Adding solutions for new questions

diff --git a/problem_sets/week_06/ps6-worked-solution.Rmd b/problem_sets/week_06/ps6-worked-solution.Rmd
index f5791b0a0b6b80e85abe67c24192cbeb2bbf8275..986a9a0147f27890d9b6c2925e9898f9204666e2 100644 (file)
--- a/problem_sets/week_06/ps6-worked-solution.Rmd
+++ b/problem_sets/week_06/ps6-worked-solution.Rmd
@@ -65,7 +65,7 @@ h_plot
 
 # In this case, faceted histograms is probably better
 
 
 # In this case, faceted histograms is probably better
 

-h_facet = df %>% ggplot(aes(x=weeks_alive, # What to summarize
+h_facet = df %>% ggplot(aes(x=weeks_alive # What to summarize

                       )) + geom_histogram(bins = 5) + facet_grid(~dose)
 
 h_facet
                       )) + geom_histogram(bins = 5) + facet_grid(~dose)
 
 h_facet


@@ -90,7 +90,7 @@ box_plot
 library(ggridges) 
 
 ridge_plot = df %>% ggplot(aes(x=weeks_alive, y = dose)) + 
 library(ggridges) 
 
 ridge_plot = df %>% ggplot(aes(x=weeks_alive, y = dose)) + 

-  geom_density_ridges(jittered_points = T)
+  geom_density_ridges(jittered_points = T, fill = 'orange') + theme_minimal()

 ridge_plot
 
 ```
 ridge_plot
 
 ```


@@ -103,7 +103,17 @@ The global mean is
 mean(df$weeks_alive)
 ```
 
 mean(df$weeks_alive)
 ```
 

-PC3. T-test between None and Any, and between None and High.
+
+PC3. Anova
+
+```{r}
+summary(aov(weeks_alive ~ dose, data = df))
+
+```
+
+This provides evidence that the group means are different.
+
+PC4. T-test between None and Any, and between None and High.

 
 ```{r}
 
 
 ```{r}
 


@@ -129,16 +139,10 @@ t.test(weeks_alive ~ dose, data = tmp)
 
 ```
 
 
 ```
 

-The t-test supports the idea that receiving a dose of RD40 reduces lifespan
-
-PC4. Anova
-
-```{r}
-summary(aov(weeks_alive ~ dose, data = df))
+These t-tests both support the idea that receiving a dose of RD40 reduces lifespan. However, we should not completely trust these p-values, since we are doing multiple comparisons. One option is to do a Bonferroni correction, where we only cnsider things significant if $\alpha < .05/m$ where $m$ is the number of tests. In this case, we would fail to reject the null for the second test because we would set $\alpha = .025$.

 
 

-```
+The Bonferroni correction is more conservative than it needs to be, ane there are other approaches; for example,  the `TukeyHSD` function takes in an anova result and does post-hoc comparisons with corrections for all of the groups.

 
 

-This provides evidence that the group means are different.

 
 ## Statistical Questions
 
 
 ## Statistical Questions
 


@@ -202,6 +206,9 @@ chisq.test(x = c(83,121,193,103), p = c(.18,.22,.37,.23))
 # Use the formula for chi-squared
 chisq = (83-90)^2/90 + (121-110)^2/110 + (193-185)^2/185 + (103-115)^2/115
 
 # Use the formula for chi-squared
 chisq = (83-90)^2/90 + (121-110)^2/110 + (193-185)^2/185 + (103-115)^2/115
 

+chisq
+
+# We could then look up the chi-square distribution for 3 degrees of freedom

 
 ```
 
 
 ```
 


@@ -218,3 +225,66 @@ x <- matrix(c(29,54,44,77,62,131,36,67), nrow = 4, # this makes a matrix with 4
             byrow=T) # And this says that we've entered it row by row
 chisq.test(x)
 ```
             byrow=T) # And this says that we've entered it row by row
 chisq.test(x)
 ```

+
+## Empirical Questions
+
+
+EQ0.
+
+a) The unit of analysis is the customer. The dependent variable is the type of board purchased and the independent variable is gender. Males, females, and unkown gender customers are being compared. This is a two-way test.
+
+b) The null hypothesis is that the board purchased is independent of the gender of the customer. The alternative hypothesis is that if we know the gender of the customer that will tell us something about the type of board they purchased.
+
+c) A $\chi^2$ test found statistically significant evidence that board purchase behavior differs by gender. This difference is convincing, but it does directly not tell us what we really want to know, which is the difference between men and women. It could be possible that it is simply identifying a significant difference in the number of unknown gender customers across board types. Many of these concerns are addressed in the text and with additional tests, giving increased confidence in the reality of this difference.
+
+d) Statistical tests help to give (or take away) confidence in a conclusion. People are not natively good at estimating how likely something is due to chance and tests help us to make these judgments. Choosing a statistical test is based on the question that you want to answer and the type of data that you have available to answer it. For example, if this were numeric data (e.g., the amount of money spent on electronics for men and women) then we could choose a t-test to compare those distributions.
+
+
+EQ1
+
+a) These are counts for two categorical variables, so the procedure used was a $\chi^2$ test. The null hypothesis is that whether or not a blog is governed by one person is independent of whether it is on the left or the right.
+
+b) It would be surprising to see these results by chance and it make sense to believe that this difference is real. However, the main reason to be skeptical is the way that the data are grouped. The authors could have grouped them differently (e.g., 1-2 people, 3-4 people, and 5+ people); if the decision on how to group was made after seeing the data then we have good reason to be skeptical.
+
+c) 
+
+```{r}
+
+# First we create the dataframe
+df = data.frame(Governance=c('Individual','Multiple', 'Individual','Multiple'),
+                Ideology = c('Left','Left','Right','Right'),
+                Count = c(13,51,27,38))
+
+# We can make sure it's the same by testing the Chi-squared                
+chisq.test(matrix(df$Count, nrow=2))
+
+percentage_data = df %>% 
+  group_by(Ideology) %>% 
+  summarize(individual_ratio = sum(Count[Governance=='Individual']) / sum(Count),
+            group_count = sum(Count))
+
+shaw_benkler_plot = percentage_data %>%
+  ggplot(aes(x=Ideology, y=individual_ratio * 100)) + 
+    geom_bar(stat='identity', aes(fill=c('red','blue')), show.legend=F) + 
+    ylab('Percentage of Blogs') + theme_minimal()
+
+shaw_benkler_plot
+
+# If we want to add error bars, we need to calculate them (Note that ggplot could do this for us if we had raw data - always share your data!)
+
+# I decided to use confidence intervals. The standard error is another reasonable choice
+
+# Remember that for a binomial distribution (we can consider individual/non-individual as binomial), confidence intervals are mu +- x * sqrt((p*(1-p))/n)
+
+ci_95 = 1.96 * sqrt(percentage_data$individual_ratio * (1 - percentage_data$individual_ratio)/percentage_data$group_count)
+
+shaw_benkler_plot + geom_errorbar(aes(ymin=(individual_ratio-ci_95)*100, ymax=(individual_ratio + ci_95)*100),
+                                  alpha = .3, 
+                                  size=1.1, 
+                                  width=.4)
+
+```
+
+The error bars do overlap in this case, but that actually doesn't tell us whether they are significantly different.
+
+d) We don't need to be very worried about the base rate fallacy because the sizes of both groups are about the same.

diff --git a/problem_sets/week_06/ps6-worked-solution.html b/problem_sets/week_06/ps6-worked-solution.html
index 7a80c67aeed51c4df7bb890b2df39bec8b32a2d8..50075ed3b0d9cc26eb3259bf3cb63f2877e3f3a8 100644 (file)
--- a/problem_sets/week_06/ps6-worked-solution.html
+++ b/problem_sets/week_06/ps6-worked-solution.html
@@ -328,12 +328,12 @@ head(raw_df)</code></pre>
 ## 6 93 74 83 91</code></pre>
 <p>PC1. Let’s reshape the data</p>
 <pre class="r"><code>library(tidyverse)</code></pre>
 ## 6 93 74 83 91</code></pre>
 <p>PC1. Let’s reshape the data</p>
 <pre class="r"><code>library(tidyverse)</code></pre>

-<pre><code>## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──</code></pre>
+<pre><code>## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──</code></pre>

 <pre><code>## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
 ## ✔ tibble  2.1.1     ✔ dplyr   0.7.7
 ## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
 ## ✔ readr   1.1.1     ✔ forcats 0.3.0</code></pre>
 <pre><code>## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
 ## ✔ tibble  2.1.1     ✔ dplyr   0.7.7
 ## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
 ## ✔ readr   1.1.1     ✔ forcats 0.3.0</code></pre>

-<pre><code>## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
+<pre><code>## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──

 ## ✖ dplyr::filter() masks stats::filter()
 ## ✖ dplyr::lag()    masks stats::lag()</code></pre>
 <pre class="r"><code># Rename the columns
 ## ✖ dplyr::filter() masks stats::filter()
 ## ✖ dplyr::lag()    masks stats::lag()</code></pre>
 <pre class="r"><code># Rename the columns


@@ -387,7 +387,7 @@ h_plot</code></pre>
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UWJRCKE0Lp163QP0qJlZPzrd3/5+fm5ubnpHaYlq3041NTUpHuW6BUVFTXsirUPh7y8vJycnMabiIYoKCjIz89P9xT8W4MfWaTOU1bYLkUQ8Pfee+/7779/2mmn7bXXXluzvry8/PXXX990eyKRyM7OTn2ezMxMRwGkXaP8KEmdh0NzkJUVwb/kzV/q/6p4ODQHHg7Njf+v08g/SrBdau6H0L/66qtPPPFEnz59TjjhhHTPAgAAAGnTrH9RvWTJkgkTJpSUlPz4xz9OHim6Nfbcc88nn3xy0+0bNmwoLS1t8DD5+fnJw5AqKioqKysbfDukKCsrq02bNqtWraqurk73LC1XXl5e8iDVNWvWrF+/Pt3jtFyZmZmFhYVlZWUbN25M9yzRa/B/EDk5Ocn3Z61du3bdunWNOhRfQUZGRlFR0erVq6uqqtI9C/+WylMvGiyRSBQXF3vK2oi25m280DSadcDPnTt3zZo1mZmZl19+ee3GNWvWhBBmz5593nnnhRCuuOKKr3/963WvlZOTs9NOO216a8uXL0/lOW5tLlZXV3uunEbJt5v6KaSXh0MzkfzNpp9Co2jw97D2BAR+EM2Bn0Jz48eRFv53gO1Ysw74pPLy8vLy8nob169f/89//jOE4DeLAAAAtATNOuAHDBgwYMCAehtfeeWVG2+8ce+9977uuuvSMhUAAAA0veZ+EjsAAAAgCHgAAACIgoAHAACACDTr98Bv1gEHHPDUU0+lewoAAABoUl6BBwAAgAgIeAAAAIiAgAcAAIAICHgAAACIgIAHAACACAh4AAAAiICABwAAgAgIeAAAAIiAgAcAAIAICHgAAACIgIAHAACIXklJSSKRuPrqq9M9CNuQgAcAAIAICHgAAACIgIAHAACACAh4AAAAiICABwAAgAgIeAAAgGg8/PDDRx99dMeOHfPy8jp37nz22WfPnTt3C+tnz559/vnnd+/evXXr1m3atOnRo8cFF1wwZ86czS4uKyu77rrrDjnkkJ133rlVq1Y9e/YcPHjwq6+++kU3vnr16rFjx/br1699+/Zt2rTZe++9zz333C3PQyqy0j0AAAAAX66iouLEE0989tlna7d8/PHHd9111+9+97vf//73m73K+PHjL7/88g0bNtRumT179uzZs++8886xY8defPHFdRe/+uqrgwYNWrFiRe2WWbNmzZo167HHHrv00ktvuummejf+1ltvfe973/vss89qt8ycOXPmzJl33XXXDTfccNlll6VyZ9ksAQ8AABCBU089NVnv7dq1O+WUU/bbb79FixY988wz06ZNO/nkkzdu3Fhv/T333HPJJZeEEHJzc88888xvf/vbiUTitdde++1vf7t+/fpLLrmkXbt2Z5xxRnLx559/fvzxx69YsSI/P3/48OH9+/cvKCh4//33b7311kWLFo0bN27gwIFHHXVU7Y3Pnz9/4MCBZWVlIYTBgwcfdthhxcXFr7322sSJEysqKi6//PKMjIzRo0c3zXem5RDwAAAAzd1TTz315JNPhhD23HPPyZMnd+7cObn9sssuu/LKK2+88cZ668vKykaNGhVCKCkpmTx5ct++fZPbTzvttNNPP33QoEHLli378Y9/fOKJJ7Zu3TqEMGXKlMWLF4cQHnvssWOOOSa5+Nhjjz3xxBP33HPPqqqqP/zhD3UD/pxzzikrK8vMzHzkkUeOP/745MYhQ4ace+65xxxzzLx586655pqTTz55p5122nbfkxbIe+ABAACau1//+tfJC5MmTaqt9xBCIpG44YYb+vTpU2/9/fffn3x5fMyYMbX1ntS3b9+rrroqhLBy5coHH3wwuXHGjBkhhJycnKOPPrru4m9+85vnnnvucccd1759+9qNs2bNeu6550III0eOrK33pN13333ixIkhhIqKirvuuiuVu8ymBDwAAEBzlwzsI488ctNWDyFceuml9bY8//zzIYTi4uKzzjpr0/U//OEPky+8v/DCC8ktyT6vrKx89NFH6y2eMGHCH//4x7ov8k+ePDl5od676JMOPfTQnj17hhBeeeWVrblrbD0BDwAA0KyVlZV9/vnnIYT9999/sws23f7uu++GELp3756Xl7fp+latWn3zm98MIbzzzjvJLccdd1x2dnYIYciQIfvuu+/tt98+b968L5rnjTfeCCHstNNOu+yyy2YX9OrVK4Tw3nvvfckd4ysS8AAAAM3ahx9+mLzQpUuXzS7YZZddsrL+4wRnpaWlIYRdd931i24z+aXkshBCr169Jk2a1LZt2xDC3//+9wsuuKBr166dOnUaNmzYE088UVlZWfe6S5YsCSEsXLgw8QWSR+aXl5c37P7yRQQ8AABAs7bpGebrSSQSmZmZdbfU1NRs+SrJ4K/7CXOnnHLK3Llzf/GLXwwYMCB5a4sXL77//vu///3v77333slX3ZMqKiq2ZuyKiorq6uqtWclWEvAAAADNWrt27ZIXPvroo80u+Oyzz9avX7/pVRYsWPBFt5m8qdpbTurQocMll1wybdq05cuXP/PMM5deeumOO+4YQpg9e/Zxxx23cuXK5LKioqIQwr777lvzZTIyJGdj8t0EAABo1jp37tymTZvwf28+39Tf//73elv23HPPEMKcOXPqHf2etG7dug8++KB2WQhh1apVq1atWrt2bfKPRUVFRx999E033fTpp5/+5Cc/CSF89tlnL774YvKru+++ewjh/fff/9JDA2hcAh4AAKBZy8jIOPTQQ0MIU6ZMSZ6Ovp5x48bV2zJw4MAQwooVKyZNmrTp+rvvvnvVqlUhhMMOOyy5pVu3bsXFxccdd1y9lVlZWaeddlry8rJly5IXktdat27dY489tumNV1RU7LPPPp07d77hhhu28g6ylQQ8AABAc3fuuecmL5xxxhn1Doz/+c9/Pn369Hrrhw0blvyguDFjxtRr/tdff/1nP/tZCKGwsHDYsGHJjfvuu28IYerUqZueOr620mvPdf/973+/U6dOIYQLL7xwzpw5dRfX1NRcfPHF77zzzscff/y9732vYXeWL5L15UsAAABIq6OOOuqEE074wx/+8O677/bp02fYsGF9+vRZunTpM88885e//CUvL69Lly7vv/9+7fri4uLx48ePGDFi8eLFBx544I9+9KNvf/vbNTU1r7766sSJE5NvmP/Vr36VfDd7COG0006bMmVKZWXlwIEDzzvvvN69e+fn53/22WeTJ09+/PHHQwj9+vWrPd4+JyfnzjvvPPbYYz///PNevXqNGDFi//33Lykp+fjjj++9997XXnsthDBq1Kja9TQWAQ8AABCB++67r6Ki4tlnn122bNnNN99cuz0/P//+++9/9NFH6wZ8COFHP/rRihUrfvKTn6xdu/aWW2655ZZbar+UlZV10003nXnmmbVbhg4dOn369DvuuGPp0qXXXnttvV3vtddeTz75ZN0z0v3Xf/3XvffeO2LEiHXr1t122211FycSibPPPnv8+PGNcq+pS8ADAABEoKCgYMqUKQ899NC99947Y8aMlStXlpSUHH744Zdddtkee+yxevXqHXfcsV+/fnWvcvnllw8aNOjWW2994YUXkh/bvtNOOx1++OEXXnhht27d6t3+7bff/oMf/OBXv/rV7NmzP/3006qqqq9//eu77bbbGWeccdxxx216Pvlhw4Ydcsght9xyy7PPPps8qr9r1669evW65JJL9tprr236rWixEl/68YDbjeXLl6dyZwsKCgoKCkII5eXl9T6hgaaUnZ1dVFRUWlrqjJdplJ+f36pVqxDC6tWr161bl+5xWq6srKzi4uKVK1fW/QTXiEyYXpLuEf7togFLG3bFvLy85DsMKyoqas/cS9PLzMxs27ZtWVnZZk+2vE2VvDfnyxc1lVef75/uEf5D14uWpXuEliiRSLRv395T1kbUoUOHdI8A/+IkdgAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABHISvcAAAAAfImampqampo0DpBIJBKJRBoHIAh4AACA5q+qqqrm6tFpHGDjmBsLCgrSOADBIfQAAAAQBQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAABsE6tWrUokEolEYsGCBZtdsHz58uSChQsXprKjBx54IJFIjBw5MpUbaf4EPAAAAM3Fueeem0gkevToke5BmiMBDwAAABHISvcAAAAA8C8nnHBC165d27Vrl+5BmiOvwAMAANBcLFmyZPTo0TNmzKi78emnnz7++OM7derUrl27733ve7NmzXr44YcTicTYsWPrXX3lypUXXnjhLrvskpeXt/vuu1988cWlpaVNOP625RV4AAAAmq/rr7/+6quvrqmpKSwsLCgomDx58l//+tfvfe97m65ctmxZv379Pvjgg549e7Zr127mzJk333zzW2+99de//jUjY3t49Xp7uA8AAABsl15++eWrrroqPz//4YcfLi0tXbRo0RtvvFFcXPzggw9uuvihhx7KyMiYO3fu22+//c477zz33HNZWVnTpk178803m37ybUHAAwAAsG317t27ZHN23333LV/x6quvDiHceOONJ510UvJV9D59+jz00ENftP6BBx7o0qVL8vLhhx8+aNCgEMI//vGPRrsnaeUQegAAALathr0Rfc2aNVOnTs3Pzz/rrLPqbu/fv3/Pnj3fe++9euv322+/ffbZp+6WZMxv2LChAXtvhrwCDwAAwLb18ccf12zOsmXLtnCt+fPn19TUdO7cuaCgoN6X9txzz03Xb/p6/vbx1vda29WdAQAAYLvxySefhBB22GGHTb/UqVOnTTe2bdt2m8+UVgIeAACA5qhjx44hhKVLl276pc1u3O4JeAAAAJqjb37zmyGEjz76aN26dfW+NGfOnHRMlGYCHgAAgOaosLBw3333XbNmzaRJk+puf+utt7abT4b7SgQ8AAAAzdT1118fQrjsssueeuqpmpqaEMKsWbOGDBmSnZ0dQkgkEmmer2kJeAAAAJqpo4466sc//nF5efmxxx7bvn37b3zjGz179mzbtu24ceNCCB06dEj3gE1KwAMAANB8jR8//ve///0RRxxRU1Ozfv36c845569//Wvyo903ey767VhWugcAAABg+1RUVJQ87v2LtG/fvt6CU0899dRTT623bOjQoUOHDq275YMPPggh7Ljjjlu4Vghh3Lhxydfqtw9egQcAAKCZOvDAA7Ozs6dNm1Z347Jlyx588MHCwsI99tgjXYOlhYAHAACgmTrppJM2bNhw5pln/vnPf169enVpaelzzz136KGHlpeXjxgxIjc3N90DNqlUD6GfMGFCCOGiiy7a8rJFixY98sgjHTp02OxRDQAAALCpCy+8cM6cOb/+9a+POuqoutuPPvroa6+9Nl1TpUuqAT9q1KiwFQG/bNmyUaNG7bzzzgIeAACArXf77bdfcMEFTz755Mcff5yZmdm1a9d999334IMPTvdcadAUJ7GrqamZPn16CGHp0qVNsDsAAAC2Jz169OjRo0e6p0i/hgR8SUnJl26pa+3atRUVFSGEr33taw3YHQAAANCQgF+2bNmXbtmsc845pwG7AwAAABoS8FdddVXt5f/+7/+ut2Xzu8nK6tOnz3e/+90G7A4AAABoSMBfd911tZeTAV93CwAAANDoUj2JnaPiAQAAoAmkGvC//vWvG2UOAAAAYAsa52Pkampq5s+fP2/evJqami0sa926df/+/RtljwAAANCiNELAv/TSSz/4wQ8WLFjwpSs7d+48f/781PcIAADQouTk5ISxt6R7CtIs1YD/6KOPDjvssMrKyq1ZvJWfNgcAAADUk2rA/+xnP6usrGzTps1PfvKTgw46qK7WIqAAACAASURBVF27do0yFgAAAHVt+Q3LTSCRSKR3AFIN+L/97W8hhCeffPLQQw9tjHkAAACor7Ky8rqnc9M4wP87sqKgoCCNAxBCyEjx+h999FH37t3VOwAAAGxTqQZ8cXFxhw4dGmUUAAAA4IukGvD777//22+/vXHjxkaZBgAAANisVAP+kksuWbNmzdixYxtlGgAAAGCzUj2J3UEHHXTTTTdddtlllZWVF198cWFhYaOMBQAAANSVasA//fTT3bt3P+uss6699tpbbrmlf//+Xbp0KSkp+aIPGPjJT36S4h4BAACgBUo14AcNGlR7ubS09E9/+tOW1wt4AAAAaIBUA75169aNMgcAAACwBakGfHl5eaPMAQAAAGxBqmehBwAAAJqAgAcAAIAIpHoI/YIFC7Z+cWFhYdu2bVPcIwAAALRAqQb8rrvuuvWLR40adfPNN6e4RwAAAKKwYMGCZDOuXr26VatW6R4neg6hBwAAgAik+gr8E088sdntGzdu/PTTT+fMmfPQQw+VlpYeeeSRd999t+PnAQAAoGFSDfhjjz12ywtuvPHG0aNHT5w4ccSIEU8//XSKuwMAAICWaZsfQl9YWPjrX//60EMPnTJlyv/8z/9s690BAAAQo6lTp5566qk9e/Zs06ZN3759zz333Dlz5tR+9cgjj0wkEnfccUftlo0bNxYWFiYSiSFDhtS9naOPPjqRSIwdO7bpRm8qTfEe+MzMzOHDh4cQJk2a1AS7AwAAIC7jx48/9NBDH3zwwblz55aUlLz55pu/+c1vevfu/Ze//CW54MgjjwwhTJ06tfYqM2fOLC8vDyFMnz69duPGjRtffvnl2vXbmSY6iV3Pnj1DCLNnz26a3QEAABCL+fPnX3HFFYlE4mc/+1lZWdn8+fNXrFhxyimnrF27dsSIEZWVlSGEI444IoTw4osv1l7rpZdeCiFkZGQsWrRo3rx5yY1vv/12eXl5x44d995773TclW2riQI++XHx69evb5rdAQAAEIsxY8ZUVVX98Ic/vPrqq/Py8kIIRUVFDzzwQO/evRcsWDBx4sQQQs+ePTt16vT555/XvjCcfKX9+OOPD3VehJ82bVoI4YgjjkgkEmm5L9tUqiex2xpVVVU33XRTCGH33Xdvgt19keTfgwbLzs5OXsjJycnI8PF7aZOZmRlCyMvLq66uTvcsLVftwyE7O3u7/JcxFsl/i3Jzc2t/IjRYfn5+w66YlfWv/0n9FNIr+W9RTk5O8r8JmokGP7JIRe3DwVNWIpJ8XX3UqFH1to8cOXL48OFTp049//zzQwhHHHHEvffe++KLL3bv3j2E8NJLL+28884/+MEPHnvssenTp59xxhnh/wJ+uzx+PqQe8H/4wx+28NWamppPPvnkvvvue+edd0IIhx9+eIq7S0VBQUGjlEZubm5ubm7qt0MqPCFoJnJzc//7mZR+Ndborvt+TbpHaGoeDo2iVatWKd5CTk5OTk5OowxDg6X4y3oaXeqPLBrsqz5lfe3qbTdLQ3z7unRPQBNav379woULs7KyunXrVu9Lyfdiz58/P/nHZMBPnTr17LPP/vjjjxcuXHjyyScPGDAgkUgkX4GvqamZPn16IpH4zne+08T3ommkGvAnnnjiVq7cYYcdrrjiihR3l4oVK1bU1DT8mX1BQUFBQUEIoby83HsB0ig7O7uoqKi0tHTjxo3pnqXlys/PTz4nW716dbpnqW/ZsmXpHqHpZGVlFRcXr1y5csOGDemeJXoN/puTl5fXunXrEEJFRcXatWsbdSi+gszMzLZt25aVlSXfJ0kz0aL+TW4+EolE+/btv/pT1g7baqAGaVZ/eTp0aF7fnO3P4sWLq6urd9hhh02PourUqVMI4dNPP03+8Tvf+U4ikUi+XJ88fr5///7t2rXr2bPnu+++u2TJkmXLli1fvnzffffdYYcdmvZONJGmOK6mTZs2gwYNeuutt9q2bdsEuwMAACAWO+64Y0ZGxrJlyzZ9ie7zzz9PLkj+saSkpHfv3osWLfrggw+SZ7A78MADQwgHH3xwCOGll17avo+fD6m/Ar948eItL0gkEiUlJd4lCwAAwKZyc3N32mmnTz/9dO7cuT169Kj7pVmzZoUQvvnNb9ZuOeKII2bMmPHiiy++9NJLhYWFe+21VwjhoIMOuu2226ZPn75kyZKwXQd8qq/Ad/wyO+ywg3oHAADgixx00EEhhAkTJtTbfsstt4QQBgwYULsl+WFyTzzxxKxZs/r165c8WWPy6tOnT582bVrr1q0POOCAJpu8iTk1JQAAAOl0zTXXZGdnT5w48aabbqqqqgohrF69+vTTT3/zzTe//vWvn3POObUr+/fvX1BQ8PTTT9fU1CSPnw8hdOzYsVu3bjNmzPjss88GDhy4HX80TKMFfHl5+Z133jlkyJAePXrsuOOOHTp02H333Y8//vjbb7+9rKyssfYCAABAdL7xjW+UbM64ceNCCF27dv35z39eXV19+eWXFxYWduvWrbi4+L777ispKZk0aVLdDxnJyck55JBDkpdrAz7839vgw3Z9/HxorM+Bv+eee3784x+vWrWq7sbly5d/8MEHf/zjH6+88spf/epXZ555ZqPsCwAAgLgsX758s9vXrFmTvDB69Oh999337rvvfueddz755JN99tlnv/32GzNmTO0Z7GodccQRzzzzTFZW1v7771+78aCDDrrrrruCgP9Sv/jFLy699NLk5TZt2nTt2nXXXXfNysr66KOP5s6dW1ZWVlZWNnz48FWrVo0aNSr13QEAABCFb3zjG1v/Yd4DBw4cOHDgly676KKLLrroonobTz311FNPPfUrzxebVA+h/8c//pH8dPeSkpJf/epXixcvnjFjxuOPP/7II4+88cYbS5YsueWWW0pKSkIIl1122dy5cxthZAAAAGh5Ug34W2+9dePGjbm5uX/6058uuuiigoKCul/Ny8sbOXLk5MmTc3Nzq6qqbr311hR3BwAAAC1TqgH//PPPhxDOOuusum8/qKdv374//OEPQwj/+7//m+LuAAAAoGVKNeAXLlwYQujbt++Wl/Xr1y+E8M9//jPF3QEAAEDLlGrAJ09IkEgktn4xAAAA8FWlGvA77bRTCOHVV1/d8rK//e1vIYSdd945xd0BAABAy5RqwB922GEhhN/+9revv/76F6156623Jk6cGELYmo8EAAAAADaVasBfeOGFmZmZ69evHzRo0B133LFu3bq6X62srLzzzju/+93vrlu3LjMzc+TIkSnuDgAAAFqmrBSv36NHj7Fjx44ePXrp0qXnn3/+VVdd1a1bt1133TWEsGDBgjlz5qxYsSK58oYbbujRo0eKuwMAAICWKdWADyFccskl7dq1GzVqVFlZWWlp6Wuvvfbaa6/VXdC6deubb745+UlyAAAAQAM0QsCHEM4888zjjz/+gQceeP755999993S0tKampp27dr17Nlz4MCBP/jBD4qLixtlRwAAANAyNU7AhxCKiorOO++88847r7FuEAAAAKjVaAEPAADANpKTk3Pd92vSPQVplupZ6JOqq6ufeuqpSy655De/+U3d7SNHjjz22GPvuOOOqqqqRtkRAAAAtEyNEPALFy4cOHDgscceO378+Hnz5tX90sqVK5966qnzzz+/f//+8+fPT31fAAAA0DKlegj9hg0bDj/88NmzZ4cQCgsLu3btWverhx9++IwZM95///033njj+9///owZMzIzM1PcIwAAQEtTWVmZ+8obaRygYv/eBQUFaRyAkPor8L/5zW+S9Z78KPizzz677ldPP/30WbNmTZw4MSMjY+bMmRMnTkxxdwAAANAypRrwjz/+eAjhyCOPHDduXE5OzmbX/PCHPxw2bFgIYfLkySnuDgAAAFqmVAP+gw8+CCGcdNJJW152xBFHhBD+8Y9/pLg7AAAAaJlSDfilS5eGEIqKira8LPlmiUWLFqW4OwAAAGiZUg34HXfcMYTw1ltvbXlZcsEOO+yQ4u4AAACgZUo14AcOHBhCuPfeexcuXPhFa5YsWXLPPfeEEA466KAUdwcAAAAtU6oBP3LkyIyMjM8+++yoo46aOnXqpgtef/31QYMGJfP+nHPOSXF3AAAA0DKl+jnwvXv3vuaaa37605++9957hx566F577bX33nvvsssuubm5n3zyyfvvv//aa68lV44cObJ///4pDwwAAAAtUaoBH0K4+uqri4uLL7/88rVr17777rvvvvtuvQUZGRmXXXbZ9ddfn/q+AAAAoGVK9RD6pJEjR86bN+/aa6/t169fbm7uv246I2OPPfa48MILZ82adcMNN2RkNM6+AAAAoAVqhFfgkzp16vTTn/70pz/9aQhh9erVVVVVRUVFoh0AAAAaRaMFfF2tW7feFjcLAAAALZZXyAEAANgmXnzxxUQikUgk2rdvX1VV9UXLHn744eSyHj16NNauk7f5ox/9qLFusDkQ8AAAAGxbK1aseO65577oq4888si2HuDcc89t3F8QpIWABwAAYBtKnhzt4Ycf3uxXV69ePWXKFCdQ2xrb5D3wAAAAkNSjR48lS5Y88cQT69aty8vLq/fVyZMnr1279pBDDpk6deq2m+GEE07o2rVru3bttt0umoBfcgAAALANZWdnn3DCCWVlZc8+++ymX00eP3/SSSdt9rpLliwZOXJk3759W7duvdtuu51xxhnvvffepsvuu+++Y445pqSkpH379sccc8wrr7yy6e2MHj16xowZyT9ef/31iURi7Nix9ZYNGzYskUg8/fTTyT8m30g/YcKETz755JRTTunQoUN+fn6fPn3+9Kc/hRA+/vjjk08+uaSkpFWrVr17977//vu/wjelQQQ8AAAA29aQIUNCCA899FC97eXl5c8++2zXrl179+696bWmTZu2zz773HbbbTNmzOjYseMnn3xy7733futb36r7nvnq6uozzzzz9NNPnzJlSllZWUZGxpQpUwYMGPDEE0801vAffvjhfvvtF0IYM2bM4MGD33rrrWOPPfb222/v06dPWVnZVVdddfrpp7/77rvDhg2bPHlyY+10swQ8AAAA29bBBx+84447Tp48uaKiou72J598ct26dUOGDEkkEvWusmbNmqFDhy5evHj06NGrVq2aN29eWVnZlVdeWVlZedppp82fPz+57IEHHpg0aVJOTs6dd95ZVla2dOnSv//97126dNn0lwUNdtttt1199dUPPvjgyJEj77vvvnPOOae6uvqCCy4444wznn766YsuuuiOO+645pprQgiTJk1qrJ1uloAHAABg28rIyBg8ePCaNWuSB5/XSr6Wnnx9vp7x48cvWrTo5JNPHjduXEFBQQghPz//+uuvHz58+Pr168eNGxdCqK6uHjNmTAjhF7/4xYgRI3Jzc0MI++yzz1/+8pesrEY749tuu+12/vnn1/7xmGOOCSEUFhZed911tRu/+93vhhDmzZvXWDvdLAEPAADANpes9Lrnol+1atWf//zn7t2777XXXpuuT75hfuTIkfW2jxgxIoTw4osvhhA++eSTjz76qG3btvU+771z587HHXdcY03+rW99q+4BAskz4fXs2TM/P7/exo0bNzbWTjfLWegBAADY5g444IBddtnlmWeeKSsrKywsDCE8+eSTlZWVm335PYQwd+7cEMLEiRPvu+++utvXrl0bQvjnP/8ZQvjwww9DCD169Nj05Pa9e/d+7LHHGmXy5LRbs3FbE/AAAABsc4lE4qSTTvrlL3/55JNPDhs2LGzx/POVlZVLly4NIdxzzz2bvbXVq1dXV1cvWbIkhNCpU6dNF3zta19rxOGbCYfQAwAA0BSGDh0a/u9c9CtXrnzuued69uy5xx57bLoyJyenffv2IYTy8vKazamurs7IyNhll11CCIsXL970FpJt/1WtWLGiAddqMgIeAACAptCnT58uXbo899xzK1aseOKJJ7Zw/HwIYbfddgsh/OMf/6i3vbKycuHChcuWLQshdO7cOYTw/vvvr1+/vt6yd99990vnqampqbflgw8+2Ir7kTYCHgAAgCYyZMiQqqqqxx9/PHk2u8GDB3/RykMOOSSEcNttt9XbfsMNN+y8887Js9DvvPPO3bp1Ky0tnThxYt01CxcufPTRR7cwRvIc9QsWLKi7cdq0aQIeAAAAQvi/o+jvvPPO559/fp999unWrdsXrbz88suLi4t/97vfXXPNNZWVlSGEmpqahx566IYbbsjJyRk+fHgIIZFIJD9GbvTo0ffcc09VVVUIYfbs2UceeeSGDRu2MEb37t1DCPfff//bb7+d3DJ79uyzzjor+UF0zZaABwAAoIn06tWre/fub775ZlVV1WZPX1erXbt2kyZNatOmzbXXXltYWLjHHnt07Njx5JNPrqqquueee2rLf8iQIWedddb69euHDx9eWFj4ta99rUePHrNnz77pppu2cONHHnlkr169Vq9evd9++/Xr16937949e/bMyckZPXp0Y97bxibgAQAAaDrJF+HDF5x/vq5jjz327bffHj58eI8ePRYsWFBUVHTyySfPnDnzlFNOqV2TkZFx991333fffUcffXRhYWFFRcVhhx32wgsvnH766UOHDt1vv/02e8t5eXnPPffceeed9/Wvf/3vf//7ihUrRo4c+eqrr377298eOnRow85gv9NOO/Xr168BV9x6iU3ftb+9Wr58eSp3tqCgoKCgIIRQXl6+6QkSaDLZ2dlFRUWlpaUbN25M9ywtV35+fqtWrUIIq1evHvtcm3SP8x8uGrA03SM0naysrOLi4pUrV275CLFma8L0knSP8G8N/puTl5fXunXrEEJFRUXyY2lJi8zMzLZt25aVlSWPsWxKJe/NaeI9bsGrz/dP9wj/oetFy9I9QkuUSCTat2//VZ+yfjihw7YbqQGa1V+eDh2axTensrIy95U30jhAxf69k0FEGnkFHgAAACIg4AEAACACAh4AAAAiIOABAAAgAgIeAAAAIiDgAQAAIAICHgAAACIg4AEAACACAh4AAAAiIOABAAAgAgIeAAAAIiDgAQAAIAJZ6R4AAACAL5GdnV198AHpnoI0E/AAAADNXSKRSPcIpJ9D6AEAACACXoEHAABo7iorKysrK9M4QGZmZn5+fhoHIAh4AACAKLw3tnUa997r/61J495Jcgg9AAAAREDAAwAAQAQEPAAAAERAwAMAAEAEBDwAAABEQMADAABABAQ8AAAAREDAAwAAQAQEPAAAAERAwAMAAEAEBDwAAABEQMADAACwTfztb39LJBKJRGLcuHFftKZTp06JRGLOnDlNOVikBDwAAADb1pgxYz788MN0TxE9AQ8AAMC2tXbt2hEjRqR7iugJeAAAALahTp067bDDDn/961/vvvvudM8SNwEPAADANlRUVDRhwoQQwqWXXrpo0aKtucrUqVNPPfXUnj17tmnTpm/fvueee269N8k/8MADiURiwoQJFRUVl112WefOnfPz87t16zZ69OjS0tJ6t7ZkyZKRI0f27du3devWu+222xlnnPHee+811r1rSgIeAACAbWvo0KGDBg1auXLl+eef/6WLx48ff+ihhz744INz584tKSl58803f/Ob3/Tu3fsvf/lLvZVr1qw5+OCDX3zxxR/96Edjxoypqan55S9/OWjQoA0bNtSumTZt2j777HPbbbfNmDGjY8eOn3zyyb333vutb33rkUceaeQ7ue0JeAAAALa5O+64o02bNn/84x8ff/zxLSybP3/+FVdckUgkfvazn5WVlc2fP3/FihWnnHJK8l30lZWVdRePGzeuW7duL7/88pVXXnnFFVe8+eabX/va11555ZWXX345uWDNmjVDhw5dvHjx6NGjV61aNW/evLKysiuvvLKysvK0006bP3/+NrzD24CABwCA/9/evcdZVdb7A3/23BiYAQYQEgVBQbwcxFAECukAWqYdDcREvECplXqUfKUe7VhHOxXiJYlIRcRUKm95xfsrTbwgJBhpRzuiiaQggjByZ/bcfn+s39lOM4jDDMzeD/N+/7XnWc/e+ztr1ppnf/Za61nALtezZ88pU6aEEM4///yPP/7407pdccUVlZWVZ5999o9+9KPi4uIQQseOHX/3u98NHDhw2bJlt9xyS93OFRUVU6dOLSgoSH7s0KHDcccdF0L429/+lrRcf/31H3zwwfjx46+99tp27dqFENq2bfuzn/3szDPPrKio2M7N7XKTAA8AAEBLOPfcc4cNG/bBBx9cfPHFn9bnueeeCyFceOGF9dovuOCCEMLcuXPrNg4ZMqRbt251W/bYY48QQnV1dfLjk08+mXluXcmU+Ml7RaQg2wUAAADQKqRSqVtuuWXgwIG33nrrqaeeOmrUqHodKioqli9fXlBQcMABB9Rb1L9//xBCvZPee/fuvf13XLJkSQjhlltumT17dt32LVu2hBDef//9pvwa2SPAAwAA0EIOOuigyy+//L/+67++853v/PWvf23btm3dpStXrqypqenWrVt+fn69J3bv3j2E8N5779VtLCkp2c57pdPp1atXhxBuu+22bXbYuHFjTU1NXl40Z6ZHUygAAAC7gcsuu6x///5///vfr7jiinqL9txzz7y8vI8++ihzDnzGqlWrkg6Nf6OioqIuXbqEEDZs2FC7LXGl9yDAAwAA0JIKCwtnzZqVl5d3/fXXv/LKK3UXtWnTZu+9966qqkpOfa/r9ddfDyHsv//+O/Reffr0CXXmtMtIp9PLly//6KOPdrj6rBLgAQAAaFFDhgyZNGlSdXX1WWedVfee7SGEL33pSyGEadOm1XvKL3/5yxDC8OHDd+iNRowYEUL41a9+Va/9qquu6tGjh1noAQAA4DP89Kc/7d2796uvvlrvMPiVV15ZWFh4yy23XHPNNZWVlSGEjRs3Tpw4cdGiRfvss88555yzQ+9y6aWXlpWV/eY3v7nyyiuTe8jX1tbefffdV111VVFR0ZlnnrkTf6MWIMADAADQ0kpKSmbMmNGwvW/fvpMnT66pqbn00ks7dOhwwAEHlJWVzZ49u2vXrrfffntyZ/jG69y58+23396+ffsf//jHHTp0OPjggz/3uc+NHz++srLytttuazjXfY4T4AEAAMiCY4455owzzmjYfvHFFz/zzDPjx4/fb7/9VqxY8fnPf/6cc8557bXXRo4c+ZmvWVRUdMghh9S9vdzXv/71v/zlL2eeeeZBBx20bNmyjh07jh8//rXXXjv11FN34u/SMtxGDgAAgF1iyJAhtbW12+kwe/bsendoT4waNarhXeLrOu2000477bSG7T/+8Y9//OMf12vcd999b7311kbUm+scgQcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAqyXQAAAACfoaCgYNAVVVksIJVqk8V3JyHAAwAA5Lq8vLy8PCdQt3a2AAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiEMd94N9777177rln2bJlK1eu7NKlS69evcaMGXPggQdmuy4AAABoIREE+D/+8Y/Tp0+vrq4uKCjo2rXrRx99tGLFigULFowdO3bChAnZrg4AAABaQq4H+PLy8pkzZ1ZXV48dO/a0004rKCioqal56qmnZs6ced999/Xr12/o0KHZrhEAAAB2uVy/Bv6ZZ57ZvHlz//79J06cWFBQEELIy8s79thjx40bF0J48skns10gAAAAtIRcD/DLli0LIQwbNqxe++DBg0MIb7/9dhZqAgAAgBaX66fQ9+nTp7S0dMCAAfXaN2zYEEIoKSnJRlEAAADQ0nI9wI8ePbphYzqdvvfee0MIgwYNarh0xYoV999/f8P20047rV27dk2upLCwMHnQpk2b5GR+siIvLy+E0LZt29ra2mzX0npldoE2bdpkt5KGWtX3epndoaamJtu1RK/JW05+fn7yoKioKPmLkBWpVCqE0KZNm8x4TS5oVf+Tc03sH1ltPLBN8e3Va9eunTx58pIlS7p27XryySc37PDhhx/ecccdDdsnTJjQtm3b5hdQVFTU/BehmYqLi7NdAiHU+WIrd+yU3TwuOfg1Soyav+UUFhbm4B7R2tgdck0r/J+cO2L/yGrjgW2KKcBv3br14YcffuCBB7Zs2dKrV6/LL7+8Y8eO2S6q6SounZTtEj7R5upfZruEf5KaOy/bJfyT2hH1Z2HIopzackIIwY0g2C3k2r+d+c/k0L+dgenc+reTa2MWALSYaAL8vHnzZs6cWV5eXlJSMmHChDFjxmTOWqynb9++N954Y8P26urqdevWNbmA4uLi5Hv9zZs3V1ZWNvl1PnnB5r/EztOcNdMa5NT6yaktJwfl1B9rV8vPzy8tLd24cWN1dXW2a4GWs83dPNkddtYYzc7Sqv4n545UKtWhQ4cd3x1y68BYTm08UR81ZDcTQYBPp9M///nP58+fX1BQcNJJJ5144omlpaXb6d++fftkjvp61qxZ05xBPXNiZHV19e4X4H3c2b6cWj85teXkoJz6Y+1qyUwQVVVVVVVV2a4FWs42d/NkJoiqqqpW9U8g9/lzZEUyJcTO+siaLVEXD7tOrgf46urqKVOmLFq0qGfPnhdddNF+++2X7YoAAAAgC3I9wD/33HOLFi066KCDfvKTn8Q+FQcAAAA0Wa4H+EcffTSEMG7cuFQq1fBEmlQqFfXtMQAAAKCRcjr9VldXL126NIRw5ZVXbrND9+7db7755hatCQAAALIhL9sFbM/q1atNrQwAAAAhx4/A77nnnnPmzMl2FQAAAJB9OX0EHgAAAEgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAdo26AwAAIABJREFUAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgXZLqDlFBYW1tbWNvnp+fn5mQeFhYU7qahcsfv9RjuX9RORVvXHSv4vFRQUpFKpbNcCLWebu3leXl7YTcfoqPlzZEUyKMS+O0RdPOw6rSjAt2/ffqd8xm3Xrl3zXySEULFTXmUn6dixY7ZLyGk5tX5yasvJQWWL/yfbJXyidsSwFniX0tLSRvasuHTSLq1khw3NdgHE6bo/lmW7hH/W6cVsV5C7iif/KNsl/JM2V/8y2yW0nJ31kTVbcurTF+SOVhTg161b15ynFxcXFxcXhxA2bdpUWVnZ/HraNv8ldp6PP/442yXktJxaPzm15bB9u3rLyc/Pb9++/caNG6uqqhrT38YDtHI5NaDvOqlUqmPHjps3b06n0zvyvNz6aiyn/lhlZbm1cmjNWlGAr66ubs4p9DU1NZkHjfysHJHd7zfauawfmqZltpyqqiqbKEBjtJL/lsk5p9XV1VH/vlEXD7uOSewAAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAARKMh2AY2yatWqu+++e/HixevXr+/UqdOgQYPGjx/fsWPHbNcFAAAALSSCI/BLly6dNGnS008/vWbNmpKSklWrVj3++OOTJk1atWpVtksDAACAFhJBgJ8+ffrmzZsHDRo0e/bs2bNn33rrrfvvv395efnMmTOzXRoAAAC0kFwP8EuWLHn77be7dOly2WWXlZWVhRC6du16xRVXFBUVLVq0aM2aNdkuEAAAAFpCrgf4p59+OoTwxS9+saioKNPYoUOHww8/vKam5tlnn81eaQAAANBycn0Su+XLl4cQDj300HrtAwYMmD9//vvvv9/wKWvWrHnuuecato8YMaJNmzZNrqSg4P+vq8LCwlQq1eTXyU3FxcXZLiGnWT80za7ecvLy8kIIRUVFmX9QAGxHKxnQk0+qsX9kbSV/LNhRuf6Zb+3atSGEhhPOJ6fTl5eXN3zKP/7xj8mTJzdsP+qoo0pLS5tf0s76b1KxU15lJ9kpa2Y3llPrJ6e2HLavZbacdu3aNbKnjQdo5XJqQN/VYg/AreqPBY2Xqq2tzXYN23PyySdv3bp1xowZe+21V932v/71r5dffnmvXr2mT59e7ymLFy/+9re/3fClnnnmGXeeAwAAIFK5fgQ+0fD8n+R7h6qqqoad99577wsuuKBhe01NzaZNm5pcQ1FRUWFhYQihoqJim+9Ly8jPzy8uLt6yZUtNTU22a2m9CgsLk2kp7A7ZlZeX17ZtW7tDdhUUFCTXZ6XT6crKymyX03olu4N/StmVjNEhhMrKynQ6ne1yWq9UKtWuXTu7w05UUlKS7RLg/8v1AN+lS5fly5dv2LChe/fudds3bNgQQujcuXPDp3Tr1m3ixIkN29esWbNly5YmV5JKpZIAn06nKyqciJo1hYWFxcXFW7dura6uznYtrVoS4CsrK7du3ZrtWlqvgoICiSXriouLkwBfWVnZnFGGZsrPz092B7kxi4qKipIAX1VVZXfIoiTA+8i6Ewnw5I5cn4U+iejr16+v15607LHHHlmoCQAAAFpcrgf45NL3119/vV77G2+8EUKod1geAAAAdle5HuCPPvroEMK8efPqXuGZTqcXLlyYl5c3atSo7JUGAAAALSfXA/wBBxzQp0+flStX3nTTTZmJ66655prNmzcPHjy4W7du2S4QAAAAWkKuT2IXQrjgggt+8IMfPPXUU/PmzevZs+fSpUu3bt3auXPnbd4rDgAAAHZLuX4EPoSw3377TZs2bdSoUYWFhW+99Vb79u2/9rWvTZs2rWvXrtkuDQAAAFpIBEfgQwh77rnnhRdemO0qAAAAIGsiOAIPAAAACPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQKsl1ANCoqKiorK0MIVVVV2a6ltautrc12Ca1dRUVFsiPYHbLO7pB16XR63bp1IYSampps19La1dbW2iOyq6qqyu6QI+wLsLtK2b0bacaMGbNmzQohTJky5eijj852OZBNv/vd76ZOnRpC+M///M8TTzwx2+VANs2ZM+e///u/QwgXXHDBxIkTs10OZNPcuXMvvvjiEMI3v/nN888/P9vlAOyGnEIPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIuA2co31/vvvr1ixIoTQt2/fzp07Z7scyKaVK1f+4x//CCH07t27W7du2S4Hsmn16tVLly4NIfTo0WOvvfbKdjmQTeXl5W+99VYIoXv37j179sx2OQC7IQEeAAAAIuAUegAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgUZLuAnPPee+/dc889y5YtW7lyZZcuXXr16jVmzJgDDzywbp+77rrrrrvu2ubTb7rppr333rtFKoVdq/Hb+apVq+6+++7FixevX7++U6dOgwYNGj9+fMeOHVuqUti1Tj/99PXr12+nw/XXX9+3b99gdGB39+STT954440zZ87cc889Gy5t5FhgyABoDgH+n/zxj3+cPn16dXV1QUFB165dP/rooxUrVixYsGDs2LETJkzIdPvggw+yWCS0jEZu50uXLv3BD36wefPmEEJZWdmqVasef/zx+fPnX3vttW4RTytRWFiYPDA6sBurrq5+8sknP21pI8cCQwZAMwnwnygvL585c2Z1dfXYsWNPO+20goKCmpqap556aubMmffdd1+/fv2GDh2a9Fy+fHkIYerUqQ0Pp7Rp06al64Zdo5Hb+fTp0zdv3jxo0KBJkyaVlZWtXr16ypQpb7311syZM3/4wx+2aMWwa8yaNWub7YsXL77qqqtGjBjRq1evpMXowG6ppqbm73//+z333PPOO+98Wp9GjgWGDIBmcg38J5555pnNmzf3799/4sSJBQUFIYS8vLxjjz123LhxIYS63zovX748Pz+/d+/exQ2kUqms/QKwUzVmO1+yZMnbb7/dpUuXyy67rKysLITQtWvXK664oqioaNGiRWvWrMnqbwA7R8NdoLi4uKKiYsaMGXvvvfd5552X6Wl0YPcze/bsk0466aKLLnr55Zc/rU8jxwJDBkDzCfCfWLZsWQhh2LBh9doHDx4cQnj77beTH9etW7d58+bu3bvn5+e3cIXQYhq5nT/99NMhhC9+8YtFRUWZxg4dOhx++OE1NTXPPvvsLi8UsuSXv/zlpk2bLrvssuLi4qTF6MBuqUePHqNGjfrKV77yla98JXO1SD2NHAsMGQDN5xT6T/Tp06e0tHTAgAH12jds2BBCKCkpSX5MzpDs0aPHokWL5s6dm8x1t++++371q19Nvk6G3UAjt/Ok26GHHlrv6QMGDJg/f/7777/fkjVDi3n66acXLlx42mmnZU6eD0YHdlOjRo0aNWpU8njBggWVlZUN+zRyLDBkADSfAP+J0aNHN2xMp9P33ntvCGHQoEFJy4oVK0IIr7zyyoIFCzLd5s+fP2fOnPPOO+/II49skWJh12rkdr527doQQsPZg5O4Ul5e3kLlQgvaunXrb37zmy5dutQbNYwOtFqNHAsMGQDNJ8Bvz9q1aydPnrxkyZKuXbuefPLJSWPyEa2mpmbChAlDhw7t0qXLe++9d++997788su/+MUv9t13XzcKYjfQyO08uWSxffv29Z7eoUOH8H+f1WA3c//995eXl1944YX15qUzOtBqNXIsMGQANJ9r4Ldt69at99xzz7nnnrtkyZJevXr97Gc/y3xhfPDBB5955pmTJ08+6aSTevTo0bZt2379+v3whz8cOHBgOp2+8847s1s57BQ7tJ03nJ2rtrY2hFBVVdVyFUOLWLt27YMPPrjPPvuMHDmy3iKjA61cI8cCQwZAczgCvw3z5s2bOXNmeXl5SUnJhAkTxowZU3dGokGDBmVOp69r9OjRixcvfuutt1qwUthVGrmdd+nSZfny5Rs2bOjevXvdbsnMEZ07d26BUqElPfHEE+l0+thjj20YQowOtFqNHAsMGQDNJ8D/k3Q6/fOf/3z+/PkFBQUnnXTSiSeeWFpa2sjnJudGrl69ura21u2C2F3V2847d+68fPny9evX1+uWtOyxxx5ZKBF2maqqqieffLKoqGjEiBGNf5bRgd1eI8cCQwZA8zmF/hPV1dVTpkyZP39+z549r7vuugkTJjRM71u3bn3kkUcee+yx5HSvupJrt3r06OHzGbFr/Ha+1157hRBef/31et3eeOONEEK9YywQuxdffHHdunXDhg3L3Jckw+hAa9bIscCQAdB8AvwnnnvuuUWLFh100EFTp07db7/9ttmnuLj48ccfv/nmm1955ZV6i1544YUQQr9+/XZ5obCLNX47P/roo0MI8+bNq6mpyfRJp9MLFy7My8vL3HkIdg9/+MMfQgjDhw9vuMjoQGvWyLHAkAHQfAL8Jx599NEQwrhx41KpVGUDmblVjjvuuBDCtGnTXn311aQlnU7//ve/f/TRR9u1azd+/Phs1Q87USO38wMOOKBPnz4rV6686aabMrMQXXPNNZs3bx48eHC3bt2yVT/sdJs3b37jjTdSqdTBBx+8zQ5GB1qtRo4FhgyA5ks1PNmvdaqurj7ppJOqq6s/rUP37t1vvvnmEEJtbe2111774osvhhDat2/frl271atX19TUlJSUfO973xs6dGjLFQ27TOO383feeecHP/jBli1bSktLe/bsuXTp0q1bt3bu3Pnaa6/t2rVr9n4D2MnmzZt39dVX9+nTZ+rUqdvsYHRgt3f66aevX79+5syZe+65Z71FjRwLDBkAzZR/5ZVXZruGnLBq1ao5c+Zsp0P79u2PP/74EEIqlfriF7/Ys2fP9evXb968eePGjb179x46dOill166//77t1S9sGs1fjvv1KnT8OHDN23atGrVqg8++KBjx44jR4685JJLzCfMbuahhx565513vvSlLx122GHb7GB0YLf3wAMPVFRUHH/88Q0nCWrkWGDIAGgmR+ABAAAgAq6BBwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAYjVww8/nEqlUqnUsmXLsl3Ljqmurk4qv+GGG7JdCwAQDQEeAAAAIiDAA0BOeO6555LD8kcccUS2awEAcpEADwAAABEQ4AEAACACBdkuAAAIIYQjjzzy448/DiHk5+dnuxYAIBc5Ag/Ato0ePTqVSpWUlKTT6brtr732WnKpdkFBwfr16+suWrZsWbLoO9/5Tt32jRs3Xn311V/4whe6dOnSvn37Qw899Nxzz12yZMmnvfWO9q9nw4YNQ4YMSaVShYWFDz30UN1F69ev/8lPfjJixIgePXqUlJT079//G9/4xvz58xv5yp/pxRdfHDdu3IABAzp16tS+ffuDDz74hBNOmDNnTk1NzWc+Nz8/v0ePHmVlZdOmTUtaLrroomR9vvLKK9t8ypgxY5Jfc/Xq1ZnGZq49ACB31QLAttx4443JSPHiiy/Wba9757Mnnnii7qI77rgjab///vszjYsWLdprr70aDkB5eXlXX311w/dtfP9MOH/33XczjZs2bRo+fHjS/6677qrb/6WXXurcufM2R8NLLrmkOeuqtrY2nU6feOKJnzbaHnfccVVVVZnOVVVVSfuvfvWrui9SWloaQvjpT3+a/PinP/0p6Xb55Zc3fMf169cXFxeHEE444YRM446ubQAgIo7AA7BtxxxzTPLg2Wefrdv+4osvZh4///zzdRclPxYUFBx11FFJyzvvvDNq1KgVK1aEEL7xjW/MmDHj7rvvvvDCC0tKSmpqai699NLrrruu7ivsaP96KioqRo8e/cILL6RSqV//+tennHJKZtGqVatOPPHEtWvXtm3b9t///d/vvPPOhx56aPLkyd27dw8hXHvttU8++eQOr6M6rr/++gceeCCE0Ldv32uuueaBBx544IEHrrrqqr59+4YQHn/88enTp+/oaw4ePLh3794hhAcffLDh0kceeWTr1q0hhIkTJyYtzVx7AECuy/Y3CADkriR8HnXUUXUbe/ToEUJIDmUPGzasYf/hw4dnWr785S+HEPLz8+sek6+trX3zzTf79OkTQigpKXn//feb1r/eEfh0Ov1v//ZvScuMGTPq/S633357suixxx6r275kyZLCwsIQwtlnn72j66euoUOHhhD69eu3bt26uu0ff/zxPvvsE0L4+te/nmls5BH42tra//iP/0h6vvnmm/XecfTo0ckfoqKiImnZ0bUNAMTFEXgAPtVXv/rVEMJLL71UUVGRtCxbtuz9998PIXzve98LISxcuHDLli3Jog8++ODtt98OIRx77LFJy+uvv/6HP/whhHDBBRfUO728X79+t9xySwhh06ZNM2fObFr/uqqrq0899dRHH300hPCLX/ziu9/9br0Of/7zn0MIRUVFmfIS+++//7nnnjt69OguXbrs0Mqpq7KycunSpWVlZd/+9rc7dOhQd1HHjh1HjRoVQvj73//ehFc++eSTkwf1LubfsGFDcsrAKaecUlRUFJq39gCAKAjwAHyq5Cz6LVu2vPzyy0lLcv583759k7PT0+n0ggULkkXPPfdc8iCJ/SGERx55JHnw/e9/v+GLjxw5sn///iGEl156qWn9M6qrq7/1rW/dd999IYTTTz89+XKhniSfp9Pp3//+9/UWTZs27cEHH5wyZco2V0JjFBYWrly5sry8/OKLL264dN26dU1+5cMPPzw5eF7vLPo5c+Yk589PmDAhaWny2gMAYiHAA/CpRo4cmRzdzVwG/8ILL4QQhg8f3q9fv+Tq8cxl8MmDz33uc5///OeTloULF4YQ9t577549e27z9QcMGBBC+J//+Z+m9c+YNGnSb37zm+TxY489VndK9ozRo0cnp8qPGzfusMMOu+GGG5p2SLyR0un0m2++eccdd5x66qn1Dp7vqOQg/J/+9KcPPvgg05h8DdGvX78hQ4YkLU1eewBALAR4AD5VSUnJsGHDQghz585NWpIj8EceeWQI4V//9V9DnQCfHIE/5phjUqlU0vLhhx+GEJYvX576FHfeeWcIYcOGDU3rn/HYY4/l5eWNHTs2hFBeXn7ZZZc1/F0GDBhw++23d+rUKYSwePHi888/v2/fvt27dz/jjDMeeuiherfKa5oPP/xw6tSpxx13XJ8+fdq1a3fggQd+85vfTGbCb87Ljhs3LoRQW1v78MMPJy3r169Pzp/PTF8XmrH2AIBYCPAAbE9yPvz8+fMrKirWrl37xhtvhBCS+7QlAX7+/PnpdHr16tV/+9vfQp0L4EMImzZtasxbbNq0KblN+o72z0ilUrNmzbr33nsPP/zwEMJtt92WObG/rlNPPXXJkiXXXXfd8OHD8/PzQwgrV6787W9/O2bMmEMPPTQ5gt1kv/3tb/v27fv973//iSeeeOedd8rKyo488sizzjpr1qxZSQJvskMPPfSAAw4Idc6inzNnTkVFRSqVOv300zPdmrz2AIBYCPAAbE9yGfzWrVsXLFgwb9682trabt267b///iGEESNGhBC2bNmyaNGiF154oba2Ni8vL5kIPdGxY8cQwmGHHfbZU6rm5TWhf8b06dO/9a1v5eXl3XDDDalUqra29rzzzttmTN1jjz0uuuii559/fs2aNY8//vgll1yy5557hhD+93//d/To0R9//HHT1tLChQsnTpy4cePGffbZ58Ybb1y6dOlHH330wgsvzJo166yzzkoO+zdHchb9s88+m1SYnD8/cuTIZH77RJPXHgAQC0M4ANtz6KGHJhH32WefTc6fTw6/hxAOPPDAz33ucyGE559/Pjl//ogjjqg7l3u/fv1CCG+88UZ1dXVj3mtH+2dk7h43ZMiQM888M4SwePHiGTNm1O2zbt26devWZebM79ix47HHHnvNNde89957l19+eQhhxYoVmXn4dtTNN99cU1NTVFT0/PPPn3vuucnN2zOa/L1ARhLgKysrH3/88fXr1z/11FPhn8+fD81YewBALAR4AD7DV77ylRDC3Llz6wX4UOcy+ORK+Mz884mjjjoqhLB169Zkfvh6Nm3a9PnPf37fffe96qqrmtZ/m6ZMmZIc8f7hD39Ydza7Aw44oKysLLl3el0FBQWZidw/+uij7bzydrzzzjshhP33379Xr171FlVVVWVmEGiy/v37H3zwwSGEBx988OGHH66oqCgpKal3r7idsvYAgFwmwAPwGZJYvmDBgkWLFoX/m8EukQnwr732WvjnC+BDCGPGjElmqp80adKbb75Zd1Ftbe33v//9V1999d133z3hhBOa1n+b9thjj5/97GehwWx2hx12WAhh7ty5DadhzyTewYMHf9bK2LZ99903hLBs2bJ6U8TV1NScddZZK1euDI2+Rv3TJAfhn3jiidmzZ4cQxo4dW1paWrfDTll7AEAuK8h2AQDkui9/+ct5eXkVFRUhhNLS0sxd4sL/XQafRNPOnTsfccQRdZ9YVFR08803f/3rX1+1atWAAQO+853vDB48uGvXru++++4dd9yRzDN34YUX/su//EvT+n+a7373u7Nmzfrzn/982223ffvb3x46dGgIYcKECU888UQ6nR41atR55503cODAtm3brlix4pFHHnnggQdCCF/4whc+85U/zdFHH/3rX/9648aNxxxzzKWXXtqnT5/y8vJXX31aBPCoAAACV0lEQVR1+vTpS5YsKS0t3bhx49KlS3/0ox+dcMIJ9dZSI40bN+7KK6/ctGnT008/Herc/j1jZ609ACB3feZUNwAwaNCgZNT48pe/XG9R165dk0WnnHLKNp87e/bs4uLihgNQKpU655xzampqmtw/c3/1d999t96LzJ8/P7mb3cCBA6urq5PG884779NGw0MOOWTVqlXNWUXJEfKGNZ933nmLFi0qKipKWkaPHl1bW1tVVZX8+Ktf/aruiyQH1X/6059u8y0OOeSQ5Fk9evTI/FL17OjaBgAi4hR6AD5b5uL2uhfAJ5Kz6EODC+AzzjjjjCVLllx88cX9+/dv3759+/btBw4cOHHixFdfffWmm27K3DS+yf23aejQoQ1ns7vhhhteeumlk08+ecCAAZ06dSotLT344IOPP/74+++//y9/+Uvmm4imufvuu++6664RI0bsvffexcXF++2339lnn71w4cIbbrjh8MMPf/rpp8eOHXvCCSd87Wtfa/JbZG5Hd8YZZ3zaTPI7Ze0BALkpVVtbm+0aAAAAgM/gCDwAAABEQIAHAACACJiFHgA+8eijj9a7B9tnatOmzfnnn7+L6gEAyHANPAB8Yvz48XffffcOPaWsrKy8vHwX1QMAkCHAAwAAQARcAw8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAEfh/Vai9JC4EDk4AAAAASUVORK5CYII=" width="672" /></p>
 <pre class="r"><code># In this case, faceted histograms is probably better
 
 <p><img 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UWJRCKE0Lp163QP0qJlZPzrd3/5+fm5ubnpHaYlq3041NTUpHuW6BUVFTXsirUPh7y8vJycnMabiIYoKCjIz89P9xT8W4MfWaTOU1bYLkUQ8Pfee+/7779/2mmn7bXXXluzvry8/PXXX990eyKRyM7OTn2ezMxMRwGkXaP8KEmdh0NzkJUVwb/kzV/q/6p4ODQHHg7Njf+v08g/SrBdau6H0L/66qtPPPFEnz59TjjhhHTPAgAAAGnTrH9RvWTJkgkTJpSUlPz4xz9OHim6Nfbcc88nn3xy0+0bNmwoLS1t8DD5+fnJw5AqKioqKysbfDukKCsrq02bNqtWraqurk73LC1XXl5e8iDVNWvWrF+/Pt3jtFyZmZmFhYVlZWUbN25M9yzRa/B/EDk5Ocn3Z61du3bdunWNOhRfQUZGRlFR0erVq6uqqtI9C/+WylMvGiyRSBQXF3vK2oi25m280DSadcDPnTt3zZo1mZmZl19+ee3GNWvWhBBmz5593nnnhRCuuOKKr3/963WvlZOTs9NOO216a8uXL0/lOW5tLlZXV3uunEbJt5v6KaSXh0MzkfzNpp9Co2jw97D2BAR+EM2Bn0Jz48eRFv53gO1Ysw74pPLy8vLy8nob169f/89//jOE4DeLAAAAtATNOuAHDBgwYMCAehtfeeWVG2+8ce+9977uuuvSMhUAAAA0veZ+EjsAAAAgCHgAAACIgoAHAACACDTr98Bv1gEHHPDUU0+lewoAAABoUl6BBwAAgAgIeAAAAIiAgAcAAIAICHgAAACIgIAHAACACAh4AAAAiICABwAAgAgIeAAAAIiAgAcAAIAICHgAAACIgIAHAACIXklJSSKRuPrqq9M9CNuQgAcAAIAICHgAAACIgIAHAACACAh4AAAAiICABwAAgAgIeAAAgGg8/PDDRx99dMeOHfPy8jp37nz22WfPnTt3C+tnz559/vnnd+/evXXr1m3atOnRo8cFF1wwZ86czS4uKyu77rrrDjnkkJ133rlVq1Y9e/YcPHjwq6+++kU3vnr16rFjx/br1699+/Zt2rTZe++9zz333C3PQyqy0j0AAAAAX66iouLEE0989tlna7d8/PHHd9111+9+97vf//73m73K+PHjL7/88g0bNtRumT179uzZs++8886xY8defPHFdRe/+uqrgwYNWrFiRe2WWbNmzZo167HHHrv00ktvuummejf+1ltvfe973/vss89qt8ycOXPmzJl33XXXDTfccNlll6VyZ9ksAQ8AABCBU089NVnv7dq1O+WUU/bbb79FixY988wz06ZNO/nkkzdu3Fhv/T333HPJJZeEEHJzc88888xvf/vbiUTitdde++1vf7t+/fpLLrmkXbt2Z5xxRnLx559/fvzxx69YsSI/P3/48OH9+/cvKCh4//33b7311kWLFo0bN27gwIFHHXVU7Y3Pnz9/4MCBZWVlIYTBgwcfdthhxcXFr7322sSJEysqKi6//PKMjIzRo0c3zXem5RDwAAAAzd1TTz315JNPhhD23HPPyZMnd+7cObn9sssuu/LKK2+88cZ668vKykaNGhVCKCkpmTx5ct++fZPbTzvttNNPP33QoEHLli378Y9/fOKJJ7Zu3TqEMGXKlMWLF4cQHnvssWOOOSa5+Nhjjz3xxBP33HPPqqqqP/zhD3UD/pxzzikrK8vMzHzkkUeOP/745MYhQ4ace+65xxxzzLx586655pqTTz55p5122nbfkxbIe+ABAACau1//+tfJC5MmTaqt9xBCIpG44YYb+vTpU2/9/fffn3x5fMyYMbX1ntS3b9+rrroqhLBy5coHH3wwuXHGjBkhhJycnKOPPrru4m9+85vnnnvucccd1759+9qNs2bNeu6550III0eOrK33pN13333ixIkhhIqKirvuuiuVu8ymBDwAAEBzlwzsI488ctNWDyFceuml9bY8//zzIYTi4uKzzjpr0/U//OEPky+8v/DCC8ktyT6vrKx89NFH6y2eMGHCH//4x7ov8k+ePDl5od676JMOPfTQnj17hhBeeeWVrblrbD0BDwAA0KyVlZV9/vnnIYT9999/sws23f7uu++GELp3756Xl7fp+latWn3zm98MIbzzzjvJLccdd1x2dnYIYciQIfvuu+/tt98+b968L5rnjTfeCCHstNNOu+yyy2YX9OrVK4Tw3nvvfckd4ysS8AAAAM3ahx9+mLzQpUuXzS7YZZddsrL+4wRnpaWlIYRdd931i24z+aXkshBCr169Jk2a1LZt2xDC3//+9wsuuKBr166dOnUaNmzYE088UVlZWfe6S5YsCSEsXLgw8QWSR+aXl5c37P7yRQQ8AABAs7bpGebrSSQSmZmZdbfU1NRs+SrJ4K/7CXOnnHLK3Llzf/GLXwwYMCB5a4sXL77//vu///3v77333slX3ZMqKiq2ZuyKiorq6uqtWclWEvAAAADNWrt27ZIXPvroo80u+Oyzz9avX7/pVRYsWPBFt5m8qdpbTurQocMll1wybdq05cuXP/PMM5deeumOO+4YQpg9e/Zxxx23cuXK5LKioqIQwr777lvzZTIyJGdj8t0EAABo1jp37tymTZvwf28+39Tf//73elv23HPPEMKcOXPqHf2etG7dug8++KB2WQhh1apVq1atWrt2bfKPRUVFRx999E033fTpp5/+5Cc/CSF89tlnL774YvKru+++ewjh/fff/9JDA2hcAh4AAKBZy8jIOPTQQ0MIU6ZMSZ6Ovp5x48bV2zJw4MAQwooVKyZNmrTp+rvvvnvVqlUhhMMOOyy5pVu3bsXFxccdd1y9lVlZWaeddlry8rJly5IXktdat27dY489tumNV1RU7LPPPp07d77hhhu28g6ylQQ8AABAc3fuuecmL5xxxhn1Doz/+c9/Pn369Hrrhw0blvyguDFjxtRr/tdff/1nP/tZCKGwsHDYsGHJjfvuu28IYerUqZueOr620mvPdf/973+/U6dOIYQLL7xwzpw5dRfX1NRcfPHF77zzzscff/y9732vYXeWL5L15UsAAABIq6OOOuqEE074wx/+8O677/bp02fYsGF9+vRZunTpM88885e//CUvL69Lly7vv/9+7fri4uLx48ePGDFi8eLFBx544I9+9KNvf/vbNTU1r7766sSJE5NvmP/Vr36VfDd7COG0006bMmVKZWXlwIEDzzvvvN69e+fn53/22WeTJ09+/PHHQwj9+vWrPd4+JyfnzjvvPPbYYz///PNevXqNGDFi//33Lykp+fjjj++9997XXnsthDBq1Kja9TQWAQ8AABCB++67r6Ki4tlnn122bNnNN99cuz0/P//+++9/9NFH6wZ8COFHP/rRihUrfvKTn6xdu/aWW2655ZZbar+UlZV10003nXnmmbVbhg4dOn369DvuuGPp0qXXXnttvV3vtddeTz75ZN0z0v3Xf/3XvffeO2LEiHXr1t122211FycSibPPPnv8+PGNcq+pS8ADAABEoKCgYMqUKQ899NC99947Y8aMlStXlpSUHH744Zdddtkee+yxevXqHXfcsV+/fnWvcvnllw8aNOjWW2994YUXkh/bvtNOOx1++OEXXnhht27d6t3+7bff/oMf/OBXv/rV7NmzP/3006qqqq9//eu77bbbGWeccdxxx216Pvlhw4Ydcsght9xyy7PPPps8qr9r1669evW65JJL9tprr236rWixEl/68YDbjeXLl6dyZwsKCgoKCkII5eXl9T6hgaaUnZ1dVFRUWlrqjJdplJ+f36pVqxDC6tWr161bl+5xWq6srKzi4uKVK1fW/QTXiEyYXpLuEf7togFLG3bFvLy85DsMKyoqas/cS9PLzMxs27ZtWVnZZk+2vE2VvDfnyxc1lVef75/uEf5D14uWpXuEliiRSLRv395T1kbUoUOHdI8A/+IkdgAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABHISvcAAAAAfImampqampo0DpBIJBKJRBoHIAh4AACA5q+qqqrm6tFpHGDjmBsLCgrSOADBIfQAAAAQBQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAAAQAQEPAAAAERDwAAAAEAEBDwAAABEQ8AAAABABAQ8AAAAREPAAAABsE6tWrUokEolEYsGCBZtdsHz58uSChQsXprKjBx54IJFIjBw5MpUbaf4EPAAAAM3Fueeem0gkevToke5BmiMBDwAAABHISvcAAAAA8C8nnHBC165d27Vrl+5BmiOvwAMAANBcLFmyZPTo0TNmzKi78emnnz7++OM7derUrl27733ve7NmzXr44YcTicTYsWPrXX3lypUXXnjhLrvskpeXt/vuu1988cWlpaVNOP625RV4AAAAmq/rr7/+6quvrqmpKSwsLCgomDx58l//+tfvfe97m65ctmxZv379Pvjgg549e7Zr127mzJk333zzW2+99de//jUjY3t49Xp7uA8AAABsl15++eWrrroqPz//4YcfLi0tXbRo0RtvvFFcXPzggw9uuvihhx7KyMiYO3fu22+//c477zz33HNZWVnTpk178803m37ybUHAAwAAsG317t27ZHN23333LV/x6quvDiHceOONJ510UvJV9D59+jz00ENftP6BBx7o0qVL8vLhhx8+aNCgEMI//vGPRrsnaeUQegAAALathr0Rfc2aNVOnTs3Pzz/rrLPqbu/fv3/Pnj3fe++9euv322+/ffbZp+6WZMxv2LChAXtvhrwCDwAAwLb18ccf12zOsmXLtnCt+fPn19TUdO7cuaCgoN6X9txzz03Xb/p6/vbx1vda29WdAQAAYLvxySefhBB22GGHTb/UqVOnTTe2bdt2m8+UVgIeAACA5qhjx44hhKVLl276pc1u3O4JeAAAAJqjb37zmyGEjz76aN26dfW+NGfOnHRMlGYCHgAAgOaosLBw3333XbNmzaRJk+puf+utt7abT4b7SgQ8AAAAzdT1118fQrjsssueeuqpmpqaEMKsWbOGDBmSnZ0dQkgkEmmer2kJeAAAAJqpo4466sc//nF5efmxxx7bvn37b3zjGz179mzbtu24ceNCCB06dEj3gE1KwAMAANB8jR8//ve///0RRxxRU1Ozfv36c845569//Wvyo903ey767VhWugcAAABg+1RUVJQ87v2LtG/fvt6CU0899dRTT623bOjQoUOHDq275YMPPggh7Ljjjlu4Vghh3Lhxydfqtw9egQcAAKCZOvDAA7Ozs6dNm1Z347Jlyx588MHCwsI99tgjXYOlhYAHAACgmTrppJM2bNhw5pln/vnPf169enVpaelzzz136KGHlpeXjxgxIjc3N90DNqlUD6GfMGFCCOGiiy7a8rJFixY98sgjHTp02OxRDQAAALCpCy+8cM6cOb/+9a+POuqoutuPPvroa6+9Nl1TpUuqAT9q1KiwFQG/bNmyUaNG7bzzzgIeAACArXf77bdfcMEFTz755Mcff5yZmdm1a9d999334IMPTvdcadAUJ7GrqamZPn16CGHp0qVNsDsAAAC2Jz169OjRo0e6p0i/hgR8SUnJl26pa+3atRUVFSGEr33taw3YHQAAANCQgF+2bNmXbtmsc845pwG7AwAAABoS8FdddVXt5f/+7/+ut2Xzu8nK6tOnz3e/+90G7A4AAABoSMBfd911tZeTAV93CwAAANDoUj2JnaPiAQAAoAmkGvC//vWvG2UOAAAAYAsa52Pkampq5s+fP2/evJqami0sa926df/+/RtljwAAANCiNELAv/TSSz/4wQ8WLFjwpSs7d+48f/781PcIAADQouTk5ISxt6R7CtIs1YD/6KOPDjvssMrKyq1ZvJWfNgcAAADUk2rA/+xnP6usrGzTps1PfvKTgw46qK7WIqAAACAASURBVF27do0yFgAAAHVt+Q3LTSCRSKR3AFIN+L/97W8hhCeffPLQQw9tjHkAAACor7Ky8rqnc9M4wP87sqKgoCCNAxBCyEjx+h999FH37t3VOwAAAGxTqQZ8cXFxhw4dGmUUAAAA4IukGvD777//22+/vXHjxkaZBgAAANisVAP+kksuWbNmzdixYxtlGgAAAGCzUj2J3UEHHXTTTTdddtlllZWVF198cWFhYaOMBQAAANSVasA//fTT3bt3P+uss6699tpbbrmlf//+Xbp0KSkp+aIPGPjJT36S4h4BAACgBUo14AcNGlR7ubS09E9/+tOW1wt4AAAAaIBUA75169aNMgcAAACwBakGfHl5eaPMAQAAAGxBqmehBwAAAJqAgAcAAIAIpHoI/YIFC7Z+cWFhYdu2bVPcIwAAALRAqQb8rrvuuvWLR40adfPNN6e4RwAAAKKwYMGCZDOuXr26VatW6R4neg6hBwAAgAik+gr8E088sdntGzdu/PTTT+fMmfPQQw+VlpYeeeSRd999t+PnAQAAoGFSDfhjjz12ywtuvPHG0aNHT5w4ccSIEU8//XSKuwMAAICWaZsfQl9YWPjrX//60EMPnTJlyv/8z/9s690BAAAQo6lTp5566qk9e/Zs06ZN3759zz333Dlz5tR+9cgjj0wkEnfccUftlo0bNxYWFiYSiSFDhtS9naOPPjqRSIwdO7bpRm8qTfEe+MzMzOHDh4cQJk2a1AS7AwAAIC7jx48/9NBDH3zwwblz55aUlLz55pu/+c1vevfu/Ze//CW54MgjjwwhTJ06tfYqM2fOLC8vDyFMnz69duPGjRtffvnl2vXbmSY6iV3Pnj1DCLNnz26a3QEAABCL+fPnX3HFFYlE4mc/+1lZWdn8+fNXrFhxyimnrF27dsSIEZWVlSGEI444IoTw4osv1l7rpZdeCiFkZGQsWrRo3rx5yY1vv/12eXl5x44d995773TclW2riQI++XHx69evb5rdAQAAEIsxY8ZUVVX98Ic/vPrqq/Py8kIIRUVFDzzwQO/evRcsWDBx4sQQQs+ePTt16vT555/XvjCcfKX9+OOPD3VehJ82bVoI4YgjjkgkEmm5L9tUqiex2xpVVVU33XRTCGH33Xdvgt19keTfgwbLzs5OXsjJycnI8PF7aZOZmRlCyMvLq66uTvcsLVftwyE7O3u7/JcxFsl/i3Jzc2t/IjRYfn5+w66YlfWv/0n9FNIr+W9RTk5O8r8JmokGP7JIRe3DwVNWIpJ8XX3UqFH1to8cOXL48OFTp049//zzQwhHHHHEvffe++KLL3bv3j2E8NJLL+28884/+MEPHnvssenTp59xxhnh/wJ+uzx+PqQe8H/4wx+28NWamppPPvnkvvvue+edd0IIhx9+eIq7S0VBQUGjlEZubm5ubm7qt0MqPCFoJnJzc//7mZR+Ndborvt+TbpHaGoeDo2iVatWKd5CTk5OTk5OowxDg6X4y3oaXeqPLBrsqz5lfe3qbTdLQ3z7unRPQBNav379woULs7KyunXrVu9Lyfdiz58/P/nHZMBPnTr17LPP/vjjjxcuXHjyyScPGDAgkUgkX4GvqamZPn16IpH4zne+08T3ommkGvAnnnjiVq7cYYcdrrjiihR3l4oVK1bU1DT8mX1BQUFBQUEIoby83HsB0ig7O7uoqKi0tHTjxo3pnqXlys/PTz4nW716dbpnqW/ZsmXpHqHpZGVlFRcXr1y5csOGDemeJXoN/puTl5fXunXrEEJFRcXatWsbdSi+gszMzLZt25aVlSXfJ0kz0aL+TW4+EolE+/btv/pT1g7baqAGaVZ/eTp0aF7fnO3P4sWLq6urd9hhh02PourUqVMI4dNPP03+8Tvf+U4ikUi+XJ88fr5///7t2rXr2bPnu+++u2TJkmXLli1fvnzffffdYYcdmvZONJGmOK6mTZs2gwYNeuutt9q2bdsEuwMAACAWO+64Y0ZGxrJlyzZ9ie7zzz9PLkj+saSkpHfv3osWLfrggw+SZ7A78MADQwgHH3xwCOGll17avo+fD6m/Ar948eItL0gkEiUlJd4lCwAAwKZyc3N32mmnTz/9dO7cuT169Kj7pVmzZoUQvvnNb9ZuOeKII2bMmPHiiy++9NJLhYWFe+21VwjhoIMOuu2226ZPn75kyZKwXQd8qq/Ad/wyO+ywg3oHAADgixx00EEhhAkTJtTbfsstt4QQBgwYULsl+WFyTzzxxKxZs/r165c8WWPy6tOnT582bVrr1q0POOCAJpu8iTk1JQAAAOl0zTXXZGdnT5w48aabbqqqqgohrF69+vTTT3/zzTe//vWvn3POObUr+/fvX1BQ8PTTT9fU1CSPnw8hdOzYsVu3bjNmzPjss88GDhy4HX80TKMFfHl5+Z133jlkyJAePXrsuOOOHTp02H333Y8//vjbb7+9rKyssfYCAABAdL7xjW+UbM64ceNCCF27dv35z39eXV19+eWXFxYWduvWrbi4+L777ispKZk0aVLdDxnJyck55JBDkpdrAz7839vgw3Z9/HxorM+Bv+eee3784x+vWrWq7sbly5d/8MEHf/zjH6+88spf/epXZ555ZqPsCwAAgLgsX758s9vXrFmTvDB69Oh999337rvvfueddz755JN99tlnv/32GzNmTO0Z7GodccQRzzzzTFZW1v7771+78aCDDrrrrruCgP9Sv/jFLy699NLk5TZt2nTt2nXXXXfNysr66KOP5s6dW1ZWVlZWNnz48FWrVo0aNSr13QEAABCFb3zjG1v/Yd4DBw4cOHDgly676KKLLrroonobTz311FNPPfUrzxebVA+h/8c//pH8dPeSkpJf/epXixcvnjFjxuOPP/7II4+88cYbS5YsueWWW0pKSkIIl1122dy5cxthZAAAAGh5Ug34W2+9dePGjbm5uX/6058uuuiigoKCul/Ny8sbOXLk5MmTc3Nzq6qqbr311hR3BwAAAC1TqgH//PPPhxDOOuusum8/qKdv374//OEPQwj/+7//m+LuAAAAoGVKNeAXLlwYQujbt++Wl/Xr1y+E8M9//jPF3QEAAEDLlGrAJ09IkEgktn4xAAAA8FWlGvA77bRTCOHVV1/d8rK//e1vIYSdd945xd0BAABAy5RqwB922GEhhN/+9revv/76F6156623Jk6cGELYmo8EAAAAADaVasBfeOGFmZmZ69evHzRo0B133LFu3bq6X62srLzzzju/+93vrlu3LjMzc+TIkSnuDgAAAFqmrBSv36NHj7Fjx44ePXrp0qXnn3/+VVdd1a1bt1133TWEsGDBgjlz5qxYsSK58oYbbujRo0eKuwMAAICWKdWADyFccskl7dq1GzVqVFlZWWlp6Wuvvfbaa6/VXdC6deubb745+UlyAAAAQAM0QsCHEM4888zjjz/+gQceeP755999993S0tKampp27dr17Nlz4MCBP/jBD4qLixtlRwAAANAyNU7AhxCKiorOO++88847r7FuEAAAAKjVaAEPAADANpKTk3Pd92vSPQVplupZ6JOqq6ufeuqpSy655De/+U3d7SNHjjz22GPvuOOOqqqqRtkRAAAAtEyNEPALFy4cOHDgscceO378+Hnz5tX90sqVK5966qnzzz+/f//+8+fPT31fAAAA0DKlegj9hg0bDj/88NmzZ4cQCgsLu3btWverhx9++IwZM95///033njj+9///owZMzIzM1PcIwAAQEtTWVmZ+8obaRygYv/eBQUFaRyAkPor8L/5zW+S9Z78KPizzz677ldPP/30WbNmTZw4MSMjY+bMmRMnTkxxdwAAANAypRrwjz/+eAjhyCOPHDduXE5OzmbX/PCHPxw2bFgIYfLkySnuDgAAAFqmVAP+gw8+CCGcdNJJW152xBFHhBD+8Y9/pLg7AAAAaJlSDfilS5eGEIqKira8LPlmiUWLFqW4OwAAAGiZUg34HXfcMYTw1ltvbXlZcsEOO+yQ4u4AAACgZUo14AcOHBhCuPfeexcuXPhFa5YsWXLPPfeEEA466KAUdwcAAAAtU6oBP3LkyIyMjM8+++yoo46aOnXqpgtef/31QYMGJfP+nHPOSXF3AAAA0DKl+jnwvXv3vuaaa37605++9957hx566F577bX33nvvsssuubm5n3zyyfvvv//aa68lV44cObJ///4pDwwAAAAtUaoBH0K4+uqri4uLL7/88rVr17777rvvvvtuvQUZGRmXXXbZ9ddfn/q+AAAAoGVK9RD6pJEjR86bN+/aa6/t169fbm7uv246I2OPPfa48MILZ82adcMNN2RkNM6+AAAAoAVqhFfgkzp16vTTn/70pz/9aQhh9erVVVVVRUVFoh0AAAAaRaMFfF2tW7feFjcLAAAALZZXyAEAANgmXnzxxUQikUgk2rdvX1VV9UXLHn744eSyHj16NNauk7f5ox/9qLFusDkQ8AAAAGxbK1aseO65577oq4888si2HuDcc89t3F8QpIWABwAAYBtKnhzt4Ycf3uxXV69ePWXKFCdQ2xrb5D3wAAAAkNSjR48lS5Y88cQT69aty8vLq/fVyZMnr1279pBDDpk6deq2m+GEE07o2rVru3bttt0umoBfcgAAALANZWdnn3DCCWVlZc8+++ymX00eP3/SSSdt9rpLliwZOXJk3759W7duvdtuu51xxhnvvffepsvuu+++Y445pqSkpH379sccc8wrr7yy6e2MHj16xowZyT9ef/31iURi7Nix9ZYNGzYskUg8/fTTyT8m30g/YcKETz755JRTTunQoUN+fn6fPn3+9Kc/hRA+/vjjk08+uaSkpFWrVr17977//vu/wjelQQQ8AAAA29aQIUNCCA899FC97eXl5c8++2zXrl179+696bWmTZu2zz773HbbbTNmzOjYseMnn3xy7733futb36r7nvnq6uozzzzz9NNPnzJlSllZWUZGxpQpUwYMGPDEE0801vAffvjhfvvtF0IYM2bM4MGD33rrrWOPPfb222/v06dPWVnZVVdddfrpp7/77rvDhg2bPHlyY+10swQ8AAAA29bBBx+84447Tp48uaKiou72J598ct26dUOGDEkkEvWusmbNmqFDhy5evHj06NGrVq2aN29eWVnZlVdeWVlZedppp82fPz+57IEHHpg0aVJOTs6dd95ZVla2dOnSv//97126dNn0lwUNdtttt1199dUPPvjgyJEj77vvvnPOOae6uvqCCy4444wznn766YsuuuiOO+645pprQgiTJk1qrJ1uloAHAABg28rIyBg8ePCaNWuSB5/XSr6Wnnx9vp7x48cvWrTo5JNPHjduXEFBQQghPz//+uuvHz58+Pr168eNGxdCqK6uHjNmTAjhF7/4xYgRI3Jzc0MI++yzz1/+8pesrEY749tuu+12/vnn1/7xmGOOCSEUFhZed911tRu/+93vhhDmzZvXWDvdLAEPAADANpes9Lrnol+1atWf//zn7t2777XXXpuuT75hfuTIkfW2jxgxIoTw4osvhhA++eSTjz76qG3btvU+771z587HHXdcY03+rW99q+4BAskz4fXs2TM/P7/exo0bNzbWTjfLWegBAADY5g444IBddtnlmWeeKSsrKywsDCE8+eSTlZWVm335PYQwd+7cEMLEiRPvu+++utvXrl0bQvjnP/8ZQvjwww9DCD169Nj05Pa9e/d+7LHHGmXy5LRbs3FbE/AAAABsc4lE4qSTTvrlL3/55JNPDhs2LGzx/POVlZVLly4NIdxzzz2bvbXVq1dXV1cvWbIkhNCpU6dNF3zta19rxOGbCYfQAwAA0BSGDh0a/u9c9CtXrnzuued69uy5xx57bLoyJyenffv2IYTy8vKazamurs7IyNhll11CCIsXL970FpJt/1WtWLGiAddqMgIeAACAptCnT58uXbo899xzK1aseOKJJ7Zw/HwIYbfddgsh/OMf/6i3vbKycuHChcuWLQshdO7cOYTw/vvvr1+/vt6yd99990vnqampqbflgw8+2Ir7kTYCHgAAgCYyZMiQqqqqxx9/PHk2u8GDB3/RykMOOSSEcNttt9XbfsMNN+y8887Js9DvvPPO3bp1Ky0tnThxYt01CxcufPTRR7cwRvIc9QsWLKi7cdq0aQIeAAAAQvi/o+jvvPPO559/fp999unWrdsXrbz88suLi4t/97vfXXPNNZWVlSGEmpqahx566IYbbsjJyRk+fHgIIZFIJD9GbvTo0ffcc09VVVUIYfbs2UceeeSGDRu2MEb37t1DCPfff//bb7+d3DJ79uyzzjor+UF0zZaABwAAoIn06tWre/fub775ZlVV1WZPX1erXbt2kyZNatOmzbXXXltYWLjHHnt07Njx5JNPrqqquueee2rLf8iQIWedddb69euHDx9eWFj4ta99rUePHrNnz77pppu2cONHHnlkr169Vq9evd9++/Xr16937949e/bMyckZPXp0Y97bxibgAQAAaDrJF+HDF5x/vq5jjz327bffHj58eI8ePRYsWFBUVHTyySfPnDnzlFNOqV2TkZFx991333fffUcffXRhYWFFRcVhhx32wgsvnH766UOHDt1vv/02e8t5eXnPPffceeed9/Wvf/3vf//7ihUrRo4c+eqrr377298eOnRow85gv9NOO/Xr168BV9x6iU3ftb+9Wr58eSp3tqCgoKCgIIRQXl6+6QkSaDLZ2dlFRUWlpaUbN25M9ywtV35+fqtWrUIIq1evHvtcm3SP8x8uGrA03SM0naysrOLi4pUrV275CLFma8L0knSP8G8N/puTl5fXunXrEEJFRUXyY2lJi8zMzLZt25aVlSWPsWxKJe/NaeI9bsGrz/dP9wj/oetFy9I9QkuUSCTat2//VZ+yfjihw7YbqQGa1V+eDh2axTensrIy95U30jhAxf69k0FEGnkFHgAAACIg4AEAACACAh4AAAAiIOABAAAgAgIeAAAAIiDgAQAAIAICHgAAACIg4AEAACACAh4AAAAiIOABAAAgAgIeAAAAIiDgAQAAIAJZ6R4AAACAL5GdnV198AHpnoI0E/AAAADNXSKRSPcIpJ9D6AEAACACXoEHAABo7iorKysrK9M4QGZmZn5+fhoHIAh4AACAKLw3tnUa997r/61J495Jcgg9AAAAREDAAwAAQAQEPAAAAERAwAMAAEAEBDwAAABEQMADAABABAQ8AAAAREDAAwAAQAQEPAAAAERAwAMAAEAEBDwAAABEQMADAACwTfztb39LJBKJRGLcuHFftKZTp06JRGLOnDlNOVikBDwAAADb1pgxYz788MN0TxE9AQ8AAMC2tXbt2hEjRqR7iugJeAAAALahTp067bDDDn/961/vvvvudM8SNwEPAADANlRUVDRhwoQQwqWXXrpo0aKtucrUqVNPPfXUnj17tmnTpm/fvueee269N8k/8MADiURiwoQJFRUVl112WefOnfPz87t16zZ69OjS0tJ6t7ZkyZKRI0f27du3devWu+222xlnnPHee+811r1rSgIeAACAbWvo0KGDBg1auXLl+eef/6WLx48ff+ihhz744INz584tKSl58803f/Ob3/Tu3fsvf/lLvZVr1qw5+OCDX3zxxR/96Edjxoypqan55S9/OWjQoA0bNtSumTZt2j777HPbbbfNmDGjY8eOn3zyyb333vutb33rkUceaeQ7ue0JeAAAALa5O+64o02bNn/84x8ff/zxLSybP3/+FVdckUgkfvazn5WVlc2fP3/FihWnnHJK8l30lZWVdRePGzeuW7duL7/88pVXXnnFFVe8+eabX/va11555ZWXX345uWDNmjVDhw5dvHjx6NGjV61aNW/evLKysiuvvLKysvK0006bP3/+NrzD24CABwCA/9/evcdZVdb7A3/23BiYAQYQEgVBQbwcxFAECukAWqYdDcREvECplXqUfKUe7VhHOxXiJYlIRcRUKm95xfsrTbwgJBhpRzuiiaQggjByZ/bcfn+s39lOM4jDDMzeD/N+/7XnWc/e+ztr1ppnf/Za61nALtezZ88pU6aEEM4///yPP/7407pdccUVlZWVZ5999o9+9KPi4uIQQseOHX/3u98NHDhw2bJlt9xyS93OFRUVU6dOLSgoSH7s0KHDcccdF0L429/+lrRcf/31H3zwwfjx46+99tp27dqFENq2bfuzn/3szDPPrKio2M7N7XKTAA8AAEBLOPfcc4cNG/bBBx9cfPHFn9bnueeeCyFceOGF9dovuOCCEMLcuXPrNg4ZMqRbt251W/bYY48QQnV1dfLjk08+mXluXcmU+Ml7RaQg2wUAAADQKqRSqVtuuWXgwIG33nrrqaeeOmrUqHodKioqli9fXlBQcMABB9Rb1L9//xBCvZPee/fuvf13XLJkSQjhlltumT17dt32LVu2hBDef//9pvwa2SPAAwAA0EIOOuigyy+//L/+67++853v/PWvf23btm3dpStXrqypqenWrVt+fn69J3bv3j2E8N5779VtLCkp2c57pdPp1atXhxBuu+22bXbYuHFjTU1NXl40Z6ZHUygAAAC7gcsuu6x///5///vfr7jiinqL9txzz7y8vI8++ihzDnzGqlWrkg6Nf6OioqIuXbqEEDZs2FC7LXGl9yDAAwAA0JIKCwtnzZqVl5d3/fXXv/LKK3UXtWnTZu+9966qqkpOfa/r9ddfDyHsv//+O/Reffr0CXXmtMtIp9PLly//6KOPdrj6rBLgAQAAaFFDhgyZNGlSdXX1WWedVfee7SGEL33pSyGEadOm1XvKL3/5yxDC8OHDd+iNRowYEUL41a9+Va/9qquu6tGjh1noAQAA4DP89Kc/7d2796uvvlrvMPiVV15ZWFh4yy23XHPNNZWVlSGEjRs3Tpw4cdGiRfvss88555yzQ+9y6aWXlpWV/eY3v7nyyiuTe8jX1tbefffdV111VVFR0ZlnnrkTf6MWIMADAADQ0kpKSmbMmNGwvW/fvpMnT66pqbn00ks7dOhwwAEHlJWVzZ49u2vXrrfffntyZ/jG69y58+23396+ffsf//jHHTp0OPjggz/3uc+NHz++srLytttuazjXfY4T4AEAAMiCY4455owzzmjYfvHFFz/zzDPjx4/fb7/9VqxY8fnPf/6cc8557bXXRo4c+ZmvWVRUdMghh9S9vdzXv/71v/zlL2eeeeZBBx20bNmyjh07jh8//rXXXjv11FN34u/SMtxGDgAAgF1iyJAhtbW12+kwe/bsendoT4waNarhXeLrOu2000477bSG7T/+8Y9//OMf12vcd999b7311kbUm+scgQcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAqyXQAAAACfoaCgYNAVVVksIJVqk8V3JyHAAwAA5Lq8vLy8PCdQt3a2AAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiEMd94N9777177rln2bJlK1eu7NKlS69evcaMGXPggQdmuy4AAABoIREE+D/+8Y/Tp0+vrq4uKCjo2rXrRx99tGLFigULFowdO3bChAnZrg4AAABaQq4H+PLy8pkzZ1ZXV48dO/a0004rKCioqal56qmnZs6ced999/Xr12/o0KHZrhEAAAB2uVy/Bv6ZZ57ZvHlz//79J06cWFBQEELIy8s79thjx40bF0J48skns10gAAAAtIRcD/DLli0LIQwbNqxe++DBg0MIb7/9dhZqAgAAgBaX66fQ9+nTp7S0dMCAAfXaN2zYEEIoKSnJRlEAAADQ0nI9wI8ePbphYzqdvvfee0MIgwYNarh0xYoV999/f8P20047rV27dk2upLCwMHnQpk2b5GR+siIvLy+E0LZt29ra2mzX0npldoE2bdpkt5KGWtX3epndoaamJtu1RK/JW05+fn7yoKioKPmLkBWpVCqE0KZNm8x4TS5oVf+Tc03sH1ltPLBN8e3Va9eunTx58pIlS7p27XryySc37PDhhx/ecccdDdsnTJjQtm3b5hdQVFTU/BehmYqLi7NdAiHU+WIrd+yU3TwuOfg1Soyav+UUFhbm4B7R2tgdck0r/J+cO2L/yGrjgW2KKcBv3br14YcffuCBB7Zs2dKrV6/LL7+8Y8eO2S6q6SounZTtEj7R5upfZruEf5KaOy/bJfyT2hH1Z2HIopzackIIwY0g2C3k2r+d+c/k0L+dgenc+reTa2MWALSYaAL8vHnzZs6cWV5eXlJSMmHChDFjxmTOWqynb9++N954Y8P26urqdevWNbmA4uLi5Hv9zZs3V1ZWNvl1PnnB5r/EztOcNdMa5NT6yaktJwfl1B9rV8vPzy8tLd24cWN1dXW2a4GWs83dPNkddtYYzc7Sqv4n545UKtWhQ4cd3x1y68BYTm08UR81ZDcTQYBPp9M///nP58+fX1BQcNJJJ5144omlpaXb6d++fftkjvp61qxZ05xBPXNiZHV19e4X4H3c2b6cWj85teXkoJz6Y+1qyUwQVVVVVVVV2a4FWs42d/NkJoiqqqpW9U8g9/lzZEUyJcTO+siaLVEXD7tOrgf46urqKVOmLFq0qGfPnhdddNF+++2X7YoAAAAgC3I9wD/33HOLFi066KCDfvKTn8Q+FQcAAAA0Wa4H+EcffTSEMG7cuFQq1fBEmlQqFfXtMQAAAKCRcjr9VldXL126NIRw5ZVXbrND9+7db7755hatCQAAALIhL9sFbM/q1atNrQwAAAAhx4/A77nnnnPmzMl2FQAAAJB9OX0EHgAAAEgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAdo26AwAAIABJREFUAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgXZLqDlFBYW1tbWNvnp+fn5mQeFhYU7qahcsfv9RjuX9RORVvXHSv4vFRQUpFKpbNcCLWebu3leXl7YTcfoqPlzZEUyKMS+O0RdPOw6rSjAt2/ffqd8xm3Xrl3zXySEULFTXmUn6dixY7ZLyGk5tX5yasvJQWWL/yfbJXyidsSwFniX0tLSRvasuHTSLq1khw3NdgHE6bo/lmW7hH/W6cVsV5C7iif/KNsl/JM2V/8y2yW0nJ31kTVbcurTF+SOVhTg161b15ynFxcXFxcXhxA2bdpUWVnZ/HraNv8ldp6PP/442yXktJxaPzm15bB9u3rLyc/Pb9++/caNG6uqqhrT38YDtHI5NaDvOqlUqmPHjps3b06n0zvyvNz6aiyn/lhlZbm1cmjNWlGAr66ubs4p9DU1NZkHjfysHJHd7zfauawfmqZltpyqqiqbKEBjtJL/lsk5p9XV1VH/vlEXD7uOSewAAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAARKMh2AY2yatWqu+++e/HixevXr+/UqdOgQYPGjx/fsWPHbNcFAAAALSSCI/BLly6dNGnS008/vWbNmpKSklWrVj3++OOTJk1atWpVtksDAACAFhJBgJ8+ffrmzZsHDRo0e/bs2bNn33rrrfvvv395efnMmTOzXRoAAAC0kFwP8EuWLHn77be7dOly2WWXlZWVhRC6du16xRVXFBUVLVq0aM2aNdkuEAAAAFpCrgf4p59+OoTwxS9+saioKNPYoUOHww8/vKam5tlnn81eaQAAANBycn0Su+XLl4cQDj300HrtAwYMmD9//vvvv9/wKWvWrHnuuecato8YMaJNmzZNrqSg4P+vq8LCwlQq1eTXyU3FxcXZLiGnWT80za7ecvLy8kIIRUVFmX9QAGxHKxnQk0+qsX9kbSV/LNhRuf6Zb+3atSGEhhPOJ6fTl5eXN3zKP/7xj8mTJzdsP+qoo0pLS5tf0s76b1KxU15lJ9kpa2Y3llPrJ6e2HLavZbacdu3aNbKnjQdo5XJqQN/VYg/AreqPBY2Xqq2tzXYN23PyySdv3bp1xowZe+21V932v/71r5dffnmvXr2mT59e7ymLFy/+9re/3fClnnnmGXeeAwAAIFK5fgQ+0fD8n+R7h6qqqoad99577wsuuKBhe01NzaZNm5pcQ1FRUWFhYQihoqJim+9Ly8jPzy8uLt6yZUtNTU22a2m9CgsLk2kp7A7ZlZeX17ZtW7tDdhUUFCTXZ6XT6crKymyX03olu4N/StmVjNEhhMrKynQ6ne1yWq9UKtWuXTu7w05UUlKS7RLg/8v1AN+lS5fly5dv2LChe/fudds3bNgQQujcuXPDp3Tr1m3ixIkN29esWbNly5YmV5JKpZIAn06nKyqciJo1hYWFxcXFW7dura6uznYtrVoS4CsrK7du3ZrtWlqvgoICiSXriouLkwBfWVnZnFGGZsrPz092B7kxi4qKipIAX1VVZXfIoiTA+8i6Ewnw5I5cn4U+iejr16+v15607LHHHlmoCQAAAFpcrgf45NL3119/vV77G2+8EUKod1geAAAAdle5HuCPPvroEMK8efPqXuGZTqcXLlyYl5c3atSo7JUGAAAALSfXA/wBBxzQp0+flStX3nTTTZmJ66655prNmzcPHjy4W7du2S4QAAAAWkKuT2IXQrjgggt+8IMfPPXUU/PmzevZs+fSpUu3bt3auXPnbd4rDgAAAHZLuX4EPoSw3377TZs2bdSoUYWFhW+99Vb79u2/9rWvTZs2rWvXrtkuDQAAAFpIBEfgQwh77rnnhRdemO0qAAAAIGsiOAIPAAAACPAAAAAQAQEeAAAAIiDAAwAAQAQEeAAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIiDAAwAAQAQKsl1ANCoqKiorK0MIVVVV2a6ltautrc12Ca1dRUVFsiPYHbLO7pB16XR63bp1IYSampps19La1dbW2iOyq6qqyu6QI+wLsLtK2b0bacaMGbNmzQohTJky5eijj852OZBNv/vd76ZOnRpC+M///M8TTzwx2+VANs2ZM+e///u/QwgXXHDBxIkTs10OZNPcuXMvvvjiEMI3v/nN888/P9vlAOyGnEIPAAAAERDgAQAAIAICPAAAAERAgAcAAIAICPAAAAAQAQEeAAAAIuA2co31/vvvr1ixIoTQt2/fzp07Z7scyKaVK1f+4x//CCH07t27W7du2S4Hsmn16tVLly4NIfTo0WOvvfbKdjmQTeXl5W+99VYIoXv37j179sx2OQC7IQEeAAAAIuAUegAAAIiAAA8AAAAREOABAAAgAgI8AAAARECABwAAgAgUZLuAnPPee+/dc889y5YtW7lyZZcuXXr16jVmzJgDDzywbp+77rrrrrvu2ubTb7rppr333rtFKoVdq/Hb+apVq+6+++7FixevX7++U6dOgwYNGj9+fMeOHVuqUti1Tj/99PXr12+nw/XXX9+3b99gdGB39+STT954440zZ87cc889Gy5t5FhgyABoDgH+n/zxj3+cPn16dXV1QUFB165dP/rooxUrVixYsGDs2LETJkzIdPvggw+yWCS0jEZu50uXLv3BD36wefPmEEJZWdmqVasef/zx+fPnX3vttW4RTytRWFiYPDA6sBurrq5+8sknP21pI8cCQwZAMwnwnygvL585c2Z1dfXYsWNPO+20goKCmpqap556aubMmffdd1+/fv2GDh2a9Fy+fHkIYerUqQ0Pp7Rp06al64Zdo5Hb+fTp0zdv3jxo0KBJkyaVlZWtXr16ypQpb7311syZM3/4wx+2aMWwa8yaNWub7YsXL77qqqtGjBjRq1evpMXowG6ppqbm73//+z333PPOO+98Wp9GjgWGDIBmcg38J5555pnNmzf3799/4sSJBQUFIYS8vLxjjz123LhxIYS63zovX748Pz+/d+/exQ2kUqms/QKwUzVmO1+yZMnbb7/dpUuXyy67rKysLITQtWvXK664oqioaNGiRWvWrMnqbwA7R8NdoLi4uKKiYsaMGXvvvfd5552X6Wl0YPcze/bsk0466aKLLnr55Zc/rU8jxwJDBkDzCfCfWLZsWQhh2LBh9doHDx4cQnj77beTH9etW7d58+bu3bvn5+e3cIXQYhq5nT/99NMhhC9+8YtFRUWZxg4dOhx++OE1NTXPPvvsLi8UsuSXv/zlpk2bLrvssuLi4qTF6MBuqUePHqNGjfrKV77yla98JXO1SD2NHAsMGQDN5xT6T/Tp06e0tHTAgAH12jds2BBCKCkpSX5MzpDs0aPHokWL5s6dm8x1t++++371q19Nvk6G3UAjt/Ok26GHHlrv6QMGDJg/f/7777/fkjVDi3n66acXLlx42mmnZU6eD0YHdlOjRo0aNWpU8njBggWVlZUN+zRyLDBkADSfAP+J0aNHN2xMp9P33ntvCGHQoEFJy4oVK0IIr7zyyoIFCzLd5s+fP2fOnPPOO+/II49skWJh12rkdr527doQQsPZg5O4Ul5e3kLlQgvaunXrb37zmy5dutQbNYwOtFqNHAsMGQDNJ8Bvz9q1aydPnrxkyZKuXbuefPLJSWPyEa2mpmbChAlDhw7t0qXLe++9d++997788su/+MUv9t13XzcKYjfQyO08uWSxffv29Z7eoUOH8H+f1WA3c//995eXl1944YX15qUzOtBqNXIsMGQANJ9r4Ldt69at99xzz7nnnrtkyZJevXr97Gc/y3xhfPDBB5955pmTJ08+6aSTevTo0bZt2379+v3whz8cOHBgOp2+8847s1s57BQ7tJ03nJ2rtrY2hFBVVdVyFUOLWLt27YMPPrjPPvuMHDmy3iKjA61cI8cCQwZAczgCvw3z5s2bOXNmeXl5SUnJhAkTxowZU3dGokGDBmVOp69r9OjRixcvfuutt1qwUthVGrmdd+nSZfny5Rs2bOjevXvdbsnMEZ07d26BUqElPfHEE+l0+thjj20YQowOtFqNHAsMGQDNJ8D/k3Q6/fOf/3z+/PkFBQUnnXTSiSeeWFpa2sjnJudGrl69ura21u2C2F3V2847d+68fPny9evX1+uWtOyxxx5ZKBF2maqqqieffLKoqGjEiBGNf5bRgd1eI8cCQwZA8zmF/hPV1dVTpkyZP39+z549r7vuugkTJjRM71u3bn3kkUcee+yx5HSvupJrt3r06OHzGbFr/Ha+1157hRBef/31et3eeOONEEK9YywQuxdffHHdunXDhg3L3Jckw+hAa9bIscCQAdB8AvwnnnvuuUWLFh100EFTp07db7/9ttmnuLj48ccfv/nmm1955ZV6i1544YUQQr9+/XZ5obCLNX47P/roo0MI8+bNq6mpyfRJp9MLFy7My8vL3HkIdg9/+MMfQgjDhw9vuMjoQGvWyLHAkAHQfAL8Jx599NEQwrhx41KpVGUDmblVjjvuuBDCtGnTXn311aQlnU7//ve/f/TRR9u1azd+/Phs1Q87USO38wMOOKBPnz4rV6686aabMrMQXXPNNZs3bx48eHC3bt2yVT/sdJs3b37jjTdSqdTBBx+8zQ5GB1qtRo4FhgyA5ks1PNmvdaqurj7ppJOqq6s/rUP37t1vvvnmEEJtbe2111774osvhhDat2/frl271atX19TUlJSUfO973xs6dGjLFQ27TOO383feeecHP/jBli1bSktLe/bsuXTp0q1bt3bu3Pnaa6/t2rVr9n4D2MnmzZt39dVX9+nTZ+rUqdvsYHRgt3f66aevX79+5syZe+65Z71FjRwLDBkAzZR/5ZVXZruGnLBq1ao5c+Zsp0P79u2PP/74EEIqlfriF7/Ys2fP9evXb968eePGjb179x46dOill166//77t1S9sGs1fjvv1KnT8OHDN23atGrVqg8++KBjx44jR4685JJLzCfMbuahhx565513vvSlLx122GHb7GB0YLf3wAMPVFRUHH/88Q0nCWrkWGDIAGgmR+ABAAAgAq6BBwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAERDgAYjVww8/nEqlUqnUsmXLsl3Ljqmurk4qv+GGG7JdCwAQDQEeAAAAIiDAA0BOeO6555LD8kcccUS2awEAcpEADwAAABEQ4AEAACACBdkuAAAIIYQjjzzy448/DiHk5+dnuxYAIBc5Ag/Ato0ePTqVSpWUlKTT6brtr732WnKpdkFBwfr16+suWrZsWbLoO9/5Tt32jRs3Xn311V/4whe6dOnSvn37Qw899Nxzz12yZMmnvfWO9q9nw4YNQ4YMSaVShYWFDz30UN1F69ev/8lPfjJixIgePXqUlJT079//G9/4xvz58xv5yp/pxRdfHDdu3IABAzp16tS+ffuDDz74hBNOmDNnTk1NzWc+Nz8/v0ePHmVlZdOmTUtaLrroomR9vvLKK9t8ypgxY5Jfc/Xq1ZnGZq49ACB31QLAttx4443JSPHiiy/Wba9757Mnnnii7qI77rgjab///vszjYsWLdprr70aDkB5eXlXX311w/dtfP9MOH/33XczjZs2bRo+fHjS/6677qrb/6WXXurcufM2R8NLLrmkOeuqtrY2nU6feOKJnzbaHnfccVVVVZnOVVVVSfuvfvWrui9SWloaQvjpT3+a/PinP/0p6Xb55Zc3fMf169cXFxeHEE444YRM446ubQAgIo7AA7BtxxxzTPLg2Wefrdv+4osvZh4///zzdRclPxYUFBx11FFJyzvvvDNq1KgVK1aEEL7xjW/MmDHj7rvvvvDCC0tKSmpqai699NLrrruu7ivsaP96KioqRo8e/cILL6RSqV//+tennHJKZtGqVatOPPHEtWvXtm3b9t///d/vvPPOhx56aPLkyd27dw8hXHvttU8++eQOr6M6rr/++gceeCCE0Ldv32uuueaBBx544IEHrrrqqr59+4YQHn/88enTp+/oaw4ePLh3794hhAcffLDh0kceeWTr1q0hhIkTJyYtzVx7AECuy/Y3CADkriR8HnXUUXUbe/ToEUJIDmUPGzasYf/hw4dnWr785S+HEPLz8+sek6+trX3zzTf79OkTQigpKXn//feb1r/eEfh0Ov1v//ZvScuMGTPq/S633357suixxx6r275kyZLCwsIQwtlnn72j66euoUOHhhD69eu3bt26uu0ff/zxPvvsE0L4+te/nmls5BH42tra//iP/0h6vvnmm/XecfTo0ckfoqKiImnZ0bUNAMTFEXgAPtVXv/rVEMJLL71UUVGRtCxbtuz9998PIXzve98LISxcuHDLli3Jog8++ODtt98OIRx77LFJy+uvv/6HP/whhHDBBRfUO728X79+t9xySwhh06ZNM2fObFr/uqqrq0899dRHH300hPCLX/ziu9/9br0Of/7zn0MIRUVFmfIS+++//7nnnjt69OguXbrs0Mqpq7KycunSpWVlZd/+9rc7dOhQd1HHjh1HjRoVQvj73//ehFc++eSTkwf1LubfsGFDcsrAKaecUlRUFJq39gCAKAjwAHyq5Cz6LVu2vPzyy0lLcv583759k7PT0+n0ggULkkXPPfdc8iCJ/SGERx55JHnw/e9/v+GLjxw5sn///iGEl156qWn9M6qrq7/1rW/dd999IYTTTz89+XKhniSfp9Pp3//+9/UWTZs27cEHH5wyZco2V0JjFBYWrly5sry8/OKLL264dN26dU1+5cMPPzw5eF7vLPo5c+Yk589PmDAhaWny2gMAYiHAA/CpRo4cmRzdzVwG/8ILL4QQhg8f3q9fv+Tq8cxl8MmDz33uc5///OeTloULF4YQ9t577549e27z9QcMGBBC+J//+Z+m9c+YNGnSb37zm+TxY489VndK9ozRo0cnp8qPGzfusMMOu+GGG5p2SLyR0un0m2++eccdd5x66qn1Dp7vqOQg/J/+9KcPPvgg05h8DdGvX78hQ4YkLU1eewBALAR4AD5VSUnJsGHDQghz585NWpIj8EceeWQI4V//9V9DnQCfHIE/5phjUqlU0vLhhx+GEJYvX576FHfeeWcIYcOGDU3rn/HYY4/l5eWNHTs2hFBeXn7ZZZc1/F0GDBhw++23d+rUKYSwePHi888/v2/fvt27dz/jjDMeeuiherfKa5oPP/xw6tSpxx13XJ8+fdq1a3fggQd+85vfTGbCb87Ljhs3LoRQW1v78MMPJy3r169Pzp/PTF8XmrH2AIBYCPAAbE9yPvz8+fMrKirWrl37xhtvhBCS+7QlAX7+/PnpdHr16tV/+9vfQp0L4EMImzZtasxbbNq0KblN+o72z0ilUrNmzbr33nsPP/zwEMJtt92WObG/rlNPPXXJkiXXXXfd8OHD8/PzQwgrV6787W9/O2bMmEMPPTQ5gt1kv/3tb/v27fv973//iSeeeOedd8rKyo488sizzjpr1qxZSQJvskMPPfSAAw4Idc6inzNnTkVFRSqVOv300zPdmrz2AIBYCPAAbE9yGfzWrVsXLFgwb9682trabt267b///iGEESNGhBC2bNmyaNGiF154oba2Ni8vL5kIPdGxY8cQwmGHHfbZU6rm5TWhf8b06dO/9a1v5eXl3XDDDalUqra29rzzzttmTN1jjz0uuuii559/fs2aNY8//vgll1yy5557hhD+93//d/To0R9//HHT1tLChQsnTpy4cePGffbZ58Ybb1y6dOlHH330wgsvzJo166yzzkoO+zdHchb9s88+m1SYnD8/cuTIZH77RJPXHgAQC0M4ANtz6KGHJhH32WefTc6fTw6/hxAOPPDAz33ucyGE559/Pjl//ogjjqg7l3u/fv1CCG+88UZ1dXVj3mtH+2dk7h43ZMiQM888M4SwePHiGTNm1O2zbt26devWZebM79ix47HHHnvNNde89957l19+eQhhxYoVmXn4dtTNN99cU1NTVFT0/PPPn3vuucnN2zOa/L1ARhLgKysrH3/88fXr1z/11FPhn8+fD81YewBALAR4AD7DV77ylRDC3Llz6wX4UOcy+ORK+Mz884mjjjoqhLB169Zkfvh6Nm3a9PnPf37fffe96qqrmtZ/m6ZMmZIc8f7hD39Ydza7Aw44oKysLLl3el0FBQWZidw/+uij7bzydrzzzjshhP33379Xr171FlVVVWVmEGiy/v37H3zwwSGEBx988OGHH66oqCgpKal3r7idsvYAgFwmwAPwGZJYvmDBgkWLFoX/m8EukQnwr732WvjnC+BDCGPGjElmqp80adKbb75Zd1Ftbe33v//9V1999d133z3hhBOa1n+b9thjj5/97GehwWx2hx12WAhh7ty5DadhzyTewYMHf9bK2LZ99903hLBs2bJ6U8TV1NScddZZK1euDI2+Rv3TJAfhn3jiidmzZ4cQxo4dW1paWrfDTll7AEAuK8h2AQDkui9/+ct5eXkVFRUhhNLS0sxd4sL/XQafRNPOnTsfccQRdZ9YVFR08803f/3rX1+1atWAAQO+853vDB48uGvXru++++4dd9yRzDN34YUX/su//EvT+n+a7373u7Nmzfrzn/982223ffvb3x46dGgIYcKECU888UQ6nR41atR55503cODAtm3brlix4pFHHnnggQdCCF/4whc+85U/zdFHH/3rX/9648aNxxxzzKWXXtqnT5/y8vJXX31aBPCoAAACV0lEQVR1+vTpS5YsKS0t3bhx49KlS3/0ox+dcMIJ9dZSI40bN+7KK6/ctGnT008/Herc/j1jZ609ACB3feZUNwAwaNCgZNT48pe/XG9R165dk0WnnHLKNp87e/bs4uLihgNQKpU655xzampqmtw/c3/1d999t96LzJ8/P7mb3cCBA6urq5PG884779NGw0MOOWTVqlXNWUXJEfKGNZ933nmLFi0qKipKWkaPHl1bW1tVVZX8+Ktf/aruiyQH1X/6059u8y0OOeSQ5Fk9evTI/FL17OjaBgAi4hR6AD5b5uL2uhfAJ5Kz6EODC+AzzjjjjCVLllx88cX9+/dv3759+/btBw4cOHHixFdfffWmm27K3DS+yf23aejQoQ1ns7vhhhteeumlk08+ecCAAZ06dSotLT344IOPP/74+++//y9/+Uvmm4imufvuu++6664RI0bsvffexcXF++2339lnn71w4cIbbrjh8MMPf/rpp8eOHXvCCSd87Wtfa/JbZG5Hd8YZZ3zaTPI7Ze0BALkpVVtbm+0aAAAAgM/gCDwAAABEQIAHAACACJiFHgA+8eijj9a7B9tnatOmzfnnn7+L6gEAyHANPAB8Yvz48XffffcOPaWsrKy8vHwX1QMAkCHAAwAAQARcAw8AAAAREOABAAAgAgI8AAAARECABwAAgAgI8AAAABABAR4AAAAiIMADAABABAR4AAAAiIAADwAAABEQ4AEAACACAjwAAABEQIAHAACACAjwAAAAEAEBHgAAACIgwAMAAEAEBHgAAACIgAAPAAAAEfh/Vai9JC4EDk4AAAAASUVORK5CYII=" width="672" /></p>
 <pre class="r"><code># In this case, faceted histograms is probably better
 

-h_facet = df %&gt;% ggplot(aes(x=weeks_alive, # What to summarize
+h_facet = df %&gt;% ggplot(aes(x=weeks_alive # What to summarize

                       )) + geom_histogram(bins = 5) + facet_grid(~dose)
 
 h_facet</code></pre>
                       )) + geom_histogram(bins = 5) + facet_grid(~dose)
 
 h_facet</code></pre>


@@ -415,15 +415,23 @@ library(ggridges) </code></pre>
 ## 
 ##     scale_discrete_manual</code></pre>
 <pre class="r"><code>ridge_plot = df %&gt;% ggplot(aes(x=weeks_alive, y = dose)) + 
 ## 
 ##     scale_discrete_manual</code></pre>
 <pre class="r"><code>ridge_plot = df %&gt;% ggplot(aes(x=weeks_alive, y = dose)) + 

-  geom_density_ridges(jittered_points = T)
+  geom_density_ridges(jittered_points = T, fill = 'orange') + theme_minimal()

 ridge_plot</code></pre>
 <pre><code>## Picking joint bandwidth of 10.5</code></pre>
 ridge_plot</code></pre>
 <pre><code>## Picking joint bandwidth of 10.5</code></pre>

-<p><img 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xugK7l+Ygpw+w/iX8jgI0B5IkBZBKguBQDolduBJiLPbt/VPzHuVntgR4vmtyzxsxGf6XmJwAYJr2TQEaA8EaAsAlSX/Af0+25A3cyAtPNO4JCiOe+mBvTj6bFFflbdy/kTm4AaH8p4LYOMAOWJAGURoLrkN6A3hgExaQFZ572SK39gfkA/n9HR8cNtDnL/xN3AA1JezOAiQHkiQFkEqC75DegcY11xU0DSeS89vATQ6r5PrC+3wwsmzXvCj+8/XgW2S1JezaAiQHkiQFkEqC75C+gWwLY4MOe8574b3evU9hIy7rP9DTmvZxARoDwRoCwCVJf8BDQzDJggFLOeboA+IPSWfXeuPrBX0gsaeAQoTwQoiwDVJf8AvRQNDBSLWbIboMPF3rTvHgXivpD1kgYaAcoTAcoiQHXJL0DfqgMkXxBr2UA3QH3th887ckzwYaftgNnSXtMAI0B5IkBZBKgu+QPoJ02B5rliJStY4QZomtfvSOseBVTpwb+XnaMnwhH5K3mvakARoDwRoCwCVJf8AJTNoFz/KZGMsfIal/iZ7PUblkY6vxrFd6IRZ0OAfhJf1kAiQHkiQFkEqC7xA3r9PqCGhMnl04pn9EzwivP+iGJfI0Wug2YZ95ot84X1PwKUJwKURYDqEj+gD8uaQTmjC5tUPqK/9ysndXN9wu8p8l5nAy30OiWeAOWJAGURoLrEDegqwCb0Q7RbJ1alrs3y/qW8KBeg0RcF3ueFJsBGqS+tvxGgPBGgLAJUl3gBPWSsJU4T6BdvGW77mMBxzQ7+NgGxn8p9cf2LAOWJAGURoLrECeh5Y0VwmEi+eDsuDdCCLsBkya+uXxGgPBGgLAJUl/gAfa0mcLeSGZTzqrj8rCbyI3xBwZEIRLwm+/X1IwKUJwKURYDqEheg7zcC7pB7dWKf3e0CtLfgm04xblL6C8wfAcoTAcoiQHWJB9Av7wAa+9jJI72DJXuRqhwWfNPZscAp+S8xbwQoTwQoiwDVJQ5Ar94D1D4mGC/+VhV9iI9eJ/ym5wDN9TmUiQDliQBlEaC6VDGgN8cYePHO9i6j9D4xQEzfI+Jv+WITYJMZrzJXBChPBCiLANWligFdCISLX/nzD7oTJ8TuPypuo7Fu/ZkZLzNPBChPBCiLANWlCgHdYXx6TpWilw4lA9NNeZ05IkB5IkBZBKguVQToU+HAWNXMySs9Qp9ZmQhQnghQFgGqSxUA+lxVoL+SA0BNyg4MMOmlrigClCcClEWA6lL5gP66LtBR8FzGesVmZTpn1otdfgQoTwQoiwDVpXIBZTMo3yZ6BmXNmg60uW7ay11eBChPBCiLANWl8gD95i4g/qRq4SSX10iXC8wRoDwRoCwCVJfKAfRaf6DGIdXASW+18VviKxNfcZ8RoDwRoCwCVJfKAXS8rBmUNastsMjEV9xnBChPBCiLANUl34AuA8JWqcbNjPbZEP2uma+5jwhQnghQFgGqSz4B3QtgtmrbzKkvMNrUF917BChPBCiLANUlX4BmRQCjVMtmUk9Gw/Zzc192bxGgPBGgLAJUl3wA+nw1oE8oH0BfqgeBria/7l4iQHkiQFkEqC55B/TtusBdIX0Afaly6wCZZr/yZSJAeSJAWQSoLnkF9OMmwG05qlkzsQXArVdNf+09IkB5IkBZBKgueQP0q3ZA/VA/gL5U+c2Ax8x/8UtHgPJEgLIIUF3yAugPfYCa6apNM7fNQC3V1zgmQHkiQFkEqC6VBfTmaKDKLtWimV1n9RODEqA8EaAsAlSXygI6Dwhfq9oz02MTg76lYgBcEaA8EaAsAlSXygC6CbCF7gz0vhsCDFIyAiURoDwRoCwCVJc8AT0aBoxXjZmKno4BLqoZg6IIUJ4IUBYBqksegJ6LMlbEVFumpilAuxuKRsERAcoTAcoiQHWpNKAv1wC6y7kApvadrw8cUDUMLAKUJwKURYDqUilAf1MPSDqnWjJVLQcafqtsIAhQvghQFgGqS+6Afnwr0DRbtWPKym8NrFQ3EgQoVwQoiwDVJTdA2QlI8SdUM6awXTbEfKRuKAhQnghQFgGqSy5Ar/aqfCcgeWS8AuPVDQUByhMByiJAdakE0BvDgOhKdwJS6TKiEP6qsqEgQHkiQFkEqC6VADoNiFivWjDVjQJ6KxsKApQnApRFgOpSMaArANsC1X4pL6c2cFrVUBCgPBGgLAJUl4oA3W0DpqrmS4NmAy2vKRoKApQnApRFgOqSE9DMcGCkarx06EIisF3RUBCgPBGgLAJUlxyA5kUBfSvNFZDKbT1Q9ws1Q0GA8kSAsghQXWKAvlQDSL6gmi5NuguYp2YoCFCeCFAWAapLBqBvxwGtK+0JnJ49EYaod5QMBQHKEwHKIkB16W+FHyQCTU+rdkufBgJ2JUNBgPJEgLIIUF362/+0BupX5hM4PTtVDXhGxVAQoDwRoCwCVJdudANqHVGNllZNAtqrmBiUAOWJAGURoJp0tT8Qs081WXp1rj7whIKxIEB5IkBZBKge3UgBoraqFku3lgMJ35g/GAQoTwQoiwDVo8lA+GrVXmkXmxj0EfMHgwDliQBlEaBatAiwLVTNlYbtsaHqe6aPBgHKEwHKIkB1aAOAaaqx0rJ7gdGmDwcByhMByiJANWifDRirmio9ezIatufNHg8ClCcClEWAqo9NIDJEtVS6Ng5IvmnygBCgPBGgLAJUeblRQB+aQMRH5+KAdJNHhADliQBlEaCqu1QN6Jyn2il9ewRoZPI1jglQnghQFgGquF/EAnfSBCK+y28FLDV3TAhQnghQFgGqtrfqAS1yVCOldbttqPquqYNCgPJEgLIIUKX9rjFwS5ZqojTvXiDF1FEhQHkiQFkEqMo+vg1oQBMwVdCJaNhMnZWJAOWJAGURoAq70gaoc0y1T/o3EWhn5qxMBChPBCiLAFXX152AmgdV62SBzjUA9po4MAQoTwQoiwBV1nc9gJjHVeNkiVYBcSZeYI4A5YkAZRGgqrraD4jeoZomi9QOmGXe0BCgPBGgLAJUUdftQORG1TBZpQPhiHzTtLEhQHkiQFkEqJpuPEATgPqT8eumj2mDQ4DyRICyCFAl3ZwM2B5RrZKFOl0TeNKs0SFAeSJAWQSokuYYfs5XjZKlmgvc8r1Jo0OA8kSAsghQFS0BMF01SdYq/zZghUnDQ4DyRICyCFAFrTL8nKhaJKu1w4aql80ZHwKUJwKURYCa3ybDzwdUe2S97gWGmDNABChPBCiLADW9nTYgRbVGFuxkNeCsKSNEgPJEgLIIULNLCwMG0QT0ATQVaHHVjCEiQHkiQFkEqMmlhwP9yc9AyksE1pgxRgQoTwQoiwA1t+MRQG/yM7A2A9XMmFqZAOWJAGURoKZ2MhLocUE1RJatF2A3YZQIUJ4IUBYBamZZUUAXuoBcwJ2oCuTIHyYClCcClEWAmlhOFaAT+RlEU4Hb5O9HIkB5IkBZBKh5nY0G7qILcAbThabAo9IHigDliQBlEaCmlWd8/mxHfgbXNhui35E9UgQoTwQoiwA1q4vVgDvPqgbI8t0L9JM9VAQoTwQoiwA1qQLDzza5qvmxfqdqAJmSx4oA5YkAZRGg5vRMDNA6R7U+Jpe/Y9rEVNFXHZ0LNPxK7mARoDwRoCwC1JSerQ60qmx+bm4EI9vdp4Tean4rYKbc0SJAeSJAWQSoGTE/W54R6oj+rQyDswYnhd5uWjjCX5I6XAQoTwQoiwA1oWeYn9lCFdG/UzEorrvYWx4BtL8uc7wIUJ4IUBYBKj+2/bPS+VnwcImfsB0Xesu58cBmmQNGgPJEgLIIUOnlV0o/Czq6AMUisTe9Fqguc1IRApQnApRFgMqOHf/ZqrJt/zS6zQ3QhwXfdg/gfolDRoDyRICyCFDJnTf8bF0J/Sy40w3QuYJv+4SxUn9c3pgRoDwRoCwCVG65VSvh8Z+OhrkBukf0jc8B6l+RNmgEKE8EKIsAlVp2lUp7/tE+W4mfTYTPIJ3fBhgnbdQIUJ4IUBYBKrNTUUDbyumn2ypo5A7xN34oCrZzsoaNAOWJAGURoBLLNPxsX2nnD8kf4VwHjd0g49YnGCu230oaNwKUJwKURYDK60gE0DHQ+evOpx+0/KrrwZHtWnSdKWcLcN6twGxJA0eA8kSAsghQaaWFA53PB8bDoR7GymtEx11i0Qmp9oQj/Dk5I0eA8kSAsghQWe0MA7oHeP2OtVWcWw/DZotFJ6QaAbSUc3kPApQnApRFgEpqow24J8Drbx6LKjkJcqtYdEKpc42AhVLGjgDliQBlEaByetTQr//FAGkY4DqCMkmoOaHVdhsiXpQxeAQoTwQoiwCV0gLDvsEBH/1Yy20ejtMiyQmx7EBrGR/iCVCeCFAWASqhm9MM+kYE7OdZt3N4sE+kOCFWboKcD/EEKE8EKIsAFd/1sQZ8DwXuwjl3QNOEcROCsQ/xz4sfQAKUJwKURYAK76rxydI2NRgX6rr8jKiM85DwNwxo8Z3wESRAeSJAWQSo6L691/AzuOmH7C5AO4qBJlRje+JnCR9CApQnApRFgAru887GauPS4Fg4UXI1jIjHxUATsu0KQ9hF0WNIgPJEgLIIULF9nARErQmWhW01nH5GLReBTEg3Bkj8UvAgEqA8EaAsAlRol5sBVTcHz8KTQ2oDMX0OBX9LoR47J36s4FEkQHkiQFkEqMjeSABqCpo9OOuU8Fk0Q7InooBMscNIgPJEgLIIUIE9VweIO6halMrWVKDO+0LHkQDliQBlEaDiyqkGNBJ7BV+q4vLbAvfcEDmQBChPBCiLABXWoUig+SnVnFTCMmsAG0SOJAHKEwHKIkBFxaZfalspLx+nvBVA1EsCh5IA5YkAZRGgYro5D8DdAU6fTAVZP2Pd/xtxg0mA8kSAsghQIf0w2vBzUKDT11FBlttQ6EU6CVCeCFAWASqir/oYfo5TzUglbk8EcETYcBKgPBGgLAJUQB+1BcLnq0akUjcFqPFrUeNJgPJEgLII0OD7ZSJQJejTN6lgyk8G2n4vaEAJUJ4IUBYBGnQFtYFYun6m4rLigMmCRpQA5YkAZRGgwXasCpBwWLUf1PZw4KiYISVAeSJAWQRokK0NA1rS4fMaNAmo/ishY0qA8kSAsgjQoLo2EUCXs6rtoAqcm0FbCzkalADliQBlEaDB9AU7fMlOh3/qUVY8MFrEsBKgPBGgLAI0iC63BsKmq3aDKm5PJLBVwLgSoDwRoCwCNPB+Vg+IXq1aDcrVXCCyIPiBJUB5IkBZBGjAHa0C1KFrFmlVfyA++LlBCVCeCFAWARpgN5fagGZPqhaDKtW5FkDHq8GOLQHKEwHKIkAD69thALrmqgaD8igzFngo2MElQHkiQFkEaEC9387wcxRdtEi/tkYEvyOJAOWJAGURoIF0qT4QuUi1FZS3ZgAROcENLwHKEwHKIkADaF8UUHunaiko7w0AYt8ManwJUJ4IUBYB6nc/TDU+vt+WqdoJykd5dwJNfh/MCBOgPBGgLALU3z7uZvjZ65xqJiifZSUAnYPZFU+A8kSAsghQP3u+IRD2MO0+0rlD1YGRNwMfYwKUJwKURYD6194qQI0NqoWgym9TBLAo8EEmQHkiQFkEqD99P874+N70iGofqIpKNcZpb8DDTIDyRICyCFA/+m1btvmTjp63QA8AEacCHWcClCcClEWA8neqlrFU0uRLlii/DxB9KcCBJkB5IkBZBChv1+bbgDo7VMtA8ZXXAaj9WmBDTYDyRICyCFDO3utifHxv+5RqFyjeclsACZcDGmsClCcClEWA8nW6DmAbQ3PPW6hTjYBmHwUy2AQoTwQoiwDl6Ye5xsf3mutVk0D5VUYc0PrTAIabAOWJAGURoBz9poPx8b0NnbxptQ7GAnd94f94E6A8EaAsArTiDtVgH98vqOaA8rt9MUDyV34POAHKEwHKIkAr6stRxupnnU2qLaACaWdVoKvflzomQHkiQFkEaAU9e4vhZ/KpQJfg48MaRVVtPbNyzj1yZuHgfqM3KZ03YFs00N1fQQlQnghQFgFabj8sDgeiZgZswCpjDYjVJEOkChYpNcbx3JsdUPkgNlcxBP3av1EnQHkiQFkEaHm9zs7dbPpEwEvv7ggUlah6HTQ7fdf65UYrNm5PzzHlHqcXP/cah0y5Px9tMgRN/tyvYSdAeSJAWQSo766vM5Y92/DzgS+8SShpmjgS/Ct7x9yhHRpFwb3oxM4jFqXJ3S12LLLk7pKk3lFFMUHb+nU0EwHKEwHKIkB99nZnY9mP3xzEonvC5jKrpTAQ/OjIgv6JNviqyh0jN8pbMR7rdk8Hpd0LT9uqAbf7c7V4ApQnApRFgProxsZoY8nvmx3MkrvFzZAYURzwlruif72Se7fVadHp3qGjRj04atSQvp1uq+1CNHmupNNT27s9+VQ5d8Hb7hpA4tv8Y0+A8kSAsghQ772RbCz3tdcEt+BucDMkSowFnGUtSC7+0N6ox4SVhzw3Q5zbv2xUhxrObwi7c4GMjaK3uT35hyXcvj+lGb8x4l7kHnwClCcClEWAeuuHFYyf3llBLrcH3QxpJEQCrs6v6OLceWVrnrKmvOeQPrub8yiB6L7iZ5nSaA20oOBIPFA9l3f4CVCeCFAWAeql5+4wFvo6q4NebPPjXYYMFcAAVweGOtcsY3ot5fgFkLdxkPPzfPMleWIfhz7bQFknmgKR+zjHnwDliQBlEaBl+mJqmLHyNiCorZ9FzXdtazTnTPoLy1o79ey3ntvDi+t7OXaYx88SukfpSMkhXGgj8nYDLJsdEbGI70pzBChPBCiLAPXsSH1jSUsIZue7q/z7igiJCH51lqPsyXGOjZodl/tJYda0BPaDdWaIJHRqsZ/V1a+AGp2/x3goKd/zvAUIUJ4IUBYBWrq3ejPuRguDZAHj2NZun6jbK6cTI6oxruLGBbKym/9oK8cPLxZ44uVc5wbWpmnibjKY8scYD6bThxxvAgKUJwKURYC6981CtvPojsBPPfLSwbWbzJjHPnOQ43P4HSsCnvR5c1vHttBt4h7T6bkDe6WsV3oufKkWRBifLZ6v+G1AgPJEgLIIUFc3Dxt7E8UAACAASURBVDc0BKmZqs8Sz92JwYzPsB67g7qVrWwt1NbrhKAHpV9bY4Goii93TIDyRICyCNCSXmRXPbINDPbYJQVlDWcrzhEDgr5gff5ytsWh2gwL/grhK+NW4/lNvFrBO4EA5YkAZRGgRb0/NsxYtlrtVb2I+9+5iWzWo4iBx8XcGDsBq5UWu31klNvLeHrtKrjWHAHKEwHKIkAdfbWY7fCoI3IXiknlL2Z73sP7C5svL7ObcXtRk0L2+nnTwoHY4+W+GwhQnghQFgFq9MN2dtZ4lQdyVS/c/re9Odvz003ohHGr6xg32fKwyJvUqe3s6U38tpz3AwHKEwHKIkB/vHG4Gdv42ceCF417sheba6nVTsE3m92fnd65UPCtatNT7CzTFi/7fkcQoDwRoKxKD+jNk+y8TbR7XPVi7X95k9n2yvjlErY7rK9r3HIfC66Rc5U/KQKIWnXN13uCAOWJAGVVdkCz72J8NtugepkOoM2JbD1xgpwZPbPYIQmN08Tc2MXtM6csD/iyUjLaww5Y6/RLH28KApQnApRVuQE93dExUdIy6+07KjjVh3167yltu0P+zEigyhIRN7WmgeNs1sE6rdDm3me8fFVWe18JJUB5IkBZlRjQG5mOU2/qL7TgFd/zU9mUS4liztj30ePs/Hh78C/O3OI58VvqJGjBOrYvKcnrJKEEKE8EKKvSAno17XbHFETzBc/iZkqH72RHDUyU/NCzuxr30jbYEwvcZmVKEfKwRJXd35A9fNoXZd8bBChPBCirkgL6xTq2EQwJC6zI54VJ7MSjjsek31H+pDDjNQryoPphrmlBq5wV87hEtZGdd1UvrcwcdwQoTwQoq1IC+tuZjjmHmzxiyaPF97OLZcQuNeW+1sUAMeuDuommLkAhdZNDAJ1NYavHnZ7zeH8QoDwRoKzKB+jNc4PDHbP8rrXgrqOCgrwHjWXe1tesM/YPJRgfc+cGcwu13AA1R31/OsC2g9tGlD63kwDliQBlVTZAv9zWki3J4UHOW6SsfWyFLv4x8+4wiwkzOojfNQlugK4V97iEtZydhRY10/3C8QQoTwQoq3IB+sIENu8GYlKEnTlubs7Vz0EyLqLp+z77Gq/YvYFvK+7u8tOm5cleZ8ex8xGqL/m85G1CgPJEgLIqEaCfbW3jWIybzNHqcBo/eoJt/WywxeR7zWeXh+sY8Gu22gVoO5EPS2AnBrKNOrGPfFb0TiFAeSJAWZUF0B9ODXVcKD2i51bVi2ugXZwUaazEqTgcfW4Y0Crgra5div2sekDkgxJaek92sGr1ec4LfhCgPBGgrEoB6M1L0+o4FuHGU7Q6odCvjrLrbcar2Y+9yvjlc0ugE9Wf7eX0M078tecFltaVERr10Bs/EqB8EaCsSgDoSwsSHQtwdL/tqpfSIJrPNtT1O6Po3rfGAA0DnrF5+8CWt3aZrfuGk7RebEptW+9TNwhQnghQVogDevNn85o49Axrv0izo7j96qlk4znUNuXayN7bF2us/h5Vd/+mdGSgYzNP4pYvTX3/ls2DKgJU30IZ0O+zHy46hqbltJOqF86gWm3whe5Pq3wIh+oaH8LTVT4CM3pqrOOw1cjBp3zOdWdGHlQRoPoWsoC+u/v+ak49m0+Sf9Kj1HIHGM+imur5jY/EA3WEznyvZecXOw4URr2ZLyl4KxflQRUBqm8hCeg3WTNbFh152HqKxfUsKNjFVqOT1B+5mtGgUghaULBvkONoYdy2+BeK3tEeVBGg+hZygH6Xt7RLpFPP6G6p1t3pXtyFceFAxMM6nHaa2dAQNOQ/xbPOLm7vnIPv1vnP3lDwjvagigDVt5AC9ErWwi5RRQcdJg577Lzq5VBAjoOXEvepfhjOnjTWhesGffF5a5T5cDPnG6numMOfVvAGFJ4HVQSovoUKoNde2TX29uKZe2N7pgq5SLr6FlYFbHY5V+0IoExD0Hj1GxNMKn1CkaFhHRaeLe8qnsLzoIoA1bcQAPTqm8fndY4uPt0ltvuMNB0+74ooqwc7eCm42eTEdjweSAj0iHoLdmxa26L5oKO6LMy+YtZb2oMq8YB+arefK/7zRPv3Ad0GAcqyNqCXs9eOuiOy5FzrRn3nHQwVPI02xxnPqYtem3GPGo8pUa+HJLkzKwfEFU+GcvvYHT+/asL72oMqKYCO/KHozwRoMFkU0O9fy1w15q7qrnkqanYcu1bpcZLCyxthA6KDmopTRum1gNtUnRClqvTZPWKL32lRd47dfF7yVlEPqqQAan+06M8EaDBZDdAPLx1eNe7uhjYXnYhLHvNo6G2XO8i2vzXXcJ93Wg0gSZutsqaVf3Bun0Zub7oek7fmXL4u6V3uQZUMQCeOtz/v/DMBGkzWAPTaBy+c2r7ogZ63RsG96q0Hzths1tzsppY/23imYaO1vGLTnqpAFwteyVRAWevHda7j9gaMuq3vtA3Hn/9Q9PvdgyoZgM5+zT72j44/FwH6/q65o5ccdPzxFXv+PzOnjpyV8TfHN/wpfcno6Xu/JkC9pS+gN37/q0tP7Vs1a3Sf1vFhKF1Eo+SUuVueUr08SeupTsaTjNd14r1NkUDfENrW7GenNk7pc1vp3+RRTbqmzFz/RM6rH/wg5L1vBqCFj9m3uAGaZ7cPnzbUPvKyA9DcRUtycmfal/7H+NuHE+3Dpw+3p7xOgHpJG0C/+uSdV57Nydj72LJZDw3p1a5JLLxkq9u6z5jUzRmWvBgcf2vYk++Vrfph+GxlmG6XKDa9i0fWTrnvzjhbmbdobLNO/cdMX7Z5f+a5F9/+6JvAFgZTAP1xjP2XJYBeGz40+1+F/9hln/pvBuiDuww6/zHJ/kFh4f+baD/+/wr/ddI+4hoBWjb5gH5xxei377zzzi9efPH5s2fPHs/I2L1725o1C1NnTBw7dNA9Xdu1aFI7xhuXJUU3Trpn2LRlOzK0/EgrutyBxlOOWaz6YZTXPAOOqaofhA6dP7hu9sherePCfb11wwYHciaTKYAWPmuf+PdiQHfbn3B8YYH9GQboqD+zv6TZLxUWZtt3Or6yz37Q+bNXZjv70//53ydj+pbUr1+/vtYv8OfQs0Pp7ry1uFtqO4v29a7iKCK2cevOfVMmLVizK8PK89D53x62q6KN5nvFJhifBh5R/SA0Kv/E3rXzxg3unpQY67lS+mUAzpgD6E/L7PuLAZ1s/9bxhRfs2xigzj30J5imy+yfFP3IHOfPXu7o7M8B3O/4IEigfBZZvV7D5kkdevS3j5k0d+na7WmZuk++K60L4yKM3x4TtN9GMcR4lJtUPwg9yzq8a/3S2RNHDeyVnNS8wXwB3EkCtPDbEfb3nID+39Dh/3V84TP7QgboPsdfHICOt+87yNprH+P82WAAPVx2mwdVuoii1c/aTZqxkthLfbextj5g9OjR42bMmLF8+fIt23YcPnwy59wLL/zmnStf/CGQgQjVrnQ1XsLGe1UbUHH5dwOx76t+uSpJsgA1Pp5P/5cD0Bv2ic4v/GgfzwBNd/yFAfpve0lDf3L867++d/aHPwbQp5dLuvLF55et3+cfcXxT8fe8e/ny+19661ogr6Ww/lFY+L9KH4CQ/lH4x//ZU834aNwy1QqzP59rAzT80PNJGKsxCl460f1X9dvJLED/M8+eWXoN9HP7vNKAFj5k/6f326CdSCxt9sIHEf914XXub4Uf9iteix9kgU0YWYnAHZ7XvvhPSCwUoX9NpCJACz8dOuxKqW2gL7Njm0oBusT+meMv//7DXwjQshGguvS3M26HaLe0wI6zjNpAb4/jHglQIZkGaOFRe+o45174A46/L7Jf9AA0077X8ZcsY2WVAC0TAapJn40qtSl5lGoeOdobDYwr/SwIUCGZB+g/p9rtDNAfhg8995/Cf+61T/mXB6B/HTs069+FP702IsXjjE8ClEWA6lFWfUPNui5Aq1lhNui1YcCaUk+DABWSeYAW/tYJaOF5u33krGH28WzHYClAC996wD5iznj70F943AYByiJAdeiLhwwzq8xMcFsF3alaR55mAGGZ7k+EABWSiYAW7nECWvjezjmjFx107L8qBvTpeb9h/7m+b8GoGTu/8bwNApRFgGpQDjt2vlV6QVU3QB9VjSNXdqDqz92eCQEqJOmAiogAZRGgyvtivA2InHSxoMDtIzw2q7aRq4vJQP13Xc+FABWSB1UEqL4RoKo7zVY/WxxgHHV2+RlukVn6cpoCSV+XPBkCVEgeVBGg+kaAqu2zBwwtIyc4p9dc6QK0q2IYuTteGxhUMmEGASokD6oIUH0jQJWWGV+y+mmUX7IKWuOoUhX9aXcUkFr8fAhQIXlQRYDqGwGqsA8GG1ZGPeya3f1sL6efDTW5EDxXS23AwaJnRIAKyYMqAlTfCFBl3dxd07CyTenLHu2yJ7XsudBa856OBao853xOBKiQPKgiQPWNAFXVm2zipWqzQuDSGPndgQbvO54UASokD6oIUH0jQNX0/RJ2VZ0umarxE1JuM+Cu79jTIkCF5EEVAapvBKiScm41+KyzQrV8ojpeCxjJnhcBKiQPqghQfSNAFfT+cINP2/36XjXO73ZEAGt/JEAF5UEVAapvBKjpXdtY3fCz6Q7V6AktFQg/TYAKyoMqAlTfCFCzu9Da4DN6yoWKUbJUQ4CavyJAxeRBFQGqbwSoub07jB3n2SM0dh65d6Et0OILAlRIHlQRoPpGgJrZdyvYhEuNN6rWTkZP1wcG/F9ILBQEKE8EKIsANa+bRxMNPqs+bK3D5LnbHw2sComFggDliQBlEaCm9Vwy2/fe54Rq6KS13AbbuVBYKAhQnghQFgFqUpdH2Ni8IZaYaD7QRgE1PlD9QguIAOWJAGURoKZ0ZV4Vg8+4RapO3Mzft2x5mvQ7v9jR+BXxuerXOvgIUJ4IUBYBakLfr6vNDl0ap+xaxYvjHXM8rZR9P9kNgf43Kn5BNI8A5YkAZRGg0ru+j805HzZA3cbPEcXTjI6XfU8HooFFql/woCNAeSJAWQSo5G5mtGB0JT8h2y7frXFNdL9F9n2ttMH2pOrXPNgIUJ4IUBYBKren72Rutd4mG67yau0CtIP0OxsFVH9T9aseZAQoTwQoiwCV2ZkOTK0mq6WzVV45NhegEdLPIL14F9D8S9UvfHARoDwRoCwCVF5nkh27bpYonjM53e1iyZC/ITarAXD/TdWvfVARoDwRoCwCVFI3T93FwIpPVT5rSKY7oKfl39/+KsAK1S9/UBGgPBGgLAJUSjeOtmFc1ZurwWmbF2q4/Kxrxh0uBcJOqx6BYCJAeSJAWQSohK7uvY1p1WCeBnwa3e8CdIQpdzgMqPUb1YMQRAQoTwQoiwAV3pfrGjCrGi1U/uG9qJO1iv2sZ8IneKMLdwJ3fKN6HAKPAOWJAGURoIJ7bx67WDGaLdPocpv76zv9bHzIpDs8FWes7KoeicAjQHkiQFkEqNBeHBXJpEparxGfRrnTkurUaTv7nGl3uDsKeEz1YAQcAcoTAcoiQMV1PbMb09PWfbdpUGlbKhCRp3pAAo0A5YkAZRGgorqy/hbGZ/TgI6rx0qKBQNy7qsckwAhQnghQFgEqplcnVHUcJjQpywOS3AMbl8x9eNKYCZNmL92Q5vnV0O18S6DDVdXDElgEKE8EKIsAFdDV9M6OnTQtl7oft3Qxbe7ApNooXc07Bs7eq8vueall1gLGKx2WgCNAeSJAWQRo0P1mfl1GY2Qf902fB6Ykx8BX0R0m71cGm2ltjQB2KxyXwCNAeSJAWQRocF3N6B3GTIybcKpEjvxdw+OLrYy6pfP9D06du2T5srnTHxrctVl0yTlBQ7brtadefDOMp/8zVQMTTAQoTwQoiwANpl/Ni3Psd++w6mIJG0fGFB11Gd3ugbXHyiCZsfahjtWc3xA/NsT3N/UBEj5WMzJBRYDyRICyCNCA+3q/c8tn7CiXg/lr7nJMHmdr8eAO3+dxXtw1vlWYU97VobwaerYZ0PUHBUMTZAQoTwQoiwANrJsFDzq2cdraL3dBeXZWgoPU5lOPV4jLyRktHd/baO55mYap7VgNYJrZQxN8BChPBCiLAA2kyytudegXNzbDpUXuxFjHCunIdE5eDo1y/ECdqeadIWR2jxkr2k+YOzYCIkB5IkBZBKjffbX/bsfn77CG9253UXF+ukPD2xb7MwNT3pJmjh1Kc0P2wKZJQPSLJg6OkAhQnghQFgHqX9dOj3AcMY8I55GfRdNz5DsvHNxhs9/CPJbEfjBxrWC4dCm/O9D492aNjqAIUJ4IUBYB6k8/m+bY6446ncOKjkaqcZAxsduxPbP9roCM2e4gtP1BoXBpU+4tQI9r5gyPqAhQnghQFgHK3S8fcW74rHLP+qerlxwU3yq/4PRAtuf9dv/XPotbx86ijxgZmptCD8cAs0wYHoERoDwRoCwClK/frXNcnxhh7RfmFBTMdDutaO8StvEzbmkwxyNdmMMmEU0InGCdW2v8ejkke3yERoDyRICyCFCOPt7axXlp4ObTnFe17OEGaGPjf5FjzwapzOkhYYDt/pzgvTKhDHsDW0SzSbwPdjwQ/bLUARIcAcoTAcoiQCvqs729wh1SNhxbcnxS29LntrflPXCpvHY1NW6p/jYBtyS7Vc7daEjgnOA+vxuQaKUdSQQoTwQoiwAtt8/T+kY6T10fvseNhO7ufNZYKOZsorwHI4Cw0dof0bQ3oviZx3Oug+YkAndb6IwkApQnApRFgPru8ycGRDmgiB20tbSRk9z8TD4hjKb9bCW0VUbF36i09q7nPp7zR9iOJAudkUSA8kSAsghQH11J6+/Us0b/x8qsFGZGFRMSPU+kTedH2ICYVSJvUnhPh7kAbcr7Q+uNH9ovfIxkRYDyRICyCFBvfba/n5PImL7rvJ5ZNKNIkNsPi5PJ0cZagG24zh/jd7mtfUdyb7yYZKWp7QhQnghQFgFapt/v6ePc7hlz71qf52XOZt9iG+nPeZt8PcV2ULV9WvjtCmubG6BhFyv+fmf5PYH67wscJZkRoDwRoCwCtHTvb+3h3Odeva9vPY1mGN9VY70Aj8p0cazxMT5e3xnrM9wAjef/MTa1XYfvRQ2T3AhQnghQFgHq1jvrksOKtnuuL3fd8vy9xje1kLW3Z10MEK3vhtBEF6CD/Pix47HAaCHDJD0ClCcClEWAFvfa8iSnCrUGbqxgG+RTrYxv6y9vHs/DhlG2SdJuPsiWlfgZ5devkG0RwNrgx8mECFCeCFAWAcq6cWme8zx3xNm3VrhdL60eEDE3cIEqLruj8VD6id/AKqYRxbuQ/FxLnguEnxYz5HIjQHkiQFkE6I9XsycWXQOu4ajdHLuV11cDam4NzB7eLg4zHk57XU/sXNmE7UHrsM/fnxsM1HhD2LDLiwDliQBlVXZArxwaWjSzUrNxT3ApMC8caCz/anCzjbtpdlL63QRYxpbtWf7/VF5boOlnIodeTgQoTwQoq1ID+ptNPZ2nJYYlTePcnJc/hh1ldNp/PPxubTSQEGKX7cxKAHrof04nAcoTAcqqtIBefza1VdGukC6ppype9p1d6G/8wL3mbJzcFQvU9vtjst4drAZMlPAWEBsByhMByqqcgF45OqaOU8/Yfqv9mInuXBfjR8aadSXi9HggZodJd2ZS7JzOTXLeBuIiQHkiQFmVENBX13Qrmk8oceR27lNpWDltAdtsWbaU7UQTIHqjefdnRtMtsCueAOWJAGVVMkA/zxzf0IlnRLtp/m5gzGpu/NhyKaz4uscWQJSU853UNRCo/prU90PQEaA8EaCsSgTo9Z+t6Fy06hnbd8UZv5f8k2x98DEJpJRTThIQuUbe7W8dN3DUCnOvxcR2xSd+LP1NEUwEKE8EKKuyAPrWjiGxRRNgtBy3J5CtmJmNjFWnnb6+mr+8W8N6bSYFcGxP+Z1tb6z1rhZ9q0UdbO489WqlpNv3XpbxQnb6zow3RqARoDwRoKzKAOjltAeKPrejdt+lARqX0cBYcfW5T/zpotNAY4VfF+5cR0PQR0XfqqODNYpeFdtCKbfvqyPG/Q67Yda7I4AIUJ4IUJYoQK++9/LZjD2bVqXOm1jUpNTUVRt2Hzn97BsfXhVzH74qD9C39z1QPPlFVPvJ+wPegZ4RD9Txeen2Cy2LTw+PTgv0Hnx1PtkQVMqn+DtKTmqPFjerPk/bIoFUue+JoCJAeSJAWcEC+tGzTyx/sHfLWJRbzC2d7hu3ZNvJ5z+4LuZhl8oHoFd/tnFI0TmaCGsxamMwm/qOGTcU53vy5FTXU20fxL14jwkauUH4zRakuQ3Qw+JvvrwW24A9Et4JgiJAeSJAWYED+snZx8Z1qFk+nGWLaNh5+Nytp37xhcDn4AXQXx+elVx82Q1bs2FrsoNb4Nn6Z1w5O+3drtJpeyq4u/LSeeNTfJT4k+9nuw1LZ+G3Xn4PGu+EMwLfAmIjQHkiQFkBAfrluVX3J5RyMfaWO3sNGj1p7uLlGzZu3LV3797tGzduXL5k7uQHh/XrmtSkTngZSWslDZq55dRrXwt4DqUAvf7Lo/PvqVXidcsRweJplFm/fD8L3FfAhW8FLSg4ZwBddZfoWx3n9qBbir7xCsrvA1T/hYCxlxIByhMByvIb0E8ypt7h4jCmZZ+xi7Yeq3hqzKcPbn1kWso9bRpEekBap7197ranX/8qiOdQBOjNyzmPPdQ+2oV650lb/TjLyHcnGlbgZ0G02xOSsbkyt7XhTZrgG53l9qA7Cb7tCmMHMyW8G8Sgy4wA5YkAZfkF6HfZM1rbitftmvWbtjGQfQ8n96ycNrTbbTVQulpt7pu25lDBbwK46sNfvn310OLhd1Zz3Vj0HcMeEXa5t1OJBvPlH3TfyO2JSDl7Pbs5UFvwzCJ73B70OLE3zVG28aq2+tz/wTYjApQnApTFD+jvtvUtXtGK7z1jV9BHX59JWz1jWJdbYzwgRe1WPUfPWpeWdemtT2+W93iufvjahcOb5o7qmlhqpbZm+5TFB/06QbOCspsZK7M+9787G+S6/1iR9+3qacOb+mL3lec3K3nQ/k0tL6aMOkB3ycdnBBgByhMByuIE9PUVdxYtawn3LXlS6IKU9fij0+xdbq3uCSmrRuOWHe4ZMnTCxBmpRc2dOHHEkHs6tWpUBl7UbDNwekDrxOWW28p4GGkVfNORiJJHMUX0AyjqyXigafCbc93bW7LlYbrQ2+VsX1VdDwclQHkiQFk8gL7xyO3O5Sym+/zj0pannAPr543t1+EWz8/2FRYRn9R33JJdcuboPN8eqLanwm9bWLxlo6ucFVCjw7FAktizLnc1cDzm6DlCb5W7x4xfO9Plv8X9jwDliQBlVQjoB+vbFK16pmw2Zx7Mc8d3rZ4/fti9ya0a1YrybqbxobPOLUndBo6duXrXCWlkGV3sBlTZxvGNmx3XVKoxSeKD2WussXUXe/t5KwZ17ztL+PmnvLHDQVeb8jb3LwKUJwKUVT6g36X3ce5xv+UhvutdCO/i6YxDe3dt3Lh2uaOVGzdu3Xvg2GmTrreW399YweWcDenAIwu3yJ2WY1OEfxcS1r/JgG2/WW91/ghQnghQVnmAvjzZeZx8wwcr2IcSso0yFnBT568rv6XGGtsE1Q9CaMONX1CnzHu3c0aA8kSAsnwC+u3jHRx6xtor3gIYqk0znr+J8ydXnPGAbI+ofhAiy+8NRF809R3PEQHKEwHK8gHo2zMdJ9eEd12t68XJTWiJTcUBkuU2DIiUfEVlc8vrCNR4yeQ3fUURoDwRoCxvgN7M6RfG+Gww0dxJejRro4abHPO7G96kq34UIjvbGqj7pvlv/PIiQHkiQFllAf1uVwumZ1i3x8y6eJqe7asG3C1zD39AnWsFJHBfRNQKnW4KNLys4r3vMwKUJwKU5Qnop8vqOg5KH5WpesFSHDtRJqnic/xN71QDoLWGjyvwTjYEmn6g5u3vPQKUJwKUVRrQ306tyvhsmmruZXI07HQi0ETsiT+COlQd6B1SHw4y4oCWv1e1BHiJAOWJAGW5A/raSHZGoq1jiF1IN5DOJwF1NV0J3xyh3b6tIEuvZaztf6ZuIfCMAOWJAGW5AP35/ex0xIi+io6Y16r8XkC1/aofha9Sjd9yK0r/0+nZ93a/b4llP9mn1QDai5xjO7gIUJ4IUFYxoM/2cZwVPUzTtS6TG2P8JtF4PTwFqPK4+z8scGx5QZyEyZzNaU8M0OlLtYuCKwKUJwKU5QS0oBdb/qqPVXZWtJnl7d9ewYFA7BJHC8x5MAF1MdnA8qTr73OLZwiw7jGiu4xfAZ1FXJ5ARAQoTwQoiwH6jIPP2ElnVC9FZpQ1mK2txU294Ptb2EbGseY9ogA6c4v7rviTrgnxG5bztPRuu/EkumkiKAHKEwHK+ukfzzk+vNeakqt6ETKlo/WKqGnv80ADK+zmPloD6Ff8l2lus1RtUvmogmpLFaBLMFd2ERcByhMByrp8P1vsak8TcvEg/bt4awk1Q3x8y9MJwB3a747ZHA7MKPpzLzdALTzXyGZD0GQt9iQRoDwRoD/++OZQtuc9doocPvNWjxo4Zr1W63IrXdSEn/T6HXlJQMLTJj+sAJplPIOifUYd3QBNUfuogmqL8Sn+riuql4kfCVC+CNDLY8MdkwBL+vC+0flpuZHw6/EGUT83axZ6/Y6+QMwhkx9VQA0AajovZdTH7UlNUvyogmpbVSBJgyPqCVCeKjugH09n071Xe1DWrqN1xRc/jtoh6R4CqJ2bNQ95+4aHjRU7jQ9gcut8K6CZ46PDHLcnpdFrHUA7Y4CW6s/qJEB5qtyAfrmYXZUtKkXagUu5dUoW6kb6TInXoYLNhattms0AWk4njFe4N/vDaddlpJpptcHE//YYT6Xpb1UvHAQoT5UZ0KsbGW8R90ucrm6xG1WcF8UwIbvbo1pR9stp0YDd/EcVYDsjgGnsp/63kwAAIABJREFUD4+GFT0lfc+e4i2tFtDgdcWLBwHKU+UF9HpaY2Nhs/U6InNJuN+NqjEy78ivtroeVLWcMl89FQ90sNCRlHOBcMeh8+vjnOuflvezoCDdeCq1f6Z2ASFAeaq0gD7dmi1snfbJXRB6uAE6UO5d+VOXkgc1rczX8toAjbScgamknFldm9/hushKf6DWk+wPeatHD3xos8U/vzs73sj45XZG6RJCgPJUSQG91JXp0VL6OX8D3QAdJfvO+Mtp63xINi/H+9wHVNd7tvcttZ2Pvn/RVuXztwOt9NnCLKZTzYHIgyqXEQKUp0oJ6C+HsAM/G6+Uv66S6gboaun3xt/FBa3DUbWLl18gM7TfAb+3SvEr2qfoXzJjfZ8RYNnYLzmbyuvFE6A8VUJAP5jIJvysO9+MzXzZrl3D9XSbntnrga/u5/ZoWmvX76TieZc2hQEhdZ1O1vm7jWc4+bqy5YQA5anSAfrlomrG+zJmoklnbS63FS3rOk8M54qdXT5A9YMov8NuK/V9i/9xEhB9QOWjklH+UOMpDvhG1ZJCgPJUyQC9upkduRQ5zLwZ61Y410HrbDDtHoMotynQWvOtiSvcAG1a/I/5XYDGZQ8osHpTjV+/bVUdUm8tQP8rz8hyq1SA3jiYyPac3Jth5kKQm9r/7oGLdfv87jV2ueA47yfH65P7ZuX4kn/NTgB6KHxUkloZBTR8Rc3SYhlAv1/bt0GM8dUtH0nG0luVCdDsNmyh65imeqnQtvFA1J6Kv01t69wAbeX65/0GNdPVPSpZ7aoFVMtUsrhYBdCd1RzvhcLCplj0k2wvy1R5AH3ecUxmC8te70F+a40PjItUP4gKy45wAep+boKxZhqh04wtgspoYnxmWnZTwQJjEUC3Ge+DpIcYoO2AOdLB9KyyAOo8cqnh8pA4ylpO6THAMNUPgiPX2V1VSl27qh9QLwSvxnKmk/FMByuYY9kagP7ehtteNr5ofPXvMxD2oXwyS1c5AH1vPFttqTNb8/0jSjvTGGhnhTM4s5sV+WlbUurfz90KdArBX5AXU9jGirdMX2isAehMVP+s0Alo4U9dMUU2mJ5VBkA/ncMOvY6ZUDku1xFgbD92fWuswJ3p7/gU3+gxj39Pr2rxmUB9tSQKqPmU2YuNNQBNwiLHFx1fPYhOcrksW+gD+tUydiCRxCnrQqNxxidiy8zCcWLpwzN2lF3ZXAaEb1PwcKT3eLyxvj3vmrkLjjUAjUG244uOr15CTblcli3UAf1uAzvwM7w/Xem9/NgOJOufyzMIqHtK9YOQ0dPtjXdx1/dNXXSsAWgtHHF80fHVJxErl8uyhTagV7c3YNvKeug9OYYGHY4Bhqt+EMF3vjlwVwhuBi0ouDjG+A1XN8fMhccagCbjAccXHV+dg7ZyuSxbKAN67fFEx4Gfj6t+92tf7i1AWyvsQKqoIzGWviRnea2vaawKzL1q3uJjDUC3AOcKiwD9JArLZIPpWegCei2tKeMzabvqd77+5fcA6oXGR98VoboZtKAgk82i0vaXpi1A1gD0rwkIX/QdA/Q/p+NR9Tv5ZJYuVAG9fsBxsEtLS0zhobrJVjgDibPBQFyI7i+8MNr4GF91h1kH1VsD0MJfG2vmaAzcFW2soT8lHUzPQhPQa0/cxvhsvlb1e94SsSnsFqh+EKI6b/ziTA7JzaBGm9lFS/qaNLuIRQAt/LzkKt0Nn5HNZdlCEdAfHnd8eG+2OlSXI7Gx6YjvV/0gxHWkGjBF9YOQ1Wl2PnLtQ6YsRlYBtLDwg60p3dvfMz37X3Kt9FroAfr9dseuo2ariE+uQu6CGEtD86T4ohwX4r7PjAOarAOoykIN0K/WxTs+vNPaJ28Dii/JFjIZz6i+3lfFC6bMu4z3d83d8reEEqA8hRagnyyMdcxytk71u9w6zTfW16RfW8/czt4C3K36QcgrP5WthHZ9Q/bCZDFA/3Th2PsKZlUOJUB//XA047MtzVjH354ob5c2Ft7WUb36TjFtHusDVYA5Zt2Zgp7szC6rsEDyxT6sAuilacuN/387wXhNunv+jPxCB9BLQ8LYWUddd6t+e1upp+OBntLvJfNOx07S8BFmHaufCkQ9YdJ9KWkZu7JzY7kTLVsE0EeA4YWF/2rpeIvdafo6aIgAeu1oR8eV2/odVP3WtlQX2wNNpE9SdSqhzEXgZNcLuMWkywaqKXsQm9+21+sSlylrAPq6sS6eWlh4Dlj96SYgTz6ZpQsJQD9b04gtnjEjQmtniPzGGC+a/HkC7nNNIb9e+p05y24A3GfSfSlq7+1sjWHSx9KWKmsAmoKGV4z/jEPCvwsLh2OYdDE9CgFAXxnv2PQZPy30rsooudU22FZJv5ezVVyAdpV+b0XtjgCWm3VnaspPZXtMY5bL2hRqDUBbYyv7T30sNf4/A3dK9rJMVgf0anoXx5LZesVF1W9oy8Vm3hgt/252uV0Frrb8uytqsmGLqVdfVVB2SiRbc9gqZ4YRawBaDReN/78C/Mz4z3OoLhtMz6wN6Nvz6rLFMvJec87kzjkVSkqfbQq0N+EJuV9GM0L+3RWV38H4rRoKE0yV27FebFNo4uM/SFi4rAFoQ8eEyk8g/C/Gf86jrmwwPbMwoN8f7snePYibYMpEQmfGNgCqdNlhxn2ZUm+TpmByXwOtY8L9FfVU7dJX7QzR9rRlr2sTCYRaA9CemG/8f3vcw/6yBu3lclk2ywL6wtRajguLdXjUnNWMI0W7km0TTbk7+c001txNOeTrXLQL0B5m3GFRj9lgqwyHBG90HMGTuPVbwUuYNQDdgKqXfsoG9hp/fr8exkoX0yNrAvrBhtbOFZoxx0x6l55vVIJAaOyb2BkBzDXnruwuQE0FbYTxBnnazDtU1doW7LWtu+z3QhcyawD6Z+ODRpQx0H8o/N8Bxn9/JZ/M0lkQ0C/TeoezN0xY8irztnHNdiEQHwqn2Z+qa95RmdmJxS/dEJPu0VleixCe2a5061qxlzd6osjzO60BaOGb7Ko9UVkGpcZ/50oH0zOrAfpdpt35iTDx4RNmvkPbu23IC4FLhFxoCzQ7Z9a9nXIeKRE13mTNjlQFZph7l8ra0pHtELD1PnFd1KJmEUALb2SmPvae8d8/Nxh0RrKWXrIUoN8cH17NsSjGDjF7AvX6boAuM/m+JTQSqH7UxPvbN/G+IXOfMvEOnS021LbMtZqDLa0vO6gJCUvfFbO0WQVQtVkH0M+eGOxc94zuvc78w1Pi3QBdavq9i+5RG2yVYrb+PsZHlZA+pbNUJ8c6dqyG3XvsewELHAHKk0UAfWvD3RHOj4HdlytZIJLcALX8bL3pxnr8WNUPwpRyEoCBqh+EieUtbeN4i9aa9EzQE4ZaBtC/po9pVb9ui5Qn/iIZS29ZANBvz0xv5pQrusdy6TNf+GiKy886Vj+aPrdJqF49vWx7jN+7K1U/CFM7OKymcyfBvJ8Ht9xZBdDjscVLZs3jsrksm+aA3nhlXe+iU6lrDVhr2l6PsuXGlQA6T92jEFMvID5EL1xZtoeBGpmqH4S5nV+R7Py4ljinIIhdShYBdKfxRGvclTK6E/u9sVc6mJ5pDOjNN7bb6xShdeuYXYpXmfYV/54bpvZxBN8MIDJULmJccfntgSSrf2bwu1MzWjrO0kPcg5lfBrj8WQPQj8NR7/F/sD/9c389RH4qG0zPNAX02vMbB9ctEqt6j1Qdpqk7ObAaYLt9terHEWw7IkJgJdqPThi/+cYFdQvHp/TpkbLRats8Mibf7jQ08u7VL94IYBm0BqCzUeXt4j+/VQXzZGLpLQ0B/eDEgu5Vi/CMTBq3S5vVhwvp+63/yfcp4/dSP9UPwtTW2hAexAwGF8c4Pw+3PCLuIZnU8eltw53LUe0hW1/3d6+SNQC9HXNcf5mLVvKo9J5egH6UvXJQyfTlkXc8sLHyHIJiUhfuNPMIej2yA/GBX6Xz/uL3Y10dPgf5W/ayPsXbnuoMXPvMd/4s2pYANAYnXH85VXmns7v6anrqva6j1at3mrC1ki3m5pQC1DBr/gBdOtcU6BXoD291HX4R8G2oLX/vpLZRxSsl7Sbve/Ua56JtCUCr4aTrL6cQ4/n1D+z2h/5T/JfX7PbZviR8zZ7mL54s9YB+8/KRpUNbRpa8TSOaDVxw0GobnKzSChtsle+azwcNPxYG+LO9XYCGW3cDzrnNY9sUI4oqd03YeuHTihdtSwDaAm4kzkdLz68bgNp/U/yXLRUBetD31/UD9ItXMtdN7OGa5ciw87b+s3bRiqe8DlUFHlT9IBQ0B4g+HNiPJri9PzeKfVQmd37HlO51XU+mXq8pW3Mul7N3yRqAznLbifRONMr494F9qL344KZ/jhwaAoBee/9nxzfNGNyuDtyL7zhycVqe6vdYiJeTCHSslCv33YDmgb27ari9SUNgEoTMR8d0rOX2lKJuHzB988lfXPG2aFsCUHYY04F/sj/9K70+wj8uC+jccQ/8y/nnV+2PVgDou3kvaAroZ796JnPbIxMGtG8QVgpORCR2HbVgN10PzoTy7wbqW/djaDBlxQEpAf2k+yekTYIflapOrJ/Up1lUqcUwpmWfsUu2nbx02bWbyRqAFrILHtTuOnZsN7ZGtqPMlz+wLzhgL5oldLP9UhGgf0pfMnr63q+d//zS+nEPrf/EAegr9vTCwjP2s45/380++79mz7+586GRi35deGPHuFELXjEN0K8/ePNnOcd2rZ43bnC3VvERKFNMs+4psx87qs1BSqHfFGOFY5/qB6GoLTbYAvoE3s/1jo08I/pRqezikbVT7kuq62W5TL7vwbmr92S++Mt3rgRy/Kiw+AAtPF6z+KFXP1r2qwag79udrv7vyBm/dwL64UT78OnD7SmvG3/+6XG7PWWcfegOX4Cmj99ZsMc+9JmHNuQfHGZ/2+P2gwT0+z/88Yt3Xnvx2bNPZ6Tt3rA6debE4fff075l41plxqW4Ko2S+oyauWZ/4MeVUIG1JRxYoPpBKGsMUCeQK0DttpW8dfsLf1AadO7AmhnDujX3tcBWb3j7XfcMHjVx/tI1Ox7POHn2mRffeOfdK94+8YuPE9DCPx8Y0SKubouUtD95+aIB6E8TRzk+479if+pTB6D/b6L9+P8r/NdJ+4hrhYUv21Oe+7/CL2bYfQFqf6aQbRy1Zxj/ybZvKbrdP73g7H/+6n+v3X1H06ZNatWq6eNFL1vVBi2T+46atmTzQXJTVZm1KtfERB5daBng9PSji9/EDUP68iDnj25bOi2lT7smtcLKW5JLiqpVq2HTpne0b9+xj1GK0UOTWfMWOVq5obht+4zeDoCZv/ICWm4GoIWH7a+xP260f+0ENNu+0/G1ffaDhT9NsxewP18f7gvQ6T8Zf37b7tiQ+oU9teh2L3d09ucAHtPYil/d2AbN7+zSZ8jYqQtXbztwgnYOqS/PAKTledWPQmHHqgU2PX3+VOc0tJ1MvQKCwvJPHdy+esGUMff37NCycZ3oChZ1vqoH4oxHgQP6sWO98R8pswudgC6zf+L42qf2OYU37A86dzFt8wXodvbnj+1L2X9u2IvPFQ0G0LNVgbDatWs3a9ayY8d7+g0a/cCMmctXbtt9+GTOc6+88+m3f/xPxbdBmdwsILaSzUrk0SNA9LuBvHT/c3zBrC2XRQ+IVfrvH7/56J0X8s8c3r9tzfI5Mx4YPaBf947tmzVrWLt2ZMVyFhXj7dO1n3kCutpbz3r+FAP0p8kj/lFY+JI9qwjQ8fZ9B1l77WMK33XKWFiY4wtQx/H1H9vXsf+4AL2639n//G9wGau3Qd6CDhX+n+pHEHzG79FynkU6EF4ZrvFbXvcCrf5gxlCExkLxE89C8acffvjhk48++ui3bxq98Awr95SzI0+4+nUgD6ACQL1KvdAboIUZ9pcLCzfYv3UC+m97SUN/esW+1fmNL/oHaHHqz0TSoZ9UH7EhoL8VFv7d5xdfqgJMUQ2Y6tj09BPMGIr/hMRCofthTHFFOY7HinDMGnzH2BMe3+QE9Ip9Q+HfU+YVFq2BPmT/Z8nXP7Qvc/7hbBlANxCgvIU6oJ8mAj0q5RH0pWLT0x83YSgIUCFVAGhReVXR6czX//nvdxd7IPp8mS87AC2cPvyvL9qziwFdYv/M8bV//+EvhT/aH/w/x192uwGa6/iXmQQobyEO6PV7gFtUXQxFpx4Gav1O/lAQoELiAvTTqlhR/OfNqPqRd0BP2J9fZ/++GNDMopM7s+yZhT/NchymVPiHlBJAz9oPsn/50E6A8hbigM4HYtJV46VDbHr6LsIunO4zAlRIXIBOR6+fSv7SH5O9A/qVfdFw9l8noH8dOzTr34U/vTYi5Xt2fueIF/5T+N3coSWAvmUf/UVh4XczRhCgvIU2oMdtsFl+In0xsenpl0ofCgJUSFyANsF211/2ItE7oIWz7Y7P5U5AC996wD5iznj70F8Yf/5pv90+cqJ9WF4JoP+abx/+yIJhc54iQHkLaUBfj6ksFzHmiE1PXyB7KAhQIXEBGlV6PtAoH4Bm2e3XXIAWXt+3YNSMnd84v+Xl9eMfWP3hX3Y8VwRo4Z8PTR8x5eg/fr3jizKAPvwEAeqlUAb082YBnoITmg0FGss+E5EAFRIXoHFIdf1lARp4/SaJEaCsEAb0Rn8ggU6gLem88ftkiOShIECF5EGVd0D7o+5XxX/+qi4GSbTSawQoK4QBXQJEP6FaLZ1KjwZ2yh0KAlRIHlR5BzQLaPOO84+/vRPIkctl2QhQVugCesIG23LVZunVAuNXymtSh4IAFZIHVd4B/WkAgPvWZGauGWj8wfQVUALUUcgC+mZ1YJRqsXSrF9DqW5lDQYAKyYMqHwfS/32Y60TOUf+QDqZnBCgrVAH9ojlwF01Z7VF2A2CizKEgQIXkQZXP2ZguDoxnetYc9pxkLL1FgLJCFNAbxsebBpXzGh7ltjsCyJA4FASokDyoKm86u7/89tef/1emkz4jQFkhCugiIDpNtVY69jAQe1neUBCgQvKgKrD5QGVHgLJCE9BM2oHko/y7gOQfpA0FASokD6oIUH0LSUBfjQFGq6ZK056qBaRKGwoCVEgeVBGg+haKgH56C9CJzkDy0UYbwnJkDQUBKiQPqghQfQtBQK/1BBrRGUg+Gw3EfShpKAhQIXlQRYDqWwgCOh2IOaRaKY270BroKemy5wSokDyoIkD1LfQA3Q/Y1qpGSuuO1wBWyBkKAlRIHlQRoPoWcoBeigImqiZK81bbEJ4vZSgIUCF5UEWA6luoAfpuPNCLdiBVkB1I+FTGUBCgQvKgigDVtxAD9Nu2QPNzqn3SvvPNgX43JQwFASokD6oIUH0LLUBvDgNqZ6rmyQIdqQaskTAUBKiQPKgiQPUttABdAUTuUo2TJVoGRDwrfigIUCF5UEWA6ltIAZppAxaqpski3Q80Er8ZlAAVkgdVBKi+hRKgL1UFUlTDZJXONQP6C98MSoAKyYMqAlTfQgjQDxsCyTQFKG/pxq+btaKHggAVkgdVBKi+hQ6g33UAbjmjmiULtRSIFL0ZlAAVkgdVBKi+hQygN1OA2GOqUbJUA4GGvxc7FASokDyoIkD1LWQAZbuVt6kmyVqxzaB9xJ4UT4AKyYMqAlTfQgXQIzZggWqRrNaRGGCZ0KEgQIXkQRUBqm8hAuhLVeganAG00oawsyKHggAVkgdVBKi+hQagX9UDutEZ8P43DIh7X+BQEKBC8qCKANW3kAD0z22A23JVY2TF8loCnQVeIokAFZIHVQSovoUCoH/qD9R9UrVF1iyzJjBb3FAQoELyoIoA1bdQAHQyEL1PtURiy518e9UqTcackn9Pj9lgOy5sKAhQIXlQRYDqWwgAug4IWyNfGjM7mABHNbfIv6+HgOq/FDUWBKiQPKgiQPXN+oBmhAHT5TtjZtl1UVTVdOl3lt8BaPm1oMEgQIXkQRUBqm+WB/RSNGCXroy5jUFJPeTfW1Y8kCJoNAhQIXlQRYDqm9UBfbsO0MWkGUQumHM3BQUJLkCjzsq/uz1RwBYxw0GACsmDKgJU3ywO6Ke3As1NOYBpS9cYRCUtNuNg0zybC1AcMOEO5wubVoQAFZIHVQSovlkb0O+SgfiTJhCTP7wItQ4maJ3r5if2yr+/goL+QP0PRQwIASokD6oIUH2zNKA3BgPVD5ohzGQzN0oWVHf5aTPhSKaCgnO3CTqengAVkgdVBKi+WRrQ6cZHz61mAJNd1UXaTvl318t1b83l3xsrowYwVcCIEKBC8qCKANU3KwO6zlhBW2qKL8vcPlMPk393aeEl97Za/r052hgGpAU/JASokDyoIkD1zcKAHrIBk83hZZwboJ1MuL/UsKI7G2PCnTmbBFR5IegxIUCF5EEVAapv1gU0N8q8A0DHugHa3ow73NqS3VXiSjPuy1l+D6DxJ8EOCgEqJA+qCFB9syygL1UHepg1g12qG6D3mXOXmRvWHTbnnorKbQJ0C3ZHEgEqJA+qCFB9syqgv4kHks6bhUtmmAvQVWbdqdkdqR78jiQCVEgeVBGg+mZRQD++FWiabR4uA127xUN33uYNxq+Jx4MbFwJUSB5UEaD6Zk1Av2wLxJ8w0ZazSUV+xmeYeK9m9zAQdSmogSFAheRBFQGqb5YE9PseQE358xS5lzeptsFn9OAsU+/V7O4xfkO8F8zIEKBC8qCKANU3KwJ6fbBB2W6zcck/uHlfntl3anLnmgPtvg1iaAhQIXlQRYDqmwUBvfkQELFRtTWhWaaxnj38ZuBjQ4AKyYMqAlTfLAjoPMC2TLU0odrOSGBF4GNDgArJgyoCVN+sB+gqALNVOxO6LTR+PWUGPDgEqJA8qCJA9c1ygO4w/ByvWplQLgWo9lKgo0OACsmDKgJU36wG6KEwYLhqY0K6i8lAwgcBDg8BKiQPqghQfbMYoCcigP6heyi7Fp25JfBd8QSokDyoIkD1zVqA5kQBPU26AlLl7VgsMORGQANEgArJgyoCVN8sBWhBVSA51I/F1KAdkcD8gEaIABWSB1UEqL5ZCdDnawBtz6nWpTK0xBbgWfEEqJA8qCJA9c1CgL5cC2hlyhU4qQeByLMBjBEBKiQPqghQfbMOoK/VAW4zcQKmSl1+H6Dma/4PEgEqJA+qCFB9swygb8QBTUN7Kg+dOp8ENPL/YCYCVEgeVBGg+mYVQN+qDySacolfylFWYyDpK3+HiQAVkgdVBKi+WQTQtxsYK0RmTgBKHY0F7vX3Eh8EqJA8qCJA9c0agL6dACQ8qZqUStbuKsCDfs7MRIAKyYMqAlTfLAEo87NBpmpQKl2rw4BH/BspAlRIHlQRoPpmBUDfMvysf1w1J5WwOQB2+jVUBKiQPKgiQPXNAoD+soGx/kl+qmg0EP6kP2NFgArJgyoCVN/0B/TNeCCBPr8rKb8vUOWiH4NFgArJgyoCVN+0B/TVOPJTXReSgZqv8o8WASokD6oIUH3THdAX6wCNaP+7ss62Auq/wz1cBKiQPKgiQPVNc0AvxQKJdPynwrISgSYf8o4XASokD6oIUH3TG9C8GOBWOv9IaZnxQOvPOAeMABWSB1UEqL5pDWhWNNCCzn9XXLrxKaDj13wjRoAKyYMqAlTfdAb0aCTQ5oxqP6h9xueAnt9zDRkBKiQPqghQfdMY0D3hwF00/6cGba8CDOA6LZ4AFZIHVQSovukL6Bob0O28ajtCtVP7Dl3g/+71EcCw6xyDRoAKyYMqAlTfdAX05jwAff1Yxik/Wt7MeHWr3sd/eMNK49PAAxzXmSNAheRBFQGqb5oCeu1BYwkfRtcvllL+/XAWu4/7ZxYbnwfGVzw1EwEqJA+qCFB90xPQ7wYai/cEiYZU6maiuDj+TcyphqCTKxSUABWSB1UEqL5pCehnnYGwuRIJqdRdiC0BFNP4f2y2Iei0igQlQIXkQRUBqm86Anq5JRC5Up4glbwdLj/R1o+fm2F8/4wKBCVAheRBFQGqbxoC+osGQMwWaX5U+pa4ARrvzw9ORYXroASokDyoIkD1TT9Ac2sAddMk4UEVFCx1AzTBr5+cYvzE1HIFJUCF5EEVAapv2gGaFgncQtMnS2yPG6Ad/ftRJujE8o5mIkCF5EEVAapvmgF6c4kNSDotBQ7KWX68C9D5fv7sNGN4xpZzRD0BKiQPqghQfdML0KujjIW6V6U6/Sg/Y99Jk+9yeYmfTfL8/dlZhqAjrvkcQAJUSB5UEaD6phWgn3Y2FurRlenw+dwHahtPudEcc5/zJJvTz0YZ/v/sPONnB1/1NYIEqJA8qCJA9U0nQN9oAkSkivdC304mFq0KJvu9KhhUO7tGA40nBDRTy6IwoPc3PoaQABWSB1UEqL5pBOiZmsD/b+8+4KOo8/+Pf1MIoQRI6FVElF4EQUWKSBE4JqEGBIkYIBBALoAhIp1QA4gYOoYQEBJKQtj4w7+KeoeenoAVG4KKCih4d3LnFb1T9j+fLclmsyTfnZ2Z73d338/HQ9N3Z2fm+2J3p1VfqXcrZGZpW/xiWjH7rg/ka/3TtHA1+Oc9L0MEVBduqUJA5SVPQNPDGGu0Xc9ISG9ZydacMD+6bsnSyoy18XyVDwRUF26pQkDlJUtAL4+l42KC7OzzQ1x2KPKnty7WVGPsllOeFiMCqgu3VCGg8pIkoB/dpTZkcLCdve4ul4DGi54Yb2yqxVjdVzwsRwRUF26pQkDlJUdAjzdgLDxZdBZM180loGNFT4xXdtRnrPqhsgsSAdWFW6oQUHlJEdB1EYzVXC06CuaLdQnoHNET452c5oxVyiyzJBFQXbilCgGVlwQBvUQnT26hYZdEv7e6pJ8R+0VPjJfy2jMWMt99WSKgunBLFQIqL/EBfbej2o/7j4gOghAlr+EfEj0pXivoqU72eLdLzSGgunBLFQIqL+EBzY1mLDxJdA0EyWvj6Gd/Pzz8yjLeBd//AAAgAElEQVRcnfA+F0otTQRUF26pQkDlJTig36WEMhazVnQLhCmc2jyEhbdfIHo6tJmmLrxWZ1yXJwKqC7dUIaDyEhvQs/eqT2I67BMdAqGO7DX3ME49LY5U//mzuCxQBFQXbqlCQOUlNKAHazMWMirY9v4MJJvqMhbxdMkSRUB14ZYqBFReAgN65bEQxqKWiG4A+CKnlfoaIqn4/HYIqC7cUoWAyktcQE/dqQ69ttmiCwC+ye9Dp3A951ioCKgu3FKFgMpLWEAzq6sv3+Px8t3vWejsok0dx3UioLpwSxUCKi9BAT0fpz5viQmqc9cFrsXVGKv8jG25IqC6cEsVAiovMQHNb6T2825/O/gGbmJHM7rY3KUfEFCduKUKAZWXiIB+Q5cmqzxd9LAH3Rymo5I6nkFAdeKWKgRUXgICevw2dbS1DK5TJwc6y6QwxmrsRkD14ZYqBFRepgf025nqUAsb6787j4NHa2ur/ywm/jMgBgUCygMBJWYH9HhLdZw1fUr0cAfdPdeFjir7JBAGBQLKAwEl5gb0q8mhjIWO0Hw9MzNkjOrRa2ym6KnwQ5ZH1NcWVbdeM3OFMgYCygMBJaYGNLeJ+iSlyTrRI708z3W1nR0ppK+mS/4GuYx66qwb+ImJa5QxEFAeCCgxMaAf0+nPwkZJ/fTzUHPnCTo7HxM9LX4ot5c652rnmLZKGQQB5YGAEtMCenVtTdr4vkn0GC9ffMk54meKnha/NKcqXd/pC5NWKoMgoDwQUGJWQF/spA6syMmSP62zRJcE9HbRE+Ofstqr865xrjlrlUEQUB4IKDEnoOcmhNKhR9Jf9yjL5SpvIThOXxNLYgRdpflcxWuFtBBQHggoMSOg362hJ3b1Fooe2hXb5BJQdkD01PirrXeoc6/OTuNXLKMgoDwQUGJCQA+1VsdTpXh/uGxctks/wyR/u0Fix2xPQvu/Y/iqZRAElAcCSgwP6J/6U4267RQ9qPnUKwloe9HT4s92dlDnYJWFlw1euQyCgPJAQInBAf34kXA68mip6AHNa2JJQOeJnha/ZnmsOm2IO2Lo2mUUBJQHAkoMDeiXc6upoygqyX8OfC9o7+xnbz+80LBUnrs/RJ2NynsGrl9GQUB5IKDEwIBeWh5Db34OyxU9lL1xeCDtL8Aqjfaf6EtrVVN6HT/3a8PWMKMgoDwQUGJYQC9n0EmTQ/o8K3oYe2tXkhI3I0f0VASEo4m0W32DTVcNWseMgoDyQECJQQG9vIGefbCukh94BAbL6U+v49vmGbKSGQYB5YGAEkMCemmtLZ9tV4sevyDcRtu7yvcdN2A1MwwCygMBJQYE9OLyBjRmWi0XPXZBCgsa0+ow8BXdVzTDIKA8EFCie0A/nWM7nrz1MtHjFmRROJ22Job87jWdVzXDIKA8EFCic0DfTKhM+eyQLnrQgkyOTIyihA5+UdeVzTAIKA8ElOgZ0GuH+tEmg5C715syKg/NHxf/2G5T7gp8dXA87RLM+uTrt7oZBwHlgYAS/QL61ZrbbXtQDthqyoi0JFe3HbE+8JApdwe+yhtnW2Cdd32n1xpnGASUBwJK9Aroqwm2Zxg1xuw1aTyOch4y1MofzlICqoOP1KIF1mTxeX3WOcMgoDwQUKJLQL/aeKetZbfOMu16HRkhxQetx5t1n+Cr/GTbDhpVE/6gw1pnHASUBwJKfA/oNctYOt6Ehfc0c7fP3iVn/YjCUZf+49j8NraF1n3LJT1WX2MgoDwQUOJrQN+e28w2IBok7DN1IMa4nLnzaVPvGXz0VF86QxeLnvJHfVZh/SGgPBBQ4lNAP0y3v3Sv1Cvd7HMXhbsE1G9Olgd2+ybUsS24Dqs+1W1F1hMCygMBJdoDenb1PaH2rTjJAi5+EeUSUHP2mgIdHVvUzbbyhA/YLuHJmhBQHggo0RjQU0u72+tZP367kBHYpaSfEYeFTAH4Zs+ERrbFVzVu9zc6r9S+QkB5IKBEQ0C/f/73rezpilHW6fLS/WjOlnXpy9PS0hamZzyTxdXDeSUBfUCPSQDzWTIG2XYNZVWUbVLt2YSA8kBAibcB/ShzuOPa6XWUNT7XM3t50pBuzWuy0io37tBv3ONPl7t/p6W787fr4Nyd/qtgQc/KzrfR3zZmDdcAAeWBgBJvAvpVbnI7x+6XzUY/5WM99y0Y2bEaK09Iwx4JK256nNGRB+y/1ULMWwigl8NpPSMdi3JKrhxviCKgPBBQwhvQrw6ldHNs+a7UeYqP19jMnTewsUspqzVtf+/AQaPIkAE9O98aU7KTPAttPjRtv+dbeXrEXZ0fXIBLF/m//AX9ajjez+7xxPNXjF3jOSCgPBBQwhPQ93ZO7hDmCFrDIYt83GazI7F9qOPGqrQamLh4a9nbK8xekzLqnobOjoa0jF+Hi7QHtmMZo5o714reaYViNyshoDwQUFJBQC8cWfC7Bs5ngzF9ZmX5OE6yJtzqiGLTQSlbK3j2eDgjqY/zQu01Bywq8PG+QXLZs3o53w6v1GXK9tPmjAAPEFAeCCi5eUA/zF2gNC9+MV2nz4xtvg6Qwynt7bcX3T/tOd4/2vP4g46IVntgaaGvkwBys2ya1K34nfGYfrOzz5g5GJyCLKBXFeU/Gv4MASWeAnrOsm5Sz+jityHDWw5NzfZ9bGwZYjtknjUavcHb9y23JXa0v4VQMw5XqQt4xzYl3VdypG6NHpM3HL9g7qBAQHkgoKRUQL89uWfx+LtdDjMPadI3aZ0eZ1iyLO5kf1IxXGMBc2d3q2S7hduS83SYHpBcVmpcm8olK2LDPomrD737vUmDAgHlgYASW0AvvV3wzOPxdzdgLiJvfzA5Q6djfAoea2J7KttjiS/bgvJ+39n2DkDlATh7SFAofOb3Q9tWcVknI1r2n5ye85rhO90joDyCPqDff/THvMzlUwd1rstKibytb8LCnfrtHpQ/1XbqiOhxvu/xnp1gP/yvTRreDQ0Sll0LJvRqHl5qDa3aqu+41PXPnThr0MntgzugH66fMXru1kvqZ4uV/1P//1u8spa+v0Q5UurPgjKg50+/fGjH6tTJw3u2qRvC3EQ07T5s+ord+u5ZeXS6LZ/NU/TZiG5Z3cf2Ur5O4kFdbg/8QuGOxYmDOtRxX2VD6rS+b8SUtLW7Cl57/yv9RklQB/SoogybHKuMfJc+XaN+4wtFSbB39IvgCugX5868fqLgQNaGFfNnJYwceE/bpu7HTDqfcjbtMvDhOWtzDNgn3TK3Pt1Du6U63vb+R2xPmauNMvcMpCBe/tbFU4f1aFnL42ocHnNrpz5DH5oye9G6LdlHjr9y5ux5jW+aBnNAvxsWm/eL9V/rlUn/s15UHla/U6TEKt9ZreeVCTf8L6Bfny/2wRm7V0+QgiOq7KysrM0bNmxYumhR6qxZUxMSRsQO7dPnzk6331K/ViXPsSxRpXHb+5QJKcu3GLhRZlULuqe2K3W+2cI022nNI4b4ulsq+Kf8navnJsb1bte4akVrea3oW5p36tS7z+DYYQkJk2fNSl20aKk6YjLVkbOHhlCRbTSddIyts/ah9pfL589fNH5w35zAgG5QNts+/l553mpNUL61WjOUNcoJq7VQecr+Gx/0tfvbDe+daB5tnIrWBZ+F12rcquv9Q8cmPb5sU7YJVy96tgfda8vlRtz2+h70ci68/y4jbhz8Rn72xqVzpowZ0qtzy4ZRZd6U0lk1DaP6kd80dEZgQB9VvrF9fJlevm9UXrBaJz76Z2WT1bpSec3+G+92tbuu4a7GGLyE9BJavWaDpi3bdb7v/kHDxzwyY86C9I3b95l2wTe7I/ER6pQ0nGfUwerbB9CWhfABeBYKTnm7MzOWpc2cPGHU7wb0vKtzy+YNYqpHCB6LX/teOBMD+t/YYb/ZPvlcSbFa/6BkqD9d94/YqdYb42IdxfTlGejLt2h7ctnQ8bG++l/dGPV/ddT/Yhq0KEf7ri7u7u8weLTNlCTVrDTVytWrV2/fvj3n4MHCl1564/TpCxeu/PUnb/6t0zAXuDxPx2xWe9TI4y+zFRocEdMvWQ17FGYKhAdhlXFR/Puv31+48M7pN1966djBg3vV0aKOmZU0eGbRMJpoH1IDHCOsr+u461LeCG0WiM9Ar9o2GKl+oPc/r8cmWF9TX8vPVH78moJail+8B2o4va4L7+aTYWraQgZwH7CpUU4sJbTqvL/905BHYaqfrKKnQAe/BsSgCOKNSMXPQC8oM9X/pyiXtypfWrcrfzqu5CCgHhgT0GdoO2mLDQbnk+wZQi/kY1ZKfJFcTgioNII4oMXvgb6qrFL/n6O8OHPMDesbys4M5azbnyGgxIiAvtdXbVrkZJP2dc/qR2fHa7Llqv4PxFQIqDSCOaAblC22j7OVY+r/P1SWxS6xWn9UUh4Z/avbnyGgxICAbqaT43Y1cdvO1rvpzfpOFt0fiakQUGkEc0CvDIvN/9X6n41K4i/qV/8bpSh56sdpipLu/mcIKNE9oOd+p8YsarZ5+SRrbVe5Gyrk3Gd6QUClEXwBHTfeLt9qLVCUkdPilIc/tP1smaLQJ5sV21GdCGgZegf0IJ28s9teQ3Npyd7pfpYTSxqdCaVyitDdn32DgEoj+ALqdED98oN100fP3vo3+88syrCf1Q+vKXQwEgJalr4BvZwcwljkTEPzeSCuJmOhbRa7fbvgUToRb4Mt1/R8PGZCQKURZAHVCAElugb0dGe1Ya18vOJcBbbVceygHOf+k+cepK1J3V/T8QGZCQGVBgLKAwElegZ0dw31qeEYYze+5zcsPsRjRpkfbmqvfjtsssnnL9cJAioNBJQHAkr0C+jlKbRD5ipD81lUlFxyjFytsqm2pNZWf1An0x9fxyOg0kBAeSCgRLeAftBVTVcXow89KuricpTxWg8/PzySdqy/53WdHpWJEFBpIKA8EFCiV0AL66gv3ycYdeKQEo1dAup5X6ltHdUfVXrsa30el3kQUGkgoDwQUKJTQFepz/tq6n3aT20BLSpKpcviNd2vywMzDwIqDQSUBwJKdAnopbFqse7Q4dLHFevqEtCMm/1SnkLb45WzOjw08yCg0kBAeSCgRI+AfnSXmqsBRp64rsSMkn7GlHOJz6daqr9QI8Ofjo9HQKWBgPJAQIkOAT3RiLHwZFPyWVR0tElxQFPK+71jSXQ93G5+tDEJAZUGAsoDASW+BzRbLVXN1Sb1s6hoZ317PkPiK/jFbDrFSMTjl/WYS2bwi4BeO3s8a1XK5IS42NjYsZPnrtj+/PuldhlDQHXhlioEVF4+B3RhCGO3mHlZjbz4Ouoz3s7pFf/mfNqYdMf/6TKfjCd7QC8eWx7f0cNF3Kq0H7Eg33lNYQRUF26pQkDl5WNAr4xXh9Bdh4zPZinPZfFd3ylvsFr30Ek6XjHcQDIH9Ku9U9qGlW1nibBOU3NpxzEEVBduqUJA5eVbQL+8n7Z2l7MxR7S19JZpk1y95paRpA3o6eU9i6+RXaPN/aOSUpdnbFStS5+XPPaBdjWdP4t4YP0nCKgu3FKFgMrLp4B+0FZ9gjdVdCTLVTCGjkwafU63GWYYOQP6wZIOjj7W75244oCnWZy7dkpvxxlewu7f8aPoKdYBAsoDASW+BPT1xoxFup9WTjqZtEdTnWf1m2UGkTCgl7b2CrU/8eyTUsFevlmzekXZ3xKN95c3nW8OAeWBgBIfAmqpxVj0RnMq6ItjiXTpzsEf6TjXjCBdQE8nR9uS2Gj0eq5DdAtXDLH/QZsN34iedt8goDwQUKI9oNmVGWts5uZ37XbSy9Baz8h9jibJApo/0PbkMzruKS9m9LH0+yNsxzikfi56+n2BgPJAQInmgGaEMdba43tiErLMoP1v+r6r66zTmUwBvZpFZ1Zlod0Web2BMC+pKf1ptcc+Ff0gtENAeSCgRGtAn6DjfI4YETtjZNPhptUzJH4SKk9Av992BzUwapS21xeW9G4h9GboLL99FoqA8kBAibaAXktSR0h/ky78rpPZtJHjvtM6zz/9yBLQq7toqxtrPMOHfx4ze9Pr/5oLvhX9YLRBQHkgoERTQL8bow6PEcaf/FNfe3vQ86J0WU8wIklAD7WjfDaf5+PS3f4AJbTxNomf8t8cAsoDASVaAnppMGMhE/WpmqnSaKfvbm/qPxf1IEVA/9DHls/5OvzjuLUHvZC/60XRD0kDBJQHAko0BPRib8ZCH/N9hAmwX510VnnRdwbMSJ9JENCPx9ueNqbq9NoiozVtiJp4XvTD8hoCygMBJd4H9EI3xsLT9Bli5ltIJxjpfNKIWekj4QG9sozeJa6VfFS3mW1JraveYt0dgh+Y1xBQHggo8Tqg5zqqz+GW6jbGTJfXnw7bTrtiyNz0heiAFraiGTNa3xPDHImnI2n7vSf2oXkLAeWBgBJvA/qx+rKsqnln/zTCMnpa1PaEMfNTO7EB/TSe3rHsof+BEdvay77/WFkIKA8ElHgZ0PdvYyxqg+6jzFwH6Sx34bMuGTRLNRIZ0GtP00GYTVYYMbcts6qrt33/++IendcQUB4IKPEuoO80ZazmM0YMM3OtbKiO6ZZynfVCYEBP9aRtawn6vflZ2t57aadQP3onFAHlgYASrwJ6qjFjMVsNGmamOhJHp1qeItPF44UF9PtldPmou541cHan0uapERcEPUCvIaA8EFDiTUD/rD5vq7vTwHFmpnV0yHazI8bNWW+JCuibdKXomqnGzu0cupOmx8U8Qq8hoDwQUOJFQP9Un7F6/nH6JR4F8WGMhYyT5mmRmIBeXVaZTrNi+FlhLNMiGAtf4B/bkhBQHggo4Q/o63UZa7jb6IFmpk0t1HY0yDFy9npBSEDfuUedBbVNOSn2lubqXT3wmYAH6TUElAcCSrgDerIOY40qOC25vzk6gc5eGfuxoXOYl4iAbqQN5P3yzJnb+UPo7MxybbnzDAHlgYAS3oBSP5vkmDPSTLStDZ00OFOGV5bmB/TTB+nIo4Xmze20qoxVSjf7YXoPAeWBgBLOgJ6sHZD9LCqyJNEm6D5nDJ7NHEwPaB4dUHDvc2bO7Z30pslwmfZ98AgB5YGAEr6A0vPPpnvNHGrm2U0biKssEX6CEZMDeikphLGqKSbP7Hx60ttWgn+uyoWA8kBACVdAXw/gfqrm1FBHdcdXDJ/X5TM3oG+0VR9zayP3/byJWRGMRR8y86F6DwHlgYASnoC+ob7WaxK4/Swqeq6vWpPw6WJfWpoa0A1VGAt7SMglBZ5S/zUOk/uNUASUBwJKOAL6Zr0Aff/TxdJ6tKP3QRNm+E2ZGNALQ9VHWy9D0LzeR+e8H3vZtEfrPQSUBwJKKg7on+sz1jjA+1lUdDiOziU8XOAeTeYF9DgdhNXLpJ2XPDg6SL3/uyXeIxQB5YGAkgoDeqohY40Cvp+qjbSFuOZ6YZdMMiug1xaFM1Z5ltB5nRzGWLPXzXm8GiCgPBBQUlFAzzRmrGGA7T9/E4WTIumSSaLOVm9SQD97gC56JPqMMCuqM1ZD2k1JCCgPBJRUENB31Vd79QPq+M3yZHVT41JpppiNSeYE9BidyG9Qvug5XbS9EWPhG8x4xBogoDwQUFJ+QD+4JaDOH1KxJ+iSSU33mTX3XZkR0Kvz1dfOVaW4oNWBDuqcniXDIWBlIaA8EFBSbkDPtmCszi7RQ81UBxXamDToXdMWQDETAvrp/XQeaUlOSHi0nzoxcZJdFsAOAeWBgJLyAvrJ7YzF7BA90My28XY6MmmB6bvZGB/QYw3UR6YUiJ7BTpbxIYzd/bnRj1oDBJQHAkrKCehnbRirtU30MDOfZVo1NTS3m32uZaMDevVJ9eV7tfmi566r2eHqfBbwZL8iCCgPBJTcPKCft2es5mbRY0yIvX3pEpVxH5i5IIwO6Gd0tFVLyd6PWan+U1X/VUMftxYIKA8ElNw0oF90ZiwqAK4fp83qZmpuqi4y83W8sQEtoq3vQ6V5+e6UWVtdy/KNfOBaIKA8EFBys4B+1U19vfeU6PElztFE2in0tjzzloSRAb22UH2xXO0J0TPVg+xbGIuQ7YKdCCgPBJTcJKBf38NYlfWiR5dQe3oz2h5/2qwlYWBAz9EG79sk2fruJrc9Y6ErDHvomiCgPBBQ4jmg36rxiFwjemyJtoqu4lM55aI5S8K4gD7fSH0cQ6R7+e6Q30O6HUIRUB4IKPEY0MsPqK+rVooeWeIVJtE1gxpsMWV0GxVQ28t3OXae9+zYYHUmjxN+QmsXCCgPBJR4CuiVQYxVWiZ6XEnhuQdpe3zXF0xYEgYF1Hbsews5X747jaNnyBLtUo+A8kBAiYeAfjeUsfBFogeVLJ6mU7eHjDJ+lyZjAlpIO88PEX/se/mS1X+m7vvSiMevCQLKAwElZQP6/QjGwqTa31osyzy6+FqVuUa/FWpEQL9/nHael/jlu1NaJcY6fqr/DNAGAeWBgJIyAb06lrHQVNEDSir542mXpvpPG3uqUAMC+sG9dEiV3C/fHdLVWXybLAclIaA8EFDiHtBrD6svWGf7OBq2JD+ctEGXcSWJPf3ordB2hp7AUv+A7otRl+Wwo6JnHp+najDW6A29Z4E2CCgPBJS4BfRaojrmHvNtKDzbiXagZC0Cajf8je3pMfX9g3FLQu+AfjtZjX6NxaJnHLetdRiLeUnfeaARAsoDASVuAU1S+5ns20DYXoPZRazWZ2RJ4knamzJ0zHtGLQmdA3qyDV2reY/oueaFLHUGVz+q60zQCAHlgYCS0gGdoQ66yb4NA0tr5lTniD4jSxJHp9I/DZVnGHQCNl0Dem11ZcbCJlhEzzOv7Guhzl4hJ7N2g4DyQEBJqYCmqIF41MdRsJ6VmKPLuJLHwfjKdOG5BYZc80PPgH7Sn7Z6ibpusWZ5bRkL36rfbNAKAeWBgBLXgD6ujroJvg6CR1wCOkCPUSWVPQPphPX11xpwmiYdA7q3tjqR94u7brFmR7oyFrpGt/mgFQLKAwElLgF9go6o83kMxLkEtJsOY0o2W++hR9Zs8/d6LwndAvolHdhTba7oGaXJ0Z7qtD+p04zQDAHlgYCSkoAuUFfdeN+HwEMuAe3j+81JaH1Hemx3ZOl8hLxeAT3aVJ269v56JcBjA9Wpny741CIIKA8ElBQHdLG64o7SYQQsdAloog63J6PlLenRtc3RdZzrE9Bvp4YyVulR/9p65MoyXJ21D+v+/N4rCCgPBJQ4A7pUXW2H6zEA8mOK+xmRrccNysgy/xZ6gB336ZhQXQJa1IL2wPXvC7FMoMupmH5NP1cIKA8ElDgCupxWWs2r/OHlj83JdD7pWRDiDKiPO0RJzZLa2JZQ/Z6F6hDQi5PVp59h8X5y7NFN0alF+n2jwyzVCgHlgYASe0DT1RYoWl/2HY2vQi1pvsrxdWpVWz7DA/UFvMOxFLrMEGu3W6dD5H0P6CF697NZABxDOyeMsXu+0GOmaoOA8kBAiS2g9PxzqNZ+FnRyPOEMmev4zoHEu2/vOn63TqNJXoX2hLbaosvJgH0N6LnR9K9WvKwnnvfKwgj1yf1nesxVTRBQHggooYD61M+isSXvefrrpl/NCmfbXsg3y9DhbMC+BfTaJnrv+bZNoueITlZGMnb7+77PVG0QUB4IKFEDusynfhZULdnqPkzPMeQfjqXaNifVW3DB1yXhU0D/eDcdZTqxUPTs0M2GKMaavu3rPNUIAeWBgJIb/1riy/ufRUVrXXZbaq7jCPIblgV30GOPmu7jaUZ8COgX08LVKejiFyf+5LVZfUZd9zXfZqlWCCgPBJTcWOZbP4vSXAJaQ7/x41dWdqZHX2nkK74sCc0Bvfo0nTI/Zp7ouaCzXQ0Yq/m8L3NUMwSUBwJKaP/PWF92u17iEtB6uo0ef7OxJx0jz+7bo30PcK0BLaQzlYbFHRI9C3SXcwtjVXI1z08fIKA8EFDVbJ/3n98dUhLQ7joNHX+0S6HrfrCmS89rXBTaAvoWXROYddoi+uEb4UAr9Wn9do2z0xcIKA8E9IcfZjLfj99sXxJQP7h6mYEOJNgOwqoyQdtp67UE9MMEevOzYaBeAvBwZ8ZCV2uamz5BQHkgoNemqKNvrK9r+dMRzn529t8jsPVxNNW2PYl12/yt90vD+4B+Oo2e9FZP9Pcjj26uoIf6AFO9n5c+QkB5BH1AryYwHc7/WVSUXtPez7sO+n5bfm99H3pOyGolnfR2cXgb0E9m0A5kleIOiH7IRjr2oPoYJxl7OdSyEFAewR7Q70YzFqLL4ZYHHunYsGnvJcH+/NNh34R6tn9Puq7zbtdQ7wL63hQ6fjZsQKAf72UZRe/Sm3xqEQSUR5AH9PJQtZ9TRY+PgGRZ3N22Tb7yiINebJT3JqB/HFNJvf3QvjtEP1QTTAph7P6vvF/BfYCA8gjugH7zgDoAZ4keHAEr++H6tqehdae8wHu6Ju6AXt3bh/Z8CO+3XfSjNMeccMY6fap1RdcCAeUR1AH98l51BKaKHhqBzLKyr22/Jtb0sRNci4QzoB8vaGp7dqsEz4kHllRmrMUZX1Z3LyGgPII5oOc6MRaxSPTA8HMFucfK/4VDKZ3sO8k2m2ap+LU8T0CvPDfEto0qenxAbzpyty6KsXo+HeblHQSURxAH9P07GItcIXpY+LXC5BYhrFKnxRX8Ws5k+45NLGbssxVsU6owoFeLEu1n+289N3B3XPJsa13Gqh/Wa/WvEALKI3gD+lYTxqLWix4Ufi2vjWPf10EV7nvw7MTb7c9Dw7s/8UI5T0TLD+iV/MmNbLdSbUimCY9PNnuaM1Zps94D4WYQUB5BG9CXa6tPiDJFDwn/dlfx0VcPc/x2dlKnMPtvRw1YcvyK58VSTkDf3zg0yvbnoV3m5hv+2KSU14GxkAUGjQh3CCiPYA1ofnXGGu4SPSD826qSw1cr53L9RV5av2jHX0R2n77rVNlt854Deu2tZ8bdav+7kDZT91C4xKoAABl4SURBVBn8uCRW0EudBRPNuVonAsojSAP6bARjt+4VPRz8nFISUMa9L4Nl06N3Rjr/KurexLX5pU65Xiag376yeWbvms5Od5sR5AvNMkydDQMumjFGEFAewRnQVaGMtcsTPRj8XVeXgI735g+PZkzsHlXyt5GtB0584pn9x9/66CtnQC9+/NYL+9Y9Pva+JqHO3wq5RVkapK/cS5kSwljHj0wYJAgoj2AM6LUZ6ni8B4PRV3e6BNT7s7FsnxPX3uVCKA7VomvVqhXh/t3IdiMXBtUuS+WZr86eJl6fZMB7CCiPIAzo5RHqkBxcwd6LULFBLolL0XQLll2LJw5oG+3eSxfhje+NT92CheVqfU3Gog4ZPk4QUB7BF9Dz96kvB716xQmeLXbpnE9vTR7Zmj77kbgHurdv2ahBg5o1GzRo2LL93f3iEueuzUI6PdjVRJ3jGUYPFASUR9AF9N1W6sr3e9FDICBYWhUHNE70tASXAx3UeT7F4I3xCCiPYAvoi3UZq7Jc9AAIEHsaOfrZLdgOChLtaH91rj/whaFDBQHlEWQBza7CWO1nRK/+AePQqBrqQG6YjJOgmu6REMbuOGXkWEFAeQRXQBeGMtYiW/S6H0gsOzfmiJ6G4LQwkrHoAgMHCwLKI5gCenksvdo8LHrNB9BDZl3GwlcZN1wQUB5BFNBPutHWDmzWhQCxj87lMvaSUeMFAeURPAF9pbH6D/YM0Ss9BJzC+YO69hi/U8A9FwxUC3rn+xWv+5ogoDyCJqA7IhmLWilgLYfAlmk7Nz4LHSHitU1yOGN1Co0ZMQgojyAJ6Pd09OYtIp4lQGDbWXxQ/yARd7+mlvq6ainvFae8goDyCI6AnutDR78fErGGQ2DrVnI41moR959NJ/sf+qUBgwYB5REUAX2pCWMh47CzIuguJ6QkoP2ETEEBnZLgtj/qP2oQUB7BENCMCMaqLRSydkOAczkfAGsqaBpS1PU7cpPuwwYB5RH4Ab04Sl23m+8QtHJDYJvjEtBoURORSYfUxut9lmUElEfAB/Tk7erK1Rd7z4MhlroEtLmwqTjYU737lq/qO3IQUB6BHtD1kYxVSha2ZkOAywsvCehQgdMxtRJjESt03RqPgPII7IBeoMv2NNwocMWGANe/uJ9hW0ROx6bGtBnrEx0HDwLKI6AD+jzt49zroMj1GgLc/sbOgD4qdkIO91Onoc5+/UYPAsojgAN6ZU4YY5UfE7taQ6Dbd7ctn1GzRE9IUWo1dTomfKXX+EFAeQRuQP/USV2dWmwVvVZDwNsyOTY+TYbNlFlt6Xg7i04DCAHlEagBvZoeyVhIXIHodRrAPMcSwhkLnfatLkMIAeURoAE9fS+9IbRC9AoNYK5Nzel11/N6jCEElEdABvTqyiq082ee6LUZwGxHR4eqT0In6fBOKALKIxAD+jqd4KHWk6JXZQARNtC+J4193xyPgPIIvIBefjxCXYF67xe9HoO/2TSwaVTjfutFT4bPCsbQ3v2xZ30cSAgoj4ALaH5LdeWJwalDwFtj7SdWCon1/9N2ZbaiHatWfOfTSEJAeQRYQM8OpyEwCO9+grcSiw8pihc9Kb6zJNFWgDY+7dGEgPIIqIBeXlJdXW2arRW9+oL/2R9ZHNDwLNETo4PsHvRUYsQH2kcTAsojkAKaS6/eIyceFb3ugh+a5XJapUQ9b9gyr2uNiHoDN+t5m1yW0knuqs77WutwQkB5BE5AT/aldb9ntukrKgSCYS4B1fPU8oe7OE41MlnHG+VTkEDPqhtmXtU2oBBQHoES0A/Hh9FhbLjsJmgzxCWgPXW83ZJrJqXqeKuc9vSlDWNtD2oaUggoj8AI6IUUes+8RnKh+esoBIYJLgEdpt/NppfcaoyI95Y20OHxrMdxDYMKAeURCAG9uCBaXUkiRmDbO2i2wSWgy/S72YEuNyvmyOL5tvPt9X/F62GFgPLw/4B+vaQ2bW/su1vI6gmBolNx6FrquCNoW5eATtXvZr1xdHoMDZDB3l7xAwHl4e8B/WpxHVo3u2eKWTkhYOyp73ypvUvHW21p1MZ9bxyZGEUJHeDdC3kElId/B/SzObVozezi/4ffec+yZ9cR0dMQUA4MoCMgQ/vs1fNG73UJaJqeN+ydg+NpD2l2b64XV01CQHn4c0DfTrTt/HxnhrgVU5gDw9V/OkLbLRE9HQElb9njSw/oe5MzSvoZrvNNeydvfA2aiNbPXOYdXggoD78N6LX8waH0uuSeDSJXS1G213GMyuGipwTKlV+vOKCxgifl8CTaWMDqpn7EN8IQUB5+GtCL61rb/lHvF5yX7MhvWDwsZ4ieFijX09UdC6pjvuhJKTo6i850xyJGFPGMMQSUh18G9LVE28uR6qP2iF4lBUkueWFYC/u+yi2rJ71SipogxRHGliWdbeecar3y8wpHGQLKw/8CeiHDvsNJ0+kyXMdLjC4umyZw6hTZ5a1a+JQ8/8xtHWzbclB5xOEKDvFEQHn4WUCv5A6vbHvt3mOl/5+1UbvGLgGdLXpiwM/kJTWzrTkNZ54sb6whoDz8KaBXLY/Wti/5CTmiV0KxEFDwSUY/+9n72iw4dfOhjYBy8JuAfn9scgPbMo/sG9RPPm26ugQ0GHfiAp8dmtXOfgb+Dk/+6SZDGwHl4B8B/TrnoRjb0g7tMjt43/ks4bJ3YfQx0RMDfurZCU3t69BtjxV972FoI6Ac/CCgb63sa3vfk4W0m7ZP9Eonh4KS1/CzRE8L+LFN8Y0c/w4Py3TfPRQB5SF5QD/d9bDjX8nwzslB/sanq52O/bNDRomeEvBzmWObO9alttP2f+k6tBFQDhIH9MNdia3tb9Owar0ezxW9osklb1QMY2Ed00VPBwSAXUl3VrIPtLAuM5475xzaCCgHOQN65cXVIx3PPFlIy/jV8uxFJ5F9WeKPbYEAcXjR7xo5B9ztYzf88btgCui/FOWq49N/KMpfPP3Ka8p2z38rXUAvn9iY2LWy8x2+xoPm7Re9bgEEhaxZfWs7B16VbpN3v3VF36HtLbdUmRjQrUqy26/4Q0CvvbN/4cg2lZyLMKT5kFS86wlgpp0p/ZuEOIdgpbajFh04o/GadL5zSxUCelPn/t+2ucPaVynZN6dmt/HpB0WvSwBB6cDiMV2iSgZjZPu4udte+Mz3Ye4tt1SZGND3j77s9ityBvSz1/atSh7asabLbuGsbvexC3BpYgCxsubH31XbdWBWbzckaUX2yx/7GAwvuKXKxIA6cnl6VcJD6V+fVI7YvvPPHY+OmPrsT8IDevnsHw5uWTx15L23Vmal1OwwJHk1NrYDyOLAqmmDO9QsPUwjmnZXpjyZeeDF9771sR0VER3Qg7HKmARl9Hp7QDOS42Y9pihP3DA9oFc+P/PqsX2bV6clxQ+467YarIzqLXvGz16PdALIKHf9nDG97/AwcKs27dR3eOKcZZuyj5x4++OvfUxJGYID+oky6vUb1vOPKvaAKtO/U1/bD1POGRHQb86f//jMmZMnThQc2Zu1ZcPqRWmzJifEDe7Tpc0t0RFlZ7xdaO1W98VOWbAJVyMGkN/BzEVJw3u1qRN+swEdUqtxy049+8WOTZiZsmjphqeysg4def7EiVNn3jnvxaWYxAT0ofF240oC+qRSRD/8xBnQL+irlcor9r+5/IzdX//tvbfvv1N1q6pJrejomjebn55ncs2m7e8bOn7GoqdysHMngB/al7kkZUJsn07NY27aUnfd/qGhM2YG1IUjoD/HjvzZ9tOZ9oDOsX2Rpbxk/5t3u9pd13B/D3kRzPDq9W5pfWfPB4c/PO3xpet3CL2uFgDo6lDWphVpMyaOHtK7W/vbGtSMvGkHLvmeOZNfwl9Uptu/lWEP6AbbF7v1CGiu81V5VHR0dMMWLVp06Nq1R//+Q0ePfjRpZtr8NWt27Dh05OWTZ85d/Nt/fX5wAOA3frzyxXunXn7+UM6Op9csS0tNSho/enT//v1X63DTJgf0tPKk/Vu7HFvhbV8UB/THl+3++g8Nrn7zjfPTG9YbWm5BMjd+Fj0FvvuP1RoQj0L0FOjgN2sgPArhg0JsQM8rM+zf2uAxoE5S7Egv3A3Rh/3q4Cer9Z+ip8F3P1lFT4EOfg2IQRHEx8JTLv+ljPrF9q3ZCGiFEFBZIKDSCPKAWlOU4/Sd8woCWiEEVBYIqDSCPaDvKPFv37B+PWWYko+AVgABlQUCKo1gD6j1WUV5KFGZbVFeRkArgIDKAgGVRtAH1Hpy8dgJW/9zVDmDgFYAAZUFAiqN4Alo+bbYj0G6CQSUIKCyQEClEewBnTfsI/rw9/gx5e3NjoASBFQWCKg0gj2gFmXKO//56b2Zyu7yfgsBJQioLBBQaQR7QK3bYm3Hxi/9ubxfQkAJAioLBFQaQR9Q6zeHt2wrPFv+7yCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHkgoAQBlQUCKg0ElAcCShBQWSCg0kBAeSCgBAGVBQIqDQSUBwJKEFBZIKDSQEB5IKAEAZUFAioNBJQHAkoQUFkgoNJAQHn4GtDr16/rMrPE+jUA0vOX69f/JnoafPdTIKTnx+s/ip4EHfyKgJpgcNdeoicBbI517ZojehrARul6r+hJCEAIKBgJAZUGAmoEBBSMhIBKAwE1AgIKRkJApYGAGgEBBSMhoNJAQI2AgIKREFBpIKBGQEDBSAioNBBQIwRmQN99+7ToSQCba2+/fUX0NIDN+2+fEj0JASgwAwoAYAIEFABAIwQUAEAjBBQAQCMEFABAIwQUAECjQAzo1U0TR0zadl30ZASzA4rDJfoKC0SUF5TvHJ+VWgZYILoJwIB+OUZRJihKwlXRExLENrgGFAtElF9/7wxoqWWABaKfAAzobGXZj9Zrc5R00RMSxOYoF/5jc8OKBSLKb5+nK86AlloGWCD6CbyAnlMm/qJ++PvIuL+InpTgNWbYr8WfY4GIkTOcXgLYA1pqGWCB6CjwArpF2Wn7uEo5LHhKgtd1JbnkCywQMV7JzMwc4QhoqWWABaKjwAvok8rbto/PKxsFT0nw+lhZeXrd3FW5P9IXWCDijHcEtNQywALRUeAFdJryme3jG8piwVMSvF5WRtg2IT30uhULRCRnQEstAywQHQVeQEcrl20fP1RmCp6S4JWjDDv87b/PpSsjL2GBiOQMaKllgAWio0AMqP38aR8o0wRPSfA6ffQT28fFSgYWiEglAXVZBlggOgq8gE5Tztk+vqE8KXhK4F1lChaISCUv4V2WARaIjgIvoE8q9pMpH1eeEjwlcFUZdgMLRKCSjUguywALREeBF9DNyh7bx/VKruApCVr/sTx/w/bJp/QuGxaIOM6AlloGWCA6CryAfqZM+U398MuYOBypJso0x3OcncozWCAiOQNaahlggego8AJqTVE237D+L11ZKXpCgpdFefh9dYQeih3zgxULRCBnQEsvAywQ/QRgQL+IVx6aN1p55JroCQleN9YqyrjJccrYt+grLBBhigNaahlggegnAANq/W5jwvDE7ThZl0A3Ts5/ZPScbX+zf4UFIkpxQEsvAywQ3QRiQAEATIGAAgBohIACAGiEgAIAaISAAgBohIACAGiEgAIAaISAAgBohIACAGiEgAIAaISAAgBohIACAGiEgAIAaISAAgBohIACAGiEgEKAKGTsova//pWxzfpNCwQLBBQCBAIK5kNAIUDoEtA/MHaXblMEgQ8BhQCBgIL5EFAIEAgomA8BhQChS0B/vX79J92mCAIfAgrSiWNVf6GPHzAW9nf65CJjU+jjT2vuianecdo55y+W+toZ0H90Z+FH6ZO/L+/TuGq7UW/e7G5ej+9Qq3ob5dhv9IVzI1J1tsJqncPYGccvDWPh1zzcM4ANAgrS2crYG/RxC2PsBfokh7F89cOZRswmdK3990p/7Qjov3qx0Fz6+s0Y+09Zqsc7+e8Ix4/ZkF+tbgF9m7EF9t/6RySL9XDPAHYIKEjnC8bS6eNDarLm0yeTWPh19ds1GBu9PS+lGmPrbL9W+mt7QH8ewEL20E+vNmBVZhwoXNXQEWF3axhrmVFQsLolYxutbgG1Nmdt7b+135Zu93sGcEBAQT4tWT/60ITFsPvsX/dS/z+AhdHzUOu521i1S2W/tgX0v0MZ2267jT2M/R99/LwSm+zpPu5hd9jeHrjejMVZ3QM6jzH7q/VhLOaXsvcM4ICAgnxmsio/2975XMYi/m21XmFsldX6EWMp9h+/ytjisl9TQH8dxdjT9m/OYhE37J8MS/NwF/+tX8vxZHIia291D+gZxmwv1tVX8NPL3hOAEwIK8ili7KTV+hxreY6xV63WXMbetVpXM/aN4+ftWf+yX6sB/WICYw87vreMsYNcdzbcQ0Ctt7F76MNzjP257D0BOCGgIJ9/RrBlVutU9qi1IVtqtSaz+uqTyRGssfPn41iDsl+rAf0dYyz6mv17H1Ri7M7NF8q/o18+2/NQiKeAzmchV6y0P8Ad1rL3BOCEgIKE+rK+Vms7lmUdyx6wWtuyBPV79zEX1cp+XUhbyUcylui4jf3R9IMGDx/9xfN9fP/U4BZhtj/2END3Gdtmtf69Mlvp4Z4BnBBQkNBaFvnzX0PY59ZtrMov10IY7ZfU2TVj7LcyX6sBDdn9W1cW8pbjRn5Y38sWyNanPN3Fvurqj2r3nPTsGE8BtbZiA9XfYSFfe7hnACcEFCSkPgH8g4XVs1o/ZexP+Sz0L+r3+rAupX7H/etCWwL/HMLuLEnc9eOpDRhr9GPZezgVyppt/Yo+m+YxoItYpR+tsfT8t+w9ATghoCCjBmzJPDZS/aQ+Wz2L3U3fmsIif3X9FfevHTvST2JsC315/fq/bd/+3wLGCsvewSQW4Tjwc6zHgJ5lbL/6Cj7H0z0BOCGgIKME1qeHbYekeDa4M1tC38pjLM/+w392ar6q7NeOgP4Qbd+OVJ9eg5NzjD1b9g76snb2T/7XwGNArW3ZqL2s2k+e7hnACQEFGR1glSNsx6NvYdVCaVciq/WXhqzeZ/TJjSTGPir7tfNY+K327UiDWcRZ202tZOzDsneQyKr/gz7+lsDYrVYPAV3KqvW3bbwqe08ATggoyOiHUMaq0+vmjxljMfY3NS0hLGLm3he23ePYr93ta2dAf+ti246Uy1jdJYUvZo9g7F4P230OqN8uPHsy8w5WnbGFp8oG9FPaYHTC6vGeARwQUJDSXYwNsH1Sl7Gxju/tjbRvBw+ZdsPD18Wns3vLvh1punOzeYdrnu4g3vG3089EMDasbECtHRhr4iyv+z0D2CGgIKWFjC23fTKKsT3Ob37zePuoqDsf+dDj1yXnA3VsR3ozvmN09bZKvuf9jm7k3t84ssXkM1bryZGxuzwEdIXjTCYe7xnABgEFANAIAQUA0AgBBQDQCAGFIFC03k2m6CmCwICAQhAYy9zUEj1FEBgQUAAAjRBQAACNEFAAAI0QUAAAjRBQAACNEFAAAI0QUAAAjRBQAACNEFAAAI0QUAAAjRBQAACNEFAAAI0QUAAAjRBQAACNEFAAAI0QUAAAjRBQAACNEFAAAI3+P0PPNbJm7mb1AAAAAElFTkSuQmCC" 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s2aJScnS2pRQW93MB0It3DgN73/ld5ar6W7dP5Kun5kZGPHjPRHjx4dO3ZshsYDcKf8TAcAAACAJzl8+HB4eLh9krMaJbXgZflYTGfC/9h9Qi9/oO9P3Tji56u2lTS1rXKF3O4Hs/hrbhdVHi2rTZMmTWrevHmpUsxMCLgLzsADAAAgvc6cOVO7du2oqChJTxfR6r4K4HyQ+1l7QFXG/KO9S0pJ1fztqjRaf15P48crPqaeNSUpOTm5S5cuqampGZQTwJ2iwAMAACBdLl++XKdOnd9//13SYw9ozSBlDTSdCf/jcqzazVbyLUr34bN6ZUHaTzKuuR4KlaS9e/fOmDHDmfkA3AMKPAAAANIWHx9fv379gwcPSsqXUxuGKk9205lwMx9sU3TM7QZ8tEtnL6fxJNkCNTvCsT1ixIjffvvNOeEA3BsKPAAAANKQmpraunVr+7pi2YO0drAK5zGdCbfw5aE0Bths2vpz2s8TVlotK0pSTExMjx49nJAMwD2jwAMAACANvXr1+uSTTyRl8dcn/VX6YdOBcGvpmW3+XPpmpH+rne7LKklr165dtmzZPcUC4AwUeAAAANzO2LFj33nnHUk+Fi3ormpPmA6E20rPxATZgtL1VHlz6M2Wju1+/fpduZK+3g8gw1DgAQAAcEvvv//+yJEj7dvT2ql5BbNxkLZSDzlnjF2n51WlmCSdO3du+PDhdx8LgDNQ4AEAAHBza9as6d69u81mkzSonvrUMh0I6dC2siyW2w0olk/PPpreZ7NY9E4nx2KB77777p49e+41H4B7QIEHAADATezZs6dFixYpKSmS2lS6cSk13NzTRdTthVs+6u+r/3tJPrdt+P/yRAENqidJVqu1e/fuLAsPGESBBwAAwL+dOHEiPDw8NjZW0otPal7XNE7qwq3M6KCXX7zJ8ZzBWt5HL5S44yeMbKAi90vS999/P2vWrHvNB+BuWezXRAFGREdHW61Wi8USGhpqOgu8QVRUVFBQUHBwsOkg8HgJCQkxMTGSQkJCgoLSN9cTcGv2d1Tu3LlNB0mvixcvVqpU6cSJE5LKFNK2EcqWjnnR4G72/aoPtum7X3UtXvlz6sUn1bmacme7y2db94PCJkpS9uzZDx8+nD9/fidGhUHR0dGBgYF8ffIUfqYDAAAAwI3ExsaGh4fb23uhPFoziPbuqcoVVrnCTnu2OqXU5Bmt2qNr164NGDDgo48+ctpTA0g3LqEHAACAQ0pKSosWLewTlYVm1brBypfTdCa4jbfaOdaoW7p06ebNm03HATIjCjwAAAAcevTosWbNGklBAfp0gIpzlTT+puB9GtnYsd2rV6/ExESjcYDMiAIPAAAASRo7duycOXMk+fpocU9VKmo6ENzPK7X1ZEFJOnr06JQpU0zHATIdCjwAAAA0f/78kSNH2rffaqdG5c3GgZvy99WslxxLEowfP/733383nQjIXCjwAAAAmd2GDRu6du1qX5xocD31qmk6ENzYf4qr9XOSFBsbO2DAANNxgMyFAg8AAJCpff/9982aNUtOTpbU+jm90dJ0ILi9ia0caxOsXLmS2ewAV6LAAwAAZF6//fZb3bp1r1+/LqnaE/qgm+PqaOA28ufSyCaO7T59+th/+wPABSjwAAAAmdTly5fr1Klz7tw5SU8W1Mf9FOBnOhM8RJ9aeqKAJB0+fPjtt982HQfILCjwAAAAmVFCQkLDhg0PHz4sqUAurR2sHMGmM8Fz+Pvq7Q6O7TFjxth/DQQgo1HgAQAAMh2r1dqhQ4ft27dLyhGstYP1YKjpTPA01Uuo6TOSdO3ataFDh5qOA2QKFHgAAIBMZ/DgwcuXL5cU4KdVffXUQ6YDwTNNaavgAElauHDhrl27TMcBvB8FHgAAIHN5++23p0yZIsli0dwuql7CdCB4rIdCNSRckmw2W58+faxWq+lEgJejwAMAAGQiq1ev7tevn3379WZqV9lsHHi8QfVUOI8kfffdd/PmzTMdB/ByFHgAAIDM4ptvvmnbtq39NGm36hrewHQgeL6gAE1p69iOjIy8evWq0TiAl6PAAwAAZArHjh1r0KBBfHy8pLplNLNDmj8BpEuj8o4bMS5evDhmzBjTcQBvRoEHAADwfhcvXgwLC/vzzz8llS+iZb3l52s6E7zIW+0d76gZM2YcPXrUdBzAa1HgAQAAvFxsbGx4ePjJkyclFc6jLwYqJIvpTPAuTxZU9+qSlJyc3L9/f9NxAK9FgQcAAPBmqamprVq12rNnj6TQrFo3RHlzmM4EbzS6iUKzStLatWvXrVtnOg7gnSjwAAAA3qxXr16ff/65pEB/fTpAxfKZDgQvdV9WjWnq2O7fv39ycrLROIB3osADAAB4rQkTJrzzzjuSfCxa1EOVipoOBK/W9QU9WVCSjhw5MnPmTNNxAC9EgQcAAPBOixcvjoyMtG9Paasmz5iNA+/n56u32ju2x4wZY580EYATUeABAAC80JdffhkREWGz2ST1q6O+tU0HQuZQvYQalJOkK1euvPbaa6bjAN6GAg8AAOBtDh482Lhx46SkJElNn9HkNqYDITOZ3EYBfpI0Z86cgwcPmo4DeBUKPAAAgFc5e/ZsWFjY1atXJVUupoU95GMxnQmZyaN51aeWJKWkpLCkHOBcFHgAAADvce3atbCwsNOnT0sqnl+f9Fegv+lMyHxebag82SVp06ZN9kUQADgFBR4AAMBLJCUlNWnS5Mcff5T0QE6tHexYlxtwsRzBN5aUGzRoEEvKAc5CgQcAAPAGNputc+fOmzdvlpQ1UF8MVOE8pjMhE+tSzbGk3NGjR2fNmmU6DuAlKPAAAADeIDIycuHChZL8fLWst8oVNh0ImZuvj6a2dWyPHTs2OjraaBzAS1DgAQAAPN7s2bMnTJhg334nQmGlzcYBJKlGSdUrI0nR0dGjR482HQfwBhR4AAAAz/bZZ5/17t3bvv1aY3V63mga4G8mt5G/ryTNnj376NGjpuMAHo8CDwAA4MF2797dqlWr1NRUSRFVNaqx6UDA3xTLp5dflKTk5OSBAweajgN4PAo8AACApzpx4kR4eHhcXJyk2qX0TidZWPLdqx0+q/e+1Jufa/ZmHfjNdJr0GdlY92WVpC+++GLLli2m4wCezc90AAAAANyNixcv1q5d+9KlS5LKFtKKPo5rleGVfrmornO15ed/HKzwqN7rrJIPGsqUPvdl1WuN1HehJA0YMGDfvn2+vrxTgbvEGXgAAADPExsbW69evZMnT0oqlEdrBilroOlMyDCHz+qZEf9u75K+PaGKI7X7hIlMd6JHDRXNJ0k//PDDBx98YDoO4MEo8AAAAB4mJSWlRYsWe/fulRSaVesG64GcpjMhw9hs6vCOomJu/mhsotr8nxKTXZvpDvn7amIrx/aIESOuX79uNA7gwSjwAAAAHubll19es2aNpKAAfTZAxfObDoSMtOOY9v5yuwEnL+jz712V5m41KKdqT0jS+fPn33zzTdNxAE9FgQcAAPAko0ePnjt3riRfHy3pqeeKmg6EDLb1f66cv8mYQxmf455NaSMfiyRNnTr19OnTpuMAHokCDwAA4DHmzp07atQo+/bb7dWwvNE0cIkL19Iec/5Kxue4Z2UKqX0VSYqPjx8+fLjpOIBHosADAAB4hjVr1rz88sv27aH11aOG2ThwkexBzhnjDl5vrpAskrR48WL7JA4A7ggFHgAAwAPs2bOnRYsWKSkpktpV1vjmpgPBVUo/nPaYMoUyPIZTFMilgXUlyWazDRgwwHQcwPNQ4AEAANzd8ePH69WrFxsbK6lmSb3fVRaL6UxwlbqllSf77QYEB6jZs65Kc88G1VP+XJL09ddfr1692nQcwMNQ4AEAANzahQsXateufenSJUllC2llX/n7ms4EF8oaqOntbvcrmwktlc9z1hEMyaLXmzm2hwwZkpSUZDQO4GEo8AAAAO7r+vXrdevW/eWXXyQVzqM1g5Qt0HQmuFyr5/R+FwUH/Pt4gJ+mtFGfWiYy3YMO/3HcF3DixImZM2eajgN4EovNZjOdAZlXdHS01Wq1WCyhoaGms8AbREVFBQUFBQcHmw4Cj5eQkBATEyMpJCQkKMhD5oaCG7O/o3Lnzn2nP5icnFyvXr2NGzdKyp1NO0eqaL4MyAcPcf6K3v9KO47p0jXlCtFzRRVRVQ/f8dvKLXz5s6qPl6RcuXIdP36cr4IGRUdHBwYG8vXJU/iZDgAAAICbsNlsERER9vYeHKAvBtLeM7sHciqyoekQTvJCCYWX1ef7dfny5TFjxkyfPt10IsAzcAk9AACAOxoyZMiiRYsk+flqeR89+6jpQIBTTWrtmM1h9uzZx44dMx0H8AwUeAAAALfz1ltvTZo0SZLFonc7qW4Z04EAZyuWT92qS1JycvKQIUNMxwE8AwUeAADAvSxbtuyvJbLHNFVEVbNxgIwysrFyBkvSJ598sm3bNtNxAA9AgQcAAHAjW7Zsad++vdVqlfTyi3rVW+55Bv5X7mw37uofMGCA/W0P4DYo8AAAAO7i+++/b9y4sX1l7MZPa0YH04Ey0tZDemWB6k5S+GT1X6Sd3ASdKfWuqcJ5JGnfvn2LFy82HQdwdywjB5NYRg7OxTJycBaWkYNzpXMZuZMnT1aqVOnChQuS/lNcG4Yq0N8l+VzuwlW1/T9tPvjv4+FlNb+b7stqIhPMWf6tWsyQpIIFCx49epTPcRdjGTnPwhl4AAAA8y5cuFC7dm17ey/5oD4d4LXtPSZBNSbcpL1L+ny/6kxUYrLLM8GoZs/quaKSdObMmalTp5qOA7g1CjwAAIBh169fr1u37okTJyQ9nFvrhzhm9vJKk9fop9O3fHTPSc3Y6MI0cAMWi6a0kcUiSW+++eb58+dNJwLcFwUeAADApMTExEaNGu3bt09S7mzaMFT5c5nOlGFsNs1La67xNAfA+1R4VC0qSFJMTMyIESNMxwHcFwUeAADAGKvV2r59+y1btkgKyaIvBqpYPtOZMtKFqzodlcaYw2cVk+CSNHAnE1o4bhv54IMPfvzxR9NxADdFgQcAADCmb9++y5cvl+TvqxWv6NlHTQfKYFfinDkM3qRQHr1SW5JSU1MHDhxoOg7gpijwAAAAZowdO3bGjBmSLBa920l1SpkOlPHyZE97jI9FubNlfBS4n8iGeiCnJG3atGnt2rWm4wDuiAIPAABgwHvvvTdy5Ej79uTWeqmq2TguEppVJQqmMeaZR7x2Bn7cXrZAvdbIsT1o0KCUlBSjcQB3RIEHAABwtdWrV/fo0cNms0kaXE/9w0wHcqHeNdMaUMslOeCWulTTEwUk6dChQ++9957pOIDbocADAAC41NatW1u3bp2amiqpQxW90dJ0INfqXE3hZW/5aKvn1KqiC9PAzfj5anIbx/aoUaOuXr1qNA7gdijwAAAArrN///6GDRsmJiZKqldGc7s4lr/OPHx9tKKPBtZVln9eJx8UoBGNtKB7pvsPgn+pU0o1S0rSpUuXxo0bZzoO4F4s9mu3ACOio6OtVqvFYgkNDTWdBd4gKioqKCgoODjYdBB4vISEhJiYGEkhISFBQUGm48Dj2d9RuXPnPn78eOXKlS9evCipUlFtGqagANPhzDl/Ret+0PHzslhULJ/qlErXFHfIDA6eUelhSrUqS5Yshw4dKlKkiOlE3iw6OjowMJCvT57Cz3QAAACATOGPP/6oWbOmvb2XfFCfD8zU7V3SAzkzy9R9uFNPFlTnanp3ixITE4cMGbJixQrTiQB3wSX0AAAAGe7y5cu1atU6deqUpMJ5tH6IcoWYzgS4sTFNlT1IklauXLljxw7TcQB3QYEHAADIWLGxsW3atDl48KCkvDm0cZjy5zKdCXBv92fX8AaO7X79+lmtVqNxAHdBgQcAAMhASUlJrVu33rt3r6QcwVo/RI/mNZ0J8ASv1FbhPJL03XffLVq0yHQcwC1Q4AEAADKK1Wrt0KHDxo0bJQUF6LMBKv2w6UyAhwj0v7HI4vDhw2NjY43GAdwCBR4AACCj9OrVa+nSpbtIfsgAACAASURBVJL8fLW0t/5T3HQgwKM0e1aVikrS2bNnJ06caDoOYB4FHgAAIEO8+uqrs2fPlmSxaG4X1S9rOhDgaSwWTWsni0WSJk+efObMGdOJAMMo8AAAAM43ZcqUcePG2bentlWHKmbjAJ7q6SJqV1mS4uLihg0bZjoOYBgFHgAAwMnmzZs3aNAg+/aIRupb22wcwLONb6GQLJK0ePHiPXv2mI4DmESBBwAAcKZVq1Z17drVZrNJ6lVTY5qaDgR4uAK5NLieJNlstn79+tn/cgGZEwUeAADAaTZt2tSmTZvU1FRJbStrenvTgQCvMKieHgyVpG+++eajjz4yHQcwhgIPAADgHLt27WrUqFFiYqKk8LKa11U+FtOZAK8QFHBjSbmhQ4fGxcUZjQMYQ4EHAABwgh9++CEsLMy+VPXzj2t5H/n7ms4EeJFWFVXxMUk6ffo0S8oh06LAAwAA3Ktjx47VqlXrypUrkp4uos8GKtDfdCbAu1gseuu/S8pNmjTp9OnTphMBBlDgAQAA7snvv/9eo0aNCxcuSCpRUOuGKFug6UyAN3rmkRtLyg0ePNh0HMAACjwAAMDdu3DhQo0aNX7//XdJRe7XxqEKzWo6E+C9JrRQ1kBJWrZs2Y4dO0zHAVyNAg8AAHCXLl++XKtWrWPHjknKn0ubhyt/LtOZAK+WP5eGN5Akm83Wt29fq9VqOhHgUhR4AACAuxETExMWFvbDDz9Iyp1Nm4apcB7TmYBMoF8dFblfkvbt2zdv3jzTcQCXosADAADcsfj4+Pr163/77beScgRrw1A9UcB0JiBzCPTXlDaO7cjIyKtXrxqNA7gUBR4AAODOJCUlNW3adOvWrZJCsmjNIJUtZDoTkJk0LK/qJSTp4sWLo0ePNh0HcB0KPAAAwB1ITU1t06bN2rVrJWXx18f9VKmo6UxA5jO9vfx8JWnmzJmHDx82HQdwEQo8AABAelmt1oiIiJUrV0ry89Wy3qpR0nQmIFMqUVAvvyhJycnJffv2NR0HcBEKPAAAQLrYbLZevXotWLBAko9FH3ZXg3KmMwGZ2Ogmyp1NkjZu3Pjxxx+bjgO4AgUeAAAgXQYNGjR79mxJFove7aTWz5kOBGRuuUI0rrlje8CAAQkJCUbjAK5AgQcAAEjba6+9NmXKFPv2tLbqXM1sHOBepaQqNtF0iHvWuZpjCslff/110qRJhtMAGY8CDwAAkIYJEyaMHTvWvj2+hV6pbTYOcPdiE/XGZyo5RAEdlDVCod3UZpYO/GY61t3ysWhGB1kskvTGG2/89pvH/kmA9KHAAwAA3M5bb701fPhw+/arDTWsvtk4wN07dUnlX9WwZTp4RjabJEXHaMk3emaEZm0yHe5uPVdU7SpLUlxc3IABA0zHATIWBR4AAOCW3nnnnf79+9u3B4RpbDOzcYC7l5SisEk68sdNHkpOVe8P9cX3Ls/kJG+2VPYgSVq1atXmzZtNxwEyEAUeAADg5ubPn9+zZ0+bzSapRw1Nam06EHAP5m3T4bO3fNRm06AlLkzjVA/k1MjGju3evXsnJSUZjQNkIAo8AADATSxZsqRz585Wq1VSRFXN/O99toCHWrk7jQFH/tDBMy6JkgF619ITBSTpyJEj06ZNMx0HyCgUeAAAgH9buXJlhw4dUlNTJbWppDldaO/weMfPpz3m2LmMz5Ex/H01s6Nje+zYsadPnzaZBsgwFHgAAIB/+PTTT1u3bp2SkiKp2bOa300+tHd4vhRrOsakZnyODFPtCbV6TpJiY2P/mroC8DIUeAAAgBvWrFnTvHnz5ORkSQ3KaXFP+fmazgQ4w8O50x5TKE/G58hIk1srW6AkrVy5cuPGjabjAM5HgQcAAHDYsGFD06ZN7TNghZXW8j7yp73DW9QtncaAB3KqXGGXRMkw+XNpVBPHdu/evRMTE43GAZyPAg8AACBJmzdvbtSoUUJCgqSaJbWqrwL8TGcCnKdnTd2f/XYDRjWWr+eXgz61VPJBSTp27NjkyZNNxwGczPP/jgIAANyzrVu3NmjQID4+XlL1EvqkvwL9TWcCnCpnsD4doJzBN3+0Rw11fcG1gTKGn69mveSYdXLcuHG//PKL6USAM1HgAQBAZrdt27bw8PC4uDhJzz+uzwYqKMB0JiADVHhU349Xm0r/+P1UyQe1tLdmdfSepRaqFFO7ypIUHx/fu3dv03EAZ7LYbDbTGZB5RUdHW61Wi8USGhpqOgu8QVRUVFBQUHDwLU4uAOmWkJAQExMjKSQkJCgoyHQcZKyvv/66Tp06sbGxkqoU09rByhpoOhOQweKTdOy8YhNUKI/y5zKdJgNcvKbHByk6RpJWrlzZpEmTtH4i84qOjg4MDOTrk6fgDDwAAMi8duzYERYWZm/vlYpqDe0dmUNQgEo9pOeKemd7l3R/dk1o4dju27fv9evXjcYBnIYCDwAAMqmdO3eGhYXZr7ao+JjWDXEsQAXAC3SupoqPSdKZM2dee+0103EA56DAAwCAzGjnzp116tSxn5er8KjW094B7+Jj0TsR8vOVpBkzZuzfv990IsAJKPAAACDT2bFjx1/t/dlHtX6IsjPXAeB1nnpI/epIUmpqardu3VJTU00nAu4VBR4AAGQu/2rvG4YoB5M3AV5qZGM9nFuSvvvuu5kzZ5qOA9wrCjwAAMgsTp8+PWvWrJo1a9rve69Aewe8XUgWzezo2B4xYsTp06dNpgHuGQUeAAB4vxUrVjz11FMPPfRQr1694uPjJd2XVW93oL0D3q9eGTV7VpKuX7/OsvDwdBR4AADg5fr379+8efOffvrp7wejY/T8WG340VQoAK4zvb3jt3WffvrpypUrTccB7h4FHgAAeLMFCxZMmzbtpg/FJanZdJ274uJEAFwtX0690dKx3adPn8uXLxuNA9w9CjwAAPBmY8aMuc2j1xM0ZY3LsgAwpusLqlJMks6dOzdo0CDTcYC7RIEHAABe6+jRoydPnrz9mLU/uCYLAJN8LHqvswL9JWnevHlbtmwxnQi4GxR4AADgtZYsWZLmmFOXXBAEgHnF8+vVhpJks9m6du0aFxdnOhFwxyjwAADAOy1dunTcuHFpDvP3dUEWAG5hcLhKPSRJv/zyy4gRI0zHAe4YBR4AAHih+fPnt23bNjU1Nc2RxfK5IA4At+Dvq7ld5OcrSdOnT9+9e7fpRMCdocADAABv884773Tq1Mne3kOzpjG46bOuiATATZQvon51JCk1NTUiIiIxMdF0IuAOUOABAIBXmTZtWo8ePaxWq6TWz2nlK46zbTf1SF71qum6bADcwegmKppPkg4dOjR69GjTcYA7QIEHAADeY9y4cf3797fZbJIiqmrBy3r+Cc3v5ph6+l8ezat1gxUc4OqQAMwKCtD7XeRjkaRJkybt27fPdCIgvSjwAADASwwbNuzVV1+1b/esobld5OsjSW0q6cAEda6mfDklyceikg9qfAt9P16PPWAuLgBzKhdzXH2TkpLSsWNHLqSHp7DYf0UNGBEdHW21Wi0WS2hoqOks8AZRUVFBQUHBwcGmg8DjJSQkxMTESAoJCQkKCjIdB2mz2Wx9+vSZOXOmfXdQPb3ZUhbLTUYmJCvAz3HmDUBmFpuo0sN04oIkDRs2bPz48aYTmREdHR0YGMjXJ0/BGXgAAODZ7DNR/dXeRzXRxFY3b++SAv1p7wAkKSSLPuh240L6PXv2mE4EpI0CDwAAPFhSUlLLli3nz58vyWLRlDYa2dh0JgAeonIxvVJbklJSUjp06BAfH286EZAGCjwAAPBUcXFxDRo0WLlypSQfi96JUP8w05kAeJRxzVUsnyQdOXIkMjLSdBwgDRR4AADgka5cuVKzZs3169dL8vfVoh7q+oLpTAA8TVCAPuzumPBy+vTp27ZtM50IuB0KPAAA8DwXLlyoVq3azp07JQX6a1VftXrOdCYAnunZRzUkXJKsVmvHjh2vXbtmOhFwSxR4AADgYU6dOlWlSpUDBw5IyhaotYMVXtZ0JgCebGRjlXpIkk6dOtWnTx/TcYBbosADAABPcujQocqVKx8/flxS7mz6MlLVnjCdCYCHC/DTop4K9JekDz/8cNWqVaYTATdHgQcAAB5j9+7d//nPf86ePSvpwVBtH6HyRUxnAuAVniyocc0d2926dbP/OwO4Gwo8AADwDBs2bKhevXpUVJSkYvm0Y6QeL2A6E/A3sYl6e4Oqva6CvfRgb1Ufr1mbFJdkOhbSrW8dvVBCkqKiojp27Gi1Wk0nAv6NAg8AADzA4sWLw8PDY2NjJZUvoq9f00OhpjMBf/PTaZUaplcW6KvDOntZZ6L15c/qNV9lhuuwuVO5Ccna96u+Oqyj54xl8CA+Fn3YXfdllaTNmzdPmzbNdCLg3yw2m810BmRe0dHRVqvVYrGEhvItDE4QFRUVFBQUHBxsOgg8XkJCQkxMjKSQkJCgoCDTcaBp06YNGDDA/qXlhRL6pL+yBZrOBPzNhasqPVznr9z80YL36cAEhWZ1aaRzVzR8mZbuUkLyjRgD66pXTceSabiVlXvUbLokBQQE7Nq1q2xZL58kMzo6OjAwkK9PnoK/vgAAwH3ZbLbBgwf379/f3t6bPqO1g2jvcDtjP75le5d0JlrjP3VhGunnMyo9TPO332jv9hh9F6r+FCWluDSMx2n6jF6qKklJSUmtW7e2X/gDuAkKPAAAcFPJycnt27efNGmSffflF7W0t7L4mw0F/FuqVcu+TWPMR9/IZZe9Jiar4VRdvMVa5msPaMQKFyXxXG+312MPSNLRo0dfeeUV03GAGyjwAADAHV2/fr1evXqLFi2y745pqv97iUt/4Y4uXtOf19MYc+6Kol11HnfJNzpx4XYDpm/QlTgXhfFQWQO1pKcC/CTp/fffX7p0qelEgAMfgwAAwO2cP3/++eef37hxoyQ/X83prBGNTGcCbiEuMV3DYtM37N6tPZDGgMRkbTnokiierHwRjW/h2O7WrduJEyeMxgEcKPAAAMC9HDlypGLFivv375cUHKDVfdW5mulMwK09kDPta0P8ffVADpekkX770zlj0L+O6paRpGvXrrVo0SIhIcF0IoACDwAA3MnXX39dqVKlU6dOScqTXVsiFe7lM0DD44Vk0XOPpTGm6uOO67FdID0v5O+b8Tk8n8WiD7ur4H2StH///v79+5tOBFDgAQCA21i+fHnNmjWjo6MlPZpXO0eqwqOmMwHpMLR+GgOGpTXAiYrmS3tMsfwZn8MrhGbVR73k5ytJs2fPXrZsmelEyOwo8AAAwC1MnDixVatW9otUn31U34xyzAINuL+w0rfr8KOa6IUSrgvT9Jk0BuTJrucfd0kUr1C5mF5v5tju0qXL0aNHjcZBZkeBBwAAhqWkpHTr1m3IkCFWq1VSg3L6MlJ5spuOBdyJCS20qIcK5/nHwUfzankfjWzs0iRhpVWj5O0GTGzluuv5vcPgeo6b4a9fv960aVNWhodBFpvLlqQE/kd0dLTVarVYLKGhoaazwBtERUUFBQUFBwebDgKPl5CQEBMTIykkJCQoKMh0HC939erV5s2b2yecl9S7lqa1Zbk4eCqrTT/8piN/SNITBVXyQflYDMS4EqfwydrxP6eKfSwa01SRDQ1E8nTRMSr3qk5dkqS2bdsuXLjQdCKniY6ODgwM5OuTp6DAwyQKPJyLAg9nocC7zKlTp8LDww8ePCjJ10dT2uiV2qYzwdudvazdJxQdowdyquJjCs1qOlDGSEnVh1/rg2367lclJit3NlUvof5heuYR08k81ne/qPIYJSZL0owZM3r16mU6kXNQ4D0LBR4mUeDhXBR4OAsF3jW+/fbbhg0bXrhwQVJIFi3ppfpMOI+M9Osl9f5Qaw/or++//r5qU0lT2ug+L63xdsmpTDvvHO9uUfd5kuTv779ly5YqVaqYTuQEFHjPwgVqAADAgGXLllWrVs3e3vPn0vbXaO/IWD/8rnKRWvO9/n72KjlV87fr6RE6d8VcsoxHe3eWbtUVUVWSkpOTmzdvfvbsWdOJkOlQ4AEAgEvZbLZRo0b9NeF8mULaM1ZlC5mOBa+WnKoWb+vyLaYe++WiOr3n2kDwWLNe0tNFJOn8+fNNmjRJTEw0nQiZCwUeAAC4TlxcXMuWLUePHm2/ia9heW0foQK5TMeCt/t0n46eu92AdT/ox99dlQaeLNBfq/rq/uyStHv37h49ephOhMyFAg8AAFzk7Nmz//nPf5YvX27fHRKuVX2VNdBsKGQKm35Ke8zmgxmfA17hwVAt7+O4MWHevHkzZ840nQiZCAUeAAC4wq5du55++ul9+/ZJyuKvD7vrjZZmVthCJvTH5bTHnE3HGMCu6uOa0sax3a9fv78WwgQyGgUeAABkuHnz5lWrVu3cuXOSHsiprZFq7w2TN8NjhGRxzhjgL71rqXM1SUpJSWnRosXhw4dNJ0KmQIEHAAAZKDk5uVevXp06dbJP9VS2kPaMUcXHTMdCJvPkg2mPKZmOMcDf/d9Lev5xSbpy5Uq9evUuXbpkOhG8HwUeAABklAsXLrz44ouzZs2y77asqB0j9WCo2VDIjJo/K7/bLqV2X1bVLuWqNPAW/r5a2VeP5JWkX375pVGjRkxKj4xGgQcAABliz5495cuX3759uyRfH73ZSkt6KijAdCxkSkXzqV+d2w2Y3FrZmE8Rdy40q74YqFwhkrRz586IiAj7EhtABqHAAwAA55s7d27VqlXPnDkj6b6sWjNIg+vJwpR1MOeNlupd6yZvQn9fTW2rl6qayASvUDy/VrzimJR+yZIlI0aMMJ0I3sx31KhRpjMg84qPj7fZbBaLJTg42HQWeIP4+Hh/f39/f3/TQeDxUlJSkpKSJAUEBPCOulMJCQndu3cfPXp0SkqKpKce0uZhevoR07GQ6VksqlNKdUrJZlNSigJ89UhetayoD7qpXhnT4eDhityvAvfps32S9PXXXxcsWLBs2bKmQ6VXfHy8n58fH3aews90AAAA4D1+/fXXpk2b7t+/377b6jnN6czk3nAjzzyiZ/h1EjJARFX9elGvfyJJL7/8ct68ecPDw02HghfiEnoAAOAcn3/+ebly5ezt3d9Xb7XTkp60dwCZxZimaldZ+u/Cct98843pRPBCFHgAAHCvUlJShgwZ0qBBg8uXL0vKn0tbX9UrtU3HAgAXslj0flfVekqS4uPjw8PDDx48aDoUvA0FHgAA3JMzZ85Uq1Zt4sSJ9rmXqz2h/eNUqajpWADgcv6+WvmKni4iSdHR0bVr1/7tt99Mh4JXocADAIC7t3bt2jJlyuzYsUOSxaLIhto0THlzmI4FAIZkDdSaQSqWT5LOnj1bo0aN8+fPmw4F70GBBwAAdyM5OXnQoEH16tX7888/JeXOprWD9Hoz+fLlAkDmlie7NgxVwfsk6fjx4zVr1oyKijIdCl6Cz1gAAHDHTp48Wbly5cmTJ9svm69STN+PV+1SpmMBgHt4OLc2DlWe7JL0008/1alT5+rVq6ZDwRtQ4AEAwJ1ZvHhx2bJl9+zZI8nHosiG+jLSca4JAGD3eAFtGKKcwZK0d+/eOnXqXL9+3XQoeDwKPAAASK9r1661bdu2bdu2165dk5QvpzYO0+vN5OdrOhkAuJ8yhbR2sLIFStKuXbvq1q0bGxtrOhQ8GwUeAACky86dO0uXLr148WL7bt0y+mGCqpcwGwoA3FrFx7RmsEKySNLXX39Nh8c9osADAIA0JCcnjxgxomrVqr/++qukQH/N6KDPBzhu7wQA3EaVYvpsgIIDJGnbtm1169aNiYkxHQqeys90AAAA4NZ+/vnn9u3b79+/37771ENa3FNPFjQbCt7japwuXNXlWF2O1fWEG8ctFuUMVqC/7s+ufLkcFyEDHuqFEvpsoOpPVlyStm3bVrNmzbVr1+bMmdN0LngeCjwAALi51NTUqVOnvvbaawkJCZIsFvWro/HNlcXfdDJ4pqtxOnhGB0/r8B868odOXdJvfyohOV0/mzVQD4bq0bwqnl9PFlTph/VEASZfgCep/rcOv2vXrurVq2/YsCF37tymc8HDWOyrvwBGREdHW61Wi8USGhpqOgu8QVRUVFBQUHBwsOkg8HgJCQn26xtDQkKCgoJMxzHj+PHjL7300s6dO+27D+fWB91U7QmzoeB5Tl7Q9iPacVS7TujIH3LiF8/gAJUtrOceU42SqlxMgfxeCZ7g66OqN0nX4iXp8ccf37RpU4ECBcxGio6ODgwM5OuTp6DAwyQKPJyLAg9nyeQF3mq1Tp8+PTIyMj4+3n6k43/0Vjvl4O8W0icmQet/1PoftOkn/R51y2EWix7IoYL3KU925c6mXCEK9JfFoix+jtPysYm6Hq9L13X+iv64ovNXbvlUwQF6oYTqlVHD8sqbw/l/IsCJ9pxUnYmKjpGkQoUKbdy48bHHHjOYhwLvWSjwMIkCD+eiwMNZMnOBP3ToUOfOnXft2mXffSCn3uuk8LJmQ8EzRMfo031avVebD978wvii+VT6YT31oB4voCcKqHCeO7sdIyFZR8/pyB868Ju+P6W9vzgq0N/5+qhKMbWvoqbPcts83NdPp1XzDcfvpO6///61a9eWK1fOVBgKvGehwMMkCjyciwIPZ8mcBT45OfmNN94YN25cYmKi/UibSnq7ve7LajYX3F1isj7/Xgu/1voflZTyj4d8LCpTSM8/rqqP67miCnX2e+noOX35szb9pM0H/zEBnqSQLGr2rLpU03NFnfyigFOcuKAaE3TqkiRly5Zt1apVNWrUMJKEAu9ZKPAwiQIP56LAw1kyYYHfvXt3165df/zxR/tu/lyaHaH6nHjHbf18RnO2atEORf3zTHiOYNUppQblVKOk80v7TSWlaPsRrd6rj7/795X25QqrXx01ryB/ZryDm/njsupM1I+/S5K/v/8777wTERHh+hgUeM9CgYdJFHg4FwUezpKpCvzVq1cjIyNnz55ttVolWSzq9LwmtVZO/ibhFlJStWqvZmzQzmP/OJ4jWI2fVrNnVb2EAgytdJRq1aaf9OHX+vg7Jf7tMv6Hc2tQPUVUVVCAmWDATV2JU8Op2nbYsRsZGTl27FiLxeLKDBR4z0KBh0kUeDgXBR7OknkK/NKlS/v373/u3Dn77iN59V4nvVDCbCi4r6txeu9Lvb1BZ6JvHPT1Uc2S6vgf1S/nRlPBR8Vo4Q7N2qgTF24czJdTQ+ur6wtulBNITFbEe1ryjWO3ZcuWH3zwQWCg66ZwoMB7Fgo8TKLAw7ko8HCWzFDgjx071qtXr02bNtl3/X01sK5GNOL8JG7u3BVNW6d3tziWv7J7MFSdn1fE8yp4n7Fgt2e16dN9mvSFdh2/cfDBUI1rrjaV5OPS05zALdlsenWFxn/q2K1QocLq1avz5cvnmlenwHsWCjxMosDDuSjwcBbvLvCxsbHjxo2bOnXqX5PVVS6m2RF6sqDZXHBTp6P0xud6/6t/XJFeuZheqa1G5eXrYy7Zndh6SGM/1tZDN46UK6xp7VSlmLlMwD99sE3d5zlmgixQoMDq1aufeeYZF7wuBd6zUOBhEgUezkWBh7N4a4G32WxLly4dPHjwmTNn7EdyZ9MbLRVRVa694xKe4Y/LGv+p5v6tuvv6qPHTGlhXzzxiNNnd2nZYw5bdOBtvsahFBU1po/y5jMYC/mv7ETWdrkvXJClLliwzZ87s3LlzRr8oBd6zUOBhEgUezkWBh7N4ZYHft29f3759d+zYYd/1sajLCxrfnFXicBNRMRr/qWZvVnyS40iAn9pX0ZBwPZrXaLJ7ZrNp5R4N+Ui/XnIcyR6k15upZ02uqIdbOHVJDafqh98du127dn377bezZMmSca9IgfcsFHiYRIGHc1Hg4SxeVuDPnj0bGRm5cOFC+zzzkio+phkdVK6w2VxwR/FJemu93vxcV+McRwL89FJVDW+gh7zoszohWdPWafynivnv6vEVH9P7XfR4AaOxAElSbKI6z9HSXY7dcuXKrVy5slChQhn0chR4z0KBh0kUeDgXBR7O4jUF/vr16xMnTpw6dWpcnKON5c+lN1uqTSWumce/WW1avFORy3U6ynHEz1ftK2tEIxXKYzRZhjkTrT4f6uPvHLuB/hrVRAPresyN/fBu09Zp8EdKSZWknDlzzpkzp2nTphnxQhR4z0KBh0kUeDgXBR7O4gUFPjk5ec6cOWPGjLlwwbGIVnCA+odpSLiyum5xIniMHUfVb5G+++XGkUblNb6Fiuc3l8lVPtuvl+fpj8uO3QqPasHLeuwBo5kASdKOo2ox48abs2vXrtOmTXP69xwKvGehwMMkCjyciwIPZ/HoAm+1WpctWzZy5Mjjxx1TdflY1KaSxjXXg/xb697ikvT+Vq3YraPnZLWpyP0KL6seL2bsPAVnL2vQEi3dpb++ElZ8TFPaqOJjGfii7uZKnAYs0rxtjt2sgZrZUR2qGM0ESJIuXFW72dr0k2O3ePHiS5YsKVOmjBNfggLvWSjwMIkCD+eiwMNZPLTA22y2zz777LXXXvvxxx//Oli9hCa2VtlC5mIhfY6dU4OpOvLHv48/kFMrX1Glos5/xeRUvbVOYz6+cR94oTx6s6WaPZtJ77BY94O6zNHZ/57tbFtZs1/iihWYZ7Vp0hcasULJqZIUEBAwcuTIwYMH+/n5OeX5KfCehQIPkyjwcC4KPJzFEwv8unXrRo4cuXfv3r+OlH5Yb7RUracMhkJ6XYvXU0P12583fzRHsPaPU5H7nfmKWw+p53wdPuvYDcmi4Q3UP0yB/s58FY8TFaOId/XZfsdu8fxa3kclHzSaCZAk7f1FbWbp+HnHboUKFT744IPixYvf+zNT4D0Lc3QAAODZ1q5dW6FChbCwsL/ae7F8+n/27ju+pvv/A/jrZu89ZIcQM/aIvfeqUXu2aPnVbKulrbZftSk1qrWpUYLYJFZskgkUvAAAIABJREFUEZtIrIjIIHvve+/vj3sbRERwc88dr+ejjz7u+Zxz73mHSO7rfta/E3FtDtO72lh8+K3pHUBaNn7YpbB7xadjxGq0n/syvQ/0RfhizOyt7ekdgK0Z9k3DipEw1AeA8Fj4zsK2C0KXRQQ0qoTrczC2rXyAzOXLl+vVqzd//vzCwkKhSyOlYg88CYk98KRY7IEnRVGLHniJROLv7z937tzr168XNVa0x6y+GNYceroClkbvzfvrlx1rJTI2QNLfMDb4qLtIpdh4Ft9uR3KmvKWGC1aOQtsaH/WyGul6JAYuxyP5EpCY2Bm/D+U/K1IJR25i7LqXK9vVrVt33bp1DRo0+OAXZA+8emEPPBERkZrJz8/fvHlzrVq1+vfvX5TePe2xdgzuL8GoVowZaqZA/DIovk1OPp4kfNRdHj5Huzn4fI08vZsYYO5A3JzH9F6y+p64+hs+aSg/XBGAzguQlFnqc4iUoltdhC7EZ63lXfE3b95s0qTJpEmT0tLShC6NlIEBnoiISG2kpaUtWrTIy8tr1KhRYWFhssbKjtgwDg+WYExb6DO6q6HcApRlQGRO/ge+fqEY8w+g9vcIkn/LoEsd3F2IGb34DVMaSxPsnYJ5A+Xbwp8KRZNZuBfzrqcRlT8rE6wfh4DvUNEeAMRi8YoVK6pWrbp582YOr9Z4HEJPQuIQelIsDqEnRVHBIfRPnjxZvnz5hg0b0tPTixpruWJGbwz0lQcMUl9WY5GW/Y5rXqyGg8V7v/KtKHz2N65Hyg8dLLBsOAY3e+/X0WZHbmLIKvlfkKUJ/v0KXeoIXRMRACA7Hz/vxh/H5AvUA/D19V22bFmTJk3K/iIcQq9e+AufiIhIdUml0pMnT/bt27dKlSrLli0rSu8tq+LgN7g9H0OaMb1rgncuN1jP873Te34hftmDRj/K07tIhFGtELaI6f29dauLy7+isiMApGWj5xL8fVLomogAACYGWDQEN+a+nAtz+fLlpk2bDhky5OnTp4KWRuWFPfAkJPbAk2KxB54URRV64NPT0//5558///zz3r17RY16uujbCF93Q2MvQYqi8nIjEg1/hOTtb8p2T0a/xu/3gqP+xu0o+aGHHdaMQSefjypSyyVnov8fOP3fP8cZvTBngHwSMpEq2HUZ325HVJL80NDQcPz48T/88IOdnV3pT2QPvHphgCchMcCTYjHAk6IIG+CvXr26Zs2aHTt2yGqQsTTBmDaY2Bke73gnRurqz+P4anPJk+GndcOSoWV9nQIx5uzD3P3yIbUiEb5sjwWDYW6ksFK1VoEYY9di8zn54YiWWDeW6wiQCsnOx5LDWHgImbnyFnNz8ylTpkybNs3Kyuptz2KAVy8M8CQkBnhSLAZ4UhRBAnxycvK2bdvWr19/69atV9trueL/OmFYc5gxgGm643cwfQduvjLutZID/tcfQ5uX9RXuRmPk6pcz3is5YP04tKmu2DK1mlSKX/bif3vlh13rwG8yTA0FrYnodXGp+HUv1geh8L+J8VZWVpMmTZo8ebKNjc2b1zPAqxcGeBISAzwpFgM8KYoyA3xBQcGxY8f++eefAwcO5OXlFbUb6KFPQ0zoiFbVyvX+pHIePkdYLCQSeDmilmtZB2mLJVh8GD/vQV4BAIhEmNAB8wfxc59yseYUJmyEWAIATavg0DewMRO6JqLXPYjDrN3wC345N8fc3PzLL7+cOnWqk5PTq1cywKsXBngSEgM8KRYDPCmKEgK8VCq9dOnSjh07/v3338TExFdPVXPG520wsiXs33/JcdJOEfEYsRoXHsgPPeywYRza1RS0Jk237yqGrJLv7VfTFcdnwOmtI5SJBHM7Cj/vwf5rL6fnGBoaDh069Ouvv65RQ77wHQO8emGAJyExwJNiMcCTopRrgL9+/bqfn9+///4bGRn5aruZET5tgs9ao0VVxd6QNNy605i69eWU19GtsWw4LFRi90MNdzYcvZbIt5erUgEnZsKdb2dIJd2OwrwD8AuWDxsBIBKJOnToMHHixO7du6empjLAqxEGeBISAzwpFgM8KYrCA7xUKg0ODt67d++ePXsiIiJePaWrg3Y1Maw5+jeBicHH34q0yIs0jFmLQzfkhxWs8Pfn6FVf0Jq0zM2n6Dwf8ekA4GGHUz+gkoPQNRG9xaMXWHQIW84ht+Blo6en5/Dhw0ePHl2xYkXhSqP3wABPQmKAJ8VigCdFUVSAz83NPX369IEDBw4ePBgTE1PsbJPKGNQUg3xRgSNv6f3tv4ax65CQLj/s2wh/fw47c0Fr0krhsegwFzEpAOBqg5Mz4e30rucQCedFGlYdx5pTeJH2slFfX79Hjx6jR4/u2rWrnp6ecNXRuzHAk5AY4EmxGOBJUT4ywD979uzo0aNHjhw5ceJEVlZWsbMNKqJ/Ywxsior2iqmWtE1mLqZuxbrT8kMLYywfiZEtBa1Juz1+gfZz8TQRAJyscOoHVHMWuiaiUuUVwC8Yq47j8qPX2h0dHQcPHjx8+PD69TmYR0UxwJOQGOBJsRjgSVE+IMBnZWUFBQWdOHEiMDDw3r17xc7qiNC0Cvo0Qt9GzO30US4/wvA/8eiF/LBVNWz+Ep78phLa00S0m4OIeIAZntTKjUisPY3tF+WrORTx9vYeMGBA//7969SpI1BpVDIGeBISAzwpFgM8KUoZA3xOTs7ly5fPnDlz8uTJ4ODggoKCYheYGaFjLfSojx714MAl5enjFIoxdz9m75Pv7Wygh9mf4pvu0CnbPnNU3qKT0W4OHj4HACcrnP4RVTmWntREdj72XsGW8zh59+W2czKVK1fu27dvr169mjZtqqOjI1CB9BIDPAmJAZ4UiwGeFKWUAJ+SknLx4sWLFy+ePXs2JCTk1Z3bi1R3QWcfdK2L1tVgqK+kmkmzRcRj+Gpc/G+juBou2PZ/qOshaE30hpgUtJktHx/hYo0zP8HLUeiaiN5HdDK2X8SOi7j5tPgpe3v7Ll26dO3atWPHjnZ2dkJURwADPAmLAZ4UiwGeFOXVAK+vr3/37t3g4ODLly8HBweHh4eX+KvTwQJta6CjDzr6cCspUrDN5zBxEzJyAUAkwledsGAQjDViz4J7MXj8AiIRvCtoyNpv0clo+5s8w3vY4cxP8GDSITX0IA5+wdgTghuRxU/p6Og0bNiwffv27dq1a9asGd93KRkDPAmJAZ4UiwGeFCI/P//mzZvBwcG3b9++ffv2nTt3cnJySrzS3gItq6J1dbStgVquEHEkMylacia+3AC/YPlhBStsHIcuGjEjdc8V/LAL9+Netvi4Yd5AdK8nXE0KEpWE1rMRmQAAlR1x5ic4WwtdE9GHepKA/ddw4BrO30eBuPhZAwODxo0bt2rVqkWLFr6+vtbW/F4vd9oY4B88ePDNN98AGDVqVN++fUu8ZuTIkSkpKatXr3ZxcVFuddqFAZ4UiwGePszTp0/v3bt3584dWVwPCwt7czZ7EW8nNK2M5lXRwhvVnBnaqRydCsXIvxCdLD/s3QBrx8BeIxZT+HkP/re3hHaRCAsH45vuSi9I0SLi0Xq2/O+uhguCftSQvzjSZuk5OH4Hx24j4DaeJZVwgUgkqlatWpMmTRo3btywYcM6deoYGGjESCEVo9W7/G3fvr1p06ZOThoxYIuIiMogNzf34cOHDx48ePDgQVhYWHh4eHh4eEZGRilPsTNHw0po7IXGldCkMvfZJmXIL8SPflhyWL6alKkhfh+Gce2ELktBjt0qOb0DkEoxfQeaVkFzb+XWpGiVHHBiJlrPxos03ItBlwU4/SMs3ntLSiIVYmGMfo3RrzEA3I/DqVCcvoczYYhPl18glUrDwsLCwsI2bdoEwMDAoGbNmnXq1Klbt66Pj0/t2rU5c14htDrA5+fnr1y5cs6cOUIXQkREihcXFxcZGRkZGfnkyZOIiIjHjx8/evQoJibmnUPPXG1QxwN1PdCgIup7cv4qKVtYDIb++XLeacNK2DpBo9Yzn3egtLNSKebux+FvlVVNuanqhOMz0PY3JGXieiR6LcHR6RqycgFRVSdUdcL4DpBKERaL8/dx/j6CH+PBK5Ni8vPzb9y4cePGjaIWBweHmjVrVqtWrXr16lWrVvX29nZzc9PV1RXgC1Bn2hvgra2tJRLJnTt3AgMDO3XqJHQ5RET03goLC+Pj42NjY+Pi4p49exYbG/vs2bPo6OioqKhnz56VuD78m6xNUcMFNV1RyxW13FDbHbZm5V04UcmkUqw+gW+2IycfAHR1ML0Hfu0PfQ16f5uZiwsP3nHNyVAUiqGn/l+1jxuOfof2c5CRizNhGLQCe6ZowtdFVEQkQg0X1HCRDxFKzkTwY4Q8xtUnuPYEsSmvXRwfHx8fH3/69OmiFgMDA09PTy8vLy8vL09PTw8PD3d3dzc3Nw6RLoX2BnhTU9PBgwcvWrRo48aNjRo1KsuKC7K0/+TJk/j4eHd3dy8vr169er06ST4oKOj3338fO3Zsx44d//333/Pnz6emptrb2zdq1GjAgAFmZq+9JUxNTd25c+fDhw+joqKsrKxq1KjRp08fDw9uCENEBAASiSQpKSk5OTkpKSkxMTExMfHFixcJCQkJCQnx8fFxcXGyBxKJpOyvaaCHivaoUgFVneDtBG8nVHeGo2X5fRFE7+FFGj5bgyM35YcedtgyHq2qCVpTOYhLhfhd/2rzChCfriELvzWqBP9p6L4IeQU4cB1j1mLjF1w7gzSWjRm61kHX/xbaTEjHzae4FYXbUQiNRlis/NPJIvn5+bJJbcVex9DQ0OU/FSpUcHFxcXBwcHR0dHJysrOzs7e319fX3j1atTfAA2jZsmVQUFBISMhff/01Y8aM0i/et2/fhg0bAOjq6trZ2cmmUJ48efKHH36oV++19VLz8vJmzpypo6PTuXNnkUh04sSJffv2hYeHz5s3r2iISGho6MKFC1NSUnR1de3t7RMSEk6dOnX27Nlp06a1aNGinL5eIiKhZGRk5OTkZGZmpqenZ2VlyR6kpaWlp6dnZGSkpaWlpaWlpqbK/p+SkpKcnJyamvrBt9MRwckKnvao6AAPO1S0R0V7VHKAmy10dRT4ZREpzMHrGLP25VTSwc3w52hYaeKKnEZle9etSUPN29fE9v/DgOUQS7D5HOwtsGiI0DURKYW9hXx3VRmxBJEJCI9FeBzux+LhczyOR3Qy3pzZlpeXFxERERER8bZXtrKysre3t7W1tbW1tbGxsba2trGxsbKysrCwsLS0tLS0lD02Nzc3Nzc3NTUtty9RAFod4AGMHz/+7t27ly5dunTpUtOmTd922fPnzzdv3iwSiYYMGdKnTx8DA4Ps7OzVq1efOXNm1apVf/31l57eyz/JvXv3NmjQYMqUKbK43q1btwkTJoSHh4eFhdWqVQtAXl6eLL336dNnyJAhhoaG+fn5O3fu9PPzW7p0aeXKlStUqKCEr52ItFNaWtqrvdapqalFc8IzMjIKCwsBiMXi9PT0YhekpKQAKCgokG2Qnp6eLhaLc3Nzc3Jy8vPzs7KyZKfy8vKys7OzsrLy8/NTU1Pz8/Nl1yucmRFcbeBgAVcbOFrCzRbO1nC1gasNnK01asgxabasPHy9DX+flB9ammDlSAzT3A/znaxgZYLU7HdcY61R77fRtxFWf4Yv1kMqxeLDcLLCtG5C10SkdLo68HKEl+Nru0XmFSAiHk8SEJmAp4mISsKzJEQlIS4VhW/sWlckNTU1NTX14cOHZbmvjo5O7969d+/eraOjCZ/ia3uAt7OzGzly5F9//fXXX3/Vrl37bR/PbN++XSwWd+7ceeDAgbIWExOTr7/++tmzZxEREQEBAd27v9zwpKCgYMyYMUWd7SYmJg0bNgwMDHz27JkswO/bty8lJaVVq1ajR4+WXWNgYDB8+PCUlJQTJ074+/uPHz++jPWnp6er7EaAERERc+bMSUoqaZeJ/xQVL+JgMlIE2XeUunw75efnZ2eX+h72dQUFBVlZWW87KxaLS1xKPTc3Nzc390PqE46+LqxNYWMGGzPYmsHGFHbmsLeAg4X8QQVLOFjCRIM66EhrhURg2J8vl31qWRVbxsPTXtCaypmeLvo3wbrTpV0z6K1dKmpsbFskpOOHXQDwzXZUsMKQZkLXRKQCDPVR3QXV39i5WyLFizTEpiAuFXGpiEtBfDqepyEhHYkZ8v8kZYtBEonE398/PDxcZTcINzY2LvuWe9oe4AF07dr1zJkzYWFhGzZsmDhxYonX3L17F0CvXr2Ktffo0WP58uV37959NcB7e3tbWr42pdLCwgJAUZfX9evXZc8t9mpdunQ5ceKE7F5lVFBQoLIBft68eXv27BG6CiISjKE+TAxgbgRDfVgYw8wIRvqwMIa5EcyMYGYEC2NYmcDCGBbGsDSBpQmsTGBtCjMjoUsnKn+FYsw7gNn+KBADgL4ufumH73pqxSyPX/rBPwRJbxma42yNmb2VW5CyzOyNuFSsDIRUitF/w9ES7WsKXRORqpLNhnOyKu2a5EwkZSI5EylZ8v/ScpCWjdRspGUjIxcZOcjKQ0oW2vUa4+DgUFBQoKzy34+hoWHZL2aAh0gk+uqrr6ZMmXL8+PHWrVvXrl272AUFBQVJSUm6urpvfmYjW3Pu+fPnrzY6OjqWfsfY2FgAgYGBr67BCEC2YHJiYuIHfR0qx9fXd/fu3Sr7+QKRZtDT0yu2QGYRc3PzYkPFTExMXv1819DQ0NhYviuxjo6Oubl8f3MDAwMTE/nUWysrq1dfTUdHR/aJpJGRkZGRUdGzLC0tRSKRhYWFrEVPT0922QfIBdRswADR+3vy5MmECROuXr0qO6zmjK0T0KCisEUpj4s1jn6HvksRnVz8VCUH7P8aduZClKUUf4zA81TsvoL8QvRdaXHo0KHq1asLXRSRGrMGNGK9y/fAAA8Abm5uAwYM2LZt28qVK1euXFlsAENKSopUKpW9MS32RNna9cUid+mfoBQWFqalpQE4ceJEiRfk5uZKpdIyjgEuem+tgiZPnjxo0KBSRvwCyMjIkH2xRcmB6GNkZGQYGBi816eYH8PQ0LDEjcp0dHRkI2709PQ+8nvb2NjYyIj90QKQzeQH/wpIQWTfUdbW1lKpdO3atd9++63s96NIhPEdsGiI1k0JaVQJdxZg9Qnsv4b7sdDRQTVn9G2EL9pp+BgcHRH+mYAXaTh3H+np6UOGDDl//ryrq+t7vYhsCRKRSKTK7wNJjaSmpr76mT4p33tNzmeAl+vfv//58+efPn26ffv2UaNGvXrK2tpaJBKlp6dLJJJif7iyKP5ePz1lb+gzMjJ27dr18W8Ki2baqyZnZ+fSL0hOTpZIJCKRyNbWVjklkWZLSkoyNjYu6j0m+mBFP+11dHRU/CctqQXZd1R8fPyYMWOOHDkia3SywoZx6FKn1GdqLisTzOiFGcWnJ2o+I33sm4YW/0NYDKKjo3v16nXu3LkPG7XEn06kECKRiL/s1IgWTLQqG11d3YkTJ4pEon379j169OjVU/r6+ra2tmKxOCYmptizoqKiUIaYWoyTkxOAZ8+eFWsvLCxMSkoqWvmZiIiINMb+/ft9fHyK0vunTXBngfamdy1nY4Yj36KCFQDcvn27f//+Kjs1l4hUDQP8S97e3j179pRIJCtWrBCLX9u1QLZ6/MGDB4s9RdZSs+b7rUAie7XDhw8Xa/fz8xs9evTevXvft3IiIiJSWcnJySNGjBgzZoxsZxYrE/wzHrsmwbbk9StIK3ja49A3MDUEgOPHj5d9ByIi0nIM8K8ZNmyYg4PDkydPinWDDx48WFdXNyAgYO/evbJsn5ubu2zZskePHtnb23fp0uW97tKvXz9TU9PTp0/v2LFDtuWyVCo9d+7c7t279fT0OnbsqMCviIiIiAR0+PDhWrVq+fn5yQ471MLt+Zq8zTuVXYOK+HeifN+B9evXL1iwQOiKiEgNcA78a4yMjCZMmPDLL78Ua3dychoxYsTGjRs3bdq0fft2e3v7uLg4iURiaWk5ZcqUsu/aJ2Nubj5lypSlS5fu2LFj9+7dFSpUSE9PT0tLE4lE06ZNU9n9CYmIiKjs0tLSpk2btmHDBtmhiQEWDMb/dUTZlqklrdCjHpYOw6QtADBz5szKlSv369dP6KKISKUxwBdXv379tm3bFtvgDUCfPn28vLwCAwOfPHmSkJBQqVKlKlWqDBo0SLYQfen09PQ8PDxe3V6uSZMmf/zxx86dOyMiImJiYmxsbOrUqTNgwAB3d3cFfz1ERESkdIGBgWPGjCla76ZpFWz6At5OwhZFqmhiZzx8gRUBkEgkI0aMcHd3b9SokdBFEZHqEnGbbhIQV6EnxeIq9KQoubm5mZmZAExNTbmzDr2XtLS0b775Zv369bK3WIb6+LUfvukuHylN9CaxBL1/x+EbAODk5BQcHOzm5lbK9SkpKbJt5Pj2iRQiOTnZyMiIb5/UBX+ZEBERESnGkSNHfHx81q1bJ0vvjSrh2m/4rifTO5VGVwc7vkJtdwCIi4vr1auX7ANEIqI38fcJERER0cdKTk4eOXJk9+7dZcPmDfUxZwAu/oKarkJXRurA3AgHv5FvLHfz5s3hw4dLJBKhiyIiVcQAT0RERPRR9uzZU7NmzS1btsgOZR3vM3tDT1fYukiduNti/zQYGwDAvn37Zs6cKXRFRKSKGOCJiIiIPlBsbGzfvn379+///PlzAEb6WDCYHe/0gRp7YcM4+T4FCxYs2Lp1q9AVEZHKYYAnIiIiem9SqXTNmjU1atTw9/eXtbSsilvzML0HO97pww1qip/6yB+PHTs2ODhY0HKISOUwwBMRERG9n7CwsNatW3/xxRdpaWkALIyxchSCfuJGcaQAv/RFv8YAkJub27dv35iYGKErIiIVwgBPREREVFa5ubmzZs2qW7fuuXPnZC296iN0If6vI3REwpZGGkIkwuYvUdcDAGJjY/v06ZOTkyN0UUSkKhjgiYiIiMokMDDQx8dn9uzZ+fn5AJys4DcZ+7+Gq43QlZFmMTXE/q/haAkAISEhY8aMEboiIlIVDPBERERE7xATEzNo0KDOnTs/evQIgI4IX7bHvUXo31joykhDudtizxQY6AHA9u3bFy5cKHRFRKQSGOCJiIiI3qqwsHDp0qXVq1ffuXOnrKW2O87NwurPYGUibGmk4Zp748/R8sczZ848cuSIoOUQkUpggCciIiIqWVBQUL169aZNm5aRkQHAzAiLh+Lab2jmLXRlpB0+b4OvOgGAWCweMmTI/fv3ha6IiATGAE9ERERU3LNnzwYOHNi2bdu7d+/KWj5tgrBF+Lobd4kjpVo6DO1qAkBaWtonn3wi2/iAiLQWAzwRERHRSzk5ObNnz65WrdquXbtkLdWcEfg9dk3iYnUkAD1d7JyIivYAEB4ePmzYMIlEInRRRCQYBngiIiIiAJBKpTt37qxevfqsWbOys7MBmBthwWDcmoeOPkIXR1rMzhz+02BqCACHDh2aP3++0BURkWAY4ImIiIgQHBzcokWLQYMGPX36FIBIhOEtEL4Y03vIVwInElAdd2wYB5EIAJYsWXLo0CGhKyIiYTDAExERkVZ7+vTpkCFDmjZtevHiRVlLo0q48DO2jIeztbClEb00wBff9QQAqVQ6ceLE8PBwoSsiIgEwwBMREZGWSklJmT59erVq1Xbs2CGVSgG42mDzlwj+H5pWEbo4ojf89im61gGAzMzMESNGpKamCl0RESkbAzwRERFpndzc3EWLFlWuXHnRokW5ubkATA3xaz/cX4IRLeUDlYlUja4Otn+FKhUAICIiggvaEWkhBngiIiLSImKxeMOGDd7e3tOnT09OTgagq4MxbfHwd8zqCxMDoesjKpWVCfynwswIAA4fPjxr1iyhKyIipWKAJyIiIq0glUr9/Pxq1ar1+eefP3v2TNbYox5uzsPaMXCyErY6orKq6YpNX8jHicydO9ff31/oiohIeRjgiYiISPMdOnSoQYMGAwYMKFr6q7k3zs7CwW9Qy1XY0ojeW7/GLxe0GzlyZFhYmNAVEZGSMMATERGRJjt27FiTJk169ux548YNWUstV+z/Gud/RsuqwpZG9OF++xSdfAAgIyOjT58+6enpQldERMrAAE9ERESa6dixY82bN+/ateuVK1dkLVUqYOsE3JqPXvWFLY3oY+nqYMdXqOQAAPfv3x8xYoRsJwUi0mwM8ERERKRRpFLpwYMHGzdu3LVr16Kt3T3tsX4c7i3E0ObQ4SLzpBFszLB3qnzlxf379//2229CV0RE5Y4BnoiIiDSERCLZuXNnvXr1evXqFRISImt0t8Vfn+H+YnzWGnq6whZIpGB13LFurPzxL7/8cuTIEUHLIaJypyd0AUREREQfKy8v759//lm0aNGDBw+KGis54PteGNkSBny/Q5prcDOERGDpUUgkkmHDhoWEhHh5eQldFBGVF/5CIyIiIjWWnp7+999/L1u2LDY2tqjR2wkze2NoM3a5k1ZYOBg3IhEUhpSUlL59+168eNHU1FToooioXHAIPREREaml6Ojo6dOnu7u7T58+vSi91/XAvxMRtggjWzK9k7bQ08XOSXCzBYDbt2+PHTv2Xc8gInXFHngiIiJSM9euXVu6dOmuXbsKCgqKGltXx3c90aU2RFyjjrSPgwV2T0br2cgtwI4dOxo2bDht2jShiyIixWMPPBEREakHsVi8d+/e1q1bN2zYcNu2bbL0riNCv8YI/h+CfkTXOkzvpL0ae2HlKPnj77777vTp00JWQ0Tlgz3wREREpOpSUlLWr1//559/PnnypKjR1BCjWmFqV3g5ClgakQr5vA2uPMaaUygsLBw0aNDVq1fd3NyELoqIFIkBnoiIiFTX7du3V65cuW3btuzs7KJGF2t81Qnj2sHG7D1eKisPkQmIS8WLNGTlITUbUikycmGsDz0KDBrmAAAgAElEQVRdWBjDygQ2ZnC2hov1+70ykepYPgK3ohD8CPHx8f379z979qyhoaHQRRGRwjDAExERkcrJy8vbs2fPn3/+eeHChVfbm1TG5M7o3wT671qgTixBaDQuPcSNpwiPxf04PE99jwLszFHNGTVdUccdjb1Qx51L4pF6MNTH7slo+CNepOHKlStfffXV2rVrhS6KiBRGJJVKha6BtFdycrJEIhGJRLa2tkLXQpogKSnJ2NjYxMRE6EJI7eXm5mZmZgIwNTU1NjYWuhzt8vjx4zVr1mzcuDEhIaGo0VAfnzbBxE5o/K79rUOjcew2Tt/D+ftIy37HxWVnaogWVdG+JjrVRh13hb0sUTk5G44Oc1EgBoC///573LhxQldEqis5OdnIyIhvn9QFAzwJiQGeFIsBnhSFAV758vPz9+/fv3bt2pMnT0okkqJ2N1t80Q5j28HB4q3PlUoR/Bh+wdhzBU8TS77GSB9ejqjkABdrOFrCwhhmRjA1hIEeJFKkZSMnH6nZSEhHTAqeJuLhc2TmlvxSLtbo0wgDfdHcm2vmUVlJpLjzDE/ioa+Lmq7wtC/3Oy4PwOQtAGBoaBgUFOTr61vutyT1xACvXhjgSUgM8KRYDPCkKAzwynT//v3169dv3rw5Pj6+qFFHhI4++LI9etaH7tv3zHn8AhvPYvsFPEkofsrUEL6V0bQKmlRGTVd42EHnPcN2ZALuRuPaE1x+hIsPkJ5T/AIPO4xoiZEtuYoevcO605jtj6ikly2NvbB4KFpWLd/7Dl+NrecBwMXF5dq1a46O/E6lEjDAqxcGeBISAzwpFgM8KQoDvBJkZmb6+fmtX7++2Cx3BwuMbo2xbUtLxRIpDt/AykCcuAvJK29kRCI0rIhuddHJB4283j1PvuzEElyNwPG7OHITVx5D/HKIgPyzhgkd0aPee39GQBpPKsUX67G2pA3ddHWwYRxGtCzHu2fno/kvuPkUAFq2bHny5El9ff1yvB+pJwZ49cIAT0JigCfFYoAnRWGALz9SqfT8+fMbN2708/OT/SHL6IjQvhbGtkXvBjB4+xq72fnYeAbLjuLRi9faG1bC4Kbo3wTu5f/75HkqdgVj+0UEP3qtvaoTpnXDyJYwZESi/2w8g8/WvPWsvi7uLEBVp3Is4EkCGv2IpEwA+Oqrr1asWFGONyP1xACvXhjgSUgM8KRYDPCkKAzw5eHp06dbtmzZsmXLo0evBV9XG4xujc9av2NWcFYelgdg6VEkpL9sdLDAqFb4vA28yzMCvU14LDadxcYziH+lJGdrfN0N4zvA2ECAkkjVVJlW/MOmYj5vg3Vjy7eGwDvotlA+bGTz5s0jRowo3/uRumGAVy8M8CQkBnhSLAZ4UhQGeAXKyMjYvXv3li1bzp49++rqdIb66FkPo1ujc+3SZrkDyCvAXycxd/9rObm+J6Z0xUDf0rrrlSO/EH7BWHxYPlBZxsUaP/XB5224+ZxWi4iH19R3XONqg2fl3yk+/wBm7AQAY2Pjc+fONWjQoNxvSeqDAV69MMCTkBjgSbEY4ElRGOA/XmFhYWBg4NatW/fv35+d/dp+bg0qYmRLDGkOW7N3vIhUim0X8NNuRL6yRl3n2pjeA+1qlkPRHyfwDubux5mwly3VXbBwMHrUE64mEtTZcLSe/Y5rdETI3/KOz7A+nlSKT5djzxUAcHd3v3r1qr19+a+DT2qCAV69CP2pNREREWmWy5cvb9++fefOna+uKg/A2RpDmmFkK9RyLdPrXHmMSVtem2fe0Qez+6NJZYWWqzidfNDJB+fv44ddOBsOAGEx6LkYHX3wx3BUdxG6PlI6ozKshmCgp4y1D0UibPoC4bEIjUZUVNTAgQMDAwP19BgEiNQPe+BJSOyBJ8ViDzwpCnvgP0B4ePiOHTu2b99ebIq7iQE+aYhhLdDJp6zdjClZmLETa0+9XGG+sRfmD0LbGoouutwcuYnpOxAaLT/U18XUrvilHyfGa5eULDiMR6G4tGvqe+LaHCXV8/A5Gv+E1GwAmDp16u+//66kG5NqYw+8emGAJyExwJNiMcCTojDAl11UVNTOnTt37Nhx48aNV9t1ddC2Boa1QN9GMDd67Sl5BThyC1ceIzULTlZoXR2tqkH0XyfktguYtvXldHc3WywcjIG+Ly9QF4VirAvCT35IzJC3VHbEmjHq9DEEfbwei3H4RmkXLByMb3soqxrg8A30WiL/aGzr1q1Dhw5V3r1JVTHAqxcGeBISAzwpFgM8KQoD/Du9ePHCz89v586dFy5cKPZeokFFDG2Ogb5wti7hiQev48sNiE15rbG+JzaPh6Uxxq5DwG15o6E+pvfA971gos691ilZmLUbq0/I1wAXifBFOywcUvxDDdJUodFo/BOy80s+W80Z1+coe1zGb/vwkx8AGBsbX7hwoV49LtKg7Rjg1QsDPAmJAZ4UiwGeFIUB/m2Sk5P37t3777//BgUFicWvjQyuUgGDm2FIs9I2tf73Eoauejkw/lUmBtDRQWau/LBtDfz1mTCbw5WH4EcYuw53nskPK9pj83i0rCpoTaQsx+9g8Er5Tuyv8nHDwW/gYafseqRS9FsG/6sA4OHhERISwgXttBwDvHphgCchMcCTYjHAk6IwwBeTmpq6b9++Xbt2nThxoqCg4NVTLtYY2BSDmqJRpXe8SGIGqkyTz78thY0ZlgzFyJbqN2a+dPmFmLsfc/ejQAwAujqY3gP/68995rRCfDpWBODITTx8DiMD1HDBoKYY3QqGZVjlrjxk5MJ3Fu7FAECbNm0CAwP19QUqhVQAA7x6YYAnITHAk2IxwJOiMMDLpKamHjhwwM/PLzAwMD//tUHA9hbo3xgDfdGyWlnX0F4egMlb3nFN93pYOwZOVh9ascq7FYURq3E7Sn7YtAp2fCVAHyzRqwvaTZw4cfny5UJXRIJhgFcvDPAkJAZ4UiwGeFIULQ/wKSkpstx+/PjxYrndxgx9GmKAL9rVeO+u44ErsOvyW8+aGeH3YRjb9oMqVit5BfhpN5Yclk8lsDLBxi/wSUOhyyLtc/QWei6Wr86wYcOG0aNHC10RCYMBXr0wwJOQGOBJsRjgSVG0M8AnJyfv379/9+7dJ06cKJbbrUzwSUN82gQdasHgQ7eObj8Xp0JLPtXYC1snoEqFD3xldXT8Doavxos0ABCJMLUrFgzicHpStgUH8f2/AGBoaBgUFOTr6yt0RSQABnj18qG/hImIiEgjJCQk7Nu3b/fu3adPny42v93KBL0a4NMm6OTz4bm9iLVpCY0iEb7tjt8GQF/LsmtHH9yahyGrcCoUUil+P4Ibkdg1CXbmQldG2mR6D9yIxM7LyMvL69u3b0hIiIuLi9BFEVFp2ANPQmIPPCkWe+BJUbShBz4mJsbf33/v3r1nz54ttp68tSl6N0D/xuioiNxeZOEhfLfjtRZbM2ydgC51FHYLtSOW4H/++M1fPpzeww77v0Ydd6HLIm2SnY8Wv+JGJAA0btz4zJkzRkbc5FC7sAdevTDAk5AY4EmxGOBJUTQ4wEdEROzZs8ff3z84OFgikbx6ytYMvRugfxO0r6nI3F7kSQK8p6Hwv3s2qgS/yVy/DQCO3MTQVfLlxMyM8M94ToknpYpKQqMfEZ8OAMOGDfvnn3+EroiUigFevTDAk5AY4EmxGOBJUTQvwN+5c8ff39/f3//mzZvFTjla4pOG6N8YbaqX4xzsF2nosxSXHsoPP2+DVaME20NLBT18jk9+l2/rpSPCnAH4vpfQNZE2OXcf7efI9zhcsGDB9OnTha6IlIcBXr0wwJOQGOBJsRjgSVE0I8BLJJLLly/v27fP39//0aNHxc662eKThujXCC2qQlenfCu5HYWeixGVBAD6ulgxEl+0L987qqO0bAxeiaO35IejW+Ovz8plKARRif4+iS83AICuru6BAwe6desmdEWkJAzw6oUBnoTEAE+KxQBPiqLWAT4vL+/UqVP79u07cODA8+fPi52tUgF9G6FvIzSqBFHZ9m//SMduYeAKpOcAgJ05dk9G6+rKuK86EkvwzTYsOyY/7FALe6fCnPORSVm+2oRVxwHAwsLi0qVLNWrUELoiUgYGePXCAE9CYoAnxWKAJ0VRxwCfkpJy5MiR/fv3Hzt2LCMjo9jZep7o0xB9GqGWq1KrWnsaEzaiUAwA1Zxx6Bt4OSq1AHW0+gQmbZH/odXzxJFvUcFK6JpIOxSI0WWBfLtHLy+vK1eu2NjYCF0UlTsGePXCAE9CYoAnxWKAJ0VRowD/+PHjgwcPHjx48OzZs4WFha+e0tVBi6r4pCE+aQBPe2UXJpXiRz/M3S8/bFsDe6aUvJMcvenwDQxcgaw8APByRMB3/OCDlCQ5E41n4fELAGjbtm1AQIC+Pher0HAM8OqFAZ6ExABPisUAT4qi4gFeLBZfvnz54MGDhw4dCg0NLXbW2AAda6F3Q/SsB3sLQQpEoRhfrMeGM/LDES2xdgync7+f4EfouQQJ6QBQwQpHp6Ouh9A1kXa4F4OmP8unvYwbN+7vv/8WuiIqXwzw6oUBnoTEAE+KxQBPiqKaAT4lJSUwMPDw4cNHjx5NTEwsdtbeAj3qoXcDdPSBiYEgBcrlFmDQCuy/Jj/8qQ9+7aek+fYa5kEcOi9AZAIAWJng8Ldo5i10TaQdjt5Cz8UQSwBg6dKlU6ZMEboiKkcM8OqFAZ6ExABPisUAT4qiUgH+9u3bR48ePXLkyMWLF4sNkgdQ3QU966FnfTStUu6LyZdFRi56L8HpewCgI8LKURjfQeia1FlsCjrPx91oADA1xL5p6FBL6Jq0kliCvEKBPxpTsj+OYco/ABel1wIM8OqFAZ6ExABPisUAT4oieIBPTU09efLksWPHjh07Fh0dXeysvi5aVUOP+uhRD5VVaWp0Uia6LMDVCAAw0MM/4zHAV+ia1F9yJrosQEgEABjpY+9UdK0jdE1aIysPKwKw7QJCYyCVws4cXepgeg/4uAldmVJ8sR5rTgGAubn5hQsXfHx8hK6IygUDvHphgCchMcCTYjHAk6IIEuDFYnFISMjx48cDAgKCg4Pf7Gx3sEC3uuheD518YKEq4/pfep6KjvNe9hXvnYpOfLevIBm56LkYZ8IAwFAffpPQs77QNWmBJwnougD344q36+ti+Uh82V6ImpTr1UXpPT09L1++7OioSh8ZkoIwwKsXBngSEgM8KRYDPCmKMgP8gwcPTp48eeLEiVOnTqWmphY7q6uDRpXQrS661EGDitBR1Znkz5LQYR4exAGAtSkOf4umVYSuSbNk56P3Epy4CwAGetg1Cb0bCF2TRssrQJ0ZJaR3GR0RDn2rFUMhkjPR9Bf5P21fX99Tp04JPquIFI4BXr0wwJOQGOBJsRjgSVHKO8A/ffo0KCjo1KlTp06denOEPIAKVujsg8610ak2bM0Ufn8Fe5qIdnMQEQ8A9hYI/J7rpZeLnHz0XYZjtwBAXxc7J6FPQ6Fr0lyrjuOrTaVdUNMVdxcoqRhhPXoB31lIygSAAQMG7NixQ0dHBdbbIMVhgFcv3NGFiEjV5eXlJSQkJCQkJCUlpaSkpKamZmVl5eTkpKWlSSSStLS0Ep8lEomsrKwAWFhYGBoampubW1hYWFhYWFpaWltbW1tb29jYGBoaKvdL0WoPHz48d+7cmTNnzpw58/Tp0zcvMNJHi6ro5INOtVHbTW2WbY9MQNs58pXSna1xYgaquwhdk4YyNsC+qej3Bw7fQIEYA5djzxSOpS8vu4PfcUFoNMJitOK7vbIj/Keh4zzkFWDXrl0VK1acP3++0EURaS8GeCIiVfH8+fPHjx8/fvw4MjIyMjIyOjo6JiYmNjb2zWHVimJmZmZvb+/o6Ghvb+/g4ODs7Ozo6Ojq6urk5OTq6uro6Kirq1tOt9YG+fn5169fv3Tp0vnz5y9evPj8+fM3r9HVQX1PtK+F9jXR3BvG6rbG9ZMEtP0NTxMBwNUGp35AlQpC16TRDPWxZzL6/4FDN1AgxqfLsX8aOtcWuixN9LCEf6/FPXiuFQEeQMuqWDsGI/+CVIoFCxZ4eXmNHTtW6KKItBQDPBGRMOLj42/fvn379u179+7duXPn/v37b+tLLz+ZmZmZmZlPnjwp8ayenl6FChU8PDxcXV3d3Nzc3d09PT1l/7e0tFRyqeri0aNHISEhISEhwcHB165dy8vLe/MaHRFqu6NNdbSriZbVYKW2gxYjX0nvbrY4/QO8uL5V+TPUx+7J+GQpjt1CXgH6LMXR6WhdXeiyNE6hpAzXiMu/DpUxvAUev8CvewFgwoQJbm5uXbp0EbooIm3EAE9EpCTp6emyXHflypWrV6/GxMSU5Vn2FnCwgL05KljBxgyWxrA0gZUJTA1hqA8LY/nW32ZG0P+vszwnH7kFACCVIjUb+YXIykNatvz/aTlIyUJKFpIykJSJhIy3vgctLCyMjo4ucYa2tbW1h4eHp6enp6dnxYoVK1asKHtgZqby07UVSiwWP3z48NatW9evX79+/fq1a9dSUlJKvFJfFw0qomU1tKyq3qG9yLMktJvD9C4MQ33snYIei3EqFDn56LkYx2egSWWhy9IsHnZ48a7PVD3tlVKKyvi5LyLi8c95FBYWDhgw4Ny5c3XqaME6fkQqhovYkZC4iB0plgouYhcfHy+b83zu3LnQ0FCx+K39NZYmqOaMKhVQyQFeDvCwg7sdnK1gqF/uRSZmID4dCemIScGLNMQkIy4Vz5IQk4KYFOQVvMdL2dnZeb7Cw8OjYsWK7u7u5ubm5VZ+uShxETupVBoVFRUeHh4aGhoaGnr79u3Q0NCcnJy3vYi9BXwro1kVNK2CRl4w+dDh8WExCH6MrDzYm6NVNVSw+sDXUaC4VLSeLR9jzPQulKw8dJ6PCw8AwNoUQT+itrvQNWmQ/+3Fz3tKu8DZGs9WqO7GEOUkvxBdFuD0PQBwcXG5dOmSm5ub0EXRx+IiduqFAZ6ExABPiqUiAT4nJycoKCggIODkyZOhoaEl/pjV10UtN9T1gI8barmipiucrZVfaZk8T8WzZDxLQlQSIhMQlYiniYhMRHLme7yItbW1u7u7u7u7bEC+q6uru7u7s7Ozi4uLkZFRudX+4bKzsx8+fBgVFRUbGxsdHf3w4cMHDx48ePAgKyurlGeZGqKeJxpWRCMvNKqkgNngt6MwfiMuPnjZoquDwc2wdBjshPtIJCEdbX7DvRgAcLbGmZ9QmeldIGnZaD8X154AQAUrnJvFvwuFScmC99dIzHjrBWvGYGxbJRakMlKz0fwX+U+AmjVrnj9/XrZgKqkvBnj1wgBPQmKAJ8USNsBHRUUdPHjw8OHDQUFBJfbKetihRVU09kJjL9T1gFH5d62Xq/QcRCYgMhGRCXgSjycJ8sO07Pd7HRsbGycnJ0dHR2dnZ3t7eycnJwcHBzs7OxsbG1tbW1tbWxsbG1E5LMien5+flJSUlJSUmJgYFxf34sWLmJiYuLi4qKioZ8+excTEFBS8e+yBnTnqeqCOO+p6oH5FVHWSz2hQiLPh6LIAOfklnPJyxPlZwnTFp2aj3RzciAQABwsE/agti3iprMQMtJ4tT1MV7XH+Z9X9NFDtXHiAbguRXtIgm//riJWjlF2P6niaiKY/Iy4VAFq3bh0QEMA9TdQaA7x6YYAnITHAk2IJEuBDQ0P37t3r7+9/48aNN89WdkTbGmhdHW1qwEU73lUnZyJS1kufIP/vaSKikpBSWtf1O1hYWFhZWZmZmZmZmck2w9PV1bWyshKJRMbGxm/rwxeLxenp6QAyMjLy8/PT0tIyMzMzMjLS09NTUlJkI+Tfi505qjujugtquKCGC2q5wancInRWHqp+jZiSJ9QDQO8G2DetvO7+Npm56DQflx4CgI0ZTv/AMdsqISYFLX6V7+Tn44azszRhkQUV8SQBP+zC3pCXk4lqu+OH3hjgK2hZKuBGJFrPRkYuwM3h1R8DvHphgCchMcCTYikzwN+7d2/nzp1+fn5hYWHFTlmaoEMtdKmNTrXhzm/t/2TkIioRUUmITkZ0Mp4lITYF0cmIS32/0fhKoK8LZ2t42qOiPSraw8sRlR3h7QRrU+XVsO40xq57xzUPlih1z7a8AvRYjBN3AcDCGCdmolEl5d2dSvfwOVr8ivh0AGhVDQHfq/0YH5WSk4/7ccjKg6e9tnwUWxaBd9BjEQrEADBp0qQ//vhD6IroAzHAqxeuQk9E9B5iY2O3b9++devWW7duFTtV2RG9G6B7PbSo+nJBeCpiboSarqjpWsKp3ALEpyM2BfHpiE/D8zQkZyIpE0mZSM5EciZSs5GeU/Jg8g9gbABrU1ibwtYMtmZwsISDBRws4GaLCpZws4WjpSJHwn+YU/fefc3JUOUFeLEEg1fK07uJAQ59y/SuWqpUwJHpaPsbMnJxNhxDV2HXJOG/jTWGsQHqeghdhOrp5IMN4zDiL0ilWL58uZOT0/fffy90UUSajwGeiOjdcnJy/P39N2/efPLkyWIrydd0xadN0K8xapUUTaksjPThbvvu0QpiCdJz5Ek+Kw8FYmTmAkBeAbLfyPa6OrAwBgADPZgawkgfJoawMnltvz1VFvf2wfNFYstwjUJIpfhiPfyvAoCBHnZPQcuqSro1lV2DivCfhm4LkV+IvSGYtAWrRgldE2m6YS0Ql4rpOwBg5syZjo6Oo0ePFrooIg3HAE9EVJobN26sXbt2+/btaWmv7QhcpQKGNMMAX9TgCl7Koqsj7znXBmZlWJvfXFnr98/chfVBAKAjwj/j0ZUbP6uq9jWx+UsMXQWJFH8eh4s1ZvYWuibSdN/2wPM0/H4EUql03LhxdnZ2PXv2FLooIk3GAE9EVILs7OwdO3asWbPmypUrr7bbW2BwUwxrwfHDVL5qu+NQCasivqaOUsb0Lj2K+Qfkj1eO4tpdqm5QUzxPw9R/AOBHP3jaY0gzoWsiTbd4CF6kYdsFFBYWDhw48OjRo61btxa6KCKNxUXsSEhcxI4USyGL2D18+PDPP//ctGlTampqUaOeLnrUw+jW6FpHPQZgk7oLjUadGRBL3nqBqw0eL4VBOX8Ov+0Chq+G7J3Cr/0wq2/53o4UZdpWLD0KAIb6CPgOrasLXRBpugIxei3BsVsAYGFhcfr06fr16wtdFJUVF7FTL1zehIgIAKRSaUBAQPfu3atVq7Zs2bKi9F7JAXMHImo5/KeiV32md1KSmq6Y3OWtZ3VEWDmq3NP78Tv4bI08vY/vwPSuThYPRb/GAJBXgL7LcD9O6IJI0+nrYs8UNPMGgPT09C5dury5RQsRKQR74ElI7IEnxfqwHvicnJwtW7YsW7YsPDy8qFFXB73q48sO6FALOiJFF0pUBhIppm3F8gAU+0VtaojVn2F4i/K9+82naD0b6TkA0L8x/p3IJc3VTE4+2s/FpYcA4OWIS7/A3kLomkjTpWShzW+4HQUArq6u586d8/T0FLgmKgP2wKsXBngSEgM8Kdb7BvjExMRVq1atWrUqISGhqNHOHGPaYkIHuPG7klTAjUisC0LIY6RkwdkabWrgi3ZwLuedqCMT0OwXxKUCQOvqCPgOhtxUXA3Fp6Ppz4iIB4Dm3jg5k3+PVO6ep6LVbDx8DgBeXl5nz551dnYWuih6BwZ49cIAT0JigCfFKnuAf/LkyZIlSzZu3JidnV3UWNsdkzpjaHMY8T0uabGULLT4FfdiAKCWK879DCu+qVNb4bFo9gtSsgBgaHP8Mx4iDimicvY0ES3/h2dJAFCjRo2goCB7e3uhi6LSMMCrF46HIyLtcufOnaFDh3p7e69atUqW3kUidKmD4zNwax4+b8P0TlotrwB9lsrTu4s1jkxneldv1ZyxZ4p88Y5tF/DbPqELIi3gYYfjM+BoCQD37t3r1KlTcnKy0EURaQ4GeCLSFiEhIZ988kmdOnW2b99eWFgIwEAPI1vi9nwcnY4OtYSuj0hoUilGr8GZMACwMMbh6ZxIogna1sDqz+SPf96D3VdKvZpIEao6IfB72JgBwM2bNzt37pyWliZ0UUQagkPoSUgcQk+K9bYh9BcuXJg9e3ZAQEBRi5kRxrbF1K7MJ0Qv/bALc/cDgL4uDn+Ljj5CF0QfKj0Hm8/i+F3EpsBQH/U8kJqNbRcAwMQA535GfU+BKyRtcDUCHeYhLRsAfH19AwICLCy4lKIq4hB69cIAT0JigCfFejPAnzt37tdffz158mRRi7UpvuqEyV1gayZEiUSqan0QxqyVP94wDqNbC1oNfYTT9zB4JV680d/pZiufluxqg5DZqGCl/NJI61x+hE7zkJELAM2bNz927JiZGX/7qhwGePXCIfREpJnOnz/foUOHVq1aFaV3BwvMG4jIP/C//kzvRK85FYrxG+SPf+rD9K7Grkag+6IS0juAZ0mwNAGA6GT0+wN5BUoujbSRb2UcmQ4zIwC4cOFC9+7dMzMzhS6KSL0xwBORpgkODu7cuXPLli2LorujJRYPRcQyfN8LFsbCVkekcsJj0f8PFIgBYGhz/NpP6ILoI0zagpz8t55Ny5b/DLz4AOM3Kq0o0motquLgNzAxAICzZ8927do1IyND6KKI1BgDPBFpjrt37/bv39/X1zcwMFDWUsEKvw9DxDJ83Q2mhsJWR6SKEjPQY7F8m7EWVbF+LLcZU2MPn+PSw3dc41sZeroAsPEMlge842IihWhT/WWGP3/+PNe0I/oYDPBEpAnCwsIGDBjQtm3bo0ePylocLLB4KB4vxdSu8jcNRFRMfiH6LcPjFwDg5Qj/qTDkNorq7ObTd1/zPA3Lhssff70Np++Va0VEcu1q4vB0+Sfply5d6tixY0pKitBFEaklBngiUm9Pnz4dPXq0j4+Pn5+fbFVOWzPMGyjvdWd0JyrFF+txNhwArExw6BvYmQtdEH0c2VJhpUvPwf91xJi2AFAoxrDjrC4AACAASURBVIDliEwo77qIAKBNdRz9DuZGABASEtKuXbuEBH7zEb03BngiUlcvXryYPHmyt7f3pk2bxGIxAAtj/NxXPtddOQPm49Nx/A4OXMf1SIglyrgjkaIsPIRNZwFATxd+k1HNWeiC6KNVsHz3NU5WALByJJpWAYDEDPRdhuy3T5snUqCWVRHwvXwxxZs3b7Zp0yY2NlbooojUDLeRIyFxGzn6MOnp6YsWLVq2bFnRYrbGBvi/jvi+l/KWl78bja+34vhdFP0QrWCF73piUmfocAoxqbz919B3KSRSAPhzNMZ3ELogUoS0bDiMR35hadf88Al++xQA4lLR8EfEpgDA0ObYOkEZFRIBuBqBzguQnAkAXl5ex48fr1ixotBFaTVuI6de2ANPROokLy9v6dKlXl5ev/32myy96+viy/Z49DsWDVFeej9+B41/QuAdvPoR6PNUTP0H/ZehUKykMog+zO0oDPtTnt4ndmZ61xyWJvi8TWkXmBriy/byx05W2D0ZBnoAsO0Clh0r7+qI5BpWwukf4GgJAI8fP27RokVoaKjQRRGpDQZ4IlIPYrF406ZN3t7e06ZNS0xMBKAjwuBmCFuE1Z/B2Vp5lSRmYMDyt27U5H8VCw8prxii95WQjt6/IzMXADrXxu9DhS6IFGr+INTzLPmUjghrx8DV5mVL0ypYMVL++NvtOBNW3tURydV2x5mf4GYLALGxsa1atbp8+bLQRRGpBwZ4IlIDhw4dqlu37ujRo6OiomQt3eri2hxs/z94OSq7mFXHkZpd2gWLDr1jCCuRUGTLzssWLavqhH8nyncUI41hYYwzP2FsW+i//jdb1QkB32Nws+LXj2sn77QvFGPgCsRwXXBSlqpOOP8zqjoBQHJycocOHY4d4zgQonfjHHgSEufA0ztdunTpu+++O3fuXFGLb2UsGIxW1QQrqdkv795p+cxPQlZI9Dbj1mHtaQCwNsXlX+HtJHRBVG5epOH0PUQnw9gA9T3RpPJbl+fIK0Cr2bjyGACaVkHQj/Jx9URKkJCOrgtx7QkA6Ovrb9iwYdiwYUIXpXU4B169sAeeiFRUeHh4v379mjdvXpTeqzljzxRc/EXgbByd/O5rohLLvw6i97QiQJ7e9XSxcyLTu4ZztMSgpvimO/6vI5pWKW1xTUN97J4MewsAuPQQX29TWo1EsLfA6R/RriYAFBQUjBgxYv78+UIXRaTSGOCJSOXExsZ+8cUXPj4+e/fulY0ScrHG2jG4Mx99G0Ek9BrvhmXomzLi/vOkYk6FYtp/wWzJUHT0EbQaUjFuttjxFXR1AGBlILZfFLog0ibmRjjyLfo3BgCpVDpjxowJEybIdoclojcxwBORCklPT//xxx+9vb3XrFlTWFgIwNIEcwfiwe8Y01ZVJutWd3n3NTXKcA2R0kTEY8By+f4IY9piUmehCyLV076mfHs5AOPWITRa0GpIyxjqY+ckTO4iP1y9evUnn3ySlZUlaFFEKooBnohUQl5e3h9//OHl5TVnzhzZ72xDfUztisdLMaMXTFSpQ3ug7zsuqO3OAE8qJDMXn/yOpEwAaO6NVaMErodU1nc90bsBAGTlof8fyMgVuiDSJjoiLBuOJUPl0z0OHTrUqlWr2NhYoesiUjnlFeAlEkk5vTIRaRiJRLJ169Zq1apNmTKlaH+4ES1xfzF+H6a8rd3LbnAzNPN+61k9XSwbrsRqiEollWLkX7jzDABcbV7u+030JpEIm75EZUcACI/F52sAICEdR27CLxgnQ+W7DxKVn2ndsGsSjA0A4Pr1602aNLl586bQRRGpFoWtQh8TE7NmzZoLFy6EhoZmZGRkZWXJXnnevHm9e/euUaOGQu5CGoar0NPRo0dnzJhx69atopaudTB/EGq7C1jU/7N333FNnYsbwJ8AYQ8FURFURMWFGyfiXgUH1L3FveuqWrcW7VUcP+usW1v3bt27orgq1K1AcaGg7B0gJL8/QqmDoSZwMp7v5376SU5ec57e64U8Oe9534JFJ6HrCgQEf3zc1BCbhqPvJxs1EQnF9yjmHAAAYzH858LVSehApPbuvkSTeUjLAIBa5fDgFWT/flQ0FmNoSyzuBUsTAQOS9rsRCq8VeJsAAGZmZjt27OjWrZvQobQZV6HXLKop8MuWLZs3b15q6gc7IyveuUKFCs+fP580adLy5ctFgq89RWqGBV6X3bp1a8aMGZcuXco50sAJS/qglYZ83SfNwq4A/OqPuy+Rko5yNuhYG991RAVboZMR/et4ELouz25fO0ZhoLvQgUhDbP0z+/J7rqrZ48oclLAowkCke55HofMyPAgHAJFING/evLlz57JKFBIWeM2iggK/ZMmSGTNmKB67uLjUqlVr9+7d+LfA16lTR3FtbcyYMWvXrlXyXKRlWOB105MnT2bPnp2zwjyAyqWxqCe6NxR+hXkirfE0Ao3mIiEVACZ2xEre2UFfYshGbPszz1e9XXF4UhGmIZ2UmIY+a3Dy3xn03t7eO3bssLDgV0eqxwKvWZQt8E+fPq1WrZpcLq9YseKmTZtatWoFQPH1mOKdU1JSJk+evHHjRj09vXv37tWoUUMluUk7sMDrmvDw8AULFmzfvl2xwjwAu2KY441hrSBWjxXmibRDYhoazcWTNwDQpgZOT1eXTRxIU6RloMk83H2Z54DHfqhapggDkU7KkmHGXiw7kf20Ro0ahw8fdnbOex0a+ios8JpF2UXsVq5cKZfLzc3NT506pWjvHzEzM9uwYUPjxo1lMtmKFSuUPB0Raajo6OipU6dWrlx58+bNOfvD+fZAyAqMbsv2TqRKcjkGrs9u7xVssW8C2zt9MRND7J8AC+M8B5y9X4RpSFfp68GvL34bk72s3cOHDxs2bPj7778LnYtISMoW+GvXrgEYPnx45cqV8xojEokGDhwI4N69e0qejog0TlJS0oIFCypWrLh8+XKJRALAWIwpHvhnJWZ5wcxI6HxEWsf3KI7dAQBTQxyZrI5bOZBGcLbDpuF5vhoeW4RRSLf1c8PVeShfAgASEhK8vLymTZuWM5WPSNcoW+CfPXsGoFGjRvkPc3R0BBAc/MmSzUSkvdLS0pYvX+7k5DR//vzExEQABvoY2hLBy7GsH0sFUaE4EYT5h7IfbxqO2uq9oQOpuV6NMaZd7i+ZGhZtFNJt9Rzxly/a1QQAuVzu5+fXunXr169fC52LSADKFnixWAxA8dE8H+/evcO/98YTkdbLyMhYt25dpUqVpk6dqtjaXSRCj0Z4sASbh6MsVzwgKhwhkei/LnvZ+cke3NGw6MjlOPIXPJaixEjo94fDOPRdixuhQsdShRX9UL9CLsdrOBR5FNJtJSxwahpmeUFPBAD+/v5169Y9c+aM0LmIipqyBV4xc/7ChQv5D7t16xaA8uXLK3k6IlJzUql0y5Ytzs7OY8eOffPmjeLgN7Xxly/2T0AVO2HTEWmzZAm8VyI+FQBaVceS3kIH0hmSTHT7P3y7EqfuIiYZMjlex2FPAJrOx+wDQodTmpEY+8bD6sPFrYqboWNtgQKRDtPXg28PHP8+exfDqKiob7755vvvv8/IyBA6GlHRUbbAe3t7A9i3b9/hw4fzGvPkyZPNmzcD6Nixo5KnIyK1JZVKd+zYUbVq1WHDhr148UJxsHlV+M/FyWmo5yhoOCJtJ5djyEY8DAeAcjbYN54L1xWdEZtx5K9cjsvlWHQUq04XeSBVq1gKmz+8GX5Jn/zWtyMqVN/URuAiuDkDgFwuX7ZsWZMmTZ48eSJ0LqIiomyBHzdunJ2dHYCePXtOnjw5PDz8/VezsrL27t3bokWLjIwMExOT8ePHK3k6IlJDWVlZv/76a/Xq1QcPHvzPP/8oDjauhLMz8OccNKsibDoinbDsBA7cBABjMQ5NhK2l0IF0xp1n+O1afgPmHkRiWlGlKTTdG2LsvzfDW5ni2waCpiGdV9YGl2fjhy7Z0+kDAwPr16+/fv16JbfHJtIIyhZ4CwuLY8eOWVpaZmVlrVy5smzZsmXLllW8VK9ePXNz8z59+rx7904kEm3cuNHBgfdLEWkVqVS6c+fO6tWrDxw4MCQkRHGwfgX8MRXXF2QvNkNEhe3iQ8zcn/14nQ9cnQRNo2P230D+lSExDafuFlWawrS8H+o6AkBCKgZvKODfmqiwGehjcS+cnwkHawBITU0dM2aMp6dnzu17RNpK2QIPoEGDBoGBgW3btlU8zbkIHxQUpNgyqkyZMn/88Uf//v2VPxcRqYnMzMwtW7ZUrVp10KBBORtM1HXE0cm4/SM61RU2HZEOeRWD3msgzQKAUW3g00LoQDrmyWeUhc8Zo/6MxNg/AZYmAHA8CCtOCR2ICGhVHff+h56Ns5+eOnWqZs2au3btEjQUUeESqXCqyYMHD44fP37r1q23b9+mpqZaWVk5Ozu3adPG29vb0JCbjVAuYmNjZTKZSCSyseG65BpDIpFs3bp16dKlOTe6A6jriLne6Fof3GuCqCilZ6L5j7j1DwA0qYzLs2FoIHQmHfPNUpwu6AL7D12wuFeRpCl8ewLQdy0AiPXhPxeNKgkdiAgA8NtVjN+RvYongM6dO69fv97e3l7QUBojNjbW2NjY1NS04KGkBlRZ4Im+FAu8ZklKStqwYcOKFSsiIyNzDro6YY43OtdldVdTgc/xywUEBCMxDcXN4F4Vo9uiOj/SaIvhm7H5EgCUssKdRbAvLnQg3TNiMzZdKmDM+iEY1aZI0hSJnL91FWwRuBjF+Jmf1EN4LIZuxNn72U+trKyWLl06fPhw7mNdIBZ4zcICT0JigdcU7969W7Vq1bp16+Lj43MONqmM2V7wqCNgLsqPXI7pe7HsxMe3qurrYWF3zOwqUCxSnc2XMHwzAIj1cX4mmlcVOpBOOnwb3f4vvwEiEcJWwtG2qAIVvtQMNJyTveVB94Y48J3QgYj+JZdj65+YsgsJ/16Kd3d337BhQ/Xq1QXNpe5Y4DWLCu6Bz0t8fPyxY8e2bNly//59mUxWeCciosITGho6evRoR0fHxYsX57T31jVwYSYC5rO9q7X5h+F3PJeFprJkmLUfq88IkYlU53YYxu/IfrykD9u7YLrWR61y+Q0Y2Eyr2jsAU0PsGw9TQwA4eAsbLggdiOhfIhGGtsTDpehcL/uIv79/nTp1fvjhh5SUFEGjEamMaq7Anzp16vDhwzY2Nv/73/8UR27duuXl5RUREaF42rRp02PHjpUoUUL5c5E24RV4dRYQELBixYojR47kfAGnJ0KX+vihCxpWFDYaFexlDCpPRoY0zwEWxni+CtbmRZiJVCc6Ca6z8SIaAHo1xl5u0iqo0Ldo8SPexOXyUsOKOD9TO7dM33IZwzYBgIkhbv0IF240RGpm3w1M/BWR/04cLFeu3IoVK7p16yZoKDXFK/CaRQVX4L///nsPD4/Nmzc/ffpUcSQjI2PgwIE57R1AQEBA69ateR2eSP1JpdJ9+/Y1btzYzc3t0KFDiv/bGhrApwUeLMGRSWzvmuHgzfzaO4AkCX4PLKo0pFJZMvRdm93eXRywebjQgXRepVIIWowhLWAk/u9gcTPM8caVOdrZ3gEMbYneTQAgLQO9VyM1Q+hARB/q1RiP/TC6bfZe8S9fvuzevXvbtm0fPnwodDQipShb4K9evbps2TIAYrG4XLnsOWTHjx9XlPnZs2c/ffrU19cXwP3793///XclT0dEhSc2NnbJkiVOTk69e/e+efOm4qCVKb7vhLD/w9YRqMaVzzTHg/DPGPOq8HNQIZh7EOfuA4CVKQ5NhLmW9kPNUtISW0YgdiOuzsPJafjLF+/WY2H3Dyq99tkwBE4lAeBhOCbuFDoN0SeKmWKdD279+N+1hwsXLtSpU2fUqFFv374VNBrR11O2wK9YsQJAmTJlHj16tGrVKsXBw4cPA7Czs5s3b56zs/OsWbO8vLwAbN++XcnTEVFh+Pvvv4cPH+7g4DBjxoxXr7JbnaMtVg7Aq9VY2odLW2uelHTVjCF183sgfvodAEQibB8JZzuhA9F7TA3h5oxvaqN+BRjoC52m8FmZYs84iPUBYNMlHLgpdCCi3NSvgOsLsGUESloCgFQq/eWXXypVqrRw4ULeGE+aSNkC/+TJEwDjx4+vVOm/nUDPnz8PoH///gYG2dvRdu7cGUBYWJiSpyMiFUpPT9+9e7e7u3vdunU3b96clpamOO7mjAPfIXQFJnbU2smfWq9MsYLH2FsXfg5SqdC3GLQ+e2HCGZ3h5Sp0INJ5DStiUc/sxyM243mUoGmI8qAnwpAWeLocUz2z58UkJyfPmzevUqVKa9asycjgHSCkSZQt8C9evADw/t4MoaGhikkprVu3zjlob28P4NmzZ0qejohUIiQkZNq0aWXLlu3Xr9/Vq1cVB43EGOiO2z/i6jx0bwj9Qtykggpdu5qfMcal8HOQ6qRm4NuViE8FgLYu+LGH0IGIAABTPNGhFgDEp6LfOkizhA5ElIdipvDri8d+6N0Eir3hIyMjx48fX6VKlS1btmRmZgodkOizKPsJvVixYgByLtwBOHPmDAB9ff2mTZvmHExNTQVgZGSk5OmISBkSiWTPnj1t2rSpUqWKn59fVFT2tZKyNvDtgZc/Y8couDoJm5FUo0Mt1M53a6uW1dCoUn4DSN2M2Iz7rwCgnA12j+VXbKQu9ETYMQqliwFAQDDmHxY6EFG+KthizzjcWojWNbKPPH/+fNiwYVWrVmWNJ42g7O//ihUrArh27VrOkc2bNwNo1qyZpaVlzsGgoCAAZcuWVfJ0RPR1/vrrr3HjxpUpU6Zv374XL15U7B+pJ0KHWjg8CWErMcsr+94w0g76etg7HrZ5/G9azgY7RxdtIFLOmrPYdQ0AjMQ48F2e/8sSCaKUFXaMyl7r+6djuMhFvkntuTrhwkyc++G/77LDwsKGDRtWuXLlNWvWKC49EqknZQt827ZtAWzevPnUqVNyuXzfvn1///03AMWqdQr379/fsGEDgKpVqyp5OiL6Im/evFm+fHnNmjUbNGiwdu3auLjsfYrtimFmV4SuxOnp8HbVicWWdFDVMrjjC2/X7E/VCgb66N8Mf/mirI1wyegLXQ/BlF3Zj1cN4FaOpI7a18QUTwCQyTFgPaKThA5E9BnauuDGAhyfigb/Tj988eLF+PHjHR0dFyxYkDNRkUitiBQX4r5aQkJChQoVFK3A0NBQsQiEtbV1cHCwjY1NWlqal5fX5cuXFcevX7/euHFjleQm7RAbGyuTyUQikY0Ny4QqJSYmHjlyZNeuXRcvXszK+u9+RH09dKyNYS3RqS5Luw6JiMeNUEQnobQVGlfixVsN8y4R9WbidRwADHLH9lFCB9Jk10Nw4m+8jIZYH7XKoVtDOHApR9XJkKLZAtwOA4BOdfH7lOzbjAtbQip2+OPSI0TEo5gpXJ0wuDkqlSqKU5M2Ofk3fI/iesh/R0xMTAYMGPDdd9+9v9qXVoqNjTU2NjY1NRU6CH0WZQs8gICAgG7dukVGRiqeGhoabt++vU+fPgASEhIUN8kDGDt27Jo1a5Q8F2kZFnjVSktLO3ny5N69e0+cOPH+yhQAqtljcHP0d0MZbghHpDmkWWj/P1x6BAB1yiNgPkwMhc6kmd7EYdAGnH/wwUGxPqZ6YmF3fqGpMqFvUW8mkiQAsGogJnQo9DOeCMKgDYhJ/uCgvh5mdsWCbkX0DQJpk8uPseQPnLmHnIYkEonatm07ZsyYzp076+tr5w8LFnjNooICD+Ddu3cnT54MCgoqUaJE165da9WqpTiekJBQtWrVevXqDRo0qGfPnvm/CekgFniVSElJOXXq1MGDB48fP/7Rjqa2lujVGAPd/5sbRkQaZPoeLD0OAMXN8JcvnEoKHUgzRSeh6XyEROb+6pAW2DKiaANptd+uYsB6ADAW48bCAlbTVNLFh+i4BJl5rHs/s+t/W9wRfZH7r7DiJPZcR/p7S9rZ29sPGzZs6NCh2reqFwu8ZlFNgSf6OizwyoiNjT1+/PjRo0dPnz790fV2c2N0rY++TdG+Jq8sEWmqI3+h2/9BLoeeCMemoFNdoQNprNFbseFCfgNOT8/eCI1UYuB6/HoVAKrZ4y9fmBbOtJEsGapPQ3BEngMM9HHvJ1SzL5Szky6IjMf6C/jlAt4m/HdQT0+vffv2Pj4+Xbp0MTY2Fi6dKrHAaxYWeBISC/xXCA4O/uOPP44fP3716lWpVPr+SyaG+KY2ejVGp3qF9YGJiIpGcAQazEFiGgDM8cbC7kIH0lhpGSgxEqkZ+Y3xdsXhSUUVSAckSVB3Jv55CwAjWuOXoYVylqtP4b6wgDG8CE/Ky5Di8G2sOwf/px8cL168eM+ePfv37+/m5ibS8Ls1WOA1i8oKfFJS0u7duy9evHjv3r24uDipVGptbe3i4tKmTZsBAwa8v6UcUQ4W+M+Umpp65cqVU6dOnTp1KiQk5KNXzY3hUQfdG8KjDsyMBAlIRKqUko7Gc/EgHADa18Sp6R9sJUBf5EYomswrYEzpYohYWyRpdMbtMLjNz57cfmgivm2g+lP8fAbf7SxgTMfaODVN9acm3fToNTZdxG/XPt5kwdHRsUePHj169GjQoBD+ohcJFnjNopoCv23btkmTJiUkJOT6qqWl5f/93//5+PgofyLSMizw+ZDJZPfu3Tt37ty5c+f8/f0lEslHA0pZoXM9dK2Pti4wFguSkYgKRd+12BMAAOVL4M4i2JgLHUiTnfwbnn4FjBHrI6OgKkhfaulxTN8DANbm+Hux6reu9D2KOQcKGNOkMgLmq/i8pOPSM/FHEHZcwZl7H6+/4Ojo2K1bt65duzZt2lSzlrtjgdcsKijwy5Yt+/777xWPLSwsKlWq5OjoaGBg8OzZs+Dg4MTERMVLK1eunDhxopLnIi3DAv8RuVz++PHjy5cvX7p06fLly9HR0Z+OqV0OnnXRuR4aVuRFOSIttPoMJuwEACMxrs6FK1egVM7NUDQu6Aq8XTG84RV4VZPJ0eF/2Sv/N6+Ki7Ogr6fK9990CSM2FzDGyxVHeHMEFY6oROy5jt0BuPUPPqpTJUuW9PT09PT0bNeunUZMQ2aB1yzKFvjHjx/XrFkzKyvL1tZ21qxZw4cPf/9/e4lEsmnTph9//DEqKkosFj948MDZ2VnpzKQ9WOABSKXSwMDAa9eu+fv7X716NSoq6tMxxc3Q1gUdaqFjbdhzHzgi7RUQjJa+2Vd1Ng7D8FZCB9J8aRmwHYWU9PzGdGuIg98VVSBdEhGP2j8gKhEAFnbHHG9VvnlwBKpMLWDM/w3Adx1VeVKiTz2PwoGbOHgLt8M+bvJisdjNza1Dhw7t2rWrW7eunp5Kv8RSHRZ4zaJsgR8zZsz69euNjIyuXLnSsGHDXMfcvHmzRYsW6enp48aNW716tTKnIy2jswX+zZs3t27dunnz5vXr12/fvp2amvrpGCMxGlVEWxe0rwlXJxVfuCAiNfQ2AfVn4XUcAPi0wFbubaYiY7dj3bn8Bpz7AW1diiqNjjkehC7LIZfDQB9/zkZTlV7H8fTDyb/zfNXGHMHLYc07UKiovIzBkds4dgf+TyH9ZHdDa2vrli1btmrVqlWrVtWrV1erde9Y4DWLsgW+SpUqwcHBY8aMWbs2v8ln48aNW7t2bdWqVR8/fqzM6UjL6E6Bj4yMDAoKunPnzp07d27fvv369etchxmJ0cAJLauhRTU0deZK8kQ6RJqFdj/h8mMAqOuIa/Ngwp8AKhKbDLcFePIm91dHt8U6rtJTmCbsxOozAOBoi78Xw0p1HeFVDJrOR3hsLi8Z6OPIJG6+SMKIT8WZezgRhDP38C4xlwE2NjZubm5ubm5NmzatX7++iYlJkWf8AAu8ZlG2wJubm6ekpOzYsWPgwIH5DNu1a1f//v3Nzc2TkpLyGUa6RlsLfEZGxpMnTx48eHDv3r27d+/evXs3IiLPnWqLm6FJZTStDPeqaODEj+xEOur73Vh2AgCszfGXLyrYCh1Iu0TGY8hGnLr7wUEjMWZ0xhxvTnEqXJJMNJqLey8BoHcT7Bmnyjd/HYfRW3E86IOpy9XssWEImldV5YmIvoJMjqDnOHcfZ+/jeggkmbmMEYvFtWrVatiwoaurq6ura/Xq1Q0MDIo4Jwu8ZlH274ei/3/mJJCv+LLgwYMHM2fOBGBhYbFz5868VnT09/f38/MD4ODgsG7dui89Sz7v2b59+3HjVPqrhrROSkpKcHDw06dPnzx58ujRo4cPH4aEhGRm5vZDGgAg1odLWTSsiEYV0agSqpWBOs2iIiIBHLyF5ScBQE+EX0ezvate6WI4OQ1/heHk33gWBWMxXMri2wawKyZ0Mh1gLMaecXCdjbQM7L2ODrUwuLnK3ty+OH6fguAIXHyEyHhYmqBhRTR15iKvpBb0RKhfAfUrYEYXpGXgegguP8blR7gd9l+Zz8zMVMzQVDw1NjauWbNmnTp1atWq5eLiUrNmTS27ykXKU7bA29vbh4SEXL9+fcCAAfkMu3nzJgAHB4evPlFSUlJQUJCrq2uur169evWr3/kzrV+//tSpUyr8goA0UUJCwrNnz/7555+wsLDQ0NCQkJDQ0NBXr17l/6cMDeDigDrlUa8CXCugdnnu+kZE/3nyBkN+yb5+OMcbHnWEDqS9XJ24qr8wqttjRX+M3goA43fAzRmVS6vy/Z3t4GynyjckUjkTQ7SugdY1ACA9E3ee4XooAoJxMzR76RMFiURy+/bt27dv5xwpVaqUi4tLlSpVqlWr5uzs7OzsXK5cObVdD4+KgLIFvk2bNiEhIVu3bh08eHBei9jduXNn06ZNAFq3bv11ZxGJRHK5/OrVq7kWeIlEcufOHcWYr3t/ovdJpdK3b9++evUqPDw8PDz8+fPnL1++fPny5fPnz2NiYj7nHcqXQHV71CyLmmVRqxyq2UOsSbuBgEAeMwAAIABJREFUElHRSZLg2/9DkgQAvqmNud8KHYiocIxqg7P3cOQvJEvQZw0C5sOwqKcJE6kLIzGaOqOpM6Z4AEB4LG6H4c4z3HmGoOd4m/DB4Ldv3759+/bChQv//XEjIycnp4oVKzo5OTk5OTk6Ojo6OpYrV654ce5UpBOU/dk5YcKETZs2paend+rUaf78+UOGDDE2Ns55NSMjY9u2bfPmzZNIJPr6+uPHj/+6s5QtWzY+Pv7GjRsZGRmGhh/fInzr1q2MjIyaNWvev3//6/9NCtK0aVM7Oztzcy5mqg1iY2OjoqKioqLevXsXERGh+GdERERkZOSbN28iIyNlMtlnvpWhASqWQhW77P/UcEA1e1gYF/wHiYjkcgz5BY9fA0AFW/w2hvN+SZttHo7bYQiPxZ1nmLUffn2FDkSkHhys4WAN738vU76Jw72XuPsSD8LxMByPX39883x6evrjx48/XRrc3Ny8XLlyDg4OZcqUKVu2bKlSpRwcHEqWLFmmTJmSJUsKvlQeqYqyBb5atWpLliyZOnVqVFTU2LFjZ8+eXaVKFUdHRwAvXrx4+vRpbGz22qA//fRTtWrVvjKlgUHTpk1Pnz4dGBjYuHHjj15VzJ9v1qxZrgU+Pj5+3759ISEhL1++LFasWPXq1b29vcuXL//RsIsXL/r7+4eEhMjlcmdn5169en00IC4ubuvWrZ6enm3btgWwf//+3377bdCgQd26dXt/2IoVKy5fvjx37lzFZAHFjfTDhw9v3Ljxjh07goKC0tPTy5Ur16dPnwYNGrx7927Hjh13795NT0+3t7f38vJq2bLl1/1XRMnJyYmJiQkJCQkJCYmJifHx8XFxcXFxcbH/ivlXdHS0VCr9ilMUM4WjLZxKooItKpZCxVKoVArlS3D1IyL6SstP4uAtADAxxKGJ3O+KtJy1OX4dgzaLIJNjxUm0r4l2NYXORKR+yhRHmeLoWDv7aZYMz6Lw5A2evEFwBIIjERr5waz7HMnJyY8ePXr06FGub2tubm5nZ1fiXzb/sra2NjAwKFmyZKlSpYoXL25lZcX5+WpOBbOXpkyZYm1tPXHixMTExLi4uBs3bty4ceP9Aebm5itXrhw2bJgyZ3F3dz99+rS/v/9HBT4tLS0wMNDOzs7JKZfb2h4+fLh06dK4uDh9fX1bW9uoqKiLFy9euXJl8uTJzZo1U4yRy+U///yzYl6KgYGBqanpnTt3AgMDcwYo782bN5MnT65du3afPn1CQkIuXbrk6+s7YsSI3bt3V6lSpVevXuHh4WfOnFmxYoWpqWledyJoH4lEEh8fL5PJFMU7JSUlIyNDIpGkpaUp/pmampqenp6UlJSZmRkfH5+enp6ampqQkCCRSFJSUuLj41NTU5OTk5OSkuLicvsx9lVMDeFgA7tiKGsD++JwsEb5EihXAuVLoBjX5iQi1bn8GD/sy368djDqOgoZhqhotKyGH7pi0VHI5Bi0AXd/gq2l0JmI1Ju+HiqVQqVSH2yLmJaBsHcIe4dnUXgehedReBmD8NiPp9+/Lzk5OSQkJCQkpMAzWlhYWFlZWVhYmJubKx6YmpqamZkVK1bMxMTExMTE0tLS0NBQ8U8zMzNjY2PFcWNjY7FYrJiwzPn8hUc1tx/5+Ph8++23u3btunDhwv379+Pi4uRyubW1tYuLS+vWrfv371+smLLLvLq4uBQvXvzWrVsSieT9Wfo3b97MyMhwd3f/dCX89PR0RXv39vbu27evkZFRRkbGvn37Dhw4sHLlykqVKpUuXRrA5cuXL1y4YGBgMHLkyNatW4vF4rCwsCVLlvj7+yuZOceJEydGjhzp6empeGpsbHzq1KlffvnFy8tryJAhioPW1taK/wK/qMBnZGSo7Z3/gYGB06ZNi4uLS01NBSCXyxMSEgDIZDLFA0FYGMPWEraWsLVACQuUskIpK5S0RJniKGWFMsXZ0omoKITHovdqSLMAYGQb+LQQOhBRUZn/LS48wI1QRMTDZyP+mMKtWIi+mIkhajigxifrg6dn4nUcXsfhdSwiE7L/GRGHtwmISkJ0ErI+4ybRpKQkVe38rWj4AExMTIyMjACYmpoqHujr61tYWCiGiUSi99tizuAc5ubmYvHHS0AbGxu/3wo/YmRk9P7GeE2aNKlevbqy/z6FxsDAIK/d1nIZrKqzWllZjRkzZsyYMap6w4+IRCI3N7fjx4/fvn3b3d0957hi/ry7u3t6evpHf+To0aNxcXHNmzf38fFRHDE0NBwwYEBcXNz58+ePHDkyevRouVy+e/duAEOGDOnQoYNimJOT08KFC0eNGpWVlaWS8KVLl/bw8Mh5Wr9+/VOnTpmamvbv3z/nYIMGDXbt2hUZGflF75yUlKS2Bd7Pz68IdgcwMYSFMcyNUdwMFsawNIGVKaxMUdwMxUxhbY7iZrA2h7UZrM1hY84lc4hIeBlS9FiVfZ2kUSWsym8XFyJtY6CP3eNQ5wckpuFEEFafxYQOQmci0hZGYjiVhFPJ3F+VyxGdhOgkxCQjJhmxyYhNQWwyYpMRl4L4VMSnIiEVCalITEPKx9Xqa2RkZGRkZABQ4WzZr2NmZnbv3j1LSzWd82Nubi5AgS8C7u7ux48fv3r1ak6BT01NDQwMdHBwKF++fHBw8EfjAwMDAXTq1Omj4x07djx//vyDBw8AREVFvX371tzcvH379u+PKVWqVOPGja9du6aS5JUqVXp/goDi26Zy5cq9vyCfYrbJ56+dpv66dOly6tQpiUQCQE9PL+f/MFZWVgDEYrHiCzkzMzOxWCwWi01NTRXDRCKR4vYbCwsLxTDFF2wWFhaGhobm5ubm5ubGxsZmZmYWFhaf/3cdQGIh/GsSEX2p77///kbodgAlLXHwOxhxX0nSMRVssX4I+q0FgGn7jGoPOVOjRg2hQxHpBBFgC9h+xkjFXa7JyckpKSmK+14lEolEIklISMjIyEhNTU1NTc3MzExOTpZKpYmJiTKZLCkpSSaTpaamZmRkZGVlKS7jp6enK+pASkpKZmZmQactLHK5XG2ven6pLyvwc+bMUeZkTZo0ef9C9JeqWrVqiRIl/vrrr9TUVMWMiBs3bkil0vcvyL/vzZs3AM6ePXvp0qX3jyuu1UdHRwOIiIgA4ODg8Oni9k5OTqoq8O/P38j/4JcyMTFR27+LPXr06NGjR/5jJBKJIj8XxiSVSEtLMzAw+HSSFdGXkkqlis8ZYrHYwECVX3b/+uuv27dvB2Cgj73j4WCtwvcm0hh9m+LMPez0R3p6+siRI/39/VXyuUhHpKeny2QykUiUz/xhos+n2C/s049PiottKpeenp6WlqZ4nJCQkHP9UvEFQc6wtLS0j2ZYK74a+PQNFctm5X/Shg0bKu6eVk9f9Enjyz6U+Pr6fmGYD0ycOFGZAi8SiZo1a3b06NGbN2+2atUK760//+lgqVSquNH6/Pnzub6bojoqpnNYW+fyASrXg+pG03/bpaeny+VykUhUSD8gSNdIJBJDQ0NN//8FqQOJRKIo8IaGhir8hjEwMHDSpEmKx//rjVbqezseUaFbMxgBwQh9i6dPn86aNWvjxo1CJ9IYOR2GH59IJdLT04vy49P7f2/t7e2L5qTa5MsKfIkSJXI9npiY+P7XIYq78N//IqR69eq1atWqW7dubn/6CzRv3vzo0aP+/v6tWrVKSUn5+++/y5cvX7Zs2U9HGhgYWFhYJCUl7d+/P5+vJ21tbZHHXRnx8fFfkTA5Ofkr/hQREWm96Ojob7/9VnF5oWdjTP5G6EBEgrIwxu5xcJuPzCxs2rSpffv23bt3FzoUEZG6+7Jd/qJys2nTJsV9yK6urvv27Xv+/LlEIklNTX316tWxY8cUl8fDwsJ69uw5cOBAJeMqlo4PCgpKSkpSzJ/PZ7M3Ozs7AK9evfrouFQqjYmJSUxMBFCqVCnFmE9vyXjx4kWBeT6dvq6Yt09ERPS+rKysPn36KH6z1HDAluFceZsIDZzg2zP78YgRI16+fCloHCIiDfBlBf5TwcHBffv2TUtLmzFjxu3bt3v27Fm+fHl9fX09PT0HB4cuXbr4+/svWrRIIpH069fv0aNHyidu1qxZVlbW9evXFdu85VPgXVxcAJw4ceKj4wcOHPDx8Tl8+DAAGxsbe3v75OTks2fPvj8mJiYm/xXUFd9ZREVFvX/w4cOHLPBERPSpmTNnKm7psjLF4Ykw542rRACAqZ5o6wIAcXFx/fr1U9UGQERE2krZAr9s2bK0tLTmzZsvXrw4rzEzZ85s165dWlrasmXLlDwdgObNmwM4ffr03bt3nZyc8rlxolu3bmZmZpcuXdqzZ49UKgUgl8v9/f0PHjxoYGDQrl07ACKRqE+fPgC2bt16/vx5xa+N8PDwefPm5f8rxMHBAcClS5fCwsIUR8LDw3/++WeunkVERB85ePCgn58fAJEIO0fD2U7oQERqQ0+EnaNhawkAV69e/fHHH4VORESk1pRdWVdx4drT01OU71xADw+Pc+fO5bWe3BdxdHR0cHAIDQ1FvpffAVhYWEycOHHlypV79uw5ePBg6dKlExMTExISRCLR5MmTc5q/u7v73bt3z5079/PPP2/YsMHMzCwuLk5PT8/Hx2fr1q15vXndunUdHR2fP38+ZcqUSpUqZWZmPn/+3N7e3tvbe//+/cr/axIRkXZ4+PChj4+P4par2V7oUk/oQERqxq4Yto1A5+WQy+Hr69umTZu8NhgiIiJlr8ArtmFT3G2eD8VacW/fvlXydAo5P9bzL/AAGjVqtGrVqrZt25YtW/bdu3empqbNmzdfvXp1ixYtcsaIRKLx48dPmjSpfv36JiYm6enptWvXXrRoUevWrd3d3StXrpzrOxsaGv74448eHh62trZhYWFJSUmdOnXy8/OrUqWKu7v7161gb2NjU7Vq1a/4g0REpJ7i4+O9vb0V65t61sX8bkIHIlJLnnUxoQMAZGVl9e/fP9fVhYmICIBIyV3EbW1to6OjJ0yYsGrVqnyGTZw4cdWqVaVLl1YUfiKF2NhYxUamNjY2QmchbRATE2NiYsJt5Eh5EolE0brNzMy+ehs5mUzWpUsXxVIslUrhti+K8e8mUR7SM9FkPoKeA8C333576NAhgQOpsbi4uKysLH58IlWJjY01NjbmxydNoewVeMXOcLt27Xr+/HleY54/f/7bb78BqFePEweJiEhXzJ8/X9HezY1xZDLbO1F+jMTYMy57fcfDhw9v2LBB6EREROpI2QLv4+MDICYmplOnTnfu3Pl0QGBgYJcuXWJiYgAMHjxYydMRERFphKNHj/r6+gIQibBlOFwchA5EpPaq2OHnf3ccnjx58oMHDwSNQ0SkjpSdQi+Xyzt27JizB1uHDh0aNmzo5OQEICws7Pbt26dPn1a85OHh8emObqTjOIWeVItT6ElVlJxC//jx40aNGiUlJQGY3hn/6636hETaqs8a7L0OADVq1Lh16xZ/pH+KU+hJtTiFXrMoW+ABpKSk9OvX79ixY/mM6d69+44dO/jXgj7CAk+qxQJPqqJMgU9ISGjUqNHTp08BtK+Jk9Ogr+x0NyIdkpCKerMQ9g4ARowY8csvvwidSO2wwJNqscBrFhV8pjAzMzt69OixY8c6duxYsmTJ91+ytLTs2rXr6dOnDxw4wL8TRESk9WQyWf/+/RXt3akk9oxjeyf6Mlam2DMOYn0A2Lhx44EDB4RORESkRlRwBf4jiYmJoaGhWVlZ1tbWFSpU0NPjJxfKE6/Ak2rxCjypyldfgZ89e/aiRYsAmBkhYD5qlSushETabelxTN8DAMWKFQsKCnJ0dBQ4kDrhFXhSLV6B1yyqb9eWlpb16tVr0KBBxYoV2d6JiEh3HDp0aPHixQBEImwbyfZO9PWmeqJ9TQCIj4/v27dvZmam0ImIiNQCCzYREZEK3Lt3b/DgwYp5bTM6o0cjoQMRaTI9EXaORikrALh+/fqcOXOETkREpBZY4ImIiJQVExPj5eWlmHXvUQe+PYUORKT5Slnh19HQEwGAn5/fmTNnhE5ERCQ8FngiIiKlSKXSnj17Pnv2DEAVO+wem105iEhJ7WpiemcAkMlkAwcOjIiIEDoREZHAWOCJiIiUMmXKlIsXLwKwMsXRybDiMkBEqrOwO9ycAeDdu3f9+/eXyWRCJyIiEhILPBER0dfbtm3bzz//DEBPhN/GoGoZoQMRaRcDfeweB2tzALh48aJilwciIp3FAk9ERPSVAgICRo8erXjs2xOd6gobh0g7lbPB1hEQiQBgwYIFV65cEToREZFgWOCJiIi+xqtXr7p165aeng6gdxPM6Cx0ICLt1bU+xrcHgKysrL59+0ZHRwudiIhIGCzwREREXyw1NdXLyysyMhJA/Qr/XR4kokKytA/qVwCA169fDxo0SLFlIxGRrmGBJyIi+jJyudzHxycwMBBA6WI4OhkmhkJnItJ2RmLsGw9LEwA4efLk0qVLhU5ERCQAFngiIqIv4+vru3//fgBGYhyZBAdroQMR6YaKpbBxWPbj2bNnX7t2TdA4REQCYIEnIiL6AocOHZo3b57i8S9D0biSsHGIdEuvxhjVBgCkUmmfPn1iYmKETkREVKRY4ImIiD5XYGDgwIEDFTffTvXEIHehAxHpnpUDUKc8ALx69Yo3wxORrmGBJyIi+iwRERFdu3ZNTU0F4FkX/+stdCAinWQsxr7xsDAGgBMnTvj5+QmdiIio6LDAExERFSwtLc3Lyys8PBxADQfsHgt9/golEoizHW+GJyIdxU8fREREBZDL5UOGDLl16xYAW0v8MTV7KWwiEkrvJhjZBgAyMzN79+4dFRUldCIioqLAAk9ERFSAhQsX7t27F4ChAQ5NRAVboQMREfB/A1DXEQDCw8P79+8vk8kEDkREVPhY4ImIiPKzd+/eBQsWKB5vGAL3KsLGIaJsxmIcmAArUwA4e/bsjz/+KHQiIqJCxwJPRESUp5s3b/r4+CiWuZ7eGT4thA5ERO+pWApbR0AkAoCFCxeeP39e6ERERIWLBZ6IiCh3r1696tmzp0QiAeDtisW9hA5ERJ/4tgG+6wAAMpmsX79+r1+/FjoREVEhYoEnIiLKRWJiYt++fRUrY9VzxK9joCcSOhMR5WZpXzSuBADv3r3r1atXZmam0ImIiAoLCzwREdHHpFLp0KFDnzx5AsC+OH6fCjMjoTMRUR7E+tg/ASUsAODatWszZswQOhERUWFhgSciIvrYxIkTL1++DMDcGH9MhX1xoQMRUb7K2mDX2OxpMitXrjx48KDQiYiICgULPBER0Qf8/Py2bNkCQF8Pu8dm71NFRGqufU3M/RYA5HL5kCFDFDNoiIi0DAs8ERHRfw4cOJAz/3Z5P3SuJ2wcIvoCc7zRoRYAJCUldevWLTk5WehEREQqxgJPRESULSAgYNCgQTKZDMCEDviuo9CBiOhL6ImwayzKlwCAR48eDR8+XOhEREQqxgJPREQEACEhIV27dk1LSwPQtT5W9Bc6EBF9ORtzHPwOxmIA2Lt3788//yx0IiIiVWKBJyIiQnR0tIeHR3R0NABXJ+waC33+hiTSTK5OWD0o+/HUqVOvXbsmaBwiIlXixxMiItJ1aWlpXbp0CQ0NBeBoiz+mcNM4Is02rBWGtACAzMzMHj16RERECJ2IiEg1WOCJiEinyWSyfv36Xb9+HUBxM5z8HqWLCZ2JiJS21gf1HAEgIiKiR48emZmZAgciIlIFFngiItJpkyZNOnLkCAAjMY5MQjV7oQMRkSoYi3FoImzMAeDatWuTJk0SOhERkQoYCB2AiIhIMH5+foo1rkQibB+JFtWEDkREecjMwpHbOHsf4bEwMUSd8ujnhkql8vsjjrbYMw7fLEWWDGvXrnV1dR08eHARxSUiKhwiuVwudAbSXbGxsTKZTCQS2djYCJ2FtEFMTIyJiYmpqanQQUgz7Nq1a8CAAYrfg0v74PtOQgciojz8/QK9ViP4wzvZ9USY7IH/9S5gycklf2DGXgAwNjb29/d3dXUtxKBFIi4uLisrix+fSFViY2ONjY358UlTcAo9ERHponPnzg0ZMkTR3id0YHsnUl9PI9B60cftHYBMjmUnMHZ7AX98Wif0aAQAEomkW7du7969K4yQRERFgwWeiIh0TmBgYLdu3TIyMgD0aISVA4QORER5+24n4lLyfPWXC7j6NL8/LhJh6wi4OADAy5cvuaAdEWk0FngiItIt//zzj6enZ1JSEoAW1fDraOiJhM5ERHkIj8XZ+wWM2fpnAQPMjXFkMoqbAcCVK1e4oB0RaS4WeCIi0iFv377t2LFjZGQkgJplcXQyjMRCZyKivP0VhgLXa7r9T8HvU6kU9ozLvlt+7dq1W7ZsUUE4IqIixwJPRES6IjEx0cPDIzQ0FED5Ejg9HcW4ZA+RektIK3hMPhPs39ehFhb3yn48duzYgICAr49FRCQQbiNHREQ6QSKReHl5BQYGAihhgTMzUKa40JmIPnEtGAdu4mkEZDI4lUTnevimNkQ6fJeHrUXBY0pZfe67TeuEuy+wOwDp6endunW7ffu2g4ODMvGIiIoYCzwREWm/rKysvn37Xrp0CYC5MU58jyp2Qmci+lB8Knx+wdG/Pji44QKaVMbe8Sinq/uFNakMsT4ys/Ib07zqF7zh5uF4GoE7zxAZGenl5XXlyhXunkVEGoRT6ImISMvJ5fIRI0YcOXIEgKEBDk9Ew4pCZyL6UGYWOvl93N4VroegzaLPnSWufYqboZ9bfgPE+hjZ5gve0MQQRyahdDEAuHPnTs52kkREGoEFnoiItNz06dO3bt0KQE+EnaPRrqbQgYg+sekirgXn+WroWyw8XIRp1IxfXzjnPWXGry+qlvmyNyxrg8MTsxew3Ldvn6+vr1L5iIiKEAs8ERFps59++snPz0/xeM1g9GosbByi3G27UsCAX69Cmu80ci1WwgL+c+Ht+vFaACUt8etofNfxa96zSWVsGJL9eN68eYcOHVI2JRFRkeA98EREpLXWr18/c+ZMxeMfe2B0W2HjEOUuS4ag5wWMiUnGsyhULl0UedRQSUscnoQnb3DuPsJjYWyIuuXRoRZMDL/+PQc3x4NXWH4Scrl80KBBFSpUqFevnuoiExEVChZ4IiLSTrt27Ro3bpzi8WQPzPYSNg5RnpIlyJIVPCw+tfCjqLeqZb54tnz+lvTBkwicCEJKSkqXLl1u3bpVpoxKT0BEpGqcQk9ERFro6NGjgwcPlslkAIa2xLK+QgciypulCYzFBQ/7/M3S6DPp62H3WNRwAIDXr1936dIlNVXnvyYhIvXGAk9ERNrm7NmzvXv3lkqlAHo1xi9DdXobbVJ/IhHcC9oIrYKt7u4kV6gsTfDHVJS0BIA7d+4MGDBA8cUfEZF6YoEnIiKt4u/v7+3tnZ6eDqBTXfw6Bvr8XUdqb0KHggZ81VJt9Dkq2OLwpOxF6Q8fPvzDDz8InYiIKE/8UENERNrj1q1bnTp1UkyCbVUdB76DWF/oTKQKD8OxJwA7/HH5MSSZQqcpBJ3q5reZecfaGNuuCNPoHjdnbBmePVVn6dKlW7ZsEToREVHuuIgdERFpiaCgoI4dOyYmJgJoUhm/T/ms+4pJzV18iEm/4d7L/45Ym2OqJ6Z10ra5Fet84FQSvkeQJPnvoJEYY9thUU9+FVXo+rkhJBILDgPA6NGjy5cv37YtN64gIrUjksvlQmcg3RUbGyuTyUQikY0Nb+wjFYiJiTExMTE1NRU6CAng/v37rVq1iomJAVDPERdmoRj/Imi+7VcwdCNkuX1U6VofB7+DgdbV2vhUnLmHx68hk6NiKXSoidLFhM6kM+RyDNyA364CgJWV1dWrV11cXIQOlYu4uLisrCx+fCJViY2NNTY25scnTcECT0JigSfVYoHXWY8ePWrVqtW7d+8A1CqHi7NgYy50JlJa6FvUmIYMaZ4DlvbB952KMBDpgPRMdFiCPx8DQNmyZW/cuKGGG8uxwJNqscBrFu2afEZERLonODi4bdu2ivZe3R7nfmB71xIrT+XX3gEsO/FZ26cTfT4jMQ5PzN5t/tWrV56enklJSUKHIiL6Dws8ERFpsJCQkFatWkVERABwtsP5mdnbQZEWOP+ggAHvEj+4N55IJazNceL77J8kf//9d8+ePTMztXHhRCLSTCzwRESkqRTt/c2bNwAqlsLFWbDj3cJa5E1cwWPCYws/B+kep5L4YypMDQHg9OnTI0eO5D2nRKQmWOCJiEgjKdr769evATiVxKVZsC8udCZSKUV9yp+ZUeHnIJ3UsCL2jM/e6WDbtm3z588XOBAREQAWeCIi0kTBwcE57b2CLS7NRlmu5aR1XMoWMEBPhBoORRKFdFKXelgzOPvxwoULN2zYIGQaIiIALPBERKRxnjx58v6198tzUI7tXRv1bVrAgDYuKGVVJFFIV41qgx+6ZD8eN27ckSNHBI1DRMQCT0REGuXx48fv3/d+eTbbu9Ya5I5GlfJ81cwIy/sVYRrSVYt6YnBzAMjKyurbt++ff/4pdCIi0mks8EREpDHu3bvXsmXLyMhIAJVK4dIszpzXZgb6+GMKmlfN5aWSlvh9CmoWNMeeSHkiETYNg2ddAJBIJF27dr13757QoYhId4m4qCYJKDY2ViaTiUQiGxt+BicViImJMTExMTU1FToIFYo7d+506NAhJiYGgLMdLnLVOt0gk+PgTey5jvuvIMmAoy0862Jka1ibC52MdElqBtouxvUQALCzs7t69aqTk5NQYeLi4rKysvjxiVQlNjbW2NiYH580BQs8CYkFnlSLBV6LBQQEeHp6xsfHA6huj/MzuWMcERWpmGQ0X4hHrwHAycnp6tWrdnZ2giRhgSfVYoHXLJxCT0RE6u7SpUsdOnRQtPfa5XB5Nts7ERU1G3OcmYHyJQAgLCysQ4cOsbGxQociIp0pHmwcAAAgAElEQVTDAk9ERGrtxIkTnp6eycnJABo44eIs2FoKnYmIdJKDNc7OQElLALh//76Hh4fiRxMRUZFhgSciIvW1b98+b2/vtLQ0AO5VcH4m73wmIiE52+H0dFiZAsDNmze7du0qkUiEDkVEOoQFnoiI1NSmTZv69euXmZkJoH1NnJoOSxOhMxGRzqvriONTYWoIABcvXuzRo4fixxQRURFggSciInW0ZMmSkSNHZmVlAfB2xe9TYGYkdCYiIgBAsyo4NBGGBgBw/Pjx/v37K35YEREVNhZ4IiJSL3K5fNq0aTNmzFDskzLIHfsnwEgsdCwiovd0rI0942CgDwD79+8fMmSITCYTOhQRaT8WeCIiUiNSqXTo0KF+fn6KpxM7YtvI7I/IRERq5dsG2DYCeiIA2Llz58iRI7k9MxEVNgOhAxAREWVLS0vr3bv377//rnjq2wOzvIRNRESUn/7NkC7F8M2Qy7F582axWLx27VqRSCR0LiLSWizwRESkFmJjY7t06XLt2jUA+npYOxgj2widiYioIENbIkOKsdshl2P9+vUGBgarVq1ihyeiQsICT0REwnvx4oWHh8ejR48AGImxawy6NRQ6ExHR5xndFplZ+G4nAKxevVpPT2/lypXs8ERUGFjgiYhIYHfv3vXw8Hjz5g0AK1McnYyW1YTORET0JSZ0gDQLU3YBwKpVq2QyGa/DE1Fh4CJ2REQkpHPnzjVv3lzR3u2L48octnci0kiTPeDXN/vx6tWrx48fzzXtiEjlWOCJiEgwO3bs8PT0TExMBFDDAQELUKuc0JmIiL7WVE8s65f9eO3ataNGjeLeckSkWizwREQkALlcPnfuXB8fn8zMTAAtqsF/LsrZCB2LiEg5UzywcgAUc+c3btw4dOjQrKwsoUMRkfbgPfBERFTU0tPThw4dumvXLsXT3k2wfSSMxMKGIiJSjYkdIdbH+B2Qy7F9+/b09PSdO3caGPBTNxGpAH+UEBFRkYqOjvby8lJsFwfghy5Y1BNc6YmItMnYdjA0wKgtkMmxZ88eiUSyZ88eIyMjoXMRkcbjFHoiIio6jx49atSokaK9i/WxZQQW92J7JyItNLwVto2Evh4AHDlypHPnzikpKUKHIiKNxwJPRERF5PTp002bNg0LCwNQ3AxnZmBIC6EzEREVmoHu2DMOhgYAcO7cufbt28fFxQkdiog0Gws8EREVhZUrV3bq1CkhIQFA5dK4vgCtqgudiYiokPVohKOTYWoIAAEBAS1atIiIiBA6FBFpMBZ4IiIqXOnp6T4+PpMnT1Ysxdy6Bm4sQBU7oWMRERWJb2rj9AxYmQLA/fv3mzVrFhoaKnQoItJULPBERFSIIiIiWrZsuX37dsXTUW1wZjqszQXNRERUtNyr4NIslLICgLCwsGbNmgUGBgodiog0Egs8EREVluvXr7u6ut64cQOAWB/rfLB+CAz0hY5FqvboNcZsg8t02IxEpcnoswYXHwqdiUjN1HWE/1xUsAWAt2/ftmzZ8uzZs0KHIiLNwwJPRESFYsOGDS1btnzz5g0AW0uc+wGj2wqdiQrB8pOo/QPWn8fDcMQm45+32HsdbRZj8AZkZgkdjkidVC6Na/NRqxwAJCUlderUaceOHUKHIiINwwJPREQqJpFIhgwZMnr06IyMDAB1yuPWQrSoJnQsKgRb/8TUXZDmVtR3+GM8uwnRh+yK4c852T8PMzMzfXx8fH19hQ5FRJqEBZ6IiFTp+fPnbm5u27ZtUzzt54Zr8+FoK2woKhSpGZi+J78BGy/i7suiSkOkIYqZ4sx09GwMAHK5fM6cOcOGDcvMzBQ6FxFpBhZ4IiJSmZMnT9avX1+xOJNYH6sG4rcx2fsnkfY5ew/RSfkNkMux93pRpSHSHEZi7BmHqZ7ZT7ds2eLh4REfHy9oKCLSDCzwRESkAlKpdNasWZ07d46NjQVgVwwXZ2FCB6FjUWF69LrgMQ/DCz8HkQbSE8GvL9YMhr4eAJw/f97NzS0sLEzoXESk7ljgiYhIWW/evGnTps3ixYtlMhmAltUQtBjNqggdiwpZasZnjEkv/BxEGmtsOxybDHNjAHj06FGjRo2uXLkidCgiUmss8EREpJTTp0/XqVNH8aFTJMKMLjj3Q/Z2x6TdHKwLHlPWpvBzEGkyz7rwn4tyNgAQHR3drl27LVu2CB2KiNQXCzwREX2lzMzM6dOne3h4REVFAbAxx/Gp+KkXd3rXFe1cIBIVMKZ9zSKJQqTJ6pTHjYVoVAkAMjIyhg0bNnHiRKlUKnQuIlJHLPBERPQ1QkND3dzcli5dKpfLATSrgr9/gkcdoWNREapYCj0a5TfA2Q7dGhZVGiJNZlcMl2ejb9Psp6tWrerYsWN0dLSgoYhIHbHAExHRF9u+fXu9evVu374NQE+EmV1xadZnTagmLbPeBy4Oub9kbY6D38HQoGgDEWksYzF+G4OfekFPBAAXLlxwdXVVbOpBRJSDBZ6IiL5AXFxcr169fHx8kpKSAJQpjnM/YFFPTpvXUdbmuL4AEzrA5L3NAvVE+LYBAhehZlnhkhFpIMUyIsemoJgpALx48cLNzW3btm1C5yIiNSJSTH0kEkRsbKxMJhOJRDY2XOaIVCAmJsbExMTU1FToIFrrwoULgwcPDg/P3hmsa31sHo4SFsKGIrWQLMHNfxCVCAtjNKiIkpZCByLSZCGR8F753y6MI0aMWLVqlbGxseJpXFxcVlYWPz6RqsTGxhobG/Pjk6ZggSchscCTarHAF57U1NQZM2asWbNG8VvD1BDL+2NUG6FjKSckEvtv4OFrZErhaAuPOmhVXehMREQAgGQJhm7C/hvZT+vVq3fgwAEnJyewwJOqscBrFhZ4EhILPKkWC3whuX79+uDBg4ODgxVPXZ3w2xhUsRM2lFIypJi6G2vPQvbh70D3KvhtbPZ+TkREwpLLsfIUpu+FNAsArKysNm/e3L17dxZ4Ui0WeM3Ce+CJiChPaWlp06ZNc3d3V7R3A33M8UbAfM1u7wAGrMfqMx+3dwD+T9HKF1GJQmQiIvqQSITJHrg0C/bFASAhIaFHjx5jx45NT08XOhoRCYYFnoiIchcQEFC3bl0/P7+srCwA1e0RMB8Lu0Os4evVHbvz36zUT4W9w6z9RZiGiChfzaogcDE61Mp+um7durZt2+ZMiSIiXcMCT0REH0tKSpowYYK7u/vTp08B6Ovh+064swgNnIROpgqbLxUwYNc1pGYUSRQios9Q0hInp+GnXtn7fTx8+LBt27bbt2/nnbBEOogFnoiIPnDy5EkXF5fVq1fLZDIAVcvAfy6W9oGxWOhkKnLznwIGpGbg/ssiiUJE9Hn0RJjRBX/ORvkSAJCWljZ16tQuXbq8fftW6GhEVKRY4ImIKFtkZGSfPn08PT1fvnwJwEAfM7ogaDGaVBY6mUrFpahmDBFREWvqjKDF6NU4++nx48dr1qx59OhRQUMRUZFigSciIshksnXr/p+9+w5r6nrcAP4GCCNsERdu60BFARXcuBAFq6jFXdRqW/euWrRqrbtqFVdrrftbrevnqqsO3Fo3BcQtQ5C9h4zk90dSHHUTOEl4P08fn9yTC7y0FvLmnnvOant7++3btytHGlfHlR8wv7fuXHgv8D4b19tyG3Mi0kjWptg+GpuHw8IEAOLi4rp37z5o0KCUlBTR0YioOLDAExGVdH///beLi8vIkSOTk5MBmBnjp89x6Xs4VhGdrGi0qPWOEyxlcKhULFGIiD7K5y1xaz7c7FWHmzZtcnBwOHTokNBQRFQcWOCJiEqu+Pj4r776qlmzZteuXVOOdHVG0EKM6wR93f39MKLDO04Y2gaGBsUShYjoY1W1xclpWNxfNU8qIiLCy8vL19c3ISFBdDQiKkK6+wKNiIjeLC8vb9WqVbVr1/7111+Vi9VVtcW+idg3UbVCkg5rVw+jPd74rGMVzOxZjGmIiD6WngQTPXF9LlxqqEa2bNlSt27dgpuhiEj3sMATEZU4x44dc3R0HDVqVGJiIgAjKaZ5I3gRujqLTlZcln2Oeb0hM3xpUCJBr6Y44QdzY0GxiIg+nL0dLszCj/1gYggAsbGxyuVIHz16JDoaEamfhBtIkkCJiYlyuVwikdjY2IjOQrogISHBxMREJpOJDqK5bt++PWnSpBfvk/RywrLP8UlZgaGEiU/DgesIDEduPqqVQeeGqGsnOhMR0ce6H4Mvf0XAbdWhTCb77rvvJkyYYGho+NaPo5IuMTHR2NiYL5+0BQs8icQCT+rFAv8WT58+nTVr1m+//ZaXl6ccsbfDkv7o3FBsLiIiUhuFAr8FYPK253th1qlTZ8WKFR06vGvxDyrBWOC1C6fQExHpuNTU1BkzZtSsWfOXX35RtncbM/j74tZ8tnciIp0ikWBoW4QuRv8WkEgAIDQ01N3d3cfHJzw8XHQ6IlIDXoEnkXgFntSLV+BfkZ2dvXr16vnz58fHxytHjKQY6Y7p3rA2FRuNiIiKVsBtjNyAkCeqQ5lMNnHixClTppia8hcAvYRX4LULCzyJxAJP6sUCXyAnJ+e3336bN29eZGSkckRPgj7NMKcXqtmKjUZERMUkNx/+RzF7D1KzVCMVKlT44YcfBg0apKfHebikwgKvXfi/LhGRTsnJyVm7dm2tWrVGjBhR0N47N8S1ufjfSLZ3IqISRKqPiZ4IXYzBbtCTAEBUVNSQIUOcnJwOHz4sOh0RfQxegSeReAWe1KuEX4HPzs5ev379okWLwsLCCgZb1sbcXmhdR2AuIiIS79ojTPwfTt9+PtKmTZt58+Y1a9ZMXCjSCLwCr11Y4EkkFnhSrxJb4NPT09euXbtkyZKoqKiCwaaf4PvP0NFBYC7SdHGp8D+KfdfwIAb6eqhTAZ+5YngHmBuLTkZERWPvVUzdjjvRz0e6dOny/fffOzs7iwtFgrHAaxcWeBKJBZ7UqwQW+NjY2BUrVqxevToxMbFgsFlNzOwBjwYCc5EWOBOKz5YjLvXV8Wq2ODAJ9SqKyERERS8vH+sCMHsPopNVIxKJxNvbe8aMGY6OjkKjkRgs8NqFBZ5EYoEn9SpRBT40NPSnn37avHlzdnZ2wWAbe0zzRof6AnORdrgfA2c/pGW//lk7awQuQCmz4s1ERMUoMwcrj2HRASSkq0YkEknXrl39/PxcXFyERqPixgKvXbiIHRGRNlEoFEePHvX09Kxbt+7atWuV7V0iQVdnXPwep6azvdN7+W7nG9s7gCdJWHSwGNMQUbGTGWJyFzxchu97wkoGAAqFYt++fa6uru7u7idOnBAdkIhej1fgSSRegSf10u0r8GlpaZs3b165cmVoaGjBoJEU/Ztjohfq2gmMRlomOxc2XyEz523nVLXFo2XFFYiIhErOxPIjWH4ESRnPBxs3bjxp0qSePXsaGBiIi0bFgVfgtQsLPInEAk/qpasF/saNGz///PPvv/+enp5eMFjaHMPaY6Q7ylkJjEZa6U406kx692mZG2BiWPRpiEgzpGZhzXH8dBgxKc8HK1euPHLkyC+//NLa2lpcNCpaLPDahQWeRGKBJ/XSsQKflpb2xx9/rF279sqVKy+ON6yM0R7o3wLGUlHRSLsFhqPht+8+LXEtrE2LPg0RaZKsHGw8g8V/4mHs80FTU9P+/fuPGDGiYcOG4qJRUWGB1y4s8CQSCzypl24UeIVCcfbs2Q0bNuzcuTMj4/l0Rqk+ujfBSHdu6k6FlZiO0sPw9t//FiZIWVdcgYhIw+TLsfcqfjqM83dfGm/RosXXX3/t4+NjbMzdJnUHC7x2YYEnkVjgSb20vcDfu3dvy5YtW7duffTo0Yvj1ctgaFt84YaylqKika5pOhOX77/thF5N8cfo4kpDRJrq6kMsP4odl5CT93ywVKlSAwYMGDJkSIMG3LBUF7DAaxcWeBKJBZ7US0sL/JMnT3bs2LFt27ZXpsobSeHdCEPaoH196ElEpSPddPAGPl38xmf19XB1DhyrFGMgItJgMSlYdwq/nEREwkvjTk5OgwYN6tOnT5kyZQRFIzVggdcuLPAkEgs8qZd2FfioqKjdu3fv3Lnz/Pnzcrn8xaeafoLPW6JPM27ETUXou52Ys/c143oS/DwEX7Yt9kBEpNny5Th0E2tP4vAt5L/wW8vAwMDDw6Nfv37dunUzNeXKGdqHBV67sMCTSCzwpF5aUeDv37+/d+/evXv3Xrx48ZXeXs0W/Vrg85aoXV5UOipZ9lzBdzsR8uT5iOsnWNgHbvbiMhGRxnuShI2nseks7j19adzU1NTLy8vHx8fT01PDfxfTi1jgtQsLPInEAk/qpbEFPj8//+LFi3/++ef+/ftDQkJeebaCNT5zQZ9maPoJJDo0VT4nDwnpSExHahYynkGuQEqm6ilDA5gawcQQZsawNIGtBUyNhGYt2e49xb2n0JOgfiVULCU6DRFpCYUCF+9h8znsuPTSBvIAZDKZp6ent7e3p6cn95/TfCzw2oUFnkRigSf10rQCHx0dfezYsSNHjhw7diwxMfGVZyuWQvcm8HFFi1paf4t7ZCJCniA0CneiER6PsHg8TUFc6gd8BpkhKtqgghWqlEa1MqhZDnUqoE4FyLgPORGRZnuWi8O38PsF/HkDmTkvPSWVSlu1atWlS5cuXbrUrFlTUEB6BxZ47cICTyKxwJN6aUKBT0tLO3v27IkTJ06cOBEYGPjfn7H1KqKrM7o3QeNqWny9PSYFF+/h4j1cfYTrj5Cc+e4P+Qh6ElQvg4ZV0KgaXGrApQbMuW8REZGmyniGP29g1984dBMZz159tmbNmp07d/bw8HBzc+Ot8hqFBV67sMCTSCzwpF6iCnxaWtqFCxdOnz4dEBBw5cqVvLy8V04wkqJ1HXg5oosTapQt5nRqk5SBY//geBDOhOJu9DtOtpShgjVszWFjBhtz6Elg/fKrtdQs5OQhNQvJmYhNQWwqYlIgf+tvJH091KuI1nXQug7a1kVp88J+R0REVBSycnA0EPuu4eANxKe9+qyhoWGzZs3at2/ftm1bV1dXqVQqIiM9xwKvXVjgSSQWeFKv4izw4eHhFy5cuHDhwrlz5wIDA/Pz8/97zidl0bEBOjVAu3pafI/3vafYdw0HruP83ZeWHS5gbox6FVG/EupUQJ3yqFYGVUp/zPebl4/oZDyOx4MY3IlGaBSCIvAw9vWtXk8C52rwaAAvR7jUgL7eB385IiIqavlyXLqPgzdw6CYCw19zgkwma9asWevWrVu3bu3i4sIOKQQLvHZhgSeRWOBJvYq0wCcnJ1+9evXKlStXrly5fPlyVFTUa08rb4W2ddGuHtrXQ1XboghSTMLisfUcdl7Grf+85JJIYF8BLWujeS24VEftCkV4D3/GM9wKw9VHuHwfF+7hcdxrzrG1wKdO8G6Mjg4w4oUcIiKNFJmIY//gWCBOhrx+kRSpVOrk5NS0aVNXV1dXV9caNWoUe8YSigVeu7DAk0gs8KRe6i3wT548uXXr1s2bN2/cuHHjxo2HDx++6QdmJRu0qo3WdeBmjzoV1PLFhcl4hl1/Y8NpnAnFK99uOSt0bojODdHGHrYWYuI9ScLp2wgIwbF/EBb/6rOWMnRrhD7N4F4fBvoi8hER0bvIFQgMx8lgnArB2TvPNyh5hY2NjYuLi7Ozs7Ozs6OjY/Xq1Ys3ZgnCAq9dWODfKDY2dujQoQB27NhhbMx1k4oECzypV2EKfEJCQnBwcEhISFBQUEhIyK1bt/67bnwBqT4aVkHTT9C8JlrWRiWd+PsbHInVx7H1HFKzXhqvWQ6fucC7MRpX16zV8kOj8OdNHLqJM6HIe/kOhjIW6NccA1vDsYqgcERE9B7y5QgMx9k7OBuKC/cQlfTGMy0tLR0dHR0cHBwcHOrXr1+3bl0rK6tiTKrLWOC1Cwv8G7HAFwMWeFKv9yzwWVlZDx48uHfv3r179+7evXvnzp3Q0ND4+P9cz32BngS1yqNxdTSpjibV4VQVxroyVVuuwKGbWHYYJ4JfGq9gjb7NMKClFnTgpAwcuI6dl/FXEJ7lvvSUc1UMaYP+LWDJlyVERBovLB4X7+HvB/j7AW48fnVTulfY2dnZ29vXqlVL+WetWrUqVaqkr8/5Vx+MBV67sMC/EQt8MWCBJ/V6pcBnZ2dHRERERESEhYU9fvz40aNHjx49evjw4ZtuX3+RqRHq2sGxiuqfBpVhpnM/Bp7lYss5LDmE0Bf+fUj10bURvnCDRwPtWxkuORP/dwXbLuJE0EtL38kM0acZRnaEc1Vh2YiI6IPk5SP4Ca4/wvXHuBmGW2FIy37HhxgaGlarVq1GjRrVq1evXr16tWrVqlatWqVKFWtr6+JIrLVY4LULC/wbscAXAxZ4KqT8/PzY2Ni4uLgnT57ExMTcvXs3MTExOjo6KioqIiIiJibmPT+PqRFql0fdiqhrh7p2qF8R1cpo1nRx9crOxbpTWHgAkS/cJVDOCsPa48u2qKD9r3MiE7HlHDacxr2nL423qIUxHujRhHfIExFpGYUCj+IQGI7gSPwTgdtRCI1Czqvbtr6eqalplSpVKlWqZGdnV6lSpfLly1eoUKFChQrly5cvU6aMgYFBEWfXdCzw2oUF/o0+qMD/888/x44de/ToUWxsbOXKlWvUqNG1a1c7OzvlszNnzrxx48awYcM8PT2VI3K5vG/fvllZWS1btpw8eXLB55k1a9b169cHDhzYs2fPovm2NAsLPL1FUlJSYmKi8s+EhISEhIT4+HjlnzExMXFxcXFxcbGxsR/6Q8xYimpl8ElZ1Cz3/J9KpSDR3br+opw8rD+NuXtfqu4NKmOCJ/o01bUl3BUKnAnF2pPYfeWlqfWVbDC2E75sCwsTceGIiKhw8vLxKA53onEnGnejcTca92PwJOnVFVjfTiKR2P6rfPnytra2NjY2pUuXtrGxsbW1LVWqlI2NjZWVlbm5eZF9H+KxwGuXkv6Gk1rs3bt3/fr1APT19UuXLq28q/bEiRPTpk1zcnIC4OTkdOPGjX/++aegwD9+/DgrKwtAcPDzu07lcvnt27eV5wv4NoiKQEpKSl5eXkpKSlZWVnZ2tvJBZmZmUlJSZmZmenp6enp6UlJSenp6ampqSkpKSkpKampqcnJycnJyIb+0qRGqlEYlG1SyQZXSqFoaVW1RvQzKW5WUrv4KuQLbLuC7nXj0wk5srWrDrxs8GujmvxOJBG72cLPH8jSsO4U1xxGeAAARCZj0P/zwfxjeAeM6oayl6KBERPThDPRV78J3eeGFc3YuHsXiYSwexeFRHMLiERaPiATEpLz+kygUitjY2NjY2Hd8LQMDKysrKysrS0tLa2trS0tLCwsL839ZWlqamprKZDJLS0uZTGZsbGxlZWVsbGxiYmJhYcHb8km9WOAL6+nTp5s2bZJIJP369evevbuhoWFmZuaaNWtOnz69atWqn3/+2cDAQFnIg4KCCj4qJCQEgEQiSUpKevr0ably5QA8fPgwKyvLysqqWrVqor4d0mQZGRk5OW9bzuVNJ6SkpMjl8reMvHiobNrKx6mpqfn5+S8OPnv2LDMzE0BaWlpeXl5+fn5qamrBZ1AGUJ6cnp6em/vyemLqZm6MCtawtUA5S5S3RjlL2JVCeSvYWcOuFKz4PvILAm5jwlbcePx8pGVtfN8T7eoJi1ScSptjaldM8sL/XcXyIzh/FwBSMrFgP5Ydxhdt8I0XqtoKDklERIVnLIW9HeztXh3PzkVkIp4kIjIRT1MQmYCnKXiSiNhURCe/uv3Kf+Xl5cXHx799vdu3sLa2lkgkVlZWyj8BKB+bmpoaGhrq6elZWloCMDIyUl4GV5Z/APr6+hYWqo1bC54FYGBg8OKkgBefetOIUsFnflFycrKxsfGzZ88ASKVSMzOzj/s2qXiwwBfW77//np+f7+Hh0bt3b+WITCabOHFiRETEw4cPjx496uXlpVw8IykpKTIysmLFigCUV9qbN29+/vz54OBgZYFXXo13cnKSvPe1sKSkpFeKmeY4e/bs2LFjle2OChT+wrLOMzSAtSmsZLA2hbUpSpmp/rQxQ2lz2JihjAVsLVDaXHfWgS9Sj+Iw6X/Yc+X5SOPqmNsLHR3EZRLEQB8+rvBxxeX7+PFP/N8VyBXIzsXqv/DrSQxoieneqF5GdEoiIioCxlJ8UhaflH39s9m5iE9DTApiU5GQhoR0xKchIR2J6UhIR1IGkjORlIGkjA+bn18gKSkJwFu2p9UNMpnM0NBQdIrX69Sp04oVK96/ZBUzMzMzIyOj9zyZBb6wlNfVu3bt+sp4ly5d/P39g4KCvLy8ADg5OZ08eTIoKEhZ4ENCQkqXLt2mTRtlgW/fvj1eKPDv/9XlcrnGrmKwadOm8PBw0SmouOnrwcIEBvowN4aRFDJDyAxhJIWVDEZSmBrB3BgmhjAzhpUMpkYwM4aZMaxNYW4MSxksTGCioT/5tU9mDubtw5I/kf3vTIgaZTG3F3q56uaE+ffn+gl2jcXdaCw6iC3nkJOH3HxsOI2t5+DbCt91R5XSoiMSEVExMpaiYilULPXuM1MykZKFlEykZSEtG6lZSM5ExjNkPkNqFtKzkZWDtGxkPENOHpIykJOHjGfIzkVWjupPHZaZmamcp6mBtm/fPmXKFGUR00AfVOhY4AslNzc3ISFBX1+/YL26AlWqVAHw9KlqBWRlgf/nn386deoUGxubkJDQunXrunXrSiQS5XR6hUIRHBwskUg+qMDr6elp7BV4X1/fa9eupaWliQ7yDkZGRsopQ6+QSqUFM8D19fVffK9EX19fObEcgEQikUql/52MVHjv+S6mgYHBm2Y6SaVSU1PTVwYtLCxefPfR2Nj4xTUaLSws9PRUW4cpp3gpz1G+KWhkZKT8Tk1MTJQjynNMTEwMDQ0NDQ0Lv/xJJlCYH/zK/0Ya+/ZqcTp+/PiUWVPCwsKUh6GXJ3AAACAASURBVJYyTOuGMR66tkxdYdQqj3VfYmYPLP4Tv55CVg5y8/FbALZcMBwwYMD48eOVc6P414nUgj+dSL34N0ogU+DVV1cf4tmzZ1lZWXl5eRkZGQBSU1Plcnlubq7yMDs7W3nTovKeROWHFMzflMvlL05uLThZKTc397/9OSUl5bXlMD8//31epSsUipSUN6wfUAj5+fnp6elq/7Rv0alTp4oVK2rs/zIfFIwFvlCSkpIUCsWLnaeAcsPJgltlHB0dJRKJ8nK9srHb29ubm5tXrlw5LCwsOTk5NTU1LS2tRo0ayntg3pMmb2vZvXv37t27v/0crkJP6vXKPvAlU0xMzNixY//44w/loZ4Eg90wrzfKWIjNpaEq2WC5L6Z2xcID+OUEsnORk5Ozfv367du3DxkyZMyYMXZ2dkXxDh2VNMqVQfjLjtQiKSkpPz+fL59IXbgKvXZ5tXbSB1GuSKF85+yVp5RvVhVcw7S0tKxevXpSUlJUVJTyBvi6desCqF+/PoCQkJCPmD9PRPSKDRs22NvbF7R3p6q4+D3Wfcn2/g7lrbDsc9z/CSPcYWgAAJmZmStWrHBxcVm2bNmL1zeIiIiIBGKBLxSpVGpjY5Ofn//kyZNXnlLe/l2hQoWCkYK16ENCQmQyWdWqVfFvgQ8ODlZenHd2di6u7ESkU8LDwzt16vTFF18oV8oxM8ZPn+Pv2XCpITqZ9rCzxqpBuLMYA1tBXw8AkpKS/Pz8ateuvWXLFo29X4mIiIhKDhb4wlI28AMHDrwyrhypV+/5Hk3KAn/p0qXw8PA6deoob3VQnqC8Am9sbGxvb19syYlIZ2zevLlBgwZHjx5VHnZuiKCFGNcJBtx69sNVtcXGYQhcgK7/vqEaHh7u6+vr4uJy5swZodGIiIiopGOBL6y+ffvq6+sfPXp0z549yoXNsrOzly1bdv/+fVtb206dOhWcaW9vb2RkdPXqVYVCUVDUrays7OzsHjx4kJiY2KBBA319vtwmog8QHx/fo0ePgQMHKm/bKWWGTcNwaDLXUS+sunbYNxFnZ6DpJ6qRa9euubm5ffbZZw8fPhQajYiIiEouLmL3bkOGDHntwoA9evTo0aNH+fLlfX19N2zYsHHjxt9//93W1jY6Oloul1taWo4bN+7FVcQNDAwcHByuXr2Kf2+AV6pfv75yBj7nzxPRBzly5MjgwYMLdrvwcsK6oShnJTaUTmlZGxdmYdffmLodD2MBYPfu3X/++ef48eP9/PzetAEEERERURHhFfh3S0tLS32dgr3HunfvPmfOnNatW5ctWzYhIaF69eqdO3f29/d3cHB45VMpZ9Hr6+vXqlWrYLBgmj0LPBG9p5ycnIkTJ3p6eirbu5kx1g7FwUls7+onkcDHFSGLsKgvLGUAkJ2dPX/+fOWN8R+0cSsRERFRIUn44oME4jZypF4lZBu5Bw8e9OnTRzmdB0DTT7BlBD4pKzZUiRCbiuk7sP408v9dz65FixYrV650dHQUmou0gHIbudKleXMLqQG3kSP14jZy2oVX4ImItMmuXbsaNWqkbO/6epjujbMz2N6LSRkLrB2Kq3PQuo5q5Pz5840bNx49enRycrLQaERERFQisMATEWmHnJycsWPH+vj4KNerq2CN4374wYdLzRc3xyoImI7fR8LOGgDy8/NXrlxZu3btTZs2cVIbERERFSkWeCIiLRAZGdm6dWt/f3/loUcD3JiHNtx3UhCJBH2bI3QxJneBVB8AYmNjBw0a5ObmFhwcLDodERER6SwWeCIiTRcQENCoUaPLly8D0NfDHB8cmowyFqJjlXhmxljYF4EL0E61FCnOnj3r5OQ0ZcqUjIwModGIiIhIN7HAExFpNH9/f3d399jYWABlLPDXt5jmDb3XbG1JYtSpgBN++H0kylsBQG5u7qJFi+rVq3fw4EHR0YiIiEjXsMATEWmo7OzsQYMGjR07Ni8vD4BLDVybi7Z1Rcei1+nbHLd/xGgP6OsBQFhY2Keffurj4xMVFSU6GhEREekOFngiIk0UHR3dtm3bTZs2KQ+HtMGZ71CxlNhQ9DaWMvj74tL3aFRNNbJr1666deuuWbNGLpe/9UOJiIiI3gsLPBGRxrl27ZqLi8ulS5cASPWxahDWfQkjqehY9B4aV8fl2VjuC3NjAEhJSRkxYkSrVq24uB0REREVnoHoAERE9JLdu3f7+vpmZmYCsLXArrHPdx0nraCvhzEe6NEEIzdg/3UAuHDhgrOz89SpU/38/IyMjEQHJKKipVAooqOjw8LCIiMjo6OjY2Ji0tLSnj17lpycXLDZpKmpqZmZmbW1dalSpcqUKVO5cuUqVapUrFhRIuEaJ0T0NhJuWksCJSYmyuVyiURiY2MjOgvpgoSEBBMTE5lMJjrIx1uwYIGfn5/yJ3P9ijgwCVVtRWeiQtj9N8ZsRlSS6tDe3v7XX39t0aKF0FAkQHZ2dnp6eunSpUUHoSKRnp5+7dq1a9eu3bx5Mzg4+M6dOx+3FYVMJqtVq5a9vb2Dg4OLi4uLi4u5ufl/T0tKSsrPz+fLJ1KXxMREY2NjrX75VKKwwJNILPCkXlpd4HNzc4cPH/7bb78pDz0dsX20aho2abWUTEzdjl9OQvn7Vk9Pb/jw4fPnz3/t63LSVSzwuictLe3kyZMnT54MCAgIDg7Oz89X+5fQ19d3dHRs1aqVu7u7m5ubqampcpwFntSLBV67sMCTSCzwpF7aW+BTU1M/++yzv/76S3k4xgNLB6jWMyfdcO4OvlyH0H/XpK9cufKaNWs8PT2FhqLiwwKvMx48eLBv376DBw+eO3cuNzf3teeUNkft8qhqi0o2KG+FMhYoZQYzo5eWMsnJQ3o2EjOQmI7oZITF41Es7kQjNvX1X9fIyKht27be3t7e3t6GhoYs8KRGLPDahQWeRGKBJ/XS0gIfGRnp5eUVGBgIQF8Pyz7HqI6iM1EReJaLOXux8ABy/71QN2DAgGXLlvEHYEnAAq/tIiMjt23btm3bths3bvz32Wq2cP0EjavDsQocq8DG7OO/UFIGgiNx7REu3cf5u4hIePUEfX395s2be3t7d+/evVq1aq/7HEQfhgVeu7DAk0gs8KRe2ljgg4ODO3fuHBERAcDMGNtHwctJdCYqSoHhGPIrrj5UHZYpU2bFihW9evUSGoqKHAu8lsrOzt61a9eGDRsCAgJe2Q+ytDk6OqBjA7Sri0pF9irmcRxOhuBYIP4KQmL6S08ZGxv7+Ph8/fXXXFaDCokFXruwwJNILPCkXlpX4M+cOePt7Z2UlASgnBUOTnq+hTjpsHw5fjqMGbuQlaMa6dat25o1a8qXLy80FxUhFnit8+DBg9WrV2/cuDExMfHF8Zrl8JkLujaCSw3oFeOC8Xn5OB2KPVew5wqeJr/0lIODw6hRowYMGKBFv/5Io7DAaxcWeBKJBZ7US7sK/O7duwcMGJCdnQ2gdnkcmcIF50uW+zH48lcE3FYdWltbL1myZNCgQdxESiexwGuREydOLFu27NChQy9eci9nhb7N0K85GlcXGA0A8uU4FYKNZ7DnyvM3AQHY2NiMGjVqzJgxpUqVEpeOtBILvHZhgSeRWOBJvbSowK9Zs2b06NHKVYub1cSBSYW6Z5K0lEKBtScxeRtSs1QjHTt2XLt2bZUqVYTmIvVjgdd8crl89+7d8+bNu3nzZsGgvh46N8RX7eDpqHELiyZnYvNZ/HwCt588HzQzMxsxYsTEiRPLlCkjLhppGRZ47cICTyKxwJN6aUuB/+GHH2bMmKF83MUJf4yBzFBsIhIpIgHD1uPQv5XB3Nx84cKFw4YN46V4XcICr8ny8/O3b98+d+7c27dvFwzamGFoWwzvgCqa/R9NocDxICw/ikM3UfCiXiaTjR49evLkybwaT++DBV67sMCTSCzwpF6aX+AVCsX48eOXL1+uPBzshrVDYKAvNhRphM1nMX7r80Wq2rRps27duho1aggNRWrDAq+Z5HL5zp07Z86ceefOnYLBWuUxoTM+b6Vlb63+E4H5+7HjEvL/nfhvZWU1efLkcePGmZiYCI1Gmo4FXruwwJNILPCkXhpe4PPy8oYMGbJ582bl4TddsLAPeJGVCjxNxsiN2HNFdSiTyebMmTN27Fg9PQ2buUsfjgVeAx09enTKlCm3bt0qGHGohGne8HEt1tXp1OtuNObuw//OP6/xlSpVmj9/fr9+/Tiph96EBV67sMCTSCzwpF6aXOCzs7P79Omzb98+ABIJFvTB5C6iM5FG2nkZozYiNlV12Lx5899++61OnTpCQ1FhscBrlFu3bk2aNOn48eMFIw0qY2YPdG+sI2+q3n6C6Tvxf1efT6pv2rTp8uXLXVxchOYiDcUCr134pj4RUZFLT0/v0qWLsr3r62HtELZ3eiMfVwQvQt/mqsMLFy44OTktXLgwLy9PaC4iXfD06dOhQ4c6OzsXtPda5bFtFG7MQ48mOtLeAdjbYfc4nJuBZjVVI5cuXWrWrNnw4cOVG5cSkfbiFXgSiVfgSb008wp8cnKyp6fnxYsXARgaYOsI+LiKzkTaYP91DF+PqH9fbDdu3Hj9+vUODg5CQ9FH4hV44XJzc/39/WfPnp2aqprfUtYSM3tgaFtIdXchEoUCf1zClG0IT1CNlC1b9qeffurbt6/QXKRZeAVeu/AKPBFREYqPj2/fvr2yvcsMsW8C2zu9r67OCF6EwW6qw6tXrzZu3Hj27Nm5ublCcxFpn4CAAEdHx0mTJinbu7EU33bFvaUY3kGX2zsAiQR9muH2Yvh1g6EBAMTExPTr18/T0zM8PFx0OiL6GCzwRERF5enTp23atLl+/ToACxMcnoJODUVnIq1iJcP6r3BkCirbAEBOTs7MmTMbN2587do10dGItENsbKyvr2+7du1CQkKUIz1dELoY83rD3FhstOIjM8TcXrg1H272qpHDhw/Xr19/zZo1nIpLpHVY4ImIikRERISbm1twcDCAUmb461u05jJk9FE8GuCfhRjWXnWDbmBgYNOmTadOnZqdnS06GpHmUigUv/32m729/ZYtW5Q1tU4FHPfDrrGavrV7EalTAaem4bevYG0KAGlpaSNGjPDw8IiIiBAdjYg+AAs8EZH6PXr0qHXr1nfv3gVQxgInp8GF+3lTIViYYM0XOOGHGmUBIC8vb+HChY6OjufPnxcdjUgT3b9/v3379kOHDk1MTAQgM8S83rg1H+3riU4mlESCL9wQsgjdG6tG/vrrrwYNGmzbtk1oLiL6ACzwRERqdv/+fTc3t8ePHwOoYI2A6WhYWXQm0glt6yJwAcZ1Um1SfefOndatW48dOzYjI0N0NCJNkZ+fv2TJkoYNG546dUo54umIoEX4tqvqJnAqZ4U94/H7SJQyA4Dk5OR+/fr169cvOTlZdDQiejeuQk8icRV6Ui9NWIX+7t277dq1e/LkCYDKNjgxDZ+UFRiHdNPFexjyK24/UR1Wq1Zt7dq1HTp0EBqK3oar0BeP0NDQQYMGXb58WXloJUOH+ihvDWtTNK+JDvWhz0tXL4hKwpBfceSW6rBq1aq///57s2bNhIYiAbgKvXbRnzVrlugMVHJlZWUpFAqJRMIfGaQWWVlZUqlUKpWKCnD79u127dpFRUUBqGqLgOmoXkZUFtJllWwwtA3kCly8D7kCycnJW7duDQ8Pd3NzMzYuMQtzaZW8vLycnBz+sis6crl86dKlvXv3DgsLU45YyZCciZAn+PsBTt/G1vPYdhGOVUvoDfCvZW6C/s1ha4FTIcjLR3Jy8qZNm6RSacuWLSXKJTeoZMjKyjIwMBD48ok+CK/Ak0i8Ak/qJfYKfHBwcPv27WNiYgDUKIuT01Qrh5MmCE/AjcfIyUMFazSuBiNdeZVy4zG+WIubqsKCChUqrFq1ytvbW2goeg1egS9Sjx49Gjhw4NmzZ5WHtuZIzcaz1+23aGiAP79Bh/rFGk+g20+w6SyuPUJaNipYoX19+LZ6zfL7wZHouxL//LuYXadOnTZv3mxra1vMaUkUXoHXLizwJBILPKmXwAIfFBTUvn372NhYADXL4eQ0VCxV/CnoNW6GYdwWnL79fMRKhvGd8W03Hdn/OTcfPx7ED/+H7H/rio+Pj7+/f7ly5YTmopewwBeddevWTZgwIS0tTXn4eUtcuo97T994fsVSuLMEMsNiiieKXIGp27HkT8hffqVfxgKbh8OjwavnZ+diwlasOa46rFix4vbt21u0aFEcWUk0FnjtwjuBiIgK68X2XqcCAqazvWuKI7fQdOZL7R1AciZm7obnotdfoNM6Un34dcONeWhRSzWyc+fOevXqbdy4ke/Rk26LjY3t2rXrl19+qWzvZS2xbyL6NX9bewcQmYh9V4spoUATt+LHg6+2dwCxqei2FOfuvDpuLMXqwdgxBhYmABAZGdm2bduffvqpOLIS0YdggSciKpSgoKB27doVtPeT01DBWnQmAgDEp6Hvyje29ONB+H5P8QYqSnUq4MwM+PuqJscmJiYOHjy4Y8eODx8+FB2NqEgcPny4QYMGBw4cUB52b4x/FqCrM87dfffHnv1PfdUxN8Pgf/SNzz7LxfD1eO37ez6uuDYXTlUBIDc3d8KECX379uU+F0QahQWeiOjjKa+9x8XFAbC3w6lpKG8lOhP96+cTSM582wnLjyAzp7jSFD09CUZ7IGgROjdUjRw/ftzBweHHH3/My8sTGo1InbKzs8eNG+fl5aVcc8TCBBu+xp7xsLUAgPi0d3+GuNQijijalnOvufb+oqBIXH30+qc+KYsLs/BlW9Xh9u3bmzdvzrcCiTQHCzwR0UdSrlqnvPZub4eTfijH9q5JjgW+44TMnNfMI9V2lW1waDK2DEdpcwDIzMycPHmyq6vr9evXRUcjUoOQkBBXV9fly5cr7xBpVhM352NQ6+cnWL3HbbzK/c912PU3lPP3PMdYirVDse5LGEsBIDAwsEmTJseOHVNbPiIqBBZ4IqKPERIS8uJ972zvGuhJ0rvPiUws+hwiDGiJkEUY0FJ1eP36dVdX10mTJnEqLGm1tWvXNmnSJDAwEICBPmb2wJnvUO3ltdJdarz787zPOVotNevd56S865whbXBmhmpJl8TERE9PT94ST6QJWOCJiD5YaGhowY5xyvve2d41kMl7rDJtoiv7yf2XrQW2DMfhyahqCwB5eXlLliypX7/+kSNHREcj+mDJycmfffbZ119/nZmZCaCqLQKmY1ZPGPxnL4nODWH31oVISpmhR5MiC6oZylq++5xy73FOk+q4OgetagNAfn7+hAkTBg8e/OzZs8LmI6JCYIEnIvowd+/ebdeu3dOnTwHULo8TfrzvXUPVtXv3OfUrFX0OoTo1RNBCjO8MfT0AePz4cefOnfv27av8C0ykFS5evOjo6Lh7927lYe+muPnCtguvMDHEL0NUf+H/SyLByoGwNi2aoBqjTd13nCCRvPscpbKWOO6Hr9qpDjdu3NihQwflyi9EJAQLPBHRB7h//367du2io6Px737vXHNeY/Vt/o4T6leEg64XeACmRlg6AJdnq1aWBrB9+3Z7e/tffvlFLpeLTEb0LgqFYuHCha1btw4LCwMgM8S6L7F9NCzfeqO7l9PzNe1eZCXD7yPf/ZNBBwxu/Y5/RT2aoLLN+342QwP8MgSrBqnmO5w7d87V1TU4OLiwKYnoo0i4SSwJlJiYKJfLJRKJjc17/xoherOEhAQTExOZ7D2WMPooDx8+bNOmTUREBIAaZbnfu6ZTKOCxEH/98/pnDfRxbCravt81KN2Qlw//o5ixCxn/ToBt3rz5zz//7ODgIDRXiZCdnZ2enl66dGnRQbRJXFycr69vwU0fDpWwffR7zaxRyniG7RdxNhTx6Shliua10K+5apPzkmDPFfTyR/7r3qOrXgbnZ37MnV8nguGzHEkZAGBpafnHH394eHgUNihpgMTERGNj46J7+UTqxQJPIrHAk3oVaYEPCwtzc3NTXgWqZouA7z7g8gWJkpKJXv449p8Ob2aM375Er6YiMokWFo+RG/HnDdWhVCodN27czJkzTU11fVaxUCzwH+r06dP9+/d/8uSJ8vDr9vhpwHstbEEFTgZjxAbciX4+IpGgZxOsGowy/5me8J7uRqPLYtx7CgAGBgarVq366quv1JCVhGKB1y4s8CQSCzypV9EV+MjISDc3N+VGuFVKI2C6amEw0nwKBf7vKjafxfXHyMpBJRu418doj5I+e2LX3xi7GVH/LtRfpUoVf3//rl27Cg2ly1jg359cLp83b96sWbPy8/MBWJjg16El9O22wpMrcOEurjxEejYqWKNtXVQvU9jPmZiOnssQcFt1OGXKlPnz50skksJ+XhKHBV67sMCTSCzwpF5FVOCjoqLatGlz7949ABVL4fR3angBRCRcaham78Tqv55Psu3atevy5curVq0qMpaOYoF/T7GxsQMGDPjrr7+Uh42q4Y/RqFFWbCh6VU4evv4NG8+oDnv16rVp0yZjY2OhoejjscBrFy5iR0T0NjExMe3bt1e29wrWODmN7Z10hIUJ/H1xeTaaVFeN7N+/v169evPnz8/JyREajUqo06dPOzk5FbT30R44P5PtXRMZGmD9V5j9GZTX3Xfs2OHu7p6YmCg6F1GJwAJPRPRGcXFxHTp0CA0NBVDWEif8ULOc6ExEatWoGi7NxspBsJIBQGZmpp+fX8OGDY8fPy46GpUgcrl87ty57du3j4qKAmAlw+5x8PeFkVR0MnoDiQTfdcfGr2FoAADnzp1r0aLF48ePBcciKgFY4ImIXi8xMbFjx45BQUEAbC1w3A91KojORFQE9CQY6Y7bP2JAS9X1tNDQUHd39969e0dGRopOR7ovLi7O09Nz+vTpypveG1fHtbno0UR0LHoPvq1weLJqy7rQ0NDmzZvfuHHjXR9ERIXCAk9E9BopKSkeHh43b94EUMoMf32L+hVFZyIqSuWssGU4Tk1DvX//qu/YsaNOnToLFy7kjHoqOmfPnnVycjp69KjycFRHnJvBO5W0Sbt6ODsDdtYAEB0d3aZNmxMnTogORaTLWOCJiF6VlpbWuXPnq1evArCS4egUNKwsOhNRsXCzx415+LEfzIwBICMjY+rUqQ4ODocPHxYdjXSNQqFYsGBBu3btlHvFWcqwcyxWDOS0ee3jUAkXv1e9zZ2amurp6blt2zbRoYh0Fgs8EdFLMjIyvLy8Ll68CMDcGIenoHH1d34Qke6Q6mOSF0IXo08z1cjdu3c9PT27dev24MEDodFId8THx3t5eX377bd5eXkAnKvi2hx85iI6Fn2sSjY4MwOt6wBATk7OgAEDli1bJjoUkW5igSciei4rK6tbt25nz54FYGqEPyej6SeiMxGJYGeNbaNwajocKqlG9u/fX79+fT8/v/T0dKHRSOudO3fOycmpYFrHCHdcmMXV5rWetSmOTlUtXiCXy8ePHz916lTuV02kdizwREQqz54969Gjh/LmPRND7J+IVrVFZyISqo09rs/Fcl9YmwJAdnb2/Pnza9euvWXLFr4up4+gnDbftm1b5fqIFib4YzRWDeK0eR1hLMWOMRjWXnW4cOHCoUOHKidZEJG6sMATEQFAbm5ur169jhw5AsBIiv8bj3b1RGci0gAG+hjjgTuL8WVb6EkAICoqytfXt1mzZpcuXRKdjrSJcrX5gmnzTlVxbS56NRUdi9RKXw9rvsCsnqrD9evX+/j4ZGdnCw1FpFNY4ImIkJeX17dv3/379wMwNMDOMfBoIDoTkSaxtcDaobgyBy3/nZZy+fLl5s2bDxgwgFvN0fsICAhwdHRUvkkKYHgHXJiFTzhtXkfN7IHVg1Vv+e3du7dz586pqamiQxHpCBZ4Iirp8vPzfX19d+/eDcBAH7+PxKfOojMRaSTnqjjzHbaPRmUbAFAoFP/73/9q1649Y8YM3hhPb5Kfnz9z5swOHTpERUUBsJRhxxisHgxjTpvXacM7YPto1c0RAQEBbdu2jY2NFR2KSBewwBNRiSaXy4cMGaLc8EZfD5uHoSeXQSZ6M4kEvZsidDFmfwZTIwDIzMz84YcfatWq9dtvv8nlctEBSbNERka2a9du9uzZ+fn5AJpUx7U58HEVHYuKhY8rDk5S7Ul5/fr1Vq1ahYWFiQ5FpPVY4Imo5FIoFF9//fWmTZsA6Emw/iv0bS46E5E2MDHEd91xZwkGtlLNko2Ojh46dKizs/OxY8dEpyNNsX//fkdHxzNnzgCQSDDBE+dmcrX5kqVDfZychtLmAHD37t2WLVuGhISIDkWk3VjgiaiEUigUo0aNWrduHQCJBL8MgW8r0ZmItIqdNTYOw98/wM1eNXLr1i0PD49OnTrdunVLaDQSLCsra/To0d7e3gkJCQBKm+PARCzpD0MD0cmo2DWpjjPfoWIpAIiMjHRzc7ty5YroUERajAWeiEqo8ePHr169GoBEgpUDMbSt6EBE2qlRNQRMx57xqFlONXL06FFnZ+eBAweGh4cLjUZiBAUFubi4rFy5UrnXYNu6uDkfXk6iY5E49nY4N1P1IyI+Pr59+/anTp0SHYpIW7HAE1FJ9M033yxfvlz5eOkAjHAXG4dI63VvjOBFWDFQNVdWLpdv3ry5du3a33zzTWJiouh0VEwUCsWKFSuaNGkSFBQEwEAfc3xw3A921qKTkWhVSuPsDDhWAYC0tDRPT0/lzi9E9KFY4ImoxPHz81u8eLHy8aK+GNdJbBwiHSHVx6iOePAT/LpBZggA2dnZixcvrlGjxrx58zIyMkQHpKIVExPTpUuXMWPGKDf9rmaL09MxzVu1SgJRWUsETFdtRZmdnd2zZ8+tW7eKDkWkfVjgiahkmTlz5vz585WP5/bCN13ExiHSNRYmmNsLd5diaFvo6wFAcnLytGnTatasuXr16pycHNEBqUjs27fPwcHh8b+FmQAAIABJREFU0KFDysP+LXBjHprXEhuKNI6lDEenolNDAMjLyxs4cOCqVatEhyLSMizwRFSC/PDDD7Nnz1Y+ntUTft3ExiHSWXbW+HUoAhfAu7FqJDo6euTIkXXq1Nm8ebNyRzHSDampqUOGDPH29o6LiwNgKcPWEdg6ApYy0clII8kMsW8CejUFALlcPmrUqLlz54oORaRN9GfNmiU6A5VcWVlZCoVCIpHIZPw9T2qQlZUllUqlUulrn50/f/706dOVj6d5Y/ZnxZiMqESytUCfZujUEA9j8TgOAJKTk/fu3btz584yZcrUrVtXIikps6vz8vJycnJ075fdqVOnOnfuXLAgmZs9jk5FqzpiQ5Gm09dDjyaISsb1xwBw8uTJ9PR0d3f3kvMDQdNkZWUZGBi86eUTaRpegSeiEmHRokV+fn7Kx1M+xRwfsXGISpCmn+DkNBydisbVVSO3b9/u1auXo6Pj3r17lQuVk9bJysoaP358hw4dHj9+DMBIikV9cXIaqpQWnYy0gb4e1g7BJC/V4ZIlS7766ivOzSF6HyzwRKT7Fi9ePGXKFOXjiZ5Y0EdsHKKSqKMD/p6N3eNQv6JqJDAwsHv37o0bN96/fz9rvHY5e/Zsw4YNly1bJpfLAThWwdU5+KYL16ujDyCR4Md+mNtLdbhu3bp+/fpxmQyid2KBJyIdt3Tp0m+++Ub5eFwnLO4vNg5RySWRoEcT3FqA30eiVnnV4PXr17t169a4cWNejdcK6enpY8aMadOmzb179wAY6GOaNy7Pfv6+DNEH8euGVYNUb/3s2LHD29s7MzNTdCgijcYCT0S67Keffpo4caLy8RgPLB0gNg4RQU+Cvs0Rsggbh6FGWdXg9evXu3fv7uTktGvXLuVFXdJAR48edXBwWLFihfK/Ub2KOD8Tc3xgaCA6GWmzEe7YNAwG+gBw+PDhTp06paSkiA5FpLlY4IlIZy1dunTChAnKx6M6Ytnn4Po4RBpCXw8DWyH0R2z4+nmNv3Xrlo+Pj4ODw5YtW/Ly8oQGpJfEx8d//vnnnTp1Ut7xLtXHNG9cmwOXGqKTkU4Y0BK7xsJYCgBnz55t166dclMDIvovFngi0k1Lly4tuPY+qiP8fdneiTSOgT4GtUboj9g0DLX/nVQfEhLi6+tbq1atNWvWZGdnCw1IUCgU69evr1OnztatW5UjzlXx9w+Y4wMjLllN6tOtEf78BmbGAHD9+vXWrVtHRESIDkWkiVjgiUgHLVmyhO2dSFsY6MO3FYIX4feRz2+lfvTo0YgRI6pVqzZ//vzk5GShAUuuwMDA1q1bDxkyJCEhAYDMEAv74vJsOFYRnYx0Ubt6OOGHUmYAEBoa2qpVK+VSC0T0IgkXjCGBEhMT5XK5RCKxsbERnYV0QUJCgomJyapVqyZPnqwcGe2B5Zw5T6Q9FArsu4b5+/H3g+eDFhYWX3755ZgxYypXriwuWqFkZ2enp6eXLq01e6ylpKR8//33K1asKLiXwaMBVg16fr8DUREJikTH+YhOBoCyZcseOXLE0dFRdCgdl5iYaGxsLJPJRAeh98ICTyKxwJN6JSQkrFq1aubMmcrDMR68751IW50MxoID+Ouf5yNSqdTHx2fixInOzs7icn0kLSrwcrl8w4YN06ZNi4mJUY5UsMbSAejdVGwuKkEexsJ9Ph7GAoCVldWBAwdatmwpOpQuY4HXLizwJBILPKnX1KlTFy5cqHw8thN+GsD2TqTdrj3CooPY/TfyX1iZvk2bNmPGjOnatau+vr64aB9GWwp8QEDA+PHjb968qTw00Mfojvj+M5gbi81FJU50MjrOR1AkAMhksp07d3p6eooOpbNY4LULCzyJxAJPajR9+vS5c+cqH0/05H7vRLrjYSyWHcH6AGQ8ez5YtWrVkSNHDhkyxNraWly096X5Bf7OnTtTpkzZt29fwUj7eljui3rc4J0ESUyH54+4fB8ApFLpxo0b+/XrJzqUbmKB1y4s8CQSCzypy+TJk3/88Ufl46ldMb+32DhEpH6J6fjlJFYeQ1TS80GZTNa/f/+RI0c2bNhQXLR30+QC/+TJk9mzZ69fv77gdvcaZbGoL3o0EZuLCOnZ6P4TjgcBgJ6e3vLly0eNGiU6lA5igdcuLPAkEgs8FZ5CoRg3bpy/v7/ycEYPfN9TbCIiKkK5+dh1GcuOvLTKHYBmzZoNGzbMx8fHxMREULS30cwCHxsbu2DBgp9//jkrK0s5Ym2Kad4Y3RGGBmKjEak8y0X/1dj9t+pw5syZs2bNEhlIF7HAaxcWeBKJBZ4KSS6XDx8+fO3atcrDOT6Y5i02EREVk8v34X8Uu/5GTt7zQWtra19f36FDh9avX19ctNfQtAIfExOzePHiNWvWZGRkKEeMpBjRAdO9VZt4EWmOfDmGr8evp1SHI0eO9Pf319PjZthqwwKvXVjgSSQWeCqMvLy8wYMHb926FYBEgh/7YSIXuCEqYWJSsO4Ufj2FsPiXxl1dXQcNGtSnTx8rKytB0V6iOQU+PDx8yZIl69aty8zMVI4Y6MO3JWb2RGX+KiZNpVDAbwcW7Fcd9unTZ9OmTYaGhkJD6Q4WeO3CAk8iscDTR8vJyenbt++ePXsASCTw98WojqIzEZEgcgUO38QvJ3Ho5kvr1RsbG3fv3t3X19fd3V3skvWaUOADAwOXLFmybdu23Nxc5Yi+Hvo2x4zuqFlOYC6i97X0ECb9DmV3cXd33717t7m5uehQuoAFXruwwJNILPD0cTIzM3v27HnkyBEA+npYOxRfuInOREQaIDIRG89gfQAexb00XrZs2d69e/ft29fV1VUiYntJgQVeLpcfOHBg5cqVJ06cKHjVJ9VHv+bw64Za5Ys/EdHH23IOQ9YiNx8AGjVqdOjQoTJlyogOpfVY4LULCzyJxAJPHyE1NfXTTz89c+YMAKk+Ng9Hn2aiMxGRJpErEBCCjWew58pLO88BqFq1aq9evXr16tWoUaPijCSkwD99+nT9+vVr164NCwsrGJQZYkhbTPREFfHT+Yk+xqGb8FmOzBwAqFmz5pEjR6pXry46lHZjgdcuLPAkEgs8faj4+PhOnTpdu3YNgLEUO8bgU2fRmYhIU6VlY88VbDmLUyGQv/x6p1q1at7e3t7e3i1atCiG2fXFWeBzc3OPHDmyfv36gwcPFuwMB6CcFUZ0wAh32HCZOtJyF+/h08VISAeAcuXK/fnnn87OfDXw8VjgtQsLPInEAk8fJCIiwsPD4/bt2wDMjLF3AtrXE52JiLRBVBL+uITtF1/dfA5A6dKlvby8PD09O3bsWHQr3hVDgZfL5efPn9++ffsff/yRkJDw4lPNamKkO3xcuTkc6Y7bT9BpIcITAMDc3Hz37t3u7u6iQ2krFnjtwgJPIrHA0/u7c+dOx44dw8PDAZQyw5/foOknojMRkbZ5FIedl7HrMq4+wiuvgAwMDFxdXT08PNzd3Zs0aaLey/JFV+Bzc3PPnDmzd+/evXv3RkZGvviUpQz9W+DrdmhQWe1flki8J0novBD/RACAVCpdt26dr6+v6FBaiQVeu7DAk0gs8PSerl696unpGRcXB6C8FY5OhUMl0ZmKWG4+9lzB0UBEJMBIioaV0a856lUUHYtIV0QkYO9V7LuG06HIy3/1WQsLi1atWrm5ubVs2bJx48ZSqbSQX07tBT4iIuLo0aNHjhz566+/UlNTX3xKXw8d6uPzlujRBCbcZot0WnImui9FwG0AkEgkc+bM8fPzEx1K+7DAaxcWeBKJBZ7ex4kTJ7p3756WlgagRlkcm4rqur7ibGA4eq9AaNRLgxIJhrXHss85CZZInZIzcSwQh2/hSCCeJr/mBBMTE2dn56ZNmzZp0qRRo0Y1atT4iHXsC1/gFQrF3bt3L1y4cO7cuTNnzty/f/+VE/QkaFYTfZrBxxVlLT/66xBpmWe5GPgz/rikOhw2bNjKlSvFbhupdVjgtQsLPInEAk/vtGPHDl9f32fPngFoWBlHpqBcUd2jqinux6DpDNXaPP/VvwW2jijeQEQlg0KBW+H46x8cD8L5u68uX1/AwsLC0dGxfv369erVq127dq1atSpVeveMoI8o8FlZWbdv3w4KCgoKCrp+/fq1a9eSk1/zBoPMEO3qoYsTujXS/R+PRK8lV+Cb37H0kOrQy8tr+/btZmZcrfF9scBrFxZ4EokFnt7O399//PjxcrkcQKva2D8JViXgl0u3Jdh//W0nHJkCjwbFlYaoRMrNx5UHOHcX5+7gwt03vqGmZGJiUr169WrVqlWsWLFChQoVK1a0tbW1sbEpVaqUhYWFmZmZubn5awt8enp6enp6SkpKUlJSbGxsVFRUdHR0WFhYWFjYw4cPIyMjlT/9/ksiQT07tKuHTg3Rxp7z5IkAYPkRTNiq2m/C2dn54MGD5cuXFx1KO7DAaxcWeBKJBZ7eRKFQTJ48efHixcpD78b4fWSJeJEam4oKI5H/+hftKr2bYvvo4gpERMDdaPz9ANce4fpj3AxDatYHfwYzMzMDA4OCufcZGRk5OTkf9BlMjeBcFc1qokVttKjFreCIXmPPFQxYjawcAKhcufLBgwcdHBxEh9ICLPDahQWeRGKBp9d69uzZ4MGDt23bpjwc1h4rB0FfT2yoYnI0EJ0WvuOcGmVxf2mxpCGi/1Ao8CgOIU8Q8gR3o3EnGvdjXn/zfGEYGqCqLerZwd4ODpXgUAl1KpSUH4NEhXHpProuQVwqAFhYWOzYscPDw0N0KE3HAq9dWOBJJBZ4+q+kpKQePXoEBAQAkEjwfU981110pmK04xJ6r3jHOTZmiP+lWNIQ0fvJzEFYHCITEZ2MqCTEpiIhHUkZSM1CejZSs1TTatKzoQDMjQFAIoGVDGbGsDSBtSlKmcGuFMpZokppVLJBxVKs60Qf6WEsvH5ULQRrYGDg7+8/fPhw0aE0Ggu8duFaxkSkQR4/fuzl5RUSEgJAqo9fhmCwm+hMxcvW4t3nlOH60kQaRmYIezvY24nOQURA9TI4PxM9lyHgNvLy8kaMGBEaGrp06VIuTU+6ge/uEpGm+Pvvv5s2baps7+bGOPhNiWvvAFxqvPtWf7c6xRKFiIhIO5Uyw9Gpz19F+Pv7d+nSJSUlRWgoIvVggScijbB79+62bdvGxMQAsLPG2ZnoWCLXnTE1whdvfdvCQB8jOxZXGiIiIu1kaID1X2FBH+hJAODIkSPNmjW7f/++6FxEhcUCT0TiLViwoFevXpmZmQAcq+DSbDSsLDqTOHN7ocGbv/35vVG/YjGmISIi0lpTPsWe8TAzBoDbt2+7urqePHlSdCiiQmGBJyKRnj17NmjQoG+//Va53XEXJ5ydgYqlRMcSylKGgOno30J10aBAGQtsGoZJXoJiERERaaFujXB+JirbAEBiYqKHh4e/v7/oUEQfj6vQk0hchb6Ei4mJ6dGjx4ULF5SH4zphcX8uvPzcw1j89Q/CE2BkAMeqcK//7tvjiYiI6L9iUtBjGS7cVR0OGjRozZo1xsbGQkNpCq5Cr11Y4EkkFviS7Pr1697e3hEREQCk+lgxEF+3F52JiIiIdNSzXIzahHWnVIdNmjTZvXt3pUqVhIbSCCzw2oWXuohIgG3btrVs2VLZ3m3McGQK2zsREREVISMpfh2KVYMg1QeAK1euNG78/+3deVyU5f7/8c+A7DuIioAbCLkAIu6puS9Zhpm55HLMzPSUeTSzTp1fm2lpix5zqSwrTT2V5pbaV8uNcBf3Ek1BBRWUfVhl5vfHbePEooA4Nze8no8ePZjrvmb4cEcX857ruq+7zc6dO1UuCygnAjwAiyosLJw+ffqIESNycnJEpKWf7H9berRQuywAAFADTOotv7wmdd1ERJKSknr37v3BBx+wJBkaQoAHYDnXr1/v16/fBx98oDyMbCPRb0lAXXWLAgAANUiXYDk0U9oFiIjcvHlz+vTpQ4YMycjIULsuoEwI8AAs5ODBgxEREdu3bxcRnU7eHCxrp4gL28cAAADL8vOU3f+5ffnemjVr2rZte/z4cVWLAsqEAA/AEhYvXtylS5eLFy+KiJujrJsqbzwuOt1dnwcAAFD57GxkydOybMKtO7zExsZ26NDhyy+/VLsu4C4I8ADur6ysrJEjR06aNCkvL09EQvzl4DsysLXaZQEAgBrvH11l31vStJ6ISE5Ozrhx40aPHp2VlaV2XUCpCPAA7qMTJ060bdv222+/VR6O7Cx7//ozCQAAoLrQBnJopgxpf+vh8uXL27RpExMTo2pRQKkI8ADul08//bR9+/Z//PGHiNjZyKKxsnyiONmpXRYAAIAZVwf5brIsGCN2NiIiZ86c6dix47x589idHlWQjt9LqCglJcVgMOh0Oi8vL7VrQWVKS0sbP378Dz/8oDwMqCv/e0EiGqtbVPVXaJATl+R6prg5Sqj/rXchAACgjGLiZNgnEnvl1sP+/fsvW7asbt1qfr+clJQUe3t7R0dHtQtBmRDgoSYCfLUUFRU1cuTI+Ph45eHQDvLZM+LqoG5R1Vxugby7ThZuk1T9rRZHWxnTVWYOEU9nVSsDAEBTsnLl+a/k6z23HtapU+eLL7545JFHVC3q/iLAawsBHmoiwFczBQUFb7/99uzZswsLC0XE0Vbmj5ZnuqtdVnWXni29Zsuh8yUcalhbdv1HGta2eE0AAGjZqmiZuEzSs289HD9+/Icffuji4qJqUfcLAV5bCPBQEwG+Ovn9999HjRp1+PBh5WGrhrLyn9LMV92iaoQRC2VVdKlH2wXI3rfEijv2AQBQHnHJMmqxRJ259bBJkybLli3r2rWrqkXdFwR4bWETOwD3ymAwfPzxxxEREUp61+lkSj/Z9xbp3RLOXpXVe+/U4cCf8vNxS1UDAEB10chbdr4us4aKbS0RkfPnz3fv3n3q1Kk5OTlql4YajQAP4J78+eef5n/P/L1k26vy8Sh2ULOQLcfkruuoNh+1SCkAAFQv1lby6kDZ/7aENhD5a8YiLCwsKipK7dJQcxHgAVSQwWCYP39+aGjo7t27lZZRneX4e9Kzhbp11SyXbty9T/z1+18HAADVVKuGcvAdeXWgWFuJiJw9e/ahhx56/vnnMzMz1S4NNREBHkBFnDp1qnPnzlOmTMnOzhaRum6y9l/yzURx5/opy1LW9d2ZXRn6AAAs70KyTFkuzaaL6zipO1H6vCff7JGbhWqXhWJsa8msoRL9pjT3FRExGAwLFy5s0aLFpk2b1C4NNQ4BHkD55OXlvfHGG61bt96799a118M6ysn3ZVAbdeuqoYJ97t7ngfr3vw4AQDmtjJaWL8v8rfJHomTmSlKGbDshY5ZIt5lynZndKqldgBx5V16PFBtrEZFLly49+uijTz75ZGJiotqloQYhwAMoh19++SUsLOztt9/Oz88Xkfoesm6qrHpealfP+6powIBwcbS9UwedTp5ob6lqAABl88spGb1YsvNLOPRbrDz2kRi4T1SVZGcj7wyRw+9K+8BbLd9//32zZs3mzZt38+ZNVUtDTUGAB1AmiYmJw4cP79Wr15kzZ0REp5MJPeX0HHksQu3KajYvZ/nPoDt1ePohCWtgqWoAAGUzdYUUGko9Gh17p/uDQnUh/hL9pswfLS72IiIZGRn/+te/WrdubdoVCLh/CPAA7iI/P3/u3LkPPPDA6tWrlZYWfrL7P7LkaXHjivcqYMaj8q/+oivpTu+D28misRYvCABwR78nyPGLd+nzv30WKQUVZaWTyX3l9FwZ3O5Wy4kTJ7p16zZixIjLly+rWhqqOQI8gDvZvHlzaGjoyy+/rGy16mQn7w+XmFnSOVjtyvAXnU4+Gik7X5fB7cTbVUTEzVH6hcmP/5IfXizTLncAAEv6vQxXTJ9OuP914J75ecoPL8qWl6VpPRERo9G4atWqBx544K233tLr9WpXh+pJZ7zrHYSB+yYlJcVgMOh0Oi8vL7VrQVEnT5586aWXfv75Z1PLkPby4VPiz38rAADuwcpoeWrhXfr4esjlTyxSDSpDXoF8tEXeXSf6vFstvr6+M2fOHD16tJVVVZ8xTUlJsbe3d3RkXaU2VPXfJwCWl5CQ8Mwzz7Rq1cqU3lv6yS//lu8mk94BALhXfp5378MfXG2xs5FXB8qZD2Vk51sXtSUkJIwdOzY8PHzr1q1qV4dqhQAP4La0tLR///vfQUFBX3zxRWFhoYjUdpGF/5CYWdKjhdrFAQBQLXQIFPe7TXb2C7NIKahUvh6yfKLse+v2lYbHjx/v379/t27doqPZlhCVgwAPQEQkKytr9uzZAQEBs2fPzs7OFhF7G5n+iJz9SCb1llrWatcHAEB1YVtLpj9ypw4eTvLP3paqBpWtXYDs+X+yZooE+dxq2bVr14MPPjhgwIAjR46oWhqqA66Bh5q4Br4q0Ov1ixYtmjt3bnJystJipZOnHpSZT0oD/rMAAHAf3CyUIf+VdYdKOORgKxumSa+WFq8Jla2gUL7YKe/8KImpt1p0Ot3AgQPfeOON8PBwVUv7G66B1xYCPNREgFdXZmbm4sWLP/zww6SkJFPjo63l3SclxF/FugAAqP4MRpm/Vd7fKNfSb7XodNInRD54Slr6qVoZKlV2vnzyfzJno9zIutWi0+kefvjhf//73506dVK1tFsI8NpCgIeaCPBqSU5O/u9//7tw4cLU1FRTY99QeWuwtA9UsS4AAGqWQoMcjZdLN8TBVlo1lLpuaheE+yMjR+ZvlY+3SKrZ3eW6du06Y8aM/v3765SN71RCgNcWAjzURIC3vLNnz86bN2/ZsmU5OTmmxj4h8sbj0ilIxboAAACqufRs+e/P8t+f5Xrm7cYWLVpMmzZtxIgRdnZ2qlRFgNcWAjzURIC3GKPR+Ouvvy5YsGDjxo0Gg0Fp1OnksQh5daC0C1C3OgAAgJoiK1c++1U+2iwJt9dBSt26dZ977rkJEyb4+PiU/tT7ggCvLQR4qIkAbwFZWVkrVqz45JNPTp06ZWq0rSUjOsn0R6S5r4qlAQAA1FD5N2XFb/LhT3I64Xajra3t4MGDJ06c2KVLF4tVQoDXFgI81ESAv6+OHj36+eeff/vtt+np6aZGDyd5toe80Fd8PVQsDQAAAGI0ypZjMm+rbD8p5rGsRYsW48ePHzVqlKen5/2ugQCvLQR4qIkAfz+kpaWtWrXqiy++OHz4sHl7c195oa+M6ixO6lxgBajpxCWJiZPcAvH3kk5NxY13KQCAquR0giz4WZZHiT7vdqO9vf2gQYPGjh3bs2dPKyur+/StCfDaQoCHmgjwlaigoGDbtm3Lly9ft25dbm6uqd3GWgZGyKRe0r25qLrFKaCO32Llha8lJu52i6OtPNdLZg4RB1vVqgIAoLi0bFm+Rz79VU5d/lu7r6/viBEjRo0aFRISUunflACvLQR4qIkAf+8MBkNUVNTq1at/+OGH5ORk80MBdeXph2TsQ+LjrlZ1gMrWHJBhn8jNwhIOdWwq215lQQoAoCr6LVY+/1V+OPC3CXkRCQkJGT58+JAhQwIDK+3GvwR4bSHAQ00E+AorLCyMiopas2bNmjVrEhMTzQ852cnjbWXsQ9KtGVPuqNGupEnQNMnKLbXDlH7y8SgLFgQAQHlk5sp3++Tr3RIVK0VCW3h4+ODBgx9//PFmzZrd43chwGsLAR5qIsCXl16v37Zt24YNGzZu3Hj9+nXzQ9ZW0quljOgkj7cVZ3u1CgSqkDfWyNtr79TB3kaSlogL/78AAKq2C8my8jdZGf23LesVQUFBkZGRjzzySKdOnaytrSvw4gR4bSHAQ00E+DI6c+bM1q1bt2zZsmvXLvPr20XESicPBsnQjvJEO6nrplaBQFXU+S35LfYufTa/LP3DLFINAAD37Gi8fL9fvt8vZ68WPeTp6dm7d+/+/fv36dOnXDeTJ8BrCwEeaiLA30FSUtKOHTu2b9++ffv2uLi4IkdtrOWhZjKojUS2kfrcEA4oSZMpciH5Ln2Wjpdx3SxRDIo4fEEOnpf0bPFxly4PSGNvtQsCAE05flHWHJQfD8qJS0UP6XS6kJCQnj179ujRo2vXrq6urnd+KQK8thDgoSYCfBEJCQlRUVG7d+/etWvX6dOni//v6e4ofUNlYIT0DxMPJ1VqBDQjZIacvHyXPqtfkKEdLFIN/nLovEz4Qo7E3W7R6eSxCFk0lh03AaDczifJphj5KUZ2/SF5BUWPWltbR0REdO3atWvXrp06dSrxLTcBXlsI8FATAb6goCAmJmb//v379u2Ljo4uPtMuIlY6CW8kfUOlb6h0aiq1KnJxE1ATPbVQVkbfpc/pOdLM1yLVQEREfj0lD88t4S2miPh5ym9vSoMa+tcAAO6VPk9+PSU/H5f/O1HCAnsR0el0wcHBHTp06NixY4cOHZo3b16rVi0hwGsNAf627OzsYcOGicjSpUvr1KlTvENmZuZTTz0lIsuWLbuXwLlz586PPvpowIABEyZMqPCLVA81MMDn5eWdPHkyJiYmJibm8OHDx44dK3JNu0mwj3RrLt2bS88WUtvFwmUC1cHmozJg7p06RDSWQzMtVQ1EsnIlaJpcSSu1Q58Q+fkVCxYEANVUXLL8elp2nJYdpyQhteQ+jo6OrVq1ioiICA4OjoiIiIiIsLGxsWyZqIhaahegbYsXL96yZYufn9+iRYvUrgVVkdFojIuLO3Xq1MmTJ0+cOHHixIk//vijoKCkuScRK5209JfOwdI5SB5qxpXtwL16uJVEtpF1h0o+amcjn/zDovVg9d47pXcR+b8TcvKytPSzVEEAUE018panH5KnHxIROXdNos7I7j9k71k5c+X27eiys7Ojo6Ojo2+tVbOxsWnWrFlISEhISEjLli2bNWvWqFEjKysrlX4ClIoAD1SaGzdunDt37ty5c2fOnImNjVX+nZ2dfYen1HGVdgHSLkA6BEr7QHG51JyWAAAeTklEQVR1sFixQI3w7T9l9GJZc6Bou6ezLJ8oHQLVqKkG23G6DH1OEeABoDIF1pXAuvKPriIiN7Jk71nZf04OnpdD5+VG1u1uBQUFx48fP378uKnFwcEhODg4KCgoODg4ODg4ICAgICDA25tNR1VGgL8nnTp18vHxcXZ2VrsQWFRycnJCQsLFixfj4uLi4+Pj4uLi4uLOnz+flnbHqSUREWngJWENpVVDad1IIhqLf025dABQh6Ot/PCi/HJKvtkjRy5IToH4e0qfUHm2h3gxclvc1fTK6QMAqBgvZ3kkXB4Jv/UwLlkOX5AjcXI0Xo7FF11sn5OTc/To0aNHj5o3uri4NGnSpHHjxg0bNmzUqFGDBg0aNGjg5+dXr149S/0QNR0B/p6kpqZ++eWXAwYM6NWrl6nx0KFD27ZtU1ZKN2/efPTo0fHx8XPnzh0zZszgwYPNn67X67/99tt9+/alp6d7e3u3bdt26NChfByguvz8/OvXrycnJ1+5ckX595UrV5KSki5fvpyYmHj58uXSrlovzttVWvhKM18J8ZfmvhLagK3jARX0bCE9W6hdBKRMi4xYiAQAFtPIWxp5y+B2tx6mZMnxS3Lqspy8JH8kyukEScoo+pTMzMxjx44dO3asSLudnV29evX8/Px8fHzq169fp04dX19fb29vb2/vevXqeXt7OzgwvlcOAnwl++6777799luj0ejo6GhnZ3fgwIHjx4+3b9++eM+MjIzp06cnJiY2bNjQ2dk5Li5u/fr1586dmzVrlk6ns3zl1VtBQUFmZmZaWlp6enpGRkZ6errydWpqaspfbty4kZKScu3atfT0ikwAeThJkzoSUFea1pOgehLsI0E+xHUAuK1Vw1K3JDAJb2SJSgAAxXk6S7dm0q3Z7ZZUvcRekdirEntF/kySP6/J+SS5nlnCc/Py8uLj4+Pj40t7cScnp9q1a9euXdvLy8vzL+7u7h4eHu7u7q6urh4eHq6uri4uLi4uLk5OvIcuFQG+Mv3+++8rVqyws7N78cUXH3zwQZ1OpwTyXbt2Fe+8Z88ef3//JUuWKAtOjh079uabb546ders2bNBQUEWr72qyM7OzsvLE5G8vDzl6nFTS3p6usFg0Ov1+fn5ubm5OTk5+fn5er1e6am0p6Wl5eXl6fX6jIyMnJwcvV6flpam9KmU8qytpJ6b+HuJn6f4ekpjb2lY+9aHl+7cegMA7mhEJ3l3nRQUltqhgdff3jgCANTl4STtA6X937eMyciR+OsSlyxxyXLxhly6IZdS5NINuZYu+TdLfSm9Xq/X6++Q8M3pdDp3d3dnZ2dHR0dnZ2dXV1d7e3tnZ2cnJydbW1s3N7datWq5ublZW1u7uroqnUXEzc3NysrK3t5eme1XHipH3d3dq80UKQG+BFOmTClxx8W73nJvxYoVIjJmzJjOnTsrLYGBgdOnT58xY0aJ/adNm2a6XCQsLKxt27b79u27fPly2QN8dnZ2lb0R4IkTJ1555ZWMjNsrb8wvES8oKMjKurVvhsFgMO+mIiud1Ha59U8dV6nnLt4uUs9dfNzFx13qe0gdV27DDgAV1LSevDJQ3vmx5KPWVrJorNjyxgQAqjZXBwnxlxD/Eg5dS5dr6XI5RZIyJDFVkjIkOUOupUtShlzPlBtZd0r45oxGY2pqampqKXfAK7+2bdtu27ZNue99FWRnZ1f22qroz6AuU7AsF+X+3ra2tr179zZvb9asWcOGDYt/2tS0adMmTZqYtyhhvrCw9LmJYnJycqpsgJ87d26JSw8sycZanO3FzVGc7cTFQVzsxc1R3B3FzVHcHMXD6dY/nk7i6SyezmxqBQD311uDxWiUWevF8Pe/Xa4O8vkzMiC8lKcBALSgrpvUdZPQBqV2yMyVlCxJyZIUvaRkSape0rIlTfl3tmTkSGaOZOVKWrZk5Yo+T/SVs4hWDh48ePHiRR8fn8p5ucpmbW1NgL8nS5curVOnTvH2zMzMp556qrRnXb161Wg01q1b187OrsihBg0aFA/wvr6+RVqqzboORb9+/TZt2pSfn1/8kLOzs+l31M7Ozt7eXvna0dHR1tZWRGxtbR0dHc2POjk52djYKO1WVlYuLi4ioqyWcXd3V1rs7OwcHBycnZ1tbW2Vi2dsbGzKXrBR5Po9/cQAgLt7sa8MmHFu5cqVR44cycrKql27dqdOnUaOHOnp6ckgDADVnpOIk0hJ8/clS09Pz83Nzc3NzczMLCgoyMjIyM/Pz8nJycnJycvLUw4pLaZVvdnZ2UoGMe1s1aVLlyqb3suLAF9pkpOT5a9IWYSHh0fxxkrZm8HFxaXKzsCPHj16yJAhyobtJZ4BEcnKyjIajTqdjr33USkyMzNtbW2Lf4gGlFdBQYEyfNnZ2SkfLKIShYeHh4fXrNl25TdK+fQZuEd6vd5gMPD2CZUlKyvLxsamyr59qgkjZ7nW9hPgK40S3UvcwPz+XeBdxd9W3nUg0Ov1ygcQVXbIgLZkZWXVqlWLXyfcO9Nno/xGoVLwxw6VSNnlV/iNQiXR6/X8sdOQErZqQ8XUr19fRK5du1Z80fjly5fVqAgAAAAAUH0Q4CuNo6NjQEBAXl7eL7/8Yt5+7ty5c+fOqVUVAAAAAKB6IMBXplGjRonIV199deDAAWWx3MWLF+fMmWNtbS3Vbo86AAAAAIAlcQ18ZWrduvVjjz22fv36mTNnOjs7Ozg4JCcnBwYGPvroo59//rmrq6vaBQIAAAAAtIoZ+Eo2bty46dOnh4eHG43GgoKC/v37z5o1S7m1e2k7sQMAAAAAcFe6KnsTsupk0aJFW7dunTdvXpMmTdSupWpJSUlR7oPi5eWldi2oDm7cuOHg4ODo6Kh2IdC83NzcrKwsEXFycnJwcFC7HGie8htVu3ZttQtBdZCamlpYWMjbJ1SWlJQUe3t73j5pBTPwlWnGjBmDBg06deqUeWNGRsauXbscHR39/f3VKgwAAAAAoHUE+MrUuXPnwsLC+fPnHzlyRPms/ejRo6+99lpOTk7fvn1tbGzULhAAAAAAoFVsYleZHn300YSEhC1btrz55pvm7RERESNGjFCpKAAAAABAdUCAr2TPPffcgAED9u/fn5SUZGVl5ePjExAQ0LJlS7XrAgAAAABoGwG+8vn7+3O5OwAAAACgcnENPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0IBaaheAGk33F7ULQTVhZWXFrxMqBUMTKh2/UagsvH1C5eJ3SVt0RqNR7RoAAAAAAMBdsIQeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0IBaaheAGiopKWn16tUxMTEZGRkeHh5t2rQZPny4m5ub2nUBqEFWrVq1atWqEg8tXrzY19fX9JAhC4BlbN26ddGiRZ999lm9evWKHy3jWMSQBVRjzMBDBRcuXJg8efL27dtv3Ljh5OSUlJS0efPmyZMnJyUlqV0agBrkypUrZenGkAXAMgoLC7du3Vra0TKORQxZQPXGDDxUsGDBguzs7DZt2kyePNnd3T05Ofm99947e/bsZ5999vrrr6tdHYCaIiEhQUQ+/vhj88l2hZ2dnelrhiwA95vBYPjzzz//97//nT9/vrQ+ZRyLGLKA6o0ZeFhabGzsuXPnvLy8XnnlFXd3dxHx9vZ+4403bG1tDx06dOPGDbULBFBTJCQkWFtbN2rUyL4YnU6n9GHIAnC/ffPNN0888cS0adMOHDhQWp8yjkUMWUC1R4CHpW3fvl1EOnXqZGtra2p0dXWNiIgwGAw7duxQrzQANUh6enp2draPj4+1tfUdujFkAbjf/Pz8evTo0adPnz59+tjY2JTYp4xjEUMWUO2xhB6WpqxZDQsLK9IeGhq6d+/ey5cvq1EUgBpHGYv8/PwOHTq0c+fOq1evenl5NW7cuF+/fsq0lXk3hiwA90+PHj169OihfL1v376CgoLifco4FjFkAdUeAR6WlpKSIiLFt0JV3jGnpqaqUBOAmicxMVFEDh8+vG/fPlPj3r17N2zYMGnSpM6dOystDFkAqoIyjkUMWUC1R4CHpSnXX7m4uBRpd3V1lb/+8ADA/aYEeIPBMHr06A4dOnh5eV26dOm77747cODAvHnzGjdurOxsx5AFoCoo41jEkAVUe1wDD3WYNogyMRqNInLz5k01ygFQ4zRv3vzpp5+eNWvWE0884efn5+DgEBQU9Prrr4eHh+fn569cudK8M0MWgKqgjGMRQxZQjRHgYWleXl4ikpmZWaRdafH09FShJgA1T5s2bSIjI5s1a1akPTIyUkTOnj2rPGTIAlAVlHEsYsgCqj0CPCxN+eORkZFRpF1pqV27tgo1AcBflJXzycnJyoQVQxaAqqCMYxFDFlDtEeBhafXr1xeRU6dOFWk/ffq0iPj4+KhQE4AaJjc3d+PGjT/99JOS0s0p14j6+fkpa1AZsgBUBWUcixiygGqPAA9L69Wrl4j89ttvBoPB1Jifn3/w4EErKyvTbVQA4P6xt7ffvHnzp59+evjw4SKH9uzZIyJBQUHKQ4YsAFVBGccihiyg2iPAw9KCg4MDAgKuXr26ePFi05Yqc+bMyc7ObteuXZ06ddQuEECN8PDDD4vI/Pnzjx07prTk5+d///33mzZtcnR0HD58uNLIkAWgKijjWMSQBVR7uuKrB4H77fz586+++mpOTo6zs7O/v/+FCxdyc3M9PT3nzp3r7e2tdnUAagSj0Th37tyoqCgRcXFxcXR0TE5ONhgMTk5OL774YocOHUw9GbIAWMzIkSMzMjI+++yzevXqFTlUxrGIIQuo3gjwUMfVq1dXr14dExOTmZnp4eHRrl27YcOGubm5qV0XgBrEaDRGRUVt2bIlMTExOzvb39+/adOmQ4cO9fDwKNKTIQuAZdwhwEuZxyKGLKAaI8ADAAAAAKABXAMPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwAAAACgAQR4AAAAAAA0gAAPAAAAAIAGEOABAAAAANAAAjwAAAAAABpAgAcAAAAAQAMI8AAAAAAAaAABHgAAAAAADSDAAwCASrZ+/XqdTqfT6eLj49WupXwKCwuVyhcuXKh2LQAAFEWABwAAAABAAwjwAAAAd7Jr1y5lWr5t27Zq1wIAqNEI8AAAAAAAaAABHgAAAAAADaildgEAAABVWufOndPS0kTE2tpa7VoAADUaM/AAAGhMZGSkTqdzcnLKz883bz9+/LhyqXatWrUyMjLMD8XHxyuHnn32WfP2rKys999/v2PHjl5eXi4uLmFhYRMnToyNjS3tW5e3fxGZmZnt27fX6XQ2Njbr1q0zP5SRkfHOO+9069bNz8/PycmpZcuWQ4YM2bt3bxlf+a6ioqKGDh0aGhrq4eHh4uLSvHnzgQMHbtiwwWAw3PW51tbWfn5+7u7u8+fPV1qmTZumnM/Dhw+X+JRBgwYpP2ZycrKp8R7PHgAAYgQAAJqyaNEi5Y94VFSUebv5nc+2bNlifujrr79W2tesWWNqPHToUP369Yu/N7Cysnr//feLf9+y9zeF87i4OFOjXq/v0qWL0n/VqlXm/aOjoz09PUt8ozJ9+vR7OVdGozE/P//xxx8v7Y3Qww8/fPPmTVPnmzdvKu2ffPKJ+Ys4OzuLyMyZM5WH+/fvV7q99tprxb9jRkaGvb29iAwcONDUWN6zDQBAcczAAwCgMX379lW+2LFjh3l7VFSU6evdu3ebH1Ie1qpVq2fPnkrL+fPne/TokZiYKCJDhgxZsmTJ6tWrp0yZ4uTkZDAYZsyY8cEHH5i/Qnn7F5GXlxcZGblnzx6dTvfll18OGzbMdCgpKenxxx9PSUlxcHD45z//uXLlynXr1s2aNcvHx0dE5s6du3Xr1nKfIzMfffTR2rVrRSQwMHDOnDlr165du3bt7NmzAwMDRWTz5s0LFiwo72u2a9euUaNGIvLjjz8WP7px48bc3FwRGTNmjNJyj2cPAIBb1P4EAQAAlJsSPnv27Gne6OfnJyLKVPaDDz5YvH+XLl1MLb179xYRa2tr8zl5o9F45syZgIAAEXFycrp8+XLF+heZgc/Pz3/kkUeUliVLlhT5Wb766ivl0E8//WTeHhsba2NjIyLPPPNMec+PuQ4dOohIUFBQenq6eXtaWlqDBg1E5LHHHjM1lnEG3mg0vvzyy0rPM2fOFPmOkZGRyn+IvLw8paW8ZxsAgBIxAw8AgPb069dPRKKjo/Py8pSW+Pj4y5cvi8iLL74oIgcPHszJyVEOXbly5dy5cyLSv39/peXUqVPbtm0TkRdeeKHI8vKgoKDPP/9cRPR6/WeffVax/uYKCwtHjBixadMmEZk3b96ECROKdDhy5IiI2NramspTNG3adOLEiZGRkV5eXuU6OeYKCgouXLjg7u4+fvx4V1dX80Nubm49evQQkT///LMCr/zkk08qXxS5mD8zM1NZMjBs2DBbW1u5t7MHAIA5AjwAANqjrKLPyck5cOCA0qKsnw8MDFRWp+fn5+/bt085tGvXLuULJfaLyMaNG5Uvpk6dWvzFu3fv3rJlSxGJjo6uWH+TwsLCsWPH/vDDDyIycuRI5cOFIpR8np+f//333xc5NH/+/B9//PG9994r8SSUhY2NzdWrV1NTU1966aXiR9PT0yv8yhEREcrkeZFV9Bs2bFDWz48ePVppqfDZAwCgCAI8AADa0717d2V213QZ/J49e0SkS5cuQUFBytXjpsvglS/q1q3bqlUrpeXgwYMi4uvr6+/vX+Lrh4aGisjJkycr1t9k8uTJy5cvV77+6aefzLdkN4mMjFSWyg8dOrR169YLFy6s2JR4GeXn5585c+brr78eMWJEkcnz8lIm4ffv33/lyhVTo/IxRFBQUPv27ZWWCp89AACKIMADAKA9Tk5ODz74oIjs3LlTaVFm4Dt37iwiDz30kJgFeGUGvm/fvjqdTmm5du2aiCQkJOhKsXLlShHJzMysWH+Tn376ycrKavDgwSKSmpr6yiuvFP9ZQkNDv/rqKw8PDxGJiYl5/vnnAwMDfXx8Ro0atW7duiK3yquYa9euffzxxw8//HBAQICjo+MDDzzwj3/8Q9kJ/15edujQoSJiNBrXr1+vtGRkZCjr503b18k9nD0AAIogwAMAoEnKevi9e/fm5eWlpKScPn1aRJT7tCkBfu/evfn5+cnJyb///ruYXQAvInq9vizfQq/XK7dJL29/E51Ot3Tp0u+++y4iIkJEli1bZlrYb27EiBGxsbEffPBBly5drK2tReTq1asrVqwYNGhQWFiYMoNdYStWrAgMDJw6deqWLVvOnz/v7u7euXPncePGLV26VEngFRYWFhYcHCxmq+g3bNiQl5en0+lGjhxp6lbhswcAQBEEeAAANEm5DD43N3ffvn2//fab0WisU6dO06ZNRaRbt24ikpOTc+jQoT179hiNRisrK2UjdIWbm5uItG7d+u673VpZVaC/yYIFC8aOHWtlZbVw4UKdTmc0GidNmlRiTK1du/a0adN2795948aNzZs3T58+vV69eiLyxx9/REZGpqWlVewsHTx4cMyYMVlZWQ0aNFi0aNGFCxeuX7++Z8+epUuXjhs3Tpn2vxfKKvodO3YoFSrr57t3767sb6+o8NkDAKAI/k4AAKBJYWFhSsTdsWOHsn5emX4XkQceeKBu3boisnv3bmX9fNu2bc33cg8KChKR06dPFxYWluV7lbe/ienuce3bt3/66adFJCYmZsmSJeZ90tPT09PTTXvmu7m59e/ff86cOZcuXXrttddEJDEx0bQPX3l9+umnBoPB1tZ29+7dEydOVG7eblLhzwVMlABfUFCwefPmjIyMn3/+Wf6+fl7u4ewBAFAEAR4AAK3q06ePiOzcubNIgBezy+CVK+FN+88revbsKSK5ubnK/vBF6PX6Vq1aNW7cePbs2RXrX6L33ntPmfF+/fXXzXezCw4Odnd3V+6dbq5WrVqmjdyvX79+h1e+g/Pnz4tI06ZNGzZsWOTQzZs3TTsIVFjLli2bN28uIj/++OP69evz8vKcnJyK3CuuUs4eAABCgAcAQLuUWL5v375Dhw7JXzvYKUwB/vjx4/L3C+BFZNCgQcpO9ZMnTz5z5oz5IaPROHXq1GPHjsXFxQ0cOLBi/UtUu3btd999V4rtZte6dWsR2blzZ/Ft2E2Jt127dnc7GSVr3LixiMTHxxfZIs5gMIwbN+7q1atS5mvUS6NMwm/ZsuWbb74RkcGDBzs7O5t3qJSzBwCAiNRSuwAAAFBBvXv3trKyysvLExFnZ2fTXeLkr8vglWjq6enZtm1b8yfa2tp++umnjz32WFJSUmho6LPPPtuuXTtvb++4uLivv/5a2WduypQpLVq0qFj/0kyYMGHp0qVHjhxZtmzZ+PHjO3ToICKjR4/esmVLfn5+jx49Jk2aFB4e7uDgkJiYuHHjxrVr14pIx44d7/rKpenVq9eXX36ZlZXVt2/fGTNmBAQEpKamHjt2bMGCBbGxsc7OzllZWRcuXPjPf/4zcODAImepjIYOHfrmm2/q9frt27eL2e3fTSrr7AEAIHfdTwUAAFRZbdq0Uf6g9+7du8ghb29v5dCwYcNKfO4333xjb29f/L2BTqd77rnnDAZDhfub7q8eFxdX5EX27t2r3M0uPDy8sLBQaZw0aVJpb1RCQkKSkpLu5RQpM+TFa540adKhQ4dsbW2VlsjISKPRePPmTeXhJ598Yv4iyqT6zJkzS/wWISEhyrP8/PxMP1QR5T3bAAAUxxJ6AAA0zHRxu/kF8AplFb0UuwDeZNSoUbGxsS+99FLLli1dXFxcXFzCw8PHjBlz7NixxYsXm24aX+H+JerQoUPx3ewWLlwYHR395JNPhoaGenh4ODs7N2/e/NFHH12zZs3Ro0dNn0RUzOrVq1etWtWtWzdfX197e/smTZo888wzBw8eXLhwYURExPbt2wcPHjxw4MABAwZU+FuYbkc3atSo0naSr5SzBwCo4XRGo1HtGgAAAAAAwF0wAw8AAAAAgAYQ4AEAAAAA0AB2oQcAABqwadOmIvdguys7O7vnn3/+PtUDAIDlcQ08AADQgOHDh69evbpcT3F3d09NTb1P9QAAYHkEeAAAAAAANIBr4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0AACPAAAAAAAGkCABwAAAABAAwjwAAAAAABoAAEeAAAAAAANIMADAAAAAKABBHgAAAAAADSAAA8AAAAAgAYQ4AEAAAAA0ID/D2wJJY16jPpyAAAAAElFTkSuQmCC" width="672" /></p>

 <p>It’s a bit tough to tell, but the overall assumptions of normality and equal variance seem reasonable.</p>
 <p>The global mean is</p>
 <pre class="r"><code>mean(df$weeks_alive)</code></pre>
 <pre><code>## [1] 75.55263</code></pre>
 <p>It’s a bit tough to tell, but the overall assumptions of normality and equal variance seem reasonable.</p>
 <p>The global mean is</p>
 <pre class="r"><code>mean(df$weeks_alive)</code></pre>
 <pre><code>## [1] 75.55263</code></pre>

-<p>PC3. T-test between None and Any, and between None and High.</p>
+<p>PC3. Anova</p>
+<pre class="r"><code>summary(aov(weeks_alive ~ dose, data = df))</code></pre>
+<pre><code>##             Df Sum Sq Mean Sq F value Pr(&gt;F)  
+## dose         3   4052  1350.7    3.55 0.0245 *
+## Residuals   34  12937   380.5                 
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+<p>This provides evidence that the group means are different.</p>
+<p>PC4. T-test between None and Any, and between None and High.</p>

 <pre class="r"><code>t.test(df[df$dose == 'None', 'weeks_alive'], # Samples with no dose
        df[df$dose != 'None','weeks_alive'] # Samples with any dose
        )</code></pre>
 <pre class="r"><code>t.test(df[df$dose == 'None', 'weeks_alive'], # Samples with no dose
        df[df$dose != 'None','weeks_alive'] # Samples with any dose
        )</code></pre>


@@ -483,15 +491,8 @@ t.test(weeks_alive ~ dose, data = tmp)</code></pre>
 ## sample estimates:
 ## mean in group High mean in group None 
 ##           65.25000           91.36364</code></pre>
 ## sample estimates:
 ## mean in group High mean in group None 
 ##           65.25000           91.36364</code></pre>

-<p>The t-test supports the idea that receiving a dose of RD40 reduces lifespan</p>
-<p>PC4. Anova</p>
-<pre class="r"><code>summary(aov(weeks_alive ~ dose, data = df))</code></pre>
-<pre><code>##             Df Sum Sq Mean Sq F value Pr(&gt;F)  
-## dose         3   4052  1350.7    3.55 0.0245 *
-## Residuals   34  12937   380.5                 
-## ---
-## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
-<p>This provides evidence that the group means are different.</p>
+<p>These t-tests both support the idea that receiving a dose of RD40 reduces lifespan. However, we should not completely trust these p-values, since we are doing multiple comparisons. One option is to do a Bonferroni correction, where we only cnsider things significant if <span class="math inline">\(\alpha &lt; .05/m\)</span> where <span class="math inline">\(m\)</span> is the number of tests. In this case, we would fail to reject the null for the second test because we would set <span class="math inline">\(\alpha = .025\)</span>.</p>
+<p>The Bonferroni correction is more conservative than it needs to be, ane there are other approaches; for example, the <code>TukeyHSD</code> function takes in an anova result and does post-hoc comparisons with corrections for all of the groups.</p>

 </div>
 <div id="statistical-questions" class="section level2">
 <h2>Statistical Questions</h2>
 </div>
 <div id="statistical-questions" class="section level2">
 <h2>Statistical Questions</h2>


@@ -536,7 +537,11 @@ print(ci)</code></pre>
 500 * c(.18,.22,.37,.23)</code></pre>
 <pre><code>## [1]  90 110 185 115</code></pre>
 <pre class="r"><code># Use the formula for chi-squared
 500 * c(.18,.22,.37,.23)</code></pre>
 <pre><code>## [1]  90 110 185 115</code></pre>
 <pre class="r"><code># Use the formula for chi-squared

-chisq = (83-90)^2/90 + (121-110)^2/110 + (193-185)^2/185 + (103-115)^2/115</code></pre>
+chisq = (83-90)^2/90 + (121-110)^2/110 + (193-185)^2/185 + (103-115)^2/115
+
+chisq</code></pre>
+<pre><code>## [1] 3.242564</code></pre>
+<pre class="r"><code># We could then look up the chi-square distribution for 3 degrees of freedom</code></pre>

 <p>The p-value for this is large, meaning that we don’t have evidence that the sample differs from the census distribution.</p>
 <ol start="2" style="list-style-type: lower-alpha">
 <li></li>
 <p>The p-value for this is large, meaning that we don’t have evidence that the sample differs from the census distribution.</p>
 <ol start="2" style="list-style-type: lower-alpha">
 <li></li>


@@ -555,6 +560,63 @@ chisq.test(x)</code></pre>
 ## data:  x
 ## X-squared = 0.66724, df = 3, p-value = 0.8809</code></pre>
 </div>
 ## data:  x
 ## X-squared = 0.66724, df = 3, p-value = 0.8809</code></pre>
 </div>

+<div id="empirical-questions" class="section level2">
+<h2>Empirical Questions</h2>
+<p>EQ0.</p>
+<ol style="list-style-type: lower-alpha">
+<li><p>The unit of analysis is the customer. The dependent variable is the type of board purchased and the independent variable is gender. Males, females, and unkown gender customers are being compared. This is a two-way test.</p></li>
+<li><p>The null hypothesis is that the board purchased is independent of the gender of the customer. The alternative hypothesis is that if we know the gender of the customer that will tell us something about the type of board they purchased.</p></li>
+<li><p>A <span class="math inline">\(\chi^2\)</span> test found statistically significant evidence that board purchase behavior differs by gender. This difference is convincing, but it does directly not tell us what we really want to know, which is the difference between men and women. It could be possible that it is simply identifying a significant difference in the number of unknown gender customers across board types. Many of these concerns are addressed in the text and with additional tests, giving increased confidence in the reality of this difference.</p></li>
+<li><p>Statistical tests help to give (or take away) confidence in a conclusion. People are not natively good at estimating how likely something is due to chance and tests help us to make these judgments. Choosing a statistical test is based on the question that you want to answer and the type of data that you have available to answer it. For example, if this were numeric data (e.g., the amount of money spent on electronics for men and women) then we could choose a t-test to compare those distributions.</p></li>
+</ol>
+<p>EQ1</p>
+<ol style="list-style-type: lower-alpha">
+<li><p>These are counts for two categorical variables, so the procedure used was a <span class="math inline">\(\chi^2\)</span> test. The null hypothesis is that whether or not a blog is governed by one person is independent of whether it is on the left or the right.</p></li>
+<li><p>It would be surprising to see these results by chance and it make sense to believe that this difference is real. However, the main reason to be skeptical is the way that the data are grouped. The authors could have grouped them differently (e.g., 1-2 people, 3-4 people, and 5+ people); if the decision on how to group was made after seeing the data then we have good reason to be skeptical.</p></li>
+<li></li>
+</ol>
+<pre class="r"><code># First we create the dataframe
+df = data.frame(Governance=c('Individual','Multiple', 'Individual','Multiple'),
+                Ideology = c('Left','Left','Right','Right'),
+                Count = c(13,51,27,38))
+
+# We can make sure it's the same by testing the Chi-squared                
+chisq.test(matrix(df$Count, nrow=2))</code></pre>
+<pre><code>## 
+##  Pearson's Chi-squared test with Yates' continuity correction
+## 
+## data:  matrix(df$Count, nrow = 2)
+## X-squared = 5.8356, df = 1, p-value = 0.01571</code></pre>
+<pre class="r"><code>percentage_data = df %&gt;% 
+  group_by(Ideology) %&gt;% 
+  summarize(individual_ratio = sum(Count[Governance=='Individual']) / sum(Count),
+            group_count = sum(Count))
+
+shaw_benkler_plot = percentage_data %&gt;%
+  ggplot(aes(x=Ideology, y=individual_ratio * 100)) + 
+    geom_bar(stat='identity', aes(fill=c('red','blue')), show.legend=F) + 
+    ylab('Percentage of Blogs') + theme_minimal()
+
+shaw_benkler_plot</code></pre>
+<p><img src="data:image/png;base64,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" width="672" /></p>
+<pre class="r"><code># If we want to add error bars, we need to calculate them (Note that ggplot could do this for us if we had raw data - always share your data!)
+
+# I decided to use confidence intervals. The standard error is another reasonable choice
+
+# Remember that for a binomial distribution (we can consider individual/non-individual as binomial), confidence intervals are mu +- x * sqrt((p*(1-p))/n)
+
+ci_95 = 1.96 * sqrt(percentage_data$individual_ratio * (1 - percentage_data$individual_ratio)/percentage_data$group_count)
+
+shaw_benkler_plot + geom_errorbar(aes(ymin=(individual_ratio-ci_95)*100, ymax=(individual_ratio + ci_95)*100),
+                                  alpha = .3, 
+                                  size=1.1, 
+                                  width=.4)</code></pre>
+<p><img 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" width="672" /></p>
+<p>The error bars do overlap in this case, but that actually doesn’t tell us whether they are significantly different.</p>
+<ol start="4" style="list-style-type: lower-alpha">
+<li>We don’t need to be very worried about the base rate fallacy because the sizes of both groups are about the same.</li>
+</ol>
+</div>