diff --git a/Exercises/Exercise 9/Radiation.ipynb b/Exercises/Exercise 9/Radiation.ipynb
new file mode 100644
index 0000000..fba3535
--- /dev/null
+++ b/Exercises/Exercise 9/Radiation.ipynb	
@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we import the raw data into a data frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radiation = read.csv(\"Radiation_KKW_Goesgen.csv\", header = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First lets compute some bare statistics on that data. Lets get the average, the standard deviation and the number of datapoints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mu=mean(radiation$Radiation.µSv.h)\n",
+    "sigma=sd(radiation$Radiation.µSv.h)\n",
+    "n=length(radiation$Radiation.µSv.h)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we plot the histogram and compare the distribution to a normal distribution that best fits the data. As we see, the distribution is slightly asymmetric, and the mean is pulled up by some outliers. You can chose to correct this here, but we will continue on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lVUVGR7+cYyBrBPnz7W0YWWNzurU6dO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\nAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "Plot with title “Histogram of radiation$Radiation.µSv.h”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist(radiation$Radiation.µSv.h, breaks=seq(0.08,0.13,by=0.001))\n",
+    "lines(seq(0.08,0.13,by=0.0001),1.3*dnorm(seq(0.08,0.13,by=0.0001),mean=mu, sd=sigma),col=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The boxplot summarizes the same picture. Its a symmetric, approximately normal distribution for the center of the data, but with an asymmetric distribution towards higher values. High radiation, worrying!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2r3T0h+qb2QcqR2/4Tkl9oLKUdq909Ifqm9kHKkdv+E5JfaCylHavdPSH6pvZBy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\nrs8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot(radiation$Radiation.µSv.h)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The legal requirement is a mean radiation below 0.1muSh, and we will perform a T-test to demonstrate this. The boxplot already hints that this may quite likely. Our research hypothesis is that the true mean is below 0.1, hence the null hypothesis H0: mu >= 0.1 and we need to perform a one sided T-test. Small values of the T statistic disprove H0, so our critical region will be in the left tail of the T-distribution. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T= ( mu-0.1)/sigma*sqrt(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we find the critical region for the one-sided T-test, using the specified level of significance alpha=0.001. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-3.09777463598355"
+      ],
+      "text/latex": [
+       "-3.09777463598355"
+      ],
+      "text/markdown": [
+       "-3.09777463598355"
+      ],
+      "text/plain": [
+       "[1] -3.097775"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qt(0.001,df=n-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-13.801065865729"
+      ],
+      "text/latex": [
+       "-13.801065865729"
+      ],
+      "text/markdown": [
+       "-13.801065865729"
+      ],
+      "text/plain": [
+       "[1] -13.80107"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our T-distribution is well within the critical region, we can safely reject the null hypothesis and are safe from radiation. Note that many actual measurement values exceed 0.1 and it is very close to the average of the data. But the large number of datapoints gives us enough statistical confidence for this test. Note also that the distribution is slightly asymmetric, which would allow us to use even stricter criteria when using asymmetric distribution functions. The true mean is most certainly below 0.1!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Exercises/Exercise 9/Radiation_KKW_Goesgen.csv b/Exercises/Exercise 9/Radiation_KKW_Goesgen.csv
new file mode 100644
index 0000000..b5f1009
--- /dev/null
+++ b/Exercises/Exercise 9/Radiation_KKW_Goesgen.csv	
@@ -0,0 +1,1085 @@
+Date,Radiation µSv/h
+28.12.2015 ,0.1011
+29.12.2015 ,0.0994
+30.12.2015 ,0.0998
+31.12.2015 ,0.1007
+01.01.2016 ,0.0967
+02.01.2016 ,0.1032
+03.01.2016 ,0.0972
+04.01.2016 ,0.1035
+05.01.2016 ,0.0987
+06.01.2016 ,0.1004
+07.01.2016 ,0.1028
+08.01.2016 ,0.101
+09.01.2016 ,0.102
+10.01.2016 ,0.1025
+11.01.2016 ,0.1034
+12.01.2016 ,0.1022
+13.01.2016 ,0.0979
+14.01.2016 ,0.0998
+15.01.2016 ,0.0962
+16.01.2016 ,0.0947
+17.01.2016 ,0.095
+18.01.2016 ,0.0964
+19.01.2016 ,0.0978
+20.01.2016 ,0.0979
+21.01.2016 ,0.0945
+22.01.2016 ,0.0946
+23.01.2016 ,0.0969
+24.01.2016 ,0.0962
+25.01.2016 ,0.0977
+26.01.2016 ,0.0986
+27.01.2016 ,0.099
+28.01.2016 ,0.0973
+29.01.2016 ,0.0958
+30.01.2016 ,0.0997
+31.01.2016 ,0.1065
+01.02.2016 ,0.0946
+02.02.2016 ,0.0952
+03.02.2016 ,0.098
+04.02.2016 ,0.0965
+11.02.2016 ,0.0975
+12.02.2016 ,0.1006
+13.02.2016 ,0.1026
+14.02.2016 ,0.1017
+15.02.2016 ,0.0974
+16.02.2016 ,0.0964
+17.02.2016 ,0.0984
+18.02.2016 ,0.098
+19.02.2016 ,0.0962
+20.02.2016 ,0.096
+21.02.2016 ,0.0935
+22.02.2016 ,0.0963
+23.02.2016 ,0.0989
+24.02.2016 ,0.0967
+25.02.2016 ,0.0985
+26.02.2016 ,0.0968
+27.02.2016 ,0.0995
+28.02.2016 ,0.0993
+29.02.2016 ,0.1024
+01.03.2016 ,0.0968
+02.03.2016 ,0.0981
+03.03.2016 ,0.0975
+04.03.2016 ,0.0982
+05.03.2016 ,0.1027
+06.03.2016 ,0.098
+07.03.2016 ,0.0971
+08.03.2016 ,0.0965
+09.03.2016 ,0.0983
+10.03.2016 ,0.0985
+11.03.2016 ,0.0958
+12.03.2016 ,0.0957
+13.03.2016 ,0.0959
+14.03.2016 ,0.0956
+15.03.2016 ,0.0988
+16.03.2016 ,0.0965
+17.03.2016 ,0.0964
+18.03.2016 ,0.0987
+19.03.2016 ,0.0989
+20.03.2016 ,0.0973
+21.03.2016 ,0.0977
+22.03.2016 ,0.0977
+23.03.2016 ,0.098
+24.03.2016 ,0.0973
+25.03.2016 ,0.0995
+26.03.2016 ,0.0964
+28.03.2016 ,0.0978
+29.03.2016 ,0.0959
+30.03.2016 ,0.0959
+31.03.2016 ,0.0984
+01.04.2016 ,0.0972
+02.04.2016 ,0.0973
+03.04.2016 ,0.0999
+04.04.2016 ,0.0998
+05.04.2016 ,0.1012
+06.04.2016 ,0.099
+07.04.2016 ,0.0964
+08.04.2016 ,0.097
+09.04.2016 ,0.0971
+10.04.2016 ,0.0973
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diff --git a/Exercises/Exercise 9/Trading.ipynb b/Exercises/Exercise 9/Trading.ipynb
new file mode 100644
index 0000000..634c568
--- /dev/null
+++ b/Exercises/Exercise 9/Trading.ipynb	
@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trading = read.csv(\"Trading_Data_DAX.csv\", header = TRUE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets first look at the histogram. We have the daily high and low, as well as the first and last. Lets just look at first. We store the observed frequencies here, and can access them by measured$counts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ck6pJGnJ16cbp7zj0+KeooQIUkhJEWK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",
+      "text/plain": [
+       "Plot with title “Histogram of trading$First”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "measured=hist(trading$First, breaks=seq(10000,14000,by=500))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to test against the theoretical prediction of a normal distribution. First we need to estimate the variance and the mean of that distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mu=mean(trading$First)\n",
+    "sigma=sd(trading$First)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we calculate the probability to find data in these brackets, assuming a normal distribution with this value. We do this by generating a vector that contains the probability to find a datapoint up to the upper bound of the box, and substract a vector containing the probability to find data up to the lower bound of each box. This difference then gives the probability to find data within each interval. This will be the p_i in a chi^2 test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "probabilities=pnorm(seq(10500,14000,by=500),mean=mu,sd=sigma)-pnorm(seq(10000,13500,by=500),mean=mu,sd=sigma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can calculate the chi^2 statistic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chisq= sum(  (measured$counts-probabilities*length(trading$First))^2/(probabilities*length(trading$First)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "30.885802649057"
+      ],
+      "text/latex": [
+       "30.885802649057"
+      ],
+      "text/markdown": [
+       "30.885802649057"
+      ],
+      "text/plain": [
+       "[1] 30.8858"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chisq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 8 brackets to consider, so the degrees of freedom are 7. Using an alpha of 0.05, we evaluate up to what value of chi^2 we can expect to find with a probability of 0.95. This is the 0.95 quantile of the distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "14.0671404493402"
+      ],
+      "text/latex": [
+       "14.0671404493402"
+      ],
+      "text/markdown": [
+       "14.0671404493402"
+      ],
+      "text/plain": [
+       "[1] 14.06714"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qchisq(p=0.95,df=7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As our chi^2 is much bigger than the critical value, we have to reject that hypothesis. Clearly the stock market has not been a random process following a normal distribution. This is clear as it is a very complex mechanism that sets market pricing in society. We can compare the observed distribution with the best fit normal distribution, clearly there is a too big gap just below the average."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UwqwdZbvnz5448/nmdh2bJlja8PYTnF\nfQs5OTnlnqFy9+7dU6dO5Zxae/78+YyMjJzlzZo1mzNnjmVzF0HpP4+AchQ7wNqmT5/+9ddf\nv/jii0888US9evXKly/v6upavXr1rl27fvDBBydOnMidWEtEnJ2dv/vuuylTpnh7exdYtlxc\nXObPn3/gwIHx48c/9thjHh4eOY8WEBAwb968EydOtG3b1nj9qlWr7tmzZ+bMmY0aNSpTpkzN\nmjVHjBhx6NChPBNnGA85mVSlSpXExMSpU6c++uijrq6u1apVCw4O/uGHH4o7EYx5FXfLlIzJ\nF8ikEmy9atWq7d27980338x5EatXrz548OD9+/cHBQWV4kcpquK+hapXr3758uWFCxeGhIR4\ne3u7ubnl9DxnZ+fKlSt37tz5448/3rt3b5UqVawQPj/zfh4B5XSGos1xAEDb/v73v+fON9G4\nceOkpCS1eWB3iv4WmjBhwldffRUZGRkSEmKtdICjYI8d4Fji4+NHjx69bdu23FGwmzdvfvbZ\nZx9//HHuOmr3tMHGlf4tZDxbIQDzcjG9CgANyczMXL58+fLly52cnHKmELt+/brxnvsWLVq8\n+uqr6gLC1pX+LWTNSaEBR8N/mwAH9eDBg5SUlJSUFOM/yQEBAZs3bzY+VxF4mBK/hfz8/AYO\nHFinTh3LZwQcDsfYAY7l+vXra9eu3b59+7Fjx5KTk2/duuXh4fHII4+0a9du2LBh/v7+qgPC\n1vEWAmwZxQ4AAEAjGIoFAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g2AEAAGgExQ4A\nAEAjKHYAAAAaQbEDAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g2AEAAGgExQ4AAEAj\nKHYAAAAaQbEDAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g2AEAAGgExQ4AAEAjKHYA\nAAAaQbEDAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g2AEAAGgExQ4AAEAjKHYAAAAa\nQbEDAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g2AEAAGgExQ4AAEAjKHYAAAAaQbED\nAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g2AEAAGgExQ4AAEAjKHYAAAAaQbEDAADQ\nCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g2AEAAGgExQ4AAEAjKHYAAAAa4aI6AADYjT/+\n+OOXX35RncKqmjZtWqtWLdUpABSVzmAwqM4AAPZh7Nix33zzjbu7u+ogVpKenj5y5MjFixer\nDgKgqNhjBwBFlZ2dPXz48KVLl6oOYiVjxozJzs5WnQJAMXCMHQAAgEZQ7AAAADSCYgcAAKAR\nFDsAAACNoNgBAABoBMUOAABAIyh2AAAAGkGxAwAA0AiKHQAAgEZQ7AAAADSCYgcAAKARFDsA\nAACNoNgBAABoBMUOAABAI1xUByg2g8GQlJSUlJSUmppqMBi8vLx8fHx8fHx0Op3qaAAAACrZ\nU7G7c+fOJ598EhoaeunSpTx31a1bd/z48dOnTy9fvrySbAAAAMrZTbFLT0/39/dPTEx0cnJq\n3bp148aNPT09dTrdzZs3k5KSjhw5MnPmzA0bNmzbts3NzU11WAAAAAXsptjNmTMnMTFxxIgR\nH330Ue3atfPce+nSpddee2316tVz5syZPXu2koQAAABq2c3JE2vWrGnTps2KFSvytzoRqVOn\nzsqVK/38/NauXWv9bAAAALbAbordxYsXu3Tp4uT00MBOTk5dunS5cOGCNVMBAADYDrspdp6e\nnmfPni18nTNnznh5eVknDwAAgK2xm2IXEBAQGxu7YsWKh62wbNmyuLg4f39/a6YCAACwHXZz\n8sR77723cePGUaNGzZ07NzAw0NfX19PTU0RSU1NPnToVHx9/+PBhLy+vWbNmqU4KAACght0U\nO29v7927d7/wwgt79+49dOhQ/hXatWu3ZMkSb29v62cDAACwBXZT7ESkWbNmiYmJBw8e3L59\n+6lTp1JTU0XE09PT19e3Z8+efn5+qgMCAACoZE/FLoefn58ZO9yZM2cef/zxzMzMwldzdnbO\nzMx0dnY21/MCQEn8/rvs2iWHD8upU3L1qly/LjqdVKkiNWuKj4/4+UnXrlKnjuqUAJSxv2Jn\nXg0aNNiyZUtWVlYh6/zyyy9Tp07Nzs6m2AFQ4/JlWbZM1q6VI0ekWjVp21Z8faVbN6lYUUTk\n5k25fFmOHJGlS+X6dfHzk6FD5bnnpEYN1bkBWJvdF7vz588fPny4bNmyHTp0yDmdolh0Ol3X\nrl0LX4drlAFQ5vhxmTNH1q2TBg3k2WdlxQpp0UJ0uoJXfvBADh+WqCj58kv55z9l2DD5xz/E\nx8e6iQGoZDfTnYjI8uXL69Wr5+bmptfrU1JSROT111/39vYOCQnp06dP7dq1Q0NDVWcEADO5\nckVeeEFatJA//pANG+TkSZk5U1q2fGirExEnJ/Hzk1mz5PRpiYiQM2ekaVOZOFFSUqyYG4BK\ndlPsEhISxowZc+HChXv37kVFRY0ePXrVqlU514195plnunbteufOnYkTJ+7YsUN1UgAoHYNB\nFi+Wxx6TgwdlyxbZtk2eeqqwPpefk5P07Su7dklsrOzaJU2ayMqVFosLwIbYTbH79NNPnZyc\nYmJiMjMzY2NjN2/ePGvWrMDAwFOnTq1bt27nzp0REREiMnfuXNVJAaAUkpOlXz955RV56y3Z\nv1969izVowUGyuHDMn26vPiiDBokf/5pppQAbJTdFLv9+/cHBgb269fPyckpKCiod+/eSUlJ\nH3zwQfny5XNWCAkJ8ff3T0xMVJsTAEpuzx7x85PLl+XAAXn1VTHLCVuurvL3v8vevXLihLRp\nIwXNAwpAM+ym2P3xxx+NGzfO/bJRo0Yi4uvra7zO448/fv36dWsnAwCz+OYb6d5dAgPlp5+k\nSRMzP3jz5rJvn3TsKJ07S1iYmR8cgM2wm2JXpUoV49KWc/vatWvG61y7do0zWAHYpdmz5fnn\n5d//lkWLpFw5izyFm5usXClvvSVDh8pnn1nkKQCoZjfFztfXNzY29sqVKyJy5cqVuLi4ihUr\nfvXVV7krXLx4MS4u7rHHHlOXEQCKz2CQV1+VOXMkLEymTLH4073+uqxcKW+8ITNnWvy5AFid\n3cxjN3HixMGDB7do0eKJJ57Yt2/fzZs3v/322xEjRpw/f7579+7Xrl2bP39+enr68OHDVScF\ngCIzGGTyZFmxQjZulO7drfSkQ4eKp6cMHCiZmfLRR1Z6UgBWYTfFbtCgQRMmTAgNDd24caOL\ni8sHH3wwbNiwEydOvPfee99++23OOgEBARMnTlSbEwCKYdo0+eYb2bRJOnWy6vP26SOxsdKv\nn5QpI7NnW/WpAViS3RQ7nU735Zdfvvbaa2fPnm3SpEmdOnVEZNasWZ06ddq4cWNWVlbXrl0H\nDx7MVb8A2I1335WvvpL4eGu3uhz+/hIRIcHBUqmSTJ+uIAAAC7CbYpejYcOGDRs2NF7Su3fv\n3r17q8oDACW0aJHMni2RkdKtm7IMgYHyzTcyfLjUqiUcxwJogp0VOwDQgs2bZdIkCQ2VoCDF\nSQYPlj/+kOefl0cekS5dFIcBUGoUOwCwrhMnZMgQmTFDXnhBdRQREXnlFfntNxkwQBIT5a9D\nIgDsjt1MdwIAWnDzpgQHS48etnXKwqefip+fhIRIerrqKABKhWIHANZiMMhzz4mrq6xYIU62\n9OvXxUVWr5aMDHnxRdVRAJSKLf1mAQBt+9e/5PvvJTxcKlRQHSWfypUlPFyiomTBAtVRAJQc\nxQ4ArGL3bpk5UxYtMv91YM2lZUv54guZPl0OHlQdBUAJUewAwPL+/FNGjJDnn5chQ1RHKdTY\nsTJggAwbJmlpqqMAKAmKHQBY3sSJ4u4un32mOkcRhIbK/fsydarqHABKgulOAMDCvv1WIiNl\nzx5xc1MdpQgqVpRvvpFu3aR/f9VRABQbe+wAwJIuX5bJk+Wdd6R1a9VRiqxjR/nb32TcOI+7\nd1VHAVA8FDsAsKTx48XHR/72N9U5iuntt6V69WcTE1XnAFA8DMUCgMWsXClbt8rBg+LsrDpK\nMZUpI8uWPfHEE3t//111FADFwB47ALCM5GR59VWZOVMef1x1lBLx84tv2nTknj2Smqo6CoCi\notgBgGVMmya1a9vfIKyRmFatMl1c5I03VAcBUFQUOwCwgG3b5Ntv5auvxNVVdZSSy3J2XtGh\ngyxcKHv2qM4CoEgodgBgbllZMmmSjBsn7durjlJax2vVkuHDZeJEyc5WnQWAaRQ7ADC3Tz+V\nGzdkzhzVOczk3/+Ws2flyy9V5wBgGsUOAMzq4kWZPVs+/FAqVVIdxUxq1pRZs2TmTElOVh0F\ngAkUOwAwq7/9TZo2ldGjVecwq5dekrp15c03VecAYALFDgDM58cfZe1a+fxzcdLWb1cXF5k7\nV77+Wg4dUh0FQGG09asHABQyGOTVV2X4cA2cM1EAf3/p10+mTVOdA0BhKHYAYCbffiu//CIf\nfKA6h8V89JH89JNER6vOAeChKHYAYA5378qbb8q0aVK3ruooFtO4sUycKK+/Lvfvq44CoGAU\nOwAwh/nz5c4du77ORJHMnClXr8rixapzACgYxQ4ASu3PP2XOHHnrLalQQXUUC6tSRd54Q959\nV9LSVEcBUACKHQCU2ocfSuXKMm6c6hxW8fLL4uwsn32mOgeAAlDsAKB0Ll+WefNk1iy7vixs\nMZQvL2+9JR9/LNevq44CIC+KHQCUzuzZ0rixDB2qOocVjRkjNWrIv/6lOgeAvCh2AFAK587J\nkiUye7bWZiQunKurvPOOLFggV66ojgLgLxzpNxEAmN1770mrVtKvn+ocVjd0qDRsKB9+qDoH\ngL+g2AFASf32m6xYIe++qzqHCk5O8vbb8tVXcumS6igA/odiBwAlNXu2PPGEBAaqzqHIwIHi\n48NOO8CmUOwAoETOnJGVK+Wdd1TnUEenk7fflsWL5fJl1VEA/JeL6gAAYJ/mzJG2baVXL9U5\nLCgrK+v27dsHDhx46Br16j1et+7tGTMuTJ9uxVwW5O3t7eXlpToFUHIUOwAovvPnZcUKiYpS\nncOy9u/fn5SUFBsbW8g6g0WWnT7dd/Xqq1aLZUnjxo376quvVKcASo5iBwDF99FH0qKF9Omj\nOodlPXjwoGHDhvv37y98pTIdOpzr0+eO/Y9KT5o0KSsrS3UKoFQodgBQTH/8IV9/LatWiU6n\nOorFOTk5VapUycRK//yn8+TJ5d5+WypXtkooSylbtqzqCEBpcfIEABTT3LnSoIGEhKjOYTOG\nD5cqVWT+fNU5AFDsAKA43LKyJDRUXn/dsS41UTgXF5kxQ+bNk4wM1VEAR8cvJgAohp4nT4qX\nlwwfrjqIjXn+eXFyksWLVecAHB3FDgCKyjU7+6kTJ2TaNHF1VZ3FxpQvL1OmyGefyf37qqMA\nDo1iBwBF1em335wfPJCxY1UHsUkvvSQpKbJuneocgEOj2AFA0Tx40PvYse1Nmoi7u+ooNqly\nZXnhBfn3v1XnABwaxQ4AiiYmpkp6+ndNmqjOYcOmTpVjx+S771TnABwXxQ4AiuaTTxK8vW+V\nL686hw2rX18GDZJPP1WdA3BcFDsAKIK9e+XHHzc//rjqHDZv+nTZtEmOH1edA3BQFDsAKIJP\nP5U+fS5zeXiT2raVzp1l7lzVOQAHRbEDAFMuXJCICJk6VXUOO/Hqq7JypSQnq84BOCKKHQCY\nMn+++PhIQIDqHHaif3+pVUu++kp1DsARUewAoFAZGbJ4sbzyiuh0qqPYCWdnmTxZ/vMfycpS\nHQVwOBQ7ACjUypWi08mzz6rOYVeef17S0iQsTHUOwOFQ7ADg4QwGmTdPXnxRmOWkWDw9ZdQo\n+eIL1TkAh0OxA4CH27FDTp6UiRNV57BDkyfL3r2yd6/qHIBjodgBwMPNny/BwVKvnuocdsjX\nV3r1kvnzVecAHAvFDgAe4vffJSZGpkxRncNuTZ4s69bJtWuqcwAOhGIHAA8RGiqPPSbduqnO\nYbeeflrq1JHFi1XnABwIxQ4ACpKZKUuWcHRdqTg5yfjx8tVXkp2tOgrgKCh2AJViRegAACAA\nSURBVFCQsDC5c0dGjlSdw849/7xcuyZxcapzAI6CYgcABfnySxk5UipUUJ3DzlWtKoMHy5df\nqs4BOAqKHQDkc+SI/PQT47DmMXGibN0qv/2mOgfgECh2AJBPaKh07izNmqnOoQnt20vLllw6\nFrAOih0A/FVamqxaJRMmqM6hIRMmyLJlkpmpOgegfRQ7APirb7+VMmVk4EDVOTRk+HDJzJTw\ncNU5AO2j2AHAXy1cKKNHS9myqnNoiIeHDB8uCxeqzgFoH8UOAIzs3y8HD8q4capzaM748bJr\nl5w4oToHoHEUOwAwsnChdO8ujRurzqE5rVpJmzZchQKwNIodAPy/tDRZs0ZefFF1Do0aN05W\nrOAUCsCiKHYA8P/WrJEyZWTAANU5NGroULl7VyIjVecAtIxiBwD/b/FiGTmS0yYspUIFGTqU\n0VjAoih2ACAiIkePSmKijB2rOoemjR0r27fLmTOqcwCaRbEDABERWbJEOnSQpk1V59C0J5+U\nZs3k669V5wA0i2IHACKZmbJypbzwguocDuCFF2TZMsnOVp0D0CaKHQCIREVJZqYMGaI6hwN4\n9llJSZHNm1XnALSJYgcAIkuXypAh4uGhOocDqFJFQkIYjQUsxEV1AABQ7fffZetW+eEH1Tkc\nxvPPS79+kpws1aqpjgJoDXvsADi85culcWPp2FF1DocRECA1asjKlapzABpEsQPg2AwGWb5c\nRo9WncORODnJc8/J0qWqcwAaRLED4Nh27ZJz52TkSNU5HMyoUXLsmBw4oDoHoDUUOwCObdky\n6dVL6tRRncPBNG4snTvLsmWqcwBaQ7ED4MDS0iQsjHFYNUaPltWrJTNTdQ5AUyh2ABxYWJi4\nukr//qpzOKTBgyUzU+LiVOcANIViB8CBLV8uQ4dKuXKqczgkDw/R62X5ctU5AE2h2AFwVOfP\ny65djMOq9NxzEh8vV66ozgFoB8UOgKNasUJ8fKRdO9U5HFjPnlK7tqxerToHoB0UOwCOatUq\nGTVKdQjH5uQkzz4r33yjOgegHRQ7AA4pIUF+/VVGjFCdw+E995wcOiRHj6rOAWgExQ6AQ1qx\nQnr0kEceUZ3D4fn6Srt27LQDzIViB8DxZGbKunVcbcJWPPecrFol2dmqcwBaQLED4Hg2bpS7\nd2XAANU5ICIiQ4ZIcrJs3646B6AFFDsAjuebbyQkRCpUUJ0DIiJStar06cNoLGAWFDsADubP\nP2XjRsZhbcvIkRIZKenpqnMAdo9iB8DBrFsnnp4SEKA6B4wEBYmLi0RHq84B2D2KHQAHs3Kl\nDBsmLi6qc8BIuXIyaBCjsUDpUewAOJLz5+XHH+XZZ1XnQD7Dh8vWrVxeDCglih0AR7Jypfj4\nSNu2qnMgn27dpHZtWbtWdQ7AvlHsADiS1avZXWejnJxk6FBZtUp1DsC+UewAOIyDB+X4cRk2\nTHUOPMSzz8q+fZKUpDoHYMcodgAcxurV0r69eHurzoGHaNFCmjeXb79VnQOwYxQ7AI7hwQNZ\ns4bddbZu2DBZvVp1CMCOUewAOIadO+XKFRk8WHUOFGrYMPn1V9m/X3UOwF5R7AA4htWrJSBA\natRQnQOFql9fOnRgpx1QYhQ7AA4gK0vCw2XoUNU5UATDh8uaNfLggeocgF2i2AFwAPHxkpEh\nISGqc6AIBg+Wa9dk507VOQC7RLED4ADWrJGgIPH0VJ0DRVCtmgQEMBoLlAzFDoDWpadLbKwM\nH646B4ps2DAJC5PMTNU5APtDsQOgdTEx4uIiffqozoEiCwmRO3dkyxbVOQD7Q7EDoHVr1khI\niJQrpzoHiqxiRenbV9asUZ0DsD8UOwCa9uefsmkT8xLbn2HDJCZGMjJU5wDsDMUOgKZFRoqn\np/j7q86BYnr6adHpJC5OdQ7AzlDsAGjamjUyaJC4uKjOgWIqX16Cg2XtWtU5ADtDsQOgXcnJ\nsmMHlxGzV0OGyMaNcuuW6hyAPbG//8UaDIakpKSkpKTU1FSDweDl5eXj4+Pj46PT6VRHA2Bj\n1q+XatWkSxfVOVAivXuLm5tER8vIkaqjAHbDnordnTt3Pvnkk9DQ0EuXLuW5q27duuPHj58+\nfXr58uWVZANgi9atk8GDxdlZdQ6UiKur6PWydi3FDig6uyl26enp/v7+iYmJTk5OrVu3bty4\nsaenp06nu3nzZlJS0pEjR2bOnLlhw4Zt27a5ubmpDgvABly+LD/8IB98oDoHSmHIEAkKkj//\nlEqVVEcB7IPdFLs5c+YkJiaOGDHio48+ql27dp57L1269Nprr61evXrOnDmzZ89WkhCAbVm/\nXh55RNq3V50DpdCjh3h5SWSkPP+86iiAfbCbkyfWrFnTpk2bFStW5G91IlKnTp2VK1f6+fmt\n5RQqADnWrpVnnhGOvrVrLi4yYAAzFQNFZzfF7uLFi126dHFyemhgJyenLl26XLhwwZqpANio\nCxdkzx4ZMkR1DpTa4MGyfbtcu6Y6B2Af7KbYeXp6nj17tvB1zpw54+XlZZ08AGza2rXSoIG0\naaM6B0qta1epVk0iIlTnAOyD3RS7gICA2NjYFStWPGyFZcuWxcXF+TO/PAARWbtWhgxhHFYL\nnJ1l0CBZt051DsA+2M3JE++9997GjRtHjRo1d+7cwMBAX19fT09PEUlNTT116lR8fPzhw4e9\nvLxmzZqlOikA1c6elQMHZOFC1TlgJoMHS48ecvWq1KihOgpg6+ym2Hl7e+/evfuFF17Yu3fv\noUOH8q/Qrl27JUuWeHt7Wz8bANuyfr00aiStW6vOATPp1Elq1JDwcHnpJdVRAFtnN8VORJo1\na5aYmHjw4MHt27efOnUqNTVVRDw9PX19fXv27Onn56c6IADbkDMvMTTDyUkGDZL16yl2gEn2\nVOxy+Pn5mbHDZWRkhIaG3rt3r5B1zp8/b66nA2BxZ87IgQOyeLHqHDCrZ56RBQvkyhWpWVN1\nFMCm2V+xM6/U1NTIyMg7d+4Usk5aWpqIGAwGa4UCUArr1omPj7RqpToHzKpjR6lZU8LDZdIk\n1VEAm2bfxW7//v379++/e/dugwYNAgIC3N3di/sItWrV+uGHHwpf56effurUqZOO0+sAuxAW\nJoMGqQ4Bc3NykoEDZf16ih1QOLspdjt27Ni2bdu0adMqV64sIlevXh0yZMjOnTtzV6haterS\npUuDgoLUZQSgWs447Ndfq84BC3jmGZk/Xy5floKuPwQgh93MY/fJJ58sXLgwZ/5hg8EQEhKy\nc+fOOnXqjB49+pVXXunZs2dKSsrAgQMPHjyoOikAddavl8aNpUUL1TlgAR07Sq1aEhmpOgdg\n0+ym2B08eLBly5Y5lxTbtm3bnj17AgMDk5KSli5dOnfu3G3btkVFRd27d+/9999XnRSAOmFh\n8swzqkPAMnJGY8PCVOcAbJrdFLuUlJScQVgRSUxMFJGPP/7Yzc0td4Xg4OA+ffrs2rVLTT4A\nyuXMS8xEJxr2zDOya5f88YfqHIDtspti5+XldfXq1ZzbOSexPvroo3nWadCgwa1bt6ydDICN\nCAsTb29p2VJ1DlhMzrmxUVGqcwC2y26KXYcOHfbs2XP58mURadq0qYjkP5zuwIEDtTmoFnBY\n69ezu07jGI0FTLGbYvfyyy9nZmYOGjTo6tWrISEhjRo1mjBhwqlTp3LuvXfv3syZM/fs2dO/\nf3+1OQGocf687N8vAweqzgELGzRIdu6Ua9dU5wBslN0UO39//9dffz0hIcHb2/vFF1/s06dP\nUlJSs2bNWrRo0aVLl9q1a8+ePbt+/fozZ85UnRSACuHh0qCBcGlBzevcWapVYzQWeBi7mcdO\nRD788ENfX99//OMfq1atyl149OhREdHpdAMGDJg3b17VqlXVBQSgzvr1nA/rEJycRK+X8HAZ\nN051FMAW2VOxE5ExY8aMGDFi+/bt+/btu3r1qsFg8PLy8vX19ff3r1Onjup0ABS5eFESE+Xz\nz1XngFUMGiSBgXL9ulSpojoKYHPsrNiJSJkyZQIDAwMDA1UHAWAzwsOlbl154gnVOWAV3bpJ\npUoSHS3PP686CmBz7OYYOwB4qIgIGTRIuKCzg3B2luBgCQ9XnQOwRRQ7AHbuyhXZvZvzYR3L\nwIHy3Xdy86bqHIDNodgBsHMREVKrlnTooDoHrKhnT/HwkNhY1TkAm0OxA2DnIiJErxcnfps5\nEldX6d+f0VggP34VArBnKSmyc6cMGqQ6B6xu4EDZvFm4jCTwVxQ7APYsOloqV5bOnVXngNU9\n9ZSUKSPx8apzALaFYgfAnoWHS0iIODurzgGrK1tWgoIkIkJ1DsC2UOwA2K2bN2XbNsZhHdfA\ngRIXJxkZqnMANoRiB8BuxcWJh4d07646BxQJDBSdTrZsUZ0DsCEUOwB2Kzxc+vcXV1fVOaCI\nm5sEBjIaCxij2AGwT+npsmWL6PWqc0CpAQMkNlayslTnAGwFxQ6Afdq4UZycpFcv1TmgVL9+\ncveu7NihOgdgKyh2AOxTZKQ8/bSUK6c6B5SqUEF69mSmYiAXxQ6AHcrMlA0buD4sREQGDpTo\naMnOVp0DsAkUOwB2aOtWycqSPn1U54ANCA6WGzdk927VOQCbQLEDYIciI6VXL6lQQXUO2IAq\nVaRrV86NBXJQ7ADYm+xsiYlhHBb/o9dLeLgYDKpzAOpR7ADYm507JTVVgoJU54DN0Ovl8mXZ\nt091DkA9ih0AexMZKd26SeXKqnPAZtSpI08+KZGRqnMA6lHsANgVg0GiohiHRV4DBjDpCSAU\nOwB2JjFRLl+W4GDVOWBj9Hr59Vc5dkx1DkAxih0AuxIZKR06SK1aqnPAxjRqJC1aMBoLUOwA\n2JXISAkJUR0CNkmvl6go1SEAxSh2AOzHsWPy66+i16vOAZuk18vBg3LunOocgEoUOwD2Izxc\nWrYUb2/VOWCTct4bzFQMx0axA2A/oqLYXYfC6PUcZgcHR7EDYCfOnZPDh2XAANU5YMP0evnp\nJ/njD9U5AGUodgDsRESEeHtL8+aqc8CGtW8vNWpIbKzqHIAyFDsAdiIignFYmODkJMHBHGYH\nR0axA2APrl6VhATGYWGaXi/bt8uff6rOAahBsQNgD6KipHp1efJJ1Tlg87p3F3d32bhRdQ5A\nDYodAHsQESEhIeLEryyYUqaM9O3LTMVwWPyWBGDzUlPl++85wA5FpdfLxo2SkaE6B6AAxQ6A\nzduwQdzcpHt31TlgJwIDxWCQ775TnQNQgGIHwOZFRkrfvlKmjOocsBPu7vLUU4zGwjFR7ADY\ntrt3ZdMmCQlRnQN2Ra+X2FjJzladA7A2ih0A2/bdd3L/vgQGqs4BuxIUJDdvyg8/qM4BWBvF\nDoBti4iQXr3Ew0N1DtiVqlWlc2euGwsHRLEDYMOysyUuToKDVeeAHdLrJSpKDAbVOQCrotgB\nsGE//CA3bki/fqpzwA4NGCAXLsjBg6pzAFZFsQNgw6KjpXNnqVZNdQ7Yobp1xc+Pc2PhaCh2\nAGxYVBTzEqPk9HoOs4OjodgBsFUHD8q5c9K/v+ocsFshIfLLL5KUpDoHYD0UOwC2KipK/Pyk\nQQPVOWC3mjaVxo0lOlp1DsB6KHYAbFVkJOOwKC1GY+FgKHYAbNJvv8mxY0x0gtIKCZHERPnj\nD9U5ACuh2AGwSVFR4u0tzZurzgE79+STUqOGxMSozgFYCcUOgE2KjJQBA1SHgP1zcpL+/Zn0\nBI6DYgfA9ly9KgkJnA8L8wgJkR075NYt1TkAa6DYAbA9MTFSrZp06KA6BzTB31/Kl5eNG1Xn\nAKyBYgfA9kRHS79+4uysOgc0wdVVAgMZjYWDoNgBsDG3b8t33zHRCcwpJETi4yUzU3UOwOIo\ndgBszMaN4uoqPXuqzgEN6dtXsrJk+3bVOQCLo9gBsDHR0dK3r5QrpzoHNMTDQ3r2ZDQWjoBi\nB8CW3Lsn8fESEqI6BzQnOFiio+XBA9U5AMui2AGwJdu2SXq6BAaqzgHNCQ6WlBTZs0d1DsCy\nKHYAbEl0tPj7i5eX6hzQnBo1pH17RmOheRQ7ADbjwQOJjmYcFpYSHCyRkapDAJZlutj9+eef\nVsgBALJ3r1y9Kv36qc4BjdLr5fRp+eUX1TkACzJd7OrUqTN69OiEhAQrpAHg0KKi5MknpXZt\n1TmgUY0aSbNm7LSDtpkudnXr1l2+fHnHjh1btmz5n//85xaX2wNgIVFREhysOgQ0LSREoqNV\nhwAsyHSxO3Xq1LZt2wYPHnzy5MlJkybVrl177Nix+/bts0I4AA7k5Ek5dYpiB8sKDpYDB+TC\nBdU5AEsxXex0Ol3Pnj3Xrl174cKFDz/8sGbNmkuWLGnXrl2bNm0WLlyYlpZmhZQAtC8yUh57\nTJo0UZ0DmtamjdSty047aFgxzoqtXr3666+//uuvv27ZsmXgwIFHjx4dP3587dq1J06ceOzY\nMctFBOAQoqPZXQeL0+kkOJhJT6BhxZ7uRKfT+fj4PPbYY5UqVRKR27dvh4aGtmjRYtiwYamp\nqRZICMABXL4se/cy0QmsISREdu0SJnyARhWj2GVnZ8fExPTt27dhw4azZ88uW7bsrFmzLl68\nuHHjxm7duq1Zs2bSpEmWCwpAy6KipFYtaddOdQ44gG7dpGJFiYtTnQOwCJeirHThwoUlS5Ys\nXrz40qVLOp0uICDgpZde6tevn7Ozs4jUqVMnMDAwODh448aNFk4LQKNyxmF1OtU54ABcXOTp\npyUqSkaOVB0FMD/Txa5fv37x8fHZ2dmVK1eeNm3axIkTGzVqlGcdnU7Xvn372NhYy4QEoGmp\nqfL99zJjhuoccBghITJypGRkiJub6iiAmZkudnFxcU888cRLL700dOjQcuXKPWy1wMDAihUr\nmjUbAMewYYO4uUm3bqpzwGH07i0Gg3z3nfTvrzoKYGami93+/fvbtGljcjU/Pz8/Pz9zRALg\nYKKi5OmnpUwZ1TngMNzd5amnJCqKYgftMX3yRFFaHQCUUGambNoker3qHHAwISESEyP376vO\nAZiZ6WK3bt26Hj16XLx4Mc/yixcvdu/ePTw83DLBADiGbdskK0t691adAw6mXz+5eVN+/FF1\nDsDMTBe7RYsW3b59u27dunmW161b9+bNm4sWLbJMMACOITJSnnpKKlRQnQMOpmpV6dyZS1BA\ne0wXu6NHj7Zt27bAu9q2bXv06FFzRwLgMB48kNhYjnOCGlyCAlpkutjduHGjSpUqBd5VvXr1\nlJQUc0cC4DASEiQ5mWIHNUJC5OxZ+fln1TkAczJd7KpUqfLrr78WeNfp06e9vLzMHQmAw4iM\nlI4dpUYN1TngkBo0kJYt2WkHjTFd7Dp37hwTE3Py5Mk8y0+cOBETE9OpUyfLBAPgAHIuOAGo\nEhJCsYPGmC5206ZNu3fvXqdOnebNm3f69Ok7d+6cPn163rx5nTt3vnfv3gwmiwdQMkePyunT\nFDuoFBIihw/L2bOqcwBmY3qC4g4dOixYsGDy5Mkvv/yy8XJnZ+cFCxZ07NjRYtkAaFp0tDRr\nJo0bq84BB9aqldSvL9HRMnWq6iiAeZgudiIyYcKEjh07/uc//0lMTLx586aXl1f79u1feuml\n5s2bWzofAM2KipKQENUh4PByRmMpdtCKIhU7EWnRokVoaKhFowBwIOfPy8GDsnCh6hxweCEh\nMm+eJCdLtWqqowBmYPoYOwAwv5gYeeQRad1adQ44vM6dpXJliYtTnQMwD4odABUiIkSvF51O\ndQ44PGdn6dePc2OhGUUqdjt37uzfv3/NmjXLli3rko+lIwLQmuvXZfduzoeFrQgJka1bJT1d\ndQ7ADEzXsri4uODg4AcPHnh6ejZu3JgmB6C04uLE01O6dFGdAxARkYAAcXKSTZtU5wDMwHRL\ne+edd3Q63apVq4YNG6Zj3ARA6UVGSr9+wv8SYSPKl5fAQImOFmdn1VGA0jI9FHvs2DG9Xj98\n+HBaHQAzyMiQrVsZh4VtCQmRuDjnBw9U5wBKy3Sxc3d3r169uhWiAHAImzeLiPTqpToHYCQo\nSNLTfa9cUZ0DKC3TxS4gICAxMdEKUQA4hKgo6d1b3NxU5wCMeHlJ165+v/+uOgdQWqaL3Ucf\nfXTx4sV33303OzvbCoEAaNn9+7JhAxecgC3S6/1+/51DjmDvTB+8/Pbbbzdt2vSdd95ZunRp\nq1atvLy88qywbNkyi0QDoD07d0pqqvTtqzoHkE9IiNekSQ1SUlTnAErFdLFbvnx5zo3z58+f\nP38+/woUOwBFFR0tXbtKlSqqcwD51K59tlo1RmNh70wXu0OHDlkhBwDtMxgkKkr+9jfVOYCC\nHaxXr9Pp06pTAKViuti1atXKCjkAaN/+/XLxIhOdwGbtf/TRQQcOyMmT0qSJ6ixACRXjWrHn\nz59PSEhITU21XBoAWhYVJW3byiOPqM4BFOxqxYqXPT0lMlJ1EKDkilTs9uzZ07Jly/r163fs\n2HHfvn05C9esWdOsWbOdO3daMh4ADYmIkAEDVIcACnPw0UcpdrBrpovdiRMnAgICzpw5E/zX\nAZSgoKBz586tX7/eYtkAaMipU3LyJOOwsHEH69X77zEDgH0yXexmz5597969n376afHixcbL\nPTw8evTosXv3botlA6AhkZHSpIk89pjqHEBhzlWtKnXrSlSU6iBACZkudtu2bdPr9c2bN89/\nV5MmTS7y3xoARREZKXq96hCACQYRCQ6m2MF+mS52169fr1+/foF3OTs7375928yJAGjPpUuy\nbx/jsLAPer3s3Ck3bqjOAZSE6WJXqVKl5OTkAu86dOhQrVq1zB0JgOZERkrt2tKuneocQBF0\n6yZeXhIXpzoHUBKmi12nTp02bNiQmZmZZ/n27du3bt3avXt3i+QCoCXR0RISIjquwwl74Ows\nfftybizslOliN2PGjOTkZL1ef/z4cRG5c+fOvn37pk+fHhgY6OLiMm3aNMuHBGDPbtyQnTs5\nwA72ZMAA2bJFMjJU5wCKzfSVJzp16rRgwYIpU6bEx8eLSP/+/XOWu7q6Ll68uEWLFpYNCMDe\nxcZKxYrSrZvqHECR9eolzs6yeTP/IYHdMV3sRGTChAldunQJDQ1NSEi4fv26p6dn+/btp0yZ\n0rRpU0vnA2D3oqIkKEhcivTbBrAJ5cpJ796cyg17VNRftU2bNp03b55FowDQoIwM2bJF1qxR\nnQMoJr1eJk2SrCwpU0Z1FKAYinGtWAAotk2bxMlJAgJU5wCKqW9fyciQ779XnQMoHoodAEuK\njJTAQClfXnUOoJg8PaVnT86Nhd0xPRTbqFGjwlc4ffq0mcIA0JZ792TDBlmwQHUOoET0ennr\nLVmwQJzYCQK7YbrYpaSk5FmSnp5+//59EalYsaKOiakAPMyOHZKRIU8/rToHUCLBwfLSS5KQ\nIJ06qY4CFJXp/4XczCcjIyMxMbFDhw7dunV72EUpAEAiIsTfXzw9VecASqRGDenYkevGwr6U\nZPeyq6tru3btNmzYsH///jlz5pg9EwAtePBAYmIkJER1DqAU9HoOs4N9KfnMUpUqVQoICFi+\nfPnbb79txkAmGQyGpKSkpKSk1NRUg8Hg5eXl4+Pj4+PDoDBgWxIS5No1CQ5WnQMoBb1epk2T\nn3+Wli1VRwGKpFRThpYtW/bSpUvmimLSnTt3Pvnkk9DQ0PxPWrdu3fHjx0+fPr08J98BNiIi\nQjp3lurVVecASqF+ffHzk8hIih3sRcmL3ZUrV2JjY+vUqWPGNIVIT0/39/dPTEx0cnJq3bp1\n48aNPT09dTrdzZs3k5KSjhw5MnPmzA0bNmzbts3Nzc06kQAUJjJSXn5ZdQig1PR6WbdO3nlH\ndQ6gSEwXu3fyvZvv379/4cKFqKioW7duzZo1yyK58pkzZ05iYuKIESM++uij2rVr57n30qVL\nr7322urVq+fMmTN79mzrRALwUAcPyrlzMmCA6hxAqQ0YIDNnyqlT4uurOgpgmuli9+677xa4\nvHz58jNmzHjzzTfNHalga9asadOmzYoVK5wKmk+oTp06K1euPHXq1Nq1ayl2gHqRkdKmjdSr\npzoHUGqPPy5NmkhUlLz+uuoogGmmi11sbGyeJU5OTpUqVWrevLmHh4dlUhXg4sWL/fv3L7DV\n5abq0qVLaGio1SIBeKiICHn2WdUhADMZMEAiIih2sAumi11QUJAVcpjk6el59uzZwtc5c+aM\nl5eXdfIAeKikJDl+XPR61TkAM9Hr5YMP5Pff2QkN22c3l0kJCAiIjY1dsWLFw1ZYtmxZXFyc\nv7+/NVMBKEBYmDRtKk2aqM4BmEnOcQXMVAx7UKrpTqzpvffe27hx46hRo+bOnRsYGOjr6+vp\n6Skiqampp06dio+PP3z4sJeXl9VO5gDwUOHhMnCg6hCA+eh0/x2N5URv2DzTxa5+/fpFf7hz\n586VOErhvL29d+/e/cILL+zdu/fQoUP5V2jXrt2SJUu8vb0tFABAkZw7J4cOyZIlqnMAZjVg\ngHzxhVy7xtSMsHGmi11aWlp2dvbNmzdzvnR3d09PT8+57eXl5ezsbMF0f9WsWbPExMSDBw9u\n37791KlTqampIuLp6enr69uzZ08/Pz+rJQHwUJGR0qCBtGqlOgdgVh07SrVqEh0tL76oOgpQ\nGNPF7ty5c3369MnIyHjvvfe6du3q4eGRlpa2a9euf/7zn+7u7vHx8dY8N1ZE/Pz8zNjhzpw5\n06xZszt37phc02AwmOtJAS1jHBaa5OQkISESEUGxg40zXexmzpx5+fLlo0eP5l7RwcPD4+mn\nn+7evXvz5s1nzpz52WefWTikBTVo0CA+Pv7evXuFrPPLL79MnTqVa9ECpl25IgkJ8u9/q84B\nWMCAARIUJDdvCtMvwIaZLnbr168fNmxY/ut0ubm5DRgwYM2aNcqL3dixY7t06TJq1KgSfK9O\np+vWrVvh63CNMqCoIiKkVi1p3151DsACevSQihUlNlZGjlQdBXgo09Od3EAnVQAAIABJREFU\nJCcnP2wU0mAwJCcnmztSsS1ZsuSHH35QnQKASGSk6PXC7m1okouLBAVJRITqHEBhinRWbHh4\n+Lvvvuvu7m68PD09PSwsrEGDBhbL9hf//Oc/C7n3wIEDuStwSTFAjevX5fvvxVqXGQQUGDhQ\nBg+WtDSx7sHlQNGZLnYTJkyYNm1ap06d3nnnna5du1auXPnGjRu7du165513zp8/b7Vx2Pff\nf7+Qew8fPnz48OGc2xQ7QI2oKPHyks6dVecALOapp8TVVTZulMGDVUcBCma62L3yyisnTpxY\ntGiRXq8XERcXl/v37+fcNW7cuJetOFujh4fHq6++Wrly5TzLX3311fbt2w8ZMsRqSQAUICJC\n9HpxsZtpz4FiK1tW+vaV8HCKHWyW6V/BTk5OCxcuHDZs2PLlyw8dOpSamurp6dm6devRo0d3\n797d8gn/KyYmZuzYsYsXL160aFHfvn2N73r11VebNm06depUq4UBkNfNm/LddxIbqzoHYGED\nB8pzz0lGhnBeHWxSUf9v3aNHjx49elg0SuH69et37NixcePGBQUFjRkzZu7cuRUrVlSYB8Bf\nxMWJu7so/S0BWEOfPiIiW7ZISIjqKEABTJ8Vm+v8+fMJCQk513tQolq1apGRkV9//XVYWFiz\nZs22bt2qKgmAvMLDpX9/cXVVnQOwMDc36dNHwsNV5wAKVqRit2fPnpYtW9avX79jx4779u3L\nWbhmzZpmzZrt3LnTkvEKMGbMmCNHjjRo0KBXr14TJ05MS0uzcgAAeaWlyebNMmiQ6hyAVQwc\nKNHRcveu6hxAAUwXuxMnTgQEBJw5cyY4ONh4eVBQ0Llz59avX2+xbA9Vv379HTt2fPTRR0uX\nLm3ZsqX1AwD4iw0bxNVVnnpKdQ7AKoKC5N492bZNdQ6gAKaL3ezZs+/du/fTTz8tXrzYeLmH\nh0ePHj12795tsWyFcXJyeu211/bt22flK9UCKEBYmPTrJ2XLqs4BWEWFCtKrF6OxsE2mT57Y\ntm2bXq9v3rx5SkpKnruaNGmSkJBgmWBF0rx588OHD2dnZzs5FeNgQQDmdOeOxMfLihWqcwBW\nNHCgTJsm9+5xXClsjek+dP369fr16xd4l7Oz8+3bt82cqJh0Op2LiwvFDlAmPl5EJDBQdQ7A\nivr1k9u3ZccO1TmAvEz3oUqVKj3sgrCHDh2qVauWuSMBsCthYfL008zpBcdSqZL4+zMaCxtk\nuth16tRpw4YNmZmZeZZv375969at1pyjGIDNuXtX4uI4HxaOaNAgiYyU/78UE2AjTBe7GTNm\nJCcn6/X648ePi8idO3f27ds3ffr0wMBAFxeXadOmWT4kAFu1aZNkZ8tfLwYDOAS9XlJTZdcu\n1TmAvzB98kSnTp0WLFgwZcqU+Ph4Eenfv3/OcldX18WLF7do0cKyAQHYsvBwCQwUd3fVOQCr\nq1RJuneX9eulZ0/VUYD/KdI5BxMmTDh8+PDkyZPbtGlTv379li1bjh8//tChQ88995yl8wGw\nXZmZEhsrzzyjOgegSM5obHa26hzA/5jeY7dnz55y5cq1atVq3rx5VggEwG5s3SqZmYzDwnHp\n9fLSS/LDD8Lh5rAZpvfYdezYcfbs2VaIAsDOrF8vgYFSoYLqHIAiVatK9+4SFqY6B/A/potd\nlSpV3JjIAEAeWVkSGyuDB6vOASg1aJCEhTEaC9thuth1795979692bxrARjbulXu3GEcFo5O\nr5eUFFF0dU0gP9PFbs6cOSkpKVOnTs3IyLBCIAD2Yf166d1bKlZUnQNQqnp16daN0VjYDtMn\nT7z//vstWrSYP3/+mjVrWrVqVbt2bZ1OZ7zCsmXLLJUOgG3KypKYGPniC9U5ABvwzDMya5Z8\n/rlwcUvYANPFbvny5Tk3UlJSvvvuu/wrUOwAh/Pdd5KRIf36qc4B2AC9XiZPlt27pWtX1VGA\nIhS7Q4cOWSEHAHuybp0EBoqnp+ocgA2oUUO6dZP16yl2sAWmi12rVq2skAOA3cjKkuhoYWJL\nIBejsbAZD30LrlmzJjEx0ZpRANiHLVvkzh3GYYH/GThQkpPlhx9U5wAeXuyGDRv25Zdf5n75\nySefBAYGWiUSANuWMy8x47BArmrV/jsaC6hW1J3GR48e3bx5s0WjALADmZkSHc28xEBezzzD\nTMWwBRwNAKA4tmyRzEzGYYG8BgyQ69dl1y7VOeDoKHYAimPdOunTh+vDAnlVqyY9esi6dapz\nwNFR7AAU2d27EhMjQ4aozgHYpMGDJSxM7t9XnQMOjWIHoMg2bZL797k+LFAwvV5SU+X771Xn\ngEMrbB67b7/9NioqKud2zoVivby88q928//au/f4nOvGj+Pva3OasDnPoZyac4wVcs7hRsgI\nMwspQlZIOjBMoYNEKQmFJeYQkVPlVIg5R3Sjci4Rs7HNYdv1+2N3+61hDdv1ua7v9Xo+7sf9\nuK/v9bm+13vXZ/e1t+/x4sXsSAbA6SxYoHbtlC+f6RyAUypcWM2ba+FCtWhhOgrcV0bF7vr1\n6zExMWmXpHsIwI0kJGjFCs2aZToH4MS6dtWwYfrwQ+XMaToK3NQti11CQoIjcwBwditXSmI/\nLJCRjh01YIDWrRNXfoUhtyx2efLkcWQOAM5uwQI99pi8vEznAJyYj4/+85//3UwZMIGTJwBk\nwuXLWrWK82GBfxcUpKVLdfWq6RxwUxQ7AJmwbJly5VKrVqZzAE6vQwddvSru1QRDKHYAMmHh\nQgUGKndu0zkAp5cvn9q21YIFpnPATVHsAPybixf19dfq1s10DsBFBAVp2TLFxZnOAXdEsQPw\nb5YuVf78at7cdA7ARbRtKw8PrVplOgfcEcUOwL+JjFTnzsqR0WUvAfw/Ly916KDISNM54I4o\ndgAydPas1q9nPyxwe4KCtGqVYmNN54DbodgByNDixSpeXI0amc4BuJT//Ed58+rv23ICDkOx\nA5Ch+fMVFCQPviuA25Erlx5/nL2xcDy+rAHc2smT+uEHde1qOgfggoKCtHat/vrLdA64F4od\ngFuLjFTZsqpTx3QOwAU98oiKFtXixaZzwL1Q7ADcWmSkgoNls5nOAbggDw917qz5803ngHuh\n2AG4hf/+V7t3KzjYdA7AZQUHa9MmHT9uOgfcCMUOwC1ERqpGDVWrZjoH4LLq1lW5clq0yHQO\nuBGKHYBbmD+fy9cBd8VmU3Awe2PhSBQ7ADezY4eOHGE/LHC3QkK0e7cOHjSdA+6CYgfgZiIj\nVb++ypY1nQNwcVWqqGZNLVhgOgfcBcUOwA2Sk7Vggbp3N50DsITgYH3+uex20zngFih2AG6w\nYYPOnlWXLqZzAJYQHKyjR7V9u+kccAsUOwA3mDdPLVuqaFHTOQBLuO8+NWjAKRRwDIodgH+6\nckVLlnDaBJCVundXZKSSkkzngPVR7AD808qVun5dgYGmcwAW0qWLoqO1dq3pHLA+ih2Af/r8\nc3XooHz5TOcALKRwYbVurc8/N50D1kexA5BGdLRWrVJIiOkcgOWEhGjpUsXHm84Bi6PYAUhj\n0SIVKKCWLU3nACznscfk6ally0zngMVR7ACk8fnn6tZNOXOazgFYTp486thRc+eazgGLo9gB\n+NuJE9q8WU88YToHYFEhIfrmG/35p+kcsDKKHYC/ff657r9fdeqYzgFYVLNmKl5cCxeazgEr\no9gB+Nu8eerRw3QIwLo8PBQSwrmxyFYUOwCSpN27deAA58MC2euJJxQVpcOHTeeAZVHsAEiS\nPvtMDRqoXDnTOQBLe+AB1azJKRTIPhQ7AFJioiIjOW0CcIQePTR3rux20zlgTRQ7ANK33yo6\nWl26mM4BuIHg4P+dgQ5kA4odACkiQu3aqVAh0zkAN1CypFq0UESE6RywJood4PZiY7VsGefD\nAo7To4cWLVJCgukcsCCKHeD2Fi5U3rxq08Z0DsBtdOyo5GQtX246ByyIYge4vc8+U3CwcuUy\nnQNwG3nz6vHH2RuL7ECxA9zbsWPatEm9epnOAbiZnj31zTc6c8Z0DlgNxQ5wb3PmqEoVPfig\n6RyAm2nSRKVKcRcKZDmKHeDG7HZ99hmb6wADPDzUo4dmzzadA1ZDsQPc2KZNOnaM6xIDZvTq\npQMHtHu36RywlBymAwAwZ84ctWypkiXveAXJyckxMTFZmMjJXbt2LWfOnKZTILvY7fbr169H\nR0c76P0KF85fp07SjBnx48c76B1v4O3t7eHBJh5LodgB7io+XosXa9q0u1nH0KFDJ0+enFWJ\nXELVqlVNR0B22bFjx8GDB+fNm+ewd+wrjYuKKj1t2jWHveU/DR48eNKkSYbeHNmCYge4qy++\nkIeHAgPvZh0XL15s165deHh4FmVydh07drx2zdSfYGS7a9eu3XvvvUuXLnXYO3pevly4deuD\nr79+8ZFHHPamqcLDwy9evOj490W2otgB7mr2bAUFycvrLldTpEiRgICALEnk/HLnzm06ArJX\n7ty5Hf37/PjjFTZt0osvOvRNJUlFihRx/Jsiu7FnHXBLx45p40Y9+aTpHIDbe/JJrV6tP/80\nnQMWQbED3NKcOapUSfXqmc4BuL1mzVSihObONZ0DFkGxA9xPcrJmz2ZzHeAUPDzUq5dmzTKd\nAxZBsQPcz8aNOnVKPXqYzgFAkvTkkzp4UNu3m84BK6DYAe5n1iy1aaMSJUznACBJKl9eTZuy\n0Q5ZgmIHuJmYGC1ZoqeeMp0DQBpPPaXISCUkmM4Bl0exA9xMZKTy51fbtqZzAEjj8cdlt2vJ\nEtM54PIodoCb+fRT9ewp7osFOBUvL3Xrxt5Y3D2KHeBO9u/X9u2cDws4o6ee0vr1+u030zng\n2ih2gDv55BPVry/udgo4oTp1VKMGG+1wl7ilGJCVDhw4sGXLFtMpbs4zMTFk5sztjz/+3+nT\ns2qdhw8fLlasWFatDXB3vXtrwgSFh8vT03QUuCqKHZCVJk6cuHz58rJly5oOchOtLlxITkgI\n27cv/sCBrFrnTz/9dN9992XV2gB398QTeuUVrVnD6U24YxQ7ICvZ7fb27dvPcs6dKS1bqnnz\n72fMyMJV+vn52e32LFwh4NYKF1ZgoGbOpNjhjlHsAPdw9KjWr9fWraZzAMhQnz5q3Vq//66S\nJU1HgUvi5AnAPcycqerVVaeO6RwAMtSsmcqU0Zw5pnPAVVHsADeQmKjZs9Wnj+kcAP6Nzaan\nntKMGUpONh0FLoliB7iBFSsUHa0nnjCdA0Am9O6tkye1fr3pHHBJFDvADUyfri5dVLCg6RwA\nMqFECbVvryw9zwnug5MnAKs7cULffKMNG0znAJBpffsqMFDnzqloUdNR4GLYYgdY3cyZqlhR\nDRuazgEg01q1UsmSmj3bdA64HoodYGmJifr0U/XrJ5vNdBQAmebhoT59NH26uE4kbhPFDrC0\nFSt04YJ69DCdA8BtevppHT/OKRS4XRQ7wNI+/lhdu6pQIdM5ANwmX1899pimTTOdAy6GkycA\n6zp6VN98oy1bTOcAcEeeeUZt23IXCtwWttgB1vXxx3rgAdWrZzoHgDvSsqXKltWnn5rOAVdC\nsQMs6to1zZql/v1N5wBwp2w2PfOMpk9XUpLpKHAZFDvAohYvVkKCQkJM5wBwF3r31rlzWrnS\ndA64DIodYFFTp6pHD+XPbzoHgLtQpIi6dNFHH5nOAZdBsQOsaP9+/fCD+vUznQPAXevfX998\no99+M50DroFiB1jR1Klq0EA1apjOAeCu1a+vGjW47gkyiWIHWE5srObO1bPPms4BIIs8+6w+\n/VQJCaZzwAVQ7ADLmTNH99yjxx83nQNAFuneXUlJiow0nQMugGIHWIvdro8+0jPPKFcu01EA\nZJF77tGTT2rqVNM54AIodoC1rFunX37htAnAagYO1O7diooynQPOjmIHWMsHHygwUKVKmc4B\nIEvdf79atdKUKaZzwNlR7AALOXZMK1YoNNR0DgDZIDRUixbpzz9N54BTo9gBFjJ1qqpXV+PG\npnMAyAatW+u++zR9uukccGoUO8Aq4uP1ySdsrgMsy8NDAwdq2jRdv246CpwXxQ6wis8/l6Tu\n3U3nAJBtevdWbKwWLzadA86LYgdYgt2uKVPUt6/y5jUdBUC28fZWr156/33TOeC8cpgOcNvs\ndvvhw4cPHz4cExNjt9t9fHwqVqxYsWJFm81mOhpgzrp1OnhQy5ebzgEgmw0apMqVFRWlunVN\nR4EzcqVil5CQMHHixGnTpp0+fTrdU6VLl+7Xr9/QoUO9vLyMZAMMe/99deyosmVN5wCQzfz8\n9J//6L33NG+e6ShwRi5T7OLi4po3bx4VFeXh4VGrVi0/Pz9vb2+bzXbx4sXDhw/v27dv5MiR\nK1euXLduXV52RcHdHDmilSu1aZPpHAAcYtAgPfaY3n5bpUubjgKn4zLFbvz48VFRUSEhIW+/\n/XbJkiXTPXv69Olhw4bNnz9//PjxY8eONZIQMGbKFAUEqH590zkAOESrVvLz09SpGj/edBQ4\nHZc5eSIyMjIgICAiIuLGViepVKlSc+fOrV279oIFCxyfDTDp4kXNmqUhQ0znAOAoNpsGDdL0\n6YqPNx0FTsdlit2pU6caNWrk4XHLwB4eHo0aNTp58qQjUwHmzZwpHx917mw6BwAH6tFDNpsi\nIkzngNNxmWLn7e199OjRjMf89ttvPj4+jskDOIXERE2ZotBQ5cxpOgoAB/LyUr9+eu892e2m\no8C5uEyxa9GixVdffRVx63+dzJ49e8WKFc2bN3dkKsCwxYt1/rz69jWdA4DDDRyoo0e1apXp\nHHAuLnPyxOuvv75q1apevXpNnjy5devWlSpV8vb2lhQTE3Po0KHVq1fv3bvXx8fntddeM50U\ncKB331Xv3ipUyHQOAA5XooSCgzVxotq2NR0FTsRlil2FChU2b9789NNPb9++fc+ePTcOqFOn\nzieffFKhQgXHZwPM+O477d6t+fNN5wBgyJAh8vfXrl0KCDAdBc7CZYqdpOrVq0dFRe3evXv9\n+vWHDh2KiYmR5O3tXalSpWbNmtWuXdt0QMCxJk5UYKD4xwzgtmrUUMuWmjiRixUjlSsVuxS1\na9fOwg6XkJAwbdq0a9euZTDm+PHjWfV2QJY5dEgrV2rzZtM5ABj14otq00Zjx6p8edNR4BRc\nr9hlrejo6MWLF1+9ejWDMZcvX5Zk58wjOJV33lH9+nr4YdM5ABjVsqUeeEBTpmjSJNNR4BTc\nvdiVLFlyy5YtGY/54YcfGjRoYLPZHBMJ+Hd//KHPPtPChaZzAHACw4apXz+NGqWCBU1HgXku\nc7kTScnJyfPnz+/fv/+gQYPWrl1744CJEye2bt3a8cEAR3v/fZUvr3btTOcA4AS6dlXhwvro\nI9M54BRcZotdUlJShw4dVq5cmfLw/fff79Sp06xZswoUKJA6Zv/+/V9//bWhgICjXLqkadM0\ncaJufSMWAG4kRw4NGaI33tALLyhPHtNpYJjL/GGYMWPGypUrixcv/uabb06dOrVOnTpLlixp\n1qzZxYsXTUcDHOvjj5U3r0JCTOcA4DSeflqJiZo923QOmOcyxS4iIiJHjhzffffdyy+/PGDA\ngK1bt44aNWrXrl2tWrWKjY01nQ5wlKtXNWmSBg9W7tymowBwGvnyaeBAvfOOkpJMR4FhLlPs\nfvrppwYNGlSqVCnloYeHx5gxY6ZMmbJ9+/ZHH300Li7ObDzAQSIiFB+v/v1N5wDgZJ57TmfO\ncE4VXKbYXbt2rVixYukWhoaGTpgwYcuWLe3bt09ISDASDHCcpCS9/bYGDlT+/KajAHAyRYqo\nTx+9+aa4OJd7c5lid++99546derG5S+++OLo0aM3bNjQqVOnjK8zDLi8RYv0++8aNMh0DgBO\naehQ/fe/WrXKdA6Y5DJnxfr7+y9fvjwmJsbb2zvdU+Hh4bGxsZMmTfL09DSSDXAEu11vvqm+\nfVW0qOkoAJzSvfeqRw+NH6+2bU1HgTEus8WuY8eO165dm3+L+52/++67ffv2TeKgUVjYypX6\n+We9+KLpHACc2MsvKypKGzeazgFjXGaLXfv27SdNmnTjYXappk2b5ufnd/78eUemAhxn3Dj1\n6qXSpU3nAODE/PzUtavGjVPTpqajwAyXKXb58+cfPHhwBgM8PDyGDRvmsDyAQ61dq507NXeu\n6RwAnN6rr6pmTW3bpnr1TEeBAS6zKxZwa6+/rm7dVKGC6RwAnN4DD6hDB40dazoHzHCZLXaA\n+/ruO23erJ9+Mp0DgIsIC9NDD2nXLgUEmI4CR2OLHeD0Xn9dXbqoShXTOQC4iIAAPfqoXn/d\ndA4YwBY7wLlt2aING7Rvn+kcAFzK6NGqW1d798rf33QUOBRb7ADnFh6uxx9XtWqmcwBwKQ89\npDZtNGaM6RxwNLbYAU5s82atX68ffzSdA4ALGjVKDz+sPXtUq5bpKHActtgBTiw8XJ07q3p1\n0zkAuKC6ddlo54bYYgc4q+++4+g6AHclPFx163J6rFthix3grEaPVlAQR9cBuHMPPaR27TR6\ntOkccBy22AFOae1abdmiAwdM5wDg4saM0YMPciMK98EWO8ApjRypJ55QxYqmcwBwcbVqqVMn\nhYWZzgEHodgBzuerr7R7t0aNMp0DgCWMGaONG7Vhg+kccASKHeBkkpMVFqY+fVSunOkoACyh\nalWFhGjECNM54AgUO8DJzJ+vX37hKxhAVho9Wrt2afly0zmQ7Sh2gDO5fl2jRys0VCVLmo4C\nwELKl1efPgoLU3Ky6SjIXhQ7wJnMnKnz5/Xyy6ZzALCcsDD99pvmzTOdA9mLYgc4jbg4vfaa\nXn5ZhQqZjgLAckqU0ODBGjlSV6+ajoJsRLEDnMakSfL01PPPm84BwKKGDdOlS/roI9M5kI0o\ndoBzOHdOEyYoPFx585qOAsCivL0VFqZx4xQTYzoKsgvFDnAOr72m0qXVu7fpHAAsbcAA5c+v\nN980nQPZhWIHOIHDh/Xxx3rrLXl6mo4CwNJy59b48XrvPZ08aToKsgXFDnACr76qhg3Vrp3p\nHADcQFCQHniAm4xZFcUOMG3TJn35pSZMMJ0DgHuw2TRhgubOLXP+vOkoyHoUO8Aou11Dhyok\nRAEBpqMAcBuNG6tDh+AdO0znQNbLYToA4N7mztWBA1qyxHQOAG7m7bcrVKoUcOKE6RzIYmyx\nA8yJi9Orr2rYMJUubToKADdz//3rqlTpumMH1yu2GIodYM7bb8tm07BhpnMAcEfLa9b0un5d\n771nOgiyEsUOMOT4cU2YoLfe0j33mI4CwB3F58q1pHZtjRunM2dMZ0GWodgBhgwbptq1FRxs\nOgcA9/W9n5/Kl9fw4aaDIMtQ7AAT1q/XF1/ovfdks5mOAsB9Jdtseu89zZmjqCjTWZA1KHaA\nwyUm6vnn9fTTXOIEgHmNG6trVz33nJKTTUdBFqDYAQ43ZYr++EPjx5vOAQCSpHfe0c8/65NP\nTOdAFqDYAY71++8KD9frr6tIEdNRAECSVKqURo3Sq6+Ke1G4Pood4Fgvvig/P/XrZzoHAKQx\neLCKFdOrr5rOgbtFsQMcaN06LVigqVPl6Wk6CgCkkTOnpk7VJ59o2zbTUXBXKHaAo1y9qoED\n9cwzqlPHdBQAuEHTpureXQMGKDHRdBTcOYod4ChvvaWLFzlnAoDzeucdHT/OvShcGsUOcIjD\nh/XGG3r3XRUsaDoKANxC8eJ6802Fh+vECdNRcIcodkD2s9vVr5+aNFH37qajAECG+vaVv7+e\nfdZ0Dtwhih2Q/T75RNu3a+pU0zkA4N/YbPr4Y337rSIjTUfBnaDYAdnsjz80bJjGjFH58qaj\nAEAmVK2qV1/VoEFc1s4V5TAdABY3adKkDz74wHQKxzl37lzlypX/sSg0VPffryFDDCUCgNs3\nfLgWL9YLL2jOHNNRcHsodshe+/btK168+JNPPmk6iIOEhYVFR0f//+NFi/TVV9q5kwvXAXAl\nuXJp5kw1bKhu3dSmjek0uA0UO2S7SpUqPfPMM6ZTOMiECRP+/8Fffyk0VK++qho1zCUCgDtS\nr54GDVK/ftq/X97eptMgszjGDsg2oaHy9dWIEaZzAMAdef115cmjoUNN58BtoNgB2WPxYi1Z\nolmzlCuX6SgAcEfy5tWsWZo9W6tXm46CzKLYAdngzz81YIBGjFDt2qajAMBdaNBAgwerb1+l\nPXoYToxiB2SDZ55RmTIaPtx0DgC4a2PHyttboaGmcyBTKHZAFuscG6tvvlFEhHLmNJ0FAO5a\nnjyKiNCiRVqwwHQU/DuKHZCV7rt+Peyvv/TGG6pa1XQWAMgiAQEaNUrPPqtTp0xHwb+g2AFZ\nJzFx4p9//pgnjwYNMh0FALLUq6+qcmU9+aSSk01HQUYodkDWee21stevDytWTDab6SgAkKU8\nPTV3rnbs0MSJpqMgIxQ7IIts2qTx44cXK3Y2B9f9BmBF5cpp6lSFhWnnTtNRcEsUOyArXLig\nkBA988y399xjOgoAZJuQEAUFqXt3XbpkOgpujmIH3DW7Xb17y8eHPRQArO/DD2W3q39/0zlw\ncxQ74K69957WrVNkpLy8TEcBgGyWP78WLNAXX2jmTNNRcBMUO+DuREXp5Zf14Ydc3wSAu6hd\nWxMn6vnntW+f6ShIj2IH3IXz5xUUpCeeUK9epqMAgAMNHKj27dWli2JjTUfBP1DsgDuVnKwn\nnpCPjz74wHQUAHC4lF2xvXvLbjcdBf+PYgfcqTFjtHWrFi3i0DoA7ih/fn3xhb7+Wu+8YzoK\n/h/FDrgjX32lceP02Wfy8zMdBQAMqV5dM2Zo+HCtW2c6Cv6HK6kCt++//1WPHgoLU/v2pqMA\ngFHBwdq5U926accOlS1rOg3YYgfcrpgYBQaqSRONGmU6CgA4gbfekr+/AgMVF2c6Cih2wG1J\nSlJwsDw99dln8uD/PgAg5cihyEhduqRevTiRwjj+MgG346WXFBWWVuVQAAAeQ0lEQVSlZctU\noIDpKADgNAoX1rJl+vZbhYebjuLuOMYOyLQZMzRlitas0f33m44CAE6menXNm6fAQFWqpO7d\nTadxX2yxAzLn2281cKCmTlWzZqajAIBTattWEyboqae0ebPpKO6LLXZAJuzfr86d9cIL6tPH\ndBQAcGKDB+vIEQUG6ocfVLGi6TTuiC12wL85fVpt26p1a40fbzoKADi9999XvXpq21bnzpmO\n4o4odkCGLl5UmzYqW1Zz5nAaLAD8O09PRUaqYEG1a8cFUByPP1TArV25og4dlJysZcuUJ4/p\nNADgIvLl04oVunBBnTvr+nXTadwLxQ64hcREBQXp2DGtWaOCBU2nAQCXUqyYvv5ae/fqySeV\nnGw6jRuh2AE3Y7erTx9t3aqvv1bp0qbTAIALKl9ea9Zo1So995zpKG6Es2KBm3n+eX35pdat\nU+XKpqMAgMuqWVMrVqhVKxUooDfeMJ3GLVDsgBu89JJmzdKaNQoIMB0FAFxcgwZaulSPPSYv\nL26x7QAUO+CfRozQBx/oq6/UsKHpKABgCS1batEiPf64cuXSK6+YTmNxFDsgjVGj9O67WrpU\nzZubjgIAFtKunSIjFRQkDw+99JLpNFZGsQP+FhamiRO1ZIlatzYdBQAsp2NHRUaqWzclJ7Pd\nLvtQ7ADJbtdLL+nDD/Xll2rVynQaALCoTp20cKGCgnTlisLDTaexJood3F5yskJDFRGhFSvU\nrJnpNABgaYGBWrJEnTsrLk5vvy2bzXQgq6HYwb1dv66nntKKFfrmG9WvbzoNALiBtm21cqU6\ndFBMjD76SJ6epgNZCsUObiw+Xl27audObdggf3/TaQDAbTRrprVr9eijio7W3LnKndt0IOvg\nzhNwV3/9pebN9fPP2ryZVgcAjla3rr7/Xtu2qXVrxcSYTmMdFDu4pV9/VYMGunpVW7bo/vtN\npwEAt1StmrZs0dmzathQJ0+aTmMRFDu4n61b9fDDKldO330nX1/TaQDAjd13nzZvVpEiqldP\ne/aYTmMFFDu4mfnz1ayZOnbUihXKn990GgBwewULas0aNW+uRo305Zem07g8ih3cRnKywsLU\ns6fGj9fHHysHZw4BgHPInVtz5uiVV9S5s8aPl91uOpAL428b3ENsrHr00HffadkyPfqo6TQA\ngH+y2RQWpqpV1bOn9u3TJ5/onntMZ3JJbLGDGzh4UHXr6tAhRUXR6gDAeXXqpB9+0I4devhh\n/fKL6TQuiWIHq4uMVN26qlJF27erUiXTaQAAGapRQzt2qFQpPfggh9zdAYodrOvKFT37rHr0\n0KhR+uILFShgOhAAIBMKFdLKlRoyRJ0764UXdO2a6UCuhGPsYFE//6zgYJ0/r/Xr1aiR6TQA\ngNvh4aHRo9WggZ54Qps3a/58VahgOpNrYIsdrGjaND34oMqU0Z49tDoAcFUtWmjvXhUsqFq1\nNHu26TSugWIHazlzRu3b64UXNGGCvvxSRYqYDgQAuAu+vlqzRqNHq39/de6sv/4yHcjZUexg\nIQsWqHp1nTmj3bv17LOy2UwHAgDcNZtNQ4dq+3YdOaLq1TmjImMUO1jCH3+oY0f17KlBg7R1\nqypXNh0IAJClUs6Wffppdemi4GCdO2c6kJOi2MHFJSfr449VtapOndLOnRo5kltKAIA15cql\nceO0bZsOHlTVqpozh3tU3IhiB1e2b58aNdLQoRo5Utu26YEHTAcCAGSzgADt3KkhQzRggJo1\n088/mw7kXCh2cE0XL2rQIAUEqFgxHTyoF16Qp6fpTAAAh8iZU8OHa/9+eXmpZk0NG6ZLl0xn\nchYUO7iaxERNmyY/P61ereXLtXSp7rvPdCYAgMNVqKBVqxQZqcWLVbGiZs1ScrLpTOZR7OBS\nVq5UzZp65RUNG6b9+9WmjelAAACjOnXSwYMaOFDPP6+AAK1dazqQYRQ7uIgfflDTpurYUU2b\n6vBhvfSScuc2nQkA4AS8vBQWpkOHFBCg1q31n/9o1y7TmYyh2MHp7dypdu3UsKGKF9dPP+nD\nD1WsmOlMAAAnU7KkZs7Ujz/qnntUp446ddK+faYzGUCxgxOLilL79qpTR56e2r1bCxaoYkXT\nmQAATqxaNS1dqm3bFB8vf3916qQ9e0xnciiKHZzSt9+qeXM9/LA8PbVjh5Ytk7+/6UwAABfx\n0ENas0abN+vKFQUEqE0bbdhgOpODUOzgTK5e1Zw58vfXo4+qRAnt26cvv1RAgOlYAAAXVL++\nVq3Srl0qUEAtW+qhh7R5s+lM2Y5iB+fw++8aPVplyuj559WsmX75RXPnqnp107EAAC6uVi0t\nWKBDh9SggY4eNZ0m23HzJRiVnKxvv9X06Vq+XBUqKCxMvXopf37TsQAA1lKhgiZPNh3CESh2\nMOS33zRnjubM0R9/qEMHrVmjZs1ks5mOBQCAC6PYwbHOn9eiRZo3T5s3q2pVhYaqZ08uXwIA\njnf69Onjx4/369fPdBDHCQwMbGP1K9tT7OAQFy5o2TItWqS1a1W4sIKC9O67evBB07EAwH0d\nPXr0zJkz0dHRpoM4yI4dO65du0axA+7C8eMtfv651okTmjtXPj7q2FGrV6tpU3l6mk4GAJCv\nr+/ChQtNp3CQ3r17m47gCBQ7ZLXr17Vli9as0erV2revdb58e++9t+rcuWrShD4HAEC2otgh\nK9jt2r9f69dr3Tpt3KgrV1S3roKCFBHx4uTJkpo3a2Y6IgAA1kexw526fl27d2vLFm3apE2b\ndP68KlZU8+aaM0fNmsnHx3Q+AADcDsUOt+PoUe3YoR07FBWlnTt15YqqVFHDhnrvPTVtqlKl\nTOcDAMCtUexwa4mJOnRI+/Zp717t3q09e3T+vLy99dBDatJEL72khx9W4cKmUwIAgP+h2OFv\nV6/q8GEdOqT//lcHDujAAR06pGvXVLSoatZUrVp66ikFBMjPj8sIAwDgnFyv2Nnt9sOHDx8+\nfDgmJsZut/v4+FSsWLFixYo22kbmRUfr2DEdParfftOvv+rXX3XkiE6cUHKyfHxUubKqVVOv\nXnrgAT3wgEqUMB0XAABkiisVu4SEhIkTJ06bNu306dPpnipdunS/fv2GDh3q5eVlJJszOndO\nf/6p06d15oxOnNDp0zp1SseP6/hxXbokSd7eKl9e5curdm0FBcnPT5UrcxMIAABcl8sUu7i4\nuObNm0dFRXl4eNSqVcvPz8/b29tms128ePHw4cP79u0bOXLkypUr161blzdvXtNhs9nVq4qO\n1oULOn/+f/99/rzOntW5c/8rc3/+qbNnde2aJOXOLV9flS6t0qVVqZJatlTZsrrvPpUtq4IF\nTf8kAAAgK7lMsRs/fnxUVFRISMjbb79dsmTJdM+ePn162LBh8+fPHz9+/NixY40kvD3Xr+vy\nZV25ooQExcTo2jVduqTLl5WQoEuXFBury5f/tyQ6WrGxiolRbKyio3XxohIS/n89BQqocGEV\nLaqiRVWkiKpWVfPmKlpUxYurZEkVL66iRc39kAAAwKFcpthFRkYGBARERER4eHjc+GypUqXm\nzp176NChBQsWOHuxe+01jR5986cKFpSXl/Lmlbe38udX/vzKl0+FCql8eXl7y9tbPj7/+0/B\ngipUSIUKKWdOx6YHAADOy2WK3alTpx577LGbtroUHh4ejRo1mjZt2m2t9ujRo3Xr1k1MTMxg\nTMqzWXVyxuT4+O/y5Yuz2RJttlib7Zp02WZLkK6mrD8hQQkJOn8+S97LGcTHxycmJi5btsx0\nEAeJiYmRVKhQIdNBHISf19r4ea3N3X7euLi4Hj16mE6R7Vym2Hl7ex89ejTjMb/99pvPbd7w\noEyZMgsXLsy42Nnt9rNnz+bMom1jQYMGVW/RIktW5RIuXLggd/ri4Oe1Nn5ea+Pntbxq1aqZ\njpDtXKbYtWjRYsGCBRERET179rzpgNmzZ69YsSI4OPi2Vuvh4dG0adMsyJdpJUqUKMEFRAAA\nQDaw2e120xky5ddffw0ICIiJialVq1br1q0rVark7e0tKSYm5tChQ6tXr967d6+Pj8/OnTsr\nVKhgOiwAAIABLlPsJP30009PP/309u3bb/psnTp1Pvnkk+rVqzs4FQAAgJNwpWKXYvfu3evX\nrz906FDKUZ/e3t6VKlVq1qxZ7dq1TUcDAAAwyfWKHQAAAG7qllcPAQAAgGuh2AEAAFgExQ4A\nAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AEAAFgExQ4AAMAi\nKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBE5TAdwOw8//PC2bdtMpwAAwO3Uq1dv\n69atplNkL4qdo5UvX75o0aKjR482HQTZYsyYMZKYX6tifq2N+bW2MWPG5M+f33SKbEexc7Rc\nuXIVLlw4ICDAdBBki8KFC0tifq2K+bU25tfaUubX8jjGDgAAwCIodgAAABZBsQMAALAIih0A\nAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCK484Sj5cqVy3QEZCPm19qYX2tj\nfq3NTebXZrfbTWdwL9HR0ZIKFixoOgiyBfNrbcyvtTG/1uYm80uxAwAAsAiOsQMAALAIih0A\nAIBFUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodgAAABZBsQMAALAIih0AAIBF\nUOwAAAAsgmIHAABgERQ7AAAAi6DYAQAAWATFDgAAwCIodre0ZMmS5557rkGDBvny5bPZbN26\ndbvVyF9//TUkJMTX1zdPnjx+fn5hYWHx8fFOMgy3ksn5dfJfA9xKZibu8uXLCxYsCA4OrlKl\nSt68eb29vRs2bDhz5szk5OQbBzO/TiUz85uUlPTaa6+1adOmTJkyefPmLVSoUK1atcaMGXPh\nwoUbBzO/TiXzX7ypvvrqK5vNZrPZwsLCbnzWvebXjlsICAiQVKBAgYoVK0oKCgq66bD9+/f7\n+PjYbLb27dsPGjSodu3akurVqxcfH298GDKQyfl15l8DZCAzEzdp0iRJuXLlqlevXpcuXRo3\nbpwjRw5Jjz32WFJSUtqRzK+zycz8JiQkSPL19W3cuHHnzp1bt25dtGhRSSVLljx27Fjakcyv\ns8nkF2+qs2fPFi9ePF++fJJGjBiR7ll3m1+K3S1t2LDhyJEjycnJX331VQa/WHXq1JE0a9as\nlIdJSUnBwcGSXn/9dePDkIFMzq8z/xogA5mZuMWLF0+dOvXixYupSw4cOFCsWDFJ8+bNSzuS\n+XU2mZnf5OTkdAXu6tWrISEhkvr27Zt2OfPrbDL5xZsqMDCwRIkSI0eOvGmxc7f5pdj9uwx+\nsXbt2iXJ398/7cJTp055eHiULl06OTnZ4DBkUia/OJzt1wCZlMn5TfXGG29I6tevX+oS5teZ\n3e78bty4UVLTpk1TlzC/ziwz8/vpp59KWrFiRco2+HTFzg3nl2Ps7sr69esltWnTJu3CUqVK\n1ahR49SpU4cPHzY4DA7D/FqJt7e3pNy5c6cuYX6t5IsvvpBUs2bN1CXMr0s7duzYoEGDevfu\n3bZt25sOcMP5pdjdlUOHDkmqVKlSuuUphwWkzrGRYXAY5tcy7HZ7RESEpPbt26cuZH5d3eDB\ng/v37x8cHOzn5zdlypQaNWqMGDEi9Vnm13UlJyf36tXLx8cnZVvdTbnh/OYwHcC1xcTE6O9/\n4qfl4+Mj6eLFiwaHwWGYX8sYM2bMtm3bOnXq1KJFi9SFzK+rmzlzZlxcXMr/bt269ezZs1PO\nokjB/LquiRMnfv/99998882NH3gqN5xftthlC7vdLslmsznhMDgM8+taPvjggzFjxtSuXXvW\nrFmZGc/8uorLly8nJyf/8ccfkZGRP//8s7+//+7du//1Vcyvk9u/f//IkSP79+/fsmXLO3i5\nheeXYndXUmp7SoVPK12pNzIMDsP8WsDEiROfe+65gICAtWvXFihQIO1TzK8F2Gw2X1/foKCg\nlStXnjlzpnfv3qlPMb+uyG639+jRo2TJkhMmTMh4pBvOL8XurqTsaE/Z6Z7WkSNH9PdOd1PD\n4DDMr6sLDw9/8cUXH3744XXr1hUsWDDds8yvlVSrVq1EiRL79u2Ljo5OWcL8uqKkpKQff/zx\n6NGj+fPnt/1tyJAhksaNG2ez2fr06ZMy0h3n19Gn4bqgf73ORa1atdIuPH36tIeHR6lSpdKd\nIO3gYcikrLrcCfPrnP51flP+GDRt2vTSpUs3HcD8OrPbvdxJbGysp6enpNjY2JQlzK8zu9X8\nJiUlPX2DevXqSfL393/66adnz56dMtIN55di9+8yc2XaOXPmpDxMSkpKuQDmTS9p6OBhyIy7\nL3Z25teJZTBxSUlJffv2ldSqVauMrxrP/DqtDOZ369ate/fuTbvkr7/+CgwMlNS4ceO0y5lf\np3Vbxf2m17Gzu9/82ux2+91u9LOoJUuWLF++XNKpU6fWrVtXtmzZJk2aSCpSpMg777yTOuyn\nn35q2LDhpUuX2rdvX65cuU2bNu3atatu3bobNmzw8vIyOwwZyOT8OvOvATKQmYmbMGHCSy+9\n5OHhERQUlCtXrrQvf+CBB4YOHZr6kPl1NpmZ3zfffPPVV18tX758uXLlChYseObMmV27diUk\nJJQoUWL9+vWVK1dOXRvz62wy+cWbzuTJk4cMGTJixIixY8emXe5282u6WTqvtBc6SqtMmTLp\nRv7yyy/BwcFFixbNlStX+fLlhw8ffvny5RtXaGQYbiWT8+vkvwa4lcxM3Msvv3yrL8ZWrVql\nWyHz61QyM78HDx4cOnRoQEBAkSJFPD09vb2969SpEx4efuHChRtXyPw6lcx/8aZ1qy12djeb\nX7bYAQAAWARnxQIAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEU\nOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAA\nAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug\n2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAAAIug2AGw\nsiJFipQtWzb14alTp2w2W2BgoAPeetu2bQ0bNryz1zoyJwArodgBcLRffvnFZrN169bNdJDs\nlZSUlJSUlJycfONTV65csd1CZGRkFmZwk48aQKocpgMAgOMUK1Zs06ZNhQsXzr63iI2NHTdu\n3Lx5806fPm2323PmzFm0aNGAgICZM2eWKFEi7cicOXN279493cvLlSvnmJwALIliB8CN5MqV\n6453j2aG3W5v27bt5s2be/bsWa1atYiIiOHDhx88eHDu3Lnnz59PV+zy5s07e/ZsIzkBWBW7\nYgE41Jtvvunn5ydpwYIFqfsf586dK2nv3r02m+3JJ5/89ddfu3XrVqxYMQ8Pj23btkmaMWNG\nYGBguXLlvLy8fHx8mjRpsmjRonRrTk5Onjx5cpUqVfLkyXPvvfcOGTLk8uXL6cbceOxa6pue\nPHmye/fuRYoU8fLyeuihh1atWpXutUlJSRMnTqxcuXLK+gcPHnz58uV0x/Dt2LFj8+bNnTt3\nnjNnTuPGjX18fLp37z527NjffvutUqVKmf+UMsh544ezevXqli1blixZMnfu3CVKlGjYsOGE\nCRMy/qgBWBVb7AA4VPv27XPmzPniiy/Wq1dv4MCBKQsbNGiQOuDkyZN169YtUqRI69at4+Li\n8uTJI6lfv3516tR55JFHihcvfvbs2RUrVnTt2vWtt9566aWXUl84YMCA6dOnlylTJjQ01Gaz\nLVmyZOfOnUlJSZlJdfLkyYceeqhUqVJdu3Y9e/bsl19+2b59+40bNzZq1Ch1zDPPPPPpp5+W\nLVs2NDTUw8NjyZIlu3btSrf+P/74Q1K1atXSrd/Dw8PDIwv+IX3jhxMREdGrVy9fX98OHToU\nK1bs3LlzBw4cmDlz5rBhw/71owZgQXYAcKwjR45ICgoKSrd8z549Kd9LoaGhiYmJaZ86ceJE\n2odxcXEPPvigl5fXhQsXUpZs2LBBUs2aNS9fvpw6platWpLKlCmT+sKTJ09K6tChw41vGhYW\nlpycnLLws88+k9S+ffvUYWvXrk23/vj4+AcffDDd+g8dOiSpYsWKp0+f3rp1a4MGDW76CSQk\nJEjKmTNnr3965513/jVnug+nfv36np6eKcfzpUr9WG71UQOwKnbFAnAuRYoUeeuttzw9PdMu\nvPfeeyXZ7faYmJg///wzNja2Y8eOCQkJmzZtShmQcrBaeHj4Pffck7Ikb968Y8eOzeSb3nff\nfaNHj7bZbCkPQ0JCvL29t2/fnjogIiJC0pgxY1LX7+XldeP6K1as2KdPn8OHD1eoUGHAgAGn\nT59evHhxdHT0Td/0+vXrc/7p22+/zTjnTT8cT0/PHDn+sfulYMGC//4zA7Aiih0A5+Lv7583\nb950C/fs2dOhQwdvb28fHx9fX98SJUqMGDFC0unTp1MHSGrcuHHaV6V7mIFatWql7UY2m610\n6dJpC1nK+tPumZV00/MbPv744xkzZvj7+//000/Hjh3r0qWLr6/vkCFDrl27lm6kt7d3un9q\nr1mzJuOcN344wcHB165dq1atWmho6OLFi8+cOZO5nxiANVHsADiXkiVLpluye/fuBg0abNq0\nacCAAZ9//vmKFStWr149dOhQSVevXk0ZExMTkyNHjkKFCqV9Yb58+VI3sGXMx8cn3ZIcOXKk\nPX4uNjb2xvXfc889N67fw8OjT58+W7du3bhxY5UqVcaNG1e4cOHJkye/8MILmUmSsRs/nNDQ\n0Llz5/r5+X300UddunQpUaJE/fr1t2zZcvfvBcAVUewAOJfU/aGp3n333YSEhIULF7711lvd\nu3dv27Zt69at0+1t9Pb2TkxMvHDhQtqFly9fjouLy5JUBQoUuHH9cXFxGazf09OzUKFCw4cP\n3717t7e39+zZs+12+13GuPHDkRQSEvLDDz9ER0evWbOmf//+O3fubNOmTcpRegDcDcUOgKOl\nHCKWyfNVJR07dkxSvXr10i5cv3592ocp50l8//33aReme3g3/P39JW3evDntwnQPb8XX17dy\n5cpxcXFXrlzJqjw3KlCgQKtWrT766KOhQ4deunQp5fO53Y8agKuj2AFwtJQbKpw4cSKT48uX\nLy8p7YkF8+bNS1fsevXqJSk8PDx1E1p8fPzIkSOzJLCknj17pqw/Pj4+ZcmVK1dGjRqVbtie\nPXtST19N9euvv+7fv79s2bJeXl5ZlSfVt99+m5iYmHbJX3/9JSnlULzb/agBuDquYwfA0QoU\nKFC3bt2oqKjg4ODKlSt7enoGBgZWr179VuNDQ0PnzZsXHBwcFBRUpkyZvXv3rlq1qkuXLmmv\nUfzII4/07dt3xowZ1atXf/zxx1OuY1eyZMkbD567My1atOjVq9ecOXNS17906VJfX18fH5+0\nF6g7dOhQcHBwkyZNWrZsefXq1TNnzgwdOvTTTz+Nj48PDw/PkiTpBAcH58iRo0mTJmXKlPH0\n9IyKitqwYUO1atXatWun2/+oAbg8A5dYAeD2jhw50q5du4IFC6YcNPbZZ5/Z/75UW69evW4c\nv2HDhkaNGhUoUKBAgQLNmjVbt25dyqXmJk2alDomKSnp3XffrVixYq5cuUqVKjV48OBLly4V\nLlw4M9exu/FNa9as6enpmXZJYmLi22+/7efnl7L+559//sKFCzly5KhZs2bqmIsXL06fPr1t\n27blypXLnTu3JF9f3+bNm69duzbtqlKuY3fjWbF3kPOjjz4KDAwsX7583rx5vb29a9SoMXbs\n2Ojo6NQBN/2oAViVzX7XB/MCgHv68ccf/f39u3XrNn/+/Buf3bx58yuvvJLJ4/AAIEtwjB0A\nZErKsWup4uPjhw0bJqljx443HZ8l9xADgNvCMXYAkCnh4eEbN25s2rSpr6/v77//vmrVquPH\nj7dp06ZLly43HX/TS5MAQLZiVywAZMqKFSvef//9ffv2RUdH58iRo1KlSt27dx80aFDOnDlN\nRwOA/6HYAQAAWASHgAAAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAA\nYBEUOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEU\nOwAAAIug2AEAAFgExQ4AAMAiKHYAAAAWQbEDAACwCIodAACARVDsAAAALIJiBwAAYBEUOwAA\nAIug2AEAAFgExQ4AAMAi/g9/rFzYKAnW5wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "Plot with title “Histogram of trading$First”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      },
+      "text/plain": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist(trading$First, breaks=seq(10000,14000,by=500))\n",
+    "lines(seq(10000,14000,by=5),130000*dnorm(seq(10000,14000,by=5),mean=mu, sd=sigma),col=\"red\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Exercises/Exercise 9/Trading_Data_DAX.csv b/Exercises/Exercise 9/Trading_Data_DAX.csv
new file mode 100644
index 0000000..709cd79
--- /dev/null
+++ b/Exercises/Exercise 9/Trading_Data_DAX.csv	
@@ -0,0 +1,254 @@
+Date,First,High,Low,Last,Volume
+27/12/2018,10607.42,10635.45,10372.43,10380.34,1778961152
+21/12/2018,10573.08,10654.66,10512.64,10633.82,9476073472
+20/12/2018,10621.18,10686.37,10563.44,10611.1,4355436032
+19/12/2018,10777.42,10831.44,10749.55,10766.21,3957748736
+18/12/2018,10744.28,10841.42,10714.97,10740.89,3794915584
+17/12/2018,10852.99,10886.8,10700.62,10772.2,3418881024
+14/12/2018,10809.58,10903.39,10733.75,10865.77,3443502336
+13/12/2018,10978.68,10988.77,10887.26,10924.7,3318145792
+12/12/2018,10832.04,10971.12,10815.51,10929.43,3871640320
+11/12/2018,10711.4,10884.62,10684.62,10780.51,4192821760
+10/12/2018,10726.17,10757.73,10585.77,10622.07,4658125312
+07/12/2018,10876.34,10927.37,10788.09,10788.09,4347385344
+06/12/2018,11053.58,11063.44,10762.41,10810.98,5539258880
+05/12/2018,11204.32,11266.28,11177.15,11200.24,3167188480
+04/12/2018,11429.82,11442.19,11330.44,11335.32,3562060544
+03/12/2018,11534.75,11566.97,11457.61,11465.46,4596474880
+30/11/2018,11311.66,11315.3,11208.6,11257.24,4767285248
+29/11/2018,11377.21,11403.72,11275.7,11298.23,3661718784
+28/11/2018,11355.8,11358.8,11279.46,11298.88,3277165568
+27/11/2018,11380.79,11400.05,11264.65,11309.11,3370348032
+26/11/2018,11315.9,11390.93,11301.82,11354.72,3557904896
+23/11/2018,11146.55,11205.99,11093.52,11192.69,2516529408
+22/11/2018,11216.29,11221.77,11119.04,11138.49,2573186560
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