPRbNUBepdH/6U3jSuqkJPv7x8vbh8svDd3Js3aoFeqT6LFoE06aF2z7zmdLW2PcdfbQrsRZ01126W0lEqkbVBliB7wE/Bi4CBnWwLQDGmF8CfwRGAU8D04GzgSnGmAszvOcS4AXgXGAO8DhwOHCnMea/C/wepJTiApmf/GTpa6/6jjkGRo0Kt2mwINXGD2QOHQof+EB5+9CpU3SBnldegSVLytsPkUI8/ji89164rYDFePIKsIILsAbPh3v2aCFGqS5btkQDmRdcAAccUN5+HHooTJwYbvvTn3Rrs1QXP3u1d+9odnapGQOf9XKYFi+OBn5FRCpUNQdYpwI3AR8FDgZ2tbexMeYs4OvAP4Gx1toLrbXnAhOAVuCPxpgDvPf0BiYBnYCPWWsnWms/BgwHlgDXG2MmFvW7kuK5777wKuP77eduiyy3uMHCwoVatVmqx5Il8MYb4bbLLy9vVkPaRz4SvXj+61/L3w+RfFgbDWKOGwcjRuS9y7xKBIC7ePYX6HnwQbcSukg1mDw5WmrDn4QrF3+ct26dq20sUg3Wr4/ein/ppS4jvNwmTHCTFkF/+Uv5+yEikoeqDbBaa39irf2+tXaytfbdLN7yrdTXH1prFwf2MxW4DTgAuMZ7z+eAnsDfrbUPBN6zFvjX1NPr8/0epIRaWuChh8JtF14YrTlXLhMnwsCB4TYNFqRa+AHM3r3h3HOT6UvXrvCxj4XbHn1U9e6kOsydG11ApMBSG3lnsAJcdln4eXOzSthIdbA2em4644zoWKtcRo+O3tqsyT+pFn//e7TUhj/WKpeGhui56cUXXT1WEZEKV7UB1lwYY/YDzkg9jRvtpNv8peXP814PegTYCZxljOlacCeluJ5+2l0oBpW7JldQQ0N0oPLUU7BxYzL9EcnW9u0ugBl00UXlL7URdPHF4ezZ7dvhkUeS649Itu6/P/x84MDoQog5avUWTcwpwDpkCIwfH267776C+iNSFq+/DitWhNv8usLl5o/zpk+H5csT6YpI1tra3MKLQR/8IPTqlUx/wK2X0a3bvudxfRQRqUB1EWAFjgSagPestatiXk8vQ+1NPTPWe30va20LMBfoChxRpH5KsfhZAyec4C4kk/TRj4aDUrt3R7NsRSrN449Hb8G8+OLk+gPQrx+cdlq47a9/VV1jqWzNzfDkk+E2f7IgDwVlsEJ08nHePHjrrYL6JFJy/jhv6FBX8z5JZ52lEjZSfV56CdauDbcllb2a1q0bnH9+uO3BB90diiIiFaxeAqzpyFpccBVr7TZgE3CgMWZ/AGNMT6BXe+8LtB+a4XVJwuLFMGdOuC3pgQK4QfdZZ4Xb7r8/XCdWpJLE3YL5gQ9A//7J9CfIDwotWwYzZybTF5FsPPxw+OKwc+doDdQ85F2DNe3UU6PHtIJCUsnWr4fnnw+3XXKJq3mfpMZGt8hW0OTJsGNHMv0RyYZ/Z8VRRxVUF7xo/Gu3DRvgueeS6YuISJbqJcDaI/V1ezvbpAv47e+9p733+e9plzFmXtwDSPBe2xrkDxT69HEF0yuBHxRavdrd5iZSiebNi9aLrITJCoDjj48ugvD3vyfTF5GOZKoX2bt3wbsuOIO1U6foApBPPRXOXBepJA89FK0XWe7VzjPxA73btikoJJVr9Wp45ZVwW6WM8w47DI49NtymcZ6IVLh6CbBKvWhpcbc0B114ocsUqgSjRsHhh4fbHn44mb6IdMQvYTFwIJx4YjJ98RkTLVXwzDMKCkllevPNaL3IIl3EFhxgBZd1FyxVsGMHPPtsgT0TKQFro+emD34Q9s8q16H0Bg6Ek04Kt02enExfRDryyCPh8ko9esA55yTXH58/+ffaa1rsSkQqWr0EWLemvnZrZ5vuqa9bvPe09z7/Pe2y1o6MewAqKFMsU6bA1q3hNv92rSQZAx/x1lJ75hmtgC6Vp6XFZbEFXXhhwfUii+pDH3LZd2k7d2oFdKlM/kTakCFFqxdZcIkAgIMOii62paCQVKI334RVXuWuiy5Kpi+Z+KU/XnsN1qxJpi8imVgbXSD03HNhv/2S6U+ciRNd0Dctrs8iIhWkgq6USyqdNjI47kVjTHfgAGCjtXYLgLW2Gdjc3vsC7W8XqZ9SKP+ke9xxcPDByfQlk3PPDQeFdu1SUEgqz5QpsCUwd2QMfPjDyfUnTu/e0aCQMsKl0sRNVpx/ftHqRbYGb5UmzwxWiC4o8sYb7vZRkUoSN1kxalQyfclkwoRoRq2CQlJp5syJTlb454GkNTZGM2onT9aipiJSseolwLoQ2AX0NcYMinl9XOqrtzISs73X9zLGdAFGATuBRf7rkoANG+Dll8NtlVKTK6h3b7dQUJAyhaTSxE1WDBiQTF/a418MzJgRvWAQSZI/WQFFnawoSokAcItd9ewZbtOEhVSSlhZ48slwWxEnK4qmsdGVLQhSUEgqjT/OO/RQGDkymb60x7/zb9UqmD07flsRkYTVRYDVWrsDSBcT+3jMJulCaH6U6xHv9aDzga7A09banQV3Ugr3xBMQvNDs2hXOPDO5/rTHHyzMmgUrVybTFxFftUxWgEqlmGIAACAASURBVAsK9eoVblOmkFSSEk9WFC3A2tjo7rAIevjh8HlVJElxZaAq7c6KNH+c9847bqwnUgniJivOO6/yJivAZaj7i5oqMUVEKlRdBFhTfp76+l1jzN5VhowxJwHXApuAP3jv+T3QDFxgjLk48J5+wE9TT39Wsh5LbvyL2DPOgG7tld1N0CmnwAEHhNseeyyZvoj44iYrzjgjuf60p0uXaFDo0UeVKSSVoQyTFVOnTg09z6sGa5qfEb56tbuNVKQSVMudFQAjRsDQoeG2Rx9Npi8ivmqarIhbv+Lpp12QWESkwlRtgNUYc54x5tX0A2hMtb8aeOy9irHWPg38CjgImGWMedAY8ygwBegMXGWt3RT8DGvtBuBqoA34qzHmWWPMX3AlB4YBP7fWPl/671Y6tGwZLFgQbqvUjDtwQSH/9rEnn1RQSCqDfxFYyZMVED3W33kH5s9Ppi8iQU8+GZ6saGoq+p0V//jHP0LP885gBTjqqGhQyM9yEknCxo3Vc2cFuKCQ379nnoE9e5Lpj0iQP86r5MkKcMHfYHbttm3gTS6KiFSCqg2wAn2B8YFH+q9usK1v8A3W2m8AVwHzgbOBk4CngQnW2gfjPsRaez8wAXgCOAb4MLAEuNJae31xvyXJm38B2LcvHH98Mn3Jlh9gXb4clixJpCsie61cGQ1OVvJFLLig0GBvLUIFhaQS+L+HZZisKCjAakx0QZGnn1aZAEnec8+VfLKi6PxjqbkZpk9Ppi8iaVu2wCuvhNsqfZzXrx8cfXS47YknkumLiEg7qjbAaq29w1prOnjckeF9x1lru1trD7TWfsha+0rMRwTf83JquwNT7zveWntnyb45yY217gIw6OyzoaHCf71HjYrOFvsrTYuUm38s9e5d+ZMVcUGhp55SUEiStXZt9PZ6f2KtCM71SmQUVCIAosfShg3wxhuF7VOkUP65acKEyr6zAmDgQDfWC9LknyRtypRwJnWXLnD66cn1J1v+uWnKFNixI5m+iIhkUOERKJEsLF3qsj+Dzj47ka7kpKEh2s8nnlCZAEmWfxF7xhmVP1kB0YH32rXw5pvJ9EUE3O3AQT16wPjxRf8YP2O1oAxWcIuJHHFEuE2Tf5KkDRvg9dfDbdUwzoPouem551Q7UpLl/z0/6SR3fqp0Z54ZHo/u3AkvvZRcf0REYlTBVbNIB/yA0IAB0YyBSuVfIKh2pCRpxQpYuDDcdtZZyfQlV+9/Pxx2WLhNmUKSJP/cdPrpLlOoyPyM1YIDrBDNtH36adWOlOT45QG6doWTT06uP7k46yzVjpTKsWULvPpquK1axnm9e7tasUGa/BORCqMAq1Q3a6Mn1zPPDA9mK1lc7UgNFiQpceUBxo1Lpi+5MiY6YaHakZKUuPIAJbqIbW1tDT0vSoDVP5ZUO1KSFFceoGvXZPqSq7jakZr8k6S88EJ4sqyxEU47Lbn+5MrPCH/pJVo2bmTNmjXJ9EdExKMAq1S3pUvh7bfDbdUyEwuZa0eqTIAkoVrLA6T5x9I//wkzZybTF6lvfnmA/feHE04oyUf5GawF12CF+NqRmvyTJMTVAK6mcR7E147ctSuZvkh98/+On3gidO+eTF/yccYZ0KnT3qetO3Zw9bBhDB48mEmTJiXYMRERp4qunEVi+AOFaioPkOZnCr37bvQ2bZFSW7ECFi0Kt1VLjbu0uNqRzz+fSFekzvnnphKVB4ASZbBCfFDI+yyRkvPLA+y3X/WUB0jza0fu2AHTpiXXH6lPzc3R37tqG+f17OmCwimrV69m9IYNtLW1cc011yTYMRERRwFWqW5+xl01lQdIGzYsWiZAQSEpt7jyAMcck0xfCnHGGeHnzz+vjHApr7gF1kqYcVeyAOvEieHnmzfD7NnF2bdItvxz06mnVk95gLTevaNlAjTOk3KbMiVaHmDChNAmS5Ys4YILLuArX/kKO3fuLHMHsxQY523ctImTgdJMX4qI5E4BVqley5dXd3mANGOiF7IaeEu5vfBC+Hm1lQdI84+lNWtg8eJEuiJ1yj+W9t8fjj++ZB/nB1iLUiIAXJkAZYRLkpqbq788QJp/bpoyRTXCpbz8c1NMeYDLLruMhx56iFtuuYU//vGPZexcDk49dW8yTUtLC/sBpSnAIyKSuyq8ehZJmTIl/LxvXxg5Mpm+FMofeC9ZAqtWJdIVqUPr18O8eeG2009Ppi+Fev/7YdCgcJuCQlJO/rnp1FNLVh4AogHVomWwQvzknzLCpVxeeSUchGxshJNOSq4/hfAXEtq0SRnhUj4tLTB1argtZpz32muv7f33b3/721L3Kj+9e8PYsaGmicn0REQkQgFWqV7+TOypp1Znxh3AmDFuwBCkoJCUix8Q6t4dxo1Lpi+FissIf+65RLoidWjrVnj99XBbiVdoLlmJAIgeS6tXKyNcyscf540f72qwVqNBg+Dww8NtOjdJuUyfDsFb/hsa4AMfaPctDZV8TeWdm04DqqxAnIjUqAr+yynSjo0bYc6ccJt/IVhNGhoidZAUYJWy8QOsp5xS0oy7kvOzMhYvhnfeSaYvUl+mTg3XuOvSpeQZdyUrEQAuIDRwYLhN5yYph9274eWXw23+OKna+OcmZYRLufjjvDFj4MAD232LqeQ1LbxrvgOBMYl0REQkTAFWqU4vvRQelO63Hxx3XHL9KQY/QDx7NmzYkEhXpI7s2OEyG4Kq/SI27sLBz4QSKQX/Iva446Bbt5J+ZEkzWFUjXJIyYwZs3x5uq/ZzU1xG+JIliXRF6khbW/TclMWdFRWdwTp4MAwbFspanZhUX0REAir4L6dIO/xgyUknudpc1eyEE8IX4tbCiy8m1x+pD6++6mpzpXXqBCefnFx/ikEZ4ZKEPXvc5F9QicsDQIlrsEI0KLRokQsMiZSSP84bNQoOOiiZvmTpnXfeYdu2bZk3OPxwOPjgcJvOTVJq8+e7WvtBWZybKjqDFdy5KdDHiaCMcBFJnAKsUn127XJBoaBqz2oAFyD2A1vKupNS87Maxo2Dnj2T6Usx+UGhWbPcitQipTJrFmzZEm4rw7mppBms4BYTOeCAcJsm/6SUrI2Ofyp8nHfFFVcwePBgevTowcKFC+M3issI98/BIsXmH0uHHgpDhnT4torOYAWYODEUBB4M8I9/JNYdERFQgFWq0WuvRQu1n3JKcv0pJv8C4rXXwtmFIsXU1hYNlFT4RWzWxo+HpqZ9z9vaohMzIsXkB0qGD4d+/Ur+sSWtwQouq91fDMWvjSlSTIsXw9q14bYKr7N/11137f338OHDM2/on2Pnz1c5KCmtPMoDQBVksB55JO/5ba+8kkRPRET2UoBVqk8ehdqrxkknhW53YccOmDkzuf5IbZszBzZtCreV4ZbmsmhsdGU3gvzbt0WKxdrorb5ZTFZMnjyZ73znO6wu4Jb7kmewQjTA+vrr4YlOkWLyj6VBg2Do0ES6UnRHHx2ty6ygkJRKXJ3fWhnnGcMrnTqF2zTOE5GEKcAq1SWuLmmtDBTABYpHjgy3KVNISsUfiA4bFl0xvJr5me2vvOIyWUWKbfnyaF3SDjLuli1bxkc/+lF+9KMf8elPfzrvjy55DVZwGeHB20VbWlyQVaQU4moZV3o2XUCPHj0yv9ilC5x4YrhN4zwpFf9YOvBAGD06q7f6k3eV6FW/jMHMmdBeHWQRkRJTgFWqy9Kl8J53Q8ippybTl1Lxg0KajZVS8bNmau1Y8msab9oEb72VTF+ktk2dGn7er59b0KYdt99++95/P1/AQjclLxEAsP/+LvMuSEEhKYWNG91t80F+BnWF69u3b/sb+OO8qVOhCoJZUoX8cd7JJ4cny9qxefPmEnSouN5oaGBPsKG1FaZNS6o7IiIKsEqV8QcKAwe6Yu21xL+QWLECVq5Mpi9Su9avd6uBB/kByWo3cCAcdli4TUEhKQX/98ov9xKjc+fORfnosmSwQjQo9PLLWrFZiu/VV8O/V/vtFw3uV7gOj0H/XLt1qyvZI1JMLS1uLYegHNasWOSPESvQDmOY4TdqnCciCVKAVaqLH2DN4iK26hx5JPTuHW5TfS4pNj/jrnv3rG8bqypxQSGRYtqxA2Z4l3hZXMQWa4VmP5hTtgDr6tWuNIJIMfnnpuOPdzW1q0iHWeR9+8IRR4TbdG6SYps5E3bt2ve8ocGVe8nS0Cqpexy5z0+TfyKSIAVYpXps3w6zZoXbai3jDtwASEEhKTX/Inb8eChSRl1F8Y+lt97Sis1SXDNmwO7d+543NLigUJmULYP1/e93pQ+CdG6SYmpri56bqnCcl1WZDv9uJR1LUmx+csbIkdCrV9Zv79q1a5E7VBqRI2f9eli8OImuiIgowCpV5PXXYU+g0k7nzmW9iC0rPyikFZulmNra3G2YQSedlExfSm3sWK3YLKXl/z6NGeNqlnbAFOnuCz+gWpIarODuFlFQSEppwQJXgzWoCs9NWR2D/jhv8WJYu7Y0HZL65E9WVOGxlI0VwDt+o9avEJGEKMAq1cO/iD366GjgpFbErdjs11ESyde8edDcHG6rwiyhrMSt2KyBtxRTnhl3xco0LVuJAIgGhWbM0IrNUjz+sTRkCAwalExfCpDVMThqFPTsGW7ThIUUy7vvwrJl4bYcx3klPZcUWeTI0ThPRBKiAKtUB2vjV8KsVXErNvsXHiL58o+lww6D/v2T6Us5+EGhadNcFq9IoVatcgsRBpU5S6isAdbjj3eTFmmtrZr8k+KpkXFeVhmsnTpFJ/80zpNi8Y+lXr1gxIhk+lIGkXDq3LnRRAIRkTJQgFWqw8qVbkGNoBq91WUv//vzb+kWyVeNXMRmzT+WtmxxtVhFCuUfS717u4UKs1B1JQLA3TVyzDHhtmnTSvd5Uj+am+HNN8NtVXpuynqSw5/8e+01N2khUig/WH/iieE742rMG0BLsKGtzZVXExEps9r9Syu1xb+I7dMHhg1Lpi/l4q/0uWKFu+VHpBCbNkWDi1V6EZu1fv1clm6QgkJSDBVwEVu2Ra7S/HOTjiUphunTw3cWNDbCsccm158CZD3JccIJ4edbt2ryT/IyadIkLrvsMmbMmOHWq/D/LucxzqumEgEtwCy/UecmEUmAAqxSHeIy7oqU/VOxhg+P1ufSYEEKNW2aK7mR1rVrtBxFLVJQSIotrjZ2ApMVZS0RAPGTf2vWlPYzpfb547xjj4WmpmT6UqCsj8G+fTX5JwVbsmQJ11xzDffccw9nnXUWzJkD27eHN/LLUdSgyH1+OpZEJAEKsErla2mBN94It9V6eQBwWVB+doMGC1IoP+Pu+ONdplCt84NCcRcgIrmYPRt27tz33Jjo71kZlD3AesQRrp5fkM5NUghro2WQqnicl1OZDj/wpWNJcvTwww/v/ffGjRujx9KRR8JBB5W5V+UXOXJWrYqWlxMRKTEFWKXyzZ4Nu3bte97QkMhFbCL879O/hU4kF9a636GgOshqAGDcOLeoSNqePW4FdJF8+cfS8OFw4IEA7N69m0mTJvHYY4+VvBtlrcEKmvyT4nv7bVi3LtxWxeemnI5B/1jS5J/kqFu3buEG/+9xnsdSNZUIAFgMcMAB4UatXyEiZdY56Q6IdMi/BTPu1vlaFQiwtuzeTZdNmzCLFrmfgUiuVqyIXsT6F3e1qls3GDMGZs7c1zZtGnzgA8n1SaqbH2ANHEs33XQTN910EwDPP/88p512Wsm6UfYMVnDnpqee2vc8PflXw4uoSAn5x1KfPjB0aDJ9KYKcAqzjxkHnzm7SD9wiV2+8AaeeWprOSc3pFJg87gHY+fMJFVGr4aSU4GKRFtx5+Mkn920wbRpcfHHZ+yUiZWbMhKLty9ophbxdAVapfO1cxNa8gQPhkEN4++WXee+99+jVsyeHT5umAKvkJ+4i9n3vS6QriRg/PhpgFcnHli0wf364zQuwpn3xi19kvr9tEZV9kSuIXrBv3gwLF8JRR5X+s6X2+BPpJ5xQ1XX2czoG05N/wTsqpk1TgFWy1hCY2BoHLkifPn4aG2Hs2Lz2a6rxGBw/Phxgfe01Tf6J1IfnSc2zFMhSYIxUAVapbHErqtZTgBWwJ5zAew8+CMDm5mbee+QR+l5xRcK9kqpUYxexORs/Hm67bd/zZctcRm+/fsn1SarTjBnRFc8zLBa3devWknal7CUCAA4+GIYMcVnxadOmKcAquWtrg9dfD7dV+Tgv52Nw/PhogFUkS8EA6wm4c8Le4OjYsVkvFuefS6ohwBqZzPAn/5qbYcECGDGifJ0SkaQsBV4u4P0fAA7rcKsOKMAqlS3uIjbPmdhq1Xb88aHnnefOdTVpq3R1XUlI3EWs97tV80aMgB493MRN2muvwXnnJdcnqU5+NviYMRn/Ju/evbukXUmkRAC4C9lggHX6dLjyyvJ8ttSOBQtcRnhQlQdYcz4Gx4+H3/xm3/N//EOTf5K1YID1eLzfvxzGef7EQDUEWCMGDIif/FOAVaQevIS1V+f9bmP+SBECrMqXl8qWw0Vsrdpz9NGEhjwtLTBrVlLdkWq1cKGbyQ+q8ovYnHXqFL3YUKaQ5CMuGzyDmg6wBs2aFV6QUiQb/rE0ZEjVBxZzzmAdMQL23z/c5o9/RTJIB1gPwkUGQueAHMZ5NRFgheiiXhrniUh2TOpREAVYpbLVc/3VlLbu3ZkXbLBWgwXJXdxFbP/+yfQlSX5Q6NVX3TElkq316115iaB2soRKHfBMLMB63HHhunYtLeEaxyLZqMFxXs7HYEODJv8kb+kAa/o3aO/vX/fuOZVtqZkAqz/Omz0bduxIpi8iUi4HAl8ucB/XpfZTEAVYpXL98585XcTWqtbWVkLDbGOU2SC5q8GL2Lz4A+8NG9ztmCLZ8icrundv9/bDcgdYy1KDFVy5jVGjwm06N0ku4u7ISejctHDhQrZt21aUfeV1DPrnpunTNfknWckYYD32WHfnTpbKNjlXav7k3+7dLsgqUu+M2Q9j/h1jFmHMToxZjTGTMGZQjvtZjjG2nUf5V+O2djPWFjaTYu0OrN1caFcUYJXKleNFbK1qbW0lcsm6cGG0ZplIJnGZZfUaYB082NXoCvJr04q0xw8i5ngRW2x+MKesF8n+pOcbb5Tvs6X6zZkTLithjAuOlNm5557L8OHD6dGjB81+KZ085BVg9Y+lf/4zXEdSJIN0pmk6RL/3HJDjOK9mMli7d4eRI8NtOjdJvTOmK/As8D2gB/B3YCVwFTATY/KpPXpnhkfBQcpqpgCrVK4sLmK3bdvGlVdeyTnnnMPChQvL2LnyaWtrYy7QknpuwGU16FZMydabb1bERWxFMMb9LQlSgFWyZW3O2eA1WyIAosfS/PlQpCxAqQP+sTR8OPTsWfZuPPHEE3v/ffnllxe8v7wCU4ccAn37htt0bpIstLW1cQiQLvq09wxQYIC1GoWyd4N0LIl8FzgRmAocgbWXYu144HqgLzAp5z1ae2WGx5qi9jwfxvTHmAkY099rfz/G/Blj5mLMoxhzYoY95E0BVqlM1kYzWGPKA9xyyy3ceeedPPXUU1x66aVl6lx5tba2shuYk25ID9w1WJBs+RexRx6ZyEVsxfCDy2+8ATVwYSFlsHIlrF0bbuvgIrbUF62JlQgAGDsWunQJfrgm/yR7WYzzyu2hhx5K5oPjJj41zpMstLa2Ejxy2lpboU8fGDo0p/3UQgbr3vOhfyzNmwfbt5e/QyKVwJhG4CupZ1/G2q17X7P257gww2kYc2zMu6vVDcBzQK+9Lcb0BF4CPg6MAM4FnsGYw4v5wQqwSmV65x1Y401+xFzE/uEPf9j779k1Wl+ntbUVgMjNLRp4S7ZyWPG8LviZDZs3R+s9i8Txj6WDDurwIjb9N7wj+WaeJprB2tQEo0eH23QrpmRj2zYX9AiqgHPTBRdcUPA+8g5MxU3+1UpdTCmZtrY2gkfOu2vXusmKHH8PayrAOnZs+K7HtrZovWeR+nEKLtC4FGvjZsH/mvr6kfJ1qeQmAm9h7aJA25W4ZP97gCOBbwH74bJ4i0YBVqlMfsZd795wWLQ0SOfOncvUoeSkBzyRcOrixVCEWmFS47Zvh7lzw20VkCWUqIED3SNIExaSjbiMuw4uQrPNKM038zTRACvoVkzJz4wZ4TsHunSBo49Orj8pDQ0JXhr5x5IWYZQstO7eTTA0v3HDhrzGedUYYPX7uPd72G8/1WEV2Wds6uuMDK+n28fktFdj/gVjbsOYX2HMFzCmb8dvKptBgJ89cx6wB/gG1i7G2l8Cs4HTivnBdRdgNcYcb4y5zxiz2hiz2xizyRjzojHmKhNzJjHGdDLGfNMY86YxZocx5r3U+49Kov91w79Ay3ARm+hAuEzS2U/zcHVY9/4UrHUXKCLtmTUrfBHbuXOHF7G7du3ijjvuYOrUqSXuXILiMoVE2hP3NzeLjLtSZrDGvafsAVb/WNIijJIN/2/u6NHQtWsyfQkoRomNvANTgwZB/3C5OJ2bpCPd164N3AObkkc2eE3VYAWV3BDZZ0jq66oMr6fbD81xvz8FrgW+BvwWWI4xV+fevZLYH9hXF8SYTsBJwBtYuz6w3QJgcDE/uPajUwHGmEtwhX0/DqwBHsBF7E/EFfa929u+AfgL8HPcD/4RXJzrY8Drxpjk72WqRXEXsRkW5OmU4MrN5ZK+ON8NzIJwoFkDb+mIfyyNHOlm9ttx/fXXc9VVV3HKKaewYMGCEnYuQX6mkOqwSkeWL3cZZUH+71GMmg+wjh4NjY37nqsOq2Qjy3FeuWV7vJaEFmGUPPRevjz0/B2AAQNy3o9/7qiGDFZfuwFWLcIo1avRGDMv7pHl+3ukvmYqRJw+MPbPcn8PARfjArLdgFG4eFkT8HuMKbzWTuFWA8MDzz+A+zk8723XmX1riRdF3QRYjTGdgVuBTsCnrbXHWmsvtdaegUuH3gBcZow5PfC2q4GLgMXAcGvtx6y1E3EB2m7A/6b2K8W0ahWsXx9uGzcudtN6yGANzihHhtkaeEtHAhexFrj7rbc44ogjePDBBzO+5ZZbbnHbW8v3v//9UvcwGf7Au7kZlixJpi9SHfyAUL9+0VITMUpZIqAiAqyNjdE6rDo3SXu2bQN/8u6YY5LpiyfRDFbQIoySMz/A+u7BB+e1n2osEeALnf/GjHF3baWpDqtIcVj7Naz9G9auwNodWDsPa68HrsPdbPuThHsILqlyDMZ8A2NGAz/EXQpP9rY7itS8VLHUfnRqn+FAP2ChtfZPwRestfPZl70aLFrzrdTXf7XWrg1sfz8ucj8MqIQIfW3xL2J794YhQ2I3racMVkgtdBUcPCxeDJs2lb1PUiV27AgtItLc3Mwtr77K4sWLueiii7Laxc6dO0vVu2T17w+DvTtCFBSS9vh3DIwbl/MiIu2p2gxWUMkNyc3s2dHSNX6QPiGJ3ybtH0ubNqkOq2RmLX1WrAg19Tv33Lx2VXMB1q5dNfkntaLFWjsy7pHl+7emvnbL8Hr31NdC6zv9AVgHHIkx7ytwX4X6MbAL+BnuJuBTgOex9pW9W7g+jgCmFfOD6ynAuivL7f4JYIwZioto78CVBvDV4mprlcG/MDv22IwXsfUWYH0LaA3eigmqwyqZvfkmBH5/tm7fzuwcd5FIsKZcdCumZMva6G3vGe6syP8jqjjA6h9LixZpEUbJLK50TQXUX4XilAgoKDA1cCD4GYg6N0kmy5fT6N32vu6QQ/LaVTUGWDtc5DGuHJRI/UnPwmSqNZpuf7ugT7G2DViaepZfKn2xWDsPVxbgbuBxXAbrhd5WH8QtcpX5ts481FOAdRnuP/xIY8xlwRdSC1Z9BtgI/C3VnF5tba61dnfM/vJbbU065g+827mIrbcSAXuA5qFDwxto4C2ZeMfSP/v2ZUdCXalIfqaQv6q1SNqqVfDee+G2LOqv5iKfzLm49yQSYB01KlyHVYswSnv8340iH0uFSDyDFbQ4j2TPO5bWAc09esRv24FqDLD6Iuc//1hasAC2bkWkzqTzazIFVdLtc4rwWQemviZf8NjaGVh7Bdaeh7Xfx9ot3uu/xdpjsDYumTJvtR+dSrHWtgJXAJtwtVPfMMb82RjzLO6XaRVwprU2vYJF0Vdba6c4cWOHb64Xa9bAu++G29oZeNdbBivApve/P7yBZmMlE2/gvda/JT4LdZXBunWry7wT8eVQuiZfVZ3B2tgIY8eG23Rukjhe6Rqg6NnghUg8gxW0CKNk7403Qn/z38AVGcxHRUwuFChy/hs9Grp02fdcizBKfXoZ2Ay8H2OOjnn9Y6mvfn3S3BgzEjgSt5hWja6S3LG6CbACWGtfBk7DZbOOAy4FTgfagKdS7WnFXm1NsuFfxPbqBe97X8bN6yGDtcMA69Kl0ZWtRVpaYO7cUJMCrJ5+/aJBMmUKSZwcStfkq6oDrKCsO8mOV7qGhga3GE2FqIggkxZhlGzE3Ckwg/x/h/1zR01ksDY1qQ6riLUtwM2pZ7dgTPe9rxnzLdwd2S9g7RuB9q9gzAKM+XFoX8Z8GGPOiHyGMWOAv+AWufp96jMrgzFDMGY8xkzI+Cii2o9OBRhjPgVMB1YC43FB1COAO4DrgWeNMU2l+vx2ihNXzi9g0uIWEWkniFoPGaz+QKl58GDYb7/wRloVU3xz57oga1pDA+9lseK5r6YDrBBfJkDE5/9eFGHF8w5rx+Wxj3z3UxT+sbR4seqwSpQ/zjvqKOiWad2N8quIDNYBA2DQoHCbzk3iW7UK1q8PLX5bSIDVf181jv9i+6xxngi4GqTTgJOBxRhzL8a8ilsE6j3gam/7PrhsVL+W6gnAMxizHGP+jjH3YMw0XAL9UcDzwA2l+zZyYMw1GLMM+AfwCvBcO4+iqZsAqzHmcOBOYD1wvrV2urV2m7V2sbX2WuBhXFZr+perXKutSVAO9VchusJ5NQ4GOuIP9tvisj10V7vMsAAAIABJREFUu4v4/IvYI45gd1Pu80fVmMGQEz9QNmuWbsWUsNWrcypdk6+qD7COGBGuwwqa/JOoCq6/ChWSwQrR8a/GeeLzjqUNuJVs8j0H1GyA1T+WFi6E7ZluUBWpUdbuxN25fRPuDu0LcaUu7wDGYe2yzG8OeQKYBDQDp+DKCwwDXgI+D5yFtckv+WHMl4DbgfcBs4AHgLvaeRRN52LurMJ9EugCPG6tjatufR9wPjAB+A3lWm1N9lm3zs3GBnUw8J7hDS52795No3+BV+UiAda2NhcUmjZtX6MG3uKLmawweWTm1F2AtbkZ/vEP8EtxSP2KK13jLzZYBFUfYO3SxS12Ffx5zZoFE4p655VUs127IqVrKqn+KlRIBiu4c9PkQDm8mTNdpmKtn5Mle6mJ9PRf/PS0ej1lsPrHWuz3PmoUdO4Me/akN4I5c+DEE8vQQ5EK4gKf3089Otr2RuDGmPapwNTidqwkvom7S/w8rH22nB9cNxms7AuIbs7wero9vfJZerW1UcaYLjHbF3O1NYFokLBHDxg2rN23+Cf/PemTZw3xvydrbTQotGgRbEt+sT6pELt3u8FjUJ5ZQjUfYB0wwD2CNGEhQXF3VpSg/nc+F8Vx70n0otg/N+lYkqB589z5Ka2hAY6OW28jOcUIsBaF/3PZsAFWrkymL1KZYuqvQvEyWCsmmzsHsd97166uFEmQzk0itW4w8Fy5g6tQXwHW9P19x2V4/fjU1+UA1tp/APOB/YDzYrYvzmprso9/S/Mxx3R4EbufV4u0GmZbcxUbYE3PxqalZ2NFAObPd5lCQRV2EVtRFBSS9uRYuiZf1ZrB2tbWxqOPPsoLL7yA9f/OvPUWeKV8pI7FlK6hR4/4bRNSjKBSUSYmDzkEevcOt6nkhqQFS9ek/ubXYwarL2OfNc4TqTcrgERKFdRTgPXvqa8TjDHXBV8wxpyISyMG+GvgpZ+nvv7UGNMvsP3FwEeBJYH9SqHyuIi94IILQs+rcba1I36Ata2tza2KOWJEeEMNFiTNv4gdNszd1pyHms9ghfiBdxVeWEgJZChd09rayne+8x0uv/xyli9f3u4usj2GqjXAeuedd3LeeecxceJEnl2/Pjwx2toavSVc6leZJisKkU8Ga0mOOWMUFJLMvGNpE24VF8j/WqgYCy8mLWOf/ck/fyFYEak1dwKnY0zvDrcssroJsFprZwD/nXp6qzFmrjHmPmPMS8DLuEWrbrfWPh142yTgb8DhwAJjzF+MMc/hgrA7gM9Ya2vvnvQkbNgA/kVqFrc0d+3aNfS8GgcDHYnNYIXoYEGZDZJWxIvYugiw+sfSunWwZk0yfZHK4h9LqdI199xzDz/60Y+4++67ueKKK/LadTEuZishwHr11fsWnj3/E5+AI48Mb6CgkEB86ZoKDLDmww9oFe28qQCrZBI4N1kg+JuhRa5i+OO8lhZYsKD0HRKRpPwEmAI8hzETy3lBWzcBVgBr7b8AFwNPAgOAi4ARwAvAZdbaa73t24CPA9cDq3GLYI0G7geOs9ZOQ4rDHzR26xa9SIuRVXHzKpcxwOoPvDUbK+AyxmbPDrdV2CrNFWfo0GiGryYsBKIB1lTpml/84hd7m6ZMmdLuLsqdwZqknTt3Kigk8d56K1q6xv9dqVL5Hoe/+c1vOPHEE7n33nvjN/B/PqtWwfr1eX2W1BjvTqXgmUolAmL07BldvFTnJpHaZW0rcC3QCXgG2IExyzFmWcxjaTE/uq4CrADW2r9Zaz9ore1jre1ire1trT3DWntPhu1brbU/t9aOstbul3rfx621b5W77zXNv4gdOxY6dcp5N3UVYB07NryabEuLq70p9W3RIti+PdxWwEVsXWSwGhPNbtDAWyAaaE9l3HXpErf2ZbxSBlgrbpEriB5Lb77pJn6kvvnHUgGlaypNPhmsq1ev5ktf+hLTpk3jk5/8ZPz49fDDoXv3cJvOTbJ+Pbzzzr7n1hIMtyqDNQNN/onUD2OOwi1afxRggEZgCPC+mMfQYn503gFWY8yQYj2K+Q1JlfIz7vIMCFXjYKAjfj2wvQOgnj3dBUqQBgviH0tDhkQXykjJ5nipiwAraOAtUc3NsGxZuC31e9IphwnATMdQrZQIiPADrDt2wMKFyfRFKkeRxnmlVq5M8nnz5oWet8TdgdTQ4CbTg3RuEu9YaunShSWB58pgzcD/mzN7tlskWERq0c+AfrharGOB/bG2IeOjiArZ2XJcPe1CH97Vi9Sd7dtd1l1Qliue13WJAFBQSKL8LKF2jqVqHDyXjH8sLV8OGzcm0hWpEH5AqLFxb+mahobi3wBUMwHW3r3h0EPDbTo31be2tui5yQ8cVrF8Mlj9bTIurqV6++Lzfgfe7d+f4F/9espgzWmi0h/nbdkCS4t6Z7CIVI5TgDlYezXWvom128r1wYVeIazDFY/N97EOl7Ir9Wzu3PAMYqdOMGJEXruqxQCrP+gODR78gbdmY+ubtdGgUIEB1rrJYD3ySPAWzdOFbJ3zj6VRoyBVGqAYGay+mgmwgib/JOztt11GeFCGAOvmzZvL0KHiyueYyzrA6h9Lixe7wJDUL+/c9G6/fqHnmX4f7777bi677DKmT58e+3ox7qpIWrt97tcPBg4Mt+ncJFKrWoBESnoWGmB9zFp7er4P4PFifBNS5fwgxlFHRQMdGdRDBmu7AVZ/4L11KyxZgtSpNWvgvffCbe0EWLM5XuomwNq5M4wZE27TwLu+tZMNnstxkW2JgHzOX1UTYJ01y00ASX3yj6V+/WDAgMhm11xzDQcccADXXXddmToWVaxayB3JOsA6cuTeiR3AHUdz5uT8eVIjtm+HBQtCTau9AGuclStXcvnll3PPPfdw2mmnxW5TjRmsvg77rIxwkXrxHDCmw61KIOlFrurkyl3a5WcJFXDbWDUOBjrSbomAvn1h0KDwGzRYqF/+//2BB8Ihh2TcXBmsHg28Ja2lxa16HhQ4N7344otF/8iaWeQKogHWTZtc2Y2UWpwMlXbE3VnhnVveffddJk2aBMBtt93Ghg0bytW7gvnHXFFLBDQ2uiBrkCb/6te8eeE71RoaePegg0KbxJ0Dnnnmmb3/3rlzZ+yuqzHAmnOiTdzdFVXwfYpIzm4ABmDMDzEm95XTC9C5gPceAxQ6+vke8IsC9yHVrLXVrTAcVECAtRYv2trNYAU3WAiuJjpzJnziE2XomVQc/yJ2zJjQRWw9ZHwXxB94L1jgskW6dUumP5KcBQtckDXNGBg9eu/TuL/L6eMrn2BL3PvyfU9FXBQffLDLUly3bl/bzJkwdCgzZ87koosuokePHjzzzDP0798/uX5KeWRRumbTpk2h55mCQJUon2POr+OcMcAK7twUnPBTgLV++cfS8OHs7hy+nI/7fezsbdPW1hb5HazGAKuvwz7747z33oPVq6PJKiJS7T4DPAz8G3ApxjwPvAPEXfxarL2pWB+cdwartXa2tXZlIR9urV1prZ3d8ZZSs5YscQGMoCwXuIpTiwGjdjNYQbOxsk8OC1yBSgREjB7takCntbVFJ4CkPvjH0mGHQc+ee59eeeWVoZeDx5ICrLiAdIaM8Ouuu463336befPm8YUvfCGBzklZbdgAK73LhZiJ9FKM31asWMENN9zAgw8+mHGbYhwv+Sxy5Qe3/LFeiD/Oe+ut8ASQ1I+YcV42tVO7eqXXdu/eHdmmLgKshx7q7u4K0oSFSC26Ebgcd8f8+4FrgO+n2uMeRVNIBqtI4fyZ2EMOcSsQ56kaBwMdySqDNWj9epfROnhwiXsmFWXLFli2LNzWQTZ4LR4vBena1dWAnjt3X9vMmTB+fHJ9kmR0ULqmZyDYCu7vdHrhq2wDRcVYUKRiA6zgzk1PPrnveeoidtq0aXubHnrooXL3SsrNDwh16wbDhkU288c67QYcs3Tdddfx6KOPArB48WKGxXxuMY6Xki5yBfvuRkl/zu7d7jw1blzOnytVrK0tWn937Fjanngi1BT3++gvzNjS0kJTU5O3+zoIsKYn/557bl/bzJlw/vml7ZiIlNtVSX1w0WqwGmOWGWMWGWMu6WC7/zLGLC3W50qV8wfeBZQHgNrMYG130A3xQWk/OCC17803w5nLjY0wfHi7b1EGawx/wkLHUv2xtsNbmtsrt9Hh3+y9H1MHAdagNWvCJQOkPvjHkn+nQIqfUReXYZerdHAV4Ne//nXsNsU4DvPJYM2pZE+PHnD44eE2nZvqT9xdf2PHZvU76wdYd+3aFdmmGMdCueXVZ43zRGqftXfm9CiiYi5y9T5gGHCfMeYH7WzXJ7WtSMEBVn9AWosB1g5LBMTdiqnBQv3xj6WRI12QtR1a5CqGfyzNnetqRUv9WLHCLcoU5P1etFc/0a8dmem8VIsB1tDfi8MOc4GhIK1+Xn+yHOe1eLe8FyPAGvTSSy/FtieVwZrz8e+fm3Qs1R//WBo0CPr0yep3yT9n+ccbVGcGa17nUf9YWr4cNm8uXqdEpPyM+SPGXIgx3ZPuSjEDrAAvAf8Avm+M+cv/z96bh8txlGf7dx3tx5aw5E2yFluLtcsb4IBJsA2xYwyYhJCFhMSQAF/CDsFAEi4gkI8khC2EkIQEAmZJCCbJxxZwsDEi2PwM2JKO9s1aLVmrrf1IOqd+f9SMTk9Nz0x1dfU2897XNZc0fXq6e86Zmq566qnnVUpNCHx8oZvYu7fZzZIwf9W+kXajwNoxIgDM8rEo0vHuPTwmK8TBGkOkkBEAJ08a14jQO9gTVBdfbIo2RUiyvHfChPiuUIjBbFwbLs2guK9P7k29zqlTpmBclBb9vCwcrFEeeeSR2O0h+o0+DtbEBgH7nr5ypeTt9xotomtc7iW2wBrnYLXvY1UYU3kJrPPngxWPIHn7glB57gS+BhxAqW+h1B+gVCF5iaEF1s3A9cBy4FeB/1UFvTGhAtiC0KRJJnw8AVVczpIUL4F1yxY4dizDqxJKxZkzsGZN47ZAAmvPMWWKid2IIo7w3iJusqKDYBL9XvbNYPVpj6V2sELzvUnaUm+xdm3jCoC+Pli6NHZXu68TWmBtRVEO1sQCq92Wjhwxbnuhd2gRXeMyFrLbV9ykYM84WEePNqu8osjknyBUnRnA64HvA88DPgVsR6lHUOq9KPX0vC4ktMCK1voQ8IvAPwLXAj9RSj079HmELiBuJrYv2UdSIgJqLFwIY8ZEd2os1CN0Nxs2gO1GcBBYJSKgBeK662r279/PRz7yER588MH4HTzc4NG25Doo7fqIAGhuS+vX0z64ROgq7LY0f74pchVDFkWuoiyzVyfUKIuDtWO7nTrVuOmjyIRF7/DEE2blX5QUAmvc575nBFaQyT9B6Da0fhyt/wGtbwcuxJg9Pw9cBrwXeBildqHU36PU7Sg1rt3h0hBcYAXQWg9prf8QeAPmDd6vlHplFucSKkyHKs0uSERAjbFjTfXzKCIK9Q52W5ozxzjCOyARAS2wv4ukLXUVr3vd63j729/O8573PPbaA9bDh5tdYQ7RNe0E1lYDvhCD2bIJrPYyVJYubZw4HRpicb6XJBSJLbC2aUtZO1gvuuii2O1lyWDteD9WSib/ehm7nzdxIlxxBeDWl3MxpDiNN0pGMIF1zRoIPKkjCEJBaH0Crf8TrX8PmAbcAHwIOAz8H+AbmCiB/0CpV6HUJSFPn4nAWkdr/Sngl4ATwGeUUh8FmkuHCr3HsWOwaVPjtoT5q1DN2dakODlYIT6fS+gNPIvFdWN76cRrXvMapk2bxt133916J7vj/fjjsH9/thcm5MY999wDmAy6T3ziE40/tL83J0wwrrsORO9FrgO+nnCw9vfDvHkNm6zWJXQrw8PNAmCbe5Pd1wktsGY50ZFLBivI5F8vY/fzrrrq3OSVy73E3tatEQHORhu7n3fqVPO4VBCE6qO1Rusfo/Ufo/UyYC7wNuBh4IXAZ4DHUepBlLolxCkzFVgBtNbfx+SyrgfeDPxm1ucUKsDAQGM4/5gxsDi5r0UcrBHszsLAgBngCN2N1i1zuWzsQZ9LgZxucrCuXbuWf/7nf2bv3r3ceeedrXecMwfOs4pQSgGErqTp+9RuS8uWwajO88JFRQSUrchV7PeFJQpFn42JRtsI3cXWrc1Z8G0E1qwdrK4THSGP3Q4vMcvu523darJYhe6njRvcR2DtFgertyh8wQUwa1bjNpmwEITuR+vH0Ppv0Pr5wMXAbwFfARYAQWJNMxdYAbTWW4CfA74NjM7jnELJsQexixaZZe4JEYE1gt3xPnFCqp/3Art2waFDjdscHay9JrD+7Gc/c9sxrvq5PbgRuhPP6BqfIlfdGBHgIrBGW1ZTpIDQPdht6bLL4JLWq/CyzmB1dbD64ONg9eq/LljQ3FeWyb/u5/jx5v585HvV5V5i79OtDtZE12ybEWTlnyBUF6Vej1IXJnqN1kfQ+t/Q+reBS4C/D3EpwXq2Wus+bXIOWv38GHAH8FxMZS+hl0mQy9WOKnYGkuIcEXDhhTB9euM2mY3tfuy2NGVK8+egBS6d8G5iwoQJDc/bDuAl6673GBw0Vc+jeAisvRwREIvVli4AZtb+P2gX5xO6h4TRNUVFBGTVDjvh1X8dMwa9eDGPbdvGoytWcPjwYRGFegF7Rdro0bBkybmnvexgTdV+pdCVIHQTf4tZ6v9fKPWyxEWstB5C6yB5cLlaB7Thf7XWP8jzvELJOHu2ucK9R4ErCNMxLjuJOjySz9V7xMUDOLpOe83BagusJ0+ebL2z3ZbWrzcCnNBVNHy+165tLHLR12ciAhxol8Haim4UWGMnaKZNg1qBofqV1VvXhRcmMxsIFcIxuqZOlSMCfMRSrwxWYCVw8OBBhoaG2LJ1q/TzeoG4VX/jRrQDl8+bi8DaDaaVVALrE0+YhyAIVeQ7gMIYOr8C7EWpT6PUc/O+EFmbJeTPxo0mTDyKfZNzpBs6A51wdrCCFLrqRRIOYqOUTZzJmvHjxzc8P3HiROudlyxprH5+9iysW5fRlQmlwG5L8+Y1Z/G2wMfB2o0RAbHnjql+Xn8mAmuXsn+/KQ4YpcNEetYCaysBM8SqDZ/JEl+B9Svr1zduWL0aYpZ7C11Eh+iaUBEBVXOwpr7/XXEFTJzYuE0mLAShmmh9OzAdeAvwM+BpwKuB76PUNpT6c5RamMeleAusSqmtKR5bQr4JoWLYN69Zs2DyZK9DiYPVwhaqd++GgwczuCqhFBw5Ao891rgtgRvcxcHQTYwb17hapK3Aet55TdXPpePd5dh/X8/JCl8Hq0/bK1uRq5bvof69VLu2+rdUNznkhQh2Luj555vigW2wJ5PzymAtysHq2389PX9+4wapft7dDA93XPXnExHgIsKWfUyVWmCNW6Ui/TxBqC5a70frT6D19cB84APAVmAW8MfAGpR6GKXehFKtQ+FTksbBekXKh9Cr2Dcvz3gAqF5nwIdEAuvcudDf37hNOgvdi93pHjsW7MFXBJ9OeDfTVmAFyefqJbRuFoUc4wHMy5MXueqZiAA415bqVzYHOD+XKxIKwe53LF3auCIghqwdrK2omoP12Jgx7LA3Sj+ve9myxRStjWL1TXzE026ICAhy/5OVf4LQnWi9Ga3fi9ZXAjdgClgdBJ4BfAzYhVLfQqnfRKnx7Q6VlDQC6+wWj/oU9T0O+wi9iC0KJRjE2vSCgzXRe5TZ2N7CbkuLFsGYMS13d+lgd2MbqmO/t7YZrBBf6KqLfz89zeOPw+HDjdsSRNe0y2Dt+YgAiK1+7n/nF0qPPVnh0JZsgTVuCXMasixylWcG6+DgIE29OhGFuhe7LV12mSlqG8Hl89eNRa6C3P/s76YNG5pj7ARBqDZa/xit3wBMw2S0fhU4A9wGfAnYG/J03gKr1np7i8e22i7H2uyzPczlC5Xj4MHmXK4UAmvVZlt9SNz5l9nY3sEWzzu0JZcOdje2oTr2e+u4BNVeIn74MOzaFfiqhDyxP/PnlqjbbWnyZDOQdSREREA3CKwtGTsWFi9umKC4CokI6ErOnGnOq166tOPLshZ3XCc6QhzbR2B1fb+Dg4M09epkIr17sQXWmLbUq0Wugtz/7Lz9oSFT8FIQhO5D6yG0/ibwW8CrMI5WBUxs+7qESJErIV/sjkJ/v1nW7knVOgM+JH6P9mzsunVw+nTgqxIKJy6Xq4NLyMfl0E3Y763jEtRp05qcIjJhUW1auuLi4gESiH8+Ra56SmAFuOoqold2NSKwdiWbNjX3ORwEVnvCK3QeeKvjlSWD1fX9njp1qkFg1QB79sC+fU6vFyqGgxs8VERATzpY+/ubo7WknycI3YlSz0SpvwEeB/4VuAgYAv475GlEYBXyxZ5lt2cOE2J3BrqxQE/iQfjSpY3CwJkzYFedFarPtm1w7Fjjtg6DWB8HQzcJIIkHtEo1O8LFKVRpEgmsbWj3vey69LcbIwLaYrWlpUBfWa9V8Mf+jrziCpg0qePLQos7vhMdWZ4rim9EwKlTp9gGHLN/IPem7uPIEdPXixIjsIaKCPD9TBaFi5Bss23bNh544IHG/eLioARB6A6UmoNS70GpDcCPgTcClwCPAm8FpqP1i0KeUgRWIV8C5q9CbzhYE7+n889vdgXLbGz3YQtCl14Kl7QviOjTCe+mNuU1eJBCV11FbCzEqVOwcWPjtg5u8HaDVVchP4vsR9/j5MKyZQ0RAROAWbK6ovuwxQnHLOPQAqtr/zBEPzJEBqvreU+ePImGkRzW+utEFOo+4gqZXnll024+BUzj9ul2B+v27duZN28eN998Mx/60IdGfhAXrVby9y4IQhuUuhClXodSDwKbgPcCVwI7gb8AFqP1M9D6b9A6+PIPEViF/Dh7FtasadwmAmtHvAbhMhvb/XgUEZEMVo8lmfbvdetWOHo04FUJeRLrYF23zuSu1enrM3mhbWj3vZynwFopB+uUKQxNn96waeHgYEEXI2SG50R66IgA19cX5WD1jQioF2es9+rOHUUm/7oPu5/XopBpqIiAqo2pkt7/3vnOd57rA/zxH//xyA/sft6RI7BjR5BrFAQhJ5Qaj1K/gVLfwEQA/C3wLOAI8FngZrS+Aq3/FK0zXdorAquQH5s3gz2YSimwVm221QevQbjMxnY/CQtcgVsHuxvbUB2vAe3ChY0DGq2bBQShMsQ6WO1B7Lx5MGFC2+MkEVizdM5VSmAFhizheoEIrN1FikKmZXGwZnmudq9xvY76fk3T5uvXN/exhWrj2M9z+ey49H+qNqZKev87dOhQ/A+mToWLL27cJhMWglA1ngC+DLyw9vxbwG8AU9H6NWj9g7wuZLTvC5VSz+2wy9R2+2itl/ueW6godkdh5ky44IJUh6zabKsPXu/Rno09dAh274YZMwJemVAYx47BY481bnMYxPosI+smvATWsWONayT6/bVyJTz72YGvTsiD2AGkhxu83feyb/ajj9BTtTZ8dsmShucLTp0q6EqETIgrZDpnjtNLQ2fqF+kkzzIioM5qYNi80OSFDw2ZVWLXXZfoOEJJSVDItFcdrElNAn2tan4oZX639903sm3lSrjjjrSXKAhCfkwEHga+APwbWh8s6kK8BVbgAaDdN+8v1R5x6JTnFqpI4PxVqF5nwAevzv+MGTB5Mhw+PLJt9WoRWLuFNWsaHcljxsCCBR1f5tPB7ukiV3WuvrpRYBUHa2Vp+j4dHvZyg7f7LLnel3ouIgA4vXBhw/OLz541rscLLyzoioSg2G1p6VLnQqa2u7xKGax5FrmqH/sksBl4evSHq1eLwNotbNsGx483bmtRyNRn8rwbBNak97+2/dmrr24UWKWfJwhVYwFabyr6IiCdyLmc9gKrIDSSsEqzC1XrDPjgNQhXyvx+l0eM4gMDcNttga9OKAR7ELtwoXFadkAcrJ4Cq/1dtXq1cZc4CgdCebA/A+cdOWIEvigp3eCt2pU9uMuiuI7vcfJi6PLLOQH0RzeuXg033ljQFQlBSTGRHro/5zvR4eOc9RFLQ0ywDGDWP55DRKHuIUEh01BFrqo2pkrah23pYIVm8XrrViNwn3ee7+UJgpAnJRFXIYXAqrW+KeB1CN3O4cOwc2fjNsfKsu2oWmfAB+9OeJzAKnQHnpMVPhms3dSm7PcSW/AoDrvjfeyYcZc4Ln0VyoP9mb90//7GHZ72NBNf04GkAuvw8DCjRo1yPoYrVWufw8Aa4JnRjatWicDaDaQsZBpC6Gz3+jyjOrJ0sEYZMCcb2bBq1UhkgFBt7In0NmMml89ON/b/gjpY63n7Z87UD2S+z66/Pu1lCoJQFEqNA34TuBGYBoxrsadG6+eHOq0s0xfywZ5VHz/eFBJJiQisbbAHNhs2mAII41p9twiVIC6Xy3EQ6zKQ7MY2VMd7QHvJJcY98sQTI9tWrxaBtYLYn++p+/Y17rBsmZM40e6z5Dro68WIAK01A1gCq0z+dQcpC5mG7s/lWWwuzyJX0WOvwlpKeOCAuU9Nnep0LKHEJJhIl4iA9tvqtBVYx441UVvR/vXAgAisglBVlJoFfA+YC3Tq2Af9spP1jUI+2B2FxYvBcvP4ULXZVh+8OzyLFzcuYR4aMlVmhWqzcyccOdK4zdPB6rJETDJYa9i/YxGFKon9N59qO1g9JytcIgI6XUsvCKzDw8M0tBylYO1ac38Sqo3tuJs1yzjCHQnh6Gz3+rI5WEP0X3cCeuLExo1yb6o+CQuZisDafludjv1Z6ecJQjfxCWAe8H3gJcAyYHaLR1DHjLfAqpS6Thll2Bul1CyllKSx9wIZ5K9C9ToDPnh3wvv7Ye7cxm3SWag+9iD24ouNu9KBXnewisAqRD8DY4CLDx1q3CGAwOoycO10DFeqKLA2pUSeOgVbthRxOUJIUvbzispgDSHs5ulgtTlrFY6Te1MXsHp1Y/RDh0KmPpPnIe5JR48e5Yg94Z8jQTMFRMAiAAAgAElEQVRYIb6fV+L7qSAIbXk+sAm4Da2/gdZr0Hp7y0dA0jhYfwq8N+X5/wz4ScpjCGVneDhVLlc7esHBmuo9iijUfcQNYh1dpi6d8G5sQ3WCCqxbtsCJEwGuSsiT6GdgEdAX/Qwo1bJKc7vj2M8lIqA1WmueBHbZP5B7U/Wx/4YJc/bLksEaoh3mFREAMQKrFLqqPnZb6lDItAgH689+9jNmzJjB9OnTeeihh1rulyUuQnKUjg5W+/7/1FOwq+luJQhCNTgNPILWZ/M+cdqIgO5ZOypkR5wQIQ5WZ1J1/u3Oggxiq08Kl5A4WFMM4BcsgNGR2PJ6AQShUkT/5suwQpfmzjXO/4THsZ+7Dvqyun+VuQ3X33PTnUjuTdXm0KFmIUIcrMFfE8cZW2Bdvx5On/Y6llASEvbzQn3eknwmf+d3focjR45w7NgxfvVXf7Xj+bMguIN12jSYMqVxm9ybBKGqPIiJCMidtEWublNK3Z/i9Qs77yJUHvvmdNllcOGFQQ4tDtYO2A6SffvM45JLAlyZkDsnTjQvpU3gEvJZItZNpBJYx42D+fNNXmSdgQF45jNbv0YoHdHPwFVmw8gPE7SlLBysIbIfW20rC1GB9QXRH8ggttrYrskJE5ojijqQdZGrVu0ry3bYzjEXyrF7Zv78xg2nT8OmTbBkidfxhILxKGTqszop7aTfunXrzv1/z549ba8vK4JnsCpl+gEPPDCybfVquP12zysUBKFA3gssR6nXoPU/5XnitALr1NojDbmPBJRSFwPvBF4MzAJOAtuA+7TWd8Xs/2Lg7cC1tU2PAH+ttf5WLhdcdTLKXwVxsHZk1iyYOBGOHh3ZNjAAz39+oKsTcmXNGtP5rjNqlFk65ojLZyntsswyk3pAu2xZs8AqVIro33wp1mfCMR4A+3V0Flglg9VQv7bVIxvMv9u3m+J9kyYVcl1CSgIUMq1yRECr9t1O0AkVETDU3w+zZzcWRVq1SgTWqrJjR3Mh0w6Tf6EiAqpmWgnuYAXTz4sKrHbdA0EQqoHWj6DUrcAXUeoVwL3AbqDVbOvdoU6dRmCdHeoi8kQp9XTgu8CFwBrg/wGTgMXAW4G7rP3fAnwMOAt8DxgEbgW+qZR6o9b6k/ldfUWxb04BBdaqdQZ8SCUi9/UZ0SCajyQCa3WxB7ELFhhnpSO97mC13+9Q0srlV10FX/nKyPN6AQTHDFyheOqf70tqj4ZPeyAHq2u7CjFBWDWBtf6eN2LCsSZEf7h6NdxwQwFXJaQmZf4qZB8R4LpfXk7yUO9Xa2361VGBdWAAXv5yr+MJBWO3pYsv7rjqzGfyPM/YmqwI7mCF5jHqxo2mEOP48UkvTxCE4rkF092/Avj5FvsozHCgeIFVB662lQc15+p3MH36l2itv279/Hrr+QLgwxhR9Wat9UO17fMxuQ4fU0p9R2u9OY/rryRHjhhnShSPjncrqtYZ8CG1iLxsWbPAKlSTlG5wH5dDWdrUoUOHmDBhAhMmTOi8cwuCOFijPPkk7N4NM2Z4X5OQL/XPwDLrORMnGsd/wuPU8clg7UUHa/33MASsBZ4V/eHAgAisVWRoKEgh0xBCZ7vXuzpYQ2Swtjtfq9f4vt/h4WHz+/56ZEgjha6qi0ch06wiAuqvcxImCyATB+uiRcacUv9dDA/DunVw7bXtXycIQrlQ6i5MTMAg8J/AVuBYHqdOGxFQNf4MuAh4vS2uAmitH7Y2vRkYBXyyLq7W9tuolPq/wEdr+7wxu0uuOHYnb+xYuPLKYIcvqxgUktTv0V72um4dnDkDY8akvDIhV7RO7RLycbCWoU1997vf5SUveQn9/f088sgjXHHFFV7HST2QrhdAOHRoZNvAgAisFaL+Nz/XcuqfiWXLzKAq4XFGDpM+g7WKAmvSc0X3HyBGYBWqx5YtcPJk47YEcRt1snawtvq+L8pJ7ns/in2dLWg//jgcPBis3oGQIx79PJfPbDcKrEknNpzex4QJMG+eca7WGRgQgVUQqscfAkeAn0PrDXme2H00UXGUUhOAVwDHgX9xfNkLa//eE/Oz+rYXp7y07sbuKCxaFFTYEwerA/ZAp14AQagWO3cax2SUlA7Wqgist912G4ODgxw+fJhXv/rV3sdJLbAq1dyeRBSqFE0O1voPArrBXR2sRS1NDknSc0Xf44A5wMgPV69uzJgWqoH9HThjRnMlbgdCZ7C6Hi8rB2un4/j2X2O/e+bMgf7+xh3FxVo9YgqZHrn8cu6++2527NjR8mU+EQG+E4FlIun1Or8Xuz8gbUkQqshU4Ad5i6vQQwIr8AxgIvCo1vqkUuoFSqmPKqU+pZR6i1LqsujOSqkLMAWwAB61D6a13gkcAC5XSklVhlZkWOAKwnfIy0jqzs2kSWA7/kQUqh52B2/KFOOoTIBLB7vskxYPP2wvNHAnyPeFdLwrzfDwMGOAemm4c5+JjAVWcbAaom2uqeUcO9YcKSSUn0D9vKwdrK0yt8uSwepd5GpoyLjv7aJW0s+rHnYh09Gj+fX3vIc777yTG264gdOnT8e+LOuIgLKS2f3Pdg2vWtU4GSgIQhXYQkFaZy8JrItr/+5TSv0X8G1MUas/xBSx2qyUiibC18XVw1rr4y2Ouav27+WhL7YrGB5uFh8C5q+aU5RbDApBkPdoD3ik41097GJxV12VuLhSVR2sUZwytFqQicC6YQMMDnpfk5AvWmsWAGNGNsQ7kx2OEyX6WfIduIZqa2UWWKP77wcO2pXm5d5UPQIVMi1KYA3RDn2EKd/ztvw92b93qX5ePazvP33llXy3VtF+9+7d3HfffbEv83GwisDaZl+7LR04AE88keTSBEEonn8AbkapK/I+cS8JrJNr/94B3Aa8npGqYh/GFL76vFLqmtp+59f+PdHmmHXhdaLLBSil1sQ9gLHub6NCbNtmHClRPHK52lF2MSgEQd6jCKzVJ4BLKJTLoUgKF1gXL27M6hwaMrnGQiXQWhNtORpg9mw4//wWr2h9nFbPXQd9RWU/hiSNgxVgw1ir+yP3pmrx1FNgL132FFhD33tcHaIhzusaC9Lu567nbfm9Yfev16419yehOlii+JlFixqet/q+dRkn+Eywtztnmr5YKNJGBLTcd+ZMs/ovitybBKFaaP13wKeBH6LUK1Fqel6nLv7bMT/q73U08B6t9ae01vu11tu11ncBX8WYWu4q7Aq7DftmdOmlcMklQU/RCw7WIAKr3fHevbuxUI9Qbk6ebM7N9RjEdoODdZTteEtAkKrN/f2mAEIU6XhXhuHh4UaBVevgkxWuYktWwk6ZBVZ7/3V2Jru0pWphr1IaN867kGno/lzsUnqH84bKYM0qIqDl6+zvsVOnmvI8hRKjddP3ny2wtur/uHx2QkcEpOmLhcJnYqPT6wHJ2xeEbkCpIUwx+unAZ4AdKDXU4nE25Kl7SWCNWin/Jebn9W03Wvv3x+xb57zav0ddLkBrvSTuAcSH6lSdjPNXofxiUAiCvMe5c01lzCiSHVkd1q1rzOXq6zMF4xLis0SsbG0qTac+mENKOt6VxXaw4imwSgar37k6Oli3bDGFXoRqYC9DX7wYRo/2OlSQCbA2r0/j/utEiAzW1BEBkyebAmNR5N5UHXbuNI7wCGcXLmyxcyM+EQG+E4F1yiqwJmm/bb9jJG9fEKrOTmAHsL32b7vHzpAnDiawKqX+RSn1rFDHy4B65YQTWuv9MT/fVvu3brGsr3marJQ6r3l3AOo9GanKEEdcZmRgyi4GhSCIwDpqlBn4RJGOd3Ww29L8+c2CuQM+y8jK1qZCCqytHE0dkY53Zek7eJCpkedaa697U4gMVokIgC2jRzcKclqbQi9CNQg4kZ51Bmur42XlYO10HN8Jv7bXK3FQ1cXuR1x4IWcvvrhhk2qRux8qIiCJwDracyIlJGkjAhIJrOvXQ4siY4IglBCtr0Dr2c6PgIR0sN4J/EgpNaCUepNSanLHV+TLo7V/JyilxsX8fErt32MAWusnGRFZr7V3VkrNBC4CtmutjwS+1upz7Bg89ljjtsD5q1B+MSgEwQYdtoggHe/qEGiywmUgWbY2ZZ+/1QDD51jeDim7471vn3kIpWe8FbVxsq8Prrgi8XHEwep3Lnv/QTATRlHk3lQNAhcyDX3v8c1gLcrBmjqDFURgrTIxxeKGHfs/ofL1k3yOu8HB2nbfpUsbC8mePt0c1SUIghBDSIH1FcByYAnwMWC3UuoLSqnnBjyHN1rrHcBKQDESAxClvu3RyLZv1f59Wcz+9W3fCHKB3caaNcaJUmf0aHBc6pIEcbAmwO54r1nTuOxcKCdaNw9iPScrfDrhRbepp6wlc7t37/Y+VjCBddYsmGjVNpSBbCWwBdYt/f2NRcscEYHV71yx30HiCK8mjz3WHOcQ0MGaV0RAWTJYfSMCGp7bfYPt2+GIeEAqgd2HuOoq51U2PhEB3ZrBGszBev75pgBmFOnnCYLgQDCBVWv9Za31zcCVwF8DTwG/DXxfKbVOKfVHSqmLQp3Pkw/V/v2wUmpafaNS6hrgj2pP/yGy/98AQ8AfROMPlFJXAn8KnK3tI9jYN6GFC8HOWgtA2cSgLAj2Hu2O98mTUgChCjz+eHNBMk+XkI+DIe0gNy0hK9UGe299fc3tyXafCKVkgiWwbho/3us4SQVWl8FsFYtcJb3m2DZoi3KrVjVO0ArlxP7Ou+wyuPBC78NlXeTKNYM1r3YYKiKg4ThXXtnc15YJi/LTopCpLbC6RgS47CMCq0Obk36eIFQHpa5DqVkpjzELpa5LeynBi1xprbdord8FzMS4PL/LiOi6Syn1b0qp54c+r+O1fRn4PLAMWKuU+pZS6n7gx5iIgH/SWn81sv8G4C5gHPBDpdS3lVL/hXHCXgi8TWu9Oe/3UQlilrpkgThYEzBlihkARZHZ2PJj/40uuACmT/c6lIugY3fou6lNBXVIyVLM6nHmDOO3bWvY5Cuwtvss5elgjaNoB2u78zs5WJ98ElI41YWcsL/zUsZAhZ7c8y1y1W5f13O12tbu50EyWMeMaS6AKfem8mMXMh01ChYtCiO6t9inFwXWxPvK6gpBqBI/Bd6b8hh/Bvwk7YUEF1jraK3Paq3/Q2t9OzAb+DtgLPBrwL1Kqc1KqbcqpfqzuoYWvAp4LbAFuAm4HngEeKXW+rX2zlrrjwF3AA8BvwA8H/MHfLHW+m9zuuZqEbekOSOBVRysCRFRqHrE5a965pC6dMLLJrCGdNAGHcDHFUA4c8b/eEL2bNqEsv5GmzNwsPoOXLslIiCJg0hrbSb+pkxp3FHuTeUnZklzGspS5Mrn3CEyWH0jAppeJ3n71SOukOn48d4OVt8JvSSf464vcgXN/bzHH4eDB52vTxBKh1ITUOr9KLURpU6h1OMo9VmUSu7cUWoySv0NSm1HqcHavx9HqQsyuHLnqyrw3OfI/NtRKfU84DXAL9c2ncQIms8GPgy8WSn1Iq11LtNC2ny7/lPt4fqabyBZq+7s3Nmc+SQOVm+Cvsdly+C73x15Lh3v8hPQJeTSuSy7wJomMiCowLpkSePzegGExYv9jylki7X0fDtwzPPzFCKDNcR3e9UE1tj3rJT5Xlu+fOQHAwPwghcEu04hMEeONBcyTdnPyzqD1bXIVX3fJPeaEAJrEAcrxLvuhoe9sqaFnGix6s/Xhe3yeXSdCGz1uewJB+ucOdDf35g1vXo13Hij8zkEoTQoNR64H3gWsAf4f8AVGPPhi1DqWWi91fFYF2HMh/OArcB/YeowvRl4AUo9G60PtTlCVtyGWZ3uS5CCQZkIrEqpSzF/rN8H5mDU5DXAPwJ3a62PKKVmAH8M/CHwCeB5WVyLUAB2R+Gii2Dq1ExOJQ7WhNjOhm3bzEBp0iT/YwrZMTgIGzc2bkvhEuoGB+uLXvQi72MFFVgnTTLV56NLzgcGRGAtM9ZkxSr8P98hMliLyn4MSav32mrw3fI7aNmyRoFVlmKWmzVrGp+PHWtcdykoKoM1hIPVJyLAt2/X8fdkT8IeO2aKXdkFe4Ry0GbVn90fa/WZ6tWIgLRtt+M9t563//DDI9sGBkRgFarKuzHi6kPArWh9DACl3gZ8BPgsZnW3Cx/HiKv/AfwGWp+tHesTwBuBjwKvDHbl7kytPdKQuhMdbDpTGV6glPoPYAfwQWAG8K/Ac7XWy7TWn9RaHwHQWu/SWr8eo6RfH+o6hBJgO+6WLfNe0tyJXnCwBhVY4wog2AMloTysWwfRDnZfXyoBr4oZrFlGBLhW6G2JRG5Ui4GBhl7TAP6fr3biaJ4ZrGUUWL2WaNptacMGM8EklJO4QqZjxqQ6ZOgJ8zQZrGmLt7U7X6tzBHOwXnKJeUSRe1N52bOnuZBpCwdrEhd2p31cxUlXgTV1f8qDzCMCoHnCQtqSUEWUGgu8ofbs9efEVQCtP4rxHNyIUk93ONY04OXAaeB158RVw13AfuAVKHVJ3MszZHagx5y0FxJyvcg24JuYKIDtwDuAGVrrV2it/7fD6yYEvA6haOIE1owQgTUhY8aYgVAU6SyUF9sNPm+eWa7kSTdEBKS5nqAOVhCBtUocOmTy0yIM4P95ave5dBVsujUioF27avmeFy9uXMI8NGRyjYVyEjh/FbKPCEjiYE16bh8nXSiBNfY8UpynOtj9vMmTzxUydXWwZhkR4Cqwnikggz7t5IjTvdJuS2vXNhofBKEaPAd4GrAFrR+N+fk9tX9f7HCs2zAa4g/R+omGn2g9iInVHAXc7n21Pmi9PdgjJSEF1suA/wRu1VrP11p/WGvtkgT9ISQeoHs4cQI2b27clqHAKhEBHogoVB3sQVHKKs3dEBFQKoHV/nvs3t3sRBHKQf17rvYZOIGpdJlFRICvM6hbBNYkDqJzz/v7Ye7cxp1t4UEoB8PDmRQyLarIVYg2FMLB6hsREHsfs/8e0pbKS5tVf679MZd7SdYRAadPn47dL0vStl0vB+vJk7Bli/M5hBx56inYt6/oqygrV9f+faTFz+vbXWZLQx6rKwkpsM7UWr9Ma/29JC/SWm/UWv8g4HUIRbJ2rel81xk1ChYtyux04mD1oFUBBKFcaN08KErpEvJxsIZcou9DyDYe2iHF3LkwwVqAIU6hclIbxNY/PWtq/88rIsBl4Brq/lW0wOrlYAWZ/KsKO3bA0aON2wIIrKEnwFy/77NysCbNYHU9p5PoZv89tmxpLNQjlIc2q/58P8O+E3rd4GANHhEweTLMmNG4Te5N5eTb34bbbzePd77TPBfqzKr9u6vFz+vbL8/5WF1JSIH1g0qp3+u0k1LqlUqpzwY8r1Am7JvO/Pkwfnxmp+sFB2twEdnueB89agZMQrnYtw8OHGjclkOVZnGwJmDUKFiypHGbdLzLifV3qT/LwsHqOugLIfiX0cHa7n20vWfLsuZqYE/8xWV+elBUkauuy2AFEwU1OlLHWGvJ2y8jg4MmbzpK5HvQNyIgDpfPTZoM1io6WJ33lcm/alD/u+zbB/fdBz/9abHXE5axSqk1cQ/H159f+7fVTNvx2r8Tcz5WVxJSYH0l8PMO+z0HuDPgeYUykWP+KmTgSCshwQfLl14qBRCqgD2InTQJZs5MdUiJCAgssIJ0vKvA0NCIuFD7DNSlu1AOVp8M1l6MCGh7z7bb0r59styvjGQQDwDVjggIkcHqGxEQ+7px44zBIYrcm8rH+vVtC5naf+tWhaQkg7X9tlY/c77/Sz+vGmR0bxKEpIQUWF0ZC0g6dLeyd2/j84y/3HrBwZrJe5SqmOXH/pssXdpYBMYDn4iAotuU6wDDhVwE1jVrJHKjbGzZAqdONWyqd8N9P99Ji1z1Ugard0TArFkw0TI8SHZk+choIr3KRa58XLC+9yPn35OIQuXH/pvMndtQyNS1P+YTEZBWYLWpooPVW2Ddvh2OHHE+j5ADBw82FTLtMoH1tNZ6SdzD8fXHav+2qpR8Xu3foy1+ntWxupJcBVallAKuA/bneV4hR770JfjmN+GDH4Tf+i249tpMTycZrJ5Ix7v8FFRExFdgfeSRR3j/+9/Ptm3bvK8vjtILrHEFEOxCf0KxRL7fNCYc6nD9eaCIgOhnyXXgGuL+VUaBNYmDqOF5X59M/pWdEyeaC7ykzAavk3UGa5YRASEcrL4Ca8vzxPXzurCPXGk6TFb4RgT4RtIk+Rzb+1bBweq975VXwtixjdskwqZc2G2pvx/mzCnmWspJPQtwRouf17dvz/lYXcnozru0Ril1v7Xptpht0XPNBaYCX0hzXqHEKAVTp5rHrbdmfjpxsHrSqgBCf6vJKCFXTp82S8eiZCCwhnKwnjp1iptuuomjR4/y1a9+lYGAokjpBdYpU+Cyyxpnzlevbl6eKRRHi/xVyC8iwNdRlPQ6Wm3LiqTuv47i0LJl8NBDI89FYC0XtkN/9GhYsCDIoYvKYE3j2mu3f14RAc4O1iefhN27mwv2CMXRQWD1nSTwXTGR5HNsX1sVHKze99zRo010w4oVI9sGBuCGG9xeL2RPBqv+uoyVtX+va/Hz+naXZUMhj9WVpBJYgZsi/9cY8XRqm/3PAN8E3p7yvIIA9IaDNZP3uGiRKdBTF6uGh83A6ZnPTH9sIT0bNxqRNYpdTMmDrCICli9fztFaVenVgWf1sxRY0xyrgauuahRYV62Cl740zLGF9ESXmWvdILCGcrCWKYM1T4I6WKFZFFq/Hs6cgTFjvK9RCEhcIdNx44IcOusM1voxzWK69ucJ4WDtdIxMi1yBmfibMgUOHRrZtmqVCKxlIS5j2tPB6vLZCR0RYF9bFRysqb5jrrqqUWCV+JpyESewClF+BDwFzEWpa9B6hfXzl9X+/YbDsb4DDAO/gFKXoPXIF5lS44AXY+JAv536qrNCqduApcBO4D/QOugXWFppf3btMQdQwD2RbfZjOnC+1volWusD8YcThGSIg9WTceOaXSfiFCoP9t9i9uzmbEIPXDqXPh1Qe8AasthcSIE1s6J4ErlRXp56CnbsOPdUM5K/CvkJrKHamstxi44ISOJgbdrXnkg6fdpMOAnlIMMiIllnsIL75zVEBmundujjpkt0HqWaRQZZ1lwe7H7CxIkmhzqCa5soQ0RAEQ7WpO7zVN8xcW1J8vbLwdAQrF3buK278lfTo/Vp4JO1Z3+HUued+5lSbwOuAn6A1j+LbH8DSq1Hqb+wjrUH+FdMXaVPoVTUsPkh4GLgiw3CaxEo9TqU2opSz7G2/zvwLeCvgC8DP0Sp8SFPnUpg1Vpvrz22AX8GfDGyzX7s0YHVYUHoBQdrZiKyiELlJaMiIj4OBh+B9ezZsx5XF0/pIwJACiCUmTVrGp4Ojx5NVK4LFREQPY7v0stQ3+1FC6ypHKyTJpkJpSjiFCoHWjf/LQLlr5rDh20PruJLiEmKEBmsLudMLAbbfx9pS+XB/lssW9a0pNl1RZHP8v807SPu2opwsOYqsNr9vOPHIXDNAcGTmEKm4mCN5c+B/w+4AdiEUl9BqR8DH8HUR/o9a/+LgAXAtJhjvQXYAvwqsB6l/g2lBoA3AZuAt2XzFhLxK5hCXCO5U8a5+jJgN/CXwMPAM4HXhDxxsHAKrfWfaa2/Hup4guCCOFhTIAUQyktGLqGsHKx9HQYFaaiEwCoFEMqLNVlxdMYMop8g3+/Tdu3E1xnUiw7W2GuVyb9ysnu3yfGM0gUO1hARASEyWF3OmVjItf8+Gzc2CxFCMTgsaa5SkauyZLC2a0e+RVwBuPhiU18kitybyoE9WTFjBkyeXMy1lBmtTwE3Ax8ATgC/DFwOfA64Dq23JjjWAeB64G8xTtZfAZ4GfAK4Hq0PtXl1XiwAVqN19EvhNzGL2V6G1n+K+X0cAF4R8sSS/itUGnGwpsB2Njz5JOzaFebYgj8HDzbmeUJmAmuoDNZuFFgPHz7MJz/5SX70ox91PvCYMaYAQhRxCpUD6+9w5PLLG54XWeQqhKAkAquQG/bfYcoUmBZnbPEj6yJXcedIsq0dPsfw6dslPs/ixY2uyOHh5qW0Qv6cOeNUyNS1TbjsFzqD1d636x2sII7wspLRqr+uROuTaP0etJ6H1uPQehpavwqtmwUArd+H1gqtX9niWIfQ+k1oPat2rFlo/Wa0fjJ2//y5GNhrbbsR2InWDwN10flBTJxpMLwFVqXUsFLqrFJqfu35UIJHuPWjQk/TCw7WzETkadPMACmKDGSLx/4b9PfDnDlBDp1VRIAtsHZDRMBb3/pW3vjGN3LTTTexffv2zgcXUah8DA83OYmfsjLu8spg7SUHa6qIAGhuS3v2wAGJ7i+cOMedFQ+ThtArDPKMCCitg3XCBJg3r3GbrK4onk2bnAqZ+jpYXfZJm0dsX1vXZ7BCs8tY+nnlQARWIZ6nMDEHBqVmYxy7D1j7HQfOIyBpHKw7MJW36lNWO2vbXB47U5xXEM4hDtYUKCWiUBmJG8T2hVlskFdEQDcIrJ///OcB814+9KEPdT643ZakAELxbNtmctIiPDlzZsPzUAJrURmsZRRYUxW5AjOh1N/fuE3uTcVj/w0C5q9C+P5cnkWuQmSw+gqsHV8nrrvyYf8NZs82+dMWeRe5SnI/KYODNen9L/V3jN2WHnsMjh1LdgwhLFYhU0AEVqHOZuC5KFV3VrwWEw/wHWu/GTQ7XVPhPWrXWl+htZ6ttX7Meu70CPcWhF6mFxysmb5HEVjLh0Muly8+GVwun7eqFLnydS+cOHGi805SAKF82G3p0ksZtAaxWUQE+C5D7haBNbWDta9PnEJl49Qpk98ZJfAgtkwZrCEcrJ2uP1SRq46vk7z98uHouHONbCpDRMD+/ftj98uS3B2s8+ebSKiRkzUV0hRyxv79jx1r6iIIAvw9MB5YhVI/A96BKeb1zXN7KDUBeAYQNDtHMliFSiMCa0rs2diNG+HkyXDHF5IxNNTcWaVAtycAACAASURBVAg4iPVxOfg4WJ+0C6GkIEsHq+uxBgcHO+8UVwBBnELFEjOIDeWSa3ccXxFHMlgjyORfuVi/3tyf6vT1waJFQU+RRwZrmRysPu3f61rttnTwoIndEIrDcSI9pIM1tMBq958eeOCB2P2yJK3Amvg7ZuzY5u896ecVi/37X7wYRo8u5lqEcqH1l4CPAOOAa4HdwMvROmo7/3WgH7gv5KmDCaxKqRcppUaFOp4gdKLogWVehBZYH3zwQd72trfx6KOPmo6CXQBh3bpUxxdSsHkz2GJezlWafWb4bQfre9/7Xo+ri6eoiIAop1yrLosoVC5iljSHynmUDFa/8zu/Z7strV0LAZ3xQkLsQey8ec0xDinJI4M1TWxAO0qbwQowcyY87WmN2ySHtTgOHWouZNoibsPVwVqGiICrAkeGuJC7gxVkdUXZkPxVoR1a3wVcAFyKKcL1fWuP+zHi6z+HPG1IB+vXgceVUp9USt0Q8LiCEItXR7OChHR1DA4Ocscdd/Cxj32MF73oRQzFLaWQzkJx2L/7mTPhgguCHT6riACbf//3f0/8mlaIwCp4cfw4bN3auG3p0mATViEyWLPKEC9aYM3EwTo4aArDCMVgC3IZDGKLymANMUnh4ywNFRHQ8T4Wl7cvrrviSFDI1LXIlUtfxkeEbbVf3LVNnDix4zWEJm0Gq5fAagvJkrdfHDGFTEVgFZrQehCt4zNMtN6J1istV2tqQgqs/wlMAl4H/FAptUUp9X6l1MKA5xCEcxTt3MmLkA7WjRs3cvDgQQAef/xxDh8+LKJQmbAHPYE7CllFBKR1GiU5dhECqzP232vrVjh6NOw5BDfWrGnMGRw9GhYuDCbiiIPV7/zOA/inPQ1mzWrcJvemYtA683sTFJfBGiIioNQOVpB+Xpmwf/dLlrQsZOp6vwrlYE2TwVpEkavcIwKguS0dOQI7pXZ3IcQUMhWBVSgDwQRWrfWvApcCrwF+AFwOvBtYo5T6iVLqLUqpqe2OIQhJ6BUHa5YZrGfPno2vMNuFv8dKYM/EBixwBW4dbNclaZ2OE4oyCKx2BEJLFixoLIAAUgChKOxB7IIFMHZsLhEBvs65KgqsScWpRNcrolA52LcPDhxo3JbBIDZ0XyeNg7WrMlih+e+1YQOcPt35dUJ4EixpdnWwFpHBau/rW9x0+/btXHfddVx11VVs2bIl0WsLiQi49FK45JLGbeIILwZ7zHTppaYegiAAKHV/gkc5M1gBtNZHtNaf0Vo/D5iFqda1Cng68FFgp1LqXqXU74Y8r9CbFD2wzIuQgw5bKPrxj3/cLOIdOiQFEIrgqadgx47GbYEzrXw62CKwJhBYx46FhdaiDRGFiiEmfxXCLUNOWuQqVN6xTdH3wbQRAa2OAYjAWhZs8WDSpGZ3cQBCO1jTiKlJ21AIB6vvvdbpWpcsMVEBdc6eNYXLhHxJWMg07yJXST7Hdl/MV2D9zGc+w6OPPsrAwACvfvWrE722kIgAkBzWsiD5q0J7bnJ43Bj5fzCCCqxRtNaPa60/rLW+FlgCfBDYAfwi8Nmsziv0Dr3qYE2D3SF6xSteEV8AQToL+WPPxI4bZwqJBMRlQNfNAqtv59pZYAURhcqA1i073mXKYO0GB2vS8yfa355g2rXLTAAK+RJX8TzJd6IjeWSwuvYbjxw54p693eIYSTNYM3WwnncezJ3buE1cd/mzZQvYn6s2K5VcVxT59O3KEhHwgQ984Nz/H3jggUSvTdoegn3H2Pcm6ecVQw7RNUKlmd3iMRcjqH4QOAH8NRAfhO1JZgKrxXZgLbCh9jx8z0zoOUIs66oCIR2sp60lYcePH48vgCCdhfyxOwqLF5vcyIBkVeQqS0GnUg5WaG5LUgAhf3buNI7wKLVBbB4RAUVnsOZ5H8zUwTp3LkyY0LhNqp/nT05FRMqUwXrDDTcwdepU1q1bF/Rc7X6eqYMVpJ9XBuzf+YwZMHlyy919IwJc9kkrsIZysKahkIgAaG5LW7bAiRN+xxL8iCtkKgKrEEXr7S0ej6H1crR+N/AS4G3AdSFPnZnAqpQapZR6oVLqS8ATwBeA2zBi619mdV6hd+gVB2tIV0fLGWapMFs8OSx18SmE0E0OVvu9uB4rlcB65Ehz9IOQLbYgNGUKTJsGhPs+TSqwhmprPsfNkhAO1pbXO2qUmWiKIqJQvpw+DbbImNEgtkwZrABPPfUUr3zlK53OFSIiIFMHK4jAWgYSOu5CFrny6f+1O2eoDNY0FFLkCkwU1KhR0QNL3n7etChkKgiJ0Pp+4KfAu0IeNrjAqpR6jlLqU8Ae4OvAy4FB4NPAL2itZ2ut/zT0eYXeo+ilkXmRpYP1HHEFEAYHvc8jJGR4OBeXUFYO1qoKrK5ukEQCa1zIvgxk8yUuf7X2N8zDwerrDAqVwRqiPR44cMBpwJypgxVEFCqajRshOjGrVPDii3WKcrC2+/w9/PDD3ufKKyLA28G6b595CPmRcCLd1cEaKiIgSfsIFRGQhsIyWMeNM4Uzo8i9KV/sMVOtkKkgeLALE2cajGACq1LqL5RSjwHLgT8AzgfuwVhvp2qt/1Br/aNQ5xOEXnGwhhRYW3aA7AIIQ0NSACFPtm1rXl6UwSBWBFY/gTURErlRPG0GsVk5WIvKYI0jTRsB+MIXvsC0adOYO3cuR48ebbtv2gFup/2bsu7WrDH3JyEf7LY0ezacf34mpwqdwerrJM/yXO3O63uvdb7+yy+HiRMbt8lqpfyIK2Sa0MGadZGr0BEBd999N7fffjvf+c53Yo+RlsIiAkD6eUUjBa6EECg1AXgm4B667kBIB+s7gZnA/cCrgEu11r+htf6G1jr/dQNC1xPKwaq1ZtWqVZzIOT9nx44dPP744x33CzkItztE02rLZmMLIEhnIT/sQc7Uqc0uyABIRICbwGqfI5GDFaTjXSQnTxrXXZTI3yPU92m7dlJ0REBagfV3f/d3OXv2LDt27OA973lP4vO3+z5IfN+2J5pOnjR5d0I+xBW4yohQ7vJ2r08qyPT393ufK2kGa+YO1r4+qX5eJHGFTK+8su1LXItc+fTtQhe5sgXWgwcPcuedd/Lf//3fvOAFL8jEAJOHwDo0NMTq1aub940rdNWFJp9SorUIrEJnlJrV5rEYpX4ZuBejXwadBQopsP4RMFNrfYvW+vNa6/a2B0FISSgH65/8yZ9w9dVXM2/ePE6ePBni0jryne98hzlz5jBnzhx+9KP2xu4sBdabb7555InksBZH3JLmDHDpXLp26DsdJxRdIbBKAYT8WLeusahYXx8sWnTuaZkjAsoisEZ59NFHE58/qIN1yhSYPr1xm4hC+ZHTvQnCO1jTFLmqs8xx0F6JDFaQyb8isX/XDoVMQ0YEuNz70uSy2ivkPvvZzzY8b7UaYnSKYq5p7z8u3zG33HILy5Yt49d//dcbf2C3pSefhF27Oh5PCMCuXeb3HUUEVqGZbcBjLR4DwNeA5wAbgbtCnjiYwKq1/pjWek+o4wlCJ0I5WP/yL03NtT179vCpT30q9XW58Fd/9VcMDQ0xODjIW9/61rb7hhx0tO3Qx1U/F/IhJ5dQNzhYtdbBXIeuAmtiFi2SAghFYX9vXXllQyX6bosIyGrJc53jx48HPb/X9YooVAwHDsAeq1uf4SC2jBmsEyLfHUnPlTSD1fdem+h7w/77rV9vCpkJ2ePhuMuyyFUaB2tcH8l2sH75y19u+/M6fX3+UkTS9uAqWNfZsmUL3//+9wH42te+1igST5tmJgCjyL0pH+zfc6SQqSBEWN7m8T3gC8DvA9cSWMMMXuRKEPIihIP1hz/8YcPzlStXpromVx544IFz///JT37Sdt+QDta2bsC4AghPPOF9LsGRY8fgsccat2U0iM0rg/Wiiy7yuDq3Y4O/AOrqXrS3J3awSgGE4uhQpTnU92kIB2sIQSlrB2unwiVJBVOv+7btmpTVFflgT1b095sM1owoYwbruHHjgp6r3c9zcbDak7enTzdHqgjhGR5ubk8OE+mugqCPeJrGrRq3n32vuPHGG9v+vI4tsCb5PKeNCOjURp+0XJINr5e8/eKIKwqctJ8udD9a34TWN7d4/BJavxKt/wWtg+avQgqBVSl1v1LqPqXUjMhz18d94d6C0KuEcLA+97nPbXj+1FNPpbqmLMgyIqDheVwBBOksZM+aNY25TWPGNItzgchLYHV1/bhQhMCaOiIApONdBA65XKFcckkF1jwdrCEF1k6/n6T3Ya/7ti1E7NhhCsYI2WIL2UuXmsiNjMgjgzXp52/MmDFBz9XuNZlnsILp49kiudybsmfbNrBXA3g4WLMucuXaPlwcrM95znPa/ryOLbB2mtSLknUGq33NTjmsQvZI/qpQctL0lG6qPfqt564PQUhFCAfry172sobnt956a6pryoLcHKxxBRDEKZQ9dkdh4UIYOzaTU4UqhNDpNVkLPJUVWKUAQrbs3QsHDzZuy8jBmrTIVVUzWH0E1uAO1vnzm78TJcIme+JcQhmSh4M17ZLitOdq93PfiIDEQrRM/uWP/Tt2LGTqmonv405NExEQt58tRtr9JlcHaxKBNesMVvs9NX0f2G1p40ZTiFHIjlOn2hYyFYQy4J8sDfUp0N3Wc0HIBZcb62c/+1nuvfde3v72t/OMZzyjaf9f+IVf4J577jn3/IILLgh/oSnpNAjXWjsLQB2FsGXL4KGHRp7LIDZ7cpyJzcvBWhWBtdVxgmRY2s6GegGEmTPTH1uIx25LkyY1/b5DueTaHcd3kJpWUGp3/qyOldZB1Gl/wBSCWbwYVqwY2TYwAJZDSgjI0FBzbnTOAmvaz3GIiABXsacyDlYwf8evf33kuQis2ePZz/ONCMjaweoisNqva+VgHRXNq2+zXxxJJxzSOlib+oyLFhlzSv04w8Om0OZ117U9rpCCdevM/amOVchU6GGUek+KV2u0/kCoS/EWWLXW29s9F4Ss6dQ52LZtG7//+78PwP/8z/9w0HY2kTzwvAjaDcLf+ta38oUvfIH3ve99vOENb+h4rI7v1+70rVtnMroyclT2PHFLmjMqcAXdI7Am6YBHydXBWi+AcOjQyLaBARFYsyRuEGv97UJ9x4fIYA0h9lbNwepdlOuqqxoFVlldkS2bNsHgYOO2DO9NEHa1TqvXJ3W8ud5rKpPBCs2Tf3v2mIJmAbPTBYsO2eCtcO2PlSEiwJ6MsPepYkRAxzHThAmmkOaGDSPbBgZEYM0Su583b57JBxcEeB+gAZ9AXg0UL7DaKKV+F9istX6ww37PAuZrre8OdW6hN+nUEXjXu9517v+HoiJHhI75Ojlx+PBhJk+eHPuzVh2n9evX8/GPfxyAN77xjbz2ta9lbAchtKMQZg+gzpwxSzEyHlj1LDt3wpEjjdsydAn5RAT4CKxx7WhwcJDly5fz9Kc/nSl25dUExwZ/Acm1cx1EYK0XQPjBD0a2DQzA7bcnP5bghoNLqNV9I+nfuF1b8hV2uiUiILiDFZrvQatXG7dQhpmgPY3dlmbOhIxX+IR2sKZZ7lwnjYM1qfs7Nwfr7NlGkDhxYmTbwADcfHOy4whuxBUydexTu5pAfCIC0kRouEx829eeRURAWoE1dUQAmAkLW2AVsiPn6BqhUryq6AuoE0xgBT5Xe7QVWIHfB34PEIFVSEWnG+tXvvKVjscoi4P19OnTLX/WahC+c+fOhu1Llixh06ZNbc/TtsgVjBRAiHYGV60SgTUrbFfDRReZbK6McBnQuWZ+tTtOXCf0jjvu4N5772Xx4sU8+uijHScD2l1jJRys0CywiusuO06fbhzkQLNTi9ZCiL1MsRMhHKwhIgLKKLAmdbA6vW97EHXihLlPzZ3b+bVCcgooIpJHBmuZHKyFZbDW8/Yffnhkmwis2RFXyHThQqeXuorwZYwIcHWwuma1xpF2gi91RACY78avfnXk+apV5u8tVe3Do7W3G1zoAbT+fNGXUKeIqf8+jA1XEFKR9MbqMhAtSmBtNyBu1XEaP358w/bNmzd3PI/TUm77ZiU5rNkR11HIsFMWyuVg0ynbdGhoiHvvvReAtWvX8nB0YNeBIgTWYN8DtsC3aZMUQMiK9euN476OUia308Jb4LNoN1DzHbiGEljzzGDNzcF68cXNk0/iFMqOAgaxeWSwJhUo88xg9RVYvYRoKXSVH3ZbWrDAOXbLdcLbZ3WSb5RN3HVBc1uxj+XaltJmsIZ0sHaKPQCa29KhQyZ2QwjP3r0mziSKCKxCCSlCYJ0DHOm4Vw4opS5USu1TSmmlVFt1Sin1SqXUw0qpY0qpQ0qpbyulbsjrWoVmkt5Y427aZYkIaNehcBVYXXAawNg3K3HdZcfKlY3PYxx3IXERGLPIYD116lTD8xPRpYkJjx13fFd8HazeTqp6AYSRE5pcYyE80XxOMM7G889v2i2UwJpFBqs4WDtgfz/KvSkbDh0yBfmiXH115qcNncGaxqFXpyszWKG5La1dC54Tl0IHUvTzXCcdXPptZXWwukYJxJF1Bqu9ujB2/xkzmuNTZMIiG+y29LSnwaxZxVyLILQhVUSAaq7WdU3Mtui5FgDPBf4nzXkD8hGgY6q7UurjwJuBk8C9wHjgFuBWpdTLtNb/lelVCrH4dJTHjBnTsK0oB6tSquFa23XiW3WcXJdYR+kYEQDNnb+9e2H/fuMgEsJx9Chs3dq4LeNBbFFFrmyBNcnAuQwRAd7fC1IAIT9ssa1FWwolUITIYA3h2KuawJrq9790KdSc8ICsrsgKexDb328KiWRMHg7WMmew5upgtWOfBgfNCgupxh2W4eFmse2aa5xf7jvZ6yuwpnGw+maw+jpdW11bSIF10Cr0F3tvreft//CHI9sGBuCXfqntsQUP4vp5EsUgtEKp+xPsrdH6+aFOnTaD9X1wrlqXBq6pPdqxD/iTlOdNjVLq+cCdwKeB17bZ7xcx4upB4Nla60217c8GHgD+RSn1gNb6ycwvWmggaUfzzJkzTJgwoWFbUQLrLbfccm7JdNx1RHFdEuSCU0RAXAGE1aslnys0q1d753L54rOMzGfQZz+vmsAadKC/bJkUQMiauFyuFgKrOFiTUZqIAGie/Nu61UxUTZzo9nrBDbstLV2aSzGxXs9gzdXBWnd+7dgxsm1gQATW0GzdCsePN25LsKTZZYwSckIvzaREp4iAMjpYO7VR28Ha8t4aJ7AK4cl51Z9QeW5y2CeqYwYjbY/pVZiCVb+Hubj/rW2Le/w2cDNwudb6kZTnTYVSagLwj8Ba4MMddn9b7d8/r4urAFrrh4B/AC7AFO4ScqbTTf85z3lOw89cIgJCDkzb4bp0BloPwn061U4Ca70AQhTpLITHHsQuWuScy+VLWRysaTvQoQTWVu096MRLXORGSuFAsNi92yxrjtKi452VgzVpBmsooTeOojNYM4sImD/fTERFWbPG7bWCO/YgNod4APBbMp/keEm21ckyg9VngiWYgxUkhzUP7H7eZZclWg3mk5vfaj+fCfYk+/lGBKRxsGYdEWA7WFvub4+ZNmwwhTeFcJw4YVz2UURgFdozu8VjLkZ8/SBwAvhrTIRpMFI5WHWkWpdS6k7gv3WJKni14b2YX+SNQMtv8poQ+7za03tidrkHeBPwYkzcgJAjnW6sfZbjIu6mXZSD1XVmF8IKrM7LjZYta64wK4SlgEGsT2fdZ9CntUZrfa46rC2wnkxQ6CmkwOrauc5UYK0XQLjsMv9jCo3Yg9gpU2D69Nhd83Cw+rarvB2s+/fv59Of/jTLli3jjjvuiN2nVA7WsWPNRFT07z0wAM96ltvrhc6cPt2cE53TIDa0g9V1MiVppEUcSSca4n6eq4MVzN/1W98aeS79vPCkdNyFdLCGzGB1KXLl6kxNYjixydrB6mzCWbrULFWvH+/sWVN4UwTAcKxZYyI36owaFVvIVBDOofX2Nj99DFheixH4LvBjoN3+iQi25kdrfbPW+kOhjpcVSqmrgD8C/kVr/cMOuy8AxgH7tda7Yn5ed+LKN2gBdOoIKCuXJe7GWJTA6pSFWqNVhyCEg7WtwBpFCiCEZWioOT8wh46YTwaXr6sm+pkOLbDmXeQq1ffCzJlmOWYUGciGJW4Q2yKXK5Sw2a6d+DpY885gfcc73sG73/1uXvKSl7B27drYfUrlYAVZXZE169dDVAhRqvl3nhFljAhwbZM+EweFZrBCcz9v167mlQBCOuzJv4T9vCyX9aeZgChLREDWGaxOq/7AxKrNndu4TYowhsXu5y1YAB7FngWhAa3vB34KvCvkYYMJrEqpeUqpNymlWvbElFJLa/sEteG6opTqA/4ZeBJ4h8NLZtX+jRNX0Vofrx1rslJKQsByplNHw0VgtW/4RQmsPg7WuPfT6fqdhSO7410vgCCEYcuWxoxbKMTB6iJe+g76otuq5mANmsFaL4AQRUShsDjmr0J2EQEhBNZQDlbX9/O5z33u3P/f//73x+5TKgcrNAsUq1c3ulqEdNhtac6c3DJug37vki5PMuk1VC6DFYwgZNUlkMJxATl0CHbubNyWsJ/n0h8rwsHqExGQRZGrtI70YGMmaL43ST8vLPa9KUGxOEHowC5gScgDhkytfwtmmfyRNvscBT6KWVZfBG8EngncpbU+6LD/+bV/T7TZp55e3rEHqpRaE/cAsg1e7FKSdmhdHKx5ZbA6z4qSLCKgk/Dk3LmoF0CIIrOx4bBnYmfMMMuaMyaviADI1sFaqYgAiM9hFcJw/Dhs3ty4rc0gNithM/oZqUpEQBR7QrJOHg7WRO3LbktHjsD2YKu6hASTFaHxySRNcrwk2+q4tiUf4TNUBqv3/Sluia3cm8JhC2wTJsC8eYkO4dIX8c1NTXOsuHbRqSBUXL9Na11oBmunNpeoL2g7/SVvPxzDw6nd4IIQi4kDfSZwqtOuSQgpsD4fWKG13tFqB22yEFYAtwQ8rxNKqVnAnwM/0Fp/Lu/zC+HpdGN1ETGr7GANIbC27VzYA1lbFBT8KaijUJaIgBO2ezfhsUMVuSpMYF2/HhKIzEIbBgYaBzFjxsDChS13D7XENq2DNavrAD+Bdf/+/bHbky6hbHVNvvs3cemlcMkljdvk3hQGrWHFisZtOQ5iQztYfYUnn2so0sGaSoi2/77SlsJh/y6XLjWidgJ8J+aKyGC1BVYX4TTu2En6d0nandbauQ/Y6ueJHKz795u8fSE927bBsWON20RgFTqh1Kw2j8Uo9cvAvcBM4DshTx1SYJ0JbHXYbwswI+B5Xfk7jFP0DxK8pt6a+9vsc17t36OdDqa1XhL3AKTUoAedbqwuAmtZHKztOhRJMlg7zfwmEo7s5Rf2wEvwp6AqzT4OVt9BX5UdrKEH+ixdCtGie8PDUv08FPZkxaJFphhSC7LKPu0ksJbdwXrffffFbg/tYA0iENn3JhGFwvD4480ZnF3mYE36+XNtSyEE1twdrNDcltasaczgFfwJ0M9z6Yv4Cqxp2odPkau4fpvLcdqRpD37tNFEY6bLL2/O25d7Uxjs3+PUqc0TrYLQzDZMMau4xwDwNeA5wEbgrpAnHh3wWEOYglCdGAckm8ILw4swean/YC2FqyckT1dKPVD7/29qrfcCdTdurCCslDoPuAA4rLXuKLAKYQnhYC1KYE1y3kIiAqC5M7hvH+zda25sgj8HDpiBbJQudLBWOYM1uIO1v98E8kerc69cCc94RrrjComrNIcSNtu1k5CD206EOk4r8ogISPz7v/pquPfekecyiA2D/XucPNnE1+RAFp/jEBEBrtfgM3EQKiIglRBtr644fdqssLC3C8k4fbrxfg9e/byQy/pDirU+EQFxwqmPUaTT61u1B5+6FUni3FDK3JuWLx/ZtnIlvOAFbc8hOCDxAIIfy4FWN8jTwB7gB8C/onXQiICQAutG4OeVUv1a69j1n0qpfuDngaKq5VwA3NjiZ+MjP6uLrhuAQeBipdR0rfVu6zXX1f6V0KIC6NQRcBExO4WyZ0WSiACbNA7WROLZFVfApEkm467OypUisKbF7iicd15z9dGM8OmslzGD1bedFiawgul4Rwdc4ghPz/Bwc85dh453KAdYu8kKl3MEF0oi+LSP66+/PvYafATWTCMCoHnyb8cO47zMIce6q4kbxLbI5g1NFu3B9bOZVURA0OXHbfZJdX+aNMkUMtsaWYC4YoUIrGnZsMGIrFE8fqd5F7lybYdxYxafDNY8Haw+bSdxX/CaaxoFVunnhaGgVX9CxdH6pqJOHTIi4B5gCvDPNWdnAzVx9Z+AybV9c0VrreIewOzaLlsi27fVXnMSuL/281+LOezLav9+I9OLF2KpsoPVdVa0XSfJx9mXqLPQ19d8E5POQnrsjsKyZY3LxzPEJyIghMBqC6plyWBt1e5cBjWJsZdirlplBELBny1bwP4sdeh4Z7U0P2kGa9kiAurXnFT0CeFgTSwQXXllc/VzcbGmp8ACV8GFwxavT9ru0hS5yisiIPX9SSI3wmO3pTlzYGLHWshNhHSdZu1g1Vo3bHeJQnMxvrQjSXv2aTuJBVb7O3PLFjgqC1xTcfiwmUSNIgKrUHJCjur/FlgL/AawWSn1UaXU62uPj2KyV38T4wr9eMDzZs1Ha/++Wyl1ZX2jUurZwP/BxA58pogL63VCOFjj9jl48CDf/OY3m1x3IXF1sLbrEGTuYIXmm5h0vNNTkgJXEM7B2qkDXrWIgOAZrNDclo4fN51vwR+7LU2fDhde2PYloQSKbstgrbuPkg4oC3GwjholRRhDc/w4bN7cuK3AAldQfQdrUoG1EAcrxAusgdz0PUsgx53PpHir/UI6WFvdY6Iu1rJFBPi0ncRt1M6A17p5lY2QDPv3N368mWQVhBITLCJAa31CKfWLwBeB5wFvYST3oL7G6PvA72itj4c6b9Zorb+nlPob4M3A3y1R2AAAIABJREFUCqXU/2CKZd2CeV+v0lo/WeQ19iohHKy2UDM4OMhFF10Ue7yQuMzstjp/bg5WaO4Ubt5sBmLnNZnUBRficrkCzcSePXuWUaNGoVos6XTtmPu4N/POYE3SAW93rFwjAi6+GC67rDF/d8UK6SimwWMQm1X2adIM1qyiCurbtNYtvwviGBwcjL2GUmawghGFHn545LmsrkjHmjWNjvrRo41YkBN5ZbAmFXLTZLAmbTuFOVjt783Dh2HnTpg1K91xexWtE2eDtyKk69R3FUUSgfXMmTNMqK0uqEJEQFIHa8fJy7Fjzfdm9O+/YgXccEP71wmtsdvS0qVmklUQXFBqHMbgeSMwjdb1ojRaPz/UaUNmsKJNYahfVEo9E/hFYGbtRzuB72mtfxLyfHmhtX6LUmoF8AaMsHoa+B7wAa31g4VeXA+ThYP1nnsa0yv27t3L1AwyR11v2u3eo0/HJPFs7JIlMGbMSFXZeubhs57V/nVCPOvWQbSTqZTpLKTkoYce4qUvfSlTpkxh+fLlXBjj4nPtXEoGa0YCKxhRKCqwrlwJvxaXPiM44eEGDyXwtfssFe1grZ9vVJtBiH09lXKwQrMotH49DA7COJdaq0IT9iB24cJcf5dZCIe+97worveaSjtYL7vMOP8PHhzZtmKFCKy+7NnT+LuEYA7WrCMC0jpYo2MQ+1hZOFjTRgQEz2AF87eOfp/K6op0SIErwRelZmH0urmMmD1bEdRRl0nwn9b6J1rrv9Bav672+IuouKqUyidw0AGt9bZa7uq8Dvt9Tmv9DK31eVrryVrrF4i4WixZZLAeO3as4fnu3XZdszC4RgQkdbAGjwioz8ZGEaeQP3ZHa968IG7gX/mVX2Hv3r2sXbuWN73pTbH7+EYE+A762gmsaTNYfQVWFxdh3PZgAqtEboTj0CHYtatxm0PHu6iIAF/3UNLraHcNUVoVeEzqYA+x5NpLSLOzq8+eNS5MwY+Ci4hkIRymceglvYZKZ7DWq59HkXuTP/bv7oILYObM+H074Dsx57uKIoSDtdU+ZXSwBo8IgObIjdWrG40VgjtnzjTf1yV/VXDnE8A8zAr6lwDLMLWX4h5zQp44V6FTKXVtLY91V8edBaEDnToCPhEB8+Y16uzjMnJwuFxb3H6Qc0QASMc7JIGWjdk88cQT5/7/jW/E19zzdfP4CqwHDhw4939bYH3wQfe5qSIyWDNzsNptac8e2LcvzLF7Dbst9febCYsOFBUR4OtESnoddTpNQtiD2L6aWJn0d5FUsAq2FLy/vzleQ+5NftRXpkTJeRAbLDrC45idBFaX6/D5XJfGwQpS6CokcY67BHEtUVz6IiH7dq7HatUHC5HBmqR/l6Q9hIgIcGprdr9+cBA2buz8OqGZDRtMtFoUO39dEFrzfGATcBtafwOt16D19paPgGQusCqlZiql3qWUWg38FJPNemnW5xW6n043S5+IgHoOXR3frMdOVMbBCjIbGwqtc6nSnKRz6bLNxS0ad5y77rrr3P/jCsalcQZVNiJgzhw4//zGbeII98NuS7ajsQVZCZudIgJcHKyhMlhdjmXfc+p5rUnbVmEOVmj+/pS25MfWrSZbPUrOg9giHaw+MRgux0jq/i7MwQrN/bxt2+BJKS/hRUA3eJ4O1lafo1ARAa4O1iT3oKwdrF59wQsugCuuaNwm9yY/7H7enDkwaVIx1yJUkdPAI2idu2iRicCqlJqolPo9pdT9wGPA/wUWA48DHwWuz+K8Qm8RwsHaSWD1dcp1IiuBtdP1ei13sWdjT56U2Vgfdu0yxSOiZCCwJukkZxkRsHz58nP/jxNYXScvQjpYXSMCMhNY+/qa25M4hfzwHMTm4WB1aVdZOPai+DpYsxZYg75vWxRatcq4MYVk2IPYyy4zRflyJK8M1hBL+eOodAYrwPz5pjp3FPtzIXTmxAlTDDZKipVKvisfyiCwuoxz0vbvkrS7uGtO2kad748y+ReGjFb9CT3Dg5iIgNwJJrAqpUYppV6olPo3YC/wT8BNkXPcBMzSWr9da/2zUOcVepc8HKxZCayuN+2k2WCdRCsv4Wjy5OZiByIKJefRRxufT5liBrKBSbs8KpSDtb+//9z/v/SlLzX9vAiBNW6wEiomwRlZipmewUFYu7Zxm6PjLpTA125wGjL/rhOhHaxJrytEpmUwgfXoUeO8E5Jh35sKWIJZFgfr61//erZZnyHfe2BeGaxB7k+jRzcX3BRRKDn2JM+oUbB4sffhQi7r7/R5CyGwJo0I6EoHK8T38wJOoPYEWjffm0RgFZLxXmARSr0m7xOPTnsApdQzgd8BfgO4CFOl6wzwdeCLwDuAZ2itl7c8iCB4EMLBau9z2sp6KdrBGjqD1SsiAExnYceOkecrV8LLX+72WsFgD1auucY7l6sdrTqAvg5W38HlnDnt88Lttpbk2KEiAsD8XpT1d8jMwQrNHe+NG43rJSJICx1Ys6YxpiTOGdyCrITNohysoTNYW2WVjxkzxvn8uTlYL7kEpk6FvXtHtq1YYZYRCu7Yg9hrr839EsqSwXrhhRcy3nJy+jpYk0YP+DpYgznfr74afvrTkecisCbHbkuLF0OKWg6+9w2Xz4nrRHKSPliIiIC0GawhBVbvyXbbwXrwIOzeDTNmuL1egJ07TTHTKHb/WRDaofUjKHUr8EWUegVwL7AbaDVQvjvUqb0FVqXUu4HfBuZjRFUwVtwvAv+utT5U2+8taS9SEOLo1KlwcbDaN8sqOFjbCaydBtWpZmO//vWR5/XZ2AwEwq4lp0FsWgdrXBXxOBGy07FvvfXWttdZBgdrfVufld2ZqcC6eLFxtdTPMTxsco2vl+QcZ+y2NH8+nHee00uziggIkcGap4O1VftrVdW5lcCadNAa3IF39dXNAutLX+p/vF5j797G3x8UIrCWxcGqlGLUqFENP+8JBys0i0Lr1pkCM2PHhjl+LxC4nxcyIqDTsbKOCHAtcpVkAj3JfdSnjXr3BWfONKv/orFgK1eKwJoEe4JnyhTzexWEZNwCXAJcAfx8i30UoIHiBVbg/ZiL2Qt8CviS1npbiIsSBBc63SxDZLBWrchVJhms0Nzx3r/fVEDPYIl7V3LwoJmNjVICgdV129DQEKNHt75dxL3mfLuYk0UagdXXwZplDq0z48fDwoXGhVlnxQoRWJOQYhBblIPVxa0WQuit06mN2PeK+v5Jo2cKjQgAc2/67ndHnkvkRjLstjRpEsyenftlZOHM9BGjlFJNE25ZOUt97jOZCqz1avf16z5zxoisGWTFdyWnT5vJ0igpHXdxE942vhEBaQTWVmONpBmsXRsRoJRpNw88MLJt5Up44QvdXi/E9/PE1CMkQam7MDEBg8B/AluBY3mcOm1EgAKmAr8E7FNKfVVrLWUnhVzo1FF2uTEW5WANIbD6dEy8IwJmzTKVMaNVZVesEIHVFbuj0N9vXHcZEDoiAPwE1ui26dOns3v37oafp4kICO1gtcnUwQpm0GULrIIbQ0PNxVcSCKxZOUc7ZbDmHRGQ1MFa/8wnXbKZ1BEcXEizBYzdu+HAAbjoIv9j9hL2vemaa0zkRs6Ecpb7HDO6X19fn5fA6nP9pXOwnn8+zJsHmzaNbFuxQgRWV9avNyJrlJQCa0gHa6d+TZK+o0sGq308VwdrnhEBmRW5AvO3jwqs0s9LRgmia4TK84fAEeDn0HpDnidO04v6OeDvgIMYy+0/AHuUUl9TSr1UKRW/nkwQAhHCwWrvk1cGq+tNu917zDUioD4bG0WcQu7YHYWrr859EOvrcmi1rdPPo5+1VsuOXehKgdVuS6tXj0QGCO2pZ9ZGSTCIDSHkJM3/jntNVsW26vg6WJO21aS/T59Kzm2ZN685HkLuTe7EZYMXQBYO1rJHBITKYA16f5J+nj92P2/uXOMIT0GeAmuIiICTJ0+23KenHKzQ3Ja2boUjR9xf38scOAC7djVuE4FVSM5U4Ad5i6uQQmDVWv9Ea/1G4DLgJcA9mMiAXwG+ihFb/xG4NMSFCoJNFkWubMrsYPURWL0drCAd7zSUYCY2bURAmmPHfb7LHBGQi4M1yokTsHlz2HN0K3ZbmjXLZHM5EkLI6fQ58o2dCCmw+jpYk0YEJB20BhfS+v5/9t483I6i3BpfdeZzMo+EKXOAACEMYRSZUVCQK4PKIAjCleE663W4er366Kfo9yleFZUHf3BFAa+ICITJCEgQEEhIGBLGEDLPc05OknNO/f7o05u33q7qru7d096n1vPkye7avbtr71PV9daqVettCGa9d2OTHTZv9ib9FAVNYrMgDpMkuUqqYE1DHVe4ghXQx3lpj3/1igzivKQ7H2z99cPuFedaPm677TbjOWX0YM2UYD3ggKB/Md9946AH70sDBgBTphRTF4daxluoTkyaGFXfVErZLaW8T0r5UXhM8VUAZgMY1vd6EgAIIX4ghHDp3xxSQ1RAa5PkKm6m5bTA614UwRorWOCk0FtvAVu32n++v2LbNnXLHVCISqia7LNR7Spuwjmg3BYBVS1E2EBn1u+2j9mB/04xJ7F5KFht+lpaW6LzULBmbRFQNUHEn6euL9mB/06trR4pUACyULAm2bWhU7BmQbAmVbBnTrDyvrR5M/DOO+ldv17R2xtc2MmAYLX9+6fpwRonJvzf//1f4zm2CtasLAKS7J6oas7U3AwcfLBa5sYmO5Rg159DXeBXAE6GEOPzvnGqrVVKuUVK+Rsp5UnwsnX9B4BX4Xm1fhnAHCHEQiHEN9O8r0P/RNSqai0pWE3BStoEa6qrsVK61VgbvPjiu0kjAH3QlQOKVLCmbRGQVMFqe63MFaxAUCnkAu9oSFm1SqgsCta0LALi2MtQxPFgLbVFABDsS6++GrSRcAiC96Vp07zxqQDk5cGaRMFqM97ErX/S75sFEa1gzBhg9Gi1zI1N0dAJDkquYLUlD+OIU6644grj9fKyCIizqyOugjV27OnivGQoiXWNQ41Dyl8AuAnAbAjxCQixd163zmw5QEq5VEr5fSnlQQBmAPhvAGsA7A/gv7K6r0P/QVQAYTMwFkGwSimtFaxhwXQSZV9VyryWFuCgg9SyOXPsP99fwSexBx4Y3DaUA/ImWOln0rYIqHkPViAYMM6ZoxLxDkEsWQJs3KiWpaBgzYNgtdmOmacHa1oWAYUnuQK8BSuqOuztdYt/NuBj0+GHF1MP5GcREHUfIUQuFgFJ+0HmClYhgmPT3LnpXb9ewfvSXnsFieoEsIlFsvZgjUOCjh071nhOFhYBcfpRkj5XdSzI+9IrrwAsmbIDw9atwV1/BY5NDjUMIXoAfBbA3gB+A2AJhOgx/EuV8MlFby2lnCul/Dy8L/hBAHfmcV+H+kbUwJyGgjULi4A4fkalsggAgoMcDyodgiiB/yqQPAg3lVEkUbAWYRGQNKlXUsVsKHhf2rABWLo0/fvUE3hfGjXKm8jGQFYWAfQaNvfIOskVLV++fDnuvvtubNu2rVKWlkVAXMInEwVre7u3cEXhSKFw7NjhKX0pClQJZUEcJiGj8kpylfT7Zk6wAsGxyS3+RaNK6xoTbMYN27aXpoLVNCbQOVMeFgFZe7BWPWc69FB1a/vu3V5SUwcz+K6/lpbg+O7gYIelAJYAeKfv/7B/qU7AcjW0kFL2SikflFJenOd9HeoTaShYbbfU9/T04NRTT4UQAl//+teTVDf0nnl5sEaZ3EfiiCPU4wULvImagx67dnkr1hQlTyKSRhDKy8psEWBz/dQ9WAHPg3XkSLXMKcLDoVusECLWJdJQUEa1I5t7ZG0R4Jd3dnZi2rRpOO+883DllVdW3ucLHH6ds7YIyGyLMyeFHMEajpdeAujfurExmCwsR5j6VDVtI6lFgGDPlCyIz9IqWIFgX1qzBli5Mt171BNSsK4xwYbgS0qw2oxJpuubxh06l0kqcslTwRrXIiB2XxswANh/f7XMxXnh4H3poIMK2fXnUAeQcjyknGD9L0U4x2CHmkUeClb/M/fccw8effRRAMD3v/99vMqVHzEQR5EXNhlIQ2kYO1iYNk3ditnT47ZihuGVV7wVax9CAIccUkhVkqo3geoVrLr2XeYkV7lYBAjhSKG44IF3AsVdHgpWm3aWFlESpWCdOXMmNvbZKvzhD3+ovP/II48o56dpEZCFci8SvC+5rZjh4Iq7Aw7wlMAFIc725GquaZPkCoBiE2DTPuP2gzQ9WFMfnyZMAIYOVcscKWTGihXA2rVqWY4Eq+2ugDw8WNNQsJbJIqDqORMQbAsuzgtHSXb9OThUA0ewOtQs8vBg9T9z3XXXKeXzqjAqjxNQlCrJFaDfiukCbzN4oDBlCjBoUCFVsQ0u0yJYwwgboBgFa9LvmwnBCritmHGwZo03kaVIEHinoaBMw4M1LwUrtQWg9+jo6NCeH3fLZimSXAH6rZgvvVT9desVJZvEmtpMNT74SRWsABSbgLwsAkqjYHWLf/HA+9Lw4QDxIq0GaVkEJLWtAbIlWKtdQM/aIqDqJFdAcOffiy96u9scgti1y9sZSeESXDnUIJqKroCDQ1KEEay23pK2gytX2/HJaRzEmcCGBU5JAhPbgCoUhx+uTlxd4G1GiTJhVmMHUI2C1SYQj3vtrBWsqagWbGDaihnTV7RfgPelQYOASZNiXyaPJFdl8mDVZURvamrCXqyNhRGsYX01r63RkfC3Yi5c+G7Z3LnAjBnVX7ve0N0dJJ9LSrBWo2BN8rxPqmBNox+URsEKeGNT364tAG4hPQy6nRUxrWtMSMsiIEzE4bf5NBSsYRYBtahgTWWx3bcy8u/lk4iOOAzi5ZfVXX8NDcD06cXVx6G2IMQJfa+ehZRd5NgOUj6RVlWcgtWhZhFGvNiSRFEDuX+9AQMGKOUtxA/m0UcfxZFHHonPf/7z0ZWOUTeghEmugOBqrNuKqUdvLzB/vlpW4CQ2yXZJH9UoWE1kaL+3CADcVsw44JPY6dNVxaIlirII4J9Ji2iMmhRzgtXvd7z/pWkRUAjBCjjVnS1efRXo6lLLCp7s2xA2cVGNgjVri4BSK1iBYF9asQJYvTr9+9QDUrCuMcHGD74agjVqYdB0/SwtAuL0+Tj9KInIJpU50+DBwOTJapkbm/TgC+lTpniLpw4OdngcwGMAxrJj23+pwRGsDjWLMJWqLZlqq2A97rjjlHIaRJx66ql4/vnnccMNN+CEE6IXS8qU5CpRsMCJDZcVU4833gC2b1fLCiRY81awhvVFIF2LgN27d+Oqq67CqFGj8GKIJ3DpCFa3FdMe/HdJ2JfSIPii2pFNO0uD6AWiJ86cYN3ZtximW+Do7e2tXYsAwG3FtAXvSxMnAkOGFFOXPmShYK3GgzVri4BSe7ACHiE0eLBa5samIDZsAJYsUctSjPNsCD6bZ6oNwRqnD5rGhDQsAuoqyZUPF+fZIaU4zyFjCPEeCPEAhNgAIbZBiGchxKUJrvMJCCFD/t0Z84q/7fu3mR3b/ksNziLAoWYR1/exmsF1xIgRSvlOg2Jz9uzZ2LZtGwYOHBh5TYq8CNZULAJ0WzHnzAlObvs7uBJxn32CWeNTgG3AV02fiLvKT8vysAi4+eabcfPNNwMApk+fju7ubmWC7MPWOiQ3ghUIbsV0gXcQGzcCb72lliUMvIuyCMjKgzVqUswzovvEqq7/9fT0FKJgTa1/+Vtz3VbMcPCxqQS/j6kN5K1gTdMioKYVrA0NXrt4guyYnDsXOPPM9O9Vy3j+efW4owPYb7/ULp+HRYDpM2GftYnr8rAIqNaDNReLAMCbH5Ekk5g/37NqaXI0TAXd3aXa9edggBDnAfgDPJHmEwDWATgVwP9AiEMg5ZcSXHU+AF1ym3/GuoqUnwg9zhFOwepQs8hDweoHBJxQNRGsAPDjH/8Y69evN74fhxiNO1FNojRM7MNK4UihIHjgnZEXoC1RaRuIJgl4i7YI+PnPf64cP/GE3kYnKaGcmsJOB96Xli/3vFgd3gXfgtnWFky2Z4k0CIoyWQREjR2cYPX7jq7/9fT01K4HK+C2YtqgpyfYn0rgU5uFgtW2rekI1jQUrFksNORGsAL6JIwOKnSLFZrF3aRIuvPBZrzh5Tbn+LCx9LBJEJWnRUAa4oHEzyO+iLVjh2fV4vAuFizwfhcK/gxyKBZCDAfw/wFoBHA+pDwJUp4P4AAAbwL4IoQ4KcGV74GUn9D8+2V6lc8XjmB1qFnEVbBWYxFgIlh1A/m3vvUtnHnmmbFWg9NSsEYFJqltz9RtxbQk+voFenuDE/uMFL7VKEHrxSKAk0hbt261vlahFgGARwgNGqSWuYmsCr5YceihiZUfeShYk5L2WShY+TWTEKw1YxEAuMW/KLz6KtDZqZaVYPdJXgrWqL7pE6tZe7Am7f+FEqxLlgAh4oF+iYwX0rNWsNLytBWsNhYBpp0UtojTHwpVsA4f7nnuU7ixSQXvS5MmAcOGFVMXBxOuBDAYwF8g5d2VUilXA/j3vqMvFlAvPYRYBCGutzjv+xDircjzYsARrA41i7gK1iTEURTBuoOvtvXhueeew8aNG7XvxalH1hYBprJI8Cyp/lZMBw+vvQZs26aWlZBgzTLJVZRFQJoK1sPYNiLuO2lTT4pcCdaGhuA2KK4w6+9IcRKbR5KrMlgE+OW8r/jPCxPBWoRFQKoEK3/O+lsxHTzwvjRhAsAskIpAkRYB9N5Jk1ylYRGQxX2qwv77e1veKRwp9C7WrQPeeUcty5hgtd11ZHuOjYI1imClau80CdZly5bhzjvvxObNmwPn+KjWIsBWZGN7fijc4l84uLCgBDsrHAL4YN//d2nemwmgC8BpEKItvyqFYjyAURbnjew7NzU4gtWhZhEWaKSlYDWpfXyCdebMmcbPmsjXtBSsSbZyJ/kNtNBtxXSqu3fBf4uxY4HRozO5VTUWAWkpWMPaYh4K1lGj1PHT1PeS/gaZEqyA24oZhg0bgEWL1LIqFivSICiSKFhtJmpJ2lmUMon3s7QtAkqnYOWLFW4rpgr+bCmBehXIxiKgGoK1CIuA0ilYGxuDW5sdKfQueF/y8xOkiGrGDdqeslSwtrW9y6XE9WA1le3YsQOHHXYYLrzwQlx22WXaeunuAWRrEVBVX+PP2hde8Ha7OXg7ILn/aknGJgcF0/v+Dw4EUu4C8DKANgBxjaiPgBA/ghC/hhDfhhAnVlfN2BgAINVtuI5gdahZhCnbbEmiKJInSsH6z3+a/ZdNq65xCNaw4CEtpWHiya1bjTUjJ/9VoBwWAWEBuE222TDYKFj5tbZv3259rcIVrECwL73zjtuK6YNPYjs6gKlTE18uDeVoVDuyec4WpWAtmmDNnCAaNgyYOFEtc2OTh+7uUvqvAnaejnGRZNdGLSpYMx2fOMnhFv/eBf8tDj88Vf9VILlFABCfYI2ztZ5+rrW1tfI6zIM1joL1kUcewbp16wAAf/nLX1JJXFWoRQAQjPO2bwdefz359eoJCxYAXV1qmSNYOVqEEK/o/uVydyEGAxjSd7TMcJZfPi7m1c8C8CUA/wrgPwE8DiEehxB7xK5nHAjRACGmAjgZwJI0L+0IVoeaRRpJrqK2KfufMSlY9w9ZrTYRrHVhEQC4rZgm9PQEJ/Q5E6y6QDaJmiesjCJMGZ2HRUDNE6xuK6YZKfqvAuk8A9NQsKZFsKatYLXpbxR5KfdiwSnC9ShxEpGyKVgpwWpTh7r0YAWC7WPRImDTpuzuV0vgY1MGhJCNRUARClY6JpgUrDYEq66sp6cnEMOZFvnijCdJdvDx96t5HmHkSG83G4WL8zzwvjRlCjBkiP5ch6IwkLzuNJzjd9xBhvc5VgL4LwCHwSNvxwD4EIBXAZwI4H4IEW/VSoieyj8Plyll6vu74alu9wBwR6z7RMARrA41i7BJrE0QKqW0TgplIlhnz55t/OyWLVtCr0lRc0muAJcV04TXXgsmEclwEqsLPLv4SjCyVbCaguSwz6ZpEWBLsCb1nM2cYHVbMc1I2ZcrD4sAm3aWFtEYRUz1O4sAIPi8nTfPW/jq7+B9aeJEL/lKDti1axeWLl1qfD8vD9aoMU9nEZCFsrQmLAIAb7dAG7PTc2MTsGaNl/SLIoOFdJtxw0agYbOAkdSDtb29vfI6zCLAdmzp7u4O9AW+i9BHtR6suSpYgaCFjVv885Djrr8axi4p5UG6f9ZXEOLPEOLVmP+OyuwbSfkwpPw2pJwHKbdAytWQ8j4ARwJ4HcAMAB+JedWl8NSoSwBIeGTwEsO/NwE8AS8x13dS+EYVOILVoWZRrYLVNGnca6+9Kq+jPFhvu+02Y/1MAUHdKFiHDw9uxXzuuWTXqifwQGH8eG/lOiPo2vFTTz0VKEuyXdJHrVkEmNSxtt9NtxiTWSIRH1z94vqSl0Rk8WK1rAYI1qQK1iTP4rgEa1SSK115TSW5AvRbMd3iX2GT2O3bt+PII4/E2LFj8Y1vfEN7ThYK1iSLikVaBGTh9Vo1mpqA6dPVMt6O+iM4MTZoELBfXNvBaGStYI1aGDR91mQRkJaC1ZZgrdYiIO7urKr7ms5yI+sF/LJj166g/6ojWLPCBAD7x/znb6+jmZvZlrsKBvT9v7WqWkq5DcB/9x29P+Znx0PKCZByAgAB4I+V4+C//SHlyZDyJ5Ay1VV4R7A61CyqTXJlImFGk2REUQrWMOhUhLp6AMV6sFYVMPBB8Nlnk1+rXpDzJFZHfgwaFNydUQ2ZWk3ytCwsAqL6cpz+ZPsbZE6wHnmkerx4saeS6c/gSqkUkogUZRFgM1GmZbfffjve//73Y+XKlaF1idr6yXdS+GNXXROsI0cCEyaoZf19bNIlEclpEnvHHXfgxRdfBAB8//vf156Tl4JVd70oBWtS4rMuPFiB4NgUknug34DHeYcdBjSkP6WuBQ/WaiwCTB6snWwXWFEEq83vHwtEbvtLAAAgAElEQVS8L23bBixcWN01ax0vv+yRrD6ECCp9HdKBlIdCShHz3+N9n90CwPc+3MdwB7/8nRRq+0bf/3tWcY2TAVyfQl1iwxGsDjWLsEHfRpVmIngGDx4cuJ4pydVRR5mV86aAIEuLgCREWFWTW/79588HLMjnukV3t7cdlSJjo3ZdgFqNt2o9KFjj+HWVhmA94ABPBUPR31WsuklslUlE0iD4otpR2OKfTT1efPFFXHzxxXjkkUeUHRW2dfHv97e//Q3f+973lPKTTz4Zq1at0o5/f/7zn2MTrHEnrUl88BKBT2T7e1/SJRHJyX/1rbfeqryOQ+IA6Xuw2hKscRWscT1Vk1g8mT6XOcHK47x33nGLfylb15hgM24U4cFqUrCGEbY9PT048MADIYTA1KlTMXfuXKNFwCbm82sSrORtEVCVBysAjBrl7Wqj6O+Lf7wv7bcfQObhDqWCv0obDB6EaAZwMIAueNv7q8Wwvv/1nm82kPLvkPK1QLkQbRBiDIRIntAhAo5gdahZZKFgbW1txXSyHSrKIiBscDYFBHVjEQB45CFdtddt9ehPWLgw6L9aAMGqa9tlswjIMslVnAWLaojnVNHQEGwr/T3wzkANXgsK1gsuuEAp38ETExGETZzPOuss7XvXXXedtv994QtfUMgwHzWnYAWCpNC8eapKpr+B96XJk4GhQ3O5tc2z3tSO01aw6tpyVhYBcftBKS0CALf4x7F6NbCMJdHOiGC1GTds5g/VKFjjEKy0v+o+t7BPrfnqq6/irLPOMloErF27VimrG4sAIDg29ee+BOSSLM4hNczs+/98zXtnAWgDMAtS6gmQeDiv7397028hBkGIoyCE3q9FiCkQ4mEAWwAsB7AVQtwJIapRyWrhCFaHmkUY8WKzzZkH/WeddRZuueUWjBgxIvAZE8EaZhVQKwrWqgKGQYO84JuiPwcLBSQR0U0Yk6paTV6jUW0kTBWUhYI1yk85C4uAzCexgD7wzlo5W1ZklEQkafbuqPPT8GD1y9pYUhlT0jbTvQCvD5gW+e6++24j6fW73/0uUBZGkJWWYHWLfypyUtzpwGOhOERH2h6stgRrERYBpSVY3eKfCt6XBg/2FiwyQFoWATb9K46ClcZYdLyKIlgpVq5caVSwbt2qWjhmRbBGjT2pWwQAwd0V/Xnxb9cu4KWX1DLnv1pm3AyPnDwHQpxbKRViNIAf9h39v8Cn3k2YtTcr/xqEGMnKmiHEtwBcAGAHgFti1O9SAE/DI3t5HcYAmA3gNABN8PxZW/vu8xiEaA98pgo4gtWhZhFXwRpmEdDQ0ID77rsPF154IZqa3lWMRxGsYRPPOB6sNgb0PvzvHYeotbleYnBSqD8H3gUkEalGwWqjjgCKtQiwaed5WATkQrDywFtHMvYXZJREJI1nYBSpaEM6hrXFiy66SCkPU7AmJaZMY5fuXmkqWHOz33CLf++i4CQivK3Zjk9A+OLzHXfcgdtvv92KYPKRlYI1DYsAm3ullRwvNtzi37vQKe4y8F8F7GI0m7ZUjUVAVIxkUrDatEuTgpXPn6qN6eKeS+sS53wruMW/d/HSSyq53NDg/FfLDCk3ALgCQC+AuyDEoxDijwBeAzAZwI8rnq0q/IRZzaz8/wBYBiGehBB3QIiZABYD+C94VgOXQMrlMWp4Yl/dgkoB4JsARgPYCOAcAIPgWR08D2AKgGti3CcSjmB1qFmEDfpxLQJaWloqr3XKBT4h8Af/JASrrm6m1dnSWwQAwcB74UJga3UJBGsSu3YFk/IURLBGTSJ98PaVlKzJ0iLAZkEibYuAwgjW8eO9BD0U/XXBgidSSSmJSFEWAXEUrM3Nagya1CKAX4fCNGHVjVtperDmpmAF3OKfD+6NLkRu/qtAsP3o2ljcseerX/0qLrroIlx88cW45Ra9wCUtgjULZWlcQjnpfVKDW/zzICXwzDNqWYZbmtNSsNqIOPKwCOAwJbmyJVjjLGwkiesyIVgHD3aLfz54nLf//kE7EodyQco/ATgBwMMADgPwAQBvAvgEpPxizKt9B8ATAPaFR3qeAqATwK8BHAop7455vUMAvAQpVZNwIRoAXAhAAvg6pLwPUm6HlPMAnAugG8C/xLxXKBzB6lCzCFOwxiVlTASrHyyYklyFkUTf/OY3jauzHEkM3NOyCKh6cjt9OkB+P/T2BonG/gC+zUeIUilYk6rqgOqsJ0yBcZmTXBU2iRXC+XMB3iSWB95HH53KpdMg+KLaR7UerHy8SapgbW8373gyjV22CzY+0lCwZta3eF9asMDL2tzfwPvS1Km5JhExxU8UcQlHSqpeeeWV2nOqIVhpHJiXBytQ0CK5Ddzinwddgq+QZLfVQGfZlKUHaxpJruISrLr+3d3dHRjzTONV3hYBVSe58sEXLPpjXwKCixUZ9SWHlCHlPyDlmZByGKQcACmPhJT/E3K+6Pu3mJV/C1K+D1KOg5QdkLIdUk6BlFdrk1NFYzQ8NS3HIQCGwiNS72R1WA7gnwCmJrifEY5gdahZpGkRQAnWOBYBYR6sAPDrX//aWEeKvAjWTILz1lbgkEPUsv4YLBQ0ie2PClZ+TX6tmvVgBYKB9/PPe4sW/Qlvvw2wJBdlIlizUrD6n+PtOSnBSscyDtv+B8QnWONujc5MweoW/zxktFhhCx4nxVGwmp7lPAmODtV4sKaR5Cps3LQhvGzvk1n/oXCLfx54Xxo1CpgwIZNbVbv4m1aSqywtAqpVsFZLsBaiYAXc4h8AbNni7XikyHlscqg7DEDQhgAA/G0GL0LKLZr3lwFIdcLuCFaHmkVeFgH+PwobBSsAfO1rX7Oqt2kCHRY8lIZgBVzgDQQD72OOyeW2aSpYk076wpTaWXiwppnkytYTOTeClfelLVuA15Is5NYweF8aPRoYNy6VS+dhEZBULe63OxtCin9GVx42PmVJsJbGIsAt/gGbNwOvvqqW5TyJ5UnawpIiclSjGMvTIsD2Xrp7UpRWwQq4xT9Av1ghRCa3so1D0iJYi1Cw2hKspvEqzgJfGgrW1Pra9OkAtfDpj4t/3Me5pQU49NDi6uNQD1gL4ABN+fHw7AFMAWAbgM1pVsQRrA41i2otAmwI1u7ubm0AYEuw6hSuurrxjJk+4ipYo/y7MkswwkmhRYuAdeuqv26tYNOmIAmW0yS2GgWrbfAYRvAA4RMBU5uMaqth105qEVDNhCW3SeweewBjx6pl/W3BQrdYkdIkNq7iUockFgE2ClaTJU1SBetIvqWXIA7BGtZX4/aV3JJc+ejvPqzPPqtOYtvagqRzxtiyRRWMmEgVHWzHCR3StAjo7OzEWWedha9+9avWiwpx+03UZ0yfc4t/OaG7O5jgKsM4z/ZZaUOw2hD6aXiw0v5VjUVA3StYdc/h/jY28Tjv8MPVHScODvHhbfUX4uxKiRCj4PmsAsAjhs8dCGBFmhXpNwSrEKJDCPEvQojfCCFeE0J0CSG2CyHmCyH+UwgxMOSznxBCPCuE2CaE2CCEeEAIcVye9XcIIiygjatgpclAuIJVR5LaWgToJrGmoOPNN9+0PhcomYJ16lRgwAC1rD+RQrpJ7LRpudzaVsFajUXAF77whdA6JLEIyNKD1XRPW4K1UAUr0L9Jod27gTlz1LIUJ7FpPAOz8mA1WdLYJrniY9eQIUOMn0uLYC21ghUIqu4WLQLWr8/ufmVDCSaxfAG5FhWso0aNwsyZM3H99dfj61//eug1fCQhWEutYO3vi38vvwx0dqplOROsceL+uEmu4ihYadtua2vTllejYM3Lg7UwghXo33EeEPRfdfYADtXj5wAEgD9CiN9CiP8H4DkAgwAsBTAz8AkhJgLYD8D8NCvSbwhWABcB+DOAKwD0ALgXwGwAEwB8G8BzQojR/ENCiBsA3ALgYACz4MmLTwfwhBAi1YxjDvEQV8Ga1INVp97buXMnenp6lGtOmjTJqt6moOOVV16JrDOQfpKrVAKGxsZgJtX+FHgXOIm1VbBWk+TKpLD2sXjx4kBZlhYBfGHDVsFqO7HPXWXHwQPvF15QE6jVM156CeCEIifJqkAeHqw2iUjSsgig16ELhT09PaF9rN8QrAce2H8X/zJMFhcHNgrWpIrOMNiSnrYKVh8/+MEPrO5VdxYBQP9OzsP70pQpwPDhmd1O11aTKliz9GA1Eaw27dJkL8UJ1qwUrIUluQL69+LfsmXACiYYdASrQ7WQ8jEA/wXPh/USAJ8DMBbADgCXQ0pdQHF13/8Pp1mV/kSw7gZwE4ADpZQHSik/IqU8A8D+AF6A59lwA/2AEOI0AJ8FsB7AdCnlv/R95gR4JO0tQoiheX4Jh3dh2urJiU8fSS0COvmKNbwJMJ+g/uu//qtVvU0DtE5tlIcHa2qTW04KPfOMquqsV+gmsTn5rwL5KFjD8DzfMseuVQTBWvMK1hkz1C3xO3cC81NdXC0veF/ab79UJ7FpPAOzUrBWaxFQCwRr7n1Lt/jHlTP1iqVLgZUr1bJ+RLAmUbCKvudu3CRXYf1Zh5olWHmcN3euNz71B+S8WFEWD9Ywj31AtQjo6empXCepghVAYN4VR8Fqo9YNKwu7Vqp97aCDgI4OtYy3sXoF/57DhwOTJxdTF4f6gpTfAXAkgP8D4GYA/wng4D7yVYedAH4K4KE0q9FvCFYp5f9IKT8lpVzIylcCuK7v8FwhBJWd+ftivyulfIN85mkAvwIwFMAnM6y2QwhMA93mzZtTtQjQTW5Xr16Nf7IB4qqrrsI111wTWW/T4G/r1xrHZ9bm/dQCBk4qrlnjrcjWO5YuBVatUstynMTm4cEahssvv1xbHqVgrdaDldaVB9817cEKAIMHe7YbFE8/nd/9i0TGixX1bBFAFwqjCNY4Spw0PVhzV7ACwTb01FNAnv25KPC+NGIEYLnTJk1wsiSORYBugdsWWSW5atHsTklLwVpqD1bAU92R3wa7dvWP5DzbtnkWARQZx3m2/SRrBWvU3IQSrP57tm3SZLPGE+PFUbDGif+ixp5MCdbGxqCK9amn0rt+mcHHpqOOUp8rDg7VQMq5kPIbkPJTkPK7kPLtkHO/CSk/Dyk3pFkF15o9+NKgVgAjAEAI0Q7glL7yuzSf8cvO1rznkANMA+NLL71kRaTQQdikYO3p6TEG+B/+8IeV45aWFnz+858PnLdp06bQevjQBRphwUNczy/TZ1Kb3I4bB+y5p1rWH4IFroYaORKYODG32+uCyaRby5IEj++88462XEcWUfuNajxYAbW/2ChYpZTWk5PCFawAcByz+e4PfWnLFmDBArUs5Ulsf7EIMCVoTIKatggAgGOPVY83bAA0nud1hxwznofBxsLF1Gb+7d/+LVD20EN2QpM0k1xR0H5mOgcI7zc2hJcOhROsgwcDBx+slvWHxb/nn1cXZZqbgcMOy/SWtoSgjQerjWLa9BzWjT9hBCtfAA+DaX61bds25dikYNV99zhkbFg9pZSp7PIKBY/znn66/hf/enuDNj3OHsChzuAIVg8+G7IbgM9g7w+PcF0rpVym+Yy/ZJtvOlaHCsKUoDakCR2EacDMPVgXLlREzxVw4rSlpQVTpkwJEK8XXXSRdb05woL2UnmwAt7EjU9k+0PgrVuJzXESW42CNU7wmFTlR/vJsGHDQuuog01/sSFY42wbKyXB+uabniq8nsEnsS0twKGHpnoLk7VMHGStYI1jEZBUwRoHcQnWuMRS5gTrvvsC++yjltX7gkVPTykmsXTLsA9TYhtbXHzxxdb35ojyJ9cpWHnf7eDbehG/H9SsRQAQjPPqvS8BwTjv0EO9ZKYZIg8Fa9S4BQBPP/10qBepjmC17c+msS1svkYRR8Gaxm6LVD1YgWBf2rwZePXVdO9RNixYAPC8Do5gdagzOILVw2f7/n9ISunPbMb2/a8jVyGl3A5gE4BhQohBGdfPQQPTwLhly5aqCFbuwfrpT386si4NDQ0VYvbnP/+58t6DDz4YWg8fthYBfr2TWARkHpxzUuiFF4JZV+sJ3d0eKUSRo/8qkK5FQFj7MSkITAmw/Gt97Wtfq5StXbs2tI5h1+EII1jjTOCr9TnLDAcdBAxiQ0u9L1hwNfihhwJs8lYtirII4O0sjJCJYxFA75UVwRp2nTiKKqAgBSvQ/xThr7wCsG22Af/MHGC7wyKsD/JrbNhgt5PPlmCNsgjgKrv29vbQa4TdK+x8oAQxnA14X1q8OJiwpt7Ax94cCCHbxaisPVi7urpwwQUXKGW0D7cxojkOwWprARLHgzWNhFhATgvte+0FjB+vltX72MT70oQJwOhAjnEHh5pGvydYhRAfgOejuhvAN8lbA/v+D3v6+9GrFcEqhHhF9w9APunG6wymAXzLli1WA6NNkquenh5s3rw5si5tbW2V5Ag82LCtt62Ctbe3t/LP9tph10t1cnvkkZ6vkI/du4E5c9K7ftnw4ouFT2KrsQjg2+bDgkeTV5YJUW2RbwEzIcoiQEpppWA1qYlKq2BtbAxO4uo58JYy+P0yWKxI4xkYRXIkteNI0yKgp6cnViKrMKSlYNVtuQRy6ltcKTR/vuerWK/4xz/U40mTgFGjUr9Nd3d3aP9JsgDIFaKmRTwK2wVnHaETZRGwePFi5XxbgjWJgrX0HqyA5w/Ok7LWc+K4JUu8rOcUOSykl8WDFQDuvvtuZTxJyyIgbPGQoiwK1kz6Wn9b/MshznNwKBr9mmAVQhwA4HcABIAvSyn7SZrm+oApYLBNcmWrYLUBPW/o0KHKe9OmTQuthw9bghXw6l66JFcAMGAAMH26WlbPqrsnn1SPDzjA82DNEdUoWAG17Ya1heXLl8eql+5aEyZMqLzu7OzEk/z30yCqv+j6aBwFa2kJViAYeP/zn97W33rE228Hk8Udf3zqt0niw2ZTloaCtRYtAuIQSzZEQGaYMcPzTfSh20JfT+AE63vek/otHnroIYwZMwZHHnmkcSE6yfh0POv3Niq3KJ9IHw8//HDovXUK1r/+9a/K+bYerHWrYG1o0CeOq1fwvjRiBLDffpnf1jYOsYltqlGw+qCJp9KyCLBVsGZFsBauYAWCi38vv+z50dcjNm0KJovLYGxycCga/ZZgFULsDeAhAMMA/FhK+VN2ii9tCJotvYsBff9HL68DkFIepPsHIB2ZST9DmEWADflo68FqA7qy29DQgM997nOV48GDB4fWw4etRQBgJlgLTXLlgw+W9Rx4c4KwgEDBdgJr+jvTc8OCx6OPPjqW/5R/7mEkEcTll1+unHP++edbX4fD7y86lV5deLACwcBbl8m4XsD70pgx3taxlBGnb9x2220YNWoULrroosiEIUV5sIYpWMvmwVoowdreHkxKU6+Lf+vWBX38Mlis+N73vof169djzpw5+MlPfqI9J4lFQEtLS2VHEGC3e0LXR2wXyHUEK40D9957b+V8/p1M7TcvD9Zc+g8Hj3WefdbbsVSP0MV5OWQ8T5qsVFdu097oOQMHDgycSxcxsvZg5cjKIqAUCtbDD/f85t+9idef6hFPPeXtVvLR1uZ9fweHOkO/JFiFEMMBPAJgHIBbAHxJc9qSvv/30bwHIcQAAEMBbJRSWhGsDukiKwUrz8RMsZ/lqvV73/veyuvtbAt5WgrWuBYBcbKoVwVOCi1b5m2xqjesXAksWqSWlYRgjeNxZ1KwNrAJxLZt2/BPnughBH5bpPXjWz9Xr15tfR0Ov7/Yfv9qLQIKmcSOGgVMmaKW1euChW4Sm0GyON1z1vS3vfTSS7F+/XrccccduO+++yrlaXiwhik/+WQyzCLApGDdvXt3am02LYLVJtt1ptBtxSyiX2cN/owYOBA4JP18rHQHAvea95FEwdrY2KiQNjYEq62CVQfduEf70vr165Xzy0awFrL4xxWsnZ2eZVK9obMTmDtXLctgsUKHav3h6efjKlgHcf93xCNYbdsknx+ZUIRFQJJdgonQ2urtsKCo1ziPq8GPOkollx0c6gT9jmAVQgwE8CCAAwHcDeAqqY+OXgOwE8CoPrUrh7/kUocRRW3ANNBt3LjRaiClgzANpqlygQfIv/zlL5VtziYMGDCg8poHEFGekjbnJrEIME0CUg/Op0zxtlBR1KNSiAdAQ4YABx+cezWqtQgwKVipVYYPGz9ifi16fZ13ne11OJYuXQpAr2yo1iIgbhKWTNEfMjZv2wbMm6eWZTSJjfOcpXiK/O5R44vNxDiOB2sSBSsnnO6//37jNaJQFwpWIEiwrlrlWVPUG/hixTHHACSuyQK6bfNAsvGpoaEhNsGq6yO6fqgb13QEK/0+mzZtUs7nY47N2GpTt7DysHsVMjYNH+5ZIlHU49j0/POqMrexMTef/WoVrPTzcW0E+M47QO2H9NzGxsaAtZotERm2eEiRhoI17sJ5rjuZ+sPiX29vcC6Y02KFg0Pe6FcEqxCiFcBfABwF4GEAF0optaOAlHIHgEf7Di/QnOLvbb1P855DDjANdLfeeqsV+UgHbJNFAPcHmjRpEsaMGRO4Nt/6HEawmgKPOAoMUwATFtTkph4Son+YtvNJ7HHH5bJtjCMriwCuYAX0flnjxo2rvOYekIAa5HMFqw2i+outgrUmLQKAYF9auBCwzKJdM/jnP73g20dLS1DRkRJ0EzWbZyDdshw1ybV5NudNsB599NEYnTBTb1wvyTgKorDy1KHLVlxvi3/d3cGEQxnsrOB/szgEa9TzOUuCtaenJ7Qv6hSsPMmWLcFat0mufPCxqd76EhCM8w491FOE5wDbGN+GrLc5h7Y9XX8OI1i5MCVtpWddJ7kCgn1p3TrgzTezuVdR0HnL8u/t4FAn6DcEqxCiEcAdAE4BMBvAuVLKKO/TH/f9/w0hRGWfphDiWACfArAJwG8yqK6DBegAPn78eOU9XdbZJB6sXLkwdOhQLUnE/YoowcqzpReV5CrXyS1X3T33HGC5Ul0T2LUr6JFUkFF7tRYBcQhWnYKVttsbb7yx8trGIsAGpjbtT3JtFaxxVHWlUrBOn+75R1LwbVa1Dv59jjgi+J1Tgq1FAG9DlGBNQ8Fqen5LKRNbBIQRrM3NzdaelBx1YxGgW/ybPTufe+eF+fO9bc0UGUxiefsy7U7IwiJAaKxDbAlW3f2jCFYew9laBGwIWQireYsAIBjnvf46YGH7UzOQMpdkcSbYWh2lZRFAXzc1NWHfffdVzv3c5z5XuRetR1NTU+YEa1YerKVRsO67L7DXXmpZvY1NfLFi0iTPa9/BoQ7RbwhWAP8G4MN9r9cBuFEIcavmXyUFuJRyFoCfAhgBYJ4Q4h4hxAMAngDQBOByKeUmOBQCOvgdzkyy39Ss/PHAxIZg5YP6gAEDtCRRW1tb4Dwf27dvVwbxKMKIIq4Ha9hEOI6Cr2occ4yq5tQRkrWMOXMAOukTIjjZyAlpWgRwFRGHzi+LTrRpu9dZBMQlWLlv8P7771957U96bb9/nC2ZpSJYm5uBo49Wy+op8O7tzXUSa/uc5Wpt2h+SEKw2Cla/PI6C1eSFxz/T3NwcSviEoW4sAoBg25o7t74yNvO+NHVq0LInBfCxQLf1HsjGImCffYKpEeIQrGEKVJ1FAL+/rYJ11apVsa2ZaopgnTYN4F6d9TQ2LVoUJIxz3NKsawtxYrtqFKyNjY348pe/rJz7+OOP47bbbgvUo7m5OUCwpt0my+LBmllfEyLYtuqpLwHBscnZAzjUMfoTwTqMvP4wgMsM/xQpopTycwAuB7AQwOkAjgUwC8AJUsp7sq+2gwl08OO+qLpBkAfJNkmuKNra2tDU1KSQSPQ9CnpOT0+PEpCnoWBds2ZNagrWTCa3gwcHMzb//e/p36co8EBh2jTPg7UAVGsRYEpypZsw6ywCKMFKCVSdgjWuByuvM/UF8wlWHWGmK6tZiwAAOOEE9fjpp71Fi3rA668DLIlMVgSraeKn6xucQIpjEWAzkQubFCclWKnqTqdgTYqsCdZc+9bRRwczNtfT1mZdsrgMENY/KJLssIgiWHWK7jgWS/x6UQpWDluCFQgmyIqqW00RrE1Nwfb1xBPF1CUL8Dhvzz09m5GcYPIMtdkNAZhjO9NnuYL14osvDpx//fXXB65dNgWrlLLqBGGm9zJJcuWDx3kvvxyMjWoVa9cCr72mlhW068/BIQ/0G4JVSvlfUkph8W+x5rO3SilnSCkHSCmHSSnPlFLWoalkbYEHxaNGjaoc8y1dQHCAtklyReGTpnEVrIA6GYlDsJrOFUKk5sGaWXB+4onq8ezZqs9irULK4MpygYFCnhYBnGCVUoYqWKWUyvW5lUYUeJsdQkhsv0/pvr9usl2zFgGAt9JPCYwdO7zkG/UA3pfGjvW2y2WAOJM03tbjEKg2z+awSbENoUTP90FJKf4Z07hmg7heknEtAjKdtHJ0dABHHqmW1cvi34oVnuqOIqOxifcPk2osDQUr77e6WClLBSvH7t27lYWFsLHBpBo39ZGa8mAFgqTQc88FLSpqFbo4z7CQkAVMbYH3H5vnrU38z0nT4cOH49BDD1XO13nf6xSsSe1oTIiTuMp0fqktAgDg8MO98cmHbq5Rq+CLFQMHAoccUkxdHBxyQL8hWB3qD9xknarjdB6sPKi2SXJFMahvK5ROwbpmzRrlmJ9DCV/TAB1HwdrZ2Rl7dTX37Zk88N6wwVuRrXW89RawfLlaViDBautBGpdgFULgwx/+sHIub6Nz5sxRjvniAydYdX0nDLw9x1Gw8nYdh9wpnYJ1+PBgMFovpNDjj6vHGfYlU7IcGwUrJXDCSA4ppVWmZ1N70hFFJgUrVzOZLAKampoghMBPfvKTwDXOOeccXH311drr+8hawZr2ZDwSfPHvqafUTOG1Ct6Xhg4FDjook1uF9Q8K3d82ysKloaFBWfS2WXBIm2ANU7AC6vcKi6GuvfbayM9T1JSCFfD8fWnMvBGC61kAACAASURBVHt3fSjCN2zw/Iwpco7zbJSpumPd55NYBADvznt8dHV1BRSifDFi165diZ7pJpsR/5o62MS1YeeWxiIA8HZW1KsinI9NxxyjPjccHOoMjmB1qFlwgpWSOzoFq61FQBTBqlOw8mCiublZCdCTKljDCNbSK1j32QeYOFEtq4dggQcKY8YAxBs0b1RrEWDyYG1sbMRFF12knMuD3Kuuuko55n2jp6fH2M98dHd3Y/PmzVqlD2+b9Pp+fzEpG3h/iuN9XDoFKxAkhZ54wlM41DJWrAhuGzv55MxuZ5qk6foGV+jZEqy2RL7pPJ3PsYm84vWgYw79jN/vjuZevgB+9rOf4ROf+IT2+j6yTnKVO8H63veqx9u2AS+8kG8dsgAfm044QfVCTxG8nZpU1rY7LLh6zmQRoEsCB6RHsPpWB1EEq2lhkuNvf/ubttzU5k2LQGH3KnRsGjjQS0pIUQ+Lf08+qe64am8PeqFnDFsFa9wEVjbn+PMgvuuoq6srcP/m5masJl61t956a6JnOt8JSBHHV9V0flwFayF9jQtTnnmm9hMEd3YGc3BkGOc5OJQBjmB1qFlwv8gogjXMIsCGYH3ppZcA6FV4usGcBgs0aE7DIiAJwVqI/52OFKp18EnsSSflum2MI02LAE6wnn/++TiCTJ54H3rjjTeU4yiCVde3HnvsMYwaNQpjx44NTEZ5e9YRrCbSjE/4TQG/7cS8cIKVB95r1wILFxZTl7TAJ+JDhgDTp2d2OxN5ofvbhin0dJMyv83YLmSZ2tMWTcIlE3nF72WyCPDHN51Fx7777qtYb+hgmtwC+t8i7mQ4d4J11CjgwAPVslonhTZuBObNU8tOOimz2/EFiDgEa5SCtbGx0Uiwmvqw7jluald0zPCtbHz4Y1SUZzG/RlyY6qbzOacoHcEKBOO8J58E8rT9yAI8zjvuONW7OQfYbn+3sZuIq2D1+wGf75gIVoo//OEPiZ7pYYsaaRCsceO6QuLA97yn/hIE85wBOu9mB4c6gyNYHWoWfEtZlEVAmIKVDuxRgbVOwbr33nsHykwThLQUrHG878Lum2kGZ04KLVoELF2a3f2yxsqVwKuvqmUZTmJtkGaSKz7JBYD3ve99lbJf/OIXWLx4ceWYk1C8b3Dys7m5Gcccc4xS9tOf/hS7d+/G9u3bceWVVyrv8TZLg32dFxiFrYLVdmKea6ZzHcaN8/xJKWp9wUKnuAvZJlgt4lgEJFGw6hKQ+ODtLw7BumPHDiulKB0Dad/0xze+3XNEX3b5oUOHautC7xMnG3rpLQKA+lOEc4/ztrZMFXfVKFijyI8wBWscgtUm1uLn+ONeHAVrkrGhrghWrgjfsiW4vb6W0NnpKQcpCojzbH16bXYM2IxLOosATrDu3r07cL+mpiace+65leNDDjkk0TPdlCgPKMYiIPckV4A+QTCPk2oNjz2mHs+Y4SnfHRzqGI5gdahZhFkE6LZZ8sQEcRWsPomqy4T+uc99LlBmQ7DS+8YhWLdv315+iwDA838bPlwtq2VSiKucdMFQzkiSRMR0LleFA8GJJrUN4GQpbfNAsE03NzfjpptuUspmzpxZeU3JW8BOwWoiWPmE39T+n+RZt1FSiwAhggsWtdyXNm8ObsvOeBIbxyIgrgcr4LUx2+es6TwdwQrY7XCguyYoUeOPM3yy7Gc4jyJYgXge3qW3CACCfWnlSuDNN/OvR1rQKe7Y8zhNhC1AUCSxCEiiYNURvNUQrFEL7bYWASbE2VFBUQjpE4U99wT2208tq2VF+DPPqIq7xsZCFHe2CtZqLAJMyRn9eRCf7zQ0NGgVrOeff75ynbQJ1iIsAgrbyVRPCYJ37/YU7RQFi1IcHPKAI1gdSom3334b9957b2j2ZE4G0UBg8+bN2s/QQTdukqv39q3S8yBg8eLFSvIdHzYEK62zbvKfpkVAIQrWhoaguqGWV2NzVtzZICuLAFOyj6dJAovx48dXXh977LGBJAU6gnXatGlaFbgOYR6s/rOhWouAp556KvSeUeW5gpNCr7/u+ZjWIp54Iqi4Y4R92ohjEcAJJJstwd3d3dYkYhwFK2BHINHxREew+opVjtbW1kp/N8HUf2oyyRUATJ7sEUMUtTo27diRu+KOL0CY+lbaClZTTJjUg5Wf48d/UQrWsOfBJZdcEvpZIF0FqymxXq7gY9Pjj9euIpw/B2bM8BbTc0aaCtakSa54TCeE0BKsdPFux44diZ7p73//+43vmfp9lgrWwhbaeV/auBHos6irOcyd63mcU/Dv5+BQh3AEq0PpsHr1ahx44IE455xz8NnPftZ4HieDKPmyadMm7WdooB5XwXr55ZcH7gsAo0eP1p5vmiDQAVqnyKNIM8lV3ElvauCrsfPmAevWZXvPLLB5sxcsUJRgJTbLJFdA+ESTeh1/7GMfC5A0PCj2+5ktwZqHgpWfW1g/scH06Z5PKYUhiUrpwSexxx6bqeIOqE7BamPzsnv3buv3TO3JtDhoY2VBCVbdTgkTiSqE0Pqzht1Ld5+oc23tE3KBEMGxqVb7Eve4a2gAjj8+01umTbBWq2Dl/YO3NdP1eFv1x72oMSpMwfqVr3wltC66+/pIQrACJVCx8r60fLm3AFhr6O72FIMUBcV5ZfBg5YISE8FKx56kBOtXvvIVDOc73sg1dTD1B91YH1fBWhjBqksQXKtjE4/zDjoIMMyZHRzqCY5gdSgd7rrrrgrhwbcTU3AyiA7wGzdu1H6GDromD1YTwTps2DAAwEQ28JkyX9oE9HTVN65FgMknMm4AnnnAcMwxAJ2sSAk8+mi298wCfJtOa2vmijsbVGsREOXBGkawUq/jQYMGBfoOD4rjEqw8SKYeklEKVq4EDJuAUlKrsH5ig8bG4GRv1qxCqlIVClDcAekpWMNUqmHtjPaHuBYBNgo901gUtd0Z0CdvpIhDsPb09IQmArO9duY45RT1+M03AWZTUhMoQHHH+0deClbTfXh9wvy7w/qyyXuSI8yDlXsd68aoNC0CgBIQrAccAOy1l1pWi2PT3LkAz+HAyeOcYKtgrcYiwHSOH8utXbs28v48B0ZSgnXcuHFYvny59r24BKttDBxXlJJbPzv1VPV41qzaswno7Q1ahZRAlOLgkAccweqQKnbu3Ik777wTt912G2655ZbQ7MMmLFmyRDk2EShhBKsJVKlmUrCaJqM+KXT66afjkEMOAQBceeWVRt8g0wSBfh8aiNt47NFzTb9t3Els5lvLWlqCAWotBt68zscc421rLhhpWgTQc0wWAT7mz5+Pv5PgaeDAgYG+kybB2tDQoF2QMPUDvsgSFvBTUqvUClYAOP109fiVV2rPJuDJJwH6vMtBcQdUp2Clnw2b+Ia1E0oCxVWwxrUIoMibYDWV2xIGueHQQ4GRI9WyWhubdu0qZBKrS3Kl60dpK1hNW4W3sW2oNrsfdOeZsqdzhFkE8H6oi+3qTsEqBHDaaWrZX/9aezYBvP8feGBhirtqFaxpWATolOp87iSEUBb3duzYoZzDRSkmNDU1oa2tDVdccUXgvTQUrLrfIGyOWqgXP4/z1qwBXn45n3unhZde8upN4QhWh34CR7A6pIrt27fjwgsvxKWXXoorrrgiMljUwVeK+vjTn/6kPY+TQTakDQ0WkhKszc3NeP7557FgwYJQhW0aBKtpME9CsBaqzOOB9wsv1JZNwJYtQcUdX2EuCEkUQqZzdQpWXb/auXNnwC9LR7Dy/u9PXsP6Kp2k0z7R2tqqBPFRCtZ7771XOabfberUqYq3WM0oWAG9Oq3Wto/99a/q8YwZQeuDDGBSv+n+5mEKvaQKVjr+pKFgDbMIoKD98stf/nLl9Uc+8pHK6yiLgDQW9EpHsDY0BFWsvG2WHU8/DVASRIhcJrG8f0gptX/HvBSsUQQr7Rs2Sa7iKFijCFYdKWxrbcNRWoIVCMZ5y5bVlk1Ad3dwLC0wzjM9F5MQrDbtRmcRcM011yjnSymVccwfW2ib7+rqCuzU+8///E/t/Sn8a/3617/G7Nmz8Y9//KPyXlYEqyl+BAomWCdOBCZMUMtqbfHvkUfU4wkTAJK3wcGhnuEIVodUwbcIpzFxMgWcSRSsS5curbw2JbkSQgSyoQMqKdTc3IypU6eGZr2kyj+6IlsUwVqoMu/YY2vbJuCxx7zg24dOlVsQsiZY+XZHAHj55ZexevVqpWzgwIEQQijPABqINzY2VvpLGMFKJ+60r7S0tCj90n8umPoB7etA8LvtSRLc0HuWXsHa1FTbpND27cGssiHJLdJEnG3MYQrWOAQrHQeqUbDqFiv5vXTjFqCOb1/60pdwxhln4PTTT8cNN9xQKeeEErcbiKtgtVXWh5XnAq4Ueust4O23i6lLEjz8sHp82GG5KO54/wDsicQsPFiTKliTerBmpWBNSrAW2od8TJ0atAmopbHp2Wc9r30K/nzIEbaxvM15NgvHOouAM888E2eeeabyGboIqCNYuYK1qakJX/va1/DZz34W11xzDa6++upAPYQQlWs1NTXh+OOPxx577FF5v7OzU6uQr1uCFQguWNSSTUBvb5AQft/7vAVAB4d+AEewOqQKrmBLYhHAJ5LvvPOO9rwkBOu3v/1tbd34NmjdtWy3Nfugq6/AuxMAOqBT1VAci4Curi7jb2uagBRmEQDUvk0AX4l9z3uACIVLXrAlMmyIEBo8+hNNXV/Qqez8tkyfAbpM5kB4X6IT5TAFq/+eKUCePHmycszVGbQONUWwAsHAe8GC2rEJ+Pvf1YQ8jY3AySfncmtTW9H1oTAP1jCikPcz+oy3IVhNClYdocXHQNPuC1o+evRoPPjgg3jkkUeURQZOsPI+GpdgjfMMKpQcmj69dm0CurqCCXlyIoTSJlg5uUNjMhuCdSvzzQwjWLP2YG1qalJ2SMQhWKM8WOPuUMoVOpuAWbNqxyaAk8EHHxwkjHNEmgrWuOf47VcIgbvvvlv5DB2jfCKWx4lc5drW1oYbbrgBN954ozLu+Ojo6AgIVqIWKni/o/Ghre9xqQnWWrYJmDsXWL9eLXvf+4qpi4NDAXAEq0OqSEPByie2lBSl4GSQDQE6m0xGTBYBgD5ZiA2BS8G/h69MMhGsnZ2d+NSnPqUEDUkUrCZbhsK3PteqTcCGDcBzz6llJQoUskpy5Xuw6pK+rdCQeXkQrHEUrDxw5mSUiWAtJQnEodtSXys2AXyx4thjM0/I46MaBautRQDtZ0IIRQFOVd9xLQJ0hBafEJv8km08WLlSvaWlRSGJ0iBY414jFzQ06BOK1AKefNJLGOdD910ygi7OyEPBauvBGmafYerLQojKuJfUIsD/vKn+uvrprG9MKGUfoqhVm4Bdu7ydShQFx3nVKlhpuc2ON52CFfDaMiU/oxSsgLrgwWNIWwELL+OLD2FJHm13cYURrHF2gmWCWrYJ4HHefvsB48YVUxcHhwLgCFaHVMEH0jQUrCZwMsiGAB01apS2bnwCyq/V0tKiJZriwJ8gmywCAOCmm27CYyTIM5Fi3ONIdx+OQhWsgN4moBaChUcfVbfltLXlkpDHFllbBBx55JEYyRRey5YtC1zHb8vVEqw0MOcWAXEUrGHBeGNjozKBtlGwJnmWZYampqDqkwe0ZYTOyzjHSayprej+5mkoWBsbG3HQQQdVjhcsWFB5XZSC1QTufc7HvLjK7prwYPXBSaG33gIWLSqmLnHA+/yMGcDw4bncWtcedURikvGJE6y076WhYDVZBNAFhaQWAT4RFYdgpXFgzROsOpsAbmNRRjzzDEBJep0aN2eY/ta8Pdm0CRuCVefBCiCQxIr2NX9s4aKUuASrTRmP6fjYU3cKVkCfOK5Mu6l00HkZ52QD5eBQFjiC1SFVCCGsVC9hWMOzDhoQpkgz4dhjj628jqNgjWsPoIM/Yaf31flb0szspqDZRKKGvVe4glVnE/DAA/ncuxrwSewJJwAx1cxZIk2LAB3BOmTIEEX5DQT9TQG9gpUGxLRcR9D6SEvByoNxW4uAOJ6ShYJvH1u4sPykUMFexnl7sDY2Nio+cnTSabrGpk2btOVRBGtTU1NVBOtwRszZEqw1neTKx/TpAFl8BVD+samzszAvYyBbD1bbJFc0foryYKULaiYFK23v1SpYo1Sp9PvSnUw1bREA6InJhx4qPylUkJdxGEx/07DYxlQel2Cl8zh+z9dee63y2h9buMqVjnX8WkOHDg3UQzfHamlpUa7JFz3DvI9tPVjD4rpSEKw8zlu7Fnj++XzrEBcl8zJ2cCgCjmB1SB10MpeElHhbk2BCN1jaWgRQBRENEkxJroAgwRoVbOvw6U9/Wjm2UbACavBBfz/6/fhkwpRYiKIU3pLMLB8LFpSbFFq1yrMyoCiRPUBvb6/27yelDASTpr9zlAcrABxwwAE45JBDKsc6gtVvn7Qv6bLNAsATTzyhrQugBuZRHqxSysQKVtqfaD3jkHCFYsaMoHdk2UmhBx9Uj3P2Mq7GgzUsMQ4t5+2MPuNp204jyRWfEJuIVJN1AEVSBWtdEKwNDcAZZ6hlDzxQblLo0UcL8zIG0rcICFOw0r5H70t3JG3dulXZjcPbpYl80S0qAkEf2LDvoLtGv1WwAsAHPqAer1lTblKos9PzBqcoASFkS7BWYxFg6gthO/Z++tOfBs7jKlc6jvHki3TR0Ydu/iaECCTPokhDwdrT0xNrDMudYJ040dteTzFzZr51iIuHHlKPC/YydnAoAo5gdUgdNpOyMOgUrOu5WTaCFgFUBUCxF3mw0wE6jkXAiBEjImodxCc/+Unl2J8YRBGsGzZsqLw2qRw4wUpJ2bgK1twsAgDg6KMB/luWmRSaOVNN0DBwIHDcccXVhyHO6nvcTLO+EscHJWDeeOONwHX88+mklLZTGvz+6Ec/Mtb75ptvrrzmFgE0UO/t7UV3d7dxEYJPVG09WE0T3LCtZIWgsTG4YFFmUmjFiuAkO+fFimoVrP6z0lbByscl2h9ME7WNGzdqy3WEFm/TJkKIT3B14ARra2urMpbbKKAoaibJlY8PflA9LjspdN996nGOXsZAcQpWStzsvffeldc9PT3KefR5zZOgmhSsNko73fWrJVjpM6IuCNbJk2uLFJo1y0sY5yNHL+Mw2CZCi2sRYLLfCFOwUlCClM6daB+jOzH4+HO8xmLLZPFG47RqCda4u5NKQbACwFlnqcePPuotCpQR27cH7QFKJEpxcMgLjmB1SB3VKlh1gXsUwcqVQhT77LNP5TWdpNK68YkpJ2v59kkbTJ8+HQcffHDl2J9cm5Jc+aAefLSOVEXLCdYhJOFNqRWstUQKSRmcxL7vfd625pIgrH/x95JaBPigE9uFCxca70v7/8sk4ykNfq+99lqccsop2s/TjLVhClbAm4zS/jSYEAxJLQJMWzRLp2AFaosU4hPsQYNytQcA4hGsnNCUUlbakG2Sq8bGRuUZb2MRYCLybQhWk4I1CcE6dOjQ/qNgBWqLFFqxApgzRy07++xcq6Brj1l5sNLr0viIEqxA+O4HGuOZ1OhctRdmDRU1bur8wimSEqyltwjwUUuk0P33q8fHH5+bl3EYbBWsNotfdFwx2WWE9YXvfve7ldc0WaOJYA1TsLa3tyuxGmDua1krWE3nms4vpJ+9//0e6e9jxw7g8cfzr4cNZs0C6POusTG4O8TBoR/AEawOqaNaBauOIKSqTt21m5qajArWfffdt/LaH6CllKEK1tHMe4kn+bEFJT51BKtu1ZZOEkxBOE/oYKNgLQXBCtQOKTR/vpcBl+JDHyqmLgaEkX58UlctwUoXC8JA+9IDRJ38yiuvVF53dHRgpgV5QfsK92AFvO9I60/7W9IkV6YJbikJ1lohhXp7g4sV739/7osVcSwCdM9R//NJLQJsFKwmRFkEcNUfRRKCddSoUVURrLbe0GHluaNWSCFOCA0eDLz3vblWwVbBqutzUQRrmIKVEqx77rmncg3av8J2P9hYBADA4sWLA/W0vYbOL5wiqQdrTShYgdohhZYtA+bOVcv4c6Ag2CpYbRSY9Bza3mwtAvYybPGm8R4lOJcvX155rRt/+BzLhmCN8mBNqmA1LbyWhmAdMcLboUDBx4CygMd5JVmscHDIG45gdUgddMCNO3HavXu3dmDkhKJ/Lr2nScGqI1j5IMkJVu4RlJRg1W0P5YE/h4lgDVOw1oxFAABMmVIbpBAPFMaPB4ifbxkQRvpFBaO6a+iSdfgII2noln+Tio63s7a2tkCQ7cMPeGng29LSEqlgDfOy48+LulCwArVBCs2b56nuKHJW3AHxJlI6QtNva2GkIidb4ipYTbBRsJrGwCQerCNHjrQay9NQsJamb9UCKdTbG5xcn3FGrosVUsrUPVj59mQTwfoISTo5dOhQY/8KS5BoaxHwsY99LFBP3XfQXSNMeQck82CVUhrH8dIRrLVCCvHYc8iQ3BcrTDD9TXkb4QvRus+bdsPRz9JzeF8wjS30frTNz5s3T3uOrg78sxQmr3wgPQWrKebTnV/YWMWFKc8954lTyoSlS71Yj6KAOM/BoQxwBKtD6rDxbTPBRA7qCFY6+IURrDqLAD74clKIbz1L4sEK6AnWMOUsPY+fa1LoAmrwE1fBWkhgzoOFv/0NYKRxodixA/jrX9Wys8/2MuSWCDzQpkExbwfVKliP5ZOlPnz0ox/Fl770pcqxiWD9zGc+Eyi74YYb0NHREVAi+SolPkluampSsspyBWuYRUAYwUp/q5rxYPWhI4VmzSquPjrwifWECcCBB+ZeDVsFa29vr3bS5bdH07O0q6srVQUrHUt19bElWG0UrNwGp6mpyUrBGqc8roIod+hIoXvuKaYuJrzwQnCxImfFXVdXl3ZhNq0kV2EKVur/vWzZMqPHMV/ITmIRQHdEhH2HKII1joI1jGANi9VKR7ACelKIt90iYVqsMMQwecM0fwqLbSjJaCJYaXujfcHk0wqYCVZ6PxNJum7dukAZn8+YFKw0NlyyZInyXloKVtOcyfbZlQtOPFFNCKqzMCsavC8NHeolMnVw6IdwBKtD6qhGwWoa6Oi2MB90oGtqatIGAI2NjcoA7ROsfJDkpNC0adOUY5qtNg7oKq3/3ajyo729XUnqA9hZBFC0trZq78NRqgQjZ5yhkkJdXeVKdjVrlqoCbGgIeseWADTQ5osMtgSrKckVJ1i5ws0H7ysmgvXaa68NlF144YXYvHlzIHD2+wBXZvBMtZ2dnco5YRYBtgrWmrIIAPSk0F13FVMXHbZvL81iBZ1M0mcm/9vyHQI+ohSsnZ2d1grWMLW4D7qTwsYigHsL+0hiEfD2229bEaxx/OvCiOnSgJNCc+cCb79dTF104ITvxInA1Km5VsEUY+gWAdJUsHJS9+ijj65awRqW2If7RNJt0lEWAXScivpd6Hfo7u5OZKVRGpsNihNP9BKD+pAS+POfi6sPx3PPAStXqmUlUtzZEqwm2zEbgpV+Nmx3XTUE63XXXRcosyVYJ02aVHn9ne98R3kvLQWrbmw1nd/d3Z3/rj8AaG0NJou6++7y5K/o6QkSvmeeWZrFCgeHvOEIVofUkZeClRMmum2Qw4cPD3gtcv9V//MU06dPN94rDnTqCvodBwwYgMsvvxwf+chHKmX0u5q29VB0dHQY/SQpSrU9c8QI4KST1LK77vIC8DLgj39Uj485BjBsZy8SlJhob28PJdptLAK4KpzCRLDydmnajmxSA/nEkG6izC0CAJV0Wr58uTHJVdg2upaWlvqxCACA885TjxcsAEISkeWKmTM9Va2PAhcrTAkG+d9Wl1SRfj6MYOWEjUlhR69herZHEaw6YoeTQgAU1bcJvH9ecsklyjPApDI1lceZ4JaKYD3pJIA/60jivUKxcWNQnV7AYoUpxti4cWOgLKmCVac4pZnJAeCcc85RiB9bgtVEKvGFiBdffFE5pmNgmhYBnGwy9YeaU7C2tgYXLP7yF6AsYymP8yZPBvbfv5i6aGBavEqTYKX9hF6Hx38mgQe9n4lgpSSp6Xo2CtYJEyYo7xVBsAIFxoLnnqser14NPPlkMXXheOKJoGVBiRYrHBzyhiNYHVJHFgpWG4IVUO0AAM9Hjg7cvb292LVrVyTBOmrUKCXA54SrLWwI1oaGBlx55ZWVsrgKVluCNW7W6sxx/vnq8aJFQf+eIrBggfePghNYJQGdiLW1tSltJIlFQJiCwUSwcmLGpGDVET+m93UEqz/5nThxYqXsjTfeUL4ntfKIUrDGTXJVWosAwNuGxRcA/vSnYupCIWVQTXviiUDCHQHVwlbBqtvSCLzbpkykIldUt7a2Gi0C6LPd1DfSIlhtJoQNDQ3493//dwDAySefjPPPP18ZO00LD3GIV5NasFQEa0tLMJnh/fd7uyyKxr33quRUS0shk1hTnLZ27dpAWRoK1m3btkFKiRVke3lLSwtGjBih+M/TZKhhSa5oewsjWMeNG6ccU7LHpGD11ehZEKw8nqZjdCkJViAYO23YADz2WDF1oVi92iOFKM47r1Q2UEksAugz2+SvavJgDYv/TPMPGwWrbnHd1oOVxnthiUt5XXTxmqlfmeZMpt+/MIJ16tSgtVIZ4jwguFhx8MHBXBsODv0IjmB1SB1ZKFijLAJ8UufBBx9UzhkxYkRgIO/s7IwkWAHgd7/7HfbZZx98/OMfx2mnnWb3BRg4wdrT06Od5NtMwsNWkE1qPIrSrcbOmAGMHauWlSFY4ITQHnt4mTBLCBpwtrW1hSpYbYLFJAQrn5Tq+lJzc7MxgPZB+4Df33WT3ylTplTK7r33XuV7UisPvtWybpNcAUBjY1Dd8NBDxfsav/CCt3BCccEFxdQFantKQrD6xH/YRI33If5s9yeFJmsLCpoEzsYiwHQtkzqI4/rrr8fWrVvx6KOPoqGhITTBiA9blRWvL/39S+PBB4yP5AAAIABJREFU6uPcc1WiZetWgCRXKgS9vcHx8bTTPJ+7nJE0Tgsr44tpPAHiAw88oBCse+21F4QQyiLE6tWrjdczjY26XRI+fvCDH1ReT5w4EQcccEDl+PXXX6+8pko6f+EganGC3nco+xvWFcE6cSJw+OFqWRksbPj26o4O4AMfKK4+Gph2XIQpWE02SbTP0THJdI4twUrjOp6EVHc/0/VMYxTNh8GFNrw/mMhlH6bYLo7lienauYELU556qnhf43feAZ59Vi0rMM5zcCgDHMHqkDryUrDqtjNzr9TW1tbAwM0JViFEwHsLAC644AIsXboUv/3tb622WOrAg3o+SdYRrHEtAqK2huuuZVOeORoagqTQ3/7mKRyKwpYtHjFFcd55HoFVQsSxCKATOtP26DCClSfB8cETg+gI1iFDhkT2IV0f0E1+aX+eNWuW8j1HjhypXNOkVOIWATZJrkpNsALAOeeUz9eYqxrGjvUWVgqCrYLVZBGwefNmAOaJFx9bWlpaAgsT/jZnG4KVkkc2Sa4AvYL1wgsv1F5fB/psoH1kocFywkSO6voRra9tYp9CsPfeel/jIi1sdBPpgiaxpkVcnXexjozQPUv585mPN9dddx2WL19eOfb9UMeMGVMpW7VqlfF6pkXsqEXF3t5edHV14a233lJiszvvvLPyOsoiQPd70TY/cOBAK0U3J1FNGeNLB65inTs3uPCWJ3bvDnrBfuADahKhEsCUwJO3D5tEn/QcungRpvqmSKpgbW1t1caEfJwyEaymuRGgzgGFEEYbEB9h47YOpYwFTz896GtctIUNX/gbMsSrp4NDP4YjWB1SR1EerIC6RRjwkiI0Nzcrddq+fXtoIJEmuIKVfz8/qKBBRFdXV+W72VoE2ChYTRPhQoOFs8/2tjm+W5li1Q333APQwKypCfiXfymuPhHgFgFhBCs9l7Y32sbiBth77703zmR+mjoVA1fo6KALpHUK1n333Vf5HPXl4wSraYJRV0mufIwaFfQ1vv324pIgrFkDPPqoWnbBBSoJnDNMiwx8IdAnUjl8hV4cBevgwYOVxQXfpzIuwWqrYOUT1yOOOAL7JdyqRyesVB1IQX/TKNVekszphYErhRYsAObPL6YuAPCHP6jHBxzgbcMsAKY4TUewJlWwAqp38JAhQwIKVkAlWG+88Ubj9WyTYXFQ4ua9732v8p6f7CaJByu/b1RSLCD4nKoZgvXkk4O+xrffXkxdAP1CfgkVdyZ/+WoVrLTPrF+/vtKOwzxY29vbtURplAerKTkWH6dMO5zoeb5ViA8usqExaxTBSueKNUWwtrcDZ52llt19t5qUN090dnrWNRTnnKPO6xwc+iEcweqQOmwyD5uQdOuZf0+upvO3bnEyRUfOZgEa1G/ZskX5fkKISlDBCSh/gm8ypqdob29XgnNTUFBKgnXIkGBmzD/8oRi/u127gDvuUMtOPRUwKDfLABowRilYTQSrrYJVp0B96qmnAgG0TmltIpAodBYBusnvZZddpnxuyZIlldd8W2kSgrWU28JswSeJy5YBjz9eSFVw++1eZlkfra3BiUHOsFWw6sYbwE7ByvtQQ0ODomL1FUP0nkkJVl1/5RNX3l/igF7flCTPZnLvw2QR0NXVVUxm5jC85z0A8dwEAPz2t8XU5fXXgaefVssuuKAwv8hqCdaenp7A31v3rKdb9Pfaay+FYPW3DvNnvo80FKwcH2QJm/znRNoEqy2xUzMEa0tLcKF65kzAsFMgU0gZ7MeHHw5oEjEVDRsFa29vr/K3Nz2DaTunBOuuXbsq/Tkq/tP1jygFq4lg5eUmH3J6npRSefbwBUZaP91zh/5ulGCtKYsAILj4t2VLkOTMC3/+s2pFJURpc1Y4OOQJR7A6pA5KWOatYOU46KCDABRHsFJF3bPPPqt8j46OjgppNWjQIGWLmK9yopPqQYMGVRIoUHR0dEQG80C8bM+54pJL1OPNm4H77su/Hg89BPAkHTG21haBpApWGszaJjngOO+88zCWe+hCT7DaKFh1Sa7od/Db+LBhw4xeX2PHjlX6s8kigCe5srEIKLyf2GDGjGAW5P/5n/y3Nm/ZEty29qEPAYbJVl6gz0DTIgMQT8FKJ7Q6ghVQ/Yt1ClaT/UYaBKuJgLIBVafrbBOklMpvQb+nrh+ZFKy9vb2xF2MzR2MjcNFFatkTTwCLF+dfF04IDRsGnHFG/vXoQxyC1XZhV9eW6fiyfv16LF26tHLsK1jfxxZo/b5rq2CNM+aNHj1aWcRfuXIlgHQIVnq+aQyi5c3NzcpYV2qCFQA++lGAxtq7dwdV2XngmWe8BQuKksZ5cbf+A3YKVjquAO8+26P6gi4Gou1WF/uZhCF8nDIRrLycLn5ykU2YgrW3t1fpc9ROTvfcAkqqYAWA8eMBpqYPLGjngd27gd//Xi078UTPYsfBoZ/DEawOqSMLBSsnWKWUSkBJA81vfvObALxsrtdddx2A4gjWSWxV/Be/+EXlNQ1GhBBKIOFPEuj3HjRokHYLW3t7u1VwXkoFKwBMngwcd5xa9vvf57u1ubdXr2ooaAumLXiSq7AtuqbV+zhqniuuuKLy+tprr9XWKQ0Fq9/uafv3+4cQIuC1DHj9fcyYMcaJLffGpIsfO3bswJtvvhn4DP0NSpeIRwchgEsvVcteecVLNpUn7rpL3bLW0BBcSCkApkUG/rc1KVj9ctpGKDlqIljpAoP/bKfn+QuBHDzJFVf92RCse3IVZgzQ54SOYO3s7FTG+CjPWPo782dCKW0CzjkH4BP/227Ltw4rVgQTbH3sY54ivCDEIVhtPeF1Clbat9atW4d7iUrLJ1j33HNP5Tntk7Dcv5uOLzt37qzcPyzJFYc/xvjwFbVJCFa+OGqjYOWfoYvypSdYR44MJpHi40Qe4HHe2LEeKVRCmCwCaDtIQrC2t7crxKdvsxSW5ArQq1Fpu9VZ0ZgUrNzOzUTEcnW3v0AJxLMI4H2Kjq2m8b7UCU8//nH1eMUKz/oiTzz8sGcFRVHFjhkHh3qCI1gdUkdaClYayPIBkBO3nGC9//77MX/+fEydOhWASvp0dnbGCqqrASdYb7755sprHnjQwMhEsOqUex0dHVb+XaUlWIFgsLBsGTBrVn73nz07qEz6xCfyu39C8CRXYV689FxKUNK+FUWw/vCHP8R//Md/4De/+Q1OOeUUbZ10BCsPpnXQEay0bvR97rUKeM+LpqYmY1/giyp77LGHMiF48cUXAcCoyDP1q9LhtNOCW5tvvTW/++/cCZAEMAC8OpVA1UCfgWF/W5OC1Z+Ihnm56foQfbbrklwNHToUFzG1ZGNjo9JPe3t7Q733/Hvx/pclwcp/Jzpm60gi+vtzIriUBGtHR3A75gMPBCeVWYIvNra1Fe4XSeM02o90BKvJ35A+j6WUWoKVtr+3335b+bxP5jc0NChkybp16wAE/bt5vOXXlX4XUyJRCtqf0lSwxo3hWltba4tgBYKLbFu2BJNNZYkFC4DnnlPLLr20UF/wMNh4sHIikZ5H+x6Pf3TzjTAPVgCBMQpQ2/n06dMD75sI1pOYX7xJwQqoC5QmgjVKwcr7FH1m6HZIAiXfzXTYYQBfmL311vx2K+lEKYcdBkybls/9HRxKjnKOKg41jWqSXNEJm69QAIIDIL8uDQaam5vxwQ9+EAcT9SHPGM6JqSzx7W9/W1vOJ742BKuNgtUUnJd2uwvgbW3uI8MruOmmfFSsvb3Ar3+tlk2aFMwiXULQv2lra6s1wWoKLqMI1hEjRuC73/2uomTl0E1SZ1hkjtd5sOoUrICeYJ0wYQIAGNXc/LcCgClTplTKFvcR7PQ8GtiXkgDSobERuPhiteypp4A+Ajlz/PGPwQQiXFVbEOjfkCtPKSixr9veT9/n2/iTKFhbWlrwrW99S6lDc3NzYNLJx0HdvSjJ2djYiH322QdJEUWw0gRzLS0tkQsSYQRraRXiuq3Nv/lNPvdevTpIQH34w0FVbc6gJAdtb0kVrHzB3G/LYQtzRx99dOU1Pc/3OOaL6Fwhp7OhsSFYaVz67LPPAtATrFGJR5N4sNa0ghUAJkwATjhBLbv11vxUrL/6lXo8YkRQVVsi0PkQjdlM1keAnYLVhmDVxX9+jEVB+9X+3J4IZoJ1wIAB+OxnPwsAOPvss5X5GgcdV+iYE8cigPcpOm6bFKwm64BSzJmECKpFX38deOyxfO4/axawaJFaVpI4z8GhDHAEq0PqoCRgnEnTjh078OMf/7hyTNVlYRNLIHqbPw92+dbqLPHpT39aW04DdUCdhPtBBCdYdcHKgAEDajfJlQ8hgMsvV8sWL/bUQllj1qygJ9dllxWWQCQO+OQwiYLVRLAmtc7QTVJtEu1Uq2D11eKmxQbdRHr8+PGVsnfeeSfwmShPydLinHOCydl++cvs79vZGVTLHn20l/G8BDCpk3lfoRNb2kZ88oa+TxfKOMHq9yGbySyfvHZ1daGjo0Px3eYTQd2WzrPPPrvy3c4+++yqFhBpP9u6dWtg3KW/w9ChQyPHIZ5kjBJEpe1fI0Z4/Yninnu8LZlZ4+abveSLPpqagosnBWD+/PmV14cddljldVIFK49NdApWio6ODoWgp4slfh/lCtampiZlfPRjLFpn0zZlU739HUk6gpXGc3xxgvtBcnsfEykdpmAtnYexCTzO27gxHy/WefO8hUaKiy8udbZz2m7oQlmYgpWONX47klIG+kPUrgodwarz86Z9prm5OdBnTQQrANxwww1Yv3497r33Xm0SVR9pK1gbGhqUepoIVpOytRRzJgA46STPj5XiV7/KXpjS0xNcrJg0yUsM6eDgAMARrA4ZwMYPVIef/exnyvFxxJdz586dyoDJA9Ao5QEnnvJUsA4bNgwnajyeOMHKJ+HUJwzwAhldMpQ999yztpNc+Tj55GCCnptu8hRDWUEXKEycWGgCkTig/WDgwIFKO+ATW9ouTBYBdLJpo+bRgRJXAPCZz3xGq7zm0CW5iqNg9dUTpq2W9Pfwvxv9HfyJec1bBABAe3vQ4uK554LbI9PG7bcDRGECADB49RYB+gykz9KdO3cqJAklDidOnFh57bcR2meogs9GwWqazOoWNIQQ2oUHH7p7jRw5EosXL8bTTz+Nu3misZjg/WzVqlXKMf2dhgwZkknm9FLgiitUIqanxxubssTSpcBf/qKWnXsuQNpbEZBSYs6cOZVjGtvwmKWnp8dqYddEsJoIT+5ZrCNYdTZQlCBa02fzEJdgPYAsFvljim6hY29iibJixQr0EtKDx6+DBg3SevBz1LyCFfC2EB9/vFr2298CBjIrFUgJ3HijWjZ8eOFWG2GQUioEK21PtB1wz3hKxC5duhRSSmzfvl3x7x48eHAiBauOYOUE6he/+MXQ9zlMCR4pdLtIgHgerPR3am9vV75/lOc6R2nmTA0NwNVXq2WLFnkJe7PE/fcDS5aoZddcU1qrDQeHIuB6g0PqsCH7dOAE62mnnaYc08klDYr5wKpDkQpWANptmlEEK1eDDBo0KEBeAV7gFVfBSsmz0kxshQiSMStWeIkQssJf/hIMFK6+umYCBU6IhilYaf/Zd999K693795duQ7dfmWTmEoHHlCHbf0yfW7Lli2QUgYU3D50BKvvCWujYPV/J13gTn+3LBSsv//97/Gtb32r4hWYGc4/H+ATop/9LDt1w4YNwQRAJ5wQ9AkrECaLAODdtiKlxCKy9Y223/Xr16O7u1tpS1TBumPHjkiCdcGCBQD0k9lfEpXxnX0+tpR44RM+04R48ODBOOaYY0JVQTZob29XtlJyH0z6vIirYK0pgnX06CAZ88ADwBtvZHfPG29U+2prq0f0FoxVq1Zh9erVlWO+eEz7hkmJCaikJCcsfIJVCIEzzzwz8Fned+mxT0rpiFNKEPnfIa5FwFVXXaUc79q1S0vm0udCd3e3Qgzxfjxo0CCtjQgHV71SO66aIVgBj4yh2LoVuOWW7O73j38Ac+eqZZ/8pLcQWVJs27ZN6SN0DtHV1VUhTGm80tHRoeyE2L59OzZs2BBobzqClfsg2xKstN3aHCcBXQinz544FgH0edDR0RG6cBlVXio7m1NOAXhysV/9yvPCzwI7dgQt1Q48sLSJ4hwcikJtsAgONQUbs34daLAIBDNS0sHORLyYUKQHK6AnWPdmSV94wMMHd5OCde+99w6QSjzbNKBOJGigZPIZKgTHHQcccoha9qtfBT0d08CWLcAvfqGWTZ3qKWlrBFzBaiJYe3p6lL/zpEmTlK3HvjKNK9KS4ATmsfbRj37U6nO0H8+bNw+dnZ2K6idKwer7qZoU9LqJtI5gpb8TDezTIIBmz56NSy65BN/5znfw5S9/uerrhaKlBbjySrVswQKAZOJOFT//OUAJFSGCE+kCwbfl8sUqv78sWbJEaQPHE7XVhg0bAs9lGwUr3Y7497//HUDQEw8Arr76arzxxht48cUXK/1G503sIw1LjyjQRI1hBGsSBStVtpeaYAU82xgaK/T2Aj/8YTZJRZ5/HvjrX9Wyj37Uy8ReMGgbGD58eMDagvYdW4KVExa0LX/jG98IfJZvQ9YpWG0J1rgK1vFsS+6aNWu0fX7w4MHK91hDEqPRZ0hzc3Ngy7aJYKV9ivefUpE+Udh/f+DUU9Wy228H+mx6UsWuXcD//b9q2ZgxnpdxicFtJbggw/97c4J12LBhylxq3bp1yrhham87d+5U5g26xQYaD/nguSQ4oaoThcSFLrEcEM8igC5ojxw5MnTh0gftp7SvmWxPCkFDQzDOWrEimIAqLdxySzDJ43XX1YSlmoNDnnAEq0PqSGIRwLfDPProo2hsbFQGedNWZpugmF6HK1iLIliPOOII5Zh7ItHBvaOjA42NjVqCda+99gqocHXBNv39KLlrWqUtBEIAn/mMWrZ9u0fepI1f/hLgE5lPf7qmAgVbBSv/Gw8fPlyZbPpBK/dUTIK2tjZs2LABs2bNwo4dO0Kzw1JQIgcA5jLFCSWaeH9qbW2tTLptLALCFKz0N6VEbhoWAddff33l9a3cqzQLfOhDwLhxatnPfuYtLqSJl14KErdnngmQJGJFgy8kcTWO3z5efvnlStnYsWMVQmXjxo0KqQgEk/zoyBb6rG9paYGUUmmPdAyaPHkyppFMvCb1DqBP3JY2qIKVq67pmD1ixIhIRSr/zjWjYAW8LcUf/7ha9sIL6W/H7O72iFuKwYODCU0KwtKlSyuvx40bh5aWFoXYoP2Mtpf29nYlVjMRrM3Nzcri3+TJkwN14AQrPb755puxa9cu7SI87av+omJcW5y2tjZlTFu9enXA3xLw1Lc6SwJALxDQeWJy0BhuyJAhkYm0So1rr/U8hX10dwM/+lH6Cxa/+x2wbJlads01pfZeBdRn65AhQwJCEv/vzeMaIURA0U3bjd92uWKaL4bQtuVDR7DycZTHjWGJ6mxhIlj5AiN9DvHxhP6eNgQrV6bTOpRKlAJ4lhuHH66W3XJL+j7hS5cGdykddZT3z8HBQYEjWB1SRxKLAK4M8ieYpm0ccQlWHojSoCQPi4CjNAMQV+iGKVj930GXxZMrWAH9ZNVEsJYuWDj00GBm13vvDW7xqgYvvQT86U9q2Smn1FygQCdigwcPNk64dFsS+WRz06ZNCoGj2w5mi2HDhuHUU0+N1bf4RPqZZ56pvG5qalKudeyxxyrn0v5lev7Q/uQ/M3QEK51oUII1DQJoDVv5f/PNN6u+ZiiamgCulN28Gfjv/07vHrt3A9//vlrW0RFcKCkYXBXGt7R3dnZi27Zt+DBRNh100EHKZLW3t1chmAYMGBDw8eWeeICaXbmrqwvr1q3TegLrQCd23AM17k6OJKB9ZAPbSbB27drK61GjRkWO/WFJsWpCgXfZZQBTkuGGG4ILddXgd78LZme+7jog4Y6CtLF8+fLKaz+OoDEYjSfo82706NFGQj2sHetInTHMh5YvPP/whz9U6uFfk/ui8vraxJL8/qtWrTJurY5DsFIiymQfwxXjNU2wjhsHXHSRWvbMM0HldjVYsgT4zW/UskMO8Rb/Sg5KCA4fPhyDBg1SPHf993ULx7wt6bzs+XyDtx8dwcptA0aMGKEshgDBuFE3Z4kLOgauIKQhF8rQZ8XKlSv///buPD6q6vwf+OeZrECAEJbIJiAGtSyyFBEUbV0qLbXuWv2iUvHb2mrrAiL9WgSXKnW31datxaLYn0vVilsVMLihgiKilDUNshogQQhknXl+f9x7x3Pv3JlMJpPMjH7er9d9TeYuMyfJPXd57jnPcbXINetU165dmwywehslmC2I0+6eScS6zjP/F/X1wB/+kLwHFqGQdZ1njomRlQVMm5ZRjVKI2goDrJR0iaQIMHPeFRQUhC8QonWPbGmA1bxJ9nZxaQ2jR492tWKaM2dORH487xNlv4vw//EZQbh9+/YRAdambm7T+mIBsIIz3gu82bPdXZATVVsLzJoVmd/u6qtb/tltzLxo7N69e9QbLvN/3759e2RnZ0fcJC5dujR8Qdq1a1fflkOtzcy3t2LFivDPHTt2dNWX7t274zdGAO+ss84K/+zXgj4YDLqOH07QqDkpAlpjkKuSkhJXGoRWcfTR1sMD0wsvAO+8k5zPf+QRYN0697xf/CItujObzDqQn5+P3NzciPryxz/+0dWybvDgwejcubNr39u4cWP458LCQtcNZSgUwhdGTmfn3FRYWOjaL7ds2eIKMMUbYDVb7wBtE2A1g1dmHknAHWDt1q1bkwHW5uZsTTt5eYBnEBfs3g3MmZOcG9n16yMHXTzssLTqzmwGCp1zSLwB1mjrmcdm737sl0fY203fG2CdOXOm7wM187rHCRR7A1nxMFt1R2vBCrjPH2Zdibd1rZe3/mR0gBWwUth4A+hz5gDG3yphoZB1zWg+uBEBrrsuI3Lsmw+znECmGTh19ie/3PLe/dOvBas3wGp+Tk5OTtSUM+bD7GnTpkUsP/LII8MPFLt27Yph3pRfCTDrrXkO9KZ662f01qmrq3M1GPC2YDWPM97WqkBk0NXbUyXtlJRE5gl/913rWi8Znn0W+PBD97zzz7cGBSaiCOl/lqGMk0gL1n8arQkHDBgQvqj2G1kccN/oxdMF2QxeVlRUuPKIeS/WW4OI4LXXXsP8+fNRVlaG6667LmIdbxcxvyBycXExfvazn4XnT5kyBUBk99CMbsEKWMEZ7+iY27YBd93V8s++997Iga0uvRRog0B7snnzSsXTgtXZz7yBG7PF6NixY1s8QE4izDQBZoDVr47fd999ePfdd7Fo0SJXsNXvAY+3y6VzPDADrHV1daisrHTdLJs3KjU1NS0Ohm7z6bLlzWvZKq6+OnJAj5tuAjwBs2ZbuRLwpjo49FArX2QKBINBvPLKK1i6dGnEMr/8wt768rin+9vQoUORlZXlOn98/vnn4Z+Li4vRqVMnV8seMwDrHLdFxHWT6G257NdayBFvgDXeVBzNFW0EZyCyBatZBr9uzuYNb5cuXRLO155Sxx0XOQr6G2+0PFVAXR1www1WV2lHIADMmJFWASG/Xg7RAqfedaOt19R+PH36dNf7k08+2fXeLzC6atWq8M9OoNOsg6WlpSgtLXW1yI23O7N5XtixY0dEblRHc1qwmgEcbyoQh1n/Onfu7HowEyvfbdpq3z7ygcXevda5qaUPHh97DPj0U/e8c86xHlhkAG/6FcC9PznHXr8WrN5gvd8DDPOcVlVV5fs5fu68804MGzYMEydOxC9+8YuI5dnZ2Xjttddwxx134M0330zKeck8B+7cuTP8ENTbgrVz586uv9HKlSvDP8fKwQpEtlg1z18dO3Z0rZ+W90yAdc/kPYbdfbfVtb8lyssjez316AF4Bvwjoq+lz1VbGhORdiJyk4isE5FaEdkmIn8Tkd5Nb/3t09wcrDt27HDlJiwx8vZFa8Fq3tjF05XZDKKuXr3aFdRIRheWeHTr1g0XXHBB1O8zA6wff/wxthh5o8yT++23345zzjkHkydPDv/dRCSiu6tJVV0BhrQPsALAT38amVfoxReB555L/DNfesl6EmsaMgS46KLEPzNFGhsbI1o5RAuwelMJAJEX4cuXLw+/HzNmTKuUuSlmq9k1a9aEf452kT5u3DiccMIJrmCwefxx/gbm8SIrKyv8ed4BGNauXRu1PEDL8hWrakSKAMDder/V9OwJXHONe15lpdWaxwzoNMfOncD06e4b4exs6+bYM2BhW3nggQcwceJEjBs3Ds8//7xrmdmy1DlnmPWluro6IoD+ve99D4A7gGMGWHv27BmRa9H8f5rnLzO4s94z+nw6t2CNlSJgkzEoTa9evVwBKu8gLfv27XPVw0MOOcRV5mgD+6QdEeD66628qKbf/z6yJXe8VK3tPfsFLrwQMPLxpoPVq1eHf3b2e/P/aJ5rzIcNffr0iSvA6rcf33zzzXj00UcxY8YMlJeXR6QNGDhwYMwHgk7Obu9AQd83BrTMy8uLu9eGGWCtqKhwBT7NwFW0AKtfwMt7PvYOVFpTU4O/Gt3di4uLM78FKwCcdBLwgx+45y1davWMSNTSpZEtwfv2tXLsZwi/AKu53zv7k1mP/AKsX375ZURPA/MVQLNS1owfPx4rV67ESy+9FHUAq/79+2PatGmuXOIt4e1l6DyAMAOszv3PkCFDwvPM4483RUCHDh1cxwxvi1WzFXlxcXHUY1da6dgRmDnTPa+mxurGn+jxobraegjivZefNQuII2c1fcOIdIDIhRD5E0Q+gEgdRBQis1v4uadCZAlE9tpTKUQmJqfQqcEAaxNEJB/AYgAzARQA+BeAzQB+BmCFiLB9vEdzW6U8/PDDrvennHJK+GdvTh2H2RLML0eXl3nhvGnTJlcLorYKsDaln2cwmjuNkU/NC4xu3brh6aefxty5c11QTyvlAAAgAElEQVQ3tGYQwJvDq6amxjXipjfA6r2YTwuBgNXFy/s0/Q9/sEZZbq6VK4Fbb3XPy8sDbrzRyiWUYbZs2eIKCPXu3dt1w1VbWxte7m1pBkQGbpYtWxZ+P3r06FYrdyzega4c3px7sfh1ozMDQX379g3nDcvPz3cdr8zgQVFRkesmBIg++Eg8vvrqK1f3c0ebtGAFgNNPB8aPd8/7+OPERkI/cMDK+eUJouHnPwc8uaXb0ty5c8M/n3nmma76Yf5vv/Od7wBwnzt27NjhOsc8/vjjOPjggwG4j63mIFjOftm3b1/f8pg3ZWZw5z//+U/450AgEHOgRbOebvEM1mIGbZIxWrMf83c3jyO1tbUoLy8Pvx84cKCr7lVWVrr+/ubNbn5+Pnr27OmqXzuT0S24rXTvDvz2t+55tbXWjai3TsTj8ceBV15xzzv0UCvVRhpRVXxodBN1riOidW83j20lJSWu4Gm8KQIAK/fjlClTcNttt0VcJwFWAPXPf/5z1IHenDrkDbCaLrroorhzhnu7YJsPHsz6Ei3A6g3eeD+zvr4+4lzz8ssvu96PGDHCdb7PyBasjhkzIlPKPPII8Prrzf+ssjLrAYj5sCwQsK7z2mBA22TxC7B6W04DkS0zAXd93LhxY0SqDiAyfYX5kCOewd7aktM61eGcp70pAgDrwZ3DPOd4UwQEAoGoDXgAdw+qAQMGZEaAFbB6V3hTymzcaAVeg8HmfVZjo1WXjOtnAFZL8BQ1wqCUKwEwD8AVAI4C0PLRAkWuAvAigHEA3oUVczsKwEsQuaLFn58iDLA27XcAjgawFMAgVT1PVccAmAqgO4C/pbJw6ai5KQLeeust13un2zvgbnlqXqyb3b+8g0X5GTJkCA4//PCI+VlZWWkTYD3ssMNcN6jmzXSsGwNHrC5m3vfm37WxsTF9Lxh69bJO8KZg0OrybFwANWn1aiuvq9H9G4B1Ye9zw5YJzNaWvXr1Qrt27SK6dvm14PS7CH/ttddc63z3u99tlTI3xa+OAvHt/w6/Fn9mIMh7g25+thk86NGjB7Kzs10X1i0JsEYb0KpNWrACVsu7mTOtrl2m554D7rkn/iBrba3VGtYINAKwBohL4UjntbW1+OSTT1zzHjPSF5hBzSOOOAKA+0HTqlWrXDeZEyZMCP9s7jPmcdmpQ9HSzJg3heZ3mSkMnFaw0ZgPHbZu3Ro+Vjc2NkaM6N4azN9tw4YN4YdxCxYsCKfT6NChA0pKSlznr1Ao5Kov5v4/cOBABAIB101+tIF90tbJJwOnneaet3271U3T09I3pmeeiex+2b699TAwzUY6X+dpoeu0FjOPoebDb28qpkRbsMbjsssuQ21tLV566SXX/GuuuSacTzJWb6frvdcZMXgDrOa50wywRsvBav6NnL9dUVFR1FQjzzzzDM7x5Fc8/vjjXQ8o/HpHZIxOnYCbb45MhTFzJvDmm/F/zhdfAL/8pZVmwHTZZdbgVhnEL8BqnkOc1BZ+KTvM8R5KS0td1z/OOua+s3fvXtd5zftgOR2YxxjnvOdNEQC4z5fmtZVfIDrWQFfmOXrs2LGZE2AFrPsjb27UJUusFDTxBlmDQav+vfuue/6gQcCVVyannJSJ9gH4K4DLAIwCcEOLPk3kMAB3AqgDcBxUfwjV0wEMB7AbwD0QafsBQZKAAdYYRCQXVpQeAC5X1fBRVVXvBvApgONFZJTf9t9WzUkRoKqum+JFixa5Rso0g5/OxXowGHTl1hkxYkSTZRKRiLxdADBy5Mi0eVorIrjxxht9l5kXVtGYF/1mXjHA3fq3W7du6N27t6u1hnkxn3ZOOSUycFNTY3X3imfE2bfesloCeVt4nHsucOqpyStnG1q9erUrAOTc6Hbp0sUVrHFaOfi1YHVa53l5W6K1pYEDB7q6WDqakyfZL8BqtmD1fpbZun3BggXhn51WiWZ5Eg2w1tXVYdKkSb7L2qwFKwAUFVl5jL2BmyeftFr4eB9AeO3aZdUlbwvyXr2sEWZT2BL87bffjph38803h1sN+7VgNY+r9957b/jnTp06ueqA2e3Q5KSzibZ/Ol2TAYQH/gDcx9umHh4cfPDBrmO1E+Ravnx5uFdCIBBotVziZrmrq6vD55Z58+aF55911lnIz89HYWGh6/xtttQzf2fnJjhaACpjXHddZODmv/8FLrnEakkXSygE/OUvVo8Mr5tuSsvBQ+655x7Xe+eBmN/gUarqCux4W4GZQVWzBWhLW2JPnDgRVVVVuPrqqzFjxgzcfPPN4WWBQMC1PztGjBjRrAcUZqD2yy+/dAVyzHoYrQWrX4BVRFyBsfnz5wMA3njjDZx77rmu77/jjjsgIq7jl7d1e8YZPToyjU0waD0Ef/rpph8Arlxp1TtvC/ITTgCMcQsyhbm/OOciv3pm7kvOPcBxxx0XflBRW1uLZ555JryOc13j7fln9syIJ+1aWzMb0jhpevxa3fq1YFVV1z2R87ub13ZmAFZV8d5774Xfjxs3LuqxKy21b2/lXvWmsfn3v61eFk2Vf98+4KqrIu+vOncG7rwTiLOlP30DqW6E6qVQfQiqHwOI7JbXPFcCyALwIFS/fqqhug7A7wFk2+tkHAZYYzsGQGcAG1XVr7mck8wxM6M0raQ5KQI2bdrkelI7fPhw13IzwOpcrG/cuDHcHUpE4s7zY6YecPzkJz+Ja9u24rSs8opnJE7zAmSFp3XnwoULwz/369cPgUDA9aT3M29rtHRz+eWRI6HX1lrdNG+80b9bZmWl1QrommusgKxp/PjIi/kM0djYiNM8LadOsP82OTk5rotwJ7BoBvGcVnejRo3yDaSe4P07tyERcY1S6zjyyCPj/gzz9//iiy8QDAZdOS+9gSgz57P5IGKknf/XvOFPpJXdvn378JOf/MTV4th8GNJmLVgdRxzh31ropZeASZOAjz6KvJkNBoEFC6zBq4wcpACAwkJr4DijtWZbU1XccsstEfPLy8vx4osvoqGhwbUPOMdZv30NsAa3Mh9URAuwHn300QDiC7BGO4ZHS4vhCAQCrmO7sx/94x//CM8bP358zDQDLVFYWOgK5CxevBgVFRV49dVXw/MusnNYBwIBV30y8zqbLVidhxrRUgB5BYNBvPfee64geVrIzQXuuAMw/s8AgC1brLze8+a5RzF3rFtnPagwcmqGXXUVYOf+TScbN27Eo48+Gn4/Y8aM8M9+LVh37Njhyut4yCGHuFp0m8dSvy7MLVFYWIi7774bt912W0SvjltvvTXiofpMb97CJpjB2NWrV7uCN+YDu2gBVrPluflA8Mwzzwz/fP/992P+/Pn40Y9+FPH9znHHPL5s3rw54VRP1dXVmDt3Lp544glX2drceedFjoQeDFppbK691hro1GvfPuD++60Bd7wPQIcNs9JMpWDAzpYyc4Y7QVFvC1ZVdfXmc469ubm5ONY7EJ/NOZe1a9fOVW/NIGw6BljN8+en9uBl3jEIgMgAq6riyy+/dLVQdf5OZiMD8yH8kiVLXJ89ZswY17nK24AlLfXpY52bvA/T33kH+J//sVq0eo8XoZDVYvynP7XyGJvy862gbTN6kxHFwcmz+qzPsoyOsaVmJIrM4dzVfxxluTM/s/qetDLzRs/vpikYDKKiogJr1qzBXcao8CUlJRGjwZo3rps2bUJVVZUrpcCgQYNcTxZjOeWUU3DeeefhqaeeAmBdzE6bNi2ubdvK4MGDI+YVFxfj+OOPb3Jbc2CiBQsWoKqqCrm5uXjmmWcwa9as8LKxY8cCsIILzpPg0tLSqC3s0kIgYA0CMmOGdWFgWrAAePVVYNw4wOlivm4d8N57/i3yxoyxWg2laCAer1AohP3792Pv3r346quvsG/fPmzYsAHPPfccNm/ejEmTJuGHP/whunTpgkceeQR/+ctfIm6CzP9dSUlJ+AJw2bJlGD9+PN40utk5wcrs7GzceuutESPBnn766a31q8bl7LPPxutG7rXc3Fwcc8wxcW9/+OGHIxAIIBQK4cCBA1ixYoWru5c30DVmzBg88MADEZ/jtCYaMGBA+CbGOzhRVVUVnn/+eRQVFeGkk06CqqK2thZr167F8uXLkZ2djQcffNA1MFJ+fj7++Mc/4rzzzgNgtUhU1ZjdxJPuxBOtVnI33ODOVVdWZgV+Bg2yWhR17gzs2GF1E/Mb2bpjR+CBB2K2tnNu+s2bf7+fE12+Y8cOzJs3z3Ve6NOnT7hF15QpU3DFFVeEW3tmZWWFb7BOPfVU9O3bN6I+fc8T4PJLmTFixIhwazi/h3z9+vVzBZSGDh2K3NzccLd6hxMsieXwww8P31S+8cYb6N69uyt3+U9/+tMmP6MlJk6cGP6+Bx98EB9++CGCdlfDPn36uP5exxxzTHiAuqeeegoXXXQR6urqXOk3nKCyGYxdtWqVqx6EQiGsXr0aixcvxuOPP47ly5cjEAjgoYcewpQpU8LrqSpCoVD41ZnM98FgELt27UIoFELPnj2Rl5eHnJwcV2vbhHXtCjz0kJV/2Lzxrq21uv7//e/WA72DD7ZyF69YYbW283PZZdZDjhaKVX/irVvma0NDA6644orw/zwrK8uVyskM9K1duxaq6jrmHnTQQSgsLHQ9MDePpWb9iyenfkuceeaZOP3007F+/Xr86U9/wsiRI5t9zhs6dCg6d+4cMTBbjx49XEEb80FaZWUlFi9ejHHjxrkeaJsPTy6//HI88MADKC8vR2Njo+812ahRo8LXcGYwac+ePdixY4crYBsMBlFdXY3KykpUVlairq4OWVlZyM7ORlZWFrKyslBcXIyTTz45fHzJy8vDE088gbPOOqttz0mAFQi99lor96NnkEKUllq9kUaPBgYPtgJHZWVWwMhvAJ8jjgDuuy8yh38SNXXO8v4c73oVFRWuB1JOQN8MiJaXl+OFF15w1Z2RxqCwI0eOdD0EA6y6ZebXnzBhAv72NyvLnXmv1sf7wCgNmA/ZP/jgA1RUVLge4DkBVvOB5YEDB3DhhRe68hN369YtfJ9pPihZuXIltm3bhsWLF2Pq1Knh+SNGjEBhYaHrwYmTKqfN60dzjRplpX+6+mr3vdC2bVZL1oMPBsaOtc5hu3YB779vpdnwys21gqvNaOgQj2jnpnhfQ6EQ9u3bh4KCAjQ2NmLfvn2uKSsrK+JajtKISCEA54QZ2YhRdTNEdgHoB5FOUN0bsU4ak7Qc3CZNiMjdAK4GcI+qRjR3E5EjAXwC4GNVTThNgIjU5uXl5TXVnT5TfPrpp66TYYcOHZCdnY36+nrU1dVFjNTsmD59Ov7g6S7X0NCA4uJi1yitpilTprhaVcQjGAwm58aqFagqRo0a5WqBev311/u2zvLau3cvevfuHTM/UG5uLj777DOUlJRg3rx5uNjoeu/tXu6Ux6+MzV0nWdtlq2JqYyN+7Bn5PN6j2KsimJOVhQbj5ry5ZWjJdq2hrKzMdeM6a9Ys3HTTTb7r5ubmYvv27a4HGWVlZbjnnntQVlaGk08+GVdeeWVKLxyDwSAuvvhizJ8/H3l5eXj00UebHfwfOnRo1FbZ27Ztc92E7t27F8XFxa50JoFAANu3b0ePHj1w3XXX4fbbbw8vKywsRH19vWsQseZYuHAhhgwZ4sqBm5eXh/bt27uCRo5kBiO9y8cHg7ihsREx2z4623hmbwNwjQjKwqulx7XE97//fdx444047rjjfJePGTMG77//fvj9hg0bXIE+APjoo49cN6uAFQj94IMPAFgPJ5YtWxbucdHQ0IC+ffu68uHNnj3b9WALAM455xw8+6z7Qf2qVauitpB13HjjjZg9e7bvssLCQmzatMmVTy7ZXn/9dd8eIADw29/+FrcagwcuXrwYJ554Yvi9iETsG0uWLMFxxx2HPXv2uFqIiwjat2+PYDCIxsZG18CMpkAgAFVt8T7Xrl27iIGNYp0DYwUruwaD+H1dHQ6LdUwwP8uYHQJwV1YWnrdblScaFG0rf/3rX3HJJZeE31dVVaFr165Ry3HuuefiqaeewiuvvIKJE78eGLhTp05obGx0tXRduHCha/9JV7/85S/xoGek+p///Od46KGHwu9VFb169XKlyjAFAgFUV1e7GiU89dRTvg9MhgwZgjlz5uCkk05yDebVu3dvVzfxgoICNDQ0oL6+vkX7hROEDQQCEZNZR5IZWHTeiyr+t7ERk8z6710vRtmXimBmVha8w361tKyp0LFjR+zatQu5ubkRAXTThAkTXAHV999/PxyIB6zj2rx581zXUt51HC+++CJOTbP0WevXr4853sabb74ZDqaNGTPG9UDPdP755+PJJ58EAPz5z3/G5ZdfHvN7n3zySZx//vnYs2cPioqKXPuDeR2djsFWp6xDGxowu6YGnb37cpRrO1MVgBk5OfjcPuean5voa1sZNGiQq+dYJsvPz0ddXV2dqqZXfgaRGQBuA3AjVGc3c9thAFYCqIJqUZR1VsDKxzoMqqt810lXzkUqp8gJwMOwjj23RFl+qL18XZyf93mUKZSXl6ffFLW1tdq1a1e1/zZxTSNHjtQ9e/b4ft6VV14ZdbtFixa18W/X+tavX6+DBw9WANqvXz+tqKiIe9sZM2ZE/Vu1b99eFy5cGF5369atmpWV1az/U7pMpwO6BNBlcU5vA3pOGpQ7mVOfPn30hRdeiNgHPv3006jbXHTRRUnZR1tbKBTSdevW6c6dOxPa/o477vD9/Y877jjf9e+55x7XerNmzQov+/DDD5Py//r1r3+tDQ0N4c+dMGFCyvchADoA0Plx1iNnmgNo5zQou3fq0KGDLl++XEOhUNTzxuOPPx7x/58/f354+Wmnnea7jyxbtkz79OmjBQUF+sgjj0QsX7RokZaUlGj37t112rRpWl9fH7HOtm3bdNy4ceHvuuGGG5ralVVVdcOGDdqxY8eI3yUQCOjLL78c12e0RH19vfbp0yfi+3v27KmVlZWudYPBoPbr1y/q/+jggw9W6z7BMn78+JTvN8macgC9rpl16V+AjkiDssc7DR8+XIPBYMQ+cuyxx0bd5h//+Ieqqu7fvz/mtWG3bt20pqamdXfmJFm/fr126NAhXPbCwkJdv359xHpz586N+vueeOKJEeuHQiG99NJLXetNnTpVQ6GQbzkmTZqU8n2itabvA/pGlHrjNy0F9FJAJQ3KnqzpzjvvdP2/TzjhhIh1AoGAfvLJJxH7xkMPPaS9e/fWww47TEtLS333Ne89w6GHHqq1tbXxVoM2EwwG9ZBDDvH9Gx166KGu821ZWZn27ds3Yr1hw4bp1q1bw+vt2rVLi4qK4v7bxzrGpftUDOjDUepNtOkBe7tUlz3RqWfPnm22f7a2vLw8hfUs1jeOpKmKlQEzFFAFZiew7Th72y0x1nnHXmdcyn7HRGOIqS5AOk9ggDVh7733ng4bNizmwa979+56zDHH6KxZs2JeVFdXV+ukSZNcwcC8vDydPXt2G/5Gbau+vl7ffvvtiJvXpjQ0NOjll1+uOTk5rr/18OHD9f33349Y/7777otYN1OmboD+DtB3Y1wgLAV0JqA906C88Ux5eXnarVs37devn55xxhl6yimnaElJiYqIAtBOnTrp9ddfr9u2bYu5H9x6660RwfMTTzxRd+/e3az9KVM1Njbqr371K83NzQ3//gMHDtQ1a9ZE3WbXrl362muv+d6o3H777ZqdnR33/1FEdMyYMTp+/Hg99thj9eGHH464Qd6+fbuOHTs25fscAA3AegDxYoy6tAzQvwN6bBqU1zsNHDhQL7nkkoj/74IFC3T48OFaWFioY8eO1SeffDLq//+dd97Rp59+2hX882poaIi5PB7BYFCXL1+umzZtatZ2n3zySXh/EREdPny4vvrqqy0qS3MsW7ZMjzrqKBURzcnJ0TPOOEM3bNjgu+6SJUu0R48eEXXi2GOP1dWrV7vWXb16te/NMGAFDoYPH67XXnut3nXXXTpy5MiE95GsrKw2e6A4GNbNaay6tBBWMCg/DepPPFNOTo6efPLJunHjRt//+apVq7R///4R/7/Jkye7ArKLFi3S4uLiiM/v3r27vv7668nbYdvAxx9/rL/5zW906tSpum7duqjrPfvsszpo0CDX73vkkUfq2rVrfdcPhUL673//W+fOnavl5eUxy7B582YdOnRok/+/wsJCPeigg7R79+5aVFSknTp1cm7aFYCefvrp+sEHH7SojrXG1BHQqxH7gfqHgN4G62FhqsubzPo2ffr0iOuGjRs3uv5HRUVFvg8Nm6OsrExvueUWnTp1qm7evLlFn9Wali9frgMHDgz/7iKiRx11lK5YsSJi3R07dui1116rI0eO1NGjR+ucOXN8z93Lly/XUaNGhT+zd+/eesEFF+gbb7wRse7SpUv1oIMOSvm+0ZLph4A+E6MuLQP0KUAnpEFZE5lyc3O1a9eu2r9/fz366KNbZT9MhaQFWIHnFVjTzOmoGJ/HAGuUiSkCYmCKgJb78ssvUVFRgcbGRuTl5SE3Nxd5eXkoKCho9mix1dXVqKioQH5+PoqKiiK69tHX9u/fj507d6J9+/bo2LFjzAFQqqqqsGnTpnB+NS+/ri/eefGs01qfFdi3D+1XrED++vXIqqoCQiEEi4pQV1KCmpEjEfIMvpOssifzdy4oKEDHjh1dXf9M1dXVqKqqQs+ePZEdZ+7YyspKbN26Fe3atUPXrl1bPDpzJqqrq8Pu3bvR2NiI3r17tyg1yO7du7F582aEQqHwcSwvLy/c1b+iogJ5eXlo164dsrOz4zo+qSrKysrw1VdffX1StvcNcx/x+7lVlodCaPf558hbvRq527ZBamoQ6tQJDX37onbECDT07w+xuzG3xvcnsk27du1atXt8utmzZ094P0uF2tpaZGVlIScnJ+Z6DQ0NKC8vRygUQl5eHnr16oVc74AbtsbGRqxZswYNDQ2u/JC9e/eOyLFeVVWF3bt3Q0Qiui3H+tkZVbuqqirchXrfvn2unLjm9XBTdTGeZTnbt6P9p58iv6wMgf37gawsNPbogZrBg1E7dChgH++bWydac5nv75GTg06dOsX1P1+/fj3q6upQUFCAbt26+Z53Ghoa8N///hcHDhxAVlYWOnbsiD59+sR9bstUNTU12LFjR/hvk6xuxcFgEOvWrcOBAweQk5MTnnJzc9GuXTt06dLF92+rag0AlJWVFc59q6rYvXs3du/eDVV3XuNgMIhgMBjzWijazy1dT2pq0OGTT5C/di2ydu+GNDYi2KUL6gcOxIGRIxHq1q3Vy5Doeols07lz54gB2hyqiu3bt6OxsRG9evX6xtcbk6qisrIynFYp4B2sM0HONVjnzp1j1suamhqsXbvWdb+UznEU32O/KtqtX4/2q1cjb8sWBGprEerQAQ09e+LAkUeibuBAiH2tHO3ckS6vBQUF2Lt3L/Ly8tCxY8eo1xiZLmkpAkQ+wdfjC8Xr+1AtjfJ5TBEQBQOsMYjIVQDuAfCMqp7rs3wigJcAPK+qZ3qXN+N7vrEBViIiIiIiIiIiit83NAdrIaw0vwBQAFVv2mxAZCeAbgA6I8MGuUrOo59vLmeo15FRljvzP22DshAREREREREREWUe1T0AvrDfjYhYLtIXVnB1U6YFVwEGWJvyLoCvAAwUkeE+y8+2Xxe0XZGIiIiIiIiIiIgyzsv269k+yzI6xsYAawyqWg/gfvvtAyLSwVkmItcAGAZgiap+lIryERERERERERERpRWRNfbU27PkPgBBAJdB5Ghj/RIA1wNotNfJOMzB2gQRyQdQCmAMgO0A3gbQz36/E8DRqlrWwu9gDlYiIiIiIiIiIkqvHKwizwPoab/rBaAvgK0AttjztkP1DM82TrBxAFTLPcuuBnA3rGDqGwDqAfwAQDsAv4Hqn5L+O7QBBljjICLtAPwWwAWwdqRKAK8BmKmqW2JtG+fnM8BKRERERERERETpFmAth9XQMJpNUO3v2SZ6gNVafiqAa/F1LtYVAG6H6kstK2zqMMCaBhhgJSIiIiIiIiIiIM0CrBQX5mAlIiIiIiIiIiIiShADrEREREREREREREQJYoCViIiIiIiIiIiIKEEMsBIREREREREREREliAFWIiIiIiIiIiIiogSJqqa6DN96IqIAkJeXl+qiEBERERERERFRCtXV1QEAVFVSXBSKEwOsaUBEgrBaE9eluixJkmu/1qe0FETfDKxPRMnBukSUPKxPRMnBukSUHN/EupQHIKSqWakuCMWHAVZKOhH5HABUdXCqy0KU6VifiJKDdYkoeVifiJKDdYkoOViXKB0wBysRERERERERERFRghhgJSIiIiIiIiIiIkoQA6xERERERERERERECWKAlYiIiIiIiIiIiChBDLASERERERERERERJUhUNdVlICIiIiIiIiIiIspIbMFKRERERERERERElCAGWImIiIiIiIiIiIgSxAArERERERERERERUYIYYCUiIiIiIiIiIiJKEAOsRERERERERERERAligJWIiIiIiIiIiIgoQQywEhERERERERERESWIAVZKGhFpJyI3icg6EakVkW0i8jcR6Z3qshG1JhEZJSIzROQ5EdkiIioiGsd2k0XkQxGpFpFKEXlFRMY1sc0x9nqV9nYfishFTWzTR0Tm2nWy1q6jN4pIfnN/V6LWIiLtReR0EfmriKy199X9IrJSRG4QkYIY27IuEXmIyDX2eWm9iHwlInUisklE5onI0BjbsT4RRSEiXUWkwr7W29DEuqxLRAYRKXXuk6JME6Jsx7pEGUFUm4wBEDXJPui8CeBoANsBvA2gP4CjAOwEcLSqlqWsgEStSEReAHCad76qSoxt7gVwJYAaAK8DyAdwIgABcLaqvuCzzVkAnoL1cOwtALvsbQoB3KWq03y2ORTAUgDdAHwGYDWA7wI4BMC7AE5U1bpm/FcrId8AAA5JSURBVLpErUJELgXwiP32P7D2104AxgHoCGANgONVtcKzHesSkQ8R2QWgA4BPAWy1Zw8GMAhAA4AzVfUlzzasT0QxiMhjAC6CVSc2quqhUdZjXSLyEJFSAMcD+CeAap9V7lLVVZ5tWJcoc6gqJ04tngDcAkABvAegwJh/jT2/NNVl5MSptSYA1wG4CcCpAA4CUGsdXqOuf5JdL3YBKDHmjwVQB6AKQKFnmyIAX9nbnWnMLwaw3p7/PZ/vesdedp8xLxvAc/b82an++3HipKoAcDGAhwAc4ZnfE8DH9v76pGcZ6xInTlEmAMcAyPeZ/yt7n90BINuYz/rEiVOMCVaARu1zlQLYEGU91iVOnHwmAKX2ftk/zvVZlzhl1JTyAnDK/AlALoA99oFnhM/ylfayUakuKydObTGh6QDrK3aduMpn2X32sqme+dPt+S/4bHOGvWyBZ/5R9vwvAeR5lhUDqAdQCeMGmxOndJzsC2m161auMZ91iROnBCYAG+x9epgxj/WJE6coE4B2dr35HEAJYgdYWZc4cfKZ0PwAK+sSp4yamIOVkuEYAJ1hdZNZ4bP8Wfv11LYrElF6EpF2AE6w3z7rs0q0+jIxxjYvwwo8neTJEeRss0A9XVpU9UtYqTy6ADg2vtITpcxK+zUPQFeAdYmohRrs13qA9YkoDrNgdRW+DF/XnwisS0TJwbpEmYgBVkqGI+3Xj6Msd+YPa4OyEKW7w2AFiXaq6haf5dHqS9R6pqr1sHIF5cPKrdfkNk18F1G6OcR+bYDVggBgXSJKiIhcCKv+rLcngPWJKCoRGQZgKoC5qvp2E6uzLhE1bYqI/FlE7heR34jIwT7rsC5RxmGAlZLBOSD6HfjM+f3aoCxE6S5mfVHV/bBSbnQRkY4AICKdYLUSj7od/OsZ6yZ9U1xpv75mtCxgXSKKg4hcKyKPicgzIvIZgHmwBiQ9X1WD9mqsT0Q+RCQA4FFY+//0ODZhXSJq2u8A/BLA5bC6+m8QkZmedViXKOMwwErJUGC/HoiyfL/92rENykKU7pqqL0BknSkwljWnnrFuUsYTkR8BmAKr9ap58c26RBSfU2ANInc2gMEANsEKrn5krMP6ROTv1wBGA7hWVXfHsT7rElF0bwG4EMBAAO1htVK9HkAjgJtE5EpjXdYlyjgMsBIREVFaEpHDATwBQGDd3K5sYhMi8lDVk1RVYOWPOw5WWoAlInJ9aktGlN7sbsu3AFiiqo+luDhEGU9Vb1DVJ1S1TFVrVHWdqt4K4HR7ldl27lWijMQAKyVDtf3aPsryDvbrvjYoC1G6a6q+AJF1ptpY1px6xrpJGUtEegN4DVZQ6G5Vvc+zCusSUTOo6h47f+SPAHwE4GYRGW0vZn0iivQAgFxYA1vFi3WJqJlU9XUAywEUAhhjz2ZdoozDACslwxf2a58oy535m9qgLETpLmZ9EZEOsC4uqlR1HwCo6l4AX8XaDv71jHWTMpKIFAF4HVauq7kApvmsxrpElABVbQDwFKyW4c7oy6xPRJF+DKvL8IMiUupMAP6fvby3Mf8gex7rElFinEEXe9qvrEuUcRhgpWRwumyOjLLcmf9pG5SFKN2tBVAHoLvdQs8rWn2JWs9EJAfAEAC1ANbFs00T30WUMiJSAOBVAN8B8ByA/1VV9VmVdYkocbvs1+72K+sTkb9CAMd7JqeFXb4xL9+ex7pElJgu9quT75R1iTIOA6yUDO/CelI0UESG+yw/235d0HZFIkpPqloDYLH99hyfVaLVl5c9y00/hnVhv1BVa322OVVE8swNRKQYwHgAVbDqMFHK2fvpvwAcBeDfcI9y7sK6RNQix9uvGwHWJyI/qip+E4AB9iobjfnl9jasS0TNJCLdYe2vAPAxwLpEGUpVOXFq8QQrAbzCOuh0MOZfY88vTXUZOXFqqwnWU1GNsfwku17sAlBizB9rb1sFoNCzTRGsBxkK4Exjfg9YXWoUwPd8vusde9m9xrxsAP+0589O9d+LEydVBYAsWC1WFdYos+3j2IZ1iRMnnwnAMQAmAAh45ufAGhU9CKvrc19jGesTJ05xTAD62/vphijLWZc4cfJMAMbBGswqyzO/v7Ef/8uzjHWJU0ZNourX646oeUQkH0AprC4z2wG8DSt33hgAOwEcraplKSsgUSsSkYkAZhqzjoKV2+4DY97Nqvqysc29AK6EdYP7BqxBFE62tztbVV/w+Z6zADxtr1MKYDesC49CWIMATfXZpgTAUgBdAawCsBrAaACHAHgPwAmqWpfI702UTCJyJYB77bfPA9gbZdVpqup0b2ZdIvIhIpNh5S/eBWtAq90AugEYCiu/XS2Ai1X1ac92rE9ETRCR/gD+C6sF66FR1mFdIjIY56UdsFqp7oEVLxgFq1Xp57D21wrPdqxLlDEYYKWkEZF2AH4L4AIAfQFUwhoBeqaqbkll2Yhak3HBEMvPVPUxn+2uAHAEgHoA78MKxL4X47uOAfA7AEfDusBYDeB+Vf17jG36ArgJVmumIliJ3P8B4FZ1d48hShkRmQ1gVhyrDlC7K6ax7WSwLhGFicgAAJfCSgVwCKzgaj2AclhdLv+oqhuibDsZrE9EUcUTYLXXmwzWJSIAgIgcAasHxRhYsYIusPKt/gfAMwD+olZaAL9tJ4N1iTIAA6xERERERERERERECeIgV0REREREREREREQJYoCViIiIiIiIiIiIKEEMsBIREREREREREREliAFWIiIiIiIiIiIiogQxwEpERERERERERESUIAZYiYiIiIiIiIiIiBLEACsRERERERERERFRghhgJSIiIiIiIiIiIkoQA6xERERERERERERECWKAlYiIiIiIiIiIiChBDLASERERERERERERJYgBViIiIqIMJSLazKnc3q6//b40tb9B84hIqef3uT/VZXKIyHCfv3f/VJeLiIiIiFpfdqoLQEREREQJ+7vPvGMBDASwEsAnnmW7Wr1EbeOfAKoBvJ/qghgq8fX/YwKA4hSWhYiIiIjaEAOsRERERBlKVSd754nIY7ACrC+o6uwom24FcASAA61VtlY2TVXLU10Ik6p+AWAyYLW0BQOsRERERN8aDLASERERfcuoagOANakuBxERERHRNwFzsBIRERF9y0TLwSoik+35s0VkoIg8LSK7RGSviLwqIt+x18sWkf8TkXUiUisiG0Tk8hjf11dE7heRjfb6lSLykoiMS/LvNdsu/2QRGWWXeY/9fU+LSB97vQ4icruIlNvl+UxEzo7ymeNE5AUR2SQidSKyQ0Q+FJE5IlKQzPITERERUWZigJWIiIiIvAYA+BDAEAALAZTDyitaKiIHAXgWwHQAnwMoBdAXwP0i8r/eDxKRsbDywV4OoAHAywA+A3AKgLdE5LxWKP8YAO8C6A7g3wB2AzgHwCIR6QzgTQAXA1gGYCmA7wB4WkRO8ZT9VABvA/gJgO0AngOwAkARgOsAdGuFshMRERFRhmGKACIiIiLyugjAHAD/p6oqIgLgb7ByjC4CEAJQoqo7AUBEToQViL0ewCPOh4hIJ1gDUnUCMElV5xvLvgvgdQCPishi57OS5DIAv1TVB+3vygHwCoCTALwHYAeAQ1R1v718CoBHAfwfrICsYxqsBglnq+o/zS8QkdGwArdERERE9C3HFqxERERE5FUG4AZVVQCwX++xl30HwFVmQFRVF8Fq2dlPRPobn3MJgJ4A7jWDq/Y2ywHcDKAAwKQkl/8dJ7hqf1cDgD/Zbw+HFXzdb6z/GIBdAMbawVhHd/t1ofcLVHWZqu5LaqmJiIiIKCMxwEpEREREXqV2UNJUZr82wEoL4OUs72nM+4H9+lyU73nbfj2quQVswus+85zylavqOnOBqgYBbAKQA3e3/4/s18dFZLSI8NqZiIiIiCLwIpGIiIiIvLZ6Z6hqtf3jDjsg6eUszzPm9bdf37UHn3JNsHKgAsnPZRpRfqN8fsvM5Wb5/w9W/thTYeWk3SUiL4rIpSKSn5SSEhEREVHGYw5WIiIiIvIKJbjMy3mY/yyA/THWW9OMz4xHUsqvqpvtXLEnAPgxgONhBVtPBTBdRMaqKvOwEhEREX3LMcBKRERERK1lC4DDAMxR1Y+aWjkdqWojrJQDrwOAiPSDNeDXCQCuAzA9daUjIiIionTAFAFERERE1FresF/PSGkpkkhVNwH4g/12SCrLQkRERETpgQFWIiIiImotDwGogNWd/ufeQaJEJFtEThGRtAxUisjVInKQz6If2a+b27I8RERERJSemCKAiIiIiFqFqu4RkdMALIAVbP2diHwGoArAQQBGAiiE1cL1s5QVNLpZAO4UkZUA1gMQAEcCGASgEsCdKSwbEREREaUJBliJiIiIqNWo6vsiMhTA1QAmwhooCgC2A1gC4HkAC1NUvKb8GsAEAKMA/NCetxnA3QDuVtWtqSoYEREREaUPUdVUl4GIiIiIqEkiUgorQDtAVctTW5roMqWcRERERJQcbMFKRERERJnmThGpBrBQVZ9IdWEAQEQOBnCT/fbwVJaFiIiIiNoWA6xERERElGnOsl+rAaRFgBVAEYCLU10IIiIiImp7TBFARERERERERERElKBAqgtARERERERERERElKkYYCUiIiIiIiIiIiJKEAOsRERERERERERERAligJWIiIiIiIiIiIgoQQywEhERERERERERESWIAVYiIiIiIiIiIiKiBDHASkRERERERERERJQgBliJiIiIiIiIiIiIEsQAKxEREREREREREVGCGGAlIiIiIiIiIiIiShADrEREREREREREREQJYoCViIiIiIiIiIiIKEEMsBIREREREREREREliAFWIiIiIiIiIiIiogQxwEpERERERERERESUoP8PRR4pGFUZaBoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1500x450 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 3), dpi=150)\n",
+    "plt.title(\"1 Hz stimulus\")\n",
+    "ax1 = plt.gca()\n",
+    "ax1.plot(model.t, model.output.T, c=\"k\")\n",
+    "ax2 = plt.gca().twinx()\n",
+    "ax2.plot(model.t, stimulus, lw=2, c=\"r\", alpha=0.8)\n",
+    "ax1.set_xlabel(\"Time [ms]\")\n",
+    "ax1.set_ylabel(\"Activity [Hz]\")\n",
+    "ax2.set_ylabel(\"Stimulus [mV/ms]\", color='r')\n",
+    "ax2.set_ylabel(\"Stimulus [mV/ms]\", color='r')\n",
+    "ax2.tick_params(axis='y', labelcolor='r')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/neurolib/models/aln/loadDefaultParams.py b/neurolib/models/aln/loadDefaultParams.py
index 4c6ef00..527de76 100644
--- a/neurolib/models/aln/loadDefaultParams.py
+++ b/neurolib/models/aln/loadDefaultParams.py
@@ -32,10 +32,12 @@ def loadDefaultParams(Cmat=None, Dmat=None, lookupTableFileName=None, seed=None)
     params.dt = 0.1  # ms 0.1ms is reasonable
     params.duration = 2000  # Simulation duration (ms)
     np.random.seed(seed)  # seed for RNG of noise and ICs
-    # set seed to 0, pypet will complain otherwise
-    if seed is None:
-        seed = 0
-    params.seed = seed
+    # set seed to 0 if None, pypet will complain otherwise
+    params.seed = seed or 0
+    
+    # make sure that seed=0 remains None
+    if seed == 0:
+        seed = None
 
     # options
     params.warn = 0  # warn if limits of lookup tables are exceeded
diff --git a/neurolib/models/aln/timeIntegration.py b/neurolib/models/aln/timeIntegration.py
index 76cf87f..0d2d7d2 100644
--- a/neurolib/models/aln/timeIntegration.py
+++ b/neurolib/models/aln/timeIntegration.py
@@ -651,7 +651,8 @@ def adjust_shape(original, target):
 
     # repeat original in y until larger (or same size) as target
 
-    # ------- y
+    # tile until N 
+
     # either (x,) shape or (y,x) shape
     if len(original.shape) == 1:
         # if original.shape[0] > 1:
@@ -661,9 +662,10 @@ def adjust_shape(original, target):
     else:
         rep_y = 1
 
+    # tile once so the array has shape (N,1)
     original = np.tile(original, (rep_y, 1))
 
-    # ------- x
+    # tile until t
 
     if target.shape[1] > original.shape[1]:
         rep_x = int(target.shape[1] / original.shape[1]) + 1
@@ -671,8 +673,8 @@ def adjust_shape(original, target):
         rep_x = 1
     original = np.tile(original, (1, rep_x))
 
-    # cut
-    original = original[: target.shape[0], : target.shape[1]]
+    # cut from end because the beginning can be initial condition
+    original = original[: target.shape[0], - target.shape[1] : ]
 
     return original
 
diff --git a/neurolib/models/fhn/loadDefaultParams.py b/neurolib/models/fhn/loadDefaultParams.py
index b2ac9f5..fa24b40 100644
--- a/neurolib/models/fhn/loadDefaultParams.py
+++ b/neurolib/models/fhn/loadDefaultParams.py
@@ -23,10 +23,12 @@ def loadDefaultParams(Cmat=None, Dmat=None, seed=None):
     params.dt = 0.1  # ms 0.1ms is reasonable
     params.duration = 2000  # Simulation duration (ms)
     np.random.seed(seed)  # seed for RNG of noise and ICs
-    # set seed to 0, pypet will complain otherwise
-    if seed is None:
-        seed = 0
-    params.seed = seed
+    # set seed to 0 if None, pypet will complain otherwise
+    params.seed = seed or 0
+    
+    # make sure that seed=0 remains None
+    if seed == 0:
+        seed = None
 
     # ------------------------------------------------------------------------
     # global whole-brain network parameters
diff --git a/neurolib/models/hopf/loadDefaultParams.py b/neurolib/models/hopf/loadDefaultParams.py
index 5bc97c3..e51426e 100644
--- a/neurolib/models/hopf/loadDefaultParams.py
+++ b/neurolib/models/hopf/loadDefaultParams.py
@@ -23,10 +23,12 @@ def loadDefaultParams(Cmat=None, Dmat=None, seed=None):
     params.dt = 0.1  # ms 0.1ms is reasonable
     params.duration = 2000  # Simulation duration (ms)
     np.random.seed(seed)  # seed for RNG of noise and ICs
-    # set seed to 0, pypet will complain otherwise
-    if seed is None:
-        seed = 0
-    params.seed = seed
+    # set seed to 0 if None, pypet will complain otherwise
+    params.seed = seed or 0
+    
+    # make sure that seed=0 remains None
+    if seed == 0:
+        seed = None
 
     # ------------------------------------------------------------------------
     # global whole-brain network parameters
diff --git a/neurolib/models/thalamus/loadDefaultParams.py b/neurolib/models/thalamus/loadDefaultParams.py
index a840b9d..92ba652 100644
--- a/neurolib/models/thalamus/loadDefaultParams.py
+++ b/neurolib/models/thalamus/loadDefaultParams.py
@@ -18,8 +18,12 @@ def loadDefaultParams(seed=None):
     params.dt = 0.01  # ms
     params.duration = 60000  # Simulation duration (ms)
     np.random.seed(seed)  # seed for RNG of noise and ICs
-    # set seed to 0, pypet will complain otherwise
-    params.seed = seed or 0.0
+    # set seed to 0 if None, pypet will complain otherwise
+    params.seed = seed or 0
+    
+    # make sure that seed=0 remains None
+    if seed == 0:
+        seed = None
 
     # local parameters for both populations
     params.tau = 20.0
diff --git a/neurolib/models/wc/loadDefaultParams.py b/neurolib/models/wc/loadDefaultParams.py
index c5c2ff1..0eab30f 100644
--- a/neurolib/models/wc/loadDefaultParams.py
+++ b/neurolib/models/wc/loadDefaultParams.py
@@ -23,10 +23,12 @@ def loadDefaultParams(Cmat=None, Dmat=None, seed=None):
     params.dt = 0.1  # ms 0.1ms is reasonable
     params.duration = 2000  # Simulation duration (ms)
     np.random.seed(seed)  # seed for RNG of noise and ICs
-    # set seed to 0, pypet will complain otherwise
-    if seed is None:
-        seed = 0
-    params.seed = seed
+    # set seed to 0 if None, pypet will complain otherwise
+    params.seed = seed or 0
+    
+    # make sure that seed=0 remains None
+    if seed == 0:
+        seed = None
 
     # ------------------------------------------------------------------------
     # global whole-brain network parameters
diff --git a/neurolib/utils/functions.py b/neurolib/utils/functions.py
index c6353f8..c12a57e 100644
--- a/neurolib/utils/functions.py
+++ b/neurolib/utils/functions.py
@@ -306,3 +306,94 @@ def getMeanPowerSpectrum(activities, dt, maxfr=70, spectrum_windowsize=1.0, norm
     if normalize:
         powers /= np.max(powers)
     return f, powers
+
+def construct_stimulus(stim='dc', duration=6000, dt=0.1, stim_amp=0.2, stim_freq=1, stim_bias=0, n_periods=None, nostim_before=0, nostim_after=0):
+    """Constructs a stimulus that can be applied to a model
+
+    :param stim: Stimulation type: 'ac':oscillatory stimulus, 'dc': stimple step current, 
+                'rect': step current in negative then positive direction with slowly
+                decaying amplitude, used for bistability detection, defaults to 'dc'
+    :type stim: str, optional
+    :param duration: Duration of stimulus in ms, defaults to 6000
+    :type duration: int, optional
+    :param dt: Integration time step in ms, defaults to 0.1
+    :type dt: float, optional
+    :param stim_amp: Amplitude of stimulus (for AdEx: in mV/ms, multiply by conductance C to get current in pA), defaults to 0.2
+    :type stim_amp: float, optional
+    :param stim_freq: Stimulation frequency, defaults to 1
+    :type stim_freq: int, optional
+    :param stim_bias: Stimulation offset (bias), defaults to 0
+    :type stim_bias: int, optional
+    :param n_periods: Numer of periods of stimulus, defaults to None
+    :type n_periods: [type], optional
+    :param nostim_before: Time before stimulation, defaults to 0
+    :type nostim_before: int, optional
+    :param nostim_after: Time after stimulation, defaults to 0
+    :type nostim_after: int, optional
+    :raises ValueError: Raises error if unsupported stimulus type is chosen.
+    :return: Stimulus timeseries
+    :rtype: numpy.ndarray
+    """
+    '''Constructs a sitmulus that can be applied as input to a model
+
+    TODO: rewrite
+
+    stim:       Stimulus type: 'ac':oscillatory stimulus, 'dc': stimple step current, 
+                'rect': step current in negative then positive direction with slowly
+                decaying amplitude, used for bistability detection
+    stim_amp:   Amplitude of stimulus (for AdEx: in mV/ms, multiply by conductance C to get current in pA)
+    '''
+    def sinus_stim(f=1, amplitude=0.2, positive=0, phase=0, cycles=1, t_pause=0):
+        x = np.linspace(np.pi, -np.pi, int(1000 / dt / f))
+        sinus_function = np.hstack(((np.sin(x + phase) + positive), np.tile(0, t_pause)))
+        sinus_function *= amplitude
+        return np.tile(sinus_function, cycles)
+
+    if stim == 'ac':
+        """Oscillatory stimulus
+        """
+        n_periods = n_periods or int(stim_freq)
+
+        stimulus = np.hstack(([stim_bias] * int(nostim_before / dt),
+                              np.tile(sinus_stim(stim_freq, stim_amp) + stim_bias, n_periods)))
+        stimulus = np.hstack((stimulus, [stim_bias] * int(nostim_after / dt)))
+    elif stim == 'dc':
+        """Simple DC input and return to baseline
+        """
+        stimulus = np.hstack(
+            ([stim_bias] * int(nostim_before / dt), [stim_bias + stim_amp] * int(1000 / dt)))
+        stimulus = np.hstack((stimulus, [stim_bias] * int(nostim_after / dt)))
+        stimulus[stimulus < 0] = 0
+    elif stim == 'rect':
+        """Rectified step current with slow decay
+        """
+        # construct input
+        stimulus = np.zeros(int(duration / dt))
+        tot_len = int(duration / dt)
+        stim_epoch = tot_len / 6
+
+        stim_increase_counter = 0
+        stim_decrease_counter = 0
+        stim_step_increase = 5.0 / stim_epoch
+
+        for i, m in enumerate(stimulus):
+            if 0 * stim_epoch <= i < 0.5 * stim_epoch:
+                stimulus[i] -= stim_amp
+            elif 0.5 * stim_epoch <= i < 3.0 * stim_epoch:
+                stimulus[i] = -np.exp(-stim_increase_counter) * stim_amp
+                stim_increase_counter += stim_step_increase
+            elif 3.0 * stim_epoch <= i < 3.5 * stim_epoch:
+                stimulus[i] += stim_amp
+            elif 3.5 * stim_epoch <= i < 5 * stim_epoch:
+                stimulus[i] = np.exp(-stim_decrease_counter) * stim_amp
+                stim_decrease_counter += stim_step_increase
+    else:
+        raise ValueError(f"Stimulus {stim} not found. Use \"ac\", \"dc\" or \"rect\".")
+
+    # repeat stimulus until full length
+    steps = int(duration / dt)
+    stimlength = int(len(stimulus))
+    stimulus = np.tile(stimulus, int(steps / stimlength + 2))
+    stimulus = stimulus[:steps]
+
+    return stimulus    
diff --git a/setup.py b/setup.py
index e7d3abd..20c47a8 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,7 @@ with open("requirements.txt") as f:
 
 setuptools.setup(
     name="neurolib",
-    version="0.5.3",
+    version="0.5.4",
     description="Easy whole-brain neural mass modeling",
     long_description=long_description,
     long_description_content_type="text/markdown",


Test patch:
diff --git a/tests/test_functions.py b/tests/test_functions.py
index e547f7c..65eaf7a 100644
--- a/tests/test_functions.py
+++ b/tests/test_functions.py
@@ -28,6 +28,8 @@ class TestFunctions(unittest.TestCase):
         cls.model = aln
         cls.ds = Dataset("gw")
 
+        cls.single_node = ALNModel()
+
     def test_kuramoto(self):
         kuramoto = func.kuramoto(self.model.rates_exc[:, ::10], dt=self.model.params["dt"], smoothing=1.0)
 
@@ -53,3 +55,10 @@ class TestFunctions(unittest.TestCase):
     def test_getMeanPowerSpectrum(self):
         fr, pw = func.getMeanPowerSpectrum(self.model.rates_exc, dt=self.model.params["dt"])
 
+    def test_construct_stimulus(self):
+        self.single_node.params['duration'] = 2000
+        stimulus = func.construct_stimulus("ac", duration=self.single_node.params.duration, dt=self.single_node.params.dt, stim_amp=1.0, stim_freq=1)
+        stimulus = func.construct_stimulus("dc", duration=self.single_node.params.duration, dt=self.single_node.params.dt, stim_amp=1.0, stim_freq=1)
+        stimulus = func.construct_stimulus("rect", duration=self.single_node.params.duration, dt=self.single_node.params.dt, stim_amp=1.0, stim_freq=1)
+        
+        self.single_node.run()
\ No newline at end of file